imports + strict & fixing Docs (#5)

* absolute imports
* rise Sphinx warnings
* fixing docs & config
* docs: fix links & API folder
* move figures
* pandas: fix as_matrix
* update CI
* pep8

.pep8speaks.yml

@@ -0,0 +1,28 @@
+# File : .pep8speaks.yml
+
+scanner:
+ diff_only: True # If False, the entire file touched by the Pull Request is scanned for errors. If True, only the diff is scanned.
+ linter: pycodestyle # Other option is flake8
+
+pycodestyle: # Same as scanner.linter value. Other option is flake8
+ max-line-length: 110 # Default is 79 in PEP 8
+ ignore: # Errors and warnings to ignore
+ - W504 # line break after binary operator
+ - E402 # module level import not at top of file
+ - E731 # do not assign a lambda expression, use a def
+ - C406 # Unnecessary list literal - rewrite as a dict literal.
+ - E741 # ambiguous variable name
+
+no_blank_comment: True # If True, no comment is made on PR without any errors.
+descending_issues_order: False # If True, PEP 8 issues in message will be displayed in descending order of line numbers in the file
+
+message: # Customize the comment made by the bot,
+ opened: # Messages when a new PR is submitted
+ header: "Hello @{name}! Thanks for opening this PR. "
+ # The keyword {name} is converted into the author's username
+ footer: "Do see the [Hitchhiker's guide to code style](https://goo.gl/hqbW4r)"
+ # The messages can be written as they would over GitHub
+ updated: # Messages when new commits are added to the PR
+ header: "Hello @{name}! Thanks for updating this PR. "
+ footer: "" # Why to comment the link to the style guide everytime? :)
+ no_errors: "There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: "

.travis.yml

@@ -18,9 +18,9 @@ sudo: false
 python:
  - 2.7
  # - 3.4 # will be deprecated for pandas
- - 3.5
  - 3.6
  - 3.7
+ - 3.8
 
 # See http://docs.travis-ci.com/user/caching/#pip-cache
 cache: pip

MANIFEST.in

@@ -32,6 +32,8 @@ recursive-include figures *.png
 # Include the sample data
 recursive-include data_images *.yml *.yaml
 
+recursive-include assets *.png
+
 prune .git
 prune venv
 prune build

README.md

@@ -14,7 +14,7 @@
 
 We present a final step of image processing pipeline which accepts a large number of images, containing spatial expression information for thousands of genes in Drosophila imaginal discs. We assume that the gene activations are binary and can be expressed as a union of a small set of non-overlapping spatial patterns, yielding a compact representation of the spatial activation of each gene. This lends itself well to further automatic analysis, with the hope of discovering new biological relationships. Traditionally, the images were labelled manually, which was very time-consuming. The key part of our work is a binary pattern dictionary learning algorithm, that takes a set of binary images and determines a set of patterns, which can be used to represent the input images with a small error.
 
-![schema](figures/pipeline_schema.png)
+![schema](assets/pipeline_schema.png)
 
 For the image segmentation and individual object detection, we used [Image segmentation toolbox](https://borda.github.io/pyImSegm/).
 
@@ -86,13 +86,13 @@ python experiments/run_dataset_generate.py \
 ```
 
 **Sample atlases**
-![atlases](figures/synth_atlases.png)
+![atlases](assets/synth_atlases.png)
 
 **Sample binary images**
-![binary samples](figures/synth_samples_binary.png)
+![binary samples](assets/synth_samples_binary.png)
 
 **Sample fuzzy images**
-![fuzzy samples](figures/synth_samples_fuzzy.png)
+![fuzzy samples](assets/synth_samples_fuzzy.png)
 
 For adding Gaussian noise with given sigmas use following script:
 ```bash
@@ -101,7 +101,7 @@ python experiments/run_dataset_add_noise.py \
  -d apdDataset_vX --sigma 0.01 0.1 0.2
 ```
 
-![gauss noise](figures/synth_gauss-noise.png)
+![gauss noise](assets/synth_gauss-noise.png)
 
 ### Real images
 
@@ -226,7 +226,7 @@ python experiments/run_reconstruction.py \
  --nb_workers 1 --visual
 ```
 
-![reconstruction](figures/reconst_imag-disc.png)
+![reconstruction](assets/reconst_imag-disc.png)
 
 ### Aggregating results
 

bpdl/data_utils.py

@@ -1,20 +1,21 @@
 """
 The basic module for generating synthetic images and also loading / exporting
 
-Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 from __future__ import absolute_import
 
-import os
 import glob
-import logging
 # import warnings
 import itertools
+import logging
 import multiprocessing as mproc
+import os
 from functools import partial
 
 # to suppress all visual, has to be on the beginning
 import matplotlib
+
 if os.environ.get('DISPLAY', '') == '' and matplotlib.rcParams['backend'] != 'agg':
  print('No display found. Using non-interactive Agg backend.')
  # https://matplotlib.org/faq/usage_faq.html
@@ -31,7 +32,7 @@
 from imsegm.utilities.experiments import WrapExecuteSequence
 from imsegm.utilities.data_io import io_imread, io_imsave
 
