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stringlengths 2
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⌀ | name
stringlengths 1
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| docstring
stringlengths 0
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⌀ | code
stringlengths 4
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| signature
stringlengths 2
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⌀ | embed_func_code
listlengths 768
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21,132 | tfields.mesh_3d | project |
Project the points of the tensor_field to a copy of the mesh
and set the face values accord to the field to the maps field.
If no field is present in tensor_field, the number of points in a mesh
is counted.
Args:
tensor_field (Tensors | TensorFields)
delta (float | None): forwarded to Mesh3D.in_faces
merge_functions (callable): if multiple Tensors lie in the same face,
they are mapped with the merge_function to one value
point_face_assignment (np.array, dtype=int): array assigning each
point to a face. Each entry position corresponds to a point of the
tensor, each entry value is the index of the assigned face
return_point_face_assignment (bool): if true, return the
point_face_assignment
Examples:
>>> import tfields
>>> import numpy as np
>>> mp = tfields.TensorFields([[0,1,2],[2,3,0],[3,2,5],[5,4,3]],
... [1, 2, 3, 4])
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [1,1,0], [0,1,0], [0,2,0], [1,2,0]],
... maps=[mp])
>>> points = tfields.Tensors([[0.5, 0.2, 0.0],
... [0.5, 0.02, 0.0],
... [0.5, 0.8, 0.0],
... [0.5, 0.8, 0.1]]) # not contained
Projecting points onto the mesh gives the count
>>> m_points = m.project(points, delta=0.01)
>>> list(m_points.maps[3].fields[0])
[2, 1, 0, 0]
TensorFields with arbitrary size are projected,
combinging the fields automatically
>>> fields = [tfields.Tensors([1,3,42, -1]),
... tfields.Tensors([[0,1,2], [2,3,4], [3,4,5], [-1] * 3]),
... tfields.Tensors([[[0, 0]] * 2,
... [[2, 2]] * 2,
... [[3, 3]] * 2,
... [[9, 9]] * 2])]
>>> tf = tfields.TensorFields(points, *fields)
>>> m_tf = m.project(tf, delta=0.01)
>>> assert m_tf.maps[3].fields[0].equal([2, 42, np.nan, np.nan], equal_nan=True)
>>> assert m_tf.maps[3].fields[1].equal([[1, 2, 3],
... [3, 4, 5],
... [np.nan] * 3,
... [np.nan] * 3],
... equal_nan=True)
>>> assert m_tf.maps[3].fields[2].equal([[[1, 1]] * 2,
... [[3, 3]] * 2,
... [[np.nan, np.nan]] * 2,
... [[np.nan, np.nan]] * 2],
... equal_nan=True)
Returning the calculated point_face_assignment can speed up multiple
results
>>> m_tf, point_face_assignment = m.project(tf, delta=0.01,
... return_point_face_assignment=True)
>>> m_tf_fast = m.project(tf, delta=0.01, point_face_assignment=point_face_assignment)
>>> assert m_tf.equal(m_tf_fast, equal_nan=True)
| def project(
self,
tensor_field,
delta=None,
merge_functions=None,
point_face_assignment=None,
return_point_face_assignment=False,
):
"""
Project the points of the tensor_field to a copy of the mesh
and set the face values accord to the field to the maps field.
If no field is present in tensor_field, the number of points in a mesh
is counted.
Args:
tensor_field (Tensors | TensorFields)
delta (float | None): forwarded to Mesh3D.in_faces
merge_functions (callable): if multiple Tensors lie in the same face,
they are mapped with the merge_function to one value
point_face_assignment (np.array, dtype=int): array assigning each
point to a face. Each entry position corresponds to a point of the
tensor, each entry value is the index of the assigned face
return_point_face_assignment (bool): if true, return the
point_face_assignment
Examples:
>>> import tfields
>>> import numpy as np
>>> mp = tfields.TensorFields([[0,1,2],[2,3,0],[3,2,5],[5,4,3]],
... [1, 2, 3, 4])
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [1,1,0], [0,1,0], [0,2,0], [1,2,0]],
... maps=[mp])
>>> points = tfields.Tensors([[0.5, 0.2, 0.0],
... [0.5, 0.02, 0.0],
... [0.5, 0.8, 0.0],
... [0.5, 0.8, 0.1]]) # not contained
Projecting points onto the mesh gives the count
>>> m_points = m.project(points, delta=0.01)
>>> list(m_points.maps[3].fields[0])
[2, 1, 0, 0]
TensorFields with arbitrary size are projected,
combinging the fields automatically
>>> fields = [tfields.Tensors([1,3,42, -1]),
... tfields.Tensors([[0,1,2], [2,3,4], [3,4,5], [-1] * 3]),
... tfields.Tensors([[[0, 0]] * 2,
... [[2, 2]] * 2,
... [[3, 3]] * 2,
... [[9, 9]] * 2])]
>>> tf = tfields.TensorFields(points, *fields)
>>> m_tf = m.project(tf, delta=0.01)
>>> assert m_tf.maps[3].fields[0].equal([2, 42, np.nan, np.nan], equal_nan=True)
>>> assert m_tf.maps[3].fields[1].equal([[1, 2, 3],
... [3, 4, 5],
... [np.nan] * 3,
... [np.nan] * 3],
... equal_nan=True)
>>> assert m_tf.maps[3].fields[2].equal([[[1, 1]] * 2,
... [[3, 3]] * 2,
... [[np.nan, np.nan]] * 2,
... [[np.nan, np.nan]] * 2],
... equal_nan=True)
Returning the calculated point_face_assignment can speed up multiple
results
>>> m_tf, point_face_assignment = m.project(tf, delta=0.01,
... return_point_face_assignment=True)
>>> m_tf_fast = m.project(tf, delta=0.01, point_face_assignment=point_face_assignment)
>>> assert m_tf.equal(m_tf_fast, equal_nan=True)
"""
if not issubclass(type(tensor_field), tfields.Tensors):
tensor_field = tfields.TensorFields(tensor_field)
inst = self.copy()
# setup empty map fields and collect fields
n_faces = len(self.maps[3])
point_indices = np.arange(len(tensor_field))
if not hasattr(tensor_field, "fields") or len(tensor_field.fields) == 0:
# if not fields is existing use int type fields and empty_map_fields
# in order to generate a sum
fields = [np.full(len(tensor_field), 1, dtype=int)]
empty_map_fields = [tfields.Tensors(np.full(n_faces, 0, dtype=int))]
if merge_functions is None:
merge_functions = [np.sum]
else:
fields = tensor_field.fields
empty_map_fields = []
for field in fields:
cls = type(field)
kwargs = {key: getattr(field, key) for key in cls.__slots__}
shape = (n_faces,) + field.shape[1:]
empty_map_fields.append(cls(np.full(shape, np.nan), **kwargs))
if merge_functions is None:
merge_functions = [lambda x: np.mean(x, axis=0)] * len(fields)
# build point_face_assignment if not given.
if point_face_assignment is not None:
if len(point_face_assignment) != len(tensor_field):
raise ValueError("Template needs same lenght as tensor_field")
else:
point_face_assignment = self.in_faces(tensor_field, delta=delta)
point_face_assignment_set = set(point_face_assignment)
# merge the fields according to point_face_assignment
map_fields = []
for field, map_field, merge_function in zip(
fields, empty_map_fields, merge_functions
):
for i, f_index in enumerate(point_face_assignment_set):
if f_index == -1:
# point could not be mapped
continue
point_in_face_indices = point_indices[point_face_assignment == f_index]
res = field[point_in_face_indices]
if len(res) == 1:
map_field[f_index] = res
else:
map_field[f_index] = merge_function(res)
map_fields.append(map_field)
inst.maps[3].fields = map_fields
if return_point_face_assignment:
return inst, point_face_assignment
return inst
| (self, tensor_field, delta=None, merge_functions=None, point_face_assignment=None, return_point_face_assignment=False) | [
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21,134 | tfields.mesh_3d | remove_faces |
Remove faces where face_delete_mask is True
| def remove_faces(self, face_delete_mask):
"""
Remove faces where face_delete_mask is True
"""
face_delete_mask = np.array(face_delete_mask, dtype=bool)
self.faces = self.faces[~face_delete_mask]
self.faces.fields = self.faces.fields[~face_delete_mask]
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] |
21,135 | tfields.core | removed |
Return copy of self without vertices where remove_condition is True
Copy because self is immutable
Examples:
>>> import tfields
>>> m = tfields.TensorMaps(
... [[0,0,0], [1,1,1], [2,2,2], [0,0,0],
... [3,3,3], [4,4,4], [5,5,5]],
... maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3],
... [3, 4, 5], [3, 4, 1],
... [3, 4, 6]],
... [1, 3, 5, 7, 9],
... [2, 4, 6, 8, 0])])
>>> c = m.keep([False, False, False, True, True, True, True])
>>> c.equal([[0, 0, 0],
... [3, 3, 3],
... [4, 4, 4],
... [5, 5, 5]])
True
>>> assert c.maps[3].equal([[0, 1, 2], [0, 1, 3]])
>>> assert c.maps[3].fields[0].equal([5, 9])
>>> assert c.maps[3].fields[1].equal([6, 0])
| def removed(self, remove_condition):
"""
Return copy of self without vertices where remove_condition is True
Copy because self is immutable
Examples:
>>> import tfields
>>> m = tfields.TensorMaps(
... [[0,0,0], [1,1,1], [2,2,2], [0,0,0],
... [3,3,3], [4,4,4], [5,5,5]],
... maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3],
... [3, 4, 5], [3, 4, 1],
... [3, 4, 6]],
... [1, 3, 5, 7, 9],
... [2, 4, 6, 8, 0])])
>>> c = m.keep([False, False, False, True, True, True, True])
>>> c.equal([[0, 0, 0],
... [3, 3, 3],
... [4, 4, 4],
... [5, 5, 5]])
True
>>> assert c.maps[3].equal([[0, 1, 2], [0, 1, 3]])
>>> assert c.maps[3].fields[0].equal([5, 9])
>>> assert c.maps[3].fields[1].equal([6, 0])
"""
remove_condition = np.array(remove_condition)
return self[~remove_condition]
| (self, remove_condition) | [
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21,137 | tfields.core | stale |
Returns:
Mask for all vertices that are stale i.e. are not refered by maps
Examples:
>>> import numpy as np
>>> import tfields
>>> vectors = tfields.Tensors(
... [[0, 0, 0], [0, 0, 1], [0, -1, 0], [4, 4, 4]])
>>> tm = tfields.TensorMaps(
... vectors,
... maps=[[[0, 1, 2], [0, 1, 2]], [[1, 1], [2, 2]]])
>>> assert np.array_equal(tm.stale(), [False, False, False, True])
| def stale(self):
"""
Returns:
Mask for all vertices that are stale i.e. are not refered by maps
Examples:
>>> import numpy as np
>>> import tfields
>>> vectors = tfields.Tensors(
... [[0, 0, 0], [0, 0, 1], [0, -1, 0], [4, 4, 4]])
>>> tm = tfields.TensorMaps(
... vectors,
... maps=[[[0, 1, 2], [0, 1, 2]], [[1, 1], [2, 2]]])
>>> assert np.array_equal(tm.stale(), [False, False, False, True])
"""
stale_mask = np.full(self.shape[0], False, dtype=bool)
used = set(ind for mp in self.maps.values() for ind in mp.flatten())
for i in range(self.shape[0]):
if i not in used:
stale_mask[i] = True
return stale_mask
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21,138 | tfields.mesh_3d | template |
'Manual' way to build a template that can be used with self.cut
Returns:
Mesh3D: template (see cut), can be used as template to retrieve
sub_mesh from self instance
Examples:
>>> import tfields
>>> from sympy.abc import y
>>> mp = tfields.TensorFields([[0,1,2],[2,3,0],[3,2,5],[5,4,3]],
... [1, 2, 3, 4])
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [1,1,0], [0,1,0], [0,2,0], [1,2,0]],
... maps=[mp])
>>> m_cut = m.cut(y < 1.5, at_intersection='split')
>>> template = m.template(m_cut)
>>> assert m_cut.equal(m.cut(template))
TODO:
fields template not yet implemented
| def template(self, sub_mesh):
"""
'Manual' way to build a template that can be used with self.cut
Returns:
Mesh3D: template (see cut), can be used as template to retrieve
sub_mesh from self instance
Examples:
>>> import tfields
>>> from sympy.abc import y
>>> mp = tfields.TensorFields([[0,1,2],[2,3,0],[3,2,5],[5,4,3]],
... [1, 2, 3, 4])
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [1,1,0], [0,1,0], [0,2,0], [1,2,0]],
... maps=[mp])
>>> m_cut = m.cut(y < 1.5, at_intersection='split')
>>> template = m.template(m_cut)
>>> assert m_cut.equal(m.cut(template))
TODO:
fields template not yet implemented
"""
cents = tfields.Tensors(sub_mesh.centroids())
face_indices = self.in_faces(cents, delta=None)
inst = sub_mesh.copy()
if inst.maps:
inst.maps[3].fields = [tfields.Tensors(face_indices, dim=1)]
else:
inst.maps = [
tfields.TensorFields([], tfields.Tensors([], dim=1), dim=3, dtype=int)
]
return inst
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21,139 | tfields.core | tmp_transform |
Temporarily change the coord_sys to another coord_sys and change it back at exit
This method is for cleaner code only.
No speed improvements go with this.
Args:
see transform
Examples:
>>> import tfields
>>> p = tfields.Tensors([[1,2,3]], coord_sys=tfields.bases.SPHERICAL)
>>> with p.tmp_transform(tfields.bases.CYLINDER):
... assert p.coord_sys == tfields.bases.CYLINDER
>>> assert p.coord_sys == tfields.bases.SPHERICAL
| @classmethod
def _from_dict(cls, content: dict):
type_ = content.get("type")
assert type_ == cls.__name__
return _from_dict(content)
| (self, coord_sys) | [
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21,140 | tfields.core | to_segment |
For circular (close into themself after
<periodicity>) coordinates at index <coordinate> assume
<num_segments> segments and transform all values to
segment number <segment>
Args:
segment (int): segment index (starting at 0)
num_segments (int): number of segments
coordinate (int): coordinate index
periodicity (float): after what lenght, the coordiante repeats
offset (float): offset in the mapping
coord_sys (str or sympy.CoordinateSystem): in which coord sys the
transformation should be done
Examples:
>>> import tfields
>>> import numpy as np
>>> pStart = tfields.Points3D([[6, 2 * np.pi, 1],
... [6, 2 * np.pi / 5 * 3, 1]],
... coord_sys='cylinder')
>>> p = tfields.Points3D(pStart)
>>> p.to_segment(0, 5, 1, offset=-2 * np.pi / 10)
>>> assert np.array_equal(p[:, 1], [0, 0])
>>> p2 = tfields.Points3D(pStart)
>>> p2.to_segment(1, 5, 1, offset=-2 * np.pi / 10)
>>> assert np.array_equal(np.round(p2[:, 1], 4), [1.2566] * 2)
| def to_segment( # pylint: disable=too-many-arguments
self,
segment,
num_segments,
coordinate,
periodicity=2 * np.pi,
offset=0.0,
coord_sys=None,
):
"""
For circular (close into themself after
<periodicity>) coordinates at index <coordinate> assume
<num_segments> segments and transform all values to
segment number <segment>
Args:
segment (int): segment index (starting at 0)
num_segments (int): number of segments
coordinate (int): coordinate index
periodicity (float): after what lenght, the coordiante repeats
offset (float): offset in the mapping
coord_sys (str or sympy.CoordinateSystem): in which coord sys the
transformation should be done
Examples:
>>> import tfields
>>> import numpy as np
>>> pStart = tfields.Points3D([[6, 2 * np.pi, 1],
... [6, 2 * np.pi / 5 * 3, 1]],
... coord_sys='cylinder')
>>> p = tfields.Points3D(pStart)
>>> p.to_segment(0, 5, 1, offset=-2 * np.pi / 10)
>>> assert np.array_equal(p[:, 1], [0, 0])
>>> p2 = tfields.Points3D(pStart)
>>> p2.to_segment(1, 5, 1, offset=-2 * np.pi / 10)
>>> assert np.array_equal(np.round(p2[:, 1], 4), [1.2566] * 2)
"""
if segment > num_segments - 1:
raise ValueError("Segment {0} not existent.".format(segment))
if coord_sys is None:
coord_sys = self.coord_sys
with self.tmp_transform(coord_sys):
# map all values to first segment
self[:, coordinate] = (
(self[:, coordinate] - offset) % (periodicity / num_segments)
+ offset
+ segment * periodicity / num_segments
)
| (self, segment, num_segments, coordinate, periodicity=6.283185307179586, offset=0.0, coord_sys=None) | [
0.01821688376367092,
0.09014852344989777,
-0.06424594670534134,
0.0035663307644426823,
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21,141 | tfields.core | transform |
Args:
coord_sys (str)
Examples:
>>> import numpy as np
>>> import tfields
CARTESIAN to SPHERICAL
>>> t = tfields.Tensors([[1, 2, 2], [1, 0, 0], [0, 0, -1],
... [0, 0, 1], [0, 0, 0]])
>>> t.transform('spherical')
r
>>> assert t[0, 0] == 3
phi
>>> assert t[1, 1] == 0.
>>> assert t[2, 1] == 0.
theta is 0 at (0, 0, 1) and pi / 2 at (0, 0, -1)
>>> assert round(t[1, 2], 10) == round(0, 10)
>>> assert t[2, 2] == -np.pi / 2
>>> assert t[3, 2] == np.pi / 2
theta is defined 0 for R == 0
>>> assert t[4, 0] == 0.
>>> assert t[4, 2] == 0.
CARTESIAN to CYLINDER
>>> tCart = tfields.Tensors([[3, 4, 42], [1, 0, 0], [0, 1, -1],
... [-1, 0, 1], [0, 0, 0]])
>>> t_cyl = tCart.copy()
>>> t_cyl.transform('cylinder')
>>> assert t_cyl.coord_sys == 'cylinder'
R
>>> assert t_cyl[0, 0] == 5
>>> assert t_cyl[1, 0] == 1
>>> assert t_cyl[2, 0] == 1
>>> assert t_cyl[4, 0] == 0
Phi
>>> assert round(t_cyl[0, 1], 10) == round(np.arctan(4. / 3), 10)
>>> assert t_cyl[1, 1] == 0
>>> assert round(t_cyl[2, 1], 10) == round(np.pi / 2, 10)
>>> assert t_cyl[1, 1] == 0
Z
>>> assert t_cyl[0, 2] == 42
>>> assert t_cyl[2, 2] == -1
>>> t_cyl.transform('cartesian')
>>> assert t_cyl.coord_sys == 'cartesian'
>>> assert round(t_cyl[0, 0], 10) == 3
| def transform(self, coord_sys, **kwargs):
"""
Args:
coord_sys (str)
Examples:
>>> import numpy as np
>>> import tfields
CARTESIAN to SPHERICAL
>>> t = tfields.Tensors([[1, 2, 2], [1, 0, 0], [0, 0, -1],
... [0, 0, 1], [0, 0, 0]])
>>> t.transform('spherical')
r
>>> assert t[0, 0] == 3
phi
>>> assert t[1, 1] == 0.
>>> assert t[2, 1] == 0.
theta is 0 at (0, 0, 1) and pi / 2 at (0, 0, -1)
>>> assert round(t[1, 2], 10) == round(0, 10)
>>> assert t[2, 2] == -np.pi / 2
>>> assert t[3, 2] == np.pi / 2
theta is defined 0 for R == 0
>>> assert t[4, 0] == 0.
>>> assert t[4, 2] == 0.
