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 myfunctions import remove_tick_ymarks import string def plot_amp(ax, mean1, dev,nrs = [3,4,5],nr_size = 12,name = 'amp',nr = 1, wide = 'gainsboro', middle = 'darkgrey', narrow = 'black'): 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, lim = [[]]*len(versions) 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].text(-0.1, 1.1, string.ascii_uppercase[nrs[i]], transform=ax[nr,i].transAxes, size=nr_size, weight='bold') ax[nr,i].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]), color=wide) ax[nr,i].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]), color=middle) if i != 0: ax[nr, i] = remove_tick_ymarks(ax[nr,i]) # 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=narrow) lim[i] = np.nanmax(std95[sim]) # ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %') for i in range(len(versions)): ax[nr, i].set_ylim(0,np.nanmax(lim)) #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(): mean1 = pd.read_pickle('mean.pkl') colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B'] inch_factor = 2.54 whole_page_width = 6.28 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)): 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'] dev = ['original', '05', '2']#'005', titels = ['binary','0.5 ms','2 ms'] fig, ax = plt.subplots(nrows=2, ncols=3, sharex=True) versions = [original, m05, m2]#m005, nrs = [0,1,2] nr_size = 12 for i in range(len(versions)): keys = [k for k in versions[i]][2::] try: data = np.array(versions[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, i].set_title(titels[i]) ax[ 0,0].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() middle = 'pink'#'LightSalmon' wide='lightpink'#mistyrose' narrow = 'crimson' ax[0,i].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]), color=wide, alpha = 0.45)# #embed() ax[0,i].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]), color=middle) ax[0,i].plot(x / 500, corr / 500 + 1, color='grey', linestyle='--', label='AMf') ax[0,i].plot(np.array(keys)[sim], data[sim], color=narrow) # plt.fill_between(np.array([0,1]),np.array([0,0]),np.array([1,1])) ax[0, i].text(-0.1, 1.1, string.ascii_uppercase[nrs[i]], transform=ax[0,i].transAxes, size=nr_size, weight='bold') if i != 0: ax[0, i] = remove_tick_ymarks(ax[0, i]) # ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %') ax[0, 2].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[ 1,0].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,nrs = [3,4,5],nr_size = nr_size,name='amp max', nr=1,wide = 'gainsboro', middle = 'lightblue', narrow = 'steelblue') # 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[ 0,0].set_ylabel('MPF [EODf]') #ax[1, 0].set_ylabel('Modulation depth') ax[1, 0].set_ylabel('Modulation Peak') #ax[2, 0].set_ylabel('Whole modulation ') plt.subplots_adjust(hspace = 0.35) for i in range(2): ax[i,0].spines['right'].set_visible(False) ax[i,0].spines['top'].set_visible(False) ax[i,1].spines['right'].set_visible(False) ax[i,1].spines['top'].set_visible(False) ax[i,2].spines['right'].set_visible(False) ax[i,2].spines['top'].set_visible(False) #ax[i,3].spines['right'].set_visible(False) #ax[i,3].spines['top'].set_visible(False) #for i in range(3): ax[1, 1].set_xlabel('stimulus frequency [EODf]') #ax[2, i].set_ylim([0, 370]) #ax[2,i].set_ylim([0, 4700]) #ax[2, 1].set_xlabel('stimulus frequency [EODf]') plt.subplots_adjust(bottom = 0.13) #fig.tight_layout()f # fig.label_axes() return fig if __name__ == "__main__": fig = plot_mean_cells() #fig.savefig() plt.savefig('MPFmodulation.pdf') plt.savefig('../highbeats_pdf/MPFmodulation.pdf') plt.show() # plt.close()