diff --git a/tfields/core.py b/tfields/core.py
index 2eef22e83a7b2d8bbc81e884782152e91eda40a6..9c30abb37d3174e8dceca379f41b64c45e5d467f 100644
--- a/tfields/core.py
+++ b/tfields/core.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env
+#!/usr/bin/env # pylint: disable=too-many-lines,super-with-arguments
# encoding: utf-8
"""
Author: Daniel Boeckenhoff
@@ -15,16 +15,16 @@ Notes:
# builtin
import warnings
import pathlib
-from six import string_types
from contextlib import contextmanager
from collections import Counter
from copy import deepcopy
import logging
+from six import string_types
# 3rd party
import numpy as np
import sympy
-import scipy as sp
+import scipy
import sortedcontainers
import rna
@@ -51,15 +51,18 @@ def dim(tensor):
return tensor.shape[1]
-class AbstractObject(object):
+class AbstractObject(object): # pylint: disable=useless-object-inheritance
+ """
+ Abstract base class for all tfields objects implementing polymorphisms
+ """
+
def save(self, path, *args, **kwargs):
"""
Saving by redirecting to the correct save method depending on path
Args:
path (str or buffer)
- *args:
- forwarded to extension specific method
+ *args: joined with path
**kwargs:
extension (str): only needed if path is buffer
... remaining:forwarded to extension specific method
@@ -73,14 +76,14 @@ class AbstractObject(object):
# get the save method
try:
- save_method = getattr(self, "_save_{extension}".format(**locals()))
- except AttributeError:
+ save_method = getattr(self, "_save_" + extension)
+ except AttributeError as err:
raise NotImplementedError(
"Can not find save method for extension: "
"{extension}.".format(**locals())
- )
+ ) from err
- path = rna.path.resolve(path)
+ path = rna.path.resolve(path, *args)
rna.path.mkdir(path)
return save_method(path, **kwargs)
@@ -91,8 +94,7 @@ class AbstractObject(object):
Args:
path (str or buffer)
- *args:
- forwarded to extension specific method
+ *args: joined with path
**kwargs:
extension (str): only needed if path is buffer
... remaining:forwarded to extension specific method
@@ -106,14 +108,14 @@ class AbstractObject(object):
try:
load_method = getattr(cls, "_load_{e}".format(e=extension))
- except AttributeError:
+ except AttributeError as err:
raise NotImplementedError(
"Can not find load method for extension: "
"{extension}.".format(**locals())
- )
+ ) from err
return load_method(path, *args, **kwargs)
- def _save_npz(self, path, **kwargs):
+ def _save_npz(self, path):
"""
Args:
path (open file or str/unicode): destination to save file to.
@@ -164,27 +166,40 @@ class AbstractObject(object):
Factory method
Given a path to a npz file, construct the object
"""
- # TODO: think about allow_pickle, wheter it really should be True or
+ # Note: think about allow_pickle, wheter it really should be True or
# wheter we could avoid pickling (potential security issue)
load_kwargs.setdefault("allow_pickle", True)
np_file = np.load(path, **load_kwargs)
- d = dict(np_file)
- d.pop("tfields_version", None)
- return cls._from_dict(d)
+ content = dict(np_file)
+ content.pop("tfields_version", None)
+ return cls._from_dict(content)
- def _args(self) -> tuple:
+ def _args(self) -> tuple: # pylint: disable=no-self-use
+ """
+ Used for allowing the polymorphic signature Class(obj) as a copy/casting constructor
+ """
return tuple()
- def _kwargs(self) -> dict:
