From 3cd02c70a48658119dc08cfed882724ef3a4504e Mon Sep 17 00:00:00 2001 From: Jon Wright Date: Wed, 13 Mar 2024 16:57:28 +0100 Subject: [PATCH] V2.0.4 (#258) * added a new method to blobcorrector.correctorclass to do distorsion correction using a pixel LUT * Attempt to manage openmp with multiprocessing * Attempt to manage openmp with multiprocessing * Attempt to manage openmp with multiprocessing * drop 3.7 for 3.8 (but keep 2.7 for now) * Update blobcorrector.py Added new property self.dim with detector dimension to blobcorrector.correctorclass, inferred from the splinefile (xmin, xmax, ymin, ymax) * Update blobcorrector.py * TODO: fix fscan2d to be easier to use * Thanks to Andrew Reader's youtube video * added 3.11 (3.x is busted due to numba) * auto reshape for fscan2d * setting threads only for multiprocessing * fixup fscan2d * fixup fscan2d * Update build_flake_pytest_ubuntu2004.yml * Update build_flake_pytest_ubuntu2004.yml * Update setup.py * Update sigma_3_matrices.py (py2, print) * Update properties.py (py2: print) * Update test_datasets.py (py2: fstring/print) * Update cImageD11.py : use OMP_NUM_THREADS and py27 * Enable manually specifying detector and motor names * matrix vs dcm with old scipy * fix py27 windows * binary wheels for win32/py27 * 'fixup' * 'fixup' * 'fixup' * Update appveyor.yml * Update appveyor.yml * Update appveyor.yml * Update appveyor.yml * Update appveyor.yml * Update appveyor.yml * Update appveyor.yml * Add build wheels and tarball * Update README.md * Update __init__.py * Create eps_sig_solver_NEW.py Create a new solver to compute strain and stress from ImageD11 ubis * Bugfix assemble_label.py set rather than list randomly permuted the order of scans. Oups. * compatible for python2.7 * fix file path * corrected mistakes in eps_sig_solve_NEW.py + updated invariant properties function - corrected a few mistakes and typos in function description - corrected a mistake in convert_tensors_to_vect - changed invariant_prop function to return von Mises stress / strain instead of octahedral shear stress / strain * Update eps_sig_solver_NEW.py * corrected typo in form_stiffness_tensor * updated Cij_symmetry * Update eps_sig_solver_NEW.py * Rename eps_sig_solver_NEW.py to stress.py * Rename ImageD11/eps_sig_solver.py to ImageD11/depreciated/eps_sig_solver.py * Update stress.py * Update stress.py * Update stress.py * Update stress.py * Update stress.py * Add scanning 3DXRD example notebooks * Remove f-string for 2.7 compat. * Exclude Python 3.12 in setup.py Until Numba updates to Python 3.12 This should fix the CI for Python 3.x * Exclude 3.12 from GitHub Actions CI * Fix GitHub actions yml typo * Third time lucky * Use GitHub Actions badges in README.md * Correct typo * Update README.md * fix the log strain - needs testing better * helper function from several notebooks * mlem and centers code * function from notebooks * function from notebooks * Change int32 and float32 to 64 * remove broken test * put stress on hold while updating it * Update __init__.py v2.0.2 * Update README.md v2.0.2 * Update release.yml : no 3.12 version * Allow a fuller mask, to mask powder rings for example * fix mask for powder removal and reformat (black) * Resurrect tests for columnfile.py, correct np.int In the Github Actions CI, the command "python -m pytest" is run to test ImageD11. By default, this only runs test files with the naming convention `test_*.py` Renamed `testcolumnfile.py` to `test_columnfile.py` to fix this. Also, replaced `np.int` references `int` to fix numpy depreciation errors. * Fix np.int64 * Add colfile tests to run_tests.py * Significant notebook layout changes * Parallelised iradon * Ensure 0 fill value for np.interp * Add mask to MLEM * First attempt at enforcing file paths * A few bugfixes * update cibuildwheel and upload|download-artifacts * eps_sig_solver was deprecated * eps_sig_solver was deprecated + black formatted * Python 3.12 support * Add col3d file to dataset paths * Try to get rid of the fork problem * point by point - needs some more docs to expain pars * fixup 2.7 * Add outsize support to MLEM * Clean up comment * convert nnz to ip function * error on typo in group names * fix forking problem/error * get the warnings to print, but only when needed * for CI only having 1 thread? * CI fails. Not sure why. * fixup * set forkserver here, todo: windows? * and for windows that does not fork anyway * and for windows that does not fork anyway * and for windows that does not fork anyway * percent, not fraction * tested on SS316 * was not tested yet. pythonpath * tested once: should not be forking * seen to run once * with a threshold image option * Notebook for par file building from poni * A few small sinogram bugfixes * self.shape is undefined * Significant scanning notebook changes General changes: - Moved quite a few common functions into utils.py. These need properly incorporating into ImageD11 in the future - All notebooks updated to obey DataSet filestructures for peaks and grains files 0_S3DXRD_segment: - Added widget written by Pierre-Olivier for adjusting segmenter parameters - Added slurm bash "listener" that waits for slurm job to complete before next cell completes - Moves individual sparse files to their own subfolder after assembling 1_S3DXRD_index: - More sensible indexer that prioritises low-multiplicity rings - Grains now saved in HDF5 format - Added another example notebook for indexing a minor phase 2_S3DXRD_sinograms: - Added whole-sample sinogram reconstruction to use as a mask for individual grain reconstructions. - Added MLEM refinement stage (uses SLURM) to grain reconstructions using run_mlem_recon.py - Added another example notebook for mapping a minor phase (uses grain recons for centre-of-mass) 3_S3DXRD_strain_maps_pbp: - Performs a point-by-point refinement of the grain-averaged UBI using 2D peak selection - Uses grain labels array from sinograms for grain shapes - Should give "relative" Type-III strains 4_S3DXRD_plot_both_phases: - Small example notebook for plotting both phases in the same map - Some initial work on orientation relationship identification using Orix (not finished yet) * Add box-beam example notebooks - Similar to scanning notebooks but for box-beam 3DXRD - Currently only working for Frelon data - Uses Frelon segmenter from ma4750 (in sandbox) for peaksearching - Global parameter refinement notebook - Uses makemap.py to refine grain positions - Some extra notebooks for importing multiple slices in a z series and looking at peak shapes (still under development) * Build parameter file from CIF and pyFAI PONI * Clear notebook outputs * Correct pyplot typo * Allow flake 2.7 linting to pass * Python 2 compat * Remove duplicate segmentation notebook * Common utils between scanning and box-beam notebooks - Moved a few more functions out of notebooks into utils - This significantly simplifies the notebooks - MLEM recon: y0 now gets written to/read from the grain HDF5 file rather than command-line option * Replace FileNotFoundError with IOError for py2.7 * Minor phase scanning notebooks now use nb_utils, needs testing * Correct ImageD11_gui bugs - broken log buttons and tth_histogram * Rename and add batch scripting * Add batch cells to scanning notebooks * All notebooks now use nb_utils.py * Remove WIP notebook from commit * Add RGB colour output to sinogram notebooks * Add widget with sliders for Frelon peaksearch data * Add RGB IPF X,Y,Z outputs to HDF, allows viewing with H5Web * py2 compat * Small bugfixes and tidying up * Small changes and bugfixes * Clear output * secret spacegroup number hidden option * secret spacegroup number hidden option * secret spacegroup number hidden option * secret spacegroup number hidden option * Correct very dodgy default cosine tolerance * Correct grain translation sign problems * Correct dodgy do_index scan * Handle missing samples in skips_dict, dset lists are now sorted too * More accurately find user home directory path * Just use default arguments for assemble_label and properties * Force only 2 threads for indexer (confirmed faster) * Improve comments about peak filtration * Correct origin for recon plots * Much faster spatial correction using lut * Lima segmenter now used in interactive notebook * Add howmany to Eiger interactive segmenter * Segment everything in parallel on SLURM * Cleaned-up local PBP indexer * Correct PBP results filename * Fix dodgy pbp name * Fix PBP output name for real this time * fixes #236 * fixes #236 * Correct home directory searching * added options to score_all_pairs for ring selection * added options to score_all_pairs for ring selection * Optionally scale sinogram rows by ring current * Forgot to also add to batch cells * Correct dodgy minor phase grain positioning * nb_utils function for ring current correction per scan * H5py in nb_utils * Make ring current correction optional and separate * More sensible default dstol * tag maintenance release --------- Co-authored-by: Jean-Baptiste Jacob <137695414+jbjacob94@users.noreply.github.com> Co-authored-by: jadball Co-authored-by: Thomas VINCENT Co-authored-by: James Ball <42615415+jadball@users.noreply.github.com> Co-authored-by: Jerome Kieffer Co-authored-by: Jonathan Wright --- .circleci/config.yml | 56 + .gitattributes | 8 + .../build_flake_pytest_ubuntu2004.yml | 51 + .github/workflows/release.yml | 115 + .gitignore | 1 + .travis.yml | 18 + ImageD11/ImageD11_file_series.py | 97 +- ImageD11/ImageD11_thread.py | 2 +- ImageD11/ImageD11options.py | 49 + ImageD11/__init__.py | 5 +- ImageD11/blobcorrector.py | 80 +- ImageD11/cImageD11.py | 161 + ImageD11/cImageD11_docstrings.py | 346 + ImageD11/columnfile.py | 274 +- {sandbox => ImageD11}/compute_fazit.py | 2 +- ImageD11/correct.py | 5 +- ImageD11/depreciated/data.py | 4 +- ImageD11/{ => depreciated}/eps_sig_solver.py | 4 +- .../depreciated}/filterout.py | 0 ImageD11/depreciated/fitgrainxy.py | 4 +- ImageD11/depreciated/fsrc/test_cf.py | 4 +- ImageD11/depreciated/hist.py | 6 +- ImageD11/depreciated/linearity.py | 8 +- .../depreciated}/mar2bruker.py | 0 ImageD11/depreciated/opendata.py | 2 +- ImageD11/depreciated/peaklabel.py | 204 + ImageD11/depreciated/test_f2py.py | 8 +- ImageD11/depreciated/ubitool.py | 4 +- ImageD11/fft_index_refac.py | 290 +- ImageD11/finite_strain.py | 143 + ImageD11/grain.py | 193 +- ImageD11/grid_index_parallel.py | 110 +- ImageD11/guicommand.py | 67 +- ImageD11/gv_general.py | 26 +- ImageD11/indexing.py | 1347 +- ImageD11/labelimage.py | 31 +- ImageD11/lattice_reduction.py | 36 +- .../nbGui/3DXRD/0_3DXRD_segment_frelon.ipynb | 570 + .../3DXRD/1_3DXRD_refine_parameters.ipynb | 547 + ImageD11/nbGui/3DXRD/2_3DXRD_index.ipynb | 855 + .../nbGui/3DXRD/3_3DXRD_look_at_peaks.ipynb | 735 + .../nbGui/3DXRD/4_3DXRD_merge_slices.ipynb | 477 + ImageD11/nbGui/3DXRD/frelon_peaksearch.py | 231 + 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| 37 - lgtm.yml | 8 + pyproject.toml | 7 + sandbox/CRL.py | 2 +- sandbox/Index1DSearch.py | 174 + sandbox/abonnin/fix_omega_angles.py | 1 + sandbox/abonnin/make_point_sino.py | 20 +- sandbox/art.py | 17 +- sandbox/avxsum/bench_imsum.py | 83 + sandbox/avxsum/imsum.c | 163 + sandbox/bgfilter_grab.py | 228 + sandbox/bravias.py | 137 + sandbox/buffered_series.py | 36 + sandbox/cleanbg.py | 10 +- sandbox/collect_peak_pixels.py | 150 + sandbox/completeness.py | 112 + sandbox/diffgeo.py | 65 + sandbox/diffracCl/diffracCl.c | 34 +- sandbox/diffracCl/diffracCl.cl | 22 +- sandbox/diffracCl/diffracCl.py | 96 +- sandbox/ev78/integrate_them.py | 16 +- sandbox/ev78/makevols.py | 4 +- sandbox/ev78/watch_direcs.py | 1 + sandbox/factors.py | 24 +- sandbox/fazit.py | 380 + sandbox/fazit_funcs.py | 137 + sandbox/find_pseudo_twins.py | 22 +- sandbox/fit_rotating.py | 365 + sandbox/fittrans.py | 10 +- sandbox/fittrans2.py | 249 + sandbox/forwards_project.py | 8 +- sandbox/fpair.py | 158 + 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| 8 +- sandbox/rad.py | 6 +- sandbox/ringselect.py | 4 +- sandbox/rl.py | 6 +- sandbox/segment.py | 364 + sandbox/segment2d.py | 403 + sandbox/segment_hdf_slurm/s2_400MPa.batch | 21 + sandbox/segment_hdf_slurm/segment1.py | 178 + sandbox/segment_ma4752_alu.py | 277 + sandbox/segmenter.py | 323 + sandbox/sharpen_search.py | 158 + sandbox/sigma_3_matrices.py | 44 + sandbox/spot_harvest.py | 4 +- sandbox/teo.py | 22 +- sandbox/twingen.py | 16 +- ...from filtered peaks - Jupyter Notebook.pdf | Bin 0 -> 237892 bytes ...RD diffractogram from filtered peaks.ipynb | 247 + sandbox/weighted_kde/sma_261N.flt.new | 33185 ++++++++++++++++ scripts/ImageD11_gui.py | 12 +- scripts/bgmaker.py | 34 +- scripts/edfheader.py | 16 +- scripts/filtergrain.py | 66 +- scripts/fitgrain.py | 90 +- scripts/fix_spline.py | 4 +- scripts/index_unknown.py | 55 +- scripts/makemap.py | 76 +- scripts/merge_flt.py | 6 +- scripts/peaksearch.py | 9 +- scripts/plotedf.py | 2 +- scripts/plotgrainhist.py | 62 +- 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+ test/simul_1000_grains/README.txt | 5 + test/simul_1000_grains/bencher.py | 39 + test/simul_1000_grains/compare.py | 10 +- test/simul_1000_grains/easy.py | 17 + test/simul_1000_grains/grid_index.py | 20 +- test/simul_1000_grains/idx.py | 24 + test/simul_1000_grains/make_u_t.py | 9 +- test/simul_1000_grains/make_u_t_easy.py | 104 + test/simul_1000_grains/run_simul.py | 23 +- test/simul_1000_grains/test_mappers.py | 19 +- test/simul_1000_grains/todo.txt | 2 + test/sparse_overlap_test.py | 88 + test/test_cImageD11.py | 84 +- test/test_clean_mask.py | 62 + test/test_closest_vec.py | 35 + .../{testcolumnfile.py => test_columnfile.py} | 34 +- test/test_compress_duplicates.py | 72 + test/test_fft_index.py | 2 - test/test_finite_strain.py | 138 + test/test_forking.py | 127 + test/test_index_unknown/.gitignore | 4 + test/test_index_unknown/__init__.py | 0 test/test_index_unknown/lattice_trans.ipynb | 162 + test/test_index_unknown/test_index_unknown.py | 37 +- test/test_indexing.py | 83 + 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test/testlabelimage/__init__.py | 0 test/testlabelimage/runtestlabelimagemerge.py | 66 + test/testlabelimage/testlabelimage.py | 2 +- test/testlattice.py | 74 + test/testscale.py | 2 +- test/twinprob/__init__.py | 0 test/twinprob/test.py | 14 - test/twinprob/test_twin.py | 28 + todo.txt | 42 + webgui/data.js | 619 + webgui/demo1.html | 426 + webgui/demo_canvas.html | 341 + webgui/gl-matrix-min.js | 28 + webgui/notes_for_new_gui.md | 210 + webgui/webgl-utils.js | 176 + 399 files changed, 79461 insertions(+), 9359 deletions(-) create mode 100644 .circleci/config.yml create mode 100644 .github/workflows/build_flake_pytest_ubuntu2004.yml create mode 100644 .github/workflows/release.yml create mode 100644 .travis.yml create mode 100644 ImageD11/ImageD11options.py create mode 100644 ImageD11/cImageD11.py create mode 100644 ImageD11/cImageD11_docstrings.py rename {sandbox => ImageD11}/compute_fazit.py (99%) rename ImageD11/{ => depreciated}/eps_sig_solver.py (99%) rename {scripts => ImageD11/depreciated}/filterout.py (100%) rename {scripts => ImageD11/depreciated}/mar2bruker.py (100%) create mode 100644 ImageD11/depreciated/peaklabel.py create mode 100644 ImageD11/finite_strain.py create mode 100755 ImageD11/nbGui/3DXRD/0_3DXRD_segment_frelon.ipynb create mode 100755 ImageD11/nbGui/3DXRD/1_3DXRD_refine_parameters.ipynb create mode 100755 ImageD11/nbGui/3DXRD/2_3DXRD_index.ipynb create mode 100755 ImageD11/nbGui/3DXRD/3_3DXRD_look_at_peaks.ipynb create mode 100755 ImageD11/nbGui/3DXRD/4_3DXRD_merge_slices.ipynb create mode 100755 ImageD11/nbGui/3DXRD/frelon_peaksearch.py create mode 100755 ImageD11/nbGui/S3DXRD/0_S3DXRD_segment_and_label.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/1_2_3_S3DXRD_pbp_indexing_local.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/1_S3DXRD_index.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/1_S3DXRD_index_minor_phase.ipynb create mode 100644 ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map_minor_phase.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/3_S3DXRD_strain_maps_pbp.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/4_S3DXRD_plot_both_phases.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/4_S3DXRD_view_PBP.ipynb create mode 100644 ImageD11/nbGui/S3DXRD/5_S3DXRD_plot_multiple_slices.ipynb create mode 100755 ImageD11/nbGui/S3DXRD/run_mlem_recon.py create mode 100644 ImageD11/nbGui/__init__.py create mode 100755 ImageD11/nbGui/build_par_file.ipynb create mode 100644 ImageD11/nbGui/fit_cen_trans.ipynb create mode 100644 ImageD11/nbGui/fit_geometry.py create mode 100644 ImageD11/nbGui/nb_utils.py create mode 100644 ImageD11/nbGui/plot3d.ipynb create mode 100644 ImageD11/nbGui/plot3d.py create mode 100644 ImageD11/nbGui/test_gui_fit.ipynb create mode 100644 ImageD11/project/h5demo1.py create mode 100644 ImageD11/project/make_h5_project_fails_no_external.py create mode 100644 ImageD11/project/plan_new_peaksearch.md create mode 100644 ImageD11/project/project.py create mode 100644 ImageD11/project/project_yaml.py create mode 100644 ImageD11/project/projects.py create mode 100644 ImageD11/project/test_json.py create mode 100644 ImageD11/silxGui/__init__.py create mode 100644 ImageD11/silxGui/readme.md create mode 100644 ImageD11/silxGui/silx_colfile.py create mode 100644 ImageD11/silxGui/silx_plot3d.py create mode 100644 ImageD11/silxGui/silx_sptview.py create mode 100644 ImageD11/sinograms/__init__.py create mode 100644 ImageD11/sinograms/assemble_label.py create mode 100644 ImageD11/sinograms/dataset.py create mode 100755 ImageD11/sinograms/lima_segmenter.py create mode 100755 ImageD11/sinograms/point_by_point.py create mode 100644 ImageD11/sinograms/polefigures.py create mode 100644 ImageD11/sinograms/pread.py create mode 100755 ImageD11/sinograms/properties.py create mode 100755 ImageD11/sinograms/properties.py.sav create mode 100644 ImageD11/sinograms/roi_iradon.py create mode 100644 ImageD11/sinograms/sinogram2crysalis.py create mode 100644 ImageD11/sparseframe.py create mode 100644 ImageD11/stress.py create mode 100644 ImageD11/tkGui/__init__.py rename ImageD11/{ => tkGui}/guiindexer.py (82%) rename ImageD11/{ => tkGui}/guimaker.py (100%) rename ImageD11/{ => tkGui}/guipeaksearch.py (100%) rename ImageD11/{ => tkGui}/guisolver.py (100%) rename ImageD11/{ => tkGui}/guitransformer.py (81%) rename ImageD11/{ => tkGui}/listdialog.py (94%) rename ImageD11/{ => tkGui}/plot3d.py (94%) rename ImageD11/{ => tkGui}/twodplot.py (84%) create mode 100644 ImageD11/weighted_kde.py delete mode 100644 MANIFEST create mode 100644 MANIFEST.in delete mode 100644 PKG-INFO delete mode 100644 README.TXT create mode 100644 README.md create mode 100644 appveyor.yml delete mode 100644 changelog.txt create mode 100644 docs/DeformationGradientTensor.ipynb create mode 100644 docs/NotesOnNewDeformationGradientTensor.ipynb create mode 100644 docs/howto_compile_win64.txt create mode 100644 docs/howto_gdb_crashing_c.txt create mode 100644 docs/howto_install_esrf.txt create mode 100644 docs/scor_gvec_z_sympy.py create mode 100644 docs/sintheta_squared_geometry.ipynb create mode 100644 docs/sphx/.gitignore create mode 100644 docs/sphx/api/ImageD11.silxGui.rst create mode 100644 docs/sphx/api/ImageD11.tkGui.rst create mode 100644 docs/sphx/changelog.rst create mode 100644 docs/sphx/parallel.rst delete mode 100644 docs/sphx/postprocessing.rst create mode 100644 docs/sphx/related.rst create mode 100644 docs/weight_gv_refine.ipynb create mode 100644 find_all_files_interfacing.py delete mode 100644 fixprint.py delete mode 100644 howto_compile_win64.txt create mode 100644 lgtm.yml create mode 100644 pyproject.toml create mode 100644 sandbox/Index1DSearch.py create mode 100644 sandbox/avxsum/bench_imsum.py create mode 100644 sandbox/avxsum/imsum.c create mode 100644 sandbox/bgfilter_grab.py create mode 100644 sandbox/bravias.py create mode 100644 sandbox/buffered_series.py create mode 100644 sandbox/collect_peak_pixels.py create mode 100644 sandbox/completeness.py create mode 100644 sandbox/diffgeo.py create mode 100644 sandbox/fazit.py create mode 100644 sandbox/fazit_funcs.py create mode 100644 sandbox/fit_rotating.py create mode 100644 sandbox/fittrans2.py create mode 100644 sandbox/fpair.py create mode 100644 sandbox/gold/grab_the_pixels.py create mode 100644 sandbox/gold/newpeaksearch.py create mode 100644 sandbox/harvest_pixels.py create mode 100644 sandbox/hdfscan.py rename ImageD11/indexer.py => sandbox/indexer_1d.py (73%) create mode 100644 sandbox/list_folder_images.py create mode 100755 sandbox/ma4750/custom_peak_search.py create mode 100755 sandbox/ma4750/custom_peak_search_ma5179.py create mode 100755 sandbox/ma4750/to_flt.py create mode 100644 sandbox/mlem.ipynb create mode 100644 sandbox/newpeaksearch.py create mode 100644 sandbox/newpeaksearch3d.py create mode 100644 sandbox/peaksearch_nxs.py create mode 100644 sandbox/peaksearcher_nxs.py create mode 100755 sandbox/pfun.py create mode 100644 sandbox/pixelmapper.ipynb create mode 100644 sandbox/segment.py create mode 100644 sandbox/segment2d.py create mode 100644 sandbox/segment_hdf_slurm/s2_400MPa.batch create mode 100755 sandbox/segment_hdf_slurm/segment1.py create mode 100644 sandbox/segment_ma4752_alu.py create mode 100644 sandbox/segmenter.py create mode 100644 sandbox/sharpen_search.py create mode 100644 sandbox/sigma_3_matrices.py create mode 100644 sandbox/weighted_kde/3DXRD diffractogram from filtered peaks - Jupyter Notebook.pdf create mode 100644 sandbox/weighted_kde/3DXRD diffractogram from filtered peaks.ipynb create mode 100644 sandbox/weighted_kde/sma_261N.flt.new create mode 100644 setup.cfg create mode 100644 src/.clang-format create mode 100644 src/.gitignore delete mode 100644 src/.indent.pro create mode 100644 src/__init__.py create mode 100644 src/_cImageD11.pyf create mode 100644 src/cImageD11.h delete mode 100644 src/cImageD11.pyf delete mode 100644 src/cImageD11module.c create mode 100644 src/cimaged11utils.c create mode 100644 src/darkflat.c create mode 100644 src/localmaxlabel.c create mode 100644 src/make_pyf.py create mode 100644 src/old/bldlib.py create mode 100644 src/old/check_cpu.c create mode 100644 src/sparse_image.c create mode 100644 src/splat.c delete mode 100644 src/testconnectedpixels.py create mode 100644 test/.gitignore create mode 100644 test/demo/.gitignore delete mode 100644 test/demo/bravias.py delete mode 100644 test/demo/latred.py delete mode 100644 test/demo/latred_new.py create mode 100644 test/demo/nbsplat.py create mode 100644 test/demo/qtsplat.py create mode 100644 test/demo/testsplat.py create mode 100644 test/demo/tksplat.py create mode 100644 test/eps_sig/__init__.py create mode 100644 test/eps_sig/test.out create mode 100644 test/eps_sig/test_eps.py create mode 100644 test/gv_general/__init__.py create mode 100644 test/index_demos/.gitignore create mode 100644 test/index_demos/__init__.py create mode 100644 test/index_demos/pairFFT.py delete mode 100644 test/jpa/plotuniq.py delete mode 100644 test/jpa/uniqify_ubi.py create mode 100644 test/ken_simul/__init__.py delete mode 100644 test/ken_simul/idx.py create mode 100644 test/ken_simul/test_all_pairs.py create mode 100644 test/ken_simul/testken.py create mode 100644 test/peaksearchtiftest/.gitignore create mode 100644 test/peaksearchtiftest/__init__.py delete mode 100644 test/peaksearchtiftest/peaks_t5.flt delete mode 100644 test/peaksearchtiftest/peaks_t5.spt delete mode 100644 test/peaksearchtiftest/peaks_t6.flt delete mode 100644 test/peaksearchtiftest/peaks_t6.spt delete mode 100644 test/peaksearchtiftest/peaks_t7.flt delete mode 100644 test/peaksearchtiftest/peaks_t7.spt create mode 100644 test/peaksearchtiftest/scriptedpeaksearch.py delete mode 100644 test/peaksearchtiftest/tiftest0006.tif delete mode 100644 test/peaksearchtiftest/tiftest0007.tif delete mode 100644 test/peaksearchtiftest/tiftest0008.tif delete mode 100644 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test/test_clean_mask.py create mode 100644 test/test_closest_vec.py rename test/{testcolumnfile.py => test_columnfile.py} (79%) create mode 100644 test/test_compress_duplicates.py delete mode 100644 test/test_fft_index.py create mode 100644 test/test_finite_strain.py create mode 100644 test/test_forking.py create mode 100644 test/test_index_unknown/.gitignore create mode 100644 test/test_index_unknown/__init__.py create mode 100644 test/test_index_unknown/lattice_trans.ipynb create mode 100644 test/test_indexing.py create mode 100644 test/test_localmaxlabel.py create mode 100644 test/test_misori.py create mode 100644 test/test_mmo/.gitignore create mode 100644 test/test_mmo/__init__.py delete mode 100644 test/test_mmo/make_test_data.py create mode 100644 test/test_mmo/make_test_data_mmo.py create mode 100644 test/test_rsv/Fe3O4.cif create mode 100644 test/test_rsv/Fe3O4.inp create mode 100644 test/test_rsv/plot_Fe3O4.ipynb create mode 100644 test/test_rsv/run_demo.py create mode 100644 test/test_score_gvec_z.py create mode 100644 test/test_sparse_image.py create mode 100644 test/testcolfile2db/.gitignore create mode 100644 test/testcolfile2db/__init__.py delete mode 100644 test/testcolfile2db/test.db delete mode 100644 test/testcolfile2db/test.flt create mode 100644 test/testconnectedpixels/__init__.py create mode 100644 test/testdatasets/test_datasets.py create mode 100644 test/testlabelimage/.gitignore create mode 100644 test/testlabelimage/__init__.py create mode 100644 test/testlabelimage/runtestlabelimagemerge.py create mode 100644 test/testlattice.py create mode 100644 test/twinprob/__init__.py delete mode 100644 test/twinprob/test.py create mode 100644 test/twinprob/test_twin.py create mode 100644 webgui/data.js create mode 100644 webgui/demo1.html create mode 100644 webgui/demo_canvas.html create mode 100644 webgui/gl-matrix-min.js create mode 100644 webgui/notes_for_new_gui.md create mode 100644 webgui/webgl-utils.js diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 00000000..1d8656db --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,56 @@ +# Python CircleCI 2.0 configuration file +# +# Check https://circleci.com/docs/2.0/language-python/ for more details +# +version: 2 +jobs: + build: + docker: + # specify the version you desire here + # use `-browsers` prefix for selenium tests, e.g. `3.6.1-browsers` + - image: circleci/python:3.8.0 + + # Specify service dependencies here if necessary + # CircleCI maintains a library of pre-built images + # documented at https://circleci.com/docs/2.0/circleci-images/ + # - image: circleci/postgres:9.4 + + working_directory: ~/repo + + steps: + - checkout + + # Download and cache dependencies + - restore_cache: + keys: + # fallback to using the latest cache if no exact match is found + - v1-dependencies- + + - run: + name: install dependencies + command: | + python3 -m venv venv + . venv/bin/activate + python3 -m pip install pip setuptools --upgrade + python3 -m pip install pytest numpy + python3 -m pip install --editable . + + - save_cache: + key: ImageD11-{{arch}}-{{ checksum "setup.py" }} + paths: + - ./venv + + # run tests + # this example uses Django's built-in test-runner + # other common Python testing frameworks include pytest and nose + # https://pytest.org + # https://nose.readthedocs.io + - run: + name: run tests + command: | + . venv/bin/activate + python3 -m pytest + + - store_artifacts: + path: test-reports + destination: test-reports diff --git a/.gitattributes b/.gitattributes index 430a4460..615fcd89 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,4 +1,12 @@ +*.out_merge_t200 linguist-generated +*.out linguist-generated *.flt linguist-generated +*.gve linguist-generated +*.ubi linguist-generated +*.par linguist-generated +*.prm linguist-generated +*.pars linguist-generated *.html linguist-documentation *.pdf linguist-generated *.odt linguist-documentation +*.pks linguist-generated diff --git a/.github/workflows/build_flake_pytest_ubuntu2004.yml b/.github/workflows/build_flake_pytest_ubuntu2004.yml new file mode 100644 index 00000000..94e10ef0 --- /dev/null +++ b/.github/workflows/build_flake_pytest_ubuntu2004.yml @@ -0,0 +1,51 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions + +name: ImageD11_pytest +on: + push: + branches: [ master ] + pull_request: + branches: [ master ] + +jobs: + build: + # because ESRF forces 20.04 for now. + runs-on: ubuntu-20.04 + strategy: + fail-fast: false + matrix: + python-version: ["2.7", "3.8", "3.9", "3.10", "3.11", "3.12"] + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + if: matrix.python-version != '2.7' + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install Ubuntu python 2.7 + if: matrix.python-version == '2.7' + run: | + sudo apt-get update + sudo apt-get install -y --no-install-recommends python2 python2-dev python3-virtualenv + virtualenv -p python2 ${HOME}/cp27 + source ${HOME}/cp27/bin/activate + ${HOME}/cp27/bin/python -m pip install -U pip + ${HOME}/cp27/bin/python -m pip install -U setuptools wheel + echo "${HOME}/cp27/bin" >> $GITHUB_PATH + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + if [ -f requirements.txt ]; then python -m pip install -r requirements.txt; fi + python -m pip install . + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + python -m flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude="*depreciated*" + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + # flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + cd test + python -m pytest diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 00000000..7177bb7c --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,115 @@ +name: Build and deploy + +on: + workflow_dispatch: + release: + types: + - published + +jobs: + build_wheels: + name: Build wheels on ${{ matrix.os }}-${{ matrix.cibw_archs }} + runs-on: ${{ matrix.os }} + strategy: + # Ensure that a wheel builder finishes even if another fails + fail-fast: false + matrix: + include: + - os: ubuntu-20.04 + cibw_archs: "auto64" + #- os: ubuntu-20.04 + # cibw_archs: "auto32" + - os: ubuntu-20.04 + cibw_archs: "aarch64" + - os: ubuntu-20.04 + cibw_archs: "ppc64le" + - os: windows-2019 + cibw_archs: "auto64" + #- os: windows-2019 + # cibw_archs: "auto32" + - os: macos-11 + cibw_archs: "universal2" + + steps: + - uses: actions/checkout@v4 + + - name: Set up QEMU + if: runner.os == 'Linux' + uses: docker/setup-qemu-action@v3 + with: + platforms: all + + - name: Build wheels + uses: pypa/cibuildwheel@v2.16.5 + env: + CIBW_PROJECT_REQUIRES_PYTHON: ">=3.7" + CIBW_BUILD: cp37-* cp38-* cp39-* cp310-* cp311-* cp312-* + # Do not build for pypy, muslinux and python3.12 on ppc64le + CIBW_SKIP: pp* *-musllinux_* cp312-*linux_ppc64le + CIBW_ARCHS: ${{ matrix.cibw_archs }} + + # Use silx wheelhouse: needed for ppc64le + CIBW_ENVIRONMENT_LINUX: "PIP_FIND_LINKS=https://www.silx.org/pub/wheelhouse/ PIP_TRUSTED_HOST=www.silx.org" + + CIBW_TEST_REQUIRES: pytest + CIBW_TEST_COMMAND: pytest {project}/test + # Skip tests for 32bits and emulated architectures, arm64 macos and on Windows + CIBW_TEST_SKIP: "*-*linux_i686 *-*linux_{aarch64,ppc64le,s390x} *-macosx_arm64 *-macosx_universal2:arm64 *-win32 *-win_amd64" + + - uses: actions/upload-artifact@v4 + with: + name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }} + path: ./wheelhouse/*.whl + + build_sdist: + name: Build source distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + cache: 'pip' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build twine + + - name: Build sdist + run: python -m build --sdist + + - name: Check the package + run: | + python -m twine check dist/* + + - name: Install and test sdist + run: | + pip install --pre dist/ImageD11*.tar.gz + pytest test + + - uses: actions/upload-artifact@v4 + with: + name: cibw-sdist + path: dist/*.tar.gz + + pypi-publish: + needs: [build_wheels, build_sdist] + name: Upload release to PyPI + runs-on: ubuntu-latest + environment: + name: pypi + permissions: + id-token: write + if: github.event_name == 'release' && github.event.action == 'published' + # or, alternatively, upload to PyPI on every tag starting with 'v' (remove on: release above to use this) + # if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') + steps: + - uses: actions/download-artifact@v4 + with: + pattern: cibw-* + path: dist + merge-multiple: true + + - uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore index d12d3c9b..414eb347 100644 --- a/.gitignore +++ b/.gitignore @@ -18,6 +18,7 @@ __pycache__/ \#*# # Distribution / packaging +.vscode .Python env/ build/ diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 00000000..1341e7e5 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,18 @@ +language: python # this works for Linux but is an error on macOS or Windows +jobs: + include: + - name: "Python 3.8.0 on Xenial Linux" + python: 3.8 # this works for Linux but is ignored on macOS or Windows + - name: "Python 3.7.4 on macOS" + os: osx + osx_image: xcode11.2 # Python 3.7.4 running on macOS 10.14.4 + language: shell # 'language: python' is an error on Travis CI macOS, could be generic +install: + - python3 -m pip install --upgrade pip setuptools +# fabio could not be installed as a dependency on mac automatically + - python3 -m pip install cython + - python3 -m pip install fabio + - python3 -m pip install --no-use-pep517 --editable . +# 'python3' is a 'command not found' error on Windows but 'py' works on Windows only +script: + - OMP_NUM_THREADS=2 python3 -m pytest diff --git a/ImageD11/ImageD11_file_series.py b/ImageD11/ImageD11_file_series.py index 1ec62a11..dae6e3e8 100644 --- a/ImageD11/ImageD11_file_series.py +++ b/ImageD11/ImageD11_file_series.py @@ -8,53 +8,57 @@ import fabio.file_series import fabio.fabioimage import fabio.openimage -import numpy +import numpy, h5py +import gzip, bz2 +from ImageD11 import ImageD11options def get_options(parser): - parser.add_option("-5","--hdf5",action="store", type="string", + parser.add_argument("-5","--hdf5",action="store", type=str, dest = "hdf5", default = None, help = "hdf file containing input image series") # or, eventually: # stem, first, last, format, (omegas better be in the headers) - parser.add_option("-n","--stem",action="store", type="string", + parser.add_argument("-n","--stem",action="store", type=str, dest = "stem", default = None, help = "stem name for input image series") - parser.add_option("-f","--first",action="store", type="int", + parser.add_argument("-f","--first",action="store", type=int, dest = "first", default = None, help = "first number for input image series") - parser.add_option("-l","--last",action="store", type="int", + parser.add_argument("-l","--last",action="store", type=int, dest = "last", default = None, help = "last number for input image series") - parser.add_option("--ndigits", action="store", type="int", + parser.add_argument("--ndigits", action="store", type=int, dest = "ndigits", default = 4, help = "Number of digits in file numbering [4]") - parser.add_option("-P", "--padding", action="store", - type="choice", choices=["Y","N"], + parser.add_argument("-P", "--padding", action="store", + choices=["Y","N"], default="Y", dest="padding", help="Is the image number to padded Y|N, e.g. "\ "should 1 be 0001 or just 1 in image name, default=Y") - parser.add_option("-F","--format",action="store", type="string", + parser.add_argument("-F","--format",action="store", type=str, dest = "format", default = ".edf", help = "format [.edf] for input image series") - parser.add_option("-O", "--flood", action="store", type="string", + parser.add_argument("-O", "--flood", action="store", + type=ImageD11options.ImageFileType(mode='r'), dest = "flood", default = None, help = "Flood") - parser.add_option("-d", "--dark", action="store", type="string", + parser.add_argument("-d", "--dark", action="store", dest = "dark", default = None, + type=ImageD11options.ImageFileType(mode='r'), help = "Dark image") - parser.add_option("-S", "--step", action="store", type="float", + parser.add_argument("-S", "--step", action="store", type=float, dest = "OMEGASTEP", default = None, help = "omega step size") - parser.add_option("-T", "--start", action="store", type="float", + parser.add_argument("-T", "--start", action="store", type=float, dest = "OMEGA", default = None, help = "start omega") - parser.add_option("--omega_motor", action="store", type="string", + parser.add_argument("--omega_motor", action="store", type=str, dest = "omegamotor", default = "Omega", help = "Header value to use for rotation motor position [Omega]") - parser.add_option("--omega_motor_step", action="store", type="string", + parser.add_argument("--omega_motor_step", action="store", type=str, dest = "omegamotorstep", default = "OmegaStep", help = "Header value to use for rotation width [OmegaStep]") @@ -69,7 +73,7 @@ def get_series_from_hdf( hdf_file, dark = None, flood = None ): im = hdf_file[group][image] om = float(im.attrs['Omega']) data = im[:,:] - if (dark, flood) is not (None, None): + if (dark, flood) != (None, None): data = data.astype(numpy.float32) if dark is not None: numpy.subtract( data, dark, data ) @@ -80,14 +84,13 @@ def get_series_from_hdf( hdf_file, dark = None, flood = None ): 'Omega': om } ) def series_from_fabioseries( fabioseries, dark, flood, options ): - for filename in fabioseries: try: fim = fabio.openimage.openimage(filename) except: print("Missing image",filename) continue - if (dark, flood) is not (None, None): + if (dark, flood) != (None, None): fim.data = fim.data.astype(numpy.float32) if dark is not None: numpy.subtract( fim.data, dark, fim.data ) @@ -158,7 +161,7 @@ def get_series_from_options( options, args ): if len(args) > 0 : # We assume unlabelled arguments are filenames fso = fabio.file_series.file_series(args) - return series_from_fabioseries( fso, dark, flood ) + return series_from_fabioseries( fso, dark, flood, options ) if options.hdf5 is not None: hf = h5py.File(options.hdf5) @@ -169,3 +172,59 @@ def get_series_from_options( options, args ): dark, flood) + +def getedfheader(filename): + """ + Reads a header from an edf file in 1024 byte chunks. + Assumes enclosing { } + Returns string enclosed + Adds a filename key at the top + """ + h = "filename = " + if filename[-3:]==".gz": + fp=gzip.GzipFile(filename,"rb") + elif filename [-4:]==".bz2": + fp=bz2.BZ2File(filename,"rb") + else: + try: + fp=open(filename,"rb") + except IOError: + return "" + h=h+filename+";\n" + s=fp.read(1024) + if s.find("{")==-1: + raise Exception("Not an edf file") + while 1: + if s.find("}")>=0: + h=h+s[0:s.find("}")+2] + break + else: + h=h+s + s=fp.read(1024) + return h + +def motor_mne(hd): + """ + expands the _mne and _pos header items of edf headers + """ + h = {} + order = [] + for line in hd.split(";"): + try: + key,vals = line.split("=") + except ValueError: + continue + key = key.lstrip().rstrip() + h[key] = vals.split(";")[0] + order.append( key ) + for k in order: + if k.endswith("_mne"): + stem = k.split("_")[0] + p = k.replace("_mne","_pos") + newkeys = h[k].split() + newvals = h[p].split() + for ik, iv in zip(newkeys, newvals): + kk = stem+":"+ik + h[kk]=iv + order.append( kk ) + return h, order diff --git a/ImageD11/ImageD11_thread.py b/ImageD11/ImageD11_thread.py index 8faee24f..a0482f10 100644 --- a/ImageD11/ImageD11_thread.py +++ b/ImageD11/ImageD11_thread.py @@ -26,7 +26,7 @@ import queue as Queue import threading -global stop_now +# global stop_now stop_now = False class ImageD11_thread(threading.Thread): diff --git a/ImageD11/ImageD11options.py b/ImageD11/ImageD11options.py new file mode 100644 index 00000000..261cda66 --- /dev/null +++ b/ImageD11/ImageD11options.py @@ -0,0 +1,49 @@ + +import os, logging + +# Try to manage the options coming and going to the different scripts +# +# Load / save / log command lines to reproduce options set when driving scripts +# Offer gui helpers to launch programs +# +# Previously we had "optparse", this becomes "argparse" for later pythons +# +# The idea is to use something like gooey or argparseui which hook to +# the parsers _actions list to see what can be offered. +# +# We define a few "types" corresponding to ImageD11 known filetypes to +# help when identifying what an optiong is so we could hook up plotting +# and editing to the different entities + +class FileType(object): + def __init__(self, mode='r'): + assert mode in 'rw' + self._mode = mode + def __call__(self, string): + if 'r' in self._mode: + if not os.path.exists( string ): + logging.error("File %s not found", string) + return string + +class ParameterFileType(FileType): + pass + +class ColumnFileType(FileType): + pass + +class UbiFileType(FileType): + pass + +class ImageFileType(FileType): + pass + +class SplineFileType(FileType): + pass + +class GvectorFileType(FileType): + pass + +class HdfFileType(FileType): + pass + + diff --git a/ImageD11/__init__.py b/ImageD11/__init__.py index f0ca9814..1c53836b 100644 --- a/ImageD11/__init__.py +++ b/ImageD11/__init__.py @@ -1,7 +1,7 @@ """ ImageD11 - image analysis software for beamline ID11 at the ESRF -Copyright (C) 2005-2018 Jon Wright +Copyright (C) 2005-2019 Jon Wright This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -18,6 +18,7 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA """ -__version__ = "1.9.0" +__version__ = "2.0.4" __author__ = 'Jon Wright', __author_email__ = 'wright@esrf.fr' + diff --git a/ImageD11/blobcorrector.py b/ImageD11/blobcorrector.py index 65ed254d..1c21575c 100644 --- a/ImageD11/blobcorrector.py +++ b/ImageD11/blobcorrector.py @@ -1,10 +1,8 @@ -## Automatically adapted for numpy.oldnumeric Sep 06, 2007 by alter_code1.py - from __future__ import print_function -# ImageD11_v0.4 Software for beamline ID11 -# Copyright (C) 2005 Jon Wright +# ImageD11 Software for beamline ID11 +# Copyright (C) 2021 Jon Wright # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -27,8 +25,11 @@ To think about doing - valid regions? What if someone uses a 1K spline file for a 2K image etc? + +2021 : added LUT method for eiger """ import logging, numpy, math +import fabio from scipy.interpolate import bisplev def readfit2dfloats(filep, nfl): @@ -50,7 +51,6 @@ def readfit2dfloats(filep, nfl): i = i + 14 if j == nfl: break - i = i + 1 return ret class correctorclass: #IGNORE:R0902 @@ -73,7 +73,7 @@ def __init__(self, argsplinefile, orientation="edf"): self.tck2 = None if self.splinefile is not None: self.readfit2dspline(self.splinefile) - + self.dim = ( int(self.xmax-self.xmin), int(self.ymax-self.ymin)) # detector dimensions read from splinefile def correct(self, xin, yin): @@ -92,9 +92,9 @@ def correct(self, xin, yin): # Unreachable code - we no longer accept this complexity # it means the spline file for ImageD11 bruker images # is not the same as for fit2d. - xpos = self.xmax - xin - xcor = xin - bisplev(yin, xpos, self.tck2) - ycor = yin + bisplev(yin, xpos, self.tck1) + # xpos = self.xmax - xin + # xcor = xin - bisplev(yin, xpos, self.tck2) + # ycor = yin + bisplev(yin, xpos, self.tck1) return xcor, ycor def make_pixel_lut(self, dims): @@ -138,6 +138,33 @@ def make_pos_lut(self, dims): self.pixel_lut[1] * self.ysize ) return self.pos_lut + + def correct_px_lut(self, pks): + """Transform x,y in raw image coordinates into x,y of an + idealised image using a LUT. Faster than standard correct method for large peakfiles. + pks : ImageD11 columnfile with raw coordinates s_raw, f_raw add new colulmns sc, fc with corrected coordinates""" + + assert self.dim is not None + assert 's_raw' in pks.titles and 'f_raw' in pks.titles + + # make pixel_lut + substract xy grid coordinate (i,j) to keep only dx and dy arrays. + self.make_pixel_lut(self.dim) + i,j = numpy.mgrid[ 0:self.dim[0], 0:self.dim[1] ] + dx = self.pixel_lut[0] - i + dy = self.pixel_lut[1] - j + + # get integer pixel index (si,fi) of each peak + si = numpy.round(pks['s_raw']).astype(int) + fi = numpy.round(pks['f_raw']).astype(int) + + # apply dx dy correction on s_raw / f_raw + sc = (dx[ si, fi ] + pks.s_raw).astype(numpy.float32) + fc = (dy[ si, fi ] + pks.f_raw).astype(numpy.float32) + # add corrected arrays as new columns + pks.addcolumn(sc, 'sc') + pks.addcolumn(fc, 'fc') + + def distort(self, xin, yin): """ Distort a pair of points xnew, ynew to find where they @@ -197,8 +224,8 @@ def readfit2dspline(self, name): if myline[:7] != "SPATIAL": raise SyntaxError(name + \ ": file does not seem to be a fit2d spline file") - myline = fin.readline() # BLANK LINE - myline = fin.readline() # VALID REGION + fin.readline() # BLANK LINE + fin.readline() # VALID REGION myline = fin.readline() # the actual valid region, # assuming xmin,ymin,xmax,ymax logging.debug("xmin,ymin,xmax,ymax, read: "+myline) @@ -210,8 +237,8 @@ def readfit2dspline(self, name): logging.debug("gridspace, xsize, ysize: "+myline) self.gridspacing, self.xsize, self.ysize = \ [float(z) for z in myline.split()] - myline = fin.readline() # BLANK - myline = fin.readline() # X-DISTORTION + fin.readline() # BLANK + fin.readline() # X-DISTORTION myline = fin.readline() # two integers nx1,ny1 logging.debug("nx1, ny1 read: "+myline) nx1, ny1 = [int(z) for z in myline.split()] @@ -220,8 +247,8 @@ def readfit2dspline(self, name): ty1 = numpy.array(readfit2dfloats(fin, ny1), numpy.float32) cf1 = numpy.array(readfit2dfloats(fin, (nx1 - 4) * (ny1 - 4)), numpy.float32) - myline = fin.readline() #BLANK - myline = fin.readline() # Y-DISTORTION + fin.readline() #BLANK + fin.readline() # Y-DISTORTION myline = fin.readline() # two integers nx2, ny2 nx2 , ny2 = [int(z) for z in myline.split()] tx2 = numpy.array(readfit2dfloats(fin, nx2), numpy.float32) @@ -270,6 +297,28 @@ def make_pixel_lut(self, dims): +class eiger_spatial(object): + + def __init__(self, + dxfile="/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dx.edf", + dyfile="/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dy.edf",): + self.dx = fabio.open(dxfile).data # x == fast direction at ID11 + self.dy = fabio.open(dyfile).data # y == slow direction + assert self.dx.shape == self.dy.shape + + def __call__(self, pks): + si = numpy.round(pks['s_raw']).astype(int) + fi = numpy.round(pks['f_raw']).astype(int) + pks['fc'] = self.dx[ si, fi ] + pks['f_raw'] + pks['sc'] = self.dy[ si, fi ] + pks['s_raw'] + return pks + + def pixel_lut(self): + """ returns (slow, fast) pixel postions of an image """ + s = self.dx.shape + i, j = numpy.mgrid[ 0:s[0], 0:s[1] ] + return self.dy + j, self.dx + i + # #""" #http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OWENS/LECT5/node5.html @@ -316,4 +365,3 @@ def make_pixel_lut(self, dims): # Hence, for now, # """ # pass - diff --git a/ImageD11/cImageD11.py b/ImageD11/cImageD11.py new file mode 100644 index 00000000..54820b3b --- /dev/null +++ b/ImageD11/cImageD11.py @@ -0,0 +1,161 @@ +# import cImageD11 compiled module and patch the docstrings + +import os +import struct +import warnings +from ImageD11 import cImageD11_docstrings + +try: + from ImageD11._cImageD11 import * + import ImageD11._cImageD11 +except ImportError: + print("Failed to import compiled C code for cImageD11 module") + print("Are you running from a source directory?") + print("Try something like:") + print(" python -m pip install --editable .") + print("or:") + print(" python setup.py develop") + print("or:") + print(" python setup.py build_ext --inplace") + raise + +# Check for the use of openmp interactions with os.fork and multiprocessing + + +def check_multiprocessing(patch=False): + """ + I tried to suppress the "fork" method for multiprocessing, but I could not. + + You cannot safely use os.fork together with threads. + But the cImageD11 codes uses threads via openmp, and you are importing them. + So please use forkserver or spawn for multiprocessing. + + https://discuss.python.org/t/concerns-regarding-deprecation-of-fork-with-alive-threads/33555 + https://github.com/FABLE-3DXRD/ImageD11/issues/177 + + > Developers should respond by adjusting their use of multiprocessing or concurrent.futures + > to explicitly specify either the "forkserver" or "spawn" start methods via a context. + """ + # We only do this if os.fork exists + if not hasattr(os, "fork"): + return + import multiprocessing + + # + # now check for the fork issue + if not hasattr(multiprocessing, "get_start_method"): + # You are on python2.7. Give up? + warnings.warn( + "python2.7 with ImageD11.cImageD11: for multiprocessing use spawn\n" + ) + return + # + # This has a side effect of fixing the start method if allow_none was not True + method = multiprocessing.get_start_method(allow_none=True) + if method == "fork": + warnings.warn(check_multiprocessing.__doc__) + # Problem cases are: + # child processes -> oversubscribe -> we will set num threads to 1 + # your numpy (etc) are also going to be a problem if this was needed + parent = None + if hasattr(multiprocessing, "parent_process"): + parent = multiprocessing.parent_process() + # only for python 3.8 and up + if parent is not None: + if "OMP_NUM_THREADS" not in os.environ: + # people wanting Nprocs * Mthreads may need to reset after import + # or use OMP_NUM_THREADS as usual + cimaged11_omp_set_num_threads(1) + if method in ("fork", None): + # warn if we are a child process + warnings.warn(check_multiprocessing.__doc__) + if method is None and patch: + # we should change it away from fork + # this is going to break code which relied on fork + # needs one of those "warn people for a year or two and then break it" + # messages. Perhaps wait on python3.14 when multiprocessing will get + # fixed and break everyones code at that time? + poss = multiprocessing.get_all_start_methods() + if "forkserver" in poss: + multiprocessing.set_start_method("forkserver") + elif "spawn" in poss: + multiprocessing.set_start_method("spawn") + else: + raise Exception("Could not set to forkserver or spawn") + # It is doubtful that this function can ever please everyone. + + +def cores_available(): + """ + Return the number of CPU cores you can use + First choice = SLURM_CPUS_PER_TASK (ESRF specific) + Second choice = os.sched_getaffinity + Third choice = os.cpu_count (may be high) + Guess at 1 when os.cpu_count fails. + """ + if "SLURM_CPUS_PER_TASK" in os.environ: + return int(os.environ["SLURM_CPUS_PER_TASK"]) + if hasattr(os, "sched_getaffinity"): + return len(os.sched_getaffinity(os.getpid())) + ncpu = os.cpu_count() + if ncpu is None: + return 1 + else: + return ncpu + + +if cimaged11_omp_get_max_threads() == 0: + # The code was compiled without openmp + OPENMP = False +else: + # Openmp threading can be used + OPENMP = True + check_multiprocessing() + + +# For 32 or 64 bits +nbyte = struct.calcsize("P") # 4 or 8 + +if nbyte == 8: + + def put_incr(*a, **k): + """redirects to put_incr64""" + return put_incr64(*a, **k) + + +if nbyte == 4: + + def put_incr(*a, **k): + """redirects to put_incr32""" + return put_incr32(*a, **k) + + +# Add / fix the docstrings + + +def fix_doc(oldstring, to_be_added): + """Adds a description of the function to the f2py string""" + if oldstring.find("Wrapper for"): + try: + head, tail = oldstring.split("Wrapper for") + except: + print(oldstring) + print(to_be_added) + raise + iname = tail.find("\n") + return head + tail[:iname] + " " + to_be_added + tail[iname:] + else: + return oldstring + to_be_added + + +def fill_in_docstrings(): + for name in cImageD11_docstrings.__all__: + doc = getattr(cImageD11_docstrings, name) + if name in globals(): + func = globals()[name] + func.__doc__ = fix_doc(func.__doc__, doc) + + +fill_in_docstrings() + +assert verify_rounding(20) == 0, "Problem with cImageD11 fast rounding code" diff --git a/ImageD11/cImageD11_docstrings.py b/ImageD11/cImageD11_docstrings.py new file mode 100644 index 00000000..cb0b0d5d --- /dev/null +++ b/ImageD11/cImageD11_docstrings.py @@ -0,0 +1,346 @@ + +"""Autogenerated from make_pyf.py Edit in _cImageD11.pyf please""" +array_histogram = """computes the histogram for an image +Go through the data to compute a histogram of the values + Previous call of array_stats would help to set up this call + compare to np.bincount - this does not gain much + better implementations can be done +img[] Input data array (1D or 2D.ravel(), so sparse or dense) +npx length of data array (contiguous) +low Lower edge of first bin +high Upper edge of last bin +hist[] Histogram to be output +nhist Number of bins in the histogram +""" +array_mean_var_cut = """computes the mean and variance of an image +with pixels above the value mean+cut*stddev removed. This is iterated +n times as the mean and variance change as pixels are removed. +""" +array_mean_var_msk = """computes the mean and variance of an image +with pixels above the value mean+cut*stddev removed. This is iterated +n times as the mean and variance change as pixels are removed. +""" +array_stats = """computes statistics for an image. + img Input data array (1D or 2D.ravel(), so sparse or dense) +npx length of data array (contiguous) +*minval minimum of the pixels +*maxval maximum of the pixels +s1 Sum of all pixel +s2 Sum of pixel^2 +""" +bgcalc = """computes a background on a 1d signal where gain +and sp and st are defined by: + diff = difference to neighbors or bg estimate + sigmap = weight for abs background value + sigmat = constant weight + gain for b += diff * gain +img - source data +bg - computed background +msk - mask +""" +blob_moments = """fills in the reduced moments in results array. +... FIXME - this would be clearer in python, fast anyway. +""" +bloboverlaps = """determines the overlaps between labels1 and labels2 +for an image series. Peaks in labels2 may be merged if they were +joined by a peak on labels1. Results in results1 are accumulated +into results2 if peaks are overlapped. +""" +blobproperties = """fills the array results with properties of each labelled +object described by data (pixel values) and labels. The omega value +is the angle for this frame. +results are FIXME +""" +checks = """the round to nearest int code is correct +""" +cimaged11_omp_get_max_threads = """reads the openmp max number of threads +see docs/sphx/parallel.rst +""" +cimaged11_omp_set_num_threads = """Sets the openmp number of +threads to use. +Change if you use multiprocessing or do not like +os.environ['OMP_NUM_THREADS'] +see also: multiprocessing.cpu_count(), +see also os.cpu_count() +see docs/sphx/parallel.rst +""" +clean_mask = """removes pixels which are not 4 connected from msk +while copying into ret. +""" +closest = """finds the value and index in x closest to a value in v. +e.g. x = cosine of angles between pairs of peaks + v = idealised values based on hkl geometry + ibest set to the x[i] matching to a v[j] with diff "best" +""" +closest_vec = """finds the closest neighbors for each row of X +ignoring the self. Treated as a X=[v1,v2,v3,...], computes +sum{(vi-vj)**2} for all i!=j and places minimum in ic. +""" +cluster1d = """is used in sandbox/friedel.py to find clusters of peaks +work in progress +""" +compress_duplicates = """removes duplicate i,j labels. On entry then +i and j are set as the labels from two images. They are sorted +and on exit i,j hold the unique pairs and oi holds the count +for the number of overlaps. oj and tmp are temporaries. +""" +compute_gv = """computes scattering vectors given thr positions of the spot +in the laboratory in xlylzl[npks], the omega rotation[npks], and +the rest of the parameters (wedge,wvln,chi,t[3] and omegasign) +""" +computes_xlylzl = """finds spot positions in the laboratory frame +using packed parameters that are more general +s = slow pixel position +f = fast pixel position +p = [s_cen, f_cen, s_size, f_size] +r[9] = dot( transform.detector_rotation_matrix, flipmatrix ) +... with flipmatrix = [1,0,0], [0,o22,o21], [0,o12,o11] +dist = [distancex, distancey, distancez] is 3D (beyond old model) +... see test/test_cImageD11.py +""" +connectedpixels = """Determines which pixels in data are above the +user supplied threshold and assigns them into connected objects +which are output in labels. Connectivity is 3x3 box (8) by default +and reduces to a +(4) is con8==0 +""" +count_shared = """takes two sorted arrays in pi and pj and counts +how many collisions there are. Useful to compare two lists of +peak to grain assignments, or pixel to peak assignments, etc +""" +frelon_lines = """Subtracts the average value of (pixels < cut) per row +""" +frelon_lines_sub = """Subtracts drk from img and then same as frelon_lines +""" +localmaxlabel = """assigns a label for each pixel so they are grouped +to the local maximum. Equal values choose to assign towards the earlier +value in memory. +cpu arg (1)0=C, (1)1=SSE2, (1)2=AVX2; if > 9 prints timing +""" +make_clean_mask = """is a lot like clean msk but it generates +the msk using img and cut. +Beware: work in progress +""" +mask_to_coo = """takes a mask and converts it to a list +of i,j coordinates in a sparse array coo format +returns : + 0 => success + 1 => ns out of range ; 2 => nf out of range + 3 => nnz < 1 empty mask + 4 => nnz did not match this mask +""" +misori_cubic = """computes the trace of the smallest misorientation + for cubic symmetry + u1 and u2 are both orientation matrices "U" + compute u1. u2.T to get the rotation from one to the other + find the permutation that will maximise the trace + one of six... + xyz yxz zxy + xzy yzx zyx +Beware : work in progress. Which point group is this? +""" +misori_cubic_pairs = """Computes the dissimilarity matrix to use for clustering +of orientations. Fills in an upper triangular matrix that you can try +to pass to scipy.cluster routines +""" +misori_monoclinic = """assumes a unique b axis and only checks +the flip of b -> -b +Not sure about the point group here. Is is 2/m ?? + Beware: work in progress +""" +misori_orthorhombic = """computes the trace of the smallest misorientation + u1 and u2 are both orientation matrices "U" + compute u1. u2.T to get the rotation from one to the other + find the flips that will maximise the trace: + abs( trace(dot(u1,u2.T) )) + Looks like point group mmm. Not sure why this is in C? + Beware: work in progress +""" +misori_tetragonal = """computes the trace of the smallest misorientation + u1 and u2 are both orientation matrices "U" + compute u1. u2.T to get the rotation from one to the other + finds the flips a/b and c->-c that will maximise the trace: + abs( trace(dot(u1,u2.T) )) + Looks like point group 4/mmm. What about 4/m ? + Beware: work in progress +""" +overlaps = """determines which of (row1,col1,labels1) and (row2,col2,labels2) +are overlapped. + +""" +put_incr32 = """does the simple loop : data[ind] += vals +not sure why this isn't in numpy +uses 32 bit addressing +""" +put_incr64 = """does the simple loop : data[ind] += vals +not sure why this isn't in numpy +uses 64 bit addressing +""" +quickorient = """takes two g-vectors in UBI[0] and UBI[1] +and overwrites with UBI orientation using cache in bt (from h1,h2) +... computes cross product 0x1 = ubi[0]xubi[1] +... normalises u0=ubi0 and u2=0x1 and computes u1=u0xu2 +... returns in UBI BT.(u1,u2,u3) +algorithm was due to Busing and Levy +""" +refine_assigned = """fits a ubi matrix to a set of g-vectors and assignments +in labels. e.g. where(labels==label) it uses the peaks. + ... perhaps this is not what you want for overlapping peaks in twins! +""" +reorder_f32_a32 = """called in sandbox/fazit.py simple +loop with openmp saying out[adr[i]] in data[i] +e.g. semi-random writing +""" +reorder_u16_a32 = """called in sandbox/fazit.py simple +loop with openmp saying out[adr[i]] in data[i] +e.g. semi-random writing +""" +reorderlut_f32_a32 = """lut called in sandbox/fazit.py simple +loop with openmp saying out[i] in data[adr[i]] +e.g. semi-random reading +""" +reorderlut_u16_a32_a16 = """called in sandbox/fazit.py +data - source data read in order +adr0 - output position for the first pixel in each row +adr1 - difference offset to output next pixel in each row +""" +reorderlut_u16_a32lut = """called in sandbox/fazit.py simple +loop with openmp saying out[i] in data[adr[i]] +e.g. semi-random reading +""" +score = """takes a ubi matrix and list of g-vectors and computes +hkl = dot(ubi, gv), then rounds these g-vectors to integer +and computes drlv2 = (h-int(h))**2 + (k-int(k))**2 + (l-int(l))**2 +If drlv2 is less than tol*tol then the peak is considered to +be indexed. Returns the number of peaks found. +""" +score_and_assign = """is similar to score but it assigns peaks to this +ubi only if they fit the data better than the current UBI. +It updates drlv2 and labels to use best fitting grain for each peak. + ... perhaps this is not what you want for overlapping peaks in twins! +""" +score_and_refine = """is very similar to score but it also refines the UB +matrix using the assigned peaks and overwrite the argument. +It returns the number of peaks and fit prior to refinement. +""" +score_gvec_z = """reads ubi, ub, gv and recompute +if (recompute) it fills directions to project errors per peak: + g0 = gv / |gv| = unit vector along g + g1 = gxy / |gxy| = unit vector perpendicular to z and g (omega) + g2 ... ought to be cross( g0, g1 ) ? (eta) +For all peaks it computes h = ubi.g, rounds to nearest ih = int(h) +and then computes gcalc = ub.ih = dot( ub, ( round( dot( ubi, g) ) ) ) +The error gv - gcalc is then projected into the co-ordinate system +g0,g1,g2 for errors along g, z and the rhs +Beware : work in progress. Is z always the right axis? +""" +sparse_blob2Dproperties = """fills the array results with properties of +each labelled object described by v and labels (pixel values and blob) +and positions i,j in the image. +results are: + results[ipk,s2D_1] = sum(1), number of pixels + results[ipk,s2D_I] = sum (I), total intensity + results[ipk,s2D_fI] = sum (f*I), intensity weighted fast index + results[ipk,s2D_sI] = sum (s*I), intensity weighted slow index + results[ipk,s2D_ffI] = sum (f*f*I), intensity weighted fast^2 index + results[ipk,s2D_sfI] = sum (s*f*I), intensity weighted slow*fast index + results[ipk,s2D_ssI] = sum (s*s*I), intensity weighted slow^2 index +""" +sparse_connectedpixels = """runs the connectedpixels algorithm on +a sparse image using a supplied threshold putting labels +into labels array and returning the number of blobs found +""" +sparse_connectedpixels_splat = """is for debugging/timing. It splats +the sparse array into a dense array and runs the old connectedpixels +code on that. +""" +sparse_is_sorted = """checks whether the indices in i and j of a sparse +coo format come in the order that they would appear inside an image +* @param i, j index arrays +* @param nnz dimension of i, j + returns 0 for all OK + k for first non-sorted element + -k for first duplicate +""" +sparse_localmaxlabel = """assigns labels to sparse array in sorted coo format +supplied in (v,(i,j)). MV and iMV are temporaries. +single threaded +""" +sparse_overlaps = """identifies the pixels in i1,j1 which overlap i2,j2. +The list of overlaps is returned in k1/k2 such that i1[k1]==i2[k2] +and j1[k1]==j2[k2]. Probably assumes that sparse_is_sorted was true. +""" +sparse_smooth = """smooths data in coo format. Workaround for avoiding +equal pixels on peak tails for localmaxlabel +single threaded +""" +splat = """draws gvectors into an rgba image. The horror of maintaining plot3d +over the years motivated this code. See test/demo/tksplat +* set the color and markersize per peak +* perhaps also a draw order (back to front, top to bottom) ? +""" +tosparse_u16 = """stores pixels from img into row/col/val. +msk determines whether pixels are masked (e.g. eiger mask) +returns the number of pixels found +""" +uint16_to_float_darkflm = """subtracts image drk(float32) from +raw data in data (uint16), multiples by flm(float32) and returns in img. +""" +uint16_to_float_darksub = """subtracts image drk(float32) from +raw data in data (uint16) and returns in img. +""" +__all__ = [ + "array_histogram", + "array_mean_var_cut", + "array_mean_var_msk", + "array_stats", + "bgcalc", + "blob_moments", + "bloboverlaps", + "blobproperties", + "checks", + "cimaged11_omp_get_max_threads", + "cimaged11_omp_set_num_threads", + "clean_mask", + "closest", + "closest_vec", + "cluster1d", + "compress_duplicates", + "compute_gv", + "computes_xlylzl", + "connectedpixels", + "count_shared", + "frelon_lines", + "frelon_lines_sub", + "localmaxlabel", + "make_clean_mask", + "mask_to_coo", + "misori_cubic", + "misori_cubic_pairs", + "misori_monoclinic", + "misori_orthorhombic", + "misori_tetragonal", + "overlaps", + "put_incr32", + "put_incr64", + "quickorient", + "refine_assigned", + "reorder_f32_a32", + "reorder_u16_a32", + "reorderlut_f32_a32", + "reorderlut_u16_a32_a16", + "reorderlut_u16_a32lut", + "score", + "score_and_assign", + "score_and_refine", + "score_gvec_z", + "sparse_blob2Dproperties", + "sparse_connectedpixels", + "sparse_connectedpixels_splat", + "sparse_is_sorted", + "sparse_localmaxlabel", + "sparse_overlaps", + "sparse_smooth", + "splat", + "tosparse_u16", + "uint16_to_float_darkflm", + "uint16_to_float_darksub"] \ No newline at end of file diff --git a/ImageD11/columnfile.py b/ImageD11/columnfile.py index 94ceabf6..9e698a2c 100644 --- a/ImageD11/columnfile.py +++ b/ImageD11/columnfile.py @@ -28,13 +28,14 @@ # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 0211-1307 USA +import warnings from ImageD11 import parameters, transform import numpy as np FLOATS = [ "fc", - "sc", + "sc", "omega", "f_raw", "s_raw", @@ -77,13 +78,14 @@ "Grain", "grainno", "grain_id", - "IKEY", + "IKEY", + "npk2d", ] # 9 elements ij = ["%d%d"%(i,j) for i in range(1,4) for j in range(1,4)] # Uij, UBIij -LONGFLOATS = [s+v for v in ij for s in ["U","UBI"]] +LONGFLOATS = [s+v for v in ij for s in ["U","UBI"]] @@ -98,9 +100,9 @@ ('sig','_s') ] ] # 'e' ij1 'e' ij2 '_s' for triangle ij -EXPONENTIALS += ["%s%d%s%d%s"%(h,ijs[i],h,ijs[j],t) - for i in range(6) - for j in range(i,6) +EXPONENTIALS += ["%s%d%s%d%s"%(h,ijs[i],h,ijs[j],t) + for i in range(6) + for j in range(i,6) for h,t in [ ('e',''),('e','_s'),('s',''),('s','_s')] ] # testing for line compression @@ -110,32 +112,38 @@ #assert set(olde) == set(EXPONENTIALS) #print "These seem to match" - + FORMATS = {} # Make a dictionary for formatstrings when writing files -for f in FLOATS: - FORMATS[f] = "%.4f" -for f in LONGFLOATS: - FORMATS[f] = "%.12f" +for f in FLOATS: + FORMATS[f] = "%.4f" +for f in LONGFLOATS: + FORMATS[f] = "%.12f" for f in INTS: FORMATS[f] = "%.0f" for f in EXPONENTIALS: FORMATS[f] = "%.4e" -def clean(str_lst): +def clean(str_lst): """ trim whitespace from titles """ - return [s.lstrip().rstrip() for s in str_lst] + return [s.lstrip().rstrip() for s in str_lst] + -class columnfile: +def fillcols(lines, cols): + for i,line in enumerate(lines): + for j,item in enumerate(line.split()): + cols[j][i] = float(item) + +class columnfile(object): """ Class to represent an ascii file containing multiple named columns """ - + def __init__(self, filename = None, new = False): self.filename = filename - self.bigarray = None + self.__data = [] self.titles = [] if filename is not None: self.parameters = parameters.parameters(filename=filename) @@ -146,6 +154,55 @@ def __init__(self, filename = None, new = False): if not new: self.readfile(filename) + def get_bigarray(self): + # if someone uses this we have to go back to the old + # representation + if not hasattr(self,"__bigarray") or len(self.__data) != len(self.__bigarray): + self.__bigarray = np.asarray( self.__data ) + self.__data = self.__bigarray + return self.__bigarray + + def set_bigarray(self, ar): +# print("setting bigarray",len(ar),len(ar[0])) +# warnings.filter("once") +# warnings.warn("Setting bigarray on colfile", stacklevel=2) + assert len(ar) == len(self.titles), \ + "Wrong length %d to set bigarray"%(len(ar))+\ + " ".join(self.titles) + nrows = len(ar[0]) + for col in ar: + assert len(col) == nrows, "ar is not rectangular" + self.nrows = nrows + # use a list of arrays + self.__bigarray = ar + self.__data = self.__bigarray + self.set_attributes() + + bigarray = property(fget=get_bigarray, fset=set_bigarray) + + def set_attributes(self): + """ + Set object vars to point into the big array + """ + if self.nrows == 0: + # use empty arrays for now... + # not sure why this was avoided in the past? + pass + #return + for i, name in enumerate(self.titles): + setattr(self, name, self.__data[i]) + a = getattr(self, name) + assert len(a) == self.nrows, "%s %d %d"%(name,len(a),self.nrows) + + def __getitem__(self, key): + if key in self.titles: + return self.getcolumn( key ) + else: + raise KeyError + + def keys(self): + return self.titles + def removerows( self, column_name, values, tol = 0 ): """ removes rows where self.column_name == values @@ -155,16 +212,15 @@ def removerows( self, column_name, values, tol = 0 ): """ col = self.getcolumn( column_name ) if tol <= 0: # integer comparisons - col = col.astype( np.int ) + col = col.astype( int ) mskfun = lambda x, val, t: x == val else: # floating point - mskfun = lambda x, val, t: np.abs( x - val ) < t + mskfun = lambda x, val, t: np.abs( x - val ) < t mask = mskfun( col, values[0], tol ) for val in values[1:]: np.logical_or( mskfun( col, val, tol ), mask, mask) self.filter( ~mask ) - def sortby( self, name ): """ Sort arrays according to column named "name" @@ -172,21 +228,22 @@ def sortby( self, name ): col = self.getcolumn( name ) order = np.argsort( col ) self.reorder( order ) - + def reorder( self, indices ): """ Put array into the order given by indices ... normally indices would come from np.argsort of something """ - self.bigarray = self.bigarray[:, indices] + for col in self.__data: + col[:] = col[indices] self.set_attributes() - + def writefile(self, filename): """ write an ascii columned file """ self.chkarray() - fout = open(filename,"w+") # appending + fout = open(filename,"w") # appending # Write as "# name = value\n" parnames = list(self.parameters.get_parameters().keys()) parnames.sort() @@ -205,7 +262,7 @@ def writefile(self, filename): fout.write("\n") format_str += "\n" for i in range(self.nrows): - fout.write(format_str % tuple( self.bigarray[:, i]) ) + fout.write(format_str % tuple( [col[i] for col in self.__data] ) ) fout.close() def readfile(self, filename): @@ -213,7 +270,6 @@ def readfile(self, filename): Reads in an ascii columned file """ self.titles = [] - self.bigarray = None self.parameters = parameters.parameters(filename=filename) self.ncols = 0 self.nrows = 0 @@ -232,18 +288,18 @@ def readfile(self, filename): while header and i < len(raw): if len(raw[i].lstrip())==0: # skip blank lines - i += 1 + i += 1 continue if raw[i][0] == "#": # title line if raw[i].find("=") > -1: # key = value line - name, value = clean(raw[i][1:].split("=")) + name, value = clean(raw[i][1:].split("=",1)) self.parameters.addpar( parameters.par( name, value ) ) else: self.titles = raw[i][1:].split() - i += 1 + i += 1 else: header = False try: @@ -251,31 +307,19 @@ def readfile(self, filename): lastrow = [ float( v ) for v in raw[-1].split() ] if len(row0) == len(lastrow ): nrows = len(raw)-i - last = None + last = len(raw) else: nrows = len(raw)-i-1 # skip the last row - last = -1 - self.bigarray = np.zeros( ( len(row0), nrows ), np.float ) - for i,line in enumerate(raw[i:last]): - self.bigarray[:,i] = [ float( v ) for v in line.split() ] + last = len(raw)-1 + cols = [ np.empty( nrows , float ) for _ in range(len(row0))] + fillcols( raw[i:last], cols ) + self.__data=cols except: - raise Exception("Problem interpreting your colfile") - (self.ncols, self.nrows) = self.bigarray.shape + raise # Exception("Problem interpreting your colfile") + self.ncols, self.nrows = len(row0), nrows self.parameters.dumbtypecheck() self.set_attributes() - def set_attributes(self): - """ - Set object vars to point into the big array - """ - if self.nrows == 0: - # use empty arrays for now... - # not sure why this was avoided in the past? - pass - #return - for i, title in enumerate(self.titles): - setattr(self, title, self.bigarray[i]) - assert getattr(self, title).shape == (self.nrows,) def filter(self, mask): """ @@ -284,31 +328,45 @@ def filter(self, mask): self.chkarray() if len(mask) != self.nrows: raise Exception("Mask is the wrong size") - self.nrows = int(np.sum( - np.compress(mask, np.ones(len(mask))))) - self.bigarray = np.compress(mask, self.bigarray, axis = 1) - assert self.bigarray.shape == (self.ncols, self.nrows) + msk = np.array( mask, dtype=bool ) + # back to list here + self.__data = [col[msk] for col in self.__data] + self.nrows = len(self.__data[0]) self.set_attributes() - + def copy(self): """ Returns a (deep) copy of the columnfile """ cnw = columnfile(self.filename, new = True) self.chkarray() - cnw.bigarray = self.bigarray.copy() cnw.titles = [t for t in self.titles ] - cnw.parameters = self.parameters - (cnw.ncols, cnw.nrows) = cnw.bigarray.shape + cnw.parameters = parameters.parameters( **self.parameters.parameters ) + cnw.bigarray = [col.copy() for col in self.__data] + cnw.ncols = self.ncols cnw.set_attributes() return cnw + def copyrows(self, rows): + """ + Returns a copy of select rows of the columnfile + """ + self.chkarray() + cnw = columnfile(self.filename, new = True) + cnw.titles = [t for t in self.titles ] + cnw.parameters = self.parameters + cnw.bigarray = [col[rows] for col in self.__data] + #cnw.ncols, cnw.nrows = cnw.bigarray.shape + #cnw.set_attributes() + return cnw + def chkarray(self): """ Ensure self.bigarray holds our attributes """ - for i, title in enumerate(self.titles): - self.bigarray[i] = getattr(self, title) + for i, name in enumerate(self.titles): + a = getattr(self, name) + self.__data[i] = a def addcolumn(self, col, name): """ @@ -318,22 +376,19 @@ def addcolumn(self, col, name): if len(col) != self.nrows: raise Exception("Wrong length column") if name in self.titles: + idx = self.titles.index(name) # Make this overwrite instead of throwing an exception - self.bigarray[self.titles.index(name)] = col + self.__data[idx] = col # raise Exception("Already got a column called "+name) - setattr(self, name, - self.bigarray[self.titles.index(name)] ) else: - assert self.bigarray.shape == (self.ncols, self.nrows) - self.titles.append(name) + # assert self.bigarray.shape == (self.ncols, self.nrows) data = np.asanyarray( col ) - assert data.shape[0] == self.nrows - self.ncols += 1 - self.bigarray = np.append( self.bigarray, - data[np.newaxis,:], - axis=0) - assert self.bigarray.shape == (self.ncols, self.nrows) - self.set_attributes() + assert data.shape[0] == self.nrows + self.titles.append(name) + idx = len(self.titles)-1 + self.ncols += 1 + self.__data.append( data ) + setattr(self, name, self.__data[idx] ) # Not obvious, but might be a useful alias setcolumn = addcolumn @@ -343,8 +398,8 @@ def getcolumn(self, name): Gets data, if column exists """ if name in self.titles: - return self.bigarray[self.titles.index(name)] - raise Exception("Name "+name+" not in file") + return self.__data[self.titles.index(name)] + raise KeyError("Name "+name+" not in file") def setparameters( self, pars ): """ @@ -352,7 +407,7 @@ def setparameters( self, pars ): """ self.parameters = pars self.parameters.dumbtypecheck() - + def updateGeometry(self, pars=None ): """ changing or not the parameters it (re)-computes: @@ -363,15 +418,16 @@ def updateGeometry(self, pars=None ): if pars is not None: self.setparameters( pars ) pars = self.parameters - if "sc" in self.titles: + if "sc" in self.titles and "fc" in self.titles: pks = self.sc, self.fc - elif "xc" in self.titles: + elif "xc" in self.titles and "yc" in self.titles: pks = self.xc, self.yc else: - raise Exception("columnfile file misses xc or sc") + raise Exception("columnfile file misses xc/yc or sc/fc") xl,yl,zl = transform.compute_xyz_lab( pks, **pars.parameters) peaks_xyz = np.array((xl,yl,zl)) + assert "omega" in self.titles,"No omega column" om = self.omega * float( pars.get("omegasign") ) tth, eta = transform.compute_tth_eta_from_xyz( peaks_xyz, om, @@ -393,7 +449,7 @@ def updateGeometry(self, pars=None ): self.addcolumn(modg, "ds") # dstar - + class newcolumnfile(columnfile): """ Just like a columnfile, but for creating new files """ @@ -401,6 +457,18 @@ def __init__(self, titles): columnfile.__init__(self, filename=None, new=True) self.titles = titles self.ncols = len(titles) + + +def colfile_from_dict( c ): + """ convert from a dictonary of numpy arrays """ + titles = list(c.keys()) + nrows = len(c[titles[0]]) + for t in titles: + assert len(c[t]) == nrows, t + colf = newcolumnfile( titles=titles ) + colf.nrows = nrows + colf.set_bigarray( [ c[t] for t in titles ] ) + return colf try: @@ -423,7 +491,10 @@ def colfile_to_hdf( colfile, hdffile, name=None, compression=None, opened = True if name is None: # Take the file name - name = os.path.split(c.filename)[-1] + try: + name = os.path.split(c.filename)[-1] + except: + name = 'peaks' if name in list(h.keys()): g = h[name] else: @@ -431,12 +502,14 @@ def colfile_to_hdf( colfile, hdffile, name=None, compression=None, g.attrs['ImageD11_type'] = 'peaks' for t in c.titles: if t in INTS: - ty = np.int32 + ty = np.int64 else: - ty = np.float32 + ty = np.float64 # print "adding",t,ty dat = getattr(c, t).astype( ty ) if t in list(g.keys()): + if g[t].shape != dat.shape: + g[t].resize( dat.shape ) g[t][:] = dat else: g.create_dataset( t, data = dat, @@ -459,12 +532,12 @@ def colfileobj_to_hdf( cf, hdffile, name=None): print(name, h) raise g.attrs['ImageD11_type'] = 'peaks' - for t in c.titles: + for t in cf.titles: if t in INTS: - ty = np.int32 + ty = np.int64 else: - ty = np.float32 - g.create_dataset( t, data = getattr(c, t).astype( ty ) ) + ty = np.float64 + g.create_dataset( t, data = getattr(cf, t).astype( ty ) ) h.close() def colfile_from_hdf( hdffile , name=None, obj=None ): @@ -485,7 +558,10 @@ def colfile_from_hdf( hdffile , name=None, obj=None ): print(groups) raise Exception("Did not find your "+str(name)+" in "+hdffile) else: - assert len(groups) == 1, "Your hdf file has many groups. Which one??" + groups = [g for g in groups + if 'ImageD11_type' in h[g].attrs and + h[g].attrs['ImageD11_type'] in ('peaks', b'peaks') ] + assert len(groups) == 1, "Your hdf file has many groups. Which one??"+str(groups) g = h[groups[0]] name = groups[0] if hasattr(g, 'listnames'): @@ -513,26 +589,19 @@ def colfile_from_hdf( hdffile , name=None, obj=None ): col = columnfile( filename=name, new=True ) else: col = obj - col.titles = newtitles - dat = g[newtitles[0]][:] - col.bigarray = np.zeros( (len(newtitles), len(dat) ), np.float32) - col.bigarray[0] = dat - col.ncols = len(newtitles) - col.nrows = len(dat) - i = 1 - for t in newtitles[1:]: - col.bigarray[i] = g[t][:] - i += 1 - col.set_attributes() + col.nrows = len( g[newtitles[0]] ) + for name in newtitles: + col.addcolumn( g[name][:].copy(), name ) + h.close() return col - + except ImportError: def hdferr(): raise Exception("You do not have h5py installed!") def colfile_to_hdf( a,b,name=None): hdferr() - + def colfile_from_hdf( hdffile , name=None ): hdferr() @@ -546,14 +615,21 @@ def bench(): """ import sys, time start = time.time() + import cProfile, pstats + pr = cProfile.Profile() + pr.enable() colf = columnfile(sys.argv[1]) + pr.disable() + ps = pstats.Stats(pr, stream=sys.stdout ) + ps.sort_stats('tottime') + ps.reverse_order() print(colf.bigarray.shape) print("ImageD11", time.time() - start) start = time.time() nolf = np.loadtxt(sys.argv[1]) print(nolf.shape) print("np", time.time() - start) - + ps.print_stats() # os.system("time -p ./a.out") @@ -596,7 +672,5 @@ def colfile2db( colfilename, dbname ): dbo.commit() dbo.close() - if __name__ == "__main__": bench() - diff --git a/sandbox/compute_fazit.py b/ImageD11/compute_fazit.py similarity index 99% rename from sandbox/compute_fazit.py rename to ImageD11/compute_fazit.py index b1ae7eed..a3761bce 100644 --- a/sandbox/compute_fazit.py +++ b/ImageD11/compute_fazit.py @@ -50,7 +50,7 @@ def __init__(self, splinefile = None, for key in self.required_pars: if key not in self.pars.parameters: - raise Exception("Missing parameter "+str(par)) + raise Exception("Missing parameter "+str(key)) def compute_tth_eta(self, dims): """ diff --git a/ImageD11/correct.py b/ImageD11/correct.py index 227f4edc..9ac4e649 100644 --- a/ImageD11/correct.py +++ b/ImageD11/correct.py @@ -2,7 +2,7 @@ from __future__ import print_function -import numpy +import numpy, fabio from PIL import ImageFilter # These don't work @@ -51,7 +51,7 @@ def correct(data_object, data_object.data = picture #print "scaled",scal, except: - print("Scale overflow",monitorcol, monitorval, dataobj.filename) + print("Scale overflow",monitorcol, monitorval, data_object.filename) if do_median: # We do this after corrections @@ -59,6 +59,7 @@ def correct(data_object, # direction of a previously radially transformed image # Gives the liquid contribution med = numpy.median( picture ) + # FIXME if True: # Suboption - save the median or not? obj = fabio.deconstruct_filename( data_object.header['filename'] ) obj.extension = ".bkm" diff --git a/ImageD11/depreciated/data.py b/ImageD11/depreciated/data.py index b0708f5a..488abd8a 100644 --- a/ImageD11/depreciated/data.py +++ b/ImageD11/depreciated/data.py @@ -80,7 +80,7 @@ def getmin(self): def getmean(self): """ return mean """ if self.m == None: - d = np.ravel(self.data.astype(np.float)) + d = np.ravel(self.data.astype(float)) self.m = np.sum(d) / len(d) return float(self.m) @@ -96,7 +96,7 @@ def getstddev(self): # # 1. For each value xi calculate the difference # x_i - between xi and the average value . - d = np.ravel(self.data.astype(np.float)) - self.m + d = np.ravel(self.data.astype(float)) - self.m # 2. Calculate the squares of these differences. S = d*d # 3. Find the average of the squared differences. diff --git a/ImageD11/eps_sig_solver.py b/ImageD11/depreciated/eps_sig_solver.py similarity index 99% rename from ImageD11/eps_sig_solver.py rename to ImageD11/depreciated/eps_sig_solver.py index cea7e3a5..dbf4648e 100644 --- a/ImageD11/eps_sig_solver.py +++ b/ImageD11/depreciated/eps_sig_solver.py @@ -42,7 +42,7 @@ def readubis(ubifile): if len(vals) == 3: u = u + [vals] if len(u)==3: - ubisread.append(n.array(u)) + ubisread.append(np.array(u)) u = [] f.close() return ubisread @@ -219,7 +219,7 @@ def compute_write_eps_sig(self,outputfile): except: print("couldn't compute stress! please check the crystal_symmetry parameters and elastic constants") writestress = False - pass + ''' writing down the results''' diff --git a/scripts/filterout.py b/ImageD11/depreciated/filterout.py similarity index 100% rename from scripts/filterout.py rename to ImageD11/depreciated/filterout.py diff --git a/ImageD11/depreciated/fitgrainxy.py b/ImageD11/depreciated/fitgrainxy.py index 545c54c5..535f6413 100644 --- a/ImageD11/depreciated/fitgrainxy.py +++ b/ImageD11/depreciated/fitgrainxy.py @@ -143,7 +143,7 @@ def makemap(self): self.scandata=self.allscandata.copy() self.compute_gv(g) h=np.dot(g.ubi,np.transpose(self.gv)) - hint=np.floor(h+0.5).astype(np.int) # rounds down + hint=np.floor(h+0.5).astype(int) # rounds down diff=h-hint drlv=np.sqrt(np.sum(diff*diff,0)) indices = np.compress(drlv < 0.05, @@ -164,7 +164,7 @@ def makemap(self): # 2nd time with refined npks = closest.score_and_refine(mat, self.gv, tol) h=np.dot(mat,np.transpose(self.gv)) - hint=np.floor(h+0.5).astype(np.int) + hint=np.floor(h+0.5).astype(int) # rounds down diff=h-hint drlv=np.sqrt(np.sum(diff*diff,0)) diff --git a/ImageD11/depreciated/fsrc/test_cf.py b/ImageD11/depreciated/fsrc/test_cf.py index 1258c011..78109e80 100644 --- a/ImageD11/depreciated/fsrc/test_cf.py +++ b/ImageD11/depreciated/fsrc/test_cf.py @@ -17,7 +17,7 @@ for tol in [0.01, 0.1, 0.2, 0.5]: int_tmpf = np.zeros( NPK, np.int32 ) - drlv2f = np.zeros( NPK, np.float) + drlv2f = np.zeros( NPK, float) start = tim() for i, gr in enumerate(ubis): @@ -26,7 +26,7 @@ fortrantime = end -start int_tmpc = np.zeros( NPK, np.int32 ) - drlv2c = np.zeros( NPK, np.float) + drlv2c = np.zeros( NPK, float) start = tim() diff --git a/ImageD11/depreciated/hist.py b/ImageD11/depreciated/hist.py index e3a90323..586bb0e1 100644 --- a/ImageD11/depreciated/hist.py +++ b/ImageD11/depreciated/hist.py @@ -35,7 +35,7 @@ def hist(data,verbose=0): returns histogram if data is a 2D numpy.uint16 array """ if data.dtype.char == np.uint16: - h = np.zeros(pow(2,16)-1,np.int) + h = np.zeros(pow(2,16)-1,int) _hist._hist(data,h,verbose) return h raise Exception("Sorry, not implemented") @@ -43,8 +43,8 @@ def hist(data,verbose=0): def test_dvhist(): import _hist - gv= np.zeros((10,3),np.float) - h = np.zeros((10,10,10),np.int) + gv= np.zeros((10,3),float) + h = np.zeros((10,10,10),int) _hist._dvhist(gv,h,-1,1,-1,1,-1,1,1) logging.info("test_dvhist maximum.reduce(h)"% np.maximum.reduce(h)) diff --git a/ImageD11/depreciated/linearity.py b/ImageD11/depreciated/linearity.py index d191e200..913dd817 100644 --- a/ImageD11/depreciated/linearity.py +++ b/ImageD11/depreciated/linearity.py @@ -58,7 +58,7 @@ def anotherimage(self, ar, scale=1.0, name=None): ar = data scale = guessed scale factor """ - t = ar.astype(np.float) + t = ar.astype(float) self.rawimages.append(t) self.scaledimages.append(scale * t * ( 1.0 + t * self.a )) self.scale_factors.append(scale) @@ -76,7 +76,7 @@ def makeaverage(self): """ logging.info("Compute average") self.average = np.zeros(self.scaledimages[0].shape, - np.float) + float) for im in self.scaledimages: self.average = self.average + im self.average = self.average / len(self.scaledimages) @@ -122,8 +122,8 @@ def lsq(diff, gradients): gradients """ nvar = len(gradients) - lsqmat = np.zeros((nvar,nvar),np.float) - rhs = np.zeros((nvar),np.float) + lsqmat = np.zeros((nvar,nvar),float) + rhs = np.zeros((nvar),float) for i in range(nvar): logging.debug(" lsq shapes: %d %d"%(gradients[i].ravel().shape[0], diff.ravel().shape[0])) diff --git a/scripts/mar2bruker.py b/ImageD11/depreciated/mar2bruker.py similarity index 100% rename from scripts/mar2bruker.py rename to ImageD11/depreciated/mar2bruker.py diff --git a/ImageD11/depreciated/opendata.py b/ImageD11/depreciated/opendata.py index 181f6410..016397c5 100644 --- a/ImageD11/depreciated/opendata.py +++ b/ImageD11/depreciated/opendata.py @@ -94,7 +94,7 @@ def openchi(filename): except: pass # titles - a=np.array(xy,np.float) + a=np.array(xy,float) h['rows']=a.shape[1] return data(a,h) diff --git a/ImageD11/depreciated/peaklabel.py b/ImageD11/depreciated/peaklabel.py new file mode 100644 index 00000000..d484059b --- /dev/null +++ b/ImageD11/depreciated/peaklabel.py @@ -0,0 +1,204 @@ + + +import h5py, os +import numpy as np +from ImageD11 import sparseframe, cImageD11 +import scipy.sparse +import scipy.sparse.csgraph + +# given a file holding pixels in _sparse.h5 format +# ... we expect to find: +# ... shape = image shape +# ... nnz = pixels per frame. Shape = number of frames. +# ... row/col/intensity + + +def pairrow( s , row): + """ + s = SparseScan + + returns SparseScan which adds a dictionary of pairs: + sourceFrame destFrame + [ idty, iomega, idty, iomega ] : nedge, array( (nedge, 3) ) + (src, dest, npixels) + """ + s.omegaorder = np.argsort( s.motors["omega"] ) + s.sinorow = row + olap = sparseframe.overlaps_linear( s.nnz.max()+1 ) + pairs = {} + for i in range(1, len(s.omegaorder)): + if (s.nnz[s.omegaorder[i]] == 0) or (s.nnz[s.omegaorder[i-1]] == 0): + continue + f0 = s.getframe( s.omegaorder[i-1] ) + f1 = s.getframe( s.omegaorder[i] ) + ans = olap( f0.row, f0.col, f0.pixels['labels'], s.nlabels[ s.omegaorder[i-1] ], + f1.row, f1.col, f1.pixels['labels'], s.nlabels[ s.omegaorder[i] ] ) + pairs[ row, s.omegaorder[i-1], row, s.omegaorder[i] ] = ans + return pairs + + + +def pairscans( s1, s2, omegatol = 0.051 ): + olap = sparseframe.overlaps_linear( max(s1.nnz.max(), s2.nnz.max())+1 ) + assert len(s1.nnz) == len(s2.nnz ) + pairs = {} + for i in range(len(s1.nnz)): + # check omega angles match + o1 = s1.motors['omega'][s1.omegaorder[i]] + o2 = s2.motors['omega'][s2.omegaorder[i]] + assert abs( o1 - o2 ) < omegatol, 'diff %f, tol %f'%(o1-o2, omegatol) + if (s1.nnz[s1.omegaorder[i]] == 0) or (s2.nnz[s2.omegaorder[i]] == 0): + continue + f0 = s1.getframe( s1.omegaorder[i] ) + f1 = s2.getframe( s2.omegaorder[i] ) + ans = olap( f0.row, f0.col, f0.pixels['labels'], s1.nlabels[ s1.omegaorder[i] ], + f1.row, f1.col, f1.pixels['labels'], s2.nlabels[ s2.omegaorder[i] ] ) + pairs[ s1.sinorow, s1.omegaorder[i], s2.sinorow, s2.omegaorder[i] ] = ans + return pairs + + + + +if 0: # remove unused code in here + opts = { + "chunks": (10000,), + "maxshape": (None,), + "compression": "lzf", + "shuffle": True, + } + + def add_connected_peaklabel( h5name, h5path, threshold=0 ): + # this will load the whole scan into memory. + scan = sparseframe.SparseScan( h5name, h5path ) + scan.cplabel( threshold, countall=False ) + with h5py.File( h5name, "a" ) as hsp: + grp = hsp.require_group( h5path ) + if 'cplabel' in grp: + grp['cplabel'][:] = scan.labels + grp['Ncplabel'][:] = scan.nlabels + else: + grp.create_dataset( name = 'cplabel', data = scan.labels, **opts ) + grp.create_dataset( name = 'Ncplabel', data = scan.nlabels ) + return scan.nlabels, scan.labels + + def add_localmax_peaklabel( h5name, h5path, smooth=True ): + scan = sparseframe.SparseScan( h5name, h5path ) + scan.lmlabel( smooth=smooth, countall=False ) + with h5py.File( h5name, "a" ) as hsp: + grp = hsp.require_group( h5path ) + if 'lmlabel' in grp: + grp['lmlabel'][:] = scan.labels + grp['Nlmlabel'][:] = scan.nlabels + else: + grp.create_dataset( name = 'lmlabel', data = scan.labels, **opts ) + grp.create_dataset( name = 'Nlmlabel', data = scan.nlabels ) + return scan.nlabels, scan.labels + + + + + +class IncrementalSymmetricCOOMatrix: + + def __init__(self, shape, dtype): + self.dtype = dtype + self.shape = shape + assert self.shape[0] == self.shape[1] + self.rows = [] + self.cols = [] + + def addeye(self): + r = np.arange(self.shape[0], dtype=np.int32) + self.append( r, r ) + + def append(self, i, j): + self.rows.append(i) + self.cols.append(j) + + def concatenate(self): + print("Concatenating") + self.rows = np.concatenate(self.rows) + self.cols = np.concatenate(self.cols) + self.data = np.ones( len(self.rows), dtype=np.uint8 ) # 1/0 + + def tocoo(self): + return scipy.sparse.coo_matrix((self.data, (self.rows, self.cols)), + shape=self.shape) + + +def add2mat( ds, r1, r2, pairs ): + # add entries into a sparse matrix + # + # key (500, 2, 501, 2893) + # (41, array([[ 1, 1, 5], + # [ 1, 2, 2], + ct = 0 + p = 0 + for k in pairs.keys(): + ky1, ko1, ky2, ko2 = k + if ky1 != r1 or ky2 != r2: + raise Exception("logic error in add2mat") + assert ky1 == r1 + assert ky2 == r2 + npairs, ijn = pairs[k] + if npairs == 0: + continue + assert ijn.shape[0] == npairs + ds.coomat.append( ds.firstpk[ky1,ko1] + ijn[:,0] - 1, + ds.firstpk[ky2,ko2] + ijn[:,1] - 1 ) + # ijn[:,2] ) + ct += npairs + + + +def build_overlap_matrix(ds, hname): + import tqdm + names='row','col','intensity','lmlabel' + lastscan = None + npk = ds.nlm.sum() + ds.firstpk = np.zeros( ds.nlm.shape, int ) + s = ds.firstpk.shape + ds.firstpk.shape=-1 + ds.firstpk[1:] = ds.nlm.ravel().cumsum()[:-1] + ds.firstpk.shape = s + ds.coomat = IncrementalSymmetricCOOMatrix( (npk, npk), np.int32 ) + ds.coomat.addeye() + + def rd1( hname, scan, row ): + spscan = sparseframe.SparseScan(hname, scan, names=names) + spscan = pairrow( spscan, ds, row ) + return spscan + + def pr2( scan1, scan2 ): + #assert scan1.sinorow == row1 + #assert scan2.sinorow == row2 + pairs = pairscans( ds, scan1, scan2 ) + return pairs #, row1, row2 + + lo = 0 + hi = len(ds.scans) + s1 = rd1( hname, ds.scans[lo], lo ) + add2mat( ds, lo, lo, s1.pairs ) + + for row in tqdm.tqdm( range(lo+1,hi) ): + s2 = rd1( hname, ds.scans[row], row ) + add2mat( ds, row, row, s2.pairs ) + p = pairscans( ds, s1, s2 ) + add2mat( ds, row-1, row, p ) + s1 = s2 + return ds + + +def find_global_peaklabel(ds, hname): + ds = build_overlap_matrix( ds, hname ) + m = ds.coomat + m.concatenate() + coo = scipy.sparse.coo_matrix( (np.ones( len(m.rows), dtype=np.uint32 ), + (m.rows, m.cols)), shape=m.shape) + csr = coo.tocsr() + csr += csr.T + cc = scipy.sparse.csgraph.connected_components( csr ) + return cc + + + diff --git a/ImageD11/depreciated/test_f2py.py b/ImageD11/depreciated/test_f2py.py index e0c8b3cf..fca514a1 100644 --- a/ImageD11/depreciated/test_f2py.py +++ b/ImageD11/depreciated/test_f2py.py @@ -18,9 +18,9 @@ import ImageD11.closest np.random.seed(42) -cosines = np.linspace( -1, 1, 3 ).astype(np.float) +cosines = np.linspace( -1, 1, 3 ).astype(float) print "cosines" -artest = np.array([ 0.99, 0.2, 0.3, 0.7, 0.8],np.float) +artest = np.array([ 0.99, 0.2, 0.3, 0.7, 0.8],float) oldy = ImageD11.closest.closest(artest, cosines ) newy = connectedpixels.closest( artest, cosines ) assert oldy == newy @@ -58,9 +58,9 @@ def pyscore( u, g, tol ): assert np.allclose( ubifitnew, ubifitold ) ubilist = [ (np.random.random((3,3))-0.5)*10 for i in range(3) ] - drlv2new = np.zeros( npk, np.float) + 2 + drlv2new = np.zeros( npk, float) + 2 labelsnew = np.ones( npk, np.int32) - 2 - drlv2old = np.zeros( npk, np.float) + 2 + drlv2old = np.zeros( npk, float) + 2 labelsold = np.ones( npk, np.int32) - 2 for i,u in enumerate(ubilist): diff --git a/ImageD11/depreciated/ubitool.py b/ImageD11/depreciated/ubitool.py index ebf6c338..d00b860b 100644 --- a/ImageD11/depreciated/ubitool.py +++ b/ImageD11/depreciated/ubitool.py @@ -70,7 +70,7 @@ def read_ubi_file(self,filename): """ Get ubi matrices from a file """ - i=0; u = np.zeros((3,3),np.float) + i=0; u = np.zeros((3,3),float) for line in open(filename,"r").readlines(): uij = [float(x) for x in line.split()] if len(uij)==3: @@ -78,4 +78,4 @@ def read_ubi_file(self,filename): i=i+1 else: self.ubilist.append(u) - u = np.zeros((3,3),np.float) + u = np.zeros((3,3),float) diff --git a/ImageD11/fft_index_refac.py b/ImageD11/fft_index_refac.py index 532dd4b0..730bb726 100644 --- a/ImageD11/fft_index_refac.py +++ b/ImageD11/fft_index_refac.py @@ -13,47 +13,106 @@ """ import logging, time, sys -import numpy +import numpy as np -from ImageD11 import labelimage, cImageD11 +from ImageD11 import labelimage, cImageD11, columnfile def get_options(parser): - parser.add_option( '-n', '--ngrid', + parser.add_argument( '-n', '--ngrid', action = 'store', - dest = 'np', - type = 'int', + dest = 'npx', + type = int, help = 'number of points in the fft grid [128]', default = 128 ) - parser.add_option( '-r', '--max_res', + parser.add_argument( '-r', '--max_res', action = 'store', dest = 'mr', - type = 'float', + type = float, help = 'Maximum resolution limit for fft (d-spacing) [1.0]', default = 1.0) - parser.add_option( '-s', '--nsig', + parser.add_argument( '-s', '--nsig', action = 'store', dest = 'nsig', - type = 'float', + type = float, help = 'Number of sigma for patterson peaksearch threshold [5]', default = 5) return parser + +def refine_vector( v, gv, tol=0.25, ncycles=25, precision=1e-6 ): + """ + Refine a (single) real space lattice vector + Input + v : real space lattice vector + gv : scattering vectors (no errors) + tol : tolerance for initial assignment to peak + ncycles : how much to refine + precision : when to stop refining in v += s when |s|/|v| < precision + Returns + vref : refined real space lattice vector + + Uses: + hr = v[0]*gv[i,0] + v[0]*gv[i,1] + v[1]*gv[i,2] 1 per peak + hi = round(hr) = calc 1 per peak + diff = calc - hr 1 per peak + gradient = d(diff)/dv[i] = gv[j,i] 3 per peak + matrix = gradient[i].gradient[j] 3x3 peaks^2 + rhs = gradient . diff 3 + + Solve for shifts and iterates reducing tolerance as 1/(ncycles+1) + Peaks that change index during refinement are removed + Stops on : + no peaks reassigned + ncycles runs out + |shift| / |vec| < precision + """ + assert gv.shape[1] == 3 + vref = v.copy() + mg = (gv*gv).sum(axis=1) + mold = None + wt = 1./(mg + mg.max()*0.01) + gvt = gv.T.copy() + for i in range(ncycles): + hr = np.dot( vref, gvt ) # npks + hi = np.round( hr ) # npks + diff = hi - hr # npks + m = abs(diff) < tol/(i+1) # keep or not ? + diff = (diff*wt)[m] # select peaks used + grad = (gvt*wt).T[m] + gvt = gvt[:,m] + wt = wt[m] + # lsq problem: + rhs = np.dot( grad.T, diff ) + mat = np.dot( grad.T, grad ) + # avoid problems if singular: + U,s,V = np.linalg.svd( mat ) + one_over_s = np.where( s/s.max() < 1e-6, 1, 1./s ) + mati = np.dot( U, np.dot(np.diag( one_over_s ), V ) ) + vshft = np.dot( mati, rhs ) + vref = vref + vshft + if m.sum()==0: + break + slen = np.sqrt((vshft * vshft).sum()) + vlen = np.sqrt((vref*vref).sum()) + if vlen < precision or abs(slen/vlen) < precision: + break + return vref + + class grid: - def __init__(self, np = 128, mr = 1.0 ,nsig = 5): + def __init__(self, npx = 128, mr = 1.0 ,nsig = 5): """ Set up the grid to use (the large unit cell) - np - number of points in the grid + npx - number of points in the grid mr - maximum resolution limit to consider """ - self.np = np + self.npx = npx self.nsig = nsig - self.grid = numpy.zeros((np,np,np),numpy.float32) - self.old_grid = numpy.zeros((np,np,np),numpy.float32) - self.cell_size = np*mr/2. + self.grid = np.zeros((npx,npx,npx),np.float32) + self.old_grid = np.zeros((npx,npx,npx),np.float32) + self.cell_size = npx * mr / 2. logging.info("Using an FFT unit cell of %s"%(str(self.cell_size))) - - def gv_to_grid_new(self, gv): """ Put gvectors into our grid @@ -63,25 +122,19 @@ def gv_to_grid_new(self, gv): logging.info("Gridding data") self.gv = gv hrkrlr = self.cell_size * gv - hkl = numpy.floor(hrkrlr + 0.5).astype(numpy.int) + hkl = np.round( hrkrlr ).astype(int) # Filter to have the peaks in asym unit # ... do we need to do this? What about wrapping? - hmx = numpy.where( hkl.max(axis=1) < self.np/2 - 2., - 1, 0 ) - hmn = numpy.where( hkl.min(axis=1) > -self.np/2 + 2., - 1, 0 ) - my_g = numpy.compress( hmx & hmn, gv, axis=0 ) - + hmx = hkl.max(axis=1) < (self.npx/2. - 2.) + hmn = hkl.min(axis=1) > (2. - self.npx/2.) + my_g = np.compress( hmx & hmn, gv, axis=0 ) # Compute hkl indices in the fft unit cell using filtered peaks hrkrlr = self.cell_size * my_g - # Integer part of hkl (eg 1.0 from 1.9) - hkl = numpy.floor(hrkrlr).astype(numpy.int) - + hkl = np.floor(hrkrlr).astype(int) # Fractional part of indices ( eg 0.9 from 1.9) remain = hrkrlr - hkl grid = self.grid - import time start = time.time() # Loop over corners with respect to floor corner ng = grid.shape[0]*grid.shape[1]*grid.shape[2] @@ -96,133 +149,104 @@ def gv_to_grid_new(self, gv): (1,1,1) ]: #8 # The corner thkl = hkl + cor - fac = 1 - numpy.absolute(remain - cor) - vol = numpy.absolute(fac[:,0]*fac[:,1]*fac[:,2]).astype(numpy.float32) - thkl = numpy.where(thkl < 0, self.np + thkl, thkl) + fac = 1 - abs(remain - cor) + vol = abs(fac[:,0]*fac[:,1]*fac[:,2]).astype(np.float32) + thkl = np.where(thkl < 0, self.npx + thkl, thkl) ind = thkl[:,0]*grid.shape[1]*grid.shape[2] + \ thkl[:,1]*grid.shape[1] + \ thkl[:,2] - cImageD11.put_incr( flatgrid , ind.astype(numpy.intp), vol ) - # print thkl,vol - - # vals = numpy.take(grid.flat, ind) - # print "vals",vals - # vals = vals + vol - # print "vol",vol - # print "ind",ind - # FIXME - # This is wrong - if ind repeats several times we - # only take the last value - # numpy.put(grid, ind, vals) - # print numpy.argmax(numpy.argmax(numpy.argmax(grid))) - # print grid[0:3,-13:-9,-4:] - logging.warn("Grid filling loop takes "+str(time.time()-start)+" /s") + cImageD11.put_incr( flatgrid , ind.astype(np.intp), vol ) + logging.info("Grid filling loop takes "+str(time.time()-start)+" /s") def fft(self): """ Compute the Patterson """ start = time.time() - self.patty = numpy.ascontiguousarray(abs(numpy.fft.fftn(self.grid)), - numpy.float32) + self.patty = np.ascontiguousarray(abs(np.fft.fftn(self.grid)), + np.float32) logging.info("Time for fft "+str(time.time()-start)) self.origin = self.patty[0,0,0] logging.info("Patterson origin height is :"+str(self.origin)) def props(self): """ Print some properties of the Patterson """ - print("Patterson info") - print(self.patty.shape, type(self.patty)) - p = numpy.ravel(self.patty) - m = numpy.mean(p) - print("Average:",m) + logging.info("Patterson info "+str(self.patty.shape) + +str(type(self.patty))) + p = np.ravel(self.patty) + m = np.mean(p) + logging.info("Average: %f"%(m)) p2 = p*p self.mean = m - v = numpy.sqrt( (numpy.sum(p2) - m*m*len(p) ) /(len(p)-1) ) - print("Sigma",v) + v = np.sqrt( (np.sum(p2) - m*m*len(p) ) /(len(p)-1) ) + logging.info("Sigma: %f"%(v)) self.sigma = v def peaksearch(self, peaksfile): """ Peaksearch in the Patterson """ - lio = labelimage.labelimage( (self.np, self.np), + lio = labelimage.labelimage( (self.npx, self.npx), peaksfile ) logging.info("Peaksearching at %f sigma"%(self.nsig)) thresh = self.mean + self.nsig * self.sigma - for i in range(self.np): + for i in range(self.npx): lio.peaksearch(self.patty[i], thresh, i) lio.mergelast() - #if i%2 == 0: - # print ".", - # sys.stdout.flush() - #print lio.finalise() - def pv(self, v): return ("%8.4f "*3)%tuple(v) + def pv(self, v): + """ print vector """ + return ("%8.4f "*3)%tuple(v) def reduce(self, vecs): raise Exception("You want lattice_reduction instead") - from ImageD11 import sym_u - g = sym_u.trans_group(tol = self.rlgrid*2) - # print self.rlgrid, len(vecs) - for i in range(len(vecs)): - print(self.pv(vecs[i]), end=' ') - assert len(vecs[i]) == 3 - t = g.reduce(vecs[i]) - if numpy.dot(t, t) < self.minlen * self.minlen: - print("Short ",self.pv(vecs[i]), i, self.pv(t)) - continue - if not g.isMember(t): - g.additem(t) - print("Adding",self.pv(vecs[i]), i, self.pv(t)) - print(len(g.group), g.group) - else: - print("Got ",self.pv(vecs[i]), i , self.pv(t)) - print(g.group) - return g.group[1:] def read_peaks(self, peaksfile): """ Read in the peaks from a peaksearch """ - from ImageD11.columnfile import columnfile - import time start = time.time() - self.colfile = columnfile(peaksfile) - print("reading file",time.time()-start) + colf = columnfile.columnfile(peaksfile) + logging.info("reading file %f/s"%(time.time()-start)) # hmm - is this the right way around? - self.rlgrid = 1.0*self.cell_size/self.np - self.px = self.colfile.omega - self.px = numpy.where(self.px > self.np/2 , - self.px - self.np , - self.px)*self.rlgrid - self.py = self.colfile.sc - self.py = numpy.where(self.py > self.np/2 , - self.py - self.np , - self.py)*self.rlgrid - self.pz = self.colfile.fc - self.pz = numpy.where(self.pz > self.np/2 , - self.pz - self.np , - self.pz)*self.rlgrid - self.UBIALL = numpy.transpose(numpy.array( [self.px, self.py, self.pz] )) - # self.UBIALL = self.reduce(self.UBIALL) - print("Number of peaks found",self.px.shape[0],time.time()-start) - # print self.UBIALL.shape, self.gv.shape - + self.rlgrid = 1.0*self.cell_size/self.npx + self.px = colf.omega + self.px = np.where(self.px > self.npx/2 , + self.px - self.npx , + self.px)*self.rlgrid + self.py = colf.sc + self.py = np.where(self.py > self.npx/2 , + self.py - self.npx , + self.py)*self.rlgrid + self.pz = colf.fc + self.pz = np.where(self.pz > self.npx/2 , + self.pz - self.npx , + self.pz)*self.rlgrid + self.UBIALL = np.array( [self.px, self.py, self.pz] ).T + logging.info("Number of peaks found %d %f/s, now fit some"%( + self.px.shape[0],time.time()-start)) + for i in range(colf.nrows): + print(".",end="") + self.UBIALL[i] = refine_vector( self.UBIALL[i], self.gv ) + + logging.info("Fitting vectors %f /s"%(time.time()-start)) + self.colfile = colf + def slow_score(self): + logging.info("running slow_score") import time start = time.time() - scores = numpy.dot( self.UBIALL, numpy.transpose( self.gv ) ) - scores_int = numpy.floor( scores + 0.5).astype(numpy.int) + scores = np.dot( self.UBIALL, np.transpose( self.gv ) ) + scores_int = np.floor( scores + 0.5).astype(int) diff = scores - scores_int nv = len(self.UBIALL) print("scoring",nv,time.time()-start) self.tol = 0.1 - scores = numpy.sqrt(numpy.average(diff*diff, axis = 1)) - n_ind = numpy.where(numpy.absolute(diff)< self.tol, 1 , 0) - nind = numpy.sum(n_ind, axis=1) - order = numpy.argsort(nind)[::-1] - mag_v = numpy.sqrt( self.px * self.px + + scores = np.sqrt(np.average(diff*diff, axis = 1)) + n_ind = np.where(np.absolute(diff)< self.tol, 1 , 0) + nind = np.sum(n_ind, axis=1) + order = np.argsort(nind)[::-1] + mag_v = np.sqrt( self.px * self.px + self.py * self.py + self.pz * self.pz ) f = open("fft.pks","w") @@ -240,7 +264,7 @@ def slow_score(self): )) for k in range(i+1,nv): l = order[k] - nij = numpy.sum( n_ind[j] * n_ind[l] ) + nij = np.sum( n_ind[j] * n_ind[l] ) f.write("%4d : %-7d "%(k,nij)) f.write("\n") @@ -273,21 +297,21 @@ def slow_score(self): - sm = numpy.zeros( (len(diff), len(diff)), numpy.float) - for k in range(len(diff)): - i = order[k] - sm[i,i] = numpy.dot(diff[i], diff[i]) - for k in range(len(diff)-1): - i = order[k] - for l in range(i+1, len(diff)): - j = order[l] - sm[i,j] = numpy.dot(diff[i],diff[j])/sm[i,i]/sm[j,j] - sm[j,i] = sm[i,j] - for i in range(len(diff)): - sm[i,i] = 1. - print(sm[:5,:5]) - print("Scoring takes",time.time()-start) - return sm + # sm = np.zeros( (len(diff), len(diff)), float) + # for k in range(len(diff)): + # i = order[k] + # sm[i,i] = np.dot(diff[i], diff[i]) + # for k in range(len(diff)-1): + # i = order[k] + # for l in range(i+1, len(diff)): + # j = order[l] + # sm[i,j] = np.dot(diff[i],diff[j])/sm[i,i]/sm[j,j] + # sm[j,i] = sm[i,j] + # for i in range(len(diff)): + # sm[i,i] = 1. + # print(sm[:5,:5]) + # print("Scoring takes",time.time()-start) + # return sm ##### # To Do @@ -304,11 +328,11 @@ def slow_score(self): def test(options): gvfile = options.gvfile mr = options.max_res - np = options.np + npx = options.npx nsig = options.nsig from ImageD11 import indexing - print(np, mr, nsig) - go = grid(np = np , mr = mr , nsig = nsig) + print(npx, mr, nsig) + go = grid(npx = npx , mr = mr , nsig = nsig) io = indexing.indexer() io.readgvfile(gvfile) go.gv_to_grid_new(io.gv ) @@ -320,7 +344,7 @@ def test(options): #sys.stdout.flush() #ubinew = go.reduce(go.UBIALL) #print "After reduction", len(ubinew) - go.UBIALL = ubinew + #go.UBIALL = ubinew go.slow_score() #from matplotlib.pylab import imshow, show #imshow(im) @@ -331,9 +355,9 @@ def test(options): go.gv_to_grid_old(io.gv) - diff = numpy.ravel(go.grid - go.old_grid) - print("All OK if this is zero",numpy.add.reduce(diff)) - print("error at",numpy.argmax(diff),diff.max(),numpy.argmin(diff),diff.min()) + diff = np.ravel(go.grid - go.old_grid) + print("All OK if this is zero",np.add.reduce(diff)) + print("error at",np.argmax(diff),diff.max(),np.argmin(diff),diff.min()) @@ -344,7 +368,7 @@ def test(options): import sys class options: max_res = 0.7 - np = 128 + npx = 128 nsig = 10 options.gvfile = sys.argv[1] diff --git a/ImageD11/finite_strain.py b/ImageD11/finite_strain.py new file mode 100644 index 00000000..88cc8f86 --- /dev/null +++ b/ImageD11/finite_strain.py @@ -0,0 +1,143 @@ + +from __future__ import print_function + +# ImageD11 Software for beamline ID11 +# Copyright (C) 2020 Jon Wright +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 2 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA + +import numpy as np, math + +# helpers +def e6_to_symm(e): + """ Follows the eps convention from xfab + eps = [e11, e12, e13, e22, e23, e33] + """ + e11, e12, e13, e22, e23, e33 = e + return np.array(((e11,e12,e13), + (e12,e22,e23), + (e13,e23,e33))) + +def symm_to_e6(m): + """ Follows the eps convention from xfab + eps = [e11, e12, e13, e22, e23, e33] + """ + return np.array( ( m[0,0], m[0,1], m[0,2], + m[1,1], m[1,2], + m[2,2] ) ) + +def cell_to_B( cell ): + from ImageD11.unitcell import unitcell + B = unitcell( cell ).B + return B + +class DeformationGradientTensor( object ): + + def __init__(self, ubi, ub0): + """ + see docs/DeformationGradientTensor.ipynb + F = dot( ubi.T, ub0.T ) + F = ui.bi.b0.u0 + """ + if hasattr(ubi, 'ubi'): # you passed in and ImageD11.grain + ubi = ubi.ubi + assert ubi.shape == (3,3) + if hasattr( ub0, 'ub'): + # you passed in and ImageD11.grain + ub0 = ub0.ub + assert ub0.shape == (3,3) + self.F = np.dot( ubi.T, ub0.T ) + self._svd = None + self._vrs = None + + @property + def SVD(self): + """ Returns the singular value decomposition of F """ + if self._svd is None: + self._svd = np.linalg.svd(self.F) + return self._svd + + @property + def VRS(self): + """ Returns the Polar decomposition of F=V.R=R.S + with R as a rotation matrix and V, S as symmetric + """ + if self._vrs is None: + w,sing,vh = self.SVD + R = np.dot( w, vh ) + S = np.dot( vh.T, np.dot( np.diag(sing), vh ) ) + V = np.dot( w , np.dot( np.diag(sing), w.T ) ) + self._vrs = V,R,S + return self._vrs + + @property + def U(self): + """ Returns the Busing and Levy U matrix relating ubi to ub0 """ + v,r,s = self.VRS + return r + + def finite_strain_ref(self, m=0.5): + """ + Returns the finite strain in the reference co-ordinate system + if the polar decomposition gives : + F = V.R = R.S = ui.bi.b0.u0 + Ft.F removes ui final orientation effect + Em = (S^m - I )/2m + """ + m2 = int(round(m*2)) + assert np.allclose( m2 * 0.5, m ) + if m2 == 0: + u,s,vt = self.SVD # == F + logs = np.diag( np.log( s ) ) + logFFT = np.dot( np.dot( vt.T, logs ), vt ) + Em = logFFT # empirically, no division by 2 for matching in small strain limit + elif (m2 % 2) == 0: # No SVD in this path + m = int(round(m)) + Cm = np.linalg.matrix_power( np.dot( self.F.T, self.F ), m ) + Em = (Cm - np.eye(3))/m2 + else: + V,R,S = self.VRS # == F + Um = np.linalg.matrix_power(S, m2) + Em = (Um - np.eye(3))/m2 + return Em + + def finite_strain_lab(self, m=0.5): + """ + Returns the finite strain in the lab co-ordinate system + if the polar decomposition gives : + F = V.R = R.S = ui.bi.b0.u0 + F.Ft removes u0 initial orientation effect + Em = (V^m - I )/2m + """ + m2 = int(round(m*2)) + assert np.allclose( m2 * 0.5, m ) + if m2 == 0: + u,s,vt = self.SVD # == F + logs = np.diag( np.log( s ) ) + logFTF = np.dot( np.dot( u, logs ), u.T ) + em = logFTF # *0.5 + elif (m2 % 2) == 0: # No SVD in this path + m = int(round(m)) + Bm = np.linalg.matrix_power( np.dot( self.F, self.F.T ), m ) + em = (Bm - np.eye(3))/m2 + else: + V,R,S = self.VRS # == F + Vm = np.linalg.matrix_power(V, m2) + em = (Vm - np.eye(3))/m2 + return em + + + + diff --git a/ImageD11/grain.py b/ImageD11/grain.py index 24baaeef..b0bbcca0 100644 --- a/ImageD11/grain.py +++ b/ImageD11/grain.py @@ -21,36 +21,189 @@ # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA -import numpy, math -import ImageD11.indexing +import numpy as np, math +import ImageD11.indexing, ImageD11.unitcell, ImageD11.finite_strain +import xfab.tools + +# helpers : put these into xfab.tools at some point? +def e6_to_symm(e): + """ Follows the eps convention from xfab + eps = [e11, e12, e13, e22, e23, e33] + """ + e11, e12, e13, e22, e23, e33 = e + return np.array(((e11,e12,e13), + (e12,e22,e23), + (e13,e23,e33))) + +def symm_to_e6(m): + """ Follows the eps convention from xfab + eps = [e11, e12, e13, e22, e23, e33] + """ + return np.array( ( m[0,0], m[0,1], m[0,2], + m[1,1], m[1,2], + m[2,2] ) ) + class grain: def __init__(self,ubi,translation=None, **kwds): - self.ubi = numpy.array(ubi,numpy.float) - self.ub = numpy.linalg.inv(ubi) - self.u = ImageD11.indexing.ubitoU(self.ubi) - try: - self.Rod = ImageD11.indexing.ubitoRod(self.ubi) - except: - print(self.ubi) - raise - self.mt = numpy.dot(self.ubi, self.ubi.T) - self.rmt = numpy.linalg.inv(self.mt) if translation is None: # If translation has not been read from ubi file make it # be None to avoid confusion with a grain which is known # to be at [0,0,0] self.translation = None else: - self.translation = numpy.array(translation,numpy.float) + self.translation = np.array(translation,float) + self.set_ubi(ubi) + def set_ubi(self, ubi): - self.ubi = numpy.array(ubi,numpy.float) - self.ub = numpy.linalg.inv(ubi) - self.u = ImageD11.indexing.ubitoU(self.ubi) - self.Rod = ImageD11.indexing.ubitoRod(self.ubi) - self.mt = numpy.dot(self.ubi, self.ubi.T) - self.rmt = numpy.linalg.inv(self.mt) + """ Update the orientation and clear cached values """ + self.ubi = np.array(ubi,float) + assert np.linalg.det(self.ubi) >= 0, 'Left handed axis system!' + self.clear_cache() + + def clear_cache(self): + # We will cache a bunch of things and access them + # via properties. Usually we return copies of these + # so we avoid people overwriting cached values by accident. + # e.g. cell = grain.unitcell + # cell[4:] = 90 # degree angle for orthorhombic + # oups : grain._unitcell was corrupted! + self._UB = None + self._U = None + self._B = None + self._rod = None + self._mt = None + self._rmt = None + self._unitcell = None + + @property + def UB(self): + """ The UB matrix from Busing and Levy + columns are the reciprocal space lattice vectors + """ + if self._UB is None: + self._UB = np.linalg.inv(self.ubi) + return self._UB.copy() + + @property + def ub(self): + return self.UB + + @property + def B(self): + if self._B is None: + self._B = ImageD11.unitcell.unitcell( self.unitcell ).B.copy() + return self._B.copy() + + @property + def U(self): + """ The orientation matrix (U) from Busing and Levy """ + if self._U is None: + # ubi = inv(UB) = inv(B)inv(U) + self._U = np.dot( self.B, self.ubi ).T + return self._U.copy() + + @property + def u(self): + return self.U + + @property + def Rod(self): + """ A Rodriguez vector. + Length proportional to angle, direction is axis""" + if self._rod is None: + self._rod = xfab.tools.u_to_rod( self.U ) + return self._rod.copy() + + @property + def mt(self): + """Metric tensor """ + if self._mt is None: + self._mt = np.dot( self.ubi, self.ubi.T ) + return self._mt.copy() + + @property + def rmt(self): + """ Reciprocal metric tensor """ + if self._rmt is None: + self._rmt = np.linalg.inv( self.mt ) + return self._rmt.copy() + + @property + def unitcell(self): + """ a,b,c,alpha,beta,gamma """ + if self._unitcell is None: + G = self.mt + a, b, c = np.sqrt( np.diag( G ) ) + al = np.degrees( np.arccos( G[1,2]/b/c ) ) + be = np.degrees( np.arccos( G[0,2]/a/c ) ) + ga = np.degrees( np.arccos( G[0,1]/a/b ) ) + self._unitcell = np.array( (a,b,c,al,be,ga) ) + return self._unitcell.copy() + + def eps_grain_matrix(self, dzero_cell, m=0.5): + """ dzero_cell can be another grain or cell parameters + [a,b,c,alpha,beta,gamma] + m is the exponent for the Seth-Hill finite strain tensors + E = 1/2m (U^2m - I) + (Negative m reverses the reference and lab systems). + Returns eps as a symmetric matrix + ... in the grain reference system of dzero_cell + """ + if hasattr( dzero_cell, "UB" ): + B = dzero_cell.UB + else: + B = ImageD11.unitcell.unitcell( dzero_cell ).B + F = ImageD11.finite_strain.DeformationGradientTensor( self.ubi, B ) + eps = F.finite_strain_ref( m ) + return eps + + def eps_grain(self, dzero_cell, m=0.5): + """ dzero_cell can be another grain or cell parameters + [a,b,c,alpha,beta,gamma] + m is the exponent for the Seth-Hill finite strain tensors + E = 1/2m (U^2m - I) + (Negative m reverses the reference and lab systems). + Returns eps as a the 6 independent entries in the symmetric matrix + e11 e12 e13 e22 e23 e33 + ... in the grain reference system of dzero_cell + """ + E = self.eps_grain_matrix( dzero_cell, m ) + return symm_to_e6( E ) + + + def eps_sample_matrix(self, dzero_cell, m=0.5): + """ dzero_cell can be another grain or cell parameters: + [a,b,c,alpha,beta,gamma] + m is the exponent for the Seth-Hill finite strain tensors + E = 1/2m (V^2m - I) + (Negative m reverses the reference and lab systems). + Returns eps as a symmetric matrix + ... in the sample system (z up, x along the beam at omega=0) + """ + if hasattr( dzero_cell, "UB" ): + B = dzero_cell.UB + else: + B = ImageD11.unitcell.unitcell( dzero_cell ).B + F = ImageD11.finite_strain.DeformationGradientTensor( self.ubi, B ) + eps = F.finite_strain_lab( m ) + return eps + + + def eps_sample(self, dzero_cell, m=0.5): + """ dzero_cell can be another grain or cell parameters: + [a,b,c,alpha,beta,gamma] + m is the exponent for the Seth-Hill finite strain tensors + E = 1/2m (V^2m - I) + (Negative m reverses the reference and lab systems). + Returns eps as a the 6 independent entries in the symmetric matrix + e11 e12 e13 e22 e23 e33 + ... in the sample system (z up, x along the beam at omega=0) + """ + E = self.eps_sample_matrix( dzero_cell, m ) + return symm_to_e6( E ) + @@ -104,7 +257,7 @@ def read_grain_file(filename): u = u + [vals] if len(u)==3: grainsread.append( grain(u, t) ) - for k in ["name","npks","nuniq","Rod","intensity_info"]: + for k in ["name","npks","nuniq","intensity_info"]: # Rod - is recomputed when needed if k in p: setattr(grainsread[-1], k, p[k]) p={} diff --git a/ImageD11/grid_index_parallel.py b/ImageD11/grid_index_parallel.py index 98a37ea3..91b57905 100644 --- a/ImageD11/grid_index_parallel.py +++ b/ImageD11/grid_index_parallel.py @@ -1,11 +1,11 @@ from __future__ import print_function -from ImageD11 import peakmerge, indexing, transformer +from ImageD11 import peakmerge, indexing, transformer, cImageD11 from ImageD11 import grain, unitcell, refinegrains, sym_u import xfab.tools import sys, os, numpy as np, time, random -import multiprocessing +import multiprocessing, traceback from multiprocessing import Pool from multiprocessing import Queue as PQueue @@ -31,6 +31,9 @@ def domap( pars, """ mapping function - does what makemap.py does, but in a function """ + if 'FITPOS' not in gridpars: + gridpars['FITPOS']=True + OmSlop = gridpars['OMEGAFLOAT'] OmFloat= OmSlop > 0 # @@ -55,13 +58,23 @@ def domap( pars, o.grainnames.append(i) o.ubisread[name] = g.ubi o.translationsread[name] = g.translation - if gridpars['SYMMETRY'] is not "triclinic": + if gridpars['SYMMETRY'] != "triclinic": o.makeuniq( gridpars['SYMMETRY'] ) o.generate_grains() - o.refinepositions() + if gridpars['FITPOS']: + o.refinepositions() + else: + o.assignlabels() + for key in o.grains.keys(): + g = o.grains[key] + g.set_ubi( o.refine( g.ubi, quiet=False ) ) # This fills in the uniq for each grain o.savegrains( nulfile, sort_npks = False) - gl = [ g for g in o.grains.values() if g.npks > gridpars['NPKS'] ] + if 'NUNIQ' in gridpars: + keep = lambda g: g.nuniq > gridpars['NUNIQ'] and g.npks > gridpars['NPKS'] + else: + keep = lambda g: g.npks > gridpars['NPKS'] + gl = [ g for g in o.grains.values() if keep(g) ] if len(gl) == 0: break grains = gl @@ -80,6 +93,10 @@ def doindex( gve, x, y, z, w, gridpars): TOLSEQ = gridpars['TOLSEQ'] COSTOL = gridpars[ 'COSTOL'] DSTOL = gridpars[ 'DSTOL'] + if "2RFIT" in gridpars: + DOFIT = gridpars[ '2RFIT' ] + else: + DOFIT = False ss = sys.stdout # turns off printing if gridpars['NUL']: NUL = open(nulfile,"w") @@ -89,8 +106,9 @@ def doindex( gve, x, y, z, w, gridpars): unitcell = UC, gv = gve.T ) - myindexer.ds = np.sqrt( (gve * gve).sum(axis=0) ) - myindexer.ga = np.zeros(len(myindexer.ds),np.int)-1 # Grain assignments + # added in indexer.__init__ + #myindexer.ds = np.sqrt( (gve * gve).sum(axis=0) ) + #myindexer.ga = np.zeros(len(myindexer.ds),int)-1 # Grain assignments for ring1 in gridpars['RING1']: for ring2 in gridpars['RING2']: myindexer.parameterobj.set_parameters( { @@ -106,7 +124,7 @@ def doindex( gve, x, y, z, w, gridpars): myindexer.assigntorings( ) try: myindexer.find( ) - myindexer.scorethem( ) + myindexer.scorethem( fitb4 = DOFIT ) except: pass # filter out crap @@ -180,7 +198,7 @@ def add(self, grains): dt2 =np.dot(dt,dt) if dt2 > self.dt2: continue - aumis = np.dot(gold.asymusT, gnew.u) + aumis = np.dot(gold.asymusT, gnew.U) arg = (aumis[:,0,0]+aumis[:,1,1]+aumis[:,2,2] - 1. )/2. angle = np.arccos(np.clip(arg, -1, 1)).min() if angle < self.tar: @@ -210,10 +228,10 @@ def initgrid( fltfile, parfile, tmp, gridpars ): gridpars[ 'UC' ] = unitcell.unitcell_from_parameters( mytransformer.parameterobj ) col = mytransformer.colfile if not "drlv2" in col.titles: - col.addcolumn( np.ones(col.nrows, np.float), + col.addcolumn( np.ones(col.nrows, float), "drlv2" ) if not "labels" in col.titles: - col.addcolumn( np.ones(col.nrows, np.float)-2, + col.addcolumn( np.ones(col.nrows, float)-2, "labels" ) if not "sc" in col.titles: assert "xc" in col.titles @@ -259,10 +277,17 @@ def grid_index_parallel( fltfile, parfile, tmp, gridpars, translations ): """ gridpars = initgrid( fltfile, parfile, tmp, gridpars ) print( "Done init" ) - - NPR = multiprocessing.cpu_count() - 1 + if 'NPROC' not in gridpars or gridpars['NPROC'] is None: + NPR = multiprocessing.cpu_count() - 1 + cImageD11.cimaged11_omp_set_num_threads(2) # assume hyperthreading is useful? + else: + NPR = int(gridpars['NPROC']) + if 'NTHREAD' in gridpars: + cImageD11.cimaged11_omp_set_num_threads(int(gridpars['NTHREAD'])) + elif NPR > 1: + cImageD11.cimaged11_omp_set_num_threads(1) tsplit = [ translations[i::NPR] for i in range(NPR) ] - args = [("%s.flt"%(tmp), sys.argv[2], t, gridpars) for i,t in enumerate(tsplit) ] + args = [("%s.flt"%(tmp), parfile, t, gridpars) for i,t in enumerate(tsplit) ] q = PQueue() p = Pool(processes=NPR, initializer=wrap_test_many_points_init, initargs=[q]) print( "Using a pool of",NPR,"processes" ) @@ -289,7 +314,7 @@ def grid_index_parallel( fltfile, parfile, tmp, gridpars, translations ): sys.stderr.write(" Caught queue empty exception\n") if pa._number_left == 0: break - except KeyBoardInterrupt: + except KeyboardInterrupt: break # write here to be on the safe side .... grain.write_grain_file( "all"+tmp+".map", ul.uniqgrains ) @@ -303,33 +328,38 @@ def grid_index_parallel( fltfile, parfile, tmp, gridpars, translations ): import sys, random from ImageD11.grid_index_parallel import grid_index_parallel -fltfile = sys.argv[1] -parfile = sys.argv[2] -tmp = sys.argv[3] - -gridpars = { - 'DSTOL' : 0.004, - 'OMEGAFLOAT' : 0.13, - 'COSTOL' : 0.002, - 'NPKS' : int( sys.argv[4] ), - 'TOLSEQ' : [ 0.02, 0.015, 0.01], - 'SYMMETRY' : "cubic", - 'RING1' : [5,10], - 'RING2' : [5,10], - 'NUL' : True, - 'tolangle' : 0.25, - 'toldist' : 100., -} +if __name__=="__main__": + # You need this idiom to use multiprocessing on windows (script is imported again) + gridpars = { + 'DSTOL' : 0.004, + 'OMEGAFLOAT' : 0.13, + 'COSTOL' : 0.002, + 'NPKS' : int( sys.argv[4] ), + 'TOLSEQ' : [ 0.02, 0.015, 0.01], + 'SYMMETRY' : "cubic", + 'RING1' : [5,10], + 'RING2' : [5,10], + 'NUL' : True, + 'FITPOS' : True, + 'tolangle' : 0.25, + 'toldist' : 100., + 'NPROC' : None, # guess from cpu_count + 'NTHREAD' : 2 , + } -# grid to search -translations = [(t_x, t_y, t_z) + # grid to search + translations = [(t_x, t_y, t_z) for t_x in range(-500, 501, 50) for t_y in range(-500, 501, 50) for t_z in range(-500, 501, 50) ] -# Cylinder: -# translations = [( x,y,z) for (x,y,z) in translations if (x*x+y*y)< 500*500 ] -# -random.seed(42) # reproducible -random.shuffle(translations) -grid_index_parallel( fltfile, parfile, tmp, gridpars, translations ) + # Cylinder: + # translations = [( x,y,z) for (x,y,z) in translations if (x*x+y*y)< 500*500 ] + # + random.seed(42) # reproducible + random.shuffle(translations) + + fltfile = sys.argv[1] + parfile = sys.argv[2] + tmp = sys.argv[3] + grid_index_parallel( fltfile, parfile, tmp, gridpars, translations ) """) diff --git a/ImageD11/guicommand.py b/ImageD11/guicommand.py index 11ebb820..7d1cb1b6 100644 --- a/ImageD11/guicommand.py +++ b/ImageD11/guicommand.py @@ -1,4 +1,3 @@ - from __future__ import print_function # ImageD11_v0.4 Software for beamline ID11 @@ -30,27 +29,32 @@ import logging, sys # Things to offer from gui -from ImageD11 import peakmerge, indexing, transformer, eps_sig_solver +from ImageD11 import peakmerge, indexing, transformer + +# eps_sig_solver # To autoconvert arrays to lists for Java XMLRPC RETURN_NUMERICS = False import numpy as np + TYPE_NUMERIC = type(np.zeros(1)) + class guicommand: """ Keeps a log of all commands issued - separates gui code from algorithmical code """ + def __init__(self): - self.objects = { "peakmerger" : peakmerge.peakmerger(), - "transformer": transformer.transformer(), - "indexer" : indexing.indexer(), - "solver" : eps_sig_solver.solver(), - } - - self.commandscript = \ -"""# Create objects to manipulate - they hold your data + self.objects = { + "peakmerger": peakmerge.peakmerger(), + "transformer": transformer.transformer(), + "indexer": indexing.indexer(), + # "solver" : eps_sig_solver.solver(), + } + + self.commandscript = """# Create objects to manipulate - they hold your data # from ImageD11 import peakmerge, indexing, transformer, eps_sig_solver mypeakmerger = peakmerge.peakmerger() @@ -77,34 +81,47 @@ def execute(self, obj, command, *args, **kwds): if obj not in list(self.objects.keys()): raise Exception("ERROR! Unknown command object") o = self.objects[obj] - ran = "my%s.%s("% (obj, command) + ran = "my%s.%s(" % (obj, command) if command.find("."): subobjs = command.split(".")[:-1] for s in subobjs: - o = getattr(o,s) + o = getattr(o, s) command = command.split(".")[-1] - func = getattr(o,command) + func = getattr(o, command) try: addedcomma = "" for a in args: - ran="%s %s %s"%(ran,addedcomma,repr(a)) - addedcomma="," - for k,v in list(kwds.items()): - ran="%s %s %s=%s "%(ran,addedcomma,k,v) - addedcomma="," - ran+=" )\n" - logging.debug("Running: "+ran) + ran = "%s %s %s" % (ran, addedcomma, repr(a)) + addedcomma = "," + for k, v in list(kwds.items()): + ran = "%s %s %s=%s " % (ran, addedcomma, k, v) + addedcomma = "," + ran += " )\n" + logging.debug("Running: " + ran) sys.stdout.flush() ret = func(*args, **kwds) except: - logging.error("Exception occurred " + "self" + str(self) + - "obj" + str(obj)+ "command" + str(command) + - "func" + str(func) + "args" + str(args) + "kwds" + str(kwds)) + logging.error( + "Exception occurred " + + "self" + + str(self) + + "obj" + + str(obj) + + "command" + + str(command) + + "func" + + str(func) + + "args" + + str(args) + + "kwds" + + str(kwds) + ) import traceback + traceback.print_exc() return "Exception occured in the python " + ran - self.commandscript+=ran + self.commandscript += ran return ret def getdata(self, obj, name): @@ -118,7 +135,7 @@ def getdata(self, obj, name): """ if obj not in list(self.objects.keys()): raise Exception("ERROR! Unknown command object") - attribute = getattr(self.objects[obj],name) + attribute = getattr(self.objects[obj], name) if RETURN_NUMERICS: # Normally python will get this logging.debug("python return array") diff --git a/ImageD11/gv_general.py b/ImageD11/gv_general.py index ff617d14..afff26ff 100644 --- a/ImageD11/gv_general.py +++ b/ImageD11/gv_general.py @@ -31,7 +31,8 @@ from numpy.linalg import det, inv import numpy as np -print("gv_general from ",__file__) +# Use python -v myscript.py args +# print("gv_general from ",__file__) def angmod(a): return np.arctan2( np.sin(a), np.cos(a) ) @@ -73,7 +74,7 @@ def rotate_vectors(self, vectors, angles = None): http://mathworld.wolfram.com/RotationFormula.html r' = r cos(t) + n(n.r)(1-cos(t)) + rxn sin(t) """ - p = np.array(vectors, np.float) + p = np.array(vectors, float) assert p.shape[0] == 3 if angles is None: self.to_matrix() @@ -93,13 +94,14 @@ def rotate_vectors_inverse(self, vectors, angles = None): """ Same as rotate vectors, but opposing sense """ - p = np.array(vectors, np.float) + p = np.array(vectors, float) assert p.shape[0] == 3 if angles is None: self.to_matrix() return np.dot(self.inversematrix, p) # Just reverse the angles here - return self.rotate_vectors(vectors, -angles) + else: + return self.rotate_vectors(vectors, -angles) def to_matrix(self): """ @@ -115,10 +117,10 @@ def to_matrix(self): e = np.array([self.direction]*3) w = np.transpose(np.array( [ [ 0, -dz, dy ] , [dz, 0, -dx] , - [-dy, dx, 0] ], np.float)) + [-dy, dx, 0] ], float)) st = math.sin( math.radians( self.angle )) ct = math.cos( math.radians( self.angle )) - self.matrix = np.identity(3, np.float)*ct - st * w + \ + self.matrix = np.identity(3, float)*ct - st * w + \ (1 - ct)*e*np.transpose(e) self.inversematrix = inv(self.matrix) return self.matrix @@ -136,12 +138,12 @@ def axis_from_matrix( m ): if arg == 1: # identity matrix - oh bugger - direction is undefined angle_rad = 0.0 - direc = np.array([0,0,1],np.float) + direc = np.array([0,0,1],float) else: angle_rad = math.acos(arg) direc = np.array([m[2,1] - m[1,2], m[0,2] - m[2,0], - m[1,0] - m[0,1] ], np.float ) + m[1,0] - m[0,1] ], float ) direc = direc / np.sqrt(np.dot(direc, direc)) o = rotation_axis( direc , math.degrees( angle_rad ) ) if not (abs(o.matrix - m) < 1e-5).all(): @@ -150,7 +152,7 @@ def axis_from_matrix( m ): raise Exception("error in axis_from_matrix") return o -rotate_identity = rotation_axis( np.array([0,0,1],np.float) , 0.0 ) +rotate_identity = rotation_axis( np.array([0,0,1],float) , 0.0 ) def k_to_g( k , # scattering vectors in the laboratory @@ -189,7 +191,7 @@ def k_to_g( k , # scattering vectors in the laboratory def g_to_k( g, # g-vectors [3,:] wavelength, # Needed - depends on curve of Ewald sphere! - axis = np.array([0,0,1], np.float), + axis = np.array([0,0,1], float), pre = None, post = None ): """ @@ -230,7 +232,7 @@ def g_to_k( g, # g-vectors [3,:] else: rg = g assert rg.shape == g.shape - beam = np.zeros(rg.shape, np.float) + beam = np.zeros(rg.shape, float) beam[0,:] = -1./wavelength beam[1,:] = 0. beam[2,:] = 0. @@ -252,7 +254,7 @@ def g_to_k( g, # g-vectors [3,:] rbda0 = np.sum(rb * a0, 0) rbda1 = np.sum(rb * a1, 0) rbda2 = np.sum(rb * a2, 0) - assert rbda0.shape == rbda1.shape == rbda2.shape == (g.shape[1],) + assert rbda0.shape == rbda1.shape == rbda2.shape == (npeaks,) modg = np.sqrt(np.sum(g * g, 0)) kdotbeam = -modg*modg/2. # print kdotbeam,"uyou" diff --git a/ImageD11/indexing.py b/ImageD11/indexing.py index 9b831738..13fc85b0 100644 --- a/ImageD11/indexing.py +++ b/ImageD11/indexing.py @@ -1,4 +1,3 @@ - from __future__ import print_function, division @@ -24,93 +23,147 @@ from . import cImageD11, unitcell from xfab.tools import ubi_to_u, u_to_rod, ubi_to_rod -import math, time, sys, logging +import math, time, sys + +loglevel = 1 + + +class clogging( + object +): # because multiprocessing. FIXME. object level logging rather than module ? + def log(self, *args): + print(" ".join(str(a) for a in args)) + + def debug(self, *args): + if loglevel <= 0: + logging.log("debug:", *args) + + def info(self, *args): + if loglevel <= 1: + logging.log("info:", *args) + + def warning(self, *args): + if loglevel <= 2: + logging.log("warning:", *args) + + def error(self, *args): + if loglevel <= 3: + logging.log("error:", *args) + + +logging = clogging() + + +def ubi_fit_2pks(ubi, g1, g2): + """ + Refine a ubi matrix so it matches the pair of g-vectors supplied + (almost) accounts for cell parameters not being quite right + """ + ub = np.linalg.inv(ubi) + h1 = np.round(np.dot(ubi, g1)) + h2 = np.round(np.dot(ubi, g2)) + g3 = np.cross(g1, g2) + h3 = np.dot(ubi, g3) # do not round to integer + g1c = np.dot(ub, h1) + g2c = np.dot(ub, h2) + g3c = np.dot(ub, h3) + R = np.outer(g1, h1) + np.outer(g2, h2) + np.outer(g3, h3) + H = np.outer(h1, h1) + np.outer(h2, h2) + np.outer(h3, h3) + ubfit = np.dot(R, np.linalg.inv(H)) + ubifit = np.linalg.inv(ubfit) + return ubifit def myhistogram(data, bins): - """ - The numpy histogram api was changed - So here is an api that will not change - It is based on that from the old Numeric manual - """ - n = np.searchsorted( np.sort(data), bins ) - n = np.concatenate( [ n, [len(data)]] ) - return n[1:] - n[:-1] + """ + The numpy histogram api was changed + So here is an api that will not change + It is based on that from the old Numeric manual + """ + n = np.searchsorted(np.sort(data), bins) + n = np.concatenate([n, [len(data)]]) + return n[1:] - n[:-1] def readubis(ubifile): - """read ubifile and return a list of ubi arrays """ + """read ubifile and return a list of ubi arrays""" f = open(ubifile, "r") ubisread = [] u = [] for line in f: - if line[0]=="#": + if line[0] == "#": continue - vals = [ float(x) for x in line.split() ] + vals = [float(x) for x in line.split()] if len(vals) == 3: u = u + [vals] - if len(u)==3: + if len(u) == 3: ubisread.append(np.array(u)) u = [] f.close() return ubisread + def write_ubi_file(filename, ubilist): - """ save 3x3 matrices into file """ - f=open(filename,"w") + """save 3x3 matrices into file""" + f = open(filename, "w") for u in ubilist: - f.write("%f %f %f\n" %(u[0][0],u[0][1],u[0][2])) - f.write("%f %f %f\n" %(u[1][0],u[1][1],u[1][2])) - f.write("%f %f %f\n\n"%(u[2][0],u[2][1],u[2][2])) + f.write("%f %f %f\n" % (u[0][0], u[0][1], u[0][2])) + f.write("%f %f %f\n" % (u[1][0], u[1][1], u[1][2])) + f.write("%f %f %f\n\n" % (u[2][0], u[2][1], u[2][2])) f.close() + def ubitocellpars(ubi): - """ convert ubi matrix to unit cell """ + """convert ubi matrix to unit cell""" g = np.dot(ubi, np.transpose(ubi)) from math import acos, degrees, sqrt - a = sqrt(g[0,0]) - b = sqrt(g[1,1]) - c = sqrt(g[2,2]) - alpha = degrees( acos(g[1,2]/b/c)) - beta = degrees( acos(g[0,2]/a/c)) - gamma = degrees( acos(g[0,1]/a/b)) + + a = sqrt(g[0, 0]) + b = sqrt(g[1, 1]) + c = sqrt(g[2, 2]) + alpha = degrees(acos(g[1, 2] / b / c)) + beta = degrees(acos(g[0, 2] / a / c)) + gamma = degrees(acos(g[0, 1] / a / b)) return a, b, c, alpha, beta, gamma + def ubitoU(ubi): """ convert ubi to Grainspotter style U The convention is B as being triangular, hopefully as Busing and Levy TODO - make some testcases please!! """ - #return np.transpose(np.dot(ubitoB(ubi),ubi)) + # return np.transpose(np.dot(ubitoB(ubi),ubi)) return ubi_to_u(ubi) + def ubitoRod(ubi): """ TODO Testcases!!! """ -# u = ubitoU(ubi) -# w, v = np.linalg.eig(u) -# print 'Eigenvalues' -# print w -# print 'Eigen vectors' -# print v -# #ehat = v[:,0] -# #angle = -1*math.acos(np.clip(w[order[1]].real,-1,1)) -# order = np.argsort(w.real) -# print order -# ehat = v[:, order[-1]] -# if order.tolist() != range(3): -# print 'HHFH' -# angle = -1*np.arccos(w[order[1]].real) -# else: -# angle = np.arccos(w[order[1]].real) -# Rod = ehat * math.tan(angle/2) -# return Rod.real + # u = ubitoU(ubi) + # w, v = np.linalg.eig(u) + # print 'Eigenvalues' + # print w + # print 'Eigen vectors' + # print v + # #ehat = v[:,0] + # #angle = -1*math.acos(np.clip(w[order[1]].real,-1,1)) + # order = np.argsort(w.real) + # print order + # ehat = v[:, order[-1]] + # if order.tolist() != range(3): + # print 'HHFH' + # angle = -1*np.arccos(w[order[1]].real) + # else: + # angle = np.arccos(w[order[1]].real) + # Rod = ehat * math.tan(angle/2) + # return Rod.real return ubi_to_rod(ubi) + def ubitoB(ubi): - """ give the B matrix from ubi """ + """give the B matrix from ubi""" g = np.dot(ubi, np.transpose(ubi)) return np.transpose(np.linalg.inv(np.linalg.cholesky(g))) @@ -128,6 +181,7 @@ def mod_360(theta, target): diff = theta - target return theta + def calc_drlv2(UBI, gv): """ Get the difference from being integer hkls @@ -136,13 +190,12 @@ def calc_drlv2(UBI, gv): returns drlv2 = (h_calc - h_int)^2 """ h = np.dot(UBI, np.transpose(gv)) - hint = np.floor(h + 0.5).astype(np.int) # rounds down + hint = np.floor(h + 0.5).astype(int) # rounds down diff = h - hint - drlv2 = np.sum(diff * diff,0) + drlv2 = np.sum(diff * diff, 0) return drlv2 - def refine(UBI, gv, tol, quiet=True): """ Refine an orientation matrix and rescore it. @@ -160,57 +213,56 @@ def refine(UBI, gv, tol, quiet=True): # print "Scores before",self.score(UBI) # Need to find hkl indices for all of the peaks which are indexed h = np.dot(UBI, np.transpose(gv)) - hint = np.floor(h+0.5).astype(np.int) # rounds down + hint = np.floor(h + 0.5).astype(int) # rounds down diff = h - hint - drlv2 = np.sum( diff * diff, 0) + drlv2 = np.sum(diff * diff, 0) tol = float(tol) tol = tol * tol # Only use peaks which are assigned to rings for refinement - ind = np.compress( np.less(drlv2,tol) , np.arange(gv.shape[0]) ) + ind = np.compress(np.less(drlv2, tol), np.arange(gv.shape[0])) # scoreb4=ind.shape[0] contribs = drlv2[ind] try: - fitb4=math.sqrt(np.sum(contribs)/contribs.shape[0]) + fitb4 = math.sqrt(np.sum(contribs) / contribs.shape[0]) if not quiet: - logging.debug("Fit before refinement %.8f %5d"% ( - fitb4, contribs.shape[0])) + logging.debug("Fit before refinement %.8f %5d" % (fitb4, contribs.shape[0])) except: - logging.error("No contributing reflections for \n%s"%(str(UBI))) + logging.error("No contributing reflections for \n%s" % (str(UBI))) raise # drlv2_old=drlv2 - R=np.zeros((3,3),np.float) - H=np.zeros((3,3),np.float) + R = np.zeros((3, 3), float) + H = np.zeros((3, 3), float) for i in ind: - r = gv[i,:] - k = hint[:,i].astype(np.float) + r = gv[i, :] + k = hint[:, i].astype(float) # print r,k - R = R + np.outer(r,k) - H = H + np.outer(k,k) + R = R + np.outer(r, k) + H = H + np.outer(k, k) try: - HI=np.linalg.inv(H) - UBoptimal=np.dot(R,HI) - UBIo=np.linalg.inv(UBoptimal) + HI = np.linalg.inv(H) + UBoptimal = np.dot(R, HI) + UBIo = np.linalg.inv(UBoptimal) except: # A singular matrix - this sucks. - UBIo=UBI - h=np.dot(UBIo,np.transpose(gv)) - hint=np.floor(h+0.5).astype(np.int) # rounds down - diff=h-hint - drlv2=np.sum(diff*diff,0) - ind = np.compress( np.less(drlv2,tol), np.arange(gv.shape[0]) ) + UBIo = UBI + h = np.dot(UBIo, np.transpose(gv)) + hint = np.floor(h + 0.5).astype(int) # rounds down + diff = h - hint + drlv2 = np.sum(diff * diff, 0) + ind = np.compress(np.less(drlv2, tol), np.arange(gv.shape[0])) # scorelastrefined=ind.shape[0] contribs = drlv2[ind] try: - fitlastrefined=math.sqrt(np.sum(contribs)/contribs.shape[0]) + fitlastrefined = math.sqrt(np.sum(contribs) / contribs.shape[0]) if not quiet: - print("after %.8f %5d"%(fitlastrefined,contribs.shape[0])) + logging.debug("after %.8f %5d" % (fitlastrefined, contribs.shape[0])) except: - print("\n\n\n") - print("No contributing reflections for\n",UBI) - print("After refinement, it was OK before ???") - print("\n\n\n") + logging.error("\n\n\n") + logging.error("No contributing reflections for %s\n" % (str(UBI))) + logging.error("After refinement, it was OK before ???") + logging.error("\n\n\n") return UBI - raise + # raise # for i in ind: # print "( %-6.4f %-6.4f %-6.4f ) %12.8f %12.8f"%(\ # h[0,i],h[1,i],h[2,i],sqrt(drlv2[i]),sqrt(drlv2_old[i])) @@ -222,85 +274,138 @@ def refine(UBI, gv, tol, quiet=True): return UBIo +def indexer_from_colfile(colfile, **kwds): + uc = unitcell.unitcell_from_parameters(colfile.parameters) + w = float(colfile.parameters.get("wavelength")) + gv = np.array((colfile.gx, colfile.gy, colfile.gz), float) + kwds.update({"unitcell": uc, "wavelength": w, "gv": gv.T}) + ind = indexer(**kwds) + if "omega" in colfile.titles: + ind.omega_fullrange = find_omega_ranges(colfile.omega) + return ind class indexer: """ A class for searching for orientation matrices """ - def __init__(self, - unitcell=None, - gv=None, - cosine_tol = 0.002, - minpks = 10 , - hkl_tol=0.01, - ring_1=1, - ring_2=2, - ds_tol=0.005, - wavelength=-1, - uniqueness=0.5, - eta_range=0., - max_grains=100): + + def __init__( + self, + unitcell=None, + gv=None, + cosine_tol=0.002, + minpks=10, + hkl_tol=0.01, + ring_1=1, + ring_2=2, + ds_tol=0.005, + wavelength=-1, + uniqueness=0.5, + eta_range=0.0, + max_grains=100, + ): """ Unitcell would be a unitcell object for generating hkls peaks gv would be a 3*n array of points in reciprocal space The rest of the arguments are parameters. """ - self.unitcell=unitcell - self.gv=gv - if gv is not None: - print('gv:', gv, gv.shape, gv.dtype) - self.wedge=0.0 # Default - if gv is not None: - self.gvflat=np.ascontiguousarray(gv,'d') - # Makes it contiguous in memory, hkl fast index - - self.cosine_tol=cosine_tol - self.wavelength=wavelength - self.hkl_tol=hkl_tol - self.ring_1=ring_1 - self.ring_2=ring_2 - self.uniqueness=uniqueness - self.minpks=minpks - self.ds_tol=ds_tol - self.max_grains=max_grains + # This stop variable allows computation to be run in a thread... + self.stop = False + self.unitcell = unitcell + self.gv = gv + self.ra = None + if gv is not None: # do init + logging.info("gv: %s %s %s" % (str(gv), str(gv.shape), str(gv.dtype))) + assert gv.shape[1] == 3 + self.gv = gv.astype(float) + self.ds = np.sqrt((gv * gv).sum(axis=1)) + self.ga = np.zeros(len(self.ds), np.int32) - 1 # Grain assignments + self.gvflat = np.ascontiguousarray(gv, float) + self.wedge = 0.0 # Default + + self.cosine_tol = cosine_tol + self.wavelength = wavelength + self.hkl_tol = hkl_tol + self.ring_1 = ring_1 + self.ring_2 = ring_2 + self.uniqueness = uniqueness + self.minpks = minpks + self.ds_tol = ds_tol + self.max_grains = max_grains self.eta_range = eta_range - self.ubis=[] - self.scores=[] - - + self.ubis = [] + self.scores = [] + self.omega = None + self.index_needs_debug = 0 # track problems quietly... + self.omega_fullrange = 0 # flags having no idea # it would make more sense to inherit the parameter object - will # have to think about this some more - how general is it? from ImageD11 import parameters - self.parameterobj = parameters.parameters(cosine_tol=self.cosine_tol, - hkl_tol=self.hkl_tol, ring_1=self.ring_1, ring_2=self.ring_2, - minpks=self.minpks, uniqueness=self.uniqueness, ds_tol=self.ds_tol, - wavelength=self.wavelength, eta_range=self.eta_range, - max_grains=self.max_grains) + self.parameterobj = parameters.parameters( + cosine_tol=self.cosine_tol, + hkl_tol=self.hkl_tol, + ring_1=self.ring_1, + ring_2=self.ring_2, + minpks=self.minpks, + uniqueness=self.uniqueness, + ds_tol=self.ds_tol, + wavelength=self.wavelength, + eta_range=self.eta_range, + max_grains=self.max_grains, + ) + + # Add a resetting functionality, adapted from + # stackoverflow.com/questions/4866587/pythonic-way-to-reset-an-objects-variables + import copy + + self.__pristine_dict = copy.deepcopy(self.__dict__) + + def __getattr__(self, name): + """got some time lost setting tol which does not exist + + this will never be clean :-( + """ + if name == "tol": + raise KeyError("tol not in indexer") + print("WARNING: creating indexer.%s" % (name)) + setattr(self, name, None) + + def reset(self): + """ + To get a really clean indexer just create a new one (e.g. via __init__) + This was added for the gui to help it forget what happened before + but keep parameters as they were set + """ + import copy + + self.__dict__ = copy.deepcopy(self.__pristine_dict) + self.__pristine_dict = copy.deepcopy(self.__dict__) - def loadpars(self,filename=None): + def loadpars(self, filename=None): if filename is not None: self.parameterobj.loadparameters(filename) - self.parameterobj.update_other(self) + # self.parameterobj.update_other(self) # busted CI for logging in windows + py2.7 + for parname in self.parameterobj.parameters: + if hasattr(self, parname): + setattr(self, parname, self.parameterobj.get(parname)) def updateparameters(self): self.savepars() - self.pars=self.parameterobj.parameters + self.pars = self.parameterobj.parameters - def savepars(self,filename=None): + def savepars(self, filename=None): self.parameterobj.update_yourself(self) if filename is not None: self.parameterobj.saveparameters(filename) - - - def out_of_eta_range(self,eta): - """ decide if an eta is going to be kept """ + def out_of_eta_range(self, eta): + """decide if an eta is going to be kept""" e = mod_360(float(eta), 0) if e < abs(self.eta_range) and e > -abs(self.eta_range): return True - if e < -180.+abs(self.eta_range) or e > 180.-abs(self.eta_range): + if e < -180.0 + abs(self.eta_range) or e > 180.0 - abs(self.eta_range): return True return False @@ -309,200 +414,292 @@ def assigntorings(self): Assign the g-vectors to hkl rings """ # rings are in self.unitcell - limit = np.maximum.reduce(self.ds) - print("Maximum d-spacing considered",limit) - self.unitcell.makerings(limit, tol = self.ds_tol) + limit = np.amax(self.ds) + logging.info("Assign to rings, maximum d-spacing considered: %f" % (limit)) + self.unitcell.makerings(limit, tol=self.ds_tol) dsr = self.unitcell.ringds - self.ra = np.zeros(self.gv.shape[0],np.int)-1 - self.na = np.zeros(len(dsr),np.int) - print("Ring assignment array shape",self.ra.shape) + # npks + npks = len(self.ds) + self.ra = np.zeros(npks, np.int32) - 1 + self.na = np.zeros(len(dsr), np.int32) + logging.info("Ring assignment array shape", self.ra.shape) tol = float(self.ds_tol) - for i in range(self.ra.shape[0]): - # Assign the peak to a ring (or no ring) - ds = self.ds[i] - best = 999. - for j in range(len(dsr)): - diff = abs(ds-dsr[j]) - if diff < tol: - if diff < best: - self.ra[i]=j - best=diff + best = np.zeros(npks, float) + tol + for j, dscalc in enumerate(dsr): + dserr = abs(self.ds - dscalc) + sel = dserr < best + self.ra[sel] = j + best[sel] = dserr[sel] # Report on assignments - ds=np.array(self.ds) - print("Ring ( h, k, l) Mult total indexed to_index ubis peaks_per_ubi") + ds = np.array(self.ds) + logging.info( + "Ring ( h, k, l) Mult total indexed to_index ubis peaks_per_ubi tth" + ) minpks = 0 # try reverse order instead for j in range(len(dsr))[::-1]: - ind = np.compress( np.equal(self.ra,j), np.arange(self.ra.shape[0]) ) - self.na[j]=ind.shape[0] - n_indexed = np.sum(np.where( self.ga[ind] > -1, 1, 0)) - n_to_index = np.sum(np.where( self.ga[ind] == -1, 1, 0)) + ind = np.compress(np.equal(self.ra, j), np.arange(self.ra.shape[0])) + self.na[j] = ind.shape[0] + n_indexed = np.sum(np.where(self.ga[ind] > -1, 1, 0)) + n_to_index = np.sum(np.where(self.ga[ind] == -1, 1, 0)) # diffs = abs(take(ds,ind) - dsr[j]) - h=self.unitcell.ringhkls[dsr[j]][0] + h = self.unitcell.ringhkls[dsr[j]][0] Mult = len(self.unitcell.ringhkls[dsr[j]]) - try: - expected_orients = int(180./self.omega_fullrange * self.na[j]/float(Mult)) - expected_npks = int(self.omega_fullrange/180. * Mult) + if self.omega_fullrange > 0: + expected_orients = int( + 180.0 / self.omega_fullrange * self.na[j] / float(Mult) + ) + expected_npks = int(self.omega_fullrange / 180.0 * Mult) minpks += expected_npks - except: - expected_orients = 'N/A' - expected_npks = 'N/A' - print("Ring %-3d (%3d,%3d,%3d) %3d %5d %5d %5d %5s %2s"%(\ - j,h[0],h[1],h[2],Mult, - self.na[j],n_indexed,n_to_index,expected_orients,expected_npks)) + else: + expected_orients = "N/A" + expected_npks = "N/A" + tth = 2 * np.degrees(np.arcsin(dsr[j] * self.wavelength / 2)) + logging.info( + "Ring %-3d (%3d,%3d,%3d) %3d %5d %5d %5d %5s %2s %.2f" + % ( + j, + h[0], + h[1], + h[2], + Mult, + self.na[j], + n_indexed, + n_to_index, + expected_orients, + expected_npks, + tth, + ) + ) if minpks > 0: - print('\nmin_pks: - Current --> %3d'%(self.minpks)) - print(' - Expected --> %3d\n'%(minpks)) + logging.info("\nmin_pks: - Current --> %3d" % (self.minpks)) + logging.info(" - Expected --> %3d\n" % (minpks)) # We will only attempt to index g-vectors which have been assigned # to hkl rings (this gives a speedup if there # are a lot of spare peaks - ind = np.compress(np.greater(self.ra,-1), np.arange(self.ra.shape[0])) + ind = np.compress(np.greater(self.ra, -1), np.arange(self.ra.shape[0])) self.gvr = self.gv[ind] - print("Using only those peaks which are assigned to rings for scoring trial matrices") - print("Shape of scoring matrix",self.gvr.shape) - self.gvflat=np.ascontiguousarray(self.gvr, 'd') # Makes it contiguous + logging.info( + "Using only those peaks which are assigned to rings for scoring trial matrices" + ) + logging.info("Shape of scoring matrix", self.gvr.shape) + self.gvflat = np.ascontiguousarray(self.gvr, float) # Makes it contiguous # in memory, hkl fast index - def friedelpairs(self,filename): + def friedelpairs(self, filename): """ Attempt to identify Freidel pairs Peaks must be assigned to the same powder ring Peaks will be the closest thing to being 180 degrees apart """ - out = open(filename,"w") - dsr=self.unitcell.ringds + out = open(filename, "w") + dsr = self.unitcell.ringds nring = len(dsr) - for j in range( nring ): - ind = np.compress(np.equal(self.ra,j), np.arange(self.ra.shape[0])) + for j in range(nring): + ind = np.compress(np.equal(self.ra, j), np.arange(self.ra.shape[0])) # ind is the indices of the ring assigment array - eg which hkl is this gv # - if len(ind)==0: + if len(ind) == 0: continue thesepeaks = self.gv[ind] # - h=self.unitcell.ringhkls[dsr[j]][0] + h = self.unitcell.ringhkls[dsr[j]][0] # - out.write("\n\n\n# h = %d \n"%(h[0])) - out.write("# k = %d \n"%(h[1])) - out.write("# l = %d \n"%(h[2])) - out.write("# npks = %d \n"%(thesepeaks.shape[0])) - out.write("# score eta1 omega1 tth1 gv1_x gv1_y gv1_z eta2 omega2 tth2 gv2_x gv2_y gv2_z\n") + out.write("\n\n\n# h = %d \n" % (h[0])) + out.write("# k = %d \n" % (h[1])) + out.write("# l = %d \n" % (h[2])) + out.write("# npks = %d \n" % (thesepeaks.shape[0])) + out.write( + "# score eta1 omega1 tth1 gv1_x gv1_y gv1_z eta2 omega2 tth2 gv2_x gv2_y gv2_z\n" + ) for k in range(thesepeaks.shape[0]): nearlyzero = thesepeaks + thesepeaks[k] - mag = np.sum(nearlyzero*nearlyzero,1) + mag = np.sum(nearlyzero * nearlyzero, 1) best = np.argmin(mag) if best > k: a = ind[k] - out.write("%f "%( np.sqrt(mag[best]) ) ) - out.write("%f %f %f %f %f %f "%(self.eta[a],self.omega[a],self.tth[a],self.gv[a][0],self.gv[a][1],self.gv[a][2])) + out.write("%f " % (np.sqrt(mag[best]))) + out.write( + "%f %f %f %f %f %f " + % ( + self.eta[a], + self.omega[a], + self.tth[a], + self.gv[a][0], + self.gv[a][1], + self.gv[a][2], + ) + ) a = ind[best] - out.write("%f %f %f %f %f %f "%(self.eta[a],self.omega[a],self.tth[a],self.gv[a][0],self.gv[a][1],self.gv[a][2])) + out.write( + "%f %f %f %f %f %f " + % ( + self.eta[a], + self.omega[a], + self.tth[a], + self.gv[a][0], + self.gv[a][1], + self.gv[a][2], + ) + ) out.write("\n") + def score_all_pairs(self, n=None, rmulmax=None, rings_to_use=None): + """ + Generate all the potential pairs of rings and go score them too + n = maximum number of pairs to try + maxmult = max multiplicity of rings to use for generating pairs + rings_to_use = the rings to use for generating ubis + """ + self.assigntorings() + if rings_to_use is not None: + rings = [r for r in rings_to_use if (self.ra == r).sum() > 0] + else: + # Which rings have peaks assigned to them? + rings = [r for r in set(self.ra) if r >= 0] + # What are the multiplicities of these rings? We will use low multiplicity first + mults = {r: len(self.unitcell.ringhkls[self.unitcell.ringds[r]]) for r in rings} + if rmulmax is not None: + rings = [r for r in rings if mults[r] <= rmulmax] + + # How many peaks per ring? We will use the sparse rings first... + # why? We assume these are the strongest peaks on a weak high angle ring + # occupation = {r:self.na[r] for r in rings} + pairs = [ + (int(mults[r1] * mults[r2]), int(self.na[r1] * self.na[r2]), r1, r2) + for r1 in rings + for r2 in rings + ] + pairs.sort() + self.tried = 0 + self.npairs = len(pairs) + self.stop = False + k = 0 + for mu, oc, r1, r2 in pairs: + k += 1 + try: + self.ring_1 = r1 + self.ring_2 = r2 + self.find() + if len(self.hits) == 0: # skip when nothing is found + continue + self.scorethem() + self.tried += 1 + except KeyboardInterrupt: + break + if self.stop: + break + if n is not None and k > n: + break + logging.info( + "Tried r1=%d r2=%d attempt %d of %d, got %d grains" + % (r1, r2, self.tried, len(pairs), len(self.ubis)) + ) + logging.info( + "\nTested", self.tried, "pairs and found", len(self.ubis), "grains so far" + ) def find(self): """ Dig out the potential hits """ + # Optionally only used unindexed peaks here. Make this obligatory + # Need indices of gvectors to test. + # Bug out early when there are none + if self.ra is None: + self.assigntorings() + iall = np.arange(self.gv.shape[0]) + i1 = np.compress( + np.logical_and(np.equal(self.ra, self.ring_1), self.ga == -1), iall + ).tolist() + i2 = np.compress( + np.logical_and(np.equal(self.ra, self.ring_2), self.ga == -1), iall + ).tolist() + if len(i1) == 0 or len(i2) == 0: + logging.info("no peaks left for those rings") + return # Which are the rings being used for indexing hkls1 = self.unitcell.ringhkls[self.unitcell.ringds[int(self.ring_1)]] hkls2 = self.unitcell.ringhkls[self.unitcell.ringds[int(self.ring_2)]] - print("hkls of rings being used for indexing") - print("Ring 1:",hkls1) - print("Ring 2:",hkls2) - cosangles=[] + logging.info("hkls of rings being used for indexing") + logging.info("Ring 1: %s" % (str(hkls1))) + logging.info("Ring 2: %s" % (str(hkls2))) + cosangles = [] for h1 in hkls1: for h2 in hkls2: - ca=self.unitcell.anglehkls(h1,h2) + ca = self.unitcell.anglehkls(h1, h2) cosangles.append(ca[1]) cosangles.sort() - coses=[] - while len(cosangles)>0: - a=cosangles.pop() - if abs(a-1.)<1e-5 or abs(a+1.)<1e-5: # Throw out 180 degree angles + coses = [] + while len(cosangles) > 0: + a = cosangles.pop() + if ( + abs(a - 1.0) < 1e-5 or abs(a + 1.0) < 1e-5 + ): # Throw out 180 degree angles continue - if len(coses)==0: + if len(coses) == 0: coses.append(a) continue - if abs(coses[-1]-a) > 1e-5: + if abs(coses[-1] - a) > 1e-5: coses.append(a) - print("Possible angles and cosines between peaks in rings:") + logging.info("Possible angles and cosines between peaks in rings:") for c in coses: - print(math.acos(c)*180/math.pi,c) + logging.info("%.6f %.6f" % (math.acos(c) * 180 / math.pi, c)) # - # Need indices of gvectors to test - iall = np.arange(self.gv.shape[0]) # - # Optionally only used unindexed peaks here? Make this obligatory - i1 = np.compress(np.logical_and(np.equal(self.ra,self.ring_1), - self.ga==-1 ) , iall).tolist() - i2 = np.compress(np.logical_and(np.equal(self.ra,self.ring_2), - self.ga==-1 ) , iall).tolist() - print("Number of peaks in ring 1:",len(i1)) - print("Number of peaks in ring 2:",len(i2)) - print("Minimum number of peaks to identify a grain",self.minpks) + logging.info("Number of peaks in ring 1: %d" % (len(i1))) + logging.info("Number of peaks in ring 2: %d" % (len(i2))) + logging.info("Minimum number of peaks to identify a grain %d" % (self.minpks)) # print self.gv.shape # ntry=0 # nhits=0 - self.hits=[] - if len(i1)==0 or len(i2)==0: + self.hits = [] + if len(i1) == 0 or len(i2) == 0: # return without crashing please return - tol=float(self.cosine_tol) + tol = float(self.cosine_tol) # ng=0 - mp=np.sqrt(np.sum(self.gv*self.gv,1)) + mp = np.sqrt(np.sum(self.gv * self.gv, 1)) # print mp.shape - ps1 = np.take(self.gv,i1,0) - mp1 = np.take(mp,i1,0) + ps1 = np.take(self.gv, i1, 0) + mp1 = np.take(mp, i1, 0) n1 = ps1.copy() - ps2 = np.take(self.gv,i2,0) - mp2 = np.take(mp,i2,0) + ps2 = np.take(self.gv, i2, 0) + mp2 = np.take(mp, i2, 0) n2 = ps2.copy() # print "mp1.shape",mp1.shape # print "n1[:,1].shape",n1[:,1].shape for i in range(3): - n1[:,i]=n1[:,i]/mp1 - n2[:,i]=n2[:,i]/mp2 - cs = np.array(coses,'d') + n1[:, i] = n1[:, i] / mp1 + n2[:, i] = n2[:, i] / mp2 + cs = np.array(coses, "d") # found=0 - hits=[] + hits = [] start = time.time() - onepercent=len(i1)/100. -# if onepercent < 1: onepercent=1 - start=time.time() - mtol = -tol # Ugly interface - set cosine tolerance negative for all - # instead of best + self.cosangles = cs + mtol = -tol # Ugly interface - set cosine tolerance negative for all + # instead of best for i in range(len(i1)): - if i == 0: - print("Percent done %6.3f%% ... potential hits %-6d" \ - % (i*100./len(i1),len(hits)), end=' ') - costheta=np.dot(n2,n1[i]) - if tol > 0: # This is the original algorithm - the closest angle - best,diff = cImageD11.closest(costheta,cs) + costheta = np.dot(n2, n1[i]) + if tol > 0: # This is the original algorithm - the closest angle + best, diff = cImageD11.closest(costheta, cs) if diff < tol: - hits.append( [ diff, i1[i], i2[best] ]) + hits.append([diff, i1[i], i2[best]]) else: - assert tol < 0, "Cosine tolerance should be positive or negative" for cval in cs: # 1d scalar 1d diff = cval - costheta - candidates = np.compress( abs(diff) < mtol, i2 ) + candidates = np.compress(abs(diff) < mtol, i2) for c in candidates: - hits.append( [ 0.0, i1[i], c ] ) - print("\rPercent done %6.3f%% ... potential hits %-6d" \ - % ((i+1)*100./len(i1),len(hits)), end=' ') - costheta=np.dot(n2,n1[i]) - - print("\rPercent done %6.3f%% ... potential hits %-6d" \ - % ((i+1)*100./len(i1),len(hits))) - print("Number of trial orientations generated",len(hits)) - print("Time taken",time.time()-start) - self.hits=hits - - def histogram_drlv_fit(self,UBI=None,bins=None): + hits.append([0.0, i1[i], c]) + logging.info("Number of trial orientations generated %d" % (len(hits))) + logging.info("Time taken %.6f /s" % (time.time() - start)) + self.hits = hits + + def histogram_drlv_fit(self, UBI=None, bins=None): """ Generate a histogram of |drlv| for a ubi matrix For use in validation of grains @@ -512,135 +709,171 @@ def histogram_drlv_fit(self,UBI=None,bins=None): else: ubilist = [UBI] if bins is None: - start=0.25 - fac=2 - bins=[start] + start = 0.25 + fac = 2 + bins = [start] while start > 1e-5: - start=start/fac + start = start / fac bins.append(start) bins.append(-start) bins.reverse() - bins=np.array(bins) - hist = np.zeros((len(ubilist),bins.shape[0]-1),np.int) - j=0 + bins = np.array(bins) + hist = np.zeros((len(ubilist), bins.shape[0] - 1), int) + j = 0 for UBI in ubilist: drlv2 = calc_drlv2(UBI, self.gv) - drlv = np.sort(np.sqrt(drlv2)) # always +ve - if drlv[-1]>0.866: - print("drlv of greater than 0.866!!!",drlv[-1]) - positions = np.searchsorted(drlv,bins) - hist[j,:] = positions[1:]-positions[:-1] - j=j+1 - #for i in range(bins.shape[0]-1): - # print "%10.7f - %10.7f %10d"%(bins[i],bins[i+1],hist[i]) - #print sum(hist),hist.shape,bins.shape - self.bins=bins - self.histogram=hist - - def scorethem(self): - start=time.time() - ts=0 - tor=0 - ng=0 - tol=float(self.hkl_tol) - gv=self.gvflat - all=len(self.hits) - print("Scoring",all,"potential orientations") - prog=0 - ng=0 - nuniq=0 - - while len(self.hits) > 0 and ng -1 or self.ga[j]>-1: - # skip things which are already assigned - continue - if i==j: + drlv = np.sort(np.sqrt(drlv2)) # always +ve + if drlv[-1] > 0.866: + print("drlv of greater than 0.866!!!", drlv[-1]) + positions = np.searchsorted(drlv, bins) + hist[j, :] = positions[1:] - positions[:-1] + j = j + 1 + self.bins = bins + self.histogram = hist + + def scorethem(self, fitb4=False): + """decide which trials listed in hits to keep""" + start = time.time() + ng = 0 + tol = float(self.hkl_tol) + gv = self.gvflat + all = len(self.hits) + logging.info("Scoring %d potential orientations" % (all)) + progress = 0 + nuniq = 0 + # for getind mallocs + drlv2tmp = np.empty(len(self.gv), float) + labelstmp = np.empty(len(self.gv), np.int32) + while len(self.hits) > 0 and ng < self.max_grains: + diff, i, j = self.hits.pop() + if self.ga[i] > -1 or self.ga[j] > -1 or i == j: + # skip things which are already assigned or errors continue try: - self.unitcell.orient(self.ring_1, self.gv[i,:], self.ring_2, self.gv[j,:],verbose=0,all=False) + self.unitcell.orient( + self.ring_1, self.gv[i, :], self.ring_2, self.gv[j, :], verbose=0 + ) except: - print(i,j,self.ring_1,self.ring_2) - print(self.gv[i]) - print(self.gv[j]) + logging.error( + " ".join([str(x) for x in (i, j, self.ring_1, self.ring_2)]) + ) + logging.error(str(self.gv[i])) + logging.error(str(self.gv[j])) + logging.error("Failed to find orientation in unitcell.orient") raise - npk = cImageD11.score(self.unitcell.UBI,gv,tol) + if fitb4: # FIXME : this does not work + self.unitcell.UBI = ubi_fit_2pks( + self.unitell.UBI, self.gv[i, :], self.gv[j, :] + ) + # npk = cImageD11.score(self.unitcell.UBI,gv,tol) + npk = self.score(self.unitcell.UBI, tol) + UBI = self.unitcell.UBI.copy() if npk > self.minpks: - self.unitcell.orient(self.ring_1, self.gv[i,:], self.ring_2, self.gv[j,:],verbose=0,all=True) - npks=[cImageD11.score(UBItest,gv,tol) for UBItest in self.unitcell.UBIlist] - choice = np.argmax(npks) - UBI = self.unitcell.UBIlist[choice] - npk = npks[choice] + # Try to get a better orientation if we can...: + self.unitcell.orient( + self.ring_1, + self.gv[i, :], + self.ring_2, + self.gv[j, :], + verbose=0, + crange=abs(self.cosine_tol), + ) + if fitb4: + for k in range(len(self.unitell.UBIlist)): + self.unitell.UBIlist[k] = ubi_fit_2pks( + self.unitell.UBIlist[k], self.gv[i, :], self.gv[j, :] + ) + if len(self.unitcell.UBIlist) > 1: + npks = [ + self.score(UBItest, tol) for UBItest in self.unitcell.UBIlist + ] + choice = np.argmax(npks) + if npks[choice] >= npk: + UBI = self.unitcell.UBIlist[choice].copy() + npk = npks[choice] + _ = cImageD11.score_and_refine(UBI, gv, tol) # See if we already have this grain... try: - ubio=self.refine(UBI.copy()) - # refine the orientation - ind=self.getind(ubio) # indices of peaks indexed - ga=self.ga[ind] # previous grain assignments - uniqueness=np.sum(np.where(ga==-1,1,0))*1.0/ga.shape[0] + ind = self.getind( + UBI, + drlv2tmp=drlv2tmp, + labelstmp=labelstmp, + ) # indices of peaks indexed + ga = self.ga[ind] # previous grain assignments + uniqueness = np.sum(np.where(ga == -1, 1, 0)) * 1.0 / ga.shape[0] if uniqueness > self.uniqueness: -# print "Writing in self.ga" - np.put(self.ga, ind, len(self.scores)+1) - self.ubis.append(ubio) + self.ga[ind] = len(self.scores) + 1 + self.ubis.append(UBI) self.scores.append(npk) - ng=ng+1 + ubistr = (" %.6f" * 9) % tuple(UBI.ravel()) + logging.info( + "new grain %d pks, i %d j %d UBI %s" % (npk, i, j, ubistr) + ) + ng = ng + 1 else: - nuniq=nuniq+1 - # put(self.ga,ind,ng) + nuniq = nuniq + 1 except: raise - print() - print("Number of orientations with more than",self.minpks,"peaks is",len(self.ubis)) - print("Time taken",time.time()-start) - if len(self.ubis)>0: - bestfitting=np.argmax(self.scores) - print("UBI for best fitting\n",self.ubis[bestfitting]) - print("Unit cell\n",ubitocellpars(self.ubis[bestfitting])) - print("Indexes",self.scorelastrefined,"peaks, with =",self.fitlastrefined) - print("That was the best thing I found so far") - notaccountedfor = np.sum(np.where( np.logical_and( - self.ga==-1, self.ra!=-1),1,0)) - print("Number of peaks assigned to rings but not indexed = ",\ - notaccountedfor) - #self.histogram(self.ubis[bestfitting]) + + logging.info( + "Number of orientations with more than %d peaks is %d" + % (self.minpks, len(self.ubis)) + ) + logging.info("Time taken %.3f/s" % (time.time() - start)) + if len(self.ubis) > 0: + bestfitting = np.argmax(self.scores) + logging.info("UBI for best fitting\n%s" % (str(self.ubis[bestfitting]))) + logging.info( + "Unit cell: %s\n" % (str(ubitocellpars(self.ubis[bestfitting]))) + ) + self.refine(self.ubis[bestfitting]) + logging.info( + "Indexes %d peaks, with =%f" + % (self.scorelastrefined, self.fitlastrefined) + ) + logging.info("That was the best thing I found so far") + notaccountedfor = ((self.ga < 0) & (self.ra >= 0)).sum() + logging.info( + "Number of peaks assigned to rings but not indexed = %d" + % (notaccountedfor) + ) else: - print("Try again, either with larger tolerance or fewer minimum peaks") + logging.info( + "Try again, either with larger tolerance or fewer minimum peaks" + ) def fight_over_peaks(self): """ Get the best ubis from those proposed Use all peaks (ring assigned or not) """ - self.drlv2 = np.zeros( self.gv.shape[0], np.float)+2 - labels = np.ones( self.gv.shape[0], np.int32) - np.subtract(labels,2,labels) + self.drlv2 = np.zeros(self.gv.shape[0], float) + 2 + labels = np.ones(self.gv.shape[0], np.int32) + np.subtract(labels, 2, labels) i = -1 for ubi in self.ubis: i += 1 try: - npk = cImageD11.score_and_assign( ubi, self.gv, self.hkl_tol, - self.drlv2, labels, i) + npk = cImageD11.score_and_assign( + ubi, self.gv, self.hkl_tol, self.drlv2, labels, i + ) except: - print(ubi.shape) - print(self.gv.shape) - print(self.hkl_tol) - print(self.drlv2.shape) - print(labels.shape) - print("Error in fight_over_peaks",__file__) - raise + print(ubi.shape) + print(self.gv.shape) + print(self.hkl_tol) + print(self.drlv2.shape) + print(labels.shape) + print("Error in fight_over_peaks", __file__) + raise self.ga = labels # For each grain we want to know how many peaks it indexes # This is a histogram of labels - bins = np.arange(-0.5, len(self.ubis)-0.99) - hst = myhistogram( labels, bins ) + bins = np.arange(-0.5, len(self.ubis) - 0.99) + hst = myhistogram(labels, bins) self.gas = hst assert len(self.gas) == len(self.ubis) - - - def saveindexing(self, filename, tol = None): + def saveindexing(self, filename, tol=None): """ Save orientation matrices @@ -648,11 +881,11 @@ def saveindexing(self, filename, tol = None): grain by grain } peak by peak } """ - f = open(filename,"w") + f = open(filename, "w") i = 0 from ImageD11 import transform - self.gv = np.ascontiguousarray( self.gv ) + self.gv = np.ascontiguousarray(self.gv) # grain assignment self.fight_over_peaks() @@ -660,166 +893,179 @@ def saveindexing(self, filename, tol = None): # Printing per grain uinverses = [] allind = np.array(list(range(len(self.ra)))) - tthcalc =np.zeros(len(self.ra),np.float) - etacalc =np.zeros(len(self.ra),np.float) - omegacalc = np.zeros(len(self.ra),np.float) + tthcalc = np.zeros(len(self.ra), float) + etacalc = np.zeros(len(self.ra), float) + omegacalc = np.zeros(len(self.ra), float) i = -1 - for ubi in self.ubis: i += 1 # Each ubi has peaks in self.ga - uinverses.append( np.linalg.inv(ubi) ) + uinverses.append(np.linalg.inv(ubi)) # self.ga was filled in during fight_over_peaks - npk , mdrlv = cImageD11.refine_assigned( - ubi.copy(), - self.gv, - self.ga, - i) + npk, mdrlv = cImageD11.refine_assigned(ubi.copy(), self.gv, self.ga, i) assert npk == self.gas[i] - f.write("Grain: %d Npeaks=%d =%f\n"%( - i, self.gas[i], np.sqrt(mdrlv) )) - f.write("UBI:\n"+str(ubi)+"\n") + f.write( + "Grain: %d Npeaks=%d =%f\n" % (i, self.gas[i], np.sqrt(mdrlv)) + ) + f.write("UBI:\n" + str(ubi) + "\n") cellpars = ubitocellpars(ubi) f.write("Cell pars: ") - for abc in cellpars[:3]: f.write("%10.6f "%(abc)) - for abc in cellpars[3:]: f.write("%10.3f "%(abc)) + for abc in cellpars[:3]: + f.write("%10.6f " % (abc)) + for abc in cellpars[3:]: + f.write("%10.3f " % (abc)) f.write("\n") # Grainspotter U - f.write("U:\n"+str( ubitoU(ubi) ) + "\n") - f.write("B:\n"+str( ubitoB(ubi) ) + "\n") + f.write("U:\n" + str(ubitoU(ubi)) + "\n") + f.write("B:\n" + str(ubitoB(ubi)) + "\n") # Compute hkls - h = np.dot( ubi, self.gv.T ) - hint = np.floor( h + 0.5 ) - gint= np.dot( uinverses[-1], hint ) + h = np.dot(ubi, self.gv.T) + hint = np.floor(h + 0.5) + gint = np.dot(uinverses[-1], hint) dr = h - hint - f.write( - "Peak ( h k l ) drlv x y ") + f.write("Peak ( h k l ) drlv x y ") if self.wavelength < 0: f.write("\n") else: f.write( - " Omega_obs Omega_calc Eta_obs Eta_calc tth_obs tth_calc\n") + " Omega_obs Omega_calc Eta_obs Eta_calc tth_obs tth_calc\n" + ) - tc, ec, oc = transform.uncompute_g_vectors( - gint, - self.wavelength, - wedge = self.wedge) - ind = np.compress( self.ga == i, allind) + tc, ec, oc = transform.uncompute_g_vectors( + gint, self.wavelength, wedge=self.wedge + ) + ind = np.compress(self.ga == i, allind) for j in ind: - f.write("%-6d ( % 6.4f % 6.4f % 6.4f ) % 12.8f "%(j,h[0,j],h[1,j],h[2,j],np.sqrt(self.drlv2[j])) ) - f.write(" % 7.1f % 7.1f "%(self.xp[j],self.yp[j]) ) + f.write( + "%-6d ( % 6.4f % 6.4f % 6.4f ) % 12.8f " + % (j, h[0, j], h[1, j], h[2, j], np.sqrt(self.drlv2[j])) + ) + f.write(" % 7.1f % 7.1f " % (self.xp[j], self.yp[j])) if self.wavelength < 0: f.write("\n") else: # # # These should be equal to - to = math.asin( self.wavelength * self.ds[j]/2)*360/math.pi + to = math.asin(self.wavelength * self.ds[j] / 2) * 360 / math.pi # tth observed eo = mod_360(self.eta[j], 0) oo = self.omega[j] tc1 = tc[j] # Choose which is closest in eta/omega, # there are two choices, {eta,omega}, {-eta,omega+180} - w = np.argmin( [ abs(ec[0][j] - eo) , abs(ec[1][j] - eo) ] ) + w = np.argmin([abs(ec[0][j] - eo), abs(ec[1][j] - eo)]) ec1 = ec[w][j] oc1 = oc[w][j] # Now find best omega within 360 degree intervals oc1 = mod_360(oc1, oo) - f.write(" % 9.4f % 9.4f % 9.4f % 9.4f % 9.4f % 9.4f"% (oo,oc1, eo,ec1 ,to,tc1)) - etacalc[ j ] = ec1 - omegacalc[ j ] = oc1 - tthcalc[ j ] = tc1 - if self.ra[j] == -1 : + f.write( + " % 9.4f % 9.4f % 9.4f % 9.4f % 9.4f % 9.4f" + % (oo, oc1, eo, ec1, to, tc1) + ) + etacalc[j] = ec1 + omegacalc[j] = oc1 + tthcalc[j] = tc1 + if self.ra[j] == -1: f.write(" *** was not assigned to ring\n") else: f.write("\n") f.write("\n\n") # peaks assigned to rings - in_rings = np.compress(np.greater(self.ra,-1),np.arange(self.gv.shape[0])) + in_rings = np.compress(np.greater(self.ra, -1), np.arange(self.gv.shape[0])) f.write("\n\nAnd now listing via peaks which were assigned to rings\n") - nleft=0 - nfitted=0 + nleft = 0 + nfitted = 0 npk = 0 for peak in in_rings: # Compute hkl for each grain - h = self.gv[peak,:] - f.write("\nPeak= %-5d Ring= %-5d gv=[ % -6.4f % -6.4f % -6.4f ] omega= % 9.4f eta= % 9.4f tth= % 9.4f\n"%(peak,self.ra[peak],h[0],h[1],h[2], - self.omega[peak],self.eta[peak],self.tth[peak])) + h = self.gv[peak, :] + f.write( + "\nPeak= %-5d Ring= %-5d gv=[ % -6.4f % -6.4f % -6.4f ] omega= % 9.4f eta= % 9.4f tth= % 9.4f\n" + % ( + peak, + self.ra[peak], + h[0], + h[1], + h[2], + self.omega[peak], + self.eta[peak], + self.tth[peak], + ) + ) if self.ga[peak] != -1: m = self.ga[peak] - hi = np.dot(self.ubis[m],h) - hint = np.floor(hi+0.5).astype(np.int) + hi = np.dot(self.ubis[m], h) + hint = np.floor(hi + 0.5).astype(int) gint = np.dot(uinverses[m], hint) - f.write("Grain %-5d (%3d,%3d,%3d)"%( m, - hint[0],hint[1],hint[2])) - f.write(" ( % -6.4f % -6.4f % -6.4f ) "%(hi[0],hi[1],hi[2])) + f.write("Grain %-5d (%3d,%3d,%3d)" % (m, hint[0], hint[1], hint[2])) + f.write(" ( % -6.4f % -6.4f % -6.4f ) " % (hi[0], hi[1], hi[2])) # Now find best omega within 360 degree intervals - f.write(" omega= % 9.4f eta= %9.4f tth= %9.4f\n"%( - omegacalc[peak],etacalc[peak],tthcalc[peak]) ) - npk=npk+1 + f.write( + " omega= % 9.4f eta= %9.4f tth= %9.4f\n" + % (omegacalc[peak], etacalc[peak], tthcalc[peak]) + ) + npk = npk + 1 else: - if len(self.ubis)>0: - f.write("Peak not assigned\m") + if len(self.ubis) > 0: + f.write("Peak not assigned\n") # , closest=[ % -6.4f % -6.4f % -6.4f ] for grain %d\n"%(hi[0],hi[1],hi[2],m)) else: f.write("Peak not assigned, no grains found\n") - nleft=nleft+1 + nleft = nleft + 1 - f.write("\n\nTotal number of peaks was %d\n"%(self.gv.shape[0])) - f.write("Peaks assigned to grains %d\n"%(npk)) - f.write("Peaks assigned to rings but remaining unindexed %d\n"%(nleft)) + f.write("\n\nTotal number of peaks was %d\n" % (self.gv.shape[0])) + f.write("Peaks assigned to grains %d\n" % (npk)) + f.write("Peaks assigned to rings but remaining unindexed %d\n" % (nleft)) - f.write("Peaks not assigned to rings at all %d\n"%(np.sum(np.where(self.ra==-1,1,0)))) + f.write( + "Peaks not assigned to rings at all %d\n" + % (np.sum(np.where(self.ra == -1, 1, 0))) + ) f.close() - - - - def getind(self,UBI,tol=None): + def getind(self, UBI, tol=None, drlv2tmp=None, labelstmp=None): """ Returns the indices of peaks in self.gv indexed by matrix UBI """ if tol == None: tol = self.hkl_tol - drlv2 = calc_drlv2( UBI, self.gv) - drlv2 = np.where( self.ra == -1, tol + 1, drlv2) - ind = np.compress( np.less(drlv2,tol*tol) , np.arange(self.gv.shape[0]) ) - return ind - + ng = len(self.gvflat) + if drlv2tmp is None: + drlv2 = np.ones(ng, float) + else: + drlv2 = drlv2tmp + drlv2[:] = 1 + if labelstmp is None: + labels = np.zeros(ng, np.int32) + else: + labels = labelstmp + labels[:] = 0 + # we only use peaks assigned to rings for scoring here + # already done in making gvflat in assigntorings + try: + npk = cImageD11.score_and_assign(UBI, self.gv, tol, drlv2, labels, 1) + except: + print(self.gvflat.shape) + print("ra", self.ra.shape) + print(drlv2.shape) + print(labels.shape) + print("logic error in getind") + raise + return labels == 1 - def score(self,UBI,tol=None): + def score(self, UBI, tol=None): """ Decide which are the best orientation matrices """ -# t0=time.time() - if tol==None: - return cImageD11.score(UBI,self.gvflat,self.hkl_tol) + if tol is None: + return cImageD11.score(UBI, self.gv, self.hkl_tol) else: - return cImageD11.score(UBI,self.gvflat,tol) - # NONE OF THIS FOLLOWING CODE IS EXECUTED, EVER! -# t1=time.time() -# h=matrixmultiply(UBI,transpose(self.gv)) -# hint=floor(h+0.5).astype(int) # rounds down -# diff=h-hint -# drlv2=sum(diff*diff,0) -# tol = float(self.hkl_tol) -# tol = tol*tol -# print "%e"%(tol) -# for i in range(10): -# print h[:,i],hint[:,i],drlv2[i] -# ind = compress( less(drlv2,tol) , arange(self.gv.shape[0]) ) -# ind = compress( less(drlv2[:npks],tol) , arange(npks) ) -# t2=time.time() -# print n-len(ind),"Time in c",t1-t0,"Time in python",t2-t1 -# print "Grain UBI" -# print UBI -# print "Number of peaks",ind.shape[0] -# return ind - - def refine(self,UBI): + return cImageD11.score(UBI, self.gv, tol) + + def refine(self, UBI): """ Refine an orientation matrix and rescore it. @@ -831,70 +1077,75 @@ def refine(self,UBI): r = g-vectors h = hkl indices """ -# print "Orientation and unit cell refinement of" -# print "UBI\n",UBI -# print "Scores before",self.score(UBI) + # print "Orientation and unit cell refinement of" + # print "UBI\n",UBI + # print "Scores before",self.score(UBI) # Need to find hkl indices for all of the peaks which are indexed - drlv2=calc_drlv2(UBI,self.gv) + drlv2 = calc_drlv2(UBI, self.gv) h = np.dot(UBI, np.transpose(self.gv)) - hint = np.floor(h + 0.5).astype(np.int) # rounds down + hint = np.floor(h + 0.5).astype(int) # rounds down tol = float(self.hkl_tol) - tol = tol*tol + tol = tol * tol # Only use peaks which are assigned to rings for refinement - ind = np.compress( np.logical_and(np.less(drlv2,tol),np.greater(self.ra,-1)) , np.arange(self.gv.shape[0]) ) - #scoreb4=ind.shape[0] + ind = np.compress( + np.logical_and(np.less(drlv2, tol), np.greater(self.ra, -1)), + np.arange(self.gv.shape[0]), + ) + # scoreb4=ind.shape[0] contribs = drlv2[ind] - if len(contribs)==0: - raise Exception("No contributing reflections for"+str(UBI)) - #try: + if len(contribs) == 0: + raise Exception("No contributing reflections for" + str(UBI)) + # try: # fitb4=sum(contribs)/contribs.shape[0] - #except: + # except: # print "No contributing reflections for\n",UBI # raise - #drlv2_old=drlv2 - R=np.zeros((3,3),np.float) - H=np.zeros((3,3),np.float) + # drlv2_old=drlv2 + R = np.zeros((3, 3), float) + H = np.zeros((3, 3), float) for i in ind: - r = self.gv[i,:] - k = hint[:,i].astype(np.float) -# print r,k - R = R + np.outer(r,k) - H = H + np.outer(k,k) + r = self.gv[i, :] + k = hint[:, i].astype(float) + # print r,k + R = R + np.outer(r, k) + H = H + np.outer(k, k) try: - HI=np.linalg.inv(H) - UBoptimal=np.dot(R,HI) - UBIo=np.linalg.inv(UBoptimal) + HI = np.linalg.inv(H) + UBoptimal = np.dot(R, HI) + UBIo = np.linalg.inv(UBoptimal) except: # A singular matrix - this sucks. - UBIo=UBI - drlv2 = calc_drlv2(UBIo,self.gv) - ind = np.compress( np.logical_and(np.less(drlv2,tol),np.greater(self.ra,-1)), np.arange(self.gv.shape[0]) ) - self.scorelastrefined=ind.shape[0] + UBIo = UBI + drlv2 = calc_drlv2(UBIo, self.gv) + ind = np.compress( + np.logical_and(np.less(drlv2, tol), np.greater(self.ra, -1)), + np.arange(self.gv.shape[0]), + ) + self.scorelastrefined = ind.shape[0] contribs = drlv2[ind] try: - self.fitlastrefined=math.sqrt(np.sum(contribs)/contribs.shape[0]) + self.fitlastrefined = math.sqrt(np.sum(contribs) / contribs.shape[0]) except: print("\n\n\n") - print("No contributing reflections for\n",UBI) + print("No contributing reflections for\n", UBI) print("After refinement, it was OK before ???") print("\n\n\n") raise -# for i in ind: -# print "( %-6.4f %-6.4f %-6.4f ) %12.8f %12.8f"%(h[0,i],h[1,i],h[2,i],sqrt(drlv2[i]),sqrt(drlv2_old[i])) -# print UBIo -# print "Scores after", self.score(UBIo,self.hkl_tol) -# print "diff\n",UBI-UBIo -# print "Mean drlv now",sum(sqrt(drlv2))/drlv2.shape[0], -# print "Mean drlv old",sum(sqrt(drlv2_old))/drlv2_old.shape[0] + # for i in ind: + # print "( %-6.4f %-6.4f %-6.4f ) %12.8f %12.8f"%(h[0,i],h[1,i],h[2,i],sqrt(drlv2[i]),sqrt(drlv2_old[i])) + # print UBIo + # print "Scores after", self.score(UBIo,self.hkl_tol) + # print "diff\n",UBI-UBIo + # print "Mean drlv now",sum(sqrt(drlv2))/drlv2.shape[0], + # print "Mean drlv old",sum(sqrt(drlv2_old))/drlv2_old.shape[0] return UBIo - def saveubis(self,filename): + def saveubis(self, filename): """ Save the generated ubi matrices into a text file """ write_ubi_file(filename, self.ubis) - def coverage(self): """ Compute the expected coverage of reciprocal space @@ -903,41 +1154,38 @@ def coverage(self): """ pass - - def readgvfile(self,filename, quiet=False): - f=open(filename,"r") + def readgvfile(self, filename, quiet=False): + f = open(filename, "r") # Lattice!!! self.unitcell = unitcell.cellfromstring(f.readline()) while 1: - line=f.readline() - if line[0]=="#": - if line.find("wavelength")>-1: + line = f.readline() + if line[0] == "#": + if line.find("wavelength") > -1: self.wavelength = float(line.split()[-1]) if not quiet: - print("Got wavelength from gv file of ",self.wavelength) + print("Got wavelength from gv file of ", self.wavelength) continue - if line.find("wedge")>-1: + if line.find("wedge") > -1: self.wedge = float(line.split()[-1]) if not quiet: - print("Got wedge from gv file of ",self.wedge) + print("Got wedge from gv file of ", self.wedge) continue - if line.find("ds h k l")>-1: - continue # reads up to comment line - if line.find("omega")>-1 and line.find("xc")>-1: + if line.find("ds h k l") > -1: + continue # reads up to comment line + if line.find("omega") > -1 and line.find("xc") > -1: break - self.eta=[] # Raw peak information - self.omega=[] - self.ds=[] - self.xr=[] - self.yr=[] - self.zr=[] - self.xp=[] - self.yp=[] - self.omega_ranges=[] - self.omega_fullrange = 0 - for line in f.readlines(): - try: - v=[float(x) for x in line.split()] + self.eta = [] # Raw peak information + self.omega = [] + self.ds = [] + self.xr = [] + self.yr = [] + self.zr = [] + self.xp = [] + self.yp = [] + try: + for line in f.readlines(): + v = [float(x) for x in line.split()] if len(v) == 0: # Skip the blank lines continue @@ -951,50 +1199,81 @@ def readgvfile(self,filename, quiet=False): self.ds.append(v[5]) self.eta.append(v[6]) self.omega.append(v[7]) - except: - print("LINE:",line) - raise -# raise "Problem interpreting the last thing I printed" + except: + print("LINE:", line) + raise + # raise "Problem interpreting the last thing I printed" f.close() - - self.omega_ranges.append(int(round(min(self.omega)))) - sorted_omega = sorted(self.omega) - for i in range(len(sorted_omega)-1): - if sorted_omega[i+1]-sorted_omega[i] > 10: - self.omega_ranges.append(int(round(sorted_omega[i]))) - self.omega_ranges.append(int(round(sorted_omega[i+1]))) - self.omega_ranges.append(int(round(max(self.omega)))) - if not quiet: - print('\nGot %d sets of omega values from gv file:'%(len(self.omega_ranges)/2)) - for i in range(len(self.omega_ranges)//2): # integer division intended here - self.omega_fullrange += self.omega_ranges[2*i+1]-self.omega_ranges[2*i] - if not quiet: - print('- Range %1d from %4d to %4d ==> %3d degrees'%(i+1, \ - self.omega_ranges[2*i], self.omega_ranges[2*i+1], \ - self.omega_ranges[2*i+1]-self.omega_ranges[2*i])) - + self.ds = np.array(self.ds) + self.omega = np.array(self.omega) + self.omega_fullrange = find_omega_ranges(self.omega) if self.wavelength > 0: - self.tth=np.arcsin(np.array(self.ds)*self.wavelength/2)*360/math.pi + self.tth = ( + np.arcsin(np.array(self.ds) * self.wavelength / 2) * 360 / math.pi + ) else: - self.tth=np.zeros(len(self.ds)) - self.gv=np.transpose(np.array( [ self.xr , self.yr, self.zr ] ,np.float)) + self.tth = np.zeros(len(self.ds)) + self.gv = np.transpose(np.array([self.xr, self.yr, self.zr], float)) self.allgv = self.gv.copy() - self.ga=np.zeros(len(self.ds),np.int32)-1 # Grain assignments + self.ga = np.zeros(len(self.ds), np.int32) - 1 # Grain assignments - self.gvflat=np.ascontiguousarray(self.gv,'d') # Makes it contiguous in memory, hkl fast index + self.gvflat = np.ascontiguousarray(self.gv, float) + self.gv = self.gvflat.copy() + # Makes it contiguous in memory, hkl fast index if not quiet: - print("Read your gv file containing",self.gv.shape) -# if self.wavelength>0: -# print "First ten peaks" -# from ImageD11 import transform -# import math -# if self.gv.shape[0]>10: -# all=10 -# else: -# all=self.gv.shape[0] -# to,eo,oo=transform.uncompute_g_vectors(transpose(self.gv)[:all,:],self.wavelength) -# print "Peak tth_calc tth_gv_obs eta_obs eta_gv_obs_1 eta_gv_obs_1 omega_obs omega_gv_obs omega_gv_obs" -# for i in range(all): -# tc=math.asin(self.ds[i]*self.wavelength/2)*360/math.pi -# print i,tc,to[i],self.eta[i],eo[0][i],eo[1][i],self.omega[i],oo[0][i],oo[1][i] -# print "...." + print("Read your gv file containing", self.gv.shape) + + +def find_omega_ranges(omega): + """ + This looks at self.omega and attempts to figure out coverage + in degrees. + + We round to 1 degree as this is only used for an approximate + guess to numbers of peaks anyway + """ + iomega = np.round(omega % 360).astype(int) + counts = np.bincount(iomega) + return (counts > 0).sum() + + +def index( + colf, + npk_tol=[(400, 0.01), (200, 0.02)], + cosine_tol=np.cos(np.radians(90 - 0.1)), + ds_tol=0.004, + max_grains=1000, + rmulmax=10, + rings_to_use=None, + maxpairs=None, + log_level=3, +): + """ + Creates an indexer from a colfile + Uses the unit cell from the colfile.parameters + + Loops over pairs of rings with multiplicity less than rmulmax + + npk_tol : list of (minpks, hkl_tol) to test + cosine_tol : for finding pairs of peaks to make an orientation + ds_tol : for assigning peaks to hkl rings in d* + + rmulmax : max multiplicity of rings to use in ubi generation (indexer.find) + rings_to_use : which rings to consider to ubi generation (default = all) + maxpairs : how many pairs of rings to use for ubi generation + + loglevel : like the logging module, but not + + returns the indexer object + """ + global loglevel + loglevel = log_level + ind = indexer_from_colfile( + colf, ds_tol=ds_tol, max_grains=max_grains, cosine_tol=cosine_tol + ) + ind.assigntorings() + for minpks, tol in npk_tol: + ind.minpks = minpks + ind.hkl_tol = tol + ind.score_all_pairs(n=maxpairs, rmulmax=rmulmax, rings_to_use=rings_to_use) + return ind diff --git a/ImageD11/labelimage.py b/ImageD11/labelimage.py index 88171db3..750e4d28 100644 --- a/ImageD11/labelimage.py +++ b/ImageD11/labelimage.py @@ -157,11 +157,22 @@ def peaksearch(self, data, threshold, omega): # data = 2D Numeric array (of your data) # threshold = float - pixels above this number are put into objects """ + d = data.astype(np.float32) + self.labelpeaks( d, threshold ) + self.measurepeaks( d, omega ) + + + def labelpeaks(self, data, threshold): self.threshold = threshold self.npk = cImageD11.connectedpixels(data.astype(np.float32), self.blim, threshold, self.verbose) + + def measurepeaks(self, data, omega, blim=None): + if blim is not None: + self.npk = blim.max() + self.blim = blim if self.npk > 0: self.res = cImageD11.blobproperties(data.astype(np.float32), self.blim, @@ -170,6 +181,8 @@ def peaksearch(self, data, threshold, omega): else: # What to do? self.res = None + + def mergelast(self): """ @@ -186,16 +199,16 @@ def mergelast(self): # Thanks to Stine West for finding a bug here # self.npk = cImageD11.bloboverlaps(self.lastbl, - self.lastnp, - self.lastres, - self.blim, - self.npk, - self.res, - self.verbose) + self.lastnp, + self.lastres, + self.blim, + self.npk, + self.res, + self.verbose) if self.lastnp > 0: # Fill out the moments of the "closed" peaks # print "calling blobmoments with",self.lastres - ret = cImageD11.blob_moments(self.lastres[:self.lastnp]) + cImageD11.blob_moments(self.lastres[:self.lastnp]) # Write them to file self.outputpeaks(self.lastres[:self.lastnp]) # lastres is now moved forward into res @@ -216,7 +229,7 @@ def output2dpeaks(self, file_obj): file_obj.write("# Threshold level %f\n"%( self.threshold)) file_obj.write("# Number_of_pixels Average_counts s f sc fc sig_s sig_f cov_sf IMax_int\n") - ret = cImageD11.blob_moments(self.res) + cImageD11.blob_moments(self.res) fs = "%d "+ "%f "*9 + "\n" for i in self.res[:self.npk]: @@ -265,7 +278,7 @@ def finalise(self): """ self.onlast = 1 if self.lastres is not None: - ret = cImageD11.blob_moments(self.lastres) + cImageD11.blob_moments(self.lastres) self.outputpeaks(self.lastres) #if hasattr(self.sptfile, "close"): # self.sptfile.close() diff --git a/ImageD11/lattice_reduction.py b/ImageD11/lattice_reduction.py index a7e607b4..65fb451c 100644 --- a/ImageD11/lattice_reduction.py +++ b/ImageD11/lattice_reduction.py @@ -3,9 +3,9 @@ from .rc_array import rc_array -from numpy import dot, round_, array, float, allclose, asarray, fabs,\ +from numpy import dot, round_, array, allclose, asarray, fabs,\ argmin, argmax, sqrt, argsort, take, sum, where, ndarray, eye,\ - zeros, cross + zeros, cross, pi, arccos, floor from numpy.linalg import inv, LinAlgError import logging @@ -221,7 +221,7 @@ def flip(self, v): assert isinstance( ret, rc_array ) assert ret.check() assert ret.shape == v.shape[::-1], "Shape mismatch, %s %s %s %s"%( - str(v.shape[::-1]),str(v.shape), str(ra.shape), v.direction) + str(v.shape[::-1]),str(v.shape), str(ret.shape), v.direction) return ret def matrix(self, direction): @@ -401,10 +401,10 @@ def cosangle_vec( ubi, v ): Angle between v in real and reciprocal space eg, is a* parallel to a or not? """ - real = np.dot( ubi.T, v ) - reci = np.dot( np.linalg.inv(ubi) , v ) - return np.dot( real, reci )/np.sqrt( - np.dot(real, real) * np.dot(reci, reci) ) + real = dot( ubi.T, v ) + reci = dot( inv(ubi) , v ) + return dot( real, reci )/sqrt( + dot(real, real) * dot(reci, reci) ) def search_2folds( ubi ): @@ -421,33 +421,33 @@ def search_2folds( ubi ): if h==0 and k==0 and l==0: continue c = cosangle_vec( ubi, [h,k,l] ) - if abs(c - np.floor( c + 0.5)) < 0.001: - print(h, k, l, c, np.arccos(c)*180/pi) + if abs(c - floor( c + 0.5)) < 0.001: + print(h, k, l, c, arccos(c)*180/pi) def get_options(parser): - parser.add_option('-v', '--min_vec2', + parser.add_argument('-v', '--min_vec2', action='store', - type='float', + type=float, dest="min_vec2", - help='Minimum axis length ^2, \AA^2 [1.5]', + help='Minimum axis length ^2, (angstrom^2) [1.5]', default = 1.5) - parser.add_option('-m', '--n_try', + parser.add_argument('-m', '--n_try', action='store', - type='int', + type=int, dest="n_try", default=None, help='Number of vectors to test in finding lattice [all]') - parser.add_option('-f', '--fraction_indexed', + parser.add_argument('-f', '--fraction_indexed', action='store', - type='float', + type=float, dest="fraction_indexed", default=0.9, help='Fraction of peaks to be indexed') - parser.add_option('-t','--tol', + parser.add_argument('-t','--tol', action='store', - type='float', + type=float, default = 0.1, dest="tol", help='tolerance in hkl error for indexing') diff --git a/ImageD11/nbGui/3DXRD/0_3DXRD_segment_frelon.ipynb b/ImageD11/nbGui/3DXRD/0_3DXRD_segment_frelon.ipynb new file mode 100755 index 00000000..8a8687d0 --- /dev/null +++ b/ImageD11/nbGui/3DXRD/0_3DXRD_segment_frelon.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8deabe5b", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 27/02/2024" + ] + }, + { + "cell_type": "markdown", + "id": "6835a47c-a552-4d1d-b605-1867dd631b2a", + "metadata": {}, + "source": [ + "This notebook will help you to extract the locations of diffraction peaks on your detector images.\n", + "\n", + "It will also merge together your 2D spots (on a stack of detector images with different omega angles).\n", + "\n", + "We merge across omega because we often see the same spot twice on multiple detector images.\n", + "\n", + "The results are saved to the PROCESSED_DATA folder of the experiment, inside the sample and dataset folders that you select within this notebook\n", + "\n", + "## NOTE: These notebooks are under active development\n", + "They require the latest version of ImageD11 from Git to run.\n", + "\n", + "If you don't have this set up yet, you can run the below cell.\n", + "\n", + "It will automatically download and install ImageD11 to your home directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2402147c-5513-4907-8ca9-76e3e252df0c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "# check whether we already have ImageD11 here\n", + "\n", + "if os.path.exists(id11_code_path):\n", + " raise FileExistsError(\"ImageD11 already present! Giving up\")\n", + "\n", + "!git clone https://github.com/FABLE-3DXRD/ImageD11 {id11_code_path}\n", + "output = !cd {id11_code_path} && python setup.py build_ext --inplace\n", + "\n", + "if not os.path.exists(os.path.join(id11_code_path, \"build\")):\n", + " raise FileNotFoundError(f\"Can't find build folder in {id11_code_path}, compilation went wrong somewhere\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)\n", + "\n", + "# if this works, we installed ImageD11 properly!\n", + "try:\n", + " import ImageD11.cImageD11\n", + "except:\n", + " raise FileNotFoundError(\"Couldn't import cImageD11, there's a problem with your Git install!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "851fcab9-7631-439f-885c-438bcefeac84", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b5c1db6-5a32-4294-abef-cfc2150d24de", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import glob, pprint\n", + "\n", + "import ImageD11.sinograms.dataset\n", + "import ImageD11.sinograms.lima_segmenter\n", + "import ImageD11.sinograms.assemble_label\n", + "import ImageD11.sinograms.properties\n", + "\n", + "import numpy as np\n", + "import fabio\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", + "from skimage import filters, measure, morphology\n", + "import ipywidgets as widgets\n", + "import h5py\n", + "from IPython.display import display\n", + "%matplotlib widget\n", + "\n", + "from ImageD11.nbGui import nb_utils as utils\n", + "\n", + "from frelon_peaksearch import worker, process\n", + "\n", + "# from utils import apply_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f45e3647-2e1b-4a31-b5de-01adbd4d7573", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Check that we're importing ImageD11 from the home directory rather than from the Jupyter kernel\n", + "\n", + "?ImageD11.sinograms.dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de77981e-c3bf-4a29-8944-95286831ac34", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\" # USER: modify this to change the destination folder if desired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "187950bd-18b5-4bd4-80da-2a0c7a984b11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR\"\n", + "dataset = \"ff1\"\n", + "\n", + "# USER: specify path to detector spline file\n", + "\n", + "spline_file = '/data/id11/inhouse1/ewoks/detectors/files/Frelon2k_C36/frelon36.spline'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad077c4b-39cc-4b90-9637-33c32f12e364", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create ImageD11 dataset object\n", + "\n", + "ds = ImageD11.sinograms.dataset.DataSet(dataroot=rawdata_path,\n", + " analysisroot=processed_data_root_dir,\n", + " sample=sample,\n", + " dset=dataset,\n", + " detector=\"frelon3\",\n", + " omegamotor=\"diffrz\",\n", + " dtymotor=\"diffty\")\n", + "ds.import_all(scans=[\"1.1\"])\n", + "ds.save()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83f17f79-87ba-4081-b6ed-e17e48fd9697", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: specify path to background file\n", + "\n", + "bg_file = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/FeAu_0p5_tR/tdxrd_all/ff_bkg.edf\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "069b343d-4695-45fe-9ead-eab2c4c4cd16", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#Define the initial parameters\n", + "start_worker_args = {\n", + " \"bgfile\":bg_file,\n", + " \"threshold\":50,\n", + " \"smoothsigma\":1.0,\n", + " \"bgc\":0.9,\n", + " \"minpx\":3,\n", + " \"m_offset_thresh\":80,\n", + " \"m_ratio_thresh\":135,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef30f6f8-8611-4f66-be3b-006c890b91fa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with h5py.File(ds.masterfile, 'r') as h5In:\n", + " test_image = h5In['1.1/measurement/frelon3'][0].astype('uint16')\n", + "\n", + "# Display the image initially\n", + "fig, axs = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(16, 5))\n", + "test_image_worker = worker(**start_worker_args)\n", + "goodpeaks = test_image_worker.peaksearch(img=test_image, omega=0)\n", + "fc, sc = goodpeaks[:, 23:25].T # 23 and 24 are the columns for fc and sc from blob properties\n", + "\n", + "im1 = axs[0].imshow(test_image, norm=LogNorm(vmax=1000))\n", + "axs[0].set_title(\"Original image\")\n", + "im2 = axs[1].imshow(test_image_worker.smoothed, cmap=\"viridis\", norm=LogNorm(vmax=1000), interpolation=\"nearest\")\n", + "axs[1].set_title(\"Background corrected\")\n", + "im3 = axs[2].imshow(test_image_worker.smoothed, cmap=\"viridis\", norm=LogNorm(vmax=1000), interpolation=\"nearest\")\n", + "axs[2].set_title(f\"{len(fc)} peaks\")\n", + "sc1, = axs[2].plot(fc, sc, marker='+', c=\"r\", ls=\"\")\n", + "axs[2].set_aspect(1)\n", + "plt.show()\n", + "\n", + "thresh_slider = widgets.IntSlider(value=start_worker_args[\"threshold\"], min=1, max=100, step=1, description='Threshold:')\n", + "smsig_slider = widgets.FloatSlider(value=start_worker_args[\"smoothsigma\"], min=0.0, max=1.0, step=0.05, description='Smoothsigma:')\n", + "bgc_slider = widgets.FloatSlider(value=start_worker_args[\"bgc\"], min=0.0, max=1.0, step=0.05, description='bgc:')\n", + "minpx_slider = widgets.IntSlider(value=start_worker_args[\"minpx\"], min=1, max=5, step=1, description='minpx:')\n", + "mofft_slider = widgets.IntSlider(value=start_worker_args[\"m_offset_thresh\"], min=1, max=200, step=1, description='m_offset_thresh:')\n", + "mratt_slider = widgets.IntSlider(value=start_worker_args[\"m_ratio_thresh\"], min=1, max=200, step=1, description='m_ratio_thresh:')\n", + "\n", + "\n", + "def update(threshold, smoothsigma, bgc, minpx, m_offset_thresh, m_ratio_thresh):\n", + " image_worker = worker(bg_file,\n", + " threshold,\n", + " smoothsigma,\n", + " bgc,\n", + " minpx,\n", + " m_offset_thresh,\n", + " m_ratio_thresh)\n", + " goodpeaks = image_worker.peaksearch(img=test_image, omega=0)\n", + " fc, sc = goodpeaks[:, 23:25].T\n", + " im2.set_data(image_worker.smoothed)\n", + " im3.set_data(image_worker.smoothed)\n", + " sc1.set_data(fc, sc)\n", + " axs[2].set_title(f\"{len(fc)} peaks\")\n", + " plt.draw()\n", + "\n", + "interactive_plot = widgets.interactive(update,\n", + " threshold=thresh_slider,\n", + " smoothsigma=smsig_slider,\n", + " bgc=bgc_slider,\n", + " minpx=minpx_slider,\n", + " m_offset_thresh=mofft_slider,\n", + " m_ratio_thresh=mratt_slider)\n", + "\n", + "display(interactive_plot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0a98042-065d-4d22-bd1e-e9c656432f44", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "end_worker_args = {\n", + " \"bgfile\":bg_file,\n", + " \"threshold\":thresh_slider.value,\n", + " \"smoothsigma\":smsig_slider.value,\n", + " \"bgc\":bgc_slider.value,\n", + " \"minpx\":minpx_slider.value,\n", + " \"m_offset_thresh\":mofft_slider.value,\n", + " \"m_ratio_thresh\":mratt_slider.value,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eee00548-3a48-44d0-b4ad-e71b71de95ca", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(end_worker_args)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a1795a9-16eb-430d-a246-a26b12c35e77", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we run the segmenter on all our data\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "cf_2d, cf_3d = process(ds, bg_file, nthreads-1, end_worker_args)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "daccc72e-0aae-4332-80b0-e9ed894056e9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we can use this to verify that the 3D merging is behaving as expected\n", + "# don't worry about this too much!\n", + "\n", + "# take a few 3d peaks with the most 2d peaks, plot them\n", + "\n", + "unique, counts = np.unique(cf_2d.spot3d_id, return_counts=True)\n", + "hits_dict = dict(zip(unique, counts))\n", + "hits_dict_max = sorted(hits_dict.items(), key=lambda x: x[1], reverse=True)\n", + "\n", + "m = np.isin(cf_3d.index, [spot3d_id for spot3d_id, count in hits_dict_max[500:501]])\n", + "cf_3d_single_peak = cf_3d.copy()\n", + "cf_3d_single_peak.filter(m)\n", + "\n", + "peak_2d_mask = np.isin(cf_2d.spot3d_id, cf_3d_single_peak.index)\n", + "cf_2d_peaks = cf_2d.copy()\n", + "cf_2d_peaks.filter(peak_2d_mask)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(cf_3d_single_peak.f_raw, cf_3d_single_peak.s_raw, marker=\"X\", c=cf_3d_single_peak.omega, s=50, label='Merged 3D peak')\n", + "cols = ax.scatter(cf_2d_peaks.f_raw, cf_2d_peaks.s_raw, c=cf_2d_peaks.o_raw, s=cf_2d_peaks.s_I / 1000, label='Contibutory 2D peaks')\n", + "fig.colorbar(cols)\n", + "ax.set_xlim(0, 2048)\n", + "ax.set_ylim(0, 2048)\n", + "ax.invert_yaxis()\n", + "ax.legend()\n", + "ax.set_title(\"Color is omega of peak. Scaled by sum intensity\")\n", + "ax.set_xlabel(\"f_raw\")\n", + "ax.set_ylabel(\"s_raw\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88fe5554-8b0a-4c17-a2a1-02536e476c42", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cf_2d = utils.apply_spatial_lut(cf_2d, spline_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e10d2350-e9f6-419a-a69d-07b575a9e8ae", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cf_3d = utils.apply_spatial_lut(cf_3d, spline_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c71ebf96-3bbe-46a2-aa27-30a7a0a2c9f2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "parfile = '/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226/Fe_tdxrd_refined.par'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "197e8418-030b-4901-8e8f-9f8b1df7c017", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cf_2d.parameters.loadparameters(parfile)\n", + "\n", + "cf_2d.updateGeometry()\n", + "ImageD11.columnfile.colfile_to_hdf(cf_2d, ds.col2dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d44b9da8-f533-4295-9453-dc2dffd0a774", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cf_3d.parameters.loadparameters(parfile)\n", + "cf_3d.updateGeometry()\n", + "ImageD11.columnfile.colfile_to_hdf(cf_3d, ds.col3dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "148fb053-420b-4534-ba48-9e6a67e7a746", + "metadata": {}, + "outputs": [], + "source": [ + "# change to 0 to allow all cells to be run automatically\n", + "if 1:\n", + " raise ValueError(\"Hello!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac6a298f-1405-4d90-b677-7a29c04de67e", + "metadata": {}, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR\": []\n", + "}\n", + "\n", + "dset_prefix = \"ff\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + " \n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + "\n", + "mask_path = '/data/id11/inhouse1/ewoks/detectors/files/Frelon2k_C36/mask.edf'\n", + "\n", + "spline_file = '/data/id11/inhouse1/ewoks/detectors/files/Frelon2k_C36/frelon36.spline'\n", + "parfile = '/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/FeAu_0p5_tR/tdxrd_all/fitted.par'\n", + "bg_file = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/FeAu_0p5_tR/tdxrd_all/ff_bkg.edf\"\n", + "\n", + "worker_args = end_worker_args\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " print(\"Importing DataSet object\")\n", + " ds = ImageD11.sinograms.dataset.DataSet(dataroot=rawdata_path,\n", + " analysisroot=processed_data_root_dir,\n", + " sample=sample,\n", + " dset=dataset,\n", + " detector=\"frelon3\",\n", + " omegamotor=\"diffrz\",\n", + " dtymotor=\"diffty\")\n", + " \n", + " if os.path.exists(ds.col2dfile):\n", + " print(f\"Found existing cf_2d for {dataset} in {sample}, skipping\")\n", + " continue\n", + " \n", + " ds.import_all(scans=[\"1.1\"])\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " ds.save()\n", + "\n", + " print(\"Peaksearching\")\n", + " cf_2d, cf_3d = process(ds, bg_file, nthreads-1, worker_args)\n", + " \n", + " print(\"Spatially correcting peaks\")\n", + " cf_2d = utils.apply_spatial(cf_2d, spline_file, nthreads)\n", + " cf_3d = utils.apply_spatial(cf_3d, spline_file, nthreads)\n", + " \n", + " print(\"Saving peaks to file\")\n", + " cf_2d.parameters.loadparameters(parfile)\n", + "\n", + " cf_2d.updateGeometry()\n", + " ImageD11.columnfile.colfile_to_hdf(cf_2d, ds.col2dfile)\n", + " \n", + " cf_3d.parameters.loadparameters(parfile)\n", + " cf_3d.updateGeometry()\n", + " ImageD11.columnfile.colfile_to_hdf(cf_3d, ds.col3dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab892957-5ce7-4f04-a01c-c04cc9a2715c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ImageD11/nbGui/3DXRD/1_3DXRD_refine_parameters.ipynb b/ImageD11/nbGui/3DXRD/1_3DXRD_refine_parameters.ipynb new file mode 100755 index 00000000..a27751a3 --- /dev/null +++ b/ImageD11/nbGui/3DXRD/1_3DXRD_refine_parameters.ipynb @@ -0,0 +1,547 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 27/02/2024" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "This notebook will help you to refine the experimental parameters (such as sample to detector distance etc).\n", + "\n", + "We do this by indexing a Z slice of a sample, refining the positions of the grains we find, then refining the parameters using the grain diffraction data\n", + "\n", + "This notebook has already been run to generate Fe_refined.par\n", + "\n", + "There's no need to run it again :)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os, glob, pprint\n", + "import numpy as np\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import matplotlib\n", + "%matplotlib widget\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# import utils\n", + "from ImageD11.nbGui import nb_utils as utils\n", + "\n", + "import ImageD11.grain\n", + "import ImageD11.indexing\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, dataset\n", + "\n", + "from ImageD11.blobcorrector import eiger_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\" # USER: modify this to change the destination folder if desired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR\"\n", + "dataset = \"ff1\"\n", + "\n", + "# USER: specify path to detector mask\n", + "\n", + "mask_path = '/data/id11/inhouse1/ewoks/detectors/files/Frelon2k_C36/mask.edf'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "parfile = '/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226/Fe_tdxrd_refined.par'\n", + "spline_file = '/data/id11/inhouse1/ewoks/detectors/files/Frelon2k_C36/frelon36.spline'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load the dataset from file\n", + "\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)\n", + "\n", + "print(ds)\n", + "print(ds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load 3d columnfile from disk\n", + "\n", + "cf_3d = ImageD11.columnfile.colfile_from_hdf(ds.col3dfile)\n", + "\n", + "cf_3d.parameters.loadparameters(parfile)\n", + "cf_3d.updateGeometry()\n", + "\n", + "\n", + "if \"index\" not in cf_3d.titles:\n", + " cf_3d.addcolumn(np.arange(cf_3d.nrows), \"index\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot the 3D peaks (fewer of them) as a cake (two-theta vs eta)\n", + "# if the parameters in the par file are good, these should look like straight lines\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(cf_3d.ds, cf_3d.eta, s=1)\n", + "\n", + "ax.set_xlabel(\"D-star\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# here we are filtering our peaks (cf_3d) to select only the strongest ones for indexing purposes only!\n", + "# dsmax is being set to limit rings given to the indexer - 6-8 rings is normally good\n", + "\n", + "# USER: modify the \"frac\" parameter below and re-run the cell until the orange dot sits nicely on the \"elbow\" of the blue line\n", + "# this indicates the fractional intensity cutoff we will select\n", + "# if the blue line does not look elbow-shaped in the logscale plot, try changing the \"doplot\" parameter (the y scale of the logscale plot) until it does\n", + "\n", + "cf_strong = utils.selectpeaks(cf_3d, frac=0.985, dsmax=0.92, doplot=0.8, dstol=0.01)\n", + "print(f\"Got {cf_strong.nrows} strong peaks for indexing\")\n", + "cf_strong.writefile(f'{sample}_{dataset}_3d_peaks_strong.flt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we will also export some additional strong peaks across all rings\n", + "# this will be useful for grain refinement later (using makemap)\n", + "\n", + "cf_strong_allrings = utils.selectpeaks(cf_3d, frac=0.95, dsmax=cf_3d.ds.max(), doplot=0.05, dstol=0.01)\n", + "print(f\"Got {cf_strong_allrings.nrows} strong peaks for makemap\")\n", + "cf_strong_allrings_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt'\n", + "cf_strong_allrings.writefile(cf_strong_allrings_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can take a look at the intensities of the remaining peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(cf_strong.ds, cf_strong.sum_intensity,',')\n", + "ax.semilogy()\n", + "\n", + "ax.set_xlabel(\"D-star\")\n", + "ax.set_ylabel(\"Intensity\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can define a unit cell from our parameters\n", + "\n", + "Fe = ImageD11.unitcell.unitcell_from_parameters(cf_strong.parameters)\n", + "Fe.makerings(cf_strong.ds.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now let's plot our peaks again, with the rings from the unitcell included, to check our lattice parameters are good\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "skip=1\n", + "ax.scatter( cf_strong.ds[::skip], cf_strong.eta[::skip], s=0.5)\n", + "ax.plot( Fe.ringds, [0,]*len(Fe.ringds), '|', ms=90, c='orange')\n", + "ax.set_xlabel('1 / d ($\\AA$)')\n", + "ax.set_ylabel('$\\\\eta$ (deg)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# specify our ImageD11 indexer with these peaks\n", + "\n", + "indexer = ImageD11.indexing.indexer_from_colfile(cf_strong)\n", + "\n", + "print(f\"Indexing {cf_strong.nrows} peaks\")\n", + "\n", + "# USER: set a tolerance in d-space (for assigning peaks to powder rings)\n", + "\n", + "indexer.ds_tol = 0.05\n", + "\n", + "# change the log level so we can see what the ring assigments look like\n", + "\n", + "ImageD11.indexing.loglevel = 1\n", + "\n", + "# assign peaks to powder rings\n", + "\n", + "indexer.assigntorings()\n", + "\n", + "# change log level back again\n", + "\n", + "ImageD11.indexing.loglevel = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we are indexing!\n", + "# indexing will select all rings with a multiplicity below max_multiplity to search\n", + "max_multiplicity = 13\n", + "# the minimum number of peaks on a ring for a ring to be indexed on\n", + "min_counts_on_ring = 0\n", + "# the sequence of hkl tolerances the indexer will iterate through\n", + "hkl_tols_seq = [0.01, 0.02, 0.03, 0.04]\n", + "# the sequence of minpks fractions the indexer will iterate through\n", + "fracs = [0.9, 0.75]\n", + "# the tolerance in g-vector angle\n", + "cosine_tol = np.cos(np.radians(90.25))\n", + "# the max number of UBIs we can find per pair of rings\n", + "max_grains = 1000\n", + "\n", + "grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer.ds_tol,\n", + " max_mult=max_multiplicity,\n", + " min_ring_count=min_counts_on_ring,\n", + " hkl_tols=hkl_tols_seq,\n", + " fracs=fracs,\n", + " cosine_tol=cosine_tol,\n", + " max_grains=max_grains\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create grain objects\n", + "grains = [ImageD11.grain.grain(ubi, translation=np.array([0., 0., 0.])) for ubi in indexer.ubis]\n", + "\n", + "# set grain GIDs (useful if we ever delete a grain)\n", + "for i, g in enumerate(grains):\n", + " g.gid = i" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tmp_ubi_path = f'{sample}_{dataset}_grains.ubi'\n", + "tmp_map_path = f'{sample}_{dataset}_grains.map'\n", + "\n", + "new_flt_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt.new' # flt file containing assignments from makemap\n", + "unindexed_flt_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt.unindexed' # remaining unassigned peaks from makemap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ImageD11.grain.write_grain_file(tmp_ubi_path, grains)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "omegas_sorted = np.sort(ds.omega)[0]\n", + "omega_slop = np.round(np.diff(omegas_sorted).mean(), 3)\n", + "\n", + "makemap_hkl_tol_seq = [0.05, 0.025, 0.01]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for inc, makemap_tol in enumerate(makemap_hkl_tol_seq):\n", + " print(f\"Running makemap {inc+1}/{len(makemap_hkl_tol_seq)}\")\n", + " if inc == 0: # ubi into map\n", + " makemap_output = !makemap.py -p {parfile} -u {tmp_ubi_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s cubic -t {makemap_hkl_tol_seq[inc]} --omega_slop={omega_slop} --no_sort\n", + " else: # map into map\n", + " makemap_output = !makemap.py -p {parfile} -u {tmp_map_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s cubic -t {makemap_hkl_tol_seq[inc]} --omega_slop={omega_slop} --no_sort" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "grains_refined_positions = ImageD11.grain.read_grain_file(tmp_map_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist([np.mean(g.unitcell[0:3]) for g in grains_refined_positions], bins=50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "refine_output = !refine_em.py {new_flt_path} {tmp_map_path} {parfile} --omega_slop={omega_slop}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# work out how many parameter files are missing\n", + "# check the missing .par files, and copy to cover the missing files\n", + "import shutil\n", + "print(f'Total number of files expected = {len(grains_refined_positions)}')\n", + "\n", + "for i in range(len(grains_refined_positions)):\n", + " filename = f\"{i}.par\"\n", + " if not (os.path.isfile(filename)):\n", + " print(f'Missing {filename}')\n", + " print(f'Copying {i-1}.par to {filename}')\n", + " shutil.copy(str(i-1) + '.par', filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "refined_parfile = 'Fe_refined.par'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "avg_par_output = !avg_par.py {refined_parfile} {len(grains_refined_positions)}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# clean up temporary files\n", + "for i in range(len(grains_refined_positions)):\n", + " pfile = str(i) + '.par'\n", + " ffile = str(i) + '.flt'\n", + " ufile = str(i) + '.ubi'\n", + " for filename in [pfile, ffile, ufile]:\n", + " if os.path.isfile(filename):\n", + " os.remove(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# refined parameter file has now been created!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/3DXRD/2_3DXRD_index.ipynb b/ImageD11/nbGui/3DXRD/2_3DXRD_index.ipynb new file mode 100755 index 00000000..eadeb064 --- /dev/null +++ b/ImageD11/nbGui/3DXRD/2_3DXRD_index.ipynb @@ -0,0 +1,855 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 27/02/2024" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have good experimental parameters, we can index more grains!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os, glob, pprint\n", + "import numpy as np\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import matplotlib\n", + "%matplotlib widget\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# import utils\n", + "from ImageD11.nbGui import nb_utils as utils\n", + "\n", + "import ImageD11.grain\n", + "import ImageD11.indexing\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, dataset\n", + "\n", + "from ImageD11.blobcorrector import eiger_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cmp = {'compression':'gzip',\n", + " 'compression_opts': 2,\n", + " 'shuffle' : True }\n", + "\n", + "def save_array(grp, name, ary):\n", + " hds = grp.require_dataset(name, \n", + " shape=ary.shape,\n", + " dtype=ary.dtype,\n", + " **cmp)\n", + " hds[:] = ary\n", + " return hds\n", + "\n", + "def save_grains(grains, ds):\n", + " with h5py.File(ds.grainsfile, 'w') as hout:\n", + " grn = hout.create_group('grains')\n", + " for g in tqdm(grains):\n", + " gg = grn.create_group(str(g.gid))\n", + " save_array(gg, 'peaks_3d_indexing', g.peaks_3d).attrs['description'] = \"Strong 3D peaks that were assigned to this grain during indexing\"\n", + " gg.attrs.update({'ubi':g.ubi,\n", + " 'translation':g.translation})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\" # USER: modify this to change the destination folder if desired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR\"\n", + "dataset = \"ff1\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "parfile = '/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226/Fe_tdxrd_refined.par'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load the dataset from file\n", + "\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)\n", + "\n", + "print(ds)\n", + "print(ds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load 3d columnfile from disk\n", + "\n", + "cf_3d = ImageD11.columnfile.colfile_from_hdf(ds.col3dfile)\n", + "\n", + "cf_3d.parameters.loadparameters(parfile)\n", + "cf_3d.updateGeometry()\n", + "\n", + "if \"index\" not in cf_3d.titles:\n", + " cf_3d.addcolumn(np.arange(cf_3d.nrows), \"index\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot the 3D peaks (fewer of them) as a cake (two-theta vs eta)\n", + "# if the parameters in the par file are good, these should look like straight lines\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(cf_3d.ds, cf_3d.eta, s=1)\n", + "\n", + "ax.set_xlabel(\"D-star\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# here we are filtering our peaks (cf_3d) to select only the strongest ones for indexing purposes only!\n", + "# dsmax is being set to limit rings given to the indexer - 6-8 rings is normally good\n", + "\n", + "# USER: modify the \"frac\" parameter below and re-run the cell until the orange dot sits nicely on the \"elbow\" of the blue line\n", + "# this indicates the fractional intensity cutoff we will select\n", + "# if the blue line does not look elbow-shaped in the logscale plot, try changing the \"doplot\" parameter (the y scale of the logscale plot) until it does\n", + "\n", + "cf_strong = utils.selectpeaks(cf_3d, frac=0.9837, dsmax=0.92, doplot=0.8, dstol=0.01)\n", + "print(f\"Got {cf_strong.nrows} strong peaks for indexing\")\n", + "cf_strong.writefile(f'{sample}_{dataset}_3d_peaks_strong.flt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we will also export some additional strong peaks across all rings\n", + "# this will be useful for grain refinement later (using makemap)\n", + "\n", + "cf_strong_allrings = utils.selectpeaks(cf_3d, frac=0.95, dsmax=cf_3d.ds.max(), doplot=0.8, dstol=0.01)\n", + "print(f\"Got {cf_strong_allrings.nrows} strong peaks for makemap\")\n", + "cf_strong_allrings_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt'\n", + "cf_strong_allrings.writefile(cf_strong_allrings_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can take a look at the intensities of the remaining peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(cf_strong.ds, cf_strong.sum_intensity,',')\n", + "ax.semilogy()\n", + "\n", + "ax.set_xlabel(\"D-star\")\n", + "ax.set_ylabel(\"Intensity\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can define a unit cell from our parameters\n", + "\n", + "Fe = ImageD11.unitcell.unitcell_from_parameters(cf_strong.parameters)\n", + "Fe.makerings(cf_strong.ds.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now let's plot our peaks again, with the rings from the unitcell included, to check our lattice parameters are good\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "skip=1\n", + "ax.scatter( cf_strong.ds[::skip], cf_strong.eta[::skip], s=0.5)\n", + "ax.plot( Fe.ringds, [0,]*len(Fe.ringds), '|', ms=90, c='orange')\n", + "ax.set_xlabel('1 / d ($\\AA$)')\n", + "ax.set_ylabel('$\\\\eta$ (deg)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# specify our ImageD11 indexer with these peaks\n", + "\n", + "indexer = ImageD11.indexing.indexer_from_colfile(cf_strong)\n", + "\n", + "print(f\"Indexing {cf_strong.nrows} peaks\")\n", + "\n", + "# USER: set a tolerance in d-space (for assigning peaks to powder rings)\n", + "\n", + "indexer.ds_tol = 0.05\n", + "\n", + "# change the log level so we can see what the ring assigments look like\n", + "\n", + "ImageD11.indexing.loglevel = 1\n", + "\n", + "# assign peaks to powder rings\n", + "\n", + "indexer.assigntorings()\n", + "\n", + "# change log level back again\n", + "\n", + "ImageD11.indexing.loglevel = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we are indexing!\n", + "# indexing will select all rings with a multiplicity below max_multiplity to search\n", + "max_multiplicity = 13\n", + "# the minimum number of peaks on a ring for a ring to be indexed on\n", + "min_counts_on_ring = 0\n", + "# the sequence of hkl tolerances the indexer will iterate through\n", + "hkl_tols_seq = [0.01, 0.02, 0.03, 0.04, 0.05, 0.1]\n", + "# the sequence of minpks fractions the indexer will iterate through\n", + "fracs = [0.9, 0.75]\n", + "# the tolerance in g-vector angle\n", + "cosine_tol = np.cos(np.radians(90 - 0.25))\n", + "# the max number of UBIs we can find per pair of rings\n", + "max_grains = 1000\n", + "\n", + "grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer.ds_tol,\n", + " max_mult=max_multiplicity,\n", + " min_ring_count=min_counts_on_ring,\n", + " hkl_tols=hkl_tols_seq,\n", + " fracs=fracs,\n", + " cosine_tol=cosine_tol,\n", + " max_grains=max_grains\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create grain objects\n", + "grains = [ImageD11.grain.grain(ubi, translation=np.array([0., 0., 0.])) for ubi in indexer.ubis]\n", + "\n", + "# set grain GIDs (useful if we ever delete a grain)\n", + "for i, g in enumerate(grains):\n", + " g.gid = i" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot pole figures?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tmp_ubi_path = f'{sample}_{dataset}_grains.ubi'\n", + "tmp_map_path = f'{sample}_{dataset}_grains.map'\n", + "\n", + "new_flt_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt.new' # flt file containing assignments from makemap\n", + "unindexed_flt_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt.unindexed' # remaining unassigned peaks from makemap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ImageD11.grain.write_grain_file(tmp_ubi_path, grains)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "omegas_sorted = np.sort(ds.omega)[0]\n", + "omega_slop = np.round(np.diff(omegas_sorted).mean(), 3)\n", + "\n", + "makemap_hkl_tol_seq = [0.05, 0.025, 0.01]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for inc, makemap_tol in enumerate(makemap_hkl_tol_seq):\n", + " print(f\"Running makemap {inc+1}/{len(makemap_hkl_tol_seq)}\")\n", + " if inc == 0: # ubi into map\n", + " makemap_output = !makemap.py -p {parfile} -u {tmp_ubi_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s cubic -t {makemap_hkl_tol_seq[inc]} --omega_slop={omega_slop} --no_sort\n", + " else: # map into map\n", + " makemap_output = !makemap.py -p {parfile} -u {tmp_map_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s cubic -t {makemap_hkl_tol_seq[inc]} --omega_slop={omega_slop} --no_sort" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# re-import our refined grains from the makemap procedure\n", + "\n", + "grains2 = ImageD11.grain.read_grain_file(tmp_map_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# remove grains with no peaks\n", + "\n", + "grains2 = [grain for grain in grains2 if \"no peaks\" not in grain.intensity_info]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "xx = [grain.translation[0] for grain in grains2]\n", + "yy = [grain.translation[1] for grain in grains2]\n", + "zz = [grain.translation[2] for grain in grains2]\n", + "# col = [utils.grain_to_rgb(grain) for grain in grains2] # IPF-Z colour instead\n", + "col = [float(grain.npks) for grain in grains2]\n", + "sizes = [0.01*(float(grain.intensity_info.split(\"mean = \")[1].split(\" , \")[0].replace(\"'\", \"\"))) for grain in grains2]\n", + "if centre_plot:\n", + " scatterplot = ax.scatter(xx-np.mean(xx), yy-np.mean(yy), zz, c=col, s=sizes)\n", + "else:\n", + " scatterplot = ax.scatter(xx, yy, zz, c=col, s=sizes)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-200,200)\n", + "plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by n peaks\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist([float(grain.npks) for grain in grains2], bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# find the spike\n", + "absolute_minpks = 25" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# filter out grains with fewer than 15 peaks\n", + "grains_filtered = [grain for grain in grains2 if float(grain.npks) > absolute_minpks]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "xx = [grain.translation[0] for grain in grains_filtered]\n", + "yy = [grain.translation[1] for grain in grains_filtered]\n", + "zz = [grain.translation[2] for grain in grains_filtered]\n", + "# col = [utils.grain_to_rgb(grain) for grain in grains_filtered] # IPF-Z colour instead\n", + "col = [float(grain.npks) for grain in grains_filtered]\n", + "sizes = [0.01*(float(grain.intensity_info.split(\"mean = \")[1].split(\" , \")[0].replace(\"'\", \"\"))) for grain in grains_filtered]\n", + "if centre_plot:\n", + " scatterplot = ax.scatter(xx-np.mean(xx), yy-np.mean(yy), zz, c=col, s=sizes)\n", + "else:\n", + " scatterplot = ax.scatter(xx, yy, zz, c=col, s=sizes)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-200,200)\n", + "plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by n peaks\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in grains_filtered:\n", + " grain.gid = int(grain.name.split(\":\")[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# now we will assign all our 3D peaks to our grains" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tol = 0.05\n", + "\n", + "utils.assign_peaks_to_grains(grains_filtered, cf_3d, tol)\n", + "\n", + "print(\"Storing peak data in grains\")\n", + "# iterate through all the grains\n", + "for g in tqdm(grains_filtered):\n", + " # store this grain's peak indices so we know which 3D peaks we used for indexing\n", + " g.peaks_3d = cf_3d.index[cf_3d.grain_id == g.gid]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist([np.mean(grain.unitcell[0:3]) for grain in grains_filtered], bins=25)\n", + "plt.show()\n", + "\n", + "print(np.mean([np.mean(grain.unitcell[0:3]) for grain in grains_filtered]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# save grain data\n", + "\n", + "save_grains(grains_filtered, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning up\n", + "\n", + "if os.path.exists(cf_strong_allrings_path):\n", + " os.remove(cf_strong_allrings_path)\n", + "\n", + "if os.path.exists(tmp_ubi_path):\n", + " os.remove(tmp_ubi_path)\n", + "\n", + "if os.path.exists(tmp_map_path):\n", + " os.remove(tmp_map_path)\n", + "\n", + "if os.path.exists(new_flt_path):\n", + " os.remove(new_flt_path)\n", + "\n", + "if os.path.exists(unindexed_flt_path):\n", + " os.remove(unindexed_flt_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# change to 0 to allow all cells to be run automatically\n", + "if 1:\n", + " raise ValueError(\"Hello!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR\": []\n", + "}\n", + "\n", + "dset_prefix = \"ff\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + "\n", + "parfile = '/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226/Fe_tdxrd_refined.par'\n", + "\n", + "cf_strong_frac = 0.95\n", + "cf_strong_dsmax = 0.92\n", + "cf_strong_dstol = 0.01\n", + "\n", + "cf_strong_allrings_frac = 0.95\n", + "cf_strong_allrings_dstol = 0.01\n", + "\n", + "indexer_dstol = 0.05\n", + "indexer_max_mult = 13\n", + "indexer_hkl_tols = [0.01, 0.02, 0.03, 0.04, 0.05, 0.1]\n", + "indexer_fracs = [0.9, 0.75]\n", + "indexer_cosine_tol = np.cos(np.radians(90 - 0.25))\n", + "indexer_max_grains = 1000\n", + "indexer_min_ring_count = 0\n", + "\n", + "makemap_hkl_tol_seq = [0.05, 0.025, 0.01]\n", + "makemap_import_minpks = 25\n", + "\n", + "peak_assignment_hkl_tol = 0.05\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " print(\"Importing DataSet object\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " \n", + " if os.path.exists(ds.grainsfile):\n", + " print(f\"Found existing grains file for {dataset} in {sample}, skipping\")\n", + " continue\n", + "\n", + "\n", + " print(\"Loading 3D peaks\")\n", + " cf_3d = ImageD11.columnfile.colfile_from_hdf(ds.col3dfile)\n", + " cf_3d.parameters.loadparameters(parfile)\n", + " cf_3d.updateGeometry()\n", + " if \"index\" not in cf_3d.titles:\n", + " cf_3d.addcolumn(np.arange(cf_3d.nrows), \"index\")\n", + "\n", + " print(\"Filtering 3D peaks\")\n", + " cf_strong = utils.selectpeaks(cf_3d, frac=cf_strong_frac, dsmax=cf_strong_dsmax, doplot=None, dstol=cf_strong_dstol)\n", + " print(f\"Got {cf_strong.nrows} strong peaks for indexing\")\n", + " cf_strong_path = f'{sample}_{dataset}_3d_peaks_strong.flt'\n", + " cf_strong.writefile(cf_strong_path)\n", + "\n", + " cf_strong_allrings = utils.selectpeaks(cf_3d, frac=cf_strong_allrings_frac, dsmax=cf_3d.ds.max(), doplot=None, dstol=cf_strong_allrings_dstol)\n", + " print(f\"Got {cf_strong_allrings.nrows} strong peaks for makemap\")\n", + " cf_strong_allrings_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt'\n", + " cf_strong_allrings.writefile(cf_strong_allrings_path)\n", + " \n", + " grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer_dstol,\n", + " max_mult=indexer_max_mult,\n", + " min_ring_count=indexer_min_ring_count,\n", + " hkl_tols=indexer_hkl_tols,\n", + " fracs=indexer_fracs,\n", + " cosine_tol=indexer_cosine_tol,\n", + " max_grains=indexer_max_grains\n", + " )\n", + "\n", + " grains = [ImageD11.grain.grain(ubi, translation=np.array([0., 0., 0.])) for ubi in indexer.ubis]\n", + "\n", + " for i, g in enumerate(grains):\n", + " g.gid = i\n", + "\n", + " tmp_ubi_path = f'{sample}_{dataset}_grains.ubi'\n", + " tmp_map_path = f'{sample}_{dataset}_grains.map'\n", + "\n", + " new_flt_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt.new' # flt file containing assignments from makemap\n", + " unindexed_flt_path = f'{sample}_{dataset}_3d_peaks_strong_all_rings.flt.unindexed' # remaining unassigned peaks from makemap\n", + "\n", + " ImageD11.grain.write_grain_file(tmp_ubi_path, grains)\n", + "\n", + " omegas_sorted = np.sort(ds.omega)[0]\n", + " omega_slop = np.round(np.diff(omegas_sorted).mean(), 3)\n", + "\n", + " makemap_hkl_tol_seq = makemap_hkl_tol_seq\n", + "\n", + " for inc, makemap_tol in enumerate(makemap_hkl_tol_seq):\n", + " print(f\"Running makemap {inc+1}/{len(makemap_hkl_tol_seq)}\")\n", + " if inc == 0: # ubi into map\n", + " makemap_output = !makemap.py -p {parfile} -u {tmp_ubi_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s cubic -t {makemap_hkl_tol_seq[inc]} --omega_slop={omega_slop} --no_sort\n", + " else: # map into map\n", + " makemap_output = !makemap.py -p {parfile} -u {tmp_map_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s cubic -t {makemap_hkl_tol_seq[inc]} --omega_slop={omega_slop} --no_sort\n", + "\n", + " grains2 = ImageD11.grain.read_grain_file(tmp_map_path)\n", + " \n", + " # remove grains with no peaks\n", + " grains2 = [grain for grain in grains2 if \"no peaks\" not in grain.intensity_info]\n", + " \n", + " absolute_minpks = makemap_import_minpks\n", + " grains_filtered = [grain for grain in grains2 if float(grain.npks) > absolute_minpks]\n", + "\n", + " for grain in grains_filtered:\n", + " grain.gid = int(grain.name.split(\":\")[0])\n", + "\n", + " tol = peak_assignment_hkl_tol\n", + "\n", + " utils.assign_peaks_to_grains(grains_filtered, cf_3d, tol)\n", + "\n", + " print(\"Storing peak data in grains\")\n", + " # iterate through all the grains\n", + " for g in tqdm(grains_filtered):\n", + " # store this grain's peak indices so we know which 3D peaks we used for indexing\n", + " g.peaks_3d = cf_3d.index[cf_3d.grain_id == g.gid]\n", + "\n", + " print(\"Saving grains\")\n", + " save_grains(grains_filtered, ds)\n", + "\n", + " if os.path.exists(cf_strong_path):\n", + " os.remove(cf_strong_path)\n", + "\n", + " if os.path.exists(cf_strong_allrings_path):\n", + " os.remove(cf_strong_allrings_path)\n", + "\n", + " if os.path.exists(tmp_ubi_path):\n", + " os.remove(tmp_ubi_path)\n", + "\n", + " if os.path.exists(tmp_map_path):\n", + " os.remove(tmp_map_path)\n", + "\n", + " if os.path.exists(new_flt_path):\n", + " os.remove(new_flt_path)\n", + "\n", + " if os.path.exists(unindexed_flt_path):\n", + " os.remove(unindexed_flt_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/3DXRD/3_3DXRD_look_at_peaks.ipynb b/ImageD11/nbGui/3DXRD/3_3DXRD_look_at_peaks.ipynb new file mode 100755 index 00000000..34e7f424 --- /dev/null +++ b/ImageD11/nbGui/3DXRD/3_3DXRD_look_at_peaks.ipynb @@ -0,0 +1,735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 16/01/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os, glob, pprint\n", + "import numpy as np\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import matplotlib\n", + "%matplotlib widget\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from ImageD11.nbGui import nb_utils as utils\n", + "\n", + "import ImageD11.grain\n", + "import ImageD11.indexing\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, dataset\n", + "\n", + "from ImageD11.blobcorrector import eiger_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\" # USER: modify this to change the destination folder if desired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR\"\n", + "dataset = \"ff1\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "parfile = '/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226/Fe_tdxrd_refined.par'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load the dataset from file\n", + "\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)\n", + "\n", + "print(ds)\n", + "print(ds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load 3d columnfile from disk\n", + "\n", + "cf_3d = ImageD11.columnfile.colfile_from_hdf(ds.col3dfile)\n", + "\n", + "cf_3d.parameters.loadparameters(parfile)\n", + "cf_3d.updateGeometry()\n", + "\n", + "if \"index\" not in cf_3d.titles:\n", + " cf_3d.addcolumn(np.arange(cf_3d.nrows), \"index\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load 2d columnfile from disk\n", + "\n", + "cf_2d = ImageD11.columnfile.colfile_from_hdf(ds.col2dfile)\n", + "\n", + "cf_2d.parameters.loadparameters(parfile)\n", + "cf_2d.updateGeometry()\n", + "\n", + "if \"index\" not in cf_2d.titles:\n", + " cf_2d.addcolumn(np.arange(cf_2d.nrows), \"index\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load grains from disk\n", + "\n", + "def read_grains(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin: \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " translation = gg.attrs['translation'][:]\n", + " g = ImageD11.grain.grain(ubi, translation=translation)\n", + " g.gid = int(gid_string)\n", + " g.peaks_3d = gg['peaks_3d_indexing'][:]\n", + " grains.append(g)\n", + " \n", + " return grains" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "grains = read_grains(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Work out what 2D and 3D peaks we have for each grain\n", + "# These will be \"masks\" on cf_2d and cf_3d\n", + "# Will save a lot of speed\n", + "\n", + "for grain in tqdm(grains):\n", + " grain.mask_3d = np.isin(cf_3d.index, grain.peaks_3d)\n", + " grain.mask_2d = np.isin(cf_2d.spot3d_id, cf_3d.index[grain.mask_3d])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# let's say we want all the omega values for the 3D peaks of a grain\n", + "# all we have to do for this is get the omega values from cf_3d (all 3d peaks)\n", + "# you do this with cf_3d.omega\n", + "# then mask it by the mask_3d of that grain\n", + "# so you get cf_3d.omega[some_grain.mask_3d]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# pick the grain with the most 3D peaks\n", + "selected_grain = sorted(grains, key=lambda g: len(g.peaks_3d), reverse=True)[0]\n", + "\n", + "# and look at its 2D and 3D peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(cf_3d.fc[selected_grain.mask_3d], cf_3d.sc[selected_grain.mask_3d], marker=\"X\", c=cf_3d.omega[selected_grain.mask_3d], label='Merged 3D peak')\n", + "cols = ax.scatter(cf_2d.fc[selected_grain.mask_2d], cf_2d.sc[selected_grain.mask_2d], c=cf_2d.o_raw[selected_grain.mask_2d], s=cf_2d.s_I[selected_grain.mask_2d] / 1000, label='Contibutory 2D peaks')\n", + "fig.colorbar(cols)\n", + "ax.set_xlim(0, 2048)\n", + "ax.set_ylim(0, 2048)\n", + "ax.invert_yaxis()\n", + "ax.legend()\n", + "ax.set_title(\"Color is omega of peak. Scaled by sum intensity\")\n", + "ax.set_xlabel(\"f_raw\")\n", + "ax.set_ylabel(\"s_raw\")\n", + "ax.set_aspect(1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# take the selected grain\n", + "# get the omega value (corresponding to some image frame) with the most associated 2D peaks\n", + "\n", + "unique, counts = np.unique(cf_2d.o_raw[selected_grain.mask_2d], return_counts=True)\n", + "hits_dict = dict(zip(unique, counts))\n", + "hits_dict_max = sorted(hits_dict.items(), key=lambda x: x[1], reverse=True)[0]\n", + "max_omega, peaks_on_frame = hits_dict_max\n", + "\n", + "# mask all the omega values for the 2D peaks of the selected grain\n", + "# so we are only selecting those of max_omega\n", + "\n", + "omega_mask = cf_2d.o_raw[selected_grain.mask_2d] == max_omega\n", + "\n", + "# now extract the fast, slow and omega values of the peaks\n", + "# we are chaining masks together\n", + "# we are first selecting 2D peaks for the selected grain with [selected_grain.mask_2d]\n", + "# then we are selecting those with the correct omega value with [omega_mask]\n", + "\n", + "fast, slow, omega = cf_2d.f_raw[selected_grain.mask_2d][omega_mask], cf_2d.s_raw[selected_grain.mask_2d][omega_mask], cf_2d.o_raw[selected_grain.mask_2d][omega_mask]\n", + "\n", + "# print(fast, slow, omega)\n", + "\n", + "# get the corresponding frame number\n", + "\n", + "omegas_sorted = np.sort(ds.omega)[0]\n", + "omega_slop = np.round(np.diff(omegas_sorted).mean(), 3)\n", + "\n", + "frame_number = np.where(np.isclose(ds.omega[0, :], max_omega, atol=omega_slop/10))[0][0]\n", + "\n", + "# get the corresponding 2D frame\n", + "with h5py.File(ds.masterfile, 'r') as h5In:\n", + " frame_2d = h5In['1.1/measurement/frelon3'][frame_number].astype('uint16')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot it\n", + "\n", + "from matplotlib.colors import LogNorm\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(frame_2d, norm=LogNorm(vmax=1000), interpolation=\"nearest\")\n", + "ax.scatter(fast, slow, marker='+', c=\"r\")\n", + "ax.set_title(f\"Frame {frame_number} at w = {max_omega:.2f}\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# unfortunately due to the segmenter used, we have very little information about the 3D peaks\n", + "# we have the following columns in the columnfile:\n", + "# \"s_raw\": centre-of-mass of peak in slow direction\n", + "# \"f_raw\": centre-of-mass of peak in fast direction\n", + "# \"omega\": centre-of-mass of peak in omega\n", + "# \"sum_intensity\": sum of intensity of peak\n", + "# \"Number_of_pixels\": number of pixels in peak\n", + "\n", + "# if you want 3D peak shape information, you need to calculate it!\n", + "# below is an example of a function (using Numba for speed) to do this\n", + "# it calculates the extent in slow, fast and omega for each 3D peak\n", + "# it uses the 2D peak information to do this\n", + "\n", + "from numba import njit, prange\n", + "\n", + "@njit(parallel=True)\n", + "def calculate_3d_peak_extents(index_3d, spot3d_id_2d, fc_2d, sc_2d, omega_2d):\n", + " # make arrays to store the results in\n", + " # same length as index_3d\n", + " \n", + " fast_extent = np.zeros_like(index_3d) - 1 # extent in fast direction\n", + " slow_extent = np.zeros_like(index_3d) - 1 # extent in slow direction\n", + " omega_extent = np.zeros_like(index_3d) - 1 # extent in omega direction\n", + " \n", + " # iterate through each of the 3D peaks\n", + " for inc in prange(index_3d.shape[0]):\n", + " # select 2D peaks that merged to form this 3D peak\n", + " mask_2d = spot3d_id_2d == index_3d[inc]\n", + " \n", + " # get the fast, slow and omega for the 2D peaks\n", + " peak_2d_fast = fc_2d[mask_2d]\n", + " peak_2d_slow = sc_2d[mask_2d]\n", + " peak_2d_omega = omega_2d[mask_2d]\n", + " \n", + " # use ptp (basically max-min) to determine extent\n", + " fast_extent[inc] = np.ptp(peak_2d_fast)\n", + " slow_extent[inc] = np.ptp(peak_2d_slow)\n", + " omega_extent[inc] = np.ptp(peak_2d_omega)\n", + " \n", + " return fast_extent, slow_extent, omega_extent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fast_extent, slow_extent, omega_extent = calculate_3d_peak_extents(cf_3d.index, cf_2d.spot3d_id, cf_2d.fc, cf_2d.sc, cf_2d.omega)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# store the results in the 3D peaks columnfile\n", + "\n", + "cf_3d.addcolumn(fast_extent, \"f_extent\") # extent in fast direction\n", + "cf_3d.addcolumn(slow_extent, \"s_extent\") # extent in slow direction\n", + "cf_3d.addcolumn(omega_extent, \"o_extent\") # extent in omega direction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# validate that this function is working\n", + "\n", + "# pick the first 3d peak with over 30 2d peaks\n", + "n_2d_peaks = -1\n", + "peak_index_3d = -1\n", + "while n_2d_peaks < 30:\n", + " peak_index_3d += 1\n", + " # get its 2D peaks\n", + " peak_2d_mask = cf_2d.spot3d_id == cf_3d.index[peak_index_3d]\n", + "\n", + " # make sure we have more than 1 2d peak\n", + " n_2d_peaks = np.sum(peak_2d_mask)\n", + " \n", + "print(f\"3D peak {peak_index_3d} has {n_2d_peaks} 2D peaks\")\n", + "\n", + "# and get its extents\n", + "first_3d_peak_f_extent = cf_3d.f_extent[peak_index_3d]\n", + "first_3d_peak_s_extent = cf_3d.s_extent[peak_index_3d]\n", + "first_3d_peak_o_extent = cf_3d.o_extent[peak_index_3d]\n", + "\n", + "# get the fast, slow and omega values\n", + "peak_2d_fast = cf_2d.fc[peak_2d_mask]\n", + "peak_2d_slow = cf_2d.sc[peak_2d_mask]\n", + "peak_2d_omega = cf_2d.omega[peak_2d_mask]\n", + "\n", + "# work out extents and assert equality\n", + "assert np.max(peak_2d_fast) - np.min(peak_2d_fast) == first_3d_peak_f_extent\n", + "assert np.max(peak_2d_slow) - np.min(peak_2d_slow) == first_3d_peak_s_extent\n", + "assert np.max(peak_2d_omega) - np.min(peak_2d_omega) == first_3d_peak_o_extent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we could plot these results for a single grain\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.hist(cf_3d.o_extent[selected_grain.mask_3d], bins=30)\n", + "ax.set_xlabel(\"Omega extent of 3D peaks\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.semilogy()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# or we could compute the mean omega extent for each grain, and plot a scatter plot:\n", + "\n", + "for grain in grains:\n", + " grain.mean_omega_extent = np.mean(cf_3d.o_extent[grain.mask_3d])\n", + "\n", + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "xx = [grain.translation[0] for grain in grains]\n", + "yy = [grain.translation[1] for grain in grains]\n", + "zz = [grain.translation[2] for grain in grains]\n", + "col = [grain.mean_omega_extent for grain in grains]\n", + "sizes = [0.0001*(cf_3d.nrows) for grain in grains]\n", + "if centre_plot:\n", + " scatterplot = ax.scatter(xx-np.mean(xx), yy-np.mean(yy), zz, c=col, s=sizes)\n", + "else:\n", + " scatterplot = ax.scatter(xx, yy, zz, c=col, s=sizes)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-200,200)\n", + "plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by omega extent\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# could plot a histogram too:\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.hist([grain.mean_omega_extent for grain in grains], bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Guide to what the 2D peaks columns mean, from ImageD11/src/blobs.h:\n", + "\n", + "# s_1 = 0, /* 1 Npix */\n", + "# s_I, /* 2 Sum intensity */\n", + "# s_I2, /* 3 Sum intensity^2 */\n", + "# s_fI, /* 4 Sum f * intensity */\n", + "# s_ffI, /* 5 Sum f * f* intensity */\n", + "# s_sI, /* 6 Sum s * intensity */\n", + "# s_ssI, /* 7 Sum s * s * intensity */\n", + "# s_sfI, /* 8 Sum f * s * intensity */\n", + "# s_oI, /* 9 sum omega * intensity */\n", + "# s_ooI, /* 10 sum omega2 * intensity */\n", + "# s_soI, /* 11 sum omega * s * intensity */\n", + "# s_foI, /* 12 sum omega * f * intensity */\n", + "\n", + "# mx_I, /* 13 Max intensity */\n", + "# mx_I_f, /* 14 fast at Max intensity */\n", + "# mx_I_s, /* 15 slow at Max intensity */\n", + "# mx_I_o, /* 16 omega at max I */\n", + "\n", + "# bb_mx_f, /* 17 max of f */\n", + "# bb_mx_s, /* 18 max of s */\n", + "# bb_mx_o, /* 19 max of omega */\n", + "# bb_mn_f, /* 20 min of f */\n", + "# bb_mn_s, /* 21 min of s */\n", + "# bb_mn_o, /* 22 min of o */\n", + "\n", + "# avg_i, /* Average intensity */\n", + "# f_raw, /* fast centre of mass */\n", + "# s_raw, /* slow centre of mass */\n", + "# o_raw, /* omega centre of mass */\n", + "# m_ss, /* moments */\n", + "# m_ff,\n", + "# m_oo,\n", + "# m_sf,\n", + "# m_so,\n", + "# m_fo," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# peak \"aspect ratio\" could be considered the ratio between bounding box size in fast and slow direction\n", + "\n", + "cf_2d_grain1.addcolumn(cf_2d_grain1.bb_mx_f - cf_2d_grain1.bb_mn_f, \"bb_size_f\")\n", + "cf_2d_grain1.addcolumn(cf_2d_grain1.bb_mx_s - cf_2d_grain1.bb_mn_s, \"bb_size_s\")\n", + "cf_2d_grain1.addcolumn(cf_2d_grain1.bb_size_f / cf_2d_grain1.bb_size_s, \"bb_aspect\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist(cf_2d_grain1.bb_aspect[cf_2d_grain1.bb_aspect != np.inf], bins=50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# the problem here is it depends where on the ring you are\n", + "# perhaps moment between slow and fast is a better metric\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.hist(cf_2d_grain1.m_sf, bins=100)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# construct a mean moment for each grain\n", + "\n", + "for grain in tqdm(grains):\n", + " grain1_3d_mask = np.isin(cf_3d.index, grain.peaks_3d)\n", + "\n", + " cf_3d_grain1 = cf_3d.copy()\n", + " cf_3d_grain1.filter(grain1_3d_mask)\n", + "\n", + " grain1_2d_mask = np.isin(cf_2d.spot3d_id, cf_3d_grain1.index)\n", + " cf_2d_grain1 = cf_2d.copy()\n", + " cf_2d_grain1.filter(grain1_2d_mask)\n", + " \n", + " grain.cf_3d = cf_3d_grain1\n", + " grain.cf_2d = cf_2d_grain1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in grains:\n", + " grain.mean_sf_moment = np.abs(np.mean(grain.cf_2d.m_sf))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "xx = [grain.translation[0] for grain in grains]\n", + "yy = [grain.translation[1] for grain in grains]\n", + "zz = [grain.translation[2] for grain in grains]\n", + "col = [grain.mean_sf_moment for grain in grains]\n", + "sizes = [0.0001*(cf_3d.nrows) for grain in grains]\n", + "if centre_plot:\n", + " scatterplot = ax.scatter(xx-np.mean(xx), yy-np.mean(yy), zz, c=col, s=sizes)\n", + "else:\n", + " scatterplot = ax.scatter(xx, yy, zz, c=col, s=sizes)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-200,200)\n", + "plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by m_sf\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# perhaps something else\n", + "# like - the mean number of 2d peaks per merged 3d peak?\n", + "# gives a terrible rough approximation for omega spread" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in grains:\n", + " spots_3d, merge_counts = np.unique(grain.cf_2d.spot3d_id, return_counts=True)\n", + " grain.mean_merges = np.mean(merge_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "xx = [grain.translation[0] for grain in grains]\n", + "yy = [grain.translation[1] for grain in grains]\n", + "zz = [grain.translation[2] for grain in grains]\n", + "col = [grain.mean_merges for grain in grains]\n", + "sizes = [0.0001*(cf_3d.nrows) for grain in grains]\n", + "if centre_plot:\n", + " scatterplot = ax.scatter(xx-np.mean(xx), yy-np.mean(yy), zz, c=col, s=sizes, vmax=4)\n", + "else:\n", + " scatterplot = ax.scatter(xx, yy, zz, c=col, s=sizes, vmax=4)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-200,200)\n", + "plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by mean merges\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/3DXRD/4_3DXRD_merge_slices.ipynb b/ImageD11/nbGui/3DXRD/4_3DXRD_merge_slices.ipynb new file mode 100755 index 00000000..b80879a1 --- /dev/null +++ b/ImageD11/nbGui/3DXRD/4_3DXRD_merge_slices.ipynb @@ -0,0 +1,477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 21/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os, glob, pprint\n", + "import numpy as np\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import matplotlib\n", + "%matplotlib widget\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# import utils\n", + "from ImageD11.nbGui import nb_utils as utils\n", + "\n", + "import ImageD11.grain\n", + "import ImageD11.indexing\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, dataset\n", + "\n", + "from ImageD11.blobcorrector import eiger_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\" # USER: modify this to change the destination folder if desired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample you want to import\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR\": []\n", + "}\n", + "\n", + "dset_prefix = \"ff\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR\"]\n", + "\n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load all 3DXRD datasets for this sample\n", + "\n", + "from collections import OrderedDict\n", + "\n", + "\n", + "# just take first sample for now\n", + "\n", + "sample = sample_list[0]\n", + "datasets = samples_dict[sample]\n", + "ds_dict = OrderedDict()\n", + "\n", + "# try to sort datasets alphabetically\n", + "\n", + "datasets_sorted = sorted(datasets)\n", + "\n", + "for dataset in datasets_sorted:\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " ds_dict[dataset] = ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def read_grains(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin: \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " translation = gg.attrs['translation'][:]\n", + " g = ImageD11.grain.grain(ubi, translation=translation)\n", + " g.gid = int(gid_string)\n", + " g.peaks_3d = gg['peaks_3d_indexing'][:]\n", + " grains.append(g)\n", + " \n", + " return grains" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# populate z translations\n", + "\n", + "z_translation_motor = \"samtz\"\n", + "\n", + "for ds in ds_dict.values():\n", + " with h5py.File(ds.masterfile, 'r' ) as hin:\n", + " this_z_trans_value = hin[\"1.1/instrument/positioners\"][z_translation_motor][()]\n", + " ds.zpos = this_z_trans_value # this is in microns for samtz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load grains for each dataset and tie them to the dataset objects\n", + "for ds in ds_dict.values():\n", + " ds.grains = read_grains(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# get positions within the sample (set middle slice as zero position)\n", + "\n", + "middle_ds = list(ds_dict.values())[len(ds_dict.values())//2]\n", + "middle_pos = middle_ds.zpos\n", + "\n", + "for ds in ds_dict.values():\n", + " # adjust so that the first letterbox (lowest z so highest on the sample) has the highest value of zpos\n", + " ds.zpos_sample = middle_pos - ds.zpos\n", + " print(ds.zpos, ds.zpos_sample)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for inc, ds in enumerate(ds_dict.values()):\n", + " for grain in ds.grains:\n", + " grain.pos_sample = grain.translation + np.array([0., 0., ds.zpos_sample * 1000])\n", + " grain.dataset = ds.dsname\n", + " grain.z_slice = inc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_grains = []\n", + "for ds in ds_dict.values():\n", + " all_grains.extend(ds.grains)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(all_grains[0].pos_sample, all_grains[0].translation)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "\n", + "import matplotlib.cm as cm\n", + "\n", + "colors = cm.rainbow(np.linspace(0, 1, len(list(ds_dict.values()))))\n", + "\n", + "for ds in ds_dict.values():\n", + " xx = [grain.pos_sample[0] for grain in ds.grains]\n", + " yy = [grain.pos_sample[1] for grain in ds.grains]\n", + " zz = [grain.pos_sample[2] for grain in ds.grains]\n", + " # col = [len(grain.peaks_3d) for grain in ds.grains]\n", + " # col = [grain.z_slice for grain in ds.grains]\n", + " scatterplot = ax.scatter(xx, yy, zz, c=colors[ds.grains[0].z_slice], label=ds.grains[0].z_slice)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-100,100)\n", + "# plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by z slice\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "centre_plot = False\n", + "\n", + "fig = plt.figure(figsize=(12, 12))\n", + "ax = fig.add_subplot(projection='3d')\n", + "xx = [grain.pos_sample[0] for grain in all_grains]\n", + "yy = [grain.pos_sample[1] for grain in all_grains]\n", + "zz = [grain.pos_sample[2] for grain in all_grains]\n", + "# col = [utils.grain_to_rgb(grain) for grain in grains_filtered] # IPF-Z colour instead\n", + "col = [len(grain.peaks_3d) for grain in all_grains]\n", + "# sizes = [0.01*(float(grain.intensity_info.split(\"mean = \")[1].split(\" , \")[0].replace(\"'\", \"\"))) for grain in all_grains]\n", + "if centre_plot:\n", + " scatterplot = ax.scatter(xx-np.mean(xx), yy-np.mean(yy), zz-np.mean(zz), c=col)\n", + "else:\n", + " scatterplot = ax.scatter(xx, yy, zz, c=col)\n", + "ax.set_xlim(-200,200)\n", + "ax.set_ylim(-200,200)\n", + "ax.set_zlim(-100,100)\n", + "plt.colorbar(scatterplot)\n", + "ax.set_title(\"Grains coloured by n peaks\")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can look for duplicate grains\n", + "# this is a very simple duplicate grain detector\n", + "\n", + "distance_tolerance = 25/2 # microns\n", + "angle_tolerance = 2 # degrees\n", + "\n", + "def eudis(v1, v2):\n", + " return np.linalg.norm(v1-v2)\n", + "\n", + "from xfab.symmetry import Umis\n", + "\n", + "def misorien_deg(U1, U2):\n", + " return np.min(Umis(U1, U2, 7), axis=0)[1] # 7 == cubic\n", + "\n", + "matches = []\n", + "\n", + "for grain_a in all_grains:\n", + " for grain_b in all_grains:\n", + " if grain_a.z_slice == grain_b.z_slice:\n", + " # grains are in the same slice, skip\n", + " continue\n", + " translation = eudis(grain_a.pos_sample, grain_b.pos_sample)\n", + " if translation < distance_tolerance:\n", + " # might have a match in translation, now check misorientation\n", + " misorien = misorien_deg(grain_a.U, grain_b.U)\n", + " if misorien < angle_tolerance:\n", + " print(f\"Found match! Grain A: {grain_a.z_slice}:{grain_a.gid} | Grain B: {grain_b.z_slice}:{grain_b.gid} | Distance: {translation:.0f} um | Angle: {misorien:.3f} deg\")\n", + " matches.append((grain_a, grain_b))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# how do we determine good choices of parameters?\n", + "# one way is to follow Louca and try a range of parameters and plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def get_n_matches(dist_tol, ang_tol):\n", + " n_matches = 0\n", + " for grain_a in all_grains:\n", + " for grain_b in all_grains:\n", + " if grain_a.z_slice == grain_b.z_slice:\n", + " # grains are in the same slice, skip\n", + " continue\n", + " translation = eudis(grain_a.pos_sample, grain_b.pos_sample)\n", + " if translation < dist_tol:\n", + " # might have a match in translation, now check misorientation\n", + " misorien = misorien_deg(grain_a.U, grain_b.U)\n", + " if misorien < ang_tol:\n", + " n_matches += 1\n", + " return n_matches" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dist_tols = [1, 2, 5, 10, 15, 20, 25]\n", + "ang_tols = [1, 2, 3, 4, 5]\n", + "\n", + "tol_check_results = {}\n", + "\n", + "print(\"dist_tol | ang_tol | n_matches\")\n", + "\n", + "for dist_tol in dist_tols:\n", + " for ang_tol in ang_tols:\n", + "\n", + " n_matches = get_n_matches(dist_tol, ang_tol)\n", + " tol_check_results[dist_tol, ang_tol] = n_matches\n", + " print(f\"{dist_tol} | {ang_tol} | {n_matches}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "for ang_tol in ang_tols:\n", + " n_matches_at_this_angle = []\n", + " for dist_tol in dist_tols:\n", + " n_matches_at_this_distance = tol_check_results[dist_tol, ang_tol]\n", + " n_matches_at_this_angle.append(n_matches_at_this_distance)\n", + " \n", + " ax.plot(dist_tols, n_matches_at_this_angle, label=ang_tol)\n", + " \n", + "ax.legend()\n", + "\n", + "ax.set_xlabel(\"Distance tolerance (um)\")\n", + "ax.set_ylabel(\"Number of merges\")\n", + "ax.set_title(\"Legend is angle tolerance (deg)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# please note that this is a very simple duplicate grain detector\n", + "# it doesn't consider things like whether grains can match to multiple other grains\n", + "# or whether there could be duplicate grains within a single slice\n", + "# we're also not currently considering how to merge grains together\n", + "# this involves averaging the positions (easy) and the UBIs (not so easy)\n", + "# hopefully this notebook gives you a starting point though!