CARTESIAN to CYLINDER
>>> tCart = tfields.Tensors([[3, 4, 42], [1, 0, 0], [0, 1, -1],
... [-1, 0, 1], [0, 0, 0]])
>>> t_cyl = tCart.copy()
>>> t_cyl.transform('cylinder')
>>> assert t_cyl.coord_sys == 'cylinder'
R
>>> assert t_cyl[0, 0] == 5
>>> assert t_cyl[1, 0] == 1
>>> assert t_cyl[2, 0] == 1
>>> assert t_cyl[4, 0] == 0
Phi
>>> assert round(t_cyl[0, 1], 10) == round(np.arctan(4. / 3), 10)
>>> assert t_cyl[1, 1] == 0
>>> assert round(t_cyl[2, 1], 10) == round(np.pi / 2, 10)
>>> assert t_cyl[1, 1] == 0
Z
>>> assert t_cyl[0, 2] == 42
>>> assert t_cyl[2, 2] == -1
>>> t_cyl.transform('cartesian')
>>> assert t_cyl.coord_sys == 'cartesian'
>>> assert round(t_cyl[0, 0], 10) == 3
"""
if self.rank == 0 or any(s == 0 for s in self.shape):
# scalar or empty
self.coord_sys = coord_sys # pylint: disable=attribute-defined-outside-init
return
if self.coord_sys == coord_sys:
# already correct
return
tfields.bases.transform(self, self.coord_sys, coord_sys, **kwargs)
# self[:] = tfields.bases.transform(self, self.coord_sys, coord_sys)
self.coord_sys = coord_sys # pylint: disable=attribute-defined-outside-init
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21,142 | tfields.core | transform_field |
Transform the field to the coordinate system of choice.
NOTE: This is not yet any way generic!!! Have a look at Einsteinpy and actual status of
sympy for further implementation
| def transform_field(self, coord_sys, field_index=0):
"""
Transform the field to the coordinate system of choice.
NOTE: This is not yet any way generic!!! Have a look at Einsteinpy and actual status of
sympy for further implementation
"""
self.fields[field_index].transform(coord_sys, position=self)
| (self, coord_sys, field_index=0) | [
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] |
21,143 | tfields.mesh_3d | triangles |
Cached method to retrieve the triangles, belonging to this mesh
Examples:
>>> import tfields
>>> mesh = tfields.Mesh3D.grid((0, 1, 3), (1, 2, 3), (2, 3, 3))
>>> assert mesh.triangles() is mesh.triangles()
| def triangles(self):
"""
Cached method to retrieve the triangles, belonging to this mesh
Examples:
>>> import tfields
>>> mesh = tfields.Mesh3D.grid((0, 1, 3), (1, 2, 3), (2, 3, 3))
>>> assert mesh.triangles() is mesh.triangles()
"""
return self._triangles
| (self) | [
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21,144 | tfields.bounding_box | Node |
This class allows to increase the performance with cuts in
x,y and z direction
An extension to arbitrary cuts might be possible in the future
Args:
parent: Parent node of self
mesh: Mesh corresponding to the node
cut_expr: Cut that determines the seperation in left and right node
cuts: List of cuts for the children nodes
Attrs:
parent (Node)
remaining_cuts (dict): key specifies dimension, value the cuts that
are still not done
cut_expr (dict): part of parents remaining_cuts. The dimension defines
what is meant by left and right
Examples:
>>> import tfields
>>> mesh = tfields.Mesh3D.grid((5.6, 6.2, 3),
... (-0.25, 0.25, 4),
... (-1, 1, 10))
>>> cuts = {'x': [5.7, 6.1],
... 'y': [-0.2, 0, 0.2],
... 'z': [-0.5, 0.5]}
>>> tree = tfields.bounding_box.Node(mesh,
... cuts,
... at_intersection='keep')
>>> leaves = tree.leaves()
>>> leaves = tfields.bounding_box.Node.sort_leaves(leaves)
>>> meshes = [leaf.mesh for leaf in leaves]
>>> templates = [leaf.template for leaf in leaves]
>>> special_leaf = tree.find_leaf([5.65, -0.21, 0])
| class Node(object):
"""
This class allows to increase the performance with cuts in
x,y and z direction
An extension to arbitrary cuts might be possible in the future
Args:
parent: Parent node of self
mesh: Mesh corresponding to the node
cut_expr: Cut that determines the seperation in left and right node
cuts: List of cuts for the children nodes
Attrs:
parent (Node)
remaining_cuts (dict): key specifies dimension, value the cuts that
are still not done
cut_expr (dict): part of parents remaining_cuts. The dimension defines
what is meant by left and right
Examples:
>>> import tfields
>>> mesh = tfields.Mesh3D.grid((5.6, 6.2, 3),
... (-0.25, 0.25, 4),
... (-1, 1, 10))
>>> cuts = {'x': [5.7, 6.1],
... 'y': [-0.2, 0, 0.2],
... 'z': [-0.5, 0.5]}
>>> tree = tfields.bounding_box.Node(mesh,
... cuts,
... at_intersection='keep')
>>> leaves = tree.leaves()
>>> leaves = tfields.bounding_box.Node.sort_leaves(leaves)
>>> meshes = [leaf.mesh for leaf in leaves]
>>> templates = [leaf.template for leaf in leaves]
>>> special_leaf = tree.find_leaf([5.65, -0.21, 0])
"""
def __init__(
self,
mesh,
cuts,
coord_sys=None,
at_intersection="split",
delta=0.0,
parent=None,
box=None,
internal_template=None,
cut_expr=None,
):
self.parent = parent
# initialize
self.mesh = copy.deepcopy(mesh)
if self.is_root():
cuts = copy.deepcopy(cuts) # dicts are mutable
self.remaining_cuts = cuts
logging.debug(cuts)
self.delta = delta
if box is None:
vertices = np.array(self.mesh)
self.box = {
"x": [min(vertices[:, 0]) - delta, max(vertices[:, 0]) + delta],
"y": [min(vertices[:, 1]) - delta, max(vertices[:, 1]) + delta],
"z": [min(vertices[:, 2]) - delta, max(vertices[:, 2]) + delta],
}
else:
self.box = box
self.left = None
self.right = None
self.at_intersection = at_intersection
self._internal_template = internal_template
if self.is_leaf():
self._template = None
if self.is_root():
self._trim_to_box()
self.cut_expr = cut_expr
self.left_template = None
self.right_template = None
self.coord_sys = coord_sys
# start the splitting process
self._build()
def _build(self):
if not self.is_last_cut():
self._choose_next_cut()
self._split()
def is_leaf(self):
if self.left is None and self.right is None:
return True
else:
return False
def is_root(self):
if self.parent is None:
return True
else:
return False
def is_last_cut(self):
for key in self.remaining_cuts:
if len(self.remaining_cuts[key]) != 0:
return False
return True
def in_box(self, point):
x, y, z = point
for key in ["x", "y", "z"]:
value = locals()[key]
if value < self.box[key][0] or self.box[key][1] < value:
return False
return True
@property
def root(self):
if self.is_root:
return self
return self.parent.root
@classmethod
def sort_leaves(cls, leaves_list):
"""
sorting the leaves first in x, then y, then z direction
"""
sorted_leaves = sorted(
leaves_list, key=lambda x: (x.box["x"][1], x.box["y"][1], x.box["z"][1])
)
return sorted_leaves
def _trim_to_box(self):
# 6 cuts to remove outer part of the box
x, y, z = sympy.symbols("x y z")
eps = 0.0000000001
x_cut = (float(self.box["x"][0] - eps) <= x) & (
x <= float(self.box["x"][1] + eps)
)
y_cut = (float(self.box["y"][0] - eps) <= y) & (
y <= float(self.box["y"][1] + eps)
)
z_cut = (float(self.box["z"][0] - eps) <= z) & (
z <= float(self.box["z"][1] + eps)
)
section_cut = x_cut & y_cut & z_cut
self.mesh, self._internal_template = self.mesh.cut(
section_cut, at_intersection=self.at_intersection, return_template=True
)
def leaves(self):
"""
Recursive function to create a list of all leaves
Returns:
list: of all leaves descending from this node
"""
if self.is_leaf():
return [self]
else:
if self.left is not None:
leftLeaves = self.left.leaves()
else:
leftLeaves = []
if self.right is not None:
rightLeaves = self.right.leaves()
else:
rightLeaves = []
return tfields.lib.util.flatten(leftLeaves + rightLeaves)
def find_leaf(self, point, _in_recursion=False):
"""
Returns:
Node / None:
Node: leaf note, containinig point
None: point outside root box
"""
x, y, z = point
if self.is_root():
if not self.in_box(point):
return None
else:
if not _in_recursion:
raise RuntimeError("Only root node can search for all leaves")
if self.is_leaf():
return self
if len(self.cut_expr) > 1:
raise ValueError("cut_expr is too long")
key = list(self.cut_expr)[0]
value = locals()[key]
if value <= self.cut_expr[key]:
return self.left.find_leaf(point, _in_recursion=True)
return self.right.find_leaf(point, _in_recursion=True)
def _split(self):
"""
Split the node in two new nodes, if there is no cut_expr set and
remaing cuts exist.
"""
if self.cut_expr is None and self.remaining_cuts is None:
raise RuntimeError(
"Cannot split the mesh without cut_expr and" "remaining_cuts"
)
else:
# create cut expression
x, y, z = sympy.symbols("x y z")
if "x" in self.cut_expr:
left_cut_expression = x <= self.cut_expr["x"]
right_cut_expression = x >= self.cut_expr["x"]
key = "x"
elif "y" in self.cut_expr:
left_cut_expression = y <= self.cut_expr["y"]
right_cut_expression = y >= self.cut_expr["y"]
key = "y"
elif "z" in self.cut_expr:
left_cut_expression = z <= self.cut_expr["z"]
right_cut_expression = z >= self.cut_expr["z"]
key = "z"
else:
raise KeyError()
# split the cuts into left / right
left_cuts = self.remaining_cuts.copy()
right_cuts = self.remaining_cuts.copy()
left_cuts[key] = [
value
for value in self.remaining_cuts[key]
if value <= self.cut_expr[key]
]
right_cuts[key] = [
value
for value in self.remaining_cuts[key]
if value > self.cut_expr[key]
]
left_box = copy.deepcopy(self.box)
right_box = copy.deepcopy(self.box)
left_box[key][1] = self.cut_expr[key]
right_box[key][0] = self.cut_expr[key]
# actually cut!
left_mesh, self.left_template = self.mesh.cut(
left_cut_expression,
at_intersection=self.at_intersection,
return_template=True,
)
right_mesh, self.right_template = self.mesh.cut(
right_cut_expression, | (mesh, cuts, coord_sys=None, at_intersection='split', delta=0.0, parent=None, box=None, internal_template=None, cut_expr=None) | [
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] |
21,145 | tfields.bounding_box | __init__ | null | def __init__(
self,
mesh,
cuts,
coord_sys=None,
at_intersection="split",
delta=0.0,
parent=None,
box=None,
internal_template=None,
cut_expr=None,
):
self.parent = parent
# initialize
self.mesh = copy.deepcopy(mesh)
if self.is_root():
cuts = copy.deepcopy(cuts) # dicts are mutable
self.remaining_cuts = cuts
logging.debug(cuts)
self.delta = delta
if box is None:
vertices = np.array(self.mesh)
self.box = {
"x": [min(vertices[:, 0]) - delta, max(vertices[:, 0]) + delta],
"y": [min(vertices[:, 1]) - delta, max(vertices[:, 1]) + delta],
"z": [min(vertices[:, 2]) - delta, max(vertices[:, 2]) + delta],
}
else:
self.box = box
self.left = None
self.right = None
self.at_intersection = at_intersection
self._internal_template = internal_template
if self.is_leaf():
self._template = None
if self.is_root():
self._trim_to_box()
self.cut_expr = cut_expr
self.left_template = None
self.right_template = None
self.coord_sys = coord_sys
# start the splitting process
self._build()
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21,146 | tfields.bounding_box | _build | null | def _build(self):
if not self.is_last_cut():
self._choose_next_cut()
self._split()
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21,147 | tfields.bounding_box | _choose_next_cut |
Set self.cut_expr by choosing the dimension with the most remaining
cuts. Remove that cut from remaining cuts
| def _choose_next_cut(self):
"""
Set self.cut_expr by choosing the dimension with the most remaining
cuts. Remove that cut from remaining cuts
"""
largest = 0
for key in self.remaining_cuts:
if len(self.remaining_cuts[key]) > largest:
largest = len(self.remaining_cuts[key])
largest_key = key
median = sorted(self.remaining_cuts[largest_key])[
int(0.5 * (len(self.remaining_cuts[largest_key]) - 1))
]
# pop median cut from remaining cuts
self.remaining_cuts[largest_key] = [
x for x in self.remaining_cuts[largest_key] if x != median
]
self.cut_expr = {largest_key: median}
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21,148 | tfields.bounding_box | _convert_field_index |
Recursively getting the fields index from root
Args:
index (int): field index on leaf
(index with respect to parent node, not to root)
Returns:
int: field index
| def _convert_field_index(self, index):
"""
Recursively getting the fields index from root
Args:
index (int): field index on leaf
(index with respect to parent node, not to root)
Returns:
int: field index
"""
if self.is_root():
return index
else:
return_value = self.parent._convert_field_index(
self.parent._internal_template.fields[0][int(index)]
)
return return_value
| (self, index) | [
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21,149 | tfields.bounding_box | _convert_map_index |
Recursively getting the map fields index from root
Args:
index (int): map field index on leaf
(index with respect to parent node, not to root)
Returns:
int: map field index
| def _convert_map_index(self, index):
"""
Recursively getting the map fields index from root
Args:
index (int): map field index on leaf
(index with respect to parent node, not to root)
Returns:
int: map field index
"""
if self.is_root():
return index
else:
return_value = self.parent._convert_map_index(
self.parent._internal_template.maps[3].fields[0][int(index)]
)
return return_value
| (self, index) | [
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] |
21,150 | tfields.bounding_box | _split |
Split the node in two new nodes, if there is no cut_expr set and
remaing cuts exist.
| def _split(self):
"""
Split the node in two new nodes, if there is no cut_expr set and
remaing cuts exist.
"""
if self.cut_expr is None and self.remaining_cuts is None:
raise RuntimeError(
"Cannot split the mesh without cut_expr and" "remaining_cuts"
)
else:
# create cut expression
x, y, z = sympy.symbols("x y z")
if "x" in self.cut_expr:
left_cut_expression = x <= self.cut_expr["x"]
right_cut_expression = x >= self.cut_expr["x"]
key = "x"
elif "y" in self.cut_expr:
left_cut_expression = y <= self.cut_expr["y"]
right_cut_expression = y >= self.cut_expr["y"]
key = "y"
elif "z" in self.cut_expr:
left_cut_expression = z <= self.cut_expr["z"]
right_cut_expression = z >= self.cut_expr["z"]
key = "z"
else:
raise KeyError()
# split the cuts into left / right
left_cuts = self.remaining_cuts.copy()
right_cuts = self.remaining_cuts.copy()
left_cuts[key] = [
value
for value in self.remaining_cuts[key]
if value <= self.cut_expr[key]
]
right_cuts[key] = [
value
for value in self.remaining_cuts[key]
if value > self.cut_expr[key]
]
left_box = copy.deepcopy(self.box)
right_box = copy.deepcopy(self.box)
left_box[key][1] = self.cut_expr[key]
right_box[key][0] = self.cut_expr[key]
# actually cut!
left_mesh, self.left_template = self.mesh.cut(
left_cut_expression,
at_intersection=self.at_intersection,
return_template=True,
)
right_mesh, self.right_template = self.mesh.cut(
right_cut_expression,
at_intersection=self.at_intersection,
return_template=True,
)
# two new Nodes
self.left = Node(
left_mesh,
left_cuts,
parent=self,
internal_template=self.left_template,
cut_expr=None,
coord_sys=self.coord_sys,
at_intersection=self.at_intersection,
box=left_box,
)
self.right = Node(
right_mesh,
right_cuts,
parent=self,
internal_template=self.right_template,
cut_expr=None,
coord_sys=self.coord_sys,
at_intersection=self.at_intersection,
box=right_box,
)
| (self) | [
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21,151 | tfields.bounding_box | _trim_to_box | null | def _trim_to_box(self):
# 6 cuts to remove outer part of the box
x, y, z = sympy.symbols("x y z")
eps = 0.0000000001
x_cut = (float(self.box["x"][0] - eps) <= x) & (
x <= float(self.box["x"][1] + eps)
)
y_cut = (float(self.box["y"][0] - eps) <= y) & (
y <= float(self.box["y"][1] + eps)
)
z_cut = (float(self.box["z"][0] - eps) <= z) & (
z <= float(self.box["z"][1] + eps)
)
section_cut = x_cut & y_cut & z_cut
self.mesh, self._internal_template = self.mesh.cut(
section_cut, at_intersection=self.at_intersection, return_template=True
)
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21,152 | tfields.bounding_box | find_leaf |
Returns:
Node / None:
Node: leaf note, containinig point
None: point outside root box
| def find_leaf(self, point, _in_recursion=False):
"""
Returns:
Node / None:
Node: leaf note, containinig point
None: point outside root box
"""
x, y, z = point
if self.is_root():
if not self.in_box(point):
return None
else:
if not _in_recursion:
raise RuntimeError("Only root node can search for all leaves")
if self.is_leaf():
return self
if len(self.cut_expr) > 1:
raise ValueError("cut_expr is too long")
key = list(self.cut_expr)[0]
value = locals()[key]
if value <= self.cut_expr[key]:
return self.left.find_leaf(point, _in_recursion=True)
return self.right.find_leaf(point, _in_recursion=True)
| (self, point, _in_recursion=False) | [
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21,153 | tfields.bounding_box | in_box | null | def in_box(self, point):
x, y, z = point
for key in ["x", "y", "z"]:
value = locals()[key]
if value < self.box[key][0] or self.box[key][1] < value:
return False
return True
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21,154 | tfields.bounding_box | is_last_cut | null | def is_last_cut(self):
for key in self.remaining_cuts:
if len(self.remaining_cuts[key]) != 0:
return False
return True
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21,155 | tfields.bounding_box | is_leaf | null | def is_leaf(self):
if self.left is None and self.right is None:
return True
else:
return False
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21,156 | tfields.bounding_box | is_root | null | def is_root(self):
if self.parent is None:
return True
else:
return False
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21,157 | tfields.bounding_box | leaves |
Recursive function to create a list of all leaves
Returns:
list: of all leaves descending from this node
| def leaves(self):
"""
Recursive function to create a list of all leaves
Returns:
list: of all leaves descending from this node
"""
if self.is_leaf():
return [self]
else:
if self.left is not None:
leftLeaves = self.left.leaves()
else:
leftLeaves = []
if self.right is not None:
rightLeaves = self.right.leaves()
else:
rightLeaves = []
return tfields.lib.util.flatten(leftLeaves + rightLeaves)
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21,158 | tfields.planes_3d | Planes3D |
Point-NormVector representaion of planes
Examples:
>>> import tfields
>>> points = [[0, 1, 0]]
>>> norms = [[0, 0, 1]]
>>> plane = tfields.Planes3D(points, norms)
>>> plane.symbolic()[0]
Plane(Point3D(0, 1, 0), (0, 0, 1))
| class Planes3D(tfields.TensorFields):
"""
Point-NormVector representaion of planes
Examples:
>>> import tfields
>>> points = [[0, 1, 0]]
>>> norms = [[0, 0, 1]]
>>> plane = tfields.Planes3D(points, norms)
>>> plane.symbolic()[0]
Plane(Point3D(0, 1, 0), (0, 0, 1))
"""
def symbolic(self):
"""
Returns:
list: list with sympy.Plane objects
"""
return [
sympy.Plane(point, normal_vector=vector)
for point, vector in zip(self, self.fields[0])
]
def plot(self, **kwargs): # pragma: no cover
"""
forward to Mesh3D plotting
"""
artists = []
centers = np.array(self)
norms = np.array(self.fields[0])
for i in range(len(self)):
artists.append(rna.plotting.plot_plane(centers[i], norms[i], **kwargs))
return artists
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21,161 | tfields.core | __getitem__ |
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors,
... [42, 21, 10.5],
... [1, 2, 3],
... [[0, 0], [-1, -1], [-2, -2]])
Slicing
>>> sliced = scalar_field[2:]
>>> assert isinstance(sliced, tfields.TensorFields)
>>> assert isinstance(sliced.fields[0], tfields.Tensors)
>>> assert sliced.fields[0].equal([10.5])
Picking
>>> picked = scalar_field[1]
>>> assert np.array_equal(picked, [0, 0, 1])
>>> assert np.array_equal(picked.fields[0], 21)
Masking
>>> masked = scalar_field[np.array([True, False, True])]
>>> assert masked.equal([[0, 0, 0], [0, -1, 0]])
>>> assert masked.fields[0].equal([42, 10.5])
>>> assert masked.fields[1].equal([1, 3])
Iteration
>>> _ = [point for point in scalar_field]
| def __getitem__(self, index):
"""
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors,
... [42, 21, 10.5],
... [1, 2, 3],
... [[0, 0], [-1, -1], [-2, -2]])
Slicing
>>> sliced = scalar_field[2:]
>>> assert isinstance(sliced, tfields.TensorFields)
>>> assert isinstance(sliced.fields[0], tfields.Tensors)
>>> assert sliced.fields[0].equal([10.5])
Picking
>>> picked = scalar_field[1]
>>> assert np.array_equal(picked, [0, 0, 1])
>>> assert np.array_equal(picked.fields[0], 21)
Masking
>>> masked = scalar_field[np.array([True, False, True])]
>>> assert masked.equal([[0, 0, 0], [0, -1, 0]])
>>> assert masked.fields[0].equal([42, 10.5])
>>> assert masked.fields[1].equal([1, 3])
Iteration
>>> _ = [point for point in scalar_field]
"""
item = super().__getitem__(index)
try:
if issubclass(type(item), TensorFields):
if isinstance(index, tuple):
index = index[0]
if item.fields:
