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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Ramona 2018-11-28 17:45:50 +01:00
commit 9f80891616
2 changed files with 23 additions and 9 deletions
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19
code/plot_eodform_spikehist.py
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@ -14,14 +14,29 @@ dataset = '2018-11-13-aa-invivo-1'
# read eod and time of baseline # read eod and time of baseline
time, eod = read_baseline_eod(os.path.join(data_dir, dataset)) time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
<<<<<<< HEAD
eod_norm = eod - np.mean(eod)
# calculate eod times and indices by zero crossings
threshold = 0
shift_eod = np.roll(eod_norm, 1)
eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
eod_duration = eod_times[2]- eod_times[1]
=======
>>>>>>> 5cd62554fa5af12a6a50661f0a60cd2b0457e702
# read spikes during baseline activity # read spikes during baseline activity
spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
# calculate interpike intervals and plot them # calculate interpike intervals and plot them
interspikeintervals = np.diff(spikes) interspikeintervals = np.diff(spikes)/eod_duration
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor)) fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue') plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
plt.xlabel("time [ms]", fontsize = 22) plt.xlabel("eod cycles", fontsize = 22)
plt.xticks(fontsize = 18) plt.xticks(fontsize = 18)
plt.ylabel("number of \n interspikeintervals", fontsize = 22) plt.ylabel("number of \n interspikeintervals", fontsize = 22)
plt.yticks(fontsize = 18) plt.yticks(fontsize = 18)

13
code/repetition_firingrate.py
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@ -54,16 +54,17 @@ for deltaf in df_map.keys():
# get spikes between 60 ms before and after the chirp # get spikes between 60 ms before and after the chirp
spikes_to_cut = np.asarray(spikes[rep][phase]) spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)] spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
spikes_raster = spikes_to_cut[(spikes_to_cut > -cut_window+5) & (spikes_to_cut < cut_window-5)]
spikes_idx = np.round(spikes_cut*sampling_rate) spikes_idx = np.round(spikes_cut*sampling_rate)
# also save as binary, 0 no spike, 1 spike # also save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx)*1 binary_spikes = np.isin(cut_range, spikes_idx)*1
# add the spikes to the dictionaries with the correct df and phase # add the spikes to the dictionaries with the correct df and phase
if idx in df_phase_time[deltaf].keys(): if idx in df_phase_time[deltaf].keys():
df_phase_time[deltaf][idx].append(spikes_cut) df_phase_time[deltaf][idx].append(spikes_raster)
df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes)) df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
else: else:
df_phase_time[deltaf][idx] = [spikes_cut] df_phase_time[deltaf][idx] = [spikes_raster]
df_phase_binary[deltaf][idx] = binary_spikes df_phase_binary[deltaf][idx] = binary_spikes
@ -80,15 +81,13 @@ for df in df_phase_time.keys():
smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate) smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
fig, ax = plt.subplots(2, 1, sharex=True, figsize=(20/inch_factor, 15/inch_factor)) fig, ax = plt.subplots(2, 1, sharex=True, figsize=(18/inch_factor, 13/inch_factor))
for i, trial in enumerate(plot_trials): for i, trial in enumerate(plot_trials):
ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k') ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
ax[1].plot(time_axis, smoothed_spikes*1000, color='royalblue', lw = 2) ax[1].plot(time_axis[0+5*sampling_rate:-5*sampling_rate], smoothed_spikes[0+5*sampling_rate:-5*sampling_rate]*1000, color='royalblue', lw = 2)
ax[0].set_title('df = %s Hz' %(df), fontsize = 18)
ax[0].set_title('df = %s Hz' %(df))
ax[0].set_ylabel('repetition', fontsize=22) ax[0].set_ylabel('repetition', fontsize=22)
ax[0].yaxis.set_label_coords(-0.1, 0.5) ax[0].yaxis.set_label_coords(-0.1, 0.5)
ax[0].set_yticks(np.arange(1, len(plot_trials)+1,2)) ax[0].set_yticks(np.arange(1, len(plot_trials)+1,2))