From b892d99fa3a42cd7d3e71c77d2204a4aef8b8e71 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Sat, 18 Feb 2023 00:43:08 +0100
Subject: [PATCH 1/8] perf: add dump context

---
 .github/workflows/commit-lint.yml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.github/workflows/commit-lint.yml b/.github/workflows/commit-lint.yml
index 9d8dc5a..765de9e 100644
--- a/.github/workflows/commit-lint.yml
+++ b/.github/workflows/commit-lint.yml
@@ -15,6 +15,11 @@ jobs:
         with:
           fetch-depth: 0
       
+      - name: Dump GitHub context
+        env:
+          GITHUB_CONTEXT: ${{ toJSON(github) }}
+        run: echo "$GITHUB_CONTEXT"
+      
       - name: Commit Lint
         uses: wagoid/commitlint-github-action@v5
         with:

From ebcaf4435da2a8b063b7b1e03f580c11400c5191 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Tue, 21 Feb 2023 13:21:44 +0100
Subject: [PATCH 2/8] fix: updated names of files

---
 README.md                                     |  5 +-
 .../0_0_angle_dist_in_group.py                |  6 +-
 .../automated_schneider/0_1_group_to_pop.py   | 22 +++++++
 .../population_matrix.py}                     | 29 ++++-----
 .../schneider_automatic.py                    |  0
 fly_pipe/preproc/0_2_join_csvs.py             | 65 -------------------
 fly_pipe/settings.py                          |  3 +
 ...eider_social.py => automated_schneider.py} |  4 ++
 8 files changed, 46 insertions(+), 88 deletions(-)
 rename fly_pipe/find_edges/{ => automated_schneider}/0_0_angle_dist_in_group.py (78%)
 create mode 100644 fly_pipe/find_edges/automated_schneider/0_1_group_to_pop.py
 rename fly_pipe/find_edges/{0_1_group_to_pop.py => automated_schneider/population_matrix.py} (55%)
 rename fly_pipe/find_edges/{ => automated_schneider}/schneider_automatic.py (100%)
 delete mode 100644 fly_pipe/preproc/0_2_join_csvs.py
 rename fly_pipe/utils/{schneider_social.py => automated_schneider.py} (97%)

diff --git a/README.md b/README.md
index 2e7ce14..090c73d 100644
--- a/README.md
+++ b/README.md
@@ -4,11 +4,12 @@
 
 - [x] Make dev env
 - [x] run whole pipeline
-- [ ] add versioning
+- [x] add versioning
 
 ### Pipeline scripts
 
 - [x] PREPROC: unpack excell to csv tables
 - [ ] MAIN: automatic detection of soc interaction
 - [ ] util: check soc space index
-- [ ] util: merge multiple track csv from one video
+
+
diff --git a/fly_pipe/find_edges/0_0_angle_dist_in_group.py b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
similarity index 78%
rename from fly_pipe/find_edges/0_0_angle_dist_in_group.py
rename to fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
index d63f769..ba1f0f0 100644
--- a/fly_pipe/find_edges/0_0_angle_dist_in_group.py
+++ b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
@@ -3,7 +3,7 @@
 import pandas as pd
 
 from fly_pipe.utils import fileio
-from fly_pipe.utils import schneider_social as ss
+from fly_pipe.utils import automated_schneider as ss
 
 
 POPULATION_NAME = "CSf"
@@ -21,6 +21,6 @@
 
 for group_name, group_path in population.items():
     group = fileio.load_files_from_folder(group_path, file_format='.csv')
-    total = ss.find_distances_and_angles_in_group(
+    result = ss.find_distances_and_angles_in_group(
         group, pxpermm[group_name])
-    total.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
+    result.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
diff --git a/fly_pipe/find_edges/automated_schneider/0_1_group_to_pop.py b/fly_pipe/find_edges/automated_schneider/0_1_group_to_pop.py
new file mode 100644
index 0000000..6d500b2
--- /dev/null
+++ b/fly_pipe/find_edges/automated_schneider/0_1_group_to_pop.py
@@ -0,0 +1,22 @@
+import pandas as pd
+
+from fly_pipe.utils import fileio
+
+
+POPULATION_NAME = "CSf"
+INPUT_PATH = "../../data/find_edges/0_0_angle_dist_in_group/" + POPULATION_NAME
+OUTPUT_PATH = "../../data/find_edges/0_1_group_to_population/" + POPULATION_NAME
+
+group = fileio.load_files_from_folder(INPUT_PATH, file_format='.csv')
+result = pd.DataFrame()
+
+for name, path in group.items():
+    df = pd.read_csv(path, index_col=0)
+    df = df.groupby(['angle', 'distance'])[
+        'counts'].sum().reset_index(name='counts')
+    result = pd.concat([result, df], axis=0)
+
+result = result.groupby(['angle', 'distance'])[
+    'counts'].sum().reset_index(name='counts')
+
+result.to_csv("{}.csv".format(OUTPUT_PATH))
diff --git a/fly_pipe/find_edges/0_1_group_to_pop.py b/fly_pipe/find_edges/automated_schneider/population_matrix.py
similarity index 55%
rename from fly_pipe/find_edges/0_1_group_to_pop.py
rename to fly_pipe/find_edges/automated_schneider/population_matrix.py
index 3e6e222..6da1f43 100644
--- a/fly_pipe/find_edges/0_1_group_to_pop.py
+++ b/fly_pipe/find_edges/automated_schneider/population_matrix.py
@@ -1,33 +1,26 @@
-# %%
-import os
+
 import numpy as np
 import pandas as pd
 