# circumvent the setter here.
with self._bypass_setters("fields", demand_existence=False):
item.fields = [
field.__getitem__(index) for field in item.fields
]
except IndexError as err: # noqa: F841 pylint: disable=possibly-unused-variable
warnings.warn(
"Index error occured for field.__getitem__. Error "
"message: {err}".format(**locals())
)
return item
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21,163 | tfields.core | __new__ | null | def __new__(cls, tensors, *fields, **kwargs):
rigid = kwargs.pop("rigid", True)
obj = super(TensorFields, cls).__new__(cls, tensors, **kwargs)
if issubclass(type(tensors), TensorFields):
obj.fields = tensors.fields
elif not fields:
obj.fields = []
if fields:
# (over)write fields
obj.fields = fields
if rigid:
olen = len(obj)
field_lengths = [len(f) for f in obj.fields]
if not all(flen == olen for flen in field_lengths):
raise ValueError(
"Length of base ({olen}) should be the same as"
" the length of all fields ({field_lengths}).".format(**locals())
)
return obj
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21,170 | tfields.core | _cut_sympy | null | def _cut_sympy(self, expression):
if len(self) == 0:
return self.copy()
mask = self.evalf(expression) # coord_sys is handled by tmp_transform
mask.astype(bool)
inst = self[mask].copy()
# template
indices = np.arange(len(self))[mask]
template = tfields.TensorFields(np.empty((len(indices), 0)), indices)
return inst, template
| (self, expression) | [
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] |
21,171 | tfields.core | _cut_template |
In principle, what we do is returning
self[template.fields[0]]
If the templates tensors is given (has no dimension 0), 0))), we switch
to only extruding the field entries according to the indices provided
by template.fields[0]. This allows the template to define additional
points, extending the object it should cut. This becomes relevant for
Mesh3D when adding vertices at the edge of the cut is necessary.
| def _cut_template(self, template):
"""
In principle, what we do is returning
self[template.fields[0]]
If the templates tensors is given (has no dimension 0), 0))), we switch
to only extruding the field entries according to the indices provided
by template.fields[0]. This allows the template to define additional
points, extending the object it should cut. This becomes relevant for
Mesh3D when adding vertices at the edge of the cut is necessary.
"""
# Redirect fields
fields = []
if template.fields and issubclass(type(self), TensorFields):
template_field = np.array(template.fields[0])
if len(self) > 0:
# if new vertices have been created in the template, it is in principle unclear
# what fields we have to refer to. Thus in creating the template, we gave np.nan.
# To make it fast, we replace nan with 0 as a dummy and correct the field entries
# afterwards with np.nan.
nan_mask = np.isnan(template_field)
template_field[nan_mask] = 0 # dummy reference to index 0.
template_field = template_field.astype(int)
for field in self.fields:
projected_field = field[template_field]
projected_field[nan_mask] = np.nan # correction for nan
fields.append(projected_field)
if dim(template) == 0:
# for speed circumvent __getitem__ of the complexer subclasses
tensors = Tensors(self)[template.fields[0]]
else:
tensors = template
return type(self)(tensors, *fields)
| (self, template) | [
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21,182 | tfields.core | cut |
Extract a part of the object according to the logic given
by <expression>.
Args:
expression (sympy logical expression|tfields.TensorFields): logical
expression which will be evaluated. use symbols x, y and z.
If tfields.TensorFields or subclass is given, the expression
refers to a template.
coord_sys (str): coord_sys to evaluate the expression in. Only
active for template expression
Examples:
>>> import tfields
>>> import sympy
>>> x, y, z = sympy.symbols('x y z')
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> p.cut(x > 0).equal([[1, 2, 3],
... [4, 5, 6],
... [1, 2, -6],
... [1, 0, -1]])
True
combinations of cuts
>>> cut_expression = (x > 0) & (z < 0)
>>> combi_cut = p.cut(cut_expression)
>>> combi_cut.equal([[1, 2, -6], [1, 0, -1]])
True
Templates can be used to speed up the repeated cuts on the same
underlying tensor with the same expression but new fields.
First let us cut a but request the template on return:
>>> field1 = list(range(len(p)))
>>> tf = tfields.TensorFields(p, field1)
>>> tf_cut, template = tf.cut(cut_expression,
... return_template=True)
Now repeat the cut with a new field:
>>> field2 = p
>>> tf.fields.append(field2)
>>> tf_template_cut = tf.cut(template)
>>> tf_template_cut.equal(combi_cut)
True
>>> tf_template_cut.fields[0].equal([2, 4])
True
>>> tf_template_cut.fields[1].equal(combi_cut)
True
Returns:
copy of self with cut applied
[optional: template - requires <return_template> switch]
| def cut(self, expression, coord_sys=None, return_template=False, **kwargs):
"""
Extract a part of the object according to the logic given
by <expression>.
Args:
expression (sympy logical expression|tfields.TensorFields): logical
expression which will be evaluated. use symbols x, y and z.
If tfields.TensorFields or subclass is given, the expression
refers to a template.
coord_sys (str): coord_sys to evaluate the expression in. Only
active for template expression
Examples:
>>> import tfields
>>> import sympy
>>> x, y, z = sympy.symbols('x y z')
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> p.cut(x > 0).equal([[1, 2, 3],
... [4, 5, 6],
... [1, 2, -6],
... [1, 0, -1]])
True
combinations of cuts
>>> cut_expression = (x > 0) & (z < 0)
>>> combi_cut = p.cut(cut_expression)
>>> combi_cut.equal([[1, 2, -6], [1, 0, -1]])
True
Templates can be used to speed up the repeated cuts on the same
underlying tensor with the same expression but new fields.
First let us cut a but request the template on return:
>>> field1 = list(range(len(p)))
>>> tf = tfields.TensorFields(p, field1)
>>> tf_cut, template = tf.cut(cut_expression,
... return_template=True)
Now repeat the cut with a new field:
>>> field2 = p
>>> tf.fields.append(field2)
>>> tf_template_cut = tf.cut(template)
>>> tf_template_cut.equal(combi_cut)
True
>>> tf_template_cut.fields[0].equal([2, 4])
True
>>> tf_template_cut.fields[1].equal(combi_cut)
True
Returns:
copy of self with cut applied
[optional: template - requires <return_template> switch]
"""
with self.tmp_transform(coord_sys or self.coord_sys):
if issubclass(type(expression), TensorFields):
template = expression
obj = self._cut_template(template)
else:
obj, template = self._cut_sympy(expression, **kwargs)
if return_template:
return obj, template
return obj
| (self, expression, coord_sys=None, return_template=False, **kwargs) | [
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21,186 | tfields.core | equal |
Test, whether the instance has the same content as other.
Args:
other (iterable)
**kwargs: see Tensors.equal
| def equal(self, other, **kwargs): # pylint: disable=arguments-differ
"""
Test, whether the instance has the same content as other.
Args:
other (iterable)
**kwargs: see Tensors.equal
"""
if not issubclass(type(other), Tensors):
return super(TensorFields, self).equal(other, **kwargs)
with other.tmp_transform(self.coord_sys):
mask = super(TensorFields, self).equal(other, **kwargs)
if issubclass(type(other), TensorFields):
if len(self.fields) != len(other.fields):
mask &= False
else:
for i, field in enumerate(self.fields):
mask &= field.equal(other.fields[i], **kwargs)
return mask
| (self, other, **kwargs) | [
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21,196 | tfields.planes_3d | plot |
forward to Mesh3D plotting
| def plot(self, **kwargs): # pragma: no cover
"""
forward to Mesh3D plotting
"""
artists = []
centers = np.array(self)
norms = np.array(self.fields[0])
for i in range(len(self)):
artists.append(rna.plotting.plot_plane(centers[i], norms[i], **kwargs))
return artists
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21,199 | tfields.planes_3d | symbolic |
Returns:
list: list with sympy.Plane objects
| def symbolic(self):
"""
Returns:
list: list with sympy.Plane objects
"""
return [
sympy.Plane(point, normal_vector=vector)
for point, vector in zip(self, self.fields[0])
]
| (self) | [
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21,204 | tfields.points_3d | Points3D |
Points3D is a general class for 3D Point operations and storage.
Points are stored in np.arrays of shape (len, 3).
Thus the three coordinates of the Points stay close.
Args:
points3DInstance -> copy constructor
[points3DInstance1, points3DInstance2, ...] -> coord_sys are correctly treated
list of coordinates (see examples)
Kwargs:
coord_sys (str):
Use tfields.bases.CARTESIAN -> x, y, z
Use tfields.bases.CYLINDER -> r, phi, z
Use tfields.bases.SPHERICAL -> r, phi, theta
Examples:
Initializing with 3 vectors
>>> import tfields
>>> import numpy as np
>>> p1 = tfields.Points3D([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
>>> assert p1.equal([[1., 2., 3.],
... [4., 5., 6.],
... [1., 2., -6.]])
Initializing with listof coordinates
>>> p2 = tfields.Points3D(np.array([[1., 2., 3., 4, 5,],
... [4., 5., 6., 7, 8],
... [1, 2, -6, -1, 0]]).T)
>>> assert p2.equal(
... [[ 1., 4., 1.],
... [ 2., 5., 2.],
... [ 3., 6., -6.],
... [ 4., 7., -1.],
... [ 5., 8., 0.]], atol=1e-8)
>>> p2.transform(tfields.bases.CYLINDER)
>>> assert p2.equal(
... [[ 4.12310563, 1.32581766, 1.],
... [ 5.38516481, 1.19028995, 2.],
... [ 6.70820393, 1.10714872, -6.],
... [ 8.06225775, 1.05165021, -1.],
... [ 9.43398113, 1.01219701, 0.]], atol=1e-8)
Copy constructor with one instance preserves coord_sys of instance
>>> assert tfields.Points3D(p2).coord_sys == p2.coord_sys
Unless you specify other:
>>> assert tfields.Points3D(p2,
... coord_sys=tfields.bases.CARTESIAN).equal(
... [[ 1., 4., 1.],
... [ 2., 5., 2.],
... [ 3., 6., -6.],
... [ 4., 7., -1.],
... [ 5., 8., 0.]], atol=1e-8)
Copy constructor with many instances chooses majority of coordinates
systems to avoid much transformation
>>> assert tfields.Points3D.merged(p1, p2, p1).equal(
... [[ 1., 2., 3.],
... [ 4., 5., 6.],
... [ 1., 2., -6.],
... [ 1., 4., 1.],
... [ 2., 5., 2.],
... [ 3., 6., -6.],
... [ 4., 7., -1.],
... [ 5., 8., 0.],
... [ 1., 2., 3.],
... [ 4., 5., 6.],
... [ 1., 2., -6.]], atol=1e-8)
>>> p1.transform(tfields.bases.CYLINDER)
unless specified other. Here it is specified
>>> assert tfields.Points3D.merged(
... p1, p2, coord_sys=tfields.bases.CYLINDER).equal(
... [[ 2.23606798, 1.10714872, 3. ],
... [ 6.40312424, 0.89605538, 6. ],
... [ 2.23606798, 1.10714872, -6. ],
... [ 4.12310563, 1.32581766, 1. ],
... [ 5.38516481, 1.19028995, 2. ],
... [ 6.70820393, 1.10714872, -6. ],
... [ 8.06225775, 1.05165021, -1. ],
... [ 9.43398113, 1.01219701, 0. ]], atol=1e-8)
Shape is always (..., 3)
>>> p = tfields.Points3D([[1., 2., 3.], [4., 5., 6.],
... [1, 2, -6], [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> p.shape
(6, 3)
Empty array will create an ndarray of the form (0, 3)
>>> tfields.Points3D([])
Points3D([], shape=(0, 3), dtype=float64)
Use it as np.ndarrays -> masking etc. is inherited
>>> mask = np.array([True, False, True, False, False, True])
>>> mp = p[mask].copy()
Copy constructor
>>> assert mp.equal(
... [[ 1., 2., 3.],
... [ 1., 2., -6.],
... [ 0., 1., -1.]])
>>> assert tfields.Points3D(mp).equal(
... [[ 1., 2., 3.],
... [ 1., 2., -6.],
... [ 0., 1., -1.]])
Coordinate system is implemented. Default is cartesian
>>> p_cart = p.copy()
>>> p.transform(tfields.bases.CYLINDER)
>>> assert p.equal(
... tfields.Points3D([[2.236, 1.107, 3.],
... [6.403, 0.896, 6.],
... [2.236, 1.107, -6.],
... [7.071, -2.356, -5.],
... [1. , 0. , -1.],
... [1. , 1.571, -1.]],
... coord_sys=tfields.bases.CYLINDER),
... atol=1e-3)
>>> p.transform(tfields.bases.CARTESIAN)
>>> assert p.equal(p_cart, atol=1e-15)
| class Points3D(tfields.Tensors):
# pylint: disable=R0904
"""
Points3D is a general class for 3D Point operations and storage.
Points are stored in np.arrays of shape (len, 3).
Thus the three coordinates of the Points stay close.
Args:
points3DInstance -> copy constructor
[points3DInstance1, points3DInstance2, ...] -> coord_sys are correctly treated
list of coordinates (see examples)
Kwargs:
coord_sys (str):
Use tfields.bases.CARTESIAN -> x, y, z
Use tfields.bases.CYLINDER -> r, phi, z
Use tfields.bases.SPHERICAL -> r, phi, theta
Examples:
Initializing with 3 vectors
>>> import tfields
>>> import numpy as np
>>> p1 = tfields.Points3D([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
>>> assert p1.equal([[1., 2., 3.],
... [4., 5., 6.],
... [1., 2., -6.]])
Initializing with listof coordinates
>>> p2 = tfields.Points3D(np.array([[1., 2., 3., 4, 5,],
... [4., 5., 6., 7, 8],
... [1, 2, -6, -1, 0]]).T)
>>> assert p2.equal(
... [[ 1., 4., 1.],
... [ 2., 5., 2.],
... [ 3., 6., -6.],
... [ 4., 7., -1.],
... [ 5., 8., 0.]], atol=1e-8)
>>> p2.transform(tfields.bases.CYLINDER)
>>> assert p2.equal(
... [[ 4.12310563, 1.32581766, 1.],
... [ 5.38516481, 1.19028995, 2.],
... [ 6.70820393, 1.10714872, -6.],
... [ 8.06225775, 1.05165021, -1.],
... [ 9.43398113, 1.01219701, 0.]], atol=1e-8)
Copy constructor with one instance preserves coord_sys of instance
>>> assert tfields.Points3D(p2).coord_sys == p2.coord_sys
Unless you specify other:
>>> assert tfields.Points3D(p2,
... coord_sys=tfields.bases.CARTESIAN).equal(
... [[ 1., 4., 1.],
... [ 2., 5., 2.],
... [ 3., 6., -6.],
... [ 4., 7., -1.],
... [ 5., 8., 0.]], atol=1e-8)
Copy constructor with many instances chooses majority of coordinates
systems to avoid much transformation
>>> assert tfields.Points3D.merged(p1, p2, p1).equal(
... [[ 1., 2., 3.],
... [ 4., 5., 6.],
... [ 1., 2., -6.],
... [ 1., 4., 1.],
... [ 2., 5., 2.],
... [ 3., 6., -6.],
... [ 4., 7., -1.],
... [ 5., 8., 0.],
... [ 1., 2., 3.],
... [ 4., 5., 6.],
... [ 1., 2., -6.]], atol=1e-8)
>>> p1.transform(tfields.bases.CYLINDER)
unless specified other. Here it is specified
>>> assert tfields.Points3D.merged(
... p1, p2, coord_sys=tfields.bases.CYLINDER).equal(
... [[ 2.23606798, 1.10714872, 3. ],
... [ 6.40312424, 0.89605538, 6. ],
... [ 2.23606798, 1.10714872, -6. ],
... [ 4.12310563, 1.32581766, 1. ],
... [ 5.38516481, 1.19028995, 2. ],
... [ 6.70820393, 1.10714872, -6. ],
... [ 8.06225775, 1.05165021, -1. ],
... [ 9.43398113, 1.01219701, 0. ]], atol=1e-8)
Shape is always (..., 3)
>>> p = tfields.Points3D([[1., 2., 3.], [4., 5., 6.],
... [1, 2, -6], [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> p.shape
(6, 3)
Empty array will create an ndarray of the form (0, 3)
>>> tfields.Points3D([])
Points3D([], shape=(0, 3), dtype=float64)
Use it as np.ndarrays -> masking etc. is inherited
>>> mask = np.array([True, False, True, False, False, True])
>>> mp = p[mask].copy()
Copy constructor
>>> assert mp.equal(
... [[ 1., 2., 3.],
... [ 1., 2., -6.],
... [ 0., 1., -1.]])
>>> assert tfields.Points3D(mp).equal(
... [[ 1., 2., 3.],
... [ 1., 2., -6.],
... [ 0., 1., -1.]])
Coordinate system is implemented. Default is cartesian
>>> p_cart = p.copy()
>>> p.transform(tfields.bases.CYLINDER)
>>> assert p.equal(
... tfields.Points3D([[2.236, 1.107, 3.],
... [6.403, 0.896, 6.],
... [2.236, 1.107, -6.],
... [7.071, -2.356, -5.],
... [1. , 0. , -1.],
... [1. , 1.571, -1.]],
... coord_sys=tfields.bases.CYLINDER),
... atol=1e-3)
>>> p.transform(tfields.bases.CARTESIAN)
>>> assert p.equal(p_cart, atol=1e-15)
"""
def __new__(cls, tensors, **kwargs):
if not issubclass(type(tensors), Points3D):
kwargs["dim"] = 3
return super(Points3D, cls).__new__(cls, tensors, **kwargs)
def balls(self, radius, spacing=(5, 3)):
"""
Args:
radius (float): radius of spheres
spacing (tuple of int): n_phi, n_theta
Returns:
tfields.Mesh3D: Builds a sphere around each point with a resolution
defined by spacing and given radius
"""
sphere = tfields.Mesh3D.grid(
(radius, radius, 1),
(-np.pi, np.pi, spacing[0]),
(-np.pi / 2, np.pi / 2, spacing[1]),
coord_sys="spherical",
)
sphere.transform("cartesian")
balls = []
with self.tmp_transform("cartesian"):
for point in self:
ball = sphere.copy()
ball += point
balls.append(ball)
return tfields.Mesh3D.merged(*balls)
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21,208 | tfields.points_3d | __new__ | null | def __new__(cls, tensors, **kwargs):
if not issubclass(type(tensors), Points3D):
kwargs["dim"] = 3
return super(Points3D, cls).__new__(cls, tensors, **kwargs)
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21,212 | tfields.core | _args | null | def _args(self):
return (np.array(self),)
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21,216 | tfields.core | _kwargs | null | def _kwargs(self):
return dict((attr, getattr(self, attr)) for attr in self._iter_slots())
| (self) | [
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21,221 | tfields.core | _weights |
transformer method for weights inputs.
