507 lines
20 KiB
Python
507 lines
20 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 functionssimulation import default_settings
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import matplotlib.gridspec as gridspec
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def ps_df(data, d = '2019-09-23-ad-invivo-1', wish_df = 310, window = 'no',sampling_rate = 40000):
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#nfft = 4096
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#trial_cut = 0.1
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#freq_step = sampling_rate / nfft
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data_cell = data[data['dataset'] == d]#
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dfs = np.unique(data_cell['df'])
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df_here = dfs[np.argmin(np.abs(dfs - wish_df))]
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dfs310 = data_cell[data_cell['df'] == df_here]
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#pp = [[]]*len(dfs310)
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pp = []
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ppp = []
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trial_cut = 0.1
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for i in range(len(dfs310)):
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duration = dfs310.iloc[i]['durations']
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#cut_vec = np.arange(0, duration, trial_cut)
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cut_vec = np.arange(0, duration, trial_cut)
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#spikes_cut = spikes[(spikes > 0.05) & (spikes < 0.95)]
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#for j, cut in enumerate(cut_vec):
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# # print(j)
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# spike_times = dfs310.iloc[i]['spike_times']
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# spikes = spike_times - spike_times[0]
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# spikes_cut = spikes[(spikes > cut) & (spikes < cut_vec[j + 1])]
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# if cut == cut_vec[-2]:
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# #counter_cut += 1
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# break
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# if len(spikes_cut) < 10:
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# #counter_spikes += 1
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# break
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# spikes_mat = np.zeros(int(trial_cut * sampling_rate) + 1)
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# spikes_idx = np.round((spikes_cut - trial_cut * j) * sampling_rate)
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# for spike in spikes_idx:
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# spikes_mat[int(spike)] = 1#
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#
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# #spikes_mat = np.zeros(int(spikes[-1]* sampling_rate + 5))
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# #spikes_idx = np.round((spikes) * sampling_rate)
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# #for spike in spikes_idx:
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# # spikes_mat[int(spike)] = 1
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# spikes_mat = spikes_mat * sampling_rate
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# if type(window) != str:
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# spikes_mat = gaussian_filter(spikes_mat, sigma=window)
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# # smoothened_spikes_mat05 = gaussian_filter(spikes_mat, sigma=window05) * sampling_rate
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# # smoothened_spikes_mat2 = gaussian_filter(spikes_mat, sigma=window2) * sampling_rate
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# else:
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# smoothened = spikes_mat * 1
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# nfft = 4096
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# p, f = ml.psd(spikes_mat - np.mean(spikes_mat), Fs=sampling_rate, NFFT=nfft, noverlap=nfft / 2)
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# pp.append(p)
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spike_times = dfs310.iloc[i]['spike_times']
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if len(spike_times) < 3:
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counter_spikes += 1
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break
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spikes = spike_times - spike_times[0]
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spikes_cut = spikes[(spikes > 0.05) & (spikes < 0.95)]
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if len(spikes_cut) < 3:
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counter_cut += 1
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break
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spikes_mat = np.zeros(int(spikes[-1] * sampling_rate + 5))
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spikes_idx = np.round((spikes) * sampling_rate)
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for spike in spikes_idx:
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spikes_mat[int(spike)] = 1
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spikes_mat = spikes_mat * sampling_rate
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if type(window) != str:
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spikes_mat = gaussian_filter(spikes_mat, sigma=window)
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# smoothened_spikes_mat05 = gaussian_filter(spikes_mat, sigma=window05) * sampling_rate
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# smoothened_spikes_mat2 = gaussian_filter(spikes_mat, sigma=window2) * sampling_rate
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else:
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spikes_mat = spikes_mat*1
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nfft = 4096
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p, f = ml.psd(spikes_mat - np.mean(spikes_mat), Fs=sampling_rate, NFFT=nfft, noverlap=nfft / 2)
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ppp.append(p)
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#spike_times = data_cell.iloc[i]['spike_times']#
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#if len(spike_times) < 3:
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# counter_spikes += 1