 from fly_pipe.utils import fileio
 
-
-POPULATION_NAME = "CSf"
-INPUT_PATH = "../../data/find_edges/0_0_angle_dist_in_group/" + POPULATION_NAME
-OUTPUT_PATH = "../../data/find_edges/0_1_group_to_population/" + POPULATION_NAME
+INPUT_PATH = "../../data/find_edges/0_1_group_to_population/"
+OUTPUT_PATH = "../../data/find_edges/"
 
 group = fileio.load_files_from_folder(INPUT_PATH, file_format='.csv')
-total = pd.DataFrame()
-
-for name, path in group.items():
-    df = pd.read_csv(path, index_col=0)
-    df = df.groupby(['angle', 'distance'])[
-        'counts'].sum().reset_index(name='counts')
-    total = pd.concat([total, df], axis=0)
-
-total = total.groupby(['angle', 'distance'])[
-    'counts'].sum().reset_index(name='counts')
-
-total.to_csv("{}.csv".format(OUTPUT_PATH))
 
 degree_bins = np.array([x for x in range(-180, 181, 5)])
 distance_bins = np.array([x*0.25 for x in range(0, 81)])
 round_bins = pd.DataFrame(0, index=distance_bins, columns=degree_bins)
 
+
+round
+group by
+round
+group by
+
+res. to df
+
 for row in total.iterrows():
     _, value = row
     degree = value["angle"]
diff --git a/fly_pipe/find_edges/schneider_automatic.py b/fly_pipe/find_edges/automated_schneider/schneider_automatic.py
similarity index 100%
rename from fly_pipe/find_edges/schneider_automatic.py
rename to fly_pipe/find_edges/automated_schneider/schneider_automatic.py
diff --git a/fly_pipe/preproc/0_2_join_csvs.py b/fly_pipe/preproc/0_2_join_csvs.py
deleted file mode 100644
index 262cffa..0000000
--- a/fly_pipe/preproc/0_2_join_csvs.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import sys
-import os
-import pandas as pd
-
-from utils import fileio
-
-
-INPUT_PATH = "./data/preproc/0_0_unpack_xlsx/"
-OUTPUT_PATH = "./data/preproc/0_1_join_csvs/"
-
-if not os.path.exists(OUTPUT_PATH):
-    os.makedirs(OUTPUT_PATH)
-
-files = fileio.load_multiple_folders(INPUT_PATH)
-
-for file_name, file_path in files.items():
-    if not os.path.exists(OUTPUT_PATH+"/"+file_name):
-        os.makedirs(OUTPUT_PATH+"/"+file_name)
-
-    arenas = fileio.load_multiple_folders(file_path)
-
-    for arena_name, arena_path in arenas.items():
-        if not os.path.exists(OUTPUT_PATH+"/"+file_name+"/"+arena_name):
-            os.makedirs(OUTPUT_PATH+"/"+file_name+"/"+arena_name)
-
-        videos = fileio.load_multiple_folders(arena_path)
-
-
-d = {}
-for video_name, video_path in videos.items():
-
-    csvs = []
-    content = fileio.load_files_from_folder(video_path)
-
-    for csv_name, csv_path in content.items():
-        csvs.append((csv_name, pd.read_csv(csv_path)))
-
-    d.update({video_name: csvs})
-
-
-v1_csvs = d['v1']
-v2_csvs = d['v2']
-
-pairs = 0
-for csv_name_v2, data_v2 in v2_csvs:
-    row_v2 = data_v2.iloc[0].values
-
-    x_v2 = row_v2[0]
-    y_v2 = row_v2[1]
-
-    for csv_name_v1, data_v1 in v1_csvs:
-
-        row_v1 = data_v1.iloc[-1].values
-
-        x_v1 = row_v1[0]
-        y_v1 = row_v1[1]
-
-        if abs(x_v2-x_v1) < 20 and abs(y_v2 - y_v1) < 20:
-
-            print(x_v1, x_v2)
-            print(y_v1, y_v2)
-            print(csv_name_v2, csv_name_v1)
-            pairs += 1
-
-print(pairs)
diff --git a/fly_pipe/settings.py b/fly_pipe/settings.py
index 17b573b..e7c081c 100644
--- a/fly_pipe/settings.py
+++ b/fly_pipe/settings.py
@@ -1,3 +1,6 @@
+import os
+root = os.path.dirname(os.path.dirname(__file__))
+
 
 # CONFIG = '../../configs/test.yaml'
 
diff --git a/fly_pipe/utils/schneider_social.py b/fly_pipe/utils/automated_schneider.py
similarity index 97%
rename from fly_pipe/utils/schneider_social.py
rename to fly_pipe/utils/automated_schneider.py
index d91ffc7..e46d6f4 100644
--- a/fly_pipe/utils/schneider_social.py
+++ b/fly_pipe/utils/automated_schneider.py
@@ -42,3 +42,7 @@ def find_distances_and_angles_in_group(group, pxpermm):
         total = pd.concat([total, df], axis=0)
 
     return total
+
+
+def population_matrix():
+    pass
\ No newline at end of file

From 7fa8f3eece68a1943c698d081a4e9b68b4ff4560 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Mon, 6 Mar 2023 01:38:32 +0100
Subject: [PATCH 3/8] fix: bug with angle rotation fixed