Args:
weights (np.ndarray | None):
If weights is None, use np.ones
Otherwise just pass the weights.
rigid (bool): demand equal weights and tensor length
Returns:
weight array
| def _weights(self, weights, rigid=True):
"""
transformer method for weights inputs.
Args:
weights (np.ndarray | None):
If weights is None, use np.ones
Otherwise just pass the weights.
rigid (bool): demand equal weights and tensor length
Returns:
weight array
"""
# set weights to 1.0 if weights is None
if weights is None:
weights = np.ones(len(self))
if rigid:
if not len(weights) == len(self):
raise ValueError("Equal number of weights as tensors demanded.")
return weights
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21,222 | tfields.points_3d | balls |
Args:
radius (float): radius of spheres
spacing (tuple of int): n_phi, n_theta
Returns:
tfields.Mesh3D: Builds a sphere around each point with a resolution
defined by spacing and given radius
| def balls(self, radius, spacing=(5, 3)):
"""
Args:
radius (float): radius of spheres
spacing (tuple of int): n_phi, n_theta
Returns:
tfields.Mesh3D: Builds a sphere around each point with a resolution
defined by spacing and given radius
"""
sphere = tfields.Mesh3D.grid(
(radius, radius, 1),
(-np.pi, np.pi, spacing[0]),
(-np.pi / 2, np.pi / 2, spacing[1]),
coord_sys="spherical",
)
sphere.transform("cartesian")
balls = []
with self.tmp_transform("cartesian"):
for point in self:
ball = sphere.copy()
ball += point
balls.append(ball)
return tfields.Mesh3D.merged(*balls)
| (self, radius, spacing=(5, 3)) | [
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] |
21,231 | tfields.core | equal |
Evaluate, whether the instance has the same content as other.
Args:
optional:
rtol (float)
atol (float)
equal_nan (bool)
see numpy.isclose
| def equal(
self, other, rtol=None, atol=None, equal_nan=False, return_bool=True
): # noqa: E501 pylint: disable=too-many-arguments
"""
Evaluate, whether the instance has the same content as other.
Args:
optional:
rtol (float)
atol (float)
equal_nan (bool)
see numpy.isclose
"""
if issubclass(type(other), Tensors) and self.coord_sys != other.coord_sys:
other = other.copy()
other.transform(self.coord_sys)
self_array, other_array = np.asarray(self), np.asarray(other)
if rtol is None and atol is None:
mask = self_array == other_array
if equal_nan:
both_nan = np.isnan(self_array) & np.isnan(other_array)
mask[both_nan] = both_nan[both_nan]
else:
if rtol is None:
rtol = 0.0
if atol is None:
atol = 0.0
mask = np.isclose(
self_array, other_array, rtol=rtol, atol=atol, equal_nan=equal_nan
)
if return_bool:
return bool(np.all(mask))
return mask
| (self, other, rtol=None, atol=None, equal_nan=False, return_bool=True) | [
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21,241 | tfields.core | plot |
Generic plotting method of Tensors.
Forwarding to rna.plotting.plot_tensor
| def plot(self, *args, **kwargs):
"""
Generic plotting method of Tensors.
Forwarding to rna.plotting.plot_tensor
"""
artist = rna.plotting.plot_tensor(
self, *args, **kwargs
) # pylint: disable=no-member
return artist
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21,247 | tfields.core | TensorFields |
Discrete Tensor Field
Args:
tensors (array): base tensors
*fields (array): multiple fields assigned to one base tensor. Fields
themself are also of type tensor
**kwargs:
rigid (bool): demand equal field and tensor lenght
... : see tfields.Tensors
Examples:
>>> import tfields
>>> from tfields import Tensors, TensorFields
>>> scalars = Tensors([0, 1, 2])
>>> vectors = Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = TensorFields(vectors, scalars)
>>> scalar_field.rank
1
>>> scalar_field.fields[0].rank
0
>>> vectorField = TensorFields(vectors, vectors)
>>> vectorField.fields[0].rank
1
>>> vectorField.fields[0].dim
3
>>> multiField = TensorFields(vectors, scalars, vectors)
>>> multiField.fields[0].dim
1
>>> multiField.fields[1].dim
3
Empty initialization
>>> empty_field = TensorFields([], dim=3)
>>> assert empty_field.shape == (0, 3)
>>> assert empty_field.fields == []
Directly initializing with lists or arrays
>>> vec_field_raw = tfields.TensorFields([[0, 1, 2], [3, 4, 5]],
... [1, 6], [2, 7])
>>> assert len(vec_field_raw.fields) == 2
Copying
>>> cp = TensorFields(vectorField)
>>> assert vectorField.equal(cp)
Copying takes care of coord_sys
>>> cp.transform(tfields.bases.CYLINDER)
>>> cp_cyl = TensorFields(cp)
>>> assert cp_cyl.coord_sys == tfields.bases.CYLINDER
Copying with changing type
>>> tcp = TensorFields(vectorField, dtype=int)
>>> assert vectorField.equal(tcp)
>>> assert tcp.dtype == int
Raises:
TypeError:
>>> import tfields
>>> tfields.TensorFields([1, 2, 3], [3]) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Length of base (3) should be the same as the length of all fields ([1]).
This error can be suppressed by setting rigid=False
>>> loose = tfields.TensorFields([1, 2, 3], [3], rigid=False)
>>> assert len(loose) != 1
| class TensorFields(Tensors):
"""
Discrete Tensor Field
Args:
tensors (array): base tensors
*fields (array): multiple fields assigned to one base tensor. Fields
themself are also of type tensor
**kwargs:
rigid (bool): demand equal field and tensor lenght
... : see tfields.Tensors
Examples:
>>> import tfields
>>> from tfields import Tensors, TensorFields
>>> scalars = Tensors([0, 1, 2])
>>> vectors = Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = TensorFields(vectors, scalars)
>>> scalar_field.rank
1
>>> scalar_field.fields[0].rank
0
>>> vectorField = TensorFields(vectors, vectors)
>>> vectorField.fields[0].rank
1
>>> vectorField.fields[0].dim
3
>>> multiField = TensorFields(vectors, scalars, vectors)
>>> multiField.fields[0].dim
1
>>> multiField.fields[1].dim
3
Empty initialization
>>> empty_field = TensorFields([], dim=3)
>>> assert empty_field.shape == (0, 3)
>>> assert empty_field.fields == []
Directly initializing with lists or arrays
>>> vec_field_raw = tfields.TensorFields([[0, 1, 2], [3, 4, 5]],
... [1, 6], [2, 7])
>>> assert len(vec_field_raw.fields) == 2
Copying
>>> cp = TensorFields(vectorField)
>>> assert vectorField.equal(cp)
Copying takes care of coord_sys
>>> cp.transform(tfields.bases.CYLINDER)
>>> cp_cyl = TensorFields(cp)
>>> assert cp_cyl.coord_sys == tfields.bases.CYLINDER
Copying with changing type
>>> tcp = TensorFields(vectorField, dtype=int)
>>> assert vectorField.equal(tcp)
>>> assert tcp.dtype == int
Raises:
TypeError:
>>> import tfields
>>> tfields.TensorFields([1, 2, 3], [3]) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Length of base (3) should be the same as the length of all fields ([1]).
This error can be suppressed by setting rigid=False
>>> loose = tfields.TensorFields([1, 2, 3], [3], rigid=False)
>>> assert len(loose) != 1
"""
__slots__ = ["coord_sys", "name", "fields"]
__slot_setters__ = [tfields.bases.get_coord_system_name, None, as_fields]
def __new__(cls, tensors, *fields, **kwargs):
rigid = kwargs.pop("rigid", True)
obj = super(TensorFields, cls).__new__(cls, tensors, **kwargs)
if issubclass(type(tensors), TensorFields):
obj.fields = tensors.fields
elif not fields:
obj.fields = []
if fields:
# (over)write fields
obj.fields = fields
if rigid:
olen = len(obj)
field_lengths = [len(f) for f in obj.fields]
if not all(flen == olen for flen in field_lengths):
raise ValueError(
"Length of base ({olen}) should be the same as"
" the length of all fields ({field_lengths}).".format(**locals())
)
return obj
def _args(self) -> tuple:
return super()._args() + tuple(self.fields)
def _kwargs(self) -> dict:
content = super()._kwargs()
content.pop("fields") # instantiated via _args
return content
def __getitem__(self, index):
"""
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors,
... [42, 21, 10.5],
... [1, 2, 3],
... [[0, 0], [-1, -1], [-2, -2]])
Slicing
>>> sliced = scalar_field[2:]
>>> assert isinstance(sliced, tfields.TensorFields)
>>> assert isinstance(sliced.fields[0], tfields.Tensors)
>>> assert sliced.fields[0].equal([10.5])
Picking
>>> picked = scalar_field[1]
>>> assert np.array_equal(picked, [0, 0, 1])
>>> assert np.array_equal(picked.fields[0], 21)
Masking
>>> masked = scalar_field[np.array([True, False, True])]
>>> assert masked.equal([[0, 0, 0], [0, -1, 0]])
>>> assert masked.fields[0].equal([42, 10.5])
>>> assert masked.fields[1].equal([1, 3])
Iteration
>>> _ = [point for point in scalar_field]
"""
item = super().__getitem__(index)
try:
if issubclass(type(item), TensorFields):
if isinstance(index, tuple):
index = index[0]
if item.fields:
# circumvent the setter here.
with self._bypass_setters("fields", demand_existence=False):
item.fields = [
field.__getitem__(index) for field in item.fields
]
except IndexError as err: # noqa: F841 pylint: disable=possibly-unused-variable
warnings.warn(
"Index error occured for field.__getitem__. Error "
"message: {err}".format(**locals())
)
return item
def __setitem__(self, index, item):
"""
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> original = tfields.TensorFields(
... [[0, 0, 0], [0, 0, 1], [0, -1, 0]],
... [42, 21, 10.5], [1, 2, 3])
>>> obj = tfields.TensorFields(
... [[0, 0, 0], [0, 0, np.nan],
... [0, -1, 0]], [42, 22, 10.5], [1, -1, 3])
>>> slice_obj = obj.copy()
>>> assert not obj.equal(original)
>>> obj[1] = original[1]
>>> assert obj[:2].equal(original[:2])
>>> assert not slice_obj.equal(original)
>>> slice_obj[:] = original[:]
>>> assert slice_obj.equal(original)
"""
super(TensorFields, self).__setitem__(index, item)
if issubclass(type(item), TensorFields):
if isinstance(index, slice):
for i, field in enumerate(item.fields):
self.fields[i].__setitem__(index, field)
elif isinstance(index, tuple):
for i, field in enumerate(item.fields):
self.fields[i].__setitem__(index[0], field)
else:
for i, field in enumerate(item.fields):
self.fields[i].__setitem__(index, field)
@classmethod
def merged(cls, *objects, **kwargs):
if not all(isinstance(o, cls) for o in objects):
# Note: could allow if all map_fields are none
raise TypeError(
"Merge constructor only accepts {cls} instances."
"Got objects of types {types} instead.".format(
cls=cls,
types=[type(o) for o in objects],
)
)
return_value = super(TensorFields, cls).merged(*objects, **kwargs)
return_templates = kwargs.get("return_templates", False)
if return_templates:
inst, templates = return_value
else:
inst, templates = (return_value, None)
fields = []
if all(len(obj.fields) == len(objects[0].fields) for obj in objects):
for fld_idx in range(len(objects[0].fields)):
field = tfields.Tensors.merged(
*[obj.fields[fld_idx] for obj in objects]
)
fields.append(field)
inst = cls.__new__(cls, inst, *fields)
if return_templates: # pylint: disable=no-else-return
return inst, templates
else:
return inst
def transform_field(self, coord_sys, field_index=0):
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] |
21,285 | tfields.core | plot |
Generic plotting method of TensorFields.
Args:
field_index: index of the field to plot (as quiver by default)
normalize: If True, normalize the field vectors to show only the direction
color: additional str argument 'norm' added. If color="norm", color with the norm.
| def plot(self, *args, **kwargs):
"""
Generic plotting method of TensorFields.
Args:
field_index: index of the field to plot (as quiver by default)
normalize: If True, normalize the field vectors to show only the direction
color: additional str argument 'norm' added. If color="norm", color with the norm.
"""
field_index = kwargs.pop("field_index", None)
field_args = ["normalize"]
if field_index is None:
for field_arg in field_args:
if field_arg in kwargs:
kwargs.pop(field_arg)
LOGGER.warning("Unused option %s", field_arg)
artist = super(TensorFields, self).plot(*args, **kwargs)
else:
normalize_field = kwargs.pop("normalize", False)
color = kwargs.get("color", None)
field = self.fields[field_index].copy()
# this tranfomration is compmlicated. Transforming the field is not yet understood
# if self.dim == field.dim:
# field.transform(self.coord_sys)
# else:
# logging.debug(
# "Careful: Plotting tensors with field of"
# "different dimension. No coord_sys check performed."
# )
if color == "norm":
norm = field.norm()
kwargs["color"] = norm
if normalize_field:
field = field.normalized()
if field.dim <= 3:
artist = (
rna.plotting.plot_tensor( # noqa: E501 pylint: disable=no-member
self, *args, field, **kwargs
)
)
else:
raise NotImplementedError(
"Field of dimension {field.dim}".format(**locals())
)
return artist
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21,292 | tfields.tensor_grid | TensorGrid |
A Tensor Grid is a TensorField which is aware of it's grid nature, which is order of iteration
(iter-order) over the base vectors (base_vectors).
Args:
*base_vectors (tuple): indices of the axes which should be iterated
**kwargs:
num (np.array): same as np.linspace 'num'
iter_order (np.array): index order of building the grid.
further: see TensorFields class
| class TensorGrid(TensorFields):
"""
A Tensor Grid is a TensorField which is aware of it's grid nature, which is order of iteration
(iter-order) over the base vectors (base_vectors).
Args:
*base_vectors (tuple): indices of the axes which should be iterated
**kwargs:
num (np.array): same as np.linspace 'num'
iter_order (np.array): index order of building the grid.
further: see TensorFields class
"""
__slots__ = ["coord_sys", "name", "fields", "base_vectors", "num", "iter_order"]
__slot_setters__ = TensorFields.__slot_setters__ + [
None,
None,
None,
]
def __new__(cls, tensors, *fields, **kwargs):
if isinstance(tensors, TensorGrid):
default_base_vectors = tensors.base_vectors
default_num = tensors.num
default_iter_order = tensors.iter_order
else:
default_base_vectors = kwargs.pop("base_vectors", None)
default_num = None
default_iter_order = None
base_vectors = kwargs.pop("base_vectors", default_base_vectors)
num = kwargs.pop("num", default_num)
iter_order = kwargs.pop("iter_order", default_iter_order)
obj = super(TensorGrid, cls).__new__(cls, tensors, *fields, **kwargs)
if len(base_vectors) == 3:
base_vectors = tuple(tuple(bv) for bv in base_vectors)
base_vectors = grid.ensure_complex(*base_vectors)
if (
isinstance(base_vectors, (tuple, list))
and base_vectors
and len(base_vectors[0]) == 3
):
if num is None:
num = np.array([int(bv[2].imag) for bv in base_vectors], dtype=int)
base_vectors = np.transpose([[bv[0], bv[1]] for bv in base_vectors])
# base_vectors
base_vectors = Tensors(base_vectors, coord_sys=obj.coord_sys)
if len(base_vectors) != 2:
raise ValueError(
f"base_vectors must be of lenght 2. Lenght is {len(base_vectors)}."
)
obj.base_vectors = base_vectors
# num
if num is None:
num = np.array([1 for _ in range(base_vectors.dim)])
else:
num = np.array(num, dtype=int)
obj.num = num
# iter_order
if iter_order is None:
iter_order = np.arange(base_vectors.dim)
else:
iter_order = np.array(iter_order, dtype=int)
obj.iter_order = iter_order
if isinstance(tensors, TensorGrid):
if (obj.num != tensors.num).all() or (
obj.is_empty() and not obj.base_vectors.equal(tensors.base_vectors)
):
# copy constructor with shape change
return obj.empty(*obj.base_num_tuples(), iter_order=iter_order)
if (obj.iter_order != tensors.iter_order).all():
# iter_order was changed in copy constructor
obj.iter_order = (
tensors.iter_order
) # set to 'default_iter_order' and change later
obj.change_iter_order(iter_order)
return obj
def __getitem__(self, index):
if not self.is_empty():
return super().__getitem__(index)
item = self.explicit()
if not util.is_full_slice(index, item.shape):
# downgrade to TensorFields
item = TensorFields(item)
return item.__getitem__(index)
@classmethod
# pylint:disable=arguments-differ
def grid(cls, *base_vectors, tensors=None, fields=None, **kwargs):
"""
Build the grid (explicitly) from base vectors
Args:
explicit args: see __new__
**kwargs: see TensorFields
"""
iter_order = kwargs.pop("iter_order", np.arange(len(base_vectors)))
if tensors is None:
tensors = TensorFields.grid(*base_vectors, iter_order=iter_order, **kwargs)
obj = cls(tensors, base_vectors=base_vectors, iter_order=iter_order, **kwargs)
if fields:
# pylint:disable=attribute-defined-outside-init
obj.fields = fields
return obj
@classmethod
def empty(cls, *base_vectors, **kwargs):
"""
Build the grid (implicitly) from base vectors
"""
base_vectors = grid.ensure_complex(*base_vectors)
bv_lengths = [int(bv[2].imag) for bv in base_vectors]
tensors = np.empty(shape=(np.prod(bv_lengths), 0))
return cls.grid(*base_vectors, tensors=tensors, **kwargs)
@classmethod
def merged(cls, *objects, **kwargs):
if "base_vectors" not in kwargs:
base_vectors = None
for obj in objects:
if base_vectors is None:
base_vectors = obj.base_vectors
else:
if not all(
((a == b).all() for a, b in zip(base_vectors, obj.base_vectors))
):
raise NotImplementedError("Non-alligned base vectors")
kwargs.setdefault("base_vectors", base_vectors)
merge = super().merged(*objects, **kwargs)
return merge
def base_num_tuples(self):
"""
Returns the grid style base_vectors + num tuples
"""
return tuple(
(bv[0], bv[1], complex(0, n))
for bv, n in zip(self.base_vectors.T, self.num)
)
@property
def rank(self):
if self.is_empty():
return 1
return super().rank
def is_empty(self):
"""
Check if the object is an implicit grid (base points are empty but base_vectors and iter
order can be used to build the explicit grid's base points).
"""
return 0 in self.shape
def explicit(self):
"""
Build the grid explicitly (e.g. after changing base_vector, iter_order or init with empty)
"""
kwargs = {
attr: getattr(self, attr)
for attr in self.__slots__
if attr not in ("base_vectors", "num", "coord_sys")
}
base_num_tuples = self.base_num_tuples()
kwargs["coord_sys"] = self.base_vectors.coord_sys
obj = self.grid(*base_num_tuples, **kwargs)
obj.transform(self.coord_sys)
return obj
def change_iter_order(self, iter_order):
"""
Transform the iter order
"""
field_swap_indices = grid.change_iter_order(
# pylint:disable=access-member-before-definition
self.num,
self.iter_order,
iter_order,
)
for field in self.fields:
field[:] = field[field_swap_indices]
# pylint:disable=attribute-defined-outside-init
self.iter_order = iter_order
self[:] = self.explicit()
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21,295 | tfields.tensor_grid | __getitem__ | null | def __getitem__(self, index):
if not self.is_empty():
return super().__getitem__(index)
item = self.explicit()
if not util.is_full_slice(index, item.shape):
# downgrade to TensorFields
item = TensorFields(item)
return item.__getitem__(index)
| (self, index) | [
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21,297 | tfields.tensor_grid | __new__ | null | def __new__(cls, tensors, *fields, **kwargs):
if isinstance(tensors, TensorGrid):
default_base_vectors = tensors.base_vectors
default_num = tensors.num
default_iter_order = tensors.iter_order
else:
default_base_vectors = kwargs.pop("base_vectors", None)
default_num = None
default_iter_order = None
base_vectors = kwargs.pop("base_vectors", default_base_vectors)
num = kwargs.pop("num", default_num)
iter_order = kwargs.pop("iter_order", default_iter_order)
obj = super(TensorGrid, cls).__new__(cls, tensors, *fields, **kwargs)
if len(base_vectors) == 3:
base_vectors = tuple(tuple(bv) for bv in base_vectors)
base_vectors = grid.ensure_complex(*base_vectors)
if (
isinstance(base_vectors, (tuple, list))
and base_vectors
and len(base_vectors[0]) == 3
):
if num is None:
num = np.array([int(bv[2].imag) for bv in base_vectors], dtype=int)
base_vectors = np.transpose([[bv[0], bv[1]] for bv in base_vectors])
# base_vectors
base_vectors = Tensors(base_vectors, coord_sys=obj.coord_sys)
if len(base_vectors) != 2:
raise ValueError(
f"base_vectors must be of lenght 2. Lenght is {len(base_vectors)}."
