722 lines
31 KiB
Python
722 lines
31 KiB
Python
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
|
|
from myfunctions import remove_tick_marks
|
|
|
|
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)
|
|
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_trans(grid,colors = ['brown'],line_col = 'black',input = ['2019-10-21-aa-invivo-1'],sigma = [0.00005,0.00025,0.0005, 0.002],wish_df = 150, color_eod = 'orange',color_stim = 'red', color_df = 'green'):
|
|
sampling_rate = 40000
|
|
plt.rcParams['lines.linewidth'] = 1.5
|
|
plt.rcParams['lines.markersize'] = 6
|
|
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)
|
|
|
|
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
|
|
ax = {}
|
|
fc = 'lightgrey'
|
|
ec = 'grey'
|
|
#fc = 'moccasin'
|
|
#ec = 'wheat'
|
|
scale = 1
|
|
ax = plot_whole_ps_trans(f, ax, grid, colors, eodf, stepsize, p, df, scale = scale, ax_nr = 0,nr=0, filter='whole' ,color_eod = color_eod,color_stim = color_stim , color_df = color_df,fc = fc, ec = ec)
|
|
ax[0].legend( loc=(0,1),
|
|
ncol=3, mode="expand", borderaxespad=0.)#bbox_to_anchor=(0.4, 1, 0.6, .1),
|
|
ax[0].set_xlim([0, 1000])
|
|
|
|
ax[0] = remove_tick_marks(ax[0])
|
|
ax = plot_whole_ps_trans(f,ax,grid, colors, eodf, stepsize, p, df,scale = scale, ax_nr = 1,nr = 0, filter = 'original',color_eod = color_eod,color_stim = color_stim , color_df = color_df,fc = fc, ec = ec)
|
|
ax[1] = remove_tick_marks(ax[1])
|
|
ax[1].set_xlim([0, 1000])
|
|
|
|
wide = 2
|
|
#embed()
|
|
for i in range(len(sigma)):
|
|
ax[i+2] = plt.subplot(grid[i+2])
|
|
plot_filter_trans(ax, i+2, f[1+i], p,i+1, colors, gauss[1+i], eodf, stepsize, wide, df[1+i],scale = scale,color_eod = color_eod,color_stim = color_stim , color_df = color_df,fc = fc, ec = ec)
|
|
#ax[i+2].set_ylim([0, eodf*1.5])
|
|
ax[i + 2].set_xlim([0, 1000])
|
|
ax[2] = remove_tick_marks(ax[2])
|
|
#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))].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)+1):
|
|
ax[i].spines['right'].set_visible(False)
|
|
ax[i].spines['top'].set_visible(False)
|
|
cols = grid.ncols
|
|
rows = grid.nrows
|
|
ax[int(len(df))].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(1,len(df)+1):
|
|
ax[i].axvline(x = eodf/2, color = line_col, linestyle = 'dashed')
|
|
plt.tight_layout()
|
|
#embed()
|
|
#fig.label_axes()
|
|
|
|
def plot_example_ps(grid,colors = ['brown'],line_col = 'black',input = ['2019-10-21-aa-invivo-1'],sigma = [0.00005,0.00025,0.0005, 0.002],wish_df = 150, color_eod = 'orange',color_stim = 'red', color_df = 'green'):
|
|
sampling_rate = 40000
|
|
#colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B']
|
|
|
|
|
|
plt.rcParams['lines.linewidth'] = 1.5
|
|
plt.rcParams['lines.markersize'] = 6
|
|
#data = pd.read_pickle('../pictures_highbeats/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)