---
 .../0_0_angle_dist_in_group.py                | 128 +++++++++++++++---
 1 file changed, 111 insertions(+), 17 deletions(-)

diff --git a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
index ba1f0f0..cfd977e 100644
--- a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
+++ b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
@@ -1,26 +1,120 @@
-import os
-import json
+# %%
+from mpl_toolkits.mplot3d import Axes3D
+import sys
+import itertools
 import pandas as pd
-
+import numpy as np
 from fly_pipe.utils import fileio
-from fly_pipe.utils import automated_schneider as ss
+import matplotlib.pyplot as plt
+from matplotlib.image import NonUniformImage
+import matplotlib.pyplot as plt
+import numpy as np
 
+treatment = fileio.load_multiple_folders("./CSf")
 
-POPULATION_NAME = "CSf"
+for group_name, group_path in treatment.items():
+    print(group_name)
+    group = fileio.load_files_from_folder(group_path, file_format='.csv')
 
-INPUT_PATH = "../../data/raw/" + POPULATION_NAME
-OUTPUT_PATH = "../../data/find_edges/0_0_angle_dist_in_group/" + POPULATION_NAME
+    total = pd.DataFrame()
+    group_dfs = {fly: pd.read_csv(path, index_col=0)
+                 for fly, path in group.items()}
+    combinations = list(itertools.permutations(group_dfs.keys(), 2))
+    print(len(combinations))
+    for fly1, fly2 in combinations:
+        df1, df2 = group_dfs[fly1], group_dfs[fly2]
 
-PXPERMM_PATH = "../../data/pxpermm/" + POPULATION_NAME + ".json"
+        df = pd.DataFrame()
+        df['distance'] = np.sqrt(
+            np.square(df1['pos x']-df2['pos x']) + np.square(df1['pos y']-df2['pos y']))
 
-if not os.path.exists(OUTPUT_PATH):
-    os.makedirs(OUTPUT_PATH)
+        df['distance'] = df['distance'] / (df1.a.mean()*4)
+        df['distance'] = round(df['distance'], 2)
 
-population = fileio.load_multiple_folders(INPUT_PATH)
-pxpermm = json.load(open(PXPERMM_PATH))
+        df["dfx"] = df2['pos x'] - df1['pos x']
+        df["dfy"] = df2['pos y'] - df1['pos y']
 
-for group_name, group_path in population.items():
-    group = fileio.load_files_from_folder(group_path, file_format='.csv')
-    result = ss.find_distances_and_angles_in_group(
-        group, pxpermm[group_name])
-    result.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
+        df1.loc[df1['ori'] < 0, 'ori'] *= -1  # 2*np.pi
+
+        cos = np.cos(df1["ori"])
+        sin = np.sin(df1["ori"])
+
+        df['qx2'] = df1['pos x'] + cos * df["dfx"] - sin * df["dfy"]
+        df['qy2'] = df1['pos y'] + sin * df["dfx"] + cos * df["dfy"]
+
+        df['angle'] = np.arctan2(
+            df["qy2"]-df1["pos y"], df["qx2"]-df1["pos x"])
+        df['angle'] = np.rad2deg(df['angle'])
+        df['angle'] = np.round(df['angle'])
+
+        df = df[df.distance <= 6]
+        df = df.groupby(['angle', 'distance']
+                        ).size().reset_index(name='counts')
+        # df = df[['angle', 'distance']]
+
+        total = pd.concat([total, df], axis=0)
+    total.to_csv("./0_0_angle_dist/" + group_name + ".csv")
+
+group = fileio.load_files_from_folder("./0_0_angle_dist/", file_format='.csv')
+tot = pd.DataFrame()
+for name, path in group.items():
+    df = pd.read_csv(path, index_col=0)
+    tot = pd.concat([tot, df], axis=0)
+
+df = tot.groupby(['angle', 'distance'])[
+    'counts'].sum().reset_index(name='counts')
+# %%
+# define the bins
+degree_bins = np.arange(-180, 181, 5)
+distance_bins = np.arange(0, 6.001, 0.25)
+
+# calculate the 2D histogram
+hist, _, _ = np.histogram2d(df['angle'], df['distance'], bins=(
+    degree_bins, distance_bins), weights=df['counts'])
+
+# plot the histogram as a polar heatmap
+# plt.style.use('dark_background')  # this makes the text and grid lines white
+fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'polar': True})
+ax.grid(False)  # pcolormesh gives an annoying warning when the grid is on
+ax.pcolormesh(np.radians(degree_bins), distance_bins, hist.T, cmap='jet')
+ax.grid(True)
+plt.tight_layout()
+plt.show()
+
+
+# %%
+degree_bins = np.arange(-180, 181, 5)
+distance_bins = np.arange(0, 6.001, 0.25)
+round_bins = pd.DataFrame(0, index=distance_bins, columns=degree_bins)
+
+for row in df.iterrows():
+    _, value = row
+    degree = value["angle"]
+    distance = value["distance"]
+    count = value["counts"]
+    degree_bin = np.digitize([degree], degree_bins)[0]-1
+    distance_bin = np.digitize([distance], distance_bins)[0]-1
+    round_bins.iloc[distance_bin, degree_bin] += count
+n = len(degree_bins)
+m = len(distance_bins)
+
+rad = np.linspace(0, 10., m)
+a = np.linspace(0, 2 * np.pi, n)
+r, th = np.meshgrid(rad, a)
+z = round_bins.to_numpy().T
+plt.figure(figsize=(10, 10))
+plt.subplot(projection="polar")
+plt.pcolormesh(th, r, z, cmap='jet')
+plt.plot(a, r, ls='none', color='k')
+plt.grid()
+plt.colorbar()
+
+
+# %%
+histogram = round(round_bins.sum(axis=1))
+histogram.plot(kind="bar")
+
+# TODO:
+# fix pxpermm and major x axis (column "a") for each individual
+# make ploting to work as function
+# make distribution to rounds bins as function

