diff --git a/PyNutil/counting_and_load.py b/PyNutil/counting_and_load.py
index f5b1f8dba50e81fab7d13622bf2db89a5de5fd06..77a0fd98e31206649e10f32b21b4cc72b66ae2f7 100644
--- a/PyNutil/counting_and_load.py
+++ b/PyNutil/counting_and_load.py
@@ -1,6 +1,9 @@
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))
"""
+
diff --git a/messing_around_files/load_workflow.py b/PyNutil/load_workflow.py
similarity index 77%
rename from messing_around_files/load_workflow.py
rename to PyNutil/load_workflow.py
index 1d7d4131b27fd525829313c9180f9223936ea640..ca271c976ae2ae5d0bf5a29ba0b7bb9fe589e994 100644
--- a/messing_around_files/load_workflow.py
+++ b/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"""
diff --git a/PyNutil/read_and_write.py b/PyNutil/read_and_write.py
index a662371a333bfb5f30d8f859829e2f712ff741f8..db9be5db3597a394623781e8249df8b808769d60 100644
--- a/PyNutil/read_and_write.py
+++ b/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
diff --git a/messing_around_files/testing_openflatfile.py b/messing_around_files/testing_openflatfile.py
index 183e403e213e8a6754851414b08f634325c79d2b..1b076bac733d8eab0f14ca1cd27879730a22992e 100644
--- a/messing_around_files/testing_openflatfile.py
+++ b/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)))