|
|
|
|
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 = {}
|
|
fc = 'lightgrey'
|
|
ec = 'grey'
|
|
#fc = 'moccasin'
|
|
#ec = 'wheat'
|
|
scale = 1
|
|
ax = plot_whole_ps(f, ax, grid, colors, eodf, stepsize, p, df, scale = scale, ax_nr = 0,nr=0, filter='whole' ,color_eod = color_eod,color_stim = color_stim , color_df = color_df,fc = fc, ec = ec)
|
|
ax[0].legend( loc=(0,1),
|
|
ncol=3, mode="expand", borderaxespad=0.)#bbox_to_anchor=(0.4, 1, 0.6, .1),
|
|
|
|
ax[0] = remove_tick_marks(ax[0])
|
|
ax = plot_whole_ps(f,ax,grid, colors, eodf, stepsize, p, df,scale = scale, ax_nr = 1,nr = 0, filter = 'original',color_eod = color_eod,color_stim = color_stim , color_df = color_df,fc = fc, ec = ec)
|
|
ax[1] = remove_tick_marks(ax[1])
|
|
#ax[0].set_ylim([0, 2000])
|
|
|
|
wide = 2
|
|
#embed()
|
|
for i in range(len(sigma)):
|
|
ax[i+2] = plt.subplot(grid[i+2])
|
|
plot_filter(ax, i+2, f[1+i], p,i+1, colors, gauss[1+i], eodf, stepsize, wide, df[1+i],scale = scale,color_eod = color_eod,color_stim = color_stim , color_df = color_df,fc = fc, ec = ec)
|
|
ax[i+2].set_ylim([0, eodf*1.5])
|
|
ax[2] = remove_tick_marks(ax[2])
|
|
#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_ylabel('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)+1):
|
|
ax[i].spines['right'].set_visible(False)
|
|
ax[i].spines['top'].set_visible(False)
|
|
cols = grid.ncols
|
|
rows = grid.nrows
|
|
ax[int(len(df))].set_xlabel(' 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(1,len(df)+1):
|
|
ax[i].axhline(y = eodf/2, color = line_col, linestyle = 'dashed')
|
|
plt.tight_layout()
|
|
#embed()
|
|
#fig.label_axes()
|
|
|
|
def plot_whole_ps_trans(f,ax,grid, colors, eodf, stepsize, p, df, ax_nr = 0,nr = 0, filter = 'original', scale = 1, color_eod = 'orange',color_stim = 'red', color_df = 'green',fc = 'lightgrey', ec = 'grey',):
|
|
ax[ax_nr] = plt.subplot(grid[ax_nr])
|
|
|
|
if filter == 'whole':
|
|
#ax[nr].set_facecolor('lightgrey')
|
|
ax[ax_nr].plot( f[nr],p[nr], color=colors[0])
|
|
ax[ax_nr].fill_between( [f[0][-1],f[0][-1]],[np.min(p), np.max(p)], color=fc,edgecolor=ec)
|
|
ax[ax_nr].plot(df[0],np.max(p[nr][int(abs(df[nr]) / stepsize) - 5:int(abs(df[nr]) / stepsize) + 5]) * scale,
|
|
color=color_df, marker='o', linestyle='None', label='Df')
|
|
ax[ax_nr].plot(df[nr] + eodf,p[nr][int((df[nr] + eodf) / stepsize) + 1], color=color_stim, marker='o',
|
|
linestyle='None',
|
|
label='stimulus')
|
|
ax[ax_nr].plot(eodf - 1,np.max(p[nr][int(eodf / stepsize) - 5:int(eodf / stepsize) + 5]) * scale, color=color_eod,
|
|
marker='o', linestyle='None', label='EODf') # = '+str(int(eodf))+' Hz')
|
|
|
|
elif filter == 'original':
|
|
#ax[nr].fill_between([eodf] * len(p[nr]), p[nr], color='lightgrey')
|
|
#ax[nr].fill_between([max(p[0])]*len(f[nr]),f[nr], color = 'lightgrey')
|
|
