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update plot_samples, plot methods

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This commit is contained in:
Dario Coscia
2023-10-31 10:54:04 +01:00
committed by Nicola Demo
parent 8cb4df13f0
commit 5336f36f08
1 changed files with 27 additions and 23 deletions
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50
pina/plotter.py
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@@ -1,8 +1,9 @@
""" Module for plotting. """
import matplotlib.pyplot as plt
import torch
from pina.callbacks import MetricTracker
from pina.utils import check_consistency
from pina import LabelTensor
@@ -11,12 +12,12 @@ class Plotter:
Implementation of a plotter class, for easy visualizations.
"""
def plot_samples(self, problem, variables=None):
def plot_samples(self, problem, variables=None, **kwargs):
"""
Plot the training grid samples.
:param SolverInterface solver: the SolverInterface object.
:param list(str) variables: variables to plot. If None, all variables
:param SolverInterface solver: The SolverInterface object.
: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.
@@ -26,7 +27,7 @@ class Plotter:
:Example:
>>> plotter = Plotter()
>>> plotter.plot_samples(solver=solver, variables='spatial')
>>> plotter.plot_samples(problem=problem, variables='spatial')
"""
if variables is None:
@@ -47,9 +48,9 @@ class Plotter:
variables).T.detach()
if coords.shape[0] == 1: # 1D samples
ax.plot(coords.flatten(), torch.zeros(coords.flatten().shape), '.',
label=location)
label=location, **kwargs)
else:
ax.plot(*coords, '.', label=location)
ax.plot(*coords, '.', label=location, **kwargs)
ax.set_xlabel(variables[0])
try:
@@ -72,7 +73,7 @@ class Plotter:
:type pts: torch.Tensor
:param pred: SolverInterface solution evaluated at 'pts'.
:type pred: torch.Tensor
:param method: not used, kept for code compatibility
:param method: Not used, kept for code compatibility
:type method: None
:param truth_solution: Real solution evaluated at 'pts',
defaults to None.
@@ -80,11 +81,11 @@ class Plotter:
"""
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
ax.plot(pts, pred.detach(), label='neural net solution', **kwargs)
ax.plot(pts, pred.detach(), label='Neural Network solution', **kwargs)
if truth_solution:
truth_output = truth_solution(pts).float()
ax.plot(pts, truth_output.detach(), label='true solution', **kwargs)
ax.plot(pts, truth_output.detach(), label='True solution', **kwargs)
plt.xlabel(pts.labels[0])
plt.ylabel(pred.labels[0])
@@ -99,7 +100,7 @@ class Plotter:
:type pts: torch.Tensor
:param pred: SolverInterface solution evaluated at 'pts'.
:type pred: torch.Tensor
:param method: matplotlib method to plot 2-dimensional data,
:param method: Matplotlib method to plot 2-dimensional data,
see https://matplotlib.org/stable/api/axes_api.html for
reference.
:type method: str
@@ -118,40 +119,44 @@ class Plotter:
cb = getattr(ax[0], method)(
*grids, pred_output.cpu().detach(), **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(), **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(),
**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(), **kwargs)
fig.colorbar(cb, ax=ax)
ax.title.set_text('Neural Network prediction')
def plot(self, trainer, components=None, fixed_variables={}, method='contourf',
def plot(self, solver, components=None, fixed_variables={}, method='contourf',
res=256, filename=None, **kwargs):
"""
Plot sample of SolverInterface output.
:param Trainer trainer: the Trainer object.
:param list(str) components: the output variable to plot. If None, all
:param SolverInterface solver: The SolverInterface object instance.
:param list(str) components: The output variable to plot. If None, all
the output variables of the problem are selected. Default value is
None.
:param dict fixed_variables: a dictionary with all the variables that
:param dict fixed_variables: A dictionary with all the variables that
should be kept fixed during the plot. The keys of the dictionary
are the variables name whereas the values are the corresponding
values of the variables. Defaults is `dict()`.
:param {'contourf', 'pcolor'} method: the matplotlib method to use for
:param {'contourf', 'pcolor'} method: The matplotlib method to use for
plotting the solution. Default is 'contourf'.
:param int res: the resolution, aka the number of points used for
:param int res: The resolution, aka the number of points used for
plotting in each axis. Default is 256.
:param str filename: the file name to save the plot. If None, the plot
:param str filename: The file name to save the plot. If None, the plot
is shown using the setted matplotlib frontend. Default is None.
"""
solver = trainer.solver
if components is None:
components = [solver.problem.output_variables]
v = [
@@ -182,9 +187,8 @@ class Plotter:
self._2d_plot(pts, predicted_output, v, res, method,
truth_solution, **kwargs)
plt.tight_layout()
if filename:
plt.title('Output {} with parameter {}'.format(components,
fixed_variables))
plt.savefig(filename)
else:
plt.show()
@@ -193,14 +197,14 @@ class Plotter:
"""
Plot the loss function values during traininig.
:param SolverInterface solver: the SolverInterface object.
:param Trainer trainer: the PINA Trainer object instance.
:param str/list(str) metric: The metrics to use in the y axis. If None, the mean loss
is plotted.
:param bool logy: If True, the y axis is in log scale. Default is
True.
:param bool logx: If True, the x axis is in log scale. Default is
True.
:param str filename: the file name to save the plot. If None, the plot
:param str filename: The file name to save the plot. If None, the plot
is shown using the setted matplotlib frontend. Default is None.
"""