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- #!/usr/bin/env python
- # coding: utf-8
- #%%
- print('initializing packages')
- from os import chdir
- import matplotlib.pyplot as plt
- from pathlib import Path
- import numpy as np
- import pandas as pd
- import sys
- import seaborn as sns
- from scipy import signal
- import matplotlib
- matplotlib.rcParams.update({'font.size': 22})
- sys.path.append('/Users/kperks/mnt/engram/scripts/Python/Analysis/')
- from ClassDef_AmplitudeShift_Stable import AmpShift_Stable
- #%%
- print('changing to data_processed folder and defining folders used in script')
- chdir('/Users/kperks/mnt/engram/spikedata/data_processed/')
- exptpath = Path.cwd().resolve().parents[0] #assumes running notebook from /data_processed
- data_folder = exptpath / 'data_raw'
- figure_folder = exptpath / 'data_processed' / 'Figures_GRC_properties' / 'Unsubtracted_CvsU'
- df_folder = exptpath / 'data_processed' / 'Figures_GRC_properties' / 'Unsubtracted_CvsU' / 'df_cmdintact'
- #%%
- print('setting sweep duration to 50 msec')
- sweepdur = 0.045
- #%%
- print('defining functions')
- def calc_peaks (xtime,sweeps, order, min_peakt, t0_offset,threshold_h,dt):
- min_peakt = min_peakt+t0_offset
- R = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- nsamp=int(order/dt) #the window of comparison in nsamp for order; 2msec seemed good
- ipsp_ = signal.argrelextrema(R,np.less_equal,order = nsamp)[0]
- epsp_ = signal.argrelextrema(R,np.greater_equal,order = nsamp)[0]
- epsp_ = epsp_[np.where((epsp_*dt)>min_peakt)[0]]
- ipsp_ = ipsp_[np.where((ipsp_*dt)>min_peakt)[0]]
- epsp = []
- measure = epsp_
- compare = ipsp_
- for i in measure:
- if len(compare[compare<i])>0:
- lb = np.max(compare[compare<i])
- elif len(compare[compare<i])==0:
- lb = int(min_peakt/dt)
- if len(compare[compare>i])>0:
- rb = np.min(compare[compare>i])
- elif len(compare[compare>i])==0:
- rb = len(R)-1
- min_height = np.min([abs(R[i]-R[lb]),abs(R[i]-R[rb])])
- if min_height>threshold_h:
- epsp.append(i)
- if len(epsp)>0:
- epsp = np.min(epsp)
- elif len(epsp)==0:
- epsp = np.NaN
- ipsp = []
- measure = ipsp_
- compare = epsp_
- for i in measure:
- if len(compare[compare<i])>0:
- lb = np.max(compare[compare<i])
- elif len(compare[compare<i])==0:
- lb = int(min_peakt/dt)
- if len(compare[compare>i])>0:
- rb = np.min(compare[compare>i])
- elif len(compare[compare>i])==0:
- rb = len(R)-1
- min_height = np.min([abs(R[i]-R[lb]),abs(R[i]-R[rb])])
- if min_height>threshold_h:
- ipsp.append(i)
- if len(ipsp)>0:
- ipsp = np.min(ipsp)
- elif len(ipsp)==0:
- ipsp = np.NaN
- R_filt = signal.medfilt(R,[11])
- y = signal.medfilt(np.concatenate([[0],np.diff(R_filt)]),[25]) #-threshold_dvdt
- accel = signal.medfilt(np.concatenate([[0],np.diff(y)]),[11])
- dvdt_start = int((0.002+t0_offset)/dt)
- if ~np.isnan([epsp]).any():
- epsp_t = xtime[epsp]
- max_dvdt = np.max(y[dvdt_start:epsp])
- dvdt_threshold = np.max([0.01,0.15*max_dvdt])
- onset_options = np.where((np.sign(y-dvdt_threshold)>0) & (np.sign(accel)>=0))[0]
- valid_onsets = onset_options[(onset_options>dvdt_start)&(onset_options<epsp)]
- if len(valid_onsets) > 0:
- if (epsp_t-(np.min(valid_onsets)*dt)*1000) > 0: #ensure that onset is before peak
- epsp_onset_ind = np.min(valid_onsets) #min after stim artifact
- epsp_amp = R[epsp]-R[0] #R[epsp]-R[epsp_onset_ind]
- epsp_onset = xtime[epsp_onset_ind]
- epsp_dvdt = np.max(y[epsp_onset_ind:epsp])
- epsp_dvdt = epsp_dvdt/dt/1000 # convert to mV/msec from mV/sample
- elif (epsp_t-(np.min(valid_onsets)*dt)*1000) <= 0:
- epsp_t = np.NaN
- epsp_onset = np.NaN
- epsp_amp = np.NaN
- epsp_dvdt = np.NaN
- elif len(valid_onsets)==0:
- epsp_t = np.NaN
- epsp_onset = np.NaN
- epsp_amp = np.NaN
- epsp_dvdt = np.NaN
