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@@ -15,4 +15,4 @@ bvals.txt | |
| .cache | ||
| nosetests.xml | ||
| coverage.xml | ||
| *.pyc | ||
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| Technique,Category,Subcategory,notes,subfolder,Link to source code,Authors,Institution,function/module,DOI,Tester,test status,wrapped | ||
| 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,,no | ||
| 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 | ||
| 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,,no | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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 | ||
| 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,,no | ||
| 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,,no | ||
| 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 | ||
| 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,,no | ||
| 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,,no | ||
| 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 | ||
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@@ -9,4 +9,5 @@ matplotlib | |
| scienceplots | ||
| cvxpy | ||
| pytest | ||
| tqdm | ||
| pandas | ||
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| import os | ||
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| import pandas as pd | ||
| import json | ||
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| # directory of the current script | ||
| SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) | ||
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| # path to the repository | ||
| REPO_DIR = os.path.dirname(SCRIPT_DIR) | ||
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| CODE_CONTRIBUTIONS_FILE = os.path.join(REPO_DIR, "doc", "code_contributions_record.csv") | ||
| ALGORITHMS_FILE = os.path.join(REPO_DIR, "tests", "IVIMmodels", "unit_tests", "algorithms.json") | ||
| SOURCE_FOLDER = os.path.join(REPO_DIR, "src", "original") | ||
| WRAPPED_FOLDER = os.path.join(REPO_DIR, "src", "standardized") | ||
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| # Read the CSV file | ||
| 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 | ||
| with open(ALGORITHMS_FILE, 'r') as f: | ||
| algorithms_data = json.load(f) | ||
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| # list of all algorithms from the JSON file | ||
| all_algorithms = algorithms_data['algorithms'] | ||
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| # Check if both code_contributions_file matches with source folder | ||
| for subfolder in unique_subfolders: | ||
| subfolder_path = os.path.join(SOURCE_FOLDER, subfolder) | ||
| if not os.path.exists(subfolder_path): | ||
| 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 | ||
| df['Tested'] = df.apply(lambda row: 'Yes' if any(algorithm in row['wrapped'] for algorithm in all_algorithms) else 'No', axis=1) | ||
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| # Select the desired columns | ||
| df_selected = df[['Technique', 'subfolder', 'Authors', 'Tested', 'wrapped']] | ||
| df_selected.columns = ['Technique', 'Subfolder', 'Contributors', 'Tested', 'wrapped'] | ||
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| # Convert the DataFrame to HTML | ||
| html_string = df_selected.to_html(index=False) | ||
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| # Save the HTML to a file | ||
| with open(os.path.join(REPO_DIR, 'website','combined_report.html'), 'w') as f: | ||
| f.write(html_string) | ||
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| # Printing message that report has been successfully generated | ||
| print("Combined HTML report generated successfully.") | ||
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| <table border="1" class="dataframe"> | ||
| <thead> | ||
| <tr style="text-align: right;"> | ||
| <th>Technique</th> | ||
| <th>Subfolder</th> | ||
| <th>Contributors</th> | ||
| <th>Tested</th> | ||
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| <th>wrapped</th> | ||
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| </tr> | ||
| </thead> | ||
| <tbody> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>OGC_AmsterdamUMC</td> | ||
| <td>Oliver Gurney-Champion</td> | ||
| <td>No</td> | ||
| <td>no</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>OGC_AmsterdamUMC</td> | ||
| <td>Oliver Gurney-Champion</td> | ||
| <td>Yes</td> | ||
| <td>OGC_AmsterdamUMC_biexp</td> | ||
| </tr> | ||
| <tr> | ||
| <td>Tri-exponential</td> | ||
| <td>OGC_AmsterdamUMC</td> | ||
| <td>Oliver Gurney-Champion</td> | ||
| <td>No</td> | ||
| <td>no</td> | ||
| </tr> | ||
| <tr> | ||
| <td>Tri-exponential</td> | ||
| <td>OGC_AmsterdamUMC</td> | ||
| <td>Oliver Gurney-Champion</td> | ||
| <td>Yes</td> | ||
| <td>OGC_AmsterdamUMC_biexp_segmented</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>OGC_AmsterdamUMC</td> | ||
| <td>Oliver Gurney-Champion/Sebastiano Barbieri</td> | ||
| <td>Yes</td> | ||
| <td>OGC_AmsterdamUMC_Bayesian_biexp</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>PvH_KB_NKI</td> | ||
| <td>Petra van Houdt/Stefan Zijlema/Koen Baas</td> | ||
| <td>Yes</td> | ||
| <td>PvH_KB_NKI_IVIMfit</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>PV_MUMC</td> | ||
| <td>Paulien Voorter</td> | ||
| <td>Yes</td> | ||
| <td>PV_MUMC_biexp</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Ivan A. Rashid</td> | ||
| <td>Yes</td> | ||
| <td>IAR_LU_biexp</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Ivan A. Rashid</td> | ||
| <td>Yes</td> | ||
| <td>IAR_LU_segmented_2step</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Ivan A. Rashid</td> | ||
| <td>Yes</td> | ||
| <td>IAR_LU_segmented_3step</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Ivan A. Rashid</td> | ||
| <td>Yes</td> | ||
| <td>IAR_LU_subtracted</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Farooq et al. Modified by Ivan A. Rashid</td> | ||
| <td>Yes</td> | ||
| <td>IAR_LU_modified_mix</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Fadnavis et al. Modified by Ivan A. Rashid</td> | ||
| <td>Yes</td> | ||
| <td>IAR_LU_modified_topopro</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Modified by Ivan A. Rashid</td> | ||
| <td>No</td> | ||
| <td>no</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>IAR_LundUniversity</td> | ||
| <td>Modified by Ivan A. Rashid</td> | ||
| <td>No</td> | ||
| <td>no</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>OJ_GU</td> | ||
| <td>Oscar Jalnefjord</td> | ||
| <td>No</td> | ||
| <td>OJ_GU_seg</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>OJ_GU</td> | ||
| <td>Oscar Jalnefjord</td> | ||
| <td>No</td> | ||
| <td>no</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>OJ_GU</td> | ||
| <td>Oscar Jalnefjord</td> | ||
| <td>No</td> | ||
| <td>no</td> | ||
| </tr> | ||
| <tr> | ||
| <td>IVIM</td> | ||
| <td>ETP_SRI</td> | ||
| <td>Eric Peterson</td> | ||
| <td>Yes</td> | ||
| <td>ETP_SRI_LinearFitting</td> | ||
| </tr> | ||
| </tbody> | ||
| </table> | ||
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Rather than
noprobably just blank is easier to handle later on.