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# break
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#spikes = spike_times - spike_times[0]
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# cut trial into snippets of 100 ms
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#cut_vec = np.arange(0, duration, trial_cut)
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#spikes_cut = spikes[(spikes > 0.05) & (spikes < 0.95)]
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#if len(spikes_cut) < 3:
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# counter_cut += 1
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# break
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#spikes_new = spikes_cut - spikes_cut[0]
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#spikes_mat = np.zeros(int(spikes_new[-1] * sampling_rate) + 2)
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# spikes_mat = np.zeros(int(trial_cut * sampling_rate) + 1)
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#spikes_idx = np.round((spikes_new) * sampling_rate)
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#for spike in spikes_idx:
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# spikes_mat[int(spike)] = 1
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#spikes_mat = spikes_mat * sampling_rate
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#nfft = 4096
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#p, f = ml.psd(smoothened - np.mean(smoothened), Fs=sampling_rate, NFFT=nfft, noverlap=nfft / 2)
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#ppp.append(p)
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#p_mean = np.mean(pp,axis = 0)
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p_mean2 = np.mean(ppp, axis=0)
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#ref = (np.max(p_mean2))
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#
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db = 10 * np.log10(p_mean2 / np.max(p_mean2))
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#ref = (np.max(p_mean2))
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#db2 = 10 * np.log10(p_mean2 / ref)
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#embed()
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return df_here,p_mean2,f,db
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def plot_example_ps(grid,input = ['2019-10-21-aa-invivo-1'],sigma = [0.00005,0.00025,0.0005, 0.002]):
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sampling_rate = 40000
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colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B']
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plt.rcParams['lines.linewidth'] = 1.5
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plt.rcParams['lines.markersize'] = 6
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#data = pd.read_pickle('data_beat.pkl')
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#iter = np.unique(data['dataset'])
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iter = ['2019-05-07-by-invivo-1']
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iter = ['2019-09-23-ad-invivo-1']
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iter = input
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for cell in iter:
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data = pd.read_pickle('data_beat.pkl')
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beat_results = pd.read_pickle('beat_results_smoothed.pkl')
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#embed()
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eodf = int(beat_results[beat_results['dataset'] == cell]['eodf'].iloc[0])
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df = [[]] * (len(sigma) + 1)
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p = [[]] * (len(sigma) + 1)
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f = [[]] * (len(sigma) + 1)
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db = [[]] * (len(sigma) + 1)
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sigmaf = [[]] * (len(sigma) + 1)
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gauss = [[]] * (len(sigma) + 1)
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wish_df = 150
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df[0], p[0], f[0], db[0] = ps_df(data, d=cell, wish_df= wish_df, window='no', sampling_rate=sampling_rate)
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for i in range(len(sigma)):
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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)
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sigmaf[i + 1] = 1 / (2 * np.pi * sigma[i])
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gauss[i + 1] = np.exp(-(f[1+i] ** 2 / (2 * sigmaf[i + 1] ** 2)))
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db = 'no'
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stepsize = f[0][1] - f[0][0]
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if db == 'db':
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p = db
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# fig.suptitle(d, labelpad = 25)
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#print(d)
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ax = {}
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ax[0] = plt.subplot(grid[0])
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ax[0].plot(f[0], p[0], color=colors[0])
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ax[0].plot(eodf - 1, np.max(p[0][int(eodf / stepsize) - 5:int(eodf / stepsize) + 5]) * 0.95, color='k',
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marker='o', linestyle='None', label='EODf') # = '+str(int(eodf))+' Hz')
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#embed()
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ax[0].fill_between(f[1], [max(p[0])]*len(f[1]), facecolor='lightgrey', edgecolor='grey')
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ax[0].plot(df[0], np.max(p[0][int(abs(df[0]) / stepsize) - 5:int(abs(df[0]) / stepsize) + 5]) * 0.95,
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color=colors[1], marker='o', linestyle='None', label='Df')
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ax[0].plot(df[0] + eodf, p[0][int((df[0] + eodf) / stepsize) + 1], color=colors[2], marker='o', linestyle='None',
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label='stimulus')
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ax[0].legend(bbox_to_anchor=(0.4, 1, 0.6, .1), loc='lower left',
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ncol=3, mode="expand", borderaxespad=0.)
