214 lines
8.4 KiB
Python
214 lines
8.4 KiB
Python
import nixio as nix
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import os
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from IPython import embed
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#from utility import *
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import matplotlib.mlab as ml
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import scipy.integrate as si
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from scipy.ndimage import gaussian_filter
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from IPython import embed
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from myfunctions import *
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from myfunctions import auto_rows
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from myfunctions import remove_tick_ymarks
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import string
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def plot_amp(ax, mean1, dev,nrs = [3,4,5],nr_size = 12,name = 'amp',nr = 1, wide = 'gainsboro', middle = 'darkgrey', narrow = 'black'):
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np.unique(mean1['type'])
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all_means = mean1[mean1['type'] == name +' mean']
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original = all_means[all_means['dev'] == 'original']
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#m005 = all_means[all_means['dev'] == '005']
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m05 = all_means[all_means['dev'] == '05']
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m2 = all_means[all_means['dev'] == '2']
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# fig, ax = plt.subplots(nrows=4, ncols = 3, sharex=True)
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versions = [original, m05, m2] #m005,
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lim = [[]]*len(versions)
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for i in range(len(versions)):
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keys = [k for k in versions[i]][2::]
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try:
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data = np.array(versions[i][keys])[0]
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except:
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break
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axis = np.arange(0, len(data), 1)
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axis_new = axis * 1
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similarity = [keys, data]
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sim = np.argsort(similarity[0])
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# similarity[sim]
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all_means = mean1[mean1['type'] == name+' std']
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std = all_means[all_means['dev'] == dev[i]]
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std = np.array(std[keys])[0]
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#ax[1, 1].set_ylabel('Modulation depth')
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#ax[nr,i].set_title(dev[i] + ' ms')
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all_means = mean1[mean1['type'] == name+' 95']
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std95 = all_means[all_means['dev'] == dev[i]]
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std95 = np.array(std95[keys])[0]
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all_means = mean1[mean1['type'] == name+' 05']
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std05 = all_means[all_means['dev'] == dev[i]]
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std05 = np.array(std05[keys])[0]
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ax[nr, i].text(-0.1, 1.1, string.ascii_uppercase[nrs[i]], transform=ax[nr,i].transAxes,
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size=nr_size, weight='bold')
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ax[nr,i].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]),
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color=wide)
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ax[nr,i].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]),
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color=middle)
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if i != 0:
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ax[nr, i] = remove_tick_ymarks(ax[nr,i])
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# ax[i].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--', label='AMf')
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ax[nr,i].plot(np.array(keys)[sim], data[sim], color=narrow)
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lim[i] = np.nanmax(std95[sim])
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# ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %')
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for i in range(len(versions)):
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ax[nr, i].set_ylim(0,np.nanmax(lim))
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#embed()
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return ax
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def create_beat_corr(hz_range, eod_fr):
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beat_corr = hz_range%eod_fr
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beat_corr[beat_corr>eod_fr/2] = eod_fr[beat_corr>eod_fr/2] - beat_corr[beat_corr>eod_fr/2]
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return beat_corr
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def plot_mean_cells():
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mean1 = pd.read_pickle('mean.pkl')
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colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B']
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inch_factor = 2.54
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whole_page_width = 6.28
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intermediate_length = 3.7
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plt.rcParams['figure.figsize'] = (whole_page_width, intermediate_length)
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plt.rcParams['font.size'] = 11
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plt.rcParams['axes.titlesize'] = 12
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plt.rcParams['axes.labelsize'] = 12
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plt.rcParams['lines.linewidth'] = 1.5
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plt.rcParams['lines.markersize'] = 8
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plt.rcParams['legend.loc'] = 'upper right'
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plt.rcParams["legend.frameon"] = False
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# load data for plot
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# data1 = pd.read_csv('ma_allcells_unsmoothed.csv')
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# data2 = pd.read_csv('ma_allcells_05.csv')
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# data3 = pd.read_csv('ma_allcells_2.csv')
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# data_tob = pd.read_csv('ma_toblerone.csv')
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# smothed = df_beat[df_beat['dev'] == 'original']
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# data1 = smothed[smothed['type'] == 'amp']
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x = np.arange(0, 2550, 50)
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corr = create_beat_corr(x, np.array([500] * len(x)))
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np.unique(mean1['type'])
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all_means = mean1[mean1['type'] == 'max mean']
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#versions = [[]]*len(dev)
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#for i in range(len(dev)):
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original = all_means[all_means['dev'] == 'original']
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m005 = all_means[all_means['dev'] == '005']
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m05 = all_means[all_means['dev'] == '05']
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m2 = all_means[all_means['dev'] == '2']
