Functions flat_to_array

PyNutil/counting_and_load.py

 import numpy as np
 import pandas as pd
 import struct
+import matplotlib.pyplot as plt
+import os
+import nrrd
 
 
 # related to counting and load
@@ -114,7 +117,7 @@ def pixel_count_per_region(
 """Read flat file and write into an np array"""
 
 
-def flat_to_array(flat_file):
+def flat_to_array(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
@@ -133,8 +136,28 @@ def flat_to_array(flat_file):
             image[y, x] = image_data[x + y * w]
 
     image_arr = np.array(image)
-    return image_arr
+    #return image_arr
 
+    """assign label file values into image array"""
+    labelfile = pd.read_csv(labelfile)
+    allen_id_image = np.zeros((h, w))  # create an empty image array
+    coordsy, coordsx = np.meshgrid(list(range(w)), list(range(h)))
+    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
+
+#def count_per_uniqueidx()
+
+    """count pixels for unique idx"""
+    unique_ids, counts = np.unique(allen_id_image, return_counts=True)
+
+    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"])
+    # create a pandas df with regions and pixel counts
+    return(df_area_per_label)
 
 # Import flat files, count pixels per label, np.unique... etc. nitrc.org/plugins/mwiki/index.php?title=visualign:Deformation
 
@@ -146,3 +169,4 @@ def flat_to_array(flat_file):
        b,w,h=struct.unpack(">BII",f.read(9))
        data=struct.unpack(">"+("xBH"[b]*(w*h)),f.read(b*w*h))
 """
+

messing_around_files/load_workflow.py → PyNutil/load_workflow.py

@@ -5,26 +5,18 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np
 
-from read_and_write import FlattoArray, LabeltoArray
+#from read_and_write import flat_to_array, label_to_array
+from counting_and_load import flat_to_array
 
 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 = FlattoArray(base)
+#image_arr = flat_to_array(base, label)
+plt.imshow(flat_to_array(base, label))
 
-plt.imshow(FlattoArray(base))
 
-"""assign label file values into image array"""
-labelfile = pd.read_csv(r"../annotation_volumes\allen2017_colours.csv")
-allen_id_image = np.zeros((h, w))  # create an empty image array
-coordsy, coordsx = np.meshgrid(list(range(w)), list(range(h)))
-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)]  # assign allen IDs into image array
 
-plt.imshow(allen_id_image)
-
-"""count pixels for unique idx"""
+"""count pixels in np array for unique idx, return pd df"""
 unique_ids, counts = np.unique(allen_id_image, return_counts=True)
 
 area_per_label = list(zip(unique_ids, counts))
@@ -34,6 +26,7 @@ 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"""
 

PyNutil/read_and_write.py

@@ -60,8 +60,8 @@ def save_dataframe_as_csv(df_to_save, output_csv):
     df_to_save.to_csv(output_csv, sep=";", na_rep="", index=False)
 
 
-def flat_to_array(flatfile):
-    """Read flat file and write into an np array, return array"""
+def flat_to_array(flatfile, labelfile):
+    """Read flat file, write into an np array, assign label file values, return array"""
     with open(flatfile, "rb") as f:
         # i dont know what b is, w and h are the width and height that we get from the
         # flat file header
@@ -80,7 +80,16 @@ def flat_to_array(flatfile):
             image[y, x] = imagedata[x + y * w]
 
     image_arr = np.array(image)
-    return image_arr
+    #return image_arr
+
+    """assign label file values into image array"""
+    labelfile = pd.read_csv(labelfile)
+    allen_id_image = np.zeros((h, w))  # create an empty image array
+    coordsy, coordsx = np.meshgrid(list(range(w)), list(range(h)))
+    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
 
 
 def label_to_array(label_path, image_array):
@@ -111,5 +120,6 @@ def files_in_directory(directory):
 
 
 def read_atlas_volume(atlas_volume_path):
+    """return data from atlas volume"""
     data, header = nrrd.read(atlas_volume_path)
     return data

messing_around_files/testing_openflatfile.py

@@ -38,7 +38,7 @@ with open(base, "rb") as f:
 
 """assign label file values into image array"""
 
-labelfile = pd.read_csv(r"metadata/annotation_volumes\allen2017_colours.csv")
+labelfile = pd.read_csv(r"annotation_volumes\allen2017_colours.csv")
 #labelfile[]
 print(list(zip(val,count)))