def run_body_speed_for_all_data():
details = load_session_details()
details['OF_body_speed_max'] = np.nan
details['OF_body_speed_mean'] = np.nan
details['OF_body_distance'] = np.nan
# OF
base_folder = r'Y:\Data 2018-2019\Complement 4 - schizophrenia Project\2019 Adult Behavior C4_for revisions\OF'
litters = ['ALC_050319_1_control','ALC_050319_2_mC4','ALC_051719_1_control','ALC_051719_2_mC4','ALC_060519_1_control','ALC_060519_2_mC4','ALC_070519_1_control'] # 'ALC_060519_1_control','ALC_060519_2_mC4',
for litter in litters:
# get the subject folders
sessions = os.listdir(os.path.join(base_folder,litter))
for session in sessions:
sub = get_subject_from_session(session)
sess_folder = os.path.join(base_folder,litter,session)
vids = get_all_videos_in_session(sess_folder)
df = get_analysis_dfs(sess_folder,vids)
bs = get_animal_speed(df,time_filter=300)
details.loc[sub,'OF_body_speed_max'] = np.nanmax(bs)/1000.
details.loc[sub,'OF_body_speed_mean'] = np.nanmean(bs)/1000.
details.loc[sub,'OF_body_distance'] = np.nansum(bs/20.)/1000.
print(litter,':',session)
details.to_csv(r'C:\Users\User\Desktop\Code\dlc_utils\OF_speed_distance.csv')
def run_open_probability_for_all_data():
details = load_session_details()
details['EZM_open_probability'] = np.nan
# OF
base_folder = r'Y:\Data 2018-2019\Complement 4 - schizophrenia Project\2019 Adult Behavior C4_for revisions\EZM'
litters = [
'ALC_050319_1_control', 'ALC_050319_2_mC4', 'ALC_051719_1_control',
'ALC_051719_2_mC4', 'ALC_060519_1_control', 'ALC_060519_2_mC4',
'ALC_070519_1_control'
] # 'ALC_070519_1_control'
for litter in litters:
# get the subject folders
sessions = os.listdir(os.path.join(base_folder, litter))
for session in sessions:
sub = get_subject_from_session(session)
sess_folder = os.path.join(base_folder, litter, session)
vids = get_all_videos_in_session(sess_folder)
df = get_analysis_dfs(sess_folder, vids)
q_loc, body_loc, zm_center, open_coord, closed_coord = get_quadrant(
df, time_filter=300
) #### change time_filter=None if you dont want a time filter
details.loc[sub, 'EZM_open_probability'] = np.nansum(
q_loc) / np.count_nonzero(~np.isnan(q_loc))
print(litter, ':', session)
details.to_csv(
r'C:\Users\User\Desktop\Code\dlc_utils\EZM_open_probability.csv')
def run_latency_for_all_data():
details = load_session_details()
details['NOR_T1_choice'] = np.nan
details['NOR_T1_object1_latency'] = np.nan
details['NOR_T1_nonobject_latency'] = np.nan
details['NOR_T2_choice'] = np.nan
details['NOR_T2_object1_latency'] = np.nan
details['NOR_T2_object2_latency'] = np.nan
details['SI_T1_choice'] = np.nan
details['SI_T1_subject1_latency'] = np.nan
details['SI_T1_nonsubject_latency'] = np.nan
details['SI_T2_choice'] = np.nan
details['SI_T2_subject1_latency'] = np.nan
details['SI_T2_subject2_latency'] = np.nan
# NOR
base_folder = r'Y:\Data 2018-2019\Complement 4 - schizophrenia Project\2019 Adult Behavior C4_for revisions\NOR'
litters = ['ALC_050319_1_control','ALC_050319_2_mC4','ALC_051719_1_control','ALC_051719_2_mC4','ALC_060519_1_control','ALC_060519_2_mC4','ALC_070519_1_control']
for litter in litters:
# get the subject folders
sessions = os.listdir(os.path.join(base_folder,litter))
for session in sessions:
sub = get_subject_from_session(session)
trials = os.listdir(os.path.join(base_folder,litter,session))
for trial in trials:
sess_folder = os.path.join(base_folder,litter,session,trial)
vids = get_all_videos_in_session(sess_folder)
df = get_analysis_dfs(sess_folder,vids)
choice,bl,tr = get_latency(df,plot_on=False,time_filter=300)
