/

figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_instantaneous_and_mean_firing_rates-checkpoint.py

@@ -8,14 +8,6 @@ def plot_instan_mean_firing_rate(M):
     rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
     
     # visualize calculate instantaneous and mean firing rates
-    nrows = 1
-    ncols = 1
-    width = 10
-    panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
-    
-    height = width / panel_wh_ratio * float(nrows) / ncols
-    pl.rcParams['figure.figsize'] = (width, height)
-
     fig = pl.figure()
     fig, ax = pl.subplots()
     ax.plot(tsteps, rate)

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

@@ -8,6 +8,7 @@ sys.path.append('./figures/Schmidt2018_dyn')
 # from helpers import original_data_path, population_labels
 from helpers import population_labels
 from multiarea_model import MultiAreaModel
+from multiarea_model import Analysis
 from plotcolors import myred, myblue
 
 import matplotlib.pyplot as pl

figures/MAM2EBRAINS/M2E_visualize_instantaneous_and_mean_firing_rates.py

@@ -8,14 +8,6 @@ def plot_instan_mean_firing_rate(M):
     rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
     
     # visualize calculate instantaneous and mean firing rates
-    nrows = 1
-    ncols = 1
-    width = 10
-    panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
-    
-    height = width / panel_wh_ratio * float(nrows) / ncols
-    pl.rcParams['figure.figsize'] = (width, height)
-
     fig = pl.figure()
     fig, ax = pl.subplots()
     ax.plot(tsteps, rate)

figures/MAM2EBRAINS/M2E_visualize_resting_state.py

@@ -8,6 +8,7 @@ sys.path.append('./figures/Schmidt2018_dyn')
 # from helpers import original_data_path, population_labels
 from helpers import population_labels
 from multiarea_model import MultiAreaModel
+from multiarea_model import Analysis
 from plotcolors import myred, myblue
 
 import matplotlib.pyplot as pl