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Ramona 2018-11-30 17:19:28 +01:00
parent ff2167a686
commit ca4185a64a
2 changed files with 82 additions and 60 deletions
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127
code/spikes_beat.py
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@ -12,67 +12,78 @@ cut_window = 100
cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
window = 1
inch_factor = 2.54
#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"
datasets = ["2018-11-13-ad-invivo-1", "2018-11-13-aj-invivo-1", \
"2018-11-13-ak-invivo-1", "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
plt.close()
fig = plt.figure()
#figsize=(5 / inch_factor, 2.5 / inch_factor))
fig.set_size_inches((5, 2.5))
ax = fig.add_subplot(111)
for i, df in enumerate(sorted(rates.keys())):
max_rep = len(sorted(rates[df].keys()))-1
for j, rep in enumerate(rates[df].keys()):
if j == max_rep:
farbe = 'royalblue'
gro = 8
else:
farbe = 'k'
gro = 6
ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
# figsize=(5 / inch_factor, 2.5 / inch_factor))
fig.set_size_inches((35/inch_factor, 15/inch_factor))
axes = []
axes.append(fig.add_subplot(221))
axes.append(fig.add_subplot(222))
axes.append(fig.add_subplot(223))
axes.append(fig.add_subplot(224))
#ax.set_xlabel('difference frequency [Hz]', fontsize=20)
ax.set_ylabel('firing modulation', fontsize=16)
ax.set_xlabel('$\Delta$f [Hz]', fontsize=16)
for dataset, ax in zip(datasets, axes):
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)
ax.yaxis.set_tick_params(labelsize=14)
ax.xaxis.set_tick_params(labelsize=14)
# read spikes during chirp stimulation
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(spikes)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
fig.tight_layout()
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)
modulation = np.std(smoothed_data)
rates[deltaf][x] = modulation
break
for i, df in enumerate(sorted(rates.keys())):
max_rep = len(sorted(rates[df].keys()))-1
for j, rep in enumerate(rates[df].keys()):
if j == max_rep:
farbe = 'royalblue'
gro = 8
else:
farbe = 'k'
gro = 6
ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
ax.set_xlim(-450, 800)
ax.set_ylim(0.1, 0.3)
ax.set_xticks(np.arange(-400, 810, 100))
ax.yaxis.set_tick_params(labelsize=18)
ax.xaxis.set_tick_params(labelsize=18)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
axes[0].set_ylabel('Firing rate modulation', fontsize=22)
axes[0].yaxis.set_label_coords(-0.15, -.125)
axes[2].set_xlabel('$\Delta$f [Hz]', fontsize=22)
axes[3].set_xlabel('$\Delta$f [Hz]', fontsize=22)
axes[1].set_yticklabels([])
axes[3].set_yticklabels([])
axes[0].set_xticklabels([])
axes[1].set_xticklabels([])
axes[2].set_xticklabels(['-400', '', '-200', '', '0', '', '200', '', '400', '', '600', '', '800'], rotation=45)
axes[3].set_xticklabels(['-400', '', '-200', '', '0', '', '200', '', '400', '', '600', '', '800'], rotation=45)
fig.subplots_adjust(left=0.09, bottom=0.175, right=0.975, top=0.95)
fig.savefig('spikes_beat_20af.png')

15
code/spikes_chirp.py
View File

@ -93,10 +93,21 @@ for df in df_phase_binary.keys():
upper_limit = np.max(sorted(csi_rates.keys()))+30
lower_limit = np.min(sorted(csi_rates.keys()))-30
fig, ax = plt.subplots()
inch_factor = 2.54
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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])
plt.xlabel("$\Delta$f", fontsize = 22)
plt.xticks(fontsize = 18)
plt.ylabel("CSI", fontsize = 22)
plt.yticks(fontsize = 18)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
fig.tight_layout()
plt.show()
#plt.show()
plt.savefig('CSI.png')