# now comes the annoying if then statements
# trial1 or trial 2?
if 'Trial1' in trial or 'TRIAL1' in trial or 'trial1' in trial:
if details.loc[sub,'NOR_obj1']=='bl':
#This is trial 1 and the object is in bl
o_pref = bl
o_nonpref = tr
if choice=='bl':details.loc[sub,'NOR_T1_choice'] = 'object'
else: details.loc[sub,'NOR_T1_choice'] = 'nonobject'
details.loc[sub,'NOR_T1_object1_latency'] = bl
details.loc[sub,'NOR_T1_nonobject_latency'] = tr
else:
#This is trial 1 and the object is in tr
o_pref = tr
o_nonpref = bl
if choice=='bl':details.loc[sub,'NOR_T1_choice'] = 'nonobject'
else: details.loc[sub,'NOR_T1_choice'] = 'object'
details.loc[sub,'NOR_T1_object1_latency'] = tr
details.loc[sub,'NOR_T1_nonobject_latency'] = bl
elif 'Trial2' in trial or 'TRIAL2' in trial or 'trial2' in trial:
if details.loc[sub,'NOR_obj1']=='bl':
#This is trial 2 and object1 was in bl in trial1 and 2
o_pref = bl
o_nonpref = tr
if choice=='bl':details.loc[sub,'NOR_T2_choice'] = 'familiar'
else: details.loc[sub,'NOR_T2_choice'] = 'novel'
details.loc[sub,'NOR_T2_object1_latency'] = bl
details.loc[sub,'NOR_T2_object2_latency'] = tr
else:
#This is trial 2 and object1 was in tr in trial1 and 2
o_pref = tr
o_nonpref = bl
if choice=='bl':details.loc[sub,'NOR_T2_choice'] = 'novel'
else: details.loc[sub,'NOR_T2_choice'] = 'familiar'
details.loc[sub,'NOR_T2_object1_latency'] = tr
details.loc[sub,'NOR_T2_object2_latency'] = bl
else:
print(sess_folder)
ValueError('why am I here?')
print(litter,':',session,':',trial,':opref:',o_pref,':ononpref:',o_nonpref)
# SI
base_folder = r'Y:\Data 2018-2019\Complement 4 - schizophrenia Project\2019 Adult Behavior C4_for revisions\SocialInteraction'
litters = ['ALC_050319_1_control','ALC_050319_2_mC4','ALC_051719_1_control','ALC_051719_2_mC4','ALC_060519_1_control','ALC_060519_2_mC4','ALC_070519_1_control']
for litter in litters:
# get the subject folders
sessions = os.listdir(os.path.join(base_folder,litter))
for session in sessions:
sub = get_subject_from_session(session)
trials = os.listdir(os.path.join(base_folder,litter,session))
trials = [x for x in trials if x !='.DS_Store']
for trial in trials:
sess_folder = os.path.join(base_folder,litter,session,trial)
vids = get_all_videos_in_session(sess_folder)
df = get_analysis_dfs(sess_folder,vids)
choice,bl,tr = get_latency(df,plot_on=False,fractional_size=0.36,time_filter=300)
# now comes the annoying if then statements
# trial1 or trial 2?
if 'Trial1' in trial or 'TRIAL1' in trial:
if details.loc[sub,'SI_mouse1']=='bl':
#This is trial 1 and the subject is in bl
o_pref = bl
o_nonpref = tr
if choice=='bl':details.loc[sub,'SI_T1_choice'] = 'subject'
else: details.loc[sub,'SI_T1_choice'] = 'nonsubject'
details.loc[sub,'SI_T1_subject1_latency'] = bl
details.loc[sub,'SI_T1_nonsubject_latency'] = tr
else:
#This is trial 1 and the object is in tr
o_pref = tr
o_nonpref = bl
if choice=='bl':details.loc[sub,'SI_T1_choice'] = 'nonsubject'
else: details.loc[sub,'SI_T1_choice'] = 'subject'
details.loc[sub,'SI_T1_subject1_latency'] = tr
details.loc[sub,'SI_T1_nonsubject_latency'] = bl
elif 'Trial2' in trial or 'TRIAL2' in trial:
if details.loc[sub,'SI_mouse1']=='bl':
#This is trial 2 and object1 was in bl in trial1 and 2
o_pref = bl
o_nonpref = tr
if choice=='bl':details.loc[sub,'SI_T2_choice'] = 'familiar'
else: details.loc[sub,'SI_T2_choice'] = 'novel'
details.loc[sub,'SI_T2_subject1_latency'] = bl
details.loc[sub,'SI_T2_subject2_latency'] = tr
else:
#This is trial 2 and object1 was in tr in trial1 and 2
o_pref = tr
o_nonpref = bl
if choice=='bl':details.loc[sub,'SI_T2_choice'] = 'novel'
else: details.loc[sub,'SI_T2_choice'] = 'familiar'
details.loc[sub,'SI_T2_subject1_latency'] = tr
details.loc[sub,'SI_T2_subject2_latency'] = bl
else:
print(sess_folder)
ValueError('why am I here?')