-from .utilities import create_clean_folder
+from bpdl.utilities import create_clean_folder
 
 NB_WORKERS = mproc.cpu_count()
 IMAGE_SIZE_2D = (128, 128)
@@ -154,7 +155,7 @@ def image_deform_elastic(im, coef=0.5, grid_size=(20, 20), rand_seed=None):
  >>> img = np.zeros((10, 15), dtype=int)
  >>> img[2:8, 3:7] = 1
  >>> img[6:, 9:] = 2
- >>> image_deform_elastic(img, coef=0.3, grid_size=(5, 5), rand_seed=0)
+ >>> image_deform_elastic(img, coef=0.3, grid_size=(2, 2), rand_seed=0)
  array([[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, 0],
  [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
@@ -164,7 +165,7 @@ def image_deform_elastic(im, coef=0.5, grid_size=(20, 20), rand_seed=None):
  [0, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2, 2, 0],
  [0, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2, 2, 0],
  [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2]], dtype=uint8)
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
  >>> img = np.zeros((10, 15, 5), dtype=int)
  >>> img[2:8, 3:7, :] = 1
  >>> im = image_deform_elastic(img, coef=0.2, grid_size=(4, 5), rand_seed=0)
@@ -734,19 +735,19 @@ def export_image(path_out, img, im_name, name_template=SEGM_PATTERN,
  (5, 10)
  >>> np.round(im.astype(float), 1).tolist() # doctest: +NORMALIZE_WHITESPACE
  [[0.6, 0.7, 0.6, 0.6, 0.4, 0.7, 0.4, 0.9, 1.0, 0.4],
-  [0.8, 0.5, 0.6, 0.9, 0.1, 0.1, 0.0, 0.8, 0.8, 0.9],
-  [1.0, 0.8, 0.5, 0.8, 0.1, 0.7, 0.1, 1.0, 0.5, 0.4],
-  [0.3, 0.8, 0.5, 0.6, 0.0, 0.6, 0.6, 0.6, 1.0, 0.7],
-  [0.4, 0.4, 0.7, 0.1, 0.7, 0.7, 0.2, 0.1, 0.3, 0.4]]
+ [0.8, 0.5, 0.6, 0.9, 0.1, 0.1, 0.0, 0.8, 0.8, 0.9],
+ [1.0, 0.8, 0.5, 0.8, 0.1, 0.7, 0.1, 1.0, 0.5, 0.4],
+ [0.3, 0.8, 0.5, 0.6, 0.0, 0.6, 0.6, 0.6, 1.0, 0.7],
+ [0.4, 0.4, 0.7, 0.1, 0.7, 0.7, 0.2, 0.1, 0.3, 0.4]]
  >>> img = np.random.randint(0, 9, [5, 10])
  >>> path_img = export_image('.', img, 'testing-image', stretch_range=False)
  >>> name, im = load_image(path_img, fuzzy_val=False)
  >>> im.tolist() # doctest: +NORMALIZE_WHITESPACE
  [[4, 4, 6, 4, 4, 3, 4, 4, 8, 4],
-  [3, 7, 5, 5, 0, 1, 5, 3, 0, 5],
-  [0, 1, 2, 4, 2, 0, 3, 2, 0, 7],
-  [5, 0, 2, 7, 2, 2, 3, 3, 2, 3],
-  [4, 1, 2, 1, 4, 6, 8, 2, 3, 0]]
+ [3, 7, 5, 5, 0, 1, 5, 3, 0, 5],
+ [0, 1, 2, 4, 2, 0, 3, 2, 0, 7],
+ [5, 0, 2, 7, 2, 2, 3, 3, 2, 3],
+ [4, 1, 2, 1, 4, 6, 8, 2, 3, 0]]
  >>> os.remove(path_img)
 
  Image - TIFF
@@ -1135,7 +1136,7 @@ def dataset_load_weights(path_base, name_csv=CSV_NAME_WEIGHTS, img_names=None):
  encoding = np.array([[int(x) for x in c.split(';')] for c in coding])
  # the new encoding with pattern names
  else:
- encoding = df.as_matrix()
+ encoding = df.values
  return np.array(encoding)
 
 

bpdl/dictionary_learning.py

@@ -2,16 +2,17 @@
 The main module for Atomic pattern dictionary, jjoiningthe atlas estimation
 and computing the encoding / weights
 
-Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 from __future__ import absolute_import
 
+import logging
 import os
 import time
-import logging
 
 # to suppress all visual, has to be on the beginning
 import matplotlib
+
 if os.environ.get('DISPLAY', '') == '' and matplotlib.rcParams['backend'] != 'agg':
  print('No display found. Using non-interactive Agg backend.')
  # https://matplotlib.org/faq/usage_faq.html
@@ -25,14 +26,14 @@
 # using https://github.com/Borda/pyGCO
 from gco import cut_general_graph, cut_grid_graph_simple
 