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dev = ['original', '05', '2']#'005',
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titels = ['binary','0.5 ms','2 ms']
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fig, ax = plt.subplots(nrows=2, ncols=3, sharex=True)
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versions = [original, m05, m2]#m005,
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nrs = [0,1,2]
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nr_size = 12
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for i in range(len(versions)):
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keys = [k for k in versions[i]][2::]
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try:
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data = np.array(versions[i][keys])[0]
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except:
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#embed()
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break
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axis = np.arange(0, len(data), 1)
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axis_new = axis * 1
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similarity = [keys, data]
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sim = np.argsort(similarity[0])
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# similarity[sim]
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all_means = mean1[mean1['type'] == 'max std']
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std = all_means[all_means['dev'] == dev[i]]
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std = np.array(std[keys])[0]
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#ax[0,i].set_title(dev[i] +' ms')
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ax[0, i].set_title(titels[i])
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ax[ 0,0].set_ylabel('MPF [EODf]')
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all_means = mean1[mean1['type'] == 'max 95']
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std95 = all_means[all_means['dev'] == dev[i]]
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std95 = np.array(std95[keys])[0]
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all_means = mean1[mean1['type'] == 'max 05']
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std05 = all_means[all_means['dev'] == dev[i]]
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std05 = np.array(std05[keys])[0]
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#std[np.where(list(data[sim] + std[sim])>std95[sim])] = std95[sim][np.where(list(data[sim] + std[sim])>std95[sim])]
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#embed()
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middle = 'pink'#'LightSalmon'
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wide='lightpink'#mistyrose'
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narrow = 'crimson'
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ax[0,i].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]),
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color=wide, alpha = 0.45)#
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#embed()
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ax[0,i].fill_between(np.array(keys)[sim], list(data[sim] + std[sim]), list(data[sim] - std[sim]), color=middle)
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ax[0,i].plot(x / 500, corr / 500 + 1, color='grey', linestyle='--', label='AMf')
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ax[0,i].plot(np.array(keys)[sim], data[sim], color=narrow)
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# plt.fill_between(np.array([0,1]),np.array([0,0]),np.array([1,1]))
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ax[0, i].text(-0.1, 1.1, string.ascii_uppercase[nrs[i]], transform=ax[0,i].transAxes,
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size=nr_size, weight='bold')
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if i != 0:
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ax[0, i] = remove_tick_ymarks(ax[0, i])
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# ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %')
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ax[0, 2].legend(bbox_to_anchor=(0.7, 1, 0.7, .1), loc='lower left',
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ncol=3, mode="expand", borderaxespad=0.)
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#ax = plot_amp(ax, mean1, dev,name = 'amp',nr = 2)
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ax[ 1,0].legend(bbox_to_anchor=(0.3, 1, 0.7, .1), loc='lower left',
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ncol=3, mode="expand", borderaxespad=0.)
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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')
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# ax[1].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--')
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# ax[1].plot(data2.x, transfer_array, color=colors[0])
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# ax[1].plot(data2.x, data2.freq20, color=colors[1])
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# ax[2].plot(data_tob.ff, data_tob.fe, color='grey', linestyle='--')
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# ax[2].plot(data3.x, transfer_array, color=colors[0])
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# ax[2].plot(data3.x, color=colors[1])
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# ax[2].plot(data_tob.ff, transfer_array, color='grey', linestyle='--')
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# ax[2].plot(data4.x, transfer_array, color=colors[0])
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# ax[2].plot(data3.x, color=colors[1])
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ax[ 0,0].set_ylabel('MPF [EODf]')
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#ax[1, 0].set_ylabel('Modulation depth')
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ax[1, 0].set_ylabel('Modulation Peak')
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#ax[2, 0].set_ylabel('Whole modulation ')
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plt.subplots_adjust(hspace = 0.35)
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for i in range(2):
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ax[i,0].spines['right'].set_visible(False)
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ax[i,0].spines['top'].set_visible(False)
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ax[i,1].spines['right'].set_visible(False)
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ax[i,1].spines['top'].set_visible(False)
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ax[i,2].spines['right'].set_visible(False)
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ax[i,2].spines['top'].set_visible(False)
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#ax[i,3].spines['right'].set_visible(False)
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#ax[i,3].spines['top'].set_visible(False)
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#for i in range(3):
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ax[1, 1].set_xlabel('stimulus frequency [EODf]')
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#ax[2, i].set_ylim([0, 370])
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#ax[2,i].set_ylim([0, 4700])
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#ax[2, 1].set_xlabel('stimulus frequency [EODf]')
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plt.subplots_adjust(bottom = 0.13)
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#fig.tight_layout()f
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# fig.label_axes()
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return fig
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if __name__ == "__main__":
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fig = plot_mean_cells()
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#fig.savefig()
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plt.savefig('MPFmodulation.pdf')
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plt.savefig('../highbeats_pdf/MPFmodulation.pdf')
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plt.show()
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# plt.close()
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