)
obj.base_vectors = base_vectors
# num
if num is None:
num = np.array([1 for _ in range(base_vectors.dim)])
else:
num = np.array(num, dtype=int)
obj.num = num
# iter_order
if iter_order is None:
iter_order = np.arange(base_vectors.dim)
else:
iter_order = np.array(iter_order, dtype=int)
obj.iter_order = iter_order
if isinstance(tensors, TensorGrid):
if (obj.num != tensors.num).all() or (
obj.is_empty() and not obj.base_vectors.equal(tensors.base_vectors)
):
# copy constructor with shape change
return obj.empty(*obj.base_num_tuples(), iter_order=iter_order)
if (obj.iter_order != tensors.iter_order).all():
# iter_order was changed in copy constructor
obj.iter_order = (
tensors.iter_order
) # set to 'default_iter_order' and change later
obj.change_iter_order(iter_order)
return obj
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21,312 | tfields.tensor_grid | base_num_tuples |
Returns the grid style base_vectors + num tuples
| def base_num_tuples(self):
"""
Returns the grid style base_vectors + num tuples
"""
return tuple(
(bv[0], bv[1], complex(0, n))
for bv, n in zip(self.base_vectors.T, self.num)
)
| (self) | [
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21,313 | tfields.tensor_grid | change_iter_order |
Transform the iter order
| def change_iter_order(self, iter_order):
"""
Transform the iter order
"""
field_swap_indices = grid.change_iter_order(
# pylint:disable=access-member-before-definition
self.num,
self.iter_order,
iter_order,
)
for field in self.fields:
field[:] = field[field_swap_indices]
# pylint:disable=attribute-defined-outside-init
self.iter_order = iter_order
self[:] = self.explicit()
| (self, iter_order) | [
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] |
21,324 | tfields.tensor_grid | explicit |
Build the grid explicitly (e.g. after changing base_vector, iter_order or init with empty)
| def explicit(self):
"""
Build the grid explicitly (e.g. after changing base_vector, iter_order or init with empty)
"""
kwargs = {
attr: getattr(self, attr)
for attr in self.__slots__
if attr not in ("base_vectors", "num", "coord_sys")
}
base_num_tuples = self.base_num_tuples()
kwargs["coord_sys"] = self.base_vectors.coord_sys
obj = self.grid(*base_num_tuples, **kwargs)
obj.transform(self.coord_sys)
return obj
| (self) | [
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] |
21,327 | tfields.tensor_grid | is_empty |
Check if the object is an implicit grid (base points are empty but base_vectors and iter
order can be used to build the explicit grid's base points).
| def is_empty(self):
"""
Check if the object is an implicit grid (base points are empty but base_vectors and iter
order can be used to build the explicit grid's base points).
"""
return 0 in self.shape
| (self) | [
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] |
21,341 | tfields.core | TensorMaps |
Args:
tensors: see Tensors class
*fields (Tensors): see TensorFields class
**kwargs:
coord_sys ('str'): see Tensors class
maps (array-like): indices indicating a connection between the
tensors at the respective index positions
Examples:
>>> import tfields
>>> scalars = tfields.Tensors([0, 1, 2])
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> maps = [tfields.TensorFields([[0, 1, 2], [0, 1, 2]], [42, 21]),
... tfields.TensorFields([[1], [2]], [-42, -21])]
>>> mesh = tfields.TensorMaps(vectors, scalars,
... maps=maps)
>>> assert isinstance(mesh.maps, tfields.Maps)
>>> assert len(mesh.maps) == 2
>>> assert mesh.equal(tfields.TensorFields(vectors, scalars))
Copy constructor
>>> mesh_copy = tfields.TensorMaps(mesh)
Copying takes care of coord_sys
>>> mesh_copy.transform(tfields.bases.CYLINDER)
>>> mesh_cp_cyl = tfields.TensorMaps(mesh_copy)
>>> assert mesh_cp_cyl.coord_sys == tfields.bases.CYLINDER
| class TensorMaps(TensorFields):
"""
Args:
tensors: see Tensors class
*fields (Tensors): see TensorFields class
**kwargs:
coord_sys ('str'): see Tensors class
maps (array-like): indices indicating a connection between the
tensors at the respective index positions
Examples:
>>> import tfields
>>> scalars = tfields.Tensors([0, 1, 2])
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> maps = [tfields.TensorFields([[0, 1, 2], [0, 1, 2]], [42, 21]),
... tfields.TensorFields([[1], [2]], [-42, -21])]
>>> mesh = tfields.TensorMaps(vectors, scalars,
... maps=maps)
>>> assert isinstance(mesh.maps, tfields.Maps)
>>> assert len(mesh.maps) == 2
>>> assert mesh.equal(tfields.TensorFields(vectors, scalars))
Copy constructor
>>> mesh_copy = tfields.TensorMaps(mesh)
Copying takes care of coord_sys
>>> mesh_copy.transform(tfields.bases.CYLINDER)
>>> mesh_cp_cyl = tfields.TensorMaps(mesh_copy)
>>> assert mesh_cp_cyl.coord_sys == tfields.bases.CYLINDER
"""
__slots__ = ["coord_sys", "name", "fields", "maps"]
__slot_setters__ = [
tfields.bases.get_coord_system_name,
None,
as_fields,
as_maps,
]
def __new__(cls, tensors, *fields, **kwargs):
if issubclass(type(tensors), TensorMaps):
default_maps = tensors.maps
else:
default_maps = {}
maps = Maps(kwargs.pop("maps", default_maps))
obj = super(TensorMaps, cls).__new__(cls, tensors, *fields, **kwargs)
obj.maps = maps
return obj
def __getitem__(self, index):
"""
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0],
... [1, 1, 1], [-1, -1, -1]])
>>> maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3], [2, 3, 4]],
... [[1, 2], [3, 4], [5, 6]]),
... tfields.TensorFields([[0], [1], [2], [3], [4]])]
>>> mesh = tfields.TensorMaps(vectors,
... [42, 21, 10.5, 1, 1],
... [1, 2, 3, 3, 3],
... maps=maps)
Slicing
>>> sliced = mesh[2:]
>>> assert isinstance(sliced, tfields.TensorMaps)
>>> assert isinstance(sliced.fields[0], tfields.Tensors)
>>> assert isinstance(sliced.maps[3], tfields.TensorFields)
>>> assert sliced.fields[0].equal([10.5, 1, 1])
>>> assert sliced.maps[3].equal([[0, 1, 2]])
>>> assert sliced.maps[3].fields[0].equal([[5, 6]])
Picking
>>> picked = mesh[1]
>>> assert np.array_equal(picked, [0, 0, 1])
>>> assert np.array_equal(picked.maps[3], np.empty((0, 3)))
>>> assert np.array_equal(picked.maps[1], [[0]])
Masking
>>> masked = mesh[np.array([True, False, True, True, True])]
>>> assert masked.equal([[0, 0, 0], [0, -1, 0],
... [1, 1, 1], [-1, -1, -1]])
>>> assert masked.fields[0].equal([42, 10.5, 1, 1])
>>> assert masked.fields[1].equal([1, 3, 3, 3])
>>> assert masked.maps[3].equal([[1, 2, 3]])
>>> assert masked.maps[1].equal([[0], [1], [2], [3]])
Iteration
>>> _ = [vertex for vertex in mesh]
"""
item = super(TensorMaps, self).__getitem__(index)
if issubclass(type(item), TensorMaps): # pylint: disable=too-many-nested-blocks
if isinstance(index, tuple):
index = index[0]
if item.maps:
item.maps = Maps(item.maps)
indices = np.arange(len(self))
keep_indices = indices[index]
if isinstance(keep_indices, (int, np.integer)):
keep_indices = [keep_indices]
delete_indices = set(indices).difference(set(keep_indices))
# correct all maps that contain deleted indices
for map_dim in self.maps:
# build mask, where the map should be deleted
map_delete_mask = np.full(
(len(self.maps[map_dim]),), False, dtype=bool
)
for i, map_ in enumerate( # pylint: disable=invalid-name
self.maps[map_dim]
):
for node_index in map_:
if node_index in delete_indices:
map_delete_mask[i] = True
break
map_mask = ~map_delete_mask
# build the correction counters
move_up_counter = np.zeros(self.maps[map_dim].shape, dtype=int)
for delete_index in delete_indices:
move_up_counter[self.maps[map_dim] > delete_index] -= 1
item.maps[map_dim] = (self.maps[map_dim] + move_up_counter)[
map_mask
]
return item
@classmethod
def merged(
cls, *objects, **kwargs
): # pylint: disable=too-many-locals,too-many-branches
if not all(isinstance(o, cls) for o in objects):
# Note: could allow if all face_fields are none
raise TypeError(
"Merge constructor only accepts {cls} instances.".format(**locals())
)
tensor_lengths = [len(o) for o in objects]
cum_tensor_lengths = [sum(tensor_lengths[:i]) for i in range(len(objects))]
return_value = super().merged(*objects, **kwargs)
return_templates = kwargs.get("return_templates", False)
if return_templates:
inst, templates = return_value
else:
inst, templates = (return_value, None)
dim_maps_dict = {} # {dim: {i: map_}
for i, obj in enumerate(objects):
for dimension, map_ in obj.maps.items():
map_ = map_ + cum_tensor_lengths[i]
if dimension not in dim_maps_dict:
dim_maps_dict[dimension] = {}
dim_maps_dict[dimension][i] = map_
maps = []
template_maps_list = [[] for i in range(len(objects))]
for dimension in sorted(dim_maps_dict):
# sort by object index
dim_maps = [
dim_maps_dict[dimension][i]
for i in range(len(objects))
if i in dim_maps_dict[dimension]
]
return_value = TensorFields.merged(
*dim_maps,
return_templates=return_templates,
)
if return_templates:
(
map_, # pylint: disable=invalid-name
dimension_map_templates,
) = return_value
for i in range(len(objects)):
template_maps_list[i].append(
(dimension, dimension_map_templates[i])
)
else:
map_ = return_value # pylint: disable=invalid-name
maps.append(map_)
inst.maps = maps
if return_templates: # pylint: disable=no-else-return
for i, template_maps in enumerate(template_maps_list):
# template maps will not have dimensions according to their
# tensors which are indices
templates[i] = tfields.TensorMaps(
templates[i], maps=Maps(template_maps)
)
return inst, templates
else:
return inst
def _cut_template(self, template):
"""
Args:
template (tfields.TensorMaps)
Examples:
>>> import | (tensors, *fields, **kwargs) | [
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21,346 | tfields.core | __new__ | null | def __new__(cls, tensors, *fields, **kwargs):
if issubclass(type(tensors), TensorMaps):
default_maps = tensors.maps
else:
default_maps = {}
maps = Maps(kwargs.pop("maps", default_maps))
obj = super(TensorMaps, cls).__new__(cls, tensors, *fields, **kwargs)
obj.maps = maps
return obj
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21,354 | tfields.core | _cut_template |
Args:
template (tfields.TensorMaps)
Examples:
>>> import tfields
>>> import numpy as np
Build mesh
>>> mmap = tfields.TensorFields([[0, 1, 2], [0, 3, 4]],
... [[42, 21], [-42, -21]])
>>> m = tfields.Mesh3D([[0]*3, [1]*3, [2]*3, [3]*3, [4]*3],
... [0.0, 0.1, 0.2, 0.3, 0.4],
... [0.0, -0.1, -0.2, -0.3, -0.4],
... maps=[mmap])
Build template
>>> tmap = tfields.TensorFields([[0, 3, 4], [0, 1, 2]],
... [1, 0])
>>> t = tfields.Mesh3D([[0]*3, [-1]*3, [-2]*3, [-3]*3, [-4]*3],
... [1, 0, 3, 2, 4],
... maps=[tmap])
Use template as instruction to make a fast cut
>>> res = m._cut_template(t)
>>> assert np.array_equal(res.fields,
... [[0.1, 0.0, 0.3, 0.2, 0.4],
... [-0.1, 0.0, -0.3, -0.2, -0.4]])
>>> assert np.array_equal(res.maps[3].fields[0],
... [[-42, -21], [42, 21]])
| def _cut_template(self, template):
"""
Args:
template (tfields.TensorMaps)
Examples:
>>> import tfields
>>> import numpy as np
Build mesh
>>> mmap = tfields.TensorFields([[0, 1, 2], [0, 3, 4]],
... [[42, 21], [-42, -21]])
>>> m = tfields.Mesh3D([[0]*3, [1]*3, [2]*3, [3]*3, [4]*3],
... [0.0, 0.1, 0.2, 0.3, 0.4],
... [0.0, -0.1, -0.2, -0.3, -0.4],
... maps=[mmap])
Build template
>>> tmap = tfields.TensorFields([[0, 3, 4], [0, 1, 2]],
... [1, 0])
>>> t = tfields.Mesh3D([[0]*3, [-1]*3, [-2]*3, [-3]*3, [-4]*3],
... [1, 0, 3, 2, 4],
... maps=[tmap])
Use template as instruction to make a fast cut
>>> res = m._cut_template(t)
>>> assert np.array_equal(res.fields,
... [[0.1, 0.0, 0.3, 0.2, 0.4],
... [-0.1, 0.0, -0.3, -0.2, -0.4]])
>>> assert np.array_equal(res.maps[3].fields[0],
... [[-42, -21], [42, 21]])
"""
inst = super()._cut_template(template) # this will set maps=Maps({})
# Redirect maps and their fields
if template.fields:
# bulk was cut so we need to correct the map references.
index_lut = np.full(len(self), np.nan) # float type
index_lut[template.fields[0]] = np.arange(len(template.fields[0]))
for map_dim, map_ in self.maps.items():
map_ = map_._cut_template( # pylint: disable=protected-access
template.maps[map_dim]
)
if template.fields:
# correct
map_ = Maps.to_map( # pylint: disable=invalid-name
index_lut[map_], *map_.fields
)
inst.maps[map_dim] = map_
return inst
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21,393 | tfields.core | Tensors |
Set of tensors with the same basis.
Args:
tensors: np.ndarray or AbstractNdarray subclass
**kwargs:
name: optional - custom name, can be anything
Examples:
>>> import numpy as np
>>> import tfields
Initialize a scalar range
>>> scalars = tfields.Tensors([0, 1, 2])
>>> scalars.rank == 0
True
Initialize vectors
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> vectors.rank == 1
True
>>> vectors.dim == 3
True
>>> assert vectors.coord_sys == 'cartesian'
Initialize the Levi-Zivita Tensor
>>> matrices = tfields.Tensors([[[0, 0, 0], [0, 0, 1], [0, -1, 0]],
... [[0, 0, -1], [0, 0, 0], [1, 0, 0]],
... [[0, 1, 0], [-1, 0, 0], [0, 0, 0]]])
>>> matrices.shape == (3, 3, 3)
True
>>> matrices.rank == 2
True
>>> matrices.dim == 3
True
Initializing in different start coordinate system
>>> cyl = tfields.Tensors([[5, np.arctan(4. / 3.), 42]],
... coord_sys='cylinder')
>>> assert cyl.coord_sys == 'cylinder'
>>> cyl.transform('cartesian')
>>> assert cyl.coord_sys == 'cartesian'
>>> cart = cyl
>>> assert round(cart[0, 0], 10) == 3.
>>> assert round(cart[0, 1], 10) == 4.
>>> assert cart[0, 2] == 42
Initialize with copy constructor keeps the coordinate system
>>> with vectors.tmp_transform('cylinder'):
... vect_cyl = tfields.Tensors(vectors)
... assert vect_cyl.coord_sys == vectors.coord_sys
>>> assert vect_cyl.coord_sys == 'cylinder'
You can demand a special dimension.
>>> _ = tfields.Tensors([[1, 2, 3]], dim=3)
>>> _ = tfields.Tensors([[1, 2, 3]], dim=2) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Incorrect dimension: 3 given, 2 demanded.
The dimension argument (dim) becomes necessary if you want to initialize
an empty array
>>> _ = tfields.Tensors([]) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Empty tensors need dimension parameter 'dim'.
>>> tfields.Tensors([], dim=7)
Tensors([], shape=(0, 7), dtype=float64)
| class Tensors(AbstractNdarray): # pylint: disable=too-many-public-methods
"""
Set of tensors with the same basis.
Args:
tensors: np.ndarray or AbstractNdarray subclass
**kwargs:
name: optional - custom name, can be anything
Examples:
>>> import numpy as np
>>> import tfields
Initialize a scalar range
>>> scalars = tfields.Tensors([0, 1, 2])
>>> scalars.rank == 0
True
Initialize vectors
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> vectors.rank == 1
True
>>> vectors.dim == 3
True
>>> assert vectors.coord_sys == 'cartesian'
Initialize the Levi-Zivita Tensor
>>> matrices = tfields.Tensors([[[0, 0, 0], [0, 0, 1], [0, -1, 0]],
... [[0, 0, -1], [0, 0, 0], [1, 0, 0]],
... [[0, 1, 0], [-1, 0, 0], [0, 0, 0]]])
>>> matrices.shape == (3, 3, 3)
True
>>> matrices.rank == 2
True
>>> matrices.dim == 3
True
Initializing in different start coordinate system
>>> cyl = tfields.Tensors([[5, np.arctan(4. / 3.), 42]],
... coord_sys='cylinder')
>>> assert cyl.coord_sys == 'cylinder'
>>> cyl.transform('cartesian')
>>> assert cyl.coord_sys == 'cartesian'
>>> cart = cyl
>>> assert round(cart[0, 0], 10) == 3.
>>> assert round(cart[0, 1], 10) == 4.
>>> assert cart[0, 2] == 42
Initialize with copy constructor keeps the coordinate system
>>> with vectors.tmp_transform('cylinder'):
... vect_cyl = tfields.Tensors(vectors)
... assert vect_cyl.coord_sys == vectors.coord_sys
>>> assert vect_cyl.coord_sys == 'cylinder'
You can demand a special dimension.
>>> _ = tfields.Tensors([[1, 2, 3]], dim=3)
>>> _ = tfields.Tensors([[1, 2, 3]], dim=2) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Incorrect dimension: 3 given, 2 demanded.
The dimension argument (dim) becomes necessary if you want to initialize
an empty array
>>> _ = tfields.Tensors([]) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Empty tensors need dimension parameter 'dim'.