+ def _kwargs(self) -> dict: # pylint: disable=no-self-use
+ """
+ Used for allowing the polymorphic signature Class(obj) as a copy/casting constructor
+ """
return dict()
_HIERARCHY_SEPARATOR = "::"
- def _as_dict(self):
- d = {}
+ def _as_dict(self) -> dict:
+ """
+ Get an object represenation in a dict format. This is necessary e.g. for saving the full
+ file uniquely in the npz format
+
+ Returns:
+ dict: object packed as nested dictionary
+ """
+ content = {}
# type
- d["type"] = type(self).__name__
+ content["type"] = type(self).__name__
# args and kwargs
for base_attr, iterable in [
@@ -194,24 +209,26 @@ class AbstractObject(object):
for attr, value in iterable:
attr = base_attr + self._HIERARCHY_SEPARATOR + attr
if hasattr(value, "_as_dict"):
- part_dict = value._as_dict()
+ part_dict = value._as_dict() # pylint: disable=protected-access
for part_attr, part_value in part_dict.items():
- d[attr + self._HIERARCHY_SEPARATOR + part_attr] = part_value
+ content[
+ attr + self._HIERARCHY_SEPARATOR + part_attr
+ ] = part_value
else:
- d[attr] = value
- return d
+ content[attr] = value
+ return content
@classmethod
- def _from_dict(cls, d: dict):
+ def _from_dict(cls, content: dict):
try:
- d.pop("type")
+ content.pop("type")
except KeyError:
# legacy
- return cls._from_dict_legacy(**d)
+ return cls._from_dict_legacy(**content)
here = {}
- for string in d: # TOO no sortelist
- value = d[string]
+ for string in content: # TOO no sortelist
+ value = content[string]
attr, _, end = string.partition(cls._HIERARCHY_SEPARATOR)
key, _, end = end.partition(cls._HIERARCHY_SEPARATOR)
@@ -221,9 +238,7 @@ class AbstractObject(object):
here[attr][key] = {}
here[attr][key][end] = value
- """
- Do the recursion
- """
+ # Do the recursion
for attr in here:
for key in here[attr]:
if "type" in here[attr][key]:
@@ -235,14 +250,14 @@ class AbstractObject(object):
# Found under nt. ???
obj_type = obj_type.decode("UTF-8")
obj_type = getattr(tfields, obj_type)
- attr_value = obj_type._from_dict(here[attr][key])
+ attr_value = obj_type._from_dict(
+ here[attr][key]
+ ) # noqa: E501 pylint: disable=protected-access
else: # if len(here[attr][key]) == 1:
attr_value = here[attr][key].pop("")
here[attr][key] = attr_value
- """
- Build the generic way
- """
+ # Build the generic way
args = here.pop("args", tuple())
args = tuple(args[key] for key in sorted(args))
kwargs = here.pop("kwargs", {})
@@ -251,17 +266,15 @@ class AbstractObject(object):
return obj
@classmethod
- def _from_dict_legacy(cls, **d):
+ def _from_dict_legacy(cls, **content):
"""
legacy method of _from_dict - Opposite of old _as_dict method
which is overridden in this version
"""
list_dict = {}
kwargs = {}
- """
- De-Flatten the first layer of lists
- """
- for key in sorted(list(d)):
+ # De-Flatten the first layer of lists
+ for key in sorted(list(content)):
if "::" in key:
attr, _, end = key.partition("::")
if attr not in list_dict:
@@ -273,13 +286,11 @@ class AbstractObject(object):
index = int(index)
if index not in list_dict[attr]:
list_dict[attr][index] = {}
- list_dict[attr][index][end] = d[key]
+ list_dict[attr][index][end] = content[key]
else:
- kwargs[key] = d[key]
+ kwargs[key] = content[key]
- """
- Build the lists (recursively)
- """
+ # Build the lists (recursively)
for key in list(list_dict):
sub_dict = list_dict[key]
list_dict[key] = []
@@ -291,19 +302,19 @@ class AbstractObject(object):