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/3DXRD/frelon_peaksearch.py b/ImageD11/nbGui/3DXRD/frelon_peaksearch.py new file mode 100755 index 00000000..e849b705 --- /dev/null +++ b/ImageD11/nbGui/3DXRD/frelon_peaksearch.py @@ -0,0 +1,231 @@ +import h5py +import numpy as np +import scipy + +import fabio +import functools +import ImageD11.cImageD11 + +import multiprocessing +from ImageD11.columnfile import columnfile +from tqdm.contrib.concurrent import process_map + +from tqdm.notebook import tqdm + +# we will now define our 2D segmenter and 3D merging functions +# this will eventually make its way into ImageD11 properly +# there are some important parameters that we need to ensure are correctly selected for good segmentation +# these are the following lines: +# self.m_offset = self.bg < 80 +# self.m_ratio = self.bg > 200 +# both in the "bgsub" function of the "worker" class +# these determine which part of the detector image is treated as the background, and which is treated as peaks +# these parameters may need adjusting when going from undeformed to deformed data +# to adjust them, change the numbers, then run the three following cells to plot the results of the segmentation on a single frame +# a good choice of numbers will mean we exclude regions between rings and powder background on the rings +# therefore only peaks created by grains should survive + +def do3dmerge(cf_2d_dict, n, omega): + s = cf_2d_dict["s_raw"] + f = cf_2d_dict["f_raw"] + o = cf_2d_dict["o_raw"] + I = cf_2d_dict["s_I"] + s1 = cf_2d_dict["s_1"] + + print('Making 3D merge') + + # pointers to frames in s,f,o,I,n,s1 + p = np.cumsum(np.concatenate(([0, ], n))) + # make a KDTree for each frame (wastes a bit of memory, but easier for sorting later) + trees = [scipy.spatial.cKDTree(np.transpose((s[p[i]:p[i + 1]], f[p[i]:p[i + 1]]))) + for i in range(len(n))] + print('made trees') + # sys.stdout.flush() + # because interlaced might not be in order + order = np.argsort(omega % 360) + # peaks that overlap, k : 0 -> npks == len(s|f|o) + # diagonal + krow = list(range(len(o))) + kcol = list(range(len(o))) + for i in range(1, len(n)): # match these to previous + flo = order[i - 1] + fhi = order[i] + tlo = trees[flo] + thi = trees[fhi] + # 1.6 is how close centers should be to overlap + lol = trees[flo].query_ball_tree(trees[fhi], r=1.6) + for srcpk, destpks in enumerate(lol): # dest is strictly higher than src + for destpk in destpks: + krow.append(srcpk + p[flo]) + kcol.append(destpk + p[fhi]) + csr = scipy.sparse.csr_matrix((np.ones(len(krow), dtype=bool), + (kcol, krow)), shape=(len(o), len(o))) + # connected components == find all overlapping peaks + ncomp, labels = scipy.sparse.csgraph.connected_components(csr, + directed=False, return_labels=True) + + print('connected components') + # sys.stdout.flush() + # Now merge the properties + npkmerged = np.bincount(labels, minlength=ncomp) # number of peaks that were merged + s3d1 = np.bincount(labels, minlength=ncomp, weights=s1) # s_1 + s3dI = np.bincount(labels, minlength=ncomp, weights=I) # s_I + ssI = np.bincount(labels, minlength=ncomp, weights=I * s) # s_sI + sfI = np.bincount(labels, minlength=ncomp, weights=I * f) # s_sI + soI = np.bincount(labels, minlength=ncomp, weights=I * o) # s_sI + s3d = ssI / s3dI + f3d = sfI / s3dI + o3d = soI / s3dI + + cf_3d_dict = { + "s_raw": s3d, + "f_raw": f3d, + "omega": o3d, + "sum_intensity": s3dI, + "Number_of_pixels": s3d1 + } + + spot3d_id = labels + + return cf_3d_dict, spot3d_id + +class worker: + """ subtracts background, custom for ma4750 """ + + def __init__(self, bgfile, threshold=50, smoothsigma=1., bgc=0.9, minpx=3, m_offset_thresh=80, m_ratio_thresh=135): + self.bg = fabio.open(bgfile).data + self.threshold = threshold # was 50 # ADU to zero out image + self.smoothsigma = smoothsigma # sigma for Gaussian before labelleing + self.bgc = bgc # fractional part of bg per peak to remove + self.minpx = minpx + + self.m_offset = self.bg < m_offset_thresh + + self.mbg = np.mean(self.bg[self.m_offset]) + + self.m_ratio = self.bg > m_ratio_thresh + + self.bg -= self.mbg # remove dark + self.invbg = 1 / self.bg[self.m_ratio] + self.bins = b = np.linspace(0, 2, 256) + self.bc = (b[1:] + b[:-1]) / 2 + + self.wrk = np.empty(self.bg.shape, 'b') + self.labels = np.empty(self.bg.shape, 'i') + + def bgsub(self, img): + img = img.astype(np.float32) + offset = np.mean(img[self.m_offset]) # remove dark + np.subtract(img, offset, img) + ratio = img[self.m_ratio] * self.invbg + h, b = np.histogram(ratio, bins=self.bins) + htrim = np.where(h < h.max() * 0.05, 0, h) + r = (htrim * self.bc).sum() / htrim.sum() + # Better to scale background to data rather than data to background? + # np.multiply(img, 1 / r, img) + np.subtract(img, self.bg * r, img) + self.offset = offset + self.scale = r + return img + + def peaksearch(self, img, omega=0): + self.cor = self.bgsub(img) + # smooth the image for labelling (removes noise maxima) + self.smoothed = scipy.ndimage.gaussian_filter(self.cor, self.smoothsigma) + assert self.smoothed.dtype == np.float32 + # zero out the background + self.mt = self.smoothed < self.threshold + self.smoothed[self.mt] = 0 + # label on smoothed image + self.npks = ImageD11.cImageD11.localmaxlabel(self.smoothed, self.labels, self.wrk) + self.labels[self.mt] = 0 + # now find the borders of each blob : first dilate + l3 = scipy.ndimage.uniform_filter(self.labels * 7, 3) + self.borders = (self.labels * 7) != l3 + # border properties - use the real data or the smoothed? Real data. Might be noisier + self.blobsouter = ImageD11.cImageD11.blobproperties(self.cor, self.labels * self.borders, self.npks) + # Computed background per peak + self.per_peak_bg = np.concatenate(([0, ], self.blobsouter[:, ImageD11.cImageD11.mx_I])) + self.bgcalc = self.per_peak_bg[self.labels] + self.m_top = self.cor > self.bgcalc * self.bgc + self.forprops = self.cor * self.m_top + self.blobs = ImageD11.cImageD11.blobproperties(self.forprops, + self.labels * self.m_top, + self.npks, + omega=omega) + ImageD11.cImageD11.blob_moments(self.blobs) + self.enoughpx = self.blobs[:, ImageD11.cImageD11.s_1] >= self.minpx + self.goodpeaks = self.blobs[self.enoughpx] + return self.goodpeaks + + def plots(self): + pass + + +@functools.lru_cache(maxsize=1) +def get_dset(h5name, dsetname): + """This avoids to re-read the dataset many times""" + dset = h5py.File(h5name, "r")[dsetname] + return dset + + +def pps(arg): + hname, dsetname, num, omega, worker_args = arg + if pps.worker is None: + pps.worker = worker(**worker_args) + frm = get_dset(hname, dsetname)[num] + pks = pps.worker.peaksearch(frm, omega=omega) + return num, pks + + +pps.worker = None + +# PKSAVE = 's_raw f_raw o_raw s_1 s_I m_ss m_ff m_oo m_sf m_so m_fo'.split() +PKSAVE = ["s_1", "s_I", "s_I2", "s_fI", "s_ffI", "s_sI", "s_ssI", "s_sfI", "s_oI", "s_ooI", "s_soI", "s_foI", "mx_I", "mx_I_f", "mx_I_s", "mx_I_o", "bb_mx_f", "bb_mx_s", "bb_mx_o", "bb_mn_f", "bb_mn_s", "bb_mn_o", "avg_i", "f_raw", "s_raw", "o_raw", "m_ss", "m_ff", "m_oo", "m_sf", "m_so", "m_fo"] +PKCOL = [getattr(ImageD11.cImageD11, p) for p in PKSAVE] + +def process(ds, bgfile, ncpu, worker_args): + hname = ds.masterfile + scan_name = ds.scans[0] + frames_dset = scan_name + "/measurement/" + ds.detector + omega = ds.omega[0, :] + + n_frames = omega.shape[0] + + args = [(hname, frames_dset, i, omega[i], worker_args) for i in range(n_frames)] + + all_peaks = process_map(pps, args, chunksize=1) + + # make a dict to hold our results in + cf_2d_dict = {} + + # populate it with empty lists + for title in PKSAVE: + cf_2d_dict[title] = [] + + npks = [] + + for peak_props in all_peaks: + this_index, props = peak_props + + for title, prop_index in zip(PKSAVE, PKCOL): + cf_2d_dict[title].extend(props[:, prop_index]) + + npks.append(props.shape[0]) + + # convert to numpy arrays: + for key, value in cf_2d_dict.items(): + cf_2d_dict[key] = np.array(value) + + cf_3d_dict, spot3d_id = do3dmerge(cf_2d_dict, npks, omega) + + cf_2d = ImageD11.columnfile.colfile_from_dict(cf_2d_dict) + + cf_2d.addcolumn(spot3d_id, "spot3d_id") + cf_2d.addcolumn(cf_2d.o_raw, "omega") + + cf_3d = ImageD11.columnfile.colfile_from_dict(cf_3d_dict) + + cf_3d.addcolumn(np.arange(cf_3d.nrows), "index") + + return cf_2d, cf_3d diff --git a/ImageD11/nbGui/S3DXRD/0_S3DXRD_segment_and_label.ipynb b/ImageD11/nbGui/S3DXRD/0_S3DXRD_segment_and_label.ipynb new file mode 100755 index 00000000..0ee848cf --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/0_S3DXRD_segment_and_label.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8deabe5b", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "markdown", + "id": "11cc9a47-acd9-4174-9aef-a7c27e7534bd", + "metadata": { + "tags": [] + }, + "source": [ + "## NOTE: These notebooks are under active development\n", + "They require the latest version of ImageD11 from Git to run.\n", + "\n", + "If you don't have this set up yet, you can run the below cell.\n", + "\n", + "It will automatically download and install ImageD11 to your home directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4e3f4e7-08a2-4826-b098-c941a667642e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "# check whether we already have ImageD11 here\n", + "\n", + "if os.path.exists(id11_code_path):\n", + " raise FileExistsError(\"ImageD11 already present! Giving up\")\n", + "\n", + "!git clone https://github.com/FABLE-3DXRD/ImageD11 {id11_code_path}\n", + "output = !cd {id11_code_path} && python setup.py build_ext --inplace\n", + "\n", + "if not os.path.exists(os.path.join(id11_code_path, \"build\")):\n", + " raise FileNotFoundError(f\"Can't find build folder in {id11_code_path}, compilation went wrong somewhere\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)\n", + "\n", + "# if this works, we installed ImageD11 properly!\n", + "try:\n", + " import ImageD11.cImageD11\n", + "except:\n", + " raise FileNotFoundError(\"Couldn't import cImageD11, there's a problem with your Git install!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "851fcab9-7631-439f-885c-438bcefeac84", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b5c1db6-5a32-4294-abef-cfc2150d24de", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import glob, pprint\n", + "import fabio\n", + "import time\n", + "import shutil\n", + "\n", + "import ImageD11.sinograms.dataset\n", + "import ImageD11.sinograms.lima_segmenter\n", + "import ImageD11.sinograms.assemble_label\n", + "import ImageD11.sinograms.properties\n", + "import ImageD11.nbGui.nb_utils as utils\n", + "\n", + "import numpy as np\n", + "import fabio\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", + "from skimage import filters, measure, morphology\n", + "from ipywidgets import interact, interactive, widgets, fixed, Layout\n", + "import h5py\n", + "\n", + "%matplotlib ipympl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f45e3647-2e1b-4a31-b5de-01adbd4d7573", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Check that we're importing ImageD11 from the home directory rather than from the Jupyter kernel\n", + "\n", + "?ImageD11.sinograms.dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fdb520f-cb00-4eeb-b740-fc7b407f1f69", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240304\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "187950bd-18b5-4bd4-80da-2a0c7a984b11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_100um\"\n", + "\n", + "# USER: specify path to detector mask\n", + "\n", + "# mask_path = '/data/id11/inhouse1/ewoks/detectors/files/eiger_E-08-0173/mask_with_gaps_E-08-0173.edf' # temporary eiger mask (Nov 2023)\n", + "# mask_path = '/data/id11/inhouse1/ewoks/detectors/files/eiger_E-08-0144/mask.edf' # normal eiger mask\n", + "\n", + "mask_path = '/data/id11/inhouse1/ewoks/detectors/files/eiger_E-08-0173/mask_with_gaps_E-08-0173.edf' # temporary eiger mask (Nov 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad077c4b-39cc-4b90-9637-33c32f12e364", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create ImageD11 dataset object\n", + "\n", + "ds = ImageD11.sinograms.dataset.DataSet(dataroot=rawdata_path,\n", + " analysisroot=processed_data_root_dir,\n", + " sample=sample,\n", + " dset=dataset)\n", + "ds.import_all()\n", + "ds.save()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d20a5bf-b3b9-489a-81d3-4b8e6c792d7c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#Define the initial parameters\n", + "start_pars = {#\"bgfile\": bg_path,\n", + " \"maskfile\": mask_path,\n", + " \"cut\": 1,\n", + " \"pixels_in_spot\": 3,\n", + " \"howmany\": 100000}\n", + "\n", + "def segment_frame_from_options( ds, options, image_file_num=None):\n", + " opts = ImageD11.sinograms.lima_segmenter.OPTIONS = ImageD11.sinograms.lima_segmenter.SegmenterOptions(**options)\n", + " opts.setup()\n", + " if image_file_num is None:\n", + " image_file_num = len(ds.imagefiles)//2\n", + " hfile = ImageD11.sinograms.lima_segmenter.os.path.join( ds.datapath, ds.imagefiles[ image_file_num ] )\n", + " with h5py.File( hfile, 'r' ) as hin:\n", + " frms = hin[ds.limapath]\n", + " for i, spf in enumerate( ImageD11.sinograms.lima_segmenter.reader( frms, opts.mask, opts.cut ) ): \n", + " ref = frms[i]\n", + " break\n", + " \n", + " spi = spf.to_dense('intensity')\n", + " \n", + " if opts.mask is not None:\n", + " rshow = ref * opts.mask\n", + " sshow = spi * opts.mask\n", + " else:\n", + " rshow = ref\n", + " sshow = spi\n", + " \n", + " return rshow, sshow\n", + "\n", + "cut_slider = widgets.IntSlider(value=start_pars[\"cut\"], min=1, max=20, step=1, description='Cut:')\n", + "pixels_in_spot_slider = widgets.IntSlider(value=start_pars[\"pixels_in_spot\"], min=1, max=20, step=1, description='Pixels in Spot:')\n", + "howmany_slider = widgets.IntSlider(value=np.log10(start_pars[\"howmany\"]), min=1, max=15, step=1, description='log(howmany):')\n", + "\n", + "# Display the image initially\n", + "fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(16, 9))\n", + "raw_image, segmented_image = segment_frame_from_options(ds, start_pars)\n", + "im1 = axs[0].imshow(raw_image, cmap=\"viridis\", norm=LogNorm(vmin=1, vmax=1000), interpolation=\"nearest\")\n", + "im2 = axs[1].imshow(segmented_image, cmap=\"viridis\", norm=LogNorm(vmin=1, vmax=1000), interpolation=\"nearest\")\n", + "axs[0].set_title(\"Raw image\")\n", + "axs[1].set_title(\"Segmented image\")\n", + "labels, nblobs = measure.label(segmented_image, connectivity=2, return_num=True)\n", + "plt.suptitle(f\"{nblobs} peaks found\\n cut={cut_slider.value}, pixels_in_spot={pixels_in_spot_slider.value}, howmany={10**howmany_slider.value}\")\n", + "plt.show()\n", + "\n", + "def update_image(cut, pixels_in_spot, howmany):\n", + " howmany_exp = 10**howmany\n", + " these_opts = start_pars.copy()\n", + " these_opts[\"cut\"] = cut\n", + " these_opts[\"pixels_in_spot\"] = pixels_in_spot\n", + " these_opts[\"howmany\"] = howmany_exp\n", + " raw_image, segmented_image = segment_frame_from_options(ds, these_opts)\n", + " labels, nblobs = measure.label(segmented_image, connectivity=2, return_num=True)\n", + " im1.set_data(raw_image)\n", + " im2.set_data(segmented_image)\n", + " plt.suptitle(f\"{nblobs} peaks found\\n cut={cut}, pixels_in_spot={pixels_in_spot}, howmany={howmany_exp}\")\n", + " plt.draw()\n", + "\n", + "interactive_plot = widgets.interactive(update_image, cut=cut_slider, pixels_in_spot=pixels_in_spot_slider, howmany=howmany_slider)\n", + "\n", + "display(interactive_plot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82e9c9ec-5301-4bf0-a7ba-2957afcc4db9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "end_pars = {# \"bgfile\": bg_path,\n", + " \"maskfile\": mask_path,\n", + " \"cut\": cut_slider.value,\n", + " \"pixels_in_spot\": pixels_in_spot_slider.value,\n", + " \"howmany\": 10**howmany_slider.value}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81929009-7445-434a-b19e-57bcd9e3e6a4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create batch file to send to SLURM cluster\n", + "\n", + "sbat = ImageD11.sinograms.lima_segmenter.setup(ds.dsfile, **end_pars)\n", + "if sbat is None:\n", + " raise ValueError(\"This scan has already been segmented!\")\n", + "print(sbat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3827f616-bfae-45c1-969c-24da93886a4e", + "metadata": {}, + "outputs": [], + "source": [ + "utils.slurm_submit_and_wait(sbat, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5de23264-4c22-43fe-8be6-8cc54039ea2d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# label sparse peaks\n", + "\n", + "ImageD11.sinograms.assemble_label.main(ds.dsfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddb029cd-7f66-4c91-abee-5f1723303360", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# generate peaks table\n", + "\n", + "ImageD11.sinograms.properties.main(ds.dsfile, options={'algorithm': 'lmlabel', 'wtmax': 70000, 'save_overlaps': False})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f9d215f-0ec9-4014-a49f-76e7c8167cf1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# make a new subfolder called \"sparse\" that holds all the individual \"scan______sparse.h5\" files\n", + "\n", + "sparse_folder_path = os.path.join(ds.analysispath, \"sparse\")\n", + "\n", + "if not os.path.exists(sparse_folder_path):\n", + " os.mkdir(sparse_folder_path)\n", + " \n", + "scan_sparse_files = glob.glob(os.path.join(ds.analysispath, \"scan*_sparse.h5\"))\n", + "\n", + "for scan_sparse_file in scan_sparse_files:\n", + " shutil.move(scan_sparse_file, sparse_folder_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b6e6575-bb8e-47ac-88e1-0ff5350ab1a5", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: incorporate DATA/visitor/ma5839/id11/20240118/SCRIPTS/0_S3DXRD_segment_and_label_single_dset.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "703d22d0-ef82-4e08-8087-c57e76e16de1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if 1:\n", + " raise ValueError(\"Change the 1 above to 0 to allow 'Run all cells' in the notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb03508f-29f8-41d3-bad2-c78055010cb6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4f70bb5-035b-48b2-9acd-39c6e3ea8666", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + "\n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + "\n", + "print(samples_dict)\n", + "\n", + "# now we have our samples_dict, we can process our data:\n", + "mask_path = '/data/id11/inhouse1/ewoks/detectors/files/eiger_E-08-0173/mask_with_gaps_E-08-0173.edf'\n", + "\n", + "# you can change these if needed, but they will default to those you selected with the widget\n", + "\n", + "try:\n", + " seg_pars = {\"maskfile\": mask_path,\n", + " \"cut\": cut_slider.value,\n", + " \"pixels_in_spot\": pixels_in_spot_slider.value,\n", + " \"howmany\": 10**howmany_slider.value}\n", + "except NameError:\n", + " seg_pars = {\"maskfile\": mask_path}\n", + "\n", + "\n", + "sbats = []\n", + "dataset_objects = []\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.DataSet(dataroot=rawdata_path,\n", + " analysisroot=processed_data_root_dir,\n", + " sample=sample,\n", + " dset=dataset)\n", + " \n", + " if os.path.exists(ds.sparsefile):\n", + " print(f\"Found existing Sparse file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " try:\n", + " ds.import_all()\n", + " except ValueError:\n", + " print(f\"Very dodgy scan! Skipping\")\n", + " continue\n", + " except KeyError:\n", + " print(f\"Very dodgy scan! Skipping\")\n", + " continue\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " ds.save()\n", + " \n", + " print(\"Segmenting\")\n", + " sbat = ImageD11.sinograms.lima_segmenter.setup(ds.dsfile, **seg_pars)\n", + " \n", + " if sbat is None:\n", + " print(f\"{dataset} in sample {sample} already segmented, skipping\")\n", + " continue\n", + " \n", + " sbats.append(sbat)\n", + " dataset_objects.append(ds)\n", + "\n", + " \n", + "utils.slurm_submit_many_and_wait(sbats, wait_time_sec=60)\n", + "\n", + "for ds in dataset_objects:\n", + " print(\"Labelling sparse peaks\")\n", + " ImageD11.sinograms.assemble_label.main(ds.dsfile)\n", + "\n", + " print(\"Generating peaks table\")\n", + " ImageD11.sinograms.properties.main(ds.dsfile, options={'algorithm': 'lmlabel', 'wtmax': 70000, 'save_overlaps': False})\n", + "\n", + " print(\"Cleaning up sparse files\")\n", + " sparse_folder_path = os.path.join(ds.analysispath, \"sparse\")\n", + "\n", + " if not os.path.exists(sparse_folder_path):\n", + " os.mkdir(sparse_folder_path)\n", + "\n", + " scan_sparse_files = glob.glob(os.path.join(ds.analysispath, \"scan*_sparse.h5\"))\n", + "\n", + " for scan_sparse_file in scan_sparse_files:\n", + " shutil.move(scan_sparse_file, sparse_folder_path)\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0efe8f8-4226-45fc-9df7-d916e61ecb8c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ImageD11/nbGui/S3DXRD/1_2_3_S3DXRD_pbp_indexing_local.ipynb b/ImageD11/nbGui/S3DXRD/1_2_3_S3DXRD_pbp_indexing_local.ipynb new file mode 100755 index 00000000..67366d49 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/1_2_3_S3DXRD_pbp_indexing_local.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)\n", + "\n", + "os.environ['OMP_NUM_THREADS']='1'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib ipympl\n", + "\n", + "import ImageD11.sinograms.point_by_point\n", + "import ImageD11.sinograms.dataset\n", + "import ImageD11.sinograms.properties\n", + "import ImageD11.columnfile\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_250um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load the dataset\n", + "dset = ImageD11.sinograms.dataset.load(dset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Import the 2D columnfile, or make it if it doesn't exist\n", + "\n", + "if os.path.exists(dset.col2dfile):\n", + " cf_2d = ImageD11.columnfile.colfile_from_hdf(dset.col2dfile)\n", + "else:\n", + " # Import 2D peaks, make a spatially corrected columnfile, save it\n", + " peaks_table = ImageD11.sinograms.properties.pks_table.load(dset.pksfile)\n", + "\n", + " # Grab the 2d peak centroids\n", + " peaks_2d = peaks_table.pk2d(dset.omega, dset.dty)\n", + " cf_2d = utils.tocolf(peaks_2d, par_path, e2dx_path, e2dy_path)\n", + "\n", + " # save the 2D peaks to file so we don't have to spatially correct them again\n", + " ImageD11.columnfile.colfile_to_hdf(cf_2d, dset.col2dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# filter the columnfile to discard weak peaks\n", + "\n", + "cf_2d.filter(cf_2d.Number_of_pixels > 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pbp_object = ImageD11.sinograms.point_by_point.PBP(par_path,\n", + " dset,\n", + " hkl_tol=0.01,\n", + " fpks=0.9,\n", + " ds_tol=0.01,\n", + " etacut=0.1,\n", + " ifrac=1./300,\n", + " cosine_tol=np.cos(np.radians(90 - 0.25)),\n", + " y0=-7.985,\n", + " symmetry=\"cubic\",\n", + " foridx=[0, 1, 2, 3, 4, 5],\n", + " forgen=[5, 1],\n", + " uniqcut=0.9)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pbp_object.setpeaks(cf_2d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = pbp_object.iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dset.pbpfile = os.path.join(dset.analysispath, dset.dsname + '_pbp_index.pbp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pbp_object.point_by_point(dset.pbpfile, loglevel=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/S3DXRD/1_S3DXRD_index.ipynb b/ImageD11/nbGui/S3DXRD/1_S3DXRD_index.ipynb new file mode 100755 index 00000000..26397904 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/1_S3DXRD_index.ipynb @@ -0,0 +1,748 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import glob, pprint\n", + "import numpy as np\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import matplotlib\n", + "%matplotlib ipympl\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils\n", + "\n", + "import ImageD11.grain\n", + "import ImageD11.indexing\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, dataset\n", + "\n", + "from ImageD11.blobcorrector import eiger_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define our functions\n", + "\n", + "cmp = {'compression':'gzip',\n", + " 'compression_opts': 2,\n", + " 'shuffle' : True }\n", + "\n", + "def save_array(grp, name, ary):\n", + " hds = grp.require_dataset(name, \n", + " shape=ary.shape,\n", + " dtype=ary.dtype,\n", + " **cmp)\n", + " hds[:] = ary\n", + " return hds\n", + "\n", + "def save_grains(grains, ds):\n", + " with h5py.File(ds.grainsfile, 'w') as hout:\n", + " grn = hout.create_group('grains')\n", + " for g in tqdm(grains):\n", + " gg = grn.create_group(str(g.gid))\n", + " save_array(gg, 'peaks_4d_indexing', g.peaks_4d).attrs['description'] = \"Strong 4D peaks that were assigned to this grain during indexing\"\n", + " gg.attrs.update({'ubi':g.ubi})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_250um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load the dataset from file\n", + "\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)\n", + "\n", + "print(ds)\n", + "print(ds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# if we already have the 2D and 4D peaks, we could just load them from file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# cf_2d = ImageD11.columnfile.colfile_from_hdf(ds.col2dfile)\n", + "# cf_2d.parameters.loadparameters(par_path)\n", + "# cf_2d.updateGeometry()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# cf_4d = ImageD11.columnfile.colfile_from_hdf(ds.col4dfile)\n", + "# cf_4d.parameters.loadparameters(par_path)\n", + "# cf_4d.updateGeometry()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# otherwise load them from the peaks table:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Import 2D peaks, make a spatially corrected columnfile, save it\n", + "\n", + "peaks_table = ImageD11.sinograms.properties.pks_table.load(ds.pksfile)\n", + "\n", + "# Grab the 2d peak centroids\n", + "peaks_2d = peaks_table.pk2d(ds.omega, ds.dty)\n", + "cf_2d = utils.tocolf(peaks_2d, par_path, e2dx_path, e2dy_path)\n", + "\n", + "if os.path.exists(ds.col2dfile):\n", + " os.remove(ds.col2dfile)\n", + "\n", + "# save the 2D peaks to file so we don't have to spatially correct them again\n", + "ImageD11.columnfile.colfile_to_hdf(cf_2d, ds.col2dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# We will now generate a cf (columnfile) object for the 4D peaks.\n", + "# Will be corrected for detector spatial distortion\n", + "\n", + "peaks_4d = peaks_table.pk2dmerge(ds.omega, ds.dty)\n", + "cf_4d = utils.tocolf(peaks_4d, par_path, e2dx_path, e2dy_path) # spatial correction\n", + "\n", + "# uncomment below if you don't want spatial correction for some reason\n", + "# cf_4d = ImageD11.columnfile.colfile_from_dict(peaks_4d)\n", + "# cf_4d.addcolumn(cf_4d.s_raw, \"sc\")\n", + "# cf_4d.addcolumn(cf_4d.f_raw, \"fc\")\n", + "# cf_4d.parameters.loadparameters(par_path)\n", + "# cf_4d.updateGeometry()\n", + "\n", + "# the first thing we should do is create an index column for our 4D peaks\n", + "index_column = np.arange(cf_4d.nrows)\n", + "cf_4d.addcolumn(index_column, 'index')\n", + "\n", + "# Delete the columnfile output file if it exists\n", + "\n", + "if os.path.exists(ds.col4dfile):\n", + " os.remove(ds.col4dfile)\n", + " \n", + "# save the 4D peaks to file so we don't have to spatially correct them again\n", + "ImageD11.columnfile.colfile_to_hdf(cf_4d, ds.col4dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate a mask that selects only 4D peaks greater than 25 pixels in size\n", + "\n", + "m = cf_4d['Number_of_pixels'] > 25\n", + "\n", + "# then plot omega vs dty for all peaks - should look sinusoidal\n", + "\n", + "fig, ax = plt.subplots()\n", + "counts, xedges, yedges, im = ax.hist2d(cf_4d['omega'][m], cf_4d['dty'][m], weights=np.sqrt(cf_4d['sum_intensity'][m]), bins=(ds.obinedges, ds.ybinedges), norm=matplotlib.colors.LogNorm())\n", + "ax.set_xlabel(\"Omega angle\")\n", + "ax.set_ylabel(\"dty\")\n", + "\n", + "fig.colorbar(im, ax=ax)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot the 4D peaks (fewer of them) as a cake (two-theta vs eta)\n", + "# if the parameters in the par file are good, these should look like straight lines\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(cf_4d.ds, cf_4d.eta, s=1)\n", + "\n", + "ax.set_xlabel(\"dstar\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# OPTIONAL: export CF to an flt so we can play with it with ImageD11_gui\n", + "# uncomment the below line\n", + "\n", + "# cf_4d.writefile(f'{sample}_{dataset}_4d_peaks.flt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# here we are filtering our peaks (cf_4d) to select only the strongest ones for indexing purposes only!\n", + "# dsmax is being set to limit rings given to the indexer - 6-8 rings is normally good\n", + "\n", + "# USER: modify the \"frac\" parameter below and re-run the cell until the orange dot sits nicely on the \"elbow\" of the blue line\n", + "# this indicates the fractional intensity cutoff we will select\n", + "# if the blue line does not look elbow-shaped in the logscale plot, try changing the \"doplot\" parameter (the y scale of the logscale plot) until it does\n", + "\n", + "cf_strong = utils.selectpeaks(cf_4d, frac=0.994, dsmax=1.4, doplot=0.95)\n", + "print(cf_4d.nrows)\n", + "print(cf_strong.nrows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# OPTIONAL: export CF to an flt so we can play with it with ImageD11_gui\n", + "# uncomment the below line\n", + "\n", + "# cf_strong.writefile(f'{sample}_{dataset}_strong_4d_peaks.flt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can take a look at the intensities of the remaining peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(cf_strong.ds, cf_strong.sum_intensity,',')\n", + "ax.semilogy()\n", + "\n", + "ax.set_xlabel(\"Dstar\")\n", + "ax.set_ylabel(\"Intensity\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can define a unit cell from our parameters\n", + "\n", + "Fe = ImageD11.unitcell.unitcell_from_parameters(cf_strong.parameters)\n", + "Fe.makerings(cf_strong.ds.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now let's plot our peaks again, with the rings from the unitcell included, to check our lattice parameters are good\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "skip=1\n", + "ax.scatter( cf_strong.ds[::skip], cf_strong.eta[::skip], s=0.5)\n", + "ax.plot( Fe.ringds, [0,]*len(Fe.ringds), '|', ms=90, c=\"red\")\n", + "ax.set_xlabel('1 / d ($\\AA$)')\n", + "ax.set_ylabel('$\\\\eta$ (deg)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# specify our ImageD11 indexer with these peaks\n", + "# we're aiming to index around 3_000 to 10_000 peaks\n", + "\n", + "indexer = ImageD11.indexing.indexer_from_colfile(cf_strong)\n", + "\n", + "print(f\"Indexing {cf_strong.nrows} peaks\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: set a tolerance in d-space (for assigning peaks to powder rings)\n", + "\n", + "indexer.ds_tol = 0.01\n", + "\n", + "# change the log level so we can see what the ring assigments look like\n", + "\n", + "ImageD11.indexing.loglevel = 1\n", + "\n", + "# assign peaks to powder rings\n", + "\n", + "indexer.assigntorings()\n", + "\n", + "# change log level back again\n", + "\n", + "ImageD11.indexing.loglevel = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# let's plot the assigned peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "# indexer.ra is the ring assignments\n", + "\n", + "ax.scatter(cf_strong.ds, cf_strong.eta, c=indexer.ra, cmap='tab20', s=1)\n", + "ax.set_xlabel(\"d-star\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we are indexing!\n", + "# indexing will select all rings with a multiplicity below max_multiplity to search\n", + "max_multiplicity = 11\n", + "# the minimum number of peaks on a ring for a ring to be indexed on\n", + "min_counts_on_ring = 0\n", + "# the sequence of hkl tolerances the indexer will iterate through\n", + "hkl_tols_seq = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.1]\n", + "# the sequence of minpks fractions the indexer will iterate through\n", + "fracs = [0.9, 0.8, 0.7, 0.6, 0.5]\n", + "# the tolerance in g-vector angle\n", + "cosine_tol = np.cos(np.radians(90 - 0.25))\n", + "# the max number of UBIs we can find per pair of rings\n", + "max_grains = 1000\n", + "\n", + "grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer.ds_tol,\n", + " max_mult=max_multiplicity,\n", + " min_ring_count=min_counts_on_ring,\n", + " hkl_tols=hkl_tols_seq,\n", + " fracs=fracs,\n", + " cosine_tol=cosine_tol,\n", + " max_grains=max_grains\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# set grain GIDs (useful if we ever delete a grain)\n", + "for i, g in enumerate(grains):\n", + " g.gid = i\n", + " \n", + " g.a = np.cbrt(np.linalg.det(g.ubi))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "mean_unit_cell_lengths = [grain.a for grain in grains]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(mean_unit_cell_lengths)\n", + "ax.set_xlabel(\"Grain ID\")\n", + "ax.set_ylabel(\"Unit cell length\")\n", + "plt.show()\n", + "\n", + "a0 = np.median(mean_unit_cell_lengths)\n", + " \n", + "print(a0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# assign peaks to grains\n", + "\n", + "tol = 0.05\n", + "\n", + "utils.assign_peaks_to_grains(grains, cf_strong, tol=tol)\n", + "\n", + "print(\"Storing peak data in grains\")\n", + "# iterate through all the grains\n", + "for g in tqdm(grains):\n", + " # store this grain's peak indices so we know which 4D peaks we used for indexing\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[cf_strong.grain_id == g.gid]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "utils.plot_index_results(indexer, cf_strong, 'First attempt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "utils.plot_grain_sinograms(grains, cf_strong)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# save grain data\n", + "\n", + "save_grains(grains, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 1:\n", + " raise ValueError(\"Change the 1 above to 0 to allow 'Run all cells' in the notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + " \n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + " \n", + "# now we have our samples_dict, we can process our data:\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')\n", + "\n", + "cf_strong_frac = 0.994\n", + "cf_strong_dsmax = 1.4\n", + "cf_strong_dstol = 0.005\n", + "\n", + "indexer_ds_tol = 0.01\n", + "max_multiplicity = 11\n", + "min_counts_on_ring = 0\n", + "hkl_tols_seq = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.1]\n", + "fracs = [0.9, 0.8, 0.7, 0.6, 0.5]\n", + "cosine_tol = np.cos(np.radians(90 - 0.25))\n", + "max_grains = 1000\n", + "\n", + "peak_assign_tol = 0.05\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " if not os.path.exists(dset_path):\n", + " print(f\"Missing DataSet file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " if os.path.exists(ds.grainsfile):\n", + " print(f\"Already have grains for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " peaks_table = ImageD11.sinograms.properties.pks_table.load(ds.pksfile)\n", + " peaks_2d = peaks_table.pk2d(ds.omega, ds.dty)\n", + " cf_2d = utils.tocolf(peaks_2d, par_path, e2dx_path, e2dy_path)\n", + " if os.path.exists(ds.col2dfile):\n", + " os.remove(ds.col2dfile)\n", + " ImageD11.columnfile.colfile_to_hdf(cf_2d, ds.col2dfile)\n", + "\n", + " peaks_4d = peaks_table.pk2dmerge(ds.omega, ds.dty)\n", + " cf_4d = utils.tocolf(peaks_4d, par_path, e2dx_path, e2dy_path) # spatial correction\n", + " index_column = np.arange(cf_4d.nrows)\n", + " cf_4d.addcolumn(index_column, 'index')\n", + " if os.path.exists(ds.col4dfile):\n", + " os.remove(ds.col4dfile)\n", + " ImageD11.columnfile.colfile_to_hdf(cf_4d, ds.col4dfile)\n", + " \n", + " cf_strong = utils.selectpeaks(cf_4d, frac=cf_strong_frac, dsmax=cf_strong_dsmax, dstol=cf_strong_dstol)\n", + " \n", + "\n", + "\n", + " grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer_ds_tol,\n", + " max_mult=max_multiplicity,\n", + " min_ring_count=min_counts_on_ring,\n", + " hkl_tols=hkl_tols_seq,\n", + " fracs=fracs,\n", + " cosine_tol=cosine_tol,\n", + " max_grains=max_grains\n", + " )\n", + " \n", + " for i, g in enumerate(grains):\n", + " g.gid = i\n", + " \n", + " utils.assign_peaks_to_grains(grains, cf_strong, tol=peak_assign_tol)\n", + "\n", + " print(\"Storing peak data in grains\")\n", + " for g in tqdm(grains):\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[cf_strong.grain_id == g.gid]\n", + " \n", + " save_grains(grains, ds)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/S3DXRD/1_S3DXRD_index_minor_phase.ipynb b/ImageD11/nbGui/S3DXRD/1_S3DXRD_index_minor_phase.ipynb new file mode 100755 index 00000000..a3261232 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/1_S3DXRD_index_minor_phase.ipynb @@ -0,0 +1,675 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os, glob, pprint\n", + "import numpy as np\n", + "import matplotlib\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "\n", + "%matplotlib widget\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils\n", + "\n", + "import ImageD11.grain\n", + "import ImageD11.unitcell\n", + "import ImageD11.indexing\n", + "import ImageD11.columnfile\n", + "import ImageD11.refinegrains\n", + "import ImageD11.sinograms.dataset\n", + "import ImageD11.sinograms.properties\n", + "import ImageD11.sinograms.lima_segmenter\n", + "import ImageD11.sinograms.assemble_label\n", + "\n", + "from ImageD11.blobcorrector import eiger_spatial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cmp = {'compression':'gzip',\n", + " 'compression_opts': 2,\n", + " 'shuffle' : True }\n", + "\n", + "def save_array(grp, name, ary):\n", + " hds = grp.require_dataset(name, \n", + " shape=ary.shape,\n", + " dtype=ary.dtype,\n", + " **cmp)\n", + " hds[:] = ary\n", + " return hds\n", + "\n", + "def save_grains_minor_phase(grains, ds):\n", + " with h5py.File(ds.grainsfile_minor_phase, 'w') as hout:\n", + " grn = hout.create_group('grains')\n", + " for g in tqdm(grains):\n", + " gg = grn.create_group(str(g.gid))\n", + " save_array(gg, 'peaks_4d_indexing', g.peaks_4d).attrs['description'] = \"Strong 4D peaks that were assigned to this grain during indexing\"\n", + " gg.attrs.update({'ubi':g.ubi})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_100um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load the dataset from file\n", + "\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)\n", + "\n", + "print(ds)\n", + "print(ds.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load 4d peaks from file\n", + "\n", + "cf_4d = ImageD11.columnfile.colfile_from_hdf(ds.col4dfile)\n", + "\n", + "cf_4d.parameters.loadparameters(par_path)\n", + "cf_4d.updateGeometry()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot the 4D peaks (fewer of them) as a cake (two-theta vs eta)\n", + "# if the parameters in the par file are good, these should look like straight lines\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(cf_4d.ds, cf_4d.eta, s=1)\n", + "\n", + "ax.set_xlabel(\"dstar\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# isolate main phase peaks, and remove them from the dataset\n", + "main_phase_peaks_mask = utils.unitcell_peaks_mask(cf_4d, dstol=0.0075, dsmax=cf_4d.ds.max())\n", + "\n", + "minor_phase_peaks = cf_4d.copy()\n", + "minor_phase_peaks.filter(~main_phase_peaks_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot the remaining peaks as a cake (d-star vs eta)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(minor_phase_peaks.ds, minor_phase_peaks.eta, s=1)\n", + "\n", + "ax.set_xlabel(\"dstar\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Update geometry for minor phase peaks\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Au.par')\n", + "minor_phase_peaks.parameters.loadparameters(par_path)\n", + "minor_phase_peaks.updateGeometry()\n", + "\n", + "cf_strong = utils.selectpeaks(minor_phase_peaks, dstol=0.005, dsmax=0.90, frac=0.875, doplot=0.01)\n", + "print(cf_strong.nrows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "phase_name = \"Au\"\n", + "ds.grainsfile_minor_phase = os.path.join(ds.analysispath, ds.dsname + f'_grains_{phase_name}.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# OPTIONAL: export CF to an flt so we can play with it with ImageD11_gui\n", + "# uncomment the below line\n", + "\n", + "# cf_4d.writefile(f'{sample}_{dataset}_4d_peaks.flt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can take a look at the intensities of the remaining peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(cf_strong.ds, cf_strong.sum_intensity,',')\n", + "ax.semilogy()\n", + "\n", + "ax.set_xlabel(\"dstar\")\n", + "ax.set_ylabel(\"Intensity\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now we can define unit cells from our parameters\n", + "\n", + "minor_phase_unitcell = ImageD11.unitcell.unitcell_from_parameters(cf_strong.parameters)\n", + "minor_phase_unitcell.makerings(cf_strong.ds.max())\n", + "\n", + "major_phase_unitcell = ImageD11.unitcell.unitcell_from_parameters(cf_4d.parameters)\n", + "major_phase_unitcell.makerings(cf_strong.ds.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now let's plot our peaks again, with the rings from the unitcell included, to check our lattice parameters are good\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "skip=1\n", + "ax.scatter( cf_strong.ds[::skip], cf_strong.eta[::skip],s=0.5)\n", + "ax.plot( minor_phase_unitcell.ringds, [0,]*len(minor_phase_unitcell.ringds), '|', ms=90, c=\"red\", label=\"Minor phase\")\n", + "ax.plot( major_phase_unitcell.ringds, [0,]*len(major_phase_unitcell.ringds), '|', ms=90, c=\"green\", label=\"Major phase\")\n", + "ax.set_xlabel('1 / d ($\\AA$)')\n", + "ax.set_ylabel('$\\\\eta$ (deg)')\n", + "\n", + "lgnd = ax.legend(loc=\"upper right\", scatterpoints=1, fontsize=10)\n", + "for handle in lgnd.legend_handles:\n", + " handle.set(markersize=6.0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# specify our ImageD11 indexer with these peaks\n", + "\n", + "indexer = ImageD11.indexing.indexer_from_colfile(cf_strong)\n", + "\n", + "print(f\"Indexing {cf_strong.nrows} peaks\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: set a tolerance in d-space (for assigning peaks to powder rings)\n", + "\n", + "indexer.ds_tol = 0.005\n", + "\n", + "# change the log level so we can see what the ring assigments look like\n", + "\n", + "ImageD11.indexing.loglevel = 1\n", + "\n", + "# assign peaks to powder rings\n", + "\n", + "indexer.assigntorings()\n", + "\n", + "# change log level back again\n", + "\n", + "ImageD11.indexing.loglevel = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# let's plot the assigned peaks\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "# indexer.ra is the ring assignments\n", + "\n", + "ax.scatter(cf_strong.ds, cf_strong.eta, c=indexer.ra, cmap='tab20', s=1)\n", + "ax.set_xlabel(\"d-star\")\n", + "ax.set_ylabel(\"eta\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# now we are indexing!\n", + "# indexing will select all rings with a multiplicity below max_multiplity to search\n", + "max_multiplicity = 23\n", + "# the minimum number of peaks on a ring for a ring to be indexed on\n", + "min_counts_on_ring = 100\n", + "# the sequence of hkl tolerances the indexer will iterate through\n", + "hkl_tols_seq = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06]\n", + "# the sequence of minpks fractions the indexer will iterate through\n", + "fracs = [0.9, 0.8]\n", + "# the tolerance in g-vector angle\n", + "cosine_tol = np.cos(np.radians(90 - 0.25))\n", + "# the max number of UBIs we can find per pair of rings\n", + "max_grains = 1000\n", + "\n", + "grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer.ds_tol,\n", + " max_mult=max_multiplicity,\n", + " min_ring_count=min_counts_on_ring,\n", + " hkl_tols=hkl_tols_seq,\n", + " fracs=fracs,\n", + " cosine_tol=cosine_tol,\n", + " max_grains=max_grains\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# set grain GIDs (useful if we ever delete a grain)\n", + "for i, g in enumerate(grains):\n", + " g.gid = i\n", + " \n", + " g.a = np.cbrt(np.linalg.det(g.ubi))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "mean_unit_cell_lengths = [grain.a for grain in grains]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(mean_unit_cell_lengths)\n", + "ax.set_xlabel(\"Grain ID\")\n", + "ax.set_ylabel(\"Unit cell length\")\n", + "plt.show()\n", + "\n", + "a0 = np.median(mean_unit_cell_lengths)\n", + " \n", + "print(a0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# assign peaks to grains\n", + "\n", + "tol=0.05\n", + "\n", + "utils.assign_peaks_to_grains(grains, cf_strong, tol=tol)\n", + "\n", + "print(\"Storing peak data in grains\")\n", + "# iterate through all the grains\n", + "for g in tqdm(grains):\n", + " # store this grain's peak indices so we know which 4D peaks we used for indexing\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[cf_strong.grain_id == g.gid]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "utils.plot_index_results(indexer, cf_strong, 'First attempt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "utils.plot_grain_sinograms(grains, cf_strong, 25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# save grain data\n", + "\n", + "save_grains_minor_phase(grains, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 1:\n", + " raise ValueError(\"Change the 1 above to 0 to allow 'Run all cells' in the notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + " \n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + " \n", + "# now we have our samples_dict, we can process our data:\n", + "\n", + "major_phase_par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "minor_phase_par_path = os.path.join(processed_data_root_dir, 'Au.par')\n", + "phase_name = \"Au\"\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')\n", + "\n", + "main_phase_cf_dstol = 0.0075\n", + "\n", + "cf_strong_frac = 0.875\n", + "cf_strong_dsmax = 0.9\n", + "cf_strong_dstol = 0.005\n", + "\n", + "indexer_ds_tol = 0.005\n", + "max_multiplicity = 23\n", + "min_counts_on_ring = 100\n", + "hkl_tols_seq = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06]\n", + "fracs = [0.9, 0.8]\n", + "cosine_tol = np.cos(np.radians(90 - 0.25))\n", + "max_grains = 1000\n", + "\n", + "peak_assign_tol = 0.05\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " if not os.path.exists(dset_path):\n", + " print(f\"Missing DataSet file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " \n", + " ds.grainsfile_minor_phase = os.path.join(ds.analysispath, ds.dsname + f'_grains_{phase_name}.h5')\n", + " \n", + " if os.path.exists(ds.grainsfile_minor_phase):\n", + " print(f\"Already have grains for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " par_path = major_phase_par_path\n", + " cf_4d = ImageD11.columnfile.colfile_from_hdf(ds.col4dfile)\n", + "\n", + " cf_4d.parameters.loadparameters(par_path)\n", + " cf_4d.updateGeometry()\n", + "\n", + " main_phase_peaks_mask = utils.unitcell_peaks_mask(cf_4d, dstol=main_phase_cf_dstol, dsmax=cf_4d.ds.max())\n", + "\n", + " minor_phase_peaks = cf_4d.copy()\n", + " minor_phase_peaks.filter(~main_phase_peaks_mask)\n", + " \n", + " par_path = minor_phase_par_path\n", + " minor_phase_peaks.parameters.loadparameters(par_path)\n", + " minor_phase_peaks.updateGeometry()\n", + "\n", + " cf_strong = utils.selectpeaks(minor_phase_peaks, dstol=cf_strong_dstol, dsmax=cf_strong_dsmax, frac=cf_strong_frac)\n", + "\n", + " grains, indexer = utils.do_index(cf=cf_strong,\n", + " dstol=indexer_ds_tol,\n", + " max_mult=max_multiplicity,\n", + " min_ring_count=min_counts_on_ring,\n", + " hkl_tols=hkl_tols_seq,\n", + " fracs=fracs,\n", + " cosine_tol=cosine_tol,\n", + " max_grains=max_grains\n", + " )\n", + " \n", + " for i, g in enumerate(grains):\n", + " g.gid = i\n", + " \n", + " utils.assign_peaks_to_grains(grains, cf_strong, tol=peak_assign_tol)\n", + "\n", + " print(\"Storing peak data in grains\")\n", + " for g in tqdm(grains):\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[g.mask_4d]\n", + " \n", + " save_grains_minor_phase(grains, ds)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map.ipynb b/ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map.ipynb new file mode 100644 index 00000000..96ab631f --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map.ipynb @@ -0,0 +1,1444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import concurrent.futures\n", + "import timeit\n", + "import glob\n", + "import pprint\n", + "from shutil import rmtree\n", + "import time\n", + "from functools import partial\n", + "\n", + "import matplotlib\n", + "%matplotlib ipympl\n", + "\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, roi_iradon\n", + "from ImageD11.blobcorrector import eiger_spatial\n", + "from ImageD11.grain import grain\n", + "\n", + "from skimage.filters import threshold_otsu\n", + "from skimage.morphology import convex_hull_image\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils\n", + "\n", + "import ipywidgets as widgets\n", + "from ipywidgets import interact" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# define our functions\n", + "\n", + "def read_grains(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin: \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " g = ImageD11.grain.grain(ubi)\n", + " g.gid = int(gid_string)\n", + " grains.append(g)\n", + " \n", + " return grains\n", + "\n", + "\n", + "def map_grain_from_peaks(g, flt, ds):\n", + " \"\"\"\n", + " Computes sinogram\n", + " flt is already the peaks for this grain\n", + " Returns angles, sino\n", + " \"\"\" \n", + " NY = len(ds.ybincens) # number of y translations\n", + " iy = np.round((flt.dty - ds.ybincens[0]) / (ds.ybincens[1]-ds.ybincens[0])).astype(int) # flt column for y translation index\n", + "\n", + " # The problem is to assign each spot to a place in the sinogram\n", + " hklmin = g.hkl_2d_strong.min(axis=1) # Get minimum integer hkl (e.g -10, -9, -10)\n", + " dh = g.hkl_2d_strong - hklmin[:,np.newaxis] # subtract minimum hkl from all integer hkls\n", + " de = (g.etasigns_2d_strong.astype(int) + 1)//2 # something signs related\n", + " # 4D array of h,k,l,+/-\n", + " # pkmsk is whether a peak has been observed with this HKL or not\n", + " pkmsk = np.zeros(list(dh.max(axis=1) + 1 )+[2,], int) # make zeros-array the size of (max dh +1) and add another axis of length 2\n", + " pkmsk[ dh[0], dh[1], dh[2], de ] = 1 # we found these HKLs for this grain\n", + " # sinogram row to hit\n", + " pkrow = np.cumsum(pkmsk.ravel()).reshape(pkmsk.shape) - 1 #\n", + " # counting where we hit an HKL position with a found peak\n", + " # e.g (-10, -9, -10) didn't get hit, but the next one did, so increment\n", + "\n", + " npks = pkmsk.sum( )\n", + " destRow = pkrow[ dh[0], dh[1], dh[2], de ] \n", + " sino = np.zeros( ( npks, NY ), 'f' )\n", + " hits = np.zeros( ( npks, NY ), 'f' )\n", + " angs = np.zeros( ( npks, NY ), 'f' )\n", + " adr = destRow * NY + iy \n", + " # Just accumulate \n", + " sig = flt.sum_intensity\n", + " ImageD11.cImageD11.put_incr64( sino, adr, sig )\n", + " ImageD11.cImageD11.put_incr64( hits, adr, np.ones(len(de),dtype='f'))\n", + " ImageD11.cImageD11.put_incr64( angs, adr, flt.omega)\n", + " \n", + " sinoangles = angs.sum( axis = 1) / hits.sum( axis = 1 )\n", + " # Normalise:\n", + " sino = (sino.T/sino.max( axis=1 )).T\n", + " # Sort (cosmetic):\n", + " order = np.lexsort((np.arange(npks), sinoangles))\n", + " sinoangles = sinoangles[order]\n", + " ssino = sino[order].T\n", + " \n", + " return sinoangles, ssino, hits[order].T\n", + "\n", + "def do_sinos(g, hkltol=0.25):\n", + " flt = utils.tocolf({p:p2d[p][g.peaks_2d] for p in p2d}, par_path, dxfile=e2dx_path, dyfile=e2dy_path) # convert it to a columnfile and spatially correct\n", + " \n", + " hkl_real = np.dot(g.ubi, (flt.gx, flt.gy, flt.gz)) # calculate hkl of all assigned peaks\n", + " hkl_int = np.round(hkl_real).astype(int) # round to nearest integer\n", + " dh = ((hkl_real - hkl_int)**2).sum(axis = 0) # calculate square of difference\n", + "\n", + " # g.dherrall = dh.mean() # mean hkl error across all assigned peaks\n", + " # g.npksall = flt.nrows # total number of assigned peaks\n", + " flt.filter(dh < hkltol*hkltol) # filter all assigned peaks to be less than hkltol squared\n", + " hkl_real = np.dot(g.ubi, (flt.gx, flt.gy, flt.gz)) # recalculate error after filtration\n", + " hkl_int = np.round(hkl_real).astype(int)\n", + " dh = ((hkl_real - hkl_int)**2).sum(axis = 0)\n", + " # g.dherr = dh.mean() # dherr is mean hkl error across assigned peaks after hkltol filtering\n", + " # g.npks = flt.nrows # total number of assigned peaks after hkltol filtering\n", + " g.etasigns_2d_strong = np.sign(flt.eta)\n", + " g.hkl_2d_strong = hkl_int # integer hkl of assigned peaks after hkltol filtering\n", + " g.sinoangles, g.ssino, g.hits = map_grain_from_peaks(g, flt, ds)\n", + " return i,g\n", + "\n", + "\n", + "def run_iradon_id11(grain, pad=20, y0=0, workers=1, sample_mask=None, apply_halfmask=False, mask_central_zingers=False):\n", + " outsize = grain.ssino.shape[0] + pad\n", + " \n", + " if apply_halfmask:\n", + " halfmask = np.zeros_like(grain.ssino)\n", + "\n", + " halfmask[:len(halfmask)//2-1, :] = 1\n", + " halfmask[len(halfmask)//2-1, :] = 0.5\n", + " \n", + " ssino_to_recon = grain.ssino * halfmask\n", + " else:\n", + " ssino_to_recon = grain.ssino\n", + " \n", + " # # pad the sample mask\n", + " # sample_mask_padded = np.pad(sample_mask, pad//2)\n", + "\n", + " \n", + " # Perform iradon transform of grain sinogram, store result (reconstructed grain shape) in g.recon\n", + " grain.recon = ImageD11.sinograms.roi_iradon.iradon(ssino_to_recon, \n", + " theta=grain.sinoangles, \n", + " mask=sample_mask,\n", + " output_size=outsize,\n", + " projection_shifts=np.full(grain.ssino.shape, -y0),\n", + " filter_name='hamming',\n", + " interpolation='linear',\n", + " workers=workers)\n", + " \n", + " if mask_central_zingers:\n", + " grs = grain.recon.shape[0]\n", + " xpr, ypr = -grs//2 + np.mgrid[:grs, :grs]\n", + " inner_mask_radius = 25\n", + " outer_mask_radius = inner_mask_radius + 2\n", + "\n", + " inner_circle_mask = (xpr ** 2 + ypr ** 2) < inner_mask_radius ** 2\n", + " outer_circle_mask = (xpr ** 2 + ypr ** 2) < outer_mask_radius ** 2\n", + "\n", + " mask_ring = inner_circle_mask & outer_circle_mask\n", + " # we now have a mask to apply\n", + " fill_value = np.median(grain.recon[mask_ring])\n", + " grain.recon[inner_circle_mask] = fill_value\n", + " \n", + " return grain\n", + "\n", + "cmp = {'compression':'gzip',\n", + " 'compression_opts': 2,\n", + " 'shuffle' : True }\n", + "\n", + "def save_array(grp, name, ary):\n", + " hds = grp.require_dataset(name, \n", + " shape=ary.shape,\n", + " dtype=ary.dtype,\n", + " **cmp)\n", + " hds[:] = ary\n", + " return hds\n", + "\n", + "def save_grains_for_mlem(grains, ds, y0):\n", + " with h5py.File(ds.grainsfile, 'r+') as hout:\n", + " try:\n", + " grp = hout.create_group('peak_assignments')\n", + " except ValueError:\n", + " grp = hout['peak_assignments']\n", + "\n", + " ds_gord = save_array( grp, 'gord', gord )\n", + " ds_gord.attrs['description'] = 'Grain ordering: g[i].pks = gord[ inds[i] : inds[i+1] ]'\n", + " ds_inds = save_array( grp, 'inds', inds )\n", + " ds_inds.attrs['description'] = 'Grain indices: g[i].pks = gord[ inds[i] : inds[i+1] ]'\n", + " \n", + " grains_group = 'grains'\n", + " for g in tqdm(grains):\n", + " gg = hout[grains_group][str(g.gid)]\n", + " # save stuff for sinograms\n", + " \n", + " save_array(gg, 'ssino', g.ssino).attrs['description'] = 'Sinogram of peak intensities sorted by omega'\n", + " save_array(gg, 'sinoangles', g.sinoangles).attrs['description'] = 'Projection angles for sinogram'\n", + " save_array(gg, 'og_recon', g.og_recon).attrs['description'] = 'Original ID11 iRadon reconstruction'\n", + " save_array(gg, 'circle_mask', whole_sample_mask).attrs['description'] = 'Reconstruction mask to use for MLEM'\n", + " \n", + " # might as well save peaks stuff while we're here\n", + " save_array(gg, 'translation', g.translation).attrs['description'] = 'Grain translation in lab frame'\n", + " save_array(gg, 'peaks_2d_sinograms', g.peaks_2d).attrs['description'] = \"2D peaks from strong 4D peaks that were assigned to this grain for sinograms\"\n", + " save_array(gg, 'peaks_4d_sinograms', g.peaks_4d).attrs['description'] = \"Strong 4D peaks that were assigned to this grain for sinograms\"\n", + "\n", + " gg.attrs['cen'] = g.cen\n", + " gg.attrs['y0'] = y0\n", + " \n", + " \n", + "def prepare_mlem_bash(ds, grains, pad, is_half_scan, n_simultaneous_jobs=50, cores_per_task=8, niter=50):\n", + " \n", + " slurm_mlem_path = os.path.join(ds.analysispath, \"slurm_mlem\")\n", + "\n", + " if os.path.exists(slurm_mlem_path):\n", + " print(f\"Removing {slurm_mlem_path}\")\n", + " rmtree(slurm_mlem_path)\n", + "\n", + " os.mkdir(slurm_mlem_path)\n", + " \n", + " recons_path = os.path.join(ds.analysispath, \"mlem_recons\")\n", + "\n", + " if os.path.exists(recons_path):\n", + " print(f\"Removing {recons_path}\")\n", + " rmtree(recons_path)\n", + "\n", + " os.mkdir(recons_path)\n", + " \n", + " if is_half_scan:\n", + " dohm = \"Yes\"\n", + " mask_cen = \"Yes\"\n", + " else:\n", + " dohm = \"No\"\n", + " mask_cen = \"No\"\n", + " \n", + " bash_script_path = os.path.join(slurm_mlem_path, ds.dsname + '_mlem_recon_slurm.sh')\n", + " python_script_path = os.path.join(id11_code_path, \"ImageD11/nbGui/S3DXRD/run_mlem_recon.py\") \n", + " outfile_path = os.path.join(slurm_mlem_path, ds.dsname + '_mlem_recon_slurm_%A_%a.out')\n", + " errfile_path = os.path.join(slurm_mlem_path, ds.dsname + '_mlem_recon_slurm_%A_%a.err')\n", + " log_path = os.path.join(slurm_mlem_path, ds.dsname + '_mlem_recon_slurm_$SLURM_ARRAY_JOB_ID_$SLURM_ARRAY_TASK_ID.log')\n", + "\n", + " reconfile = os.path.join(recons_path, ds.dsname + \"_mlem_recon_$SLURM_ARRAY_TASK_ID.txt\")\n", + "\n", + " bash_script_string = f\"\"\"#!/bin/bash\n", + "#SBATCH --job-name=mlem-recon\n", + "#SBATCH --output={outfile_path}\n", + "#SBATCH --error={errfile_path}\n", + "#SBATCH --array=0-{len(grains)-1}%{n_simultaneous_jobs}\n", + "#SBATCH --time=02:00:00\n", + "# define memory needs and number of tasks for each array job\n", + "#SBATCH --ntasks=1\n", + "#SBATCH --cpus-per-task={cores_per_task}\n", + "#\n", + "date\n", + "echo python3 {python_script_path} {ds.grainsfile} $SLURM_ARRAY_TASK_ID {reconfile} {pad} {niter} {dohm} {mask_cen} > {log_path} 2>&1\n", + "python3 {python_script_path} {ds.grainsfile} $SLURM_ARRAY_TASK_ID {reconfile} {pad} {niter} {dohm} {mask_cen} > {log_path} 2>&1\n", + "date\n", + " \"\"\"\n", + " \n", + " with open(bash_script_path, \"w\") as bashscriptfile:\n", + " bashscriptfile.writelines(bash_script_string)\n", + " \n", + " return bash_script_path, recons_path\n", + "\n", + "\n", + "def save_grains(grains, ds):\n", + " with h5py.File(ds.grainsfile, 'r+') as hout:\n", + " try:\n", + " grp = hout.create_group('slice_recon')\n", + " except ValueError:\n", + " grp = hout['slice_recon']\n", + " save_array(grp, 'intensity', raw_intensity_array).attrs['description'] = 'Raw intensity array for all grains'\n", + " save_array(grp, 'labels', grain_labels_array).attrs['description'] = 'Grain labels array for all grains'\n", + " \n", + " ipfxdset = save_array(grp, 'ipf_x_col_map', rgb_x_array)\n", + " ipfxdset.attrs['description'] = 'IPF X color at each pixel'\n", + " ipfxdset.attrs['CLASS'] = 'IMAGE'\n", + " ipfydset = save_array(grp, 'ipf_y_col_map', rgb_y_array)\n", + " ipfydset.attrs['description'] = 'IPF Y color at each pixel'\n", + " ipfydset.attrs['CLASS'] = 'IMAGE'\n", + " ipfzdset = save_array(grp, 'ipf_z_col_map', rgb_z_array)\n", + " ipfzdset.attrs['description'] = 'IPF Z color at each pixel'\n", + " ipfzdset.attrs['CLASS'] = 'IMAGE'\n", + " \n", + " grains_group = 'grains'\n", + "\n", + " for g in tqdm(grains):\n", + " gg = hout[grains_group][str(g.gid)]\n", + " \n", + " save_array(gg, 'recon', g.recon).attrs['description'] = 'Final reconstruction'\n", + " \n", + " \n", + "# without a mask, MLEM can introduce artifacts in the corners\n", + "# so we can manually mask those out\n", + "\n", + "# we can incoporate our own mask too\n", + "# by modifying the below function\n", + "\n", + "def apply_manual_mask(mask_in):\n", + " mask_out = mask_in.copy()\n", + " \n", + " # mask_out[200:, 250:] = 0\n", + " \n", + " return mask_out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_250um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load the dataset (for motor positions, not sure why these are not in peaks)\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# determine ring currents for sinogram row-by-row intensity correction\n", + "\n", + "utils.get_ring_current_per_scan(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Import 4D peaks\n", + "\n", + "cf_4d = ImageD11.columnfile.columnfile(ds.col4dfile)\n", + "\n", + "cf_4d.parameters.loadparameters(par_path)\n", + "cf_4d.updateGeometry()\n", + "\n", + "print(f\"Read {cf_4d.nrows} 4D peaks\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "grains = read_grains(ds)\n", + "\n", + "for grain in grains:\n", + " # print(grain.gid)\n", + " grain.a = np.cbrt(np.linalg.det(grain.ubi))\n", + " \n", + "print(f\"{len(grains)} grains imported\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# here we are filtering our peaks (cf_4d) to select only the strongest ones\n", + "# this time as opposed to indexing, our frac is slightly weaker but we are NOT filtering in dstar!!!!!\n", + "# this means many more peaks per grain = stronger sinograms\n", + "\n", + "# USER: modify the \"frac\" parameter below and re-run the cell until the orange dot sits nicely on the \"elbow\" of the blue line\n", + "# this indicates the fractional intensity cutoff we will select\n", + "# if the blue line does not look elbow-shaped in the logscale plot, try changing the \"doplot\" parameter (the y scale of the logscale plot) until it does\n", + "\n", + "cf_strong = utils.selectpeaks(cf_4d, frac=0.995, dsmax=cf_4d.ds.max(), doplot=0.9)\n", + "print(cf_4d.nrows)\n", + "cf_strong.nrows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# # now let's do a whole-sample tomographic reconstruction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# If the sinograms are only half-sinograms (we scanned dty across half the sample rather than the full sample), set the below to true:\n", + "is_half_scan = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if is_half_scan:\n", + " utils.correct_half_scan(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "utils.assign_peaks_to_grains(grains, cf_strong, tol=0.25)\n", + "\n", + "print(\"Storing peak data in grains\")\n", + "# iterate through all the grains\n", + "for g in tqdm(grains):\n", + " # store this grain's peak indices so we know which 4D peaks we used for indexing\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[cf_strong.grain_id == g.gid]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in grains:\n", + " # grain.peaks_4d_selected, grain.cen, grain.dx, grain.dy = utils.graincen(grain.gid, cf_strong, doplot=True, nsigma=1)\n", + " grain.rgb_z = utils.grain_to_rgb(grain, ax=(0,0,1),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_y = utils.grain_to_rgb(grain, ax=(0,1,0),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_x = utils.grain_to_rgb(grain, ax=(1,0,0),)# symmetry = Symmetry.cubic)\n", + " utils.fit_grain_position_from_sino(grain, cf_strong)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# make sure we get cen right (centre of rotation should be the middle of dty)\n", + "fig, ax = plt.subplots()\n", + "ax.plot([g.cen for g in grains])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "c0 = np.median([g.cen for g in grains])\n", + "\n", + "print('Center of rotation in dty', c0)\n", + "\n", + "y0 = c0/2\n", + "\n", + "print('y0 is', y0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# generate sinogram for whole sample\n", + "\n", + "whole_sample_sino, xedges, yedges = np.histogram2d(cf_4d.dty, cf_4d.omega, bins=[ds.ybinedges, ds.obinedges])\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(whole_sample_sino, interpolation=\"nearest\", vmin=0)\n", + "ax.set_aspect(4)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# \"quick\" whole-sample reconstruction\n", + "\n", + "pad = 50\n", + "\n", + "outsize = whole_sample_sino.shape[0] + pad\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "if is_half_scan:\n", + " halfmask = np.zeros_like(whole_sample_sino)\n", + "\n", + " halfmask[:len(halfmask)//2-1, :] = 1\n", + " halfmask[len(halfmask)//2-1, :] = 0.5\n", + "\n", + " ssino_to_recon = whole_sample_sino * halfmask\n", + "else:\n", + " ssino_to_recon = whole_sample_sino\n", + "\n", + "recon = ImageD11.sinograms.roi_iradon.iradon(ssino_to_recon, \n", + " theta=ds.obincens, \n", + " output_size=outsize,\n", + " projection_shifts=np.full(whole_sample_sino.shape, -y0),\n", + " filter_name='hamming',\n", + " interpolation='linear',\n", + " workers=nthreads)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we should be able to easily segment this using scikit-image\n", + "recon_man_mask = apply_manual_mask(recon)\n", + "\n", + "thresh = threshold_otsu(recon_man_mask)\n", + "\n", + "# we can also override the threshold if we don't like it:\n", + "\n", + "# thresh = 0.05\n", + "\n", + "binary = recon_man_mask > thresh\n", + "\n", + "chull = convex_hull_image(binary)\n", + "\n", + "fig, axs = plt.subplots(1, 3, sharex=True, sharey=True, constrained_layout=True)\n", + "axs[0].imshow(recon_man_mask, vmin=0, origin=\"lower\")\n", + "axs[1].imshow(binary, origin=\"lower\")\n", + "axs[2].imshow(chull, origin=\"lower\")\n", + "\n", + "axs[0].set_title(\"Reconstruction\")\n", + "axs[1].set_title(\"Binarised threshold\")\n", + "axs[2].set_title(\"Convex hull\")\n", + "\n", + "fig.supxlabel(\"<-- Y axis\")\n", + "fig.supylabel(\"Beam >\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "whole_sample_mask = chull" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "m = cf_strong.grain_id >= 0\n", + "ax.scatter(cf_strong.omega[m], cf_strong.dty[m], c=cf_strong.grain_id[m])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "mean_unit_cell_lengths = [grain.a for grain in grains]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(mean_unit_cell_lengths)\n", + "ax.set_xlabel(\"Grain ID\")\n", + "ax.set_ylabel(\"Unit cell length\")\n", + "plt.show()\n", + "\n", + "a0 = np.median(mean_unit_cell_lengths)\n", + " \n", + "print(a0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "utils.plot_grain_sinograms(grains, cf_strong)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.style.use('dark_background')\n", + "fig, ax = plt.subplots(2,2, figsize=(12,12))\n", + "a = ax.ravel()\n", + "x = [g.dy for g in grains]\n", + "y = [g.dx for g in grains]\n", + "s = [g.mask_4d.sum()/10 for g in grains]\n", + "a[0].scatter(x, y, c=[g.rgb_z for g in grains], s=s)\n", + "a[0].set(title='IPF color Z', aspect='equal')\n", + "a[1].scatter(x, y, c=[g.rgb_y for g in grains], s=s)\n", + "a[1].set(title='IPF color Y', aspect='equal')\n", + "a[2].scatter(x, y, c=[g.rgb_x for g in grains], s=s)\n", + "a[2].set(title='IPF color X', aspect='equal')\n", + "a[3].scatter(x, y, c=s)\n", + "a[3].set(title='Number of 4D peaks', aspect='equal')\n", + "\n", + "fig.supxlabel(\"Lab y (transverse)\")\n", + "fig.supylabel(\"Lab x (beam)\")\n", + "\n", + "for a in ax.ravel():\n", + " a.invert_xaxis()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# populate translations of grains\n", + "for g in grains:\n", + " g.translation = np.array([g.dx, g.dy, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Big scary block\n", + "# Must understand what this does!\n", + "\n", + "# Ensure cf is sorted by spot3d_id\n", + "# NOTE: spot3d_id should be spot4d_id, because we have merged into 4D?\n", + "assert (np.argsort(cf_strong.spot3d_id) == np.arange(cf_strong.nrows)).all()\n", + "\n", + "# load the 2d peak labelling output\n", + "pks = ImageD11.sinograms.properties.pks_table.load(ds.pksfile)\n", + "\n", + "# Grab the 2d peak centroids\n", + "p2d = pks.pk2d(ds.omega, ds.dty)\n", + "\n", + "# NOTE: These are not spatially corrected?!\n", + "\n", + "numba_order, numba_histo = utils.counting_sort(p2d['spot3d_id'])\n", + "\n", + "grain_2d_id = utils.palloc(p2d['spot3d_id'].shape, np.dtype(int))\n", + "\n", + "cleanid = cf_strong.grain_id.copy()\n", + "\n", + "utils.find_grain_id(cf_strong.spot3d_id, cleanid, p2d['spot3d_id'], grain_2d_id, numba_order)\n", + "\n", + "gord, counts = utils.counting_sort(grain_2d_id)\n", + "\n", + "inds = np.concatenate(((0,), np.cumsum(counts)))\n", + "\n", + "# I think what we end up with is:\n", + "# inds\n", + "# this is an array which tells you which 2D spots each grain owns\n", + "# the 2D spots are sorted by spot ID\n", + "# inds tells you for each grain were you can find its associated 2D spots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now our 2D peak assignments are known, let's populate our grain objects with our 2D peaks\n", + "\n", + "for grain in tqdm(grains):\n", + " i = grain.gid\n", + " grain.peaks_2d = gord[inds[i+1] : inds[i+2]]\n", + " # grain.mask_2d = np.isin(cf_2d.index, grain.peaks_2d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Determine sinograms of all grains\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "with concurrent.futures.ThreadPoolExecutor(max_workers= max(1,nthreads-1)) as pool:\n", + " for i in tqdm(pool.map(do_sinos, grains), total=len(grains)):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we can optionally correct the grain sinograms by scaling each row by the ring current:\n", + "\n", + "for grain in tqdm(grains):\n", + " utils.correct_sinogram_rows_with_ring_current(grain, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Show sinogram of single grain\n", + "\n", + "g = grains[0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.imshow((g.ssino/g.ssino.mean(axis=0)), norm=matplotlib.colors.LogNorm(), interpolation='nearest', origin=\"lower\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# if you want, you can override the y0 value here\n", + "\n", + "# y0 = 1.5 # for example!\n", + "\n", + "y0 = c0/2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "g = grains[1]\n", + "\n", + "run_iradon_id11(g, pad=pad, y0=y0, workers=max(nthreads, 20), sample_mask=whole_sample_mask, apply_halfmask=is_half_scan, mask_central_zingers=is_half_scan)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "g = grains[1]\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10,5))\n", + "axs[0].imshow(g.recon, vmin=0, origin=\"lower\")\n", + "axs[0].set_title(\"ID11 iradon\")\n", + "axs[1].imshow(g.ssino, aspect='auto')\n", + "axs[1].set_title(\"ssino\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "run_this_iradon = partial(run_iradon_id11, pad=pad, y0=y0, sample_mask=whole_sample_mask, workers=1, apply_halfmask=is_half_scan, mask_central_zingers=is_half_scan)\n", + "\n", + "with concurrent.futures.ThreadPoolExecutor( max_workers= max(1,nthreads-1) ) as pool:\n", + " for i in tqdm(pool.map(run_this_iradon, grains), total=len(grains)):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in grains:\n", + " grain.og_recon = grain.recon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, a = plt.subplots(1,2,figsize=(10,5))\n", + "rec = a[0].imshow(grains[8].og_recon, vmin=0, origin=\"lower\")\n", + "sin = a[1].imshow(grains[8].ssino, aspect='auto')\n", + "\n", + "# Function to update the displayed image based on the selected frame\n", + "def update_frame(i):\n", + " rec.set_array(grains[i].og_recon)\n", + " sin.set_array(grains[i].ssino)\n", + " a[0].set(title=str(i))\n", + " fig.canvas.draw()\n", + "\n", + "# Create a slider widget to select the frame number\n", + "frame_slider = widgets.IntSlider(\n", + " value=0,\n", + " min=0,\n", + " max=len(grains) - 1,\n", + " step=1,\n", + " description='Grain:'\n", + ")\n", + "\n", + "interact(update_frame, i=frame_slider)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "f,a = plt.subplots( 1,3, figsize=(15,5) )\n", + "ty, tx = utils.triangle().T\n", + "for i,title in enumerate( 'xyz' ):\n", + " ax = np.zeros(3)\n", + " ax[i] = 1.\n", + " hkl = [utils.crystal_direction_cubic( g.ubi, ax ) for g in grains]\n", + " xy = np.array([utils.hkl_to_pf_cubic(h) for h in hkl ])\n", + " rgb = np.array([utils.hkl_to_color_cubic(h) for h in hkl ])\n", + " for j in range(len(grains)):\n", + " grains[j].rgb = rgb[j]\n", + " a[i].scatter( xy[:,1], xy[:,0], c = rgb ) # Note the \"x\" axis of the plot is the 'k' direction and 'y' is h (smaller)\n", + " a[i].set(title=title, aspect='equal', facecolor='silver', xticks=[], yticks=[])\n", + " a[i].plot( tx, ty, 'k-', lw = 1 )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level=0.4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot initial output\n", + "\n", + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(rgb_z_array, origin=\"lower\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(grain_labels_array, origin=\"lower\") # originally 1,2,0\n", + "ax.set_title(\"Grain label map\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(raw_intensity_array, origin=\"lower\")\n", + "ax.set_title(\"Raw intensity array\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we can clean up these reconstructions using an MLEM iterative recon\n", + "# we can use the whole sample shape mask for this" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "save_grains_for_mlem(grains, ds, y0=y0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bash_script_path, recons_path = prepare_mlem_bash(ds, grains, pad, is_half_scan, n_simultaneous_jobs=1000, cores_per_task=8, niter=50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "utils.slurm_submit_and_wait(bash_script_path, 30)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# collect results into grain attributes\n", + "# the filenames are element position not gid\n", + "\n", + "for i, grain in enumerate(tqdm(grains)):\n", + " grain.recon = np.loadtxt(os.path.join(recons_path, ds.dsname + f\"_mlem_recon_{i}.txt\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# look at all our grains\n", + "\n", + "n_grains_to_plot = 25\n", + "\n", + "grains_step = len(grains)//n_grains_to_plot\n", + "\n", + "grid_size = np.ceil(np.sqrt(len(grains[::grains_step]))).astype(int)\n", + "nrows = (len(grains[::grains_step])+grid_size-1)//grid_size\n", + "\n", + "fig, axs = plt.subplots(grid_size, nrows, figsize=(10,10), layout=\"constrained\", sharex=True, sharey=True)\n", + "for i, ax in enumerate(axs.ravel()):\n", + " if i < len(grains[::grains_step]):\n", + " # get corresponding grain for this axis\n", + " g = grains[::grains_step][i]\n", + " ax.imshow(g.recon, vmin=0, origin=\"lower\")\n", + " # ax.invert_yaxis()\n", + " ax.set_title(i)\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(rgb_z_array, origin=\"lower\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(raw_intensity_array, origin=\"lower\")\n", + "ax.set_title(\"Sinogram raw intensity map\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(grain_labels_array, origin=\"lower\")\n", + "ax.set_title(\"Grain label map\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "save_grains(grains, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 1:\n", + " raise ValueError(\"Change the 1 above to 0 to allow 'Run all cells' in the notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + " \n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + " \n", + "# now we have our samples_dict, we can process our data:\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')\n", + "\n", + "cf_strong_frac = 0.995\n", + "cf_strong_dstol = 0.005\n", + "\n", + "is_half_scan = False\n", + "correct_sinos_with_ring_current = True\n", + "\n", + "peak_assign_tol = 0.25\n", + "\n", + "manual_threshold = None\n", + "# manual_threshold = 0.025\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "pad = 50\n", + "\n", + "# y0 = -1.4\n", + "\n", + "mlem_n_simultaneous_jobs = 1000\n", + "mlem_cores_per_task = 8\n", + "mlem_niter = 50\n", + "\n", + "cutoff_level = 0.2\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " if not os.path.exists(dset_path):\n", + " print(f\"Missing DataSet file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " \n", + " if not os.path.exists(ds.grainsfile):\n", + " print(f\"Missing grains file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " # check grains file for existance of slice_recon, skip if it's there\n", + " with h5py.File(ds.grainsfile, \"r\") as hin:\n", + " if \"slice_recon\" in hin.keys():\n", + " print(f\"Already reconstructed {dataset} in {sample}, skipping\")\n", + " continue\n", + " \n", + " # determine ring currents for sinogram row-by-row intensity correction\n", + " utils.get_ring_current_per_scan(ds)\n", + " \n", + " cf_4d = ImageD11.columnfile.columnfile(ds.col4dfile)\n", + " cf_4d.parameters.loadparameters(par_path)\n", + " cf_4d.updateGeometry()\n", + " \n", + " grains = read_grains(ds)\n", + " \n", + " cf_strong = utils.selectpeaks(cf_4d, frac=cf_strong_frac, dsmax=cf_4d.ds.max(), dstol=cf_strong_dstol)\n", + " \n", + " if is_half_scan:\n", + " utils.correct_half_scan(ds)\n", + " \n", + " utils.assign_peaks_to_grains(grains, cf_strong, tol=peak_assign_tol)\n", + " \n", + " for g in tqdm(grains):\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[cf_strong.grain_id == g.gid]\n", + " \n", + " for grain in tqdm(grains):\n", + " # grain.peaks_4d_selected, grain.cen, grain.dx, grain.dy = utils.graincen(grain.gid, cf_strong, doplot=False)\n", + " grain.rgb_z = utils.grain_to_rgb(grain, ax=(0,0,1),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_y = utils.grain_to_rgb(grain, ax=(0,1,0),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_x = utils.grain_to_rgb(grain, ax=(1,0,0),)# symmetry = Symmetry.cubic)\n", + " utils.fit_grain_position_from_sino(grain, cf_strong)\n", + " \n", + " c0 = np.median([g.cen for g in grains])\n", + " \n", + " y0 = c0/2\n", + " \n", + " whole_sample_sino, xedges, yedges = np.histogram2d(cf_4d.dty, cf_4d.omega, bins=[ds.ybinedges, ds.obinedges])\n", + " \n", + " print(\"Whole sample mask\")\n", + " outsize = whole_sample_sino.shape[0] + pad\n", + "\n", + " if is_half_scan:\n", + " halfmask = np.zeros_like(whole_sample_sino)\n", + "\n", + " halfmask[:len(halfmask)//2-1, :] = 1\n", + " halfmask[len(halfmask)//2-1, :] = 0.5\n", + "\n", + " ssino_to_recon = whole_sample_sino * halfmask\n", + " else:\n", + " ssino_to_recon = whole_sample_sino\n", + " \n", + " recon = ImageD11.sinograms.roi_iradon.iradon(ssino_to_recon, \n", + " theta=ds.obincens, \n", + " output_size=outsize,\n", + " projection_shifts=np.full(whole_sample_sino.shape, -y0),\n", + " filter_name='hamming',\n", + " interpolation='linear',\n", + " workers=nthreads)\n", + " \n", + " recon_man_mask = apply_manual_mask(recon)\n", + " if manual_threshold is None:\n", + " thresh = threshold_otsu(recon_man_mask)\n", + " else:\n", + " thresh = manual_threshold\n", + " \n", + " binary = recon_man_mask > thresh\n", + " whole_sample_mask = convex_hull_image(binary)\n", + " \n", + " for g in grains:\n", + " g.translation = np.array([g.dx, g.dy, 0])\n", + " \n", + " print(\"Peak 2D organise\")\n", + " pks = ImageD11.sinograms.properties.pks_table.load(ds.pksfile)\n", + " p2d = pks.pk2d(ds.omega, ds.dty)\n", + " numba_order, numba_histo = utils.counting_sort(p2d['spot3d_id'])\n", + " grain_2d_id = utils.palloc(p2d['spot3d_id'].shape, np.dtype(int))\n", + " cleanid = cf_strong.grain_id.copy()\n", + " utils.find_grain_id(cf_strong.spot3d_id, cleanid, p2d['spot3d_id'], grain_2d_id, numba_order)\n", + " gord, counts = utils.counting_sort(grain_2d_id)\n", + " inds = np.concatenate(((0,), np.cumsum(counts)))\n", + " \n", + " for grain in tqdm(grains):\n", + " i = grain.gid\n", + " grain.peaks_2d = gord[inds[i+1] : inds[i+2]]\n", + " \n", + " print(\"Making sinograms\")\n", + " with concurrent.futures.ThreadPoolExecutor(max_workers= max(1,nthreads-1)) as pool:\n", + " for i in tqdm(pool.map(do_sinos, grains), total=len(grains)):\n", + " pass\n", + " \n", + " if correct_sinos_with_ring_current:\n", + " for grain in tqdm(grains):\n", + " utils.correct_sinogram_rows_with_ring_current(grain, ds)\n", + " \n", + " print(\"Running iradon\")\n", + " \n", + " run_this_iradon = partial(run_iradon_id11, pad=pad, y0=y0, sample_mask=whole_sample_mask, workers=1, apply_halfmask=is_half_scan, mask_central_zingers=is_half_scan)\n", + "\n", + " with concurrent.futures.ThreadPoolExecutor( max_workers= max(1,nthreads-1) ) as pool:\n", + " for i in tqdm(pool.map(run_this_iradon, grains), total=len(grains)):\n", + " pass\n", + " \n", + " for grain in grains:\n", + " grain.og_recon = grain.recon\n", + " \n", + " save_grains_for_mlem(grains, ds, y0)\n", + " \n", + " bash_script_path, recons_path = prepare_mlem_bash(ds, grains, pad, is_half_scan, mlem_n_simultaneous_jobs, mlem_cores_per_task, mlem_niter)\n", + " \n", + " utils.slurm_submit_and_wait(bash_script_path, 30)\n", + " \n", + " for i, grain in enumerate(tqdm(grains)):\n", + " grain.recon = np.loadtxt(os.path.join(recons_path, ds.dsname + f\"_mlem_recon_{i}.txt\"))\n", + " \n", + " rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level)\n", + " \n", + " save_grains(grains, ds)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map_minor_phase.ipynb b/ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map_minor_phase.ipynb new file mode 100755 index 00000000..4ad0999e --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/2_S3DXRD_sinograms_map_minor_phase.ipynb @@ -0,0 +1,1346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os\n", + "import concurrent.futures\n", + "import timeit\n", + "\n", + "import matplotlib\n", + "%matplotlib widget\n", + "\n", + "from skimage.feature import blob_log\n", + "\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "import numba\n", + "import pprint\n", + "import numpy as np\n", + "import skimage.transform\n", + "import ipywidgets as ipyw\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from functools import partial\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils\n", + "\n", + "import ImageD11.refinegrains\n", + "import ImageD11.columnfile\n", + "import ImageD11.sinograms.properties\n", + "import ImageD11.sinograms.roi_iradon\n", + "from ImageD11.blobcorrector import eiger_spatial\n", + "from ImageD11.grain import grain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# define our functions\n", + "\n", + "def read_grains_minor_phase(ds):\n", + " with h5py.File(ds.grainsfile_minor_phase, 'r') as hin: \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " g = ImageD11.grain.grain(ubi)\n", + " g.gid = int(gid_string)\n", + " g.peaks_4d = gg['peaks_4d_indexing'][:]\n", + " grains.append(g)\n", + " \n", + " return grains\n", + "\n", + "\n", + "\n", + "def read_grains_main_phase(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin: \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + "\n", + " g = ImageD11.grain.grain(ubi)\n", + " g.gid = int(gid_string)\n", + " g.y0 = gg.attrs['y0'][()]\n", + " g.sample_mask = gg['circle_mask'][:]\n", + " g.recon = gg['recon'][:]\n", + " g.ssino = gg['ssino'][:]\n", + " grains.append(g)\n", + " \n", + " return grains\n", + "\n", + "\n", + "def map_grain_from_peaks(g, flt, ds):\n", + " \"\"\"\n", + " Computes sinogram\n", + " flt is already the peaks for this grain\n", + " Returns angles, sino\n", + " \"\"\" \n", + " NY = len(ds.ybincens) # number of y translations\n", + " iy = np.round((flt.dty - ds.ybincens[0]) / (ds.ybincens[1]-ds.ybincens[0])).astype(int) # flt column for y translation index\n", + "\n", + " # The problem is to assign each spot to a place in the sinogram\n", + " hklmin = g.hkl_2d_strong.min(axis=1) # Get minimum integer hkl (e.g -10, -9, -10)\n", + " dh = g.hkl_2d_strong - hklmin[:,np.newaxis] # subtract minimum hkl from all integer hkls\n", + " de = (g.etasigns_2d_strong.astype(int) + 1)//2 # something signs related\n", + " # 4D array of h,k,l,+/-\n", + " # pkmsk is whether a peak has been observed with this HKL or not\n", + " pkmsk = np.zeros(list(dh.max(axis=1) + 1 )+[2,], int) # make zeros-array the size of (max dh +1) and add another axis of length 2\n", + " pkmsk[ dh[0], dh[1], dh[2], de ] = 1 # we found these HKLs for this grain\n", + " # sinogram row to hit\n", + " pkrow = np.cumsum(pkmsk.ravel()).reshape(pkmsk.shape) - 1 #\n", + " # counting where we hit an HKL position with a found peak\n", + " # e.g (-10, -9, -10) didn't get hit, but the next one did, so increment\n", + "\n", + " npks = pkmsk.sum( )\n", + " destRow = pkrow[ dh[0], dh[1], dh[2], de ] \n", + " sino = np.zeros( ( npks, NY ), 'f' )\n", + " hits = np.zeros( ( npks, NY ), 'f' )\n", + " angs = np.zeros( ( npks, NY ), 'f' )\n", + " adr = destRow * NY + iy \n", + " # Just accumulate \n", + " sig = flt.sum_intensity\n", + " ImageD11.cImageD11.put_incr64( sino, adr, sig )\n", + " ImageD11.cImageD11.put_incr64( hits, adr, np.ones(len(de),dtype='f'))\n", + " ImageD11.cImageD11.put_incr64( angs, adr, flt.omega)\n", + " \n", + " sinoangles = angs.sum( axis = 1) / hits.sum( axis = 1 )\n", + " # Normalise:\n", + " sino = (sino.T/sino.max( axis=1 )).T\n", + " # Sort (cosmetic):\n", + " order = np.lexsort((np.arange(npks), sinoangles))\n", + " sinoangles = sinoangles[order]\n", + " ssino = sino[order].T\n", + " \n", + " return sinoangles, ssino, hits[order].T\n", + "\n", + "def do_sinos(g, hkltol=0.25):\n", + " flt = utils.tocolf({p:p2d[p][g.peaks_2d] for p in p2d}, par_path, dxfile=e2dx_path, dyfile=e2dy_path) # convert it to a columnfile and spatially correct\n", + " \n", + " hkl_real = np.dot(g.ubi, (flt.gx, flt.gy, flt.gz)) # calculate hkl of all assigned peaks\n", + " hkl_int = np.round(hkl_real).astype(int) # round to nearest integer\n", + " dh = ((hkl_real - hkl_int)**2).sum(axis = 0) # calculate square of difference\n", + "\n", + " # g.dherrall = dh.mean() # mean hkl error across all assigned peaks\n", + " # g.npksall = flt.nrows # total number of assigned peaks\n", + " flt.filter(dh < hkltol*hkltol) # filter all assigned peaks to be less than hkltol squared\n", + " hkl_real = np.dot(g.ubi, (flt.gx, flt.gy, flt.gz)) # recalculate error after filtration\n", + " hkl_int = np.round(hkl_real).astype(int)\n", + " dh = ((hkl_real - hkl_int)**2).sum(axis = 0)\n", + " # g.dherr = dh.mean() # dherr is mean hkl error across assigned peaks after hkltol filtering\n", + " # g.npks = flt.nrows # total number of assigned peaks after hkltol filtering\n", + " g.etasigns_2d_strong = np.sign(flt.eta)\n", + " g.hkl_2d_strong = hkl_int # integer hkl of assigned peaks after hkltol filtering\n", + " g.sinoangles, g.ssino, g.hits = map_grain_from_peaks(g, flt, ds)\n", + " return i,g\n", + "\n", + "\n", + "def run_iradon_id11(grain, pad=20, y0=0, workers=1, sample_mask=None, apply_halfmask=False, mask_central_zingers=False):\n", + " outsize = grain.ssino.shape[0] + pad\n", + " \n", + " if apply_halfmask:\n", + " halfmask = np.zeros_like(grain.ssino)\n", + "\n", + " halfmask[:len(halfmask)//2-1, :] = 1\n", + " halfmask[len(halfmask)//2-1, :] = 0.5\n", + " \n", + " ssino_to_recon = grain.ssino * halfmask\n", + " else:\n", + " ssino_to_recon = grain.ssino\n", + " \n", + " # # pad the sample mask\n", + " # sample_mask_padded = np.pad(sample_mask, pad//2)\n", + "\n", + " \n", + " # Perform iradon transform of grain sinogram, store result (reconstructed grain shape) in g.recon\n", + " grain.recon = ImageD11.sinograms.roi_iradon.iradon(ssino_to_recon, \n", + " theta=grain.sinoangles, \n", + " mask=sample_mask,\n", + " output_size=outsize,\n", + " projection_shifts=np.full(grain.ssino.shape, -y0),\n", + " filter_name='hamming',\n", + " interpolation='linear',\n", + " workers=workers)\n", + " \n", + " if mask_central_zingers:\n", + " grs = grain.recon.shape[0]\n", + " xpr, ypr = -grs//2 + np.mgrid[:grs, :grs]\n", + " inner_mask_radius = 25\n", + " outer_mask_radius = inner_mask_radius + 2\n", + "\n", + " inner_circle_mask = (xpr ** 2 + ypr ** 2) < inner_mask_radius ** 2\n", + " outer_circle_mask = (xpr ** 2 + ypr ** 2) < outer_mask_radius ** 2\n", + "\n", + " mask_ring = inner_circle_mask & outer_circle_mask\n", + " # we now have a mask to apply\n", + " fill_value = np.median(grain.recon[mask_ring])\n", + " grain.recon[inner_circle_mask] = fill_value\n", + " \n", + " return grain\n", + "\n", + "\n", + "def find_cens_from_recon(grain):\n", + " grain.bad_recon = False\n", + " blobs = blob_log(grain.recon, min_sigma=1, max_sigma=10, num_sigma=10, threshold=.01)\n", + " blobs_sorted = sorted(blobs, key=lambda x: x[2], reverse=True)\n", + " try:\n", + " largest_blob = blobs_sorted[0]\n", + " \n", + " # we now have the blob position in recon space\n", + " # we need to go back to microns\n", + " \n", + " # first axis (vertical) is x\n", + " # second axis (horizontal) is y\n", + " \n", + " x_recon_space = largest_blob[0]\n", + " y_recon_space = largest_blob[1]\n", + " \n", + " # centre of the recon image is centre of space\n", + " \n", + " x_microns = (x_recon_space - grain.recon.shape[0]//2) * ds.ystep + y0\n", + " y_microns = -(y_recon_space - grain.recon.shape[1]//2) * ds.ystep + y0\n", + " \n", + " grain.x_blob = x_microns\n", + " grain.y_blob = y_microns\n", + " except IndexError:\n", + " # didn't find any blobs\n", + " # for small grains like these, if we didn't find a blob, normally indicates recon is bad\n", + " # we will exclude it from maps and export\n", + " grain.bad_recon = True\n", + "\n", + " \n", + "cmp = {'compression':'gzip',\n", + "'compression_opts': 2,\n", + "'shuffle' : True }\n", + "\n", + "def save_array(grp, name, ary):\n", + " hds = grp.require_dataset(name, \n", + " shape=ary.shape,\n", + " dtype=ary.dtype,\n", + " **cmp)\n", + " hds[:] = ary\n", + " return hds\n", + "\n", + "def save_grains_minor_phase(grains, ds):\n", + "\n", + " # delete existing file, because our grain numbers have changed\n", + " if os.path.exists(ds.grainsfile_minor_phase):\n", + " os.remove(ds.grainsfile_minor_phase)\n", + " \n", + " with h5py.File(ds.grainsfile_minor_phase, 'w-') as hout: # fail if exists\n", + " try:\n", + " grp = hout.create_group('peak_assignments')\n", + " except ValueError:\n", + " grp = hout['peak_assignments']\n", + "\n", + " ds_gord = save_array( grp, 'gord', gord )\n", + " ds_gord.attrs['description'] = 'Grain ordering: g[i].pks = gord[ inds[i] : inds[i+1] ]'\n", + " ds_inds = save_array( grp, 'inds', inds )\n", + " ds_inds.attrs['description'] = 'Grain indices: g[i].pks = gord[ inds[i] : inds[i+1] ]'\n", + " \n", + " try:\n", + " grp = hout.create_group('slice_recon')\n", + " except ValueError:\n", + " grp = hout['slice_recon']\n", + " save_array(grp, 'intensity', raw_intensity_array).attrs['description'] = 'Raw intensity array for all grains'\n", + " save_array(grp, 'labels', grain_labels_array).attrs['description'] = 'Grain labels array for all grains'\n", + " \n", + " ipfxdset = save_array(grp, 'ipf_x_col_map', rgb_x_array)\n", + " ipfxdset.attrs['description'] = 'IPF X color at each pixel'\n", + " ipfxdset.attrs['CLASS'] = 'IMAGE'\n", + " ipfydset = save_array(grp, 'ipf_y_col_map', rgb_y_array)\n", + " ipfydset.attrs['description'] = 'IPF Y color at each pixel'\n", + " ipfydset.attrs['CLASS'] = 'IMAGE'\n", + " ipfzdset = save_array(grp, 'ipf_z_col_map', rgb_z_array)\n", + " ipfzdset.attrs['description'] = 'IPF Z color at each pixel'\n", + " ipfzdset.attrs['CLASS'] = 'IMAGE'\n", + " \n", + " grains_group = hout.create_group('grains')\n", + " for g in tqdm(grains):\n", + " gg = grains_group.create_group(str(g.gid))\n", + " # save stuff for sinograms\n", + " \n", + " gg.attrs.update({'ubi':g.ubi})\n", + " \n", + " save_array(gg, 'peaks_4d_indexing', g.peaks_4d).attrs['description'] = \"Strong 4D peaks that were assigned to this grain during indexing\"\n", + " \n", + " save_array(gg, 'ssino', g.ssino).attrs['description'] = 'Sinogram of peak intensities sorted by omega'\n", + " save_array(gg, 'sinoangles', g.sinoangles).attrs['description'] = 'Projection angles for sinogram'\n", + " save_array(gg, 'og_recon', g.recon).attrs['description'] = 'Original ID11 iRadon reconstruction'\n", + " save_array(gg, 'recon', g.recon).attrs['description'] = 'Final reconstruction'\n", + " save_array(gg, 'circle_mask', whole_sample_mask).attrs['description'] = 'Reconstruction mask to use for MLEM'\n", + " \n", + " # might as well save peaks stuff while we're here\n", + " save_array(gg, 'translation', g.translation).attrs['description'] = 'Grain translation in lab frame'\n", + " save_array(gg, 'peaks_2d_sinograms', g.peaks_2d).attrs['description'] = \"2D peaks from strong 4D peaks that were assigned to this grain for sinograms\"\n", + " save_array(gg, 'peaks_4d_sinograms', g.peaks_4d).attrs['description'] = \"Strong 4D peaks that were assigned to this grain for sinograms\"\n", + "\n", + " gg.attrs['cen'] = g.cen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_250um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load the dataset (for motor positions, not sure why these are not in peaks)\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# determine ring currents for sinogram row-by-row intensity correction\n", + "\n", + "utils.get_ring_current_per_scan(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Import 4D peaks\n", + "\n", + "cf_4d = ImageD11.columnfile.columnfile(ds.col4dfile)\n", + "\n", + "cf_4d.parameters.loadparameters(par_path)\n", + "cf_4d.updateGeometry()\n", + "\n", + "print(f\"Read {cf_4d.nrows} 4D peaks\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "phase_name = \"Au\"\n", + "\n", + "ds.grainsfile_minor_phase = os.path.join(ds.analysispath, ds.dsname + f'_grains_{phase_name}.h5')\n", + "\n", + "grains = read_grains_minor_phase(ds)\n", + "\n", + "for grain in grains:\n", + " # print(grain.gid)\n", + " grain.a = np.cbrt(np.linalg.det(grain.ubi))\n", + " \n", + "print(f\"{len(grains)} grains imported\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# isolate main phase peaks, and remove them from the dataset\n", + "main_phase_peaks_mask = utils.unitcell_peaks_mask(cf_4d, dstol=0.0075, dsmax=cf_4d.ds.max())\n", + "\n", + "minor_phase_peaks = cf_4d.copy()\n", + "minor_phase_peaks.filter(~main_phase_peaks_mask)\n", + "\n", + "# Update geometry for minor phase peaks\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Au.par')\n", + "minor_phase_peaks.parameters.loadparameters(par_path)\n", + "minor_phase_peaks.updateGeometry()\n", + "\n", + "cf_strong = utils.selectpeaks(minor_phase_peaks, dstol=0.005, dsmax=minor_phase_peaks.ds.max(), frac=0.9, doplot=0.01)\n", + "print(cf_strong.nrows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# If the sinograms are only half-sinograms (we scanned dty across half the sample rather than the full sample), set the below to true:\n", + "is_half_scan = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if is_half_scan:\n", + " utils.correct_half_scan(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load major phase grain reconstruction\n", + "# for pad and y0\n", + "\n", + "major_phase_grains = read_grains_main_phase(ds)\n", + "whole_sample_mask = major_phase_grains[0].sample_mask\n", + "y0 = major_phase_grains[0].y0\n", + "\n", + "pad = ((major_phase_grains[0].recon.shape[0] - major_phase_grains[0].ssino.shape[0]))\n", + "pad" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tol = 0.25\n", + "\n", + "utils.assign_peaks_to_grains(grains, cf_strong, tol=tol)\n", + "\n", + "print(\"Storing peak data in grains\")\n", + "# iterate through all the grains\n", + "for g in tqdm(grains):\n", + " # store this grain's peak indices so we know which 4D peaks we used for sinograms\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[g.mask_4d]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "m = cf_strong.grain_id >= 0\n", + "ax.scatter(cf_strong.omega[m], cf_strong.dty[m], c=cf_strong.grain_id[m])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "mean_unit_cell_lengths = [grain.a for grain in grains]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(mean_unit_cell_lengths)\n", + "ax.set_xlabel(\"Grain ID\")\n", + "ax.set_ylabel(\"Unit cell length\")\n", + "plt.show()\n", + "\n", + "a0 = np.median(mean_unit_cell_lengths)\n", + " \n", + "print(a0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "utils.plot_grain_sinograms(grains, cf_strong, 25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in tqdm(grains):\n", + " # grain.peaks_4d_selected, grain.cen, grain.dx, grain.dy = utils.graincen(grain.gid, cf_strong, doplot=False)\n", + " grain.rgb_z = utils.grain_to_rgb(grain, ax=(0,0,1),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_y = utils.grain_to_rgb(grain, ax=(0,1,0),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_x = utils.grain_to_rgb(grain, ax=(1,0,0),)# symmetry = Symmetry.cubic)\n", + " utils.fit_grain_position_from_sino(grain, cf_strong)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# make sure we get cen right (centre of rotation should be the middle of dty)\n", + "fig, ax = plt.subplots()\n", + "ax.plot([g.cen for g in grains])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "c0 = np.median([g.cen for g in grains])\n", + "\n", + "print('Center of rotation in dty', c0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.style.use('dark_background')\n", + "fig, ax = plt.subplots(2,2, figsize=(12,12))\n", + "a = ax.ravel()\n", + "x = [g.dy for g in grains]\n", + "y = [g.dx for g in grains]\n", + "s = [g.mask_4d.sum()/10 for g in grains]\n", + "a[0].scatter(x, y, c=[g.rgb_z for g in grains], s=s)\n", + "a[0].set(title='IPF color Z', aspect='equal')\n", + "a[1].scatter(x, y, c=[g.rgb_y for g in grains], s=s)\n", + "a[1].set(title='IPF color Y', aspect='equal')\n", + "a[2].scatter(x, y, c=[g.rgb_x for g in grains], s=s)\n", + "a[2].set(title='IPF color X', aspect='equal')\n", + "a[3].scatter(x, y, c=s)\n", + "a[3].set(title='Number of 4D peaks', aspect='equal')\n", + "\n", + "fig.supxlabel(\"Lab y (transverse)\")\n", + "fig.supylabel(\"Lab x (beam)\")\n", + "\n", + "for a in ax.ravel():\n", + " a.invert_xaxis()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# populate translations of grains\n", + "for g in grains:\n", + " g.translation = np.array([g.dx, g.dy, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Big scary block\n", + "# Must understand what this does!\n", + "\n", + "# Ensure cf is sorted by spot3d_id\n", + "# NOTE: spot3d_id should be spot4d_id, because we have merged into 4D?\n", + "assert (np.argsort(cf_strong.spot3d_id) == np.arange(cf_strong.nrows)).all()\n", + "\n", + "# load the 2d peak labelling output\n", + "pks = ImageD11.sinograms.properties.pks_table.load(ds.pksfile)\n", + "\n", + "# Grab the 2d peak centroids\n", + "p2d = pks.pk2d(ds.omega, ds.dty)\n", + "\n", + "# NOTE: These are not spatially corrected?!\n", + "\n", + "numba_order, numba_histo = utils.counting_sort(p2d['spot3d_id'])\n", + "\n", + "grain_2d_id = utils.palloc(p2d['spot3d_id'].shape, np.dtype(int))\n", + "\n", + "cleanid = cf_strong.grain_id.copy()\n", + "\n", + "utils.find_grain_id(cf_strong.spot3d_id, cleanid, p2d['spot3d_id'], grain_2d_id, numba_order)\n", + "\n", + "gord, counts = utils.counting_sort(grain_2d_id)\n", + "\n", + "inds = np.concatenate(((0,), np.cumsum(counts)))\n", + "\n", + "# I think what we end up with is:\n", + "# inds\n", + "# this is an array which tells you which 2D spots each grain owns\n", + "# the 2D spots are sorted by spot ID\n", + "# inds tells you for each grain were you can find its associated 2D spots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# now our 2D peak assignments are known, let's populate our grain objects with our 2D peaks\n", + "\n", + "for grain in tqdm(grains):\n", + " i = grain.gid\n", + " grain.peaks_2d = gord[inds[i+1] : inds[i+2]]\n", + " # grain.mask_2d = np.isin(cf_2d.index, grain.peaks_2d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Determine sinograms of all grains\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "with concurrent.futures.ThreadPoolExecutor(max_workers= max(1,nthreads-1)) as pool:\n", + " for i in tqdm(pool.map(do_sinos, grains), total=len(grains)):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we can optionally correct the grain sinograms by scaling each row by the ring current:\n", + "\n", + "for grain in tqdm(grains):\n", + " utils.correct_sinogram_rows_with_ring_current(grain, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Show sinogram of single grain\n", + "\n", + "g = grains[0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.imshow((g.ssino/g.ssino.mean(axis=0)), norm=matplotlib.colors.LogNorm(), interpolation='nearest', origin=\"lower\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# you can pick a grain and investigate the effects of changing y0 that gets passed to iradon\n", + "# it' best to pick the grain AFTER reconstructing all grains, so you can pick a grain of interest\n", + "\n", + "g = grains[5]\n", + " \n", + "vals = np.linspace(-8.5, -7.5, 9)\n", + "\n", + "grid_size = np.ceil(np.sqrt(len(vals))).astype(int)\n", + "nrows = (len(vals)+grid_size-1)//grid_size\n", + "\n", + "fig, axs = plt.subplots(grid_size, nrows, sharex=True, sharey=True)\n", + "\n", + "for inc, val in enumerate(tqdm(vals)):\n", + " run_iradon_id11(g, y0=val)\n", + " # crop = g.recon[200:240, 230:290]\n", + " crop = g.recon\n", + " \n", + " axs.ravel()[inc].imshow(crop, origin=\"lower\", vmin=0)\n", + " axs.ravel()[inc].set_title(val)\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# you can overwrite y0 here\n", + "\n", + "# y0 = -7.875\n", + "# pad = 50" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "run_this_iradon = partial(run_iradon_id11, pad=pad, y0=y0, sample_mask=whole_sample_mask, workers=1, apply_halfmask=is_half_scan, mask_central_zingers=is_half_scan)\n", + "\n", + "with concurrent.futures.ThreadPoolExecutor( max_workers= max(1,nthreads-1) ) as pool:\n", + " for i in tqdm(pool.map(run_this_iradon, grains), total=len(grains)):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from ipywidgets import interact\n", + "%matplotlib ipympl\n", + "\n", + "fig, a = plt.subplots(1,2,figsize=(10,5))\n", + "rec = a[0].imshow(grains[8].recon, vmin=0, origin=\"lower\")\n", + "sin = a[1].imshow(grains[8].ssino, aspect='auto')\n", + "\n", + "# Function to update the displayed image based on the selected frame\n", + "def update_frame(i):\n", + " rec.set_array(grains[i].recon)\n", + " sin.set_array(grains[i].ssino)\n", + " a[0].set(title=str(grains[i].gid))\n", + " fig.canvas.draw()\n", + "\n", + "# Create a slider widget to select the frame number\n", + "frame_slider = widgets.IntSlider(\n", + " value=0,\n", + " min=0,\n", + " max=len(grains) - 1,\n", + " step=1,\n", + " description='Grain:'\n", + ")\n", + "\n", + "interact(update_frame, i=frame_slider)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with concurrent.futures.ThreadPoolExecutor(max_workers= max(1, nthreads-1)) as pool:\n", + " for i in tqdm(pool.map(find_cens_from_recon, grains), total=len(grains)):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# remove bad recon grains from future analysis\n", + "print(f\"{len(grains)} grains before filtration\")\n", + "grains = [grain for grain in grains if not grain.bad_recon]\n", + "print(f\"{len(grains)} grains after filtration\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for g in grains:\n", + " g.translation = np.array([g.x_blob, -g.y_blob, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.style.use('dark_background')\n", + "fig, ax = plt.subplots(2,2, figsize=(12,12))\n", + "a = ax.ravel()\n", + "x = [g.translation[1] for g in grains]\n", + "y = [g.translation[0] for g in grains]\n", + "s = [g.mask_4d.sum()/10 for g in grains]\n", + "a[0].scatter(x, y, c=[g.rgb_z for g in grains], s=s)\n", + "a[0].set(title='IPF color Z', aspect='equal')\n", + "a[1].scatter(x, y, c=[g.rgb_y for g in grains], s=s)\n", + "a[1].set(title='IPF color Y', aspect='equal')\n", + "a[2].scatter(x, y, c=[g.rgb_x for g in grains], s=s)\n", + "a[2].set(title='IPF color X', aspect='equal')\n", + "a[3].scatter(x, y, c=s)\n", + "a[3].set(title='Number of 4D peaks', aspect='equal')\n", + "\n", + "fig.supxlabel(\"Lab y (transverse)\")\n", + "fig.supylabel(\"Lab x (beam)\")\n", + "\n", + "for a in ax.ravel():\n", + " a.invert_xaxis()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "f,a = plt.subplots( 1,3, figsize=(15,5) )\n", + "ty, tx = utils.triangle().T\n", + "for i,title in enumerate( 'xyz' ):\n", + " ax = np.zeros(3)\n", + " ax[i] = 1.\n", + " hkl = [utils.crystal_direction_cubic( g.ubi, ax ) for g in grains]\n", + " xy = np.array([utils.hkl_to_pf_cubic(h) for h in hkl ])\n", + " rgb = np.array([utils.hkl_to_color_cubic(h) for h in hkl ])\n", + " for j in range(len(grains)):\n", + " grains[j].rgb = rgb[j]\n", + " a[i].scatter( xy[:,1], xy[:,0], c = rgb ) # Note the \"x\" axis of the plot is the 'k' direction and 'y' is h (smaller)\n", + " a[i].set(title=title, aspect='equal', facecolor='silver', xticks=[], yticks=[])\n", + " a[i].plot( tx, ty, 'k-', lw = 1 )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot initial output\n", + "\n", + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(rgb_z_array, origin=\"lower\") # originally 1,2,0\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(grain_labels_array, origin=\"lower\") # originally 1,2,0\n", + "ax.set_title(\"Grain label map\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(raw_intensity_array, origin=\"lower\") # originally 1,2,0\n", + "ax.set_title(\"Raw intensity array\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "labels, counts = np.unique(grain_labels_array, return_counts=True)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(labels[labels > 0], counts[labels > 0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# filter out grains with more than 25 pixels in the label map\n", + "# this normally indicates a dodgy reconstruction for this grain\n", + "# only really applies if the grains are very small!\n", + "\n", + "bad_gids = [int(label) for (label, count) in zip(labels, counts) if count > 25 and label > 0]\n", + "bad_gids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(f\"{len(grains)} grains before filtration\")\n", + "grains = [grain for grain in grains if grain.gid not in bad_gids]\n", + "print(f\"{len(grains)} grains after filtration\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot initial output\n", + "\n", + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(rgb_z_array, origin=\"lower\") # originally 1,2,0\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(grain_labels_array, origin=\"lower\") # originally 1,2,0\n", + "ax.set_title(\"Grain label map\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(raw_intensity_array, origin=\"lower\") # originally 1,2,0\n", + "ax.set_title(\"Raw intensity array\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write grains to disk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "save_grains_minor_phase(grains, ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 1:\n", + " raise ValueError(\"Change the 1 above to 0 to allow 'Run all cells' in the notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + " \n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + " \n", + "# now we have our samples_dict, we can process our data:\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "minor_phase_par_path = os.path.join(processed_data_root_dir, 'Au.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')\n", + "\n", + "main_phase_cf_dstol = 0.0075\n", + "phase_name = \"Au\"\n", + "\n", + "cf_strong_frac = 0.9\n", + "cf_strong_dstol = 0.005\n", + "\n", + "is_half_scan = False\n", + "correct_sinos_with_ring_current = True\n", + "\n", + "peak_assign_tol = 0.25\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "# pad = 50\n", + "\n", + "cutoff_level = 0.7\n", + "\n", + "grain_too_many_px = 22\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " if not os.path.exists(dset_path):\n", + " print(f\"Missing DataSet file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " \n", + " ds.grainsfile_minor_phase = os.path.join(ds.analysispath, ds.dsname + f'_grains_{phase_name}.h5')\n", + " \n", + " if not os.path.exists(ds.grainsfile_minor_phase):\n", + " print(f\"Missing grains file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " # check grains file for existance of slice_recon, skip if it's there\n", + " with h5py.File(ds.grainsfile_minor_phase, \"r\") as hin:\n", + " if \"slice_recon\" in hin.keys():\n", + " print(f\"Already reconstructed {dataset} in {sample}, skipping\")\n", + " continue\n", + " \n", + " # determine ring currents for sinogram row-by-row intensity correction\n", + " utils.get_ring_current_per_scan(ds)\n", + " \n", + " cf_4d = ImageD11.columnfile.columnfile(ds.col4dfile)\n", + " cf_4d.parameters.loadparameters(par_path)\n", + " cf_4d.updateGeometry()\n", + " \n", + " grains = read_grains_minor_phase(ds)\n", + " \n", + " main_phase_peaks_mask = utils.unitcell_peaks_mask(cf_4d, dstol=main_phase_cf_dstol, dsmax=cf_4d.ds.max())\n", + "\n", + " minor_phase_peaks = cf_4d.copy()\n", + " minor_phase_peaks.filter(~main_phase_peaks_mask)\n", + "\n", + " # Update geometry for minor phase peaks\n", + "\n", + " minor_phase_peaks.parameters.loadparameters(minor_phase_par_path)\n", + " minor_phase_peaks.updateGeometry()\n", + " \n", + " cf_strong = utils.selectpeaks(minor_phase_peaks, frac=cf_strong_frac, dsmax=cf_4d.ds.max(), dstol=cf_strong_dstol)\n", + " \n", + " if is_half_scan:\n", + " utils.correct_half_scan(ds)\n", + " \n", + " main_phase_grains = read_grains_main_phase(ds)\n", + " whole_sample_mask = main_phase_grains[0].sample_mask\n", + " y0 = main_phase_grains[0].y0\n", + " pad = ((main_phase_grains[0].recon.shape[0] - main_phase_grains[0].ssino.shape[0]))\n", + " \n", + " utils.assign_peaks_to_grains(grains, cf_strong, tol=peak_assign_tol)\n", + " \n", + " for g in tqdm(grains):\n", + " g.mask_4d = cf_strong.grain_id == g.gid\n", + " g.peaks_4d = cf_strong.index[g.mask_4d]\n", + " \n", + " for grain in tqdm(grains):\n", + " # grain.peaks_4d_selected, grain.cen, grain.dx, grain.dy = utils.graincen(grain.gid, cf_strong, doplot=False)\n", + " grain.rgb_z = utils.grain_to_rgb(grain, ax=(0,0,1),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_y = utils.grain_to_rgb(grain, ax=(0,1,0),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_x = utils.grain_to_rgb(grain, ax=(1,0,0),)# symmetry = Symmetry.cubic)\n", + " utils.fit_grain_position_from_sino(grain, cf_strong)\n", + " \n", + " c0 = np.median([g.cen for g in grains])\n", + " \n", + " for g in grains:\n", + " g.translation = np.array([g.dx, g.dy, 0])\n", + " \n", + " print(\"Peak 2D organise\")\n", + " pks = ImageD11.sinograms.properties.pks_table.load(ds.pksfile)\n", + " p2d = pks.pk2d(ds.omega, ds.dty)\n", + " numba_order, numba_histo = utils.counting_sort(p2d['spot3d_id'])\n", + " grain_2d_id = utils.palloc(p2d['spot3d_id'].shape, np.dtype(int))\n", + " cleanid = cf_strong.grain_id.copy()\n", + " utils.find_grain_id(cf_strong.spot3d_id, cleanid, p2d['spot3d_id'], grain_2d_id, numba_order)\n", + " gord, counts = utils.counting_sort(grain_2d_id)\n", + " inds = np.concatenate(((0,), np.cumsum(counts)))\n", + " \n", + " for grain in tqdm(grains):\n", + " i = grain.gid\n", + " grain.peaks_2d = gord[inds[i+1] : inds[i+2]]\n", + " \n", + " print(\"Making sinograms\")\n", + " with concurrent.futures.ThreadPoolExecutor(max_workers= max(1,nthreads-1)) as pool:\n", + " for i in tqdm(pool.map(do_sinos, grains), total=len(grains)):\n", + " pass\n", + " \n", + " if correct_sinos_with_ring_current:\n", + " for grain in tqdm(grains):\n", + " utils.correct_sinogram_rows_with_ring_current(grain, ds)\n", + " \n", + " print(\"Running iradon\")\n", + " \n", + " run_this_iradon = partial(run_iradon_id11, pad=pad, y0=y0, sample_mask=whole_sample_mask, workers=1, apply_halfmask=is_half_scan, mask_central_zingers=is_half_scan)\n", + "\n", + " with concurrent.futures.ThreadPoolExecutor( max_workers= max(1,nthreads-1) ) as pool:\n", + " for i in tqdm(pool.map(run_this_iradon, grains), total=len(grains)):\n", + " pass\n", + " \n", + " with concurrent.futures.ThreadPoolExecutor(max_workers= max(1, nthreads-1)) as pool:\n", + " for i in tqdm(pool.map(find_cens_from_recon, grains), total=len(grains)):\n", + " pass\n", + " \n", + " grains = [grain for grain in grains if not grain.bad_recon]\n", + " \n", + " for g in grains:\n", + " g.translation = np.array([g.x_blob, -g.y_blob, 0])\n", + " \n", + " rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level)\n", + " \n", + " labels, counts = np.unique(grain_labels_array, return_counts=True)\n", + " bad_gids = [int(label) for (label, count) in zip(labels, counts) if count > grain_too_many_px and label > 0]\n", + " \n", + " grains = [grain for grain in grains if grain.gid not in bad_gids]\n", + " \n", + " rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array = utils.build_slice_arrays(grains, cutoff_level)\n", + " \n", + " save_grains_minor_phase(grains, ds)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/S3DXRD/3_S3DXRD_strain_maps_pbp.ipynb b/ImageD11/nbGui/S3DXRD/3_S3DXRD_strain_maps_pbp.ipynb new file mode 100755 index 00000000..cf297aae --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/3_S3DXRD_strain_maps_pbp.ipynb @@ -0,0 +1,898 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bbc4bc96-cbc7-436a-a174-c99388869cbb", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94b89030-fdb2-47d2-bc26-3e5cfb0d6509", + "metadata": {}, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f4b91e0-7a83-462b-85cb-27f65721ff93", + "metadata": {}, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os\n", + "import concurrent.futures\n", + "import timeit\n", + "\n", + "import matplotlib\n", + "%matplotlib ipympl\n", + "\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from xfab.symmetry import Umis\n", + "\n", + "\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, roi_iradon\n", + "from ImageD11.blobcorrector import eiger_spatial\n", + "from ImageD11.grain import grain\n", + "from ImageD11 import cImageD11\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01541645-a0ee-47c2-ab99-7eecd35392c8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def read_grains(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin:\n", + " grp = hin['slice_recon']\n", + " \n", + " raw_intensity_array = grp['intensity'][:]\n", + " grain_labels_array = grp['labels'][:]\n", + " \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " g = ImageD11.grain.grain(ubi)\n", + " # general grain properties\n", + " g.gid = int(gid_string)\n", + " g.translation = gg['translation'][:]\n", + " g.cen = gg.attrs['cen']\n", + " g.y0 = gg.attrs['y0']\n", + " # sinogram stuff\n", + " g.ssino = gg['ssino'][:]\n", + " g.sinoangles = gg['sinoangles'][:]\n", + " # reconstructions\n", + " g.og_recon = gg['og_recon'][:]\n", + " g.recon = gg['recon'][:]\n", + " grains.append(g)\n", + " \n", + " return grains, raw_intensity_array, grain_labels_array\n", + "\n", + "\n", + "# save recons and 2d properties to existing grain file\n", + "\n", + "cmp = {'compression':'gzip',\n", + " 'compression_opts': 2,\n", + " 'shuffle' : True }\n", + "\n", + "def save_array(grp, name, ary):\n", + " hds = grp.require_dataset(name, \n", + " shape=ary.shape,\n", + " dtype=ary.dtype,\n", + " **cmp)\n", + " hds[:] = ary\n", + " return hds\n", + "\n", + "\n", + "def save_ubi_map(ds):\n", + " with h5py.File(ds.pbpubifile, 'w') as hout:\n", + " grp = hout.create_group('arrays')\n", + " save_array(grp, 'ubi_map', ubi_map).attrs['description'] = 'Refined UBI values at each pixel'\n", + " save_array(grp, 'eps_map', eps_map).attrs['description'] = 'Strain matrices (sample ref) at each pixel'\n", + " save_array(grp, 'misorientation_map', misorientation_map).attrs['description'] = 'Misorientation to grain avg at each pixel'\n", + " ipfxdset = save_array(grp, 'ipf_x_col_map', ipf_x_col_map)\n", + " ipfxdset.attrs['description'] = 'IPF X color at each pixel'\n", + " ipfxdset.attrs['CLASS'] = 'IMAGE'\n", + " ipfydset = save_array(grp, 'ipf_y_col_map', ipf_y_col_map)\n", + " ipfydset.attrs['description'] = 'IPF Y color at each pixel'\n", + " ipfydset.attrs['CLASS'] = 'IMAGE'\n", + " ipfzdset = save_array(grp, 'ipf_z_col_map', ipf_z_col_map)\n", + " ipfzdset.attrs['description'] = 'IPF Z color at each pixel'\n", + " ipfzdset.attrs['CLASS'] = 'IMAGE'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3c559a5-0202-4f54-be32-20830f5fd6a1", + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1635a8ba-5cbb-4ce3-8cfc-13c19b38c574", + "metadata": {}, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_100um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ef8df24-9a41-479e-9f3c-b76fe4b27d5c", + "metadata": {}, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20ff052b-cca8-4310-8b29-4c82e0e513c8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load the dataset (for motor positions, not sure why these are not in peaks)\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfb81175-ace9-4d13-8c05-bc7a0953d881", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Import 2D peaks\n", + "\n", + "cf_2d = ImageD11.columnfile.columnfile(ds.col2dfile)\n", + "\n", + "cf_2d.parameters.loadparameters(par_path)\n", + "cf_2d.updateGeometry()\n", + "\n", + "print(f\"Read {cf_2d.nrows} 2D peaks\")\n", + "\n", + "index_column = np.arange(cf_2d.nrows)\n", + "cf_2d.addcolumn(index_column, 'index')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5ff57a-0a7f-44cd-b437-eb4cc4e2ea25", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "grains, raw_intensity_array, grain_labels_array = read_grains(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8feef60-367b-478a-9ce4-8a94e3cedd60", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# filter 2D peaks by intensity\n", + "\n", + "cf_2d_strong = utils.selectpeaks(cf_2d, frac=0.95, dsmax=cf_2d.ds.max(), doplot=0.5)\n", + "print(cf_2d.nrows)\n", + "print(cf_2d_strong.nrows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a85cb2c4-50d1-4264-aef4-af903e4ce941", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cf_2d_strong.addcolumn(np.cos(np.radians(cf_2d_strong.omega)), 'cosomega')\n", + "cf_2d_strong.addcolumn(np.sin(np.radians(cf_2d_strong.omega)), 'sinomega')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4a9c316-66ed-4935-aa82-687668a41180", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tol = 0.025\n", + "\n", + "utils.assign_peaks_to_grains(grains, cf_2d_strong, tol=tol)\n", + "\n", + "print(\"Storing peak data in grains\")\n", + "# iterate through all the grains\n", + "\n", + "gvecs_2d_strong = np.transpose((cf_2d_strong.gx, cf_2d_strong.gy, cf_2d_strong.gz)).astype(float)\n", + "\n", + "# make lookup table for grain IDs so we can quickly get the grain given a GID (might not be contiguous or start at 0)\n", + "grain_lut = {}\n", + "for g in tqdm(grains):\n", + " grain_lut[g.gid] = g\n", + " g.mask_2d_strong = cf_2d_strong.grain_id == g.gid\n", + " \n", + " g.gve_2d_strong = gvecs_2d_strong[g.mask_2d_strong].T\n", + " \n", + " g.cosomega = cf_2d_strong.cosomega[g.mask_2d_strong]\n", + " g.sinomega = cf_2d_strong.sinomega[g.mask_2d_strong]\n", + " \n", + " g.dty = cf_2d_strong.dty[g.mask_2d_strong]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f07f3a90-ede1-412d-b1d0-a36e1be9c617", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in grains:\n", + " grain.label_mask = grain_labels_array == grain.gid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e855673a-a871-426e-ab58-543f16906684", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# refine each grain's ubi using all contributory pixels\n", + "# use the tolerance we used for assignment earlier\n", + "\n", + "for grain in tqdm(grains):\n", + "\n", + " ubifit = grain.ubi.copy()\n", + " _ = cImageD11.score_and_refine(ubifit, np.transpose(grain.gve_2d_strong), tol)\n", + " \n", + " grain.set_ubi(ubifit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bee32ebb-12df-419e-856f-4112ba3b8fdd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# WORKING\n", + "\n", + "# for each grain\n", + "# for each pixel in the mask\n", + "# local refinement\n", + "# global container\n", + "\n", + "n_ysteps_tol = 10\n", + "\n", + "per_pixel_ubis = {}\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "for ginc, grain in enumerate(tqdm(grains[:])):\n", + " def refine_ubis(pixel_position):\n", + " i, j = pixel_position\n", + " \n", + " # convert pixel position to real space micron position\n", + " \n", + " a = (j - grains[0].recon.shape[0]//2) * ds.ystep\n", + " b = (i - grains[0].recon.shape[0]//2) * ds.ystep\n", + " \n", + " dty_calc = a*grain.cosomega - b*grain.sinomega + grain.y0 # microns\n", + " \n", + " mask = np.abs(grain.dty - dty_calc) < n_ysteps_tol*ds.ystep\n", + " \n", + " gve = np.transpose(grain.gve_2d_strong[:, mask])\n", + " \n", + " ubifit = grain.ubi.copy()\n", + " _ = cImageD11.score_and_refine(ubifit, gve, tol)\n", + "\n", + " return ubifit\n", + "\n", + " pixel_positions = np.argwhere(grain.label_mask == True)\n", + " \n", + " with concurrent.futures.ThreadPoolExecutor(max_workers = max(1, nthreads-1)) as pool:\n", + " pixel_ubis = pool.map(refine_ubis, pixel_positions)\n", + "\n", + " for pixel_position, ubi in zip(pixel_positions, pixel_ubis):\n", + " per_pixel_ubis[tuple(pixel_position)] = (ginc, ubi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d55a7b0-47da-4be2-9618-7b8fd0055619", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# a UBI per pixel\n", + "\n", + "ubi_map = np.empty((grains[0].recon.shape + (3,3)))\n", + "ubi_map.fill(np.nan)\n", + "for pxi in tqdm(range(grains[0].recon.shape[0])):\n", + " for pxj in range(grains[0].recon.shape[1]):\n", + " try:\n", + " graininc, this_ubi = per_pixel_ubis[pxi, pxj]\n", + " ubi_map[pxi, pxj, :, :] = this_ubi\n", + " except KeyError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b6a7866-7ce2-4c54-b9d1-89d094757f4e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pixel_grain_lut = {}\n", + "for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " this_ubi = ubi_map[i, j]\n", + " if not np.isnan(this_ubi[0,0]):\n", + " this_grain = ImageD11.grain.grain(this_ubi)\n", + " pixel_grain_lut[i, j] = this_grain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc67cb11-ef15-4f5a-adb7-3f152e5cf5d8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "eps_map = np.empty((grains[0].recon.shape + (3,3)))\n", + "eps_map.fill(np.nan)\n", + "for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ref_gid = grain_labels_array[i, j]\n", + " this_ref_grain = grain_lut[this_ref_gid]\n", + " this_eps = this_grain.eps_sample_matrix(dzero_cell=this_ref_grain.unitcell)\n", + " eps_map[i, j] = this_eps\n", + " except KeyError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6da68a30-4650-4bea-8181-6ef75a1fdf5c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "misorientation_map = np.empty((grains[0].recon.shape))\n", + "misorientation_map.fill(np.nan)\n", + "for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ref_gid = grain_labels_array[i, j]\n", + " this_ref_grain = grain_lut[this_ref_gid]\n", + " this_misorien = np.min(Umis(this_ref_grain.U, this_grain.U, 7), axis=0)[1]\n", + " misorientation_map[i, j] = this_misorien\n", + " except KeyError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21ce0f7d-6be5-4dcd-9d35-cd6178d077bc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ipf_x_col_map = np.empty((grains[0].recon.shape + (3,)))\n", + "ipf_x_col_map.fill(np.nan)\n", + "for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ipf_x_col = utils.hkl_to_color_cubic(utils.crystal_direction_cubic(this_grain.ubi, (1, 0, 0)))\n", + " ipf_x_col_map[i, j] = this_ipf_x_col\n", + " except KeyError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08768b2d-0146-4163-8e8d-231fa14bd4a0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ipf_y_col_map = np.empty((grains[0].recon.shape + (3,)))\n", + "ipf_y_col_map.fill(np.nan)\n", + "for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ipf_y_col = utils.hkl_to_color_cubic(utils.crystal_direction_cubic(this_grain.ubi, (0, 1, 0)))\n", + " ipf_y_col_map[i, j] = this_ipf_y_col\n", + " except KeyError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bf1dddf-5025-4780-8dd4-8a3694d7f136", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ipf_z_col_map = np.empty((grains[0].recon.shape + (3,)))\n", + "ipf_z_col_map.fill(np.nan)\n", + "for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ipf_z_col = utils.hkl_to_color_cubic(utils.crystal_direction_cubic(this_grain.ubi, (0, 0, 1)))\n", + " ipf_z_col_map[i, j] = this_ipf_z_col\n", + " except KeyError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b93014e9-7ea8-4243-9299-27eaa83bb700", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "eps_s_11_map = eps_map[:, :, 0, 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "im = ax.imshow(eps_s_11_map, cmap='RdBu_r', vmin=-1e-3, vmax=1e-3, origin=\"lower\")\n", + "plt.colorbar(im)\n", + "ax.set_title(\"eps_11\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0ac6555-4e70-46fd-8f67-1dcaeb510406", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "eps_s_22_map = eps_map[:, :, 1, 1]\n", + "\n", + "fig, ax = plt.subplots()\n", + "im = ax.imshow(eps_s_22_map, cmap='RdBu_r', vmin=-1e-3, vmax=1e-3, origin=\"lower\")\n", + "plt.colorbar(im)\n", + "ax.set_title(\"eps_22\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28d88a16-c89d-4800-9ef2-7a55dd572666", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "eps_s_33_map = eps_map[:, :, 2, 2]\n", + "\n", + "fig, ax = plt.subplots()\n", + "im = ax.imshow(eps_s_33_map, cmap='RdBu_r', vmin=-1e-3, vmax=1e-3, origin=\"lower\")\n", + "plt.colorbar(im)\n", + "ax.set_title(\"eps_33\")\n", + "plt.show()\n", + "\n", + "# add quivers!!!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dd330b0-c575-498f-bbf4-b79a50b5ccf8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "image_to_show = np.transpose((ipf_z_col_map[:, :, 0], ipf_z_col_map[:, :, 1], ipf_z_col_map[:, :, 2]), axes=(1, 2, 0))\n", + "fig, ax = plt.subplots(constrained_layout=True)\n", + "ax.imshow(image_to_show, origin=\"lower\") # originally 1,2,0\n", + "ax.set_title(\"IPF-Z\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fe56c6b-fa74-409c-ab68-02fc041a5673", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "im = ax.imshow(misorientation_map, vmax=0.15, origin=\"lower\")\n", + "plt.colorbar(im)\n", + "ax.set_title(\"Misorientation to grain mean (degrees)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bb83b62-8e14-46ca-8514-10ff0e5cafb1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ds.pbpubifile = os.path.join(ds.analysispath, ds.dsname + '_pbp_map.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d5a6c02-657e-4dc9-ba78-740ed242672b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "save_ubi_map(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c559091-97bd-4b2d-9dc6-d99eb1b6e038", + "metadata": {}, + "outputs": [], + "source": [ + "if 1:\n", + " raise ValueError(\"Change the 1 above to 0 to allow 'Run all cells' in the notebook\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51473acc-dfa2-4a1e-8380-dd5c5c953e54", + "metadata": {}, + "outputs": [], + "source": [ + "# Now that we're happy with our indexing parameters, we can run the below cell to do this in bulk for many samples/datasets\n", + "# by default this will do all samples in sample_list, all datasets with a prefix of dset_prefix\n", + "# you can add samples and datasets to skip in skips_dict\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + " \n", + "samples_dict = utils.find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list)\n", + " \n", + "# manual override:\n", + "# samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_200um\"]}\n", + " \n", + "# now we have our samples_dict, we can process our data:\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')\n", + "\n", + "cf_2d_strong_frac = 0.95\n", + "peak_assign_tol = 0.025\n", + "n_ysteps_tol = 10\n", + "\n", + "nthreads = len(os.sched_getaffinity(os.getpid()))\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " if not os.path.exists(dset_path):\n", + " print(f\"Missing DataSet file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " print(f\"I have a DataSet {ds.dset} in sample {ds.sample}\")\n", + " \n", + " if not os.path.exists(ds.grainsfile):\n", + " print(f\"Missing grains file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " ds.pbpubifile = os.path.join(ds.analysispath, ds.dsname + '_pbp_map.h5')\n", + " \n", + " if os.path.exists(ds.pbpubifile):\n", + " print(f\"PBP file already exists for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " # Import 2D peaks\n", + "\n", + " cf_2d = ImageD11.columnfile.columnfile(ds.col2dfile)\n", + " cf_2d.parameters.loadparameters(par_path)\n", + " cf_2d.updateGeometry()\n", + " print(f\"Read {cf_2d.nrows} 2D peaks\")\n", + " index_column = np.arange(cf_2d.nrows)\n", + " cf_2d.addcolumn(index_column, 'index')\n", + " \n", + " grains, raw_intensity_array, grain_labels_array = read_grains(ds)\n", + " \n", + " cf_2d_strong = utils.selectpeaks(cf_2d, frac=cf_2d_strong_frac, dsmax=cf_2d.ds.max())\n", + " cf_2d_strong.addcolumn(np.cos(np.radians(cf_2d_strong.omega)), 'cosomega')\n", + " cf_2d_strong.addcolumn(np.sin(np.radians(cf_2d_strong.omega)), 'sinomega')\n", + " \n", + " utils.assign_peaks_to_grains(grains, cf_2d_strong, tol=peak_assign_tol)\n", + "\n", + " print(\"Storing peak data in grains\")\n", + " # iterate through all the grains\n", + "\n", + " gvecs_2d_strong = np.transpose((cf_2d_strong.gx, cf_2d_strong.gy, cf_2d_strong.gz)).astype(float)\n", + "\n", + " # make lookup table for grain IDs so we can quickly get the grain given a GID (might not be contiguous or start at 0)\n", + " grain_lut = {}\n", + " for g in tqdm(grains):\n", + " grain_lut[g.gid] = g\n", + " g.mask_2d_strong = cf_2d_strong.grain_id == g.gid\n", + "\n", + " g.gve_2d_strong = gvecs_2d_strong[g.mask_2d_strong].T\n", + "\n", + " g.cosomega = cf_2d_strong.cosomega[g.mask_2d_strong]\n", + " g.sinomega = cf_2d_strong.sinomega[g.mask_2d_strong]\n", + "\n", + " g.dty = cf_2d_strong.dty[g.mask_2d_strong]\n", + " \n", + " g.label_mask = grain_labels_array == g.gid\n", + " \n", + " \n", + " ubifit = g.ubi.copy()\n", + " _ = cImageD11.score_and_refine(ubifit, np.transpose(g.gve_2d_strong), peak_assign_tol)\n", + " g.set_ubi(ubifit)\n", + " \n", + " per_pixel_ubis = {}\n", + " \n", + " for ginc, grain in enumerate(tqdm(grains[:])):\n", + " def refine_ubis(pixel_position):\n", + " i, j = pixel_position\n", + "\n", + " # convert pixel position to real space micron position\n", + "\n", + " a = (j - grains[0].recon.shape[0]//2) * ds.ystep\n", + " b = (i - grains[0].recon.shape[0]//2) * ds.ystep\n", + "\n", + " dty_calc = a*grain.cosomega - b*grain.sinomega + grain.y0 # microns\n", + "\n", + " mask = np.abs(grain.dty - dty_calc) < n_ysteps_tol*ds.ystep\n", + "\n", + " gve = np.transpose(grain.gve_2d_strong[:, mask])\n", + "\n", + " ubifit = grain.ubi.copy()\n", + " _ = cImageD11.score_and_refine(ubifit, gve, peak_assign_tol)\n", + "\n", + " return ubifit\n", + "\n", + " pixel_positions = np.argwhere(grain.label_mask == True)\n", + "\n", + " with concurrent.futures.ThreadPoolExecutor(max_workers = max(1, nthreads-1)) as pool:\n", + " pixel_ubis = pool.map(refine_ubis, pixel_positions)\n", + "\n", + " for pixel_position, ubi in zip(pixel_positions, pixel_ubis):\n", + " per_pixel_ubis[tuple(pixel_position)] = (ginc, ubi)\n", + " \n", + " ubi_map = np.empty((grains[0].recon.shape + (3,3)))\n", + " ubi_map.fill(np.nan)\n", + " for pxi in tqdm(range(grains[0].recon.shape[0])):\n", + " for pxj in range(grains[0].recon.shape[1]):\n", + " try:\n", + " graininc, this_ubi = per_pixel_ubis[pxi, pxj]\n", + " ubi_map[pxi, pxj, :, :] = this_ubi\n", + " except KeyError:\n", + " continue\n", + " \n", + " pixel_grain_lut = {}\n", + " for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " this_ubi = ubi_map[i, j]\n", + " if not np.isnan(this_ubi[0,0]):\n", + " this_grain = ImageD11.grain.grain(this_ubi)\n", + " pixel_grain_lut[i, j] = this_grain\n", + " \n", + " eps_map = np.empty((grains[0].recon.shape + (3,3)))\n", + " eps_map.fill(np.nan)\n", + " for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ref_gid = grain_labels_array[i, j]\n", + " this_ref_grain = grain_lut[this_ref_gid]\n", + " this_eps = this_grain.eps_sample_matrix(dzero_cell=this_ref_grain.unitcell)\n", + " eps_map[i, j] = this_eps\n", + " except KeyError:\n", + " continue\n", + " \n", + " misorientation_map = np.empty((grains[0].recon.shape))\n", + " misorientation_map.fill(np.nan)\n", + " for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ref_gid = grain_labels_array[i, j]\n", + " this_ref_grain = grain_lut[this_ref_gid]\n", + " this_misorien = np.min(Umis(this_ref_grain.U, this_grain.U, 7), axis=0)[1]\n", + " misorientation_map[i, j] = this_misorien\n", + " except KeyError:\n", + " continue\n", + " \n", + " ipf_x_col_map = np.empty((grains[0].recon.shape + (3,)))\n", + " ipf_x_col_map.fill(np.nan)\n", + " for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ipf_x_col = utils.hkl_to_color_cubic(utils.crystal_direction_cubic(this_grain.ubi, (1, 0, 0)))\n", + " ipf_x_col_map[i, j] = this_ipf_x_col\n", + " except KeyError:\n", + " continue\n", + " \n", + " ipf_y_col_map = np.empty((grains[0].recon.shape + (3,)))\n", + " ipf_y_col_map.fill(np.nan)\n", + " for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ipf_y_col = utils.hkl_to_color_cubic(utils.crystal_direction_cubic(this_grain.ubi, (0, 1, 0)))\n", + " ipf_y_col_map[i, j] = this_ipf_y_col\n", + " except KeyError:\n", + " continue\n", + " \n", + " ipf_z_col_map = np.empty((grains[0].recon.shape + (3,)))\n", + " ipf_z_col_map.fill(np.nan)\n", + " for i in tqdm(range(grains[0].recon.shape[0])):\n", + " for j in range(grains[0].recon.shape[1]):\n", + " try:\n", + " this_grain = pixel_grain_lut[i, j]\n", + " this_ipf_z_col = utils.hkl_to_color_cubic(utils.crystal_direction_cubic(this_grain.ubi, (0, 0, 1)))\n", + " ipf_z_col_map[i, j] = this_ipf_z_col\n", + " except KeyError:\n", + " continue\n", + " \n", + " save_ubi_map(ds)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c73580e-108c-4d95-8437-a6ce7f890326", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ImageD11/nbGui/S3DXRD/4_S3DXRD_plot_both_phases.ipynb b/ImageD11/nbGui/S3DXRD/4_S3DXRD_plot_both_phases.ipynb new file mode 100755 index 00000000..f810f409 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/4_S3DXRD_plot_both_phases.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bbc4bc96-cbc7-436a-a174-c99388869cbb", + "metadata": {}, + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94b89030-fdb2-47d2-bc26-3e5cfb0d6509", + "metadata": {}, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f4b91e0-7a83-462b-85cb-27f65721ff93", + "metadata": {}, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import os\n", + "import concurrent.futures\n", + "import timeit\n", + "\n", + "import matplotlib\n", + "%matplotlib widget\n", + "\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "import ImageD11.columnfile\n", + "from ImageD11.sinograms import properties, roi_iradon\n", + "from ImageD11.blobcorrector import eiger_spatial\n", + "from ImageD11.grain import grain\n", + "from ImageD11 import cImageD11\n", + "\n", + "from ImageD11.nbGui import nb_utils as utils" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3c559a5-0202-4f54-be32-20830f5fd6a1", + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1635a8ba-5cbb-4ce3-8cfc-13c19b38c574", + "metadata": {}, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_100um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ef8df24-9a41-479e-9f3c-b76fe4b27d5c", + "metadata": {}, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20ff052b-cca8-4310-8b29-4c82e0e513c8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load the dataset (for motor positions, not sure why these are not in peaks)\n", + "ds = ImageD11.sinograms.dataset.load(dset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21a2c05d-db13-45fc-9ff5-d419479b3457", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def read_grains(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin:\n", + " grp = hin['slice_recon']\n", + " \n", + " raw_intensity_array = grp['intensity'][:]\n", + " grain_labels_array = grp['labels'][:]\n", + " \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " g = ImageD11.grain.grain(ubi)\n", + " # general grain properties\n", + " g.gid = int(gid_string)\n", + " g.translation = gg['translation'][:]\n", + " g.cen = gg.attrs['cen']\n", + " # sinogram stuff\n", + " g.ssino = gg['ssino'][:]\n", + " g.sinoangles = gg['sinoangles'][:]\n", + " # reconstructions\n", + " g.og_recon = gg['og_recon'][:]\n", + " g.recon = gg['recon'][:]\n", + " grains.append(g)\n", + " \n", + " return grains, raw_intensity_array, grain_labels_array\n", + "\n", + "\n", + "def read_gold_grains(ds):\n", + " ds.grainsfile_gold = os.path.join(ds.analysispath, ds.dsname + '_grains_Au.h5')\n", + " with h5py.File(ds.grainsfile_gold, 'r') as hin:\n", + " grp = hin['slice_recon']\n", + " \n", + " raw_intensity_array = grp['intensity'][:]\n", + " grain_labels_array = grp['labels'][:]\n", + " \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " g = ImageD11.grain.grain(ubi)\n", + " # general grain properties\n", + " g.gid = int(gid_string)\n", + " g.translation = gg['translation'][:]\n", + " g.cen = gg.attrs['cen']\n", + " # sinogram stuff\n", + " g.ssino = gg['ssino'][:]\n", + " g.sinoangles = gg['sinoangles'][:]\n", + " # reconstructions\n", + " g.og_recon = gg['og_recon'][:]\n", + " g.recon = gg['recon'][:]\n", + " grains.append(g)\n", + " \n", + " return grains, raw_intensity_array, grain_labels_array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec5ff57a-0a7f-44cd-b437-eb4cc4e2ea25", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "grains_Fe, raw_intensity_array_Fe, grain_labels_array_Fe = read_grains(ds)\n", + "grains_Au, raw_intensity_array_Au, grain_labels_array_Au = read_gold_grains(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2aa7d96-f2b6-4a88-863b-89352193c6cb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for grain in tqdm(grains_Fe):\n", + " grain.rgb_z = utils.grain_to_rgb(grain, ax=(0,0,1),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_y = utils.grain_to_rgb(grain, ax=(0,1,0),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_x = utils.grain_to_rgb(grain, ax=(1,0,0),)# symmetry = Symmetry.cubic)\n", + " \n", + "for grain in tqdm(grains_Au):\n", + " grain.rgb_z = utils.grain_to_rgb(grain, ax=(0,0,1),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_y = utils.grain_to_rgb(grain, ax=(0,1,0),)# symmetry = Symmetry.cubic)\n", + " grain.rgb_x = utils.grain_to_rgb(grain, ax=(1,0,0),)# symmetry = Symmetry.cubic)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9cafd61-177a-4baa-8c42-667871bee9e2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "red_Fe = np.zeros_like(grains_Fe[0].recon)\n", + "grn_Fe = np.zeros_like(grains_Fe[0].recon)\n", + "blu_Fe = np.zeros_like(grains_Fe[0].recon)\n", + "\n", + "for g in tqdm(grains_Fe):\n", + " red_Fe[grain_labels_array_Fe == g.gid] = g.rgb_z[0]\n", + " grn_Fe[grain_labels_array_Fe == g.gid] = g.rgb_z[1]\n", + " blu_Fe[grain_labels_array_Fe == g.gid] = g.rgb_z[2]\n", + " \n", + "rgb_Fe = np.transpose((red_Fe, grn_Fe, blu_Fe), axes=(1, 2, 0))\n", + "\n", + "red_Au = np.zeros_like(grains_Au[0].recon)\n", + "grn_Au = np.zeros_like(grains_Au[0].recon)\n", + "blu_Au = np.zeros_like(grains_Au[0].recon)\n", + "\n", + "for g in tqdm(grains_Au):\n", + " red_Au[grain_labels_array_Au == g.gid] = g.rgb_z[0]\n", + " grn_Au[grain_labels_array_Au == g.gid] = g.rgb_z[1]\n", + " blu_Au[grain_labels_array_Au == g.gid] = g.rgb_z[2]\n", + " \n", + "rgb_Au = np.transpose((red_Au, grn_Au, blu_Au), axes=(1, 2, 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c559091-97bd-4b2d-9dc6-d99eb1b6e038", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, constrained_layout=True, sharex=True, sharey=True)\n", + "axs[0].imshow(rgb_Fe, origin=\"lower\")\n", + "axs[1].imshow(rgb_Au, origin=\"lower\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b30cc703-9c1a-4bf2-b1c2-0f35cc4639bb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rgb_Au_alpha = np.append(rgb_Au, np.zeros((rgb_Au.shape[0], rgb_Au.shape[1], 1)), axis=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9339a35-ba31-4fa6-a9d7-b6476bcd71f3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rgb_Au_alpha[rgb_Au_alpha[:, :, 0] != 0, 3] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d32bf91-2f9d-49aa-a465-b9ea2bc553be", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "(raw_intensity_array_Au != 0).astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1df3e31-7e64-41f6-a13a-8836db221e7e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, constrained_layout=True, sharex=True, sharey=True)\n", + "axs[0].imshow(raw_intensity_array_Fe, cmap=\"viridis\", origin=\"lower\")\n", + "axs[0].imshow(rgb_Au_alpha, origin=\"lower\")\n", + "axs[1].imshow(raw_intensity_array_Fe, cmap=\"viridis\", origin=\"lower\")\n", + "axs[0].set_title(\"Ni intensity map with Aus overlaid\")\n", + "axs[1].set_title(\"Ni intensity map\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e531a918-4478-45cb-9e85-bc24668dba9c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(constrained_layout=True, sharex=True, sharey=True)\n", + "ax.imshow(rgb_Fe, origin=\"lower\")\n", + "ax.imshow(raw_intensity_array_Au, alpha=(raw_intensity_array_Au != 0).astype(float), cmap=\"grey\", origin=\"lower\")\n", + "ax.set_title(\"Fe IPF Z colours with white Au\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45816ead-aabe-4151-b4dc-e143d1325d05", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# orientation relationship stuff\n", + "\n", + "# pick a Au grain\n", + "\n", + "Au_grain = grains_Au[25]\n", + "\n", + "# find out where it is in the sample\n", + "\n", + "fig, ax = plt.subplots(constrained_layout=True, sharex=True, sharey=True)\n", + "ax.imshow(rgb_Fe, origin=\"lower\")\n", + "ax.imshow(Au_grain.recon, alpha=(Au_grain.recon > 0.05).astype(float), origin=\"lower\")\n", + "ax.set_title(\"Ni IPF Z colours with single Au\")\n", + "\n", + "vr = 50\n", + "\n", + "ax.set_xlim(Au_grain.translation[0]-vr, Au_grain.translation[0]+vr)\n", + "ax.set_ylim(Au_grain.translation[1]+vr, Au_grain.translation[1]-vr)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b2b8e3f-04bc-4bb1-81de-ae3d9109db66", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# find Ni grain surrounding Au grain\n", + "\n", + "Au_grain_pos_nearest_px = Au_grain.translation.copy()\n", + "Au_grain_pos_nearest_px = np.round(Au_grain_pos_nearest_px).astype(int)\n", + "\n", + "print(Au_grain_pos_nearest_px)\n", + "\n", + "Fe_grain_gid = grain_labels_array_Fe[Au_grain_pos_nearest_px[1], Au_grain_pos_nearest_px[0]].astype(int)\n", + "\n", + "print(Fe_grain_gid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1cb8e2a-95c1-4561-8eda-fb1109ca378c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# confirm Ni grain selection\n", + "\n", + "# rgb_Fe_masked = rgb_\n", + "\n", + "fig, ax = plt.subplots(constrained_layout=True, sharex=True, sharey=True)\n", + "ax.imshow(grain_labels_array_Fe == Fe_grain_gid, cmap=\"grey\", origin=\"lower\")\n", + "ax.imshow(Au_grain.recon, alpha=(Au_grain.recon > 0.05).astype(float), origin=\"lower\")\n", + "ax.set_title(\"Ni IPF Z colours with single Au\")\n", + "\n", + "vr = 50\n", + "\n", + "ax.set_xlim(Au_grain.translation[0]-vr, Au_grain.translation[0]+vr)\n", + "ax.set_ylim(Au_grain.translation[1]+vr, Au_grain.translation[1]-vr)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "355e11ea-8066-43f0-88de-3b62e281ab47", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we now have a Au grain and the Fe grain that it's inside\n", + "\n", + "# look for orientation relationships\n", + "\n", + "Fe_grain = [grain for grain in grains_Fe if grain.gid == Fe_grain_gid][0]\n", + "\n", + "print(Fe_grain.U)\n", + "print(Au_grain.U)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f5cc20b-fdcd-4b66-9f84-22f1ad4e7148", + "metadata": {}, + "outputs": [], + "source": [ + "# https://doi.org/10.1016/S1005-0302(12)60169-8\n", + "# we are looking for MC Aus\n", + "# 4 different types of OR present\n", + "# A {001} Au // {001} matrix , <100> Au // <100> matrix\n", + "# B [001] Au // [310] matrix , (020) Au // (1-31) matrix\n", + "# C [110] Au // [310] matrix , (-11-1) Au // (002) matrix\n", + "# D [001] Au // [001] matrix , (-260) Au // (020) matrix\n", + "\n", + "# let's check A" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3faf7ec-63ea-4fa1-bf23-409e3b3e071d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from xfab.parameters import read_par_file\n", + "\n", + "par_file_Fe = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "par_file_Au = os.path.join(processed_data_root_dir, 'Au.par')\n", + "\n", + "pars_Fe = read_par_file(par_file_Fe)\n", + "pars_Au = read_par_file(par_file_Au)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51ef1565-5cae-4385-9c9a-b291652e1531", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ucell_Fe = [pars_Fe.get(\"cell__a\"), pars_Fe.get(\"cell__b\"), pars_Fe.get(\"cell__c\"), pars_Fe.get(\"cell_alpha\"), pars_Fe.get(\"cell_beta\"), pars_Fe.get(\"cell_gamma\")]\n", + "ucell_Au = [pars_Au.get(\"cell__a\"), pars_Au.get(\"cell__b\"), pars_Au.get(\"cell__c\"), pars_Au.get(\"cell_alpha\"), pars_Au.get(\"cell_beta\"), pars_Au.get(\"cell_gamma\")]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa86d845-761e-4864-9ac2-aad8d93dbcd2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ucell_Au" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0df1597a-a444-487c-9fd7-41012f106853", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "from diffpy.structure import Lattice, Structure\n", + "from orix.crystal_map import Phase\n", + "from orix.quaternion import Orientation, Rotation, symmetry\n", + "from orix.vector import Miller, Vector3d\n", + "\n", + "struc_Fe = Structure(lattice=Lattice(*ucell_Fe))\n", + "struc_Au = Structure(lattice=Lattice(*ucell_Au))\n", + "\n", + "cubic_Fe = Phase(point_group=\"m-3m\", structure=struc_Fe)\n", + "cubic_Au = Phase(point_group=\"m-3m\", structure=struc_Au)\n", + "\n", + "# crystal frame:\n", + "\n", + "matrix_plane = Miller(hkl=[2, 2, 0], phase=cubic_Fe)\n", + "Au_plane = Miller(hkl=[0, 0, 2], phase=cubic_Au)\n", + "\n", + "matrix_dir = Miller(uvw=[1, 0, 0], phase=cubic_Fe)\n", + "Au_dir = Miller(hkl=[1, 0, 0], phase=cubic_Au)\n", + "\n", + "# orientations in Orix are sample-to-crystal, so we have to invert\n", + "o_Fe = Orientation.from_matrix(Fe_grain.U.T)\n", + "o_Au = Orientation.from_matrix(Au_grain.U.T)\n", + "\n", + "matrix_plane_lab = ~o_Fe * matrix_plane.symmetrise(unique=True)\n", + "Au_plane_lab = ~o_Au * Au_plane.symmetrise(unique=True)\n", + "\n", + "matrix_dir_lab = ~o_Fe * matrix_dir.symmetrise(unique=True)\n", + "Au_dir_lab = ~o_Au * Au_dir.symmetrise(unique=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b23c41f1-6b9e-4695-bf66-0a71affe997b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, constrained_layout=True, sharex=True, sharey=True)\n", + "\n", + "axs[0].imshow(grain_labels_array_Fe == Fe_grain_gid, cmap=\"grey\", origin=\"lower\")\n", + "axs[0].imshow(Au_grain.recon, alpha=(Au_grain.recon > 0.05).astype(float), origin=\"lower\")\n", + "axs[0].set_title(\"Directions\")\n", + "\n", + "axs[1].imshow(grain_labels_array_Fe == Fe_grain_gid, cmap=\"grey\", origin=\"lower\")\n", + "axs[1].imshow(Au_grain.recon, alpha=(Au_grain.recon > 0.05).astype(float), origin=\"lower\")\n", + "axs[1].set_title(\"Planes\")\n", + "\n", + "vr = 100\n", + "\n", + "axs[0].set_xlim(Au_grain.translation[0]-vr, Au_grain.translation[0]+vr)\n", + "axs[0].set_ylim(Au_grain.translation[1]+vr, Au_grain.translation[1]-vr)\n", + "\n", + "ars = 5\n", + "txs = (50*5)*1/ars\n", + "\n", + "for inc in range(len(Au_dir.symmetrise(unique=True).coordinates)):\n", + " arrow_centre = Au_grain.translation[0:2]\n", + " axs[0].quiver(arrow_centre[0], arrow_centre[1], Au_dir_lab.coordinates[inc, 0], Au_dir_lab.coordinates[inc, 1], color=\"r\", scale=ars)\n", + " axs[0].annotate(np.array_str(Au_dir.symmetrise(unique=True).coordinates[inc], precision=None, suppress_small=True), arrow_centre + Au_dir_lab.coordinates[inc, 0:2]*[1, -1]*txs, ha=\"center\", va=\"center\", c=\"red\")\n", + "\n", + "ars = 5\n", + "txs = (50*5)*1/ars\n", + "\n", + "for inc in range(len(matrix_dir.symmetrise(unique=True).coordinates)):\n", + " arrow_centre = Au_grain.translation[0:2] + [-20, -20]\n", + " axs[0].quiver(arrow_centre[0], arrow_centre[1], matrix_dir_lab.coordinates[inc, 0], matrix_dir_lab.coordinates[inc, 1], color=\"b\", scale=ars)\n", + " axs[0].annotate(np.array_str(matrix_dir.symmetrise(unique=True).coordinates[inc], precision=None, suppress_small=True), arrow_centre + matrix_dir_lab.coordinates[inc, 0:2]*[1, -1]*txs, ha=\"center\", va=\"center\", c=\"blue\")\n", + " \n", + " \n", + " \n", + "ars = 5\n", + "txs = (50*5)*1/ars\n", + "\n", + "for inc in range(len(Au_dir.symmetrise(unique=True).coordinates)):\n", + " arrow_centre = Au_grain.translation[0:2]\n", + " axs[1].quiver(arrow_centre[0], arrow_centre[1], Au_plane_lab.coordinates[inc, 0], Au_plane_lab.coordinates[inc, 1], color=\"r\", scale=ars)\n", + " axs[1].annotate(np.array_str(Au_plane.symmetrise(unique=True).coordinates[inc], precision=None, suppress_small=True), arrow_centre + Au_plane_lab.coordinates[inc, 0:2]*[1, -1]*txs, ha=\"center\", va=\"center\", c=\"red\")\n", + "\n", + "ars = 5\n", + "txs = (50*5)*1/ars\n", + "\n", + "for inc in range(len(matrix_dir.symmetrise(unique=True).coordinates)):\n", + " arrow_centre = Au_grain.translation[0:2] + [-20, -20]\n", + " axs[1].quiver(arrow_centre[0], arrow_centre[1], matrix_plane_lab.coordinates[inc, 0], matrix_plane_lab.coordinates[inc, 1], color=\"b\", scale=ars)\n", + " axs[1].annotate(np.array_str(matrix_plane.symmetrise(unique=True).coordinates[inc], precision=None, suppress_small=True), arrow_centre + matrix_plane_lab.coordinates[inc, 0:2]*[1, -1]*txs, ha=\"center\", va=\"center\", c=\"blue\")\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36049644-d04e-4477-9bb3-1efd1519b350", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from scipy.spatial.transform import Rotation as R\n", + "import ImageD11.sym_u\n", + "cubic = ImageD11.sym_u.cubic()\n", + "for op in cubic.group:\n", + " v = R.from_matrix((Fe_grain.U.T @ op.T) @ Au_grain.U).as_rotvec( degrees=True)\n", + " ang = np.linalg.norm(v)\n", + " print(ang, v/ang)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "987b1b1d-ba35-4f84-a8ca-18abc56b0bd1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# matrix is 1\n", + "# Au is 2\n", + "\n", + "# define an orientation matrix for each of the ORs\n", + "from orix.quaternion import Misorientation, Orientation\n", + "\n", + "# A {001} Au // {001} matrix , <100> Au // <100> matrix\n", + "\n", + "matrix_dirs = Miller(hkl=[0, 0, 2], phase=cubic_Fe)\n", + "Au_dirs = Miller(hkl=[2, 2, 0], phase=cubic_Au)\n", + "\n", + "misorien_A = Misorientation.from_align_vectors(Au_dirs, matrix_dirs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12d1bfc5-a1db-4cc4-8a54-237ce7de87c9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "\n", + "# print(misorien_B)\n", + "\n", + "min_misoriens = []\n", + "\n", + "for Au_grain in grains_Au[:]:\n", + " Au_grain_pos_nearest_px = Au_grain.translation.copy()\n", + " Au_grain_pos_nearest_px = np.round(Au_grain_pos_nearest_px).astype(int)\n", + " Fe_grain_gid = grain_labels_array_Fe[Au_grain_pos_nearest_px[1], Au_grain_pos_nearest_px[0]].astype(int)\n", + " try:\n", + " Fe_grain = [grain for grain in grains_Fe if grain.gid == Fe_grain_gid][0]\n", + " o_Fe = Orientation.from_matrix(Fe_grain.U.T, symmetry=cubic_Fe.point_group)\n", + " o_Au = Orientation.from_matrix(Au_grain.U.T, symmetry=cubic_Au.point_group)\n", + " \n", + " misorien_actual = Misorientation(o_Fe * (~o_Au), symmetry=(o_Au.symmetry, o_Fe.symmetry))\n", + " misorien_actual = misorien_actual.map_into_symmetry_reduced_zone()\n", + " \n", + " misorien_ref = misorien_A\n", + " misorien_ref = misorien_ref.map_into_symmetry_reduced_zone()\n", + " \n", + " misorien_diff = Misorientation(misorien_ref * (~misorien_actual), symmetry=misorien_actual.symmetry)\n", + " \n", + " misorien_diff = misorien_diff.map_into_symmetry_reduced_zone()\n", + " \n", + " min_misorien = np.rad2deg(misorien_diff.angle)\n", + " \n", + " print(Fe_grain.gid, Au_grain.gid, min_misorien)\n", + " \n", + " min_misoriens.append(min_misorien)\n", + " except IndexError:\n", + " continue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95a14487-809f-4714-953d-4d218d62fbee", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "np.rad2deg(misorien_actual.angle)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6eb1e67b-cc93-4b01-a3eb-7dbe18e42208", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "misorien_actual.axis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e43b838a-1977-4196-9c4b-485e46bbcc1f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "o_Au.to_euler(\"mtex\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93102d3f-6f1e-40c1-b7fe-4ab48a514b62", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "np.savetxt(os.path.join(processed_data_root_dir, 'Fe_grain_matrix.txt'), o_Fe.to_matrix()[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81eba9dd-4807-4335-afe7-fb4a5e1f1b9d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "np.savetxt(os.path.join(processed_data_root_dir, 'Au_grain_matrix.txt'), o_Au.to_matrix()[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe6c906b-ad2c-4711-bd6b-a9f9dbe2c3fb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "np.savetxt(os.path.join(processed_data_root_dir, 'OR_matrix.txt'), misorien_A.to_matrix()[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c747456-eaac-4c91-b46c-50b8207fd13a", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "ax.hist(np.array(min_misoriens), bins=50)\n", + "# ax.set_xlim(0,10)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6c55fd6-f828-4deb-a17c-3c4d89bc3ee8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "min_misoriens" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c57ba658-173f-4b99-ae72-111f6f72dbdf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ImageD11/nbGui/S3DXRD/4_S3DXRD_view_PBP.ipynb b/ImageD11/nbGui/S3DXRD/4_S3DXRD_view_PBP.ipynb new file mode 100755 index 00000000..c7a9b5d3 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/4_S3DXRD_view_PBP.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "import scipy.ndimage as ndi\n", + "\n", + "from ImageD11.nbGui import nb_utils as utils\n", + "import ImageD11.sinograms.dataset\n", + "\n", + "from ImageD11.grain import grain\n", + "%matplotlib ipympl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: pick a sample and a dataset you want to segment\n", + "\n", + "sample = \"FeAu_0p5_tR_nscope\"\n", + "dataset = \"top_250um\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# desination of H5 files\n", + "\n", + "dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + "\n", + "# USER: specify the path to the parameter file\n", + "\n", + "par_path = os.path.join(processed_data_root_dir, 'Fe_refined.par')\n", + "\n", + "e2dx_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dx_E-08-0173_20231127.edf')\n", + "e2dy_path = os.path.join(processed_data_root_dir, '../../CeO2/e2dy_E-08-0173_20231127.edf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load the dataset\n", + "dset = ImageD11.sinograms.dataset.load(dset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dset.pbpfile = os.path.join(dset.analysispath, dset.dsname + '_pbp_index.pbp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# pbp_array = np.loadtxt('/home/esrf/james1997a/Code/ImageD11/ImageD11/nbGui/S3DXRD/grainsout_all.pbp')\n", + "\n", + "pbp_array = np.loadtxt(dset.pbpfile)\n", + "ubi = {}\n", + "i,j,n,u,ubi[0,0],ubi[0,1],ubi[0,2],ubi[1,0],ubi[1,1],ubi[1,2],ubi[2,0],ubi[2,1],ubi[2,2] = pbp_array.T\n", + "i = i.astype(int) \n", + "i -= i.min() # position in array / space\n", + "j = j.astype(int) \n", + "j -= j.min()\n", + "n = n.astype(int) # total peaks indexing with hkl==int with 0.03\n", + "u = u.astype(int) # unique (h,k,l) labels on indexed peaks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "NY = int(i.max()-i.min())+1\n", + "i.min(),i.max(),NY" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist(u, bins=128)\n", + "ax.set_xlabel('Unique spots per pixel')\n", + "ax.set_ylabel('Count')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "minpeaks = 50\n", + "\n", + "uni = np.ones((NY,NY))\n", + "uni.fill(minpeaks) # peak cutorr\n", + "npk = np.zeros((NY,NY))\n", + "ubifit = np.zeros((NY,NY,3,3), float)\n", + "ubifit.fill(np.nan)\n", + "ubilist = []\n", + "for k in range(len(i)):\n", + " if u[k] > uni[i[k],j[k]]:\n", + " uni[i[k],j[k]] = u[k]\n", + " npk[i[k],j[k]] = n[k]\n", + " for ii in range(3):\n", + " for jj in range(3):\n", + " ubifit[i[k],j[k],ii,jj] = ubi[ii,jj][k]\n", + " ubilist.append(ubifit[i[k],j[k]].copy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "f,a = plt.subplots( 1,3, figsize=(15,5) )\n", + "ty, tx = utils.triangle().T\n", + "for i,title in enumerate( 'xyz' ):\n", + " ax = np.zeros(3)\n", + " ax[i] = 1.\n", + " hkl = [utils.crystal_direction_cubic( ubi, ax ) for ubi in ubilist]\n", + " xy = np.array( [utils.hkl_to_pf_cubic(h) for h in hkl ] )\n", + " rgb = np.array( [utils.hkl_to_color_cubic(h) for h in hkl ])\n", + " \n", + " a[i].scatter( xy[:,1], xy[:,0], c = rgb, s=1 ) # Note the \"x\" axis of the plot is the 'k' direction and 'y' is h (smaller)\n", + " a[i].set(title=title, aspect='equal', facecolor='silver', xticks=[], yticks=[])\n", + " a[i].plot( tx, ty, 'k-', lw = 1 )\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def normalise( r ):\n", + " negatives = r [r < 0 ]\n", + " noise = np.std(negatives)\n", + " s = ndi.uniform_filter( r, 5 )\n", + " positives = s > noise\n", + " height = r[positives].mean()\n", + " return np.where(s>noise, r/r.max(), 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "r = npk.copy()\n", + "ubisel = ubifit.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rgb = np.zeros( (3, ubisel.shape[0], ubisel.shape[1], 3 ))\n", + "for i in range(ubisel.shape[0]):\n", + " for j in range(ubisel.shape[1]):\n", + " for k,axis in enumerate( ((1,0,0),(0,1,0),(0,0,1)) ):\n", + " if np.isnan( ubisel[i,j,0,0] ):\n", + " rgb[k,i,j] = np.nan\n", + " else:\n", + " rgb[k,i,j] = utils.hkl_to_color_cubic( utils.crystal_direction_cubic( ubisel[i,j], axis ) )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, a = plt.subplots(2,2,sharex=True,sharey=True, figsize=(10,10), constrained_layout=True)\n", + "a[0][0].imshow(rgb[0].transpose(1, 0, 2), origin=\"lower\")\n", + "a[0][0].set_title(\"IPF X\")\n", + "a[0][1].imshow(rgb[1].transpose(1, 0, 2), origin=\"lower\")\n", + "a[0][1].set_title(\"IPF Y\")\n", + "a[1][0].imshow(rgb[2].transpose(1, 0, 2), origin=\"lower\")\n", + "a[1][0].set_title(\"IPF Z\")\n", + "m = r > 0.02\n", + "a[1][1].imshow(np.where(m, r, np.nan).T, origin=\"lower\")\n", + "a[1][1].set_title(\"Number of peaks\")\n", + "for ax in a.ravel():\n", + " ax.set(yticks=[], xticks=[])\n", + " ax.invert_xaxis()\n", + " ax.invert_yaxis()\n", + "\n", + "fig.supxlabel(\"< Lab Y direction\")\n", + "fig.supylabel(\"Lab X direction >\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/S3DXRD/5_S3DXRD_plot_multiple_slices.ipynb b/ImageD11/nbGui/S3DXRD/5_S3DXRD_plot_multiple_slices.ipynb new file mode 100644 index 00000000..40b84bd2 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/5_S3DXRD_plot_multiple_slices.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a545dd7f-4140-4abc-9c00-6edd96a2e501", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Jupyter notebook based on ImageD11 to process scanning 3DXRD data\n", + "# Written by Haixing Fang, Jon Wright and James Ball\n", + "## Date: 26/02/2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92baa2cd-4e85-4998-979b-88ef0a674c48", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# USER: Change the path below to point to your local copy of ImageD11:\n", + "\n", + "import os\n", + "\n", + "home_dir = !echo $HOME\n", + "home_dir = str(home_dir[0])\n", + "\n", + "# USER: You can change this location if you want\n", + "\n", + "id11_code_path = os.path.join(home_dir, \"Code/ImageD11\")\n", + "\n", + "import sys\n", + "\n", + "sys.path.insert(0, id11_code_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74e066b0-ae13-46e9-81b0-a11ea524a469", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import functions we need\n", + "\n", + "import concurrent.futures\n", + "import timeit\n", + "import glob\n", + "import pprint\n", + "from shutil import rmtree\n", + "import time\n", + "from functools import partial\n", + "\n", + "import matplotlib\n", + "%matplotlib ipympl\n", + "\n", + "import h5py\n", + "from tqdm.notebook import tqdm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import ImageD11.columnfile\n", + "# from ImageD11.sinograms.dataset import DataSet\n", + "from ImageD11.sinograms import properties, roi_iradon, dataset\n", + "# from ImageD11.blobcorrector import eiger_spatial\n", + "from ImageD11.grain import grain\n", + "\n", + "import ImageD11.nbGui.nb_utils as utils\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac85f820-7358-4ab6-87bf-fa386793cedb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# NOTE: For old datasets before the new directory layout structure, we don't distinguish between RAW_DATA and PROCESSED_DATA\n", + "\n", + "### USER: specify your experimental directory\n", + "\n", + "rawdata_path = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/RAW_DATA\"\n", + "\n", + "!ls -lrt {rawdata_path}\n", + "\n", + "### USER: specify where you want your processed data to go\n", + "\n", + "processed_data_root_dir = \"/home/esrf/james1997a/Data/ihma439/id11/20231211/PROCESSED_DATA/James/20240226\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1ff363a-8106-45c4-b4de-f557f33c852a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "samples_dict = {}\n", + "\n", + "skips_dict = {\n", + " \"FeAu_0p5_tR_nscope\": [\"top_100um\", \"top_-100um\"]\n", + "}\n", + "\n", + "dset_prefix = \"top\"\n", + "\n", + "sample_list = [\"FeAu_0p5_tR_nscope\"]\n", + "\n", + "for sample in sample_list:\n", + " all_dset_folders_for_sample = os.listdir(os.path.join(rawdata_path, sample))\n", + " dsets_list = []\n", + " for folder in all_dset_folders_for_sample:\n", + " if dset_prefix in folder:\n", + " dset_name = folder.split(f\"{sample}_\")[1]\n", + " if dset_name not in skips_dict[sample]:\n", + " dsets_list.append(dset_name)\n", + "\n", + " samples_dict[sample] = dsets_list\n", + " \n", + "# manually override:\n", + "\n", + "samples_dict = {\"FeAu_0p5_tR_nscope\": [\"top_150um\", \"top_200um\", \"top_250um\"]}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "968d1fcd-46e7-43ad-8f09-5c5b3a639ae1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def read_grains(ds):\n", + " with h5py.File(ds.grainsfile, 'r') as hin:\n", + " grp = hin['slice_recon']\n", + " \n", + " raw_intensity_array = grp['intensity'][:]\n", + " grain_labels_array = grp['labels'][:]\n", + " \n", + " grains_group = 'grains'\n", + " \n", + " grains = []\n", + " for gid_string in tqdm(sorted(hin[grains_group].keys(), key=lambda x: int(x))):\n", + " gg = hin[grains_group][gid_string]\n", + " ubi = gg.attrs['ubi'][:]\n", + " g = ImageD11.grain.grain(ubi)\n", + " # general grain properties\n", + " g.gid = int(gid_string)\n", + " g.translation = gg['translation'][:]\n", + " g.cen = gg.attrs['cen']\n", + " g.y0 = gg.attrs['y0']\n", + " # sinogram stuff\n", + " g.ssino = gg['ssino'][:]\n", + " g.sinoangles = gg['sinoangles'][:]\n", + " # reconstructions\n", + " g.og_recon = gg['og_recon'][:]\n", + " g.recon = gg['recon'][:]\n", + " grains.append(g)\n", + " \n", + " return grains, raw_intensity_array, grain_labels_array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cc2f6e-8cf1-4ec5-b8a0-2cab2a4f44d9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "z_translation_motor = \"pz\"\n", + "\n", + "ds_dict = []\n", + "\n", + "for sample, datasets in samples_dict.items():\n", + " for dataset in datasets:\n", + " print(f\"Processing dataset {dataset} in sample {sample}\")\n", + " dset_path = os.path.join(processed_data_root_dir, sample, f\"{sample}_{dataset}\", f\"{sample}_{dataset}_dataset.h5\")\n", + " if not os.path.exists(dset_path):\n", + " print(f\"Missing DataSet file for {dataset} in sample {sample}, skipping\")\n", + " continue\n", + " \n", + " print(\"Importing DataSet object\")\n", + " \n", + " ds = ImageD11.sinograms.dataset.load(dset_path)\n", + " \n", + " print(\"Importing grains\")\n", + " \n", + " ds.grains, ds.raw_intensity_array, ds.grain_labels_array = read_grains(ds)\n", + " \n", + " print(\"Importing z positions\")\n", + " \n", + " with h5py.File(ds.masterfile, 'r' ) as hin:\n", + " this_z_trans_value = hin[\"1.1/instrument/positioners\"][z_translation_motor][()]\n", + " ds.zpos = this_z_trans_value # this is in microns for samtz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16775ee0-1d44-4557-874d-8c06fe607e2c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ImageD11/nbGui/S3DXRD/run_mlem_recon.py b/ImageD11/nbGui/S3DXRD/run_mlem_recon.py new file mode 100755 index 00000000..ab70e8b6 --- /dev/null +++ b/ImageD11/nbGui/S3DXRD/run_mlem_recon.py @@ -0,0 +1,102 @@ +import os + +username = os.environ.get("USER") + +id11_code_path = "/home/esrf/" + username + "/Code/ImageD11" + +import sys + +sys.path.insert(0, id11_code_path) + +import numpy as np +import h5py + +import ImageD11.sinograms.roi_iradon + +def run_iradon_mlem(ssino, sinoangles, mask=None, pad=20, y0=0, workers=1, niter=20, apply_halfmask=False, mask_central_zingers=False): + outsize = ssino.shape[0] + pad + if apply_halfmask: + halfmask = np.zeros_like(ssino) + + halfmask[:len(halfmask)//2-1, :] = 1 + halfmask[len(halfmask)//2-1, :] = 0.5 + + ssino_to_recon = ssino * halfmask + else: + ssino_to_recon = ssino + + # Perform iradon transform of grain sinogram, store result (reconstructed grain shape) in g.recon + recon = ImageD11.sinograms.roi_iradon.mlem(ssino_to_recon, + theta=sinoangles, + mask=mask, + workers=workers, + output_size=outsize, + projection_shifts=np.full(ssino_to_recon.shape, -y0), + niter=niter) + + if mask_central_zingers: + grs = recon.shape[0] + xpr, ypr = -grs//2 + np.mgrid[:grs, :grs] + inner_mask_radius = 25 + outer_mask_radius = inner_mask_radius + 2 + + inner_circle_mask = (xpr ** 2 + ypr ** 2) < inner_mask_radius ** 2 + outer_circle_mask = (xpr ** 2 + ypr ** 2) < outer_mask_radius ** 2 + + mask_ring = inner_circle_mask & outer_circle_mask + # we now have a mask to apply + fill_value = np.median(recon[mask_ring]) + recon[inner_circle_mask] = fill_value + + return recon + +def read_hdf5(h5name, ginc): + with h5py.File(h5name, 'r') as hin: + grains_group = 'grains' + gg = hin[grains_group][sorted(hin[grains_group].keys(), key=lambda x: int(x))[ginc]] + # gg = hin[grains_group][str(gid)] + + ssino = gg['ssino'][:] + sinoangles = gg['sinoangles'][:] + circle_mask = gg['circle_mask'][:] + y0 = float(gg.attrs['y0'][()]) + + return ssino, sinoangles, circle_mask, y0 + +def main(h5name, ginc, dest, pad, niter, dohm, maskcen): + # read the HDF5 file + ssino, sinoangles, circle_mask, y0 = read_hdf5(h5name, ginc) + # feed ssino, sinoangles, mask to run_iradon_mlem + nthreads = len(os.sched_getaffinity(os.getpid())) + # print(f"Running on {nthreads} threads") + recon = run_iradon_mlem(ssino, sinoangles, mask=circle_mask, pad=pad, y0=y0, workers=nthreads, niter=niter, apply_halfmask=dohm, mask_central_zingers=maskcen) + # write result to disk as Numpy array + # save as TIFF instead? + np.savetxt(dest, recon) + +if __name__ == "__main__": + h5name = sys.argv[1] + ginc = int(sys.argv[2]) + dest = sys.argv[3] + # y0 = float(sys.argv[4]) + # pad = int(sys.argv[5]) + # niter = int(sys.argv[6]) + # dohm = sys.argv[7] + # maskcen = sys.argv[8] + + pad = int(sys.argv[4]) + niter = int(sys.argv[5]) + dohm = sys.argv[6] + maskcen = sys.argv[7] + + if dohm == "Yes": + apply_halfmask = True + else: + apply_halfmask = False + + if maskcen == "Yes": + mask_central_zingers = True + else: + mask_central_zingers = False + + main(h5name, ginc, dest, pad, niter, apply_halfmask, mask_central_zingers) diff --git a/ImageD11/nbGui/__init__.py b/ImageD11/nbGui/__init__.py new file mode 100644 index 00000000..72024a5c --- /dev/null +++ b/ImageD11/nbGui/__init__.py @@ -0,0 +1 @@ +""" nbGui """ \ No newline at end of file diff --git a/ImageD11/nbGui/build_par_file.ipynb b/ImageD11/nbGui/build_par_file.ipynb new file mode 100755 index 00000000..dd45fcf9 --- /dev/null +++ b/ImageD11/nbGui/build_par_file.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3ff7a14d-db7c-4c71-9de9-12bda564f827", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from xfab.structure import build_atomlist" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "22ae1142-2cc4-40bf-801c-96a03ab82af1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pyFAI" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1c02aaae-6c95-4aaa-b2b8-93154688b999", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-02-13 16:57:27,125 - xfab.structure - WARNING - No _atom_type_symbol found in CIF\n", + "WARNING:xfab.structure:No _atom_type_symbol found in CIF\n", + "2024-02-13 16:57:27,126 - xfab.structure - WARNING - No dispersion factors for Si in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for Si in cif file - set to zero\n", + "2024-02-13 16:57:27,126 - xfab.structure - WARNING - No dispersion factors for Si in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for Si in cif file - set to zero\n", + "2024-02-13 16:57:27,127 - xfab.structure - WARNING - No dispersion factors for Si in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for Si in cif file - set to zero\n", + "2024-02-13 16:57:27,127 - xfab.structure - WARNING - No dispersion factors for O in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for O in cif file - set to zero\n", + "2024-02-13 16:57:27,128 - xfab.structure - WARNING - No dispersion factors for O in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for O in cif file - set to zero\n", + "2024-02-13 16:57:27,128 - xfab.structure - WARNING - No dispersion factors for O in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for O in cif file - set to zero\n", + "2024-02-13 16:57:27,128 - xfab.structure - WARNING - No dispersion factors for O in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for O in cif file - set to zero\n", + "2024-02-13 16:57:27,129 - xfab.structure - WARNING - No dispersion factors for O in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for O in cif file - set to zero\n", + "2024-02-13 16:57:27,129 - xfab.structure - WARNING - No dispersion factors for O in cif file - set to zero\n", + "WARNING:xfab.structure:No dispersion factors for O in cif file - set to zero\n" + ] + } + ], + "source": [ + "# Import the CIF, save the results in unit_cell\n", + "\n", + "cif_path = 'SiO2_mp-7000_conventional_standard.cif'\n", + "\n", + "mylist = build_atomlist()\n", + "mylist.CIFread(cif_path)\n", + "unit_cell = mylist.atomlist.cell\n", + "cell_lattice = mylist.atomlist.sgname[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "7c8234be-a3d9-4695-bdac-56a613a78753", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import calibration results from PONI\n", + "\n", + "poni_path = \"CeO2_20240210_1348_175mm.poni\"\n", + "poni_results = pyFAI.load(poni_path).getImageD11()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "e536b2ec-6047-4a0d-a342-f21bee268276", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# build parameter string\n", + "\n", + "par_string = f\"\"\"cell__a {unit_cell[0]}\n", + "cell__b {unit_cell[1]}\n", + "cell__c {unit_cell[2]}\n", + "cell_alpha {unit_cell[3]}\n", + "cell_beta {unit_cell[4]}\n", + "cell_gamma {unit_cell[5]}\n", + "cell_lattice_[P,A,B,C,I,F,R] {cell_lattice}\n", + "chi 0.0\n", + "distance {poni_results[\"distance\"]}\n", + "fit_tolerance 0.1\n", + "min_bin_prob 1e-05\n", + "no_bins 10000\n", + "o11 {poni_results[\"o11\"]}\n", + "o12 {poni_results[\"o12\"]}\n", + "o21 {poni_results[\"o21\"]}\n", + "o22 {poni_results[\"o22\"]}\n", + "omegasign 1.0\n", + "t_x 0\n", + "t_y 0\n", + "t_z 0\n", + "tilt_x {poni_results[\"tilt_x\"]}\n", + "tilt_y {poni_results[\"tilt_y\"]}\n", + "tilt_z {poni_results[\"tilt_z\"]}\n", + "wavelength {poni_results[\"wavelength\"]*10}\n", + "wedge 0.0\n", + "weight_hist_intensities 0\n", + "y_center {poni_results[\"y_center\"]}\n", + "y_size {poni_results[\"y_size\"]}\n", + "z_center {poni_results[\"z_center\"]}\n", + "z_size {poni_results[\"z_size\"]}\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "f3dde877-751f-438d-9c64-eb3c0da72c77", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Save it to file\n", + "\n", + "par_path = \"output.par\"\n", + "with open(par_path, \"w\") as par_file:\n", + " par_file.writelines(par_string)" + ] + }, + { + "cell_type": "raw", + "id": "59c34763-f1b4-4881-91cf-2c1757e5184e", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ImageD11/nbGui/fit_cen_trans.ipynb b/ImageD11/nbGui/fit_cen_trans.ipynb new file mode 100644 index 00000000..e58629a1 --- /dev/null +++ b/ImageD11/nbGui/fit_cen_trans.ipynb @@ -0,0 +1,125 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook\n", + "import ipywidgets, pylab as pl\n", + "from ImageD11 import transformer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# input data from peaksearch and starting parameter file:\n", + "trans=transformer.transformer()\n", + "trans.loadfiltered(\"github/FABLE-3DXRD/ImageD11/test/nac_demo/peaks.out_merge_t200\")\n", + "trans.loadfileparameters(\"github/FABLE-3DXRD/ImageD11/test/nac_demo/nac.prm\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "def parCallBack( arg ):\n", + " trans.parameterobj.parameters.update( { arg['owner'].description : arg['new'] } )\n", + " drawPlot()\n", + " \n", + "def fixVaryCallBack( arg ):\n", + " name = arg['owner'].description.split(\" \")[1]\n", + " vars = trans.getvars()\n", + " if arg.new and not (name in vars):\n", + " vars.append( name )\n", + " if name in vars and not arg.new:\n", + " vars.remove(name)\n", + " trans.parameterobj.set_varylist(vars)\n", + " \n", + "def fitCallBack(arg):\n", + " \"\"\" fit call back - runs fit \"\"\"\n", + " lo, hi = ax1.get_xlim()\n", + " trans.fit( lo, hi )\n", + " for i, pname in enumerate(vars):\n", + " layout[i,0].value = trans.parameterobj.get(pname)\n", + " drawPlot()\n", + " \n", + "def drawPlot():\n", + " tth, eta = trans.compute_tth_eta()\n", + " pt1.set_data( tth, eta )\n", + "\n", + "# Things to be edited in the UI\n", + "vars = \"y_center z_center distance tilt_y tilt_z\".split()\n", + "steps = (1, 1, 100, 0.01, 0.01, 0)\n", + "nv = len(vars)\n", + "\n", + "# Draw the widgets:\n", + "layout = ipywidgets.GridspecLayout(nv+1,2)\n", + "for i,( pname, pstep ) in enumerate( zip( vars, steps ) ) :\n", + " layout[i,0] = ipywidgets.FloatText( description=pname, \n", + " value = trans.parameterobj.parameters.get(pname),\n", + " step=pstep)\n", + " layout[i,0].observe( parCallBack , names='value' )\n", + " layout[i,1] = ipywidgets.ToggleButton( description=\"Vary \"+pname, \n", + " value = pname in trans.getvars() )\n", + " layout[i,1].observe( fixVaryCallBack, names='value' )\n", + " \n", + "layout[nv,0] = ipywidgets.FloatText( description='fit_tolerance', \n", + " value = trans.parameterobj.parameters.get(\"fit_tolerance\"), step=0,)\n", + "layout[nv,0].observe( parCallBack , names='value' )\n", + "\n", + "layout[nv,1] = ipywidgets.Button(description=\"Run Fit (blocks)\")\n", + "layout[nv,1].on_click( fitCallBack )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Draw plot\n", + "fig1 = pl.figure(1, figsize=(9,6))\n", + "ax1 = fig1.add_subplot()\n", + "tth, eta = trans.compute_tth_eta()\n", + "trans.addcellpeaks()\n", + "pt1, = ax1.plot( tth, eta, \",\")\n", + "ax1.set(xlabel=\"tth\", ylabel=\"eta\")\n", + "ax1.plot( trans.theorytth, [0,]*len(trans.theorytth), \"r|\", ms=360, alpha=0.2 )\n", + "# Add controls\n", + "display(layout)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ImageD11/nbGui/fit_geometry.py b/ImageD11/nbGui/fit_geometry.py new file mode 100644 index 00000000..9fc90b34 --- /dev/null +++ b/ImageD11/nbGui/fit_geometry.py @@ -0,0 +1,114 @@ +import ipywidgets, pylab as pl +from ImageD11 import transformer +from tkinter import * +from tkinter import filedialog + + +class FitGeom(transformer.transformer): + + "Gives an IPython notebook UI for the experimental calibrations" + try: + if get_ipython().__class__.__name__ == 'ZMQInteractiveShell': + interactive = True + else: + interactive = False + except NameError: + interactive = False + + def __init__(self): + transformer.transformer.__init__(self) + self.vars = "y_center z_center distance tilt_y tilt_z".split() + self.steps = (1, 1, 100, 0.01, 0.01, 0) + self.nv = len(self.vars) + + def __parCallBack( self, arg ): + self.parameterobj.parameters.update( { arg['owner'].description : arg['new'] } ) + self.__drawPlot() + + def __fixVaryCallBack( self, arg ): + name = arg['owner'].description.split(" ")[1] + vars = self.getvars() + if arg.new and not (name in self.vars): + vars.append( name ) + if name in self.vars and not arg.new: + vars.remove(name) + self.parameterobj.set_varylist(vars) + + def __fitCallBack(self, arg): + """ fit call back - runs fit """ + lo, hi = self.ax1.get_xlim() + self.fit( lo, hi ) + self.__updateGui() + + def __drawPlot(self): + tth, eta = self.compute_tth_eta() + self.pt1.set_data( tth, eta ) + + def __loadCallBack(self, arg): + rootTk = Tk() + rootTk.withdraw() + rootTk.call('wm', 'attributes', '.', '-topmost', True) + filename = filedialog.askopenfilename() + get_ipython().run_line_magic('gui', 'tk') + if not filename == '': + self.loadfileparameters(filename) + self.__updateGui() + + def __saveCallBack(self, arg): + rootTk = Tk() + rootTk.withdraw() + rootTk.call('wm', 'attributes', '.', '-topmost', True) + filename = filedialog.asksaveasfilename() + get_ipython().run_line_magic('gui', 'tk') + if not filename == '': + self.parameterobj.saveparameters(filename) + + def __updateGui(self): + for i, pname in enumerate(self.vars): + self.layout[i,0].value = self.parameterobj.get(pname) + self.__drawPlot() + + def __drawWidgets(self): + nv = self.nv + vars = self.vars + steps = self.steps + self.layout = ipywidgets.GridspecLayout(nv+1,3) + for i,( pname, pstep ) in enumerate( zip( vars, steps ) ) : + self.layout[i,0] = ipywidgets.FloatText( description=pname, + value = self.parameterobj.parameters.get(pname), + step=pstep) + self.layout[i,0].observe( self.__parCallBack , names='value' ) + self.layout[i,1] = ipywidgets.ToggleButton( description="Vary "+pname, + value = pname in self.getvars() ) + self.layout[i,1].observe( self.__fixVaryCallBack, names='value' ) + + self.layout[nv,0] = ipywidgets.FloatText( description='fit_tolerance', + value = self.parameterobj.parameters.get("fit_tolerance"), step=0,) + self.layout[nv,0].observe( self.__parCallBack , names='value' ) + + self.layout[nv,1] = ipywidgets.Button(description="Run Fit (blocks)") + self.layout[nv,1].on_click( self.__fitCallBack ) + + self.layout[0,2] = ipywidgets.Button(description="Load parameters") + self.layout[0,2].on_click( self.__loadCallBack ) + + self.layout[1,2] = ipywidgets.Button(description="Save to a file") + self.layout[1,2].on_click( self.__saveCallBack ) + + def fitGui(self): + if self.__class__.interactive: + self.fig1 = pl.figure(1, figsize=(9,6)) + self.ax1 = self.fig1.add_subplot() + tth, eta = self.compute_tth_eta() + self.addcellpeaks() + self.pt1, = self.ax1.plot( tth, eta, ",") + self.ax1.set(xlabel="tth", ylabel="eta") + self.ax1.plot( self.theorytth, [0,]*len(self.theorytth), "r|", ms=360, alpha=0.2 ) + # Add controls + self.__drawWidgets() + display(self.layout) + else: + print('Sorry, this Gui works only in IPython notebooks!') + + + diff --git a/ImageD11/nbGui/nb_utils.py b/ImageD11/nbGui/nb_utils.py new file mode 100644 index 00000000..4350ce4b --- /dev/null +++ b/ImageD11/nbGui/nb_utils.py @@ -0,0 +1,760 @@ +import os +import subprocess +import time + +import h5py +import numba +import numpy as np +from matplotlib import pyplot as plt +from tqdm import tqdm +from tqdm.contrib.concurrent import process_map + +import ImageD11.cImageD11 +import ImageD11.columnfile +import ImageD11.grain +import ImageD11.indexing +import ImageD11.refinegrains +import ImageD11.unitcell + +from ImageD11.blobcorrector import eiger_spatial, correctorclass + +from scipy.optimize import curve_fit + + + +def correct_sinogram_rows_with_ring_current(grain, ds): + grain.ssino = grain.ssino / ds.ring_currents_per_scan_scaled[:, None] + + +def get_ring_current_per_scan(ds): + """Gets the ring current for each scan (i.e rotation/y-step) + Stores it inside ds.ring_currents_per_scan and a scaled version inside ds.ring_currents_per_scan_scaled""" + if not hasattr(ds, "ring_currents_per_scan"): + ring_currents = [] + with h5py.File(ds.masterfile, "r") as h5in: + for scan in ds.scans: + ring_current = float(h5in[scan]["instrument/machine/current"][()]) + ring_currents.append(ring_current) + + ds.ring_currents_per_scan = np.array(ring_currents) + ds.ring_currents_per_scan_scaled = np.array(ring_currents/np.max(ring_currents)) + +def correct_pixel(pixel, spline_file): + sr, fr = pixel + sc, fc = ImageD11.blobcorrector.correctorclass(spline_file).correct(sr, fr) + return (sc, fc) + + +def apply_spatial(cf, spline_file, workers): + # sc = np.zeros(cf.nrows) + # fc = np.zeros(cf.nrows) + + print("Spatial correction...") + + raw_pixels = np.vstack((cf['s_raw'], cf['f_raw'])).T + + corrected_pixels = process_map(correct_pixel, raw_pixels, [spline_file] * len(raw_pixels), max_workers=workers, chunksize=len(raw_pixels)//workers) + + sc, fc = [list(t) for t in zip(*corrected_pixels)] + + cf.addcolumn(sc, "sc") + cf.addcolumn(fc, "fc") + + return cf + +def apply_spatial_lut(cf, spline_file): + # sc = np.zeros(cf.nrows) + # fc = np.zeros(cf.nrows) + + print("Spatial correction...") + + corrector = ImageD11.blobcorrector.correctorclass(spline_file) + corrector.correct_px_lut(cf) + + return cf + +def find_datasets_to_process(rawdata_path, skips_dict, dset_prefix, sample_list): + samples_dict = {} + + for sample in sample_list: + all_dset_folders_for_sample = os.listdir(os.path.join(rawdata_path, sample)) + dsets_list = [] + for folder in all_dset_folders_for_sample: + if dset_prefix in folder: + dset_name = folder.split(sample + "_")[1] + if sample in skips_dict.keys(): + if dset_name not in skips_dict[sample]: + dsets_list.append(dset_name) + else: + dsets_list.append(dset_name) + + samples_dict[sample] = sorted(dsets_list) + + return samples_dict + + +def sine_function(x, offset, a, b): + return b * np.sin(np.radians(x)) + a * np.cos(np.radians(x)) + offset + +def fit_sine_wave(x_data, y_data, initial_guess): + # Fit the sine function to the data + popt, _ = curve_fit(sine_function, x_data, y_data, p0=initial_guess, method='trf', loss='soft_l1', max_nfev=10000) + + offset, a, b = popt + + return offset, a, b + +def fit_grain_position_from_sino(grain, cf_strong): + initial_guess = (0, 0.5, 0.5) + + offset, a, b = fit_sine_wave(cf_strong.omega[grain.mask_4d], cf_strong.dty[grain.mask_4d], initial_guess) + + grain.cen = offset + + grain.dx = -b + grain.dy = -a + +def grain_to_rgb(g, ax=(0, 0, 1)): + return hkl_to_color_cubic(crystal_direction_cubic(g.ubi, ax)) + + +def crystal_direction_cubic(ubi, axis): + hkl = np.dot(ubi, axis) + # cubic symmetry implies: + # 24 permutations of h,k,l + # one has abs(h) <= abs(k) <= abs(l) + hkl = abs(hkl) + hkl.sort() + return hkl + + +def hkl_to_color_cubic(hkl): + """ + https://mathematica.stackexchange.com/questions/47492/how-to-create-an-inverse-pole-figure-color-map + [x,y,z]=u⋅[0,0,1]+v⋅[0,1,1]+w⋅[1,1,1]. + These are: + u=z−y, v=y−x, w=x + This triple is used to assign each direction inside the standard triangle + + makeColor[{x_, y_, z_}] := + RGBColor @@ ({z - y, y - x, x}/Max@{z - y, y - x, x}) + """ + x, y, z = hkl + assert x <= y <= z + assert z >= 0 + u, v, w = z - y, y - x, x + m = max(u, v, w) + r, g, b = u / m, v / m, w / m + return (r, g, b) + + +def hkl_to_pf_cubic(hkl): + x, y, z = hkl + assert x <= y <= z + assert z >= 0 + m = np.sqrt((hkl ** 2).sum()) + return x / (z + m), y / (z + m) + + +def triangle(): + """ compute a series of point on the edge of the triangle """ + xy = [np.array(v) for v in ((0, 1, 1), (0, 0, 1), (1, 1, 1))] + xy += [xy[2] * (1 - t) + xy[0] * t for t in np.linspace(0.1, 1, 5)] + return np.array([hkl_to_pf_cubic(np.array(p)) for p in xy]) + + +# def calcy(cos_omega, sin_omega, sol): +# return sol[0] + cos_omega * sol[1] + sin_omega * sol[2] + + +# def fity(y, cos_omega, sin_omega, wt=1): +# """ +# Fit a sinogram to get a grain centroid +# # calc = d0 + x*co + y*so +# # dc/dpar : d0 = 1 +# # : x = co +# # : y = so +# # gradients +# # General linear least squares +# # Solution by the normal equation +# # wt is weights (1/sig? or 1/sig^2?) +# # +# """ +# g = [wt * np.ones(y.shape, float), wt * cos_omega, wt * sin_omega] # gradient +# nv = len(g) +# m = np.zeros((nv, nv), float) +# r = np.zeros(nv, float) +# for i in range(nv): +# r[i] = np.dot(g[i], wt * y) # A^T . b +# for j in range(i, nv): +# m[i, j] = np.dot(g[i], g[j]) # (A^T . A) . a = A^T . b +# m[j, i] = m[i, j] +# sol = np.dot(np.linalg.inv(m), r) +# return sol + + +# def fity_robust(dty, co, so, nsigma=5, doplot=False): +# cen, dx, dy = fity(dty, co, so) +# calc2 = calc1 = calcy(co, so, (cen, dx, dy)) +# # mask for columnfile, we're selecting specific 4D peaks +# # that come from the right place in y +# selected = np.ones(co.shape, bool) +# for i in range(3): +# err = dty - calc2 +# estd = max(err[selected].std(), 1.0) # 1 micron +# # print(i,estd) +# es = estd * nsigma +# selected = abs(err) < es +# cen, dx, dy = fity(dty, co, so, selected.astype(float)) +# calc2 = calcy(co, so, (cen, dx, dy)) +# # bad peaks are > 5 sigma +# if doplot: +# f, a = plt.subplots(1, 2) +# theta = np.arctan2(so, co) +# a[0].plot(theta, calc1, ',') +# a[0].plot(theta, calc2, ',') +# a[0].plot(theta[selected], dty[selected], "o") +# a[0].plot(theta[~selected], dty[~selected], 'x') +# a[1].plot(theta[selected], (calc2 - dty)[selected], 'o') +# a[1].plot(theta[~selected], (calc2 - dty)[~selected], 'x') +# a[1].set(ylim=(-es, es)) +# plt.show() +# return selected, cen, dx, dy + + +# def graincen(gid, colf, doplot=True, nsigma=5): +# # Get peaks beloging to this grain ID +# m = colf.grain_id == gid +# # Get omega values of peaks in radians +# romega = np.radians(colf.omega[m]) +# # Calculate cos and sin of omega +# co = np.cos(romega) +# so = np.sin(romega) +# # Get dty values of peaks +# dty = colf.dty[m] +# selected, cen, dx, dy = fity_robust(dty, co, so, nsigma=nsigma, doplot=doplot) +# return selected, cen, dx, dy + + +@numba.njit(parallel=True) +def pmax(ary): + """ Find the min/max of an array in parallel """ + mx = ary.flat[0] + mn = ary.flat[0] + for i in numba.prange(1, ary.size): + mx = max(ary.flat[i], mx) + mn = min(ary.flat[i], mn) + return mn, mx + + +@numba.njit(parallel=True) +def palloc(shape, dtype): + """ Allocate and fill an array with zeros in parallel """ + ary = np.empty(shape, dtype=dtype) + for i in numba.prange(ary.size): + ary.flat[i] = 0 + return ary + + +# counting sort by grain_id +@numba.njit +def counting_sort(ary, maxval=None, minval=None): + """ Radix sort for integer array. Single threaded. O(n) + Numpy should be doing this... + """ + if maxval is None: + assert minval is None + minval, maxval = pmax(ary) # find with a first pass + maxval = int(maxval) + minval = int(minval) + histogram = palloc((maxval - minval + 1,), np.int64) + indices = palloc((maxval - minval + 2,), np.int64) + result = palloc(ary.shape, np.int64) + for gid in ary: + histogram[gid - minval] += 1 + indices[0] = 0 + for i in range(len(histogram)): + indices[i + 1] = indices[i] + histogram[i] + i = 0 + for gid in ary: + j = gid - minval + result[indices[j]] = i + indices[j] += 1 + i += 1 + return result, histogram + + +@numba.njit(parallel=True) +def find_grain_id(spot3d_id, grain_id, spot2d_label, grain_label, order, nthreads=20): + """ + Assignment grain labels into the peaks 2d array + spot3d_id = the 3d spot labels that are merged and indexed + grain_id = the grains assigned to the 3D merged peaks + spot2d_label = the 3d label for each 2d peak + grain_label => output, which grain is this peak + order = the order to traverse spot2d_label sorted + """ + assert spot3d_id.shape == grain_id.shape + assert spot2d_label.shape == grain_label.shape + assert spot2d_label.shape == order.shape + T = nthreads + print("Using", T, "threads") + for tid in numba.prange(T): + pcf = 0 # thread local I hope? + for i in order[tid::T]: + grain_label[i] = -1 + pkid = spot2d_label[i] + while spot3d_id[pcf] < pkid: + pcf += 1 + if spot3d_id[pcf] == pkid: + grain_label[i] = grain_id[pcf] + + +def tocolf(pkd, parfile, dxfile, dyfile): + """ Converts a dictionary of peaks into an ImageD11 columnfile + adds on the geometric computations (tth, eta, gvector, etc) """ + spat = eiger_spatial(dxfile=dxfile, dyfile=dyfile) + cf = ImageD11.columnfile.colfile_from_dict(spat(pkd)) + cf.parameters.loadparameters(parfile) + cf.updateGeometry() + return cf + + +def unitcell_peaks_mask(cf, dstol, dsmax): + cell = ImageD11.unitcell.unitcell_from_parameters(cf.parameters) + cell.makerings(dsmax) + m = np.zeros(cf.nrows, bool) + for v in cell.ringds: + if v < dsmax: + m |= (abs(cf.ds - v) < dstol) + + return m + + +def strongest_peaks(colf, uself=True, frac=0.995, B=0.2, doplot=None): + # correct intensities for structure factor (decreases with 2theta) + cor_intensity = colf.sum_intensity * (np.exp(colf.ds * colf.ds * B)) + if uself: + lf = ImageD11.refinegrains.lf(colf.tth, colf.eta) + cor_intensity *= lf + order = np.argsort(cor_intensity)[::-1] # sort the peaks by intensity + sortedpks = cor_intensity[order] + cums = np.cumsum(sortedpks) + cums /= cums[-1] + enough = np.searchsorted(cums, frac) + # Aim is to select the strongest peaks for indexing. + cutoff = sortedpks[enough] + mask = cor_intensity > cutoff + if doplot is not None: + fig, axs = plt.subplots(1, 2, figsize=(10, 5)) + axs[0].plot(cums / cums[-1], ',') + axs[0].set(xlabel='npks', ylabel='fractional intensity') + axs[0].plot([mask.sum(), ], [frac, ], "o") + axs[1].plot(cums / cums[-1], ',') + axs[1].set(xlabel='npks logscale', ylabel='fractional intensity', xscale='log', ylim=(doplot, 1.), + xlim=(np.searchsorted(cums, doplot), len(cums))) + axs[1].plot([mask.sum(), ], [frac, ], "o") + plt.show() + return mask + + +def selectpeaks(cf, dstol=0.005, dsmax=10, frac=0.99, doplot=None): + m = unitcell_peaks_mask(cf, dstol=dstol, dsmax=dsmax) + cfc = cf.copy() + cfc.filter(m) + ms = strongest_peaks(cfc, frac=frac, doplot=doplot) + cfc.filter(ms) + return cfc + + +def plot_index_results(ind, colfile, title): + # Generate a histogram of |drlv| for a ubi matrix + ind.histogram_drlv_fit() + # indexer.fight_over_peaks() + + fig, axs = plt.subplots(3, 2, layout="constrained", figsize=(9, 12)) + axs_flat = axs.ravel() + + # For each grain, plot the error in hkl vs the number of peaks with that error + + for grh in ind.histogram: + axs_flat[0].plot(ind.bins[1:-1], grh[:-1], "-") + + axs_flat[0].set(ylabel="number of peaks", + xlabel="error in hkl (e.g. hkl versus integer)", + title=title) + + # set a mask of all non-assigned g-vectors + + m = ind.ga == -1 + # m = colfile.grain_id == -1 + + # plot the assigned g-vectors omega vs dty (sinograms) + + axs_flat[1].scatter(colfile.omega[~m], + colfile.dty[~m], + c=ind.ga[~m], + s=2, + cmap='tab20') + + axs_flat[1].set(title='Sinograms of {} grains'.format(ind.ga.max() + 1), + xlabel='Omega/deg', + ylabel='dty/um') + + # Define weak peaks as all non-assigned peaks with intensity 1e-4 of max + cut = colfile.sum_intensity[m].max() * 1e-4 + weak = colfile.sum_intensity[m] < cut + + # Plot unassigned peaks in omega vs dty + + axs_flat[2].scatter(colfile.omega[m][weak], colfile.dty[m][weak], s=2, label='weak') + axs_flat[2].scatter(colfile.omega[m][~weak], colfile.dty[m][~weak], s=2, label='not weak') + + axs_flat[2].set(title='Sinograms of unassigned peaks', + xlabel='Omega/deg', + ylabel='dty/um') + axs_flat[2].legend() + + # Plot d-star vs intensity for all assigned peaks + + axs_flat[3].scatter(colfile.ds[~m], colfile.sum_intensity[~m], s=2) + axs_flat[3].set(title='Intensity of all assigned peaks', + xlabel='d-star', + ylabel='Intensity', + yscale='log') + + # Plot d-star vs intensity for all unassigned peaks + + axs_flat[4].scatter(colfile.ds[m][weak], colfile.sum_intensity[m][weak], s=2, label='weak') + axs_flat[4].scatter(colfile.ds[m][~weak], colfile.sum_intensity[m][~weak], s=2, label='not weak') + + axs_flat[4].set(title='Intensity of all unassigned peaks', + xlabel='d-star', + ylabel='Intensity', + yscale='log') + axs_flat[4].legend() + + # Get the number of peaks per grain + + npks = [(colfile.grain_id == i).sum() for i in range(len(ind.ubis))] + + # Plot histogram of number of peaks per grain + + axs_flat[5].hist(npks, bins=32) + axs_flat[5].set(title='Hist of peaks per grain', + xlabel='Number of peaks', + ylabel='Number of grains') + + for ax in axs_flat: + ax.set_box_aspect(0.7) + + plt.show() + + +def plot_grain_sinograms(grains, cf, n_grains_to_plot=None): + if n_grains_to_plot is None: + n_grains_to_plot = len(grains) + + grains_step = len(grains) // n_grains_to_plot + + grid_size = np.ceil(np.sqrt(len(grains[::grains_step]))).astype(int) + nrows = (len(grains[::grains_step]) + grid_size - 1) // grid_size + + fig, axs = plt.subplots(grid_size, nrows, figsize=(10, 10), layout="constrained", sharex=True, sharey=True) + for i, ax in enumerate(axs.ravel()): + if i < len(grains[::grains_step]): + # get corresponding grain for this axis + g = grains[::grains_step][i] + m = cf.grain_id == g.gid + ax.scatter(cf.omega[m], cf.dty[m], c=cf.sum_intensity[m], s=2) + ax.set_title(g.gid) + + fig.supxlabel("Omega") + fig.supylabel("Y translation (um)") + + plt.show() + + +def assign_peaks_to_grains(grains, cf, tol): + # assign peaks to grains + + # column to store the grain labels + labels = np.zeros(cf.nrows, 'i') + # get all g-vectors from columnfile + gv = np.transpose((cf.gx, cf.gy, cf.gz)).astype(float) + # column to store drlv2 (error in hkl) + drlv2 = np.ones(cf.nrows, 'd') + # iterate over all grains + print("Scoring and assigning {} grains".format(len(grains))) + for g in tqdm(grains): + n = ImageD11.cImageD11.score_and_assign(g.ubi, gv, tol, drlv2, labels, g.gid) + + # add the labels column to the columnfile + cf.addcolumn(labels, 'grain_id') + + +def do_index(cf, + dstol=0.05, + max_mult=13, + min_ring_count=0, + hkl_tols=(0.01, 0.02, 0.03, 0.04, 0.05, 0.1), + fracs=(0.9, 0.8, 0.7, 0.6, 0.5), + cosine_tol=np.cos(np.radians(90 - 0.25)), + max_grains=1000): + print("Indexing {} peaks".format(cf.nrows)) + Fe = ImageD11.unitcell.unitcell_from_parameters(cf.parameters) + Fe.makerings(cf.ds.max()) + indexer = ImageD11.indexing.indexer_from_colfile(cf) + + ImageD11.indexing.loglevel = 3 + + indexer.ds_tol = dstol + indexer.assigntorings() + indexer.max_grains = max_grains + + ImageD11.cImageD11.cimaged11_omp_set_num_threads(2) + + n_peaks_expected = 0 + rings = [] + for i, dstar in enumerate(indexer.unitcell.ringds): + multiplicity = len(indexer.unitcell.ringhkls[indexer.unitcell.ringds[i]]) + counts_on_this_ring = (indexer.ra == i).sum() + if counts_on_this_ring > min_ring_count: + n_peaks_expected += multiplicity + if multiplicity < max_mult: + rings.append((counts_on_this_ring, multiplicity, i)) + + rings.sort() + + print("{} peaks expected".format(n_peaks_expected)) + print("Trying these rings (counts, multiplicity, ring number): {}".format(rings)) + indexer.cosine_tol = np.abs(cosine_tol) + + for frac in fracs: + for tol in hkl_tols: + indexer.minpks = n_peaks_expected * frac + indexer.hkl_tol = tol + for i in range(len(rings)): + for j in range(i, len(rings)): + indexer.ring_1 = rings[i][2] + indexer.ring_2 = rings[j][2] + + indexer.find() + indexer.scorethem() + + print(frac, tol, len(indexer.ubis)) + + grains = [ImageD11.grain.grain(ubi, translation=np.array([0., 0., 0.])) for ubi in indexer.ubis] + print("Found {} grains".format(len(grains))) + + return grains, indexer + + +def refine_grain_positions(cf_3d, ds, grains, parfile, symmetry="cubic", cf_frac=0.85, cf_dstol=0.01, + hkl_tols=(0.05, 0.025, 0.01)): + sample = ds.sample + dataset = ds.dset + cf_strong_allrings = selectpeaks(cf_3d, frac=cf_frac, dsmax=cf_3d.ds.max(), doplot=None, dstol=cf_dstol) + print("Got {} strong peaks for makemap".format(cf_strong_allrings.nrows)) + cf_strong_allrings_path = '{}_{}_3d_peaks_strong_all_rings.flt'.format(sample, dataset) + cf_strong_allrings.writefile(cf_strong_allrings_path) + + tmp_ubi_path = '{}_{}_grains.ubi'.format(sample, dataset) + tmp_map_path = '{}_{}_grains.map'.format(sample, dataset) + + new_flt_path = '{}_{}_3d_peaks_strong_all_rings.flt.new'.format(sample, + dataset) # flt file containing assignments from makemap + unindexed_flt_path = '{}_{}_3d_peaks_strong_all_rings.flt.unindexed'.format(sample, + dataset) # remaining unassigned peaks from makemap + + ImageD11.grain.write_grain_file(tmp_ubi_path, grains) + + omegas_sorted = np.sort(ds.omega)[0] + omega_slop = np.round(np.diff(omegas_sorted).mean(), 3) + + for inc, makemap_tol in enumerate(hkl_tols): + print("Running makemap {}/{}".format(inc + 1, len(hkl_tols))) + if inc == 0: # ubi into map + makemap_command = "makemap.py -p {} -u {} -U {} -f {} -F {} -s {} -t {} --omega_slop={} --no_sort".format( + parfile, tmp_ubi_path, tmp_map_path, cf_strong_allrings_path, unindexed_flt_path, symmetry, + hkl_tols[inc], omega_slop) + makemap_output = subprocess.run(makemap_command, capture_output=True, shell=True).stdout.decode("utf-8") + + # makemap_output = !makemap.py -p {parfile} -u {tmp_ubi_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s {symmetry} -t {hkl_tols[inc]} --omega_slop={omega_slop} --no_sort + else: # map into map + makemap_command = "makemap.py -p {} -u {} -U {} -f {} -F {} -s {} -t {} --omega_slop={} --no_sort".format( + parfile, tmp_map_path, tmp_map_path, cf_strong_allrings_path, unindexed_flt_path, symmetry, + hkl_tols[inc], omega_slop) + makemap_output = subprocess.run(makemap_command, capture_output=True, shell=True).stdout.decode("utf-8") + # makemap_output = !makemap.py -p {parfile} -u {tmp_map_path} -U {tmp_map_path} -f {cf_strong_allrings_path} -F {unindexed_flt_path} -s {symmetry} -t {hkl_tols[inc]} --omega_slop={omega_slop} --no_sort + + grains2 = ImageD11.grain.read_grain_file(tmp_map_path) + + return grains2 + + +def build_slice_arrays(grains, cutoff_level=0.0): + grain_labels_array = np.zeros_like(grains[0].recon) - 1 + + redx = np.zeros_like(grains[0].recon) + grnx = np.zeros_like(grains[0].recon) + blux = np.zeros_like(grains[0].recon) + + redy = np.zeros_like(grains[0].recon) + grny = np.zeros_like(grains[0].recon) + bluy = np.zeros_like(grains[0].recon) + + redz = np.zeros_like(grains[0].recon) + grnz = np.zeros_like(grains[0].recon) + bluz = np.zeros_like(grains[0].recon) + + raw_intensity_array = np.zeros_like(grains[0].recon) + + raw_intensity_array.fill(cutoff_level) + + def norm(r): + m = r > r.max() * 0.2 + return (r / r[m].mean()).clip(0, 1) + + for g in tqdm(grains): + i = g.gid + + g_raw_intensity = norm(g.recon) + + g_raw_intensity_mask = g_raw_intensity > raw_intensity_array + + g_raw_intensity_map = g_raw_intensity[g_raw_intensity_mask] + + raw_intensity_array[g_raw_intensity_mask] = g_raw_intensity_map + + redx[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_x[0] + grnx[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_x[1] + blux[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_x[2] + + redy[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_y[0] + grny[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_y[1] + bluy[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_y[2] + + redz[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_z[0] + grnz[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_z[1] + bluz[g_raw_intensity_mask] = g_raw_intensity_map * g.rgb_z[2] + + grain_labels_array[g_raw_intensity_mask] = i + + raw_intensity_array[raw_intensity_array == cutoff_level] = 0 + + rgb_x_array = np.transpose((redx, grnx, blux), axes=(1, 2, 0)) + rgb_y_array = np.transpose((redy, grny, bluy), axes=(1, 2, 0)) + rgb_z_array = np.transpose((redz, grnz, bluz), axes=(1, 2, 0)) + + return rgb_x_array, rgb_y_array, rgb_z_array, grain_labels_array, raw_intensity_array + + +def slurm_submit_and_wait(bash_script_path, wait_time_sec=60): + if not os.path.exists(bash_script_path): + raise IOError("Bash script not found!") + submit_command = "sbatch {}".format(bash_script_path) + sbatch_submit_result = subprocess.run(submit_command, capture_output=True, shell=True).stdout.decode("utf-8") + + print(sbatch_submit_result.replace("\n", "")) + + slurm_job_number = None + + if sbatch_submit_result.startswith("Submitted"): + slurm_job_number = sbatch_submit_result.replace("\n", "").split("job ")[1] + + print(slurm_job_number) + + assert slurm_job_number is not None + + slurm_job_finished = False + + while not slurm_job_finished: + squeue_results = subprocess.run("squeue -u $USER", capture_output=True, shell=True).stdout.decode("utf-8") + + if slurm_job_number not in squeue_results: + print("Slurm job finished!") + slurm_job_finished = True + else: + print("Slurm job not finished! Waiting {} seconds...".format(wait_time_sec)) + time.sleep(wait_time_sec) + + +def slurm_submit_many_and_wait(bash_script_paths, wait_time_sec=60): + + for bash_script_path in bash_script_paths: + if not os.path.exists(bash_script_path): + raise IOError("Bash script not found!") + + + slurm_job_numbers = [] + for bash_script_path in bash_script_paths: + submit_command = "sbatch {}".format(bash_script_path) + sbatch_submit_result = subprocess.run(submit_command, capture_output=True, shell=True).stdout.decode("utf-8") + + print(sbatch_submit_result.replace("\n", "")) + + slurm_job_number = None + + if sbatch_submit_result.startswith("Submitted"): + slurm_job_number = sbatch_submit_result.replace("\n", "").split("job ")[1] + + # print(slurm_job_number) + + assert slurm_job_number is not None + + slurm_job_numbers.append(slurm_job_number) + + slurm_job_finished = False + + while not slurm_job_finished: + squeue_results = subprocess.run("squeue -u $USER", capture_output=True, shell=True).stdout.decode("utf-8") + + jobs_still_running = False + for slurm_job_number in slurm_job_numbers: + if slurm_job_number in squeue_results: + jobs_still_running = True + + if jobs_still_running: + print("Slurm jobs not finished! Waiting {} seconds...".format(wait_time_sec)) + time.sleep(wait_time_sec) + else: + print("Slurm jobs all finished!") + slurm_job_finished = True + + +def correct_half_scan(ds): + c0 = 0 + # check / fix the centre of rotation + # get value of bin closest to c0 + central_bin = np.argmin(abs(ds.ybincens - c0)) + # get centre dty value of this vin + central_value = ds.ybincens[central_bin] + + lo_side = ds.ybincens[:central_bin + 1] + hi_side = ds.ybincens[central_bin:] + + # get the hi/lo side which is widest + # i.e if you go from -130 to +20, it selects -130 + yrange = max(hi_side[-1] - hi_side[0], lo_side[-1] - lo_side[0]) + + # round to nearest multiple of ds.ystep + yrange = np.ceil(yrange / ds.ystep) * ds.ystep + + # make new ymin and ymax that are symmetric around central_value + ds.ymin = central_value - yrange + ds.ymax = central_value + yrange + + new_yrange = ds.ymax - ds.ymin + + # determine new number of y bins + ny = int(new_yrange // ds.ystep) + 1 + + ds.ybincens = np.linspace(ds.ymin, ds.ymax, ny) + ds.ybinedges = np.linspace(ds.ymin - ds.ystep / 2, ds.ymax + ds.ystep / 2, ny + 1) + + print(len(ds.ybincens)) + print(ds.ybincens) + print(ds.ystep) + print(yrange) + print(ny) diff --git a/ImageD11/nbGui/plot3d.ipynb b/ImageD11/nbGui/plot3d.ipynb new file mode 100644 index 00000000..af755095 --- /dev/null +++ b/ImageD11/nbGui/plot3d.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plot3d\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p = plot3d.Plot3d( w=400, h=300, rx=10, ry=20, rz=30, npx=1 )\n", + "display( p.widget )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/nbGui/plot3d.py b/ImageD11/nbGui/plot3d.py new file mode 100644 index 00000000..8de3b360 --- /dev/null +++ b/ImageD11/nbGui/plot3d.py @@ -0,0 +1,44 @@ + +import io +import numpy as np +import ipywidgets +import scipy.spatial.transform +from ImageD11 import cImageD11 +from PIL import Image + +def demodata(): + h,k,l = np.mgrid[-3:4,-3:4,-3:4] + gve = np.dot( np.eye(3)*0.1 , (h.ravel(), k.ravel(), l.ravel() )) + return gve + +class Plot3d: + + def __init__(self, xyz=demodata(), w=386, h=256, rx=0., ry=0., rz=0., npx = 1 ): + self.rgba = np.empty( (h, w, 4), 'B') + self.xyz = xyz + self.npx = npx + self.ipyimg = ipywidgets.Image( ) + self.wrx = ipywidgets.FloatSlider( value=rx, min=-360, max=360.0, step=1, description='rx:', disabled=False, + continuous_update=True, orientation='vertical', readout=True, readout_format='.1f' ) + self.wry = ipywidgets.FloatSlider( value=ry, min=-360, max=360.0, step=1, description='ry:', disabled=False, + continuous_update=True, orientation='vertical', readout=True, readout_format='.1f' ) + self.wrz = ipywidgets.FloatSlider( value=rz, min=-360, max=360.0, step=1, description='rz:', disabled=False, + continuous_update=True, orientation='vertical', readout=True, readout_format='.1f' ) + self.wrx.observe( self.redraw, names='value' ) + self.wry.observe( self.redraw, names='value' ) + self.wrz.observe( self.redraw, names='value' ) + self.redraw(None) + self.widget = ipywidgets.HBox([ self.ipyimg, self.wrx, self.wry, self.wrz] ) + + def redraw(self,change): + u = scipy.spatial.transform.Rotation.from_euler('XYZ', + (self.wrx.value, self.wry.value, self.wrz.value), + degrees=True).as_matrix() + rotated = u.dot(self.xyz) + order = np.argsort( (rotated[2]*100).astype(np.int16) ) + cImageD11.splat( self.rgba, rotated[:,order].T, u.ravel(), self.npx ) + img = Image.fromarray(self.rgba) + with io.BytesIO() as buffer: + img.save( buffer, format='gif' ) + self.ipyimg.value = buffer.getvalue() + diff --git a/ImageD11/nbGui/test_gui_fit.ipynb b/ImageD11/nbGui/test_gui_fit.ipynb new file mode 100644 index 00000000..35446bb7 --- /dev/null +++ b/ImageD11/nbGui/test_gui_fit.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:titles are xc yc omega\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "09846b15c06c4d86a4e711f4d7e180a7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridspecLayout(children=(FloatText(value=1024.0, description='y_center', layout=Layout(grid_area='widget001'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from ImageD11.nbGui.fit_geometry import FitGeom\n", + "test_fit_geom = FitGeom()\n", + "test_fit_geom.loadfiltered(\"../../test/nac_demo/peaks.out_merge_t200\")\n", + "test_fit_geom.fitGui()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ImageD11/parameters.py b/ImageD11/parameters.py index ecb03ae6..b3234f62 100644 --- a/ImageD11/parameters.py +++ b/ImageD11/parameters.py @@ -1,233 +1 @@ - -from __future__ import print_function - - - -# ImageD11_v0.4 Software for beamline ID11 -# Copyright (C) 2005 Jon Wright -# -# This program is free software; you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation; either version 2 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program; if not, write to the Free Software -# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA - - -""" -Class to handle groups of parameters to be saved in and out -of files and edited in guis with the fixed/varied info etc -""" - -class par: - """ - Represents a thing which can vary - """ - def __init__(self, name, value, helpstring = None , - vary=False, can_vary=False , stepsize = None): - """ - name : unique key used as keyword arg to some functions - value : value of the parameter - helpstring : optional string to help user - vary : value should be optimised - can_vary : value is not fixed - stepsize : guessestimated ball park step size (eg not 1e99!) - """ - self.name = name - self.value = value - if helpstring is None: - self.helpstring = "parameter : "+name - else: - self.helpstring = helpstring - self.vary = vary - self.can_vary = can_vary - self.stepsize = stepsize - - def fromstringlist(self, sl): - """ to send to Java """ - [self.name, - self.value, - self.helpstring, - self.vary, - self.can_vary, - self.stepsize] = sl - - def tostringlist(self): - """ to catch from Java """ - return [self.name , - self.value , - self.helpstring , - self.vary , - self.can_vary, - self.stepsize ] - -import logging -class parameters: - """ - Class to hold a set of named parameters - """ - def __init__(self,**kwds): - """ - name=value style arg list - """ - self.parameters = kwds - self.varylist = [] - self.can_vary = {} - self.variable_list = [] - self.stepsizes = {} - self.par_objs = {} - for k,v in list(self.parameters.items()): - self.addpar(par(k,v)) - - def addpar(self,par): - """ - add a parameter object - """ - self.parameters[par.name] = par.value - self.can_vary[par.name] = par.can_vary - if par.vary and par.name not in self.varylist: - self.varylist.append(par.name) - if par.can_vary and par.name not in self.variable_list: - self.variable_list.append(par.name) - self.stepsizes[par.name]=par.stepsize - self.par_objs[par.name]=par - - def get_variable_list(self): - return self.variable_list - - def get_variable_values(self): - """ values of the parameters """ - return [self.parameters[name] for name in self.varylist] - - def get_variable_stepsizes(self): - """ stepsizes for optimisers """ - return [self.stepsizes[name] for name in self.varylist] - - def set_varylist(self, vl): - ks = list(self.parameters.keys()) - for v in vl: - assert v in ks - assert v in self.variable_list - self.varylist = vl - - def set_variable_values(self,values): - """ set values of the parameters""" - assert len(values)==len(self.varylist) - for name, value in zip(self.varylist,values): - self.parameters[name]=value - - def set_parameters(self,d): - """ - Updates the values of parameters - """ - self.parameters.update(d) - self.dumbtypecheck() - - def get_parameters(self): - """ - Returns a dictionary of parameters - """ - return self.parameters - - def get(self,name): - return self.parameters[name] - - def set(self,name,value): - self.parameters[name] = value - - def update_yourself(self,other): - """ - Sychronise this parameter objects list of values with another object - """ - for k,v in list(self.parameters.items()): - if hasattr(other,k): - var = getattr(other,k) - print("setting: pars[%s] from %s to %s"%(k,v,var)) - self.parameters[k]=var - else: - print("error: %s has no attribute %s, ignoring"%(other,k)) - - def update_other(self,other): - """ - Synchronise an object with the values in this object - """ - for k,v in list(self.parameters.items()): - if hasattr(other,k): - var = getattr(other,k) - logging.debug("setting: %s.%s from %s to %s"%(other,k,var,v)) - setattr(other,k,v) - else: - logging.debug("error: %s has no attribute %s, ignoring"% - (other,k)) - - def saveparameters(self,filename): - """ - Write parameters to a file - """ - f=open(filename,"w") - keys=list(self.parameters.keys()) - keys.sort() - for key in keys: - f.write("%s %s\n"%(key,str(self.parameters[key]))) - f.close() - - def loadparameters(self,filename): - """ - Load parameters from a file - """ - lines = open(filename,"r").readlines() - for line in lines: - try: - [name, value] = line.split(" ") - name=name.replace("-","_") - self.parameters[name]=value - except ValueError: - print("Failed to read:",line) - self.dumbtypecheck() - - def dumbtypecheck(self): - """ - Eventually parameter types (and units and fixed/varied to be - specifieable - For now it just tries to coerce to float, then does nothing - """ - for name, value in list(self.parameters.items()): - if type(value) == type("string"): - try: - vf = float(value) - except ValueError: - # it really is a string - self.parameters[name] = value.lstrip().rstrip() - continue - # here if float worked - try: - vi = int(value) - except ValueError: - # it really is a float - self.parameters[name] = vf - continue - - # here if float and int worked - # should not be needed, depends on int valueerror - if abs(vi - vf) < 1e-9: - # use int - self.parameters[name] = vi - continue - else: - self.parameters[name] = vf - continue - else: - # int/float preserve type - self.parameters[name] = value - -def read_par_file( filename ): - p = parameters() - p.loadparameters( filename ) - return p +from xfab.parameters import * diff --git a/ImageD11/peakmerge.py b/ImageD11/peakmerge.py index fa3a2ea1..3e387ef7 100644 --- a/ImageD11/peakmerge.py +++ b/ImageD11/peakmerge.py @@ -279,8 +279,8 @@ def readpeaks(self, filename, startom = 0., omstep = 1.): if line[0] == "#": currentimage.addtoheader(line) continue - self.imagenumbers = np.zeros(len(self.images), np.int) - self.omegas = np.zeros(len(self.images), np.float) + self.imagenumbers = np.zeros(len(self.images), int) + self.omegas = np.zeros(len(self.images), float) i = 0 while i < len(self.images): self.imagenumbers[i] = int(self.images[i].imagenumber) diff --git a/ImageD11/peaksearcher.py b/ImageD11/peaksearcher.py index 109f5cbd..4235f8ef 100644 --- a/ImageD11/peaksearcher.py +++ b/ImageD11/peaksearcher.py @@ -31,33 +31,26 @@ Defines one function (peaksearch) which might be reused """ -try: - import Queue as queue -except: - import queue - -import threading - - -import time # For benchmarking +import time reallystart = time.time() -from ImageD11 import ImageD11_thread - -ImageD11_thread.stop_now = False +from six.moves import queue +# import threading from math import sqrt import sys , glob , os.path - -from ImageD11 import blobcorrector -from ImageD11.labelimage import labelimage import numpy # Generic file format opener from fabio import fabio from fabio.openimage import openimage -from fabio import fabioimage + +from ImageD11 import blobcorrector, ImageD11options +from ImageD11.correct import correct +from ImageD11.labelimage import labelimage +from ImageD11 import ImageD11_thread +ImageD11_thread.stop_now = False class timer: @@ -76,26 +69,25 @@ def tock(self,msg=""): print(" ".join(self.msgs),"%.2f/s"% (self.now-self.start)) sys.stdout.flush() -from ImageD11.correct import correct -def peaksearch( filename , - data_object , - corrector , - thresholds , +def peaksearch( filename , + data_object , + corrector , + thresholds , labims ): """ - filename : The name of the image file for progress info - data_object : Fabio object containing data and header + filename : The name of the image file for progress info + data_object : Fabio object containing data and header corrector : spatial and dark/flood , linearity etc - + thresholds : [ float[1], float[2] etc] labims : label image objects, one for each threshold - + """ t = timer() picture = data_object.data.astype(numpy.float32) - + assert "Omega" in data_object.header, "Bug in peaksearch headers" for lio in list(labims.values()): @@ -122,7 +114,7 @@ def peaksearch( filename , ome = float(data_object.header["Omega"]) # print "Reading from header" # - # Now peaksearch at each threshold level + # Now peaksearch at each threshold level t.tick(filename) for threshold in thresholds: labelim = labims[threshold] @@ -140,12 +132,12 @@ def peaksearch( filename , if labelim.npk > 0: labelim.output2dpeaks(f) labelim.mergelast() - t.msg("T=%-5d n=%-5d;" % (int(threshold),labelim.npk)) + t.msg("T=%-5d n=%-5d;" % (int(threshold),labelim.npk)) # Close the output file # Finish progress indicator for this file t.tock() sys.stdout.flush() - return None + return None def peaksearch_driver(options, args): @@ -155,7 +147,7 @@ def peaksearch_driver(options, args): ################## debugging still for a in args: print("arg: "+str(a)+","+str(type(a))) - for o in list(options.__dict__.keys()): + for o in list(options.__dict__.keys()): # FIXME if getattr(options,o) in [ "False", "FALSE", "false" ]: setattr(options,o,False) if getattr(options,o) in [ "True", "TRUE", "true" ]: @@ -179,7 +171,7 @@ def peaksearch_driver(options, args): raise ValueError("No files to process") # What to do about spatial - + if options.perfect=="N" and os.path.exists(options.spline): print("Using spatial from",options.spline) corrfunc = blobcorrector.correctorclass(options.spline) @@ -187,71 +179,72 @@ def peaksearch_driver(options, args): print("Avoiding spatial correction") corrfunc = blobcorrector.perfect() - - - # Get list of filenames to process - # if len(args) > 0 : - # # We no longer assume unlabelled arguments are filenames - # file_series_object = file_series.file_series(args) - # This is always the case now corrfunc.orientation = "edf" - + scan = None if options.format in ['bruker', 'BRUKER', 'Bruker']: extn = "" - if options.perfect is not "N": + if options.perfect != "N": print("WARNING: Your spline file is ImageD11 specific") print("... from a fabio converted to edf first") elif options.format == 'GE': extn = "" # KE: This seems to be a mistake and keeps PeakSearch from working in # some cases. Should be revisited if commenting it out causes problems. -# options.ndigits = 0 - + # options.ndigits = 0 + elif options.format == 'py': + import importlib + sys.path.append( '.' ) + scan = importlib.import_module( options.stem ) + first_image = scan.first_image + file_series_object = scan.file_series_object else: extn = options.format - import fabio - - if options.interlaced: - f0 = ["%s0_%04d%s"%(options.stem,i,options.format) for i in range( + if scan is None: + if options.interlaced: + f0 = ["%s0_%04d%s"%(options.stem,i,options.format) for i in range( options.first, options.last+1)] - f1 = ["%s1_%04d%s"%(options.stem,i,options.format) for i in range( + f1 = ["%s1_%04d%s"%(options.stem,i,options.format) for i in range( options.first, options.last+1)] - if options.iflip: - f1 = [a for a in f1[::-1]] - - def fso(f0,f1): - for a,b in zip(f0,f1): - try: - yield fabio.open(a) - yield fabio.open(b) - except: - print(a,b) - raise - file_series_object = fso(f0,f1) - first_image = openimage( f0[0] ) + if options.iflip: + f1 = [a for a in f1[::-1]] - else: - file_name_object = fabio.filename_object( - options.stem, - num = options.first, - extension = extn, - digits = options.ndigits) - - # Output files: - - first_image = openimage( file_name_object ) - import fabio.file_series - # Use traceback = True for debugging - file_series_object = fabio.file_series.new_file_series( - first_image, - nimages = options.last - options.first + 1, - traceback = True ) - + def fso(f0,f1): + for a,b in zip(f0,f1): + try: + yield fabio.open(a) + yield fabio.open(b) + except: + print(a,b) + raise + file_series_object = fso(f0,f1) + first_image = openimage( f0[0] ) + + else: + import fabio + if options.ndigits > 0: + file_name_object = fabio.filename_object( + options.stem, + num = options.first, + extension = extn, + digits = options.ndigits) + else: + file_name_object = options.stem + + + first_image = openimage( file_name_object ) + import fabio.file_series + # Use traceback = True for debugging + file_series_object = fabio.file_series.new_file_series( + first_image, + nimages = options.last - options.first + 1, + traceback = True ) + + # Output files: if options.outfile[-4:] != ".spt": options.outfile = options.outfile + ".spt" print("Your output file must end with .spt, changing to ",options.outfile) @@ -261,33 +254,29 @@ def fso(f0,f1): # global OMEGA, OMEGASTEP, OMEGAOVERRIDE OMEGA = options.OMEGA OMEGASTEP = options.OMEGASTEP - OMEGAOVERRIDE = options.OMEGAOVERRIDE + OMEGAOVERRIDE = options.OMEGAOVERRIDE # Make a blobimage the same size as the first image to process - # List comprehension - convert remaining args to floats - must be unique list - thresholds_list = [float(t) for t in options.thresholds] - try: - thresholds_list = list(set(thresholds_list)) - except: - import sets - thresholds_list = list(sets.Set(thresholds_list)) + # List comprehension - convert remaining args to floats + # must be unique list so go via a set + thresholds_list = list( set( [float(t) for t in options.thresholds] ) ) thresholds_list.sort() - + li_objs={} # label image objects, dict of - + s = first_image.data.shape # data array shape - + # Create label images for t in thresholds_list: # the last 4 chars are guaranteed to be .spt above mergefile="%s_t%d.flt"%(options.outfile[:-4], t) spotfile = "%s_t%d.spt"%(options.outfile[:-4], t) - li_objs[t]=labelimage(shape = s, - fileout = mergefile, + li_objs[t]=labelimage(shape = s, + fileout = mergefile, spatial = corrfunc, - sptfile=spotfile) + sptfile=spotfile) print("make labelimage",mergefile,spotfile) # Not sure why that was there (I think if glob was used) # files.sort() @@ -304,32 +293,32 @@ def fso(f0,f1): darkimage += options.darkoffset if options.flood is not None: floodimage=openimage(options.flood).data - cen0 = floodimage.shape[0]/6 - cen1 = floodimage.shape[0]/6 + cen0 = int(floodimage.shape[0]/6) + cen1 = int(floodimage.shape[0]/6) middle = floodimage[cen0:-cen0, cen1:-cen1] nmid = middle.shape[0]*middle.shape[1] floodavg = numpy.mean(middle) print("Using flood",options.flood,"average value",floodavg) if floodavg < 0.7 or floodavg > 1.3: print("Your flood image does not seem to be normalised!!!") - + else: floodimage=None start = time.time() print("File being treated in -> out, elapsed time") - # If we want to do read-ahead threading we fill up a Queue object with data + # If we want to do read-ahead threading we fill up a Queue object with data # objects # THERE MUST BE ONLY ONE peaksearching thread for 3D merging to work # there could be several read_and_correct threads, but they'll have to get the order right, # for now only one if options.oneThread: # Wrap in a function to allow profiling (perhaps? what about globals??) - def go_for_it(file_series_object, darkimage, floodimage, + def go_for_it(file_series_object, darkimage, floodimage, corrfunc , thresholds_list , li_objs, OMEGA, OMEGASTEP, OMEGAOVERRIDE ): for data_object in file_series_object: t = timer() - if not isinstance( data_object, fabio.fabioimage.fabioimage ): + if not hasattr( data_object, "data"): # Is usually an IOError if isinstance( data_object[1], IOError): sys.stdout.write(data_object[1].strerror + '\n') @@ -352,74 +341,46 @@ def go_for_it(file_series_object, darkimage, floodimage, monitorcol = options.monitorcol, ) t.tick(filein+" io/cor") - peaksearch( filein, data_object , corrfunc , + peaksearch( filein, data_object , corrfunc , thresholds_list , li_objs ) for t in thresholds_list: li_objs[t].finalise() - if options.profile_file is not None: - print("Profiling output") - try: - import cProfile as Prof - except ImportError: - try: - import profile as Prof - except: - print("Your package manager is having a laugh") - print("install python-profile please") - raise - Prof.runctx( "go_for_it(file_series_object, darkimage, floodimage, \ - corrfunc , thresholds_list, li_objs , \ - OMEGA, OMEGASTEP, OMEGAOVERRIDE )", - globals(), - locals(), - options.profile_file ) - import pstats - try: - p = pstats.Stats(options.profile_file, - stream = open(options.profile_file+".txt","w")) - except: - p = pstats.Stats(options.profile_file) - p.strip_dirs().sort_stats(-1).print_stats() + go_for_it(file_series_object, darkimage, floodimage, + corrfunc , thresholds_list, li_objs, + OMEGA, OMEGASTEP, OMEGAOVERRIDE ) - else: - go_for_it(file_series_object, darkimage, floodimage, - corrfunc , thresholds_list, li_objs, - OMEGA, OMEGASTEP, OMEGAOVERRIDE ) - else: print("Going to use threaded version!") try: # TODO move this to a module ? - + class read_only(ImageD11_thread.ImageD11_thread): def __init__(self, queue, file_series_obj , myname="read_only", OMEGA=0, OMEGAOVERRIDE = False, OMEGASTEP = 1): """ Reads files in file_series_obj, writes to queue """ - self.queue = queue + self.queue = queue self.file_series_obj = file_series_obj self.OMEGA = OMEGA self.OMEGAOVERRIDE = OMEGAOVERRIDE self.OMEGASTEP = OMEGASTEP - ImageD11_thread.ImageD11_thread.__init__(self , + ImageD11_thread.ImageD11_thread.__init__(self , myname=myname) print("Reading thread initialised", end=' ') - + def ImageD11_run(self): """ Read images and copy them to self.queue """ for data_object in self.file_series_obj: if self.ImageD11_stop_now(): print("Reader thread stopping") break - if not isinstance( data_object, fabio.fabioimage.fabioimage ): + if not hasattr( data_object, "data" ): + import pdb; pdb.set_trace() # Is usually an IOError if isinstance( data_object[1], IOError): -# print data_object -# print data_object[1] sys.stdout.write(str(data_object[1].strerror) + '\n') -# ': ' + data_object[1].filename + '\n') else: import traceback traceback.print_exception(data_object[0],data_object[1],data_object[2]) @@ -451,7 +412,6 @@ def ImageD11_run(self): if self.ImageD11_stop_now(): print("Reader thread stopping") break - # Flag the end of the series self.queue.put( (None, None) , block = True) @@ -464,16 +424,16 @@ def __init__(self, queue_read, queues_out, thresholds_list, corrects and copies images to output queues doing correction once """ self.queue_read = queue_read - self.queues_out = queues_out + self.queues_out = queues_out self.dark = dark self.flood = flood self.do_median = do_median self.monitorcol = monitorcol self.monitorval = monitorval self.thresholds_list = thresholds_list - ImageD11_thread.ImageD11_thread.__init__(self , + ImageD11_thread.ImageD11_thread.__init__(self , myname=myname) - + def ImageD11_run(self): while not self.ImageD11_stop_now(): ti = timer() @@ -483,7 +443,7 @@ def ImageD11_run(self): self.queues_out[t].put( (None, None) , block = True) # exit the while 1 - break + break data_object = correct(data_object, self.dark, self.flood, do_median = self.do_median, @@ -517,11 +477,11 @@ def __init__(self, q, corrfunc, threshold, li_obj, def run(self): while not self.ImageD11_stop_now(): filein, data_object = self.q.get(block = True) - if not isinstance( data_object, fabio.fabioimage.fabioimage ): + if not hasattr( data_object, "data" ): break - peaksearch( filein, data_object , self.corrfunc , - [self.threshold] , - { self.threshold : self.li_obj } ) + peaksearch( filein, data_object , self.corrfunc , + [self.threshold] , + { self.threshold : self.li_obj } ) self.li_obj.finalise() # 8 MB images - max 40 MB in this queue @@ -536,7 +496,7 @@ def run(self): for t in thresholds_list: print("make queue and peaksearch for threshold",t) queues[t] = queue.Queue(3) - searchers[t] = peaksearch_one(queues[t], corrfunc, + searchers[t] = peaksearch_one(queues[t], corrfunc, t, li_objs[t] ) corrector = correct_one_to_many( read_queue, queues, @@ -551,7 +511,6 @@ def run(self): for t in thresholds_list[::-1]: searchers[t].start() my_threads.append(searchers[t]) - looping = True nalive = len(my_threads) def empty_queue(q): while 1: @@ -564,7 +523,7 @@ def empty_queue(q): try: nalive = 0 for thr in my_threads: - if thr.isAlive(): + if thr.is_alive(): nalive += 1 if options.killfile is not None and \ os.path.exists(options.killfile): @@ -582,124 +541,127 @@ def empty_queue(q): q = queues[t] empty_queue(q) for thr in my_threads: - if thr.isAlive(): - thr.join(timeout=1) + if thr.is_alive(): + thr.join(timeout=1) print("finishing from waiting loop") except: print("Caught exception in waiting loop") - ImageD11.thread_stop_now = True + ImageD11_thread.stop_now = True time.sleep(1) empty_queue(read_queue) for t in thresholds_list: q = queues[t] empty_queue(q) for thr in my_threads: - if thr.isAlive(): + if thr.is_alive(): thr.join(timeout=1) raise - + except ImportError: print("Probably no threading module present") raise - + def get_options(parser): """ Add our options to a parser object """ - parser.add_option("-n", "--namestem", action="store", - dest="stem", type="string", default="data", + parser.add_argument("-n", "--namestem", action="store", + dest="stem", type=str, default="data", help="Name of the files up the digits part "+\ "eg mydata in mydata0000.edf" ) - parser.add_option("-F", "--format", action="store", - dest="format",default=".edf", type="string", - help="Image File format, eg edf or bruker" ) - parser.add_option("-f", "--first", action="store", - dest="first", default=0, type="int", + parser.add_argument("-F", "--format", action="store", + dest="format",default=".edf", type=str, + help="Image File format, eg edf or bruker or GE or py" ) + parser.add_argument("-f", "--first", action="store", + dest="first", default=0, type=int, help="Number of first file to process, default=0") - parser.add_option("-l", "--last", action="store", - dest="last", type="int",default =0, + parser.add_argument("-l", "--last", action="store", + dest="last", type=int,default =0, help="Number of last file to process") - parser.add_option("-o", "--outfile", action="store", - dest="outfile",default="peaks.spt", type="string", + parser.add_argument("-o", "--outfile", action="store", + dest="outfile",default="peaks.spt", type=str, help="Output filename, default=peaks.spt") - parser.add_option("-d", "--darkfile", action="store", - dest="dark", default=None, type="string", + parser.add_argument("-d", "--darkfile", action="store", + dest="dark", default=None, type=ImageD11options.ImageFileType(mode='r'), help="Dark current filename, to be subtracted, default=None") dod = 0 - parser.add_option("-D", "--darkfileoffset", action="store", - dest="darkoffset", default=dod, type="float", + parser.add_argument("-D", "--darkfileoffset", action="store", + dest="darkoffset", default=dod, type=float, help= "Constant to subtract from dark to avoid overflows, default=%d"%(dod)) - s="/data/opid11/inhouse/Frelon2K/spatial2k.spline" - parser.add_option("-s", "--splinefile", action="store", - dest="spline", default=s, type="string", - help="Spline file for spatial distortion, default=%s" % (s)) - parser.add_option("-p", "--perfect_images", action="store", - type="choice", choices=["Y","N"], default="N", dest="perfect", + # s="/data/opid11/inhouse/Frelon2K/spatial2k.spline" + parser.add_argument("-s", "--splinefile", action="store", + dest="spline", default=None, type=ImageD11options.SplineFileType(mode='r'), + help="Spline file for spatial distortion, default=None" ) + parser.add_argument("-p", "--perfect_images", action="store", + choices=["Y","N"], default="N", dest="perfect", help="Ignore spline Y|N, default=N") - parser.add_option("-O", "--flood", action="store", type="string", + parser.add_argument("-O", "--flood", action="store", + type=ImageD11options.ImageFileType(mode='r'), default=None, dest="flood", help="Flood file, default=None") - parser.add_option("-t", "--threshold", action="append", type="float", + parser.add_argument("-t", "--threshold", action="append", type=float, dest="thresholds", default=None, help="Threshold level, you can have several") - parser.add_option("--OmegaFromHeader", action="store_false", - dest="OMEGAOVERRIDE", default=False, + parser.add_argument("--OmegaFromHeader", action="store_false", + dest="OMEGAOVERRIDE", default=False, help="Read Omega values from headers [default]") - parser.add_option("--OmegaOverRide", action="store_true", - dest="OMEGAOVERRIDE", default=False, + parser.add_argument("--OmegaOverRide", action="store_true", + dest="OMEGAOVERRIDE", default=False, help="Override Omega values from headers") - parser.add_option("--singleThread", action="store_true", - dest="oneThread", default=False, + parser.add_argument("--singleThread", action="store_true", + dest="oneThread", default=False, help="Do single threaded processing") - parser.add_option("--profile", action="store", type="string", - dest="profile_file", default=None, - help="Write profiling information (you will want singleThread too)") - parser.add_option("-S","--step", action="store", - dest="OMEGASTEP", default=1.0, type="float", + # if you want to do this then instead I think you want + # python -m cProfile -o xx.prof peaksearch.py ... + # python -m pstats xx.prof + # ... sort + # ... stats +# parser.add_argument("--profile", action="store", +# type=ImageD11options.ProfilingFileType, +# dest="profile_file", default=None, +# help="Write profiling information (you will want singleThread too)") + parser.add_argument("-S","--step", action="store", + dest="OMEGASTEP", default=1.0, type=float, help="Step size in Omega when you have no header info") - parser.add_option("-T","--start", action="store", - dest="OMEGA", default=0.0, type="float", + parser.add_argument("-T","--start", action="store", + dest="OMEGA", default=0.0, type=float, help="Start position in Omega when you have no header info") - parser.add_option("-k","--killfile", action="store", - dest="killfile", default=None, type="string", + parser.add_argument("-k","--killfile", action="store", + dest="killfile", default=None, + type=ImageD11options.FileType(), help="Name of file to create stop the peaksearcher running") - parser.add_option("--ndigits", action="store", type="int", + parser.add_argument("--ndigits", action="store", type=int, dest = "ndigits", default = 4, help = "Number of digits in file numbering [4]") - parser.add_option("-P", "--padding", action="store", - type="choice", choices=["Y","N"], default="Y", dest="padding", + parser.add_argument("-P", "--padding", action="store", + choices=["Y","N"], default="Y", dest="padding", help="Is the image number to padded Y|N, e.g. "\ "should 1 be 0001 or just 1 in image name, default=Y") - - parser.add_option("-m", "--median1D", action="store_true", + parser.add_argument("-m", "--median1D", action="store_true", default=False, dest="median", help="Computes the 1D median, writes it to file .bkm and" \ +" subtracts it from image. For liquid background"\ +" on radially transformed images") - - parser.add_option("--monitorcol", action="store", type="string", + parser.add_argument("--monitorcol", action="store", type=str, dest="monitorcol", default = None, help="Header value for incident beam intensity") - - - parser.add_option("--monitorval", action="store", type="float", + parser.add_argument("--monitorval", action="store", type=float, dest="monitorval", default = None, help="Incident beam intensity value to normalise to") - - parser.add_option("--omega_motor", action="store", type="string", + parser.add_argument("--omega_motor", action="store", type=str, dest = "omegamotor", default = "Omega", help = "Header value to use for rotation motor position [Omega]") - parser.add_option("--omega_motor_step", action="store", type="string", + parser.add_argument("--omega_motor_step", action="store", type=str, dest = "omegamotorstep", default = "OmegaStep", help = "Header value to use for rotation width [OmegaStep]") - parser.add_option("--interlaced", action="store_true", + parser.add_argument("--interlaced", action="store_true", dest = "interlaced", default = False, help = "Interlaced DCT scan") - parser.add_option("--iflip", action="store_true", + parser.add_argument("--iflip", action="store_true", dest="iflip", default=False, help = "Reverse second half of interlaced scan") return parser @@ -715,11 +677,11 @@ def get_help(usage = True): except: # python3 import io - import optparse + import argparse if usage: - o = get_options(optparse.OptionParser()) + o = get_options(argparse.ArgumentParser()) else: - o = get_options(optparse.OptionParser(optparse.SUPPRESS_USAGE)) + o = get_options(argparse.ArgumentParser(usage=argparse.SUPPRESS)) f = io.StringIO() o.print_help(f) return f.getvalue() diff --git a/ImageD11/project/h5demo1.py b/ImageD11/project/h5demo1.py new file mode 100644 index 00000000..1cc7d7c1 --- /dev/null +++ b/ImageD11/project/h5demo1.py @@ -0,0 +1,88 @@ + +from __future__ import print_function + +# Make a project file in a hdf. + +# example : +# +from __future__ import division +import os +import numpy as np +import h5py +import fabio + +# how the scan was done +fmt = "/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y%03d_/interlaced_1_%d/Frelon/interlaced_1_%d_Frelon%04d.edf" +ysteps = np.arange( -60, 60.1, 1) +npts = 360 * 2 +ostep = 0.5 +ostart = 0.25 +# We recorded a series of scans +omegas = [ np.arange( npts, dtype = np.float32 ) * ostep + ostart, ] * len( ysteps ) +dtys = [ np.full( npts, ystep, dtype = np.float32 ) for ystep in ysteps ] + +def interlacedscan( ystep, fmt, npts ): + scan = [] + for i in range( npts//2 ): + scan.append( fmt % ( ystep, 1, 1, i ) ) # forward interlaced + scan.append( fmt % ( ystep, 2, 2, npts//2 - 1 - i) ) + return scan + +filenames = [ interlacedscan( ystep, fmt, npts ) for ystep in ysteps ] + +# Information about the edf files +def binary_info( filename ): + """ info to read the edf files - assume flat regular files """ + im = fabio.open( filename ) + return im._frames[0].start, im._frames[0].size, im.data.shape, im.data.dtype + +def writesino(h5name, omegas, dtys, filenames): + offset, size, shape, dtype = binary_info( filenames[0][0] ) + print(offset,size,shape,dtype) + nframes = len( omegas[0] ) * len( omegas ) + print(nframes, len(omegas), sum(len(o) for o in omegas)) + # Now create a hdf5 file: + with h5py.File(h5name, "w", libver='latest' ) as h: + # now create a VDS linking within the same file + layout = h5py.VirtualLayout( shape = (nframes, shape[0], shape[1] ), + dtype = dtype ) + j = 0 + graw = h.require_group('scans') + for i, scan in enumerate(filenames): + g = graw.require_group('scan%04d'%(i)) + g.create_dataset( "data", + shape = (len(scan), shape[0], shape[1]), + dtype = dtype, + external = [(fname, offset, size) for fname in scan] ) + g.create_dataset( "omega" , data = omegas[i] ) + g.create_dataset( "dty" , data = dtys[i] ) + vsource = h5py.VirtualSource( h.filename, # ok - circular? + 'scans/scan%04d/data'%(i), + shape = (len(scan), shape[0], shape[1]) ) + layout[ j:j+len(scan), :, :] = vsource + j += len(scan) + g = h.require_group('sinogram') + g.create_dataset('omega', data = np.concatenate(omegas) ) + g.create_dataset('dty', data = np.concatenate(dtys) ) + g.create_virtual_dataset( 'data', layout ) + +h5name = 'demo1.h5' +writesino( h5name, omegas, dtys, filenames ) + +# Now some tests ...: + +for iy in range(0,len(dtys), 31): + print('iy',iy) + for jo in (0,1,123): + fname = filenames[iy][jo] + print(' ', fname, iy*npts + jo, end= ' ' ) + fab = fabio.open( fname ).data + h5v = h5py.File( h5name, 'r' )[ 'sinogram/data' ][ iy * npts + jo ] + # external data + # h5e = h5py.File( h5name, 'r' )[ 'scans/scan%04d/data'%( iy ) ] [jo] + if (h5v == fab).all(): + print('ok') + else: + print('debug please') + print(fab.shape,fab) + print(h5v.shape,h5v) diff --git a/ImageD11/project/make_h5_project_fails_no_external.py b/ImageD11/project/make_h5_project_fails_no_external.py new file mode 100644 index 00000000..c8bc9efa --- /dev/null +++ b/ImageD11/project/make_h5_project_fails_no_external.py @@ -0,0 +1,75 @@ + + +# Make a project file in a hdf. + +# example : +# +import os +import h5py, fabio +fmt = "/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y%03d_/interlaced_1_%d/Frelon/interlaced_1_%d_Frelon%04d.edf" +args = "ystep", "ipass", "ipass", "fnum" +ysteps = range(-60,61) +# interlaced +frames = [] +for ystep in ysteps: + for fnum in range(360): + # forward + frames.append( fmt % ( ystep, 1, 1, fnum ) ) + # and back + frames.append( fmt % ( ystep, 2, 2, 359-fnum ) ) + +print("total frames:",len(frames)) +for f in frames[:10]: + print(f) +for f in frames[-10:]: + print(f) + +im = fabio.open( frames[0] ) +# hacky +bsize = im._frames[0].size +boffset = im._frames[0].start +bshape = im.data.shape + +with h5py.File("/tmp/demo.hdf" , "w" ) as h: + g = h.require_group('sinogram') + g.create_dataset( "data", + shape = (len(frames), bshape[0], bshape[1]), + dtype = im.data.dtype, + external = [(fname, boffset, bsize) for fname in frames] ) + +# outputs: +""" +total frames: 87120 +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0000.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0359.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0001.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0358.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0002.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0357.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0003.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0356.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0004.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y-60_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0355.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0355.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0004.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0356.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0003.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0357.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0002.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0358.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0001.