From 856538661e585fa1250063f007923e4c1a4ea983 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Tue, 7 Mar 2023 01:33:36 +0100
Subject: [PATCH 4/8] fix: angle and distance working good

---
 .../0_0_angle_dist_in_group.py                | 37 ++++++++++++-------
 1 file changed, 24 insertions(+), 13 deletions(-)

diff --git a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
index cfd977e..6113ff9 100644
--- a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
+++ b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
@@ -10,19 +10,22 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-treatment = fileio.load_multiple_folders("./CSf")
+PATH = "../../../data/raw\CSf"
+treatment = fileio.load_multiple_folders(PATH)
 
 for group_name, group_path in treatment.items():
     print(group_name)
     group = fileio.load_files_from_folder(group_path, file_format='.csv')
 
     total = pd.DataFrame()
-    group_dfs = {fly: pd.read_csv(path, index_col=0)
-                 for fly, path in group.items()}
-    combinations = list(itertools.permutations(group_dfs.keys(), 2))
+    # group_dfs = {fly: pd.read_csv(path, index_col=0)
+    #              for fly, path in group.items()}
+
+    combinations = list(itertools.permutations(group.keys(), 2))
     print(len(combinations))
     for fly1, fly2 in combinations:
-        df1, df2 = group_dfs[fly1], group_dfs[fly2]
+        df1 = pd.read_csv(group[fly1], index_col=0)
+        df2 = pd.read_csv(group[fly2], index_col=0)
 
         df = pd.DataFrame()
         df['distance'] = np.sqrt(
@@ -31,6 +34,12 @@
         df['distance'] = df['distance'] / (df1.a.mean()*4)
         df['distance'] = round(df['distance'], 2)
 
+        df2["pos x"] = df2["pos x"] - df1["pos x"]
+        df2["pos y"] = df2["pos y"] - df1["pos y"]
+
+        df1["pos x"] = df1["pos x"] - df1["pos x"]
+        df1["pos y"] = df1["pos y"] - df1["pos y"]
+
         df["dfx"] = df2['pos x'] - df1['pos x']
         df["dfy"] = df2['pos y'] - df1['pos y']
 
@@ -42,20 +51,23 @@
         df['qx2'] = df1['pos x'] + cos * df["dfx"] - sin * df["dfy"]
         df['qy2'] = df1['pos y'] + sin * df["dfx"] + cos * df["dfy"]
 
-        df['angle'] = np.arctan2(
-            df["qy2"]-df1["pos y"], df["qx2"]-df1["pos x"])
+        # df['angle'] = np.arctan2(
+        #     df["qy2"]-df1["pos y"], df["qx2"]-df1["pos x"])
+        df['angle'] = np.arctan2(df["qy2"], df["qx2"])
         df['angle'] = np.rad2deg(df['angle'])
         df['angle'] = np.round(df['angle'])
 
-        df = df[df.distance <= 6]
+        df = df[df.distance <= 20]
         df = df.groupby(['angle', 'distance']
                         ).size().reset_index(name='counts')
         # df = df[['angle', 'distance']]
 
         total = pd.concat([total, df], axis=0)
-    total.to_csv("./0_0_angle_dist/" + group_name + ".csv")
+    total.to_csv(
+        "../../../data/find_edges/0_0_angle_dist_in_group/CSf/" + group_name + ".csv")
 
-group = fileio.load_files_from_folder("./0_0_angle_dist/", file_format='.csv')
+group = fileio.load_files_from_folder(
+    "../../../data/find_edges/0_0_angle_dist_in_group/CSf/", file_format='.csv')
 tot = pd.DataFrame()
 for name, path in group.items():
     df = pd.read_csv(path, index_col=0)
@@ -63,10 +75,10 @@
 
 df = tot.groupby(['angle', 'distance'])[
     'counts'].sum().reset_index(name='counts')
-# %%
+
 # define the bins
 degree_bins = np.arange(-180, 181, 5)
-distance_bins = np.arange(0, 6.001, 0.25)
+distance_bins = np.arange(0, 20.001, 0.25)
 
 # calculate the 2D histogram
 hist, _, _ = np.histogram2d(df['angle'], df['distance'], bins=(
@@ -81,7 +93,6 @@
 plt.tight_layout()
 plt.show()
 
-
 # %%
 degree_bins = np.arange(-180, 181, 5)
 distance_bins = np.arange(0, 6.001, 0.25)

