def __init__(
self,
session_name: str,
events_window_s: int = 3,
firing_rate_window: int = 100,
):
self.session_name = session_name
self.hairpin = Session.on_hairpin(self.session_name)
self.events_window = events_window_s * 60
self.firing_rate_window = firing_rate_window
# start logging
recorder.start(base_folder=self.base_folder,
name=session_name,
timestamp=False)
# get session data
self.fetch()
from loguru import logger
from pathlib import Path
from tpd import recorder
from myterial import blue_grey_dark
import draw
from data.dbase.db_tables import ROICrossing
from data import arena
from analysis._visuals import move_figure
from analysis import visuals
folder = Path(r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\analysis\behavior")
recorder.start(base_folder=folder,
folder_name="roi_crossings",
timestamp=False)
'''
Plots an overview of ROI crossings for each ROI and saves
the data as a .h5 file for further analysis
'''
# TODO save with tracking data from all body parts for animation making
for ROI in arena.ROIs_dict.keys():
# create figure and draw ROI image
f = plt.figure(figsize=(20, 12))
f.suptitle(ROI)
f._save_name = f"roi_{ROI}"
axes = f.subplot_mosaic("""
AABC
AADE
from data.dbase._tracking import calc_angular_velocity
from kinematics.msd import MSD
from kinematics import time
from geometry import Path
from matplotlib import rc
rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"]})
rc("text", usetex=False)
folder = Path(
# r"/Users/federicoclaudi/Dropbox (UCL)/Rotation_vte/Presentations/Presentations/Fiete lab"
r'D:\Dropbox (UCL)\Rotation_vte\Presentations\Presentations\Fiete lab'
)
recorder.start(
base_folder=folder.parent, folder_name=folder.name, timestamp=False
)
"""
Fit MSD model to turn trajectories in mice
"""
# %%
# load and clean roi crossings
ROI = "T3"
MIN_DUR = 1.3
_bouts = pd.read_hdf(
paths.analysis_folder / "behavior" / "saved_data" / f"{ROI}_crossings.h5"
).sort_values("duration")
from fcutils.progress import track
from fcutils.path import files, from_json
sys.path.append("./")
from data.dbase.db_tables import ROICrossing, LocomotionBouts, Tracking
from data import arena
# base_folder = Path(r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\analysis\behavior")
base_folder = Path(
"/Users/federicoclaudi/Dropbox (UCL)/Rotation_vte/Locomotion/analysis/behavior"
)
recorder.start(
base_folder=base_folder,
folder_name="saved_data",
timestamp=False,
)
save_folder = base_folder / "saved_data"
# ---------------------------------------------------------------------------- #
# save roi crossings #
# ---------------------------------------------------------------------------- #
def save_rois():
tracking = {}
for ROI in arena.ROIs_dict.keys():
# fetch from database
crossings = pd.DataFrame((ROICrossing * ROICrossing.InitialCondition
& f'roi="{ROI}"'
& "mouse_exits=1").fetch())
base_folder = Path(r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\analysis")
"""
Plot a few exploratory plots for each session a recording was performed on.