# Found under nt. ???
bulk_type = bulk_type.decode("UTF-8")
bulk_type = getattr(tfields, bulk_type)
- list_dict[key].append(bulk_type._from_dict_legacy(**sub_dict[index]))
-
- with cls._bypass_setters("fields", demand_existence=False):
- """
- Build the normal way
- """
+ list_dict[key].append(
+ bulk_type._from_dict_legacy(**sub_dict[index])
+ ) # noqa: E501 pylint: disable=protected-access
+
+ with cls._bypass_setters(
+ "fields", demand_existence=False
+ ): # noqa: E501 pylint: disable=protected-access,no-member
+ # Build the normal way
bulk = kwargs.pop("bulk")
bulk_type = kwargs.pop("bulk_type")
obj = cls.__new__(cls, bulk, **kwargs)
- """
- Set list attributes
- """
+ # Set list attributes
for attr, list_value in list_dict.items():
setattr(obj, attr, list_value)
return obj
@@ -354,8 +365,10 @@ class AbstractNdarray(np.ndarray, AbstractObject):
for attr in self._iter_slots():
setattr(self, attr, getattr(obj, attr, None))
- def __array_wrap__(self, out_arr, context=None):
- return np.ndarray.__array_wrap__(self, out_arr, context)
+ def __array_wrap__(self, out_arr, context=None): # pylint: disable=arguments-differ
+ return np.ndarray.__array_wrap__(
+ self, out_arr, context
+ ) # noqa: E501 pylint: disable=too-many-function-args
@classmethod
def _iter_slots(cls):
@@ -384,7 +397,7 @@ class AbstractNdarray(np.ndarray, AbstractObject):
except Exception as err:
raise ValueError(
str(attr) + str(dtype) + str(kwargs[attr]) + str(err)
- )
+ ) from err
def __setattr__(self, name, value):
if name in self.__slots__:
@@ -460,11 +473,9 @@ class AbstractNdarray(np.ndarray, AbstractObject):
# set the __slot__ attributes
valid_slot_attrs = list(self._iter_slots())
- """
- attributes that have been added later have not been pickled with the
- full information and thus need to be excluded from the __setstate__
- need to be in the same order as they have been added to __slots__
- """
+ # attributes that have been added later have not been pickled with the full information
+ # and thus need to be excluded from the __setstate__ need to be in the same order as they
+ # have been added to __slots__
added_slot_attrs = ["name"]
n_np = 5 # number of numpy array states
n_old = len(valid_slot_attrs) - len(state[n_np:])
@@ -528,7 +539,7 @@ class AbstractNdarray(np.ndarray, AbstractObject):
for slot_index, setter in zip(slot_indices, setters):
cls.__slot_setters__[slot_index] = setter
- def copy(self, *args, **kwargs):
+ def copy(self, **kwargs): # pylint: disable=arguments-differ
"""
The standard ndarray copy does not copy slots. Correct for this.
@@ -555,11 +566,15 @@ class AbstractNdarray(np.ndarray, AbstractObject):
False
"""
+ if kwargs:
+ raise NotImplementedError(
+ "Copying with arguments {kwargs} not yet supported"
+ )
# works with __reduce__ / __setstate__
return deepcopy(self)
-class Tensors(AbstractNdarray):
+class Tensors(AbstractNdarray): # pylint: disable=too-many-public-methods
"""
Set of tensors with the same basis.
@@ -646,12 +661,12 @@ class Tensors(AbstractNdarray):
__slot_defaults__ = ["cartesian"]
__slot_setters__ = [tfields.bases.get_coord_system_name]
- def __new__(cls, tensors, **kwargs):
+ 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)
+ dim = kwargs.pop("dim", None) # pylint: disable=redefined-outer-name
- """ copy constructor extracts the kwargs from tensors"""
+ # copy constructor extracts the kwargs from tensors
if issubclass(type(tensors), Tensors):
if dim is not None:
dim = tensors.dim
@@ -669,15 +684,15 @@ class Tensors(AbstractNdarray):
else:
dtype = np.float64
- """ demand iterable structure """
+ # demand iterable structure
try:
len(tensors)
- except TypeError:
+ except TypeError as err:
raise TypeError(
"Iterable structure necessary." " Got {tensors}".format(**locals())
- )
+ ) from err
- """ process empty inputs """
+ # process empty inputs
if len(tensors) == 0:
if issubclass(type(tensors), tfields.Tensors):
tensors = np.empty(tensors.shape, dtype=tensors.dtype)
@@ -694,9 +709,9 @@ class Tensors(AbstractNdarray):
tensors = np.asarray(tensors, dtype=dtype, order=order)
obj = tensors.view(cls)
- """ check dimension(s) """
- for d in obj.shape[1:]:
- if not d == obj.dim:
+ # check dimension(s)
+ for obj_dim in obj.shape[1:]:
+ if not obj_dim == obj.dim:
raise ValueError(
"Dimensions are inconstistent. "
"Manifold dimension is {obj.dim}. "
@@ -711,10 +726,10 @@ class Tensors(AbstractNdarray):
" {dim} demanded.".format(**locals())
)
- """ update kwargs with defaults from slots """
+ # update kwargs with defaults from slots
cls._update_slot_kwargs(kwargs)
- """ set kwargs to slots attributes """
+ # set kwargs to slots attributes
for attr in kwargs:
if attr not in cls._iter_slots():
raise AttributeError(
@@ -809,14 +824,14 @@ class Tensors(AbstractNdarray):
"""
- """ get most frequent coord_sys or predefined coord_sys """
+ # get most frequent coord_sys or predefined coord_sys
coord_sys = kwargs.get("coord_sys", None)
return_templates = kwargs.pop("return_templates", False)
if coord_sys is None:
bases = []
- for t in objects:
+ for tensors in objects:
try:
- bases.append(t.coord_sys)
+ bases.append(tensors.coord_sys)
except AttributeError:
pass
if bases:
@@ -827,22 +842,22 @@ class Tensors(AbstractNdarray):
default = cls.__slot_defaults__[cls.__slots__.index("coord_sys")]
kwargs["coord_sys"] = default
- """ transform all raw inputs to cls type with correct coord_sys. Also
- automatically make a copy of those instances that are of the correct
- type already."""
