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test labrotation freq phenomenon

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a.ott 2020-09-11 17:22:18 +02:00
parent 5af4117d4c
commit 3c06be9917
1 changed files with 39 additions and 26 deletions
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test.py
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@ -22,29 +22,42 @@ from matplotlib import gridspec
from plottools.axes import labelaxes_params from plottools.axes import labelaxes_params
def demo():
""" Run a demonstration of the axes module.
"""
# sp = self.spikes(index)
def afigure(): # binary = np.zeros(t.shape)
fig = plt.figure() # spike_indices = ((sp - t[0]) / dt).astype(int)
gs = gridspec.GridSpec(2, 3, width_ratios=[5, 1.5, 2.4]) # binary[spike_indices[(spike_indices >= 0) & (spike_indices < len(binary))]] = 1
gs.update(left=0.075, bottom=0.14, right=0.985, top=0.9, wspace=0.6, hspace=0.6) # g = gaussian_kernel(kernel_width, dt)
ax1 = fig.add_subplot(gs[:, 0]) # rate = np.convolve(binary, g, mode='same')
ax2 = fig.add_subplot(gs[0, 1:])
ax3 = fig.add_subplot(gs[1, 1]) fit = ModelFit("results/final_2/2012-07-12-ag-invivo-1/start_parameter_4_err_6.11/")
ax4 = fig.add_subplot(gs[1, 2]) model = fit.get_model()
for ax in [ax1, ax2, ax3, ax4]:
ax.set_xlabel('xlabel') base_stim = SinusoidalStepStimulus(fit.get_cell_data().get_eod_frequency(), 0, 0)
ax.set_ylabel('ylabel') time_list = []
return fig, (ax1, ax2, ax3, ax4) freq_list = []
con_freq_list = []
labelaxes_params(xoffs='auto', yoffs=-1, labels='A', font=dict(fontweight='bold')) duration = 10
step = model.get_sampling_interval()
fig, axs = afigure() g = gaussian_kernel(0.005, step)
# axs[0].text(0.5, 0.5, 'fig.label_axes()', transform=axs[0].transAxes, ha='center') for i in range(20):
# axs[0].text(0.0, 0.0, 'X', transform=fig.transFigure, ha='left', va='bottom') print(i)
fig.label_axes() v1, spikes = model.simulate(base_stim, duration)
plt.show()
binary = np.zeros(int(np.rint(duration / step)))
demo() for s in spikes:
binary[int(np.rint(s/step))] = 1
rate = np.convolve(binary, g, mode='same')
con_freq_list.append(rate)
time, freq = hF.calculate_time_and_frequency_trace(spikes, model.get_sampling_interval())
time_list.append(time)
freq_list.append(freq)
rates = np.array(con_freq_list)
mean_rate = np.mean(rates, axis=0)
mean_time, mean_freq = hF.calculate_mean_of_frequency_traces(time_list, freq_list, model.get_sampling_interval())
plt.plot(np.arange(0, 10, step), mean_rate, alpha=0.5)
plt.plot(mean_time, mean_freq, alpha=0.5)
plt.show()