From b39b74b066a369d538c04e6190128bd2e3ec0fd6 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Mon, 13 Mar 2023 00:54:44 +0100
Subject: [PATCH 5/8] fix: better histogram and ploting

---
 .../0_0_angle_dist_in_group.py                | 145 +++++++++---------
 1 file changed, 69 insertions(+), 76 deletions(-)

diff --git a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
index 6113ff9..39d12b5 100644
--- a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
+++ b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
@@ -1,39 +1,74 @@
 # %%
-from mpl_toolkits.mplot3d import Axes3D
 import sys
+import json
 import itertools
 import pandas as pd
 import numpy as np
-from fly_pipe.utils import fileio
-import matplotlib.pyplot as plt
-from matplotlib.image import NonUniformImage
+
 import matplotlib.pyplot as plt
-import numpy as np
+from scipy.signal import find_peaks
 
-PATH = "../../../data/raw\CSf"
-treatment = fileio.load_multiple_folders(PATH)
+from fly_pipe.utils import fileio
+
+
+FPS = 22.8
+POP = "CSf"
+PATH = "../../../data/raw/" + POP
+OUTPUT_PATH = "../../../data/find_edges/0_0_angle_dist_in_group/" + POP
 
+normalization = json.load(open("../../../data/normalization.json"))
+pxpermm = json.load(open("../../../data/pxpermm/" + POP + ".json"))
+
+treatment = fileio.load_multiple_folders(PATH)
 for group_name, group_path in treatment.items():
     print(group_name)
     group = fileio.load_files_from_folder(group_path, file_format='.csv')
 
     total = pd.DataFrame()
-    # group_dfs = {fly: pd.read_csv(path, index_col=0)
-    #              for fly, path in group.items()}
-
+    group_dfs = {fly: pd.read_csv(path, index_col=0)
+                 for fly, path in group.items()}
     combinations = list(itertools.permutations(group.keys(), 2))
-    print(len(combinations))
-    for fly1, fly2 in combinations:
-        df1 = pd.read_csv(group[fly1], index_col=0)
-        df2 = pd.read_csv(group[fly2], index_col=0)
 
+    norm = normalization[group_name]
+
+    # for fly in group.keys():
+    #     df1 = group_dfs[fly]
+
+    for fly1, fly2 in combinations:
+        df1 = group_dfs[fly1].copy(deep=True)
+        df2 = group_dfs[fly2].copy(deep=True)
         df = pd.DataFrame()
+
+        # df['movement'] = ((df1['pos x'] - df1['pos x'].shift())
+        #                   ** 2 + (df1['pos y'] - df1['pos y'].shift())**2)**0.5
+
+        # df['movement'] = df['movement']/pxpermm[group_name]/FPS
+        # df.loc[0, 'movement'] = df.loc[1, 'movement']
+
+        # n, c = np.histogram(df['movement'].values,
+        #                     bins=np.arange(0, 2.51, 0.01))
+        # opp = np.max(n) - n
+        # peaks, properties = find_peaks(opp/np.max(opp), prominence=0.05)
+
+        # if len(peaks) == 0:
+        #     movecut = 0
+
+        # else:
+        #     movecut = c[peaks[0]]
+
+        df2["pos x"] = (df2["pos x"] - norm["x"])  # / norm["radius"]
+        df2["pos y"] = (df2["pos y"] - norm["y"])  # / norm["radius"]
+        df1["pos x"] = (df1["pos x"] - norm["x"])  # / norm["radius"]
+        df1["pos y"] = (df1["pos y"] - norm["y"])  # / norm["radius"]
+
         df['distance'] = np.sqrt(
             np.square(df1['pos x']-df2['pos x']) + np.square(df1['pos y']-df2['pos y']))
 
         df['distance'] = df['distance'] / (df1.a.mean()*4)
         df['distance'] = round(df['distance'], 2)
 
+        df1.loc[df1['ori'] < 0, 'ori'] *= -1
+
         df2["pos x"] = df2["pos x"] - df1["pos x"]
         df2["pos y"] = df2["pos y"] - df1["pos y"]
 
@@ -43,89 +78,47 @@
         df["dfx"] = df2['pos x'] - df1['pos x']
         df["dfy"] = df2['pos y'] - df1['pos y']
 
-        df1.loc[df1['ori'] < 0, 'ori'] *= -1  # 2*np.pi
-
         cos = np.cos(df1["ori"])
         sin = np.sin(df1["ori"])
 
         df['qx2'] = df1['pos x'] + cos * df["dfx"] - sin * df["dfy"]
         df['qy2'] = df1['pos y'] + sin * df["dfx"] + cos * df["dfy"]
 
-        # df['angle'] = np.arctan2(
-        #     df["qy2"]-df1["pos y"], df["qx2"]-df1["pos x"])
         df['angle'] = np.arctan2(df["qy2"], df["qx2"])
         df['angle'] = np.rad2deg(df['angle'])
         df['angle'] = np.round(df['angle'])
 
-        df = df[df.distance <= 20]
+        df = df[df.distance <= 7]
+        # df = df[df.movement >= movecut]
+
         df = df.groupby(['angle', 'distance']
                         ).size().reset_index(name='counts')
-        # df = df[['angle', 'distance']]
 
         total = pd.concat([total, df], axis=0)
-    total.to_csv(
-        "../../../data/find_edges/0_0_angle_dist_in_group/CSf/" + group_name + ".csv")
 
