fixed errors after merge with main

oliverchampion · oliverchampion · commit 889a2c115f28 · 2025-01-04T21:45:17.000+01:00
Also changed fit_result["D*"] back to fit_result["Dp"], as now both names were used throughout the scripts
diff --git a/WrapImage/nifti_wrapper.py b/WrapImage/nifti_wrapper.py
@@ -111,7 +111,7 @@ def loop_over_first_n_minus_1_dimensions(arr):
         for idx, view in tqdm(loop_over_first_n_minus_1_dimensions(data), desc=f"{args.algorithm} is fitting", dynamic_ncols=True, total=total_iteration):
             fit_result = fit.osipi_fit(view, bvals)
             f_image.append(fit_result["f"])
-            Dp_image.append(fit_result["D*"])
+            Dp_image.append(fit_result["Dp"])
             D_image.append(fit_result["D"])
 
         # Convert lists to NumPy arrays
diff --git a/doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb b/doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb
@@ -532,7 +532,7 @@
     {
      "data": {
       "text/plain": [
-       "{'f': array(0.04609779), 'D*': array(0.01136011), 'D': array(0.00071134)}"
+       "{'f': array(0.04609779), 'Dp': array(0.01136011), 'D': array(0.00071134)}"
       ]
      },
      "execution_count": 15,
@@ -569,7 +569,7 @@
     {
      "data": {
       "text/plain": [
-       "{'f': array(0.04611801), 'D*': array(0.0113541), 'D': array(0.0007113)}"
+       "{'f': array(0.04611801), 'Dp': array(0.0113541), 'D': array(0.0007113)}"
       ]
      },
      "execution_count": 16,
@@ -636,10 +636,10 @@
     "#plot the results of algorithm 1\n",
     "plt.subplot(121)\n",
     "plt.plot(np.unique(bval),signal_1dir,'x')\n",
-    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*'])+(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
-    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*']))\n",
+    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['Dp'])+(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
+    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['Dp']))\n",
     "plt.plot(np.unique(bval),(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
-    "plt.legend(['measured data','model fit','D*','D'])\n",
+    "plt.legend(['measured data','model fit','Dp','D'])\n",
     "plt.ylabel('S/S0')\n",
     "plt.xlabel('b-value [s/mm^2]')\n",
     "plt.title('algorithm 1')\n",
@@ -650,10 +650,10 @@
     "#plot the results of algorithm 2\n",
     "plt.subplot(122)\n",
     "plt.plot(np.unique(bval),signal_1dir,'x')\n",
-    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*'])+(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
-    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*']))\n",
+    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['Dp'])+(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
+    "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['Dp']))\n",
     "plt.plot(np.unique(bval),(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
-    "plt.legend(['measured data','model fit','D*','D'])\n",
+    "plt.legend(['measured data','model fit','Dp','D'])\n",
     "plt.ylabel('S/S0')\n",
     "plt.xlabel('b-value [s/mm^2]')\n",
     "plt.title('algorithm 2')\n"
@@ -818,7 +818,7 @@
      "data": {
       "text/plain": [
        "{'f': array([0., 0., 0., ..., 0., 0., 0.]),\n",
-       " 'D*': array([0., 0., 0., ..., 0., 0., 0.]),\n",
+       " 'Dp': array([0., 0., 0., ..., 0., 0., 0.]),\n",
        " 'D': array([0., 0., 0., ..., 0., 0., 0.])}"
       ]
      },
diff --git a/src/standardized/ETP_SRI_LinearFitting.py b/src/standardized/ETP_SRI_LinearFitting.py
@@ -71,14 +71,14 @@ def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
             f, Dstar = ETP_object.linear_fit(bvalues, signals)
 
             results["f"] = f
-            results["D*"] = Dstar
+            results["Dp"] = Dstar
 
             return results
         else: 
             f, D, Dstar = ETP_object.ivim_fit(bvalues, signals)
 
             results["f"] = f
-            results["D*"] = Dstar
+            results["Dp"] = Dstar
             results["D"] = D
 
             return results
diff --git a/src/standardized/IAR_LU_biexp.py b/src/standardized/IAR_LU_biexp.py
@@ -78,7 +78,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         
         results = {}
         results["f"] = fit_results.model_params[1]
-        results["D*"] = fit_results.model_params[2]
+        results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
         return results
@@ -110,7 +110,7 @@ def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
         
         results = {}
         results["f"] = fit_results.model_params[..., 1]
-        results["D*"] = fit_results.model_params[..., 2]
+        results["Dp"] = fit_results.model_params[..., 2]
         results["D"] = fit_results.model_params[..., 3]
         
