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Co-authored-by: celprov <[email protected]>

eddymotion/model.py

@@ -115,11 +115,11 @@ def __init__(self, gtab, **kwargs):
         gtab : :obj:`~numpy.ndarray`
             An :math:`N \times 4` table, where rows (*N*) are diffusion gradients and
             columns are b-vector components and corresponding b-value, respectively.
-        b_low : :obj:`~numbers.Number`
+        th_low : :obj:`~numbers.Number`
             A lower bound for the b-value corresponding to the diffusion weighted images
             that will be averaged.
-        b_high : :obj:`~numbers.Number`
-            A lower bound for the b-value corresponding to the diffusion weighted images
+        th_high : :obj:`~numbers.Number`
+            An upper bound for the b-value corresponding to the diffusion weighted images
             that will be averaged.
         bias : :obj:`bool`
             Whether the overall distribution of each diffusion weighted image will be
@@ -129,16 +129,16 @@ def __init__(self, gtab, **kwargs):
 
         """
         self._gtab = gtab
-        self._th_low = kwargs.get("b_low", 50)
-        self._th_high = kwargs.get("b_high", self._gtab[3, ...].max() + 1)
+        self._th_low = kwargs.get("th_low", 50)
+        self._th_high = kwargs.get("th_high", self._gtab[3, ...].max())
         self._bias = kwargs.get("bias", True)
         self._stat = kwargs.get("stat", "median")
 
     def fit(self, data, **kwargs):
         """Calculate the average."""
-        # Select the interval of b-values for which DWIs will be averages
-        b_mask = (self._gtab[3, ...] > self._th_low) & (
-            self._gtab[3, ...] < self._th_high
+        # Select the interval of b-values for which DWIs will be averaged
+        b_mask = (self._gtab[3, ...] >= self._th_low) & (
+            self._gtab[3, ...] <= self._th_high
         )
         shells = data[..., b_mask]
 
@@ -151,7 +151,7 @@ def fit(self, data, **kwargs):
             shells = shells * drift
 
         # Select the summary statistic
-        avg_func = np.mean if self._stat == "median" else np.mean
+        avg_func = np.median if self._stat == "median" else np.mean
         # Calculate the average
         self._data = avg_func(shells, axis=-1)