160 lines
6.3 KiB
Python
160 lines
6.3 KiB
Python
""" Module for plotting. """
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import matplotlib
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#matplotlib.use('Qt5Agg')
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from pina import LabelTensor
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from pina import PINN
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from .problem import SpatialProblem, TimeDependentProblem
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#from pina.tdproblem1d import TimeDepProblem1D
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class Plotter:
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def _plot_2D(self, obj, method='contourf'):
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"""
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"""
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if not isinstance(obj, PINN):
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raise RuntimeError
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res = 256
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pts = obj.problem.spatial_domain.discretize(res, 'grid')
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grids_container = [
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pts[:, 0].reshape(res, res),
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pts[:, 1].reshape(res, res),
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]
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pts = LabelTensor(torch.tensor(pts), obj.problem.input_variables)
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predicted_output = obj.model(pts.tensor)
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if hasattr(obj.problem, 'truth_solution'):
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truth_output = obj.problem.truth_solution(*pts.tensor.T).float()
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fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
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cb = getattr(axes[0], method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[0])
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cb = getattr(axes[1], method)(*grids_container, truth_output.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[1])
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cb = getattr(axes[2], method)(*grids_container, (truth_output-predicted_output.tensor.float().flatten()).detach().reshape(res, res))
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fig.colorbar(cb, ax=axes[2])
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else:
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
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cb = getattr(axes, method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes)
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def _plot_1D_TimeDep(self, obj, method='contourf'):
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"""
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"""
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if not isinstance(obj, PINN):
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raise RuntimeError
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res = 256
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grids_container = np.meshgrid(
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obj.problem.spatial_domain.discretize(res, 'grid'),
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obj.problem.temporal_domain.discretize(res, 'grid'),
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)
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pts = np.hstack([
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grids_container[0].reshape(-1, 1),
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grids_container[1].reshape(-1, 1),
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])
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pts = LabelTensor(torch.tensor(pts), obj.problem.input_variables)
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predicted_output = obj.model(pts.tensor)
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if hasattr(obj.problem, 'truth_solution'):
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truth_output = obj.problem.truth_solution(*pts.tensor.T).float()
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fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
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cb = getattr(axes[0], method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[0])
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cb = getattr(axes[1], method)(*grids_container, truth_output.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[1])
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cb = getattr(axes[2], method)(*grids_container, (truth_output-predicted_output.tensor.float().flatten()).detach().reshape(res, res))
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fig.colorbar(cb, ax=axes[2])
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else:
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
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cb = getattr(axes, method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes)
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def plot(self, obj, method='contourf', filename=None):
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"""
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"""
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res = 256
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pts = obj.problem.domain.sample(res, 'grid')
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print(pts)
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grids_container = [
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pts.tensor[:, 0].reshape(res, res),
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pts.tensor[:, 1].reshape(res, res),
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]
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predicted_output = obj.model(pts)
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predicted_output = predicted_output['p']
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if hasattr(obj.problem, 'truth_solution'):
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truth_output = obj.problem.truth_solution(*pts.tensor.T).float()
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fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
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cb = getattr(axes[0], method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[0])
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cb = getattr(axes[1], method)(*grids_container, truth_output.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[1])
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cb = getattr(axes[2], method)(*grids_container, (truth_output-predicted_output.tensor.float().flatten()).detach().reshape(res, res))
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fig.colorbar(cb, ax=axes[2])
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else:
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
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# cb = getattr(axes, method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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cb = getattr(axes, method)(*grids_container, predicted_output.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes)
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if filename:
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plt.savefig(filename)
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else:
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plt.show()
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def plot(self, obj, method='contourf', filename=None):
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"""
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"""
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res = 256
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pts = obj.problem.domain.sample(res, 'grid')
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print(pts)
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grids_container = [
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pts.tensor[:, 0].reshape(res, res),
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pts.tensor[:, 1].reshape(res, res),
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]
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predicted_output = obj.model(pts)
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predicted_output = predicted_output['ux']
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if hasattr(obj.problem, 'truth_solution'):
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truth_output = obj.problem.truth_solution(*pts.tensor.T).float()
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fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
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cb = getattr(axes[0], method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[0])
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cb = getattr(axes[1], method)(*grids_container, truth_output.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes[1])
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cb = getattr(axes[2], method)(*grids_container, (truth_output-predicted_output.tensor.float().flatten()).detach().reshape(res, res))
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fig.colorbar(cb, ax=axes[2])
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else:
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
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# cb = getattr(axes, method)(*grids_container, predicted_output.tensor.reshape(res, res).detach())
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cb = getattr(axes, method)(*grids_container, predicted_output.reshape(res, res).detach())
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fig.colorbar(cb, ax=axes)
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if filename:
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plt.savefig(filename)
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else:
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plt.show()
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def plot_samples(self, obj):
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for location in obj.input_pts:
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plt.plot(*obj.input_pts[location].tensor.T.detach(), '.', label=location)
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plt.legend()
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plt.show()
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