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ax[0].set_xlim([0, 2000])
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wide = 2
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for i in range(len(sigma)):
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ax[i+1] = plt.subplot(grid[i+1])
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plot_filter(ax, i+1, f[1+i], p, colors, gauss[1+i], eodf, stepsize, wide, df[1+i])
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ax[i+1].set_xlim([0, 2000])
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#embed()
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#if db == 'db':
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# ax[0].set_ylim([np.min([p]),0])#p[0][,p[1][0:2000],p[2][0:2000],p[3][0:2000]
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#else:
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# ax[0].set_ylim([ 0,np.max([p])])
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ax[int(len(df))-1].set_xlabel('frequency [Hz]')
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# ax[1].set_ylabel(r'power [Hz$^2$/Hz]')
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ax[0].ticklabel_format(axis='y', style='sci', scilimits=[0, 0])
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#print(df[3])
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for i in range(len(df)):
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ax[i].spines['right'].set_visible(False)
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ax[i].spines['top'].set_visible(False)
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cols = grid.ncols
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rows = grid.nrows
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ax[int(cols*(rows/2))-1].set_ylabel(' power spectral density [Hz²/Hz]')
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#ax[2].set_ylabel('Hz²/Hz')
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#ax[3].set_ylabel('Hz²/Hz')
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#ax[0].set_ylabel('Hz²/Hz')
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for i in range(len(df)):
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ax[i].axvline(x = eodf/2, color = 'black', linestyle = 'dashed')
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plt.tight_layout()
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#fig.label_axes()
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def plot_filter(ax, ax_nr, f, p4, colors, gauss3, eodf, stepsize, wide, df):
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ax[ax_nr].plot(f, p4[ax_nr], color=colors[0])
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ax[ax_nr].fill_between(f, max(p4[0]) * gauss3 ** 2, facecolor='lightgrey', edgecolor='grey')
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ax[ax_nr].plot(eodf, np.max(p4[ax_nr][int(eodf / stepsize) - wide:int(eodf / stepsize) + wide]) * 0.95, color='k', marker='o',
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linestyle='None')
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ax[ax_nr].plot(abs(df), np.max(p4[ax_nr][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) * 0.95,
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color=colors[1], marker='o', linestyle='None')
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ax[ax_nr].plot(df + eodf,
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np.max(p4[ax_nr][int((df + eodf) / stepsize) - wide:int((df + eodf) / stepsize) + wide]) * 0.95,
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color=colors[2], marker='o', linestyle='None')
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return ax
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def plot_amp(ax, mean1, dev,name = 'amp',nr = 1):
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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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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].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]),
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color='gainsboro')
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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='darkgrey')
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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='black')
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# ax[0].plot(data1.x, data1.freq20, color=colors[1], label='20 %')
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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(grid, sigma = ['original','05','2'],lw = 0.7):
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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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half_page_width = 7.9 / inch_factor
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intermediate_width = 12 / inch_factor
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whole_page_width = 16 * 2 / inch_factor
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small_length = 6 / inch_factor
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intermediate_length = 12 * 1.5 / inch_factor
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max_length = 25 / inch_factor
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whole_page_width = 6.7
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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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version =[[]]*len(sigma)
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version2 = [[]] * len(sigma)
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dev = [[]] * len(sigma)
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all_means2 = mean1[mean1['type'] == 'amp max' + ' mean']
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for i in range(len(sigma)):
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try:
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version[i] = all_means[all_means['dev'] == sigma[i]]
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version2[i] = all_means2[all_means2['dev'] == sigma[i]]
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except:
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version[i] = []
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print(sigma[i])
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dev[i] = sigma[i]
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#if sigma[i] == 'original':
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# titles[i] = 'binary'
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#else:
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# titles[i] = str(np.float(sigma[i])/1000
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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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#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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#dev = ['original', '05', '2']#'005',
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titels = ['binary','0.005 ms','0.25 ms','0.5 ms','2 ms']
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#fig, ax = plt.subplots(nrows=2, ncols=len(sigma), sharex=True)
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#plt.subplot(axis)
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#grid = gridspec.GridSpec(2, 3, wspace=0.0, height_ratios=[6, 2], width_ratios=[1,0.5,3], hspace=0.2)
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#versions = [original, m05, m2]#m005,
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ax = {}
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for i in range(len(version)):
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if len(version[i])>0:
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print(i)
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plots = gridspec.GridSpecFromSubplotSpec(2, 1,
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subplot_spec=grid[i], wspace=0, hspace=0.5)
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keys = [k for k in version[i]][2::]
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try:
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data = np.array(version[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] = plt.subplot(plots[0])
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ax[0].set_title(titels[i])
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ax[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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ax[0].fill_between(np.array(keys)[sim], list(std95[sim]), list(std05[sim]),
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color='mistyrose')
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#embed()
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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)
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|
axis_new = axis * 1
|
|
similarity = [keys, data]
|
|
sim = np.argsort(similarity[0])
|
|
# similarity[sim]
|
|
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]
|
|
# ax[1, 1].set_ylabel('Modulation depth')
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|
# 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 |