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data is ready for plotting

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This commit is contained in:
Ramona 2018-11-20 17:40:37 +01:00
parent ae6e78f9a7
commit 1daf5c244e
1 changed files with 26 additions and 16 deletions
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42
code/spikes_analysis.py
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@ -4,6 +4,7 @@ from read_chirp_data import *
from utility import *
from IPython import embed
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-09-ad-invivo-1"
@ -21,37 +22,46 @@ for k in spikes.keys():
# make phases together, 12 phases
phase_vec = np.arange(0, 1+1/12, 1/12)
phase_mat_df = {}
cut_range = np.arange(-50*sampling_rate, 50*sampling_rate, 1)
df_phase_time = {}
df_phase_binary = {}
for deltaf in df_map.keys():
phase_df = {}
df_phase_time[deltaf] = {}
df_phase_binary[deltaf] = {}
for rep in df_map[deltaf]:
for phase in spikes[rep]:
#print(phase)
for idx in range(len(phase_vec)-1):
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
if phase_vec[idx] in phase_df.keys():
phase_df[phase_vec[idx]].append(phase)
else:
phase_df[phase_vec[idx]] = [phase]
#spikes_one_chirp = spikes[rep][phase]
spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
spikes_idx = np.round(spikes_cut*sampling_rate)
binary_spikes = np.isin(cut_range, spikes_idx)*1
phase_mat_df[deltaf] = phase_df
if phase_vec[idx] in df_phase_time[deltaf].keys():
df_phase_time[deltaf][phase_vec[idx]].append(spikes[rep][phase])
df_phase_binary[deltaf][phase_vec[idx]] = np.vstack((df_phase_binary[deltaf][phase_vec[idx]], binary_spikes))
else:
df_phase_time[deltaf][phase_vec[idx]] = [spikes[rep][phase]]
df_phase_binary[deltaf][phase_vec[idx]] = binary_spikes
#df_spikes = spikes[df_map['-50Hz'][0]]
#phase_spikes = df_spikes[phase_mat_df['-50Hz'][0.0][0]]
trial_spikes = np.asarray(spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)])
plot_spikes = trial_spikes[(trial_spikes < 50.0) & (trial_spikes > -50.0)]
plot_trials = df_phase_binary['-50Hz'][0.0]
#hist_data = plt.hist(plot_trials)
#ax.plot(hist_data[1][:-1], hist_data[0])
hist_data = plt.hist(plot_spikes, color='w')
fig, ax = plt.subplots()
ax.scatter(plot_spikes, np.ones(len(plot_spikes))*10, marker='|', color='k')
ax.plot(hist_data[1][:-1], hist_data[0])
for i, trial in enumerate(plot_trials):
embed()
exit()
trial[trial == 0] = np.nan
ax.scatter(np.ones(len(trial)), trial, marker='|', color='k', size=12)
plt.show()
#mu = 1
#sigma = 1
#time_gauss = np.arange(-4, 4, 1)