core.py 85.3 KB
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#!/usr/bin/env
# encoding: utf-8
"""
Author:     Daniel Boeckenhoff
Mail:       daniel.boeckenhoff@ipp.mpg.de

core of tfields library
contains numpy ndarray derived bases of the tfields package
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Notes:
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    It could be worthwhile concidering `np.li.mixins.NDArrayOperatorsMixin <https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html>`_
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"""
import warnings
import os
import pathlib
from six import string_types
from contextlib import contextmanager
from collections import Counter

import numpy as np
import sympy
import scipy as sp
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import rna
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import tfields.bases
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from nltk.misc.chomsky import objects
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np.seterr(all='warn', over='raise')


def rank(tensor):
    """
    Tensor rank
    """
    return len(tensor.shape) - 1


def dim(tensor):
    """
    Manifold dimension
    """
    if rank(tensor) == 0:
        return 1
    return tensor.shape[1]


class AbstractNdarray(np.ndarray):
    """
    All tensors and subclasses should derive from AbstractNdarray.
    AbstractNdarray implements all the inheritance specifics for np.ndarray
    Whene inheriting, three attributes are of interest:
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    Attributes:
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        __slots__ (list of str): If you want to add attributes to
            your AbstractNdarray subclass, add the attribute name to __slots__
        __slot_defaults__ (list): if __slot_defaults__ is None, the
            defaults for the attributes in __slots__ will be None
            other values will be treaded as defaults to the corresponding
            arg at the same position in the __slots__ list.
        __slotDtype__ (list of types): for the conversion of the
            args in __slots__ to numpy arrays. None values mean no
            conversion.

    Args:
        array (array-like): input array
        **kwargs: arguments corresponding to __slots__
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    TODO:
        equality check
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    """
    __slots__ = []
    __slot_defaults__ = []
    __slotDtypes__ = []
    __slot_setters__ = []

    def __new__(cls, array, **kwargs):  # pragma: no cover
        raise NotImplementedError("{clsType} type must implement '__new__'"
                                  .format(clsType=type(cls)))

    def __array_finalize__(self, obj):
        if obj is None:
            return
        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)

    @classmethod
    def _iter_slots(cls):
        return [att for att in cls.__slots__ if att != '_cache']

    @classmethod
    def _update_slot_kwargs(cls, kwargs):
        """
        set the defaults in kwargs according to __slot_defaults__
        and convert the kwargs according to __slotDtypes__
        """
        slotDefaults = cls.__slot_defaults__ + \
            [None] * (len(cls.__slots__) - len(cls.__slot_defaults__))
        slotDtypes = cls.__slotDtypes__ + \
            [None] * (len(cls.__slots__) - len(cls.__slotDtypes__))
        for attr, default, dtype in zip(cls.__slots__, slotDefaults, slotDtypes):
            if attr == '_cache':
                continue
            if attr not in kwargs:
                kwargs[attr] = default
            if dtype is not None:
                try:
                    kwargs[attr] = np.array(kwargs[attr], dtype=dtype)
                except Exception as err:
                    raise ValueError(str(attr) + str(dtype) + str(kwargs[attr]) + str(err))

    def __setattr__(self, name, value):
        if name in self.__slots__:
            index = self.__slots__.index(name)
            try:
                setter = self.__slot_setters__[index]
            except IndexError:
                setter = None
            if setter is not None:
                value = setter(value)
        super(AbstractNdarray, self).__setattr__(name, value)

    def __reduce__(self):
        """
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        important for pickling (see `here <https://stackoverflow.com/questions/26598109/preserve-custom-attributes-when-pickling-subclass-of-numpy-array>`_)

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        Examples:
            >>> from tempfile import NamedTemporaryFile
            >>> import pickle
            >>> import tfields

            Build a dummy scalar field
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            >>> from tfields import Tensors, TensorFields
            >>> scalars = Tensors([0, 1, 2])
            >>> vectors = Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
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            >>> scalar_field = TensorFields(vectors, scalars, coord_sys='cylinder')
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            Save it and restore it
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            >>> out_file = NamedTemporaryFile(suffix='.pickle')
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            >>> pickle.dump(scalar_field,
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            ...             out_file)
            >>> _ = out_file.seek(0)
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            >>> sf = pickle.load(out_file)
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            >>> sf.coord_sys == 'cylinder'
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            True
            >>> sf.fields[0][2] == 2.
            True

        """
        # Get the parent's __reduce__ tuple
        pickled_state = super(AbstractNdarray, self).__reduce__()

