Merge branch 'main' into test-results-file

Author: AhmedBasem20

.github/workflows/website.yml

@@ -50,11 +50,15 @@ jobs:
         run: |
           pytest tests/IVIMmodels/unit_tests/test_ivim_fit.py
 
+      - name: 'Filter and compress results file.'
+        run: python utilities/reduce_output_size.py test_output.csv test_output.csv.gz
+
       - name: move data to the dashboard folder
         run: |
-          mv test_output.csv website/dashboard
+          mv test_output.csv.gz website/dashboard
           mv test_results_report.json website/dashboard
 
+
       - name: Build documentation
         run: |
           mkdir docs/_static

.gitignore

@@ -10,6 +10,8 @@ __pycache__/
 *.raw
 bvals.txt
 download
+.Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb
+.ipynb_checkpoints
 md5sums.txt
 *.gz
 *.zip

README.md

@@ -6,6 +6,8 @@ The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) is an initiative
 
 This **IVIM code collection** code library is maintained by OSIPI [Taskforce 2.4](https://www.osipi.org/task-force-2-4/) (*currently not available*) and aims to collect, test and share open-source code related to intravoxel incoherent motion (IVIM) analysis of diffusion encoded MRI data to be used in research and software development. Code contributions can include any code related IVIM analysis (denoising, motion correction, model fitting, etc.), but at an initial phase, development of tests and other features of the repository will predominantly focus on fitting algorithms. A goal of the IVIM OSIPI task force is to develop a fully tested and harmonized code library, building upon the contributions obtained through this initiative. Documentation and analysis are available on the [OSIPI TF2.4](https://osipi.github.io/TF2.4_IVIM-MRI_CodeCollection/).
 
+We have some useful tools and further documentation on https://osipi.github.io/TF2.4_IVIM-MRI_CodeCollection/.
+
 ## How to contribute
 
 If you would like to get involve in OSIPI and work within the task force, please email the contacts listed on our website.
@@ -17,6 +19,9 @@ If you would like to contribute with code, please follow the instructions below:
 *   [Guidelines for IVIM code contribution](doc/guidelines_for_contributions.md)
 *   [Guidelines to creating a test file](doc/creating_test.md) 
 
+If you would like to use code from the repository and/or are new to Github or IVIM, please see the jupyter notebook below:
+*   [Introduction to TF2.4_IVIM-MRI_CodeCollection github and IVIM Analysis using Python](doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb)
+
 ## Repository Organization
 
 The repository is organized in four main folders along with configuration files for automated testing. 

doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb

@@ -3,6 +3,7 @@
 from scipy.stats import norm
 import pathlib
 import sys
+from tqdm import tqdm
 
 class OsipiBase:
     """The base class for OSIPI IVIM fitting"""
@@ -47,7 +48,7 @@ def initialize(**kwargs):
         pass
 
     #def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initial_guess=None, **kwargs):
-    def osipi_fit(self, data, bvalues, **kwargs):
+    def osipi_fit(self, data, bvalues=None, **kwargs):
         """Fits the data with the bvalues
         Returns [S0, f, Dstar, D]
         """
@@ -68,7 +69,6 @@ def osipi_fit(self, data, bvalues, **kwargs):
         #kwargs["bvalues"] = use_bvalues
 
         #args = [data, use_bvalues, use_thresholds]
-        args = [data, use_bvalues]
         #if self.required_bounds or self.required_bounds_optional:
             #args.append(use_bounds)
         #if self.required_initial_guess or self.required_initial_guess_optional:
@@ -83,7 +83,13 @@ def osipi_fit(self, data, bvalues, **kwargs):
 
         #args = [data, use_bvalues, use_initial_guess, use_bounds, use_thresholds]
         #args = [arg for arg in args if arg is not None]
-        results = self.ivim_fit(*args, **kwargs)
+
+        # Assuming the last dimension of the data is the signal values of each b-value
+        results = np.empty(list(data.shape[:-1])+[3]) # Create an array with the voxel dimensions + the ones required for the fit
+        for ijk in tqdm(np.ndindex(data.shape[:-1]), total=np.prod(data.shape[:-1])):
+            args = [data[ijk], use_bvalues]
+            fit = list(self.ivim_fit(*args, **kwargs))
+            results[ijk] = fit
 
         #self.parameter_estimates = self.ivim_fit(data, bvalues)
         return results

src/wrappers/OsipiBase.py

@@ -0,0 +1,43 @@
+import os
+import gzip
+import csv
+import sys
+
+def reduce_output_file_size(input_file:str, output_file:str):
+    """
+    Simplify the data generated by the analysis pipeline by retaining only the essential information required for the frontend.
+    """
+    if os.path.exists(input_file):
+        # Open the input and output files
+        with open(input_file, 'r') as infile, gzip.open(output_file, 'wt', newline='') as outfile:
+            reader = csv.DictReader(infile)
+
+            # Drop b_values columns
+            fieldnames = [field for field in reader.fieldnames if not field.startswith('bval_')]
+            writer = csv.DictWriter(outfile, fieldnames=fieldnames)
+            writer.writeheader()
+
+            columns_to_round = ['f', 'Dp', 'D', 'f_fitted', 'Dp_fitted', 'D_fitted']
+
+            for row in reader:
+                #Delete columns starting with 'bval_'
+                for key in list(row.keys()):
+                    if key.startswith('bval_'):
+                        del row[key]
+
+                # Round values in the remaining relevant columns
+                for column in columns_to_round:
+                    if column in row:
+                        row[column] = round(float(row[column]), 4)
+                writer.writerow(row)
+    else:
+        print(f"File '{input_file}' not found.")
+
+if __name__ == '__main__':
+    if len(sys.argv) != 3:
+        print("Usage: python reduce_output_size.py <input_file> <output_file>")
+        sys.exit(1)
+    
+    input_file = sys.argv[1]
+    output_file = sys.argv[2]
+    reduce_output_file_size(input_file, output_file)

utilities/reduce_output_size.py

@@ -8,6 +8,7 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.7.1/jszip.min.js"></script>
   <script src="https://cdn.plot.ly/plotly-2.30.0.min.js"></script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/PapaParse/5.3.0/papaparse.min.js"></script>
+  <script src="https://cdn.jsdelivr.net/npm/pako@1.0.11/dist/pako.min.js"></script>
   <script src="index.js"></script>
   <script src="test_plots.js"></script>
   <link rel="stylesheet" href="index.css">

website/dashboard/index.html

@@ -205,17 +205,31 @@ document.addEventListener('DOMContentLoaded', function() {
 
     showLoading();
 
-    Papa.parse('test_output.csv', {
-        download: true,
-        header: true,
-        complete: results => {
-            data = results;
-            hideLoading();
-            populateOptions(data);
-            drawBoxPlot();
-            drawRegionBoxPlot();
-
+    fetch('test_output.csv.gz')
+    .then(response => {
+        if (!response.ok) {
+            throw new Error('Network response was not ok');
         }
+        return response.arrayBuffer();
+    })
+    .then(buffer => {
+        // Use pako to decompress the data
+        var decompressed = pako.inflate(new Uint8Array(buffer), { to: 'string' });
+        // Now use Papa Parse to parse the decompressed CSV data
+        Papa.parse(decompressed, {
+            header: true,
+            complete: results => {
+                console.log(results);
+                data = results;
+                hideLoading();
+                populateOptions(data);
+                drawBoxPlot();
+                drawRegionBoxPlot();
+            }
+        });
+    })
+    .catch(error => {
+        console.error('There has been a problem with your fetch operation:', error);
     });
 
     function populateOptions(data) {