         return results
diff --git a/src/standardized/IAR_LU_modified_mix.py b/src/standardized/IAR_LU_modified_mix.py
@@ -81,7 +81,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         #D = fit_results.model_params[3]
         results = {}
         results["f"] = fit_results.model_params[1]
-        results["D*"] = fit_results.model_params[2]
+        results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
         return results
diff --git a/src/standardized/IAR_LU_modified_topopro.py b/src/standardized/IAR_LU_modified_topopro.py
@@ -83,7 +83,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         #return f, Dstar, D
         results = {}
         results["f"] = fit_results.model_params[1]
-        results["D*"] = fit_results.model_params[2]
+        results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
         return results
diff --git a/src/standardized/IAR_LU_segmented_2step.py b/src/standardized/IAR_LU_segmented_2step.py
@@ -84,7 +84,7 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
         #return f, Dstar, D
         results = {}
         results["f"] = fit_results.model_params[1]
-        results["D*"] = fit_results.model_params[2]
+        results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
         return results
diff --git a/src/standardized/IAR_LU_segmented_3step.py b/src/standardized/IAR_LU_segmented_3step.py
@@ -83,7 +83,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         #return f, Dstar, D
         results = {}
         results["f"] = fit_results.model_params[1]
-        results["D*"] = fit_results.model_params[2]
+        results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
         return results
diff --git a/src/standardized/IAR_LU_subtracted.py b/src/standardized/IAR_LU_subtracted.py
@@ -83,7 +83,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         #return f, Dstar, D
         results = {}
         results["f"] = fit_results.model_params[1]
-        results["D*"] = fit_results.model_params[2]
+        results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
         return results
diff --git a/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py b/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py
@@ -81,6 +81,6 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         results = {}
         results["D"] = fit_results[0]
         results["f"] = fit_results[1]
-        results["D*"] = fit_results[2]
+        results["Dp"] = fit_results[2]
 
         return results
diff --git a/src/standardized/OGC_AmsterdamUMC_biexp.py b/src/standardized/OGC_AmsterdamUMC_biexp.py
@@ -71,6 +71,6 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         results = {}
         results["D"] = fit_results[0]
         results["f"] = fit_results[1]
-        results["D*"] = fit_results[2]
+        results["Dp"] = fit_results[2]
 
         return results
diff --git a/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py b/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py
@@ -72,6 +72,6 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         results = {}
         results["D"] = fit_results[0]
         results["f"] = fit_results[1]
-        results["D*"] = fit_results[2]
+        results["Dp"] = fit_results[2]
 
         return results
diff --git a/src/standardized/OJ_GU_seg.py b/src/standardized/OJ_GU_seg.py
@@ -66,7 +66,7 @@ def ivim_fit(self, signals, bvalues=None):
 
         results = {} 
         results["f"] = fit_results['f']
-        results["D*"] = fit_results['Dstar']
+        results["Dp"] = fit_results['Dstar']
         results["D"] = fit_results['D']
         
         return results
diff --git a/src/standardized/PV_MUMC_biexp.py b/src/standardized/PV_MUMC_biexp.py
@@ -51,7 +51,7 @@ def ivim_fit(self, signals, bvalues=None):
 
         results = {} 
         results["f"] = fit_results[1]
-        results["D*"] = fit_results[2]
+        results["Dp"] = fit_results[2]
         results["D"] = fit_results[0]
         
         return results
diff --git a/src/standardized/PvH_KB_NKI_IVIMfit.py b/src/standardized/PvH_KB_NKI_IVIMfit.py
@@ -59,6 +59,6 @@ def ivim_fit(self, signals, bvalues=None):
         results = {}
         results["D"] = fit_results[0][0,0,0]/1000
         results["f"] = fit_results[1][0,0,0]
-        results["D*"] = fit_results[2][0,0,0]/1000
+        results["Dp"] = fit_results[2][0,0,0]/1000
 
         return results
diff --git a/src/wrappers/OsipiBase.py b/src/wrappers/OsipiBase.py
@@ -89,8 +89,8 @@ def osipi_fit(self, data, bvalues=None, **kwargs):
             # result_keys is a list of strings of parameter names, e.g. "S0", "f1", "f2", etc.
             result_keys = self.result_keys
         else:
-            # Default is ["f", "D*", "D"]
-            self.result_keys = ["f", "D*", "D"]
+            # Default is ["f", "Dp", "D"]
+            self.result_keys = ["f", "Dp", "D"]
         
         results = {}
         for key in self.result_keys:
@@ -132,8 +132,8 @@ def osipi_fit_full_volume(self, data, bvalues=None, **kwargs):
                 # result_keys is a list of strings of parameter names, e.g. "S0", "f1", "f2", etc.
                 result_keys = self.result_keys
             else:
-                # Default is ["f", "D*", "D"]
-                self.result_keys = ["f", "D*", "D"]
+                # Default is ["f", "Dp", "D"]
+                self.result_keys = ["f", "Dp", "D"]
 