ax[ax_nr].plot(f[nr][f[nr]<eodf/2],p[nr][f[nr]<eodf/2], color=colors[0])
|
|
ax[ax_nr].plot(f[nr][f[nr] > eodf / 2],np.zeros(len(f[nr][f[nr] > eodf / 2])), color=colors[0])
|
|
#embed()
|
|
ax[ax_nr].fill_between( [eodf/2,eodf/2],[np.min(p),np.max(p)], color=fc,edgecolor=ec)
|
|
ax[ax_nr].plot( df[0],np.max(p[nr][int(abs(df[nr]) / stepsize) - 5:int(abs(df[nr]) / stepsize) + 5]) * scale,
|
|
color=color_df, marker='o',zorder = 2, linestyle='None', label='Df')#edgecolors = 'black'
|
|
ax[ax_nr].plot(df[nr] + eodf,0, color=color_stim, marker='o',
|
|
linestyle='None',
|
|
label='stimulus',zorder = 2)#,edgecolors = 'black'
|
|
ax[ax_nr].plot(eodf - 1,0, color=color_eod,
|
|
marker='o', linestyle='None', label='EODf',zorder = 2) #edgecolors = 'black', # = '+str(int(eodf))+' Hz')
|
|
|
|
#ax[ax_nr].set_ylim([0, eodf * 1.5])
|
|
#ax[ax_nr].set_xlim(ax[ax_nr].get_xlim()[::-1])
|
|
return ax
|
|
|
|
def plot_whole_ps(f,ax,grid, colors, eodf, stepsize, p, df, ax_nr = 0,nr = 0, filter = 'original', scale = 1, color_eod = 'orange',color_stim = 'red', color_df = 'green',fc = 'lightgrey', ec = 'grey',):
|
|
ax[ax_nr] = plt.subplot(grid[ax_nr])
|
|
|
|
if filter == 'whole':
|
|
#ax[nr].set_facecolor('lightgrey')
|
|
ax[ax_nr].plot(p[nr], f[nr], color=colors[0])
|
|
ax[ax_nr].fill_between([np.min(p), np.max(p)], [f[0][-1],f[0][-1]], color=fc,edgecolor=ec)
|
|
ax[ax_nr].plot(np.max(p[nr][int(abs(df[nr]) / stepsize) - 5:int(abs(df[nr]) / stepsize) + 5]) * scale, df[0],
|
|
color=color_df, marker='o', linestyle='None', label='Df')
|
|
ax[ax_nr].plot(p[nr][int((df[nr] + eodf) / stepsize) + 1], df[nr] + eodf, color=color_stim, marker='o',
|
|
linestyle='None',
|
|
label='stimulus')
|
|
ax[ax_nr].plot(np.max(p[nr][int(eodf / stepsize) - 5:int(eodf / stepsize) + 5]) * scale, eodf - 1, color=color_eod,
|
|
marker='o', linestyle='None', label='EODf') # = '+str(int(eodf))+' Hz')
|
|
|
|
elif filter == 'original':
|
|
#ax[nr].fill_between([eodf] * len(p[nr]), p[nr], color='lightgrey')
|
|
#ax[nr].fill_between([max(p[0])]*len(f[nr]),f[nr], color = 'lightgrey')
|
|
ax[ax_nr].plot(p[nr][f[nr]<eodf/2], f[nr][f[nr]<eodf/2], color=colors[0])
|
|
ax[ax_nr].plot(np.zeros(len(f[nr][f[nr] > eodf / 2])), f[nr][f[nr] > eodf / 2], color=colors[0])
|
|
#embed()
|
|
ax[ax_nr].fill_between([np.min(p),np.max(p)], [eodf/2,eodf/2], color=fc,edgecolor=ec)
|
|
ax[ax_nr].plot(np.max(p[nr][int(abs(df[nr]) / stepsize) - 5:int(abs(df[nr]) / stepsize) + 5]) * scale, df[0],
|
|
color=color_df, marker='o',zorder = 2, linestyle='None', label='Df')#edgecolors = 'black'
|
|
ax[ax_nr].plot(0, df[nr] + eodf, color=color_stim, marker='o',
|
|
linestyle='None',
|
|
label='stimulus',zorder = 2)#,edgecolors = 'black'
|
|
ax[ax_nr].plot(0, eodf - 1, color=color_eod,
|
|
marker='o', linestyle='None', label='EODf',zorder = 2) #edgecolors = 'black', # = '+str(int(eodf))+' Hz')
