changed for multiple repros
This commit is contained in:
parent
3bcc6b72ad
commit
ccd7623421
@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
|
||||
from IPython import embed
|
||||
import sys
|
||||
from icr_analysis import *
|
||||
from open_nixio import *
|
||||
from open_nixio_new import *
|
||||
|
||||
# Parameters
|
||||
sampling_rate = 20000
|
||||
@ -19,7 +19,7 @@ delay = 1.5 # delay in seconds after comb reaches one end, before commencing mo
|
||||
cell_name = sys.argv[1].split('/')[-2]
|
||||
|
||||
# Open Nixio File
|
||||
curr_comb, intervals_dict = open_nixio(sys.argv[1], sys.argv[2])
|
||||
curr_comb, intervals_dict = open_nixio_new(sys.argv[1], sys.argv[2])
|
||||
|
||||
# Kernel Density estimator: gaussian fit
|
||||
t = np.arange(-sigma*4, sigma*4, 1/sampling_rate)
|
||||
@ -53,7 +53,7 @@ if sys.argv[2] == 'average':
|
||||
plt.close(fig)
|
||||
|
||||
elif sys.argv[2] == 'nonaverage':
|
||||
for (speed, direct, pos, comb) in intervals_dict:
|
||||
for (repro, speed, direct, pos, comb) in intervals_dict:
|
||||
spike_train = intervals_dict[speed, direct, pos, comb]
|
||||
avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate)
|
||||
p, freq, std_four, mn_four = fourier_psd(avg_convolve_spikes, sampling_rate)
|
||||
@ -73,7 +73,7 @@ elif sys.argv[2] == 'nonaverage':
|
||||
ax2.axhline(y=(mn_four+std_four), xmin=0, xmax=1, linestyle='--', color='red')
|
||||
# ax2.axvline(x=max_four,linestyle='--', color='green')
|
||||
|
||||
plt.savefig(('nonavg_' + '_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
|
||||
plt.savefig(('nonavg_' + str(repro) +'_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
|
||||
+ '_' + str(comb) + '_' + str(direct) + '.png'))
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
53
analysis_graphs_new.py
Normal file
53
analysis_graphs_new.py
Normal file
@ -0,0 +1,53 @@
|
||||
# ---------------------------------------------------------------------------------------------------------------------
|
||||
# Name: Firing Rate and Fourier Script (moving comb repro)
|
||||
# Purpose: Takes nixio spike data from moving comb repro and plots firing rate and power spectrum density graph
|
||||
# Usage: python3 analysis_graphs.py average
|
||||
# Author: Carolin Sachgau, University of Tuebingen
|
||||
# Created: 20/09/2018
|
||||
# ---------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from IPython import embed
|
||||
import sys
|
||||
from icr_analysis import *
|
||||
from open_nixio_new import *
|
||||
|
||||
# Parameters
|
||||
sampling_rate = 20000
|
||||
sigma = 0.01 # for Gaussian
|
||||
delay = 1.5 # delay in seconds after comb reaches one end, before commencing movement again
|
||||
cell_name = sys.argv[1].split('/')[-2]
|
||||
|
||||
# Open Nixio File
|
||||
intervals_dict = open_nixio_new(sys.argv[1])
|
||||
|
||||
# Kernel Density estimator: gaussian fit
|
||||
t = np.arange(-sigma*4, sigma*4, 1/sampling_rate)
|
||||
fxn = np.exp(-0.5*(t/sigma)**2) / np.sqrt(2*np.pi) / sigma # gaussian function
|
||||
|
||||
|
||||
for (repro, speed, direct, pos, comb) in intervals_dict:
|
||||
spike_train = intervals_dict[speed, direct, pos, comb]
|
||||
avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate)
|
||||
p, freq, std_four, mn_four = fourier_psd(avg_convolve_spikes, sampling_rate)
|
||||
|
||||
# Graphing
|
||||
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)
|
||||
|
||||
# Firing Rate Graph
|
||||
firing_times = np.arange(0, len(avg_convolve_spikes))
|
||||
ax1.plot((firing_times / sampling_rate), avg_convolve_spikes)
|
||||
ax1.set_title('Firing Rate of trial ' + str((speed, pos)) + ' comb = ' + str(comb) + '\n')
|
||||
ax1.set_xlabel('Time (s)')
|
||||
ax1.set_ylabel('Firing rate (Hz)')
|
||||
|
||||