             # Create the results dictionary
             results = {}
diff --git a/tests/IVIMmodels/unit_tests/test_ivim_fit.py b/tests/IVIMmodels/unit_tests/test_ivim_fit.py
@@ -61,7 +61,7 @@ def test_ivim_fit_saved(name, bvals, data, algorithm, xfail, kwargs, tolerances,
     if xfail["xfail"]:
         mark = pytest.mark.xfail(reason="xfail", strict=xfail["strict"])
         request.node.add_marker(mark)
-    signal, ratio = signal_helper(data["data"])    
+    signal = signal_helper(data["data"])
     tolerances = tolerances_helper(tolerances, data)
     start_time = time.time()  # Record the start time
     fit = OsipiBase(algorithm=algorithm, **kwargs)
@@ -73,7 +73,7 @@ def to_list_if_needed(value):
         "name": name,
         "algorithm": algorithm,
         "f_fit": to_list_if_needed(fit_result['f']),
-        "Dp_fit": to_list_if_needed(fit_result['D*']),
+        "Dp_fit": to_list_if_needed(fit_result['Dp']),
         "D_fit": to_list_if_needed(fit_result['D']),
         "f": to_list_if_needed(data['f']),
         "Dp": to_list_if_needed(data['Dp']),
@@ -158,12 +158,12 @@ def test_bounds(name, bvals, data, algorithm, xfail, kwargs, tolerances, request
     # deliberately have silly bounds to see whether they are used
     fit = OsipiBase(algorithm=algorithm, bounds=bounds, initial_guess = [0.001, 0.25, 0.015, 1.2], **kwargs)
     signal = signal_helper(data["data"])
-    tolerances = tolerances_helper(tolerances, data)
-    fit_results = fit.osipi_fit(signal, bvals)
+    fit_result = fit.osipi_fit(signal, bvals)
 
     assert bounds[0][0] <= fit_result['D'] <= bounds[1][0],  f"Result {fit_result['D']} out of bounds for data: {name}"
     assert bounds[0][1] <= fit_result['f'] <= bounds[1][1], f"Result {fit_result['f']} out of bounds for data: {name}"
     assert bounds[0][2] <= fit_result['Dp'] <= bounds[1][2], f"Result {fit_result['Dp']} out of bounds for data: {name}"
-    assert bounds[0][3] <= fit_result['S0'] <= bounds[1][3], f"Result {fit_result['S0']} out of bounds for data: {name}"
+    # S0 is not returned as argument...
+    #assert bounds[0][3] <= fit_result['S0'] <= bounds[1][3], f"Result {fit_result['S0']} out of bounds for data: {name}"
 
     
diff --git a/tests/IVIMmodels/unit_tests/test_ivim_synthetic.py b/tests/IVIMmodels/unit_tests/test_ivim_synthetic.py
@@ -40,9 +40,9 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
         fit_result = fit.osipi_fit(signal, bvals) #, prior_in=prior
         time_delta += datetime.datetime.now() - start_time
         if save_file is not None:
-            save_file.writerow([ivim_algorithm, name, SNR, idx, f, Dp, D, fit_result["f"], fit_result["D*"], fit_result["D"], *signal])
+            save_file.writerow([ivim_algorithm, name, SNR, idx, f, Dp, D, fit_result["f"], fit_result["Dp"], fit_result["D"], *signal])
             # save_results(save_file, ivim_algorithm, name, SNR, idx, [f, Dp, D], [f_fit, Dp_fit, D_fit])
-        npt.assert_allclose([f, Dp, D], [fit_result["f"], fit_result["D*"], fit_result["D"]], rtol, atol)
+        npt.assert_allclose([f, Dp, D], [fit_result["f"], fit_result["Dp"], fit_result["D"]], rtol, atol)
     if save_duration_file is not None:
         save_duration_file.writerow([ivim_algorithm, name, SNR, time_delta/datetime.timedelta(microseconds=1), fit_count])
         # save_duration(save_duration_file, ivim_algorithm, name, SNR, time_delta, fit_count)