|
|
|
|
ax[ax_nr].set_ylim([0, eodf * 1.5])
|
|
ax[ax_nr].set_xlim(ax[ax_nr].get_xlim()[::-1])
|
|
return ax
|
|
|
|
|
|
def plot_filter_trans(ax, ax_nr, f, p4,array_nr, colors, gauss3, eodf, stepsize, wide, df, fc = 'lightgrey', scale = 1, ec = 'grey',color_eod = 'orange',color_stim = 'red', color_df = 'green'):
|
|
ax[ax_nr].plot(f, p4[array_nr],color=colors[0])
|
|
prev_height = np.max((p4[0][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) * scale)
|
|
now_height = np.max((p4[array_nr][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) *scale)
|
|
|
|
ax[ax_nr].plot([np.abs(df), np.abs(df)],[prev_height, now_height+440], color = 'black')
|
|
ax[ax_nr].scatter( np.abs(df), now_height+440, marker = 'v', color='black', zorder = 2)
|
|
#embed()
|
|
|
|
|
|
ax[ax_nr].fill_between(f, max(p4[0]) * gauss3 ** 2, facecolor=fc, edgecolor=ec)
|
|
ax[ax_nr].plot( eodf, np.max(p4[array_nr][int(eodf / stepsize) - wide:int(eodf / stepsize) + wide]) * scale,color=color_eod, marker='o',
|
|
linestyle='None')
|
|
ax[ax_nr].plot( abs(df),np.max(p4[array_nr][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) * scale,
|
|
color=color_df, marker='o', linestyle='None')
|
|
#ax[ax_nr].plot(df + eodf,
|
|
# np.max(df + eodf,p4[array_nr][int((df + eodf) / stepsize) - wide:int((df + eodf) / stepsize) + wide]) * scale,
|
|
# color=color_stim, marker='o', linestyle='None')
|
|
#ax[ax_nr].set_xlim(ax[ax_nr].get_xlim()[::-1])
|
|
return ax
|
|
|
|
def plot_filter(ax, ax_nr, f, p4,array_nr, colors, gauss3, eodf, stepsize, wide, df, fc = 'lightgrey', scale = 1, ec = 'grey',color_eod = 'orange',color_stim = 'red', color_df = 'green'):
|
|
ax[ax_nr].plot( p4[array_nr],f, color=colors[0])
|
|
prev_height = np.max((p4[0][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) * scale)
|
|
now_height = np.max((p4[array_nr][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) *scale)
|
|
|
|
#ax[ax_nr].plot([prev_height, now_height+440],[np.abs(df), np.abs(df)], color = 'black')
|
|
#ax[ax_nr].scatter( now_height+440, np.abs(df), marker = '>', color='black', zorder = 2)
|
|
#embed()
|
|
|
|
|
|
ax[ax_nr].fill_between(max(p4[0]) * gauss3 ** 2,f, facecolor=fc, edgecolor=ec)
|
|
ax[ax_nr].plot(np.max(p4[array_nr][int(eodf / stepsize) - wide:int(eodf / stepsize) + wide]) * scale, eodf, color=color_eod, marker='o',
|
|
linestyle='None')
|
|
ax[ax_nr].plot( np.max(p4[array_nr][int(abs(df) / stepsize) - wide:int(abs(df) / stepsize) + wide]) * scale,abs(df),
|
|
color=color_df, marker='o', linestyle='None')
|
|
ax[ax_nr].plot(
|
|
np.max(p4[array_nr][int((df + eodf) / stepsize) - wide:int((df + eodf) / stepsize) + wide]) * scale,df + eodf,
|
|
color=color_stim, marker='o', linestyle='None')
|
|
ax[ax_nr].set_xlim(ax[ax_nr].get_xlim()[::-1])
|
|