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_1/Frelon/interlaced_1_1_Frelon0359.edf +/data/id11/nanoscope/Commissioning/sonja_fib_Al_z500__nobackup/difftomo_Al_y060_/interlaced_1_2/Frelon/interlaced_1_2_Frelon0000.edf + +Traceback (most recent call last): + File "make_h5_project_fails_no_external.py", line 40, in + g.create_dataset( "data", + File "/usr/lib/python3/dist-packages/h5py/_hl/group.py", line 136, in create_dataset + dsid = dataset.make_new_dset(self, shape, dtype, data, **kwds) + File "/usr/lib/python3/dist-packages/h5py/_hl/dataset.py", line 167, in make_new_dset + dset_id = h5d.create(parent.id, None, tid, sid, dcpl=dcpl) + File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper + File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper + File "h5py/h5d.pyx", line 80, in h5py.h5d.create +ValueError: Unable to create dataset (object header message is too large) +""" diff --git a/ImageD11/project/plan_new_peaksearch.md b/ImageD11/project/plan_new_peaksearch.md new file mode 100644 index 00000000..8eb47350 --- /dev/null +++ b/ImageD11/project/plan_new_peaksearch.md @@ -0,0 +1,118 @@ + + +Making a new peaksearch. + +Input: + Scan information: + # Counters (including monitor, etc) + Detector_1: + List of images (URL's or counts) + Detector_2: + List of images (URL's) + ... + # Motors (including Epoch, etc) + Motor_1: + List of positions (values or URL's) + Motor_2: + List of positions (values or URL's) + ... + Trajectories: + Lists of connections between scan points + Might be actual or virtual (e.g. what is the inner loop) + + +One big table of _all_ of the images/counters/positions recorded. Assuming +single points in each position. + +Links between these would give subsets of points. e.g. + 1D scan on omega + Move dty + 1D scan on omega + Move dty + ... == 2D scan on omega/dty + +We can want to see a rocking curve in any scan direction (dty or omega). + +Realistic case: + Continuous rotation on omega + Sinusoidal movement on dty (prime factor on rotation speed) + Covers sinogram in diagonal directions + +"Experiment" is a database of "scan_points". + Point_id, results (images, motor pos, monitor, time, etc) + +Trajectories: + Lists of links between the scan points (directed for now) + Link_id(k) : Point_id(i) -> Point_id(j) + +Examples: + + sweep_musst type scan: + images = ["data%04d.edf"%(i) for i in range(npts)] + omega = [ start + step*i for i in range(npts)] + ... deal with missing images on the file system + interlaced scans: + ... iflip or not + 2D scans y/omega: + ... sweep_musst style or interlaced + + +Processing +========== + +"Store": an intermediate result that the user should be able +to inspect. It might be written to disk, or the recipe to +make it again on the fly is stored (or linked). + + +Point by point : + Corrections (dark, flat, ...) + Store: + Point_id/detector_id/corrected_image + Normalisation (monitor or srcur, ...) + Store: + Point_id/detector_id/normalised_image + Radial integration + Store: + Point_id/detector_id/1D_integrations + Background estimation / removal (can depend on neighbor images or 1D) + Store: + Point_id/detector_id/background + Masking (diamond peaks, ice rings, detector gaps) + Thresholding (peak versus smooth background) + Store: + Point_id/detector_id/mask [bg/peak/static_mask/dynamic_mask] + Object labelling (group pixels into lists of spots) + Store: + Point_id/detector_id/peak_id/lists_of_[pixels/values/weights] + Object properties (intensity, center of mass, etc) + Store: + Point_id/detector_id/peak_id/[list_of_properties] + + +Trajectories : + 1D case was scan on omega + Check overlap of peaks objects from one frame to next + Score for whether peaks are overlapped could be fraction of pixels + overlapped or mismatch in position, etc + image_id(i) -> image_id(j): + list of peak_id(i) -> peak_id(j) : overlap_score(s) + Collect "unique" peaks (cliques) or clusters + Any clique has 1 or more members: + list of (2X point_id/image_id/peak_id) + Store: + + + + + + + + + + + + + + + diff --git a/ImageD11/project/project.py b/ImageD11/project/project.py new file mode 100644 index 00000000..fa304748 --- /dev/null +++ b/ImageD11/project/project.py @@ -0,0 +1,236 @@ + +from __future__ import print_function, division +import collections, json + +""" +Load and save a data analysis project + + Images part - all the frames in one long table + - which frames are adjacent + We use a namedtuple for each scan point and record all the things + that we care about as a tuple: + (filename, Omega, dty, z, Load, etc) <- can be hashed + The edges tell us which frames want/need merging. + + Processing parts: + foreach node: + Corrected images (dark, flat, ... ) + Background image(s) + Threshold estimation + Label images + List of peaks + +""" + + +RotationNode = collections.namedtuple( "RotationNode", + "filename Omega" ) + +DiffTomoNode = collections.namedtuple( "DiffTomoNode", + "filename Omega dty" ) + +class Scan( object ): + """ Scan data as a graph + nodes = list of datapoints + edges = which nodes are adjacent to this node + """ + def __init__(self, + nodes = None, # a List[] + edges = None, # a List[[int,]] + ): + """ + nodes = measurement points in the scan + edges = edges[i] = connections to point i + hidden self.node_index that gives you position in scan + """ + if nodes is None: + self.nodes = [] + else: + self.nodes = nodes + if edges is None: + self.edges = [] + else: + self.edges = edges + # Given a node, node_index finds it in the list + self.node_index = {} + for i, node in enumerate(self.nodes): + self.node_index[node] = i + if __debug__: + for i, e in enumerate(self.edges): # i is implicit + for j in e: + assert j>=0 and i 0 and inode < adr: + neighbor = inode + elif inode < 0 and inode >= -adr: + neighbor = inode + adr + else: + err +=1 # Error + continue + self.edges[ neighbor ].append( adr ) + nbs.append( neighbor ) + self.edges.append( nbs ) + assert len(self.edges) == len(self.nodes) + return err + + def neighbors( self, node ): + """ + node = scan point (Image, Omega, etc) + node = integer + """ + if isinstance( node, int ): + return self.edges[node] + if isinstance( node, tuple): + i = self.node_index[ node ] + return self.edges[i] + raise TypeError("node not understood, want tuple or int row index") + + def __len__(self): + return len(self.nodes) + + def __getitem__(self, i): + """ We index to the node, not the edges ? """ + return self.nodes[i] + + def todict( self ): + """ + Convert to a dictionary representation for serialisation + """ + data = { "titles": self.nodes[0]._fields, + "rows" : [[field for field in node ] + for node in self.nodes], + "edges" : self.edges } + return data + + def fromdict( self, dct ): + """ + Convert from a dictionary representation for serialisation + """ + self.edges = dct['edges'] + # named tuple + tup = collections.namedtuple( "Node", dct['titles'] ) + for i, item in enumerate( dct['rows'] ): + t = tup( *item ) + self.nodes.append( t ) + self.node_index[t] = i + + +def add_motor( inputscan, motorname, motorpos ): + """ + Adds another motor to a scan + e.g. dty position when not in header + """ + fields = inputscan[0]._fields + (motorname,) + tup = collections.namedtuple( "Node", fields ) + try: + if len( inputscan ) == len( motorpos ): + nodes = [ tup(*(vals + (mot,))) for vals, mot in + zip(inputscan, motorpos)] + else: + raise Exception("Motorpos has wrong length") + except TypeError: # no len + mot = float( motorpos ) + nodes = [ tup( *(vals + (mot,))) for vals in inputscan ] + edges = [e for e in inputscan.edges] + return Scan( nodes, edges ) + + +def fablescandata_from_spt( spt, headeritems=["Omega",], + nodeclass = RotationNode ): + """ + Read an ImageD11 peaksearch spt file to return a Scan object + """ + myscan = Scan() + # Unpack the [motor|counter]_mne/_pos pairs + motor_mne = counter_mne = [] + with open(spt, "r") as sptfile: + for line in sptfile.readlines(): + if line[0] != "#": # skip blank and peaks + continue + # 0123456 + if line.find("# File ")==0: + filename = line[7:-1] # trim newline + header = {} + continue + if line.find("# motor_mne") == 0: + motor_mne = line.split("=")[1].split() + continue + if line.find("# motor_pos") == 0: + motor_pos = [float(v) for v in line.split("=")[1].split()] + for k,v in zip( motor_mne, motor_pos ): + header[ k ] = v + continue + if line.find("# counter_mne") == 0: + counter_mne = line.split("=")[1].split() + continue + if line.find("# counter_pos") == 0: + counter_pos = [float(v) for v in line.split("=")[1].split()] + for k,v in zip( counter_mne, counter_pos ): + header[ k ] = v + continue + if line.find("# npks") == 0: + args = { key:float(header[key]) for key in headeritems } + mynode = nodeclass( filename=filename, **args ) + myscan.addNode( mynode, (-1,) ) + continue + for hitem in headeritems: + if line.find(hitem) > 0: + val = line[1:].split( "=" )[-1].strip() + header[ hitem ] = val + # end of frame marker + return myscan + + +def sinogram_from_spt_list( sptlist ): + scans = [fablescandata_from_spt( sptfile ) for sptfile in sptlist ] + + +def mergeScans( scan1, scan2 ): + """ + Merge two scans together + """ + n1 = len(scan1) + n2 = len(scan2) + # "+" joins lists: + nodes = scan1.nodes + scan2.nodes + edges = scan1.edges + [ (i + n1, j + n1) for i,j in nodes ] + return Scan( nodes, edges ) + + +class Project( object ): + """ + Holds all the information about the project + + scans / experiment data + processing steps and results + + Mostly links to external files ? + """ + def __init__(self): + self.Scans = [] + self.processing = [] + + def load(self): + pass + + def save(self): + pass + diff --git a/ImageD11/project/project_yaml.py b/ImageD11/project/project_yaml.py new file mode 100644 index 00000000..3f77e044 --- /dev/null +++ b/ImageD11/project/project_yaml.py @@ -0,0 +1,202 @@ + +from __future__ import print_function +import yaml, pprint + + +my_yml = """ + +Detectors: + Frelon21 : + name : "Frelon21" + type : "2Ddetector" + dark : "/data/id11/.../dark1s.edf" + flat : "/data/id11/.../flat.edf" + spline : "/data/id11/.../frelon21_mar16.spline" + Frelon4M : + name : "Frelon4M" + type : "2Ddetector" + dark : "/data/id11/.../dark1s.edf" + flat : "/data/id11/.../flat.edf" + spline : "/data/id11/.../Frelon4M.spline" + pico0 : + name : "pico0" + type : "0Ddetector" + # e.g. photons to pA current converter + gain : 1.e7 + mca : + name : "mca" + type : "1Ddetector" + nchannels : 4096 + inifile : SDD_53keV.ini + gain : # 4096/53.0 + +Positioners: + FF_Detector_Mount : + - {type : translation, name : ffdtx1, axis : [1.0, 0.0, 0.0], pos : 20.0} + - {name : ffdtz1, type: translation, axis : [0.0, 0.0, 1.0]} + - {name : ffdtilt, type: rotation, axis : [0.0, 1.0, 0.0]} + 3DXRD_Huber_Tower : + - {name : diffty, type: translation, axis : [0.0, 1.0, 0.0] } + - {name : difftz, type: translation, axis : [0.0, 0.0, 1.0] } + - {name : diffry, type: rotation, axis : [0.0, 1.0, 0.0] } + - {name : diffrz, type: rotation, axis : [0.0, 0.0, 1.0] } + - {name : samry, type: rotation, axis : [0.0, 1.0, 0.0] } + - {name : samrx, type: rotation, axis : [1.0, 0.0, 0.0] } + - {name : samtx, type: translation, axis : [1.0, 0.0, 0.0] } + - {name : samty, type: translation, axis : [0.0, 1.0, 0.0] } + - {name : samtz, type: translation, axis : [0.0, 0.0, 1.0] } + Fable_diffractometer : + - { name : wedge, type : rotation, axis : [0.0, 1.0, 0.0] } + - { name : omega, type : rotation, axis : [0.0, 0.0, 1.0] } + - { name : t_x, type : translation, axis : [1.0, 0.0, 0.0] } + - { name : t_y, type : translation, axis : [0.0, 1.0, 0.0] } + - { name : t_z, type : translation, axis : [0.0, 0.0, 1.0] } + Fable_detector : + - { name : distance, type : translation, axis : [1.0, 0.0, 0.0] } + - { name : tilt_x, type : rotation, axis : [1.0, 0.0, 0.0] } + - { name : tilt_y, type : rotation, axis : [0.0, 1.0, 0.0] } + - { name : tilt_z, type : rotation, axis : [0.0, 0.0, 1.0] } + - { name : Oij, type : positioner, + mat4 : [[1, 0, 0, 0], + [0, o22, o21, 0], + [0, o12, o11, 0], + [0, 0, 0, 1]] } + - { name : z_size, type : scale, axis : [0, 0, 1] } + - { name : y_size, type : scale, axis : [0, 1, 0] } + - { name : z_center, type : translation, axis : [0.0, 0.0,-1.0] } + - { name : y_center, type : translation, axis : [0.0,-1.0, 0.0] } + +Fe3O4: + - name : magnetite + - space_group : Fd-3m + - composition : { 'Fe':3, 'O':4 } + - unit_cell : [ 8.39, 8.39, 8.39, 90., 90., 90. ] + +Silicon: + - name : silicon + - space_group : Fd-3m + - composition : { 'Si':1 } + - unit_cell : [ 5.43094,5.43094, 5.43094,90,90,90 ] + +Experiment: + Radiation : + # energy : 0.124 # Units match unit cell + wavelength : 0.124 + direction : [ 1.0, 0.0, 0.0 ] + bandpass : 2.0e-3 + divergence : 1.0e-6 # Units? + SampleMount : # from 0,0,0 to sample position + 3DXRD_Huber_Tower + Detectors : # from 0,0,0 to detector position + - { positioner : FF_Detector_Mount, detector : Frelon21 } + Scans : + scan1 : + - Motor : diffrz + Step : 0.25 + Start : 0.0 + - measurement : + - monitor : + name : pico6 + normvalue : 1e7 # to normalise to + dataURL : "place:///to_find_the_data.dat" + - Frelon21 : + dark : dark1s.edf + darkoffset : 12 + binning : [1, 1] + flips : [No, No] + dataURL : + namefmt : "{stem:s}{pass:d}_{number:04d}.edf.gz" + folder : /data/id11/inhouse3/blah/toto + stem : toto17_ + first : 0 + last : 899 + interlaced : Yes + iflip : No + + scan2 : + - Motor : diffry + Step : 0.05 + Start : 0.0 + - measurement : + - monitor : + name : pico6 + normvalue : 1e7 # to normalise to + dataURL : "place:///to_find_the_data.dat" + - Frelon21 : + dark : dark1s.edf + darkoffset : 12 + binning : [1, 1] + flips : [No, No] + dataURL : + namefmt : "{stem:s}{number:04d}.edf" + folder : /data/id11/inhouse3/blah/toto2 + stem : toto17_ + first : 0 + last : 20 + + + scan3 : + - Motorpos : { "diffty": 0.1 } + - Motor : diffrz + Step : 0.05 + Start : 0.0 + - measurement : + - monitor : + name : pico6 + normvalue : 1e7 # to normalise to + dataURL : "place:///to_find_the_data.dat" + - Frelon21 : + dark : dark1s.edf + darkoffset : 12 + binning : [1, 1] + flips : [No, No] + dataURL : + namefmt : "{stem:s}{number:04d}.edf" + folder : /data/id11/inhouse3/blah/toto3 + stem : toto17_ + first : 0 + last : 20 + +""" + +ImageD11_project_schema = """ + TODO : figure out how to format and validate this stuff properly +""" + + + +y = yaml.load(my_yml) +pprint.pprint(y) + + + + +class ImageD11Project( object ): + + def __init__(self, project_dict = None, filename=None ): + """ Takes filename or existing project dictionary """ + self.project_dict = { } + if filename is not None: + self.load( filename ) + if project_dict is not None: + self.project_dict.update( project_dict ) + + def load(self, filename): + """ Updates the existing project from the file (e.g. adds and overwrites) """ + with open(filename, "r") as stream: + pd = yaml.load( stream ) + self.project_dict.update( pd ) + + def save(self, filename): + """ Writes the current project to a file """ + with open(filename, "w") as stream: + stream.write( yaml.dump( self.project_dict, default_flow_style=False, encoding='utf-8' ) ) + + def validate(self): + """ Check that we have what we need in the dictionary + TODO - try to use schema from nexus or cif or something ? """ + pass + + + + diff --git a/ImageD11/project/projects.py b/ImageD11/project/projects.py new file mode 100644 index 00000000..76474540 --- /dev/null +++ b/ImageD11/project/projects.py @@ -0,0 +1,60 @@ + +"""ImageD11 project files + +We want to be able to: + - Load / save your work + - Log what has and has not been done + +Use cases: + + - Single 3DXRD scan, far field detector for strain / center of mass + - List of source images, omega angles + - Files / information to get corrected data (dark, flat, spline, monitor) + - Background estimation + - Thresholding + - Peak locations (per image) + - Merging peaks in 3D across omega steps + - Conversion to scattering vectors, geometry calibration + - Assignment of peaks to grains + - Refinement of grain sizes, positions, unit cells, intensities per (h,k,l) + + - Near field scan + - Interface to DCT code ! + - List of source images, omega angles + - Files / information to get corrected data (dark, flat, spline, monitor) + - Background estimation + - Thresholding + - Peak locations (per image) + - Merging peaks in 3D across omega steps + + - Scanning 3DXRD scans (difftomo) + - Same as single 3DXRD but also with a y-step, so: + - List of source images, omega angles, y-steps + + - 3DXRD scans (difftomo) as a function of temperature, time, load, etc + - As above but with extra "load" column + +We need to save data with "parent"-"child" relationships so we can see what +items "depend-on" other items. This should map onto typical file formats +and work across programming languages. We should try to take inspiration from +NeXus and ImageCIF (etc) so far as resources allow. Also work with existing +experimental data. Also get something working in the short term (e.g. for the +next 5 years). + +For now we will use a nested python dictionary to hold the project. This +will be loaded and saved to a yaml (or json, pickle, etc) file. + +Initial jobs from this experiment description file: + - Locate the interesting things in it + - Validate whether the dataURLs exist (md5 matches if recorded, etc) + - Assert that all of the dataURLs are unique (no repeats) + - Load / save a description file + + +Processing Description: + - Reference to the raw data description + - Get corrected images (monitor, dark, flat, mask) + - Concept of "previous" and "next" images in scan + - Estimate background (single image method, file series method) + +""" \ No newline at end of file diff --git a/ImageD11/project/test_json.py b/ImageD11/project/test_json.py new file mode 100644 index 00000000..353479eb --- /dev/null +++ b/ImageD11/project/test_json.py @@ -0,0 +1,77 @@ + +from __future__ import print_function +import yaml, pprint + + +my_yml = """ + +# Detectors +Frelon21 : + name : "Frelon21" + type : "2Ddetector" + dark : "/data/id11/.../dark1s.edf" + flat : "/data/id11/.../flat.edf" + spline : "/data/id11/.../frelon21_mar16.spline" + +Frelon4M : + name : "Frelon4M" + type : "2Ddetector" + dark : "/data/id11/.../dark1s.edf" + flat : "/data/id11/.../flat.edf" + spline : "/data/id11/.../Frelon4M.spline" + +pico0 : + name : "pico0" + type : "0Ddetector" + # e.g. photons to pA current converter + gain : 1.e7 + +mca : + name : "mca" + type : "1Ddetector" + nchannels : 4096 + inifile : SDD_53keV.ini + gain : # 4096/53.0 + +# Experiment Stations +3dxrd: + - name : 3dxrd + - wavelength : 0.1234 + - Positioners: + - omega : 0.0 + - wedge : 0.0 + - samtz : 0.0 + - Detectors: + - name : Frelon21 + detx : 120.0 + - name : Frelon4M + ffdtz1 : 220.0 + +Fe3O4: + - name : magnetite + - space_group : Fd-3m + - composition : { 'Fe':3, 'O':4 } + - unit_cell : [ 8.39, 8.39, 8.39, 90., 90., 90. ] + +Silicon: + - name : silicon + - space_group : Fd-3m + - composition : { 'Si':1 } + - unit_cell : [ 5.43094,5.430945.43094,90,90,90 ] + +scan1: + - instrument: + - 3dxrd + - sample: + - Silicon + - Fe3O4 + - measurement: + - Frelon21: ["test0001.edf", "test0002.edf", "test0003.edf" ] + - omega: [ 0.0 , 1.0, 2.0] + - epoch: [ 0.0 , 5.0, 10.0] + - srcur: [ 190.0 , 189.9, 189.8] + +""" + +y = yaml.safe_load(my_yml) +pprint.pprint(y) diff --git a/ImageD11/rc_array.py b/ImageD11/rc_array.py index e621fa0f..c279079b 100644 --- a/ImageD11/rc_array.py +++ b/ImageD11/rc_array.py @@ -10,7 +10,7 @@ peaks equally well and in a coherent way. """ -from numpy import dot, round_, array, float, allclose, asarray, fabs,\ +from numpy import dot, round_, array, allclose, asarray, fabs,\ argmin, argmax, sqrt, argsort, take, sum, where, ndarray, eye,\ zeros, cross from numpy.linalg import inv, LinAlgError @@ -138,7 +138,7 @@ def inv(self): """ assert self.shape == (3,3) ret = inv(self) - return rr_array(ret, self.other_direction()) + return rc_array(ret, self.other_direction()) if __name__=="__main__": diff --git a/ImageD11/refinegrains.py b/ImageD11/refinegrains.py index 46ff8461..87080748 100644 --- a/ImageD11/refinegrains.py +++ b/ImageD11/refinegrains.py @@ -24,12 +24,14 @@ import numpy -from ImageD11 import transform, indexing, parameters +from ImageD11 import transform, indexing, parameters, ImageD11options from ImageD11 import grain, columnfile, cImageD11, simplex import xfab.tools -print(__file__) +# print(__file__) + + def triclinic( cp ): @@ -37,13 +39,15 @@ def triclinic( cp ): def monoclinic_a( cp ): a,b,c,al,be,ga = cp - return [a,b,c,90.,be,90.] + return [a,b,c,al,90.,90.] + def monoclinic_b( cp ): a,b,c,al,be,ga = cp - return [a,b,c,90.,90.,ga] + return [a,b,c,90.,be,90.] + def monoclinic_c( cp ): a,b,c,al,be,ga = cp - return [a,b,c,90.,be,90.] + return [a,b,c,90.,90.,ga] def orthorhombic( cp ): """ a=b, c, 90,90,90 """ @@ -58,25 +62,74 @@ def tetragonal( cp ): def trigonalP( cp ): """ a=b=c, alpha=beta=gamma """ a,b,c,al,be,ga = cp - a = (a+b+c)/3. - al = (al+be+ga)/3. - return [a,a,a,al,al,al] + anew = (a+b+c)/3. + alnew = (al+be+ga)/3. + return [anew,anew,anew,alnew,alnew,alnew] def trigonalH( cp ): """ a=b,c, alpha=beta=90,gamma=120 """ a,b,c,al,be,ga = cp - a = (a+b)/2. - return [ a, a, c, 90., 90., 120.] + anew = (a+b)/2. + return [ anew, anew, c, 90., 90., 120.] hexagonal = trigonalH def cubic( cp ): """ a=b=c, alpha=beta=gamma=90 """ - a = (cp[0]+cp[1]+cp[2]) - return [ a, a, a, 90., 90., 90.] + anew = (cp[0]+cp[1]+cp[2])/3. + return [ anew, anew, anew, 90., 90., 90.] - +def get_options(parser): + parser.add_argument("-p", "--parfile", action="store", + dest="parfile", + type=ImageD11options.ParameterFileType(mode='r'), + help="Name of input parameter file") + parser.add_argument("-u", "--ubifile", action="store", + dest="ubifile", + type=ImageD11options.UbiFileType(mode='r'), + help="Name of ubi file") + parser.add_argument("-U", "--newubifile", action="store", + dest="newubifile", + type=ImageD11options.UbiFileType(mode='w'), + help="Name of new ubi file to output") + parser.add_argument("-f", "--fltfile", action="store", + dest="fltfile", + type=ImageD11options.ColumnFileType(mode='r'), + help="Name of flt file") + parser.add_argument("-F", "--newfltfile", action="store", + dest="newfltfile", + type=ImageD11options.ColumnFileType(mode='w'), + help="Name of flt file containing unindexed peaks") + lattices = ["cubic", "hexagonal", "trigonalH","trigonalP", + "tetragonal", "orthorhombic", "monoclinic_a", + "monoclinic_b","monoclinic_c","triclinic"] + parser.add_argument("-s", "--sym", action="store", + dest="symmetry", # type="choice", + default = "triclinic", + choices = lattices, + help="Lattice symmetry for choosing orientation") + parser.add_argument("-l", "--lattice", action="store", + dest="latticesymmetry", #type="choice", + default = "triclinic", + choices = lattices, + help="Lattice symmetry for choosing orientation from "+ + "|".join(lattices)) + parser.add_argument("-t", "--tol", action="store", + dest="tol", type=float, + default = 0.25, + help="Tolerance to use in peak assignment, default=%f"%(0.25)) + parser.add_argument( "--omega_no_float", action="store_false", + dest = "omega_float", + default = True, + help= "Use exact observed omega values") + + parser.add_argument( "--omega_slop", action="store", type=float, + dest = "omega_slop", + default = 0.5, + help= "Omega slop (step) size") + + return parser class refinegrains: @@ -182,7 +235,7 @@ def readubis(self, filename): print(filename, type(filename)) raise for i, g in enumerate(ul): - name = filename + "_" + str(i) + # name = filename + "_" + str(i) # Hmmm .... multiple grain files? name = i self.grainnames.append(i) @@ -255,10 +308,10 @@ def loadfiltered(self, filename): self.scannames.append(filename) self.scantitles[filename] = col.titles if not "drlv2" in col.titles: - col.addcolumn( numpy.ones(col.nrows, numpy.float), + col.addcolumn( numpy.ones(col.nrows, float), "drlv2" ) if not "labels" in col.titles: - col.addcolumn( numpy.ones(col.nrows, numpy.float)-2, + col.addcolumn( numpy.ones(col.nrows, float)-2, "labels" ) if not "sc" in col.titles: assert "xc" in col.titles @@ -336,7 +389,7 @@ def compute_gv(self, thisgrain , update_columns = False ): junk = cImageD11.score_and_refine(mat , gvT, self.tolerance) hklf = numpy.dot( mat, gv ) - hkli = numpy.floor( hklf + 0.5 ) + hkli = numpy.round( hklf ) gcalc = numpy.dot( numpy.linalg.inv(mat) , hkli ) tth,[eta1,eta2],[omega1,omega2] = transform.uncompute_g_vectors( @@ -360,11 +413,12 @@ def compute_gv(self, thisgrain , update_columns = False ): omega_calc = best_fitting * omega2 + ( 1 - best_fitting ) * omega1 # Take a weighted average within the omega error of the observed omerr = (om*sign - omega_calc) + # Clip to 360 degree range + omerr = omerr - ( 360 * numpy.round( omerr / 360.0 ) ) # print omerr[0:5] omega_calc = om*sign - numpy.clip( omerr, -self.slop , self.slop ) # print omega_calc[0], om[0] thisgrain.omega_calc = omega_calc - # Now recompute with improved omegas... (tth, eta do not change much) #self.tth, self.eta = transform.compute_tth_eta( # numpy.array([x, y]), @@ -637,7 +691,7 @@ def assignlabels(self, quiet=False): start = time.time() for s in self.scannames: self.scandata[s].labels = self.scandata[s].labels*0 - 2 # == -1 - drlv2 = numpy.zeros(len(self.scandata[s].drlv2), numpy.float)+1 + drlv2 = numpy.zeros(len(self.scandata[s].drlv2), float)+1 nr = self.scandata[s].nrows sc = self.scandata[s].sc fc = self.scandata[s].fc @@ -653,20 +707,17 @@ def assignlabels(self, quiet=False): if not quiet: print("Start first grain loop",time.time()-start) start = time.time() - gv = numpy.zeros((nr,3),numpy.float ) + gv = numpy.zeros((nr,3), float ) wedge = self.parameterobj.parameters['wedge'] omegasign = self.parameterobj.parameters['omegasign'] chi = self.parameterobj.parameters['chi'] wvln = self.parameterobj.parameters['wavelength'] first_loop = time.time() - drlv2 = self.scandata[s].drlv2*0 + 1 # == 1 + drlv2 = (self.scandata[s].drlv2*0 + 1).astype(float) # == 1 int_tmp = numpy.zeros(nr , numpy.int32 )-1 for ig, g in enumerate(self.grainnames): gr = self.grains[ ( g, s) ] self.set_translation( g, s) - gr.peaks_xyz = peaks_xyz - gr.om = self.scandata[s].omega - gr.omega_calc=self.scandata[s].omega*0 cImageD11.compute_gv( peaks_xyz, self.scandata[s].omega, omegasign, @@ -675,15 +726,6 @@ def assignlabels(self, quiet=False): chi, gr.translation, gv) - if False: # For testing / debugging - omf = self.OMEGA_FLOAT - self.OMEGA_FLOAT = False - self.compute_gv( gr ) - import pylab - pylab.plot( self.gv[:,0], self.gv[:,0]-gv2[:,0], ".") - pylab.plot( self.gv[:,1], self.gv[:,1]-gv2[:,1], ".") - pylab.plot( self.gv[:,2], self.gv[:,2]-gv2[:,2], ".") - pylab.title("C") cImageD11.score_and_assign( gr.ubi, gv, self.tolerance, @@ -745,6 +787,7 @@ def assignlabels(self, quiet=False): gr.sc = numpy.take( sc, ind) gr.fc = numpy.take( fc, ind) gr.om = numpy.take(self.scandata[s].omega , ind) + gr.omega_calc = gr.om.copy() gr.npks = len(gr.ind) self.set_translation( g, s) try: @@ -826,37 +869,33 @@ def compute_lp_factor( colfile, **kwds ): colfile.addcolumn(lpg, "Lorentz_per_grain") -import math +def lf( tth, eta ): + """ + INPUT: 2*theta and eta in degrees. Can be floats or numpy arrays. -def cosd(x): return numpy.cos( x * math.pi/180 ) -def sind(x): return numpy.sin( x * math.pi/180 ) + OUTPUT: Lorentz scaling factor for intensity. Same data type as input. -def lf( tth, eta ): + EXPLANATION: + Compute the Lorentz Factor defined for 3DXRD as - sin_2t = sind( tth ) - sin_eta = sind( numpy.abs( eta ) ) - sin2_t = sind( tth/2 )*sind( tth/2 ) - # pure guesswork - return sin_2t * ( sin_eta + sin2_t ) + L( theta,eta ) = sin( 2*theta )*|sin( eta )| - # unreachable code here: - # ... problem that cos^2_p - sin^2 can be < 0 - # must mean that p is NOT eta + This is verified by: - cos_2t = cosd( tth ) + * Kabsch, W. (1988). Evaluation of single-crystal X-ray di raction data + from a position-sensitive detector + * Poulsen, H. F. (2004). 3DXRD { a new probe for materials science. + Roskilde: Riso National Laboratory - cos_t = cosd( tth/2 ) - sin_t = sind( tth/2 ) - # our eta is their psi + 90 degrees - cos_p = cosd( eta + 90 ) + and can be derived with support of + * Als-Nielsen, J. and McMorrow, D. (2017). Elements of Modern X-ray Physics - bot = sin_2t * numpy.sqrt( cos_p * cos_p - sin_t * sin_t ) + MODIFIED: 7 Feb 2019 by Axel Henningsson. + """ - top = (1 + cos_2t * cos_2t)*cos_2t - # Wondering if there is a div0 to fear - # Certainly lf can be infinite, so mask this problem as 1e6 - bot = numpy.where( numpy.abs ( bot ) > 1e-6 , bot, 1e-6 ) - return top / bot + sin_tth = numpy.sin( numpy.radians(tth) ) + sin_eta = numpy.sin( numpy.radians(eta) ) + return sin_tth*abs( sin_eta ) def compute_total_intensity( colfile, indices, tth_range, ntrim = 2, quiet = False ): diff --git a/ImageD11/rotdex.py b/ImageD11/rotdex.py index cfd49d20..eb7b2f82 100644 --- a/ImageD11/rotdex.py +++ b/ImageD11/rotdex.py @@ -77,7 +77,7 @@ def compute_dgdt( txyz, peaks_Cxyz, beam_Cxyz, wavelength): drdT = np.zeros((3,3,len(f))) # d( r/f )/dT -> r is a vector, r[0], r[1], r[2] # f is a scalar - o = np.ones(len(f)) + # o = np.ones(len(f)) # please derive/explain this... note r is already r/f drdT[0] = ( (dfdT[0] * r).T + (1,0,0) ).T / f drdT[1] = ( (dfdT[1] * r).T + (0,1,0) ).T / f @@ -130,15 +130,14 @@ def fit_ub_t( ub, translation, hkl, peaks_Cxyz, beam_Cxyz, wavelength): ubnew = ub.copy() # empirically it converges to 3 decimal places in 1 cycle # ...since: dgobsdt seems to depend on t we run a couple of cycles - for iloop in range(2): + for _ in range(2): gobs, dgobsdt = compute_dgdt( tnew, peaks_Cxyz, beam_Cxyz, wavelength ) # Note dgdub=h does not change here for i in range(3): dg[i+9] = dgobsdt[i] # translation at 9,10,11 gcalc = np.dot( ubnew , hkl ) # 3xn gdiff = gcalc - gobs - if 0: - print((gdiff*gdiff).ravel().sum(),tnew) + # print((gdiff*gdiff).ravel().sum(),tnew) dg.shape = 12,3*npk mat = np.dot( dg, dg.T ) rhs = np.dot( dg, gdiff.ravel() ) diff --git a/ImageD11/rsv.py b/ImageD11/rsv.py index 36fa78b0..b86dc7db 100644 --- a/ImageD11/rsv.py +++ b/ImageD11/rsv.py @@ -1,5 +1,5 @@ -from __future__ import print_function +from __future__ import print_function, division """ Reciprocal Space Volume @@ -47,7 +47,7 @@ def __init__(self, dimensions, bounds, np, **kwds ): assert len(dimensions) == 3 self.SIG = None # signal self.MON = None # monitor - self.NR = dimensions # dimensions + self.NR = [int(x) for x in dimensions] # dimensions self.NORMED = None self.bounds = bounds # boundary in reciprocal space self.np = np # px per hkl @@ -62,7 +62,7 @@ def allocate_vol( self ): """ if self.NR is None: raise Exception("Cannot allocate rsv") - total = self.NR[0]*self.NR[1]*self.NR[2] + total = int(self.NR[0]*self.NR[1]*self.NR[2]) print("rsv: memory used = %.2f MB"%(total*8.0/1024/1024)) print("dim: %d %d %d"%(self.NR[0],self.NR[1],self.NR[2])) self.SIG = numpy.zeros( total, numpy.float32 ) @@ -109,7 +109,7 @@ def slice(self, plane, num): str(self.plnames))) p = self.plnames[plane] # floor(x+0.5) is nearest integer - ind = numpy.floor( num * self.np + 0.5) - self.bounds[p][0] + ind = int(numpy.floor( num * self.np + 0.5) - self.bounds[p][0]) # convert this back to num testnum = 1.0*(self.bounds[p][0] + ind )/self.np if abs(testnum - num)>1e-6: @@ -152,8 +152,9 @@ def writevol(vol, filename): """ if not isinstance( vol, rsv ): raise Exception("First arg to writevol should be an rsv object") - if None in [vol.NR, vol.SIG, vol.MON]: - raise Exception("Cannot save rsv, has not data in it") + for a in [vol.NR, vol.SIG, vol.MON]: + if a is None: + raise Exception("Cannot save rsv, has not data in it") volout = h5py.File( filename,"w") if vol.SIG.dtype != numpy.float32: logging.warning("rsv SIG was not float32, converting") @@ -191,8 +192,9 @@ def writenormedvol(vol, filename): if not isinstance( vol, rsv ): raise Exception("First arg to writevol should be an rsv object") - if None in [vol.NR, vol.NORMED]: - raise Exception("Cannot save rsv, has not data in it") + for a in [vol.NR, vol.NORMED]: + if a is None: + raise Exception("Cannot save rsv, has not data in it") volout = h5py.File( filename,"w") if vol.NORMED.dtype != numpy.float32: logging.warning("rsv NORMED was not float32, converting") diff --git a/ImageD11/rsv_mapper.py b/ImageD11/rsv_mapper.py index 068383af..104b68a4 100644 --- a/ImageD11/rsv_mapper.py +++ b/ImageD11/rsv_mapper.py @@ -1,4 +1,4 @@ - +#!/usr/bin/env python from __future__ import print_function @@ -9,7 +9,7 @@ import numpy, logging from ImageD11 import parameters, transform, indexing, \ - cImageD11, blobcorrector, rsv + cImageD11, blobcorrector, rsv, ImageD11options class rsv_mapper(object): @@ -198,10 +198,10 @@ def add_image( self, om, data ): assert ind.dtype == numpy.intp # hkls[1] is faster. Steps by NR[2] only numpy.add( ind, NR[2]*numpy.floor( - hkls[1] + 0.5 - self.bounds[1][0]).astype(numpy.int32), + hkls[1] + 0.5 - self.bounds[1][0]).astype(numpy.intp), ind ) numpy.add( ind, numpy.floor( - hkls[2] + 0.5 - self.bounds[2][0]).astype(numpy.int32), + hkls[2] + 0.5 - self.bounds[2][0]).astype(numpy.intp), ind ) # # @@ -259,47 +259,51 @@ def get_options(parser): """ parser = ImageD11_file_series.get_options( parser ) - parser.add_option("-p", "--pars", action="store",type="string", + parser.add_argument("-p", "--pars", action="store", dest = "pars", default = None, + type=ImageD11options.ParameterFileType(mode='r'), help = "ImageD11 parameter file for experiment") - parser.add_option("-o", "--output", action="store", type="string", + parser.add_argument("-o", "--output", action="store", dest = "output", default = None, + type=ImageD11options.HdfFileType(mode='r'), help = "Name of hdf5 output file") - parser.add_option("-s", "--splinefile", action="store", type="string", + parser.add_argument("-s", "--splinefile", action="store", dest = "spline", default = None, + type=ImageD11options.SplineFileType(mode='r'), help = "Name of fit2d spline file for spatial dist") - parser.add_option("-u", "--ubifile", action="store", type="string", + parser.add_argument("-u", "--ubifile", action="store", dest = "ubifile", default = None, + type = ImageD11options.UbiFileType(mode='r'), help = "Name of ubi file (first matrix is used)") - parser.add_option("-x", "--npixels", action="store", type="int", + parser.add_argument("-x", "--npixels", action="store", type=int, dest = "npixels", default = 16, help = "Number of pixels in reciprocal space map per integer hkl [16]") - parser.add_option("-i", "--images", action="store", type="int", + parser.add_argument("-i", "--images", action="store", type=int, dest = "images", default = None, help = "Number of images to process [all]") - parser.add_option("-b", "--border", action="store", type="int", + parser.add_argument("-b", "--border", action="store", type=int, dest = "border", default = 10, help = "Border around images to allocate space, px [10]") - parser.add_option("-t", "--saturation", action="store", type="float", + parser.add_argument("-t", "--saturation", action="store", type=float, dest = "maxpix", default = None, help = "Saturation value for excluding pixels") - #parser.add_option("-t", "--testcolfile", action="store", type="string", + #parser.add_argument("-t", "--testcolfile", action="store", type="string", # dest = "testcolfile", default=None, # help = "A columnfile to test geometry") - parser.add_option("-c", "--subslice", action="store", type="int", + parser.add_argument("-c", "--subslice", action="store", type=int, dest = "subslice", default=1, help = "Number of omega subslices to repeat images") - parser.add_option("--maskfilename", action="store", type="string", + parser.add_argument("--maskfilename", action="store", type=str, dest = "maskfilename", default=None, help = "Mask image (fit2d style)" ) @@ -319,10 +323,10 @@ def main(): root.setLevel(logging.WARNING) try: - from optparse import OptionParser - parser = OptionParser() + from argparse import ArgumentParser + parser = ArgumentParser() parser = get_options( parser ) - options, args = parser.parse_args() + options, args = parser.parse_known_args() except SystemExit: raise except: diff --git a/ImageD11/saintraw.py b/ImageD11/saintraw.py index e7b0abd2..3b5730ff 100644 --- a/ImageD11/saintraw.py +++ b/ImageD11/saintraw.py @@ -177,7 +177,7 @@ def parsedocs(self): Parse the saint documentation for the Bruker format """ self.titles = [] - title = None + title = help = format = None for line in self.doc.split("\n"): if len(line.rstrip()) == 0: if title is not None: @@ -294,7 +294,7 @@ def tocolumnfile(self): """ cof = columnfile.newcolumnfile( self.alltitles ) dlist = [ self.data[t] for t in self.alltitles ] - cof.bigarray = numpy.array( dlist, numpy.float ) + cof.bigarray = numpy.array( dlist, float ) cof.nrows = len( self.data[ self.alltitles[0] ] ) cof.ncols = len( self.alltitles ) cof.set_attributes() diff --git a/ImageD11/scale.py b/ImageD11/scale.py index 6360b90b..cb9b6649 100644 --- a/ImageD11/scale.py +++ b/ImageD11/scale.py @@ -45,8 +45,7 @@ """ -import numpy -from fabio.openimage import openimage +import numpy, fabio class scale: def __init__( self, im1, threshold = None): @@ -56,8 +55,8 @@ def __init__( self, im1, threshold = None): im1 = a * im2 + b returns a, b """ - lsqmat = numpy.zeros((2, 2), numpy.float) - dyda = numpy.ravel(im1).astype(numpy.float) + lsqmat = numpy.zeros((2, 2), float) + dyda = numpy.ravel(im1).astype(float) self.threshold = threshold if threshold is None: self.indices = None @@ -103,14 +102,14 @@ def scale(self, im2): ...use scale image for that """ if self.indices is None: - rhs0 = numpy.sum(self.dyda * numpy.ravel(im2).astype(numpy.float)) - rhs1 = numpy.sum(numpy.ravel(im2).astype(numpy.float)) + rhs0 = numpy.sum(self.dyda * numpy.ravel(im2).astype(float)) + rhs1 = numpy.sum(numpy.ravel(im2).astype(float)) ans = numpy.dot(self.inverse, [rhs0, rhs1]) return ans[0], ans[1] else: usedata = numpy.take(numpy.ravel(im2) , self.indices) - rhs0 = numpy.sum(self.dyda * usedata.astype(numpy.float)) - rhs1 = numpy.sum(usedata.astype(numpy.float)) + rhs0 = numpy.sum(self.dyda * usedata.astype(float)) + rhs1 = numpy.sum(usedata.astype(float)) ans = numpy.dot(self.inverse, [rhs0, rhs1]) return ans[0], ans[1] @@ -122,7 +121,7 @@ def scaleseries( target, stem, first, last, Scale a series of [bruker] images to the target TODO - make it work with fabio file series """ - # d0 = numpy.ravel(target.data.astype(numpy.float)) + # d0 = numpy.ravel(target.data.astype(float)) scaler = scale(target.data, thresh) print("# Scaling with respect to:", sys.argv[1]) if thresh is not None: @@ -135,14 +134,14 @@ def scaleseries( target, stem, first, last, # we only look to see for i in range(first, last+1): name = "%s.%04d" % (stem, i) - secondimage = openimage.openimage(name) + secondimage = fabio.open(name) a, b = scaler.scale(secondimage.data) print(i, name , a, b, end=' ') else: # we correct the image for i in range(first, last+1): name = "%s.%04d" % (stem, i) newname = "cor_%s.%04d" % (stem.split("/")[-1], i) - secondimage = openimage.openimage(name) + secondimage = fabio.open(name) newdata = scaler.scaleimage(secondimage.data) # write out the file secondimage.data = newdata @@ -154,7 +153,7 @@ def scaleseries( target, stem, first, last, if __name__ == "__main__": import sys - FIRSTIMAGE = openimage.openimage(sys.argv[1]) + FIRSTIMAGE = fabio.open(sys.argv[1]) STEM = sys.argv[2] FIRST = int(sys.argv[3]) LAST = int(sys.argv[4]) diff --git a/ImageD11/silxGui/__init__.py b/ImageD11/silxGui/__init__.py new file mode 100644 index 00000000..3d252078 --- /dev/null +++ b/ImageD11/silxGui/__init__.py @@ -0,0 +1 @@ +""" silx gui code """ diff --git a/ImageD11/silxGui/readme.md b/ImageD11/silxGui/readme.md new file mode 100644 index 00000000..92a08177 --- /dev/null +++ b/ImageD11/silxGui/readme.md @@ -0,0 +1,18 @@ + +Some gui utilities using the silx library from http//github.com/silx-kit/silx + +# silx_colfile.py + +Load / save columnfile, choose axes to plot, apply masking operations. + +# silx_sptview.py + +Display peak search output (sptfile) overlaying image and spots + +- crashes on exit on windows, bug or PyQT feature? + +# silx_plot3d.py + +Displays a 3D plot of g-vectors + +- opengl not working at beamline (ESRF rnice to debian 8 desktop) diff --git a/ImageD11/silxGui/silx_colfile.py b/ImageD11/silxGui/silx_colfile.py new file mode 100644 index 00000000..c355c83b --- /dev/null +++ b/ImageD11/silxGui/silx_colfile.py @@ -0,0 +1,166 @@ + +from ImageD11.columnfile import columnfile + +import sys, os +import numpy as np +import silx.gui.qt , silx.gui.plot + +class silxqtcolfile( object ): + + def __init__(self, filename=None, xlabel=None, ylabel=None, zlabel=None ): + """ + Reads an ImageD11 peak search output for applying masks on 2D scatter plots + """ + self.xlabel = None + self.ylabel = None + self.zlabel = None + self.drawUI() + self.loadColfile(filename) + + def drawUI( self ): + """ + Sets up the UI + | cfname | load | save + | parname | load | + | xaxis | yaxis | color | apply_mask | + """ + self.win = silx.gui.qt.QWidget() + self.scatter_widget = silx.gui.plot.ScatterView( backend='gl' ) + # import pdb; pdb.set_trace() + if not self.scatter_widget._plot()._backend.isValid(): + self.scatter_widget = silx.gui.plot.ScatterView( backend='matplotlib' ) + self.scatter_widget.setColormap( silx.gui.colors.Colormap(name='viridis') ) + self.scatter_widget.getMaskToolsWidget().parent().setFloating(True) + self.scatter_widget.getMaskToolsWidget().show() + # glayout.addWidget( self.scatter_widget, 3, 0, 1, 4 ) + + glayout = silx.gui.qt.QGridLayout(self.win) + # 0 1 2 3 + # fname load save 0 + # pname load 1 + # x y z domask 2 + # plots + + self.fnamelabel = silx.gui.qt.QLabel( 'Columnfile: ' ) + blc=silx.gui.qt.QPushButton( "Load colfile" ) + glayout.addWidget( self.fnamelabel, 0, 0, 1, 2 ) + blc.clicked.connect( self.loadColfile ) + glayout.addWidget(blc, 0, 2) + bsc=silx.gui.qt.QPushButton( "Save colfile" ) + bsc.clicked.connect( self.savecolfile ) + glayout.addWidget(bsc, 0, 3) + + self.pnamelabel = silx.gui.qt.QLabel( 'Parameter file: ' ) + blp=silx.gui.qt.QPushButton( "Load parfile" ) + glayout.addWidget( blp, 1, 3) + blp.clicked.connect(self.loadparameters) + glayout.addWidget( self.pnamelabel, 1, 0, 1, 2 ) + + bxa=silx.gui.qt.QComboBox( None ) + bya=silx.gui.qt.QComboBox( None ) + bza=silx.gui.qt.QComboBox( None ) + self.axisboxes = [bxa,bya,bza] + self.ignoreselect= True + for b in self.axisboxes: + b.currentIndexChanged.connect( self.select ) + glayout.addWidget(silx.gui.qt.QLabel("x-plot"),2,0) + glayout.addWidget(silx.gui.qt.QLabel("y-plot"),2,1) + glayout.addWidget(silx.gui.qt.QLabel("color"),2,2) + glayout.addWidget(bxa,3,0) + glayout.addWidget(bya,3,1) + glayout.addWidget(bza,3,2) + + bdel=silx.gui.qt.QPushButton( "Delete selected" ) + bdel.clicked.connect( self.applymask ) + glayout.addWidget(bdel,2,3) + + self.scatter_widget.show() + self.win.show() + + def setTitles(self): + """ Read the colfile titles into the dropdowns """ + self.ignoreselect=True + for i,b in enumerate(self.axisboxes): + t = b.currentText() + b.clear() + b.addItems( self.colfile.titles ) + if t in self.colfile.titles: + b.setCurrentIndex( self.colfile.titles.index( t ) ) + else: + b.setCurrentIndex(i) + self.ignoreselect=False + + def select(self,col): + """ + Choose the x,y,z axes for plotting + """ + if self.ignoreselect: + return + self.update() + self.scatter_widget.resetZoom() + + def savecolfile(self): + """ Write after editing """ + filename = silx.gui.qt.QFileDialog(None,"Columnfile").getSaveFileName() + print(filename) + try: + self.colfile.writefile( filename[0] ) + except: + m = silx.gui.qt.QMessageBox.about(self.scatter_widget, + "Fail", "Saving failed - bad filename?" ) + + def loadColfile(self, filename=None): + """ Read in a new file """ + if filename is None or filename is False: + filename = silx.gui.qt.QFileDialog(self.win,"Columnfile").getOpenFileName()[0] + try: + c = columnfile( filename ) + self.colfile = c + except: + print("problem opening file",filename) + return + print("loaded file",filename) + self.fnamelabel.setText( "Columnfile: " + self.colfile.filename ) + self.setTitles() + self.update() + self.scatter_widget.resetZoom() + self.scatter_widget.activateWindow() + self.win.activateWindow() + + def update(self): + """ Refreshes the plot """ + self.x = self.colfile.getcolumn(self.axisboxes[0].currentText()) + self.y = self.colfile.getcolumn(self.axisboxes[1].currentText()) + self.z = self.colfile.getcolumn(self.axisboxes[2].currentText()) + self.scatter_widget.getXAxis().setLabel(self.axisboxes[0].currentText()) + self.scatter_widget.getYAxis().setLabel(self.axisboxes[1].currentText()) + self.scatter_widget.setGraphTitle('color: ' + self.axisboxes[2].currentText()) + self.scatter_widget.setData( self.x, self.y, self.z ) + + def applymask(self): + """ Applies the scatterplot mask to the colfile """ + m = self.scatter_widget.getMaskToolsWidget().getSelectionMask() + print("Masking",(m!=0).sum()) + self.colfile.filter( m == 0 ) + self.update() + + def loadparameters(self): + fname = silx.gui.qt.QFileDialog(self.win,"Parfile").getOpenFileName()[0] + if not os.path.exists(fname): + m = silx.gui.qt.QMessageBox.about(self.scatter_widget, + "Fail", "Setting parameters failed - bad filename?" ) + self.colfile.parameters.loadparameters( fname ) + self.colfile.updateGeometry() + self.setTitles() + self.pnamelabel.setText("Parameters: "+fname) + self.update() + +if __name__=="__main__": + app = silx.gui.qt.QApplication( sys.argv ) + if len(sys.argv) > 1: + qp = silxqtcolfile( sys.argv[1] ) + else: + qp = silxqtcolfile( ) + # Avoid segfault on exceptions + sys.excepthook = silx.gui.qt.exceptionHandler + app.exec_() diff --git a/ImageD11/silxGui/silx_plot3d.py b/ImageD11/silxGui/silx_plot3d.py new file mode 100644 index 00000000..62acb70b --- /dev/null +++ b/ImageD11/silxGui/silx_plot3d.py @@ -0,0 +1,56 @@ + + + +from ImageD11.columnfile import columnfile +import sys +import numpy as np +import silx.gui.qt , silx.gui.plot3d.SceneWindow +from silx.gui.plot3d.tools.PositionInfoWidget import PositionInfoWidget +from silx.gui.widgets.BoxLayoutDockWidget import BoxLayoutDockWidget + + + + +if __name__ == "__main__": + colf = columnfile( sys.argv[1] ) + colf.parameters.loadparameters( sys.argv[2] ) + colf.updateGeometry() + + qapp = silx.gui.qt.QApplication([]) + + # Create a SceneWindow widget + window = silx.gui.plot3d.SceneWindow.SceneWindow() + + sceneWidget = window.getSceneWidget() + sceneWidget.setBackgroundColor((0.1, 0.18, 0.08, 1.)) + sceneWidget.setForegroundColor((1., 1., 1., 1.)) + sceneWidget.setTextColor((0.5, 0.7, 0.7, 1.)) + sceneWidget.setProjection('orthographic') + + positionInfo = PositionInfoWidget() + positionInfo.setSceneWidget(sceneWidget) + dock = BoxLayoutDockWidget() + dock.setWindowTitle("Selection Info") + dock.setWidget(positionInfo) + window.addDockWidget(silx.gui.qt.Qt.BottomDockWidgetArea, dock) + + x,y,z,values = colf.gx, colf.gy, colf.gz, colf.avg_intensity + + scatter3d = silx.gui.plot3d.items.Scatter3D() + scatter3d.setData(x, y, z, values) + # Set scatter3d properties + scatter3d.getColormap().setName('viridis') # Use 'magma' colormap + scatter3d.setSymbol('.') # Use point markers + scatter3d.setSymbolSize(11) # Set the size of the markers + + # Add scatter3d to the scene + sceneWidget.addItem( scatter3d ) + + # Set scatter3d transform + SIZE=1 + scatter3d.setScale(SIZE, SIZE, SIZE) + + window.show() + # Avoid segfault on exceptions + sys.excepthook = silx.gui.qt.exceptionHandler + qapp.exec_() \ No newline at end of file diff --git a/ImageD11/silxGui/silx_sptview.py b/ImageD11/silxGui/silx_sptview.py new file mode 100644 index 00000000..09eab0bc --- /dev/null +++ b/ImageD11/silxGui/silx_sptview.py @@ -0,0 +1,86 @@ + + +import sys, os +import numpy as np, fabio +import silx.gui.qt , silx.gui.plot + +from ImageD11.peakmerge import peakmerger +qapp=None + +class sptview(object): + + def __init__(self, fname=None): + self.pm = peakmerger() + self.fname = fname + self.pm.readpeaks( fname ) + self.select_image(0) + self.drawUI() + + def select_image(self, i): + if i < 0 or i > len(self.pm.images)-1: + return False + im = self.pm.images[i] + self.currentnum = i + j = im.imagenumber + self.pm.harvestpeaks( numlim=(j-.1,j+0.1) ) + self.frame = fabio.open(im.name) + self.pkx = [ p.x for p in self.pm.allpeaks ] + self.pky = [ p.y for p in self.pm.allpeaks ] + self.pkI = [ p.avg for p in self.pm.allpeaks ] + return True + + def next(self): + if self.select_image( self.currentnum + 1): + self.changeframe() + else: + self.warn("No next image") + + def prev(self): + if self.select_image( self.currentnum - 1): + self.changeframe() + else: + self.warn("No previous image") + + def drawUI(self): + self.widget = silx.gui.qt.QWidget() + self.widget.setWindowTitle("spot viewer") + self.layout = silx.gui.qt.QGridLayout() + self.widget.setLayout( self.layout ) + self.spot_label = silx.gui.qt.QLabel( self.fname ) + self.framelabel = silx.gui.qt.QLabel( self.frame.filename ) + self.framelabel.setAlignment( silx.gui.qt.Qt.AlignCenter ) + self.layout.addWidget(self.spot_label, 0, 0, 1, 3) + self.layout.addWidget(self.framelabel, 1, 1) + self.n = silx.gui.qt.QPushButton( "next" ) + self.p = silx.gui.qt.QPushButton( "prev" ) + self.n.clicked.connect( self.next ) + self.p.clicked.connect( self.prev ) + self.layout.addWidget(self.p, 1, 0 ) + self.layout.addWidget(self.n, 1, 2 ) + self.plot = silx.gui.plot.Plot2D( backend='gl') + self.imglabel = self.plot.addImage( self.frame.data, z=0, origin=(-0.5,-0.5), + colormap=silx.gui.colors.Colormap(name='magma')) + self.plotlabel = self.plot.addScatter( self.pky, self.pkx, self.pkI, z=1, symbol='+', + colormap=silx.gui.colors.Colormap(name='viridis')) + self.plot.getScatter( self.plotlabel ).setSymbolSize(10) + self.plot.setKeepDataAspectRatio(True) + self.layout.addWidget(self.plot, 2, 0, 1, 3) + self.widget.show() + self.plot.show() + + def changeframe(self): + d = self.frame.data + im = self.plot.getImage( self.imglabel ) + im.setData(d) + pl = self.plot.getScatter( self.plotlabel ) + pl.setData( self.pky, self.pkx, self.pkI ) + self.framelabel.setText( self.frame.filename ) + + def warn(self, message): + silx.gui.qt.QMessageBox.warning(None, "Warning", message) + +if __name__=="__main__": + qapp = silx.gui.qt.QApplication([]) + s=sptview( sys.argv[1] ) + s.widget.show() + sys.exit(qapp.exec_()) diff --git a/ImageD11/simplex.py b/ImageD11/simplex.py index d0ec30a8..78958b2f 100644 --- a/ImageD11/simplex.py +++ b/ImageD11/simplex.py @@ -157,7 +157,7 @@ def minimize(self, epsilon = 0.0001, maxiters = 250, monitor = 1): if T <= epsilon: # We converged! Break out of loop! - break; + break else: # Didn't converge. Keep crunching. diff --git a/ImageD11/sinograms/__init__.py b/ImageD11/sinograms/__init__.py new file mode 100644 index 00000000..87c20973 --- /dev/null +++ b/ImageD11/sinograms/__init__.py @@ -0,0 +1,38 @@ + + + +""" +Instructions: + + +# create the dataset file + +python3 -m ImageD11.sinograms.dataset \ + /data/visitor/ma5415/id11/20221027 \ + /data/visitor/ma5415/id11/20221027/analysis/test \ + NSCOPE_SiMo1000_6 \ + DT600nm_Z50 \ + dstest_NSCOPE_SiMo1000_6_DT600nm_Z50.h5 + + +# do some segmentation +python3 -m ImageD11.sinograms.lima_segmenter setup dataset.h5 +sbatch /data/visitor/ma5415/id11/20221027/analysis/test/NSCOPE_SiMo1000_6/NSCOPE_SiMo1000_6_DT600nm_Z50/slurm/lima_segmenter_slurm.sh + +# next step(s) +label the pixels (save that or not?) + cost = np.cumsum( dataset.nnz ) + +save the peaks from each frame? + s_raw f_raw S_I float32, float32, float32 == 4 * 3 = 12 bytes (number of pixels?) + sc fc 1 pixel is (uint16, uint16, uint16) = (2 * 3) = 6 bytes + + +determine dataset.npks_per_frame # keeping all? Keeping Some? + +make the graph of all the p + + + +""" + diff --git a/ImageD11/sinograms/assemble_label.py b/ImageD11/sinograms/assemble_label.py new file mode 100644 index 00000000..855fd0bb --- /dev/null +++ b/ImageD11/sinograms/assemble_label.py @@ -0,0 +1,226 @@ +from __future__ import print_function +import h5py, os, time, numpy as np +import numba +import fabio + +"""WARNING: work in progresss""" + +import ImageD11.sinograms.dataset + + +# given sample + dset -> make a single file with all the sparse pixels in it. +# ... this is not really needed, but saves redoing the pixel labelling code + + +SCANMOTORS = ( + "diffrz", + "diffrz_cen360", + "diffrz_center", + "fpico4", + "fpico3", + "diffty", + "diffty_center", + "rot", + "rot_cen360", + "rot_center", + "fpico6", + "dty", + "dty_center", +) +HEADERMOTORS = ( + "diffty", + "diffrz", + "samtx", + "samty", + "samtz", + "diffry", + "samrx", + "samry", + "dty", + "rot", + "pz", + "px", + "py", + "shtz", + "shty", + "shtx", +) + + +def testready(dsobj): + """assume they are finish if they exist. Might be still writing ???""" + done, todo = dsobj.check_sparse() + return (done > 0) and (todo == 0) + + +def getsparse(dsobj, num): + """ + dsobj = DataSet object + returns the pixels from the sparse segmentation + """ + with h5py.File( + os.path.join(dsobj.analysispath, dsobj.sparsefiles[num]), "r" + ) as hin: + row = hin[dsobj.limapath]["row"][:] + col = hin[dsobj.limapath]["col"][:] + intensity = hin[dsobj.limapath]["intensity"][:] + nnz = hin[dsobj.limapath]["nnz"][:] + return row, col, intensity, nnz + + +@numba.njit(boundscheck=True) +def filterpixels(cutimage, row, col, intensity, nnz): + """ + cutimage = 2D image of thresholds to use + row, col, intensity = pixels + nnz = pixels per frame for a stack of images in row/col/intensity + """ + # 1: scan all of the pixels to decide on the output space required + nnz_out = np.zeros_like(nnz) + ntotal = 0 + ip = 0 + for iframe in range(len(nnz)): + for j in range(nnz[iframe]): + if intensity[j + ip] > cutimage[row[j + ip], col[j + ip]]: + nnz_out[iframe] += 1 + ntotal += 1 + ip += nnz[iframe] + row_out = np.zeros((ntotal,), dtype=row.dtype) + col_out = np.zeros((ntotal,), dtype=col.dtype) + intensity_out = np.zeros((ntotal,), dtype=intensity.dtype) + nout = 0 + ip = 0 + for iframe in range(len(nnz)): + for j in range(nnz[iframe]): + if intensity[j + ip] > cutimage[row[j + ip], col[j + ip]]: + row_out[nout] = row[j + ip] + col_out[nout] = col[j + ip] + intensity_out[nout] = intensity[j + ip] + nout += 1 + ip += nnz[iframe] + return row_out, col_out, intensity_out, nnz_out + + +def harvest_masterfile( + dset, outname, scanmotors=SCANMOTORS, headermotors=HEADERMOTORS, cutimage=None +): + """ + dset = ImageD11.sinograms.dataset.DataSet object + outname = sparse file to write + + cut = image = 2D image of cutoffs for keeping pixels (e.g. keep high angle + and use a higher threshold for low angle or noisy) + """ + opts = { + "chunks": (10000,), + "maxshape": (None,), + "compression": "lzf", + "shuffle": True, + } + with h5py.File(outname, "a") as hout: + hout.attrs["h5input"] = dset.masterfile + print("Harvesting", dset.masterfile, end=": ") + with h5py.File(dset.masterfile, "r") as hin: + done = [] + for scan in dset.scans: + if scan.find("::"): + scan = scan.split("::")[0] + if scan in done: + continue + gin = hin[scan] + bad = False + for check in ("title", "measurement", "measurement/" + dset.detector): + if check not in hin[scan]: + print(scan, "missing", check, "skipping") + bad = True + if bad: + print("Skipping", scan) + continue + title = hin[scan]["title"][()] + g = hout.require_group(scan) + gm = g.require_group("measurement") + for m in scanmotors: # vary : many + if m in gin["measurement"]: + data = data = gin["measurement"][m][:] + ds = gm.require_dataset(m, shape=data.shape, dtype=data.dtype) + ds[()] = data + gip = g.require_group("instrument/positioners") + for m in headermotors: # fixed : scalar + if "instrument/positioners/%s" % (m) in gin: + data = gin["instrument/positioners"][m][()] + ds = gip.require_dataset(m, shape=data.shape, dtype=data.dtype) + ds[()] = data + try: + frms = gin["measurement"][dset.detector] + except Exception as e: + print(e) + print(list(gin)) + print(list(gin["measurement"])) + print(dset.detector) + raise + g.attrs["itype"] = frms.dtype.name + g.attrs["nframes"] = frms.shape[0] + g.attrs["shape0"] = frms.shape[1] + g.attrs["shape1"] = frms.shape[2] + print(scan, end=", ") + done.append(scan) + print() + + # Finished with master file. Now harvest the segmented files. + idx = 0 + print("Loading pixels:", end=" ") + for scan in done: + g = hout.require_group(scan) + for name in "row", "col": + if name not in g: + g.create_dataset(name, shape=(0,), dtype=np.uint16, **opts) + if "intensity" not in g: + g.create_dataset( + "intensity", shape=(0,), dtype=g.attrs["itype"], **opts + ) + nfrm = g.attrs["nframes"] + g.require_dataset("nnz", shape=(nfrm,), dtype=np.uint32) + nstart = nread = npx = pstart = 0 + while nread < nfrm: + row, col, intensity, nnz = getsparse(dset, idx) + if cutimage is not None: + row, col, intensity, nnz = filterpixels( + cutimage, row, col, intensity, nnz + ) + idx += 1 # loop over sparse files in this scan + nread = nstart + len(nnz) # number of frames in this limafile + g["nnz"][nstart:nread] = nnz + nstart = nread + pread = pstart + len(row) # number of pixels in this limafile + g["row"].resize((pread,)) + g["row"][pstart:pread] = row + g["col"].resize((pread,)) + g["col"][pstart:pread] = col + g["intensity"].resize((pread,)) + g["intensity"][pstart:pread] = intensity + pstart = pread + print(scan, end=", ") + print() + return outname + + +def main(dsname, outname=None, cutimage=None): + """ + dsname = Dataset describing the masterfile + segmentation etc + outname = sparse pixels file to write. Defaults to dset.sparsefile + """ + if isinstance(dsname, ImageD11.sinograms.dataset.DataSet): + dset = dsname + else: + dset = ImageD11.sinograms.dataset.load(dsname) + if outname is None: + outname = dset.sparsefile + if cutimage is not None: + cutimage = fabio.open(cutimage).data + harvest_masterfile(dset, outname, cutimage=cutimage) + + +if __name__ == "__main__": + import sys + + main(sys.argv[1], sys.argv[2]) diff --git a/ImageD11/sinograms/dataset.py b/ImageD11/sinograms/dataset.py new file mode 100644 index 00000000..40a7d5f6 --- /dev/null +++ b/ImageD11/sinograms/dataset.py @@ -0,0 +1,623 @@ +from __future__ import print_function, division + +import os, h5py, numpy as np +import fast_histogram +import logging + +""" +TO DO: + +- debug / convert this to work on f2scan and/or fscan2d data +- treat 360 scans starting near the center (monitor/hits) +- send pyFAI style integration jobs +- mca harvesting for fluo tomo +- scalar reconstructions (counters, roi, etc) +- 1d reconstructions (fluoct xrdct) +- run peaksearch/segmentations +- peak sinograms +- linking or merging peaks across dty/omega +- indexing and reconstructing grains + +""" + + +# POSSIBLE_DETECTOR_NAMES = ("frelon3", "eiger") + +def guess_chunks(name, shape): + if name == 'omega': + return (shape[0], 1) + if name == 'dty': + return (1, shape[1]) + return shape + + +class DataSet: + """One DataSet instance per detector!""" + + # simple strings or ints + ATTRNAMES = ("dataroot", "analysisroot", "sample", "dset", "shape", "dsname", + "datapath", "analysispath", "masterfile", "limapath", + "detector", "omegamotor", "dtymotor", + "pksfile", "sparsefile", "parfile" + ) + STRINGLISTS = ("scans", "imagefiles", "sparsefiles") + # sinograms + NDNAMES = ("omega", "dty", "nnz", "frames_per_file", "nlm", "frames_per_scan") + + def __init__(self, + dataroot=".", + analysisroot=".", + sample="sample", + dset="dataset", + detector="eiger", + omegamotor="rot_center", + dtymotor="dty"): + """ The things we need to know to process data """ + + # defaults to eiger and nanoscope station, can be overwritten with init parameters detector, omegamotor and dtymotor + self.detector = detector # frelon3 + self.limapath = None # where is the data in the Lima files + + self.omegamotor = omegamotor # diffrz + self.dtymotor = dtymotor # diffty + + self.dataroot = dataroot # folder to find {sample}/{sample}_{dset} + self.analysisroot = analysisroot # where to write or find sparse data + self.sample = sample # from bliss path + self.dset = dset # from bliss path + + self.dsname = "_".join((self.sample, self.dset)) + + # paths for raw data + + self.datapath = os.path.join(self.dataroot, self.sample, self.dsname) + self.masterfile = os.path.join(self.datapath, self.dsname + '.h5') + + # paths for processed data + self.update_paths() + + # These are in order ! The order of the lists is important - all things should match. + self.scans = None # read from master or load from analysis + self.frames_per_scan = None # how many frames (and motor positions) in each scan row. + self.imagefiles = None # List of strings. w.r.t self.datapath + self.frames_per_file = None # how many frames in this file (Lima files) + self.sparsefiles = None # maps sparse files to self.imagefiles + + self.shape = (0, 0) + self.omega = None + self.dty = None + + def update_paths(self): + # paths for processed data + # root of analysis for this dataset for this sample: + self.analysispath = os.path.join(self.analysisroot, self.sample, self.dsname) + + self.dsfile_default = os.path.join(self.analysispath, self.dsname + '_dataset.h5') + # at the moment, set self.dsfile to be the default + # if save or load is ever called, this will be replaced + self.dsfile = self.dsfile_default + self.pksfile = os.path.join(self.analysispath, self.dsname + '_peaks_table.h5') + self.col4dfile = os.path.join(self.analysispath, self.dsname + '_peaks_4d.h5') + self.col3dfile = os.path.join(self.analysispath, self.dsname + '_peaks_3d.h5') # useful for regular 3DXRD scans + self.col2dfile = os.path.join(self.analysispath, self.dsname + '_peaks_2d.h5') + self.grainsfile = os.path.join(self.analysispath, self.dsname + '_grains.h5') + self.sparsefile = os.path.join(self.analysispath, self.dsname + '_sparse.h5') + + def __repr__(self): + r = [] + for name in "dataroot analysisroot sample dset".split(): + r.append('%s = "%s"' % (name, getattr(self, name))) + r.append('shape = ( %d, %d)' % tuple(self.shape)) + if self.scans is not None: + r.append('# scans %d from %s to %s' % ( + len(self.scans), self.scans[0], self.scans[-1])) + return "\n".join(r) + + def compare(self, other): + '''Try to see if the load/save is working''' + from types import FunctionType + sattrs = set([name for name in vars(self) if name[0] != '_']) + oattrs = set([name for name in vars(self) if name[0] != '_']) + if sattrs != oattrs: + logging.info('Attribute mismatch ' + str(sattrs) + ' != ' + str(oattrs)) + return False + for a in sattrs: + s = getattr(self, a) + if isinstance(s, FunctionType): + continue + o = getattr(other, a) + t = type(s) + if type(o) != type(s): + logging.info('Type mismatch %s %s' % (str(t), str(a))) + return False + if t == np.ndarray: + if s.shape != o.shape: + logging.info('Shape mismatch %s %s' % (str(s.shape), str(o.shape))) + return False + if (s != o).all(): + logging.info('Data mismatch ' + str(a)) + return False + else: + if s != o: + logging.info('Data mismatch ') + return False + logging.info('Dataset objects seem to match!') + return True + + def report(self): + print(self) + print("# Collected %d missing %d" % (self.check_images())) + print("# Segmented %d missing %d" % (self.check_sparse())) + + def import_all(self, scans=None, shape=None): + # collect the data + self.import_scans(scans=scans) + # lima frames + self.import_imagefiles() + # motor positions + self.import_motors_from_master() + self.guess_shape() + self.guessbins() + # pixels per frame + try: + self.import_nnz() + except: + logging.info("nnz not available. Segmentation done?") + + def import_scans(self, scans=None, hname=None): + """ Reads in the scans from the bliss master file """ + # we need to work out what detector we have at this point + # self.guess_detector() + if hname is None: + hname = self.masterfile + frames_per_scan = [] + with h5py.File(hname, 'r') as hin: + if scans is None: + scans = [scan for scan in list(hin['/']) if + (scan.endswith('.1') and + ('measurement' in hin[scan]) and + (self.detector in hin[scan]['measurement']))] + goodscans = [] + for scan in scans: + # Make sure that this scan has a measurement from our detector + if self.detector not in hin[scan]['measurement']: + print('Bad scan', scan) + else: + try: + frames = hin[scan]['measurement'][self.detector] + except KeyError as e: # Thrown by h5py + print('Bad scan', scan, ', h5py error follows:') + print(e) + continue + if len(frames.shape) == 3: # need 1D series of frames + goodscans.append(scan) + frames_per_scan.append(frames.shape[0]) + else: + print('Bad scan', scan) + + self.scans = goodscans + self.frames_per_scan = frames_per_scan + + logging.info('imported %d scans from %s' % (len(self.scans), hname)) + return self.scans + + def import_imagefiles(self): + """ Get the Lima file names from the bliss master file, also scan_npoints """ + # self.import_scans() should always be called before this function, so we know the detector + npts = None + self.imagefiles = [] + self.frames_per_file = [] + with h5py.File(self.masterfile, 'r') as hin: + bad = [] + for i, scan in enumerate(self.scans): + if ('measurement' not in hin[scan]) or (self.detector not in hin[scan]['measurement']): + print('Bad scan', scan) + bad.append(scan) + continue + frames = hin[scan]['measurement'][self.detector] + self.imageshape = frames.shape[1:] + for vsrc in frames.virtual_sources(): + self.imagefiles.append(vsrc.file_name) + self.frames_per_file.append(vsrc.src_space.shape[0]) # not sure about this + # check limapath + if self.limapath is None: + self.limapath = vsrc.dset_name + assert self.limapath == vsrc.dset_name + self.frames_per_file = np.array(self.frames_per_file, int) + self.sparsefiles = [name.replace('/', '_').replace('.h5', '_sparse.h5') for name in + self.imagefiles] + logging.info('imported %d lima filenames' % (np.sum(self.frames_per_file))) + + def import_motors_from_master(self): # could also get these from sparse files if saved + """ read the motors from the lima file + you need to import the imagefiles first + these will be the motor positions to accompany the images + """ + # self.guess_motornames() + self.omega = [None, ] * len(self.scans) + self.dty = [None, ] * len(self.scans) + with h5py.File(self.masterfile, 'r') as hin: + bad = [] + for i, scan in enumerate(self.scans): + # Should always be there, if not, filter scans before you get to here + om = hin[scan]['measurement'][self.omegamotor][()] + if len(om) == self.frames_per_scan[i]: + self.omega[i] = om + else: # hope the first point was good ? Probably corrupted MUSST data. + self.omega[i] = [om[0], ] + bad.append(i) + # this can be an array or a scalar + dty = hin[scan]['instrument/positioners'][self.dtymotor] + if len(dty.shape) == 0: + self.dty[i] = np.full(self.frames_per_scan[i], dty[()]) + elif dty.shape[0] == self.frames_per_scan[i]: + self.dty[i] = dty[:] + else: + # corrupted MUSST? + self.dty[i] = np.full(self.frames_per_scan[i], dty[0]) + for b in bad: + dom = [(abs(self.omega[i][0] - self.omega[b]), i) for i in range(len(self.scans)) + if i not in bad] + if len(dom) > 0: + j = np.argmin(dom[0][1]) + self.omega[b] = self.omega[j] # best match + print("replace bad scan omega", b, self.scans[b], "with", j, self.scans[j]) + logging.info('imported omega/dty') + + def guess_shape(self): + """Guess the shape if it was not given """ + npts = np.sum(self.frames_per_scan) + if len(self.scans) == 1: # probably fscan2d or f2scan + with h5py.File(self.masterfile, 'r') as hin: + s = hin[self.scans[0]] + title = s['title'].asstr()[()] + print('Scan title', title) + if title.split()[0] == 'fscan2d': + s0 = s['instrument/fscan_parameters/slow_npoints'][()] + s1 = s['instrument/fscan_parameters/fast_npoints'][()] + file_nums = np.arange(s0 * s1).reshape((s0, s1)) + # slice notation means last frame+1 to be inclusive + self.scans = ["%s::[%d:%d]" % (self.scans[0], row[0], row[-1] + 1) + for row in file_nums] + elif title.split()[0] == 'f2scan': + # good luck ? Assuming rotation was the inner loop here: + step = s['instrument/fscan_parameters/step_size'][()] + s1 = int(np.round(360 / step)) + s0 = npts // s1 + logging.warning("Dataset might need to be reshaped") + else: + s0 = 1 + s1 = npts + elif len(self.scans) > 1: + s0 = len(self.scans) + s1 = npts // s0 + self.shape = s0, s1 + if np.prod(self.shape) != npts: + print("Warning: irregular scan - might be bugs in here") + print(npts, len(self.scans)) + self.omega = np.array(self.omega).reshape(self.shape) + self.dty = np.array(self.dty).reshape(self.shape) + logging.info('sinogram shape = ( %d , %d ) imageshape = ( %d , %d)' % ( + self.shape[0], self.shape[1], self.imageshape[0], self.imageshape[1])) + + def guessbins(self): + ny, nomega = self.shape + self.omin = self.omega.min() + self.omax = self.omega.max() + if (self.omax - self.omin) > 360: + # multi-turn scan... + self.omin = 0. + self.omax = 360. + self.omega_for_bins = self.omega % 360 + else: + self.omega_for_bins = self.omega + # values 0, 1, 2 + # shape = 3 + # step = 1 + self.ostep = (self.omax - self.omin) / (nomega - 1) + self.ymin = self.dty.min() + self.ymax = self.dty.max() + if ny > 1: + self.ystep = (self.ymax - self.ymin) / (ny - 1) + else: + self.ystep = 1 + self.obincens = np.linspace(self.omin, self.omax, nomega) + self.ybincens = np.linspace(self.ymin, self.ymax, ny) + self.obinedges = np.linspace(self.omin - self.ostep / 2, self.omax + self.ostep / 2, nomega + 1) + self.ybinedges = np.linspace(self.ymin - self.ystep / 2, self.ymax + self.ystep / 2, ny + 1) + + def guess_detector(self): + '''Guess which detector we are using from the masterfile''' + + # open the masterfile + hname = self.masterfile + detectors_seen = [] + scan = "1.1" + + with h5py.File(hname, 'r') as hin: + # go through the first scan, and see what detectors we can see + for measurement in list(hin[scan]['measurement']): + if measurement.attrs.get("interpretation") == "image": + detectors_seen.append(measurement) + + if len(detectors_seen) != 1: + raise ValueError("More than one detector seen! Can't work out which one to process.") + else: + self.detector = detectors_seen[0] + + # def guess_motornames(self): + # '''Guess which station we were using (Nanoscope or 3DXRD) from which motors are in instrument/positioners''' + # from ImageD11.sinograms.assemble_label import HEADERMOTORS_NSCOPE, HEADERMOTORS_TDXRD, HEADERMOTORS + # # open the masterfile + # hname = self.masterfile + # motors_seen = [] + # scan = "1.1" + + # with h5py.File( hname, 'r' ) as hin: + # # go through the first scan, and see what motors we can see + # for positioner in list(hin[scan]['instrument/positioners']): + # if positioner in HEADERMOTORS: + # motors_seen.append(positioner) + + # using_nscope = False + # using_tdxrd = False + # for motor in motors_seen: + # if motor in HEADERMOTORS_NSCOPE: + # using_nscope = True + # elif motor in HEADERMOTORS_TDXRD: + # using_tdxrd = True + + # if using_nscope and using_tdxrd: + # raise ValueError("Found both nscope and tdxrd motors in positioners, not sure which one we were using!") + + # if using_nscope: + # self.omegamotor = 'rot_center' + # self.dtymotor = 'dty' + # elif using_tdxrd: + # self.omegamotor = 'diffrz' + # self.dtymotor = 'diffty' + + def sinohist(self, weights=None, omega=None, dty=None, method='fast'): + """ Bin some data onto the sinogram histogram """ + bins = len(self.obincens), len(self.ybincens) + rng = ((self.obinedges[0], self.obinedges[-1]), + (self.ybinedges[0], self.ybinedges[-1])) + if isinstance(weights, np.ndarray): + wt = weights.ravel() + else: + wt = weights + if omega is None: + omega = self.omega_for_bins + if dty is None: + dty = self.dty + if method == 'numpy': + ret = np.histogram2d(omega.ravel(), dty.ravel(), + weights=wt, bins=bins, range=rng) + histo = ret[0] + elif method == 'fast': + histo = fast_histogram.histogram2d(omega.ravel(), dty.ravel(), + weights=wt, bins=bins, range=rng) + return histo + + def import_nnz(self): + """ Read the nnz arrays from the scans """ + nnz = [] + for spname in self.sparsefiles: + with h5py.File(os.path.join(self.analysispath, spname), "r") as hin: + nnz.append(hin[self.limapath]['nnz'][:]) + self.nnz = np.concatenate(nnz).reshape(self.shape).astype(np.int32) + logging.info('imported nnz, average %f' % (self.nnz.mean())) # expensive if you are not logging it. + + # def compute_pixel_labels(self): + # this should instead from from the pk2d file generated by sinograms/properties.py + # nlm = [] + # for spname in self.sparsefiles: + # n, l = peaklabel.add_localmax_peaklabel( os.path.join( self.analysispath, spname ), + # self.limapath ) + # nlm.append(n) + # self.nlm = np.concatenate( nlm ).reshape( self.shape ) + + # def import_nlm(self): + # this should instead from from the pk2d file generated by sinograms/properties.py + # """ Read the Nlmlabels + # These are the number of localmax peaks per frame + # """ + # nlm = [] + # for spname in self.sparsefiles: + # with h5py.File( os.path.join( self.analysispath, spname ), "r" ) as hin: + # nlm.append( hin[self.limapath]['Nlmlabel'][:] ) + # self.nlm = np.concatenate( nlm ).reshape( self.shape ) + # logging.info('imported nlm, max %d'%(self.nlm.max())) + + def check_files(self, path, filenames, verbose=0): + """ See whether files are created or not """ + # images collected + done = 0 + missing = 0 + for fname in filenames: + fullname = os.path.join(path, fname) + if os.path.exists(fullname): + done += 1 + else: + missing += 1 + if verbose > 0: + print("missing", fullname) + verbose -= 1 + return done, missing + + def check_images(self): + """ Is the experiment finished ? """ + return self.check_files(self.datapath, self.imagefiles) + + def check_sparse(self): + """ Has the segmentation been done ? """ + return self.check_files(self.analysispath, self.sparsefiles, verbose=2) + + def save(self, h5name=None, h5group='/'): + if h5name is None: + # none supplied, so use default path + h5name = self.dsfile_default + # make sure parent directories exist + # ensure that the analysis path exists + dsfile_folder = os.path.dirname(self.dsfile_default) + if not os.path.exists(dsfile_folder): + os.makedirs(dsfile_folder) + + ZIP = {'compression': 'gzip'} + + with h5py.File(h5name, "a") as hout: + grp = hout[h5group] + # Simple small objects + for name in self.ATTRNAMES: + data = getattr(self, name, None) + if data is not None: + grp.attrs[name] = data + # The string lists + for name in self.STRINGLISTS: + data = getattr(self, name, None) + if data is not None and len(data): + sdata = np.array(data, "S") + ds = grp.require_dataset(name, + shape=sdata.shape, + chunks=sdata.shape, + dtype=h5py.string_dtype(), + **ZIP) + ds[:] = sdata + # + for name in self.NDNAMES: + data = getattr(self, name, None) + if data is not None: + data = np.asarray(data) + try: + chunks = guess_chunks(name, data.shape) + ds = grp.require_dataset(name, + shape=data.shape, + chunks=chunks, + dtype=data.dtype, + **ZIP) + ds[:] = data + except: + print(name) + print(len(data)) + print(data.shape) + print(chunks) + raise + + # if we got here, we saved the file successfully + self.dsfile = h5name + + def load(self, h5name=None, h5group='/'): + if h5name is None: + # none supplied, so use default path + h5name = self.dsfile_default + + """ Recover this from a hdf5 file """ + with h5py.File(h5name, "r") as hin: + grp = hin[h5group] + for name in self.ATTRNAMES: + if name in grp.attrs: + setattr(self, name, grp.attrs.get(name)) + self.shape = tuple(self.shape) # hum + for name in self.NDNAMES: + if name in grp: + data = grp[name][()] + setattr(self, name, data) + for name in self.STRINGLISTS: + if name in grp: + stringlist = list(grp[name][()]) + if hasattr(stringlist[0], 'decode') or isinstance(stringlist[0], np.ndarray): + data = [s.decode() for s in stringlist] + else: + data = stringlist + setattr(self, name, data) + self.guessbins() + + # analysis paths can only be calculated once + self.update_paths() + # if we got here, we loaded the file successfully + self.dsfile = h5name + + return self + + +def load(h5name, h5group='/'): + ds_obj = DataSet().load(h5name, h5group) + + return ds_obj + + +def import_from_sparse(hname, + omegamotor='instrument/positioners/rot', + dtymotor='instrument/positioners/dty', + ): + ds = DataSet() + with h5py.File(hname, 'r') as hin: + scans = list(hin['/']) + order = np.argsort([float(v) for v in scans if v.endswith('.1')]) + scans = [scans[i] for i in order] + dty = [hin[scan][dtymotor][()] for scan in scans] + omega = [hin[scan][omegamotor][()] for scan in scans] + nnz = [hin[scan]['nnz'][()] for scan in scans] + # nlm = [hin[scan]['Nlmlabel'][()] for scan in scans] + ds.scans = scans + ds.nnz = nnz + ds.nnz = np.array(nnz) + ds.shape = ds.nnz.shape + ds.omega = np.zeros(ds.nnz.shape, float) + for i, o in enumerate(omega): + if isinstance(o, float) or (len(o) == len(ds.nnz[i])): + ds.omega[i] = o + if len(o) > len(ds.nnz[i]): + ds.omega[i] = ds.omega[i - 2] # guess zig zag + # warning here + + ds.dty = np.zeros(ds.nnz.shape, float) + for i, o in enumerate(dty): + if isinstance(o, float) or (len(o) == len(ds.nnz[i])): + ds.dty[i] = o + else: + raise Exception('Cannot read %d dty %s %s' % (i, str(o), str(o.shape))) + # assert ds.nlm.shape == ds.shape + try: + ds.guess_scans() + except: + print("warning, guess scans failed") + try: + ds.guessbins() + except: + print("warning, guessbins failed") + return ds + + +# Example +# s = dataset( +# dataroot = "/data/visitor/ma5415/id11/20221027", +# analysisroot = "/data/visitor/ma5415/id11/20221027/analysis/SparsePixels", +# sample = "NSCOPE_SiMo1000_6", +# dset = "DT600nm_Z1" ) +# s.import_all() + + +def check(dataroot, analysisroot, sample, dset, destination, scans=None): + h5o = DataSet(dataroot=dataroot, analysisroot=analysisroot, sample=sample, dset=dset) + h5o.import_all(scans=scans) + h5o.save(destination) + + print("Checking: Read back from hdf5") + t = load(destination) + t.report() + return t.compare(h5o) + + +if __name__ == "__main__": + import sys + + logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) + + dataroot = sys.argv[1] + analysisroot = sys.argv[2] + sample = sys.argv[3] + dset = sys.argv[4] + destination = sys.argv[5] + + check(dataroot, analysisroot, sample, dset, destination) diff --git a/ImageD11/sinograms/lima_segmenter.py b/ImageD11/sinograms/lima_segmenter.py new file mode 100755 index 00000000..e36037ea --- /dev/null +++ b/ImageD11/sinograms/lima_segmenter.py @@ -0,0 +1,490 @@ +from __future__ import print_function, division + +""" Do segmentation of lima/eiger files with no notion of metadata +Blocking will come via lima saving, so about 1000 frames per file + +Make the code parallel over lima files ... + minimal interprocess communication intended + - read an input file which has the source/destination file names for this job +""" + + +import sys +import time +import os +import math +import logging +import numpy as np +import h5py +import hdf5plugin +import fabio +import numba +import warnings + +from ImageD11 import sparseframe +from ImageD11.sinograms import dataset +import ImageD11.cImageD11 + +try: + from bslz4_to_sparse import chunk2sparse +except ImportError: + chunk2sparse = None + +# Code to clean the 2D image and reduce it to a sparse array: +# things we might edit +class SegmenterOptions: + # These are the stuff that belong to us in the hdf5 file + # (in our group: lima_segmenter) + jobnames = ( + "cut", + "howmany", + "pixels_in_spot", + "maskfile", + "bgfile", + "cores_per_job", + "files_per_core", + ) + + # There are things that DO NOT belong to us + datasetnames = ("limapath", "analysispath", "datapath", "imagefiles", "sparsefiles") + + def __init__( + self, + cut=1, # keep values abuve cut in first look at image + howmany=100000, # max pixels per frame to keep + pixels_in_spot=3, + maskfile="", + bgfile="", + cores_per_job=8, + files_per_core=8, + ): + self.cut = cut + self.howmany = howmany + self.pixels_in_spot = pixels_in_spot + self.maskfile = maskfile + self.bgfile = bgfile + self.bg = None + self.files_per_core = files_per_core + self.cores_per_job = cores_per_job + + def __repr__(self): + return "\n".join( + [ + "%s:%s" % (name, getattr(self, name, None)) + for name in self.jobnames + self.datasetnames + ] + ) + + def setup(self): + self.thresholds = tuple([self.cut * pow(2, i) for i in range(6)]) + # validate input + if len(self.maskfile): + # The mask must have: + # 0 == active pixel + # 1 == masked pixel + self.mask = 1 - fabio.open(self.maskfile).data.astype(np.uint8) + assert self.mask.min() < 2 + assert self.mask.max() >= 0 + print( + "# Opened mask", + self.maskfile, + " %.2f %% pixels are active" % (100 * self.mask.mean()), + ) + if len(self.bgfile): + self.bg = fabio.open(self.bgfile).data + + def load(self, h5name, h5group): + + with h5py.File(h5name, "r") as hin: + grp = hin[h5group] + pgrp = grp.parent + for name in self.jobnames: + if name in grp.attrs: + setattr(self, name, grp.attrs.get(name)) + for name in self.datasetnames: + # datasetnames = ( 'limapath', 'analysispath', 'datapath', + # 'imagefiles', 'sparsefiles' ) + if name in pgrp.attrs: + data = pgrp.attrs.get(name) + setattr(self, name, data) + elif name in pgrp: + data = pgrp[name][()] + if name.endswith("s"): # plural + if isinstance(data, np.ndarray): + data = list(data) + if isinstance(data[0], np.ndarray) or isinstance( + data[0], bytes + ): + data = [x.decode() for x in data] + else: + data = str(data) + setattr(self, name, data) + else: + logging.warning("Missing " + name) + self.setup() + + def save(self, h5name, h5group): + logging.info("saving to " + h5name + "::" + h5group) + with h5py.File(h5name, "a") as hout: + grp = hout.require_group(h5group) + for name in self.jobnames: + value = getattr(self, name, None) + print(name, value) + if value is not None: + grp.attrs[name] = value + + +@numba.njit +def select(img, mask, row, col, val, cut): + # TODO: This is in now cImageD11.tosparse_{u16|f32} + # Choose the pixels that are > cut and put into sparse arrays + k = 0 + for s in range(img.shape[0]): + for f in range(img.shape[1]): + if img[s, f] * mask[s, f] > cut: + row[k] = s + col[k] = f + val[k] = img[s, f] + k += 1 + return k + + +@numba.njit +def top_pixels(nnz, row, col, val, howmany, thresholds): + """ + selects the strongest pixels from a sparse collection + - THRESHOLDS should be a sorted array of potential cutoff values to try + that are higher than the original cutoff used to select data + - howmany is the maximum number of pixels to return + """ + # quick return if there are already few enough pixels + if nnz <= howmany: + return nnz + # histogram of how many pixels are above each threshold + h = np.zeros(len(thresholds), dtype=np.uint32) + for k in range(nnz): + for i, t in enumerate(thresholds): + if val[k] > t: + h[i] += 1 + else: + break + # choose the one to use. This is the first that is lower than howmany + tcut = thresholds[-1] + for n, t in zip(h, thresholds): + if n < howmany: + tcut = t + break + # now we filter the pixels + n = 0 + for k in range(nnz): + if val[k] > tcut: + row[n] = row[k] + col[n] = col[k] + val[n] = val[k] + n += 1 + if n >= howmany: + break + return n + + +OPTIONS = None # global. Nasty. + + +class frmtosparse: + def __init__(self, mask, dtype): + # cache the mallocs on this function. Should be one per process + self.row = np.empty(mask.size, np.uint16) + self.col = np.empty(mask.size, np.uint16) + self.val = np.empty(mask.size, dtype) + self.mask = mask + + def __call__(self, frm, cut): + nnz = select(frm, self.mask, self.row, self.col, self.val, cut) + return nnz, self.row[:nnz], self.col[:nnz], self.val[:nnz] + + +def clean(nnz, row, col, val): + global OPTIONS + if nnz == 0: + return None + if nnz > OPTIONS.howmany: + nnz = top_pixels(nnz, row, col, val, OPTIONS.howmany, OPTIONS.thresholds) + # Now get rid of the single pixel 'peaks' + # (for the mallocs, data is copied here) + s = sparseframe.sparse_frame( + row[:nnz].copy(), col[:nnz].copy(), OPTIONS.mask.shape + ) + s.set_pixels("intensity", val[:nnz].copy()) + else: + s = sparseframe.sparse_frame(row, col, OPTIONS.mask.shape) + s.set_pixels("intensity", val) + if OPTIONS.pixels_in_spot <= 1: + return s + # label them according to the connected objects + s.set_pixels("f32", s.pixels["intensity"].astype(np.float32)) + npk = sparseframe.sparse_connected_pixels( + s, threshold=0, data_name="f32", label_name="cp" + ) + # only keep spots with more than 3 pixels ... + # mom = sparseframe.sparse_moments( s, + # intensity_name="f32", + # labels_name="cp" ) + # npx = mom[:, cImageD11.s2D_1] + npx = np.bincount(s.pixels["cp"], minlength=npk) + pxcounts = npx[s.pixels["cp"]] + pxmsk = pxcounts >= OPTIONS.pixels_in_spot + if pxmsk.sum() == 0: + return None + sf = s.mask(pxmsk) + return sf + + +def reader(frms, mask, cut): + """ + iterator to read chunks or frames and segment them + returns sparseframes + """ + if ( + (chunk2sparse is not None) + and ("32008" in frms._filters) + and (not frms.is_virtual) + and (OPTIONS.bg is None) + ): + print("# reading compressed chunks") + fun = chunk2sparse(mask, dtype=frms.dtype) + for i in range(frms.shape[0]): + filters, chunk = frms.id.read_direct_chunk((i, 0, 0)) + npx, row, col, val = fun.coo(chunk, cut) + spf = clean(npx, row, col, val) + yield spf + else: + fun = frmtosparse(mask, frms.dtype) + for i in range(frms.shape[0]): + frm = frms[i] + if OPTIONS.bg is not None: + frm = frm.astype(np.float32) - OPTIONS.bg + npx, row, col, val = fun(frm, cut) + spf = clean(npx, row, col, val) + yield spf + + +def segment_lima(args): + """Does segmentation on a single hdf5 + srcname, + destname, + dataset + """ + srcname, destname, dataset = args + # saving compression style: + opts = { + "chunks": (10000,), + "maxshape": (None,), + "compression": "lzf", + "shuffle": True, + } + start = time.time() + with h5py.File(destname, "a") as hout: + with h5py.File(srcname, "r") as hin: + if dataset not in hin: + print("Missing", dataset, "in", srcname) + return + # TODO/fixme - copy some headers over + print("# ", srcname, destname, dataset) + print("# time now", time.ctime(), "\n#", end=" ") + frms = hin[dataset] + g = hout.require_group(dataset) + row = g.create_dataset("row", (0,), dtype=np.uint16, **opts) + col = g.create_dataset("col", (0,), dtype=np.uint16, **opts) + # can go over 65535 frames in a scan + # num = g.create_dataset("frame", (1,), dtype=np.uint32, **opts) + sig = g.create_dataset("intensity", (0,), dtype=frms.dtype, **opts) + nnz = g.create_dataset("nnz", (frms.shape[0],), dtype=np.uint32) + g.attrs["itype"] = np.dtype(np.uint16).name + g.attrs["nframes"] = frms.shape[0] + g.attrs["shape0"] = frms.shape[1] + g.attrs["shape1"] = frms.shape[2] + npx = 0 + nframes = frms.shape[0] + for i, spf in enumerate(reader(frms, OPTIONS.mask, OPTIONS.cut)): + if i % 100 == 0: + if spf is None: + print("%4d 0" % (i), end=",") + else: + print("%4d %d" % (i, spf.nnz), end=",") + sys.stdout.flush() + if spf is None: + nnz[i] = 0 + continue + if spf.nnz + npx > len(row): + row.resize(spf.nnz + npx, axis=0) + col.resize(spf.nnz + npx, axis=0) + sig.resize(spf.nnz + npx, axis=0) + row[npx:] = spf.row[:] + col[npx:] = spf.col[:] + sig[npx:] = spf.pixels["intensity"] + # num[npx:] = i + nnz[i] = spf.nnz + npx += spf.nnz + g.attrs["npx"] = npx + end = time.time() + print("\n# Done", nframes, "frames", npx, "pixels fps", nframes / (end - start)) + return destname + + # the output file should be flushed and closed when this returns + + +OPTIONS = None + + +def initOptions(h5name, jobid): + global OPTIONS + OPTIONS = SegmenterOptions() + OPTIONS.load(h5name, "lima_segmenter") + OPTIONS.jobid = jobid + + +def main(h5name, jobid): + global OPTIONS + initOptions(h5name, jobid) + options = OPTIONS + assert options is not None + assert OPTIONS is not None + args = [] + files_per_job = options.cores_per_job * options.files_per_core # 64 files per job + start = options.jobid * files_per_job + end = min((options.jobid + 1) * files_per_job, len(options.imagefiles)) + for i in range(start, end): + args.append( + ( + os.path.join(options.datapath, options.imagefiles[i]), # src + os.path.join(options.analysispath, options.sparsefiles[i]), # dest + options.limapath, + ) + ) + if 1: + ImageD11.cImageD11.check_multiprocessing(patch=True) # avoid fork + import multiprocessing + + with multiprocessing.Pool( + processes=options.cores_per_job, + initializer=initOptions, + initargs=(h5name, jobid), + ) as mypool: + donefile = sys.stdout + for fname in mypool.map(segment_lima, args, chunksize=1): + donefile.write(fname + "\n") + donefile.flush() + else: + for arg in args: + fname = segment_lima(arg) + print(fname) + sys.stdout.flush() + + print("All done") + + +def setup_slurm_array(dsname, dsgroup="/", pythonpath=None): + """Send the tasks to slurm""" + dso = dataset.load(dsname, dsgroup) + nfiles = len(dso.sparsefiles) + dstlima = [os.path.join(dso.analysispath, name) for name in dso.sparsefiles] + done = 0 + for d in dstlima: + if os.path.exists(d): + done += 1 + print("total files to process", nfiles, "done", done) + if done == nfiles: + return None + sdir = os.path.join(dso.analysispath, "slurm") + if not os.path.exists(sdir): + os.makedirs(sdir) + options = SegmenterOptions() + options.load(dsname, dsgroup + "/lima_segmenter") + + files_per_job = options.files_per_core * options.cores_per_job + jobs_needed = math.ceil(nfiles / files_per_job) + sbat = os.path.join(sdir, "lima_segmenter_slurm.sh") + if pythonpath is None: + cmd = sys.executable + else: + cmd = "PYTHONPATH=%s %s" % (pythonpath, sys.executable) + command = ( + "%s -m ImageD11.sinograms.lima_segmenter segment %s $SLURM_ARRAY_TASK_ID" + % (cmd, dsname) + ) + with open(sbat, "w") as fout: + fout.write( + """#!/bin/bash +#SBATCH --job-name=array-lima_segmenter +#SBATCH --output=%s/lima_segmenter_%%A_%%a.out +#SBATCH --error=%s/lima_segmenter_%%A_%%a.err +#SBATCH --array=0-%d +#SBATCH --time=02:00:00 +# define memory needs and number of tasks for each array job +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=%d +# +date +echo Running on $HOSTNAME : %s +OMP_NUM_THREADS=1 %s > %s/lima_segmenter_$SLURM_ARRAY_TASK_ID.log 2>&1 +date +""" + % (sdir, sdir, jobs_needed, options.cores_per_job, command, command, sdir) + ) + logging.info("wrote " + sbat) + return sbat + + +def setup( + dsname, + cut=None, # keep values abuve cut in first look at image + howmany=100000, # max pixels per frame to keep + pixels_in_spot=3, + maskfile="", + bgfile="", + cores_per_job=8, + files_per_core=8, + pythonpath=None, +): + """ + Writes options into the dataset file + cut=None is replaced by 1 for eiger, 25 otherwise + pythonpath -> point to a non-default install + """ + dso = dataset.load(dsname) + if cut is None: + if "eiger" in dso.limapath: + cut = 1 + else: + cut = 25 # 5 sigma + if len(maskfile) == 0 and ("eiger" in dso.limapath): + warnings.warn("Eiger detector needs a maskfile that is missing") + options = SegmenterOptions( + cut=cut, + howmany=howmany, + pixels_in_spot=pixels_in_spot, + maskfile=maskfile, + bgfile=bgfile, + cores_per_job=cores_per_job, + files_per_core=files_per_core, + ) + options.save(dsname, "lima_segmenter") + return setup_slurm_array(dsname, pythonpath=pythonpath) + + +def segment(): + # Uses a hdffile from dataset.py to steer the processing + # everything is passing via this file. + # + h5name = sys.argv[2] + jobid = int(sys.argv[3]) + main(h5name, jobid) + + +if __name__ == "__main__": + + if sys.argv[1] == "setup": + setup(sys.argv[2]) + + if sys.argv[1] == "segment": + segment() diff --git a/ImageD11/sinograms/point_by_point.py b/ImageD11/sinograms/point_by_point.py new file mode 100755 index 00000000..570df6e3 --- /dev/null +++ b/ImageD11/sinograms/point_by_point.py @@ -0,0 +1,430 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Try to build the point-by-point mapping code ... +from __future__ import print_function, division + +import multiprocessing +# method = multiprocessing.set_start_method("forkserver") +from ImageD11 import cImageD11 # NO!!! patches to forkserver + +import sys, os +from multiprocessing.managers import SharedMemoryManager +import time, random +import numpy as np +import numba +import pprint +from ImageD11 import sym_u, unitcell, parameters +import ImageD11.sinograms.dataset +import ImageD11.indexing + + +@numba.njit(boundscheck=True) +def hkluniq(ubi, gx, gy, gz, eta, m, tol, hmax): + """count uniq peaks - move to cImageD11 in the future""" + # index from -hmax to hmax + hcount = np.zeros((2 * hmax + 1, 2 * hmax + 1, 2 * hmax + 1, 2), dtype=np.int32) + tcount = 0 + for i in range(len(gx)): + if ~m[i]: + continue + H = ubi[0][0] * gx[i] + ubi[0][1] * gy[i] + ubi[0][2] * gz[i] + K = ubi[1][0] * gx[i] + ubi[1][1] * gy[i] + ubi[1][2] * gz[i] + L = ubi[2][0] * gx[i] + ubi[2][1] * gy[i] + ubi[2][2] * gz[i] + ih = np.round(H) + ik = np.round(K) + il = np.round(L) + if eta[i] > 0: + s = 1 + else: + s = 0 + dh2 = (H - ih) ** 2 + (K - ik) ** 2 + (L - il) ** 2 + if dh2 < tol * tol: + # fixme sign(yl) + if abs(ih) < hmax and abs(ik) < hmax and abs(il) < hmax: + hcount[int(ih) + hmax, int(ik) + hmax, int(il) + hmax, s] = 1 + tcount += 1 + ucount = 0 + for i in range(hcount.size): + if hcount.flat[i] > 0: + ucount += 1 + return tcount, ucount + + +def dtymask(i, j, cosomega, sinomega, dtyi, step, y0): + """ + Selects the peaks using integer bins in dty. + Needs a UI / plot to see what is happening (map the i,j to the sinogram and recon) + """ + dty_icalc = np.round((step * (i * cosomega + j * sinomega) + y0) / step).astype(int) + return dtyi == dty_icalc + + +def idxpoint( + i, + j, + isel, + cosomega, + sinomega, + dtyi, + gx, + gy, + gz, + eta, + ystep=2.00, + y0=0.0, + minpks=1000, + hkl_tol=0.1, + ds_tol=0.005, + cosine_tol=np.cos(np.radians(90 - 0.1)), + forgen=None, + hmax=-1, # auto + uniqcut=0.75, +): + """ + Indexing function called at one point in space. + Selects peaks from the sinogram and attempts to index. + More of this could be indexing.py I guess. + + i,j = integer step from the rotation axis center + + Effectively a columnfile: + isel = column of rings to use + cosomega = column of cosine omega + sinomega = column of sin omega + dtyi = column of dty on integer bins + gx/gy/gz/eta = peak co-ordinates + + ystep, y0 = compute the dtyi bins to select peaks (dtymask function above) + + forgen = the rings to use for INDEXING / orientation searching (a speedup effect) + + minpks = number of peaks to find with rings + hkl_tol = ImageD11 tolerance of indexed == | h-int(h) | < hkl_tol + cosine_tol = ImageD11 tolerance for finding pairs of peaks + + uniqcut = unique peaks cutoff (uniq> ucut * max are returned) + + returns a list of : + (npks, nuniq, ubi) + """ + cImageD11.cimaged11_omp_set_num_threads(1) + # args isel, cosomega, sinomega, dtyi, gx, gy, gz + mr = isel + m = dtymask(i, j, cosomega, sinomega, dtyi, ystep, y0) + # the arrays to use for indexing (this point in space) + igx = gx[m & mr] + igy = gy[m & mr] + igz = gz[m & mr] + ind = ImageD11.indexing.indexer( + unitcell=ucglobal, + wavelength=parglobal.get("wavelength"), + gv=np.array((igx, igy, igz)).T, + ) + ind.minpks = minpks + ind.hkl_tol = hkl_tol + ind.cosine_tol = cosine_tol # degrees + ind.ds_tol = ds_tol + ind.assigntorings() + for ind.ring_1 in forgen: + for ind.ring_2 in forgen: + ind.find() + ind.scorethem() + if ImageD11.indexing.loglevel <= 3: + sys.stdout.flush() + global symglobal + ind.ubis = [sym_u.find_uniq_u(ubi, symglobal) for ubi in ind.ubis] + # count the unique peaks per grain + uniqs = np.empty((len(ind.ubis), 2), int) + for i, ubi in enumerate(ind.ubis): + # pass in the dty mask for peak counting with all peaks + uniqs[i] = hkluniq(ubi, gx, gy, gz, eta, m, hkl_tol, hmax) + if len(ind.ubis) == 0: + return [ + (0, 0, np.eye(3)), + ] + if len(ind.ubis) == 1: + return [ + (uniqs[0][0], uniqs[0][1], ind.ubis[0]), + ] + order = np.lexsort(uniqs.T)[::-1] # decreasing ... + best = uniqs[order[0]][1] # number of unique spots + last = int(best * uniqcut) + grains = [] + for i in order: + ntotal, nuniq = uniqs[i] + if nuniq < last: + break + grains.append((ntotal, nuniq, ind.ubis[i])) + return grains + + +def proxy(args): + """Wrapper function for multiprocessing""" + i, j, memcolf, idxopts = args + sm = {} + for col in "gx", "gy", "gz", "dtyi", "isel", "cosomega", "sinomega", "eta": + shape, dtype, mem = memcolf[col] + sm[col] = np.ndarray(shape, dtype, buffer=mem.buf) + g = idxpoint( + i, + j, + sm["isel"], + sm["cosomega"], + sm["sinomega"], + sm["dtyi"], + sm["gx"], + sm["gy"], + sm["gz"], + sm["eta"], + **idxopts + ) + return i, j, g + + +# one of these per process: +ucglobal = None +symglobal = None +parglobal = None + + +def initializer(parfile, symmetry, loglevel=3): + global ucglobal, symglobal, parglobal + parglobal = parameters.read_par_file(parfile) + ucglobal = unitcell.unitcell_from_parameters(parglobal) + symglobal = sym_u.getgroup(symmetry)() + ImageD11.indexing.loglevel = loglevel + + +class PBP: + def __init__( + self, + parfile, + dset, + hkl_tol=0.01, + fpks=0.7, + ds_tol=0.005, + etacut=0.1, + ifrac=None, + gmax=5, + cosine_tol=np.cos(np.radians(90 - 0.1)), + y0=0, + symmetry="cubic", + loglevel=3, + foridx=None, + forgen=None, + uniqcut=0.75, + ): + """ + parfile = ImageD11 parameter file (for the unit cell + geometry) + dsname = name of dset file, or object with "ybincens" array + + """ + self.parfile = parfile + self.hkl_tol = hkl_tol + self.fpks = fpks + self.ds_tol = ds_tol + self.etacut = etacut + self.symmetry = symmetry + self.foridx = foridx + self.forgen = forgen + self.gmax = gmax + self.dset = dset + self.ybincens = np.array(dset.ybincens) + if ifrac is None: + self.ifrac = 1.0 / len(self.ybincens) + else: + self.ifrac = ifrac + self.ystep = abs(self.ybincens[-1] - self.ybincens[0]) / ( + len(self.ybincens) - 1 + ) + self.y0 = y0 + self.uniqcut = uniqcut + self.cosine_tol = cosine_tol + self.loglevel = loglevel + + def setpeaks(self, colf): + """ + This is called on the main parent process + The workers only need the init and they will pick up the shared + memory defined in here + """ + # Load the peaks + colf.parameters.loadparameters(self.parfile) + colf.updateGeometry() # for ds + # + uc = unitcell.unitcell_from_parameters(colf.parameters) + uc.makerings(colf.ds.max()) + # peaks that are on rings + sel = np.zeros(colf.nrows, bool) + # rings to use for indexing + isel = np.zeros(colf.nrows, bool) + npks = 0 + if self.foridx is None: + self.foridx = range(len(uc.ringds)) + hmax = 0 + for i in range(len(uc.ringds)): + ds = uc.ringds[i] + rm = abs(colf.ds - ds) < self.ds_tol + hkls = uc.ringhkls[ds] + if i in self.foridx and rm.sum() > 0: + icut = np.max(colf.sum_intensity[rm]) * self.ifrac + rm = rm & (colf.sum_intensity > icut) + isel |= rm + npks += len(hkls) + print( + i, + "%.4f" % (ds), + hkls[-1], + len(hkls), + rm.sum(), + "used, sum_intensity>", + icut, + ) + sel |= rm + else: + print(i, "%.4f" % (ds), hkls[-1], len(hkls), rm.sum(), "skip") + for hkl in hkls: + hmax = max(np.abs(hkl).max(), hmax) + if self.forgen is None: + self.forgen = self.foridx + + isel = isel & ((np.abs(np.sin(np.radians(colf.eta)))) > self.etacut) + + colf.addcolumn(isel, "isel") # peaks selected for indexing + colf.addcolumn(np.sin(np.radians(colf.omega)), "sinomega") + colf.addcolumn(np.cos(np.radians(colf.omega)), "cosomega") + colf.addcolumn(np.round((colf.dty - self.y0) / self.ystep).astype(int), "dtyi") + # peaks that are on any rings + self.colf = colf + self.icolf = colf.copy() + self.icolf.filter(sel) + self.npks = npks + if self.fpks < 1: + self.minpks = int(npks * self.fpks) + else: + self.minpks = self.fpks + print( + "Using for indexing:", + self.icolf.nrows, + "npks, minpks, forgen", + self.npks, + self.minpks, + self.forgen, + ) + self.hmax = hmax + + def iplot(self): + import pylab as pl + + f, ax = pl.subplots(2, 1) + ax[0].plot(self.colf.tth, self.colf.sum_intensity, ",") + ax[0].plot(self.icolf.tth, self.icolf.sum_intensity, ",") + ax[0].set(yscale="log", ylabel="sum intensity", xlabel="tth") + histo = self.dset.sinohist( + omega=self.icolf.omega, dty=self.icolf.dty, weights=self.icolf.sum_intensity + ) + f.colorbar( + ax[1].pcolormesh( + self.dset.obinedges, + self.dset.ybinedges, + histo.T, + norm=pl.matplotlib.colors.LogNorm(), + ), + ax=ax[1], + ) + ax[1].set(ylabel="dty", xlabel="omega") + return f, ax + + def point_by_point( + self, + grains_filename, + nprocs=None, + gridstep=1, + debugpoints=None, + loglevel=None, + ): + if loglevel is not None: + self.loglevel = loglevel + start = time.time() + # FIXME - look at dataset ybincens and roi_iradon output_size ? + if nprocs is None: + nprocs = len(os.sched_getaffinity(os.getpid())) + + idxopt = { + "ystep": self.ystep, + "y0": self.y0, + "minpks": self.minpks, + "hkl_tol": self.hkl_tol, + "ds_tol": self.ds_tol, + "cosine_tol": self.cosine_tol, + "forgen": self.forgen, + "hmax": self.hmax, + "uniqcut": self.uniqcut, + } + pprint.pprint(idxopt) + + nlo = np.floor((self.ybincens.min() - self.y0) / self.ystep).astype(int) + nhi = np.ceil((self.ybincens.max() - self.y0) / self.ystep).astype(int) + rng = range(nlo, nhi + 1, gridstep) + + with SharedMemoryManager() as smm: # this runs in the parent process + memcolf = {} + for col in "gx", "gy", "gz", "dtyi", "isel", "cosomega", "sinomega", "eta": + ar = getattr(self.icolf, col) + mem = smm.SharedMemory(size=ar.itemsize * ar.size) + mar = np.ndarray(ar.shape, dtype=ar.dtype, buffer=mem.buf) + mar[:] = ar[:] # copy into shm + memcolf[col] = ( + ar.shape, + ar.dtype, + mem, + ) # this is a shared memory reference + + if debugpoints is None: + args = [(i, j, memcolf, idxopt) for i in rng for j in rng] + random.shuffle(args) + else: + args = [(i, j, memcolf, idxopt) for i, j in debugpoints] + gmap = {} + t0 = time.time() + with open(grains_filename, "w") as gout: + gout.write( + "# i j ntotal nuniq ubi00 ubi01 ubi02 ubi10 ubi11 ubi12 ubi20 ubi21 ubi22\n" + ) + done = 0 + ng = 0 + with multiprocessing.Pool( + nprocs, + initializer=initializer, + initargs=(self.parfile, self.symmetry, self.loglevel), + ) as p: + for i, j, g in p.imap_unordered(proxy, args): + gmap[i, j] = g + done += 1 + ng += len(g) + if done % nprocs == 0: + sys.stdout.flush() # before! + dt = time.time() - t0 + print( + "Done %7.3f %%, average grains/point %6.2f, %.3f /s/point, total %.1f /s" + % ( + 100.0 * done / len(args), + float(ng) / done, + dt / done, + dt, + ), + end="\r", + ) + sys.stdout.flush() + for k in range(min(self.gmax, len(g))): + # only output gmax grains per point max + n, u, ubi = g[k] + # print(g[k]) + gout.write("%d %d %d %d " % (i, j, n, u)) + gout.write(("%f " * 9) % tuple(ubi.ravel()) + "\n") + gout.flush() + + end = time.time() + print(end - start, "seconds", (end - start) / len(args), "s per point") + print(end - t0, "seconds", (end - t0) / len(args), "s per point without setup") diff --git a/ImageD11/sinograms/polefigures.py b/ImageD11/sinograms/polefigures.py new file mode 100644 index 00000000..2230e824 --- /dev/null +++ b/ImageD11/sinograms/polefigures.py @@ -0,0 +1,91 @@ + +"""WARNING: work in progress""" + + +# given a dataset + parameters + unit cell (+ rings) + sparsepixels : create polefigues +import numpy as np +import pylab as pl +import os +import ImageD11.sinograms.dataset +import ImageD11.sparseframe +import ImageD11.transform +import ImageD11.unitcell +import ImageD11.parameters + + +def main( pars, + dataset, + + ): + pass + + +class splatter: + """ splats pixels onto polefigures """ + def __init__(self, + parfile, + dxfile = '/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dx.edf', + dyfile = '/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dy.edf'): + self.pardict = ImageD11.parameters.parameters(parfile).parameters + self.pardict['dxfile'] = dxfile + self.pardict['dyfile'] = dyfile + self.pLUT = ImageD11.transform.pixelLUT( self.pardict ) + + def process(self, dsfile): + ds = ImageD11.sinograms.dataset( dsfile ) + + + + + +if 0: + import numba + ds = ImageD11.sinograms.dataset.load('/data/visitor/blc14570/id11/20230425/PROCESSED_DATA/ds_Martensite500C_DTz50.h5') + ds.omega.shape, len(ds.sparsefiles) + chosen = ds.omega.shape[0]//2 + 1 + sps = ImageD11.sparseframe.SparseScan( os.path.join(ds.analysispath, ds.sparsefiles[chosen]), ds.limapath ) + pars = ImageD11.parameters.read_par_file( '/data/visitor/blc14570/id11/20230425/PROCESSED_DATA/FeBCC.par' ).parameters + pars['dxfile'] = '/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dx.edf' + pars['dyfile'] = '/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dy.edf' + plut = ImageD11.transform.PixelLUT( pars ) + ds = 2 * np.sin( np.radians( plut.tth / 2 ) ) / pars['wavelength'] + a0 = 2.86 + uc = ImageD11.unitcell.unitcell( [a0,a0,a0,90,90,90,],'I') + uc.makerings( ds.max() ) + labels = np.zeros( ds.shape, dtype=np.uint32 ) + for i, dsr in enumerate( uc.ringds ): + tol = 4e-3 * ds + 1e-2 + m = abs( ds - dsr ) < tol + labels[m] = i + 1 + # eta values + etabin = np.round( ( plut.eta % 360 ) / 0.1 ).astype(int) + etabin.min(), etabin.max() + output_shape = (labels.max()+1, sps.shape[0], 3601) + pfs = np.zeros( output_shape, int) + ipf = labels[ sps.row, sps.col ] + ifrm = np.zeros( len(sps.row), int ) + for i in range( len(sps.ipt) - 1): + ifrm[ sps.ipt[i]: sps.ipt[i+1] ] = i + ieta = etabin[ sps.row, sps.col ] + tth_step = plut.tth.max() / 3000 + @numba.njit + def accumulate( ipf, ifrm, ieta, intensity, output ): + for j in range(ipf.size): + output[ ipf.flat[j], ifrm.flat[j], ieta.flat[j] ] += intensity[j] + accumulate( ipf, ifrm, ieta, sps.intensity, pfs ) + + tthpf = np.zeros( output_shape, int) + tthbin = np.round( plut.tth / tth_step ).astype(int) + assert tthbin.max() <= 3001 + itth = tthbin[ sps.row, sps.col ] + accumulate( ipf, ifrm, itth, sps.intensity, tthpf ) + + f,a = pl.subplots(3,7, figsize=(21,7)) + for ax, p, j in zip(a.ravel(), pfs[1:], range(len(pfs[1:]))): + vmx = max( p.max(), 1 ) + ax.imshow(p, norm=pl.matplotlib.colors.LogNorm(vmin=0.1, vmax=vmx)) + hkls = uc.ringhkls[ uc.ringds[j] ] + title=str(hkls[-1]) + " M=%d"%(len(hkls)) + ax.set( xlabel= 'eta', ylabel='omega', xticks = [], yticks = [], title=title ) + + \ No newline at end of file diff --git a/ImageD11/sinograms/pread.py b/ImageD11/sinograms/pread.py new file mode 100644 index 00000000..dd360724 --- /dev/null +++ b/ImageD11/sinograms/pread.py @@ -0,0 +1,140 @@ + +"""WARNING: work in progress - look at mpi and/or uncompressed data instead?""" +from __future__ import print_function + +import os +import h5py +import sys +import numpy as np +import ImageD11.sinograms.properties +import multiprocessing +import concurrent.futures + +def read_segment( args ): + hname, dset, shmd, start, end = args + tbl = ImageD11.sinograms.properties.pks_table.fromSHM( shmd ) + with h5py.File( open(hname, "rb"), 'r') as hin: + sys.stdout.flush() + s = start + b = 1024*1024*10 + assert end <= tbl.glabel.shape[0] + assert end <= tbl.pk_props.shape[1] + while s < end: + e = min( end, s+b ) + hin[dset]['pk_props'].read_direct( tbl.pk_props, + np.s_[:, s:e], np.s_[:, s:e] ) + hin[dset]['glabel'].read_direct( tbl.glabel, + np.s_[s:e], np.s_[s:e] ) + s = e + total_bytes = tbl.pk_props[:,start:end].nbytes + tbl.glabel[start:end].nbytes + del tbl + return start, end + +def create( hname, dset ): + with h5py.File(hname, 'r') as hin: + tbl = ImageD11.sinograms.properties.pks_table() + tbl.npk = hin['pks2d/npk'][:] + tbl.nlabel = hin['pks2d'].attrs['nlabel'] + tbl.ipk = hin['pks2d/ipk'][:] + for name in 'pk_props', 'glabel': + dset = hin['pks2d'][name] + setattr( tbl, name, tbl.share( name, + shape = dset.shape, + dtype = dset.dtype ) ) + del dset + + return tbl + +def pread( tbl, hname, dset='pks2d', nproc=0 ): + if nproc == 0: + nproc = len( os.sched_getaffinity( os.getpid() )) + print("Using", nproc, 'processes to read') + if tbl is None: + tbl = create( hname, dset ) + shmd = tbl.export() + n = shmd[ 'glabel' ][ 'shape' ][0] + se = list(range(0,n,n//nproc)) + se[-1] = n + args = [(hname, dset, shmd, start, end) for start, end in zip(se[:-1],se[1:])] + nbytes = 0 + with multiprocessing.Pool( nproc ) as pool: + for rb in pool.map( read_segment, args ): + pass + return tbl + +def read2pipe( dest, hname, dset, selection ): + with h5py.File( open( hname, 'rb' ), 'r' ) as hin: + dest.send_bytes( hin[ dset ][ selection ][:].tobytes() ) + dest.close() + +def readpartial( args ): + ary, hname, dset, selection = args + write_end, read_end = multiprocessing.Pipe() + p = multiprocessing.Process( target = read2pipe, args = ( write_end, hname, dset, selection ) ) + p.start() + output = ary[ selection ] + output[:] = np.frombuffer( read_end.recv_bytes(), dtype = output.dtype ).reshape(output.shape) + p.join() + read_end.close() + return selection + + +def getse( shape, chunks, nproc ): + dim = len(shape) + n = [ (s+c-1) // c for s,c in zip(shape, chunks)] + print(n, 'chunks in the dataset', shape, chunks ) + ax = np.argmax(n) + nperproc = n[ax]//nproc + start = 0 + block = chunks[ax] + slices = [] + for p in range(nproc): + slc = [] + for axis in range(len(shape)): + if axis == ax: + end = start + block * nperproc + slc.append( slice( start, min(end, shape[ax] ) ) ) + start = end + else: + slc.append( slice(None, None, None) ) + slices.append( tuple(slc) ) + return slices + + +def readN( hname, dset, NPROC=40 ): + with h5py.File(hname, 'r') as hin: + ds = hin[dset] + output = np.empty( ds.shape, ds.dtype ) + chunks = ds.chunks + se = getse(output.shape, chunks, NPROC) + print(se) + with concurrent.futures.ThreadPoolExecutor( max_workers=NPROC ) as pool: + jobs = {} + for s in se: + jobs[ pool.submit( readpartial, (output, hname, dset, s ) ) ] = s + for future in concurrent.futures.as_completed(jobs): + print(future.result()) + + +if __name__=="__main__": + + hname = '/data/projects/3dipolyplast/NS_Analysis_jw/et12_z95_2/20230405/pks_ET12_7ns_slice_pz15_2.h5' + import timeit + + start = timeit.default_timer() + tbl = pread( None, hname ) + end = timeit.default_timer() + print("Reading took {end-start:.2f}/s".format(**locals())) + + # Single threaded + start = timeit.default_timer() + chk = ImageD11.sinograms.properties.pks_table.load( hname ) + end = timeit.default_timer() + print("One core reading took {end-start:.2f}/s".format(**locals())) + + start = timeit.default_timer() + assert (tbl.pk_props == chk.pk_props).all() + assert (tbl.glabel == chk.glabel).all() + end = timeit.default_timer() + print("Matches {end-start:.2f}/s".format(**locals())) + diff --git a/ImageD11/sinograms/properties.py b/ImageD11/sinograms/properties.py new file mode 100755 index 00000000..61453130 --- /dev/null +++ b/ImageD11/sinograms/properties.py @@ -0,0 +1,1007 @@ +from __future__ import print_function + + +import os +import logging +import sys +import time +import tqdm +from timeit import default_timer +import numpy as np +import h5py +import hdf5plugin +import scipy.sparse +import scipy.sparse.csgraph +import ImageD11.sinograms.dataset +import ImageD11.sparseframe +import numba +import ImageD11.cImageD11 +ImageD11.cImageD11.check_multiprocessing(patch=True) + +# The first part of the code is all to run in parallel on a multicore machine +# +# The 2D sinogram needs to be processed. Each frame has 4 neighbors. +# 2 in the same rotation and 2 in the previous and next rotation +# Work is split up to be one process doing two rows and joining them +# For each worker added you process a row twice +# +# A sparse matrix is built in shared memory. +# +# perhaps some refactoring is needed +# +# it is building a sparse matrix of (npks x npks) which tags the overlaps. +# the number of entries in the matrix depends on how many peaks are overlapping. +# ... so first the overlaps are counted +# ... then each process shares their data +# ... for now the matrix is not saved. Should only be about npks * noverlaps. +# + +import multiprocessing as mp +from multiprocessing import shared_memory, resource_tracker + +##################################################################################### +def remove_shm_from_resource_tracker(): + """Monkey-patch multiprocessing.resource_tracker so SharedMemory won't be tracked + More details at: https://bugs.python.org/issue38119 + """ + self = None + + def fix_register(name, rtype): + if rtype == "shared_memory": + return + return resource_tracker._resource_tracker.register(name, rtype) + + if resource_tracker.register is not fix_register: + resource_tracker.register = fix_register + + def fix_unregister(name, rtype): + if rtype == "shared_memory": + return + return resource_tracker._resource_tracker.unregister(name, rtype) + + if resource_tracker.unregister is not fix_register: + resource_tracker.unregister = fix_register + resource_tracker.unregister = fix_unregister + if "shared_memory" in resource_tracker._CLEANUP_FUNCS: + del resource_tracker._CLEANUP_FUNCS["shared_memory"] + + +###################################################################################### + + +class shared_numpy_array: + """See: https://bugs.python.org/issue38119 + The multiprocessing pool must stick around until the process exits + """ + + def __init__(self, ary=None, shape=None, dtype=None, shmname=None, fill=None): + if ary is not None: + shape = ary.shape + dtype = ary.dtype + self.nbytes = ary.nbytes + else: + self.nbytes = np.prod(shape) * np.dtype(dtype).itemsize + + if shmname is None: + self.shm = shared_memory.SharedMemory(create=True, size=self.nbytes) + self.creator = True + else: + self.shm = shared_memory.SharedMemory(create=False, name=shmname) + self.creator = False + self.array = np.ndarray(shape, dtype, buffer=self.shm.buf) + if ary is not None: + self.array[:] = ary + if fill is not None: + self.array[:] = fill + + def __del__(self): + del self.array + self.shm.close() + if self.creator: + try: + self.shm.unlink() + except Exception as e: + print("Error: ", e) + + def export(self): + return { + "shape": self.array.shape, + "dtype": self.array.dtype, + "shmname": self.shm.name, + } + + +class tictoc: + def __init__(self): + self.t = default_timer() + + def __call__(self, msg=""): + t = default_timer() + print("%s : %.6f /s" % (msg, t - self.t)) + self.t = default_timer() + + +def pairrow(s, row): + """ + s = SparseScan + + returns SparseScan which adds a dictionary of pairs: + sourceFrame destFrame + [ idty, iomega, idty, iomega ] : nedge, array( (nedge, 3) ) + (src, dest, npixels) + """ + s.omegaorder = np.argsort(s.motors["omega"]) # not mod 360 here + s.sinorow = row + olap = ImageD11.sparseframe.overlaps_linear(s.nnz.max() + 1) + pairs = {} + for i in range(1, len(s.omegaorder)): + if (s.nnz[s.omegaorder[i]] == 0) or (s.nnz[s.omegaorder[i - 1]] == 0): + continue + f0 = s.getframe(s.omegaorder[i - 1]) + f1 = s.getframe(s.omegaorder[i]) + ans = olap( + f0.row, + f0.col, + f0.pixels["labels"], + s.nlabels[s.omegaorder[i - 1]], + f1.row, + f1.col, + f1.pixels["labels"], + s.nlabels[s.omegaorder[i]], + ) + pairs[row, s.omegaorder[i - 1], row, s.omegaorder[i]] = ans + return pairs + + +def pairscans(s1, s2, omegatol=0.051): + olap = ImageD11.sparseframe.overlaps_linear(max(s1.nnz.max(), s2.nnz.max()) + 1) + assert len(s1.nnz) == len(s2.nnz) + pairs = {} + omega_1 = s1.motors["omega"] % 360 + omega_2 = s2.motors["omega"] % 360 + for i in range(len(s1.nnz)): + # check omega angles match + o1 = omega_1[i] + j = np.argmin(abs(omega_2 - o1)) + o2 = omega_2[j] + if abs(o1 - o2) > omegatol: + # this frame has no neighbor + continue + if (s1.nnz[i] == 0) or (s2.nnz[j] == 0): + continue + f0 = s1.getframe(i) + f1 = s2.getframe(j) + ans = olap( + f0.row, + f0.col, + f0.pixels["labels"], + s1.nlabels[i], + f1.row, + f1.col, + f1.pixels["labels"], + s2.nlabels[j], + ) + pairs[s1.sinorow, i, s2.sinorow, j] = ans + return pairs + + +def props(scan, i, algorithm="lmlabel", wtmax=None): + """ + scan = sparseframe.SparseScan object + i = sinogram row id : used for tagging pairs + algorithm = 'lmlabel' | 'cplabel' + + Labels the peaks with lmlabel + Assumes a regular scan for labelling frames + returns ( row, properties[(s1,sI,sRow,sCol,frame),:], pairs, scan ) + """ + scan.sinorow = i + getattr(scan, algorithm)(countall=False) # labels all the pixels in the scan. + npks = scan.total_labels + r = np.empty((5, npks), np.int64) + s = 0 + j0 = i * scan.shape[0] + for j in range(scan.shape[0]): + if scan.nnz[j] == 0: + continue + f0 = scan.getframe(j) + e = s + scan.nlabels[j] + # [1:] means skip the background labels == 0 output + r[0, s:e] = np.bincount(f0.pixels["labels"])[1:] + wt = f0.pixels["intensity"].astype(np.int64) + if wtmax is not None: + m = wt > wtmax + n = m.sum() + if n > 0: + wt[m] = wtmax + # print(scan,'replaced',n) + signal = np.bincount(f0.pixels["labels"], weights=wt)[1:] + if signal.min() < 1: + print( + "Bad data", + scan.hname, + scan.scan, + i, + j, + scan.nlabels[j], + scan.nnz[j], + f0.pixels["intensity"].min(), + ) + raise Exception("bad data") + r[1, s:e] = signal + r[2, s:e] = np.bincount(f0.pixels["labels"], weights=f0.row * wt)[1:] + r[3, s:e] = np.bincount(f0.pixels["labels"], weights=f0.col * wt)[1:] + r[4, s:e] = j + j0 + s = e + # Matrix entries for this scan with itself: + pairs = pairrow(scan, i) + return r, pairs + + +###### testing / debug +if 0: + + def checkprops(i): + hname = "ds_MA4752_S4_2_XRD_DTL1z60_sparsefull.h5" + ds = ImageD11.sinograms.dataset.load(hname) + scan = ImageD11.sparseframe.SparseScan(hname[3:], ds.scans[i]) + return props(scan, i) + + r, p = checkprops(400) + for i, (k, (n, v)) in enumerate(p.items()): + if i < 3 or i > (len(p) - 3): + print(i, k, n) + print(v.T) +###### + + +def get_start_end(n, p): + """For splitting up the scan row over processes + n = number of jobs + p = number of processes + + All processes must do at least two rows to get the overlaps between rows. + The work sharing is based on these overlaps. + + rows : 0 1 2 3 4 5 etc. + overlap 01 12 23 34 45 + """ + overlaps = [(i - 1, i) for i in range(1, n)] + assert len(overlaps) >= p + joins_per_job = np.zeros(p, int) + for i, o in enumerate(overlaps): + joins_per_job[i % p] += 1 + assert np.sum(joins_per_job) == len(overlaps) + start = 0 + slices = [] + for i in range(p): + end = start + joins_per_job[i] + ss = overlaps[start][0] + se = overlaps[end - 1][1] + slices.append((ss, se)) + start += joins_per_job[i] + return slices + + +def countse(se): + """counts the points in a block of start/end""" + k = 0 + for s, e in se: + for i in range(s, e + 1): + k += 1 + return k + + +def testit(NPROC): + """sanity check""" + for t in range(95, 110): + pairs = set() + s = np.arange(t) + r = set() + for st, en in get_start_end(t, NPROC): + # last = None + for i in range(st, en + 1): + _ = s[i] # indexerror? + r.add(i) + if i > st: + pairs.add((i, i - 1)) + assert len(pairs) == t - 1 + assert len(r) == t + + +# testit() + + +def compute_storage(peaks): + """make the cumulative sums of peaks to figure out storage space + holds the row, npks, nii, nij + """ + P = {} + M = {} + for row, npks, nii, nij in peaks: + if row in P: + # verify duplicates + assert P[row] == npks + assert M[row][0] == nii + if nij > M[row][1]: + M[row] = nii, nij + else: + P[row] = npks + M[row] = nii, nij + ks = sorted(P.keys()) + npk = np.array([(P[k], M[k][0], M[k][1]) for k in ks]) + return ks, npk + + +class pks_table: + def __init__( + self, + npk=None, + ipk=None, + pk_props=None, + rc=None, + rpk=None, + glabel=None, + nlabel=0, + use_shm=False, + ): + """ + Cases: + Create from npks counting -> here + Read from a file -> classmethod pks_table.load( h5name ) + Read from shared memory -> classmethod pks_table.fromSHM( h5name ) + """ + self.npk = npk + self.ipk = ipk + self.pk_props = pk_props + self.rc = rc + self.rpk = rpk + self.glabel = glabel + self.nlabel = nlabel + self.use_shm = use_shm + self.shared = {} + # otherwise create + if self.npk is not None: + self.create(self.npk) + + def share(self, name, *args, **kwds): + """puts the array into shared memory + returns the shared copy + """ + self.shared[name] = shared_numpy_array(*args, **kwds) + return self.shared[name].array + + def create(self, npk): + # [ nscans, 3 ] + # number_of_peaks_in_scan + # number_of_pairs_ii + # number_of_pairs_ij + self.npk = npk + s = npk.sum(axis=0) + # pointers to peak tables per scan + ipk = np.empty(npk.shape[0] + 1, int) + ipk[0] = 0 + ipk[1:] = np.cumsum(npk[:, 0]) + self.ipk = self.share("ipk", ipk) + # pointers to r/c positions + rpk = np.empty(npk.shape[0] + 1, int) + rpk[0] = 0 + rpk[1:] = np.cumsum(npk[:, 1] + npk[:, 2]) + self.rpk = self.share("rpk", rpk) + self.pk_props = self.share("pk_props", shape=(5, s[0]), dtype=np.int64) + self.rc = self.share("rc", shape=(3, s[1] + s[2]), dtype=np.int64) + + def export(self): + return {name: self.shared[name].export() for name in self.shared} + + def __del__(self): + del self.ipk + del self.rpk + del self.pk_props + del self.rc + names = list(self.shared.keys()) + for name in names: + o = self.shared.pop(name) + del o + + def guesschunk(self, ar, m=64 * 40): + return None + """ for parallel / compressed """ + chunk = list(ar.shape) + nbytes = np.prod(chunk) * ar.dtype.itemsize + if (nbytes // m) < pow(2, 16): + print("Guessing 1 chunk for", ar.shape, ar.dtype) + return tuple(chunk) + axsplit = np.argmax(chunk) + n = chunk[axsplit] + chunk[axsplit] = max(1, n // m) + print("Guessing chunks for", ar.shape, ar.dtype, chunk) + return tuple(chunk) + + def save(self, h5name, group="pks2d", rc=False): + opts = {} # No compression is faster + with h5py.File(h5name, "a") as hout: + grp = hout.require_group(group) + ds = grp.require_dataset( + name="ipk", + shape=self.ipk.shape, + chunks=self.guesschunk(self.ipk), + dtype=self.ipk.dtype, + **opts + ) + ds[:] = self.ipk + ds.attrs["description"] = "pointer to start of a scan" + ds = grp.require_dataset( + name="pk_props", + shape=self.pk_props.shape, + chunks=self.guesschunk(self.pk_props), + dtype=self.pk_props.dtype, + **opts + ) + ds[:] = self.pk_props + ds.attrs["description"] = "[ ( s1, sI, srI, scI, id ), Npks ]" + ds = grp.require_dataset( + name="npk", + shape=self.npk.shape, + chunks=self.guesschunk(self.npk), + dtype=self.npk.dtype, + **opts + ) + ds[:] = self.npk + ds.attrs[ + "description" + ] = "[ nscans, (N_peaks_in_scan, N_pairs_ii, N_pairs_ij) ]" + if hasattr(self, "glabel") and self.glabel is not None: + ds = grp.require_dataset( + name="glabel", + shape=self.glabel.shape, + chunks=self.guesschunk(self.glabel), + dtype=self.glabel.dtype, + **opts + ) + ds[:] = self.glabel + grp.attrs["nlabel"] = self.nlabel + if rc and hasattr(self, "rc") and self.rc is not None: + ds = grp.require_dataset( + name="rc", + shape=self.rc.shape, + chunks=self.guesschunk(self.rc), + dtype=self.rc.dtype, + **opts + ) + ds[:] = self.rc + ds.attrs[ + "description" + ] = "row/col array for sparse connected pixels COO matrix" + + @classmethod + def fromSHM(cls, dct): + names = "ipk", "rpk", "pk_props", "rc", "glabel" + sharrays = { + name: shared_numpy_array(**dct[name]) for name in names if name in dct + } + arrays = {name: sharrays[name].array for name in names if name in dct} + o = cls(**arrays) + o.shared = sharrays + return o + + @classmethod + def load(cls, h5name, h5group="pks2d"): + with h5py.File(h5name, "r") as hin: + grp = hin[h5group] + ipk = grp["ipk"][:] + pk_props = grp["pk_props"][:] + if "glabel" in grp: + glabel = grp["glabel"][:] + nlabel = grp.attrs["nlabel"] + else: + glabel = None + nlabel = 0 + # rc? + npk = grp["npk"][:] + nlabel = grp.attrs["nlabel"] + obj = cls(ipk=ipk, pk_props=pk_props, glabel=glabel, nlabel=nlabel) + obj.npk = npk # this is ugly. Sending as arg causes allocate. + return obj + + def find_uniq(self, outputfile=None, use_scipy=False): + """find the unique labels from the rc array""" + t = tictoc() + n = self.ipk[-1] + # print("Row/col sparse array") + # for i in range(3): + # print(self.rc[i].dtype,self.rc[i].shape)i + if outputfile is not None: + with h5py.File(outputfile, "w") as hout: + hout["data"] = self.rc[2] + hout["i"] = self.rc[0] + hout["j"] = self.rc[1] + return None, None + if use_scipy: + coo = scipy.sparse.coo_matrix( + (self.rc[2], (self.rc[0], self.rc[1])), shape=(n, n) + ) + t("coo") + cc = scipy.sparse.csgraph.connected_components( + coo, directed=False, return_labels=True + ) + t("find connected components") + else: + cc = find_ND_labels(self.rc[0], self.rc[1], n) + self.cc = cc + self.nlabel, self.glabel = cc + return cc + + def pk2dmerge(self, omega, dty): + """ + creates a dictionary of the 3D peaks + """ + assert omega.shape == dty.shape + assert omega.size > self.pk_props[4].max() + + out = np.zeros((7, self.nlabel), float) + n = numbapkmerge(self.glabel, self.pk_props, omega, dty, out) + allpks = { + "s_raw": out[2] / out[1], + "f_raw": out[3] / out[1], + "omega": out[4] / out[1], + "Number_of_pixels": out[0], + "sum_intensity": out[1], + "dty": out[5] / out[1], + "spot3d_id": np.arange(self.nlabel), # points back to labels in pk2d + "npk2d": out[6], + } + return allpks + + def pk2d(self, omega, dty): + s1, sI, srI, scI, frm = self.pk_props + allpks = { + "s_raw": srI / sI, + "f_raw": scI / sI, + "omega": omega.flat[frm], + "dty": dty.flat[frm], + "Number_of_pixels": s1, + "sum_intensity": sI, + "spot3d_id": self.glabel, + } + return allpks + + +if 0: + + def load_and_transpose(hname, itype, vtype): + """Read in a coo file saved by pks_table.find_uniq""" + with h5py.File(hname, "r") as hin: + di = hin["i"] + ii = np.empty(len(di) * 2, itype) + jj = np.empty(len(di) * 2, itype) + vv = np.empty(len(di) * 2, vtype) + ii[: len(di)] = hin["i"][:] # this should cast when filling + ii[len(di) :] = hin["j"][:] + jj[: len(di)] = hin["j"][:] + jj[len(di) :] = hin["i"][:] + vv[: len(di)] = hin["data"][:] + vv[len(di) :] = hin["data"][:] + return ii, jj, vv + + +@numba.njit +def numbapkmerge(labels, pks, omega, dty, out): + for k in range(len(labels)): + frm = pks[4, k] + o = omega.flat[frm] + y = dty.flat[frm] + j = labels[k] + out[0, j] += pks[0, k] # s1 == number of pixels in a peak + out[1, j] += pks[1, k] # sI == sum of the intensity + out[2, j] += pks[2, k] # srI === sum of intensity * row + out[3, j] += pks[3, k] # scI === sum of intensity * column + out[4, j] += o * pks[1, k] + out[5, j] += y * pks[1, k] + out[6, j] += 1 # s0 == number of 2D peaks + return k + + +@numba.njit(parallel=True) +def numbalabelNd(i, j, pkid, flip=0): + """ + i, j are the pairs of overlapping peaks + pkid are the current labels of the peaks + + This scans all pairs and for each pair it makes + pkid[i] = pkid[j] = min(pkid[i], pkid[j]) + + Run this enough times and eventually all peaks point + back to the first one they overlap. + + Using parallel seems dodgy to me. Might be a race condition, + but it seems like the code is legal so long as only these + addresses are touched? + """ + # scanning in forwards direction + nbad = 0 + N = len(i) - 1 + for k in numba.prange(len(i)): + p = k + flip * (N - 2 * k) + # numba was fussy, no idea why, gets confused + # when indexing using an if statement. + # so made it like this: + # flip == 0 -> k + # flip == 1 -> k + flip * ( N - 2 * k ) + # -> N - k + # Changing array iteration direction seemed to + # speed up convergence. DFS or BFS is likely + # better. But stacks can overflow. + pi = pkid[i[p]] + pj = pkid[j[p]] + if pi != pj: + m = min(pi, pj) + pkid[i[p]] = m + pkid[j[p]] = m + nbad += 1 + return nbad + + +@numba.njit(parallel=True) +def get_clean_labels(labels): + """ + Given the labelling in l, put clean labels in the place. + + l = integer array with value = label of lowest 2d peak in this ND group. + """ + n = 0 + assert labels[0] == 0, "first label should be zero" + for i in range(len(labels)): # count the labels. NOT PARALLEL! + if labels[i] == i: + labels[i] = n + n += 1 + else: + labels[i] = -labels[i] # for inplace, tag the ones to be redone + for i in numba.prange(len(labels)): + j = labels[i] + if j < 0: # zeros and pointers to zero should not change. + labels[i] = labels[-j] + return n + + +def find_ND_labels(i, j, npks, verbose=1): + start = time.time() + labels = np.arange(npks, dtype=int) + flip = 0 + b = numbalabelNd(i, j, labels, flip=flip) + while 1: + if verbose > 1: + dt = time.time() - start + print("%.1f %.6f" % (dt, b / 1e6)) + if verbose == 1: + print(".", end="") + if b == 0: + break + flip = (1, 0)[flip] # exchange 1 and 0 + b = numbalabelNd(i, j, labels, flip=flip) + n = get_clean_labels(labels) + return n, labels + + +def pks_table_from_scan(sparsefilename, ds, row): + """ + Labels one rotation scan to a peaks table + + sparsefilename = sparse pixels file + dataset = ImageD11.sinograms.dataset + row = index for dataset.scan[ row ] + + returns a pks_table. + You might want to call one of "save" or "pk2d" or "pk2dmerge" on the result + + This is probably not threadsafe + """ + sps = ImageD11.sparseframe.SparseScan(sparsefilename, ds.scans[row]) + sps.motors["omega"] = ds.omega[row] + peaks, pairs = ImageD11.sinograms.properties.props(sps, row) + # which frame/peak is which in the peaks array + # For the 3D merging + n1 = sum(pairs[k][0] for k in pairs) # how many overlaps were found: + npk = np.array( + [ + (peaks.shape[1], n1, 0), + ] + ) + pkst = ImageD11.sinograms.properties.pks_table(npk=npk, use_shm=False) + pkst.pk_props = peaks + rc = pkst.rc + s = 0 + pkid = ImageD11.sparseframe.nnz_to_pointer( sps.nlabels ) + for (row1, frame1, row2, frame2), (npairs, ijn) in pairs.items(): + # add entries into a sparse matrix + # key: (500, 2, 501, 2893) + # value: (41, array([[ 1, 1, 5], + # [ 1, 2, 2], ... + if npairs == 0: + continue + # assert (row1 == i) and (row2 == i), (row1,row2,i) + e = s + npairs + assert e <= rc.shape[1] + # rc[ 0, s : e ] = ip[row1] + pkid[row1][frame1] + ijn[:,0] - 1 # col + # rc[ 1, s : e ] = ip[row2] + pkid[row2][frame2] + ijn[:,1] - 1 # row + rc[0, s:e] = pkid[frame1] + ijn[:, 0] - 1 # col + rc[1, s:e] = pkid[frame2] + ijn[:, 1] - 1 # row + rc[2, s:e] = ijn[:, 2] # num pixels + s = e + uni = pkst.find_uniq() + """ + if 0: # future TODO : scoring overlaps better. + ks = list(pairs.keys()) + k = ks[100] + ipt = pkid + npx1 = peaks[0][ipt[k[1]] : ipt[k[1] + 1]] # number of pixels in pk1 (allpeaks) + npx2 = peaks[0][ipt[k[3]] : ipt[k[3] + 1]] # number of pixels in pk2 (allpeaks) + ijo = pairs[k][1] # which peaks overlap each other + # npx1.shape, npx2.shape, ijo.shape, ijo.max(axis=0) + n1 = npx1[ijo[:, 0] - 1] # number of pixels in pk1 (overlapping) + n2 = npx2[ijo[:, 1] - 1] # number of pixels in pk2 (overlapping) + no = ijo[:, 2] + """ + # pk3d = pkst.pk2dmerge(ds.omega, ds.dty) + return pkst + + +def process(qin, qshm, qout, hname, dsfilename, options): + """ + Worker process. + qin : gives a block of scans from qin to label (start, end) + qshm : gives us shared memory to store/return results + qout : numbers of overlaps + hname = sparse pixels + dsfilename = load omega + options (passed through) + """ + dset = ImageD11.sinograms.dataset.load(dsfilename) + scans = dset.scans + remove_shm_from_resource_tracker() + start, end = qin.get() + n2 = 0 + # allocate lists to hold results + pii = {} + pij = {} + mypks = {} + pkid = {} + prev = None # suppress flake8 idiocy + # This is the 1D scan within the same row + for i in range(start, end + 1): + scan = ImageD11.sparseframe.SparseScan(hname, scans[i]) + scan.motors["omega"] = dset.omega[i] + mypks[i], pii[i] = props( + scan, i, algorithm=options["algorithm"], wtmax=options["wtmax"] + ) + pkid[i] = ImageD11.sparseframe.nnz_to_pointer( scan.nlabels ) + n1 = sum(pii[i][k][0] for k in pii[i]) + if i > start: + pij[i] = pairscans(scan, prev) + n2 = sum(pij[i][k][0] for k in pij[i]) + # number of pair overlaps required for the big matrix + qout.put((i, len(mypks[i][0]), n1, n2)) + prev = scan + # Now we are waiting for the shared memory to save the results + shm = qshm.get() + pkst = pks_table.fromSHM(shm) + ip = pkst.ipk + rc = pkst.rc + # For each row, save our local results + for i in range(start, end + 1): + if (i == start) and (i > 0): + # will be done by the previous worker + continue + # now copy our results to shared memory. First the peaks: + pkst.pk_props[:, ip[i] : ip[i + 1]] = mypks[i] + # + # find the unique starting id for each frame: + # Where to store these in the shared memory + s = ipstart = pkst.rpk[i] + ipend = pkst.rpk[i + 1] + # + if 0: # debugging + n1 = sum(pii[i][k][0] for k in pii[i]) + if i > start: + n2 = sum(pij[i][k][0] for k in pij[i]) + else: + n2 = 0 + # # will be done by the previous worker + # print('Debugging:',i, ipstart, ipend, n1, n2, ipend-ipstart, n1+n2) + assert (ipend - ipstart) == (n1 + n2) + # + for (row1, frame1, row2, frame2), (npairs, ijn) in pii[i].items(): + # add entries into a sparse matrix + # key: (500, 2, 501, 2893) + # value: (41, array([[ 1, 1, 5], + # [ 1, 2, 2], ... + if npairs == 0: + continue + assert (row1 == i) and (row2 == i) + e = s + npairs + assert e <= ipend + rc[0, s:e] = ip[row1] + pkid[row1][frame1] + ijn[:, 0] - 1 # col + rc[1, s:e] = ip[row2] + pkid[row2][frame2] + ijn[:, 1] - 1 # row + rc[2, s:e] = ijn[:, 2] # num pixels + s = e + if i == 0: + continue # no pairs to previous row exit + for (row1, frame1, row2, frame2), (npairs, ijn) in pij[i].items(): + if npairs == 0: + continue + assert (row1 == i) and (row2 == i - 1) + e = s + npairs + assert e <= ipend + rc[0, s:e] = ip[row1] + pkid[row1][frame1] + ijn[:, 0] - 1 # col + rc[1, s:e] = ip[row2] + pkid[row2][frame2] + ijn[:, 1] - 1 # row + rc[2, s:e] = ijn[:, 2] # num pixels + s = e + qout.put(start) + + +def goforit(ds, sparsename, options): + """ + dsfilename = ImageD11.sinograms.dataset FILE + This is how processes all get omega data + sparsename = pixels to read + options + """ + qin = mp.Queue(maxsize=options["nproc"]) + qshm = mp.Queue(maxsize=options["nproc"]) + qresult = mp.Queue(maxsize=len(ds.scans)) + out = [] + with mp.Pool( + options["nproc"], + initializer=process, + initargs=(qin, qshm, qresult, sparsename, ds.dsfilename, options), + ) as pool: + slices = get_start_end(len(ds.scans), options["nproc"]) + for i, (s, e) in enumerate(slices): + qin.put((s, e)) + waiting = countse(slices) + for i in tqdm.tqdm(range(waiting)): + ans = qresult.get() + out.append(ans) + ks, P = compute_storage(out) + assert len(ks) == len(ds.scans) + mem = pks_table(P) + shm = mem.export() + # workers fill the shared memory + for i in range(options["nproc"]): + qshm.put(shm) + dones = set() + for i in tqdm.tqdm(range(options["nproc"])): + # check done + f = qresult.get() + dones.add(f) + if len(dones) == options["nproc"]: + break + return out, ks, P, mem + + +default_options = { + "algorithm": "lmlabel", + "wtmax": None, # value to replace saturated pixels + "save_overlaps": False, + "nproc": None, +} + + +def main(dsfilename, sparsefile=None, pksfile=None, options={}): + """ + dsname = ImageD11.sinograms.dataset file + sparsename = File containing sparse pixels (None = use dsname.sparsefile) + pkname = File to receive output peaks (None = use dsname.pksfile) + + options : dictionary of options, to override + + """ + default_options = { + "algorithm": "lmlabel", # | cplabel ] + "wtmax": None, # value to replace saturated pixels + "save_overlaps": False, # for debug + "nproc": None, # None == guess + } + for k in options: + if k in default_options: + default_options[k] = options[k] + else: + raise Exception("I do not understand %s in options" % (str(k))) + options = default_options + + t = tictoc() + ds = ImageD11.sinograms.dataset.load(dsfilename) + ds.dsfilename = dsfilename + if sparsefile is None: + sparsefile = ds.sparsefile + assert os.path.exists(sparsefile) + if pksfile is None: + pksfile = ds.pksfile + if os.path.exists(pksfile): + logging.warning( + "Your output file already exists. Processing likely to fail! %s" % (pksfile) + ) + try: + rmem = None + t("read ds %s" % (dsfilename)) + nscans = len(ds.scans) + if options["nproc"] is None: + if "SLURM_CPUS_PER_TASK" in os.environ: + options["nproc"] = max(1, int(os.environ["SLURM_CPUS_PER_TASK"]) - 1) + else: + options["nproc"] = len(os.sched_getaffinity(0)) + if options["nproc"] > nscans - 1: + options["nproc"] = max(1, nscans - 1) + print("Nscans", nscans) + print("Options", options) + peaks, ks, P, rmem = goforit(ds, sparsefile, options) # ds.omega is passed here + t("%d label and pair" % (len(rmem.pk_props[0]))) + if "save_overlaps" in options and options["save_overlaps"]: + rmem.save(pksfile + "_mat.h5", rc=True) + t("cache") + cc = rmem.find_uniq() + t("%s connected components" % (str(cc[0]))) + rmem.save(pksfile) + t("write hdf5") + except Exception as e: + print("Unhandled exception:", e) + raise + finally: + if rmem is not None: + print("Trying to clean up shared memory") + del rmem + return + + +if __name__ == "__main__": + dsname = sys.argv[1] + sparsename = sys.argv[2] + pkname = sys.argv[3] + algorithm = "lmlabel" + options = default_options + if len(sys.argv) >= 5: + options["algorithm"] = sys.argv[4] + + main(dsname, sparsename, pkname, options) + + print("Your stuff left in shm:") + os.system("ls -l /dev/shm | grep %s" % (os.environ["USER"])) + print("... all done") + + +""" +import numba +@numba.njit +def pkmerge( labels, pks, omega, dty, nlm, out): + k = 0 + for i in range(nlm.size): + o = omega[i] + y = dty[i] + for j in range(nlm[i]): # peaks on this frame + l = labels[k] + out[0,l] += pks[0,k] + out[1,l] += pks[1,k] + out[2,l] += pks[2,k] + out[3,l] += pks[3,k] + out[4,l] += o * pks[1,k] + out[5,l] += y * pks[1,k] + k += 1 + return k + +class h5pks_table: + def __init__(self, h5name, group='pks2d'): + with h5py.File( h5name, 'r' ) as hin: + grp = hin[group] + for name in 'npk ipk pk_props rpk'.split(): + setattr( self, name, grp[name][:] ) + +s1, sI, sr, sc, frm = pks +gpks = np.empty( (6,labels.max()+1), float ) + + +pkmerge( labels, pks, ds.omega.ravel(), ds.dty.ravel(), ds.nlm.ravel(), gpks) +sc = gpks[2] / gpks[1] +fc = gpks[3] / gpks[1] +om = gpks[4] / gpks[1] +ty = gpks[5] / gpks[1] +""" diff --git a/ImageD11/sinograms/properties.py.sav b/ImageD11/sinograms/properties.py.sav new file mode 100755 index 00000000..2ce1ef48 --- /dev/null +++ b/ImageD11/sinograms/properties.py.sav @@ -0,0 +1,597 @@ + +import os +import sys +import time +import tqdm +from timeit import default_timer +import numpy as np +import h5py +import scipy.sparse +import scipy.sparse.csgraph +import ImageD11.sinograms.dataset +from ImageD11 import sparseframe + + +### The first part of the code is all to run in parallel on a multicore machine +# +# The 2D sinogram needs to be processed. Each frame has 4 neighbors. +# 2 in the same rotation and 2 in the previous and next rotation +# Work is split up to be one process doing two rows and joining them +# For each worker added you process a row twice +# +# A sparse matrix is built in shared memory. +# +# perhaps some refactoring is needed +# +# it is building a sparse matrix of (npks x npks) which tags the overlaps. +# the number of entries in the matrix depends on how many peaks are overlapping. +# ... so first the overlaps are counted +# ... then each process shares their data +# ... for now the matrix is not saved. Should only be about npks * noverlaps. +# +import multiprocessing as mp +from multiprocessing import shared_memory, resource_tracker + +##################################################################################### +def remove_shm_from_resource_tracker(): + """Monkey-patch multiprocessing.resource_tracker so SharedMemory won't be tracked + More details at: https://bugs.python.org/issue38119 + """ + self = None + def fix_register(name, rtype): + if rtype == "shared_memory": + return + return resource_tracker._resource_tracker.register(self, name, rtype) + resource_tracker.register = fix_register + def fix_unregister(name, rtype): + if rtype == "shared_memory": + return + return resource_tracker._resource_tracker.unregister(self, name, rtype) + resource_tracker.unregister = fix_unregister + if "shared_memory" in resource_tracker._CLEANUP_FUNCS: + del resource_tracker._CLEANUP_FUNCS["shared_memory"] +###################################################################################### +remove_shm_from_resource_tracker() +NPROC = max( 1, int(os.environ['SLURM_CPUS_PER_TASK']) - 1 ) + +class shared_numpy_array: + """See: https://bugs.python.org/issue38119 + The multiprocessing pool must stick around until the process exits + """ + def __init__(self, ary=None, shape=None, dtype=None, shmname=None, + fill = None): + if ary is not None: + shape = ary.shape + dtype = ary.dtype + self.nbytes = ary.nbytes + else: + self.nbytes = np.prod(shape) * np.dtype(dtype).itemsize + if shmname is None: + self.shm = shared_memory.SharedMemory( + create=True, size = self.nbytes ) + self.creator=True + else: + self.shm = shared_memory.SharedMemory( + create=False, + name = shmname ) + self.creator=False + self.array = np.ndarray( shape, dtype, buffer=self.shm.buf) + if ary is not None: + self.array[:] = ary + if fill is not None: + self.array[:] = fill + + def __del__(self): + del self.array + self.shm.close() + if self.creator: + try: + self.shm.unlink() + except Exception as e: + print('Error: ',e) + + def export(self): + """ Returns a description of the shared memory to use for attaching """ + return { 'shape' : self.array.shape, + 'dtype' : self.array.dtype, + 'shmname' : self.shm.name } + +########################## Now to the real code + +class tictoc: + def __init__(self): + self.t = default_timer() + def __call__(self, msg = '' ): + t = default_timer() + print("%s : %.6f /s"%( msg, t - self.t ) ) + self.t = default_timer() + +def props(scan, i, firstframe=None): + """ + scan = sparseframe.SparseScan object + i = sinogram row id : used for tagging pairs + + Labels the peaks with lmlabel + Assumes a regular scan for labelling frames (e.g NxM) + returns ( row, properties[(s1,sI,sRow,sCol,frame),:], pairs, scan ) + """ + scan.sinorow = i + scan.lmlabel(countall=False) + npks = scan.total_labels + r = np.empty( (5,npks), np.int64 ) + s = 0 + if firstframe is None: # not tested or used ... + j0 = i * scan.shape[0] + else: + j0 = firstframe + for j in range(scan.shape[0]): + if scan.nnz[j] == 0: + continue + f0 = scan.getframe(j) + e = s + scan.nlabels[j] + r[0,s:e] = np.bincount( f0.pixels['labels']-1 ) + r[1,s:e] = np.bincount( f0.pixels['labels']-1, weights=f0.pixels['intensity'] ) + r[2,s:e] = np.bincount( f0.pixels['labels']-1, weights=f0.row*f0.pixels['intensity'] ) + r[3,s:e] = np.bincount( f0.pixels['labels']-1, weights=f0.col*f0.pixels['intensity'] ) + r[4,s:e] = j + j0 + s = e + # Matrix entries for this scan with itself: + pairs = pairrow( scan, i ) + return r, pairs + +###### testing / debug +if 0: + def checkprops(i): + hname = 'ds_MA4752_S4_2_XRD_DTL1z60_sparsefull.h5' + ds = ImageD11.sinograms.dataset.load(hname) + scan = sparseframe.SparseScan( hname[3:], ds.scans[i] ) + return props( scan, i ) + r, p = checkprops(400) + for i,(k,(n,v)) in enumerate(p.items()): + if i < 3 or i > (len(p)-3): + print(i,k,n) + print(v.T) +###### + +def get_start_end( n, p ): + """ For splitting up the scan row over processes + n = number of jobs + p = number of processes + + All processes must do at least two rows to get the overlaps between rows. + The work sharing is based on these overlaps. + + rows : 0 1 2 3 4 5 etc. + overlap 01 12 23 34 45 + """ + overlaps = [ (i-1,i) for i in range(1,n) ] + assert len(overlaps) >= p + joins_per_job = np.zeros(p, int) + for i,o in enumerate(overlaps): + joins_per_job[i%p] += 1 + assert np.sum(joins_per_job) == len(overlaps) + start = 0 + slices=[] + for i in range(p): + end = start + joins_per_job[i] + ss = overlaps[start][0] + se = overlaps[end-1][1] + slices.append( (ss, se) ) + start += joins_per_job[i] + return slices + +def countse( se ): + """ counts the points in a block of start/end""" + k = 0 + for s, e in se: + for i in range(s, e+1): + k += 1 + return k + +def testit(): + """ sanity check """ + for t in range(95,110): + pairs = set() + s = np.arange(t) + r = set() + for st, en in get_start_end( t, NPROC): + last = None + for i in range(st,en+1): + x = s[i] # indexerror? + r.add(i) + if i > st: + pairs.add((i,i-1)) + assert len(pairs) == t-1 + assert len(r) == t +# testit() + +def compute_storage( peaks ): + '''make the cumulative sums of peaks to figure out storage space + holds the row, npks, nii, nij + ''' + P = {} + M = {} + for row, npks, nii, nij in peaks: + if row in P: + # verify duplicates + assert P[row] == npks + assert M[row][0] == nii + if nij > M[row][1]: + M[row] = nii, nij + else: + P[row] = npks + M[row] = nii, nij + ks = sorted( P.keys() ) + npk = np.array( [ (P[k],M[k][0],M[k][1]) for k in ks ] ) + return ks, npk + + +def pairrow( s , row): + """ + s = SparseScan + + returns SparseScan which adds a dictionary of pairs: + sourceFrame destFrame + [ idty, iomega, idty, iomega ] : nedge, array( (nedge, 3) ) + (src, dest, npixels) + """ + s.omegaorder = np.argsort( s.motors["omega"] ) + s.sinorow = row + olap = sparseframe.overlaps_linear( s.nnz.max()+1 ) + pairs = {} + for i in range(1, len(s.omegaorder)): + if (s.nnz[s.omegaorder[i]] == 0) or (s.nnz[s.omegaorder[i-1]] == 0): + continue + f0 = s.getframe( s.omegaorder[i-1] ) + f1 = s.getframe( s.omegaorder[i] ) + ans = olap( f0.row, f0.col, f0.pixels['labels'], s.nlabels[ s.omegaorder[i-1] ], + f1.row, f1.col, f1.pixels['labels'], s.nlabels[ s.omegaorder[i] ] ) + pairs[ row, s.omegaorder[i-1], row, s.omegaorder[i] ] = ans + return pairs + + + +def pairscans( s1, s2, omegatol = 0.05 ): + """ + Pair peaks across two scans. + """ + olap = sparseframe.overlaps_linear( max(s1.nnz.max(), s2.nnz.max())+1 ) + assert len(s1.nnz) == len(s2.nnz ) + pairs = {} + for i in range(len(s1.nnz)): + # check omega angles match + o1 = s1.motors['omega'][s1.omegaorder[i]] + o2 = s2.motors['omega'][s2.omegaorder[i]] + assert abs( o1 - o2 ) < omegatol, 'diff %f, tol %f'%(o1-o2, omegatol) + if (s1.nnz[s1.omegaorder[i]] == 0) or (s2.nnz[s2.omegaorder[i]] == 0): + continue + f0 = s1.getframe( s1.omegaorder[i] ) + f1 = s2.getframe( s2.omegaorder[i] ) + ans = olap( f0.row, f0.col, f0.pixels['labels'], s1.nlabels[ s1.omegaorder[i] ], + f1.row, f1.col, f1.pixels['labels'], s2.nlabels[ s2.omegaorder[i] ] ) + pairs[ s1.sinorow, s1.omegaorder[i], s2.sinorow, s2.omegaorder[i] ] = ans + return pairs + + + + +class pks_table: + + def __init__(self, + npk=None, + ipk=None, + pk_props=None, + rc=None, + rpk=None, + glabel=None, + nlabel=0, + use_shm=False, + ): + """ + Cases: + Create from npks counting -> here + Read from a file -> classmethod pks_table.load( h5name ) + Read from shared memory -> classmethod pks_table.fromSHM( h5name ) + """ + self.npk = npk + self.ipk = ipk + self.pk_props=pk_props + self.rc = rc + self.rpk = rpk + self.glabel = glabel + self.nlabel = nlabel + self.use_shm = use_shm + # otherwise create + if self.npk is not None: + self.create( self.npk ) + + def export(self): + return { 'ipk': self.ipk.export(), + 'rpk': self.rpk.export(), + 'pk_props' : self.pk_props.export(), + 'rc' : self.rc.export() } + + def fromSHM(cls, dct): + return cls( ipk = shared_numpy_array( dct['ipk'] ), + rpk = shared_numpy_array( dct['rpk'] ), + pk_props = shared_numpy_array( dct['pk_props'] ), + rc = shared_numpy_array( dct['rc'] ) ) + + def create(self, npk): + # [ nscans, 3 ] + # number_of_peaks_in_scan + # number_of_pairs_ii + # number_of_pairs_ij + self.npk = npk + s = npk.sum(axis=0) + # pointers to peak tables per scan + ipk = np.empty( npk.shape[0]+1, int ) + ipk[0] = 0 + ipk[1:] = np.cumsum( npk[:,0] ) + self.ipk = shared_numpy_array( ipk ) + # pointers to r/c positions + rpk = np.empty( npk.shape[0]+1, int ) + rpk[0] = 0 + rpk[1:] = np.cumsum( npk[:,1]+npk[:,2] ) + self.rpk = shared_numpy_array( rpk ) + self.pk_props = shared_numpy_array( shape=(5, s[0]), dtype=np.int64 ) + self.rc = shared_numpy_array( shape=(3, s[1]+s[2]), dtype=np.int32 ) + + + def __del__(self): + """ get rid of the shared memory things """ + del self.ipk + del self.rpk + del self.pk_props + del self.rc + + def save(self, h5name, group='pks2d'): + opts = {'compression':'gzip', + 'compression_opts':2, + 'shuffle':True, + } + with h5py.File( h5name, 'a' ) as hout: + grp = hout.require_group( group ) + ds = grp.require_dataset( name = 'ipk', shape = self.ipk.shape, dtype = self.ipk.dtype, **opts ) + ds[:] = self.ipk + ds.attrs['description']='pointer to start of a scan' + ds = grp.require_dataset( name = 'pk_props', shape = self.pk_props.shape, dtype = self.pk_props.dtype, **opts ) + ds[:] = self.pk_props + ds.attrs['description']='[ ( s1, sI, srI, scI, id ), Npks ]' + ds = grp.require_dataset( name = 'npk', shape = self.npk.shape, dtype = self.npk.dtype, **opts ) + ds[:] = self.npk + ds.attrs['description']="[ nscans, (N_peaks_in_scan, N_pairs_ii, N_pairs_ij) ]" + if hasattr(self,'glabel'): + ds = grp.require_dataset( name = 'glabel', + shape = self.glabel.shape, + dtype = self.glabel.dtype, **opts ) + ds[:] = self.glabel[1] + grp.attrs['nlabel'] = self.nlabel + + @classmethod + def load(cls, h5name, h5group='pks2d'): + with h5py.File( h5name, 'r' ) as hin: + grp = hout[ h5group ] + ipk = grp[ 'ipk' ][:] + pk_props = grp[ 'ipk' ][:] + if glabel in grp: + glabel = grp['glabel'][:] + nlabel = grp['glabel'].attrs['nlabel'] + else: + glabel = None + nlabel = 0 + # rc? + npk = grp['npk'][:] + nlabel = grp.attrs['nlabel'] + obj = cls( ipk=ipk, pk_props=pk_props, glabel=glabel, nlabel=nlabel ) + obj.npks = npks # hum + return obj + + def find_uniq(self): + """ find the unique labels from the rc array """ + t = tictoc() + n = self.ipk.array[-1] + coo = scipy.sparse.coo_matrix( (self.rc.array[2], (self.rc.array[0], self.rc.array[1])), shape=(n,n)) + t('coo') + csr = coo.tocsr() + t('tocsr') + csr += csr.T + t('transpose') + cc = scipy.sparse.csgraph.connected_components( csr ) + t('find connected components') + self.nlabel, self.glabel = cc + return cc + + +def process(qin, qshm, qout, hname, scans): + remove_shm_from_resource_tracker() + start, end = qin.get() + n2 = 0 + # allocate lists to hold results + nrows = end - start + 1 + pii = {} + pij = {} + mypks = {} + pkid = {} + prev = None # suppress flake8 + for i in range(start, end+1): + scan = sparseframe.SparseScan( hname, scans[i] ) + global omega + scan.motors['omega'] = omega[i] + mypks[i], pii[i] = props(scan, i) +# nlabels[i] = scan.nlabels # peaks per frame information + pkid[i] = np.concatenate(([0,], np.cumsum(scan.nlabels))) + n1 = sum( pii[i][k][0] for k in pii[i] ) + if i > start: + pij[i] = pairscans( scan, prev ) + n2 = sum( pij[i][k][0] for k in pij[i] ) + # number of pair overlaps required for the big matrix + qout.put( (i, len(mypks[i][0]), n1, n2) ) + prev = scan + # Now we are waiting for the shared memory to save the results + shm = qshm.get() + print('got from shm',shm) + pkst = pks_table.fromSHM(pks_table, shm ) + ip = pkst.ipk.array + rc = pkst.rc.array + # For each row, save our local results + for i in range(start, end+1): + if (i == start) and (i > 0): + # will be done by the previous worker + continue + # now copy our results to shared memory. First the peaks: + pkst.pk_props.array[:, ip[i]: ip[i+1] ] = mypks[i] + # + # find the unique starting id for each frame: + # Where to store these in the shared memory + s = ipstart = pkst.rpk.array[i] + ipend = pkst.rpk.array[i+1] + # + if 0: # debugging + n1 = sum( pii[i][k][0] for k in pii[i] ) + if i > start: + n2 = sum( pij[i][k][0] for k in pij[i] ) + else: + n2 = 0 + # # will be done by the previous worker + # print('Debugging:',i, ipstart, ipend, n1, n2, ipend-ipstart, n1+n2) + assert (ipend - ipstart)==(n1 + n2) + # + for (row1, frame1, row2, frame2), (npairs, ijn) in pii[i].items(): + # add entries into a sparse matrix + # key: (500, 2, 501, 2893) + # value: (41, array([[ 1, 1, 5], + # [ 1, 2, 2], ... + if npairs == 0: + continue + assert (row1 == i) and (row2 == i) + e = s + npairs + assert e <= ipend + rc[ 0, s : e ] = ip[row1] + pkid[row1][frame1] + ijn[:,0] - 1 # col + rc[ 1, s : e ] = ip[row2] + pkid[row2][frame2] + ijn[:,1] - 1 # row + rc[ 2, s : e ] = ijn[:,2] # num pixels + s = e + if i == 0: + continue # no pairs to previous row exit + for (row1, frame1, row2, frame2), (npairs, ijn) in pij[i].items(): + if npairs == 0: + continue + assert (row1 == i) and (row2 == i-1) + e = s + npairs + assert e <= ipend + rc[ 0, s : e ] = ip[row1] + pkid[row1][frame1] + ijn[:,0] - 1 # col + rc[ 1, s : e ] = ip[row2] + pkid[row2][frame2] + ijn[:,1] - 1 # row + rc[ 2, s : e ] = ijn[:,2] # num pixels + s = e + qout.put(start) + + +def goforit(ds, sparsename): + """ + Runs the multiprocessing code + """ + qin = mp.Queue(maxsize=NPROC) + qshm = mp.Queue(maxsize=NPROC) + qresult = mp.Queue(maxsize=len(ds.scans)) + out = [] + with mp.Pool(NPROC, initializer=process, + initargs=(qin, qshm, qresult, sparsename, ds.scans)) as pool: + slices = get_start_end( len(ds.scans), NPROC ) + for i,(s,e) in enumerate(slices): + qin.put((s,e)) + waiting = countse( slices ) + for i in tqdm.tqdm(range(waiting)): + ans = qresult.get() + out.append(ans) + ks, P = compute_storage( out ) + assert len(ks) == len(ds.scans) + mem = pks_table( P ) + shm = mem.export() + # workers fill the shared memory + for i in range(NPROC): + qshm.put( shm ) + dones = set() + for i in tqdm.tqdm(range(NPROC)): + # check done + f = qresult.get() + dones.add( f ) + if len(dones) == NPROC: + break + return out, ks, P, mem + +def main( dsname, sparsename, pkname ): + global NPROC, omega + try: + rmem = None + t = tictoc() + ds = ImageD11.sinograms.dataset.load(dsname) + omega = ds.omega + t('read ds %s'%(dsname)) + nscans = len(ds.scans) + if NPROC > nscans-1: + NPROC = nscans-1 + print('Nscans',nscans,'NPROC', NPROC) + peaks, ks, P, rmem = goforit(ds, sparsename) + t('%d label and pair'%(len(rmem.pk_props.array[0]))) +# rmem.save( pkname ) +# t('cache') + cc = rmem.find_uniq() + t('%d connected components'%(cc[0])) + rmem.save( pkname ) + t('write hdf5') + except Exception as e: + print('Unhandled exception:',e) + raise + finally: + if rmem is not None: + del rmem + return + + +if __name__=="__main__": + dsname = sys.argv[1] + sparsename = sys.argv[2] + pkname = sys.argv[3] + main( dsname, sparsename, pkname ) + + print("Your stuff left in shm:") + os.system("ls -l /dev/shm | grep %s"%(os.environ['USER'])) + print("... all done") + + +""" +import numba +@numba.njit +def pkmerge( labels, pks, omega, dty, nlm, out): + k = 0 + for i in range(nlm.size): + o = omega[i] + y = dty[i] + for j in range(nlm[i]): # peaks on this frame + l = labels[k] + out[0,l] += pks[0,k] + out[1,l] += pks[1,k] + out[2,l] += pks[2,k] + out[3,l] += pks[3,k] + out[4,l] += o * pks[1,k] + out[5,l] += y * pks[1,k] + k += 1 + return k + +class h5pks_table: + def __init__(self, h5name, group='pks2d'): + with h5py.File( h5name, 'r' ) as hin: + grp = hin[group] + for name in 'npk ipk pk_props rpk'.split(): + setattr( self, name, grp[name][:] ) + +s1, sI, sr, sc, frm = pks +gpks = np.empty( (6,labels.max()+1), float ) + + +pkmerge( labels, pks, ds.omega.ravel(), ds.dty.ravel(), ds.nlm.ravel(), gpks) +sc = gpks[2] / gpks[1] +fc = gpks[3] / gpks[1] +om = gpks[4] / gpks[1] +ty = gpks[5] / gpks[1] +""" diff --git a/ImageD11/sinograms/roi_iradon.py b/ImageD11/sinograms/roi_iradon.py new file mode 100644 index 00000000..58116f98 --- /dev/null +++ b/ImageD11/sinograms/roi_iradon.py @@ -0,0 +1,483 @@ +"""This code came from skimage.transform and then was modified + +- Added mask ROI for back projection. Optimisation for small grains + in big maps. Should allow threading (one thread per tile) + +- Added projection shifts (sinogram shaped array of dty shifts). + Should work the same way for both forward and back projection. + Probably only for small offsets + +""" + +""" +License: BSD-3-Clause + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. +3. Neither the name of the University nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. +. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE HOLDERS OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +""" + +from scipy.fft import fft, ifft, fftfreq, fftshift +from scipy.interpolate import interp1d +import numpy as np +import concurrent.futures, os +import skimage.transform.radon_transform +import numba +from ImageD11 import cImageD11 + + +def _sinogram_pad(n, o=None): + if o is None: + diagonal = int(np.ceil(np.sqrt(2) * n)) + else: + diagonal = int(np.ceil(np.sqrt(2) * o)) + pad = diagonal - n + old_center = n // 2 + new_center = diagonal // 2 + pad_before = new_center - old_center + pad_width = ((pad_before, pad - pad_before), (0, 0)) + return pad_width + + +def _get_fourier_filter(size, filter_name): + """Construct the Fourier filter. + """ + n = np.concatenate((np.arange(1, size / 2 + 1, 2, dtype=int), + np.arange(size / 2 - 1, 0, -2, dtype=int))) + f = np.zeros(size) + f[0] = 0.25 + f[1::2] = -1 / (np.pi * n) ** 2 + + # Computing the ramp filter from the fourier transform of its + # frequency domain representation lessens artifacts and removes a + # small bias as explained in [1], Chap 3. Equation 61 + fourier_filter = 2 * np.real(fft(f)) # ramp filter + if filter_name == "ramp": + pass + elif filter_name == "shepp-logan": + # Start from first element to avoid divide by zero + omega = np.pi * fftfreq(size)[1:] + fourier_filter[1:] *= np.sin(omega) / omega + elif filter_name == "cosine": + freq = np.linspace(0, np.pi, size, endpoint=False) + cosine_filter = fftshift(np.sin(freq)) + fourier_filter *= cosine_filter + elif filter_name == "hamming": + fourier_filter *= fftshift(np.hamming(size)) + elif filter_name == "hann": + fourier_filter *= fftshift(np.hanning(size)) + elif filter_name is None: + fourier_filter[:] = 1 + return fourier_filter[:, np.newaxis] + + +# skimage.transform.iradon +def iradon(radon_image, + theta, + output_size=None, + filter_name="ramp", + interpolation="linear", + projection_shifts=None, + mask=None, + workers=1): + """Inverse radon transform. From skimage.transform. Simplified then ruined. + + - allow projection offset/shifts to be used + 1D = constant offset for this projection (why?) + 2D = offset versus dty for this projection + - allow a mask to be used for roi reconstruction + - run over several threads (each one doing a different roi) + """ + angles_count = len(theta) + if angles_count != radon_image.shape[1]: + raise ValueError("The given ``theta`` does not match the number of " + "projections in ``radon_image``.") + if output_size is None: + output_size = radon_image.shape[0] + to_pad = _sinogram_pad(radon_image.shape[0], output_size) + if projection_shifts is not None: + assert projection_shifts.shape == radon_image.shape + projection_shifts = np.pad(projection_shifts, to_pad, + mode='constant', constant_values=0) + radon_image = np.pad(radon_image, to_pad, + mode='constant', constant_values=0) + img_shape = radon_image.shape[0] + + # Resize image to next power of two (but no less than 64) for + # Fourier analysis; speeds up Fourier and lessens artifacts + projection_size_padded = max(64, int(2 ** np.ceil(np.log2(2 * img_shape)))) + pad_width = ((0, projection_size_padded - img_shape), (0, 0)) + img = np.pad(radon_image, pad_width, mode='constant', constant_values=0) + # return img + # Apply filter in Fourier domain + if filter_name is not None: + fourier_filter = _get_fourier_filter(projection_size_padded, filter_name) + projection = fft(img, axis=0, workers=workers) * fourier_filter + radon_filtered = np.real(ifft(projection, axis=0, workers=workers)[:img_shape, :]) + else: + radon_filtered = radon_image + + # Reconstruct image by interpolation + reconstructed = np.zeros((output_size, output_size), + dtype=radon_image.dtype) + radius = output_size // 2 + xpr, ypr = np.mgrid[:output_size, :output_size] - radius + + if mask is not None: + xpr = xpr[mask] + ypr = ypr[mask] + recm = reconstructed[mask] + else: + recm = reconstructed + + x = np.arange(img_shape) - img_shape // 2 + rtheta = np.deg2rad(theta) + + # interpolate data in parallel + # if we are linear interpolating, np.interp is much faster + + if interpolation == "linear": + def run_interp(i): + t = ypr * np.cos(rtheta[i]) - xpr * np.sin(rtheta[i]) + if projection_shifts is not None: + xi = x + projection_shifts.T[i] + else: + xi = x + + result = np.interp(t, xi, radon_filtered[:, i], left=0, right=0) + return result + else: + def run_interp(i): + t = ypr * np.cos(rtheta[i]) - xpr * np.sin(rtheta[i]) + if projection_shifts is not None: + xi = x + projection_shifts.T[i] + else: + xi = x + + interpolant = interp1d(xi, radon_filtered[:, i], + kind=interpolation, + copy=False, + assume_sorted=True, + bounds_error=False, + fill_value=0) + result = interpolant(t) + return result + + if workers == 1: + for angle in range(angles_count): + recm += run_interp(angle) + else: + if workers is None or workers < 1: + workers = cImageD11.cores_available() + + # apply interpolants in parallel + with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as pool: + for interp_result in pool.map(run_interp, range(angles_count)): + recm += interp_result + + recm *= np.pi / (2 * angles_count) + + if mask is not None: + reconstructed[mask] = recm + return reconstructed + + +# TODO : fixme +# It would be 'nice' for the radon transform to be closer to iradon +# in using the same xpr/ypr/shifts formula. That will give pixel co-ords +# for the 1D projections. The problem is to then 'cut up' the pixels on +# a 1D projection (so trapezium integrations). Doing bilinear interpolation +# gave nasty artifacts at 45 degrees angle. This needs a bit of thought. +# +# 4 corners. +# Sort into order(?) or just abs(sin), abs(cos)? +# First triangle. Middle Part. Last triangle. +# + +def radon(image, theta, + output_size=None, # sinogram width + projection_shifts=None, + mask=None, + workers=1, + ): + """ + From skimage.transform. Modified to have projection shifts and roi + to match the masked iradon here. And spread theta over threads. + + Calculates the radon transform of an image given specified + projection angles. + + Parameters + ---------- + image : array_like + Input image. The rotation axis will be located in the pixel with + indices ``(image.shape[0] // 2, image.shape[1] // 2)``. + theta : array_like + Projection angles (in degrees). + output_size : shape of the output sinogram. Matches [:, len(theta)] + + Returns + ------- + radon_image : ndarray + Radon transform (sinogram). The tomography rotation axis will lie + at the pixel index ``radon_image.shape[0] // 2`` along the 0th + dimension of ``radon_image``. + + References + ---------- + .. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic + Imaging", IEEE Press 1988. + .. [2] B.R. Ramesh, N. Srinivasa, K. Rajgopal, "An Algorithm for Computing + the Discrete Radon Transform With Some Applications", Proceedings of + the Fourth IEEE Region 10 International Conference, TENCON '89, 1989 + + Notes + ----- + Based on code of Justin K. Romberg + (https://www.clear.rice.edu/elec431/projects96/DSP/bpanalysis.html) + + """ + if image.ndim != 2: + raise ValueError('The input image must be 2-D') + + assert image.dtype == np.float32 + assert len(image.shape) == 2 + assert image.shape[0] == image.shape[1] + + if output_size is None: + output_size = image.shape[0] + + if projection_shifts is not None: + assert projection_shifts.shape[1] == len(theta) + assert projection_shifts.shape[0] == output_size + + radius = output_size // 2 + # padding the image. Shall we bother? Apparently yes. + pad = [int(np.ceil(output_size - s)) for s in image.shape] + new_center = [(s + p) // 2 for s, p in zip(image.shape, pad)] + old_center = [s // 2 for s in image.shape] + pad_before = [nc - oc for oc, nc in zip(old_center, new_center)] + pad_width = [(pb, p - pb) for pb, p in zip(pad_before, pad)] + padded_image = np.pad(image, pad_width, mode='constant', + constant_values=0) + # padded_image is always square + if padded_image.shape[0] != padded_image.shape[1]: + raise ValueError('padded_image must be a square') + center = padded_image.shape[0] // 2 + radon_image = np.zeros((len(theta), padded_image.shape[0]), + dtype=image.dtype).T + + angles_count = len(theta) + rtheta = np.deg2rad(theta) + + def roti(i): + if projection_shifts is not None: + dx = projection_shifts.T[i] # measured positions are shifted + else: + dx = None + rotated = skimage.transform.radon_transform.warp(padded_image, fxyrot, + map_args={'angle': rtheta[i], + 'center': center, + 'projection_shifts': dx}, + clip=False) + radon_image[:, i] = rotated.sum(0) + + slices = list(range(angles_count)) + if workers == 1: + for i in slices: + roti(i) + return radon_image + if workers is None or workers < 1: + workers = cImageD11.cores_available() + with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as pool: + for _ in pool.map(roti, slices): + pass + return radon_image + + +def fxyrot(colrow, angle=0, center=0, projection_shifts=None): + # used in radon above + # apply the projection shifts in reverse + # t = ypr * np.cos(rtheta[i]) - xpr * np.sin(rtheta[i]) + # if projection_shifts is not None: + # xi = x + projection_shifts.T[i] # measured positions are shifted + # else: + # xi = x + col, row = colrow.T - center + n = int(np.sqrt(col.shape[0])) + assert n * n == col.shape[0] + col.shape = n, n + row.shape = n, n + cos_a, sin_a = np.cos(angle), np.sin(angle) + if projection_shifts is not None: + ct = col.T + ct += projection_shifts + x = cos_a * col + sin_a * row + y = -sin_a * col + cos_a * row + colrow[:, 0] = x.ravel() + colrow[:, 1] = y.ravel() + return colrow + center + + +@numba.njit(boundscheck=False) +def recon_cens(omega, dty, ybins, imsize, wt, y0=0.0): + """ Back project the peak centers into a map + + omega, dty = peak co-ordinates in the sinogram + ybins = spatial binning + imsize = probably len(ybins)+1 + wt = intensity, probably ones() + """ + r = np.zeros((imsize, imsize), dtype=np.float32) + rc = imsize // 2 + for i in range(len(omega)): + s, c = np.sin(omega[i]), np.cos(omega[i]) + yv = (dty[i] - y0) / (ybins[1] - ybins[0]) + if abs(c) > abs(s): + # going across image, beam is along x + for p in range(imsize): + k = p - rc # -rc -> rc + v = ((-yv - k * s) / c) + rc + j = int(np.floor(v)) + f = (j + 1) - v + if (j >= 0) & (j < imsize): + r[j, p] += wt[i] * f + f = v - j + if ((j + 1) >= 0) & ((j + 1) < imsize): + r[j + 1, p] += wt[i] * f + else: + # going along image + for p in range(imsize): + j = p - rc # -rc -> rc + v = ((-yv - j * c) / s) + rc + k = int(np.floor(v)) + f = (k + 1) - v + if (k >= 0) & (k < imsize): + r[p, k] += wt[i] * f + f = v - k + if ((k + 1) >= 0) & ((k + 1) < imsize): + r[p, k + 1] += wt[i] * f + return r + + +def mlem(sino, + theta, + startvalue=1, + projection_shifts=None, + output_size=None, + mask=None, + workers=1, + niter=50, + divtol=1e-5): + """ + # Also called "MART" for Multiplicative ART + # This keeps a positivity constraint for both the data and reconstruction + # + # This implementation was inspired from from: + # https://www.youtube.com/watch?v=IhETD4nSJec + # by Andrew Reader + # + # ToDo : implement a mask + # ToDo : check about the corners / circle=False aspects + # + + An "MLEM" algorithm from XRDUA was used in this paper: + + "Impurity precipitation in atomized particles evidenced by nano x-ray diffraction computed tomography" + Anne Bonnin; Jonathan P. Wright; Rémi Tucoulou; Hervé Palancher + Appl. Phys. Lett. 105, 084103 (2014) https://doi.org/10.1063/1.4894009 + + This python code implements something similar based on a youtube video (https://www.youtube.com/watch?v=IhETD4nSJec) + + There are lots of papers from mathematicians in the literature about MART (multiplicative ART). + The conversion of latex algebra back and forth into computer code seems to be a bit of a + problem for me (Jon Wright - Nov 2023). + """ + + # + # Also called "MART" for Multiplicative ART + # This keeps a positivity constraint for both the data and reconstruction + # + # This implementation was inspired from from: + # https://www.youtube.com/watch?v=IhETD4nSJec + # by Andrew Reader + # + + # output_size here is the shape of the output in real space + + if output_size is None: + output_size = sino.shape[0] + + sensitivity_image = iradon(np.ones_like(sino), + theta, + output_size=output_size, + mask=None, + filter_name=None, + workers=workers, + ) + + recip_sensitivity_image = 1. / sensitivity_image + # The image reconstruction: + mlem_rec = np.empty(sensitivity_image.shape, np.float32) + mlem_rec[:] = startvalue + + # modify projection_shifts for radon + # only need to pad [0] + + if projection_shifts is not None: + to_pad = (mlem_rec.shape[0] - projection_shifts.shape[0]) // 2 + proj_shifts_padded = np.pad(projection_shifts, ((to_pad, to_pad), (0, 0))) + else: + proj_shifts_padded = None + + for i in range(niter): + calc_sino = radon(mlem_rec, + theta, + mask=None, + projection_shifts=proj_shifts_padded, + workers=workers + ) + + # pad sino to the size of calc_sino + + sino_padded = np.pad(sino, ( + ((calc_sino.shape[0] - sino.shape[0]) // 2, (calc_sino.shape[0] - sino.shape[0]) // 2), (0, 0))) + + ratio = sino_padded / (calc_sino + divtol) + + correction = recip_sensitivity_image * iradon(ratio, + theta, + output_size=output_size, + mask=None, + projection_shifts=proj_shifts_padded, + filter_name=None, + workers=workers, + ) + + mlem_rec *= correction + + if mask is not None: + mlem_rec[~mask] = 0. + + return mlem_rec diff --git a/ImageD11/sinograms/sinogram2crysalis.py b/ImageD11/sinograms/sinogram2crysalis.py new file mode 100644 index 00000000..86b07eb0 --- /dev/null +++ b/ImageD11/sinograms/sinogram2crysalis.py @@ -0,0 +1,226 @@ + +"""WARNING: work in in progress""" + +from __future__ import print_function + +import h5py +import numpy as np +import numba +import sys, os +import ImageD11.sinograms.dataset +import ImageD11.sinograms.lima_segmenter +import fabio.app.eiger2crysalis, fabio.limaimage +import bslz4_to_sparse +import tqdm +import concurrent.futures +import threading +import hdf5plugin + +readlock = threading.Lock() + +# Dummy object +class empty: + pass + +# Dummy fabio image for the data +class SinoScan( fabio.limaimage.LimaImage ): + """ Inherit limaimage to be allowed by converter """ + def __init__(self, dataset, num=0): + self.header = {} + self.dataset = dataset + self.data = self.dataset[num] + self._nframes = len(self.dataset) + self.h5 = empty() # fake this out + self.h5.attrs = {'default': None} + + def close(self): + pass + + def __iter__(self): + o = empty() + o.header = {} + for o.data in self.dataset: + yield o + + +def name_decorator(method): + def decorate_name(self = None): + headers = method(self) + print("Patching header") + headers["drealpixelsizex"] = 0.075 + headers["drealpixelsizey"] = 0.075 + headers["dexposuretimeinsec"] = 0.1 # fixme + return headers + return decorate_name + +if not hasattr( fabio.app.eiger2crysalis.Converter, 'patched' ): + fabio.app.eiger2crysalis.Converter.common_headers = name_decorator(fabio.app.eiger2crysalis.Converter.common_headers) + +# patch the converter headers +fabio.app.eiger2crysalis.Converter.patched = True + + +@numba.njit(nogil=True) +def accumulate( npixels, values, indices, dest, num ): + d0 = dest[0].size * num + for i in range(npixels): + dest.flat[ d0 + indices[i] ] += values[i] + +@numba.njit(parallel=True) +def padd( values, destination ): + for i in numba.prange( values.size ): + destination.flat[i] += values.flat[i] + + +def integrate_sino( ds, image_mask, sinomask=None, nomega = 10): + """ + ds = ImageD11.sinograms.dataset that describes scandata + sinomask = which frames to use + nomega = number of omegasteps to combine + """ + global readlock + + if sinomask is not None: + assert sinomask.shape == ds.shape + else: + sinomask = np.ones( ds.shape, bool ) + + # inverse of omega step size + istep = (len(ds.obincens)-1)/(ds.obincens[-1]-ds.obincens[0]) + # which omega bin is this frame ? + obin = np.round( istep * (ds.omega - ds.obincens[0])).astype(int) + + nout = ds.shape[1] // nomega + destination = np.zeros( (nout, 2162, 2068), np.uint32 ) + address = 0 + npx = 0 + dt = None + for num_frames, sourcefile in zip( ds.frames_per_file, ds.imagefiles ): + todo = np.nonzero( sinomask.flat[address : address + num_frames] )[0] + if len(todo) > 0: + with readlock: + with h5py.File( os.path.join(ds.datapath, sourcefile), 'r' ) as hin: + dset = hin[ ds.limapath ] + dt = dset.dtype + chunks = [] + for t in todo: + num = obin.flat[address + t] // nomega + try: + chunks.append( (num, dset.id.read_direct_chunk((t,0,0))[1]) ) + except: + print("FAIL",ds.datapath, sourcefile,t,dset) + raise + print('.' ,end='') + dc = bslz4_to_sparse.chunk2sparse(image_mask, dt) + for t,(o,b) in zip(todo, chunks): + npx, (vals, inds ) = dc( b, 0 ) + accumulate( npx, vals, inds, destination, o ) + print(',', end='') + address += num_frames + print('/',end='') + return destination + + +def makecmdline( + wvln = 12.3985 / 43.569, + xbeam = 1024, + ybeam = 1024, + omegastart = 90, + step = 0.1, + distance = 140, + savepath = '.', + run = 0, + ): + return "eiger2crysalis /dev/null -w %f --beam %.2f %.2f --omega=-%d+index*%f --distance=%.3f -o %s/esperanto/frame_%d_{index}.esperanto" %( + wvln, xbeam, ybeam, omegastart, step, distance, savepath, run) + + +def sum_sinogram( dsname, output_name, nomega = 10, nthreads = None ): + if nthreads is None: + nthreads = min(max( len(os.sched_getaffinity( os.getpid() )) - 2, 1 ), 20) + ds = ImageD11.sinograms.dataset.load( dsname ) + with h5py.File(dsname ,'r') as hin: + image_mask = fabio.open(hin['lima_segmenter'].attrs['maskfile']).data + # ds.import_nnz() + print("Going to sum sinogram of", dsname, 'output to', output_name) + with concurrent.futures.ThreadPoolExecutor( max_workers=nthreads ) as pool: + args = [] + for i in range(nthreads): + rows = np.zeros( ds.shape, bool ) + rows[i::nthreads] = True + args.append( (ds, image_mask, rows, nomega) ) + + def fun(args): + localds, image_mask, sinomask, nomega = args + return integrate_sino( localds, image_mask, sinomask=sinomask, nomega=nomega ) + + spx = None + for ans in pool.map(fun, args): + if spx is None: + spx = ans + else: + padd( ans, spx ) + # fill mask? + mval = pow(2,32)-1 + for i,frm in enumerate(spx): + spx[i] = np.where( image_mask, spx[i], mval ) + + with h5py.File( output_name, 'w' ) as hout: + ds = hout.create_dataset('data', + shape = spx.shape, + dtype = spx.dtype, + data = spx, + chunks = (1,spx.shape[1],spx.shape[2]), + **hdf5plugin.Bitshuffle(nelems=0, lz4=True) + ) + return spx + + +def makecmdline( + wvln = 12.3985 / 43.569, + xbeam = 1024, + ybeam = 1024, + omegastart = 90, + step = 0.1, + distance = 140, + savepath = '.', + run = 1, + ): + print("cp /data/id11/nanoscope/Eiger/frame_1_.set %s/frame.set"%(savepath)) + return "eiger2crysalis sum_lpcmoX1_slice1.h5 -w %f --flip-lr --calc-mask False --beam %.2f %.2f --omega=-%d+index*%f --distance=%.3f -o %s/frame_%d_{index}.esperanto" %( + wvln, xbeam, ybeam, omegastart, step, distance, savepath, run) + +if __name__=="__main__": + sample = 'lpcmo_x3' + dset = 'z50_RT' + dsname = '../ds_{sample}_{dset}.h5'.format(**locals()) + guessroot = os.getcwd() + if guessroot.startswith( '/data' ): + items = guessroot.split('/') + id11 = items.index('id11') + dataroot = "/".join( items[:id11+1] ) + "/RAW_DATA" + print(dataroot) + if not os.path.exists(dsname): + ds = ImageD11.sinograms.dataset.DataSet( + dataroot = '/data/visitor/hc5185/id11/20230505/RAW_DATA', + analysisroot = os.getcwd(), + sample = sample, + dset = dset, ) + ds.import_all() + ds.save(dsname) + ImageD11.sinograms.lima_segmenter.setup(dsname) + + spx = sum_sinogram( dsname, "sum_{sample}_{dset}.h5".format(**locals()), nthreads = 20) + + # Dummy file opener + def myopenimage( filename ): + global spx + '''flips to reverse order - need to fix this properly ... ''' + return SinoScan( spx[::-1] ) + + # replace the file opener by our thing + fabio.app.eiger2crysalis.fabio_open = myopenimage + + cmd = makecmdline(savepath='lpcmoX1_slice1', step=0.5, ) + sys.argv = cmd.split() + fabio.app.eiger2crysalis.main() \ No newline at end of file diff --git a/ImageD11/sparseframe.py b/ImageD11/sparseframe.py new file mode 100644 index 00000000..fb0e80e5 --- /dev/null +++ b/ImageD11/sparseframe.py @@ -0,0 +1,636 @@ + +from __future__ import print_function, division + +import time, sys +import h5py, scipy.sparse, numpy as np #, pylab as pl +from ImageD11 import cImageD11 + +SAFE = True + +# see also sandbox/harvest_pixels.py + +NAMES = { + "filename" : "original filename used to create a sparse frame", + "intensity" : "corrected pixel values", + "nlabel": "Number of unique labels for an image labelling", + "threshold" : "Cut off used for thresholding", + } + + +class sparse_frame( object ): + """ + Indices / shape mapping + + This was developed for a single 2D frame + See SparseScan below for something aiming towards many frames + """ + def __init__(self, row, col, shape, itype=np.uint16, pixels=None, + SAFE=SAFE ): + """ row = slow direction + col = fast direction + shape = size of full image + itype = the integer type to store the indices + our c codes currently use unsigned short... + nnz is implicit as len(row)==len(col) + pixels = numpy arrays in a dict to name them + throw in a ary.attrs if you want to save some + """ + if SAFE: + self.check( row, col, shape, itype, SAFE ) + self.shape = shape + self.row = np.asarray(row, dtype = itype ) + self.col = np.asarray(col, dtype = itype ) + self.nnz = len(self.row) + # Things we could have using those indices: + # raw pixel intensities + # corrected intensities + # smoothed pixel intensities + # labelling via different algorithms + self.pixels = {} + self.meta = {} + if pixels is not None: + for name, val in pixels.items(): + assert len(val) == self.nnz + self.pixels[name] = val + + def __repr__(self): + h = "Sparse Frame ( %d , %d ) nnz = %d, data: "%( self.shape[0], self.shape[1], self.nnz ) + h += " ".join(list(self.pixels.keys())) + return h + + def __eq__(self, other): + if self.shape != other.shape: + print("Shape mismatch") + return False + if self.nnz != other.nnz: + print("nnz mismatch") + return False + if not (self.row == other.row).all(): + print("row mismatch") + return False + if not (self.col == other.col).all(): + print("row mismatch") + return False + for k in self.pixels.keys(): + if not (self.pixels[k] == other.pixels[k]).all(): + print("pixels mismatch",k) + print(self.pixels[k]) + print(other.pixels[k]) + return False + return True + + + def check(self, row, col, shape, itype, SAFE=SAFE): + """ Ensure the index data makes sense and fits """ + if SAFE: + lo = np.iinfo(itype).min + hi = np.iinfo(itype).max + assert len(shape) == 2 + assert shape[0] >= lo and shape[0] < hi + assert shape[1] >= lo and shape[1] < hi + assert np.min(row) >= lo and np.max(row) < hi + assert np.min(col) >= lo and np.max(col) < hi + assert len(row) == len(col) + + def is_sorted(self): + """ Tests whether the data are sorted into slow/fast order + rows are slow direction + columns are fast """ + # TODO: non uint16 cases + assert self.row.dtype == np.uint16 and \ + cImageD11.sparse_is_sorted( self.row, self.col ) == 0 + + def to_dense(self, data=None, out=None): + """ returns the full 2D image + data = name in self.pixels or 1D array matching self.nnz + Does not handle repeated indices + e.g. obj.to_dense( obj.pixels['raw_intensity'] ) + """ + if data in self.pixels: + data = self.pixels[data] # give back this array + else: + ks = list( self.pixels.keys() ) + if len(ks)==1: + data = self.pixels[ks[0]] # default for only one + else: + data = np.ones( self.nnz, bool ) # give a mask + if out is None: + out = np.zeros( self.shape, data.dtype ) + else: + assert out.shape == self.shape + assert len(data) == self.nnz + scipy.sparse.coo_matrix((data, (self.row, self.col)), shape=(self.shape)).todense(out=out) + # does not handle duplicate indices if they were present: + # adr = self.row.astype(np.intp) * self.shape[1] + self.col + # out.flat[adr] = data + return out + + def mask( self, msk ): + """ returns a subset of itself """ + spf = sparse_frame( self.row[msk], + self.col[msk], + self.shape, self.row.dtype ) + for name, px in self.pixels.items(): + if name in self.meta: + m = self.meta[name].copy() + else: + m = None + spf.set_pixels( name, px[msk], meta = m ) + return spf + + def set_pixels( self, name, values, meta=None ): + """ Named arrays sharing these labels """ + if SAFE: assert len(values) == self.nnz + self.pixels[name] = values + if meta is not None: + self.meta[name] = meta + + + def sort_by( self, name ): + """ Not sure when you would do this. For sorting + by a peak labelling to get pixels per peak """ + order = np.argsort( self.pixels[name] ) + self.reorder( self, order ) + + def sort( self ): + """ Puts you into slow / fast looping order """ + order = np.lexsort( ( self.col, self.row ) ) + self.reorder( self, order ) + + def reorder( self, order ): + """ Put the pixels into a different order (in place) """ + if SAFE: assert len(order) == self.nnz + self.row[:] = self.row[order] + self.col[:] = self.col[order] + for name, px in self.pixels.items(): + px[:] = px[order] + + def threshold(self, threshold, name='intensity'): + """ + returns a new sparse frame with pixels > threshold + """ + return self.mask( self.pixels[name] > threshold ) + + def to_hdf_group( frame, group ): + """ Save a 2D sparse frame to a hdf group + Makes 1 single frame per group + """ + itype = np.dtype( frame.row.dtype ) + meta = { "itype" : itype.name, + "shape0" : frame.shape[0], + "shape1" : frame.shape[1] } + for name, value in meta.items(): + group.attrs[name] = value + opts = { "compression": "lzf", + "shuffle" : True, + } + #opts = {} + group.require_dataset( "row", shape=(frame.nnz,), + dtype=itype, **opts ) + group.require_dataset( "col", shape=(frame.nnz,), + dtype=itype, **opts ) + group['row'][:] = frame.row + group['col'][:] = frame.col + for pxname, px in frame.pixels.items(): + group.require_dataset( pxname, shape=(frame.nnz,), + dtype=px.dtype, + **opts ) + group[pxname][:] = px + if pxname in frame.meta: + group[pxname].attrs = dict( frame.meta[pxname] ) + + +omeganames = ['measurement/rot_center', 'measurement/rot', + 'measurement/diffrz_center', 'measurement/diffrz'] +dtynames = ['measurement/dty_center', 'measurement/dty', + 'measurement/diffty_center', 'measurement/diffty'] + + +class SparseScan( object ): + + + def __init__( self, hname, scan, start = 0, n=None, + names = ('row','col','intensity'), + omeganames = omeganames, + dtynames = dtynames ): + """ + hname : file coming from a sparse segmentation + scan : a scan within that file + motors : which motor channels to (try) to read + + start : first frame to read + n = number of frames to read + + if the scan does not fit into memory use (start, n) + """ + self.hname = hname + self.scan = scan + self.names = list(names) + self.omeganames = list(omeganames) + self.dtynames = list(dtynames) + if scan.find('::') >= 0: # Format is "1.1::[start:end]" + scan, indexes = scan.split("::") + start, end = [int(s) for s in indexes[1:-1].split(':')] + n = end - start + with h5py.File(hname,"r") as hin: + grp = hin[scan] + self.shape = tuple( [ int(v) for v in ( grp.attrs['nframes'], + grp.attrs['shape0'], + grp.attrs['shape1'] ) ] ) + if n is None: + end = self.shape[0] # nframes + else: + end = start + n + self.shape = end-start, self.shape[1], self.shape[2] + # read the motors - if any + self.motors = {} + for name, motors in [ ('omega',self.omeganames), + ('dty',self.dtynames) ]: + for motor in motors: + if motor in grp: + self.motors[ name ] = grp[motor][start:end] + break + # read the pixels - all pointers + nnz = grp['nnz'][:] + ipt = nnz_to_pointer( nnz ) + s = ipt[start] + e = ipt[end] + for name in self.names: + if name in grp: + setattr( self, name, grp[name][s:e] ) + # pointers into this scan + self.nnz = nnz[start:end] + self.ipt = nnz_to_pointer( self.nnz ) + + def getframe(self, i, SAFE=SAFE): + # (self, row, col, shape, itype=np.uint16, pixels=None): + s = self.ipt[i] + e = self.ipt[i+1] + if s == e: + return None # empty frame + return sparse_frame( self.row[ s: e], + self.col[ s: e], + self.shape[1:], + pixels = { name : getattr( self, name)[s:e] for name in self.names }, + SAFE=SAFE ) + + + def cplabel(self, threshold = 0, countall=True ): + """ Label pixels using the connectedpixels assigment code + Fills in: + self.nlabels = number of peaks per frame + self.labels = peak labels (should be unique) + self.total_labels = total number of peaks + + if countall == True : labels all peaks from zero + == False : labels from 1 on each frame + """ + self.nlabels = np.zeros( len(self.nnz), np.int32 ) + self.labels = np.zeros( len(self.row), "i") + if 'labels' not in self.names: + self.names.append('labels') + nl = 0 + # TODO: run this in parallel with threads? + for i, npx in enumerate( self.nnz ): + s = self.ipt[i] + e = self.ipt[i+1] + if npx > 0: + self.nlabels[i] = cImageD11.sparse_connectedpixels( + self.intensity[ s : e ], + self.row[ s : e ], + self.col[ s : e ], + threshold, + self.labels[ s : e ] ) + # zero label is the background! + self.labels[ s : e ] = np.where( self.labels[ s : e ] > 0, + self.labels[ s : e ] + nl, 0 ) + else: + self.nlabels[i] = 0 + if countall: + nl += self.nlabels[i] + self.total_labels = self.nlabels.sum() + + + def lmlabel(self, threshold = 0, countall=True, smooth=True ): + """ Label pixels using the localmax assigment code + Fills in: + self.nlabels = number of peaks per frame + self.labels = peak labels (should be unique) + self.total_labels = total number of peaks + if countall == True : labels all peaks from zero + == False : labels from 1 on each frame + """ + self.nlabels = np.zeros( len(self.nnz), np.int32 ) + self.labels = np.zeros( len(self.row), "i") + if 'labels' not in self.names: + self.names.append('labels') + if smooth: + self.signal = np.empty( self.intensity.shape, np.float32 ) + else: + self.signal = self.intensity + # temporary workspaces + npxmax = self.nnz.max() + vmx = np.zeros( npxmax, np.float32 ) + imx = np.zeros( npxmax, 'i' ) + nl = 0 + # TODO: run this in parallel with threads? + for i, npx in enumerate( self.nnz ): + s = self.ipt[i] + e = self.ipt[i+1] + if npx > 0: + if smooth: + cImageD11.sparse_smooth( self.intensity[ s: e], + self.row[s:e], + self.col[s:e], + self.signal[s:e] ) + self.nlabels[i] = cImageD11.sparse_localmaxlabel( + self.signal[ s : e ], + self.row[ s : e ], + self.col[ s : e ], + vmx[:npx], + imx[:npx], + self.labels[s : e] ) + assert (self.labels[s:e] > 0).all() + self.labels[ s : e ] += nl + else: + self.nlabels[i] = 0 + if countall: + nl += self.nlabels[i] + self.total_labels = self.nlabels.sum() + + def moments(self): + """ Computes the center of mass in s/f/omega + """ + pks = {} + i32 = self.intensity.astype(np.float32) + pks['Number_of_pixels'] = np.bincount(self.labels, + weights=None, + minlength = self.total_labels+1 )[1:] + pks['sum_intensity'] = np.bincount(self.labels, + weights=i32, + minlength = self.total_labels+1 )[1:] + pks['s_raw'] = np.bincount(self.labels, + weights=i32*self.row, + minlength = self.total_labels+1 )[1:] + pks['s_raw'] /= pks['sum_intensity'] + pks['f_raw'] = np.bincount(self.labels, + weights=i32*self.col, + minlength = self.total_labels+1 )[1:] + pks['f_raw'] /= pks['sum_intensity'] + frame = np.empty( self.row.shape, np.int32 ) + for i in range(len(self.nnz)): + frame[ self.ipt[i]:self.ipt[i+1] ] = i + for name in 'omega','dty': + if name in self.motors: + pks[name] = np.bincount(self.labels, + weights=i32*self.motors[name][frame], + minlength = self.total_labels+1 )[1:] + pks[name] /= pks['sum_intensity'] + return pks + + +def from_data_mask( mask, data, header ): + """ + Create a sparse from a dense array + """ + assert mask.shape == data.shape + # using uint16 here - perhaps make this general in the future + # ... but not for now + assert data.shape[0] < pow(2,16)-1 + assert data.shape[1] < pow(2,16)-1 + nnz = (mask>0).sum() + tmp = np.empty( data.shape[0],'i') # tmp hold px per row cumsums + row = np.empty( nnz, np.uint16 ) + col = np.empty( nnz, np.uint16 ) + cImageD11.mask_to_coo( mask, row, col, tmp ) + intensity = data[ mask > 0 ] + # intensity.attrs = dict(header) # FIXME USE xarray ? + spf = sparse_frame( row, col, data.shape, itype=np.uint16 ) + spf.set_pixels( "intensity" , intensity, dict( header ) ) + return spf + + +def from_data_cut( data, cut, header={}, detectormask=None): + assert data.dtype in (np.uint16, np.float32) + if detectormask is None: + msk = np.ones(data.shape, bool ) + else: + msk = detectormask + row = np.empty( data.shape, np.uint16 ) + col = np.empty( data.shape, np.uint16 ) + if data.dtype == np.uint16: + val = np.empty( data.shape, np.uint16 ) + nnz = cImageD11.tosparse_u16( data, msk, row, col, val, cut) + if data.dtype == np.float32: + val = np.empty( data.shape, np.float32 ) + nnz = cImageD11.tosparse_f32( data, msk, row, col, val, cut) + spf = sparse_frame( row.ravel()[:nnz].copy(), + col.ravel()[:nnz].copy(), + data.shape ) + spf.set_pixels( 'intensity', val.ravel()[:nnz].copy(), dict(header) ) + return spf + + + +def from_hdf_group( group ): + itype = np.dtype( group.attrs['itype'] ) + shape = group.attrs['shape0'], group.attrs['shape1'] + row = group['row'][:] # read it + col = group['col'][:] + spf = sparse_frame( row, col, shape, itype=itype ) + for pxname in list(group): + if pxname in ["row", "col"]: + continue + data = group[pxname][:] + header = dict( group[pxname].attrs ) + spf.set_pixels( pxname, data, header ) + return spf + +def sparse_moments( frame, intensity_name, labels_name ): + """ We rely on a labelling array carrying nlabel metadata (==labels.data.max())""" + nl = frame.meta[ labels_name ][ "nlabel" ] + return cImageD11.sparse_blob2Dproperties( + frame.pixels[intensity_name].astype(np.float32), # limitations of f2py here. + frame.row, + frame.col, + frame.pixels[labels_name], + nl ) + + +class overlaps_linear: + """ Memory caching object for the linear time algorithm to find + peak overlaps + + Given (row1, col1, label1) and (row2, col2, label2) it finds pixels + where (row[i] == row2[i]) and (col1[i] == col2[i]) + and returns (labels1[i], labels2[i], sum_pixels[i]) + ... so the number of overlapping pixels for that pair of labels + """ + def __init__(self, nnzmax=4096*4): + """ nnzmax = max pixels on a frame """ + self.nnzmax = nnzmax + self.realloc() + + def realloc(self): + nnzmax = self.nnzmax + self.ki = np.empty( nnzmax,'i' ) + self.kj = np.empty( nnzmax,'i' ) + self.ect = np.empty( nnzmax, 'i' ) + self.tj = np.empty( nnzmax, 'i' ) + self.tmp = np.empty( nnzmax+1,'i') + + def __call__(self, row1, col1, labels1, n1, + row2, col2, labels2, n2, checkmem=True ): + if checkmem: + assert len(row1)==len(col1)==len(labels1) + assert len(row2)==len(col2)==len(labels2) + nnz = max( max(len(row1), len(row2)), max(n1,n2)) + if nnz > self.nnzmax: + self.nnzmax = nnz + print("realloc",nnz) + self.realloc() + npx = cImageD11.sparse_overlaps( row1, col1, self.ki[:len(row1)], + row2, col2, self.kj[:len(row2)] ) + if npx == 0: # there are no overlaps + return 0, None + r = labels1[ self.ki[:npx] ] # my labels + c = labels2[ self.kj[:npx] ] # your labels + nedge = cImageD11.compress_duplicates( r, c, self.ect[:npx], self.tj[:npx], self.tmp ) + # overwrites r/c in place : ignore the zero label (hope it is not there) + rcl = np.zeros( (nedge, 3), 'i') + rcl[:,0] = r[:nedge] + rcl[:,1] = c[:nedge] + rcl[:,2] = self.ect[:nedge] + return nedge, rcl + + +class overlaps_matrix: + """ Memory caching object for the quadratic time algorithm to find + peak overlaps + + Given (row1, col1, label1) and (row2, col2, label2) it finds pixels + where (row[i] == row2[i]) and (col1[i] == col2[i]) + and returns (labels1[i], labels2[i], sum_pixels[i]) + ... so the number of overlapping pixels for that pair of labels + + This is easier to understand and faster for small number of peaks per frame + """ + def __init__(self, npkmax=256): + self.npkmax = npkmax + self.realloc() + + def realloc(self): + self.matmem = np.empty( (self.npkmax* self.npkmax,), 'i') + # potentially n^2 overlaps. Really? + self.results = np.empty( (3*self.npkmax*self.npkmax), 'i') + + def __call__(self, row1, col1, labels1, n1, + row2, col2, labels2, n2, checkmem=True ): + assert labels1.max()-1 < n1, "%d %d %d"%(labels1.min(), + labels1.max(), + n1) + assert labels2.max()-1 < n2 + mx = max(n1, n2) + if max(n1, n2) > self.npkmax: + self.npkmax = mx + print("realloc",mx) + self.realloc() + mat = self.matmem[:n1*n2] + mat.shape = n1, n2 + nov = cImageD11.coverlaps( row1, col1, labels1, + row2, col2, labels2, + mat, self.results ) + return nov, self.results[:nov*3].reshape((nov,3)) + + + +def overlaps(frame1, labels1, frame2, labels2): + """ + figures out which label of self matches which label of other + Assumes the zero label does not exist (background) + Returns sparse array of: + label in self (row) + label in other (col) + number of shared pixels (data) + """ + ki = np.empty( frame1.nnz, 'i' ) + kj = np.empty( frame2.nnz, 'i' ) + npx = cImageD11.sparse_overlaps( frame1.row, frame1.col, ki, + frame2.row, frame2.col, kj) + # self.data and other.data filled during init + row = frame1.pixels[labels1][ ki[:npx] ] # my labels + col = frame2.pixels[labels2][ kj[:npx] ] # your labels + ect = np.empty( npx, 'i') # ect = counts of overlaps + tj = np.empty( npx, 'i') # tj = temporary for sorting + n1 = frame1.meta[labels1][ "nlabel" ] + n2 = frame2.meta[labels2][ "nlabel" ] + tmp = np.empty( max(n1, n2)+1, 'i') # for histogram + nedge = cImageD11.compress_duplicates( row, col, ect, tj, tmp ) + # overwrites row/col in place : ignore the zero label (hope it is not there) + crow = row[:nedge]-1 + ccol = col[:nedge]-1 + cdata = ect[:nedge] + cedges = scipy.sparse.coo_matrix( ( cdata, (crow, ccol)), shape=(n1, n2) ) + # really? + return cedges + + +def sparse_connected_pixels( frame, + label_name="connectedpixels", + data_name="intensity", + threshold=None ): + """ + frame = a sparse frame + label_name = the array to save labels to in that frame + data_name = an array in that frame + threshold = float value or take data.threshold + """ + labels = np.zeros( frame.nnz, "i" ) + if threshold is None: + threshold = frame.meta[data_name]["threshold"] + nlabel = cImageD11.sparse_connectedpixels( + frame.pixels[data_name], frame.row, frame.col, + threshold, labels ) + frame.set_pixels( label_name, labels, { 'nlabel' : nlabel } ) + return nlabel + + +def sparse_localmax( frame, + label_name="localmax", + data_name = "intensity" ): + labels = np.zeros( frame.nnz, "i" ) + vmx = np.zeros( frame.nnz, np.float32 ) + imx = np.zeros( frame.nnz, 'i') + nlabel = cImageD11.sparse_localmaxlabel( + frame.pixels[data_name], frame.row, frame.col, + vmx, imx, labels ) + frame.set_pixels( label_name, labels, { "nlabel" : nlabel } ) + return nlabel + + +def sparse_smooth( frame, data_name='intensity' ): + smoothed = np.zeros( frame.nnz, np.float32 ) + cImageD11.sparse_smooth( frame.pixels[data_name], + frame.row, + frame.col, + smoothed ) + return smoothed + + +def nnz_to_pointer( nnz, out = None ): + """ + nnz = number of pixels per frame + pointer = position in a single flat array + """ + if out is None: + out = np.empty( len(nnz)+1, int ) + else: + assert len(out) == len(nnz)+1 + out[0] = 0 + np.cumsum( nnz, out=out[1:] ) + return out + + + + + diff --git a/ImageD11/stress.py b/ImageD11/stress.py new file mode 100644 index 00000000..72690c0a --- /dev/null +++ b/ImageD11/stress.py @@ -0,0 +1,669 @@ + +# coding: utf-8 +from __future__ import print_function, division + +import numpy as np +import ImageD11.finite_strain, ImageD11.unitcell, ImageD11.parameters +from ImageD11.grain import read_grain_file + + +# Compute strain and stress from a list of ImageD11 UBI matrices +# Copyright (C) 2023 Jean-Baptiste Jacob +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 2 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. + + + +class EpsSigSolver: + """ A class to handle strain and stress computations on a list of ubis. """ + def __init__(self, + name = 'default', + symmetry = None, + stress_unit = 'GPa', + UBI_list = [], + unitcell = None, + c11=None,c12=None,c13=None,c14=None,c15=None,c16=None, + c22=None,c23=None,c24=None,c25=None,c26=None, + c33=None,c34=None,c35=None,c36=None, + c44=None,c45=None,c46=None, + c55=None,c56=None, + c66=None): + + """ + Initialize the EpsSigSolver class. + + Parameters: + ----------- + cij (float) : elastic constants of the crystal + Cij_symmetry (str) : symmetry considered for the Stiffness and Compliance matrices. Should be one of the following: + 'cubic', 'trigonal_high', 'trigonal_low', 'tetragonal_high', 'tetragonal_low', 'hexagonal', 'orthorhombic', 'monoclinic', 'triclinic' + + unitcell (array_like) : Unstrained unit cell parameters [a, b, c, alpha,beta, gamma] + UBI_list (list of 3x3 arrays) : List of real-space unit cell vectors (ubi in ImageD11). + """ + + assert symmetry in Cij_symmetry.keys(), 'symmetry not recognized!' + + self.phase_name = name + self.unitcell = unitcell + + if unitcell is not None: + if len(unitcell)==6: + self.cell__a = unitcell[0] + self.cell__b = unitcell[1] + self.cell__c = unitcell[2] + self.cell_alpha = unitcell[3] + self.cell_beta = unitcell[4] + self.cell_gamma = unitcell[5] + else: + raise Exception("The unit cell must be defined by six parameters!") + + self.parameterobj = ImageD11.parameters.parameters(cell__a=self.cell__a, + cell__b=self.cell__b, + cell__c=self.cell__c, + cell_alpha=self.cell_alpha, + cell_beta=self.cell_beta, + cell_gamma=self.cell_gamma, + c11=c11,c12=c12,c13=c13,c14=c14,c15=c15,c16=c16, + c22=c22,c23=c23,c24=c24,c25=c25,c26=c26, + c33=c33,c34=c34,c35=c35,c36=c36, + c44=c44,c45=c45,c46=c46, + c55=c55,c56=c56, + c66=c66) + + self.symmetry = symmetry + self.stress_unit = 'GPa' + self.Cij_symmetry = Cij_symmetry[self.symmetry] + self.Cij = self.form_stiffness_tensor() + if not np.alltrue(self.Cij == 0): + self.Sij = np.linalg.inv(self.Cij) + + self.UBIs = UBI_list + self.F_list = None + + @property + def nUBIs(self): + return len(self.UBIs) + + def __str__(self): + return ("EpsSigSolver:\n phase name: {phase_name}\n reference unitcell: {unitcell}\n symmetry:" +\ + "{symmetry}\n unit:{stress_unit}\n Stiffness:\n {Cij}\n n ubis: {nUBIs)}").format(self) + + # Load / save / update functions for parameters (from former eps_sig_solver.py) + ######################################## + def loadmap(self,filename): + try: + self.glist = ImageD11.grain.read_grain_file(filename) + self.UBIs=[g.ubi for g in self.glist] + except: + print("error when reading %s\n",filename) + raise + + + def loadpars(self,filename=None): + if filename is not None: + self.parameterobj.loadparameters(filename) + self.parameterobj.update_other(self) + #update also the unitcell list (because the element are included in parameterobj but not the list): + self.unitcell=[self.cell__a, self.cell__b, self.cell__c, self.cell_alpha, self.cell_beta, self.cell_gamma] + + def updateparameters(self): + self.savepars() + self.pars=self.parameterobj.parameters + #update also the unitcell list (because the element are included in parameterobj but not the list): + self.unitcell=[self.cell__a, self.cell__b, self.cell__c, self.cell_alpha, self.cell_beta, self.cell_gamma] + + def savepars(self,filename=None): + self.parameterobj.update_yourself(self) + if filename is not None: + self.parameterobj.saveparameters(filename) + + + + + # Methods for Strain & stress computation + ######################################### + + def form_stiffness_tensor(self): + """ + Form the Voigt 6x6 stiffness tensor (Cij) from elastic constants (cij), using the symmetry specified in self.symmetry. + By definition, this is only valid for strain in grain coordinates, which must be provided as a 6-component vector + in voigt notation (e11, e22, e33, 2.e23, 2.e13, 2.e12). Stress in lab coordinates can be obtained by rotating the 3x3 stress + tensor from the grain to the lab coordinate system (see strain2stress_lab for details). + + Return + ---------- + Cij : 6x6 matrix containing the elastic components + """ + Cij = np.zeros((6,6)) + pattern = self.Cij_symmetry # pattern for the stiffness matrix. From Mouhat & Coudert (2014). Necessary and sufficient elastic stability conditions in various crystal systems. Physical Review + + + parlist = 'c11,c22,c33,c44,c55,c66,c12,c13,c14,c15,c16,c23,c24,c25,c26,c34,c35,c36,c45,c46,c56'.split(',') + + for i,parname in enumerate(parlist): + v = self.parameterobj.parameters[parname] + if v is None: + continue + if self.symmetry in ['hexagonal','trigonal_high','trigonal_low'] and parname == 'c66': + v = 0.5 * (self.parameterobj.parameters['c11'] - self.parameterobj.parameters['c12']) + + c_ij = np.where( np.abs(pattern) == i+1, np.sign(pattern) * v, 0 ) + Cij += c_ij + return Cij + + + + def compute_Deformation_Gradient_Tensors(self): + """ + compute the deformation gradient tensor F for all ubis + + Return + -------- + New instance F_list added to EpsSiSolver, containing a list of F tensors + """ + F_list = [] + B0 = ImageD11.unitcell.unitcell(self.unitcell).B + + for ubi in self.UBIs: + F = ImageD11.finite_strain.DeformationGradientTensor(ubi, B0) + F_list.append(F) + self.F_list = F_list + + + + def strain2stress_Ref(self, m=1): + """ + Compute elastic strain and stress in grain reference coordinates for all ubis, using the stiffness matrix in self.Cij. + Returns strain and stress as two lists of 3x3 symmetric tensors 'eps_Ref' and 'sigma_Ref' + + Parameters + ---------- + m (float) : exponent for the Seth-Hill finite strain tensors E = 1/2m (U^2m - I). see documentation in ImageD11.finite_strain.py + for more detail + + Return + ---------- + New instances 'eps_Ref' and 'sigma_Ref' added to EpsSigSolver, containing respectively strain and stress as lists of 3x3 tensors + """ + + assert self.F_list is not None, 'No deformation gradient tensors to process. Run "self.compute_Deformation_Gradient_Tensors" first' + + self.eps_Ref = [] + self.sigma_Ref = [] + + for F in self.F_list: + + # compute strain and stress + eRef = F.finite_strain_ref(m) + eRef_v = full_3x3_to_vector(eRef, 'voigt', is_strain=True) + sRef_v = np.dot(self.Cij, eRef_v) + sRef = vector_to_full_3x3(sRef_v, 'voigt', is_strain=False) + + # append strain and stress to lists + self.eps_Ref.append(eRef) + self.sigma_Ref.append(sRef) + + + + def strain2stress_Lab(self, m=1, debug=0): + """ + Compute elastic strain and stress in Lab coordinates for all ubis, using the stiffness matrix in self.Cij. + Computation is done first in the grain coordinate system, and then stress in lab coordinates is obtained by + rotating the 3x3 stress tensor from the grain to the lab coordinate system using the following transormation + σ' = RT.σ.R where R is the rotation matrix yielded by the polar decomposition of the finite deformation + gradient tensor F. + + Returns strain and stress as two lists of 3x3 symmetric tensors 'eps_Lab' and 'sigma_Lab'. + + Parameters + ---------- + m (float) : exponent for the Seth-Hill finite strain tensors E = 1/2m (U^2m - I). see documentation in ImageD11.finite_strain.py + for more detail + + Return + ---------- + New instances 'eps_Lab' and 'sigma_Lab' added to EpsSigSolver, containing respectively strain and stress as lists of 3x3 tensors + """ + + self.eps_Lab = [] + self.sigma_Lab = [] + + for F in self.F_list: + + # compute strain in lab coordinates + eLab = F.finite_strain_lab(m) + + # compute strain and stress in grain coordinates + eRef = F.finite_strain_ref(m) + eRef_v = full_3x3_to_vector(eRef, 'voigt', is_strain=True) + sRef = vector_to_full_3x3( np.dot(self.Cij, eRef_v), 'voigt', is_strain=False ) + + # Rotate stress to lab coordinates + sLab = F.U.dot(sRef).dot(F.U.T) + + # check rotation is working ok for strain + assert np.allclose(eLab, F.U.dot(eRef).dot(F.U.T)), 'eRef and eLab are inconsistent. Rotation matrix might be dodgy' + + # Check stress tensor has the same eigenvalues in lab and grain coordinates + eigv_Ref,_ = eigen_decomposition(sRef) + eigv_Lab,_ = eigen_decomposition(sLab) + assert np.allclose(eigv_Ref, eigv_Lab), 'sRef and sLab are inconsistent' + + # append strain and stress to lists + self.eps_Lab.append(eLab) + self.sigma_Lab.append(sLab) + + + def convert_tensors_to_vecs(self, output_format = 'voigt', debug=0): + """ + convert strain and stress tensors to 6-component vectors in different possible formats + + output_format (str) : select one of the output formats below: + + default : e11, e22, e33, e23, e13, e12 | s11, s22, s33, s23, s13, s12 + xfab : e11, e12, e13, e22, e23, e33 | s11, s12, s13, s22, s23, s33 + mandel : e11, e22, e33, √2.e22, √2.e23, √2.e33 | s11, s22, s33, √2.s22, √2.s23, √2.s33 + voigt : e11, e22, e33, 2.e23, 2.e13, 2.e12 | s11, s22, s33, s23, s13, s12 + + Return + ---------- + New instances 'eps_xx_fmt' and 'sigma_xx_fmt' added to EpsSigSolver, containing respectively strain and stress as 6-component vectors + instances names refer explicitely to the format specified in output_format, e.g. esp_Ref_voigt, etc. + """ + formats = 'default,voigt,mandel,xfab'.split(',') + assert output_format in formats, 'output format not recognized!' + + # select all strain and stress tensors list + dnames = [attr for attr in dir(self) if any([attr.startswith(s) for s in ['eps','sigma']]) ] + # filter out all data that begins with 'eps' or 'sigma' but are not strain or stress tensors + dnames = [d for d in dnames if any([d.endswith(s) for s in ['Lab','Ref','_d']]) ] + + # stop if no strain / stress tensor list fond + if len(dnames) == 0: + print('No strain or stress tensors list found') + return + + # convert all tensors lists to selected vector format + for d in dnames: + if debug: + print(d) + tensor_list = self.__getattribute__(d) + assert np.all([T.shape == (3,3) for T in tensor_list]), 'data in input list are not 3x3 tensors' + + if 'eps' in d: + strain = True + else: + strain = False + + vector_list = [full_3x3_to_vector(T, output_format, is_strain=strain) for T in tensor_list] + outname = '_'.join([d,output_format]) + + setattr(self, outname, vector_list) + + + def deviatoric_tensors(self, debug=0): + """ + compute deviatoric component for all strain and stress tensors lists + + Return + ---------- + New instances 'eps_xx_d' and 'sigma_xx_d' added to EpsSigSolver, containing lists of deviatoric 3x3 tensors + """ + + # select all strain and stress tensors list that are not deviatoric + dnames = [attr for attr in dir(self) if any([attr.startswith(s) for s in ['eps','sigma']]) ] + dnames = [d for d in dnames if any([d.endswith(s) for s in ['Lab','Ref']]) ] + + # stop if no strain / stress tensor list fond + assert len(dnames) > 0, 'No strain or stress tensors list found' + + # compute deviatoric tensors + for d in dnames: + if debug: + print(d) + + tensor_list = self.__getattribute__(d) + assert np.all([T.shape == (3,3) for T in tensor_list]), 'data in input list are not 3x3 tensors' + tensors_dev = [deviatoric(T) for T in tensor_list] + + setattr(self, d+'_d', tensors_dev) + + + + def invariant_props(self, dname): + # NOTE : not sure about the expression of von Mises strain. In any case it is related to √J2 by a multiplication factor k, + # but it seems to be different from the definition of von Mises stress √(3.J2). + # see https://www.continuummechanics.org/vonmisesstress.html + """ + compute invariant properties for selected data column + compute invariant properties for selected data column: volumetric strain / pressure (-I1/3) and von mises strain /stress (√3.J2) + + dname (str) : name of the input data column. Must be a non-deviatoric 3x3 tensors + + Returns + ---------- + New instances added to EpsSigSolver, containing list of floats + if strain tensor in input + dname+'_vol' : volumetric strain + dname+'_vM' : von Mises strain (√2.J2) + if stress tensor in input: + dname+'_P_hyd' : hydrostatic Pressure (if stress tensor in input) + dname+'_vM' : von Mises stress (√3.J2) + """ + assert dname in dir(self), 'dname not recognized' + assert '_d_' not in dname, 'tensor is deviatoric. Please use the non-deviatoric tensor' + + tensor_list = self.__getattribute__(dname) + assert np.all([T.shape == (3,3) for T in tensor_list]) + + tensor_list_dev = [deviatoric(T) for T in tensor_list] + Invts = [invariants(T) for T in tensor_list] + Invts_dev = [invariants(T) for T in tensor_list_dev] + + Inv1 = [-i[0]/3 for i in Invts] + Inv2 = [np.sqrt(3*i[1]) for i in Invts_dev] + + if 'eps' in dname: + setattr(self, dname+'_vol', Inv1) + else: + setattr(self, dname+'_P_hyd', Inv1) + setattr(self, dname+'_vM', Inv2) + + + + def compute_principal_components(self, dname): + """ + compute principal components resulting from eigen decomposition of the strain / stress tensor for selected data column + + dname (str) : name of the input data column + + Returns + ---------- + New instances dname+'_eigvals' , dname+'_eigvecs' added to EpsSigSolver, containing respectively a list of eigenvalues and eigenvectors + + eigvals (1x3 array) : Principal components in decreasing order from the largest positive to largest negative + for strain : ε1 > ε2 > ε3 (positive strain = elongation) + for stress σ3 > σ2 > σ1 (positive stress = tension) + eigvecs (3x3 array) : Normalized principal component vectors by columns sorted accordingly to eigvals + """ + assert dname in dir(self), 'dname not recognized' + + tensor_list = self.__getattribute__(dname) + assert np.all([T.shape == (3,3) for T in tensor_list]), 'data in input list are not 3x3 tensors' + + PC = [eigen_decomposition(T) for T in tensor_list] + eigvals = [p[0] for p in PC] + eigvecs = [p[1] for p in PC] + + setattr(self, dname+'_eigvals', eigvals) + setattr(self, dname+'_eigvecs', eigvecs) + + + + +# GENERAL FUNCTIONS +################################################################################ + +def full_3x3_to_vector(T, output_format = 'default', is_strain=True): + """ + Form a 6 component strain or stress vector from a 3x3 tensor, using specified convention + + Parameters + ----------- + T (array_like) : strain or stress tensor of the form [e11 e12 e13] + [e12 e22 e23] + [e13 e23 e33] + + is_strain (bool) : specify whether T is a strain or stress tensor + + output_format (str) : specify convention for the 6-component vector: must be one of the following: 'default', 'xfab', 'mandel', 'voigt' + + default : e11, e22, e33, e23, e13, e12 | s11, s22, s33, s23, s13, s12 + xfab : e11, e12, e13, e22, e23, e33 | s11, s12, s13, s22, s23, s33 + mandel : e11, e22, e33, √2.e22, √2.e23, √2.e33 | s11, s22, s33, √2.s22, √2.s23, √2.s33 + voigt : e11, e22, e33, 2.e23, 2.e13, 2.e12 | s11, s22, s33, s23, s13, s12 + """ + + assert output_format in 'default,voigt,mandel,xfab'.split(','), 'Format not recognized' + if output_format == 'default' or not is_strain: + return np.array([T[0, 0], T[1, 1], T[2, 2], T[1, 2], T[0, 2], T[0, 1]]) + + elif output_format == 'xfab': + return np.array([T[0, 0], T[0, 1], T[0, 2], T[1, 1], T[1, 2], T[2, 2]]) + + elif output_format == 'mandel': + sqr2 = np.sqrt(2) + return np.array([T[0, 0], T[1, 1], T[2, 2], sqr2*T[1, 2], sqr2*T[0, 2], sqr2*T[0, 1]]) + + else: + return np.array([T[0, 0], T[1, 1], T[2, 2], 2*T[1, 2], 2*T[0, 2], 2*T[0, 1]]) + + + +def vector_to_full_3x3(vec, input_format='default', is_strain=True): + """ + Reconstruct a 3x3 strain or stress tensor from a 6-component vector, using specified convention + + Parameters + ----------- + vec (array_like) : 6-component strain or stress vector + + is_strain (bool) : specify whether the intput vector is a strain or stress vector + + input_format (str) : specify convention for the 6-component vector: must be one of the following: 'default', 'xfab', 'mandel', 'voigt' + + default : e11, e22, e33, e23, e13, e12 | s11, s22, s33, s23, s13, s12 + xfab : e11, e12, e13, e22, e23, e33 | s11, s12, s13, s22, s23, s33 + mandel : e11, e22, e33, √2.e22, √2.e23, √2.e33 | s11, s22, s33, √2.s22, √2.s23, √2.s33 + voigt : e11, e22, e33, 2.e23, 2.e13, 2.e12 | s11, s22, s33, s23, s13, s12 + """ + + assert input_format in 'default,voigt,mandel,xfab'.split(','), 'Format not recognized' + + if input_format == 'default' or not is_strain: + return np.array([[vec[0], vec[5], vec[4]], + [vec[5], vec[1], vec[3]], + [vec[4], vec[3], vec[2]]]) + + elif input_format == 'xfab': + return np.array([[vec[0], vec[1], vec[2]], + [vec[1], vec[3], vec[4]], + [vec[2], vec[4], vec[5]]]) + + elif input_format == 'mandel': + sqr2 = np.sqrt(2) + return np.array([[vec[0] , vec[5]/sqr2, vec[4]/sqr2], + [vec[5]/sqr2, vec[1] , vec[3]/sqr2], + [vec[4]/sqr2, vec[3]/sqr2, vec[2] ]]) + + else: + return np.array([[vec[0] , vec[5]/2, vec[4]/2], + [vec[5]/2, vec[1] , vec[3]/2], + [vec[4]/2, vec[3]/2, vec[2]]]) + + + + +def rotate_3x3_tensor(S, R, tol = 1e-6): + """Return 3x3 matrix in rotated coordinate system + + Parameters + ----------- + S (array_like) : 3x3 strain / stress matrix + R (array_like) : 3x3 Rotation matrix + tol (float) : tolerance to consider R as a rotation matrix + + Returns + ----------- + S' (array_like) : S in rotated coordinate system + """ + # check R is a rotation matrix + assert np.allclose(np.dot(R,R.T), np.eye(3)) and abs(np.linalg.det(R) - 1) < tol, 'R is not a rotation matrix' + # return rotated coordinates + return np.linalg.multi_dot([R,S,R.T]) + + + + +def build_6x6_rot_mat(R, tol): + """ + Return 6x6 transormation matrix corresponding to rotation R for a Voigt 6x6 stiffness tensor + + Parameters + ----------- + R (array_like) : 3x3 rotation matrix + tol (float) : tolerance to consider R as a rotation matrix + + Returns + ----------- + K (array_like) : 6x6 transformation matrix for the 6x6 stiffness tensor + NOTE : K is not a rotation matrix, and unlike R it does not satisfy K.KT = I + """ + + assert np.allclose(np.dot(R,R.T), np.eye(3)) and abs(np.linalg.det(R) - 1) < tol, 'R is not a rotation matrix' + + q11,q12,q13,q21,q22,q23,q31,q32,q33 = R.flatten() + + k1 = np.array( [q11**2, q12**2, q13**2, 2*q12*q13, 2*q11*q13, 2*q11*q12] ) + k2 = np.array( [q21**2, q22**2, q23**2, 2*q22*q23, 2*q21*q23, 2*q21*q22] ) + k3 = np.array( [q31**2, q32**2, q33**2, 2*q32*q33, 2*q31*q33, 2*q31*q32] ) + k4 = np.array( [q21*q31, q22*q32, q23*q33, q22*q33 + q23*q32, q21*q33 + q23*q31, q21*q32 + q22*q31] ) + k5 = np.array( [q11*q31, q12*q32, q13*q33, q12*q33 + q13*q32, q11*q33 + q13*q31, q11*q32 + q12*q31] ) + k6 = np.array( [q11*q21, q12*q22, q13*q23, q12*q23 + q13*q22, q11*q23 + q13*q21, q11*q22 + q12*q21] ) + + return np.array([k1,k2,k3,k4,k5,k6]) + + +def eigen_decomposition(T): + """ + Compute the norm and orientation of principal components resulting from eigen decomposition of a strain / stress tensor + + Parameters + ---------- + T (array_like) : 3x3 symmetric tensor + + Returns + ---------- + eigvals_s (1x3 array) : Principal components values in decreasing order + eigvecs_s (3x3 array) : Normalized principal component vectors by columns sorted accordingly to eigenvalues + """ + eigenvalues, eigenvectors = np.linalg.eig(T) + + # Sort eigenvalues and eigenvectors in descending order + idx = np.argsort(eigenvalues) + eigvals_s = eigenvalues[idx][::-1] + eigvecs_s = eigenvectors[:, idx[::-1]] + + return eigvals_s, eigvecs_s + + + +def deviatoric(T, return_iso = False): + """ + Decompose the strain / stress tensor into an isotropic and a deviatoric component : T = H + D, where H = tr(T) / 3 + + Parameters + ---------- + T (array_like) : 3x3 symmetric tensor + return_iso (bool) : if True, the isotropic component will be returned in addition to the deviatoric tensor. Default is False + + Returns + ---------- + pi (float) : Hydrostatic strain / stress. Hydrostatic pressure then equals to -Pi + D (array_like): 3x3 deviatoric tensor. + """ + pi = np.trace(T) / 3.0 + D = T - pi * np.identity(3) + + if return_iso: + return pi, D + else: + return D + + +def invariants(T): + """ + Returns the three invariants I1, I2, I3 of a 3x3 tensor + """ + I1 = np.trace(T) + I2 = 0.5 * (np.trace(np.dot(T, T)) - np.trace(T) ** 2) + I3 = np.linalg.det(T) + + return I1, I2, I3 + + + +# Cij_symmetry for stiffness tensor (from Mouhat & Coudert 2014) +######################## + +Cij_symmetry = { + 'cubic': np.array([[1, 7, 7, 0, 0, 0], + [7, 1, 7, 0, 0, 0], + [7, 7, 1, 0, 0, 0], + [0, 0, 0, 4, 0, 0], + [0, 0, 0, 0, 4, 0], + [0, 0, 0, 0, 0, 4]]), + + 'trigonal_high': np.array([[1, 7, 8, 9, 0, 0], + [7, 1, 8, -9, 0, 0], + [8, 8, 3, 0, 0, 0], + [9, -9, 0, 4, 0, 0], + [0, 0, 0, 0, 4, 9], + [0, 0, 0, 0, 9, 6]]), + + 'trigonal_low': np.array([[1, 7, 8, 9, 10, 0 ], + [7, 1, 8, -9, -10, 0 ], + [8, 8, 3, 0, 0, 0 ], + [9, -9, 0, 4, 0, -10], + [10,-10, 0, 0, 4, 9 ], + [0, 0, 0, -10 , 9, 6 ]]), + + 'tetragonal_high': np.array([[1, 7, 8, 0, 0, 0], + [7, 1, 8, 0, 0, 0], + [8, 8, 3, 0, 0, 0], + [0, 0, 0, 4, 0, 0], + [0, 0, 0, 0, 4, 0], + [0, 0, 0, 0, 0, 6]]), + + 'tetragonal_low': np.array([[1, 7, 8, 0, 0, 11], + [7, 1, 8, 0, 0, -11], + [8, 8, 3, 0, 0, 0], + [0, 0, 0, 4, 0, 0], + [0, 0, 0, 0, 4, 0], + [11, -11, 0, 0, 0, 6]]), + + 'orthorhombic': np.array([[ 1, 7, 8, 0, 0, 0], + [ 7, 2, 12, 0, 0, 0], + [ 8, 12, 3, 0, 0, 0], + [ 0, 0, 0, 4, 0, 0], + [ 0, 0, 0, 0, 5, 0], + [ 0, 0, 0, 0, 0, 6]]), + + 'monoclinic': np.array([[ 1, 7, 8, 0, 10, 0], + [ 7, 2, 12, 0, 14, 0], + [ 8, 12, 3, 0, 17, 0], + [ 0, 0, 0, 4, 0, 20], + [10, 14, 17, 0, 5, 0], + [ 0, 0, 0, 20, 0, 6]]), + + 'triclinic': np.array([[ 1, 7, 8, 9, 10, 11], + [ 7, 2, 12, 13, 14, 15], + [ 8, 12, 3, 16, 17, 18], + [ 9, 13, 16, 4, 19, 20], + [10, 14, 17, 19, 5, 21], + [11, 15, 18, 20, 21, 6 ]]), + } + + +Cij_symmetry['hexagonal'] = Cij_symmetry['tetragonal_high'] +Cij_symmetry['tetragonal'] = Cij_symmetry['tetragonal_high'] +Cij_symmetry[None] = Cij_symmetry['triclinic'] diff --git a/ImageD11/sym_u.py b/ImageD11/sym_u.py index 634ebc6d..bfc0371d 100644 --- a/ImageD11/sym_u.py +++ b/ImageD11/sym_u.py @@ -22,12 +22,12 @@ def m_from_string(s): m.append(np.array(r)-t) return np.array(m) -def fmt(c): +def fmt(c): if c == 1: return "+" if c == -1: return "-" - else: + else: return "%f"%(c) def m_to_string(m): @@ -36,19 +36,17 @@ def m_to_string(m): """ st = [] for i in range(3): - needplus = 0 for v,s in zip([ [ 1,0,0] , [ 0,1,0], [0,0,1] ], "xyz"): c = np.dot(v,m.T[i]) if abs(c)>0: st.append( "%s%s"%(fmt(c),s)) - needplus = 1 if i<2: st.append(",") return "".join(st) - - + + @@ -60,7 +58,7 @@ def __init__(self, tol=1e-5): Basic group is identity tol is for numerical comparison of group membership """ - self.group = [ np.identity(3, np.float) ] + self.group = [ np.identity(3, float) ] self.tol = 1e-5 def op(self, x, y): """ @@ -103,7 +101,7 @@ def makegroup(self): global DEBUG if DEBUG: print("making new group") - new = True + new = True while new: for a in self.group: for b in self.group: @@ -115,15 +113,80 @@ def makegroup(self): if DEBUG: print("adding",c,"to group") self.group.append(c) +symcache = {} + def generate_group(*args): + global symcache + if args in symcache: + return symcache[args] g=group() for a in args: g.additem(m_from_string(a)) + symcache[args]=g return g +# From International tables the "Laue classes" are: +# +# -1 Triclinic +# 2/m Monoclinic +# mmm Orthorhombic +# 4/m or 4/mmm Tetragonal +# -3 or -3/m Trigonal +# 6/m or 6/mmm Hexagonal +# m-3 or m-3m Cubic +# +# Note: the Laue class is the symmetry of the reflection intensities assuming friedel's +# law is obeyed. e.g. these are the 32 point groups combined with inversion. +# Needed for peak intensities +# +# These groups are used for lattice permutations, they should preserve the handedness +# and so they DO NOT CONTAIN MIRROR PLANES +# +crystallographic_point_groups = """ +# index Crystal_system Hermann-Mauguin Order +1 Triclinic 1 1 +2 Triclinic -1 2 +3 Monoclinic 2 2 +4 Monoclinic m 2 +5 Monoclinic 2/m 4 +6 Orthorhombic 222 4 +7 Orthorhombic mm2 4 +8 Orthorhombic mmm 8 +9 Tetragonal 4 4 +10 Tetragonal -4 4 +11 Tetragonal 4/m 8 +12 Tetragonal 422 8 +13 Tetragonal 4mm 8 +14 Tetragonal -42m 8 +15 Tetragonal 4/mmm 16 +16 Trigonal 3 3 +17 Trigonal -3 3 +18 Trigonal 32 6 +19 Trigonal 3m 6 +20 Trigonal -3m 12 +21 Hexagonal 6 6 +22 Hexagonal -6 6 +23 Hexagonal 6/m 12 +24 Hexagonal 622 12 +25 Hexagonal 6mm 12 +26 Hexagonal -62m 12 +27 Hexagonal 6/mmm 24 +28 Cubic 23 12 +29 Cubic m-3 24 +30 Cubic 432 24 +31 Cubic -43m 24 +32 Cubic m3m 48""" + +proper_point_groups = """1 2 3 4 6 222 322 422 622 23 432""".split() +assert len(proper_point_groups) == 11 +improper_centrosymmetrical_point_groups = """-1 2/m -3 4/m 6/m mmm -3m 4/mmm 6/mmm m-3 m3m""".split() +assert len(improper_centrosymmetrical_point_groups) == 11 +improper_acentric_point_groups = """m -4 -6 mm2 3mm 4mm -42m 6mm -62m -43m""".split() +assert len(improper_acentric_point_groups) == 10 def cubic(): + """ P32 """ return generate_group( "z,x,y", "-y,x,z" ) def hexagonal(): @@ -133,8 +196,8 @@ def hexagonal(): return generate_group ( "-y,x-y,z", "-x,-y,z", "y,x,-z" ) def trigonal(): - """ P3 143 """ - return generate_group ( "y,-x-y,z" ) + """ P321 150 """ + return generate_group ( "y,-x-y,z", "y,x,-z" ) def rhombohedralP(): """ R3 primitive """ @@ -143,7 +206,7 @@ def rhombohedralP(): def tetragonal(): """ P4 75""" return generate_group ( "-y,x,z", "-x,y,-z" ) - + def orthorhombic(): """ P222 16 """ return generate_group( "-x,-y,z", "-x,y,-z" ) @@ -190,7 +253,7 @@ def find_uniq_hkls( hkls, grp, func=hklmax): uniq[i] = np.where( msk , cand[i], uniq[i] ) tmax = np.where( msk , t, tmax ) return uniq - + class trans_group(group): """ @@ -202,8 +265,8 @@ def __init__(self, tol = 1e-5): """ Identity is to not move at all """ - self.group = [ np.zeros(3, np.float) ] - self.tol = tol + group.__init__(self,tol) + def op(self, x, y): """ Means of generating new thing from two others @@ -251,36 +314,35 @@ def test(): [ 0, 0,1]] )) print("testing1") for op in [ "x,y,z", "-y,x-y,z", "-y,x,z"]: - d = np.linalg.det(m_from_string(op)) + d = np.linalg.det(m_from_string(op)) assert d == 1.0, "Determinant = %f %s"%(d,op) - print("testing2") + print("testing2") assert len(cubic().group) == 24, "not 24 ops found for cubic !" - print(len(hexagonal().group)) - assert len(hexagonal().group) == 12 ,"not 6 ops found for hexagonal !" - assert len(trigonal().group) == 3 ,"not 3 ops found for trigonal !"+\ + assert len(hexagonal().group) == 12 ,"not 12 ops found for hexagonal !" + assert len(trigonal().group) == 6 ,"not 7 ops found for trigonal !"+\ str(trigonal().group) - assert len(tetragonal().group) == 4 ,"not 8 ops found for tetragonal !" + assert len(tetragonal().group) == 8 ,"not 8 ops found for tetragonal !" assert len(orthorhombic().group) == 4 ,"not 4 ops found for orthorhombic !" print("testing3") for f in [ monoclinic_a, monoclinic_b, monoclinic_c]: r = f().group assert len(r) == 2, " not 2 ops for monoclinic " - assert np.allclose( - find_uniq_u( np.array( + assert np.allclose( + find_uniq_u( np.array( [[0,1,0],[-1,0,0],[0,0,1]]),cubic()), np.identity(3) ), "Should easily get this unique choice" # translational groups g1 = trans_group() g2 = trans_group() - ops = [ np.array( [ 1,0,0], np.float) , - np.array( [ 0,1,0], np.float) , - np.array( [ 0,0,1], np.float) ] + ops = [ np.array( [ 1,0,0], float) , + np.array( [ 0,1,0], float) , + np.array( [ 0,0,1], float) ] for op in ops: g1.additem(op) g2.additem(op) - g2.additem( np.array( [ 5,6,7], np.float) ) + g2.additem( np.array( [ 5,6,7], float) ) for op2 in g2.group: found = False for op1 in g1.group: @@ -295,24 +357,25 @@ def test(): g2.additem([0.1, 0.45, 10]) print(g2.group) DEBUG = False - + def getgroup(s): """ convert a user supplied string to a group ... a little vague still """ - if s in ['cubic', 'hexagonal','trigonal','tetragonal', + groups =['cubic', 'hexagonal','trigonal','tetragonal', 'orthorhombic','monoclinic_c','monoclinic_a', - 'monoclinic_b','triclinic','rhombohedralP']: - import ImageD11.sym_u - return getattr(ImageD11.sym_u, s) + 'monoclinic_b','triclinic','rhombohedralP'] + if s in groups: + return globals()[s] + else: + raise Exception( s,"not in", groups ) + - if __name__=="__main__": test() - u = np.array([[ 0.71850787 , 0.69517833, 0.02176059], [-0.62925889 , 0.66306714, -0.40543213], @@ -320,6 +383,6 @@ def getgroup(s): find_uniq_u(u,cubic(),debug=0) - - + + diff --git a/ImageD11/symops.py b/ImageD11/symops.py index 868004f8..928639b3 100644 --- a/ImageD11/symops.py +++ b/ImageD11/symops.py @@ -98,7 +98,7 @@ def test_absence(h, k, l, sg): # So use an unambiguous string like "P 1 21/c 1" symmop=sgstring.split() if len(symmop) != 4: - print("Only read %d symmops"%symops) + print("Only read %d symmops"%symmop) raise Exception("You must supply at least 4 space separated symmetry operations"\ " and optional compound operations separated by '/'") diff --git a/ImageD11/tkGui/__init__.py b/ImageD11/tkGui/__init__.py new file mode 100644 index 00000000..3c0e9a26 --- /dev/null +++ b/ImageD11/tkGui/__init__.py @@ -0,0 +1 @@ +""" Tkinter gui code """ diff --git a/ImageD11/guiindexer.py b/ImageD11/tkGui/guiindexer.py similarity index 82% rename from ImageD11/guiindexer.py rename to ImageD11/tkGui/guiindexer.py index 5f7a00c8..57c0ee55 100644 --- a/ImageD11/guiindexer.py +++ b/ImageD11/tkGui/guiindexer.py @@ -30,6 +30,41 @@ from .listdialog import listdialog from . import twodplot +import threading +try: + import Tkinter as Tk +except: + import tkinter as Tk + + +class run_idx: + def __init__(self, parent, idxer): + self.top = Tk.Toplevel(parent) + self.label = Tk.Label(self.top, text="Found so far %8d"%(0)) + self.label.pack() + self.btn = Tk.Button(self.top, text="Stop", command=self.stop) + self.btn.pack() + self.idxer = idxer + self.thr = threading.Thread( target=self.idxer.score_all_pairs ) + self.thr.start() + self.top.after(1000, self.update) + + def stop(self): + self.label.configure(text="Got a stop, so far %8d"%(len(self.idxer.ubis))) + self.btn.configure(text='Stopping') + self.idxer.stop=True + self.thr.join() + self.top.destroy() + + def update(self): + self.label.configure( text="Tested %8d of %8d\nFound so far %8d"%( + self.idxer.tried, + self.idxer.npairs, + len(self.idxer.ubis)) ) + if self.idxer.tried == self.idxer.npairs: + self.top.destroy() + else: + self.top.after(1000, self.update) class guiindexer: @@ -50,13 +85,20 @@ def __init__(self,parent): ( "Make Friedel pair file", 5, self.makefriedel), ( "Generate trial orientations",0, self.find), ( "Score trial orientations",0, self.scorethem), + ( "Auto-find",2, self.autofind), ( "Histogram fit quality",0, self.histogram_drlv_fit), ( "Save parameters", 0, self.saveparameters), ( "Save UBI matrices", 5, self.saveubis), - ( "Write out indexed peaks",0,self.saveindexing) + ( "Write out indexed peaks",0,self.saveindexing), + ( "Reset indexer",0,self.reset), ] ) self.plot3d=None + def autofind(self): + self.parent.guicommander.commandscript += "myindexer.score_all_pairs()\n" + run_idx( self.parent, self.parent.guicommander.objects['indexer']) + + def loadgv(self): """ see indexing.readgvfile """ filename=self.parent.opener.show( @@ -176,3 +218,9 @@ def saveindexing(self): """ see indexing.saveindexing """ filename=self.parent.saver.show(title="File to save indexing output") self.parent.guicommander.execute("indexer","saveindexing",filename) + + + def reset(self): + """ see indexing.reset """ + self.parent.guicommander.execute("indexer","reset") + diff --git a/ImageD11/guimaker.py b/ImageD11/tkGui/guimaker.py similarity index 100% rename from ImageD11/guimaker.py rename to ImageD11/tkGui/guimaker.py diff --git a/ImageD11/guipeaksearch.py b/ImageD11/tkGui/guipeaksearch.py similarity index 100% rename from ImageD11/guipeaksearch.py rename to ImageD11/tkGui/guipeaksearch.py diff --git a/ImageD11/guisolver.py b/ImageD11/tkGui/guisolver.py similarity index 100% rename from ImageD11/guisolver.py rename to ImageD11/tkGui/guisolver.py diff --git a/ImageD11/guitransformer.py b/ImageD11/tkGui/guitransformer.py similarity index 81% rename from ImageD11/guitransformer.py rename to ImageD11/tkGui/guitransformer.py index 38b63f88..5e45462e 100644 --- a/ImageD11/guitransformer.py +++ b/ImageD11/tkGui/guitransformer.py @@ -26,9 +26,8 @@ # # python3? # from tkinter import * -from .listdialog import listdialog - -from ImageD11 import twodplot +from .listdialog import listdialog, columnchooser +from . import twodplot import logging @@ -50,7 +49,9 @@ def __init__(self,parent,quiet="No"): ( "Add unit cell peaks",0, self.addcellpeaks), ( "Fit",0, self.fit), ( "Save parameters", 0, self.saveparameters), +# ( "Plot selected columns", 0, self.plotcols), ( "Plot tth histogram", 0, self.plothisto ), + ( "Export tth histogram", 0, self.savehisto ), ( "Filter peaks based on tth histogram", 0, self.filterhisto ), ( "Compute g-vectors", 0, self.computegv), ( "Save g-vectors", 0, self.savegv), @@ -153,7 +154,8 @@ def chooseyz(self): pass def plotcols(self): - names = self.parent.guicommander.execute("transformer",getcols) + names = self.parent.guicommander.execute("transformer","getcols") + print(names) d = columnchooser(self.parent, names) print(d.result) @@ -189,11 +191,15 @@ def plothisto(self, nbins = None): nbins = self.parent.guicommander.execute("transformer", "parameterobj.get", "no_bins") + doweight = self.parent.guicommander.execute("transformer", + "parameterobj.get", + "weight_hist_intensities") d = listdialog( self.parent, - items={"no_bins": nbins}, + items={"no_bins": nbins, "weight_hist_intensities": doweight}, title="Histogram - no of bins") nbins = int(d.result['no_bins']) + doweight = int(d.result['weight_hist_intensities']) self.parent.guicommander.execute("transformer", "parameterobj.set_parameters", @@ -217,6 +223,35 @@ def plothisto(self, nbins = None): } ))) + def savehisto(self, nbins = None): + if nbins is None: + nbins = self.parent.guicommander.execute("transformer", + "parameterobj.get", + "no_bins") + doweight = self.parent.guicommander.execute("transformer", + "parameterobj.get", + "weight_hist_intensities") + d = listdialog( self.parent, + items={"no_bins": nbins, "weight_hist_intensities": doweight}, + title="Histogram - no of bins") + + nbins = int(d.result['no_bins']) + doweight = int(d.result['weight_hist_intensities']) + + self.parent.guicommander.execute("transformer", + "parameterobj.set_parameters", + d.result) + else: + self.parent.guicommander.execute("transformer", + "parameterobj.set", + "no_bins", nbins) + + bins, hist = self.parent.guicommander.execute("transformer", + "compute_tth_histo") + filename=self.parent.saver.show(title="File to save histogram") + self.parent.guicommander.execute("transformer","save_tth_his",filename,bins,hist) + + def filterhisto(self): """ Call plot histo, then filter on it """ nbins = self.parent.guicommander.execute("transformer", @@ -226,10 +261,14 @@ def filterhisto(self): min_bin_prob = self.parent.guicommander.execute("transformer", "parameterobj.get", "min_bin_prob") + + doweight = self.parent.guicommander.execute("transformer", + "parameterobj.get", + "weight_hist_intensities") d=listdialog(self.parent,items={ "no_bins": nbins, - "min_bin_prob": min_bin_prob}, + "min_bin_prob": min_bin_prob, "weight_hist_intensities": doweight}, title="Histogram filter") diff --git a/ImageD11/listdialog.py b/ImageD11/tkGui/listdialog.py similarity index 94% rename from ImageD11/listdialog.py rename to ImageD11/tkGui/listdialog.py index e9146412..8905f94c 100644 --- a/ImageD11/listdialog.py +++ b/ImageD11/tkGui/listdialog.py @@ -122,16 +122,17 @@ def __init__(self, parent, items, title="Choose two columns"): if title: self.title(title) body = Tk.Frame(self) - listbox1 = Listbox(body) - listbox2 = Listbox(body) + listbox1 = Tk.Listbox(body) + listbox2 = Tk.Listbox(body) for i in items: - listbox1.insert(END,i) - listbox2.insert(END,i) + listbox1.insert(Tk.END,i) + listbox2.insert(Tk.END,i) body.pack(padx=5, pady=5) + listbox1.pack() + listbox2.pack() + self.initial_focus=body self.buttonbox() self.grab_set() - if not self.initial_focus: - self.initial_focus = self self.protocol("WM_DELETE_WINDOW", self.cancel) self.geometry("+%d+%d" % (parent.winfo_rootx()+50, parent.winfo_rooty()+50)) diff --git a/ImageD11/plot3d.py b/ImageD11/tkGui/plot3d.py similarity index 94% rename from ImageD11/plot3d.py rename to ImageD11/tkGui/plot3d.py index 1839aab6..95acd5c0 100644 --- a/ImageD11/plot3d.py +++ b/ImageD11/tkGui/plot3d.py @@ -1,4 +1,4 @@ - +#!/usr/bin/env python from __future__ import print_function @@ -97,7 +97,7 @@ def __init__(self,parent,data=None,lines=None, Tk.Label(self,text="Red=[1,0,0] Green=[0,1,0] Blue=[0,0,1]").pack( side=Tk.BOTTOM,expand=Tk.NO,fill=Tk.X) self.ubis=ubis - self.color=numpy.ones((xyz.shape[0],3),numpy.float) + self.color=numpy.ones((xyz.shape[0],3),float) print(self.color.shape) self.tex=False if ubis is not None: @@ -201,9 +201,9 @@ def scorecolor(self,i=0): if self.ubis is not None: from ImageD11 import indexing for u,i in zip(self.ubis,list(range(len(self.ubis)))): - scores=indexing.calc_drlv2(self.ubis[i],self.xyz) + scores=indexing.calc_drlv2(u,self.xyz) print(self.xyz.shape,scores.shape) - ind = numpy.compress( numpy.less(scores,0.02) , + ind = numpy.compress( numpy.less(scores,0.05*0.05) , numpy.arange(self.xyz.shape[0]) ) print("Grain",i,scores.shape,ind.shape) for j in range(3): @@ -253,10 +253,10 @@ def redraw(self,o): if self.ubis is not None and len(self.ubis)==1: hkl = numpy.dot(numpy.linalg.inv(self.ubis[0]), - numpy.identity(3,numpy.float)).T + numpy.identity(3,float)).T # print hkl else: - hkl = numpy.identity(3,numpy.float) + hkl = numpy.identity(3,float) # print hkl GL.glBegin(GL.GL_LINE_LOOP) @@ -307,7 +307,7 @@ def redraw(self,o): except: print("Usage %s gvector_file [ubifile] [image parfile]"%(sys.argv[0])) raise - sys.exit() + # sys.exit() on=0 xyz=[] @@ -318,11 +318,14 @@ def redraw(self,o): xyz.append( [ vals[0],vals[1],vals[2] ]) except: pass - if line.find("xr yr zr")>0 or line.find("gx gy gz ")>0: - on=1 - - npeaks = len(xyz) + if line.find("xr yr zr")>0 or line.find("gx ")>0: + on = 1 xyz=numpy.array(xyz) + if len(xyz) == 0 and lines[0][0]=="#": + from ImageD11 import columnfile + c = columnfile.columnfile( sys.argv[1] ) + xyz = numpy.array( (c.gx, c.gy, c.gz )).T + npeaks = len(xyz) if len(sys.argv)==3: o=plot3d(None,data=xyz,ubis=sys.argv[2]) elif len(sys.argv)==6: diff --git a/ImageD11/twodplot.py b/ImageD11/tkGui/twodplot.py similarity index 84% rename from ImageD11/twodplot.py rename to ImageD11/tkGui/twodplot.py index e0a689ed..8cb89b99 100644 --- a/ImageD11/twodplot.py +++ b/ImageD11/tkGui/twodplot.py @@ -65,7 +65,7 @@ def __init__(self,parent=None,data=None,quiet="No"): self.xr=self.yr=None # a tk.DrawingArea self.canvas = FigureCanvasTkAgg(self.f, master=self) - self.canvas.show() + self.canvas.draw() self.tkc=self.canvas.get_tk_widget() self.tkc.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1) self.tkc.bind("",self.on_down) @@ -133,21 +133,21 @@ def keypress(self,*arg): arg[0](*arg[1:]) else: arg[0]() - self.canvas.show() + self.canvas.draw() def bindkeys(self): return - self.bind_all('' ,lambda e: self.keypress(self.a.panx,1 ) ) - self.bind_all('',lambda e: self.keypress(self.a.panx,-1) ) - self.bind_all('' ,lambda e: self.keypress(self.a.pany,-1 ) ) - self.bind_all('' ,lambda e: self.keypress(self.a.pany,1) ) - self.bind_all('' ,lambda e: self.keypress(self.a.zoomx,-1 ) ) - self.bind_all('',lambda e: self.keypress(self.a.zoomx,1) ) - self.bind_all('' ,lambda e: self.keypress(self.a.zoomy,1 ) ) - self.bind_all('' ,lambda e: self.keypress(self.a.zoomy,-1) ) - self.bind_all('' , lambda e : self.keypress(self.autoscale) ) - self.bind_all('', lambda e : self.keypress(self.autoscaley, e ) ) + # self.bind_all('' ,lambda e: self.keypress(self.a.panx,1 ) ) + # self.bind_all('',lambda e: self.keypress(self.a.panx,-1) ) + # self.bind_all('' ,lambda e: self.keypress(self.a.pany,-1 ) ) + # self.bind_all('' ,lambda e: self.keypress(self.a.pany,1) ) + # self.bind_all('' ,lambda e: self.keypress(self.a.zoomx,-1 ) ) + # self.bind_all('',lambda e: self.keypress(self.a.zoomx,1) ) + # self.bind_all('' ,lambda e: self.keypress(self.a.zoomy,1 ) ) + # self.bind_all('' ,lambda e: self.keypress(self.a.zoomy,-1) ) + # self.bind_all('' , lambda e : self.keypress(self.autoscale) ) + # self.bind_all('', lambda e : self.keypress(self.autoscaley, e ) ) def autoscaley(self,e): print(dir(self.a.dataLim)) @@ -215,17 +215,17 @@ def replot(self): self.a.set_title(item.d["title"]) try: if "plottype" in item.d: - ret = self.a.hist(item.y,item.x) + self.a.hist(item.y,item.x) elif item.x.shape[0]>self.maxpoints: if self.quiet=="No": if messagebox.askyesno("Slow plotting workaround","Shall I plot only the first %d points for increased speed?"%(self.maxpoints)): - ret = self.a.plot(item.x[:self.maxpoints],item.y[:self.maxpoints],**po) + self.a.plot(item.x[:self.maxpoints],item.y[:self.maxpoints],**po) else: - ret = self.a.plot(item.x,item.y,**po) + self.a.plot(item.x,item.y,**po) else: - ret = self.a.plot(item.x[:self.maxpoints],item.y[:self.maxpoints],**po) + self.a.plot(item.x[:self.maxpoints],item.y[:self.maxpoints],**po) else: - ret = self.a.plot(item.x,item.y,**po) + self.a.plot(item.x,item.y,**po) except: print("plotting exception ignored") raise @@ -233,23 +233,23 @@ def replot(self): self.a.set_xlim(self.xr) if self.yr is not None: self.a.set_ylim(self.yr) - self.canvas.show() + self.canvas.draw() def logy(self): # FIXME - clip negative values before making logscaled? - if self.a.yaxis.is_log(): + if self.a.get_yscale() == "log": self.a.set_yscale('linear') else: self.a.set_yscale('log') - self.canvas.show() + self.canvas.draw() def logx(self): # FIXME - clip negative values before making logscaled? - if self.a.xaxis.is_log(): + if self.a.get_xscale() == "log": self.a.set_xscale('linear') else: self.a.set_xscale('log') - self.canvas.show() + self.canvas.draw() def on_3(self,event): self.autoscale() @@ -333,31 +333,18 @@ def on_up(self,event): self.yr=yr self.a.set_xlim(xr) self.a.set_ylim(yr) - self.canvas.show() + self.canvas.draw() if __name__=="__main__": - import epffile, powbase, mcadata, ciidata - if len(sys.argv)<3: - import numpy as np - from math import pi - print("Usage: %s filename format"%(sys.argv[0])) - x=np.arange(0.0,3.0,0.01) - dat=epffile.powderdata(x, - np.sin(2*pi*x)+5, - np.sqrt(sin(2*pi*x)+5), - { "title":"sin x" }) - else: - try: - if sys.argv[2]=="powbase": - dat=powbase.powbase(sys.argv[1]) - if sys.argv[2]=="epf": - dat=epffile.epffile(sys.argv[1]) - if sys.argv[2]=="mca": - dat=mcadata.mcadata(sys.argv[1]) - except: - print("Could not read your file %s" % (sys.argv[1])) - raise + + import numpy as np + from math import pi + x=np.arange(0.0,3.0,0.01) + dat=data(x, + np.sin(2*pi*x)+5, + np.sqrt(np.sin(2*pi*x)+5), + { "title":"sin x" }) root = Tk.Tk() root.wm_title("Two dimensional plotting") diff --git a/ImageD11/transform.py b/ImageD11/transform.py index f926fc3d..3bcb9ba6 100644 --- a/ImageD11/transform.py +++ b/ImageD11/transform.py @@ -23,12 +23,13 @@ """ import logging import numpy as np -from ImageD11 import gv_general +from ImageD11 import gv_general, cImageD11 from numpy import radians, degrees +import fabio # for LUT try: # crazy debug - test = np.arccos(np.zeros(10, np.float)) + _ = np.arccos(np.zeros(10, float)) except: print(dir()) raise @@ -49,18 +50,18 @@ def cross_product_2x2(a, b): def detector_rotation_matrix(tilt_x, tilt_y, tilt_z): """ Return the tilt matrix to apply to peaks - tilts in radians + tilts are in radians typically applied to peaks rotating around beam center """ r1 = np.array([[np.cos(tilt_z), -np.sin(tilt_z), 0], # note this is r.h. [np.sin(tilt_z), np.cos(tilt_z), 0], - [0, 0, 1]], np.float) + [0, 0, 1]], float) r2 = np.array([[np.cos(tilt_y), 0, np.sin(tilt_y)], [0, 1, 0], - [-np.sin(tilt_y), 0, np.cos(tilt_y)]], np.float) + [-np.sin(tilt_y), 0, np.cos(tilt_y)]], float) r3 = np.array([[1, 0, 0], [0, np.cos(tilt_x), -np.sin(tilt_x)], - [0, np.sin(tilt_x), np.cos(tilt_x)]], np.float) + [0, np.sin(tilt_x), np.cos(tilt_x)]], float) r2r1 = np.dot(np.dot(r3, r2), r1) return r2r1 @@ -88,6 +89,8 @@ def compute_xyz_lab(peaks, ((-1, 0),( 0, 1)) for (-x, y) (( 0,-1),(-1, 0)) for (-y,-x) etc... + + kwds are not used (but lets you pass in a dict with other things in it) """ assert len(peaks) == 2, "peaks must be a 2D array" # Matrix for the tilt rotations @@ -100,13 +103,13 @@ def compute_xyz_lab(peaks, # detector_orientation = [[o11, o12], [o21, o22]] # logging.debug("detector_orientation = "+str(detector_orientation)) - flipped = np.dot(np.array(detector_orientation, np.float), + flipped = np.dot(np.array(detector_orientation, float), peaks_on_detector) # vec = np.array([np.zeros(flipped.shape[1]), # place detector at zero, # sample at -dist flipped[1, :], # x in search, frelon +z - flipped[0, :]], np.float) # y in search, frelon -y + flipped[0, :]], float) # y in search, frelon -y # Position of diffraction spots in 3d space after detector tilts about # the beam centre on the detector rotvec = np.dot(r2r1, vec) @@ -128,7 +131,10 @@ def compute_tth_eta(peaks, chi=0.0, # == chi - 90 **kwds): # spare args are ignored """ - 0/10 for style + Finds x,y,z co-ordinates of peaks in the laboratory frame + Computes tth/eta from these (in degrees) + + kwds are not used (left for convenience if you have a parameter dict) """ peaks_xyz = compute_xyz_lab( peaks, @@ -157,12 +163,15 @@ def compute_tth_eta_from_xyz(peaks_xyz, omega, **kwds): # last line is for laziness - """ Peaks is a 3 d array of x,y,z peak co-ordinates - crystal_translation is the position of the grain giving rise - to a diffraction spot - in x,y,z ImageD11 co-ordinates - x,y with respect to axis of rotation and or beam centre ?? - z with respect to beam height, z centre - omega data needed if crystal translations used + crystal_translation is the position of the grain giving rise to a diffraction spot + in x,y,z ImageD11 co-ordinates + x,y is with respect to the axis of rotation (usually also beam centre). + z with respect to beam height, z centre + omega data are needed if crystal translations are used + + computed via the arctan recipe. + + returns tth/eta in degrees """ assert len(peaks_xyz) == 3 # Scattering vectors @@ -182,6 +191,46 @@ def compute_tth_eta_from_xyz(peaks_xyz, omega, return tth, eta +def compute_sinsqth_from_xyz(xyz): + """ Computes sin(theta)**2 + x,y,z = co-ordinates of the pixel in cartesian space + + if you need grain translations then use func(peaks_xyz - compute_grain_origins(...) ) + + seems to be competitive with arctan2 (docs/sintheta_squared_geometry.ipynb) + + returns sin(theta)**2 + """ + # R = hypotenuse of component normal to incident beam (defines x). e.g. y*y+z*z + R = xyz[1]*xyz[1] + xyz[2]*xyz[2] + # Q = hypotenuse along the scattered beam, e.g. x*x+y*y+z*z + Q = xyz[0]*xyz[0] + R + # if Q == 0 then this is undefined + sinsqth = 0.5*R/( Q + xyz[0]*np.sqrt(Q) ) + return sinsqth + + +def sinth2_sqrt_deriv(xyz): + """ sin(theta)**2 from xyz, and derivatives w.r.t x,y,z + """ + x,y,z = xyz + R = z*z + y*y + Q = R + x*x + SQ = np.sqrt(Q) + R2 = R/2 + # at x==y==0 this is undefined. ac + rQ_xSQ = 1/(Q + x*SQ) + sinth2 = R2*rQ_xSQ + # some simplification and collecting terms from expressions above to get: + sr = sinth2*rQ_xSQ + p = (x / SQ + 2)*sr # p should be in the range 3sr -> 2sr for x/x to 0/sqrt(R) + t = (rQ_xSQ - p) # + sinth2_dx = -(SQ*sr+x*p) + sinth2_dy = y*t + sinth2_dz = z*t + return sinth2, sinth2_dx, sinth2_dy, sinth2_dz + + def compute_xyz_from_tth_eta(tth, eta, omega, t_x=0.0, t_y=0.0, t_z=0.0, # == phi at chi=90 @@ -200,7 +249,7 @@ def compute_xyz_from_tth_eta(tth, eta, omega, omega data needed if crystal translations used """ # xyz = unit vectors along the scattered vectors - xyz = np.zeros((3, tth.shape[0]), np.float) + xyz = np.zeros((3, tth.shape[0]), float) rtth = np.radians(tth) reta = np.radians(eta) xyz[0, :] = np.cos(rtth) @@ -211,7 +260,7 @@ def compute_xyz_from_tth_eta(tth, eta, omega, # Find vectors in the fast, slow directions in the detector plane pks = np.array([(1, 0), (0, 1), - (0, 0) ], np.float).T + (0, 0) ], float).T dxyzl = compute_xyz_lab(pks, **kwds) # == [xpos, ypos, zpos] shape (3,n) # @@ -284,12 +333,12 @@ def compute_grain_origins(omega, wedge=0.0, chi=0.0, w = np.radians(wedge) WI = np.array([[np.cos(w), 0, -np.sin(w)], [0, 1, 0], - [np.sin(w), 0, np.cos(w)]], np.float) + [np.sin(w), 0, np.cos(w)]], float) c = np.radians(chi) CI = np.array([[1, 0, 0], [0, np.cos(c), -np.sin(c)], - [0, np.sin(c), np.cos(c)]], np.float) - t = np.zeros((3, omega.shape[0]), np.float) # crystal translations + [0, np.sin(c), np.cos(c)]], float) + t = np.zeros((3, omega.shape[0]), float) # crystal translations # Rotations in reverse order compared to making g-vector # also reverse directions. this is trans at all zero to # current setting. gv is scattering vector to all zero @@ -301,7 +350,7 @@ def compute_grain_origins(omega, wedge=0.0, chi=0.0, if chi != 0.0: c = np.cos(np.radians(chi)) s = np.sin(np.radians(chi)) - u = np.zeros(t.shape, np.float) + u = np.zeros(t.shape, float) u[0, :] = t[0, :] u[1, :] = c * t[1, :] + -s * t[2, :] u[2, :] = s * t[1, :] + c * t[2, :] @@ -309,7 +358,7 @@ def compute_grain_origins(omega, wedge=0.0, chi=0.0, if wedge != 0.0: c = np.cos(np.radians(wedge)) s = np.sin(np.radians(wedge)) - u = np.zeros(t.shape, np.float) + u = np.zeros(t.shape, float) u[0, :] = c * t[0, :] + -s * t[2, :] u[1, :] = t[1, :] u[2, :] = s * t[0, :] + c * t[2, :] @@ -317,39 +366,37 @@ def compute_grain_origins(omega, wedge=0.0, chi=0.0, return t -def compute_tth_histo(tth, no_bins=100, +def compute_tth_histo(tth, no_bins=100, weight = False, weights = None, **kwds): """ Compute a histogram of tth values + Uses numpy's histogram rather that doing it by hand as above + New feature: weight by something (peak intensity for instance), send true for weight and weights values Returns a normalised histogram (should make this a probability *and* - For each datapoint, the number of other points in the same bin + For each datapoint, the corresponding histogram weight + + Updated and modernized 2021-02-11 S. Merkel """ - tthsort = np.sort(tth) - maxtth = tthsort[-1] - mintth = tthsort[0] - logging.debug("maxtth=%f , mintth=%f" % (maxtth, mintth)) - binsize = (maxtth - mintth) / (no_bins + 1) - tthbin = np.arange(mintth, maxtth + binsize, binsize) - # print len(tthbin),tthbin[:10] - nn = np.searchsorted(tthsort, tthbin) # position of bin in sorted - nn = np.concatenate([nn, [len(tthsort)]]) # add on last position - histogram = (nn[1:] - nn[:-1]).astype(np.float32) - # this would otherwise be integer - logging.debug("max(histogram) = %d" % (max(histogram))) - # Change from max - # histogram = histogram/max(histogram) - histogram = histogram / len(tth) - # Vectorised version - # bin for each two theta - bins = np.floor((tth - mintth) / binsize).astype(np.int) - # print "got bins",len(bins),len(tth),len(histogram) - # print "bins",bins[:10] - # print "tth",tth[:10] - # print "histogram",histogram[:10] - hpk = np.take(histogram, bins) # histogram value for each peak - # print "hpk",hpk[:10] + maxtth = tth.max() + mintth = tth.min() + logging.debug("Histogram: maxtth=%f , mintth=%f, bins=%d" % (maxtth, mintth, no_bins)) + if (weight): + logging.debug("Weighted histogram") + histogram,binedges = np.histogram(tth, bins=no_bins, weights=weights, density=True) + else: + logging.debug("Un-weighted histogram") + histogram,binedges = np.histogram(tth, bins=no_bins, density=True) + tthbin = 0.5 *(binedges[:-1] + binedges[1:]) + histogram = histogram/histogram.sum() + # histogram value for each peak + # len(hpk) = number of peaks + # Tried to use numpy's digitize but failed. Edges are treated differently between np.histogram and np.digitize (both are inclusive in np.histogram) + # Tried many combinations and gave up + binsize = (maxtth - mintth) / (no_bins-1) + bins = np.floor((tth - mintth) / binsize).astype(np.int32) + hpk = np.take(histogram, bins) return tthbin, histogram, hpk @@ -362,7 +409,7 @@ def compute_k_vectors(tth, eta, wvln): c = np.cos(tth / 2) # cos theta s = np.sin(tth / 2) # sin theta ds = 2 * s / wvln - k = np.zeros((3, tth.shape[0]), np.float) + k = np.zeros((3, tth.shape[0]), float) # x - along incident beam k[0, :] = -ds * s # this is negative x # y - towards door @@ -385,7 +432,6 @@ def compute_g_vectors(tth, ... unless a wedge angle is specified """ k = compute_k_vectors(tth, eta, wvln) -# print k[:,0] return compute_g_from_k(k, omega, wedge, chi) @@ -395,8 +441,8 @@ def compute_g_from_k(k, omega, wedge=0, chi=0): """ om = np.radians(omega) # G-vectors - rotate k onto the crystal axes - g = np.zeros((3, k.shape[1]), np.float) - t = np.zeros((3, k.shape[1]), np.float) + g = np.zeros((3, k.shape[1]), float) + t = np.zeros((3, k.shape[1]), float) # # g = R . W . k where: # R = ( cos(omega) , sin(omega), 0 ) @@ -508,21 +554,21 @@ def compute_lorentz_factors(tth, eta, omega, wavelength, wedge=0., chi=0.): # (-sin(omega) , cos(omega), 0 ) # ( 0 , 0 , 1 ) # - W = [[cos(wedge), 0, sin(wedge)], + W = [[np.cos(wedge), 0, np.sin(wedge)], [0, 1, 0], - [-sin(wedge), 0, cos(wedge)]] + [-np.sin(wedge), 0, np.cos(wedge)]] # C = [[1, 0, 0], - [0, cos(chi), sin(chi)], - [0, -sin(chi), cos(chi)]] - u = np.matrixmultiply(C, np.matrixmultiply(W, u)) + [0, np.cos(chi), np.sin(chi)], + [0, -np.sin(chi), np.cos(chi)]] + u = np.dot(C, np.dot(W, u)) u_x_So = cross_product_2x2(u, So) # if DEBUG: print "axis orientation",u # # S = scattered vectors. Length 1/lambda. - S = np.array([cos(np.radians(tth) / 2.) * sin(np.radians(eta)) / wavelength, - cos(np.radians(tth) / 2.) * cos(np.radians(eta)) / wavelength, - sin(np.radians(tth) / 2.) / wavelength]) + S = np.array([np.cos(np.radians(tth) / 2.) * np.sin(np.radians(eta)) / wavelength, + np.cos(np.radians(tth) / 2.) * np.cos(np.radians(eta)) / wavelength, + np.sin(np.radians(tth) / 2.) / wavelength]) try: S_dot_u_x_So = np.dot(S, u_x_So) except: @@ -556,7 +602,138 @@ def compute_polarisation_factors(args): """ n = [0, 0, 1] - +class Ctransform(object): + pnames = ( "y_center", "z_center", "y_size", "z_size", + "distance", "wavelength","omegasign", + "tilt_x","tilt_y","tilt_z", + "o11", "o12", "o21", "o22", + "wedge", "chi" ) + def __init__(self, pars ): + + """ To Do ...: + origin = xyz(0,0), ds = xyz(1,0), df = xyz(0,1) + xyz(s,f) = origin + ds*s + df*f + """ + self.pars={} + for p in self.pnames: + self.pars[p] = pars[p] # copy + self.reset() + + def reset(self): + p = self.pars + self.distance_vec = np.array( (p['distance'],0.,0.)) + self.dmat = detector_rotation_matrix( p['tilt_x'], p['tilt_y'], p['tilt_z']) + self.fmat = np.array( [[ 1, 0, 0], + [ 0, p['o22'], p['o21']], + [ 0, p['o12'], p['o11']]] ) + self.rmat = np.dot(self.dmat, self.fmat).ravel() + self.cen = np.array( ( p["z_center"], p["y_center"], p["z_size"], p["y_size"] )) + + def sf2xyz(self, sc, fc, tx=0, ty=0, tz=0, out=None): + assert len(sc) == len(fc) + if out is None: + out = np.empty( (len(sc),3), float) + t = np.array( (tx,ty,tz) ) + cImageD11.compute_xlylzl( sc, fc, self.cen, self.rmat, self.distance_vec, out) + return out + + def xyz2gv(self, xyz, omega, tx=0, ty=0, tz=0, out=None ): + assert len(omega) == len(xyz) + if out is None: + out = np.empty( (len(xyz),3), float) + cImageD11.compute_gv( xyz, + omega, + self.pars['omegasign'], + self.pars['wavelength'], + self.pars['wedge'], + self.pars['chi'], + np.array((tx,ty,tz)), + out) + return out + + def sf2gv( self, sc, fc, omega, tx=0, ty=0, tz=0, out=None ): + xyz = self.sf2xyz( sc, fc, tx, ty, tz ) + return self.xyz2gv( xyz, omega, tx, ty, tz, out ) + + +class PixelLUT( object ): + + """ A look up table for a 2D image to store pixel-by-pixel values + """ + + # parameters that can be used to create this LUT + pnames = ( "y_center", "z_center", "y_size", "z_size", + "distance", "wavelength", "omegasign", + "tilt_x","tilt_y","tilt_z", + "o11", "o12", "o21", "o22", + "wedge", "chi", "dxfile", "dyfile", "spline", "shape" ) + + def __init__( self, pars ): + """ + pars is a dictionary containing the calibration parameters + """ + self.pars = {} + for p in self.pnames: + if p in pars: + self.pars[p] = pars[p] # make a copy + if 'dxfile' in pars: + # slow/fast coordinates on image at pixel centers + self.df = fabio.open( pars['dxfile'] ).data + self.ds = fabio.open( pars['dyfile'] ).data + self.shape = s = self.ds.shape # get shape from file + self.pars['shape'] = s + slow, fast = np.mgrid[ 0:s[0], 0:s[1] ] + self.sc = slow + self.ds + self.fc = fast + self.df + elif 'spline' in pars: # need to test this... + from ImageD11 import blobcorrector + b = blobcorrector.correctorclass( self.pars['spline'] ) + s = int(b.ymax - b.ymin), int(b.xmax - b.xmin) + if 'shape' in self.pars: # override. Probabl + s = self.pars['shape'] + self.shape = s + self.fc, self.sc = b.make_pixel_lut( s ) + slow, fast = np.mgrid[ 0:s[0], 0:s[1] ] + self.df = self.fc - fast + self.ds = self.sc - slow + else: + s = self.shape = self.pars.get("shape") + self.sc, self.fc = np.mgrid[0:s[0], 0:s[1]] + self.df = None + self.ds = None + + self.xyz = compute_xyz_lab( (self.sc.ravel(), self.fc.ravel()), **self.pars ) + self.sinthsq = compute_sinsqth_from_xyz( self.xyz ) + self.tth, self.eta = compute_tth_eta_from_xyz(self.xyz, None, **self.pars) + # scattering angles: + self.tth, self.eta = compute_tth_eta( (self.sc.ravel(), self.fc.ravel()), **self.pars ) + # scattering vectors: + self.k = compute_k_vectors( self.tth, self.eta, self.pars.get('wavelength') ) + self.sinthsq.shape = s + self.tth.shape = s + self.eta.shape = s + self.k.shape = (3, s[0], s[1]) + self.xyz.shape = (3, s[0], s[1]) + + def spatial(self, sraw, fraw): + """ applies a spatial distortion to sraw, fraw (for peak centroids) """ + if self.df is None: + return sraw, fraw + else: + si = np.round(sraw.astype(int)).clip( 0, self.shape[1] - 1 ) + fi = np.round(fraw.astype(int)).clip( 0, self.shape[1] - 1 ) + sc = sraw + self.ds[ si, fi ] + fc = fraw + self.df[ si, fi ] + return sc, fc + + def __repr__(self): + """ print yourself in a way we can use for eval """ + sp = "\n".join( [ "%s : %s,"%(repr(p), repr(self.pars[p])) for p in self.pnames + if p in self.pars ] ) + return "PixelLUT( { %s } )"%(sp) + + + if __name__ == "__main__": # from indexing import mod_360 def mod_360(theta, target): @@ -572,9 +749,9 @@ def mod_360(theta, target): diff = theta - target return theta - tth = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], np.float) - eta = np.array([10, 40, 70, 100, 130, 160, 190, 220, 270, 340], np.float) - om = np.array([0, 20, 40, 100, 60, 240, 300, 20, 42, 99], np.float) + tth = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], float) + eta = np.array([10, 40, 70, 100, 130, 160, 190, 220, 270, 340], float) + om = np.array([0, 20, 40, 100, 60, 240, 300, 20, 42, 99], float) for wavelength in [0.1, 0.2, 0.3]: for wedge in [-10., -5., 0., 5., 10.]: diff --git a/ImageD11/transformer.py b/ImageD11/transformer.py index 1700c5d8..964b75e8 100644 --- a/ImageD11/transformer.py +++ b/ImageD11/transformer.py @@ -164,6 +164,10 @@ helpstring="Number of bins to use in histogram based filters", vary=False, can_vary=False), + par('weight_hist_intensities', False, + helpstring="If True or 1, weight histograms by peak intensities. If False or 0, histogram by number of peaks.", + vary=False, + can_vary=False), ] @@ -282,10 +286,23 @@ def compute_tth_eta(self): def compute_histo(self, colname): """ Compute the histogram over twotheta for peaks previous read in - Filtering is moved to a separate function """ + Filtering is moved to a separate function + + colname is most-often "tth" + + other parameters are set in the parameter object + no_bins = number of bins + weight_hist_intensities: True or False + False: histogram by number of measured peaks + True: weight by peak intensities + """ if colname not in self.colfile.titles: raise Exception("Cannot find column " + colname) - bins, hist, hpk = transform.compute_tth_histo(self.getcolumn(colname), + weight = self.parameterobj.get("weight_hist_intensities") + if (weight): + bins, hist, hpk = transform.compute_tth_histo(self.getcolumn(colname), weight = True, weights = self.getcolumn("sum_intensity"), **self.parameterobj.get_parameters()) + else: + bins, hist, hpk = transform.compute_tth_histo(self.getcolumn(colname), **self.parameterobj.get_parameters()) self.addcolumn(hpk, colname + "_hist_prob") return bins, hist @@ -326,12 +343,18 @@ def tth_entropy(self): def gof(self, args): """ Compute how good is the fit of obs/calc peak positions in tth """ self.applyargs(args) + # + if self.update_fitds: + cell = unitcell.unitcell_from_parameters( self.parameterobj ) # Here, pars is a dictionary of name/value pairs to pass to compute_tth_eta tth, eta = self.compute_tth_eta() w = self.parameterobj.get("wavelength") gof = 0. npeaks = 0 for i in range(len(self.tthc)):# (twotheta_rad_cell.shape[0]): + if self.update_fitds: + b4 = self.fitds[i] + self.fitds[i] = cell.ds( self.fithkls[i] ) self.tthc[i] = transform.degrees(math.asin(self.fitds[i] * w / 2) * 2) diff = numpy.take(tth, self.indices[i]) - self.tthc[i] # print "peak",i,"diff",maximum.reduce(diff),minimum.reduce(diff) @@ -352,6 +375,7 @@ def fit(self, tthmin=0, tthmax=180): self.indices = [] # which peaks used self.tthc = [] # computed two theta values self.fitds = [] # hmm? + self.fithkls = [] self.fit_tolerance = 1. pars = self.parameterobj.get_parameters() w = float(pars['wavelength']) @@ -375,8 +399,13 @@ def fit(self, tthmin=0, tthmax=180): if sum(logicals) > 0: self.tthc.append(tthcalc) self.fitds.append(dsc) + self.fithkls.append( self.unitcell.ringhkls[dsc][0] ) ind = numpy.compress(logicals, list(range(len(tth)))) self.indices.append(ind) + self.update_fitds = False + for p in self.parameterobj.varylist: + if p.startswith('cell'): + self.update_fitds = True guess = self.parameterobj.get_variable_values() inc = self.parameterobj.get_variable_stepsizes() if len(guess) == 0: @@ -393,6 +422,9 @@ def fit(self, tthmin=0, tthmax=180): self.wavelength = self.parameterobj.get("wavelength") self.gof(newguess) print(newguess) + if self.update_fitds: + self.addcellpeaks() + def addcellpeaks(self, limit=None): @@ -474,7 +506,7 @@ def getaxis(self): This handles omegasign in a more elegant way """ # unit vector along z - v = numpy.array([0, 0, 1], numpy.float) + v = numpy.array([0, 0, 1], float) p = self.parameterobj.get("omegasign") return v * p @@ -541,7 +573,7 @@ def write_colfile(self, filename): def write_graindex_gv(self, filename): from ImageD11 import write_graindex_gv if ("gx" not in self.colfile.titles): - self.compute_gv() + self.computegv() gv = [ self.getcolumn("gx"), self.getcolumn("gy"), self.getcolumn("gz") ] @@ -632,3 +664,17 @@ def write_pyFAI(self, filename, tthmin=0, tthmax=180): # print geoRef #geoRef.save(os.path.splitext(filename)[0] + ".poni") + def save_tth_his(self, filename, bins, hist): + """ + Saves a 2 theta histogram into a file + bins and histogram should have been calculated previously + """ + # self.parameterobj.update_other(self) + + f = open(filename, "w") + f.write("# Peaks: %s\n" % self.colfile.filename) + f.write("# N. bins: %d\n" % len(bins)) + f.write("# 2tth intensity\n") + for i in range(0,len(hist)): + f.write("%10.7f %.7g \n" % (bins[i], hist[i])) + f.close() diff --git a/ImageD11/unitcell.py b/ImageD11/unitcell.py index 34c3b2b9..33c57684 100644 --- a/ImageD11/unitcell.py +++ b/ImageD11/unitcell.py @@ -1,6 +1,6 @@ -from __future__ import print_function +from __future__ import print_function, division ## Automatically adapted for numpy.oldnumeric Sep 06, 2007 by alter_code1.py @@ -27,14 +27,15 @@ # # # - -import numpy - +import logging, math +from math import fabs +import numpy as np from numpy.linalg import inv +from ImageD11 import cImageD11 +from xfab import tools, sg + -import math -import logging def radians(x): return x*math.pi/180. @@ -48,9 +49,9 @@ def cross(a,b): """ a x b has length |a||b|sin(theta) """ - return numpy.array([ a[1]*b[2]-a[2]*b[1] , - a[2]*b[0]-b[2]*a[0] , - a[0]*b[1]-b[0]*a[1] ],numpy.float) + return np.array([ a[1]*b[2]-a[2]*b[1] , + a[2]*b[0]-b[2]*a[0] , + a[0]*b[1]-b[0]*a[1] ],float) @@ -58,7 +59,7 @@ def norm2(a): """ Compute the unit 2 norm """ - return numpy.sqrt(numpy.dot(a,a)) + return np.sqrt(np.dot(a,a)) def unit(a): @@ -91,7 +92,7 @@ def I(h,k,l): def F(h,k,l): return (h+k)%2!=0 or (h+l)%2!=0 or (k+l)%2!=0 - + def R(h,k,l): return (-h+k+l)%3 != 0 @@ -105,6 +106,42 @@ def R(h,k,l): "F" : F , "R" : R} +def orient_BL( B, h1, h2, g1, g2): + """Algorithm was intended to follow this one using 2 indexed + reflections. + W. R. Busing and H. A. Levy. 457. Acta Cryst. (1967). 22, 457 + """ + h1c=np.dot(B,h1) # cartesian H1 + h2c=np.dot(B,h2) # cartesian H2 + t1c=unit(h1c) # unit vector along H1 + t3c=unit(np.cross(h1c,h2c)) + t2c=unit(np.cross(h1c,t3c)) + t1g=unit(g1) + t3g=unit(np.cross(g1,g2)) + t2g=unit(np.cross(g1,t3g)) + T_g = np.transpose(np.array([t1g,t2g,t3g])) # Array are stored by rows and + T_c = np.transpose(np.array([t1c,t2c,t3c])) # these are columns + U=np.dot(T_g , np.linalg.inv(T_c)) + UB=np.dot(U,B) + UBI=np.linalg.inv(UB) + return UBI, UB + +def cosangles_many(ha, hb, gi): + """ Finds the cosines of angles between two lists of hkls in + reciprocal metric gi """ + assert len(ha[0])==3 and len(hb[0])==3 + na = len(ha) + nb = len(hb) + hag = np.dot(ha, gi) + hbg = np.dot(hb, gi) + hagha = np.sqrt((hag*ha).sum(axis=1)) + hbghb = np.sqrt((hbg*hb).sum(axis=1)) + haghb = np.dot(ha, hbg.T) + ca = haghb / np.outer( hagha, hbghb ) + assert ca.shape == (na, nb) + return ca + + def cellfromstring(s): items = s.split() # print items @@ -122,18 +159,22 @@ def __init__(self, lattice_parameters, symmetry = "P", verbose = 0 ): """ Unit cell class supply a list (tuple etc) of a,b,c,alpha,beta,gamma - optionally a symmetry, one of "P","A","B","C","I","F","R" + optionally a lattice symmetry, one of "P","A","B","C","I","F","R" + or : a space group number (integer) """ - self.lattice_parameters = numpy.array(lattice_parameters) + self.lattice_parameters = np.array(lattice_parameters) if self.lattice_parameters.shape[0]!=6: raise Exception("You must supply 6 lattice parameters\n"+\ " a,b,c,alpha,beta,gamma") self.symmetry = symmetry - if self.symmetry not in ["P","A","B","C","I","F","R"]: - raise Exception("Your symmetry "+self.symmetry+\ - " was not recognised") - # assigning a function here! - self.absent = outif[self.symmetry] + if isinstance( symmetry, int ): + self.symmetry = symmetry + else: + if self.symmetry not in ["P","A","B","C","I","F","R"]: + raise Exception("Your symmetry "+self.symmetry+\ + " was not recognised") + # assigning a function here! + self.absent = outif[self.symmetry] a = self.lattice_parameters[0] b = self.lattice_parameters[1] c = self.lattice_parameters[2] @@ -142,9 +183,9 @@ def __init__(self, lattice_parameters, symmetry = "P", verbose = 0 ): cb= math.cos(radians(self.lattice_parameters[4])) cg= math.cos(radians(self.lattice_parameters[5])) if verbose==1: print("Unit cell",self.lattice_parameters) - self.g = numpy.array( [[ a*a , a*b*cg, a*c*cb ], + self.g = np.array( [[ a*a , a*b*cg, a*c*cb ], [ a*b*cg , b*b , b*c*ca ], - [ a*c*cb , b*c*ca, c*c ]],numpy.float) + [ a*c*cb , b*c*ca, c*c ]],float) if verbose==1: print("Metric tensor\n",self.g) try: self.gi = inv(self.g) @@ -152,39 +193,41 @@ def __init__(self, lattice_parameters, symmetry = "P", verbose = 0 ): raise Exception("Unit cell was degenerate, could not determine"+\ "reciprocal metric tensor") if verbose==1: print("Reciprocal Metric tensor\n",self.gi) - self.astar=numpy.sqrt(self.gi[0,0]) - self.bstar=numpy.sqrt(self.gi[1,1]) - self.cstar=numpy.sqrt(self.gi[2,2]) + self.astar=np.sqrt(self.gi[0,0]) + self.bstar=np.sqrt(self.gi[1,1]) + self.cstar=np.sqrt(self.gi[2,2]) self.alphas=degrees(math.acos(self.gi[1,2]/self.bstar/self.cstar)) self.betas =degrees(math.acos(self.gi[0,2]/self.astar/self.cstar)) self.gammas=degrees(math.acos(self.gi[0,1]/self.astar/self.bstar)) if verbose==1: print("Reciprocal cell") - if verbose==1: + if verbose==1: print(self.astar, self.bstar, self.cstar, \ self.alphas, self.betas, self.gammas) # Equation 3 from Busing and Levy - self.B = numpy.array ( - [ [ self.astar , - self.bstar*math.cos(radians(self.gammas)) , + self.B = np.array ( + [ [ self.astar , + self.bstar*math.cos(radians(self.gammas)) , self.cstar*math.cos(radians(self.betas)) ] , - [ 0 , - self.bstar*math.sin(radians(self.gammas)) , + [ 0 , + self.bstar*math.sin(radians(self.gammas)) , -self.cstar*math.sin(radians(self.betas))*ca ], [ 0 , 0 , - 1./c ] ] , numpy.float) + 1./c ] ] , float) if verbose == 1: print(self.B) - if verbose == 1: - print(numpy.dot( numpy.transpose(self.B), + if verbose == 1: + print(np.dot( np.transpose(self.B), self.B)-self.gi) # this should be zero self.hkls = None self.peaks = None self.limit = 0 - + + self.ringtol = 0.001 # used for caching - self.anglehkl_rings = None - self.anglehkl_cache = None - self.ringtol = 0.001 + self.anglehkl_cache = { "ringtol" : self.ringtol , + "B" : self.B, + "BI" : np.linalg.inv(self.B) } + def tostring(self): """ @@ -199,22 +242,28 @@ def tostring(self): self.symmetry) - def gethkls_xfab(self,dsmax,spg): + def gethkls_xfab(self,dsmax,spg=None): """ Generate hkl list Argument dsmax is the d* limit (eg 1/d) Argument spg is the space group name, e.g. 