def somefunc(exp_prep):
# print(f"\n{'-' * 5} RUNNING PRE4AP TRIALS {'-' * 5}")
prep = exp_prep[:-6]
pre4aptrial = exp_prep[-5:]
expobj, _ = aoutils.import_expobj(prep=prep,
trial=pre4aptrial,
verbose=False)
return expobj
def inner(*args, **kwargs):
def somefunc(exp_prep):
pass
for exp_prep in pj.flattenOnce(allopticalResults.post_4ap_trials):
prep = exp_prep[:-6]
pre4aptrial = exp_prep[-5:]
expobj, _ = aoutils.import_expobj(prep=prep,
trial=pre4aptrial,
verbose=False)
aoutils.working_on(expobj)
res_ = func(expobj=expobj, **kwargs)
aoutils.end_working_on(expobj)
# with concurrent.futures.ThreadPoolExecutor() as executor:
# trials = pj.flattenOnce(allopticalResults.post_4ap_trials)
# executor.map(somefunc, trials)
# # _ = [executor.submit(somefunc, exp_prep) for exp_prep in trials]
t_end = time.time()
pj.timer(t_start, t_end)
print(f" {'--' * 5} COMPLETED FOR LOOP ACROSS EXPS {'--' * 5}\n")
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import alloptical_utils_pj as aoutils import alloptical_plotting_utils as aoplot from funcsforprajay import funcs as pj from skimage import draw # # import results superobject that will collect analyses from various individual experiments results_object_path = '/home/pshah/mnt/qnap/Analysis/alloptical_results_superobject.pkl' allopticalResults = aoutils.import_resultsobj(pkl_path=results_object_path) expobj, experiment = aoutils.import_expobj(prep='RL109', trial='t-013') """######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### """ # sys.exit() """# ########### END OF // ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ########### END OF // ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ########### END OF // ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ###########
from skimage import draw
# import results superobject that will collect analyses from various individual experiments
results_object_path = '/home/pshah/mnt/qnap/Analysis/alloptical_results_superobject.pkl'
allopticalResults = aoutils.import_resultsobj(pkl_path=results_object_path)
results_object_path = '/home/pshah/mnt/qnap/Analysis/onePstim_results_superobject.pkl'
onePresults = aoutils.import_resultsobj(pkl_path=results_object_path)
save_path_prefix = '/home/pshah/mnt/qnap/Analysis/Procesing_figs/sz_processing_boundaries_2022-01-06/'
os.makedirs(save_path_prefix) if not os.path.exists(save_path_prefix) else None
# %% 5.0-dc) ANALYSIS: cross-correlation between mean FOV 2p calcium trace and LFP seizure trace TODO
import scipy.signal as signal
expobj, experiment = aoutils.import_expobj(prep='RL109', trial='t-017')
sznum = 1
slice = np.s_[
expobj.convert_frames_to_paqclock(expobj.seizure_lfp_onsets[sznum]):expobj.
convert_frames_to_paqclock(expobj.seizure_lfp_offsets[sznum])]
# detrend
detrended_lfp = signal.detrend(
expobj.lfp_signal[expobj.frame_start_time_actual:expobj.