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_modul( grid,data = ['2019-10-21-aa-invivo-1'],line_col = 'black',lw = 0.5, sigma = ['original','05','2'],colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B'], wish_df = 150, color_eod = 'black',color_df = 'orange', size = 17, color_modul = ['steelblue']):
|
|
#mean1 = pd.read_pickle('mean.pkl')
|
|
data_all = pd.read_pickle('beat_results_smoothed.pkl')
|
|
d = data_all[data_all['dataset'] == data[0]]
|
|
|
|
#embed()
|
|
|
|
inch_factor = 2.54
|
|
|
|
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
|
|
|
|
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)
|
|
limits = [[]]*len(sigma)
|
|
minimum = [[]] * len(sigma)
|
|
y_max = [[]] * len(sigma)
|
|
y_min = [[]] * len(sigma)
|
|
ax ={}
|
|
|
|
for i, e in enumerate(sigma):
|
|
y2 = d['result_amplitude_max_' + e]
|
|
y_max[i] = np.max(y2)
|
|
y_min[i] = np.min(y2)
|
|
for i,e in enumerate(sigma):
|
|
dev[i] = sigma[i]
|
|
plots = gridspec.GridSpecFromSubplotSpec( 1,1,
|
|
subplot_spec=grid[i], wspace=0.4, hspace=0.5)
|
|
d = data_all[data_all['dataset'] == data[0]]
|
|
x = d['delta_f'] / d['eodf'] + 1
|
|
#embed()
|
|
data = ['2019-10-21-aa-invivo-1']
|
|
#end = ['original', '005', '05', '2']
|
|
y = d['result_frequency_' + e]
|
|
#embed()
|
|
y2 = d['result_amplitude_max_' + e]
|
|
#y_sum[i] = np.nanmax(y)
|
|
ff = d['delta_f'] / d['eodf'] + 1
|
|
fe = d['beat_corr']
|
|
ax[0] = plt.subplot(plots[0])
|
|
eod = d['eodf'].iloc[0]
|
|
|
|
if np.max(y)<d['eodf'].iloc[0]*0.6:
|
|
color_chosen = color_df
|
|
else:
|
|
color_chosen = color_eod
|
|
x_scatter = x.iloc[np.argmin(np.abs(np.array(x) - (wish_df / eod + 1)))]
|
|
|
|
ax[0] = plt.subplot(plots[0])
|
|
if e != sigma[-1]:
|
|
ax[0] = remove_tick_marks(ax[0])
|
|
ax[0].plot(x, y2, color=color_modul[0],zorder = 1, linewidth = lw)
|
|
height = y2.iloc[np.argmin(np.abs(np.array(x)-(wish_df/eod+1)))]
|
|
if e == 'original':
|
|
whole_height = height
|
|
whole_height = np.max(y2)
|
|
if (e != 'whole') and (e!= 'original'):
|
|
ax[0].scatter(x_scatter, height+70, zorder=2, marker = 'v', color='black', s=size)
|
|
#embed()
|
|
ax[0].plot([x_scatter,x_scatter], [whole_height,height +70], zorder=3,
|
|
color = 'black')
|
|
ax[0].scatter(x_scatter,height,zorder = 2, color = color_chosen, s =size)
|
|
#y_all[i] = np.max(y2)
|
|
if i == len(sigma)-1:
|
|
ax[0].set_xlabel('stimulus frequency [EODf]')
|
|
ax[0].set_ylim([np.min(y_min)*0.8, np.max(y_max)*1.2])
|
|
#ax[1].spines['top'].set_visible(False)
|
|
#ax[1].spines['right'].set_visible(False)
|
|
ax[0].spines['right'].set_visible(False)
|
|
ax[0].spines['top'].set_visible(False)
|
|
#ax[0].spines['left'].set_visible(False)
|
|
ax[0].spines['right'].set_visible(False)
|
|
ax[0].spines['top'].set_visible(False)
|
|
#ax[0].set_xlim([0, 5])
|
|
#embed()
|
|
#ax[0].set_ylim([0, d['eodf'].iloc[0]*1.5])
|
|
ax[0].set_yticks([])
