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provide group level FNs
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5 files changed

+14
-6
lines changed

5 files changed

+14
-6
lines changed

pyproject.toml

Lines changed: 8 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,14 +4,21 @@ build-backend = "setuptools.build_meta"
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[project]
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name="fmripnet"
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version="1.0.1a"
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version="1.0.1c"
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description="pNet: a python package for computing personalized fucntional networks"
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readme = "README.md"
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requires-python = ">=3.8"
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classifiers=[
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"Programming Language :: Python :: 3",
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"Operating System :: OS Independent",
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]
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dependencies = [
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'ggplot',
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'matplotlib',
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'plotnine',
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'statsmodels',
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'surfplot',
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'tomli']
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[project.urls]
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Homepage= "https://github.com/MLDataAnalytics/pNet"

setup.py

Lines changed: 3 additions & 2 deletions
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@@ -5,7 +5,7 @@
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setup(
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name="fmri_pnet",
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version="1.0.1a",
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version="1.0.1c",
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description="pNet: a python package for computing personalized fucntional networks",
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long_description=long_description,
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long_description_content_type="text/markdown",
@@ -24,7 +24,8 @@
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"pnet.Brain_Template.HCP_Surface_Volume": ["*.mat", "*.zip", "*.log", "*.gz"],
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"pnet.Brain_Template.HCP_Volume": ["*.mat", "*.zip", "*.log", "*.gz"],
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"pnet.Brain_Template.MNI_Volume": ["*.mat", "*.zip", "*.log", "*.gz"],
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"pnet.examples":["*.toml", "*.txt"]
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"pnet.examples":["*.toml", "*.txt"],
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"pnet.Group_FNs":["*.mat"]
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},
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include_package_data = True,
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install_requires=[

src/pnet/.idea/.name

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src/pnet/Module/FN_Computation_torch.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -506,7 +506,7 @@ def run_FN_computation_torch_cluster(dir_pnet_result: str):
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else: # use precomputed gFNs
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file_gFN = setting['FN_Computation']['Group_FN']['file_gFN']
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gFN, _, _ = load_matlab_single_array(file_gFN)
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gFN = load_matlab_single_array(file_gFN)
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if dataType == 'Volume':
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Brain_Mask = load_brain_template(os.path.join(dir_pnet_dataInput, 'Brain_Template.json.zip'))['Brain_Mask']
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gFN = reshape_FN(gFN, dataType=dataType, Brain_Mask=Brain_Mask)
@@ -625,4 +625,4 @@ def run_FN_computation_torch_cluster(dir_pnet_result: str):
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flag_complete[scan - 1] = 1
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# ============================================= #
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print('Finished FN computation at ' + time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())), flush=True)
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print('Finished FN computation at ' + time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())), flush=True)

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