import nixio as nix import os from IPython import embed #from utility import * import matplotlib.pyplot as plt import numpy as np import pandas as pd import matplotlib.mlab as ml import scipy.integrate as si from scipy.ndimage import gaussian_filter from IPython import embed from myfunctions import * from myfunctions import auto_rows from functionssimulation import default_settings import matplotlib.gridspec as gridspec def ps_df(data, d = '2019-09-23-ad-invivo-1', wish_df = 310, window = 'no',sampling_rate = 40000): #nfft = 4096 #trial_cut = 0.1 #freq_step = sampling_rate / nfft data_cell = data[data['dataset'] == d]# dfs = np.unique(data_cell['df']) df_here = dfs[np.argmin(np.abs(dfs - wish_df))] dfs310 = data_cell[data_cell['df'] == df_here] #pp = [[]]*len(dfs310) pp = [] ppp = [] trial_cut = 0.1 for i in range(len(dfs310)): duration = dfs310.iloc[i]['durations'] #cut_vec = np.arange(0, duration, trial_cut) cut_vec = np.arange(0, duration, trial_cut) #spikes_cut = spikes[(spikes > 0.05) & (spikes < 0.95)] #for j, cut in enumerate(cut_vec): # # print(j) # spike_times = dfs310.iloc[i]['spike_times'] # spikes = spike_times - spike_times[0] # spikes_cut = spikes[(spikes > cut) & (spikes < cut_vec[j + 1])] # if cut == cut_vec[-2]: # #counter_cut += 1 # break # if len(spikes_cut) < 10: # #counter_spikes += 1 # break # spikes_mat = np.zeros(int(trial_cut * sampling_rate) + 1) # spikes_idx = np.round((spikes_cut - trial_cut * j) * sampling_rate) # for spike in spikes_idx: # spikes_mat[int(spike)] = 1# # # #spikes_mat = np.zeros(int(spikes[-1]* sampling_rate + 5)) # #spikes_idx = np.round((spikes) * sampling_rate) # #for spike in spikes_idx: # # spikes_mat[int(spike)] = 1 # spikes_mat = spikes_mat * sampling_rate # if type(window) != str: # spikes_mat = gaussian_filter(spikes_mat, sigma=window) # # smoothened_spikes_mat05 = gaussian_filter(spikes_mat, sigma=window05) * sampling_rate # # smoothened_spikes_mat2 = gaussian_filter(spikes_mat, sigma=window2) * sampling_rate # else: # smoothened = spikes_mat * 1 # nfft = 4096 # p, f = ml.psd(spikes_mat - np.mean(spikes_mat), Fs=sampling_rate, NFFT=nfft, noverlap=nfft / 2) # pp.append(p) spike_times = dfs310.iloc[i]['spike_times'] if len(spike_times) < 3: counter_spikes += 1 break spikes = spike_times - spike_times[0] spikes_cut = spikes[(spikes > 0.05) & (spikes < 0.95)] if len(spikes_cut) < 3: counter_cut += 1 break spikes_mat = np.zeros(int(spikes[-1] * sampling_rate + 5)) spikes_idx = np.round((spikes) * sampling_rate) for spike in spikes_idx: spikes_mat[int(spike)] = 1 spikes_mat = spikes_mat * sampling_rate if type(window) != str: spikes_mat = gaussian_filter(spikes_mat, sigma=window) # smoothened_spikes_mat05 = gaussian_filter(spikes_mat, sigma=window05) * sampling_rate # smoothened_spikes_mat2 = gaussian_filter(spikes_mat, sigma=window2) * sampling_rate else: spikes_mat = spikes_mat*1 nfft = 4096 p, f = ml.psd(spikes_mat - np.mean(spikes_mat), Fs=sampling_rate, NFFT=nfft, noverlap=nfft / 2) ppp.append(p) #spike_times = data_cell.iloc[i]['spike_times']# #if len(spike_times) < 3: # counter_spikes += 1 # break #spikes = spike_times - spike_times[0] # cut trial into snippets of 100 ms #cut_vec = np.arange(0, duration, trial_cut) #spikes_cut = spikes[(spikes > 0.05) & (spikes < 0.95)] #if len(spikes_cut) < 3: # counter_cut += 1 # break #spikes_new = spikes_cut - spikes_cut[0] #spikes_mat = np.zeros(int(spikes_new[-1] * sampling_rate) + 2) # spikes_mat = np.zeros(int(trial_cut * sampling_rate) + 1) #spikes_idx = np.round((spikes_new) * sampling_rate) #for spike in spikes_idx: # spikes_mat[int(spike)] = 1 #spikes_mat = spikes_mat * sampling_rate #nfft = 4096 #p, f = ml.psd(smoothened - np.mean(smoothened), Fs=sampling_rate, NFFT=nfft, noverlap=nfft / 2) #ppp.append(p) #p_mean = np.mean(pp,axis = 0) p_mean2 = np.mean(ppp, axis=0) #ref = (np.max(p_mean2)) # db = 10 * np.log10(p_mean2 / np.max(p_mean2)) #ref = (np.max(p_mean2)) #db2 = 10 * np.log10(p_mean2 / ref) #embed() return df_here,p_mean2,f,db def plot_example_ps(grid,input = ['2019-10-21-aa-invivo-1'],sigma = [0.00005,0.00025,0.0005, 0.002]): sampling_rate = 40000 colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B'] plt.rcParams['lines.linewidth'] = 1.5 plt.rcParams['lines.markersize'] = 6 #data = pd.read_pickle('data_beat.pkl') #iter = np.unique(data['dataset']) iter = ['2019-05-07-by-invivo-1'] iter = ['2019-09-23-ad-invivo-1'] iter = input for cell in iter: data = pd.read_pickle('data_beat.pkl') beat_results = pd.read_pickle('beat_results_smoothed.pkl') #embed() eodf = int(beat_results[beat_results['dataset'] == cell]['eodf'].iloc[0]) df = [[]] * (len(sigma) + 1) p = [[]] * (len(sigma) + 1) f = [[]] * (len(sigma) + 1) db = [[]] * (len(sigma) + 1) sigmaf = [[]] * (len(sigma) + 1) gauss = [[]] * (len(sigma) + 1) wish_df = 150 df[0], p[0], f[0], db[0] = ps_df(data, d=cell, wish_df= wish_df, window='no', sampling_rate=sampling_rate) for i in range(len(sigma)): df[1+i], p[1+i], f[1+i], db[1+i] = ps_df(data, d=cell, wish_df= wish_df, window = sigma[i]*sampling_rate,sampling_rate = sampling_rate) sigmaf[i + 1] = 1 / (2 * np.pi * sigma[i]) gauss[i + 1] = np.exp(-(f[1+i] ** 2 / (2 * sigmaf[i + 1] ** 2))) db = 'no' stepsize = f[0][1] - f[0][0] if db == 'db': p = db # fig.suptitle(d, labelpad = 25) #print(d) ax = {} ax[0] = plt.subplot(grid[0]) ax[0].plot(f[0], p[0], color=colors[0]) ax[0].plot(eodf - 1, np.max(p[0][int(eodf / stepsize) - 5:int(eodf / stepsize) + 5]) * 0.95, color='k', marker='o', linestyle='None', label='EODf') # = '+str(int(eodf))+' Hz') #embed() ax[0].fill_between(f[1], [max(p[0])]*len(f[1]), facecolor='lightgrey', edgecolor='grey') ax[0].plot(df[0], np.max(p[0][int(abs(df[0]) / stepsize) - 5:int(abs(df[0]) / stepsize) + 5]) * 0.95, color=colors[1], marker='o', linestyle='None', label='Df') ax[0].plot(df[0] + eodf, p[0][int((df[0] + eodf) / stepsize) + 1], color=colors[2], marker='o', linestyle='None', label='stimulus') ax[0].legend(bbox_to_anchor=(0.4, 1, 0.6, .1), loc='lower left', ncol=3, mode="expand", borderaxespad=0.) ax[0].set_xlim([0, 2000]) wide = 2 for i in range(len(sigma)): ax[i+1] = plt.subplot(grid[i+1]) plot_filter(ax, i+1, f[1+i], p, colors, gauss[1+i], eodf, stepsize, wide, df[1+i]) ax[i+1].set_xlim([0, 2000]) #embed() #if db == 'db': # ax[0].set_ylim([np.min([p]),0])#p[0][,p[1][0:2000],p[2][0:2000],p[3][0:2000] #else: # ax[0].set_ylim([ 0,np.max([p])]) ax[int(len(df))-1].set_xlabel('frequency [Hz]') # ax[1].set_ylabel(r'power [Hz$^2$/Hz]') ax[0].ticklabel_format(axis='y', style='sci', scilimits=[0, 0]) #print(df[3]) for i in range(len(df)): ax[i].spines['right'].set_visible(False) ax[i].spines['top'].set_visible(False) cols = grid.ncols rows = grid.nrows ax[int(cols*(rows/2))-1].set_ylabel(' power