- elif np.isnan([epsp]).any():
- epsp_t = np.NaN
- epsp_onset = np.NaN
- epsp_amp = np.NaN
- epsp_dvdt = np.NaN
- if ~np.isnan([ipsp]).any():
- ipsp_t = xtime[ipsp]
- max_dvdt = np.min(y[dvdt_start:ipsp])
- dvdt_threshold = np.min([-0.01,0.15*max_dvdt])
- onset_options = np.where((np.sign(y-dvdt_threshold)<0) & (np.sign(accel)<=0))[0]
- valid_onsets = onset_options[(onset_options>dvdt_start)&(onset_options<ipsp)]
- if len(valid_onsets) > 0:
- if (ipsp_t-(np.min(valid_onsets)*dt)*1000) > 0: #ensure that onset is before peak
- ipsp_onset_ind = np.min(valid_onsets) #min after stim artifact
- ipsp_amp = R[ipsp]-R[0] #R[ipsp_onset_ind]
- ipsp_onset = xtime[ipsp_onset_ind]
- ipsp_dvdt = np.min(y[ipsp_onset_ind:ipsp])
- ipsp_dvdt = ipsp_dvdt/dt/1000 # convert to mV/msec from mV/sample
- elif (ipsp_t-(np.min(valid_onsets)*dt)*1000) <= 0:
- ipsp_t = np.NaN
- ipsp_onset = np.NaN
- ipsp_amp = np.NaN
- ipsp_dvdt = np.NaN
- elif len(valid_onsets)==0:
- ipsp_t = np.NaN
- ipsp_onset = np.NaN
- ipsp_amp = np.NaN
- ipsp_dvdt = np.NaN
- elif np.isnan([ipsp]).any():
- ipsp_t = np.NaN
- ipsp_onset = np.NaN
- ipsp_amp = np.NaN
- ipsp_dvdt = np.NaN
- #calculate std of response starting at first psp onset with duration 20ms
- onset_vals = np.array([epsp_onset,ipsp_onset])
- if len(onset_vals[~np.isnan(onset_vals)])==2:
- if epsp<ipsp:
- onset_ind = int(epsp_onset/1000/dt)
- offset_ind = onset_ind + int(20/1000/dt)
- response_std = np.mean(np.std(sweeps[onset_ind:offset_ind,:],1))
- peak_std = np.std(sweeps[epsp])
- if ipsp<epsp:
- onset_ind = int(ipsp_onset/1000/dt)
- offset_ind = onset_ind + int(20/1000/dt)
- response_std = np.mean(np.std(sweeps[onset_ind:offset_ind,:],1))
- peak_std = np.std(sweeps[ipsp])
- elif len(onset_vals[~np.isnan(onset_vals)])==1:
- if (~np.isnan(epsp_onset) & np.isnan(ipsp_onset)):
- onset_ind = int(epsp_onset/1000/dt)
- offset_ind = onset_ind + int(20/1000/dt)
- response_std = np.mean(np.std(sweeps[onset_ind:offset_ind,:],1))
- peak_std = np.std(sweeps[epsp])
- if (~np.isnan(ipsp_onset) & np.isnan(epsp_onset)):
- onset_ind = int(ipsp_onset/1000/dt)
- offset_ind = onset_ind + int(20/1000/dt)
- response_std = np.mean(np.std(sweeps[onset_ind:offset_ind,:],1))
- peak_std = np.std(sweeps[ipsp])
- elif len(onset_vals[~np.isnan(onset_vals)])==0:
- response_std = np.NaN
- peak_std = np.NaN
- #calculate halfwidth of response
- if (~np.isnan(epsp_t) & ~np.isnan(epsp_amp)):
- if R[int(epsp_t/1000/dt)] >= epsp_amp:
- R_shifted = R
- elif R[int(epsp_t/1000/dt)] < epsp_amp: #if epsp peak is negative need to offset response to calc halfwidth
- R_shifted = R - R[int(epsp_t/1000/dt)] + epsp_amp
- if ~np.isnan(epsp_amp):
- rise_options = np.where((np.sign((R_shifted-(epsp_amp/2)))>0) & (np.sign(y)>0))[0]
- valid_rise = rise_options[rise_options>int(2/1000/dt)]
- rise_t = np.min(valid_rise) * dt
- fall_options = np.where((np.sign((R_shifted-(epsp_amp/2)))>0) & (np.sign(y)<0))[0]
- valid_fall = fall_options[fall_options>int(2/1000/dt)]
- fall_t = np.max(valid_fall) * dt
- epsp_hw = (fall_t - rise_t)*1000 #convert to ms
- elif (np.isnan(epsp_t) | np.isnan(epsp_amp)):
- epsp_hw = np.NaN
- return epsp_t, epsp_amp, epsp_onset, ipsp_t, ipsp_amp, ipsp_onset, response_std, peak_std, epsp_hw, epsp_dvdt, ipsp_dvdt
- def get_results(expt,cmd_t,u_t,c_t,c_latency,do_plot,ax):
- #get command response
- xtime,sweeps = expt.get_sweepsmat('lowgain',cmd_t,sweepdur)
- cmd_ = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- # use these cmd trials to get Vm when measuring Estim responses
- Vm_baseline = np.mean(sweeps,1)[0]
- cell_data = {
- 'exptname' : exptname,
- 'Vm_baseline' : Vm_baseline,
- 'c_latency' : c_latency}
- # calculate peak data for cmdR
- min_peakt = 0.003 #(s)