-group = fileio.load_files_from_folder(
-    "../../../data/find_edges/0_0_angle_dist_in_group/CSf/", file_format='.csv')
-tot = pd.DataFrame()
+    total.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
+
+group = fileio.load_files_from_folder(OUTPUT_PATH, file_format='.csv')
+
+degree_bins = np.arange(0, 361, 5)
+distance_bins = np.arange(0, 6.251, 0.25)
+res = np.zeros((len(degree_bins)-1, len(distance_bins)-1))
+
 for name, path in group.items():
     df = pd.read_csv(path, index_col=0)
-    tot = pd.concat([tot, df], axis=0)
+    df.loc[df['angle'] < 0, 'angle'] += 360
 
-df = tot.groupby(['angle', 'distance'])[
-    'counts'].sum().reset_index(name='counts')
+    hist, _, _ = np.histogram2d(df['angle'], df['distance'], bins=(
+        degree_bins, distance_bins), weights=df['counts'])
 
-# define the bins
-degree_bins = np.arange(-180, 181, 5)
-distance_bins = np.arange(0, 20.001, 0.25)
+    norm_hist = np.ceil((hist / np.max(hist)) * 256)
+    res += hist
 
-# calculate the 2D histogram
-hist, _, _ = np.histogram2d(df['angle'], df['distance'], bins=(
-    degree_bins, distance_bins), weights=df['counts'])
-
-# plot the histogram as a polar heatmap
-# plt.style.use('dark_background')  # this makes the text and grid lines white
 fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'polar': True})
-ax.grid(False)  # pcolormesh gives an annoying warning when the grid is on
-ax.pcolormesh(np.radians(degree_bins), distance_bins, hist.T, cmap='jet')
+img = ax.pcolormesh(np.radians(degree_bins), distance_bins, res.T, cmap='jet')
+
+ax.set_rgrids(np.arange(0, 6.251, 0.5), angle=0)
 ax.grid(True)
+
 plt.tight_layout()
 plt.show()
-
-# %%
-degree_bins = np.arange(-180, 181, 5)
-distance_bins = np.arange(0, 6.001, 0.25)
-round_bins = pd.DataFrame(0, index=distance_bins, columns=degree_bins)
-
-for row in df.iterrows():
-    _, value = row
-    degree = value["angle"]
-    distance = value["distance"]
-    count = value["counts"]
-    degree_bin = np.digitize([degree], degree_bins)[0]-1
-    distance_bin = np.digitize([distance], distance_bins)[0]-1
-    round_bins.iloc[distance_bin, degree_bin] += count
-n = len(degree_bins)
-m = len(distance_bins)
-
-rad = np.linspace(0, 10., m)
-a = np.linspace(0, 2 * np.pi, n)
-r, th = np.meshgrid(rad, a)
-z = round_bins.to_numpy().T
-plt.figure(figsize=(10, 10))
-plt.subplot(projection="polar")
-plt.pcolormesh(th, r, z, cmap='jet')
-plt.plot(a, r, ls='none', color='k')
-plt.grid()
-plt.colorbar()
-
-
-# %%
-histogram = round(round_bins.sum(axis=1))
-histogram.plot(kind="bar")
-
-# TODO:
-# fix pxpermm and major x axis (column "a") for each individual
-# make ploting to work as function
-# make distribution to rounds bins as function

From 7932b7184dc0e45f3a3e329ddcbf65a1fe37bcbe Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Mon, 13 Mar 2023 01:22:49 +0100
Subject: [PATCH 6/8] fix: better histogram and ploting

---
 .../0_0_angle_dist_in_group.py                | 29 ++++++++++---------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
index 39d12b5..4c3eda0 100644
--- a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
+++ b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
@@ -39,22 +39,22 @@
         df2 = group_dfs[fly2].copy(deep=True)
         df = pd.DataFrame()
 
-        # df['movement'] = ((df1['pos x'] - df1['pos x'].shift())
-        #                   ** 2 + (df1['pos y'] - df1['pos y'].shift())**2)**0.5
+        df['movement'] = ((df1['pos x'] - df1['pos x'].shift())
+                          ** 2 + (df1['pos y'] - df1['pos y'].shift())**2)**0.5
 
-        # df['movement'] = df['movement']/pxpermm[group_name]/FPS
-        # df.loc[0, 'movement'] = df.loc[1, 'movement']
+        df['movement'] = df['movement']/pxpermm[group_name]*FPS
+        df.loc[0, 'movement'] = df.loc[1, 'movement']
 
-        # n, c = np.histogram(df['movement'].values,
-        #                     bins=np.arange(0, 2.51, 0.01))
-        # opp = np.max(n) - n
-        # peaks, properties = find_peaks(opp/np.max(opp), prominence=0.05)
+        n, c = np.histogram(df['movement'].values,
+                            bins=np.arange(0, 2.51, 0.01))
+        opp = np.max(n) - n
+        peaks, properties = find_peaks(opp/np.max(opp), prominence=0.05)
 
-        # if len(peaks) == 0:
-        #     movecut = 0
+        if len(peaks) == 0:
+            movecut = 0  # 0.01
 