>>> tfields.Tensors([], dim=7)
Tensors([], shape=(0, 7), dtype=float64)
"""
__slots__ = ["coord_sys", "name"]
__slot_defaults__ = ["cartesian"]
__slot_setters__ = [tfields.bases.get_coord_system_name]
def __new__(cls, tensors, **kwargs): # pylint: disable=too-many-branches
dtype = kwargs.pop("dtype", None)
order = kwargs.pop("order", None)
dim_ = kwargs.pop("dim", None)
# copy constructor extracts the kwargs from tensors
if issubclass(type(tensors), Tensors):
if dim_ is not None:
dim_ = tensors.dim
coord_sys = kwargs.pop("coord_sys", tensors.coord_sys)
tensors = tensors.copy()
tensors.transform(coord_sys)
kwargs["coord_sys"] = coord_sys
kwargs["name"] = kwargs.pop("name", tensors.name)
if dtype is None:
dtype = tensors.dtype
else:
if dtype is None:
if hasattr(tensors, "dtype"):
dtype = tensors.dtype
else:
dtype = np.float64
# demand iterable structure
try:
len(tensors)
except TypeError as err:
raise TypeError(
"Iterable structure necessary." " Got {tensors}".format(**locals())
) from err
# process empty inputs
if len(tensors) == 0:
if issubclass(type(tensors), tfields.Tensors):
tensors = np.empty(tensors.shape, dtype=tensors.dtype)
elif dim_ is not None:
tensors = np.empty((0, dim_))
if issubclass(type(tensors), np.ndarray):
# np.empty
pass
elif hasattr(tensors, "shape"):
dim_ = dim_(tensors)
else:
raise ValueError("Empty tensors need dimension parameter 'dim'.")
tensors = np.asarray(tensors, dtype=dtype, order=order)
obj = tensors.view(cls)
if dim_ is not None:
if dim_ != obj.dim:
raise ValueError(
"Incorrect dimension: {obj.dim} given,"
" {dim_} demanded.".format(**locals())
)
# update kwargs with defaults from slots
cls._update_slot_kwargs(kwargs)
# set kwargs to slots attributes
# pylint:disable=consider-using-dict-items
for attr in kwargs:
if attr not in cls._iter_slots():
raise AttributeError(
"Keyword argument {attr} not accepted "
"for class {cls}".format(**locals())
)
setattr(obj, attr, kwargs[attr])
return obj
def __iter__(self):
"""
Forwarding iterations to the bulk array. Otherwise __getitem__ would
kick in and slow down imensely.
Examples:
>>> import tfields
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors, [42, 21, 10.5], [1, 2, 3])
>>> [(point.rank, point.dim) for point in scalar_field]
[(0, 1), (0, 1), (0, 1)]
"""
for index in range(len(self)):
yield super(Tensors, self).__getitem__(index).view(Tensors)
@classmethod
def _load_txt(cls, path, set_header: bool = False, **load_kwargs):
"""
Factory method
Given a path to a txt file, construct the object
Args:
path: see :meth:`Tensors.load`
set_header: if True, set the header to the `name` attribute
(the type of the name attribute will be a list of strings
(header separated by delimiter)
)
**loadkwargs: see :meth:`np.loadtxt`
"""
load_txt_keys = [
k
for k, v in inspect.signature(np.loadtxt).parameters.items()
if v.default is not inspect._empty # pylint:disable=protected-access
]
load_txt_kwargs = {}
for key in load_txt_keys:
if key in load_kwargs:
load_txt_kwargs[key] = load_kwargs.pop(key)
# use the same defaults as np.savetxt
load_txt_kwargs.setdefault("comments", "# ") # default is "#"
comments = load_txt_kwargs.get("comments")
load_txt_kwargs.setdefault("delimiter", " ")
skiprows = 0
header = []
with open(rna.path.resolve(path)) as file_:
while True:
line = file_.readline()
if line.startswith(comments):
header.append(line.lstrip(comments).rstrip("\n"))
else:
file_.seek(0)
break
skiprows += 1
load_txt_kwargs["skiprows"] = max(skiprows, load_txt_kwargs.pop("skiprows", 0))
arr = np.loadtxt(path, **load_txt_kwargs)
obj = cls(arr, **load_kwargs)
i = 0
for key, line in zip(cls._iter_slots(), header):
slot, rest = line.split(": ")
tpe, val_str = rest.split(" = ")
if tpe == "NoneType":
val = None
else:
tpe = __builtins__[tpe]
val = tpe(val_str)
setattr(obj, slot, val)
i += 1
header = header[i:]
if set_header:
obj.name = (
obj.name,
header,
) # pylint:disable=attribute-defined-outside-init
return obj
def _save_txt(self, path, **kwargs):
"""
Save as text file.
Args:
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] |
21,397 | tfields.core | __new__ | null | def __new__(cls, tensors, **kwargs): # pylint: disable=too-many-branches
dtype = kwargs.pop("dtype", None)
order = kwargs.pop("order", None)
dim_ = kwargs.pop("dim", None)
# copy constructor extracts the kwargs from tensors
if issubclass(type(tensors), Tensors):
if dim_ is not None:
dim_ = tensors.dim
coord_sys = kwargs.pop("coord_sys", tensors.coord_sys)
tensors = tensors.copy()
tensors.transform(coord_sys)
kwargs["coord_sys"] = coord_sys
kwargs["name"] = kwargs.pop("name", tensors.name)
if dtype is None:
dtype = tensors.dtype
else:
if dtype is None:
if hasattr(tensors, "dtype"):
dtype = tensors.dtype
else:
dtype = np.float64
# demand iterable structure
try:
len(tensors)
except TypeError as err:
raise TypeError(
"Iterable structure necessary." " Got {tensors}".format(**locals())
) from err
# process empty inputs
if len(tensors) == 0:
if issubclass(type(tensors), tfields.Tensors):
tensors = np.empty(tensors.shape, dtype=tensors.dtype)
elif dim_ is not None:
tensors = np.empty((0, dim_))
if issubclass(type(tensors), np.ndarray):
# np.empty
pass
elif hasattr(tensors, "shape"):
dim_ = dim_(tensors)
else:
raise ValueError("Empty tensors need dimension parameter 'dim'.")
tensors = np.asarray(tensors, dtype=dtype, order=order)
obj = tensors.view(cls)
if dim_ is not None:
if dim_ != obj.dim:
raise ValueError(
"Incorrect dimension: {obj.dim} given,"
" {dim_} demanded.".format(**locals())
)
# update kwargs with defaults from slots
cls._update_slot_kwargs(kwargs)
# set kwargs to slots attributes
# pylint:disable=consider-using-dict-items
for attr in kwargs:
if attr not in cls._iter_slots():
raise AttributeError(
"Keyword argument {attr} not accepted "
"for class {cls}".format(**locals())
)
setattr(obj, attr, kwargs[attr])
return obj
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21,435 | tfields.triangles_3d | Triangles3D |
Points3D child restricted to n * 3 Points.
Three Points always group together to one triangle.
Args:
tensors (Iterable | tfields.TensorFields)
*fields (Iterable | tfields.Tensors): Fields with the same length as tensors
**kwargs: passed to base class
Attributes:
see :class:`~tfields.TensorFields`
Examples:
>>> import tfields
>>> t = tfields.Triangles3D([[1,2,3], [3,3,3], [0,0,0]])
You can add fields to each triangle
>>> t = tfields.Triangles3D(t, tfields.Tensors([42]))
>>> assert t.fields[0].equal([42])
| class Triangles3D(tfields.TensorFields):
# pylint: disable=R0904
"""
Points3D child restricted to n * 3 Points.
Three Points always group together to one triangle.
Args:
tensors (Iterable | tfields.TensorFields)
*fields (Iterable | tfields.Tensors): Fields with the same length as tensors
**kwargs: passed to base class
Attributes:
see :class:`~tfields.TensorFields`
Examples:
>>> import tfields
>>> t = tfields.Triangles3D([[1,2,3], [3,3,3], [0,0,0]])
You can add fields to each triangle
>>> t = tfields.Triangles3D(t, tfields.Tensors([42]))
>>> assert t.fields[0].equal([42])
"""
def __new__(cls, tensors, *fields, **kwargs):
kwargs["dim"] = 3
kwargs["rigid"] = False
obj = super(Triangles3D, cls).__new__(cls, tensors, *fields, **kwargs)
if not len(obj) % 3 == 0:
warnings.warn(
"Input object of size({0}) has no divider 3 and"
" does not describe triangles.".format(len(obj))
)
return obj
def ntriangles(self):
"""
Returns:
int: number of triangles
"""
return len(self) // 3
def _to_triangles_mask(self, mask):
mask = np.array(mask)
mask = mask.reshape((self.ntriangles(), 3))
mask = mask.all(axis=1)
return mask
def __getitem__(self, index):
"""
In addition to the usual, also slice fields
Examples:
>>> import numpy as np
>>> import tfields
>>> vectors = tfields.Tensors(np.array([range(30)] * 3).T)
>>> triangles = tfields.Triangles3D(vectors, range(10))
>>> assert np.array_equal(triangles[3:6],
... [[3] * 3,
... [4] * 3,
... [5] * 3])
>>> assert triangles[3:6].fields[0][0] == 1
"""
item = super(tfields.TensorFields, self).__getitem__(index)
try: # __iter__ will try except __getitem__(i) until IndexError
if issubclass(type(item), Triangles3D): # block int, float, ...
if len(item) % 3 != 0:
item = tfields.Tensors(item)
elif item.fields:
# build triangle index / indices / mask when possible
tri_index = None
if isinstance(index, tuple):
index = index[0]
if isinstance(index, int):
pass
elif isinstance(index, slice):
start = index.start or 0
stop = index.stop or len(self)
step = index.step
if start % 3 == 0 and (stop - start) % 3 == 0 and step is None:
tri_index = slice(start // 3, stop // 3)
else:
try:
tri_index = self._to_triangles_mask(index)
except ValueError:
pass
# apply triangle index to fields
if tri_index is not None:
item.fields = [
field.__getitem__(tri_index) for field in item.fields
]
else:
item = tfields.Tensors(item)
except IndexError as err:
logging.warning(
"Index error occured for field.__getitem__. Error message: %s", err
)
return item
def _save_stl(self, path, **kwargs):
"""
Save the object to a stl file
"""
import stl
shape = stl.Mesh(np.zeros(self.ntriangles(), dtype=stl.Mesh.dtype))
shape.vectors = self.bulk.reshape((self.ntriangles(), 3, 3))
shape.save(path, **kwargs)
@classmethod
def _load_stl(cls, path):
"""
Factory method
Given a path to a stl file, construct the object
"""
import stl.mesh
triangles = stl.mesh.Mesh.from_file(path)
obj = cls(triangles.vectors.reshape(-1, 3))
return obj
@classmethod
def merged(cls, *objects, **kwargs):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
obj = super(Triangles3D, cls).merged(*objects, **kwargs)
if not len(obj) % 3 == 0:
warnings.warn(
"Input object of size({0}) has no divider 3 and"
" does not describe triangles.".format(len(obj))
)
return obj
def evalf(self, expression=None, coord_sys=None):
"""
Triangle3D implementation
Examples:
>>> from sympy.abc import x
>>> import numpy as np
>>> import tfields
>>> t = tfields.Triangles3D([[1., 2., 3.], [-4., 5., 6.], [1, 2, -6],
... [5, -5, -5], [1,0,-1], [0,1,-1],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> mask = t.evalf(x >= 0)
>>> assert np.array_equal(t[mask],
... tfields.Triangles3D([[ 5., -5., -5.],
... [ 1., 0., -1.],
... [ 0., 1., -1.]]))
Returns:
np.array: mask which is True, where expression evaluates True
"""
mask = super(Triangles3D, self).evalf(expression, coord_sys=coord_sys)
mask = self._to_triangles_mask(mask)
mask = np.array([mask] * 3).T.reshape((len(self)))
return mask
def cut(self, expression, coord_sys=None):
"""
Default cut method for Triangles3D
Examples:
>>> import sympy
>>> import numpy as np
>>> import tfields
>>> x, y, z = sympy.symbols('x y z')
>>> t = tfields.Triangles3D([[1., 2., 3.], [-4., 5., 6.], [1, 2, -6],
... [5, -5, -5], [1, 0, -1], [0, 1, -1],
... [-5, -5, -5], [1, 0, -1], [0, 1, -1]])
>>> tc = t.cut(x >= 0)
>>> assert tc.equal(tfields.Triangles3D([[ 5., -5., -5.],
... [ 1., 0., -1.],
... [ 0., 1., -1.]]))
>>> t.fields.append(tfields.Tensors([1,2,3]))
>>> tc2 = t.cut(x >= 0)
>>> assert np.array_equal(tc2.fields[-1], np.array([2.]))
"""
# mask = self.evalf(expression, coord_sys=coord_sys)
# inst = self[mask].copy()
# return inst
return super().cut(expression, coord_sys)
def mesh(self):
"""
Returns:
tfields.Mesh3D
"""
mp = tfields.TensorFields(np.arange(len(self)).reshape((-1, 3)), *self.fields)
mesh = tfields.Mesh3D(self, maps=[mp])
return mesh.cleaned(stale=False) # stale vertices can not occure here
@cached_property()
def _areas(self):
"""
Cached method to retrieve areas of triangles
"""
transform = np.eye(3)
return self.areas(transform=transform)
def areas(self, transform=None):
"""
Calculate area with "heron's formula"
Args:
transform (np.ndarray): optional transformation matrix
The triangle points are transformed with transform if given
before calclulating the area
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[1,0,0], [0,0,1], [0,0,0]],
... faces=[[0, 1, 2]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5]))
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0], [0,0,1]],
... faces=[[0, 1, 2], [1, 2, 3]])
>>> assert np.allclose(m.triangles().areas(), np.array( | (tensors, *fields, **kwargs) | [
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21,438 | tfields.triangles_3d | __getitem__ |
In addition to the usual, also slice fields
Examples:
>>> import numpy as np
>>> import tfields
>>> vectors = tfields.Tensors(np.array([range(30)] * 3).T)
>>> triangles = tfields.Triangles3D(vectors, range(10))
>>> assert np.array_equal(triangles[3:6],
... [[3] * 3,
... [4] * 3,
... [5] * 3])
>>> assert triangles[3:6].fields[0][0] == 1
| def __getitem__(self, index):
"""
In addition to the usual, also slice fields
Examples:
>>> import numpy as np
>>> import tfields
>>> vectors = tfields.Tensors(np.array([range(30)] * 3).T)
>>> triangles = tfields.Triangles3D(vectors, range(10))
>>> assert np.array_equal(triangles[3:6],
... [[3] * 3,
... [4] * 3,
... [5] * 3])
>>> assert triangles[3:6].fields[0][0] == 1
"""
item = super(tfields.TensorFields, self).__getitem__(index)
try: # __iter__ will try except __getitem__(i) until IndexError
if issubclass(type(item), Triangles3D): # block int, float, ...
if len(item) % 3 != 0:
item = tfields.Tensors(item)
elif item.fields:
# build triangle index / indices / mask when possible
tri_index = None
if isinstance(index, tuple):
index = index[0]
if isinstance(index, int):
pass
elif isinstance(index, slice):
start = index.start or 0
stop = index.stop or len(self)
step = index.step
if start % 3 == 0 and (stop - start) % 3 == 0 and step is None:
tri_index = slice(start // 3, stop // 3)
else:
try:
tri_index = self._to_triangles_mask(index)
except ValueError:
pass
# apply triangle index to fields
if tri_index is not None:
item.fields = [
field.__getitem__(tri_index) for field in item.fields
]
else:
item = tfields.Tensors(item)
except IndexError as err:
logging.warning(
"Index error occured for field.__getitem__. Error message: %s", err
)
return item
| (self, index) | [
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21,440 | tfields.triangles_3d | __new__ | null | def __new__(cls, tensors, *fields, **kwargs):
kwargs["dim"] = 3
kwargs["rigid"] = False
obj = super(Triangles3D, cls).__new__(cls, tensors, *fields, **kwargs)
if not len(obj) % 3 == 0:
warnings.warn(
"Input object of size({0}) has no divider 3 and"
" does not describe triangles.".format(len(obj))
)
return obj
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21,447 | tfields.triangles_3d | _baricentric |
Determine baricentric coordinates like
[u,v,w] = [ab, ac, ab x ac]^-1 * ap
where ax is vector from point a to point x
Examples:
empty Meshes return right formatted array
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([], faces=[])
>>> m.triangles()._baricentric(np.array([0.2, 0.2, 0]))
array([], dtype=float64)
>>> m2 = tfields.Mesh3D([[0,0,0], [2,0,0], [0,2,0], [0,0,2]],
... faces=[[0, 1, 2], [0, 2, 3]]);
>>> assert np.array_equal(
... m2.triangles()._baricentric(np.array([0.2, 0.2, 0]),
... delta=2.),
... [[0.1, 0.1, 0.0],
... [0.1, 0.0, 0.1]])
if point lies in the plane, return np.nan, else inf for w if delta
is exactly 0.
>>> baric = m2.triangles()._baricentric(np.array([0.2, 0.2, 0]),
... delta=0.),
>>> baric_expected = np.array([[0.1, 0.1, np.nan],
... [0.1, 0.0, np.inf]])
>>> assert ((baric == baric_expected) | (np.isnan(baric) &
... np.isnan(baric_expected))).all()
Raises:
If you define triangles that have colinear side vectors or in general lead to
not invertable matrices [ab, ac, ab x ac] the values will be nan and
a warning will be triggered:
>>> import warnings
>>> import numpy as np
>>> import tfields
>>> m3 = tfields.Mesh3D([[0,0,0], [2,0,0], [4,0,0], [0,1,0]],
... maps=[[[0, 1, 2], [0, 1, 3]]]);
>>> with warnings.catch_warnings(record=True) as wrn:
... warnings.simplefilter("ignore")
... bc = m3.triangles()._baricentric(np.array([0.2, 0.2, 0]), delta=0.3)
>>> bc_exp = np.array([[ np.nan, np.nan, np.nan], [ 0.1, 0.2, 0. ]])
>>> assert ((bc == bc_exp) | (np.isnan(bc) &
... np.isnan(bc_exp))).all()
The warning would be:
UserWarning('Singular matrix: Could not invert matrix ...
... [[ 2. 4. 0.], [ 0. 0. 0.], [ 0. 0. 0.]]. Return nan matrix.',)
Returns:
np.ndarray: barycentric coordinates u, v, w of point with respect to each triangle
| def _baricentric(self, point, delta=0.0):
"""
Determine baricentric coordinates like
[u,v,w] = [ab, ac, ab x ac]^-1 * ap
where ax is vector from point a to point x
Examples:
empty Meshes return right formatted array
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([], faces=[])
>>> m.triangles()._baricentric(np.array([0.2, 0.2, 0]))
array([], dtype=float64)
>>> m2 = tfields.Mesh3D([[0,0,0], [2,0,0], [0,2,0], [0,0,2]],
... faces=[[0, 1, 2], [0, 2, 3]]);
>>> assert np.array_equal(
... m2.triangles()._baricentric(np.array([0.2, 0.2, 0]),
... delta=2.),
... [[0.1, 0.1, 0.0],
... [0.1, 0.0, 0.1]])
if point lies in the plane, return np.nan, else inf for w if delta
is exactly 0.
>>> baric = m2.triangles()._baricentric(np.array([0.2, 0.2, 0]),
... delta=0.),
>>> baric_expected = np.array([[0.1, 0.1, np.nan],
... [0.1, 0.0, np.inf]])
>>> assert ((baric == baric_expected) | (np.isnan(baric) &
... np.isnan(baric_expected))).all()
Raises:
If you define triangles that have colinear side vectors or in general lead to
not invertable matrices [ab, ac, ab x ac] the values will be nan and
a warning will be triggered:
>>> import warnings
>>> import numpy as np
>>> import tfields
>>> m3 = tfields.Mesh3D([[0,0,0], [2,0,0], [4,0,0], [0,1,0]],
... maps=[[[0, 1, 2], [0, 1, 3]]]);
>>> with warnings.catch_warnings(record=True) as wrn:
... warnings.simplefilter("ignore")
... bc = m3.triangles()._baricentric(np.array([0.2, 0.2, 0]), delta=0.3)
>>> bc_exp = np.array([[ np.nan, np.nan, np.nan], [ 0.1, 0.2, 0. ]])
>>> assert ((bc == bc_exp) | (np.isnan(bc) &
... np.isnan(bc_exp))).all()
The warning would be:
UserWarning('Singular matrix: Could not invert matrix ...