- threshold_h = 0.25 #(mV)
- order = 0.0025 #(s)
- #get command response
- xtime,sweeps = expt.get_sweepsmat('lowgain',cmd_t,sweepdur)
- result = calc_peaks (xtime,sweeps, order, min_peakt,0,threshold_h,dt)
- if do_plot==1:
- plot_peaks_result(ax,xtime,sweeps,result,'purple')
- this_dict = result_to_dict(result,'cmd')
- cell_data.update(this_dict)
- # calculate peak data for uncoupled response
- #get uncoupled response
- xtime,sweeps = expt.get_sweepsmat('lowgain',u_t-c_latency,sweepdur)
- result = calc_peaks (xtime,sweeps, order, min_peakt,c_latency,threshold_h,dt)
- if do_plot==1:
- plot_peaks_result(ax,xtime,sweeps,result,'green')
- this_dict = result_to_dict(result,'u')
- cell_data.update(this_dict)
- # calculate peak data for coupled response
- #need to subtract cmd Response
- #first get command response offset by c_latency
- # xtime,sweeps = expt.get_sweepsmat('lowgain',cmd_t+c_latency,sweepdur)
- # cmd_ = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- #get coupled response
- xtime,sweeps = expt.get_sweepsmat('lowgain',c_t-c_latency,sweepdur)
- # sweeps = np.asarray([sweep - cmd_ for sweep in sweeps.T]).T
- result = calc_peaks (xtime,sweeps, order, min_peakt,c_latency,threshold_h,dt)
- if do_plot==1:
- plot_peaks_result(ax,xtime,sweeps,result,'orange')
- this_dict = result_to_dict(result,'c')
- cell_data.update(this_dict)
- mean_c = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- # calculate peak data for predicted response
- #need to subtract cmd Response
- #first get command response offset by c_latency
- xtime,sweeps = expt.get_sweepsmat('lowgain',cmd_t,sweepdur)
- cmd_ = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- #get uncoupled response
- xtime,sweeps = expt.get_sweepsmat('lowgain',u_t-c_latency,sweepdur)
- sweeps = np.asarray([sweep + cmd_ for sweep in sweeps.T]).T
- result = calc_peaks (xtime,sweeps, order, min_peakt,c_latency,threshold_h,dt)
- if do_plot==1:
- plot_peaks_result(ax,xtime,sweeps,result,'gray')
- this_dict = result_to_dict(result,'p')
- cell_data.update(this_dict)
- mean_p = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- this_dict = {'max_diff_predicted' : np.max(mean_c-mean_p)}
- cell_data.update(this_dict)
- return cell_data
- def plot_response(ax,xtime,sweeps,color_r):
- R = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- ax.plot(xtime,R,color = color_r)
- def plot_peaks_result(ax,xtime,sweeps,result,color_r):
- R = np.mean(sweeps,1)-np.mean(sweeps,1)[0]
- epsp_t = result[0],
- epsp_amp = result[1],
- epsp_onset = result[2],
- ipsp_t = result[3],
- ipsp_amp = result[4],
- ipsp_onset = result[5],
- ax.plot(xtime,R,color = color_r)
- ax.vlines(epsp_onset,-2,2,color = 'red',linestyles='dashed')
- ax.vlines(ipsp_onset,-2,2,color = 'blue',linestyles='dashed')
- ax.plot(epsp_t, epsp_amp,"*",color = color_r)
- ax.plot(ipsp_t, ipsp_amp,"^",color = color_r)
- def result_to_dict(result, rtype):
- this_dict = {
- rtype + '_epsp_t' : result[0],
- rtype + '_epsp_amp' : result[1],
- rtype + '_epsp_onset' : result[2],
- rtype + '_ipsp_t' : result[3],
- rtype + '_ipsp_amp' : result[4],
- rtype + '_ipsp_onset' : result[5],
- rtype + '_response_std' : result[6],
- rtype + '_peak_std' : result[7],
- rtype + '_epsp_hw' : result[8],
- rtype + '_epsp_dvdt' : result[9],
- rtype + '_ipsp_dvdt' : result[10]
- }
- return this_dict
-
- #################################################
- #################################################
- #%%
- print('initializing a figure')
- save_plot = 1
- fig = plt.figure(num=1)
- ax = fig.add_axes([0.1,0.1,0.8,0.8])
- # ax.set_visible(True)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200606_005'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # #from command only trials get times for command responses