'R3-c' """ stl_max = dsmax/2. - from xfab import tools,sg - raw_peaks = tools.genhkl_all(self.lattice_parameters, + if isinstance(self.symmetry, int): + sgnum = self.symmetry + else: + sgnum = None + raw_peaks = tools.genhkl_all(self.lattice_parameters, 0 , stl_max, sgname=spg, + sgno = sgnum, output_stl=True) peaks = [] for i in range(len(raw_peaks)): - peaks.append([raw_peaks[i,3]*2,(raw_peaks[i,0],raw_peaks[i,1],raw_peaks[i,2])]) + peaks.append([raw_peaks[i,3]*2, + (int(raw_peaks[i,0]),int(raw_peaks[i,1]),int(raw_peaks[i,2]))]) self.peaks = peaks + self.limit = dsmax return peaks def gethkls(self,dsmax): @@ -227,6 +276,8 @@ def gethkls(self,dsmax): """ if dsmax == self.limit and self.peaks is not None: return self.peaks + if isinstance(self.symmetry, int): + return self.gethkls_xfab( dsmax, spg=None ) h=k=0 l=1 # skip 0,0,0 hs=ks=ls=1 @@ -274,14 +325,14 @@ def gethkls(self,dsmax): break peaks.sort() - + self.peaks=peaks self.limit=dsmax return peaks def ds(self,h): """ computes 1/d for this hkl = hgh """ - return math.sqrt(numpy.dot(h,numpy.dot(self.gi,h))) # 1/d or d* + return math.sqrt(np.dot(h,np.dot(self.gi,h))) # 1/d or d* def makerings(self,limit,tol=0.001): @@ -309,9 +360,9 @@ def anglehkls(self,h1,h2): """ Compute the angle between reciprocal lattice vectors h1, h2 """ - g1 = numpy.dot(h1,numpy.dot(self.gi,h1)) - g2 = numpy.dot(h2,numpy.dot(self.gi,h2)) - g12= numpy.dot(h1,numpy.dot(self.gi,h2)) + g1 = np.dot(h1,np.dot(self.gi,h1)) + g2 = np.dot(h2,np.dot(self.gi,h2)) + g12= np.dot(h1,np.dot(self.gi,h2)) costheta = g12/math.sqrt(g1*g2) try: return degrees(math.acos(costheta)),costheta @@ -326,26 +377,29 @@ def anglehkls(self,h1,h2): def getanglehkls(self, ring1, ring2): """ - Cache the last pair called for + Cache the previous pairs called for + sorted by cos2angle """ - if self.anglehkl_rings == (ring1, ring2, self.ringtol): - return self.anglehkl_cache + if self.ringtol != self.anglehkl_cache['ringtol'] or \ + (self.B != self.anglehkl_cache['B']).any(): + self.anglehkl_cache = {'ringtol':self.ringtol, + 'B':self.B, + 'BI':np.linalg.inv(self.B) } + key = (ring1,ring2) + B = self.anglehkl_cache['B'] + BI = self.anglehkl_cache['BI'] + if key not in self.anglehkl_cache: + h1 = self.ringhkls[self.ringds[ring1]] + h2 = self.ringhkls[self.ringds[ring2]] + cangs = cosangles_many( h1, h2, self.gi ) + val = filter_pairs( h1, h2, cangs, B, BI ) + self.anglehkl_cache[key] = val else: - self.anglehkl_rings = (ring1, ring2, self.ringtol) - cache = [] - hcach = [] - for ha in self.ringhkls[self.ringds[ring1]]: - for hb in self.ringhkls[self.ringds[ring2]]: - if ha == hb: - continue - hcach.append( ( ha, hb) ) - # Note - returns angle, cosangle - cache.append( self.anglehkls(ha,hb)[1] ) - self.anglehkl_cache = [ hcach , numpy.array(cache) ] - return self.anglehkl_cache - - - def orient(self,ring1,g1,ring2,g2,verbose=0, all = True): + val = self.anglehkl_cache[key] + return val + + + def orient(self,ring1,g1,ring2,g2,verbose=0, crange = -1.): """ Compute an orientation matrix using cell parameters and the indexing of two reflections @@ -357,80 +411,161 @@ def orient(self,ring1,g1,ring2,g2,verbose=0, all = True): t2 is in the plane of both (unit vector along g1x(g1xg2)) t3 is perpendicular to both (unit vector along g1xg2) """ - costheta = numpy.dot(g1,g2)/norm2(g2)/norm2(g1) - if verbose==1: print("observed costheta",costheta) - best=5. - hab , angles_ab = self.getanglehkls( ring1, ring2 ) - diffs = abs( angles_ab - costheta ) - if all: - order = numpy.argsort( diffs ) - best = [ order[0],] - for i in range(1,len(order)): - if diffs[order[i]] < diffs[order[0]]+1e-5: - best.append(order[i]) - else: - break + costheta = np.dot( g1, g2 ) / np.sqrt( (g1*g1).sum()*(g2*g2).sum() ) + hab, c2ab, matrs = self.getanglehkls( ring1, ring2 ) + if crange > 0: + best = np.arange( len(c2ab), dtype=int)[ abs( c2ab - costheta ) < crange ] + if verbose == 1: + print("possible best", best, len(c2ab)) else: - best = [numpy.argmin( diffs ),] + i = np.searchsorted(c2ab, costheta, side='left' ) + if i > 0 and (i == len(c2ab) or + (fabs(costheta - c2ab[i-1]) < fabs(costheta - c2ab[i]))): + best = [i-1,] + else: + best = [i,] + if verbose==1: + print("g1, g2",g1,g2) + print("observed cos2theta",costheta) + print("hab, c2ab",hab,c2ab) + print("best",best) self.UBIlist = [] + UBlist=[] for b in best: - try: - h1, h2 = hab[b] - except: - print("hab=",hab) - print("best=",b) - print("len(hab)",len(hab)) - print("len(angles_ab)", len(angles_ab)) - print("costheta",costheta) - print("abs( angles_ab - costheta ) )",abs( angles_ab - costheta )) - print("len(abs( angles_ab - costheta )) )",len(abs( angles_ab - costheta ) )) - print(numpy.argmin( abs( angles_ab - costheta ) )) - raise + h1, h2 = hab[b] if verbose==1: print("Assigning h1",h1,g1,self.ds(h1),\ - math.sqrt(numpy.dot(g1,g1)),\ - self.ds(h1)-math.sqrt(numpy.dot(g1,g1))) + math.sqrt(np.dot(g1,g1)),\ + self.ds(h1)-math.sqrt(np.dot(g1,g1))) print("Assigning h2",h2,g2,self.ds(h2),\ - math.sqrt(numpy.dot(g2,g2)),\ - self.ds(h1)-math.sqrt(numpy.dot(g1,g1))) - print("Cos angle calc",self.anglehkls(h1,h2),"obs",costheta) - try: - h1c=numpy.dot(self.B,h1) - h2c=numpy.dot(self.B,h2) - t1c=unit(h1c) - t3c=unit(cross(h1c,h2c)) - t2c=unit(cross(h1c,t3c)) - t1g=unit(g1) - t3g=unit(cross(g1,g2)) - t2g=unit(cross(g1,t3g)) - T_g = numpy.transpose(numpy.array([t1g,t2g,t3g])) # Array are stored by rows and - T_c = numpy.transpose(numpy.array([t1c,t2c,t3c])) # these are columns - U=numpy.dot(T_g , inv(T_c)) - UB=numpy.dot(U,self.B) - UBI=inv(UB) - except: - logging.error("unitcell.orient h1 %s g1 %s h2 %s g2 %s self.B %s"%( - str(h1), str(g1), str(h2), str(g2), str(self.B))) - print("angles_ab = ",angles_ab) - print("hab",hab) - print("best",b) - print("ring1",ring1,"ring2",ring2) - import traceback - traceback.print_exc() + math.sqrt(np.dot(g2,g2)),\ + self.ds(h1)-math.sqrt(np.dot(g1,g1))) + print("Cos angle calc",self.anglehkls(h1,h2), + "obs",costheta,"c2ab",c2ab[b]) + BT = matrs[b] + UBI = np.empty((3,3), float) + UBI[0] = g1 + UBI[1] = g2 + cImageD11.quickorient( UBI, BT ) if verbose==1: print("UBI") print(UBI) - print("Grain gi") - print(numpy.dot(numpy.transpose(UB),UB)) - print("Cell gi") - print(self.gi) - h=numpy.dot(UBI,g1) + h=np.dot(UBI,g1) print("(%9.3f, %9.3f, %9.3f)"%(h[0],h[1],h[2])) - h=numpy.dot(UBI,g2) + h=np.dot(UBI,g2) print("(%9.3f, %9.3f, %9.3f)"%(h[0],h[1],h[2])) self.UBI=UBI - self.UB=UB + self.UB=np.linalg.inv(UBI) self.UBIlist.append(UBI) + UBlist.append(self.UB) + # trim to uniq list? What about small distortions... + self.UBIlist = ubi_equiv( self.UBIlist, UBlist ) + +def BTmat( h1, h2, B, BI ): + """ used for computing orientations + """ + g1 = np.dot( B, h1 ) # gvectors for these hkl + g2 = np.dot( B, h2 ) + g3 = np.cross( g1, g2 ) + u1 = unit(g1) # normalised + u3 = unit(g3) + u2 = np.cross(u1, u3) + BT = np.dot( BI, np.transpose((u1, u2, u3))) + return BT + +HKL0 = np.array( [ [0,0,1,1,-1,1,-1,0, 0, 1, -1, 1, 1, 3, 11], + [0,1,0,1, 1,0, 0,1,-1, 1, 1,-1, 1, 2, 12], + [1,0,0,0, 0,1, 1,1, 1, 1, 1, 1,-1, 1, 13] ], float ) # first unit cell + + +def filter_pairs( h1, h2, c2a, B, BI, tol = 1e-5): + """ remove duplicate pairs for orientation searches + h1 = reflections of ring1, N1 peaks + h2 = reflections of ring2, N2 peaks + c2a = cos angle between them, N1xN2 + B = B matrix in reciprocal space + BI = inverse in real space + """ + assert c2a.shape == (len(h1), len(h2)) + order = np.argsort( c2a.ravel() ) # increasing in cosine of angle + c2as = c2a.flat[order] + hi, hj= np.mgrid[0:len(h1),0:len(h2)] + hi = hi.ravel()[order] # to get back the peaks + hj = hj.ravel()[order] + # Results holders: + pairs = [ ] + cangs = [ ] + matrs = [ ] + # cluster the cangs assuming a sensible threshold + dc = (c2as[1:] - c2as[:-1]) > 1e-8 # differences + inds = list(np.arange( 1, len(dc) + 1, dtype=int )[dc]) + [len(c2as)-1,] + p = 0 # previous + for i in inds: + c = c2as[p:i] # block is p:i + if abs(c2as[p]) < 0.98: # always keep the first one + ha = h1[hi[p]] + hb = h2[hj[p]] + pairs.append( (ha, hb) ) + cangs.append( c2as[p] ) + BT = BTmat( ha, hb, B, BI) + matrs.append( BT ) + else: + p = i + continue + if len(c) == 1: + p = i + continue + assert (c.max()-c.min()) < 2.1e-8, "Angles blocking error in filter_pairs" + # here we have a bunch of hkl pairs which all give the same angle + # between them. They are not all the same. We generate a pair of peaks + # from the first one and see which other pairs index differently + ga = np.dot(B, ha ) + gb = np.dot(B, hb ) + assert abs( np.dot(ga,gb)/np.sqrt(np.dot(ga,ga)*np.dot(gb,gb)) - c2as[p] ) < 2e-8, "mixup in filter_pairs" + gobs = np.array( (ga, gb, (0,0,0)), float) + UBI = gobs.copy() + cImageD11.quickorient( UBI, BT ) + gtest = [ np.dot( np.linalg.inv(UBI), HKL0 ).T.copy(), ] + for j in range(p+1,i): + ha = h1[hi[j]] + hb = h2[hj[j]] + BT = BTmat( ha, hb, B, BI) + newpair = True + for gt in gtest: + UBI = gobs.copy() + cImageD11.quickorient( UBI, BT ) + npk = cImageD11.score(UBI, gt, 1e-6) + if npk == len(HKL0[0]): + newpair = False + break + if newpair: + pairs.append( (ha, hb) ) + cangs.append( c2as[j] ) + matrs.append( BT ) + gtest.append( np.dot( np.linalg.inv(UBI), HKL0 ).T.copy() ) + p = i + return pairs, cangs, matrs + +def ubi_equiv( ubilist, ublist, tol=1e-8): + """ Two ubi are considered equivalent if they both index the peaks + in the HKL0 array exactly""" + if len(ubilist) < 2: + return ubilist + order = np.argsort( [np.trace( ubi ) for ubi in ubilist ] ) # low to high + uniq = [ ubilist[ order[-1] ], ] + refgv = [ np.dot( ublist[ order[-1]] , HKL0 ), ] + for i in order[:-1][::-1]: + ubi = ubilist[i] + score = 1 + for pks in refgv: # pks is (n,3) for an orientation + hcalc = np.dot( ubi, pks ) + score = min(score, np.abs( np.round( hcalc ) - hcalc).sum() ) + if score <= tol: # matches a previous grain + break + if score > tol: # is a new orientation + uniq.append( ubi ) + refgv.append( np.dot( np.linalg.inv( ubi ) , HKL0 ) ) + return uniq def unitcell_from_parameters(pars): @@ -443,11 +578,5 @@ def unitcell_from_parameters(pars): import sys,time start=time.time() cell = unitcell([float(x) for x in sys.argv[1:7]],sys.argv[7]) - p=cell.gethkls(float(sys.argv[8])) - n=len(p) - print("Generated",n,"peaks in",time.time()-start,"/s") - for k in p: - try: - print(k[1],k[0],1/k[0],k[1]) - except: - pass + cell.makerings(2) + cell.getanglehkls(11,12) diff --git a/ImageD11/weighted_kde.py b/ImageD11/weighted_kde.py new file mode 100644 index 00000000..214541c7 --- /dev/null +++ b/ImageD11/weighted_kde.py @@ -0,0 +1,298 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Jul 17 21:15:51 2020 + +@author: younes +""" + + +# from: https://nbviewer.jupyter.org/gist/tillahoffmann/f844bce2ec264c1c8cb5 + +import numpy as np +from scipy.spatial.distance import cdist +from six import string_types + +class gaussian_kde(object): + """Representation of a kernel-density estimate using Gaussian kernels. + + Kernel density estimation is a way to estimate the probability density + function (PDF) of a random variable in a non-parametric way. + `gaussian_kde` works for both uni-variate and multi-variate data. It + includes automatic bandwidth determination. The estimation works best for + a unimodal distribution; bimodal or multi-modal distributions tend to be + oversmoothed. + + Parameters + ---------- + dataset : array_like + Datapoints to estimate from. In case of univariate data this is a 1-D + array, otherwise a 2-D array with shape (# of dims, # of data). + bw_method : str, scalar or callable, optional + The method used to calculate the estimator bandwidth. This can be + 'scott', 'silverman', a scalar constant or a callable. If a scalar, + this will be used directly as `kde.factor`. If a callable, it should + take a `gaussian_kde` instance as only parameter and return a scalar. + If None (default), 'scott' is used. See Notes for more details. + weights : array_like, shape (n, ), optional, default: None + An array of weights, of the same shape as `x`. Each value in `x` + only contributes its associated weight towards the bin count + (instead of 1). + + Attributes + ---------- + dataset : ndarray + The dataset with which `gaussian_kde` was initialized. + d : int + Number of dimensions. + n : int + Number of datapoints. + neff : float + Effective sample size using Kish's approximation. + factor : float + The bandwidth factor, obtained from `kde.covariance_factor`, with which + the covariance matrix is multiplied. + covariance : ndarray + The covariance matrix of `dataset`, scaled by the calculated bandwidth + (`kde.factor`). + inv_cov : ndarray + The inverse of `covariance`. + + Methods + ------- + kde.evaluate(points) : ndarray + Evaluate the estimated pdf on a provided set of points. + kde(points) : ndarray + Same as kde.evaluate(points) + kde.pdf(points) : ndarray + Alias for ``kde.evaluate(points)``. + kde.set_bandwidth(bw_method='scott') : None + Computes the bandwidth, i.e. the coefficient that multiplies the data + covariance matrix to obtain the kernel covariance matrix. + .. versionadded:: 0.11.0 + kde.covariance_factor : float + Computes the coefficient (`kde.factor`) that multiplies the data + covariance matrix to obtain the kernel covariance matrix. + The default is `scotts_factor`. A subclass can overwrite this method + to provide a different method, or set it through a call to + `kde.set_bandwidth`. + + Notes + ----- + Bandwidth selection strongly influences the estimate obtained from the KDE + (much more so than the actual shape of the kernel). Bandwidth selection + can be done by a "rule of thumb", by cross-validation, by "plug-in + methods" or by other means; see [3]_, [4]_ for reviews. `gaussian_kde` + uses a rule of thumb, the default is Scott's Rule. + + Scott's Rule [1]_, implemented as `scotts_factor`, is:: + + n**(-1./(d+4)), + + with ``n`` the number of data points and ``d`` the number of dimensions. + Silverman's Rule [2]_, implemented as `silverman_factor`, is:: + + (n * (d + 2) / 4.)**(-1. / (d + 4)). + + Good general descriptions of kernel density estimation can be found in [1]_ + and [2]_, the mathematics for this multi-dimensional implementation can be + found in [1]_. + + References + ---------- + .. [1] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and + Visualization", John Wiley & Sons, New York, Chicester, 1992. + .. [2] B.W. Silverman, "Density Estimation for Statistics and Data + Analysis", Vol. 26, Monographs on Statistics and Applied Probability, + Chapman and Hall, London, 1986. + .. [3] B.A. Turlach, "Bandwidth Selection in Kernel Density Estimation: A + Review", CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993. + .. [4] D.M. Bashtannyk and R.J. Hyndman, "Bandwidth selection for kernel + conditional density estimation", Computational Statistics & Data + Analysis, Vol. 36, pp. 279-298, 2001. + + Examples + -------- + Generate some random two-dimensional data: + + >>> from scipy import stats + >>> def measure(n): + >>> "Measurement model, return two coupled measurements." + >>> m1 = np.random.normal(size=n) + >>> m2 = np.random.normal(scale=0.5, size=n) + >>> return m1+m2, m1-m2 + + >>> m1, m2 = measure(2000) + >>> xmin = m1.min() + >>> xmax = m1.max() + >>> ymin = m2.min() + >>> ymax = m2.max() + + Perform a kernel density estimate on the data: + + >>> X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j] + >>> positions = np.vstack([X.ravel(), Y.ravel()]) + >>> values = np.vstack([m1, m2]) + >>> kernel = stats.gaussian_kde(values) + >>> Z = np.reshape(kernel(positions).T, X.shape) + + Plot the results: + + >>> import matplotlib.pyplot as plt + >>> fig = plt.figure() + >>> ax = fig.add_subplot(111) + >>> ax.imshow(np.rot90(Z), cmap=plt.cm.gist_earth_r, + ... extent=[xmin, xmax, ymin, ymax]) + >>> ax.plot(m1, m2, 'k.', markersize=2) + >>> ax.set_xlim([xmin, xmax]) + >>> ax.set_ylim([ymin, ymax]) + >>> plt.show() + + """ + def __init__(self, dataset, bw_method=None, weights=None): + self.dataset = np.atleast_2d(dataset) + if not self.dataset.size > 1: + raise ValueError("`dataset` input should have multiple elements.") + self.d, self.n = self.dataset.shape + + if weights is not None: + self.weights = weights / np.sum(weights) + else: + self.weights = np.ones(self.n) / self.n + + # Compute the effective sample size + # http://surveyanalysis.org/wiki/Design_Effects_and_Effective_Sample_Size#Kish.27s_approximate_formula_for_computing_effective_sample_size + self.neff = 1.0 / np.sum(self.weights ** 2) + + self.set_bandwidth(bw_method=bw_method) + + def evaluate(self, points): + """Evaluate the estimated pdf on a set of points. + + Parameters + ---------- + points : (# of dimensions, # of points)-array + Alternatively, a (# of dimensions,) vector can be passed in and + treated as a single point. + + Returns + ------- + values : (# of points,)-array + The values at each point. + + Raises + ------ + ValueError : if the dimensionality of the input points is different than + the dimensionality of the KDE. + + """ + points = np.atleast_2d(points) + + d, m = points.shape + if d != self.d: + if d == 1 and m == self.d: + # points was passed in as a row vector + points = np.reshape(points, (self.d, 1)) + m = 1 + else: + msg = "points have dimension %s, dataset has dimension %s" % (d, + self.d) + raise ValueError(msg) + + # compute the normalised residuals + chi2 = cdist(points.T, self.dataset.T, 'mahalanobis', VI=self.inv_cov) ** 2 + # compute the pdf + result = np.sum(np.exp(-.5 * chi2) * self.weights, axis=1) / self._norm_factor + + return result + + __call__ = evaluate + + def scotts_factor(self): + return np.power(self.neff, -1./(self.d+4)) + + def silverman_factor(self): + return np.power(self.neff*(self.d+2.0)/4.0, -1./(self.d+4)) + + # Default method to calculate bandwidth, can be overwritten by subclass + covariance_factor = scotts_factor + + def set_bandwidth(self, bw_method=None): + """Compute the estimator bandwidth with given method. + + The new bandwidth calculated after a call to `set_bandwidth` is used + for subsequent evaluations of the estimated density. + + Parameters + ---------- + bw_method : str, scalar or callable, optional + The method used to calculate the estimator bandwidth. This can be + 'scott', 'silverman', a scalar constant or a callable. If a + scalar, this will be used directly as `kde.factor`. If a callable, + it should take a `gaussian_kde` instance as only parameter and + return a scalar. If None (default), nothing happens; the current + `kde.covariance_factor` method is kept. + + Notes + ----- + .. versionadded:: 0.11 + + Examples + -------- + >>> x1 = np.array([-7, -5, 1, 4, 5.]) + >>> kde = stats.gaussian_kde(x1) + >>> xs = np.linspace(-10, 10, num=50) + >>> y1 = kde(xs) + >>> kde.set_bandwidth(bw_method='silverman') + >>> y2 = kde(xs) + >>> kde.set_bandwidth(bw_method=kde.factor / 3.) + >>> y3 = kde(xs) + + >>> fig = plt.figure() + >>> ax = fig.add_subplot(111) + >>> ax.plot(x1, np.ones(x1.shape) / (4. * x1.size), 'bo', + ... label='Data points (rescaled)') + >>> ax.plot(xs, y1, label='Scott (default)') + >>> ax.plot(xs, y2, label='Silverman') + >>> ax.plot(xs, y3, label='Const (1/3 * Silverman)') + >>> ax.legend() + >>> plt.show() + + """ + if bw_method is None: + pass + elif bw_method == 'scott': + self.covariance_factor = self.scotts_factor + elif bw_method == 'silverman': + self.covariance_factor = self.silverman_factor + elif np.isscalar(bw_method) and not isinstance(bw_method, string_types): + self._bw_method = 'use constant' + self.covariance_factor = lambda: bw_method + elif callable(bw_method): + self._bw_method = bw_method + self.covariance_factor = lambda: self._bw_method(self) + else: + msg = "`bw_method` should be 'scott', 'silverman', a scalar " \ + "or a callable." + raise ValueError(msg) + + self._compute_covariance() + + def _compute_covariance(self): + """Computes the covariance matrix for each Gaussian kernel using + covariance_factor(). + """ + self.factor = self.covariance_factor() + # Cache covariance and inverse covariance of the data + if not hasattr(self, '_data_inv_cov'): + # Compute the mean and residuals + _mean = np.sum(self.weights * self.dataset, axis=1) + _residual = (self.dataset - _mean[:, None]) + # Compute the biased covariance + self._data_covariance = np.atleast_2d(np.dot(_residual * self.weights, _residual.T)) + # Correct for bias (http://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance) + self._data_covariance /= (1 - np.sum(self.weights ** 2)) + self._data_inv_cov = np.linalg.inv(self._data_covariance) + + self.covariance = self._data_covariance * self.factor**2 + self.inv_cov = self._data_inv_cov / self.factor**2 + self._norm_factor = np.sqrt(np.linalg.det(2*np.pi*self.covariance)) #* self.n \ No newline at end of file diff --git a/ImageD11/write_graindex_gv.py b/ImageD11/write_graindex_gv.py index dc5fa275..2b749dad 100644 --- a/ImageD11/write_graindex_gv.py +++ b/ImageD11/write_graindex_gv.py @@ -54,7 +54,7 @@ def make_ds_list(cell,limit=2.): hmax = h kmax = k ds_string="(%d%d%d)"%(h,k,l) - ds_list.append([ky,ptype,ds_string]) + ds_list.append([ds,ptype,ds_string]) return ds_list def get_ds_string(g,ds_list): diff --git a/MANIFEST b/MANIFEST deleted file mode 100644 index a3ea2b9a..00000000 --- a/MANIFEST +++ /dev/null @@ -1,203 +0,0 @@ -LICENSE -todo.txt -changelog.txt -README.TXT 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-docs/icons.zip -docs/icon.svg -docs/icons.svg -docs/48x48.ico -docs/32x32.ico -docs/16x16.ico -docs/multi.ico - diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..f020e800 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,66 @@ +include *.md +include *.bat +include *.py +include *.sh +include *.txt +include *.yml +include LICENSE +recursive-include ImageD11 *.c +recursive-include ImageD11 *.md +recursive-include ImageD11 *.py +recursive-include data *.spline +recursive-include docs *.bat +recursive-include docs *.docx +recursive-include docs *.ico +recursive-include docs *.odt +recursive-include docs *.pdf +recursive-include docs *.png +recursive-include docs *.py +recursive-include docs *.rst +recursive-include docs *.rtf +recursive-include docs *.svg +recursive-include docs *.txt +recursive-include docs *.zip +recursive-include docs Makefile +recursive-include sandbox *.c +recursive-include sandbox *.cl +recursive-include sandbox *.gif +recursive-include sandbox *.par +recursive-include sandbox *.py +recursive-include sandbox *.sh +recursive-include sandbox *.txt +recursive-include scripts *.py +recursive-include src *.c +recursive-include src *.h +recursive-include src *.py +recursive-include src *.pyf +recursive-include src *.txt +recursive-include test *.cif +recursive-include test *.cpp +recursive-include test *.doc +recursive-include test *.flt +recursive-include test *.gve +recursive-include test *.gz +recursive-include test *.idx +recursive-include test *.ini +recursive-include test *.inp +recursive-include test *.ipynb +recursive-include test *.map +#recursive-include test *.out +#recursive-include test *.out_merge_t100 +#recursive-include test *.out_merge_t200 +recursive-include test *.par +recursive-include test *.pars +recursive-include test *.pdf +#recursive-include test *.pks +recursive-include test *.prm +recursive-include test *.py +recursive-include test *.sh +recursive-include test *.spline +recursive-include test *.txt +recursive-include test *.ubi +recursive-include webgui *.html +recursive-include webgui *.js +recursive-include webgui *.md +prune ImageD11src/depreciated +prune src/old diff --git a/PKG-INFO b/PKG-INFO deleted file mode 100644 index f17ff6d8..00000000 --- a/PKG-INFO +++ /dev/null @@ -1,10 +0,0 @@ -Metadata-Version: 1.7 -Name: ImageD11 -Version: 1.7.0 -Summary: ImageD11 -Home-page: UNKNOWN -Author: Jon Wright -Author-email: wright@esrf.fr -License: GPL -Description: UNKNOWN -Platform: UNKNOWN diff --git a/README.TXT b/README.TXT deleted file mode 100644 index 017f8c69..00000000 --- a/README.TXT +++ /dev/null @@ -1,35 +0,0 @@ -ImageD11 -Version 1.7 -Jon Wright -wright@esrf.fr - -This is the source code for ImageD11. Probably you wanted a compiled version. - -Some documentation is here: https://pythonhosted.org/ImageD11/ - -To use it, try this: - - Download and install python 2.7, perhaps from www.python.org - Add the packages numpy, matplotlib and pyopengl. - - Then try: - python setup.py build - or - python setup.py build --compiler=mingw32 - Followed by: - python setup.py install - -After it is installed, you should find a script ImageD11_gui.py, somewhere in your path - - -Until 2017 this code was mostly developed on sourceforge at http://sourceforge.net/projects/fable/ - -It moves to github.com/jonwright/ImageD11 for a spring clean. - - -Good luck! - - - - - diff --git a/README.md b/README.md new file mode 100644 index 00000000..43d095e4 --- /dev/null +++ b/README.md @@ -0,0 +1,72 @@ + + +ImageD11 is a python code for identifying individual grains in spotty area detector X-ray diffraction images. + +Version 2.0.2, Jon Wright, wright@esrf.fr + +This is the source code for ImageD11. Probably you wanted a compiled version. + +If your pip is up-to-date, you can try to install it like this: +``` + python -m pip install --upgrade pip setuptools + python -m pip install ImageD11 +``` +If you want to use an existing numpy installation add a `--no-build-isolation` flag. + +To get all the possible dependencies too, you can try: + `python -m pip install ImageD11[full]` + +Some (dated) documentation is here: https://imaged11.readthedocs.io/ + +If you are at ESRF on an old linux computer you can try "module load fable". + +To use from git, try this: + + - Download and install python 3.8+, perhaps from www.python.org but probably from conda. + - Preload binary packages from conda (or your system package manager): + numpy, scipy, matplotlib, h5py, pillow, pycifrw, xfab, pyqt, pillow, silx[full] etc + - `pip install git+https://github.com/FABLE-3DXRD/ImageD11.git` + +If you want to work with the sources then you can try like this: + ``` + $ python -m pip install --upgrade pip + $ git clone https://github.com/FABLE-3DXRD/ImageD11.git && cd ImageD11 + $ python -m pip install --editable . + ``` + +If you want multiple binaries in your home (on recent pythons) you can do and get the compiled code +for each platform in .so files that are labelled by platform. This is potentially useful for a +heterogenous cluster (like at ESRF): +``` + # on ppc64le: + python3 -m pip install dist/ImageD11-1.9.8-cp38-cp38-linux_ppc64le.whl --user --ignore-installed + # on x86_64: + python3 -m pip install dist/ImageD11-1.9.8-cp38-cp38-linux_x86_64.whl --user --ignore-installed + # etc + # ~/.local/lib/python3.8/site-packages/ImageD11 % ls *.so + _cImageD11.cpython-38-powerpc64le-linux-gnu.so _cImageD11.cpython-38-x86_64-linux-gnu.so +``` + +After it is installed, you should find a script ImageD11_gui.py, somewhere in your path. + +Until 2017 this code was mostly developed on sourceforge at http://sourceforge.net/projects/fable/ + +It is now developed at http://github.com/FABLE-3DXRD/ImageD11 + +Bug reports are always welcome! + +Good luck! + +## CI Status + +[![Flake, Build and PyTest](https://github.com/FABLE-3DXRD/ImageD11/actions/workflows/build_flake_pytest_ubuntu2004.yml/badge.svg)](https://github.com/FABLE-3DXRD/ImageD11/actions/workflows/build_flake_pytest_ubuntu2004.yml) + + diff --git a/appveyor.yml b/appveyor.yml new file mode 100644 index 00000000..600d09d0 --- /dev/null +++ b/appveyor.yml @@ -0,0 +1,30 @@ + +image: Visual Studio 2015 + +build: false + + +environment: + matrix: + - CONDA: C:\Python27 + - CONDA: C:\Python27-x64 + - CONDA: C:\Python38-x64 +# Meson errors, no binaries for 32-bits - CONDA: C:\Python38 + +init: + - "ECHO %CONDA%" + +install: + - "SET PATH=%CONDA%;%CONDA%\\Scripts;%CONDA%\\Library\\bin;%CONDA%\\DLLS;%PATH%" + - "mkdir C:\\tmp" + - "set TMPDIR=C:\\tmp" + - python -c "import sys; print(sys.executable, sys.version)" + - python -m pip install --upgrade pip setuptools + - python -m pip install pytest numpy + - python -m pip install --ignore-installed certifi --upgrade-strategy only-if-needed --only-binary=h5py --only-binary=scipy --only-binary=hdf5plugin . + + +test_script: + - cd test + - python -m pytest + diff --git a/changelog.txt b/changelog.txt deleted file mode 100644 index ca2df89e..00000000 --- a/changelog.txt +++ /dev/null @@ -1,6 +0,0 @@ - - -Friedel pair files should be implemented in indexer.py - -Separation of the gui from the working code via parameters.py and guicommand.py - diff --git a/docs/DeformationGradientTensor.ipynb b/docs/DeformationGradientTensor.ipynb new file mode 100644 index 00000000..b7fd82e0 --- /dev/null +++ b/docs/DeformationGradientTensor.ipynb @@ -0,0 +1,498 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Strains from 3DXRD\n", + "\n", + "*Jon Wright, 26 April (-> 1st May -> +AH 19th May) 2020*\n", + "\n", + "The observed data from a 3DXRD experiment are the scattering vectors in the sample frame, the \"g-vectors\". If we succeed to index these using orientation matrices, (UB), we assign hkl indices to spots.\n", + "The matrix inverse of (UB) gives the real space unit cell vectors, called $\\mathbf{L}$ here (for lattice, it is the **ubi** from ImageD11). In the ImageD11 code there is often confusion between row and column vectors as the code usually applies transformations from the left and does not distinguish row and column vectors. If you substitute *h,k,l* as unit vectors in (1) you can identify the unit cell vectors in reciprocal space:\n", + "\n", + "\\begin{equation} \n", + "\\begin{pmatrix} g_x \\\\ g_y \\\\ g_z \\end{pmatrix} =\n", + "\\mathbf{L^{-1}} \n", + "\\begin{pmatrix} h \\\\ k \\\\ l \\end{pmatrix}\n", + "= (\\mathbf{UB})\n", + "\\begin{pmatrix} h \\\\ k \\\\ l \\end{pmatrix}\n", + "= \n", + "\\begin{pmatrix} \n", + "\\begin{pmatrix}\n", + "x_a^* \\\\\n", + "y_a^* \\\\\n", + "z_a^*\n", + "\\end{pmatrix} \n", + "\\begin{pmatrix}\n", + "x_b^* \\\\\n", + "y_b^* \\\\\n", + "z_b^*\n", + "\\end{pmatrix} \n", + "\\begin{pmatrix}\n", + "x_c^* \\\\\n", + "y_c^* \\\\\n", + "z_c^*\n", + "\\end{pmatrix} \n", + "\\end{pmatrix} \n", + "\\begin{pmatrix} h \\\\ k \\\\ l \\end{pmatrix} \n", + "\\tag{1}\n", + "\\end{equation}\n", + "\n", + "\\begin{equation} \n", + "\\begin{pmatrix} h \\\\ k \\\\ l \\end{pmatrix} =\n", + "\\mathbf{L} \\begin{pmatrix} g_x \\\\ g_y \\\\ g_z \\end{pmatrix} = \n", + "( \\mathbf{UB} )^{-1}\\begin{pmatrix} g_x \\\\ g_y \\\\ g_z \\end{pmatrix} = \\begin{pmatrix} \n", + "(x_a & y_a & z_a ) \\\\ \n", + "(x_b & y_b & z_b ) \\\\\n", + "(x_c & y_c & z_c ) \\end{pmatrix}\n", + "\\begin{pmatrix} g_x \\\\ g_y \\\\ g_z \\end{pmatrix}\n", + "\\tag{2}\n", + "\\end{equation}\n", + "\n", + "The real space lattice as row vectors is not quite so obvious. Instead you need to multiply from the left with a row vector in crystal space $(c_x,c_y,c_z)$ as unit vector and compute a column vector in laboratory space, $(l_x,l_y,l_z)$ as in (3):\n", + "\n", + "\\begin{equation}\n", + " (c_x, c_y, c_z) \\begin{pmatrix}\n", + "(x_a & y_a & z_a ) \\\\ \n", + "(x_b & y_b & z_b ) \\\\\n", + "(x_c & y_c & z_c ) \\end{pmatrix}\n", + "= \\begin{pmatrix} l_x \\\\ l_y \\\\ l_z \\end{pmatrix} \n", + "\\tag{3}\n", + "\\end{equation}\n", + "\n", + "The difference of row versus column vectors and coming from the left or the right of a matrix has been the cause of many years of confusion. Many thanks to Axel Henningsson for pointing out some errors here. A quick note to self: when we multiply two matrices A, B with shapes (rows,cols):(M,K) and (K,N), then the K has to be the same and $ C = A.B $ with shapes $ C[m,n] = A[m,k].B[k,n] $." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If a crystal is strained then we look how the atoms move compared to a reference which was strain free. If we are sitting on some atom in the initial state, the positions of the atoms in the next unit cells are given by a matrix $\\mathbf{L_0}$. After strain the neighbor positions will now be given by the 3 lattice vectors:\n", + "\n", + "$ \\mathbf{a} = \\mathbf{F . a_0 } $\n", + "\n", + "$ \\mathbf{b} = \\mathbf{F . b_0 } $\n", + "\n", + "$ \\mathbf{c} = \\mathbf{F . c_0 } $\n", + "\n", + "$ \\mathbf{L^T} = \\mathbf{F . L_0^T }$\n", + "\n", + "[***To be confirmed: second attempt***: I think this is the deformation gradient tensor from finite strain theory, but neglecting any rigid body translations].\n", + "\n", + "This is easier for most people to read in terms of column vectors with $\\mathbf{A = L^T}$ and $\\mathbf{A = F.A_0}$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming this is the $\\mathbf{F}$ of finite strain theory then everything else falls beautifully into place. If we have a reference unit cell, in any orientation, then we can compute $ \\mathbf{F} $ which includes both a change in orientation and strain:\n", + "\n", + "$ \\mathbf{L^T} = \\mathbf{F.L_0^T } $\n", + "\n", + "$ \\mathbf{F} = \\mathbf{ L^T . L_0^{-T}} = \\mathbf{ (U.B)^{-T} (U_0.B_0)^{T}} $\n", + "\n", + "$ \\mathbf{F} = \\mathbf{ U^{-T}.B^{-T}.B_0^{T}.U_0^{T}} $\n", + "\n", + "We see that when we form the products $\\mathbf{F^T.F}$ and $\\mathbf{F.F^T}$ we are going to rotate the strain into the laboratory or reference axes and annihilate the central rotation matrices.\n", + "\n", + "Note that we can use *any* axis choice that we like, provided that we are consistent for $\\mathbf{L}$ and $\\mathbf{L_0}$. There is no requirement here for a selection of a set of orthogonal axes. If we change our definition of $\\mathbf{U_0}$ then we may change $\\mathbf{F}$, but it will come out in the wash.\n", + "\n", + "The code to compute $\\mathbf{F}$ from an ImageD11 grain and reference pair of ubi matrices is in the next cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'OK, all three axes transformed as they should'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.random.seed(4567) # prime, try to get reproducible numbers\n", + "\n", + "def deformation_gradient_tensor(ubi, ubi_0):\n", + " \"\"\"\n", + " Computes the Deformation Gradient Tensor F such that :\n", + " ubi = np.dot( F, ubi_0 )\n", + " \"\"\"\n", + " F = np.dot( ubi.T, np.linalg.inv( ubi_0 ).T )\n", + " return F\n", + "\n", + "def test_F():\n", + " ubi1 = np.random.random((3,3))\n", + " ubi0 = np.random.random((3,3))\n", + " F = deformation_gradient_tensor( ubi1, ubi0 )\n", + " for i in range(3):\n", + " assert np.allclose( np.dot(F, ubi0[i]), ubi1[i] )\n", + " return \"OK, all three axes transformed as they should\"\n", + "\n", + "test_F()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have $\\mathbf{F}$ then it is fairly straightforward to copy and paste various equations from wikipedia. To get the orientation matrix we need to decompose into a rotation **R** and a strain (**V** or **S**). There are two options, first rotate and then strain (***RS***), or first do the strain and then rotate (***VR***). In the language of finite strain theory these are the left and right polar decompositions:\n", + "\n", + "$\\mathbf{F} = \\mathbf{R.S} = \\mathbf{V.R} $\n", + "\n", + "I have replaced the **U** which is on wikipedia for **S** to avoid collisions with the orientation above. This decomposition can be computed using a singular value decompositon of **F**, as in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Polar decomposition looks OK'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def polar_decomposition( F ):\n", + " \"\"\" \n", + " Uses an SVD to compute the polar decomposition of F\n", + " F = V.R = R.S \n", + " V and S are symmetric deformations in lab and sample respectively\n", + " R is a rotation (orthonormal) that relates them\n", + " Can be done quicker iteratively, apparently.\n", + " Returns V,R,S\n", + " \"\"\"\n", + " w,sing,vh = np.linalg.svd( F )\n", + " R = np.dot( w, vh )\n", + " S = np.dot( vh.T, np.dot( np.diag(sing), vh ) )\n", + " V = np.dot( w , np.dot( np.diag(sing), w.T ) )\n", + " return V, R, S\n", + "\n", + "def test_polar():\n", + " F = np.random.random((3,3))\n", + " V,R,S = polar_decomposition(F)\n", + " assert np.allclose( np.dot(V, R), F )\n", + " assert np.allclose( np.dot(R, S), F )\n", + " return \"Polar decomposition looks OK\"\n", + "\n", + "test_polar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The thing we wanted to compute is the strain. It seems there are an infinite number of different measures we could use in finite strain theory and they should all come down to the same thing for infinitesimal strains. When there are changes in orientation as well as strain, then **V** is constant and **S** depends on how/where someone defined the standard orientation. In the past I think we have been using **S** to get an infinitesimal strain in fable/FitAllB (I am not 100% sure). For the Lagrangian and Eulerian Finite Strains we have:\n", + "\n", + "Lagrangian Finite Strain in the Reference Crystal Axes:\n", + "\\begin{equation}\n", + "\\mathbf{E} = \\frac{1}{2}( \\mathbf{F^T.F - I } ) = \n", + "\\frac{1}{2} \\mathbf{ ( (L^T . L_0^{-T})^T .( L^T . L_0^{-T} ) - I )} =\n", + "\\frac{1}{2} \\mathbf{ ( L_0^{-1}.(L.L^T) . L_0^{-T} - I )} \n", + "\\end{equation}\n", + "\n", + "Eulerian Finite Strain, in the Sample x,y,z axes:\n", + "\\begin{equation}\n", + " \\mathbf{e} = \\frac{1}{2}( \\mathbf{F.F^T - I } ) = \\frac{1}{2} \n", + " \\mathbf{ (( L^T . L_0^{-T} ).(L^T . L_0^{-T})^T - I )} = \\frac{1}{2} \n", + " \\mathbf{ (L^T .(L_0^{-T}.L_0^{-1}).L - I) }\n", + "\\end{equation}\n", + "\n", + "Note: these come from the infinitesimal strain theory wikipedia page. Both of these equations correspond to a formula where $\\epsilon = (d-d_0)/d_0$. If you want other strains you will need to adjust accordingly. The finite strain page has the opposite sign for the Eulerian strain." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def strains_from_deformation(ubi, ubi_0):\n", + " \"\"\" \n", + " Gets the sample (Lagrangian) and lab (Eulerian) system strain tensors\n", + " ubi and ubi_0 are 3x3 numpy arrays where the rows are the real\n", + " space unit cell vectors.\n", + " Also gives back the Polar Decompostion of F = V.R = R.S\n", + " \n", + " Returns E_grain, E_sample, V, R, S\n", + " \"\"\"\n", + " F = deformation_gradient_tensor(ubi, ubi_0)\n", + " I = np.eye(3)\n", + " E_ref = 0.5 * ( np.dot(F.T,F) - I ) \n", + " e_lab = 0.5 * ( np.dot(F,F.T) - I ) \n", + " return E_ref, e_lab" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Relation to the metric tensor\n", + "\n", + "In both of the previous expressions I have substituted for $\\mathbf{F}$ and we see the metric tensors have shown up. For the reference state the metric tensor is $( \\mathbf{G} = \\mathbf{L.L^T} = \\mathbf{L^T.L} )$. For the current lab axis state it is the reciprocal metric tensor:\n", + "$( \\mathbf{g_0} = \\mathbf{L_0^{-1}.L_0^{-T}} = \\mathbf{L_0^{-T}.L_0^{-1}}) $. Finding out how the strains relate to the metric tensor has been a critical problem in Jon's mind. It appears to come down to:\n", + "\n", + "$ \\mathbf{ E } = \\frac{1}{2} \\mathbf{ L_0^{-1}.( G - G_0 ).L_0^{-T} } $\n", + "\n", + "$ \\mathbf{e} = \\frac{1}{2} \\mathbf{ L^{T} .(g_0 - g ). L } $\n", + "\n", + "If I now take the difference in metric tensors of the deformed and undeformed states as being the main observable from the experiment then I can then read finally read some sense into all of these equations. Taking the difference in real or reciprocal space and then how to transform them was not obvious at first sight(!)\n", + "\n", + "The change in metric tensor was always a symmetric matrix that looked like it was going to be strain. Also it is easy to compute in the laboratory axes independently of grains and reference system choices. The problem has been to relate this strain in the crystallographic coordinate system to the sample x/y/z and the crystal orthogonal axes. The strains we found above are just this tensor transformed into the sample or reference axis system. \n", + "\n", + "At last, we attempt to compute these different things in code:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Seems the strains are OK'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def strains_from_metric(ubi_1, ubi_0):\n", + " \"\"\"\n", + " Strains computed via the shifts in metric tensors\n", + " - no matrix decomposition is used\n", + " - no choice of reference axes\n", + " - no orientation matrix is found\n", + " \n", + " input: ubi and ubi_0 are 3x3 numpy arrays with rows as real\n", + " space unit cell vectors.\n", + " \n", + " Returns E_sample E_grain\n", + " \"\"\"\n", + " G0 = np.dot( ubi_0, ubi_0.T ) # reference metric tensor\n", + " G = np.dot( ubi_1, ubi_1.T ) # measured metric tensor\n", + " ub_0 = np.linalg.inv(ubi_0) # to go to reference axes\n", + " # F = np.dot( ubi.T, ubi_0.-T ) = ubi.T, ub0.T \n", + " # 2e = FT.F - I = ub_0 ubi ubi.T ub_0.T \n", + " # 2e = FT.F - I = ub_0 (G - G0) ub_0.T \n", + " E_ref = 0.5*np.dot( np.dot( ub_0, G - G0 ) , ub_0.T ) # into ref space\n", + " # 2e = F.FT - I = ubi.T, ub_0.T ub_0 ubi - I \n", + " # 2e = F.FT - I = ubi.T( g_0 - g )ubi\n", + " dg = np.linalg.inv(G0) - np.linalg.inv(G)\n", + " e_lab = 0.5 * np.dot( np.dot( ubi_1.T , dg), ubi_1)\n", + " return E_ref, e_lab\n", + "\n", + "def test_strain():\n", + " ubi1 = np.random.random((3,3))+np.eye(3)\n", + " ubi0 = np.random.random((3,3))+np.eye(3)\n", + " Eref1, elab1 = strains_from_deformation(ubi1, ubi0)\n", + " Eref2, elab2 = strains_from_metric(ubi1, ubi0)\n", + " assert np.allclose(Eref1, Eref2)\n", + " assert np.allclose(elab1, elab2)\n", + " return \"Seems the strains are OK\"\n", + "\n", + "test_strain()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Worked example\n", + "\n", + "We make a pure shear strain on a cubic unit cell and compute the various strain tensors. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eref\n", + "[[0.03125 0.25 0. ]\n", + " [0.25 0.03125 0. ]\n", + " [0. 0. 0. ]]\n", + "elab\n", + "[[0.03125 0.25 0. ]\n", + " [0.25 0.03125 0. ]\n", + " [0. 0. 0. ]]\n" + ] + } + ], + "source": [ + "a0 = 4.064 # Aluminium\n", + "e12 = a0*0.25 # 25% strain\n", + "\n", + "ubi_0 = np.diag([a0,a0,a0])\n", + "\n", + "ubi = np.array( [[a0, e12, 0],\n", + " [e12, a0, 0],\n", + " [0, 0, a0]] )\n", + "\n", + "Eref1, elab1 = strains_from_metric( ubi, ubi_0 )\n", + "\n", + "Eref2, elab2 = strains_from_deformation(ubi, ubi_0)\n", + "\n", + "assert np.allclose(Eref1, Eref2)\n", + "assert np.allclose(elab1, elab2)\n", + "print(\"Eref\\n\"+str(Eref1))\n", + "print(\"elab\\n\"+str(elab1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ref,grain:\n", + "[[ 0.03125 0.25 -0. ]\n", + " [ 0.25 0.03125 -0. ]\n", + " [-0. -0. 0. ]]\n", + "[[ 0.03125 0.25 -0. ]\n", + " [ 0.25 0.03125 -0. ]\n", + " [-0. -0. 0. ]]\n", + "LabXYZ:\n", + "[[ 0.10448217 0.15266425 -0.11944166]\n", + " [ 0.15266425 -0.14634993 -0.06549923]\n", + " [-0.11944166 -0.06549923 0.10436776]]\n", + "[[ 0.10448217 0.15266425 -0.11944166]\n", + " [ 0.15266425 -0.14634993 -0.06549923]\n", + " [-0.11944166 -0.06549923 0.10436776]]\n" + ] + } + ], + "source": [ + "# Apply a large random rotation to the grain\n", + "from scipy.spatial.transform import Rotation\n", + "rot = Rotation.from_euler(\"XYZ\",(123,456,789)).as_matrix()\n", + "ubi_rotated = np.dot( ubi , rot )\n", + "\n", + "np.set_printoptions(suppress=True)\n", + "Eref3, elab3 = ret = strains_from_deformation(ubi_rotated, ubi_0) \n", + "\n", + "F = deformation_gradient_tensor( ubi_rotated, ubi_0 )\n", + "V,R,S = polar_decomposition(F)\n", + "Eref4, elab4 = strains_from_metric( ubi_rotated, ubi_0 )\n", + "print(\"Ref,grain:\")\n", + "print(Eref3)\n", + "print(Eref4)\n", + "print(\"LabXYZ:\")\n", + "print(elab3)\n", + "print(elab4)\n", + "# Ensure we get a compatible rotation back\n", + "assert np.allclose( np.dot(R, rot), np.eye(3) )\n", + "# Ensure the Eulerian Strain is the same as before rotation\n", + "assert np.allclose( Eref3, Eref4 )\n", + "assert np.allclose( elab3, elab4 )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ref,grain:\n", + "[[-0.00224369 -0.06130724 -0.00836564]\n", + " [-0.06130724 0.09592113 -0.23357876]\n", + " [-0.00836564 -0.23357876 -0.03117745]]\n", + "[[-0.00224369 -0.06130724 -0.00836564]\n", + " [-0.06130724 0.09592113 -0.23357876]\n", + " [-0.00836564 -0.23357876 -0.03117745]]\n", + "LabXYZ:\n", + "[[ 0.10448217 0.15266425 -0.11944166]\n", + " [ 0.15266425 -0.14634993 -0.06549923]\n", + " [-0.11944166 -0.06549923 0.10436776]]\n", + "[[ 0.10448217 0.15266425 -0.11944166]\n", + " [ 0.15266425 -0.14634993 -0.06549923]\n", + " [-0.11944166 -0.06549923 0.10436776]]\n" + ] + } + ], + "source": [ + "# Apply a large random rotation to the reference too grain\n", + "from scipy.spatial.transform import Rotation\n", + "rot1 = Rotation.from_euler(\"XYZ\",(123,456,789)).as_matrix()\n", + "ubi_rotated = np.dot( ubi , rot1 )\n", + "rot2 = Rotation.from_euler(\"XYZ\",(789,5,123)).as_matrix()\n", + "ubi_ref = np.dot( ubi_0 , rot2 )\n", + "\n", + "np.set_printoptions(suppress=True)\n", + "Eref6, elab6 = ret = strains_from_deformation(ubi_rotated, ubi_ref) \n", + "\n", + "F = deformation_gradient_tensor( ubi_rotated, ubi_ref )\n", + "V,R,S = polar_decomposition(F)\n", + "Eref7, elab7 = strains_from_metric( ubi_rotated, ubi_ref )\n", + "print(\"Ref,grain:\")\n", + "print(Eref6)\n", + "print(Eref7)\n", + "print(\"LabXYZ:\")\n", + "print(elab6)\n", + "print(elab7)\n", + "# Ensure we get a compatible rotation back\n", + "assert np.allclose( np.dot(R, np.dot(rot2.T, rot1)), np.eye(3) )\n", + "# Ensure the Eulerian Strain is the same as before rotation\n", + "assert np.allclose( Eref6, Eref7 )\n", + "assert np.allclose( elab6, elab7 )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/NotesOnNewDeformationGradientTensor.ipynb b/docs/NotesOnNewDeformationGradientTensor.ipynb new file mode 100644 index 00000000..66c69753 --- /dev/null +++ b/docs/NotesOnNewDeformationGradientTensor.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finite strains\n", + "\n", + "A quick notebook to try to reduce the problem to looking at some simple pictures. There seems to be a lot more than one way to define the strain tensor components. We use a 2D example for now so it is easier to draw pictures. This is a part two which attempts to get to the point of choosing between some options of different strain tensors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline \n", + "import matplotlib.pyplot as pl, numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference State\n", + "\n", + "Use the case of an in-situ loading experiment where the initial state is known. \n", + "This is also like the case of mapping around a crack tip in a deforming single crystal.\n", + "It will not work for residual strain where there is no reference orientation.\n", + "We will assume the crystal is cubic (square) and was carefully aligned on the diffractometer\n", + "so the axes are parallel to the axes. The unit cell and lattice vectors in real space are:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def unstrained_lattice(a0, f):\n", + " a = ( a0, 0, 0 )\n", + " b = ( 0, a0, 0 )\n", + " c = ( 0, 0, a0 )\n", + " return a, b, c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pure shear strain\n", + "We apply a pure shear strain as drawn in some pictures found on the internet. \n", + "The atoms which were on the laboratory axes move at exactly 90 degrees to the axes and not\n", + "along the axes. We have \"f\" as symbol for the fractional finite movement. \n", + "It is not clear whether this \"f\" corresponds to a shear strain $\\epsilon_{12}$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def pure_shear_lattice(a0, f):\n", + " a = ( a0, f*a0, 0) # vector in x/y/z laboratory space\n", + " b = (f*a0, a0, 0)\n", + " c = ( 0, 0, a0)\n", + " return (a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple shear strain\n", + "This case does not seem simple at all. Nevertheless, \n", + "there are pictures on the internet showing this definition.\n", + "The loss of symmetry is appalling. We will re-use the same f as before but we are choosing to keep the x-axis parallel to the a axis. A different choice would place it along b." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def simple_shear_lattice(a0, f):\n", + " a = ( a0, 0, 0)\n", + " b = ( 2*f*a0, a0, 0)\n", + " c = ( 0, 0, a0)\n", + " return (a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experimental shear strain\n", + "We imagine a careful experimentalist who realigns their strained crystal on the diffractometer after is has undergone a **pure** strain. Their crystal is beautiful and symmetric but because the realign it this is no longer to easy to see. The lattice vectors are now harder to compute:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def experimental_shear_lattice(a0, f):\n", + " a1 = a0 * np.sqrt(1+f*f) # length of a and b after straining\n", + " phi = 2 * np.arctan(f) # Rotation angle to realign crystal\n", + " a = ( a1, 0, 0 )\n", + " b = ( a1*np.sin(phi), a1*np.cos(phi), 0 )\n", + " c = ( 0, 0, a0 )\n", + " return (a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A new Strain Tensor?\n", + "\n", + "There is another notebook about this. We ended up finding the deformation gradient tensor and then giving all of the Seth-Hill family as options for ImageD11.finite_strain. Here is a fast run through:\n", + "\n", + "The lattice in real space row vectors is **ubi** in ImageD11. The deformation gradient tensor transforms column vectors. Also $\\mathbf{(UBI) = (UB)^{-1}}$. Therefore:\n", + "\n", + "$ \\mathbf{ (UBI)^T = F.(UBI_0)^T } $\n", + "\n", + "$ \\mathbf{ F = (UBI)^T . (UB_0)^T } $\n", + "\n", + "Once you have $\\mathbf{F}$ then the rest plugs into finite strain theory. This can be decomposed into a \"left\" or a \"right\" pair of matrices that correspond to rotation followed by stretch or stretch followed by rotation:\n", + "\n", + "$ \\mathbf{ F = V.R = R.S } $\n", + "\n", + "The $\\mathbf{R}$ matrix is rhe same as the conventional $\\mathbf{U}$ above. Note that the finite strain literature uses the $\\mathbf{U}$ for the stretch that we have as $\\mathbf{S}$. There are now a family of strain tensors given by m=0,0.5,1,...:\n", + "\n", + "$ \\mathbf{ e_m = \\frac{1}{2m} ( V^{2m} - I ) = \\frac{1}{2m} ( (F.F^{T})^{m} - I ) } $\n", + "\n", + "$ \\mathbf{ E_m = \\frac{1}{2m} ( S^{2m} - I ) = \\frac{1}{2m} ( (F^{T}.F)^{m} - I ) } $\n", + "\n", + "$ \\mathbf{ E_0 } = \\log(\\mathbf{ S }) = \\frac{1}{2} \\log( \\mathbf{F^{T}.F} ) $\n", + "\n", + "Numerically some of these are less pretty because of the difference between:\n", + "\n", + "$ \\epsilon = \\frac{d - d0}{d0} \\neq \\frac{d - d0}{d} $\n", + "\n", + "In what follows: note that **ubi**$ = \\mathbf{(UB)^{-1} = B^{-1}U^{-1} }$ so that whenever we use this matrix we want to try ensure the transpose is done to keep the $U$ out of the results. Many hours have been spent with transposes the wrong way around, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " grain.eps_grain_matrix:\n", + " dzero_cell can be another grain or cell parameters\n", + " [a,b,c,alpha,beta,gamma]\n", + " m is the exponent for the Seth-Hill finite strain tensors\n", + " E = 1/2m (U^2m - I)\n", + " (Negative m reverses the reference and lab systems).\n", + " Returns eps as a symmetric matrix\n", + " ... in the grain reference system of dzero_cell\n", + " \n", + " grain.eps_sample_matrix:\n", + " dzero_cell can be another grain or cell parameters:\n", + " [a,b,c,alpha,beta,gamma]\n", + " m is the exponent for the Seth-Hill finite strain tensors\n", + " E = 1/2m (V^2m - I)\n", + " (Negative m reverses the reference and lab systems).\n", + " Returns eps as a symmetric matrix\n", + " ... in the sample system (z up, x along the beam at omega=0)\n", + " \n" + ] + } + ], + "source": [ + "# In the implementation we have:\n", + "from ImageD11.grain import grain\n", + "print(\" grain.eps_grain_matrix:\")\n", + "print( grain.eps_grain_matrix.__doc__)\n", + "print(\" grain.eps_sample_matrix:\")\n", + "print( grain.eps_sample_matrix.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do these look like?\n", + "We will plot the three lattices on three plots for some different values of f." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pure Shear unit cell [ 4.019950 4.019950 4.000000 90.000000 90.000000 78.578814]\n", + "dzero unit cell [ 4.000000 4.000000 4.000000 90.000000 90.000000 90.000000]\n", + "UBI:\n", + "[[4.000000 0.400000 0.000000]\n", + " [0.400000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "UBI0:\n", + "[[4.000000 0.000000 0.000000]\n", + " [0.000000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[-2512.6 50167.7 0.0]\n", + " [50167.7 -2512.6 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[-2512.6 50167.7 0.0]\n", + " [50167.7 -2512.6 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[ -0.0 100000.0 0.0]\n", + " [100000.0 -0.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[ 0.0 100000.0 0.0]\n", + " [100000.0 -0.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[ 5000.0 100000.0 0.0]\n", + " [100000.0 5000.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[ 5000.0 100000.0 0.0]\n", + " [100000.0 5000.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experimental pure unit cell [ 4.019950 4.019950 4.000000 90.000000 90.000000 78.578814]\n", + "dzero unit cell [ 4.000000 4.000000 4.000000 90.000000 90.000000 90.000000]\n", + "UBI:\n", + "[[4.019950 0.000000 0.000000]\n", + " [0.796030 3.940347 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "UBI0:\n", + "[[4.000000 0.000000 0.000000]\n", + " [0.000000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[-2512.6 50167.7 0.0]\n", + " [50167.7 -2512.6 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[ 7421.6 49174.3 0.0]\n", + " [ 49174.3 -12446.8 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[ 0.0 100000.0 0.0]\n", + " [100000.0 -0.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[ 19802.0 98019.8 0.0]\n", + " [ 98019.8 -19802.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[ 5000.0 100000.0 0.0]\n", + " [100000.0 5000.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[ 24802.0 98019.8 0.0]\n", + " [ 98019.8 -14802.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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jrTE4eLTUJRA8wkhrDA4eLXUJBI8wkhqD3/FoqUvgHY87jJzG4ODRUpdI8AgjqTE4eLTUJRI8wkhqDA4eLXWJBI8wkhqDg0dLXSLBI4yUxuDg0VKXUPAII6UxOHi01CUUPMJIaQwOHi11CQWPMFIag4NHS11CwSMUNwxWkhZKWilphaSjWlPuEDl4tNQlFDxCc3sMO4bBHggcA5wj6aA+65xANrHpALIJTVcUWuVwOHi01CUWPEJxw2BPBq6NzBJggqSphVc7FA4eLXWJBY9Q3DDYacCqmturSWWStINHS11iwSPsQmMYZBhsf4Nc6iY4SVogaamkpevXr9+1SofKwaOlLrHgEQoaBku2hzCj5vZ0YE3flUoZBuvg0VK35uGkgkcoaBgscCtwRn524higJyLWFljn0Dh4tNRtfwNeWJFU8AjFDYNdDJwIrAS2AGcVX+oQOHi01O0MHtN6jhY1DDaAc4oqqjAOHi11O4PHtJ6jnf3ORwePlroEg0fo9Mbg4NFSl2DwCJ3cGBw8WuoSDR6hkxuDg0dLXaLBI3RyY3DwaKlLNHiETm4MDh4tdYkGj9DJjcHBo6Uu0eAROrUxOHi01CUcPEKnNgYHj5a6hINH6NTG4ODRUpdw8Aid2hgcPFrqEg4eoVMbg4NHS13CwSM097HrqyWtk/RYg+XHSuqRtDy/XFR8mbvAwaOlLvHgEZr72PU1wCLg2gHWuTci5hdS0XA5eLTUJR48QnPDYH8O/KYNtRTDwaOlLvHgEYrLGOZKekTSbZIOLmibQ+Pg0VKXePAIzR1KDOYhYL+I2CzpROAWsu+XqCNpAdn3TjBz5swCHrofDh4tdYkHj1DAHkNEbIqIzfn1xUCXpEkN1m3tMFgHj5a6CgSPUEBjkDQlHxiLpDn5NjcMd7tD4uDRUleB4BGaOJSQdB1wLDBJ0mrgfwJdsHMQ7KnA5yVtA7YCp+UzINvPwaOlrgLBIzQ3DPYTgyxfRHY6s3wOHi11FQgeodPe+ejg0VJXgeAROqkxOHi01FUkeIROagwOHi11FQkeoZMag4NHS11FgkfopMbg4NFSV5HgETqpMTh4tNRVJHiETmkMDh4tdRUKHqFTGoODR0tdhYJH6JTG4ODRUleh4BE6pTE4eLTUVSh4hE5pDA4eLXUVCh6hExqDg0dLXcWCRyhmGKwkLZS0UtIKSUcVX+YAHDxa6ioWPEJzewzXAMcPsPwEsolNB5BNZ7pi+GXtAgePlrqKBY9QzDDYk4FrI7MEmCBpalEFDsrBo6WuYsEjFJMxTANW1dxend/XHg4eLXUVCx6hmMagfu7rd4KTpAWSlkpaun79+uE/soNHS10Fg0copjGsBmbU3J4OrOlvxcKHwTp4tNRVMHiEYhrDrcAZ+dmJY4CeiFhbwHYH5+DRUlfB4BGKGQa7GDgRWAlsAc5qVbF1HDxa6ioYPEIxw2ADOKewinaFg0dLXQWDR6jyOx8dPFrqKho8QpUbg4NHS11Fg0eocmNw8Gipq2jwCFVuDA4eLXUVDR6hyo3BwaOlrqLBI1S1MTh4tNRVOHiEqjYGB4+WugoHj1DVxuDg0VJX4eARqtoYHDxa6iocPEJVG4ODR0tdhYNHqGJjcPBoqat48AhVbAwOHi11FQ8eoYqNwcGjpa7iwSM02RgkHS/pqXwS9AX9LD9WUo+k5fnlouJLzTl4tNRVPHiE5uYxjAYuA95PNq3pQUm3RsQTfVa9NyLmt6DG3hw8WuoqHjxCc3sMc4CVEfFvEfEa8GOyydDt5+DRUtcBwSM01xianQI9V9Ijkm6TdHB/Gxr2MFgHj5a6DggeobnG0MwU6IeA/SLicOA7wC39bWjYw2AdPFrqOiB4hOYaw6BToCNiU0Rszq8vBrokTSqsyh0cPFrqOiB4hOYaw4PAAZL2l7QbcBrZZOidJE2RpPz6nHy7G4ou1sGjJa8Dgkdo7ivqtgFfAG4HngSuj4jHJX1O0ufy1U4FHpP0CLAQOC0fElscB4+Wug4JHqGJ05Ww8/BgcZ/7rqy5vghYVGxpfTh4tNR1SPAIVXrno4NHS12HBI9Qpcbg4NFS1yHBI1SpMTh4tNR1SPAIVWkMDh4tdR0UPEJVGoODR0tdBwWPUJXG4ODRUtdBwSNUpTE4eLTUdVDwCFVpDA4eLXUdFDxCBRpD9/aXHTxa0hSdFTxCBRrD/q+vzK50SKhjnWffbas7KniECjSG33n9meyKg0dL1I7n6NQPnsvuu+/O2LFjOf3000uuaniq0RgcPFrCfuf1ldC1O/fd9wu2bNnCmjVruPHGG3nmmWfKLm3IihoGK0kL8+UrJB013MLuX/gpfrTmRN699R62b1zF/Qs/NdxNmhXqwVu/y7fXnM4JL9/Ca6+/xsc+Mp/u7m6mTZvGq6++yj333FN2iUM2aGOoGQZ7AnAQ8AlJB/VZ7QTggPyyALhiOEXdv/BTHLPhZsZoOxKMEhyz4WY3B0vGg7d+l0OWfYVpehEJLl/yMiufeYZbv3cxW7duZfz48WzevLnsMoesmY9d7xwGCyBpxzDY2inRJwPX5jMYlkiaIGlqRKwdSlFHb/h71GegnARzNtzC9779laFs0qxQH+u5mm69tvP2upeD7i5x6MorWLz4QHp6ekqsbviaaQz9DYP9vSbWmQb0agySFpDtUTBz5syGDzia7f3fr+BPNn2niZLNWqzPC9cF83bjew+9xv4X/4qJUxYwfvz4cuoqSDONoZlhsM2sQ0RcBVwFMHv27IYTnt5gFGP6aQ7bYhRj/tu/DlisWTus+9Zc9qmZXjhu7CjWnz+OF5jMlK+uLLGyYjTTGAYdBtvkOk17cOLJHLPh5l6HExHZ/XP3eutQN2tWmOeO+jJ7LftKr8OJrbEbq951PlNKrKsohQyDzW+fkZ+dOAboGWq+ADD3i9ewZOJH2BajiMj2FJZM/Ahzv3jNUDdpVqijT/osj73rYl5gMttDvMBkHnvXxRx90mfLLq0QamZmq6QTgUuB0cDVEfH1HYNgI+LKfEL0IuB4YAtwVkQsHWibs2fPjqVLB1zFzFpA0rKImD3QOkUNgw3gnKEUaWbpSf6dj2bWfm4MZlbHjcHM6jQVPrbkgaX1wHNNrDoJeLHF5RSlKrW6zmJVpU7Iat0jIgb8VunSGkOzJC0dLEFNRVVqdZ3Fqkqd0HytPpQwszpuDGZWpwqN4aqyC9gFVanVdRarKnVCk7UmnzGYWftVYY/BzNos6cYw2Ei5VEi6WtI6SY+VXUsjkmZIukvSk5Iel3Ru2TU1ImmspAckPZLX+hdl1zQQSaMlPSzpp2XX0oikZyU9Kmm5pEE/pJTsoUQ+Uu5p4P1kH+t+EPhERDwx4A+WQNJ/AjaTTbE6pOx6+iNpKjA1Ih6StBewDDgl0f+eIjvXvllSF3AfcG5ELCm5tH5J+hIwGxgXEfPLrqc/kp4FZkdEU++3SHmPYedIuYh4DdgxUi45EfFz4Ddl1zGQiFgbEQ/l118CniSbspWcyOwYmNiVX5J8BZM0HfgQ8Ndl11KklBtDo3FxNkySZgFHAv9SbiWN5bvny4F1wB0RkWqtlwJ/Bg3mEaYjgH+StCwfsTiglBtDU+PibNdI2hP4CXBeRGwqu55GIuKNiDiCbBrYHEnJHaJJmg+si4hlZdfShHkRcRTZRPdz8sPfhlJuDIWOizPIj9d/AvwoIm4qu55mRMRG4G6yIUCpmQeclB+//xg4TtIPyy2pfxGxJv93HXAz2aF6Qyk3hmZGylmT8kDv+8CTEfHtsusZiKTJkibk17uB9wHJTQGOiAsjYnpEzCJ7ft4ZEcl9N52kPfLAGUl7AB8ABjyDlmxjiIhtwBeA28mCsusj4vFyq+qfpOuA+4F3SFot6dNl19SPecAnyV7VlueXE8suqoGpwF2SVpC9QNwREcmeCqyAtwL3SXoEeAD4WUT840A/kOzpSjMrT7J7DGZWHjcGM6vjxmBmddwYzKyOG4OZ1XFjMLM6bgxmVseNwZom6WhJK/J5CXvksxKS+wyDDZ/f4GS7RNLFwFigG1gdEd8ouSRrATcG2yX551YeBF4B3h0Rb5RckrWADyVsV+0N7AnsRbbnYB3Iewy2SyTdSvYR4/3JRsV9oeSSrAXGlF2AVYekM4BtEfG3+UzOX0o6LiLuLLs2K5b3GMysjjMGM6vjxmBmddwYzKyOG4OZ1XFjMLM6bgxmVseNwczquDGYWZ3/DzD0PiojyjshAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple Shear unit cell [ 4.000000 4.079216 4.000000 90.000000 90.000000 78.690068]\n", + "dzero unit cell [ 4.000000 4.000000 4.000000 90.000000 90.000000 90.000000]\n", + "UBI:\n", + "[[4.000000 0.000000 0.000000]\n", + " [0.800000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "UBI0:\n", + "[[4.000000 0.000000 0.000000]\n", + " [0.000000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[-4966.9 49669.3 0.0]\n", + " [49669.3 4966.9 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[ 4966.9 49669.3 0.0]\n", + " [49669.3 -4966.9 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[-4962.8 99503.7 0.0]\n", + " [99503.7 14937.9 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[14937.9 99503.7 0.0]\n", + " [99503.7 -4962.8 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[ 0.0 100000.0 0.0]\n", + " [100000.0 20000.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[ 20000.0 100000.0 0.0]\n", + " [100000.0 0.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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MwKeBiXBg9uPFwAcldQC7gEvjOZBh8RmPLnQZzXgslGQY7KOU/kKZrmsWAAvSKqpqPHh0oQsgeISB9s5HDx5d6AIIHmGgNQYPHl3oAggeYaA1Bg8eXegCCB5hIDUGf8ejC10A73jsNHAagwePLnSBBI8wkBqDB48udIEEjzCQGoMHjy50gQSPMJAagwePLnSBBI8wUBqDB48udAEFjzBQGoMHjy50AQWPMFAagwePLnQBBY8wUBqDB48udAEFj5DeMFhJmi9praRWSWdWp9w+8uDRhS6g4BGS7TF0DoN9AzANuFbSSQVrziea2HQC0YSm21Ktsj88eHShCyx4hPSGwV4I3GWR5cBwSWNSr7YvPHh0oQsseIT0hsGOA9Z3Ob2BUCZJe/DoQhdY8Ai9aAw9DIMtNcilaIKTpDmSmiQ1bdmypXeV9pUHjy50gQWPkNIwWKI9hAldTo8HNhUuymQYrAePLnSbnggqeISUhsECi4HL41cnpgHtZtaWYp1948GjC93+ffBca1DBI6Q3DHYJMAtYC+wErkq/1D7w4NGF7kDwGNZ9NK1hsAZcm1ZRqfHg0YXuQPAY1n20vt/56MGjC12AwSPUe2Pw4NGFLsDgEeq5MXjw6EIXaPAI9dwYPHh0oQs0eIR6bgwePLrQBRo8Qj03Bg8eXegCDR6hnhuDB48udIEGj1CvjcGDRxe6gINHqNfG4MGjC13AwSPUa2Pw4NGFLuDgEeq1MXjw6EIXcPAI9doYPHh0oQs4eIRkH7u+Q9JmSavKXD5TUruklfHhxvTL7AUPHl3oAg8eIdnHru8EFgB3VVjziJnNTqWi/vLg0YUu8OARkg2D/QXwYg1qSYcHjy50gQePkF7GMF1Si6SlkiantM2+8eDRhS7w4BGSPZXoyQrgWDPbIWkWsIjo+yWKSJpD9L0TTJw4MYWbLsGDRxe6wINHSGGPwcy2m9mO+PgSoEHSyDJrqzsM1oNHF7ocBI+QQmOQNDoeGIuks+Ntbu3vdvvEg0cXuhwEj5DgqYSke4CZwEhJG4DPAg1wYBDsxcAHJXUAu4BL4xmQtefBowtdDoJHSDYM9t09XL6A6OXM7Hnw6EKXg+AR6u2djx48utDlIHiEemoMHjy60OUkeIR6agwePLrQ5SR4hHpqDB48utDlJHiEemoMHjy60OUkeIR6agwePLrQ5SR4hHppDB48utDlKHiEemkMHjy60OUoeIR6aQwePLrQ5Sh4hHppDB48utDlKHiEemkMHjy60OUoeIR6aAwePLrQ5Sx4hHSGwUrSfElrJbVKOjP9Mivw4NGFLmfBIyTbY7gTOK/C5ecTTWw6gWg60239L6sXPHh0octZ8AjpDIO9ELjLIsuB4ZLGpFVgjzx4dKHLWfAI6WQM44D1XU5viM+rDQ8eXehyFjxCOo1BJc4rOcFJ0hxJTZKatmzZ0v9b9uDRhS6HwSOk0xg2ABO6nB4PbCq1MPVhsB48utDlMHiEdBrDYuDy+NWJaUC7mbWlsN2eefDoQpfD4BHSGQa7BJgFrAV2AldVq9giHjy60OUweIR0hsEacG1qFfWGB48udDkMHiHP73z04NGFLqfBI+S5MXjw6EKX0+AR8twYPHh0octp8Ah5bgwePLrQ5TR4hDw3Bg8eXehyGjxCXhuDB48udDkOHiGvjcGDRxe6HAePkNfG4MGjC12Og0fIa2Pw4NGFLsfBI+S1MXjw6EKX4+AR8tgYPHh0oct58Ah5bAwePLrQ5Tx4hDw2Bg8eXehyHjxCwsYg6TxJa+JJ0J8qcflMSe2SVsaHG9MvNebBowtdzoNHSDaPYTDwNeDtRNOaHpe02Mx+W7D0ETObXYUau/Pg0YUu58EjJNtjOBtYa2bPmNke4HtEk6Frz4NHF7o6CB4hWWNIOgV6uqQWSUslTS61oX4Pg/Xg0YWuDoJHSNYYkkyBXgEca2anAV8FFpXaUL+HwXrw6EJXB8EjJGsMPU6BNrPtZrYjPr4EaJA0MrUqO3nw6EJXB8EjJGsMjwMnSDpO0iHApUSToQ+QNFqS4uNnx9vdmnaxHjy64NVB8AjJvqKuA/gQ8CCwGrjXzJ6S9AFJH4iXXQysktQCzAcujYfEpseDRxe6OgkeIcHLlXDg6cGSgvNu73J8AbAg3dIKePDoQlcnwSPk6Z2PHjy60NVJ8Ah5agwePLrQ1UnwCHlqDB48utDVSfAIeWkMHjy60NVR8Ah5aQwePLrQ1VHwCHlpDB48utDVUfAIeWkMHjy60NVR8Ah5aQwePLrQ1VHwCDloDI37X/Hg0QVNto91q1dw8j89kXUpqQm+MRy3d210pE5CHVd/xnZsgI7dMGRo1qWkJvjG8Ed7n46OePDoAtV5H332uRcZMWIEQ4cOZcqUKezcuTPjyvouH43Bg0cXsD/auxaGDGXHKztZvHgxL730Es888wy33HJL1qX1WVrDYCVpfnx5q6Qz+1vYY/Ov5DubZvGmXT9n/7b1PDb/yv5u0rlUPb7463xl02Wc/8oi9nTsZdihQ7nmmmuYNm0ae/bsYdmyZVmX2Gc9NoYuw2DPB04C3i3ppIJl5wMnxIc5wG39Keqx+VcybesDDNF+JBgkmLb1AW8OLhiPL/46JzffwDi9gAQrNrzK3ld3s+CG99HS0sLxxx/P/v37sy6zz5J87PrAMFgASZ3DYLtOib4QuCuewbBc0nBJY8ysrS9FnbX1v1DBQDkJzt66iG985Ya+bNK5VP1V+x00as+B09tfNfbsg/Yf/Su/O+XtrFq1iquvvjrDCvsnSWMoNQz2jQnWjAO6NQZJc4j2KJg4cWLZGxxM6U47WMb7t381QcnOVVnBA9ebjx3CsIZXueK7G9l33zSOOuooLrjggmxqS0GSxpBkGGySNZjZQmAhwNSpU8tOeNrHIIaUaA4dNogh1/2uYrHO1cLmL0/n6C7TC08cOZgdnz6C5xjF6M+tzbCydCRpDD0Og024JrHHR1zItK0PdHs6YRadP/3wY/q6WedS8+yZn+Tw5hu6PZ3YZYewfspcRmdYV1pSGQYbn748fnViGtDe13wBYPqH72T5iL+kwwZhFu0pLB/xl0z/8J193aRzqTrrgqtZNeVmnmMU+008xyhWTbmZsy7Ib67QlZLMbJU0C5gHDAbuMLMvdA6CNbPb4wnRC4DzgJ3AVWbWVGmbU6dOtaamikucc1UgqdnMplZak9YwWAOu7UuRzrnwBP/OR+dc7XljcM4V8cbgnCuSKHysyg1LW4BnEywdCbxQ5XLSkpdavc505aVOiGodZmYVv1U6s8aQlKSmnhLUUOSlVq8zXXmpE5LX6k8lnHNFvDE454rkoTEszLqAXshLrV5nuvJSJySsNfiMwTlXe3nYY3DO1VjQjaGnkXKhkHSHpM2SVmVdSzmSJkj6maTVkp6S9JGsaypH0lBJv5HUEtd6U9Y1VSJpsKQnJP1P1rWUI2mdpCclrZTU44eUgn0qEY+U+z3wdqKPdT8OvNvMflvxihmQ9GfADqIpVidnXU8pksYAY8xshaTDgWbgokD/PUX0WvsOSQ3Ao8BHzGx5xqWVJOljwFTgCDObnXU9pUhaB0w1s0Tvtwh5j+HASDkz2wN0jpQLjpn9Angx6zoqMbM2M1sRH38ZWE00ZSs4FtkRn2yID0E+gkkaD/wF8B9Z15KmkBtDuXFxrp8kTQLOAH6dbSXlxbvnK4HNwENmFmqt84BPQJl5hOEw4MeSmuMRixWF3BgSjYtzvSPpMOA+4KNmtj3resoxs31mdjrRNLCzJQX3FE3SbGCzmTVnXUsCM8zsTKKJ7tfGT3/LCrkxpDouzkH8fP0+4Dtmdn/W9SRhZtuAh4mGAIVmBnBB/Pz9e8A5ku7OtqTSzGxT/HMz8ADRU/WyQm4MSUbKuYTiQO+bwGoz+0rW9VQiaZSk4fHxRuBtQHBTgM3sejMbb2aTiO6fy8zssozLKiJpWBw4I2kY8OdAxVfQgm0MZtYBfAh4kCgou9fMnsq2qtIk3QM8BpwoaYOkv8u6phJmAO8lelRbGR9mZV1UGWOAn0lqJXqAeMjMgn0pMAeOAR6V1AL8Bvihmf2o0hWCfbnSOZedYPcYnHPZ8cbgnCvijcE5V8Qbg3OuiDcG51wRbwzOuSLeGJxzRbwxuMQknSWpNZ6XMCyelRDcZxhc//kbnFyvSLoZGAo0AhvM7IsZl+SqwBuD65X4cyuPA7uBN5nZvoxLclXgTyVcbx0FHAYcTrTn4OqQ7zG4XpG0mOgjxscRjYr7UMYluSoYknUBLj8kXQ50mNl345mcv5J0jpkty7o2ly7fY3DOFfGMwTlXxBuDc66INwbnXBFvDM65It4YnHNFvDE454p4Y3DOFfHG4Jwr8v8Qeg0i/FxdpwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Flipped Simple unit cell [ 4.079216 4.000000 4.000000 90.000000 90.000000 78.690068]\n", + "dzero unit cell [ 4.000000 4.000000 4.000000 90.000000 90.000000 90.000000]\n", + "UBI:\n", + "[[ 0.800000 4.000000 0.000000]\n", + " [ 4.000000 0.000000 0.000000]\n", + " [-0.000000 -0.000000 -4.000000]]\n", + "UBI0:\n", + "[[4.000000 0.000000 0.000000]\n", + " [0.000000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[ 4966.9 49669.3 0.0]\n", + " [49669.3 -4966.9 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[ 4966.9 49669.3 0.0]\n", + " [49669.3 -4966.9 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[14937.9 99503.7 0.0]\n", + " [99503.7 -4962.8 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[14937.9 99503.7 0.0]\n", + " [99503.7 -4962.8 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[ 20000.0 100000.0 0.0]\n", + " [100000.0 0.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[ 20000.0 100000.0 0.0]\n", + " [100000.0 0.0 0.0]\n", + " [ 0.0 0.0 0.0]]\n" + ] + } + ], + "source": [ + "from ImageD11.grain import grain\n", + "from ImageD11.unitcell import unitcell\n", + "\n", + "def plot_lattice( ubi, ax ):\n", + " # walk around the x/y square:\n", + " points = [ (0,0,0), (1,0,0), (1,1,0), (0,1,0), (0,0,0) ]\n", + " x,y,z = np.dot( points, ubi).T\n", + " ax.plot( x, y, \"o-\" )\n", + " ax.text(x[1],y[1],'a')\n", + " ax.text(x[-2],y[-2],'b')\n", + " ax.set_xlabel(\"x\")\n", + " ax.set_aspect(\"equal\")\n", + " \n", + "def plotstrain(lattice, a0, f, name, reference_cell):\n", + " # reference as a grain\n", + " if len(reference_cell)==3:\n", + " g0 = grain( reference_cell )\n", + " else: # as a unit cell\n", + " g0 = grain( np.linalg.inv( unitcell(reference_cell).B ))\n", + " # distorted sample\n", + " g = grain( lattice(a0,f) )\n", + " fig, ax = pl.subplots(1,1,\n", + " figsize=(4,4))\n", + " plot_lattice( g0.ubi , ax )\n", + " plot_lattice( g.ubi, ax )\n", + " ax.set_title(\"%.2f %s\"%(f, name))\n", + " pl.show()\n", + " np.set_printoptions(floatmode='fixed', precision=6, suppress=True) \n", + " print(name,\"unit cell\",g.unitcell)\n", + " print(\"dzero unit cell\",g0.unitcell)\n", + " print(\"UBI:\")\n", + " print(g.ubi)\n", + " print(\"UBI0:\")\n", + " print(g0.ubi)\n", + " np.set_printoptions(floatmode='fixed', precision=1, suppress=True)\n", + " for m in (0,0.5,1):\n", + " print(\"Grain co-ordinates micro eps, m=\",m)\n", + " print(g.eps_grain_matrix(g0, m)*1e6)\n", + " print(\"Sample co-ordinates micro eps, m=\",m)\n", + " print(g.eps_sample_matrix(g0, m)*1e6) \n", + "\n", + " \n", + "a0 = 4.\n", + "f = 0.1\n", + "reference_cell = np.eye(3)*a0\n", + "\n", + "def flipped(a0,f):\n", + " a, b, c = np.array( simple_shear_lattice( a0, f ) )\n", + " return (b, a, -c )\n", + " \n", + "lattices = {\n", + " \"Pure Shear\" : pure_shear_lattice,\n", + " \"Experimental pure\" : experimental_shear_lattice,\n", + " \"Simple Shear\" : simple_shear_lattice,\n", + " \"Flipped Simple\" : flipped\n", + "} \n", + "\n", + "for name in lattices:\n", + " print(\"============================================================\")\n", + " plotstrain(lattices[name], a0, f, name, reference_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we bring the reference cell closer to distorted" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pure unit cell [ 4.019950 4.019950 4.000000 90.000000 90.000000 78.578814]\n", + "dzero unit cell [ 4.019900 4.019900 4.000000 90.000000 90.000000 78.579000]\n", + "UBI:\n", + "[[4.000000 0.400000 0.000000]\n", + " [0.400000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "UBI0:\n", + "[[3.940301 0.796007 0.000000]\n", + " [0.000000 4.019900 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[ 5.9 0.8 -0.0]\n", + " [ 0.8 6.2 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[ 6.1 0.8 -0.0]\n", + " [ 0.8 6.1 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[11.8 1.6 -0.0]\n", + " [ 1.6 12.5 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[12.2 1.7 -0.0]\n", + " [ 1.7 12.2 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[11.8 1.6 -0.0]\n", + " [ 1.6 12.5 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[12.2 1.7 -0.0]\n", + " [ 1.7 12.2 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "experimental pure unit cell [ 4.019950 4.019950 4.000000 90.000000 90.000000 78.578814]\n", + "dzero unit cell [ 4.019900 4.019900 4.000000 90.000000 90.000000 78.579000]\n", + "UBI:\n", + "[[4.019950 0.000000 0.000000]\n", + " [0.796030 3.940347 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "UBI0:\n", + "[[3.940301 0.796007 0.000000]\n", + " [0.000000 4.019900 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[ 5.9 0.8 -0.0]\n", + " [ 0.8 6.2 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[ 6.2 0.8 -0.0]\n", + " [ 0.8 5.9 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[11.8 1.6 -0.0]\n", + " [ 1.6 12.5 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[12.5 1.6 -0.0]\n", + " [ 1.6 11.8 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[11.8 1.6 -0.0]\n", + " [ 1.6 12.5 -0.0]\n", + " [-0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[12.5 1.6 -0.0]\n", + " [ 1.6 11.8 0.0]\n", + " [-0.0 0.0 0.0]]\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simple unit cell [ 4.000000 4.079216 4.000000 90.000000 90.000000 78.690068]\n", + "dzero unit cell [ 4.019900 4.019900 4.000000 90.000000 90.000000 78.579000]\n", + "UBI:\n", + "[[4.000000 0.000000 0.000000]\n", + " [0.800000 4.000000 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "UBI0:\n", + "[[3.940301 0.796007 0.000000]\n", + " [0.000000 4.019900 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[-2282.2 -1484.2 -0.0]\n", + " [-1484.2 7319.5 0.0]\n", + " [ -0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[-2480.8 505.8 0.0]\n", + " [ 505.8 7518.2 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[-4549.5 -2983.5 -0.0]\n", + " [-2983.5 14751.1 0.0]\n", + " [ -0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[-4948.9 1016.8 0.0]\n", + " [ 1016.8 15150.5 0.0]\n", + " [ 0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[-4534.7 -2998.7 -0.0]\n", + " [-2998.7 14864.4 -0.0]\n", + " [ -0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[-4936.1 1022.0 -0.0]\n", + " [ 1022.0 15265.8 0.0]\n", + " [ -0.0 0.0 0.0]]\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flipped unit cell [ 4.079216 4.000000 4.000000 90.000000 90.000000 78.690068]\n", + "dzero unit cell [ 4.019900 4.019900 4.000000 90.000000 90.000000 78.579000]\n", + "UBI:\n", + "[[ 0.800000 4.000000 0.000000]\n", + " [ 4.000000 0.000000 0.000000]\n", + " [-0.000000 -0.000000 -4.000000]]\n", + "UBI0:\n", + "[[3.940301 0.796007 0.000000]\n", + " [0.000000 4.019900 0.000000]\n", + " [0.000000 0.000000 4.000000]]\n", + "Grain co-ordinates micro eps, m= 0\n", + "[[ 7519.2 495.8 -0.0]\n", + " [ 495.8 -2481.8 0.0]\n", + " [ -0.0 0.0 -0.0]]\n", + "Sample co-ordinates micro eps, m= 0\n", + "[[-2480.8 505.8 -0.0]\n", + " [ 505.8 7518.2 0.0]\n", + " [ -0.0 0.0 -0.0]]\n", + "Grain co-ordinates micro eps, m= 0.5\n", + "[[15152.5 996.7 -0.0]\n", + " [ 996.7 -4950.9 0.0]\n", + " [ -0.0 0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 0.5\n", + "[[-4948.9 1016.8 -0.0]\n", + " [ 1016.8 15150.5 0.0]\n", + " [ -0.0 0.0 0.0]]\n", + "Grain co-ordinates micro eps, m= 1\n", + "[[15267.8 1001.7 -0.0]\n", + " [ 1001.7 -4938.1 -0.0]\n", + " [ -0.0 -0.0 0.0]]\n", + "Sample co-ordinates micro eps, m= 1\n", + "[[-4936.1 1022.0 0.0]\n", + " [ 1022.0 15265.8 0.0]\n", + " [ 0.0 0.0 0.0]]\n" + ] + } + ], + "source": [ + "a0 = 4.\n", + "f = 0.1\n", + "reference_cell = [a0+0.0199,a0+0.0199,a0,90,90,78.579]\n", + "\n", + "def flipped(a0,f):\n", + " a, b, c = np.array( simple_shear_lattice( a0, f ) )\n", + " return (b, a, -c )\n", + " \n", + "lattices = {\n", + " \"pure\" : pure_shear_lattice,\n", + " \"experimental pure\" : experimental_shear_lattice,\n", + " \"simple\" : simple_shear_lattice,\n", + " \"flipped\" : flipped\n", + "} \n", + "\n", + "for name in lattices:\n", + " print(\"============================================================\")\n", + " plotstrain(lattices[name], a0, f, name, reference_cell)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/howto_compile_win64.txt b/docs/howto_compile_win64.txt new file mode 100644 index 00000000..a3346811 --- /dev/null +++ b/docs/howto_compile_win64.txt @@ -0,0 +1,79 @@ +For python2.7 on windows 64 bit: + +December 2018 +============= + +Grab miniconda, git and the Microsoft Visual C++ Compiler for Python 2.7: + https://www.microsoft.com/en-us/download/details.aspx?id=44266 + +Now: + conda create -name py27_64 python=2.7 + conda activate py27_64 + conda install numpy scipy matplotlib pyopengl pillow + pip install h5py fabio xfab pycifrw --no-deps + git clone http://github.com/jonwright/ImageD11.git + python setup.py build + pip install . --no-deps + cd test + python run_tests.py all + ... rejoice + ... improve + pip install . --no-deps -U + python run_tests.py all + conda deactivate + +Getting SxsErrors on a new computer? You might need the 2008 SP1 redistributable packages: + http://www.microsoft.com/en-us/download/details.aspx?id=5582 x86 + http://www.microsoft.com/en-us/download/details.aspx?id=2092 x64 + http://msdn.microsoft.com/en-us/library/hh506443.aspx .net + +If you want to use 32 bits then: + set CONDA_FORCE_32BIT=1 + conda create -name py27_32 python=2.7 + conda activate py27_32 + ... and repeat as above + +If you want to use python3.x then: + conda create -name py37_64 python=3.7 + conda activate py37_64 + ... and repeat as above + + + +Older instructions: +=================== + +Install compiler: +MS Windows SDK for Windows 7 and .NET Framework 3.5 SP1 +http://www.microsoft.com/en-us/download/details.aspx?id=18950 + +Open a winpython command prompt + +Set up compiler: + +C:\Windows\System32\cmd.exe /E:ON /V:ON /T:0E /K "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" + +Set up environment: + +set DISTUTILS_USE_SDK=1 +setenv /x64 /release + +... and "python setup.py build" should work + + +# Using compiler from microsoft for python 2.7 I seem to need this for cffi + +set VS90COMNTOOLS="C:\Users\wright\AppData\Local\Programs\Common\Microsoft\Visual C++ for Python\9.0" + +=========== Using mingwpy =============== + +pip install -i https://pypi.anaconda.org/carlkl/simple mingwpy +python setup.py build --compiler=mingw32 +python setup.py bdist_wheel +cd dist +pip install ImageD11-1.6.0-cp27-cp27m-win_amd64.whl + +Tested on Anaconda2 / win7 / amd64 + + + diff --git a/docs/howto_gdb_crashing_c.txt b/docs/howto_gdb_crashing_c.txt new file mode 100644 index 00000000..6341b21a --- /dev/null +++ b/docs/howto_gdb_crashing_c.txt @@ -0,0 +1,15 @@ + + +First set environment variables then run python interpreter under gdb: + + +$ OMP_NUM_THREADS=8 PYTHONPATH=~/.local/lib/python2.7/site-packages gdb python +... +> set args test_sparseimage.py +> run +... boom +> bt + + +The traceback usually is enough to figure things out. Otherwise you can +set breakpoints etc as usual. diff --git a/docs/howto_install_esrf.txt b/docs/howto_install_esrf.txt new file mode 100644 index 00000000..32375e1e --- /dev/null +++ b/docs/howto_install_esrf.txt @@ -0,0 +1,44 @@ + +The new way (modules): + +1) Search for "modulefile(4)" to locate manual (or man module) + +2) "module load fable" + -> modulefile is from /sware/exp/modules/debian9/fable/.version + -> writes a fable_py27.pth in users ~.local/lib/python2.7/site-packages + sets $FABLE_ROOT + sets $FABLE_PY27 + +3) Check you are clean and where it goes for install: + +$ git pull +$ git status # <--- clean please ! + +4) Install it (from a devel machine): + +$ env | grep FABLE +OK for FABLE_ROOT +$ /usr/bin/python2.7 python setup.py build +$ /usr/bin/python2.7 -m pip install . --prefix=$FABLE_ROOT + +5) Verify: + +$ which peaksearch.py # is it in $PATH? +$ head `which peaksearch.py` # is the #! OK? +$ python -c "import ImageD11.cImageD11_sse2 as e;print(e.__file__)" +$ cd test && python run_tests.py + + +=============================================================================== +The old way (install_debianX.sh) + +$ python setup.py build --force install \ + --prefix=/sware/exp/fable/standalone/debian8 \ + --exec-prefix=/sware/exp/fable/standalone/debian8 + +Then in fable.bash (or .bashrc etc): + +export PATH=/sware/exp/fable/standalone/debian8/bin +export PYTHONPATH=/sware/exp/fable/standalone/debian8/lib/python2.7/site-packages + +/sware only writeable from the devel machines. diff --git a/docs/scor_gvec_z_sympy.py b/docs/scor_gvec_z_sympy.py new file mode 100644 index 00000000..48a5141c --- /dev/null +++ b/docs/scor_gvec_z_sympy.py @@ -0,0 +1,23 @@ + +from __future__ import print_function +import sympy +sympy.init_printing() +x,y,z=sympy.symbols("x,y,z") +gvec = sympy.Matrix( [x,y,z] ) +axis=sympy.Matrix([0,0,1]) +omegadir=axis.cross(gvec) +etadir = omegadir.cross(gvec) + +print("# gvec:",gvec) +print("# axis:",axis) +print("# omegadir:",omegadir) +print("# etadir:",etadir) +print("# |etadir|2:",sympy.simplify(etadir.dot(etadir))) +print("# |omegadir|2:",sympy.simplify(omegadir.dot(omegadir))) + +# gvec: Matrix([[x], [y], [z]]) +# axis: Matrix([[0], [0], [1]]) +# omegadir: Matrix([[-y], [x], [0]]) +# etadir: Matrix([[x*z], [y*z], [-x**2 - y**2]]) +# |etadir|2: x**2*z**2 + y**2*z**2 + (x**2 + y**2)**2 +# |omegadir|2: x**2 + y**2 diff --git a/docs/sintheta_squared_geometry.ipynb b/docs/sintheta_squared_geometry.ipynb new file mode 100644 index 00000000..a6b9d952 --- /dev/null +++ b/docs/sintheta_squared_geometry.ipynb @@ -0,0 +1,417 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d29237e2", + "metadata": {}, + "source": [ + "# 2D Detector Geometry\n", + "\n", + "Various issues come up when calibrating 2D detector geometry and doing strain refinements:\n", + "\n", + "- Numerical instabilities can arise for small angles in certain formulations\n", + "\n", + "- Exact answers for angles like 30,45,60,90,120 etc are hard to reproduce\n", + "\n", + "- ...leading to some philosophical misgivings about trigonometric functions\n", + "\n", + "- People want to get strains that are independent of orientation (despite JW resisting this idea for years)\n", + "\n", + "- There is a correlation between wavelength and distance (not addressed here)\n", + "\n", + "- Youtube videos from Norman J. Wildberger suggested looking a quadrance (length^2) and spread (sin^2 angle). He has a book \"DIVINE PROPORTIONS : Rational Trigonometry to Universal Geometry\" Wild Egg Books, Sydney 2005. ISBN: 0-9757492-0-X. It might be useful to get a copy and try to read it." + ] + }, + { + "cell_type": "markdown", + "id": "8b1b83cb", + "metadata": {}, + "source": [ + "We set up the geometry to be ready to work on data where the detector was moved. This happens to follow the fable convention and it is a bit more common than moving the X-ray beam (but that obviously happens too). Anyway, the detector itself doest not define the geometry here. We place the x-axis along the X-ray beam. The detector is wherever it is, and some function gives the x,y,z co-ordinates of each pixel. A photon is detected at pixel co-ordinates $(x,y,z)$ in 3D cartesian space, usually in the sensitive surface of the detector.\n", + "\n", + "The origin of the coordinate system is usually in the centre of the sample. If the sample is not at the origin, then subtract the sample coordinates from the detector coordinates to get a vector along the direction of the output ray below.\n", + "\n", + "The goal here is to get a formula for the metric tensor in terms of the pixel co-ordinates. Elsewhere we have formulae for the various finite strain tensors in terms of the metric tensor.\n", + "\n", + "Consider the cross product of vectors along the incident and scattered rays. This is:\n", + "\n", + "$ \\mathbf{s_0} \\times \\mathbf{s_1} = (1,0,0) \\times (x,y,z) = |s_0| |s_1| \\sin2\\theta \\mathbf{n} $\n", + "\n", + "$ \\mathbf{s_0} \\times \\mathbf{s_1} = (0,z,-y) = (1).(\\sqrt{x^2+y^2+z^2}) \\sin2\\theta \\mathbf{n} $\n", + "\n", + "...where $\\mathbf{n}$ is a unit vector normal to the incident and scattered rays. We dot this vector with itself to arrive at a scalar value:\n", + "\n", + "$ (\\mathbf{s_0} \\times \\mathbf{s_1}).(\\mathbf{s_0} \\times \\mathbf{s_1})\n", + "= (y^2 + z^2) = (x^2 + y^2 + z^2) \\sin^22\\theta $\n", + "\n", + "This gives us a relationship between $\\sin^22\\theta$ and the detector coordinates. From Bragg's law, $\\lambda = 2d\\sin\\theta$, we can find the metric tensor in terms of $\\sin^2\\theta$, (note the $\\theta$ versus $2\\theta$):\n", + "\n", + "$ \\mathbf{ h g h^T } = \\frac{1}{d^2} = \\frac{ 4 \\sin^2\\theta } {\\lambda^2} $\n", + "\n", + "... where $\\mathbf{g}$ is the reciprocal metric tensor. " + ] + }, + { + "cell_type": "markdown", + "id": "841bf876", + "metadata": {}, + "source": [ + "We convert the $2\\theta$ into $\\theta$ via trig identities:\n", + "\n", + "$ \\sin^22\\theta = (2\\sin\\theta\\cos\\theta)^2 = 4 \\sin^2\\theta \\cos^2\\theta = 4 \\sin^2\\theta (1 - \\sin^2\\theta)$\n", + "\n", + "This gives us a quadratic in $\\sin^2\\theta$:\n", + "\n", + "$ (\\sin^2\\theta)^2 - (\\sin^2\\theta) + \\frac { (y^2 + z^2) } {4 (x^2 + y^2 + z^2) } = 0 $\n", + "\n", + "This can be solved using a quadratic formula for $ax^2+bx+c$ that avoids roundoff for small scattering angles where $c$ approaches zero (https://web.physics.utah.edu/~detar/lessons/python/quadratic/node3.html):\n", + "\n", + "$ x = \\frac{2.c}{(-b \\mp \\sqrt( b^2 - 4ac )} $\n", + "\n", + "We set, $ R = z^2 + y^2 $ and $ Q = x^2 + y^2 + z^2 $ to get:\n", + "\n", + "$ Q (\\sin^2\\theta)^2 - Q (\\sin^2\\theta) + R/4 = 0 $\n", + "\n", + "$ \\sin^2\\theta = \\frac{R}{2 ( Q \\mp \\sqrt{ Q^2 - Q R } ) } $\n", + "\n", + "Note that $ Q = R + x^2 $:\n", + "\n", + "$ \\sin^2\\theta = \\frac{R}{2 ( Q \\mp \\sqrt{ Q (R + x^2) - Q R })} = \\frac{R}{2 ( Q \\mp x\\sqrt{ Q })} $\n", + "\n", + "$ \\mathbf{ h g h^T } = \\frac{1}{d^2} = \\frac{ 4 \\sin^2\\theta } {\\lambda^2} = \\frac{2 R}{ \\lambda^2 ( Q \\mp x\\sqrt{ Q } ) } $" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "36a38723", + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import arctan2, sin, cos, sqrt, allclose, where, isfinite\n", + "\n", + "def sinth2_atan( x, y, z ):\n", + " \"\"\" Compute sin(theta)^2\n", + " Conventional approach in ImageD11 now\n", + " \"\"\"\n", + " twotheta = arctan2( sqrt(z*z + y*y), x )\n", + " sinth = sin( twotheta/2 )\n", + " return sinth**2\n", + "\n", + "def sinth2_sqrt( x, y, z ):\n", + " \"\"\" Compute sin(theta)^2 \n", + " x,y,z = co-ordinates of the pixel in cartesian space\n", + " R = hypotenuse normal to incident beam (along x)\n", + " Q = hypotenuse along the scattered beam\n", + " \"\"\"\n", + " R = y*y+z*z\n", + " Q = x*x + R\n", + " sinsqth = 0.5*R/( Q + x*sqrt(Q) ) # postive root only, not 0.5*R/( Q - x*sqrt(Q) )\n", + " return sinsqth" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "918ba0ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1.1102230246251565e-16 0 1000 1000 0.4999999999999999 0.5\n", + "-5.551115123125783e-17 1 1.7320508075688772 0 0.24999999999999994 0.25\n", + "5.551115123125783e-17 1e-09 1000 1000 0.4999999999996465 0.49999999999964645\n", + "0.0 1000000000.0 0 1 2.5e-19 2.5e-19\n", + "0.0 1e+20 1 0 2.5e-41 2.5e-41\n", + "0.0 100 0 0 0.0 0.0\n", + "-2.7755575615628914e-17 1000 1000 1000 0.2113248654051871 0.21132486540518713\n", + "1.1102230246251565e-16 -123 456 789 0.5668799937442661 0.566879993744266\n" + ] + } + ], + "source": [ + "tests = [(0,1000,1000), # two theta == 90 degrees, sin(45)=1/sqrt(2), ans=0.5\n", + " (1, sqrt(3), 0), # two theta == 60 degrees, sin(30)=1/2, ans=0.25\n", + " (1e-9,1000,1000), \n", + " (1e9,0,1),\n", + " (1e20,1,0),\n", + " (100,0,0),\n", + " (1000,1000,1000),\n", + " (-123,456,789)]\n", + "\n", + "for x,y,z in tests:\n", + " v1 = sinth2_atan(x,y,z) \n", + " v2 = sinth2_sqrt(x,y,z)\n", + " e = v1-v2\n", + " print(e,x,y,z,v1,v2)\n", + " assert allclose( v1, v2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "099f44e9", + "metadata": {}, + "outputs": [], + "source": [ + "# diversion : derivatives of the atan expression:\n", + "\"\"\"\n", + "[jon@pine docs]$ maxima \n", + ";;; Loading #P\"/usr/lib/ecl-21.2.1/sb-bsd-sockets.fas\"\n", + ";;; Loading #P\"/usr/lib/ecl-21.2.1/sockets.fas\"\n", + "Maxima 5.44.0 http://maxima.sourceforge.net\n", + "using Lisp ECL 21.2.1\n", + "Distributed under the GNU Public License. See the file COPYING.\n", + "Dedicated to the memory of William Schelter.\n", + "The function bug_report() provides bug reporting information.\n", + "(%i1) display2d : false;\n", + "(%o1) false\n", + "(%i2) s : sin( atan2( sqrt( y*y+z*z), x ) / 2 ); \n", + "(%o2) sin(atan2(sqrt(z^2+y^2),x)/2)\n", + "(%i3) diff( s,x,1); \n", + "(%o3) -(sqrt(z^2+y^2)*cos(atan2(sqrt(z^2+y^2),x)/2))/(2*(z^2+y^2+x^2))\n", + "(%i4) diff(s,y,1);\n", + "(%o4) (x*y*cos(atan2(sqrt(z^2+y^2),x)/2))/(2*sqrt(z^2+y^2)*(z^2+y^2+x^2))\n", + "(%i5) diff(s,z,1); \n", + "(%o5) (x*z*cos(atan2(sqrt(z^2+y^2),x)/2))/(2*sqrt(z^2+y^2)*(z^2+y^2+x^2))\n", + "\"\"\"\n", + "\n", + "# import numba # no great effect here\n", + "# @numba.njit \n", + "def sinth2_atan_deriv( x, y, z ):\n", + " \"\"\" Compute sin(theta)**2 and derivatives w.r.t x,y,z \"\"\"\n", + " R2 = z*z+y*y\n", + " r = sqrt( R2 )\n", + " D2 = R2 + x*x # x*x+y*y+z*z\n", + " theta = arctan2( r, x ) / 2\n", + " sinth = sin( theta )\n", + " costh = cos( theta )\n", + " div_D2 = where(isfinite(1/D2),1/D2,0)\n", + " p = sinth*costh*div_D2\n", + " dsinth2_dx = - r*p\n", + " # if r == 0 this blows up. For r==0 then y==0 as well. Should somehow determine y==0...\n", + " div_r = where(isfinite(1/r), 1/r, 0)\n", + " dsinth2_dy = x*y*p*div_r # at r == 0?\n", + " dsinth2_dz = x*z*p*div_r\n", + " return sinth*sinth, dsinth2_dx, dsinth2_dy, dsinth2_dz" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f8ceff20", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"# maxima:\n", + "(%i3) r:z*z+y*y\n", + "(%o3) z^2+y^2\n", + "(%i4) q:r+x*x\n", + "(%o4) z^2+y^2+x^2\n", + "(%i5) s:r/(2*(q+x*sqrt(q)))\n", + "(%o5) (z^2+y^2)/(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2))\n", + "(%i6) diff(s,x,1)\n", + "(%o6) -((z^2+y^2)*(sqrt(z^2+y^2+x^2)+x^2/sqrt(z^2+y^2+x^2)+2*x))\n", + " /(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)^2)\n", + "(%i7) diff(s,y,1)\n", + "(%o7) y/(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)\n", + " -((z^2+y^2)*((x*y)/sqrt(z^2+y^2+x^2)+2*y))\n", + " /(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)^2)\n", + "(%i8) diff(s,z,1)\n", + "(%o8) z/(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)\n", + " -((z^2+y^2)*((x*z)/sqrt(z^2+y^2+x^2)+2*z))\n", + " /(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)^2)\n", + "\"\"\"\n", + "# @numba.njit\n", + "def sinth2_sqrt_deriv(x, y, z):\n", + " R = z*z + y*y\n", + " Q = R + x*x\n", + " SQ = sqrt(Q)\n", + " R2 = R/2\n", + " # at x==y==0 this is undefined.\n", + " rQ_xSQ = 1/(Q + x*SQ)\n", + " sinth2 = R2*rQ_xSQ\n", + " # some simplification and collecting terms from expressions above to get:\n", + " sr = sinth2*rQ_xSQ\n", + " p = (x/SQ+2)*sr # p should be in the range 3sr -> 2sr for x/x to 0/sqrt(R)\n", + " t = (rQ_xSQ - p) # may cancel? \n", + " sinth2_dx = -(SQ*sr+x*p)\n", + " sinth2_dy = y*t\n", + " sinth2_dz = z*t\n", + " return sinth2, sinth2_dx, sinth2_dy, sinth2_dz" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf36f991", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xyz: 0 1000 1000 \n", + "sqrt\t (0.5, -0.00035355339059327376, 0.0, 0.0) \n", + "atan\t (0.4999999999999999, -0.00035355339059327376, 0.0, 0.0)\n", + "xyz: 1 1.7320508075688772 0 \n", + "sqrt\t (0.25, -0.1875, 0.10825317547305482, 0.0) \n", + "atan\t (0.24999999999999994, -0.18750000000000003, 0.10825317547305487, 0.0)\n", + "xyz: 1e-09 1000 1000 \n", + "sqrt\t (0.49999999999964645, -0.00035355339059327376, 1.768181277393352e-16, 1.768181277393352e-16) \n", + "atan\t (0.4999999999996465, -0.00035355339059327376, 1.767766952966369e-16, 1.767766952966369e-16)\n", + "xyz: 1000000000.0 0 1 \n", + "sqrt\t (2.5e-19, -5e-28, 0.0, 5e-19) \n", + "atan\t (2.5e-19, -5.000000000000001e-28, 0.0, 5.000000000000001e-19)\n", + "xyz: 1e+20 1 0 \n", + "sqrt\t (2.5e-41, -4.9999999999999985e-61, 5e-41, 0.0) \n", + "atan\t (2.5e-41, -4.999999999999999e-61, 5e-41, 0.0)\n", + "xyz: 100 0 0 \n", + "sqrt\t (0.0, -0.0, 0.0, 0.0) \n", + "atan\t (0.0, -0.0, 0.0, 0.0)\n", + "xyz: 1000 1000 1000 \n", + "sqrt\t (0.21132486540518713, -0.00019245008972987527, 9.622504486493763e-05, 9.622504486493763e-05) \n", + "atan\t (0.2113248654051871, -0.00019245008972987527, 9.622504486493763e-05, 9.622504486493763e-05)\n", + "xyz: -123 456 789 \n", + "sqrt\t (0.5668799937442661, -0.0005340113387752514, -3.606644048906357e-05, -6.240443321462973e-05) \n", + "atan\t (0.5668799937442661, -0.0005340113387752512, -3.60664404890636e-05, -6.240443321462979e-05)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_751591/3023911578.py:37: RuntimeWarning: divide by zero encountered in double_scalars\n", + " div_r = where(isfinite(1/r), 1/r, 0)\n" + ] + } + ], + "source": [ + "for x,y,z in tests:\n", + " a1 = sinth2_sqrt_deriv(x,y,z)\n", + " a2 = sinth2_atan_deriv(x,y,z)\n", + " print('xyz:', x,y,z,\"\\nsqrt\\t\",a1,\"\\natan\\t\",a2)\n", + " assert allclose(a1,a2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "265a2ad1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 629650 -4.2043328642028395e-07 -3.2161595933277716e-10 9.70597750455382e-10\n", + "-7.0338134765625 -7.033922666496423\n", + "2 54669 -3.3884238262201716e-07 5.078959545993244e-10 4.734340882978083e-10\n", + "-2211.7232267291006 -2211.8036421740667\n", + "3 629650 -4.2043328642028395e-07 -3.2161595933277716e-10 9.70597750455382e-10\n", + "-2745.3556259377906 -2745.4355099133227\n" + ] + } + ], + "source": [ + "from numpy import isclose, random, arange\n", + "random.seed(11)\n", + "for s,o in [(1e6,0.5), (1,.25), (1e-6,0.5)]:\n", + " x,y,z = (random.random( (3,1_000_000) )-o)*s\n", + " a1 = sinth2_sqrt_deriv(x,y,z)\n", + " a2 = sinth2_atan_deriv(x,y,z)\n", + " for i in range(4):\n", + " if allclose(a1[i], a2[i]):\n", + " continue\n", + " absdiff = abs(a1[i]-a2[i])\n", + " e = absdiff.argmax()\n", + " print(i,e, x[e],y[e],z[e])\n", + " print(a1[i][e],a2[i][e])\n", + " # The derivatives start to lose precision for small values of x,y,z:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "57c81e75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "110 ms ± 4.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%timeit a1 = sinth2_sqrt_deriv(x,y,z)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c2cdd334", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "274 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%timeit a2 = sinth2_atan_deriv(x,y,z)" + ] + }, + { + "cell_type": "markdown", + "id": "b52f4575", + "metadata": {}, + "source": [ + "## conclusion\n", + "\n", + "This method seems to be more accurate for angles where exact answers are known and appears to be slightly faster.\n", + "\n", + "Still need to do something useful with it.\n", + "\n", + "Could set up to refine the metric tensor elements directly from spot positions on the detector. Does not depend on rotation angles (except for grain origins).\n", + "\n", + "There should be some way to look at orientations to go along with this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "700c16ca", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphx/.gitignore b/docs/sphx/.gitignore new file mode 100644 index 00000000..69fa449d --- /dev/null +++ b/docs/sphx/.gitignore @@ -0,0 +1 @@ +_build/ diff --git a/docs/sphx/advanced.rst b/docs/sphx/advanced.rst index 6b943565..8916eaeb 100644 --- a/docs/sphx/advanced.rst +++ b/docs/sphx/advanced.rst @@ -1,17 +1,26 @@ +======== Advanced ======== -Take advanced to mean without graphical interface, without prejudice to -people who prefer touching mice to keyboards. -The program was mainly developed for online analysis during experiments to get an idea if the setup and sample etc are OK. Other programs exist which might to a better job (eg GRAINDEX [link]) and you should consider trying for serious data analysis. Notably [Grainspotter] reads ImageD11 gve files and tends to make a better indexing. -Nevertheless, the free nature of ImageD11 and convenience of the python language could tempt you to process large volumes of data using the functions in the program. Currently the indexing module is the most easily accessible for script writers. The other modules should be separated from the gui very soon. -edit?]#Using_the_indexing_module_interactively? + +Take advanced to mean without graphical interface, without prejudice to people +who prefer touching mice to keyboards. The program was mainly developed for +online analysis during experiments to get an idea if the setup and sample etc +are OK. Other programs exist which might to a better job, and you should +consider trying them for serious data analysis. Notably, `Grainspotter +`_ reads ImageD11 gve files +and tends to make a better indexing. Nevertheless, the free nature of ImageD11 +and convenience of the python language could tempt you to process large volumes +of data using the functions in the program. Currently the indexing module is the +most easily accessible for script writers. The other modules should be separated +from the gui very soon. Using the indexing module interactively +--------------------------------------- -An example interactive session is given here. A trial orientation is -generated from one g-vector file and then used to index another:: +An example interactive session is given here. A trial orientation is generated +from one g-vector file and then used to index another:: - fable4:~/id11/lab6grains % /bliss/users/blissadm/bin/python + fable4:~/id11/lab6grains % python Python 2.4 (#3, Mar 11 2005, 10:10:40) [GCC 3.3 20030226 (prerelease) (SuSE Linux)] on linux2 Type "help", "copyright", "credits" or "license" for more information. @@ -99,7 +108,7 @@ generated from one g-vector file and then used to index another:: >>> # These look a bit too good to be true...? Got an orientation now - test it on another dataset (could be another -rotation or a different detector etc). +rotation or a different detector etc):: >>> # Anyway, apply these to another dataset with weak peaks in it too (peak.gve)... >>> another_indexing_object = indexing.indexer() @@ -167,44 +176,6 @@ rotation or a different detector etc). Transformations and peak merging without the gui ================================================ -Use the history from the Tk gui... - -Separating_U_from_UBI -===================== - -Seems to come up a lot for some people? -UBI is inverse of (UB), so (UB)^{-1}=B^{-1}U^{-1}, but U^{-1} is also -transpose(U), so inevitably it gets flipped, even if you know what "B" is. -There is now a ubitocellpars routine in (svn) indexing.py, which you can -use to construct a "B" matrix according to some convention. "B" is chosen -as a Cholesky factor of the product (UBI).(UBI)^T (=g, the metric tensor). -Since you are free to chose "B" however you like (x along a, y along b*, z -orthogonal etc) then you also chose "U" however you like (eg x along a*, y -along b, z orthogonal). Take a triclinic or hexagonal unit cell instead of -cubic and you see the mess. So perhaps this is not the "U" you want - -please correct it - or add some sort of "U" labelling:: +Use the history from Help>History to capture the commands run when you click on +the tk gui. - import Numeric as n - import LinearAlgebra as l - def getU(ubi): - # start by getting metric tensor - g=n.matrixmultiply(ubi,n.transpose(ubi)) - # one convention for B is a Cholesky factor of g (triangular) - bi=l.cholesky_decomposition(g) - # get inverse to separate this from u - b = l.inverse(bi) - # ubi = (UB)^{-1} = B^{-1} U^{-1} - # ... premultiply B.B^{-1}.U^{-1} - ui=n.matrixmultiply(b,ubi) - # transpose is inverse - roughly 50% chance I got this wrong - # ... or C/fortran array ordering confuses as well - u=n.transpose(ui) - return u - >>> ubi - [[2.59661965, -2.99631727, 1.24883939], - [-1.0962173, 0.695891380, 3.94892308], - [-3.0554582, -2.79602729, -0.35546775]] - >>> getU(ubi) - array([[ 0.6246505 , -0.26370927, -0.73503005], - [-0.72080294, 0.16740569, -0.67262059], - [ 0.3004245 , 0.94996461, -0.08551237]]) \ No newline at end of file diff --git a/docs/sphx/api/ImageD11.rst b/docs/sphx/api/ImageD11.rst index 762cfdad..b468f2dd 100644 --- a/docs/sphx/api/ImageD11.rst +++ b/docs/sphx/api/ImageD11.rst @@ -1,339 +1,343 @@ -ImageD11 Package +ImageD11 package ================ -:mod:`ImageD11` Package ------------------------ +Subpackages +----------- -.. automodule:: ImageD11.__init__ - :members: - :undoc-members: - :show-inheritance: +.. toctree:: + :maxdepth: 4 -:mod:`ImageD11_file_series` Module ----------------------------------- + ImageD11.silxGui + ImageD11.tkGui + +Submodules +---------- + +ImageD11.ImageD11\_file\_series module +-------------------------------------- .. automodule:: ImageD11.ImageD11_file_series - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`ImageD11_thread` Module ------------------------------ +ImageD11.ImageD11\_thread module +-------------------------------- .. automodule:: ImageD11.ImageD11_thread - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`bisplev` Module ---------------------- +ImageD11.ImageD11options module +------------------------------- -.. automodule:: ImageD11.bisplev - :members: - :undoc-members: - :show-inheritance: +.. automodule:: ImageD11.ImageD11options + :members: + :undoc-members: + :show-inheritance: -:mod:`blobcorrector` Module ---------------------------- +ImageD11.blobcorrector module +----------------------------- .. automodule:: ImageD11.blobcorrector - :members: - :undoc-members: - :show-inheritance: - -:mod:`columnfile` Module ------------------------- + :members: + :undoc-members: + :show-inheritance: + +ImageD11.cImageD11 module +------------------------- + +.. automodule:: ImageD11.cImageD11 + :members: + :undoc-members: + :show-inheritance: + +ImageD11.cImageD11\_docstrings module +------------------------------------- + +.. automodule:: ImageD11.cImageD11_docstrings + :members: + :undoc-members: + :show-inheritance: + +ImageD11.columnfile module +-------------------------- .. automodule:: ImageD11.columnfile - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`correct` Module ---------------------- +ImageD11.compute\_fazit module +------------------------------ -.. automodule:: ImageD11.correct - :members: - :undoc-members: - :show-inheritance: - -:mod:`fft_index_refac` Module ------------------------------ +.. automodule:: ImageD11.compute_fazit + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.fft_index_refac - :members: - :undoc-members: - :show-inheritance: +ImageD11.correct module +----------------------- -:mod:`grain` Module -------------------- +.. automodule:: ImageD11.correct + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.grain - :members: - :undoc-members: - :show-inheritance: +ImageD11.eps\_sig\_solver module +-------------------------------- -:mod:`guicommand` Module ------------------------- +.. automodule:: ImageD11.eps_sig_solver + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.guicommand - :members: - :undoc-members: - :show-inheritance: +ImageD11.fft\_index\_refac module +--------------------------------- -:mod:`guiindexer` Module ------------------------- +.. automodule:: ImageD11.fft_index_refac + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.guiindexer - :members: - :undoc-members: - :show-inheritance: +ImageD11.finite\_strain module +------------------------------ -:mod:`guimaker` Module ----------------------- +.. automodule:: ImageD11.finite_strain + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.guimaker - :members: - :undoc-members: - :show-inheritance: +ImageD11.grain module +--------------------- -:mod:`guipeaksearch` Module ---------------------------- +.. automodule:: ImageD11.grain + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.guipeaksearch - :members: - :undoc-members: - :show-inheritance: +ImageD11.grid\_index\_parallel module +------------------------------------- -:mod:`guitransformer` Module ----------------------------- +.. automodule:: ImageD11.grid_index_parallel + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.guitransformer - :members: - :undoc-members: - :show-inheritance: +ImageD11.guicommand module +-------------------------- -:mod:`gv_general` Module ------------------------- +.. automodule:: ImageD11.guicommand + :members: + :undoc-members: + :show-inheritance: + +ImageD11.gv\_general module +--------------------------- .. automodule:: ImageD11.gv_general - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`hist` Module ------------------- +ImageD11.indexer module +----------------------- -.. automodule:: ImageD11.hist - :members: - :undoc-members: - :show-inheritance: +.. automodule:: ImageD11.indexer + :members: + :undoc-members: + :show-inheritance: -:mod:`indexing` Module ----------------------- +ImageD11.indexing module +------------------------ .. automodule:: ImageD11.indexing - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`labelimage` Module ------------------------- +ImageD11.labelimage module +-------------------------- .. automodule:: ImageD11.labelimage - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`lattice_reduction` Module -------------------------------- +ImageD11.lattice\_reduction module +---------------------------------- .. automodule:: ImageD11.lattice_reduction - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`license` Module ---------------------- +ImageD11.license module +----------------------- .. automodule:: ImageD11.license - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`listdialog` Module ------------------------- - -.. automodule:: ImageD11.listdialog - :members: - :undoc-members: - :show-inheritance: - -:mod:`parameters` Module ------------------------- +ImageD11.parameters module +-------------------------- .. automodule:: ImageD11.parameters - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`peakmerge` Module ------------------------ +ImageD11.peakmerge module +------------------------- .. automodule:: ImageD11.peakmerge - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`peaksearcher` Module --------------------------- +ImageD11.peaksearcher module +---------------------------- .. automodule:: ImageD11.peaksearcher - :members: - :undoc-members: - :show-inheritance: - -:mod:`plot3d` Module --------------------- + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.plot3d - :members: - :undoc-members: - :show-inheritance: - -:mod:`rc_array` Module ----------------------- +ImageD11.rc\_array module +------------------------- .. automodule:: ImageD11.rc_array - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`refinegrains` Module --------------------------- +ImageD11.refinegrains module +---------------------------- .. automodule:: ImageD11.refinegrains - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: + +ImageD11.rotdex module +---------------------- + +.. automodule:: ImageD11.rotdex + :members: + :undoc-members: + :show-inheritance: -:mod:`rsv` Module ------------------ +ImageD11.rsv module +------------------- .. automodule:: ImageD11.rsv - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`rsv_mapper` Module ------------------------- +ImageD11.rsv\_mapper module +--------------------------- .. automodule:: ImageD11.rsv_mapper - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`saintraw` Module ----------------------- +ImageD11.saintraw module +------------------------ .. automodule:: ImageD11.saintraw - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`scale` Module -------------------- +ImageD11.scale module +--------------------- .. automodule:: ImageD11.scale - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`scalem` Module --------------------- +ImageD11.simplex module +----------------------- -.. automodule:: ImageD11.scalem - :members: - :undoc-members: - :show-inheritance: +.. automodule:: ImageD11.simplex + :members: + :undoc-members: + :show-inheritance: -:mod:`simplex` Module ---------------------- +ImageD11.sparseframe module +--------------------------- -.. automodule:: ImageD11.simplex - :members: - :undoc-members: - :show-inheritance: +.. automodule:: ImageD11.sparseframe + :members: + :undoc-members: + :show-inheritance: -:mod:`sym_u` Module -------------------- +ImageD11.sym\_u module +---------------------- .. automodule:: ImageD11.sym_u - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`symops` Module --------------------- +ImageD11.symops module +---------------------- .. automodule:: ImageD11.symops - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`test_transform` Module ----------------------------- +ImageD11.threshold\_image module +-------------------------------- -.. automodule:: ImageD11.test_transform - :members: - :undoc-members: - :show-inheritance: +.. automodule:: ImageD11.threshold_image + :members: + :undoc-members: + :show-inheritance: -:mod:`transform` Module ------------------------ +ImageD11.transform module +------------------------- .. automodule:: ImageD11.transform - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`transformer` Module -------------------------- +ImageD11.transformer module +--------------------------- .. automodule:: ImageD11.transformer - :members: - :undoc-members: - :show-inheritance: - -:mod:`twodplot` Module ----------------------- - -.. automodule:: ImageD11.twodplot - :members: - :undoc-members: - :show-inheritance: - -:mod:`ubitool` Module ---------------------- + :members: + :undoc-members: + :show-inheritance: -.. automodule:: ImageD11.ubitool - :members: - :undoc-members: - :show-inheritance: - -:mod:`unitcell` Module ----------------------- +ImageD11.unitcell module +------------------------ .. automodule:: ImageD11.unitcell - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: -:mod:`write_graindex_gv` Module -------------------------------- +ImageD11.write\_graindex\_gv module +----------------------------------- .. automodule:: ImageD11.write_graindex_gv - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- +.. automodule:: ImageD11 + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/sphx/api/ImageD11.silxGui.rst b/docs/sphx/api/ImageD11.silxGui.rst new file mode 100644 index 00000000..287827c8 --- /dev/null +++ b/docs/sphx/api/ImageD11.silxGui.rst @@ -0,0 +1,38 @@ +ImageD11.silxGui package +======================== + +Submodules +---------- + +ImageD11.silxGui.silx\_colfile module +------------------------------------- + +.. automodule:: ImageD11.silxGui.silx_colfile + :members: + :undoc-members: + :show-inheritance: + +ImageD11.silxGui.silx\_plot3d module +------------------------------------ + +.. automodule:: ImageD11.silxGui.silx_plot3d + :members: + :undoc-members: + :show-inheritance: + +ImageD11.silxGui.silx\_sptview module +------------------------------------- + +.. automodule:: ImageD11.silxGui.silx_sptview + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: ImageD11.silxGui + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/sphx/api/ImageD11.tkGui.rst b/docs/sphx/api/ImageD11.tkGui.rst new file mode 100644 index 00000000..4ab5f124 --- /dev/null +++ b/docs/sphx/api/ImageD11.tkGui.rst @@ -0,0 +1,78 @@ +ImageD11.tkGui package +====================== + +Submodules +---------- + +ImageD11.tkGui.guiindexer module +-------------------------------- + +.. automodule:: ImageD11.tkGui.guiindexer + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.guimaker module +------------------------------ + +.. automodule:: ImageD11.tkGui.guimaker + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.guipeaksearch module +----------------------------------- + +.. automodule:: ImageD11.tkGui.guipeaksearch + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.guisolver module +------------------------------- + +.. automodule:: ImageD11.tkGui.guisolver + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.guitransformer module +------------------------------------ + +.. automodule:: ImageD11.tkGui.guitransformer + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.listdialog module +-------------------------------- + +.. automodule:: ImageD11.tkGui.listdialog + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.plot3d module +---------------------------- + +.. automodule:: ImageD11.tkGui.plot3d + :members: + :undoc-members: + :show-inheritance: + +ImageD11.tkGui.twodplot module +------------------------------ + +.. automodule:: ImageD11.tkGui.twodplot + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: ImageD11.tkGui + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/sphx/batchprocessing.rst b/docs/sphx/batchprocessing.rst index 518e2d0e..ac4c6092 100644 --- a/docs/sphx/batchprocessing.rst +++ b/docs/sphx/batchprocessing.rst @@ -35,16 +35,11 @@ image numbers and header information (svn version only for now):: Usage would be:: + python getheader.py "mydatafiles*.edf" samz Omega > headerinfo.txt -The quotes around the first command line argument are critical to stop -the shell expanding the "*". -It will create a file with the image number, filename and header -information you want. We will put the Omega entry as the last column and -last argument on the command line. -We now work out what the individual sweepscans were from this header -information file. It is sorted by image number and we assume a scan -corresponds to an increasing sequence of Omega angles:: +The quotes around the first command line argument are critical to stop the shell expanding the "*". +It will create a file with the image number, filename and header information you want. We will put the Omega entry as the last column and last argument on the command line. We now work out what the individual sweepscans were from this header information file. It is sorted by image number and we assume a scan corresponds to an increasing sequence of Omega angles:: % more getscans.py import sys @@ -85,25 +80,17 @@ corresponds to an increasing sequence of Omega angles:: first = True last = -1e9 -Assuming you have made a directory ImageD11 in with your sample to pick -up the output should now have 4 shell scripts. Run each one on a -different machine, the peaksearch output should end up in the ImageD11 -directory. +Assuming you have made a directory ImageD11 in with your sample to pick up the output should now have 4 shell scripts. Run each one on a different machine, the peaksearch output should end up in the ImageD11 directory. Work out how to index one scan ============================== -Take the output from the peaksearch into the gui (probably the -merge_txxx) file. Go through the transformation menu until you are happy -you have nice parameters. Save the parameter file. Save the g-vectors -and go through the indexing menu, finding grains. Once you are happy go -to the Help menu and select history. Copy and paste this into a file. -#Edit_the_history_file_to_take_a_command_line_argument? +Take the output from the peaksearch into the gui (probably the merge_txxx) file. Go through the transformation menu until you are happy you have nice parameters. Save the parameter file. Save the g-vectors and go through the indexing menu, finding grains. Once you are happy go to the Help menu and select history. Copy and paste this into a file. Edit the history file to take a command line argument +===================================================== -Now edit the history file to replace the input and output file names to -be a variable from the command line: eg:: +Now edit the history file to replace the input and output file names to be a variable from the command line: eg:: % more history.py # Create objects to manipulate - they hold your data @@ -221,4 +208,4 @@ Indexing a peaksearch output and filtering peaks at the same time:: myindexer.find( ) myindexer.scorethem( ) - myindexer.saveubis( sys.argv[4] ) \ No newline at end of file + myindexer.saveubis( sys.argv[4] ) diff --git a/docs/sphx/calibration.rst b/docs/sphx/calibration.rst index 1e487bcd..3716f39a 100644 --- a/docs/sphx/calibration.rst +++ b/docs/sphx/calibration.rst @@ -1,9 +1,7 @@ +=========== Calibration =========== -Calibration of the sample-detector distance, detector tilts and beam centre -are one of the fundamental reasons for creating the ImageD11 program and -having the sometimes annoying graphical interface. The menu offers the -option to read in previously determined parameters from a file:: +Calibration of the sample-detector distance, detector tilts and beam centre are one of the fundamental reasons for creating the ImageD11 program and having the sometimes annoying graphical interface. The menu offers the option to read in previously determined parameters from a file:: cell__a 5.8 cell__b 5.8 @@ -31,20 +29,7 @@ option to read in previously determined parameters from a file:: z-center 984.924425 z-size 46.77648 -The meaning of the parameters is mostly self explanatory. Begins with the -unit cell parameters (in angstroms/degrees), the lattice centering using -the first letter of the space group symbol. The sample detector distance is -normally in millimeters. The fit tolerance is the difference in degrees -between observed and computed peak positions to use in parameter -refinement. The tilts are in radians about the y/z axes in the ID11 -co-ordinate system (z is vertical, y is horizontal). The position of the -beam centre and pixel sizes are used to transform the spatially corrected -peak positions into real space peaks positions. You MUST use the values for -pixel size recorded in the spline file if you have made a spatial -distortion for things to work out properly. -Having read in some data which was output from the peaksearching menu you -can make a plot of two theta / eta and then add unit cell peaks. - +The meaning of the parameters is mostly self explanatory. Begins with the unit cell parameters (in angstroms/degrees), the lattice centering using the first letter of the space group symbol. The sample detector distance is normally in millimeters. The fit tolerance is the difference in degrees between observed and computed peak positions to use in parameter refinement. The tilts are in radians about the y/z axes in the ID11 co-ordinate system (z is vertical, y is horizontal). The position of the beam centre and pixel sizes are used to transform the spatially corrected peak positions into real space peaks positions. You MUST use the values for pixel size recorded in the spline file if you have made a spatial distortion for things to work out properly. Having read in some data which was output from the peaksearching menu you can make a plot of two theta / eta and then add unit cell peaks. Perhaps you will need to go to the plotting menu and clear plot before @@ -53,41 +38,16 @@ making this plot to get a sensible x/y range. The horizontal axis represents the peak positions in two theta with the eta value being the angle around a powder ring measured from the vertical. -If the initial parameters for the transformation are good then you will -hopefully find nearly vertical lines made up of the diffraction spots from -all of your grains with crosses overlaying them indicating the computed -peak positions. If the parameters are not so good then the lines will be -deformed and perhaps not match the computed positions. -To improve the fit you can change the parameters manually via the edit -parameters menu option and then replot. -You can also choose to fit the parameters where peaks which are within fit_tolerance of the computed positions are used for a simplex minimisation procedure. Usually this works better if you zoom in on the low angle data first and then repeat the fit after replotting and unzooming to use the full range of data. It works better that way because you are less likely to mis-assign low angle peaks as there is less peak overlap. +If the initial parameters for the transformation are good then you will hopefully find nearly vertical lines made up of the diffraction spots from all of your grains with crosses overlaying them indicating the computed peak positions. If the parameters are not so good then the lines will be deformed and perhaps not match the computed positions. To improve the fit you can change the parameters manually via the edit parameters menu option and then replot. You can also choose to fit the parameters where peaks which are within fit_tolerance of the computed positions are used for a simplex minimisation procedure. Usually this works better if you zoom in on the low angle data first and then repeat the fit after replotting and unzooming to use the full range of data. It works better that way because you are less likely to mis-assign low angle peaks as there is less peak overlap. -Once the parameters are refined and the transformation gives vertical lines -you can hope to make a good computation of g-vectors. Note that the -wavelength is not normally refined and should be given as correctly as -possible to obtain the correct curvature of the Ewald sphere (calibrate it -via some other method please). -Changing wavelength is eventually equivalent to changing the unit cell -parameters so you might get away with a poor initial value. +Once the parameters are refined and the transformation gives vertical lines you can hope to make a good computation of g-vectors. Note that the wavelength is not normally refined and should be given as correctly as possible to obtain the correct curvature of the Ewald sphere (calibrate it via some other method please). Changing wavelength is eventually equivalent to changing the unit cell parameters so you might get away with a poor initial value. -Before computing the g-vectors (scattering vectors or normals to the -diffracting hkl planes) you should check the value of the wedge angle: this -is zero when the incident beam is perpendicular to the rotation axis. In -cases where a heavy stress rig is used at beamline ID11 together with the -Laue optics there is an inclination angle between the rotation axis and the -beam - named wedge after the motor in spec for adjusting that angle. You -should use the negative value of the two theta angle of the monochromator -(eg about -2.8 degrees at 80keV) -Once you have clicked on compute g-vectors you can save these vectors into -a file. There is also an option for saving a file suitable for introduction -into the Graindex software developed by the Riso group (FIXME encourage an -expert to explain how to do that) -#Making_the_transformations_independent_of_the_gui_for_scripting? +Before computing the g-vectors (scattering vectors or normals to the diffracting hkl planes) you should check the value of the wedge angle: this is zero when the incident beam is perpendicular to the rotation axis. In cases where a heavy stress rig is used at beamline ID11 together with the Laue optics there is an inclination angle between the rotation axis and the beam - named wedge after the motor in spec for adjusting that angle. You should use the negative value of the two theta angle of the monochromator (eg about -2.8 degrees at 80keV). Once you have clicked on compute g-vectors you can save these vectors into a file. There is also an option for saving a file suitable for introduction into the Graindex software developed by the Riso group (FIXME encourage an expert to explain how to do that). Making the transformations independent of the gui for scripting +=============================================================== -If you have a parameter file from a calibration you can directly make the -gve file like this:: +If you have a parameter file from a calibration you can directly make the gve file like this:: $ python from ImageD11 import transformer @@ -99,4 +59,4 @@ gve file like this:: obj.computegv( ) obj.savegv( 'peaks_t20.gve' ) -... and that's why they call it "object oriented" programming. \ No newline at end of file +... and that's why they call it "object oriented" programming. diff --git a/docs/sphx/changelog.rst b/docs/sphx/changelog.rst new file mode 100644 index 00000000..1dfe45d3 --- /dev/null +++ b/docs/sphx/changelog.rst @@ -0,0 +1,18 @@ +========= +Changelog +========= + +In the unlikely event that I remember to update this file here is +an attempt to keep track of progress. + + + + +2021-12-17 +========== +Added a working example in ImageD11/project/h5demo1.py which is +building a virtual dataset and a series of external links to find +frames in external edf files. + + + diff --git a/docs/sphx/conf.py b/docs/sphx/conf.py index df00c7bd..2e8d85f1 100644 --- a/docs/sphx/conf.py +++ b/docs/sphx/conf.py @@ -25,7 +25,7 @@ # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax'] +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -41,16 +41,16 @@ # General information about the project. project = u'ImageD11' -copyright = u'2017, Jon Wright' +copyright = u'2020, Jon Wright' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = '1.7.0' +version = '1.9.8' # The full version, including alpha/beta/rc tags. -release = '1.7.0' +release = '1.9.8' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -121,7 +121,8 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +#html_static_path = ['_static'] +html_static_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. diff --git a/docs/sphx/fileformats.rst b/docs/sphx/fileformats.rst index 6dc6c2f2..b7981da2 100644 --- a/docs/sphx/fileformats.rst +++ b/docs/sphx/fileformats.rst @@ -1,11 +1,7 @@ +============ File Formats ============ -Unfortunately ImageD11 has evolved with some very poor file formats. These should be -replaced as soon as -possible with something more powerful, but in the meantime, here is some documentation -about what is -actually there right now. -Mostly the files are ascii following a simplistic model of: +Unfortunately ImageD11 has evolved with some very poor file formats. These should be replaced as soon as possible with something more powerful, but in the meantime, here is some documentation about what is actually there right now. Mostly the files are ascii following a simplistic model of: # name = value @@ -25,10 +21,8 @@ FIXME - it uses fabio.open() Input/Output : 2D Peak search results ===================================== -The peaksearch.py script saves results in an ascii file which is read in again in the -graphical interface (peaksearch menu). For each image processed, the file contains the -header information on lines beginning with '#' a series of threshold levels and peaks. -The file is typically named peaks.out +The peaksearch.py script saves results in an ascii file which is read in again in the graphical interface (peaksearch menu). For each image processed, the file contains the header information on lines beginning with '#' a series of threshold levels and peaks. The file is typically named peaks.out. + Example with most of the header information snipped:: # File plat_a0050.edf @@ -47,23 +41,21 @@ Example with most of the header information snipped:: 119 2371.050420 377.755365 1467.049632 372.961306 1468.529932 3.122391 2.406393 -0.363226 44 -The Omega header entry is currently the only thing which is relied on. If your raw -images did not contain Omega header entries then a helper script recoveromegas.py is -available to help to insert these values. The numerical values for each peak found in -the image which are recorded are: - -Number_of_pixels - the number of pixels in the blob (units: pure integer) -Average_counts - the average pixel intensity in the blob (units : ADU) -x - the centre of mass peak position in the raw image in the fast/slow (CHECK) array direction (units: pixels) -y - the centre of mass peak position in the raw image in the fast/slow (CHECK) array direction (units: pixels) -xc - the x value corrected for spatial distortion, using the spline file mentioned in the header (units: pixels) -yc - the x value corrected for spatial distortion, using the spline file mentioned in the header (units: pixels) -sig_x - the second moment of the intensity distribution in the x direction (eg: Gaussian sigma, related to the peak width) (units: pixels) -sig_y - the second moment of the intensity distribution in the y direction (eg: Gaussian sigma, related to the peak width) (units: pixels) -cov_xy - the x/y covariance of the intensity distribution. Ranges from -1 to 1 with zero being a circular peak and extreme values representing ellipses. (units: pure float) -Written by : scripts/peaksearch.py scripts/recoveromega.py -Read by : ImageD11/guipeaksearch.py -#Input.2FOutput_:_Merged_peaks? +The Omega header entry is currently the only thing which is relied on. If your raw images did not contain Omega header entries then a helper script recoveromegas.py is available to help to insert these values. +The numerical values for each peak found in the image which are recorded are: + +:Number_of_pixels: the number of pixels in the blob (units: pure integer) +:Average_counts: the average pixel intensity in the blob (units : ADU) +:x: the centre of mass peak position in the raw image in the fast/slow (CHECK) array direction (units: pixels) +:y: the centre of mass peak position in the raw image in the fast/slow (CHECK) array direction (units: pixels) +:xc: the x value corrected for spatial distortion, using the spline file mentioned in the header (units: pixels) +:yc: the x value corrected for spatial distortion, using the spline file mentioned in the header (units: pixels) +:sig_x: the second moment of the intensity distribution in the x direction (eg: Gaussian sigma, related to the peak width) (units: pixels) +:sig_y: the second moment of the intensity distribution in the y direction (eg: Gaussian sigma, related to the peak width) (units: pixels) +:cov_xy: the x/y covariance of the intensity distribution. Ranges from -1 to 1 with zero being a circular peak and extreme values representing ellipses. (units: pure float) + +| Written by : scripts/peaksearch.py scripts/recoveromega.py +| Read by : ImageD11/guipeaksearch.py Input/Output : Merged peaks =========================== @@ -72,61 +64,53 @@ Versions > 1.0 Many columns, they are: -1: sc distortion corrected centre of mass, slow index direction -2: fc distortion corrected centre of mass, fast index direction -3: omega omega (rotation sequence) centre of mass -4: Number_of_pixels ... in the 3D connected object -5: avg_intensity average intensity of the pixels in the object -6: s_raw raw position (no spatial), slow direction -7: f_raw raw position (no spatial), fast direction -8: sigs second moment, slow direction -9: sigf second moment, fast direction -10: covsf covariance for fast/slow -11: sigo second moment, omega direction (out of plane of image) -12: covso covariance for slow/omega -13: covfo covariance for fast/omega -14: sum_intensity total intensity for pixels above threshold -15: sum_intensity^2 summed intensity squared [not useful?] -16: IMax_int maximum pixel -17: IMax_s array index slow direction for maximum pixel -18: IMax_f array index fast direction for maximum pixel -19: IMax_o array index omega direction for maximum pixel -20: Min_s minimum pixel position in slow direction -21: Max_s maximum pixel position in slow direction -22: Min_f minimum pixel position in fast direction -23: Max_f maximum pixel position in fast direction -24: Min_o minimum pixel position in omega direction -25: Max_o maximum pixel position in omega direction -26: dety unlikely to be correct flipped direction -27: detz unlikely to be correct flipped direction -28: onfirst blob is present on first image -29: onlast blob is present on last image -30: spot3d_id line number - 1 (titles) in the file on creation by peaksearch.py - -The peaksearching menu in the gui reads in the previous peaksearch output and merges peaks which have adjacent Omega values (and centre of mass within some tolerance) to give a new file which contains only the peaks. Typically named peaks.flt - -# xc yc omega npixels avg_intensity x_raw y_raw sigx sigy covxy -1020.355425 1458.043233 -37.802212 170.000000 2696.852941 1024.230029 1458.756529 2.060744 2.430557 -0.124729 -1060.621433 1456.483538 -37.750000 4.000000 1227.250000 1064.493991 1457.499083 0.499964 0.499999 -0.003485 -665.542804 728.847852 -37.750000 6.000000 1243.166667 672.161282 732.332082 1.060509 0.743424 0.767337 - -Column labels are as before but with the addition of an omega column giving the centre -of mass of the blob in omega too. - -Written by : ImageD11/guipeaksearch.py -Read by : ImageD11/guitransformer.py ImageD11/refinegrains.py +:sc: distortion corrected centre of mass, slow index direction +:fc: distortion corrected centre of mass, fast index direction +:omega: omega (rotation sequence) centre of mass +:Number_of_pixels: ... in the 3D connected object +:avg_intensity: average intensity of the pixels in the object +:s_raw: raw position (no spatial), slow direction +:f_raw: raw position (no spatial), fast direction +:sigs: second moment, slow direction +:sigf: second moment, fast direction +:covsf: covariance for fast/slow +:sigo: second moment, omega direction (out of plane of image) +:covso: covariance for slow/omega +:covfo: covariance for fast/omega +:sum_intensity: total intensity for pixels above threshold +:sum_intensity^2: summed intensity squared [not useful?] +:IMax_int: maximum pixel +:IMax_s: array index slow direction for maximum pixel +:IMax_f: array index fast direction for maximum pixel +:IMax_o: array index omega direction for maximum pixel +:Min_s: minimum pixel position in slow direction +:Max_s: maximum pixel position in slow direction +:Min_f: minimum pixel position in fast direction +:Max_f: maximum pixel position in fast direction +:Min_o: minimum pixel position in omega direction +:Max_o: maximum pixel position in omega direction +:dety: unlikely to be correct flipped direction +:detz: unlikely to be correct flipped direction +:onfirst: blob is present on first image +:onlast: blob is present on last image +:spot3d_id: line number - 1 (titles) in the file on creation by peaksearch.py + +The peaksearching menu in the gui reads in the previous peaksearch output and merges peaks which have adjacent Omega values (and centre of mass within some tolerance) to give a new file which contains only the peaks. Typically named peaks.flt:: + + # xc yc omega npixels avg_intensity x_raw y_raw sigx sigy covxy + 1020.355425 1458.043233 -37.802212 170.000000 2696.852941 1024.230029 1458.756529 2.060744 2.430557 -0.124729 + 1060.621433 1456.483538 -37.750000 4.000000 1227.250000 1064.493991 1457.499083 0.499964 0.499999 -0.003485 + 665.542804 728.847852 -37.750000 6.000000 1243.166667 672.161282 732.332082 1.060509 0.743424 0.767337 + +Column labels are as before but with the addition of an omega column giving the centre of mass of the blob in omega too. + +| Written by : ImageD11/guipeaksearch.py +| Read by : ImageD11/guitransformer.py ImageD11/refinegrains.py gvectorfile ============ -Scattering vectors computed by the transformation module, normally via the -guitransformer module... -The first line of the file contains the unit cell parameters and lattice centering (one -of P,A,B,C,I,F). The wavelength and wedge angle are expected on the next lines (needed -to compute ideal two theta, omega and azimuth angles for computed orientations). A list -of computed d* values expected h,k,l values for the unit cell follow, I don't think the -program uses them anymore. Note that d* is 1/d-spacing in Angstrom. The actual -scattering vectors follow:: +Scattering vectors computed by the transformation module, normally via the guitransformer module... The first line of the file contains the unit cell parameters and lattice centering (one of P,A,B,C,I,F). The wavelength and wedge angle are expected on the next lines (needed to compute ideal two theta, omega and azimuth angles for computed orientations). A list of computed d* values expected h,k,l values for the unit cell follow, I don't think the program uses them anymore. Note that d* is 1/d-spacing in Angstrom. The actual scattering vectors follow:: 78.712000 78.712000 78.712000 90.000000 90.000000 90.000000 I # wavelength = 0.939500 @@ -159,28 +143,29 @@ scattering vectors follow:: -0.013099 -0.009687 -0.007903 1566.912500 1513.290200 0.018108 115.878145 53.000000 44665 211439.526323 -3236.750899 -1570.156341 0.017023 -0.001041 -0.006196 1502.350000 1516.761000 0.018145 -109.965181 93.999200 78237 211439.811196 3388.245269 -1230.889139 ... - -gx x-component of scattering vector (along the beam) with all angles at -zero (units 1/Angstrom) -gy y-component of scattering vector (toward the door) with all angles at -zero (units 1/Angstrom) -gz z-component of scattering vector (roughly up) with all angles at zero -(units 1/Angstrom) -xc spatially corrected peak x-position on detector (units: pixels) -yc spatially corrected peak y-position on detector (units: pixels) -ds 1/d-spacing - modulus of scattering vector - ds = \frac{\lambda}{ 2 \sin \theta} -eta - azimuthal angle. -omega rotation angle of scan -spotid is an spot identifier to follow the individual spot at any process step -xl x-component of scattering vector in the laboratory coordinate system (along the beam) with all angles at zero (units microns) -yl y-component of scattering vector in the laboratory coordinate system (toward the door) with all angles at zero (units microns) -zl z-component of scattering vector in the laboratory coordinate system (roughly up) with all angles at zero (units microns) -Written by: ImageD11/guitransformer.py -Read by: ImageD11/indexing.py and GrainSpotter and possibly other programs.. + +The definitions follow: + +:gx: x-component of scattering vector (along the beam) with all angles at zero (units 1/Angstrom) +:gy: y-component of scattering vector (toward the door) with all angles at zero (units 1/Angstrom) +:gz: z-component of scattering vector (roughly up) with all angles at zero (units 1/Angstrom) +:xc: spatially corrected peak x-position on detector (units: pixels) +:yc: spatially corrected peak y-position on detector (units: pixels) +:ds: 1/d-spacing - modulus of scattering vector: :math:`ds = \frac{\lambda}{ 2 \sin \theta}`. +:eta: azimuthal angle. +:omega: rotation angle of scan +:spotid: an spot identifier to follow the individual spot at any process step +:xl: x-component of scattering vector in the laboratory coordinate system (along the beam) with all angles at zero (units microns) +:yl: y-component of scattering vector in the laboratory coordinate system (toward the door) with all angles at zero (units microns) +:zl: z-component of scattering vector in the laboratory coordinate system (roughly up) with all angles at zero (units microns) + +| Written by: ImageD11/guitransformer.py +| Read by: ImageD11/indexing.py and GrainSpotter and possibly other programs.. Input/Output : UBI matrices =========================== + After successful(?) completion of some indexing a set of UBI matrices can be saved from the indexing.py script or guiindexing interface. These are just 3x3 matrices separated by blank lines:: @@ -193,44 +178,42 @@ by blank lines:: -3.311710 -4.157822 -2.286774 -3.261669 -0.019438 4.770287 -It should be the case that (CHECK/TESTCASE) - \begin{matrix} h & k & l \end{matrix} \begin{matrix} UBI_{11} & UBI_{12} & -UBI_{13} \\ UBI_{21} & UBI_{22} & UBI_{23} \\ UBI_{31} & UBI_{32} & UBI_{33} -\end{matrix} \begin{matrix} xr & yr & zr \end{matrix} -Written by: ImageD11/indexing.py ImageD11/guiindexer.py -Read by: ImageD11/refinegrains.py - +| It should be the case that (CHECK/TESTCASE) +| \begin{matrix} h & k & l \end{matrix} \begin{matrix} UBI_{11} & UBI_{12} & +| UBI_{13} \\ UBI_{21} & UBI_{22} & UBI_{23} \\ UBI_{31} & UBI_{32} & UBI_{33} +| \end{matrix} \begin{matrix} xr & yr & zr \end{matrix} +| Written by: ImageD11/indexing.py ImageD11/guiindexer.py +| Read by: ImageD11/refinegrains.py Parameter files =============== -This files should all have the format name value. -For the transformation module the parameters are: -Unit cell - -cell_a -cell_b -cell_c -cell_alpha -cell_beta -cell_gamma -cell_lattice_[P,A,B,C,I,F,R] F -Diffractometer angles and geometry: +This files should all have the format name value. +For the transformation module the parameters are:: -chi : rotation of detector around beam (not tested, probably only in CVS) -wedge : tilt of axis around y -distance : sample detector distance (units: millimetres) -tilt-y : first detector tilt -tilt-z : second detector tilt -wavelength : of the incoming x-rays -y-center : beam centre on the detector (units: pixels) -y-size : pixel size in the y direction (units: microns) -z-center : beam centre on the detector (units: pixels) -z-size : pixel size in the z direction (units: microns) - -CHECK that y/z are the right way around and also the hidden detector flip matrix. -fit_tolerance is used to decide which spots to assign to hkl rings in fitting the -geometrical parameters (units: degrees). + Unit cell + cell_a + cell_b + cell_c + cell_alpha + cell_beta + cell_gamma + cell_lattice_[P,A,B,C,I,F,R] F + +Diffractometer angles and geometry: + +:chi: rotation of detector around beam (not tested, probably only in CVS) +:wedge: tilt of axis around y +:distance: sample detector distance (units: millimetres) +:tilt-y: first detector tilt +:tilt-z: second detector tilt +:wavelength: of the incoming x-rays +:y-center: beam centre on the detector (units: pixels) +:y-size: pixel size in the y direction (units: microns) +:z-center: beam centre on the detector (units: pixels) +:z-size: pixel size in the z direction (units: microns) + +CHECK that y/z are the right way around and also the hidden detector flip matrix. fit_tolerance is used to decide which spots to assign to hkl rings in fitting the geometrical parameters (units: degrees). The indexing parameters are: -FIXME \ No newline at end of file +FIXME diff --git a/docs/sphx/filtering.rst b/docs/sphx/filtering.rst index 93c77713..0fae3b9f 100644 --- a/docs/sphx/filtering.rst +++ b/docs/sphx/filtering.rst @@ -1,268 +1,269 @@ -Filtering -========= - -How to clean up a peaksearch output to only get the nice peaks, or -whatever criteria you like:: - - #!/usr/bin/env fable.python - from ImageD11.columnfile import * - import sys - f = columnfile(sys.argv[1]) - m = (f.fc < 1020.0) | (f.fc > 1027.0) - f.filter(m) - m = (f.fc < 2044.0) | (f.fc > 2051.0) - f.filter(m) - m = (f.sc < 1020.0) | (f.sc > 1027.0) - f.filter(m) - m = (f.sc < 2044.0) | (f.sc > 2051.0) - f.filter(m) - f.writefile(sys.argv[2]) - -Since version 1.0 of ImageD11 a "columnfile" facility has been added. This -python object allows for easy filtering of your data by generating -attributes representing the names of columns, and also a filter function. -Here is an example:: - - D:\wright\Grain_Stuff\sim_test\simdata_oPPA_5grains>python - Python 2.4.4 (#71, Oct 18 2006, 08:34:43) [MSC v.1310 32 bit (Intel)] on win32 - Type "help", "copyright", "credits" or "license" for more information. - >>> from ImageD11.columnfile import columnfile - >>> **obj = columnfile("peaks_t11.flt")** - >>> dir(obj) - ['IMax_f', 'IMax_int', 'IMax_o', 'IMax_s', 'Max_f', 'Max_o', 'Max_s', 'Min_f', 'Min_o', 'Min_s', 'Number_of_pixels', - '__doc__', '__init__', '__module__', 'avg_intensity', 'bigarray', 'covfo', 'covsf', 'covso', 'dety', 'detz', 'f_raw', 'fc', - 'filename', 'filter', 'ncols', 'nrows', 'omega', 'onfirst', 'onlast', 'parameters', 'readfile', 's_raw', 'sc', - 'set_attributes', 'sigo', 'sigs', 'spot3d_id', 'sum_intensity', 'sum_intensity^2', 'titles', 'writefile'] - >>> from matplotlib.pylab import * - >>> plot(obj.Number_of_pixels, obj.sum_intensity) - >>> show() - [close plot window] - >>> - >>> obj.nrows - 13985 - >>> **obj.filter( obj.Number_of_pixels > 5 )** - >>> obj.nrows - 13577 - >>> # Make a more complicated mask - >>> mask = (obj.detz > 20) & (obj.detz < 130) - >>> # see how many peaks will survive - >>> mask.cumsum() - array([ 0, 0, 0, ..., 482, 482, 482]) - >>> **obj.filter( mask )** - >>> obj.nrows - 482 - >>> **obj.writefile( "my_filtered_peaks.flt" )** - -Some possible filtering operations clipped out of the numpy array object -help. - - >>> help(mask) - Help on ndarray object: - class ndarray(__builtin__.object) - | An array object represents a multidimensional, homogeneous array - | of fixed-size items. [...] - | - | Methods defined here: - | - | __abs__(...) - | x.__abs__() <==> abs(x) - | - | __add__(...) - | x.__add__(y) <==> x+y - | - | __and__(...) - | x.__and__(y) <==> x&y - | - | __contains__(...) - | x.__contains__(y) <==> y in x - | - | __div__(...) - | x.__div__(y) <==> x/y - | - | __divmod__(...) - | x.__divmod__(y) <==> divmod(x, y) - | - | __eq__(...) - | x.__eq__(y) <==> x==y - | - | __float__(...) - | x.__float__() <==> float(x) - | - | __floordiv__(...) - | x.__floordiv__(y) <==> x//y - | - | __ge__(...) - | x.__ge__(y) <==> x>=y - | - | __gt__(...) - | x.__gt__(y) <==> x>y - | - | __iadd__(...) - | x.__iadd__(y) <==> x+y - | - | __iand__(...) - | x.__iand__(y) <==> x&y - | - | __idiv__(...) - | x.__idiv__(y) <==> x/y - | - | __ifloordiv__(...) - | x.__ifloordiv__(y) <==> x//y - | - | __ilshift__(...) - | x.__ilshift__(y) <==> x< x%y - | - | __imul__(...) - | x.__imul__(y) <==> x*y - | - | __int__(...) - | x.__int__() <==> int(x) - | - | __invert__(...) - | x.__invert__() <==> ~x - | - | __ior__(...) - | x.__ior__(y) <==> x|y - | - | __ipow__(...) - | x.__ipow__(y) <==> x**y - | - | __irshift__(...) - | x.__irshift__(y) <==> x>>y - | - | __isub__(...) - | x.__isub__(y) <==> x-y - | - | __itruediv__(...) - | x.__itruediv__(y) <==> x/y - | - | __ixor__(...) - | x.__ixor__(y) <==> x^y - | - | __le__(...) - | x.__le__(y) <==> x<=y - | - | __lshift__(...) - | x.__lshift__(y) <==> x< x x%y - | - | __mul__(...) - | x.__mul__(y) <==> x*y - | - | __ne__(...) - | x.__ne__(y) <==> x!=y - | - | __neg__(...) - | x.__neg__() <==> -x - | - | __nonzero__(...) - | x.__nonzero__() <==> x != 0 - | - | __or__(...) - | x.__or__(y) <==> x|y - | - | __pow__(...) - | x.__pow__(y[, z]) <==> pow(x, y[, z]) - | - | __radd__(...) - | x.__radd__(y) <==> y+x - | - | __rand__(...) - | x.__rand__(y) <==> y&x - | - | __rdiv__(...) - | x.__rdiv__(y) <==> y/x - | - | __rdivmod__(...) - | x.__rdivmod__(y) <==> divmod(y, x) - | - | __rfloordiv__(...) - | x.__rfloordiv__(y) <==> y//x - | - | __rlshift__(...) - | x.__rlshift__(y) <==> y< y%x - | - | __rmul__(...) - | x.__rmul__(y) <==> y*x - | - | __ror__(...) - | x.__ror__(y) <==> y|x - | - | __rpow__(...) - | y.__rpow__(x[, z]) <==> pow(x, y[, z]) - | - | __rrshift__(...) - | x.__rrshift__(y) <==> y>>x - | - | __rshift__(...) - | x.__rshift__(y) <==> x>>y - | - | __rsub__(...) - | x.__rsub__(y) <==> y-x - | - | __rtruediv__(...) - | x.__rtruediv__(y) <==> y/x - | - | __rxor__(...) - | x.__rxor__(y) <==> y^x - | - | __sub__(...) - | x.__sub__(y) <==> x-y - | - | __truediv__(...) - | x.__truediv__(y) <==> x/y - | - | __xor__(...) - | x.__xor__(y) <==> x^y - | - | all(...) - | a.all(axis=None) - | - | any(...) - | a.any(axis=None, out=None) - | - | argmax(...) - | a.argmax(axis=None, out=None) - | - | argmin(...) - | a.argmin(axis=None, out=None) - | - | choose(...) - | a.choose(b0, b1, ..., bn, out=None, mode='raise') - | - | Return an array that merges the b_i arrays together using 'a' as - | the index The b_i arrays and 'a' must all be broadcastable to the - | same shape. The output at a particular position is the input - | array b_i at that position depending on the value of 'a' at that - | position. Therefore, 'a' must be an integer array with entries - | from 0 to n+1.; - | - | clip(...) - | a.clip(min=, max=, out=None) - | - | nonzero(...) - | a.nonzero() returns a tuple of arrays - | - | Returns a tuple of arrays, one for each dimension of a, - | containing the indices of the non-zero elements in that - | dimension. The corresponding non-zero values can be obtained - | with - | a[a.nonzero()]. - | - | To group the indices by element, rather than dimension, use - | transpose(a.nonzero()) - | instead. The result of this is always a 2d array, with a row for - | each non-zero element.; - -We think it is Turing complete! \ No newline at end of file +Filtering +========= + +There is a new gui option, try doing: + python -m ImageD11.silxGui.silx_colfile.py mypeaks.flt + + +How to clean up a peaksearch output to only get the nice peaks, or +whatever criteria you like:: + + #!/usr/bin/env fable.python + from ImageD11.columnfile import * + import sys + f = columnfile(sys.argv[1]) + m = (f.fc < 1020.0) | (f.fc > 1027.0) + f.filter(m) + m = (f.fc < 2044.0) | (f.fc > 2051.0) + f.filter(m) + m = (f.sc < 1020.0) | (f.sc > 1027.0) + f.filter(m) + m = (f.sc < 2044.0) | (f.sc > 2051.0) + f.filter(m) + f.writefile(sys.argv[2]) + +Since version 1.0 of ImageD11 a "columnfile" facility has been added. This python object allows for easy filtering of your data by generating attributes representing the names of columns, and also a filter function. Here is an example:: + + D:\wright\Grain_Stuff\sim_test\simdata_oPPA_5grains>python + Python 2.4.4 (#71, Oct 18 2006, 08:34:43) [MSC v.1310 32 bit (Intel)] on win32 + Type "help", "copyright", "credits" or "license" for more information. + >>> from ImageD11.columnfile import columnfile + >>> **obj = columnfile("peaks_t11.flt")** + >>> dir(obj) + ['IMax_f', 'IMax_int', 'IMax_o', 'IMax_s', 'Max_f', 'Max_o', 'Max_s', 'Min_f', 'Min_o', 'Min_s', 'Number_of_pixels', + '__doc__', '__init__', '__module__', 'avg_intensity', 'bigarray', 'covfo', 'covsf', 'covso', 'dety', 'detz', 'f_raw', 'fc', + 'filename', 'filter', 'ncols', 'nrows', 'omega', 'onfirst', 'onlast', 'parameters', 'readfile', 's_raw', 'sc', + 'set_attributes', 'sigo', 'sigs', 'spot3d_id', 'sum_intensity', 'sum_intensity^2', 'titles', 'writefile'] + >>> from matplotlib.pylab import * + >>> plot(obj.Number_of_pixels, obj.sum_intensity) + >>> show() + [close plot window] + >>> + >>> obj.nrows + 13985 + >>> **obj.filter( obj.Number_of_pixels > 5 )** + >>> obj.nrows + 13577 + >>> # Make a more complicated mask + >>> mask = (obj.detz > 20) & (obj.detz < 130) + >>> # see how many peaks will survive + >>> mask.cumsum() + array([ 0, 0, 0, ..., 482, 482, 482]) + >>> **obj.filter( mask )** + >>> obj.nrows + 482 + >>> **obj.writefile( "my_filtered_peaks.flt" )** + +Some possible filtering operations clipped out of the numpy array object +help:: + + >>> help(mask) + Help on ndarray object: + class ndarray(__builtin__.object) + | An array object represents a multidimensional, homogeneous array + | of fixed-size items. [...] + | + | Methods defined here: + | + | __abs__(...) + | x.__abs__() <==> abs(x) + | + | __add__(...) + | x.__add__(y) <==> x+y + | + | __and__(...) + | x.__and__(y) <==> x&y + | + | __contains__(...) + | x.__contains__(y) <==> y in x + | + | __div__(...) + | x.__div__(y) <==> x/y + | + | __divmod__(...) + | x.__divmod__(y) <==> divmod(x, y) + | + | __eq__(...) + | x.__eq__(y) <==> x==y + | + | __float__(...) + | x.__float__() <==> float(x) + | + | __floordiv__(...) + | x.__floordiv__(y) <==> x//y + | + | __ge__(...) + | x.__ge__(y) <==> x>=y + | + | __gt__(...) + | x.__gt__(y) <==> x>y + | + | __iadd__(...) + | x.__iadd__(y) <==> x+y + | + | __iand__(...) + | x.__iand__(y) <==> x&y + | + | __idiv__(...) + | x.__idiv__(y) <==> x/y + | + | __ifloordiv__(...) + | x.__ifloordiv__(y) <==> x//y + | + | __ilshift__(...) + | x.__ilshift__(y) <==> x< x%y + | + | __imul__(...) + | x.__imul__(y) <==> x*y + | + | __int__(...) + | x.__int__() <==> int(x) + | + | __invert__(...) + | x.__invert__() <==> ~x + | + | __ior__(...) + | x.__ior__(y) <==> x|y + | + | __ipow__(...) + | x.__ipow__(y) <==> x**y + | + | __irshift__(...) + | x.__irshift__(y) <==> x>>y + | + | __isub__(...) + | x.__isub__(y) <==> x-y + | + | __itruediv__(...) + | x.__itruediv__(y) <==> x/y + | + | __ixor__(...) + | x.__ixor__(y) <==> x^y + | + | __le__(...) + | x.__le__(y) <==> x<=y + | + | __lshift__(...) + | x.__lshift__(y) <==> x< x x%y + | + | __mul__(...) + | x.__mul__(y) <==> x*y + | + | __ne__(...) + | x.__ne__(y) <==> x!=y + | + | __neg__(...) + | x.__neg__() <==> -x + | + | __nonzero__(...) + | x.__nonzero__() <==> x != 0 + | + | __or__(...) + | x.__or__(y) <==> x|y + | + | __pow__(...) + | x.__pow__(y[, z]) <==> pow(x, y[, z]) + | + | __radd__(...) + | x.__radd__(y) <==> y+x + | + | __rand__(...) + | x.__rand__(y) <==> y&x + | + | __rdiv__(...) + | x.__rdiv__(y) <==> y/x + | + | __rdivmod__(...) + | x.__rdivmod__(y) <==> divmod(y, x) + | + | __rfloordiv__(...) + | x.__rfloordiv__(y) <==> y//x + | + | __rlshift__(...) + | x.__rlshift__(y) <==> y< y%x + | + | __rmul__(...) + | x.__rmul__(y) <==> y*x + | + | __ror__(...) + | x.__ror__(y) <==> y|x + | + | __rpow__(...) + | y.__rpow__(x[, z]) <==> pow(x, y[, z]) + | + | __rrshift__(...) + | x.__rrshift__(y) <==> y>>x + | + | __rshift__(...) + | x.__rshift__(y) <==> x>>y + | + | __rsub__(...) + | x.__rsub__(y) <==> y-x + | + | __rtruediv__(...) + | x.__rtruediv__(y) <==> y/x + | + | __rxor__(...) + | x.__rxor__(y) <==> y^x + | + | __sub__(...) + | x.__sub__(y) <==> x-y + | + | __truediv__(...) + | x.__truediv__(y) <==> x/y + | + | __xor__(...) + | x.__xor__(y) <==> x^y + | + | all(...) + | a.all(axis=None) + | + | any(...) + | a.any(axis=None, out=None) + | + | argmax(...) + | a.argmax(axis=None, out=None) + | + | argmin(...) + | a.argmin(axis=None, out=None) + | + | choose(...) + | a.choose(b0, b1, ..., bn, out=None, mode='raise') + | + | Return an array that merges the b_i arrays together using 'a' as + | the index The b_i arrays and 'a' must all be broadcastable to the + | same shape. The output at a particular position is the input + | array b_i at that position depending on the value of 'a' at that + | position. Therefore, 'a' must be an integer array with entries + | from 0 to n+1.; + | + | clip(...) + | a.clip(min=, max=, out=None) + | + | nonzero(...) + | a.nonzero() returns a tuple of arrays + | + | Returns a tuple of arrays, one for each dimension of a, + | containing the indices of the non-zero elements in that + | dimension. The corresponding non-zero values can be obtained + | with + | a[a.nonzero()]. + | + | To group the indices by element, rather than dimension, use + | transpose(a.nonzero()) + | instead. The result of this is always a 2d array, with a row for + | each non-zero element.; + +We think it is Turing complete! diff --git a/docs/sphx/index.rst b/docs/sphx/index.rst index 9349097c..87174736 100644 --- a/docs/sphx/index.rst +++ b/docs/sphx/index.rst @@ -3,35 +3,35 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. +==================================== Welcome to ImageD11's documentation! ==================================== -Beware: you might find some of this information is out of date or wrong. -Please fix anything you find that is wrong and send a pull request. -This documentation lives in the docs/sphx part of the sources. - -ImageD11 is for identifying individual grains in spotty area detector -diffraction images. -It finds the spots and helps you to try to index them. -After you index the spots you can then work on extracting useful information -on a grain by grain basis. -ImageD11 is intended to be complementary to the rest of the FABLE software. -ImageD11 has a strong focus on fast preliminary data analysis which can be -carried out during experiments in order to make decisions about the -experimental setup etc. - -The package includes facilities for peak searching in two dimensional -area detector images using a simple threshold. -A graphical interface is available to assist with the calibration -of the extracted peak positions and their transformation into -scattering vectors (or g-vectors) in reciprocal space. -In cases where "relatively few" grains are illuminated by the beam the -package is capable of indexing the diffraction spots and refining -unit cell parameters and orientation matrices. -ImageD11 is mainly written using the python language, with a few -extension modules in c for increased performance. +Beware: you might find some of this information is out of date or wrong. Please +fix anything you find that is wrong and send a pull request. This documentation +lives in the docs/sphx part of the sources. + +ImageD11 is for identifying individual grains in spotty area detector +diffraction images. It finds the spots and helps you to try to index them. After +you index the spots you can then work on extracting useful information on a +grain by grain basis. ImageD11 is intended to be complementary to the rest of +the FABLE software. ImageD11 has a strong focus on fast preliminary data +analysis which can be carried out during experiments in order to make decisions +about the experimental setup etc. + +The package includes facilities for peak searching in two dimensional area +detector images using a simple threshold. A graphical interface is available to +assist with the calibration of the extracted peak positions and their +transformation into scattering vectors (or g-vectors) in reciprocal space.In +cases where "relatively few" grains are illuminated by the beam the package is +capable of indexing the diffraction spots and refining unit cell parameters and +orientation matrices. ImageD11 is mainly written using the python language, with +a few extension modules in c for increased performance. +Here is a pdf file with a worked example for making a centre of mass grain map +:download:`com_guide.pdf<../com_guide.pdf>` + Contents: @@ -41,23 +41,30 @@ Contents: installation programs peaksearching - postprocessing filtering calibration indexing index_unknown refinement advanced + parallel fileformats batchprocessing + rsv_mapper + related + changelog -Here is a pdf file with a worked example for making a centre of -mass grain map :download:`com_guide.pdf<../com_guide.pdf>` Indices and tables ================== +All the code docstrings can be found via: + +.. toctree:: + :maxdepth: 1 + + api/modules + * :ref:`genindex` * :ref:`modindex` * :ref:`search` - diff --git a/docs/sphx/indexing.rst b/docs/sphx/indexing.rst index 38c6a9dd..6f1bd1df 100644 --- a/docs/sphx/indexing.rst +++ b/docs/sphx/indexing.rst @@ -1,59 +1,21 @@ Indexing ======== -Once your diffraction spots have been found and converted to g-vectors -(scattering vectors) you can attempt to find the orientations of the grains -in the sample. ImageD11 only includes rudimentary facilities for indexing -suitable for relatively small numbers of grains in the sample (probably <100, -although the limit depends on the data and unit cell). -Indexing can be carried out via the indexing menu on the graphical user -interface. Begin by reading in the g-vectors which were saved at the and of -the transformation procedure. If you have collected data for a single crystal -then a useful diagnostic is produced by the plot x/y/z menu option which -gives a 3 dimensional representation of the observed data. For a single -crystal these should form a nice regular lattice if everything has gone well. -For samples containing many grains you get something like a 3d representation -of the pole figure measured, but with no intensity information currently +Once your diffraction spots have been found and converted to g-vectors (scattering vectors) you can attempt to find the orientations of the grains in the sample. ImageD11 only includes rudimentary facilities for indexing suitable for relatively small numbers of grains in the sample (probably <100, although the limit depends on the data and unit cell). + +Indexing can be carried out via the indexing menu on the graphical user interface. Begin by reading in the g-vectors which were saved at the and of the transformation procedure. If you have collected data for a single crystal then a useful diagnostic is produced by the plot x/y/z menu option which gives a 3 dimensional representation of the observed data. For a single crystal these should form a nice regular lattice if everything has gone well. For samples containing many grains you get something like a 3d representation of the pole figure measured, but with no intensity information currently. (FIXME - colorize the plot). -Indexing then proceeds by assigning hkl values to two peaks and using these -indexed peaks together with the unit cell parameters to determine the -orientation of a grain. Trial orientations are scored according to how many -peaks they index. The algorithm needs some parameters to carry out this -procedure. The ds_tol parameter is used to determine which powder style -diffraction rings overlap and which peaks to assign hkl values according to -the length of the scattering vector. The units are normally 1/angstroms if -you input unit cell parameters and wavelength in angstroms. Peaks are -considered if they coincide with computed positions within a tolerance of -1/d. -Once the peaks are assigned to powder rings then the program outputs -information in the console window about the assignments. Orientations can -be generated using two rings selected via the indexing parameters. For each -peak (i) found in the first ring the (cosine of the) angle between peak (i) -and all of the peaks in the second ring in computed. The two peaks can be -in the same ring if it has a non-parallel lattice components (eg (100) and -(010) are OK for cubic unit cells, but not for triclinic). For each peak -the first ring the program identifies the best agreeing peak in the second -ring in terms of the (cosine of the) angle between them, compared to the -computed values from the unit cell parameters. A trial orientation is -generated if the cosine is within cosine_tol of the theoretical value. -Trial orientations can then be scored to see how many peaks they index, -within a tolerance of hkl_tol which is applied in terms of the computed hkl -indices from the positions the observed peaks. Also a threshold level of -minpks is used to ensure that a new orientation matrix indexes at least -minpks peaks which were previously unindexed. Usually problems with -indexing just need an increase of the hkl_tol value, perhaps combined with -a review of the parameters and data quality. -The generated orientation gives a matrix relating hkl indices to g-vectors -via the equation:: + +Indexing then proceeds by assigning hkl values to two peaks and using these indexed peaks together with the unit cell parameters to determine the orientation of a grain. Trial orientations are scored according to how many peaks they index. The algorithm needs some parameters to carry out this procedure. The ds_tol parameter is used to determine which powder style diffraction rings overlap and which peaks to assign hkl values according to the length of the scattering vector. The units are normally 1/angstroms if you input unit cell parameters and wavelength in angstroms. Peaks are considered if they coincide with computed positions within a tolerance of 1/d. + +Once the peaks are assigned to powder rings then the program outputs information in the console window about the assignments. Orientations can be generated using two rings selected via the indexing parameters. For each peak (i) found in the first ring the (cosine of the) angle between peak (i) and all of the peaks in the second ring in computed. The two peaks can be in the same ring if it has a non-parallel lattice components (eg (100) and (010) are OK for cubic unit cells, but not for triclinic). For each peak the first ring the program identifies the best agreeing peak in the second ring in terms of the (cosine of the) angle between them, compared to the computed values from the unit cell parameters. A trial orientation is generated if the cosine is within cosine_tol of the theoretical value. Trial orientations can then be scored to see how many peaks they index, within a tolerance of hkl_tol which is applied in terms of the computed hkl indices from the positions the observed peaks. Also a threshold level of minpks is used to ensure that a new orientation matrix indexes at least minpks peaks which were previously unindexed. Usually problems with indexing just need an increase of the hkl_tol value, perhaps combined with a review of the parameters and data quality. + +The generated orientation gives a matrix relating hkl indices to g-vectors via the equation:: h = UBI g g = UB h -where UBI is the matrix the program writes out and h and g are the hkl -indices and reciprocal vector respectively. UBI is the inverse of UB. -ImageD11 does NOT introduce a factor of 2pi into the g-vectors as many -programs do. Unit cell parameters can then be derived from -(UBI).transpose(UBI) giving the metric tensor. The program does this when -you write out indexed peaks +where UBI is the matrix the program writes out and h and g are the hkl indices and reciprocal vector respectively. UBI is the inverse of UB. ImageD11 does NOT introduce a factor of 2pi into the g-vectors as many programs do. Unit cell parameters can then be derived from (UBI).transpose(UBI) giving the metric tensor. The program does this when you write out indexed peaks. + By default the UBI matrices are refined according to the following algorithm:: From Paciorek et al Acta A55 543 (1999) @@ -64,11 +26,7 @@ By default the UBI matrices are refined according to the following algorithm:: r = g-vectors h = hkl indices -The values determined depend on the hkl_tol value used and correpondingly -the peaks used for refinement (FIXME future, introduce a weighting -depending on the goodness of fit of a particular peak). -(Dated) sample output from writing out indexing is shown below. Mostly it -should be self explanatory.:: +The values determined depend on the hkl_tol value used and correpondingly the peaks used for refinement (FIXME future, introduce a weighting depending on the goodness of fit of a particular peak). (Dated) sample output from writing out indexing is shown below. Mostly it should be self explanatory:: Grain: 0 Npeaks=18 =0.044535 UBI: @@ -89,13 +47,4 @@ should be self explanatory.:: Peaks assigned to rings but remaining unindexed 195 Peaks not assigned to rings at all 64 -Nowadays the unit cell parameters should be shown as well. The orientation -matrix can be determined from the (UBI) matrix by experts. Non experts -should just substitute (1,0,0), then (0,1,0) and finally (0,0,1) into the -equations for h and g above to find out where their diffraction planes are -pointing in the sample. -A recent feature added to the program is a plot of the number of peaks -found with gradually increasing hkl_tol value to use for validation and -setting of this parameter. A real grain should have a peak close to zero -tolerance, and then randomly appearing peaks should increase as something -roughly proportional to the cube of the tolerance for a texture free sample. +Nowadays the unit cell parameters should be shown as well. The orientation matrix can be determined from the (UBI) matrix by experts. Non experts should just substitute (1,0,0), then (0,1,0) and finally (0,0,1) into the equations for h and g above to find out where their diffraction planes are pointing in the sample. A recent feature added to the program is a plot of the number of peaks found with gradually increasing hkl_tol value to use for validation and setting of this parameter. A real grain should have a peak close to zero tolerance, and then randomly appearing peaks should increase as something roughly proportional to the cube of the tolerance for a texture free sample. diff --git a/docs/sphx/installation.rst b/docs/sphx/installation.rst index 763465fa..6116b546 100644 --- a/docs/sphx/installation.rst +++ b/docs/sphx/installation.rst @@ -1,179 +1,156 @@ -Getting ImageD11 -================ - -ImageD11 is a python module which depends on the prior installation -python itself and several other packages. It is usually better to -get these from a package manager or in a batteries-included python -installation. Perhaps Anaconda or Python(x,y) etc. - -General purpose packages that are required: - -- `Python itself `_ (2.5, 2.6 or 2.7) -- `numpy `_ for numerical arrays -- `matplotlib `_ for plotting -- `PIL `_, the python imaging library -- `PyOpenGl `_ (with the Togl widget) - -Specific packages from fable: - -- fabio (from fable, now silx) for imageio -- xfab (from fable) for some crystallographic calculations - -Recommended packages to pick up when you are installing a system: - -- scipy for many different scientific computations -- h5py for access to hdf files -- pyminuit optimiser (needed for fitallb) -- pmw python metawidgets for fabian gui -- pyFAI for radial integration - -For development you also need: - -- C compiler for your system -- opencl system -- sphinx docutils to build this documentation -- latex for the pdf manual - -Finally, you need to download ImageD11 itself. The file release area was historically at -`sourceforge `_. -Nowadays the sources are on `github `_ and the -package itself is on pypi. Install it using: - -.. - pip install ImageD11 - - - -Linux platforms where you have root access ------------------------------------------- - -Use the package manager to install pre-built versions of the main python -packages. Python itself usually comes with the system. -There are some hints at the `scipy site -`_ about how the packages are named for -different vendors. - -On debian based systems (also ubuntu etc): - -.. - sudo apt-get install python-numpy python-numpy-dev python-image python-scipy python-matplotlib python-opengl build-essential - -To install the bleeding edge version do an svn checkout: - -.. - svn co http://fable.svn.sourceforge.net/projects/ImageD11/trunk fabio - svn co http://fable.svn.sourceforge.net/projects/ImageD11/trunk xfab - svn co http://fable.svn.sourceforge.net/projects/ImageD11/trunk ImageD11 - -Then you can install system wide via: - -.. - cd fabio - python setup.py build install - cd ../xfab - python setup.py build install - cd ../ImageD11 - python setup.py build install - -Or in some local area by adding that location as --prefix - -.. - export PATH=$PATH:/somewhere/bin export PYTHONPATH=/somewhere/lib/python2.6/site-packages - python setup.py build install --prefix=/somewhere - -Historically the pyopengl builds are broken for the Togl widget. -Get it from Togl.sourceforge.net and do something like the following: - -.. - tar -zxf Togl2.0-8.4-Linux.tar.gz - cd Togl2.0-8.4-Linux/lib - cp -r Togl2.0 /usr/lib/pymodules/python2.6/OpenGL/Tk/linux2-tk8.5 - - - - - -Linux platforms where you don't have root access ------------------------------------------------- - -This is possible, but surprisingly difficult. The best solution is to go talk -to your system administrator and ask them to help. It is really much easier to -install via the system package manager. A few keystrokes and you have everything -installed for free. - -For linux there are some prebuilt versions with batteries included: - -- Enthought python distribution http://www.enthought.com/products/epd.php. -- Anaconda https://store.continuum.io/cshop/anaconda - -Otherwise build from source code. You download the source distribution for each -dependency and then compile it and install it. -Usually you will need to compile further dependencies for each of the -packages you are installing. It can be done, and has some value for -education and character building, but it is not worth the effort. -If you can succeed to do that you'll probably now agree you were better off -to talk to the sysadmin or buy the Enthought version. - -It should be possible to install using "easy_install" or "pip" via -the pypi package index. FIXME: add the command for doing that. - -Windows -------- - -The easiest solution is to install a "batteries included" distribution of -python. For example: - -- The fable gui http://sourceforge.net/projects/fable -- Enthought python distribution http://www.enthought.com/products/epd.php -- pythonxy www.pythonxy.com -- Anaconda https://store.continuum.io/cshop/anaconda - -The author usually installs the python binaries from www.python.org and then -add on the extension packages from each individual project if they are available. -Otherwise Christoph Gohlke maintains a -`handy collection `_ of -compiled modules, especially for 64bit windows systems, in case you -cannot find a build from the upstream project. - -To develop ImageD11 on windows 64 bit you need to get hold of the right -compiler. This was freely available from microsoft, but a little non-obvious. -You need to get VC++ 2008 (msvcrt9, the free 2008 express edition is OK) and -then add on the .net sdk 3.5 to get the 64bit compiler. See the notes on the -`cython wiki `_. - -Mac ---- - -ImageD11 has been installed by a number of mac owners but the author is not -sure how they did it. FIXME... - - +=================== +Installing ImageD11 +=================== + +:: + + pip install ImageD11 + +If you go for --user or systemwide the we suggest to skip the +dependencies and fix them one at a time later as needed (pip can +get into a fight with the system package manager or conda). + +:: + + pip install ImageD11 --no-deps -U --user + +If that did not work, then read on. + +ImageD11 is a python module which depends on the prior installation +python itself and several other packages. It is usually better to +get these from a package manager or in a batteries-included python +installation. Perhaps Anaconda or Python(x,y) etc. + +Several dependency packages that are required are listed in the setup.py +file of ImageD11 as `install_requires`. + +Specific packages from fable that ImageD11 needs are: + +- fabio (from fable, now silx) for imageio +- xfab (from fable) for some crystallographic calculations + +Recommended packages to pick up when you are installing a system: + +- numpy for arrays of numbers +- matplotlib for plotting +- scipy for many different scientific computations +- h5py for access to hdf files +- pyminuit optimiser (needed for fitallb) +- pmw python metawidgets for fabian gui +- pillow (PIL) imaging library +- pyFAI for radial integration +- pyopengl and pyopengltk for 3D plots +- six for the python2 vs python3 breakup + +For development you would also need: + +- C compiler for your system +- sphinx docutils to build this documentation +- latex if you want to make a pdf manual + +Finally, you need to download ImageD11 itself. The file release area was +historically at +`sourceforge `_. +Nowadays the sources are on `github `_ +and the package itself is on pypi. Install it in a virtualenv using: + + + +Getting python +-------------- + +Historically we had a lot of notes here about installing python. + +These are removed for now. It is a bigger problam that just for us. You can +either go with the package manager for your system, or the version from +python.org, or a batteries included system like miniconda or anaconda. Or you +have a mac and all bets are off. + +Whatever you do, please learn about virtualenv or conda env before you install +something. Python environments are relatively fragile and as soon as you install +one broken package the whole thing will be ruined. + +Python2 versus Python3 +---------------------- + +A wide range of python versions are routinely tested by the CI services, including +python2.7. From about python 3.5 and upwards we hope it will be working, but there +may be difficulties with the earliest python 3 iterations. + +Compiling from source +--------------------- + +There are some continuous integration scripts in the source which can give some +inspiration. See .circleci, .travis.yml and appveyor.yml + +The C wrapper codes are interfaced using f2py in numpy. +If you modify the C sources then the wrappers are re-built using a +script that adds some docstrings. Critical files are currently (May 2020) :: + + src/makepyf.py : builds interface declarations in + src/_cImageD11.pyf : interface description + src/_cImageD11module.c : interface made by f2py + ImageD11src/cImageD11.py : wrapper that adds docstrings + ImageD11src/cImageD11_docstrings.py + setup.py : drives the build + +To optimise for a specific machine then setup.py should look at your CFLAGS +environment variable for anything you want to add:: + + export CFLAGS=-march=native + pip install . + +Please test if you do this and remember not to copy it to another machine. + +Testing +------- + +It should be possible to test using pytest:: + + python -m pytest + +Otherwise in the test folder there is a run_tests.py script. + + +Windows +------- + +To develop ImageD11 on windows 64 bit you need to get hold of the right +compiler. This was freely available from microsoft, but a little non-obvious. +You need to get VC++ 2008 (msvcrt9, the free 2008 express edition is OK) and +then add on the .net sdk 3.5 to get the 64bit compiler. See the notes on the +`cython wiki `_. Nowadays +ths mostly just works as microsoft started releasing an old compiler that was +needed for 2.7. For newer python versions you need a newer compiler. + +Mac +--- + +ImageD11 has been installed by a number of mac owners but the author was never +sure how they did it. Recently a CI has been set up on Travis that tests For +macos as well, so hopefully this will improve in the future. The main missing +point is the 3D opengl plots which need pyopngltk to be ported to mac, or perhaps +easier to find some other package to do then job. Ubuntu ------ -.. +To install on Ubuntu :: + sudo apt-get install build-essential - sudo apt-get install gfortran - sudo apt-get install git subversion - sudo apt-get install python-numpy python-matplotlib python-pillow python-pmw - sudo apt-get install python-scipy - sudo apt-get install python-setuptools - sudo pip install fabio - sudo pip install wheel - wget https://bootstrap.pypa.io/get-pip.py - sudo python get-pip.py - - svn co https://svn.code.sf.net/p/fable/code/xfab/trunk xfab - cd xfab/ - python setup.py build bdist_wheel - sudo pip install dist/xfab-0.0.2-py2-none-any.whl - + sudo apt-get install git + sudo apt-get install python-numpy git clone http://github.com/jonwright/ImageD11 cd ImageD11/ python setup.py build bdist_wheel - sudo pip install dist/ImageD11-1.7.0-cp27-cp27mu-linux_x86_64.whl + pip install dist/ImageD11-1.7.0-cp27-cp27mu-linux_x86_64.whl cd ImageD11/ cd test/ python run_tests.py +Installing at ESRF +------------------ +Historically various ways. Currently (may 2020):: + + debian : module load fable + ubuntu : make your own conda or virtual environment for now diff --git a/docs/sphx/parallel.rst b/docs/sphx/parallel.rst new file mode 100644 index 00000000..7bed8057 --- /dev/null +++ b/docs/sphx/parallel.rst @@ -0,0 +1,129 @@ +================================ +Parallel code in ImageD11 +================================ + +The main reason to write this code was to have something which is as fast, +or faster, than the data collection at the beamline. Performance is +supposed to be a feature. If you cannot see what you are doing during +an experiment then you can waste a lot of beamtime. Over time this +is easier as the detectors get older. + +Now, in 2020, ID11 is about to get a new detector and so speed is a +bit of problem again. + +Compiled C codes +---------------- + +In the beginning there were hand written wrappers which compiled +against the Numeric python module. These were never ported +to numarray but following the great numpy reunification they +were ported to numpy. For a very long time we resisted +the move to python3 because it broke these wrappers. Accepting +that python3 is coming to end of life, we looked into a range +of options here : https://github.com/jonwright/cython_example + +Many of the experts suggested cython (and now numba). There +is also ctypes and cffi as plausible options. At the time we +had some Fortran code in place and this was being built by f2py. +Fortran has the huge advantage of being a standardised language. +The meaning of the code does not change when someone decides to +deprecate a feature you are using. For long term maintenance +this is quite appealing. Sadly no one has Fortran comilers and +so it is painful to install. Also there are no unsigned or +small integer types. + +The C code is nominally portable/ansi conforming and can run without +openmp if needed. There are a few nasty things which rely on ieee +and an optimiser which is not too agressive. These should have +testcases. + +Looking to the future: it would be handy to find a nice parallel +language which is easier than opencl. The main drawback +of the current system is that you need to compile and optimise +the C code for the machine you are running on. If you run on a +cluster where each CPU is different then you miss having a JIT. + + +Performance measurements +------------------------ + +To check the C code is actually performing OK we plan try to set something +up using "airspeed velocity" : https://github.com/airspeed-velocity/asv + +Over the years there have been a few different cases of introducing parallel +programming into ImageD11. They have all introduced regrettable bugs or +other problems to distribute the code. It should be worth the cost. + +To avoid exploding the git repository, this is planned for a seperate github +project http://github.com/jonwright/ImageD11_benchmarks . A bunch +of things that are currently in testcases (like examples and so on) +will probably go there. Eventually. + +OpenMP Threading +---------------- + +Many of the compiled C codes have openmp loops inside. In the vast +majority of cases this is a simple for-loop decorator that should +not be too hard to debug. The main problem is when you are calling +several C codes at the same time from the main python process. +Or if you have a lot of python processes running at the same +time, then you overload the CPU's. To avoid that you can: + +- set OMP_NUM_THREADS to the number per process +- set dynamically via: cImageD11.cimaged11_omp_set_num_threads + +Python Threading + Queue modules +-------------------------------- + +Used in ImageD11.peaksearcher. There is a thread for each of the following +tasks: +- reading frames (blocking IO) +- correcting frames for dark / flat +- one thread per threshold of a multi-threshold peaksearch + +Within each of these tasks there may be underlying openmp threads. + +Python Multiprocessing module +----------------------------- + +Used in grid_index_parallel.py and also refine_em.py. Convenient +but remember to set OMP_NUM_THREADS to something sensible you +you get access to a big CPU. Problems show up as Ncpu^2 if +openmp and multiprocessing co-exist. + +SIMD parallelism +---------------- + +Many CPU's have an instruction level parallelism that can +give large speedups and also do what you would want. See, e.g.: +https://software.intel.com/sites/landingpage/IntrinsicsGuide/ + +Take some examples from avx2:: + + __m256i _mm256_subs_epu16 (__m256i a, __m256i b) + Subtract packed unsigned 16-bit integers in b from packed unsigned + 16-bit integers in a using saturation, and store the results in dst. + +This is what you might want to subtract a dark image +and it does 16 pixels in a single instruction. Notably it +sounds like it deals with the overflow problems correctly instead of +wrapping. Another example, if you want to normalise a vector you have:: + + __m256 _mm256_rsqrt_ps (__m256 a) + Compute the approximate reciprocal square root of packed + single-precision (32-bit) floating-point elements in a, and store + the results in dst. The maximum relative error for this + approximation is less than 1.5*2^-12. + +This one is harder to justify. It depends on how often you need to +repeat the computation as to whether you need it. + +For various reasons of portability, these are NOT currently used +in ImageD11 source code. If there were to be a nice portable library +that gave access to these across different CPUs with runtime +instruction selection then it could have a big impact. + +The current hope is that the C compilers will use these things +if they are appropriate. Then we re-compile to code for each +machine and put it in a different conda or virtualenv. This +is controlled via CFLAGS when compiling. \ No newline at end of file diff --git a/docs/sphx/peaksearching.rst b/docs/sphx/peaksearching.rst index be7f3b96..2aa33adc 100644 --- a/docs/sphx/peaksearching.rst +++ b/docs/sphx/peaksearching.rst @@ -2,9 +2,7 @@ Peaksearching ============= -Peaksearching in two dimensional images can be carried out in a relatively automated -way using the peaksearch.py script. There is a command line interface and the -underlying code can also be used via the fable gui. +Peaksearching in two dimensional images can be carried out in a relatively automated way using the peaksearch.py script. There is a command line interface and the underlying code can also be used via the fable gui. Giving the command "peaksearch.ph -h" should print the following help message:: @@ -40,45 +38,19 @@ Giving the command "peaksearch.ph -h" should print the following help message:: Threshold level, you can have several -Usually data images are collected as a numerical series while the sample rotates -and are named using the convention stem0000.edf, stem0001.edf etc. The first -three options "-n", "-f" and "-l" specify the series of images in terms of -stem name start and end number. -Currently the script will automatically assume ".edf" or ".edf.gz" extensions -and corresponding format. If you want for example ".tif.gz" say "-F .tif.gz". -It should accept the following formats: - -`` ".edf",".edf.gz",".edf.bz2", +Usually data images are collected as a numerical series while the sample rotates and are named using the convention stem0000.edf, stem0001.edf etc. The first three options "-n", "-f" and "-l" specify the series of images in terms of stem name start and end number. Currently the script will automatically assume ".edf" or ".edf.gz" extensions and corresponding format. If you want for example ".tif.gz" say "-F .tif.gz". +It should accept the following formats:: + + ".edf",".edf.gz",".edf.bz2", ".tif",".tif.gz",".tif.bz2", ".mccd",".mccd.gz",".mccd.bz2" - `` -Others are present in spirit, but not perhaps via a user interface. -The "-d" option is optional, for a dark current image that can be subtracted. -If you use the Frelon2K CCD camera at ESRF the please allow for the offset of -1000 ADU if you do not decide to subtract a dark current. The "-D" option -alters the offset to try to avoid overflow when subtraction gives a negative -number (it is though to be marginally faster to remain with unsigned short -data than going over to int) -The "-s" option specifies a fit2d spline file characterising the spatial -distortion of the camera. The default is probably OK for recent data from ID11, -but more generally you will want to change this option. Be sure to use the pixel -size data from this file when computing the detector calibration parameters. -In the case where no spatial corrections are needed then please be sure to -specify the "-p Y" option to indicate there is no need for spatial distortion -correction (eg a nice image plate). Corrections are applied to peaks positions, -NOT to the raw images. -Threshold levels are used to determine which pixels contribute to diffraction -spots. A segmentation of the image will be made where all pixels above the -threshold are the collected into connected objects. Try to choose levels which -are significantly above the background level. You may use more than one level -if you like, eg "-t 1050 -t 5000" for levels 1050 ADU and 5000 ADU. -The output file contains all of the information contained in the header as -well as the obtained peak positions. You can specify a more logical name for -this file if you do not like "peaks.out". +Others are present in spirit, but not perhaps via a user interface. The "-d" option is optional, for a dark current image that can be subtracted. If you use the Frelon2K CCD camera at ESRF the please allow for the offset of 1000 ADU if you do not decide to subtract a dark current. The "-D" option alters the offset to try to avoid overflow when subtraction gives a negative number (it is though to be marginally faster to remain with unsigned short data than going over to int). +The "-s" option specifies a fit2d spline file characterising the spatial distortion of the camera. The default is probably OK for recent data from ID11, but more generally you will want to change this option. Be sure to use the pixel size data from this file when computing the detector calibration parameters. In the case where no spatial corrections are needed then please be sure to specify the "-p Y" option to indicate there is no need for spatial distortion correction (eg a nice image plate). Corrections are applied to peaks positions, NOT to the raw images. +Threshold levels are used to determine which pixels contribute to diffraction spots. A segmentation of the image will be made where all pixels above the threshold are the collected into connected objects. Try to choose levels which are significantly above the background level. You may use more than one level if you like, eg "-t 1050 -t 5000" for levels 1050 ADU and 5000 ADU. The output file contains all of the information contained in the header as well as the obtained peak positions. You can specify a more logical name for this file if you do not like "peaks.out". Some examples:: -``peaksearch.py -n plat_a -f 50 -l 90 -o peaks_50_90.out -s spatial.spline -t 5000 `` + peaksearch.py -n plat_a -f 50 -l 90 -o peaks_50_90.out -s spatial.spline -t 5000 This will peaksearch images:: @@ -87,9 +59,7 @@ This will peaksearch images:: ... plat_a0090.edf -A spatial distortion computed from the file spatial.spline (in the current directory) -will be applied. A threshold level of 5000 is used to decide which pixels -contribute to peaks. +A spatial distortion computed from the file spatial.spline (in the current directory) will be applied. A threshold level of 5000 is used to decide which pixels contribute to peaks. Example output:: # File plat_a0050.edf @@ -117,97 +87,112 @@ The numbers in the output file have the following meanings: **Number_of_pixels** is the number of pixels in the connected object. -**Average_counts** is the average counts in each pixel in that object. -If the camera has a constant offset (in this case about 1000) then you -need to subtract that number before computing intensities. In this case -intensity is approximately number_of_pixels*(Average_counts-1000.0) - -**x** and **y** are the position of the centre of mass of the peak in the -uncorrected image (first moments of the intensity distributions in the blobs -is the mean) -**xc** and **yc** are the x and y positions corrected for spatial distortion. - -**sig_x** and **sig_y** are the second moments of the intensity distribution -in the blob. Something like the width, but off by some factor of twopi perhaps -(FIXME) - -**cov_xy** is the covariance (the third one of the second moments). It ranges -between -1 and 1 with a value of 0 for a circular peak and +1 and -1 refering -to elliptical shapes rotated by 90 degrees from each other. (FIXME a picture -would help) -The definition of x and y are in terms of the fast and slow array indices as -the image comes into memory, so it depends on how the image was stored in the -file and the routine which read it in as to what you might finally get. This -should later be better defined by the use of a better image file format, like -ImageCIF, which defines such things. -For most experiments at ID11 the position in the rotation scan should be -specified by a value "Omega" in the image headers. If this is not the case -there is a script called recoveromega.py which will read a text file containing -lines with omega values followed by filenames to recover the appropriate -information. +**Average_counts** is the average counts in each pixel in that object. If the camera has a constant offset (in this case about 1000) then you need to subtract that number before computing intensities. In this case intensity is approximately number_of_pixels*(Average_counts-1000.0) + +**x** and **y** are the position of the centre of mass of the peak in the uncorrected image (first moments of the intensity distributions in the blobs is the mean). **xc** and **yc** are the x and y positions corrected for spatial distortion. + +**sig_x** and **sig_y** are the second moments of the intensity distribution in the blob. Something like the width, but off by some factor of twopi perhaps (FIXME). + +**cov_xy** is the covariance (the third one of the second moments). It ranges between -1 and 1 with a value of 0 for a circular peak and +1 and -1 refering to elliptical shapes rotated by 90 degrees from each other (FIXME a picture would help). The definition of x and y are in terms of the fast and slow array indices as the image comes into memory, so it depends on how the image was stored in the file and the routine which read it in as to what you might finally get. This should later be better defined by the use of a better image file format, like ImageCIF, which defines such things. For most experiments at ID11 the position in the rotation scan should be specified by a value "Omega" in the image headers. If this is not the case there is a script called recoveromega.py which will read a text file containing lines with omega values followed by filenames to recover the appropriate information. How does it work? ================= For now, we are talking about the most up to date SVN version (0.8.1), a new release -will be made soon. -The algorithm used is based on a disjoint set, which is described in "Introduction -to Algorithms" by Cormen, Leiserson., Rivest and Stein. An image is scanned row -by row and each pixel is compared to a threshold value. If the pixel is above -the threshold, it will be labelled as a peak. To determine the labels the pixel -is compared to the previous pixel, and the pixels on the previous row. If one -of these pixels is already labelled, the current pixel takes the same label. -When there is a disagreement about labelling, the two labels are made the same -using the "disjoint set" - -oooooooooooo o = pixel below threshold -oo1ooooo2ooo 1,2 = labels -o111ooo222oo X pixel where label 1 and label 2 must be made equivalent -oo1111X -Within the sourcecode the routine "connectedpixels" is a compiled extension in -C which takes a data image and computes an image of peak assignment labels. -It optionally applies the dark and flood corrections when thresholding. -The connectivity in the 2D image is therefore:: +will be made soon. The algorithm used is based on a disjoint set, which is described in "Introduction to Algorithms" by Cormen, Leiserson., Rivest and Stein. An image is scanned row by row and each pixel is compared to a threshold value. If the pixel is above the threshold, it will be labelled as a peak. To determine the labels the pixel is compared to the previous pixel, and the pixels on the previous row. If one of these pixels is already labelled, the current pixel takes the same label. When there is a disagreement about labelling, the two labels are made the same using the "disjoint set". + +| oooooooooooo o = pixel below threshold +| oo1ooooo2ooo 1,2 = labels +| o111ooo222oo X pixel where label 1 and label 2 must be made equivalent +| oo1111X + +Within the sourcecode the routine "connectedpixels" is a compiled extension in C which takes a data image and computes an image of peak assignment labels. It optionally applies the dark and flood corrections when thresholding. The connectivity in the 2D image is therefore:: 0 0 0 0 X 0 0 0 0 -To compute the properties of each connected object the routine "blobproperties" -is then called with the data image and label image (this could be more efficient). -It scans through the image forming properties on a pixel by pixel basis. -In order to merge peaks on adjacent frames, the label images are compared. -When exactly overlapping pixels are both labelled, then these labels should -be made equivalent. The code which does it is called "bloboverlaps". +To compute the properties of each connected object the routine "blobproperties" is then called with the data image and label image (this could be more efficient). It scans through the image forming properties on a pixel by pixel basis. In order to merge peaks on adjacent frames, the label images are compared. When exactly overlapping pixels are both labelled, then these labels should be made equivalent. The code which does it is called "bloboverlaps". Files which are produced +======================== + +| Historically: +| +| name [default: peaks.out] : 2D peaks, header info +| name_merge_t500 [default: peaks.out_merge_t500] : 3D peaks for threshold at 500 +| +| In the Brave New World (tm): +| +| name.spt [add ".spt" to users request] : 2D peaks, header info +| foreach threshold: +| +| name_t500.flt [add "_t500.flt" to users request] : 3D peaks for threshold at 500 +| "users request" to default to stem_first_last in place of peaks.out. +| ouch - forgot the first and last ... c'est la vie + +Using peaksearch for programmers +-------------------------------- + +Well, it used to be simpler than it is now. For the full pleasures of 3D peaksearching in a program designed for fast 2D, one should read the ImageD11.labelimage code. The quick way to get an idea what thresholding will do is this:: + + data = fabio.openimage("my_lovely_data.edf") + blobs = numpy.zeros(data.shape, numpy.int) + threshold = 1500.0 + npks = connectedpixels.connectedpixels(data, blobs, threshold, verbose=0) + +On exit from connectedpixels the npks is the number of peaks found and blobs is an array of integer peak assignments (the argument is supposed to be modified during the function call). You can then have a look at blobs to see where the peaks are. In practice the peaksearch code will be doing a dark +and flood correction too, which can add to the confusion. + +For the 3D version it could be something like this:: -Historically: + with h5py.File('mydata.h5','r') as h: + frames = h['/entry/scan/detector/data'][()] + omegas = h['/entry/scan/omega'][()] + nframes, rows, cols = frames.shape + lio = labelimage.labelimage( (rows, cols), peaksfile ) + for i in np.argsort(omegas): + lio.peaksearch(frames[i], threshold, omegas[i]) + lio.mergelast() + lio.finalise() -name [default: peaks.out] : 2D peaks, header info -name_merge_t500 [default: peaks.out_merge_t500] : 3D peaks for threshold at 500 -In the Brave New World (tm): -name.spt [add ".spt" to users request] : 2D peaks, header info -foreach threshold:: - name_t500.flt [add "_t500.flt" to users request] : 3D peaks for threshold at 500 - ... "users request" to default to stem_first_last in place of peaks.out. - ... ouch - forgot the first and last ... c'est la vie + +Various dataset input formats - python class input format +--------------------------------------------------------- + +Image input was handled by fabio, the code attempts to build a fabio file series from the command line. +At some point in 2018 the ESRF control system upgraded to using hdf5 files and the peaksearch.py code +has never quite adapted. A workaround is to have a python class defining the data for peaksearching, +see sandbox/hdfscan.py in the source code. + +Example cases of input image data include: + +#. edf files with omega angles in headers. The image number order is the order to peasearch:: + data0000.edf ... Omega=0 + data0001.edf ... Omega=0.5 + ... + data0360.edf ... Omega=180. + +#. edf files with no angles in headers (ftomo). The image number order may be wrong due to interlacing (--interlaced and --iflip options). You have to supply the omega angles in command line arguments. -Using peaksearch for programmers +#. bliss files (2018). Interlaced frames in two distant folders:: + Omega = scanfolder/interlaced_1_1/data.h5::/measurement/rot_master/mean_pos:rot_mean + scanfolder/interlaced_1_1/Frelon/interlaced_1_1_Frelon0000.edf + scanfolder/interlaced_1_1/Frelon/interlaced_1_1_Frelon0359.edf + Omega = scanfolder/interlaced_1_2/data.h5::/measurement/rot_master/mean_pos:rot_mean + scanfolder/interlaced_1_2/Frelon/interlaced_1_2_Frelon0000.edf + scanfolder/interlaced_1_2/Frelon/interlaced_1_2_Frelon0359.edf + +#. hdf5 files (2020). Frames in order. + +#. hdf5 files (2020). Interlaced rewind. + +#. hdf5 files (2020). Interlaced zigzag. + +#. hdf5 files (2020). Interlaced forwards (e.g. mod 360). + + + -Well, it used to be simpler than it is now. For the full pleasures of 3D -peaksearching in a program designed for fast 2D, one should read the -ImageD11.labelimage code. The quick way to get an idea what thresholding -will do is this: - -data = fabio.openimage("my_lovely_data.edf") -blobs = numpy.zeros(data.shape, numpy.int) -threshold = 1500.0 -npks = connectedpixels.connectedpixels(data, blobs, threshold, verbose=0) -On exit from connectedpixels the npks is the number of peaks found and blobs -is an array of integer peak assignments (the argument is supposed to be -modified during the function call). You can then have a look at blobs to -see where the peaks are. In practice the peaksearch code will be doing a dark -and flood correction too, which can add to the confusion. diff --git a/docs/sphx/postprocessing.rst b/docs/sphx/postprocessing.rst deleted file mode 100644 index 3b8121f3..00000000 --- a/docs/sphx/postprocessing.rst +++ /dev/null @@ -1,84 +0,0 @@ -Post-processing -=============== - -The 3D merge is now done during the peaksearch. This section is -obsolete -After peaksearching all of your diffraction images you will have -reduced your data -analysis problem from a multi-gigabyte scale to something which is -hopefully less only a -few megabytes. The output from the peaksearch.py script should -contain all the -information you could possibly need, but currently ImageD11 only -provides tools to use a -small part of that information. Specifically it processes peak -positions in terms of -xc,yc and Omega (with the possible addition of wedge, manually, -later). - -Currently this processing can only be carried out in the gui, but -it is a very high priority to make this possible via a command line -script (FIXME!!!). Start up the graphical interface via the script -"ImageD11_gui.py", either by typing that command at a unix terminal -or double clicking it under windows (windows users are likely to -find it installed somewhere like -c:\python24\scripts\ImageD11_gui.py). -Eventually a message giving a list of things to do should appear on -the screen. You are warmly invited to work on any of those tasks! -Click the "OK" button to get rid of it and the graphical interface -should appear after a few seconds. If it is still not there after a -few minutes then either you have a very slow computer or you have -some installation or configuration problems. Try to start the -program again from a command line, READ the error message and act -accordingly. -This page describes the use of the "PeakSearching" menu. The first -item (search raw images) does not do anything, see -ImageD11:peaksearching for carrying out a peaksearch, or -alternatively implement a graphical interface for carrying out that -task. The second item (read .pks file) is the item you want to -begin with. It will read the output of a peaksearch run and then -display a plot of image number versus omega. This plot allows you -to interpret whether or not a single continuous rotation was -carried out and to select (via a mouse zoom) the range of images -you wish to use. If your plot is garbage or you get an error -message then probably you do not have good Omega values in your -peaksearch output. Use the script recoveromega.py to fix that. -Having selected the range of images you then need to ask the -program to Harvest peaks, which means to go and read in all of the -peaks from the output of the peaksearch for the range of images you -want to use. It will tell you how many peaks it found. -Once those peaks are read from the file they probably should be -merged via the merge peaks menu option. This command tells the -program to compute weighted averages of peaks on adjacent frames -and is a poor mans version of a 3 dimensional peaksearch. Whether -or not peaks are merged currently depends on the lines in file -guipeaksearch.py:: - - class peak: - def __init__(self,line,omega,threshold,num,tolerance=2): - self.TOLERANCE = tolerance # Pixel separation for combining peaks - ... -FIXME Clearly this should be configurable by the average user from the -interface and the whole processing business should not contain any -reference to Tk! -The next menu option is filter peaks which currently does not really do -anything useful except to plot the peak positions on the screen. This will -give you an idea how many spots you have observed, and perhaps indicate -potential problems (eg: peak positions due to the direct beam). -You are now ready to save good peaks into an output file. This output file -contains all the information from the peaksearch merged together for peaks -on adjacent images.:: - - # xc yc omega npixels avg_intensity x_raw y_raw sigx sigy covxy - 1105.310387 1934.819179 -38.750000 102.000000 2022.323529 1119.384182 1953.811254 1.815357 3.546957 -0.082630 - 372.770569 1469.674753 -38.750000 21.000000 1326.047619 377.557906 1468.206162 1 .761871 1.075446 -0.469539 - - You can filter this file as you like. For example, using the awk program -(standard for most unix systems, also available for windows [find a link]). -For example, to remove peaks containing less the 10 pixels, try to following: - - awk '($4>10){print}' < originalpeaks.flt > filteredpeaks.flt -(You might have to adjust the first line of the file to be the same as -before) -The filtered peaks are now ready to me read into the transform menu, see -ImageD11:Calibration. \ No newline at end of file diff --git a/docs/sphx/programs.rst b/docs/sphx/programs.rst index 36a7df9f..a392e12e 100644 --- a/docs/sphx/programs.rst +++ b/docs/sphx/programs.rst @@ -2,82 +2,81 @@ Program List ============ -This is a list of the different things which can be run from -a command line. +This is a list of the different things which can be run from a command line. Image Related ============= -peaksearch.py: Peaksearches the images, see the detailed help +peaksearch.py + Peaksearches the images, see the detailed help. -recoveromega.py Attempts to repair the problem of having no -header information about sample orientation. Largely superceeded -by command line options to peaksearch.py nowadays. +recoveromega.py + Attempts to repair the problem of having no header information about sample orientation. Largely superceeded by command line options to peaksearch.py nowadays. -fit2dcake.py Drives the fit2d program to do radial integration. -See the detailed help and also the official fit2d site +fit2dcake.py + Drives the fit2d program to do radial integration. See the detailed help and also the official fit2d site. -edfheader.py Reads the header information from ID11/ESRF edf files +edfheader.py + Reads the header information from ID11/ESRF edf files. -id11_summarize.py Runs through a set of images trying to figure -out what happened during an experiment. +id11_summarize.py + Runs through a set of images trying to figure out what happened during an experiment. -bgmaker.py Runs through a series of images finding the minimum. +bgmaker.py + Runs through a series of images finding the minimum. -edf2bruker.py Converts ID11/ESRF edf files into bruker format -for processing with Bruker tools (eg smart, saint etc) +edf2bruker.py + Converts ID11/ESRF edf files into bruker format for processing with Bruker tools (eg smart, saint etc). -powderimagetopeaks.py Draws a grid onto a powder image to cut -the rings up into spots. The resulting image can then be -peaksearched and used for calibration +powderimagetopeaks.py + Draws a grid onto a powder image to cut the rings up into spots. The resulting image can then be peaksearched and used for calibration. Graphical applications ======================= -ImageD11_gui.py Your one stop solution for peak transformation, -calibration and indexing +ImageD11_gui.py + Your one stop solution for peak transformation, calibration and indexing. -plotedf.py OpenGl based image display. Should be obsoleted. -See also imageviewer and fabian. +plotedf.py + OpenGl based image display. Should be obsoleted. See also imageviewer and fabian. -rubber.py Tk based Image display. Should be obsoleted. -See also imageviewer and fabian. +rubber.py + Tk based Image display. Should be obsoleted. See also imageviewer and fabian. Grain refinement ================ -fitgrain.py : Fits a grain (eg ubi) and diffractometer parameters +fitgrain.py + Fits a grain (eg ubi) and diffractometer parameters. -filtergrain.py : Selects peaks which belong to a grain for fitgrain +filtergrain.py + Selects peaks which belong to a grain for fitgrain. -filterout.py : Tries to remove peaks belonging to a grain from a -filtered peaks file so you can then index the rest +filterout.py + Tries to remove peaks belonging to a grain from a filtered peaks file so you can then index the rest. -makemap.py : Refines the position and orientation of grains with -diffractometer pars fixed to make a 3D centre of mass map +makemap.py + Refines the position and orientation of grains with diffractometer pars fixed to make a 3D centre of mass map. -plotgrainhist.py : Shows how well a set of grains fit a set of -peaks in an flt file, via a list of parameters. +plotgrainhist.py + Shows how well a set of grains fit a set of peaks in an flt file, via a list of parameters. -ubi2cellpars.py : Computes the unit cell parameters for the -UBI matrices in a ubi file +ubi2cellpars.py + Computes the unit cell parameters for the UBI matrices in a ubi file -pars_2_sweeper.py : Converts the ImageD11 grain refinement -parameters into something closert to format which is needed by -Soren Schmidt's grainsweeper program. +pars_2_sweeper.py + Converts the ImageD11 grain refinement parameters into something closert to format which is needed by Soren Schmidt's grainsweeper program. Miscellaneaous ============== -index_unknown.py : A new indexing program aimed at small numbers -of unknown single crystals. +index_unknown.py + A new indexing program aimed at small numbers of unknown single crystals. -ImageD11_2_shelx.py : Rather untested, unlikely to work yet. -Gives integrated intensities for peaks in shelx format. -You are better off with fabric. - -ImageD11Server.py : Obsolete - an xmlrpc server for the Java -Gui's which now use jepp instead. +ImageD11_2_shelx.py + Rather untested, unlikely to work yet. Gives integrated intensities for peaks in shelx format. You are better off with fabric. +ImageD11Server.py + Obsolete - an xmlrpc server for the Java Gui's which now use jepp instead. The Java/Eclipse/RCP based graphical interfaces to ImageD11: @@ -91,6 +90,3 @@ probably still want the command line versions. Beware that the was an early version of the Java based gui which was obsoleted after a change from xmlrpc based communication to using JNI hosted python interpreter. - - - diff --git a/docs/sphx/refinement.rst b/docs/sphx/refinement.rst index e75ee6f3..034f6b11 100644 --- a/docs/sphx/refinement.rst +++ b/docs/sphx/refinement.rst @@ -1,24 +1,18 @@ Refinement ========== -Assuming you have managed to find some trial orientation matrices the quality -of the derived parameters will be quite poor without post refinement of the -experimental setup. For each grain the translation of the grain from the -rotation axis perturbs the position of the diffraction spots. Also the -precise orientation of the rotation axis affects the final peak positions. -How to do this? +Assuming you have managed to find some trial orientation matrices the quality of the derived parameters will be quite poor without post refinement of the experimental setup. For each grain the translation of the grain from the rotation axis perturbs the position of the diffraction spots. Also the precise orientation of the rotation axis affects the final peak positions. How to do this? Input data +========== -Filtered diffraction peak positions (output from peaksearch menu) for one or -more scans. -UBI matrices for grains to be treated. -Starting geometry parameters (as used in transformation menu). +| Filtered diffraction peak positions (output from peaksearch menu) for one or more scans. +| UBI matrices for grains to be treated. +| Starting geometry parameters (as used in transformation menu). Output results +============== -Refined UBI matrix and displacement (x,y,z) for at least one grain in each -rotation. -"Observed" and calculated values of indexed reflections - +| Refined UBI matrix and displacement (x,y,z) for at least one grain in each rotation. +| "Observed" and calculated values of indexed reflections -FIXME: This should write about fitgrain/filtergrain/refine_em/avg_par \ No newline at end of file +FIXME: This should write about fitgrain/filtergrain/refine_em/avg_par diff --git a/docs/sphx/related.rst b/docs/sphx/related.rst new file mode 100644 index 00000000..70c3ef97 --- /dev/null +++ b/docs/sphx/related.rst @@ -0,0 +1,149 @@ +============================================== +Links to related open source software packages +============================================== + +Here are a list of links to other useful sites or documents +which might be interesting if you are looking at ImageD11. No endorsement is +implied. Please send a pull request to add or remove links here. + +Synchrotron + Multigrain +------------------------ + +https://github.com/FABLE-3DXRD/ (several sub-projects here) + +https://sourceforge.net/projects/fable/ + +http://multigrain.texture.rocks/doku.php + +https://sourceforge.net/projects/dct/ + +https://github.com/hexrd + +https://github.com/praxes/hexrd + +https://github.com/marinerhemant/MIDAS + +https://github.com/MechanicsNext/MechanicsNext + +https://github.com/MechanicsNext/MechanicsNext/wiki/MIDAS-Analysis-Workflow + +https://github.com/HeLiuCMU/HEXOMAP + +https://github.com/FrankieLi/IceNine + +https://github.com/HeLiuCMU/nfHEDMtools + +https://github.com/beamteamco/hedm-tools + +https://github.com/haixingfang/LabDCT-forward-simu-model + +https://github.com/pyxem/pyxem + +https://github.com/junspark/matlab_tools + +https://github.com/junspark/python_tools + +https://github.com/rachelelim/diffraction_simulator + +https://www.aps.anl.gov/files/APS-Uploads/SECTORS33-34/coordinates-PE-system.pdf + +Modelling / Microstructures +---------------------------- + +https://github.com/BlueQuartzSoftware/DREAM3D + +https://github.com/heprom/pymicro + +https://github.com/rquey/neper + +https://github.com/mrgprasad/kanapy + +https://github.com/tdegeus/GooseFFT/ + +https://materialsdatafacility.org/ + +Pole figures / orientations +--------------------------- + +https://mtex-toolbox.github.io/ + +https://github.com/siddharth-maddali/snsampler + +https://github.com/siddharth-maddali/SphereMesh + +https://github.com/youngung/upf + +General / physics / Crystallography +----------------------------------- + +https://github.com/mantidproject/mantid + +https://github.com/tschoonj/xraylib + +https://github.com/silx-kit/silx + +https://github.com/pmla/auguste + +https://github.com/hua2001/PyStrucTrans + +https://github.com/KedoKudo/cyxtal + +http://ncorr.com/ + +https://github.com/yayahjb/bgaol + +https://github.com/slaclab/psgeom + +https://github.com/stefsmeets/xcore + +https://github.com/McStasMcXtrace + +IO/File formats +--------------- + +https://github.com/silx-kit/fabio + +https://github.com/silx-kit/hdf5plugin + +https://github.com/nexusformat + +https://github.com/data-exchange/dxchange + +https://github.com/kiyo-masui/bitshuffle + +https://doi.org/10.1107/S2052252520008672 (IUCrJ Gold Standard paper) + +https://github.com/yayahjb/cbflib + +Powders and Rietveld +-------------------- + +https://github.com/silx-kit/pyFAI + +https://subversion.xray.aps.anl.gov/trac/pyGSAS + +https://github.com/tproffen/DiffuseCode + +Electrons/ EBSD +--------------- + +https://github.com/BYU-MicrostructureOfMaterials/OpenXY + +https://github.com/EMsoft-org/EMsoft + +https://github.com/pyxem + +https://github.com/stefsmeets/edtools + +Tomo +---- + +https://github.com/astra-toolbox/astra-toolbox + +https://github.com/tomopy/tomopy + +https://gitlab.esrf.fr/tomotools + +https://github.com/jcesardasilva/toupy + +https://github.com/iternlm/microtomodenoise diff --git a/docs/sphx/rsv_mapper.rst b/docs/sphx/rsv_mapper.rst index 4a2eeac3..9049ffa8 100644 --- a/docs/sphx/rsv_mapper.rst +++ b/docs/sphx/rsv_mapper.rst @@ -1,6 +1,5 @@ - ===================================================== - rsv_mapper.py +rsv_mapper.py ===================================================== :Info: :Author: Jon Wright @@ -10,18 +9,10 @@ Introduction ============ -We wish to convert a series of diffraction images into -a 3D reciprocal space volume. The rsv_mapper.py script -which is in ImageD11 accomplishes this task. - -The program reads the geometry and computes k vectors for -each pixel in the image and stores these in a cache. -Then for each image, a rotation matrix is constructed representing -the rotation of the crystal axes compared to the laboratory axes. -This matrix includes the current rotation of the crystal due to -the diffractometer and also the orientation matrix. -By applying the matrix, we can get to hkl values, or reciprocal -space co-ordinates, to use directly as array indices. + +We wish to convert a series of diffraction images into a 3D reciprocal space volume. The rsv_mapper.py script which is in ImageD11 accomplishes this task. + +The program reads the geometry and computes k vectors for each pixel in the image and stores these in a cache. Then for each image, a rotation matrix is constructed representingthe rotation of the crystal axes compared to the laboratory axes. This matrix includes the current rotation of the crystal due to the diffractometer and also the orientation matrix. By applying the matrix, we can get to hkl values, or reciprocalspace co-ordinates, to use directly as array indices. The pixel values are accumulated in a 3D array and a second array counts the number of contributing pixels. This volume is written @@ -31,21 +22,19 @@ Running the program =================== In common with many other scripts in ImageD11 you type:: - rsv_mapper.py --help + + $ rsv_mapper.py --help Usage: rsv_mapper.py [options] Options: -h, --help show this help message and exit -The following options are all used to describe a series -of images corresponding to a rotation experiment. They are the same -for peaksearch.py:: +The following options are all used to describe a series of images corresponding to a rotation experiment. They are the same for peaksearch.py:: -5 HDF5, --hdf5=HDF5 hdf file containing input image series -This reads the series of images from an hdf file, in the event that -you have them in that format (it is currently unlikely for raw data):: +This reads the series of images from an hdf file, in the event that you have them in that format (it is currently unlikely for raw data):: -n STEM, --stem=STEM stem name for input image series -f FIRST, --first=FIRST @@ -67,19 +56,11 @@ you have them in that format (it is currently unlikely for raw data):: -T OMEGA, --start=OMEGA start omega -Some particular points to note: the dark and flood and spline -images are quite important at this stage for avoiding artifacts. -If your images are not already corrected it is important to -use the right ones.:: +Some particular points to note: the dark and flood and spline images are quite important at this stage for avoiding artifacts. If your images are not already corrected it is important to use the right ones:: -p PARS, --pars=PARS ImageD11 parameter file for experiment -See the transform module documentation. You need good instrument -parameters to map the pixels to correct positions in reciprocal space. -The program *does not* recompute k vectors based on the crystal -translation, so it is better to set this to zero and align your -crystal as well as you can.:: - +See the transform module documentation. You need good instrument parameters to map the pixels to correct positions in reciprocal space. The program *does not* recompute k vectors based on the crystal translation, so it is better to set this to zero and align your crystal as well as you can:: -u UBIFILE, --ubifile=UBIFILE Name of ubi file (first matrix is used) @@ -87,8 +68,8 @@ crystal as well as you can.:: An orientation matrix in ImageD11's format. This means the matrix which is used to make scattering vectors onto crystal axes (the axes you will use in reciprocal space):: - hkl = (UBI).(g) + hkl = (UBI).(g) -x NPIXELS, --npixels=NPIXELS Number of pixels in reciprocal space map per integer hkl [16] @@ -98,9 +79,11 @@ axes you will use in reciprocal space):: This option removes a border around the images. Useful in the case that the detector image does not go to the edge:: + -b BORDER, --border=BORDER Border around images to allocate space, px [10] -FIXME: radius and mask + +FIXME: radius and mask:: -o OUTPUT, --output=OUTPUT Name of hdf5 output file @@ -122,11 +105,6 @@ Things to do + Radius and mask active pixel selections? + Process only specific planes + Rebin the image before processing (an optimisation) -+ Split pixels in 3D (at least add in a couple of -intermediate angular steps) ++ Split pixels in 3D (at least add in a couple of intermediate angular steps) + Apply a polarisation correction + Write directly a format which can be read by chimera - - - - diff --git a/docs/weight_gv_refine.ipynb b/docs/weight_gv_refine.ipynb new file mode 100644 index 00000000..879750b4 --- /dev/null +++ b/docs/weight_gv_refine.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.spatial.transform import Rotation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Introduce a weighting factor into the UB/UBI refinement. Effectively like adding in the contribution of the same peak over and over again.\n", + "\n", + "This weighting factor could be:\n", + "- the pixel by pixel intensity ... this is just taking the center of mass position in g-vector space\n", + "- a robust function which downweights outliers ... seems non-linear or iterative for two grains (which one do you pick for the solution?)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def robust_weights(UBI, gv, sigma = 0.005, eta = 0.5 ):\n", + " \"\"\" \n", + " Given an approximate UBI and experimental g-vectors ...\n", + " Computes integer hkl : hint= VVVVVVVVVVVVVVVVVV\n", + " Finds computed g-vectors : dot(UB), round(dot(UBI,gv))\n", + " gerr**2 = sum_{xyz} (gobs - gcalc)**2 \n", + " Weights are:\n", + " Gauss term : exp( -gerr^2 / (2*sigma*sigma) ) ... local\n", + " Cauchy term : sigma*sigma / (sigma*sigma + )\n", + " Computes p = exp()\n", + " \n", + " \"\"\"\n", + " hint = np.round( np.dot( UBI, gv ) )\n", + " gcalc = np.dot( np.linalg.inv(UBI), hint )\n", + " dg2 = ((gv - gcalc)**2).sum( axis = 0 )\n", + " isig = 1/sigma\n", + " # probability this peak is indexed : Gaussian errors\n", + " pg = np.exp( - dg2*(isig*isig/2) ) # exp(0) == 1\n", + " # probability this peak is indexed : Cauchy errors\n", + " pe = sigma * sigma / ( sigma * sigma + dg2 )\n", + " p = pg * eta + pe * (1-eta)\n", + " pm = p.max()\n", + " assert pm <= 1 and pm > 0\n", + " return hint, p, dg2.mean() # estimate for what sigma might be\n", + "\n", + "def weighted_refine(hkl, gv, weights ):\n", + " \"\"\"\n", + " Refine an orientation matrix with weights\n", + "\n", + " Based on Paciorek et al Acta A55 543 (1999)\n", + " UB = R H-1\n", + " ... add a weighting per peak (like adding the peak w times)\n", + " \n", + " where:\n", + " R = sum_n r_n h_n^t\n", + " H = sum_n h_n h_n^t\n", + " w = weights for each peak\n", + " r = g-vectors\n", + " h = hkl indices\n", + " \"\"\"\n", + " R = np.zeros((3,3),float)\n", + " H = np.zeros((3,3),float)\n", + " assert gv.shape[0] == 3\n", + " for w,h,g in zip(weights, hkl.T, gv.T ):\n", + " R += (np.outer(g,h)*w)\n", + " H += (np.outer(h,h)*w)\n", + " HI = np.linalg.pinv(H, hermitian=True) # might fail\n", + " UBoptimal = np.dot(R,HI)\n", + " UBIo = np.linalg.inv(UBoptimal)\n", + " return UBIo, UBoptimal, R, H" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(13)\n", + "a = np.random.normal(loc=0,scale=1,size=3)\n", + "r = Rotation.from_euler('xyz', a, degrees=True )\n", + "a0 = 5.43094\n", + "ubi0 = np.eye(3)*a0\n", + "ubi1 = np.dot( ubi0, r.as_dcm() )\n", + "print(ubi0,'\\n', ubi1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hi = np.mgrid[ -4:5, -4:5, -4:5 ]\n", + "hi.shape = 3,-1\n", + "ub0 = np.linalg.inv( ubi0 )\n", + "g0 = np.dot( ub0, hi )\n", + "ub1 = np.linalg.inv( ubi1 )\n", + "g1 = np.dot( ub1, hi )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g0.T[3],g1.T[3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gall = np.concatenate( (g0, g1), axis=1 )\n", + "gall.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "w = np.ones( len(gall[0]) )\n", + "ubiavg = 0.5*(ubi0 + ubi1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = (ubi0+ubi1*2)/3\n", + "e = 1e-4\n", + "for i in range(10):\n", + " hint, w, e = robust_weights( m, gall, e, eta=0.5)\n", + " ret = weighted_refine( hint, gall, 1-w )\n", + " m = ret[0]\n", + " print('e',e, 'err',((ubi0 - m)**2).sum(), ((ubi1 - m)**2).sum(), end=' ')\n", + " ret = weighted_refine( hint, gall, w )\n", + " m = ret[0]\n", + " print('e',e, 'err',((ubi0 - m)**2).sum(), ((ubi1 - m)**2).sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/find_all_files_interfacing.py b/find_all_files_interfacing.py new file mode 100644 index 00000000..e3c49ed8 --- /dev/null +++ b/find_all_files_interfacing.py @@ -0,0 +1,34 @@ + +import os + +apui = [] +syui = [] +noui = [] + +root = "." +for dirName, subdirList, fileList in os.walk(root): + if True in [dirName.find(s)>=0 for s in ("__","build",".git",".vscode") ]: + continue + for f in fileList: + if f.endswith(".py"): + p = os.path.join(dirName, f) + s = open( p , "r" ).read() + if "optparse" in s or "add_option" in s: + apui.append( p ) + elif "argv" in s: + syui.append( p ) + else: + noui.append( p ) + +print("Neither:") +for f in noui: + print(f) + + +print("argv:") +for f in syui: + print(f) + +print("optparse:") +for f in apui: + print(f) diff --git a/fixprint.py b/fixprint.py deleted file mode 100644 index acbe1661..00000000 --- a/fixprint.py +++ /dev/null @@ -1,48 +0,0 @@ - - -import glob, shutil - - -def got_future(lines): - for line in lines: - if line.find("from __future__ import print_function")==0: - return True - return False - -def got_print(lines): - for line in lines: - if line.find("print") > -1: - return True - return False - -def got_input(lines): - for line in lines: - if line.find("input") > -1: - return True - return False - -fl = glob.glob("*.py") - -def fix(fname, lines): - shutil.move(fname, fname+".bak2") - g = open(fname,"w") - g.write("\nfrom __future__ import print_function\n") - if got_input(lines): - g.write("from six.moves import input\n") - for line in lines: - g.write(line) - g.close() - - -for fname in fl: - lines = open(fname).readlines() - p = got_print(lines) - f = got_future(lines) - if p and f: - print "OK",fname - continue - if p: - print "Need to fix",fname - fix(fname, lines) - - diff --git a/howto_compile_win64.txt b/howto_compile_win64.txt deleted file mode 100644 index 3e1d44d3..00000000 --- a/howto_compile_win64.txt +++ /dev/null @@ -1,37 +0,0 @@ -For python2.7 on windows 64 bit: - - -Install compiler: -MS Windows SDK for Windows 7 and .NET Framework 3.5 SP1 -http://www.microsoft.com/en-us/download/details.aspx?id=18950 - -Open a winpython command prompt - -Set up compiler: - -C:\Windows\System32\cmd.exe /E:ON /V:ON /T:0E /K "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" - -Set up environment: - -set DISTUTILS_USE_SDK=1 -setenv /x64 /release - -... and "python setup.py build" should work - - -# Using compiler from microsoft for python 2.7 I seem to need this for cffi - -set VS90COMNTOOLS="C:\Users\wright\AppData\Local\Programs\Common\Microsoft\Visual C++ for Python\9.0" - -=========== Using mingwpy =============== - -pip install -i https://pypi.anaconda.org/carlkl/simple mingwpy -python setup.py build --compiler=mingw32 -python setup.py bdist_wheel -cd dist -pip install ImageD11-1.6.0-cp27-cp27m-win_amd64.whl - -Tested on Anaconda2 / win7 / amd64 - - - diff --git a/lgtm.yml b/lgtm.yml new file mode 100644 index 00000000..1e0e0473 --- /dev/null +++ b/lgtm.yml @@ -0,0 +1,8 @@ +path_classifiers: + - exclude: "ImageD11src/depreciated/*" +queries: + - exclude: "py/unreachable-statement" +extraction: + python: + python_setup: + version: 3 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..a59ec013 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,7 @@ +[build-system] +requires = ["setuptools", + "six", + "wheel", + "numpy; python_version=='2.7'", + "oldest-supported-numpy; python_version>='3.0'" ] +build-backend = "setuptools.build_meta" diff --git a/sandbox/CRL.py b/sandbox/CRL.py index b9d9fb38..5d02da1a 100644 --- a/sandbox/CRL.py +++ b/sandbox/CRL.py @@ -248,7 +248,7 @@ # x^2 + y^2 = 2*Ry # ... x>>y : x^2 = 2Ry # so Y = x^2/(2R) -x = np.linspace( -.5e-3, .5e-3, 100) +#x = np.linspace( -.5e-3, .5e-3, 100) x = np.linspace( -25e-6, 25e-6, 100) #pl.plot( 1e6*x, 1e6*x*x/(2*R_SI), "-" ) E = 40 diff --git a/sandbox/Index1DSearch.py b/sandbox/Index1DSearch.py new file mode 100644 index 00000000..e464ce32 --- /dev/null +++ b/sandbox/Index1DSearch.py @@ -0,0 +1,174 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.3.2 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# Some exploration of the intexing in Joel's code. Orient along a scattering vector and then do a 1D search rotating around that vector. The idea is to get to something which searches all of the places something can be found in orientation space. But avoiding the 3D problem for small datasets. + +# %matplotlib inline +#notebook +import numpy as np, pylab as pl +from ImageD11 import indexing, unitcell, grain, transform, cImageD11 +from scipy.spatial.transform import Rotation + +# Generate some peaks +h,k,l = [x.ravel() for x in np.mgrid[-4:5,-4:5,-4:5]] + +uc = unitcell.unitcell( [7.89, 8.910, 9.1011, 92, 97, 99], "P") + +orient = Rotation.from_euler("XYZ",(10,20,31)).as_matrix() +ub = np.dot( orient, uc.B ) +ubi = np.linalg.inv( ub ) + +gcalc = np.dot( ub, (h,k,l) ) + +modg = np.sqrt(gcalc*gcalc).sum(axis=0) + +tth, eta, omega = transform.uncompute_g_vectors( gcalc, 0.3 ) + +pl.plot(tth, eta[0],".") +pl.plot(tth, eta[1],".") + +selected_peak = sp = np.argmin(abs(tth - 4.17)) +print(tth[sp], eta[0][sp], omega[0][sp], eta[1][sp], omega[1][sp], gcalc[:,sp], h[sp], k[sp], l[sp]) + +# Now we get to the interesting part. We have simulated some data for a triclinic crystal and picked out some specific peak that we want to work with. The idea is to come up with a "new" way of doing indexing. It is mostly based on Joel Berniers fibre texture thing. +# - We assume that we have assigned this peak h,k,l indices based on the two theta. +# - We find a rotation which takes unitcell B matrix to put the gvector parallel to this gvector. +# - We then apply rotations around this g-vector to generate new orientations. + +gx,gy,gz = gobs = gcalc[:,sp].copy() +h0,k0,l0 = -1,2,0 +g0 = np.dot( uc.B, (h0,k0,l0)) +# angle between observed reflection and this computed peak: +cosa = np.dot( g0, gobs )/ np.sqrt( np.dot(g0,g0) * np.dot(gobs,gobs )) +ang = np.arccos(cosa) +vec = np.cross( g0, gobs ) +print(vec, ang) +direc = vec / np.sqrt( np.dot(vec,vec)) + +u0 = Rotation.from_rotvec( ang * direc).as_matrix() +ub0 = np.dot( u0, uc.B) +gtest = np.dot( ub0, (h0,k0,l0)) +print(gtest, gobs) + +# Now we want to rotate around gtest. This is a vector in reciprocal space. Ideally we want to apply the rotation in real space, such that dot(UBI, g0) == h0 == ( B-1. U-1 ) is invariant. The vector to rotate around is therefore the g-vector which is in real space(?) +# +# + +ubi0 = np.linalg.inv( ub0 ) +hkl0 = np.array((h0,k0,l0),float) +unitH = hkl0 / np.sqrt( np.dot(hkl0,hkl0) ) +unitG = gtest / np.sqrt( np.dot(gtest,gtest) ) +angles_to_test = np.linspace( -np.pi, np.pi, 360 ) + +testmats = [ np.dot( ubi0, Rotation.from_rotvec( unitG*x ).as_matrix() ) for x in angles_to_test ] +rmats = [ Rotation.from_rotvec( unitG*x ).as_matrix() for x in angles_to_test ] +print(np.dot(testmats[0],gtest)) + +gobs = gcalc.T.copy() +import timeit +start = timeit.default_timer() +npk = [cImageD11.score( m, gobs, 0.05) for m in testmats] +print("Scoring takes", timeit.default_timer()-start) + +pl.figure() +pl.plot( angles_to_test, npk,"-") + +# Conclusion: This appears to work. It takes a while to test all the potential orientations however. +# Perhaps we can expand the mathematics to try to figure out some contributions a bit quicker? +# +# Our equation is: +# hkl = integer = UBI0 . rotation_on_g0 . gvobs +# +# Each g-vector has a component along the g0 axis which will be invariant. +# +# The other components could be found in an axis/angle expansion. +# - gaxis + gs.sin(angle) + gc.cos(angle) +# - h,k,l = ubi0.gaxis + ubi0.gs.sin(angle) + ubi0.gc.cos(angle) +# We can try to find h, k, l in terms of the rotation angle as solutions to this equation? +# +# sin/cos have range -1,1 : gives ranges for hmax, hmin, kmax, kmin, lmax, lmin +# + +ux,uy,uz = unitG +ubi0_sin = np.dot(ubi0, np.array( ((0,-uz, uy),(uz, 0, -ux),(-uy, ux, 0)))) +ubi0_cos = np.dot(ubi0,np.outer( unitG, unitG )) + +tcos = np.dot(ubi0 - ubi0_cos, gobs.T) +tsin = np.dot(ubi0_sin , gobs.T) +tconst = np.dot(ubi0_cos, gobs.T ) + +pl.figure() +j = 567 +pl.plot( angles_to_test, tconst[0,j] + np.cos(angles_to_test)*tcos[0,j] + np.sin(angles_to_test)*tsin[0,j],"-",label='h') +pl.plot( angles_to_test, tconst[1,j] + np.cos(angles_to_test)*tcos[1,j] + np.sin(angles_to_test)*tsin[1,j],"-",label='k') +pl.plot( angles_to_test, tconst[2,j] + np.cos(angles_to_test)*tcos[2,j] + np.sin(angles_to_test)*tsin[2,j],"-",label='l') +pl.legend() + +# + +# hvalues +hmin, hmax = tconst[0,j] - np.sqrt( tsin[0,j]**2 + tcos[0,j]**2 ), tconst[0,j] + np.sqrt( tsin[0,j]**2 + tcos[0,j]**2 ) +kmin, kmax = tconst[1,j] - np.sqrt( tsin[1,j]**2 + tcos[1,j]**2 ), tconst[1,j] + np.sqrt( tsin[1,j]**2 + tcos[1,j]**2 ) +lmin, lmax = tconst[2,j] - np.sqrt( tsin[2,j]**2 + tcos[2,j]**2 ), tconst[2,j] + np.sqrt( tsin[2,j]**2 + tcos[2,j]**2 ) + # http://en.wikipedia.org/wiki/List_of_trigonometric_identities#Linear_combinations + # a.cos(x) + b.sin(x) = c cos(x+p) + # with p = atan(-b/a) +def aclip(x): + return np.arctan2( np.sin(x),np.cos(x)) + +assert np.allclose(aclip(angles_to_test), angles_to_test) + +p = np.arctan2( -tsin[0,j], tcos[0,j] ) +sab = np.sign(tcos[0,j])*np.sqrt( tcos[0,j]**2 + tsin[0,j]**2 ) +#np.fix rounds towards 0 +hr = np.arange(np.fix(hmin), np.fix(hmax)+1) +rhs = hr - tconst[0,j] +sth = np.clip(rhs/sab,-2,2) +a1 = aclip( np.arccos(sth) - p ) +a2 = aclip(-np.arccos(sth) - p ) +# sin(arccos(x)) = sqrt(1-x**2) +# sin(arctan(x)) = x/sqrt(1+x**2) +# cos(arccos(x)) = x +# cos(arctan(x)) = 1/sqrt(1+x**2) +# sin(A - B) = sin A cos B - cos A sin B +# cos(A - B) = cos A cos B + sin A sin B +# ... this leads directly to sin/cos without finding angle +#print(h,a1,a2) +pl.plot(a1,hr,"o") +pl.plot(a2,hr,"+") +p = np.arctan2( -tsin[1,j], tcos[1,j] ) +sab = np.sign(tcos[1,j])*np.sqrt( tcos[1,j]**2 + tsin[1,j]**2 ) +for k in range(int(np.round(kmin)), int(np.round(kmax))+1): + rhs = float(k) - tconst[1,j] + sth = rhs/sab + if sth > -1 and sth < 1: + a1 = aclip( np.arccos( sth ) - p ) + a2 = aclip(-np.arccos( sth ) - p ) +# print(k,a1,a2) + pl.plot([a1,],[k,],"o") + pl.plot([a2,],[k,],"+") +p = np.arctan2( -tsin[2,j], tcos[2,j] ) +sab = np.sign(tcos[2,j])*np.sqrt( tcos[2,j]**2 + tsin[2,j]**2 ) +for l in range(int(np.round(lmin)), int(np.round(lmax))+1): + rhs = float(l) - tconst[2,j] + sth = rhs/sab + if sth > -1 and sth < 1: + a1 = aclip(np.arccos( sth ) - p ) + a2 = aclip(- np.arccos( sth ) - p ) + #print(l,a1,a2) + pl.plot([a1,],[l,],"o") + pl.plot([a2,],[l,],"+") + +# - + + diff --git a/sandbox/abonnin/fix_omega_angles.py b/sandbox/abonnin/fix_omega_angles.py index d4096388..09772685 100644 --- a/sandbox/abonnin/fix_omega_angles.py +++ b/sandbox/abonnin/fix_omega_angles.py @@ -1,4 +1,5 @@ from ImageD11.columnfile import columnfile +from pylab import plot c = columnfile("mainphase.flt") c.addcolumn(c.omega.copy(), "image_number") c.omega[:] = (c.image_number/147).astype(int) diff --git a/sandbox/abonnin/make_point_sino.py b/sandbox/abonnin/make_point_sino.py index 25223e23..c618df89 100644 --- a/sandbox/abonnin/make_point_sino.py +++ b/sandbox/abonnin/make_point_sino.py @@ -1,7 +1,7 @@ from __future__ import print_function -import numpy as np +import numpy as np, pylab as pl def read_log(fname): @@ -91,13 +91,13 @@ def fit_sino( omega, obs, debug=False ): print("# spot3d_id omega xpos intensity") for i in range(len(usedpks)): print("%-7d %8.1f %9.5f %15g"%(usedpks[i], omused[i], xposused[i], intens[i])) - if 0: - pl.clf() - pl.title("Grain number %d"%(ng)) - pl.plot(om, xpos,"o") - pl.plot(omused, xposused,"o") - pl.plot(omused, calc,"+") - pl.ion() - pl.show() - pl.savefig("grainfit_%d.png"%(ng)) + # if 0: + # pl.clf() + # pl.title("Grain number %d"%(ng)) + # pl.plot(om, xpos,"o") + # pl.plot(omused, xposused,"o") + # pl.plot(omused, calc,"+") + # pl.ion() + # pl.show() + # pl.savefig("grainfit_%d.png"%(ng)) ng += 1 diff --git a/sandbox/art.py b/sandbox/art.py index ea00895d..843d77f3 100644 --- a/sandbox/art.py +++ b/sandbox/art.py @@ -1,5 +1,5 @@ -from __future__ import print_function +from __future__ import print_function, division """ @@ -31,7 +31,7 @@ def update_wtd( recon, proj, angle, msk, dbg=True ): pos = np.zeros( x.shape, np.float32).ravel() np.add( x.ravel()*cth, y.ravel()*sth, pos) # Lower & upper pixel: - idx_lo = np.floor(pos).astype(np.int) + idx_lo = np.floor(pos).astype(int) idx_hi = idx_lo+1 # idx will go from -xxx to xxx mn = idx_lo.min() @@ -50,14 +50,14 @@ def update_wtd( recon, proj, angle, msk, dbg=True ): cImageD11.put_incr( calc_proj, idx_lo, r*wt_lo ) cImageD11.put_incr( calc_proj, idx_hi, r*wt_hi ) error = np.zeros( calc_proj.shape, np.float32 ) - start = (len(calc_proj)- len(proj))/2 + start = int((len(calc_proj)- len(proj))/2) error[ start:start+len(proj) ] = proj error = error - calc_proj npcalc_proj = np.zeros( (mx-mn), np.float32 ) cImageD11.put_incr( npcalc_proj, idx_hi, wt_hi ) cImageD11.put_incr( npcalc_proj, idx_lo, wt_lo ) npcalc_proj = np.where(npcalc_proj >0 ,npcalc_proj, 1) - update = np.zeros( r.shape, np.float32) + # update = np.zeros( r.shape, np.float32) update = wt_hi*np.take( error/npcalc_proj, idx_hi ) update += wt_lo*np.take( error/npcalc_proj, idx_lo ) @@ -78,7 +78,7 @@ def update_wtd( recon, proj, angle, msk, dbg=True ): #pl.colorbar() #pl.show() - update = update + # update = update update.shape = recon.shape print(update.sum(),error.sum(), end=' ') if dbg: @@ -91,7 +91,8 @@ def update_wtd( recon, proj, angle, msk, dbg=True ): def make_data(): - import rad, Image + import rad + from PIL import Image im = Image.open("phantom3.gif") a = np.asarray(im).astype(np.float32) print(a.min(),a.max()) @@ -123,7 +124,7 @@ def test_u_n(): for j in range(3): for proj, angle in zip(projections, theta): print(j, angle, end=' ') - start = time.clock() + start = time.time() update = update_wtd( recon, proj, angle, msk, dbg=False) recon = recon + update*0.9 # clip to zero - constructing positive intensity @@ -135,7 +136,7 @@ def test_u_n(): pl.imshow(recon) pl.colorbar() # pl.show() - end = time.clock() + end = time.time() sumtime += end-start ncalc +=1 # print err.sum() diff --git a/sandbox/avxsum/bench_imsum.py b/sandbox/avxsum/bench_imsum.py new file mode 100644 index 00000000..21f218d4 --- /dev/null +++ b/sandbox/avxsum/bench_imsum.py @@ -0,0 +1,83 @@ + + +from __future__ import print_function, division + +import ctypes, numpy, sys, time, os +if 0: + os.system("gcc -shared -fPIC -O3 -fopenmp -march=native imsum.c -o imsum.so") + +timer = time.perf_counter + +data = (numpy.random.random( 2048*2048*32 )*10000).astype(numpy.uint16) +if "win" in sys.platform: + lib = ctypes.CDLL( "imsum.dll" ) + cfuns = [lib.imsum_sse2_0 ,lib.imsum_sse2_1 , lib.imsum_c_simd, + lib.imsum_c,lib.imsum_avx2_0,lib.imsum_avx2_1,lib.imsum_avx2_2] +else: + lib = ctypes.CDLL( "imsum.so" ) + cfuns = [lib.imsum_sse2_0 ,lib.imsum_sse2_1 , lib.imsum_c_simd, + lib.imsum_c] + if "avx2" in open("/proc/cpuinfo").read(): + print("You have avx2") + cfuns += [lib.imsum_avx2_0,lib.imsum_avx2_1,lib.imsum_avx2_2] + +lib.imsum_sse2_0.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_sse2_0.restype = ctypes.c_int64 +lib.imsum_sse2_1.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_sse2_1.restype = ctypes.c_int64 +lib.imsum_avx2_0.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_avx2_0.restype = ctypes.c_int64 +lib.imsum_avx2_1.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_avx2_1.restype = ctypes.c_int64 +lib.imsum_avx2_2.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_avx2_2.restype = ctypes.c_int64 +lib.imsum_c.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_c.restype = ctypes.c_int64 +lib.imsum_c_simd.argtypes = [ ctypes.POINTER( ctypes.c_uint16 ), ctypes.c_int64 ] +lib.imsum_c_simd.restype = ctypes.c_int64 + + + + + +val = data.sum(dtype=numpy.int64) + +N=100 +t0=timer() +vals = [ data.sum(dtype=numpy.int64) for i in range(N)] +t1=timer() +avg = (t1-t0)/N +print("numpy",avg) +for cfunc in cfuns[::-1]: + t0=timer() + valc = [cfunc( data.ctypes.data_as( ctypes.POINTER( ctypes.c_uint16 ) ), + data.shape[0] ) + for i in range(N)] + t1=timer() + avc = (t1-t0)/N + ok = (numpy.array(valc) == vals).all() + print(cfunc.__name__,"%.6f"%(avc),"%.6f"%(avg/avc),ok) + if not ok: + print(valc[0],vals[0]) + +print(len(data)*5000) + +""" + gcc -shared -fPIC -Ofast -fopenmp -march=native -fprofile-generate=. imsum.c -o imsum.so + python3 ./bench_imsum.py + gcc -shared -fPIC -Ofast -fopenmp -march=native -fprofile-use=. imsum.c -o imsum.so + python3 ./bench_imsum.py + + . /data/id11/jon/spack/ + spack load intel-oneapi-compilers@2021.3.0 + icc -prof-gen -shared -fPIC -Ofast -fopenmp -xHost imsum.c -o imsum.so + python3 ./bench_imsum.py + icc -prof-use -shared -fPIC -Ofast -fopenmp -xHost imsum.c -o imsum.so + python3 ./bench_imsum.py + + /usr/bin/clang -shared -fPIC -Ofast -march=native imsum.c -o imsum.so -fprofile-instr-generate + python3 bench_imsum.py + /usr/bin/llvm-profdata-10 merge default.profraw -o default.profdata + /usr/bin/clang -shared -fPIC -Ofast -march=native imsum.c -o imsum.so -fprofile-instr-use + python3 bench_imsum.py + """ \ No newline at end of file diff --git a/sandbox/avxsum/imsum.c b/sandbox/avxsum/imsum.c new file mode 100644 index 00000000..d31fd487 --- /dev/null +++ b/sandbox/avxsum/imsum.c @@ -0,0 +1,163 @@ + +#include +#include +#include + +int64_t imsum_avx2_2( uint16_t im[], int64_t n ){ + __m256i s0,s1,s2,s3; + __m128i l0, l1; + int i; + int64_t sum; + s0 = _mm256_setzero_si256(); + s1 = _mm256_setzero_si256(); + s2 = _mm256_setzero_si256(); + s3 = _mm256_setzero_si256(); + for( i = 0; i < n; i = i+16){ + /* read 4*16=64 bytes, so not aligned + * goes to lower 4*32=128 in t + */ + l0 = _mm_loadu_si128( (__m128i*) &im[i] ) ; + s0 = _mm256_add_epi64( s0, _mm256_cvtepu16_epi64 ( l0 ) ); + s1 = _mm256_add_epi64( s1, _mm256_cvtepu16_epi64 ( _mm_bsrli_si128( l0, 8 ))); + l1 = _mm_loadu_si128( (__m128i*) &im[i+8] ) ; + s2 = _mm256_add_epi64( s2, _mm256_cvtepu16_epi64 ( l1 ) ); + s3 = _mm256_add_epi64( s3, _mm256_cvtepu16_epi64 ( _mm_bsrli_si128( l1, 8 ))); + } + sum = _mm256_extract_epi64 (s0, 0) + \ + _mm256_extract_epi64 (s0, 1) + \ + _mm256_extract_epi64 (s0, 2) + \ + _mm256_extract_epi64 (s0, 3) ; + sum +=_mm256_extract_epi64 (s1, 0) + \ + _mm256_extract_epi64 (s1, 1) + \ + _mm256_extract_epi64 (s1, 2) + \ + _mm256_extract_epi64 (s1, 3); + sum += _mm256_extract_epi64 (s2, 0) + \ + _mm256_extract_epi64 (s2, 1) + \ + _mm256_extract_epi64 (s2, 2) + \ + _mm256_extract_epi64 (s2, 3); + sum+= _mm256_extract_epi64 (s3, 0) + \ + _mm256_extract_epi64 (s3, 1) + \ + _mm256_extract_epi64 (s3, 2) + \ + _mm256_extract_epi64 (s3, 3) ; + return sum; +} + + +int64_t imsum_avx2_1( uint16_t im[], int64_t n ){ + __m256i s0,s1; + __m128i l, lo, hi, z; + int i; + int64_t sum; + s0 = _mm256_setzero_si256(); + s1 = _mm256_setzero_si256(); + for( i = 0; i < n; i = i+8){ + /* read 4*16=64 bytes, so not aligned + * goes to lower 4*32=128 in t + */ + l = _mm_loadu_si128( (__m128i*) &im[i] ) ; + s0 = _mm256_add_epi64( s0, _mm256_cvtepu16_epi64 ( l ) ); + s1 = _mm256_add_epi64( s1, _mm256_cvtepu16_epi64 ( _mm_bsrli_si128( l, 8 ))); + } + sum = _mm256_extract_epi64 (s0, 0) + \ + _mm256_extract_epi64 (s0, 1) + \ + _mm256_extract_epi64 (s0, 2) + \ + _mm256_extract_epi64 (s0, 3) + \ + _mm256_extract_epi64 (s1, 0) + \ + _mm256_extract_epi64 (s1, 1) + \ + _mm256_extract_epi64 (s1, 2) + \ + _mm256_extract_epi64 (s1, 3); + return sum; +} + +int64_t imsum_avx2_0( uint16_t im[], int64_t n ){ + __m256i s; + __m128i l; + int i; + int64_t sum; + s = _mm256_setzero_si256(); + for( i = 0; i < n; i = i+4){ + /* read 4*16=64 bytes, so not aligned + * goes to lower 4*32=128 in t + */ + l = _mm_loadu_si128( (__m128i*) &im[i] ) ; + s = _mm256_add_epi64( s, _mm256_cvtepu16_epi64 ( l ) ); + } + sum = _mm256_extract_epi64 (s, 0) + \ + _mm256_extract_epi64 (s, 1) + \ + _mm256_extract_epi64 (s, 2) + \ + _mm256_extract_epi64 (s, 3); + return sum; +} + + + +int64_t imsum_sse2_0( uint16_t im[], int64_t n ){ + __m128i s0,s1,s2,s3,t,s32,l; + int i; + int64_t sum; + s0 = _mm_setzero_si128(); + s1 = _mm_setzero_si128(); + s2 = _mm_setzero_si128(); + s3 = _mm_setzero_si128(); + for( i = 0; i < n; i = i+8){ + /* read 4*16=64 bytes, so not aligned + * goes to lower 4*32=128 in t + */ + l = _mm_loadu_si128( (__m128i*) &im[i] ); + + s0 = _mm_add_epi64( s0, _mm_cvtepu16_epi64( l )); + s1 = _mm_add_epi64( s1, _mm_cvtepu16_epi64( _mm_bsrli_si128( l, 4 ))); + s2 = _mm_add_epi64( s2, _mm_cvtepu16_epi64( _mm_bsrli_si128( l, 8 ))); + s3 = _mm_add_epi64( s3, _mm_cvtepu16_epi64( _mm_bsrli_si128( l, 12 ))); + } + sum = _mm_extract_epi64 (s0, 0) + \ + _mm_extract_epi64 (s0, 1) + \ + _mm_extract_epi64 (s1, 0) + \ + _mm_extract_epi64 (s1, 1) + \ + _mm_extract_epi64 (s2, 0) + \ + _mm_extract_epi64 (s2, 1) + \ + _mm_extract_epi64 (s3, 0) + \ + _mm_extract_epi64 (s3, 1) ; + return sum; +} + +int64_t imsum_sse2_1( uint16_t im[], int64_t n ){ + __m128i s1,s2,t,s32,l; + int i; + int64_t sum; + s1 = _mm_setzero_si128(); + s2 = _mm_setzero_si128(); + for( i = 0; i < n; i = i+8){ + /* read 4*16=64 bytes, so not aligned + * goes to lower 4*32=128 in t + */ + l = _mm_loadu_si128( (__m128i*) &im[i] ); + s32 = _mm_add_epi32( _mm_cvtepu16_epi32( l ), + _mm_cvtepu16_epi32( _mm_bsrli_si128( l, 8 ))); + s1 = _mm_add_epi64( s1, _mm_cvtepi32_epi64( s32)); + s2 = _mm_add_epi64( s2, _mm_cvtepi32_epi64( _mm_bsrli_si128( s32, 8 ))); + } + sum = _mm_extract_epi64 (s1, 0) + \ + _mm_extract_epi64 (s1, 1)+ \ + _mm_extract_epi64 (s2, 0)+ \ + _mm_extract_epi64 (s2, 1); + return sum; +} + + + +int64_t imsum_c( uint16_t im[], int64_t n ){ + int64_t sum = 0; + for( int i = 0; i < n; i++) + sum += im[i]; + return sum; +} + +int64_t imsum_c_simd( uint16_t im[], int64_t n ){ + int64_t sum = 0; +#pragma omp simd reduction(+:sum) + for( int i = 0; i < n; i++ ) + sum += im[i]; + return sum; +} + diff --git a/sandbox/bgfilter_grab.py b/sandbox/bgfilter_grab.py new file mode 100644 index 00000000..975008dd --- /dev/null +++ b/sandbox/bgfilter_grab.py @@ -0,0 +1,228 @@ + + +from __future__ import print_function +#import multiprocessing, os +#cpu = str(multiprocessing.cpu_count())-1 +#os.environ['OPENBLAS_NUM_THREADS']=cpu +#os.environ['NUMBA_NUM_THREADS']=cpu +#os.environ['OMP_NUM_THREADS']=cpu + +import timeit +from ImageD11 import cImageD11, sparseframe +import numpy as np, fabio, h5py, os +import pylab as pl +import numba +import scipy.ndimage + +""" Generator function based image pipeline """ + +timer = timeit.default_timer +LASTTIME = timer() +def bench(name): + def outer(func): + def inner(*args, **kwds): + global LASTTIME + gen = func(*args, **kwds) + for value in gen: + end = timer() + print(name,"%.2f ms"%(1e3*(end-LASTTIME)),end=" ") + LASTTIME=end + yield value + return inner + return outer + + + +def filename_series( stem, first, last, fmt="%s%04d.edf"): + """ Generates the series of filenames to process """ + i = first + while i <= last: + fname = fmt%(stem,i) + print("\n%s"%(os.path.split(fname)[-1]),end=" ") + yield fname + i += 1 + +@bench("open+dark") +def open_sub_dark( names, darkfilename, DARK_N=1 ): + """ Opens each file in names and subtracts the dark + yields data with dark subtracted + """ + drk = fabio.open(darkfilename).data.astype(np.float32) + drk /= DARK_N + for name in names: + img = fabio.open(name) + img.data = img.data.astype(np.float32) - drk + yield img + +@bench("mask") +def do_mask( imgs, maskfilename ): + """ applies a mask to the data """ + msk = fabio.open(maskfilename).data.astype(bool) + for img in imgs: + img.data[msk] = 0 + yield img + + +@numba.njit(fastmath=True,parallel=True) +def bgest( buffer, nsigma, out): + """ guesses the background using a clipped sigma """ + for i in numba.prange(buffer.shape[1]): # slow image + for j in range(buffer.shape[2]): # along lines + # first pass with no mask + # out[i,j] = np.median( buffer[:,i,j] ) + # continue + s0 = buffer[0,i,j] + sm = buffer[0,i,j]*0 + sm2 = buffer[0,i,j]*0 + npx = buffer.shape[0] + for k in range(1,buffer.shape[0]): # one from each frame + t = buffer[k,i,j] - s0 + sm += t + sm2 += t*t + avg = sm/npx + s0 + if (sm2 - sm*sm/npx) > 0: + std = np.sqrt( (sm2 - sm*sm/npx)/npx ) + else: + std = 1.0 + for _ in range(2): + npx = 0 + s0 = avg + cut = std * nsigma + sm *= 0 + sm2*= 0 + for k in range(buffer.shape[0]): # one from each frame + t = buffer[k,i,j] - s0 + if t < cut: + sm += t + sm2 += t*t + npx += 1 + avg = sm/npx + s0 + if (sm2 - sm*sm/npx) > 0: + std = np.sqrt( (sm2 - sm*sm/npx)/npx ) + else: + std = 1.0 + out[i,j] = avg + + + +@bench('bgsub') +def buffered_bgsub( series, nsigma, bufsize=11): + """ + Sets up a buffers of a series of frames + """ + buffer = None + bufo = [] + # init + nread = 0 + # import pdb; pdb.set_trace() + for i in range(bufsize): + frame = next(series) + if buffer is None: + buffer = np.zeros( (bufsize, frame.data.shape[0], frame.data.shape[1]), + np.float32 ) + bg = np.zeros( frame.data.shape, np.float32 ) + var = np.zeros( frame.data.shape, np.float32 ) + buffer[ i ] = frame.data + bufo.append(frame) + nread += 1 + bgest( buffer, nsigma, bg ) + for i in range(bufsize//2+1): + o = bufo[i%bufsize] + o.bg = bg + yield o + i = i + 1 + for frame in series: + # mask using old estimae + bufo[nread%bufsize] = frame + buffer[nread%bufsize] = frame.data + bgest( buffer, nsigma, bg ) + o.bg = bg + yield o + nread += 1 + i += 1 + while i < nread: + o = bufo[i%bufsize] + o.bg = bg + yield o + i += 1 + + +@bench('gauss filter') +def filter_gauss( series, sigma ): + """ applies a 2D gaussian filter prior the thresholding """ + smoothed= None + for img in series: + signal = img.data - img.bg + if smoothed is None: + smoothed = np.empty_like(img.data) + scipy.ndimage.gaussian_filter( signal, + sigma, + output = smoothed ) + img.signal= signal + img.smoothed = smoothed + yield img + +@bench("threshold") +def apply_threshold( series, nsigma ): + """ converts the data to sparse array """ + msk = None + for img in series: + if msk is None: + msk = np.empty( img.data.shape, np.uint8) + cln = np.empty( img.data.shape, np.uint8) + tmp = np.empty( img.data.shape[0], 'i' ) + row = np.empty( img.data.size, np.uint16) + col = np.empty( img.data.size, np.uint16) + a,s = cImageD11.array_mean_var_cut( img.smoothed, nsigma ) + nnz = cImageD11.make_clean_mask( img.smoothed, a+nsigma*s, msk, cln ) + cImageD11.mask_to_coo( cln, row[:nnz], col[:nnz], tmp ) + header = {"filename":img.filename} + img.mask = cln + intensity = sparseframe.Container( img.signal[cln>0], **header ) + img.sparseframe = sparseframe.sparse_frame( row[:nnz], col[:nnz], + img.data.shape, + itype=np.uint16, + pixels = {"intensity": + intensity } ) + yield img + + +def grab_folder(): + import pylab as pl + ROOT = "/data/id11/wolfgang/new_segmentation/erik/Al4Cu_dct1_" + os.chdir(ROOT) + # dark_n is 21 + nsigma = 8. + stem = os.path.join(ROOT, "0_rawdata", "Orig", "Al4Cu_dct1_") + first = 0 + last = 3599 + names = filename_series( stem, first, last ) + minus_dark = open_sub_dark( names, "0_rawdata/Orig/darkend0000.edf", DARK_N=21 ) + masked = do_mask( minus_dark, "median_image-mask.edf" ) + #print(masked, type(masked)) + nsigma = 3 + rolling_bg = buffered_bgsub( masked, nsigma, bufsize=21 ) + smoothed = filter_gauss( rolling_bg, 2. ) + nsigma = 5 + thresholded = apply_threshold( smoothed, nsigma ) + + with h5py.File(os.path.join(ROOT, "0_rawdata","sparse", "peaksmed.hdf"),"w") as h5: + gr = h5.require_group("peaks") + for j, frm in enumerate(thresholded): + g = gr.require_group(str(j)) + frm.sparseframe.to_hdf_group( g ) + print("nnz",frm.sparseframe.nnz, end=" ") + if 0: + pl.figure(1) + pl.imshow( frm.signal ) + pl.figure(2) + pl.imshow( frm.mask ) + pl.show() + + +if __name__=="__main__": + grab_folder() + + + + diff --git a/sandbox/bravias.py b/sandbox/bravias.py new file mode 100644 index 00000000..e566d4e6 --- /dev/null +++ b/sandbox/bravias.py @@ -0,0 +1,137 @@ + +from __future__ import print_function, division +""" +Determine Bravias lattice for UBI + +Return transformation matrix + +Due to: +ferraris_and_ivaldi + + Acta Cryst (1983) A39 595-596 + + Determine the multiplicities of vectors in a computed powder pattern + Match these to multiplicities according to lattice types + ... appeals to powder person + Tolerance is for grouping vector lengths. + + +""" + +import sys +import numpy as np, pylab as pl + + +from ImageD11.unitcell import unitcell +from ImageD11.grain import read_grain_file +from ImageD11.indexing import ubitocellpars + + + +def fi_triclinic(ubi): + """ Ensure we have the 3 shortest non-coplanar vectors """ + return ubi + +def fi_monoclinic(ubi, cell, mult): + """ + Choose vectors having mult == 2 + These are either along b or in ac plane + If centred then one mult 4 vector is shorter and cell par + """ + possibles = [] + for i in range(len(mult)): + if mult[i] == 2: + possibles.append( cell.ringhkls(cell.ringds[i])[0] ) + possibles = [cell.ring] + + +def ferraris_and_ivaldi(ubi, tol = 0.001): + """ + Acta Cryst (1983) A39 595-596 + + Determine the multiplicities of vectors in a computed powder pattern + Match these to multiplicities according to lattice types + ... appeals to powder person + Tolerance is for grouping vector lengths. + """ + cpar = ubitocellpars(ubi) + cell = unitcell( cpar ) + dlim = cell.ds( np.array( [7,7,7] )) + cell.makerings( dlim , tol) + mult = [ len(cell.ringhkls[d]) for d in cell.ringds ] + mx = max(mult) + mn = min(mult) + cases = { + (2, 2) : fi_triclinic, + (4, 2) : fi_monoclinic, +# (8, 2) : fi_orthorhombic, +# (12, 2) : fi_rhombohedral, +# (16, 2) : fi_tetragonal, +# (24, 2) : fi_hexagonal, +# (48, 2) : fi_cubic + } + +def vecang( v1, v2 ): + cosa = np.dot(v1,v2)/np.sqrt( np.dot( v1, v1 ) * np.dot( v2, v2 )) + return np.degrees( np.arccos( cosa )) + +def lattice_powder( ubi, tol=0.01): + a,b,c = np.asarray( ubi ) + nmax = 5 + vecs = [] + lens = [] + for ai in range(-nmax,nmax): + for bi in range(-nmax,nmax): + for ci in range(-nmax,nmax): + vec = ai*a + bi*b + ci*c + modv = np.sqrt( np.dot(vec, vec) ) + lens.append( modv ) + vecs.append( vec ) + order = np.argsort( lens ) + lens = np.take( lens, order ) + vecs = np.take( vecs, order ,axis=0 ) + steps = (lens[1:]-lens[:-1])/lens[1:] # diff divided by upper + gaps = steps > tol + rings = np.cumsum(gaps) + mults, bins = np.histogram( rings, bins=np.arange(-0.5, rings[-1]+0.5, 1 ) ) + print(rings) + print(mults) + nr = min( 10, rings.max() ) + for i in range( nr, 0 , -1 ): + # this ring: + M = mults[i] + print("Powder ring",i,"multiplicity", M ) + print(" Angles between vectors:") + vl = vecs[ 1: ][ rings==i ] + vll = lens[ 1: ][ rings==i ] + for j in range(M): + print ("%2d:"%(j),vl[j],vll[j]) + print(" ",end="") + for j in range(M-1,0,-1): + print("%7d"%(j),end=" ") + print() + for j in range(M-1): + print(" %2d: "%(j), end="") + for k in range(M-1, j, -1): + print( "%7.3f"%( vecang( vl[j],vl[k] ) ) , end=" ") + print() + print( "Cell pars before",ubitocellpars(ubi)) + + +def katayama(ubi): + """ + J. Appl. Cryst (1986) 19 69-72 + + Take four vectors a,b,c and d=-(a+b+c) + Compute the 6 pairwise dot products (ab, ac, ad, bc, bd, cd) + Apply transformations (up to 12) and recompute + + etc + """ + pass + + +if __name__=="__main__": + for grain in read_grain_file( sys.argv[1] ): + print ("ubi:\n",grain.ubi) + lattice_powder(grain.ubi) diff --git a/sandbox/buffered_series.py b/sandbox/buffered_series.py new file mode 100644 index 00000000..ec3340f8 --- /dev/null +++ b/sandbox/buffered_series.py @@ -0,0 +1,36 @@ + + +def filename_series( stem, first, last, fmt="%s%04d.edf"): + i = first + while i <= last: + yield fmt%(stem,i) + i += 1 + +def buffered( names, bufsize ): + buffer = [] + # init + while len( buffer ) < bufsize: + name = next(names) + buffer.append( name ) +# print("buffer", name ) + for i in range(bufsize//2+1): +# print("from first block") + yield buffer[i] + nread = bufsize + i = i + 1 + for name in names: + buffer[nread%bufsize] = name +# print("in main loop") + yield buffer[i%bufsize] + nread += 1 + i += 1 + while i < nread: +# print("last part") + yield buffer[i%bufsize] + i += 1 + + +for name in buffered( filename_series( "data", 0, 10 ), 7 ): + print (name) + + diff --git a/sandbox/cleanbg.py b/sandbox/cleanbg.py index ad93c3b6..f4cf7a4f 100644 --- a/sandbox/cleanbg.py +++ b/sandbox/cleanbg.py @@ -1,5 +1,5 @@ -from __future__ import print_function +from __future__ import print_function, division #!