|
|
# fig.tight_layout()
|
|
# fig.label_axes()
|
|
print(sigma[i])
|
|
#embed()
|
|
plt.subplots_adjust(bottom = 0.13)
|
|
|
|
|
|
def plot_mean_cells( grid,data = ['2019-10-21-aa-invivo-1'],line_col = 'black',lw = 0.5, sigma = ['original','05','2'],colors = ['#BA2D22', '#F47F17', '#AAB71B', '#3673A4', '#53379B'], wish_df = 150, color_eod = 'black',color_df = 'orange', size = 17, color_modul = ['steelblue']):
|
|
#mean1 = pd.read_pickle('mean.pkl')
|
|
data_all = pd.read_pickle('beat_results_smoothed.pkl')
|
|
d = data_all[data_all['dataset'] == data[0]]
|
|
|
|
#embed()
|
|
|
|
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)
|
|
limits = [[]]*len(sigma)
|
|
minimum = [[]] * len(sigma)
|
|
y_max = [[]] * len(sigma)
|
|
y_min = [[]] * len(sigma)
|
|
ax ={}
|
|
|
|
for i, e in enumerate(sigma):
|
|
y2 = d['result_amplitude_max_' + e]
|
|
y_max[i] = np.max(y2)
|
|
y_min[i] = np.min(y2)
|
|
for i,e in enumerate(sigma):
|
|
dev[i] = sigma[i]
|
|
plots = gridspec.GridSpecFromSubplotSpec( 1,1,
|
|
subplot_spec=grid[i], wspace=0.4, hspace=0.5)
|
|
d = data_all[data_all['dataset'] == data[0]]
|
|
x = d['delta_f'] / d['eodf'] + 1
|
|
#embed()
|
|
data = ['2019-10-21-aa-invivo-1']
|
|
#end = ['original', '005', '05', '2']
|
|
y = d['result_frequency_' + e]
|
|
#embed()
|
|
y2 = d['result_amplitude_max_' + e]
|
|
#y_sum[i] = np.nanmax(y)
|
|
ff = d['delta_f'] / d['eodf'] + 1
|
|
fe = d['beat_corr']
|
|
#fig.suptitle(set)
|
|
ax[0] = plt.subplot(plots[0])
|
|
if e != sigma[-1]:
|
|
ax[0] = remove_tick_marks(ax[0])
|
|
if e != 'whole':
|
|
ax[0].plot(ff, fe, color='grey', zorder = 1, linestyle='--', linewidth = lw)
|
|
ax[0].axhline(y=eod / 2, color=line_col, linestyle='dashed')
|
|
ax[0].plot(x, y, color=colors[0], zorder = 2,linewidth = lw)
|
|
#embed()
|
|
eod = d['eodf'].iloc[0]
|
|
|
|
if np.max(y)<d['eodf'].iloc[0]*0.6:
|
|
color_chosen = color_df
|
|
else:
|
|
color_chosen = color_eod
|
|
#embed()
|
|
x_scatter = x.iloc[np.argmin(np.abs(np.array(x) - (wish_df / eod + 1)))]
|
|
#ax[0].scatter(x_scatter, y.iloc[np.argmin(np.abs(np.array(x) - (wish_df/eod+1)))], zorder=3, color=color_chosen, s = size)
|
|
|
|
#ax[0].set_ylabel('MPF [EODf]')
|
|
#ax[0].set_ylabel('Modulation ')
|
|
#ax[0, dd].set_title(e + ' ms')
|
|
ax[0].set_xlim([0, 4])
|
|
|
|
#ax[1] = plt.subplot(plots[1])
|
|
#if e != sigma[-1]:
|
|
# ax[1] = remove_tick_marks(ax[1])
|
|
#ax[1].plot(x, y2, color=color_modul[0],zorder = 1, linewidth = lw)
|
|
#height = y2.iloc[np.argmin(np.abs(np.array(x)-(wish_df/eod+1)))]
|
|
#if e == 'original':
|
|
# whole_height = height
|
|
# whole_height = np.max(y2)
|
|
#if (e != 'whole') and (e!= 'original'):
|
|
#ax[1].scatter(x_scatter, height+70, zorder=2, marker = 'v', color='black', s=size)
|
|
#embed()
|
|
#ax[1].plot([x_scatter,x_scatter], [whole_height,height +70], zorder=3,
