Merge branch 'main' into Unit_Testing_Tolerances

Author: oliverchampion

.github/workflows/analysis.yml

@@ -70,20 +70,21 @@ jobs:
         run: |
           python -m pytest -m slow --selectAlgorithm ${{ matrix.algorithm }} --saveFileName test_output_${{ matrix.algorithm }}_${{ matrix.SNR }}.csv --SNR ${{ matrix.SNR }} --fitCount 300 --saveDurationFileName test_duration_${{ matrix.algorithm }}_${{ matrix.SNR }}.csv
       - name: Upload raw data
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
             name: Working_Data
             retention-days: 1
             path: |
               test_output_${{ matrix.algorithm }}_${{ matrix.SNR }}.csv
               test_duration_${{ matrix.algorithm }}_${{ matrix.SNR }}.csv
+            overwrite: true
 
   merge:
     runs-on: ubuntu-latest
     needs: build
     steps:
       - name: Download artifacts
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           path: artifacts
       - name: Merge fitting results
@@ -95,12 +96,13 @@ jobs:
           head -n 1 $(ls artifacts/Working_Data/test_duration_*.csv | head -n 1) > test_duration.csv
           tail -q -n +2 artifacts/Working_Data/test_duration_*.csv >> test_duration.csv
       - name: Upload merged artifacts
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: Data
           path: |
             test_output.csv
             test_duration.csv
+          overwrite: true
 
   analyze:
     runs-on: ubuntu-latest
@@ -121,13 +123,13 @@ jobs:
             any::data.table
             any::ggplot2
       - name: Download artifacts
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: Data
       - name: Generate figures
         run: Rscript --vanilla tests/IVIMmodels/unit_tests/analyze.r test_output.csv test_duration.csv
       - name: Upload figures
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         if: always()
         with:
             name: Figures
@@ -141,6 +143,8 @@ jobs:
               durations.pdf
               curve_plot.pdf
               fitted_curves.pdf
+            overwrite: true
+
 
   compare:
     runs-on: ubuntu-latest
@@ -158,16 +162,17 @@ jobs:
             any::tidyverse
             any::assertr
       - name: Download artifacts
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: Data
       - name: Test against previous results
         run: Rscript --vanilla tests/IVIMmodels/unit_tests/compare.r test_output.csv test_reference.csv tests/IVIMmodels/unit_tests/reference_output.csv test_results.csv
       - name: Upload data
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         if: always()
         with:
             name: Comparison
             path: |
               test_reference.csv
               test_results.csv
+            overwrite: true

src/original/IAR_LundUniversity/ivim_fit_method_biexp.py

@@ -61,13 +61,13 @@ def ivim_model(self, b, S0, f, D_star, D):
 
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
 
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess

src/original/IAR_LundUniversity/ivim_fit_method_linear.py

@@ -83,13 +83,13 @@ def sivim_model(self, b, S0, f, D):
 
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0), (np.inf, 1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
 
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.001)
         else:
             self.initial_guess = initial_guess

src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py

@@ -78,7 +78,7 @@ def __init__(self, gtab, bounds=None, maxiter=10, xtol=1e-8, rescale_units=False
         if gtab.bvals[-1] >= 10:
             self.bvals = gtab.bvals/1000
 
-        if bounds == None:
+        if bounds is None:
             # Bounds expressed as (lower bound, upper bound) for [f, D*, D].
             self.bounds = np.array([(0, 1), (5, 100), (0, 4)])
         elif (bounds[0][1] <= 1) or rescale_units: # Realistically, if mm2/s units are used, D* bound is <= 1

src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py

@@ -76,7 +76,7 @@ def __init__(self, gtab, bounds=[[0, 0.005, 1e-5], [1, 0.1, 0.004]], \
         # and are thus not changed.
         if gtab.bvals[-1] >= 10:
             self.bvals = gtab.bvals/1000
-        if bounds == None:
+        if bounds is None:
             # Bounds expressed as (lower bound, upper bound) for [f, D*, D].
             self.bounds = np.array([(0, 1), (5, 100), (0, 4)])
         elif (bounds[0][1] <= 1) or rescale_units: # Realistically, if mm2/s units are used, D* bound is <= 1

src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py

@@ -86,13 +86,13 @@ def ivim_signal(self, b, S0, f, D_star, D):
 
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
 
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess

src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py

@@ -105,13 +105,13 @@ def ivim_signal(self, b, S0, f, D_star, D):
 
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
 
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess

src/original/IAR_LundUniversity/ivim_fit_method_sivim.py

@@ -77,13 +77,13 @@ def sivim_model(self, b, S0, f, D):
 
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0), (np.inf, 1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
 
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.001)
         else:
             self.initial_guess = initial_guess

src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py

@@ -93,13 +93,13 @@ def perfusion_signal(self, b, S0, D_star):
 
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
 
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess

src/original/IAR_LundUniversity/simple_test_of_fits.py

@@ -27,6 +27,11 @@ class ETP_SRI_LinearFitting(OsipiBase):
     required_initial_guess_optional = False
     accepted_dimensions = 1
     # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
+
+    # Supported inputs in the standardized class
+    supported_bounds = False
+    supported_initial_guess = False
+    supported_thresholds = True
 
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
@@ -36,11 +41,13 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             Our OsipiBase object could contain functions that compare the inputs with
             the requirements.
         """
+
         super(ETP_SRI_LinearFitting, self).__init__(bvalues, thresholds, bounds, initial_guess)
         if bounds is not None:
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
+
         # Could be a good idea to have all the submission-specfic variable be 
         # defined with initials?
         self.ETP_weighting = weighting
@@ -71,7 +78,7 @@ def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
 
         results = {}
         if linear_fit_option:
-            f, Dstar = ETP_object.linear_fit(bvalues, signals)
+            f, Dstar = ETP_object.linear_fit(bvalues, signals, self.ETP_weighting, self.ETP_stats)
 
             results["f"] = f
             results["Dp"] = Dstar