- # bout = [expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('R','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('N','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
-
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200606_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200607_005'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200607_004'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200607_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200607_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('R','Keyboard')[2],
- # expt.get_bout_win('N','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[2]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200525_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200525_006'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200524_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # # %%
- # ax.cla()
- # exptname = '20200312_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # ################################################
- # ################################################
- # #%%
- # ax.cla()
- # exptname = '20200227_000'
- # print(exptname)
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200226_002'
- # print(exptname)
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200225_000' #* had to manually change dvdt_start param in calc_peaks to 0.001
- # print(exptname)
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('N','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200115_002'
- # print(exptname)
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # ################################################
- # ################################################
- # #%%
- # ax.cla()
- # exptname = '20200113_003'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200113_004'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20191218_005'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20191218_009'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('R','Keyboard')[1],
- # expt.get_bout_win('N','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200122_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200115_004'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200312_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200309_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[1]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200122_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200121_006'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # ################################################
- # ################################################
- # #%%
- # ax.cla()
- # exptname = '20200109_004'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # # # hyperpol but drifting (also did a bout without bias current)
- # # bout = [expt.get_bout_win('R','Keyboard')[2],
- # # expt.get_bout_win('N','Keyboard')[1]]
- # # at rest near beginning of expt so see spikes
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20191218_007'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # bout = [expt.get_bout_win('R','Keyboard')[0],
- # expt.get_bout_win('N','Keyboard')[0]]
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # u_t = u_df.time.values
- # c_t = c_df.time.values
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180122_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # #note with exptname that this a hyperpolarized version of this cell
- # cell_data['exptname'] = exptname + '_hyperpolarized'
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # ############## then do at rest for this cell
- # ax.cla()