frame_end_time_actual])[slice] * -1
# downsample LFP signal to the same # of datapoints as # of frames in 2p calcium trace
CaUpsampled1 = signal.resample(expobj.meanRawFluTrace,
len(detrended_lfp))[slice]
import alloptical_utils_pj as aoutils
import alloptical_plotting_utils as aoplot
from funcsforprajay import funcs as pj
import tifffile as tf
from skimage.transform import resize
from mpl_toolkits import mplot3d
# import results superobject that will collect analyses from various individual experiments
results_object_path = '/home/pshah/mnt/qnap/Analysis/alloptical_results_superobject.pkl'
allopticalResults = aoutils.import_resultsobj(pkl_path=results_object_path)
save_path_prefix = '/home/pshah/mnt/qnap/Analysis/Results_figs/Nontargets_responses_2021-11-11'
os.makedirs(save_path_prefix) if not os.path.exists(save_path_prefix) else None
expobj, experiment = aoutils.import_expobj(
aoresults_map_id='post e.1'
) # PLACEHOLDER IMPORT OF EXPOBJ TO MAKE THE CODE WORK
# %% 1) SEIZURE WAVEFRONT PLOTTING AND ANALYSIS
"""################################# SEIZURE EVENTS PLOTTING ##############################################################
########################################################################################################################
# PLOT HEATMAP of SEIZURE EVENTS
"""
sz = 2
sz_onset, sz_offset = expobj.stims_bf_sz[sz], expobj.stims_af_sz[sz + 1]
# -- approach of dFF normalize to the mean of the Flu data 2 seconds before the seizure
pre_sz = 2 * int(expobj.fps)
sz_flu = expobj.raw[[expobj.cell_id.index(cell) for cell in expobj.good_cells],
sz_onset - pre_sz:sz_offset]
matlab_pairedmeasurements_path=matlab_pairedmeasurement_path,
processed_tiffs=False,
discard_all=True,
analysis_save_path=analysis_save_path)
# %% 2) ### the below is usually run from jupyter notebooks dedicated to each experiment prep.
to_suite2p = [
't-002', 't-006', 't-007', 't-008', 't-009', 't-011', 't-016', 't-017'
] # specify all trials that were used in the suite2p runtotal_frames_stitched = 0
baseline_trials = ['t-002',
't-006'] # specify which trials to use as spont baseline
# note ^^^ this only works currently when the spont baseline trials all come first, and also back to back
trials = ['t-011']
for trial in trials:
###### IMPORT pkl file containing expobj
pkl_path = "/home/pshah/mnt/qnap/Analysis/%s/%s/%s_%s/%s_%s.pkl" % (
date, animal_prep, date, trial, date, trial)
expobj, experiment = aoutils.import_expobj(trial=trial,
date=date,
pkl_path=pkl_path,
do_processing=False)
expobj.s2p_path = '/home/pshah/mnt/qnap/Analysis/%s/suite2p/alloptical-2p-1_25x-alltrials/plane0' % date
aoutils.run_alloptical_processing_photostim(
expobj,
to_suite2p=to_suite2p,
baseline_trials=baseline_trials,
force_redo=True)
from funcsforprajay import funcs as pj import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # # import results superobject that will collect analyses from various individual experiments results_object_path = '/home/pshah/mnt/qnap/Analysis/alloptical_results_superobject.pkl' allopticalResults = aoutils.import_resultsobj(pkl_path=results_object_path) # %% IMPORT expobj # expobj, experiment = aoutils.import_expobj(aoresults_map_id='pre h.0') expobj, experiment = aoutils.import_expobj(prep='RL109', trial='t-017') # %% useful general plots # aoplot.plotMeanRawFluTrace(expobj=expobj, stim_span_color=None, x_axis='Time', figsize=(20,3)) fig, axs = plt.subplots(2, 1, figsize=(20, 6)) fig, ax = aoplot.plotMeanRawFluTrace(expobj=expobj, stim_span_color=None, x_axis='frames', fig=fig, ax=axs[0], show=False) fig, ax = aoplot.plotLfpSignal(expobj=expobj, stim_span_color='', x_axis='time', fig=fig, ax=axs[1], show=False) fig.show() aoplot.plot_SLMtargets_Locs(expobj=expobj, background=expobj.meanFluImg_registered) aoplot.plot_lfp_stims(expobj=expobj, x_axis='Time') expobj.plot_single_frame_tiff(frame_num=10040)
