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Modify pina.__init__.py and rm useless .py files

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* rm meta.py, plotter.py, writer.py
* modify __init__ file
* modify tests due to __init__ import
This commit is contained in:
Dario Coscia
2025-02-19 11:14:53 +01:00
committed by Nicola Demo
parent 9c9d4fe7e4
commit 810d215ca0
8 changed files with 12 additions and 485 deletions
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21
pina/__init__.py
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@@ -1,18 +1,15 @@
__all__ = [
"Trainer", "LabelTensor", "Plotter", "Condition",
"PinaDataModule", 'TorchOptimizer', 'Graph',
"RadiusGraph", "KNNGraph"
"Trainer",
"LabelTensor",
"Condition",
"PinaDataModule",
'Graph',
"SolverInterface",
"MultiSolverInterface"
]
from .meta import *
from .label_tensor import LabelTensor
from .solvers.solver import SolverInterface
from .graph import Graph
from .solvers.solver import SolverInterface, MultiSolverInterface
from .trainer import Trainer
from .plotter import Plotter
from .condition.condition import Condition
from .data import PinaDataModule
from .optim import TorchOptimizer
from .optim import TorchScheduler
from .graph import Graph, RadiusGraph, KNNGraph

22
pina/meta.py
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@@ -1,22 +0,0 @@
__all__ = [
"__project__",
"__title__",
"__author__",
"__copyright__",
"__license__",
"__version__",
"__mail__",
"__maintainer__",
"__status__",
]
__project__ = "PINA"
__title__ = "pina"
__author__ = "PINA Contributors"
__copyright__ = "2021-2025, PINA Contributors"
__license__ = "MIT"
__version__ = "0.2.0"
__mail__ = 'demo.nicola@gmail.com, dario.coscia@sissa.it' # TODO
__maintainer__ = __author__
__status__ = "Alpha"
__packagename__ = "pina-mathlab"