/usr/bin/python2.7 import numpy as np, fabio @@ -43,7 +43,7 @@ def cleanbg( stem, nimages, newstem ): print(i,name) bg = np.clip( buffy.min( axis = 0 ), 90, 65535 ) - 90 # Deal with images up to bufsize/2 - for i in range(bufsize/2): + for i in range(bufsize//2): outname = newstem + "%04d.edf"%(i) print("write",outname, buffo[i].name) np.subtract( buffo[i].data , bg, buffo[i].data ) @@ -64,15 +64,15 @@ def cleanbg( stem, nimages, newstem ): buffy[bp] = o.data.copy() bg = np.clip( buffy.min( axis = 0 ), 90, 65535 ) - 90 # Out image is i - bufsize/2 - op = (i-bufsize/2-1)%bufsize + op = (i-bufsize//2-1)%bufsize np.subtract( buffo[op].data , bg, buffo[op].data ) outname = newstem + "%04d.edf"%(i-bufsize/2-1) print(i,outname, buffo[op].name) buffo[op].write( outname ) n=i - for i in range(bufsize/2): + for i in range(bufsize//2): outname = newstem + "%04d.edf"%(n+i-bufsize/2) - bp = (n+i-bufsize/2)%bufsize + bp = (n+i-bufsize//2)%bufsize print("write",outname, buffo[bp].name) np.subtract( buffo[bp].data , bg, buffo[bp].data ) buffo[bp].write( outname ) diff --git a/sandbox/collect_peak_pixels.py b/sandbox/collect_peak_pixels.py new file mode 100644 index 00000000..13366e8a --- /dev/null +++ b/sandbox/collect_peak_pixels.py @@ -0,0 +1,150 @@ + +from __future__ import print_function, division + +import multiprocessing, time +import numpy as np +import scipy.sparse, h5py +import fabio +from ImageD11 import cImageD11 +#from . +import sparse_peak_searching +import sys, os, glob + +# Estimate a noise level and determine a threshold + +def sigma_cut( img, nsigma=3. ): + """ + Assume the image has been background corrected and + follows a Gaussian noise distribution (e.g. CCD) + """ + means=[img.mean(),] + stds =[img.std(),] + cuts = [means[-1] + stds[-1]*nsigma,] + for i in range(3): + imc = img[img < cuts[-1]] + means.append( imc.mean() ) + stds.append( imc.std() ) + cuts.append( means[-1] + stds[-1]*nsigma ) + # return means, stds, cuts + c = cuts[-1].copy() + return c + + +def fix_frelon_lines(img, cut=None, block=256): + """ + Reduce readout artifacts from frelon fast readout + """ + if cut is None: + cut = sigma_cut( img, nsigma=2 ) + assert img.shape[1]%block == 0 + blocked = np.reshape( np.where(img 0).astype(int) + np.add( m[1:], mask[:-1], m[1:] ) + np.add( m[:-1], mask[1:], m[:-1] ) + np.add( m[:,1:], mask[:,:-1], m[:,1:] ) + np.add( m[:,:-1], mask[:,1:], m[:,:-1] ) + return (mask > 0) & (m > 1) + + +def to_coo( data, mask, name ): + """ + Convert an image (2D, probably float) and mask (0 versus !0) + to a sparse array (could be more efficient...) + """ + m = mask.astype(bool) + j = np.outer(np.ones( data.shape[0], dtype=np.uint16), + np.arange(data.shape[1], dtype=np.uint16) ) + ind0, ind1 = j.T[m], j[m] + # round to the nearest 0.125 + values = np.round( data[m] * 8 ) / 8 + obj = sparse_peak_searching.sparse_frame( ind0, ind1, values, + shape = data.shape, + name = name) + return obj + + +def segment( fname, bg ): + """ Does a segmentation of a single file/frame """ + imo = fabio.open(fname) + imb = imo.data.astype( np.float32 ) - bg + #print (type(imb)) + imc = fix_frelon_lines( imb, block=128 ) + s = sigma_cut(imc) + m = imc > s + cleanmask = m.astype(np.int8) + cImageD11.clean_mask( m.astype(np.int8), cleanmask ) + spar = to_coo( imc, cleanmask, fname ) + spar.sigma_cut = s + spar.con_labels() + spar.max_labels() + print(fname,spar.nmlabel) + return spar + +def write_frames_to_hdf( frames, + hdfname, + hdffolder, + opts ): + h = h5py.File( hdfname, "w" ) + for f in frames: + f.tohdf( h ) + h.close() + + + +def segment_folder( folder , nimages=900 + , extn=".edf.gz" ): + print("Starting", folder) + bg = fabio.open("pks/bkg.edf").data + start = time.time() + fnames = [ os.path.join( folder, folder + "%04d%s"%(i, extn)) for i + in range(nimages) ] + spars = [segment(fname, bg) for fname in fnames ] + return (spars, folder) + + +def dofolder(f): + start = time.time() + spars, folder = segment_folder( f ) + print("Segmented", folder, time.time()-start) + hname = "hdfs3/%s.hdf"%(folder) + opts = { "compression" : "gzip", + "compression_opts" : 4 } + sparse_peak_searching.sparse_frames_to_hdf( hname, f, spars ) + #write_frames_to_hdf( spars, hname, folder, opts ) + print("Wrote", folder, time.time()-start) + +if __name__=="__main__": + folders =[ f for f in sorted( glob.glob("Au6_s0*") ) if os.path.isdir(f) ] + if not os.path.exists("hdfs3"): + os.mkdir("hdfs3") + if 1: + import multiprocessing + p = multiprocessing.Pool( processes = multiprocessing.cpu_count() ) + for x in p.map(dofolder, folders): + pass + else: + for x in folders: + dofolder(x) + + + + + + + + + diff --git a/sandbox/completeness.py b/sandbox/completeness.py new file mode 100644 index 00000000..01de63b3 --- /dev/null +++ b/sandbox/completeness.py @@ -0,0 +1,112 @@ + +import sys +import numpy as np, pylab as pl +from ImageD11 import refinegrains, unitcell, transform + + +# Need: + + + +parfile = sys.argv[1] +peaksfile = sys.argv[2] +grainsfile = sys.argv[3] +tolerance = 0.05 +OmFloat = True +OmSlop = 0.5 +nrings = 20 + +# one way ... +rgr = refinegrains.refinegrains( tolerance=tolerance, + OmFloat=OmFloat, + OmSlop=OmSlop) +rgr.loadparameters( parfile ) +rgr.loadfiltered( peaksfile ) +rgr.readubis( grainsfile ) +rgr.generate_grains( ) +print("Assigning peak labels") +rgr.assignlabels() + +# Compute a histogram of omega + +def anorm(x): + o = np.radians(x) + return np.arctan2( np.sin( o ), np.cos( o ) ) + +omr = anorm( rgr.scandata[peaksfile].omega ) +print(omr.min(), omr.max()) +osl = np.radians( OmSlop ) +obins = np.arange( -np.pi - osl/2, np.pi+osl, osl ) +omega_histogram, _ = np.histogram( omr, bins = obins ) + +pl.figure() +pl.plot( (obins[1:]+obins[:-1])/2, omega_histogram, "-") +pl.title("Omega histogram") + +print('Grain #peaks') + +for k in list(rgr.grains.keys()): + g = rgr.grains[k] + rgr.set_translation( *k ) + rgr.compute_gv( g, update_columns=True ) + print(k, g.ind.shape) + hkl_real = np.dot( g.ubi, rgr.gv.T ) + mod_g = np.sqrt( rgr.gv * rgr.gv ).sum( axis = 1 ) + hkl_int = np.round( hkl_real ).astype(int) + g_calc = np.dot( g.UB, hkl_int ) + g_error = rgr.gv.T - g_calc + mod_g_error = np.sqrt( ( g_error * g_error ).sum(axis=0) ) + sign_eta = np.sign( rgr.scandata[peaksfile].eta_per_grain[ g.ind ] ).astype(int) + # Got h,k,l,sign_eta + # pl.figure(2) + # pl.cla() + # pl.hist( mod_g_error, bins=np.linspace(0,0.01,100) ) + h,k,l = hkl_int + + observed_peaks = set( zip( h,k,l,sign_eta ) ) + print( len(observed_peaks) ) + + # Lattice / symmetry come in here ! + uc = unitcell.unitcell( g.unitcell, rgr.parameterobj.get('cell_lattice_[P,A,B,C,I,F,R]') ) + uc.makerings( mod_g.max()*1.05 ) + + wvln, wedg, chi = (rgr.parameterobj.get(p) for p in ('wavelength', 'wedge', 'chi')) + + for ds in uc.ringds[:nrings][::-1]: + hkls = uc.ringhkls[ds] + gcalc = np.dot( g.UB, np.transpose(hkls)) + tth, eta12, omega12 = transform.uncompute_g_vectors( gcalc, wvln, wedg, chi ) + m = 0 + hit = 0 + expected = 0 + for j,(h,k,l) in enumerate(hkls): + for i in range(2): + s = np.sign( eta12[i][j] ).astype(int) + if (h,k,l,s) in observed_peaks: + hit += 1 + m += 1 + obin = np.round(( anorm( omega12[i][j] ) - obins[0])/osl).astype(int) + if omega_histogram[ obin ] == 0 or tth[j] < 1e-3: + continue + # tth/eta map? + expected += 1 + + if expected > 0: + p = 100 * hit / expected + else: + p = 0 + + print("( %4d %4d % 4d ) %4d [ %4d / %4d ] = %6.2f %%"%(hkls[-1][0], + hkls[-1][1], + hkls[-1][2], + m, hit, expected, p) ) + + + + + +# TODO: +# Needs a space group symbol per grain +# Needs the list of rings (or hkls) to be tested +# Needs to check detector position for module gaps +# Ideally taking account of error in position and/or error in intensity diff --git a/sandbox/diffgeo.py b/sandbox/diffgeo.py new file mode 100644 index 00000000..e1ebcb31 --- /dev/null +++ b/sandbox/diffgeo.py @@ -0,0 +1,65 @@ + + +from __future__ import print_function, division + +import numpy as np +import timeit + +timer = timeit.default_timer + +# spots at x,y,z positions in 3D space. +# computed tth/eta in the past +# ... tth = angle with X axis, atan2( sqrt( Y*Y + Z*Z), X ) +# ... eta = angle with Z axis, sense from atan2( -Y, Z ) + +# later we use: +# -sin(theta) +# -cos(theta) sin(eta) [ note this is also minus due to the earlier -Y ] +# cos(theta) cos(eta) + +# Given X, Y, Z +def k_from_xyz(xyz, wvln): + """ + x,y,z = 3D peak positions, origin as 0,0,0 + wvln = wavelength + returns the k-vectors + + this was already buried inside src/cdiffraction.c + """ + w1 = 1.0 / wvln + M = w1 / np.linalg.norm( xyz, axis=0 ) + k = xyz * M + k[0] -= w1 + return k + +from ImageD11.transform import compute_k_vectors, compute_tth_eta_from_xyz + + +def test1(): + np.random.seed(42) + w = 40 / 12. + nv = 1024*1024 + xyz = 1e5 * (np.random.random( (nv, 3) ) + ( 0.2, -0.5, -0.5 )).T + old = [] ; new = [] + for i in range(5): + start = timer() + t, e = compute_tth_eta_from_xyz( xyz, None ) + k = compute_k_vectors( t, e, w ) + old.append( timer()-start ) + knew = k_from_xyz(xyz, w ) + start = timer() + ok = np.allclose( k, knew ) + new.append( timer() - start ) + if ok: + print("Matches", nv) + else: + print("Fails") + print("old perf", np.min(old), np.mean(old), np.std(old)) + print("new perf", np.min(new), np.mean(new), np.std(new)) + print("speedup", np.min(old)/np.min(new)) + + +if __name__ == "__main__": + test1() + + diff --git a/sandbox/diffracCl/diffracCl.c b/sandbox/diffracCl/diffracCl.c index c1d05cbf..0f82f134 100644 --- a/sandbox/diffracCl/diffracCl.c +++ b/sandbox/diffracCl/diffracCl.c @@ -27,6 +27,7 @@ #define DISTANCE 4 #define R 5 + #include @@ -41,29 +42,30 @@ /* This line is disgusting */ __declspec(dllexport) #endif -void ttheta( float *tth, - float *eta, - float *p , - int nx, - int ny) +void ttheta( float * restrict tth, + float * restrict eta, + float * restrict p , + int ns, + int nf) { // data[i,j] => i == slow, j == fast - int i, j, address,row; - float s,f,x,y,z,th,et; -#pragma omp parallel for private(s,f,x,y,z,j,address,i) - for(i=0;i i == fast, j == slow int i; int j; - float4 s,f,x,y,z; - i = get_global_id(0); - j = get_global_id(1); - int address = i + j * get_global_size(0); + float s,f,r,x,y,z; + j = get_global_id(0); + i = get_global_id(1); + int address = j * get_global_size(1) + i; - f = (4*i + range4 - p[FC]) * p[FS]; - s = (j - p[SC]) * p[SS]; + f = (i - p[FC]) * p[FS]; + s = (j - p[SC]) * p[SS]; /* |R0 R1 R2| |0| |R3 R4 R5|.|f| |R6 R7 R8| |s| */ @@ -41,7 +40,8 @@ __kernel void tthetaf4( __global float4 *tth, y = p[R+4]*f + p[R+5]*s; eta[address] = atan2pi( z, y ); x = p[R+1]*f + p[R+2]*s + p[DISTANCE]; - tth[address] = atan2pi( hypot( y, z) , x ); + r = sqrt( y*y + z*z ); + tth[address] = atan2pi( r , x ); } diff --git a/sandbox/diffracCl/diffracCl.py b/sandbox/diffracCl/diffracCl.py index 94526f01..33c3a2d1 100644 --- a/sandbox/diffracCl/diffracCl.py +++ b/sandbox/diffracCl/diffracCl.py @@ -1,16 +1,18 @@ +from __future__ import print_function, division import pyopencl as cl import numpy +import numpy as np import time, sys, os import ctypes +import timeit +timer = timeit.default_timer if "win" in sys.platform: - timer = time.clock dll = ctypes.CDLL("diffracCl.dll") print("# time.clock", sys.platform, sys.version) else: - timer = time.time dll = ctypes.CDLL("./diffracCl.so") print("# time.time", sys.platform, sys.version) @@ -26,7 +28,7 @@ def __init__(self, npx): print("#",d.name) self.npx = npx self.queue = cl.CommandQueue(self.ctx) - self.pars = numpy.zeros(14, dtype=numpy.float32) + self.pars = np.zeros(14, dtype=np.float32) def loadProgram(self, filename): #read in the OpenCL source file as a string @@ -42,7 +44,7 @@ def loadProgram(self, filename): def popCorn(self): mf = cl.mem_flags - nb = self.npx*self.npx*4 + nb = self.npx[0]*self.npx[1]*4 #create OpenCL buffers self.par_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, @@ -50,37 +52,32 @@ def popCorn(self): self.tth_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, nb ) self.eta_buf = cl.Buffer(self.ctx, mf.WRITE_ONLY, nb ) - + self.tthl = np.empty( self.npx, np.float32 ) + self.etal = np.empty( self.npx, np.float32 ) def execute(self): # start = timer() - evtcompute = self.program.tthetaf4(self.queue, - (self.npx/4, self.npx), - None, - self.tth_buf, - self.eta_buf, - self.par_buf) + evtcompute = self.program.tthetaf(self.queue, + self.npx, + None, + #(32,32), + self.tth_buf, + self.eta_buf, + self.par_buf) +# is_blocking=False ) #evtcompute.wait() #print timer()-start - self.tthl,evtt = cl.enqueue_map_buffer( self.queue, - self.tth_buf, - cl.map_flags.READ, - 0, - (self.npx,self.npx), - numpy.float32, - 'C', - wait_for = [evtcompute], - is_blocking=False) - self.etal,evte = cl.enqueue_map_buffer( self.queue, - self.eta_buf, - cl.map_flags.READ, - 0, - (self.npx,self.npx), - numpy.float32, - 'C', - wait_for = [evtcompute], - is_blocking=False) + evtt = cl.enqueue_copy( self.queue, + self.tthl, + self.tth_buf, + wait_for = [evtcompute], + is_blocking=False) + evte = cl.enqueue_copy( self.queue, + self.etal, + self.eta_buf, + wait_for = [evtcompute, evtt], + is_blocking=False) evtcompute.wait() evtt.wait() evte.wait() @@ -89,9 +86,10 @@ def execute(self): def setpars(self, pars ): self.pars = pars # print pars - cl.enqueue_write_buffer( self.queue, - self.par_buf, - self.pars).wait() + evt = cl.enqueue_copy( self.queue, + self.par_buf, + self.pars, is_blocking=True) + @@ -116,10 +114,10 @@ def compute( self, tth, eta, p): p = pars.ctypes.data_as(ctypes.POINTER(ctypes.c_float)) times = [] # warmup - self.dll.ttheta(t , e , p, self.npx, self.npx ) + self.dll.ttheta(t , e , p, self.npx[0], self.npx[1] ) for i in range(10): start = timer() - self.dll.ttheta(t , e , p, self.npx, self.npx ) + self.dll.ttheta(t , e , p, self.npx[0], self.npx[1] ) times.append( timer() - start ) return times @@ -132,24 +130,24 @@ def make_pars( parfile ): float(p.parameters["tilt_x"]), float(p.parameters["tilt_y"]), float(p.parameters["tilt_z"]) ) - fmat = numpy.array( [[ 1 , 0 , 0], + fmat = np.array( [[ 1 , 0 , 0], [ 0 , float(p.parameters['o11']), float(p.parameters['o12']) ], [ 0 , float(p.parameters['o21']), float(p.parameters['o22']) ]], - numpy.float32) - pars = numpy.array( [ float(p.parameters["y_center"]) , + np.float32) + pars = np.array( [ float(p.parameters["y_center"]) , float(p.parameters["y_size"]) , float(p.parameters["z_center"]) , float(p.parameters["z_size"]) , float(p.parameters["distance"]) ] + - list( numpy.dot( rmat, fmat).ravel() ) , - numpy.float32) + list( np.dot( rmat, fmat).ravel() ) , + np.float32) return pars if __name__ == "__main__": start = timer() - npx = 4096 + npx = 1024,2048 example = myCL(npx) example.loadProgram("diffracCl.cl") example.popCorn() @@ -159,29 +157,27 @@ def make_pars( parfile ): times = [] # Warmup tth, eta = example.execute() - for i in range(100): + for i in range(10): start = timer() - tth, eta = example.execute() + tth_cl, eta_cl = example.execute() times.append(timer()-start) - times = numpy.array(times) + times = np.array(times) print("# mean min max std") print("%.4f %.4f %.4f %.4f"%( times.mean(), times.min(), times.max(), times.std())) - t = numpy.median(times) + t = np.median(times) print("%.1f ms, %.1f fps,"%(1e3*t,1.0/t), end=' ') print(tth.max(),tth.min()) - eta_cl = eta.copy() - eta_ct = eta.copy() - tth_cl = tth.copy() - tth_ct = tth.copy() + eta_ct = np.empty( npx, np.float32) + tth_ct = np.empty( npx, np.float32) o = ctp( npx ) - times = numpy.array( o.compute( tth_ct, eta_ct, pars ) ) + times = np.array( o.compute( tth_ct, eta_ct, pars ) ) print("# ctypes module, hopefully with openmp") print("# mean min max std") print("%.4f %.4f %.4f %.4f"%( times.mean(), times.min(), times.max(), times.std())) - t = numpy.median(times) + t = np.median(times) print("%.1f ms, %.1f fps,"%(1e3*t,1.0/t), end=' ') print(tth.max(),tth.min()) diff --git a/sandbox/ev78/integrate_them.py b/sandbox/ev78/integrate_them.py index e965fbad..32c3176f 100755 --- a/sandbox/ev78/integrate_them.py +++ b/sandbox/ev78/integrate_them.py @@ -1,5 +1,5 @@ #!/usr/bin/python - +from __future__ import print_function import sys #sys.path.append('/users/wright/software/lib/python') @@ -43,9 +43,9 @@ def edffilenameseries(stem, first=None, last=None, glob=False, extn=".edf"): """ if glob: import glob, os - files = glob.glob("%s*%s"%(stem,extn)) + files = glob.glob("%s*%s"%(stem,extn)) filesdone = glob.glob("%s*%s"%(stem,".dat")) - filesdone = [f.replace(".dat",extn) for f in filesdone] + filesdone = [f.replace(".dat",extn) for f in filesdone] for f in files: if f not in filesdone: yield f @@ -382,16 +382,6 @@ def check_pars( o ): -def polstuff(): - # Now look for the polarisation component - stth = numpy.sin(integrator.twoThetaArray(data.shape)) - cchi = numpy.cos( integrator.chiArray(data.shape)*2 ) - pfac = cchi*stth*stth - #print p.min(),p.max(),stth.min(),stth.max() - #numpy.divide( calcimage, p , calcimage ) - t.bench("computed image") - poldifference = (data - calcimage)*mask*pfac - if __name__ == "__main__": from optparse import OptionParser diff --git a/sandbox/ev78/makevols.py b/sandbox/ev78/makevols.py index b4903926..2dbb4a52 100644 --- a/sandbox/ev78/makevols.py +++ b/sandbox/ev78/makevols.py @@ -1,4 +1,6 @@ +from __future__ import print_function + import fabio, numpy as np, h5py # read in .txt file and .dat files @@ -8,7 +10,7 @@ def readtxt(fname): Reads txt file written by spec and fills in table of: filenumber, diffty, diffrz, pico3, current """ - cnext = None + cnext = cindex = None h = {} vals = [] ctrs = "diffty pico3 notsrcu".split() diff --git a/sandbox/ev78/watch_direcs.py b/sandbox/ev78/watch_direcs.py index 3aa82482..3268b6a4 100644 --- a/sandbox/ev78/watch_direcs.py +++ b/sandbox/ev78/watch_direcs.py @@ -1,4 +1,5 @@ +from __future__ import print_function import glob, os, sys, time diff --git a/sandbox/factors.py b/sandbox/factors.py index 0dbaec8c..ce1af77a 100644 --- a/sandbox/factors.py +++ b/sandbox/factors.py @@ -45,7 +45,7 @@ def generatedata(self): if self.svd is not None: l,s,r=self.svd # left , singularvals, right nf=self.nfactors - I=np.identity(nf,np.float) + I=np.identity(nf,float) suse=I*s[:nf] ls = np.dot(l[:,:nf], suse) self.gendata=np.dot(ls,r[:nf,:]) @@ -74,10 +74,10 @@ def savesvd(self,filename): s.shape[0], r.shape[0], r.shape[1])) - print(len(l.astype(np.float).tostring())) - out.write(l.astype(np.float).tostring()) - out.write(s.astype(np.float).tostring()) - out.write(r.astype(np.float).tostring()) + print(len(l.astype(float).tostring())) + out.write(l.astype(float).tostring()) + out.write(s.astype(float).tostring()) + out.write(r.astype(float).tostring()) out.close() def loadsvd(self,filename): @@ -87,9 +87,9 @@ def loadsvd(self,filename): out=open(filename,"rb") dims=struct.unpack("lllll",out.read(struct.calcsize("lllll"))) print(dims,8*dims[0]*8*dims[1]) - l=np.fromstring(out.read(8*dims[0]*dims[1]),np.float) - s=np.fromstring(out.read(8*dims[2]),np.float) - r=np.fromstring(out.read(8*dims[3]*dims[4]),np.float) + l=np.fromstring(out.read(8*dims[0]*dims[1]),float) + s=np.fromstring(out.read(8*dims[2]),float) + r=np.fromstring(out.read(8*dims[3]*dims[4]),float) print(l.shape,s.shape,r.shape,dims) l=np.reshape(l,(dims[0],dims[1])) r=np.reshape(r,(dims[3],dims[4])) @@ -122,8 +122,8 @@ def saveobsdata(self,filename): out.write(struct.pack("lll",self.x.shape[0], self.obsdata.shape[0], self.obsdata.shape[1])) - out.write(self.x.astype(np.float).tostring()) - out.write(self.obsdata.astype(np.float).tostring()) + out.write(self.x.astype(float).tostring()) + out.write(self.obsdata.astype(float).tostring()) out.close() def readobsdata(self,filename): @@ -132,8 +132,8 @@ def readobsdata(self,filename): sizes=struct.unpack("lll",infile.read(struct.calcsize("lll"))) # Type is Float therefore 8 bytes per item print(sizes) - self.x=np.fromstring(infile.read(sizes[0]*8),np.float) - self.obsdata=np.fromstring(infile.read(8*sizes[1]*sizes[2]),np.float) + self.x=np.fromstring(infile.read(sizes[0]*8),float) + self.obsdata=np.fromstring(infile.read(8*sizes[1]*sizes[2]),float) print(self.obsdata.shape) self.obsdata=np.reshape(self.obsdata,(sizes[1],sizes[2])) print(self.x.shape,self.obsdata.shape) diff --git a/sandbox/fazit.py b/sandbox/fazit.py new file mode 100644 index 00000000..6ba2c5b3 --- /dev/null +++ b/sandbox/fazit.py @@ -0,0 +1,380 @@ + +from __future__ import print_function, division +""" +fast radial regrouping method proposed by Peter Boesecke +""" +import numpy as np, pylab as pl +import timeit, sys +from ImageD11 import cImageD11 +timer = timeit.default_timer + +try: + from numba import jit, prange + GOTNUMBA = True + print("Got numba") +except: + print("No numba") + GOTNUMBA = False + def jit(pyfunc=None, **kwds): + def wrap(func): + return func + if pyfunc is not None: + return wrap(pyfunc) + else: + return wrap + def prange(*a): + return range(*a) + +if sys.version_info[0] == 2: + prange = range + +# 2018 code: +# compute radius / azimuth +# integer bin for radius +# sort on radial bin, then azimuth (or vice versa) +# +# Re-order an input array to write output +# no splits or sums n->n mapping +# +@jit(nopython=True, parallel=sys.version_info[0]>2) +def compute_r_chi( dims, pars ): + """ + Replace this with pyFAI / ImageD11 / whatever + dims = array dimension + pars = center in slow/fast + returns r/chi (float) + """ + r = np.empty( dims, np.float32 ) + c = np.empty( dims, np.float32 ) + p0 = pars[0] + p1 = pars[1] + ns = dims[0] + nf = dims[1] + for i in prange( ns ): + di = i - p0 + for j in range( nf ): + dj = j - p1 + r[i,j] = np.sqrt( di*di + dj*dj ) + c[i,j] = np.arctan2( di, dj ) + return r, c + + + + +N = 2048 +M = 2048 +dims = ( N, M ) +pars = 1022.1, 1018.5, 1.25 +t0 = timer() +rad, chi = compute_r_chi( dims, pars ) +t1 = timer() +rad, chi = compute_r_chi( dims, pars ) +t2 = timer() +print("Setting up, r_chi",t1-t0,t2-t1) + +arg = np.round( rad.ravel() / pars[2] ) + chi.ravel()/2/np.pi +start=timer() +lut = np.argsort(arg).astype(np.uint32) +adrout = np.argsort(lut).astype(np.uint32) +# required to save +# length of radial rows, r, IR and chi value for each +# TODO: optimise the order to apply lut +# assert len(set(lut)) == len(lut) + +gdata = (np.random.random( (N,M) )*256).astype( np.uint16 ) +for i in range(0,N,256): + gdata[i:i+120] += 256 + +p = np.empty( (N,M), np.float32 ) +pk = 50 +while pk < rad.max(): + dr = ((rad - pk)**2)/20. + p += 500*np.exp( -dr ) + pk = pk + 200 + pk/10. +gdata += p.astype(np.uint16) + +def measure( func, name, lut, adrout, f32=False ): + NFRAME = 500//8 # overfill the cache (500 MB) + if f32: + data = [gdata.copy().astype(np.float32) for i in range(NFRAME)] + else: + data = [gdata.copy() for i in range(NFRAME)] + out = np.zeros_like(data[0]) + w, r = func( lut, adrout, data[NFRAME-1], out ) + dt = [] + for i in range(NFRAME): + d = data[i] + t0 = timer() + func ( lut, adrout, d, out ) + dt.append(timer()-t0) + if np.sum(dt) > 15: + break + dt = np.array(dt) + t = np.median(dt)#.mean() + te = dt.std() + s = name + "% 6.3f ms (std % 6.2f), write % 7.0f MB/s, read % 7.0f MB/s %g MB"%( + t*1e3, te*1e3, w/t/1e6, r/t/1e6,r/1e6) + print(s) + if f32: + return out.astype( gdata.dtype ) + else: + return out + +@jit(nopython=True, parallel=sys.version_info[0]>2, nogil=True) +def memcp(a,b): + for i in prange(a.size): + b.flat[i] = a.flat[i] + +dest = np.empty_like(gdata) +memcp(gdata,dest) +t = [timer(),] +for i in range(10): + memcp(gdata, dest) + t.append(timer()) +t = np.array(t) +t = t[1:]-t[:-1] +print("memcpy %.2f s %.2f MB/s"%( t.min(), gdata.nbytes/t.min()/1e6 )) + +ao = np.arange( N*M, dtype=np.uint32 ) +# lut is sorted for reads. Use cachelen blocks instead +a32 = lut.copy() +a32.shape = a32.size//32 , 32 +maxread = a32.max( axis=1 ) +run_order = np.argsort( maxread ) +print(run_order.shape) +l32 = a32[ run_order ].ravel() +print(l32.shape) +i32 = ao.reshape( a32.shape )[ run_order ].ravel() +assert l32.shape == lut.shape + +# lut is sorted for reads. Use cachelen blocks instead +d32 = adrout.copy() +d32.shape = d32.size//32 , 32 +maxread = d32.max( axis=1 ) +run_order = np.argsort( maxread ) +print(run_order.shape) +m32 = d32[ run_order ].ravel() +print(l32.shape) +j32 = ao.reshape( d32.shape )[ run_order ].ravel() +assert j32.shape == lut.shape +assert m32.shape == lut.shape + + + +def fazit_numpy_write( lut, adrout, data, out ): + out.ravel()[:] = data.ravel()[lut] + writes = out.itemsize * out.size + reads = data.itemsize*data.size + lut.itemsize*lut.size + return writes, reads + +trans = measure( fazit_numpy_write, "Numpy write ", lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + + + +@jit(nopython=True, parallel=sys.version_info[0]>2, nogil=True) +def fazit_sortedwrite( lut, adrout, data, out ): + npx = lut.size + for i in prange(npx): + out.flat[i] = data.flat[lut.flat[i]] + writes = out.itemsize * out.size + reads = data.itemsize*data.size + lut.itemsize*lut.size + return writes, reads + +trans = measure( fazit_sortedwrite, "Sorted write ", lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + +@jit(nopython=True, parallel=sys.version_info[0]>2, nogil=True) +def fazit_sortedread( lut, adrout, data, out ): + npx = lut.size + for i in prange(npx): + out.flat[adrout.flat[i]] = data.flat[i] + writes = out.itemsize * out.size + reads = data.itemsize*data.size + adrout.itemsize*adrout.size + return writes, reads + +trans = measure( fazit_sortedread, "Sorted read ", lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + + +@jit(nopython=True, parallel=sys.version_info[0]>2, nogil=True) +def fazit_both( lut, adrout, data, out ): + npx = lut.size + for i in prange(npx): + out.flat[adrout.flat[i]] = data.flat[lut.flat[i]] + writes = out.itemsize * out.size + reads = data.itemsize*data.size + adrout.itemsize*adrout.size + lut.itemsize*lut.size + return writes, reads + +@jit(nopython=True, parallel=sys.version_info[0]>2, nogil=True) +def fazit_b32out( lut, a32, data, out ): + npx = lut.size + for i in prange(0,npx//32): + p = a32[i] + for j in range(32): + out.flat[p+j] = data.flat[lut.flat[i*32+j]] + writes = out.itemsize * out.size + reads = data.itemsize*data.size + a32.itemsize*a32.size + lut.itemsize*lut.size + return writes, reads + + + + + +trans = measure( fazit_both, "sort write 2Id", lut, ao ) +assert (trans.ravel() == gdata.flat[lut]).all() + +trans = measure( fazit_both, "sort reads 2Id", ao, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + +trans = measure( fazit_both, "sort blk32r2Id", j32 , m32) +assert (trans.ravel() == gdata.flat[lut]).all() + +trans = measure( fazit_both, "sort blk32o2Id", l32 , i32) +assert (trans.ravel() == gdata.flat[lut]).all() + +io32 = i32.reshape(i32.size//32,32)[:,0].copy() +trans = measure( fazit_b32out , "sort b32o Id", l32 , io32) +assert (trans.ravel() == gdata.flat[lut]).all() + + + +@jit(nopython=True, parallel=sys.version_info[0]>2, nogil=True) +def fazit_u32_i16( u32, i16, data, out ): + ns = i16.shape[0] + nf = i16.shape[1] + for i in prange( ns ): + a0 = u32[i] + i0 = i*nf + for j in range( nf ): + a0 = a0 + i16.flat[i0 + j] + out.flat[a0] = data.flat[i0 + j] + writes = out.itemsize * out.size + reads = data.itemsize*data.size + u32.itemsize*u32.size + i16.itemsize*i16.size + return writes, reads + +trans = measure( fazit_sortedread, "Sorted read ", lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + + +def fazit_f2py( lut, adrout, data, out): + cImageD11.reorder_u16_a32( data.ravel(), adrout, out.ravel()) + writes = out.nbytes + reads = data.nbytes + adrout.nbytes + return writes, reads + +trans= measure( fazit_f2py, "f2py sort read", lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + +def fazit_f2py_lut( lut, adrout, data, out): + cImageD11.reorderlut_u16_a32( data.ravel(), lut.ravel(), out.ravel()) + writes = out.nbytes + reads = data.nbytes + lut.nbytes + return writes, reads + +othr = cImageD11.cimaged11_omp_get_max_threads() +trans= measure( fazit_f2py_lut, "f sortwrite %d"%(othr), lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + + +def fazit_f2py32( lut, adrout, data, out ): + cImageD11.reorder_f32_a32( data.ravel(), adrout, out.ravel()) + writes = out.nbytes + reads = data.nbytes + adrout.nbytes + return writes, reads + +trans= measure( fazit_f2py32, "f32 sort read", lut, adrout, f32=True ) +assert (trans.ravel() == gdata.flat[lut]).all() + +def fazit_f2py_lut32( lut, adrout, data, out): + cImageD11.reorderlut_f32_a32( data.ravel(), lut.ravel(), out.ravel()) + writes = out.nbytes + reads = data.nbytes + lut.nbytes + return writes, reads + +othr = cImageD11.cimaged11_omp_get_max_threads() +trans= measure( fazit_f2py_lut32, "f32 sortwrt %d"%(othr), lut, adrout, f32=True ) +assert (trans.ravel() == gdata.flat[lut]).all() + + + +for nthr in range(7): + cImageD11.cimaged11_omp_set_num_threads(2**nthr) + gthr = cImageD11.cimaged11_omp_get_max_threads() + trans= measure( fazit_f2py_lut, "f sortwrite %d"%(gthr), lut, adrout ) + assert (trans.ravel() == gdata.flat[lut]).all() + +cImageD11.cimaged11_omp_set_num_threads(othr) +gthr = cImageD11.cimaged11_omp_get_max_threads() +trans= measure( fazit_f2py_lut, "f sortwrite %d"%(gthr), lut, adrout ) +assert (trans.ravel() == gdata.flat[lut]).all() + + + + + +def fazit_f2py_i16( u32, i16, data, out): + cImageD11.reorder_u16_a32_a16( data.reshape(i16.shape), + u32, + i16, + out.reshape(i16.shape) ) + writes = out.nbytes + reads = data.nbytes + u32.nbytes + i16.nbytes + return writes, reads + + +k = 1 +while k<2048: + try: + a = adrout.reshape(N*k,M//k) + except: + k *=2 + continue + u32 = a[:,0].copy().astype(np.int32) + i16 = np.zeros(a.shape, np.int16) + da = a[:,1:].astype(int) - a[:,:-1] + i16[:,1:] = da + assert np.alltrue( i16[:,1:] == da ) , "clipping issue" + # if k == 1: + print("highest jump was",abs(da).max()) + trans = measure( fazit_u32_i16, "u32 i16 %4d "%(k), u32, i16 ) + assert (trans.ravel() == gdata.flat[lut]).all() + for nthr in range(7): + cImageD11.cimaged11_omp_set_num_threads(2**nthr) + gthr = cImageD11.cimaged11_omp_get_max_threads() + trans = measure( fazit_f2py_i16, "f2pyi16 %2d%4d"%(k, gthr), u32, i16 ) + assert (trans.ravel() == gdata.flat[lut]).all() + + cImageD11.cimaged11_omp_set_num_threads(othr) + gthr = cImageD11.cimaged11_omp_get_max_threads() + trans = measure( fazit_f2py_i16, "f2pyi16 %2d%4d"%(k, gthr), u32, i16 ) + assert (trans.ravel() == gdata.flat[lut]).all() + k *= 2 + + + + + + +if 0: + pl.subplot(221) + pl.imshow(arg.reshape(N,N)) + pl.title("radius") + pl.colorbar() + pl.subplot(223) + pl.imshow(chi) + pl.title("azimuth") + pl.colorbar() + pl.subplot(222) + pl.imshow(lut.reshape(dims),aspect='auto', + interpolation='nearest') + pl.title("Source address to come from") + pl.subplot(224) + pl.imshow(adrout.reshape(dims),aspect='auto', + interpolation='nearest') + pl.title("Output address to go to") + pl.figure() + pl.imshow(gdata,interpolation='nearest' ,aspect='auto') + pl.figure() + pl.imshow( trans.reshape(N,N).T, interpolation='nearest', aspect='auto' ) + pl.show() diff --git a/sandbox/fazit_funcs.py b/sandbox/fazit_funcs.py new file mode 100644 index 00000000..9cf549be --- /dev/null +++ b/sandbox/fazit_funcs.py @@ -0,0 +1,137 @@ + +import numpy as np +import hdf5plugin, fabio +from ImageD11 import blobcorrector, parameters, transform +import numba + + + +def compute_tth_eta_lut( splinefile, pars, dims): + """ + Computes look up values of tth, eta for each pixel + """ + c = blobcorrector.correctorclass( splinefile ) + p = parameters.read_par_file( pars ) + xp, yp = c.make_pixel_lut( dims ) + t, e = transform.compute_tth_eta( (xp.ravel(), yp.ravel()), + **p.parameters ) + t.shape=dims + e.shape=dims + return t, e + + +def tth_guess_bins( tth, npx = 1 ): + one_px = (tth[1:] - tth[:-1]).max() + binsize = one_px * npx + itth = np.round(tth / binsize) + return itth + + +def make_lut( itth, eta ): + ie = 1./361 + sorter = (itth + (ie*eta)).ravel() + lut = np.argsort( sorter ).astype( np.uint32 ) # read from here + adrout = np.argsort( lut ).astype( np.uint32 ) # write to here + return lut, adrout + + +def forward( data, lut, adrout ): + out = np.empty_like( data ) + out.flat = data.flat[lut] + return out + +def back( data, lut, adrout ): + out = np.empty_like( data ) + out.flat = data.flat[adrout] + return out + +@numba.njit +def forward_filt( data ): + bg = np.empty( data.shape, np.float32 ) + b = data[0] + bg[0] = b + gain = 0.5 + sigmap = 0.2 + sigmat = 25. + msk = np.empty( data.shape, np.uint8 ) + for i in range( 1, len(data) ): + diff = data[i] - b + t = sigmap * b + sigmat + if diff > t: + diff = t * diff / abs(diff) / 16 + msk[i] = 1 + elif diff < -t: + diff = t * diff / abs(diff) / 4 + msk[i] = 1 + else: + msk[i] = 0 + b += diff * gain + bg[i] = b + b = data[len(data)-1] + for i in range( len(data)-2, -2, -1 ): + diff = data[i] - b + t = sigmap * b + sigmat + if diff > t: + diff = t * diff / abs(diff) / 16 + msk[i] += 1 + elif diff < -t: + diff = t * diff / abs(diff) / 4 + msk[i] += 1 + else: + if msk[i] == 1: + bg[i] = b + if msk[i] == 0 or msk[i] == 2: + bg[i] = (bg[i]+b)/2 + b += diff * gain + return bg, msk + + + + +if __name__=="__main__": + import sys + splinefile = sys.argv[1] + pars = sys.argv[2] + # dims = int(sys.argv[3]), int(sys.argv[4]) + fname = sys.argv[3] + try: + mask = fabio.open(sys.argv[4]).data > 0 + except: + mask = None + data = fabio.open(fname).data + dims = data.shape + tth, eta = compute_tth_eta_lut( splinefile, pars, dims) + print("tth: %.3f - %.3f eta: %.3f - %.3f"%( + tth.min(), tth.max(), eta.min(), eta.max()) ) + itth = tth_guess_bins( tth, npx = 0.7) + print(itth.min(), itth.max()) + end = itth.max() + 1 + if mask is not None: + itth[mask] += end # throw the masked to the end + lut, adrout = make_lut( itth, eta ) + print(lut[0]) + + trans = forward( data, lut, adrout ) + check = back( trans, lut, adrout ) + + assert (check == data).all() + + bg, mr =[x.reshape(trans.shape) for x in forward_filt( trans.ravel() )] + + bgim = back( bg, lut, adrout ) + mr2 = back( mr, lut, adrout ) + bg1d, m2 =[x.reshape(data.shape) for x in forward_filt( data.ravel() )] + + print(bg, bg.min(), bg.max()) + from matplotlib.colors import LogNorm + import pylab as pl + f, ((ax1, ax2),(ax3,ax4)) = pl.subplots( 2,2, figsize=(12,3), + sharex=True, sharey=True ) + ax1.imshow( data , vmax=1000, norm = LogNorm() ) + ax2.imshow( mr2, vmax=2,) + ax3.imshow( data-bg1d, vmin=-10, vmax=200 ) + ax4.imshow( data-bgim, vmin=-10, vmax=200 ) + + pl.show() + + diff --git a/sandbox/find_pseudo_twins.py b/sandbox/find_pseudo_twins.py index fcf1c821..883d2b32 100644 --- a/sandbox/find_pseudo_twins.py +++ b/sandbox/find_pseudo_twins.py @@ -1,4 +1,6 @@ +from __future__ import print_function + import glob, sys import numpy as np from ImageD11 import grain, sym_u @@ -48,13 +50,13 @@ hklr = np.dot( gl[0].ub, gv1 ) hkli = hklr.copy() rmodg2 = 1/(gv1*gv1).sum(axis=0) -angerr2 = np.zeros( npks, np.float ) +angerr2 = np.zeros( npks, float ) tol = 2.35*angtol*np.pi/180.0 tol2 = tol*tol -print "#", +print( "#", end = ' ') for i,g1 in enumerate(gl): np.dot( g1.ub, hkls, out=gv1 ) for j in range(i+1, len(gl)): @@ -80,14 +82,14 @@ dist = np.sqrt( ((g1.translation-g2.translation)**2).sum() ) if dist > 500: continue - print "\n# grains %d %d frac %.3f scor %.3f"%(i,j,tscor,scor.sum()), + print("\n# grains %d %d frac %.3f scor %.3f"%(i,j,tscor,scor.sum()),end=' ') rotangs = xfab.symmetry.Umis( g1.u, g2.u, 7 ) angs = [a[1] for a in rotangs] dist = np.sqrt( ((g1.translation-g2.translation)**2).sum() ) - print " misori %.3f dist %.1f"%(np.min(angs), dist) - print "# g1.t % 7.3f % 7.3f % 7.3f"%tuple(g1.translation) - print "# g2.t % 7.3f % 7.3f % 7.3f"%tuple(g2.translation) - print "# i (h k l) -> (h k l) Angle Scor " + print(" misori %.3f dist %.1f"%(np.min(angs), dist)) + print("# g1.t % 7.3f % 7.3f % 7.3f"%tuple(g1.translation)) + print("# g2.t % 7.3f % 7.3f % 7.3f"%tuple(g2.translation)) + print("# i (h k l) -> (h k l) Angle Scor ") ip = 0 order = np.argsort(scor) for k in order[::-1]: @@ -95,7 +97,7 @@ h1,k1,l1 = hkls.T[k] h2,k2,l2 = hklr.T[k] ip += 1 - print "%d ( % d % d % d ) -> ( % .3f % .3f % .3f ) %.3f %.5f"%( ip, - h1,k1,l1, h2,k2,l2, np.degrees(np.sqrt(angerr2[k])),scor[k]) - print "#", + print( "%d ( % d % d % d ) -> ( % .3f % .3f % .3f ) %.3f %.5f"%( ip, + h1,k1,l1, h2,k2,l2, np.degrees(np.sqrt(angerr2[k])),scor[k])) + print("#",end=' ') diff --git a/sandbox/fit_rotating.py b/sandbox/fit_rotating.py new file mode 100644 index 00000000..f411d728 --- /dev/null +++ b/sandbox/fit_rotating.py @@ -0,0 +1,365 @@ + +from __future__ import print_function + +from scipy.spatial.transform import Rotation, RotationSpline +from ImageD11 import grain, columnfile, cImageD11, transform +import sys, numpy as np, pylab as pl, os + +folder = os.path.split(os.getcwd())[-1] + +c = columnfile.columnfile( folder + "_t500.flt")# sys.argv[1] ) +c.sortby('omega') +c.parameters.loadparameters( "../e2.par" ) # sys.argv[2] ) +c.parameters.parameters['t_x']=0. +c.parameters.parameters['t_y']=0. +c.parameters.parameters['t_z']=0. +c.updateGeometry() +c.filter( abs( np.sin( np.radians( c.eta ))) > 0.05 ) + +g = grain.read_grain_file( folder+".map" )[0]# sys.argv[3] )[0] + +try: + NROT = int(sys.argv[1]) +except: + NROT = 1 +try: + JUMP = float(sys.argv[2]) #-116.5 +except: + JUMP = None + + + +gv = np.array( (c.gx, c.gy, c.gz) ).T.copy() + +for name in 'h,k,l'.split(','): + c.addcolumn(np.zeros(c.nrows),name) + +step = c.nrows//10 + 1 +start = 0 + +results = [] + +while start < c.nrows: + end = min(start + step, c.nrows) + gsel = gv[start:end].copy() + order = np.argsort( c.ds[start:end] ) + gfit = gsel[order] + ubi = g.ubi.copy() + print(start,end,c.omega[start:end].mean(),end=" " ) + cImageD11.score_and_refine( ubi, gfit[:len(gfit)//3], 0.5) + cImageD11.score_and_refine( ubi, gfit[:len(gfit)//3], 0.5) + cImageD11.score_and_refine( ubi, gfit, 0.5) + cImageD11.score_and_refine( ubi, gfit, 0.5) + n, e = cImageD11.score_and_refine( ubi, gfit, 0.25) + print(e,n,len(gfit)) + results.append(( c.omega[start:end].mean(), n, e, end-start, ubi )) + hkl = np.round( np.dot( ubi, gsel.T ) ) + c.h[start:end]=hkl[0] + c.k[start:end]=hkl[1] + c.l[start:end]=hkl[2] + start = end + +# c.writefile("assigned.flt") + +c.filter( (abs(c.h)+abs(c.k)+abs(c.l)) > 0) + + +class calcgrain: + + def __init__(self, hkl, sign_eta): + assert hkl.shape[0] == 3 + self.hkl = hkl # peaks to be used + self.sign_eta = sign_eta + + def gcalc( self, ub_or_spline ): + """ send and average orientation of average plus rotation spline """ + if isinstance( ub_or_spline[0], RotationSpline ): + rs, ub = ub_or_spline + self.gve = np.dot( ub, self.hkl ) + bins = np.round( np.linspace( 0, len(self.gve[0]), NROT*10) ).astype(int) + for i in range(len(bins)-1): + bi = (bins[i]+bins[i+1])/2 + self.gve[:, bins[i]:bins[i+1]] = np.dot(rs(bi).as_dcm(), + self.gve[:, bins[i]:bins[i+1]]) + else: + ub = ub_or_spline + self.gve = np.dot( ub, self.hkl ) + return self.gve + + def angcalc( self, ub, wvln, wedge=0., chi=0.): + gc = self.gcalc( ub ) + self.tth, (e0,e1), (o0,o1) = transform.uncompute_g_vectors( + gc, wvln, wedge=wedge, chi=chi ) + assert e0.max() > 0 and e0.min() >= 0. + assert e1.max() <=0 and e1.min() < 0 # negatives + self.eta = np.where( self.sign_eta > 0, e0, e1 ) + self.omega = np.where( self.sign_eta > 0, o0, o1 ) + return self.tth, self.eta, self.omega + +detparskeys = ( 'y_center', 'y_size', 'z_center', 'z_size', + 'tilt_x', 'tilt_y', 'tilt_z', 'wavelength', + 'distance', 'o11', 'o12', 'o21', 'o22' ) + +class calcdata: + + def __init__(self, scfc): + self.sc, self.fc = scfc + self.xlylzl=None + + def getxlylzl( self, **detpars ): + self.xlylzl = transform.compute_xyz_lab( + (self.sc, self.fc), + **detpars ) + + def getttheta( self, trans, omega, wedge=0., chi=0. ): + #print("got trans",trans) + self.tth, self.eta = transform.compute_tth_eta_from_xyz( + self.xlylzl, omega, + t_x=trans[0], t_y=trans[1], t_z=trans[2], + wedge=wedge, chi=chi ) + return self.tth, self.eta + +og = calcgrain( np.array( (c.h, c.k, c.l) ), np.sign(c.eta) ) +od = calcdata ( np.array( (c.sc, c.fc)) ) +od.getxlylzl( **c.parameters.parameters ) + +ubiavg = np.array( [r[-1] for r in results] ).mean(axis=0) +ub = np.linalg.inv( ubiavg ) + + +tthc, etac, omegac = og.angcalc( ub, c.parameters.get('wavelength') ) +tx,ty,tz=0,0,0 +ttho, etao = od.getttheta( (tx, ty, tz), omegac ) # wedge, chi = 0 here + +def mod360( x ): + return np.mod( x + 180, 360 ) - 180 + + + + +import lmfit + +def guess_weight( diff, sigma_cut=5 ): + s = diff.std() + c = sigma_cut * s + m = abs(diff) < c + s = diff[m].std() + c = sigma_cut * s + m = abs(diff) < c + return m, diff/s + +def guess_flat( diff, slop=0.475 ): + d = mod360( diff ) + m0 = abs(d) < (slop*10) + m = abs(d) < slop + e = np.where( d > slop, d-slop, + np.where( d < -slop, d+slop, 0 ) ) + return m0, e + + +def B( a, b, c, al, be, ga): + ca, cb, cg = [ np.cos( np.radians( x )) for x in (al, be, ga)] + g = np.array( (( a*a, a*b*cg, a*c*cb), + ( a*b*cg, b*b, b*c*ca), + ( a*c*cb, b*c*ca, c*c)) ) + gi = np.linalg.inv( g ) + return np.linalg.cholesky( gi ).T + + +def U( rx, ry, rz ): + u = Rotation.from_euler( 'XYZ', [np.radians(x) for x in (rx,ry,rz)]).as_dcm() + return u + +def getUB(p): + x= np.dot( U0, + B( p['a'].value,p['b'].value,p['c'].value, + p['al'].value,p['be'].value,p['ga'].value ) ) + return x + +def residual( pars, x, cgr, cda, omega_obs, plot=False ): + """ + pars = lmfit.Parameters + x = ? + cgr = calc grain object + cda = calc data object + """ +# aub = np.array( [[pars['ub%d%d'%(i,j)].value for j in range(3)] for i in range(3)] ) + aub = getUB( pars ) + #Uxyz = U( pars['rx'].value, pars['ry'].value, pars['rz'].value ) + #aub = np.dot( Uxyz, aub ) + if NROT == 1: + aub = np.dot( Rotation.from_euler('XYZ', + (pars['rx0'].value, + pars['ry0'].value, + pars['rz0'].value), + degrees=True).as_dcm(), aub ) + else: + times = np.linspace( 0, c.nrows, NROT) + if JUMP is not None: + ijump = np.searchsorted( c.omega, JUMP ) + ji = np.searchsorted( times, ijump ) + times[ji] = ijump+1 + times[ji-1] = ijump-1 + angles = [ ( pars['rx%d'%(i)].value, pars['ry%d'%(i)].value, pars['rz%d'%(i)].value) + for i in range(NROT) ] + rs = RotationSpline( times , Rotation.from_euler('XYZ', angles, degrees=True) ) + aub = rs,aub + # FIXMEHERE SEND IN THE ROTATIONSPLINE ... +# print(aub) + # 1/0 + tthc, etac, omegac = cgr.angcalc( aub, pars['wavelength'], + wedge=pars['wedge'].value ) + arg = {k:pars[k].value for k in detparskeys} + cda.getxlylzl( **arg ) + ttho, etao = cda.getttheta( (pars['tx'].value, pars['ty'].value, pars['tz'].value), + omegac, wedge=pars['wedge'].value ) # wedge, chi = 0 here + mtth, etth = guess_weight( ttho - tthc ) + meta, eeta = guess_weight( etao - etac ) + mome, eome = guess_flat( omega_obs- omegac) +# m = np.ones( ttho.shape, dtype=bool) + m = mtth & meta & mome + e= np.concatenate( (np.where( m, etth,0), + np.where( m, eeta,0), + np.where( m, eome,0) ) ) + + if plot: + print('in residual', m.sum(), (e*e).sum()) + pl.figure() + pl.subplot(231) + pl.plot( tthc, tthc-ttho, "." ) + pl.plot( tthc[~m], (tthc-ttho)[~m], "+" ) + pl.title(' tth ') + pl.subplot(232) + pl.plot( etac, etac-etao, ".") + pl.plot( etac[~m], (etac-etao)[~m], "+") + pl.title(' eta ');pl.ylabel('deg') + pl.subplot(233) + pl.plot( c.omega, mod360(omegac-c.omega), ".") + pl.plot( c.omega[~m], mod360(omegac-c.omega)[~m], "+") + pl.title(' omega ') + pl.subplot(234) + pl.plot( tthc, etth*m, "." ) + pl.title(' tth ') + pl.subplot(235) + pl.plot( omegac, eeta*m, ".") + pl.title(' eta ') + pl.subplot(236) + pl.plot( omegac, eome*m, ".") + pl.title(' omega ') + if NROT > 1: + pl.figure() + j = np.round(np.linspace( 0, len(c.omega), NROT*10)).astype(int) + oj = [c.omega[j[i]:j[i+1]].mean() for i in range(len(j)-1) ] + r = [rs((j[i]+j[i+1])/2) for i in range(len(j)-1)] + pl.plot( oj, [x.as_euler('XYZ',degrees=True)[0] for x in r], 'o-', label='rx') + pl.plot( oj, [x.as_euler('XYZ',degrees=True)[1] for x in r], 'o-', label='ry') + pl.plot( oj, [x.as_euler('XYZ',degrees=True)[2] for x in r], 'o-', label='rz') + pl.figure() + if NROT == 1: + ubi = np.linalg.inv( aub ) + else: + ub = np.dot( aub[0](0).as_dcm(), aub[1] ) + ubi = np.linalg.inv( ub ) + gve = transform.compute_g_vectors( ttho, etao, c.omega, + pars['wavelength'].value, + wedge = pars['wedge'].value ) + h,k,l = np.dot( ubi, gve ) + + + pl.scatter( abs(h-np.round(h)), abs(k-np.round(k)), c=abs(l-np.round(l)), s = pow( c.sum_intensity, 1/3)) + pl.axes().set_aspect('equal') + pl.xlim(-0.6,0.6) + pl.ylim(-0.6,0.6) + pl.xticks(np.linspace(-0.5,0.5,3)) + pl.yticks(np.linspace(-0.5,0.5,3)) + pl.grid(True) + pl.colorbar() + pl.show() + return e + + + + + +print(ub) +print("check") + + +p = lmfit.Parameters() +for k in detparskeys: + p.add( k, c.parameters.get( k ), vary = False ) + +p.add('tx',0, vary=True) +p.add('ty',0, vary=True) +p.add('tz',0, vary=True) +p.add('wedge',0,vary=False) + +import ImageD11.indexing +U0 = ImageD11.indexing.ubi_to_u( ubiavg ) +for name, val in zip( 'a,b,c,al,be,ga'.split(','), + ImageD11.indexing.ubitocellpars( ubiavg) ): + p.add( name, val, vary= True) + + +for i in range(NROT): + p.add('rx%d'%(i),0,vary=True) + p.add('ry%d'%(i),0,vary=True) + p.add('rz%d'%(i),0,vary=True) + +#for i in range(3): +# for j in range(3): +# p.add( "ub%d%d"%(i,j), ub[i,j], vary=True) + +x = np.arange(c.nrows*3, dtype=np.int ) + + +#e0 = residual( p, x, og, od, c.omega , plot=False) +#p['ub00'].value+=1e-8 +#e1 = residual( p, x, og, od, c.omega , plot=False) +#pl.plot( (e0-e1)*1e8,".") +#pl.show() +#1/0 + +resids={} +def iter_cb( p, i, r, *a, **k): + resids[i] = r.copy() + print(i, (r*r).sum())#, end=" " ) +# for k in resids.keys(): +# if i == k : continue +# if (resids[i] == r).all(): +# print("==",k,end=" ") +# print() + +#import pdb;pdb.set_trace() +out = lmfit.minimize( residual, p, args=(x, og, od, c.omega ), + nan_policy='raise', + iter_cb=iter_cb, + ) +print(lmfit.fit_report( out )) + + + +p = out.params +residual( p, x, og, od, c.omega , plot=True) +sys.exit() +p['tilt_x'].vary = True +p['tilt_y'].vary = True +p['tilt_z'].vary = True +p['wedge'].vary = True +p['y_center'].vary=True +p['distance'].vary=True +#p['z_center'].vary=True + +out = lmfit.minimize( residual, p, args=(x, og, od, c.omega ) ) +print(lmfit.fit_report( out )) +p=out.params +residual( p, x, og, od, c.omega , plot=True) +p['distance'].vary = False +out = lmfit.minimize( residual, p, args=(x, og, od, c.omega ) ) +print(lmfit.fit_report( out )) +p=out.params +residual( p, x, og, od, c.omega , plot=True) + +for k in detparskeys: + print(k,p[k].value) +print('wedge',p['wedge'].value) diff --git a/sandbox/fittrans.py b/sandbox/fittrans.py index 3787b4e2..c6f80169 100644 --- a/sandbox/fittrans.py +++ b/sandbox/fittrans.py @@ -60,7 +60,7 @@ def XL_to_gv_old( omega, wedge, chi, XL, wavelength, tx, ty, tz): def XL_to_gv( omega, wedge, chi, XL, wavelength, tx, ty, tz): """ Compute gv by rotation of experiment around crystal """ - beam = np.zeros( (3,len(omega)), np.float) + beam = np.zeros( (3,len(omega)), float) beam[0,:] = 1.0 XB = k_to_g( beam, omega, wedge, chi) #print "XB",XB @@ -191,14 +191,14 @@ def refine(self, gr, tol=0.05): #print "after",len(pks),pks gr.pks = pks[use] else: - use = np.arange( len(gr.pks), dtype=np.int ) + use = np.arange( len(gr.pks), dtype=int ) #print "score = ", scor[pks].sum()/len(pks), len(pks), tol # peaks to use are those with scor OK # # UB.h = gvcalc # dg/dUB = h # dg/dT = found above - gcalc = np.dot( gr.ub, hkli ) + gcalc = np.dot( gr.UB, hkli ) diff = np.take(gv - gcalc, use, axis=1) # print diff.shape, pks.shape # gv[0],[1],[2] = 3 @@ -242,7 +242,7 @@ def refine(self, gr, tol=0.05): ty = ty - shifts[1] tz = tz - shifts[2] gr.translation = [tx,ty,tz] - ub = gr.ub.ravel() + ub = gr.UB.ravel() np.add(ub, shifts[3:], ub) gr.set_ubi( np.linalg.inv( np.reshape(ub,(3,3)))) gr.npks = len(use) @@ -319,7 +319,7 @@ def find_triplets(self, peak): # Take old peak assignments: if 1: print("Using existing peak assignments") - inds = np.arange(o.colfile.nrows,dtype=np.int) + inds = np.arange(o.colfile.nrows,dtype=int) for i,gref in enumerate(gl): gref.pks = np.compress( o.colfile.labels == i, inds ) tols = [None,]*3 diff --git a/sandbox/fittrans2.py b/sandbox/fittrans2.py new file mode 100644 index 00000000..c10c1176 --- /dev/null +++ b/sandbox/fittrans2.py @@ -0,0 +1,249 @@ + +from __future__ import print_function + + +from ImageD11.columnfile import columnfile +from ImageD11 import transform, parameters, unitcell, grain, gv_general +import numpy as np, time +from matplotlib import pylab +import numba + +def k_to_g(k, cosomega, sinomega, chiwedge, out = None ): + """ Rotate k vector to g vector, e.g. from lab to crystal """ + if out is None: + g = np.empty_like( k ) + else: + g = out + assert (g.shape == k.shape) and (g.dtype == float) + cwk = np.dot( chiwedge, k ) + g[0,:] = cosomega*cwk[0,:] + sinomega*cwk[1,:] + g[1,:] = -sinomega*cwk[0,:] + cosomega*cwk[1,:] + g[2,:] = cwk[2,:] + return g + +def XSXB_to_gv( XS, XB, tx, ty, tz, wavelength): + """ + XS - position of spot on detector (rotated) + XB - direction of X-ray beam (rotated) + tx, ty, tz - grain position + wavelength + Computes g-vector + """ + xst = (XS.T - [tx,ty,tz]).T + modxst = np.sqrt( (xst*xst).sum(axis=0) ) + g = (xst/modxst - XB)/wavelength + return g + +# derivatives: + +def d_XSXB_to_gv( XS, XB, tx, ty, tz, wavelength): + """ + As XSXB_to_gv but with derivative on tx, ty, tz + """ + dgdt = np.zeros( (3, 3, XS.shape[1]), dtype=float ) + rscale = 1/wavelength + # Xspot - X0 = scattered output rays + xst = XS.copy() + xst[0] -= tx + xst[1] -= ty + xst[2] -= tz + # xst = XS - np.array((tx, ty, tz))[:,np.newaxis] + # dxst_dt = -1 # derivatice w.r.t translation + # reciprocal length of vector squared + rmodxst2 = 1/(xst[0]*xst[0] + xst[1]*xst[1] + xst[2]*xst[2]) + # dmodxst2 = 2 + rmodxst = np.sqrt( rmodxst2 ) + # dmodxst_dt = dmodxst_dxst . dxst_dt + # dmodxst2_dt = -2*xst[0] , -2*xst[1], -2*xst[2] + # d(sqrt(f))/dx = 1/2 1/sqrt(f) . df/dx + dmodxst_dt = -xst*rmodxst + # From XSXB_to_gv + # g = np.array(( (xst[0]/modxst - XB[0])/wavelength,\ + # (xst[1]/modxst - XB[1])/wavelength,\ + # (xst[2]/modxst - XB[2])/wavelength ) ) + g = rscale * (xst * rmodxst - XB ) + # g[0] = (xst[0]/modxst - XB[0])/wavelength + # g[1] = (xst[1]/modxst - XB[1])/wavelength + # g[2] = (xst[2]/modxst - XB[2])/wavelength + # Want dg/dt + # dg/dt = (dxst/dt*1/modxst + xst.dmodxst_dt/modxst2)/w + for i in range(3): + for j in range(3): + if i==j: + dgdt[i,j] = -rmodxst-xst[i]*dmodxst_dt[j]*rmodxst2 + else: + dgdt[i,j] = -xst[i]*dmodxst_dt[j]*rmodxst2 + np.multiply( dgdt, rscale, dgdt ) + return g, dgdt + +# TODO: +# test this derivative to check whether it is correct (seems OK on simul_1000_grains) +# make the 'units' of position match the 'units' of UB (GSAS normalising diagonal) +# error bars, sh/esd for convergence +# weights on the data... set up 3x3 weight matrices +# impact of using omega obs? +# + + + +class fittrans(object): + """ Fits grain translations """ + def __init__(self, fname, parfile): + print("Reading", fname) + self.colfile = columnfile( fname ) + self.colfile.parameters.loadparameters( parfile ) + print("Setting up") + self.compute_XLYLZL() + + def refine(self, gr, tol = 0.05): + tx,ty,tz = gr.translation + wvln = float(self.colfile.parameters.get("wavelength")) + if hasattr(gr, "pks") and gr.pks is not None: + #print "Got pks" + pks = gr.pks + XS = self.XS[:,pks] # 3xn + XB = self.XB[:,pks] # 3xn + else: + #print "New pks" + XS = self.XS + XB = self.XB + pks = np.arange( len(XS[0]) ) + npks = len(pks) + # Compute the g-vectors and derivatives with respect to translations + gv, dgdt = d_XSXB_to_gv( XS, XB, tx, ty, tz, wvln) + hklr = np.dot( gr.ubi, gv ) + hkli = np.round(hklr) + hkle = hklr - hkli + scor = (hkle*hkle).sum(axis=0) + #print "before",len(pks),pks + if tol is not None: + msk = scor < tol*tol + wt = np.where( msk, 1., 0.) + use = np.compress( scor < tol*tol, np.arange(npks) ) + #print "after",len(pks),pks + gr.pks = pks[use] + else: + wt = 1 + use = np.arange( 0, len(gv[0]), dtype=int) + #print "score = ", scor[pks].sum()/len(pks), len(pks), tol + # peaks to use are those with scor OK + # + # UB.h = gvcalc + # dg/dUB = h + # dg/dT = found above + gcalc = np.dot( gr.UB, hkli ) # 3x3, 3xn + diff = np.take(gv - gcalc, use, axis=1) # 3xn + # print diff.shape, pks.shape + # gv[0],[1],[2] = 3 + # tx, ty, ty = 3 + # UB00 ... UB22 = 9 + # want derivative of diff w.r.t each variable + grads = np.zeros( (12, 3, npks ) ) # 12 vars, 3xn + #for j in range(3): # tx, ty, tz + # for i in range(3): #print 1+j*3+i, + # #print ret[1+j*3+i].shape + # grads[j, i] = dgdt[j*3+i][use] + grads[:3, :] = dgdt[:,:] + + for i in range(3): + for j in range(3): + # gx = 0h + 1k + 2l + # gy = 3h + 4k + 5l + # gz = 6h + 7k + 8l + # i is gx, gy, gz + # j is ub elements + grads[3+j+i*3,i] = hkli[j] + # print grads[3+j+i*3,i] + # ... there are a lot of zero here. + # ... dgx / dub[yz] == 0 ... of course. + # + # + # future: put some weights in. Not necessarily diagonal + # + # M.A.M.s = M.A.d + # M = gradients + # A = weight matrix + # s = shifts + # d = differences + # + grd = grads.reshape( (12, -1) ) + # (12,n),(n,12) -> 12,12 + # weights will be 3x3 blocked per peak + # wmat = np.dot( grd, wt, grd.T ) + # wrhs = np.dot( grd, wt, diff ) + mat = np.dot( grd, grd.T ) + rhs = np.dot( grd, diff.ravel() ) + shifts, residuals, rank, s = np.linalg.lstsq( mat, rhs ) + tx = tx - shifts[0] + ty = ty - shifts[1] + tz = tz - shifts[2] + gr.translation = [tx,ty,tz] + ub = gr.UB.ravel() + np.add(ub, shifts[3:], ub) + gr.set_ubi( np.linalg.inv( np.reshape(ub,(3,3)))) + gr.npks = len(use) + #1/0 + return gr + + def compute_XLYLZL(self): + self.peaks_xyz = transform.compute_xyz_lab( [self.colfile.sc, + self.colfile.fc], + **self.colfile.parameters.parameters) + # These are the peak positions in the laboratory frame + # + # Now get the beam and peaks in the crystal frame + wedge = float(self.colfile.parameters.get('wedge')) + chi = float(self.colfile.parameters.get('chi')) + self.chiwedge = gv_general.chiwedge( chi, wedge ) + omega = np.radians( self.colfile.omega ) + cosomega = np.cos( omega ) + sinomega = np.sin( omega ) + beam = np.zeros( (3,len(omega)), float) + beam[0,:] = 1. + self.XB = k_to_g( beam, cosomega, sinomega, self.chiwedge) + self.XS = k_to_g( self.peaks_xyz, cosomega, sinomega, self.chiwedge ) + + + +if __name__=="__main__": + import sys + from ImageD11.indexing import ubitocellpars + o = fittrans( sys.argv[1], sys.argv[2] ) + gl = grain.read_grain_file( sys.argv[3] ) + gref = gl[0] + import time + start = time.time() + gfl = [] + ng = 0 + # Take old peak assignments: + if 1: + print("Using existing peak assignments") + inds = np.arange(o.colfile.nrows,dtype=int) + for i,gref in enumerate(gl): + gref.pks = np.compress( o.colfile.labels == i, inds ) + tols = [None,]*3 + else: + tols = [0.05,0.01,0.0075] + for gref in gl: + print("BEFORE: ", + 3*"% 7.3f "%tuple(gref.translation), 6*"%.6f "%(ubitocellpars(gref.ubi))) + for ii,tol in enumerate(tols): + #print gref.translation + gref = o.refine(gref, tol=tol) + #print i,gref.npks +# gref.pks = None + # re-assign after convergence +# gref = o.refine( gref, tol=0.0075) + gfl.append( gref ) + ng += 1 + print(ng,ng*100.0/len(gl), "%.2f "%(time.time()-start), gref.npks, end=' ') + print((3*"%.4f ")%tuple(gref.translation), end=' ') + print((6*"%.6f ")%ubitocellpars(gref.ubi)) + print(time.time()-start) + grain.write_grain_file(sys.argv[4], gfl) +# 1/0 +# for i in range(2): +# o.find_triplets( i*17 ) +# pylab.show() + +# ~/ImageD11/test/simul_1000_grains % python ../../sandbox/fittrans.py Al1000/Al1000.flt Al1000/Al1000.par allgrid.map allgridfittrans.map diff --git a/sandbox/forwards_project.py b/sandbox/forwards_project.py index cd1c105b..a451df27 100644 --- a/sandbox/forwards_project.py +++ b/sandbox/forwards_project.py @@ -34,8 +34,8 @@ def forwards_project( gr, cell = unitcell.unitcell( indexing.ubitocellpars( gr.ubi ), latt ) ds_hkl = cell.gethkls( dsmax ) hkls = np.array( [ x[1] for x in ds_hkl] ) - gvecs = np.dot( gr.ub, hkls.T ) - wvln, wedge, chi = [ pars.get(x) for x in "wavelength", "wedge", "chi" ] + gvecs = np.dot( gr.UB, hkls.T ) + wvln, wedge, chi = [ pars.get(x) for x in ["wavelength", "wedge", "chi"] ] tth, (eta1, eta2), (omega1, omega2) = transform.uncompute_g_vectors( gvecs, wvln, wedge=wedge, chi=chi) osign = float(pars.get("omegasign")) @@ -91,7 +91,7 @@ def forwards_project( gr, spatial = blobcorrector.correctorclass( spline ) outfile = sys.argv[4] tthmax, dsmax = tth_ds_max( pars, detector_size ) - print "# id h k l tth eta omega sc fc s_raw f_raw" + print ("# id h k l tth eta omega sc fc s_raw f_raw") peaks = [] for gid, gr in enumerate(grains): newpeaks = forwards_project( gr, @@ -101,7 +101,7 @@ def forwards_project( gr, dsmax, gid ) peaks += newpeaks - print "# Grain",gid,"npks",len(newpeaks) + print ("# Grain",gid,"npks",len(newpeaks)) peaks.sort() out = open(outfile, "w") out.write("# id h k l tth eta omega sc fc s_raw f_raw\n") diff --git a/sandbox/fpair.py b/sandbox/fpair.py new file mode 100644 index 00000000..925f9af5 --- /dev/null +++ b/sandbox/fpair.py @@ -0,0 +1,158 @@ + +from __future__ import print_function, division +import sys, os +import numpy as np, pylab as pl +from ImageD11 import columnfile, unitcell + +def find_pair( c, + omegatol = 0.25, + etatol = 0.25, + tthtol = 0.1): + omi = np.round( c.omega / omegatol ).astype( int ) + f180 = 180./omegatol + indsc = np.arange(c.nrows, dtype=int) + pi = [] + pj = [] + for oi in range( omi.min(), omi.max()+1): + i = omi == oi + j = abs(omi - (oi+f180)) < 1.5 + if i.sum() > 0 and j.sum() > 0: + # potential pairs + ei = c.eta[i] + ej = c.eta[j] + eji = np.where( ej < 0, -180-ej, 180-ej ) + deta = abs(ei - eji[:,np.newaxis]) # ni x nj + dtth = abs( c.tth[i] - c.tth[j][:,np.newaxis] ) + #print(deta, deta.shape, i.sum(),j.sum()) + #print(dtth) + m = (deta < etatol)&(dtth < tthtol) + inds = np.arange( len(ei)*len(ej), dtype=np.int ) [ m.ravel() ] + ip = inds % len(ei) + jp = inds // len(ei) + #print("Found",m.ravel().sum()) + + #for k in range(len(inds)): + #print(k, ip[k], jp[k], m[jp[k],ip[k]], i.sum(), j.sum()) + ic = np.compress( i, indsc)[ ip ] + jc = np.compress( j, indsc)[ jp ] + pi.append( ic ) + pj.append( jc ) + # print( k,ip[k], jp[k], c.tth[ic],c.tth[jc],c.eta[ic],c.eta[jc],c.omega[ic],c.omega[jc]) + #print(len(pi)) + pi = np.concatenate( pi ) + pj = np.concatenate( pj ) + print(pi.shape,pj.shape,c.nrows) + return pi, pj + +def calc_tth_eta( c, pi, pj ): + dX = c.xl[pi] + c.xl[pj] + dY = c.yl[pi] + c.yl[pj] + dZ = c.zl[pi] - c.zl[pj] + r = np.sqrt(dY*dY + dZ*dZ) + tth = np.degrees( np.arctan2( r, dX ) ) + eta = np.degrees(np.arctan2( dY, dZ )) + return tth, eta + +def tth_ds( ds, w ): + return np.degrees( 2 * np.arcsin( w * ds / 2. ) ) + +def strain( tthvals, etavals ): + # cot(theta) . d(theta) + th = np.radians(tthvals / 2) + th0 = th.mean() + strain = (th0 - th) / np.tan( th0 ) + # Fit this as being linear + s2e = np.sin( np.radians(etavals) )**2 + p = np.polyfit( s2e, strain, 1 ) + scalc = np.polyval(p , s2e ) + return strain, th0, p, scalc, s2e + +def d_tth( tth, w ): + return w / 2 / np.sin(np.radians(tth/2)) + +def fit_hkl( c, pi, pj, tth, eta, stem ): + u = unitcell.unitcell_from_parameters( c.parameters ) + u.makerings( c.ds.max()*1.1, tol=0.001 ) + w = c.parameters.get( "wavelength" ) + tthv = [ tth_ds( ds, w ) for ds in u.ringds ] + print("(h, k, l) tthcalc Gradient_sin2p Intercept_sin2p") + for tth0,d in zip(tthv[:46], u.ringds): + print( u.ringhkls[d][0], end=" " ) + m = abs(tth - tth0) < 0.025 # tolerance in tth + try: + s,th0,p, sc, s2e = strain( tth[m], eta[m] ) + print( tth0, np.degrees(th0*2), p[0], p[1] ) + sys.stdout.flush() + except: + print() + continue + try: + f=pl.figure(2, dpi=200, figsize=(20,15)) + f.clf() + f.add_subplot(2,3,1) + #pl.subplot(231) + pl.plot( eta[m], tth[m],"+" ) + pl.ylabel("tth") + f.add_subplot(2,3,2) + #pl.subplot(23) + pl.plot( eta[m], d_tth(tth[m],w),"+" ) + pl.ylabel("d-spacing") + #pl.subplot(233) + f.add_subplot(2,3,3) + pl.plot( eta[m], s, "+") + pl.plot( eta[m], sc, "x") + #pl.subplot(234) + f.add_subplot(2,3,4) + pl.plot( s2e,tth[m],"+" ) + pl.ylabel("tth") + #pl.subplot(235) + f.add_subplot(2,3,5) + pl.plot( s2e,d_tth(tth[m],w),"+" ) + pl.ylabel("d-spacing") + f.add_subplot(2,3,6) + pl.plot( s2e, s, "+") + pl.plot( s2e, sc, "x") + pl.ylabel("strain") + hkl = "_%d_%d_%d.png"%(tuple(u.ringhkls[d][0])) + f.savefig( stem + hkl) +# pl.show() + + + except: + print("Fail") + raise + + +if __name__=="__main__": + c = columnfile.columnfile(sys.argv[1]) + stem = os.path.join("png", sys.argv[1].split(".")[0]) + c.parameters.loadparameters( sys.argv[2] ) + c.updateGeometry() + d=c.copy() + pi, pj = find_pair(c) + tth, eta = calc_tth_eta( c, pi, pj ) + pl.subplot(221) + pl.plot(d.tth, d.eta,"r.") + pl.plot( tth, eta, "g.") + pl.subplot(222) + pl.plot(d.tth, d.eta,"r.") + pl.plot( tth, eta, "g.") + pl.xlim( 3,15.1) + pl.subplot(223) + pl.plot(d.tth, d.eta,"r.") + pl.plot( tth, eta, "g.") + pl.xlim( 11,11.75) + pl.subplot(224) + pl.plot(d.tth, d.eta,"r.") + pl.plot( tth, eta, "g.") + pl.xlim( 14.3,14.85) +# pl.show() + pl.savefig( stem+"_all.png") + f = open( sys.argv[1].replace(".hdf",".tth_eta"), "w") + f.write("# From %s\n"%(sys.argv[1])) + f.write("# tth eta \n") + for i in range(len(tth)): + f.write("%f %f\n"%(tth[i], eta[i])) + f.close() + fit_hkl( c, pi, pj, tth, eta, stem ) + diff --git a/sandbox/friedel.py b/sandbox/friedel.py index 220840ab..a9e26186 100644 --- a/sandbox/friedel.py +++ b/sandbox/friedel.py @@ -233,7 +233,7 @@ def clusterpeaks( cf, pks, d_ds ): ds = cf.ds[ pks] order = np.argsort( ds ).astype( np.intc ) ids = np.zeros( len(pks), np.intc ) - avgs = np.zeros( len(pks), np.float ) + avgs = np.zeros( len(pks), float ) nclusters = cImageD11.cluster1d( ds , order, d_ds, ids, avgs) # print(ds) # print( nclusters, ids, avgs ) diff --git a/sandbox/gold/grab_the_pixels.py b/sandbox/gold/grab_the_pixels.py new file mode 100644 index 00000000..57b0bd39 --- /dev/null +++ b/sandbox/gold/grab_the_pixels.py @@ -0,0 +1,77 @@ + + + +from ImageD11 import cImageD11, sparseframe +import numpy as np, fabio, h5py, os + + +def grab2d( im, dark, nsigma=3., blocksize=256 ): + """ returns the fabio image adding + .corr = corrected data + .sparseframe = segmented spots + """ + data = np.empty( im.size, np.float32 ) # corrected data + msk = np.empty( im.shape, 'b' ) # mask + csk = np.empty( im.shape, 'b' ) # cleaned mask + # subtract the background + shape0 = im.shape + data.shape = data.size + dark.shape = dark.size + im.shape = im.size + cImageD11.uint16_to_float_darksub( data, dark, im ) + # compute the mean and std of the data trimming at nsigma + avg, std = cImageD11.array_mean_var_cut( data, nsigma ) + threshold = avg + nsigma * std + # remove the readout drift + data.shape = im.size // blocksize, blocksize + cImageD11.frelon_lines( data, threshold ) + data.shape = shape0 + dark.shape = shape0 + im.shape = shape0 + # threshold and clean + nnz = cImageD11.make_clean_mask( data, threshold, msk, csk ) + row = np.empty( nnz, np.uint16 ) + col = np.empty( nnz, np.uint16 ) + tmp = np.empty( shape0[0], 'i' ) + cImageD11.mask_to_coo( csk, row, col, tmp ) + header = { 'threshold' : threshold, + } + spar = sparseframe.from_data_mask( csk, data, header ) + return spar, data + + +def test(): + img = fabio.open( '../Au6_s0_003_a/Au6_s0_003_a0001.edf.gz' ).data + drk = fabio.open("../pks/bkg.edf").data + return grab2d( img, drk, nsigma=2.5, blocksize=128 ) + +def do_img(fname, drk, nsigma=2.5, blocksize=128 ): + imo = fabio.open( fname ) + imo.spar, imo.corr = grab2d( imo.data, drk, nsigma=2.5, blocksize=128 ) + return imo + + +def grab_folder(folder): + ROOT= "/data/id11/nanoscope/Commissioning/2017Feb/gold" + drk = fabio.open("../pks/bkg.edf").data + h5 = h5py.File(os.path.join(ROOT, "Jan2020", folder+".hdf")) + gr = h5.require_group(folder) + for j in range(900): + edf = os.path.join(ROOT, folder, folder+"%04d.edf.gz"%(j)) + if os.path.exists( edf ): + imo = do_img( edf, drk ) + g = gr.require_group( str(j) ) + imo.spar.to_hdf_group( g ) + print(edf,imo.spar.nnz) + +if __name__=="__main__": + import multiprocessing + folders=[] + for i in range(1,83): + for d in 'ab': + folders.append(os.path.join("Au6_s0_%03d_"%(i)+d)) + + with multiprocessing.Pool(8) as p: + p.map( grab_folder, folders ) + + diff --git a/sandbox/gold/newpeaksearch.py b/sandbox/gold/newpeaksearch.py new file mode 100644 index 00000000..b94fbece --- /dev/null +++ b/sandbox/gold/newpeaksearch.py @@ -0,0 +1,57 @@ + +import pylab as pl, numpy as np, h5py, time, glob +import multiprocessing +from ImageD11 import sparseframe, cImageD11 + + +def newpks(hf): + """ do a peak search in a sparse frame series """ + blbs = [] + with h5py.File(hf,"r") as h: + stem = list(h)[0] + grp = h[stem] + for g in range(900): + f = sparseframe.from_hdf_group(grp[str(g)]) + sparseframe.sparse_localmax(f) + blbs.append( sparseframe.sparse_moments(f, "intensity", + "localmax") ) + return stem, blbs + + +def main(): + ################### scan specific motorpos ######## + dtyomepos = {} + oa = np.arange(0.,180.,0.2) + ob = np.arange(180.,0.,-0.2) + assert len(oa)==len(ob) + + with open( "dty.pos", "r") as f: + for line in f.readlines()[1:]: + scan = line.split("/")[0] + dty = float(line.split()[1]) + if scan[-1] == 'a': + dtyomepos[scan]=dty, oa + if scan[-1] == 'b': + dtyomepos[scan]=dty, ob + + + + hdfs = [ "Au6_s0_%03d_%s.hdf"%(i,ab) + for i in range(1,81) + for ab in 'ab'] + + pks = {} + start = time.time() + with multiprocessing.Pool(multiprocessing.cpu_count()-1) as p: + for ret, h in zip(p.imap_unordered(newpks, hdfs), hdfs): + s,b = ret + d,o = dtyomepos[s] + pks[d] = o, b + end = time.time() + print(h,d,o[0],len(b),end-start) + + + + +if __name__=="__main__": + main() diff --git a/sandbox/grid_index.py b/sandbox/grid_index.py index cf7396e8..47f92ba3 100644 --- a/sandbox/grid_index.py +++ b/sandbox/grid_index.py @@ -42,10 +42,10 @@ col = mytransformer.colfile if not "drlv2" in col.titles: - col.addcolumn( np.ones(col.nrows, np.float), + col.addcolumn( np.ones(col.nrows, float), "drlv2" ) if not "labels" in col.titles: - col.addcolumn( np.ones(col.nrows, np.float)-2, + col.addcolumn( np.ones(col.nrows, float)-2, "labels" ) if not "sc" in col.titles: assert "xc" in col.titles @@ -91,7 +91,7 @@ def domap( OmFloat, OmSlop, o.scandata["internal"] = colfile o.tolerance = tol o.readubis( grainsfile ) - if symmetry is not "triclinic": + if symmetry != "triclinic": o.makeuniq( symmetry ) o.generate_grains() o.refinepositions() @@ -121,7 +121,7 @@ def doindex( gve, x, y, z, w): gv = gve.T ) myindexer.ds = np.sqrt( (gve * gve).sum(axis=0) ) - myindexer.ga = np.zeros(len(myindexer.ds),np.int)-1 # Grain assignments + myindexer.ga = np.zeros(len(myindexer.ds),int)-1 # Grain assignments # myindexer.readgvfile( gve ) for ring1 in RING1: diff --git a/sandbox/harvest_pixels.py b/sandbox/harvest_pixels.py new file mode 100644 index 00000000..a1cb5f18 --- /dev/null +++ b/sandbox/harvest_pixels.py @@ -0,0 +1,118 @@ + +from __future__ import print_function, division + +import glob, os, multiprocessing, time +import numpy as np +import fabio, h5py +from ImageD11 import sparseframe, cImageD11 + +def ftomonames( scanpars, folder): + """ nimages, extn, interlaced, iflip in scanpars + Generate the ftomo filename series """ + nums = range(scanpars['nimages']) + if scanpars['interlaced']: + stems = [ os.path.join( folder, folder+"0_%04d"+"%s"%(scanpars['extn']) ), + os.path.join( folder, folder+"1_%04d"+"%s"%(scanpars['extn']) ) ] + if scanpars['iflip']: + s1 = [stems[0]%num for num in nums] + s2 = [stems[1]%num for num in nums[::-1] ] + else: + s1,s2 = [[stem%num for num in nums] for stem in stems] + # interleave two lists of same length + return [val for pair in zip(s1,s2) for val in pair] + else: + stem = os.path.join( folder, folder+"%04d"+"%s"%(scanpars['extn']) ) + return [stem%num for num in nums] + + +def segment( fname, bg, datamem, datapath, block=128, nsigma=3. ): + """ Does a segmentation of a single file/frame """ + imo = fabio.open(os.path.join( datapath, fname) ) # data +# print(fname) + oname = os.path.split( os.path.splitext(fname)[0] )[-1] + # subtract dark and promote to float32 (without overflow on uint16 I hope) + cImageD11.uint16_to_float_darksub( datamem.ravel(), bg.ravel(), imo.data.ravel()) + # check offset and noise + avg,sig = cImageD11.array_mean_var_cut( datamem, cut=nsigma ) + # remove the frelon lines + datamem.shape = imo.data.shape[0]*(imo.data.shape[1]//block) , block + cImageD11.frelon_lines( datamem, avg + sig*nsigma ) + datamem.shape = imo.data.shape + # overwrites datamem (should copy before if you want it + avg,sig = cImageD11.array_mean_var_cut( datamem, cut=nsigma ) + threshold = avg + sig * nsigma + # select the pixels to keep: + rawmask = datamem > threshold + cleanmask = rawmask.astype(np.int8) + cImageD11.clean_mask( rawmask.astype(np.int8), cleanmask ) + # save some metadata: + header = {"filename": fname } + for k in ("SRCUR","time_of_day"): + if k in imo.header: + header[k] = imo.header[k] + spar = sparseframe.from_data_mask( cleanmask, datamem, header ) + return spar + + +def write_frames_to_hdf( frames, + datapath, + fnames, + hdfname, + hdffolder ): + """ saves a 1D scan (single ftomo) to a single hdf """ + h = h5py.File( hdfname, "a" ) + g = h.require_group( hdffolder ) + g.attrs['datapath'] = datapath + for f,name in zip(frames,fnames): + gname = os.path.split( os.path.splitext(name)[0])[-1] + f.to_hdf_group( g.require_group( gname ) ) + h.close() + + +def dofolder( args ): + """ Processes the files in one folder (single ftomo scan) """ + folder, scanpars = args + start = time.time() + fnames = ftomonames( scanpars, folder ) # , nimages, extn, interlaced, iflip ) + hname = os.path.join("hdfs", folder+".hdf") + bg = fabio.open( + os.path.join( scanpars['bgfolder'], + folder,folder+"_median.edf") + ).data.astype(np.float32) + datamem = np.zeros_like( bg ) + spars = [segment(fname, bg, datamem, scanpars['datapath'], block=128) + for fname in fnames ] + print("Segmented", folder, time.time()-start) + write_frames_to_hdf( spars, scanpars['datapath'], fnames, hname, "/" ) + ret = "Wrote %s %.4f /s"%(folder, time.time()-start) + return ret + + +def harvest_pixels(folders): + if not os.path.exists("hdfs"): + os.mkdir( "hdfs" ) + import multiprocessing + nproc = multiprocessing.cpu_count()//4 # io + with multiprocessing.Pool( processes = nproc ) as p: + for x in p.imap_unordered( dofolder, folders ): + print("Done",x) + + + +if __name__=="__main__": + + + scanpars = { + "extn" : '.edf', + "nimages" : 360, + "interlaced" : True, + "iflip" : True, + "datapath" : '/data/id11/nanoscope/Commissioning/2018Apr/difftomo_al_quick', + "bgfolder" : "/data/id11/jon/difftomo_al_quick/oldmethod", + } + + folders = [("Al_y%03d_"%(i),scanpars) for i in range(-60,61)] + + harvest_pixels( folders ) + + diff --git a/sandbox/hdfscan.py b/sandbox/hdfscan.py new file mode 100644 index 00000000..e5c22eaa --- /dev/null +++ b/sandbox/hdfscan.py @@ -0,0 +1,53 @@ + +""" +Wrapper to map hdf onto the old peaksearch code + +Try to decide if this belongs in peaksearch or if we make something new +""" + + +from fabio.fabioimage import fabioimage +import numpy as np +import hdf5plugin +import h5py +import os +filename = os.environ['H5NAME'] +dataset = os.environ['H5GROUP'] + + +def fso( filename, dataset ): + h = h5py.File( filename, "r" ) + omega = h['/1.1/measurement/diffrz'][:] % 360 # forwards scan + nframes = len(omega) + data = h[dataset] + assert data.shape[0]==nframes + header = {'Omega' : omega[0] } + filename = "%s::%s"%(filename, dataset) + order = np.argsort( omega ) + + def frm(i): + header['Omega'] = omega[i] + f = fabioimage( data[i], header ) + f.filename = filename + f.currentframe = i + return f + + # + yield( frm(order[0]) ) # first + for i in order: + yield frm(i) + +file_series_object = fso( filename, dataset ) +first_image = next(file_series_object) + + +if __name__=="__main__": + from timeit import default_timer as timer + t0 = timer() + for i in file_series_object: + pass + t1 = timer() + print( "Read time", t1-t0 ) + + + diff --git a/sandbox/heightsquare.py b/sandbox/heightsquare.py index 8dd6b93a..0e8b8b02 100644 --- a/sandbox/heightsquare.py +++ b/sandbox/heightsquare.py @@ -3,7 +3,8 @@ def heightsquare(r,theta): """ Height of a projected square versus x """ - p = ((-1,-1,1,1),(1,-1,-1,1)) + p = ((-1,-1,1,1), + (1,-1,-1,1)) s = np.sin(np.radians(theta)) c = np.cos(radians(theta)) m = ((c, -s),(s, c)) @@ -12,7 +13,7 @@ def heightsquare(r,theta): o = [] xp = rp[0][order] yp = rp[1][order] - for v in x: + for v in r: if v < xp[0]: o.append(0) continue @@ -43,5 +44,6 @@ def heightsquare(r,theta): plot(x,h(x,10),"-") plot(x,h(x,50),"-") plot(x,h(x,45),"-") + plot(x,h(x,79),"-") plot(x,h(x,99),"-") show() diff --git a/sandbox/indexer.py b/sandbox/indexer.py index 88023e47..1b411e8c 100644 --- a/sandbox/indexer.py +++ b/sandbox/indexer.py @@ -19,8 +19,7 @@ import numpy as np -from ImageD11 import cImageD11, grain, transform -import unitcell +from ImageD11 import cImageD11, grain, transform, unitcell import math, time, sys, logging @@ -67,7 +66,7 @@ def updatecolfile(self): elif "xc" in self.cf.titles: pks = self.cf.xc, self.cf.yc else: - raise "peaks file misses xc or sc" + raise Exception("peaks file misses xc or sc") xl,yl,zl = transform.compute_xyz_lab( pks, **self.transformpars.parameters) self.cf.addcolumn(xl,"xl") @@ -164,7 +163,7 @@ def pairs(self, hkl1, hkl2, cos_tol = 0.02, hkl_tol = 0.05): ind1 = allinds[ abs(self.cf.tth - tth1) < tthtol ] ind2 = allinds[ abs(self.cf.tth - tth2) < tthtol ] angle, cosangle = self.unitcell.anglehkls( hkl1, hkl2 ) - g = np.array( (self.cf.gx, self.cf.gy, self.cf.gz), np.float ) + g = np.array( (self.cf.gx, self.cf.gy, self.cf.gz), float ) n = g/self.cf.modg gvf = g.T.copy() n1 = n[:,ind1] diff --git a/ImageD11/indexer.py b/sandbox/indexer_1d.py similarity index 73% rename from ImageD11/indexer.py rename to sandbox/indexer_1d.py index 55e15d68..ced7b7b3 100644 --- a/ImageD11/indexer.py +++ b/sandbox/indexer_1d.py @@ -20,8 +20,9 @@ import numpy as np -from ImageD11 import grain, transform, cImageD11, indexing, unitcell +from ImageD11 import grain, transform, cImageD11, indexing, unitcell, refinegrains import scipy.optimize +from scipy.spatial.transform import Rotation import math, time, sys, logging def unit(a): @@ -55,7 +56,6 @@ def setcell(self): self.unitcell = unitcell.unitcell(cp, self.transformpars.get("cell_lattice_[P,A,B,C,I,F,R]")) - def loadcolfile(self, colfile): self.cf = colfile self.updatecolfile() @@ -69,13 +69,13 @@ def updatecolfile(self): elif "xc" in self.cf.titles: pks = self.cf.xc, self.cf.yc else: - raise "peaks file misses xc or sc" + raise Exception("peaks file misses xc or sc") xl,yl,zl = transform.compute_xyz_lab( pks, **self.transformpars.parameters) self.cf.addcolumn(xl,"xl") self.cf.addcolumn(yl,"yl") self.cf.addcolumn(zl,"zl") - self.peaks_xyzT = np.array(zip(self.cf.xl,self.cf.yl,self.cf.zl)) + self.peaks_xyzT = np.array((self.cf.xl,self.cf.yl,self.cf.zl)).T.copy() om = self.cf.omega osign = self.transformpars.get("omegasign") tth, eta = transform.compute_tth_eta_from_xyz( @@ -83,6 +83,7 @@ def updatecolfile(self): **self.transformpars.parameters) self.cf.addcolumn( tth, "tth", ) self.cf.addcolumn( eta, "eta", ) + self.cf.addcolumn( refinegrains.lf(tth,eta), 'lf') gx, gy, gz = transform.compute_g_vectors( tth, eta, om*osign, wvln = self.transformpars.get("wavelength"), @@ -96,7 +97,6 @@ def updatecolfile(self): gz * gz ), "modg") - def reset(self): """ when pars change or colfile changes etc """ if "ring" not in self.cf.titles: @@ -104,8 +104,8 @@ def reset(self): self.cf.addcolumn( 42*np.ones(self.cf.nrows), "ringerr" ) if "labels" not in self.cf.titles: self.cf.addcolumn( -np.ones(self.cf.nrows), "labels" ) - - + self.mok = np.zeros( self.cf.nrows, bool ) + def tthcalc(self, hkls = None): """ @@ -113,7 +113,7 @@ def tthcalc(self, hkls = None): hkls or from the unit cell generating a list of hkls. """ if hkls is None: - dslimit = np.maximum.reduce(self.cf.modg) + dslimit = self.cf.modg.max() wvln = self.transformpars.get( "wavelength" ) ttol = self.transformpars.get( "fit_tolerance" ) # tth units # at limit this ttol make which ds range? @@ -123,7 +123,7 @@ def tthcalc(self, hkls = None): self.unitcell.makerings(dslimit+dstol/2, tol = dstol) else: self.unitcell.peaks = [ (self.unitcell.ds(h),h) for h in hkls ] - self.unitcell.makerings( dslimit, tol=dstol, newpeaks=False ) + self.unitcell.makerings( dslimit, tol=dstol ) # , newpeaks=False ) self.unitcell.ringtth = [ 2 * np.degrees( np.arcsin( ds * wvln / 2 ) ) for ds in self.unitcell.ringds ] @@ -132,7 +132,6 @@ def assigntorings(self): """ Assign the g-vectors to hkl rings that are in self.unitcell """ - dsr = self.unitcell.ringds self.cf.ring[:]=-1 self.cf.ringerr[:]=42. tol = self.transformpars.get( "fit_tolerance" ) @@ -143,6 +142,9 @@ def assigntorings(self): if mask.sum()>0: self.cf.ring[mask] = i self.cf.ringerr[mask] = diff[mask] + + + def printringassign(self): # Report on assignments print("Ring ( h, k, l) Mult total indexed to_index ") # try reverse order instead @@ -152,12 +154,106 @@ def assigntorings(self): n_indexed = (self.cf.labels[ind] > -0.5).sum() n_to_index = (self.cf.labels[ind] < -0.5).sum() h=self.unitcell.ringhkls[dsr[j]][0] - print("Ring %-3d (%3d,%3d,%3d) %3d %5d %5d %5d"%(\ + print("Ring %-3d (%3d,%3d,%3d) %3d %5d %5d %5d %.4f"%(\ j,h[0],h[1],h[2],len(self.unitcell.ringhkls[dsr[j]]), - ind.sum(),n_indexed,n_to_index)) + ind.sum(),n_indexed,n_to_index, + self.unitcell.ringtth[j] )) print("Total peaks",self.cf.nrows,"assigned",(self.cf.ring>=0).sum()) + + def rings_2_use(self, rings = None, multimin = 12 ): + """Filter rings as having low multiplicity for indexing searches + + Give rings = [list of rings to use] + Or multimin = all rings with low multiplicity + """ + ring = self.cf.ring.astype(int) + if rings is None: + mok = np.zeros( (self.cf.nrows,), bool ) + for i, ds in enumerate( self.unitcell.ringds ): + mult = len(self.unitcell.ringhkls[ds]) + if mult <= multimin: + mok[ ring == i ] = True + else: + mok = np.zeros( (self.cf.nrows,), bool ) + for r in rings: + mok[ ring == r ] = True + self.mok = mok + def search1d(self, gvec_id, + hkl = None, + angstart = -180, + angend = 180, + nang = 3600, + tol = 0.1, + ): + """ + gvec_id = an integer for a row of self.colfile + hkl = hkl indices to assign to the peak (None + means guess from self.cf.ring) + This is inspired from Bernier's fibre texture method (citation: + https://github.com/HEXRD/hexrd/blob/master/hexrd/findorientations.py + ) + """ + gv = np.array( (self.cf.gx[gvec_id], + self.cf.gy[gvec_id], + self.cf.gz[gvec_id]), float) + print(gvec_id) + if hkl is None: + ring = self.cf.ring[gvec_id] + ds = self.unitcell.ringds[int(ring)] + hkls = self.unitcell.ringhkls[ ds ] + hkl = np.array( hkls[0], int ) + print("Choosing",hkl,"from hkls", hkls) + assert hkl.shape == (3,) + g0 = np.dot( self.unitcell.B, hkl ) # non rotated g + # normalised vectors + n0 = g0/np.linalg.norm( g0 ) + nobs = gv/np.linalg.norm( gv ) + cosa = np.dot( n0, nobs ) + ang = np.arccos(cosa) + # if the vectors are already parallel ? + if ang < np.radians(0.001): + u0 = np.eye(3) + else: + vec = np.cross( n0, nobs ) + sina = np.linalg.norm( vec ) + u0 = Rotation.from_rotvec( ang * vec / sina ).as_matrix() + ub0 = np.dot( u0, self.unitcell.B ) + ubi0 = np.linalg.inv( ub0 ) + # Now we want to rotate around nobs + allgve = np.array( (self.cf.gx,self.cf.gy,self.cf.gz) ).T.copy() + scores = [] + ubis = [] + gc = np.dot( ub0, hkl ) + angs = np.radians( np.linspace( angstart, angend, nang) ) + ubis = [ np.dot(ubi0, + Rotation.from_rotvec( nobs * a ).as_matrix()) + for a in angs ] + scores = [ cImageD11.score( ubi, allgve, tol ) + for ubi in ubis ] + return scores, ubis + + def choose( self, gnum, tol): + isig = self.cf.npixels * self.cf.avg_intensity * self.cf.lf + idp = np.argmax( self.mok * (self.cf.labels<0) * isig ) + angstart = 0 + angend = 360 + nang = 3600 + s,ubis=self.search1d(idp, tol=tol, + angstart = angstart, angend=angend, nang=nang) + matfit = ubis[np.argmax(s)].copy() + tfit = np.zeros(3) + tol = 0.05 + inds, hkls = self.assign( matfit, tfit, tol ) + matfit, tfit = self.refine( matfit, tfit, inds, hkls, tol ) + inds, hkls = self.assign( matfit, tfit, tol ) + print( inds.shape, tfit ) + print( indexing.ubitocellpars( matfit ) ) + self.cf.labels[ inds ] = gnum + self.grains[ gnum ] = grain.grain( matfit, tfit ) + + def pairs(self, hkl1, hkl2, cos_tol = 0.02, hkl_tol = 0.1): """ We only look for reflection pairs matching a single hkl pairing @@ -174,7 +270,7 @@ def pairs(self, hkl1, hkl2, cos_tol = 0.02, hkl_tol = 0.1): angle, cosangle = self.unitcell.anglehkls( hkl1, hkl2 ) print("Angle, cosangle",angle,cosangle,hkl1,hkl2) assert angle > 1 and angle < 179, "hkls are parallel" - g = np.array( (self.cf.gx, self.cf.gy, self.cf.gz), np.float ) + g = np.array( (self.cf.gx, self.cf.gy, self.cf.gz), float ) n = g/self.cf.modg gvf = g.T.copy() n1 = n[:,ind1] @@ -210,20 +306,20 @@ def pairs(self, hkl1, hkl2, cos_tol = 0.02, hkl_tol = 0.1): pairs.append( (ind1[i], ind2[k], U, ubi ) ) print(npks, end=' ') - ubi, trans = self.refine( ubi, np.zeros(3,np.float), tol=hkl_tol ) + ubi, trans = self.refine( ubi, np.zeros(3,float), tol=hkl_tol ) inds, hkls = self.assign( ubi, trans, hkl_tol ) ubi, trans = self.refine( ubi, trans, inds = inds, hkls= hkls, tol=hkl_tol ) print(npks, ubi) print("cell: ",6*"%.6f "%( indexing.ubitocellpars(ubi) )) print("position: ",trans) print() - self.pairs=pairs + self.pairscache=pairs print(time.time()-start,"for",len(pairs),n1.shape, n2.shape) return pairs def assign(self, ubi, translation, tol): - gv = np.zeros(self.peaks_xyzT.shape, np.float ) + gv = np.zeros(self.peaks_xyzT.shape, float ) cImageD11.compute_gv( self.peaks_xyzT, self.cf.omega, self.transformpars.get('omegasign'), @@ -292,8 +388,8 @@ def Dfun( x0, *args ): epsilon = np.ones(12)*1e-6 epsilon[-3:] = 1. return deriv( x0, fun, epsilon, *args) - res, ier = scipy.optimize.leastsq( fun, x0, args, Dfun, - col_deriv=True) + res, ier = scipy.optimize.leastsq( fun, x0, args, )# + # Dfun, col_deriv=True) ub = np.reshape(res[:9], (3,3)) t = res[-3:] ubi = np.linalg.inv( ub ) @@ -322,7 +418,7 @@ def deriv( xk, f, epsilon, *args): # 0 1 2 3 4 5 # \test\simul_1000_grains>python ..\..\ImageD11\indexer.py Al1000\Al1000.par Al1000\Al1000.flt fit allgrid.map allgridfitscipy.map gl = ImageD11.grain.read_grain_file( sys.argv[4] ) - inds = np.arange( len(gl), dtype=np.int ) + inds = np.arange( len(gl), dtype=int ) allhkls = np.array( (c.h, c.k, c.l) ) for k,g in enumerate(gl): if len(sys.argv[3]) == len("fit"): diff --git a/sandbox/knife.py b/sandbox/knife.py index d7da628b..abc0d110 100644 --- a/sandbox/knife.py +++ b/sandbox/knife.py @@ -105,7 +105,7 @@ def convoluted_cylinder_absorbtion( x, """ # the cylinder absorbtion function puts x at 0 psv = pseudo_voigt_peak( x, position, fwhm, eta ) - cyl_abs = np.zeros( x.shape, np.float ) + cyl_abs = np.zeros( x.shape, float ) middle = x.mean() for i in range(len(x)): x0 = x[i] @@ -129,7 +129,7 @@ def convoluted_cylinder_thickness( x, """ # the cylinder absorbtion function puts x at 0 psv = pseudo_voigt_peak( x, position, fwhm, eta ) - cyl = np.zeros( x.shape, np.float ) + cyl = np.zeros( x.shape, float ) middle = x.mean() for i in range(len(x)): x0 = x[i] diff --git a/sandbox/list_folder_images.py b/sandbox/list_folder_images.py new file mode 100644 index 00000000..06b9ea17 --- /dev/null +++ b/sandbox/list_folder_images.py @@ -0,0 +1,103 @@ + +from __future__ import print_function +""" +For a peak search interface + Try to navigate the output of 'ls' in folders with lots of files + Compress the list of names when there are a lot of numbers +""" + + +import re, os, time, sys +try: + from os import scandir +except ImportError: + class wrap: + def __init__(self, name ): + self.name = name + def is_dir(self): + return os.path.isdir( self.name ) + def scandir( folder ): + for name in os.listdir( folder ): + yield wrap( name ) + + + +def list_a_folder( folder ): + """ + Lists files in the folder + Groups into: + directories + names of type (stem)(num)(extn) + things it doesn't understand (no number) + """ +# NotGreedyAnything:Digit:~Digit:End + reg = re.compile("(.*?)(\d*)(\D*)$") + direcs = [] + names = [] + files = {} # keyed by extension, then stem, then num sorted + for f in scandir( folder ): + if f.is_dir(): + direcs.append( f.name ) + continue + items = reg.match( f.name ) + if items: + stem, num, extn = items.groups() + if len(num)==0: + names.append( f.name ) + continue + if extn in files: + if stem in files[extn]: + files[extn][stem].append( num ) + else: + files[extn][stem]=[num,] + else: + files[extn]={stem:[num,]} + else: + names.append( f.name ) + # sort the numbers + for extn in files: + for stem in files[extn]: + try: + dsu = [ (int(s),s) for s in files[extn][stem] ] + except: + print(files[extn][stem] ) + raise + dsu.sort() + files[extn][stem] = [s[1] for s in dsu] + return direcs, files, names + +def print_a_folder( folder ): + direcs , files, names = list_a_folder( folder ) + print("In folder:",folder) + if len(direcs)>0: + print(" Directories:") + for d in sorted(direcs): + print("\t",d) + if len(names)>0: + print(" Files:") + for n in sorted(names): + print("\t",n) + if len(files)>0: + for extn in files: + for stem in files[extn]: + nums = files[extn][stem] + if len(nums)>3: + print("\t",stem+nums[0]+extn) + print("\t",stem+("?"*len(nums[0]))+extn, + " ... skip ",len(nums)-2,"files") + print("\t",stem+nums[-1]+extn, + " ... total ",len(nums),"files") + else: + for num in nums: + print("\t",stem+num+extn) + + +if __name__=="__main__": + if len(sys.argv)>1: + print_a_folder( sys.argv[1] ) + else: + print_a_folder( "." ) + + + + diff --git a/sandbox/ma4750/custom_peak_search.py b/sandbox/ma4750/custom_peak_search.py new file mode 100755 index 00000000..c13e94db --- /dev/null +++ b/sandbox/ma4750/custom_peak_search.py @@ -0,0 +1,233 @@ + +from __future__ import print_function + +import sys +import timeit +import multiprocessing +import h5py, hdf5plugin, fabio +import functools +import numpy as np +import tqdm +import ImageD11.cImageD11 +import scipy.sparse, scipy.spatial, scipy.ndimage + +def do3dmerge( honame, scan ): + # read the 2d peak search results + t0 = timeit.default_timer() + print('Making 3D merge', end=' ') + sys.stdout.flush() + with h5py.File( honame, 'r') as hin: + g = hin[scan]['peaks2d'] + h5input = g.attrs['h5input'] + h5scan = g.attrs['h5scan'] + detector_path = g.attrs['detector_path'] + omega_path = g.attrs['omega_path'] + omega = g['omega'][:] + s = g['s_raw'][:] + f = g['f_raw'][:] + o = g['o_raw'][:] + I = g['s_I'][:] + n = g['npks'][:] + s1 = g['s_1'][:] + # pointers to frames in s,f,o,I,n,s1 + p= np.cumsum( np.concatenate(([0,], n ) ) ) + # make a KDTree for each frame (wastes a bit of memory, but easier for sorting later) + trees = [ scipy.spatial.cKDTree( np.transpose( (s[p[i]:p[i+1]], f[p[i]:p[i+1]]) ) ) + for i in range(len(n)) ] + print('made trees',end=' ') + sys.stdout.flush() + # because interlaced might not be in order + order = np.argsort( omega % 360 ) + # peaks that overlap, k : 0 -> npks == len(s|f|o) + # diagonal + krow = list(range(len(o))) + kcol = list(range(len(o))) + for i in range(1,len(n)): # match these to previous + flo = order[i-1] + fhi = order[i] + tlo = trees[flo] + thi = trees[fhi] + # 1.6 is how close centers should be to overlap + lol = trees[flo].query_ball_tree( trees[fhi], r = 1.6 ) + for srcpk, destpks in enumerate(lol): # dest is strictly higher than src + for destpk in destpks: + krow.append( srcpk + p[flo] ) + kcol.append( destpk + p[fhi] ) + csr = scipy.sparse.csr_matrix( ( np.ones(len(krow), dtype=bool), + (kcol, krow) ), shape=(len(o),len(o)) ) + # connected components == find all overlapping peaks + ncomp, labels= scipy.sparse.csgraph.connected_components( csr, + directed=False, return_labels=True ) + print('connected components', end=' ') + sys.stdout.flush() + # Now merge the properties + npkmerged = np.bincount( labels, minlength = ncomp ) # number of peaks that were merged + s3d1 = np.bincount( labels, minlength = ncomp, weights=s1 ) # s_1 + s3dI = np.bincount( labels, minlength = ncomp, weights=I ) # s_I + ssI = np.bincount( labels, minlength = ncomp, weights=I*s ) # s_sI + sfI = np.bincount( labels, minlength = ncomp, weights=I*f ) # s_sI + soI = np.bincount( labels, minlength = ncomp, weights=I*o ) # s_sI + s3d = ssI / s3dI + f3d = sfI / s3dI + o3d = soI / s3dI + with h5py.File( honame, 'a') as hin: + g = hin[scan].require_group('peaks3d') + g['s_raw'] = s3d + g['f_raw'] = f3d + g['omega'] = o3d + g['sum_intensity'] = s3dI + g['number_of_pixels'] = s3d1 + t1 = timeit.default_timer() + print('wrote %.3f/s'%(t1-t0)) + + +class worker: + """ subtracts background, custom for ma47050 """ + def __init__(self, bgfile): + self.bg = fabio.open(bgfile).data.astype(np.float32) + self.threshold = 50 # ADU to zero out image + self.smoothsigma = 1. # sigma for Gaussian before labelleing + self.bgc = 0.9 # fractional part of bg per peak to remove + self.minpx = 3 + + self.m_offset = self.bg < 100 + self.mbg = np.mean( self.bg[self.m_offset] ) + self.m_ratio = self.bg > 300 + self.invbg = 1 / self.bg[self.m_ratio] + self.bins = b = np.linspace(0,2,256) + self.bc = (b[1:]+b[:-1])/2 + + self.wrk = np.empty( self.bg.shape, 'b') + self.labels = np.empty( self.bg.shape, 'i' ) + + def bgsub(self, img): + img = img.astype( np.float32 ) + offset = np.mean( img[self.m_offset] ) - self.mbg + np.subtract( img, offset, img) + ratio = img[self.m_ratio] * self.invbg + h,b = np.histogram( ratio, bins=self.bins ) + htrim = np.where(h < h.max()*0.05, 0, h) + r = (htrim * self.bc).sum()/htrim.sum() + np.multiply( img, 1/r, img ) + np.subtract( img, self.bg, img ) + self.offset = offset + self.scale = r + return img + + def peaksearch(self, img, omega = 0): + self.cor = self.bgsub( img ) + # smooth the image for labelling (removes noise maxima) + self.smoothed = scipy.ndimage.gaussian_filter(self.cor , self.smoothsigma) + assert self.smoothed.dtype == np.float32 + # zero out the background + self.mt = self.smoothed < self.threshold + self.smoothed[self.mt] = 0 + # label on smoothed image + self.npks = ImageD11.cImageD11.localmaxlabel(self.smoothed, self.labels, self.wrk) + self.labels[self.mt] = 0 + # now find the borders of each blob : first dilate + l3 = scipy.ndimage.uniform_filter(self.labels*7, 3) + self.borders = (self.labels*7) != l3 + # border properties - use the real data or the smoothed? Real data. Might be noisier + self.blobsouter = ImageD11.cImageD11.blobproperties( self.cor, self.labels*self.borders, self.npks ) + # Computed background per peak + self.per_peak_bg = np.concatenate( ([0,], self.blobsouter[:,ImageD11.cImageD11.mx_I])) + self.bgcalc = self.per_peak_bg[self.labels] + self.m_top = self.cor > self.bgcalc * self.bgc + self.forprops = self.cor*self.m_top + self.blobs = ImageD11.cImageD11.blobproperties( self.forprops, + self.labels * self.m_top, + self.npks, + omega = omega) + ImageD11.cImageD11.blob_moments(self.blobs) + self.enoughpx = self.blobs[:,ImageD11.cImageD11.s_1] >= self.minpx + self.goodpeaks = self.blobs[self.enoughpx] + return self.goodpeaks + + def plots(self): + pass + + +@functools.lru_cache(maxsize=1) +def get_dset(h5name, dsetname): + """This avoids to re-read the dataset many times""" + dset = h5py.File(h5name, "r")[dsetname] + return dset + +def pps( arg ): + hname, dsetname, num, omega, bgfile = arg + if pps.worker is None: + pps.worker = worker(bgfile) + frm = get_dset( hname, dsetname)[num] + pks = pps.worker.peaksearch( frm, omega=omega ) + return num, pks + +pps.worker = None + +PKSAVE = 's_raw f_raw o_raw s_1 s_I'.split() +PKCOL = [getattr( ImageD11.cImageD11, p) for p in PKSAVE] +# s_1, s_I, s_I2,\ +# s_fI, s_ffI, s_sI, s_ssI, s_sfI, s_oI, s_ooI, s_foI, s_soI, \ +# bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s, bb_mn_o, bb_mx_o, \ +# mx_I, mx_I_f, mx_I_s, mx_I_o, dety, detz, \ +# avg_i, f_raw, s_raw, o_raw, f_cen, s_cen, \ +# m_ss, m_ff, m_oo, m_sf, m_so, m_fo + + +def process( hname, scan, detector, omeganame, bgfile, houtname ): + + with h5py.File( hname, 'r') as hin: + shp = hin[scan][detector].shape + omega = hin[scan][omeganame][()] + + assert len(omega) == shp[0] + detname = "/".join((scan, detector)) + args = [ (hname, detname, i, omega[i], bgfile) for i in range(shp[0]) ] + t0 = timeit.default_timer() + + with h5py.File( houtname, "a") as hout: + g = hout.require_group( scan + '/peaks2d') + g.attrs['h5input']=hname + g.attrs['h5scan']=scan + g.attrs['detector_path']=detector + g.attrs['omega_path']=omeganame + g.attrs['bgfile']=bgfile + g.attrs['script_source'] = open(__file__, 'rb').read() + g['omega'] = omega + g['npks'] = np.zeros( len(omega), int ) + for name in PKSAVE: + g.create_dataset( name, + dtype = np.float32, + shape = (1,), + chunks = (4096,), + maxshape = (None, ) ) + + npks = 0 + with multiprocessing.Pool(9) as mypool: + i = -1 + for num, pks in tqdm.tqdm( mypool.imap( pps, args ), total=shp[0] ): + i += 1 + if len(pks) == 0: + continue + npk = len(pks) + npks += npk + g['npks'][i] = npk + for name, col in zip(PKSAVE, PKCOL): + g[name].resize( (npks,) ) + g[name][-npk:] = pks[:, col] + # closes, flushes, now merge + do3dmerge( houtname, scan ) + +if __name__=="__main__": + houtname = sys.argv[1] + hname = sys.argv[2] + detector = sys.argv[3] + omega = sys.argv[4] + bgfile = '/data/visitor/ma4750/id11/analysis_jw/march2022/background_big_beam.edf' + scans = sys.argv[5:] + for scan in scans: + process( hname, scan, detector, omega, bgfile, houtname ) + + + + diff --git a/sandbox/ma4750/custom_peak_search_ma5179.py b/sandbox/ma4750/custom_peak_search_ma5179.py new file mode 100755 index 00000000..0feb72af --- /dev/null +++ b/sandbox/ma4750/custom_peak_search_ma5179.py @@ -0,0 +1,242 @@ + +from __future__ import print_function + +import sys, os +import timeit +import multiprocessing +import h5py, hdf5plugin, fabio +import functools +import numpy as np +import tqdm +import ImageD11.cImageD11 +import scipy.sparse, scipy.spatial, scipy.ndimage + +nthreads = int( os.environ[ 'SLURM_CPUS_PER_TASK'] ) + +def do3dmerge( honame, scan ): + # read the 2d peak search results + t0 = timeit.default_timer() + print('Making 3D merge', end=' ') + sys.stdout.flush() + with h5py.File( honame, 'r') as hin: + g = hin[scan]['peaks2d'] + h5input = g.attrs['h5input'] + h5scan = g.attrs['h5scan'] + detector_path = g.attrs['detector_path'] + omega_path = g.attrs['omega_path'] + omega = g['omega'][:] + s = g['s_raw'][:] + f = g['f_raw'][:] + o = g['o_raw'][:] + I = g['s_I'][:] + n = g['npks'][:] + s1 = g['s_1'][:] + # pointers to frames in s,f,o,I,n,s1 + p= np.cumsum( np.concatenate(([0,], n ) ) ) + # make a KDTree for each frame (wastes a bit of memory, but easier for sorting later) + trees = [ scipy.spatial.cKDTree( np.transpose( (s[p[i]:p[i+1]], f[p[i]:p[i+1]]) ) ) + for i in range(len(n)) ] + print('made trees',end=' ') + sys.stdout.flush() + # because interlaced might not be in order + order = np.argsort( omega % 360 ) + # peaks that overlap, k : 0 -> npks == len(s|f|o) + # diagonal + krow = list(range(len(o))) + kcol = list(range(len(o))) + for i in range(1,len(n)): # match these to previous + flo = order[i-1] + fhi = order[i] + tlo = trees[flo] + thi = trees[fhi] + # 1.6 is how close centers should be to overlap + lol = trees[flo].query_ball_tree( trees[fhi], r = 1.6 ) + for srcpk, destpks in enumerate(lol): # dest is strictly higher than src + for destpk in destpks: + krow.append( srcpk + p[flo] ) + kcol.append( destpk + p[fhi] ) + csr = scipy.sparse.csr_matrix( ( np.ones(len(krow), dtype=bool), + (kcol, krow) ), shape=(len(o),len(o)) ) + # connected components == find all overlapping peaks + ncomp, labels= scipy.sparse.csgraph.connected_components( csr, + directed=False, return_labels=True ) + print('connected components', end=' ') + sys.stdout.flush() + # Now merge the properties + npkmerged = np.bincount( labels, minlength = ncomp ) # number of peaks that were merged + s3d1 = np.bincount( labels, minlength = ncomp, weights=s1 ) # s_1 + s3dI = np.bincount( labels, minlength = ncomp, weights=I ) # s_I + ssI = np.bincount( labels, minlength = ncomp, weights=I*s ) # s_sI + sfI = np.bincount( labels, minlength = ncomp, weights=I*f ) # s_sI + soI = np.bincount( labels, minlength = ncomp, weights=I*o ) # s_sI + s3d = ssI / s3dI + f3d = sfI / s3dI + o3d = soI / s3dI + with h5py.File( honame, 'a') as hin: + g = hin[scan].require_group('peaks3d') + g['s_raw'] = s3d + g['f_raw'] = f3d + g['omega'] = o3d + g['sum_intensity'] = s3dI + g['number_of_pixels'] = s3d1 + t1 = timeit.default_timer() + print('wrote %.3f/s'%(t1-t0)) + + +class worker: + """ subtracts background, custom for ma47050 """ + def __init__(self, bgfile, offset=100, ratio=120): + self.bg = fabio.open(bgfile).data.astype(np.float32) + self.threshold = 50 # ADU to zero out image + self.smoothsigma = 1. # sigma for Gaussian before labelleing + self.bgc = 0.9 # fractional part of bg per peak to remove + self.minpx = 3 + + self.m_offset = self.bg < offset + self.mbg = np.mean( self.bg[self.m_offset] ) + self.m_ratio = self.bg > ratio + self.invbg = 1 / self.bg[self.m_ratio] + self.bins = b = np.linspace(0,2,256) + self.bc = (b[1:]+b[:-1])/2 + + self.wrk = np.empty( self.bg.shape, 'b') + self.labels = np.empty( self.bg.shape, 'i' ) + + def bgsub(self, img): + img = img.astype( np.float32 ) + offset = np.mean( img[self.m_offset] ) - self.mbg + np.subtract( img, offset, img) + ratio = img[self.m_ratio] * self.invbg + h,b = np.histogram( ratio, bins=self.bins ) + htrim = np.where(h < h.max()*0.05, 0, h) + r = (htrim * self.bc).sum()/htrim.sum() + np.multiply( img, 1/r, img ) + np.subtract( img, self.bg, img ) + self.offset = offset + self.scale = r + return img + + def peaksearch(self, img, omega = 0): + self.cor = self.bgsub( img ) + # smooth the image for labelling (removes noise maxima) + self.smoothed = scipy.ndimage.gaussian_filter(self.cor , self.smoothsigma) + assert self.smoothed.dtype == np.float32 + # zero out the background + self.mt = self.smoothed < self.threshold + self.smoothed[self.mt] = 0 + # label on smoothed image + self.npks = ImageD11.cImageD11.localmaxlabel(self.smoothed, self.labels, self.wrk) + self.labels[self.mt] = 0 + # now find the borders of each blob : first dilate + l3 = scipy.ndimage.uniform_filter(self.labels*7, 3) + self.borders = (self.labels*7) != l3 + # border properties - use the real data or the smoothed? Real data. Might be noisier + self.blobsouter = ImageD11.cImageD11.blobproperties( self.cor, self.labels*self.borders, self.npks ) + # Computed background per peak + self.per_peak_bg = np.concatenate( ([0,], self.blobsouter[:,ImageD11.cImageD11.mx_I])) + self.bgcalc = self.per_peak_bg[self.labels] + self.m_top = self.cor > self.bgcalc * self.bgc + self.forprops = self.cor*self.m_top + self.blobs = ImageD11.cImageD11.blobproperties( self.forprops, + self.labels * self.m_top, + self.npks, + omega = omega) + ImageD11.cImageD11.blob_moments(self.blobs) + self.enoughpx = self.blobs[:,ImageD11.cImageD11.s_1] >= self.minpx + self.goodpeaks = self.blobs[self.enoughpx] + return self.goodpeaks + + def plots(self, ax): + import pylab as pl + ax.pcolormesh( abs(self.cor), norm=pl.matplotlib.colors.LogNorm() ) + ax.scatter( self.blobs[:, ImageD11.cImageD11.f_raw], + self.blobs[:, ImageD11.cImageD11.s_raw], + s = np.sqrt(self.blobs[:, ImageD11.cImageD11.s_1])/np.pi, + edgecolors='m', + facecolors='None',) + + +@functools.lru_cache(maxsize=1) +def get_dset(h5name, dsetname): + """This avoids to re-read the dataset many times""" + dset = h5py.File(h5name, "r")[dsetname] + return dset + +def pps( arg ): + hname, dsetname, num, omega, bgfile = arg + if pps.worker is None: + pps.worker = worker(bgfile) + frm = get_dset( hname, dsetname)[num] + pks = pps.worker.peaksearch( frm, omega=omega ) + return num, pks + +pps.worker = None + +PKSAVE = 's_raw f_raw o_raw s_1 s_I'.split() +PKCOL = [getattr( ImageD11.cImageD11, p) for p in PKSAVE] +# s_1, s_I, s_I2,\ +# s_fI, s_ffI, s_sI, s_ssI, s_sfI, s_oI, s_ooI, s_foI, s_soI, \ +# bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s, bb_mn_o, bb_mx_o, \ +# mx_I, mx_I_f, mx_I_s, mx_I_o, dety, detz, \ +# avg_i, f_raw, s_raw, o_raw, f_cen, s_cen, \ +# m_ss, m_ff, m_oo, m_sf, m_so, m_fo + + +def process( hname, scan, detector, omeganame, bgfile, houtname, mypool ): + + with h5py.File( hname, 'r') as hin: + shp = hin[scan][detector].shape + omega = hin[scan][omeganame][()] + + assert len(omega) == shp[0] + detname = "/".join((scan, detector)) + args = [ (hname, detname, i, omega[i], bgfile) for i in range(shp[0]) ] + t0 = timeit.default_timer() + + with h5py.File( houtname, "a") as hout: + g = hout.require_group( scan + '/peaks2d') + g.attrs['h5input']=hname + g.attrs['h5scan']=scan + g.attrs['detector_path']=detector + g.attrs['omega_path']=omeganame + g.attrs['bgfile']=bgfile + g.attrs['script_source'] = open(__file__, 'rb').read() + g['omega'] = omega + g['npks'] = np.zeros( len(omega), int ) + for name in PKSAVE: + g.create_dataset( name, + dtype = np.float32, + shape = (1,), + chunks = (4096,), + maxshape = (None, ) ) + + npks = 0 + i = -1 + for num, pks in tqdm.tqdm( mypool.imap( pps, args ), total=shp[0] ): + i += 1 + if len(pks) == 0: + continue + npk = len(pks) + npks += npk + g['npks'][i] = npk + for name, col in zip(PKSAVE, PKCOL): + g[name].resize( (npks,) ) + g[name][-npk:] = pks[:, col] + # closes, flushes, now merge + do3dmerge( houtname, scan ) + +if __name__=="__main__": + houtname = sys.argv[1] + hname = sys.argv[2] + detector = sys.argv[3] + omega = sys.argv[4] + bgfile = sys.argv[5] + # '/data/visitor/ma4750/id11/analysis_jw/march2022/background_big_beam.edf' + scans = sys.argv[6:] + with multiprocessing.Pool(nthreads) as mypool: + for scan in scans: + process( hname, scan, detector, omega, bgfile, houtname, mypool ) + + + + diff --git a/sandbox/ma4750/to_flt.py b/sandbox/ma4750/to_flt.py new file mode 100755 index 00000000..715274ed --- /dev/null +++ b/sandbox/ma4750/to_flt.py @@ -0,0 +1,29 @@ + +import sys + + +""" +Script for repairing use of incorrect spline file +""" +import h5py, tqdm, numpy as np +from ImageD11 import columnfile, blobcorrector + +spline = sys.argv[1] +cor = blobcorrector.correctorclass( spline ) +h5in = sys.argv[2] +pks = sys.argv[3] +outname = sys.argv[4] + +with h5py.File( h5in, 'r') as hin: + print(list(hin),pks) + g = hin[pks] + cd = { name:g[name][:] for name in list(g) } + cd['sc'] = np.zeros_like( cd['s_raw'] ) + cd['fc'] = np.zeros_like( cd['f_raw'] ) + +inc = columnfile.colfile_from_dict( cd ) + +for i in tqdm.tqdm(range( inc.nrows )): + inc.sc[i], inc.fc[i] = cor.correct( inc.s_raw[i], inc.f_raw[i] ) + +columnfile.colfile_to_hdf( inc, outname ) diff --git a/sandbox/make_powder_mask.py b/sandbox/make_powder_mask.py index 83445ca2..b13c9a60 100644 --- a/sandbox/make_powder_mask.py +++ b/sandbox/make_powder_mask.py @@ -33,7 +33,7 @@ def make_powder_mask( parfile, # lim = tth * eta # need some idea how to cut it up... # degree bins - m = (eta.astype(np.int) % 2)==0 + m = (eta.astype(int) % 2)==0 return m if __name__=="__main__": diff --git a/sandbox/map/gridgrain.py b/sandbox/map/gridgrain.py index 0d82745e..7e9671f5 100644 --- a/sandbox/map/gridgrain.py +++ b/sandbox/map/gridgrain.py @@ -4,7 +4,7 @@ # from ImageD11 import transform, transformer, indexing, parameters -from ImageD11.closest import score +from ImageD11.cImageD11 import score import sys diff --git a/sandbox/map/gridmap.py b/sandbox/map/gridmap.py index 4b6f0ff9..bf5a7d7b 100644 --- a/sandbox/map/gridmap.py +++ b/sandbox/map/gridmap.py @@ -4,7 +4,7 @@ # from ImageD11 import transform, transformer, indexing, parameters -from ImageD11.closest import score +from ImageD11.cImageD11 import score import sys diff --git a/sandbox/map/grids.py b/sandbox/map/grids.py index 6c9c1daa..03094d11 100644 --- a/sandbox/map/grids.py +++ b/sandbox/map/grids.py @@ -57,9 +57,9 @@ def grids( flt, tol, npk ): mytransformer.getcolumn("gz") ] ).T myindexer.gv = gv myindexer.ubis = [] - myindexer.ga = -1*np.ones( (gv.shape[0],) ,np.int) + myindexer.ga = -1*np.ones( (gv.shape[0],) ,int) myindexer.gvflat = np.reshape(np.fromstring( - myindexer.gv.tostring(),np.float), myindexer.gv.shape) + myindexer.gv.tostring(),float), myindexer.gv.shape) # ds for rings print(gv.shape) myindexer.ds = np.sqrt( gv[:,0]*gv[:,0] + diff --git a/sandbox/map/uniqify_ubi.py b/sandbox/map/uniqify_ubi.py index b3b52ccd..c0d8c15c 100644 --- a/sandbox/map/uniqify_ubi.py +++ b/sandbox/map/uniqify_ubi.py @@ -58,7 +58,7 @@ ] #print len(pks) -hkl = np.array(pks,np.float).T +hkl = np.array(pks,float).T uniq_ubis = [] names = list(ubi_all.keys()) @@ -95,7 +95,7 @@ ubi = uo[1] # print "\n# found",len(uo[0]),"times" # Find average ubi matrix for each time... - usum = np.zeros((3,3),np.float) + usum = np.zeros((3,3),float) j = 0 for name, i in uo[0]: usum = usum + ubi_all[name][i] diff --git a/sandbox/match.py b/sandbox/match.py index d05a26b8..26ee1f51 100644 --- a/sandbox/match.py +++ b/sandbox/match.py @@ -13,7 +13,9 @@ g1l = grain.read_grain_file(sys.argv[1]) g2l = grain.read_grain_file(sys.argv[2]) - +for g in g1l + g2l: + if g.translation is None: + g.translation = np.zeros(3) try: h = getattr( sym_u, sys.argv[3] )() except: @@ -43,11 +45,11 @@ -da=np.zeros( (len(g1l),len(g2l)), np.float) -dt2=np.zeros( (len(g1l),len(g2l)), np.float) +da=np.zeros( (len(g1l),len(g2l)), float) +dt2=np.zeros( (len(g1l),len(g2l)), float) for g in g1l + g2l: - g.u = xfab.tools.ubi_to_u_b(g.ubi)[0] + # g.u = xfab.tools.ubi_to_u_b(g.ubi)[0] assert (abs(np.dot(g.u, g.u.T) - np.eye(3)).ravel()).sum() < 1e-6 dtsum = np.zeros(3) @@ -62,7 +64,6 @@ trace = np.trace pi = np.pi for j,g2 in enumerate(g2l): - angle = 180. sg = None aumis = np.dot(asymusT, g2.u) # print aumis.shape @@ -89,8 +90,9 @@ if not printed: ja = np.argmin(da[i]) jd = np.argmin(dt2[i]) - print("# not matched %d min angle %d %.4f %.3f"%(i,ja,da[i,ja],dt2[i,ja])) - print("# not matched %d min dist %d %.4f %.3f"%(i,ja,da[i,jd],dt2[i,jd])) + print( "# not matched %d min angle %d %.4f %.3f"%(i,ja,da[i,ja],dt2[i,ja])) + print( "# not matched %d min dist %d %.4f %.3f"%(i,jd,da[i,jd],dt2[i,jd])) + np.save( "da",da) diff --git a/sandbox/matchquick.py b/sandbox/matchquick.py index 9fa7f23e..ee084cf8 100644 --- a/sandbox/matchquick.py +++ b/sandbox/matchquick.py @@ -1,30 +1,34 @@ - +from __future__ import print_function, division from ImageD11 import sym_u from ImageD11 import grain import sys, numpy as np, xfab.tools -print "# Usage: %s grains1.map grains2.map group angletol distancetol"%(sys.argv[0]) -print "# Matches grains using symmetry group from: " -print "# [cubic|hexagonal|trigonal|rhombohedralP|tetragonal|orthorhombic|monoclinic_[a|b|c]|triclinic]" +print ("# Usage: %s grains1.map grains2.map group angletol distancetol"%(sys.argv[0])) +print ("# Matches grains using symmetry group from: ") +print ("# [cubic|hexagonal|trigonal|rhombohedralP|tetragonal|orthorhombic|monoclinic_[a|b|c]|triclinic]") g1l = grain.read_grain_file(sys.argv[1]) g2l = grain.read_grain_file(sys.argv[2]) +for g in g1l + g2l: + if g.translation is None: + g.translation = np.zeros(3) -print "read in the grains" +print( "read in the grains") try: h = getattr( sym_u, sys.argv[3] )() except: - print "# No group!" - print "#Using cubic" + print( "# No group!") + print( "#Using cubic") h = sym_u.cubic() -print "# Symmetry considered" + print ("Made a cubic group") + +print ( "# Symmetry considered") for o in h.group: - print o + print(o) -print "Made a cubic group" try: tolangle = float(sys.argv[4]) except: @@ -44,12 +48,12 @@ -da=np.zeros( (len(g1l),len(g2l)), np.float) -dt2=np.zeros( (len(g1l),len(g2l)), np.float) +da=np.zeros( (len(g1l),len(g2l)), float) +dt2=np.zeros( (len(g1l),len(g2l)), float) for g in g1l + g2l: - g.u = xfab.tools.ubi_to_u_b(g.ubi)[0] - assert (abs(np.dot(g.u, g.u.T) - np.eye(3)).ravel()).sum() < 1e-6 + #g.u = xfab.tools.ubi_to_u_b(g.ubi)[0] + assert (abs(np.dot(g.U, g.U.T) - np.eye(3)).ravel()).sum() < 1e-6 dtsum = np.zeros(3) ndt = 0 @@ -80,9 +84,8 @@ #if dt2 > toldist2: # continue - angle = 180. sg = None - aumis = np.dot(asymusT, g2.u) + aumis = np.dot(asymusT, g2.U) arg = (aumis[:,0,0]+aumis[:,1,1]+aumis[:,2,2] - 1. )/2. asymangle = np.arccos(np.clip(arg, -1, 1)) sg = np.argmin(asymangle) @@ -92,15 +95,12 @@ #print "# Found a match!", h.group[sg].ravel() dtsum += dt ndt += 1 - print i,j,"angle %.4f"%(angle),"distance %.3f"%(np.sqrt(dT2[j])),"\t", - print 3*"%.1f "%tuple(dt),"\t", - print 3*"%.1f "%tuple(dtsum/ndt) - #print "# grain ubi,t" - #print g1.ubi,g1.translation - #print g2.ubi,g2.translation + print( i,j,"angle %.4f"%(angle),"distance %.3f"%(np.sqrt(dT2[j])),"\t", end="") + print( 3*"%.1f "%tuple(dt),"\t",end="") + print( 3*"%.1f "%tuple(dtsum/ndt)) printed = True if not printed: - print "# not matched %d "%(i) + print ("# not matched %d "%(i)) diff --git a/sandbox/mlem.ipynb b/sandbox/mlem.ipynb new file mode 100644 index 00000000..1fe04510 --- /dev/null +++ b/sandbox/mlem.ipynb @@ -0,0 +1,281 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d9f646df-ac47-4e8f-a65f-53922d9acaf1", + "metadata": {}, + "source": [ + "An \"MLEM\" algorithm from XRDUA was used in this paper:\n", + "\n", + "\"Impurity precipitation in atomized particles evidenced by nano x-ray diffraction computed tomography\"\n", + "Anne Bonnin; Jonathan P. Wright; Rémi Tucoulou; Hervé Palancher\n", + "Appl. Phys. Lett. 105, 084103 (2014)\n", + "https://doi.org/10.1063/1.4894009\n", + "\n", + "This python code implements something similar based on a youtube video (https://www.youtube.com/watch?v=IhETD4nSJec)\n", + "\n", + "There are lots of papers from mathematicians in the literature about MART (multiplicative ART). The conversion of latex algebra back and forth into computer code seems to be a bit of a problem for me (Jon Wright - 17 Nov 2023)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc820bc1-a9e9-4daa-b10f-fc23ef6a96d4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from ImageD11.sinograms.roi_iradon import iradon, radon # to have projection_shifts\n", + "from skimage.transform import iradon_sart\n", + "import numpy as np, pylab as pl\n", + "import skimage.data\n", + "\n", + "def backproject( sino, theta, projection_shifts = None ):\n", + " \"\"\" project the sinogram into the sample \"\"\"\n", + " return iradon( sino, theta, filter_name=None, projection_shifts = projection_shifts )\n", + "\n", + "def forwardproject( sample, theta, projection_shifts = None ):\n", + " \"\"\" project the sample into the experiment (sinogram) \"\"\"\n", + " return radon( sample, theta, projection_shifts = projection_shifts )\n", + "\n", + "def mlem( sino, theta, \n", + " startvalue = 1,\n", + " projection_shifts = None,\n", + " pad=0, niter=50, \n", + " divtol=1e-5, ):\n", + " # \n", + " # Also called \"MART\" for Multiplicative ART\n", + " # This keeps a positivity constraint for both the data and reconstruction\n", + " #\n", + " # This implementation was inspired from from:\n", + " # https://www.youtube.com/watch?v=IhETD4nSJec\n", + " # by Andrew Reader\n", + " #\n", + " # ToDo : implement a mask\n", + " # ToDo : check about the corners / circle=False aspects\n", + " #\n", + " # Number of pixels hitting each output in the sample:\n", + " sensitivity_image = backproject( np.ones_like(sino), theta,\n", + " projection_shifts = projection_shifts )\n", + " recip_sensitivity_image = 1./sensitivity_image\n", + " # The image reconstruction:\n", + " mlem_rec = np.empty( sensitivity_image.shape, np.float32)\n", + " mlem_rec[:] = startvalue\n", + " for i in range(niter):\n", + " calc_sino = forwardproject( mlem_rec, theta, projection_shifts = projection_shifts )\n", + " ratio = sino / (calc_sino + divtol )\n", + " correction = recip_sensitivity_image * backproject( ratio, theta, \n", + " projection_shifts = projection_shifts )\n", + " mlem_rec *= correction\n", + " return mlem_rec" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78c6d5b3-a575-4286-a26a-da0cfbcbe588", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "im = skimage.data.shepp_logan_phantom().astype(np.float32)\n", + "pl.imshow(im)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "305bf8e6-64db-4c97-9d97-af7a85ebd639", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# generate some angles from a diffraction pattern\n", + "\n", + "import ImageD11.transform, ImageD11.unitcell\n", + "import scipy.spatial.transform\n", + "\n", + "# Angles to use are going to correspond to 5 rings of fcc\n", + "a = 3.06\n", + "cell = ImageD11.unitcell.unitcell( [a,a,a,90,90,90],'F')\n", + "cell.makerings(2)\n", + "hkls = []\n", + "for ds in cell.ringds[:5]:\n", + " hkls += cell.ringhkls[ds]\n", + "hkls = np.array( hkls )\n", + "hkls.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e7634c-a59d-4134-9055-8eba0ef50c99", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "UB = scipy.spatial.transform.Rotation.random( random_state = 97 ).as_matrix()\n", + "gvecs = np.dot( UB/a, hkls.T )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38c231c6-bbfa-407c-9ae3-999c233883a4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tth, (eta0, eta1), (omega0, omega1) = ImageD11.transform.uncompute_g_vectors(gvecs, 12.39/50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f745a86b-4a7b-4a22-8a74-e27a7e894678", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "measured = np.concatenate( [ omega0[ np.abs( np.sin( np.radians( eta0 ) ) ) > 0.1 ],\n", + " omega1[ np.abs( np.sin( np.radians( eta1 ) ) ) > 0.1 ] ] )\n", + "print(eta0.shape, measured.shape, measured.min(), measured.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dffc1087-70f5-4ca0-ad09-b67250dceaa1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "order = np.argsort( measured )\n", + "theta = measured[ order ]\n", + "theta = theta[ theta > 0 ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b17d387-bdc8-4633-9c48-46a1f700f435", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sino = forwardproject( im, theta )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6314be77-51a6-445a-814a-be1f305814de", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pl.imshow(sino, aspect='auto', interpolation='nearest')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "009c4844-6b8b-47a0-9ced-287e96fd4f54", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def disp(im, title):\n", + " f, a = pl.subplots(1,2,figsize=(12,5))\n", + " f.colorbar( a[1].imshow(im), ax=a[0] )\n", + " a[1].set(title=title, xticks=(), yticks=())\n", + " a[0].plot( im[im.shape[0]//2] )\n", + " a[0].plot( im[:,im.shape[1]//2] )\n", + " pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69a4c8d3-bb3c-4a4a-adcc-0ad024391234", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fbp = iradon( sino, theta, output_size=im.shape[0])\n", + "disp(fbp,'Hann filter')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd83a9a-41b5-44f8-a7a7-da920b6e649f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "mlr = mlem( sino, theta, niter=1)\n", + "n = 1\n", + "disp( mlr, f'mlem {n} steps' )\n", + "for niter in (1,4,20,100):\n", + " mlr = mlem( sino, theta, startvalue = mlr, niter=niter)\n", + " n += niter\n", + " disp( mlr, f'mlem {n} steps' )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fef2e35e-9330-475b-b8ff-56bcb2180247", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rsart = skimage.transform.iradon_sart(sino, theta )\n", + "disp( rsart, 'iradon_sart')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62356c3e-9f7b-48a4-a0b4-6b29f01a75cf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (main)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sandbox/newpeaksearch.py b/sandbox/newpeaksearch.py new file mode 100644 index 00000000..20043140 --- /dev/null +++ b/sandbox/newpeaksearch.py @@ -0,0 +1,48 @@ + +from __future__ import print_function +import sys +import h5py, timeit +import numpy as np +from ImageD11 import sparseframe + +timer= timeit.default_timer + +def newpks(hf, g='measurement/frelon3'): + """ do a peak search in a sparse frame series """ + blbs = [] + with h5py.File(hf,"r") as h: + # sort as numbers: + dsu = [ (float(v), v) for v in list(h)] + dsu.sort() + for _, scan in dsu: + print(scan,end=" ") + sys.stdout.flush() + if g is None: + gscan=h[scan] + else: + gscan=h[scan][g] + for fgrp in sorted(list(gscan)): + f = sparseframe.from_hdf_group(gscan[fgrp]) + sparseframe.sparse_localmax(f) + blbs.append( sparseframe.sparse_moments(f, "intensity", + "localmax") ) + return blbs + + +def main(): + ################### scan specific motorpos ######## + dtyomepos = {} + oa = np.linspace(0.,360.,3600) + pks = {} + start = timer() + blobs = newpks( sys.argv[1] ) + end = timer() + print() + print("Time",end-start, len(blobs)) + return blobs + + + + +if __name__=="__main__": + blobs = main() diff --git a/sandbox/newpeaksearch3d.py b/sandbox/newpeaksearch3d.py new file mode 100644 index 00000000..e8cbafd0 --- /dev/null +++ b/sandbox/newpeaksearch3d.py @@ -0,0 +1,198 @@ + + +import sys, os +import hdf5plugin, h5py, timeit +import fabio +import numpy as np +import scipy.sparse +from ImageD11 import sparseframe, cImageD11, columnfile, blobcorrector + +timer= timeit.default_timer +T0 = START = timer() +def bench(msg): + global T0, START + end = timer() + print(msg, "%.4f /s %.4f /s"%( end - START, end - T0 ) ) + START = end + + +class sparsedatafile(object): + + def __init__(self, hname, folder): + self.hname = hname + self.h = h5py.File( hname, "r" ) + gin = self.h[folder] + self.shape = gin.attrs['shape0'], gin.attrs['shape1'] + self.row = gin['row'] + self.col = gin['col'] + self.sig = gin['intensity'] + self.omega = gin['omega'] + self.ipt = np.cumsum( np.concatenate( ( (0,), gin['nnz'][:] ) ) ) + self.nframes = gin['nnz'].shape[0] + + def getframe(self, i): + assert i>=0 and i f.pixels['localmax'] + sparseframe.sparse_localmax(frm) + # -> f.pixels['connectedpixels'] + sparseframe.sparse_connected_pixels(frm, threshold = np.finfo( np.float32 ).min ) + # which localmax peaks overlap each other + adj = sparseframe.overlaps(frm, 'localmax', frm, 'connectedpixels' ) + r, c = self.adj2overlap( adj ) + return frm, r, c + + def adj2overlap(self, adj): + """ Locate the overlaps on the same frame """ + cts = np.bincount( adj.col, minlength=adj.shape[1] ) + pairs = np.arange( adj.shape[1], dtype=int)[ cts > 1 ] + rows = [] + cols = [] + for k in pairs: + overlaps = adj.row[ adj.col == k ] + for i,p1 in enumerate(overlaps): + for p2 in overlaps[i+1:]: + rows.append( p1 ) + cols.append( p2 ) + return np.array(rows), np.array(cols) + + def peaksearch_all(self): + rows = [] + cols = [] + + frm, r, c = self.peaksearch(self.getframe(0)) + frames = [ frm, ] + + npks = [ frames[0].meta['localmax']['nlabel'], ] + cpks = [0, npks[-1]] # cumulative peak count + + for i in range(1,self.nframes): + frm, r, c = self.peaksearch( self.getframe(i)) # labelled + n = frm.meta['localmax']['nlabel'] + frames.append( frm ) + rows.append(r+cpks[i]) + cols.append(c+cpks[i]) + npks.append( n ) + cpks.append( cpks[-1] + n ) # cumsum + m = sparseframe.overlaps(frames[i-1], 'localmax', + frames[i ], 'localmax') + rows.append( m.row + cpks[i-1] ) + cols.append( m.col + cpks[i] ) + self.rows = np.concatenate( rows ) + self.cols = np.concatenate( cols ) + self.frames = frames # needed? + self.cpks = cpks + + def overlaps(self): + ntot = self.cpks[-1] + g = scipy.sparse.coo_matrix( (np.ones(len(self.rows),np.uint8), + (self.rows, self.cols)), + shape=(ntot,ntot)) + self.ncomp, self.labels = scipy.sparse.csgraph.connected_components( + g, return_labels=True, directed=False ) + + def moments(self, spline=None): + titles = "1 I fI sI".split() # for now + npx = cImageD11.s2D_1 + I = cImageD11.s2D_I + sI = cImageD11.s2D_sI + fI = cImageD11.s2D_fI + # results + c = { '1' : np.zeros( self.ncomp, 'f' ), + 'I' : np.zeros( self.ncomp, 'f' ), + 'sI': np.zeros( self.ncomp, 'f' ), + 'fI': np.zeros( self.ncomp, 'f' ), + 'oI': np.zeros( self.ncomp, 'f' ), + } + + for i,f in enumerate(self.frames): + m = sparseframe.sparse_moments( f, 'intensity', 'localmax' ) + # data , ind, vals : data[ind] += vals + inds = self.labels[ self.cpks[i]:self.cpks[i+1] ] + cImageD11.put_incr( c[ '1'], inds, m[:, npx] ) + cImageD11.put_incr( c[ 'I'], inds, m[:, I] ) + cImageD11.put_incr( c['sI'], inds, m[:, sI] ) + cImageD11.put_incr( c['fI'], inds, m[:, fI] ) + cImageD11.put_incr( c['oI'], inds, m[:, I]*self.omega[i] ) + s_raw = c['sI']/c['I'] + f_raw = c['fI']/c['I'] + omega = c['oI']/c['I'] + if spline is not None: + sc, fc = spatial_correct( s_raw, f_raw, spline ) + else: + sc, fc = s_raw, f_raw + results = { + 'sc': sc, + 'fc': fc, + 'omega': omega, + 'Number_of_pixels' : c['1'], + 'avg_intensity' : c['I']/c['1'], + 's_raw' : s_raw, + 'f_raw' : f_raw, + 'sum_intensity': c['I'], + } + return results + + +def spatial_correct( s_raw, f_raw, spline): + si = np.round( s_raw ).clip(0,2047).astype(int) + fi = np.round( f_raw ).clip(0,2047).astype(int) + try: + dx, dy = spline + fc = f_raw + dx[si, fi] + sc = s_raw + dy[si, fi] + except: + corr = blobcorrector.correctorclass(spline) + dx, dy = corr.make_pixel_lut( (2048, 2048) ) + fc = f_raw - fi + dy[si, fi] + sc = s_raw - si + dx[si, fi] + return sc, fc + + +def dict_to_colfile( c ): + titles = list(c.keys()) + nrows = len(c[titles[0]]) + for t in titles: + assert len(c[t]) == nrows, t + colf = columnfile.newcolumnfile( titles=titles ) + colf.nrows = nrows + colf.set_bigarray( [ c[t] for t in titles ] ) + return colf + +# /data/id11/jon/inhouse/jon/mar10/segment2d +hdffile = "fe3o4_peaks.hdf" +folder = "entry" +parfile = "avg.par" +splinefile = "frelon4m.spline" +splinefile = (fabio.open("F4M_EO_dx.edf").data,fabio.open("F4M_EO_dy.edf").data) + + + +bench( "startup" ) +obj = sparsedatafile( hdffile, folder ) ; bench("openhdf") +obj.peaksearch_all() ; bench("peaksearch") +obj.overlaps() ; bench("overlaps") +res = obj.moments(splinefile) ; bench("moments") +c = dict_to_colfile( res ) ; bench("convert") +c.parameters.loadparameters( parfile ) +c.updateGeometry() ; bench("geometry") +if os.path.exists('peaks.hdf'): + os.remove('peaks.hdf') + columnfile.colfileobj_to_hdf( c, "peaks.hdf" ) ; bench("save hdf") + +if 1: + import pylab as pl + pl.plot( c.tth, c.eta, ",") + pl.title("%d peaks"%(c.nrows)) + pl.show() + diff --git a/sandbox/offset_image.py b/sandbox/offset_image.py index afeac68f..879a8cbf 100644 --- a/sandbox/offset_image.py +++ b/sandbox/offset_image.py @@ -2,7 +2,8 @@ from __future__ import print_function import numpy as np -REAL = np.float +from PIL import Image +REAL = float def normalise( im ): """ Set mean to 0, std to 1 """ @@ -39,7 +40,6 @@ def find_offset( c ): def register_image( rname, cname ): """ We take the current and place it onto reference """ - import Image ref = normalise(np.asarray(Image.open(rname)).sum(axis=2, dtype=REAL)) cur = normalise(np.asarray(Image.open(cname)).sum(axis=2, dtype=REAL)) fftshape = ( ref.shape[0] + cur.shape[0] + 4, @@ -77,7 +77,6 @@ def display_registered( current, reference, xf, yf ): def test(filename): - import Image rgb = np.asarray(Image.open(filename)) vals = 1.0*rgb[:,:,0] + rgb[:,:,1] + rgb[:,:,2] print(vals.shape) diff --git a/sandbox/peaksearch_nxs.py b/sandbox/peaksearch_nxs.py new file mode 100644 index 00000000..5fddb436 --- /dev/null +++ b/sandbox/peaksearch_nxs.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python + +from __future__ import print_function + +# Command line driver only - calls ImageD11.peaksearcher +# ... which can also be called by gui + +## Automatically adapted for numpy.oldnumeric Sep 06, 2007 by alter_code1.py + + + +# ImageD11_v1.0 Software for beamline ID11 +# Copyright (C) 2005-2007 Jon Wright +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 2 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 0211-1307 USA + + + +""" +Script for peaksearching images from the command line + +Uses the connectedpixels extension for finding blobs above a threshold +and the blobcorrector(+splines) for correcting them for spatial distortion + +Defines one function (peaksearch) which might be reused +""" + +import time + +# For benchmarking +reallystart = time.time() + +if __name__=="__main__": + # If we are running from a command line: + myparser = None + try: + from argparse import ArgumentParser + import peaksearcher_nxs as peaksearcher + parser = ArgumentParser() + myparser = peaksearcher.get_options(parser) + options , args = myparser.parse_known_args() + peaksearcher.peaksearch_driver(options, args) + except: + if myparser is not None: + myparser.print_help() + print("\n\n And here is the problem:\n") + raise +end = time.time() +t = end-reallystart +print("Total time = %f /s" % ( t )) + diff --git a/sandbox/peaksearcher_nxs.py b/sandbox/peaksearcher_nxs.py new file mode 100644 index 00000000..be6a5531 --- /dev/null +++ b/sandbox/peaksearcher_nxs.py @@ -0,0 +1,587 @@ + +from __future__ import print_function + + + +# ImageD11_v1.0 Software for beamline ID11 +# Copyright (C) 2005-2007 Jon Wright +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 2 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 0211-1307 USA + + + +""" + +Adaptation of ImageD11src/peaksearcher.py by James Ball, Mar 2020 + + +Script for peaksearching images from a supplied nexus file + +Uses the connectedpixels extension for finding blobs above a threshold +and the blobcorrector(+splines) for correcting them for spatial distortion + +Defines one function (peaksearch) which might be reused +""" + +# For benchmarking +import time +reallystart = time.time() + +from six.moves import queue +# import threading +import sys , os.path +import numpy + +# Generic file format opener from fabio +from fabio.openimage import openimage + +from ImageD11 import blobcorrector, ImageD11options +from ImageD11.correct import correct +from ImageD11.labelimage import labelimage +from ImageD11 import ImageD11_thread +ImageD11_thread.stop_now = False + + +class timer: + def __init__(self): + self.start = time.time() + self.now = self.start + self.msgs = [] + def msg(self ,msg): + self.msgs.append(msg) + def tick(self ,msg=""): + now = time.time() + self.msgs.append("%s %.2f/s " %(msg ,now-self.now)) + self.now = now + def tock(self ,msg=""): + self.tick(msg) + print(" ".join(self.msgs) ,"%.2f/s "% (self.now-self.start)) + sys.stdout.flush() + + +def peaksearch( filename , + data_object , + corrector , + thresholds , + labims ): + """ + filename : The name of the image file for progress info + data_object : Fabio object containing data and header + corrector : spatial and dark/flood , linearity etc + + thresholds : [ float[1], float[2] etc] + + labims : label image objects, one for each threshold + + """ + t = timer() + picture = data_object.data.astype(numpy.float32) + + assert "Omega" in data_object.header, "Bug in peaksearch headers" + + for lio in list(labims.values()): + f = lio.sptfile + f.write("\n\n# File %s\n" % (filename)) + f.write("# Frame %d\n" % (data_object.currentframe) ) + f.write("# Processed on %s\n" % (time.asctime())) + try: + f.write("# Spatial correction from %s\n" % (corrector.splinefile)) + f.write("# SPLINE X-PIXEL-SIZE %s\n" % (str(corrector.xsize))) + f.write("# SPLINE Y-PIXEL-SIZE %s\n" % (str(corrector.ysize))) + except: + pass + for item in list(data_object.header.keys()): + if item == "headerstring": # skip + continue + try: + f.write("# %s = %s\n" % (item, + str(data_object.header[item]).replace("\n" ," "))) + except KeyError: + pass + + # Get the rotation angle for this image + ome = float(data_object.header["Omega"]) + # print "Reading from header" + # + # Now peaksearch at each threshold level + t.tick(filename) + for threshold in thresholds: + labelim = labims[threshold] + f = labelim.sptfile + if labelim.shape != picture.shape: + raise "Incompatible blobimage buffer for file %s" %(filename) + # + # + # Do the peaksearch + f.write("# Omega = %f\n" % (ome)) + labelim.peaksearch(picture, threshold, ome) + f.write("# Threshold = %f\n" % (threshold)) + f.write("# npks = %d\n" % (labelim.npk)) + # + if labelim.npk > 0: + labelim.output2dpeaks(f) + labelim.mergelast() + t.msg("T=%-5d n=%-5d;" % (int(threshold), labelim.npk)) + # Close the output file + # Finish progress indicator for this file + t.tock() + sys.stdout.flush() + return None + + +def peaksearch_driver(options, args): + """ + To be called with options from command line + """ + ################## debugging still + for a in args: + print("arg: " + str(a) + "," + str(type(a))) + for o in list(options.__dict__.keys()): # FIXME + if getattr(options, o) in ["False", "FALSE", "false"]: + setattr(options, o, False) + if getattr(options, o) in ["True", "TRUE", "true"]: + setattr(options, o, True) + print("option:", str(o), str(getattr(options, o)), ",", \ + str(type(getattr(options, o)))) + ################### + print("This peaksearcher is from", __file__) + + if options.killfile is not None and os.path.exists(options.killfile): + print("The purpose of the killfile option is to create that file") + print("only when you want peaksearcher to stop") + print("If the file already exists when you run peaksearcher it is") + print("never going to get started") + raise ValueError("Your killfile " + options.killfile + " already exists") + + if options.thresholds is None: + raise ValueError("No thresholds supplied [-t 1234]") + + if len(args) == 0 and options.nexusfile is None: + raise ValueError("No files to process") + + # What to do about spatial + + if options.perfect == "N" and os.path.exists(options.spline): + print("Using spatial from", options.spline) + corrfunc = blobcorrector.correctorclass(options.spline) + else: + print("Avoiding spatial correction") + corrfunc = blobcorrector.perfect() + + # Get list of filenames to process + # if len(args) > 0 : + # # We no longer assume unlabelled arguments are filenames + # file_series_object = file_series.file_series(args) + + # This is always the case now + corrfunc.orientation = "edf" + + import h5py + + # Read list of files and list of motor positions from Nexus file: + nexus_path = options.nexusfile + nexus_file = h5py.File(nexus_path, "r") + group = nexus_file[options.group_path] + omega_dset = group.get(options.omega_dset) + image_dset = group.get(options.image_dset) + omega_list = [x for x in omega_dset[..., :]] + image_list = [x.decode("utf-8") for x in image_dset[..., :]] + + import fabio + + # Output files: + + import fabio.file_series + # Use traceback = True for debugging + first_image = openimage(image_list[0]) + + file_series_object = fabio.file_series.new_file_series( + first_image, + nimages=len(image_list), + traceback=True) + + if options.outfile[-4:] != ".spt": + options.outfile = options.outfile + ".spt" + print("Your output file must end with .spt, changing to ", options.outfile) + + # Make a blobimage the same size as the first image to process + + # List comprehension - convert remaining args to floats + # must be unique list so go via a set + thresholds_list = list(set([float(t) for t in options.thresholds])) + thresholds_list.sort() + + li_objs = {} # label image objects, dict of + + s = first_image.data.shape # data array shape + + # Create label images + for t in thresholds_list: + # the last 4 chars are guaranteed to be .spt above + mergefile = "%s_t%d.flt" % (options.outfile[:-4], t) + spotfile = "%s_t%d.spt" % (options.outfile[:-4], t) + li_objs[t] = labelimage(shape=s, + fileout=mergefile, + spatial=corrfunc, + sptfile=spotfile) + print("make labelimage", mergefile, spotfile) + if options.dark is not None: + print("Using dark (background)", options.dark) + darkimage = openimage(options.dark).data.astype(numpy.float32) + else: + darkimage = None + if options.darkoffset != 0: + print("Adding darkoffset", options.darkoffset) + if darkimage is None: + darkimage = options.darkoffset + else: + darkimage += options.darkoffset + if options.flood is not None: + floodimage = openimage(options.flood).data + cen0 = int(floodimage.shape[0] / 6) + cen1 = int(floodimage.shape[0] / 6) + middle = floodimage[cen0:-cen0, cen1:-cen1] + nmid = middle.shape[0] * middle.shape[1] + floodavg = numpy.mean(middle) + print("Using flood", options.flood, "average value", floodavg) + if floodavg < 0.7 or floodavg > 1.3: + print("Your flood image does not seem to be normalised!!!") + + else: + floodimage = None + start = time.time() + print("File being treated in -> out, elapsed time") + # If we want to do read-ahead threading we fill up a Queue object with data + # objects + # THERE MUST BE ONLY ONE peaksearching thread for 3D merging to work + # there could be several read_and_correct threads, but they'll have to get the order right, + # for now only one + if options.oneThread: + # Wrap in a function to allow profiling (perhaps? what about globals??) + def go_for_it(file_series_object, darkimage, floodimage, + corrfunc, thresholds_list, li_objs): + for inc, data_object in enumerate(file_series_object): + t = timer() + if not isinstance(data_object, fabio.fabioimage.fabioimage): + # Is usually an IOError + if isinstance(data_object[1], IOError): + sys.stdout.write(data_object[1].strerror + '\n') + # data_object[1].filename + else: + import traceback + traceback.print_exception(data_object[0], data_object[1], data_object[2]) + sys.exit() + continue + filein = data_object.filename + + data_object.header["Omega"] = float(omega_list[inc]) + + data_object = correct(data_object, darkimage, floodimage, + do_median=options.median, + monitorval=options.monitorval, + monitorcol=options.monitorcol, + ) + t.tick(filein + " io/cor") + peaksearch(filein, data_object, corrfunc, + thresholds_list, li_objs) + for t in thresholds_list: + li_objs[t].finalise() + + go_for_it(file_series_object, darkimage, floodimage, + corrfunc, thresholds_list, li_objs) + + + else: + print("Going to use threaded version!") + try: + # TODO move this to a module ? + + class read_only(ImageD11_thread.ImageD11_thread): + def __init__(self, queue, file_series_obj, myname="read_only"): + """ Reads files in file_series_obj, writes to queue """ + self.queue = queue + self.file_series_obj = file_series_obj + ImageD11_thread.ImageD11_thread.__init__(self, + myname=myname) + print("Reading thread initialised", end=' ') + + def ImageD11_run(self): + """ Read images and copy them to self.queue """ + for inc, data_object in enumerate(self.file_series_obj): + if self.ImageD11_stop_now(): + print("Reader thread stopping") + break + if not isinstance(data_object, fabio.fabioimage.fabioimage): + # Is usually an IOError + if isinstance(data_object[1], IOError): + # print data_object + # print data_object[1] + sys.stdout.write(str(data_object[1].strerror) + '\n') + # ': ' + data_object[1].filename + '\n') + else: + import traceback + traceback.print_exception(data_object[0], data_object[1], data_object[2]) + sys.exit() + continue + ti = timer() + filein = data_object.filename + "[%d]" % (data_object.currentframe) + try: + data_object.header["Omega"] = float(omega_list[inc]) + except KeyboardInterrupt: + raise + except: + continue + ti.tick(filein) + self.queue.put((filein, data_object), block=True) + ti.tock(" enqueue ") + if self.ImageD11_stop_now(): + print("Reader thread stopping") + break + + # Flag the end of the series + self.queue.put((None, None), block=True) + + class correct_one_to_many(ImageD11_thread.ImageD11_thread): + def __init__(self, queue_read, queues_out, thresholds_list, + dark=None, flood=None, myname="correct_one", + monitorcol=None, monitorval=None, + do_median=False): + """ Using a single reading queue retains a global ordering + corrects and copies images to output queues doing + correction once """ + self.queue_read = queue_read + self.queues_out = queues_out + self.dark = dark + self.flood = flood + self.do_median = do_median + self.monitorcol = monitorcol + self.monitorval = monitorval + self.thresholds_list = thresholds_list + ImageD11_thread.ImageD11_thread.__init__(self, + myname=myname) + + def ImageD11_run(self): + while not self.ImageD11_stop_now(): + ti = timer() + filein, data_object = self.queue_read.get(block=True) + if filein is None: + for t in self.thresholds_list: + self.queues_out[t].put((None, None), + block=True) + # exit the while 1 + break + data_object = correct(data_object, self.dark, + self.flood, + do_median=self.do_median, + monitorval=self.monitorval, + monitorcol=self.monitorcol, + ) + ti.tick(filein + " correct ") + for t in self.thresholds_list: + # Hope that data object is read only + self.queues_out[t].put((filein, data_object), + block=True) + ti.tock(" enqueue ") + print("Corrector thread stopping") + + class peaksearch_one(ImageD11_thread.ImageD11_thread): + def __init__(self, q, corrfunc, threshold, li_obj, + myname="peaksearch_one"): + """ This will handle a single threshold and labelimage + object """ + self.q = q + self.corrfunc = corrfunc + self.threshold = threshold + self.li_obj = li_obj + ImageD11_thread.ImageD11_thread.__init__( + self, + myname=myname + "_" + str(threshold)) + + def run(self): + while not self.ImageD11_stop_now(): + filein, data_object = self.q.get(block=True) + if not isinstance(data_object, fabio.fabioimage.fabioimage): + break + peaksearch(filein, data_object, self.corrfunc, + [self.threshold], + {self.threshold: self.li_obj}) + self.li_obj.finalise() + + # 8 MB images - max 40 MB in this queue + read_queue = queue.Queue(5) + reader = read_only(read_queue, file_series_object) + reader.start() + queues = {} + searchers = {} + for t in thresholds_list: + print("make queue and peaksearch for threshold", t) + queues[t] = queue.Queue(3) + searchers[t] = peaksearch_one(queues[t], corrfunc, + t, li_objs[t]) + corrector = correct_one_to_many(read_queue, + queues, + thresholds_list, + dark=darkimage, + flood=floodimage, + do_median=options.median, + monitorcol=options.monitorcol, + monitorval=options.monitorval) + corrector.start() + my_threads = [reader, corrector] + for t in thresholds_list[::-1]: + searchers[t].start() + my_threads.append(searchers[t]) + nalive = len(my_threads) + + def empty_queue(q): + while 1: + try: + q.get(block=False, timeout=1) + except: + break + q.put((None, None), block=False) + + while nalive > 0: + try: + nalive = 0 + for thr in my_threads: + if thr.isAlive(): + nalive += 1 + if options.killfile is not None and \ + os.path.exists(options.killfile): + raise KeyboardInterrupt() + time.sleep(1) + except KeyboardInterrupt: + print("Got keyboard interrupt in waiting loop") + ImageD11_thread.stop_now = True + try: + time.sleep(1) + except: + pass + empty_queue(read_queue) + for t in thresholds_list: + q = queues[t] + empty_queue(q) + for thr in my_threads: + if thr.isAlive(): + thr.join(timeout=1) + print("finishing from waiting loop") + except: + print("Caught exception in waiting loop") + ImageD11_thread.stop_now = True + time.sleep(1) + empty_queue(read_queue) + for t in thresholds_list: + q = queues[t] + empty_queue(q) + for thr in my_threads: + if thr.isAlive(): + thr.join(timeout=1) + raise + + + except ImportError: + print("Probably no threading module present") + raise + + +def get_options(parser): + """ Add our options to a parser object """ + parser.add_argument("-n", "--nexusfile", action="store", + dest="nexusfile", type=str, default=None, + help="The Nexus file path") + parser.add_argument("-g", "--group_path", action="store", + dest="group_path", type=str, default=None, + help="Internal NXS path to datasets group") + parser.add_argument("--image_dset", action="store", + dest="image_dset", type=str, default=None, + help="Name of image files dataset") + parser.add_argument("--omega_dset", action="store", + dest="omega_dset", type=str, default=None, + help="Name of omegas dataset") + parser.add_argument("-o", "--outfile", action="store", + dest="outfile", default="peaks.spt", type=str, + help="Output filename, default=peaks.spt") + parser.add_argument("-d", "--darkfile", action="store", + dest="dark", default=None, type=ImageD11options.ImageFileType(mode='r'), + help="Dark current filename, to be subtracted, default=None") + dod = 0 + parser.add_argument("-D", "--darkfileoffset", action="store", + dest="darkoffset", default=dod, type=float, + help= + "Constant to subtract from dark to avoid overflows, default=%d" % (dod)) + parser.add_argument("-s", "--splinefile", action="store", + dest="spline", default=None, type=ImageD11options.SplineFileType(mode='r'), + help="Spline file for spatial distortion, default=None") + parser.add_argument("-p", "--perfect_images", action="store", + choices=["Y", "N"], default="Y", dest="perfect", + help="Ignore spline Y|N, default=N") + parser.add_argument("-O", "--flood", action="store", + type=ImageD11options.ImageFileType(mode='r'), + default=None, dest="flood", + help="Flood file, default=None") + parser.add_argument("-t", "--threshold", action="append", type=float, + dest="thresholds", default=None, + help="Threshold level, you can have several") + parser.add_argument("--singleThread", action="store_true", + dest="oneThread", default=False, + help="Do single threaded processing") + parser.add_argument("-k", "--killfile", action="store", + dest="killfile", default=None, + type=ImageD11options.FileType(), + help="Name of file to create stop the peaksearcher running") + parser.add_argument("-m", "--median1D", action="store_true", + default=False, dest="median", + help="Computes the 1D median, writes it to file .bkm and" \ + + " subtracts it from image. For liquid background" \ + + " on radially transformed images") + parser.add_argument("--monitorcol", action="store", type=str, + dest="monitorcol", + default=None, + help="Header value for incident beam intensity") + parser.add_argument("--monitorval", action="store", type=float, + dest="monitorval", + default=None, + help="Incident beam intensity value to normalise to") + parser.add_argument("--interlaced", action="store_true", + dest="interlaced", default=False, + help="Interlaced DCT scan") + parser.add_argument("--iflip", action="store_true", + dest="iflip", default=False, + help="Reverse second half of interlaced scan") + return parser + + +def get_help(usage=True): + """ return the help string for online help """ + try: + import StringIO as io + except: + # python3 + import io + import argparse + if usage: + o = get_options(argparse.ArgumentParser()) + else: + o = get_options(argparse.ArgumentParser(usage=argparse.SUPPRESS)) + f = io.StringIO() + o.print_help(f) + return f.getvalue() + + +if __name__ == "__main__": + raise Exception("Please use the driver script peaksearch.py") + + diff --git a/sandbox/pfun.py b/sandbox/pfun.py new file mode 100755 index 00000000..8d7f9b7f --- /dev/null +++ b/sandbox/pfun.py @@ -0,0 +1,71 @@ + +import h5py +import numpy as np +from ImageD11 import sparseframe, cImageD11 + +def moments( bfrm ): + I = bfrm[:, cImageD11.s2D_I] + s = bfrm[:, cImageD11.s2D_sI] / I + f = bfrm[:, cImageD11.s2D_fI] / I + n = bfrm[:, cImageD11.s2D_1] + a = I / n + return s, f, a, n + +def newpks(hf, scans=None, npixels=1, monitor=None, monval=1e3, ): + """ do a peak search in a sparse frame series """ + titles = 's_raw f_raw avg_intensity Number_of_pixels sum_intensity omega dty'.split() + c = {} + for name in titles: + c[name] = [] + # using the python file open overcomes some threading crap + with h5py.File(open(hf,"rb"),'r') as hin: + # scan numbers + if scans is None: + scans = list(hin['/']) + for scan in scans: + gin = hin[scan] + shape = gin.attrs['shape0'], gin.attrs['shape1'] + # import pdb; pdb.set_trace() + omega = gin['measurement/rot'][:] + difty = gin['instrument/positioners/dty'][()] + row = gin['row'][()] + col = gin['col'][()] + sig = gin['intensity'][()] + if monitor is not None: + mon = monval/gin[monitor][()] + ipt = np.cumsum( gin['nnz'][:] ) + iprev = 0 + for k,nnz in enumerate( gin['nnz'][()] ) : + inext = iprev + nnz + if nnz == 0: + continue + f = sparseframe.sparse_frame( row[iprev:inext], + col[iprev:inext], shape ) + f.set_pixels("intensity", sig[iprev:inext] ) + sparseframe.sparse_connected_pixels(f, threshold=0.1) + pks = sparseframe.sparse_moments(f, "intensity", "connectedpixels") + # sparseframe.sparse_localmax(f) + # pks = sparseframe.sparse_moments(f, "intensity", "localmax") + s,f,a,n = moments( pks ) + m = n > npixels + c['s_raw'].append( s[m] ) + c['f_raw'].append( f[m] ) + if monitor is not None: + c['avg_intensity'].append( a[m]*mon[k] ) + c['sum_intensity'].append( a[m]*n[m]*mon[k] ) + else: + c['avg_intensity'].append( a[m] ) + c['sum_intensity'].append( a[m]*n[m] ) + c['Number_of_pixels'].append( n[m] ) + npk = m.sum() + c['omega' ].append( np.full( npk , omega[k] ) ) + c['dty'].append( np.full( npk , difty ) ) + iprev = inext + for t in titles: + c[t] = np.concatenate( c[t] ) + return c + +def pfun( *args ): +# print(args[0]) + s, h5name, npixels = args[0] + return (s, newpks( h5name, [s,], npixels = npixels)) diff --git a/sandbox/pixelmapper.ipynb b/sandbox/pixelmapper.ipynb new file mode 100644 index 00000000..17e669fa --- /dev/null +++ b/sandbox/pixelmapper.ipynb @@ -0,0 +1,863 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given:\n", + "- sparseframe pixels file (s, f, omega, intensity)\n", + "- parameters file\n", + "- spatial distortion images (sraw -> sc, fraw -> fc)\n", + "- ubi file for list of grains\n", + " \n", + "ForEach pixel:\n", + "- assign it to one or more grains with h,k,l,sign(eta) label\n", + "- best grain + hklid\n", + "- second best grain + hklid -> to determine whether this is an overlap problem / twin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys, time\n", + "start = time.time()\n", + "import numpy as np, pylab as pl\n", + "from ImageD11 import transform, parameters, cImageD11, indexing, columnfile, sym_u, blobcorrector, grain\n", + "import fabio, h5py\n", + "\n", + "\n", + "PLOT = False\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# put in transform or blobcorrector ?\n", + "\n", + "class Lut( object ):\n", + " \n", + " pnames = ( \"y_center\", \"z_center\", \"y_size\", \"z_size\",\n", + " \"distance\", \"wavelength\",\n", + " \"tilt_x\",\"tilt_y\",\"tilt_z\",\n", + " \"o11\", \"o12\", \"o21\", \"o22\",\n", + " \"wedge\", \"chi\", \"dxfile\", \"dyfile\", \"spline\", \"shape\" )\n", + " \n", + " def __init__( self, pars ):\n", + " \n", + " self.pars = {}\n", + " for p in self.pnames:\n", + " if p in pars:\n", + " self.pars[p] = pars[p] # make a copy\n", + " if 'dxfile' in pars:\n", + " # slow/fast coordinates on image at pixel centers\n", + " self.df = fabio.open( pars['dxfile'] ).data\n", + " self.ds = fabio.open( pars['dyfile'] ).data\n", + " self.shape = s = self.ds.shape\n", + " self.pars['shape'] = s\n", + " slow, fast = np.mgrid[ 0:s[0], 0:s[1] ]\n", + " self.sc = slow + self.ds\n", + " self.fc = fast + self.df\n", + " elif 'spline' in pars: # need to test this...\n", + " b = blobcorrector.correctorclass( self.pars['spline'] )\n", + " s = self.pars['shape']\n", + " self.fc, self.sc = b.make_pixel_lut( s ) \n", + " slow, fast = np.mgrid[ 0:s[0], 0:s[1] ]\n", + " self.df = self.fc - fast\n", + " self.ds = self.sc - slow\n", + " # scattering angles:\n", + " self.tth, self.eta = transform.compute_tth_eta( \n", + " (self.sc.ravel(), self.fc.ravel()), **self.pars )\n", + " # scattering vectors:\n", + " self.k = transform.compute_k_vectors( self.tth, self.eta, \n", + " self.pars.get('wavelength') )\n", + " self.tth.shape = s\n", + " self.eta.shape = s\n", + " self.k.shape = (3, s[0], s[1])\n", + " \n", + " def spatial(self, sraw, fraw):\n", + " \"\"\" applies a spatial distortion to sraw, fraw (for peak centroids) \"\"\"\n", + " si = np.round(sraw.astype(int)).clip( 0, self.shape[1] - 1 )\n", + " fi = np.round(fraw.astype(int)).clip( 0, self.shape[1] - 1 )\n", + " sc = sraw + self.ds[ si, fi ]\n", + " fc = fraw + self.df[ si, fi ]\n", + " return sc, fc \n", + " \n", + " def __repr__(self):\n", + " \"\"\" print yourself in a way we can use for eval \"\"\"\n", + " sp = \"\\n\".join( [ \"%s : %s,\"%(repr(p), repr(self.pars[p])) for p in self.pnames\n", + " if p in self.pars ] )\n", + " return \"Lut( { %s } )\"%(sp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time \n", + "pars = parameters.read_par_file( \"../test/pixelmapper/CeO2.par\" ).parameters\n", + "pars['dxfile'] = \"/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dx.edf\"\n", + "pars['dyfile'] = \"/data/id11/nanoscope/Eiger/spatial_20210415_JW/e2dy.edf\"\n", + "tabl = Lut( pars )\n", + "print(tabl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "si, fi = int( tabl.pars['z_center'] ), int( tabl.pars['y_center'])\n", + "ref = tabl.k[:,si,fi]\n", + "sub_pixel_factor = 0\n", + "nfac = 0\n", + "for i in range(-1,2):\n", + " for j in range(-1,2):\n", + " dk = (tabl.k[:,si+i,fi+j]-ref)\n", + " if i!=0 or j!=0:\n", + " # actual distance will be from the center to 1/4 and 3/4 points\n", + " sf = np.dot(dk,dk)/(i*i+j*j)/16\n", + " sub_pixel_factor += sf\n", + " nfac += 1\n", + "\n", + "sub_pixel_factor /= 8\n", + "print(sub_pixel_factor) # FIXME : do the integral!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def MB(num):\n", + " return \"%.3f MB\"%(num / (1024*1024) )\n", + "\n", + "with h5py.File(\"../test/pixelmapper/silicon_fullscan_sparse.h5\" ,\"r\") as hin:\n", + " for scan in hin:\n", + " s = hin[scan]\n", + " print(scan,list(s))\n", + " print(dict(s.attrs))\n", + " for column in list(s['measurement']):\n", + " print(column, s['measurement'][column].shape)\n", + " nnz = s['nnz'][:]\n", + " ipt = np.cumsum(nnz)\n", + " print(\"pixels\",ipt[-1],\"per frame avg:\", ipt[-1]/len(nnz))\n", + " nbytes = 0\n", + " for name in 'row','col','frame','intensity','measurement/rot_center':\n", + " a = s[name]\n", + " b = a.dtype.itemsize * a.size\n", + " print(name, a.shape, a.dtype, a.size, MB(b))\n", + " nbytes += b\n", + " print(MB(nbytes))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def tocolf(pks):\n", + " titles = list(pks.keys())\n", + " colf = columnfile.newcolumnfile( titles=titles )\n", + " nrows = len(pks[titles[0]])\n", + " colf.nrows = nrows\n", + " colf.set_bigarray( [ pks[t] for t in titles ] )\n", + " return colf\n", + "\n", + "class SparseScan( object ):\n", + " \n", + " omeganames = ['measurement/rot_center', 'measurement/rot',\n", + " 'measurement/diffrz_center', 'measurement/diffrz']\n", + " dtynames = ['measurement/dty_center', 'measurement/dty',\n", + " 'measurement/diffty_center', 'measurement/diffty']\n", + " \n", + " def __init__( self, hname, scan ):\n", + " \"\"\"\n", + " hname : file coming from a sparse segmentation\n", + " scan : a scan within that file\n", + " motors : which motor channels to (try) to read\n", + " \n", + " assumes the scan fits into memory (could be problematic)\n", + " \"\"\"\n", + " with h5py.File(hname,\"r\") as hin:\n", + " grp = hin[scan]\n", + " self.shape = ( int(v) for v in ( grp.attrs['nframes'], \n", + " grp.attrs['shape0'], \n", + " grp.attrs['shape1'] ) )\n", + " self.motors = {}\n", + " for name, motors in [ ('omega',self.omeganames),\n", + " ('dty',self.dtynames) ]:\n", + " for motor in motors:\n", + " if motor in grp:\n", + " self.motors[ name ] = grp[motor][:]\n", + " break\n", + " \n", + " self.nnz = grp['nnz'][:]\n", + " self.ipt = np.concatenate( ( (0,) , np.cumsum(self.nnz, dtype=int) ) )\n", + " self.frame = grp['frame'][:]\n", + " self.row = grp['row'][:]\n", + " self.col = grp['col'][:]\n", + " self.intensity = grp['intensity'][:]\n", + " \n", + " def cplabel(self, threshold = 0 ):\n", + " \"\"\" Label pixels using the connectedpixels assigment code\n", + " Fills in:\n", + " self.nlabels = number of peaks per frame\n", + " self.labels = peak labels (should be unique)\n", + " self.total_labels = total number of peaks\n", + " \"\"\"\n", + " self.nlabels = np.zeros( len(self.nnz), np.int32 )\n", + " self.labels = np.zeros( len(self.row), \"i\")\n", + " nl = 0\n", + " for i, npx in enumerate( self.nnz ):\n", + " s = self.ipt[i]\n", + " e = self.ipt[i+1]\n", + " if npx > 0:\n", + " self.nlabels[i] = cImageD11.sparse_connectedpixels(\n", + " self.intensity[ s : e ],\n", + " self.row[ s : e ],\n", + " self.col[ s : e ],\n", + " threshold,\n", + " self.labels[ s : e ] )\n", + " assert (self.labels[ s : e ] > 0).all()\n", + " self.labels[ s : e ] += nl\n", + " else:\n", + " self.nlabels[i] = 0\n", + " nl += self.nlabels[i]\n", + " self.total_labels = nl\n", + "\n", + " \n", + " def lmlabel(self, threshold = 0 ):\n", + " \"\"\" Label pixels using the localmax assigment code\n", + " Fills in:\n", + " self.nlabels = number of peaks per frame\n", + " self.labels = peak labels (should be unique)\n", + " self.total_labels = total number of peaks\n", + " \"\"\"\n", + " self.nlabels = np.zeros( len(self.nnz), np.int32 )\n", + " self.labels = np.zeros( len(self.row), \"i\")\n", + " # temporary workspaces\n", + " npxmax = self.nnz.max()\n", + " vmx = np.zeros( npxmax, np.float32 )\n", + " imx = np.zeros( npxmax, 'i' )\n", + " nl = 0\n", + " for i, npx in enumerate( self.nnz ):\n", + " s = self.ipt[i]\n", + " e = self.ipt[i+1]\n", + " if npx > 0:\n", + " self.nlabels[i] = cImageD11.sparse_localmaxlabel(\n", + " self.intensity[ s : e ],\n", + " self.row[ s : e ],\n", + " self.col[ s : e ],\n", + " vmx[:npx],\n", + " imx[:npx],\n", + " self.labels[s : e] )\n", + " assert (self.labels[s:e] > 0).all()\n", + " self.labels[ s : e ] += nl\n", + " else:\n", + " self.nlabels[i] = 0\n", + " nl += self.nlabels[i]\n", + " self.total_labels = nl\n", + "\n", + " \n", + " def moments(self):\n", + " \"\"\" Computes the center of mass in s/f/omega \n", + " returns a columnfile\n", + " \"\"\"\n", + " pks = {}\n", + " for name , weights in [ ('Number_of_pixels', None),\n", + " ('s_raw', self.row ),\n", + " ('f_raw', self.col ),\n", + " ('omega', self.motors['omega'][self.frame]) ]:\n", + " pks[name] = np.bincount( self.labels, weights,\n", + " minlength = self.total_labels+1 )[1:]\n", + " if weights is not None:\n", + " pks[name] /= pks['Number_of_pixels']\n", + " return tocolf(pks)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s = SparseScan( \"../test/pixelmapper/silicon_fullscan_sparse.h5\", \"1.1\" )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.motors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "s.cplabel()\n", + "if PLOT:\n", + " pl.figure()\n", + " pl.plot(s.nlabels)\n", + "print(s.total_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time\n", + "c = s.moments()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sc, fc = tabl.spatial( c.s_raw, c.f_raw )\n", + "c.addcolumn( sc, 'sc')\n", + "c.addcolumn( fc, 'fc')\n", + "c.parameters.loadparameters(\"../test/pixelmapper/CeO2.par\")\n", + "c.updateGeometry()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if PLOT:\n", + " pl.figure()\n", + " pl.plot(c.tth, c.Number_of_pixels * c.tth,\",\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask = (c.tth * c.Number_of_pixels) > 1000\n", + "c.filter(mask)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a0 = 5.43094\n", + "[c.parameters.set(\"cell__%s\"%(abc), a0) for abc in 'abc']\n", + "i = indexing.indexer_from_colfile( c )\n", + "i.assigntorings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i.minpks = 10000\n", + "i.hkl_tol = 0.1\n", + "i.ring_1 = i.ring_2 = 21\n", + "i.find()\n", + "i.scorethem()\n", + "print(\"Got\",len(i.ubis),\"grains\")\n", + "i.histogram_drlv_fit()\n", + "if PLOT:\n", + " pl.figure()\n", + " for j in range(len(i.ubis)):\n", + " pl.plot(i.bins[1:], i.histogram[j],\"-\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cubic = sym_u.cubic()\n", + "ubis = i.ubis = [ sym_u.find_uniq_u( ubi, cubic ) for ubi in i.ubis]\n", + "ubis[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indexing.ubitocellpars(ubis[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tabl.k.shape, len(s.row)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "pomega = s.motors['omega'][s.frame]\n", + "gve = transform.compute_g_from_k( tabl.k[:, s.row, s.col] , \n", + " s.motors['omega'][s.frame] )\n", + "gve.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time \n", + "ga = np.empty( s.col.shape )\n", + "ga.fill(-1)\n", + "ge = np.ones( s.col.shape )\n", + "for k, ubi in enumerate( i.ubis ):\n", + " hkl = np.dot( ubi, gve )\n", + " print(hkl.shape)\n", + " gcalc = np.dot( np.linalg.inv(ubi), np.round(hkl) )\n", + " gerr = gcalc - gve\n", + " modge = (gerr**2).sum(axis=0)\n", + " best = (modge < ge)\n", + " ga[best] = k" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ptth = tabl.tth[ s.row, s.col ]\n", + "peta = tabl.eta[ s.row, s.col ]\n", + "if PLOT:\n", + " pl.figure()\n", + " pl.plot( ptth, modge, \",\" )\n", + " pl.semilogy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = modge<0.001\n", + "if PLOT:\n", + " pl.figure()\n", + " pl.plot( ptth[ m ], peta[ m], \",\")\n", + " pl.plot( ptth[ ~m ], peta[~m], \",\")\n", + " pl.figure()\n", + " pl.plot( pomega[m], s.intensity[m], ',')\n", + " pl.plot( pomega[~m], s.intensity[~m], ',')\n", + " pl.semilogy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can now have all the pixels assigned to their grain and hkls \n", + "\n", + "... to do next:\n", + "- build a refinement engine using intensity weighting\n", + "- deal with saturated peaks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Total time\",time.time()-start)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ubi = i.ubis[0]\n", + "print(gve.shape)\n", + "h,k,l = np.round( np.dot( ubi, gve ) ).astype(int)\n", + "gcalc = np.dot( np.linalg.inv(ubi), (h,k,l) )\n", + "mgerr = ((gve - gcalc)**2).sum(axis=0)\n", + "m = mgerr < 0.001\n", + "se = np.sign(peta).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "sortkeys = ( h,k,l,se )\n", + "order = np.lexsort( sortkeys )\n", + "print(order.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# indexing from 0 -> h.max() ... -h.min()->end\n", + "nh = h.max() - h.min() + 1\n", + "nk = k.max() - k.min() + 1\n", + "nl = l.max() - l.min() + 1\n", + "ne = 3\n", + "gvar = np.zeros( (3, 3, nh, nk, nl, ne), float )\n", + "gavg = np.zeros( (3, nh, nk, nl, ne), float )\n", + "f = np.zeros( (nh, nk, nl, ne), float )\n", + "e = np.zeros( (9,) ,dtype=np.float).reshape(3,3)\n", + "# this does not accumulate !\n", + "# f[h[m],k[m],l[m],se[m]] += s.intensity[m]\n", + "# gavg[:, h[m], k[m], l[m], se[m]] += gve[:,m]*s.intensity[m]\n", + "import numba\n", + "@numba.njit\n", + "def incr( m, h, k, l, se, gve, sig, gavg, f, gvar, e ):\n", + " # Compute the mean g-vectors in mask:\n", + " for p in range(len(m)):\n", + " if m[p]:\n", + " gavg[:, h[p],k[p],l[p],se[p]] += sig[p] * gve[:,p]\n", + " f[ h[p],k[p],l[p],se[p]] += sig[p]\n", + " # Get the mean\n", + " for i in range(f.size):\n", + " v = f.flat[i] \n", + " if v > 0:\n", + " gavg[0].flat[i] /= v\n", + " gavg[1].flat[i] /= v\n", + " gavg[2].flat[i] /= v\n", + " # Now get the variances:\n", + " # subpixel_factor = 1.7e-6 # From above. This is the in-plane error\n", + " # # not rotational, which depends on omega step.\n", + " # FIXME - not entirely correct yet. Problem goes back a long time.\n", + " for p in range(len(m)):\n", + " if m[p]:\n", + " dg = gve[:,p] - gavg[:,h[p],k[p],l[p],se[p]]\n", + " # this loop is np.outer( dg, dg )\n", + " for i in range(3):\n", + " for j in range(3):\n", + " gvar[i, j, h[p],k[p],l[p],se[p]] += (dg[i]*dg[j])*sig[p]\n", + " for i in range(3):\n", + " gvar[ i, i, h[p], k[p], l[p], se[p]] += sub_pixel_factor*sig[p]\n", + " # We are missing a pixel size contribution here. The pixel \n", + " # is not a point in space. This wants to sum up over, for \n", + " # example 2 subpixels as (sig/2 times:)\n", + " # outer( dg + p, dg + p ) + ...-> dg(x)dg + 2 dg(x)p + p(x)p\n", + " # outer( dg - p, dg - p ) + ...-> dg(x)dg - 2 dx(x)p + p(x)p\n", + " \n", + " for p in range(f.size):\n", + " v = f.flat[p]\n", + " if v > 0:\n", + " for i in range(3):\n", + " for j in range(3):\n", + " e[i,j] = gvar[i,j].flat[p] / v\n", + " d = np.linalg.det(e)\n", + " if d <= 0:\n", + " print('error')\n", + " print(e, p, v, d)\n", + " return None\n", + " ie = np.linalg.inv(e)\n", + " if ie[0,0] < 0:\n", + " print('error -ve')\n", + " print( e, p, v, d )\n", + " print(ie)\n", + " print( 'y',gvar[:,:].flat[p] )\n", + " return None\n", + " for i in range(3):\n", + " for j in range(3):\n", + " gvar[i,j].flat[p] = ie[i,j]\n", + " \n", + "\n", + "incr( m, h, k, l, se, gve, s.intensity, gavg, f, gvar, e )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "gvar = np.zeros( (3, 3, nh, nk, nl, ne), float )\n", + "gavg = np.zeros( (3, nh, nk, nl, ne), float )\n", + "f = np.zeros( (nh, nk, nl, ne), float )\n", + "incr( m, h, k, l, se, gve, s.intensity, gavg, f, gvar, e )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# option 1: take the average of the observed pixels\n", + "np.dot(ubi, gavg[:,1,1,1,1]),np.dot(ubi, gavg[:,3,1,1,-1]),\n", + "# option 2: take the computed gcalc position\n", + "gcalc = np.dot( np.linalg.inv( ubi ), np.round( np.dot( ubi, gve ) ) )\n", + "gerr = gcalc - gve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check\n", + "gvar[:,:,3,1,1,1], gavg[:,3,1,1,1], f[3,1,1,1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example \n", + "dg = np.dot( np.linalg.inv(ubi), (3,1,1) ) - gavg[:,3,1,1,1]\n", + "np.dot(dg, np.dot( gvar[:,:,3,1,1,1], dg ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example\n", + "dg = np.dot( np.linalg.inv(ubi), (5,3,3) ) - gavg[:,5,3,3,1]\n", + "print(dg,'\\n', gvar[:,:,5,3,3,1], f[5,3,3,1])\n", + "np.dot(dg, np.dot( gvar[:,:,5,3,3,1], dg ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "gouter = np.einsum( 'ik,jk->ijk', gerr, gerr )\n", + "assert gouter.shape == (3,3,gerr.shape[1])\n", + "assert np.allclose( np.outer( gerr[:,3], gerr[:,3] ), gouter[:,:,3] )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# MAM'x = MAd\n", + "# ... A == gvar 3x3\n", + "# ... d == gobs - gcalc 3\n", + "# ... M' == dg/dvariable 9x3\n", + "# ... x == variables 9\n", + "#\n", + "# 9x3.3x3.3x9.9 = 9x3.3x3.3\n", + "\n", + "\n", + "M = np.zeros( (9,3), float )\n", + "LSM = np.zeros( (9,9), float )\n", + "RHS = np.zeros( (9,), float)\n", + "\n", + "def dg_dUB( M, h, k, l ):\n", + " \"\"\" \n", + " dg_dUB :\n", + " gx = UB_00.h UB_01.h UB_02.l\n", + " gy = UB_10.h UB_11.h UB_12.l\n", + " gz = UB_20.h UB_21.h UB_22.l\n", + " M is 9x3 :\n", + " UB, g\n", + " \"\"\"\n", + " # x y z\n", + " M[:,:] = ((h,0,0),\n", + " (k,0,0),\n", + " (l,0,0),\n", + " (0,h,0),\n", + " (0,k,0),\n", + " (0,l,0),\n", + " (0,0,h),\n", + " (0,0,k),\n", + " (0,0,l) )\n", + " return M\n", + " \n", + "ub = np.zeros((3,3), float) # Linear problem. hkl already assigned\n", + "ub = np.linalg.inv( ubis[0]) # not for error estimation - need chi^2\n", + "npk = 0\n", + "g_to_fit = []\n", + "h_to_fit = []\n", + "XI2 = []\n", + "fsum = f.sum()\n", + "for ih in range(h.min(), h.max()+1):\n", + " for ik in range(k.min(), k.max()+1):\n", + " for il in range(l.min(), l.max()+1):\n", + " for sign_eta in (-1,1):\n", + " # total intensity for this peak\n", + " signal = f[ih,ik,il,sign_eta]/fsum\n", + " if signal == 0:\n", + " continue\n", + " npk += 1\n", + " # This matrix already carries an intensity weighting factor\n", + " A = gvar[:,:,ih,ik,il,sign_eta] # 3x3\n", + " # A = np.eye(3)\n", + " gobs = gavg[:,ih,ik,il,sign_eta] # 3\n", + " gcalc = np.dot( ub, (ih,ik,il) ) # 3\n", + " ge = gobs - gcalc\n", + " # Contribution to fit\n", + " scor = np.dot(ge, np.dot( A, ge ) ) \n", + " assert scor >= 0\n", + " cut = 2 * 10\n", + " weight = cut*cut/(cut*cut+scor*scor) # weight goes down as scor goes up\n", + " XI2.append( scor*weight )\n", + " # For later debugging\n", + " if 1:\n", + " g_to_fit.append( gobs )\n", + " h_to_fit.append( (ih,ik,il) )\n", + " M = dg_dUB( M, ih, ik, il )\n", + " MA = np.dot( M , A )\n", + " LSM += np.dot( MA, M.T ) * weight\n", + " RHS += np.dot( MA, ge ) * weight\n", + " # import pdb;pdb.set_trace()\n", + "#LSM *= npk/XI2\n", + "#RHS *= npk/XI2\n", + "iMAT = np.linalg.inv( LSM )\n", + "ecorfac = np.sum( XI2 ) / (npk - len(RHS) ) # potentially a factor of 2 here in scipy?\n", + "pCOV = iMAT * ecorfac\n", + "shifts = np.dot( iMAT, RHS) + ub.ravel()\n", + "print(npk)\n", + "print(shifts)\n", + "print(np.sqrt(np.diag(pCOV)))\n", + "pl.figure()\n", + "pl.hist( XI2, bins=128 )\n", + "pl.ylabel(\"Number of peaks\")\n", + "pl.xlabel(\"chi^2 contribution\")\n", + "pl.figure()\n", + "pl.imshow(pCOV)\n", + "pl.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ubfit = shifts.reshape(3,3)\n", + "ubi_fit = np.linalg.inv( ubfit )\n", + "print(ubi_fit)\n", + "print(indexing.ubitocellpars(ubi_fit))\n", + "afit = pow(np.linalg.det(ubi_fit),1/3)\n", + "print(grain.grain( ubi_fit ).eps_grain( [afit,afit,afit,90,90,90] ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g_to_fit = np.array(g_to_fit, float)\n", + "h_to_fit = np.array(h_to_fit, float)\n", + "sum_h_outer_h = np.einsum( 'ki,kj->ij', h_to_fit, h_to_fit )\n", + "sum_h_outer_g = np.einsum( 'ki,kj->ij', h_to_fit, g_to_fit )\n", + "ubi_fit2 = np.dot( np.linalg.inv( sum_h_outer_g ), sum_h_outer_h ).T\n", + "print(ubi_fit2)\n", + "print(indexing.ubitocellpars(ubi_fit2))\n", + "afit = pow(np.linalg.det( ubi_fit2 ),1/3)\n", + "print(grain.grain( ubi_fit2 ).eps_grain( [afit,afit,afit,90,90,90] ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i.ubis[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ubi_fit3 = indexing.refine( ubi_fit, g_to_fit, 0.4 )\n", + "print(ubi_fit3)\n", + "print(indexing.ubitocellpars(ubi_fit3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sandbox/plotvol.py b/sandbox/plotvol.py index ffe1a112..f3b289ea 100644 --- a/sandbox/plotvol.py +++ b/sandbox/plotvol.py @@ -54,7 +54,6 @@ def __init__(self, im ): def tkTranslate(self, event): self.activate() - pass diff --git a/sandbox/qtcam.py b/sandbox/qtcam.py index b65cadd4..820a8e75 100644 --- a/sandbox/qtcam.py +++ b/sandbox/qtcam.py @@ -57,8 +57,8 @@ def find_offset( c ): def register_image( im1, im2 ): """ We take the current and place it onto reference """ - ref = normalise(numpy.asarray(im1).sum(axis=2, dtype=numpy.float)) - cur = normalise(numpy.asarray(im2).sum(axis=2, dtype=numpy.float)) + ref = normalise(numpy.asarray(im1).sum(axis=2, dtype=float)) + cur = normalise(numpy.asarray(im2).sum(axis=2, dtype=float)) fftshape = ( ref.shape[0] + cur.shape[0] , ref.shape[1] + cur.shape[1] ) @@ -114,9 +114,9 @@ def __init__(self, npyar): assert npyar.shape[2] == 3 assert npyar.dtype == numpy.uint8 h , w = npyar.shape[0:2] - try: + try: self.__data = npyar.data - Qt.QImage.__init__(self, self.__data, w, h, Qt.QImage.Format_RGB888 ) + Qt.QImage.__init__(self, self.__data, w, h, Qt.QImage.Format_RGB888 ) except: im = numpy.zeros( (h, w, 4), numpy.uint8) diff --git a/sandbox/rad.py b/sandbox/rad.py index c43175c3..3ecdd622 100644 --- a/sandbox/rad.py +++ b/sandbox/rad.py @@ -6,12 +6,16 @@ from fabio.openimage import openimage print("Using class version") +def smooth(y, window_len=25): + kernel = numpy.ones(window_len) / window_len + convolved = numpy.convolve(y, kernel, mode='same') + return convolved def clean(sinogram): s0 = sinogram.mean(axis=0) numpy.divide( sinogram, s0, sinogram) s1 = sinogram.mean(axis=1) - c = s1 / smooth.smooth(s1,window_len=25) + c = s1 / smooth(s1,window_len=25) return (sinogram.T/c).T diff --git a/sandbox/ringselect.py b/sandbox/ringselect.py index edd31920..f9711e85 100644 --- a/sandbox/ringselect.py +++ b/sandbox/ringselect.py @@ -24,10 +24,10 @@ tth, eta = compute_tth_eta( (c.sc, c.fc), **p.parameters) -dsmax = 2*np.sin(1.03*tth.max()*np.pi/360)/w +dsmax = 2*np.sin(1.03*tthmax*np.pi/360)/w u.makerings(dsmax) -mask = np.zeros( c.nrows, dtype=np.bool ) +mask = np.zeros( c.nrows, dtype=bool ) for d in u.ringds: tthc = np.arcsin(w*d/2)*360/np.pi M = len(u.ringhkls[d]) diff --git a/sandbox/rl.py b/sandbox/rl.py index 1d66c3c4..673c5400 100755 --- a/sandbox/rl.py +++ b/sandbox/rl.py @@ -102,7 +102,7 @@ def convolve_transp(self, image): print("Using numpy fft for convolution") print("You might get better performance from fftw, why not try installing:") print("http://prdownload.berlios.de/pyfftw/PyFFTW3-0.2.tar.gz") - REAL = np.float + REAL = np.float32 class convolver(object): @@ -316,7 +316,8 @@ def test_prescale(): assert (nim[5,5] - 1.0) < 1./65535 def testg1(): - + from pylab import figure, imshow, colorbar, show + x = np.arange(20) gauss = np.exp( -(x*x+x.T*x.T)/10 ) dims = (1526,1024) ret = centre_psf( gauss, dims ) @@ -344,6 +345,7 @@ def testg5(): from fabio.openimage import openimage im = openimage("c_Al_s1_000__quantix_0037.edf") + g = gauss2d gaus = g( (50,60), 4.1 )+ g( (50,60), 10.1 )*0.1 from matplotlib.pylab import imshow, show imshow(gaus) diff --git a/sandbox/segment.py b/sandbox/segment.py new file mode 100644 index 00000000..b8fd28f0 --- /dev/null +++ b/sandbox/segment.py @@ -0,0 +1,364 @@ + +from __future__ import print_function + +import sys, timeit +import numpy as np #, numba +import scipy.ndimage +import hdf5plugin, h5py, fabio +from ImageD11 import cImageD11, sparseframe, blobcorrector, \ + parameters, transform +import pylab as pl + +# Timer (could be a decorator ...) +timer = timeit.default_timer +class ticktock(object): + def __init__(self): + self.tick = timer() + def tock(self, msg = ""): + tock = timer() + print(msg, "%.4f /s"%(tock - self.tick), end=" ") + self.tick = timer() + +# Generic filter, keeps input and out around +class imgfilt(object): + def __init__(self, *args): + pass + def __call__(self, input): + t = ticktock() + self.input = input + self.output = self.filt( input ) + t.tock(self.__class__.__name__) + return self.output + def filt( self, input ): + return input.copy() + + +# Dark subtraction and flat normalisation +# faster with omp +#@numba.njit(parallel=True) +#def _darkflat( inp, dark, flmult, out ): +# for i in numba.prange(inp.shape[0]): +# for j in range(inp.shape[1]): +# out[i,j] = ( inp[i,j] - dark[i,j] ) * flmult[i,j] + +class darkflat(imgfilt): + def __init__(self, dark, flat): + self.dark = dark + self.flmult = 1./flat + self.out = np.empty( dark.shape, np.float32 ) + + def filt(self, input): + cImageD11.uint16_to_float_darkflm( self.out.ravel(), + self.dark.ravel(), + self.flmult.ravel(), + input.ravel()) + return self.out + +class uniform(imgfilt): + def __init__(self, n=3, mode='nearest'): + self.n = n + self.mode = mode + def filt( self, input ): + return scipy.ndimage.uniform_filter( input, self.n, mode = self.mode ) + +class gauss_bgsub(imgfilt): + def __init__(self, sigma, mode='nearest' ): + self.sigma = sigma + self.mode = mode + def filt( self, input ): + bg = scipy.ndimage.gaussian_filter( input.astype(np.float32), + self.sigma, mode = self.mode ) + return input - bg + + +class gauss(imgfilt): + def __init__(self, sigma, mode='nearest' ): + self.sigma = sigma + self.mode = mode + def filt( self, input ): + bg = scipy.ndimage.gaussian_filter( input.astype(np.float32), + self.sigma, mode = self.mode ) + return bg + +class laplace(imgfilt): + def __init__(self, mode='nearest'): + self.mode = mode + def filt( self, input ): + return abs(scipy.ndimage.laplace( input.astype(np.float32), + mode = self.mode )) + +class sobel(imgfilt): + def __init__(self, mode='nearest'): + self.mode = mode + def filt( self, input ): + s0 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + s1 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + return abs(s0) + abs(s1) + +class laplace_sobel(imgfilt): + def __init__(self, mode='nearest'): + self.mode = mode + def filt( self, input ): + s0 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + s1 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + s3 = scipy.ndimage.laplace( input.astype(np.float32), + mode = self.mode ) + return abs(s0) + abs(s1) + abs(s3) + +class median( imgfilt ): + def __init__(self, size=3, mode='nearest'): + self.mode = mode + self.size = size + def filt( self, input ): + return scipy.ndimage.median_filter( input, size=self.size, mode=self.mode ) + +class median_bgsub( imgfilt ): + def __init__(self, size=3, mode='nearest'): + self.mode = mode + self.size = size + def filt( self, input ): + bg = scipy.ndimage.median_filter( input, size=self.size, mode=self.mode ) + return input - bg + +class median_footprints( imgfilt): + def __init__(self, footprints, mode='nearest'): + assert len(footprints)==2 + self.footprints = footprints + self.mode=mode + def filt( self, input ): + bg0 = scipy.ndimage.median_filter( abs(input), footprint = self.footprints[0], + mode=self.mode ) + bg1 = scipy.ndimage.median_filter( abs(input), footprint = self.footprints[1], + mode=self.mode ) + bg = np.where( bg0 < bg1, bg0, bg1 ) + den = 1/np.sqrt( bg + 1 ) # simd?? + sig = (input-bg)*den + return np.where( sig > -3 , sig, -3) + + + + +class bgsub( imgfilt ): + def __init__(self, gain=0.5, sigmap=0.2, sigmat=25 ): + self.sigmap = sigmap + self.sigmat = sigmat + self.gain = gain + self.bg = None + def filt(self, input): + if self.bg is None: + self.msk = np.empty( input.shape, np.uint8 ) + self.bg = np.empty( input.shape, np.float32 ) + cImageD11.bgcalc( input, + self.bg, + self.msk, + self.gain, + self.sigmap, + self.sigmat ) + # + #self.bg = bg1d(input.ravel(), self.gain, + # self.sigmap, self.sigmat).reshape(input.shape) + return input - self.bg + + +class variance( imgfilt ): + def __init__(self, size=5, mode='nearest'): + self.mode = mode + self.size = size + def filt( self, input ): + avg = scipy.ndimage.uniform_filter( input.astype(np.float32), + size=self.size, + mode=self.mode ) + d = input - avg + v = scipy.ndimage.uniform_filter( d*d , + size=self.size, + mode=self.mode ) + return np.sqrt(v+1) + + +class radial(imgfilt): + def __init__(self, splinefile, pars, dims, npx=0.77, mask=None ): + t, e = self.compute_tth_eta_lut( splinefile, pars, dims) + one_px = (t[1:] - t[:-1]).max() + binsize = one_px * npx + itth = np.round(t / binsize) + end = itth.max() + 1 + if mask is not None: # throw to the end + itth[ mask ] += end + self.nmask = mask.sum() + else: + self.nmask = 0 + self.output = None + self.lut, self.adrout = self.make_lut( itth, e ) + + def filt( self, input ): + if self.output is None: + self.output = np.empty_like( input ) + if input.dtype == np.float32: + cImageD11.reorder_f32_a32( input.ravel(), self.adrout, self.output.ravel()) + elif input.dtype == np.uint16: + cImageD11.reorder_u16_a32( input.ravel(), self.adrout, self.output.ravel()) + else: + self.output.flat = input.flat[self.lut] + return self.output + + def compute_tth_eta_lut( self, splinefile, pars, dims): + """ + Computes look up values of tth, eta for each pixel + """ + c = blobcorrector.correctorclass( splinefile ) + p = parameters.read_par_file( pars ) + xp, yp = c.make_pixel_lut( dims ) + t, e = transform.compute_tth_eta( (xp.ravel(), yp.ravel()), + **p.parameters ) + t.shape=dims + e.shape=dims + return t, e + + def make_lut( self, itth, eta ): + ie = 1./361 + sorter = (itth + (ie*eta)).ravel() + lut = np.argsort( sorter ).astype( np.uint32 ) # read from here + adrout = np.argsort( lut ).astype( np.uint32 ) # write to here + return lut, adrout + +class undoradial(radial): + def __init__(self, aradial): + self.output = None + self.lut, self.adrout = aradial.adrout, aradial.lut + self.nmask = aradial.nmask + + def filt(self, input): + if self.nmask >0: + input.flat[-self.nmask:] = 0 + return radial.filt( self, input ) + + +# Masking with a sigma cutoff +class maskcut(imgfilt): + def __init__(self, sigma=5): + self.sigma = sigma + self.output = None + def filt( self, input ): + if self.output is None: + self.output = np.empty( input.shape, np.uint8) + sigma = self.sigma + m, s = cImageD11.array_mean_var_msk(input.ravel(), self.output.ravel(), + cut=self.sigma, + n = 4, + verbose=0) + return self.output + + +class clean(imgfilt): + def filt( self, input ): + cln = np.empty( input.shape, 'b' ) + npx = cImageD11.clean_mask( input, cln ) + return cln + +class domask( imgfilt ): + def __init__(self,mask): + self.mask = mask + def filt( self, input): + return np.where( self.mask, 0, input ) + + + +# one in - one out pipeline +class pipeline(imgfilt): + def __init__(self, filters): + self.filters = filters + def filt(self, input): + x = input.copy() + for f in self.filters: + x = f(x) + return x + def __getitem__(self, i): + if i >= 0 and i < len(self.filters): + return self.filters[i] + def show( self ): + n = len(self.filters) + fig0, ax0 = pl.subplots(1,n+1,sharex=True, sharey = True , figsize=(5*n,4)) + fig1, ax1= pl.subplots(1,n+1,sharex=True, sharey = False, figsize=(5*n,4)) + im1 = ax0[0].imshow( self.input, origin='lower', vmin=0, + vmax = self.input.mean() + self.input.std()*3 ) + pl1 = ax1[0].plot( self.input[1024] ) + ax0[0].set_title(self.__class__.__name__+ " input") + fig0.colorbar( im1, ax=ax0[0] ) + for i in range(n): + im = self.filters[i].output + ims = ax0[i+1].imshow( im, origin = 'lower', + vmin = 0, + vmax = im.mean() + im.std()*3 ) + ax0[i+1].set_title( self.filters[i].__class__.__name__ ) + fig0.colorbar( ims, ax=ax0[i+1] ) + ims = ax1[i+1].plot(im[1024]) + fig0.tight_layout() + fig1.tight_layout() + fig0.show() + + +drk = fabio.open("dark02s.edf").data +flt = fabio.open("F29_oct2016.edf").data +flt = np.where( flt < 0.55 , 1, flt ) +mask = fabio.open("mask.edf").data > 0 + +#m = median_footprints( footprints=[np.ones((9,1)), np.ones((1,9))] ) +#df = m( flt ) - flt +#pl.imshow(df*df) +#pl.show() + +rad = radial( "frelon4m.spline", "ceo2_imaged11.par", dims=drk.shape, mask=mask ) +unrad = undoradial( rad ) + + +mypipeline = pipeline( + [ darkflat( dark = drk, flat = flt ), + rad, + bgsub(), + unrad, + maskcut( sigma = 10 ), + clean(), ] + ) + + +def segment_scan(fname, scan, outname): + with h5py.File(fname,"r") as hin: + with h5py.File( outname, "a") as hout: + g = hout.create_group( scan ) + for i,frm in enumerate(hin[scan]): + print("\n %4d"%(i), end=" ") + msk = mypipeline(frm.astype(np.float32)) + spf = sparseframe.from_data_mask( msk, frm, {} ) + spf.to_hdf_group( g.create_group( "%d"%(i) )) + +def check( fname, scan, num ): + with h5py.File( fname, "r") as hin: + frm = hin[scan][num] + for i in range(5): + print("\n",i,end=" ") + ret = mypipeline( frm ) + sys.stdout.flush() + mypipeline.show() + + +#import multiprocessing +#N = multiprocessing.cpu_count() // 2 +#cImageD11.cimaged11_omp_set_num_threads( N ) +#print("Using",cImageD11.cimaged11_omp_get_max_threads( ),"threads") + +if 1: + fname = "carbonfibre_dt50um.h5" + outname = "carbonfibre_dt50um_sparse_fast.h5" + for iscan in range(1,32): + scan = '%d.1/measurement/frelon3'%(iscan) + segment_scan( fname, scan, outname ) +else: + check( "carbonfibre_dt50um.h5", + '10.1/measurement/frelon3', + 0 ) + pl.show() + diff --git a/sandbox/segment2d.py b/sandbox/segment2d.py new file mode 100644 index 00000000..8269ce36 --- /dev/null +++ b/sandbox/segment2d.py @@ -0,0 +1,403 @@ + +from __future__ import print_function +import sys, timeit +import numpy as np #, numba +import scipy.ndimage +import hdf5plugin, h5py, fabio +from ImageD11 import cImageD11, sparseframe, blobcorrector, \ + parameters, transform +import pylab as pl + +# Timer (could be a decorator ...) +timer = timeit.default_timer +class ticktock(object): + def __init__(self): + self.tick = timer() + def tock(self, msg = ""): + tock = timer() + print(msg, "%.4f /s"%(tock - self.tick), end=" ") + self.tick = timer() + +# Generic filter, keeps input and out around +class imgfilt(object): + def __init__(self, *args): + pass + def __call__(self, input): + t = ticktock() + self.input = input + self.output = self.filt( input ) + t.tock(self.__class__.__name__) + return self.output + def filt( self, input ): + return input.copy() + + +# Dark subtraction and flat normalisation +# faster with omp +#@numba.njit(parallel=True) +#def _darkflat( inp, dark, flmult, out ): +# for i in numba.prange(inp.shape[0]): +# for j in range(inp.shape[1]): +# out[i,j] = ( inp[i,j] - dark[i,j] ) * flmult[i,j] + +class darkflat(imgfilt): + def __init__(self, dark, flat): + self.dark = dark + self.flmult = 1./flat + self.out = np.empty( dark.shape, np.float32 ) + + def filt(self, input): + cImageD11.uint16_to_float_darkflm( self.out.ravel(), + self.dark.ravel(), + self.flmult.ravel(), + input.ravel()) + return self.out + +class uniform(imgfilt): + def __init__(self, n=3, mode='nearest'): + self.n = n + self.mode = mode + def filt( self, input ): + return scipy.ndimage.uniform_filter( input, self.n, mode = self.mode ) + +class gauss_bgsub(imgfilt): + def __init__(self, sigma, mode='nearest' ): + self.sigma = sigma + self.mode = mode + def filt( self, input ): + bg = scipy.ndimage.gaussian_filter( input.astype(np.float32), + self.sigma, mode = self.mode ) + return input - bg + + +class gauss(imgfilt): + def __init__(self, sigma, mode='nearest' ): + self.sigma = sigma + self.mode = mode + def filt( self, input ): + bg = scipy.ndimage.gaussian_filter( input.astype(np.float32), + self.sigma, mode = self.mode ) + return bg + +class laplace(imgfilt): + def __init__(self, mode='nearest'): + self.mode = mode + def filt( self, input ): + return abs(scipy.ndimage.laplace( input.astype(np.float32), + mode = self.mode )) + +class sobel(imgfilt): + def __init__(self, mode='nearest'): + self.mode = mode + def filt( self, input ): + s0 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + s1 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + return abs(s0) + abs(s1) + +class laplace_sobel(imgfilt): + def __init__(self, mode='nearest'): + self.mode = mode + def filt( self, input ): + s0 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + s1 = scipy.ndimage.sobel( input.astype(np.float32), + mode = self.mode ) + s3 = scipy.ndimage.laplace( input.astype(np.float32), + mode = self.mode ) + return abs(s0) + abs(s1) + abs(s3) + +class median( imgfilt ): + def __init__(self, size=3, mode='nearest'): + self.mode = mode + self.size = size + def filt( self, input ): + return scipy.ndimage.median_filter( input, size=self.size, mode=self.mode ) + +class median_bgsub( imgfilt ): + def __init__(self, size=3, mode='nearest'): + self.mode = mode + self.size = size + def filt( self, input ): + bg = scipy.ndimage.median_filter( input, size=self.size, mode=self.mode ) + return input - bg + +class median_footprints( imgfilt): + def __init__(self, footprints, mode='nearest'): + assert len(footprints)==2 + self.footprints = footprints + self.mode=mode + def filt( self, input ): + bg0 = scipy.ndimage.median_filter( abs(input), footprint = self.footprints[0], + mode=self.mode ) + bg1 = scipy.ndimage.median_filter( abs(input), footprint = self.footprints[1], + mode=self.mode ) + bg = np.where( bg0 < bg1, bg0, bg1 ) + den = 1/np.sqrt( bg + 1 ) # simd?? + sig = (input-bg)*den + return np.where( sig > -3 , sig, -3) + + + + +class bgsub( imgfilt ): + def __init__(self, gain=0.5, sigmap=0.2, sigmat=25 ): + self.sigmap = sigmap + self.sigmat = sigmat + self.gain = gain + self.bg = None + def filt(self, input): + if self.bg is None: + self.msk = np.empty( input.shape, np.uint8 ) + self.bg = np.empty( input.shape, np.float32 ) + cImageD11.bgcalc( input, + self.bg, + self.msk, + self.gain, + self.sigmap, + self.sigmat ) + # + #self.bg = bg1d(input.ravel(), self.gain, + # self.sigmap, self.sigmat).reshape(input.shape) + return input - self.bg + + +class variance( imgfilt ): + def __init__(self, size=5, mode='nearest'): + self.mode = mode + self.size = size + def filt( self, input ): + avg = scipy.ndimage.uniform_filter( input.astype(np.float32), + size=self.size, + mode=self.mode ) + d = input - avg + v = scipy.ndimage.uniform_filter( d*d , + size=self.size, + mode=self.mode ) + return np.sqrt(v+1) + + +class radial(imgfilt): + def __init__(self, splinefile, pars, dims, npx=0.77, mask=None ): + t, e = self.compute_tth_eta_lut( splinefile, pars, dims) + one_px = (t[1:] - t[:-1]).max() + binsize = one_px * npx + itth = np.round(t / binsize) + end = itth.max() + 1 + if mask is not None: # throw to the end + itth[ mask ] += end + self.nmask = mask.sum() + else: + self.nmask = 0 + self.output = None + self.lut, self.adrout = self.make_lut( itth, e ) + + def filt( self, input ): + if self.output is None: + self.output = np.empty_like( input ) + if input.dtype == np.float32: + cImageD11.reorder_f32_a32( input.ravel(), self.adrout, self.output.ravel()) + elif input.dtype == np.uint16: + cImageD11.reorder_u16_a32( input.ravel(), self.adrout, self.output.ravel()) + else: + self.output.flat = input.flat[self.lut] + return self.output + + def compute_tth_eta_lut( self, splinefile, pars, dims): + """ + Computes look up values of tth, eta for each pixel + """ + c = blobcorrector.correctorclass( splinefile ) + p = parameters.read_par_file( pars ) + xp, yp = c.make_pixel_lut( dims ) + t, e = transform.compute_tth_eta( (xp.ravel(), yp.ravel()), + **p.parameters ) + t.shape=dims + e.shape=dims + return t, e + + def make_lut( self, itth, eta ): + ie = 1./361 + sorter = (itth + (ie*eta)).ravel() + lut = np.argsort( sorter ).astype( np.uint32 ) # read from here + adrout = np.argsort( lut ).astype( np.uint32 ) # write to here + return lut, adrout + +class undoradial(radial): + def __init__(self, aradial): + self.output = None + self.lut, self.adrout = aradial.adrout, aradial.lut + self.nmask = aradial.nmask + + def filt(self, input): + if self.nmask >0: + input.flat[-self.nmask:] = 0 + return radial.filt( self, input ) + + +# Masking with a sigma cutoff +class maskcut(imgfilt): + def __init__(self, sigma=5): + self.sigma = sigma + self.output = None + def filt( self, input ): + if self.output is None: + self.output = np.empty( input.shape, np.uint8) + sigma = self.sigma + m, s = cImageD11.array_mean_var_msk(input.ravel(), self.output.ravel(), + cut=self.sigma, + n = 4, + verbose=0) + return self.output + + +class clean(imgfilt): + def filt( self, input ): + cln = np.empty( input.shape, 'b' ) + npx = cImageD11.clean_mask( input, cln ) + return cln + +class domask( imgfilt ): + def __init__(self,mask): + self.mask = mask + def filt( self, input): + return np.where( self.mask, 0, input ) + + + +# one in - one out pipeline +class pipeline(imgfilt): + def __init__(self, filters): + self.filters = filters + def filt(self, input): + x = input.copy() + for f in self.filters: + x = f(x) + return x + def __getitem__(self, i): + if i >= 0 and i < len(self.filters): + return self.filters[i] + def show( self ): + n = len(self.filters) + fig0, ax0 = pl.subplots(1,n+1,sharex=True, sharey = True , figsize=(5*n,4)) + fig1, ax1= pl.subplots(1,n+1,sharex=True, sharey = False, figsize=(5*n,4)) + im1 = ax0[0].imshow( self.input, origin='lower', vmin=0, + vmax = self.input.mean() + self.input.std()*3 ) + pl1 = ax1[0].plot( self.input[1024] ) + ax0[0].set_title(self.__class__.__name__+ " input") + fig0.colorbar( im1, ax=ax0[0] ) + for i in range(n): + im = self.filters[i].output + ims = ax0[i+1].imshow( im, origin = 'lower', + vmin = im.mean() - im.std()*3 , + vmax = im.mean() + im.std()*3 ) + ax0[i+1].set_title( self.filters[i].__class__.__name__ ) + fig0.colorbar( ims, ax=ax0[i+1] ) + ims = ax1[i+1].plot(im[1024]) + fig0.tight_layout() + fig1.tight_layout() + fig0.show() + + +def segment_scan(fname, scan, detector, outname, + scanmotors = [], + headermotors = []): + with h5py.File(fname,"r") as hin: + gin = hin[scan] + hdr = {} + with h5py.File( outname, "a") as hout: + g = hout.create_group( scan ) + for m in scanmotors: # vary : many + print(m) + g.create_dataset(m, data = gin['measurement'][m][:] ) + for m in headermotors: # fixed : scalar + g.create_dataset(m, data = gin['instrument/positioners'][m][()] ) + + opts = { 'chunks' : (10000,), + 'maxshape' : (None,), + 'dtype': np.uint16, + 'compression':'lzf', + 'shuffle': True } + row = g.create_dataset( 'row', (1,), **opts ) + col = g.create_dataset( 'col', (1,), **opts ) + num = g.create_dataset( 'frame', (1,), **opts ) + opts['dtype'] = np.float32 + sig = g.create_dataset( 'intensity', (1,), **opts ) + frm = gin['measurement'][detector] + nnz = g.create_dataset( 'nnz', (frm.shape[0],), dtype=np.int32) + npx = 0 + g.attrs['itype']=np.dtype(np.uint16).name + g.attrs['nframes']= frm.shape[0] + g.attrs['shape0'] = frm.shape[1] + g.attrs['shape1'] = frm.shape[2] + for i,frm in enumerate(frm): + print("%4d"%(i), end=" ") + msk = mypipeline(frm.astype(np.float32)) + spf = sparseframe.from_data_mask( msk, frm, {} ) + print("%d px"%(spf.nnz)) + if spf.nnz + npx > len(row): + row.resize( spf.nnz + npx, axis=0 ) + col.resize( spf.nnz + npx, axis=0 ) + sig.resize( spf.nnz + npx, axis=0 ) + num.resize( spf.nnz + npx, axis=0 ) + row[npx:] = spf.row + col[npx:] = spf.col + sig[npx:] = spf.pixels['intensity'] + num[npx:] = i + nnz[i] = spf.nnz + npx += spf.nnz + # spf.to_hdf_group( g.create_group( "%d"%(i) )) + +def check( fname, scan, num ): + with h5py.File( fname, "r") as hin: + frm = hin[scan][num] + for i in range(5): + print("\n",i,end=" ") + ret = mypipeline( frm ) + sys.stdout.flush() + mypipeline.show() + + +#import multiprocessing +#N = multiprocessing.cpu_count() // 2 +#cImageD11.cimaged11_omp_set_num_threads( N ) +#print("Using",cImageD11.cimaged11_omp_get_max_threads( ),"threads") + +drk = fabio.open("median_image.edf").data +flt = fabio.open("F4M_flood_Ce_aug2010.edf").data +flt = np.where( flt < 0.55 , 1, flt ) +mask = fabio.open("mask.edf").data > 0 + +#m = median_footprints( footprints=[np.ones((9,1)), np.ones((1,9))] ) +#df = m( flt ) - flt +#pl.imshow(df*df) +#pl.show() + +rad = radial( "frelon4m.spline", "f.par", dims=drk.shape, mask=mask ) +unrad = undoradial( rad ) + + +mypipeline = pipeline( + [ darkflat( dark = drk, flat = flt ), + bgsub(), + maskcut( sigma = 10 ), + clean(), ] + ) + + +fname = "fe3o4map01.hdf" +scan = 'entry' # '%d.1'% iscan # /measurement/frelon3'%(iscan) +outname = "fe3o4_peaks.hdf" +camera = "frelon4m" + +if 1: + segment_scan( fname, scan, camera, outname, + scanmotors = ('omega', ), + headermotors = () ) +else: + check( fname, + scan + "/measurement/" + camera, + 0 ) + pl.show() + diff --git a/sandbox/segment_hdf_slurm/s2_400MPa.batch b/sandbox/segment_hdf_slurm/s2_400MPa.batch new file mode 100644 index 00000000..74bd6dff --- /dev/null +++ b/sandbox/segment_hdf_slurm/s2_400MPa.batch @@ -0,0 +1,21 @@ +#!/bin/bash +#SBATCH --job-name=400jobs/arrayJob-s2_0MPa +#SBATCH --output=400jobs/arrayJob_%A_%a.out +#SBATCH --error=400jobs/arrayJob_%A_%a.err +#SBATCH --array=1-50 +#SBATCH --time=06:00:00 +# define memory needs and number of tasks for each array job +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=4 +# +# segmenting frames with input lines like: h5filenames +# +# hpc5-0102:/data/visitor/ma4494/id11/analysis % find /data/visitor/ma4494/id11/s2_400MPa_r1 -name 's2_400MPa_r1_z???.h5' -print | sort -n > s2_400MPa_r1.params +# +# python3 /data/visitor/ma4494/id11/analysis/segment1.py $@ + +echo Running on $HOSTNAME : python3 /data/visitor/ma4494/id11/analysis/segment1.py $(sed -n ${SLURM_ARRAY_TASK_ID}p s2_400MPa_r1.params) +OMP_NUM_THREADS=4 python3 /data/visitor/ma4494/id11/analysis/segment1.py $(sed -n ${SLURM_ARRAY_TASK_ID}p s2_400MPa_r1.params) + + + diff --git a/sandbox/segment_hdf_slurm/segment1.py b/sandbox/segment_hdf_slurm/segment1.py new file mode 100755 index 00000000..6246af06 --- /dev/null +++ b/sandbox/segment_hdf_slurm/segment1.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python + +from __future__ import print_function +import os, time +os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" +import h5py, hdf5plugin +import numpy as np, fabio, numba +from ImageD11 import sparseframe, cImageD11 + + +# Code to clean the 2D image and reduce it to a sparse array: + +@numba.njit +def select( img, msk, row, col, val, cut=1 ): + # Choose the pixels that are > cut and put into sparse arrays + k = 0 + for so in (0,550,1100,1650): + for s in range(so,so+512): + for fo in (0,515,1040,1555): + for f in range(fo,fo+512): + if img[s,f]>cut: + if msk[s,f]: # skip masked + continue + row[k] = s + col[k] = f + val[k] = img[s,f] + k += 1 + return k + +class chooser( object ): + def __init__(self, msk, pixels_in_spot = 3, cut = 1): + """ + msk = mask to say which pixels to ignore + pixels_in_spot = how many pixels touching to keep the spot + cut = intensity threshold + """ + self.msk = msk + self.pixels_in_spot=pixels_in_spot + self.cut=cut + self.row = np.zeros(msk.shape, np.uint16).ravel() + self.col = np.zeros(msk.shape, np.uint16).ravel() + self.val = np.zeros(msk.shape, np.float32).ravel() + + def __call__(self, img): + # choose the pixels that are != 0 and not masked + nnz = select( img, self.msk, self.row, self.col, self.val, + cut=self.cut ) + # copy these into a "sparse" format + s = sparseframe.sparse_frame( self.row[:nnz], self.col[:nnz], + img.shape ) + s.set_pixels("intensity", self.val[:nnz]) + # label them according to the connected objects + sparseframe.sparse_connected_pixels( s, threshold = self.cut, + data_name='intensity', + label_name='cp') + # only keep spots with more than 3 pixels ... + mom = sparseframe.sparse_moments( s, intensity_name='intensity', + labels_name='cp' ) + npx = mom[:,cImageD11.s2D_1] + pxcounts = npx[s.pixels['cp']-1] + msk = pxcounts > self.pixels_in_spot + if msk.sum() == 0: + return None + r = s.mask( msk ) + sparseframe.sparse_connected_pixels( r, threshold = self.cut, + label_name='connectedpixels', + data_name='intensity' ) + return r + +def segment_scans(fname, scans, outname, sparsify_func, + scanmotors = ['rot','rot_center','fpico6'], + headermotors = ['dty', 'rot'], + detector = 'eiger', ): + """ Does segmentation on a series of scans in hdf files: + fname : input hdf file + scans : input groups in the hdf files [h[s] for s in scans] will be treated + outname : output hdf file to put the results into + sparsify_func : function to select which pixels to keep + headermotors : things to copy from input instrument/positioners to output + scanmotors : datasets to copy from input measurement/xxx to output + """ + opts = { 'chunks' : (10000,), 'maxshape' : (None,), + 'compression':'lzf', 'shuffle': True } + with h5py.File( outname, "a") as hout: + with h5py.File(fname,"r") as hin: + for scan in scans: + if scan.endswith(".2"): # for fscans + continue + print(fname,outname,scan,time.ctime()) + gin = hin[scan] + bad = 0 + for m in scanmotors: + if ('measurement' in gin) and m in gin['measurement']: + pass + else: + bad += 1 + if bad: + print("SKIPPING BAD SCAN",scan) + continue + hdr = {} + g = hout.create_group( scan ) + gm = g.create_group('measurement') + for m in scanmotors: # vary : many + gm.create_dataset(m, data = gin['measurement'][m][:] ) + gip = g.create_group('instrument/positioners') + for m in headermotors: # fixed : scalar + gip.create_dataset(m, data = gin['instrument/positioners'][m][()] ) + + frms = gin['measurement'][detector] + row = g.create_dataset( 'row', (1,), dtype=np.uint16, **opts ) + col = g.create_dataset( 'col', (1,), dtype=np.uint16, **opts ) + # can go over 65535 frames in a scan + num = g.create_dataset( 'frame', (1,), dtype=np.uint32, **opts ) + sig = g.create_dataset( 'intensity', (1,), dtype=frms.dtype, **opts ) + nnz = g.create_dataset( 'nnz', (frms.shape[0],), dtype=np.uint32) + g.attrs['itype'] = np.dtype(np.uint16).name + g.attrs['nframes']= frms.shape[0] + g.attrs['shape0'] = frms.shape[1] + g.attrs['shape1'] = frms.shape[2] + npx = 0 + for i,frm in enumerate(frms): + spf = sparsify_func( frm ) + if spf is None: + continue + if spf.nnz + npx > len(row): + row.resize( spf.nnz + npx, axis=0 ) + col.resize( spf.nnz + npx, axis=0 ) + sig.resize( spf.nnz + npx, axis=0 ) + num.resize( spf.nnz + npx, axis=0 ) + row[npx:] = spf.row + col[npx:] = spf.col + sig[npx:] = spf.pixels['intensity'] + num[npx:] = i + nnz[i] = spf.nnz + npx += spf.nnz + if i%100==0: + print("%4d %d"%(i, spf.nnz), end=" ") + sys.stdout.flush() + + +def main( hname, msk, outname): + msk = fabio.open(mskname).data.astype(np.uint8) + print("Masked pixels",msk.sum(),"should be 269126") + print("hname",hname) + print("outname",outname) + with h5py.File(hname,"r") as h: + scans = [snum for _,snum in sorted( + [(float(num),num) for num in list(h)])] + print(scans) + print(scans[0]) + try: + print(list(h[scans[0]]['measurement'])) + except: + print(list(h[scans[0]])) + raise + chf = chooser(msk) + segment_scans(hname, scans, outname, chf ) + + + +if __name__ =="__main__": + import sys + hname = os.path.abspath(sys.argv[1]) + mskname = "/data/id11/nanoscope/Eiger/20200925.msk" + path, fname = os.path.split(hname) + outpath = path.replace("/id11/", "/id11/analysis/") # unix !! + if not os.path.exists(outpath): + os.makedirs(outpath) + outname = os.path.join( outpath, fname.replace(".h5","_sparse.h5")) + if os.path.exists( outname ): + print("Output already exists, exiting") + print(outname) + sys.exit() + print( hname, outname) + main( hname, mskname, outname ) + + + diff --git a/sandbox/segment_ma4752_alu.py b/sandbox/segment_ma4752_alu.py new file mode 100644 index 00000000..04c046b4 --- /dev/null +++ b/sandbox/segment_ma4752_alu.py @@ -0,0 +1,277 @@ + +from __future__ import print_function +# Code to clean the 2D image and reduce it to a sparse array: +# things we might edit + +CUT = 2 # keep values abuve cut in first look at image +howmany = 10000 # max pixels per frame to keep +# additional thresholds to test if you have too many pixels (tuple) +thresholds = (4,8,16,32,64,128,256) +# Remove single pixel spots. How many 8-connected pixels needed in a spot to keep it? +pixels_in_spot = 3 +# measurements that might be involved in scanning to transfer to output +SCANMOTORS = ['rot', 'rot_cen360', 'rot_center', 'fpico6', 'dty', 'dty_center'] +# scalars from scan/instrument/positioners/XXXX to transfer to output +HEADERMOTORS = ['dty', 'rot', 'pz', 'px', 'py','shtz','shty','shtx' ] +SCANTYPES = ['fscan','f2scan','fscan2d'] +DETECTORNAME = 'eiger' +MASKFILE = "/data/id11/nanoscope/Eiger/mask_20210428.edf" +CORES = None # guess the number of cores to use + +import sys, os +HNAME = sys.argv[1] + + +###################### should not need to change much below here + +# validate input + +assert CUT >= 0 +def checkth(): + for i in range(1,len(thresholds)): + assert thresholds[i] > thresholds[i-1] +checkth() + +# we will use multiple processes, each with 1 core +# all sub-processes better do this! +os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE' +os.environ['OMP_NUM_THREADS'] = "1" +os.environ['NUMBA_NUM_THREADS'] = "1" +os.environ['MKL_NUM_THREADS'] = "1" + +import time +import h5py, hdf5plugin, numpy as np, numba, fabio + +# pip install ImageD11 --no-deps # if you do not have it yet: +from ImageD11 import sparseframe, cImageD11 + + + + +@numba.njit +def select( img, msk, row, col, val ): + global CUT + cut = CUT + # Choose the pixels that are > cut and put into sparse arrays + k = 0 + for s in range(img.shape[0]): + for f in range(img.shape[1]): + if img[s,f]>cut: + if msk[s,f]: # skip masked + continue + row[k] = s + col[k] = f + val[k] = img[s,f] + k += 1 + return k + +@numba.njit +def top_pixels( nnz, row, col, val, howmany ): + """ + selects the strongest pixels from a sparse collection + - thresholds should be a sorted array of potential cutoff values to try + that are higher than the original cutoff used to select data + - howmany is the maximum number of pixels to return + """ + # quick return if there are already few enough pixels + global thresholds + if nnz <= howmany: + return nnz + # histogram of how many pixels are above each threshold + h = np.zeros(len(thresholds), dtype=np.uint32) + for k in range(nnz): + for i,t in enumerate(thresholds): + if val[k] > t: + h[i] += 1 + else: + break + # choose the one to use. This is the first that is lower than howmany + tcut = thresholds[-1] + for n,t in zip( h, thresholds ): + if n < howmany: + tcut = t + break + # now we filter the pixels + n = 0 + for k in range(nnz): + if val[k] > tcut: + row[n] = row[k] + col[n] = col[k] + val[n] = val[k] + n += 1 + if n >= howmany: + break + return n + + +def choose_parallel( args ): + """ reads a frame and sends back a sparse frame """ + h5name, address, frame_num = args + with h5py.File( h5name, "r" ) as h: + frm = h[address][frame_num] + row = np.empty(msk.size, np.uint16) + col = np.empty(msk.size, np.uint16) + val = np.empty(msk.size, frm.dtype) + nnz = select( frm, msk, row, col, val) + global howmany + if nnz == 0: + sf = None + else: + if nnz > howmany: + nnz = top_pixels( nnz, row, col, val, howmany ) + # Now get rid of the single pixel 'peaks' + s = sparseframe.sparse_frame( row[:nnz].copy(), col[:nnz].copy(), + frm.shape ) + s.set_pixels("intensity", val[:nnz].copy()) + global pixels_in_spot + if pixels_in_spot <= 1: + sf = s + else: + # label them according to the connected objects + sparseframe.sparse_connected_pixels( s, threshold = CUT, + data_name='intensity', + label_name='cp') + # only keep spots with more than 3 pixels ... + mom = sparseframe.sparse_moments( s, intensity_name='intensity', + labels_name='cp' ) + npx = mom[:,cImageD11.s2D_1] + pxcounts = npx[s.pixels['cp']-1] + pxmsk = pxcounts >= pixels_in_spot + if pxmsk.sum() == 0: + sf = None + else: + sf = s.mask( pxmsk ) + return frame_num, sf + +def segment_scans(fname, scans, outname, mypool, + scanmotors = SCANMOTORS, + headermotors = HEADERMOTORS, + detector = DETECTORNAME): + """ Does segmentation on a series of scans in hdf files: + fname : input hdf file + scans : input groups in the hdf files [h[s] for s in scans] will be treated + outname : output hdf file to put the results into + sparsify_func : function to select which pixels to keep + headermotors : things to copy from input instrument/positioners to output + scanmotors : datasets to copy from input measurement/xxx to output + """ + opts = { 'chunks' : (10000,), 'maxshape' : (None,), + 'compression':'lzf', 'shuffle': True } + ndone = 0 + with h5py.File( outname, "a") as hout: + for scan in scans: + if scan.endswith(".2"): # for fscans + continue + with h5py.File(fname, "r") as hin: + hout.attrs['h5input'] = fname + gin = hin[scan] + bad = 1 + title = hin[scan]['title'][()] + print('# ',scan,title) + print('# time now',time.ctime(),"\n#", end =' ') + for scantype in SCANTYPES: + if title.find(scantype) >= 0: + bad = 0 + if ('measurement' in gin) and (detector not in gin['measurement']): + bad += 1 + if bad: + print("# SKIPPING BAD SCAN",scan) + continue + hdr = {} + g = hout.create_group( scan ) + gm = g.create_group('measurement') + for m in scanmotors: # vary : many + if m in gin['measurement']: + gm.create_dataset(m, data = gin['measurement'][m][:] ) + # else: + # the thing requested is not there - ignore the problem for now + gip = g.create_group('instrument/positioners') + for m in headermotors: # fixed : scalar + gip.create_dataset(m, data = gin['instrument/positioners'][m][()] ) + try: + frms = gin['measurement'][detector] + except: + print(list(gin)) + print(list(gin['measurement'])) + print(detector) + raise + row = g.create_dataset( 'row', (1,), dtype=np.uint16, **opts ) + col = g.create_dataset( 'col', (1,), dtype=np.uint16, **opts ) + # can go over 65535 frames in a scan + num = g.create_dataset( 'frame', (1,), dtype=np.uint32, **opts ) + sig = g.create_dataset( 'intensity', (1,), dtype=frms.dtype, **opts ) + nnz = g.create_dataset( 'nnz', (frms.shape[0],), dtype=np.uint32) + g.attrs['itype'] = np.dtype(np.uint16).name + g.attrs['nframes']= frms.shape[0] + g.attrs['shape0'] = frms.shape[1] + g.attrs['shape1'] = frms.shape[2] + npx = 0 + address = scan+"/measurement/"+detector + nimg = frms.shape[0] + args = [ (fname, address, i) for i in range(nimg) ] + # CLOSE the input file here + global CORES + # approx 1440 frames, 40 cores + chunksize = max(1,len(args)//CORES//8) + for i, spf in mypool.map( choose_parallel, args, + chunksize=chunksize, timeout=60): + if spf is None: + nnz[i] = 0 + continue + if i % 500 == 0: + print("%4d %d"%(i, spf.nnz), end=",") + if spf.nnz + npx > len(row): + row.resize( spf.nnz + npx, axis=0 ) + col.resize( spf.nnz + npx, axis=0 ) + sig.resize( spf.nnz + npx, axis=0 ) + num.resize( spf.nnz + npx, axis=0 ) + row[npx:] = spf.row[:] + col[npx:] = spf.col[:] + sig[npx:] = spf.pixels['intensity'] + num[npx:] = i + nnz[i] = spf.nnz + npx += spf.nnz + ndone += nimg + print("\n# Done",scan,nimg,ndone) + sys.stdout.flush() + return ndone + + # the output file should be flushed and closed when this returns + +def main( hname, outname, mypool ): + # read all the scans + print("hname = ",repr(hname)) + print("outname = ",repr(outname)) + with h5py.File(hname,"r") as h: + scans = list(h['/']) + print('scans = ', repr(scans)) + print('#',time.ctime()) + start = time.time() + nfrm = segment_scans(hname, scans, outname, mypool ) + end = time.time() + print('#',time.ctime()) + print("# Elapsed",end-start,"/s, f.p.s %.2f"%(nfrm/(end-start))) + + +if __name__=="__main__": + + import concurrent.futures + + + outh5 = os.path.split(HNAME)[-1].replace(".h5","_sparsefull.h5") + outpath= "/data/visitor/ma4752/id11/segmented" + OUTNAME = os.path.join( outpath, outh5 ) + + # each subprocess gets their own, no need if we fork + msk = 1 - fabio.open(MASKFILE).data + + if CORES is None: + try: + CORES = int(os.environ['SLURM_JOB_CPUS_PER_NODE'])-1 + except: + CORES = os.cpu_count() - 1 + CORES = max(1,CORES) + print("# Aiming to use",CORES,"processes") + with concurrent.futures.ProcessPoolExecutor( max_workers = CORES ) as mypool: + main( HNAME, OUTNAME, mypool ) + diff --git a/sandbox/segmenter.py b/sandbox/segmenter.py new file mode 100644 index 00000000..73962fa5 --- /dev/null +++ b/sandbox/segmenter.py @@ -0,0 +1,323 @@ + +from __future__ import print_function + +""" Does segmentation from a master file to produce an _sparse.h5 with +segmented images in it """ + + +import sys +# Code to clean the 2D image and reduce it to a sparse array: +# things we might edit +class Options: + CUT = 1 # keep values abuve cut in first look at image + HOWMANY = 10000 # max pixels per frame to keep + # additional thresholds to test if you have too many pixels (tuple) + THRESHOLDS = (2, 4, 8, 16, 32, 64, 128, 256) + # Remove single pixel spots. How many 8-connected pixels needed in a spot to keep it? + PIXELS_IN_SPOT = 3 + # measurements that might be involved in scanning to transfer to output + SCANMOTORS = ("rot", "rot_cen360", "rot_center", "fpico6", "dty", "dty_center") + # scalars from scan/instrument/positioners/XXXX to transfer to output + HEADERMOTORS = ("dty", "rot", "pz", "px", "py", "shtz", "shty", "shtx") + SCANTYPES = ("fscan", "f2scan", "fscan2d") + DETECTORNAME = "eiger" + MASKFILE = "/data/id11/nanoscope/Eiger/mask_20210428.edf" + CORES = None # guess the number of cores to use + HNAME = sys.argv[1] + OUTPATH = "." + +########################## should not need to change much below here + +import time +import os +import functools +import numpy as np +import hdf5plugin +import h5py +import numba +import fabio + +# pip install ImageD11 --no-deps # if you do not have it yet: +from ImageD11 import sparseframe, cImageD11 + + + +def init(self): + # validate input + print("Calling init") + assert self.CUT >= 0 + for i in range(1, len(self.THRESHOLDS)): + assert self.THRESHOLDS[i] > self.THRESHOLDS[i - 1] + self.MASK = 1 - fabio.open(self.MASKFILE).data + print("# Opened mask", self.MASKFILE) + if self.CORES is None: + try: + self.CORES = int(os.environ["SLURM_JOB_CPUS_PER_NODE"]) - 1 + except: + self.CORES = os.cpu_count() - 1 + self.CORES = max(1, self.CORES) + print("# Aiming to use", self.CORES, "processes") + outh5 = os.path.split(self.HNAME)[-1].replace(".h5", "_sparse.h5") + self.OUTNAME = os.path.join(self.OUTPATH, outh5) + print("# Output to ", self.OUTNAME) + +Options.__init__ = init +# each subprocess gets their own options (no need if we fork?) +OPTIONS = Options() + + +# we will use multiple processes, each with 1 core +# all sub-processes better do this! +os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" # not sure we want/need this here? +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["NUMBA_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + + +@numba.njit +def select(img, mask, row, col, val, cut): + # Choose the pixels that are > cut and put into sparse arrays + k = 0 + for s in range(img.shape[0]): + for f in range(img.shape[1]): + if img[s, f] > cut: + if mask[s, f]: # skip masked + continue + row[k] = s + col[k] = f + val[k] = img[s, f] + k += 1 + return k + + +@numba.njit +def top_pixels(nnz, row, col, val, howmany, thresholds): + """ + selects the strongest pixels from a sparse collection + - THRESHOLDS should be a sorted array of potential cutoff values to try + that are higher than the original cutoff used to select data + - howmany is the maximum number of pixels to return + """ + # quick return if there are already few enough pixels + if nnz <= howmany: + return nnz + # histogram of how many pixels are above each threshold + h = np.zeros(len(thresholds), dtype=np.uint32) + for k in range(nnz): + for i, t in enumerate(thresholds): + if val[k] > t: + h[i] += 1 + else: + break + # choose the one to use. This is the first that is lower than howmany + tcut = thresholds[-1] + for n, t in zip(h, thresholds): + if n < howmany: + tcut = t + break + # now we filter the pixels + n = 0 + for k in range(nnz): + if val[k] > tcut: + row[n] = row[k] + col[n] = col[k] + val[n] = val[k] + n += 1 + if n >= howmany: + break + return n + + +@functools.lru_cache(maxsize=1) +def get_dset(h5name, dsetname): + """This avoids to re-read the dataset many times""" + dset = h5py.File(h5name, "r")[dsetname] + return dset + + +def choose_parallel(args): + """reads a frame and sends back a sparse frame""" + h5name, address, frame_num = args + frm = get_dset(h5name, address)[frame_num] + if choose_parallel.cache is None: + # cache the mallocs on this function. Should be one per process + row = np.empty(OPTIONS.MASK.size, np.uint16) + col = np.empty(OPTIONS.MASK.size, np.uint16) + val = np.empty(OPTIONS.MASK.size, frm.dtype) + choose_parallel.cache = row, col, val + else: + row, col, val = choose_parallel.cache + nnz = select(frm, OPTIONS.MASK, row, col, val, OPTIONS.CUT) + if nnz == 0: + sf = None + else: + if nnz > OPTIONS.HOWMANY: + nnz = top_pixels(nnz, row, col, val, OPTIONS.HOWMANY, OPTIONS.THRESHOLDS) + # Now get rid of the single pixel 'peaks' + # (for the mallocs, data is copied here) + s = sparseframe.sparse_frame(row[:nnz].copy(), col[:nnz].copy(), frm.shape) + s.set_pixels("intensity", val[:nnz].copy()) + if OPTIONS.PIXELS_IN_SPOT <= 1: + sf = s + else: + # label them according to the connected objects + sparseframe.sparse_connected_pixels( + s, threshold=OPTIONS.CUT, data_name="intensity", label_name="cp" + ) + # only keep spots with more than 3 pixels ... + mom = sparseframe.sparse_moments( + s, intensity_name="intensity", labels_name="cp" + ) + npx = mom[:, cImageD11.s2D_1] + pxcounts = npx[s.pixels["cp"] - 1] + pxmsk = pxcounts >= OPTIONS.PIXELS_IN_SPOT + if pxmsk.sum() == 0: + sf = None + else: + sf = s.mask(pxmsk) + return frame_num, sf + + +choose_parallel.cache = None # cache for malloc per process + + +def segment_scans( fname, + scans, + outname, + mypool, + scanmotors=OPTIONS.SCANMOTORS, + headermotors=OPTIONS.HEADERMOTORS, + detector=OPTIONS.DETECTORNAME ): + """Does segmentation on a series of scans in hdf files: + fname : input hdf file + scans : input groups in the hdf files [h[s] for s in scans] will be treated + outname : output hdf file to put the results into + sparsify_func : function to select which pixels to keep + headermotors : things to copy from input instrument/positioners to output + scanmotors : datasets to copy from input measurement/xxx to output + """ + opts = { + "chunks": (10000,), + "maxshape": (None,), + "compression": "lzf", + "shuffle": True, + } + ndone = 0 + with h5py.File(outname, "a") as hout: + for scan in scans: + if scan.endswith(".2"): # for fscans + continue + with h5py.File(fname, "r") as hin: + hout.attrs["h5input"] = fname + gin = hin[scan] + bad = 1 + if "title" not in hin[scan]: + print(scan,"missing title, skipping") + continue + if "measurement" not in hin[scan]: + print(scan,"missing measurement group, skipping") + continue + if detector not in hin[scan]['measurement']: + print(scan,"missing measurement/%s, skipping"%(detector)) + continue + title = hin[scan]["title"][()] + print("# ", scan, title) + print("# time now", time.ctime(), "\n#", end=" ") + for scantype in OPTIONS.SCANTYPES: + if title.find(scantype) >= 0: + bad = 0 + if ("measurement" in gin) and (detector not in gin["measurement"]): + bad += 1 + if bad: + print("# SKIPPING BAD SCAN", scan) + continue + g = hout.create_group(scan) + gm = g.create_group("measurement") + for m in scanmotors: # vary : many + if m in gin["measurement"]: + gm.create_dataset(m, data=gin["measurement"][m][:]) + # else: + # the thing requested is not there - ignore the problem for now + gip = g.create_group("instrument/positioners") + for m in headermotors: # fixed : scalar + if "instrument/positioners/%s"%(m) in gin: + gip.create_dataset(m, data=gin["instrument/positioners"][m][()]) + try: + frms = gin["measurement"][detector] + except: + print(list(gin)) + print(list(gin["measurement"])) + print(detector) + raise + row = g.create_dataset("row", (1,), dtype=np.uint16, **opts) + col = g.create_dataset("col", (1,), dtype=np.uint16, **opts) + # can go over 65535 frames in a scan + num = g.create_dataset("frame", (1,), dtype=np.uint32, **opts) + sig = g.create_dataset("intensity", (1,), dtype=frms.dtype, **opts) + nnz = g.create_dataset("nnz", (frms.shape[0],), dtype=np.uint32) + g.attrs["itype"] = np.dtype(np.uint16).name + g.attrs["nframes"] = frms.shape[0] + g.attrs["shape0"] = frms.shape[1] + g.attrs["shape1"] = frms.shape[2] + npx = 0 + address = scan + "/measurement/" + detector + nimg = frms.shape[0] + args = [(fname, address, i) for i in range(nimg)] + # CLOSE the input file here + # max chunksize is for load balancing. Originally tuned to be 7200/40/8 == 22 + max_chunksize = 64 + chunksize = min( max(1, len(args) // OPTIONS.CORES // 8), max_chunksize ) + # set the timeout to be 1 second per frame plus a minute. If we can't segment at + # 1 f.p.s better to give up. + timeout = len(args) + 60 + for i, spf in mypool.map( + choose_parallel, args, chunksize=chunksize, timeout=timeout + ): + if i % 500 == 0: + if spf is None: + print("%4d 0" % (i), end=",") + else: + print("%4d %d" % (i, spf.nnz), end=",") + sys.stdout.flush() + if spf is None: + nnz[i] = 0 + continue + if spf.nnz + npx > len(row): + row.resize(spf.nnz + npx, axis=0) + col.resize(spf.nnz + npx, axis=0) + sig.resize(spf.nnz + npx, axis=0) + num.resize(spf.nnz + npx, axis=0) + row[npx:] = spf.row[:] + col[npx:] = spf.col[:] + sig[npx:] = spf.pixels["intensity"] + num[npx:] = i + nnz[i] = spf.nnz + npx += spf.nnz + ndone += nimg + print("\n# Done", scan, nimg, ndone) + return ndone + + # the output file should be flushed and closed when this returns + + +def main(hname, outname, mypool): + # read all the scans : just the master process + print("hname = ", repr(hname)) + print("outname = ", repr(outname)) + with h5py.File(hname, "r") as h: + scans = list(h["/"]) + print("scans = ", repr(scans)) + print("#", time.ctime()) + start = time.time() + nfrm = segment_scans(hname, scans, outname, mypool) + end = time.time() + print("#", time.ctime()) + print("# Elapsed", end - start, "/s, f.p.s %.2f" % (nfrm / (end - start))) + + + +if __name__ == "__main__": + import concurrent.futures + + with concurrent.futures.ProcessPoolExecutor(max_workers=OPTIONS.CORES) as thepool: + main(OPTIONS.HNAME, OPTIONS.OUTNAME, thepool) diff --git a/sandbox/sharpen_search.py b/sandbox/sharpen_search.py new file mode 100644 index 00000000..dcd17eee --- /dev/null +++ b/sandbox/sharpen_search.py @@ -0,0 +1,158 @@ + +from __future__ import division, print_function +import numpy as np, scipy.ndimage, sys, fabio +from ImageD11 import cImageD11, labelimage + +def gauss_1d( N, center, fwhm ): + """ + Normalised 1D gaussian peak + """ + sigma = fwhm / 2.35482 + dx = (np.arange(0, N, 1, dtype=np.float32 ) - center)/sigma + f = np.exp( -dx*dx / 2 ) / sigma / np.sqrt( 2 * np.pi ) + return f + +def lorentz_1d( N, center, fwhm ): + """ + Normalised 1D Lorentz peak + """ + dx = (np.arange(0, N, 1, dtype=np.float32 ) - center) + top = fwhm / 2. / np.pi + bottom = dx*dx + fwhm*fwhm / 4. + f = top / bottom + return f + + + + +def add_gaussian_2d( img, center, sigma, weight ): + """ adds a 2d Gaussian into a test image + (missing sig_ij for now) + """ + ns, nf = img.shape + i, j = np.mgrid[0:ns, 0:nf] + di = i.astype(np.float32) - center[0] + dj = j.astype(np.float32) - center[1] + s2_2 = 2*sigma*sigma + arg = (di*di+dj*dj)/s2_2 + np.add( img, + np.exp( -arg )/s2_2/np.pi, + img ) + return img + +# gaussian_2d = np.outer( gauss_1d, gauss_1d ) +# +# Separation will be interesting for 2theta, azimuth representations... +# + +def make_test_image( dims = (256,256), + sigmas = [3.5,], + centers = [(128,128),], + weights = [1,] ): + """ produce a test image with some peaks in it """ + imt = np.zeros( dims, np.float32 ) + for (i,j),sigma,weight in zip(centers, sigmas, weights): + imt = add_gaussian_2d( imt, (i,j), sigma, weight ) + return imt + + + +class convolutepeak(object): + """ + Gets ready to convolve your image with some functions + pk - peak width + bg - flat + peak - function to produce 1d peak + Uses these convolutions to do a pixel by pixel peak fit (of a local box) + ... e.g. attempts to strip the background + """ + def __init__(self, dims, fwhm, peak=gauss_1d ): + """ + Fits y = pk + bkg pixel by pixel... + """ + # Dimension of pk and bg images to convolve + N = int(3*fwhm+1)*2+1 + p = peak( N, N//2, fwhm ) + o = np.ones(N, np.float32 ) + # 2D peak + pk = np.outer( p, p ) + bg = np.outer( o, o ) + # Matrix for a least squares problem fitting peak in this box + m = [ [ (bg*bg).sum(), (bg*pk).sum()], + [ (bg*pk).sum(), (pk*pk).sum()] ] + detM = 1./(m[0][0]*m[1][1] - m[0][1]*m[1][0]) + invM = [[ detM*m[1][1], -detM*m[0][1]], + [ -detM*m[1][0], detM*m[0][0]]] + # convolve me to fit peak + pkc = invM[1][0]*bg + invM[1][1]*pk + # ... pk = np.outer( pk1d, pk1d ) + # ... bg = np.outer( ones, ones ) + # p0 = invM[1][0] * np.ones( N, np.float32 ) + \ + # invM[1][1] * gauss_1d( N, N//2, sigma ) + # print( invM ) + # return p0 + self.p, self.o, self.invM = p, o, invM + + def conv( self, im ): + """ Fits pk + bg """ + p, o, invM = self.p, self.o, self.invM + # 2D as a pair of 1D + # pk + imp = scipy.ndimage.correlate1d( im, invM[1][1] * p, + axis=0, mode= 'nearest' ) + imp = scipy.ndimage.correlate1d( imp, p, + axis=1, mode= 'nearest' ) + # bg + imb = scipy.ndimage.correlate1d( im, invM[1][0] * o, + axis=0, mode= 'nearest' ) + imb = scipy.ndimage.correlate1d( imb, o, + axis=1, mode= 'nearest' ) + # fitted + return imb + imp + + +def process_edf( fname, bkg , sigma_threshold, sigma_peak, sptfile ): + im = fabio.open( fname ) + cpk = convolutepeak( im.data.shape, sigma_peak ) + lio = labelimage.labelimage( im.data.shape, sptfile ) + # for i in range(im.nframes): + i = 0 + while True: + try: + im = im.next() + except: + break + data = im.data.astype( np.float32 ) + np.subtract( data, bkg, data ) + pkdata = cpk.conv( data ) + s0 = pkdata.std()*sigma_threshold + m0 = abs(pkdata) < s0 + s1 = pkdata[m0].std()*sigma_threshold + m1 = abs(pkdata) < s1 + s2 = pkdata[m1].std()*sigma_threshold + lio.peaksearch( pkdata, s2, i ) + print( "%s[%d] %d"%( im.filename, i, lio.npk ) ) + lio.mergelast() + i+=1 + lio.finalise() + + + + +if __name__ == "__main__": + edf = sys.argv[1] + bkgname = sys.argv[2] + bkg = fabio.open( bkgname ).data + sptfile = edf.replace(".edf",".flt") + try: + sigma_threshold = float(sys.argv[3]) + except: + sigma_threshold = 5. + sigma_peak = 2. + try: + fwhm_peak = float(sys.argv[4]) + except: + fwhm_peak = 3. + process_edf( edf, bkg, sigma_threshold , fwhm_peak , sptfile) + + diff --git a/sandbox/sigma_3_matrices.py b/sandbox/sigma_3_matrices.py new file mode 100644 index 00000000..d86fd4fc --- /dev/null +++ b/sandbox/sigma_3_matrices.py @@ -0,0 +1,44 @@ +from __future__ import print_function + + +import ImageD11.grain +import ImageD11.grid_index_parallel +import scipy.spatial.transform +import numpy as np + + +def make_sigma3_mats( ): + """ Rotations of 60 degrees on 111 """ + v = 60/np.sqrt( 3 ) + S3_matrices = [ scipy.spatial.transform.Rotation.from_rotvec( (x,y,z), degrees=True ).as_matrix() + for x in (-v,v) + for y in (-v,v) + for z in (-v,) ] # only need 1 z as -60 == 60 + return S3_matrices + + +def applytwinmat( g, mat ): + """ U.B -> U.M'.B """ + ubinew = np.linalg.inv( g.U.dot( mat.T ).dot( g.B ) ) + return ImageD11.grain.grain( ubinew, g.translation ) + + +def twin_check( ind, matrices, symmetry='cubic', poserr=1000, angerr=0.5 ): + gl = ImageD11.grid_index_parallel.uniq_grain_list( symmetry, poserr, angerr ) + gl.add( [ ImageD11.grain.grain( ubi, np.array((0,0,0)) ) for ubi in ind.ubis ] ) + for ubi in ind.ubis: + g = ImageD11.grain.grain( ubi ) + sg1 = ImageD11.cImageD11.score( ubi, ind.gvflat, ind.hkl_tol ) + print("%4d "%(sg1), end='') + for mat in matrices: + gnew = applytwinmat( g, mat ) + s1 = ImageD11.cImageD11.score( gnew.ubi, ind.gvflat, ind.hkl_tol ) + if s1 > ind.minpks: # should be accepted assuming uniq + b4 = len(gl.uniqgrains) + gl.add( [gnew,] ) + if len(gl.uniqgrains) == b4: + e = 'r' + else: + e = '!' + print("%4d "%(s1), end=e) + print() diff --git a/sandbox/spot_harvest.py b/sandbox/spot_harvest.py index 39250acb..6b9dbe8d 100644 --- a/sandbox/spot_harvest.py +++ b/sandbox/spot_harvest.py @@ -225,8 +225,8 @@ def writespot(filename, roi, box, hdr, flt, ispt): del boxes[i] if False: - # from matplotlib.pylab import imshow, show - imshow(box) + from matplotlib.pylab import imshow, show + imshow(boxes[i]) show() sys.exit() diff --git a/sandbox/teo.py b/sandbox/teo.py index bdb3ebea..eecbd1aa 100644 --- a/sandbox/teo.py +++ b/sandbox/teo.py @@ -55,7 +55,7 @@ def Dcalc( UB, hkls, wedge, chi, wavelength, etao, omega): """ # computed g-vectors g = np.dot( UB , hkls ) - assert g.dtype == np.float + assert g.dtype == float if 0: print(g.shape) print(hkls.shape) @@ -66,11 +66,11 @@ def Dcalc( UB, hkls, wedge, chi, wavelength, etao, omega): # wedge/chi matrix for axis orientation wc = gv_general.wedgechi( wedge=wedge, chi=chi ) - assert wc.dtype == np.float + assert wc.dtype == float # computed omega angles for reflections omega1, omega2, valid = gv_general.g_to_k( g, wavelength,axis=[0,0,-1], pre=None, post=wc ) - assert omega1.dtype == np.float + assert omega1.dtype == float # hum - isn't this wc-1 ? cwT = gv_general.chiwedge( wedge=wedge, chi=chi ).T # k vectors, scattering in laboratory at angle omega1/2 @@ -78,7 +78,7 @@ def Dcalc( UB, hkls, wedge, chi, wavelength, etao, omega): pre = cwT, post=None) k2 = gv_general.k_to_g( g, omega2, axis=[0,0,1], pre = cwT, post=None) - assert k1.dtype == np.float + assert k1.dtype == float # Computed azimuth angles # # k[1,:] = -ds*c*sin(eta) @@ -192,8 +192,8 @@ def solve( self ): for diff in self.differences: # block addins M += len(diff) # Least squares problem matrix A.x=b - A = np.zeros( (N, M), np.float ) - b = np.zeros( M, np.float ) + A = np.zeros( (N, M), float ) + b = np.zeros( M, float ) iM = 0 #print N, M, [len(x) for x in self.differences] # FIXME: This loop is doing a numpy.concatenate @@ -224,7 +224,7 @@ def solve( self ): errmat = np.linalg.inv( lsqmat ) print(solution) print(errmat) - print(svderrmat) +# print(errmat) assert np.allclose( self.errmat, errmat ) self.esds = np.sqrt( np.diag( self.errmat ) ) self.condition = s.max() / s.min() # sqrt ? @@ -268,9 +268,9 @@ def fitone( UB, t, sc, fc, omega, hkls, pars): s = 1e-7 # FIXME: Gradient arrays (move to Dcalc) - dtthUB = np.zeros((3,3,npks), np.float) - detaUB = np.zeros((3,3,npks), np.float) - domUB = np.zeros((3,3,npks), np.float) + dtthUB = np.zeros((3,3,npks), float) + detaUB = np.zeros((3,3,npks), float) + domUB = np.zeros((3,3,npks), float) for i in range(3): for j in range(3): @@ -340,7 +340,7 @@ def refit_makemap( colf, pars, grains ): if i == 1004: TESTING=3 # Get new t, UB, S - t, UB, S = fitone( g.ub, g.translation, sc, fc, om, hkls, pars) + t, UB, S = fitone( g.UB, g.translation, sc, fc, om, hkls, pars) for ncycle in range(5): t, UB, S = fitone( UB, t, sc, fc, om, hkls, pars) if S.sh_esd.max() < 1e-10: diff --git a/sandbox/twingen.py b/sandbox/twingen.py index b0b587d2..66212569 100644 --- a/sandbox/twingen.py +++ b/sandbox/twingen.py @@ -1,5 +1,5 @@ - +from __future__ import print_function from ImageD11 import unitcell, parameters, transform import numpy as np @@ -31,7 +31,7 @@ # print scattering vectors in order order = np.argsort(modg) for i in order[10::-1]: - print i,hkls.T[i],modg[i],gve.T[i] + print (i,hkls.T[i],modg[i],gve.T[i]) def make_uniq( UBIlist ): @@ -96,9 +96,9 @@ def get_ubis( h1, h2, UB0, u ): UB1 = np.linalg.inv( UBi1 ) UBIs+=[x for x in get_ubis( h1, h2, UB1, u )] -print len(uniqs) +print( len(uniqs)) uniqs2 = make_uniq( UBIs) -print len(uniqs2) +print( len(uniqs2)) # Now do that again for each uniq one: UBIs=[x for x in uniqs2] @@ -114,7 +114,7 @@ def get_ubis( h1, h2, UB0, u ): UB1 = np.linalg.inv( UBi1 ) UBIs+=[x for x in get_ubis( h1, h2, UB1, u )] uniqs3 = make_uniq( UBIs) -print len(uniqs3) +print( len(uniqs3)) # Now do that again for each uniq one: @@ -131,7 +131,7 @@ def get_ubis( h1, h2, UB0, u ): UB1 = np.linalg.inv( UBi1 ) UBIs+=[x for x in get_ubis( h1, h2, UB1, u )] uniqs4 = make_uniq( UBIs) -print len(uniqs4) +print( len(uniqs4)) uniqs= uniqs4 @@ -165,8 +165,8 @@ def doplot(ubi, col): XX=False for i,ubi in enumerate(uniqs[1:]): col = "%s+"%(colors[i%len(colors)]) - print i,col - print ubi + print( i,col) + print( ubi) doplot( ubi, col ) doplot( uniqs[0], "ko" ) diff --git a/sandbox/weighted_kde/3DXRD diffractogram from filtered peaks - Jupyter Notebook.pdf b/sandbox/weighted_kde/3DXRD diffractogram from filtered peaks - Jupyter Notebook.pdf new file mode 100644 index 0000000000000000000000000000000000000000..760001deb2009ee907d1ebb6d79965b3e00d5e03 GIT 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b/sandbox/weighted_kde/3DXRD diffractogram from filtered peaks.ipynb new file mode 100644 index 00000000..63553fe9 --- /dev/null +++ b/sandbox/weighted_kde/3DXRD diffractogram from filtered peaks.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "\n", + "# Weighted kernel density estimation to quickly reproduce the profile of a diffractometer\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "This example shows a work-arround for a quick visualization of a diffractorgram (similar to experimental powder diffractograms) from **ImageD11** \".flt\" or \".new\" columnfile containing peaks information.\n", + "\n", + "It is basically a **probability density function** (pdf) of the _$2\\theta$ position_ of the peak, which is weighted by the _peak intensity_.\n", + "
The smoothing of such gaussian kde is decided by the _**bandwidht**_ value.\n", + "\n", + "[Weighted kde](https://nbviewer.jupyter.org/gist/tillahoffmann/f844bce2ec264c1c8cb5) : The original Scipy gaussian kde was modified by [Till Hoffmann](http://tillahoffmann.github.io/) to allow for heterogeneous sampling weights.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "from ImageD11.columnfile import columnfile\n", + "from ImageD11 import weighted_kde as wkde" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (6,4)\n", + "plt.rcParams['figure.dpi'] = 150\n", + "plt.rcParams['mathtext.fontset'] = 'cm'\n", + "plt.rcParams['font.size'] = 12" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Loading and visualizing the input data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '$Diffraction \\\\, angles$')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# read the peaks\n", + "flt = columnfile('sma_261N.flt.new')\n", + "\n", + "# peaks indexed to phase 1\n", + "phase1 = flt.copy()\n", + "phase1.filter( phase1.labels > -1 )\n", + "\n", + "# unindexed peaks (phase 2 + unindexed phase 1?)\n", + "phase2 = flt.copy()\n", + "phase2.filter( phase2.labels == -1 )\n", + "\n", + "#plot radial transform for phase 1\n", + "plt.plot( phase1.tth_per_grain, phase1.eta_per_grain, 'x')\n", + "plt.xlabel( r'$ 2 \\theta \\, (\\degree) $' )\n", + "plt.ylabel( r'$ \\eta \\, (\\degree) $' )\n", + "plt.title( r'$Diffraction \\, angles$' )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting the diffraction profile\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, ' With bandwidth = 0.247')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Probability density function (pdf) of 2theta\n", + "# weighted by the peak intensity and using default 2theta bandwidth\n", + "I_phase1 = phase1.sum_intensity * phase1.Lorentz_per_grain\n", + "pdf = wkde.gaussian_kde( phase1.tth_per_grain, weights = I_phase1)\n", + "\n", + "# Plotting it over 2theta range\n", + "x = np.linspace( min(flt.tth), max(flt.tth), 500 )\n", + "y = pdf(x)\n", + "plt.plot(x, y)\n", + "plt.xlabel( r'$ 2 \\theta \\, (\\degree) $' )\n", + "plt.ylabel( r'$ I $' )\n", + "plt.yticks([])\n", + "plt.title( ' With bandwidth = %.3f'%pdf.factor )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The profile showed above is highly smoothed and the hkl peaks are merged.
