Avoid using import * in Python MR and PET demo scripts (#1215)

* switched to using importlib in MR examples

* switched to using importlib in PET examples

* trying to placate Codacy...

* more Codacy issues attended to

* removed unused import warnings from PET demo scripts

* [ci skip] updated CHANGES.md

* removed MessageRedirector.quit, using assignment to _ instead

CHANGES.md

@@ -1,11 +1,18 @@
 # ChangeLog
 
-## xx.xx.xx
+## v3.5.1
 
 * CMake/building:
   - default `DISABLE_MATLAB` to `ON` as our Matlab support is out-of-date and could
   generate conflicts with Python shared libraries.
 
+* Demo scripts:
+  - replaced importing reconstruction engines by
+```
+ exec('from sirf.' + args['--engine'] + ' import *')
+```
+  with importing via `importlib.import_module` thus getting rid of Codacy complaints about undefined modules.
+
 ## v3.5.0
 
 * GitHub Action: remove temporarily the Ubuntu 20.04 build, #1178

examples/Python/MR/acquisition_data.py

@@ -38,10 +38,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
-from ast import literal_eval
+from sirf.Utilities import error, examples_data_path, existing_filepath
 
-# import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+# import MR engine module
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -57,7 +58,7 @@ def main():
     input_file = existing_filepath(data_path, data_file)
 
     # acquisition data will be read from an HDF file input_file
-    acq_data = AcquisitionData(input_file, True)
+    acq_data = mr.AcquisitionData(input_file, True)
 
     # the raw k-space data is a list of different readouts
     # of different data type (e.g. noise correlation data, navigator data,
@@ -85,7 +86,7 @@ def main():
 
     # inspect the first readout flag
     flags0 = acq_data.get_ISMRMRD_info('flags', range(1))
-    if flags0 & IMAGE_DATA_MASK:
+    if flags0 & mr.IMAGE_DATA_MASK:
         print('first readout is image data')
     else:
         # should see this if input data file is test_2D_2x.h5
@@ -143,7 +144,7 @@ def main():
     # direction. So far only the removal of readout oversampling and noise and
     # asymmetric echo adjusting is implemented
     print('pre-processing acquisition data...')
-    processed_acq_data = preprocess_acquisition_data(acq_data).abs()
+    processed_acq_data = mr.preprocess_acquisition_data(acq_data).abs()
     acq_max = processed_acq_data.maximum(processed_acq_data*10)
     acq_min = processed_acq_data.minimum(processed_acq_data*10)
     print('norm of acq_data: %f' % processed_acq_data.norm())

examples/Python/MR/acquisition_model.py

@@ -38,8 +38,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -55,23 +58,21 @@ def main():
     input_file = existing_filepath(data_path, data_file)
 
     # acquisition data will be read from an HDF file input_file
-    AcquisitionData.set_storage_scheme('memory')
-    acq_data = AcquisitionData(input_file)
+    mr.AcquisitionData.set_storage_scheme('memory')
+    acq_data = mr.AcquisitionData(input_file)
     print('---\n acquisition data norm: %e' % acq_data.norm())
 
     # pre-process acquisition data
     print('---\n pre-processing acquisition data...')
-    processed_data = preprocess_acquisition_data(acq_data)
+    processed_data = mr.preprocess_acquisition_data(acq_data)
     print('---\n processed acquisition data norm: %e' % processed_data.norm())
 
     # perform reconstruction to obtain a meaningful ImageData object
-    # (cannot be obtained in any other way at present)
-#    if processed_data.is_undersampled():
     if acq_data.is_undersampled():
-        recon = CartesianGRAPPAReconstructor()
+        recon = mr.CartesianGRAPPAReconstructor()
         recon.compute_gfactors(False)
     else:
-        recon = FullySampledReconstructor()
+        recon = mr.FullySampledReconstructor()
     recon.set_input(processed_data)
     print('---\n reconstructing...')
     recon.process()
@@ -104,24 +105,20 @@ def main():
     print('patient table positions:')
     print(ptp)
 