1. Overview of tracking data
"""
sessions = (db_tables.ValidatedSession * db_tables.Session
& "is_recording=1"
& 'arena="openarena"')
for session in sessions:
# start a new recorder sesssion
recorder.start(base_folder=base_folder,
name=session["name"],
timestamp=False)
# load tracking data
body_tracking = db_tables.Tracking.get_session_tracking(session["name"],
body_only=True)
downsampled_tracking = db_tables.Tracking.get_session_tracking(
session["name"], body_only=True)
data_utils.downsample_tracking_data(downsampled_tracking, factor=10)
# get locomotion bouts
bouts = db_tables.LocomotionBouts.get_session_bouts(session["name"])
# crate figure
f = plt.figure(figsize=(24, 12))
axes = f.subplot_mosaic("""
f = plot_roi_crossing(bout, step=4)
recorder.add_figures(svg=False)
plt.close(f)
def animate_all(ROI: str, FPS: int):
"""
Creates an amimation for each bout in a ROI
"""
bouts = pd.read_hdf(paths.analysis_folder / "behavior" / "roi_crossings" /
f"{ROI}_crossings.h5")
for bout_n in range(len(bouts)):
animate_one(ROI, bout_n, FPS)
if __name__ == "__main__":
recorder.start(
paths.analysis_folder / "behavior",
"roi_crossings_animations",
timestamp=False,
)
ROI = "T2"
FPS = 10
BOUT_ID = 2
animate_one(ROI, BOUT_ID, FPS)
# animate_all(ROI, FPS)
recorder.describe()
sys.path.append("./")
import matplotlib.pyplot as plt
import pandas as pd
from loguru import logger
from pathlib import Path
from tpd import recorder
from myterial import salmon, teal, indigo
import draw
from data.dbase.db_tables import Tracking, ValidatedSession
from fcutils.progress import track
folder = Path(r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\analysis")
recorder.start(base_folder=folder, folder_name="all_tracking", timestamp=False)
'''
Plot the tracking for each session to check that everything is ok
'''
sessions = ValidatedSession().fetch('name')
for session in track(sessions):
save_name = folder / 'all_tracking' / (session + '.png')
if save_name.exists() or 'open' in session:
continue
tracking = Tracking.get_session_tracking(session,
body_only=False,
movement=False)
"RSPagl5",
"RSPagl6",
"RSPd1",
"RSPd2",
)
# ---------------------------------------------------------------------------- #
# plot probes #
# ---------------------------------------------------------------------------- #
# get all recordings
recordings = pd.DataFrame(Recording.fetch())
for i, recording in recordings.iterrows():
# start logging
rname = recording["name"]
recorder.start(base_folder=base_folder, name=rname, timestamp=False)
# get units
units = pd.DataFrame((Unit * Unit.Spikes & dict(recording)).fetch())
logger.info(f'processing recording: "{rname}" - {len(units)} units')
# get tracking data
# load tracking data
tracking = Tracking.get_session_tracking(rname, body_only=True)
# get probe
rsites = pd.DataFrame((Probe.RecordingSite & dict(recording)).fetch())
for n, unit in units.iterrows():
x, y, speed = (
tracking["x"].values,
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pathlib import Path
sys.path.append("./")
from fcutils.plot.figure import clean_axes
from tpd import recorder
from data.dbase.db_tables import Probe
from analysis.visuals import plot_probe_electrodes
base_folder = Path(r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\analysis")
recorder.start(base_folder=base_folder,
name="probes_reconstructions",
timestamp=False)
'''
For each reconstructed probe it plots the position of the electros
'''
TARGETS = (
"PRNr",
"PRNc",
"CUN",
"GRN",
"MB",
"PPN",
"RSPagl1",
"RSPagl2/3",
"RSPagl5",
import matplotlib.pyplot as plt
import pandas as pd
from loguru import logger
from pathlib import Path
import numpy as np
from tpd import recorder
import draw
from data.dbase import db_tables
from fcutils.progress import track
from data import data_utils
from analysis import visuals
folder = Path(r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\analysis")
recorder.start(base_folder=folder, folder_name="all_units", timestamp=False)
# %%
'''
Save a plot with basic stuff for each unit
'''
# get sessions and tracking
sessions = (db_tables.ValidatedSession * db_tables.Session
& 'is_recording=1').fetch('name')
logger.info(f'Found {len(sessions)} recordings')
for session in track(sessions):
if 'open' in session:
continue
logger.info('Fetching tracking')
tracking = db_tables.Tracking.get_session_tracking(session, movement=False)
from loguru import logger
from rich.logging import RichHandler
from pyinspect import install_traceback
install_traceback()
logger.configure(handlers=[{
"sink": RichHandler(markup=True),
"format": "{message}"
}])
from tpd import recorder
try:
recorder.start(base_folder=".", folder_name="logs", timestamp=False)
except PermissionError:
recorder.start(base_folder=".", folder_name="logs", timestamp=True)