src/extraction/core/functions/custom/localisation.py

@@ -129,7 +129,7 @@ def nuc_est_conv(
 
 
 def nuc_conv_3d(cell_mask, trap_image, pixel_size=0.23, spacing=0.6):
-    cell_mask = np.dstack([cell_mask] * trap_image.shape[-1])
+    cell_mask = np.stack([cell_mask] * trap_image.shape[0])
     ratio = spacing / pixel_size
     cell_fluo = trap_image[cell_mask]
     num_cell_fluo = len(np.nonzero(cell_fluo)[0])

src/extraction/core/functions/distributors.py

@@ -13,7 +13,7 @@ def trap_apply(cell_fun, cell_masks, *args, **kwargs):
     cell_fun: function
         Function to apply to the cell (from extraction/cell.py)
     cell_masks: 3d array
-        Segmentation masks for the cells. Note that cells are the last dimension N (Y,X,N)
+        Segmentation masks for the cells. Note that cells are in the first dimension (N, Y,X)
     *args: tuple
         Trap_image and any other arguments to pass if needed to custom functions.
     **kwargs: dict
@@ -23,7 +23,7 @@ def trap_apply(cell_fun, cell_masks, *args, **kwargs):
     return [cell_fun(mask, *args, **kwargs) for mask in cell_masks]
 
 
-def reduce_z(trap_image: np.ndarray, fun: t.Callable):
+def reduce_z(trap_image: np.ndarray, fun: t.Callable, axis: int = 0):
     """
     Reduce the trap_image to 2d.
 
@@ -33,15 +33,16 @@ def reduce_z(trap_image: np.ndarray, fun: t.Callable):
         Images for all the channels associated with a trap
     fun: function
         Function to execute the reduction
-
+    axis: int (default 0)
+        Axis in which we apply the reduction operation.
     """
     # FUTURE replace with py3.10's match-case.
     if (
         hasattr(fun, "__module__") and fun.__module__[:10] == "bottleneck"
     ):  # Bottleneck type
-        return getattr(bn.reduce, fun.__name__)(trap_image, axis=2)
+        return getattr(bn.reduce, fun.__name__)(trap_image, axis=axis)
     elif isinstance(fun, np.ufunc):
         # optimise the reduction function if possible
-        return fun.reduce(trap_image, axis=2)
+        return fun.reduce(trap_image, axis=axis)
     else:  # WARNING: Very slow, only use when no alternatives exist
-        return np.apply_along_axis(fun, 2, trap_image)
+        return np.apply_along_axis(fun, axis, trap_image)

src/extraction/core/functions/math_utils.py

@@ -20,7 +20,6 @@ def div0(array, fill=0, axis=-1):
     slices_0, slices_1 = [[slice(None)] * len(array.shape)] * 2
     slices_0[axis] = 0
     slices_1[axis] = 1
-
     with np.errstate(divide="ignore", invalid="ignore"):
         c = np.true_divide(
             array[tuple(slices_0)],

src/extraction/core/functions/trap.py

@@ -5,14 +5,14 @@ import numpy as np
 
 def imBackground(cell_masks, trap_image):
     """
-    Finds the median background (pixels not comprising cells) from trap_image
+    Find the median background (pixels not comprising cells) from trap_image.
 
     Parameters
     ----------
     cell_masks: 3d array
        Segmentation masks for cells
     trap_image:
-        The image (all channels) for the tile containing the cell
+        The image (all channels) for the tile containing the cell.
     """
     if not len(cell_masks):
         # create cell_masks if none are given
@@ -25,14 +25,14 @@ def imBackground(cell_masks, trap_image):
 
 def background_max5(cell_masks, trap_image):
     """
-    Finds the mean of the maximum five pixels of the background (pixels not comprising cells) from trap_image
+    Finds the mean of the maximum five pixels of the background.
 
     Parameters
     ----------
     cell_masks: 3d array
-        Segmentation masks for cells
+        Segmentation masks for cells.
     trap_image:
-        The image (all channels) for the tile containing the cell
+        The image (all channels) for the tile containing the cell.
     """
     if not len(cell_masks):
         # create cell_masks if none are given

src/postprocessor/core/processes/detrend.py

@@ -54,4 +54,6 @@ class detrend(PostProcessABC):
         ).mean()
         # Detrend: subtract normalised time series by moving average
         signal_detrend = signal.subtract(signal_movavg)
-        return signal_detrend.dropna(axis=1)  # Remove columns with NaNs
+        # Rolling window operations create columns that are all NaNs at the left
+        # and right edges because of the maths.  This line removes these columns.
+        return signal_detrend.dropna(axis=1, how="all")