-    # sort processed acquisition data;
-    # sorting currently performed with respect to (in this order):
-    #    - repetition
-    #    - slice
-    #    - kspace encode step 1
+    # sort processed acquisition data
     print('---\n sorting acquisition data...')
     processed_data.sort()
 
     # compute coil sensitivity maps
     print('---\n computing coil sensitivity maps...')
-    csms = CoilSensitivityData()
+    csms = mr.CoilSensitivityData()
     csms.calculate(processed_data)
 
     # create acquisition model based on the acquisition parameters
     # stored in processed_data and image parameters stored in reconstructed_images
-##    acq_model = AcquisitionModel(processed_data, reconstructed_images)
-    acq_model = AcquisitionModel()
-    acq_model.set_up(processed_data, reconstructed_images)
+    acq_model = mr.AcquisitionModel(processed_data, reconstructed_images)
+##    acq_model = mr.AcquisitionModel()
+##    acq_model.set_up(processed_data, reconstructed_images)
     acq_model.set_coil_sensitivity_maps(csms)
 
     # use the acquisition model (forward projection) to produce simulated
@@ -134,7 +131,7 @@ def main():
         simulated_acq_data.write(output_file)
 
     # display simulated acquisition data
-    #simulated_acq_data.show(title = 'Simulated acquisition data (magnitude)')
+    simulated_acq_data.show(title = 'Simulated acquisition data (magnitude)')
 
     print('\n--- Computing the norm of the acquisition model operator...')
     acqm_norm = acq_model.norm()

examples/Python/MR/coil_sensitivity_maps.py

@@ -38,10 +38,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
-from pUtilities import *
+from sirf.Utilities import error, examples_data_path, existing_filepath, show_3D_array
 
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -59,20 +60,20 @@ def main():
     input_file = existing_filepath(data_path, data_file)
     #
     # acquisition data will be read from an HDF file input_file
-    AcquisitionData.set_storage_scheme('memory')
+    mr.AcquisitionData.set_storage_scheme('memory')
 
-    acq_data = AcquisitionData(input_file)
+    acq_data = mr.AcquisitionData(input_file)
     #
     # pre-process acquisition data
-    processed_data = preprocess_acquisition_data(acq_data)
+    processed_data = mr.preprocess_acquisition_data(acq_data)
     #
     # sort k-space data into a 2D Cartesian matrix for each coil
     processed_data.sort()
 
     # 2. Calculate coil sensitivity maps directly from the raw k-space data:
 
     # create coil sensitivity object
-    CSMs = CoilSensitivityData()
+    CSMs = mr.CoilSensitivityData()
     #
     # set number of smoothing iterations to suppress noise
     CSMs.smoothness = nit
@@ -98,13 +99,13 @@ def main():
     # SSRS and Inati methods:
 
     # create coil images object
-    CIs = CoilImagesData()
+    CIs = mr.CoilImagesData()
     # calculate coil sensitivity maps by dividing each coil image data by the
     # Square-Root-of-the-Sum-of-Squares over all coils (SRSS);
     # (niter = nit) sets the number of smoothing iterations applied
     # to the image data prior to the calculation of the coil sensitivity maps
     CIs.calculate(processed_data)
-    CSs = CoilSensitivityData()
+    CSs = mr.CoilSensitivityData()
     print('B) calculating from coil images...')
     CSs.calculate(CIs, method='SRSS(niter=%d)' % nit)
     diff = CSs - CSMs

examples/Python/MR/fully_sampled_recon.py

@@ -38,8 +38,13 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+import numpy
+
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -58,7 +63,7 @@ def main():
     # bruker_to_ismrmrd on https://github.com/ismrmrd/.
     # Acquisition data will be read from an HDF file input_file
     print('---\n reading in file %s...' % input_file)
-    acq_data = AcquisitionData(input_file)
+    acq_data = mr.AcquisitionData(input_file)
 