-        # else:
-        #     movecut = c[peaks[0]]
+        else:
+            movecut = c[peaks[0]]
 
         df2["pos x"] = (df2["pos x"] - norm["x"])  # / norm["radius"]
         df2["pos y"] = (df2["pos y"] - norm["y"])  # / norm["radius"]
@@ -89,7 +89,7 @@
         df['angle'] = np.round(df['angle'])
 
         df = df[df.distance <= 7]
-        # df = df[df.movement >= movecut]
+        df = df[df.movement > movecut]
 
         df = df.groupby(['angle', 'distance']
                         ).size().reset_index(name='counts')
@@ -98,6 +98,7 @@
 
     total.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
 
+
 group = fileio.load_files_from_folder(OUTPUT_PATH, file_format='.csv')
 
 degree_bins = np.arange(0, 361, 5)
@@ -112,7 +113,7 @@
         degree_bins, distance_bins), weights=df['counts'])
 
     norm_hist = np.ceil((hist / np.max(hist)) * 256)
-    res += hist
+    res += norm_hist
 
 fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'polar': True})
 img = ax.pcolormesh(np.radians(degree_bins), distance_bins, res.T, cmap='jet')

From ce3277bf34a29f1b2f227a7654785e91bddd1721 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Mon, 13 Mar 2023 23:45:53 +0100
Subject: [PATCH 7/8] fix: better histogram and ploting

---
 .../0_0_angle_dist_in_group.py                | 53 +++++++++++--------
 1 file changed, 32 insertions(+), 21 deletions(-)

diff --git a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
index 4c3eda0..f8fa86d 100644
--- a/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
+++ b/fly_pipe/find_edges/automated_schneider/0_0_angle_dist_in_group.py
@@ -20,6 +20,10 @@
 pxpermm = json.load(open("../../../data/pxpermm/" + POP + ".json"))
 
 treatment = fileio.load_multiple_folders(PATH)
+
+degree_bins = np.arange(0, 361, 5)
+distance_bins = np.arange(0, 6.251, 0.25)
+
 for group_name, group_path in treatment.items():
     print(group_name)
     group = fileio.load_files_from_folder(group_path, file_format='.csv')
@@ -31,30 +35,22 @@
 
     norm = normalization[group_name]
 
-    # for fly in group.keys():
-    #     df1 = group_dfs[fly]
-
     for fly1, fly2 in combinations:
         df1 = group_dfs[fly1].copy(deep=True)
         df2 = group_dfs[fly2].copy(deep=True)
         df = pd.DataFrame()
 
-        df['movement'] = ((df1['pos x'] - df1['pos x'].shift())
-                          ** 2 + (df1['pos y'] - df1['pos y'].shift())**2)**0.5
+        df['movement_df1'] = ((df1['pos x'] - df1['pos x'].shift())
+                              ** 2 + (df1['pos y'] - df1['pos y'].shift())**2)**0.5
 
-        df['movement'] = df['movement']/pxpermm[group_name]*FPS
-        df.loc[0, 'movement'] = df.loc[1, 'movement']
+        df.loc[0, 'movement_df1'] = df.loc[1, 'movement_df1']
 
-        n, c = np.histogram(df['movement'].values,
+        df['movement_df1'] = df['movement_df1']/pxpermm[group_name]/FPS
+        n, c = np.histogram(df['movement_df1'].values,
                             bins=np.arange(0, 2.51, 0.01))
         opp = np.max(n) - n
         peaks, properties = find_peaks(opp/np.max(opp), prominence=0.05)
-
-        if len(peaks) == 0:
-            movecut = 0  # 0.01
-
-        else:
-            movecut = c[peaks[0]]
+        movecut_df1 = 0 if len(peaks) == 0 else c[peaks[0]]
 
         df2["pos x"] = (df2["pos x"] - norm["x"])  # / norm["radius"]
         df2["pos y"] = (df2["pos y"] - norm["y"])  # / norm["radius"]
@@ -84,21 +80,33 @@
         df['qx2'] = df1['pos x'] + cos * df["dfx"] - sin * df["dfy"]
         df['qy2'] = df1['pos y'] + sin * df["dfx"] + cos * df["dfy"]
 
-        df['angle'] = np.arctan2(df["qy2"], df["qx2"])
+        df['angle'] = np.arctan2(df["dfy"], df["dfx"])
         df['angle'] = np.rad2deg(df['angle'])
         df['angle'] = np.round(df['angle'])
 
+        df["angle_diff"] = (np.rad2deg(df1["ori"])) - df["angle"]
+
+        while df["angle_diff"].min() < -180 and df["angle_diff"].max() > 180:
+            df.loc[df['angle_diff'] < -180, 'angle_diff'] += 360
+            df.loc[df['angle_diff'] > 180, 'angle_diff'] -= 360
+
+        df["angle"] = np.round(df["angle_diff"])
+
         df = df[df.distance <= 7]
-        df = df[df.movement > movecut]
+        # df = df[df.movement_df1 > (movecut_df1*pxpermm[group_name]/FPS)]
 
         df = df.groupby(['angle', 'distance']
                         ).size().reset_index(name='counts')
 
         total = pd.concat([total, df], axis=0)
 
-    total.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
+    # histogram and bins
+    # hist, _, _ = np.histogram2d(df['angle'], df['distance'], bins=(
+    #     degree_bins, distance_bins), weights=df['counts'])
 
+    total.to_csv("{}/{}.csv".format(OUTPUT_PATH, group_name))
 
