diff --git a/boldness_darkside.py b/boldness_darkside.py
new file mode 100644
index 0000000..2853aa1
--- /dev/null
+++ b/boldness_darkside.py
@@ -0,0 +1,120 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import image_marker as im
+import tracking_result as tr
+import os
+import glob
+import argparse
+from IPython import embed
+
+#1. Tankkoordinaten
+def tankcoordinates(video, dontask=False):
+ redo = True
+ if os.path.exists('tankcoordinates.py'):
+ from tankcoordinates import bottom_left, bottom_right, top_left, top_right
+ print("Found tank coordinates top left: %s, top right: %s" % (top_left, top_right))
+ if dontask:
+ return bottom_left, top_left, top_right, bottom_right
+ answer = input('Redo markers? y/n')
+ if answer == 'y' or answer == 'Y':
+ redo = True
+ else:
+ redo = False
+ if redo:
+ tank_task = im.MarkerTask("tank limits", ["bottom left corner", "top left corner", "top right corner", "bottom right corner"], "Mark tank corners")
+ image_marker = im.ImageMarker([tank_task])
+ marker_positions = image_marker.mark_movie(video, 100)
+ bottom_right = marker_positions[0]['bottom right corner']
+ bottom_left = marker_positions[0]['bottom left corner']
+ top_right = marker_positions[0]['top right corner']
+ top_left = marker_positions[0]['top left corner']
+ with open('tankcoordinates.py', 'w') as f:
+ f.write('bottom_left = %s\n' % str(marker_positions[0]['bottom left corner']))
+ f.write('top_left = %s\n' % str(marker_positions[0]['top left corner']))
+ f.write('top_right = %s\n' % str(marker_positions[0]['top right corner']))
+ f.write('bottom_right = %s\n' % str(marker_positions[0]['bottom right corner']))
+
+ return bottom_left, top_left, top_right, bottom_right
+#2. Feederkoordinaten
+
+#3. dark_light Koordinaten
+def dark_light_coordinates(video, dontask=False):
+ redo = True
+ if os.path.exists('dark_light_coordinates.py'):
+ from dark_light_coordinates import left, right, dark_center
+ print("Found dark_light_coordinates left: %s, right: %s, dark_center: %s" % (left, right, dark_center))
+ if dontask:
+ return left, right, dark_center
+ answer = input('Redo markers? y/n')
+ if answer == 'y' or answer == 'Y':
+ redo = True
+ else:
+ redo = False
+ if redo:
+ dark_light_task = im.MarkerTask('Dark side', ['left', 'right', 'dark_center'], 'Mark light dark separator line')
+ image_marker = im.ImageMarker([dark_light_task])
+ marker_positions = image_marker.mark_movie(video, 100)
+
+ right = tr.coordinate_transformation(marker_positions[0]['right'])
+ left = tr.coordinate_transformation(marker_positions[0]['left'])
+ dark_center = tr.coordinate_transformation(marker_positions[0]['dark_center'])
+
+ with open('dark_light_coordinates.py', 'w') as f:
+ f.write('left = %s\n' % str(left))
+ f.write('right = %s\n' % str(right))
+ f.write('dark_center = %s\n' % str(dark_center))
+
+ return left, right, dark_center
+
+#4. Laden der Trackingresults
+def load_tracking_results(dlc_results_file):
+ trs = tr.TrackingResult(dlc_results_file)
+ t, x, y, l, name = trs.position_values(bodypart="snout", framerate=30)
+ return t, x, y, l
+
+#5. Wie lange hält sich der Fisch im Hellen/Dunklen auf?
+ # Anzahl Frames in der Fisch in definiertem, dunklen Bereich ist, bzw. in der der Fisch nicht im Hellen ist
+def aufenthaltsort(left, center, fish_y):
+ top_is_dark=left[1]>=center[1]
+ #bei wie vielen Frames ist der Fisch im Hellen?
+ if top_is_dark:
+ hell_count = len(fish_y[fish_y >= left[1]])
+ else:
+ hell_count = len(fish_y[fish_y < left[1]])
+ dark_count=len(fish_y) - hell_count
+ total_count = len(fish_y)
+ return hell_count, dark_count, total_count
+
+
+def analysiere_video(v, dlc, left, center):
+ t, fish_x, fish_y, likelihood = load_tracking_results(dlc)
+ hc, dc, tc = aufenthaltsort(left, center, fish_y)
+ print('Der Fisch hat sich %.2f %% im Dunklen aufgehalten.'%(dc/tc*100))
+ return (dc/tc*100)
+
+
+def main():
+ parser = argparse.ArgumentParser(description="")
+ parser.add_argument("day", type=str, help="The day you want to work on")
+ parser.add_argument("-f", "--folder", type=str, default="/data/boldness/labeled_videos", help="The base folder in which the labeled videos are stored. Default is /data/boldness/labeled_videos")
+ parser.add_argument("-a", "--animal", type=str, default="*", help="The animal id, default is * for all animals")
+ parser.add_argument("-e", "--extension", type=str, default=".mp4", help="The video file extension, default is .mp4")
+ parser.add_argument("-na", "--noask", action="store_true", help="do not ask for coordinates")
+ args = parser.parse_args()
+
+ videos = sorted(glob.glob(os.path.join(args.folder, args.day, '*%s*%s' % (args.animal, args.extension))))