|
|
#color = 'black')
|
|
#ax[1].scatter(x_scatter,height,zorder = 2, color = color_chosen, s =size)
|
|
#y_all[i] = np.max(y2)
|
|
#if i == len(sigma)-1:
|
|
# ax[1].set_xlabel('stimulus frequency [EODf]')
|
|
#ax[1].set_ylim([np.min(y_min)*0.8, np.max(y_max)*1.2])
|
|
#ax[1].spines['top'].set_visible(False)
|
|
#ax[1].spines['right'].set_visible(False)
|
|
ax[0].spines['right'].set_visible(False)
|
|
ax[0].spines['top'].set_visible(False)
|
|
ax[0].spines['left'].set_visible(False)
|
|
#ax[1].spines['right'].set_visible(False)
|
|
#ax[1].spines['top'].set_visible(False)
|
|
ax[0].set_xlim([0, 5])
|
|
#embed()
|
|
ax[0].set_ylim([0, d['eodf'].iloc[0]*1.5])
|
|
ax[0].set_yticks([])
|
|
# fig.tight_layout()
|
|
# fig.label_axes()
|
|
print(sigma[i])
|
|
#embed()
|
|
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 = False
|
|
sigma = [0.0005, 0.002] # 0.00005,0.00025,
|
|
if trans == True:
|
|
col = 1
|
|
row = 2
|
|
col_small = len(sigma)+2
|
|
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)+2
|
|
col_small = 1
|
|
t = 'vertical'
|
|
l = 9
|
|
wd = [3, 4]
|
|
hd = [1]
|
|
left = 0
|
|
right = 1
|
|
default_settings(data, intermediate_width=6.7, intermediate_length=9, ts=6, ls=10, fs=10)
|
|
grid1 = gridspec.GridSpec(1, 1, left=0.15, right=0.95, wspace=0.25, height_ratios=hd,
|
|
hspace=0.2) # ,
|
|
|
|
grid3 = gridspec.GridSpecFromSubplotSpec(row, col,subplot_spec=grid1[0], wspace=0.35, height_ratios=hd,
|
|
width_ratios=wd, hspace=0.2) # ,
|
|
|
|
|
|
hs = 0.1
|
|
axis = gridspec.GridSpecFromSubplotSpec(row_small, col_small,
|
|
subplot_spec=grid3[0, 0], wspace=0.15, hspace=hs)
|
|
wish_df = 240 # 324
|
|
color_eod = 'darkgreen' # 'orange'
|
|
color_stim = 'navy'
|
|
color_df = 'orange' # 'green'
|
|
colors = ['brown']
|
|
colors_mpf = ['brown']
|
|
colors_mod = ['brown']
|
|
line_col = 'black'
|
|
plot_example_ps_trans(axis, input=['2019-10-21-aa-invivo-1'], line_col=line_col, colors=colors, sigma=sigma,
|
|
wish_df=wish_df, color_eod=color_eod, color_stim=color_stim, color_df=color_df)
|
|
# plt.show()
|
|
# embed()
|
|
# fig.savefig()
|
|
axis = gridspec.GridSpecFromSubplotSpec(row_small, col_small,
|
|
subplot_spec=grid3[left, right], wspace=0.25, hspace=hs)
|
|
# embed()
|
|
plot_mean_cells_modul(axis, data=['2019-10-21-aa-invivo-1'], line_col=line_col, lw=1.23, size=22,
|
|
sigma=['whole', 'original', '05', '2'], colors=colors_mpf, wish_df=wish_df, color_eod=color_eod,
|
|
color_df=color_df, color_modul=colors_mod) # '005','025',
|
|
|
|
plt.savefig('rotatedps_singleamodul.pdf')
|
|
plt.savefig('../highbeats_pdf/rotatedps_singleamodul.pdf')
|
|
#plt.savefig('.pdf')
|
|
plt.show()
|
|
# plt.savefig('../results/Ramona/ma_powerspecs_negative_df' + d + '.pdf')
|
|
# plt.show()
|
|
# plt.close()
|
|
# embed()
|
|
# plot_single_tublerones() # original beat_activity |