spectral density [Hz²/Hz]') #ax[2].set_ylabel('Hz²/Hz') #ax[3].set_ylabel('Hz²/Hz') #ax[0].set_ylabel('Hz²/Hz') for i in range(len(df)): ax[i].axvline(x = eodf/2, color = 'black', linestyle = 'dashed') plt.tight_layout() #fig.label_axes() def plot_filter(ax, ax_nr, f, p4, colors, gauss3, eodf, stepsize, wide, df): ax[ax_nr].plot(f, p4[ax_nr], color=colors[0]) ax[ax_nr].fill_between(f, max(p4[0]) * gauss3 ** 2, facecolor='lightgrey', edgecolor='grey') ax[ax_nr].plot(eodf, np.max(p4[ax_nr][int(eodf / stepsize) - wide:int(eodf / stepsize) + wide]) * 0.95, color='k', marker='o', linestyle='None') ax[ax_nr].plot(abs(df), np.max(p4[ax_nr][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) * 0.95, color=colors[1], marker='o', linestyle='None') ax[ax_nr].plot(df + eodf, np.max(p4[ax_nr][int((df + eodf) / stepsize) - wide:int((df + eodf) / stepsize) + wide]) * 0.95, color=colors[2], marker='o', linestyle='None') return ax def plot_amp(ax, mean1, dev,name = 'amp',nr = 1): np.unique(mean1['type']) all_means = mean1[mean1['type'] == name +' mean'] original = all_means[all_means['dev'] == 'original'] #m005 = all_means[all_means['dev'] == '005'] m05 = all_means[all_means['dev'] == '05'] m2 = all_means[all_means['dev'] == '2'] # fig, ax = plt.subplots(nrows=4, ncols = 3, sharex=True) versions = [original, m05, m2] #m005, for i in range(len(versions)): keys = [k for k in versions[i]][2::] try: data = np.array(versions[i][keys])[0] except: break axis = np.arange(0, len(data), 1) axis_new = axis * 1 similarity = [keys, data] sim = np.argsort(similarity[0]) # similarity[sim] all_means = mean1[mean1['type'] == name+' std'] std = all_means[all_means['dev'] == dev[i]] std = np.array(std[keys])[0] #ax[1, 1].set_ylabel('Modulation depth') #ax[nr,i].set_title(dev[i] + ' ms') all_means = mean1[mean1['type'] == name+' 95'] std95 = all_means[all_means['dev'] == dev[i]] std95 = np.array(std95[keys])[0] all_means = mean1[mean1['type'] == name+' 05'] std05 = all_means[all_means['dev'] == dev[i]] std05 = np.array(std05[keys])[0] ax[nr,i].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]), color='gainsboro') ax[nr,i].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]), color='darkgrey') # ax[i].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--', label='AMf') ax[nr,i].plot(np.array(keys)[sim], data[sim], color='black') # ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %') #embed() return ax def create_beat_corr(hz_range, eod_fr): beat_corr = hz_range%eod_fr beat_corr[beat_corr>eod_fr/2] = eod_fr[beat_corr>eod_fr/2] - beat_corr[beat_corr>eod_fr/2] return beat_corr def plot_mean_cells(grid, sigma = ['original','05','2'],lw = 0.7): mean1 = pd.read_pickle('mean.pkl') colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B'] inch_factor = 2.54 half_page_width = 7.9 / inch_factor intermediate_width = 12 / inch_factor whole_page_width = 16 * 2 / inch_factor small_length = 6 / inch_factor intermediate_length = 12 * 1.5 / inch_factor max_length = 25 / inch_factor whole_page_width = 6.7 intermediate_length = 3.7 #plt.rcParams['figure.figsize'] = (whole_page_width, intermediate_length) plt.rcParams['font.size'] = 11 plt.rcParams['axes.titlesize'] = 12 plt.rcParams['axes.labelsize'] = 12 plt.rcParams['lines.linewidth'] = 1.5 plt.rcParams['lines.markersize'] = 8 plt.rcParams['legend.loc'] = 'upper