print(litter,':',session,':',trial,':opref:',o_pref,':ononpref:',o_nonpref)
details.to_csv(r'C:\Users\User\Desktop\Code\C4_behavior_utils\SIandNOR_details_NOR_SI_latency_300s.csv')
if choice=='bl':details.loc[sub,'SI_T2_choice'] = 'novel'
else: details.loc[sub,'SI_T2_choice'] = 'familiar'
details.loc[sub,'SI_T2_subject1_latency'] = tr
details.loc[sub,'SI_T2_subject2_latency'] = bl
else:
print(sess_folder)
ValueError('why am I here?')
print(litter,':',session,':',trial,':opref:',o_pref,':ononpref:',o_nonpref)
details.to_csv(r'C:\Users\User\Desktop\Code\C4_behavior_utils\SIandNOR_details_NOR_SI_latency_300s.csv')
if __name__=='__main__':
import cv2
# get the occupancy
folder = r'C:\Users\bsriram\Desktop\Data\ACM_Data\AllData\SI_sample'
vids = get_all_videos_in_session(folder)
df = get_analysis_dfs(folder,vids)
plot_pos_vs_frame(df)
bl,tr = get_occupancy(df,plot_on=True,fractional_size=0.36)
print('bl',bl)
print('tr',tr)
# get the latency
# folder = r'Y:\Data 2018-2019\Complement 4 - schizophrenia Project\2019 Adult Behavior C4_for revisions\NOR\ALC_051719_1_control\ALC_051719_1_45C_NOR\ALC_051719_1_45C_NOR_Trial2'
# vids = get_all_videos_in_session(folder)
# df = get_analysis_dfs(folder,vids)
# bl,tr = get_latency(df,plot_on=True,fractional_size=0.33)
# run_occupancy_for_all_data()
# run_latency_for_all_data()
def show_analysis(
session=r'Y:\Data 2018-2019\Complement 4 - schizophrenia Project\2019 Adult Behavior C4_for revisions\EZM\ALC_060519_2_mC4\ALC_060519_2_57G_EZM',
output=r"C:\Users\User\Desktop\Code\annotated_SI_part.avi",
speedX=10,
):
import cv2
import tqdm
import collections
import sys
vids = get_all_videos_in_session(session)
df = get_analysis_dfs(session, vids)
location, body_loc, zm_center, open_coord, closed_coord = get_quadrant(df)
try:
curr_filename = os.path.join(session, vids[0] + '.avi')
cap = cv2.VideoCapture(curr_filename)
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
writer = cv2.VideoWriter(output, fourcc, 1 / cap.get(2),
(int(cap.get(3)), int(cap.get(4))))
for idx, row in tqdm.tqdm(df.iterrows(), total=df.shape[0]):
if not idx % int(speed) == 0: continue
if row['source'][0] in curr_filename:
# cv is open to the right place
pass
else:
cap.release()
curr_filename = os.path.join(session,
row['source'][0] + '.avi')
cap = cv2.VideoCapture(curr_filename)
# get the frame
cap.set(1, row['frame_number'][0]) # 1==set frame number
succ, img = cap.read()
# draw the object circles
img = cv2.circle(img, (int(zm_center[0]), int(zm_center[1])), 5,
(0, 0, 255))
img = cv2.circle(img, (int(open_coord[0]), int(open_coord[1])), 5,
(0, 0, 255))
img = cv2.circle(img, (int(closed_coord[0]), int(closed_coord[1])),
5, (0, 0, 255))
if not np.isnan(location[idx]):
if location[idx] == 1:
img = cv2.circle(
img, (int(body_loc[0][idx]), int(body_loc[1][idx])), 5,
(255, 0, 0))
else:
img = cv2.circle(
img, (int(body_loc[0][idx]), int(body_loc[1][idx])), 5,
(0, 255, 0))
# add to writer
writer.write(img)
except Exception as er:
raise er.with_traceback(sys.exc_info()[2])
finally:
# for index, row in df.iterrows():
cap.release()
writer.release()
cv2.destroyAllWindows()