jgrewe/gp_neurobio
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Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio

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Jan Grewe 2018-11-21 12:45:49 +01:00
commit 12748df4f0
2 changed files with 21 additions and 16 deletions
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27
code/spikes_analysis.py
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@ -42,33 +42,28 @@ for deltaf in df_map.keys():
binary_spikes = np.isin(cut_range, spikes_idx)*1 binary_spikes = np.isin(cut_range, spikes_idx)*1
if phase_vec[idx] in df_phase_time[deltaf].keys(): if phase_vec[idx] in df_phase_time[deltaf].keys():
df_phase_time[deltaf][phase_vec[idx]].append(spikes[rep][phase]) df_phase_time[deltaf][phase_vec[idx]].append(spikes_cut)
df_phase_binary[deltaf][phase_vec[idx]] = np.vstack((df_phase_binary[deltaf][phase_vec[idx]], binary_spikes)) df_phase_binary[deltaf][phase_vec[idx]] = np.vstack((df_phase_binary[deltaf][phase_vec[idx]], binary_spikes))
else: else:
df_phase_time[deltaf][phase_vec[idx]] = [spikes[rep][phase]] df_phase_time[deltaf][phase_vec[idx]] = [spikes_cut]
df_phase_binary[deltaf][phase_vec[idx]] = binary_spikes df_phase_binary[deltaf][phase_vec[idx]] = binary_spikes
plot_trials = df_phase_binary['-50Hz'][0.0]
#hist_data = plt.hist(plot_trials) plot_trials = df_phase_time['-50Hz'][0.0]
#ax.plot(hist_data[1][:-1], hist_data[0]) plot_trials_binary = np.mean(df_phase_binary['-50Hz'][0.0], axis=0)
window = 100
smoothed_spikes = smooth(plot_trials_binary, window)
time_axis = np.arange(-50, 50, 1/sampling_rate)
fig, ax = plt.subplots() fig, ax = plt.subplots()
for i, trial in enumerate(plot_trials): for i, trial in enumerate(plot_trials):
embed() ax.scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
exit() ax.plot(time_axis, smoothed_spikes)
trial[trial == 0] = np.nan
ax.scatter(np.ones(len(trial)), trial, marker='|', color='k', size=12)
plt.show() plt.show()
#mu = 1
#sigma = 1
#time_gauss = np.arange(-4, 4, 1)
#gauss = gaussian(time_gauss, mu, sigma)
# spikes during time vec (00010000001)?
#smoothed_spikes = np.convolve(plot_spikes, gauss, 'same')
#window = np.mean(np.diff(plot_spikes)) #window = np.mean(np.diff(plot_spikes))
#time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window) #time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window)

10
code/utility.py
View File

@ -23,6 +23,16 @@ def gaussian(x, mu, sig):
y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.))) y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))
return y return y
def smooth(data, window):
mu = 1
sigma = window
time_gauss = np.arange(-4 * sigma, 4 * sigma, 1)
gauss = gaussian(time_gauss, mu, sigma)
smoothed_data = np.convolve(data, gauss, 'same')
return smoothed_data
def map_keys(input): def map_keys(input):
df_map = {} df_map = {}
for k in input.keys(): for k in input.keys():