jgrewe/gp_neurobio
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

haha

Browse Source
This commit is contained in:
efish 2018-11-27 14:52:49 +01:00
commit fb5627a794
2 changed files with 188 additions and 146 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
code/response_beat.py Normal file
View File

@ -0,0 +1,30 @@
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
from read_chirp_data import *
from utility import *
from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
cut_window = 20
data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
"2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
for dataset in data:
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(spikes)
print(dataset)
for df in df_map.keys():
beat_duration = 1/df
beat_window = 0
while beat_window + beat_duration <= cut_window:
beat_window = beat_window + beat_duration
for rep in df_map[df]:
for phase in spikes[rep]:
response = spikes[rep][phase]
break
#cut = response[response[]]

304
code/spikes_analysis.py
View File

@ -8,14 +8,19 @@ from IPython import embed
# define sampling rate and data path # define sampling rate and data path
sampling_rate = 40 #kHz sampling_rate = 40 #kHz
data_dir = "../data" data_dir = "../data"
dataset = "2018-11-14-ad-invivo-1" #dataset = "2018-11-13-al-invivo-1"
#data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",\ '''
# "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", \ data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",
# "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", \ "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
# "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", \ "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1",
# "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1",\ "2018-11-14-ad-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1",
# "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", \ "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1",
# "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1") "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1",
"2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
"2018-11-20-ai-invivo-1"]
'''
data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
"2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
# parameters for binning, smoothing and plotting # parameters for binning, smoothing and plotting
cut_window = 20 cut_window = 20
@ -31,12 +36,12 @@ time_axis = np.arange(-cut_window*2, cut_window*2, 1/sampling_rate) #steps
spike_bins = np.arange(-cut_window*2, cut_window*2) #ms spike_bins = np.arange(-cut_window*2, cut_window*2) #ms
# read data from files # read data from files
spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) #spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
#eod = read_chirp_eod(os.path.join(data_dir, dataset)) #eod = read_chirp_eod(os.path.join(data_dir, dataset))
#chirp_times = read_chirp_times(os.path.join(data_dir, dataset)) #chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
# make a delta f map for the quite more complicated keys # make a delta f map for the quite more complicated keys
df_map = map_keys(spikes) #df_map = map_keys(spikes)
# differentiate between phases # differentiate between phases
phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins) phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins)
@ -48,140 +53,147 @@ df_phase_binary = {}
#embed() #embed()
#exit() #exit()
# iterate over delta f, repetition, phases and a single chirp for dataset in data:
for deltaf in df_map.keys(): spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_phase_time[deltaf] = {} df_map = map_keys(spikes)
df_phase_binary[deltaf] = {} print(dataset)
for rep in df_map[deltaf]: # iterate over delta f, repetition, phases and a single chirp
chirp_size = int(rep[-1].strip('Hz')) for deltaf in df_map.keys():
#print(chirp_size) df_phase_time[deltaf] = {}
if chirp_size == 150: df_phase_binary[deltaf] = {}
continue for rep in df_map[deltaf]:
for phase in spikes[rep]: chirp_size = int(rep[-1].strip('Hz'))
for idx in np.arange(number_bins): #print(chirp_size)
# check the phase if chirp_size == 150:
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: continue
for phase in spikes[rep]:
# get spikes between 50 ms before and after the chirp for idx in np.arange(number_bins):
spikes_to_cut = np.asarray(spikes[rep][phase]) # check the phase
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)] if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
spikes_idx = np.round(spikes_cut*sampling_rate)
# also save as binary, 0 no spike, 1 spike # get spikes between 50 ms before and after the chirp
binary_spikes = np.isin(cut_range, spikes_idx)*1 spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)]
# add the spikes to the dictionaries with the correct df and phase spikes_idx = np.round(spikes_cut*sampling_rate)
if idx in df_phase_time[deltaf].keys(): # also save as binary, 0 no spike, 1 spike
df_phase_time[deltaf][idx].append(spikes_cut) binary_spikes = np.isin(cut_range, spikes_idx)*1
df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
# add the spikes to the dictionaries with the correct df and phase
if idx in df_phase_time[deltaf].keys():
df_phase_time[deltaf][idx].append(spikes_cut)
df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
else:
df_phase_time[deltaf][idx] = [spikes_cut]
df_phase_binary[deltaf][idx] = binary_spikes
# make dictionaries for csi and beat
csi_trains = {}
csi_rates = {}
beat = {}
# for plotting and calculating iterate over delta f and phases
for df in df_phase_time.keys():
csi_trains[df] = []
csi_rates[df] = []
beat[df] = []
beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
beat_window = 0
# beat window is at most 20 ms long, multiples of beat_duration
while beat_window+beat_duration <= cut_window*sampling_rate:
beat_window = beat_window+beat_duration
