def get_trials_in_recording(exp_path,
return_se=False,
ignore_dprime=False,
se=None,
suppress_dprime_error=False,
use_sparse=False):
'''
Retrieve all the trials in a recording as Trial objects
Parameters
----------
exp_path : String
Path to the experiment folder.
return_se : Bool, optional
Whether to also return a StatisticExtractor object for the
whole experiment. The default is False.
Returns
-------
[Trial] or ([Trial], StatisticExtractor)
'''
#Get the appropriate paths for the suite2p info, the timeline,
#and the trial metadata
files = os.listdir(exp_path)
s2p_path = os.path.join(exp_path, 'suite2p', 'plane0')
timeline_path = os.path.join(
exp_path, item([s for s in files if 'Timeline.mat' in s]))
psychstim_path = os.path.join(
exp_path, item([s for s in files if 'psychstim.mat' in s]))
trials = []
if calc_d_prime(psychstim_path) > 1 or ignore_dprime:
if se == None and not use_sparse:
se = Recording(exp_path)
#We need the total number of frames:
structs = _get_trial_structs(psychstim_path)
if not use_sparse:
nframes = se.ops["nframes"]
times = get_neural_frame_times(timeline_path, nframes)
licks = get_lick_state_by_frame(timeline_path, times)
for struct in structs:
if use_sparse:
trial = SparseTrial(struct, tolerant=False)
else:
trial = Trial(exp_path, struct, se, times, licks)
trials.append(trial)
return trials if not return_se else (trials, se)
elif suppress_dprime_error:
return None if not return_se else (None, None)
else:
raise ValueError("Dprime below 1")
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from mpl_toolkits.mplot3d import Axes3D
from sklearn.linear_model import TheilSenRegressor
from scipy.ndimage.filters import minimum_filter1d, uniform_filter1d
from accdatatools.Observations.recordings import Recording
import pickle as pkl
#Load a recording
try:
with open(r"C:/Users/viviani/desktop/cache.pkl", "rb") as file:
rec = pkl.load(file)
except (FileNotFoundError, EOFError):
with open(r"C:/Users/viviani/desktop/cache.pkl", "wb") as file:
rec = Recording(r"D:\Local_Repository\CFEB013\2016-05-31_02_CFEB013")
pkl.dump(rec, file)
def heaviside(X, k=200):
'''
Analytic approximation of the heaviside function.
Approaches Heaviside(x) as K goes to +inf
'''
return 1 / (1 + np.exp(-2 * k * X))
def ramp(X):
return X * (X > 0)
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 8 20:51:41 2020
@author: viviani
Simple script to determine what proportion
"""
from accdatatools.Observations.recordings import Recording
from accdatatools.Utils.map_across_dataset import apply_to_all_recordings_of_class
import numpy as np
if __name__ == "__main__":
ls = []
f = lambda pth: ls.append(Recording(pth).gen_iscell())
apply_to_all_recordings_of_class("left_only_high_contrast", "H:", f)
all_iscells = np.concatenate(ls)
rate_of_inclusion = np.count_nonzero(all_iscells) / len(all_iscells)
exl = 1 - rate_of_inclusion
print("On average, {exl*100}% of ROIs weere excluded from analysis.")
from scipy.stats import pearsonr
from accdatatools.Utils.path import get_exp_path
from accdatatools.Observations.recordings import Recording
from accdatatools.Timing.synchronisation import (get_neural_frame_times,
get_lick_state_by_frame)
experiment_ID = "2016-10-07_03_CFEB027"
experiment_path = get_exp_path(experiment_ID, "H:\\")
suite2p_path = os.path.join(experiment_path, "suite2p", "plane0")
timeline_path = os.path.join(experiment_path,
"2016-10-07_03_CFEB027_Timeline.mat")
exp_recording = Recording(suite2p_path)
frame_times = get_neural_frame_times(timeline_path,
exp_recording.ops["nframes"])
licking = get_lick_state_by_frame(timeline_path, frame_times)
corrs = [pearsonr(x, licking)[0] for x in exp_recording.dF_on_F]
corrs_isort = np.argsort(corrs)
to_plot = exp_recording.dF_on_F[corrs_isort]
fig, ax = plt.subplots()
lick_frame = np.nonzero(licking)
max_brightness = np.percentile(to_plot, 99)
ax.imshow(np.clip(to_plot[-20:], -0.2, max_brightness),
origin='lower',
aspect=5)
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 28 19:23:32 2020
@author: viviani
"""
import numpy as np
from accdatatools.Observations.recordings import Recording
from accdatatools.Utils.map_across_dataset import iterate_across_recordings
for exp in iterate_across_recordings(drive="H:\\"):
print(f"\rProcessing {exp}...")
try:
a = Recording(exp)
if np.any(a.F < 0) and np.any(a.Fneu < 0):
for i in range(a.F.shape[0]):
if np.any(a.F < 0) and np.any(a.Fneu < 0):
df = pd.DataFrame
df["F"] = a.F[i, :]
df["Fneu"] = a.Fneu[i, :]
df.to_csv(
"C:/users/viviani/desktop/single_recording_F_Fneu.csv")
else:
print(f"min F = {np.min(a.F).item():.1f}" +
f"min Fneu = {np.min(a.Fneu).item():.1F}")
except Exception as e:
pass
else:
del a