323
pina/plotter.py
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@@ -1,323 +0,0 @@
""" Module for plotting. """
import matplotlib.pyplot as plt
import torch
from pina.callbacks import MetricTracker
from .label_tensor import LabelTensor
class Plotter:
"""
Implementation of a plotter class, for easy visualizations.
"""
def plot_samples(self, problem, variables=None, filename=None, **kwargs):
"""
Plot the training grid samples.
: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.
- Fix support for more complex problems.
:Example:
>>> plotter = Plotter()
>>> plotter.plot_samples(problem=problem, variables='spatial')
"""
if variables is None:
variables = problem.domain.variables
elif variables == "spatial":
variables = problem.spatial_domain.variables
elif variables == "temporal":
variables = problem.temporal_domain.variables
if len(variables) not in [1, 2, 3]:
raise ValueError(
"Samples can be plotted only in " "dimensions 1, 2 and 3."
)
fig = plt.figure()
proj = "3d" if len(variables) == 3 else None
ax = fig.add_subplot(projection=proj)
for location in problem.input_pts:
coords = problem.input_pts[location].extract(variables).T.detach()
if len(variables) == 1: # 1D samples
ax.plot(
coords.flatten(),
torch.zeros(coords.flatten().shape),
".",
label=location,
**kwargs,
)
elif len(variables) == 2:
ax.plot(*coords, ".", label=location, **kwargs)
elif len(variables) == 3:
ax.scatter(*coords, ".", label=location, **kwargs)
ax.set_xlabel(variables[0])
try:
ax.set_ylabel(variables[1])
except (IndexError, AttributeError):
pass
try:
ax.set_zlabel(variables[2])
except (IndexError, AttributeError):
pass
plt.legend()
if filename:
plt.savefig(filename)
plt.close()
else:
plt.show()
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',
defaults to None.
:type truth_solution: torch.Tensor, optional
"""
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
ax.plot(pts.extract(v), pred, label="Neural Network solution", **kwargs)
if truth_solution:
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()
def _2d_plot(
self, pts, pred, v, res, method, truth_solution=None, **kwargs
):
"""Plot solution for two 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: Matplotlib method to plot 2-dimensional data,
see https://matplotlib.org/stable/api/axes_api.html for
reference.
:type method: str
:param truth_solution: Real solution evaluated at 'pts',
defaults to None.
:type truth_solution: torch.Tensor, optional
"""
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, **kwargs)
fig.colorbar(cb, ax=ax[0])
ax[0].title.set_text("Neural Network prediction")
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), **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, **kwargs)
fig.colorbar(cb, ax=ax)
ax.title.set_text("Neural Network prediction")
def plot(
self,
solver,
components=None,
fixed_variables={},
method="contourf",
res=256,
filename=None,
title=None,
**kwargs,
):
"""
Plot sample of SolverInterface output.
:param SolverInterface solver: The ``SolverInterface`` object instance.
:param str | list(str) components: The output variable(s) 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
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 str method: The matplotlib method to use for
plotting the solution. Available methods are {'contourf', 'pcolor'}.
Default is 'contourf'.
:param int res: The resolution, aka the number of points used for
plotting in each axis. Default is 256.
:param str title: The title for the plot. If None, the plot
is shown without a title. Default is 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.
"""
if components is None:
components = solver.problem.output_variables
if isinstance(components, str):
components = [components]
if not isinstance(components, list):
raise NotImplementedError(
"Output variables must be passed"
"as a string or a list of strings."
)
if len(components) > 1:
raise NotImplementedError(
"Multidimensional plots are not implemented, "
"set components to an available components of"
" the problem."
)
v = [
var
for var in solver.problem.input_variables
if var not in fixed_variables.keys()
]
pts = solver.problem.domain.sample(res, "grid", variables=v)
fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))
fixed_pts *= torch.tensor(list(fixed_variables.values()))
fixed_pts = fixed_pts.as_subclass(LabelTensor)
fixed_pts.labels = list(fixed_variables.keys())
pts = pts.append(fixed_pts)
pts = pts.to(device=solver.device)
# computing soluting and sending to cpu
predicted_output = solver.forward(pts).extract(components)
predicted_output = (
predicted_output.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, predicted_output, v, method, truth_solution, **kwargs
)
elif len(v) == 2:
self._2d_plot(
pts, predicted_output, v, res, method, truth_solution, **kwargs
)
plt.tight_layout()
if title is not None:
plt.title(title)
if filename:
plt.savefig(filename)
plt.close()
else:
plt.show()
def plot_loss(
self,
trainer,
metrics=None,
logy=False,
logx=False,
filename=None,
**kwargs,
):
"""
Plot the loss function values during traininig.
:param trainer: the PINA Trainer object instance.
:type trainer: Trainer
: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
is shown using the setted matplotlib frontend. Default is None.
"""
# check that MetricTracker has been used
list_ = [
idx
for idx, s in enumerate(trainer.callbacks)
if isinstance(s, MetricTracker)
]
if not bool(list_):
raise FileNotFoundError(
"MetricTracker should be used as a callback during training to"
" use this method."
)
# extract trainer metrics
trainer_metrics = trainer.callbacks[list_[0]].metrics
if metrics is None:
metrics = ["mean_loss"]
elif not isinstance(metrics, list):
raise ValueError("metrics must be class list.")
# loop over metrics to plot
for metric in metrics:
if metric not in trainer_metrics:
raise ValueError(
f"{metric} not a valid metric. Available metrics are {list(trainer_metrics.keys())}."
)
loss = trainer_metrics[metric]
epochs = range(len(loss))
plt.plot(epochs, loss.cpu(), **kwargs)
# plotting
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend()
# log axis
if logy:
plt.yscale("log")
if logx:
plt.xscale("log")
# saving in file
if filename:
plt.savefig(filename)
plt.close()