# NOTE: NEED TO RERUN FOR PS15 WITH THE CORRECT TIFF FILES AND STUFF
# %%
# import experiment object and look for number
date = '2021-01-29'
prep = 'PS15'
trials = ['t-001', 't-002', 't-003', 't-005', 't-004', 't-006']
for trial in trials:
pkl_path = "/home/pshah/mnt/qnap/Analysis/%s/%s/%s_%s.pkl" % (date, prep,
date, trial)
expobj, experiment = aoutils.import_expobj(trial=trial,
date=date,
pkl_path=pkl_path,
verbose=False)
# expobj.mean_raw_flu_trace(plot=True)
# aoplot.plotMeanRawFluTrace(expobj=expobj, stim_span_color=None, stim_lines=False, x_axis='frames', figsize=[20, 3],
# title='Mean raw Flu trace -')
if len(expobj.tiff_path) == 2:
pj.plot_single_tiff(expobj.tiff_path[1],
frame_num=201,
title='%s - frame# 201' % trial)
print(expobj.tiff_path[1])
else:
pj.plot_single_tiff(expobj.tiff_path,
frame_num=201,
title='%s - frame# 201' % trial)
print(expobj.tiff_path)
import alloptical_plotting_utils as aoplot
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import utils.funcs_pj as pj
import tifffile as tf
###### IMPORT pkl file containing expobj
trial = 't-016'
date = '2020-12-19'
pkl_path = "/home/pshah/mnt/qnap/Analysis/%s/%s_%s/%s_%s.pkl" % (
date, date, trial, date, trial)
# pkl_path = "/home/pshah/mnt/qnap/Data/%s/%s_%s/%s_%s.pkl" % (date, date, trial, date, trial)
expobj, experiment = aoutils.import_expobj(trial=trial,
date=date,
pkl_path=pkl_path)
expobj.sz_boundary_csv_done = False # i know this is a rather very precarious thing here...
if not hasattr(expobj, 's2p_path'):
expobj.s2p_path = input(
'input the suite2p path for this trial (to the plane0 folder)!!')
expobj.save()
if not hasattr(expobj, 'meanRawFluTrace'):
expobj.mean_raw_flu_trace(plot=True)
expobj.save()
if not hasattr(expobj, 'stims_in_sz'):
expobj.seizures_lfp_timing_matarray = expobj.seizures_info_array
expobj.responses_vs_distance_to_seizure_SLMTargets[
'distance_to_sz_um'] = round(
expobj.
responses_vs_distance_to_seizure_SLMTargets['distance_to_sz'] /
expobj.pix_sz_x, 2)
expobj.save()
# run_calculating_min_distance_to_seizure(no_slmtargets_szboundary_stim)
response_type = 'dFF (z scored)'
collect_responses_vs_distance_to_seizure_SLMTargets(
response_type=response_type)
key = 'f'
exp = 'post'
expobj, experiment = aoutils.import_expobj(aoresults_map_id=f"{exp} {key}.0")
# %%
import concurrent.futures
import time
def somefunc(exp_prep):
# print(f"\n{'-' * 5} RUNNING PRE4AP TRIALS {'-' * 5}")
prep = exp_prep[:-6]
pre4aptrial = exp_prep[-5:]
expobj, _ = aoutils.import_expobj(prep=prep,
trial=pre4aptrial,
verbose=False)
return expobj
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import alloptical_utils_pj as aoutils import alloptical_plotting_utils as aoplot from funcsforprajay import funcs as pj from skimage import draw # # import results superobject that will collect analyses from various individual experiments results_object_path = '/home/pshah/mnt/qnap/Analysis/alloptical_results_superobject.pkl' allopticalResults = aoutils.import_resultsobj(pkl_path=results_object_path) expobj, experiment = aoutils.import_expobj(prep='RL109', trial='t-013') """######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ######### ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### """ # sys.exit() """# ########### END OF // ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ########### END OF // ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ########### ########### END OF // ZONE FOR CALLING THIS SCRIPT DIRECTLY FROM THE SSH SERVER ###########