... [[ 2. 4. 0.], [ 0. 0. 0.], [ 0. 0. 0.]]. Return nan matrix.',)
Returns:
np.ndarray: barycentric coordinates u, v, w of point with respect to each triangle
"""
if self.ntriangles() == 0:
return np.array([])
a, _, _ = self.corners()
ap = point - a
# matrix vector product for matrices and vectors
barCoords = np.einsum("...ji,...i", self._baricentric_matrix, ap)
with warnings.catch_warnings():
# python2.7
warnings.filterwarnings(
"ignore", message="invalid value encountered in divide"
)
warnings.filterwarnings(
"ignore", message="divide by zero encountered in divide"
)
# python3.x
warnings.filterwarnings(
"ignore", message="invalid value encountered in true_divide"
)
warnings.filterwarnings(
"ignore", message="divide by zero encountered in true_divide"
)
barCoords[:, 2] /= delta # allow devide by 0.
return barCoords
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21,450 | tfields.triangles_3d | _in_triangles |
Barycentric method to optain, wheter a point is in any of the triangles
Args:
point (list of len 3)
delta (float / None):
float: acceptance in +- norm vector direction
None: accept the face with the minimum distance to the point
Returns:
np.array: boolean mask, True where point in a triangle within delta
Examples:
see tests/test_triangles_3d.py
| def _in_triangles(self, point, delta=0.0):
"""
Barycentric method to optain, wheter a point is in any of the triangles
Args:
point (list of len 3)
delta (float / None):
float: acceptance in +- norm vector direction
None: accept the face with the minimum distance to the point
Returns:
np.array: boolean mask, True where point in a triangle within delta
Examples:
see tests/test_triangles_3d.py
"""
if self.ntriangles() == 0:
return np.array([], dtype=bool)
try:
point = np.reshape(point, 3)
except ValueError:
raise ValueError(
"point must be castable to shape 3 but is of shape {0}".format(
point.shape
)
)
# min_dist_method switch if delta is None
if delta is None:
delta = 1.0
min_dist_method = True
else:
min_dist_method = False
u, v, w = self._baricentric(point, delta=delta).T
if delta == 0.0:
w[np.isnan(w)] = 0.0 # division by 0 in baricentric makes w = 0 nan.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="invalid value encountered in less_equal"
)
barycentric_bools = (
((v <= 1.0) & (v >= 0.0)) & ((u <= 1.0) & (u >= 0.0)) & ((v + u <= 1.0))
)
if all(~barycentric_bools):
return barycentric_bools
if min_dist_method:
orthogonal_acceptance = np.full(
barycentric_bools.shape, False, dtype=bool
)
closest_indices = np.argmin(abs(w)[barycentric_bools])
# transform the indices to the whole array, not only the
# barycentric_bools selection
closest_indices = np.arange(len(barycentric_bools))[barycentric_bools][
closest_indices
]
orthogonal_acceptance[closest_indices] = True
else:
orthogonal_acceptance = abs(w) <= 1
return np.array(barycentric_bools & orthogonal_acceptance)
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21,452 | tfields.triangles_3d | _on_edges |
Determine whether a point is on the edge / side ray of a triangle
TODO:
on_edges like in_triangles
Returns:
np.array: boolean mask which is true, if point is on one side ray
of a triangle
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4]]);
Corner points are found
>>> assert np.array_equal(
... m.triangles()._on_edges(tfields.Points3D([[0,1,0]])),
... np.array([ True, True, False], dtype=bool))
Side points are found, too
>>> assert np.array_equal(
... m.triangles()._on_edges(tfields.Points3D([[0.5,0,0.5]])),
... np.array([False, False, True], dtype=bool))
| def _on_edges(self, point):
"""
Determine whether a point is on the edge / side ray of a triangle
TODO:
on_edges like in_triangles
Returns:
np.array: boolean mask which is true, if point is on one side ray
of a triangle
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4]]);
Corner points are found
>>> assert np.array_equal(
... m.triangles()._on_edges(tfields.Points3D([[0,1,0]])),
... np.array([ True, True, False], dtype=bool))
Side points are found, too
>>> assert np.array_equal(
... m.triangles()._on_edges(tfields.Points3D([[0.5,0,0.5]])),
... np.array([False, False, True], dtype=bool))
"""
u, v, w = self._baricentric(point, 1.0).T
orthogonal_acceptance = w == 0 # point should lie in triangle
barycentric_bools = (
(((0.0 <= v) & (v <= 1.0)) & (u == 0.0))
| (((0.0 <= u) & (u <= 1.0)) & (v == 0.0))
| (v + u == 1.0)
)
return np.array(barycentric_bools & orthogonal_acceptance)
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21,456 | tfields.triangles_3d | _save_stl |
Save the object to a stl file
| def _save_stl(self, path, **kwargs):
"""
Save the object to a stl file
"""
import stl
shape = stl.Mesh(np.zeros(self.ntriangles(), dtype=stl.Mesh.dtype))
shape.vectors = self.bulk.reshape((self.ntriangles(), 3, 3))
shape.save(path, **kwargs)
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21,458 | tfields.triangles_3d | _to_triangles_mask | null | def _to_triangles_mask(self, mask):
mask = np.array(mask)
mask = mask.reshape((self.ntriangles(), 3))
mask = mask.all(axis=1)
return mask
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21,459 | tfields.triangles_3d | _weights |
transformer method for weights inputs.
Args:
weights (np.ndarray | int | None):
If weights is integer it will be used as index for fields and fields are
used as weights.
If weights is None it will
Otherwise just pass the weights.
Returns:
TODO: Better docs
| def _weights(self, weights, rigid=False):
"""
transformer method for weights inputs.
Args:
weights (np.ndarray | int | None):
If weights is integer it will be used as index for fields and fields are
used as weights.
If weights is None it will
Otherwise just pass the weights.
Returns:
TODO: Better docs
"""
# set weights to 1.0 if weights is None
if weights is None:
weights = np.ones(self.ntriangles())
return super(Triangles3D, self)._weights(weights, rigid=rigid)
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21,460 | tfields.triangles_3d | areas |
Calculate area with "heron's formula"
Args:
transform (np.ndarray): optional transformation matrix
The triangle points are transformed with transform if given
before calclulating the area
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[1,0,0], [0,0,1], [0,0,0]],
... faces=[[0, 1, 2]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5]))
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0], [0,0,1]],
... faces=[[0, 1, 2], [1, 2, 3]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5, 0.5]))
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [1,1,0], [0,0,1], [1,0,1]],
... faces=[[0, 1, 2], [0, 3, 4]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5, 0.5]))
| def areas(self, transform=None):
"""
Calculate area with "heron's formula"
Args:
transform (np.ndarray): optional transformation matrix
The triangle points are transformed with transform if given
before calclulating the area
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[1,0,0], [0,0,1], [0,0,0]],
... faces=[[0, 1, 2]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5]))
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0], [0,0,1]],
... faces=[[0, 1, 2], [1, 2, 3]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5, 0.5]))
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [1,1,0], [0,0,1], [1,0,1]],
... faces=[[0, 1, 2], [0, 3, 4]])
>>> assert np.allclose(m.triangles().areas(), np.array([0.5, 0.5]))
"""
if transform is None:
return self._areas
else:
indices = range(self.ntriangles())
aIndices = [i * 3 for i in indices]
bIndices = [i * 3 + 1 for i in indices]
cIndices = [i * 3 + 2 for i in indices]
# define 3 vectors building the triangle, transform it back and take their norm
if not np.array_equal(transform, np.eye(3)):
a = np.linalg.norm(
np.linalg.solve(
transform.T, (self[aIndices, :] - self[bIndices, :]).T
),
axis=0,
)
b = np.linalg.norm(
np.linalg.solve(
transform.T, (self[aIndices, :] - self[cIndices, :]).T
),
axis=0,
)
c = np.linalg.norm(
np.linalg.solve(
transform.T, (self[bIndices, :] - self[cIndices, :]).T
),
axis=0,
)
else:
a = np.linalg.norm(self[aIndices, :] - self[bIndices, :], axis=1)
b = np.linalg.norm(self[aIndices, :] - self[cIndices, :], axis=1)
c = np.linalg.norm(self[bIndices, :] - self[cIndices, :], axis=1)
# sort by length for numerical stability
lengths = np.concatenate(
(a.reshape(-1, 1), b.reshape(-1, 1), c.reshape(-1, 1)), axis=1
)
lengths.sort()
a, b, c = lengths.T
return 0.25 * np.sqrt(
(a + (b + c)) * (c - (a - b)) * (c + (a - b)) * (a + (b - c))
)
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21,461 | tfields.triangles_3d | centroids |
Returns:
:func:`~tfields.Triangles3D._centroids`
Examples:
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4], [1, 3, 4]]);
>>> assert m.triangles().centroids().equal(
... [[1./3, 1./3, 0.],
... [-1./3, 1./3, 0.],
... [0., 0., 1./3],
... [1./3, 1./3, 1./3]])
| def centroids(self):
"""
Returns:
:func:`~tfields.Triangles3D._centroids`
Examples:
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4], [1, 3, 4]]);
>>> assert m.triangles().centroids().equal(
... [[1./3, 1./3, 0.],
... [-1./3, 1./3, 0.],
... [0., 0., 1./3],
... [1./3, 1./3, 1./3]])
"""
return self._centroids
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21,462 | tfields.triangles_3d | circumcenters |
Semi baricentric method to calculate circumcenter points of the
triangles
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4], [1, 3, 4]]);
>>> assert np.allclose(
... m.triangles().circumcenters(),
... [[0.5, 0.5, 0.0],
... [-0.5, 0.5, 0.0],
... [0.0, 0.0, 0.0],
... [1.0 / 3, 1.0 / 3, 1.0 / 3]])
| def circumcenters(self):
"""
Semi baricentric method to calculate circumcenter points of the
triangles
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4], [1, 3, 4]]);
>>> assert np.allclose(
... m.triangles().circumcenters(),
... [[0.5, 0.5, 0.0],
... [-0.5, 0.5, 0.0],
... [0.0, 0.0, 0.0],
... [1.0 / 3, 1.0 / 3, 1.0 / 3]])
"""
pointA, pointB, pointC = self.corners()
a = np.linalg.norm(
pointC - pointB, axis=1
) # side length of side opposite to pointA
b = np.linalg.norm(pointC - pointA, axis=1)
c = np.linalg.norm(pointB - pointA, axis=1)
bary1 = a**2 * (b**2 + c**2 - a**2)
bary2 = b**2 * (a**2 + c**2 - b**2)
bary3 = c**2 * (a**2 + b**2 - c**2)
matrices = np.concatenate((pointA, pointB, pointC), axis=1).reshape(
pointA.shape + (3,)
)
# transpose the inner matrix
matrices = np.einsum("...ji", matrices)
vectors = np.array((bary1, bary2, bary3)).T
# matrix vector product for matrices and vectors
P = np.einsum("...ji,...i", matrices, vectors)
P /= vectors.sum(axis=1).reshape((len(vectors), 1))
return tfields.Points3D(P)
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21,466 | tfields.triangles_3d | corners |
Returns:
three np.arrays with corner points of triangles
| def corners(self):
"""
Returns:
three np.arrays with corner points of triangles
"""
indices = range(self.ntriangles())
aIndices = [i * 3 for i in indices]
bIndices = [i * 3 + 1 for i in indices]
cIndices = [i * 3 + 2 for i in indices]
a = self.bulk[aIndices, :]
b = self.bulk[bIndices, :]
c = self.bulk[cIndices, :]
return a, b, c
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21,468 | tfields.triangles_3d | cut |
Default cut method for Triangles3D
Examples:
>>> import sympy
>>> import numpy as np
>>> import tfields
>>> x, y, z = sympy.symbols('x y z')
>>> t = tfields.Triangles3D([[1., 2., 3.], [-4., 5., 6.], [1, 2, -6],
... [5, -5, -5], [1, 0, -1], [0, 1, -1],
... [-5, -5, -5], [1, 0, -1], [0, 1, -1]])
>>> tc = t.cut(x >= 0)
>>> assert tc.equal(tfields.Triangles3D([[ 5., -5., -5.],
... [ 1., 0., -1.],
... [ 0., 1., -1.]]))
>>> t.fields.append(tfields.Tensors([1,2,3]))
>>> tc2 = t.cut(x >= 0)
>>> assert np.array_equal(tc2.fields[-1], np.array([2.]))
| def cut(self, expression, coord_sys=None):
"""
Default cut method for Triangles3D
Examples:
>>> import sympy
>>> import numpy as np
>>> import tfields
>>> x, y, z = sympy.symbols('x y z')
>>> t = tfields.Triangles3D([[1., 2., 3.], [-4., 5., 6.], [1, 2, -6],
... [5, -5, -5], [1, 0, -1], [0, 1, -1],
... [-5, -5, -5], [1, 0, -1], [0, 1, -1]])
>>> tc = t.cut(x >= 0)
>>> assert tc.equal(tfields.Triangles3D([[ 5., -5., -5.],
... [ 1., 0., -1.],
... [ 0., 1., -1.]]))
>>> t.fields.append(tfields.Tensors([1,2,3]))
>>> tc2 = t.cut(x >= 0)
>>> assert np.array_equal(tc2.fields[-1], np.array([2.]))
"""
# mask = self.evalf(expression, coord_sys=coord_sys)
# inst = self[mask].copy()
# return inst
return super().cut(expression, coord_sys)
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21,471 | tfields.triangles_3d | edges |
Retrieve two of the three edge vectors
Returns:
two np.ndarrays: vectors ab and ac, where a, b, c are corners (see
self.corners)
| def edges(self):
"""
Retrieve two of the three edge vectors
Returns:
two np.ndarrays: vectors ab and ac, where a, b, c are corners (see
self.corners)
"""
a, b, c = self.corners()
ab = b - a
ac = c - a
return ab, ac
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21,474 | tfields.triangles_3d | evalf |
Triangle3D implementation
Examples:
>>> from sympy.abc import x
>>> import numpy as np
>>> import tfields
>>> t = tfields.Triangles3D([[1., 2., 3.], [-4., 5., 6.], [1, 2, -6],
... [5, -5, -5], [1,0,-1], [0,1,-1],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> mask = t.evalf(x >= 0)
>>> assert np.array_equal(t[mask],
... tfields.Triangles3D([[ 5., -5., -5.],
... [ 1., 0., -1.],
... [ 0., 1., -1.]]))
Returns:
np.array: mask which is True, where expression evaluates True
| def evalf(self, expression=None, coord_sys=None):
"""
Triangle3D implementation
Examples:
>>> from sympy.abc import x
>>> import numpy as np
>>> import tfields
>>> t = tfields.Triangles3D([[1., 2., 3.], [-4., 5., 6.], [1, 2, -6],
... [5, -5, -5], [1,0,-1], [0,1,-1],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> mask = t.evalf(x >= 0)
>>> assert np.array_equal(t[mask],
... tfields.Triangles3D([[ 5., -5., -5.],
... [ 1., 0., -1.],
... [ 0., 1., -1.]]))
Returns:
np.array: mask which is True, where expression evaluates True
"""
mask = super(Triangles3D, self).evalf(expression, coord_sys=coord_sys)
mask = self._to_triangles_mask(mask)
mask = np.array([mask] * 3).T.reshape((len(self)))
return mask
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21,475 | tfields.triangles_3d | in_triangles |
Barycentric method to obtain, which tensors are containes in any of the triangles
Args:
tensors (Points3D instance)
optional:
delta:
:obj:`float`: Normal distance to a triangle, that the points
are concidered to be contained in the triangle.
:obj:`None`: Find the minimum distance.
Default is 0.
assign_multiple: If True, one point may belong to multiple
triangles at the same time. In the other case the first
occurence will be True the other False
Returns:
list: [index(or indices if assign_multiple) of triangle for point in tensors]
| def in_triangles(
self,
tensors,
delta: typing.Optional[float] = 0.0,
assign_multiple: bool = False,
) -> typing.Union[typing.List[typing.List[int]], np.array]:
"""
Barycentric method to obtain, which tensors are containes in any of the triangles
Args:
tensors (Points3D instance)
optional:
delta:
:obj:`float`: Normal distance to a triangle, that the points
are concidered to be contained in the triangle.
:obj:`None`: Find the minimum distance.
Default is 0.
assign_multiple: If True, one point may belong to multiple
triangles at the same time. In the other case the first
occurence will be True the other False
Returns:
list: [index(or indices if assign_multiple) of triangle for point in tensors]
"""
indices = np.full(tensors.shape[0], -1, dtype=int)
if self.ntriangles() == 0:
if assign_multiple:
return [[-1] * len(indices)]
else:
return indices
with tensors.tmp_transform(self.coord_sys):
for i, point in enumerate(iter(tensors)):
mask = self._in_triangles(point, delta)
if np.any(mask):
if assign_multiple:
index = np.argwhere(mask == np.amax(mask))
index.flatten().tolist()
indices.append(index)
else:
indices[i] = np.argmax(mask)
else:
if assign_multiple:
indices.append([-1])
else:
indices[i] = -1
return indices
| (self, tensors, delta: Optional[float] = 0.0, assign_multiple: bool = False) -> Union[List[List[int]], <built-in function array>] | [
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21,479 | tfields.triangles_3d | mesh |
Returns:
tfields.Mesh3D
| def mesh(self):
"""
Returns:
tfields.Mesh3D
"""
mp = tfields.TensorFields(np.arange(len(self)).reshape((-1, 3)), *self.fields)
mesh = tfields.Mesh3D(self, maps=[mp])
return mesh.cleaned(stale=False) # stale vertices can not occure here
| (self) | [
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21,485 | tfields.triangles_3d | norms |
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4], [1, 3, 4]]);
>>> assert np.allclose(m.triangles().norms(),
... [[0.0, 0.0, 1.0],
... [0.0, 0.0, -1.0],
... [0.0, 1.0, 0.0],
... [0.57735027] * 3],
... atol=1e-8)
| def norms(self):
"""
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.Mesh3D([[0,0,0], [1,0,0], [-1,0,0], [0,1,0], [0,0,1]],
... faces=[[0, 1, 3],[0, 2, 3],[1,2,4], [1, 3, 4]]);
>>> assert np.allclose(m.triangles().norms(),
... [[0.0, 0.0, 1.0],
... [0.0, 0.0, -1.0],
... [0.0, 1.0, 0.0],
... [0.57735027] * 3],
... atol=1e-8)
"""
ab, ac = self.edges()
vectors = np.cross(ab, ac)
norms = np.apply_along_axis(np.linalg.norm, 0, vectors.T).reshape(-1, 1)
# cross product may be zero, so replace zero norms by ones to divide vectors by norms
np.place(norms, norms == 0.0, 1.0)
return vectors / norms
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21,486 | tfields.triangles_3d | ntriangles |
Returns:
int: number of triangles
| def ntriangles(self):
"""
Returns:
int: number of triangles
"""
return len(self) // 3
| (self) | [
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] |
21,497 | tfields.core | dim |
Manifold dimension
| def dim(tensor):
"""
Manifold dimension
"""
tensor = np.asarray(tensor)
if rank(tensor) == 0:
return 1
return tensor.shape[1]
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] |
21,498 | tfields.mask | evalf |
Linking sympy and numpy by retrieving a mask according to the cut_expression
Args:
array (numpy ndarray)
cut_expression (sympy logical expression)
coord_sys (str): coord_sys to evalfuate the expression in.
Returns:
np.array: mask which is True, where cut_expression evalfuates True.
Examples:
>>> import sympy
>>> import numpy as np
>>> import tfields
>>> x, y, z = sympy.symbols('x y z')
>>> a = np.array([[1., 2., 3.], [4., 5., 6.], [1, 2, -6],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> assert np.array_equal(
... tfields.evalf(a, x > 0),
... np.array([ True, True, True, False, True, False]))
And combination
>>> assert np.array_equal(
... tfields.evalf(a, (x > 0) & (y < 3)),
... np.array([True, False, True, False, True, False]))
Or combination
>>> assert np.array_equal(
... tfields.evalf(a, (x > 0) | (y > 3)),
... np.array([True, True, True, False, True, False]))
If array of other shape than (?, 3) is given, the coords need to be
specified
>>> a0, a1 = sympy.symbols('a0 a1')
>>> assert np.array_equal(
... tfields.evalf([[0., 1.], [-1, 3]], a1 > 2, coords=[a0, a1]),
... np.array([False, True], dtype=bool))
>= is taken care of
>>> assert np.array_equal(
... tfields.evalf(a, x >= 0),
... np.array([ True, True, True, False, True, True]))
| def evalf(array, cut_expression=None, coords=None):
"""
Linking sympy and numpy by retrieving a mask according to the cut_expression
Args:
array (numpy ndarray)
cut_expression (sympy logical expression)
coord_sys (str): coord_sys to evalfuate the expression in.