right' plt.rcParams["legend.frameon"] = False # load data for plot # data1 = pd.read_csv('ma_allcells_unsmoothed.csv') # data2 = pd.read_csv('ma_allcells_05.csv') # data3 = pd.read_csv('ma_allcells_2.csv') # data_tob = pd.read_csv('ma_toblerone.csv') # smothed = df_beat[df_beat['dev'] == 'original'] # data1 = smothed[smothed['type'] == 'amp'] x = np.arange(0, 2550, 50) corr = create_beat_corr(x, np.array([500] * len(x))) np.unique(mean1['type']) all_means = mean1[mean1['type'] == 'max mean'] #versions = [[]]*len(dev) #for i in range(len(dev)): version =[[]]*len(sigma) version2 = [[]] * len(sigma) dev = [[]] * len(sigma) all_means2 = mean1[mean1['type'] == 'amp max' + ' mean'] for i in range(len(sigma)): try: version[i] = all_means[all_means['dev'] == sigma[i]] version2[i] = all_means2[all_means2['dev'] == sigma[i]] except: version[i] = [] print(sigma[i]) dev[i] = sigma[i] #if sigma[i] == 'original': # titles[i] = 'binary' #else: # titles[i] = str(np.float(sigma[i])/1000 #original = all_means[all_means['dev'] == 'original'] # m005 = all_means[all_means['dev'] == '005'] #m2 = all_means[all_means['dev'] == '2'] # fig, ax = plt.subplots(nrows=4, ncols = 3, sharex=True) #versions = [original, m05, m2] # m005, #dev = ['original', '05', '2']#'005', titels = ['binary','0.005 ms','0.25 ms','0.5 ms','2 ms'] #fig, ax = plt.subplots(nrows=2, ncols=len(sigma), sharex=True) #plt.subplot(axis) #grid = gridspec.GridSpec(2, 3, wspace=0.0, height_ratios=[6, 2], width_ratios=[1,0.5,3], hspace=0.2) #versions = [original, m05, m2]#m005, ax = {} for i in range(len(version)): if len(version[i])>0: print(i) plots = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=grid[i], wspace=0, hspace=0.5) keys = [k for k in version[i]][2::] try: data = np.array(version[i][keys])[0] except: #embed() break axis = np.arange(0, len(data), 1) axis_new = axis * 1 similarity = [keys, data] sim = np.argsort(similarity[0]) # similarity[sim] all_means = mean1[mean1['type'] == 'max std'] std = all_means[all_means['dev'] == dev[i]] std = np.array(std[keys])[0] #ax[0,i].set_title(dev[i] +' ms') ax[0] = plt.subplot(plots[0]) ax[0].set_title(titels[i]) ax[0].set_ylabel('MPF [EODf]') all_means = mean1[mean1['type'] == 'max 95'] std95 = all_means[all_means['dev'] == dev[i]] std95 = np.array(std95[keys])[0] all_means = mean1[mean1['type'] == 'max 05'] std05 = all_means[all_means['dev'] == dev[i]] std05 = np.array(std05[keys])[0] #std[np.where(list(data[sim] + std[sim])>std95[sim])] = std95[sim][np.where(list(data[sim] + std[sim])>std95[sim])] #embed() ax[0].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]), color='mistyrose') #embed() ax[0].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]), color='pink') ax[0].plot(x / 500, corr / 500 + 1, color='grey', linestyle='--', label='AMf') ax[0].plot(np.array(keys)[sim], data[sim], color='red', linewidht = lw) # plt.fill_between(np.array([0,1]),np.array([0,0]),np.array([1,1])) #embed() ax[1] = plt.subplot(plots[1]) name = 'amp max' np.unique(mean1['type']) keys = [k for k in version2[i]][2::] try: data = np.array(version2[i][keys])[0] except: break axis = np.arange(0, len(data), 1) axis_new = axis * 1 similarity = [keys, data] sim = np.argsort(similarity[0]) # similarity[sim] all_means = mean1[mean1['type'] == name + ' std'] std = all_means[all_means['dev'] == dev[i]] std = np.array(std[keys])[0] # ax[1, 1].set_ylabel('Modulation depth') # ax[nr,i].set_title(dev[i] + ' ms') all_means = mean1[mean1['type'] == name + ' 95'] std95 = all_means[all_means['dev'] == dev[i]] std95 = np.array(std95[keys])[0] all_means = mean1[mean1['type'] == name + ' 05'] std05 = all_means[all_means['dev'] == dev[i]] std05 = np.array(std05[keys])[0] ax[1].