+ # transform all raw inputs to cls type with correct coord_sys. Also
+ # automatically make a copy of those instances that are of the correct
+ # type already.
objects = [cls.__new__(cls, t, **kwargs) for t in objects]
- """ check rank and dimension equality """
- if not len(set([t.rank for t in objects])) == 1:
+ # check rank and dimension equality
+ if not len(set(t.rank for t in objects)) == 1:
raise TypeError("Tensors must have the same rank for merging.")
- if not len(set([t.dim for t in objects])) == 1:
+ if not len(set(t.dim for t in objects)) == 1:
raise TypeError("Tensors must have the same dimension for merging.")
- """ merge all objects """
- remainingObjects = objects[1:] or []
+ # merge all objects
+ remaining_objects = objects[1:] or []
tensors = objects[0]
- for i, obj in enumerate(remainingObjects):
+ for i, obj in enumerate(remaining_objects):
tensors = np.append(tensors, obj, axis=0)
if len(tensors) == 0 and not kwargs.get("dim", None):
@@ -851,7 +866,7 @@ class Tensors(AbstractNdarray):
kwargs["dim"] = dim(objects[0])
inst = cls.__new__(cls, tensors, **kwargs)
- if not return_templates:
+ if not return_templates: # pylint: disable=no-else-return
return inst
else:
tensor_lengths = [len(o) for o in objects]
@@ -1030,14 +1045,16 @@ class Tensors(AbstractNdarray):
>>> assert t_cyl[0, 0] == 3
"""
- # scalars empty already there
- if self.rank == 0 or self.shape[0] == 0 or self.coord_sys == coord_sys:
- self.coord_sys = coord_sys
+ # scalars empty already there
+ if (
+ self.rank == 0 or self.shape[0] == 0 or self.coord_sys == coord_sys
+ ): # noqa: E501 pylint: disable=access-member-before-definition
+ self.coord_sys = coord_sys # pylint: disable=attribute-defined-outside-init
return
tfields.bases.transform(self, self.coord_sys, coord_sys)
# self[:] = tfields.bases.transform(self, self.coord_sys, coord_sys)
- self.coord_sys = coord_sys
+ self.coord_sys = coord_sys # pylint: disable=attribute-defined-outside-init
@contextmanager
def tmp_transform(self, coord_sys):
@@ -1057,15 +1074,15 @@ class Tensors(AbstractNdarray):
>>> assert p.coord_sys == tfields.bases.SPHERICAL
"""
- baseBefore = self.coord_sys
- if baseBefore == coord_sys:
+ base_before = self.coord_sys
+ if base_before == coord_sys:
yield
else:
self.transform(coord_sys)
yield
- self.transform(baseBefore)
+ self.transform(base_before)
def mirror(self, coordinate, condition=None):
"""
@@ -1102,15 +1119,15 @@ class Tensors(AbstractNdarray):
condition = np.array([True for i in range(len(self))])
elif isinstance(condition, sympy.Basic):
condition = self.evalf(condition)
- if isinstance(coordinate, list) or isinstance(coordinate, tuple):
- for c in coordinate:
- self.mirror(c, condition=condition)
+ if isinstance(coordinate, (list, tuple)):
+ for coord in coordinate:
+ self.mirror(coord, condition=condition)
elif isinstance(coordinate, int):
self[:, coordinate][condition] *= -1
else:
raise TypeError()
- def to_segment(
+ def to_segment( # pylint: disable=too-many-arguments
self,
segment,
num_segments,
@@ -1162,7 +1179,9 @@ class Tensors(AbstractNdarray):
+ segment * periodicity / num_segments
)
- def equal(self, other, rtol=None, atol=None, equal_nan=False, return_bool=True):
+ 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.