for phase in df_phase_time[df].keys():
# csi calculation
# trains for synchrony and rate
trials_binary = df_phase_binary[df][phase]
train_chirp = []
train_beat = []
#csi_spikerate = []
for i, trial in enumerate(trials_binary):
smoothed_trial = smooth(trial, window, 1/sampling_rate)
train_chirp.append(smoothed_trial[chirp_start:chirp_end])
train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
#std_chirp = np.std(smoothed_trial[chirp_start:chirp_end])
#std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start])
#csi = (std_chirp - std_beat)/(std_chirp + std_beat)
#csi_spikerate.append(csi)
std_chirp = np.std(np.mean(train_chirp, axis=0))
std_beat = np.std(np.mean(train_beat, axis=0))
beat[df].append(std_beat)
csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
rcs = []
rbs = []
for i, train in enumerate(train_chirp):
for j, train2 in enumerate(train_chirp):
if i >= j:
continue
else: else:
df_phase_time[deltaf][idx] = [spikes_cut] rc, _ = ss.pearsonr(train, train2)
df_phase_binary[deltaf][idx] = binary_spikes rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
rcs.append(rc)
rbs.append(rb)
# make dictionaries for csi
csi_trains = {} r_train_chirp = np.mean(rcs)
csi_rates = {} r_train_beat = np.mean(rbs)
# for plotting and calculating iterate over delta f and phases
for df in df_phase_time.keys(): csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
csi_trains[df] = []
csi_rates[df] = [] # add the csi to the dictionaries with the correct df and phase
beat_duration = int(abs(1/df*1000)*sampling_rate) #steps csi_trains[df].append(csi_train)
beat_window = 0 csi_rates[df].append(np.mean(csi_spikerate))
# beat window is at most 20 ms long, multiples of beat_duration
while beat_window+beat_duration <= cut_window*sampling_rate: '''
beat_window = beat_window+beat_duration # plot
for phase in df_phase_time[df].keys(): plot_trials = df_phase_time[df][phase]
plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
# csi calculation
# trains for synchrony and rate # calculation
trials_binary = df_phase_binary[df][phase] #overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
train_chirp = [] smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
train_beat = []
#csi_spikerate = [] fig, ax = plt.subplots(2, 1, sharex=True)
for i, trial in enumerate(trials_binary): for i, trial in enumerate(plot_trials):
smoothed_trial = smooth(trial, window, 1/sampling_rate) ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
train_chirp.append(smoothed_trial[chirp_start:chirp_end]) ax[1].plot(time_axis, smoothed_spikes*1000)
train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
#std_chirp = np.std(smoothed_trial[chirp_start:chirp_end]) ax[0].set_title(df)
#std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start]) ax[0].set_ylabel('repetition', fontsize=12)
#csi = (std_chirp - std_beat)/(std_chirp + std_beat)
#csi_spikerate.append(csi) ax[1].set_xlabel('time [ms]', fontsize=12)
ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
std_chirp = np.std(np.mean(train_chirp, axis=0)) plt.show()
std_beat = np.std(np.mean(train_beat, axis=0)) '''
csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
'''
rcs = [] fig, ax = plt.subplots()
rbs = [] for i, k in enumerate(sorted(csi_rates.keys())):
for i, train in enumerate(train_chirp): ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
for j, train2 in enumerate(train_chirp): #ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
if i >= j: ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
continue ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
else: #ax.set_xticklabels(sorted(csi_rates.keys()))
rc, _ = ss.pearsonr(train, train2) fig.tight_layout()
rb, _ = ss.pearsonr(train_beat[i], train_beat[j]) plt.show()
rcs.append(rc)
rbs.append(rb) fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_trains.keys())):
r_train_chirp = np.mean(rcs) ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
r_train_beat = np.mean(rbs) #ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat) ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_trains.keys()))
# add the csi to the dictionaries with the correct df and phase fig.tight_layout()
csi_trains[df].append(csi_train) plt.show()
csi_rates[df].append(np.mean(csi_spikerate)) '''
''' fig, ax = plt.subplots()
# plot for i, k in enumerate(sorted(beat.keys())):
plot_trials = df_phase_time[df][phase] ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0) ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
#ax.set_xticklabels(sorted(csi_trains.keys()))
# calculation fig.tight_layout()
#overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000 plt.show()
smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
fig, ax = plt.subplots(2, 1, sharex=True)
for i, trial in enumerate(plot_trials):
ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
ax[1].plot(time_axis, smoothed_spikes*1000)
ax[0].set_title(df)
ax[0].set_ylabel('repetition', fontsize=12)
ax[1].set_xlabel('time [ms]', fontsize=12)
ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
plt.show()
'''
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_rates.keys())):
ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
#ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_rates.keys()))
fig.tight_layout()
plt.show()
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_trains.keys())):
ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_trains.keys()))
fig.tight_layout()
plt.show()
# spikerate_chirp = np.zeros(len(trials_binary))
# spikerate_beat = np.zeros(len(trials_binary))
# csi_trains[df][phase] = csi_train
# csi_rates[df][phase] = csi_rate
# csi_trains[df].append(abs(csi_train))
# csi_rates[df].append(abs(csi_rate))
#csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
# spikerate_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
# spikerate_beat[i] = np.mean(smoothed_trial[chirp_start-beat_window:chirp_start])