def pop(self):
# Populate the main table
mice_data = load_excel_file(mice_log)
in_table = list(self.fetch("mouse_id"))
for mouse in mice_data:
if mouse['ID'] in in_table:
continue
key = dict(mouse_id=mouse['ID'].upper(),
strain=mouse['MouseLine'].upper(),
sex=mouse['Gender'].upper())
self.insert1(key)
# Populate the surgeries tables
surgery_data = load_yaml(surgeries_file)
in_table = list(self.fetch("mouse_id"))
for mouse in surgery_data.keys():
if mouse not in in_table: continue
surgery = surgery_data[mouse]
for inj in [k for k in list(surgery.keys()) if 'injection' in k]:
part_key = dict(mouse_id=mouse,
target=surgery[inj]['target'],
compound=surgery[inj]['compound'])
manual_insert_skip_duplicate(self.Injection, part_key)
def _make_tuples(self, key):
session_fld = get_session_folder(**key)
recs = load_yaml(metadatafile)['sessions'][key['mouse']][
key['sess_name']]
for n, rec in enumerate(sorted(recs)):
if rec != key['rec_name']: continue
# Get files
rec_files = [f for f in os.listdir(session_fld) if rec in f]
tracking_file = [
f for f in rec_files
if 'dlc' in f.lower() and f.endswith('.h5')
]
if len(tracking_file) != 1: raise ValueError
else: tracking_file = os.path.join(session_fld, tracking_file[0])
# Insert entry into main class
self.insert1(key)
# Load and clean tracking data
bp_tracking = prepare_tracking_data(tracking_file,
median_filter=True,
likelihood_th=0.9,
fisheye=False,
common_coord=False,
compute=True)
bones_tracking = compute_body_segments(bp_tracking, self.bsegments)
# Insert into the bodyparts tracking
for bp, tracking in bp_tracking.items():
bp_key = key.copy()
bp_key['bp'] = bp
bp_key['x'] = tracking.x.values
bp_key['y'] = tracking.y.values
bp_key['speed'] = tracking.speed.values
bp_key['dir_of_mvmt'] = tracking.direction_of_movement.values
bp_key['angular_velocity'] = tracking.angular_velocity.values
self.BodyPartTracking.insert1(bp_key)
# Insert into the body segment data
for bone, (bp1, bp2) in self.bsegments.items():
segment_key = key.copy()
segment_key['bp1'] = bp1
segment_key['bp2'] = bp2
segment_key['orientation'] = bones_tracking[bone][
'orientation'].values
segment_key['angular_velocity'] = bones_tracking[bone][
'angular_velocity'].values
self.BodySegmentTracking.insert1(segment_key)
def _make_tuples(self, key):
session_fld = get_session_folder(**key)
recs = load_yaml(metadatafile)['sessions'][key['mouse']][
key['sess_name']]
for n, rec in enumerate(sorted(recs)):
# Get files
rec_files = [f for f in os.listdir(session_fld) if rec in f]
videos = [
f for f in rec_files
if f.endswith(".mp4") or f.endswith(".avi")
]
if len(videos) != 1:
if len(set([get_file_name(f) for f in videos])) == 1:
video = get_file_name(videos[0]) + ".mp4"
else:
raise ValueError
else:
video = videos[0]
temp_fld = os.path.join(self.temp_files_fld, key['mouse'])
temp_rec_files = [f for f in os.listdir(temp_fld) if rec in f]
ais = [fl for fl in temp_rec_files if fl == f"{rec}_Fiji-tag.hdf5"]
if not ais:
continue
if len(ais) != 1: raise ValueError(f'Found ais: {ais}')
else: ai = ais[0]
# Open video and get number of frames
nframes, width, height, fps, _ = get_video_params(
get_cap_from_file(os.path.join(session_fld, video)))
# Open AI file and get number of samples
f, keys, subkeys, allkeys = open_hdf(os.path.join(session_fld, ai))
n_samples = len(f['AI']['0'][()])
rkey = key.copy()
rkey['rec_name'] = rec
rkey['rec_n'] = n
rkey['videofile'] = video
rkey['aifile'] = ai
rkey['n_frames'] = nframes
rkey['fps_behav'] = fps
rkey['n_samples'] = n_samples
manual_insert_skip_duplicate(self, rkey)
def _make_tuples(self, key):
session_fld = get_session_folder(**key)
recs = load_yaml(metadatafile)['sessions'][key['mouse']][
key['sess_name']]
# Get each session's recordin AI file
for n, rec in enumerate(sorted(recs)):
rec_files = [f for f in os.listdir(session_fld) if rec in f]
ais = [fl for fl in rec_files if fl == f"{rec}.hdf5"]
if len(ais) != 1: raise ValueError
else: ai = ais[0]
f, keys, subkeys, allkeys = open_hdf(os.path.join(session_fld, ai))
# Get stim start and protocol name
try:
protocol_names = dict(
f['Audio Stimulation'])['Audio Stimuli Names'][(
)].decode("utf-8")
except KeyError as err:
return
protocol_names = protocol_names.replace("[", "").replace(
"]", "").replace("'", "")
protocol_names = protocol_names.split(", u")
protocol_names = [pc.split("\\\\")[-1] for pc in protocol_names]
start_frames = np.where(
dict(f['Audio Stimulation'])['Audio Stimuli Start Indices'][
()] > 0)[0]
if len(start_frames) != len(protocol_names): raise ValueError
for sf, pc in zip(start_frames, protocol_names):
skey = key.copy()
skey['frame'] = sf
skey['protocol_name'] = pc
skey['rec_name'] = rec
manual_insert_skip_duplicate(self, skey)
from fcutils.file_io.io import load_yaml
from fcutils.plotting.utils import save_figure
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from pathlib import Path
from rich import print
from pyinspect._colors import mocassin, orange
print(f'[{mocassin}]Generating [{orange}]figures[/{orange}][{mocassin}]...\n')
metadata = load_yaml('figures/composed_figures.yml')
figs_folder = Path('figures')
for figname, figs in metadata.items():
if len(figs) < 2:
ncols, nros = 1, 1
elif len(figs) < 3:
ncols, nrows = 2, 1
elif len(figs) < 5:
ncols, nrows = 2, 2
elif len(figs) < 7:
ncols, nrows = 3, 2
f, axarr = plt.subplots(ncols=ncols, nrows=nrows, figsize=(18, 13))
f.suptitle(figname)
for ax in axarr.flatten():
ax.axis('off')
for ax, (title, fig) in zip(axarr.flatten(), figs):
ax.imshow(mpimg.imread(figs_folder / fig))
ax.set(title=title)
def populate(self):
mice = load_yaml(metadatafile)['mice']
for mouse in mice:
manual_insert_skip_duplicate(self, {'mouse': mouse})
def pop(self):
exps = load_yaml(experiments_file)
for exp in exps.keys():
key = dict(exp_name=exp, arena=exps[exp]['arena'])
manual_insert_skip_duplicate(self, key)
def pop(self):
exps = load_yaml(experiments_file)
for exp in exps.keys():
for subexp in exps[exp]['subexps']:
key = dict(exp_name=exp, subname=list(subexp.keys())[0])
manual_insert_skip_duplicate(self, key)