- # bout = [expt.get_bout_win('B','Keyboard')[0],expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # #note with exptname that this a hyperpolarized version of this cell
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190325_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[0],expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # ################################################
- # ################################################
- # #%%
- # ax.cla()
- # exptname = '20171031_004'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # #first look at hyperpolarized trials
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[1],expt.get_bout_win('B','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # #note with exptname that this a hyperpolarized version of this cell
- # cell_data['exptname'] = exptname + '_hyperpolarized'
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # ############## then do at rest for this cell
- # ###spikes are almost 10mV here!!!! good example to match in vitro
- # ax.cla()
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # #note with exptname that this a hyperpolarized version of this cell
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171010_006'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # #first look at hyperpolarized trials
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180130_000'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # #first look at hyperpolarized trials
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180108_004'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # #first look at hyperpolarized trials
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[1],expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # #note with exptname that this a hyperpolarized version of this cell
- # cell_data['exptname'] = exptname + '_hyperpolarized'
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # ############## then do at rest for this cell
- # ###spikes are almost 10mV here!!!! good example to match in vitro
- # ax.cla()
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = exptname + '.png'
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # #note with exptname that this a hyperpolarized version of this cell
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = exptname + '.csv'
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20170912_003'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # #first look at hyperpolarized trials
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20170912_005'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #######also at rest but later in trace. spikes worse but cmd better
- # ax.cla()
- # bout = [expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('B','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # cell_data['exptname'] = exptname + '_bout2'
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190107_000'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3],
- # expt.get_bout_win('U','Keyboard')[4],
- # expt.get_bout_win('U','Keyboard')[5],
- # expt.get_bout_win('U','Keyboard')[6]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[2],
- # expt.get_bout_win('C','Keyboard')[3],
- # expt.get_bout_win('C','Keyboard')[4],
- # expt.get_bout_win('C','Keyboard')[5]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171027_000'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # #first look at hyperpolarized trials
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180103_001'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #######also at rest but later in trace.
- # ax.cla()
- # bout = [expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # cell_data['exptname'] = exptname + '_bout2'
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #######then finally hyperpolarized trials.