@@ -1176,23 +1195,25 @@ class Tensors(AbstractNdarray):
if issubclass(type(other), Tensors) and self.coord_sys != other.coord_sys:
other = other.copy()
other.transform(self.coord_sys)
- x, y = np.asarray(self), np.asarray(other)
+ self_array, other_array = np.asarray(self), np.asarray(other)
if rtol is None and atol is None:
- mask = x == y
+ mask = self_array == other_array
if equal_nan:
- both_nan = np.isnan(x) & np.isnan(y)
+ 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(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan)
+ 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
- def contains(self, other, **kwargs):
+ def contains(self, other):
"""
Inspired by a speed argument @
stackoverflow.com/questions/14766194/testing-whether-a-numpy-array-contains-a-given-row
@@ -1231,20 +1252,20 @@ class Tensors(AbstractNdarray):
array([1, 3])
"""
- x, y = np.asarray(self), np.asarray(tensor)
+ self_array, other_array = np.asarray(self), np.asarray(tensor)
if rtol is None and atol is None:
equal_method = np.equal
else:
equal_method = lambda a, b: np.isclose(a, b, rtol=rtol, atol=atol) # NOQA
- """
- inspired by https://stackoverflow.com/questions/19228295/\
-find-ordered-vector-in-numpy-array
- """
+ # inspired by
+ # https://stackoverflow.com/questions/19228295/find-ordered-vector-in-numpy-array
if self.rank == 0:
- indices = np.where(equal_method((x - y), 0))[0]
+ indices = np.where(equal_method((self_array - other_array), 0))[0]
elif self.rank == 1:
- indices = np.where(np.all(equal_method((x - y), 0), axis=1))[0]
+ indices = np.where(
+ np.all(equal_method((self_array - other_array), 0), axis=1)
+ )[0]
else:
raise NotImplementedError()
return indices
@@ -1318,7 +1339,9 @@ find-ordered-vector-in-numpy-array
"""
with other.tmp_transform(self.coord_sys):
# balanced_tree option gives huge speedup!
- kd_tree = sp.spatial.cKDTree(other, 1000, balanced_tree=False)
+ kd_tree = scipy.spatial.cKDTree(
+ other, 1000, balanced_tree=False
+ ) # noqa: E501 pylint: disable=no-member
res = kd_tree.query(self, **kwargs)
array = res[1]
@@ -1390,22 +1413,15 @@ find-ordered-vector-in-numpy-array
points, extending the object it should cut. This becomes relevant for
Mesh3D when adding vertices at the edge of the cut is necessary.
"""
- # TODO: in merged return np.empty() for tensors to save space.
- # use tensors only when adding. I think this function already suits the
- # needs
-
# 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.
- """
+ # 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)
@@ -1491,7 +1507,7 @@ find-ordered-vector-in-numpy-array
Args:
other(Iterable)
**kwargs:
- ... is forwarded to sp.spatial.distance.cdist
+ ... is forwarded to scipy.spatial.distance.cdist
Examples:
>>> import tfields
@@ -1510,7 +1526,7 @@ find-ordered-vector-in-numpy-array
if issubclass(type(other), Tensors) and self.coord_sys != other.coord_sys:
other = other.copy()
other.transform(self.coord_sys)
- return sp.spatial.distance.cdist(self, other, **kwargs)
+ return scipy.spatial.distance.cdist(self, other, **kwargs)
def min_dists(self, other=None, **kwargs):
"""
@@ -1519,7 +1535,7 @@ find-ordered-vector-in-numpy-array
**kwargs:
memory_saving (bool): for very large array comparisons
default False
- ... rest is forwarded to sp.spatial.distance.cdist
+ ... rest is forwarded to scipy.spatial.distance.cdist
Returns:
np.array: minimal distances of self to other
@@ -1554,13 +1570,13 @@ find-ordered-vector-in-numpy-array
try:
if memory_saving:
raise MemoryError()
- d = self.distances(other, **kwargs)
- return d[d > 0].reshape(d.shape[0], -1).min(axis=1)