\n", + "$\\to$ _A Smaller bandwidth should be used._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Choosing the right bandwidth of the estimator\n", + "\n", + "The _bandwidth_ can be passed as argument to the **gaussian_kde()** object or set afterward using the later **set_badwidth()** method. For example, the bandwidth can be reduced by a factor of 100 with respect to its previous value:\n", + "```Python\n", + "gaussian_kde().set_bandwidth( gaussian_kde().factor / 100 )\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f08047acb5d4b0c8a20a362e9981a24", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.043000000000000003, description='bandwidth', max=0.08523560743665533…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf_phase1 = wkde.gaussian_kde( phase1.tth, weights = phase1.sum_intensity )\n", + "pdf_phase2 = wkde.gaussian_kde( phase2.tth, weights = phase2.sum_intensity )\n", + "frac_phase1 = np.sum( phase1.sum_intensity ) / np.sum( flt.sum_intensity )\n", + "frac_phase2 = np.sum( phase2.sum_intensity ) / np.sum( flt.sum_intensity )\n", + "\n", + "from ipywidgets import interact\n", + "bw_range = ( 0.001, pdf_phase1.factor/3, 0.001)\n", + "@interact( bandwidth = bw_range)\n", + "def plot_pdf(bandwidth):\n", + " pdf_phase1.set_bandwidth(bandwidth)\n", + " pdf_phase2.set_bandwidth(bandwidth)\n", + " y_phase1 = pdf_phase1(x)\n", + " y_phase2 = pdf_phase2(x)\n", + " plt.plot( x, frac_phase1 * y_phase1, label = r'$Phase \\, 1$' )\n", + " plt.plot( x, frac_phase2 * y_phase2, label = r'$Phase \\, 2$' )\n", + " plt.legend(loc='best')\n", + " plt.xlabel( r'$ 2 \\theta \\, (\\degree) $' )\n", + " plt.ylabel( r'$ I $' )\n", + " plt.yticks([])\n", + " plt.title( r'$ 3DXRD \\, diffractogram $' )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sandbox/weighted_kde/sma_261N.flt.new b/sandbox/weighted_kde/sma_261N.flt.new new file mode 100644 index 00000000..4a15dd93 --- /dev/null +++ b/sandbox/weighted_kde/sma_261N.flt.new @@ -0,0 +1,33185 @@ +# cell__a = 5.810006 +# cell__b = 5.810006 +# cell__c = 5.810006 +# cell_alpha = 90.0 +# cell_beta = 90.0 +# cell_gamma = 90.0 +# cell_lattice_[P,A,B,C,I,F,R] = F +# chi = 0.0 +# distance = 241985.0 +# filename = /data/visitor/ma3379/id11/3DXRD_Analysis/janvier19/peaksprocessing/R117_700_1_261N_z0/R117_700_1_261N_z0.flt +# fit_tolerance = 0.5 +# min_bin_prob = 1e-05 +# no_bins = 10000 +# o11 = 1 +# o12 = 0 +# o21 = 0 +# o22 = -1 +# omegasign = 1.0 +# t_x = 0 +# t_y = 0 +# t_z = 0 +# tilt_x = -0.002253 +# tilt_y = 0.000325 +# tilt_z = -0.010878 +# wavelength = 0.189869 +# wedge = 0.016342 +# y_center = 1028.434785 +# y_size = 47.2 +# z_center = 1028.968762 +# z_size = 47.2 +# sc fc omega 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0.0000 1250.0000 260546.0000 218.0000 970 368 -138.8750 969 971 366 370 -138.8750 -138.8750 -368.0040 969.6712 0 0 1899 242324.4415 31277.2864 -2756.5465 7.3828 -95.0366 -0.4166 -0.5456 -0.0543 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.128002 -0.000000 0.000000 0.000000 0.000000 + 1110.0507 1480.0567 -138.8366 178 139.0843 1109.2804 1471.0981 2.6043 1.5031 -0.0925 1.0664 -0.0946 0.0697 24757.0000 3655773.0000 248.0000 1110 1471 -138.8750 1103 1114 1469 1474 -139.6250 -138.1250 -1471.0981 1109.2804 0 0 1901 241754.3669 -21306.6166 3875.1562 5.1188 79.6920 0.3128 0.3271 0.0894 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.087781 -0.000000 0.000000 0.000000 0.000000 + 1119.8750 539.1106 -138.7991 88 587.2273 1117.4193 543.9208 2.7298 1.8887 0.3897 1.0081 0.0460 0.0494 51676.0000 33051898.0000 1115.0000 1117 543 -138.8750 1112 1123 540 547 -139.1250 -138.6250 -543.9208 1117.4193 0 0 1902 242237.6289 23104.3434 4238.6493 5.5387 -79.6043 -0.3168 -0.3984 0.0838 0.0002 156 5.614339 -80.641618 -138.669100 -1.9973 -2.0066 -0.993132 -2 -2 -1 0.094934 0.096530 0.009900 9.000000 0.000000 + 1155.0227 299.6935 -138.9754 20 111.7500 1151.3087 303.7512 1.8508 1.3275 0.2578 1.0075 -0.0189 -0.0173 2235.0000 250631.0000 125.0000 1150 304 -138.8750 1148 1154 302 305 -139.1250 -138.8750 -303.7512 1151.3087 0 0 1904 242361.0924 34407.8708 5872.1180 8.1955 -80.3151 -0.4456 -0.5952 0.1247 0.0001 11 8.208981 -80.455182 -139.021491 -3.0056 -2.9951 1.008085 -3 -3 1 0.140520 0.140808 0.010988 19.000000 1.000000 + 1160.6429 195.2898 -138.8272 11 110.8182 1156.4495 198.3675 1.4016 1.2512 0.1612 1.0048 -0.0330 -0.0140 1219.0000 135837.0000 126.0000 1157 199 -138.8750 1155 1158 197 200 -138.8750 -138.6250 -198.3675 1156.4495 0 0 1905 242414.7827 39336.0190 6126.2715 9.3261 -81.1478 -0.5085 -0.6899 0.1365 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.160123 -0.000000 0.000000 0.000000 0.000000 + 1209.4062 1584.7615 -138.7298 93 155.3871 1206.7797 1573.0364 2.3027 1.8274 -0.2482 1.0104 -0.0091 -0.0257 14451.0000 2418639.0000 255.0000 1208 1573 -138.8750 1202 1211 1570 1576 -138.8750 -138.3750 -1573.0364 1206.7797 0 0 1906 241702.1323 -26237.8125 8575.8750 6.5153 71.8999 0.4012 0.4054 0.1771 0.0000 30 6.500726 72.725538 -138.696590 1.9998 2.0001 1.999153 2 2 2 0.107854 0.108109 0.000876 12.000000 1.000000 + 1253.1344 611.0822 -138.7888 123 690.9593 1249.1470 616.6618 2.1064 1.9578 0.4066 1.0439 0.1629 0.1599 84988.0000 66485270.0000 1459.0000 1249 616 -138.8750 1245 1253 613 620 -139.3750 -138.1250 -616.6618 1249.1470 0 0 1907 242202.7206 19721.6645 10536.1418 5.2745 -61.8870 -0.2646 -0.3365 0.2331 0.0000 94 5.304516 -61.399882 -138.814392 -2.0006 -2.0018 -0.001070 -2 -2 0 0.081082 0.081169 0.002178 8.000000 1.000000 + 1270.2048 641.6224 -138.7617 301 1287.6213 1266.0091 647.1166 2.8450 1.9018 0.3170 1.0852 0.0199 -0.0822 387574.0000 572553854.0000 3225.0000 1267 648 -138.8750 1258 1272 643 651 -139.3750 -137.6250 -647.1166 1266.0091 0 0 1909 242187.3022 18282.0713 11345.1152 5.0769 -58.1779 -0.2518 -0.3202 0.2511 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.075191 -0.000000 0.000000 0.000000 0.000000 + 1270.7542 1470.0909 -138.7816 8 107.7500 1267.0023 1460.7401 1.4265 1.2969 -0.2054 1.0073 -0.0674 0.0186 862.0000 93084.0000 114.0000 1268 1460 -138.8750 1265 1268 1459 1462 -138.8750 -138.6250 -1460.7401 1267.0023 0 0 1910 241761.9489 -20819.1704 11459.2804 5.6140 61.1707 0.3092 0.3157 0.2538 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.085702 -0.000000 0.000000 0.000000 0.000000 + 1283.8925 914.0899 -139.0709 120 268.6167 1279.7764 916.4819 1.6226 1.7899 0.3570 1.0805 -0.1019 -0.1122 32234.0000 9069258.0000 452.0000 1280 917 -138.8750 1277 1282 913 919 -140.1250 -138.3750 -916.4819 1279.7764 0 0 1912 242047.6188 5423.8545 12020.1913 3.1185 -24.2863 -0.0773 -0.1023 0.2690 0.0000 82 3.242543 -25.432473 -139.181083 1.0002 -1.0026 1.000095 1 -1 1 0.022375 0.024291 0.002609 3.000000 1.000000 + 1321.5282 699.3897 -138.5987 15 109.0667 1316.5471 704.6754 1.3764 1.2772 0.2116 1.0198 -0.0026 -0.0303 1636.0000 178912.0000 122.0000 1317 706 -138.8750 1315 1318 704 706 -138.8750 -138.1250 -704.6754 1316.5471 0 0 1914 242158.4300 15561.0808 13773.7257 4.9049 -48.4867 -0.2149 -0.2743 0.3038 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.064024 -0.000000 0.000000 0.000000 0.000000 + 1323.5925 1081.3015 -140.7022 1046 128.5880 1318.7106 1080.7279 2.1894 2.5992 -0.4474 1.7938 0.2360 -0.1159 134503.0000 17775267.0000 198.0000 1321 1080 -138.8750 1314 1323 1076 1086 -144.3750 -137.1250 -1080.7279 1318.7106 0 0 1915 241962.3761 -2463.8242 13911.8348 3.3417 10.0431 0.0342 0.0274 0.3077 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.010165 -0.000000 0.000000 0.000000 0.000000 + 1325.7059 1124.6484 -138.5866 53 109.9434 1320.8069 1123.1898 1.2966 1.5181 -0.2753 1.2201 -0.1204 0.2718 5827.0000 643655.0000 134.0000 1321 1123 -138.8750 1319 1322 1121 1126 -139.8750 -137.3750 -1123.1898 1320.8069 0 0 1916 241940.1529 -4509.4469 14016.2036 3.4826 17.8346 0.0652 0.0592 0.3100 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.018605 -0.000000 0.000000 0.000000 0.000000 + 1346.2695 253.4252 -138.9142 301 1369.1528 1339.7757 258.7380 3.6562 2.0903 0.2767 1.0387 0.1205 -0.0068 412115.0000 696649347.0000 4289.0000 1341 259 -138.8750 1329 1346 255 263 -139.8750 -138.3750 -258.7380 1339.7757 0 0 1917 242387.7817 36611.9372 14894.0159 9.2615 -67.8631 -0.4627 -0.6339 0.3101 0.0000 12 9.190117 -68.367221 -138.936275 -4.0009 -2.0057 1.996667 -4 -2 2 0.149077 0.148461 0.006664 24.000000 1.000000 + 1428.6277 1452.9719 -138.8750 8 110.5000 1421.2613 1443.8778 1.3772 1.2704 -0.2539 1.0000 0.0000 0.0000 884.0000 98198.0000 123.0000 1422 1444 -138.8750 1420 1423 1443 1445 -138.8750 -138.8750 -1443.8778 1421.2613 0 0 1921 241773.1601 -19994.4149 18909.0671 6.4937 46.5981 0.3043 0.2961 0.4173 0.0001 82 6.479465 45.403577 -138.745000 1.9969 2.0012 2.000118 2 2 2 0.082169 0.080355 0.003326 12.000000 1.000000 + 1459.5713 1388.9950 -139.4261 686 173.6006 1451.5040 1381.4724 3.6249 3.1513 -0.5492 1.1971 -0.4035 0.3294 119090.0000 23821166.0000 439.0000 1449 1383 -138.8750 1445 1460 1375 1389 -141.1250 -138.1250 -1381.4724 1451.5040 0 0 1922 241806.4825 -16971.6010 20362.7876 6.2561 39.8099 0.2586 0.2547 0.4339 0.0001 31 6.157107 39.820836 -139.346228 0.9930 0.9947 3.006252 1 1 3 0.069769 0.068685 0.010779 11.000000 1.000000 + 1539.3289 1090.0394 -138.8750 5 108.8000 1529.5993 1089.6048 1.4172 1.1131 -0.2216 1.0000 0.0000 0.0000 544.0000 59250.0000 112.0000 1529 1090 -138.8750 1528 1531 1089 1090 -138.8750 -138.8750 -1089.6048 1529.5993 0 0 1924 241961.1992 -2853.2859 24095.4971 5.7265 6.7533 0.0538 0.0212 0.5274 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.011734 -0.000000 0.000000 0.000000 0.000000 + 1578.6643 410.0595 -138.7176 952 874.8729 1568.2489 417.2069 4.0478 4.3540 0.6325 1.0485 0.0344 -0.0013 832879.0000 963494177.0000 2823.0000 1568 417 -138.8750 1555 1580 405 429 -139.6250 -137.8750 -417.2069 1568.2489 0 0 1925 242310.9256 29243.9677 25879.7039 9.1551 -48.4925 -0.3632 -0.5158 0.5527 0.0000 22 9.133598 -48.620748 -138.790599 1.9979 -2.0024 3.997429 2 -2 4 0.119151 0.119108 0.004096 24.000000 1.000000 + 1737.0173 455.2256 -138.8741 328 145.5305 1723.2995 462.5086 2.9373 2.7077 0.4191 1.0413 0.0651 0.1053 47734.0000 7485564.0000 296.0000 1724 463 -138.8750 1715 1729 455 468 -139.3750 -138.3750 -462.5086 1723.2995 0 0 1928 242290.1651 27129.0989 33358.7564 10.0631 -39.1198 -0.3265 -0.4998 0.7194 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.110247 -0.000000 0.000000 0.000000 0.000000 + 1739.6785 886.0993 -139.6484 156 113.9487 1725.7656 889.6224 1.3374 2.2973 0.2281 1.2015 0.0418 0.2654 17776.0000 2037178.0000 146.0000 1725 891 -138.8750 1724 1727 885 893 -140.8750 -137.8750 -889.6224 1725.7656 0 0 1929 242068.9818 6793.3982 33530.2537 8.0443 -11.4534 -0.0597 -0.1512 0.7291 0.0001 27 8.133119 -11.908577 -139.518400 -0.9915 -3.0023 3.003264 -1 -3 3 0.027788 0.029193 0.009391 19.000000 1.000000 + 1829.9052 1263.5849 -137.8511 1278 207.3263 1813.4548 1260.0746 2.6561 4.9570 -0.6047 1.4110 -0.4517 0.5561 264963.0000 71361239.0000 710.0000 1815 1256 -138.8750 1807 1819 1248 1272 -140.3750 -135.3750 -1260.0746 1813.4548 0 0 1931 241876.5529 -11013.2277 37829.1459 9.2518 16.2319 0.2082 0.1313 0.8032 0.0000 106 9.148780 16.437405 -137.981100 2.0001 -2.0010 3.998908 2 -2 4 0.044940 0.044991 0.001484 24.000000 1.000000 + 309.4370 1656.0318 -138.7997 129 157.2093 300.8214 1652.9286 2.3433 2.2244 0.4755 1.0155 0.0082 0.0144 20280.0000 3507224.0000 307.0000 302 1654 -138.6250 296 305 1648 1657 -139.1250 -138.3750 -1652.9286 300.8214 0 0 1942 241651.7343 -29697.2672 -33894.9708 10.5635 138.7766 0.4803 0.4127 -0.7371 0.0000 91 10.587194 139.622734 -138.828497 3.9985 -0.0019 -3.999620 4 0 -4 0.120811 0.119025 0.002450 32.000000 1.000000 + 382.8138 315.7434 -138.7162 50 239.6200 369.4479 308.0654 2.3162 2.0060 -0.4918 1.0093 0.0811 -0.0642 11981.0000 2919699.0000 328.0000 371 306 -138.6250 365 373 305 312 -139.1250 -138.6250 -308.0654 369.4479 0 0 1943 242341.0062 33568.2444 -30574.3383 10.6119 -132.3276 -0.4118 -0.6060 -0.6442 0.0000 96 10.628282 -131.541267 -138.703227 -4.0008 -0.0016 -3.996538 -4 0 -4 0.136147 0.138047 0.003897 32.000000 1.000000 + 419.1892 475.9103 -138.3321 2385 335.3115 407.8881 471.0634 4.0411 4.6779 -0.2916 1.0958 -0.1168 0.0705 799718.0000 520531966.0000 2120.0000 409 471 -138.6250 398 420 459 486 -139.3750 -136.8750 -471.0634 407.8881 0 0 1945 242259.3294 26012.7015 -28840.3658 9.1080 -137.9510 -0.3193 -0.4602 -0.6295 0.0000 32 9.176225 -138.548467 -138.315331 -4.0032 -2.0024 -2.001750 -4 -2 -2 0.106021 0.105568 0.004366 24.000000 1.000000 + 421.8520 283.8403 -138.6677 146 588.5411 409.7998 276.3265 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0 0 1948 242313.8903 30913.1566 -24686.6459 9.2724 -128.6102 -0.3838 -0.5400 -0.5233 0.0002 68 9.193848 -128.456767 -138.723000 -4.0062 -2.0056 -2.000511 -4 -2 -2 0.125907 0.125116 0.008370 24.000000 1.000000 + 585.2744 186.9619 -138.5570 18 1773.3889 577.7381 180.9104 1.6135 1.2366 -0.2613 1.0093 0.0025 -0.0267 31921.0000 56982123.0000 2102.0000 578 181 -138.6250 575 580 180 182 -138.8750 -138.3750 -180.9104 577.7381 0 0 1951 242410.2324 39667.8861 -21031.9382 10.4933 -117.9326 -0.5009 -0.7026 -0.4444 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.160903 -0.000000 0.000000 0.000000 0.000000 + 700.9071 746.9055 -138.6473 33 113.6970 699.0421 747.4478 1.6774 1.8104 -0.4692 1.0116 -0.0329 0.0569 3752.0000 430162.0000 150.0000 698 748 -138.6250 696 701 745 750 -138.8750 -138.3750 -747.4478 699.0421 0 0 1952 242124.5135 13252.4757 -15514.4538 4.8170 -139.4960 -0.1826 -0.2362 -0.3313 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.054541 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1.0187 0.0862 0.0454 9035.0000 1146695.0000 177.0000 1197 104 -138.6250 1191 1199 101 106 -138.8750 -138.1250 -103.7483 1195.3769 0 0 1970 242463.6252 43773.8293 7999.6837 10.3996 -79.6435 -0.5585 -0.7686 0.1761 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.177571 -0.000000 0.000000 0.000000 0.000000 + 1206.3431 595.6299 -138.6250 1 809.0000 1203.0000 601.0000 1.0000 1.0000 0.0000 1.0000 0.0000 0.0000 809.0000 654481.0000 809.0000 1203 601 -138.6250 1203 1203 601 601 -138.6250 -138.6250 -601.0000 1203.0000 0 0 1971 242209.9366 20445.9922 8325.9524 5.2078 -67.8430 -0.2827 -0.3554 0.1856 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.084065 -0.000000 0.000000 0.000000 0.000000 + 1225.2811 1552.8150 -138.5074 47 118.2766 1222.3941 1541.7267 2.3224 1.3428 0.0128 1.0078 -0.0107 0.0084 5559.0000 664285.0000 152.0000 1222 1542 -138.6250 1219 1226 1540 1543 -138.6250 -138.3750 -1541.7267 1222.3941 0 0 1972 241718.7781 -24728.3426 9321.7659 6.2393 69.3452 0.3783 0.3812 0.1936 0.0001 49 6.213541 70.148571 -138.510689 0.9945 0.9953 3.004123 1 1 3 0.101695 0.101802 0.008327 11.000000 1.000000 + 1230.3400 1800.7523 -138.4398 222 164.4820 1227.1720 1784.3679 3.0605 2.1630 -0.1773 1.0195 -0.0402 -0.0043 36515.0000 6807125.0000 323.0000 1230 1784 -138.6250 1221 1233 1780 1789 -138.8750 -137.8750 -1784.3679 1227.1720 0 0 1973 241591.5569 -36429.7230 9586.9512 8.8625 75.2562 0.5602 0.5391 0.2119 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.148991 -0.000000 0.000000 0.000000 0.000000 + 1238.9354 767.3415 -138.8549 395 390.8000 1235.4604 771.5861 2.0886 2.5136 0.1215 1.0778 -0.0519 -0.0777 154366.0000 76386550.0000 1168.0000 1235 770 -138.6250 1231 1239 766 777 -139.8750 -138.1250 -771.5861 1235.4604 0 0 1974 242122.2744 12345.1707 9882.5927 3.7368 -51.3219 -0.1661 -0.2068 0.2184 0.0000 53 3.738988 -50.510372 -138.823172 0.0013 0.0010 1.999701 0 0 2 0.050879 0.050326 0.001667 4.000000 1.000000 + 1254.4915 386.4175 -138.5140 11 426.8182 1249.9054 391.9029 1.2023 1.4641 -0.0537 1.0077 -0.0285 0.0676 4695.0000 2008793.0000 471.0000 1250 391 -138.6250 1249 1251 390 394 -138.6250 -138.3750 -391.9029 1249.9054 0 0 1975 242318.0914 30325.3282 10576.2695 7.5499 -70.7733 -0.3957 -0.5161 0.2263 0.0001 129 7.495801 -70.772235 -138.558121 -0.0030 -4.0044 0.007710 0 -4 0 0.124061 0.123177 0.009370 16.000000 1.000000 + 1342.1482 670.9393 -138.6469 35 239.9429 1336.7502 676.5495 1.9199 1.7238 0.5543 1.0138 0.0727 0.0826 8398.0000 2054840.0000 337.0000 1338 677 -138.6250 1333 1340 674 679 -138.8750 -138.3750 -676.5495 1336.7502 0 0 1980 242173.3536 16906.0496 14743.9572 5.2921 -48.9079 -0.2314 -0.2984 0.3246 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.069512 -0.000000 0.000000 0.000000 0.000000 + 1355.6474 1693.7940 -138.9580 78 116.4487 1349.8343 1678.9407 1.6577 2.3222 -0.3193 1.0296 0.0264 -0.0973 9083.0000 1070441.0000 158.0000 1349 1678 -138.6250 1347 1353 1675 1684 -139.3750 -138.6250 -1678.9407 1349.8343 0 0 1981 241648.3949 -31368.2774 15490.0539 8.2377 63.7193 0.4809 0.4734 0.3246 0.0001 16 8.153160 64.243730 -139.067607 2.9937 0.9945 2.999624 3 1 3 0.128470 0.127730 0.008371 19.000000 1.000000 + 1388.6742 1459.6979 -138.7098 124 317.7500 1382.2377 1450.4334 2.1623 2.0783 0.1047 1.0138 -0.0046 0.0441 39401.0000 13875509.0000 637.0000 1382 1451 -138.6250 1378 1386 1447 1455 -139.1250 -138.3750 -1450.4334 1382.2377 0 0 1983 241769.0938 -20316.1113 17023.9831 6.2565 50.0385 0.3081 0.3040 0.3732 0.0000 21 6.220947 49.158440 -138.709642 0.9992 2.9994 1.001799 1 3 1 0.083530 0.081979 0.002058 11.000000 1.000000 + 1462.7617 892.1029 -138.6559 374 399.6364 1454.9191 895.3024 1.9450 2.5441 0.2565 1.0767 0.0230 0.0781 149464.0000 76546728.0000 1299.0000 1455 895 -138.6250 1451 1459 890 901 -139.8750 -137.8750 -895.3024 1454.9191 0 0 1984 242061.6515 6480.5981 20460.4540 5.0668 -17.5750 -0.0839 -0.1274 0.4489 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.026668 -0.000000 0.000000 0.000000 0.000000 + 1519.5554 757.1745 -138.6394 171 127.4912 1510.5751 762.3970 1.6241 2.2839 0.2003 1.1146 0.2204 0.1719 21801.0000 2873393.0000 223.0000 1511 762 -138.6250 1508 1513 758 767 -139.6250 -137.8750 -762.3970 1510.5751 0 0 1985 242131.7992 12854.8651 23126.7395 6.2363 -29.0672 -0.1628 -0.2324 0.4959 0.0000 35 6.217799 -29.628464 -138.749554 0.9997 1.0004 3.000048 1 1 3 0.052776 0.053545 0.000502 11.000000 1.000000 + 1523.7231 931.0369 -138.4498 10 412.3000 1514.4911 933.6959 1.2021 1.1007 0.1129 1.0247 0.0299 0.0465 4123.0000 1701217.0000 438.0000 1514 933 -138.6250 1514 1516 933 934 -138.8750 -138.1250 -933.6959 1514.4911 0 0 1986 242042.5967 4649.5094 23341.9712 5.6160 -11.2654 -0.0544 -0.1004 0.5110 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.019117 -0.000000 0.000000 0.000000 0.000000 + 1574.6425 1021.4754 -138.3928 16 109.8125 1564.1821 1022.4559 1.1283 1.4104 -0.2042 1.0329 -0.0102 0.0023 1757.0000 193441.0000 123.0000 1564 1024 -138.6250 1563 1565 1021 1024 -138.6250 -137.8750 -1022.4559 1564.1821 0 0 1990 241996.9438 386.4905 25754.9925 6.0756 -0.8597 0.0166 -0.0269 0.5652 0.0001 76 6.161605 -0.925582 -138.522800 0.9932 -0.9971 3.003722 1 -1 3 0.001588 0.001734 0.008277 11.000000 1.000000 + 1597.6496 1186.5217 -138.4655 114 137.8070 1586.4267 1184.0543 1.4385 1.9520 -0.1569 1.0667 -0.0581 0.1324 15710.0000 2276898.0000 232.0000 1587 1183 -138.6250 1584 1589 1181 1188 -139.1250 -137.6250 -1184.0543 1586.4267 0 0 1992 241912.5568 -7400.7675 26858.5026 6.5694 15.4054 0.1340 0.1005 0.5699 0.0001 52 6.464980 16.067316 -138.335500 1.9937 1.9986 2.004811 2 2 2 0.030392 0.031163 0.008050 12.000000 1.000000 + 1644.4033 1306.2351 -138.6250 1 815.0000 1632.0000 1301.0000 1.0000 1.0000 0.0000 1.0000 0.0000 0.0000 815.0000 664225.0000 815.0000 1632 1301 -138.6250 1632 1632 1301 1301 -138.6250 -138.6250 -1301.0000 1632.0000 0 0 1993 241851.8093 -13045.9195 29078.0212 7.5070 24.1635 0.2132 0.1738 0.6334 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.053479 -0.000000 0.000000 0.000000 0.000000 + 1656.9004 1692.4092 -138.5528 42 113.3333 1643.6922 1677.7460 1.7583 1.5320 -0.2430 1.0118 -0.0119 -0.0254 4760.0000 543440.0000 139.0000 1644 1678 -138.6250 1641 1646 1676 1680 -138.8750 -138.3750 -1677.7460 1643.6922 0 0 1994 241653.7271 -31270.8832 29709.0112 10.1203 46.4672 0.4985 0.4418 0.6431 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.127390 -0.000000 0.000000 0.000000 0.000000 + 1673.9641 1625.9918 -138.8552 30 108.1667 1660.4357 1613.0148 2.1811 1.7660 -0.4914 1.0246 -0.1406 0.1331 3245.0000 351975.0000 123.0000 1660 1614 -138.6250 1657 1664 1610 1616 -139.1250 -138.6250 -1613.0148 1660.4357 0 0 1995 241688.0897 -28134.3608 30507.3421 9.7432 42.6827 0.4478 0.3980 0.6609 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.114729 -0.000000 0.000000 0.000000 0.000000 + 1791.1382 441.1598 -138.7026 25 111.3200 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2010 242234.9333 23928.2024 -34559.6958 9.8445 -145.3023 -0.2895 -0.4429 -0.7357 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.097327 -0.000000 0.000000 0.000000 0.000000 + 387.8074 631.5412 -138.0865 77 111.2468 376.4063 627.8795 2.5042 2.0682 -0.5124 1.0383 -0.1838 0.1380 8566.0000 958968.0000 147.0000 379 627 -138.3750 372 381 624 632 -138.6250 -137.6250 -627.8795 376.4063 0 0 2012 242178.9422 18664.0391 -30305.0079 8.3605 -148.3722 -0.2334 -0.3445 -0.6474 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.076248 -0.000000 0.000000 0.000000 0.000000 + 528.8928 1493.9165 -138.4372 29 2024.4138 526.8213 1489.4883 1.4719 1.5003 0.1612 1.0093 -0.0095 -0.0387 58708.0000 121440732.0000 2749.0000 526 1489 -138.3750 525 529 1487 1491 -138.6250 -138.1250 -1489.4883 526.8213 0 0 2015 241738.3359 -22022.5599 -23553.9495 7.5979 136.9244 0.3460 0.3212 -0.4982 0.0001 54 7.472325 136.670153 -138.327788 -0.0051 -0.0046 -3.998868 0 0 -4 0.090301 0.089239 0.006961 16.000000 1.000000 + 622.9087 1258.2973 -138.2889 1374 228.5357 622.3010 1255.4260 2.6409 3.9969 0.5790 1.3423 0.2040 0.2871 314008.0000 104262872.0000 997.0000 622 1255 -138.3750 616 628 1245 1266 -140.3750 -135.3750 -1255.4260 622.3010 0 0 2017 241860.7524 -10892.0222 -19141.5044 5.2029 150.3590 0.1740 0.1606 -0.4236 0.0000 153 5.281104 150.899995 -138.158900 1.9961 -0.0050 -2.000571 2 0 -2 0.044849 0.044764 0.006367 8.000000 1.000000 + 711.0809 639.9729 -138.5434 112 247.6964 708.6388 640.4699 1.9130 2.1677 -0.4886 1.0473 0.1501 -0.1435 27742.0000 6998924.0000 342.0000 710 639 -138.3750 705 712 636 644 -139.1250 -138.1250 -640.4699 708.6388 0 0 2018 242179.5721 18300.4648 -15045.6402 5.5872 -129.4252 -0.2502 -0.3219 -0.3207 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.075207 -0.000000 0.000000 0.000000 0.000000 + 734.5192 551.6106 -138.3750 9 112.2222 731.7970 552.0713 1.2568 1.3948 -0.1640 1.0000 0.0000 0.0000 1010.0000 115106.0000 144.0000 732 552 -138.3750 731 733 551 554 -138.3750 -138.3750 -552.0713 731.7970 0 0 2019 242225.2997 22473.4005 -13948.7661 6.2319 -121.8270 -0.3016 -0.3852 -0.2988 0.0000 71 6.238930 -121.467800 -138.275696 -0.9991 -3.0003 -1.001079 -1 -3 -1 0.092231 0.092692 0.001437 11.000000 1.000000 + 730.2054 1418.8546 -138.5796 147 126.7959 731.2912 1413.5608 2.0577 1.9679 0.4511 1.0666 0.1351 0.0246 18639.0000 2435953.0000 215.0000 732 1414 -138.3750 726 735 1410 1417 -139.3750 -137.6250 -1413.5608 731.2912 0 0 2020 241779.9632 -18458.4490 -14060.0136 5.4818 127.2969 0.2754 0.2772 -0.3000 1.0000 -1 -1.000000 0.000000 -0.000000 0.0000 0.0000 0.000000 0 0 0 0.075994 -0.000000 0.000000 0.000000 0.000000 + 736.8482 525.6187 -138.4771 168 161.4881 733.9812 525.9695 2.1199 2.1219 -0.2858 1.0352 -0.0666 0.0884 27130.0000 4857864.0000 334.0000 734 526 -138.3750 730 739 522 530 -139.3750 -138.1250 -525.9695 733.9812 0 0 2021 242238.6805 23700.3901 -13841.6058 6.4642 -120.2860 -0.3128 -0.4046 -0.3091 0.0000 20 6.504838 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40.6250 288 295 857 870 38.8750 44.3750 -864.2902 292.0809 0 0 1900 242056.7502 7539.1725 -34175.7500 8.2270 -167.5598 0.0619 0.1517 -0.7292 0.0001 27 8.137579 -168.000278 41.163708 1.0025 3.0076 -2.997714 1 3 -3 0.030826 0.029429 0.008321 19.000000 1.000000 + 468.2007 1016.2981 39.2965 68 108.7353 462.5732 1014.7944 1.1156 1.6827 0.0315 1.5527 -0.0544 0.3336 7394.0000 806226.0000 129.0000 463 1015 40.6250 462 463 1012 1018 37.3750 41.1250 -1014.7944 462.5732 0 0 1905 241982.6292 513.1833 -26469.4765 6.2437 -178.8893 -0.0171 0.0274 -0.5648 0.0001 76 6.157776 -178.944657 39.166500 -0.9932 1.0002 -3.000360 -1 1 -3 0.002108 0.001976 0.006812 11.000000 1.000000 + 477.0096 406.2304 40.6250 7 432.8571 467.2782 401.4347 1.4310 1.1161 -0.0794 1.0000 0.0000 0.0000 3030.0000 1312804.0000 453.0000 467 402 40.6250 466 469 401 402 40.6250 40.6250 -401.4347 467.2782 0 0 1906 242295.9922 29307.5385 -26118.6710 9.2032 -131.7072 0.3523 0.5122 -0.5551 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 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624123.0000 261.0000 530 1769 40.6250 528 534 1768 1771 40.6250 40.6250 -1769.4742 530.7408 0 0 1910 241593.4684 -35343.7935 -23367.6761 9.9473 123.4708 -0.5614 -0.5312 -0.4962 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.144096 -0.000000 0.000000 0.000000 0.000000 + 607.9294 287.7066 40.6250 14 133.2143 601.2552 283.4483 1.4830 1.3836 0.0200 1.0000 0.0000 0.0000 1865.0000 252727.0000 164.0000 602 283 40.6250 599 603 282 285 40.6250 40.6250 -283.4483 601.2552 0 0 1914 242358.8544 34915.4388 -19951.8955 9.4211 -119.7451 0.4279 0.6050 -0.4224 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.142122 -0.000000 0.000000 0.000000 0.000000 + 670.5757 360.9124 40.4583 6 105.0000 665.5048 358.6683 1.3798 1.1053 -0.2170 1.0069 -0.0591 0.0248 630.0000 66184.0000 109.0000 665 359 40.6250 664 667 358 359 40.3750 40.6250 -358.6683 665.5048 0 0 1915 242322.2292 31467.0002 -16987.2012 8.3945 -118.3620 0.3955 0.5494 -0.3735 0.0003 17 8.418642 -118.972144 40.568311 1.9970 -0.0041 -4.012417 2 0 -4 0.128464 0.128083 0.013416 20.000000 1.000000 + 675.5224 718.8358 40.4394 249 140.0763 673.0727 719.0954 2.3795 2.2677 -0.5614 1.2467 0.1161 0.0734 34879.0000 5204059.0000 281.0000 674 718 40.6250 667 678 715 724 39.1250 41.8750 -719.0954 673.0727 0 0 1917 242138.5360 14574.5843 -16715.5980 5.2331 -138.9143 0.1818 0.2458 -0.3567 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.059941 -0.000000 0.000000 0.000000 0.000000 + 779.2058 1540.1525 40.6396 1884 271.0653 780.6703 1532.7591 5.0046 7.9285 0.7190 1.0330 0.0695 0.1132 510687.0000 235620671.0000 1573.0000 782 1534 40.6250 767 794 1506 1559 40.1250 41.3750 -1532.7591 780.6703 0 0 1920 241718.4367 -24178.1458 -11734.2823 6.3443 115.8885 -0.3727 -0.3834 -0.2487 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.099413 -0.000000 0.000000 0.000000 0.000000 + 923.2463 529.9717 40.5862 207 272.4686 921.7586 532.7746 3.4734 1.9960 -0.2100 1.0207 -0.0736 0.0092 56401.0000 16174909.0000 485.0000 921 534 40.6250 914 929 529 536 40.1250 41.1250 -532.7746 921.7586 0 0 1922 242239.3048 23514.7630 -5043.1746 5.6697 -102.1048 0.3052 0.3942 -0.1039 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.096597 -0.000000 0.000000 0.000000 0.000000 + 980.0956 1317.8002 40.8863 461 192.0000 980.8880 1312.7732 3.4835 1.9951 0.2424 1.0735 -0.0112 0.0697 88512.0000 21076164.0000 569.0000 983 1313 40.6250 969 987 1309 1317 40.1250 42.1250 -1312.7732 980.8880 0 0 1923 241835.6810 -13662.4020 -2275.9893 3.2779 99.4579 -0.1972 -0.2157 -0.0596 0.0001 51 3.245332 101.527322 40.772792 -1.0024 -1.0021 0.993373 -1 -1 1 0.056402 0.055470 0.007354 3.000000 1.000000 + 1028.1076 1988.0514 40.6710 614 440.1352 1027.7093 1971.1720 5.0767 3.2102 -0.1431 1.0147 -0.0028 0.0173 270243.0000 167202713.0000 1774.0000 1032 1972 40.6250 1012 1037 1963 1978 40.3750 41.1250 -1971.1720 1027.7093 0 0 1926 241492.2894 -45291.2007 61.5544 10.6223 89.9221 -0.7029 -0.6791 -0.0002 0.0000 11 10.647860 90.012707 40.665405 -3.9952 -3.9957 0.000214 -4 -4 0 0.184334 0.184773 0.006448 32.000000 1.000000 + 1079.3383 1349.0905 40.6023 191 165.0157 1078.9880 1343.0069 2.5117 1.7523 -0.2593 1.0383 -0.0681 0.0210 31518.0000 5781572.0000 352.0000 1079 1343 40.6250 1073 1084 1340 1346 40.1250 41.1250 -1343.0069 1078.9880 0 0 1928 241821.1379 -15128.6594 2411.5865 3.6249 80.9430 -0.2290 -0.2496 0.0600 0.0000 82 3.742796 79.944067 40.643561 0.0001 -1.9980 -0.000090 0 -2 0 0.062436 0.064275 0.002005 4.000000 1.000000 + 1085.2576 1315.4358 40.7731 141 216.8794 1084.7995 1310.0107 1.9270 1.8396 0.0347 1.0207 -0.0205 -0.0053 30580.0000 8789926.0000 604.0000 1085 1310 40.6250 1081 1088 1306 1314 40.3750 41.1250 -1310.0107 1084.7995 0 0 1929 241838.5081 -13539.6261 2687.3925 3.2668 78.7736 -0.1984 -0.2170 0.0500 0.0000 49 3.245385 80.347612 40.787369 -0.9991 -0.9991 -0.998337 -1 -1 -1 0.055895 0.055811 0.002094 3.000000 1.000000 + 1089.3318 1856.3644 40.4890 71 577.7465 1088.4712 1840.4831 2.3377 1.6261 -0.1621 1.0077 -0.0091 0.0308 41020.0000 24985332.0000 916.0000 1088 1841 40.6250 1084 1093 1838 1843 40.3750 40.6250 -1840.4831 1088.4712 0 0 1930 241560.8409 -39069.4472 2937.3042 9.2128 85.7005 -0.5970 -0.5955 0.0570 0.0000 101 9.203612 86.123227 40.459091 -3.9979 -1.9995 1.998170 -4 -2 2 0.159651 0.159577 0.002830 24.000000 1.000000 + 1094.4672 560.8021 40.4926 93 4042.7849 1092.3220 565.3695 3.2127 1.6600 -0.0839 1.0078 -0.0063 -0.0294 375979.0000 1551458873.0000 5596.0000 1094 564 40.6250 1085 1098 562 568 40.3750 40.6250 -565.3695 1092.3220 0 0 1931 242226.1021 22077.8658 3041.7144 5.2568 -82.1556 0.2961 0.3814 0.0722 0.0000 68 5.313103 -81.486904 40.500418 2.0007 2.0019 -0.001551 2 2 0 0.090762 0.091578 0.002551 8.000000 1.000000 + 1118.5075 190.9448 40.6188 209 1481.5981 1114.6985 193.4801 3.4820 2.0943 0.2039 1.0134 0.0656 -0.0187 309654.0000 564224444.0000 4075.0000 1114 193 40.6250 1106 1123 189 197 40.3750 40.8750 -193.4801 1114.6985 0 0 1932 242416.3672 39536.6096 4137.0231 9.3127 -84.0264 0.4938 0.6810 0.0835 0.0000 28 9.205725 -84.312822 40.573986 1.9998 4.0013 2.004163 2 4 2 0.160944 0.159192 0.004366 24.000000 1.000000 + 1137.3016 471.3923 40.6425 12 485.1667 1134.4737 476.3224 1.4256 1.2971 0.0515 1.0020 -0.0181 -0.0043 5822.0000 2862854.0000 603.0000 1135 476 40.6250 1133 1136 475 478 40.6250 40.8750 -476.3224 1134.4737 0 0 1935 242272.6650 26302.3030 5053.9705 6.3085 -79.1232 0.3412 0.4458 0.1188 0.0001 40 6.245812 -78.037656 40.620908 3.0062 1.0032 1.001447 3 1 1 0.107908 0.106432 0.007126 11.000000 1.000000 + 1140.6723 750.6710 40.6122 429 801.4219 1138.6160 754.6316 3.0590 2.1645 0.2420 1.1046 -0.2773 -0.1127 343810.0000 350378270.0000 2798.0000 1137 754 40.6250 1133 1146 750 759 39.3750 41.6250 -754.6316 1138.6160 0 0 1936 242129.3262 13121.5203 5242.8109 3.3399 -68.2204 0.1751 0.2172 0.1065 0.0000 31 3.249739 -69.096652 40.611608 1.0000 1.0008 1.003808 1 1 1 0.054101 0.052958 0.003891 3.000000 1.000000 + 1142.2406 198.2171 40.7178 8 103.3750 1138.2491 201.1197 1.3873 1.1659 -0.0169 1.0073 0.0499 -0.0095 827.0000 85535.0000 108.0000 1138 201 40.6250 1137 1140 200 202 40.6250 40.8750 -201.1197 1138.2491 0 0 1937 242412.9975 39195.9020 5257.9970 9.2655 -82.3596 0.4904 0.6726 0.1178 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.159580 -0.000000 0.000000 0.000000 0.000000 + 1143.5303 293.0732 40.6487 18 113.7778 1139.8960 296.9521 1.6106 1.1779 -0.0296 1.0093 0.0242 -0.0094 2048.0000 234618.0000 134.0000 1141 297 40.6250 1137 1142 296 298 40.3750 40.8750 -296.9521 1139.8960 0 0 1938 242364.3151 34719.1075 5328.9730 8.2464 -81.2739 0.4407 0.5957 0.1210 0.0000 27 8.174785 -80.701046 40.628762 0.9992 2.9991 3.006596 1 3 3 0.141770 0.140325 0.006705 19.000000 1.000000 + 1145.3036 579.4006 40.3912 309 227.0065 1142.6856 584.3639 2.3759 2.0288 0.1520 1.0589 -0.1058 -0.0801 70145.0000 22392689.0000 787.0000 1143 584 40.6250 1137 1148 580 589 39.3750 41.1250 -584.3639 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40.3750 40.6250 -1942.0152 1249.8050 0 0 1948 241509.3382 -44018.7018 10727.5380 10.6252 76.3038 -0.6820 -0.6600 0.2215 0.0001 17 10.617367 76.808825 40.492360 -3.9928 0.0030 4.001325 -4 0 4 0.179141 0.179388 0.007912 32.000000 1.000000 + 1266.9871 1278.7867 40.7261 712 228.7289 1263.2799 1273.8531 3.3997 3.0237 -0.3193 1.0699 -0.1833 0.1193 162855.0000 51232129.0000 782.0000 1265 1273 40.6250 1254 1270 1266 1281 39.8750 41.6250 -1273.8531 1263.2799 0 0 1950 241860.1127 -11790.5699 11261.0994 3.8566 46.3158 -0.1721 -0.1835 0.2345 0.0001 51 3.742043 46.978751 40.627465 -0.0049 -0.0049 1.996072 0 0 2 0.048639 0.047715 0.007965 4.000000 1.000000 + 1293.4080 628.8737 40.6607 12 949.4167 1288.8017 634.5607 1.3258 1.2990 0.1840 1.0038 0.0053 -0.0018 11393.0000 11120057.0000 1223.0000 1289 634 40.6250 1287 1290 633 636 40.6250 40.8750 -634.5607 1288.8017 0 0 1951 242194.2037 18886.2403 12438.9456 5.3344 -56.6301 0.2443 0.3176 0.2778 0.0001 155 5.305760 -55.228857 40.790700 1.9997 2.0028 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-0.5281 -0.5078 0.4161 0.0000 3 9.175778 60.304449 40.852599 -4.0002 -2.0001 -1.995899 -4 -2 -2 0.139164 0.138521 0.004107 24.000000 1.000000 + 1429.0570 785.8203 40.6250 2 6992.5000 1422.0000 790.5336 1.0000 1.1175 0.0000 1.0000 0.0000 -0.0000 13985.0000 98232853.0000 7463.0000 1422 791 40.6250 1422 1422 790 791 40.6250 40.6250 -790.5336 1422.0000 0 0 1960 242115.7033 11493.2430 18858.2770 5.2118 -31.3604 0.1388 0.1949 0.4134 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.047274 -0.000000 0.000000 0.000000 0.000000 + 1491.8720 414.1905 40.5185 89 123.9101 1483.2092 421.0821 2.4196 2.0626 0.4570 1.0111 0.0244 -0.0170 11028.0000 1395736.0000 169.0000 1483 420 40.6250 1478 1488 417 425 40.3750 40.8750 -421.0821 1483.2092 0 0 1963 242307.4733 29039.7669 21783.5579 8.5206 -53.1254 0.3551 0.5034 0.4731 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.118525 -0.000000 0.000000 0.000000 0.000000 + 1495.4075 1328.7397 40.6519 448 153.6987 1486.5251 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241687.8170 -28059.3608 26884.7527 9.1342 46.2247 -0.4419 -0.4134 0.5883 0.0001 18 9.190815 45.631317 40.509234 -1.9960 -4.0008 2.005914 -2 -4 2 0.114625 0.114179 0.007184 24.000000 1.000000 + 1643.6592 1278.8090 40.4533 218 2001.5780 1631.2965 1274.2551 1.7069 2.6607 -0.0344 1.0861 -0.1093 -0.1270 436344.0000 900514806.0000 3159.0000 1631 1275 40.6250 1628 1634 1269 1280 39.3750 41.3750 -1274.2551 1631.2965 0 0 1967 241865.8794 -11751.5666 29039.9789 7.3801 22.0316 -0.1993 -0.1661 0.6306 0.0000 58 7.423445 22.071612 40.435654 0.0044 0.0025 3.998159 0 0 4 0.048184 0.048549 0.005385 16.000000 1.000000 + 1674.5427 625.2252 40.5017 10 104.6000 1662.3059 631.8011 1.3407 1.3947 0.3475 1.0078 0.0458 0.0521 1046.0000 109526.0000 111.0000 1663 632 40.6250 1661 1664 630 633 40.3750 40.6250 -631.8011 1662.3059 0 0 1969 242201.9236 19098.9693 30428.0683 8.4371 -32.1155 0.2168 0.3409 0.6592 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.078002 -0.000000 0.000000 0.000000 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41.1250 -1700.0593 709.5133 0 0 1989 241630.8025 -32141.5321 -15060.1955 8.3568 115.1058 -0.4952 -0.4836 -0.3346 0.0001 32 8.371187 115.873830 41.023446 -3.9946 -1.9986 0.001540 -4 -2 0 0.131606 0.130992 0.005787 20.000000 1.000000 + 718.3364 1684.8062 40.8876 151 136.0993 719.5642 1676.2799 2.5191 1.7402 0.3484 1.0462 0.0904 0.0479 20551.0000 2928821.0000 230.0000 719 1676 40.8750 713 725 1673 1679 40.3750 41.3750 -1676.2799 719.5642 0 0 1990 241643.2332 -31011.8521 -14591.9046 8.0727 115.1982 -0.4812 -0.4737 -0.3244 0.0000 16 8.156721 115.732857 40.916679 -2.9957 -0.9964 -2.999741 -3 -1 -3 0.127066 0.127811 0.005614 19.000000 1.000000 + 742.0189 644.5242 40.8750 5 109.2000 740.0000 645.3993 1.7342 1.4316 -0.4869 1.0000 0.0000 0.0000 546.0000 59666.0000 112.0000 739 646 40.8750 738 742 644 647 40.8750 40.8750 -645.3993 740.0000 0 0 1992 242177.7100 18088.9467 -13584.8857 5.3366 -126.9067 0.2328 0.3023 -0.2888 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.074369 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5545630.0000 212.0000 1005 1805 40.8750 996 1016 1801 1808 40.3750 41.3750 -1804.7404 1004.1352 0 0 2006 241578.7835 -37308.4658 -1107.0232 8.7830 91.6996 -0.5804 -0.5735 -0.0187 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.152625 -0.000000 0.000000 0.000000 0.000000 + 1086.1189 741.4933 40.8107 737 457.9308 1084.6422 745.2121 2.8925 2.8872 -0.0059 1.0254 0.0100 -0.0167 337495.0000 432779725.0000 4458.0000 1085 746 40.8750 1076 1093 737 754 40.1250 41.6250 -745.2121 1084.6422 0 0 2009 242133.2015 13548.8797 2666.9197 3.2640 -78.8644 0.1841 0.2262 0.0668 0.0000 0 3.255005 -77.088968 40.809400 1.0012 1.0013 -1.001824 1 1 -1 0.055865 0.055344 0.002541 3.000000 1.000000 + 1164.5271 1478.1609 40.8106 1305 1283.0467 1162.8374 1469.0092 3.7084 3.2503 -0.1416 1.0323 -0.0093 0.0220 1674376.0000 6008999150.0000 10199.0000 1162 1470 40.8750 1151 1174 1459 1479 40.3750 41.3750 -1469.0092 1162.8374 0 0 2013 241756.1759 -21211.3472 6446.2337 5.2394 73.0957 -0.3231 -0.3397 0.1323 0.0000 56 5.301351 74.224223 40.819861 0.0015 -1.9963 1.999304 0 -2 2 0.087372 0.088913 0.004053 8.000000 1.000000 + 1186.2392 724.7278 41.1445 212 690.9340 1183.4943 729.1507 1.9972 2.2262 0.1350 1.0471 0.0723 0.1270 146478.0000 118330018.0000 1766.0000 1183 728 40.8750 1179 1187 724 733 40.6250 41.8750 -729.1507 1183.4943 0 0 2014 242143.3452 14350.8094 7390.8000 3.8139 -62.7511 0.1927 0.2380 0.1573 0.0000 29 3.745854 -62.790079 41.088995 -0.0005 2.0004 0.000453 0 2 0 0.059134 0.058102 0.000784 4.000000 1.000000 + 1245.2424 222.7752 40.9913 161 327.2360 1240.0968 226.9962 3.4612 2.1745 0.3467 1.0112 -0.0835 -0.0531 52685.0000 19419353.0000 711.0000 1243 228 40.8750 1233 1247 223 231 40.6250 41.3750 -226.9962 1240.0968 0 0 2015 242401.9686 38047.7846 10122.2848 9.2255 -75.1020 0.4840 0.6598 0.2064 0.0000 106 9.190112 -75.798224 40.998243 4.0016 1.9998 2.002100 4 2 2 0.154931 0.154829 0.002648 24.000000 1.000000 + 1247.6367 844.2201 40.8244 342 194.1170 1244.1335 847.5537 1.8917 1.9316 0.1312 1.1395 -0.0928 -0.0325 66388.0000 15702908.0000 529.0000 1244 848 40.8750 1240 1248 844 851 39.3750 41.8750 -847.5537 1244.1335 0 0 2016 242082.9360 8717.6500 10301.4807 3.1907 -40.2396 0.1153 0.1459 0.2338 0.0000 44 3.250092 -38.479184 40.911786 1.0005 1.0028 1.000082 1 1 1 0.035955 0.035276 0.002845 3.000000 1.000000 + 1249.8384 210.0763 40.9226 155 256.4129 1244.5576 214.2102 5.0388 2.6084 0.4135 1.0068 -0.0685 -0.0546 39744.0000 10491562.0000 360.0000 1250 214 40.8750 1234 1254 209 220 40.6250 41.1250 -214.2102 1244.5576 0 0 2017 242408.5591 38647.6244 10337.8630 9.3714 -75.0245 0.4837 0.6629 0.2266 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.157303 -0.000000 0.000000 0.000000 0.000000 + 1254.2819 1560.5644 40.9049 47 140.0213 1250.8005 1549.1662 1.6877 1.5204 -0.1820 1.0110 0.0040 -0.0056 6581.0000 986657.0000 251.0000 1250 1550 40.8750 1248 1254 1547 1552 40.6250 41.1250 -1549.1662 1250.8005 0 0 2018 241715.2442 -25091.0077 10691.4255 6.4377 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0.2038 -0.3066 0.0002 73 5.290183 129.149545 119.283200 0.0036 2.0025 -1.992050 0 2 -2 0.072707 0.071501 0.009078 8.000000 1.000000 + 729.1280 1945.1942 119.2638 154 145.3831 730.3660 1933.6903 2.8620 1.9080 -0.1433 1.0180 -0.0458 -0.0369 22389.0000 3466321.0000 281.0000 731 1933 119.3750 724 736 1930 1937 118.8750 119.6250 -1933.6903 730.3660 0 0 24539 241509.7073 -43300.2596 -14054.8105 10.6749 107.9829 -0.7584 0.5260 -0.3120 0.0002 139 10.613518 108.761488 119.133800 -3.9977 4.0039 -0.004787 -4 4 0 0.176187 0.174398 0.006589 32.000000 1.000000 + 739.0738 250.9314 119.3148 12 220.1667 734.5874 248.4235 1.3848 1.1817 -0.0452 1.0057 -0.0034 -0.0125 2642.0000 583696.0000 250.0000 735 248 119.3750 733 736 247 249 119.1250 119.3750 -248.4235 734.5874 0 0 24540 242379.7477 36665.0672 -13765.8125 9.1786 -110.5785 0.7187 -0.3265 -0.2896 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.149334 -0.000000 0.000000 0.000000 0.000000 + 840.5093 1458.5018 119.2032 45 123.2000 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0.0854 -0.0498 247024.0000 162513396.0000 2109.0000 1644 1729 144.8750 1636 1652 1719 1737 143.8750 144.8750 -1729.0859 1643.4038 0 1 31570 241626.7094 -33754.1390 29702.0926 10.5410 48.6537 -0.3455 0.6435 0.6459 0.0000 14 10.622529 48.295262 144.682780 -3.9984 3.9994 0.002365 -4 4 0 0.137338 0.137625 0.002918 32.000000 1.000000 + 1661.3041 1283.1148 144.4362 362 225.4475 1648.5292 1278.4981 2.1640 2.4353 0.0197 1.1181 -0.0481 0.1830 81612.0000 25914690.0000 892.0000 1648 1279 144.8750 1643 1653 1274 1285 142.6250 144.8750 -1278.4981 1648.5292 0 1 31571 241863.9393 -11952.9114 29873.2746 7.5777 21.8074 -0.1101 0.2311 0.6342 0.0001 93 7.444978 21.681631 144.566200 -0.0075 0.0012 3.998858 0 0 4 0.048988 0.047872 0.007681 16.000000 1.000000 + 1673.3110 1545.7350 144.8008 32 114.5625 1659.9561 1534.7741 1.8887 1.6192 -0.2967 1.0065 -0.0480 0.0108 3666.0000 423346.0000 146.0000 1659 1535 144.8750 1657 1663 1532 1537 144.6250 144.8750 -1534.7741 1659.9561 0 1 31572 241729.2860 -24346.5431 30467.9684 9.1651 38.6279 -0.2431 0.4606 0.6594 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.099432 -0.000000 0.000000 0.000000 0.000000 + 1678.4114 534.1361 144.8750 14 113.1429 1666.0884 541.2210 1.2757 1.7597 0.2017 1.0000 0.0000 0.0000 1584.0000 180414.0000 136.0000 1666 541 144.8750 1665 1667 539 544 144.8750 144.8750 -541.2210 1666.0884 0 1 31573 242248.7509 23398.5209 30600.9693 9.0353 -37.4027 0.3462 -0.3771 0.6609 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.095390 -0.000000 0.000000 0.000000 0.000000 + 1691.0876 335.7835 144.8286 287 200.0174 1677.9939 343.0234 4.4825 3.8078 0.6300 1.0047 0.0233 0.0316 57405.0000 15188187.0000 650.0000 1677 344 144.8750 1668 1688 334 352 144.6250 144.8750 -343.0234 1677.9939 0 1 31575 242350.7859 32761.5338 31178.1594 10.5706 -46.4186 0.4798 -0.5240 0.6699 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.132888 -0.000000 0.000000 0.000000 0.000000 + 1727.7911 1487.8532 144.7378 199 217.4372 1713.2430 1478.6534 2.1256 2.9161 -0.3103 1.0146 -0.0250 0.0140 43270.0000 12387580.0000 681.0000 1713 1480 144.8750 1709 1717 1472 1485 144.3750 144.8750 -1478.6534 1713.2430 0 1 31576 241759.8401 -21608.8972 33033.2579 9.2731 33.1909 -0.2082 0.4132 0.7141 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.088213 -0.000000 0.000000 0.000000 0.000000 + 1801.0686 736.4480 144.4691 43 1780.3256 1786.0657 741.8289 1.2663 1.6047 0.2377 1.0563 0.0476 0.1074 76554.0000 137175296.0000 2282.0000 1786 742 144.8750 1785 1788 739 744 143.8750 144.8750 -741.8289 1786.0657 0 1 31577 242146.7592 13863.0320 36411.9207 9.1406 -20.8432 0.2331 -0.2107 0.7778 0.0002 24 9.134973 -21.545472 144.339100 -1.9942 1.9949 4.006910 -2 2 4 0.056523 0.058305 0.010363 24.000000 1.000000 + 1814.8187 767.4919 144.8750 6 847.1667 1799.5040 772.5001 1.1180 1.3793 0.1597 1.0000 -0.0000 0.0000 5083.0000 4307799.0000 873.0000 1800 773 144.8750 1799 1800 771 774 144.8750 144.8750 -772.5001 1799.5040 0 1 31578 242131.0312 12399.3126 37064.2300 9.1692 -18.4969 0.2120 -0.1834 0.7995 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.050554 -0.000000 0.000000 0.000000 0.000000 + 1818.1027 1295.9082 144.5075 1179 212.9695 1801.8521 1291.6434 3.4695 6.6374 -0.6495 1.0886 0.0188 -0.0279 251091.0000 69095675.0000 743.0000 1802 1290 144.8750 1794 1810 1274 1307 142.6250 144.8750 -1291.6434 1801.8521 0 1 31579 241859.7761 -12540.0484 37275.5118 9.2359 18.5937 -0.0970 0.2537 0.8049 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.051176 -0.000000 0.000000 0.000000 0.000000 + 1857.6480 1493.0985 144.8299 1248 587.3045 1840.1639 1484.5083 5.1637 6.6774 -0.5770 1.0062 -0.0016 -0.0003 732956.0000 1855773692.0000 8096.0000 1844 1482 144.8750 1823 1854 1469 1513 144.1250 144.8750 -1484.5083 1840.1639 0 1 31580 241759.1390 -21842.6509 39163.0464 10.5080 29.1500 -0.1935 0.4274 0.8425 1.0000 -1 -1.000000 0.000000 0.000000 0.0000 0.0000 0.000000 0 0 0 0.088833 -0.000000 0.000000 0.000000 0.000000 + 1964.4802 1228.7056 143.6605 2106 197.2802 1945.3751 1226.4095 2.5562 5.4327 -0.3284 1.3948 -0.0745 0.3639 415472.0000 102627608.0000 589.0000 1946 1229 144.8750 1939 1951 1214 1241 140.8750 144.8750 -1226.4095 1945.3751 0 1 31583 241896.5254 -9352.7154 44177.3547 10.5741 11.9535 -0.0412 0.2072 0.9475 0.0003 73 10.575295 11.430389 143.790500 -4.0039 -0.0145 3.994851 -4 0 4 0.038008 0.036371 0.015874 32.000000 1.000000 diff --git a/scripts/ImageD11_gui.py b/scripts/ImageD11_gui.py index b14fc6a1..7214f56c 100644 --- a/scripts/ImageD11_gui.py +++ b/scripts/ImageD11_gui.py @@ -47,8 +47,10 @@ # GuiMaker is for building up the windows etc -from ImageD11.guimaker import GuiMaker -from ImageD11 import twodplot, guicommand, guipeaksearch, guiindexer, guisolver, __version__ +from ImageD11.tkGui.guimaker import GuiMaker +from ImageD11.tkGui import twodplot, guipeaksearch, guitransformer, guiindexer +# guisolver +from ImageD11 import __version__, guicommand from ImageD11.license import license # This does not do anything unless you call it as a program: @@ -150,8 +152,6 @@ def start(self): self.peaksearcher = guipeaksearch.guipeaksearcher(self) - # For the transformation menu - from ImageD11 import guitransformer self.transformer = guitransformer.guitransformer(self) @@ -159,7 +159,7 @@ def start(self): # unitcell and g-vectors self.indexer = guiindexer.guiindexer(self) - self.solver = guisolver.guisolver(self) +# self.solver = guisolver.guisolver(self) # Configure the menubar (lists of Tuples of (name, # underline_char, command_or_submenu) ) @@ -169,7 +169,7 @@ def start(self): self.peaksearcher.menuitems, self.transformer.menuitems, self.indexer.menuitems, - self.solver.menuitems, +# self.solver.menuitems, ("Plotting", 0, [("Autoscale", 0, self.autoscaleplot), ("Clear plot", 0, self.clearplot), diff --git a/scripts/bgmaker.py b/scripts/bgmaker.py index 46b3018d..bf7682d1 100755 --- a/scripts/bgmaker.py +++ b/scripts/bgmaker.py @@ -37,6 +37,8 @@ from fabio.openimage import openimage import numpy import random # to do images in random order +import logging +from ImageD11 import ImageD11options class minimum_image(object): """ @@ -126,28 +128,29 @@ def add_file(self, filename): def get_options(parser): """ add the command line options to parser """ - parser.add_option("-n", "--namestem", action = "store", - type = "string", dest = "stem", + parser.add_argument("-n", "--namestem", action = "store", + type = str, dest = "stem", help="Name of the files up the digits part, eg mydata in mydata0000.edf" ) - parser.add_option("-f", "--first", action = "store", type = "int", + parser.add_argument("-f", "--first", action = "store", type = int, dest = "first",default = 0, help = "Number of first file to process, default=0") - parser.add_option("-l", "--last", action = "store", type = "int", + parser.add_argument("-l", "--last", action = "store", type = int, dest = "last",default = 0, help = "Number of last file to process") - parser.add_option("-o", "--outfile", action = "store", type = "string", + parser.add_argument("-o", "--outfile", action = "store", + type = ImageD11options.ImageFileType(mode='w'), dest = "outfile", default = "bkg.edf", help = "Output filename, default=bkg.edf") - parser.add_option("-F", "--Format", action = "store", - type = "string", dest = "format", default = ".edf", + parser.add_argument("-F", "--Format", action = "store", + type = str, dest = "format", default = ".edf", help = "File format [edf|bruker]") - parser.add_option("-s", "--step", action = "store", type = "int", + parser.add_argument("-s", "--step", action = "store", type = int, dest = "step",default = 1, help = "step - every nth image") - parser.add_option("--ndigits", action = "store", type = "int", + parser.add_argument("--ndigits", action = "store", type = int, dest = "ndigits", default = 4, help = "Number of digits in file numbering [4]") - parser.add_option("-k", "--kalman-error", action="store", type = "float", + parser.add_argument("-k", "--kalman-error", action="store", type = float, dest = "kalman_error", default = 0, help = "Error value to use Kalman style filter (read noise)" ) @@ -157,7 +160,8 @@ def check_options( options ): """ Validate the command line options """ for att in [ "stem", "first", "last"]: if getattr( options, att) is None: - raise Exception("You must supply an option for "+att) + logging.error("You must supply an option for %s",att) + raise Exception("Missing "+att) def bgmaker( options ): @@ -226,7 +230,7 @@ def bgmaker( options ): except: print("problem writing") print("trying to write",options.outfile,"in edf format") - im = fabio.edfimage.edfimage( data = minim.minimum_image ) + im = fabio.edfimage.edfimage( data = bko.minimum_image ) try: im.write(options.outfile, force_type = im.data.dtype) except TypeError: @@ -240,10 +244,10 @@ def bgmaker( options ): START = time.time() try: - from optparse import OptionParser - MYPARSER = OptionParser() + from argparse import ArgumentParser + MYPARSER = ArgumentParser() MYPARSER = get_options( MYPARSER ) - OPTS , DUMMY = MYPARSER.parse_args() + OPTS = MYPARSER.parse_args() check_options( OPTS ) bgmaker( OPTS ) diff --git a/scripts/edfheader.py b/scripts/edfheader.py index faa5a9eb..8e93c05a 100644 --- a/scripts/edfheader.py +++ b/scripts/edfheader.py @@ -20,6 +20,8 @@ # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA import gzip, bz2 + + def getheader(filename): """ Reads a header from an edf file in 1024 byte chunks. @@ -38,7 +40,7 @@ def getheader(filename): except IOError: return "" h=h+filename+";\n" - s=fp.read(1024) + s=fp.read(1024).decode() if s.find("{")==-1: raise Exception("Not an edf file") while 1: @@ -47,7 +49,7 @@ def getheader(filename): break else: h=h+s - s=fp.read(1024) + s=fp.read(1024).decode() return h def mne(hd): @@ -73,8 +75,7 @@ def mne(hd): order.append( kk ) return h, order - -if __name__=="__main__": +def main(): keys=[] args = [] for arg in sys.argv[1:]: @@ -98,9 +99,14 @@ def mne(hd): sys.stdout.write(arg+" ") if len(keys)>0: for key in keys: - if hd.has_key(key): + if key in hd: sys.stdout.write(" "+hd[key]+" ") else: for k in order: sys.stdout.write("%s = %s ;\n"%(k, hd[k])) sys.stdout.write("\n") + + +if __name__=="__main__": + main() + diff --git a/scripts/filtergrain.py b/scripts/filtergrain.py index 1e0a8e1b..ffe8cb1c 100644 --- a/scripts/filtergrain.py +++ b/scripts/filtergrain.py @@ -12,8 +12,10 @@ import sys, logging import numpy as np +from argparse import ArgumentParser + +from ImageD11 import refinegrains, indexing, grain, ImageD11options -from ImageD11 import refinegrains, indexing, grain def filtergrain(options): @@ -81,6 +83,23 @@ def filtergrain(options): logging.info("Refined ubi in %s "%( options.newubifile)) +def get_options(parser): + parser=refinegrains.get_options(parser) + parser.add_argument("-N", "--notindexed", action="store", + dest="notindexed", + type=ImageD11options.ColumnFileType(mode='w'), + help="Name of flt file for unindexed peaks") + parser.add_argument("-g","--grain", action="store", + dest = "grain", type=int, default=None, + help = "Which grain to choose") + + parser.description = """ +Filtergrain should choose the peaks from a filtered +peaks output file according to those which are closest +to a particular grain + """ + return parser + if __name__=="__main__": console = logging.StreamHandler(sys.stdout) @@ -94,51 +113,10 @@ def filtergrain(options): root.addHandler(console) root.setLevel(logging.DEBUG) # should we process everything...? - from optparse import OptionParser - - parser = OptionParser() - parser.add_option("-p", "--parfile", action="store", - dest="parfile", type="string", - help="Name of parameter file") - parser.add_option("-u", "--ubifile", action="store", - dest="ubifile", type="string", - help="Name of ubi file") - parser.add_option("-U", "--newubifile", action="store", - dest="newubifile", type="string", - help="Name of new ubi file") - parser.add_option("-f", "--fltfile", action="store", - dest="fltfile", type="string", - help="Name of flt file") - parser.add_option("-F", "--newfltfile", action="store", - dest="newfltfile", type="string", - help="Name of new flt file") - parser.add_option("-N", "--notindexed", action="store", - dest="notindexed", type="string", - help="Name of flt file for unindexed peaks") - parser.add_option("-t", "--tol", action="store", - dest="tol", type="float", - default = None, - help="Tolerance to use in peak assignment") - parser.add_option("-g","--grain", action="store", - dest = "grain", type="string", default=None, - help = "Which grain to choose") - parser.add_option( "--omega_no_float", action="store_false", - dest = "omega_float", - default = True, - help= "Use exact observed omega values") - parser.add_option( "--omega_slop", action="store", type="float", - dest = "omega_slop", - default = 0.5, - help= "Omega slop (step) size") - - parser.description = """ -Filtergrain should choose the peaks from a filtered -peaks output file according to those which are closest -to a particular grain - """ + parser = get_options( ArgumentParser() ) - options, args = parser.parse_args() + options = parser.parse_args() if None in [options.parfile, options.ubifile, diff --git a/scripts/fitgrain.py b/scripts/fitgrain.py index b78d78ea..db68b440 100644 --- a/scripts/fitgrain.py +++ b/scripts/fitgrain.py @@ -8,8 +8,8 @@ all peaks in a dataset (so that hkl assignments are not a problem). """ - -from ImageD11 import refinegrains, indexing +from argparse import ArgumentParser +from ImageD11 import refinegrains, indexing, ImageD11options import logging, sys def fitgrain(options): @@ -45,6 +45,31 @@ def fitgrain(options): # Keep the original ordering and add translation information o.savegrains(options.newubifile, sort_npks=False) +def get_options(parser): + parser = refinegrains.get_options( parser ) + parser.add_argument("-P", "--newparfile", action="store", + dest="newparfile", + type=ImageD11options.ParameterFileType(mode='w'), + help="Name of new parameter file") + parser.add_argument("-v", "--vary", action="append", + dest="varylist", type=str, + default = [ "y_center","z_center", + "tilt_y","tilt_x","tilt_z","wedge", + "t_x","t_y","distance"], + help="Parameters to vary" ) + parser.add_argument("-x", "--fiX", action="append", + dest="fixlist", type=str, default = [], + help="Parameters to fix (overrides vary)") + parser.add_argument("-S", "--steps", action="store", + dest="steps", type=int, + default = 1000, + help="Number of simplex iterations") + + parser.description = """ +Fitgrain should attempt to fit one or more grains to a dataset +using the parameters specified on the command line. + """ + return parser if __name__=="__main__": @@ -60,68 +85,11 @@ def fitgrain(options): root.addHandler(console) root.setLevel(logging.DEBUG) # should we process everything...? - from optparse import OptionParser - - parser = OptionParser() - parser.add_option("-p", "--parfile", action="store", - dest="parfile", type="string", - help="Name of parameter file") - parser.add_option("-u", "--ubifile", action="store", - dest="ubifile", type="string", - help="Name of ubi file") - parser.add_option("-f", "--fltfile", action="store", - dest="fltfile", type="string", - help="Name of flt file") - parser.add_option("-P", "--newparfile", action="store", - dest="newparfile", type="string", - help="Name of new parameter file") - parser.add_option("-U", "--newubifile", action="store", - dest="newubifile", type="string", - help="Name of new ubi file") - parser.add_option("-v", "--vary", action="append", - dest="varylist", type="string", - default = [ "y_center","z_center", - "tilt_y","tilt_x","tilt_z","wedge", - "t_x","t_y","distance"], - help="Parameters to vary" ) - parser.add_option("-x", "--fiX", action="append", - dest="fixlist", type="string", default = [], - help="Parameters to fix (overrides vary)") - parser.add_option("-t", "--tol", action="store", - dest="tol", type="float", - default = 1.0, - help="Tolerance to use in peak assignment") - parser.add_option("-s", "--steps", action="store", - dest="steps", type="int", - default = 1000, - help="Number of simplex iterations") - parser.add_option( "--omega_no_float", action="store_false", - dest = "omega_float", - default = True, - help= "Use exact observed omega values") - - parser.add_option( "--omega_slop", action="store", type="float", - dest = "omega_slop", - default = 0.5, - help= "Omega slop (step) size") - - lattices = ["cubic", "hexagonal", "trigonalH","trigonalP", - "tetragonal", "orthorhombic", "monoclinic_a", - "monoclinic_b","monoclinic_c","triclinic"] - parser.add_option("-l", "--lattice", action="store", - dest="latticesymmetry", type="choice", - default = "triclinic", - choices = lattices, - help="Lattice symmetry for choosing orientation from "+ - "|".join(lattices)) + parser = get_options( ArgumentParser() ) - parser.description = """ -Fitgrain should attempt to fit one or more grains to a dataset -using the parameters specified on the command line. - """ - options, args = parser.parse_args() + options = parser.parse_args() for name in ["parfile" , "newparfile", "ubifile", "newubifile", diff --git a/scripts/fix_spline.py b/scripts/fix_spline.py index 5c51ba88..80e8c6ed 100644 --- a/scripts/fix_spline.py +++ b/scripts/fix_spline.py @@ -23,7 +23,8 @@ from six.moves import input - +import sys, os + """ Script for repairing use of incorrect spline file @@ -45,7 +46,6 @@ def help(): def mymain(): # If we are running from a command line: - import sys, os inname = sys.argv[1] if not os.path.exists(inname) or len(sys.argv) < 4: help() diff --git a/scripts/index_unknown.py b/scripts/index_unknown.py index e19c4141..830d7102 100755 --- a/scripts/index_unknown.py +++ b/scripts/index_unknown.py @@ -20,71 +20,74 @@ # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 0211-1307 USA +import os, sys, logging +from argparse import ArgumentParser +from ImageD11 import ImageD11options +from ImageD11 import fft_index_refac, rc_array,\ + lattice_reduction, indexing +import numpy as np +from ImageD11.fft_index_refac import grid -if __name__=="__main__": - - import os, sys, logging - from optparse import OptionParser - from ImageD11 import fft_index_refac, rc_array,\ - lattice_reduction, indexing - import numpy as np - dot = np.dot - from ImageD11.fft_index_refac import grid - - parser = OptionParser() - parser.add_option('-g', '--gve', +def get_options(parser): + parser.add_argument('-g', '--gve', action = 'store', + type=ImageD11options.GvectorFileType(mode='r'), dest = 'gvefilename', default = None, help = "Filename for g-vectors") - - parser.add_option('-k', '--ngrains', + parser.add_argument('-k', '--ngrains', action = 'store', dest = 'ngrains', - type = 'int', + type = int, default = 1, help = "number of grains to try to find") - - - parser.add_option('-o', '--output', + parser.add_argument('-o', '--output', action = 'store', + type=ImageD11options.UbiFileType(mode='w'), default = 'grains.ubi', dest = 'outfile', help = "Name of ubi file to save grains in") parser = lattice_reduction.get_options(parser) - parser.add_option('--fft', + parser.add_argument('--fft', action = 'store_true', dest = 'use_fft', default = False, help = "Use fft to generate lattice vectors [False]") - parser.add_option('--score_fft', + parser.add_argument('--score_fft', action = 'store_true', dest = 'score_fft', default = False, help = "Score fft peaks using fft peaks first [True]") - parser.add_option('--no_sort', + parser.add_argument('--no_sort', action = 'store_false', dest = 'sort_gve', default = True, help = "Sorting the gvector by length before indexing [True]") - parser.add_option('--noisy', + parser.add_argument('--noisy', action = 'store_true', dest = 'noisy', default = False, help = "Print more output") + fft_index_refac.get_options( parser ) + return parser + + - parser = fft_index_refac.get_options(parser) +if __name__=="__main__": + + logging.basicConfig( level=logging.INFO ) + parser = get_options( ArgumentParser() ) - options , args = parser.parse_args() + options = parser.parse_args() o = indexing.indexer() @@ -114,7 +117,7 @@ # print "Peak remaining",len(cur_gvecs) if options.use_fft: # do fft - g = grid( np = options.np, + g = grid( npx = options.npx, mr = options.mr, nsig = options.nsig) g.gv_to_grid_new(cur_gvecs) @@ -123,7 +126,7 @@ g.peaksearch(open("eu.patterson_pks","w")) g.read_peaks("eu.patterson_pks") vecs = rc_array.rc_array(g.UBIALL.T , direction='col') - assert vecs.shape == (3, g.colfile.nrows) + assert vecs.shape == (3, len(g.UBIALL)) order = np.argsort( g.colfile.sum_intensity )[::-1] vecs = rc_array.rc_array( np.take( vecs, order, axis = 1), direction = 'col') diff --git a/scripts/makemap.py b/scripts/makemap.py index 5b05b9fd..5c0b58ed 100755 --- a/scripts/makemap.py +++ b/scripts/makemap.py @@ -6,7 +6,8 @@ from ImageD11.indexing import readubis, write_ubi_file from ImageD11.refinegrains import refinegrains import ImageD11.refinegrains -import sys, os +from ImageD11 import ImageD11options +import sys, os, argparse def makemap(options): @@ -31,7 +32,7 @@ def makemap(options): o.loadfiltered(options.fltfile) print("got filtered") o.readubis(options.ubifile) - if options.symmetry is not "triclinic": + if options.symmetry != "triclinic": # Grainspotter will have already done this print("transform to uniq") o.makeuniq(options.symmetry) @@ -58,6 +59,16 @@ def makemap(options): col.writefile(options.newfltfile) +def get_options(parser): + parser = ImageD11.refinegrains.get_options(parser) + parser.add_argument("--no_sort", action="store_false", + dest="sort_npks", default = True, + help="Sort grains by number of peaks indexed") + parser.add_argument( "--tthrange", action="append", + dest = "tthrange", type=float, + default = None, + help= "Two theta range for getting median intensity") + return parser if __name__ == "__main__": @@ -74,67 +85,10 @@ def makemap(options): root.addHandler(console) root.setLevel(logging.DEBUG) # should we process everything...? - from optparse import OptionParser - parser = OptionParser() - + parser = get_options( argparse.ArgumentParser() ) - parser.add_option("-p", "--parfile", action="store", - dest="parfile", type="string", - help="Name of parameter file") - parser.add_option("-u", "--ubifile", action="store", - dest="ubifile", type="string", - help="Name of ubi file") - parser.add_option("-U", "--newubifile", action="store", - dest="newubifile", type="string", - help="Name of new ubi file to output") - parser.add_option("-f", "--fltfile", action="store", - dest="fltfile", type="string", - help="Name of flt file") - parser.add_option("-F", "--newfltfile", action="store", - dest="newfltfile", type="string", - help="Name of flt file containing unindexed peaks") - parser.add_option("-t", "--tol", action="store", - dest="tol", type="float", - default = 0.5, - help="Tolerance to use in peak assignment") - parser.add_option("--no_sort", action="store_false", - dest="sort_npks", default = True, - help="Sort grains by number of peaks indexed") - lattices = ["cubic", "hexagonal", "trigonal","rhombohedralP", - "tetragonal", "orthorhombic", "monoclinic_a", - "monoclinic_b","monoclinic_c","triclinic"] - parser.add_option("-s", "--sym", action="store", - dest="symmetry", type="choice", - default = "triclinic", - choices = lattices, - help="Lattice symmetry for choosing orientation") - parser.add_option( "--tthrange", action="append", - dest = "tthrange", type="float", - default = None, - help= "Two theta range for getting median intensity") - - parser.add_option( "--omega_no_float", action="store_false", - dest = "omega_float", - default = True, - help= "Use exact observed omega values") - - parser.add_option( "--omega_slop", action="store", type="float", - dest = "omega_slop", - default = 0.5, - help= "Omega slop (step) size") - - lattices = ["cubic", "hexagonal", "trigonalH","trigonalP", - "tetragonal", "orthorhombic", "monoclinic_a", - "monoclinic_b","monoclinic_c","triclinic"] - parser.add_option("-l", "--lattice", action="store", - dest="latticesymmetry", type="choice", - default = "triclinic", - choices = lattices, - help="Lattice symmetry for choosing orientation from "+ - "|".join(lattices)) - - options, args = parser.parse_args() + options = parser.parse_args() for name in ["parfile" , "ubifile", diff --git a/scripts/merge_flt.py b/scripts/merge_flt.py index baf0db9f..457f827a 100644 --- a/scripts/merge_flt.py +++ b/scripts/merge_flt.py @@ -115,11 +115,11 @@ always_ignore[key] = 1 else: # Replace the stronger peak with the weaker peak - allpeaks[key] = mytransformer.colfile.bigarray[:,i] + allpeaks[key] = mytransformer.colfile.bigarray[:,i].copy() nold = nold + 1 else: nnew = nnew + 1 - allpeaks[key] = mytransformer.colfile.bigarray[:,i] + allpeaks[key] = mytransformer.colfile.bigarray[:,i].copy() print("total peaks",len(list(allpeaks.keys())), "ignored", nignore, "new",nnew, "replacements",nold) assert nignore + nold + nnew == mytransformer.colfile.nrows @@ -136,7 +136,7 @@ bigarray = [allpeaks[k] for k in keys] -c.bigarray = numpy.array(bigarray).T +c.bigarray = numpy.array(bigarray).T.copy() print(c.bigarray.shape) c.nrows = len(keys) c.set_attributes() diff --git a/scripts/peaksearch.py b/scripts/peaksearch.py index f821f547..f4b9afd8 100644 --- a/scripts/peaksearch.py +++ b/scripts/peaksearch.py @@ -36,7 +36,8 @@ Defines one function (peaksearch) which might be reused """ - +import h5py +import hdf5plugin # first! import time # For benchmarking @@ -46,11 +47,11 @@ # If we are running from a command line: myparser = None try: - from optparse import OptionParser + from argparse import ArgumentParser from ImageD11 import peaksearcher - parser = OptionParser() + parser = ArgumentParser() myparser = peaksearcher.get_options(parser) - options , args = myparser.parse_args() + options , args = myparser.parse_known_args() peaksearcher.peaksearch_driver(options, args) except: if myparser is not None: diff --git a/scripts/plotedf.py b/scripts/plotedf.py index 54e93dd9..a6e08db3 100644 --- a/scripts/plotedf.py +++ b/scripts/plotedf.py @@ -120,7 +120,7 @@ def makeImage(self): # import pylab as pl # pl.imshow(self.image) # pl.show() - self.image = self.image # .tostring() + # self.image = self.image # .tostring() self.imageWidth = newshape[1] self.imageHeight = newshape[0] print("Returning") diff --git a/scripts/plotgrainhist.py b/scripts/plotgrainhist.py index 8671db5f..7a66a5ee 100644 --- a/scripts/plotgrainhist.py +++ b/scripts/plotgrainhist.py @@ -23,7 +23,8 @@ -from ImageD11.refinegrains import * +from ImageD11.refinegrains import refinegrains +import numpy as np import sys @@ -34,8 +35,10 @@ tol = float(sys.argv[4]) if len(sys.argv)>5: nbins = int(sys.argv[5]) + else: + nbins = 30 except: - print("Usage: %s flt par ubi tol [nbins=30]"%(sys.argv[0])) + print("Usage: %s flt par ubi tol [nbins=30] [omega_slop]"%(sys.argv[0])) sys.exit() if len(sys.argv)>6: @@ -50,34 +53,39 @@ o.tolerance=tol o.generate_grains() o.assignlabels() -from matplotlib.pylab import * -d = o.scandata[flt] -bins = array(arange(0.0,tol*11./10,tol/nbins)) -ng = int(maximum.reduce(d.labels))+1 -drl = [ compress(d.labels==i, d.drlv2) for i in range(ng)] -dp5 = [sqrt(di) for di in drl] -hl = [ hist(dpi, bins)[0] for dpi in dp5] -print("hl0:",hl[0].shape, bins.shape) -if bins.shape[0] != hl[0].shape[0]: - bins = (bins[1:] + bins[:-1])/2 -cla() - -for i in range(ng): - plot(bins,hl[i],label=str(i)) - +import matplotlib.pylab as pl +# indexed peaks only +d = o.scandata[flt] +d.filter(d.labels >= 0) + +drlv_bins = np.linspace( 0, tol, nbins ) +ng = int(d.labels.max())+1 +drlv = np.sqrt( d.drlv2 ) +dp5 = [ drlv[d.labels==i] for i in range(ng)] +hl = [ np.histogram(dpi, drlv_bins)[0] for dpi in dp5 ] +print("hl0:",hl[0].shape, drlv_bins.shape) +if drlv_bins.shape[0] != hl[0].shape[0]: + plotbins = (drlv_bins[1:] + drlv_bins[:-1])/2 +pl.subplot(211) +for i in range(ng): + pl.plot(plotbins,hl[i],label=str(i)) -print(" "*10, end=' ') -for j in range(ng): - print("%5d"%(j), end=' ') -print() -for i in range(len(bins)): - print("%10.6f"%(bins[i]), end=' ') +if 1: + print(" "*10, end=' ') for j in range(ng): - print("%5d"%(hl[j][i]), end=' ') + print("%5d"%(j), end=' ') print() - - -show() + for i in range(len(drlv_bins)-1): + print("%10.6f"%(drlv_bins[i]), end=' ') + for j in range(ng): + print("%5d"%(hl[j][i]), end=' ') + print() +pl.subplot(212) +pl.hist2d( drlv, d.labels, (drlv_bins, np.arange(-0.5,ng,1.)),vmin=0.5) +pl.colorbar() +pl.ylabel("Grain") +pl.xlabel("drlv") +pl.show() diff --git a/scripts/powderimagetopeaks.py b/scripts/powderimagetopeaks.py index 2986d827..93ea29dc 100644 --- a/scripts/powderimagetopeaks.py +++ b/scripts/powderimagetopeaks.py @@ -71,11 +71,7 @@ def usage(): d1 = inputdata.data*m1 d2 = inputdata.data*m2 - inputdata.data = d1 + inputdata.data = d1.astype(inputdata.data.dtype) inputdata.write( sys.argv[2] ) - inputdata.data = d2 + inputdata.data = d2.astype(inputdata.data.dtype) inputdata.write( sys.argv[3] ) - - - - diff --git a/scripts/refine_em.py b/scripts/refine_em.py index b95b96dd..1bc3404a 100644 --- a/scripts/refine_em.py +++ b/scripts/refine_em.py @@ -1,32 +1,54 @@ -#!/usr/bin/env python -from __future__ import print_function - -from ImageD11.columnfile import columnfile -from ImageD11.grain import read_grain_file, write_grain_file -import sys, os, multiprocessing - -try: - c = columnfile( sys.argv[1] ) - g = read_grain_file( sys.argv[2] ) - parfile = sys.argv[3] - cmds = [] -except: - print( "Usage: %s colfile.flt.new grains.map parameters.par --omega_slop=1 etc"%(sys.argv[0])) - sys.exit() - - -for i in range(len(g)): - #g[i].translation[2] = 0.0 - write_grain_file("%d.ubi"%(i),[g[i]]) - d = c.copy() - d.filter( d.labels == i ) - d.writefile("%d.flt"%(i)) - cmd = "fitgrain.py -p %s -u %d.ubi -U %d.ubi -P %d.par -f %d.flt -x t_z"%( - parfile,i,i,i,i) - for extra_arg in sys.argv[4:]: - cmd += " "+extra_arg - cmds.append( cmd ) - -p = multiprocessing.Pool( multiprocessing.cpu_count() ) -p.map( os.system, cmds ) - +#!/usr/bin/env python +from __future__ import print_function + +from ImageD11.columnfile import columnfile +from ImageD11.grain import read_grain_file, write_grain_file +import sys, os, multiprocessing, platform + +def setup(): + try: + c = columnfile( sys.argv[1] ) + g = read_grain_file( sys.argv[2] ) + parfile = sys.argv[3] + cmds = [] + except: + print( "Usage: %s colfile.flt.new grains.map parameters.par --omega_slop=1 etc"%(sys.argv[0])) + sys.exit() + + if platform.system() != "Windows": + fmt = "%s %s" + else: + fmt = '%s "%s"' + + cmd0 = fmt%( sys.executable, + os.path.join( os.path.split(__file__)[0], + "fitgrain.py" ) ) + + for i in range(len(g)): + #g[i].translation[2] = 0.0 + write_grain_file("%d.ubi"%(i),[g[i]]) + d = c.copy() + d.filter( d.labels == i ) + d.writefile("%d.flt"%(i)) + cmd = cmd0 + " -p %s -u %d.ubi -U %d.ubi -P %d.par -f %d.flt -x t_z"%( + parfile,i,i,i,i) + for extra_arg in sys.argv[4:]: + cmd += " "+extra_arg + cmds.append( cmd ) + return cmds + +if __name__=="__main__": + cmds = setup() + + if 'SLURM_CPUS_PER_TASK' in os.environ: + njobs = int(os.environ['SLURM_CPUS_PER_TASK']) + else: + njobs = multiprocessing.cpu_count() + p = multiprocessing.Pool( njobs ) + p.map( os.system, cmds ) + sys.exit() + for c in cmds: + print(c) + if os.system(c) != 0: + break + diff --git a/scripts/rubber.py b/scripts/rubber.py index 1455913f..7f5a0227 100644 --- a/scripts/rubber.py +++ b/scripts/rubber.py @@ -106,7 +106,7 @@ def __init__(self, datafile=None, bkgfile=None, master=None): if type(datafile)==type("string"): self.datafile = datafile dataobj=openimage(datafile) - self.data=dataobj.data.astype(np.int) + self.data=dataobj.data.astype(int) try: self.omega=float(dataobj.header["Omega"]) except: @@ -119,8 +119,8 @@ def __init__(self, datafile=None, bkgfile=None, master=None): if type(bkgfile)==type("string"): self.bkgfile=bkgfile bkgobj=openimage(bkgfile) - self.bkg=bkgobj.data.astype(np.int) - self.data=self.data.astype(np.int)-self.bkg + self.bkg=bkgobj.data.astype(int) + self.data=self.data.astype(int)-self.bkg print("Got your background from",bkgfile,self.bkg.shape) else: self.bkg = None @@ -203,7 +203,7 @@ def __init__(self, datafile=None, bkgfile=None, master=None): while x <= self.maxi: l.append(x) x+=step - self.legend=np.transpose(np.array([l],np.float)) + self.legend=np.transpose(np.array([l],float)) self.legendimage,(scalesx,scalesy)=NumerictoImage(self.legend,(self.scale[0],10)) self.legendimage=ImageTk.PhotoImage(self.legendimage) self.key.configure(image=self.legendimage) @@ -248,8 +248,8 @@ def rdbkg(self, bkgfile=None): bkgfile=filedialog.askopenfilename(initialdir=os.getcwd()) self.bkgfile=bkgfile bkgobj=openimage(bkgfile) - self.bkg=bkgobj.data.astype(np.int) - self.data=self.data.astype(np.int)-self.bkg + self.bkg=bkgobj.data.astype(int) + self.data=self.data.astype(int)-self.bkg print("Got your background from",bkgfile,self.bkg.shape) def jump(self): @@ -277,10 +277,10 @@ def prev(self): def readdata(self): dataobj=openimage(self.datafile) self.status.config(text=self.datafile) - self.data=dataobj.data.astype(np.int) + self.data=dataobj.data.astype(int) if self.bkg is not None: try: - self.data=self.data.astype(np.int)-self.bkg + self.data=self.data.astype(int)-self.bkg except: print("Failed to subtract bkg",self.bkg.shape,self.data.shape) try: @@ -319,7 +319,7 @@ def setmin(self): def getstats(self): # Convert to float to avoid overflow issues - t=self.data.astype(np.float) + t=self.data.astype(float) sumi=np.sum(np.ravel(t)) sumisq=np.sum(np.ravel(t*t)) npixels=t.shape[0]*t.shape[1] diff --git a/scripts/spatialfix.py b/scripts/spatialfix.py index 462225f9..1e836d3a 100644 --- a/scripts/spatialfix.py +++ b/scripts/spatialfix.py @@ -1,7 +1,7 @@ #!/usr/bin/env python from __future__ import print_function - +from six.moves import input """ Applies a new spatial correction based on spd style distortion files """ @@ -10,6 +10,21 @@ import sys, os import numpy as np +def spatialfix( cf, dx, dy, flip=""): + if "V" in flip: + cf.s_raw = 2048 - cf.s_raw + print("did a vertical flip") + if "H" in flip: + cf.f_raw = 2048 - cf.f_raw + print("did a horizontal flip") + # Closest pixel indices + si = (cf.s_raw+0.5).astype(int).clip(0,2047) + fi = (cf.f_raw+0.5).astype(int).clip(0,2047) + # Add shifts + cf.sc[:] = cf.s_raw + dy[ si, fi ] + cf.fc[:] = cf.f_raw + dx[ si, fi ] + return cf + try: dx = fabio.open(sys.argv[1]).data dy = fabio.open(sys.argv[2]).data @@ -17,28 +32,18 @@ except: print("Usage dxfile dyfile oldcolfile newcolfile") raise + if os.path.exists( sys.argv[4] ): print("About to overwrite",sys.argv[4], end=' ') - if input("OK? ")[0] not in 'yY': + response = input("OK? ") + if response[0] not in "yY": print("So I will exit then") sys.exit() - if len(sys.argv)>5: - if "V" in sys.argv[5]: - cf.s_raw = 2048 - cf.s_raw - print("did a vertical flip") - if "H" in sys.argv[5]: - cf.f_raw = 2048 - cf.f_raw - print("did a horizontal flip") - -# Closest pixel indices -si = (cf.s_raw+0.5).astype(np.int).clip(0,2047) -fi = (cf.f_raw+0.5).astype(np.int).clip(0,2047) - -# Add shifts -cf.sc[:] = cf.s_raw + dy[ si, fi ] -cf.fc[:] = cf.f_raw + dx[ si, fi ] + flip = sys.argv[5] + +cf = spatialfix( cf, dx, dy ) cf.writefile( sys.argv[4] ) diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..685039fc --- /dev/null +++ b/setup.cfg @@ -0,0 +1,2 @@ +[tool:pytest] +norecursedirs = ImageD11/depreciated .ipynb_checkpoints diff --git a/setup.py b/setup.py index 970e066d..bc3ea863 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,6 @@ +from __future__ import print_function + # ImageD11_v1.0 Software for beamline ID11 # Copyright (C) 2005-2018 Jon Wright # @@ -18,113 +20,206 @@ """ Setup script -""" +You should run src/make_pyf.py to update the pyf wrapper and docs +""" +import sys +from io import open # this misery may never end. +# For pip / bdist_wheel etc +import os, platform, os.path +from setuptools import setup, Extension +from setuptools.command import build_ext +# +import numpy, numpy.f2py # force wrapper re-generation +if not hasattr( numpy.f2py, 'get_include'): + numpy.f2py.get_include = lambda : os.path.join( + os.path.dirname(os.path.abspath(numpy.f2py.__file__)), + 'src') +############################################################################ +def get_version(): + with open("ImageD11/__init__.py","r") as f: + for line in f.readlines(): + if line.find("__version__")>-1: + return eval(line.split("=")[1].strip()) -import setuptools -import sys -from numpy.distutils.core import setup, Extension -from numpy import get_include - - -if sys.platform == "win32" and "--compiler=mingw32" not in sys.argv: - ecomparg = ["/openmp","-DF2PY_REPORT_ON_ARRAY_COPY"] - elinkarg = ["/openmp","-DF2PY_REPORT_ON_ARRAY_COPY"] - elibs = None -else: - ecomparg = ["-fopenmp -O2","-DF2PY_REPORT_ON_ARRAY_COPY"] - elinkarg = ["-fopenmp -O2","-DF2PY_REPORT_ON_ARRAY_COPY"] - elibs = ["gomp","pthread"] - - -nid = [get_include(),] - -# Compiled extension: -cImageD11extension = Extension( "cImageD11", - sources = [ "src/cImageD11.pyf", - "src/connectedpixels.c", - "src/closest.c", - "src/cdiffraction.c", - "src/blobs.c"], - include_dirs = nid + ["src",], - extra_compile_args=ecomparg, - extra_link_args=elinkarg, - libraries = elibs - ) - - -# Removed list of dependencies from setup file -# Do a miniconda (or something) instead... -#if sys.platform == 'win32': -# needed = [ -# 'six', -# 'numpy>=1.0.0', -# 'scipy', -# 'xfab>=0.0.2', -# 'pycifrw' -# 'fabio>=0.0.5', -# 'matplotlib>=0.90.0', -# ... -# ] - -needed =[]# -# ["xfab", -# "fabio", -# "pyopengl", -# "matplotlib", -# "numpy", -# "scipy", -# "six", -# "h5py", -# ] +print("Building version |%s|"%get_version(), "on system:", platform.system()) +############################################################################ -# See the distutils docs... -setup(name='ImageD11', - version='1.9.0-beta', - author='Jon Wright', - author_email='wright@esrf.fr', - description='ImageD11', - license = "GPL", - ext_package = "ImageD11", # Puts extensions in the ImageD11 directory - ext_modules = [cImageD11extension,], - install_requires = needed, - packages = ["ImageD11"], - package_dir = {"ImageD11":"ImageD11"}, - url = "http://github.com/jonwright/ImageD11", -# download_url = ["http://sourceforge.net/project/showfiles.php?group_id=82044&package_id=147869"], - package_data = {"ImageD11" : ["doc/*.html", "data/*" ]}, - scripts = ["ImageD11/rsv_mapper.py", - "scripts/peaksearch.py", +############################################################################# +# Set the openmp flag if needed. Also CFLAGS and LDSHARED from sys.argv ? +# +# JW https://stackoverflow.com/questions/724664/python-distutils-how-to-get-a-compiler-that-is-going-to-be-used + + +copt = { + 'msvc': ['/openmp', '/O2'] , + 'unix': ['-fopenmp', '-O2', ], # '-DF2PY_REPORT_ON_ARRAY_COPY=100', '-DNPY_DISABLE_OPTIMIZATION=1' ] , + 'mingw32': ['-fopenmp', '-O2'] , + } + +lopt = { k : [a for a in l] for k,l in copt.items() } +lopt['msvc'] = [] +if platform.system() == "Darwin": + copt['unix'].remove("-fopenmp") + lopt['unix'].remove("-fopenmp") + + +# might try: +# set CFLAGS=/arch:AVX2 for msvc +# CFLAGS=-march=native -mtune=native +# LDFLAGS=-march=native -mtune=native + + +class build_ext_subclass( build_ext.build_ext ): + def build_extension(self, ext): + print('Building _cImageD11 module') + c = self.compiler.compiler_type + CF = [] ; LF=[] + if "CFLAGS" in os.environ: + CF = os.environ.get("CFLAGS").split(" ") + if "LDFLAGS" in os.environ: + LF = os.environ.get("LDFLAGS").split(" ") + for e in self.extensions: + if c in copt: + e.extra_compile_args = copt[ c ] + CF + e.extra_link_args = lopt[ c ] + LF + print("Customised compiler",c,e.extra_compile_args, + e.extra_link_args) + if ext.sources[0].endswith('.pyf'): + name = ext.sources[0] + # generate wrappers + print('Creating f2py wrapper for', name) + numpy.f2py.run_main( [ + #'--quiet', + name,]) + ext.sources[0] = os.path.split(name)[-1].replace('.pyf', 'module.c') + ext.sources.append( os.path.join(numpy.f2py.get_include(), 'fortranobject.c' ) ) + build_ext.build_ext.build_extension(self, ext) + +# note that the pyf must come first +cnames = "_cImageD11.pyf blobs.c cdiffraction.c cimaged11utils.c"+\ +" closest.c connectedpixels.c darkflat.c localmaxlabel.c sparse_image.c "+\ +" splat.c" + +csources = [os.path.join('src',c) for c in cnames.split()] + +extension = Extension( "_cImageD11", csources, + include_dirs = [ 'src',numpy.get_include(), numpy.f2py.get_include() ]) + +################################################################################ + +# Try to further reduce this long list +scripts = ["ImageD11/rsv_mapper.py", + "ImageD11/tkGui/plot3d.py", + "scripts/peaksearch.py", "scripts/fitgrain.py", - "scripts/tomapper.py", "scripts/ubi2cellpars.py", "scripts/filtergrain.py", - "scripts/filterout.py", - "ImageD11/plot3d.py", - "scripts/pars_2_sweeper.py", "scripts/ImageD11_2_shelx.py", - "scripts/fit2dcake.py", "scripts/fix_spline.py", "scripts/edfheader.py", - "ImageD11/plot3d.py", - "scripts/huber2bruker.py", - "scripts/id11_summarize.py", "scripts/ImageD11_gui.py", "scripts/bgmaker.py", "scripts/merge_flt.py", "scripts/makemap.py", "scripts/plotlayer.py", - "scripts/plotedf.py", "scripts/plotgrainhist.py", - "scripts/rubber.py", "scripts/plotImageD11map.py", "scripts/cutgrains.py", "scripts/index_unknown.py", "scripts/spatialfix.py", "scripts/refine_em.py", "scripts/avg_par.py", - "scripts/powderimagetopeaks.py"]) + "scripts/powderimagetopeaks.py"] + +################################################################################ +# Things we depend on. This generally borks things if pip +# tries to do source installs of all of these. + +# sfood -I ImageD11/depreciated -I build/ -v -u >sfood.out 2>sfood.err + +minimal = [ # can't compile without this + "six", + "numpy", + "setuptools", + ] + +useful = [ # stuff you probably want, and should be able to get easily + 'fabio==0.2.2 ; python_version < "3" and sys_platform == "win32" ', + 'fabio ; python_version >= "3" or sys_platform != "win32" ', + "xfab>=0.0.4", # + # comes from xfab : "PyCifRW", + "matplotlib", # tkGui + "pyopengltk", # plot3d in tkGui + "scipy", # + 'hdf5plugin==1.4.1 ; python_version < "3" and sys_platform == "win32" ', + 'hdf5plugin ; python_version >= "3" ', + 'h5py', + 'pyyaml', + "pytest", # for the CI + 'numba==0.46.0 ; python_version < "3" ', # for some test cases + 'llvmlite==0.30.0 ; python_version < "3" ', # for some test cases + 'numba ; python_version >= "3" ', # for some test cases + "bslz4_to_sparse", + "fast_histogram", + "scikit-image", + "tqdm", +] + + +more = [ + # Used in sandbox / test / not completely essential, but should work for CI + "pillow", # in sandbox + "lmfit", # in sandbox + "PyMca5", # in sandbox + "sympy", # for maths + 'ipywidgets', # for notebook nbGui + 'pyopencl', # (was?) in sandbox + 'pyFAI ; python_version >= "3" ', # pypi problematic + 'pyFAI <= 0.18.0 ; python_version < "3" ', + 'silx[full] ; python_version >= "3" ', # for silxGui +] + +rare = [ # + "FitAllB", # not for python3 + "minuit", # for fitallb + "PyTango", # sandbox + ] + + # read the contents of your README file +this_directory = os.path.abspath(os.path.dirname(__file__)) +with open(os.path.join(this_directory, 'README.md'), encoding='utf-8') as f: + readme = f.read() + + +# See the distutils docs... +setup(name='ImageD11', + version=get_version(), + author='Jon Wright', + author_email='wright@esrf.fr', + cmdclass={ 'build_ext' : build_ext_subclass }, + description='ImageD11', + license = "GPL", +# python_requires='<3.12', # Numba still not working for 3.12 + ext_package = "ImageD11", # Puts extensions in the ImageD11 directory + ext_modules = [extension,], + setup_requires = minimal, # to compile + install_requires = minimal + useful, + extras_require = { 'full' : more, 'rare' : rare }, + packages = ["ImageD11", + "ImageD11.tkGui", + "ImageD11.silxGui", + "ImageD11.nbGui", + "ImageD11.sinograms", + ], + package_dir = {"ImageD11":"ImageD11"}, + url = "http://github.com/jonwright/ImageD11", + package_data = {"ImageD11" : ["doc/*.html", "data/*", "sandbox/*.py" ]}, + scripts = scripts, + long_description = readme, + long_description_content_type='text/markdown', +) diff --git a/src/.clang-format b/src/.clang-format new file mode 100644 index 00000000..a1ad6616 --- /dev/null +++ b/src/.clang-format @@ -0,0 +1,111 @@ +--- +Language: Cpp +# BasedOnStyle: LLVM +AccessModifierOffset: -2 +AlignAfterOpenBracket: Align +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlines: Right +AlignOperands: true +AlignTrailingComments: true +AllowAllParametersOfDeclarationOnNextLine: true +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: All +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterDefinitionReturnType: None +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: false +AlwaysBreakTemplateDeclarations: false +BinPackArguments: true +BinPackParameters: true +BraceWrapping: + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false + SplitEmptyFunction: true + SplitEmptyRecord: true + SplitEmptyNamespace: true +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Attach +BreakBeforeInheritanceComma: false +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakConstructorInitializers: BeforeColon +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: true +ColumnLimit: 80 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: false +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +ForEachMacros: + - foreach + - Q_FOREACH + - BOOST_FOREACH +IncludeBlocks: Preserve +IncludeCategories: + - Regex: '^"(llvm|llvm-c|clang|clang-c)/' + Priority: 2 + - Regex: '^(<|"(gtest|gmock|isl|json)/)' + Priority: 3 + - Regex: '.*' + Priority: 1 +IncludeIsMainRegex: '(Test)?$' +IndentCaseLabels: false +IndentPPDirectives: None +IndentWidth: 4 +IndentWrappedFunctionNames: false +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: true +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBlockIndentWidth: 2 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: true +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 19 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 60 +PointerAlignment: Right +ReflowComments: true +SortIncludes: true +SortUsingDeclarations: true +SpaceAfterCStyleCast: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeParens: ControlStatements +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 8 +UseTab: Never +... + diff --git a/src/.gitignore b/src/.gitignore new file mode 100644 index 00000000..791f1fdb --- /dev/null +++ b/src/.gitignore @@ -0,0 +1,9 @@ +cImageD11.pyf +Linux_??bit_?.?_* +Windows_??bit_?.?_* +cImageD11_????module.c +*.a +*.o +*.lib +cImageD11_avx2.pyf +cImageD11_sse2.pyf diff --git a/src/.indent.pro b/src/.indent.pro deleted file mode 100644 index 6f4ba5d8..00000000 --- a/src/.indent.pro +++ /dev/null @@ -1,4 +0,0 @@ --nbad -bap -nbc -bbo -hnl -br -brs -c33 -cd33 -ncdb -ce -ci4 --cli0 -d0 -di1 -nfc1 -i4 -ip0 -l80 -lp -npcs -nprs -npsl -sai --saf -saw -ncs -nsc -sob -nfca -cp33 -ss -ts8 -il1 - diff --git a/src/ImageD11_cmath.h b/src/ImageD11_cmath.h index dc334da8..64a4b24e 100644 --- a/src/ImageD11_cmath.h +++ b/src/ImageD11_cmath.h @@ -8,44 +8,38 @@ #define M_PI 3.14159265358979323846 #endif -#define RAD M_PI/180.0 -#define DEG 180.0/M_PI - - -#define vec3sub(a,b,c) \ - (c)[0] = (a)[0] - (b)[0]; \ - (c)[1] = (a)[1] - (b)[1]; \ - (c)[2] = (a)[2] - (b)[2]; - -#define crossProduct(a,b,c) \ - (c)[0] = (a)[1] * (b)[2] - (b)[1] * (a)[2]; \ - (c)[1] = (a)[2] * (b)[0] - (b)[2] * (a)[0]; \ - (c)[2] = (a)[0] * (b)[1] - (b)[0] * (a)[1]; - - - -#define matvec(a,b,c) \ - (c)[0] = (a)[0]*(b)[0] + (a)[1]*(b)[1] + (a)[2]*(b)[2]; \ - (c)[1] = (a)[3]*(b)[0] + (a)[4]*(b)[1] + (a)[5]*(b)[2]; \ - (c)[2] = (a)[6]*(b)[0] + (a)[7]*(b)[1] + (a)[8]*(b)[2]; - - -#define matTvec(a,b,c) \ - (c)[0] = (a)[0]*(b)[0] + (a)[3]*(b)[1] + (a)[6]*(b)[2]; \ - (c)[1] = (a)[1]*(b)[0] + (a)[4]*(b)[1] + (a)[7]*(b)[2]; \ - (c)[2] = (a)[2]*(b)[0] + (a)[5]*(b)[1] + (a)[8]*(b)[2]; - - -#define matmat(a,b,c) \ - (c)[0] = (a)[0]*(b)[0] + (a)[3]*(b)[1] + (a)[6]*(b)[2]; \ - (c)[1] = (a)[1]*(b)[0] + (a)[4]*(b)[1] + (a)[7]*(b)[2]; \ - (c)[2] = (a)[2]*(b)[0] + (a)[5]*(b)[1] + (a)[8]*(b)[2]; \ - (c)[3] = (a)[0]*(b)[3] + (a)[3]*(b)[4] + (a)[6]*(b)[5]; \ - (c)[4] = (a)[1]*(b)[3] + (a)[4]*(b)[4] + (a)[7]*(b)[5]; \ - (c)[5] = (a)[2]*(b)[3] + (a)[5]*(b)[4] + (a)[8]*(b)[5]; \ - (c)[6] = (a)[0]*(b)[6] + (a)[3]*(b)[7] + (a)[6]*(b)[8]; \ - (c)[7] = (a)[1]*(b)[6] + (a)[4]*(b)[7] + (a)[7]*(b)[8]; \ - (c)[8] = (a)[2]*(b)[6] + (a)[5]*(b)[7] + (a)[8]*(b)[8]; +#define RAD M_PI / 180.0 +#define DEG 180.0 / M_PI + +#define vec3sub(a, b, c) \ + (c)[0] = (a)[0] - (b)[0]; \ + (c)[1] = (a)[1] - (b)[1]; \ + (c)[2] = (a)[2] - (b)[2]; + +#define crossProduct(a, b, c) \ + (c)[0] = (a)[1] * (b)[2] - (b)[1] * (a)[2]; \ + (c)[1] = (a)[2] * (b)[0] - (b)[2] * (a)[0]; \ + (c)[2] = (a)[0] * (b)[1] - (b)[0] * (a)[1]; + +#define matvec(a, b, c) \ + (c)[0] = (a)[0] * (b)[0] + (a)[1] * (b)[1] + (a)[2] * (b)[2]; \ + (c)[1] = (a)[3] * (b)[0] + (a)[4] * (b)[1] + (a)[5] * (b)[2]; \ + (c)[2] = (a)[6] * (b)[0] + (a)[7] * (b)[1] + (a)[8] * (b)[2]; + +#define matTvec(a, b, c) \ + (c)[0] = (a)[0] * (b)[0] + (a)[3] * (b)[1] + (a)[6] * (b)[2]; \ + (c)[1] = (a)[1] * (b)[0] + (a)[4] * (b)[1] + (a)[7] * (b)[2]; \ + (c)[2] = (a)[2] * (b)[0] + (a)[5] * (b)[1] + (a)[8] * (b)[2]; + +#define matmat(a, b, c) \ + (c)[0] = (a)[0] * (b)[0] + (a)[3] * (b)[1] + (a)[6] * (b)[2]; \ + (c)[1] = (a)[1] * (b)[0] + (a)[4] * (b)[1] + (a)[7] * (b)[2]; \ + (c)[2] = (a)[2] * (b)[0] + (a)[5] * (b)[1] + (a)[8] * (b)[2]; \ + (c)[3] = (a)[0] * (b)[3] + (a)[3] * (b)[4] + (a)[6] * (b)[5]; \ + (c)[4] = (a)[1] * (b)[3] + (a)[4] * (b)[4] + (a)[7] * (b)[5]; \ + (c)[5] = (a)[2] * (b)[3] + (a)[5] * (b)[4] + (a)[8] * (b)[5]; \ + (c)[6] = (a)[0] * (b)[6] + (a)[3] * (b)[7] + (a)[6] * (b)[8]; \ + (c)[7] = (a)[1] * (b)[6] + (a)[4] * (b)[7] + (a)[7] * (b)[8]; \ + (c)[8] = (a)[2] * (b)[6] + (a)[5] * (b)[7] + (a)[8] * (b)[8]; #endif /* IMAGED11_CMATH_H */ - diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/_cImageD11.pyf b/src/_cImageD11.pyf new file mode 100644 index 00000000..ce2f18b0 --- /dev/null +++ b/src/_cImageD11.pyf @@ -0,0 +1,970 @@ + +python module _cImageD11 + +usercode ''' +#include "blobs.h" +''' +interface + +usercode ''' +/* This runs inside the init part of the module as it is after interface */ +#define str(x) (#x) +PyDict_SetItemString(d,str(s_1) ,PyInt_FromLong(s_1)); +PyDict_SetItemString(d,str(s_I) ,PyInt_FromLong(s_I)); +PyDict_SetItemString(d,str(s_I2) ,PyInt_FromLong(s_I2)); +PyDict_SetItemString(d,str(s_fI) ,PyInt_FromLong(s_fI)); +PyDict_SetItemString(d,str(s_ffI) ,PyInt_FromLong(s_ffI)); +PyDict_SetItemString(d,str(s_sI) ,PyInt_FromLong(s_sI)); +PyDict_SetItemString(d,str(s_ssI) ,PyInt_FromLong(s_ssI)); +PyDict_SetItemString(d,str(s_sfI) ,PyInt_FromLong(s_sfI)); +PyDict_SetItemString(d,str(s_oI) ,PyInt_FromLong(s_oI)); +PyDict_SetItemString(d,str(s_ooI) ,PyInt_FromLong(s_ooI)); +PyDict_SetItemString(d,str(s_soI) ,PyInt_FromLong(s_soI)); +PyDict_SetItemString(d,str(s_foI) ,PyInt_FromLong(s_foI)); +PyDict_SetItemString(d,str(mx_I) ,PyInt_FromLong(mx_I)); +PyDict_SetItemString(d,str(mx_I_f) ,PyInt_FromLong(mx_I_f)); +PyDict_SetItemString(d,str(mx_I_s) ,PyInt_FromLong(mx_I_s)); +PyDict_SetItemString(d,str(mx_I_o) ,PyInt_FromLong(mx_I_o)); +PyDict_SetItemString(d,str(bb_mx_f),PyInt_FromLong(bb_mx_f)); +PyDict_SetItemString(d,str(bb_mx_s),PyInt_FromLong(bb_mx_s)); +PyDict_SetItemString(d,str(bb_mx_o),PyInt_FromLong(bb_mx_o)); +PyDict_SetItemString(d,str(bb_mn_f),PyInt_FromLong(bb_mn_f)); +PyDict_SetItemString(d,str(bb_mn_s),PyInt_FromLong(bb_mn_s)); +PyDict_SetItemString(d,str(bb_mn_o),PyInt_FromLong(bb_mn_o)); + +PyDict_SetItemString(d,str(avg_i),PyInt_FromLong(avg_i)); +PyDict_SetItemString(d,str(f_raw),PyInt_FromLong(f_raw)); +PyDict_SetItemString(d,str(s_raw),PyInt_FromLong(s_raw)); +PyDict_SetItemString(d,str(f_cen),PyInt_FromLong(f_cen)); +PyDict_SetItemString(d,str(s_cen),PyInt_FromLong(s_cen)); +PyDict_SetItemString(d,str(o_raw),PyInt_FromLong(o_raw)); +PyDict_SetItemString(d,str(m_ff),PyInt_FromLong(m_ff)); +PyDict_SetItemString(d,str(m_ss),PyInt_FromLong(m_ss)); +PyDict_SetItemString(d,str(m_oo),PyInt_FromLong(m_oo)); +PyDict_SetItemString(d,str(m_sf),PyInt_FromLong(m_sf)); +PyDict_SetItemString(d,str(m_so),PyInt_FromLong(m_so)); +PyDict_SetItemString(d,str(m_fo),PyInt_FromLong(m_fo)); +PyDict_SetItemString(d,str(dety),PyInt_FromLong(dety)); +PyDict_SetItemString(d,str(detz),PyInt_FromLong(detz)); + +PyDict_SetItemString(d,str(NPROPERTY),PyInt_FromLong(NPROPERTY)); + +PyDict_SetItemString(d,str(s2D_1),PyInt_FromLong(s2D_1)); +PyDict_SetItemString(d,str(s2D_I),PyInt_FromLong(s2D_I)); +PyDict_SetItemString(d,str(s2D_fI),PyInt_FromLong(s2D_fI)); +PyDict_SetItemString(d,str(s2D_sI),PyInt_FromLong(s2D_sI)); +PyDict_SetItemString(d,str(s2D_ffI),PyInt_FromLong(s2D_ffI)); +PyDict_SetItemString(d,str(s2D_sfI),PyInt_FromLong(s2D_sfI)); +PyDict_SetItemString(d,str(s2D_ssI),PyInt_FromLong(s2D_ssI)); +PyDict_SetItemString(d,str(s2D_bb_mx_f),PyInt_FromLong(s2D_bb_mx_f)); +PyDict_SetItemString(d,str(s2D_bb_mx_s),PyInt_FromLong(s2D_bb_mx_s)); +PyDict_SetItemString(d,str(s2D_bb_mn_f),PyInt_FromLong(s2D_bb_mn_f)); +PyDict_SetItemString(d,str(s2D_bb_mn_s),PyInt_FromLong(s2D_bb_mn_s)); +PyDict_SetItemString(d,str(NPROPERTY2D),PyInt_FromLong(NPROPERTY2D)); +/* ends user code */ +''' +end interface + +interface + subroutine compute_gv(xlylzl,omega,omegasign,wvln,wedge,chi,t,gv,ng) +!DOC compute_gv computes scattering vectors given thr positions of the spot +!DOC in the laboratory in xlylzl[npks], the omega rotation[npks], and +!DOC the rest of the parameters (wedge,wvln,chi,t[3] and omegasign) + intent(c) compute_gv + intent(c) + integer, intent(c,hide), depend( xlylzl ) :: ng + double precision, intent(in):: xlylzl(ng,3) + double precision, intent(in):: omega(ng) + double precision, intent(in):: omegasign, wvln, wedge, chi + double precision, intent(in):: t(3) + double precision, intent(inout):: gv(ng,3) + ! NOT threadsafe since gv may be shared + end subroutine compute_gv + + subroutine compute_xlylzl(s,f,p,r,dist,xlylzl,n) +!DOC computes_xlylzl finds spot positions in the laboratory frame +!DOC using packed parameters that are more general +!DOC s = slow pixel position +!DOC f = fast pixel position +!DOC p = [s_cen, f_cen, s_size, f_size] +!DOC r[9] = dot( transform.detector_rotation_matrix, flipmatrix ) +!DOC ... with flipmatrix = [1,0,0], [0,o22,o21], [0,o12,o11] +!DOC dist = [distancex, distancey, distancez] is 3D (beyond old model) +!DOC ... see test/test_cImageD11.py + intent(c) compute_xlylzl + intent(c) + double precision, intent(in):: s(n), f(n) + double precision, intent(inout):: xlylzl(n,3) + double precision, intent(in):: p(4), r(9), dist(3) + integer, intent(c,hide), depend( s ) :: n + ! NOT threadsafe since xl may be shared + end subroutine compute_xlylzl + + subroutine quickorient( ubi, bt ) +!DOC quickorient takes two g-vectors in UBI[0] and UBI[1] +!DOC and overwrites with UBI orientation using cache in bt (from h1,h2) +!DOC ... computes cross product 0x1 = ubi[0]xubi[1] +!DOC ... normalises u0=ubi0 and u2=0x1 and computes u1=u0xu2 +!DOC ... returns in UBI BT.(u1,u2,u3) +!DOC algorithm was due to Busing and Levy + intent(c) quickorient + intent(c) + double precision, dimension(3,3), intent (inout) :: ubi + double precision, dimension(3,3), intent (in) :: bt + end subroutine orient + + subroutine cimaged11_omp_set_num_threads(n) +!DOC cimaged11_omp_set_num_threads Sets the openmp number of +!DOC threads to use. +!DOC Change if you use multiprocessing or do not like +!DOC os.environ['OMP_NUM_THREADS'] +!DOC see also: multiprocessing.cpu_count(), +!DOC see also os.cpu_count() +!DOC see docs/sphx/parallel.rst + intent(c) cimaged11_omp_set_num_threads + intent(c) + integer, intent(in) :: n + end subroutine cImaged11_set_omp_num_threads + + function cimaged11_omp_get_max_threads() +!DOC cimaged11_omp_get_max_threads reads the openmp max number of threads +!DOC see docs/sphx/parallel.rst + intent(c) cimaged11_omp_get_max_threads + integer :: cimaged11_omp_get_max_threads + end function cImaged11_omp_get_max_threads + + subroutine uint16_to_float_darksub( img, drk, data, npx ) +!DOC uint16_to_float_darksub subtracts image drk(float32) from +!DOC raw data in data (uint16) and returns in img. + intent(c) uint16_to_float_darksub + intent(c) + real, intent(inout), dimension(npx) :: img + real, intent(in), dimension(npx) :: drk + integer(kind=-2), intent(in), dimension(npx) :: data + integer, intent(hide), depend( img ) :: npx + end subroutine uint16_to_float_darksub + + subroutine uint16_to_float_darkflm( img, drk, flm, data, npx ) +!DOC uint16_to_float_darkflm subtracts image drk(float32) from +!DOC raw data in data (uint16), multiples by flm(float32) and returns in img. + intent(c) uint16_to_float_darkflm + intent(c) + real, intent(inout), dimension(npx) :: img + real, intent(in), dimension(npx) :: drk, flm + integer(kind=-2), intent(in), dimension(npx) :: data + integer, intent(hide), depend( img ) :: npx + end subroutine uint16_to_float_darkflm + + subroutine frelon_lines(img, ns, nf, cut) +!DOC frelon_lines Subtracts the average value of (pixels < cut) per row + intent(c) frelon_lines + intent(c) + real, intent(inout), dimension(ns,nf) :: img + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real, intent(in) :: cut + end subroutine frelon_lines + + subroutine frelon_lines_sub(img, drk, ns, nf, cut) +!DOC frelon_lines_sub Subtracts drk from img and then same as frelon_lines + intent(c) frelon_lines_sub + intent(c) + real, intent(inout), dimension(ns,nf) :: img, drk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real, intent(in) :: cut + threadsafe + end subroutine frelon_lines + + subroutine array_mean_var_cut( img, npx, mean, var, n, cut, verbose ) +!DOC array_mean_var_cut computes the mean and variance of an image +!DOC with pixels above the value mean+cut*stddev removed. This is iterated +!DOC n times as the mean and variance change as pixels are removed. + intent(c) array_mean_var_cut + real, intent(in,c), dimension(npx) :: img + integer, intent(hide,c), depend(img) :: npx = shape(img,0) + real, intent(out) :: mean, var + integer, intent(in,c), optional :: n = 3 + integer, intent(in,c), optional :: verbose = 0 + real, intent(in,c), optional :: cut = 3. + end subroutine array_mean_var_cut + + subroutine array_mean_var_msk( img, msk, npx, mean, var, n, cut, verbose ) +!DOC array_mean_var_msk computes the mean and variance of an image +!DOC with pixels above the value mean+cut*stddev removed. This is iterated +!DOC n times as the mean and variance change as pixels are removed. + intent(c) array_mean_var_msk + real, intent(in,c), dimension(npx) :: img + integer(kind=-1) , intent(in,c), dimension(npx) :: msk + integer, intent(hide,c), depend(img) :: npx = shape(img,0) + real, intent(out) :: mean, var + integer, intent(in,c), optional :: n = 3 + integer, intent(in,c), optional :: verbose = 0 + real, intent(in,c), optional :: cut = 3. + end subroutine array_mean_var_msk + + + subroutine array_stats( img, npx, minval, maxval, mean, var ) +!DOC array_stats computes statistics for an image. +!DOC img Input data array (1D or 2D.ravel(), so sparse or dense) +!DOC npx length of data array (contiguous) +!DOC *minval minimum of the pixels +!DOC *maxval maximum of the pixels +!DOC*s1 Sum of all pixel +!DOC*s2 Sum of pixel^2 + intent(c) array_stats + real, intent(c, in), dimension(npx) :: img + integer, intent(c, hide), depend(img) :: npx = shape(img,0) + ! these are intent(fortran) pointers + real, intent(out) :: minval, maxval, mean, var + threadsafe + end subroutine array_stats + + subroutine array_histogram( img, npx, low, high, hist, nhist ) +!DOC array_histogram computes the histogram for an image +!DOC Go through the data to compute a histogram of the values +!DOC Previous call of array_stats would help to set up this call +!DOC compare to np.bincount - this does not gain much +!DOC better implementations can be done +!DOC +!DOC img[] Input data array (1D or 2D.ravel(), so sparse or dense) +!DOC npx length of data array (contiguous) +!DOC low Lower edge of first bin +!DOC high Upper edge of last bin +!DOC hist[] Histogram to be output +!DOC nhist Number of bins in the histogram + intent(c) array_histogram + intent(c) + real, intent(in), dimension(npx) :: img + integer, intent(hide), depend(img) :: npx = shape(img,0) + real, intent(in) :: low, high + integer*4, intent(inout), dimension(nhist) :: hist + integer, intent(hide), depend(hist) :: nhist = shape(hist, 0 ) + threadsafe + end subroutine array_histogram + + subroutine reorder_u16_a32(data, adr, out, N) +!DOC reorder_u16_a32 called in sandbox/fazit.py simple +!DOC loop with openmp saying out[adr[i]] in data[i] +!DOC e.g. semi-random writing + intent(c) reorder_u16_a32 + intent(c) + integer(kind=-2), dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + integer(kind=-2), dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorder_u16_a32 + + subroutine reorder_f32_a32(data, adr, out, N) +!DOC reorder_f32_a32 called in sandbox/fazit.py simple +!DOC loop with openmp saying out[adr[i]] in data[i] +!DOC e.g. semi-random writing + intent(c) reorder_f32_a32 + intent(c) + real, dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + real, dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorder_u16_a32 + + subroutine reorderlut_u16_a32(data, adr, out, N) +!DOC reorderlut_u16_a32lut called in sandbox/fazit.py simple +!DOC loop with openmp saying out[i] in data[adr[i]] +!DOC e.g. semi-random reading + intent(c) reorderlut_u16_a32 + intent(c) + integer(kind=-2), dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + integer(kind=-2), dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorderlut_u16_a32 + + subroutine reorderlut_f32_a32(data, adr, out, N) +!DOC reorderlut_f32_a32 lut called in sandbox/fazit.py simple +!DOC loop with openmp saying out[i] in data[adr[i]] +!DOC e.g. semi-random reading + intent(c) reorderlut_f32_a32 + intent(c) + real, dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + real, dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorderlut_f32_a32 + + subroutine reorder_u16_a32_a16(data, adr0, adr1, out, ns, nf) +!DOC reorderlut_u16_a32_a16 called in sandbox/fazit.py +!DOC data - source data read in order +!DOC adr0 - output position for the first pixel in each row +!DOC adr1 - difference offset to output next pixel in each row + intent(c) reorder_u16_a32_a16 + intent(c) + integer(kind=-2), dimension(ns,nf), intent(in) :: data + integer*4, dimension(ns), intent(in) :: adr0 + integer*2, dimension(ns,nf), intent(in) :: adr1 + integer(kind=-2), dimension(ns,nf), intent(inout) :: out + integer, intent(hide), depend(adr1) :: ns = shape(adr1,0) + integer, intent(hide), depend(adr1) :: nf = shape(adr1,1) + end subroutine reorder_u16_a32_a16 + + subroutine bgcalc(img, bg, msk, ns, nf, gain, sp, st) +!DOC bgcalc computes a background on a 1d signal where gain +!DOC and sp and st are defined by: +!DOC diff = difference to neighbors or bg estimate +!DOC sigmap = weight for abs background value +!DOC sigmat = constant weight +!DOC gain for b += diff * gain +!DOC img - source data +!DOC bg - computed background +!DOC msk - mask + intent(c) bgcalc + intent(c) + real, dimension(ns,nf), intent(in) :: img + real, dimension(ns,nf), intent(inout) :: bg + integer*1, dimension(ns,nf), intent(inout) :: msk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real :: gain, sp, st + end subroutine bgcalc + + function verify_rounding( n ) +!DOC checks the round to nearest int code is correct + intent(c) verify_rounding + intent(c) + integer :: verify_rounding, n + end function verify_rounding + + subroutine closest_vec( x, dim, nv, ic ) +!DOC closest_vec finds the closest neighbors for each row of X +!DOC ignoring the self. Treated as a X=[v1,v2,v3,...], computes +!DOC sum{(vi-vj)**2} for all i!=j and places minimum in ic. + intent(c) closest_vec + intent(c) + double precision, intent(in) :: x(nv, dim) + integer, intent(hide), depend(x) :: dim=shape(x,1) + integer, intent(hide), depend(x) :: nv=shape(x,0) + integer, intent(inout) :: ic( nv ) + end subroutine closest_vec + + subroutine closest( x, v, ibest, best, nx, nv ) +!DOC closest finds the value and index in x closest to a value in v. +!DOC e.g. x = cosine of angles between pairs of peaks +!DOC v = idealised values based on hkl geometry +!DOC ibest set to the x[i] matching to a v[j] with diff "best" + intent(c) closest + double precision, intent(in) :: x(nx) + double precision, intent(in) :: v(nv) + ! Note : these are intent(fortran) to pass as pointers + integer, intent( out ) :: ibest + double precision, intent( out ) :: best + ! Note : these are intent(c) to pass by reference + integer, intent(c, hide), depend(x) :: nx=shape(x,0) + integer, intent(c, hide), depend(v) :: nv=shape(v,0) + threadsafe + end subroutine closest + + + function score( ubi, gv, tol, ng ) +!DOC score takes a ubi matrix and list of g-vectors and computes +!DOC hkl = dot(ubi, gv), then rounds these g-vectors to integer +!DOC and computes drlv2 = (h-int(h))**2 + (k-int(k))**2 + (l-int(l))**2 +!DOC If drlv2 is less than tol*tol then the peak is considered to +!DOC be indexed. Returns the number of peaks found. + intent(c) score + intent(c) + integer :: score + integer, intent(hide), depend(gv) :: ng + double precision, intent(in) :: ubi(3,3) + double precision, intent(in) :: gv(ng, 3) + double precision, intent(in) :: tol + ! only reads gvectors + end function score + + + subroutine score_and_refine( ubi, gv, tol, n, sumdrlv2, ng) +!DOC score_and_refine is very similar to score but it also refines the UB +!DOC matrix using the assigned peaks and overwrite the argument. +!DOC It returns the number of peaks and fit prior to refinement. + intent(c) score_and_refine + ! Note: fortran, pass by ref here = C has double *sumdrlv2 etc. + integer, intent( out ) :: n + double precision, intent(out) :: sumdrlv2 + double precision, intent(c, inout) :: ubi(3,3) + double precision, intent(c, in) :: gv(ng,3) + integer, intent(c, hide), depend( gv ) :: ng + double precision, intent(c, in) :: tol + threadsafe + end subroutine score_and_refine + + function score_and_assign( ubi, gv, tol, drlv2, labels,label, ng ) +!DOC score_and_assign is similar to score but it assigns peaks to this +!DOC ubi only if they fit the data better than the current UBI. +!DOC It updates drlv2 and labels to use best fitting grain for each peak. +!DOC ... perhaps this is not what you want for overlapping peaks in twins! + intent(c) score_and_assign + intent(c) + integer score_and_assign + double precision, intent(in) :: ubi(3,3) + double precision, intent(in) :: gv(ng,3) + integer, intent(hide), depend( gv ) :: ng + double precision, intent(in) :: tol + double precision, intent(inout) :: drlv2(ng) + integer*4, intent(inout) :: labels(ng) + integer, intent(in) :: label + ! NOT threadsafe - labels will be shared + end function score_and_assign + + subroutine refine_assigned( ubi, gv, labels, label, npk, drlv2, ng ) +!DOC refine_assigned fits a ubi matrix to a set of g-vectors and assignments +!DOC in labels. e.g. where(labels==label) it uses the peaks. +!DOC ... perhaps this is not what you want for overlapping peaks in twins! + intent(c) refine_assigned + double precision, intent(c,in) :: ubi(3,3) + double precision, intent(c,in) :: gv(ng,3) + integer, intent(c,in) :: labels(ng) + integer, intent(c,in) :: label + ! Note : pass by ref + integer, intent(out) :: npk + double precision, intent(out) :: drlv2 + integer, intent(c,hide), depend( gv ) :: ng + threadsafe + end subroutine refine_assigned + + subroutine put_incr64( data, ind, vals, boundscheck, n, m) +!DOC put_incr64 does the simple loop : data[ind] += vals +!DOC not sure why this isn't in numpy +!DOC uses 64 bit addressing + intent(c) put_incr64 + intent(c) + real, intent(inout) :: data(m) + real, intent(in) :: vals(n) + integer(kind=8), dimension(n), intent(in) :: ind + integer, intent(hide), depend( data ) :: m + integer, intent(hide), depend( ind) :: n + integer, optional :: boundscheck = 0 + ! NOT threadsafe? meant for updating data with increments + end subroutine put_incr + + + subroutine put_incr32( data, ind, vals, boundscheck, n, m) +!DOC put_incr32 does the simple loop : data[ind] += vals +!DOC not sure why this isn't in numpy +!DOC uses 32 bit addressing + intent(c) put_incr32 + intent(c) + real, intent(inout) :: data(m) + real, intent(in) :: vals(n) + integer(kind=4), dimension(n), intent(in) :: ind + integer, intent(hide), depend( data ) :: m + integer, intent(hide), depend( ind) :: n + integer, optional :: boundscheck = 0 + ! NOT threadsafe? meant for updating data with increments + end subroutine put_incr + + + + subroutine cluster1d( ar, n, order, tol, nclusters, ids, avgs) +!DOC cluster1d is used in sandbox/friedel.py to find clusters of peaks +!DOC work in progress + intent(c) cluster1d + double precision, intent(c, in) :: ar(n) + integer, intent(c, hide), depend( ar ) :: n + integer, intent(c, in) :: order(n) + double precision, intent(c, in) :: tol + integer, intent(out) :: nclusters + integer, intent(c,inout) :: ids(n) + double precision, intent(c, inout) :: avgs(n) + ! NOT threadsafe since ids may be shared + end subroutine cluster1d + + subroutine score_gvec_z( ubi, ub, gv, g0, g1, g2, e, recompute, n ) +!DOC score_gvec_z reads ubi, ub, gv and recompute +!DOC if (recompute) it fills directions to project errors per peak: +!DOC g0 = gv / |gv| = unit vector along g +!DOC g1 = gxy / |gxy| = unit vector perpendicular to z and g (omega) +!DOC g2 ... ought to be cross( g0, g1 ) ? (eta) +!DOC For all peaks it computes h = ubi.g, rounds to nearest ih = int(h) +!DOC and then computes gcalc = ub.ih = dot( ub, ( round( dot( ubi, g) ) ) ) +!DOC The error gv - gcalc is then projected into the co-ordinate system +!DOC g0,g1,g2 for errors along g, z and the rhs +!DOC Beware : work in progress. Is z always the right axis? + intent(c) score_gvec_z + intent(c) + double precision, intent(in) :: ubi(3,3) + double precision, intent(in) :: ub(3,3) + integer, intent(c, hide), depend( gv ) :: n + double precision, intent(in) :: gv(n,3) + double precision, intent(inout) :: g0(n,3) + double precision, intent(inout) :: g1(n,3) + double precision, intent(inout) :: g2(n,3) + double precision, intent(inout) :: e(n,3) + integer, intent(in) :: recompute + ! NOT threadsafe since gi may be shared + end subroutine score_gvec_z + + + function misori_cubic( u1, u2) +!DOC misori_cubic computes the trace of the smallest misorientation +!DOC for cubic symmetry +!DOC u1 and u2 are both orientation matrices "U" +!DOC compute u1. u2.T to get the rotation from one to the other +!DOC find the permutation that will maximise the trace +!DOC one of six... +!DOC xyz yxz zxy +!DOC xzy yzx zyx +!DOC Beware : work in progress. Which point group is this? + intent(c) misori_cubic + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_cubic + threadsafe + end function misori_cubic + + + function misori_orthorhombic( u1, u2) +!DOC misori_orthorhombic computes the trace of the smallest misorientation +!DOC u1 and u2 are both orientation matrices "U" +!DOC compute u1. u2.T to get the rotation from one to the other +!DOC find the flips that will maximise the trace: +!DOC abs( trace(dot(u1,u2.T) )) +!DOC Looks like point group mmm. Not sure why this is in C? +!DOC Beware: work in progress + intent(c) misori_orthorhombic + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_orthorhombic + threadsafe + end function misori_orthorhombic + + function misori_tetragonal( u1, u2) +!DOC misori_tetragonal computes the trace of the smallest misorientation +!DOC u1 and u2 are both orientation matrices "U" +!DOC compute u1. u2.T to get the rotation from one to the other +!DOC finds the flips a/b and c->-c that will maximise the trace: +!DOC abs( trace(dot(u1,u2.T) )) +!DOC Looks like point group 4/mmm. What about 4/m ? +!DOC Beware: work in progress + intent(c) misori_tetragonal + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_tetragonal + threadsafe + end function misori_tetragonal + + + function misori_monoclinic( u1, u2) +!DOC misori_monoclinic assumes a unique b axis and only checks +!DOC the flip of b -> -b +!DOC Not sure about the point group here. Is is 2/m ?? +!DOC Beware: work in progress + intent(c) misori_monoclinic + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_monoclinic + threadsafe + end function misori_monoclinic + + subroutine misori_cubic_pairs( u, dist, n) +!DOC misori_cubic_pairs Computes the dissimilarity matrix to use for clustering +!DOC of orientations. Fills in an upper triangular matrix that you can try +!DOC to pass to scipy.cluster routines + intent(c) misori_cubic_pairs + intent(c) + double precision, intent(in) :: u(n,3,3) + integer, intent( hidden ), depend(u) :: n + double precision, intent(inout) :: dist( (n*(n-1)/2) ) + ! NOT threadsafe + end subroutine misori_cubic_pairs + + function count_shared( pi, ni, pj, nj ) +!DOC count_shared takes two sorted arrays in pi and pj and counts +!DOC how many collisions there are. Useful to compare two lists of +!DOC peak to grain assignments, or pixel to peak assignments, etc + intent(c) count_shared + intent(c) + integer, intent(in) :: pi(ni) + integer, intent( hidden ), depend(pi) :: ni + integer, intent(in) :: pj(nj) + integer, intent( hidden ), depend(pj) :: nj + ! return value is int too + integer :: count_shared + threadsafe + end function count_shared + + function connectedpixels( data, labels, threshold, & + verbose, con8, ns, nf) + intent(c) connectedpixels +!DOC connectedpixels Determines which pixels in data are above the +!DOC user supplied threshold and assigns them into connected objects +!DOC which are output in labels. Connectivity is 3x3 box (8) by default +!DOC and reduces to a +(4) is con8==0 + intent(c) + real, intent(in) :: data(ns,nf) + integer, intent(inout) :: labels(ns,nf) + integer, intent(hide), depend(data) :: ns=shape(data,0) + integer, intent(hide), depend(data) :: nf=shape(data,1) + integer, optional :: con8 = 1 + integer, optional :: verbose = 0 + real threshold + ! Returns + integer :: connectedpixels + end function connectedpixels + + subroutine blobproperties( data, labels, np, omega, & + verbose, ns, nf, results) + intent(c) blobproperties +!DOC blobproperties fills the array results with properties of each labelled +!DOC object described by data (pixel values) and labels. The omega value +!DOC is the angle for this frame. +!DOC results are FIXME + intent(c) + real, intent(in) :: data(ns, nf) + integer, intent(in) :: labels(ns, nf) + integer, intent(hide), depend(data) :: ns=shape(data,0) + integer, intent(hide), depend(data) :: nf=shape(data,1) + integer, intent(in) :: np + double precision, intent(out) :: results( np, NPROPERTY ) + real, intent(in), optional :: omega = 0 + integer, optional :: verbose = 0 + threadsafe + end subroutine blobproperties + + function bloboverlaps( labels1, npk1, results1, & + labels2, npk2, results2, & + verbose, ns, nf) +!DOC bloboverlaps determines the overlaps between labels1 and labels2 +!DOC for an image series. Peaks in labels2 may be merged if they were +!DOC joined by a peak on labels1. Results in results1 are accumulated +!DOC into results2 if peaks are overlapped. + intent(c) bloboverlaps + intent(c) + integer :: bloboverlaps + integer, intent( inout ) :: labels1( ns, nf ) + integer, intent( inout ) :: labels2( ns, nf ) + integer, intent(hide), depend(labels1) :: ns=shape(labels1,0) + integer, intent(hide), depend(labels1) :: nf=shape(labels1,1) + integer, intent(in) :: npk1, npk2 + double precision, intent( inout ) :: results1( :, NPROPERTY ) + double precision, intent( inout ) :: results2( :, NPROPERTY ) + integer, intent(in) :: verbose = 0 + threadsafe + end subroutine bloboverlaps + + subroutine blob_moments( results, np ) +!DOC blob_moments fills in the reduced moments in results array. +!DOC ... FIXME - this would be clearer in python, fast anyway. + intent(c) blob_moments + intent(c) + double precision, intent( inout ) :: results( np, NPROPERTY ) + integer, intent(hide), depend(results) :: np=shape(results,0) + threadsafe + end subroutine blob_moments + + function clean_mask( msk, ret, ns, nf ) +!DOC clean_mask removes pixels which are not 4 connected from msk +!DOC while copying into ret. + intent(c) clean_mask + intent(c) + integer*1, intent(in) :: msk( ns, nf ) + integer, intent(hide), depend(msk) :: ns=shape(msk,0) + integer, intent(hide), depend(msk) :: nf=shape(msk,1) + integer*1, intent(inout), dimension(ns, nf) :: ret + ! returns an int + integer :: clean_mask + end function clean_mask + + + function make_clean_mask( img, cut, msk, ret, ns, nf ) +!DOC make_clean_mask is a lot like clean msk but it generates +!DOC the msk using img and cut. +!DOC Beware: work in progress + intent(c) make_clean_mask + intent(c) + real, intent(in), dimension(ns,nf) :: img + real, intent(in) :: cut + integer*1, intent(in) :: msk( ns, nf ) + integer, intent(hide), depend(msk) :: ns=shape(msk,0) + integer, intent(hide), depend(msk) :: nf=shape(msk,1) + integer*1, intent(inout), dimension(ns, nf) :: ret + ! returns an int + integer :: make_clean_mask + end function make_clean_mask + + function localmaxlabel( data, labels, wrk, ns, nf ) + intent(c) localmaxlabel +!DOC localmaxlabel assigns a label for each pixel so they are grouped +!DOC to the local maximum. Equal values choose to assign towards the earlier +!DOC value in memory. +!DOC cpu arg (1)0=C, (1)1=SSE2, (1)2=AVX2; if > 9 prints timing + intent(c) + real, intent(in) :: data(ns,nf) + integer*4, intent(inout), note(hello) :: labels(ns,nf) + integer*1, intent(inout) :: wrk(ns,nf) + integer, intent(hide), depend(data) :: ns=shape(data,0) + integer, intent(hide), depend(data) :: nf=shape(data,1) + ! Returns + integer :: localmaxlabel + end function localmaxlabel + + function mask_to_coo( msk, ns, nf, i, j, nnz, w ) +!DOC mask_to_coo takes a mask and converts it to a list +!DOC of i,j coordinates in a sparse array coo format +!DOC returns : +!DOC 0 => success +!DOC 1 => ns out of range ; 2 => nf out of range +!DOC 3 => nnz < 1 empty mask +!DOC 4 => nnz did not match this mask + intent(c) mask_to_coo + intent(c) + integer*1, intent(in) :: msk( ns, nf ) + integer, intent(hide), depend(msk) :: ns=shape(msk,0) + integer, intent(hide), depend(msk) :: nf=shape(msk,1) + integer(kind=-2), intent(inout), dimension(nnz) :: i, j + integer, intent(inout) :: w(ns) + integer, intent(hide), depend(i) :: nnz=shape(i,0) + integer, intent(inout) :: w(ns) + ! returns an int + integer :: mask_to_coo + end function mask_to_coo + + function sparse_is_sorted( i, j, nnz ) +!DOC sparse_is_sorted checks whether the indices in i and j of a sparse +!DOC coo format come in the order that they would appear inside an image +!DOC * @param i, j index arrays +!DOC * @param nnz dimension of i, j +!DOC returns 0 for all OK +!DOC k for first non-sorted element +!DOC -k for first duplicate + intent(c) sparse_is_sorted + intent(c) + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(i) :: nnz = shape( i, 0) + threadsafe + ! returns + integer sparse_is_sorted + end function spare_is_sorted + + function sparse_connectedpixels( v, i, j, nnz, threshold, labels ) +!DOC sparse_connectedpixels runs the connectedpixels algorithm on +!DOC a sparse image using a supplied threshold putting labels +!DOC into labels array and returning the number of blobs found + intent(c) sparse_connectedpixels + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(i) :: nnz = shape( i, 0) + real threshold + integer, intent(inout), dimension(nnz) :: labels + ! Returns + integer :: sparse_connectedpixels + threadsafe + end function sparse_connectedpixels + + function sparse_connectedpixels_splat( v, i, j, nnz, th, lbl, Z,ni, nj ) +!DOC sparse_connectedpixels_splat is for debugging/timing. It splats +!DOC the sparse array into a dense array and runs the old connectedpixels +!DOC code on that. + intent(c) sparse_connectedpixels_splat + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(i) :: nnz = shape( i, 0) + real th + integer, intent(inout), dimension(nnz) :: lbl + integer, intent(inout), dimension((ni+2)*(nj+2)) :: Z + integer :: ni, nj + ! Returns + integer :: sparse_connectedpixels_splat + threadsafe + end function sparse_connectedpixels_splat + + subroutine sparse_blob2Dproperties( v, i, j, nnz, labels, results, npk) + intent(c) sparse_blob2Dproperties +!DOC sparse_blob2Dproperties fills the array results with properties of +!DOC each labelled object described by v and labels (pixel values and blob) +!DOC and positions i,j in the image. +!DOC results are: +!DOC results[ipk,s2D_1] = sum(1), number of pixels +!DOC results[ipk,s2D_I] = sum (I), total intensity +!DOC results[ipk,s2D_fI] = sum (f*I), intensity weighted fast index +!DOC results[ipk,s2D_sI] = sum (s*I), intensity weighted slow index +!DOC results[ipk,s2D_ffI] = sum (f*f*I), intensity weighted fast^2 index +!DOC results[ipk,s2D_sfI] = sum (s*f*I), intensity weighted slow*fast index +!DOC results[ipk,s2D_ssI] = sum (s*s*I), intensity weighted slow^2 index + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(v) :: nnz = shape( v, 0) + integer, intent(in), dimension(nnz) :: labels + integer, intent(in) :: npk + double precision, intent(out) :: results( npk, NPROPERTY2D ) + threadsafe + end subroutine sparse_blob2Dproperties + + subroutine sparse_smooth( v, i, j, nnz, s) + intent(c) sparse_smooth +!DOC sparse_smooth smooths data in coo format. Workaround for avoiding +!DOC equal pixels on peak tails for localmaxlabel +!DOC single threaded + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(v) :: nnz = shape( v, 0) + real, intent(inout) :: s( nnz ) + threadsafe + end subroutine sparse_smooth + + function sparse_localmaxlabel( v, i, j, nnz, MV, iMV, labels) + intent(c) sparse_localmaxlabel +!DOC sparse_localmaxlabel assigns labels to sparse array in sorted coo format +!DOC supplied in (v,(i,j)). MV and iMV are temporaries. +!DOC single threaded + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(v) :: nnz = shape( v, 0) + real, intent(inout) :: MV( nnz ) + integer, intent(inout) :: iMV( nnz ) + integer, intent(inout) :: labels( nnz ) + ! returns + integer :: sparse_localmaxlabel + threadsafe + end function sparse_localmaxlabel + + function sparse_overlaps( i1, j1, k1, nnz1, & + i2, j2, k2, nnz2 ) +!DOC sparse_overlaps identifies the pixels in i1,j1 which overlap i2,j2. +!DOC The list of overlaps is returned in k1/k2 such that i1[k1]==i2[k2] +!DOC and j1[k1]==j2[k2]. Probably assumes that sparse_is_sorted was true. + intent(c) sparse_overlaps + intent(c) + integer(kind=-2), dimension(nnz1), intent(c) :: i1, j1 + integer, intent(inout), dimension(nnz1) :: k1 + integer, intent(hide), depend(i1), intent(c) :: nnz1=shape(i1,0) + integer(kind=-2), dimension(nnz2), intent(c) :: i2, j2 + integer, intent(inout), dimension(nnz2) :: k2 + integer, intent(hide), depend(i2), intent(c) :: nnz2=shape(i2,0) + ! returns + integer :: sparse_overlaps + threadsafe + end function sparse_overlaps + + function compress_duplicates( i, j, oi, oj, tmp, n, nt ) +!DOC compress_duplicates removes duplicate i,j labels. On entry then +!DOC i and j are set as the labels from two images. They are sorted +!DOC and on exit i,j hold the unique pairs and oi holds the count +!DOC for the number of overlaps. oj and tmp are temporaries. + intent(c) compress_duplicates + intent(c) + integer, dimension(n), intent(c) :: i, j + integer, dimension(n), intent(c, inout) :: oi, oj + integer, dimension(nt), intent(c, inout) :: tmp + integer, intent(hide), depend(i) :: n = shape(i,0) + integer, intent(hide), depend(tmp) :: nt = shape(tmp,0) + ! returns + integer :: compress_duplicates + threadsafe + end function compress_duplicates + + function coverlaps( row1, col1, labels1, nnz1, row2, col2, labels2, nnz2, mat, npk1, npk2, results) +!DOC overlaps determines which of (row1,col1,labels1) and (row2,col2,labels2) +!DOC are overlapped. +!DOC + intent(c) coverlaps + intent(c) + integer(kind=-2), dimension(nnz1), intent(c) :: row1, col1 + integer, intent(hide), depend(row1) :: nnz1 = shape(row1,0) + integer(kind=-2), dimension(nnz2), intent(c) :: row2, col2 + integer, intent(hide), depend(row2) :: nnz2 = shape(row2,0) + integer, dimension(nnz1), intent(c) :: labels1 + integer, dimension(nnz2), intent(c) :: labels2 + integer, dimension(npk1, npk2), intent(c) :: mat + integer, intent(hide), depend(mat) :: npk1 = shape(mat, 0) + integer, intent(hide), depend(mat) :: npk2 = shape(mat, 1) + integer, intent(inout), dimension(*) :: results + ! returns + integer :: coverlaps + threadsafe + end function coverlaps + + function tosparse_u16( img, msk, row, col, val, cut, ns, nf) +!DOC tosparse_u16 stores pixels from img into row/col/val. +!DOC msk determines whether pixels are masked (e.g. eiger mask) +!DOC returns the number of pixels found + intent(c) tosparse_u16 + intent(c) + integer(kind=-2), dimension(ns, nf), intent(c) :: img, row, col, val + integer(kind=-1), dimension(ns, nf), intent(c) :: msk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + integer :: cut + ! returns + integer :: tosparse_u16 + threadsafe + end function tosparse_u16 + + function tosparse_u32( img, msk, row, col, val, cut, ns, nf) +!DOC tosparse_u16 stores pixels from img into row/col/val. +!DOC msk determines whether pixels are masked (e.g. eiger mask) +!DOC returns the number of pixels found + intent(c) tosparse_u32 + intent(c) + integer(kind=-4), dimension(ns, nf), intent(c) :: img + integer(kind=-1), dimension(ns, nf), intent(c) :: msk + integer(kind=-2), dimension(*), intent(c) :: row, col + integer(kind=-4), dimension(*), intent(c) :: val + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real :: cut + ! returns + integer :: tosparse_u32 + threadsafe + end function tosparse_u32 + + function tosparse_f32( img, msk, row, col, val, cut, ns, nf) +!DOC tosparse_u16 stores pixels from img into row/col/val. +!DOC msk determines whether pixels are masked (e.g. eiger mask) +!DOC returns the number of pixels found + intent(c) tosparse_f32 + intent(c) + real, dimension(ns, nf), intent(c) :: img, val + integer(kind=-2), dimension(ns, nf), intent(c) :: row, col + integer(kind=-1), dimension(ns, nf), intent(c) :: msk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real :: cut + ! returns + integer :: tosparse_f32 + threadsafe + end function tosparse_f32 + + subroutine splat( rgba, w, h, gve, ng, u, npx ) +!DOC splat draws gvectors into an rgba image. The horror of maintaining plot3d +!DOC over the years motivated this code. See test/demo/tksplat +!DOC * set the color and markersize per peak +!DOC * perhaps also a draw order (back to front, top to bottom) ? + intent(c) splat + intent(c) + integer(kind=-1), dimension(w,h,4) :: rgba + integer,intent(hide), depend(rgba) :: h = shape(rgba, 0) + integer,intent(hide), depend(rgba) :: w = shape(rgba, 1) + double precision, intent(inout), dimension(ng,3) :: gve + integer, intent(hide), depend(gve):: ng + double precision, intent(in), dimension(9) :: u + integer :: npx + ! NOT threadsafe, rgba will be recycled and on screen + end subroutine splat + + + +end interface +end python module _cImageD11 diff --git a/src/blobs.c b/src/blobs.c index 90464734..0adc4e60 100644 --- a/src/blobs.c +++ b/src/blobs.c @@ -18,13 +18,26 @@ # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ -#include /* malloc */ -#include -#include /* sqrt */ #include "blobs.h" +#include /* sqrt */ +#include +#include /* malloc */ -void compute_moments(double b[], int nb) +/* Fill in an image of peak assignments for pixels +DLL_LOCAL +void match(int32_t * new, int32_t * old, int32_t * S) { + if (*new == 0) { + *new = *old; + } else { + if (*new != *old) { + dset_makeunion(S, *old, *new); + } + } +} +*/ + +DLL_LOCAL void compute_moments(double b[], int nb) { /* Convert the blob (summing) representation to a human readable form */ int i, off; double ts, to, tf, us, uo, uf, vs, vo, vf, tc; @@ -32,83 +45,80 @@ void compute_moments(double b[], int nb) /* loop over a set of blobs in a flat array */ for (i = 0; i < nb; i++) { - /* avg_i */ - - off = i * NPROPERTY; - - if (b[s_1 + off] == 0) { - /* Zero pixels - skip this fella! */ - /* printf("Skipped %d\n",i); */ - continue; - } - - tc = b[s_I + off]; /* tc = total counts */ - - b[avg_i + off] = b[s_I + off] / b[s_1 + off]; - - /* First and second moments */ - - uf = b[s_fI + off] / tc; - tf = b[s_ffI + off] / tc; - b[f_raw + off] = uf; - vf = tf - uf * uf; - if ((vf + 1.0) > 0.0) - b[m_ff + off] = sqrt(vf + 1.); - else - b[m_ff + off] = 1.0; - - us = b[s_sI + off] / tc; - ts = b[s_ssI + off] / tc; - b[s_raw + off] = us; - vs = ts - us * us; - if ((vs + 1.0) > 0.0) - b[m_ss + off] = sqrt(vs + 1.); - else - b[m_ss + off] = 1.0; - - uo = b[s_oI + off] / tc; - to = b[s_ooI + off] / tc; - b[o_raw + off] = uo; - vo = to - uo * uo; - if ((vo + 1.0) > 0.0) - b[m_oo + off] = sqrt(vo + 1.); - else - b[m_oo + off] = 1.; - - b[m_so + off] = - (b[s_soI + off] / tc - us * uo) / b[m_ss + off] / b[m_oo + off]; - - b[m_fo + off] = - (b[s_foI + off] / tc - uf * uo) / b[m_ff + off] / b[m_oo + off]; - - b[m_sf + off] = - (b[s_sfI + off] / tc - us * uf) / b[m_ss + off] / b[m_ff + off]; - + /* avg_i */ + + off = i * NPROPERTY; + + if (b[s_1 + off] == 0) { + /* Zero pixels - skip this fella! */ + /* printf("Skipped %d\n",i); */ + continue; + } + + tc = b[s_I + off]; /* tc = total counts */ + /* average intensity */ + b[avg_i + off] = b[s_I + off] / b[s_1 + off]; + + /* First moment */ + uf = b[s_fI + off] / tc; + b[f_raw + off] = uf; + /* Secoond moment */ + tf = b[s_ffI + off] / tc; + vf = tf - uf * uf; // variance : better to have returned this! + if ((vf + 1.0) > 0.0) + b[m_ff + off] = sqrt(vf + 1.); + else + b[m_ff + off] = 1.0; + /* first and second in other direction */ + us = b[s_sI + off] / tc; + b[s_raw + off] = us; + ts = b[s_ssI + off] / tc; + vs = ts - us * us; + if ((vs + 1.0) > 0.0) + b[m_ss + off] = sqrt(vs + 1.); + else + b[m_ss + off] = 1.0; + /* And now in omega direction */ + uo = b[s_oI + off] / tc; + to = b[s_ooI + off] / tc; + b[o_raw + off] = uo; + vo = to - uo * uo; + if ((vo + 1.0) > 0.0) + b[m_oo + off] = sqrt(vo + 1.); + else + b[m_oo + off] = 1.; + /* The three covariances */ + b[m_so + off] = + (b[s_soI + off] / tc - us * uo) / b[m_ss + off] / b[m_oo + off]; + + b[m_fo + off] = + (b[s_foI + off] / tc - uf * uo) / b[m_ff + off] / b[m_oo + off]; + + b[m_sf + off] = + (b[s_sfI + off] / tc - us * uf) / b[m_ss + off] / b[m_ff + off]; } - } -void add_pixel(double b[], int s, int f, double I, double o) -{ - - b[s_1] += 1; /* Npix */ - b[s_I] += I; /* Sum intensity */ - b[s_I2] += I * I; /* Sum intensity^2 */ - b[s_fI] += f * I; /* Sum f * intensity */ - b[s_ffI] += f * f * I; /* Sum f * f* intensity */ - b[s_sI] += s * I; /* Sum s * intensity */ - b[s_ssI] += s * s * I; /* Sum s * s * intensity */ - b[s_sfI] += s * f * I; /* Sum f * s * intensity */ - b[s_oI] += o * I; /* Sum o * intensity */ - b[s_ooI] += o * o * I; /* Sum o * intensity */ - b[s_soI] += s * o * I; /* Sum s * o * intensity */ - b[s_foI] += f * o * I; /* Sum f * o * intensity */ +DLL_LOCAL void add_pixel(double b[], int s, int f, double I, double o) { + + b[s_1] += 1; /* Npix */ + b[s_I] += I; /* Sum intensity */ + b[s_I2] += I * I; /* Sum intensity^2 */ + b[s_fI] += f * I; /* Sum f * intensity */ + b[s_ffI] += f * f * I; /* Sum f * f* intensity */ + b[s_sI] += s * I; /* Sum s * intensity */ + b[s_ssI] += s * s * I; /* Sum s * s * intensity */ + b[s_sfI] += s * f * I; /* Sum f * s * intensity */ + b[s_oI] += o * I; /* Sum o * intensity */ + b[s_ooI] += o * o * I; /* Sum o * intensity */ + b[s_soI] += s * o * I; /* Sum s * o * intensity */ + b[s_foI] += f * o * I; /* Sum f * o * intensity */ if (I > b[mx_I]) { - b[mx_I] = I; /* Max intensity */ - b[mx_I_f] = f; /* fast at Max intensity */ - b[mx_I_s] = s; /* slow at Max intensity */ - b[mx_I_o] = o; + b[mx_I] = I; /* Max intensity */ + b[mx_I_f] = f; /* fast at Max intensity */ + b[mx_I_s] = s; /* slow at Max intensity */ + b[mx_I_o] = o; } /* Bounding box */ @@ -118,32 +128,30 @@ void add_pixel(double b[], int s, int f, double I, double o) b[bb_mn_f] = ((f < b[bb_mn_f]) ? f : b[bb_mn_f]); b[bb_mn_s] = ((s < b[bb_mn_s]) ? s : b[bb_mn_s]); b[bb_mn_o] = ((o < b[bb_mn_o]) ? o : b[bb_mn_o]); - } -void merge(double b1[], double b2[]) -{ +DLL_LOCAL void merge(double b1[], double b2[]) { /* b2 is killed, b1 is kept */ int i; - b1[s_1] += b2[s_1]; /* Npix */ - b1[s_I] += b2[s_I]; /* Sum intensity */ - b1[s_I2] += b2[s_I2]; /* Sum intensity^2 */ - b1[s_fI] += b2[s_fI]; /* Sum f * intensity */ - b1[s_ffI] += b2[s_ffI]; /* Sum f * f* intensity */ - b1[s_sI] += b2[s_sI]; /* Sum s * intensity */ - b1[s_ssI] += b2[s_ssI]; /* Sum s * s * intensity */ - b1[s_sfI] += b2[s_sfI]; /* Sum f * s * intensity */ - b1[s_oI] += b2[s_oI]; /* Sum o * intensity */ - b1[s_ooI] += b2[s_ooI]; /* Sum o * intensity */ - b1[s_soI] += b2[s_soI]; /* Sum s * o * intensity */ - b1[s_foI] += b2[s_foI]; /* Sum f * o * intensity */ + b1[s_1] += b2[s_1]; /* Npix */ + b1[s_I] += b2[s_I]; /* Sum intensity */ + b1[s_I2] += b2[s_I2]; /* Sum intensity^2 */ + b1[s_fI] += b2[s_fI]; /* Sum f * intensity */ + b1[s_ffI] += b2[s_ffI]; /* Sum f * f* intensity */ + b1[s_sI] += b2[s_sI]; /* Sum s * intensity */ + b1[s_ssI] += b2[s_ssI]; /* Sum s * s * intensity */ + b1[s_sfI] += b2[s_sfI]; /* Sum f * s * intensity */ + b1[s_oI] += b2[s_oI]; /* Sum o * intensity */ + b1[s_ooI] += b2[s_ooI]; /* Sum o * intensity */ + b1[s_soI] += b2[s_soI]; /* Sum s * o * intensity */ + b1[s_foI] += b2[s_foI]; /* Sum f * o * intensity */ if (b2[mx_I] > b1[mx_I]) { - b1[mx_I] = b2[mx_I]; /* Max intensity */ - b1[mx_I_f] = b2[mx_I_f]; /* fast at Max intensity */ - b1[mx_I_s] = b2[mx_I_s]; /* slow at Max intensity */ - b1[mx_I_o] = b2[mx_I_o]; /* slow at Max intensity */ + b1[mx_I] = b2[mx_I]; /* Max intensity */ + b1[mx_I_f] = b2[mx_I_f]; /* fast at Max intensity */ + b1[mx_I_s] = b2[mx_I_s]; /* slow at Max intensity */ + b1[mx_I_o] = b2[mx_I_o]; /* slow at Max intensity */ } /* Bounding box */ @@ -156,8 +164,7 @@ void merge(double b1[], double b2[]) /* Trash b2 to be on the safe side */ for (i = 0; i < NPROPERTY; i++) - b2[i] = 0; - + b2[i] = 0; } /* ******************************************************************** @@ -171,7 +178,7 @@ void merge(double b1[], double b2[]) * Stored in array S * * S[0] = malloc'ed length of the array - * S[:-1] = current end element + * S[:-1] = current end element * * dset_initialise(int size) => S[:] = 0 * dset_new(S,v) => new item (in v) at end of working part of array @@ -186,108 +193,97 @@ void merge(double b1[], double b2[]) * * ******************************************************************** */ -INTEGER *dset_initialise(INTEGER size) -{ - INTEGER i, *S; - S = (INTEGER *) (malloc(size * sizeof(INTEGER))); +DLL_LOCAL int32_t *dset_initialise(int32_t size) { + int32_t *S; + S = (int32_t *)(calloc(size, sizeof(int32_t))); + /* calloc sets to zero also */ if (S == NULL) { - printf("Memory allocation error in dset_initialise\n"); - exit(1); + printf("Memory allocation error in dset_initialise\n"); + exit(1); } - for (i = 0; i < size; i++) - S[i] = 0; S[0] = size; return S; } -INTEGER *dset_compress(INTEGER ** pS, INTEGER * np) -{ - INTEGER *S, *T, i, j, npk; - S = (INTEGER *) * pS; - T = (INTEGER *) malloc((S[S[0] - 1] + 3) * sizeof(INTEGER)); - if (T == NULL) { - printf("Memory allocation error in dset_compress\n"); - exit(1); - } - npk = 0; +DLL_LOCAL int32_t *dset_compress(int32_t **pS, int32_t *np) { + int32_t *S, *T, i, j, npk; + S = (int32_t *)*pS; // existing set + T = dset_initialise(S[S[0] - 1] + 3); // new set + npk = 0; // number of entries for (i = 1; i < S[S[0] - 1] + 1; i++) { - if (S[i] == i) { - T[i] = ++npk; - } else { - j = dset_find(i, S); - T[i] = T[j]; - if (j >= i) - printf("Issue compressing disjoint set\n"); - if (S[j] != j) - printf("Disjoint set is squiff\n"); - } + if (S[i] == i) { + T[i] = ++npk; + } else { + j = dset_find(i, S); + T[i] = T[j]; + if (j >= i) + printf("Issue compressing disjoint set\n"); + if (S[j] != j) + printf("Disjoint set is squiff\n"); + } } *np = npk; return T; } -INTEGER *dset_new(INTEGER ** pS, INTEGER * v) -{ +DLL_LOCAL int32_t *dset_new(int32_t **pS, int32_t *v) { /* S[0] will always hold the length of the array */ /* S[:-1] holds the current element */ - INTEGER length, current, i; - INTEGER *S; - S = (INTEGER *) * pS; + int32_t length, current, i; + int32_t *S; + S = (int32_t *)*pS; length = S[0]; current = (++S[S[0] - 1]); *v = current; if (current + 3 > length) { - S = (INTEGER *) (realloc(S, length * 2 * sizeof(INTEGER))); - if (S == NULL) { - printf("Memory allocation error in dset_new\n"); - exit(1); - } - /* printf("Realloced S to %d in dset_new\n",length*2); */ - /* Fails on gcc 4 but worked on gcc 3.x - dont actually what it does or why it is there - hence commented it out - (INTEGER *) pS = S; */ - S[0] = length * 2; - S[length - 1] = 0; - for (i = length - 1; i < length * 2; i++) - S[i] = 0; - S[length * 2 - 1] = current; + S = (int32_t *)(realloc(S, length * 2 * sizeof(int32_t))); + if (S == NULL) { + printf("Memory allocation error in dset_new\n"); + exit(1); + } + /* printf("Realloced S to %d in dset_new\n",length*2); */ + /* Fails on gcc 4 but worked on gcc 3.x + dont actually what it does or why it is there + hence commented it out + (int32_t *) pS = S; */ + S[0] = length * 2; + S[length - 1] = 0; + for (i = length - 1; i < length * 2; i++) + S[i] = 0; + S[length * 2 - 1] = current; } S[current] = current; return S; } -void dset_makeunion(INTEGER * S, INTEGER r1, INTEGER r2) -{ - INTEGER a, b; +DLL_LOCAL void dset_makeunion(int32_t *S, int32_t r1, int32_t r2) { + int32_t a, b; a = dset_find(r1, S); b = dset_find(r2, S); dset_link(S, a, b); } -void dset_link(INTEGER * S, INTEGER r2, INTEGER r1) -{ +DLL_LOCAL void dset_link(int32_t *S, int32_t r2, int32_t r1) { if (r1 > r2) { - /* The higher # r1 is changed to point to the lower */ - S[r1] = r2; + /* The higher # r1 is changed to point to the lower */ + S[r1] = r2; } if (r1 < r2) { - /* Again, the higher number, r2 is the one to point to lower */ - S[r2] = r1; + /* Again, the higher number, r2 is the one to point to lower */ + S[r2] = r1; } /* if r1==r2 then they are already a union */ } -INTEGER dset_find(INTEGER x, INTEGER * S) -{ +DLL_LOCAL int32_t dset_find(int32_t x, int32_t *S) { if (x == 0) { - /* oups */ - printf("Oh dear, you tried to find zero in your " - "disjoint set, and it is not there!\n"); - return 0; + /* oups */ + printf("Oh dear, you tried to find zero in your " + "disjoint set, and it is not there!\n"); + return 0; } if (S[x] != x) { - S[x] = dset_find(S[x], S); + S[x] = dset_find(S[x], S); } return S[x]; } diff --git a/src/blobs.h b/src/blobs.h index 353d7c08..0a81686a 100644 --- a/src/blobs.h +++ b/src/blobs.h @@ -20,74 +20,113 @@ #ifndef _blobs_h #define _blobs_h +#include "cImageD11.h" -#ifdef _MSC_VER -typedef __int32 int32_t; -#else -#include -#endif +/* DLL_LOCAL +void match(int32_t * new, int32_t * old, int32_t * S); +*/ +#define match(X, Y, Z) \ + do { \ + if ((X) == 0) { \ + (X) = (Y); \ + } else { \ + if ((X) != (Y)) { \ + dset_makeunion((Z), (X), (Y)); \ + } \ + } \ + } while (0) + +DLL_LOCAL +int32_t *dset_initialise(int32_t size); /* array to hold real values of each */ + +DLL_LOCAL +int32_t *dset_new(int32_t **S, int32_t *v); + +DLL_LOCAL +void dset_makeunion(int32_t *S, int32_t r1, int32_t r2); + +DLL_LOCAL +void dset_link(int32_t *S, int32_t r1, int32_t r2); -#define INTEGER int32_t +DLL_LOCAL +int32_t dset_find(int32_t x, int32_t *S); -INTEGER *dset_initialise(INTEGER size); /* array to hold real values of each */ -INTEGER *dset_new(INTEGER ** S, INTEGER * v); -void dset_makeunion(INTEGER * S, INTEGER r1, INTEGER r2); -void dset_link(INTEGER * S, INTEGER r1, INTEGER r2); -INTEGER dset_find(INTEGER x, INTEGER * S); -INTEGER *dset_compress(INTEGER ** pS, INTEGER * np); +DLL_LOCAL +int32_t *dset_compress(int32_t **pS, int32_t *np); - /* Spot_ID - to be generated when writing out */ +DLL_PUBLIC +/* Spot_ID - to be generated when writing out */ enum { - s_1 = 0, /* 1 Npix */ - s_I, /* 2 Sum intensity */ - s_I2, /* 3 Sum intensity^2 */ - s_fI, /* 4 Sum f * intensity */ - s_ffI, /* 5 Sum f * f* intensity */ - s_sI, /* 6 Sum s * intensity */ - s_ssI, /* 7 Sum s * s * intensity */ - s_sfI, /* 8 Sum f * s * intensity */ - s_oI, /* 9 sum omega * intensity */ - s_ooI, /* 10 sum omega2 * intensity */ - s_soI, /* 11 sum omega * s * intensity */ - s_foI, /* 12 sum omega * f * intensity */ - - mx_I, /* 13 Max intensity */ - mx_I_f, /* 14 fast at Max intensity */ - mx_I_s, /* 15 slow at Max intensity */ - mx_I_o, /* 16 omega at max I */ - - bb_mx_f, /* 17 max of f */ - bb_mx_s, /* 18 max of s */ - bb_mx_o, /* 19 max of omega */ - bb_mn_f, /* 20 min of f */ - bb_mn_s, /* 21 min of s */ - bb_mn_o, /* 22 min of o */ - - avg_i, /* Average intensity */ - f_raw, /* fast centre of mass */ - s_raw, /* slow centre of mass */ - o_raw, /* omega centre of mass */ - m_ss, /* moments */ + s_1 = 0, /* 1 Npix */ + s_I, /* 2 Sum intensity */ + s_I2, /* 3 Sum intensity^2 */ + s_fI, /* 4 Sum f * intensity */ + s_ffI, /* 5 Sum f * f* intensity */ + s_sI, /* 6 Sum s * intensity */ + s_ssI, /* 7 Sum s * s * intensity */ + s_sfI, /* 8 Sum f * s * intensity */ + s_oI, /* 9 sum omega * intensity */ + s_ooI, /* 10 sum omega2 * intensity */ + s_soI, /* 11 sum omega * s * intensity */ + s_foI, /* 12 sum omega * f * intensity */ + + mx_I, /* 13 Max intensity */ + mx_I_f, /* 14 fast at Max intensity */ + mx_I_s, /* 15 slow at Max intensity */ + mx_I_o, /* 16 omega at max I */ + + bb_mx_f, /* 17 max of f */ + bb_mx_s, /* 18 max of s */ + bb_mx_o, /* 19 max of omega */ + bb_mn_f, /* 20 min of f */ + bb_mn_s, /* 21 min of s */ + bb_mn_o, /* 22 min of o */ + + avg_i, /* Average intensity */ + f_raw, /* fast centre of mass */ + s_raw, /* slow centre of mass */ + o_raw, /* omega centre of mass */ + m_ss, /* moments */ m_ff, m_oo, m_sf, m_so, m_fo, - f_cen, /* Filled in elsewhere - spatial correction */ - s_cen, /* ditto */ + f_cen, /* Filled in elsewhere - spatial correction */ + s_cen, /* ditto */ - dety, /*Filled in elsewhere - flip to HFPO book */ - detz, /*Filled in elsewhere - flip to HFPO book */ + dety, /*Filled in elsewhere - flip to HFPO book */ + detz, /*Filled in elsewhere - flip to HFPO book */ - NPROPERTY /* Number of properties if starting at 0 */ + NPROPERTY /* Number of properties if starting at 0 */ +}; + +DLL_PUBLIC +enum { + s2D_1 = 0, + s2D_I = 1, + s2D_fI = 2, + s2D_sI = 3, + s2D_ffI = 4, + s2D_sfI = 5, + s2D_ssI = 6, + s2D_bb_mx_f = 7, + s2D_bb_mx_s = 8, + s2D_bb_mn_f = 9, + s2D_bb_mn_s = 10, + NPROPERTY2D }; /*void new_blob(double blob[], int i, int j, double val);*/ +DLL_LOCAL void add_pixel(double blob[], int i, int j, double val, double omega); +DLL_LOCAL void merge(double blob1[], double blob2[]); +DLL_LOCAL void compute_moments(double blobs[], int nblobs); -#endif /* _blobs_h */ + +#endif /* _blobs_h */ diff --git a/src/cImageD11.h b/src/cImageD11.h new file mode 100644 index 00000000..84b4224f --- /dev/null +++ b/src/cImageD11.h @@ -0,0 +1,66 @@ +/* Try to collect all the compiler workarounds in one place */ + +#ifndef _cImageD11_h +#define _cImageD11_h + +#include + +/* These did not expand properly anyway + * #if _OPENMP >= 201307 + * #define SIMDCLAUSE simd + * #define SIMDFOR omp simd + * #else + * #define SIMDCLAUSE + * #define SIMDFOR ignore + * #endif + */ + +#ifdef _OPENMP +#include +#if _OPENMP >= 201307 +#define GOT_OMP_SIMD +#endif +#endif + +/* No really, thanks for standardising C99 20 years ago */ +#define restrict __restrict + +/* If we define functions as local they can be inlined at link time + * in a shared library (e.g. not shared and overridden by LD_PRELOAD) + * unlcear if/how this works currently across compilers... + */ +#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER) +#if __GNUC__ >= 4 +#define DLL_PUBLIC __attribute__((visibility("default"))) +#define DLL_LOCAL __attribute__((visibility("hidden"))) +#else +#define DLL_PUBLIC +#define DLL_LOCAL +#endif +#else +#define DLL_PUBLIC +#define DLL_LOCAL +#endif + +#ifdef _MSC_VER +typedef __int8 int8_t; +typedef __int16 int16_t; +typedef __int32 int32_t; +typedef __int64 int64_t; +typedef unsigned char uint8_t; +typedef unsigned __int16 uint16_t; +typedef unsigned __int32 uint32_t; +typedef unsigned __int64 uint64_t; + +#define inline __inline +#else +#include +#endif + +/* implemented in imaged11utils.c */ +void cimaged11_omp_set_num_threads(int); +int cimaged11_omp_get_max_threads(void); +DLL_LOCAL +double my_get_time(void); + +#endif diff --git a/src/cImageD11.pyf b/src/cImageD11.pyf deleted file mode 100644 index 5f45e709..00000000 --- a/src/cImageD11.pyf +++ /dev/null @@ -1,266 +0,0 @@ - - -python module cImageD11 - - usercode ''' -#include "blobs.h" - ''' - interface - - usercode ''' -#define str(x) (#x) - PyDict_SetItemString(d,str(s_1) ,PyInt_FromLong(s_1)); - PyDict_SetItemString(d,str(s_I) ,PyInt_FromLong(s_I)); - PyDict_SetItemString(d,str(s_I2) ,PyInt_FromLong(s_I2)); - PyDict_SetItemString(d,str(s_fI) ,PyInt_FromLong(s_fI)); - PyDict_SetItemString(d,str(s_ffI) ,PyInt_FromLong(s_ffI)); - PyDict_SetItemString(d,str(s_sI) ,PyInt_FromLong(s_sI)); - PyDict_SetItemString(d,str(s_ssI) ,PyInt_FromLong(s_ssI)); - PyDict_SetItemString(d,str(s_sfI) ,PyInt_FromLong(s_sfI)); - PyDict_SetItemString(d,str(s_oI) ,PyInt_FromLong(s_oI)); - PyDict_SetItemString(d,str(s_ooI) ,PyInt_FromLong(s_ooI)); - PyDict_SetItemString(d,str(s_soI) ,PyInt_FromLong(s_soI)); - PyDict_SetItemString(d,str(s_foI) ,PyInt_FromLong(s_foI)); - PyDict_SetItemString(d,str(mx_I) ,PyInt_FromLong(mx_I)); - PyDict_SetItemString(d,str(mx_I_f) ,PyInt_FromLong(mx_I_f)); - PyDict_SetItemString(d,str(mx_I_s) ,PyInt_FromLong(mx_I_s)); - PyDict_SetItemString(d,str(mx_I_o) ,PyInt_FromLong(mx_I_o)); - PyDict_SetItemString(d,str(bb_mx_f),PyInt_FromLong(bb_mx_f)); - PyDict_SetItemString(d,str(bb_mx_s),PyInt_FromLong(bb_mx_s)); - PyDict_SetItemString(d,str(bb_mx_o),PyInt_FromLong(bb_mx_o)); - PyDict_SetItemString(d,str(bb_mn_f),PyInt_FromLong(bb_mn_f)); - PyDict_SetItemString(d,str(bb_mn_s),PyInt_FromLong(bb_mn_s)); - PyDict_SetItemString(d,str(bb_mn_o),PyInt_FromLong(bb_mn_o)); - - PyDict_SetItemString(d,str(avg_i),PyInt_FromLong(avg_i)); - PyDict_SetItemString(d,str(f_raw),PyInt_FromLong(f_raw)); - PyDict_SetItemString(d,str(s_raw),PyInt_FromLong(s_raw)); - PyDict_SetItemString(d,str(f_cen),PyInt_FromLong(f_cen)); - PyDict_SetItemString(d,str(s_cen),PyInt_FromLong(s_cen)); - PyDict_SetItemString(d,str(o_raw),PyInt_FromLong(o_raw)); - PyDict_SetItemString(d,str(m_ff),PyInt_FromLong(m_ff)); - PyDict_SetItemString(d,str(m_ss),PyInt_FromLong(m_ss)); - PyDict_SetItemString(d,str(m_oo),PyInt_FromLong(m_oo)); - PyDict_SetItemString(d,str(m_sf),PyInt_FromLong(m_sf)); - PyDict_SetItemString(d,str(m_so),PyInt_FromLong(m_so)); - PyDict_SetItemString(d,str(m_fo),PyInt_FromLong(m_fo)); - PyDict_SetItemString(d,str(dety),PyInt_FromLong(dety)); - PyDict_SetItemString(d,str(detz),PyInt_FromLong(detz)); - - PyDict_SetItemString(d,str(NPROPERTY),PyInt_FromLong(NPROPERTY)); -''' - - end interface - - - - interface - function connectedpixels( data, labels, threshold, & - verbose, con8, ns, nf) - intent(c) connectedpixels - intent(c) - real, intent(in) :: data(ns,nf) - integer, intent(inout) :: labels(ns,nf) - integer, intent(hide), depend(data) :: ns=shape(data,0) - integer, intent(hide), depend(data) :: nf=shape(data,1) - integer, optional :: con8 = 1 - integer, optional :: verbose = 0 - real threshold -! Returns - integer :: connectedpixels -! we say threadsafe but it writes in labels, so we assume no one else -! is using at the same time - threadsafe - end function connectedpixels - end interface - - interface - subroutine blobproperties( data, labels, np, omega, & - verbose, ns, nf, results) - intent(c) blobproperties - intent(c) - real, intent(in) :: data(ns, nf) - integer, intent(in) :: labels(ns, nf) - integer, intent(hide), depend(data) :: ns=shape(data,0) - integer, intent(hide), depend(data) :: nf=shape(data,1) - integer, intent(in) :: np - double precision, intent(out) :: results( np, NPROPERTY ) - real, intent(in), optional :: omega = 0 - integer, optional :: verbose = 0 - threadsafe - end subroutine blobproperties - endinterface - - interface - subroutine blob_moments( results, np ) - intent(c) blob_moments - intent(c) - double precision, intent( inout ) :: results( np, NPROPERTY ) - integer, intent(hide), depend(results) :: np=shape(results,0) - threadsafe - end subroutine blob_moments - end interface - - interface - function bloboverlaps( labels1, npk1, results1, & - labels2, npk2, results2, & - verbose, ns, nf) - intent(c) bloboverlaps - intent(c) - integer :: bloboverlaps - integer, intent( inout ) :: labels1( ns, nf ) - integer, intent( inout ) :: labels2( ns, nf ) - integer, intent(hide), depend(labels1) :: ns=shape(labels1,0) - integer, intent(hide), depend(labels1) :: nf=shape(labels1,1) - integer, intent(in) :: npk1, npk2 - double precision, intent( inout ) :: results1( :, NPROPERTY ) - double precision, intent( inout ) :: results2( :, NPROPERTY ) - integer, intent(in) :: verbose = 0 - - end subroutine bloboverlaps - - - end interface - - interface - subroutine closest( x, v, ibest, best, nx, nv ) - intent(c) closest - double precision, intent(in) :: x(nx) - double precision, intent(in) :: v(nv) -! Note : these are intent(fortran) to pass as pointers - integer, intent( out ) :: ibest - double precision, intent( out ) :: best -! Note : these are intent(c) to pass by reference - integer, intent(c, hide), depend(x) :: nx=shape(x,0) - integer, intent(c, hide), depend(v) :: nv=shape(v,0) - end subroutine closest - end interface - - interface - function score( ubi, gv, tol, ng ) - intent(c) score - intent(c) - integer :: score - integer, intent(hide), depend(gv) :: ng - double precision, intent(in) :: ubi(3,3) - double precision, intent(in) :: gv(ng, 3) - double precision, intent(in) :: tol - - end function score - end interface - - interface - subroutine score_and_refine( ubi, gv, tol, n, sumdrlv2, ng) - intent(c) score_and_refine -! Note: fortran, pass by ref - integer, intent( out ) :: n - double precision, intent(out) :: sumdrlv2 - double precision, intent(c, inout) :: ubi(3,3) - double precision, intent(c, in) :: gv(ng,3) - integer, intent(c, hide), depend( gv ) :: ng - double precision, intent(c, in) :: tol - end subroutine score_and_refine - - end interface - - interface - function score_and_assign( ubi, gv, tol, drlv2, labels,label, ng ) - intent(c) score_and_assign - intent(c) - integer score_and_assign - double precision, intent(in) :: ubi(3,3) - double precision, intent(in) :: gv(ng,3) - integer, intent(hide), depend( gv ) :: ng - double precision, intent(in) :: tol - double precision, intent(inout) :: drlv2(ng) - integer*4, intent(inout) :: labels(ng) - integer, intent(in) :: label - end function score_and_assign - end interface - - - interface - subroutine refine_assigned( ubi, gv, labels, label, npk, drlv2, ng ) - intent(c) refine_assigned - double precision, intent(c,in) :: ubi(3,3) - double precision, intent(c,in) :: gv(ng,3) - integer, intent(c,in) :: labels(ng) - integer, intent(c,in) :: label -! Note : pass by ref - integer, intent(out) :: npk - double precision, intent(out) :: drlv2 - integer, intent(c,hide), depend( gv ) :: ng - end subroutine refine_assigned - end interface - - - interface - subroutine put_incr( data, ind, vals, boundscheck, n, m) - intent(c) put_incr - intent(c) - real, intent(inout) :: data(m) - real, intent(in) :: vals(n) -! Assumes 64 bit addressing, usually true - integer*8, dimension(n), intent(in) :: ind - integer, intent(hide), depend( data ) :: m - integer, intent(hide), depend( ind) :: n - integer, optional :: boundscheck = 0 - end subroutine put_incr - end interface - - - interface - subroutine assign( ubi, gv, tol, drlv2, labels, ig, ng) - intent(c) assign - intent(c) - double precision, intent(c,in) :: ubi(3,3) - double precision, intent(c,in) :: gv(ng,3) - double precision, intent(c,in) :: tol - double precision, intent(c,inout) :: drlv2(ng) - integer*4, intent(inout) :: labels(ng) - integer, intent(c,in) :: ig - integer, intent(c,hide), depend( gv ) :: ng - end subroutine assign - end interface - - - interface - subroutine compute_gv(xlylzl,omega,omegasign,wvln,wedge,chi,t,gv,ng) - intent(c) compute_gv - intent(c) - integer, intent(c,hide), depend( xlylzl ) :: ng - double precision, intent(in):: xlylzl(ng,3) - double precision, intent(in):: omega(ng) - double precision, intent(in):: omegasign, wvln, wedge, chi - double precision, intent(in):: t(3) - double precision, intent(inout):: gv(ng,3) - end subroutine compute_gv - end interface - - - - interface - subroutine compute_xlylzl(s,f,p,r,dist,xlylzl,n) - intent(c) compute_xlylzl - intent(c) - double precision, intent(in):: s(n), f(n) - double precision, intent(inout):: xlylzl(n,3) - double precision, intent(in):: p(4), r(9), dist(3) - integer, intent(c,hide), depend( s ) :: n - end subroutine compute_xlylzl - end interface - - interface - subroutine cluster1d( ar, n, order, tol, nclusters, ids, avgs) - intent(c) cluster1d - double precision, intent(c, in) :: ar(n) - integer, intent(c, hide), depend( ar ) :: n - integer, intent(c, in) :: order(n) - double precision, intent(c, in) :: tol - integer, intent(out) :: nclusters - integer, intent(c,inout) :: ids(n) - double precision, intent(c, inout) :: avgs(n) - end subroutine cluster1d - end interface - -end python module cImageD11 diff --git a/src/cImageD11module.c b/src/cImageD11module.c deleted file mode 100644 index 05a4710b..00000000 --- a/src/cImageD11module.c +++ /dev/null @@ -1,1802 +0,0 @@ -/* File: cImageD11module.c - * This file is auto-generated with f2py (version:2). - * f2py is a Fortran to Python Interface Generator (FPIG), Second Edition, - * written by Pearu Peterson . - * See http://cens.ioc.ee/projects/f2py2e/ - * Generation date: Tue Sep 26 14:08:14 2017 - * $Revision:$ - * $Date:$ - * Do not edit this file directly unless you know what you are doing!!! - */ - -#ifdef __cplusplus -extern "C" { -#endif - -/*********************** See f2py2e/cfuncs.py: includes ***********************/ -#include "Python.h" -#include -#include "fortranobject.h" -#include - -/**************** See f2py2e/rules.py: mod_rules['modulebody'] ****************/ -static PyObject *cImageD11_error; -static PyObject *cImageD11_module; - -/*********************** See f2py2e/cfuncs.py: typedefs ***********************/ -#ifdef _WIN32 -typedef __int64 long_long; -#else -typedef long long long_long; -typedef unsigned long long unsigned_long_long; -#endif - - -/****************** See f2py2e/cfuncs.py: typedefs_generated ******************/ -/*need_typedefs_generated*/ - -/********************** See f2py2e/cfuncs.py: cppmacros **********************/ -#if defined(PREPEND_FORTRAN) -#if defined(NO_APPEND_FORTRAN) -#if defined(UPPERCASE_FORTRAN) -#define F_FUNC(f,F) _##F -#else -#define F_FUNC(f,F) _##f -#endif -#else -#if defined(UPPERCASE_FORTRAN) -#define F_FUNC(f,F) _##F##_ -#else -#define F_FUNC(f,F) _##f##_ -#endif -#endif -#else -#if defined(NO_APPEND_FORTRAN) -#if defined(UPPERCASE_FORTRAN) -#define F_FUNC(f,F) F -#else -#define F_FUNC(f,F) f -#endif -#else -#if defined(UPPERCASE_FORTRAN) -#define F_FUNC(f,F) F##_ -#else -#define F_FUNC(f,F) f##_ -#endif -#endif -#endif -#if defined(UNDERSCORE_G77) -#define F_FUNC_US(f,F) F_FUNC(f##_,F##_) -#else -#define F_FUNC_US(f,F) F_FUNC(f,F) -#endif - -#define rank(var) var ## _Rank -#define shape(var,dim) var ## _Dims[dim] -#define old_rank(var) (PyArray_NDIM((PyArrayObject *)(capi_ ## var ## _tmp))) -#define old_shape(var,dim) PyArray_DIM(((PyArrayObject *)(capi_ ## var ## _tmp)),dim) -#define fshape(var,dim) shape(var,rank(var)-dim-1) -#define len(var) shape(var,0) -#define flen(var) fshape(var,0) -#define old_size(var) PyArray_SIZE((PyArrayObject *)(capi_ ## var ## _tmp)) -/* #define index(i) capi_i ## i */ -#define slen(var) capi_ ## var ## _len -#define size(var, ...) f2py_size((PyArrayObject *)(capi_ ## var ## _tmp), ## __VA_ARGS__, -1) - -#ifdef DEBUGCFUNCS -#define CFUNCSMESS(mess) fprintf(stderr,"debug-capi:"mess); -#define CFUNCSMESSPY(mess,obj) CFUNCSMESS(mess) \ - PyObject_Print((PyObject *)obj,stderr,Py_PRINT_RAW);\ - fprintf(stderr,"\n"); -#else -#define CFUNCSMESS(mess) -#define CFUNCSMESSPY(mess,obj) -#endif - -#ifndef max -#define max(a,b) ((a > b) ? (a) : (b)) -#endif -#ifndef min -#define min(a,b) ((a < b) ? (a) : (b)) -#endif -#ifndef MAX -#define MAX(a,b) ((a > b) ? (a) : (b)) -#endif -#ifndef MIN -#define MIN(a,b) ((a < b) ? (a) : (b)) -#endif - -#define CHECKSCALAR(check,tcheck,name,show,var)\ - if (!(check)) {\ - char errstring[256];\ - sprintf(errstring, "%s: "show, "("tcheck") failed for "name, var);\ - PyErr_SetString(cImageD11_error,errstring);\ - /*goto capi_fail;*/\ - } else - -/************************ See f2py2e/cfuncs.py: cfuncs ************************/ -static int double_from_pyobj(double* v,PyObject *obj,const char *errmess) { - PyObject* tmp = NULL; - if (PyFloat_Check(obj)) { -#ifdef __sgi - *v = PyFloat_AsDouble(obj); -#else - *v = PyFloat_AS_DOUBLE(obj); -#endif - return 1; - } - tmp = PyNumber_Float(obj); - if (tmp) { -#ifdef __sgi - *v = PyFloat_AsDouble(tmp); -#else - *v = PyFloat_AS_DOUBLE(tmp); -#endif - Py_DECREF(tmp); - return 1; - } - if (PyComplex_Check(obj)) - tmp = PyObject_GetAttrString(obj,"real"); - else if (PyString_Check(obj) || PyUnicode_Check(obj)) - /*pass*/; - else if (PySequence_Check(obj)) - tmp = PySequence_GetItem(obj,0); - if (tmp) { - PyErr_Clear(); - if (double_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;} - Py_DECREF(tmp); - } - { - PyObject* err = PyErr_Occurred(); - if (err==NULL) err = cImageD11_error; - PyErr_SetString(err,errmess); - } - return 0; -} - -static int f2py_size(PyArrayObject* var, ...) -{ - npy_int sz = 0; - npy_int dim; - npy_int rank; - va_list argp; - va_start(argp, var); - dim = va_arg(argp, npy_int); - if (dim==-1) - { - sz = PyArray_SIZE(var); - } - else - { - rank = PyArray_NDIM(var); - if (dim>=1 && dim<=rank) - sz = PyArray_DIM(var, dim-1); - else - fprintf(stderr, "f2py_size: 2nd argument value=%d fails to satisfy 1<=value<=%d. Result will be 0.\n", dim, rank); - } - va_end(argp); - return sz; -} - -static int int_from_pyobj(int* v,PyObject *obj,const char *errmess) { - PyObject* tmp = NULL; - if (PyInt_Check(obj)) { - *v = (int)PyInt_AS_LONG(obj); - return 1; - } - tmp = PyNumber_Int(obj); - if (tmp) { - *v = PyInt_AS_LONG(tmp); - Py_DECREF(tmp); - return 1; - } - if (PyComplex_Check(obj)) - tmp = PyObject_GetAttrString(obj,"real"); - else if (PyString_Check(obj) || PyUnicode_Check(obj)) - /*pass*/; - else if (PySequence_Check(obj)) - tmp = PySequence_GetItem(obj,0); - if (tmp) { - PyErr_Clear(); - if (int_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;} - Py_DECREF(tmp); - } - { - PyObject* err = PyErr_Occurred(); - if (err==NULL) err = cImageD11_error; - PyErr_SetString(err,errmess); - } - return 0; -} - -static int float_from_pyobj(float* v,PyObject *obj,const char *errmess) { - double d=0.0; - if (double_from_pyobj(&d,obj,errmess)) { - *v = (float)d; - return 1; - } - return 0; -} - - -/********************* See f2py2e/cfuncs.py: userincludes *********************/ -/*need_userincludes*/ - -/********************* See f2py2e/capi_rules.py: usercode *********************/ - /* start usercode multiline (0) */ - -#include "blobs.h" - - /* end multiline (0)*/ - -/* See f2py2e/rules.py */ -extern int connectedpixels(float*,int*,float,int,int,int,int); -extern void blobproperties(float*,int*,int,float,int,int,int,double*); -extern void blob_moments(double*,int); -extern int bloboverlaps(int*,int,double*,int*,int,double*,int,int,int); -extern void closest(double*,double*,int*,double*,int,int); -extern int score(double*,double*,double,int); -extern void score_and_refine(double*,double*,double,int*,double*,int); -extern int score_and_assign(double*,double*,double,double*,int*,int,int); -extern void refine_assigned(double*,double*,int*,int,int*,double*,int); -extern void put_incr(float*,long_long*,float*,int,int,int); -/*eof externroutines*/ - -/******************** See f2py2e/capi_rules.py: usercode1 ********************/ - - -/******************* See f2py2e/cb_rules.py: buildcallback *******************/ -/*need_callbacks*/ - -/*********************** See f2py2e/rules.py: buildapi ***********************/ - -/****************************** connectedpixels ******************************/ -static char doc_f2py_rout_cImageD11_connectedpixels[] = "\ -connectedpixels = connectedpixels(data,labels,threshold,[verbose,con8])\n\nWrapper for ``connectedpixels``.\ -\n\nParameters\n----------\n" -"data : input rank-2 array('f') with bounds (ns,nf)\n" -"labels : in/output rank-2 array('i') with bounds (ns,nf)\n" -"threshold : input float\n" -"\nOther Parameters\n----------------\n" -"verbose : input int, optional\n Default: 0\n" -"con8 : input int, optional\n Default: 1\n" -"\nReturns\n-------\n" -"connectedpixels : int"; -/* extern int connectedpixels(float*,int*,float,int,int,int,int); */ -static PyObject *f2py_rout_cImageD11_connectedpixels(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - int (*f2py_func)(float*,int*,float,int,int,int,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - int connectedpixels_return_value=0; - float *data = NULL; - npy_intp data_Dims[2] = {-1, -1}; - const int data_Rank = 2; - PyArrayObject *capi_data_tmp = NULL; - int capi_data_intent = 0; - PyObject *data_capi = Py_None; - int *labels = NULL; - npy_intp labels_Dims[2] = {-1, -1}; - const int labels_Rank = 2; - PyArrayObject *capi_labels_tmp = NULL; - int capi_labels_intent = 0; - PyObject *labels_capi = Py_None; - float threshold = 0; - PyObject *threshold_capi = Py_None; - int verbose = 0; - PyObject *verbose_capi = Py_None; - int con8 = 0; - PyObject *con8_capi = Py_None; - int ns = 0; - int nf = 0; - static char *capi_kwlist[] = {"data","labels","threshold","verbose","con8",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOO|OO:cImageD11.connectedpixels",\ - capi_kwlist,&data_capi,&labels_capi,&threshold_capi,&verbose_capi,&con8_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable verbose */ - if (verbose_capi == Py_None) verbose = 0; else - f2py_success = int_from_pyobj(&verbose,verbose_capi,"cImageD11.connectedpixels() 1st keyword (verbose) can't be converted to int"); - if (f2py_success) { - /* Processing variable con8 */ - if (con8_capi == Py_None) con8 = 1; else - f2py_success = int_from_pyobj(&con8,con8_capi,"cImageD11.connectedpixels() 2nd keyword (con8) can't be converted to int"); - if (f2py_success) { - /* Processing variable threshold */ - f2py_success = float_from_pyobj(&threshold,threshold_capi,"cImageD11.connectedpixels() 3rd argument (threshold) can't be converted to float"); - if (f2py_success) { - /* Processing variable data */ - ; - capi_data_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_data_tmp = array_from_pyobj(NPY_FLOAT,data_Dims,data_Rank,capi_data_intent,data_capi); - if (capi_data_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `data' of cImageD11.connectedpixels to C/Fortran array" ); - } else { - data = (float *)(PyArray_DATA(capi_data_tmp)); - - /* Processing variable nf */ - nf = shape(data,1); - /* Processing variable ns */ - ns = shape(data,0); - /* Processing variable labels */ - labels_Dims[0]=ns,labels_Dims[1]=nf; - capi_labels_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_labels_tmp = array_from_pyobj(NPY_INT,labels_Dims,labels_Rank,capi_labels_intent,labels_capi); - if (capi_labels_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `labels' of cImageD11.connectedpixels to C/Fortran array" ); - } else { - labels = (int *)(PyArray_DATA(capi_labels_tmp)); - -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - Py_BEGIN_ALLOW_THREADS - connectedpixels_return_value = (*f2py_func)(data,labels,threshold,verbose,con8,ns,nf); - Py_END_ALLOW_THREADS -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("i",connectedpixels_return_value); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - if((PyObject *)capi_labels_tmp!=labels_capi) { - Py_XDECREF(capi_labels_tmp); } - } /*if (capi_labels_tmp == NULL) ... else of labels*/ - /* End of cleaning variable labels */ - /* End of cleaning variable ns */ - /* End of cleaning variable nf */ - if((PyObject *)capi_data_tmp!=data_capi) { - Py_XDECREF(capi_data_tmp); } - } /*if (capi_data_tmp == NULL) ... else of data*/ - /* End of cleaning variable data */ - } /*if (f2py_success) of threshold*/ - /* End of cleaning variable threshold */ - } /*if (f2py_success) of con8*/ - /* End of cleaning variable con8 */ - } /*if (f2py_success) of verbose*/ - /* End of cleaning variable verbose */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/*************************** end of connectedpixels ***************************/ - -/******************************* blobproperties *******************************/ -static char doc_f2py_rout_cImageD11_blobproperties[] = "\ -results = blobproperties(data,labels,np,[omega,verbose])\n\nWrapper for ``blobproperties``.\ -\n\nParameters\n----------\n" -"data : input rank-2 array('f') with bounds (ns,nf)\n" -"labels : input rank-2 array('i') with bounds (ns,nf)\n" -"np : input int\n" -"\nOther Parameters\n----------------\n" -"omega : input float, optional\n Default: 0\n" -"verbose : input int, optional\n Default: 0\n" -"\nReturns\n-------\n" -"results : rank-2 array('d') with bounds (np,(NPROPERTY))"; -/* extern void blobproperties(float*,int*,int,float,int,int,int,double*); */ -static PyObject *f2py_rout_cImageD11_blobproperties(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - void (*f2py_func)(float*,int*,int,float,int,int,int,double*)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - float *data = NULL; - npy_intp data_Dims[2] = {-1, -1}; - const int data_Rank = 2; - PyArrayObject *capi_data_tmp = NULL; - int capi_data_intent = 0; - PyObject *data_capi = Py_None; - int *labels = NULL; - npy_intp labels_Dims[2] = {-1, -1}; - const int labels_Rank = 2; - PyArrayObject *capi_labels_tmp = NULL; - int capi_labels_intent = 0; - PyObject *labels_capi = Py_None; - int np = 0; - PyObject *np_capi = Py_None; - float omega = 0; - PyObject *omega_capi = Py_None; - int verbose = 0; - PyObject *verbose_capi = Py_None; - int ns = 0; - int nf = 0; - double *results = NULL; - npy_intp results_Dims[2] = {-1, -1}; - const int results_Rank = 2; - PyArrayObject *capi_results_tmp = NULL; - int capi_results_intent = 0; - static char *capi_kwlist[] = {"data","labels","np","omega","verbose",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOO|OO:cImageD11.blobproperties",\ - capi_kwlist,&data_capi,&labels_capi,&np_capi,&omega_capi,&verbose_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable verbose */ - if (verbose_capi == Py_None) verbose = 0; else - f2py_success = int_from_pyobj(&verbose,verbose_capi,"cImageD11.blobproperties() 2nd keyword (verbose) can't be converted to int"); - if (f2py_success) { - /* Processing variable data */ - ; - capi_data_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_data_tmp = array_from_pyobj(NPY_FLOAT,data_Dims,data_Rank,capi_data_intent,data_capi); - if (capi_data_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `data' of cImageD11.blobproperties to C/Fortran array" ); - } else { - data = (float *)(PyArray_DATA(capi_data_tmp)); - - /* Processing variable np */ - f2py_success = int_from_pyobj(&np,np_capi,"cImageD11.blobproperties() 3rd argument (np) can't be converted to int"); - if (f2py_success) { - /* Processing variable omega */ - if (omega_capi == Py_None) omega = 0; else - f2py_success = float_from_pyobj(&omega,omega_capi,"cImageD11.blobproperties() 1st keyword (omega) can't be converted to float"); - if (f2py_success) { - /* Processing variable results */ - results_Dims[0]=np,results_Dims[1]=(NPROPERTY); - capi_results_intent |= F2PY_INTENT_OUT|F2PY_INTENT_HIDE|F2PY_INTENT_C; - capi_results_tmp = array_from_pyobj(NPY_DOUBLE,results_Dims,results_Rank,capi_results_intent,Py_None); - if (capi_results_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting hidden `results' of cImageD11.blobproperties to C/Fortran array" ); - } else { - results = (double *)(PyArray_DATA(capi_results_tmp)); - - /* Processing variable nf */ - nf = shape(data,1); - /* Processing variable ns */ - ns = shape(data,0); - /* Processing variable labels */ - labels_Dims[0]=ns,labels_Dims[1]=nf; - capi_labels_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_labels_tmp = array_from_pyobj(NPY_INT,labels_Dims,labels_Rank,capi_labels_intent,labels_capi); - if (capi_labels_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `labels' of cImageD11.blobproperties to C/Fortran array" ); - } else { - labels = (int *)(PyArray_DATA(capi_labels_tmp)); - -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - Py_BEGIN_ALLOW_THREADS - (*f2py_func)(data,labels,np,omega,verbose,ns,nf,results); - Py_END_ALLOW_THREADS -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("N",capi_results_tmp); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - if((PyObject *)capi_labels_tmp!=labels_capi) { - Py_XDECREF(capi_labels_tmp); } - } /*if (capi_labels_tmp == NULL) ... else of labels*/ - /* End of cleaning variable labels */ - /* End of cleaning variable ns */ - /* End of cleaning variable nf */ - } /*if (capi_results_tmp == NULL) ... else of results*/ - /* End of cleaning variable results */ - } /*if (f2py_success) of omega*/ - /* End of cleaning variable omega */ - } /*if (f2py_success) of np*/ - /* End of cleaning variable np */ - if((PyObject *)capi_data_tmp!=data_capi) { - Py_XDECREF(capi_data_tmp); } - } /*if (capi_data_tmp == NULL) ... else of data*/ - /* End of cleaning variable data */ - } /*if (f2py_success) of verbose*/ - /* End of cleaning variable verbose */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/*************************** end of blobproperties ***************************/ - -/******************************** blob_moments ********************************/ -static char doc_f2py_rout_cImageD11_blob_moments[] = "\ -blob_moments(results)\n\nWrapper for ``blob_moments``.\ -\n\nParameters\n----------\n" -"results : in/output rank-2 array('d') with bounds (np,(NPROPERTY))"; -/* extern void blob_moments(double*,int); */ -static PyObject *f2py_rout_cImageD11_blob_moments(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - void (*f2py_func)(double*,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - double *results = NULL; - npy_intp results_Dims[2] = {-1, -1}; - const int results_Rank = 2; - PyArrayObject *capi_results_tmp = NULL; - int capi_results_intent = 0; - PyObject *results_capi = Py_None; - int np = 0; - static char *capi_kwlist[] = {"results",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "O:cImageD11.blob_moments",\ - capi_kwlist,&results_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable results */ - results_Dims[1]=(NPROPERTY); - capi_results_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_results_tmp = array_from_pyobj(NPY_DOUBLE,results_Dims,results_Rank,capi_results_intent,results_capi); - if (capi_results_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `results' of cImageD11.blob_moments to C/Fortran array" ); - } else { - results = (double *)(PyArray_DATA(capi_results_tmp)); - - /* Processing variable np */ - np = shape(results,0); -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - Py_BEGIN_ALLOW_THREADS - (*f2py_func)(results,np); - Py_END_ALLOW_THREADS -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue(""); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - /* End of cleaning variable np */ - if((PyObject *)capi_results_tmp!=results_capi) { - Py_XDECREF(capi_results_tmp); } - } /*if (capi_results_tmp == NULL) ... else of results*/ - /* End of cleaning variable results */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/**************************** end of blob_moments ****************************/ - -/******************************** bloboverlaps ********************************/ -static char doc_f2py_rout_cImageD11_bloboverlaps[] = "\ -bloboverlaps = bloboverlaps(labels1,npk1,results1,labels2,npk2,results2,[verbose])\n\nWrapper for ``bloboverlaps``.\ -\n\nParameters\n----------\n" -"labels1 : in/output rank-2 array('i') with bounds (ns,nf)\n" -"npk1 : input int\n" -"results1 : in/output rank-2 array('d') with bounds (:,(NPROPERTY))\n" -"labels2 : in/output rank-2 array('i') with bounds (ns,nf)\n" -"npk2 : input int\n" -"results2 : in/output rank-2 array('d') with bounds (:,(NPROPERTY))\n" -"\nOther Parameters\n----------------\n" -"verbose : input int, optional\n Default: 0\n" -"\nReturns\n-------\n" -"bloboverlaps : int"; -/* extern int bloboverlaps(int*,int,double*,int*,int,double*,int,int,int); */ -static PyObject *f2py_rout_cImageD11_bloboverlaps(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - int (*f2py_func)(int*,int,double*,int*,int,double*,int,int,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - int bloboverlaps_return_value=0; - int *labels1 = NULL; - npy_intp labels1_Dims[2] = {-1, -1}; - const int labels1_Rank = 2; - PyArrayObject *capi_labels1_tmp = NULL; - int capi_labels1_intent = 0; - PyObject *labels1_capi = Py_None; - int npk1 = 0; - PyObject *npk1_capi = Py_None; - double *results1 = NULL; - npy_intp results1_Dims[2] = {-1, -1}; - const int results1_Rank = 2; - PyArrayObject *capi_results1_tmp = NULL; - int capi_results1_intent = 0; - PyObject *results1_capi = Py_None; - int *labels2 = NULL; - npy_intp labels2_Dims[2] = {-1, -1}; - const int labels2_Rank = 2; - PyArrayObject *capi_labels2_tmp = NULL; - int capi_labels2_intent = 0; - PyObject *labels2_capi = Py_None; - int npk2 = 0; - PyObject *npk2_capi = Py_None; - double *results2 = NULL; - npy_intp results2_Dims[2] = {-1, -1}; - const int results2_Rank = 2; - PyArrayObject *capi_results2_tmp = NULL; - int capi_results2_intent = 0; - PyObject *results2_capi = Py_None; - int verbose = 0; - PyObject *verbose_capi = Py_None; - int ns = 0; - int nf = 0; - static char *capi_kwlist[] = {"labels1","npk1","results1","labels2","npk2","results2","verbose",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOOOOO|O:cImageD11.bloboverlaps",\ - capi_kwlist,&labels1_capi,&npk1_capi,&results1_capi,&labels2_capi,&npk2_capi,&results2_capi,&verbose_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable results2 */ - results2_Dims[1]=(NPROPERTY); - capi_results2_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_results2_tmp = array_from_pyobj(NPY_DOUBLE,results2_Dims,results2_Rank,capi_results2_intent,results2_capi); - if (capi_results2_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 6th argument `results2' of cImageD11.bloboverlaps to C/Fortran array" ); - } else { - results2 = (double *)(PyArray_DATA(capi_results2_tmp)); - - /* Processing variable verbose */ - if (verbose_capi == Py_None) verbose = 0; else - f2py_success = int_from_pyobj(&verbose,verbose_capi,"cImageD11.bloboverlaps() 1st keyword (verbose) can't be converted to int"); - if (f2py_success) { - /* Processing variable results1 */ - results1_Dims[1]=(NPROPERTY); - capi_results1_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_results1_tmp = array_from_pyobj(NPY_DOUBLE,results1_Dims,results1_Rank,capi_results1_intent,results1_capi); - if (capi_results1_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 3rd argument `results1' of cImageD11.bloboverlaps to C/Fortran array" ); - } else { - results1 = (double *)(PyArray_DATA(capi_results1_tmp)); - - /* Processing variable labels1 */ - ; - capi_labels1_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_labels1_tmp = array_from_pyobj(NPY_INT,labels1_Dims,labels1_Rank,capi_labels1_intent,labels1_capi); - if (capi_labels1_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `labels1' of cImageD11.bloboverlaps to C/Fortran array" ); - } else { - labels1 = (int *)(PyArray_DATA(capi_labels1_tmp)); - - /* Processing variable npk2 */ - f2py_success = int_from_pyobj(&npk2,npk2_capi,"cImageD11.bloboverlaps() 5th argument (npk2) can't be converted to int"); - if (f2py_success) { - /* Processing variable npk1 */ - f2py_success = int_from_pyobj(&npk1,npk1_capi,"cImageD11.bloboverlaps() 2nd argument (npk1) can't be converted to int"); - if (f2py_success) { - /* Processing variable nf */ - nf = shape(labels1,1); - /* Processing variable ns */ - ns = shape(labels1,0); - /* Processing variable labels2 */ - labels2_Dims[0]=ns,labels2_Dims[1]=nf; - capi_labels2_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_labels2_tmp = array_from_pyobj(NPY_INT,labels2_Dims,labels2_Rank,capi_labels2_intent,labels2_capi); - if (capi_labels2_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 4th argument `labels2' of cImageD11.bloboverlaps to C/Fortran array" ); - } else { - labels2 = (int *)(PyArray_DATA(capi_labels2_tmp)); - -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - bloboverlaps_return_value = (*f2py_func)(labels1,npk1,results1,labels2,npk2,results2,verbose,ns,nf); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("i",bloboverlaps_return_value); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - if((PyObject *)capi_labels2_tmp!=labels2_capi) { - Py_XDECREF(capi_labels2_tmp); } - } /*if (capi_labels2_tmp == NULL) ... else of labels2*/ - /* End of cleaning variable labels2 */ - /* End of cleaning variable ns */ - /* End of cleaning variable nf */ - } /*if (f2py_success) of npk1*/ - /* End of cleaning variable npk1 */ - } /*if (f2py_success) of npk2*/ - /* End of cleaning variable npk2 */ - if((PyObject *)capi_labels1_tmp!=labels1_capi) { - Py_XDECREF(capi_labels1_tmp); } - } /*if (capi_labels1_tmp == NULL) ... else of labels1*/ - /* End of cleaning variable labels1 */ - if((PyObject *)capi_results1_tmp!=results1_capi) { - Py_XDECREF(capi_results1_tmp); } - } /*if (capi_results1_tmp == NULL) ... else of results1*/ - /* End of cleaning variable results1 */ - } /*if (f2py_success) of verbose*/ - /* End of cleaning variable verbose */ - if((PyObject *)capi_results2_tmp!=results2_capi) { - Py_XDECREF(capi_results2_tmp); } - } /*if (capi_results2_tmp == NULL) ... else of results2*/ - /* End of cleaning variable results2 */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/**************************** end of bloboverlaps ****************************/ - -/********************************** closest **********************************/ -static char doc_f2py_rout_cImageD11_closest[] = "\ -ibest,best = closest(x,v)\n\nWrapper for ``closest``.\ -\n\nParameters\n----------\n" -"x : input rank-1 array('d') with bounds (nx)\n" -"v : input rank-1 array('d') with bounds (nv)\n" -"\nReturns\n-------\n" -"ibest : int\n" -"best : float"; -/* extern void closest(double*,double*,int*,double*,int,int); */ -static PyObject *f2py_rout_cImageD11_closest(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - void (*f2py_func)(double*,double*,int*,double*,int,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - double *x = NULL; - npy_intp x_Dims[1] = {-1}; - const int x_Rank = 1; - PyArrayObject *capi_x_tmp = NULL; - int capi_x_intent = 0; - PyObject *x_capi = Py_None; - double *v = NULL; - npy_intp v_Dims[1] = {-1}; - const int v_Rank = 1; - PyArrayObject *capi_v_tmp = NULL; - int capi_v_intent = 0; - PyObject *v_capi = Py_None; - int ibest = 0; - double best = 0; - int nx = 0; - int nv = 0; - static char *capi_kwlist[] = {"x","v",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OO:cImageD11.closest",\ - capi_kwlist,&x_capi,&v_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable best */ - /* Processing variable v */ - ; - capi_v_intent |= F2PY_INTENT_IN; - capi_v_tmp = array_from_pyobj(NPY_DOUBLE,v_Dims,v_Rank,capi_v_intent,v_capi); - if (capi_v_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `v' of cImageD11.closest to C/Fortran array" ); - } else { - v = (double *)(PyArray_DATA(capi_v_tmp)); - - /* Processing variable x */ - ; - capi_x_intent |= F2PY_INTENT_IN; - capi_x_tmp = array_from_pyobj(NPY_DOUBLE,x_Dims,x_Rank,capi_x_intent,x_capi); - if (capi_x_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `x' of cImageD11.closest to C/Fortran array" ); - } else { - x = (double *)(PyArray_DATA(capi_x_tmp)); - - /* Processing variable ibest */ - /* Processing variable nx */ - nx = shape(x,0); - /* Processing variable nv */ - nv = shape(v,0); -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - (*f2py_func)(x,v,&ibest,&best,nx,nv); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("id",ibest,best); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - /* End of cleaning variable nv */ - /* End of cleaning variable nx */ - /* End of cleaning variable ibest */ - if((PyObject *)capi_x_tmp!=x_capi) { - Py_XDECREF(capi_x_tmp); } - } /*if (capi_x_tmp == NULL) ... else of x*/ - /* End of cleaning variable x */ - if((PyObject *)capi_v_tmp!=v_capi) { - Py_XDECREF(capi_v_tmp); } - } /*if (capi_v_tmp == NULL) ... else of v*/ - /* End of cleaning variable v */ - /* End of cleaning variable best */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/******************************* end of closest *******************************/ - -/*********************************** score ***********************************/ -static char doc_f2py_rout_cImageD11_score[] = "\ -score = score(ubi,gv,tol)\n\nWrapper for ``score``.\ -\n\nParameters\n----------\n" -"ubi : input rank-2 array('d') with bounds (3,3)\n" -"gv : input rank-2 array('d') with bounds (ng,3)\n" -"tol : input float\n" -"\nReturns\n-------\n" -"score : int"; -/* extern int score(double*,double*,double,int); */ -static PyObject *f2py_rout_cImageD11_score(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - int (*f2py_func)(double*,double*,double,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - int score_return_value=0; - double *ubi = NULL; - npy_intp ubi_Dims[2] = {-1, -1}; - const int ubi_Rank = 2; - PyArrayObject *capi_ubi_tmp = NULL; - int capi_ubi_intent = 0; - PyObject *ubi_capi = Py_None; - double *gv = NULL; - npy_intp gv_Dims[2] = {-1, -1}; - const int gv_Rank = 2; - PyArrayObject *capi_gv_tmp = NULL; - int capi_gv_intent = 0; - PyObject *gv_capi = Py_None; - double tol = 0; - PyObject *tol_capi = Py_None; - int ng = 0; - static char *capi_kwlist[] = {"ubi","gv","tol",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOO:cImageD11.score",\ - capi_kwlist,&ubi_capi,&gv_capi,&tol_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable gv */ - gv_Dims[1]=3; - capi_gv_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_gv_tmp = array_from_pyobj(NPY_DOUBLE,gv_Dims,gv_Rank,capi_gv_intent,gv_capi); - if (capi_gv_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `gv' of cImageD11.score to C/Fortran array" ); - } else { - gv = (double *)(PyArray_DATA(capi_gv_tmp)); - - /* Processing variable ubi */ - ubi_Dims[0]=3,ubi_Dims[1]=3; - capi_ubi_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_ubi_tmp = array_from_pyobj(NPY_DOUBLE,ubi_Dims,ubi_Rank,capi_ubi_intent,ubi_capi); - if (capi_ubi_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `ubi' of cImageD11.score to C/Fortran array" ); - } else { - ubi = (double *)(PyArray_DATA(capi_ubi_tmp)); - - /* Processing variable tol */ - f2py_success = double_from_pyobj(&tol,tol_capi,"cImageD11.score() 3rd argument (tol) can't be converted to double"); - if (f2py_success) { - /* Processing variable ng */ - ng = shape(gv,0); - CHECKSCALAR(shape(gv,0)==ng,"shape(gv,0)==ng","hidden ng","score:ng=%d",ng) { -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - score_return_value = (*f2py_func)(ubi,gv,tol,ng); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("i",score_return_value); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - } /*CHECKSCALAR(shape(gv,0)==ng)*/ - /* End of cleaning variable ng */ - } /*if (f2py_success) of tol*/ - /* End of cleaning variable tol */ - if((PyObject *)capi_ubi_tmp!=ubi_capi) { - Py_XDECREF(capi_ubi_tmp); } - } /*if (capi_ubi_tmp == NULL) ... else of ubi*/ - /* End of cleaning variable ubi */ - if((PyObject *)capi_gv_tmp!=gv_capi) { - Py_XDECREF(capi_gv_tmp); } - } /*if (capi_gv_tmp == NULL) ... else of gv*/ - /* End of cleaning variable gv */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/******************************** end of score ********************************/ - -/****************************** score_and_refine ******************************/ -static char doc_f2py_rout_cImageD11_score_and_refine[] = "\ -n,sumdrlv2 = score_and_refine(ubi,gv,tol)\n\nWrapper for ``score_and_refine``.\ -\n\nParameters\n----------\n" -"ubi : in/output rank-2 array('d') with bounds (3,3)\n" -"gv : input rank-2 array('d') with bounds (ng,3)\n" -"tol : input float\n" -"\nReturns\n-------\n" -"n : int\n" -"sumdrlv2 : float"; -/* extern void score_and_refine(double*,double*,double,int*,double*,int); */ -static PyObject *f2py_rout_cImageD11_score_and_refine(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - void (*f2py_func)(double*,double*,double,int*,double*,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - double *ubi = NULL; - npy_intp ubi_Dims[2] = {-1, -1}; - const int ubi_Rank = 2; - PyArrayObject *capi_ubi_tmp = NULL; - int capi_ubi_intent = 0; - PyObject *ubi_capi = Py_None; - double *gv = NULL; - npy_intp gv_Dims[2] = {-1, -1}; - const int gv_Rank = 2; - PyArrayObject *capi_gv_tmp = NULL; - int capi_gv_intent = 0; - PyObject *gv_capi = Py_None; - double tol = 0; - PyObject *tol_capi = Py_None; - int n = 0; - double sumdrlv2 = 0; - int ng = 0; - static char *capi_kwlist[] = {"ubi","gv","tol",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOO:cImageD11.score_and_refine",\ - capi_kwlist,&ubi_capi,&gv_capi,&tol_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable gv */ - gv_Dims[1]=3; - capi_gv_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_gv_tmp = array_from_pyobj(NPY_DOUBLE,gv_Dims,gv_Rank,capi_gv_intent,gv_capi); - if (capi_gv_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `gv' of cImageD11.score_and_refine to C/Fortran array" ); - } else { - gv = (double *)(PyArray_DATA(capi_gv_tmp)); - - /* Processing variable sumdrlv2 */ - /* Processing variable tol */ - f2py_success = double_from_pyobj(&tol,tol_capi,"cImageD11.score_and_refine() 3rd argument (tol) can't be converted to double"); - if (f2py_success) { - /* Processing variable n */ - /* Processing variable ubi */ - ubi_Dims[0]=3,ubi_Dims[1]=3; - capi_ubi_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_ubi_tmp = array_from_pyobj(NPY_DOUBLE,ubi_Dims,ubi_Rank,capi_ubi_intent,ubi_capi); - if (capi_ubi_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `ubi' of cImageD11.score_and_refine to C/Fortran array" ); - } else { - ubi = (double *)(PyArray_DATA(capi_ubi_tmp)); - - /* Processing variable ng */ - ng = shape(gv,0); - CHECKSCALAR(shape(gv,0)==ng,"shape(gv,0)==ng","hidden ng","score_and_refine:ng=%d",ng) { -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - (*f2py_func)(ubi,gv,tol,&n,&sumdrlv2,ng); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("id",n,sumdrlv2); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - } /*CHECKSCALAR(shape(gv,0)==ng)*/ - /* End of cleaning variable ng */ - if((PyObject *)capi_ubi_tmp!=ubi_capi) { - Py_XDECREF(capi_ubi_tmp); } - } /*if (capi_ubi_tmp == NULL) ... else of ubi*/ - /* End of cleaning variable ubi */ - /* End of cleaning variable n */ - } /*if (f2py_success) of tol*/ - /* End of cleaning variable tol */ - /* End of cleaning variable sumdrlv2 */ - if((PyObject *)capi_gv_tmp!=gv_capi) { - Py_XDECREF(capi_gv_tmp); } - } /*if (capi_gv_tmp == NULL) ... else of gv*/ - /* End of cleaning variable gv */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/************************** end of score_and_refine **************************/ - -/****************************** score_and_assign ******************************/ -static char doc_f2py_rout_cImageD11_score_and_assign[] = "\ -score_and_assign = score_and_assign(ubi,gv,tol,drlv2,labels,label)\n\nWrapper for ``score_and_assign``.\ -\n\nParameters\n----------\n" -"ubi : input rank-2 array('d') with bounds (3,3)\n" -"gv : input rank-2 array('d') with bounds (ng,3)\n" -"tol : input float\n" -"drlv2 : in/output rank-1 array('d') with bounds (ng)\n" -"labels : in/output rank-1 array('i') with bounds (ng)\n" -"label : input int\n" -"\nReturns\n-------\n" -"score_and_assign : int"; -/* extern int score_and_assign(double*,double*,double,double*,int*,int,int); */ -static PyObject *f2py_rout_cImageD11_score_and_assign(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - int (*f2py_func)(double*,double*,double,double*,int*,int,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - int score_and_assign_return_value=0; - double *ubi = NULL; - npy_intp ubi_Dims[2] = {-1, -1}; - const int ubi_Rank = 2; - PyArrayObject *capi_ubi_tmp = NULL; - int capi_ubi_intent = 0; - PyObject *ubi_capi = Py_None; - double *gv = NULL; - npy_intp gv_Dims[2] = {-1, -1}; - const int gv_Rank = 2; - PyArrayObject *capi_gv_tmp = NULL; - int capi_gv_intent = 0; - PyObject *gv_capi = Py_None; - double tol = 0; - PyObject *tol_capi = Py_None; - double *drlv2 = NULL; - npy_intp drlv2_Dims[1] = {-1}; - const int drlv2_Rank = 1; - PyArrayObject *capi_drlv2_tmp = NULL; - int capi_drlv2_intent = 0; - PyObject *drlv2_capi = Py_None; - int *labels = NULL; - npy_intp labels_Dims[1] = {-1}; - const int labels_Rank = 1; - PyArrayObject *capi_labels_tmp = NULL; - int capi_labels_intent = 0; - PyObject *labels_capi = Py_None; - int label = 0; - PyObject *label_capi = Py_None; - int ng = 0; - static char *capi_kwlist[] = {"ubi","gv","tol","drlv2","labels","label",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOOOOO:cImageD11.score_and_assign",\ - capi_kwlist,&ubi_capi,&gv_capi,&tol_capi,&drlv2_capi,&labels_capi,&label_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable gv */ - gv_Dims[1]=3; - capi_gv_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_gv_tmp = array_from_pyobj(NPY_DOUBLE,gv_Dims,gv_Rank,capi_gv_intent,gv_capi); - if (capi_gv_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `gv' of cImageD11.score_and_assign to C/Fortran array" ); - } else { - gv = (double *)(PyArray_DATA(capi_gv_tmp)); - - /* Processing variable label */ - f2py_success = int_from_pyobj(&label,label_capi,"cImageD11.score_and_assign() 6th argument (label) can't be converted to int"); - if (f2py_success) { - /* Processing variable tol */ - f2py_success = double_from_pyobj(&tol,tol_capi,"cImageD11.score_and_assign() 3rd argument (tol) can't be converted to double"); - if (f2py_success) { - /* Processing variable ubi */ - ubi_Dims[0]=3,ubi_Dims[1]=3; - capi_ubi_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_ubi_tmp = array_from_pyobj(NPY_DOUBLE,ubi_Dims,ubi_Rank,capi_ubi_intent,ubi_capi); - if (capi_ubi_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `ubi' of cImageD11.score_and_assign to C/Fortran array" ); - } else { - ubi = (double *)(PyArray_DATA(capi_ubi_tmp)); - - /* Processing variable ng */ - ng = shape(gv,0); - CHECKSCALAR(shape(gv,0)==ng,"shape(gv,0)==ng","hidden ng","score_and_assign:ng=%d",ng) { - /* Processing variable labels */ - labels_Dims[0]=ng; - capi_labels_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_labels_tmp = array_from_pyobj(NPY_INT,labels_Dims,labels_Rank,capi_labels_intent,labels_capi); - if (capi_labels_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 5th argument `labels' of cImageD11.score_and_assign to C/Fortran array" ); - } else { - labels = (int *)(PyArray_DATA(capi_labels_tmp)); - - /* Processing variable drlv2 */ - drlv2_Dims[0]=ng; - capi_drlv2_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_drlv2_tmp = array_from_pyobj(NPY_DOUBLE,drlv2_Dims,drlv2_Rank,capi_drlv2_intent,drlv2_capi); - if (capi_drlv2_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 4th argument `drlv2' of cImageD11.score_and_assign to C/Fortran array" ); - } else { - drlv2 = (double *)(PyArray_DATA(capi_drlv2_tmp)); - -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - score_and_assign_return_value = (*f2py_func)(ubi,gv,tol,drlv2,labels,label,ng); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("i",score_and_assign_return_value); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - if((PyObject *)capi_drlv2_tmp!=drlv2_capi) { - Py_XDECREF(capi_drlv2_tmp); } - } /*if (capi_drlv2_tmp == NULL) ... else of drlv2*/ - /* End of cleaning variable drlv2 */ - if((PyObject *)capi_labels_tmp!=labels_capi) { - Py_XDECREF(capi_labels_tmp); } - } /*if (capi_labels_tmp == NULL) ... else of labels*/ - /* End of cleaning variable labels */ - } /*CHECKSCALAR(shape(gv,0)==ng)*/ - /* End of cleaning variable ng */ - if((PyObject *)capi_ubi_tmp!=ubi_capi) { - Py_XDECREF(capi_ubi_tmp); } - } /*if (capi_ubi_tmp == NULL) ... else of ubi*/ - /* End of cleaning variable ubi */ - } /*if (f2py_success) of tol*/ - /* End of cleaning variable tol */ - } /*if (f2py_success) of label*/ - /* End of cleaning variable label */ - if((PyObject *)capi_gv_tmp!=gv_capi) { - Py_XDECREF(capi_gv_tmp); } - } /*if (capi_gv_tmp == NULL) ... else of gv*/ - /* End of cleaning variable gv */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/************************** end of score_and_assign **************************/ - -/****************************** refine_assigned ******************************/ -static char doc_f2py_rout_cImageD11_refine_assigned[] = "\ -npk,drlv2 = refine_assigned(ubi,gv,labels,label)\n\nWrapper for ``refine_assigned``.\ -\n\nParameters\n----------\n" -"ubi : input rank-2 array('d') with bounds (3,3)\n" -"gv : input rank-2 array('d') with bounds (ng,3)\n" -"labels : input rank-1 array('i') with bounds (ng)\n" -"label : input int\n" -"\nReturns\n-------\n" -"npk : int\n" -"drlv2 : float"; -/* extern void refine_assigned(double*,double*,int*,int,int*,double*,int); */ -static PyObject *f2py_rout_cImageD11_refine_assigned(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - void (*f2py_func)(double*,double*,int*,int,int*,double*,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - double *ubi = NULL; - npy_intp ubi_Dims[2] = {-1, -1}; - const int ubi_Rank = 2; - PyArrayObject *capi_ubi_tmp = NULL; - int capi_ubi_intent = 0; - PyObject *ubi_capi = Py_None; - double *gv = NULL; - npy_intp gv_Dims[2] = {-1, -1}; - const int gv_Rank = 2; - PyArrayObject *capi_gv_tmp = NULL; - int capi_gv_intent = 0; - PyObject *gv_capi = Py_None; - int *labels = NULL; - npy_intp labels_Dims[1] = {-1}; - const int labels_Rank = 1; - PyArrayObject *capi_labels_tmp = NULL; - int capi_labels_intent = 0; - PyObject *labels_capi = Py_None; - int label = 0; - PyObject *label_capi = Py_None; - int npk = 0; - double drlv2 = 0; - int ng = 0; - static char *capi_kwlist[] = {"ubi","gv","labels","label",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOOO:cImageD11.refine_assigned",\ - capi_kwlist,&ubi_capi,&gv_capi,&labels_capi,&label_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable gv */ - gv_Dims[1]=3; - capi_gv_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_gv_tmp = array_from_pyobj(NPY_DOUBLE,gv_Dims,gv_Rank,capi_gv_intent,gv_capi); - if (capi_gv_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `gv' of cImageD11.refine_assigned to C/Fortran array" ); - } else { - gv = (double *)(PyArray_DATA(capi_gv_tmp)); - - /* Processing variable npk */ - /* Processing variable drlv2 */ - /* Processing variable label */ - f2py_success = int_from_pyobj(&label,label_capi,"cImageD11.refine_assigned() 4th argument (label) can't be converted to int"); - if (f2py_success) { - /* Processing variable ubi */ - ubi_Dims[0]=3,ubi_Dims[1]=3; - capi_ubi_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_ubi_tmp = array_from_pyobj(NPY_DOUBLE,ubi_Dims,ubi_Rank,capi_ubi_intent,ubi_capi); - if (capi_ubi_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `ubi' of cImageD11.refine_assigned to C/Fortran array" ); - } else { - ubi = (double *)(PyArray_DATA(capi_ubi_tmp)); - - /* Processing variable ng */ - ng = shape(gv,0); - CHECKSCALAR(shape(gv,0)==ng,"shape(gv,0)==ng","hidden ng","refine_assigned:ng=%d",ng) { - /* Processing variable labels */ - labels_Dims[0]=ng; - capi_labels_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_labels_tmp = array_from_pyobj(NPY_INT,labels_Dims,labels_Rank,capi_labels_intent,labels_capi); - if (capi_labels_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 3rd argument `labels' of cImageD11.refine_assigned to C/Fortran array" ); - } else { - labels = (int *)(PyArray_DATA(capi_labels_tmp)); - -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - (*f2py_func)(ubi,gv,labels,label,&npk,&drlv2,ng); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue("id",npk,drlv2); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - if((PyObject *)capi_labels_tmp!=labels_capi) { - Py_XDECREF(capi_labels_tmp); } - } /*if (capi_labels_tmp == NULL) ... else of labels*/ - /* End of cleaning variable labels */ - } /*CHECKSCALAR(shape(gv,0)==ng)*/ - /* End of cleaning variable ng */ - if((PyObject *)capi_ubi_tmp!=ubi_capi) { - Py_XDECREF(capi_ubi_tmp); } - } /*if (capi_ubi_tmp == NULL) ... else of ubi*/ - /* End of cleaning variable ubi */ - } /*if (f2py_success) of label*/ - /* End of cleaning variable label */ - /* End of cleaning variable drlv2 */ - /* End of cleaning variable npk */ - if((PyObject *)capi_gv_tmp!=gv_capi) { - Py_XDECREF(capi_gv_tmp); } - } /*if (capi_gv_tmp == NULL) ... else of gv*/ - /* End of cleaning variable gv */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/*************************** end of refine_assigned ***************************/ - -/********************************** put_incr **********************************/ -static char doc_f2py_rout_cImageD11_put_incr[] = "\ -put_incr(data,ind,vals,[boundscheck])\n\nWrapper for ``put_incr``.\ -\n\nParameters\n----------\n" -"data : in/output rank-1 array('f') with bounds (m)\n" -"ind : input rank-1 array('q') with bounds (n)\n" -"vals : input rank-1 array('f') with bounds (n)\n" -"\nOther Parameters\n----------------\n" -"boundscheck : input int, optional\n Default: 0"; -/* extern void put_incr(float*,long_long*,float*,int,int,int); */ -static PyObject *f2py_rout_cImageD11_put_incr(const PyObject *capi_self, - PyObject *capi_args, - PyObject *capi_keywds, - void (*f2py_func)(float*,long_long*,float*,int,int,int)) { - PyObject * volatile capi_buildvalue = NULL; - volatile int f2py_success = 1; -/*decl*/ - - float *data = NULL; - npy_intp data_Dims[1] = {-1}; - const int data_Rank = 1; - PyArrayObject *capi_data_tmp = NULL; - int capi_data_intent = 0; - PyObject *data_capi = Py_None; - long_long *ind = NULL; - npy_intp ind_Dims[1] = {-1}; - const int ind_Rank = 1; - PyArrayObject *capi_ind_tmp = NULL; - int capi_ind_intent = 0; - PyObject *ind_capi = Py_None; - float *vals = NULL; - npy_intp vals_Dims[1] = {-1}; - const int vals_Rank = 1; - PyArrayObject *capi_vals_tmp = NULL; - int capi_vals_intent = 0; - PyObject *vals_capi = Py_None; - int boundscheck = 0; - PyObject *boundscheck_capi = Py_None; - int n = 0; - int m = 0; - static char *capi_kwlist[] = {"data","ind","vals","boundscheck",NULL}; - -/*routdebugenter*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_clock(); -#endif - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "OOO|O:cImageD11.put_incr",\ - capi_kwlist,&data_capi,&ind_capi,&vals_capi,&boundscheck_capi)) - return NULL; -/*frompyobj*/ - /* Processing variable ind */ - ; - capi_ind_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_ind_tmp = array_from_pyobj(NPY_LONGLONG,ind_Dims,ind_Rank,capi_ind_intent,ind_capi); - if (capi_ind_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 2nd argument `ind' of cImageD11.put_incr to C/Fortran array" ); - } else { - ind = (long_long *)(PyArray_DATA(capi_ind_tmp)); - - /* Processing variable boundscheck */ - if (boundscheck_capi == Py_None) boundscheck = 0; else - f2py_success = int_from_pyobj(&boundscheck,boundscheck_capi,"cImageD11.put_incr() 1st keyword (boundscheck) can't be converted to int"); - if (f2py_success) { - /* Processing variable data */ - ; - capi_data_intent |= F2PY_INTENT_INOUT|F2PY_INTENT_C; - capi_data_tmp = array_from_pyobj(NPY_FLOAT,data_Dims,data_Rank,capi_data_intent,data_capi); - if (capi_data_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 1st argument `data' of cImageD11.put_incr to C/Fortran array" ); - } else { - data = (float *)(PyArray_DATA(capi_data_tmp)); - - /* Processing variable m */ - m = len(data); - CHECKSCALAR(len(data)>=m,"len(data)>=m","hidden m","put_incr:m=%d",m) { - /* Processing variable n */ - n = len(ind); - CHECKSCALAR(len(ind)>=n,"len(ind)>=n","hidden n","put_incr:n=%d",n) { - /* Processing variable vals */ - vals_Dims[0]=n; - capi_vals_intent |= F2PY_INTENT_IN|F2PY_INTENT_C; - capi_vals_tmp = array_from_pyobj(NPY_FLOAT,vals_Dims,vals_Rank,capi_vals_intent,vals_capi); - if (capi_vals_tmp == NULL) { - if (!PyErr_Occurred()) - PyErr_SetString(cImageD11_error,"failed in converting 3rd argument `vals' of cImageD11.put_incr to C/Fortran array" ); - } else { - vals = (float *)(PyArray_DATA(capi_vals_tmp)); - -/*end of frompyobj*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_start_call_clock(); -#endif -/*callfortranroutine*/ - (*f2py_func)(data,ind,vals,boundscheck,n,m); -if (PyErr_Occurred()) - f2py_success = 0; -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_call_clock(); -#endif -/*end of callfortranroutine*/ - if (f2py_success) { -/*pyobjfrom*/ -/*end of pyobjfrom*/ - CFUNCSMESS("Building return value.\n"); - capi_buildvalue = Py_BuildValue(""); -/*closepyobjfrom*/ -/*end of closepyobjfrom*/ - } /*if (f2py_success) after callfortranroutine*/ -/*cleanupfrompyobj*/ - if((PyObject *)capi_vals_tmp!=vals_capi) { - Py_XDECREF(capi_vals_tmp); } - } /*if (capi_vals_tmp == NULL) ... else of vals*/ - /* End of cleaning variable vals */ - } /*CHECKSCALAR(len(ind)>=n)*/ - /* End of cleaning variable n */ - } /*CHECKSCALAR(len(data)>=m)*/ - /* End of cleaning variable m */ - if((PyObject *)capi_data_tmp!=data_capi) { - Py_XDECREF(capi_data_tmp); } - } /*if (capi_data_tmp == NULL) ... else of data*/ - /* End of cleaning variable data */ - } /*if (f2py_success) of boundscheck*/ - /* End of cleaning variable boundscheck */ - if((PyObject *)capi_ind_tmp!=ind_capi) { - Py_XDECREF(capi_ind_tmp); } - } /*if (capi_ind_tmp == NULL) ... else of ind*/ - /* End of cleaning variable ind */ -/*end of cleanupfrompyobj*/ - if (capi_buildvalue == NULL) { -/*routdebugfailure*/ - } else { -/*routdebugleave*/ - } - CFUNCSMESS("Freeing memory.\n"); -/*freemem*/ -#ifdef F2PY_REPORT_ATEXIT -f2py_stop_clock(); -#endif - return capi_buildvalue; -} -/****************************** end of put_incr ******************************/ -/*eof body*/ - -/******************* See f2py2e/f90mod_rules.py: buildhooks *******************/ -/*need_f90modhooks*/ - -/************** See f2py2e/rules.py: module_rules['modulebody'] **************/ - -/******************* See f2py2e/common_rules.py: buildhooks *******************/ - -/*need_commonhooks*/ - -/**************************** See f2py2e/rules.py ****************************/ - -static FortranDataDef f2py_routine_defs[] = { - {"connectedpixels",-1,{{-1}},0,(char *)connectedpixels,(f2py_init_func)f2py_rout_cImageD11_connectedpixels,doc_f2py_rout_cImageD11_connectedpixels}, - {"blobproperties",-1,{{-1}},0,(char *)blobproperties,(f2py_init_func)f2py_rout_cImageD11_blobproperties,doc_f2py_rout_cImageD11_blobproperties}, - {"blob_moments",-1,{{-1}},0,(char *)blob_moments,(f2py_init_func)f2py_rout_cImageD11_blob_moments,doc_f2py_rout_cImageD11_blob_moments}, - {"bloboverlaps",-1,{{-1}},0,(char *)bloboverlaps,(f2py_init_func)f2py_rout_cImageD11_bloboverlaps,doc_f2py_rout_cImageD11_bloboverlaps}, - {"closest",-1,{{-1}},0,(char *)closest,(f2py_init_func)f2py_rout_cImageD11_closest,doc_f2py_rout_cImageD11_closest}, - {"score",-1,{{-1}},0,(char *)score,(f2py_init_func)f2py_rout_cImageD11_score,doc_f2py_rout_cImageD11_score}, - {"score_and_refine",-1,{{-1}},0,(char *)score_and_refine,(f2py_init_func)f2py_rout_cImageD11_score_and_refine,doc_f2py_rout_cImageD11_score_and_refine}, - {"score_and_assign",-1,{{-1}},0,(char *)score_and_assign,(f2py_init_func)f2py_rout_cImageD11_score_and_assign,doc_f2py_rout_cImageD11_score_and_assign}, - {"refine_assigned",-1,{{-1}},0,(char *)refine_assigned,(f2py_init_func)f2py_rout_cImageD11_refine_assigned,doc_f2py_rout_cImageD11_refine_assigned}, - {"put_incr",-1,{{-1}},0,(char *)put_incr,(f2py_init_func)f2py_rout_cImageD11_put_incr,doc_f2py_rout_cImageD11_put_incr}, - -/*eof routine_defs*/ - {NULL} -}; - -static PyMethodDef f2py_module_methods[] = { - - {NULL,NULL} -}; - -#if PY_VERSION_HEX >= 0x03000000 -static struct PyModuleDef moduledef = { - PyModuleDef_HEAD_INIT, - "cImageD11", - NULL, - -1, - f2py_module_methods, - NULL, - NULL, - NULL, - NULL -}; -#endif - -#if PY_VERSION_HEX >= 0x03000000 -#define RETVAL m -PyMODINIT_FUNC PyInit_cImageD11(void) { -#else -#define RETVAL -PyMODINIT_FUNC initcImageD11(void) { -#endif - int i; - PyObject *m,*d, *s; -#if PY_VERSION_HEX >= 0x03000000 - m = cImageD11_module = PyModule_Create(&moduledef); -#else - m = cImageD11_module = Py_InitModule("cImageD11", f2py_module_methods); -#endif - Py_TYPE(&PyFortran_Type) = &PyType_Type; - import_array(); - if (PyErr_Occurred()) - {PyErr_SetString(PyExc_ImportError, "can't initialize module cImageD11 (failed to import numpy)"); return RETVAL;} - d = PyModule_GetDict(m); - s = PyString_FromString("$Revision: $"); - PyDict_SetItemString(d, "__version__", s); -#if PY_VERSION_HEX >= 0x03000000 - s = PyUnicode_FromString( -#else - s = PyString_FromString( -#endif - "This module 'cImageD11' is auto-generated with f2py (version:2).\nFunctions:\n" -" connectedpixels = connectedpixels(data,labels,threshold,verbose=0,con8=1)\n" -" results = blobproperties(data,labels,np,omega=0,verbose=0)\n" -" blob_moments(results)\n" -" bloboverlaps = bloboverlaps(labels1,npk1,results1,labels2,npk2,results2,verbose=0)\n" -" ibest,best = closest(x,v)\n" -" score = score(ubi,gv,tol)\n" -" n,sumdrlv2 = score_and_refine(ubi,gv,tol)\n" -" score_and_assign = score_and_assign(ubi,gv,tol,drlv2,labels,label)\n" -" npk,drlv2 = refine_assigned(ubi,gv,labels,label)\n" -" put_incr(data,ind,vals,boundscheck=0)\n" -"."); - PyDict_SetItemString(d, "__doc__", s); - cImageD11_error = PyErr_NewException ("cImageD11.error", NULL, NULL); - Py_DECREF(s); - for(i=0;f2py_routine_defs[i].name!=NULL;i++) - PyDict_SetItemString(d, f2py_routine_defs[i].name,PyFortranObject_NewAsAttr(&f2py_routine_defs[i])); - - - - - - - - - - -/*eof initf2pywraphooks*/ -/*eof initf90modhooks*/ - -/*eof initcommonhooks*/ - /* start usercode multiline (0) */ - -#define str(x) (#x) - PyDict_SetItemString(d,str(s_1) ,PyInt_FromLong(s_1)); - PyDict_SetItemString(d,str(s_I) ,PyInt_FromLong(s_I)); - PyDict_SetItemString(d,str(s_I2) ,PyInt_FromLong(s_I2)); - PyDict_SetItemString(d,str(s_fI) ,PyInt_FromLong(s_fI)); - PyDict_SetItemString(d,str(s_ffI) ,PyInt_FromLong(s_ffI)); - PyDict_SetItemString(d,str(s_sI) ,PyInt_FromLong(s_sI)); - PyDict_SetItemString(d,str(s_ssI) ,PyInt_FromLong(s_ssI)); - PyDict_SetItemString(d,str(s_sfI) ,PyInt_FromLong(s_sfI)); - PyDict_SetItemString(d,str(s_oI) ,PyInt_FromLong(s_oI)); - PyDict_SetItemString(d,str(s_ooI) ,PyInt_FromLong(s_ooI)); - PyDict_SetItemString(d,str(s_soI) ,PyInt_FromLong(s_soI)); - PyDict_SetItemString(d,str(s_foI) ,PyInt_FromLong(s_foI)); - PyDict_SetItemString(d,str(mx_I) ,PyInt_FromLong(mx_I)); - PyDict_SetItemString(d,str(mx_I_f) ,PyInt_FromLong(mx_I_f)); - PyDict_SetItemString(d,str(mx_I_s) ,PyInt_FromLong(mx_I_s)); - PyDict_SetItemString(d,str(mx_I_o) ,PyInt_FromLong(mx_I_o)); - PyDict_SetItemString(d,str(bb_mx_f),PyInt_FromLong(bb_mx_f)); - PyDict_SetItemString(d,str(bb_mx_s),PyInt_FromLong(bb_mx_s)); - PyDict_SetItemString(d,str(bb_mx_o),PyInt_FromLong(bb_mx_o)); - PyDict_SetItemString(d,str(bb_mn_f),PyInt_FromLong(bb_mn_f)); - PyDict_SetItemString(d,str(bb_mn_s),PyInt_FromLong(bb_mn_s)); - PyDict_SetItemString(d,str(bb_mn_o),PyInt_FromLong(bb_mn_o)); - - PyDict_SetItemString(d,str(avg_i),PyInt_FromLong(avg_i)); - PyDict_SetItemString(d,str(f_raw),PyInt_FromLong(f_raw)); - PyDict_SetItemString(d,str(s_raw),PyInt_FromLong(s_raw)); - PyDict_SetItemString(d,str(f_cen),PyInt_FromLong(f_cen)); - PyDict_SetItemString(d,str(s_cen),PyInt_FromLong(s_cen)); - PyDict_SetItemString(d,str(o_raw),PyInt_FromLong(o_raw)); - PyDict_SetItemString(d,str(m_ff),PyInt_FromLong(m_ff)); - PyDict_SetItemString(d,str(m_ss),PyInt_FromLong(m_ss)); - PyDict_SetItemString(d,str(m_oo),PyInt_FromLong(m_oo)); - PyDict_SetItemString(d,str(m_sf),PyInt_FromLong(m_sf)); - PyDict_SetItemString(d,str(m_so),PyInt_FromLong(m_so)); - PyDict_SetItemString(d,str(m_fo),PyInt_FromLong(m_fo)); - PyDict_SetItemString(d,str(dety),PyInt_FromLong(dety)); - PyDict_SetItemString(d,str(detz),PyInt_FromLong(detz)); - - PyDict_SetItemString(d,str(NPROPERTY),PyInt_FromLong(NPROPERTY)); - - /* end multiline (0)*/ - -#ifdef F2PY_REPORT_ATEXIT - if (! PyErr_Occurred()) - on_exit(f2py_report_on_exit,(void*)"cImageD11"); -#endif - - return RETVAL; -} -#ifdef __cplusplus -} -#endif diff --git a/src/cdiffraction.c b/src/cdiffraction.c index 609832a4..c2361826 100644 --- a/src/cdiffraction.c +++ b/src/cdiffraction.c @@ -1,132 +1,206 @@ - +#include "cdiffraction.h" #include -#include #include -#include "cdiffraction.h" +#include #define PI 3.14159265358979323846 -#define RAD PI/180.0 -#define DEG 180.0/PI +#define RAD PI / 180.0 +#define DEG 180.0 / PI #define NOISY 0 -void assign( double ubi[9], double gv[][3], double tol, - double drlv2[], int labels[], int ig, int n){ - int i; - double dr, h[3], ttol, dh[3]; - ttol = tol * tol; -#pragma omp parallel for private(h,dr,dh) - for(i=0;i + +void cimaged11_omp_set_num_threads(int n) { omp_set_num_threads(n); } + +int cimaged11_omp_get_max_threads(void) { return omp_get_max_threads(); } + +#else + +void cimaged11_omp_set_num_threads(int n) {} + +int cimaged11_omp_get_max_threads(void) { + return 0; /* e.g: zero parallelism via openmp */ +} + +#endif +/* F2PY_WRAPPER_START + subroutine cimaged11_omp_set_num_threads(n) +!DOC cimaged11_omp_set_num_threads Sets the openmp number of +!DOC threads to use. +!DOC Change if you use multiprocessing or do not like +!DOC os.environ['OMP_NUM_THREADS'] +!DOC see also: multiprocessing.cpu_count(), +!DOC see also os.cpu_count() +!DOC see docs/sphx/parallel.rst + intent(c) cimaged11_omp_set_num_threads + intent(c) + integer, intent(in) :: n + end subroutine cImaged11_set_omp_num_threads +F2PY_WRAPPER_END */ + +/* F2PY_WRAPPER_START + function cimaged11_omp_get_max_threads() +!DOC cimaged11_omp_get_max_threads reads the openmp max number of threads +!DOC see docs/sphx/parallel.rst + intent(c) cimaged11_omp_get_max_threads + integer :: cimaged11_omp_get_max_threads + end function cImaged11_omp_get_max_threads +F2PY_WRAPPER_END */ + +#if defined(_MSC_VER) || defined(__MINGW32__) + +#include +double my_get_time() { + LARGE_INTEGER t, f; + QueryPerformanceCounter(&t); + QueryPerformanceFrequency(&f); + return (double)t.QuadPart / (double)f.QuadPart; +} + +#else + +#include +#include + +double my_get_time() { + struct timeval t; + gettimeofday(&t, NULL); + return t.tv_sec + t.tv_usec * 1e-6; +} +#endif diff --git a/src/closest.c b/src/closest.c index 1b8c2028..65f99ae8 100644 --- a/src/closest.c +++ b/src/closest.c @@ -1,9 +1,14 @@ -#include + +#include "cImageD11.h" #include +#include + typedef double vec[3]; -/* +#define DEG(x) ((x)*180. / 3.14159265358979323846264338327950288) + +/* # ImageD11_v1.x Software for beamline ID11 # Copyright (C) 2005-2017 Jon Wright # @@ -22,146 +27,166 @@ typedef double vec[3]; # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ -static char moduledocs[] = - "/* *******************************************************************\n" - " * closest.c Two useful functions for indexing grains \n" - " *\n" - " * 1. From python: closest(cosines_allowed, cosine_measured)\n" - " * Take 1 peak with hkl's assigned, compute the cosines to\n" - " * another powder ring (angles between this peak and peaks in\n" - " * that ring). This extension finds the best peak in the\n" - " * other ring to pair up with.\n" - " *\n" - " * More generally closest(array, values) finds the closest\n" - " * item in values to one of the values in array. Returns the\n" - " * difference and the index of the closest thing it finds.\n" - " *\n" - " * Both arguments should be one dimensional Numeric arrays\n" - " * of type Numeric.Float\n" - " *\n" - " * 2. From python: score(ubi, gv, tol) where ubi is an orientation\n" - " * matrix and gv are an array of g-vectors. Returns the number\n" - " * of g-vectors which have integer hkl peaks within tolerance\n" - " * tol. Uses the conv_double_to_int_fast function in here for \n" - " * factor of !EIGHT! speed increase compared to rounding in \n" - " * C. In fact this gives the nearest even integer, instead\n" - " * of the nearest integer, but we don't care, as a peak having\n" - " * hkl of 0.5 is nowhere near being indexed anyway.\n" - " *\n" - " * Returns and integer - number of peaks indexed\n" - " * UBI is a 3x3 Numeric.Float array (figure out the transposing yourself)\n" - " * GV is a nx3 Numeric.Float array, and you should try to make the 3 \n" - " * be the fast index for best performance\n" - " * \n" - " * 3. score_and_refine(ubi, gv, tol) \n" - " * From python: same as score, I hope, but ubi is overwritten\n" - " * with refined matrix following paciorek algorithm which is \n" - " * in indexing.py\n" - " * \n" - " * \n" - " * 4. score_and_assign( ubi, gv, tol, drlv, labels, (int) label)\n" - " * as for score, but assignments are only made if drlv is lower than\n" - " * the previous. Fills in label with the new label\n" - " *\n" - " * 5. refine_assigned( ubi, gv, labels, label, weight) \n" - " * Use only the peaks where label matches labels in refinement \n" - " * ...following again the Paciorek algorithm \n" - " *\n" - " * 6. put_incr( data, indices, values)\n" - " * pretty much as numeric.put but as increments\n" - " *\n" - " * 7. weighted_refine(ubi, gv, tol, weights) \n" - " * From python: same as score, but with weights, and ubi is overwritten\n" - " * with refined matrix following paciorek algorithm which is \n" - " * in indexing.py\n" - " * \n" - " * ****************************************************************** */ "; - -int conv_double_to_int_fast(double); - -int conv_double_to_int_safe(double); +/* inline double conv_double_to_int_fast(double); */ + +double conv_double_to_int_safe(double); int inverse3x3(double A[3][3]); /* Utils */ - -int inverse3x3(double H[3][3]) -{ +DLL_LOCAL +int inverse3x3(double H[3][3]) { double det, inverse[3][3]; int i, j; /* - # | a11 a12 a13 |-1 | a33a22-a32a23 -(a33a12-a32a13) a23a12-a22a13 | - # | a21 a22 a23 | = 1/DET * | -(a33a21-a31a23) a33a11-a31a13 -(a23a11-a21a13) | - # | a31 a32 a33 | | a32a21-a31a22 -(a32a11-a31a12) a22a11-a21a12 | + # | a11 a12 a13 |-1 | a33a22-a32a23 -(a33a12-a32a13) + a23a12-a22a13 | # | a21 a22 a23 | = 1/DET * | -(a33a21-a31a23) + a33a11-a31a13 -(a23a11-a21a13) | # | a31 a32 a33 | | + a32a21-a31a22 -(a32a11-a31a12) a22a11-a21a12 | - # DET=a11 (a33 a22 -a32 a23)- - a21 (a33 a12 -a32 a13)+ + # DET=a11 (a33 a22 -a32 a23)- + a21 (a33 a12 -a32 a13)+ a31 (a23 a12 -a22 a13) */ det = H[0][0] * (H[2][2] * H[1][1] - H[2][1] * H[1][2]) - - H[1][0] * (H[2][2] * H[0][1] - H[2][1] * H[0][2]) + - H[2][0] * (H[1][2] * H[0][1] - H[1][1] * H[0][2]); + H[1][0] * (H[2][2] * H[0][1] - H[2][1] * H[0][2]) + + H[2][0] * (H[1][2] * H[0][1] - H[1][1] * H[0][2]); if (det != 0.) { - inverse[0][0] = (H[2][2] * H[1][1] - H[2][1] * H[1][2]) / det; - inverse[0][1] = -(H[2][2] * H[0][1] - H[2][1] * H[0][2]) / det; - inverse[0][2] = (H[1][2] * H[0][1] - H[1][1] * H[0][2]) / det; - inverse[1][0] = -(H[2][2] * H[1][0] - H[2][0] * H[1][2]) / det; - inverse[1][1] = (H[2][2] * H[0][0] - H[2][0] * H[0][2]) / det; - inverse[1][2] = -(H[1][2] * H[0][0] - H[1][0] * H[0][2]) / det; - inverse[2][0] = (H[2][1] * H[1][0] - H[2][0] * H[1][1]) / det; - inverse[2][1] = -(H[2][1] * H[0][0] - H[2][0] * H[0][1]) / det; - inverse[2][2] = (H[1][1] * H[0][0] - H[1][0] * H[0][1]) / det; - - for (i = 0; i < 3; i++) - for (j = 0; j < 3; j++) - H[i][j] = inverse[i][j]; - - return 0; + inverse[0][0] = (H[2][2] * H[1][1] - H[2][1] * H[1][2]) / det; + inverse[0][1] = -(H[2][2] * H[0][1] - H[2][1] * H[0][2]) / det; + inverse[0][2] = (H[1][2] * H[0][1] - H[1][1] * H[0][2]) / det; + inverse[1][0] = -(H[2][2] * H[1][0] - H[2][0] * H[1][2]) / det; + inverse[1][1] = (H[2][2] * H[0][0] - H[2][0] * H[0][2]) / det; + inverse[1][2] = -(H[1][2] * H[0][0] - H[1][0] * H[0][2]) / det; + inverse[2][0] = (H[2][1] * H[1][0] - H[2][0] * H[1][1]) / det; + inverse[2][1] = -(H[2][1] * H[0][0] - H[2][0] * H[0][1]) / det; + inverse[2][2] = (H[1][1] * H[0][0] - H[1][0] * H[0][1]) / det; + + for (i = 0; i < 3; i++) + for (j = 0; j < 3; j++) + H[i][j] = inverse[i][j]; + + return 0; } else { - return -1; + return -1; } } -int conv_double_to_int_safe(double x) -{ - int a; - a = (int)floor(x + 0.5); - return a; +DLL_LOCAL +double conv_double_to_int_safe(double x) { return floor(x + 0.5); } + +/* See + * https://stackoverflow.com/questions/59632005/why-does-this-code-beat-rint-and-how-to-i-protect-it-from-ffast-math-and-frie + */ +#define MAGIC 6755399441055744.0 +#define conv_double_to_int_fast(x) ((x+MAGIC)-MAGIC) + +/* DLL_LOCAL +inline double conv_double_to_int_fast(double x) { + return ((x + MAGIC) - MAGIC); +} */ + +/* F2PY_WRAPPER_START + function verify_rounding( n ) +!DOC checks the round to nearest int code is correct + intent(c) verify_rounding + intent(c) + integer :: verify_rounding, n + end function verify_rounding +F2PY_WRAPPER_END */ +int verify_rounding( int n ){ + int i, hfast, hslow, bad = 0; + double v; + + for( i = -100; i < 200; i++ ){ + v = n + i*50.0; + hfast = conv_double_to_int_fast( v ); + hslow = conv_double_to_int_safe( v ); + if ( hfast != hslow ) bad++; + } + for( i = -100; i < 200; i++ ){ + v = -n + i*50.0; + hfast = conv_double_to_int_fast( v ); + hslow = conv_double_to_int_safe( v ); + if ( hfast != hslow ) bad++; + } + return bad; } -typedef union { - int i; - double d; -} a_union; - -int conv_double_to_int_fast(double x) -{ - /*return conv_double_to_int_safe(x); */ - /* This was benched as about eight times faster than the safe mode!! */ - /* Put in the reference for where this was found on the web TODO */ - const int p = 52; - const double c_p1 = (1L << (p / 2)); - const double c_p2 = (1L << (p - p / 2)); - const double c_mul = c_p1 * c_p2; - const double cs = 1.5 * c_mul; - /* Hopefully this notes the aliasing - * perhaps better to just return the safe version - * not clear it is really faster any more? + +/* F2PY_WRAPPER_START + subroutine closest_vec( x, dim, nv, ic ) +!DOC closest_vec finds the closest neighbors for each row of X +!DOC ignoring the self. Treated as a X=[v1,v2,v3,...], computes +!DOC sum{(vi-vj)**2} for all i!=j and places minimum in ic. + intent(c) closest_vec + intent(c) + double precision, intent(in) :: x(nv, dim) + integer, intent(hide), depend(x) :: dim=shape(x,1) + integer, intent(hide), depend(x) :: nv=shape(x,0) + integer, intent(inout) :: ic( nv ) + end subroutine closest_vec +F2PY_WRAPPER_END */ +void closest_vec(double x[], int dim, int nv, int closest[]) { + /* + * For each x it finds the closest neighbor + * this will grow as n^2, which means it rapidly becomes slow */ - a_union t; - t.d = x + cs; - return t.i; - /* x += cs - // const int a = *(int *)(&x); - // return (a); */ + int i, j, k, ib; + double scor, best, t; + +#pragma omp parallel for private(i, j, k, ib, scor, best, t) + for (i = 0; i < nv; i++) { /* source vector */ + /* init with something */ + j = (i + 1) % nv; + best = 0.; + for (k = 0; k < dim; k++) { + t = x[i * dim + k] - x[j * dim + k]; + best += t * t; + } + ib = j; + /* now check all the others */ + for (j = 0; j < nv; j++) { + if (i == j) + continue; + scor = 0.; + for (k = 0; k < dim; k++) { + t = x[i * dim + k] - x[j * dim + k]; + scor += t * t; + } + if (scor < best) { + ib = j; + best = scor; + } + } + closest[i] = ib; + } } -void closest(double x[], double v[], int *ribest, double *rbest, int nx, int nv) -{ - /* +/* F2PY_WRAPPER_START + subroutine closest( x, v, ibest, best, nx, nv ) +!DOC closest finds the value and index in x closest to a value in v. +!DOC e.g. x = cosine of angles between pairs of peaks +!DOC v = idealised values based on hkl geometry +!DOC ibest set to the x[i] matching to a v[j] with diff "best" + intent(c) closest + double precision, intent(in) :: x(nx) + double precision, intent(in) :: v(nv) + ! Note : these are intent(fortran) to pass as pointers + integer, intent( out ) :: ibest + double precision, intent( out ) :: best + ! Note : these are intent(c) to pass by reference + integer, intent(c, hide), depend(x) :: nx=shape(x,0) + integer, intent(c, hide), depend(v) :: nv=shape(v,0) + threadsafe + end subroutine closest +F2PY_WRAPPER_END */ +void closest(double x[], double v[], int *ribest, double *rbest, int nx, + int nv) { + /* * Finds value and index in x closest to a value in v */ int i, j, ibest; @@ -169,115 +194,148 @@ void closest(double x[], double v[], int *ribest, double *rbest, int nx, int nv) best = 99.; ibest = 0; for (i = 0; i < nx; i++) { - for (j = 0; j < nv; j++) { - if (fabs(x[i] - v[j]) < best) { - best = fabs(x[i] - v[j]); - ibest = i; - } - } + for (j = 0; j < nv; j++) { + if (fabs(x[i] - v[j]) < best) { + best = fabs(x[i] - v[j]); + ibest = i; + } + } } *ribest = ibest; *rbest = best; } -int score(vec ubi[3], vec gv[], double tol, int ng) -{ +/* F2PY_WRAPPER_START + + function score( ubi, gv, tol, ng ) +!DOC score takes a ubi matrix and list of g-vectors and computes +!DOC hkl = dot(ubi, gv), then rounds these g-vectors to integer +!DOC and computes drlv2 = (h-int(h))**2 + (k-int(k))**2 + (l-int(l))**2 +!DOC If drlv2 is less than tol*tol then the peak is considered to +!DOC be indexed. Returns the number of peaks found. + intent(c) score + intent(c) + integer :: score + integer, intent(hide), depend(gv) :: ng + double precision, intent(in) :: ubi(3,3) + double precision, intent(in) :: gv(ng, 3) + double precision, intent(in) :: tol + ! only reads gvectors + end function score +F2PY_WRAPPER_END */ +int score(vec ubi[3], vec gv[], double tol, int ng) { /* * Counts g-vectors indexed by ubi within tol */ - double sumsq, h, atol; - int n, k, j; + double sumsq, h0, h1, h2, atol; + int n, k; n = 0; atol = tol * tol; for (k = 0; k < ng; k++) { - sumsq = 0.; - for (j = 0; j < 3; j++) { - h = ubi[j][0] * gv[k][0] + ubi[j][1] * gv[k][1] + - ubi[j][2] * gv[k][2]; - h -= conv_double_to_int_fast(h); - sumsq += h * h; - } - if (sumsq < atol) { - n = n + 1; - } + h0 = ubi[0][0] * gv[k][0] + ubi[0][1] * gv[k][1] + ubi[0][2] * gv[k][2]; + h0 -= conv_double_to_int_fast(h0); + h1 = ubi[1][0] * gv[k][0] + ubi[1][1] * gv[k][1] + ubi[1][2] * gv[k][2]; + h1 -= conv_double_to_int_fast(h1); + h2 = ubi[2][0] * gv[k][0] + ubi[2][1] * gv[k][1] + ubi[2][2] * gv[k][2]; + h2 -= conv_double_to_int_fast(h2); + sumsq = h0 * h0 + h1 * h1 + h2 * h2; + if (sumsq < atol) { + n = n + 1; + } } return n; } -void score_and_refine(vec ubi[3], vec gv[], double tol, - int *n_arg, double *sumdrlv2_arg, int ng) -{ - - double h0, h1, h2, t0, t1, t2; +/* F2PY_WRAPPER_START + + subroutine score_and_refine( ubi, gv, tol, n, sumdrlv2, ng) +!DOC score_and_refine is very similar to score but it also refines the UB +!DOC matrix using the assigned peaks and overwrite the argument. +!DOC It returns the number of peaks and fit prior to refinement. + intent(c) score_and_refine + ! Note: fortran, pass by ref here = C has double *sumdrlv2 etc. + integer, intent( out ) :: n + double precision, intent(out) :: sumdrlv2 + double precision, intent(c, inout) :: ubi(3,3) + double precision, intent(c, in) :: gv(ng,3) + integer, intent(c, hide), depend( gv ) :: ng + double precision, intent(c, in) :: tol + threadsafe + end subroutine score_and_refine +F2PY_WRAPPER_END */ +void score_and_refine(vec ubi[3], vec gv[], double tol, int *n_arg, + double *sumdrlv2_arg, int ng) { + /* ng = number of g vectors */ + double h0, h1, h2, t0, t1, t2, ih[3]; double sumsq, tolsq, sumdrlv2; double R[3][3], H[3][3], UB[3][3]; - int n, k, i, j, l, ih[3]; + int n, k, i, j, l; /* Zero some stuff for refinement */ for (i = 0; i < 3; i++) { - ih[i] = 0; - for (j = 0; j < 3; j++) { - R[i][j] = 0.; - H[i][j] = 0.; - UB[i][j] = 0.; - } + ih[i] = 0; + for (j = 0; j < 3; j++) { + R[i][j] = 0.; + H[i][j] = 0.; + UB[i][j] = 0.; + } } tolsq = tol * tol; n = 0; sumdrlv2 = 0.; /* Test peaks */ for (k = 0; k < ng; k++) { - h0 = ubi[0][0] * gv[k][0] + ubi[0][1] * gv[k][1] + ubi[0][2] * gv[k][2]; - h1 = ubi[1][0] * gv[k][0] + ubi[1][1] * gv[k][1] + ubi[1][2] * gv[k][2]; - h2 = ubi[2][0] * gv[k][0] + ubi[2][1] * gv[k][1] + ubi[2][2] * gv[k][2]; - t0 = h0 - conv_double_to_int_fast(h0); - t1 = h1 - conv_double_to_int_fast(h1); - t2 = h2 - conv_double_to_int_fast(h2); - sumsq = t0 * t0 + t1 * t1 + t2 * t2; - if (sumsq < tolsq) { /* Add into lsq problem */ - n = n + 1; - sumdrlv2 += sumsq; - /* From Paciorek et al Acta A55 543 (1999) - * UB = R H-1 - * where: - * R = sum_n r_n h_n^t - * H = sum_n h_n h_n^t - * r = g-vectors - * h = hkl indices - * The hkl integer indices are: */ - ih[0] = conv_double_to_int_fast(h0); - ih[1] = conv_double_to_int_fast(h1); - ih[2] = conv_double_to_int_fast(h2); - /* The g-vector is: gv[k][012] */ - for (i = 0; i < 3; i++) { - for (j = 0; j < 3; j++) { - /* Robust weight factor, fn(tol), would go here */ - R[i][j] = R[i][j] + ih[j] * gv[k][i]; - H[i][j] = H[i][j] + ih[j] * ih[i]; - } - } /* End lsq addins */ - } /* End selected peaks */ - } /* End first loop over spots */ + h0 = ubi[0][0] * gv[k][0] + ubi[0][1] * gv[k][1] + ubi[0][2] * gv[k][2]; + h1 = ubi[1][0] * gv[k][0] + ubi[1][1] * gv[k][1] + ubi[1][2] * gv[k][2]; + h2 = ubi[2][0] * gv[k][0] + ubi[2][1] * gv[k][1] + ubi[2][2] * gv[k][2]; + t0 = h0 - conv_double_to_int_fast(h0); + t1 = h1 - conv_double_to_int_fast(h1); + t2 = h2 - conv_double_to_int_fast(h2); + sumsq = t0 * t0 + t1 * t1 + t2 * t2; + if (sumsq < tolsq) { /* Add into lsq problem */ + n = n + 1; + sumdrlv2 += sumsq; + /* From Paciorek et al Acta A55 543 (1999) + * UB = R H-1 + * where: + * R = sum_n r_n h_n^t + * H = sum_n h_n h_n^t + * r = g-vectors + * h = hkl indices + * The hkl integer indices are: */ + ih[0] = conv_double_to_int_fast(h0); + ih[1] = conv_double_to_int_fast(h1); + ih[2] = conv_double_to_int_fast(h2); + /* The g-vector is: gv[k][012] */ + for (i = 0; i < 3; i++) { + for (j = 0; j < 3; j++) { + /* Robust weight factor, fn(tol), would go here */ + R[i][j] = R[i][j] + ih[j] * gv[k][i]; + H[i][j] = H[i][j] + ih[j] * ih[i]; + } + } /* End lsq addins */ + } /* End selected peaks */ + } /* End first loop over spots */ /* Now solve the least squares problem */ /* inverse overwrites H with the inverse */ k = inverse3x3(H); if (k == 0) { - for (i = 0; i < 3; i++) - for (j = 0; j < 3; j++) - for (l = 0; l < 3; l++) - UB[i][j] += R[i][l] * H[l][j]; + for (i = 0; i < 3; i++) + for (j = 0; j < 3; j++) + for (l = 0; l < 3; l++) + UB[i][j] += R[i][l] * H[l][j]; } /* Now form ubi and copy to argument */ if ((k == 0) && (inverse3x3(UB) == 0)) { - for (i = 0; i < 3; i++) - for (j = 0; j < 3; j++) - ubi[i][j] = UB[i][j]; + for (i = 0; i < 3; i++) + for (j = 0; j < 3; j++) + ubi[i][j] = UB[i][j]; } else { - /* Determinant was zero - leave ubi as it is */ + /* Determinant was zero - leave ubi as it is */ } if (n > 0) { - sumdrlv2 /= n; + sumdrlv2 /= n; } /* return values */ @@ -285,143 +343,594 @@ void score_and_refine(vec ubi[3], vec gv[], double tol, *sumdrlv2_arg = sumdrlv2; } -int score_and_assign(vec * __restrict ubi, vec * __restrict gv, double tol, - double *__restrict drlv2, int *__restrict labels, - int label, int ng) -{ +/* F2PY_WRAPPER_START + function score_and_assign( ubi, gv, tol, drlv2, labels,label, ng ) +!DOC score_and_assign is similar to score but it assigns peaks to this +!DOC ubi only if they fit the data better than the current UBI. +!DOC It updates drlv2 and labels to use best fitting grain for each peak. +!DOC ... perhaps this is not what you want for overlapping peaks in twins! + intent(c) score_and_assign + intent(c) + integer score_and_assign + double precision, intent(in) :: ubi(3,3) + double precision, intent(in) :: gv(ng,3) + integer, intent(hide), depend( gv ) :: ng + double precision, intent(in) :: tol + double precision, intent(inout) :: drlv2(ng) + integer*4, intent(inout) :: labels(ng) + integer, intent(in) :: label + ! NOT threadsafe - labels will be shared + end function score_and_assign +F2PY_WRAPPER_END */ +int score_and_assign(vec *restrict ubi, vec *restrict gv, double tol, + double *restrict drlv2, int *restrict labels, int label, + int ng) { double h0, h1, h2, t0, t1, t2, sumsq, tolsq; int k, n; tolsq = tol * tol; n = 0; -#pragma omp parallel for private(h0,h1,h2,t0,t1,t2,sumsq) reduction(+:n) +#pragma omp parallel for private(h0, h1, h2, t0, t1, t2, sumsq) reduction(+ : n) schedule(static, 4096) for (k = 0; k < ng; k++) { - h0 = ubi[0][0] * gv[k][0] + ubi[0][1] * gv[k][1] + ubi[0][2] * gv[k][2]; - h1 = ubi[1][0] * gv[k][0] + ubi[1][1] * gv[k][1] + ubi[1][2] * gv[k][2]; - h2 = ubi[2][0] * gv[k][0] + ubi[2][1] * gv[k][1] + ubi[2][2] * gv[k][2]; - t0 = h0 - conv_double_to_int_fast(h0); - t1 = h1 - conv_double_to_int_fast(h1); - t2 = h2 - conv_double_to_int_fast(h2); - sumsq = t0 * t0 + t1 * t1 + t2 * t2; - /* If this peak fits better than the one in drlv2 then we - * assign it: - */ - if ((sumsq < tolsq) && (sumsq < drlv2[k])) { - labels[k] = label; - drlv2[k] = sumsq; - n++; - } else if (labels[k] == label) { - /* We thought it belonged but it does not */ - labels[k] = -1; - } + h0 = ubi[0][0] * gv[k][0] + ubi[0][1] * gv[k][1] + ubi[0][2] * gv[k][2]; + h1 = ubi[1][0] * gv[k][0] + ubi[1][1] * gv[k][1] + ubi[1][2] * gv[k][2]; + h2 = ubi[2][0] * gv[k][0] + ubi[2][1] * gv[k][1] + ubi[2][2] * gv[k][2]; + t0 = h0 - conv_double_to_int_fast(h0); + t1 = h1 - conv_double_to_int_fast(h1); + t2 = h2 - conv_double_to_int_fast(h2); + sumsq = t0 * t0 + t1 * t1 + t2 * t2; + /* If this peak fits better than the one in drlv2 then we + * assign it: + */ + if ((sumsq < tolsq) && (sumsq < drlv2[k])) { + labels[k] = label; + drlv2[k] = sumsq; + n++; + } else if (labels[k] == label) { + /* We thought it belonged but it does not */ + labels[k] = -1; + } } return n; } -void refine_assigned(vec ubi[3], vec gv[], int labels[], int label, - int *npk, double *sumdrlv2, int ng) -{ +/* F2PY_WRAPPER_START + subroutine refine_assigned( ubi, gv, labels, label, npk, drlv2, ng ) +!DOC refine_assigned fits a ubi matrix to a set of g-vectors and assignments +!DOC in labels. e.g. where(labels==label) it uses the peaks. +!DOC ... perhaps this is not what you want for overlapping peaks in twins! + intent(c) refine_assigned + double precision, intent(c,in) :: ubi(3,3) + double precision, intent(c,in) :: gv(ng,3) + integer, intent(c,in) :: labels(ng) + integer, intent(c,in) :: label + ! Note : pass by ref + integer, intent(out) :: npk + double precision, intent(out) :: drlv2 + integer, intent(c,hide), depend( gv ) :: ng + threadsafe + end subroutine refine_assigned +F2PY_WRAPPER_END */ +void refine_assigned(vec ubi[3], vec gv[], int labels[], int label, int *npk, + double *sumdrlv2, int ng) { /* Skip the part about weights, not used */ - double sumsqtot, sumsq, h[3], t[3]; + double sumsqtot, sumsq, h[3], t[3], ih[3]; double R[3][3], H[3][3], UB[3][3]; - int i, j, n, k, l, ih[3]; + int i, j, n, k, l; n = 0; sumsqtot = 0; for (k = 0; k < ng; k++) { - if (label != labels[k]) { - continue; - } - n++; - for (j = 0; j < 3; j++) { - h[j] = - ubi[j][0] * gv[k][0] + ubi[j][1] * gv[k][1] + - ubi[j][2] * gv[k][2]; - ih[j] = conv_double_to_int_fast(h[j]); - t[j] = h[j] - ih[j]; - } - sumsq = t[0] * t[0] + t[1] * t[1] + t[2] * t[2]; - sumsqtot += sumsq; - for (i = 0; i < 3; i++) { - for (j = 0; j < 3; j++) { - R[i][j] = R[i][j] + ih[j] * gv[k][i]; - H[i][j] = H[i][j] + ih[j] * ih[i]; - } - } + if (label != labels[k]) { + continue; + } + n++; + for (j = 0; j < 3; j++) { + h[j] = ubi[j][0] * gv[k][0] + ubi[j][1] * gv[k][1] + + ubi[j][2] * gv[k][2]; + ih[j] = conv_double_to_int_fast(h[j]); + t[j] = h[j] - ih[j]; + } + sumsq = t[0] * t[0] + t[1] * t[1] + t[2] * t[2]; + sumsqtot += sumsq; + for (i = 0; i < 3; i++) { + for (j = 0; j < 3; j++) { + R[i][j] = R[i][j] + ih[j] * gv[k][i]; + H[i][j] = H[i][j] + ih[j] * ih[i]; + } + } } /* outputs */ *npk = n; if (n > 0) { - *sumdrlv2 = sumsqtot / n; + *sumdrlv2 = sumsqtot / n; } else { - *sumdrlv2 = 0.; + *sumdrlv2 = 0.; } /* And the fitted matrix */ k = inverse3x3(H); if (k == 0) { - /* Form best fit UB */ - for (i = 0; i < 3; i++) - for (j = 0; j < 3; j++) - for (l = 0; l < 3; l++) - UB[i][j] = UB[i][j] + R[i][l] * H[l][j]; + /* Form best fit UB */ + for (i = 0; i < 3; i++) + for (j = 0; j < 3; j++) + for (l = 0; l < 3; l++) + UB[i][j] = UB[i][j] + R[i][l] * H[l][j]; } if (k == 0 && inverse3x3(UB) == 0) { - /* Copy to output */ - for (i = 0; i < 3; i++) - for (j = 0; j < 3; j++) - ubi[i][j] = UB[i][j]; + /* Copy to output */ + for (i = 0; i < 3; i++) + for (j = 0; j < 3; j++) + ubi[i][j] = UB[i][j]; } } -/* weighted_refine was never used */ +/* F2PY_WRAPPER_START + subroutine put_incr64( data, ind, vals, boundscheck, n, m) +!DOC put_incr64 does the simple loop : data[ind] += vals +!DOC not sure why this isn't in numpy +!DOC uses 64 bit addressing + intent(c) put_incr64 + intent(c) + real, intent(inout) :: data(m) + real, intent(in) :: vals(n) + integer(kind=8), dimension(n), intent(in) :: ind + integer, intent(hide), depend( data ) :: m + integer, intent(hide), depend( ind) :: n + integer, optional :: boundscheck = 0 + ! NOT threadsafe? meant for updating data with increments + end subroutine put_incr +F2PY_WRAPPER_END */ +void put_incr64(float data[], int64_t ind[], float vals[], int boundscheck, + int n, int m) { + int64_t k, ik; + if (boundscheck == 0) { + for (k = 0; k < n; k++) + data[ind[k]] += vals[k]; + } else { + for (k = 0; k < n; k++) { + ik = ind[k]; + if (ik < 0 || ik >= m) { + printf("Array bounds error! k=%d ind[k]=%d\n", (int)k, + (int)ind[k]); + } else { + data[ind[k]] += vals[k]; + } + } + } +} -void put_incr(float data[], size_t ind[], float vals[], int boundscheck, - int n, int m) -{ - int k, ik; +/* F2PY_WRAPPER_START + + subroutine put_incr32( data, ind, vals, boundscheck, n, m) +!DOC put_incr32 does the simple loop : data[ind] += vals +!DOC not sure why this isn't in numpy +!DOC uses 32 bit addressing + intent(c) put_incr32 + intent(c) + real, intent(inout) :: data(m) + real, intent(in) :: vals(n) + integer(kind=4), dimension(n), intent(in) :: ind + integer, intent(hide), depend( data ) :: m + integer, intent(hide), depend( ind) :: n + integer, optional :: boundscheck = 0 + ! NOT threadsafe? meant for updating data with increments + end subroutine put_incr + +F2PY_WRAPPER_END */ +void put_incr32(float data[], int32_t ind[], float vals[], int boundscheck, + int n, int m) { + int32_t k, ik; if (boundscheck == 0) { - for (k = 0; k < n; k++) - data[ind[k]] += vals[k]; + for (k = 0; k < n; k++) + data[ind[k]] += vals[k]; + } else { + for (k = 0; k < n; k++) { + ik = ind[k]; + if (ik < 0 || ik >= m) { + printf("Array bounds error! k=%d ind[k]=%d\n", (int)k, + (int)ind[k]); + } else { + data[ind[k]] += vals[k]; + } + } + } +} + +/* F2PY_WRAPPER_START + + subroutine cluster1d( ar, n, order, tol, nclusters, ids, avgs) +!DOC cluster1d is used in sandbox/friedel.py to find clusters of peaks +!DOC work in progress + intent(c) cluster1d + double precision, intent(c, in) :: ar(n) + integer, intent(c, hide), depend( ar ) :: n + integer, intent(c, in) :: order(n) + double precision, intent(c, in) :: tol + integer, intent(out) :: nclusters + integer, intent(c,inout) :: ids(n) + double precision, intent(c, inout) :: avgs(n) + ! NOT threadsafe since ids may be shared + end subroutine cluster1d +F2PY_WRAPPER_END */ +void cluster1d(double ar[], int n, int order[], double tol, // IN + int *nclusters, int ids[], double avgs[]) { // OUT + // Used in sandbox/friedel.py + int i, ncl; + double dv; + // order is the order of the peaks to get them sorted + avgs[0] = ar[order[0]]; + ncl = 1; // number in this cluster + ids[0] = 0; // cluster assignments ( in order ) + for (i = 1; i < n; i++) { + dv = ar[order[i]] - ar[order[i - 1]]; // difference in values + if (dv > tol) { // make a new cluster + if (ncl > 1) { // make avg for the last one + avgs[ids[i - 1]] = avgs[ids[i - 1]] / ncl; + } + ids[i] = ids[i - 1] + 1; // increment id + ncl = 1; // pks in this cluster + avgs[ids[i]] = ar[order[i]]; // store value for avg + } else { + ids[i] = ids[i - 1]; // copy last id + ncl = ncl + 1; + avgs[ids[i]] = avgs[ids[i]] + ar[order[i]]; // sum on for avg + } + } // end for(i ... + // make the last average if necessary + if (ncl > 1) { + avgs[ids[i - 1]] /= ncl; + } + *nclusters = ids[n - 1] + 1; +} + +/* F2PY_WRAPPER_START + subroutine score_gvec_z( ubi, ub, gv, g0, g1, g2, e, recompute, n ) +!DOC score_gvec_z reads ubi, ub, gv and recompute +!DOC if (recompute) it fills directions to project errors per peak: +!DOC g0 = gv / |gv| = unit vector along g +!DOC g1 = gxy / |gxy| = unit vector perpendicular to z and g (omega) +!DOC g2 ... ought to be cross( g0, g1 ) ? (eta) +!DOC For all peaks it computes h = ubi.g, rounds to nearest ih = int(h) +!DOC and then computes gcalc = ub.ih = dot( ub, ( round( dot( ubi, g) ) ) ) +!DOC The error gv - gcalc is then projected into the co-ordinate system +!DOC g0,g1,g2 for errors along g, z and the rhs +!DOC Beware : work in progress. Is z always the right axis? + intent(c) score_gvec_z + intent(c) + double precision, intent(in) :: ubi(3,3) + double precision, intent(in) :: ub(3,3) + integer, intent(c, hide), depend( gv ) :: n + double precision, intent(in) :: gv(n,3) + double precision, intent(inout) :: g0(n,3) + double precision, intent(inout) :: g1(n,3) + double precision, intent(inout) :: g2(n,3) + double precision, intent(inout) :: e(n,3) + integer, intent(in) :: recompute + ! NOT threadsafe since gi may be shared + end subroutine score_gvec_z +F2PY_WRAPPER_END */ +void score_gvec_z(vec ubi[3], // in + vec ub[3], // in + vec gv[], // in + vec g0[], // inout normed(g) + vec g1[], // inout normed(axis x g) + vec g2[], // inout normed(axis x (axis x g)) + vec e[], // inout + int recompute, // in + int n) { + /* Axis is z and we hard wire it here + * Compute errors in a co-ordinate system given by: + * parallel to gv (gv*imodg) + * parallel to cross( axis, gv ) + * parallel to cross( axis, cross( axis, gv ) ) + */ + int i; + double t, txy; + vec g, h, d; +#pragma omp parallel for private(i, t, txy, g, h, d) + for (i = 0; i < n; i++) { + g[0] = gv[i][0]; + g[1] = gv[i][1]; + g[2] = gv[i][2]; + // Test - is it faster to recompute or cache ? + // for many ubi ? Loop over peaks or ubis or ? + if (recompute) { // Fill in ub, modg, ax_x_gv, ax_ax_x_gv + t = 1. / sqrt(g[0] * g[0] + g[1] * g[1] + g[2] * g[2]); + g0[i][0] = g[0] * t; + g0[i][1] = g[1] * t; + g0[i][2] = g[2] * t; + txy = g[0] * g[0] + g[1] * g[1]; + t = 1. / sqrt(txy); + g1[i][0] = -g[1] * t; // [-y,x,0] + g1[i][1] = g[0] * t; + g1[i][2] = 0.; + t = 1. / sqrt(g[0] * g[0] * g[2] * g[2] + + g[1] * g[1] * g[2] * g[2] + txy * txy); + g2[i][0] = g[0] * g[2] * t; + g2[i][1] = g[1] * g[2] * t; + g2[i][2] = -(g[0] * g[0] + g[1] * g[1]) * t; + } // end recompute + + // Find integer h,k,l + h[0] = (double)conv_double_to_int_fast( + ubi[0][0] * g[0] + ubi[0][1] * g[1] + ubi[0][2] * g[2]); + h[1] = (double)conv_double_to_int_fast( + ubi[1][0] * g[0] + ubi[1][1] * g[1] + ubi[1][2] * g[2]); + h[2] = (double)conv_double_to_int_fast( + ubi[2][0] * g[0] + ubi[2][1] * g[1] + ubi[2][2] * g[2]); + + // Compute diff, the computed g-vector - original + d[0] = ub[0][0] * h[0] + ub[0][1] * h[1] + ub[0][2] * h[2] - g[0]; + d[1] = ub[1][0] * h[0] + ub[1][1] * h[1] + ub[1][2] * h[2] - g[1]; + d[2] = ub[2][0] * h[0] + ub[2][1] * h[1] + ub[2][2] * h[2] - g[2]; + + // Now dot this into the local co-ordinate system + e[i][0] = g0[i][0] * d[0] + g0[i][1] * d[1] + g0[i][2] * d[2]; + e[i][1] = g1[i][0] * d[0] + g1[i][1] * d[1] + g1[i][2] * d[2]; + e[i][2] = g2[i][0] * d[0] + g2[i][1] * d[1] + g2[i][2] * d[2]; + } +} + +/* F2PY_WRAPPER_START + + function misori_cubic( u1, u2) +!DOC misori_cubic computes the trace of the smallest misorientation +!DOC for cubic symmetry +!DOC u1 and u2 are both orientation matrices "U" +!DOC compute u1. u2.T to get the rotation from one to the other +!DOC find the permutation that will maximise the trace +!DOC one of six... +!DOC xyz yxz zxy +!DOC xzy yzx zyx +!DOC Beware : work in progress. Which point group is this? + intent(c) misori_cubic + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_cubic + threadsafe + end function misori_cubic +F2PY_WRAPPER_END */ +double misori_cubic(vec u1[3], vec u2[3]) { + /* Compute the trace of the smallest misorientation + * for cubic symmetry + * u1 and u2 are both orientation matrices "U" + * + * compute u1. u2.T to get the rotation from one to the other + * find the permutation that will maximise the trace + * one of six... + * xyz yxz zxy + * xzy yzx zyx + */ + int i, j, k; + double t[6], m1, m2, m3; + vec r[3]; + for (i = 0; i < 3; i++) { + for (j = 0; j < 3; j++) { + r[i][j] = 0.; + for (k = 0; k < 3; k++) + r[i][j] += u1[k][i] * u2[k][j]; + } + } + /* 6 possibilities, 18 entries, each appears twice + [0,0][1,1][2,2] + [0,0][1,2][2,1] + [0,1][1,0][2,2] + [0,1][2,0][1,2] + [0,2][1,0][2,1] + [0,2][2,0][1,1] + */ + t[0] = fabs(r[0][0]) + fabs(r[1][1]) + fabs(r[2][2]); + t[1] = fabs(r[0][0]) + fabs(r[1][2]) + fabs(r[2][1]); + t[2] = fabs(r[0][1]) + fabs(r[1][0]) + fabs(r[2][2]); + t[3] = fabs(r[0][1]) + fabs(r[2][0]) + fabs(r[1][2]); + t[4] = fabs(r[0][2]) + fabs(r[1][0]) + fabs(r[2][1]); + t[5] = fabs(r[0][2]) + fabs(r[2][0]) + fabs(r[1][1]); + /* select the maximum */ + m1 = (t[0] > t[1]) ? t[0] : t[1]; + m2 = (t[2] > t[3]) ? t[2] : t[3]; + m3 = (t[4] > t[5]) ? t[4] : t[5]; + m2 = (m2 > m3) ? m2 : m3; + m1 = (m1 > m2) ? m1 : m2; + return m1; +} + +/* F2PY_WRAPPER_START + + function misori_orthorhombic( u1, u2) +!DOC misori_orthorhombic computes the trace of the smallest misorientation +!DOC u1 and u2 are both orientation matrices "U" +!DOC compute u1. u2.T to get the rotation from one to the other +!DOC find the flips that will maximise the trace: +!DOC abs( trace(dot(u1,u2.T) )) +!DOC Looks like point group mmm. Not sure why this is in C? +!DOC Beware: work in progress + intent(c) misori_orthorhombic + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_orthorhombic + threadsafe + end function misori_orthorhombic +F2PY_WRAPPER_END */ +double misori_orthorhombic(vec u1[3], vec u2[3]) { + /* Compute the trace of the smallest misorientation + * for orthorhombic symmetry + * u1 and u2 are both orientation matrices "U" + * + * compute u1. u2.T to get the rotation from one to the other + * find the flips - just abs(trace) + */ + int i, k; + double ti, t; + t = 0; + for (i = 0; i < 3; i++) { + ti = 0.; + for (k = 0; k < 3; k++) { + ti += u1[k][i] * u2[k][i]; + } + t += fabs(ti); + } + return t; +} + +/* F2PY_WRAPPER_START + function misori_tetragonal( u1, u2) +!DOC misori_tetragonal computes the trace of the smallest misorientation +!DOC u1 and u2 are both orientation matrices "U" +!DOC compute u1. u2.T to get the rotation from one to the other +!DOC finds the flips a/b and c->-c that will maximise the trace: +!DOC abs( trace(dot(u1,u2.T) )) +!DOC Looks like point group 4/mmm. What about 4/m ? +!DOC Beware: work in progress + intent(c) misori_tetragonal + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_tetragonal + threadsafe + end function misori_tetragonal +F2PY_WRAPPER_END */ +double misori_tetragonal(vec u1[3], vec u2[3]) { + /* Compute the trace of the smallest misorientation + * for orthorhombic symmetry + * u1 and u2 are both orientation matrices "U" + * + * compute u1. u2.T to get the rotation from one to the other + * find the flips for c and select ab versus ba + */ + int i, j, k; + double m1, m2, m3; + vec r[3]; + /* c-axis */ + m3 = 0.; + for (k = 0; k < 3; k++) { + m3 += u1[k][2] * u2[k][2]; + } + m3 = fabs(m3); + /* ab */ + for (i = 0; i < 2; i++) { + for (j = 0; j < 2; j++) { + r[i][j] = 0.; + for (k = 0; k < 3; k++) { + r[i][j] += u1[k][i] * u2[k][j]; + } + } + } + m1 = fabs(r[0][0]) + fabs(r[1][1]); + m2 = fabs(r[1][0]) + fabs(r[0][1]); + if (m2 > m3) { + return m1 + m3; } else { - for (k = 0; k < n; k++) { - ik = ind[k]; - if (ik < 0 || ik >= m) { - printf("Array bounds error! k=%d ind[k]=%d\n", k, (int)ind[k]); - } else { - data[ind[k]] += vals[k]; - } - } + return m2 + m3; } } +/* F2PY_WRAPPER_START + + function misori_monoclinic( u1, u2) +!DOC misori_monoclinic assumes a unique b axis and only checks +!DOC the flip of b -> -b +!DOC Not sure about the point group here. Is is 2/m ?? +!DOC Beware: work in progress + intent(c) misori_monoclinic + intent(c) + double precision, intent(in) :: u1(3,3), u2(3,3) + ! Returns + double precision :: misori_monoclinic + threadsafe + end function misori_monoclinic +F2PY_WRAPPER_END */ +double misori_monoclinic(vec u1[3], vec u2[3]) { + /* Compute the trace of the smallest misorientation + * for orthorhombic symmetry + * u1 and u2 are both orientation matrices "U" + * + * compute u1. u2.T to get the rotation from one to the other + * find the flips - can only flip b -> -b + */ + int i, k; + double ti, t; + t = 0; + for (i = 0; i < 3; i++) { + ti = 0.; + for (k = 0; k < 3; k++) { + ti += u1[k][i] * u2[k][i]; + } + if (i == 1) { /* can only flip b to -b */ + t += fabs(ti); + } else { + t += ti; + } + } + return t; +} -void cluster1d( double ar[], int n, int order[], double tol, // IN - int *nclusters, int ids[], double avgs[]){ // OUT - int i, ncl; - double dv; - // order is the order of the peaks to get them sorted - avgs[0] = ar[order[0]]; - ncl = 1; // number in this cluster - ids[0] = 0; // cluster assignments ( in order ) - for( i=1 ; i tol ){ // make a new cluster - if( ncl > 1 ) { // make avg for the last one - avgs[ ids[i-1] ] = avgs[ids[i-1]] / ncl; - } - ids[i] = ids[i-1] + 1; // increment id - ncl = 1; // pks in this cluster - avgs[ ids[i] ] = ar[order[i]]; // store value for avg - } else { - ids[i] = ids[i-1]; // copy last id - ncl = ncl + 1; - avgs[ids[i]] = avgs[ids[i]] + ar[order[i]]; // sum on for avg +/* F2PY_WRAPPER_START + subroutine misori_cubic_pairs( u, dist, n) +!DOC misori_cubic_pairs Computes the dissimilarity matrix to use for clustering +!DOC of orientations. Fills in an upper triangular matrix that you can try +!DOC to pass to scipy.cluster routines + intent(c) misori_cubic_pairs + intent(c) + double precision, intent(in) :: u(n,3,3) + integer, intent( hidden ), depend(u) :: n + double precision, intent(inout) :: dist( (n*(n-1)/2) ) + ! NOT threadsafe + end subroutine misori_cubic_pairs +F2PY_WRAPPER_END */ +void misori_cubic_pairs(vec u[], double pairs[], int n) { + /* Computes the dissimilarity matrix to use for clustering + */ + int i, j, k; + /* static, 1 means interleaved - leads to false sharing in pairs write + * but tries to deal with the rows getting shorter + */ +#pragma omp parallel for private(i, j, k) schedule(static, 1) + for (i = 0; i < n; i++) { + for (j = i + 1; j < n; j++) { + /* https://stackoverflow.com/questions/27086195/linear-index-upper-triangular-matrix + */ + k = (n * (n - 1) / 2) - (n - i) * ((n - i) - 1) / 2 + j - i - 1; + pairs[k] = misori_cubic(&u[i * 3], &u[j * 3]); + } } - } // end for(i ... - // make the last average if necessary - if( ncl > 1 ) { - avgs[ ids[i-1] ] /= ncl; - } - *nclusters = ids[n-1]+1; } +/* F2PY_WRAPPER_START + function count_shared( pi, ni, pj, nj ) +!DOC count_shared takes two sorted arrays in pi and pj and counts +!DOC how many collisions there are. Useful to compare two lists of +!DOC peak to grain assignments, or pixel to peak assignments, etc + intent(c) count_shared + intent(c) + integer, intent(in) :: pi(ni) + integer, intent( hidden ), depend(pi) :: ni + integer, intent(in) :: pj(nj) + integer, intent( hidden ), depend(pj) :: nj + ! return value is int too + integer :: count_shared + threadsafe + end function count_shared +F2PY_WRAPPER_END */ +int count_shared(int pi[], int ni, int pj[], int nj) { + /* Given two sorted arrays compute how many collisions + * For comparing list of grain - peak indices for overlap + */ + int i, j, c; + i = 0; + j = 0; + c = 0; + while ((i < ni) && (j < nj)) { + if (pi[i] > pj[j]) { + j++; + } else if (pi[i] < pj[j]) { + i++; + } else { + i++; + j++; + c++; + } + } + return c; +} diff --git a/src/connectedpixels.c b/src/connectedpixels.c index 6a77490a..ce0a33b6 100644 --- a/src/connectedpixels.c +++ b/src/connectedpixels.c @@ -1,18 +1,19 @@ +#include "blobs.h" /* Disjoint sets thing for blob finding */ +#include #include #include -#include -#include "blobs.h" /* INTEGER */ +#include -void boundscheck(int jpk, int n2, int ipk, int n1) -{ +DLL_LOCAL +void boundscheck(int jpk, int n2, int ipk, int n1) { if ((jpk < 0) || jpk >= n2) { - printf("Bounds check error, jpk, n2\n"); - exit(0); + printf("Bounds check error, jpk, n2\n"); + exit(0); } if ((ipk < 0) || ipk >= n1) { - printf("Bounds check error, jpk, n1\n"); - exit(0); + printf("Bounds check error, jpk, n1\n"); + exit(0); } } @@ -36,52 +37,43 @@ void boundscheck(int jpk, int n2, int ipk, int n1) */ -/* Updated to use f2py to generate bindings, 2017 */ - -static char moduledocs[] = - " C extensions for image analysis, part of ImageD11\n" " "; - -#include "blobs.h" /* Disjoint sets thing for blob finding */ - /* ==== connectedpixels ======================================== */ -static char connectedpixels_doc[] = - " nblobs = connectedpixels ( data=Numeric.array(data, 2D) , \n" - " labels=Numeric.array(blob, 2D, Int) ,\n" - " threshold=float threshold ,\n" - " verbose=Int verbose )\n" - " data is normally an image \n" - " blob is an array to receive pixel -> blob assignments\n" - " threshold is the value above which a pixel is considered to be in a blob\n" - " verbose flags printing on stdout"; - -/* Fill in an image of peak assignments for pixels */ - -void match(INTEGER * new, INTEGER * old, INTEGER * S) -{ - /* printf("match %d %d\n",*new,*old); */ - if (*new == 0) { - *new = *old; - } else { - if (*new != *old) { - dset_makeunion(S, *old, *new); - } - } -} - -int connectedpixels(float *data, INTEGER * labels, float threshold, int verbose, - int eightconnected, int ns, int nf) -{ +/* F2PY_WRAPPER_START + function connectedpixels( data, labels, threshold, & + verbose, con8, ns, nf) + intent(c) connectedpixels +!DOC connectedpixels Determines which pixels in data are above the +!DOC user supplied threshold and assigns them into connected objects +!DOC which are output in labels. Connectivity is 3x3 box (8) by default +!DOC and reduces to a +(4) is con8==0 + intent(c) + real, intent(in) :: data(ns,nf) + integer, intent(inout) :: labels(ns,nf) + integer, intent(hide), depend(data) :: ns=shape(data,0) + integer, intent(hide), depend(data) :: nf=shape(data,1) + integer, optional :: con8 = 1 + integer, optional :: verbose = 0 + real threshold + ! Returns + integer :: connectedpixels + end function connectedpixels +F2PY_WRAPPER_END */ + +// Exported function is in the C wrapper, not here! +DLL_LOCAL +int connectedpixels(float *data, int32_t *labels, float threshold, int verbose, + int eightconnected, int ns, int nf) { int i, j, irp, ir, ipx; - INTEGER k, *S, *T, np; + int32_t k, *S, *T, np; if (verbose) { - printf("Welcome to connectedpixels "); - if (eightconnected) - printf("Using connectivity 8\n"); - else - printf("Using connectivity 4\n"); + printf("Welcome to connectedpixels "); + if (eightconnected) + printf("Using connectivity 8\n"); + else + printf("Using connectivity 4\n"); } /* lots of peaks possible */ @@ -96,259 +88,280 @@ int connectedpixels(float *data, INTEGER * labels, float threshold, int verbose, /* First point */ /* i = 0; j = 0; */ if (data[0] > threshold) { - S = dset_new(&S, &labels[0]); + S = dset_new(&S, &labels[0]); } else { - labels[0] = 0; + labels[0] = 0; } /* First row */ for (j = 1; j < nf; j++) { - labels[j] = 0; /* initialize */ - if (data[j] > threshold) { - if (labels[j - 1] > 0) { - labels[j] = labels[j - 1]; - } else { - S = dset_new(&S, &labels[j]); - } - } + labels[j] = 0; /* initialize */ + if (data[j] > threshold) { + if (labels[j - 1] > 0) { + labels[j] = labels[j - 1]; + } else { + S = dset_new(&S, &labels[j]); + } + } } /* === Mainloop ============================================= */ - for (i = 1; i < ns; i++) { /* i-1 prev row always exists, see above */ - ir = i * nf; /* this row */ - irp = ir - nf; /* prev row */ - /* First point */ - /* j=0; */ - labels[ir] = 0; - if (data[ir] > threshold) { - if (labels[irp] > 0) { - labels[ir] = labels[irp]; - } - if (eightconnected && (labels[irp + 1] > 0)) { - match(&labels[ir], &labels[irp + 1], S); - } - if (labels[ir] == 0) { - S = dset_new(&S, &labels[ir]); - } - } - /* Run along row to just before end */ - for (j = 1; j < nf - 1; j++) { - ipx = ir + j; - irp = ipx - nf; - labels[ipx] = 0; - if (data[ipx] > threshold) { - /* Pixel needs to be assigned */ - if (eightconnected && (labels[irp - 1] > 0)) { - match(&labels[ipx], &labels[irp - 1], S); - } - if (labels[irp] > 0) { - match(&labels[ipx], &labels[irp], S); - } - if (eightconnected && (labels[irp + 1] > 0)) { - match(&labels[ipx], &labels[irp + 1], S); - } - if (labels[ipx - 1] > 0) { - match(&labels[ipx], &labels[ipx - 1], S); - } - if (labels[ipx] == 0) { /* Label is new ! */ - S = dset_new(&S, &labels[ipx]); - } - } /* (val > threshold) */ - } /* Mainloop j */ - /* Last pixel on the row */ - ipx = ir + nf - 1; - irp = ipx - nf; - labels[ipx] = 0; - if (data[ipx] > threshold) { - if (eightconnected && (labels[irp - 1] > 0)) { - match(&labels[ipx], &labels[irp - 1], S); - } - if (labels[irp] > 0) { - match(&labels[ipx], &labels[irp], S); - } - if (labels[ipx - 1] > 0) { - match(&labels[ipx], &labels[ipx - 1], S); - } - if (labels[ipx] == 0) { /* Label is new ! */ - S = dset_new(&S, &labels[ipx]); - } - } - + for (i = 1; i < ns; i++) { /* i-1 prev row always exists, see above */ + ir = i * nf; /* this row */ + irp = ir - nf; /* prev row */ + /* First point */ + /* j=0; */ + labels[ir] = 0; + if (data[ir] > threshold) { + if (labels[irp] > 0) { + labels[ir] = labels[irp]; + } + if (eightconnected && (labels[irp + 1] > 0)) { + match(labels[ir], labels[irp + 1], S); + } + if (labels[ir] == 0) { + S = dset_new(&S, &labels[ir]); + } + } + /* Run along row to just before end */ + for (j = 1; j < nf - 1; j++) { + ipx = ir + j; + irp = ipx - nf; + labels[ipx] = 0; + if (data[ipx] > threshold) { + /* Pixel needs to be assigned */ + if (eightconnected && (labels[irp - 1] > 0)) { + match(labels[ipx], labels[irp - 1], S); + } + if (labels[irp] > 0) { + match(labels[ipx], labels[irp], S); + } + if (eightconnected && (labels[irp + 1] > 0)) { + match(labels[ipx], labels[irp + 1], S); + } + if (labels[ipx - 1] > 0) { + match(labels[ipx], labels[ipx - 1], S); + } + if (labels[ipx] == 0) { /* Label is new ! */ + S = dset_new(&S, &labels[ipx]); + } + } /* (val > threshold) */ + } /* Mainloop j */ + /* Last pixel on the row */ + ipx = ir + nf - 1; + irp = ipx - nf; + labels[ipx] = 0; + if (data[ipx] > threshold) { + if (eightconnected && (labels[irp - 1] > 0)) { + match(labels[ipx], labels[irp - 1], S); + } + if (labels[irp] > 0) { + match(labels[ipx], labels[irp], S); + } + if (labels[ipx - 1] > 0) { + match(labels[ipx], labels[ipx - 1], S); + } + if (labels[ipx] == 0) { /* Label is new ! */ + S = dset_new(&S, &labels[ipx]); + } + } } - /* Now compress the disjoint set to make single list of + /* Now compress the disjoint set to make single list of * unique labels going from 1->n */ T = dset_compress(&S, &np); /* Now scan through image re-assigning labels as needed */ +#pragma omp parallel for private(j, ipx, k) shared(labels) for (i = 0; i < ns; i++) { - for (j = 0; j < nf; j++) { - ipx = i * nf + j; - k = labels[ipx]; - if (k > 0) { - if (T[k] == 0) { - printf("Error in connectedpixels\n"); - } - if (T[k] != k) { - labels[i * nf + j] = T[k]; - } - } - } + for (j = 0; j < nf; j++) { + ipx = i * nf + j; + k = labels[ipx]; + if (k > 0) { + if (T[k] == 0) { + printf("Error in connectedpixels\n"); + } + if (T[k] != k) { + labels[i * nf + j] = T[k]; + } + } + } } free(S); free(T); return np; } -static char blobproperties_doc[] = - "res = blobproperties ( Numeric.array(data, 2D) , \n" - " Numeric.array(blob, 2D, Int) ,\n" - " Int np , \n" - " Int verbose )\n" - "\n" - " Computes various properties of a blob image " - " (created by connectedpixels)\n" - " data = image data \n" - " blob = integer peak assignments from connectedpixels \n" - " np = number of peaks to treat \n" - " verbose - flag about whether to print\n" " \n"; - -void blobproperties(float *data, INTEGER * labels, INTEGER np, float omega, - int verbose, int ns, int nf, double *res) -{ +/* F2PY_WRAPPER_START + subroutine blobproperties( data, labels, np, omega, & + verbose, ns, nf, results) + intent(c) blobproperties +!DOC blobproperties fills the array results with properties of each labelled +!DOC object described by data (pixel values) and labels. The omega value +!DOC is the angle for this frame. +!DOC results are FIXME + intent(c) + real, intent(in) :: data(ns, nf) + integer, intent(in) :: labels(ns, nf) + integer, intent(hide), depend(data) :: ns=shape(data,0) + integer, intent(hide), depend(data) :: nf=shape(data,1) + integer, intent(in) :: np + double precision, intent(out) :: results( np, NPROPERTY ) + real, intent(in), optional :: omega = 0 + integer, optional :: verbose = 0 + threadsafe + end subroutine blobproperties +F2PY_WRAPPER_END */ +void blobproperties(float *data, int32_t *labels, int32_t npk, float omega, + int verbose, int ns, int nf, double *res) { int i, j, bad, ipx; double fval; - INTEGER ipk; + int32_t ipk; if (verbose) { - printf("Computing blob moments, ns %d, nf %d, np %d\n", ns, nf, np); + printf("Computing blob moments, ns %d, nf %d, npk %d\n", ns, nf, npk); } /* Initialise the results */ - for (i = 0; i < np; i++) { - for (j = 0; j < NPROPERTY; j++) { - res[i * NPROPERTY + j] = 0.; - } - /* Set min to max +1 and vice versa */ - res[i * NPROPERTY + bb_mn_f] = nf + 1; - res[i * NPROPERTY + bb_mn_s] = ns + 1; - res[i * NPROPERTY + bb_mx_f] = -1; - res[i * NPROPERTY + bb_mx_s] = -1; - /* All pixels have the same omega in this frame */ - res[i * NPROPERTY + bb_mx_o] = omega; - res[i * NPROPERTY + bb_mn_o] = omega; + for (i = 0; i < npk; i++) { + for (j = 0; j < NPROPERTY; j++) { + res[i * NPROPERTY + j] = 0.; + } + /* Set min to max +1 and vice versa */ + res[i * NPROPERTY + bb_mn_f] = nf + 1; + res[i * NPROPERTY + bb_mn_s] = ns + 1; + res[i * NPROPERTY + bb_mx_f] = -1; + res[i * NPROPERTY + bb_mx_s] = -1; + /* All pixels have the same omega in this frame */ + res[i * NPROPERTY + bb_mx_o] = omega; + res[i * NPROPERTY + bb_mn_o] = omega; } if (verbose != 0) - printf("Scanning image\n"); + printf("Scanning image\n"); bad = 0; /* i,j is looping along the indices data array */ for (i = 0; i < ns; i++) { - for (j = 0; j < nf; j++) { - ipx = i * nf + j; - ipk = labels[ipx]; - if (ipk > 0 && ipk <= np) { - fval = (double)data[ipx]; - add_pixel(&res[NPROPERTY * (ipk - 1)], i, j, fval, omega); - } else { - if (ipk != 0) { - bad++; - if (bad < 10) { - printf("Found %d in your blob image at i=%d, j=%d\n", - ipk, i, j); - } - } - } - } /* j */ - } /* i */ + for (j = 0; j < nf; j++) { + ipx = i * nf + j; + ipk = labels[ipx]; + if (ipk > 0 && ipk <= npk) { + fval = (double)data[ipx]; + add_pixel(&res[NPROPERTY * (ipk - 1)], i, j, fval, omega); + } else { + if (ipk != 0) { + bad++; + if (bad < 10) { + printf("Found %d in your blob image at i=%d, j=%d\n", + ipk, i, j); + } + } + } + } /* j */ + } /* i */ if (verbose) { - printf("\nFound %d bad pixels in the blob image\n", bad); + printf("\nFound %d bad pixels in the blob image\n", bad); } } -static char bloboverlaps_doc[] = - " success = bloboverlaps ( Numeric.array(blob1, 2D, Int), n1 , res1 \n" - " Numeric.array(blob2, 2D, Int), n2 , res2,\n" - " verbose=0) \n" - " \n" - " merges the results from blob1/res1 into blob2/res2\n" - " blob1 would be the previous image from the series, with its results\n" - " if it does not overlap it will stay untouched in res\n" - " if it overlaps with next image it is passed into that ones res\n"; - -int bloboverlaps(INTEGER * b1, INTEGER n1, double *res1, - INTEGER * b2, INTEGER n2, double *res2, - int verbose, int ns, int nf) -{ +/* F2PY_WRAPPER_START + function bloboverlaps( labels1, npk1, results1, & + labels2, npk2, results2, & + verbose, ns, nf) +!DOC bloboverlaps determines the overlaps between labels1 and labels2 +!DOC for an image series. Peaks in labels2 may be merged if they were +!DOC joined by a peak on labels1. Results in results1 are accumulated +!DOC into results2 if peaks are overlapped. + intent(c) bloboverlaps + intent(c) + integer :: bloboverlaps + integer, intent( inout ) :: labels1( ns, nf ) + integer, intent( inout ) :: labels2( ns, nf ) + integer, intent(hide), depend(labels1) :: ns=shape(labels1,0) + integer, intent(hide), depend(labels1) :: nf=shape(labels1,1) + integer, intent(in) :: npk1, npk2 + double precision, intent( inout ) :: results1( :, NPROPERTY ) + double precision, intent( inout ) :: results2( :, NPROPERTY ) + integer, intent(in) :: verbose = 0 + threadsafe + end subroutine bloboverlaps +F2PY_WRAPPER_END */ +int bloboverlaps(int32_t *b1, int32_t n1, double *res1, int32_t *b2, int32_t n2, + double *res2, int verbose, int ns, int nf) { int i, j, safelyneed, ipx; - INTEGER *link, p1, p2, ipk, jpk, npk, *T; + int32_t *link, p1, p2, ipk, jpk, npk, *T; - /* Initialise a disjoint set in link + /* Initialise a disjoint set in link * image 2 has peak[i]=i ; i=1->n2 * image 1 has peak[i]=i+n1+1 ; i=1->n1 * 0, 1, 2, 3, n2 * 4, 5, 6, 7, 8, 9, n1 need 0, 4 == n1+n2+3 - * link to hold 0->n1-1 ; n1->n2+n2-1 + * link to hold 0->n1-1 ; n1->n2+n2-1 * */ /* This is a disjoint merge operation ... */ if (verbose) - printf("Enter bloboverlaps\n"); + printf("Enter bloboverlaps\n"); safelyneed = n1 + n2 + 3; - link = (int *)malloc(safelyneed * sizeof(INTEGER)); + link = (int *)malloc(safelyneed * sizeof(int32_t)); link[0] = safelyneed; for (i = 1; i < safelyneed; i++) { - link[i] = i; + link[i] = i; } /* flag the start of image number 2 */ - link[n2 + 1] = -99999; /* ==n2=0 Should never be touched by anyone */ + link[n2 + 1] = -99999; /* ==n2=0 Should never be touched by anyone */ /* results lists of pairs of number */ /* link holds a disjoint set, we label directly overlapping pixels (i==j) */ for (i = 0; i < ns; i++) { - for (j = 0; j < nf; j++) { - ipx = i * nf + j; - if ((p1 = b1[ipx]) == 0) - continue; - if ((p2 = b2[ipx]) == 0) - continue; - if (link[p2] < 0 || link[p1 + n2 + 1] < 0) { - printf("Whoops!!\n"); - return 0; - } - dset_makeunion(link, p2, p1 + n2 + 1); - } + for (j = 0; j < nf; j++) { + ipx = i * nf + j; + if ((p1 = b1[ipx]) == 0) + continue; + if ((p2 = b2[ipx]) == 0) + continue; + if (link[p2] < 0 || link[p1 + n2 + 1] < 0) { + printf("Whoops!!