# Fourier Graph
|
||||
ax2.semilogy(freq[freq < 200], p[freq < 200])
|
||||
ax2.axhline(y=(mn_four+std_four), xmin=0, xmax=1, linestyle='--', color='red')
|
||||
# ax2.axvline(x=max_four,linestyle='--', color='green')
|
||||
|
||||
plt.savefig(('nonavg_' + str(repro) +'_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
|
||||
+ '_' + str(comb) + '_' + str(direct) + '.png'))
|
||||
plt.close(fig)
|
||||
|
||||
# ---------------------------------------------------------------------------------------------------------------------
|
||||
@ -39,6 +39,5 @@ def open_nixio(nix_file, avg_opt):
|
||||
|
||||
# Spike data at baseline
|
||||
# comb_baseline = spikes[(spikes < comb_pos[0])]
|
||||
embed()
|
||||
quit()
|
||||
|
||||
return curr_comb, intervals_dict
|
||||
|
||||
@ -3,40 +3,40 @@ from IPython import embed
|
||||
from collections import defaultdict
|
||||
import numpy as np
|
||||
|
||||
nix_file = '/home/sachgau/Documents/combproject/comb_data/2018-11-16-ag/2018-11-16-ag.nix'
|
||||
|
||||
|
||||
f = nix.File.open(nix_file, nix.FileMode.ReadOnly)
|
||||
b = f.blocks[0] # first block
|
||||
mt = b.multi_tags['moving object-1']
|
||||
comb_pos = mt.positions[:]
|
||||
comb_ext = np.array(mt.extents[:])
|
||||
spikes = b.data_arrays["Spikes-1"][:]
|
||||
|
||||
feature_dict = {}
|
||||
for feat in mt.features:
|
||||
feature_dict.update({feat.data.name[16:]: mt.features[feat.data.name].data[:]})
|
||||
|
||||
tags = b.tags
|
||||
intervals_dict = defaultdict(list)
|
||||
|
||||
for tag in tags:
|
||||
if tag.name.startswith('Baseline'):
|
||||
continue
|
||||
curr_comb = tag.metadata["RePro-Info"]["settings"]["object"]
|
||||
repro_pos, = tag.position
|
||||
repro_ext, = tag.extent
|
||||
idx_qry = np.logical_and(repro_pos < comb_pos, comb_pos < (repro_pos + repro_ext))
|
||||
tag_idx = np.flatnonzero(idx_qry)
|
||||
tag_pos = comb_pos[idx_qry]
|
||||
|
||||
for idx, position in zip(tag_idx, tag_pos):
|
||||
if idx == (len(comb_pos)-1):
|
||||
break
|
||||
curr_speed = feature_dict['speed'][idx]
|
||||
curr_pos = comb_pos[idx]
|
||||
curr_dir = feature_dict['direction'][idx]
|
||||
curr_spikes = spikes[(spikes < comb_pos[idx + 1]) & (spikes > comb_pos[idx])]
|
||||
intervals_dict.update({(tag.name, curr_speed, curr_dir, curr_pos, curr_comb): curr_spikes})
|
||||
embed()
|
||||
quit()
|
||||
|
||||
def open_nixio_new(nix_file):
|
||||
|
||||
f = nix.File.open(nix_file, nix.FileMode.ReadOnly)
|
||||
b = f.blocks[0] # first block
|
||||
mt = b.multi_tags['moving object-1']
|
||||
comb_pos = mt.positions[:]
|
||||
comb_ext = np.array(mt.extents[:])
|
||||
spikes = b.data_arrays["Spikes-1"][:]
|
||||
|
||||
feature_dict = {}
|
||||
for feat in mt.features:
|
||||
feature_dict.update({feat.data.name[16:]: mt.features[feat.data.name].data[:]})
|
||||
|
||||
tags = b.tags
|
||||
intervals_dict = defaultdict(list)
|
||||
|
||||
for tag in tags:
|
||||
if tag.name.startswith('Baseline'):
|
||||
continue
|
||||
curr_comb = tag.metadata["RePro-Info"]["settings"]["object"]
|
||||
repro_pos, = tag.position
|
||||
repro_ext, = tag.extent
|
||||
idx_qry = np.logical_and(repro_pos < comb_pos, comb_pos < (repro_pos + repro_ext))
|
||||
tag_idx = np.flatnonzero(idx_qry)
|
||||
tag_pos = comb_pos[idx_qry]
|
||||
|
||||
for idx, position in zip(tag_idx, tag_pos):
|
||||
if idx == (len(comb_pos)-1):
|
||||
break
|
||||
curr_speed = feature_dict['speed'][idx]
|
||||
curr_pos = comb_pos[idx]
|
||||
curr_dir = feature_dict['direction'][idx]
|
||||
curr_spikes = spikes[(spikes < comb_pos[idx + 1]) & (spikes > comb_pos[idx])]
|
||||
intervals_dict.update({(tag.name, curr_speed, curr_dir, curr_pos, curr_comb): curr_spikes})
|
||||
|
||||
return intervals_dict
|
||||
Loading…
Reference in New Issue
Block a user