     # pre-process acquired k-space data:
     # prior to image reconstruction several pre-processing steps such as 
@@ -67,12 +72,12 @@ def main():
     # direction. So far only the removal of readout oversampling and noise and
     # asymmetric echo adjusting is implemented
     print('---\n pre-processing acquisition data...')
-    processed_data = preprocess_acquisition_data(acq_data)
+    processed_data = mr.preprocess_acquisition_data(acq_data)
 
     # set up reconstruction:
     # create a reconstruction object using 2D inverse Fourier transform and
     # provide pre-processed k-space data as input
-    recon = FullySampledReconstructor()
+    recon = mr.FullySampledReconstructor()
     recon.set_input(processed_data)
 
     # perform reconstruction

examples/Python/MR/grappa_and_steepest_descent.py

@@ -42,8 +42,13 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+import numpy
+
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -70,16 +75,16 @@ def main():
     input_file = existing_filepath(data_path, data_file)
 
     # acquisition data will be read from an HDF file input_data
-    AcquisitionData.set_storage_scheme('memory')
-    acq_data = AcquisitionData(input_file)
+    mr.AcquisitionData.set_storage_scheme('memory')
+    acq_data = mr.AcquisitionData(input_file)
 
     # pre-process acquisition data
     print('---\n pre-processing acquisition data...')
-    preprocessed_data = preprocess_acquisition_data(acq_data)
+    preprocessed_data = mr.preprocess_acquisition_data(acq_data)
     preprocessed_data_norm = preprocessed_data.norm()
 
     # perform reconstruction
-    recon = CartesianGRAPPAReconstructor()
+    recon = mr.CartesianGRAPPAReconstructor()
     recon.set_input(preprocessed_data)
     recon.compute_gfactors(False)
     print('---\n reconstructing...')
@@ -95,7 +100,7 @@ def main():
         return
 
     # compute coil sensitivity maps
-    csms = CoilSensitivityData()
+    csms = mr.CoilSensitivityData()
     print('---\n sorting acquisition data...')
     preprocessed_data.sort()
     print('---\n computing sensitivity maps...')
@@ -104,7 +109,7 @@ def main():
     # create acquisition model based on the acquisition parameters
     # stored in preprocessed_data and image parameters stored in
     # image_data
-    acq_model = AcquisitionModel(preprocessed_data, image_data)
+    acq_model = mr.AcquisitionModel(preprocessed_data, image_data)
     acq_model.set_coil_sensitivity_maps(csms)
 
     start_val = 0.001
@@ -131,7 +136,7 @@ def main():
         image_data = image_data - grad * tau
         if (i%10 == 0 or i == niter - 1) and show_plot:
             it = i + 1
-            title = 'Steepest-descent-refined image data, iteration %d' % it
+            title = 'Image data computed by steepest descent at iteration %d' % it
             image_data.show(zyx=zyx, slice=slc, title=title, cmap=None, \
                             postpone=(i < niter - 1))
 

examples/Python/MR/grappa_basic.py

@@ -57,8 +57,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -82,19 +85,19 @@ def main():
 
     # Create an acquisition container of type AcquisitionData
     print('---\n reading in file %s...' % input_file)
-    AcquisitionData.set_storage_scheme('memory')
+    mr.AcquisitionData.set_storage_scheme('memory')
 
-    acq_data = AcquisitionData(input_file)
+    acq_data = mr.AcquisitionData(input_file)
 
     # Pre-process this input data.
     # (Currently this is a Python script that just sets up a 3 chain gadget.
     # In the future it will be independent of the MR recon engine.)
     print('---\n pre-processing acquisition data...')
-    preprocessed_data = preprocess_acquisition_data(acq_data)
+    preprocessed_data = mr.preprocess_acquisition_data(acq_data)
 
     # Perform reconstruction of the preprocessed data.
     # 1. set the reconstruction to be for Cartesian GRAPPA data.
-    recon = CartesianGRAPPAReconstructor();
+    recon = mr.CartesianGRAPPAReconstructor();
 
     # 2. set the reconstruction input to be the data we just preprocessed.
     recon.set_input(preprocessed_data);