-from .pattern_atlas import (
+from bpdl.pattern_atlas import (
  compute_positive_cost_images_weights, edges_in_image2d_plane, init_atlas_mosaic,
  atlas_split_indep_ptn, reinit_atlas_likely_patterns, compute_relative_penalty_images_weights)
-from .pattern_weights import (
+from bpdl.pattern_weights import (
  weights_image_atlas_overlap_major, weights_image_atlas_overlap_partial)
-from .metric_similarity import compare_atlas_adjusted_rand
-from .data_utils import export_image
-from .registration import register_images_to_atlas_demons
+from bpdl.metric_similarity import compare_atlas_adjusted_rand
+from bpdl.data_utils import export_image
+from bpdl.registration import register_images_to_atlas_demons
 
 NB_GRAPH_CUT_ITER = 5
 TEMPLATE_NAME_ATLAS = 'BPDL_{}_{}_iter_{:04d}'

bpdl/metric_similarity.py

@@ -1,7 +1,7 @@
 """
 Introducing some used similarity measures fro atlases and etc.
 
-Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 
 # from __future__ import absolute_import

bpdl/pattern_atlas.py

@@ -1,19 +1,19 @@
 """
 Estimating the pattern dictionary module
 
-Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 # from __future__ import absolute_import
 import logging
 
 # import numba
 import numpy as np
-from sklearn.decomposition import SparsePCA, FastICA, DictionaryLearning, NMF
-from skimage import morphology, measure, segmentation, filters
 from scipy import ndimage as ndi
+from skimage import morphology, measure, segmentation, filters
+from sklearn.decomposition import SparsePCA, FastICA, DictionaryLearning, NMF
 
-from .data_utils import image_deform_elastic, extract_image_largest_element
-from .pattern_weights import (
+from bpdl.data_utils import image_deform_elastic, extract_image_largest_element
+from bpdl.pattern_weights import (
  weights_label_atlas_overlap_threshold, convert_weights_binary2indexes)
 
 REINIT_PATTERN_COMPACT = True
@@ -403,7 +403,7 @@ def init_atlas_sparse_pca(imgs, nb_patterns, nb_iter=5, bg_threshold=0.1):
  >>> atlas[3:7, 6:12] = 2
  >>> luts = np.array([[0, 1, 0]] * 99 + [[0, 0, 1]] * 99 + [[0, 1, 1]] * 99)
  >>> imgs = [lut[atlas] for lut in luts]
- >>> init_atlas_sparse_pca(imgs, 2)
+ >>> init_atlas_sparse_pca(imgs, 2, bg_threshold=0.05)
  array([[0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],
  [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],
  [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],

bpdl/pattern_weights.py

@@ -1,7 +1,7 @@
 """
 Estimating pattern weight vector for each image
 
-Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 
 # from __future__ import absolute_import

bpdl/registration.py

@@ -5,21 +5,21 @@
 * http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/
 * https://bic-berkeley.github.io/psych-214-fall-2016/dipy_registration.html
 
-Copyright (C) 2017-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2017-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 
-import time
 import logging
+import time
 # import multiprocessing as mproc
 from functools import partial
 
 import numpy as np
-from scipy import ndimage, interpolate
 # from scipy.ndimage import filters
 from dipy.align import VerbosityLevels
 from dipy.align.imwarp import SymmetricDiffeomorphicRegistration, DiffeomorphicMap
 from dipy.align.metrics import SSDMetric
 from imsegm.utilities.experiments import WrapExecuteSequence, nb_workers
+from scipy import ndimage, interpolate
 
 NB_WORKERS = nb_workers(0.8)
 
@@ -93,7 +93,6 @@ def register_demons_sym_diffeom(img_sense, img_ref, smooth_sigma=1.,
  [ 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],
  [ 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],
  [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
- >>> np.round(img_warp - img_sense, 1) # doctest: +SKIP
  >>> img_sense = np.zeros(img_ref.shape, dtype=int)
  >>> img_sense[4:9, 3:10] = 1
  >>> img_sense

bpdl/utilities.py

@@ -1,24 +1,26 @@
 """
 The basic module for generating synthetic images and also loading / exporting
 
-Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
+Copyright (C) 2015-2020 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
 
+import logging
 # from __future__ import absolute_import
 import os
 import re
-import types
-import logging
 import shutil
-# import multiprocessing.pool
-# import multiprocessing as mproc
-# from functools import wraps
+import types
 
 import numpy as np
 from scipy import stats
 from scipy.spatial import distance
 
 
+# import multiprocessing.pool
+# import multiprocessing as mproc
+# from functools import wraps
+
+
 # def update_path(path_file, lim_depth=5, absolute=True):
 # """ bubble in the folder tree up intil it found desired file
 # otherwise return original one