def __init__(self, exp_id, drive="H:"):
exp_path = get_exp_path(exp_id,drive)
timeline_path = get_timeline_path(exp_path)
trials = get_trials_in_recording(exp_path,ignore_dprime=True,
use_sparse=True)
licks = get_lick_times(timeline_path)
self.fig,ax = plt.subplots(ncols=2,figsize=[8,5])
for idx,trial in enumerate(trials):
trial_licks = licks-trial.start_stimulus
trial_licks = trial_licks[np.logical_and(trial_licks>0,trial_licks<5)]
ax[0 if trial.isgo else 1].plot(
trial_licks,
np.full(trial_licks.shape,idx),
'o',markersize=2,
color="k")
for a in ax:
ymin,ymax = a.get_ylim()
a.vlines((0,1,3),ymin,ymax,linestyles='dashed',color = 'k')
for name,pos in zip(('Tone','Stimulus','Response'),(0,1,3)):
a.text(pos+0.1,ymax,name,
horizontalalignment='left',
verticalalignment='bottom')
a.set_xlabel(r'$\Delta$t from trial onset (s)')
a.set_xlim((-1,5))
ax[0].set_title("Go Trials")
ax[1].set_title("No-Go Trials")
ax[0].set_ylabel('Trial Number')
def __init__(self,df,signature,dic,name,drive = "H://"):
ls_of_recordings = dic[signature]
self.recordings = ls_of_recordings
self.df = df
recording_paths = list(map(lambda p:get_exp_path(p,drive),
self.recordings))
psychstims = [get_psychstim_path(r) for r in recording_paths]
self.dprimes = [calc_d_prime(r) for r in psychstims]
self.df_repr = pd.DataFrame((self.recordings,self.dprimes)).transpose()
self.df_repr.columns = ["recording_id","dprime"]
self.df_repr[self.df_repr==np.inf] = np.nan
self.df_repr = self.df_repr[self.df_repr.dprime>1]
self.recordings = self.df_repr.recording_id.values
subset = self.df[self.df.recording_id.isin(self.recordings)]
self.pooled_stats = get_count_and_stats(subset,subsetting=False)
self.name = name
self.signature = signature
self.drive = drive
self.recording_paths = list(map(lambda p:get_exp_path(p,drive),
self.recordings))
def dataframe_from_ls_of_ROIs(ls):
return pd.DataFrame.from_records(ROI.to_dict() for ROI in ls)
def flat_dataframe_from_ls_of_ROIs(ls):
records = []
for roi in ls:
records.extend(roi.to_unrolled_records())
return pd.DataFrame(records)
def load_all_trials(path):
all_trials = []
with open(path, 'rb') as file:
while True:
try:
all_trials.append(pkl.load(file))
except EOFError:
break
return all_trials
if __name__ == "__main__":
from accdatatools.Observations.trials import get_trials_in_recording
from accdatatools.Utils.path import get_exp_path
trials = get_trials_in_recording(
get_exp_path("2016-11-01_03_CFEB027", "D:\\"))
rois = ls_of_ROIs_from_ls_of_Trials(trials)
del trials
plotter = ROIActivitySummaryFactory(rois)
@author: viviani
"""
import os
import numpy as np
import matplotlib.pyplot as plt
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)
def __init__(self,
mouse_id=None,
drive="H://",
ls_of_exp_ids=None,
ls_of_exp_paths=None):
if sum((not mouse_id, not ls_of_exp_ids, not ls_of_exp_paths)) != 2:
raise ValueError("Supply exactly one of a mouse_id, a list of " +
"exp_ids or a list of exp_paths!")
seaborn.set_style("dark")
if mouse_id:
experiment_paths = []
animal_path = get_mouse_path(mouse_id, drive)
for experiment in os.listdir(animal_path):
try:
experiment_paths.append(get_exp_path(experiment, drive))
except FileNotFoundError:
pass
elif ls_of_exp_ids:
experiment_paths = [
get_exp_path(id, drive) for id in ls_of_exp_ids
]
elif ls_of_exp_paths:
experiment_paths = ls_of_exp_paths
mean_images = {}
self.ls_of_exp_paths = []
for experiment_path in experiment_paths:
try:
ops_path = os.path.join(experiment_path, "suite2p", "plane0",
"ops.npy")
ops = np.load(ops_path, allow_pickle=True).item()
mean_image = ops["meanImgE"]
mean_images[experiment_path] = mean_image
self.ls_of_exp_paths.append(experiment_path)
except FileNotFoundError:
pass
n_exps = len(mean_images)
for width in range(ceil(sqrt(n_exps)), 1, -1):
if n_exps % width == 0:
height = n_exps // width
break
else:
width = ceil(sqrt(n_exps))
height = width
self.fig = plt.figure(figsize=(2 * width + 1, 2 * height + 1))
self.ax = np.empty((width, height), dtype=object)
gs = gridspec.GridSpec(width,
height,
wspace=0.0,
hspace=0.0,
top=1. - 0.5 / (height + 1),
bottom=0.5 / (height + 1),
left=0.5 / (width + 1),
right=1 - 0.5 / (width + 1))
iterator = iter(mean_images.items())
im_idx = 0
for i in range(width):
for j in range(height):
try:
experiment, im = next(iterator)
except StopIteration:
break
self.ax[i][j] = plt.subplot(gs[i, j])
self.ax[i][j].imshow(im)
self.ax[i][j].text(0.5,
0.5,
str(im_idx),
horizontalalignment='center',
verticalalignment='center',
transform=self.ax[i][j].transAxes,
size=16,
color='k')
self.ax[i][j].set_xticklabels([])
self.ax[i][j].set_yticklabels([])
im_idx += 1
self.canvas = self.fig.canvas
self.show = self.fig.show
self.draw = self.fig.draw