Changes in ES computation and plotting functions

SA_v2/analysis/plotting.py

@@ -2,17 +2,18 @@
 from matplotlib import pyplot as plt
 import sys,os
 from sklearn.neighbors import KernelDensity
+from sklearn import preprocessing as prep
 
-def density_map(X,kde,savefile='test.png',show=True,xlabel=None,ylabel=None):
+def density_map(X, kde, savefile='test.png', show=True, xlabel=None, ylabel=None, n_mesh = 400):
 
     plt.rc('text', usetex=True)
-    font = {'family' : 'serif', 'size'   : 40}
+    font = {'family' : 'serif', 'size': 18}
     plt.rc('font', **font)
 
     fig =  plt.figure(figsize=(8,6))
     ax = fig.add_subplot(111) 
-    n_mesh=400
-    xmin,xmax = np.percentile(X[:,0],q=10.),np.max(X[:,0])
+    #n_mesh=400
+    xmin, xmax = np.percentile(X[:,0],q=10.),np.max(X[:,0])
     #xmin, xmax = np.min(X[:,0]),np.max(X[:,0])
     dx = xmax - xmin
     ymin, ymax = np.min(X[:,1]),np.max(X[:,1])
@@ -22,30 +23,27 @@ def density_map(X,kde,savefile='test.png',show=True,xlabel=None,ylabel=None):
     y = np.linspace(ymin-0.1*dy,ymax+0.1*dy, n_mesh)
     extent = (xmin-0.1*dx,xmax+0.1*dx,ymin-0.1*dy,ymax+0.1*dy)
 
-    from sklearn import preprocessing as prep
     mms=prep.MinMaxScaler()
-
     my_map=plt.get_cmap(name='BuGn')
 
-    xy=np.array([[xi,yi] for yi in y for xi in x])
+    xy=np.array([[xi, yi] for yi in y for xi in x])
     #print("kk")
-    z = np.exp(kde.evaluate_density(xy))
+    z = np.exp(kde.evaluate_density(xy)) # this should be the computationally expensive part 
     #z = np.exp(rho)
     #print("ksdjfk")
     z=mms.fit_transform(z.reshape(-1,1))
     Z=z.reshape(n_mesh, n_mesh)
     z=my_map(z)
     Zrgb = z.reshape(n_mesh, n_mesh, 4)
 
-
     Zrgb[Z < 0.005] = (1.0,1.0,1.0,1.0)
 
     plt.imshow(Zrgb, interpolation='bilinear',cmap='BuGn', extent=extent,origin='lower',aspect='auto',zorder=1)
     cb=plt.colorbar()
     cb.set_label(label='Density',labelpad=10)
 
     X1, Y1 = np.meshgrid(x,y)
-    plt.contour(X1, Y1, Z, levels=np.linspace(0.05,0.8,5), linewidths=0.3, colors='k', extent=extent,zorder=2)
+    plt.contour(X1, Y1, Z, levels=np.linspace(0.03,0.8,6), linewidths=0.3, colors='k', extent=extent,zorder=2)
     ax.grid(False)
 
     if xlabel is not None:

SA_v2/main.py

@@ -593,7 +593,7 @@ def run_ES(parameters, model:MODEL, utils):
     outfile = utils.make_file_name(parameters, root=parameters['root'])
     with open(outfile,'wb') as f:
         pickle.dump(exact_data, f, protocol=4)
-        
+
     print("Saved results in %s"%outfile)
     print("Total run time : \t %.3f s"%(time.time()-st))
     print("\n Thank you and goodbye !")

SA_v2/para.dat

@@ -1,5 +1,5 @@
 task	ES
-L	8
+L	6
 J	1.0
 hz	1.0
 hx_i	-2.