+ dists = self.distances(other, **kwargs)
+ return dists[dists > 0].reshape(dists.shape[0], -1).min(axis=1)
except MemoryError:
min_dists = np.empty(self.shape[0])
for i, point in enumerate(np.array(other)):
- d = self.distances([point], **kwargs)
- min_dists[i] = d[d > 0].reshape(-1).min()
+ dists = self.distances([point], **kwargs)
+ min_dists[i] = dists[dists > 0].reshape(-1).min()
return min_dists
def epsilon_neighbourhood(self, epsilon):
@@ -1583,8 +1599,8 @@ find-ordered-vector-in-numpy-array
"""
indices = np.arange(self.shape[0])
dists = self.distances(self) # this takes long
- distsInEpsilon = dists <= epsilon
- indices = [indices[die] for die in distsInEpsilon] # this takes long
+ dists_in_epsilon = dists <= epsilon
+ indices = [indices[die] for die in dists_in_epsilon] # this takes long
return indices
def _weights(self, weights, rigid=True):
@@ -1623,8 +1639,8 @@ find-ordered-vector-in-numpy-array
idx = evalfs.argsort()[::-1]
evalfs = evalfs[idx]
evecs = evecs[:, idx]
- e = np.concatenate((evecs, evalfs.reshape(1, 3)))
- return e.T.reshape(
+ res = np.concatenate((evecs, evalfs.reshape(1, 3)))
+ return res.T.reshape(
12,
)
@@ -1646,7 +1662,7 @@ find-ordered-vector-in-numpy-array
"""
Forwarding to rna.plotting.plot_array
"""
- artist = rna.plotting.plot_array(self, **kwargs)
+ artist = rna.plotting.plot_array(self, **kwargs) # pylint: disable=no-member
return artist
@@ -1787,13 +1803,13 @@ class TensorFields(Tensors):
)
return obj
- def _args(self):
+ def _args(self) -> tuple:
return super()._args() + tuple(self.fields)
- def _kwargs(self):
- d = super()._kwargs()
- d.pop("fields")
- return d
+ def _kwargs(self) -> dict:
+ content = super()._kwargs()
+ content.pop("fields")
+ return content
def __getitem__(self, index):
"""
@@ -1844,7 +1860,7 @@ class TensorFields(Tensors):
item.fields = [
field.__getitem__(index) for field in item.fields
]
- except IndexError as err: # noqa: F841
+ except IndexError as err: # noqa: F841 pylint: disable=possibly-unused-variable
warnings.warn(
"Index error occured for field.__getitem__. Error "
"message: {err}".format(**locals())
@@ -1890,8 +1906,10 @@ class TensorFields(Tensors):
@classmethod
def merged(cls, *objects, **kwargs):
if not all([isinstance(o, cls) for o in objects]):
- types = [type(o) for o in objects]
- # TODO: could allow if all face_fields are none
+ types = [
+ type(o) for o in objects
+ ] # pylint: disable=possibly-unused-variable
+ # Note: could allow if all map_fields are none
raise TypeError(
"Merge constructor only accepts {cls} instances."
"Got objects of types {types} instead.".format(**locals())
@@ -1912,7 +1930,7 @@ class TensorFields(Tensors):
)
fields.append(field)
inst = cls.__new__(cls, inst, *fields)
- if return_templates:
+ if return_templates: # pylint: disable=no-else-return
return inst, templates
else:
return inst
@@ -1946,7 +1964,7 @@ class TensorFields(Tensors):
for i, name in enumerate(names):
self.fields[i].name = name
- def equal(self, other, **kwargs):
+ def equal(self, other, **kwargs): # pylint: disable=arguments-differ
"""
Test, whether the instance has the same content as other.
@@ -1957,16 +1975,15 @@ class TensorFields(Tensors):
"""
if not issubclass(type(other), Tensors):
return super(TensorFields, self).equal(other, **kwargs)
- else:
- 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
+ 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
def _weights(self, weights, rigid=True):
"""
@@ -1999,7 +2016,9 @@ class TensorFields(Tensors):
"different dimension. No coord_sys check performed."
