rename aux_dict -> auxiliary_images

Depends on #193

notebooks/ISS_Pipeline_-_Breast_-_1_FOV.ipynb

@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image(s.aux_dict['dots'])"
+    "image(s.auxiliary_images['dots'])"
    ]
   },
   {
@@ -104,7 +104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image(s.aux_dict['nuclei'])"
+    "image(s.auxiliary_images['nuclei'])"
    ]
   },
   {
@@ -156,7 +156,7 @@
     "\n",
     "# filter 'dots' auxiliary file\n",
     "print(\"filtering dots\")\n",
-    "dots_filt = white_top_hat(s.aux_dict['dots'], disk_size)\n",
+    "dots_filt = white_top_hat(s.auxiliary_images['dots'], disk_size)\n",
     "\n",
     "# convert the unstacked data back into a tensor\n",
     "s.set_stack(s.un_squeeze(stack_filt))\n",
@@ -385,7 +385,7 @@
     "stain = stain/stain.max()\n",
     "\n",
     "\n",
-    "seg = WatershedSegmenter(s.aux_dict['nuclei'], stain)  # uses skimage watershed. \n",
+    "seg = WatershedSegmenter(s.auxiliary_images['nuclei'], stain)  # uses skimage watershed.\n",
     "cells_labels = seg.segment(dapi_thresh, stain_thresh, size_lim, disk_size_markers, disk_size_mask, min_dist)\n",
     "seg.show()"
    ]
@@ -412,8 +412,8 @@
     "results = pd.merge(res, p.spots_df_viz, on='spot_id', how='left')\n",
     "\n",
     "rgb = np.zeros(s.image.tile_shape + (3,))\n",
-    "rgb[:,:,0] = s.aux_dict['nuclei']\n",
-    "rgb[:,:,1] = s.aux_dict['dots']\n",
+    "rgb[:,:,0] = s.auxiliary_images['nuclei']\n",
+    "rgb[:,:,1] = s.auxiliary_images['dots']\n",
     "do = rgb2gray(rgb)\n",
     "do = do/(do.max())\n",
     "\n",

notebooks/ISS_Pipeline_-_Breast_-_1_FOV.py

@@ -56,15 +56,15 @@
 # EPY: END markdown
 
 # EPY: START code
-image(s.aux_dict['dots'])
+image(s.auxiliary_images['dots'])
 # EPY: END code
 
 # EPY: START markdown
 # Below is a DAPI auxiliary image, which specifically marks nuclei. This is useful cell segmentation later on in the processing.
 # EPY: END markdown
 
 # EPY: START code
-image(s.aux_dict['nuclei'])
+image(s.auxiliary_images['nuclei'])
 # EPY: END code
 
 # EPY: START markdown
@@ -97,7 +97,7 @@
 
 # filter 'dots' auxiliary file
 print("filtering dots")
-dots_filt = white_top_hat(s.aux_dict['dots'], disk_size)
+dots_filt = white_top_hat(s.auxiliary_images['dots'], disk_size)
 
 # convert the unstacked data back into a tensor
 s.set_stack(s.un_squeeze(stack_filt))
@@ -249,7 +249,7 @@
 stain = stain/stain.max()
 
 
-seg = WatershedSegmenter(s.aux_dict['nuclei'], stain)  # uses skimage watershed. 
+seg = WatershedSegmenter(s.auxiliary_images['nuclei'], stain)  # uses skimage watershed.
 cells_labels = seg.segment(dapi_thresh, stain_thresh, size_lim, disk_size_markers, disk_size_mask, min_dist)
 seg.show()
 # EPY: END code
@@ -268,8 +268,8 @@
 results = pd.merge(res, p.spots_df_viz, on='spot_id', how='left')
 
 rgb = np.zeros(s.image.tile_shape + (3,))
-rgb[:,:,0] = s.aux_dict['nuclei']
-rgb[:,:,1] = s.aux_dict['dots']
+rgb[:,:,0] = s.auxiliary_images['nuclei']
+rgb[:,:,1] = s.auxiliary_images['dots']
 do = rgb2gray(rgb)
 do = do/(do.max())
 

notebooks/ISS_Simple_tutorial_-_Mouse_vs._Human_Fibroblasts.py

@@ -38,7 +38,7 @@
 # EPY: END code
 
 # EPY: START code
-image(s.aux_dict['dots'], size=10)
+image(s.auxiliary_images['dots'], size=10)
 # EPY: END code
 
 # EPY: START markdown
@@ -68,7 +68,7 @@
 stack_filt = s.un_squeeze(stack_filt)
 
 # filter dots
-dots_filt = white_top_hat(s.aux_dict['dots'], disk_dize)
+dots_filt = white_top_hat(s.auxiliary_images['dots'], disk_dize)
 
