changed model plotting routine

scripts/plotting/plot_model.py

@@ -3,36 +3,107 @@
 
 import argparse, yaml, pprint, os, shutil, datetime, sys, pickle
 import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib.patches import FancyArrowPatch
 from cyclejet.cyclegan import CycleGAN
-from cyclejet.tools import loss_calc, plot_model
+from cyclejet.tools import loss_calc, plot_model, xval, yval
 from cyclejet.scripts.run import load_yaml
+from random import randrange
+
+def plot_event(fn, refA, refB, predictA, predictB,
+               predictA2, predictB2, averager, titleA=None, titleB=None):
+    with PdfPages(fn) as pdf:
+        fig, axs = plt.subplots(2,3, figsize=(7.5,5.5))
+        plt.subplots_adjust(wspace=0.5,hspace=0.52)
+        i = randrange(len(refA))
+        # figtr = fig.transFigure.inverted()
+        # ptB = figtr.transform(ax0tr.transform((225., -10.)))
+        # ptE = figtr.transform(ax1tr.transform((225., 1.)))
+        # arrow=FancyArrowPatch(
+        #     ptB, ptE, transform=fig.transFigure,  # Place arrow in figure coord system
+        #     fc = "g", connectionstyle="arc3,rad=0.2", arrowstyle='simple', alpha = 0.3,
+        #     mutation_scale = 40.)
+        # fig.patches.append(arrow)
+        axs[0,0].imshow(refA[i].transpose(),vmin=0.0,vmax=0.2,origin='lower',
+                        aspect='auto', extent=[xval[0], xval[1], yval[0], yval[1]])
+        axs[0,0].set_title('A' if not titleA else titleA)
+        axs[0,0].set_xticks([])
+        axs[0,0].set_yticks([])
+        axs[0,1].imshow(predictB[i].transpose(),vmin=0.0,vmax=0.2,origin='lower',
+                        aspect='auto', extent=[xval[0], xval[1], yval[0], yval[1]])
+        # axs[0,1].set_xlabel('$\ln(1 / \Delta_{ab})$')
+        # axs[0,1].set_ylabel('$\ln(k_{t} / \mathrm{GeV})$',labelpad=-2)
+        axs[0,1].set_title('B' if not titleB else titleB)
+        axs[0,1].set_xticks([])
+        axs[0,1].set_yticks([])
+        axs[0,2].imshow(predictB2[i].transpose(),vmin=0.0,vmax=0.2,origin='lower',
+                        aspect='auto', extent=[xval[0], xval[1], yval[0], yval[1]])
+        axs[0,2].set_title('A' if not titleA else titleA)
+
+        axs[0,2].set_xticks([])
+        axs[0,2].set_yticks([])
+        axs[1,0].imshow(averager.inverse(refA)[i].transpose(),
+                        vmin=0.0,vmax=0.2,origin='lower',aspect='auto',
+                        extent=[xval[0], xval[1], yval[0], yval[1]])
+        #axs[1,0].set_title('A' if not titleA else titleA)
+        axs[1,0].set_xticks([])
+        axs[1,0].set_yticks([])
+        axs[1,1].imshow(averager.inverse(predictB)[i].transpose(),
+                        vmin=0.0,vmax=0.2,origin='lower',aspect='auto',
+                        extent=[xval[0], xval[1], yval[0], yval[1]])
+        # axs[0,1].set_xlabel('$\ln(1 / \Delta_{ab})$')
+        # axs[0,1].set_ylabel('$\ln(k_{t} / \mathrm{GeV})$',labelpad=-2)
+        #axs[1,1].set_title('B' if not titleB else titleB)
+        axs[1,1].set_xticks([])
+        axs[1,1].set_yticks([])
+        axs[1,2].imshow(averager.inverse(predictB2)[i].transpose(),
+                        vmin=0.0,vmax=0.2,origin='lower',aspect='auto',
+                        extent=[xval[0], xval[1], yval[0], yval[1]])
+        #axs[1,2].set_title('A' if not titleA else titleA)
+        axs[1,2].set_xticks([])
+        axs[1,2].set_yticks([])
+        # pdf.savefig()
+        # plt.close()
+
+        # fig, axs = plt.subplots(3, 2, figsize=(6,8))
+        plt.close()
+        pdf.savefig(fig)
+
 
 def main(args):
-    if os.path.isfile(args.model.strip('/')+'/best-model.yaml'):
-        fn=args.model.strip('/')+'/best-model.yaml'
+    model=args.model.strip('/')
+    if os.path.isfile(model+'/best-model.yaml'):
+        fn=model+'/best-model.yaml'
     else:
-        fn=args.model.strip('/')+'/input-runcard.json'
+        fn=model+'/input-runcard.json'
     hps=load_yaml(fn)
     cgan = CycleGAN(hps)
-    cgan.load(args.model.strip('/'))
+    cgan.load(model)
     refA=np.array(cgan.imagesA)
     refB=np.array(cgan.imagesB)
     # generating predicted sample
     predictA=cgan.g_BA.predict(refA)
     predictB=cgan.g_AB.predict(refB)
+    predictA2=cgan.g_AB.predict(predictA)
+    predictB2=cgan.g_BA.predict(predictB)
     refA = cgan.preproc.inverse(refA)
     refB = cgan.preproc.inverse(refB)
     predictA = cgan.preproc.inverse(predictA)
     predictB = cgan.preproc.inverse(predictB)
     if args.savefull:
-        np.save('%s/referenceA'%args.model.strip('/'), refA)
-        np.save('%s/referenceB'%args.model.strip('/'), refB)
-        np.save('%s/predictedA'%args.model.strip('/'), predictA)
-        np.save('%s/predictedB'%args.model.strip('/'), predictB)
+        np.save('%s/referenceA'%model, refA)
+        np.save('%s/referenceB'%model, refB)
+        np.save('%s/predictedA'%model, predictA)
+        np.save('%s/predictedB'%model, predictB)
 
-    # now create diagnostic plots
-    figfn='%s/result.pdf' % args.model.strip('/')
-    plot_model(figfn, refA, refB, predictA, predictB,
+    # now create plots
+    figfn1='%s/result.pdf' % model
+    plot_model(figfn1, refA, refB, predictA, predictB,
+                   titleA=args.titleA, titleB=args.titleB)
+    figfn2='%s/result_event.pdf' % model
+    plot_event(figfn2, refA, refB, predictA, predictB,
+               predictA, predictB, cgan.avg,
                titleA=args.titleA, titleB=args.titleB)
 
 #----------------------------------------------------------------------
@@ -46,7 +117,6 @@ def main(args):
                         help='Title of sample A.')
     parser.add_argument('--titleB', type=str, default=None,
                         help='Title of sample A.')
-
     parser.add_argument('--savefull', action='store_true')
     args = parser.parse_args()
     main(args)