plotter update

pina/plotter.py

@@ -11,14 +11,16 @@ class Plotter:
     Implementation of a plotter class, for easy visualizations.
     """
 
-    def plot_samples(self, problem, variables=None, **kwargs):
+    def plot_samples(self, problem, variables=None, filename=None, **kwargs):
         """
         Plot the training grid samples.
 
-        :param SolverInterface solver: The ``SolverInterface`` object.
+        :param AbstractProblem problem: The PINA problem from where to plot the domain.
         :param list(str) variables: Variables to plot. If None, all variables
             are plotted. If 'spatial', only spatial variables are plotted. If
             'temporal', only temporal variables are plotted. Defaults to None.
+        :param str filename: The file name to save the plot. If None, the plot
+            is shown using the setted matplotlib frontend. Default is None.
 
         .. todo::
             - Add support for 3D plots.
@@ -65,15 +67,21 @@ class Plotter:
             pass
 
         plt.legend()
-        plt.show()
+        if filename:
+            plt.savefig(filename)
+            plt.close()
+        else:
+            plt.show()
 
-    def _1d_plot(self, pts, pred, method, truth_solution=None, **kwargs):
+    def _1d_plot(self, pts, pred, v, method, truth_solution=None, **kwargs):
         """Plot solution for one dimensional function
 
         :param pts: Points to plot the solution.
         :type pts: torch.Tensor
         :param pred: SolverInterface solution evaluated at 'pts'.
         :type pred: torch.Tensor
+        :param v: Fixed variables when plotting the solution.
+        :type v: torch.Tensor
         :param method: Not used, kept for code compatibility
         :type method: None
         :param truth_solution: Real solution evaluated at 'pts',
@@ -82,18 +90,17 @@ class Plotter:
         """
         fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
 
-        ax.plot(pts, pred.detach(), label='Neural Network solution', **kwargs)
+        ax.plot(pts.extract(v), pred, label='Neural Network solution', **kwargs)
 
         if truth_solution:
-            truth_output = truth_solution(pts).float()
-            ax.plot(pts, truth_output.detach(), label='True solution', **kwargs)
+            truth_output = truth_solution(pts).detach()
+            ax.plot(pts.extract(v), truth_output, label='True solution', **kwargs)
 
         # TODO: pred is a torch.Tensor, so no labels is available
         #      extra variable for labels should be
         #      passed in the function arguments.
         # plt.ylabel(pred.labels[0]) 
         plt.legend()
-        plt.show()
 
     def _2d_plot(self,
                  pts,
@@ -109,6 +116,8 @@ class Plotter:
         :type pts: torch.Tensor
         :param pred: ``SolverInterface`` solution evaluated at 'pts'.
         :type pred: torch.Tensor
+        :param v: Fixed variables when plotting the solution.
+        :type v: torch.Tensor
         :param method: Matplotlib method to plot 2-dimensional data,
             see https://matplotlib.org/stable/api/axes_api.html for
             reference.
@@ -118,30 +127,30 @@ class Plotter:
         :type truth_solution: torch.Tensor, optional
         """
 
-        grids = [p_.reshape(res, res) for p_ in pts.extract(v).cpu().T]
+        grids = [p_.reshape(res, res) for p_ in pts.extract(v).T]
 
         pred_output = pred.reshape(res, res)
         if truth_solution:
             truth_output = truth_solution(pts).float().reshape(res, res).as_subclass(torch.Tensor)
             fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
 
-            cb = getattr(ax[0], method)(*grids, pred_output.cpu().detach(),
+            cb = getattr(ax[0], method)(*grids, pred_output,
                                         **kwargs)
             fig.colorbar(cb, ax=ax[0])
             ax[0].title.set_text('Neural Network prediction')
-            cb = getattr(ax[1], method)(*grids, truth_output.cpu().detach(),
+            cb = getattr(ax[1], method)(*grids, truth_output,
                                         **kwargs)
             fig.colorbar(cb, ax=ax[1])
             ax[1].title.set_text('True solution')
             cb = getattr(ax[2],
                          method)(*grids,
-                                 (truth_output - pred_output).cpu().detach(),
+                                 (truth_output - pred_output),
                                  **kwargs)
             fig.colorbar(cb, ax=ax[2])
             ax[2].title.set_text('Residual')
         else:
             fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
-            cb = getattr(ax, method)(*grids, pred_output.cpu().detach(),
+            cb = getattr(ax, method)(*grids, pred_output,
                                      **kwargs)
             fig.colorbar(cb, ax=ax)
             ax.title.set_text('Neural Network prediction')
@@ -201,11 +210,13 @@ class Plotter:
         pts = pts.append(fixed_pts)
         pts = pts.to(device=solver.device)
 
-        predicted_output = solver.forward(pts).extract(components).as_subclass(torch.Tensor)
+        # computing soluting and sending to cpu
+        predicted_output = solver.forward(pts).extract(components).as_subclass(torch.Tensor).cpu().detach()
+        pts = pts.cpu()
         truth_solution = getattr(solver.problem, 'truth_solution', None)
 
         if len(v) == 1:
-            self._1d_plot(pts.extract(v), predicted_output, method, truth_solution,
+            self._1d_plot(pts, predicted_output, v, method, truth_solution,
                           **kwargs)
         elif len(v) == 2:
             self._2d_plot(pts, predicted_output, v, res, method, truth_solution,
@@ -214,6 +225,7 @@ class Plotter:
         plt.tight_layout()
         if filename:
             plt.savefig(filename)
+            plt.close()
         else:
             plt.show()
 
@@ -280,3 +292,4 @@ class Plotter:
         # saving in file
         if filename:
             plt.savefig(filename)
+            plt.close()