 # create a 'stain' for segmentation
 stain = np.mean(s.max_proj('ch'), axis=0)
@@ -81,8 +81,8 @@
 
 # visualize
 tile(s.squeeze(), bar=False, size=10);
-image(s.aux_dict['dots'])
-image(s.aux_dict['stain'])
+image(s.auxiliary_images['dots'])
+image(s.auxiliary_images['stain'])
 # EPY: END code
 
 # EPY: START markdown
@@ -126,7 +126,7 @@
 disk_size_mask = None
 min_dist = 57
 
-seg = WatershedSegmenter(s.aux_dict['dapi'], s.aux_dict['stain'])
+seg = WatershedSegmenter(s.auxiliary_images['dapi'], s.auxiliary_images['stain'])
 cells_labels = seg.segment(dapi_thresh, stain_thresh, size_lim, disk_size_markers, disk_size_mask, min_dist)
 seg.show()
 # EPY: END code
@@ -174,11 +174,11 @@
 dec_filt = pd.merge(dec, spots_viz, on='spot_id',how='left')
 dec_filt = dec_filt[dec_filt.qual>.25]
 
-assert s.aux_dict['dapi'].shape == s.aux_dict['dots'].shape
+assert s.auxiliary_images['dapi'].shape == s.auxiliary_images['dots'].shape
 
-rgb = np.zeros(s.aux_dict['dapi'].shape + (3,))
-rgb[:,:,0] = s.aux_dict['dapi']
-rgb[:,:,1] = s.aux_dict['dots']
+rgb = np.zeros(s.auxiliary_images['dapi'].shape + (3,))
+rgb[:,:,0] = s.auxiliary_images['dapi']
+rgb[:,:,1] = s.auxiliary_images['dots']
 do = rgb2gray(rgb)
 do = do/(do.max())
 
@@ -193,7 +193,7 @@
 v = pd.merge(spots_viz, ass, on='spot_id')
 
 r = label_to_regions(cells_labels)
-im = r.mask(background=[0.9, 0.9, 0.9], dims=s.aux_dict['dots'].shape, stroke=None, cmap='rainbow')
+im = r.mask(background=[0.9, 0.9, 0.9], dims=s.auxiliary_images['dots'].shape, stroke=None, cmap='rainbow')
 image(im,size=10)
 
 v_ass = v[~v.cell_id.isnull()]

starfish/io.py

@@ -19,7 +19,7 @@ def __init__(self):
         self.image = None
 
         # auxiliary images
-        self.aux_dict = dict()
+        self.auxiliary_images = dict()
 
         # readers and writers
         self.write_fn = np.save  # asserted for now
@@ -41,7 +41,7 @@ def read(self, in_json_path_or_url):
 
     def _read_aux(self):
         for aux_key, aux_data in self.org['auxiliary_images'].items():
-            self.aux_dict[aux_key] = ImageStack.from_url(aux_data, self.baseurl)
+            self.auxiliary_images[aux_key] = ImageStack.from_url(aux_data, self.baseurl)
 
     @classmethod
     def from_experiment_json(cls, json_url: str):
@@ -79,7 +79,7 @@ def _write_stack(self, dir_name):
 
     def _write_aux(self, dir_name):
         for aux_key, aux_data in self.org['auxiliary_images'].items():
-            self.aux_dict[aux_key].write(os.path.join(dir_name, aux_data))
+            self.auxiliary_images[aux_key].write(os.path.join(dir_name, aux_data))
 
     def set_stack(self, new_stack):
         if self.image.raw_shape != new_stack.shape:
@@ -89,13 +89,13 @@ def set_stack(self, new_stack):
         self.image.numpy_array = new_stack
 
     def set_aux(self, key, img):
-        if key in self.aux_dict:
-            old_img = self.aux_dict[key]
+        if key in self.auxiliary_images:
+            old_img = self.auxiliary_images[key]
             if old_img.shape != img.shape:
                 msg = "Shape mismatch. Current data shape: {}, new data shape: {}".format(
                     old_img.shape, img.shape)
                 raise AttributeError(msg)
-            self.aux_dict[key].numpy_array = img
+            self.auxiliary_images[key].numpy_array = img
         else:
             # TODO: (ttung) major hack alert.  we don't have a convenient mechanism to build an ImageStack from a single
             # numpy array, which we probably should.
@@ -128,7 +128,7 @@ def set_aux(self, key, img):
             tile.numpy_array = img
             tileset.add_tile(tile)
 
-            self.aux_dict[key] = ImageStack(tileset)
+            self.auxiliary_images[key] = ImageStack(tileset)
             self.org['auxiliary_images'][key] = f"{key}.json"
 
     def max_proj(self, *dims):

starfish/pipeline/features/spots/detector/gaussian.py

@@ -32,7 +32,7 @@ def __init__(self, min_sigma, max_sigma, num_sigma, threshold, blobs_image_name,
             than thresh are ignored. Reduce this to detect blobs with less
             intensities.
         blobs_image_name : str
-            name of the image containing blobs. Must be present in the aux_dict of the Stack passed to `find`
+            name of the image containing blobs. Must be present in the auxiliary_images of the Stack passed to `find`
         measurement_type : str ['max', 'mean']
             name of the function used to calculate the intensity for each identified spot area
 