+# %%
 group = fileio.load_files_from_folder(OUTPUT_PATH, file_format='.csv')
 
 degree_bins = np.arange(0, 361, 5)
@@ -115,11 +123,14 @@
     norm_hist = np.ceil((hist / np.max(hist)) * 256)
     res += norm_hist
 
-fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'polar': True})
-img = ax.pcolormesh(np.radians(degree_bins), distance_bins, res.T, cmap='jet')
+res = np.ceil((res / np.max(res)) * 256)
 
-ax.set_rgrids(np.arange(0, 6.251, 0.5), angle=0)
-ax.grid(True)
+fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={'polar': True})
 
+img = ax.pcolormesh(np.radians(degree_bins), distance_bins, res.T, cmap="jet")
+
+ax.set_rgrids(np.arange(0, 6.251, 1.0), angle=0)
+ax.grid(True)
+plt.title("MOVECUT * pxpermm[group_name] / FPS")
 plt.tight_layout()
 plt.show()

From 50d5c4da451efb350cf4a26f11fb2e8a341019e1 Mon Sep 17 00:00:00 2001
From: milanXpetrovic <dr.milojko@gmail.com>
Date: Tue, 14 Mar 2023 17:38:02 +0100
Subject: [PATCH 8/8] Add files via upload

---
 .../find_edges/automated_schneider/movie1.csv | 401 ++++++++++++++++++
 .../find_edges/automated_schneider/prog.txt   |  49 +++
 2 files changed, 450 insertions(+)
 create mode 100644 fly_pipe/find_edges/automated_schneider/movie1.csv
 create mode 100644 fly_pipe/find_edges/automated_schneider/prog.txt

diff --git a/fly_pipe/find_edges/automated_schneider/movie1.csv b/fly_pipe/find_edges/automated_schneider/movie1.csv
new file mode 100644
index 0000000..94ed540
--- /dev/null
+++ b/fly_pipe/find_edges/automated_schneider/movie1.csv
@@ -0,0 +1,401 @@
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1,1,1,1,2,2,1,3,4,3,4,3,5,5,8,6,6,6,5,5,7,9,7,5,5,4,3,4,4,3,1,1,1,1,1,1,0,1,1,2,1,1,1,1,2,2,4,4,5,5,6,7,13,9,6,7,7,5,4,6,4,5,4,4,2,4,2,1,1,2,1,1,1
+11,18,19,18,37,44,28,36,36,43,58,47,53,72,98,73,84,80,110,105,93,109,84,99,73,75,52,55,39,40,27,23,22,20,15,17,21,22,17,22,27,27,27,35,42,36,44,53,66,66,76,72,81,74,90,108,86,93,77,85,66,50,47,48,45,32,25,25,33,45,21,17,11
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diff --git a/fly_pipe/find_edges/automated_schneider/prog.txt b/fly_pipe/find_edges/automated_schneider/prog.txt
new file mode 100644
index 0000000..a2fde31
--- /dev/null
+++ b/fly_pipe/find_edges/automated_schneider/prog.txt
@@ -0,0 +1,49 @@
+INPUTI U PROGRAM ZA ODREDIVANJE KRITERIJA
+
+INPUTS:
+    -strains is a cell containing string names
+    -n is a whole number for how many trials to use each pass
+    -nrand1 is a whole number for how many passes
+    -nrand2 is a whole number for how many pseudo trials used in each pass
+    -distance_bin is a number for distance bins (in body lengths)
+    [default 0.25]
+    -angle_bin same but for angles [default 5]
+    -start indicates what time index to start calculations (in min)
+    [default 0]
+    -exptime indicates when to stop calculations (in min) [default 30]
+    Note that you need to have enough frames for exptime+start
+    -offsettime if ~0, will stagger pseudo trials randomly in time
+    from 0 to offsettime (in min) [default 0]
+    -resample indicates whether or not to re-use trials when making pseudo-
+    trials (only use when also giving offsettime!) [default 0]
+    -distance_max is the upper limit to look for spatial configurations
+    [default = 50 body lengths]
+
+- seed za random odabir fileova
+
+- config file sa postavkama 'Bootstrap Criteria Settings.txt'
+
+- provjera i ucitavanje dataseta
+
+- provjera ako postoji i ucitavanje normalizacije
+    - ako ne postoji, generira se pomocu skripe Normalize
+
+- provjera ako podaci imaju duzinu od zadanog broja minuta
+
+- ucitavaju se sojevi, tj. populacije i onda program ide populaciju po populaciju i racuna parametre nad grupama koje pronadje u tom folderu
+
+GLAVNI DIO PROGRAMA 
+
+- prvo iterira po strainsima, tj. onoliko koliko ima foldera u .\raw 
+
+- nakon sto krene iteracija po strainsima odabire se jedan strain koji se obraduje
+
+- prvo provjera ako je vec obradjen, ako je onda ide dalje i ispisuje da je vec obradjen
+
+- opcija za eliminaciju musica koje se ne krecu, kod njih je postavljeno na 0 al ako je na 1 onda se poziva funkcija get_movecut()
+
+- u paraleli se racunaju za svaku grupu distance i kutovi sa funkcijom fast_fly_space()
+- racuna se matrica koja ima dimenzije 401x73x26
+distance bins, angle bins, za svaku populaciju
+
+sprema se u temp_home.mat