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beat

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This commit is contained in:
Ramona 2018-11-28 11:14:15 +01:00
parent 350dc24ad5
commit 99274191f8
2 changed files with 52 additions and 10 deletions
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60
code/response_beat.py
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@ -1,30 +1,72 @@
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
# define data path and important parameters
data_dir = "../data"
cut_window = 20
sampling_rate = 40 #kHz
cut_window = 40
cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
window = 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",
'''
# norm: -150, 150, 300
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"]
# norm: -50
data = ["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 = ["2018-11-14-aa-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", "2018-11-14-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"]
rates = {}
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
'''
if df == 50:
pass
else:
continue
'''
print(df)
rep_rates = []
beat_duration = int(abs(1 / df) * 1000)
beat_window = 0
while beat_window + beat_duration <= cut_window:
while beat_window + beat_duration <= cut_window/2:
beat_window = beat_window + beat_duration
for rep in df_map[df]:
for phase in spikes[rep]:
response = spikes[rep][phase]
# get spikes 40 ms before the chirp first chirp
spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < 0)]
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
smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
train = smoothed_data[window:beat_window+window]
rep_rates.append(np.std(train))
break
#cut = response[response[]]
df_rate = np.mean(rep_rates)
if df in rates.keys():
rates[df].append(df_rate)
else:
rates[df] = [df_rate]
fig, ax = plt.subplots()
for i, k in enumerate(sorted(rates.keys())):
ax.plot(np.ones(len(rates[k]))*k, rates[k], 'o')
#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
fig.tight_layout()
plt.show()

2
code/spikes_analysis.py
View File

@ -71,7 +71,7 @@ for dataset in data:
# 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
# get spikes between 40 ms before and after the chirp
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)]
spikes_idx = np.round(spikes_cut*sampling_rate)