50
pina/writer.py
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@@ -1,50 +0,0 @@
""" Module for plotting. """
import matplotlib.pyplot as plt
import numpy as np
import torch
from pina import LabelTensor
class Writer:
"""
Implementation of a writer class, for textual output.
"""
def __init__(self, frequency_print=10, header="any") -> None:
"""
The constructor of the class.
:param int frequency_print: the frequency in epochs of printing.
:param ['any', 'begin', 'none'] header: the header of the output.
"""
self._frequency_print = frequency_print
self._header = header
def header(self, trainer):
"""
The method for printing the header.
"""
header = []
for condition_name in trainer.problem.conditions:
header.append(f"{condition_name}")
return header
def write_loss(self, trainer):
"""
The method for writing the output.
"""
pass
def write_loss_in_loop(self, trainer, loss):
"""
The method for writing the output within the training loop.
:param pina.trainer.Trainer trainer: the trainer object.
"""
if trainer.trained_epoch % self._frequency_print == 0:
print(f"Epoch {trainer.trained_epoch:05d}: {loss.item():.5e}")

2
tests/test_optimizer.py
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@@ -1,6 +1,6 @@
import torch
import pytest
from pina import TorchOptimizer
from pina.optim import TorchOptimizer
opt_list = [
torch.optim.Adam, torch.optim.AdamW, torch.optim.SGD, torch.optim.RMSprop

75
tests/test_plotter.py
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@@ -1,75 +0,0 @@
from pina.domain import CartesianDomain
from pina import Condition, Plotter
from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt
from pina.problem import SpatialProblem
from pina.equation import FixedValue
"""
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
TODO : Fix the tests once the Plotter class is updated
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
class FooProblem1D(SpatialProblem):
# assign output/ spatial and temporal variables
output_variables = ['u']
spatial_domain = CartesianDomain({'x' : [-1, 1]})
# problem condition statement
conditions = {
'D': Condition(location=CartesianDomain({'x': [-1, 1]}), equation=FixedValue(0.)),
}
class FooProblem2D(SpatialProblem):
# assign output/ spatial and temporal variables
output_variables = ['u']
spatial_domain = CartesianDomain({'x' : [-1, 1], 'y': [-1, 1]})
# problem condition statement
conditions = {
'D': Condition(location=CartesianDomain({'x' : [-1, 1], 'y': [-1, 1]}), equation=FixedValue(0.)),
}
class FooProblem3D(SpatialProblem):
# assign output/ spatial and temporal variables
output_variables = ['u']
spatial_domain = CartesianDomain({'x' : [-1, 1], 'y': [-1, 1], 'z':[-1,1]})
# problem condition statement
conditions = {
'D': Condition(location=CartesianDomain({'x' : [-1, 1], 'y': [-1, 1], 'z':[-1,1]}), equation=FixedValue(0.)),
}
def test_constructor():
Plotter()
def test_plot_samples_1d():
problem = FooProblem1D()
problem.discretise_domain(n=10, mode='grid', variables = 'x', locations=['D'])
pl = Plotter()
pl.plot_samples(problem=problem, filename='fig.png')
import os
os.remove('fig.png')
def test_plot_samples_2d():
problem = FooProblem2D()
problem.discretise_domain(n=10, mode='grid', variables = ['x', 'y'], locations=['D'])
pl = Plotter()
pl.plot_samples(problem=problem, filename='fig.png')
import os
os.remove('fig.png')
def test_plot_samples_3d():
problem = FooProblem3D()
problem.discretise_domain(n=10, mode='grid', variables = ['x', 'y', 'z'], locations=['D'])
pl = Plotter()
pl.plot_samples(problem=problem, filename='fig.png')
import os
os.remove('fig.png')
"""

2
tests/test_problem_zoo/test_supervised_problem.py
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@@ -2,7 +2,7 @@ import torch
from pina.problem import AbstractProblem
from pina.condition import InputOutputPointsCondition
from pina.problem.zoo.supervised_problem import SupervisedProblem
from pina import RadiusGraph
from pina.graph import RadiusGraph
def test_constructor():
input_ = torch.rand((100,10))

2
tests/test_scheduler.py
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@@ -1,7 +1,7 @@
import torch
import pytest
from pina import TorchOptimizer, TorchScheduler
from pina.optim import TorchOptimizer, TorchScheduler
opt_list = [
torch.optim.Adam,