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Merge pull request #56 from abhicodes369/created_repostatus/statistics
report on repo
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‎.gitignore

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@@ -21,4 +21,4 @@ md5sums.txt
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.cache
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nosetests.xml
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coverage.xml
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*.pyc
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*.pyc

‎doc/code_contributions_record.csv

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Technique,Category,Subcategory,notes,subfolder,Link to source code,Authors,Institution,function/module,DOI,Tester,test status
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IVIM,Fitting,LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_least_squares/fit_least_squares_array,,tbd,
3-
IVIM,Fitting,segmented LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_segmented/fit_segmented_array,,tbd,
4-
Tri-exponential,Fitting,LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_least_squares_tri_exp/fit_least_squares_array_tri_exp,,tbd,
5-
Tri-exponential,Fitting,Segmented LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_segmented_tri_exp/fit_segmented_array_tri_exp,https://doi.org/10.3389/fphys.2022.942495,tbd,
6-
IVIM,Fitting,Bayesian,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion/Sebastiano Barbieri,Amsterdam UMC,fit_bayesian_array,https://doi.org/10.1002/mrm.28852,tbd,
7-
IVIM,Fitting,two-step segmented fit approach,also includes ADC calculation as a separate function,PvH_KB_NKI,TF2.4_IVIM-MRI_CodeCollection/src/original/PvH_KB_NKI/,Petra van Houdt/Stefan Zijlema/Koen Baas,the Netherlands Cancer Institute,DWI_functions_standalone.py,https://doi.org/10.3389/fonc.2021.705964,tbd,
8-
IVIM,Fitting,two-step (segmented) LSQ fitting, cut-off chosen for brain data; option to fit IVIM with inversion recovery or without IR,PV_MUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/PV_MUMC/,Paulien Voorter,Maastricht University Medical Center,two_step_IVIM_fit.py,,tbd,
9-
IVIM,Fitting,bi-exponential NLLS,Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_biexp.py,Ivan A. Rashid,Lund University,IvimModelBiexp,tba,tbd,
10-
IVIM,Fitting,2-step segmented NLLS,First estimates and fixes D before a bi-exponential NLLS fit. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py,Ivan A. Rashid,Lund University,IvimModelSegmented2Step,tba,tbd,
11-
IVIM,Fitting,3-step segmented NLLS,First estimates and fixes D followed by an estimate of D* followed by a bi-exponential NLLS fit. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py,Ivan A. Rashid,Lund University,IvimModelSegmented3Step,tba,tbd,
12-
IVIM,Fitting,2-step segmented NLLS,First estimates and fixes D. Subtracts the diffusion signal and estimated D*. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py,Ivan A. Rashid,Lund University,IvimModelSubtracted,tba,tbd,
13-
IVIM,Fitting,Variable projection,See referenced article. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py,Farooq et al. Modified by Ivan A. Rashid,Lund University,IvimModelVP,https://doi.org/10.1038/srep38927,tbd,
14-
IVIM,Fitting,Variable projection,See referenced article. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py,Fadnavis et al. Modified by Ivan A. Rashid,Lund University,IvimModelTopoPro,https://doi.org/10.3389/fnins.2021.779025,tbd,
15-
IVIM,Fitting,Linear fit,Linear fit for D with extrapolation for f. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_linear.py,Modified by Ivan A. Rashid,Lund University,IvimModelLinear,tba,tbd,
16-
IVIM,Fitting,sIVIM fit,NLLS of the simplified IVIM model (sIVIM). Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_sivim.py,Modified by Ivan A. Rashid,Lund University,IvimModelsIVIM,tba,tbd,
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IVIM,Fitting,Segmented NLLS fitting,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_seg,https://doi.org/10.1007/s10334-018-0697-5,tbd,
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IVIM,Fitting,Bayesian,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_bayes,https://doi.org/10.1002/mrm.26783,tbd,
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IVIM,Fitting,Segmented NLLS fitting,Specifically tailored algorithm for NLLS segmented fitting,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,seg,https://doi.org/10.1007/s10334-018-0697-5,tbd,
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IVIM,Fitting,Linear fit,Linear fit for D and D* and f. Intended to be extremely fast but not always accurate,ETP_SRI,TF2.4_IVIM-MRI_CodeCollection/src/original/ETP_SRI/LinearFitting.py,Eric Peterson,SRI International,,,tbd,
