'F:\\Users\\Gittis\\Dropbox\\Manuscripts\\Isett_Gittis_2021\\')
basepath = Path(
'F:\\Users\\Gittis\\Dropbox\\Gittis Lab Data\\OptoBehavior\\')
else:
savepath = Path('/home/brian/Dropbox/Manuscripts/Isett_Gittis_2021/')
basepath = Path('/home/brian/Dropbox/Gittis Lab Data/OptoBehavior/')
fig_name = 'Figure 1_a2a_chr2_str'
newbase = savepath.joinpath(fig_name).joinpath('data').joinpath('raw')
# %%
# inc = [x + ['AG3233_5'] for x in inc]
ethovision_tools.raw_csv_to_preprocessed_csv(newbase,
inc,
exc,
force_replace=True,
win=10)
# %%
pns = dataloc.raw_csv(newbase, inc[0], exc[0])
if not isinstance(pns, list):
pns = [pns]
saveit = True
closeit = True
for pn in pns:
df, meta = ethovision_tools.csv_load(pn, columns='All', method='preproc')
plots.plot_openloop_day(df,
meta,
save=saveit,
"""
import pandas as pd
import json
import numpy as np
from pathlib import Path
from gittislab import dataloc, ethovision_tools, signals, plots
import matplotlib.pyplot as plt
basepath = '/home/brian/Dropbox/Gittis Lab Data/OptoBehavior/Str/Naive/A2A/Ai32/Bilateral/10x10/'
inc = [['AG5477_4']]
# basepath = '/home/brian/Dropbox/Gittis Lab Data/OptoBehavior/GPe/Naive/CAG/Arch/Right/5x30/'
# inc=[['AG4486_1']]
exc = [['exclude']]
ethovision_tools.unify_raw_to_csv(basepath, inc, exc)
ethovision_tools.raw_csv_to_preprocessed_csv(basepath,
inc,
exc,
force_replace=True)
pns = dataloc.raw_csv(basepath, inc[0], exc[0])
raw, meta = ethovision_tools.csv_load(pns, method='preproc')
ethovision_tools.boris_prep(basepath,
inc,
exc,
plot_cols=['time', 'mouse_height', 'vel'],
event_col='rear',
event_thresh=0.5,
method='preproc')
dlc = ethovision_tools.add_dlc_helper(raw,
meta,
pns.parent,
force_replace=True)
'Exclude',
]
# inc=[['AG','GPe','CAG','Arch','10x']]
exc = [ex0]
inc = [
['AG', 'GPe', 'FoxP2', 'ChR2', '10x10_20mW'],
]
basepath = '/home/brian/Dropbox/Gittis Lab Data/OptoBehavior/'
ethovision_tools.unify_raw_to_csv(basepath,
inc,
exc,
force_replace=False,
win=10)
ethovision_tools.raw_csv_to_preprocessed_csv(basepath,
inc,
exc,
force_replace=False,
win=10)
for ii, ee in zip(inc, exc):
pns = dataloc.raw_csv(basepath, ii, ee)
for pn in pns:
temp = {}
raw, meta = ethovision_tools.csv_load(pn, method='preproc')
plots.plot_openloop_day(raw, meta)
# %%
# def test_batch_analyze(inc,exc):
data = pd.DataFrame([],
columns=[
'anid', 'proto', 'cell_area_opsin', 'amb_vel',
def combine_raw_csv_for_modeling(raw_pns,
boris_obs_names,
use_cols,
uniform_boris_fn='Rearing Observations.boris',
rescale=False,
avg_model_detrend=False,
z_score_x_y=False,
flip_y=False,
meta_to_raw=[]):
combined = pd.DataFrame([], columns=use_cols)
for pn, obs in zip(raw_pns, boris_obs_names):
p = Path(pn)
inc = [['AG']]
exc = [['exclude']]
ethovision_tools.unify_raw_to_csv(p, inc, exc)
ethovision_tools.raw_csv_to_preprocessed_csv(p,
inc,
exc,
force_replace=False)
pns = dataloc.raw_csv(p, inc[0], exc[0])
raw, meta = ethovision_tools.csv_load(pns, method='raw')
fn_ka = p.joinpath(uniform_boris_fn)
f = open(fn_ka, 'r')
boris = json.loads(f.read())
# Get human scored rearing events and add as vector!
human_scored_rearing = behavior.boris_to_logical_vector(
raw, boris, obs, 'a', 'd')
dlc = ethovision_tools.add_dlc_helper(raw, meta, p, force_replace=True)
dlc = dlc[0]
# pdb.set_trace()
if meta['exp_room_number'][0] == 228:
x_scale = 512 / 480
y_scale = 1.455 #Scale pixels #Video takes up only half of screen in these recordings
vid_res = np.ones((dlc.shape[0], 1)) * 704 #Video width 704 x 480
# pdb.set_trace()
else:
x_scale = 1
y_scale = 1
vid_res = np.ones(
(dlc.shape[0], 1)) * 1280 #Video width height = 512
if rescale == True:
for col in dlc.columns:
if ('dlc' in col) and ('x' in col):
dlc[col] = dlc[col].values * x_scale
if ('dlc' in col) and ('y' in col):
dlc[col] = dlc[col].values * y_scale
dlc['video_resolution'] = vid_res
dlc['human_scored_rear'] = human_scored_rearing
# dlc['head_hind_px_height']= dlc ['dlc_rear_centroid_y'] - dlc['dlc_side_head_y']
dlc['front_hind_px_height'] = dlc['dlc_rear_centroid_y'] - dlc[
'dlc_front_centroid_y']
dlc['head_hind_5hz_pw'] = signals.get_spectral_band_power(
dlc['front_hind_px_height'], meta['fs'][0], 4.5, 6.5)
dlc['snout_hind_px_height'] = dlc['dlc_rear_centroid_y'] - dlc[
'dlc_snout_y']
dlc['side_length_px'] = signals.calculateDistance(
dlc['dlc_front_centroid_x'].values,
dlc['dlc_front_centroid_y'].values,
dlc['dlc_rear_centroid_x'].values,
dlc['dlc_rear_centroid_y'].values)
# dlc['top_length_px']=signals.calculateDistance(dlc['dlc_top_head_x'].values,
# dlc['dlc_top_head_y'].values,
# dlc ['dlc_top_tail_base_x'].values,
# dlc ['dlc_top_tail_base_y'].values)
#Detrend effect of mouse distance from camera using an average pre-fitted
#z-score approach:
if avg_model_detrend == True:
detrend_cols = [
'snout_hind_px_height', 'front_hind_px_height',
'side_length_px'
]
for col in detrend_cols:
y = dlc[col]
x = dlc['x']
dlc[col + '_detrend'] = average_z_score(x, y)
if z_score_x_y == True:
for col in dlc.columns:
if ('dlc' in col) and (('x' in col) or ('y' in col)):
temp = dlc[col].values
temp = temp - np.nanmean(temp)
temp = temp / np.nanstd(temp)
dlc[col] = temp
if flip_y == True:
for col in dlc.columns:
if ('dlc' in col) and ('y' in col):
dlc[col] = -1 * dlc[col]
# dlc[col] = dlc[col] - np.nanmin(dlc[col])
for col in meta_to_raw:
dlc[col] = meta[col][0]
temp = dlc[use_cols]
combined = pd.concat([combined, temp])
#Add time lags (?)
combined.reset_index(drop=True, inplace=True)
return combined
