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'''File functions'''
import os
import sys
import numpy as np
import gzip
import hashlib
import tempfile
import filecmp
from soma import aims
from soma.aims.graph_comparison import same_graphs
from soma.aims.volumetools import compare_images
def compare_gzip_files(file1, file2):
same = False
# file1
with gzip.open(file1, 'rb') as f:
f1 = f.read()
tmp_file1 = tempfile.mkstemp()
os.close(tmp_file1[0])
tf1 = open(tmp_file1[1], 'wb')
tf1.write(f1)
tf1.close()
# file2
with gzip.open(file2, 'rb') as f:
f2 = f.read()
tmp_file2 = tempfile.mkstemp()
os.close(tmp_file2[0])
tf2 = open(tmp_file2[1], 'wb')
tf2.write(f2)
tf2.close()
same = filecmp.cmp(tmp_file1[1], tmp_file2[1])
os.unlink(tmp_file1[1])
os.unlink(tmp_file2[1])
return same
[docs]def compare_text_files(file1, file2, thresh=1e-6):
'''
Compare text files (.txt, .csv,..) which may contain nan.
'''
arr1 = np.genfromtxt(file1)
arr2 = np.genfromtxt(file2)
#if not np.any(np.isnan(arr1)) and not np.any(np.isnan(arr2)):
#return filecmp.cmp(file1, file2)
if np.any(np.isnan(arr1)) and np.any(np.isnan(arr2)):
if np.all(np.isnan(arr1) == np.isnan(arr2)):
arr1[np.isnan(arr1)] = 0
arr2[np.isnan(arr2)] = 0
return np.max(np.abs(arr2 - arr1)) <= thresh
elif not np.any(np.isnan(arr1)) and not np.any(np.isnan(arr2)):
return np.max(np.abs(arr2 - arr1)) <= thresh
return False
[docs]def compare_nii_files(file1, file2, thresh=50, out_stream=sys.stdout):
'''
Compare nifti files (.nii, .nii.gz)
'''
# Get md5 for each file
if os.path.splitext(file1)[-1] == '.gz':
md5_file1 = hashlib.md5(gzip.open(file1, 'rb').read()).hexdigest()
else:
md5_file1 = hashlib.md5(open(file1, 'rb').read()).hexdigest()
if os.path.splitext(file2)[-1] == '.gz':
md5_file2 = hashlib.md5(gzip.open(file2, 'rb').read()).hexdigest()
else:
md5_file2 = hashlib.md5(open(file2, 'rb').read()).hexdigest()
if md5_file1 == md5_file2:
return True
# md5 are differents
# Check the voxels
a_1 = aims.read(file1)
a_2 = aims.read(file2)
if a_1.np.shape == a_2.np.shape:
d = a_1.np - a_2.np
if abs(np.max(d) - np.min(d)) < thresh:
print('WARNING, use aims, absolute difference value:'
' %s' % abs(np.max(d) - np.min(d)), file=out_stream)
return True
return False
[docs]def cmp(ref_file, test_file, skip_suffixes=None):
'''
Compare files, taking into account their neuroimaging nature.
Some specific comparison function will be called for graphs, meshes,
images, CSV files.
'''
if skip_suffixes is None:
skip_suffixes = [
".minf",
# SPAM probas differ up tp 0.15 in energy and I don't know why, so
# skip the test
"_proba.csv",
# this .dat file contains full paths of filenames
"_global_TO_local.dat",
# referentials with registration are allocated differently (uuids)
"_auto.referential"]
for ext in skip_suffixes:
if ref_file.endswith(ext):
return True
if ref_file.endswith(".arg"):
return same_graphs(ref_file, test_file)
elif ref_file.endswith(".csv"):
if filecmp.cmp(ref_file, test_file):
return True
arr1 = np.genfromtxt(ref_file)
if len(arr1.shape) >= 2 and np.any(np.isnan(arr1[0, :])):
arr1 = arr1[1:, :]
arr2 = np.genfromtxt(test_file)
if len(arr2.shape) >= 2 and np.any(np.isnan(arr2[0, :])):
arr2 = arr2[1:, :]
return np.max(np.abs(arr2 - arr1)) <= 1e-5
try:
obj1 = aims.read(ref_file)
obj2 = aims.read(test_file)
if type(obj1).__name__.startswith('soma.aims.Volume_') \
and type(obj2).__name__.startswith('soma.aims.Volume_'):
return compare_images(obj1, obj2)
if obj1 == obj2:
return True
if type(obj1) != type(obj2):
return False
if hasattr(obj1, 'header'):
filter_header_for_cmp(obj1.header())
if hasattr(obj2, 'header'):
filter_header_for_cmp(obj2.header())
if obj1 == obj2:
return True
except Exception:
# aims cannot read them: use the standard filecmp
pass
return filecmp.cmp(ref_file, test_file)