@@ -97,7 +97,7 @@ def fit(self, blobs_image):
         return SpotAttributes(fitted_blobs)
 
     def find(self, image_stack) -> Tuple[SpotAttributes, EncodedSpots]:
-        blobs = image_stack.aux_dict[self.blobs].max_proj(Indices.HYB, Indices.CH, Indices.Z)
+        blobs = image_stack.auxiliary_images[self.blobs].max_proj(Indices.HYB, Indices.CH, Indices.Z)
         spot_attributes = self.fit(blobs)
         encoded_spots = self.encode(image_stack, spot_attributes.data)
         return spot_attributes, encoded_spots

starfish/pipeline/filter/bandpass.py

@@ -67,5 +67,5 @@ def filter(self, stack) -> None:
         stack.image.apply(bandpass_)
 
         # apply to aux dict too:
-        for k, val in stack.aux_dict.items():
-            stack.aux_dict[k].apply(bandpass_)
+        for auxiliary_image in stack.auxiliary_images.values():
+            auxiliary_image.apply(bandpass_)

starfish/pipeline/filter/clip.py

@@ -74,5 +74,5 @@ def filter(self, stack) -> None:
         stack.image.apply(clip)
 
         # apply to aux dict too:
-        for k, val in stack.aux_dict.items():
-            stack.aux_dict[k].apply(clip)
+        for auxiliary_image in stack.auxiliary_images.values():
+            auxiliary_image.apply(clip)

starfish/pipeline/filter/gaussian_high_pass.py

@@ -72,5 +72,5 @@ def filter(self, stack: Stack) -> None:
         stack.image.apply(high_pass)
 
         # apply to aux dict too:
-        for k, val in stack.aux_dict.items():
-            stack.aux_dict[k].apply(high_pass)
+        for auxiliary_image in stack.auxiliary_images.values():
+            auxiliary_image.apply(high_pass)

starfish/pipeline/filter/gaussian_low_pass.py

@@ -69,5 +69,5 @@ def filter(self, stack) -> None:
         stack.image.apply(low_pass)
 
         # apply to aux dict too:
-        for k, val in stack.aux_dict.items():
-            stack.aux_dict[k].apply(low_pass)
+        for auxiliary_image in stack.auxiliary_images.values():
+            auxiliary_image.apply(low_pass)

starfish/pipeline/filter/richardson_lucy_deconvolution.py

@@ -87,5 +87,5 @@ def filter(self, stack: Stack) -> None:
         func: Callable = partial(self.richardson_lucy_deconv, num_iter=self.num_iter, psf=self.psf, clip=self.clip)
         stack.image.apply(func)
 
-        for k, val in stack.aux_dict.items():
-            stack.aux_dict[k].apply(func)
+        for auxiliary_image in stack.auxiliary_images.values():
+            auxiliary_image.apply(func)

starfish/pipeline/filter/white_tophat.py

@@ -53,5 +53,5 @@ def white_tophat(image):
         stack.image.apply(white_tophat)
 
         # apply to aux dict too.
-        for aux_img in stack.aux_dict.values():
-            aux_img.apply(white_tophat)
+        for auxiliary_image in stack.auxiliary_images.values():
+            auxiliary_image.apply(white_tophat)

starfish/pipeline/registration/fourier_shift.py

@@ -26,7 +26,7 @@ def add_arguments(cls, group_parser):
     def register(self, stack):
         # TODO: (ambrosejcarr) is this the appropriate way of dealing with Z in registration?
         mp = stack.max_proj(Indices.CH, Indices.Z)
-        dots = stack.aux_dict['dots'].max_proj(Indices.HYB, Indices.CH, Indices.Z)
+        dots = stack.auxiliary_images['dots'].max_proj(Indices.HYB, Indices.CH, Indices.Z)
 
         for h in range(stack.image.num_hybs):
             # compute shift between maximum projection (across channels) and dots, for each hyb round

starfish/pipeline/segmentation/watershed.py

@@ -36,7 +36,7 @@ def segment(self, stack):
         disk_size_markers = None
         disk_size_mask = None
 
-        nuclei = stack.aux_dict['nuclei'].max_proj(Indices.HYB, Indices.CH, Indices.Z)
+        nuclei = stack.auxiliary_images['nuclei'].max_proj(Indices.HYB, Indices.CH, Indices.Z)
         seg = WatershedSegmenter(nuclei, stain)
         cells_labels = seg.segment(
             self.dapi_threshold, self.input_threshold, size_lim, disk_size_markers, disk_size_mask, self.min_distance)