117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from read_chirp_data import *
|
|
from read_baseline_data import *
|
|
from utility import *
|
|
from IPython import embed
|
|
|
|
# define data path and important parameters
|
|
data_dir = "../data"
|
|
sampling_rate = 40 #kHz
|
|
cut_window = 100
|
|
cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
|
|
window = 1
|
|
|
|
'''
|
|
# norm: -150, 150, 300 aa, #ac, aj??
|
|
data = ["2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-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-ad-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-am-invivo-1"]
|
|
|
|
#data = ["2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ak-invivo-1"]
|
|
#data = ["2018-11-09-ad-invivo-1", "2018-11-14-af-invivo-1"]
|
|
|
|
#data = ["2018-11-20-ad-invivo-1", "2018-11-13-ad-invivo-1"]
|
|
#data = ["2018-11-09-ad-invivo-1"]
|
|
|
|
rates = {}
|
|
|
|
for dataset in data:
|
|
rates[dataset] = {}
|
|
print(dataset)
|
|
# read baseline spikes
|
|
base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
|
|
base_spikes = base_spikes[1000:2000]
|
|
spikerate = len(base_spikes)/base_spikes[-1]
|
|
print(spikerate)
|
|
|
|
# read spikes during chirp stimulation
|
|
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
|
|
df_map = map_keys(spikes)
|
|
|
|
# iterate over df
|
|
for df in df_map.keys():
|
|
'''
|
|
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/2:
|
|
beat_window = beat_window + beat_duration
|
|
for rep in df_map[df]:
|
|
for phase in spikes[rep]:
|
|
# 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]
|
|
#norm_train = train*1000/spikerate
|
|
#df_rate = np.std(norm_train)
|
|
#rates[dataset][df] = [df_rate]
|
|
rep_rates.append(np.std(smoothed_data))#/spikerate)
|
|
'''
|
|
if df in rates[dataset].keys():
|
|
rates[dataset][df].append(np.std(norm_train))
|
|
else:
|
|
rates[dataset][df] = [np.std(norm_train)]
|
|
'''
|
|
break
|
|
#break
|
|
df_rate = np.mean(rep_rates)
|
|
#df_rate = rep_rates
|
|
rates[dataset][df] = df_rate
|
|
|
|
#embed()
|
|
#exit()
|
|
'''
|
|
if df in rates.keys():
|
|
rates[dataset][df].append(df_rate)
|
|
else:
|
|
rates[dataset][df] = [df_rate]
|
|
'''
|
|
|
|
colors = ['royalblue', 'red', 'green', 'violet', 'orange', 'black', 'gray']
|
|
|
|
fig, ax = plt.subplots()
|
|
for i, cell in enumerate(rates.keys()):
|
|
for j, df in enumerate(sorted(rates[cell].keys())):
|
|
ax.plot(df, rates[cell][df], 'o', color=colors[i])
|
|
#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
|
|
fig.tight_layout()
|
|
plt.show()
|
|
|
|
'''
|
|
fig, ax = plt.subplots()
|
|
for i, cell in enumerate(rates.keys()):
|
|
for j, df in enumerate(sorted(rates[cell].keys())):
|
|
ax.plot(np.ones(len(rates[cell][df]))*df, rates[cell][df], 'o', color=colors[i])
|
|
#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
|
|
fig.tight_layout()
|
|
plt.show()
|
|
''' |