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]), color='gainsboro') ax[1].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]), color='darkgrey') # ax[i].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--', label='AMf') ax[1].plot(np.array(keys)[sim], data[sim], color='black') # ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %') # ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %') ax[0].spines['right'].set_visible(False) ax[0].spines['top'].set_visible(False) ax[1].spines['right'].set_visible(False) ax[1].spines['top'].set_visible(False) ax[1].legend(bbox_to_anchor=(0.7, 1, 0.7, .1), loc='lower left', ncol=3, mode="expand", borderaxespad=0.) #ax = plot_amp(ax, mean1, dev,name = 'amp',nr = 2) ax[0].legend(bbox_to_anchor=(0.3, 1, 0.7, .1), loc='lower left', ncol=3, mode="expand", borderaxespad=0.) #ax = plot_amp(ax,mean1, dev,name='amp max', nr=1) # ax[1].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--') # ax[1].plot(data2.x, transfer_array, color=colors[0]) # ax[1].plot(data2.x, data2.freq20, color=colors[1]) # ax[2].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--') # ax[2].plot(data3.x, transfer_array, color=colors[0]) # ax[2].plot(data3.x, color=colors[1]) # ax[2].plot(data_tob.ff, transfer_array, color='grey', linestyle='--') # ax[2].plot(data4.x, transfer_array, color=colors[0]) # ax[2].plot(data3.x, color=colors[1]) ax[0].set_ylabel('MPF [EODf]') #ax[1, 0].set_ylabel('Modulation depth') ax[1].set_ylabel('Modulation Peak') #ax[2, 0].set_ylabel('Whole modulation ') #for i in range(3): ax[0].set_xlabel('stimulus frequency [EODf]') #ax[2, 1].set_xlabel('stimulus frequency [EODf]') plt.subplots_adjust(bottom = 0.13) #fig.tight_layout() # fig.label_axes() if __name__ == "__main__": data = ['2019-10-21-aa-invivo-1'] #fig, ax = plt.subplots(nrows=5, sharex=True, sharey=True) trans = True sigma = [0.0005, 0.002] # 0.00005,0.00025, if trans == True: col = 1 row = 2 col_small = len(sigma)+1 row_small = 1 l = 6 t = 'horizontal' wd = [1] hd = [1,2.5] left = 1 right = 0 else: col = 2 row = 1 row_small = len(sigma)+1 col_small = 1 t = 'vertical' l = 9 wd = [4, 4] hd = [1] left = 0 right = 1 default_settings(data, intermediate_width=7.4, intermediate_length=6, ts=6, ls=10, fs=10) grid = gridspec.GridSpec(row, col, wspace=0.35,height_ratios = hd, width_ratios=wd, hspace=0.2)#, axis = gridspec.GridSpecFromSubplotSpec(row_small,col_small, subplot_spec=grid[0,0], wspace=0.15, hspace=0.1) plot_example_ps(axis,input = ['2019-10-21-aa-invivo-1'],sigma = sigma) #plt.show() #embed() #fig.savefig() axis = gridspec.GridSpecFromSubplotSpec(row_small,col_small, subplot_spec=grid[left,right], wspace=0.15, hspace=0.3) #embed() plot_mean_cells(axis, sigma = ['original','05','2'])#'005','025', plt.savefig('temporalplot.pdf') plt.savefig('../highbeats_pdf/temporalplot.pdf') plt.savefig('temporalplot'+t+'.pdf') plt.show() # plt.savefig('../results/Ramona/ma_powerspecs_negative_df' + d + '.pdf') # plt.show() # plt.close() # embed() # plot_single_tublerones() # original beat_activity