- # ax.cla()
- # bout = [expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # cell_data['exptname'] = exptname + '_bout2'
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190102_000'
- # # need to adjust stimulus times by -0.003 because of how event marker detected artifact position
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[2],
- # expt.get_bout_win('C','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # ################################################
- # ################################################
- # #%%
- # ax.cla()
- # exptname = '20180103_003'
- # # need to adjust stimulus times by -0.0005 because of how event marker detected artifact position
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190227_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[4]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[4],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190104_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190128_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3],
- # expt.get_bout_win('U','Keyboard')[4]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3],
- # expt.get_bout_win('U','Keyboard')[4],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[2],
- # expt.get_bout_win('C','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190312_005'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171010_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180122_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171010_005'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20181213_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190107_003'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('U','Keyboard')[3],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20190110_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('U','Keyboard')[2],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0],
- # expt.get_bout_win('C','Keyboard')[1],
- # expt.get_bout_win('C','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200309_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('N','Keyboard')[1],
- # expt.get_bout_win('R','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # u_t = u_df.time.values
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20200303_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('B','Keyboard')[1],
- # expt.get_bout_win('R','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['U'])
- # u_t = u_df.time.values
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # bout = [expt.get_bout_win('R','Keyboard')[0]]
- # c_df = expt.filter_marker_df_code(bout_df,['C'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180105_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values -0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180112_003'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20180108_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1],
- # expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # #from uncoupled trials get times for command responses
- # cmd_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in u_t])
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20170206_003'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t_all = u_df.time.values - 0.0003
- # #from uncoupled trials get times for command responses
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_t = []
- # for u in u_t_all:
- # cmd_pre=np.NaN
- # cmd_post=np.NaN
- # if (len(b_df.time.values[b_df.time.values<u])>0):
- # cmd_pre = u-np.max(b_df.time.values[b_df.time.values<u])
- # if (len(b_df.time.values[b_df.time.values>u])>0):
- # cmd_post = np.min(b_df.time.values[b_df.time.values>u])-u
- # if ((cmd_pre>0.05) & (cmd_post>0.05)):
- # u_t.append(u)
- # u_t = np.asarray(u_t)
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171107_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171011_001'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('B','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171010_000'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20171010_003'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0],
- # expt.get_bout_win('U','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # ######## then super hyperpolarized to prevent spiking
- # ax.cla()
- # bout = [expt.get_bout_win('U','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('U','Keyboard')[3]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[2]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '_hyperpolarized' + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '_hyperpolarized' + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # ax.cla()
- # exptname = '20170502_002'
- # expt = AmpShift_Stable()
- # expt.load_expt(exptname, data_folder)
- # expt.set_channels('CmdTrig','lowgain','spikes','SIU','DigMark')
- # marker_df = expt.get_marker_table()
- # dt = expt.get_dt('lowgain')
- # bout = [expt.get_bout_win('U','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # u_df = expt.filter_marker_df_code(bout_df,['E'])
- # u_t = u_df.time.values - 0.0003
- # bout = [expt.get_bout_win('B','Keyboard')[0],
- # expt.get_bout_win('B','Keyboard')[1]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # cmd_t = b_df.time.values
- # bout = [expt.get_bout_win('C','Keyboard')[0]]
- # bout_df = expt.filter_marker_df_time(marker_df,bout)
- # c_df = expt.filter_marker_df_code(bout_df,['E'])
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # c_t = c_df.time.values - 0.0003
- # #calculate time will need to offset command response by to subtract from coupled estim response
- # #use coupled trials
- # cmd_coupled_t = np.asarray([np.max(b_df.time.values[b_df.time.values<t]) for t in c_t])
- # c_latency = np.median(c_t-cmd_coupled_t)
- # cell_data = get_results(expt,cmd_t,u_t,c_t,c_latency,1,ax)
- # if save_plot:
- # savename = cell_data['exptname'] + '.png' #altered name to save figure as
- # fig.savefig(figure_folder / savename,format = 'png',dpi = 75)
- # df = pd.DataFrame(cell_data,index=[0])
- # savename = cell_data['exptname'] + '.csv' #altered name to save df as
- # df.to_csv(df_folder / savename)
- # #################################################
- # #################################################
- # #%%
- # #############
- # # %%
- # ####### if freerun stim and need to elminate some
- # ####### stim trials because too close to cmd
- # ###############
- # u_t_all = u_df.time.values - 0.0003
- # #from uncoupled trials get times for command responses
- # b_df = expt.filter_marker_df_code(bout_df,['B'])
- # u_t = []
- # for u in u_t_all:
- # cmd_pre=np.NaN
- # cmd_post=np.NaN
- # if (len(b_df.time.values[b_df.time.values<u])>0):
- # cmd_pre = u-np.max(b_df.time.values[b_df.time.values<u])
- # if (len(b_df.time.values[b_df.time.values>u])>0):
- # cmd_post = np.min(b_df.time.values[b_df.time.values>u])-u
- # if ((cmd_pre>0.05) & (cmd_post>0.05)):
- # u_t.append(u)
- # u_t = np.asarray(u_t)
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