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Ramona 2018-11-27 11:21:08 +01:00
parent bf0fb6025b
commit 6fdf60f69e
2 changed files with 91 additions and 32 deletions
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76
code/spikes_analysis.py
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@ -8,7 +8,7 @@ from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-14-aa-invivo-1"
dataset = "2018-11-14-ad-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", \
# "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", \
@ -18,28 +18,29 @@ dataset = "2018-11-14-aa-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")
# parameters for binning, smoothing and plotting
#cut_window = 60
cut_window = 20
chirp_size = 14 #ms
#cut_window_csi = 20 #ms
#cut_window_plot = 50 #ms
chirp_duration = 14 #ms
neuronal_delay = 5 #ms
chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
chirp_end = int((chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
num_bin = 12
chirp_start = int((-chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
chirp_end = int((chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
number_bins = 12
window = 1 #ms
time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps
spike_bins = np.arange(-cut_window, cut_window+1) #ms
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
# read data from files
spikes = read_chirp_spikes(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))
#eod = read_chirp_eod(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
df_map = map_keys(spikes)
# differentiate between phases
phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
cut_range = np.arange(-cut_window*sampling_rate, cut_window*sampling_rate, 1)
phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins)
cut_range = np.arange(-cut_window*2*sampling_rate, cut_window*2*sampling_rate, 1)
# make dictionaries for spiketimes
df_phase_time = {}
@ -52,14 +53,18 @@ for deltaf in df_map.keys():
df_phase_time[deltaf] = {}
df_phase_binary[deltaf] = {}
for rep in df_map[deltaf]:
chirp_size = int(rep[-1].strip('Hz'))
#print(chirp_size)
if chirp_size == 150:
continue
for phase in spikes[rep]:
for idx in np.arange(num_bin):
for idx in np.arange(number_bins):
# check the phase
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
# get spikes between 50 ms before and after the chirp
spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)]
spikes_idx = np.round(spikes_cut*sampling_rate)
# also save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx)*1
@ -80,27 +85,32 @@ csi_rates = {}
for df in df_phase_time.keys():
csi_trains[df] = []
csi_rates[df] = []
beat_duration = int(abs(1/df*1000)) #ms
beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
beat_window = 0
while beat_window+beat_duration <= cut_window:
# 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 synchronity and rate
# trains for synchrony and rate
trials_binary = df_phase_binary[df][phase]
train_chirp = []
train_beat = []
spikerate_chirp = np.zeros(len(trials_binary))
spikerate_beat = np.zeros(len(trials_binary))
#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])
spikerate_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
spikerate_beat[i] = np.mean(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))
csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
rcs = []
rbs = []
@ -116,20 +126,12 @@ for df in df_phase_time.keys():
r_train_chirp = np.mean(rcs)
r_train_beat = np.mean(rbs)
embed()
exit()
csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
# add the csi to the dictionaries with the correct df and phase
#csi_trains[df][phase] = csi_train
#csi_rates[df][phase] = csi_rate
csi_trains[df].append(csi_train)
csi_rates[df].append(csi_rate)
#csi_trains[df].append(abs(csi_train))
#csi_rates[df].append(abs(csi_rate))
csi_rates[df].append(np.mean(csi_spikerate))
'''
# plot
@ -173,3 +175,13 @@ ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())
#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])

47
code/stimulus_chirp.py Normal file
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@ -0,0 +1,47 @@
import numpy as np
import matplotlib.pyplot as plt
stimulusrate = 500. # the eod frequency of the fake fish
currentchirptimes = [0.1]
chirpwidth = 0.05 # ms
chirpsize = 100.
chirpampl = 0.02
chirpkurtosis = 1.
p = 0.
stepsize = 0.00001
time = np.arange(0.0, 0.2, stepsize)
signal = np.zeros(time.shape)
ampl = np.ones(time.shape)
freq = np.ones(time.shape)
ck = 0
csig = 0.5 * chirpwidth / np.power(2.0*np.log(10.0), 0.5/chirpkurtosis)
for k, t in enumerate(time):
a = 1.
f = stimulusrate
if ck < len(currentchirptimes):
if np.abs(t - currentchirptimes[ck]) < 2.0 * chirpwidth:
x = t - currentchirptimes[ck]
g = np.exp(-0.5 * (x/csig)**2)
f = chirpsize * g + stimulusrate
a *= 1.0 - chirpampl * g
elif t > currentchirptimes[ck] + 2.0 * chirpwidth:
ck += 1
freq[k] = f
ampl[k] = a
p += f * stepsize
signal[k] = a * np.sin(6.28318530717959 * p)
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(time, signal)
ax2.plot(time, freq)
ax1.set_ylabel("fake fish field [rel]")
ax2.set_xlabel("time [s]")
ax2.set_ylabel("frequency [Hz]")
plt.show()