)
if field.dim <= 3:
- artist = rna.plotting.plot_tensor_field(self, field, **kwargs)
+ artist = rna.plotting.plot_tensor_field(
+ self, field, **kwargs
+ ) # noqa: E501 pylint: disable=no-member
else:
raise NotImplementedError(
"Field of dimension {field.dim}".format(**locals())
@@ -2008,6 +2027,11 @@ class TensorFields(Tensors):
class Fields(list, AbstractObject):
+ """
+ Container for fields which should be attached to tensors with the <>.fields attribute to make
+ them tensor fields
+ """
+
def _args(self):
return super()._args() + tuple(self)
@@ -2049,6 +2073,9 @@ class Container(Fields):
@property
def items(self):
+ """
+ items of the container as a list
+ """
return list(self)
@items.setter
@@ -2095,25 +2122,25 @@ class Maps(sortedcontainers.SortedDict, AbstractObject):
if issubclass(type(entry), tuple):
if np.issubdtype(type(entry[0]), np.integer):
# Maps([(key, value), ...]), Maps({key: value, ...})
- mp = self.to_map(entry[1], copy=True)
+ maps = self.to_map(entry[1], copy=True)
dimension = entry[0]
else:
# Maps([(tensors, field1, field2), ...])
- mp = self.to_map(*entry, copy=True)
+ maps = self.to_map(*entry, copy=True)
else:
# Maps([mp, mp, ...])
- mp = self.to_map(entry, copy=True)
+ maps = self.to_map(entry, copy=True)
if dimension is None:
- dimension = dim(mp)
- new_args.append((dimension, mp))
+ dimension = dim(maps)
+ new_args.append((dimension, maps))
args = (new_args,)
super().__init__(*args, **kwargs)
@staticmethod
- def to_map(mp, *fields, copy=False, **kwargs):
+ def to_map(mp, *fields, copy=False, **kwargs): # pylint: disable=invalid-name
"""
Args:
mp (TensorFields)
@@ -2137,7 +2164,7 @@ class Maps(sortedcontainers.SortedDict, AbstractObject):
super().__setitem__(dimension, mp)
def _args(self):
- return super()._args() + ([(k, v) for k, v in self.items()],)
+ return super()._args() + (list(self.items()),)
def equal(self, other, **kwargs):
"""
@@ -2272,17 +2299,19 @@ class TensorMaps(TensorFields):
map_delete_mask = np.full(
(len(self.maps[map_dim]),), False, dtype=bool
)
- for i, mp in enumerate(self.maps[map_dim]):
- for index in mp:
- if index in delete_indices:
+ for i, mp in enumerate(
+ self.maps[map_dim]
+ ): # pylint: disable=invalid-name
+ for node_index in mp:
+ 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 p in delete_indices:
- move_up_counter[self.maps[map_dim] > p] -= 1
+ 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
@@ -2293,7 +2322,7 @@ class TensorMaps(TensorFields):
@classmethod
def merged(cls, *objects, **kwargs):
if not all([isinstance(o, cls) for o in objects]):
- # TODO: could allow if all face_fields are none
+ # Note: could allow if all face_fields are none
raise TypeError(
"Merge constructor only accepts {cls} instances.".format(**locals())
)
@@ -2309,8 +2338,8 @@ class TensorMaps(TensorFields):
dim_maps_dict = {} # {dim: {i: mp}
for i, obj in enumerate(objects):
- for dimension, mp in obj.maps.items():
- mp = mp + cum_tensor_lengths[i]
+ for dimension, mp in obj.maps.items(): # pylint: disable=invalid-name
+ mp = mp + cum_tensor_lengths[i] # pylint: disable=invalid-name
if dimension not in dim_maps_dict:
dim_maps_dict[dimension] = {}
dim_maps_dict[dimension][i] = mp
@@ -2326,17 +2355,20 @@ class TensorMaps(TensorFields):
return_templates=return_templates,
)
if return_templates:
- mp, dimension_map_templates = return_value
+ (
+ mp,
+ dimension_map_templates,
+ ) = return_value # pylint: disable=invalid-name
for i in range(len(objects)):
template_maps_list[i].append(
(dimension, dimension_map_templates[i])
)
else:
- mp = return_value
+ mp = return_value # pylint: disable=invalid-name
maps.append(mp)
inst.maps = maps
- if return_templates:
+ 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
