def flat_to_dataframe

PyNutil/coordinate_extraction.py

@@ -93,7 +93,7 @@ def folder_to_atlas_space(
     object_cutoff=0,
 ):
     """Apply Segmentation to atlas space to all segmentations in a folder."""
-
+    """Return pixel_points, centroids, points_len, centroids_len, segmentation_filenames, """
     # This should be loaded above and passed as an argument
     slices = load_visualign_json(quint_alignment)
 

PyNutil/counting_and_load.py

@@ -117,7 +117,7 @@ def pixel_count_per_region(
 """Read flat file and write into an np array"""
 """Read flat file, write into an np array, assign label file values, return array"""
 
-def flat_to_array(flat_file, labelfile):
+def flat_to_dataframe(flat_file, labelfile):
     with open(flat_file, "rb") as f:
         # I don't know what b is, w and h are the width and height that we get from the
         # flat file header
@@ -145,9 +145,9 @@ def flat_to_array(flat_file, labelfile):
     values = image_arr[coordsx, coordsy]  # assign x,y coords from image_array into values
     lbidx = labelfile["idx"].values
     allen_id_image = lbidx[values.astype(int)]
-    return allen_id_image
+    #return allen_id_image
 
-#def count_per_uniqueidx()
+    #def count_per_uniqueidx()
 
     """count pixels for unique idx"""
     unique_ids, counts = np.unique(allen_id_image, return_counts=True)

PyNutil/load_workflow.py

@@ -6,27 +6,27 @@ import pandas as pd
 import numpy as np
 
 #from read_and_write import flat_to_array, label_to_array
-from counting_and_load import flat_to_array
+from counting_and_load import flat_to_dataframe
 
 base = r"../test_data/tTA_2877_NOP_s037_atlasmap/2877_NOP_tTA_lacZ_Xgal_s037_nl.flat"
 label = r"../annotation_volumes\allen2017_colours.csv"
 
 #image_arr = flat_to_array(base, label)
-plt.imshow(flat_to_array(base, label))
 
+#plt.imshow(flat_to_array(base, label))
 
+df_area_per_label = flat_to_dataframe(base, label)
 
 """count pixels in np array for unique idx, return pd df"""
-unique_ids, counts = np.unique(allen_id_image, return_counts=True)
+#unique_ids, counts = np.unique(allen_id_image, return_counts=True)
 
-area_per_label = list(zip(unique_ids, counts))
+#area_per_label = list(zip(unique_ids, counts))
 # create a list of unique regions and pixel counts per region
 
-df_area_per_label = pd.DataFrame(area_per_label, columns=["idx", "area_count"])
+#df_area_per_label = pd.DataFrame(area_per_label, columns=["idx", "area_count"])
 # create a pandas df with regions and pixel counts
 
 
-
 """add region name and colours corresponding to each idx into dataframe.
 This could be a separate function"""