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Technique,Category,Subcategory,notes,subfolder,Link to source code,Authors,Institution,function/module,DOI,Tester,test status,Wrapped
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IVIM,Fitting,LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_least_squares/fit_least_squares_array,,tbd,,
3+
IVIM,Fitting,segmented LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_segmented/fit_segmented_array,,tbd,,OGC_AmsterdamUMC_biexp
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Tri-exponential,Fitting,LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_least_squares_tri_exp/fit_least_squares_array_tri_exp,,tbd,,
5+
Tri-exponential,Fitting,Segmented LSQ fitting,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion,Amsterdam UMC,fit_segmented_tri_exp/fit_segmented_array_tri_exp,https://doi.org/10.3389/fphys.2022.942495,tbd,,OGC_AmsterdamUMC_biexp_segmented
6+
IVIM,Fitting,Bayesian,,OGC_AmsterdamUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/OGC_AmsterdamUMC/,Oliver Gurney-Champion/Sebastiano Barbieri,Amsterdam UMC,fit_bayesian_array,https://doi.org/10.1002/mrm.28852,tbd,,OGC_AmsterdamUMC_Bayesian_biexp
7+
IVIM,Fitting,two-step segmented fit approach,also includes ADC calculation as a separate function,PvH_KB_NKI,TF2.4_IVIM-MRI_CodeCollection/src/original/PvH_KB_NKI/,Petra van Houdt/Stefan Zijlema/Koen Baas,the Netherlands Cancer Institute,DWI_functions_standalone.py,https://doi.org/10.3389/fonc.2021.705964,tbd,,PvH_KB_NKI_IVIMfit
8+
IVIM,Fitting,two-step (segmented) LSQ fitting, cut-off chosen for brain data; option to fit IVIM with inversion recovery or without IR,PV_MUMC,TF2.4_IVIM-MRI_CodeCollection/src/original/PV_MUMC/,Paulien Voorter,Maastricht University Medical Center,two_step_IVIM_fit.py,,tbd,,PV_MUMC_biexp
9+
IVIM,Fitting,bi-exponential NLLS,Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_biexp.py,Ivan A. Rashid,Lund University,IvimModelBiexp,tba,tbd,,IAR_LU_biexp
10+
IVIM,Fitting,2-step segmented NLLS,First estimates and fixes D before a bi-exponential NLLS fit. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py,Ivan A. Rashid,Lund University,IvimModelSegmented2Step,tba,tbd,,IAR_LU_segmented_2step
11+
IVIM,Fitting,3-step segmented NLLS,First estimates and fixes D followed by an estimate of D* followed by a bi-exponential NLLS fit. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py,Ivan A. Rashid,Lund University,IvimModelSegmented3Step,tba,tbd,,IAR_LU_segmented_3step
12+
IVIM,Fitting,2-step segmented NLLS,First estimates and fixes D. Subtracts the diffusion signal and estimated D*. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py,Ivan A. Rashid,Lund University,IvimModelSubtracted,tba,tbd,,IAR_LU_subtracted
13+
IVIM,Fitting,Variable projection,See referenced article. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py,Farooq et al. Modified by Ivan A. Rashid,Lund University,IvimModelVP,https://doi.org/10.1038/srep38927,tbd,,IAR_LU_modified_mix
14+
IVIM,Fitting,Variable projection,See referenced article. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py,Fadnavis et al. Modified by Ivan A. Rashid,Lund University,IvimModelTopoPro,https://doi.org/10.3389/fnins.2021.779025,tbd,,IAR_LU_modified_topopro
15+
IVIM,Fitting,Linear fit,Linear fit for D with extrapolation for f. Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_linear.py,Modified by Ivan A. Rashid,Lund University,IvimModelLinear,tba,tbd,,
16+
IVIM,Fitting,sIVIM fit,NLLS of the simplified IVIM model (sIVIM). Supports units in mm2/s and µm2/ms,IAR_LundUniversity,TF2.4_IVIM-MRI_CodeCollection/src/original/IAR_LundUniversity/ivim_fit_method_modified_sivim.py,Modified by Ivan A. Rashid,Lund University,IvimModelsIVIM,tba,tbd,,
17+
IVIM,Fitting,Segmented NLLS fitting,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_seg,https://doi.org/10.1007/s10334-018-0697-5,tbd,,OJ_GU_seg
18+
IVIM,Fitting,Bayesian,MATLAB code,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,IVIM_bayes,https://doi.org/10.1002/mrm.26783,tbd,,
19+
IVIM,Fitting,Segmented NLLS fitting,Specifically tailored algorithm for NLLS segmented fitting,OJ_GU,TF2.4_IVIM-MRI_CodeCollection/src/original/OJ_GU/,Oscar Jalnefjord,University of Gothenburg,seg,https://doi.org/10.1007/s10334-018-0697-5,tbd,,
20+
IVIM,Fitting,Linear fit,Linear fit for D and D* and f. Intended to be extremely fast but not always accurate,ETP_SRI,TF2.4_IVIM-MRI_CodeCollection/src/original/ETP_SRI/LinearFitting.py,Eric Peterson,SRI International,,,tbd,,ETP_SRI_LinearFitting

‎requirements.txt

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zenodo-get
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pytest
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tqdm
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pandas
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sphinx
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sphinx_rtd_theme