@@ -2388,11 +2420,15 @@ class TensorMaps(TensorFields):
# 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 mp_dim, mp in self.maps.items():
- mp = mp._cut_template(template.maps[mp_dim])
+ for mp_dim, mp in self.maps.items(): # pylint: disable=invalid-name
+ mp = mp._cut_template(
+ template.maps[mp_dim]
+ ) # noqa: E501 pylint: disable=invalid-name,protected-access
if template.fields:
# correct
- mp = Maps.to_map(index_lut[mp], *mp.fields)
+ mp = Maps.to_map(
+ index_lut[mp], *mp.fields
+ ) # pylint: disable=invalid-name
inst.maps[mp_dim] = mp
return inst
@@ -2423,12 +2459,11 @@ class TensorMaps(TensorFields):
"""
if not issubclass(type(other), Tensors):
return super(TensorMaps, self).equal(other, **kwargs)
- else:
- with other.tmp_transform(self.coord_sys):
- mask = super(TensorMaps, self).equal(other, **kwargs)
- if issubclass(type(other), TensorMaps):
- mask &= self.maps.equal(other.maps, **kwargs)
- return mask
+ with other.tmp_transform(self.coord_sys):
+ mask = super(TensorMaps, self).equal(other, **kwargs)
+ if issubclass(type(other), TensorMaps):
+ mask &= self.maps.equal(other.maps, **kwargs)
+ return mask
def stale(self):
"""
@@ -2445,12 +2480,12 @@ class TensorMaps(TensorFields):
>>> assert np.array_equal(tm.stale(), [False, False, False, True])
"""
- staleMask = np.full(self.shape[0], False, dtype=bool)
- used = set([ind for mp in self.maps.values() for ind in mp.flatten()])
+ 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:
- staleMask[i] = True
- return staleMask
+ stale_mask[i] = True
+ return stale_mask
def cleaned(self, stale=True, duplicates=True):
"""
@@ -2503,11 +2538,15 @@ class TensorMaps(TensorFields):
# redirect maps. Note: work on inst.maps instead of
# self.maps in case stale vertices where removed
for map_dim in inst.maps:
- for f in range(len(inst.maps[map_dim])): # face index
- mp = np.array(inst.maps[map_dim], dtype=int)
- if tensor_index in mp[f]:
- index = tfields.lib.util.index(mp[f], tensor_index)
- inst.maps[map_dim][f][index] = duplicate_index
+ for face_index in range(len(inst.maps[map_dim])): # face index
+ mp = np.array(
+ inst.maps[map_dim], dtype=int
+ ) # noqa: E501 pylint: disable=invalid-name
+ if tensor_index in mp[face_index]:
+ index = tfields.lib.util.index(
+ mp[face_index], tensor_index
+ )
+ inst.maps[map_dim][face_index][index] = duplicate_index
if remove_mask.any():
# prevent another copy
inst = inst.removed(remove_mask)
@@ -2591,10 +2630,7 @@ class TensorMaps(TensorFields):
obj = self.copy()
map_indices = set(map_indices) # for speed up
map_delete_mask = np.array(
- [
- True if i not in map_indices else False
- for i in range(len(self.maps[map_dim]))
- ]
+ [i not in map_indices for i in range(len(self.maps[map_dim]))]
)
obj.maps[map_dim] = obj.maps[map_dim][~map_delete_mask]
obj = obj.cleaned(duplicates=False)
@@ -2682,7 +2718,7 @@ class TensorMaps(TensorFields):
flat_map = np.array(obj.maps[map_dim].flat)
values, counts = np.unique(flat_map, return_counts=True)
- counts = {v: n for v, n in zip(values, counts)}
+ counts = dict(zip(values, counts))
# last is a helper
last = np.full(max(flat_map) + 1, -3, dtype=int)
@@ -2721,9 +2757,9 @@ class TensorMaps(TensorFields):
values, counts = np.unique(path.maps[map_dim].flat, return_counts=True)
first_node = None
- for v, c in zip(values, counts):
- if c == 1:
- first_node = v
+ for value, count in zip(values, counts):
+ if count == 1:
+ first_node = value
break
edges = [list(edge) for edge in path.maps[map_dim]]
if first_node is None:
@@ -2735,8 +2771,7 @@ class TensorMaps(TensorFields):
if found_first_node:
edge[edge.index(first_node)] = len(path) - 1
break
- else:
- found_first_node = True
+ found_first_node = True
# follow the edges until you hit the end
chain = [first_node]