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Ramona 2018-11-29 17:06:17 +01:00
parent d77d377849
commit a7217fb4c6
5 changed files with 242 additions and 43 deletions
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4
code/plot_eodform_spikehist.py
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@ -9,8 +9,8 @@ from IPython import embed
inch_factor = 2.54
sampling_rate = 40000
data_dir = '../data'
dataset = '2018-11-09-ad-invivo-1'
#dataset = '2018-11-13-aa-invivo-1'
#dataset = '2018-11-09-ad-invivo-1'
dataset = '2018-11-14-ad-invivo-1'
# read eod and time of baseline
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))

61
code/response_beat.py
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@ -8,29 +8,33 @@ from IPython import embed
# define data path and important parameters
data_dir = "../data"
sampling_rate = 40 #kHz
cut_window = 40
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"]
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-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"]
'''
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))
@ -66,21 +70,48 @@ for dataset in data:
# 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
rep_rates.append(np.std(norm_train))#/spikerate)
#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[df].append(df_rate)
rates[dataset][df].append(df_rate)
else:
rates[df] = [df_rate]
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, k in enumerate(sorted(rates.keys())):
ax.plot(np.ones(len(rates[k]))*k, rates[k], 'o')
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()
'''

56
code/spikes_analysis.py
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@ -8,7 +8,8 @@ from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-13-ah-invivo-1"
dataset = "2018-11-13-al-invivo-1"
#dataset = "2018-11-09-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",
@ -90,14 +91,14 @@ for deltaf in df_map.keys():
# make dictionaries for csi and beat
csi_trains = {}
#csi_trains = {}
csi_rates = {}
beat = {}
#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] = []
#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
@ -123,9 +124,12 @@ for df in df_phase_time.keys():
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)
#beat[df].append(std_beat)
csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
csi_rates[df][phase] = np.mean(csi_spikerate)
'''
rcs = []
rbs = []
for i, train in enumerate(train_chirp):
@ -145,9 +149,7 @@ for df in df_phase_time.keys():
# add the csi to the dictionaries with the correct df and phase
csi_trains[df].append(csi_train)
csi_rates[df].append(np.mean(csi_spikerate))
'''
# plot
plot_trials = df_phase_time[df][phase]
plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
@ -170,37 +172,39 @@ for df in df_phase_time.keys():
plt.show()
'''
colors = ['k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'firebrick']
sizes = [12, 12, 12,
12, 12, 12,
12, 12, 12,
12, 12, 18]
upper_limit = np.max(sorted(csi_rates.keys()))+30
lower_limit = np.min(sorted(csi_rates.keys()))-30
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_rates.keys())):
ax.scatter(np.ones(len(csi_rates[k]))*k, 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([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_rates.keys()))
for i, df in enumerate(sorted(csi_rates.keys())):
for j, phase in enumerate(sorted(csi_rates[df].keys())):
ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
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()))
for i, k in enumerate(sorted(beat.keys())):
ax.plot(np.ones(len(beat[k]))*k, beat[k], 'o')
fig.tight_layout()
plt.show()
'''
'''
fig, ax = plt.subplots()
for i, k in enumerate(sorted(beat.keys())):
ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
#ax.set_xticklabels(sorted(csi_trains.keys()))
for i, k in enumerate(sorted(csi_trains.keys())):
ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
fig.tight_layout()
plt.show()
'''

62
code/spikes_beat.py Normal file
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@ -0,0 +1,62 @@
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
#dataset = "2018-11-13-ad-invivo-1"
#dataset = "2018-11-13-aj-invivo-1"
#dataset = "2018-11-13-ak-invivo-1" #al
#dataset = "2018-11-14-ad-invivo-1"
dataset = "2018-11-20-af-invivo-1"
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)
rates = {}
# iterate over df
for deltaf in df_map.keys():
rates[deltaf] = {}
beat_duration = int(abs(1 / deltaf) * 1000)
beat_window = 0
while beat_window + beat_duration <= cut_window/2:
beat_window = beat_window + beat_duration
for x, repetition in enumerate(df_map[deltaf]):
for phase in spikes[repetition]:
# get spikes some ms before the chirp first chirp
spikes_to_cut = np.asarray(spikes[repetition][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*sampling_rate:beat_window*sampling_rate+window*sampling_rate]
modulation = np.std(smoothed_data)
rates[deltaf][x] = modulation
break
fig, ax = plt.subplots()
for i, df in enumerate(sorted(rates.keys())):
for j, rep in enumerate(rates[df].keys()):
if j == 15:
farbe = 'royalblue'
gro = 18
else:
farbe = 'k'
gro = 12
ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
fig.tight_layout()
plt.show()

102
code/spikes_chirp.py Normal file
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@ -0,0 +1,102 @@
import matplotlib.pyplot as plt
import numpy as np
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"
dataset = "2018-11-13-al-invivo-1"
# parameters for binning, smoothing and plotting
cut_window = 20
chirp_duration = 14 #ms
neuronal_delay = 5 #ms
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*2, cut_window*2, 1/sampling_rate) #steps
spike_bins = np.arange(-cut_window*2, cut_window*2) #ms
colors = ['k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'firebrick']
sizes = [12, 12, 12,
12, 12, 12,
12, 12, 12,
12, 12, 18]
# differentiate between phases
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)
df_phase_binary = {}
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(spikes)
for deltaf in df_map.keys():
df_phase_binary[deltaf] = {}
for rep in df_map[deltaf]:
chirp_size = int(rep[-1].strip('Hz'))
if chirp_size == 150:
continue
for phase in spikes[rep]:
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 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)
# save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx)*1
# add the spikes to the dictionary with the correct df and phase
if idx in df_phase_binary[deltaf].keys():
df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
else:
df_phase_binary[deltaf][idx] = binary_spikes
csi_rates = {}
for df in df_phase_binary.keys():
csi_rates[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_binary[df].keys():
# csi calculation
trials_binary = df_phase_binary[df][phase]
train_chirp = []
train_beat = []
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(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)
csi_rates[df][phase] = np.mean(csi_spikerate)
upper_limit = np.max(sorted(csi_rates.keys()))+30
lower_limit = np.min(sorted(csi_rates.keys()))-30
fig, ax = plt.subplots()
ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
for i, df in enumerate(sorted(csi_rates.keys())):
for j, phase in enumerate(sorted(csi_rates[df].keys())):
ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
fig.tight_layout()
plt.show()