From ea54b405f754a1b142da284576170e67cf6369ee Mon Sep 17 00:00:00 2001
From: polarbean <harry.carey95@gmail.com>
Date: Wed, 23 Oct 2024 21:35:27 +0200
Subject: [PATCH] remove redundant code from propagation

---
 PyNutil/load_workflow.py | 67 ----------------------------------------
 PyNutil/propagation.py   |  9 ------
 2 files changed, 76 deletions(-)
 delete mode 100644 PyNutil/load_workflow.py

diff --git a/PyNutil/load_workflow.py b/PyNutil/load_workflow.py
deleted file mode 100644
index 72ba0c3..0000000
--- a/PyNutil/load_workflow.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""Create workflow for calculating load based on atlas maps and segmentations"""
-
-import pandas as pd
-import cv2
-
-# from read_and_write import flat_to_array, label_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"
-##optional
-seg = r"../test_data/tTA_2877_NOP_s037_seg/2877_NOP_tTA_lacZ_Xgal_resize_Simple_Seg_s037.png"
-segim = cv2.imread(seg)
-# the indexing [:2] means the first two values and [::-1] means reverse the list
-segXY = segim.shape[:2][::-1]
-# image_arr = flat_to_array(base, label)
-
-# plt.imshow(flat_to_array(base, label))
-
-df_area_per_label = flat_to_dataframe(base, label, segXY)
-
-"""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))
-# 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
-
-
-"""add region name and colours corresponding to each idx into dataframe.
-This could be a separate function"""
-
-df_label_colours = pd.read_csv(label, sep=",")
-# find colours corresponding to each region ID and add to the pandas dataframe
-
-# look up name, r, g, b in df_allen_colours in df_area_per_label based on "idx"
-new_rows = []
-for index, row in df_area_per_label.iterrows():
-    mask = df_label_colours["idx"] == row["idx"]
-    current_region_row = df_label_colours[mask]
-    current_region_name = current_region_row["name"].values
-    current_region_red = current_region_row["r"].values
-    current_region_green = current_region_row["g"].values
-    current_region_blue = current_region_row["b"].values
-
-    row["name"] = current_region_name[0]
-    row["r"] = current_region_red[0]
-    row["g"] = current_region_green[0]
-    row["b"] = current_region_blue[0]
-
-    new_rows.append(row)
-
-df_area_per_label_name = pd.DataFrame(new_rows)
-
-print(df_area_per_label_name)
-df_area_per_label_name.to_csv(
-    "../outputs/NOP_s037_regionareas.csv", sep=";", na_rep="", index=False
-)
-
-
-# Count area per unique label in one flat file - done.
-# Scale up to size of corresponding segmentation/ or size of reference atlas if points are already scaled?
-# divide "segmentation value per idx per slice" by "area per idx per slice"
-# also do for whole brain - need to loop through and match up section with corresponding atlasmap
-# output reports per brain and per slice
diff --git a/PyNutil/propagation.py b/PyNutil/propagation.py
index d0ae3a6..02b728b 100644
--- a/PyNutil/propagation.py
+++ b/PyNutil/propagation.py
@@ -122,12 +122,3 @@ def orthonormalize(arr):
         arr[i + 6] -= arr[i + 3] * dot
     normalize(arr, 6)
 
-
-if __name__ == "__main__":
-    import json, sys
-
-    with open(sys.argv[1]) as f:
-        series = json.load(f)
-    propagate(series["slices"])
-    with open(sys.argv[2], "w") as f:
-        json.dump(series, f)