version 0.0.1

examples/run_parametric_poisson.py

@@ -1,27 +1,21 @@
-import sys
-import numpy as np
-import torch
 import argparse
-from pina.pinn import PINN
-from pina.ppinn import ParametricPINN as pPINN
-from pina.label_tensor import LabelTensor
-from torch.nn import ReLU, Tanh, Softplus
-from problems.parametric_poisson import ParametricPoisson2DProblem as Poisson2D
-from pina.deep_feed_forward import DeepFeedForward
+import torch
+from torch.nn import Softplus
 
-from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
+from pina import PINN as pPINN
+from problems.parametric_poisson import ParametricPoisson
+from pina.model import FeedForward
 
 
 class myFeature(torch.nn.Module):
     """
-    Feature: sin(x)
     """
-
     def __init__(self):
         super(myFeature, self).__init__()
 
     def forward(self, x):
-        return torch.exp(- 2*(x[:, 0] - x[:, 2])**2 - 2*(x[:, 1] - x[:, 3])**2)
+        return torch.exp(- 2*(x['x'] - x['mu1'])**2 - 2*(x['y'] - x['mu2'])**2)
+
 
 if __name__ == "__main__":
 
@@ -35,11 +29,11 @@ if __name__ == "__main__":
 
     feat = [myFeature()] if args.features else []
 
-    poisson_problem = Poisson2D()
-    model = DeepFeedForward(
+    poisson_problem = ParametricPoisson()
+    model = FeedForward(
         layers=[200, 40, 10],
         output_variables=poisson_problem.output_variables,
-        input_variables=poisson_problem.input_variables+['mu1', 'mu2'],
+        input_variables=poisson_problem.input_variables,
         func=Softplus,
         extra_features=feat
     )
@@ -53,105 +47,10 @@ if __name__ == "__main__":
 
     if args.s:
 
-        pinn.span_pts(30, 'chebyshev', ['D'])
-        pinn.span_pts(50, 'grid', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
-        #pinn.plot_pts()
+        pinn.span_pts(2000, 'random', ['D'])
+        pinn.span_pts(200, 'random', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
         pinn.train(10000, 10)
         pinn.save_state('pina.poisson_param')
 
     else:
         pinn.load_state('pina.poisson_param')
-        #pinn.plot(40, torch.tensor([-0.8, -0.8]))
-        #pinn.plot(40, torch.tensor([ 0.8,  0.8]))
-
-        from smithers.io import VTUHandler
-        from scipy.interpolate import griddata
-        import matplotlib
-        matplotlib.use('GTK3Agg')
-        import matplotlib.pyplot as plt
-
-        res = 64
-        fname_minus = 'Poisson_param_08minus000000.vtu'
-        param = torch.tensor([-0.8, -0.8])
-        pts_container = []
-        for mn, mx in [[-1, 1], [-1, 1]]:
-            pts_container.append(np.linspace(mn, mx, res))
-        grids_container = np.meshgrid(*pts_container)
-        unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T
-        unrolled_pts = torch.cat([unrolled_pts, param.double().repeat(unrolled_pts.shape[0]).reshape(-1, 2)], axis=1)
-
-        #unrolled_pts.to(dtype=self.dtype)
-        unrolled_pts = LabelTensor(unrolled_pts, ['x1', 'x2', 'mu1', 'mu2'])
-
-        Z_pred = pinn.model(unrolled_pts.tensor)
-        data = VTUHandler.read(fname_minus)
-
-
-        print(data['points'][:, :2].shape)
-        print(data['point_data']['f_16'].shape)
-        print(grids_container[0].shape)
-        print(grids_container[1].shape)
-        Z_truth = griddata(data['points'][:, :2], data['point_data']['f_16'], (grids_container[0], grids_container[1]))
-
-
-        err = np.abs(Z_truth + Z_pred.tensor.reshape(res, res).detach().numpy())
-
-        plt.subplot(1, 3, 1)
-        plt.pcolor(-Z_pred.tensor.reshape(res, res).detach())
-        plt.colorbar()
-        plt.subplot(1, 3, 2)
-        plt.pcolor(Z_truth)
-        plt.colorbar()
-        plt.subplot(1, 3, 3)
-        plt.pcolor(err, vmin=0, vmax=0.009)
-        plt.colorbar()
-        plt.show()
-
-        print(unrolled_pts.tensor.shape)
-        with open('parpoisson_minus_plot.txt', 'w') as f_:
-            f_.write('x y truth pred e\n')
-            for (x, y), tru, pre, e in zip(unrolled_pts[:, :2], Z_truth.reshape(-1, 1), -Z_pred.tensor.reshape(-1, 1), err.reshape(-1, 1)):
-                f_.write('{} {} {} {} {}\n'.format(x.item(), y.item(), tru.item(), pre.item(), e.item()))
-
-        fname_plus = 'Poisson_param_08plus000000.vtu'
-        param = torch.tensor([0.8, 0.8])
-        pts_container = []
-        for mn, mx in [[-1, 1], [-1, 1]]:
-            pts_container.append(np.linspace(mn, mx, res))
-        grids_container = np.meshgrid(*pts_container)
-        unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T
-        unrolled_pts = torch.cat([unrolled_pts, param.double().repeat(unrolled_pts.shape[0]).reshape(-1, 2)], axis=1)
-
-        #unrolled_pts.to(dtype=self.dtype)
-        unrolled_pts = LabelTensor(unrolled_pts, ['x1', 'x2', 'mu1', 'mu2'])
-
-        Z_pred = pinn.model(unrolled_pts.tensor)
-        data = VTUHandler.read(fname_plus)
-
-
-        print(data['points'][:, :2].shape)
-        print(data['point_data']['f_16'].shape)
-        print(grids_container[0].shape)
-        print(grids_container[1].shape)
-        Z_truth = griddata(data['points'][:, :2], data['point_data']['f_16'], (grids_container[0], grids_container[1]))
-
-
-        err = np.abs(Z_truth + Z_pred.tensor.reshape(res, res).detach().numpy())
-
-        plt.subplot(1, 3, 1)
-        plt.pcolor(-Z_pred.tensor.reshape(res, res).detach())
-        plt.colorbar()
-        plt.subplot(1, 3, 2)
-        plt.pcolor(Z_truth)
-        plt.colorbar()
-        plt.subplot(1, 3, 3)
-        print('gggggg')
-        plt.pcolor(err, vmin=0, vmax=0.001)
-        plt.colorbar()
-        plt.show()
-        with open('parpoisson_plus_plot.txt', 'w') as f_:
-            f_.write('x y truth pred e\n')
-            for (x, y), tru, pre, e in zip(unrolled_pts[:, :2], Z_truth.reshape(-1, 1), -Z_pred.tensor.reshape(-1, 1), err.reshape(-1, 1)):
-                f_.write('{} {} {} {} {}\n'.format(x.item(), y.item(), tru.item(), pre.item(), e.item()))
-
-