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Documentation for v0.1 version (#199)

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* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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91
pina/geometry/cartesian.py
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@@ -8,12 +8,12 @@ from ..utils import torch_lhs, chebyshev_roots
class CartesianDomain(Location):
"""PINA implementation of Hypercube domain."""
def __init__(self, span_dict):
def __init__(self, cartesian_dict):
"""
:param span_dict: A dictionary with dict-key a string representing
:param cartesian_dict: A dictionary with dict-key a string representing
the input variables for the pinn, and dict-value a list with
the domain extrema.
:type span_dict: dict
:type cartesian_dict: dict
:Example:
>>> spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
@@ -21,7 +21,7 @@ class CartesianDomain(Location):
self.fixed_ = {}
self.range_ = {}
for k, v in span_dict.items():
for k, v in cartesian_dict.items():
if isinstance(v, (int, float)):
self.fixed_[k] = v
elif isinstance(v, (list, tuple)) and len(v) == 2:
@@ -33,28 +33,27 @@ class CartesianDomain(Location):
def variables(self):
"""Spatial variables.
:return: Spatial variables defined in '__init__()'
:return: Spatial variables defined in ``__init__()``
:rtype: list[str]
"""
return list(self.fixed_.keys()) + list(self.range_.keys())
def update(self, new_span):
"""Adding new dimensions on the span
def update(self, new_domain):
"""Adding new dimensions on the ``CartesianDomain``
:param new_span: A new span object to merge
:type new_span: Span
:param CartesianDomain new_domain: A new ``CartesianDomain`` object to merge
:Example:
>>> spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
>>> spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
>>> spatial_domain.variables
['x', 'y']
>>> spatial_domain_2 = Span({'z': [3, 4], 'w': [0, 1]})
>>> spatial_domain_2 = CartesianDomain({'z': [3, 4], 'w': [0, 1]})
>>> spatial_domain.update(spatial_domain_2)
>>> spatial_domain.variables
['x', 'y', 'z', 'w']
"""
self.fixed_.update(new_span.fixed_)
self.range_.update(new_span.range_)
self.fixed_.update(new_domain.fixed_)
self.range_.update(new_domain.range_)
def _sample_range(self, n, mode, bounds):
"""Rescale the samples to the correct bounds
@@ -62,11 +61,11 @@ class CartesianDomain(Location):
:param n: Number of points to sample, see Note below
for reference.
:type n: int
:param mode: Mode for sampling, defaults to 'random'.
Available modes include: random sampling, 'random';
latin hypercube sampling, 'latin' or 'lh';
chebyshev sampling, 'chebyshev'; grid sampling 'grid'.
:type mode: str, optional
:param mode: Mode for sampling, defaults to ``random``.
Available modes include: random sampling, ``random``;
latin hypercube sampling, ``latin`` or ``lh``;
chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
:type mode: str
:param bounds: Bounds to rescale the samples.
:type bounds: torch.Tensor
:return: Rescaled sample points.
@@ -97,25 +96,27 @@ class CartesianDomain(Location):
:param n: Number of points to sample, see Note below
for reference.
:type n: int
:param mode: Mode for sampling, defaults to 'random'.
Available modes include: random sampling, 'random';
latin hypercube sampling, 'latin' or 'lh';
chebyshev sampling, 'chebyshev'; grid sampling 'grid'.
:type mode: str, optional
:param variables: pinn variable to be sampled, defaults to 'all'.
:type variables: str or list[str], optional
:param mode: Mode for sampling, defaults to ``random``.
Available modes include: random sampling, ``random``;
latin hypercube sampling, ``latin`` or ``lh``;
chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
:type mode: str
:param variables: pinn variable to be sampled, defaults to ``all``.
:type variables: str | list[str]
:return: Returns ``LabelTensor`` of n sampled points.
:rtype: LabelTensor
.. note::
The total number of points sampled in case of multiple variables
is not 'n', and it depends on the chosen 'mode'. If 'mode' is
'grid' or 'chebyshev', the points are sampled independentely
is not ``n``, and it depends on the chosen ``mode``. If ``mode`` is
'grid' or ``chebyshev``, the points are sampled independentely
across the variables and the results crossed together, i.e. the
final number of points is 'n' to the power of the number of
variables. If 'mode' is 'random', 'lh' or 'latin', the variables
final number of points is ``n`` to the power of the number of
variables. If 'mode' is 'random', ``lh`` or ``latin``, the variables
are sampled all together, and the final number of points
.. warning::
The extrema values of Span are always sampled only for 'grid' mode.
The extrema values of Span are always sampled only for ``grid`` mode.
:Example:
>>> spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
@@ -142,6 +143,7 @@ class CartesianDomain(Location):
[0.6667, 1.0000],
[1.0000, 1.0000]])
"""
def _1d_sampler(n, mode, variables):
""" Sample independentely the variables and cross the results"""
tmp = []
@@ -161,8 +163,8 @@ class CartesianDomain(Location):
for variable in variables:
if variable in self.fixed_.keys():
value = self.fixed_[variable]
pts_variable = torch.tensor([[value]]).repeat(
result.shape[0], 1)
pts_variable = torch.tensor([[value]
]).repeat(result.shape[0], 1)
pts_variable = pts_variable.as_subclass(LabelTensor)
pts_variable.labels = [variable]
@@ -175,13 +177,13 @@ class CartesianDomain(Location):
:param n: Number of points to sample.
:type n: int
:param mode: Mode for sampling, defaults to 'random'.
Available modes include: random sampling, 'random';
latin hypercube sampling, 'latin' or 'lh';
chebyshev sampling, 'chebyshev'; grid sampling 'grid'.
:type mode: str, optional.
:param variables: pinn variable to be sampled, defaults to 'all'.
:type variables: str or list[str], optional.
:param mode: Mode for sampling, defaults to ``random``.
Available modes include: random sampling, ``random``;
latin hypercube sampling, ``latin`` or ``lh``;
chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
:type mode: str.
:param variables: pinn variable to be sampled, defaults to ``all``.
:type variables: str or list[str].
:return: Sample points.
:rtype: list[torch.Tensor]
"""
@@ -195,8 +197,8 @@ class CartesianDomain(Location):
for variable in variables:
if variable in self.fixed_.keys():
value = self.fixed_[variable]
pts_variable = torch.tensor([[value]]).repeat(
result.shape[0], 1)
pts_variable = torch.tensor([[value]
]).repeat(result.shape[0], 1)
pts_variable = pts_variable.as_subclass(LabelTensor)
pts_variable.labels = [variable]
@@ -241,16 +243,15 @@ class CartesianDomain(Location):
else:
raise ValueError(f'mode={mode} is not valid.')
def is_inside(self, point, check_border=False):
"""Check if a point is inside the ellipsoid.
:param point: Point to be checked
:type point: LabelTensor
:param check_border: Check if the point is also on the frontier
of the hypercube, default False.
of the hypercube, default ``False``.
:type check_border: bool
:return: Returning True if the point is inside, False otherwise.
:return: Returning ``True`` if the point is inside, ``False`` otherwise.
:rtype: bool
"""
is_inside = []
@@ -268,7 +269,7 @@ class CartesianDomain(Location):
check = bound[0] <= point.extract([variable]) <= bound[1]
else:
check = bound[0] < point.extract([variable]) < bound[1]
is_inside.append(check)
return all(is_inside)