Remove dependence of seaborn from SchueckerSchmidt2017 scripts

figures/SchueckerSchmidt2017/stabilization.py

@@ -5,8 +5,6 @@ from multiarea_model import MultiAreaModel
 from multiarea_model.multiarea_helpers import create_vector_mask
 from multiarea_model.stabilize import stabilize
 import utils
-import seaborn as sns
-cp = sns.color_palette()
 
 """
 Initialization
@@ -68,7 +66,8 @@ for iteration in [1, 2, 3, 4, 5]:
         # Scan parameter space to find a good approximation of the
         # critical parameter value where the model crosses the
         # separatrix for the initial condition of zero rates
-        if iteration < 5: # For iteration 5, we just analyze the behavior without performing the stabilization
+        if iteration < 5:
+            # For iteration 5, we just analyze the behavior without performing the stabilization
             data[iteration] = utils.compute_iteration(7, fac_nu_ext_5E_list,
                                                       theory_params, M_base, threads=THREADS)
         else:

figures/SchueckerSchmidt2017/utils.py

@@ -53,15 +53,12 @@ def velocity_peaks(time, result, threshold=0.05):
 
 
 def plot_iteration(results, theory_params, threshold=0.05, full=True):
-    import seaborn as sns
-
     traj = np.mean(results, axis=1)
     if full:
         ind = list(range(0, len(traj)))
     else:
         i = np.argmax(np.diff(traj[:, -1]))
         ind = [i, i+1]
-    cp = sns.color_palette(n_colors=len(ind))
 
     time = np.arange(0., theory_params['T'], theory_params['dt'])
     fig = pl.figure()
@@ -76,13 +73,13 @@ def plot_iteration(results, theory_params, threshold=0.05, full=True):
     ax = fig.add_subplot(122)
     for n, i in enumerate(ind):
         d_nu, minima = velocity_peaks(time, results[i], threshold=threshold)
-        ax.plot(time[:-1], d_nu, color=cp[n])
+        ax.plot(time[:-1], d_nu)
         if not full:
             ax.vlines(time[minima],
                       1e-6,
                       1e0,
                       linestyles='dashed',
-                      color=cp[n])
+                      color='k')
     ax.set_yscale('Log')
     pl.show()