\n"); + return 0; + } + dset_makeunion(link, p2, p1 + n2 + 1); + if (verbose>10) printf( "link %d %d\n",p2, p1+n2+1 ); + } } if (verbose) - printf("Scanning images\n"); + printf("Scanning images\n"); /* Now we re-label and merge peaks scanning disjoint set */ for (i = 1; i < safelyneed; i++) { - if (link[i] != i && i != n2 + 1) { - j = dset_find(i, link); - if ((i > n2 + 1) && (j < n2 + 1)) { /* linking between images */ - jpk = j - 1; - ipk = i - n2 - 2; - /* Bounds checking */ - boundscheck(jpk, n2, ipk, n1); - merge(&res2[NPROPERTY * jpk], &res1[NPROPERTY * ipk]); - continue; - } - if ((i > n2 + 1) && (j > n2 + 1)) { /* linking on the same image (1) */ - jpk = j - n2 - 2; - ipk = i - n2 - 2; - boundscheck(jpk, n1, ipk, n1); - assert((n1 > jpk) && (jpk >= 0) && (n1 > ipk) && (ipk >= 0)); - merge(&res1[NPROPERTY * jpk], &res1[NPROPERTY * ipk]); - continue; - } - if (i < n2 + 1 && j < n2 + 1) { /* linking on the same image (2) */ - jpk = j - 1; - ipk = i - 1; - boundscheck(jpk, n2, ipk, n2); - merge(&res2[NPROPERTY * jpk], &res2[NPROPERTY * ipk]); - continue; - } - assert("I am not here!"); - } + if (link[i] != i && i != n2 + 1) { + j = dset_find(i, link); + if ((i > n2 + 1) && (j < n2 + 1)) { /* linking between images */ + jpk = j - 1; + ipk = i - n2 - 2; + /* Bounds checking */ + boundscheck(jpk, n2, ipk, n1); + merge(&res2[NPROPERTY * jpk], &res1[NPROPERTY * ipk]); + if (verbose>10) printf( "merged res2[%d] res1[%d]\n", jpk, ipk ); + continue; + } + if ((i > n2 + 1) && + (j > n2 + 1)) { /* linking on the same image (1) */ + jpk = j - n2 - 2; + ipk = i - n2 - 2; + boundscheck(jpk, n1, ipk, n1); + assert((n1 > jpk) && (jpk >= 0) && (n1 > ipk) && (ipk >= 0)); + merge(&res1[NPROPERTY * jpk], &res1[NPROPERTY * ipk]); + if (verbose>10) printf( "merge res1[%d] res1[%d]\n", jpk, ipk ); + continue; + } + if (i < n2 + 1 && j < n2 + 1) { /* linking on the same image (2) */ + jpk = j - 1; + ipk = i - 1; + boundscheck(jpk, n2, ipk, n2); + merge(&res2[NPROPERTY * jpk], &res2[NPROPERTY * ipk]); + if (verbose>10) printf( "merge res2[%d] res2[%d]\n", jpk, ipk ); + continue; + } + assert("I am not here!"); + } } - /* This is the case where two spots on the current image become linked by + /* This is the case where two spots on the current image become linked by * by a spot overlap on the previous one * * The labels are now wrong, in fact there is a single peak in 3D and @@ -360,79 +373,213 @@ int bloboverlaps(INTEGER * b1, INTEGER n1, double *res1, /* First, work out the new labels */ /* Make each T[i] contain the unique ascending integer for the set */ if (verbose) - printf("Compress set\n"); - T = (int *)(malloc((n2 + 3) * sizeof(INTEGER))); + printf("Compress set\n"); + T = (int32_t *)(calloc((n2 + 3) , sizeof(int32_t))); assert(T != NULL); npk = 0; for (i = 1; i < n2 + 1; i++) { - if (link[i] == i) { - npk = npk + 1; - T[i] = npk; - } else { - j = dset_find(i, link); - assert(j < i); - T[i] = T[j]; - } + if (link[i] == i) { + npk = npk + 1; + T[i] = npk; + } else { + j = dset_find(i, link); + assert(j < i); + T[i] = T[j]; + } } if (verbose) { - for (i = 0; i < 10; i++) - printf("T[%d]=%d ", i, T[i]); + printf("n1 = %d n2 = %d ",n1,n2); + for (i = 0; i < n2+3; i++) + printf("T[%d]=%d ", i, T[i]); } /* T is now compressed, merge the peaks */ if (verbose) - printf("Merge peaks in res2\n"); + printf("Merge peaks in res2\n"); for (i = 1; i < n2 + 1; i++) { - /* dest = T[i]; */ - /* src = link[i]; */ - if (link[i] == T[i]) - continue; - if (T[i] < link[i]) { /* copy and zero out */ - if (link[i] == i) { /* This is the place accumulating */ - for (j = 0; j < NPROPERTY; j++) { - res2[NPROPERTY * (T[i] - 1) + j] = - res2[NPROPERTY * (link[i] - 1) + j]; - res2[NPROPERTY * (link[i] - 1) + j] = 0; - } - } else { - /* assert this is empty */ - assert(res2[NPROPERTY * (i - 1) + s_1] < 0.01); - } - if (verbose > 1) { - printf("np i %d j %d %f \n", T[i], link[i], - res2[NPROPERTY * T[i] + 1]); - } - } else { - assert("Bad logic in bloboverlaps"); - } + /* dest = T[i]; */ + /* src = link[i]; */ + if (link[i] == T[i]) + continue; + if (T[i] < link[i]) { /* copy and zero out */ + if (link[i] == i) { /* This is the place accumulating */ + for (j = 0; j < NPROPERTY; j++) { + res2[NPROPERTY * (T[i] - 1) + j] = + res2[NPROPERTY * (link[i] - 1) + j]; + res2[NPROPERTY * (link[i] - 1) + j] = 0; + } + } else { + /* assert this is empty */ + assert(res2[NPROPERTY * (i - 1) + s_1] < 0.01); + } + if (verbose > 1) { + printf("np i %d j %d %f \n", T[i], link[i], + res2[NPROPERTY * T[i] + 1]); + } + } else { + assert("Bad logic in bloboverlaps"); + } } if (verbose) - printf("Relabel image of blobs\n"); + printf("Relabel image of blobs\n"); /* Relabel the image where they change */ for (i = 0; i < ns; i++) { - for (j = 0; j < nf; j++) { - ipx = i * nf + j; - if ((p2 = b2[ipx]) == 0) - continue; - ipk = T[p2]; - if (ipk != p2) { - assert(ipk > 0 && ipk <= n2); - b2[ipx] = ipk; - } - } + for (j = 0; j < nf; j++) { + ipx = i * nf + j; + if ((p2 = b2[ipx]) == 0) + continue; + ipk = T[p2]; + if (ipk != p2) { + assert(ipk > 0 && ipk <= n2); + b2[ipx] = ipk; + } + } } if (verbose) - printf("Done relabelling\n"); + printf("Done relabelling\n"); free(T); free(link); return npk; } -static char blob_moments_doc[] = - " None = blob_moments(Numeric.array(peaks1))\n" - " \n" " Loop over array filling out moments from sums\n"; +/* F2PY_WRAPPER_START + subroutine blob_moments( results, np ) +!DOC blob_moments fills in the reduced moments in results array. +!DOC ... FIXME - this would be clearer in python, fast anyway. + intent(c) blob_moments + intent(c) + double precision, intent( inout ) :: results( np, NPROPERTY ) + integer, intent(hide), depend(results) :: np=shape(results,0) + threadsafe + end subroutine blob_moments +F2PY_WRAPPER_END */ +void blob_moments(double *res, int np) { compute_moments(res, np); } + +/* F2PY_WRAPPER_START + function clean_mask( msk, ret, ns, nf ) +!DOC clean_mask removes pixels which are not 4 connected from msk +!DOC while copying into ret. + intent(c) clean_mask + intent(c) + integer*1, intent(in) :: msk( ns, nf ) + integer, intent(hide), depend(msk) :: ns=shape(msk,0) + integer, intent(hide), depend(msk) :: nf=shape(msk,1) + integer*1, intent(inout), dimension(ns, nf) :: ret + ! returns an int + integer :: clean_mask + end function clean_mask +F2PY_WRAPPER_END */ +int clean_mask(const int8_t *restrict msk, int8_t *restrict ret, int ns, + int nf) { + /* cleans pixels with no 4 connected neighbors */ + int i, j, q, npx; + int8_t t; + npx = 0; +#pragma omp parallel for private(i) + for (i = 0; i < ns * nf; i++) { + if (msk[i] > 0) { + ret[i] = 1; + } else { + ret[i] = 0; + } + } + i = 0; + for (j = 0; j < nf; j++) { + q = i * nf + j; + if (ret[q] > 0) { + t = msk[q + nf]; + if (j > 0) + t += msk[q - 1]; + if (j < (nf - 1)) + t += msk[q + 1]; + if (t > 0) { + npx++; + } else { + ret[q] = 0; + } + } + } +#pragma omp parallel for private(i, j, q, t) reduction(+ : npx) + for (i = 1; i < (ns - 1); i++) { + /* j==0 */ + q = i * nf; + if (ret[q] > 0) { + t = msk[q - nf] + msk[q + nf] + msk[q + 1]; + if (t > 0) { + npx++; + } else { + ret[q] = 0; + } + } + for (j = 1; j < (nf - 1); j++) { + q = i * nf + j; + if (ret[q] > 0) { + t = msk[q - nf] + msk[q + nf] + msk[q - 1] + msk[q + 1]; + if (t > 0) { + npx++; + } else { + ret[q] = 0; + } + } + } + q = (i + 1) * nf - 1; + if (ret[q] > 0) { + t = msk[q - nf] + msk[q + nf] + msk[q - 1]; + if (t > 0) { + npx++; + } else { + ret[q] = 0; + } + } + } + i = ns - 1; + for (j = 0; j < nf; j++) { + q = i * nf + j; + if (ret[q] > 0) { + t = msk[q - nf]; + if (j > 0) + t += msk[q - 1]; + if (j < (nf - 1)) + t += msk[q + 1]; + if (t > 0) { + npx++; + } else { + ret[q] = 0; + } + } + } + return npx; +} -void blob_moments(double *res, int np) -{ - compute_moments(res, np); +/* F2PY_WRAPPER_START + + function make_clean_mask( img, cut, msk, ret, ns, nf ) +!DOC make_clean_mask is a lot like clean msk but it generates +!DOC the msk using img and cut. +!DOC Beware: work in progress + intent(c) make_clean_mask + intent(c) + real, intent(in), dimension(ns,nf) :: img + real, intent(in) :: cut + integer*1, intent(in) :: msk( ns, nf ) + integer, intent(hide), depend(msk) :: ns=shape(msk,0) + integer, intent(hide), depend(msk) :: nf=shape(msk,1) + integer*1, intent(inout), dimension(ns, nf) :: ret + ! returns an int + integer :: make_clean_mask + end function make_clean_mask +F2PY_WRAPPER_END */ +int make_clean_mask(float *restrict img, float cut, int8_t *restrict msk, + int8_t *restrict ret, int ns, int nf) { + /* cleans pixels with no 4 connected neighbors */ + int i; +#pragma omp parallel for + for (i = 0; i < ns * nf; i++) { + if (img[i] > cut) { + msk[i] = 1; + } else { + msk[i] = 0; + } + } + return clean_mask(&msk[0], &ret[0], ns, nf); } diff --git a/src/darkflat.c b/src/darkflat.c new file mode 100644 index 00000000..c6904d38 --- /dev/null +++ b/src/darkflat.c @@ -0,0 +1,627 @@ +/* stdint */ + +#include +#include +#include + +#include +#include + +#include "cImageD11.h" + +/* To compare to numpy (numba ought to do this anyway) + +__m256i _mm256_cvtepu16_epi32 (__m128i a) +__m256 _mm256_cvtepi32_ps (__m256i a) + +SSE: __m128 _mm_cvtpu16_ps (__m64 a) + + +AVX +__m256 _mm256_sub_ps (__m256 a, __m256 b) +__m256 _mm256_mul_ps (__m256 a, __m256 b) + Architecture Latency Throughput (CPI) + Skylake 4 0.5 + [divide is much slower] + + + _m256_load_ps (float const * mem_addr) + _m256_store_ps (float * mem_addr, __m256 a) + aligned load/store (32 byte boundary) + + _mm256_loadu_ps (float const * mem_addr) + _mm256_storeu_ps (float const * mem_addr) + unaligned load/store (32 byte boundary) +*/ + +/* F2PY_WRAPPER_START + subroutine uint16_to_float_darksub( img, drk, data, npx ) +!DOC uint16_to_float_darksub subtracts image drk(float32) from +!DOC raw data in data (uint16) and returns in img. + intent(c) uint16_to_float_darksub + intent(c) + real, intent(inout), dimension(npx) :: img + real, intent(in), dimension(npx) :: drk + integer(kind=-2), intent(in), dimension(npx) :: data + integer, intent(hide), depend( img ) :: npx + end subroutine uint16_to_float_darksub +F2PY_WRAPPER_END */ +void uint16_to_float_darksub(float *restrict img, const float *restrict drk, + const uint16_t *restrict data, int npx) { + int i; +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd +#else +#pragma omp parallel for +#endif + for (i = 0; i < npx; i++) { + img[i] = ((float)data[i]) - drk[i]; + } +} + +/* F2PY_WRAPPER_START + subroutine uint16_to_float_darkflm( img, drk, flm, data, npx ) +!DOC uint16_to_float_darkflm subtracts image drk(float32) from +!DOC raw data in data (uint16), multiples by flm(float32) and returns in img. + intent(c) uint16_to_float_darkflm + intent(c) + real, intent(inout), dimension(npx) :: img + real, intent(in), dimension(npx) :: drk, flm + integer(kind=-2), intent(in), dimension(npx) :: data + integer, intent(hide), depend( img ) :: npx + end subroutine uint16_to_float_darkflm +F2PY_WRAPPER_END */ +void uint16_to_float_darkflm(float *restrict img, const float *restrict drk, + const float *restrict flm, + const uint16_t *restrict data, int npx) { + int i; +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd +#else +#pragma omp parallel for +#endif + for (i = 0; i < npx; i++) { + img[i] = (((float)data[i]) - drk[i]) * flm[i]; + } +} + +/* F2PY_WRAPPER_START + subroutine frelon_lines(img, ns, nf, cut) +!DOC frelon_lines Subtracts the average value of (pixels < cut) per row + intent(c) frelon_lines + intent(c) + real, intent(inout), dimension(ns,nf) :: img + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real, intent(in) :: cut + end subroutine frelon_lines +F2PY_WRAPPER_END */ +void frelon_lines(float *img, int ns, int nf, float cut) { + int i, j, p, npx; + float rowsum, avg; + avg = img[0]; +#pragma omp parallel for private(i, j, rowsum, npx, p) firstprivate(avg) + for (i = 0; i < ns; i++) { + rowsum = 0.; + npx = 0; + p = i * nf; +#ifdef GOT_OMP_SIMD +#pragma omp simd +#endif + for (j = 0; j < nf; j++) { + if (img[p + j] < cut) { + rowsum += img[p + j]; + npx++; + } + } + if (npx > 0) + avg = rowsum / npx; +#ifdef GOT_OMP_SIMD +#pragma omp simd +#endif + for (j = 0; j < nf; j++) + img[p + j] = img[p + j] - avg; + } +} + +/* F2PY_WRAPPER_START + subroutine frelon_lines_sub(img, drk, ns, nf, cut) +!DOC frelon_lines_sub Subtracts drk from img and then same as frelon_lines + intent(c) frelon_lines_sub + intent(c) + real, intent(inout), dimension(ns,nf) :: img, drk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real, intent(in) :: cut + threadsafe + end subroutine frelon_lines +F2PY_WRAPPER_END */ +void frelon_lines_sub(float *restrict img, float *restrict drk, int ns, int nf, + float cut) { + int i, j, p, npx; + float rowsum, avg; + avg = img[0]; +#pragma omp parallel for private(i, j, rowsum, npx, p) firstprivate(avg) + for (i = 0; i < ns; i++) { + rowsum = 0.; + npx = 0; + p = i * nf; +#ifdef GOT_OMP_SIMD +#pragma omp simd +#endif + for (j = 0; j < nf; j++) { + img[p + j] = img[p + j] - drk[p + j]; + if (img[p + j] < cut) { + rowsum += img[p + j]; + npx++; + } + } + if (npx > 0) + avg = rowsum / npx; +#ifdef GOT_OMP_SIMD +#pragma omp simd +#endif + for (j = 0; j < nf; j++) + img[p + j] = img[p + j] - avg; + } +} + +/* F2PY_WRAPPER_START + subroutine array_mean_var_cut( img, npx, mean, var, n, cut, verbose ) +!DOC array_mean_var_cut computes the mean and variance of an image +!DOC with pixels above the value mean+cut*stddev removed. This is iterated +!DOC n times as the mean and variance change as pixels are removed. + intent(c) array_mean_var_cut + real, intent(in,c), dimension(npx) :: img + integer, intent(hide,c), depend(img) :: npx = shape(img,0) + real, intent(out) :: mean, var + integer, intent(in,c), optional :: n = 3 + integer, intent(in,c), optional :: verbose = 0 + real, intent(in,c), optional :: cut = 3. + end subroutine array_mean_var_cut +F2PY_WRAPPER_END */ +void array_mean_var_cut(float *restrict img, int npx, float *mean, float *std, + int n, float cut, int verbose) { + int i, nactive; + float t, s1, s2, wt, y0; + y0 = img[0]; + s1 = 0; + s2 = 0; + if (verbose) + printf("Args, img[0] %f npx %d n %d cut %f verbose %d\n", img[0], npx, + n, cut, verbose); +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd private(t) reduction(+ : s1, s2) +#else +#pragma omp parallel for private(t) reduction(+ : s1, s2) +#endif + for (i = 0; i < npx; i++) { + t = img[i] - y0; + s1 = s1 + t; + s2 = s2 + t * t; + } + /* mean and std */ + *mean = (float)(s1 / npx + y0); + *std = sqrtf((float)((s2 - (s1 * s1 / npx)) / npx)); + if (verbose > 0) + printf("n=%d Mean %f, Std %f\n", n, *mean, *std); + while (--n > 0) { + y0 = *mean; + wt = y0 + cut * (*std); + s1 = 0; + s2 = 0; + nactive = 0; +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd private(t) reduction(+ : s1, s2, nactive) +#else +#pragma omp parallel for private(t) reduction(+ : s1, s2, nactive) +#endif + for (i = 0; i < npx; i++) { + if (img[i] < wt) { + t = img[i] - y0; + s1 = s1 + t; + s2 = s2 + t * t; + nactive++; + } + } + *mean = (float)(s1 / nactive + *mean); + *std = sqrtf(((s2 - (s1 * s1 / nactive)) / nactive)); + if (verbose > 0) + printf("n=%d Mean %f, Std %f\n", n, *mean, *std); + } +} + +/* F2PY_WRAPPER_START + subroutine array_mean_var_msk( img, msk, npx, mean, var, n, cut, verbose ) +!DOC array_mean_var_msk computes the mean and variance of an image +!DOC with pixels above the value mean+cut*stddev removed. This is iterated +!DOC n times as the mean and variance change as pixels are removed. + intent(c) array_mean_var_msk + real, intent(in,c), dimension(npx) :: img + integer(kind=-1) , intent(in,c), dimension(npx) :: msk + integer, intent(hide,c), depend(img) :: npx = shape(img,0) + real, intent(out) :: mean, var + integer, intent(in,c), optional :: n = 3 + integer, intent(in,c), optional :: verbose = 0 + real, intent(in,c), optional :: cut = 3. + end subroutine array_mean_var_msk +F2PY_WRAPPER_END */ +void array_mean_var_msk(float *restrict img, uint8_t *restrict msk, int npx, + float *mean, float *std, int n, float cut, + int verbose) { + int i, nactive; + float t, s1, s2, wt, y0; + y0 = img[0]; + s1 = 0; + s2 = 0; + if (verbose) + printf("Args, img[0] %f npx %d n %d cut %f verbose %d\n", img[0], npx, + n, cut, verbose); +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd private(t) reduction(+ : s1, s2) +#else +#pragma omp parallel for private(t) reduction(+ : s1, s2) +#endif + for (i = 0; i < npx; i++) { + t = img[i] - y0; + s1 = s1 + t; + s2 = s2 + t * t; + } + /* mean and std */ + *mean = (float)(s1 / npx + y0); + *std = sqrtf((float)((s2 - (s1 * s1 / npx)) / npx)); + if (verbose > 0) + printf("n=%d Mean %f, Std %f\n", n, *mean, *std); + while (--n > 1) { + y0 = *mean; + wt = y0 + cut * (*std); + s1 = 0; + s2 = 0; + nactive = 0; +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd private(t) reduction(+ : s1, s2, nactive) +#else +#pragma omp parallel for private(t) reduction(+ : s1, s2, nactive) +#endif + for (i = 0; i < npx; i++) { + if (img[i] < wt) { + t = img[i] - y0; + s1 = s1 + t; + s2 = s2 + t * t; + nactive++; + } + } + *mean = (float)(s1 / nactive + *mean); + *std = sqrtf(((s2 - (s1 * s1 / nactive)) / nactive)); + if (verbose > 0) + printf("n=%d Mean %f, Std %f\n", n, *mean, *std); + } + + /* Fill in mask */ + y0 = *mean; + wt = y0 + cut * (*std); + if (verbose > 0) + printf("Cutting img > %f\n", wt); +#ifdef GOT_OMP_SIMD +#pragma omp parallel for simd +#else +#pragma omp parallel for +#endif + for (i = 0; i < npx; i++) { + if (img[i] < wt) { + msk[i] = 0; + } else { + msk[i] = 1; + } + } +} + +/* F2PY_WRAPPER_START + + subroutine array_stats( img, npx, minval, maxval, mean, var ) +!DOC array_stats computes statistics for an image. +!DOC img Input data array (1D or 2D.ravel(), so sparse or dense) +!DOC npx length of data array (contiguous) +!DOC *minval minimum of the pixels +!DOC *maxval maximum of the pixels +!DOC*s1 Sum of all pixel +!DOC*s2 Sum of pixel^2 + intent(c) array_stats + real, intent(c, in), dimension(npx) :: img + integer, intent(c, hide), depend(img) :: npx = shape(img,0) + ! these are intent(fortran) pointers + real, intent(out) :: minval, maxval, mean, var + threadsafe + end subroutine array_stats +F2PY_WRAPPER_END */ +void array_stats(float img[], int npx, float *minval, float *maxval, + float *mean, float *var) { + int i; + /* Use double to reduce rounding and subtraction errors */ + double t, s1, s2, y0, ts1, ts2; + float mini, maxi, tmin, tmax; + mini = FLT_MAX; + maxi = FLT_MIN; + s1 = 0.; + s2 = 0.; + y0 = img[0]; + /* Merge results - openmp 2.0 for windows has no min/max */ +#pragma omp parallel private(i, t, ts1, ts2, tmin, tmax) + { + tmin = FLT_MAX; + tmax = FLT_MIN; + ts1 = 0.; + ts2 = 0.; +#ifdef GOT_OMP_SIMD +#pragma omp for simd +#else +#pragma omp for +#endif + for (i = 0; i < npx; i++) { + t = img[i] - y0; + ts1 = ts1 + t; + ts2 = ts2 + t * t; + if (img[i] < tmin) + tmin = img[i]; + if (img[i] > tmax) + tmax = img[i]; + } // for +#pragma omp critical + { + s1 += ts1; + s2 += ts2; + if (tmin < mini) + mini = tmin; + if (tmax > maxi) + maxi = tmax; + } + } // parallel + /* results */ + *mean = (float)(s1 / npx + y0); + *var = (float)((s2 - (s1 * s1 / npx)) / npx); + *minval = mini; + *maxval = maxi; +} + +/* F2PY_WRAPPER_START + subroutine array_histogram( img, npx, low, high, hist, nhist ) +!DOC array_histogram computes the histogram for an image +!DOC Go through the data to compute a histogram of the values +!DOC Previous call of array_stats would help to set up this call +!DOC compare to np.bincount - this does not gain much +!DOC better implementations can be done +!DOC +!DOC img[] Input data array (1D or 2D.ravel(), so sparse or dense) +!DOC npx length of data array (contiguous) +!DOC low Lower edge of first bin +!DOC high Upper edge of last bin +!DOC hist[] Histogram to be output +!DOC nhist Number of bins in the histogram + intent(c) array_histogram + intent(c) + real, intent(in), dimension(npx) :: img + integer, intent(hide), depend(img) :: npx = shape(img,0) + real, intent(in) :: low, high + integer*4, intent(inout), dimension(nhist) :: hist + integer, intent(hide), depend(hist) :: nhist = shape(hist, 0 ) + threadsafe + end subroutine array_histogram +F2PY_WRAPPER_END */ +void array_histogram(float img[], int npx, float low, float high, + int32_t hist[], int nhist) { + int i, ibin; + float ostep; + memset(hist, 0, nhist * sizeof(int32_t)); + /* Compute the multiplier to get the bin numbers */ + ostep = nhist / (high - low); + for (i = 0; i < npx; i++) { + ibin = (int)floorf((img[i] - low) * ostep); + /* clip into range at ends */ + if (ibin < 0) { + ibin = 0; + } + if (ibin >= nhist) { + ibin = nhist - 1; + } + hist[ibin] = hist[ibin] + 1; + } +} + +/* F2PY_WRAPPER_START + subroutine reorder_u16_a32(data, adr, out, N) +!DOC reorder_u16_a32 called in sandbox/fazit.py simple +!DOC loop with openmp saying out[adr[i]] in data[i] +!DOC e.g. semi-random writing + intent(c) reorder_u16_a32 + intent(c) + integer(kind=-2), dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + integer(kind=-2), dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorder_u16_a32 +F2PY_WRAPPER_END */ +void reorder_u16_a32(const uint16_t *restrict data, + const uint32_t *restrict adr, uint16_t *restrict out, + int N) { + int i; + /* printf("Hello, got N=%d\n",N);*/ +#pragma omp parallel for + for (i = 0; i < N; i++) { + out[adr[i]] = data[i]; + } +} + +/* F2PY_WRAPPER_START + subroutine reorder_f32_a32(data, adr, out, N) +!DOC reorder_f32_a32 called in sandbox/fazit.py simple +!DOC loop with openmp saying out[adr[i]] in data[i] +!DOC e.g. semi-random writing + intent(c) reorder_f32_a32 + intent(c) + real, dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + real, dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorder_u16_a32 +F2PY_WRAPPER_END */ +void reorder_f32_a32(const float *restrict data, const uint32_t *restrict adr, + float *restrict out, int N) { + int i; + /* printf("Hello, got N=%d\n",N);*/ +#pragma omp parallel for + for (i = 0; i < N; i++) { + out[adr[i]] = data[i]; + } +} + +/* F2PY_WRAPPER_START + subroutine reorderlut_u16_a32(data, adr, out, N) +!DOC reorderlut_u16_a32lut called in sandbox/fazit.py simple +!DOC loop with openmp saying out[i] in data[adr[i]] +!DOC e.g. semi-random reading + intent(c) reorderlut_u16_a32 + intent(c) + integer(kind=-2), dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + integer(kind=-2), dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorderlut_u16_a32 +F2PY_WRAPPER_END */ +void reorderlut_u16_a32(uint16_t *restrict data, uint32_t *restrict lut, + uint16_t *restrict out, int N) { + int i; + /* printf("Hello, got N=%d\n",N);*/ +#pragma omp parallel for + for (i = 0; i < N; i++) { + out[i] = data[lut[i]]; + } +} + +/* F2PY_WRAPPER_START + subroutine reorderlut_f32_a32(data, adr, out, N) +!DOC reorderlut_f32_a32 lut called in sandbox/fazit.py simple +!DOC loop with openmp saying out[i] in data[adr[i]] +!DOC e.g. semi-random reading + intent(c) reorderlut_f32_a32 + intent(c) + real, dimension(N), intent(in) :: data + integer*4, dimension(N), intent(in) :: adr + real, dimension(N), intent(inout) :: out + integer, intent(hide), depend(data) :: N + end subroutine reorderlut_f32_a32 +F2PY_WRAPPER_END */ +void reorderlut_f32_a32(const float *restrict data, uint32_t *restrict lut, + float *restrict out, int N) { + int i; +#pragma omp parallel for + for (i = 0; i < N; i++) { + out[i] = data[lut[i]]; + } +} + +/* F2PY_WRAPPER_START + subroutine reorder_u16_a32_a16(data, adr0, adr1, out, ns, nf) +!DOC reorderlut_u16_a32_a16 called in sandbox/fazit.py +!DOC data - source data read in order +!DOC adr0 - output position for the first pixel in each row +!DOC adr1 - difference offset to output next pixel in each row + intent(c) reorder_u16_a32_a16 + intent(c) + integer(kind=-2), dimension(ns,nf), intent(in) :: data + integer*4, dimension(ns), intent(in) :: adr0 + integer*2, dimension(ns,nf), intent(in) :: adr1 + integer(kind=-2), dimension(ns,nf), intent(inout) :: out + integer, intent(hide), depend(adr1) :: ns = shape(adr1,0) + integer, intent(hide), depend(adr1) :: nf = shape(adr1,1) + end subroutine reorder_u16_a32_a16 +F2PY_WRAPPER_END */ +void reorder_u16_a32_a16(uint16_t *restrict data, uint32_t *restrict a0, + int16_t *restrict a1, uint16_t *restrict out, int ns, + int nf) { + int i, j, p; + /* printf("Hello, got ns=%d nf=%d\n",ns, nf);*/ +#pragma omp parallel for private(p, j) + for (i = 0; i < ns; i++) { + p = a0[i]; + for (j = 0; j < nf; j++) { + p += a1[i * nf + j]; + out[p] = data[i * nf + j]; + } + } +} + +/* F2PY_WRAPPER_START + subroutine bgcalc(img, bg, msk, ns, nf, gain, sp, st) +!DOC bgcalc computes a background on a 1d signal where gain +!DOC and sp and st are defined by: +!DOC diff = difference to neighbors or bg estimate +!DOC sigmap = weight for abs background value +!DOC sigmat = constant weight +!DOC gain for b += diff * gain +!DOC img - source data +!DOC bg - computed background +!DOC msk - mask + intent(c) bgcalc + intent(c) + real, dimension(ns,nf), intent(in) :: img + real, dimension(ns,nf), intent(inout) :: bg + integer*1, dimension(ns,nf), intent(inout) :: msk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real :: gain, sp, st + end subroutine bgcalc +F2PY_WRAPPER_END */ +void bgcalc(const float *restrict img, float *restrict bg, + uint8_t *restrict msk, int ns, int nf, float gain, float sigmap, + float sigmat) { + int ir, i; + float b, diff, t; + /* + printf("gain %f sigmap %f sigmat %f ns %d nf %d\n", + gain, sigmap, sigmat, ns, nf); */ +#pragma omp parallel for private(b, diff, t, ir, i) + for (ir = 0; ir < ns; ir++) { // in range( 1, len(data) ): + i = ir * nf; + b = img[i]; + bg[i] = b; + msk[i] = 1; + for (i = ir * nf; i < (ir + 1) * nf; i++) { + diff = img[i] - b; + t = sigmap * fabsf(b) + sigmat; + if (diff > t) { + diff = t * diff / fabsf(diff) / 16; + msk[i] = 1; + } else if (diff < -t) { + diff = t * diff / fabsf(diff) / 4; + msk[i] = 1; + } else { + msk[i] = 0; + } + b += diff * gain; + bg[i] = b; + } + i = (ir + 1) * nf - 1; + b = img[i]; + bg[i] = b; + msk[i] = 1; + for (i = (ir + 1) * nf - 1; i >= ir * nf; i--) { + diff = img[i] - b; + t = sigmap * fabsf(b) + sigmat; + if (diff > t) { + diff = t * diff / fabsf(diff) / 16; + msk[i] += 1; + } else if (diff < -t) { + diff = t * diff / fabsf(diff) / 4; + msk[i] += 1; + } else { + if (msk[i] == 1) { + bg[i] = b; + } + if ((msk[i] == 0) || (msk[i] == 2)) { + bg[i] = (bg[i] + b) / 2; + } + } + b += diff * gain; + } + } +} diff --git a/src/deriv.c b/src/deriv.c index 22a8b583..82810a50 100644 --- a/src/deriv.c +++ b/src/deriv.c @@ -1,26 +1,22 @@ -#include #include "ImageD11_cmath.h" - +#include struct grain { double UB[9]; double t[3]; }; - -double norm3( double v[3] ){ - return sqrt( v[0]*v[0] + v[1]*v[1] + v[2]*v[2] ); +double norm3(double v[3]) { + return sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); } - - /* def anglevecs2D(a, b, c, d): - return np.arctan2( a*d - b*c, a*c + b*d) - + return np.arctan2( a*d - b*c, a*c + b*d) + def derivanglevecs2d( a, b, c, d, ab2=None, cd2=None): - if ab2 is None: + if ab2 is None: ab2 = a*a+b*b if cd2 is None: cd2 = c*c+d*d @@ -32,7 +28,6 @@ def derivanglevecs2d( a, b, c, d, ab2=None, cd2=None): } */ - // axis_system matrix in laboratory co-ordinates is: // 012 axis[0], axis[1], axis[2] // 345 b[0] b[1] b[2] = cross product of c and a @@ -40,72 +35,62 @@ def derivanglevecs2d( a, b, c, d, ab2=None, cd2=None): // a.b = dot of axis and beam // ax[9]...[19] = inverse transpose matrix for conversion back -int g_to_k( double h[4], - double UB[9], - double ax[19], - double pre[9], - double wvln, - double g[3], - double k[3], - double tth_calc[10], - double eta_calc[10], - double omega_calc[10] - ){ +int g_to_k(double h[4], double UB[9], double ax[19], double pre[9], double wvln, + double g[3], double k[3], double tth_calc[10], double eta_calc[10], + double omega_calc[10]) { double gr[3], modg, modg2, ka[3], b, c, t; double gao[3], kao[3]; - int i, j ; + int i, j; - matvec( UB, h, g ); + matvec(UB, h, g); modg = norm3(g); t = wvln * modg / 2.0; - tth_calc[0] = DEG * asin( t ); + tth_calc[0] = DEG * asin(t); // d(tth_c) = DEGREES * tmp4 * dtmp3_dUB - c = DEG * wvln / (2.0 * modg * sqrt( 1 - t*t ) ); - for(i=0;i<3;i++) - for(j=0;j<3;j++) - tth_calc[i*3+j+1] = c * g[i] * h[j]; + c = DEG * wvln / (2.0 * modg * sqrt(1 - t * t)); + for (i = 0; i < 3; i++) + for (j = 0; j < 3; j++) + tth_calc[i * 3 + j + 1] = c * g[i] * h[j]; - - - matvec( pre, g, gr ); // dgr_dUB + matvec(pre, g, gr); // dgr_dUB // gr is g-vector on rotating stage in orthogonal co-ordinate axes // scale space to wavelength - gr[0]=gr[0]*wvln; - gr[1]=gr[1]*wvln; - gr[2]=gr[2]*wvln; - modg2 = gr[0]*gr[0]+gr[1]*gr[1]+gr[2]*gr[2]; + gr[0] = gr[0] * wvln; + gr[1] = gr[1] * wvln; + gr[2] = gr[2] * wvln; + modg2 = gr[0] * gr[0] + gr[1] * gr[1] + gr[2] * gr[2]; // Now find diffraction vector in axis co-ordinate system // x along axis // y along beam (not orthogonal) // z makes right handed set // Component of gr along axis = g.axis - ka[0] = gr[0]*ax[0] + gr[1]*ax[1] + gr[2]*ax[2]; // AXIS READ + ka[0] = gr[0] * ax[0] + gr[1] * ax[1] + gr[2] * ax[2]; // AXIS READ // Component along beam (rotated) from Laue condition |g|^2/2 - ka[1] = -modg2/2.; + ka[1] = -modg2 / 2.; // Solve quadratic for ka[0] // |g|^2 = |k|^2 // = k[0]*k[0] + k[1]*k[1] + k[2]*k[2] + 2*k[0]*k[1]*ax[9] // ax[9] is axis.beam - c = ka[0]*ka[0] + ka[1]*ka[1] - modg2; - b = 2*ka[0]*ka[1]*ax[9]; + c = ka[0] * ka[0] + ka[1] * ka[1] - modg2; + b = 2 * ka[0] * ka[1] * ax[9]; // sign to use is in hkl[3] - t = b*b - c*4.; - if( t >= 0.0 ){ - ka[2] = -b + h[3] * sqrt( t ) / 2.; + t = b * b - c * 4.; + if (t >= 0.0) { + ka[2] = -b + h[3] * sqrt(t) / 2.; } else { printf("Cannot find it!\n"); return 1; } // Now we have gr and ka // Put ka into laboratory orthogonal system - matvec( &ax[10], ka, k ); - k[0] = k[0]/wvln; - k[1] = k[1]/wvln; - k[2] = k[2]/wvln; + matvec(&ax[10], ka, k); + k[0] = k[0] / wvln; + k[1] = k[1] / wvln; + k[2] = k[2] / wvln; // eta is found from k... // eta = atan2( -ky, kz ) - eta_calc[0] = DEG*atan2( -k[1], k[2]); + eta_calc[0] = DEG * atan2(-k[1], k[2]); // Now find the diffractometer angle // kao = kvector in orthogonal axis system kao[0] = ka[0]; @@ -113,53 +98,54 @@ int g_to_k( double h[4], kao[1] = ka[1] * (1 - ax[9]); kao[2] = ka[2]; - gao[1] = ax[3]*gr[0] + ax[4]*gr[1] + ax[5]*gr[2]; - gao[2] = ax[6]*gr[0] + ax[7]*gr[1] + ax[8]*gr[2]; - // return np.arctan2( a*d - b*c, a*c + b*d) - omega_calc[0] = DEG*atan2( kao[1]*gao[2] - kao[2]*gao[1], kao[1]*gao[1] - kao[2]*gao[2] ); + gao[1] = ax[3] * gr[0] + ax[4] * gr[1] + ax[5] * gr[2]; + gao[2] = ax[6] * gr[0] + ax[7] * gr[1] + ax[8] * gr[2]; + // return np.arctan2( a*d - b*c, a*c + b*d) + omega_calc[0] = DEG * atan2(kao[1] * gao[2] - kao[2] * gao[1], + kao[1] * gao[1] - kao[2] * gao[2]); return 0; } -double determinant3x3( double m[9] ){ - return m[0]*m[4]*m[8] - m[0]*m[5]*m[7] + m[1]*m[5]*m[6] - - m[1]*m[3]*m[8] + m[2]*m[3]*m[7] - m[2]*m[4]*m[6]; +double determinant3x3(double m[9]) { + return m[0] * m[4] * m[8] - m[0] * m[5] * m[7] + m[1] * m[5] * m[6] - + m[1] * m[3] * m[8] + m[2] * m[3] * m[7] - m[2] * m[4] * m[6]; } -int inverse_mat3(double m[9], double r[9]){ +int inverse_mat3(double m[9], double r[9]) { double d; - d = determinant3x3( m ); - if (fabs(d) == 0.0){ + d = determinant3x3(m); + if (fabs(d) == 0.0) { return 1; - } - d = 1.0/d; - r[0] = d*( m[4]*m[8] - m[7]*m[5]); - r[1] = d*( m[7]*m[2] - m[8]*m[1]); - r[2] = d*( m[1]*m[5] - m[2]*m[4]); - r[3] = d*( m[5]*m[6] - m[3]*m[8]); - r[4] = d*( m[8]*m[0] - m[2]*m[6]); - r[5] = d*( m[2]*m[3] - m[0]*m[5]); - r[6] = d*( m[3]*m[7] - m[6]*m[4]); - r[7] = d*( m[1]*m[6] - m[0]*m[7]); - r[8] = d*( m[0]*m[4] - m[3]*m[1]); + } + d = 1.0 / d; + r[0] = d * (m[4] * m[8] - m[7] * m[5]); + r[1] = d * (m[7] * m[2] - m[8] * m[1]); + r[2] = d * (m[1] * m[5] - m[2] * m[4]); + r[3] = d * (m[5] * m[6] - m[3] * m[8]); + r[4] = d * (m[8] * m[0] - m[2] * m[6]); + r[5] = d * (m[2] * m[3] - m[0] * m[5]); + r[6] = d * (m[3] * m[7] - m[6] * m[4]); + r[7] = d * (m[1] * m[6] - m[0] * m[7]); + r[8] = d * (m[0] * m[4] - m[3] * m[1]); return 0; } -int make_ax( double post[9], double axis[3], double beam[3], double ax[19] ){ +int make_ax(double post[9], double axis[3], double beam[3], double ax[19]) { double rb[3]; ax[0] = axis[0]; ax[1] = axis[1]; ax[2] = axis[2]; matTvec(post, beam, rb); - crossProduct( axis, rb, &ax[6] ); - crossProduct( axis, &ax[6], &ax[3]); - ax[9] = ax[0]*ax[3] + ax[1]*ax[4] + ax[2]*ax[5]; - return inverse_mat3( &ax[0], &ax[10] ); + crossProduct(axis, rb, &ax[6]); + crossProduct(axis, &ax[6], &ax[3]); + ax[9] = ax[0] * ax[3] + ax[1] * ax[4] + ax[2] * ax[5]; + return inverse_mat3(&ax[0], &ax[10]); } -int main(){ - double tth_calc[10],tth_calc1[10]; - double omega_calc[10],omega_calc1[10]; - double eta_calc[10],eta_calc1[10]; +int main() { + double tth_calc[10], tth_calc1[10]; + double omega_calc[10], omega_calc1[10]; + double eta_calc[10], eta_calc1[10]; int i; double p, wvln, testval, h[4]; double post[9], pre[9], axis[3], beam[3], ax[19]; @@ -167,60 +153,50 @@ int main(){ struct grain gr, gr_test; wvln = 0.12345; - for(i=0;i<9;i++){ - gr.UB[i]=0; - post[i]=0; - pre[i]=0; + for (i = 0; i < 9; i++) { + gr.UB[i] = 0; + post[i] = 0; + pre[i] = 0; } - gr.UB[0]=0.22; - gr.UB[4]=0.57; - gr.UB[8]=0.42; - h[0]=5; - h[1]=3; - h[2]=2; - h[3]=1; - post[0]=1.; post[4]=1.; post[8]=1.; - pre[0] =1.; pre[4] =1.; pre[8] =1.; - axis[0]=0.; axis[1]=0.; axis[2]=1.; - beam[0]=1.; beam[1]=0.; beam[2]=0.; - make_ax( post, axis, beam, ax ); - for(i=0;i<19;i++) - printf("ax[%d]=%f\n",i,ax[i]); - - - g_to_k( h, - gr.UB, - ax, - pre, - wvln, - gcalc, - kcalc, - tth_calc, - eta_calc, - omega_calc); - printf( "gcalc %f %f %f |%f|\n",gcalc[0], gcalc[1], gcalc[2], norm3(gcalc)); - printf( "kcalc %f %f %f |%f|\n",kcalc[0], kcalc[1], kcalc[2], norm3(kcalc)); - printf( "eta_calc %f omega_calc %f tth_calc %f\n", eta_calc[0], omega_calc[0], tth_calc[0]); - + gr.UB[0] = 0.22; + gr.UB[4] = 0.57; + gr.UB[8] = 0.42; + h[0] = 5; + h[1] = 3; + h[2] = 2; + h[3] = 1; + post[0] = 1.; + post[4] = 1.; + post[8] = 1.; + pre[0] = 1.; + pre[4] = 1.; + pre[8] = 1.; + axis[0] = 0.; + axis[1] = 0.; + axis[2] = 1.; + beam[0] = 1.; + beam[1] = 0.; + beam[2] = 0.; + make_ax(post, axis, beam, ax); + for (i = 0; i < 19; i++) + printf("ax[%d]=%f\n", i, ax[i]); + + g_to_k(h, gr.UB, ax, pre, wvln, gcalc, kcalc, tth_calc, eta_calc, + omega_calc); + printf("gcalc %f %f %f |%f|\n", gcalc[0], gcalc[1], gcalc[2], norm3(gcalc)); + printf("kcalc %f %f %f |%f|\n", kcalc[0], kcalc[1], kcalc[2], norm3(kcalc)); + printf("eta_calc %f omega_calc %f tth_calc %f\n", eta_calc[0], + omega_calc[0], tth_calc[0]); p = 1e-8; - for(i=0; i<9; i++){ + for (i = 0; i < 9; i++) { // Now test gr_test = gr; gr_test.UB[i] += p; - g_to_k( h, gr_test.UB, ax, - pre, - wvln, - gcalc, - kcalc, - tth_calc1, - eta_calc1, - omega_calc1); - testval = (tth_calc1[0] - tth_calc[0])/p; - printf(" dtth_dUB[%d] = %f ",i,tth_calc[i+1]); - printf(" %f \n",testval); + g_to_k(h, gr_test.UB, ax, pre, wvln, gcalc, kcalc, tth_calc1, eta_calc1, + omega_calc1); + testval = (tth_calc1[0] - tth_calc[0]) / p; + printf(" dtth_dUB[%d] = %f ", i, tth_calc[i + 1]); + printf(" %f \n", testval); } - - } - diff --git a/src/localmaxlabel.c b/src/localmaxlabel.c new file mode 100644 index 00000000..be772262 --- /dev/null +++ b/src/localmaxlabel.c @@ -0,0 +1,208 @@ + +#include "cImageD11.h" +#include +#include +#include + +/** + * neighbormax assigns values in "l" to address highest pixel + * of the 8 neighbors. + * l == 0 => this pixel is the max + * otherwise o[ l-1 ] give the offset + */ + +#define pick(A, B, I, J) \ + if ((A) > (B)) { \ + (B) = (A); \ + (I) = (J); \ + } + +DLL_LOCAL +int neighbormax(const float *restrict im, // input + int32_t *restrict lout, // output + uint8_t *restrict l, // workspace temporary + int dim0, // Image dimensions + int dim1) { + // Fill in the first label as npeaks on this row + int i, j, p, k0, k1, k2, npks; + float mx0, mx1, mx2; + + npks = 0; + /* The image borders */ + for (i = 0; i < dim1; i++) { // first and last row here: + lout[i] = 0; // ends of rows are below + l[i] = 0; + lout[dim1 * (dim0 - 1) + i] = 0; + l[dim1 * (dim0 - 1) + i] = 0; + } +#pragma omp parallel for private( j, p, k0, k1, k2, mx0, mx1, mx2 ) reduction(+:npks) + for (i = dim1; i < (dim0 - 1) * dim1; i = i + dim1) { + // skipping 1 pixel border + lout[i] = 0; // set edges to zero: pixel j=0: + l[i] = 0; + + p = i + 1; + mx0 = im[p - 1 - dim1]; + k0 = 1; + pick(im[p - 1], mx0, k0, 2); + pick(im[p - 1 + dim1], mx0, k0, 3); + k1 = 4; + mx1 = im[p - dim1]; + pick(im[p], mx1, k1, 5); + pick(im[p + dim1], mx1, k1, 6); + for (j = 1; j < dim1 - 1; j++) { + p = i + j; + mx2 = im[p + 1 - dim1]; + k2 = 7; + pick(im[p + 1], mx2, k2, 8); + pick(im[p + 1 + dim1], mx2, k2, 9); + pick(mx1, mx0, k0, k1); + pick(mx2, mx0, k0, k2); + l[p] = k0; + if (k0 == 5) { /* This peak is a maximum, count in lout[i] */ + lout[i]++; + } + mx0 = mx1; + k0 = k1 - 3; + mx1 = mx2; + k1 = k2 - 3; + } + // set edges to zero: pixel j=dim1: + lout[i + dim1 - 1] = 0; + l[i + dim1 - 1] = 0; + npks += lout[i]; + } // i + return npks; +} + +/* F2PY_WRAPPER_START + function localmaxlabel( data, labels, wrk, ns, nf ) + intent(c) localmaxlabel +!DOC localmaxlabel assigns a label for each pixel so they are grouped +!DOC to the local maximum. Equal values choose to assign towards the earlier +!DOC value in memory. +!DOC cpu arg (1)0=C, (1)1=SSE2, (1)2=AVX2; if > 9 prints timing + intent(c) + real, intent(in) :: data(ns,nf) + integer*4, intent(inout), note(hello) :: labels(ns,nf) + integer*1, intent(inout) :: wrk(ns,nf) + integer, intent(hide), depend(data) :: ns=shape(data,0) + integer, intent(hide), depend(data) :: nf=shape(data,1) + ! Returns + integer :: localmaxlabel + end function localmaxlabel +F2PY_WRAPPER_END */ +int localmaxlabel(const float *restrict im, // input + int32_t *restrict lout, // output + uint8_t *restrict l, // workspace temporary + int dim0, // Image dimensions + int dim1) { + // old msvc for python 2.7 requires ALL variables declared up here. + int i, j, p, k, q, npk, t, nt, tid, lo, hi, npks; + // int noisy=0; +#define noisy 0 + double tic, toc; + int o[10] = {0, // special case + -1 - dim1, -1, -1 + dim1, -dim1, 0, + +dim1, +1 - dim1, +1, +1 + dim1}; // 7,8,9 + + if (noisy) + printf("Not using intrinsics\n"); + npks = neighbormax(im, lout, l, dim0, dim1); //, o); + if (noisy) { + toc = my_get_time(); + printf(" neighbormax %.3f ms %d peaks\n", 1000 * (toc - tic), npks); + tic = toc; + } + // Cumulate lout[i] so that lout[i] holds cumulative sums + npk = 0; + for (i = 0; i < dim0 * dim1; i = i + dim1) { + t = npk; + npk += lout[i]; + lout[i] = t; + } + if (noisy) { + toc = my_get_time(); + printf(" cumsum %.3f ms %d\n", 1000 * (toc - tic), npk); + tic = toc; + } + // Now pass with row offsets in place +#pragma omp parallel for private(nt, t, j, p) schedule(dynamic) + for (i = 0; i < (dim0 - 1); i++) { + t = lout[i * dim1]; + nt = lout[(i + 1) * dim1]; + if (t == nt) + continue; + // Break early if there is no peak on this row or you already got it + for (j = 1; j < (dim1 - 1); j++) { + p = dim1 * i + j; + if (l[p] == 5) { // pointing to self : k+1==5 + t++; + lout[p] = t; // final label + l[p] = 0; // done, so tagged as zero + if (t == nt) + break; // no more peaks + } + } + } + /* overwrite front border as zero again */ + for (i = 0; i < dim0 * dim1; i = i + dim1) { + lout[i] = 0; + } + if (noisy) { + toc = my_get_time(); + printf(" relabel %.3f ms\n", 1000 * (toc - tic)); + tic = toc; + } + // + // Now make all point to their max + // If we re-write the paths we cannot run in parallel + // ... this was the slowest part, so try openmp + // 105 ms to always walk to max + // 40 ms if path relabelled + // + // This is the same for all versions (optimised or not) + // ... perhaps re-write to be a manual loop and fill in + // ... the steps that are thread local +#pragma omp parallel private(q, i, tid, nt, k, lo, hi) + { +#ifdef _OPENMP + tid = omp_get_thread_num(); + nt = omp_get_num_threads(); +#else + tid = 0; + nt = 1; +#endif + lo = dim0 * dim1 * tid / nt; + hi = dim0 * dim1 * (tid + 1) / nt; + for (i = lo; i < hi; i++) { + if (l[i] == 0) + continue; // done + // Now we need to walk to find a label + k = 0; + q = i + o[l[i]]; + while (l[q]) { + q = q + o[l[q]]; + k++; + } // Now q addresses a max or a correct label + lout[i] = lout[q]; // take label from max + if (k > 0) { // relabel the path taken while we know the top label value + q = i + o[l[i]]; + while (l[q]) { + if ((q >= lo) && (q < hi)) { + l[q] = 0; + lout[q] = lout[i]; + } + q = q + o[l[q]]; + } + } + l[i] = 0; // sharing problems?? + } + } + if (noisy) { + toc = my_get_time(); + printf(" write %.3f ms\n", 1000 * (toc - tic)); + tic = toc; + } + return npk; +} diff --git a/src/make_pyf.py b/src/make_pyf.py new file mode 100644 index 00000000..e26a8c03 --- /dev/null +++ b/src/make_pyf.py @@ -0,0 +1,161 @@ + + +import os, glob, shutil, numpy.f2py +import sys + +head = """ +python module %s + +usercode ''' +#include "blobs.h" +''' +interface + +usercode ''' +/* This runs inside the init part of the module as it is after interface */ +#define str(x) (#x) +PyDict_SetItemString(d,str(s_1) ,PyInt_FromLong(s_1)); +PyDict_SetItemString(d,str(s_I) ,PyInt_FromLong(s_I)); +PyDict_SetItemString(d,str(s_I2) ,PyInt_FromLong(s_I2)); +PyDict_SetItemString(d,str(s_fI) ,PyInt_FromLong(s_fI)); +PyDict_SetItemString(d,str(s_ffI) ,PyInt_FromLong(s_ffI)); +PyDict_SetItemString(d,str(s_sI) ,PyInt_FromLong(s_sI)); +PyDict_SetItemString(d,str(s_ssI) ,PyInt_FromLong(s_ssI)); +PyDict_SetItemString(d,str(s_sfI) ,PyInt_FromLong(s_sfI)); +PyDict_SetItemString(d,str(s_oI) ,PyInt_FromLong(s_oI)); +PyDict_SetItemString(d,str(s_ooI) ,PyInt_FromLong(s_ooI)); +PyDict_SetItemString(d,str(s_soI) ,PyInt_FromLong(s_soI)); +PyDict_SetItemString(d,str(s_foI) ,PyInt_FromLong(s_foI)); +PyDict_SetItemString(d,str(mx_I) ,PyInt_FromLong(mx_I)); +PyDict_SetItemString(d,str(mx_I_f) ,PyInt_FromLong(mx_I_f)); +PyDict_SetItemString(d,str(mx_I_s) ,PyInt_FromLong(mx_I_s)); +PyDict_SetItemString(d,str(mx_I_o) ,PyInt_FromLong(mx_I_o)); +PyDict_SetItemString(d,str(bb_mx_f),PyInt_FromLong(bb_mx_f)); +PyDict_SetItemString(d,str(bb_mx_s),PyInt_FromLong(bb_mx_s)); +PyDict_SetItemString(d,str(bb_mx_o),PyInt_FromLong(bb_mx_o)); +PyDict_SetItemString(d,str(bb_mn_f),PyInt_FromLong(bb_mn_f)); +PyDict_SetItemString(d,str(bb_mn_s),PyInt_FromLong(bb_mn_s)); +PyDict_SetItemString(d,str(bb_mn_o),PyInt_FromLong(bb_mn_o)); + +PyDict_SetItemString(d,str(avg_i),PyInt_FromLong(avg_i)); +PyDict_SetItemString(d,str(f_raw),PyInt_FromLong(f_raw)); +PyDict_SetItemString(d,str(s_raw),PyInt_FromLong(s_raw)); +PyDict_SetItemString(d,str(f_cen),PyInt_FromLong(f_cen)); +PyDict_SetItemString(d,str(s_cen),PyInt_FromLong(s_cen)); +PyDict_SetItemString(d,str(o_raw),PyInt_FromLong(o_raw)); +PyDict_SetItemString(d,str(m_ff),PyInt_FromLong(m_ff)); +PyDict_SetItemString(d,str(m_ss),PyInt_FromLong(m_ss)); +PyDict_SetItemString(d,str(m_oo),PyInt_FromLong(m_oo)); +PyDict_SetItemString(d,str(m_sf),PyInt_FromLong(m_sf)); +PyDict_SetItemString(d,str(m_so),PyInt_FromLong(m_so)); +PyDict_SetItemString(d,str(m_fo),PyInt_FromLong(m_fo)); +PyDict_SetItemString(d,str(dety),PyInt_FromLong(dety)); +PyDict_SetItemString(d,str(detz),PyInt_FromLong(detz)); + +PyDict_SetItemString(d,str(NPROPERTY),PyInt_FromLong(NPROPERTY)); + +PyDict_SetItemString(d,str(s2D_1),PyInt_FromLong(s2D_1)); +PyDict_SetItemString(d,str(s2D_I),PyInt_FromLong(s2D_I)); +PyDict_SetItemString(d,str(s2D_fI),PyInt_FromLong(s2D_fI)); +PyDict_SetItemString(d,str(s2D_sI),PyInt_FromLong(s2D_sI)); +PyDict_SetItemString(d,str(s2D_ffI),PyInt_FromLong(s2D_ffI)); +PyDict_SetItemString(d,str(s2D_sfI),PyInt_FromLong(s2D_sfI)); +PyDict_SetItemString(d,str(s2D_ssI),PyInt_FromLong(s2D_ssI)); +PyDict_SetItemString(d,str(s2D_bb_mx_f),PyInt_FromLong(s2D_bb_mx_f)); +PyDict_SetItemString(d,str(s2D_bb_mx_s),PyInt_FromLong(s2D_bb_mx_s)); +PyDict_SetItemString(d,str(s2D_bb_mn_f),PyInt_FromLong(s2D_bb_mn_f)); +PyDict_SetItemString(d,str(s2D_bb_mn_s),PyInt_FromLong(s2D_bb_mn_s)); +PyDict_SetItemString(d,str(NPROPERTY2D),PyInt_FromLong(NPROPERTY2D)); +/* ends user code */ +''' +end interface + +interface +""" + +tail = """ +end interface +end python module _cImageD11 +""" + +def add_wrappers( pyf, csource ): + """ reads a wrapper out of a C file which is delimited by: + F2PY_WRAPPER_START and F2PY_WRAPPER_END """ + with open(csource, "r") as cfile: + in_wrapper = False + for line in cfile.readlines(): + if in_wrapper: + if line.find("F2PY_WRAPPER_END") >= 0: + pyf.write("\n") + in_wrapper = False + else: + pyf.write(line) + else: + if line.find("F2PY_WRAPPER_START") >= 0: + in_wrapper = True + + +def makepyf( pyfname, sources ): + import os + if pyfname.endswith('.pyf'): + pyfname = pyfname[:-4] + with open(pyfname+".pyf","w") as pyf: + pyf.write(head%os.path.split(pyfname)[-1]) + for sfile in sources: + add_wrappers(pyf, sfile) + pyf.write(tail) + + +def write_docs( inp, outf ): + """ One single block of !DOC per item, first word is key + """ + with open(inp , "r") as pyf: + fname = None + docs = {} + for line in pyf.readlines(): + if line.startswith("!DOC"): + if fname is not None: + docs[fname] += line[5:] + else: + words = line.split() + fname = words[1] + docs[fname] = " ".join(words[2:]) + "\n" + else: + fname = None + with open(outf, "w") as docf: + docf.write('\n"""Autogenerated from make_pyf.py Edit in %s please"""\n'%(inp)) + keys = list(docs.keys()) + keys.sort() + for fname in keys: + docf.write( '%s = """%s"""\n'%(fname, docs[fname])) + docf.write("__all__ = [\n" + ",\n".join([' "%s"'%(k) for k in keys ])+ "]") + + +for name in ['"c:\Program Files\LLVM\bin\clang-format" ', + 'clang-format-10', + 'clang-format' ]: + if os.path.exists(name) or os.system(name+" -version")==0: + CF = name + ' --style=file -i ' + break +else: + print("clang-format was not found, please do not send to git!") + CF = 'echo ' + + +def clean(sources): + """ Runs clang-format on all the C source files + Breaks a lot of things but hopefully stops it looking bad + """ + for fname in sources + glob.glob("*.h"): + print("cleaning",fname) + os.system(CF+fname) + +if __name__ == "__main__": + + PYF = "_cImageD11.pyf" + sources = [s for s in glob.glob("*.c") if s.find("module")<0 ] + clean( sources ) + makepyf( PYF , sources ) + DOCF = os.path.join("..", "ImageD11", "cImageD11_docstrings.py") + write_docs( PYF, DOCF ) + diff --git a/src/old/bldlib.py b/src/old/bldlib.py new file mode 100644 index 00000000..a4fac927 --- /dev/null +++ b/src/old/bldlib.py @@ -0,0 +1,140 @@ + + +""" +We started using some intrinsics in the C code to make it run faster +when processors support specific instructions. This makes it less +portable and harder to build. These notes explain how things could +work. + +If a CPU has some feature (sse2, avx2, fma, etc) then we will want +to use it in specific places that we are aware of. We might as well +also let the compiler use those instructions everywhere else where +we have not written specific things too. That means: + +- compile everything as avx2 -> avx2 version +- compile everything as sse2 -> sse2 version +- compile everything as ansi -> ansi version + +So we are going to build a series of extensions and use #ifdef inside +them. + +-- May 2020 - all that gets to be a horrific mess. +We have a "generic" build that takes the regular python option +and a host platform build that take -mhost=native flags. + +Which one we get is determined by the cImageD11.py module. +""" + +import os, sys, platform +import setuptools +import distutils.ccompiler + +need_build = True + +sources = ("blobs.c cdiffraction.c cimaged11utils.c closest.c connectedpixels.c"+ + " darkflat.c localmaxlabel.c sparse_image.c splat.c").split() + +plat = platform.system() +bits = platform.architecture()[0] +mach = platform.machine() +vers = "%d.%d"%(sys.version_info[:2]) +tmpdir = "%s_%s_%s_%s"%(plat, bits, mach, vers) +# Hopefully this keeps thing separate for ppc vs AMD64 etc in a build folder. +safelibname = "cImageD11_"+tmpdir+"_safe" +fastlibname = "cImageD11_"+tmpdir+"_fast" + +compiler = None +for a in sys.argv: + if "mingw32" in a: + compiler = "mingw32" + +def getfastarg(args): + cc = distutils.ccompiler.new_compiler( verbose=1 , compiler=compiler ) + with open("dummy.c","w") as cfile: + cfile.write("\n") + farg = [] + if platform.machine in ("x86_64", "AMD64"): + poss = ("-mavx2", "-mtune=native" ) + else: + poss = ("-mtune=native",) + for a in poss: + try: + cc.compile(["dummy.c",], output_dir=".",extra_preargs=args+[a,]) + farg.append(a) + except: + pass + print("Fast args are",farg) + return farg + + +if plat == "Linux" or compiler == "mingw32": + arg=["-O2", "-fopenmp", "-fPIC", "-std=c99" ] + fa = getfastarg(arg) + fastarg = arg + fa + # link args + lfastarg = arg + fa +elif plat == "Windows": # Needs to be MSVC for now. + arg=["/O2", "/openmp" ] + # the /arch switches are ignored by the older MSVC compilers + fastarg = arg + ["/arch:AVX2",] + lfastarg = arg + ["/arch:AVX2",] +else: + fastarg = lfastarg = arg = [ ] + +def run_cc( cc, flags, libname ): + objs = cc.compile( sources , + output_dir=libname.replace("cImageD11_",""), + extra_preargs = flags ) + ok = cc.create_static_lib( objs, libname, output_dir="." ) + return libname + +def write_docs( inp, outf ): + """ One single block of !DOC per item, first word is key + """ + with open(inp , "r") as pyf: + fname = None + docs = {} + for line in pyf.readlines(): + if line.startswith("!DOC"): + if fname is not None: + docs[fname] += line[5:] + else: + words = line.split() + fname = words[1] + docs[fname] = " ".join(words[2:]) + "\n" + else: + fname = None + with open(outf, "w") as docf: + docf.write('\n"""Autogenerated by src/bldlib.py. Edit in src/cImageD11_interface.pyf please"""\n') + keys = list(docs.keys()) + keys.sort() + for fname in keys: + docf.write( '%s = """%s"""\n'%(fname, docs[fname])) + docf.write("__all__ = [\n" + ",\n".join([' "%s"'%(k) for k in keys ])+ "]") + + +def make_pyf( inp, name ): + out = open(name+".pyf", "w") + out.write("python module %s\n"%(name)) + pyf = open(inp , "r").read() + out.write( pyf ) + out.write("end python module %s\n"%(name)) + out.close() + +def docs(): + write_docs( "cImageD11_interface.pyf", os.path.join("..","ImageD11src", "cImageD11_docstrings.py")) + + +def main(): + cc = distutils.ccompiler.new_compiler( verbose=1 , compiler=compiler ) + cc.add_include_dir( "." ) + docs() + make_pyf( "cImageD11_interface.pyf", "cImageD11_safe") + safelib = run_cc(cc, arg, safelibname ) + make_pyf( "cImageD11_interface.pyf", "cImageD11_fast") + fastlib = run_cc(cc, fastarg, fastlibname ) + return 0 + + +if __name__=="__main__": + main() diff --git a/src/old/cdiffraction.py b/src/old/cdiffraction.py index 4b732999..84ddeb56 100644 --- a/src/old/cdiffraction.py +++ b/src/old/cdiffraction.py @@ -1,3 +1,5 @@ +from __future__ import print_function + import numpy as n def compute_grain_origins(omega, wedge = 0.0, chi = 0.0, t_x = 0.0, t_y = 0.0, t_z = 0.0): @@ -29,22 +31,22 @@ def compute_grain_origins(omega, wedge = 0.0, chi = 0.0, [ 0, 1, 0], [ n.sin(w), 0, n.cos(w)] ] , n.float) c=n.radians(chi) - print "WI",WI + print ("WI",WI) CI = n.array( [ [ 1, 0, 0], [ 0, n.cos(c), -n.sin(c)], [ 0, n.sin(c), n.cos(c)] ] , n.float) - print "CI",CI + print( "CI",CI) t = n.zeros((3,omega.shape[0]),n.float) # crystal translations # Rotations in reverse order compared to making g-vector # also reverse directions. this is trans at all zero to # current setting. gv is scattering vector to all zero om_r = n.radians(omega) - print "om_r",om_r + print( "om_r",om_r) # This is the real rotation (right handed, g back to k) t[0,:] = n.cos(om_r)*t_x - n.sin(om_r)*t_y t[1,:] = n.sin(om_r)*t_x + n.cos(om_r)*t_y t[2,:] = t_z - print "om.t",t + print ("om.t",t) if chi != 0.0: c = n.cos(n.radians(chi)) s = n.sin(n.radians(chi)) @@ -53,7 +55,7 @@ def compute_grain_origins(omega, wedge = 0.0, chi = 0.0, u[1,:]= c * t[1,:] + -s * t[2,:] u[2,:]= s * t[1,:] + c * t[2,:] t = u - print "chi.om.t",t + print ("chi.om.t",t) if wedge != 0.0: c = n.cos(n.radians(wedge)) s = n.sin(n.radians(wedge)) @@ -62,7 +64,7 @@ def compute_grain_origins(omega, wedge = 0.0, chi = 0.0, u[1,:]= t[1,:] u[2,:]= s * t[0,:] + c * t[2,:] t = u - print "wedge.chi.om.t",t + print( "wedge.chi.om.t",t) return t import ctypes, numpy as np @@ -125,7 +127,7 @@ def flatfloat(a, shape=None): sf = c.xc,c.yc # c.nrows = 2 # c.bigarray = c.bigarray[:,:2] - print c.bigarray.shape + print (c.bigarray.shape) c.set_attributes() XL = flatfloat( compute_xyz_lab( sf , **pars.parameters ).T ) @@ -143,13 +145,13 @@ def flatfloat(a, shape=None): labels = np.zeros( c.nrows, np.int32 ) hkls = np.zeros( ( c.nrows, 3 ), NPREAL ) - print XL.shape - print om.shape - print UBs.shape - print UBIs.shape - print Ts.shape - print labels.shape - print hkls.shape + print( XL.shape) + print( om.shape) + print( UBs.shape) + print( UBIs.shape) + print( Ts.shape) + print( labels.shape) + print( hkls.shape) dll.c_choosegrains( XL.ctypes.data_as( ctypes.POINTER( REAL) ), @@ -172,26 +174,26 @@ def flatfloat(a, shape=None): peaks_xyz = ImageD11.transform.compute_xyz_lab( sf, **pars.parameters ) - print peaks_xyz[:,-1] + print( peaks_xyz[:,-1]) - print "translation",pars.get('t_x'),pars.get('t_y'),pars.get('t_z') + print( "translation",pars.get('t_x'),pars.get('t_y'),pars.get('t_z')) origin = compute_grain_origins(c.omega, wedge = pars.get('wedge'), chi=pars.get('chi'), t_x = pars.get('t_x'), t_y = pars.get('t_y'), t_z = pars.get('t_z')) - print origin[:,-1], origin.shape - print "I got to the end of the script" + print (origin[:,-1], origin.shape) + print( "I got to the end of the script") tth,eta = ImageD11.transform.compute_tth_eta( sf, omega=c.omega, **pars.parameters) kvecs = ImageD11.transform.compute_k_vectors(tth, eta, pars.get('wavelength')) - print kvecs.T + print( kvecs.T) gvecs = ImageD11.transform.compute_g_from_k( kvecs, c.omega, wedge=pars.get('wedge'), chi=pars.get('chi')) - print gvecs.T + print( gvecs.T) for i,gr in enumerate(grains): oldhkls = np.dot( gr.ubi, gvecs ) - print "grain %d"%(i) - print oldhkls.T - print hkls + print( "grain %d"%(i)) + print( oldhkls.T) + print( hkls) #C:\Users\wright\Programming\fable\ImageD11\test\makemap>c:\mingw64\bin\gcc ..\..\src\diffraction.c -O3 -march=native -ffast-math -static-libgcc -share d -o diffraction.dll diff --git a/src/old/check_cpu.c b/src/old/check_cpu.c new file mode 100644 index 00000000..c65ec5b0 --- /dev/null +++ b/src/old/check_cpu.c @@ -0,0 +1,21 @@ + +/** + * http://newbiz.github.io/cpp/2010/12/20/Playing-with-cpuid.html + * ... with corrections and modifications... + */ + +#include "check_cpu_auto.c" +#include + +int main() { + int i; + char name[13]; + cpuidProcessorName(name); + printf("%s\n", name); + printf("SSE2 %d\n", i_have_SSE2()); + printf("SSE42 %d\n", i_have_SSE42()); + printf("AVX %d\n", i_have_AVX()); + printf("AVX2 %d\n", i_have_AVX2()); + printf("AVX512F %d\n", i_have_AVX512F()); + return 0; +} diff --git a/src/sparse_image.c b/src/sparse_image.c new file mode 100644 index 00000000..46f6c8f1 --- /dev/null +++ b/src/sparse_image.c @@ -0,0 +1,1040 @@ + +#include "blobs.h" +#include +#include /* sqrt */ +#include /* printf */ +#include /* abs(int) */ +#include /* memset */ + +/* F2PY_WRAPPER_START + function mask_to_coo( msk, ns, nf, i, j, nnz, w ) +!DOC mask_to_coo takes a mask and converts it to a list +!DOC of i,j coordinates in a sparse array coo format +!DOC returns : +!DOC 0 => success +!DOC 1 => ns out of range ; 2 => nf out of range +!DOC 3 => nnz < 1 empty mask +!DOC 4 => nnz did not match this mask + intent(c) mask_to_coo + intent(c) + integer*1, intent(in) :: msk( ns, nf ) + integer, intent(hide), depend(msk) :: ns=shape(msk,0) + integer, intent(hide), depend(msk) :: nf=shape(msk,1) + integer(kind=-2), intent(inout), dimension(nnz) :: i, j + integer, intent(inout) :: w(ns) + integer, intent(hide), depend(i) :: nnz=shape(i,0) + integer, intent(inout) :: w(ns) + ! returns an int + integer :: mask_to_coo + end function mask_to_coo +F2PY_WRAPPER_END */ +int mask_to_coo(int8_t msk[], int ns, int nf, uint16_t i[], uint16_t j[], + int nnz, int nrow[]) { + int mi, mj, idx; + /* int *nrow; + nrow = (int*) malloc(ns*sizeof(int)); */ + if ((ns < 1) || (ns > 65535)) + return 1; + if ((nf < 1) || (nf > 65535)) + return 2; + if (nnz < 1) + return 3; + /* pixels per row , 2D image */ +#pragma omp parallel for private(mi, mj) + for (mi = 0; mi < ns; mi++) { + nrow[mi] = 0; + for (mj = 0; mj < nf; mj++) { + if (msk[mi * nf + mj] != 0) { + nrow[mi]++; + } + } + } + /* cumsum */ + for (mi = 1; mi < ns; mi++) { + nrow[mi] += nrow[mi - 1]; + } + if (nrow[ns - 1] != nnz) { + return 4; + } + /* fill in */ +#pragma omp parallel for private(mi, mj, idx) + for (mi = 0; mi < ns; mi++) { + if (mi == 0) { + idx = 0; + } else { + idx = nrow[mi - 1]; + } + if (nrow[mi] > idx) { + for (mj = 0; mj < nf; mj++) { + if (msk[mi * nf + mj] != 0) { + i[idx] = (uint16_t)mi; + j[idx] = (uint16_t)mj; + idx++; + } + } + } + } + /* free(nrow); */ + return 0; +} + +/* + * https://fr.mathworks.com/help/matlab/ref/sparse.html + * S = sparse(i,j,v,m,n,nz) + * i = array of index i + * j = array of index j + * v = array of values + * S( i(k), j(k) ) == v(k) + * m = dimension 0 of S + * n = dimension 1 of S + * nz = number of non-zero values + * + * https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html + * scipy.sparse.coo_matrix(arg1, shape=None, dtype=None, copy=False) + * arg1 = ( v, i, j ) as above + * shape = (m, n) as above + * dtype = float/double/int, etc + */ + +/* F2PY_WRAPPER_START + function sparse_is_sorted( i, j, nnz ) +!DOC sparse_is_sorted checks whether the indices in i and j of a sparse +!DOC coo format come in the order that they would appear inside an image +!DOC * @param i, j index arrays +!DOC * @param nnz dimension of i, j +!DOC returns 0 for all OK +!DOC k for first non-sorted element +!DOC -k for first duplicate + intent(c) sparse_is_sorted + intent(c) + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(i) :: nnz = shape( i, 0) + threadsafe + ! returns + integer sparse_is_sorted + end function spare_is_sorted +F2PY_WRAPPER_END */ +int sparse_is_sorted(uint16_t i[], uint16_t j[], int nnz) { + int k, es, ed; + es = nnz + 1; + ed = nnz + 1; + for (k = 1; k < nnz; k++) { + if (i[k] < i[k - 1]) { /* bad, not sorted */ + es = (k < es) ? k : es; + continue; + } + if (i[k] == i[k - 1]) { /* Same row, j must be gt prev */ + if (j[k] < j[k - 1]) { /* bad */ + es = (k < es) ? k : es; + } else if (j[k] == j[k - 1]) { + ed = (k < ed) ? k : ed; + } else { + continue; + } + } + } + if ((es == (nnz + 1)) && (ed == (nnz + 1))) + return 0; + if (es > ed) + return -ed; + else + return es; +} + +/* F2PY_WRAPPER_START + function sparse_connectedpixels( v, i, j, nnz, threshold, labels ) +!DOC sparse_connectedpixels runs the connectedpixels algorithm on +!DOC a sparse image using a supplied threshold putting labels +!DOC into labels array and returning the number of blobs found + intent(c) sparse_connectedpixels + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(i) :: nnz = shape( i, 0) + real threshold + integer, intent(inout), dimension(nnz) :: labels + ! Returns + integer :: sparse_connectedpixels + threadsafe + end function sparse_connectedpixels +F2PY_WRAPPER_END */ +#define NOISY 0 +int sparse_connectedpixels(float *restrict v, uint16_t *restrict i, + uint16_t *restrict j, int nnz, float threshold, + int32_t *restrict labels /* nnz */ +) { + int k, p, pp, ir; + int32_t *S, *T, np; + /* Read k = kurrent + p = prev */ + double start, mid, end; + if (NOISY) { + start = my_get_time(); + k = sparse_is_sorted(i, j, nnz); + if (k != 0) + return k; + } + pp = 0; + p = 0; + S = dset_initialise(16384); + /* Main loop */ + if (NOISY) + printf("ok to main loop\n"); + for (k = 0; k < nnz; k++) { + labels[k] = 0; + if (v[k] <= threshold) { + continue; + } + if (k == 0) + goto newlabel; + /* Decide on label for this one ... + * + * 4 neighbors : k-1 is prev + */ + p = k - 1; /* previous pixel, same row */ + if (((j[p] + 1) == j[k]) && (i[p] == i[k]) && (labels[p] > 0)) { + labels[k] = labels[p]; + /* check for unions below */ + } + if (i[k] == 0) + goto newlabel; + ir = i[k] - 1; + /* pp should be on row above, on or after j-1 */ + while (ir > i[pp]) + pp++; + /* out if nothing on row above */ + if (i[pp] == i[k]) + goto newlabel; + /* Locate previous pixel on row above */ + while (((j[k] - j[pp]) > 1) && (i[pp] == ir)) + pp++; + for (p = pp; j[p] <= j[k] + 1; p++) { + if (i[p] == ir) { + if (labels[p] > 0) { + // Union p, k + match(labels[k], labels[p], S); + } + } else { + break; // not same row + } + } + newlabel: + if (labels[k] == 0) + S = dset_new(&S, &labels[k]); + } // end loop over data + if (NOISY) + mid = my_get_time(); + T = dset_compress(&S, &np); + // renumber labels + for (k = 0; k < nnz; k++) { + if (labels[k] > 0) { + /* if( T[labels[k]] == 0 ){ + printf("Error in sparse_connectedpixels\n"); + } */ + labels[k] = T[labels[k]]; + } + } + free(S); + free(T); + if (NOISY) { + end = my_get_time(); + printf("Time in sparse image %f ms %f ms\n", 1000 * (end - mid), + 1000 * (mid - start)); + } + return np; +} + +/* F2PY_WRAPPER_START + function sparse_connectedpixels_splat( v, i, j, nnz, th, lbl, Z,ni, nj ) +!DOC sparse_connectedpixels_splat is for debugging/timing. It splats +!DOC the sparse array into a dense array and runs the old connectedpixels +!DOC code on that. + intent(c) sparse_connectedpixels_splat + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(i) :: nnz = shape( i, 0) + real th + integer, intent(inout), dimension(nnz) :: lbl + integer, intent(inout), dimension((ni+2)*(nj+2)) :: Z + integer :: ni, nj + ! Returns + integer :: sparse_connectedpixels_splat + threadsafe + end function sparse_connectedpixels_splat +F2PY_WRAPPER_END */ +#define NOISY 0 +int sparse_connectedpixels_splat(float *restrict v, uint16_t *restrict i, + uint16_t *restrict j, int nnz, float threshold, + int32_t *restrict labels, /* nnz */ + int32_t *restrict Z, + /* workspace, at least (imax+2)*(jmax+2) */ + int imax, int jmax) { + int k, p, pp, ir, jdim, ik, jk; + int32_t *S, *T, np; + /* Read k = kurrent + p = prev */ + double start, mid; + if (NOISY) { + start = my_get_time(); + k = sparse_is_sorted(i, j, nnz); + if (k != 0) + return k; + } + if (NOISY) { + mid = my_get_time(); + printf("check sorted %.3f ms\n", (mid - start) * 1000); + start = my_get_time(); + } + + jdim = jmax + 2; + /* This is not! delivered with zeros, we put a border in too + * Z = (int32_t *) malloc(idim*jdim* sizeof(int32_t)); + * later we will write into Z as a scratch area for labels (filled at very + * end) */ + pp = 0; + p = 0; + S = dset_initialise(16384); + if (NOISY) { + mid = my_get_time(); + printf("mallocs %.3f ms\n", (mid - start) * 1000); + start = my_get_time(); + } + /* zero the parts of Z that we will read from (pixel neighbors) */ + for (k = 0; k < nnz; k++) { + ik = i[k] + 1; /* the plus 1 is because we padded Z */ + jk = j[k] + 1; + p = ik * jdim + jk; + Z[p] = 0; + Z[p - 1] = 0; + Z[p - jdim - 1] = 0; + Z[p - jdim] = 0; + Z[p - jdim + 1] = 0; + } + if (NOISY) { + mid = my_get_time(); + printf("zeros %.3f ms\n", (mid - start) * 1000); + start = my_get_time(); + } + + /* Main loop */ + for (k = 0; k < nnz; k++) { + if (v[k] <= threshold) { + continue; + } + /* Decide on label for this one ... + * + * 4 neighbors : k-1 is prev + */ + ik = i[k] + 1; /* the plus 1 is because we padded Z */ + jk = j[k] + 1; + p = ik * jdim + jk; + /* previous pixel, same row */ + if (Z[p - 1] > 0) { + Z[p] = Z[p - 1]; + } + /* 3 pixels on previous row */ + ir = (ik - 1) * jdim + jk; + for (pp = ir - 1; pp <= ir + 1; pp++) { + if (Z[pp] > 0) { + // Union p, k + match(Z[p], Z[pp], S); + } + } + if (Z[p] == 0) + S = dset_new(&S, &Z[p]); + } // end loop over data + if (NOISY) { + mid = my_get_time(); + printf("main loop %.3f ms\n", (mid - start) * 1000); + start = my_get_time(); + } + T = dset_compress(&S, &np); + // renumber labels + for (k = 0; k < nnz; k++) { + ik = i[k] + 1; /* the plus 1 is because we padded Z */ + jk = j[k] + 1; + p = ik * jdim + jk; + if (Z[p] > 0) { + labels[k] = T[Z[p]]; + } + } + if (NOISY) { + mid = my_get_time(); + printf("Relabelling %f ms\n", 1000 * (mid - start)); + start = my_get_time(); + } + free(S); + free(T); + if (NOISY) { + mid = my_get_time(); + printf("Free %f ms\n", 1000 * (mid - start)); + } + return np; +} + +/* F2PY_WRAPPER_START + subroutine sparse_blob2Dproperties( v, i, j, nnz, labels, results, npk) + intent(c) sparse_blob2Dproperties +!DOC sparse_blob2Dproperties fills the array results with properties of +!DOC each labelled object described by v and labels (pixel values and blob) +!DOC and positions i,j in the image. +!DOC results are: +!DOC results[ipk,s2D_1] = sum(1), number of pixels +!DOC results[ipk,s2D_I] = sum (I), total intensity +!DOC results[ipk,s2D_fI] = sum (f*I), intensity weighted fast index +!DOC results[ipk,s2D_sI] = sum (s*I), intensity weighted slow index +!DOC results[ipk,s2D_ffI] = sum (f*f*I), intensity weighted fast^2 index +!DOC results[ipk,s2D_sfI] = sum (s*f*I), intensity weighted slow*fast index +!DOC results[ipk,s2D_ssI] = sum (s*s*I), intensity weighted slow^2 index + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(v) :: nnz = shape( v, 0) + integer, intent(in), dimension(nnz) :: labels + integer, intent(in) :: npk + double precision, intent(out) :: results( npk, NPROPERTY2D ) + threadsafe + end subroutine sparse_blob2Dproperties +F2PY_WRAPPER_END */ +/* blob_properties for sparse - in image only... */ +void sparse_blob2Dproperties(float *restrict data, uint16_t *restrict i, + uint16_t *restrict j, int nnz, + int32_t *restrict labels, double *restrict res, + int32_t npk) { + int k, kpk, f, s; + double fval; + /* init to zero */ + for (k = 0; k < npk * NPROPERTY2D; k++) { + res[k] = 0.0; + } + for (k = 0; k < npk; k++) { + res[k * NPROPERTY2D + s2D_bb_mn_f] = 65534.; + res[k * NPROPERTY2D + s2D_bb_mn_s] = 65534.; + } + /* printf("nnz : %d\n",nnz); */ + for (k = 0; k < nnz; k++) { + if (labels[k] == 0) { + continue; /* background pixel */ + } + if (labels[k] > npk) { + printf("Error,k %d,labels[k] %d, npk %d \n", k, labels[k], npk); + } + kpk = (labels[k] - 1) * NPROPERTY2D; + fval = (double)data[k]; + s = (int)i[k]; + f = (int)j[k]; + res[kpk + s2D_1] += 1.; + res[kpk + s2D_I] += fval; + res[kpk + s2D_fI] += (fval * f); + res[kpk + s2D_sI] += (fval * s); + res[kpk + s2D_ffI] += (fval * f * f); + res[kpk + s2D_sfI] += (fval * s * f); + res[kpk + s2D_ssI] += (fval * s * s); + + if (res[kpk + s2D_bb_mx_s] < s) + res[kpk + s2D_bb_mx_s] = s; + if (res[kpk + s2D_bb_mx_f] < f) + res[kpk + s2D_bb_mx_f] = f; + if (res[kpk + s2D_bb_mn_s] > s) + res[kpk + s2D_bb_mn_s] = s; + if (res[kpk + s2D_bb_mn_f] > f) + res[kpk + s2D_bb_mn_f] = f; + } +} + + +/* F2PY_WRAPPER_START + subroutine sparse_smooth( v, i, j, nnz, s) + intent(c) sparse_smooth +!DOC sparse_smooth smooths data in coo format. Workaround for avoiding +!DOC equal pixels on peak tails for localmaxlabel +!DOC single threaded + intent(c) + real, intent(in), dimension(nnz) :: v + integer(kind=-2), intent(in), dimension(nnz) :: i + integer(kind=-2), intent(in), dimension(nnz) :: j + integer, intent(hide), depend(v) :: nnz = shape( v, 0) + real, intent(inout) :: s( nnz ) + threadsafe + end subroutine sparse_smooth +F2PY_WRAPPER_END */ +#define CHECKSANITY 0 +void sparse_smooth(float *restrict v, // input image + uint16_t *restrict i, // indices + uint16_t *restrict j, + int nnz, // bounds + float *restrict s ) { // smoothed output + int k, di, dj, r, prow, p; + float m; + m = 1.0/16.0; + /* First make a copy */ + for( k = 0; k i[pp]) { + pp++; + if (CHECKSANITY) { + assert((pp >= 0) && (pp < nnz)); + } + } + if (TRACE) printf("k %d i[k] %d j[k] %d v[k] %f MV[k] %f\n",k,i[k],j[k],v[k],MV[k]); + /* skip if nothing on row above */ + if (i[pp] < i[k]) { + /* Locate previous pixel on row above */ + while ( ((j[pp] + 1) < j[k]) && (i[pp] == ir)) { + pp++; + if (CHECKSANITY) { + assert((pp >= 0) && (pp < nnz)); + } + } + /* Now the 3 pixels on the row above, if they are present */ + for (p = pp; j[p] <= j[k] + 1; p++) { + if (CHECKSANITY) { + assert((p >= 0) && (p < nnz)); + assert(p v[p]) { /* This one is higher */ + /* Steal if we are higher than neighbor currently points to + */ + if (v[k] > MV[p]) { + iMV[p] = k; + MV[p] = v[k]; + } + } else { + if (v[p] > MV[k]) { /* this one is higher than our max, point to it */ + iMV[k] = p; + MV[k] = v[p]; + } + } + } /* 3 previous */ + } /* row above */ + /* 4 preceding neighbors : k-1 is prev */ + p = k - 1; + if (CHECKSANITY) { + assert((p >= 0) && (p < nnz)); + } + if ((i[k] == i[p]) && (j[k] == (j[p] + 1))) { /* previous pixel, same row */ + if (TRACE) printf("p %d i[p] %d j[p] %d v[p] %f\n",p,i[p],j[p],v[p]); + if (v[k] > v[p]) { /* This one is higher */ + /* Steal if we are higher than neighbor currently points to */ + if (v[k] > MV[p]) { + iMV[p] = k; + MV[p] = v[k]; + } + } else { + if (v[p] > MV[k]) { /* Previous one was higher */ + iMV[k] = p; + MV[k] = v[p]; + } + } + } + /* Finally, check for your own value */ + /* Steal if we are higher than neighbor currently points to */ + if (v[k] > MV[k]) { + iMV[k] = k; + MV[k] = v[k]; + } + } // end loop over data + /* Count max values and assign unique labels */ + pp = 0; + for (k = 0; k < nnz; k++) { + labels[k] = -1; + if (iMV[k] == k) { + pp = pp + 1; /* Labels start at one */ + labels[k] = pp; + } + } + /* Now make all labels point to their root */ + for (k = 0; k < nnz; k++) { + p = iMV[k]; + pnext = 0; + while (iMV[p] != p) { + p = iMV[p]; // pointing to uphill + pnext++; + if (CHECKSANITY) { + assert((p >= 0) && (p < nnz)); + } + } + labels[k] = labels[p]; + + if( pnext > 0 ){ // Fill in all the pixels in L1 + p = iMV[k]; // Back to the start + iMV[k] = k; // labels[k] is set already + while (iMV[p] != p) { + labels[p] = labels[k]; + pnext = iMV[p]; + iMV[p] = p; + p = pnext; + } + } + } + return pp; +} + +/* F2PY_WRAPPER_START + function sparse_overlaps( i1, j1, k1, nnz1, & + i2, j2, k2, nnz2 ) +!DOC sparse_overlaps identifies the pixels in i1,j1 which overlap i2,j2. +!DOC The list of overlaps is returned in k1/k2 such that i1[k1]==i2[k2] +!DOC and j1[k1]==j2[k2]. Probably assumes that sparse_is_sorted was true. + intent(c) sparse_overlaps + intent(c) + integer(kind=-2), dimension(nnz1), intent(c) :: i1, j1 + integer, intent(inout), dimension(nnz1) :: k1 + integer, intent(hide), depend(i1), intent(c) :: nnz1=shape(i1,0) + integer(kind=-2), dimension(nnz2), intent(c) :: i2, j2 + integer, intent(inout), dimension(nnz2) :: k2 + integer, intent(hide), depend(i2), intent(c) :: nnz2=shape(i2,0) + ! returns + integer :: sparse_overlaps + threadsafe + end function sparse_overlaps +F2PY_WRAPPER_END */ +int sparse_overlaps(uint16_t *restrict i1, uint16_t *restrict j1, + int *restrict k1, int nnz1, uint16_t *restrict i2, + uint16_t *restrict j2, int *restrict k2, int nnz2 + +) { + /* + * Identify the overlapping pixels that are common to both + * i1[k1]==i2[k2] ; j1[k1]==j2[k2]; + * fill in k1/k2 + */ + int p1, p2, nhit; + p1 = 0; + p2 = 0; + nhit = 0; + while ((p1 < nnz1) && (p2 < nnz2)) { + /* Three cases: + * k1 and k2 are the same pixel or one or the other is ahead */ + if (i1[p1] > i2[p2]) { + p2++; + } else if (i1[p1] < i2[p2]) { + p1++; + } else { /* i1==i2 */ + if (j1[p1] > j2[p2]) { + p2++; + } else if (j1[p1] < j2[p2]) { + p1++; + } else { /* i1=i2,j1=j2 */ + k1[nhit] = p1; + k2[nhit] = p2; + nhit++; + p1++; + p2++; + } + } + } + for (p1 = nhit; p1 < nnz1; p1++) + k1[p1] = 0; + for (p2 = nhit; p2 < nnz2; p2++) + k2[p2] = 0; + return nhit; +} + +/* F2PY_WRAPPER_START + function compress_duplicates( i, j, oi, oj, tmp, n, nt ) +!DOC compress_duplicates removes duplicate i,j labels. On entry then +!DOC i and j are set as the labels from two images. They are sorted +!DOC and on exit i,j hold the unique pairs and oi holds the count +!DOC for the number of overlaps. oj and tmp are temporaries. + intent(c) compress_duplicates + intent(c) + integer, dimension(n), intent(c) :: i, j + integer, dimension(n), intent(c, inout) :: oi, oj + integer, dimension(nt), intent(c, inout) :: tmp + integer, intent(hide), depend(i) :: n = shape(i,0) + integer, intent(hide), depend(tmp) :: nt = shape(tmp,0) + ! returns + integer :: compress_duplicates + threadsafe + end function compress_duplicates +F2PY_WRAPPER_END */ +int compress_duplicates(int *restrict i, int *restrict j, int *restrict oi, + int *restrict oj, int *restrict tmp, int n, int nt) { + int k, vmax, c, t, ik, jk; + /* First sort on j */ + vmax = i[0]; + for (k = 0; k < n; k++) { /* length of histogram */ + if (i[k] > vmax) + vmax = i[k]; + if (j[k] > vmax) + vmax = j[k]; + } + assert(vmax < nt); + for (k = 0; k <= vmax; k++) { /* Zero the histogram */ + tmp[k] = 0; + } + for (k = 0; k < n; k++) { /* Make the histogram */ + tmp[j[k]] = tmp[j[k]] + 1; + } + c = 0; + for (k = 0; k <= vmax; k++) { /* Cumsum */ + t = tmp[k]; + tmp[k] = c; + c = c + t; + } + for (k = 0; k < n; k++) { /* Now the order is: */ + oi[tmp[j[k]]] = i[k]; + oj[tmp[j[k]]] = j[k]; + tmp[j[k]]++; + } + /* Now sort on i */ + for (k = 0; k <= vmax; k++) { /* Zero the histogram */ + tmp[k] = 0; + } + for (k = 0; k < n; k++) { /* Make the histogram */ + tmp[i[k]]++; + } + c = 0; + for (k = 0; k <= vmax; k++) { /* Cumsum */ + t = tmp[k]; + tmp[k] = c; + c = c + t; + } + for (k = 0; k < n; k++) { /* Now the order is: */ + /* t = order to read the original array to get sorted on j */ + j[tmp[oi[k]]] = oj[k]; + i[tmp[oi[k]]] = oi[k]; + tmp[oi[k]]++; + } + /* init */ + ik = i[0]; + jk = j[0]; + t = 1; /* nhits */ + c = 0; /* write pos */ + for (k = 1; k < n; k++) { + if ((ik == i[k]) && (jk == j[k])) { + t++; /* add one */ + } else { + /* write prev */ + i[c] = ik; + j[c] = jk; + oi[c] = t; + /* init next */ + c++; + t = 1; + ik = i[k]; + jk = j[k]; + } + } + /* write last */ + i[c] = ik; + j[c] = jk; + oi[c] = t; + c++; + return c; +} + + +#include + +/* F2PY_WRAPPER_START + function coverlaps( row1, col1, labels1, nnz1, row2, col2, labels2, nnz2, mat, npk1, npk2, results) +!DOC overlaps determines which of (row1,col1,labels1) and (row2,col2,labels2) +!DOC are overlapped. +!DOC + intent(c) coverlaps + intent(c) + integer(kind=-2), dimension(nnz1), intent(c) :: row1, col1 + integer, intent(hide), depend(row1) :: nnz1 = shape(row1,0) + integer(kind=-2), dimension(nnz2), intent(c) :: row2, col2 + integer, intent(hide), depend(row2) :: nnz2 = shape(row2,0) + integer, dimension(nnz1), intent(c) :: labels1 + integer, dimension(nnz2), intent(c) :: labels2 + integer, dimension(npk1, npk2), intent(c) :: mat + integer, intent(hide), depend(mat) :: npk1 = shape(mat, 0) + integer, intent(hide), depend(mat) :: npk2 = shape(mat, 1) + integer, intent(inout), dimension(*) :: results + ! returns + integer :: coverlaps + threadsafe + end function coverlaps +F2PY_WRAPPER_END */ + +int coverlaps( uint16_t* restrict row1, uint16_t* restrict col1, int* restrict labels1, int nnz1, + uint16_t* restrict row2, uint16_t* restrict col2, int* restrict labels2, int nnz2, + int* restrict mat, int npk1, int npk2, int* restrict results ){ + int npk, i1, i2; + uint32_t p1, p2; +// printf("nnz %d %d %d %d\n",nnz1,nnz2,npk1,npk2); + for( i1 = 0 ; i1 < npk1*npk2; i1++) { + mat[i1] = 0; + } + i1 = 0; + i2 = 0; + while ((i1 p2) i2++; + if (p1 < p2) i1++; + } + npk = 0; + for( i1 = 0; i1 < npk1; i1++){ + for( i2 = 0; i2 < npk2; i2++){ + if (mat[i1*npk2 + i2]>0){ + results[npk*3] = i1+1; + results[npk*3+1] = i2+1; + results[npk*3+2] = mat[i1*npk2 + i2]; + npk++; + } + } + } + return npk; +} + + +/* F2PY_WRAPPER_START + function tosparse_u16( img, msk, row, col, val, cut, ns, nf) +!DOC tosparse_u16 stores pixels from img into row/col/val. +!DOC msk determines whether pixels are masked (e.g. eiger mask) +!DOC returns the number of pixels found + intent(c) tosparse_u16 + intent(c) + integer(kind=-2), dimension(ns, nf), intent(c) :: img, row, col, val + integer(kind=-1), dimension(ns, nf), intent(c) :: msk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + integer :: cut + ! returns + integer :: tosparse_u16 + threadsafe + end function tosparse_u16 +F2PY_WRAPPER_END */ + +int tosparse_u16( uint16_t* restrict img, uint8_t* restrict msk, uint16_t* restrict row, + uint16_t* restrict col, uint16_t* restrict val, int cut, int ns, int nf ) { + int k = 0, i, j; + for( i = 0; i < ns ; i++){ + for( j = 0; j < nf ; j++ ){ + if ((msk[i*nf+j]) && (img[i*nf+j] > (uint16_t)cut)){ + row[k] = i; + col[k] = j; + val[k] = img[i*nf+j]; + k++; + } + } + } + return k; +} + + +/* F2PY_WRAPPER_START + function tosparse_u32( img, msk, row, col, val, cut, ns, nf) +!DOC tosparse_u16 stores pixels from img into row/col/val. +!DOC msk determines whether pixels are masked (e.g. eiger mask) +!DOC returns the number of pixels found + intent(c) tosparse_u32 + intent(c) + integer(kind=-4), dimension(ns, nf), intent(c) :: img + integer(kind=-1), dimension(ns, nf), intent(c) :: msk + integer(kind=-2), dimension(*), intent(c) :: row, col + integer(kind=-4), dimension(*), intent(c) :: val + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real :: cut + ! returns + integer :: tosparse_u32 + threadsafe + end function tosparse_u32 +F2PY_WRAPPER_END */ + +int tosparse_u32( uint32_t* restrict img, uint8_t* restrict msk, uint16_t* restrict row, + uint16_t* restrict col, uint32_t* restrict val, float cut, int ns, int nf ) { + int k, i; + uint32_t uicut; + uicut = cut; + k = 0; + for(i = 0; i < ns*nf ; i++ ){ + if (msk[i] && (img[i] > uicut)){ + row[k] = i / nf; + col[k] = i % nf; + val[k] = img[i]; + k++; + } + } + return k; +} + + + +/* F2PY_WRAPPER_START + function tosparse_f32( img, msk, row, col, val, cut, ns, nf) +!DOC tosparse_u16 stores pixels from img into row/col/val. +!DOC msk determines whether pixels are masked (e.g. eiger mask) +!DOC returns the number of pixels found + intent(c) tosparse_f32 + intent(c) + real, dimension(ns, nf), intent(c) :: img, val + integer(kind=-2), dimension(ns, nf), intent(c) :: row, col + integer(kind=-1), dimension(ns, nf), intent(c) :: msk + integer, intent(hide), depend(img) :: ns = shape(img,0) + integer, intent(hide), depend(img) :: nf = shape(img,1) + real :: cut + ! returns + integer :: tosparse_f32 + threadsafe + end function tosparse_f32 +F2PY_WRAPPER_END */ + +int tosparse_f32( float* restrict img, uint8_t* restrict msk, uint16_t* restrict row, + uint16_t* restrict col, float* restrict val, float cut, int ns, int nf ) { + int k = 0, i, j; + for( i = 0; i < ns ; i++){ + for( j = 0; j < nf ; j++ ){ + if ((msk[i*nf+j]) && (img[i*nf+j] > cut)){ + row[k] = i; + col[k] = j; + val[k] = img[i*nf+j]; + k++; + } + } + } + return k; +} + + +#ifdef AVX512 +#include + +int tosparse_u16_avx512( uint16_t* restrict img, uint8_t* restrict msk, uint16_t* restrict row, + uint16_t* restrict col, uint16_t* restrict val, int cut, int ns, int nf ) { + int p, bit, npx = 0; + __m256i m0 = _mm256_setzero_si256(); + /* sign. ho hum, need to test that: */ + __m512i mcut = _mm512_set1_epi16( (int16_t)( (uint16_t) cut) ); + for( p = 0; p < ( ( ns * nf ) / 32 ) * 32 ; p += 32 ) { + /* gcc misses loadu_epi8 and 16. Could be align error here? */ + __mmask32 bitmask = _mm256_cmpneq_epu8_mask ( _mm256_loadu_si256 ( (__m256i_u*) &msk[ p ] ), m0 ) + & _mm512_cmpgt_epu16_mask ( _mm512_loadu_si512 ( (__m512i_u*) &img[ p ] ), mcut); + while( bitmask ){ + bit = _lzcnt_u32( bitmask ); + row[npx] = p / nf; + col[npx] = p % nf; + val[npx] = img[ p + bit ]; + bitmask &= (~( 1u << (31 - bit))); /* clear that bit */ + npx++; + } + } + /* and the end of the image */ + while ( p < nf * ns ){ + if( img[ p ] > (uint16_t) cut ) { + row[npx] = p / nf; + col[npx] = p % nf; + val[npx] = img[ p ]; + npx++; + } + p++; + } + return npx; +} +#endif + + +/* +maxpk = nm.max() +matmem = np.empty( (maxpk, maxpk), 'i').ravel() +ctmem = np.empty( (3, maxpk), 'i') + +def mato(k): + nnz1 = nm[k] + nnz2 = nm[k+1] + mat = matmem[:nnz1*nnz2].reshape((nnz1,nnz2)) + + npk = olap( s.row[ s.ipt[k] : s.ipt[k+1] ],s.col[ s.ipt[k] : s.ipt[k+1] ], lam[ s.ipt[k] : s.ipt[k+1]], + s.row[ s.ipt[k+1] : s.ipt[k+2] ],s.col[ s.ipt[k+1] : s.ipt[k+2] ], lam[ s.ipt[k+1] : s.ipt[k+2]], + mat, ctmem ) + return npk, ctmem[:,:npk] + + */ \ No newline at end of file diff --git a/src/splat.c b/src/splat.c new file mode 100644 index 00000000..9124b461 --- /dev/null +++ b/src/splat.c @@ -0,0 +1,61 @@ + +#include "cImageD11.h" +#include + +/* F2PY_WRAPPER_START + subroutine splat( rgba, w, h, gve, ng, u, npx ) +!DOC splat draws gvectors into an rgba image. The horror of maintaining plot3d +!DOC over the years motivated this code. See test/demo/tksplat +!DOC * set the color and markersize per peak +!DOC * perhaps also a draw order (back to front, top to bottom) ? + intent(c) splat + intent(c) + integer(kind=-1), dimension(w,h,4) :: rgba + integer,intent(hide), depend(rgba) :: h = shape(rgba, 0) + integer,intent(hide), depend(rgba) :: w = shape(rgba, 1) + double precision, intent(inout), dimension(ng,3) :: gve + integer, intent(hide), depend(gve):: ng + double precision, intent(in), dimension(9) :: u + integer :: npx + ! NOT threadsafe, rgba will be recycled and on screen + end subroutine splat + +F2PY_WRAPPER_END */ +void splat(uint8_t rgba[], int w, int h, double gve[][3], int ng, double u[9], + int npx) { + int32_t i, j, k, imx, imy, imz, w2, h2; + double s[9]; + + /* init */ + h2 = h / 2; + w2 = w / 2; + for (i = 0; i < 6; i++) { + s[i] = u[i] * ((w + h) / 4); + } + s[6] = u[6] * 64; + s[7] = u[7] * 64; + s[8] = u[8] * 64; + /* Not parallel - seems to be fast anyway and rgba is shared on write */ + for (i = 0; i < w * h * 4; i = i + 4) { + rgba[i] = 0; + rgba[i + 1] = 0; + rgba[i + 2] = 0; + rgba[i + 3] = 255; + } + for (i = 0; i < ng; i++) { + imx = (int)(s[0] * gve[i][0] + s[1] * gve[i][1] + s[2] * gve[i][2]) + w2; + imy = (int)(s[3] * gve[i][0] + s[4] * gve[i][1] + s[5] * gve[i][2]) + h2; + imz = (int)(s[6] * gve[i][0] + s[7] * gve[i][1] + s[8] * gve[i][2]) + 128; + if ((imx > npx) && (imx < w - npx) && (imy > npx) && (imy < h - npx) && + (imz >= 0) && (imz < 256)) { + for (j = -npx; j <= npx; j++) { + for (k = -npx; k <= npx; k++) { + rgba[w * (imy + j) * 4 + (imx + k) * 4 + 0] = 255; + rgba[w * (imy + j) * 4 + (imx + k) * 4 + 1] = 255; + rgba[w * (imy + j) * 4 + (imx + k) * 4 + 2] = 255; + rgba[w * (imy + j) * 4 + (imx + k) * 4 + 3] = imz; + } + } + } + } +} \ No newline at end of file diff --git a/src/testconnectedpixels.py b/src/testconnectedpixels.py deleted file mode 100644 index 5488d52e..00000000 --- a/src/testconnectedpixels.py +++ /dev/null @@ -1,327 +0,0 @@ -import unittest - -import numpy as np - -import cImageD11 as connectedpixels - - -class test_connectedpixels(unittest.TestCase): - def setUp(self): - dims = (10,10) - dark = np.ones(dims) - flood = np.ones(dims) - - def test_1(self): - for t in [np.uint8, np.int8, np.uint16, np.int16, - np.int32, np.uint32, np.float32, np.float]: - data = np.array( [[ 0, 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 1, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0, 0]],t) - bl = np.zeros(data.shape,np.int32) - connectedpixels.connectedpixels( - data, - bl, - 0.1) # threshold - err = np.sum(np.ravel(data-bl)) - self.assertEqual(err, 0) - - def test_1_shape(self): - for t in [np.uint8, np.int8, np.uint16, np.int16, - np.int32, np.uint32, np.float32, np.float]: - data = np.array( [[ 1, 0, 1, 0, 1, 0, 1], - [ 1, 0, 1, 0, 1, 0, 1], - [ 1, 0, 1, 0, 0, 1, 0], - [ 1, 1, 1, 1, 1, 0, 1]],t) - bl = np.zeros(data.shape,np.int32) - connectedpixels.connectedpixels( - data, - bl, - 0.1) # threshold - err = np.sum(np.ravel(data-bl)) - self.assertEqual(err, 0) - - def test_2_shapes(self): - for t in [np.uint8, np.int8, np.uint16, np.int16, - np.int32, np.uint32, np.float32, np.float]: - data = np.array( [[ 1, 0, 1, 0, 2, 0, 2], - [ 1, 0, 1, 0, 2, 0, 2], - [ 1, 0, 1, 0, 0, 2, 0], - [ 1, 1, 1, 0, 2, 0, 2]],t) - bl = np.zeros(data.shape,np.int32) - connectedpixels.connectedpixels( - data, - bl, - 0.1) # threshold - err = np.sum(np.ravel(data-bl)) - self.assertEqual(err, 0) - - def skip_test_2_transpose(self): - for t in [np.uint8, np.int8, np.uint16, np.int16, - np.int32, np.uint32, np.float32, np.float]: - data = np.array( [[ 1, 0, 1, 0, 2, 0, 2], - [ 1, 0, 1, 0, 2, 0, 2], - [ 1, 0, 1, 0, 0, 2, 0], - [ 1, 1, 1, 0, 2, 0, 2]],t) - bl = np.zeros(data.shape,np.int32) - self.assertRaises(ValueError, - connectedpixels.connectedpixels, - *(np.transpose(data),bl,0.1)) - connectedpixels.connectedpixels( - np.transpose(data),np.transpose(bl),0.1) - err = np.sum(np.ravel(data-bl)) - self.assertEqual(err, 0) - - - -class test_blobproperties(unittest.TestCase): - - def test_prop_names(self): - names=""" s_1, /* 1 Npix */ - s_I, /* 2 Sum intensity */ - s_I2, /* 3 Sum intensity^2 */ - s_fI, /* 4 Sum f * intensity */ - s_ffI, /* 5 Sum f * f* intensity */ - s_sI, /* 6 Sum s * intensity */ - s_ssI, /* 7 Sum s * s * intensity */ - s_sfI, /* 8 Sum f * s * intensity */ - s_oI, /* 9 sum omega * intensity */ - s_ooI, /* */ - s_soI, /* 10 sum omega * s * intensity */ - s_foI, /* 11 sum omega * f * intensity */ - mx_I, /* 12 Max intensity */ - mx_I_f, /* 13 fast at Max intensity */ - mx_I_s, /* 14 slow at Max intensity */ - mx_I_o, /* 15 omega at max I */ - bb_mx_f, /* 16 max of f */ - bb_mx_s, /* 17 max of s */ - bb_mx_o, /* 18 max of omega */ - bb_mn_f, /* 19 min of f */ - bb_mn_s, /* 20 min of s */ - bb_mn_o, /* 22 min of o */ - avg_i, /* Average intensity */ - f_raw, /* fast centre of mass */ - s_raw, /* slow centre of mass */ - o_raw, /* omega centre of mass */ - m_ss, /* moments */ - m_ff, - m_oo, - m_sf, - m_so, - m_fo, - f_cen, /* Filled in elsewhere - spatial correction */ - s_cen, /* ditto */ - dety, /*Filled in elsewhere - flip to HFPO book */ - detz, /*Filled in elsewhere - flip to HFPO book */ - NPROPERTY , /* Number of properties if starting at 0 */ """ - nl = names.split("\n") - namelist = [n.split(",")[0].lstrip().rstrip() - for n in nl] - i = 0 - while i < len(namelist): - self.assertEqual(i,getattr(connectedpixels,namelist[i])) - i += 1 - - def test_find_max(self): - for t in [np.uint8, np.int8, np.uint16, np.int16, - np.int32, np.uint32, np.float32, np.float]: - data = np.array( [[ 1, 0, 1], - [ 1, 0, 1], - [ 1, 8, 1], - [ 1, 1, 1]],t) - bl = np.zeros(data.shape,np.int32) - npx = connectedpixels.connectedpixels( - data,bl,0.1) - self.assertEqual(npx,1) - err = np.sum(np.ravel(data-bl)) - self.assertEqual(err, 7) # 8-1 - res = connectedpixels.blobproperties(data, - bl, - npx, - omega=22.) - from ImageD11.connectedpixels import s_1, s_I, s_I2, \ - s_fI, s_ffI, s_sI, s_ssI, s_sfI, \ - bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s,\ - bb_mn_o, bb_mx_o, \ - mx_I, mx_I_f, mx_I_s, mx_I_o - # print res,res.shape - self.assertAlmostEqual(res[0][s_1],10) - self.assertAlmostEqual(res[0][mx_I],8) - self.assertAlmostEqual(res[0][mx_I_f],1) - self.assertAlmostEqual(res[0][mx_I_s],2) - self.assertAlmostEqual(res[0][mx_I_o],22) - - - - def skip_test_2_transpose(self): - for t in [np.uint8, np.int8, np.uint16, np.int16, - np.int32, np.uint32, np.float32, np.float]: - data = np.array( [[ 1, 0, 1, 0, 2, 0, 2], - [ 1, 0, 1, 0, 2, 0, 2], - [ 1, 0, 1, 0, 0, 2, 0], - [ 1, 1, 1, 0, 2, 0, 2]],t) - bl = np.zeros(data.shape,np.int32) - self.assertRaises(ValueError, - connectedpixels.connectedpixels, - *(np.transpose(data),bl,0.1)) - npx = connectedpixels.connectedpixels( - np.transpose(data),np.transpose(bl),0.1) - self.assertEqual(npx,2) - err = np.sum(np.ravel(data-bl)) - self.assertEqual(err, 0) - res = connectedpixels.blobproperties(data, bl, npx) - from ImageD11.connectedpixels import s_1, s_I, s_I2, \ - s_fI, s_ffI, s_sI, s_ssI, s_sfI, \ - bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s,\ - bb_mn_o, bb_mx_o, \ - mx_I, mx_I_f, mx_I_s - # print res,res.shape - self.assertAlmostEqual(res[0][s_1],9) - self.assertAlmostEqual(res[1][s_1],7) - self.assertAlmostEqual(res[0][s_I],9) - self.assertAlmostEqual(res[1][s_I],14) - self.assertAlmostEqual(res[0][s_I2],9) - self.assertAlmostEqual(res[1][s_I2],28) - # [[ 1, 0, 1, 0, 2, 0, 2], --> Fast - # [ 1, 0, 1, 0, 2, 0, 2], | - # [ 1, 0, 1, 0, 0, 2, 0], | - # [ 1, 1, 1, 0, 2, 0, 2]],t) V Slow - # f*I: - # f= 0, 1, 2, 3, 4, 5, 6 - # [[ 0, 0, 2, 0, 8, 0, 12], --> Fast - # [ 0, 0, 2, 0, 8, 0, 12], | - # [ 0, 0, 2, 0, 0,10, 0 ], | - # [ 0, 1, 2, 0, 8, 0, 12]],t) V Slow - # =9 =70 - self.assertAlmostEqual(res[0][s_fI],9) - self.assertAlmostEqual(res[1][s_fI],70) - # s*I: - # s= - # 0[[ 0, 0, 0, 0, 0, 0, 0], --> Fast - # 1 [ 1, 0, 1, 0, 2, 0, 2], | - # 2 [ 2, 0, 2, 0, 0, 4, 0], | - # 3 [ 3, 3, 3, 0, 6, 0, 6]],t) V Slow - # =15 =20 - self.assertAlmostEqual(res[0][s_sI],15) - self.assertAlmostEqual(res[1][s_sI],20) - # Bounding box - self.assertAlmostEqual(res[0][bb_mn_f],0) - self.assertAlmostEqual(res[1][bb_mn_f],4) - self.assertAlmostEqual(res[0][bb_mx_f],2) - self.assertAlmostEqual(res[1][bb_mx_f],6) - self.assertAlmostEqual(res[0][bb_mn_s],0) - self.assertAlmostEqual(res[1][bb_mn_s],0) - self.assertAlmostEqual(res[0][bb_mx_s],3) - self.assertAlmostEqual(res[1][bb_mx_s],3) - self.assertAlmostEqual(res[1][bb_mn_o],0) - self.assertAlmostEqual(res[0][bb_mx_o],0) - - -class testbloboverlaps(unittest.TestCase): - def test1(self): - import sys - data1 = np.array([[ 1, 0, 1, 0, 0, 0, 0], - [ 1, 0, 1, 0, 0, 0, 0], - [ 1, 0, 1, 1, 0, 0, 0], - [ 1, 1, 1, 0, 0, 0, 0]]) - bl1 = np.zeros(data1.shape, np.intc) - np1 = connectedpixels.connectedpixels(data1,bl1,0.1) - data2 = np.array([[ 0, 0, 0, 0, 2, 0, 2], - [ 0, 0, 0, 0, 2, 0, 2], - [ 0, 0, 0, 2, 0, 2, 0], - [ 0, 0, 0, 0, 2, 0, 2]]) - bl2 = np.zeros(data2.shape,np.intc) - np2 = connectedpixels.connectedpixels(data2,bl2,0.1) - r1 = connectedpixels.blobproperties(data1, bl1, np1, omega=-10.0) - r2 = connectedpixels.blobproperties(data2, bl2, np2, omega=10.0) - - connectedpixels.bloboverlaps(bl1,np1,r1, - bl2,np2,r2, verbose=0) - # check r1 is zeroed - err = np.sum(np.ravel(r1)) - self.assertAlmostEqual(err,0.,6) - from ImageD11.connectedpixels import s_1, s_I, s_I2, \ - s_fI, s_ffI, s_sI, s_ssI, s_sfI, \ - bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s,\ - bb_mn_o, bb_mx_o,\ - mx_I, mx_I_f, mx_I_s - # check total pixels - self.assertAlmostEqual(r2[0,s_1], 18.0, 6) - self.assertAlmostEqual(r2[0,s_I], 26.0, 6) - self.assertAlmostEqual(r2[0,bb_mn_f], 0.0, 6) - self.assertAlmostEqual(r2[0,bb_mx_f], 6.0, 6) - self.assertAlmostEqual(r2[0,bb_mn_s], 0.0, 6) - self.assertAlmostEqual(r2[0,bb_mx_s], 3.0, 6) - self.assertAlmostEqual(r2[0,bb_mn_o], -10.0, 6) - self.assertAlmostEqual(r2[0,bb_mx_o], 10.0, 6) - - def test2(self): - import sys - data1 = np.array([[ 1, 0, 0, 0, 0, 1, 1], - [ 1, 0, 2, 0, 0, 1, 1], - [ 1, 0, 2, 2, 0, 0, 0], - [ 0, 0, 2, 0, 0, 0, 0]]) - bl1 = np.zeros(data1.shape, np.intc) - np1 = connectedpixels.connectedpixels(data1,bl1,0.1) - data2 = np.array([[ 0, 0, 0, 0, 2, 0, 0], - [ 0, 0, 0, 0, 2, 2, 0], - [ 0, 0, 0, 2, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0, 0]]) - bl2 = np.zeros(data2.shape, np.intc) - np2 = connectedpixels.connectedpixels(data2,bl2,0.1) - r1 = connectedpixels.blobproperties(data1, bl1, np1) - r2 = connectedpixels.blobproperties(data2, bl2, np2) - - connectedpixels.bloboverlaps(bl1,np1,r1, - bl2,np2,r2, verbose=0) - # check which peaks are zeroed - self.assertAlmostEqual(np.sum(r1[1]),0.,6) - self.assertAlmostEqual(np.sum(r1[2]),0.,6) - - from ImageD11.connectedpixels import s_1, s_I, s_I2, \ - s_fI, s_ffI, s_sI, s_ssI, s_sfI, \ - bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s,\ - bb_mn_o, bb_mn_f,\ - mx_I, mx_I_f, mx_I_s - self.assertAlmostEqual(r1[0,s_1], 3, 6) - self.assertAlmostEqual(r1[0,s_I], 3, 6) - - # check total pixels - self.assertAlmostEqual(r2[0,s_1], 12.0, 6) - self.assertAlmostEqual(r2[0,s_I], 20.0, 6) - - def test3(self): - import sys - data1 = np.array([[ 0, 0, 0, 0, 0, 0, 0], - [ 0, 0, 0, 0, 0, 0, 0], - [ 0, 1, 1, 1, 1, 1, 0], - [ 0, 0, 0, 0, 0, 0, 0]]) - bl1 = np.zeros(data1.shape,np.intc) - np1 = connectedpixels.connectedpixels(data1,bl1,0.1) - data2 = np.array([[ 0, 0, 0, 0, 0, 0, 0], - [ 0, 2, 0, 0, 0, 2, 0], - [ 0, 2, 0, 0, 0, 2, 0], - [ 0, 2, 0, 0, 0, 2, 0]]) - bl2 = np.zeros(data2.shape,np.intc) - np2 = connectedpixels.connectedpixels(data2,bl2,0.1) - r1 = connectedpixels.blobproperties(data1, bl1, np1) - r2 = connectedpixels.blobproperties(data2, bl2, np2) - - connectedpixels.bloboverlaps(bl1,np1,r1, - bl2,np2,r2, verbose=0) - - # check which peaks are zeroed - self.assertAlmostEqual(np.sum(r1[0]),0.,6) - # Results pile up all in r2[0] - self.assertAlmostEqual(np.sum(r2[1]),0.,6) - from ImageD11.connectedpixels import s_1, s_I, s_I2, \ - s_fI, s_ffI, s_sI, s_ssI, s_sfI, \ - bb_mn_f, bb_mn_s, bb_mx_f, bb_mx_s,\ - mx_I, mx_I_f, mx_I_s - self.assertAlmostEqual(r2[0,s_1],11, 6) - self.assertAlmostEqual(r2[0,s_I],17, 6) - - - -if __name__=="__main__": - unittest.main() diff --git a/test/.gitignore b/test/.gitignore new file mode 100644 index 00000000..12ed69cc --- /dev/null +++ b/test/.gitignore @@ -0,0 +1,4 @@ +test.col +testcolfile.hdf +testgeom.flt +testoverlaps0000.edf diff --git a/test/demo/.gitignore b/test/demo/.gitignore new file mode 100644 index 00000000..420cd83f --- /dev/null +++ b/test/demo/.gitignore @@ -0,0 +1,5 @@ +eu.flt +eu3.gve +eu3.idx +eu3.ubi +eu3fitted.pars diff --git a/test/demo/bravias.py b/test/demo/bravias.py deleted file mode 100644 index 64b360d6..00000000 --- a/test/demo/bravias.py +++ /dev/null @@ -1,71 +0,0 @@ - - -""" -Determine Bravias lattice for UBI - -Return transformation matrix - - -""" - - -from ImageD11.unitcell import unitcell -from ImageD11.indexing import ubitocellpars -from numpy import array - - -def fi_triclinic(ubi): - """ Ensure we have the 3 shortest non-coplanar vectors """ - return ubi - -def fi_monoclinic(ubi, cell, mult): - """ - Choose vectors having mult == 2 - These are either along b or in ac plane - If centred then one mult 4 vector is shorter and cell par - """ - possibles = [] - for i in range(len(mult)): - if mult[i] == 2: - possibles.append( cell.ringhkls(cell.ringds[i])[0] ) - possibles = [cell.ring] - - -def ferraris_and_ivaldi(ubi, tol = 0.001): - """ - Acta Cryst (1983) A39 595-596 - - Determine the multiplicities of vectors in a computed powder pattern - Match these to multiplicities according to lattice types - ... appeals to powder person - Tolerance is for grouping vector lengths. - """ - cpar = ubitocellpars(ubi) - cell = unitcell( cp ) - dlim = cell.ds( array( [7,7,7] )) - u.makerings( dlim , tol) - mult = [ len(cell.ringhkls[d]) for d in cell.ringds ] - mx = max(mult) - mn = min(mult) - cases = { - (2, 2) : fi_triclinic, - (4, 2) : fi_monoclinic, - (8, 2) : fi_orthorhombic, - (12, 2) : fi_rhombohedral, - (16, 2) : fi_tetragonal, - (24, 2) : fi_hexagonal, - (48, 2) : fi_cubic } - - -def katayama(ubi): - """ - J. Appl. Cryst (1986) 19 69-72 - - Take four vectors a,b,c and d=-(a+b+c) - Compute the 6 pairwise dot products (ab, ac, ad, bc, bd, cd) - Apply transformations (up to 12) and recompute - - etc - """ - pass - diff --git a/test/demo/eu2.pars b/test/demo/eu2.pars index b3efb3c6..e19dd9ad 100644 --- a/test/demo/eu2.pars +++ b/test/demo/eu2.pars @@ -16,6 +16,7 @@ o22 -1 omegasign 1 tilt_y -0.00214871811248 tilt_z 0.00229338714282 +tilt_x 0.0 wavelength 0.153684 wedge 0.0 y_center 502.164103919 diff --git a/test/demo/latred.py b/test/demo/latred.py deleted file mode 100644 index b0913bd0..00000000 --- a/test/demo/latred.py +++ /dev/null @@ -1,310 +0,0 @@ - -from numpy import dot, round_, array, float, allclose, asarray, fabs,\ - argmax, sqrt, argsort, take, sum, where -from numpy.linalg import inv - -DEBUG = False - -# Some sort of round off -MIN_VEC2 = 1e-9*1e-9 - - -def fparl(x, y): - """ fraction of x parallel to y """ - ly2 = dot(y ,y) - if ly2 > 1e-9: - return dot(x, y) / ly2 - return 0 - -def mod(x,y): - """ - Returns the part of x with integer multiples of y removed - assert that ||mod(x,y)|| <= ||x|| for all y - """ - if __debug__: - b4 = dot(x,x) - n = round_(fparl(x,y)) - ret = x - n * y - if __debug__: - af = dot(ret,ret) - assert b4 >= af , "Bad mod "+str(x) + " " + str(y) - return ret - - -def sortvec_len( vl ): - """ - Sorts according to length (d-s-u) - """ - ul = [ (dot(v,v),tuple(v)) for v in vl ] - # Choose x,y,z - ul.sort() - return [ asarray(v[1]) for v in ul ] - -def sortvec_xyz( vl ): - """ - For each choose between x, -x - Sorts according to x then y then z components - """ - ul = [] - # Choose x/-x - for v in vl: - # Wonder how to do this with numpy? - ul.append( max( tuple(v),tuple(-asarray(v)) ) ) - # Choose x,y,z - ul.sort() - return [ asarray(v) for v in ul[::-1] ] - - - -class lattice(object): - """ - Represents a 3D crystal lattice built from 3 vectors - """ - - def __init__(self, v1, v2, v3, space='reciprocal', min_vec2=MIN_VEC2): - """ Make the lattice - Currently v1, v2, v3 are vectors - which gives 3D - space [ 'real' | 'reciprocal' ] - ... means they are real space lattice vectors or measured scattering - vectors from crystallography - It will attempt to find a primitive basis from the supplied vectors - """ - # 3 supplied vectors (these are lattice vectors) - vl=[v1, v2, v3] - # 9 difference vectors (these are also lattice vectors) - # Assuming : We can always add or subtract integer amounts of any - # lattice vector to get another lattice vector. - # We look for the 3 shortest which are non-coplanar - # - # We could do this recursively adding and subtracting - # ... We *need* to do it only until the solution is stable - # one would hope this is enough, but more testcases should be added? - # ... currently the algorith is vague - dsu = sortvec_len( vl + [ vi - vj for vi in vl for vj in vl ] + - [ vi + vj for vi in vl for vj in vl ] ) - # Sort by length - # Take shortest as first vector - vl = [] - for i in range(len(dsu)): - t = dsu[i] - for v in vl: # remove previous vectors - t = mod( t, v ) - if len(vl) == 2: # And try to catch hexagonal planes too - t = mod( t, vl[0] + vl[1] ) - t = mod( t, vl[0] - vl[1] ) - if dot(t,t) < min_vec2: # Skip if zeroed out - continue - vl.append( t ) - if len(vl) == 3: break - # Remove this from future vectors ??? - dsu = [ mod( d , t ) for d in dsu] - assert len(vl) == 3, "Have not got 3 vectors" - # print "vl:",vl - vl = sortvec_xyz(vl) - # print "vl",vl - if space == 'reciprocal': - self.v = array(vl).T # We got column vectors - self.vi = inv(self.v) - elif space == 'real': - self.vi = array(vl) # We got row vectors - self.v = inv(self.vi) - else: - raise Exception("Space must be real or reciprocal "+str(space)) - def ijks(self, u): - """ - Give back the nearest lattice point indices - ... only present for symmetry with hkls - """ - return round_(dot(self.v, u)) - def rem_ijks(self, u): - """ - Remainder of u (distance from a lattice point) - """ - return g - dot(self.vi, self.hkls(g)) - def hkls(self, g): - """ - Give back the hkl indices - ... these are the nearest lattice point - """ - return round_(dot(self.vi, asarray(g).T)).T - def rem_hkls(self, g): - """ - Remainder of x after removing closest lattice point in reciprocal space - """ - h = dot(self.v, self.hkls(g).T).T - print 'h',h - assert g.shape == h.shape, "bug!" - return g - h - def withvec(self, x, space="reciprocal"): - """ - Try to fit x into the lattice - Make the remainder together with current vectors - Index it as hkl indices - whichever vector has the biggest projection is replaced - remake the lattice with these 3 vectors - """ - x = asarray(x) - print "withvec",x - print 'v',self.v - print 'vi',self.vi - if space == 'reciprocal': - r = self.rem_hkls(x) - print "r",r - vd = argmax(fabs(dot(self.vi, x))) - print "vd", vd - v = list(self.v.T) # reciprocal space is columns - v[vd] = r - if space == 'real': - r = self.rem_ijks(x) - print "r",r - vd = argmax(fabs(dot(self.vi, x))) - print "vd",vd - v = list(self.vi) - v[vd] = r - print "v",v - return lattice( v[0], v[1], v[2] , space=space ) - def score_recip(self, g, tol=0.1): - """ - How many peaks have rem less than tol - """ - ga = asarray(g) - assert ga.shape[1] == 3, "gv shape" - #print ga.shape - r = self.rem_hkls(ga) - assert ga.shape == r.shape - r2 = sum( r*r, axis=1 ) - s = sum( where( r2 < tol * tol, 1, 0) ) - return s - -def get_eu_gv(): - from ImageD11.indexing import indexer, ubitocellpars - o = indexer() - o.readgvfile("eu3.gve" , quiet = True) - return o.gv - -def test_fft(): - gv = get_eu_gv() - from ImageD11.fft_index_refac import grid - from ImageD11.indexing import ubitocellpars, write_ubi_file,\ - refine - g = grid( np = 128, - mr = 1.0, - nsig = 20, - minlen = 3. ) - g.gv_to_grid_new(gv) - g.fft() - g.props() - g.peaksearch(open("eu.patterson_pks","w")) - g.read_peaks("eu.patterson_pks") - vecs = g.UBIALL - order = argsort( g.colfile.sum_intensity )[::-1] - min_pks = 300 - try: - for i in order: - print vecs[i], g.colfile.sum_intensity[i] - for j in order[i:]: - for k in order[j:]: - print i,j,k - try: - l = lattice( vecs[i], vecs[j], vecs[k], - space='real') - t = 0.1 - ref = refine( l.vi, gv, t) - l = lattice( ref[0], ref[1], ref[2], space='real') - print "UBI:" - print l.vi - print ubitocellpars(l.vi) - s = l.score_recip( gv , t ) - print "Indexes",s ,"at tolerance",t - - except: - import traceback - print traceback.print_exc() - raise Exception("error") - if s > min_pks: - write_ubi_file( "trial.ubi", [ l.vi ] ) - raise Exception("Really bad control structure") - except: - import traceback - print traceback.print_exc() - print "Got something" - -def test_eu(): - #Conventional cell gives: - #0 = (0, 1, 1) - #1 = (0, 1, -1) - #6 = (1, 0, -1) - gv = get_eu_gv() - v1 = gv[0] - v2 = gv[1] - v3 = gv[6] - l = lattice ( v1, v2, v3) - print l.v, l.vi - esum = 0.0 - for v in gv: - print ("%8.5f "*3+"%4f "*3)%tuple( list(v)+list( - dot(l.vi,v.T))), - err = l.rem_hkls(v) - esum += sqrt(dot(err,err)) - print "%.5f"%(sqrt(dot(err,err))) - # print esum / len(o.gv) - assert esum / len(gv) < 0.0102, "Did not fit" - -def test2(): - """ Adding a fourth vector """ - o = lattice( array( [ 1, 0, 0] , float) , - array( [ 0, 1, 0] , float) , - array( [ 0, 0, 2] , float) ) - # ... how to do it? - r = o.rem_hkls( array( [ 0, 0, 1] , float) ) - assert ( r == array( [ 0, 0, 1] , float) ).all() - o = o.withvec( r ) - assert allclose(o.v[0], array( [ 1, 0, 0] , float) ) - assert allclose(o.v[1], array( [ 0, 1, 0] , float) ) - assert allclose(o.v[2], array( [ 0, 0, 1] , float) ) - -def test1(): - """ Make a lattice from 3 vectors""" - o = lattice( array( [ 1, 0, 0] , float) , - array( [ 2, 1, 0] , float) , - array( [ 3, 4, 1] , float) ) - assert allclose(o.v[:,0], array( [ 1, 0, 0] , float) ) - assert allclose(o.v[:,1], array( [ 0, 1, 0] , float) ) - assert allclose(o.v[:,2], array( [ 0, 0, 1] , float) ) - """ Assign hkl indices in a strange way (no matrix inversing) """ - for x in [ [ 1,3,4 ], [ -9, 10, 13] ]: - h = o.hkls(x) - assert (asarray(h) == asarray(x)).all(), x - """ 3 more difficult vectors """ - o = lattice( array( [ 1, 0, 1] , float) , - array( [ 0, 1, 0] , float) , - array( [ 0, 0, 1] , float) ) - assert allclose(o.v[:,0], array( [ 1, 0, 0] , float) ) - assert allclose(o.v[:,1], array( [ 0, 1, 0] , float) ) - assert allclose(o.v[:,2], array( [ 0, 0, 1] , float) ) - - """ 3 really hard vectors """ - global DEBUG - DEBUG = True - o = lattice( array( [ 991, 990, 990] , float) , - array( [ 990, 991, 990] , float) , - array( [ 990, 990, 991] , float) ) - print "o.v",o.v - print o.hkls( [990,990,990] ) - o = o.withvec( [990,990,990] ) # Remove long - assert allclose(o.v[:,0], array( [ 1, 0, 0] , float) ) - assert allclose(o.v[:,1], array( [ 0, 1, 0] , float) ) - assert allclose(o.v[:,2], array( [ 0, 0, 1] , float) ) - DEBUG = False - - - -if __name__=="__main__": - import time - start = time.time() - test1() - test2() - test_eu() - test_fft() - print time.time()-start - diff --git a/test/demo/latred_new.py b/test/demo/latred_new.py deleted file mode 100644 index a6876b96..00000000 --- a/test/demo/latred_new.py +++ /dev/null @@ -1,288 +0,0 @@ - - -from numpy import dot, round_, array, float, allclose, asarray, fabs,\ - argmin, argmax, sqrt, argsort, take, sum, where, ndarray, eye,\ - zeros, cross -from numpy.linalg import inv, LinAlgError - -from ImageD11.lattice_reduction import lattice, find_lattice -from ImageD11.rc_array import rc_array - -def get_eu_gv(): - from ImageD11.indexing import indexer, ubitocellpars - o = indexer() - o.readgvfile("eu3.gve" , quiet = True) - return o.gv - -def test_fft(): - gv = get_eu_gv() - from ImageD11.fft_index_refac import grid - from ImageD11.indexing import ubitocellpars, write_ubi_file,\ - refine - g = grid( np = 128, - mr = 1.0, - nsig = 20) - g.gv_to_grid_new(gv) - g.fft() - g.props() - g.peaksearch(open("eu.patterson_pks","w")) - g.read_peaks("eu.patterson_pks") - vecs = rc_array(g.UBIALL.T , direction='col') - assert vecs.shape == (3, g.colfile.nrows) - order = argsort( g.colfile.sum_intensity )[::-1] - vecs = take( vecs, order, axis = 1) - min_pks = 300 - ntry = 0 - - assert g.gv.shape[1] == 3 - tv = rc_array( g.gv, direction='row' ) - print "Finding lattice l1 from patterson" - l1 = find_lattice( vecs, - min_vec2 = 9, - test_vecs = tv, - n_try = 20 ) - print "r2c == ubi matrix" - print l1.r2c - print "scores", l1.score( tv ) - print "cell",ubitocellpars(l1.r2c) - l1.r2c = refine( l1.r2c, g.gv, tol=0.1) - l1.c2r = inv(l1.r2c) - print "With refine",l1.score( tv ) - print "cell",ubitocellpars(l1.r2c) - print "Finding lattice l2 with gvectors to test" - l2 = find_lattice( vecs, - min_vec2 = 9, - n_try = 20, - test_vecs = tv) - print "r2c == ubi matrix" - print l2.r2c - print "scores", l2.score (tv) - print "cell",ubitocellpars(l2.r2c) - l2.r2c = refine( l2.r2c, g.gv, tol=0.1) - l2.c2r = inv(l2.r2c) - print "With refine",l2.score( tv ) - print "cell",ubitocellpars(l2.r2c) - return - -def test_eu(): - #Conventional cell gives: - #0 = (0, 1, 1) - #1 = (0, 1, -1) - #6 = (1, 0, -1) - gv = get_eu_gv() - v1 = gv[0] - v2 = gv[1] - v3 = gv[6] - assert len(v1) == 3 - print v1,v2,v3 - # This means that the g-vectors are in row direction - l = lattice ( v1, v2, v3, direction='row') - esum = 0.0 - gv = rc_array( gv , direction='row' ) - # print v1, v2, v3 - print "ubi/r2c",l.r2c - print "ub /c2r",l.c2r - print "dot(l.r2c, gv[0])",dot(l.r2c, gv[0]) - for v in gv: - #print ("%8.5f "*3+"%8.5f "*3)%tuple( list(v)+list(l.flip(v))), - assert len(v) == 3 - err = l.remainders( v ) - esum += sqrt(dot(err,err)) - #print "%.5f"%(sqrt(dot(err,err))) - #import sys - #sys.exit() - # print - assert esum / len(gv) < 0.0102, "Did not fit" - s = l.score(gv, tol = 0.1) - assert s == 583, "Expecting to index 582 peaks, got %s"%(s) - print "Indexing of eu3.gve, Average",esum / len(gv),"for",s,"peaks" - print "UBI is",l.r2c - -def test_eu_find(): - gv = get_eu_gv() - vecs = rc_array(gv, direction="row") - l = find_lattice( vecs, - # This next bit is important - tolerance for gve - # is very different to patterson - min_vec2=1./81., - n_try=20, - test_vecs=vecs) - s = l.score(vecs, tol=0.1) - print "Eu3 using find_lattice scores",s - -def test2(): - """ Adding a fourth vector """ - o = lattice( array( [ 1, 0, 0] , float) , - array( [ 0, 1, 0] , float) , - array( [ 0, 0, 2] , float) , direction = 'row') - # ... how to do it? - #print o.r2c - #print o.c2r - u = rc_array( [ 0, 0, 1] , dtype=float, direction = 'row') - r = o.remainders( u ) - assert ( r == array( [ 0, 0, 1] , float) ).all() - o = o.withvec( r ) - assert allclose(o.c2r[0], array( [ 1, 0, 0] , float) ) - assert allclose(o.c2r[1], array( [ 0, 1, 0] , float) ) - assert allclose(o.c2r[2], array( [ 0, 0, 1] , float) ) - assert allclose(o.c2r, o.r2c) - -def test1(): - """ Make a lattice from 3 vectors""" - o = lattice( array( [ 1, 0, 0] , float) , - array( [ 2, 1, 0] , float) , - array( [ 3, 4, 1] , float) , direction = 'col') - assert allclose(o.c2r[:,0], array( [ 1, 0, 0] , float) ) - assert allclose(o.c2r[:,1], array( [ 0, 1, 0] , float) ) - assert allclose(o.c2r[:,2], array( [ 0, 0, 1] , float) ) - # Both are identity - assert allclose(o.c2r, eye(3)) - assert allclose(o.r2c, eye(3)) - - """ 3 more difficult vectors """ - o = lattice( array( [ 1, 0, 1] , float) , - array( [ 0, 1, 0] , float) , - array( [ 0, 0, 1] , float) , direction = 'col') - assert allclose(o.c2r[:,0], array( [ 1, 0, 0] , float) ), str(o.c2r) - assert allclose(o.c2r[:,1], array( [ 0, 1, 0] , float) ), str(o.c2r) - assert allclose(o.c2r[:,2], array( [ 0, 0, 1] , float) ), str(o.c2r) - assert allclose(o.c2r, eye(3)) - assert allclose(o.r2c, eye(3)) - - - """ 3 shorter vectors """ - # print '3 short' - o = lattice( array( [1e-2, 0, 0] , float) , - array( [ 0,1e-2, 0] , float) , - array( [ 0, 0,1e-2] , float) , direction='row' ) - # Consider those as 3 g-vectors - # supplied rows go into c2r matrix as columns - # The r2c matrix (==ubi) is then the inverse of this - assert allclose(o.r2c[:,0], array( [ 100, 0, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,1], array( [ 0, 100, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,2], array( [ 0, 0, 100] , float) ), str(o.r2c) - - # g-vectors written as rows here - note transpose - g = array( [[1e-2, 0.,0.], - [1e-2, 1e-2,0.], - [1e-2, 0.,1e-2], - [ 0., 0.,1e-2]]) - #print g, g.shape - g = rc_array( g , direction='row') - assert g.shape == ( 4, 3) - g.check() - #print g.shape - #print g - hkl = o.flip( g ) - assert hkl.direction == 'col', hkl.direction - # print g,"\nFlips to give\n",hkl - assert allclose(hkl, - rc_array(array([[1.,0.,0.], - [1.,1.,0.], - [1.,0.,1.], - [0.,0.,1.]]).T, - direction='col') ), str(hkl) - - - # print '3 long' - """ 3 longer vectors """ - o = lattice( array( [ 10, 0, 0] , float) , - array( [ 0, 10, 0] , float) , - array( [ 0, 0, 10] , float) , direction='col' ) - assert allclose(o.r2c[:,0], array( [ 10, 0, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,1], array( [ 0, 10, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,2], array( [ 0, 0, 10] , float) ), str(o.r2c) - test_col_vec = rc_array([10.,10.,10.], direction='col') - flipped = o.flip( test_col_vec ) - assert flipped.direction == 'row' - assert allclose( flipped, - rc_array([1.,1.,1.], direction='col') ), str(flipped) - - """ 3 shorter vectors row""" - # print '3 short' - o = lattice( array( [1e-2, 0, 0] , float) , - array( [ 0,1e-2, 0] , float) , - array( [ 0, 0,1e-2] , float) , direction='row' ) - # Consider those as 3 g-vectors - assert allclose(o.r2c[:,0], array( [ 100, 0, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,1], array( [ 0, 100, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,2], array( [ 0, 0, 100] , float) ), str(o.r2c) - g = rc_array([[1e-2, 0.,0.], - [1e-2, 1e-2,0.], - [1e-2, 0.,1e-2], - [ 0., 0.,1e-2]], - direction='row') - hkl = o.flip( g ) - # print g,"\nFlips to give\n",hkl - - assert allclose(hkl, - rc_array([[1.,0.,0.], - [1.,1.,0.], - [1.,0.,1.], - [0.,0.,1.]], - direction='col').T ), str(hkl) - assert hkl.direction == 'col' - - - # print '3 long' - """ 3 longer vectors """ - o = lattice( array( [ 10, 0, 0] , float) , - array( [ 0, 10, 0] , float) , - array( [ 0, 0, 10] , float) , direction='col' ) - assert allclose(o.r2c[:,0], array( [ 10, 0, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,1], array( [ 0, 10, 0] , float) ), str(o.r2c) - assert allclose(o.r2c[:,2], array( [ 0, 0, 10] , float) ), str(o.r2c) - hkl = o.flip( rc_array([10.,10.,10.], direction='col') ) - assert allclose(hkl, - rc_array([1.,1.,1.], direction='row') ), str(hkl) - - - # FIXME - check is transpose of row is OK or not - # this break the row / col symmetry? - - # TODO - think on whether h / g are really col pairs - # Find the Sands book. - - - - # print hkl - - """ 3 really hard vectors """ - global DEBUG - DEBUG = True - o = lattice( array( [ 991, 990, 990] , float) , - array( [ 990, 991, 990] , float) , - array( [ 990, 990, 991] , float) ,direction='row') - # Eg, these were long g-vectors far from origin but making a basis - # Anticipate them making a ubi matrix with 990 - #assert allclose( o.c2r , array( [[ 991. , 1. , 1.], - # [ 990. , 0. , -1.], - # [ 990. , -1. , 0.],] )), str(o.c2r) - # OK - u = rc_array([990.,990.,990.], direction='row') - assert (o.nearest( u ) == rc_array([991.,990.,990], - direction='row')).all(),\ - str(o.nearest( u ))+" "+str( u ) - - - o = o.withvec( u ) # Remove long - # print o.c2r - # print o.r2c - assert allclose(o.c2r[:,0], array( [ 1, 0, 0] , float) ), o.c2r - assert allclose(o.c2r[:,1], array( [ 0, 1, 0] , float) ), o.c2r - assert allclose(o.c2r[:,2], array( [ 0, 0, 1] , float) ), o.c2r - DEBUG = False - - - -if __name__=="__main__": - import time - start = time.time() - test1() - test2() - test_eu() - test_eu_find() - test_fft() - - print time.time()-start - diff --git a/test/demo/nbsplat.py b/test/demo/nbsplat.py new file mode 100644 index 00000000..605727d5 --- /dev/null +++ b/test/demo/nbsplat.py @@ -0,0 +1,78 @@ + + +import numba, numpy as np +import timeit +timer = timeit.default_timer + +def nbsplat( + rgba, gve, M, + npx = 2, + zsort=True, + fg = (255,255,255,255), + bg = (0,0,0,255), + colors = None ): + """ Type checking wrapper - does the z sort and clip """ + astart = start = timer() + h,w,d = rgba.shape + rve = np.dot( M, gve ) + order = np.empty( len(rve[0]), np.int ) + tmp = np.empty( len(rve[0]), np.int ) + npk = do_zsort_clip( rve, tmp, w//2-npx, h//2-npx, zsort, order ) + i = (rve[1]+h//2).astype(int) + j = (rve[0]+w//2).astype(int) + if bg is not None: + # surprising but quite a lot quicker to use fill + rgba.view( np.uint32 ).fill( np.array( bg, np.uint8).view(np.uint32)[0] ) + if colors is None: + fgpx = np.array( fg, np.uint8 ) + nbsplat_onecolor( rgba, i, j, fgpx, order[:npk], npx ) + else: + nbsplat_manycolor( rgba, i, j, colors, order[:npk], npx ) + +@numba.njit(boundscheck=False) +def do_zsort_clip( ar, tmp, xlim, ylim, zsort, order ): + k = 0 + zmin = 1e20 + zmax = -1e20 + if zsort: + for i in range(len(ar[0])): + if abs( ar[0][i] ) < xlim and abs( ar[1][i] ) < ylim: + tmp[k] = i + k += 1 + zmin = min(zmin, ar[2][i]) + zmax = max(zmax, ar[2][i]) + npk = k + s = 255.0/(zmax - zmin) + ha = np.zeros( 257, numba.int32 ) + v8 = np.empty( ar[2].shape, numba.int32 ) + h = ha[1:] + for i in tmp[:npk]: + v8[i] = int(np.round((ar[2][i] - zmin)*s)) + h[v8[i]] = h[v8[i]] + 1 + for i in range(1,len(ha)): + ha[i] = ha[i] + ha[i-1] + for i in tmp[:npk]: + order[ha[v8[i]]] = i + ha[v8[i]] += 1 + else: + for i in range(len(ar[0])): + if abs( ar[0][i] ) < xlim and abs( ar[1][i] ) < ylim: + order[k] = i + k += 1 + zmin = min(zmin, ar[2][i]) + zmax = max(zmax, ar[2][i]) + npk = k + return npk + + +@numba.njit(boundscheck = False ) +def nbsplat_onecolor( rgba, i, j, fg, order, npx): + for k in order: + rgba[ i[k]-npx:i[k]+npx+1 , j[k]-npx:j[k]+npx+1 ] = fg + + +@numba.njit(boundscheck = False ) +def nbsplat_manycolor( rgba, i, j, c, order, npx): + for k in order: + rgba[ i[k]-npx:i[k]+npx+1 , j[k]-npx:j[k]+npx+1 ] = c[k] + diff --git a/test/demo/qtsplat.py b/test/demo/qtsplat.py new file mode 100644 index 00000000..3f27f5ee --- /dev/null +++ b/test/demo/qtsplat.py @@ -0,0 +1,398 @@ + + +from __future__ import print_function, division + +import os, sys +from timeit import default_timer as timer +import numpy as np +from scipy.spatial.transform import Rotation +if not hasattr( Rotation, 'as_matrix' ): + Rotation.as_matrix = Rotation.as_dcm +from ImageD11 import columnfile, transform, grain, cImageD11 +import nbsplat + +# This is not using Qt.py +from PyQt5 import QtWidgets, QtCore, QtGui + +class dataset: + def __init__(self, + colfilename, + parfilename=None, + ubifilename=None ): + self.loadcolf( colfilename ) + if parfilename is not None: + self.loadpars( parfilename ) + self.grains = [ ] + self.tol = 1.0 + if ubifilename is not None: + self.loadgrains( ubifilename ) + self.names ='gx','gy','gz' + self.colors=np.empty( (self.nrows, 4), np.uint8 ) + self.colors.fill(255) + + def loadcolf(self, colfilename): + self.colf = columnfile.columnfile( colfilename ) + self.nrows = self.colf.nrows + + def loadpars(self, fname): + self.colf.parameters.loadparameters( fname ) + self.colf.updateGeometry() + + def loadgrains(self, fname): + self.grains = grain.read_grain_file( fname ) + if len(self.grains)>0: + self.resetlabels() + self.assignlabels() + + def get(self, name): + if name in self.colf.titles: + return self.colf.getcolumn(name) + else: + raise Exception("name "+name+" not found") + + def xyz(self): + return [self.colf.getcolumn(name) for name in self.names] + + def grain_drlv2(self, grainid): + x,y,z = self.xyz() + hkl = np.dot( self.grains[grainid].ubi, self.xyz ) + dhkl = np.round(hkl)-hkl + drlv2 = (dhkl*dhkl).sum(axis=0) + self.colf.addcolumn("drlv2_%d"%(grainid)) + + def resetlabels(self): + self.labels = np.empty( self.nrows, 'i') + self.labels.fill(-1) + self.gvecs = np.array( (self.colf.gx, + self.colf.gy, + self.colf.gz ) ).T.copy() + self.drlv2 = np.ones ( self.nrows, float) + #print(self.colf.titles) + self.peaks_xyz = np.array( (self.colf.xl, + self.colf.yl, + self.colf.zl ) ).T.copy() + self.colf.addcolumn( self.labels, "labels" ) + self.colf.addcolumn( self.drlv2, "drlv2" ) + + def assignlabels( self ): + self.resetlabels() + nr = self.nrows + pks_xyz = np.empty((nr,3), float) + pks_xyz[:,0] = self.colf.xl + pks_xyz[:,1] = self.colf.yl + pks_xyz[:,2] = self.colf.zl + #print(self.gvecs.shape, pks_xyz.shape) + for i, g in enumerate( self.grains ): + if g.translation is None: + t = np.array((0,0,0), float) + else: + t = np.array((g.translation[0],g.translation[1],g.translation[2]), + float) + cImageD11.compute_gv( pks_xyz, + self.colf.omega, + self.colf.parameters.get('omegasign'), + self.colf.parameters.get('wavelength'), + self.colf.parameters.get('wedge'), + self.colf.parameters.get('chi'), + t, + self.gvecs ) + cImageD11.score_and_assign( g.ubi, + self.gvecs, + self.tol, + self.drlv2, + self.labels, i ) + self.colf.labels[:]= self.labels + self.colf.drlv2[:] = self.drlv2 + + +class splat3dview: + def __init__(self, w=2000, h=1500): + self.w = self.h = None + self.resize(w,h) + self.bg = (0,0,0,0) # background + self.fg = (255,255,255,0) # foreground + self.ps = 1 # pointsize + self.origin = (0,0,0) # origin in reciprocal space + self.scale = 0.7*min(w,h) # scale for Angstrom to px + self.zsort = True + self.u0 = np.eye(3, dtype=float ) + self.u = np.eye(3, dtype=float ) + self.zname = "gz" + self.need_redraw = True + + def resize(self, w, h): + if w != self.w or h != self.h: + self.w = w + self.h = h + self.rgba = np.zeros((h,w,4),np.uint8) + + def rotate(self, dx, dy, axes="XYZ"): + self.need_redraw = True + r = Rotation.from_euler(axes, (dx, dy, 0), + degrees=True ) + if hasattr(r, "as_dcm"): + self.u = r.as_dcm() + else: + self.u = r.as_matrix() + + def resetrot(self): + self.need_redraw = True + self.u0 = np.dot(self.u, self.u0) + self.u = np.eye(3, dtype=float) + + def matrix(self): + return self.scale*np.dot(self.u, self.u0) + + + + def drawPixMapOnWidget(self, datas, target ): + if self.need_redraw: + start = timer() + w, h = target.width(), target.height() + self.resize( w, h ) + #print("wh %d %d %d %d"%( + # self.w,target.width(),self.h,target.height())) + DOPROF=False + if DOPROF: + import cProfile, pstats + x = cProfile.Profile() + x.enable() + nbsplat.nbsplat( self.rgba, + datas.xyz(), + self.matrix(), + self.ps, + self.zsort, + self.fg, + self.bg, + datas.colors ) + # recompute or cache ? self.colors + # order should be here too. + # print("draw",timer()-start) + # fixme - can we write on i.bits() directly ? + p = rgbtopm( self.rgba ) + target.setPixmap( p ) + if DOPROF: + x.disable() + p = pstats.Stats( x, stream = sys.stdout).sort_stats("tottime") + p.reverse_order() + p.print_stats() + print("drawPixMapOnWidget %.3f ms %s"%( + 1e3*(timer()-start), self.need_redraw))#, end="\r") + sys.stdout.flush() + self.need_redraw = False + return(timer()-start) + return 0 + +def rgbtopm( rgb ): + i = QtGui.QImage(rgb, rgb.shape[1], rgb.shape[0], + QtGui.QImage.Format_RGB32 ) + return QtGui.QPixmap.fromImage(i) + + +def colormap(x, colors): + # fixme: load save or matplotlib or select + xmin = x.min() + xmax = x.max() + xcen = 0.5*(xmin+xmax) + print("Colormap",xmin,xmax,xcen) + # colors = np.empty( (len(x), 4), np.uint8 ) + colors[:,0] = np.interp( x, [ xmin, xcen, xmax ], [ 64, 64, 255]) # R + colors[:,1] = np.interp( x, [ xmin, xcen, xmax ], [ 64, 255, 64]) # G + colors[:,2] = np.interp( x, [ xmin, xcen, xmax ], [255, 64, 64]) # B + colors[:,3] = 0 # alpha + + + + + + +class Example( QtWidgets.QWidget ): + def __init__(self, vu, da): + QtWidgets.QWidget.__init__(self) + self.vu = vu + self.da = da + self.initUI() + self.x0 = 0 + self.y0 = 0 + self.ex = 0 + self.ey = 0 + self.doRot = False + self.animate() + self.show() + + def mousePressEvent(self, e): + #print('down', e.x(),e.y(),e.button() ) + self.doRot = True + self.resetrot( e.x(), e.y()) + + def resetrot(self, x0, y0 ): + self.x0 = x0 + self.y0 = y0 + self.vu.resetrot() + + def mouseReleaseEvent(self, e): + self.mouseMoveEvent(e) + #print('up', e.x(),e.y()) + self.vu.resetrot( ) + self.doRot = False + + def mouseMoveEvent(self,e): + # print('move',e.x(),e.y()) + axes = "XYZ" + if e.modifiers() == QtCore.Qt.ShiftModifier: + axes="ZYX" + if e.modifiers() == QtCore.Qt.ControlModifier: + axes="XZY" + self.rotateview( e.x(), e.y(), axes ) + + def rotateview(self, ex, ey, axes="XYZ"): + self.ex = ex + self.ey = ey + dx = (self.x0 - self.ex)/10. # 0.1 deg/px + dy = (self.y0 - self.ey)/10. + self.vu.rotate( dy, -dx, axes ) + + rotateactions = { + QtCore.Qt.Key_X : ( 90, 0, "XYZ"), + QtCore.Qt.Key_Y : ( 90, 0, "YZX"), + QtCore.Qt.Key_Z : ( 90, 0, "ZXY"), + } + + coloractions = { + QtCore.Qt.Key_A : "avg_intensity", + QtCore.Qt.Key_D : "drlv2", + QtCore.Qt.Key_E : "eta", + QtCore.Qt.Key_T : "tth", + QtCore.Qt.Key_V : "gz", + QtCore.Qt.Key_O : "omega", + QtCore.Qt.Key_L : "labels", + } + + + def hlp(self): + h = """ X,Y,Z = Rotate by 90 in X, Y, Z +Shift/Control = modify mouse rotate ++ = Zoom in +- = Zoom out +[0-9] = Pointsize +A : "avg_intensity", +D : "drlv2", +E : "eta", +T : "tth", +V : "gz", +O : "omega", +L : "labels", +S = Toggle zsort +Q = Quit +""" + print(h) + qmb= QtWidgets.QMessageBox.information( + self, "Help", h ) + #qmb.setText(h) + #qmb.show() + + + def keyPressEvent(self,e): + k = e.key() + print("Got key",k) + if k == QtCore.Qt.Key_H: + self.hlp() + if self.doRot and k == QtCore.Qt.Key_Shift: + self.x0 = self.ex + self.y0 = self.ey + self.vu.resetrot() + if k == QtCore.Qt.Key_Q: + self.close() + if k == QtCore.Qt.Key_S: + self.vu.zsort = not self.vu.zsort + self.status.setText( "zsort %s"%(str(self.vu.zsort))) + if k == QtCore.Qt.Key_Plus: + self.vu.scale = self.vu.scale * ( 1 + 1/8) + self.status.setText( "Scale %f"%(self.vu.scale)) + if k == QtCore.Qt.Key_Minus: + self.vu.scale = self.vu.scale * ( 1 - 1/8) + self.status.setText( "Scale %f"%(self.vu.scale)) + if k >= QtCore.Qt.Key_0 and k <= QtCore.Qt.Key_9: + self.vu.ps = int(k - QtCore.Qt.Key_0) + self.status.setText( "Pointsize %d"%(self.vu.ps)) + if k in self.rotateactions: + d1,d2,a = self.rotateactions[k] + self.status.setText("Rotated by %d %d %s"%(d1,d2,a)) + self.vu.rotate( d1, d2, a) + self.vu.resetrot() + if k in self.coloractions: + if self.coloractions[k] in self.da.colf.titles: + self.status.setText("color by %s"%(self.coloractions[k])) + colormap( self.da.get( self.coloractions[k]), + self.da.colors ) + else: + self.status.setText("No column %s"%(self.coloractions[k])) + self.vu.need_redraw=True + + def keyReleaseEvent(self,e): + if self.doRot and e.key() == QtCore.Qt.Key_Shift: + self.x0 = self.ex + self.y0 = self.ey + self.vu.resetrot() + + + def initUI(self): + + self.layout = QtWidgets.QVBoxLayout() + + self.lbl = QtWidgets.QLabel(self) + self.lbl.resize( self.vu.w, self.vu.h ) + self.lbl.setMinimumSize( 128, 128 ) + e = QtWidgets.QSizePolicy.Expanding + self.lbl.setSizePolicy( e, e ) + self.layout.addWidget( self.lbl ) + + self.status = QtWidgets.QLabel(self) + self.status.setText("Hello") + self.layout.addWidget( self.status ) + + self.buttons = QtWidgets.QHBoxLayout() + hb= QtWidgets.QPushButton("Help", self) + hb.clicked.connect( self.hlp ) + self.buttons.addWidget(hb) + qb= QtWidgets.QPushButton("Quit", self) + qb.clicked.connect( self.close ) + self.buttons.addWidget(qb) + + self.layout.addLayout( self.buttons ) + self.setLayout( self.layout ) + + + def animate(self): + self.vu.drawPixMapOnWidget( self.da, self.lbl) + QtCore.QTimer.singleShot(1000//24, self.animate ) + + + def resizeEvent(self, evt): + self.vu.need_redraw=True + + + + +def run( colf, pars, grains): + # colf pars grains + da = dataset( colf, pars, grains ) + vu = splat3dview( ) + app = QtWidgets.QApplication( sys.argv ) + l = Example( vu, da ) + sys.exit( app.exec_() ) + + + +if __name__=="__main__": + colf = sys.argv[1] + pars = None + grains = None + try: + pars = sys.argv[2] + grains = sys.argv[3] + except: + pass + + run( colf, pars, grains ) + diff --git a/test/demo/test.py b/test/demo/test.py index 09077600..ffed8f6f 100644 --- a/test/demo/test.py +++ b/test/demo/test.py @@ -1,3 +1,6 @@ + +import logging +logging.basicConfig(level=logging.INFO) # Create objects to manipulate - they hold your data # from ImageD11 import peakmerge, indexing, transformer @@ -17,7 +20,7 @@ mytransformer.compute_tth_eta( ) mytransformer.addcellpeaks( ) mytransformer.fit( 0.0 , 14.0 ) -mytransformer.saveparameters( 'eu3.pars' ) +mytransformer.saveparameters( 'eu3fitted.pars' ) mytransformer.computegv( ) mytransformer.savegv( 'eu3.gve' ) myindexer.readgvfile( 'eu3.gve' ) diff --git a/test/demo/testsplat.py b/test/demo/testsplat.py new file mode 100644 index 00000000..9a72f5c4 --- /dev/null +++ b/test/demo/testsplat.py @@ -0,0 +1,33 @@ +from __future__ import print_function, division +from ImageD11 import cImageD11, indexing + + +try: + from time import perf_counter_ns +except: + from time import time + def perf_counter_ns(): + return time()*1e9 + +import numpy as np +i = indexing.indexer() +i.readgvfile("eu3.gve") + +gve = i.gv.copy().astype(float) +#gve = np.random.random( size=(100000,3) )-0.5 +rgba = np.zeros( (512, 512, 4), np.uint8 ) +u = np.eye(3, dtype=float).ravel() +t0 = perf_counter_ns() +cImageD11.splat( rgba, gve, u, 1 ) +t1 = perf_counter_ns() +for j in range(5): + cts = [] + for i in range(10): + t2 = perf_counter_ns() + cImageD11.splat( rgba, gve, u, j ) + t3 = perf_counter_ns() + cts.append(t3-t2) + print("Ran that for npx=",j,"s, fps:", 1e9/np.mean(cts),1e9/np.max(cts),1e9/np.min(cts)) +import pylab as pl +pl.imshow(rgba) +pl.show() diff --git a/test/demo/tksplat.py b/test/demo/tksplat.py new file mode 100644 index 00000000..2e89a0dd --- /dev/null +++ b/test/demo/tksplat.py @@ -0,0 +1,61 @@ +from __future__ import print_function, division +from ImageD11 import cImageD11, indexing +from PIL import Image, ImageTk +from timeit import default_timer as timer +import numpy as np, os, sys +import nbsplat +import cProfile +try: + import tkinter as Tk +except: + import Tkinter as Tk + +i = indexing.indexer() +try: + i.readgvfile(sys.argv[1]) +except: + i.readgvfile(os.path.join(os.path.split(__file__)[0],"eu3.gve")) +gve = i.gv.copy().astype(float)/3. + +r = Tk.Tk(sync=1) +w,h = r.winfo_screenwidth()*2//3,r.winfo_screenheight()*2//3 + +rgba = np.empty((h,w,4), np.uint8) +u = np.eye(3, dtype=float).ravel() +t0 = timer() +cImageD11.splat( rgba, gve, u, 2 ) +t1 = timer() + +p = ImageTk.PhotoImage( Image.fromarray(rgba.copy(), "RGBA") ) +l = Tk.Label( r, width=w, height=h , background='black', image = p ) +l.photo = p +l.pack() + +s = np.sin(np.radians(2)) +c = np.cos(np.radians(2)) +ry = np.array( [[c,0,s],[0,1,0],[-s,0,c]] ) +s = np.sin(np.radians(2/np.sqrt(2))) +c = np.cos(np.radians(2/np.sqrt(2))) +rx = np.array( [[1,0,0],[0,c,s],[0,-s,c]] ) +rz = np.dot( ry, rx) + +colors = (np.random.random( (len(gve),4))*255).astype( np.uint8 ) + + +def rotate(): + global u, rz, rgba, gve, l, nframes + t0 = timer() + u = np.dot( rz, np.reshape(u,(3,3) )) + nbsplat.nbsplat( rgba, gve.T, u*(w+h)/2, 2 , colors=colors ) + x = Image.fromarray( rgba, "RGBA") + t1 = timer() + # import pdb;pdb.set_trace() + l.photo.paste( x ) + t2 = timer() + r.update() + print("splat %8.3f blit %8.3f total %8.3f ms per frame"%( + (t1-t0)*1e3,(t2-t1)*1e3,(t2-t0)*1e3), end="\n") + r.after(1, rotate) + +r.after(60, rotate) +r.mainloop() diff --git a/test/eps_sig/__init__.py b/test/eps_sig/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/eps_sig/test.out b/test/eps_sig/test.out new file mode 100644 index 00000000..ee96a9ef --- /dev/null +++ b/test/eps_sig/test.out @@ -0,0 +1,131 @@ +c11 141600.0 +c12 127400.0 +c13 None +c14 None +c15 None +c16 None +c22 None +c23 None +c24 None +c25 None +c26 None +c33 None +c34 None +c35 None +c36 None +c44 94200.0 +c45 None +c46 None +c55 None +c56 None +c66 None +cell__a 5.77 +cell__b 5.77 +cell__c 5.77 +cell_alpha 90.0 +cell_beta 90.0 +cell_gamma 90.0 +crystal_symmetry cubic +############################################## +cell__a cell__b cell__c cell_alpha cell_beta cell_gamma u11 u12 u13 u21 u22 u23 u31 u32 u33 eps11_c eps22_c eps33_c eps12_c eps13_c eps23_c eps11_s eps22_s eps33_s eps12_s eps13_s eps23_s sig11_c 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a/test/eps_sig/test_eps.py b/test/eps_sig/test_eps.py new file mode 100644 index 00000000..f1f5c2c1 --- /dev/null +++ b/test/eps_sig/test_eps.py @@ -0,0 +1,65 @@ + +from __future__ import print_function, division + + +import unittest, os +import numpy as np +from ImageD11.grain import read_grain_file + + + +class test_eps_grain( unittest.TestCase): + def setUp(self): + pth = os.path.split(__file__)[0] + self.testdata = np.loadtxt(os.path.join(pth,"test.out"), skiprows = 30) + self.titles = list( + "cell__a cell__b cell__c cell_alpha cell_beta cell_gamma u11 u12 u13 u21 u22 u23 u31 u32 u33".split() + + "eps11_c eps22_c eps33_c eps12_c eps13_c eps23_c eps11_s eps22_s eps33_s eps12_s eps13_s eps23_s".split() + + "sig11_c sig22_c sig33_c sig12_c sig13_c sig23_c sig11_s sig22_s sig33_s sig12_s sig13_s sig23_s".split() + ) + self.grains = read_grain_file(os.path.join(pth,"CuAlBe_scan10.map")) + a = 5.77 + self.dzero = ( a, a, a, 90., 90., 90. ) + + def test_e6_sample(self): + t11 = self.testdata[:,self.titles.index("eps11_s")]/ 100.0 + t22 = self.testdata[:,self.titles.index("eps22_s")]/ 100.0 + t33 = self.testdata[:,self.titles.index("eps33_s")]/ 100.0 + t12 = self.testdata[:,self.titles.index("eps12_s")]/ 100.0 + t23 = self.testdata[:,self.titles.index("eps23_s")]/ 100.0 + t13 = self.testdata[:,self.titles.index("eps13_s")]/ 100.0 + for i, g in enumerate(self.grains): + e11,e12,e13,e22,e23,e33 = g.eps_sample( self.dzero ) + ok = np.allclose( + ( e11, e12, e13, e22, e23, e33 ), + ( t11[i], t12[i], t13[i], t22[i], t23[i], t33[i] ), rtol=.1, atol=1e-4) + if not ok: + print(i) + for x,y in zip( ( e11, e12, e13, e22, e23, e33 ), + ( t11[i], t12[i], t13[i], t22[i], t23[i], t33[i] )): + print(x,y,(x-y),(x-y)/(x+y)) + self.assertTrue( ok ) + + def test_e6_grain(self): + t11 = self.testdata[:,self.titles.index("eps11_c")]/ 100.0 + t22 = self.testdata[:,self.titles.index("eps22_c")]/ 100.0 + t33 = self.testdata[:,self.titles.index("eps33_c")]/ 100.0 + t12 = self.testdata[:,self.titles.index("eps12_c")]/ 100.0 + t23 = self.testdata[:,self.titles.index("eps23_c")]/ 100.0 + t13 = self.testdata[:,self.titles.index("eps13_c")]/ 100.0 + for i, g in enumerate(self.grains): + e11,e12,e13,e22,e23,e33 = g.eps_grain( self.dzero ) + ok = np.allclose( + ( e11, e12, e13, e22, e23, e33 ), + ( t11[i], t12[i], t13[i], t22[i], t23[i], t33[i] ),rtol=0.1, atol=1e-4) + if not ok: + print(i) + for x,y in zip( ( e11, e12, e13, e22, e23, e33 ), + ( t11[i], t12[i], t13[i], t22[i], t23[i], t33[i] )): + print(x,y,(x-y)/(x+y)) + self.assertTrue( ok ) + + + +if __name__=="__main__": + unittest.main() diff --git a/test/gv_general/__init__.py b/test/gv_general/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/gv_general/test_gv_general.py b/test/gv_general/test_gv_general.py index 30781052..087805a0 100644 --- a/test/gv_general/test_gv_general.py +++ b/test/gv_general/test_gv_general.py @@ -20,10 +20,10 @@ def array_diff(a1,a2): class test_g_to_k(unittest.TestCase): def setUp(self): - self.tth = np.array( [60, 10, 15, 20,25, 1.9555],np.float) + self.tth = np.array( [60, 10, 15, 20,25, 1.9555],float) self.wvln = 0.5 - self.eta = np.array( [90, 10,120,-20,340, -73 ],np.float) - self.omega = np.array([60,90,180, 60,97, 131],np.float) + self.eta = np.array( [90, 10,120,-20,340, -73 ],float) + self.omega = np.array([60,90,180, 60,97, 131],float) self.np = len(self.tth) def test_0_0(self): @@ -37,7 +37,7 @@ def test_0_0(self): sol1, sol2, valid = gv_general.g_to_k( g, # self.wvln, axis = np.array([0,0,-1], - np.float), + float), pre = np.eye(3), post = np.eye(3) ) @@ -69,7 +69,7 @@ def test_10_0(self): sol1, sol2, valid = gv_general.g_to_k( g, # self.wvln, axis = np.array([0,0,-1], - np.float), + float), pre = None, post = post ) @@ -102,7 +102,7 @@ def test_0_10(self): sol1, sol2, valid = gv_general.g_to_k( g, # self.wvln, axis = np.array([0,0,-1], - np.float), + float), pre = None, post = post ) @@ -136,7 +136,7 @@ def test_5_10(self): sol1, sol2, valid = gv_general.g_to_k( g, # self.wvln, axis = np.array([0,0,-1], - np.float), + float), pre = None, post = post ) @@ -167,7 +167,7 @@ def setUp(self): self.tth = np.random.random(self.np)*2+1 self.wvln = 0.154 self.eta = np.random.random(self.np)*360. - self.omega = RandomArray.random(self.np)*360. + self.omega = np.random.random(self.np)*360. @@ -178,13 +178,13 @@ def setUp(self): self.tth = np.random.random(self.np)*20 self.wvln = 0.154 self.eta = (np.random.random(self.np)-0.5)*180. - self.omega = np.array([90,180,12,97,230,199],np.float) + self.omega = np.array([90,180,12,97,230,199],float) # print "Called setup" def test_0_0_0(self): """ wedge, chi = 0 """ SANITY = False - om = np.zeros(self.np,np.float) + om = np.zeros(self.np,float) g_old = transform.compute_g_vectors(self.tth, self.eta, om, @@ -205,7 +205,7 @@ def test_0_0(self): """ wedge, chi = 0 """ SANITY = False if SANITY: print ("*"*80) - om = np.zeros(self.np,np.float) + om = np.zeros(self.np,float) g_old = transform.compute_g_vectors(self.tth, self.eta, self.omega, @@ -229,7 +229,7 @@ def test_25_30(self): w,c=25,30 SANITY = False if SANITY: print ("*"*80) - om = np.zeros(self.np,np.float) + om = np.zeros(self.np,float) g_old = transform.compute_g_vectors(self.tth, self.eta, self.omega, @@ -256,7 +256,7 @@ def test_Rz0(self): """ zero rotation around z give identity""" o = gv_general.rotation_axis( [ 0, 0, 1] , 0 ) self.assertAlmostEqual( array_diff( o.to_matrix() , - np.identity(3, np.float) ) , + np.identity(3, float) ) , 0.0, 6) def test_Rz30(self): @@ -294,18 +294,18 @@ def test_rotate_vectors(self): vecs = np.transpose(np.array([[ 0, 0, 1 ], [ 0, 1, 0 ], [ 1, 0, 0 ], - [ 1, 0, 0 ]], np.float)) + [ 1, 0, 0 ]], float)) res = np.transpose(np.array([[ 0, 0, 1 ], [-1, 0, 0 ], [ 0, 1, 0 ], - [ 0, 1, 0 ]], np.float)) + [ 0, 1, 0 ]], float)) m1, m2 = o.rotate_vectors(vecs), res self.assertEqual(m1.shape, m2.shape) - err = "\n\n"+str(m1.astype(np.int))+" != \n\n"+str(m2) + err = "\n\n"+str(m1.astype(int))+" != \n\n"+str(m2) self.assertAlmostEqual(array_diff(m1, m2),0,6,err) - angs = np.array([90.]*4,np.float) + angs = np.array([90.]*4,float) m3, m4 = o.rotate_vectors(vecs,angs), res - err = "\n\n"+str(m3.astype(np.int))+" != \n\n"+str(m4) + err = "\n\n"+str(m3.astype(int))+" != \n\n"+str(m4) self.assertAlmostEqual(array_diff(m3, m4),0,6,err) @@ -351,14 +351,14 @@ def test_rotate_vectors2(self): [ 1, 1, 0 ], #4 [-1, 1, 0 ], #5 [ 1, 0, 0 ], #6 - [ 0, 1, 1 ]],np.float)) + [ 0, 1, 1 ]],float)) res = np.transpose(np.array([[ 0, 0, 1 ], #1 [ 0, -1, 0 ], #2 [ 0, -1, 1 ], #3 [-1, -1, 0 ], #4 [ 1, -1, 0 ], #5 [-1, 0 , 0 ], #6 - [0, -1 , 1]],np.float)) + [0, -1 , 1]],float)) r1 = o.rotate_vectors(vecs) #print np.array_str(r1,precision=1,suppress_small=1) #print res diff --git a/test/index_demos/.gitignore b/test/index_demos/.gitignore new file mode 100644 index 00000000..533a7a30 --- /dev/null +++ b/test/index_demos/.gitignore @@ -0,0 +1,7 @@ +ideal.ubi +pairFFT.pks +test.vecs +fft.pks +grains.ubi +eu.patterson_pks + diff --git a/test/index_demos/__init__.py b/test/index_demos/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/index_demos/pairFFT.py b/test/index_demos/pairFFT.py new file mode 100644 index 00000000..9a58f656 --- /dev/null +++ b/test/index_demos/pairFFT.py @@ -0,0 +1,108 @@ + +import numpy as np +from ImageD11 import indexing, fft_index_refac + +o = indexing.indexer() +o.readgvfile("test.vecs") +g = fft_index_refac.grid( 128, 1., 5.) +g.gv_to_grid_new( o.gv ) +g.fft() +g.props() +g.peaksearch( "pairFFT.pks" ) +g.read_peaks( "pairFFT.pks" ) +tol=0.1 +tol2=tol*tol + +def refine_vector( v, gv, tol=0.25, ncycles=25, precision=1e-6 ): + """ + Input + v : real space lattice vector + gv : scattering vectors (no errors) + tol : tolerance for initial assignment to peak + ncycles : how much to refine + precision : when to stop refining in v += s when |s|/|v| < precision + Returns + vref : refined real space lattice vector + + Uses: + hr = v[0]*gv[i,0] + v[0]*gv[i,1] + v[1]*gv[i,2] 1 per peak + hi = round(hr) = calc 1 per peak + diff = calc - hr 1 per peak + gradient = d(diff)/dv[i] = gv[j,i] 3 per peak + matrix = gradient[i].gradient[j] 3x3 peaks^2 + rhs = gradient . diff 3 + + Solve for shifts and iterates reducing tolerance as 1/(ncycles+1) + Peaks that change index during refinement are removed + Stops on : + no peaks reassigned + ncycles runs out + |shift| / |vec| < precision + """ + assert gv.shape[1] == 3 + vref = v.copy() + mg = (gv*gv).sum(axis=1) + mold = None + wt = 1./mg # + mg.max()) + for i in range(ncycles): + hr = np.dot( vref, gv.T ) # npks + hi = np.round( hr ) # npks + diff = hi - hr # npks + m = abs(diff) < tol/(i+1) # keep or not ? + diff = (diff*wt)[m] + grad = (gv.T*wt).T[m] + rhs = np.dot( grad.T, diff ) + mat = np.dot( grad.T, grad ) + U,s,V = np.linalg.svd( mat ) + one_over_s = np.where( s/s.max() < 1e-6, 1, 1./s ) + mati = np.dot( U, np.dot(np.diag( one_over_s ), V ) ) + vshft = np.dot( mati, rhs ) + vref = vref + vshft + if mold is not None: + changed = mold ^ m # XOR + nch = changed.sum() + # Down weight peaks that are different to before for next round + if nch == 0: + break + wt[ changed ] = 0. + #print nch,i + mold = m + slen = np.sqrt((vshft * vshft).sum()) + vlen = np.sqrt((vref*vref).sum()) +# print "shift = ",slen,vlen,slen/vlen, tol/(i+1) + if abs(slen/vlen) < precision: + break + return vref + +def scr(v): + h=np.dot(v, o.gv.T) + return h-np.round(h) + + + +idx = [] +drl = [] +vs = [] +for v in g.UBIALL: + v = refine_vector( v, o.gv ) + vs.append(v) + hr = np.dot( v, o.gv.T ) + hi = np.round( hr ) + dr = (hr - hi) + drlv2 = dr*dr + drl.append( drlv2 ) + m = drlv2 < tol2 + idx.append( m ) + print( v, m.sum() ) + +idx = np.array( idx ) # dims vecs x npk +pair = np.zeros( (len(idx),len(idx))) +for i in range(len(g.UBIALL)): + for j in range(i, len(g.UBIALL)): + pair[i,j] = pair[j,i] = (idx[i]*idx[j]).sum() + + +import pylab as pl +pl.imshow( pair, interpolation='nearest' ) +pl.show() + diff --git a/test/jpa/plotuniq.py b/test/jpa/plotuniq.py deleted file mode 100644 index 627e9fcb..00000000 --- a/test/jpa/plotuniq.py +++ /dev/null @@ -1,78 +0,0 @@ -from matplotlib.pylab import * -from matplotlib.colors import cnames -keys = cnames.keys() - - - - -def HSVtoRGB( h, s, v ): - """ http://www.cs.rit.edu/~ncs/color/t_convert.html """ - if s == 0 : - return v,v,v - h /= 60; # sector 0 to 5 - i = floor( h ); - f = h - i; # factorial part of h - p = v * ( 1 - s ); - q = v * ( 1 - s * f ); - t = v * ( 1 - s * ( 1 - f ) ); - - if i == 0: return v,t,p - if i == 1: return q,v,p - if i == 2: return p,v,t - if i == 3: return p,q,v - if i ==4 : return t,p,v - return v,p,q - - - - - - - - -z = [] -i = [] -g=0 -filloff = 0. -for line in open(sys.argv[1],"r").readlines(): - if line.find("ubi[")>0 and len(z)>0: - a = argsort - g+=12. - c = HSVtoRGB(g%360,0.8,0.9) - # (g*0.4)%1, (g*0.45)%1, (g*0.55)%1 - if max(c)>1.: - print c,g - if z[0]>filloff and max(i)>1.5e9: - cf = HSVtoRGB(g%360,0.3,0.95) - - fill([0]+i+[0],array([z[0]]+z+[z[-1]])-3.15,label="%d"%(g),facecolor=cf, - edgecolor=c) - filloff = z[-1] - plot(i,array(z)-3.15,marker="+",linestyle="-", color = c) - z=[] - i=[] - if line.find("ubi[")>0: - z=[] - i=[] - if line.find("520\\scan")==-1: - continue - items = line.split() - if int(items[0])<15: - continue - zh = float(items[1]) - if len(z) > 0 and zh > z[-1]: - z.append(float(items[1])) - diff = zh - z[0] - i.append(float(items[-1])) - else: - if len(z) == 0: - z.append(float(items[1])) - diff = zh - z[0] - i.append(float(items[-1])) - else: - print line.rstrip(),z,zh,len(z) -#legend(loc=0) -title("Diffracted intensity versus height") -xlabel("Total intensity") -ylabel("Height in sample") -show() diff --git a/test/jpa/uniqify_ubi.py b/test/jpa/uniqify_ubi.py deleted file mode 100644 index 116f4743..00000000 --- a/test/jpa/uniqify_ubi.py +++ /dev/null @@ -1,120 +0,0 @@ -import glob, sys - -import numpy as np - -from ImageD11 import cImageD11, indexing, transformer - - -# open up a set of ubi files - -parfilename = sys.argv[1] - -ubifl = glob.glob(sys.argv[2]) - -#for a in glob.glob(sys.argv[3:]): -# ubifl += glob.glob(a) - -ubi_all = {} -for fname in ubifl: - print fname - ubi_all[fname] = indexing.readubis(fname) -print "OK" - - - - -# Now try to make these into a unique set. -# -# For each ubi: -# compute the reciprocal peak positions of central lattice -# see if these match a previous one. - -pks = [ - ( 0, 0, 1), ( 0, 1, 0), ( 1, 0, 0), - ( 0, 0,-1), ( 0,-1, 0), (-1, 0, 0), - ( 0, 1, 1), ( 1, 1, 0), ( 1, 0, 1), - ( 0,-1, 1), ( 1,-1, 0), (-1, 0, 1), - ( 0, 1,-1), (-1, 1, 0), ( 1, 0,-1), - ( 0,-1,-1), (-1,-1, 0), (-1, 0,-1), - ( 1, 1, 1), (-1, 1, 1), ( 1,-1, 1), - ( 1, 1,-1), (-1,-1, 1), (-1, 1,-1), - ( 1,-1,-1), (-1,-1,-1) - ] - -hkl = np.array(pks,np.float).T - -uniq_ubis = [] -names = ubi_all.keys() -#dsu = [ (int( np.split(".")[0].split("_")[-1] ), n) for n in names ] -#dsu.sort() -#names = [d[1] for d in dsu] - -tol = 0.05 -for name in names[15:]: - this_list = ubi_all[name] - for ubi, i in zip(this_list,range(len(this_list))): - gv = np.dot(np.linal.inv(ubi),hkl) - seen = 0 - for j in range(len(uniq_ubis)): - u = uniq_ubis[j][0] - npk=cImageD11.score(u,gv.T,tol) - if npk == len(pks): - # seen you before - uniq_ubis[j][1].append((name, i)) - seen += 1 - if npk > 12 and npk < len(pks): - print "close...",npk, - if seen == 0: - uniq_ubis.append([ubi,[(name,i)]]) - if seen > 1: - print "uniq seen more than once",ubi,i,name - -print "Found",len(uniq_ubis),"unique ubi matrices" - - -z={} -for line in open("job_z.txt","r").readlines(): - j,zh = line.split() - - z [ int(j)-1 ] = float(zh) - -def get_z(name): - j=int(name.split(".")[0].split("_")[-1]) - return j,z[j] - - -for entry in uniq_ubis: - print "\n\n" - for i in range(3): - for j in range(3): - print "# ubi[%d,%d] = %f"%(i,j,ubi[i,j]) - ubi = entry[0] - - for (name,i) in entry[1]: - j,zh = get_z(name) - print j,"%7.5f"%(zh),name,i, - - # we have the ubi matrix here. - # we want to refine and score this ubi against some data - try: - t = transformer.transformer() - t.loadfileparameters(parfilename) - t.loadfiltered(name.replace(".ubi","")) - t.computegv() - except: - print name - raise - - # bit of a hack - between 10 and 11 degrees - h = np.matrixmultiply(ubi, t.gv) - hint = np.floor(h + 0.5).astype(np.int) # rounds down - diff = h - hint - drlv2 = np.sum(diff * diff,0) - ind = np.compress( drlv2 < tol*tol, np.arange(t.twotheta.shape[0])) - avg = 4 - npix = 3 - intensity = np.sum((t.finalpeaks[avg,:]*t.finalpeaks[avg,:])[ind]) - print cImageD11.score(entry[0],t.gv.T,tol), intensity - - -# now re diff --git a/test/ken_simul/__init__.py b/test/ken_simul/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/ken_simul/gcalc.py b/test/ken_simul/gcalc.py index 3a85549e..618d679d 100644 --- a/test/ken_simul/gcalc.py +++ b/test/ken_simul/gcalc.py @@ -1,3 +1,4 @@ +from __future__ import print_function from ImageD11.indexing import readubis from ImageD11.transform import uncompute_g_vectors from ImageD11 import unitcell @@ -18,11 +19,11 @@ ng = 0 for g, ubi in zip(gcalc, ul): ng +=1 - print "# Grain",ng, "\n# Energy",energy,"keV, wavelength %.5f"%(wvln) + print ("# Grain",ng, "\n# Energy",energy,"keV, wavelength %.5f"%(wvln)) tth, eta, omega = uncompute_g_vectors( g.T, wvln ) order = np.argsort(tth) # hkls = np.dot( ubi, g.T).T - print "# h k l tth eta omega" + print ("# h k l tth eta omega") for j in range(len(order)): i = order[j] for s in (0,1): @@ -31,7 +32,7 @@ abs(eta[s][i])< 45 and not unitcell.F(h,k,l): - print (" %4d"*3 + " %7.2f"*3) % tuple([h,k,l, + print ((" %4d"*3 + " %7.2f"*3) % tuple([h,k,l, tth[i], - eta[s][i], omega[s][i]]) - print + eta[s][i], omega[s][i]])) + print() diff --git a/test/ken_simul/idx.py b/test/ken_simul/idx.py deleted file mode 100644 index 5b784121..00000000 --- a/test/ken_simul/idx.py +++ /dev/null @@ -1,26 +0,0 @@ - -import time -start = time.time() -# Create objects to manipulate - they hold your data -# -from ImageD11 import indexing -myindexer = indexing.indexer() -# -# Your work starts here: -# -myindexer.readgvfile( 'simAl.gve' ) -myindexer.updateparameters( ) -myindexer.parameterobj.set_parameters( - {'minpks': '80', - 'ring_1': '0', - 'ring_2': '1', - } ) -myindexer.loadpars( ) -myindexer.assigntorings( ) -myindexer.find( ) -myindexer.scorethem( ) -myindexer.saveubis( 'test.ubi' ) -myindexer.assigntorings( ) -print "Time:",time.time()-start -myindexer.saveindexing( 'test.idx' ) -print "Time:", time.time()-start diff --git a/test/ken_simul/test_all_pairs.py b/test/ken_simul/test_all_pairs.py new file mode 100644 index 00000000..1731e16f --- /dev/null +++ b/test/ken_simul/test_all_pairs.py @@ -0,0 +1,8 @@ +import sys, os +from ImageD11.indexing import indexer + +def test(): + i = indexer() + i.readgvfile(os.path.join(os.path.dirname(__file__),'simAl.gve')) + i.score_all_pairs() + assert len(i.ubis)==20 \ No newline at end of file diff --git a/test/ken_simul/testken.py b/test/ken_simul/testken.py new file mode 100644 index 00000000..533c8adf --- /dev/null +++ b/test/ken_simul/testken.py @@ -0,0 +1,39 @@ + +# Create objects to manipulate - they hold your data +# +from ImageD11 import indexing +import unittest, sys, os + +class fake(): + def write(self,*a,**k): + pass + def flush(self,*a,**k): + pass + +class testKen(unittest.TestCase): + def test12(self): + ss=sys.stdout + sys.stdout = fake() + try: + myindexer = indexing.indexer() + myindexer.readgvfile( os.path.join( os.path.split(__file__)[0] ,'simAl.gve' ) ) + + myindexer.updateparameters( ) + myindexer.parameterobj.set_parameters( + {'minpks': '80', + 'ring_1': '0', + 'ring_2': '1', + } ) + myindexer.loadpars( ) + myindexer.assigntorings( ) + myindexer.find( ) + myindexer.scorethem( ) + # myindexer.saveubis( 'test.ubi' ) + myindexer.assigntorings( ) + # myindexer.saveindexing( 'test.idx' ) + self.assertEqual( len( myindexer.ubis ) , 20 ) + finally: + sys.stdout=ss + +if __name__=="__main__": + unittest.main() diff --git a/test/nac_demo/pyqttv.py b/test/nac_demo/pyqttv.py index 30b18b4c..03fb2383 100644 --- a/test/nac_demo/pyqttv.py +++ b/test/nac_demo/pyqttv.py @@ -18,9 +18,9 @@ class PeaksTableModel(QAbstractTableModel): """Model class that drives the population of tabular display""" def __init__(self): super(PeaksTableModel,self).__init__() - start = time.time() - self.colfile = columnfile( sys.argv[1] ) - print time.time()-start + start = time.time() + self.colfile = columnfile( sys.argv[1] ) + print( time.time()-start) def rowCount(self,index=QModelIndex()): return self.colfile.nrows @@ -35,8 +35,8 @@ def columnCount(self,index=QModelIndex()): def data(self,index,role=Qt.DisplayRole): if role == Qt.DisplayRole: - return QVariant( float(self.colfile.bigarray[ index.column(), index.row()] )) - else: + return QVariant( float(self.colfile.bigarray[ index.column(), index.row()] )) + else: return QVariant() def headerData(self,section,orientation,role=Qt.DisplayRole): diff --git a/test/peaksearchtiftest/.gitignore b/test/peaksearchtiftest/.gitignore new file mode 100644 index 00000000..4da91b99 --- /dev/null +++ b/test/peaksearchtiftest/.gitignore @@ -0,0 +1,3 @@ +peaks_t?.flt +peaks_t?.spt +tiftest????.tif \ No newline at end of file diff --git a/test/peaksearchtiftest/__init__.py b/test/peaksearchtiftest/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/peaksearchtiftest/make_test_data.py b/test/peaksearchtiftest/make_test_data.py index 7aee4985..c1054cb0 100644 --- a/test/peaksearchtiftest/make_test_data.py +++ b/test/peaksearchtiftest/make_test_data.py @@ -46,7 +46,7 @@ def testpeaksearch(self): " -f %d -l %d"%(self.FIRST, self.LAST)) results = columnfile.columnfile( "peaks_t5.flt" ) self.assertEqual( results.nrows, self.NPK) - if os.path.exists( 'peaks_t5.flt' ): + if os.path.exists( 'peaks_t6.flt' ): self.assertEqual( open( 'peaks_t5.flt').read().rstrip(), open( 'peaks_t6.flt').read().rstrip() ) @@ -68,7 +68,7 @@ def testpeaksearch_singlethread(self): " -f %d -l %d"%(self.FIRST, self.LAST)) results = columnfile.columnfile( "peaks_t7.flt" ) self.assertEqual( results.nrows, self.NPK) - if os.path.exists( 'peaks_t7.flt' ): + if os.path.exists( 'peaks_t6.flt' ): self.assertEqual( open( 'peaks_t7.flt').read().rstrip(), open( 'peaks_t6.flt').read().rstrip() ) diff --git a/test/peaksearchtiftest/peaks_t5.flt b/test/peaksearchtiftest/peaks_t5.flt deleted file mode 100644 index 413c0c81..00000000 --- a/test/peaksearchtiftest/peaks_t5.flt +++ /dev/null @@ -1,7 +0,0 @@ -# sc fc omega Number_of_pixels avg_intensity s_raw f_raw sigs sigf covsf sigo covso covfo sum_intensity sum_intensity^2 IMax_int IMax_s IMax_f IMax_o Min_s Max_s Min_f Max_f Min_o Max_o dety detz onfirst onlast spot3d_id - 12.5000 15.5000 11.0000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 11.0000 12 13 15 16 11.0000 11.0000 -15.5000 12.5000 1 0 0 - 12.5000 15.5000 11.6000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 -0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 11.6000 12 13 15 16 11.6000 11.6000 -15.5000 12.5000 0 0 1 - 12.5000 15.5000 12.2000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 12.2000 12 13 15 16 12.2000 12.2000 -15.5000 12.5000 0 0 2 - 12.5000 15.5000 12.8000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 12.8000 12 13 15 16 12.8000 12.8000 -15.5000 12.5000 0 0 3 - 12.5000 15.5000 13.4000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 13.4000 12 13 15 16 13.4000 13.4000 -15.5000 12.5000 0 0 4 - 12.5000 15.5000 14.0000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 -0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 14.0000 12 13 15 16 14.0000 14.0000 -15.5000 12.5000 0 1 5 diff --git a/test/peaksearchtiftest/peaks_t5.spt b/test/peaksearchtiftest/peaks_t5.spt deleted file mode 100644 index 852a5819..00000000 --- a/test/peaksearchtiftest/peaks_t5.spt +++ /dev/null @@ -1,156 +0,0 @@ - - -# File tiftest0006.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.0 -# filename = tiftest0006.tif -# Omega = 11.000000 -# Threshold = 5.000000 -# npks = 1 -# Threshold level 5.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0007.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.3 -# filename = tiftest0007.tif -# Omega = 11.300000 -# Threshold = 5.000000 -# npks = 0 - - -# File tiftest0008.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.6 -# filename = tiftest0008.tif -# Omega = 11.600000 -# Threshold = 5.000000 -# npks = 1 -# Threshold level 5.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0009.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.9 -# filename = tiftest0009.tif -# Omega = 11.900000 -# Threshold = 5.000000 -# npks = 0 - - -# File tiftest0010.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.2 -# filename = tiftest0010.tif -# Omega = 12.200000 -# Threshold = 5.000000 -# npks = 1 -# Threshold level 5.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0011.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.5 -# filename = tiftest0011.tif -# Omega = 12.500000 -# Threshold = 5.000000 -# npks = 0 - - -# File tiftest0012.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.8 -# filename = tiftest0012.tif -# Omega = 12.800000 -# Threshold = 5.000000 -# npks = 1 -# Threshold level 5.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0013.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.1 -# filename = tiftest0013.tif -# Omega = 13.100000 -# Threshold = 5.000000 -# npks = 0 - - -# File tiftest0014.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.4 -# filename = tiftest0014.tif -# Omega = 13.400000 -# Threshold = 5.000000 -# npks = 1 -# Threshold level 5.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0015.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.7 -# filename = tiftest0015.tif -# Omega = 13.700000 -# Threshold = 5.000000 -# npks = 0 - - -# File tiftest0016.tif -# Processed on Fri Dec 05 22:24:41 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 14.0 -# filename = tiftest0016.tif -# Omega = 14.000000 -# Threshold = 5.000000 -# npks = 1 -# Threshold level 5.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - diff --git a/test/peaksearchtiftest/peaks_t6.flt b/test/peaksearchtiftest/peaks_t6.flt deleted file mode 100644 index 413c0c81..00000000 --- a/test/peaksearchtiftest/peaks_t6.flt +++ /dev/null @@ -1,7 +0,0 @@ -# sc fc omega Number_of_pixels avg_intensity s_raw f_raw sigs sigf covsf sigo covso covfo sum_intensity sum_intensity^2 IMax_int IMax_s IMax_f IMax_o Min_s Max_s Min_f Max_f Min_o Max_o dety detz onfirst onlast spot3d_id - 12.5000 15.5000 11.0000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 11.0000 12 13 15 16 11.0000 11.0000 -15.5000 12.5000 1 0 0 - 12.5000 15.5000 11.6000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 -0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 11.6000 12 13 15 16 11.6000 11.6000 -15.5000 12.5000 0 0 1 - 12.5000 15.5000 12.2000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 12.2000 12 13 15 16 12.2000 12.2000 -15.5000 12.5000 0 0 2 - 12.5000 15.5000 12.8000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 12.8000 12 13 15 16 12.8000 12.8000 -15.5000 12.5000 0 0 3 - 12.5000 15.5000 13.4000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 13.4000 12 13 15 16 13.4000 13.4000 -15.5000 12.5000 0 0 4 - 12.5000 15.5000 14.0000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 -0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 14.0000 12 13 15 16 14.0000 14.0000 -15.5000 12.5000 0 1 5 diff --git a/test/peaksearchtiftest/peaks_t6.spt b/test/peaksearchtiftest/peaks_t6.spt deleted file mode 100644 index deb35839..00000000 --- a/test/peaksearchtiftest/peaks_t6.spt +++ /dev/null @@ -1,156 +0,0 @@ - - -# File tiftest0006.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.0 -# filename = tiftest0006.tif -# Omega = 11.000000 -# Threshold = 6.000000 -# npks = 1 -# Threshold level 6.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0007.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.3 -# filename = tiftest0007.tif -# Omega = 11.300000 -# Threshold = 6.000000 -# npks = 0 - - -# File tiftest0008.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.6 -# filename = tiftest0008.tif -# Omega = 11.600000 -# Threshold = 6.000000 -# npks = 1 -# Threshold level 6.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0009.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.9 -# filename = tiftest0009.tif -# Omega = 11.900000 -# Threshold = 6.000000 -# npks = 0 - - -# File tiftest0010.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.2 -# filename = tiftest0010.tif -# Omega = 12.200000 -# Threshold = 6.000000 -# npks = 1 -# Threshold level 6.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0011.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.5 -# filename = tiftest0011.tif -# Omega = 12.500000 -# Threshold = 6.000000 -# npks = 0 - - -# File tiftest0012.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.8 -# filename = tiftest0012.tif -# Omega = 12.800000 -# Threshold = 6.000000 -# npks = 1 -# Threshold level 6.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0013.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.1 -# filename = tiftest0013.tif -# Omega = 13.100000 -# Threshold = 6.000000 -# npks = 0 - - -# File tiftest0014.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.4 -# filename = tiftest0014.tif -# Omega = 13.400000 -# Threshold = 6.000000 -# npks = 1 -# Threshold level 6.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0015.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.7 -# filename = tiftest0015.tif -# Omega = 13.700000 -# Threshold = 6.000000 -# npks = 0 - - -# File tiftest0016.tif -# Processed on Fri Dec 05 22:24:45 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 14.0 -# filename = tiftest0016.tif -# Omega = 14.000000 -# Threshold = 6.000000 -# npks = 1 -# Threshold level 6.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - diff --git a/test/peaksearchtiftest/peaks_t7.flt b/test/peaksearchtiftest/peaks_t7.flt deleted file mode 100644 index 413c0c81..00000000 --- a/test/peaksearchtiftest/peaks_t7.flt +++ /dev/null @@ -1,7 +0,0 @@ -# sc fc omega Number_of_pixels avg_intensity s_raw f_raw sigs sigf covsf sigo covso covfo sum_intensity sum_intensity^2 IMax_int IMax_s IMax_f IMax_o Min_s Max_s Min_f Max_f Min_o Max_o dety detz onfirst onlast spot3d_id - 12.5000 15.5000 11.0000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 11.0000 12 13 15 16 11.0000 11.0000 -15.5000 12.5000 1 0 0 - 12.5000 15.5000 11.6000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 -0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 11.6000 12 13 15 16 11.6000 11.6000 -15.5000 12.5000 0 0 1 - 12.5000 15.5000 12.2000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 12.2000 12 13 15 16 12.2000 12.2000 -15.5000 12.5000 0 0 2 - 12.5000 15.5000 12.8000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 12.8000 12 13 15 16 12.8000 12.8000 -15.5000 12.5000 0 0 3 - 12.5000 15.5000 13.4000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 13.4000 12 13 15 16 13.4000 13.4000 -15.5000 12.5000 0 0 4 - 12.5000 15.5000 14.0000 4 42.0000 12.5000 15.5000 1.1180 1.1180 0.0000 1.0000 -0.0000 0.0000 168.0000 7056.0000 42.0000 12 15 14.0000 12 13 15 16 14.0000 14.0000 -15.5000 12.5000 0 1 5 diff --git a/test/peaksearchtiftest/peaks_t7.spt b/test/peaksearchtiftest/peaks_t7.spt deleted file mode 100644 index c3139da6..00000000 --- a/test/peaksearchtiftest/peaks_t7.spt +++ /dev/null @@ -1,156 +0,0 @@ - - -# File tiftest0006.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.0 -# filename = tiftest0006.tif -# Omega = 11.000000 -# Threshold = 7.000000 -# npks = 1 -# Threshold level 7.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0007.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.3 -# filename = tiftest0007.tif -# Omega = 11.300000 -# Threshold = 7.000000 -# npks = 0 - - -# File tiftest0008.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.6 -# filename = tiftest0008.tif -# Omega = 11.600000 -# Threshold = 7.000000 -# npks = 1 -# Threshold level 7.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0009.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 11.9 -# filename = tiftest0009.tif -# Omega = 11.900000 -# Threshold = 7.000000 -# npks = 0 - - -# File tiftest0010.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.2 -# filename = tiftest0010.tif -# Omega = 12.200000 -# Threshold = 7.000000 -# npks = 1 -# Threshold level 7.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0011.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.5 -# filename = tiftest0011.tif -# Omega = 12.500000 -# Threshold = 7.000000 -# npks = 0 - - -# File tiftest0012.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 12.8 -# filename = tiftest0012.tif -# Omega = 12.800000 -# Threshold = 7.000000 -# npks = 1 -# Threshold level 7.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0013.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.1 -# filename = tiftest0013.tif -# Omega = 13.100000 -# Threshold = 7.000000 -# npks = 0 - - -# File tiftest0014.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.4 -# filename = tiftest0014.tif -# Omega = 13.400000 -# Threshold = 7.000000 -# npks = 1 -# Threshold level 7.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - - - -# File tiftest0015.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 13.7 -# filename = tiftest0015.tif -# Omega = 13.700000 -# Threshold = 7.000000 -# npks = 0 - - -# File tiftest0016.tif -# Processed on Fri Dec 05 22:24:49 2008 -# Spatial correction from None -# SPLINE X-PIXEL-SIZE 1.0 -# SPLINE Y-PIXEL-SIZE 1.0 -# Omega = 14.0 -# filename = tiftest0016.tif -# Omega = 14.000000 -# Threshold = 7.000000 -# npks = 1 -# Threshold level 7.000000 -# Number_of_pixels Average_counts f s fc sc sig_f sig_s cov_fs -4 42.000000 12.500000 15.500000 12.500000 15.500000 1.118034 1.118034 0.000000 - diff --git a/test/peaksearchtiftest/scriptedpeaksearch.py b/test/peaksearchtiftest/scriptedpeaksearch.py new file mode 100644 index 00000000..c80ae480 --- /dev/null +++ b/test/peaksearchtiftest/scriptedpeaksearch.py @@ -0,0 +1,31 @@ + + +import fabio, numpy as np +from ImageD11.labelimage import labelimage +from ImageD11.peaksearcher import peaksearch +from ImageD11.blobcorrector import correctorclass, perfect + +lines = [ ("tiftest%04d.tif"%(i),i-6) for i in range(6,17) ] + +# give somes minimal options +thresholds = [1, 10, 100] +corrector = perfect() # no spatial disortion +dims = fabio.open(lines[0][0]).data.shape +label_ims = { t : labelimage( shape=dims, + fileout="peaks_t%d.flt"%( t ), + sptfile="peaks_t%d.spt"%( t ), + spatial=corrector ) + for t in thresholds } + +for filename, omega in lines: + frame = fabio.open(filename) + frame.header['Omega'] = omega + frame.data = frame.data.astype( np.float32 ) + # corrections like dark/flat/normalise would be added here + peaksearch( filename, frame, corrector, thresholds, label_ims ) + +for t in thresholds: + label_ims[t].finalise() + + + diff --git a/test/peaksearchtiftest/tiftest0006.tif b/test/peaksearchtiftest/tiftest0006.tif deleted file mode 100644 index 8e86437778d105a12e7e41d3cb61f0ef6bd1362d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2426 zcmebD)MDUZU|`^6U|?isU<9%xfS3`9%>-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q vGCml}W(KMm1*0J_8UmvsFmyve!-;_bglPq6Z9c8sJ!-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q iGCml}W(KMm1*0J_8UmvsFd71*Aut*OqaiR7LjVAVHUfzN diff --git a/test/peaksearchtiftest/tiftest0008.tif b/test/peaksearchtiftest/tiftest0008.tif deleted file mode 100644 index 8e86437778d105a12e7e41d3cb61f0ef6bd1362d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2426 zcmebD)MDUZU|`^6U|?isU<9%xfS3`9%>-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q vGCml}W(KMm1*0J_8UmvsFmyve!-;_bglPq6Z9c8sJ!-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q iGCml}W(KMm1*0J_8UmvsFd71*Aut*OqaiR7LjVAVHUfzN diff --git a/test/peaksearchtiftest/tiftest0010.tif b/test/peaksearchtiftest/tiftest0010.tif deleted file mode 100644 index 8e86437778d105a12e7e41d3cb61f0ef6bd1362d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2426 zcmebD)MDUZU|`^6U|?isU<9%xfS3`9%>-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q vGCml}W(KMm1*0J_8UmvsFmyve!-;_bglPq6Z9c8sJ!-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q iGCml}W(KMm1*0J_8UmvsFd71*Aut*OqaiR7LjVAVHUfzN diff --git a/test/peaksearchtiftest/tiftest0012.tif b/test/peaksearchtiftest/tiftest0012.tif deleted file mode 100644 index 8e86437778d105a12e7e41d3cb61f0ef6bd1362d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2426 zcmebD)MDUZU|`^6U|?isU<9%xfS3`9%>-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q vGCml}W(KMm1*0J_8UmvsFmyve!-;_bglPq6Z9c8sJ!-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q iGCml}W(KMm1*0J_8UmvsFd71*Aut*OqaiR7LjVAVHUfzN diff --git a/test/peaksearchtiftest/tiftest0014.tif b/test/peaksearchtiftest/tiftest0014.tif deleted file mode 100644 index 8e86437778d105a12e7e41d3cb61f0ef6bd1362d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2426 zcmebD)MDUZU|`^6U|?isU<9%xfS3`9%>-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q vGCml}W(KMm1*0J_8UmvsFmyve!-;_bglPq6Z9c8sJ!-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q iGCml}W(KMm1*0J_8UmvsFd71*Aut*OqaiR7LjVAVHUfzN diff --git a/test/peaksearchtiftest/tiftest0016.tif b/test/peaksearchtiftest/tiftest0016.tif deleted file mode 100644 index 8e86437778d105a12e7e41d3cb61f0ef6bd1362d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2426 zcmebD)MDUZU|`^6U|?isU<9%xfS3`9%>-pD0L7W1Y>+xOB(@+U3s`RzP+Sa2uQ-$q vGCml}W(KMm1*0J_8UmvsFmyve!-;_bglPq6Z9c8sJ!-1: + MOD = importlib.import_module(M) + if M.find(".")>-1 and 0: path = M.split(".") for direc in path[:-1]: os.chdir( direc ) M = path[-1] - MOD = importlib.import_module(M) mySuite = unittest.loader.findTestCases( MOD ) runner = unittest.TextTestRunner() try: runner.run(mySuite) except: raise + +import ImageD11 +print("ImageD11 was from",ImageD11.__file__) +print("You might do better to run python -m pytest") diff --git a/test/simul_1000_grains/.gitignore b/test/simul_1000_grains/.gitignore new file mode 100644 index 00000000..cbeaad92 --- /dev/null +++ b/test/simul_1000_grains/.gitignore @@ -0,0 +1,12 @@ +Al1000.inp +Al1000/ +allgrid.map +allgrid_fittrans.map +allgrid_indexer.map +allgrid_makemap.map +allgrid_teo.map +ideal.map +shaken.map +allgridfit.map +grid.flt +grid.ubi diff --git a/test/simul_1000_grains/README.txt b/test/simul_1000_grains/README.txt new file mode 100644 index 00000000..f84b350b --- /dev/null +++ b/test/simul_1000_grains/README.txt @@ -0,0 +1,5 @@ + +1) run_simul.py : generates grains, runs makemap, runs shake + updates timing.txt + +2) test_mappers.py : trys out the different mappers to fit the grains diff --git a/test/simul_1000_grains/bencher.py b/test/simul_1000_grains/bencher.py new file mode 100644 index 00000000..7b642ad1 --- /dev/null +++ b/test/simul_1000_grains/bencher.py @@ -0,0 +1,39 @@ + +from __future__ import print_function +import multiprocessing, sys, timeit, os + +timer = timeit.default_timer +ncpu = multiprocessing.cpu_count() + +r = {} + +for ib in range(1,ncpu+1): + for io in range(1,ncpu+1): + cmd0 = "OPENBLAS_NUM_THREADS=%d OMP_NUM_THREADS=%d "%(ib,io) + cmd1 = "python idx.py > /dev/null" + start = timer() + x = os.system( cmd0+cmd1 ) + end = timer() + print( cmd0, "%.2f/s "%(end-start)) + if x!=0: + break + +""" +jon@brainsIntel:~/Documents/ImageD11/test/simul_1000_grains$ python bencher.py +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 5.79/s +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 5.02/s +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=3 5.17/s +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=4 5.51/s +OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=1 6.01/s +OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=2 6.08/s +OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=3 5.92/s +OPENBLAS_NUM_THREADS=2 OMP_NUM_THREADS=4 56.00/s +OPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=1 6.02/s +OPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=2 6.46/s +OPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=3 23.10/s +OPENBLAS_NUM_THREADS=3 OMP_NUM_THREADS=4 58.63/s +OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=1 11.46/s +OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=2 11.29/s +OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=3 20.34/s +OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 60.67/s +""" diff --git a/test/simul_1000_grains/compare.py b/test/simul_1000_grains/compare.py index 937a2bf9..1fdafa1e 100644 --- a/test/simul_1000_grains/compare.py +++ b/test/simul_1000_grains/compare.py @@ -1,12 +1,12 @@ - +from __future__ import print_function from ImageD11.grain import read_grain_file, write_grain_file from ImageD11.columnfile import columnfile import numpy as np ideal = read_grain_file("ideal.map") -fnames = "allgrid_fittrans.map allgrid_indexer.map allgrid_makemap.map allgrid_teo.map".split() +fnames = "f2.map allgrid_fittrans.map allgrid_indexer.map allgrid_makemap.map allgrid_teo.map".split() #fnames = "teo.map", def check(gl1, gl2): @@ -26,13 +26,13 @@ def check(gl1, gl2): mxTi, mxT = i, t if abs(dvol) > mxV: mxVi, mxV = i, abs(dvol) - print dTs/i, dVs/i, mxT, mxTi, mxV, mxVi + print( dTs/i, dVs/i, mxT, mxTi, mxV, mxVi ) -print "fname avg_pos_err avg_vol_err max_pos_err grain_max_pos_err max_vol_err grain_max_vol_err" +print( "fname avg_pos_err avg_vol_err max_pos_err grain_max_pos_err max_vol_err grain_max_vol_err") for f in fnames: - print f, + print (f,end=" ") check(read_grain_file(f), ideal) diff --git a/test/simul_1000_grains/easy.py b/test/simul_1000_grains/easy.py new file mode 100644 index 00000000..b4ec5957 --- /dev/null +++ b/test/simul_1000_grains/easy.py @@ -0,0 +1,17 @@ +import timeit +start = timeit.default_timer() +import sys +from ImageD11 import indexing +myindexer = indexing.indexer() +myindexer.parameterobj.set_parameters( { + 'max_grains': 10000, + 'minpks' : 50, +} ) +myindexer.loadpars( ) +myindexer.readgvfile( sys.argv[1] ) +myindexer.score_all_pairs(10) +myindexer.saveubis( sys.argv[2] ) +assert len(myindexer.ubis)==1000 +print("Got 1000 grains") +end = timeit.default_timer() +print("Time %.3f /s"%(end-start)) \ No newline at end of file diff --git a/test/simul_1000_grains/grid_index.py b/test/simul_1000_grains/grid_index.py index 6f6917aa..a5dd7d5a 100644 --- a/test/simul_1000_grains/grid_index.py +++ b/test/simul_1000_grains/grid_index.py @@ -1,4 +1,4 @@ - +from __future__ import print_function from ImageD11 import peakmerge, indexing, transformer from ImageD11 import grain, unitcell, refinegrains import sys, os, numpy as np, time, random @@ -42,10 +42,10 @@ col = mytransformer.colfile if not "drlv2" in col.titles: - col.addcolumn( np.ones(col.nrows, np.float), + col.addcolumn( np.ones(col.nrows, float), "drlv2" ) if not "labels" in col.titles: - col.addcolumn( np.ones(col.nrows, np.float)-2, + col.addcolumn( np.ones(col.nrows, float)-2, "labels" ) if not "sc" in col.titles: assert "xc" in col.titles @@ -92,7 +92,7 @@ def fit(tol): o.scandata["internal"] = colfile o.tolerance = tol o.readubis( grainsfile ) - if symmetry is not "triclinic": + if symmetry != "triclinic": o.makeuniq( symmetry ) o.generate_grains() o.refinepositions() @@ -104,10 +104,10 @@ def fit(tol): gl = filter( lambda x: x.npks > NPKS, o.grains.values() ) sys.stdout = ss if len(gl) == 0: - print "I killed all your grains!" + print( "I killed all your grains!") break else: - print "Keeping",len(gl),"from",len(o.grains.values()),"grains with at least",NPKS,"peaks",tol + print( "Keeping",len(gl),"from",len(o.grains.values()),"grains with at least",NPKS,"peaks",tol) grain.write_grain_file( grainsfile , gl ) # re-assign after last filter fit(tol) @@ -130,7 +130,7 @@ def doindex( gve, x, y, z, w): gv = gve.T ) myindexer.ds = np.sqrt( (gve * gve).sum(axis=0) ) - myindexer.ga = np.zeros(len(myindexer.ds),np.int)-1 # Grain assignments + myindexer.ga = np.zeros(len(myindexer.ds),int)-1 # Grain assignments # myindexer.readgvfile( gve ) for ring1 in RING1: @@ -193,7 +193,7 @@ def doindex( gve, x, y, z, w): if first: first=False ng = doindex(gve, t_x,t_y,t_z, w) - print "Position",t_x, t_y, t_z,"Grains", ng, + print ("Position",t_x, t_y, t_z,"Grains", ng,end=' ') ticker.tick() if ng > 0: #ret = os.system("makemap.py -u %s.ubi -U %s.map -s cubic --omega_slop=0.13 "%(tmp,tmp) + @@ -211,10 +211,10 @@ def doindex( gve, x, y, z, w): totalgrains += nfit ticker.tick() if mytransformer.colfile.nrows == 0: - print "All peaks indexed" + print ("All peaks indexed") break else: - print "still got",mytransformer.colfile.nrows," to index, found",totalgrains,"in total" + print ("still got",mytransformer.colfile.nrows," to index, found",totalgrains,"in total") diff --git a/test/simul_1000_grains/idx.py b/test/simul_1000_grains/idx.py new file mode 100644 index 00000000..6230d76b --- /dev/null +++ b/test/simul_1000_grains/idx.py @@ -0,0 +1,24 @@ + + +# Create objects to manipulate - they hold your data +# +from ImageD11 import indexing +myindexer = indexing.indexer() + +# +# Your work starts here: +# +myindexer.readgvfile( 'Al1000/Al1000.gve' ) +myindexer.updateparameters( ) +myindexer.parameterobj.set_parameters( { + 'minpks': 100, + 'max_grains': 1001, + 'ring_1': 5, + 'ring_2': 5, + 'cosine_tol': -0.001, + } ) +myindexer.loadpars( ) +myindexer.assigntorings( ) +myindexer.find( ) +myindexer.scorethem( ) +myindexer.saveubis( 'allgrid.map' ) diff --git a/test/simul_1000_grains/make_u_t.py b/test/simul_1000_grains/make_u_t.py index 94185d97..d3304085 100644 --- a/test/simul_1000_grains/make_u_t.py +++ b/test/simul_1000_grains/make_u_t.py @@ -1,3 +1,4 @@ +from __future__ import print_function, division inp = """ #File names and Format @@ -85,7 +86,7 @@ dz = np.sin(np.arange(1000))/7 t = np.array( (x.ravel()+dx, y.ravel()+dy, z.ravel()+dz ) )/10 -print t.shape +#print t.shape np.savetxt("t",t.T) # orientations.... @@ -98,6 +99,6 @@ for i,v in enumerate(t.T): f.write("pos_grains_%d %f %f %f\n"%(i,v[0],v[1],v[2])) for i,v in enumerate(u): - f.write("U_grains_%d %f %f %f %f %f %f %f %f %f\n"%(i, - v[0,0],v[0,1],v[0,2],v[1,0],v[1,1],v[1,2],v[2,0],v[2,1],v[2,2] )) - + f.write("U_grains_%d "%(i)) + f.write(" ".join( repr(x) for x in v.ravel() ) ) + f.write('\n') diff --git a/test/simul_1000_grains/make_u_t_easy.py b/test/simul_1000_grains/make_u_t_easy.py new file mode 100644 index 00000000..82296598 --- /dev/null +++ b/test/simul_1000_grains/make_u_t_easy.py @@ -0,0 +1,104 @@ +from __future__ import print_function, division + +inp = """ +#File names and Format +#Directory to save output files +direc 'easy1000' +stem 'easy1000' +make_image 0 +output '.gve' '.flt' '.par' '.ubi' +# Structural parameters +structure_phase_0 'Al.cif' +# Crystal/grains parameters +no_grains 1000 +# Total number of grains summed over all phases to be simulated. +# Need to match the number of U_Grains_X key word +gen_size 1 -0.05 0.01 0.2 +#Grain phase : If you want to let the PolyXSim appoint which grain +# belongs to which phase the following keyword can be used. +gen_phase 1 1 0 +# Grain orientation : random (1) or specify specific orientation matrices (0) +gen_U 0 + +# 1 random, box or cylinder +gen_pos 0 0 +# pos_grains_0 0.1 -0.1 0.05 + +#sample_xyz 1.0 1.0 1.0 +gen_eps 1 0 0 0 0 + +# Instrumentation +# Detector pixel size [mm] +y_size 0.05 +z_size 0.05 +#Detector size [pixels] +dety_size 2048 +detz_size 2048 +#Distance from sample to detector [mm] +distance 300 +#Detector tilt +tilt_x 0.005 +tilt_y 0.01 +tilt_z 0.008 +#Detector orientation +o11 1 +o12 0 +o21 0 +o22 -1 +#Noise +noise 0 +#Reflection +intensity_const 1 +lorentz_apply 1 +beampol_apply 1 +peakshape 0 +#Instrumental +#Beam specs, Pt edge +wavelength 0.158154 +beamflux 1e-12 +beampol_factor 1 +beampol_direct 0 +#Beam center on detector [pixels] +dety_center 1022.1 +detz_center 1028.3 +#Omega scan range [degrees] +omega_start 0 +omega_end 180 +#Omega step size [degrees] +omega_step 0.25 +#Omega rotation direction [degrees] +omega_sign 1 +#Wedge angle of omega axis +wedge 0.0 +""" + +"U_grains_0 7.712806e-01 -6.337184e-01 5.939117e-02 6.130920e-01 7.146102e-01 -3.368241e-01 1.710101e-01 2.961981e-01 9.396926e-01" + +import numpy as np, xfab.tools + +# generate a 10x10x10 grid of grains. +# add something to this + +x,y,z = np.mgrid[0:10,0:10,0:10]-5 + +dx = np.sin(np.arange(1000))/3 # +/- 1 +dy = np.cos(np.arange(1000))/5 +dz = np.sin(np.arange(1000))/7 + +t = np.array( (x.ravel()+dx, y.ravel()+dy, z.ravel()+dz ) )/10 +#print t.shape +np.savetxt("t",t.T) + +# orientations.... +# t is in range [-0.5 -> 0.5] - make it rod also. + +u = [xfab.tools.rod_to_u( v.astype(float)) for v in t.T] + +f=open("easy1000.inp","w") +f.write(inp) +for i,v in enumerate(t.T): + f.write("pos_grains_%d %f %f %f\n"%(i,v[0]/10,v[1]/10,v[2]/10)) +for i,v in enumerate(u): + f.write( ("U_grains_%d"+ " %.12f"*9+'\n')%( i, + v[0,0],v[0,1],v[0,2],v[1,0],v[1,1],v[1,2],v[2,0],v[2,1],v[2,2] )) + diff --git a/test/simul_1000_grains/run_simul.py b/test/simul_1000_grains/run_simul.py index 58f33d9b..6f7f190e 100644 --- a/test/simul_1000_grains/run_simul.py +++ b/test/simul_1000_grains/run_simul.py @@ -1,26 +1,33 @@ +from __future__ import print_function, division -import os , time +import os , time, sys # generate the inp file def measure(cmd): start = time.time() ret = os.system(cmd) end = time.time() - if os.path.exists("timing.txt"): + if os.path.exists("timing.log"): open("timing.log","a").write("%f %s\n"%(end-start, cmd)) else: open("timing.log","w+").write("%f %s\n"%(end-start, cmd)) + if ret!=0: + print("Problem, stopping") + sys.exit() if os.path.exists("allgrid.map"): os.remove("allgrid.map") if os.path.exists("timing.log"): os.remove("timing.log") -measure("python make_u_t.py") +measure(sys.executable + " make_u_t.py") measure("PolyXSim.py -i Al1000.inp") -measure("python grid_index.py Al1000/Al1000.flt Al1000/Al1000.par grid") -measure("makemap.py -u allgrid.map -U allgridfit.map -f Al1000/Al1000.flt -p Al1000/Al1000.par --omega_slop=0.125 -t 0.0075") -measure("makemap.py -u allgridfit.map -U allgridfit.map -f Al1000/Al1000.flt -p Al1000/Al1000.par --omega_slop=0.125 -t 0.0075") +# measure("python grid_index.py Al1000/Al1000.flt Al1000/Al1000.par grid 9") +measure(sys.executable + " make_u_t.py") +measure("makemap.py -u allgrid.map -U allgridfit.map -f Al1000/Al1000.flt -p Al1000/Al1000.par --omega_no_float -t 0.05") +measure("makemap.py -u allgridfit.map -U allgridfit.map -f Al1000/Al1000.flt -p Al1000/Al1000.par --omega_no_float -t 0.02") +for i in range(3): + measure("makemap.py -u allgridfit.map -U allgridfit.map -f Al1000/Al1000.flt -p Al1000/Al1000.par --omega_slop=0.125 -t 0.0075") -os.system("python res2map.py Al1000/Al1000.ubi Al1000/Al1000.res ideal.map") -os.system("python shake.py ideal.map shaken.map") +os.system(sys.executable + " res2map.py Al1000/Al1000.ubi Al1000/Al1000.res ideal.map") +os.system(sys.executable + " shake.py ideal.map shaken.map") diff --git a/test/simul_1000_grains/test_mappers.py b/test/simul_1000_grains/test_mappers.py index d8881d88..94caee52 100644 --- a/test/simul_1000_grains/test_mappers.py +++ b/test/simul_1000_grains/test_mappers.py @@ -1,6 +1,9 @@ +from __future__ import print_function import sys, os + + args = { 'flt' : os.path.join("Al1000","Al1000.flt"), @@ -9,7 +12,7 @@ } # 1: Run makemap.py -cmd1 = "makemap.py -f %(flt)s -p %(par)s -s cubic --omega_no_float --no_sort "%args +cmd1 = sys.executable + " ../../scripts/makemap.py -f %(flt)s -p %(par)s -s cubic --omega_no_float --no_sort "%args if 1: cmd2 = "-U allgrid_makemap.map -t 0.015 -u %(ubi)s"%args os.system(cmd1+cmd2) @@ -28,21 +31,21 @@ if 1: # 2: Run sandbox/fittrans using assigned grains - cmd = "python ../../sandbox/fittrans.py %(flt)s.new %(par)s %(ubi)s allgrid_fittrans.map "%args + cmd = sys.executable + " ../../sandbox/fittrans.py %(flt)s.new %(par)s %(ubi)s allgrid_fittrans.map "%args os.system( cmd) - print cmd + print( cmd) if 1: # 3: Run ImageD11/indexer using assigned grains - cmd = "python ../../ImageD11/indexer.py %(par)s %(flt)s.new fit %(ubi)s allgrid_indexer.map"%args + cmd = sys.executable + " ../../sandbox/indexer_1d.py %(par)s %(flt)s.new fit %(ubi)s allgrid_indexer.map"%args os.system(cmd) - print cmd + print (cmd) # 4: Run sandbox/teo.py using assigned grains if 1: - cmd = "python ../../sandbox/teo.py %(flt)s.new %(par)s %(ubi)s allgrid_teo.map"%(args) + cmd = sys.executable + " ../../sandbox/teo.py %(flt)s.new %(par)s %(ubi)s allgrid_teo.map"%(args) os.system( cmd) if not os.path.exists("ideal.map"): - os.system("python res2map.py Al1000/Al1000.ubi Al1000/Al1000.res ideal.map") + os.system(sys.executable + " res2map.py Al1000/Al1000.ubi Al1000/Al1000.res ideal.map") -os.system("python compare.py") +os.system(sys.executable + " compare.py") diff --git a/test/simul_1000_grains/todo.txt b/test/simul_1000_grains/todo.txt index d09d6c62..cf0c718d 100644 --- a/test/simul_1000_grains/todo.txt +++ b/test/simul_1000_grains/todo.txt @@ -5,6 +5,8 @@ - ImageD11/indeder.py : indexer.refine TO DO: + Improve indexing to be faster (single script with simple run through) + compare results (currently makemap is the only one that works on simul1000) ... probably due to better peak assignments ? Try taking these ... diff --git a/test/sparse_overlap_test.py b/test/sparse_overlap_test.py new file mode 100644 index 00000000..862b06e1 --- /dev/null +++ b/test/sparse_overlap_test.py @@ -0,0 +1,88 @@ + +from __future__ import print_function + +import ImageD11.sparseframe +import scipy.sparse +import numpy as np +from timeit import default_timer + +def maketestim(N=64, M=2048, SIG=3): + # tile co-ordinates + i,j = np.mgrid[0:N,0:N]-N/2. + def pk(x,y,s): + # 2D peak shape (with tails) + dx = i-x + dy = j-y + gg= np.exp( -(dx*dx+dy*dy)/s/s ) + ll=s*s/(s*s+dx*dx+dy*dy) + return ll+gg + # Image for testing + im = np.zeros( (M,M), np.float32 ) + # Make a grid of points (tiles) + for ii in range(0,M,N): + for jj in range(0,M,N): + # offset of centroids + oi = 3*SIG*ii/M + oj = SIG*ii/M + # Ratio of heights + sc = jj*1.0/M + # put in two peaks, offset increases one direction, + # scale in the other + im[ii:ii+N,jj:jj+N] = pk( -oi, -oj, SIG ) + pk( oi, oj, SIG)*sc + return im + +class ticker: + def __init__(self): + self.t = default_timer() + def __call__(self, msg=''): + now = default_timer() + print( msg, '%.3f ms'%((now-self.t)*1e3), end=' ') + self.t = default_timer() + +def runthrough( im1, t1, im2, t2): + t = ticker() + # s1 = ImageD11.sparseframe.from_data_mask( im1 > t1, im1.copy(), {} ); t('s1') + s1 = ImageD11.sparseframe.from_data_cut( im1, t1 ); t('s1c') + #assert s1 == s1c + #s2 = ImageD11.sparseframe.from_data_mask( im2 > t2, im2.copy(), {} ); t('s2') + s2 = ImageD11.sparseframe.from_data_cut( im2, t2 ); t('s2c') + n1 = ImageD11.sparseframe.sparse_localmax( s1 ); t('n1') + n2 = ImageD11.sparseframe.sparse_localmax( s2 ); t('n2') + coo = ImageD11.sparseframe.overlaps(s1, 'localmax', s2, 'localmax'); t('coo') + olin = ImageD11.sparseframe.overlaps_linear(max(s1.nnz, s2.nnz)+1); + novlin, olinlap = olin( s1.row, s1.col, + s1.pixels['localmax'], + s1.meta['localmax']['nlabel'], + s2.row, s2.col, + s2.pixels['localmax'], + s2.meta['localmax']['nlabel'] ); t('olinlap') + assert olinlap.shape[0] == coo.nnz + assert (coo.row == olinlap[:,0]-1).all() + assert (coo.col == olinlap[:,1]-1).all() + assert (coo.data == olinlap[:,2]).all() + t.t = default_timer() + omat = ImageD11.sparseframe.overlaps_matrix(max(n1,n2)+1); + novmat, olinlap = omat( s1.row, s1.col, + s1.pixels['localmax'], + s1.meta['localmax']['nlabel'], + s2.row, s2.col, + s2.pixels['localmax'], + s2.meta['localmax']['nlabel'] ); t('omatlap') + assert olinlap.shape[0] == coo.nnz + assert (coo.row == olinlap[:,0]-1).all() + assert (coo.col == olinlap[:,1]-1).all() + assert (coo.data == olinlap[:,2]).all() + print('\n nnz1=%d nnz2=%d pk1=%d pk2=%d nolap=%d'%(s1.nnz, s2.nnz, n1, n2, coo.nnz)) + +for N in (256, 1024, 2048): + M = 64 + while M < N: + im1 = maketestim( M, N, 3) + im2 = maketestim( M, N, 2.5) + print(N, M) + runthrough( im1, 0.1, im2, 0.25) + runthrough( im1, 0.1, im2, 0.25) + M *= 2 + + + diff --git a/test/test_cImageD11.py b/test/test_cImageD11.py index a950b4fe..6d96d8e3 100644 --- a/test/test_cImageD11.py +++ b/test/test_cImageD11.py @@ -2,7 +2,7 @@ -from ImageD11 import cImageD11, transform +from ImageD11 import cImageD11, transform, parameters import unittest import numpy as np @@ -33,13 +33,13 @@ def runTest(self): self.wvln, wedge=self.wedge, chi=self.chi) t = np.array( (self.t_x, self.t_y, self.t_z)) - gve2 = np.empty( (len(tth), 3), np.float) + gve2 = np.empty( (len(tth), 3), float) cImageD11.compute_gv(self.XLYLZLT, self.omega,self.omegasign, self.wvln, self.wedge,self.chi, t,gve2) - + # print t, self.wedge,self.chi # for i in range( len(tth)): # print i, gve1[:,i],gve1[:,i]-gve2[i],gve1[:,i]-gve3[:,i] @@ -47,7 +47,7 @@ def runTest(self): self.assertTrue( np.allclose(gve1, gve2.T )) # print "Test Fortran" if TEST_FORTRAN: - gve3 = np.empty( (3, len(tth)), np.float, order='F') + gve3 = np.empty( (3, len(tth)), float, order='F') fImageD11.compute_gv(self.XLYLZL, self.omega,self.omegasign, self.wvln, @@ -55,7 +55,7 @@ def runTest(self): t, gve3) - + def test_5_10(self): self.wedge = 5. self.chi = 10. @@ -65,7 +65,7 @@ def test_5_10(self): self.t_y=30. self.t_z=40. self.runTest() - + def test_5_0(self): self.wedge = 5. self.chi = 0. @@ -86,7 +86,7 @@ def test_0_5(self): self.t_z=40. self.runTest() - + def test_5_10m(self): self.wedge = 5. self.chi = 10. @@ -96,7 +96,7 @@ def test_5_10m(self): self.t_y=300. self.t_z=40. self.runTest() - + def test_5_0_tt(self): self.wedge = 5. self.chi = 0. @@ -106,7 +106,7 @@ def test_5_0_tt(self): self.t_y=300. self.t_z=-400. self.runTest() - + def test_0_0_t0(self): self.wedge = 0. self.chi = 0. @@ -127,7 +127,71 @@ def test_0_0_t10(self): self.t_z=400. self.runTest() - + + +class test_compute_xlylzl(unittest.TestCase): + def setUp(self): + npk = 10240 + self.sc = np.random.random( npk )*2048 + self.fc = np.random.random( npk )*2048 + self.pars = parameters.parameters() + self.pars.set('z_center', 980. ) + self.pars.set('y_center', 1010. ) + self.pars.set('z_size', 48. ) + self.pars.set('y_size', 49. ) + self.pars.set('distance', 100100. ) + + def test1(self): + pks = self.sc, self.fc + p = self.pars + testtilts = [-0.5, 0., 0.24] + tilts = [(x,y,z) for x in testtilts for y in testtilts for z in testtilts] + pars = np.array( ( p.get("z_center"), + p.get("y_center"), + p.get("z_size"), + p.get("y_size"))) + dist = np.array( (p.get("distance"),0.,0.)) + ok = 0 + for o11,o12,o21,o22 in [ [ 1,0,0,1], + [-1,0,0,1], + [-1,0,0,-1], + [ 1,0,0,-1], + [ 0,1,1,0], + [ 0,-1,1,0], + [ 0,-1,-1,0], + [ 0,1,-1,0]]: + for tx,ty,tz in tilts: + p.parameters['tilt_x']=tx + p.parameters['tilt_y']=ty + p.parameters['tilt_z']=tz + p.parameters['o11']=o11 + p.parameters['o12']=o12 + p.parameters['o21']=o21 + p.parameters['o22']=o22 + + xlylzl = transform.compute_xyz_lab( pks, + **p.parameters ) + + dmat = transform.detector_rotation_matrix( + p.get('tilt_x'), p.get('tilt_y'), p.get('tilt_z')) + fmat = np.array( [[ 1, 0, 0], + [ 0, p.get('o22'), p.get('o21')], + [ 0, p.get('o12'), p.get('o11')]]) + r = np.dot(dmat, fmat) + + outxyz = np.zeros( (len(self.sc), 3), float ) + cImageD11.compute_xlylzl( self.sc, + self.fc, + pars, r.ravel(), dist, + outxyz) + error = np.abs(outxyz - xlylzl.T) + self.assertTrue( error.max() < 1e-6 ) + ok += 1 + print("tested xlylzl %d times"%(ok)) + + + + if __name__ == "__main__": unittest.main() diff --git a/test/test_clean_mask.py b/test/test_clean_mask.py new file mode 100644 index 00000000..929d8dc2 --- /dev/null +++ b/test/test_clean_mask.py @@ -0,0 +1,62 @@ + +import unittest +import numpy as np +from ImageD11 import cImageD11 + +# print(cImageD11.cImageD11_avx.__file__) + +def py_coo( msk ): + i, j = np.mgrid[0:msk.shape[0], 0:msk.shape[1]] + return i[msk].astype(np.uint16), j[msk].astype(np.uint16) + + +class test_clean_mask( unittest.TestCase ): + def setUp( self ): + shape = (9,11) + data = np.zeros( shape, bool ) + data[3:5, 3:5] = 1 # keep + self.target = data.copy() + data[1,1] = 1 # isolated, to be removed + data[3,0] = 1 + data[-1,-1] = 1 + data[-1,5] = 1 + data[5,-1] = 1 + data[6,0] = 1 # adjacent to prev in mem but on edge + self.src = data.copy() + def test1(self): + testmask = np.zeros( self.src.shape, np.int8 ) + npx = cImageD11.clean_mask( self.src.astype(np.int8), testmask ) + if 0: + print('npx=',npx) + print( self.src) + print(self.src.view(dtype=np.int8)) + print(testmask) + print(testmask.sum()) + self.assertTrue( self.target.sum() == npx ) + self.assertTrue( (testmask == self.target).all() ) + def test_coo(self): + npx = self.target.sum() + i = np.empty( npx, np.uint16) + j = np.empty( npx, np.uint16) + tmp = np.empty( self.target.shape[0], np.int32 ) + ok = cImageD11.mask_to_coo( self.target.astype(np.int8), i, j, tmp ) + self.assertTrue( ok == 0 ) + pi, pj = py_coo( self.target ) + self.assertTrue( ( pi == i ).all() ) + self.assertTrue( ( pj == j ).all() ) + def test_coo2(self): + npx = self.src.sum() + i = np.empty( npx, np.uint16) + j = np.empty( npx, np.uint16) + tmp = np.empty( self.target.shape[0], np.int32 ) + ok = cImageD11.mask_to_coo( self.src.astype(np.int8), i, j, tmp ) + self.assertTrue( ok == 0 ) + pi, pj = py_coo( self.src ) + self.assertTrue( ( pi == i ).all() ) + self.assertTrue( ( pj == j ).all() ) + + +if __name__=="__main__": + unittest.main() + + diff --git a/test/test_closest_vec.py b/test/test_closest_vec.py new file mode 100644 index 00000000..4e0b5995 --- /dev/null +++ b/test/test_closest_vec.py @@ -0,0 +1,35 @@ + +from ImageD11 import cImageD11 +import numpy as np, time +import unittest + + +def closest_pyvec( X ): + nv = X.shape[0] + nd = X.shape[1] + res = np.zeros( nv, int ) + for i in range(nv): + dif = X - X[i] + err = (dif*dif).sum(axis=1) + err[i] = 1e9 + res[i] = np.argmin( err ) + return res + +class test_closest_vec( unittest.TestCase ): + def setUp( self ): + np.random.seed( 42 ) + self.data = np.random.random( (2048,12) ) + + def test_same_as_python( self ): + t1 = time.time() + r1 = closest_pyvec( self.data ) + t2 = time.time() + r2 = np.zeros( self.data.shape[0], 'i' ) + cImageD11.closest_vec( self.data, r2 ) + t3 = time.time() + print( "python",t2-t1,"c",t3-t2) + self.assertTrue( (r1 == r2).all() ) + +if __name__=="__main__": + unittest.main() + diff --git a/test/testcolumnfile.py b/test/test_columnfile.py similarity index 79% rename from test/testcolumnfile.py rename to test/test_columnfile.py index 200366e6..d129de82 100644 --- a/test/testcolumnfile.py +++ b/test/test_columnfile.py @@ -39,6 +39,30 @@ def test1( self ): c = columnfile.columnfile("testgeom.flt") c.updateGeometry( ) + def testfilter(self): + c = columnfile.columnfile("testgeom.flt") + d = c.copy() + d.filter( abs(d.sc-22) > .1 ) + self.assertTrue( d.nrows == 4 ) + d.filter( abs(d.sc-22) > .1 ) + self.assertTrue( d.nrows == 4 ) + self.assertTrue( c.nrows == 5 ) + d = c.copy() + self.assertTrue( d.nrows == 5 ) + + def testfilter2(self): + c = columnfile.columnfile("testgeom.flt") + d = c.copy() + a = d.bigarray + d.filter( abs(d.sc-22) > .1 ) + self.assertTrue( d.nrows == 4 ) + d.filter( abs(d.sc-22) > .1 ) + self.assertTrue( d.nrows == 4 ) + self.assertTrue( c.nrows == 5 ) + d = c.copy() + self.assertTrue( d.nrows == 5 ) + + class test1( unittest.TestCase ): def setUp( self ): @@ -119,8 +143,14 @@ def testreorder(self): assert( list(c.a) == [ 7, 5, 4, 6, 1] ) def testmissingfile(self): - with self.assertRaises( (IOError, FileNotFoundError) ): - columnfile.columnfile( "a_filename_that_does_not_exist.flt") + e = False + try: + columnfile.columnfile( "a_filename_that_does_not_exist.flt") + except: + # got an exception + e = True + assert( e ) + if __name__ == '__main__': unittest.main() diff --git a/test/test_compress_duplicates.py b/test/test_compress_duplicates.py new file mode 100644 index 00000000..5efecf3c --- /dev/null +++ b/test/test_compress_duplicates.py @@ -0,0 +1,72 @@ + +import unittest +import numpy as np +import scipy.sparse +from ImageD11 import cImageD11 + +def _sum_duplicates(row, col, data): + """ for testing - to be moved or removed """ + # From scipy.sparse.coo_matrix + # Assumes (data, row, col) not in canonical format. + if len(data) == 0: + return row, col, data + order = np.lexsort((col,row)) + row = row[order] + col = col[order] + data = data[order] + unique_mask = ((row[1:] != row[:-1]) | + (col[1:] != col[:-1])) + unique_mask = np.append(True, unique_mask) + row = row[unique_mask] + col = col[unique_mask] + unique_inds, = np.nonzero(unique_mask) + data = np.add.reduceat(data, unique_inds, dtype=data.dtype) + return row, col, data + + +class testpls(unittest.TestCase): + N=1000 + def test1(self): + np.random.seed(42) + N = self.N + a = np.random.random(N*2) + i, j = (a*10).astype('i').reshape(2,N) + oj = np.empty( N, 'i') + oi = np.empty( N, 'i') + tmp = np.empty( 20, 'i' ) + d = np.ones( N, int ) + r,c,d = _sum_duplicates( i, j, d ) + n = cImageD11.compress_duplicates( i, j, oi, oj, tmp ) + self.assertTrue( n == len(r) ) + self.assertTrue((r==i[:n]).all()) + self.assertTrue((c==j[:n]).all()) + self.assertTrue((d==oi[:n]).all()) + def test2(self): + np.random.seed(42) + N = self.N + a = np.random.random(N*2) + i, j = (a*10).astype('i').reshape(2,N) + oj = np.empty( N, 'i') + oi = np.empty( N, 'i') + tmp = np.empty( 20, 'i' ) + d = np.ones( N, int ) + coo = scipy.sparse.coo_matrix((d,(i.copy(),j.copy()))) + csr = coo.tocsr() + coo = csr.tocoo() + #print("",i,"\n",j) + n = cImageD11.compress_duplicates( i, j, oi, oj, tmp ) + self.assertEqual(cImageD11.sparse_is_sorted( i[:n].astype(np.uint16), + j[:n].astype(np.uint16) + ), 0) + r = coo.row + c = coo.col + d = coo.data + self.assertTrue( n == len(r) ) + self.assertTrue((r==i[:n]).all()) + self.assertTrue((c==j[:n]).all()) + self.assertTrue((d==oi[:n]).all()) + + +if __name__=="__main__": + unittest.main() + diff --git a/test/test_fft_index.py b/test/test_fft_index.py deleted file mode 100644 index 139597f9..00000000 --- a/test/test_fft_index.py +++ /dev/null @@ -1,2 +0,0 @@ - - diff --git a/test/test_finite_strain.py b/test/test_finite_strain.py new file mode 100644 index 00000000..1a1c3dcc --- /dev/null +++ b/test/test_finite_strain.py @@ -0,0 +1,138 @@ + + +import unittest +from ImageD11 import finite_strain, indexing +import numpy as np + +class setupRandom(object): + def setUp(self): + np.random.seed(4567) + self.ubi = np.random.random((3,3)) + np.eye(3) + self.ub = np.linalg.inv( self.ubi ) + self.ubi0 = np.random.random((3,3)) + np.eye(3) + self.ub0 = np.linalg.inv( self.ubi0 ) + +class test_m(setupRandom, unittest.TestCase): + def test_lab(self): + forwards = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + backward = finite_strain.DeformationGradientTensor( self.ubi0, self.ub ) + for i in range(5): + m = i * 0.5 + ef = forwards.finite_strain_lab( m ) + # this is a bit misleading - negative m puts you in the other frame + eb = backward.finite_strain_ref( -m ) + self.assertTrue( np.allclose( ef, -eb ) ) + def test_ref(self): + forwards = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + backward = finite_strain.DeformationGradientTensor( self.ubi0, self.ub ) + for i in range(5): + m = i * 0.5 + ef = forwards.finite_strain_ref( m ) + eb = backward.finite_strain_lab( -m ) + self.assertTrue( np.allclose( ef, -eb ) ) + + +class test_polar(setupRandom, unittest.TestCase): + def test_polar(self): + dgt = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + v,r,s = dgt.VRS + self.assertTrue( np.allclose( np.dot( v, r), dgt.F ) ) + self.assertTrue( np.allclose( np.dot( r, s), dgt.F ) ) + +class test_svd(setupRandom, unittest.TestCase): + def test_svd(self): + dgt = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + u,s,vt = dgt.SVD + fcalc = np.dot( np.dot( u, np.diag(s)) , vt ) + self.assertTrue( np.allclose( fcalc, dgt.F ) ) + +class test_xfab_strain( unittest.TestCase ): + def setUp(self): + a0 = 4.04 + b0 = 5.04 + c0 = 6.04 + from scipy.spatial.transform import Rotation + r = Rotation.from_euler("XYZ",(123,456,789),degrees=True) + if hasattr(r, "as_matrix"): + self.u = r.as_matrix() + else: + self.u = r.as_dcm() + self.ubi = np.dot(((a0*1.1, 0,0), + (0,b0,0), + (0,0,c0)),self.u.T) + self.ubi0 = np.diag((a0,b0,c0)) + self.ub0 = np.diag((1/a0,1/b0,1/c0)) + + def test_strain_ref(self): + from xfab.tools import ubi_to_u_and_eps + dzero_cell = indexing.ubitocellpars( self.ubi0 ) + u, eps_xfab_ref_e6 = ubi_to_u_and_eps( self.ubi, dzero_cell ) + dgt = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + eps_new = dgt.finite_strain_ref( m=0.5 ) + self.assertTrue( np.allclose( dgt.U, u ) ) + self.assertTrue( np.allclose( dgt.U, self.u ) ) + self.assertTrue( np.allclose( finite_strain.symm_to_e6(eps_new), eps_xfab_ref_e6 ) ) + self.assertTrue( np.allclose( finite_strain.e6_to_symm(eps_xfab_ref_e6), eps_new ) ) + + def test_strain_lab(self): + from xfab.tools import ubi_to_u_and_eps + dzero_cell = indexing.ubitocellpars( self.ubi0 ) + u, eps_xfab_ref_e6 = ubi_to_u_and_eps( self.ubi, dzero_cell ) + emat = finite_strain.e6_to_symm( eps_xfab_ref_e6 ) + Exfab = np.dot(np.dot( u, emat), u.T ) + dgt = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + eps_new = dgt.finite_strain_lab( m=0.5 ) + self.assertTrue( np.allclose( dgt.U, u ) ) + self.assertTrue( np.allclose( dgt.U, self.u ) ) + self.assertTrue( np.allclose( eps_new, Exfab ) ) + +class test_xfab_largestrain( test_xfab_strain ): + def setUp(self): + a0 = 4.04 + b0 = 5.04 + c0 = 6.04 + from scipy.spatial.transform import Rotation + r = Rotation.from_euler("XYZ",(1230,4560,7890),degrees=True) + if hasattr(r, "as_matrix"): + self.u = r.as_matrix() + else: + self.u = r.as_dcm() + self.ubi = np.dot(((a0*12.1, 0,0), + (0,b0,0), + (0,0,c0)),self.u.T) + self.ubi0 = np.diag((a0,b0,c0)) + self.ub0 = np.diag((1/a0,1/b0,1/c0)) + + +class test_y_strain(unittest.TestCase): + def setUp(self): + # longer along the x-axis in the lab + # this is the a axis in the reference + self.ubi = np.array([[0,1,0],[-1.1,0,0],[0,0,1]],float) + self.ubi0 = np.eye(3) + self.ub0 = np.linalg.inv(self.ubi0) + self.dgt = finite_strain.DeformationGradientTensor( self.ubi, self.ub0 ) + + def test_strainlab(self): + elab = self.dgt.finite_strain_lab(m=0.5) + self.assertAlmostEqual( elab[0,0], 0.1 ) + for i in range(3): + for j in range(3): + self.assertAlmostEqual( elab[i,j], elab[j,i]) + if i == j == 0: + continue + self.assertAlmostEqual( elab[i,j], 0. ) + + def test_strainref(self): + eref = self.dgt.finite_strain_ref(m=0.5) + self.assertAlmostEqual( eref[1,1], 0.1 ) + for i in range(3): + for j in range(3): + self.assertAlmostEqual( eref[i,j], eref[j,i]) + if i == j == 1: + continue + self.assertAlmostEqual( eref[i,j], 0. ) + + +if __name__=="__main__": + unittest.main() diff --git a/test/test_forking.py b/test/test_forking.py new file mode 100644 index 00000000..010988ae --- /dev/null +++ b/test/test_forking.py @@ -0,0 +1,127 @@ +# There is a problem of forking / multiprocessing. +# There are no imports here! + +# Each function is intended to run in a subprocess + + +def run_test_fun(function, env=None): + """Runs a function in a subprocess, returns the output""" + import sys + from subprocess import PIPE, Popen + + args = [sys.executable, __file__, function] + proc = Popen(args, stdin=PIPE, stdout=PIPE, stderr=PIPE, env=env) + stdout, stderr = proc.communicate() + return stdout.decode(), stderr.decode() + + +def omp_call(N=1024): + """returns the number of threads in use for an example call using openmp""" + import ImageD11.cImageD11 + import numpy as np + + dat = np.full((N, N), 101, dtype=np.uint16) + drk = np.full((N, N), 100, dtype=np.float32) + cor = np.empty((N, N), dtype=np.float32) + ImageD11.cImageD11.uint16_to_float_darksub(cor.ravel(), drk.ravel(), dat.ravel()) + threads_avail = ImageD11.cImageD11.cores_available() + threads_used = ImageD11.cImageD11.cimaged11_omp_get_max_threads() + return threads_avail, threads_used + + +def should_warn_on_fork_in_parent(): + """This is the problem case, we want a warning""" + import multiprocessing + + try: + if "fork" in multiprocessing.get_start_methods(): + multiprocessing.set_start_method("fork") + print("Set to fork") + except AttributeError: + pass + threads_avail, threads_used = omp_call() + assert threads_avail == threads_used, "not threaded?" + + +def test_should_warn_on_fork_in_parent(): + """this is called by py.test to run the previous function""" + stdout, stderr = run_test_fun("should_warn_on_fork_in_parent") + import sys + + if stdout.find("Set to fork") != -1: # we set to fork + if sys.version_info[0] > 2: + assert stderr.find("please use forkserver or spawn") > 0 + else: + assert stderr.find("cImageD11: for multiprocessing use spawn") != -1 + + +def should_warn_on_fork_in_child(): + """This is a problem case, we want a warning""" + import multiprocessing + + try: + if "fork" in multiprocessing.get_start_methods(): + multiprocessing.set_start_method("fork") + print("Set to fork") + except AttributeError: + pass + p = multiprocessing.Pool(2) + for threads_avail, threads_used in p.map(omp_call, (1024, 1024)): + assert threads_used == 1, "failed to set omp threads == 1 for child" + + +def test_should_warn_on_fork_in_child(): + stdout, stderr = run_test_fun("should_warn_on_fork_in_child") + import sys + + if stdout.find("Set to fork") != -1: # we set to fork + if sys.version_info[0] > 2: + assert stderr.find("please use forkserver or spawn ") > 0 + else: + assert stderr.find("cImageD11: for multiprocessing use spawn") != -1 + + +def threads_in_child_no_warn(): + """This is a working case, we do not want a warning""" + import multiprocessing + + try: + if "forkserver" in multiprocessing.get_start_methods(): + multiprocessing.set_start_method("forkserver") + print("Set to forkserver") + else: + multiprocessing.set_start_method("spawn") + print("Set to spawn") + except AttributeError: + pass + p = multiprocessing.Pool(2) + for threads_avail, threads_used in p.map(omp_call, (1024, 1024)): + print("threads available, threads used", threads_avail, threads_used) + if threads_used == 1 and threads_avail >= 2: + raise ("Bad thread setting, should have 2") + if threads_used == 2: # from OMP_NUM_THREADS + pass + + +def test_threads_in_child_no_warn(): + import sys, os + + env = os.environ.copy() + env["OMP_NUM_THREADS"] = "2" + stdout, stderr = run_test_fun("threads_in_child_no_warn", env=env) + if stdout.find("Set to ") != -1: # we set to forkserver or spawn + if sys.version_info[0] > 2: + assert len(stderr) == 0 + else: + # python2.... + assert stderr.find("cImageD11: for multiprocessing use spawn") != -1 + + +if __name__ == "__main__": + import sys + + try: + result = globals()[sys.argv[1]]() + except Exception as e: + print(e) + raise diff --git a/test/test_index_unknown/.gitignore b/test/test_index_unknown/.gitignore new file mode 100644 index 00000000..333aaed6 --- /dev/null +++ b/test/test_index_unknown/.gitignore @@ -0,0 +1,4 @@ +?.ubi.fftnew +?.ubi.gve +?.ubi.new +eu.patterson_pks diff --git a/test/test_index_unknown/__init__.py b/test/test_index_unknown/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/test_index_unknown/lattice_trans.ipynb b/test/test_index_unknown/lattice_trans.ipynb new file mode 100644 index 00000000..2751d89a --- /dev/null +++ b/test/test_index_unknown/lattice_trans.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The idea here is to try to index some unknown unit cell by using lattice translational symmetry. \n", + "\n", + "Pick one of the g-vectors at random and then create a new dataset which is shifted by this g-vector. (g-vector is a scattering vector from the sample, one of the points in the reciprocal lattice of the crystal).\n", + "\n", + "Then we look for coincident points between the original and shifted data. \n", + "\n", + "Points that correspond to overlaps after lattice translation might come from the same lattice as the chosen point, or you might be unlucky and get an overlap.\n", + "\n", + "This is similar to the idea in grainspotter which used rotational symmetry. The interest here is that using a g-vector from the dataset means you are looking for other spots which come from the same lattice as the spot you picked.\n", + "\n", + "The big \"problem\" is the $N^2$ search of overlapping the original with the shifted data. This is reduced by using a KDTree structure from scipy. You could also mess about with grids (e.g. put the g-vectors onto a fine 3D grid first, now intersect grid points to reduce the problem sizes). In principle you need to repeat for all possible shift vectors in the dataset, but this is probably more like the number of lattices as you can remove peaks when you find lattices.\n", + "\n", + "We can think about using +/- the vector as there is always a Friedel pair in diffraction pattern. Probably better to choose shift vectors where we observed both +/- g from a real experiment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np, pylab as pl, scipy.spatial\n", + "from ImageD11 import indexing\n", + "\n", + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def readgv(f):\n", + " i = indexing.indexer()\n", + " i.readgvfile(f)\n", + " return i.gv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# demo dataset\n", + "g3 = readgv( \"3.ubi.gve\" )\n", + "# Note: the omega values are missing in the simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pl.figure()\n", + "pl.plot(g3[:,0],g3[:,1],\".\")\n", + "\n", + "# select a 'random' peak\n", + "np.random.seed(10037) # reproducible\n", + "j = np.round( g3.shape[0] * np.random.random() ).astype(int)\n", + "g3s = g3 - g3[j]\n", + "pl.plot(g3s[:,0],g3s[:,1],\".\",alpha=0.5)\n", + "g3s = g3 + g3[j]\n", + "pl.plot(g3s[:,0],g3s[:,1],\".\",alpha=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "kd0 = scipy.spatial.cKDTree( g3 )\n", + "kd1 = scipy.spatial.cKDTree( g3 + g3[j] )\n", + "kd2 = scipy.spatial.cKDTree( g3 - g3[j] )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# Which number to pick for max_distance is a problem\n", + "# ideally maje some error bar for scattering vector\n", + "# ... all the time is in here ?!\n", + "sd1 = kd0.sparse_distance_matrix( kd1, max_distance=0.01 ).tocoo()\n", + "sd2 = kd0.sparse_distance_matrix( kd2, max_distance=0.01 ).tocoo()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# distances between pairs of peaks after shifting\n", + "peaks = np.concatenate( (sd1.row, sd1.col, sd2.row, sd2.col ))\n", + "distances = np.concatenate(( sd1.data, sd2.data) )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pl.figure()\n", + "_=pl.hist(distances,bins=128)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pl.figure()\n", + "pl.plot(g3[:,0],g3[:,1],\".\", alpha=0.2)\n", + "pl.plot(g3[peaks,0],g3[peaks,1],\".\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/test/test_index_unknown/test_index_unknown.py b/test/test_index_unknown/test_index_unknown.py index f0108c5f..15c4908a 100644 --- a/test/test_index_unknown/test_index_unknown.py +++ b/test/test_index_unknown/test_index_unknown.py @@ -13,7 +13,7 @@ [-0.52591148, 0.82984375, -6.15279178], [ 4.54824731, 0.40771549, 1.56440062]])] >>> def norm(v): return v/np.sqrt(np.dot(v,v)) -... +... >>> import numpy as np >>> d1 = norm(np.array([-0.4,0.5,0.1])) >>> d1 @@ -27,7 +27,7 @@ """ -UBIS = [ +UBIS = [ ("1.ubi", "-k 1 -o 1.ubi.new -v 0.01 -m 20 -f 0.75 -t 0.1"), ("1.ubi", "-k 1 -o 1.ubi.fftnew -v 1 -m 20 -f 0.75 -t 0.1 --fft"), ("2.ubi", "-k 2 -o 2.ubi.new -v 0.01 -m 20 -f 0.45 -t 0.1"), @@ -38,8 +38,11 @@ ] - -SCRIPT = os.path.join(sys.executable+" ..","..","scripts","index_unknown.py") +d = os.path.dirname(__file__) +if len(d.strip()) == 0: + d = '.' +SCRIPT = os.path.join(sys.executable+' "'+d, + "..","..","scripts",'index_unknown.py"') def generate_hkls( n ): """ Makes solid hkl cube """ @@ -52,7 +55,7 @@ def generate_hkls( n ): for k in r: # print [ (l,l+lim) for l in [i,j,k]] hkls[:,i+lim,j+lim,k+lim] = i,j,k - + hkls = hkls.reshape( (3,npk*npk*npk)) return hkls @@ -77,26 +80,28 @@ def write_gve( gvecs, name): f.close() + class testGve(unittest.TestCase): def setUp(self): + d = os.path.dirname(__file__) + self.fnames = [] for u, cmd in UBIS: - write_gve( - generate_gve(u), - u+".gve" - ) - + fname=os.path.join(d, u+".gve") + ufile=os.path.join(d, u ) + write_gve( generate_gve(ufile), fname ) + self.fnames.append(fname) + def test1(self): - for u, cmd in UBIS: - assert os.path.exists(u+".gve") - e = SCRIPT +" -g %s "%(u+".gve")+cmd + for fname, (u,cmd) in zip(self.fnames, UBIS): + assert os.path.exists(fname) + e = SCRIPT +' -g "%s" '%(fname)+cmd print ("\n",e) - os.system(e) - + assert os.system(e) == 0 def tearDown(self): """ explicitely leave the files here for debugging """ pass - + if __name__ == "__main__": diff --git a/test/test_indexing.py b/test/test_indexing.py new file mode 100644 index 00000000..90a805de --- /dev/null +++ b/test/test_indexing.py @@ -0,0 +1,83 @@ + +from __future__ import print_function + +from ImageD11.indexing import ubi_fit_2pks +from ImageD11.unitcell import unitcell +import numpy as np +import time +import unittest, cProfile, pstats + +def make_random_orientations( N ): + """ + Generate random quaternions and convert to U matrices + """ + q = np.random.standard_normal( (4, N) ) + s = 1/(q*q).sum( axis=0 ) + U = np.zeros( (N, 3, 3), float ) + r,i,j,k = 0,1,2,3 + U[:,0,0] = 1 - 2*s*(q[j]*q[j]+q[k]*q[k]) + U[:,0,1] = 2*s*(q[i]*q[j]-q[k]*q[r]) + U[:,0,2] = 2*s*(q[i]*q[k]+q[j]*q[r]) + U[:,1,0] = 2*s*(q[i]*q[j]+q[k]*q[r]) + U[:,1,1] = 1 - 2*s*(q[i]*q[i]+q[k]*q[k]) + U[:,1,2] = 2*s*(q[j]*q[k]-q[i]*q[r]) + U[:,2,0] = 2*s*(q[i]*q[k]-q[j]*q[r]) + U[:,2,1] = 2*s*(q[j]*q[k]+q[i]*q[r]) + U[:,2,2] = 1 - 2*s*(q[i]*q[i]+q[j]*q[j]) + return U + +class test_2pks( unittest.TestCase ): + def setUp(self): + self.a = 6 + self.c = 5 + self.U = make_random_orientations( 1 )[0] + + def test_tetragonal(self): + a,c = self.a, self.c + cell = unitcell( [ a*1.05, a*1.05, c*0.95, 90., 90.,90.], "P" ) + ub = np.dot( self.U, cell.B ) + h1 = (1,0,0) + h2 = (0,0,1) + g1 = np.dot( ub, h1 ) # a calc, wrong + g2 = np.dot( ub, h2 ) # c + ideal = unitcell( [ a, a, c, 90., 90.,90.], "P" ) + ideal.makerings(0.3) + ideal.orient( 0, g1, 1, g2 ) + gve = np.vstack( (g1 , g2) ).T + for ubi in ideal.UBIlist: + ufit = ubi_fit_2pks( ubi, g1, g2 ) + hold = np.dot( ubi, gve ) + drlvold = abs(hold - np.round(hold)).sum() + hnew = np.dot( ufit, gve ) + drlvnew = abs(hnew - np.round(hnew)).sum() + self.assertTrue( drlvold > 0 ) + self.assertAlmostEqual( drlvnew, 0 ) + + def test_hexagonal(self): + a,c = self.a, self.c + cell = unitcell( [ a*1.05, a*1.05, c*0.95, 90., 90.,120.], "P" ) + ub = np.dot( self.U, cell.B ) + h1 = (1,0,0) + h2 = (0,0,1) + g1 = np.dot( ub, h1 ) # a calc, wrong + g2 = np.dot( ub, h2 ) # c + ideal = unitcell( [ a, a, c, 90., 90.,120.], "P" ) + ideal.makerings(0.3) + ideal.orient( 0, g1, 1, g2 ) + gve = np.vstack( (g1 , g2) ).T + for ubi in ideal.UBIlist: + ufit = ubi_fit_2pks( ubi, g1, g2 ) + hold = np.dot( ubi, gve ) + drlvold = abs(hold - np.round(hold)).sum() + hnew = np.dot( ufit, gve ) + drlvnew = abs(hnew - np.round(hnew)).sum() + self.assertTrue( drlvold > 0 ) + self.assertAlmostEqual( drlvnew, 0 ) + + + + + +if __name__=="__main__": + unittest.main() + diff --git a/test/test_localmaxlabel.py b/test/test_localmaxlabel.py new file mode 100644 index 00000000..a2c1c62c --- /dev/null +++ b/test/test_localmaxlabel.py @@ -0,0 +1,335 @@ + +from __future__ import division, print_function +import numpy as np +import timeit +from ImageD11.cImageD11 import localmaxlabel, sparse_localmaxlabel +import unittest, sys + +timer = timeit.default_timer + +def sparselocalmax( im, labels, wk ): + row, col = np.mgrid[ 0: im.shape[0], 0:im.shape[1] ] + mask = im > 0 + row = row[mask].astype(np.uint16) + col = col[mask].astype(np.uint16) + signal = im[mask].astype(np.float32) + vmx = np.empty( len(signal), np.float32 ) + imx = np.empty( len(signal), np.int32 ) + slabel = np.empty( len(signal), np.int32 ) + labels.fill(0) +# print('row',row) +# print('col',col) +# print('signal',signal) + nlabel = sparse_localmaxlabel( signal, row, col, vmx, imx, slabel ) +# print('nlabel',nlabel) +# print('slabel',slabel) +# print('vmx ',vmx) +# print('imx ',imx) + labels[mask] = slabel + return nlabel + + +try: + from numba import jit, prange + GOTNUMBA = True + print("Got numba") +except: + print("No numba") + GOTNUMBA = False + def jit(pyfunc=None, **kwds): + def wrap(func): + return func + if pyfunc is not None: + return wrap(pyfunc) + else: + return wrap + def prange(*a): + return range(*a) + + +@jit(nopython=True) +def localmax( im, labels, wk ): + """ reference implementation + ... very slow + """ + out = wk + nf = im.shape[1] + for i in prange(1,im.shape[0]-1): + out[i,0] = nf*i + out[i,nf-1] = nf*(i+1)-1 + for j in range(1,nf-1): + mx = im[i-1,j-1] + out[i,j] = (i-1) * nf + j-1 + for k,l in ( (i-1, j ), + (i-1, j+1), + (i , j-1), + (i , j ), + (i , j+1), + (i+1, j-1), + (i+1, j ), + (i+1, j+1) ): + if im[k,l] > mx: + mx = im[k,l] + out[i,j] = k * nf + l + + # now out addresses a neighbour + # Final labels + npk = 0 + labels[0]=0 # borders + labels[-1]=0 + for i in prange(1,im.shape[0]-1): + labels[i,0] = 0 + labels[i,nf-1] = 0 + for j in range(1,nf-1): + if out[i,j] == i*nf + j: # maxpx + npk += 1 + labels[i,j]=npk + out[i,j]=0 + else: + labels[i,j] = -1 + # Now relabel each pixel to point to the max... + pon=False + for i in range(1,im.shape[0]-1): + for j in range(1,nf-1): + adr = out[i,j] + if adr == 0: + continue + while 1: + adrnew = out.flat[adr] + if adrnew == 0 or labels.flat[adr]==0: + break + adr = adrnew + l = labels.flat[adr] # take from peak + labels[i,j] = l + adr = out[i,j] + while 1: + labels.flat[adr] = l + adrnew = out.flat[adr] + if adrnew == 0: + break + out.flat[adr] = 0 + adr = adrnew + + +def make_test_image( N=256, w=3 ): + im = np.zeros( (N, N), np.float32 ) + # patch size to hold peaks + pN = w*5+1 + pk = np.zeros( (pN,pN), np.float32 ) + pi,pj = np.mgrid[ 0:pN, 0:pN ] + # two peaks, separated by i + ni = N//pN + s = 5. + for i in range(0, ni): + # at max separation we want 0 - pk - pk - pN + # so: + dj = (pN/3.0)*(i/(ni-1)) + di = dj / 5. + + ci1 = pN//2 + di + ci2 = pN//2 - di + cj1 = pN//2 + dj + cj2 = pN//2 - dj + p1 = np.exp( - (( pi - ci1 )**2 + (pj - cj1)**2)/s) + p2 = np.exp( - (( pi - ci2 )**2 + (pj - cj2)**2)/s) + for j in range(0, N//pN): + im[ i*pN:(i+1)*pN, j*pN:(j+1)*pN ] = p1 + p2*(j+1)/ni + return im + +def demo(): + import pylab as pl + im = make_test_image() + l1 = np.zeros(im.shape, np.int32) + wp = np.zeros(im.shape, np.int32) + localmax(im,l1,wp) + l2 = np.zeros(im.shape,np.int32) + wk = np.zeros(im.shape,np.int8) + npks = localmaxlabel(im, l2 , wk, cpu=0 ) + ax1 = pl.subplot(2,2,1) + pl.imshow(im) + pl.xlim( 0,im.shape[0]) + pl.ylim( 0,im.shape[1]) + ax1 = pl.subplot(2,2,2, sharex=ax1, sharey=ax1) + pl.imshow((l1*13.0)%37) + pl.xlim( 0,im.shape[0]) + pl.ylim( 0,im.shape[1]) + ax1 = pl.subplot(2,2,3, sharex=ax1, sharey=ax1) + pl.imshow((l2*13.0)%37) + pl.xlim( 0,im.shape[0]) + pl.ylim( 0,im.shape[1]) + pl.show() + +class test_localmaxlabel( unittest.TestCase ): + def setUp( self ): + self.im = make_test_image(128)[:107,:109].copy() + l1 = np.zeros(self.im.shape, np.int32) + wp = np.zeros(self.im.shape, np.int32) + # numba runs + localmax(self.im, l1, wp) + self.dopy = True + def test1( self): + im = self.im + if self.dopy: + l1 = np.zeros(im.shape, np.int32) + wp = np.zeros(im.shape, np.int32) + start = timer() + localmax(im, l1, wp) + end = timer() + pytime = (end-start)*1000. + l = [l1,] + wk = [0,] + times = [pytime,] + names = ["python",] + for cpu, name in [(0,"ansi")]:#,(1,"sse2"),(2,"avx2")]: + li = np.zeros(im.shape,np.int32) + wi = np.zeros(im.shape,np.int8) + start = timer() + # print("cpu",cpu) + npks = localmaxlabel(im.copy(), li , wi ) + end=timer() + ctime = (end-start)*1000. + l.append( li ) + times.append( ctime ) + names.append( name ) + lref = l[0] + print("Timing",end=" ") + for i in range(len(names)): + print( "%s %.3f ms"%( names[i], times[i]), end=" ") + print() + for li in l[1:]: + self.assertTrue((lref == l1).all()) + def test3( self): + # print("in test 2") + im = make_test_image(N=2048) + l0 = np.zeros(im.shape,np.int32) + l1 = np.zeros(im.shape,np.int32) + wp = np.zeros(im.shape,np.int32) + wk = np.zeros(im.shape,np.int8) + N = 4 + if GOTNUMBA: + npks = localmax(im, l0, wp) + start = timer() + for i in range(N): + npks = localmax(im, l0, wp) + end=timer() + ptime = (end-start)*1000./N + else: + ptime = np.nan + npks = localmaxlabel(im, l1 , wk ) + start = timer() + for i in range(N): + npks = localmaxlabel(im, l1 , wk ) + end=timer() + ctime = (end-start)*1000./N + print("Timing 2048 numba %.3f ms c %.3f ms"%( + ptime, ctime)) + + def testbug(self): + ''' case caught with image from Mariana Mar Lucas, + caused a segfault''' + im = np.array([ + [ 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 2, 0, 0, 0, 0, 0, 0], + [ 0, 1, 1, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 0]], np.float32) + la = np.empty( im.shape, 'i' ) + wk = np.empty( im.shape, 'b' ) + localmaxlabel( im, la, wk ) + self.assertEqual(la[1,1],1) + + def testsmall(self): + ''' sse / avx issue for tiny? ''' +# print("testsmall, fixme, segfault/infinite loop so no CI") + im = np.array([ + [ 0, 0, 0, 0, 0], + [ 0, 2, 0, 0, 0], + [ 0, 1, 1, 0, 0], + [ 0, 0, 0, 0, 0]], np.float32) + la = np.empty( im.shape, 'i' ) + wk = np.empty( im.shape, 'b' ) + localmaxlabel( im, la, wk ) + self.assertEqual(la[1,1],1) + + + def testX(self): + ''' sse / avx issue for tiny? ''' + im = np.array([ + [ 0, 0, 0, 0, 0], + [ 0, 1, 0, 1, 0], + [ 0, 0, 2, 0, 0], + [ 0, 1, 0, 1, 0], + [ 0, 0, 0, 0, 0]], np.float32) + la = np.empty( im.shape, 'i' ) + wk = np.empty( im.shape, 'b' ) + localmaxlabel( im, la, wk ) + lp = np.empty( im.shape, 'i' ) + wp = np.empty( im.shape, 'b' ) + npks = localmax(im, lp, wp) + if not (lp == la).all(): + print(lp) + print(la) + + self.assertTrue( (lp == la).all() ) + + + def test_one(self): + im = np.array([ + [0, 0, 0, 0, 0,], + [0, 0, 2, 2, 0,], + [0, 0, 0, 3, 0,], + [0, 0, 0, 0, 0,], + [0, 0, 0, 0, 0,], + [0, 0, 0, 3, 0,], + [0, 0, 2, 3, 0,], + [0, 0, 0, 0, 0,], + ]) + la = np.empty( im.shape, 'i' ) + wk = np.empty( im.shape, 'b' ) + npks = localmaxlabel( im, la, wk ) + lp = np.empty( im.shape, 'i' ) + wp = np.empty( im.shape, 'b' ) + localmax(im, lp, wp) + if npks != 2: + print(la) + assert(npks == 2), npks + if not (lp == la).all(): + print(lp) + print(la) + + + def test_L_sparse(self): + im = np.array([ + [0, 0, 0, 0, 0,], + [0, 0, 2, 2, 0,], + [0, 0, 0, 4, 0,], + [0, 0, 0, 0, 0,], + [0, 0, 0, 0, 0,], + [0, 0, 0, 3, 0,], + [0, 0, 2, 3, 0,], + [0, 0, 0, 0, 0,], + ]) + la = np.empty( im.shape, 'i' ) + wk = np.empty( im.shape, 'b' ) + npks = localmaxlabel( im, la, wk ) + assert npks == 2 + las = np.empty( im.shape, 'i' ) + npks = sparselocalmax( im, las, wk ) + assert npks == 2 + m = im > 0 + assert (las[m] == la[m]).all() + +if __name__== "__main__": + import sys + if len(sys.argv)>1: + demo() + unittest.main() + +else: + im = make_test_image(256) + l2 = np.zeros( im.shape, 'i' ) + wk = np.zeros( im.shape, np.uint8 ) + npks = localmaxlabel(im, l2 , wk ) + + diff --git a/test/test_misori.py b/test/test_misori.py new file mode 100644 index 00000000..0a9c7fbd --- /dev/null +++ b/test/test_misori.py @@ -0,0 +1,199 @@ + +from __future__ import print_function +from ImageD11.cImageD11 import misori_cubic, misori_cubic_pairs, \ + misori_orthorhombic, misori_tetragonal, misori_monoclinic + +import xfab.symmetry +import numpy as np +import numba +import timeit, time +import unittest, cProfile, pstats +timer = timeit.default_timer +# 1: Triclinic +# 2: Monoclinic +# 3: Orthorhombic +# 4: Tetragonal +# 5: Trigonal +# 6: Hexagonal +# 7: Cubic + +ROTATIONS = [None] + [np.ascontiguousarray(xfab.symmetry.rotations(i)) for i in range(1,8)] + +def isrotation(mat): + d = np.linalg.det(mat) + i = np.linalg.inv(mat) + return np.allclose(d,1) and np.allclose( i, mat.T ) + +for grp in ROTATIONS[1:]: + for mat in grp: + assert isrotation(mat), "Not a rotation"+str(mat) +print("Rotation are all rotation matrices") +#print(ROTATIONS) +def Umis(umat_1, umat_2, crystal_system): + rot = ROTATIONS[crystal_system] + return _Umis(umat_1, umat_2, rot) + +#@numba.njit(fastmath=True) +def _Umis(umat_1, umat_2, rot): + misorientations = np.empty( (len(rot),2)) + lengths = 0.5 * (rot * np.dot( umat_1.T, umat_2 )).sum(axis=(2,1)) - 0.5 + misorientations[:,0] = np.arange(len(rot)) + misorientations[:,1] = np.arccos(lengths.clip(-1,1)) * 180./np.pi + return misorientations + +from scipy.spatial.transform import Rotation as R +if not hasattr( R, 'as_matrix' ): + R.as_matrix = R.as_dcm + +from xfab import symmetry +start = time.time() +def test_Umis(): + for crystal_system in range(1,8): + U1 = R.random(200).as_matrix() + U2 = R.random(200).as_matrix() + for u1, u2 in zip(U1, U2): + m1 = Umis(u1, u2, crystal_system) + m2 = symmetry.Umis(u1, u2, crystal_system) + assert np.max(np.abs(m1 - m2)) < 1e-5, str(np.max(np.abs(m1 - m2))) + +test_Umis() +print("OK",time.time()-start) + +def misori_py( u1, u2, sym=7 ): + # 7 is cubic +# ans = xfab.symmetry.Umis( u1, u2, sym) + ans = Umis( u1, u2, sym ) + ang = np.min( ans[:,1] ) + trc = np.cos(np.radians(ang))*2+1 + return trc + +def make_random_orientations( N ): + """ + Generate random quaternions and convert to U matrices + """ + q = np.random.standard_normal( (4, N) ) + s = 1/(q*q).sum( axis=0 ) + U = np.zeros( (N, 3, 3), float ) + r,i,j,k = 0,1,2,3 + U[:,0,0] = 1 - 2*s*(q[j]*q[j]+q[k]*q[k]) + U[:,0,1] = 2*s*(q[i]*q[j]-q[k]*q[r]) + U[:,0,2] = 2*s*(q[i]*q[k]+q[j]*q[r]) + U[:,1,0] = 2*s*(q[i]*q[j]+q[k]*q[r]) + U[:,1,1] = 1 - 2*s*(q[i]*q[i]+q[k]*q[k]) + U[:,1,2] = 2*s*(q[j]*q[k]-q[i]*q[r]) + U[:,2,0] = 2*s*(q[i]*q[k]-q[j]*q[r]) + U[:,2,1] = 2*s*(q[j]*q[k]+q[i]*q[r]) + U[:,2,2] = 1 - 2*s*(q[i]*q[i]+q[j]*q[j]) + return U + + +NROT = 200 + +class test_random_orientations( unittest.TestCase ): + DOPROFILE = False + DOBENCH = True + def setUp(self): + np.random.seed(42) + if self.DOPROFILE: + self.pr = cProfile.Profile() + self.pr.enable() + + def tearDown(self): + if self.DOPROFILE: + p = pstats.Stats( self.pr ) + p.strip_dirs() + p.sort_stats ('cumtime') + p.print_stats () + print( "\n--->>>" ) + + def test_are_rotations( self ): + self.U = make_random_orientations( 1000 ) + i = np.eye(3) + # transpose is inverse + ids = [ abs( (np.dot( m, m.T )-i ) ).ravel().sum() for m in self.U ] + self.assertAlmostEqual( np.max( ids ), 0 ) + # determinant is 1 + dts = [ np.linalg.det(m) - 1 for m in self.U ] + self.assertAlmostEqual( abs(np.array( dts )).max(), 0 ) + + def test_cubic(self): + U = make_random_orientations( NROT ) # xfab is a bit slow + t0 = timer() + pairs = [ (u1, u2) for u1 in U for u2 in U ] + c = [ misori_cubic( u1, u2 ) for u1, u2 in pairs ] + t1 = timer() + p = [ misori_py( u1, u2, 7 ) for u1, u2 in pairs ] + t2 = timer() + if self.DOBENCH: + print("C time %f s , pytime %f s"%(t1-t0, t2-t1)) + self.assertTrue(np.allclose( np.array(c) ,np.array(c) )) + + def test_monoclinic(self): + U = make_random_orientations( NROT ) # xfab is a bit slow + t0 = timer() + pairs = [ (u1, u2) for u1 in U for u2 in U ] + c = [ misori_monoclinic( u1, u2 ) for u1, u2 in pairs ] + t1 = timer() + p = [ misori_py( u1, u2, 2 ) for u1, u2 in pairs ] + t2 = timer() + if self.DOBENCH: + print("C time %f s , pytime %f s"%(t1-t0, t2-t1)) + self.assertTrue(np.allclose( np.array(c) ,np.array(c) )) + + def test_orthorhombic(self): + U = make_random_orientations( NROT ) # xfab is a bit slow + t0 = timer() + pairs = [ (u1, u2) for u1 in U for u2 in U ] + c = [ misori_orthorhombic( u1, u2 ) for u1, u2 in pairs ] + t1 = timer() + p = [ misori_py( u1, u2, 3 ) for u1, u2 in pairs ] + t2 = timer() + if self.DOBENCH: + print("C time %f s , pytime %f s"%(t1-t0, t2-t1)) + self.assertTrue(np.allclose( np.array(c) ,np.array(c) )) + + def test_tetragonal(self): + U = make_random_orientations( NROT ) # xfab is a bit slow + t0 = timer() + pairs = [ (u1, u2) for u1 in U for u2 in U ] + c = [ misori_tetragonal( u1, u2 ) for u1, u2 in pairs ] + t1 = timer() + p = [ misori_py( u1, u2, 4 ) for u1, u2 in pairs ] + t2 = timer() + if self.DOBENCH: + print("C time %f s , pytime %f s"%(t1-t0, t2-t1)) + self.assertTrue(np.allclose( np.array(c) ,np.array(c) )) + + def test_cubic_reverse(self): + U = make_random_orientations( NROT ) + t0 = timer() + for u1 in U: + for u2 in U: + tr1 = misori_cubic( u1, u2 ) + tr2 = misori_cubic( u2, u1 ) + self.assertAlmostEqual( tr1, tr2 ) + t1 = timer() + if self.DOBENCH: + print("C %d pairs in %f s, %f s per pair"%( + NROT*NROT*2, t1-t0, (t1-t0)/NROT)) + + def test_cubic_pairmat(self): + N = 500 + U = make_random_orientations( N ) + m0 = [] + t0 = timer() + for i in range(N): + for j in range(i+1,N): + m0.append( misori_cubic( U[i], U[j] ) ) + t1 = timer() + m1 = np.zeros( len(m0), float ) + misori_cubic_pairs( U, m1 ) + t2 = timer() + if self.DOBENCH: + print( "time for distance matrix",t1-t0, t2-t1) + self.assertTrue( np.allclose( m0, m1 ) ) + + +if __name__=="__main__": + unittest.main() + diff --git a/test/test_mmo/.gitignore b/test/test_mmo/.gitignore new file mode 100644 index 00000000..eceb5353 --- /dev/null +++ b/test/test_mmo/.gitignore @@ -0,0 +1,12 @@ +peaks_t1.flt +peaks_t1.spt +test0000.tif +test0001.tif +test0002.tif +test0003.tif +test0004.tif +test0005.tif +test0006.tif +test0007.tif +test0008.tif +test0009.tif diff --git a/test/test_mmo/__init__.py b/test/test_mmo/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/test_mmo/make_test_data.py b/test/test_mmo/make_test_data.py deleted file mode 100644 index 765e7fbb..00000000 --- a/test/test_mmo/make_test_data.py +++ /dev/null @@ -1,67 +0,0 @@ -################################################# make_test_data.py -# -# save as a .py file and run it from a command line via: -# python make_test_data.py -""" -Generate a series of test images -""" -from PIL import Image -# make a test image -im = Image.new( "L", ( 264, 299) ) -for i in [0, 1, 8, 9]: - im.save("test%04d.tif"%(i)) -# Put a 2x3 peak at (23, 45) for images 3-7 in a 0-9 series - -# peak 1 - simple blob -for p in [ (23,45), (23,46), (23,47), - (24,45), (24,46), (24,47)]: - im.putpixel( p , 128) - -# peak 2 - two will merge to one -for p in [ (123,45), (123,47), - (124,45), (124,47)]: - im.putpixel( p , 129) - -# peak 3 - one becomes two -for p in [ (23,145), (23,146), (23,147), - (24,145), (24,146), (24,147)]: - im.putpixel( p , 127) - -for i in [2, 3, 4]: - im.save("test%04d.tif"%(i)) - -for p in [ (123,46), - (124,46) ]: - im.putpixel( p , 129) - -# peak 3 - one becomes two -for p in [ (23,146), - (24,146)]: - im.putpixel( p , 0) - - -for i in [5, 6, 7]: - im.save("test%04d.tif"%(i)) - -import os -from ImageD11.columnfile import columnfile - -def test(start, step): - import sys - if sys.platform == "win32": - pksh = "%s ..\\..\\scripts\\peaksearch.py "%( sys.executable ) - else: - pksh = "peaksearch.py " - - os.system(pksh + - "-n test -F .tif -f 0 -l 9 -t 1.0 -D 0 " + - "-T %f -S %f >> testpksearch.log"%(start,step)) - c = columnfile("peaks_t1.flt") - print "start",start,"step", step,"finds:", - print "Min_o",c.Min_o,"Max_o",c.Max_o,"omega",c.omega - if len(c.Min_o) == 3: - print "I think it might be OK" -test( -4, +1) -test( -40, +1) -test( 40, +1) - diff --git a/test/test_mmo/make_test_data_mmo.py b/test/test_mmo/make_test_data_mmo.py new file mode 100644 index 00000000..7d090d51 --- /dev/null +++ b/test/test_mmo/make_test_data_mmo.py @@ -0,0 +1,68 @@ + +from __future__ import print_function, division +################################################# make_test_data.py +# +# save as a .py file and run it from a command line via: +# python make_test_data.py +""" +Generate a series of test images +""" +import os +from ImageD11.columnfile import columnfile +import unittest +from PIL import Image +import platform, sys + +def runtest(start, step): + """ check tif range """ + pksh = sys.executable + ' ' + os.path.join("..","..","scripts","peaksearch.py") + os.system(pksh + + " -n test -F .tif -f 0 -l 9 -t 1.0 -D 0 " + + "-T %f -S %f > testpksearch.log"%(start,step)) + c = columnfile("peaks_t1.flt") + print ("start",start,"step", step,"finds:", end=" " ) + print ("Min_o",c.Min_o,"Max_o",c.Max_o,"omega",c.omega) + if len(c.Min_o) == 3: + print("I think it might be OK") + return len(c.Min_o)==3 + + +class test_mmo(unittest.TestCase): + def setUp(self): + # make a test image + im = Image.new( "L", ( 264, 299) ) + for i in [0, 1, 8, 9]: + im.save("test%04d.tif"%(i)) + # Put a 2x3 peak at (23, 45) for images 3-7 in a 0-9 series + # peak 1 - simple blob + for p in [ (23,45), (23,46), (23,47), + (24,45), (24,46), (24,47)]: + im.putpixel( p , 128) + # peak 2 - two will merge to one + for p in [ (123,45), (123,47), + (124,45), (124,47)]: + im.putpixel( p , 129) + # peak 3 - one becomes two + for p in [ (23,145), (23,146), (23,147), + (24,145), (24,146), (24,147)]: + im.putpixel( p , 127) + for i in [2, 3, 4]: + im.save("test%04d.tif"%(i)) + for p in [ (123,46), + (124,46) ]: + im.putpixel( p , 129) + # peak 3 - one becomes two + for p in [ (23,146), + (24,146)]: + im.putpixel( p , 0) + for i in [5, 6, 7]: + im.save("test%04d.tif"%(i)) + + def test1(self): + runtest( -4, +1) + runtest( -40, +1) + runtest( 40, +1) + +if __name__=="__main__": + unittest.main() + diff --git a/test/test_overlapimage.py b/test/test_overlapimage.py index 93bb8bdd..81b1a2ab 100644 --- a/test/test_overlapimage.py +++ b/test/test_overlapimage.py @@ -1,43 +1,128 @@ -import numpy as np, fabio - -M = 2048 # Overall image size -N = 64 # tile size -SIG = 3. # sigma for peaks (width) - -# tile co-ordinates -i,j = np.mgrid[0:N,0:N]-N/2. - -def pk(x,y,s): - # 2D peak shape (with tails) - dx = i-x - dy = j-y - gg= np.exp( -(dx*dx+dy*dy)/s/s ) - ll=s*s/(s*s+dx*dx+dy*dy) - return ll+gg - -# Image for testing -im = np.zeros( (M,M) ) - -# Make a grid of points (tiles) -for ii in range(0,M,N): - for jj in range(0,M,N): - # offset of centroids - oi = 3*SIG*ii/M - oj = SIG*ii/M - # Ratio of heights - sc = jj*1.0/M - # put in two peaks, offset increases one direction, scale in the other - im[ii:ii+N,jj:jj+N] = pk( -oi, -oj, SIG ) + pk( oi, oj, SIG)*sc - -if 1: - import pylab as pl - pl.imshow( pl.log(im )) - pl.show() - -s = 30000/im.max() -int_im = (im*s).astype( np.uint16) - -fabio.edfimage.edfimage( int_im ).write( "testoverlaps0000.edf" ) - +from __future__ import print_function, division +import sys +import numpy as np, fabio, time, os +from ImageD11 import cImageD11 +#print(cImageD11.__file__) + +OVIM = os.path.join( + os.path.dirname(__file__), + "testoverlaps0000.edf" ) + +def maketestim(): + + M = 2048 # Overall image size + N = 64 # tile size + SIG = 3. # sigma for peaks (width) + # tile co-ordinates + i,j = np.mgrid[0:N,0:N]-N/2. + + def pk(x,y,s): + # 2D peak shape (with tails) + dx = i-x + dy = j-y + gg= np.exp( -(dx*dx+dy*dy)/s/s ) + ll=s*s/(s*s+dx*dx+dy*dy) + return ll+gg + + # Image for testing + im = np.zeros( (M,M) ) + + # Make a grid of points (tiles) + pks = [] + for ii in range(0,M,N): + for jj in range(0,M,N): + # offset of centroids + oi = 3*SIG*ii/M + oj = SIG*ii/M + # Ratio of heights + sc = jj*1.0/M + # put in two peaks, offset increases one direction, + # scale in the other + im[ii:ii+N,jj:jj+N] = pk( -oi, -oj, SIG ) + pk( oi, oj, SIG)*sc + pks.append( (ii+N//2-oi,jj+N//2-oj) ) + pks.append( (ii+N//2+oi,jj+N//2+oj) ) + if 0: + import pylab as pl + pl.imshow( pl.log(im )) + pl.show() + + s = 30000/im.max() + int_im = (im*s).astype( np.uint16 ) + if not os.path.exists( OVIM ): + fabio.edfimage.edfimage( int_im ).write( OVIM ) + return int_im, pks +def bench(int_im, pks): + im = int_im + float_im=int_im.astype(np.float32) + labelc = np.zeros( im.shape, np.int32 ) + labelm = np.zeros( im.shape, np.int32 ) + work = np.zeros( im.shape, np.int8 ) + + end0 = time.time() + nc = cImageD11.connectedpixels( float_im, labelc, 1000 ) + end1 = time.time() + rc = cImageD11.blobproperties( float_im, labelc, nc, 0 ) + cImageD11.blob_moments(rc) + end2 = time.time() + print("cptime",nc,"%.3f %.3f"%((end1-end0)*1000,(end2-end1)*1000)) + + wfloatim = np.where( float_im > 1000, float_im, 0) + #import scipy.ndimage as ndi + #wfloatim= (float_im - ndi.gaussian_filter( float_im , 32)).clip(0,1e9) + + end0 = time.time() + nw = cImageD11.localmaxlabel( wfloatim, labelm, work ) + end1 = time.time() + rw = cImageD11.blobproperties( wfloatim, labelm, nw, 0 ) + cImageD11.blob_moments(rw) + end2 = time.time() + print("lmtime",nw,"%.3f %.3f"%((end1-end0)*1000,(end2-end1)*1000)) + if "plot" in sys.argv: + pks=np.array(pks).T + import pylab as pl + pl.imshow( pl.log(float_im), origin='lower', + interpolation='nearest', aspect='auto') + pl.plot( pks[1], pks[0],"r+",label="ideal" ) + pl.plot( rc[:,cImageD11.f_raw],rc[:,cImageD11.s_raw],"wx", + label="connect") + pl.plot( rw[:,cImageD11.f_raw],rw[:,cImageD11.s_raw],"ko", + label="localmax", + markerfacecolor='None') + pl.legend() + pl.figure() + dci= [ np.sqrt(np.min( (pks[1]-x)**2 + (pks[0]-y)**2 )) + for x,y in zip( rc[:, cImageD11.f_raw],rc[:, cImageD11.s_raw])] + pl.subplot(121) + pl.plot( rc[:, cImageD11.s_raw], + dci , '.', label= "sconnected") + pl.plot( rc[:, cImageD11.f_raw], + dci , '.', label= "fconnected") + pl.ylabel("position error") + pl.title("Connected pixels = 1 blob") + pl.legend() + dwi= [ np.sqrt(np.min( (pks[1]-x)**2 + (pks[0]-y)**2 )) + for x,y in zip( rw[:, cImageD11.f_raw],rw[:, cImageD11.s_raw])] + pl.subplot(122) + pl.plot( rw[:, cImageD11.s_raw], + dwi , '.', label= "slocalmax") + pl.plot( rw[:, cImageD11.f_raw], + dwi , '.', label= "flocalmax") + pl.title("Localmax = split blobs") + pl.legend() + pl.show() + + +import unittest +class testimexists(unittest.TestCase): + def setUp(self): + maketestim() + def testit(self): + self.assertTrue(os.path.exists(OVIM)) + +if __name__=="__main__": + bench(*maketestim()) + # unittest.main() + # this is not a unit test ! + diff --git a/test/test_peaksearch.py b/test/test_peaksearch.py index 459c88f6..6fff9de2 100644 --- a/test/test_peaksearch.py +++ b/test/test_peaksearch.py @@ -1,3 +1,5 @@ + +from __future__ import print_function # testing for the peaksearch command line script # These are not really unit tests @@ -7,13 +9,14 @@ def w(name,image,omega=0): f=open(name,"wb") - f.write("{%1021s}\n"%("""Omega = %.2f ;\n Dim_1 = 256 ;\n Dim_2 = 256 ; + h = "{%1021s}\n"%("""Omega = %.2f ;\n Dim_1 = 256 ;\n Dim_2 = 256 ; DataType = UnsignedShort ; ByteOrder = LowByteFirst ; Image = 1 ; - \n\n"""%(omega))) + \n\n"""%(omega)) + f.write(h.encode('utf-8')) f.write(np.ravel(image).astype(np.uint16).tostring()) f.close() @@ -35,7 +38,7 @@ def makedata(): def losedata(): for i in range(4): name = "data%04d.edf"%(i) - print "removing",name + print("removing",name) os.remove(name) os.remove("dark_bad.edf") os.remove("dark_good.edf") @@ -47,7 +50,7 @@ def losedata(): ps = os.path.join("..","scripts") ps = os.path.join(ps,"peaksearch.py") -print ps +print (ps) cmds = [sys.executable + " " + ps + " -n data -f 0 "] @@ -74,11 +77,11 @@ def losedata(): import os -def testcmdlines(clist): +def atestcmdlines(clist): makedata() - print len(clist) + print(len(clist)) for c in clist: - print c + print (c) ret = os.system(c) if ret != 0: raise Exception("Bombed") @@ -89,9 +92,9 @@ def testcmdlines(clist): # FIXME import sys if len(sys.argv)>1 and sys.argv[1] == "ALL": - testcmdlines(clist) + atestcmdlines(clist) - testcmdlines([ sys.executable + ' ' + ps + ' -n data -f 0 -l 3 -t 15000 -t 1000 -p Y -t 0']) + atestcmdlines([ sys.executable + ' ' + ps + ' -n data -f 0 -l 3 -t 15000 -t 1000 -p Y -t 0']) assert len(open("peaks_t0.flt").readlines())==2 assert len(open("peaks_t1000.flt").readlines())==2 assert len(open("peaks_t15000.flt").readlines())==3 diff --git a/test/test_put_incr.py b/test/test_put_incr.py index d04204d4..ce15c32f 100644 --- a/test/test_put_incr.py +++ b/test/test_put_incr.py @@ -20,9 +20,9 @@ def test_put_twice(self): ind = np.ones(10,np.intp) vals = np.ones(10,np.float32) cImageD11.put_incr( data, ind, vals ) - assert (data == np.array( [0, 10] + [0]*8 , np.float)).all() + assert (data == np.array( [0, 10] + [0]*8 , float)).all() cImageD11.put_incr( data, ind, vals ) - assert (data == np.array( [0, 20] + [0]*8 , np.float)).all() + assert (data == np.array( [0, 20] + [0]*8 , float)).all() def test_as_flat(self): data = np.zeros( (10, 10), np.float32 ) diff --git a/test/test_rsv/Fe3O4.cif b/test/test_rsv/Fe3O4.cif new file mode 100644 index 00000000..64d4e471 --- /dev/null +++ b/test/test_rsv/Fe3O4.cif @@ -0,0 +1,27 @@ +data_Fe3O4 +_cell_length_a 8.3985 +_cell_length_b 8.3985 +_cell_length_c 8.3985 +_cell_angle_alpha 90. +_cell_angle_beta 90. +_cell_angle_gamma 90. +_symmetry_space_group_name_H-M 'F d -3 m' +_symmetry_Int_Tables_number 227 +loop_ +_atom_type_symbol +Fe +O +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_symmetry_multiplicity +_atom_site_Wyckoff_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +_atom_site_B_iso_or_equiv +_atom_site_occupancy +_atom_site_attached_hydrogens +Fe1 Fe 8 a 0 0 0 . 1. 0.1 +Fe2 Fe 16 d 0.625 0.625 0.625 . 1. 0.1 +O1 O 32 e 0.375 0.375 0.375 . 1. 0.1 diff --git a/test/test_rsv/Fe3O4.inp b/test/test_rsv/Fe3O4.inp new file mode 100644 index 00000000..1487c651 --- /dev/null +++ b/test/test_rsv/Fe3O4.inp @@ -0,0 +1,70 @@ +#File names and Format +direc 'rsvdemo' +stem 'rsv' +make_image 1 +output '.edf.gz' '.flt' '.gve' '.par' '.ubi' +#Structural parameters +structure_phase_0 'Fe3O4.cif' +#Structural parameters +# Crystal/grains parameters +no_grains 1 +#Total number of grains summed over all phases to be simulated. +#Need to match the number of U_Grains_X key word +gen_size 0 0 0 0 +#Grain phase : If you want to let the PolyXSim appoint which grain belongs +# to which phase the following keyword can be used. +gen_phase 1 1 0 +#Grain orientation : random (1) or specify specific orientation matrices (0) +gen_U 0 +gen_pos 0 0 + +U_grains_0 7.712806e-01 -6.337184e-01 5.939117e-02 6.130920e-01 7.146102e-01 -3.368241e-01 1.710101e-01 2.961981e-01 9.396926e-01 +size_grains_0 0.05 +pos_grains_0 0.01 -0.01 0.005 + +sample_xyz 0.2 0.2 0.2 +gen_eps 1 0 0 0 0 + +#Instrumentation +#Detector pixel size [mm] +y_size 0.2 +z_size 0.2 +#Detector size [pixels] +dety_size 512 +detz_size 512 +#Distance from sample to detector [mm] +distance 250. +#Detector tilt +tilt_x 0. +tilt_y 0. +tilt_z 0. +#Detector orientation +o11 1 +o12 0 +o21 0 +o22 1 +#Noise +noise 0 +#Reflection +lorentz_apply 1 +beampol_apply 1 +peakshape 1 0.5 0.25 +psf 1.5 +#Instrumental +#Beam specs +wavelength 0.1588 +beamflux 1e12 +beampol_factor 1 +beampol_direct 0 +#Beam center on detector [pixels] +dety_center 256.0 +detz_center 256.0 +#Omega scan range [degrees] +omega_start -90 +omega_end 90 +#Omega step size [degrees] +omega_step 0.5 +#Omega rotation direction [degrees] +omega_sign 1 +#Wedge angle of omega axis +wedge 0 diff --git a/test/test_rsv/plot_Fe3O4.ipynb b/test/test_rsv/plot_Fe3O4.ipynb new file mode 100644 index 00000000..da8abc2c --- /dev/null +++ b/test/test_rsv/plot_Fe3O4.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pylab as pl, matplotlib\n", + "from ImageD11 import rsv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v = rsv.readvol(\"Fe3O4.h5\")\n", + "v.normalise()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pl.imshow( v.slice(\"h\",0), extent=rsv.getbounds(v,\"h\"), norm=matplotlib.colors.SymLogNorm(1))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/test/test_rsv/run_demo.py b/test/test_rsv/run_demo.py new file mode 100644 index 00000000..9cb16504 --- /dev/null +++ b/test/test_rsv/run_demo.py @@ -0,0 +1,17 @@ + + +import fabio, sys, os, gzip, numpy as np + +z = np.zeros( (512,512), np.uint16) +fabio.edfimage.edfimage(z).write( gzip.GzipFile("dark.edf.gz","wb") ) +fabio.edfimage.edfimage(z+1).write( gzip.GzipFile("flat.edf.gz","wb") ) + +# Generates simulated data + +os.system("PolyXSim.py -i Fe3O4.inp") +# Reconstruct the data using the given ubi +stem=os.path.join("rsvdemo","rsv") +os.system("rsv_mapper.py -n " + stem + " -f 0 -l 356 -F .edf.gz -O flat.edf.gz -d dark.edf.gz" + + " -p " + stem + ".par -u " + stem + ".ubi -x 4 -b 10 -c 3 -o Fe3O4.h5" ) + + diff --git a/test/test_score_gvec_z.py b/test/test_score_gvec_z.py new file mode 100644 index 00000000..c01d23cb --- /dev/null +++ b/test/test_score_gvec_z.py @@ -0,0 +1,86 @@ + +from __future__ import print_function, division + +from ImageD11 import cImageD11 +import numpy as np, time +import unittest + + +def norm(v): + return v/np.linalg.norm(v, axis=1)[:,np.newaxis] + +def py_scor_z( ubi, + ub, + gv ): + """ + Decomposing the error into 3 orthogonal directions + aligned with gvector (length, like two theta) + normal with gvector and axis (e.g. omega direction) + normal with that (e.g. azimuth direction) + """ + nv = gv.shape[0] + assert gv.shape[1] == 3 + assert ubi.shape == (3,3) + assert ub.shape == (3,3) + assert np.allclose( np.dot( ubi, ub), np.eye(3), 6) + x,y,z = 0,1,2 + # local co-ordinates + g0 = norm( gv ) + g1 = np.zeros( gv.shape, float ) + g1[:,x] = -g0[:,y] + g1[:,y] = g0[:,x] + g1 = norm( g1 ) + g2 = np.zeros( gv.shape, float ) + g2[:,0] = g0[:,x]*g0[:,z] + g2[:,1] = g0[:,y]*g0[:,z] + g2[:,2] = -g0[:,x]*g0[:,x]-g0[:,y]*g0[:,y] + g2 = norm( g2 ) + # error computations + hkl = np.round( np.dot( ubi, gv.T ).T ) # integer hkl + d = np.dot(ub, hkl.T).T - gv # error in g + e = np.zeros( gv.shape, float ) + e[:,0] = (d*g0).sum(axis=1) + e[:,1] = (d*g1).sum(axis=1) + e[:,2] = (d*g2).sum(axis=1) + # now into local co-ordinates + return g0, g1, g2, e + + + +class test_closest_vec( unittest.TestCase ): + + def setUp( self ): + + UBI =np.array( [ [ 9.095614, -4.379317, -4.899265], + [ 6.253395 , 6.110302, 2.711559], + [ 2.786115, 9.817030 , 14.233333]]) + UB = np.linalg.inv( UBI ) + + HKL = np.mgrid[-5:6, -5:6, -5:6].T.copy() + HKL.shape = HKL.shape[1]*HKL.shape[1]*HKL.shape[1] , 3 + for i in range(len(HKL)): + if abs(HKL[i]).sum()==0: + HKL[i] = (1,1,1) + GVE = np.dot( UB, HKL.T ).T.copy() + GVE *= ( 0.95 + np.random.random( GVE.shape )/10. ) + self.UBI = UBI + self.UB = UB + self.GVE = GVE + + + def test_same_as_python( self ): + GVE, UBI, UB = self.GVE, self.UBI, self.UB + pg0,pg1,pg2,pe = py_scor_z( UBI, UB, GVE ) + cg0 = np.zeros( GVE.shape, float ) + cg1 = np.zeros( GVE.shape, float ) + cg2 = np.zeros( GVE.shape, float ) + ce = np.zeros( GVE.shape, float ) + cImageD11.score_gvec_z( UBI, UB, GVE, + cg0, cg1, cg2, ce, 1 ) + self.assertTrue( np.allclose( pg0, cg0, 6 ) ) + self.assertTrue( np.allclose( pg1, cg1, 6 ) ) + self.assertTrue( np.allclose( pg2, cg2, 6 ) ) + self.assertTrue( np.allclose( pe, ce, 6 ) ) + +if __name__=="__main__": + unittest.main() diff --git a/test/test_sparse_image.py b/test/test_sparse_image.py new file mode 100644 index 00000000..3fda08b4 --- /dev/null +++ b/test/test_sparse_image.py @@ -0,0 +1,272 @@ + +from __future__ import print_function +from ImageD11 import cImageD11 +import fabio +import numpy as np +import scipy.sparse +import unittest +import timeit +import os +timer = timeit.default_timer +OVIM = os.path.join( + os.path.dirname(__file__), + "testoverlaps0000.edf" ) + + +class test_array_bounds_localmax( unittest.TestCase): + def setUp(self): + self.row = np.array([ 0, 1, 26, 27, 143, 144, 147, 147, + 188, 188, 196, 196, 196, + 197, 197, 197, 197, 197, 198, 198, 198, 198, 198, 198, 199, 199, + 199, 200, 200, 201], dtype=np.uint16) + self.col = np.array([ 1, 1, 321, 321, 332, 332, 696, 697, + 616, 617, 767, 768, 769, + 767, 768, 769, 770, 771, 1, 2, 767, 768, 769, 770, 768, 769, + 770, 772, 773, 773], dtype=np.uint16) + self.vals = np.array([ 18.757812, 20.311476, 20.125 , + 20.809525, 30.141733, + 18.84375 , 20.023438, 20.023438, 19.328125, 19.328125, + 22.373016, 30.491804, 44.491806, 33.220474, 126.64 , + 134.64 , 39.64 , 22.64 , 20.882812, 19.882812, + 44.04762 , 109.70248 , 129.70248 , 32.702477, 43.264 , + 41.264 , 33.264 , 20.593496, 33.5935 , 35.02362 ], + dtype=np.float32) + def test1(self): + import h5py + nnz = self.row.shape[0] + labels = np.zeros( nnz, "i" ) + vmx = np.zeros( nnz, np.float32 ) + imx = np.zeros( nnz, 'i') + nlabel = cImageD11.sparse_localmaxlabel( + self.vals, self.row, self.col, + vmx, imx, labels ) + self.assertTrue( nlabel > 0) + # This does a double-free on exit + +class test_array_stats( unittest.TestCase ): + def test1( self ): + ar = np.arange( 6*4, dtype=np.float32 ).reshape( 6,4 ) + mini, maxi, mean, var = cImageD11.array_stats( ar.ravel() ) + t = np.allclose( (mini, maxi, mean, var), + (ar.min(), ar.max(), ar.mean(), ar.var()) ) + if not t: + print((mini, maxi, mean, var)) + print((ar.min(), ar.max(), ar.mean(), ar.var()) ) + self.assertTrue(t) + + def test2( self ): + ar = np.ones( 6*4, dtype=np.float32 ).reshape( 6,4 ) + mini, maxi, mean, var = cImageD11.array_stats( ar.ravel() ) + self.assertTrue( np.allclose( + (mini, maxi, mean, var), + (ar.min(), ar.max(), ar.mean(), ar.var()) )) + def test3( self ): + ar = np.random.random( 2048*2048).astype( np.float32 ).reshape( 2048, 2048 ) + nloop = 10 + ctime = 0 + nt = 0 + mini, maxi, mean, var = cImageD11.array_stats( ar.ravel() ) + for i in range(nloop): + start = timer() + mini, maxi, mean, var = cImageD11.array_stats( ar.ravel() ) + endc = timer() + dt = endc - start + ctime += dt + nt += 1 + if ctime > 0.1: + break + ctime /= nt + check = ar.min(), ar.max(), ar.mean(), ar.var() + start = timer() + check = ar.min(), ar.max(), ar.mean(), ar.var() + endn = timer() + numpytime = endn - start + ok = np.allclose( (mini, maxi, mean, var), check ) + if not ok: + for (a,b) in zip((mini, maxi, mean, var), check ): + print( np.allclose(a,b),a,b ) + self.assertTrue( ok ) + MB_s = ar.nbytes / ctime / 1000000 + FPS = ar.nbytes / ctime / (2048*2048*4) # Frelon float32 frame + print("array_stats: %.1f MB/s %.1f FPS(4M,f32) %.3f ms vs %.3f ms, speedup %.1f"%( MB_s, FPS, + 1e3*ctime, 1e3*numpytime, numpytime/ctime)) + + +class test_array_histogram( unittest.TestCase ): + def test1(self): + N = 255 + a = (np.random.random( (2048, 2048))*(N-0.1)).astype(np.float32) + start = timer() + hc = np.zeros( N, dtype=np.int32 ) + cImageD11.array_histogram( a.ravel(), 0., N, hc ) + endc= timer() + hn = np.bincount( a.ravel().astype(int) ) + endn = timer() + if( hc.shape[0] != hn.shape[0]): + print("shape error") + print( a.max()) + print(hc) + print(hn) + self.assertTrue( (hc == hn).all() ) + numpytime = endn - endc + ctime = endc - start + print("array_histogram: %.3f ms vs %.3f ms, speedup %.1f"%( + 1e3*ctime, 1e3*numpytime, numpytime/ctime)) + + +class test_sparse_is_sorted( unittest.TestCase ): + def setUp( self ): + self.i = np.arange(10, dtype = np.uint16 ) + self.j = np.arange(10, dtype = np.uint16 ) + def testOK( self ): + ret = cImageD11.sparse_is_sorted( self.i, self.j) + self.assertEqual( ret, 0) + def testrpt( self ): + self.i[5]=self.i[4] + self.j[5]=self.j[4] + ret = cImageD11.sparse_is_sorted( self.i, self.j) + self.assertEqual( ret, -5) + def testbadi( self ): + self.i[5]=self.i[4]-1 + ret = cImageD11.sparse_is_sorted( self.i, self.j) + self.assertEqual( ret, 5) + def testbadj( self ): + self.i[5]=self.i[4] + self.j[5]=self.j[4]-1 + ret = cImageD11.sparse_is_sorted( self.i, self.j) + self.assertEqual( ret, 5) + + +class test_sparse_connected_pixels( unittest.TestCase ): + def setUp( self ): + img =""" +00110100000000000000000000000000000000000000000000000000 +00001000000000000000000000000000000000000000000000000000 +00000000001111000000111110000000000000000000000000000010 +00000000011001000001100011000000000000000000000000010010 +11000000000000000000000000000000000000000000000000001111 +00000000000000000000000000000000020000000000000000000000 +00000000000000000000000000000000020000000000000000000000 +00000000000000000000000000000000000000000000000000000000 +00000000000000000000000000000000000000000000000000000001 +00000000000000000001100000000000000000000000000000010001 +00000000000000000010011000000000000000000000000000001110 +00000000000000000100000100000000000000000000000000000000 +00000000000000001000200010000000000000000000000000000000 +00000000000000001000000100000000000000000000000000000000 +00000000000000000100001000000000000000000000000000000000 +00000000000000000011110000000000000000000000000000000000 +00000000000000000000000000000000000000000000000000000000 +00001100000000000000000000000000000000000000000000000000 +11111100000000000000000000000000000000000000000000000000 +00010000000000000000000100000000000000000000000000000000 +00000000000000000000000000000000000000000000000000000000 +00000000000000000000000010000000000000000000000100000000 +""" + self.a = np.array([ [int(c) for c in line ] for line in img.split() ], + dtype=np.float32) + self.s = scipy.sparse.coo_matrix( self.a ) + self.threshold=0.5 + if 0: + print(self.s.data) + print(self.s.row) + print(self.s.col) + + def testOK( self ): + #import pdb; pdb.set_trace() + i = self.s.row.astype( np.uint16 ) + j = self.s.col.astype( np.uint16 ) + v = self.s.data + self.assertTrue( (self.s.todense() == self.a).all() ) + dl = np.zeros( self.a.shape, 'i' ) + nd = cImageD11.connectedpixels( self.a , dl, self.threshold ) + dstart = timer() + nd = cImageD11.connectedpixels( self.a , dl, self.threshold ) + dend = timer() + sl = np.zeros( v.shape, 'i' ) + ns = cImageD11.sparse_connectedpixels( v, i, j, self.threshold, sl) + sbegin = timer() + ns = cImageD11.sparse_connectedpixels( v, i, j, self.threshold, sl) + send = timer() + sld = scipy.sparse.coo_matrix( (sl, (i, j)), shape=self.a.shape) + testcase = (sld.todense() == dl).all() + + densetime = dend - dstart + sparsetime = send-sbegin + print("sp_cp: %.3f ms vs %.3f ms, ratio %.1f nnz %d"%( + 1e3*sparsetime, 1e3*densetime, densetime/sparsetime, sld.nnz), + " t=%.0f"%(self.threshold)) + if ~testcase: + import pylab as pl + pl.ioff() + pl.figure() + pl.subplot(311) + pl.title("t=%f"%(self.threshold)) + pl.imshow( dl, interpolation='nearest') + pl.colorbar() + pl.subplot(312) + pl.imshow( sld.todense(), interpolation='nearest') + pl.colorbar() + pl.subplot(313) + pl.imshow( sld.todense() - dl, interpolation='nearest') + pl.colorbar() + pl.show() + self.assertTrue( testcase ) + + def testsplat( self ): + i = self.s.row.astype( np.uint16 ) + j = self.s.col.astype( np.uint16 ) + v = self.s.data + self.assertTrue( (self.s.todense() == self.a).all() ) + ni, nj = self.s.shape + z = np.empty( (ni+2)*(nj+2), np.int32) + sl = np.zeros( v.shape, 'i' ) + sls = np.zeros( v.shape, 'i' ) + ns = cImageD11.sparse_connectedpixels( v, i, j, self.threshold, sl) + s0 = timer() + ns = cImageD11.sparse_connectedpixels( v, i, j, self.threshold, sl) + s1 = timer() + ns = cImageD11.sparse_connectedpixels_splat( v, i, j, self.threshold, sls, z, ni, nj) + s2 = timer() + ns = cImageD11.sparse_connectedpixels_splat( v, i, j, self.threshold, sls, z, ni, nj) + s3 = timer() + sld = scipy.sparse.coo_matrix( (sl, (i, j)), shape=self.a.shape) + testcase = (sls == sl).all() + print("\nsparse_connectedpixels %.3f ms"%(1e3*(s1-s0)), + "\nsparse_connectedpixels_splat %.3f ms"%(1e3*(s3-s2))) + self.assertTrue( testcase ) + +class test_sparse_connected_pixels_overlapim1(test_sparse_connected_pixels): + def setUp(self): + im = fabio.open(OVIM).data.astype( np.float32 ) + self.a = np.where( im > 1500, im, 0 ) + self.s = scipy.sparse.coo_matrix( self.a ) + self.threshold = 2000.1 + +class test_sparse_connected_pixels_overlapim2(test_sparse_connected_pixels): + def setUp(self): + im = fabio.open(OVIM).data.astype( np.float32 ) + self.a = np.where( im > 2000.1, im, 0 ) + self.s = scipy.sparse.coo_matrix( self.a ) + self.threshold = 2000.1 + +class test_sparse_connected_pixels_overlapim3(test_sparse_connected_pixels): + def setUp(self): + im = fabio.open(OVIM).data.astype( np.float32 ) + self.a = np.where( im > 500.1, im, 0 ) + self.s = scipy.sparse.coo_matrix( self.a ) + self.threshold = 500.1 + +class test_sparse_connected_pixels_overlapim4(test_sparse_connected_pixels): + def setUp(self): + im = fabio.open(OVIM).data.astype( np.float32 ) + self.a = np.where( im > 5000.1, im, 0 ) + self.s = scipy.sparse.coo_matrix( self.a ) + self.threshold = 5000.1 + + +if __name__=="__main__": + unittest.main() + + +# cd .. && python setup.py build && cd test && PYTHONPATH=../build/lib.linux-x86_64-2.7 python test_sparse_image.py diff --git a/test/test_sym_u.py b/test/test_sym_u.py index bc361789..a551e9c0 100644 --- a/test/test_sym_u.py +++ b/test/test_sym_u.py @@ -1,5 +1,5 @@ - +import numpy as np import ImageD11.sym_u import unittest @@ -16,6 +16,10 @@ def test_hexagonal(self): def test_tetragonal(self): c = ImageD11.sym_u.tetragonal() assert len(c.group) == 8 + + def test_trigonal(self): + c = ImageD11.sym_u.trigonal() + assert len(c.group) == 6 def test_orthorhombic(self): c = ImageD11.sym_u.orthorhombic() @@ -33,7 +37,41 @@ def test_triclinic(self): c = ImageD11.sym_u.triclinic() assert len(c.group) == 1 - + +class trh( object ): + def test_right_handed(self): + self.det = np.linalg.det( self.ubi ) + for op in self.c.group: + ubi_trans = np.dot( op, self.ubi ) + self.assertAlmostEqual( np.linalg.det( ubi_trans ) , self.det ) + + +class test_tetragonal_rh( unittest.TestCase , trh): + def setUp(self): + self.ubi = np.array( [ (1.3,0,0), ( 0,1.3,0 ), (0,0,2.1) ] ) + self.c = ImageD11.sym_u.tetragonal() + +class test_cubic_rh( unittest.TestCase , trh): + def setUp(self): + self.ubi = np.array( [ (1.3,0,0), ( 0,1.3,0 ), (0,0,1.3) ] ) + self.c = ImageD11.sym_u.cubic() + +class test_hexagonal_rh( unittest.TestCase , trh): + def setUp(self): + self.ubi = np.array( [ (1,0,0), ( -0.5, np.sqrt(3)/2, 0 ), (0,0,2.1) ] ) + self.c = ImageD11.sym_u.hexagonal() + +class test_trigonal_rh( unittest.TestCase , trh): + def setUp(self): + self.ubi = np.array( [ (1,0,0), ( -0.5, np.sqrt(3)/2, 0 ), (0,0,2.1) ] ) + self.c = ImageD11.sym_u.trigonal() + +class test_orthorhombic_rh( unittest.TestCase , trh): + def setUp(self): + self.ubi = np.array( [ (1,0,0), ( 0,1.3,0 ), (0,0,2.1) ] ) + self.c = ImageD11.sym_u.orthorhombic() + + if __name__=="__main__": unittest.main() diff --git a/test/test_transform.py b/test/test_transform.py index 0988b082..7ea4a29b 100644 --- a/test/test_transform.py +++ b/test/test_transform.py @@ -29,7 +29,7 @@ class testtransform(unittest.TestCase): def setUp(self): self.peaks = np.array([ [ 10, 20 , 50, 100, 512, 2000 ], - [ 100, 10, 50, 99, 518, 2010 ] ], np.float) + [ 100, 10, 50, 99, 518, 2010 ] ], float) def test_compute_tth_eta1(self): # Check translation of 0,0,0 has no effect @@ -40,7 +40,7 @@ def test_compute_tth_eta1(self): y_center=yc, y_size=ys, tilt_y=ty, z_center=zc, z_size=zs, tilt_z=tz, distance=dist) - om = np.ones(self.peaks.shape[1], np.float) + om = np.ones(self.peaks.shape[1], float) trans = transform.compute_tth_eta(self.peaks, y_center=yc, y_size=ys, tilt_y=ty, z_center=zc, z_size=zs, tilt_z=tz, diff --git a/test/test_uncomputegv.py b/test/test_uncomputegv.py index d294981f..1ac4e5aa 100644 --- a/test/test_uncomputegv.py +++ b/test/test_uncomputegv.py @@ -7,10 +7,10 @@ class test_uncomputegv( unittest.TestCase ): def setUp(self): - self.tth = np.array( [60, 10, 15, 20, 25, 1.9555],np.float) + self.tth = np.array( [60, 10, 15, 20, 25, 1.9555],float) self.wvln = 0.5 - self.eta = np.array( [20, 10, 120, -20, 340, -73 ],np.float) - self.omega = np.array([60, 90, 180, 60, 97, 131],np.float) + self.eta = np.array( [20, 10, 120, -20, 340, -73 ],float) + self.omega = np.array([60, 90, 180, 60, 97, 131],float) self.np = len(self.tth) def test_5_10(self): diff --git a/test/testcol.py b/test/testcol.py index 0a847334..81408793 100644 --- a/test/testcol.py +++ b/test/testcol.py @@ -1,36 +1,40 @@ - +import unittest from ImageD11 import columnfile -open("test.col","w").write("""# x a b c +class testcol( unittest.TestCase): + def setUp(self): + open("test.col","w").write("""# x a b c 0 1 2 3 5 6 1 2 0 4 2 3 32 5 6 2 """) + def test1(self): + c = columnfile.columnfile( "test.col" ) + self.assertEqual( list(c.b) , [ 2, 1, 2, 6] ) + c.removerows( "b" , [1] ) + self.assertEqual( c.nrows , 3) + self.assertEqual( list(c.b) , [ 2, 2, 6] ) + def test2(self): + c = columnfile.columnfile( "test.col" ) + self.assertEqual( list(c.b) , [ 2, 1, 2, 6]) + c.removerows( "b" , [2] ) + self.assertEqual( c.nrows , 2) + self.assertEqual( list(c.b) , [ 1, 6]) + def test3(self): + c = columnfile.columnfile( "test.col" ) + self.assertEqual( list(c.a) , [ 1, 6, 4, 5]) + c.removerows( "a" , [1] ) + c.removerows( "a" , [4] ) + self.assertEqual( c.nrows , 2) + self.assertEqual( list(c.b) , [ 1, 6]) + def test4(self): + c = columnfile.columnfile( "test.col" ) + self.assertEqual( list(c.a) , [ 1, 6, 4, 5]) + c.removerows( "a" , [1, 4] ) + self.assertEqual( c.nrows , 2) + self.assertEqual( list(c.b) , [ 1, 6]) -c = columnfile.columnfile( "test.col" ) -assert list(c.b) == [ 2, 1, 2, 6] -c.removerows( "b" , [1] ) -assert c.nrows == 3 -assert list(c.b) == [ 2, 2, 6] - -c = columnfile.columnfile( "test.col" ) -assert list(c.b) == [ 2, 1, 2, 6] -c.removerows( "b" , [2] ) -assert c.nrows == 2 -assert list(c.b) == [ 1, 6] - -c = columnfile.columnfile( "test.col" ) -assert list(c.a) == [ 1, 6, 4, 5] -c.removerows( "a" , [1] ) -c.removerows( "a" , [4] ) -assert c.nrows == 2 -assert list(c.b) == [ 1, 6] - -c = columnfile.columnfile( "test.col" ) -assert list(c.a) == [ 1, 6, 4, 5] -c.removerows( "a" , [1, 4] ) -assert c.nrows == 2 -assert list(c.b) == [ 1, 6] -print "Passed" +if __name__=="__main__": + unittest.main() diff --git a/test/testcolfile2db/.gitignore b/test/testcolfile2db/.gitignore new file mode 100644 index 00000000..6033ab00 --- /dev/null +++ b/test/testcolfile2db/.gitignore @@ -0,0 +1,3 @@ +nac.db +test.db +test.flt diff --git a/test/testcolfile2db/__init__.py b/test/testcolfile2db/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/testcolfile2db/test.db b/test/testcolfile2db/test.db deleted file mode 100644 index 538d33a00cce4f1ebfe99fefe7a28f6ff6759a5d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2048 zcmWFz^vNtqRY=P(%1ta$FlJz3U}R))P*7lCU|<4bWWWgIupkM5_(TC-pejb@^$b8X z3PwXG0e?m|adC0R`jW(?oYaEU#Oz`)!Q>p|>KNjx5aQ_M 0: + lio.output2dpeaks( sptfile ) + lio.mergelast() + lio.finalise() + + +if __name__=="__main__": + testcase() diff --git a/test/testlabelimage/testlabelimage.py b/test/testlabelimage/testlabelimage.py index 8d0e6f09..0c10a482 100644 --- a/test/testlabelimage/testlabelimage.py +++ b/test/testlabelimage/testlabelimage.py @@ -1,5 +1,5 @@ import unittest - +import os import numpy as np from ImageD11 import labelimage diff --git a/test/testlattice.py b/test/testlattice.py new file mode 100644 index 00000000..4f4a2395 --- /dev/null +++ b/test/testlattice.py @@ -0,0 +1,74 @@ + +""" Write some testcases for the lattice transforming stuff """ +import unittest + +import numpy as np +from ImageD11 import refinegrains, unitcell + + +class testlattices(unittest.TestCase): + def setUp(self): + self.uc = [ 4.31, 4.33, 4.39, 90.1, 90.2, 90.3 ] + def test_triclinic(self): + cp = refinegrains.triclinic( self.uc ) + for i,j in zip(cp,self.uc): + self.assertAlmostEqual( i, j, 6 ) + def test_mono_c(self): + a,b,c,al,be,ga = refinegrains.monoclinic_c( self.uc ) + self.assertAlmostEqual( al, 90, 5 ) + self.assertAlmostEqual( be, 90, 5 ) + def test_mono_b(self): + a,b,c,al,be,ga = refinegrains.monoclinic_b( self.uc ) + self.assertAlmostEqual( al, 90, 5 ) + self.assertAlmostEqual( ga, 90, 5 ) + def test_mono_a(self): + a,b,c,al,be,ga = refinegrains.monoclinic_a( self.uc ) + self.assertAlmostEqual( be, 90, 5 ) + self.assertAlmostEqual( ga, 90, 5 ) + def test_ortho(self): + a,b,c,al,be,ga = refinegrains.orthorhombic( self.uc ) + self.assertAlmostEqual( al, 90, 5 ) + self.assertAlmostEqual( be, 90, 5 ) + self.assertAlmostEqual( ga, 90, 5 ) + def test_tetra(self): + a,b,c,al,be,ga = refinegrains.tetragonal( self.uc ) + self.assertAlmostEqual( al, 90, 5 ) + self.assertAlmostEqual( be, 90, 5 ) + self.assertAlmostEqual( ga, 90, 5 ) + self.assertAlmostEqual( a , b, 5 ) + def test_trigP(self): + a,b,c,al,be,ga = refinegrains.trigonalP( self.uc ) + self.assertAlmostEqual( a , b, 5 ) + self.assertAlmostEqual( a , c, 5 ) + self.assertAlmostEqual( al , be, 5 ) + self.assertAlmostEqual( al , ga, 5 ) + def test_trigH(self): + """ This one is quite far from the ideal cell ... """ + a,b,c,al,be,ga = refinegrains.trigonalH( self.uc ) + self.assertAlmostEqual( a , b, 5 ) + self.assertAlmostEqual( al , 90, 5 ) + self.assertAlmostEqual( be , 90, 5 ) + self.assertAlmostEqual( ga , 120, 5 ) + def test_hexagonal(self): + """ This one is quite far from the ideal cell ... """ + a,b,c,al,be,ga = refinegrains.hexagonal( self.uc ) + self.assertAlmostEqual( a , b, 5 ) + self.assertAlmostEqual( al , 90, 5 ) + self.assertAlmostEqual( be , 90, 5 ) + self.assertAlmostEqual( ga , 120, 5 ) + self.assertAlmostEqual( c, self.uc[2] ) + def test_cubic(self): + """ This one is quite far from the ideal cell ... """ + a,b,c,al,be,ga = refinegrains.cubic( self.uc ) + self.assertAlmostEqual( a, np.mean(self.uc[0:3]), 5) + self.assertAlmostEqual( a , b, 5 ) + self.assertAlmostEqual( a , c, 5 ) + self.assertAlmostEqual( al , 90, 5 ) + self.assertAlmostEqual( be , 90, 5 ) + self.assertAlmostEqual( ga , 90, 5 ) + + + + +if __name__ == "__main__": + unittest.main() diff --git a/test/testscale.py b/test/testscale.py index 2dcfdf18..e64a5fd1 100644 --- a/test/testscale.py +++ b/test/testscale.py @@ -8,7 +8,7 @@ class testscale(unittest.TestCase): """old testcase """ def testscaleimage(self): """check the object works """ - im1 = numpy.ones((10, 10), numpy.float) + im1 = numpy.ones((10, 10), float) im1[2:8, 2:8] *= 2 im1[4:6, 4:6] *= 2 im2 = im1 * 2. + 3. diff --git a/test/twinprob/__init__.py b/test/twinprob/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/twinprob/test.py b/test/twinprob/test.py deleted file mode 100644 index d2ce6fed..00000000 --- a/test/twinprob/test.py +++ /dev/null @@ -1,14 +0,0 @@ -# -from ImageD11 import indexing -myindexer = indexing.indexer() -myindexer.readgvfile( 'Al.gve' ) -myindexer.updateparameters( ) -myindexer.parameterobj.set_parameters( - {'minpks': 3, 'ring_1': 20, 'ring_2': 20} ) -myindexer.loadpars( ) -myindexer.assigntorings( ) -myindexer.find( ) -myindexer.scorethem( ) -assert len(myindexer.ubis) == 1, "Should only have one grain" -print "Seems to be OK" - diff --git a/test/twinprob/test_twin.py b/test/twinprob/test_twin.py new file mode 100644 index 00000000..086849dd --- /dev/null +++ b/test/twinprob/test_twin.py @@ -0,0 +1,28 @@ +# + +import unittest +from ImageD11 import indexing +import sys, os + +class fake(): + def write(self,*a,**k): + pass + def flush(self,*a,**k): + pass + +class testtwin( unittest.TestCase ): + def testtwin(self): + myindexer = indexing.indexer() + myindexer.readgvfile(os.path.join( + os.path.dirname(__file__), 'Al.gve' )) + myindexer.updateparameters( ) + myindexer.parameterobj.set_parameters( + {'minpks': 3, 'ring_1': 20, 'ring_2': 20} ) + myindexer.loadpars( ) + myindexer.assigntorings( ) + myindexer.find( ) + myindexer.scorethem( ) + self.assertEqual( len(myindexer.ubis), 1) + +if __name__=="__main__": + unittest.main() diff --git a/todo.txt b/todo.txt index 0bc89289..23c7f331 100644 --- a/todo.txt +++ b/todo.txt @@ -1,3 +1,45 @@ + +# 26-2-2019 + +Overhaul or new package? + +peaksearcher: + return to image-by-image processing + background? + pixels at radius / azimuth. Assume azimuth doesn't matter. + Histogram of intensity values (different intensities) at each tth + ... characterize this distribution + - tail = Bragg peaks + - azimuthal components? + powder1D with a few fourier components in azimuth + record pixel labelling for peaks + merge across frames in nDimensions and more intelligent overlaps + (fraction of pixels in peak, central part of intensity) + +Geometry: + General purpose package - look at GREWGG + Geometry of transations+rotations is in both ImageCIF and NeXus + +Indexing: + Identification of scattering vectors / g-vectors as groups of peaks: + h, -h, 2h, -2h, h@180, ... + Known unit cells (angles between g or grainspotter) + assigned to 2theta rings + Unknown (index_unknown code) + using 2theta rings and typical powder methods + additional zone searching information from 3D + +Refinement: + Geometrical for strains from far-field + Diffractometer zeros, camera distortions + Intensities (point groups needed) + Rint, Rmerge + Difftomo 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+ WebGL based demo of plot3d from ImageD11 +

