Minor changes to plotted figures

mirams · Jan 10, 2017 · e348229 · e348229
1 parent ab98e7b
commit e348229
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Showing 2 changed files with 57 additions and 6 deletions.
diff --git a/python/PyHillFit.py b/python/PyHillFit.py
@@ -6,6 +6,7 @@
 import os
 import argparse
 import scipy.stats as st
+import matplotlib.patches as mpatches
 
 try:
     import cma
@@ -346,6 +347,9 @@ def run_hierarchical(drug_channel):
     mins = [0,0,-5,0,0]
     maxs = [8,22,20,2,25]
 
+    prior_xs = []
+    priors = []
+
     total_axes = (6,4)
     fig = plt.figure(figsize=(6,7))
     for i in range(len(labels)-1):
@@ -360,6 +364,8 @@ def run_hierarchical(drug_channel):
         ax = plt.subplot2grid(total_axes, axloc,colspan=2,rowspan=2)
         x_prior = np.linspace(mins[i],maxs[i],501)
         prior = st.gamma.pdf(x_prior,a=shapes[i],scale=scales[i],loc=locs[i])
+        prior_xs.append(x_prior)
+        priors.append(prior)
         ax.plot(x_prior,prior,label='Gamma prior',lw=2)
         ax.set_xlabel(labels[i])
         ax.set_ylabel('Probability density')
@@ -375,7 +381,11 @@ def run_hierarchical(drug_channel):
     i = len(labels)-1
     ax = plt.subplot2grid(total_axes, (4,1),colspan=2,rowspan=2)
     x_prior = np.linspace(mins[i],maxs[i],501)
-    ax.plot(x_prior,st.gamma.pdf(x_prior,a=shapes[i],scale=scales[i],loc=locs[i]),label='Gamma prior',lw=2)
+    prior = st.gamma.pdf(x_prior,a=shapes[i],scale=scales[i],loc=locs[i])
+    ax.plot(x_prior,prior,label='Gamma prior',lw=2)
+    prior_xs.append(x_prior)
+    priors.append(prior)
+
     ax.set_xlabel(labels[i])
     ax.set_ylabel('Probability density')
     ax.set_xlim(mins[i],maxs[i])
@@ -559,6 +569,47 @@ def run_hierarchical(drug_channel):
         #fig.savefig(marginals_dir+'{}_{}_{}_marginal.pdf'.format(drug,channel,filename))
         plt.close()
 
+    total_axes = (6,4)
+    fig = plt.figure(figsize=(6,7))
+    for i in range(5): # have to do sigma separately
+        if i==0:
+            axloc = (0,0)
+        elif i==1:
+            axloc = (0,2)
+        elif i==2:
+            axloc = (2,0)
+        elif i==3:
+            axloc = (2,2)
+        elif i==4:
+            axloc = (4,0)
+        ax = plt.subplot2grid(total_axes, axloc,colspan=2,rowspan=2)
+
+        ax.set_xlabel(labels[i])
+        ax.set_ylabel('Probability density')
+
+        ax.grid()
+        if (i<4):
+            min_sample = np.min(chain[burn:,i])
+            max_sample = np.max(chain[burn:,i])
+            ax.hist(chain[burn:,i],bins=50,normed=True,color='blue',edgecolor='blue')
+        elif (i==4):
+            min_sample = np.min(chain[burn:,-2])
+            max_sample = np.max(chain[burn:,-2])
+            ax.hist(chain[burn:,-2],bins=50,normed=True,color='blue',edgecolor='blue')  # -1 would be log-target
+        ax.set_xlim(min_sample,max_sample)
+        pts_in_this_range = np.where((prior_xs[i] >= min_sample) & (prior_xs[i] <= max_sample))
+        x_in_this_range = prior_xs[i][pts_in_this_range]
+        prior_in_this_range = priors[i][pts_in_this_range]
+        line = ax.plot(x_in_this_range,prior_in_this_range,lw=2,color='red',label='Prior distributions')
+    leg_ax = plt.subplot2grid(total_axes, (4,2),colspan=2,rowspan=2)
+    leg_ax.axis('off')
+    hist = mpatches.Patch(color='blue', label='Normalised histograms')
+    leg_ax.legend(handles=line+[hist],loc="center",fontsize=12,bbox_to_anchor=[0.38,0.7])
+    fig.tight_layout()
+    fig.savefig(figs_dir+'all_prior_distributions_and_marginals.png')
+    fig.savefig(figs_dir+'all_prior_distributions_and_marginals.pdf')
+    plt.close()
+
     print "Marginal plots saved in", marginals_dir
 
     print "\n\n{} + {} complete!\n\n".format(drug,channel)

diff --git a/python/plot_all_predicted_curves.py b/python/plot_all_predicted_curves.py
@@ -73,7 +73,7 @@ def run(drug_channel):
     ax1.set_xlim(10**xmin,10**xmax)
 
     for expt in experiment_numbers:
-        ax1.scatter(experiments[expt][:,0],experiments[expt][:,1],label='Expt {}'.format(expt+1),color=colors[expt],s=100)
+        ax1.scatter(experiments[expt][:,0],experiments[expt][:,1],label='Expt {}'.format(expt+1),color=colors[expt],s=100,zorder=10)
 
     for i, conc in enumerate(args.concs):
         ax1.axvline(conc,color=colors[3+i],lw=2,label=r"{} $\mu$M".format(conc),alpha=0.8)
@@ -112,7 +112,7 @@ def run(drug_channel):
     ax3.set_xlim(10**xmin,10**xmax)
 
     for expt in experiment_numbers:
-        ax3.scatter(experiments[expt][:,0],experiments[expt][:,1],label='Expt {}'.format(expt+1),color=colors[expt],s=100)#,zorder=10)
+        ax3.scatter(experiments[expt][:,0],experiments[expt][:,1],label='Expt {}'.format(expt+1),color=colors[expt],s=100,zorder=10)
 
     chain = np.loadtxt(chain_file)
     end = chain.shape[0]
@@ -173,9 +173,9 @@ def run(drug_channel):
 
     for expt in experiment_numbers:
         if expt==1:
-            ax5.scatter(experiments[expt][:,0],experiments[expt][:,1],color='orange',s=100,label='All expts')
+            ax5.scatter(experiments[expt][:,0],experiments[expt][:,1],color='orange',s=100,label='All expts',zorder=10)
         else:
-            ax5.scatter(experiments[expt][:,0],experiments[expt][:,1],color='orange',s=100)
+            ax5.scatter(experiments[expt][:,0],experiments[expt][:,1],color='orange',s=100,zorder=10)
 
     for i, conc in enumerate(args.concs):
         ax5.axvline(conc,color=colors[3+i],alpha=0.8,lw=2,label=r"{} $\mu$M".format(conc))
@@ -203,7 +203,7 @@ def run(drug_channel):
 
     fig.tight_layout()
     fig.savefig(plot_dir+'{}_{}_all_predictions.png'.format(drug,channel))
-    #fig.savefig(plot_dir+'{}_{}_all_predictions.pdf'.format(drug,channel)) # uncomment to save as pdf, or change extension to whatever you want
+    fig.savefig(plot_dir+'{}_{}_all_predictions.pdf'.format(drug,channel)) # uncomment to save as pdf, or change extension to whatever you want
 
 
     plt.close()