Returns:
np.array: mask which is True, where cut_expression evalfuates True.
Examples:
>>> import sympy
>>> import numpy as np
>>> import tfields
>>> x, y, z = sympy.symbols('x y z')
>>> a = np.array([[1., 2., 3.], [4., 5., 6.], [1, 2, -6],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> assert np.array_equal(
... tfields.evalf(a, x > 0),
... np.array([ True, True, True, False, True, False]))
And combination
>>> assert np.array_equal(
... tfields.evalf(a, (x > 0) & (y < 3)),
... np.array([True, False, True, False, True, False]))
Or combination
>>> assert np.array_equal(
... tfields.evalf(a, (x > 0) | (y > 3)),
... np.array([True, True, True, False, True, False]))
If array of other shape than (?, 3) is given, the coords need to be
specified
>>> a0, a1 = sympy.symbols('a0 a1')
>>> assert np.array_equal(
... tfields.evalf([[0., 1.], [-1, 3]], a1 > 2, coords=[a0, a1]),
... np.array([False, True], dtype=bool))
>= is taken care of
>>> assert np.array_equal(
... tfields.evalf(a, x >= 0),
... np.array([ True, True, True, False, True, True]))
"""
if isinstance(array, list):
array = np.array(array)
if cut_expression is None:
return np.full((array.shape[0]), False, dtype=bool)
if len(array.shape) != 2:
raise NotImplementedError("Array shape other than 2")
if coords is None:
if array.shape[1] == 3:
coords = sympy.symbols("x y z")
else:
raise ValueError("coords are None and shape is not (?, 3)")
elif len(coords) != array.shape[1]:
raise ValueError(
"Length of coords is not {0} but {1}".format(array.shape[1], len(coords))
)
pre_mask = sympy.utilities.lambdify(
coords, cut_expression, modules={"&": np.logical_and, "|": np.logical_or}
)
mask = np.array([pre_mask(*vals) for vals in array], dtype=bool)
return mask
| (array, cut_expression=None, coords=None) | [
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21,504 | tfields.core | rank |
Tensor rank
| def rank(tensor):
"""
Tensor rank
"""
tensor = np.asarray(tensor)
return len(tensor.shape) - 1
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21,508 | fp2md4roam.convert | convert | null | def convert(path, target_directory):
loglevel(WARN)
filer = FSFiler(target_directory)
logger.info('converting %s %s' % (path, filer.target_directory))
mindmap = RawMap(read(path), path)
Author(filer).visit(mindmap)
| (path, target_directory) | [
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21,509 | fp2md4roam | converter | null | def converter(): # pragma: no cover
if len(sys.argv) != 3:
print('usage: python3 convert.py path_to_map target_directory')
sys.exit(1)
map_path = sys.argv[1]
target_dir = sys.argv[2]
convert(map_path, target_dir)
| () | [
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] |
21,513 | numbers | Number | All numbers inherit from this class.
If you just want to check if an argument x is a number, without
caring what kind, use isinstance(x, Number).
| class Number(metaclass=ABCMeta):
"""All numbers inherit from this class.
If you just want to check if an argument x is a number, without
caring what kind, use isinstance(x, Number).
"""
__slots__ = ()
# Concrete numeric types must provide their own hash implementation
__hash__ = None
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] |
21,514 | tempora | _Saved_NS |
Bundle a timedelta with nanoseconds.
>>> _Saved_NS.derive('microseconds', .001)
_Saved_NS(td=datetime.timedelta(0), nanoseconds=1)
| class _Saved_NS:
"""
Bundle a timedelta with nanoseconds.
>>> _Saved_NS.derive('microseconds', .001)
_Saved_NS(td=datetime.timedelta(0), nanoseconds=1)
"""
td = datetime.timedelta()
nanoseconds = 0
multiplier = dict(
seconds=1000000000,
milliseconds=1000000,
microseconds=1000,
)
def __init__(self, **kwargs):
vars(self).update(kwargs)
@classmethod
def derive(cls, unit, value):
if unit == 'nanoseconds':
return _Saved_NS(nanoseconds=value)
try:
raw_td = datetime.timedelta(**{unit: value})
except TypeError:
raise ValueError(f"Invalid unit {unit}")
res = _Saved_NS(td=raw_td)
with contextlib.suppress(KeyError):
res.nanoseconds = int(value * cls.multiplier[unit]) % 1000
return res
def __add__(self, other):
return _Saved_NS(
td=self.td + other.td, nanoseconds=self.nanoseconds + other.nanoseconds
)
def resolve(self):
"""
Resolve any nanoseconds into the microseconds field,
discarding any nanosecond resolution (but honoring partial
microseconds).
"""
addl_micros = round(self.nanoseconds / 1000)
return self.td + datetime.timedelta(microseconds=addl_micros)
def __repr__(self):
return f'_Saved_NS(td={self.td!r}, nanoseconds={self.nanoseconds!r})'
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21,515 | tempora | __add__ | null | def __add__(self, other):
return _Saved_NS(
td=self.td + other.td, nanoseconds=self.nanoseconds + other.nanoseconds
)
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21,516 | tempora | __init__ | null | def __init__(self, **kwargs):
vars(self).update(kwargs)
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21,517 | tempora | __repr__ | null | def __repr__(self):
return f'_Saved_NS(td={self.td!r}, nanoseconds={self.nanoseconds!r})'
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] |
21,518 | tempora | resolve |
Resolve any nanoseconds into the microseconds field,
discarding any nanosecond resolution (but honoring partial
microseconds).
| def resolve(self):
"""
Resolve any nanoseconds into the microseconds field,
discarding any nanosecond resolution (but honoring partial
microseconds).
"""
addl_micros = round(self.nanoseconds / 1000)
return self.td + datetime.timedelta(microseconds=addl_micros)
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] |
21,519 | tempora | _check_unmatched |
Ensure no words appear in unmatched text.
| def _check_unmatched(matches, text):
"""
Ensure no words appear in unmatched text.
"""
def check_unmatched(unmatched):
found = re.search(r'\w+', unmatched)
if found:
raise ValueError(f"Unexpected {found.group(0)!r}")
pos = 0
for match in matches:
check_unmatched(text[pos : match.start()])
yield match
pos = match.end()
check_unmatched(text[pos:])
| (matches, text) | [
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] |
21,520 | tempora | _parse_timedelta_composite | null | def _parse_timedelta_composite(raw_value, unit):
if unit != 'seconds':
raise ValueError("Cannot specify units with composite delta")
values = raw_value.split(':')
units = 'hours', 'minutes', 'seconds'
composed = ' '.join(f'{value} {unit}' for value, unit in zip(values, units))
return _parse_timedelta_nanos(composed)
| (raw_value, unit) | [
0.0050397468730807304,
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21,521 | tempora | _parse_timedelta_nanos | null | def _parse_timedelta_nanos(str):
parts = re.finditer(r'(?P<value>[\d.:]+)\s?(?P<unit>[^\W\d_]+)?', str)
chk_parts = _check_unmatched(parts, str)
deltas = map(_parse_timedelta_part, chk_parts)
return sum(deltas, _Saved_NS())
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21,522 | tempora | _parse_timedelta_part | null | def _parse_timedelta_part(match):
unit = _resolve_unit(match.group('unit'))
if not unit.endswith('s'):
unit += 's'
raw_value = match.group('value')
if ':' in raw_value:
return _parse_timedelta_composite(raw_value, unit)
value = float(raw_value)
if unit == 'months':
unit = 'years'
value = value / 12
if unit == 'years':
unit = 'days'
value = value * days_per_year
return _Saved_NS.derive(unit, value)
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] |
21,523 | tempora | _prorated_values |
Given a rate (a string in units per unit time), and return that same
rate for various time periods.
>>> for period, value in _prorated_values('20/hour'):
... print('{period}: {value:0.3f}'.format(**locals()))
minute: 0.333
hour: 20.000
day: 480.000
month: 14609.694
year: 175316.333
| def _prorated_values(rate: str) -> Iterable[Tuple[str, Number]]:
"""
Given a rate (a string in units per unit time), and return that same
rate for various time periods.
>>> for period, value in _prorated_values('20/hour'):
... print('{period}: {value:0.3f}'.format(**locals()))
minute: 0.333
hour: 20.000
day: 480.000
month: 14609.694
year: 175316.333
"""
match = re.match(r'(?P<value>[\d.]+)/(?P<period>\w+)$', rate)
res = cast(re.Match, match).groupdict()
value = float(res['value'])
value_per_second = value / get_period_seconds(res['period'])
for period in ('minute', 'hour', 'day', 'month', 'year'):
period_value = value_per_second * get_period_seconds(period)
yield period, period_value
| (rate: str) -> Iterable[Tuple[str, numbers.Number]] | [
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21,524 | tempora | _resolve_unit | null | def _resolve_unit(raw_match):
if raw_match is None:
return 'second'
text = raw_match.lower()
return _unit_lookup.get(text, text)
| (raw_match) | [
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21,525 | tempora | calculate_prorated_values |
>>> monkeypatch = getfixture('monkeypatch')
>>> import builtins
>>> monkeypatch.setattr(builtins, 'input', lambda prompt: '3/hour')
>>> calculate_prorated_values()
per minute: 0.05
per hour: 3.0
per day: 72.0
per month: 2191.454166666667
per year: 26297.45
| def calculate_prorated_values():
"""
>>> monkeypatch = getfixture('monkeypatch')
>>> import builtins
>>> monkeypatch.setattr(builtins, 'input', lambda prompt: '3/hour')
>>> calculate_prorated_values()
per minute: 0.05
per hour: 3.0
per day: 72.0
per month: 2191.454166666667
per year: 26297.45
"""
rate = input("Enter the rate (3/hour, 50/month)> ")
for period, value in _prorated_values(rate):
print(f"per {period}: {value}")
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] |
21,528 | tempora | date_range |
Much like the built-in function range, but works with dates
>>> range_items = date_range(
... datetime.datetime(2005,12,21),
... datetime.datetime(2005,12,25),
... )
>>> my_range = tuple(range_items)
>>> datetime.datetime(2005,12,21) in my_range
True
>>> datetime.datetime(2005,12,22) in my_range
True
>>> datetime.datetime(2005,12,25) in my_range
False
>>> from_now = date_range(stop=datetime.datetime(2099, 12, 31))
>>> next(from_now)
datetime.datetime(...)
| def date_range(start=None, stop=None, step=None):
"""
Much like the built-in function range, but works with dates
>>> range_items = date_range(
... datetime.datetime(2005,12,21),
... datetime.datetime(2005,12,25),
... )
>>> my_range = tuple(range_items)
>>> datetime.datetime(2005,12,21) in my_range
True
>>> datetime.datetime(2005,12,22) in my_range
True
>>> datetime.datetime(2005,12,25) in my_range
False
>>> from_now = date_range(stop=datetime.datetime(2099, 12, 31))
>>> next(from_now)
datetime.datetime(...)
"""
if step is None:
step = datetime.timedelta(days=1)
if start is None:
start = datetime.datetime.now()
while start < stop:
yield start
start += step
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] |
21,530 | tempora | datetime_mod |
Find the time which is the specified date/time truncated to the time delta
relative to the start date/time.
By default, the start time is midnight of the same day as the specified
date/time.
>>> datetime_mod(datetime.datetime(2004, 1, 2, 3),
... datetime.timedelta(days = 1.5),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 1, 0, 0)
>>> datetime_mod(datetime.datetime(2004, 1, 2, 13),
... datetime.timedelta(days = 1.5),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 2, 12, 0)
>>> datetime_mod(datetime.datetime(2004, 1, 2, 13),
... datetime.timedelta(days = 7),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 1, 0, 0)
>>> datetime_mod(datetime.datetime(2004, 1, 10, 13),
... datetime.timedelta(days = 7),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 8, 0, 0)
| def datetime_mod(dt, period, start=None):
"""
Find the time which is the specified date/time truncated to the time delta
relative to the start date/time.
By default, the start time is midnight of the same day as the specified
date/time.
>>> datetime_mod(datetime.datetime(2004, 1, 2, 3),
... datetime.timedelta(days = 1.5),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 1, 0, 0)
>>> datetime_mod(datetime.datetime(2004, 1, 2, 13),
... datetime.timedelta(days = 1.5),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 2, 12, 0)
>>> datetime_mod(datetime.datetime(2004, 1, 2, 13),
... datetime.timedelta(days = 7),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 1, 0, 0)
>>> datetime_mod(datetime.datetime(2004, 1, 10, 13),
... datetime.timedelta(days = 7),
... start = datetime.datetime(2004, 1, 1))
datetime.datetime(2004, 1, 8, 0, 0)
"""
if start is None:
# use midnight of the same day
start = datetime.datetime.combine(dt.date(), datetime.time())
# calculate the difference between the specified time and the start date.
delta = dt - start
# now aggregate the delta and the period into microseconds
# Use microseconds because that's the highest precision of these time
# pieces. Also, using microseconds ensures perfect precision (no floating
# point errors).
def get_time_delta_microseconds(td):
return (td.days * seconds_per_day + td.seconds) * 1000000 + td.microseconds
delta, period = map(get_time_delta_microseconds, (delta, period))
offset = datetime.timedelta(microseconds=delta % period)
# the result is the original specified time minus the offset
result = dt - offset
return result
| (dt, period, start=None) | [
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] |
21,531 | tempora | datetime_round |
Find the nearest even period for the specified date/time.
>>> datetime_round(datetime.datetime(2004, 11, 13, 8, 11, 13),
... datetime.timedelta(hours = 1))
datetime.datetime(2004, 11, 13, 8, 0)
>>> datetime_round(datetime.datetime(2004, 11, 13, 8, 31, 13),
... datetime.timedelta(hours = 1))
datetime.datetime(2004, 11, 13, 9, 0)
>>> datetime_round(datetime.datetime(2004, 11, 13, 8, 30),
... datetime.timedelta(hours = 1))
datetime.datetime(2004, 11, 13, 9, 0)
| def datetime_round(dt, period, start=None):
"""
Find the nearest even period for the specified date/time.
>>> datetime_round(datetime.datetime(2004, 11, 13, 8, 11, 13),
... datetime.timedelta(hours = 1))
datetime.datetime(2004, 11, 13, 8, 0)
>>> datetime_round(datetime.datetime(2004, 11, 13, 8, 31, 13),
... datetime.timedelta(hours = 1))
datetime.datetime(2004, 11, 13, 9, 0)
>>> datetime_round(datetime.datetime(2004, 11, 13, 8, 30),
... datetime.timedelta(hours = 1))
datetime.datetime(2004, 11, 13, 9, 0)
"""
result = datetime_mod(dt, period, start)
if abs(dt - result) >= period // 2:
result += period
return result
| (dt, period, start=None) | [
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] |
21,532 | tempora | ensure_datetime |
Given a datetime or date or time object from the ``datetime``
module, always return a datetime using default values.
| def ensure_datetime(ob: AnyDatetime) -> datetime.datetime:
"""
Given a datetime or date or time object from the ``datetime``
module, always return a datetime using default values.
"""
if isinstance(ob, datetime.datetime):
return ob
date = cast(datetime.date, ob)
time = cast(datetime.time, ob)
if isinstance(ob, datetime.date):
time = datetime.time()
if isinstance(ob, datetime.time):
date = datetime.date(1900, 1, 1)
return datetime.datetime.combine(date, time)
| (ob: Union[datetime.datetime, datetime.date, datetime.time]) -> datetime.datetime | [
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] |
21,534 | tempora | get_date_format_string |
For a given period (e.g. 'month', 'day', or some numeric interval
such as 3600 (in secs)), return the format string that can be
used with strftime to format that time to specify the times
across that interval, but no more detailed.
For example,
>>> get_date_format_string('month')
'%Y-%m'
>>> get_date_format_string(3600)
'%Y-%m-%d %H'
>>> get_date_format_string('hour')
'%Y-%m-%d %H'
>>> get_date_format_string(None)
Traceback (most recent call last):
...
TypeError: period must be a string or integer
>>> get_date_format_string('garbage')
Traceback (most recent call last):
...
ValueError: period not in (second, minute, hour, day, month, year)
| def get_date_format_string(period):
"""
For a given period (e.g. 'month', 'day', or some numeric interval
such as 3600 (in secs)), return the format string that can be
used with strftime to format that time to specify the times
across that interval, but no more detailed.
For example,
>>> get_date_format_string('month')
'%Y-%m'
>>> get_date_format_string(3600)
'%Y-%m-%d %H'
>>> get_date_format_string('hour')
'%Y-%m-%d %H'
>>> get_date_format_string(None)
Traceback (most recent call last):
...
TypeError: period must be a string or integer
>>> get_date_format_string('garbage')
Traceback (most recent call last):
...
ValueError: period not in (second, minute, hour, day, month, year)
"""
# handle the special case of 'month' which doesn't have
# a static interval in seconds
if isinstance(period, str) and period.lower() == 'month':
return '%Y-%m'
file_period_secs = get_period_seconds(period)
format_pieces = ('%Y', '-%m-%d', ' %H', '-%M', '-%S')
seconds_per_second = 1
intervals = (
seconds_per_year,
seconds_per_day,
seconds_per_hour,
seconds_per_minute,
seconds_per_second,
)
mods = list(map(lambda interval: file_period_secs % interval, intervals))
format_pieces = format_pieces[: mods.index(0) + 1]
return ''.join(format_pieces)
| (period) | [
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] |
21,535 | tempora | get_nearest_year_for_day |
Returns the nearest year to now inferred from a Julian date.
>>> freezer = getfixture('freezer')
>>> freezer.move_to('2019-05-20')
>>> get_nearest_year_for_day(20)
2019
>>> get_nearest_year_for_day(340)
2018
>>> freezer.move_to('2019-12-15')
>>> get_nearest_year_for_day(20)
2020
| def get_nearest_year_for_day(day):
"""
Returns the nearest year to now inferred from a Julian date.
>>> freezer = getfixture('freezer')
>>> freezer.move_to('2019-05-20')
>>> get_nearest_year_for_day(20)
2019
>>> get_nearest_year_for_day(340)
2018
>>> freezer.move_to('2019-12-15')
>>> get_nearest_year_for_day(20)
2020
"""
now = time.gmtime()
result = now.tm_year
# if the day is far greater than today, it must be from last year
if day - now.tm_yday > 365 // 2:
result -= 1
# if the day is far less than today, it must be for next year.
if now.tm_yday - day > 365 // 2:
result += 1
return result
| (day) | [
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] |
21,536 | tempora | get_period_seconds |
return the number of seconds in the specified period
>>> get_period_seconds('day')
86400
>>> get_period_seconds(86400)
86400
>>> get_period_seconds(datetime.timedelta(hours=24))
86400
>>> get_period_seconds('day + os.system("rm -Rf *")')
Traceback (most recent call last):
...
ValueError: period not in (second, minute, hour, day, month, year)
| def get_period_seconds(period):
"""
return the number of seconds in the specified period
>>> get_period_seconds('day')
86400
>>> get_period_seconds(86400)
86400
>>> get_period_seconds(datetime.timedelta(hours=24))
86400
>>> get_period_seconds('day + os.system("rm -Rf *")')
Traceback (most recent call last):
...
ValueError: period not in (second, minute, hour, day, month, year)
"""
if isinstance(period, str):
try:
name = 'seconds_per_' + period.lower()
result = globals()[name]
except KeyError:
msg = "period not in (second, minute, hour, day, month, year)"
raise ValueError(msg)
elif isinstance(period, numbers.Number):
result = period
elif isinstance(period, datetime.timedelta):
result = period.days * get_period_seconds('day') + period.seconds
else:
raise TypeError('period must be a string or integer')
return result
| (period) | [
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