+
Drop your g-vectors here
+

+ +
Scale Factor: + +
+
Pointsize: + +
+

+ Click and drag to rotate. Zoom with mouse wheel or sliders below +

+

+ +

+ +

+ Things to fix. Linux fails drag and drop - add in file upload selector. + Android fails mouse move, add in touchmove http://developer.sonymobile.com/2011/11/29/xperia-phones-first-to-support-webgl/ + Android fails sliders, find hack at http://touchpunch.furf.com/ + Clean and comment the entire code. etc. + + + + + diff --git a/webgui/demo_canvas.html b/webgui/demo_canvas.html new file mode 100644 index 00000000..da0836a6 --- /dev/null +++ b/webgui/demo_canvas.html @@ -0,0 +1,341 @@ + + + + + Plot3D - Some WebGL programming + + + + + + + + + + + + + + +

+ WebGL based demo of plot3d from ImageD11 +

+
Drop your g-vectors here
+

+ +
Scale Factor: + +
+
Pointsize: + +
+

+ Click and drag to rotate. Zoom with mouse wheel or sliders below +

+

+ +

+ +

+ Things to fix. Linux fails drag and drop - add in file upload selector. + Android fails mouse move, add in touchmove http://developer.sonymobile.com/2011/11/29/xperia-phones-first-to-support-webgl/ + Android fails sliders, find hack at http://touchpunch.furf.com/ + Clean and comment the entire code. etc. + + + + + diff --git a/webgui/gl-matrix-min.js b/webgui/gl-matrix-min.js new file mode 100644 index 00000000..b52c0821 --- /dev/null +++ b/webgui/gl-matrix-min.js @@ -0,0 +1,28 @@ +/*! +@fileoverview gl-matrix - High performance matrix and vector operations +@author Brandon Jones +@author Colin MacKenzie IV +@version 2.7.0 + +Copyright (c) 2015-2018, Brandon Jones, Colin MacKenzie IV. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. + +*/ +!function(t,n){if("object"==typeof exports&&"object"==typeof module)module.exports=n();else if("function"==typeof define&&define.amd)define([],n);else{var r=n();for(var a in r)("object"==typeof exports?exports:t)[a]=r[a]}}("undefined"!=typeof self?self:this,function(){return function(t){var n={};function r(a){if(n[a])return n[a].exports;var e=n[a]={i:a,l:!1,exports:{}};return t[a].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return r.m=t,r.c=n,r.d=function(t,n,a){r.o(t,n)||Object.defineProperty(t,n,{enumerable:!0,get:a})},r.r=function(t){"undefined"!=typeof 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m[0]=t,m[1]=n,m[2]=r,m[3]=e,m[4]=u,m[5]=o,m[6]=i,m[7]=s,m[8]=c,m[9]=f,m[10]=M,m[11]=h,m[12]=l,m[13]=v,m[14]=d,m[15]=b,m},n.set=function(t,n,r,a,e,u,o,i,s,c,f,M,h,l,v,d,b){return t[0]=n,t[1]=r,t[2]=a,t[3]=e,t[4]=u,t[5]=o,t[6]=i,t[7]=s,t[8]=c,t[9]=f,t[10]=M,t[11]=h,t[12]=l,t[13]=v,t[14]=d,t[15]=b,t},n.identity=e,n.transpose=function(t,n){if(t===n){var r=n[1],a=n[2],e=n[3],u=n[6],o=n[7],i=n[11];t[1]=n[4],t[2]=n[8],t[3]=n[12],t[4]=r,t[6]=n[9],t[7]=n[13],t[8]=a,t[9]=u,t[11]=n[14],t[12]=e,t[13]=o,t[14]=i}else t[0]=n[0],t[1]=n[4],t[2]=n[8],t[3]=n[12],t[4]=n[1],t[5]=n[5],t[6]=n[9],t[7]=n[13],t[8]=n[2],t[9]=n[6],t[10]=n[10],t[11]=n[14],t[12]=n[3],t[13]=n[7],t[14]=n[11],t[15]=n[15];return t},n.invert=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=n[4],i=n[5],s=n[6],c=n[7],f=n[8],M=n[9],h=n[10],l=n[11],v=n[12],d=n[13],b=n[14],m=n[15],p=r*i-a*o,P=r*s-e*o,A=r*c-u*o,E=a*s-e*i,O=a*c-u*i,R=e*c-u*s,y=f*d-M*v,q=f*b-h*v,x=f*m-l*v,_=M*b-h*d,Y=M*m-l*d,L=h*m-l*b,S=p*L-P*Y+A*_+E*x-O*q+R*y;if(!S)return null;return S=1/S,t[0]=(i*L-s*Y+c*_)*S,t[1]=(e*Y-a*L-u*_)*S,t[2]=(d*R-b*O+m*E)*S,t[3]=(h*O-M*R-l*E)*S,t[4]=(s*x-o*L-c*q)*S,t[5]=(r*L-e*x+u*q)*S,t[6]=(b*A-v*R-m*P)*S,t[7]=(f*R-h*A+l*P)*S,t[8]=(o*Y-i*x+c*y)*S,t[9]=(a*x-r*Y-u*y)*S,t[10]=(v*O-d*A+m*p)*S,t[11]=(M*A-f*O-l*p)*S,t[12]=(i*q-o*_-s*y)*S,t[13]=(r*_-a*q+e*y)*S,t[14]=(d*P-v*E-b*p)*S,t[15]=(f*E-M*P+h*p)*S,t},n.adjoint=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=n[4],i=n[5],s=n[6],c=n[7],f=n[8],M=n[9],h=n[10],l=n[11],v=n[12],d=n[13],b=n[14],m=n[15];return t[0]=i*(h*m-l*b)-M*(s*m-c*b)+d*(s*l-c*h),t[1]=-(a*(h*m-l*b)-M*(e*m-u*b)+d*(e*l-u*h)),t[2]=a*(s*m-c*b)-i*(e*m-u*b)+d*(e*c-u*s),t[3]=-(a*(s*l-c*h)-i*(e*l-u*h)+M*(e*c-u*s)),t[4]=-(o*(h*m-l*b)-f*(s*m-c*b)+v*(s*l-c*h)),t[5]=r*(h*m-l*b)-f*(e*m-u*b)+v*(e*l-u*h),t[6]=-(r*(s*m-c*b)-o*(e*m-u*b)+v*(e*c-u*s)),t[7]=r*(s*l-c*h)-o*(e*l-u*h)+f*(e*c-u*s),t[8]=o*(M*m-l*d)-f*(i*m-c*d)+v*(i*l-c*M),t[9]=-(r*(M*m-l*d)-f*(a*m-u*d)+v*(a*l-u*M)),t[10]=r*(i*m-c*d)-o*(a*m-u*d)+v*(a*c-u*i),t[11]=-(r*(i*l-c*M)-o*(a*l-u*M)+f*(a*c-u*i)),t[12]=-(o*(M*b-h*d)-f*(i*b-s*d)+v*(i*h-s*M)),t[13]=r*(M*b-h*d)-f*(a*b-e*d)+v*(a*h-e*M),t[14]=-(r*(i*b-s*d)-o*(a*b-e*d)+v*(a*s-e*i)),t[15]=r*(i*h-s*M)-o*(a*h-e*M)+f*(a*s-e*i),t},n.determinant=function(t){var n=t[0],r=t[1],a=t[2],e=t[3],u=t[4],o=t[5],i=t[6],s=t[7],c=t[8],f=t[9],M=t[10],h=t[11],l=t[12],v=t[13],d=t[14],b=t[15];return(n*o-r*u)*(M*b-h*d)-(n*i-a*u)*(f*b-h*v)+(n*s-e*u)*(f*d-M*v)+(r*i-a*o)*(c*b-h*l)-(r*s-e*o)*(c*d-M*l)+(a*s-e*i)*(c*v-f*l)},n.multiply=u,n.translate=function(t,n,r){var a=r[0],e=r[1],u=r[2],o=void 0,i=void 0,s=void 0,c=void 0,f=void 0,M=void 0,h=void 0,l=void 0,v=void 0,d=void 0,b=void 0,m=void 0;n===t?(t[12]=n[0]*a+n[4]*e+n[8]*u+n[12],t[13]=n[1]*a+n[5]*e+n[9]*u+n[13],t[14]=n[2]*a+n[6]*e+n[10]*u+n[14],t[15]=n[3]*a+n[7]*e+n[11]*u+n[15]):(o=n[0],i=n[1],s=n[2],c=n[3],f=n[4],M=n[5],h=n[6],l=n[7],v=n[8],d=n[9],b=n[10],m=n[11],t[0]=o,t[1]=i,t[2]=s,t[3]=c,t[4]=f,t[5]=M,t[6]=h,t[7]=l,t[8]=v,t[9]=d,t[10]=b,t[11]=m,t[12]=o*a+f*e+v*u+n[12],t[13]=i*a+M*e+d*u+n[13],t[14]=s*a+h*e+b*u+n[14],t[15]=c*a+l*e+m*u+n[15]);return t},n.scale=function(t,n,r){var a=r[0],e=r[1],u=r[2];return t[0]=n[0]*a,t[1]=n[1]*a,t[2]=n[2]*a,t[3]=n[3]*a,t[4]=n[4]*e,t[5]=n[5]*e,t[6]=n[6]*e,t[7]=n[7]*e,t[8]=n[8]*u,t[9]=n[9]*u,t[10]=n[10]*u,t[11]=n[11]*u,t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],t},n.rotate=function(t,n,r,e){var u=e[0],o=e[1],i=e[2],s=Math.sqrt(u*u+o*o+i*i),c=void 0,f=void 0,M=void 0,h=void 0,l=void 0,v=void 0,d=void 0,b=void 0,m=void 0,p=void 0,P=void 0,A=void 0,E=void 0,O=void 0,R=void 0,y=void 0,q=void 0,x=void 0,_=void 0,Y=void 0,L=void 0,S=void 0,w=void 0,I=void 0;if(s0?(r[0]=2*(c*s+h*e+f*i-M*u)/l,r[1]=2*(f*s+h*u+M*e-c*i)/l,r[2]=2*(M*s+h*i+c*u-f*e)/l):(r[0]=2*(c*s+h*e+f*i-M*u),r[1]=2*(f*s+h*u+M*e-c*i),r[2]=2*(M*s+h*i+c*u-f*e));return o(t,n,r),t},n.getTranslation=function(t,n){return t[0]=n[12],t[1]=n[13],t[2]=n[14],t},n.getScaling=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[4],o=n[5],i=n[6],s=n[8],c=n[9],f=n[10];return t[0]=Math.sqrt(r*r+a*a+e*e),t[1]=Math.sqrt(u*u+o*o+i*i),t[2]=Math.sqrt(s*s+c*c+f*f),t},n.getRotation=function(t,n){var r=n[0]+n[5]+n[10],a=0;r>0?(a=2*Math.sqrt(r+1),t[3]=.25*a,t[0]=(n[6]-n[9])/a,t[1]=(n[8]-n[2])/a,t[2]=(n[1]-n[4])/a):n[0]>n[5]&&n[0]>n[10]?(a=2*Math.sqrt(1+n[0]-n[5]-n[10]),t[3]=(n[6]-n[9])/a,t[0]=.25*a,t[1]=(n[1]+n[4])/a,t[2]=(n[8]+n[2])/a):n[5]>n[10]?(a=2*Math.sqrt(1+n[5]-n[0]-n[10]),t[3]=(n[8]-n[2])/a,t[0]=(n[1]+n[4])/a,t[1]=.25*a,t[2]=(n[6]+n[9])/a):(a=2*Math.sqrt(1+n[10]-n[0]-n[5]),t[3]=(n[1]-n[4])/a,t[0]=(n[8]+n[2])/a,t[1]=(n[6]+n[9])/a,t[2]=.25*a);return t},n.fromRotationTranslationScale=function(t,n,r,a){var e=n[0],u=n[1],o=n[2],i=n[3],s=e+e,c=u+u,f=o+o,M=e*s,h=e*c,l=e*f,v=u*c,d=u*f,b=o*f,m=i*s,p=i*c,P=i*f,A=a[0],E=a[1],O=a[2];return t[0]=(1-(v+b))*A,t[1]=(h+P)*A,t[2]=(l-p)*A,t[3]=0,t[4]=(h-P)*E,t[5]=(1-(M+b))*E,t[6]=(d+m)*E,t[7]=0,t[8]=(l+p)*O,t[9]=(d-m)*O,t[10]=(1-(M+v))*O,t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},n.fromRotationTranslationScaleOrigin=function(t,n,r,a,e){var u=n[0],o=n[1],i=n[2],s=n[3],c=u+u,f=o+o,M=i+i,h=u*c,l=u*f,v=u*M,d=o*f,b=o*M,m=i*M,p=s*c,P=s*f,A=s*M,E=a[0],O=a[1],R=a[2],y=e[0],q=e[1],x=e[2],_=(1-(d+m))*E,Y=(l+A)*E,L=(v-P)*E,S=(l-A)*O,w=(1-(h+m))*O,I=(b+p)*O,N=(v+P)*R,g=(b-p)*R,T=(1-(h+d))*R;return t[0]=_,t[1]=Y,t[2]=L,t[3]=0,t[4]=S,t[5]=w,t[6]=I,t[7]=0,t[8]=N,t[9]=g,t[10]=T,t[11]=0,t[12]=r[0]+y-(_*y+S*q+N*x),t[13]=r[1]+q-(Y*y+w*q+g*x),t[14]=r[2]+x-(L*y+I*q+T*x),t[15]=1,t},n.fromQuat=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=r+r,i=a+a,s=e+e,c=r*o,f=a*o,M=a*i,h=e*o,l=e*i,v=e*s,d=u*o,b=u*i,m=u*s;return t[0]=1-M-v,t[1]=f+m,t[2]=h-b,t[3]=0,t[4]=f-m,t[5]=1-c-v,t[6]=l+d,t[7]=0,t[8]=h+b,t[9]=l-d,t[10]=1-c-M,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},n.frustum=function(t,n,r,a,e,u,o){var i=1/(r-n),s=1/(e-a),c=1/(u-o);return t[0]=2*u*i,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*u*s,t[6]=0,t[7]=0,t[8]=(r+n)*i,t[9]=(e+a)*s,t[10]=(o+u)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*u*2*c,t[15]=0,t},n.perspective=function(t,n,r,a,e){var u=1/Math.tan(n/2),o=void 0;t[0]=u/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=u,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=-1,t[12]=0,t[13]=0,t[15]=0,null!=e&&e!==1/0?(o=1/(a-e),t[10]=(e+a)*o,t[14]=2*e*a*o):(t[10]=-1,t[14]=-2*a);return t},n.perspectiveFromFieldOfView=function(t,n,r,a){var e=Math.tan(n.upDegrees*Math.PI/180),u=Math.tan(n.downDegrees*Math.PI/180),o=Math.tan(n.leftDegrees*Math.PI/180),i=Math.tan(n.rightDegrees*Math.PI/180),s=2/(o+i),c=2/(e+u);return t[0]=s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-i)*s*.5,t[9]=(e-u)*c*.5,t[10]=a/(r-a),t[11]=-1,t[12]=0,t[13]=0,t[14]=a*r/(r-a),t[15]=0,t},n.ortho=function(t,n,r,a,e,u,o){var i=1/(n-r),s=1/(a-e),c=1/(u-o);return t[0]=-2*i,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*s,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(n+r)*i,t[13]=(e+a)*s,t[14]=(o+u)*c,t[15]=1,t},n.lookAt=function(t,n,r,u){var o=void 0,i=void 0,s=void 0,c=void 0,f=void 0,M=void 0,h=void 0,l=void 0,v=void 0,d=void 0,b=n[0],m=n[1],p=n[2],P=u[0],A=u[1],E=u[2],O=r[0],R=r[1],y=r[2];if(Math.abs(b-O)0&&(l=1/Math.sqrt(l),f*=l,M*=l,h*=l);var v=s*h-c*M,d=c*f-i*h,b=i*M-s*f;(l=v*v+d*d+b*b)>0&&(l=1/Math.sqrt(l),v*=l,d*=l,b*=l);return t[0]=v,t[1]=d,t[2]=b,t[3]=0,t[4]=M*b-h*d,t[5]=h*v-f*b,t[6]=f*d-M*v,t[7]=0,t[8]=f,t[9]=M,t[10]=h,t[11]=0,t[12]=e,t[13]=u,t[14]=o,t[15]=1,t},n.str=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"},n.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2)+Math.pow(t[9],2)+Math.pow(t[10],2)+Math.pow(t[11],2)+Math.pow(t[12],2)+Math.pow(t[13],2)+Math.pow(t[14],2)+Math.pow(t[15],2))},n.add=function(t,n,r){return t[0]=n[0]+r[0],t[1]=n[1]+r[1],t[2]=n[2]+r[2],t[3]=n[3]+r[3],t[4]=n[4]+r[4],t[5]=n[5]+r[5],t[6]=n[6]+r[6],t[7]=n[7]+r[7],t[8]=n[8]+r[8],t[9]=n[9]+r[9],t[10]=n[10]+r[10],t[11]=n[11]+r[11],t[12]=n[12]+r[12],t[13]=n[13]+r[13],t[14]=n[14]+r[14],t[15]=n[15]+r[15],t},n.subtract=i,n.multiplyScalar=function(t,n,r){return t[0]=n[0]*r,t[1]=n[1]*r,t[2]=n[2]*r,t[3]=n[3]*r,t[4]=n[4]*r,t[5]=n[5]*r,t[6]=n[6]*r,t[7]=n[7]*r,t[8]=n[8]*r,t[9]=n[9]*r,t[10]=n[10]*r,t[11]=n[11]*r,t[12]=n[12]*r,t[13]=n[13]*r,t[14]=n[14]*r,t[15]=n[15]*r,t},n.multiplyScalarAndAdd=function(t,n,r,a){return t[0]=n[0]+r[0]*a,t[1]=n[1]+r[1]*a,t[2]=n[2]+r[2]*a,t[3]=n[3]+r[3]*a,t[4]=n[4]+r[4]*a,t[5]=n[5]+r[5]*a,t[6]=n[6]+r[6]*a,t[7]=n[7]+r[7]*a,t[8]=n[8]+r[8]*a,t[9]=n[9]+r[9]*a,t[10]=n[10]+r[10]*a,t[11]=n[11]+r[11]*a,t[12]=n[12]+r[12]*a,t[13]=n[13]+r[13]*a,t[14]=n[14]+r[14]*a,t[15]=n[15]+r[15]*a,t},n.exactEquals=function(t,n){return t[0]===n[0]&&t[1]===n[1]&&t[2]===n[2]&&t[3]===n[3]&&t[4]===n[4]&&t[5]===n[5]&&t[6]===n[6]&&t[7]===n[7]&&t[8]===n[8]&&t[9]===n[9]&&t[10]===n[10]&&t[11]===n[11]&&t[12]===n[12]&&t[13]===n[13]&&t[14]===n[14]&&t[15]===n[15]},n.equals=function(t,n){var r=t[0],e=t[1],u=t[2],o=t[3],i=t[4],s=t[5],c=t[6],f=t[7],M=t[8],h=t[9],l=t[10],v=t[11],d=t[12],b=t[13],m=t[14],p=t[15],P=n[0],A=n[1],E=n[2],O=n[3],R=n[4],y=n[5],q=n[6],x=n[7],_=n[8],Y=n[9],L=n[10],S=n[11],w=n[12],I=n[13],N=n[14],g=n[15];return 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a=function(t){if(t&&t.__esModule)return t;var n={};if(null!=t)for(var r in t)Object.prototype.hasOwnProperty.call(t,r)&&(n[r]=t[r]);return n.default=t,n}(r(0));function e(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}function u(t,n,r){var a=n[0],e=n[1],u=n[2],o=n[3],i=n[4],s=n[5],c=n[6],f=n[7],M=n[8],h=n[9],l=n[10],v=n[11],d=n[12],b=n[13],m=n[14],p=n[15],P=r[0],A=r[1],E=r[2],O=r[3];return t[0]=P*a+A*i+E*M+O*d,t[1]=P*e+A*s+E*h+O*b,t[2]=P*u+A*c+E*l+O*m,t[3]=P*o+A*f+E*v+O*p,P=r[4],A=r[5],E=r[6],O=r[7],t[4]=P*a+A*i+E*M+O*d,t[5]=P*e+A*s+E*h+O*b,t[6]=P*u+A*c+E*l+O*m,t[7]=P*o+A*f+E*v+O*p,P=r[8],A=r[9],E=r[10],O=r[11],t[8]=P*a+A*i+E*M+O*d,t[9]=P*e+A*s+E*h+O*b,t[10]=P*u+A*c+E*l+O*m,t[11]=P*o+A*f+E*v+O*p,P=r[12],A=r[13],E=r[14],O=r[15],t[12]=P*a+A*i+E*M+O*d,t[13]=P*e+A*s+E*h+O*b,t[14]=P*u+A*c+E*l+O*m,t[15]=P*o+A*f+E*v+O*p,t}function o(t,n,r){var a=n[0],e=n[1],u=n[2],o=n[3],i=a+a,s=e+e,c=u+u,f=a*i,M=a*s,h=a*c,l=e*s,v=e*c,d=u*c,b=o*i,m=o*s,p=o*c;return t[0]=1-(l+d),t[1]=M+p,t[2]=h-m,t[3]=0,t[4]=M-p,t[5]=1-(f+d),t[6]=v+b,t[7]=0,t[8]=h+m,t[9]=v-b,t[10]=1-(f+l),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}function i(t,n,r){return t[0]=n[0]-r[0],t[1]=n[1]-r[1],t[2]=n[2]-r[2],t[3]=n[3]-r[3],t[4]=n[4]-r[4],t[5]=n[5]-r[5],t[6]=n[6]-r[6],t[7]=n[7]-r[7],t[8]=n[8]-r[8],t[9]=n[9]-r[9],t[10]=n[10]-r[10],t[11]=n[11]-r[11],t[12]=n[12]-r[12],t[13]=n[13]-r[13],t[14]=n[14]-r[14],t[15]=n[15]-r[15],t}n.mul=u,n.sub=i},function(t,n,r){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.sub=n.mul=void 0,n.create=function(){var t=new a.ARRAY_TYPE(9);a.ARRAY_TYPE!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0);return t[0]=1,t[4]=1,t[8]=1,t},n.fromMat4=function(t,n){return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[4],t[4]=n[5],t[5]=n[6],t[6]=n[8],t[7]=n[9],t[8]=n[10],t},n.clone=function(t){var n=new a.ARRAY_TYPE(9);return n[0]=t[0],n[1]=t[1],n[2]=t[2],n[3]=t[3],n[4]=t[4],n[5]=t[5],n[6]=t[6],n[7]=t[7],n[8]=t[8],n},n.copy=function(t,n){return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t},n.fromValues=function(t,n,r,e,u,o,i,s,c){var f=new a.ARRAY_TYPE(9);return f[0]=t,f[1]=n,f[2]=r,f[3]=e,f[4]=u,f[5]=o,f[6]=i,f[7]=s,f[8]=c,f},n.set=function(t,n,r,a,e,u,o,i,s,c){return t[0]=n,t[1]=r,t[2]=a,t[3]=e,t[4]=u,t[5]=o,t[6]=i,t[7]=s,t[8]=c,t},n.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},n.transpose=function(t,n){if(t===n){var r=n[1],a=n[2],e=n[5];t[1]=n[3],t[2]=n[6],t[3]=r,t[5]=n[7],t[6]=a,t[7]=e}else t[0]=n[0],t[1]=n[3],t[2]=n[6],t[3]=n[1],t[4]=n[4],t[5]=n[7],t[6]=n[2],t[7]=n[5],t[8]=n[8];return t},n.invert=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=n[4],i=n[5],s=n[6],c=n[7],f=n[8],M=f*o-i*c,h=-f*u+i*s,l=c*u-o*s,v=r*M+a*h+e*l;if(!v)return null;return v=1/v,t[0]=M*v,t[1]=(-f*a+e*c)*v,t[2]=(i*a-e*o)*v,t[3]=h*v,t[4]=(f*r-e*s)*v,t[5]=(-i*r+e*u)*v,t[6]=l*v,t[7]=(-c*r+a*s)*v,t[8]=(o*r-a*u)*v,t},n.adjoint=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=n[4],i=n[5],s=n[6],c=n[7],f=n[8];return t[0]=o*f-i*c,t[1]=e*c-a*f,t[2]=a*i-e*o,t[3]=i*s-u*f,t[4]=r*f-e*s,t[5]=e*u-r*i,t[6]=u*c-o*s,t[7]=a*s-r*c,t[8]=r*o-a*u,t},n.determinant=function(t){var n=t[0],r=t[1],a=t[2],e=t[3],u=t[4],o=t[5],i=t[6],s=t[7],c=t[8];return n*(c*u-o*s)+r*(-c*e+o*i)+a*(s*e-u*i)},n.multiply=e,n.translate=function(t,n,r){var a=n[0],e=n[1],u=n[2],o=n[3],i=n[4],s=n[5],c=n[6],f=n[7],M=n[8],h=r[0],l=r[1];return t[0]=a,t[1]=e,t[2]=u,t[3]=o,t[4]=i,t[5]=s,t[6]=h*a+l*o+c,t[7]=h*e+l*i+f,t[8]=h*u+l*s+M,t},n.rotate=function(t,n,r){var a=n[0],e=n[1],u=n[2],o=n[3],i=n[4],s=n[5],c=n[6],f=n[7],M=n[8],h=Math.sin(r),l=Math.cos(r);return t[0]=l*a+h*o,t[1]=l*e+h*i,t[2]=l*u+h*s,t[3]=l*o-h*a,t[4]=l*i-h*e,t[5]=l*s-h*u,t[6]=c,t[7]=f,t[8]=M,t},n.scale=function(t,n,r){var a=r[0],e=r[1];return t[0]=a*n[0],t[1]=a*n[1],t[2]=a*n[2],t[3]=e*n[3],t[4]=e*n[4],t[5]=e*n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t},n.fromTranslation=function(t,n){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=n[0],t[7]=n[1],t[8]=1,t},n.fromRotation=function(t,n){var r=Math.sin(n),a=Math.cos(n);return t[0]=a,t[1]=r,t[2]=0,t[3]=-r,t[4]=a,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},n.fromScaling=function(t,n){return t[0]=n[0],t[1]=0,t[2]=0,t[3]=0,t[4]=n[1],t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},n.fromMat2d=function(t,n){return t[0]=n[0],t[1]=n[1],t[2]=0,t[3]=n[2],t[4]=n[3],t[5]=0,t[6]=n[4],t[7]=n[5],t[8]=1,t},n.fromQuat=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=r+r,i=a+a,s=e+e,c=r*o,f=a*o,M=a*i,h=e*o,l=e*i,v=e*s,d=u*o,b=u*i,m=u*s;return t[0]=1-M-v,t[3]=f-m,t[6]=h+b,t[1]=f+m,t[4]=1-c-v,t[7]=l-d,t[2]=h-b,t[5]=l+d,t[8]=1-c-M,t},n.normalFromMat4=function(t,n){var r=n[0],a=n[1],e=n[2],u=n[3],o=n[4],i=n[5],s=n[6],c=n[7],f=n[8],M=n[9],h=n[10],l=n[11],v=n[12],d=n[13],b=n[14],m=n[15],p=r*i-a*o,P=r*s-e*o,A=r*c-u*o,E=a*s-e*i,O=a*c-u*i,R=e*c-u*s,y=f*d-M*v,q=f*b-h*v,x=f*m-l*v,_=M*b-h*d,Y=M*m-l*d,L=h*m-l*b,S=p*L-P*Y+A*_+E*x-O*q+R*y;if(!S)return null;return S=1/S,t[0]=(i*L-s*Y+c*_)*S,t[1]=(s*x-o*L-c*q)*S,t[2]=(o*Y-i*x+c*y)*S,t[3]=(e*Y-a*L-u*_)*S,t[4]=(r*L-e*x+u*q)*S,t[5]=(a*x-r*Y-u*y)*S,t[6]=(d*R-b*O+m*E)*S,t[7]=(b*A-v*R-m*P)*S,t[8]=(v*O-d*A+m*p)*S,t},n.projection=function(t,n,r){return t[0]=2/n,t[1]=0,t[2]=0,t[3]=0,t[4]=-2/r,t[5]=0,t[6]=-1,t[7]=1,t[8]=1,t},n.str=function(t){return"mat3("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+")"},n.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2))},n.add=function(t,n,r){return t[0]=n[0]+r[0],t[1]=n[1]+r[1],t[2]=n[2]+r[2],t[3]=n[3]+r[3],t[4]=n[4]+r[4],t[5]=n[5]+r[5],t[6]=n[6]+r[6],t[7]=n[7]+r[7],t[8]=n[8]+r[8],t},n.subtract=u,n.multiplyScalar=function(t,n,r){return t[0]=n[0]*r,t[1]=n[1]*r,t[2]=n[2]*r,t[3]=n[3]*r,t[4]=n[4]*r,t[5]=n[5]*r,t[6]=n[6]*r,t[7]=n[7]*r,t[8]=n[8]*r,t},n.multiplyScalarAndAdd=function(t,n,r,a){return t[0]=n[0]+r[0]*a,t[1]=n[1]+r[1]*a,t[2]=n[2]+r[2]*a,t[3]=n[3]+r[3]*a,t[4]=n[4]+r[4]*a,t[5]=n[5]+r[5]*a,t[6]=n[6]+r[6]*a,t[7]=n[7]+r[7]*a,t[8]=n[8]+r[8]*a,t},n.exactEquals=function(t,n){return t[0]===n[0]&&t[1]===n[1]&&t[2]===n[2]&&t[3]===n[3]&&t[4]===n[4]&&t[5]===n[5]&&t[6]===n[6]&&t[7]===n[7]&&t[8]===n[8]},n.equals=function(t,n){var r=t[0],e=t[1],u=t[2],o=t[3],i=t[4],s=t[5],c=t[6],f=t[7],M=t[8],h=n[0],l=n[1],v=n[2],d=n[3],b=n[4],m=n[5],p=n[6],P=n[7],A=n[8];return 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t[0]=a*i+u*s,t[1]=e*i+o*s,t[2]=a*c+u*f,t[3]=e*c+o*f,t}function u(t,n,r){return t[0]=n[0]-r[0],t[1]=n[1]-r[1],t[2]=n[2]-r[2],t[3]=n[3]-r[3],t}n.mul=e,n.sub=u},function(t,n,r){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.vec4=n.vec3=n.vec2=n.quat2=n.quat=n.mat4=n.mat3=n.mat2d=n.mat2=n.glMatrix=void 0;var a=l(r(0)),e=l(r(9)),u=l(r(8)),o=l(r(5)),i=l(r(4)),s=l(r(3)),c=l(r(7)),f=l(r(6)),M=l(r(2)),h=l(r(1));function l(t){if(t&&t.__esModule)return t;var n={};if(null!=t)for(var r in t)Object.prototype.hasOwnProperty.call(t,r)&&(n[r]=t[r]);return n.default=t,n}n.glMatrix=a,n.mat2=e,n.mat2d=u,n.mat3=o,n.mat4=i,n.quat=s,n.quat2=c,n.vec2=f,n.vec3=M,n.vec4=h}])}); diff --git a/webgui/notes_for_new_gui.md b/webgui/notes_for_new_gui.md new file mode 100644 index 00000000..350e0857 --- /dev/null +++ b/webgui/notes_for_new_gui.md @@ -0,0 +1,210 @@ + + +# ImageD11_webgui : Feb 2019 + +We would like to make a graphical interface to access the pieces of ImageD11 in a way that helps new users to get started quickly while also being useful for experts and doing things like managing batch jobs. + +This is also a moment to review what is in the program in terms of functionality and what to do to improve it. + +## What should it do ? + +### background and peaksearching from images + +- open a single image (via fabio) and show it on screen +- apply corrections (dark, flat) to see they are done right +- display 1D line cuts, numbers inside pixels, image statistics +- estimate the background (single image), e.g. via filters on radial transform +- create a file series (sequence of images) for rotation scan or difftomo or serial crystallography +- estimate the background from a sequence, e.g. via filters on image number +- single threshold blob identification (was peaksearch.py) +- multi-threshold merging (was merge_flt.py) +- localmax labeling algorithm (not yet user visible) +- One True PeakSearch : local max tends to over-segment background +- saving output : ... + +### Peak search output + +Per frame. Should be much smaller than the frame itself. Can back reference to the frame data. + +For each spot: +- Pixel indices? +- slow / fast / sum_i / sum_1 / bounding_box +- implicit "assigned to background" +- 1D integration ? + +Typical case. Difftomo at 10 fps for 48 hours, 1.728x10^6 images, 13 Tb for raw unsigned short. + +Some data storage notes for pixel indices. Per peak requirement appears to be of the order 100 bytes assuming compression: + >>> import numpy, zlib + >>> im = numpy.zeros((2048,2048),numpy.uint8) + >>> im[40:80,400:450]=1 + >>> indices = numpy.arange( im.shape[0]*im.shape[1] )[ im.ravel() > 0 ] + >>> dI = indices.copy() + >>> dI[1:]= indices[1:]-indices[:-1] + >>> print( "Full image",len( im.ravel().tostring() ) ) + Full image 4194304 + >>> print( "Full image",len(zlib.compress( im.ravel() ) ) ) + Full image 4177 + >>> print( "Indices as float32",len(zlib.compress( indices.astype(numpy.uint32) ) ) ) + Indices as float32 2294 + >>> print( "Diff indices",len(zlib.compress( dI.astype(numpy.uint32) ) ) ) + Diff indices 81 + +This is 10 kbytes for some hypothetical peak. If we use the localmaxlabel code to label all pixels as belonging to a local maximum then we run into a problem (144K peaks for some typical image and labels compresses to 680 Kb). There must be a threshold to decide whether a peak should be stored or not and so there must be a way to decide what is "signal" and what is "noise". + +Per scan dimension (over omega, diffractometer_y, sample_z, etc) +- Links to say peaks are overlapping, thought to the the same peak + +Storage requirements: +- Peaks : Nimages * npk_per_image * sizeof(pk) +- Links : Nimages * npk_per_image * ndims * sizeof(link) + +Will need some code to make the unique peaks out of the peaks + links. + +TODO: pixel index format + +TODO: linking nD + +TODO: UI + +### calibration and scattering vectors + +- powder style : beam centre, tilts, distance, etc. ImageD11_gui/transform or fable/transformation +- Friedel pairs : to be added to fit wedge / tilt_x +- more generalised geometry descriptions. Use imgCIF/CBF, see, e.g.: + +https://sites.google.com/site/nexuscbf/mapping-draft/functional-mapping + +loop_ + _axis.id + _axis.depends_on + _axis.equipment + _axis.offset[1] + _axis.offset[2] + _axis.offset[3] + _axis.type + _axis.vector[1] + _axis.vector[2] + _axis.vector[3] +y_pixel_offset GONIOMETER_OMEGA detector -38.6 39.95 0 translation 0 -1 0 +x_pixel_offset y_pixel_offset detector 0 0 0 translation 1 0 0 +DETECTOR_Z '.' detector 0 0 0 translation 0 0 -1 +GONIOMETER_OMEGA DETECTOR_Z detector 0 0 0 rotation 0 0 -1 +GONIOMETER_PHI GONIOMETER_KAPPA goniometer 0 0 0 rotation 1 0 0 +GONIOMETER_KAPPA '.' goniometer 0 0 0 rotation 0 -1 0 + +/detector:/NXdetector + /transformations:NXtransformations + DATASET "GONIOMETER_OMEGA" = [0] + @depends_on="/entry/instrument/detector/transformations/DETECTOR_Z” + @equipment="detector + @long_name="GONIOMETER_OMEGA + @transformation_type="rotation" + @units="degrees + @vector=[0, 0, 1] + +Most simple (to program) will be to make an internal format that (theoretically) imports and exports to those formats, in order to be independent from other libraries. Some work done in grewgg. + +### Grain mapping - big beam + +Centre of mass + position refinement in makemap.py. There is also FitAllB and a series of test programs that are tested in the test/simul_1000_grains folder. Mostly needs a better UI. Perhaps look at nlopt or other more professional minimisers. minuit in FitAllB is good but hard to install. Scipy has a range of minimisers. + +### Grain mapping - smaller beam Difftomo + +Need some gui for seeing grains and sinograms and doing selections on each. Thus far we had things doing: + +- point_by_point peak counting (uniq or total) : TODO look at intensities +- sinogram reconstruction for indexed peaks + +### Unknown indexing + +Need some gui for seeing lattices (e.g. list of real space vectors, cross tables) and 3D viewer. + +### Data integration + +Was the "fabric" program. Perhaps out of scope for now. Can orientations be sent to dials? + +### Results display + +TODO: color grains according to orientation + +TODO: group orientations / grains together if they are the same + +TODO: check for twin relations and coincident lattices systematically. + +TODO: Look at the Rint for intensities + + +## Historical background + +A uI seems to be a hard thing to make. Historically we had/have: + +- Tkinter for a gui with: matplotlib for 2D plotting, pyopengl/togl for 3D plotting, and a home-made macro recorder. Still runs but now uses home-made pyopengltk for 3D which still has opengl problems. + +- Fable/RCP java eclipse based gui that also hosted the rest of the fable suite. Initially use xmlrpc to call python and then changed to a jepp embedded python interpreter. Nice but no java progammers currently active on the project. + +- This "webgui" folder was a proof-of-concept for a webgl widget, but was blocked web security preventing files being opened (no simple file selector). + +- The silx_guis folder has a few mini utilities using PyQT based widgets. This is the toolbox being developed at ESRF. + +## Options + +### PyQT + +This seems to be the good choice for cross platform desktop applications. Jon would need to learn QT. Easy to get expert help here at ESRF. A concern is that C++ is first class and there is more than one python binding (PyQT3, PyQT4, PyQT5, PySide, PySide2). + +There are plenty of recipes for making a program that someone goes and runs. Probably it will have a web backend in the future, if not already. + +There are a lot of developments around silx at ESRF that seem to use pyqt for gui. Probably you can use silx without using the gui. + +### Jupyter + +This is well funded outside and seems to see wide adoption. Perhaps silx will target it. + +There are several options: +- jupyterlab "next generation web-based user interface" +- jupyternotebook. Presumably the thing making .ipynb files +- jupyterconsole. Looks like it talks to backends +- qtconsole. Python console with inline plots + +It has many nice widgets: +- pythreejs widget offering 3D +- bqplot / matplotlib / altair / bokeh for 2D plotting +- csv viewer for large files (can scroll million rows) + +For batch jobs and clusters there is: +- ipyparallel (uses zeromq) +- dask.distributed and/or dask-jobqueue can send jobs to a cluster. + +It was less obvious how someone would "run a program" in the more traditional sense, but this seems to be a thing too: + https://github.com/jupyterlab/jupyterlab/blob/master/examples/filebrowser + https://github.com/jupyterlab/jupyterlab/tree/master/examples/app + +Jupyterlab looks a bit like eclipse/rcp running in a web browser. Written in typescript, it seems to be a new version of ipython for the web. + +Drawbacks / concerns: +- Installation and distribution fears : do you just need conda + recent web browser ? +- Fast moving technologies +- Can you see it running on OAR from a debian desktop ? +- Can you make a standalone exe ? +- ipynb has some drawbacks as you can edit cells that were previously run (not reproducible) + +### Bokeh + +Youtube video : https://www.youtube.com/watch?v=HmI1foA0MZc + +Developing Dashboard Applications Using Bokeh - Bryan Van de Ven + +### Home-made web application + +This got stuck and abandoned in the past, it was just too difficult for a one person effort. + +### Tkinter + +This still runs and is used. It seems to be very difficult to add things to it and maintain it. + + + + + + diff --git a/webgui/webgl-utils.js b/webgui/webgl-utils.js new file mode 100644 index 00000000..2435116f --- /dev/null +++ b/webgui/webgl-utils.js @@ -0,0 +1,176 @@ +/* + * Copyright 2010, Google Inc. + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * * Neither the name of Google Inc. nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + + +/** + * @fileoverview This file contains functions every webgl program will need + * a version of one way or another. + * + * Instead of setting up a context manually it is recommended to + * use. This will check for success or failure. On failure it + * will attempt to present an approriate message to the user. + * + * gl = WebGLUtils.setupWebGL(canvas); + * + * For animated WebGL apps use of setTimeout or setInterval are + * discouraged. It is recommended you structure your rendering + * loop like this. + * + * function render() { + * window.requestAnimFrame(render, canvas); + * + * // do rendering + * ... + * } + * render(); + * + * This will call your rendering function up to the refresh rate + * of your display but will stop rendering if your app is not + * visible. + */ + +WebGLUtils = function() { + +/** + * Creates the HTLM for a failure message + * @param {string} canvasContainerId id of container of th + * canvas. + * @return {string} The html. + */ +var makeFailHTML = function(msg) { + return '' + + '' + + '
' + + '
' + + '
' + msg + '
' + + '
' + + '
'; +}; + +/** + * Mesasge for getting a webgl browser + * @type {string} + */ +var GET_A_WEBGL_BROWSER = '' + + 'This page requires a browser that supports WebGL.
' + + 'Click here to upgrade your browser.'; + +/** + * Mesasge for need better hardware + * @type {string} + */ +var OTHER_PROBLEM = '' + + "It doesn't appear your computer can support WebGL.
" + + 'Click here for more information.'; + +/** + * Creates a webgl context. If creation fails it will + * change the contents of the container of the + * tag to an error message with the correct links for WebGL. + * @param {Element} canvas. The canvas element to create a + * context from. + * @param {WebGLContextCreationAttirbutes} opt_attribs Any + * creation attributes you want to pass in. + * @return {WebGLRenderingContext} The created context. + */ +var setupWebGL = function(canvas, opt_attribs) { + function showLink(str) { + var container = canvas.parentNode; + if (container) { + container.innerHTML = makeFailHTML(str); + } + }; + + if (!window.WebGLRenderingContext) { + showLink(GET_A_WEBGL_BROWSER); + return null; + } + + var context = create3DContext(canvas, opt_attribs); + if (!context) { + showLink(OTHER_PROBLEM); + } + return context; +}; + +/** + * Creates a webgl context. + * @param {!Canvas} canvas The canvas tag to get context + * from. If one is not passed in one will be created. + * @return {!WebGLContext} The created context. + */ +var create3DContext = function(canvas, opt_attribs) { + var names = ["webgl", "experimental-webgl", "webkit-3d", "moz-webgl"]; + var context = null; + for (var ii = 0; ii < names.length; ++ii) { + try { + context = canvas.getContext(names[ii], opt_attribs); + } catch(e) {} + if (context) { + break; + } + } + return context; +} + +return { + create3DContext: create3DContext, + setupWebGL: setupWebGL +}; +}(); + +/** + * Provides requestAnimationFrame in a cross browser way. + */ +window.requestAnimFrame = (function() { + return window.requestAnimationFrame || + window.webkitRequestAnimationFrame || + window.mozRequestAnimationFrame || + window.oRequestAnimationFrame || + window.msRequestAnimationFrame || + function(/* function FrameRequestCallback */ callback, /* DOMElement Element */ element) { + return window.setTimeout(callback, 1000/60); + }; +})(); + +/** + * Provides cancelRequestAnimationFrame in a cross browser way. + */ +window.cancelRequestAnimFrame = (function() { + return window.cancelCancelRequestAnimationFrame || + window.webkitCancelRequestAnimationFrame || + window.mozCancelRequestAnimationFrame || + window.oCancelRequestAnimationFrame || + window.msCancelRequestAnimationFrame || + window.clearTimeout; +})(); + +