test labrotation freq phenomenon

test.py

@@ -22,29 +22,42 @@ from matplotlib import gridspec
 from plottools.axes import labelaxes_params
 
 
-def demo():
-    """ Run a demonstration of the axes module.
-    """
-
-    def afigure():
-        fig = plt.figure()
-        gs = gridspec.GridSpec(2, 3, width_ratios=[5, 1.5, 2.4])
-        gs.update(left=0.075, bottom=0.14, right=0.985, top=0.9, wspace=0.6, hspace=0.6)
-        ax1 = fig.add_subplot(gs[:, 0])
-        ax2 = fig.add_subplot(gs[0, 1:])
-        ax3 = fig.add_subplot(gs[1, 1])
-        ax4 = fig.add_subplot(gs[1, 2])
-        for ax in [ax1, ax2, ax3, ax4]:
-            ax.set_xlabel('xlabel')
-            ax.set_ylabel('ylabel')
-        return fig, (ax1, ax2, ax3, ax4)
-
-    labelaxes_params(xoffs='auto', yoffs=-1, labels='A', font=dict(fontweight='bold'))
-
-    fig, axs = afigure()
-    # axs[0].text(0.5, 0.5, 'fig.label_axes()', transform=axs[0].transAxes, ha='center')
-    # axs[0].text(0.0, 0.0, 'X', transform=fig.transFigure, ha='left', va='bottom')
-    fig.label_axes()
-    plt.show()
-
-demo()
+
+
+
+# sp = self.spikes(index)
+# binary = np.zeros(t.shape)
+# spike_indices = ((sp - t[0]) / dt).astype(int)
+# binary[spike_indices[(spike_indices >= 0) & (spike_indices < len(binary))]] = 1
+# g = gaussian_kernel(kernel_width, dt)
+# rate = np.convolve(binary, g, mode='same')
+
+fit = ModelFit("results/final_2/2012-07-12-ag-invivo-1/start_parameter_4_err_6.11/")
+model = fit.get_model()
+
+base_stim = SinusoidalStepStimulus(fit.get_cell_data().get_eod_frequency(), 0, 0)
+time_list = []
+freq_list = []
+con_freq_list = []
+duration = 10
+step = model.get_sampling_interval()
+g = gaussian_kernel(0.005, step)
+for i in range(20):
+    print(i)
+    v1, spikes = model.simulate(base_stim, duration)
+
+    binary = np.zeros(int(np.rint(duration / step)))
+    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()