Nilearn-style reporting (Issue #341)

examples/easy_start/nipype_preproc_spm_auditory.py

@@ -9,7 +9,6 @@
 import nibabel
 from pypreprocess.nipype_preproc_spm_utils import do_subjects_preproc
 from pypreprocess.datasets import fetch_spm_auditory
-from pypreprocess.reporting.glm_reporter import generate_subject_stats_report
 import pandas as pd
 from nilearn.glm.first_level.design_matrix import (make_first_level_design_matrix,
                                                          check_design_matrix)

examples/pipelining/nipype_preproc_spm_multimodal_faces.py

@@ -20,8 +20,6 @@
 
 # pypreprocess imports
 from pypreprocess.datasets import fetch_spm_multimodal_fmri
-from pypreprocess.reporting.base_reporter import ProgressReport
-from pypreprocess.reporting.glm_reporter import generate_subject_stats_report
 from pypreprocess.nipype_preproc_spm_utils import do_subject_preproc
 from pypreprocess.subject_data import SubjectData
 

examples/pipelining/nistats_glm_fsl_feeds_fmri.py

@@ -14,8 +14,6 @@
 import time
 from pypreprocess.nipype_preproc_spm_utils import (SubjectData,
                                                    do_subjects_preproc)
-from pypreprocess.reporting.glm_reporter import generate_subject_stats_report
-from pypreprocess.reporting.base_reporter import ProgressReport
 from pypreprocess.datasets import fetch_fsl_feeds
 from pypreprocess.io_utils import compute_mean_3D_image
 

pypreprocess/conf_parser.py

@@ -445,9 +445,4 @@ def _ignore_subject(subject_id):
     return subjects, preproc_params
 
 # this pseudo is better
-import_data = _generate_preproc_pipeline
-
-
-if __name__ == '__main__':
-    from pypreprocess.reporting.base_reporter import dict_to_html_ul
-    print(dict_to_html_ul(_parse_job("job.conf")))
+import_data = _generate_preproc_pipeline

pypreprocess/coreg.py

@@ -60,10 +60,14 @@ def compute_similarity_from_jhist(jh, fwhm=None, cost_fun='nmi'):
     krn1 = centered_smoothing_kernel(fwhm[0],
                                      np.linspace(-1 * lim[0], lim[0],
                                                  num=2 * lim[0]))
+    # Pad with zero to get odd length
+    krn1 = np.append(krn1, 0)
     krn1 = krn1 / np.sum(krn1)
     krn2 = centered_smoothing_kernel(fwhm[1],
                                      np.linspace(-1 * lim[1], lim[1],
                                                  num=2 * lim[1]))
+    # Pad with zero to get odd length
+    krn2 = np.append(krn2, 0)
     krn2 = krn2 / np.sum(krn2)
 
     # smooth the histogram with kern1 x kern2

pypreprocess/external/tempita/_tempita.py

@@ -34,6 +34,7 @@ def foo(bar):
 import re
 import sys
 import cgi
+from html import escape
 try:
     from urllib import quote as url_quote
 except ImportError:  # Py3
@@ -440,10 +441,10 @@ def html_quote(value, force=True):
     if not isinstance(value, basestring_):
         value = coerce_text(value)
     if sys.version >= "3" and isinstance(value, bytes):
-        value = cgi.escape(value.decode('latin1'), 1)
+        value = escape(value.decode('latin1'), 1)
         value = value.encode('latin1')
     else:
-        value = cgi.escape(value, 1)
+        value = escape(value, 1)
     if sys.version < "3":
         if is_unicode(value):
             value = value.encode('ascii', 'xmlcharrefreplace')

pypreprocess/io_utils.py

@@ -261,7 +261,11 @@ def is_3D(image):
     """Check whether image is 3D"""
 
     if isinstance(image, str):
-        image = nibabel.load(image)
+        if os.path.exists(image):
+            image = nibabel.load(image)
+        else:
+            raise FileNotFoundError("File {} not found.".format(
+                image))
     elif isinstance(image, list):
         image = nibabel.concat_images(image,
                                       check_affines=False