fixed deep learning for single data points

Author: oliverchampion

src/standardized/IVIM_NEToptim.py

@@ -88,7 +88,8 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         """
         if not np.array_equal(bvalues, self.bvalues):
             raise ValueError("bvalue list at fitting must be identical as the one at initiation, otherwise it will not run")
-
+        if np.shape(np.shape(signals)) == (1,):
+            signals=signals[np.newaxis, :]
         paramsNN = deep.predict_IVIM(signals, self.bvalues, self.net, self.arg)
 
         results = {}

src/standardized/Super_IVIM_DC.py

@@ -99,7 +99,8 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         """
         if not np.array_equal(bvalues, self.bvalues):
             raise ValueError("bvalue list at fitting must be identical as the one at initiation, otherwise it will not run")
-
+        if np.shape(np.shape(signals)) == (1,):
+            signals=signals[np.newaxis, :]
         Dp, Dt, f, S0_superivimdc = infer_from_signal(
             signal=signals,
             bvalues=self.bvalues,

tests/IVIMmodels/unit_tests/algorithms.json

@@ -1,14 +1,14 @@
 {
     "algorithms": [
         "IVIM_NEToptim",
+        "Super_IVIM_DC",
         "TCML_TechnionIIT_lsqlm",
         "TCML_TechnionIIT_lsqtrf",
         "TCML_TechnionIIT_lsqBOBYQA",
         "TCML_TechnionIIT_lsq_sls_lm",
         "TCML_TechnionIIT_lsq_sls_trf",
         "TCML_TechnionIIT_lsq_sls_BOBYQA",
         "TCML_TechnionIIT_SLS",
-        "Super_IVIM_DC",
         "ASD_MemorialSloanKettering_QAMPER_IVIM",
         "ETP_SRI_LinearFitting",
         "IAR_LU_biexp",

utilities/data_simulation/GenerateData.py

@@ -192,15 +192,14 @@ def simulate_training_data(self, bvalues, SNR = (5,100), n = 1000000, Drange = (
         #data_sim = np.zeros([len(D), len(bvalues)])
         bvalues = np.array(bvalues)
         if type(SNR) == tuple:
-            noise_std = 1/SNR[1] + test[:,3] * (1/SNR[0] - 1/SNR[1])
+            noise_std = np.array(1/SNR[1] + test[:,3] * (1/SNR[0] - 1/SNR[1]))
             addnoise = True
         elif SNR == 0:
             addnoise = False
             noise_std = np.ones((n, 1))
         else:
             noise_std = np.full((n, 1), 1/SNR)
             addnoise = True
-        noise_std = noise_std[:, np.newaxis]
         # loop over array to fill with simulated IVIM data
         bvalues = np.array(bvalues).reshape(1, -1)
         data_sim = 1 * (f * np.exp(-bvalues * Dp) + (1 - f) * np.exp(-bvalues * D))