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 ###########