‎utilities/repostatus.py

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import os
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import pandas as pd
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import json
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# directory of the current script
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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# path to the repository
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REPO_DIR = os.path.dirname(SCRIPT_DIR)
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CODE_CONTRIBUTIONS_FILE = os.path.join(REPO_DIR, "doc", "code_contributions_record.csv")
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ALGORITHMS_FILE = os.path.join(REPO_DIR, "tests", "IVIMmodels", "unit_tests", "algorithms.json")
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SOURCE_FOLDER = os.path.join(REPO_DIR, "src", "original")
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WRAPPED_FOLDER = os.path.join(REPO_DIR, "src", "standardized")
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def generate_html():
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"""
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Generates an HTML report based on the code contributions and algorithm information.
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The report includes the following columns:
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- Technique
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- Subfolder
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- Contributors
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- Wrapped
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- Tested
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The report is saved as 'combined_report.html' in the 'website' directory of the repository.
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"""
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# Read the CSV file
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df = pd.read_csv(CODE_CONTRIBUTIONS_FILE)
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unique_subfolders = df['subfolder'].unique().tolist()
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# Read the JSON file
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with open(ALGORITHMS_FILE, 'r') as f:
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algorithms_data = json.load(f)
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# list of all algorithms from the JSON file
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all_algorithms = algorithms_data['algorithms']
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# Check if both code_contributions_file matches with source folder
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for subfolder in unique_subfolders:
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subfolder_path = os.path.join(SOURCE_FOLDER, subfolder)
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if not os.path.exists(subfolder_path):
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print(f"Warning: Subfolder '{subfolder}' does not exist in the source folder.")
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# Add column 'Tested' to the DataFrame based on a match with algorithms and wrapped column
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df['Wrapped'] = df['Wrapped'].fillna('')
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df['Tested'] = df.apply(lambda row: 'Yes' if any(algorithm in row['Wrapped'] for algorithm in all_algorithms) else '', axis=1)
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# Select the desired columns
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df_selected = df[['Technique', 'subfolder', 'Authors', 'Wrapped', 'Tested']]
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df_selected.columns = ['Technique', 'Subfolder', 'Contributors', 'Wrapped', 'Tested']
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# Convert the DataFrame to HTML
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html_string = df_selected.to_html(index=False)
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# Save the HTML to a file
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with open(os.path.join(REPO_DIR, 'website', 'combined_report.html'), 'w') as f:
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f.write(html_string)
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# Printing message that report has been successfully generated
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print("Combined HTML report generated successfully.")
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if __name__ == "__main__":
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generate_html()

‎website/combined_report.html

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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th>Technique</th>
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<th>Subfolder</th>
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<th>Contributors</th>
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<th>Wrapped</th>
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<th>Tested</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>IVIM</td>
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<td>OGC_AmsterdamUMC</td>
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<td>Oliver Gurney-Champion</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>OGC_AmsterdamUMC</td>
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<td>Oliver Gurney-Champion</td>
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<td>OGC_AmsterdamUMC_biexp</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>Tri-exponential</td>
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<td>OGC_AmsterdamUMC</td>
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<td>Oliver Gurney-Champion</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>Tri-exponential</td>
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<td>OGC_AmsterdamUMC</td>
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<td>Oliver Gurney-Champion</td>
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<td>OGC_AmsterdamUMC_biexp_segmented</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>OGC_AmsterdamUMC</td>
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<td>Oliver Gurney-Champion/Sebastiano Barbieri</td>
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<td>OGC_AmsterdamUMC_Bayesian_biexp</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>PvH_KB_NKI</td>
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<td>Petra van Houdt/Stefan Zijlema/Koen Baas</td>
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<td>PvH_KB_NKI_IVIMfit</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>PV_MUMC</td>
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<td>Paulien Voorter</td>
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<td>PV_MUMC_biexp</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Ivan A. Rashid</td>
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<td>IAR_LU_biexp</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Ivan A. Rashid</td>
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<td>IAR_LU_segmented_2step</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Ivan A. Rashid</td>
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<td>IAR_LU_segmented_3step</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Ivan A. Rashid</td>
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<td>IAR_LU_subtracted</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Farooq et al. Modified by Ivan A. Rashid</td>
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<td>IAR_LU_modified_mix</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Fadnavis et al. Modified by Ivan A. Rashid</td>
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<td>IAR_LU_modified_topopro</td>
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<td>Yes</td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Modified by Ivan A. Rashid</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>IAR_LundUniversity</td>
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<td>Modified by Ivan A. Rashid</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>OJ_GU</td>
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<td>Oscar Jalnefjord</td>
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<td>OJ_GU_seg</td>
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<td></td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>OJ_GU</td>
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<td>Oscar Jalnefjord</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>OJ_GU</td>
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<td>Oscar Jalnefjord</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>IVIM</td>
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<td>ETP_SRI</td>
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<td>Eric Peterson</td>
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<td>ETP_SRI_LinearFitting</td>
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<td>Yes</td>
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</tr>
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</tbody>
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</table>

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