Various Small Fixes/Improvements (#1985)

* Initial commit * Updated comments * Changed filter_tally to rounds_used in decoded_intensity_table * Fixed validation errors * added tests for checkAll decoder * Fixed imports order * include test updates * annotation fixes and speed improvements * added ray to requirements * More speed and memory improvements * Replaced ray w/ multiprocessing * Fix import order * few more fixes * Changed test to account for randomness * fix test * Added new filter step * Added input error checking and condition for empty results * Fixed channel bug * March 30 2022 big update * Updated comments * Updated comments and fixed mypy errors * Swapped ray multiprocessing w/ standard Python * fixed function arg bug * mypy fix * bug fixes * fix * Added sphinx update to reqs * fixed error correction bug * reqs fix * Added better error catching * Added error catch for no decoded targets * fix * Added spot saving/loading * Replaced old seqfish notebook * Added ability to read in 3d external label image as mask * Modified BlobDetector to work when is_volume=False * Speed improvements * fix for windows test * retrigger checks * retrigger checks
spacetx · Sep 6, 2022 · 853f56c · 853f56c
1 parent c8327a8
commit 853f56c
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Showing 7 changed files with 475 additions and 392 deletions.
diff --git a/notebooks/SeqFISH.ipynb b/notebooks/SeqFISH.ipynb
diff --git a/starfish/core/morphology/binary_mask/binary_mask.py b/starfish/core/morphology/binary_mask/binary_mask.py
@@ -355,13 +355,28 @@ def from_external_labeled_image(cls, path_to_labeled_image: Union[str, Path],
         # Load the label image generated from another program
         label_image = io.imread(path_to_labeled_image)
 
-        # Get the physical ticks from the original dapi image
-        physical_ticks = {Coordinates.Y: original_image.xarray.yc.values,
-                          Coordinates.X: original_image.xarray.xc.values}
+        # Fixes dimension order. Tiff files saved with skimage.io.imsave and tifffile.imsave will
+        # have different dimension order when read in.
+        if len(label_image.shape) == 3 and label_image.shape[0] > label_image.shape[-1]:
+            label_image = np.transpose(label_image, [2, 0, 1])
+
+        if len(label_image.shape) == 3:
+            # Get the physical ticks from the original dapi image
+            physical_ticks = {Coordinates.Y: original_image.xarray.yc.values,
+                              Coordinates.X: original_image.xarray.xc.values,
+                              Coordinates.Z: original_image.xarray.zc.values}
+
+            # Get the pixel values from the original dapi image
+            pixel_coords = {Axes.Y: original_image.xarray.y.values,
+                            Axes.X: original_image.xarray.x.values,
+                            Axes.ZPLANE: original_image.xarray.z.values}
+        else:
+            physical_ticks = {Coordinates.Y: original_image.xarray.yc.values,
+                              Coordinates.X: original_image.xarray.xc.values}
 
-        # Get the pixel values from the original dapi image
-        pixel_coords = {Axes.Y: original_image.xarray.y.values,
-                        Axes.X: original_image.xarray.x.values}
+            # Get the pixel values from the original dapi image
+            pixel_coords = {Axes.Y: original_image.xarray.y.values,
+                            Axes.X: original_image.xarray.x.values}
 
         # Create the label image
         label_im = LabelImage.from_label_array_and_ticks(

diff --git a/starfish/core/spots/DecodeSpots/check_all_decoder.py b/starfish/core/spots/DecodeSpots/check_all_decoder.py
@@ -325,8 +325,8 @@ def run(self,
                                     # Adds spot_codes column to roundData
 
                                     roundData = buildBarcodes(roundData, neighborDict,
-                                                              currentRoundOmitNum, channelDict,
-                                                              strictness, r, numJobs)
+                                                              currentRoundOmitNum, strictness, r,
+                                                              numJobs)
 
                                     # When strictness is positive the filter-first methods is used
                                     # and distanceFilter is run first on all the potential barcodes
@@ -400,7 +400,12 @@ def run(self,
         # create empty IntensityTable filled with np.nan
         data = np.full((len(allCodes), len(channels), len(rounds)), fill_value=np.nan)
         dims = (Features.AXIS, Axes.CH.value, Axes.ROUND.value)
-        centers = allCodes['center']
+
+        if len(allCodes) == 0:
+            centers = []
+        else:
+            centers = allCodes['center']
+
         coords: Mapping[Hashable, Tuple[str, Any]] = {
             Features.SPOT_RADIUS: (Features.AXIS, np.full(len(allCodes), 1)),
             Axes.ZPLANE.value: (Features.AXIS, np.asarray([round(c[0]) for c in centers])),
@@ -413,28 +418,37 @@ def run(self,
         }
         int_table = IntensityTable(data=data, dims=dims, coords=coords)
 
-        # Fill in data values
-        table_codes = []
-        for i in range(len(allCodes)):
-            code = []
-            # ints = allCodes.loc[i, 'intensities']
-            for j, ch in enumerate(allCodes.loc[i, 'best_barcodes']):
-                # If a round is not used, row will be all zeros
-                code.append(np.asarray([0 if k != ch - 1 else 1 for k in range(len(channels))]))
-            table_codes.append(np.asarray(code).T)
-        int_table.values = np.asarray(table_codes)
-        int_table = transfer_physical_coords_to_intensity_table(intensity_table=int_table,
-                                                                spots=spots)
-
-        # Validate results are correct shape
-        self.codebook._validate_decode_intensity_input_matches_codebook_shape(int_table)
-
-        # Create DecodedIntensityTable
-        result = DecodedIntensityTable.from_intensity_table(
-            int_table,
-            targets=(Features.AXIS, allCodes['targets'].astype('U')),
-            distances=(Features.AXIS, allCodes["distance"]),
-            passes_threshold=(Features.AXIS, np.full(len(allCodes), True)),
-            rounds_used=(Features.AXIS, allCodes['rounds_used']))
+        # If no targets found returns empty DecodedIntensityTable
+        if len(allCodes) > 0:
+            # Fill in data values
+            table_codes = []
+            for i in range(len(allCodes)):
+                code = []
+                # ints = allCodes.loc[i, 'intensities']
+                for j, ch in enumerate(allCodes.loc[i, 'best_barcodes']):
+                    # If a round is not used, row will be all zeros
+                    code.append(np.asarray([0 if k != ch - 1 else 1 for k in range(len(channels))]))
+                table_codes.append(np.asarray(code).T)
+            int_table.values = np.asarray(table_codes)
+            int_table = transfer_physical_coords_to_intensity_table(intensity_table=int_table,
+                                                                    spots=spots)
+
+            # Validate results are correct shape
+            self.codebook._validate_decode_intensity_input_matches_codebook_shape(int_table)
+
+            # Create DecodedIntensityTable
+            result = DecodedIntensityTable.from_intensity_table(
+                int_table,
+                targets=(Features.AXIS, allCodes['targets'].astype('U')),
+                distances=(Features.AXIS, allCodes["distance"]),
+                passes_threshold=(Features.AXIS, np.full(len(allCodes), True)),
+                rounds_used=(Features.AXIS, allCodes['rounds_used']))
+        else:
+            result = DecodedIntensityTable.from_intensity_table(
+                int_table,
+                targets=(Features.AXIS, np.array([])),
+                distances=(Features.AXIS, np.array([])),
+                passes_threshold=(Features.AXIS, np.array([])),
+                rounds_used=(Features.AXIS, np.array([])))
 
         return result