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 axes import label_axes, labelaxes_params from myfunctions import auto_rows from myfunctions import default_settings from myfunctions import remove_tick_marks import matplotlib.gridspec as gridspec import string def plot_single_cells(ax, data = ['2019-10-21-aa-invivo-1','2019-11-18-af-invivo-1','2019-10-28-aj-invivo-1']): colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B'] # labelaxes_params(xoffs=-3, yoffs=0, labels='A', font=dict(fontweight='bold')) #baseline = pd.read_pickle('data_baseline.pkl') #data_beat = pd.read_pickle('data_beat.pkl') data_all = pd.read_pickle('beat_results_smoothed.pkl') #data = np.unique(data_all['dataset'])[0] #data = np.unique(data_all['dataset']) end = ['original', '005','05', '2' ] end = ['original','005'] y_sum = [[]]*len(data)*len(end) counter = 0 for dd,set in enumerate(data): for ee, e in enumerate(end): d = data_all[data_all['dataset'] == set] #x = d['delta_f'] / d['eodf'] + 1 #embed() #y = d['result_frequency_' + e] y = d['result_amplitude_max_' + e] #y2 = d['result_amplitude_max_' + e] y_sum[counter] = np.nanmax(y) counter += 1 #print(np.nanmax(y)) #embed() lim = np.max(y_sum) #embed() grid = gridspec.GridSpec(1,3,width_ratios = [0.2,4,4],) grid0 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=grid[0], wspace=0.02, hspace=0.1) # , label1 = plt.subplot(grid0[0]) label2 = plt.subplot(grid0[1]) label3 = plt.subplot(grid0[2]) labels = [label1,label2,label3] titels = ['Cell 1','Cell 2','Cell 3']# < 0.5 EODf,with 0.5 ms wide fs_big = 11 weight = 'bold' for ll,l in enumerate(labels): #embed() l.spines['right'].set_visible(False) l.spines['left'].set_visible(False) l.spines['top'].set_visible(False) l.spines['bottom'].set_visible(False) l.set_yticks([]) l.set_xticks([]) l.set_ylabel(titels[ll],labelpad = 15, fontsize = fs_big, fontweight=weight, rotation = 0) hd = 0.3 grid1 = gridspec.GridSpecFromSubplotSpec(3,1,subplot_spec=grid[1], wspace=0.02, hspace=0.3)#, end = ['original'] color_modul1 = 'steelblue' color_mpf = 'red' y_sum1,moduls = plot_single(lim, data, end, data_all, grid1, color_mpf, color_modul1,arrows = False,nrs = [0,1,2], title = 'Binary spike trains',xlabel = True,yticks = False) grid2 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=grid[2], wspace=0.02, hspace=0.4)#, end = ['05'] end = ['2'] #y_sum = [[]] * len(data) color_mpf = 'red' color_modul = 'royalblue' color_modul = 'steelblue' color_mpf = 'darkred' color_mpf = 'tomato' color_mpf = 'red' alpha = 1 y_sum2, moduls = plot_single(lim, data, end, data_all, grid2, color_mpf, color_modul,color_upper = color_modul1, alpha = alpha, arrows = True, mods = moduls, nrs = [3,4,5], title = '2 ms Gaussian',xlabel = False) #for dd, d in enumerate(data): # embed() #embed() #ax[1].set_ylim([0, np.nanmax([y_sum1,y_sum2])]) #ax[1, dd].set_ylim([0, 350]) plt.subplots_adjust(wspace = 0.4,left = 0.1,top = 0.94, right = 0.96,bottom = 0.1) def plot_single(lim, data, end, data_all, grid1, color_mpf, color_modul,alpha = 1, color_upper = 'grey',mods = [],tob_col = 'black', tob_lw = '0.8', arrows = True, nr_size = 12, nrs = [0,1,2], xlabel = True,title = '',yticks =True): baseline = pd.read_pickle('data_baseline.pkl') y_sum = [[]] * len(data) ax = {} moduls = [[]]*len(data) for dd,set in enumerate(data): for ee, e in enumerate(end): d = data_all[data_all['dataset'] == set] x = d['delta_f'] / d['eodf'] + 1 #embed() y = d['result_frequency_' + e] y2 = d['result_amplitude_max_' + e] y_sum[dd] = np.nanmax(y) ff = d['delta_f'] / d['eodf'] + 1 fe = d['beat_corr'] #fig.suptitle(set) grid2 = gridspec.GridSpecFromSubplotSpec(2, 1, height_ratios = [1,2], subplot_spec=grid1[dd], wspace=0.02, hspace=0.2) # , ax[0] = plt.subplot(grid2[0]) ax[0].plot(ff, fe, color=tob_col,linestyle = '--', linewidth = tob_lw, zorder = 2) ax[0].plot(x, y, color=color_mpf, zorder = 3,alpha = alpha) ax[0].text(-0.1, 1.1, string.ascii_uppercase[nrs[dd]], transform=ax[0].transAxes, size=nr_size, weight='bold') b = baseline[baseline['dataset'] == set]['spike_rate'] ax[0].axhline(y = b.iloc[0], color = 'grey', linestyle = 'dotted', zorder = 1) if dd == 0: plt.title(title) ax[set+e+str(1)] = plt.subplot(grid2[1]) #ax[0, dd].set_title(e + ' ms') ax[0].set_xlim([0, 4]) #if (e == 2) and xlabel == True: ax[set+e+str(1)].plot(x, y2, color=color_modul,alpha = alpha,zorder = 2) mod_tob = d['result_toblerone_max_' + e] ax[set + e + str(1)].plot(x, mod_tob, color=tob_col,linestyle = '--', alpha = alpha,linewidth = tob_lw, zorder = 1) if arrows == True: plt.fill_between(x, y2,mods[dd], color = 'gainsboro', edgecolor= 'grey',zorder = 1) plt.plot(x, mods[dd], alpha = 0.6, color = color_upper)#linewidth = 0.4, array = [0.65, ]#2.651.65, small_arrows = True if small_arrows == True: for a in range(len(array)): #embed() pos = np.argmin(np.abs(np.array(x)-array[a])) x_present = np.array(x)[pos] y2 = np.array(y2) #embed() pos_change = 2 nr = 19 nr2 = 5 #embed() if (np.array(mods[dd])[pos]-y2[pos])>nr: plt.plot([x_present, x_present], [y2[pos] + nr, np.max(np.array(mods[dd])[pos - pos_change:pos + pos_change])-nr2], color='black') plt.scatter([x_present],[y2[pos]+nr], marker = 'v',s = 10, color='black') moduls[dd] = y2 # ax[1,0].set_ylabel('modulation depth [Hz]') #ax[2, ee].plot(x, y2, color=colors[0]) #ax[2, 0].set_ylabel(' modulation depth [Hz]') # ax[1,ee].annotate("", xy=(0.53, 16.83), xytext=(0.53, 17.33), arrowprops=dict(arrowstyle="->")) # ax[1,ee].annotate("", xy=(1.51, 16.83), xytext=(1.51, 17.33), arrowprops=dict(arrowstyle="->")) #ax[1, 0].set_xlabel('stimulus frequency [EODf]') if (dd == 2) and xlabel == True: ax[set+e+str(1)].set_xlabel('stimulus frequency [EODf]') ax[0].set_ylabel('[Hz]') ax[set + e + str(1)].set_ylabel('Modulation ') #ax[1, 2].set_xlabel('stimulus frequency [EODf]') #ax[2, 3].set_xlabel('stimulus frequency [EODf]') ax[0].spines['right'].set_visible(False) ax[0].spines['top'].set_visible(False) ax[set+e+str(1)].spines['right'].set_visible(False) ax[set+e+str(1)].spines['top'].set_visible(False) #ax[2, ee].spines['right'].set_visible(False) #ax[2, ee].spines['top'].set_visible(False) ax[0].set_xlim([0, 5]) ax[set+e+str(1)].set_xlim([0, 5]) #plt.tight_layout() # fig.label_axes() ax[set+e+str(1)].set_ylim([0, lim]) #ax[0].set_ylim([0, 240]) ax[0] = remove_tick_marks(ax[0]) if set != data[-1]: ax[set+e+str(1)] = remove_tick_marks(ax[set+e+str(1)]) if yticks == True: remove_tick_ymarks(ax[set+e+str(1)]) remove_tick_ymarks(ax[0]) #ax[0].set_ylim([0, lim]) return y_sum, moduls if __name__ == "__main__": data = ['2019-10-21-aa-invivo-1','2019-11-18-af-invivo-1','2019-10-28-aj-invivo-1'] data = ['2019-10-21-aa-invivo-1', '2019-10-21-au-invivo-1', '2019-10-28-aj-invivo-1'] data = ['2019-09-23-ad-invivo-1', '2019-10-21-au-invivo-1', '2019-10-28-aj-invivo-1'] data = ['2019-10-21-aa-invivo-1','2019-11-18-ac-invivo-1', '2019-10-28-aj-invivo-1']#'2019-11-18-ag-invivo-1''2019-11-18-ab-invivo-1' data = ['2019-11-18-ac-invivo-1','2019-11-08-aa-invivo-1', '2019-10-28-aj-invivo-1'] default_settings(data,intermediate_width = 6.28,intermediate_length = 6) #fig, ax = plt.subplots(nrows=2, ncols=3, sharex=True) ax = {} plot_single_cells(ax, data = data) #fig.savefig() plt.savefig('differentcells_trans.pdf') plt.savefig('../highbeats_pdf/differentcells_trans.pdf') # plt.subplots_adjust(left = 0.25) plt.show() #plt.close()