+ dlc_files = sorted(glob.glob(os.path.join(args.folder, args.day, '*%s*%s' % (args.animal, '.h5'))))
+ results = {}
+ if len(videos) > 0:
+ left, right, center = dark_light_coordinates(videos[0], args.noask)
+ # bl, tl, tr, br = tankcoordinates(v, args.noask)
+ for video, dlc_file in zip(videos, dlc_files):
+ animal = video.split(os.sep)[-1].split('_')[1]
+ p_dark = analysiere_video(video, dlc_file, left, center)
+ results[animal] = p_dark
+ np.save('results_%s.npy' % args.day, results)
+ print(results)
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/dark_light_preference.py b/dark_light_preference.py
new file mode 100644
index 0000000..2d171bc
--- /dev/null
+++ b/dark_light_preference.py
@@ -0,0 +1,29 @@
+import numpy as np
+import os
+import glob
+import matplotlib.pyplot as pyplot
+from IPython import embed
+
+
+if __name__ == "__main__":
+ result_files = sorted(glob.glob("./results_day*.npy"))
+ results = {}
+
+ for r in result_files:
+ day = r.split(os.sep)[-1].split('_')[-1].split('.')[0]
+ if "day" in results.keys():
+ results["day"].append(day)
+ else:
+ results["day"] = [day]
+ data = np.load(r, allow_pickle=True)
+ d = data.item()
+ for k in d.keys():
+ if k in results.keys():
+ results[k].append(d[k])
+ else:
+ results[k] =[d[k]]
+
+
+ #res= dict((k, results[k]) for k in ['lepto03DLC' , 'lepto48DLC'] if k in results)
+ #print(res)
+ print(results)
\ No newline at end of file
diff --git a/feeder_positions.py b/feeder_positions.py
new file mode 100644
index 0000000..ae0e8bf
--- /dev/null
+++ b/feeder_positions.py
@@ -0,0 +1,64 @@
+import image_marker as im
+import matplotlib.pyplot as plt
+import numpy as np
+import tracking_result as tr
+from IPython import embed
+import argparse
+import glob
+import sys
+import dark_light_coordinates as dl
+
+x_limits = np.array([0, 124])
+y_limits = np.array([0, 81])
+light_area_y = [45.5, 81]
+
+def get_risk(position, light_area_y, light_risk_scale=3, x_limits=np.array([0, 124]), y_limits = np.array([0, 81])):
+ """Calculates the risk associated with a certain position in the arena
+
+ Args:
+ position (iterable): two-element vector of position i.e. (x,y)
+ light_area_y (iterable): two element vactor with the start and stop y-coordinates of light area
+ light_risk_scale (int, optional): if position is on th birght side, risk is scaled by this number. Defaults to 2.
+ x_limits : extent of the tank on x axis in cm
+ y_limits : extent of the tank on y axis in cm
+
+ Returns:
+ float: the risk for this position
+ """
+ min_wall_dist_x = min(np.abs(position[0] - x_limits))
+ min_wall_dist_y = min(np.abs(position[1] - y_limits))
+
+ risk_x = 1/(max(x_limits)/2) * min_wall_dist_x
+ risk_y = 1/(max(y_limits)/2) * min_wall_dist_y
+ #risk_y = 1/(np.abs(np.diff(light_area_y))/2) * min_wall_dist_y
+ total_risk = min(risk_x,risk_y) #+ 0.25 * (risk_x + risk_y)
+
+ is_position_on_the_bright_side = position[1] >= light_area_y[0] and position[1] < light_area_y[1]
+ if is_position_on_the_bright_side:
+ total_risk = total_risk + 1
+ return total_risk
+
+x_positions = np.arange(0,125, 5)
+y_positions = np.arange(0, 81, 5)
+
+risk_matrix = np.zeros((len(x_positions), len(y_positions)))
+print(risk_matrix.shape)
+for i, x in enumerate(x_positions):
+ for j, y in enumerate(y_positions):
+ risk_matrix[i, j] = get_risk([x, y], light_area_y)
+
+plt.imshow(risk_matrix)
+plt.show()
+"""
+if __name__ == '__main__':
+ vid = '/mnt/movies/merle_verena/boldness/labeled_videos/day_5/2020.12.07_lepto48.m4v'
+ feeder_task = im.MarkerTask("Feeder positions", list(map(str, range(1, 9))), "Mark feeder positions")
+ tasks = [feeder_task]
+ image_marker = im.ImageMarker(tasks)
+ # vid1 = "2020.12.11_lepto48DLC_resnet50_boldnessDec11shuffle1_200000_labeled.mp4"
+ print(sys.argv[0])
+ print (sys.argv[1])
+ vid1 = sys.argv[1]
+ marker_positions = im.mark_movie(vid1, 10)
+ print(marker_positions)
+"""
diff --git a/speed.py b/speed.py
new file mode 100644
index 0000000..3cce575
--- /dev/null
+++ b/speed.py
@@ -0,0 +1,28 @@
+import sys
+import tracking_result as tr
+import numpy as np
+import matplotlib.pyplot as plt
+from IPython import embed
+#tracking_tools]$ python3 speed.py /mnt/movies/merle_verena/boldness/labeled_videos/day_1/2020.12.01_lepto03DLC_resnet50_boldnessDec11shuffle1_200000.h5
+
+if __name__ == '__main__' :
+ dlc_results = sys.argv[1]
+
+ trs = tr.TrackingResult(dlc_results)
+ t, x, y, l, name = trs.position_values(bodypart="snout", framerate=30)
+ t = t[l > 0.975]
+ x = x[l > 0.975]
+ y = y[l > 0.975]
+
+ # Dann Differenzen berechnen
+ dx= np.diff(x)
+ dy= np.diff(y)
+ dt= np.diff(t)
+
+
+ s = np.sqrt(dx**2 + dy**2)
+ v = s/dt
+ embed ()
+
+ plt.scatter(x[:-1],y[:-1],c=v)
+ plt.show()