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figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py → figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_VISUALIZATION-checkpoint.py

@@ -17,6 +17,29 @@ from matplotlib import gridspec
 icolor = myred
 ecolor = myblue
 
+# label_spikes = M.simulation.label
+label = M.simulation.label
+
+from MAM2EBRAINS_LOAD_DATA import load_and_create_data
+A = load_and_create_data(M)
+
+def plot_instan_mean_firing_rate(M):
+    # load spike data and calculate instantaneous and mean firing rates
+    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+    
+    # visualize calculate instantaneous and mean firing rates
+    ax = pl.subplot()
+    ax.plot(tsteps, rate)
+    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
+    ax.set_title('Instantaneous and mean firing rate across all populations')
+    ax.set_xlabel('time (ms)')
+    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_xlim(0, sim_params['t_sim'])
+    ax.set_ylim(0, 50)
+    ax.legend()
+
 def set_boxplot_props(d):
     for i in range(len(d['boxes'])):
         if i % 2 == 0:

figures/MAM2EBRAINS/M2E_visualize_resting_state.py → figures/MAM2EBRAINS/M2E_VISUALIZATION.py

@@ -17,6 +17,29 @@ from matplotlib import gridspec
 icolor = myred
 ecolor = myblue
 
+# label_spikes = M.simulation.label
+label = M.simulation.label
+
+from MAM2EBRAINS_LOAD_DATA import load_and_create_data
+A = load_and_create_data(M)
+
+def plot_instan_mean_firing_rate(M):
+    # load spike data and calculate instantaneous and mean firing rates
+    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+    
+    # visualize calculate instantaneous and mean firing rates
+    ax = pl.subplot()
+    ax.plot(tsteps, rate)
+    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
+    ax.set_title('Instantaneous and mean firing rate across all populations')
+    ax.set_xlabel('time (ms)')
+    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_xlim(0, sim_params['t_sim'])
+    ax.set_ylim(0, 50)
+    ax.legend()
+
 def set_boxplot_props(d):
     for i in range(len(d['boxes'])):
         if i % 2 == 0:

figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py

-def plot_instan_mean_firing_rate(M):
-    # load spike data and calculate instantaneous and mean firing rates
-    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
-    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
-    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
-
-    ax = pl.subplot()
-    ax.plot(tsteps, rate)
-    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
-    ax.set_title('Instantaneous and mean firing rate across all populations')
-    ax.set_xlabel('time (ms)')
-    ax.set_ylabel('firing rate (spikes / s)')
-    ax.set_xlim(0, sim_params['t_sim'])
-    ax.set_ylim(0, 50)
-    ax.legend()
\ No newline at end of file