        # Create our own tuple to pass to __setstate__
        new_state = pickled_state[2] + tuple([getattr(self, slot) for slot in
                                              self._iter_slots()])

        # Return a tuple that replaces the parent's __setstate__ tuple with our own
        return (pickled_state[0], pickled_state[1], new_state)

    def __setstate__(self, state):
        """
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        Counterpart to __reduce__. Important for unpickling.
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        """
        # Call the parent's __setstate__ with the other tuple elements.
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        # numpy ndarray state has 5 entries
        super(AbstractNdarray, self).__setstate__(state[:5])
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        # set the __slot__ attributes
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        valid_slot_attrs = list(self._iter_slots())
        added_slot_attrs = ['name']  # 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__
        n_old = len(valid_slot_attrs) - len(state[5:])
        if n_old > 0:
            for latest_index in range(n_old):
                new_slot = added_slot_attrs[-latest_index]
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                warnings.warn("Slots with names '{new_slot}' appears to have been "
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                              "added after the creation of the reduced state. "
                              "No corresponding state found in __setstate__."
                              .format(**locals()))
                valid_slot_attrs.pop(valid_slot_attrs.index(new_slot))
                setattr(self, new_slot, None)

        for slot_index, slot in enumerate(valid_slot_attrs):
            state_index = 5 + slot_index
            setattr(self, slot, state[state_index])
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    @property
    def bulk(self):
        """
        The pure ndarray version of the actual state
            -> nothing attached
        """
        return np.array(self)

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    @classmethod
    @contextmanager
    def _bypass_setter(cls, slot, demand_existence=False):
        """
        Temporarily remove the setter in __slot_setters__ corresponding to slot
        position in __slot__. You should know what you do, when using this.
        """
        slot_index = cls.__slots__.index(slot) if slot in cls.__slots__ else None
        if slot_index is None:
            if demand_existence:
                raise ValueError("Slot {slot} not existing".format(**locals()))
            else:
                yield
                return
        setter = cls.__slot_setters__[slot_index]
        cls.__slot_setters__[slot_index] = None
        yield
        cls.__slot_setters__[slot_index] = setter

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    def copy(self, *args, **kwargs):
        """
        The standard ndarray copy does not copy slots. Correct for this.
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        Examples:
            >>> import tfields
            >>> m = tfields.TensorMaps([[1,2,3], [3,3,3], [0,0,0], [5,6,7]],
            ...                        maps=[tfields.TensorFields([[0, 1, 2], [1, 2, 3]],
            ...                                                   [1, 2])])
            >>> mc = m.copy()
            >>> mc is m
            False
            >>> mc.maps[0].fields[0] is m.maps[0].fields[0]
            False

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        TODO:
            This function implementation could be more general or maybe redirect to deepcopy?
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        """
        inst = super(AbstractNdarray, self).copy(*args, **kwargs)
        for attr in self._iter_slots():
            value = getattr(self, attr)
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            if hasattr(value, 'copy') and not isinstance(value, list):
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                setattr(inst, attr, value.copy(*args, **kwargs))
            elif isinstance(value, list):
                list_copy = []
                for item in value:
                    if hasattr(item, 'copy'):
                        list_copy.append(item.copy(*args, **kwargs))
                    else:
                        list_copy.append(item)
                setattr(inst, attr, list_copy)

        return inst

    def save(self, path, *args, **kwargs):
        """
        Saving a tensors object by redirecting to the correct save method depending on path
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        Args:
            path (str or buffer)
            *args:
                forwarded to extension specific method
            **kwargs:
                extension (str): only needed if path is buffer
                ... remaining:forwarded to extension specific method
        """
        # get the extension
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        if isinstance(path, (string_types, pathlib.Path)):
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            extension = pathlib.Path(path).suffix.lstrip('.')
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        else:
            raise ValueError("Wrong path type {0}".format(type(path)))
        path = str(path)
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        # get the save method
        try:
            save_method = getattr(self,
                                  '_save_{extension}'.format(**locals()))
        except:
            raise NotImplementedError("Can not find save method for extension: "
                                      "{extension}.".format(**locals()))

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        path = rna.path.resolve(path)
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        return save_method(path, **kwargs)

    @classmethod
    def load(cls, path, *args, **kwargs):
        """
        load a file as a tensors object.
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        Args:
            path (str or buffer)
            *args:
                forwarded to extension specific method
            **kwargs:
                extension (str): only needed if path is buffer
                ... remaining:forwarded to extension specific method
        """
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        if isinstance(path, (string_types, pathlib.Path)):
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            extension = pathlib.Path(path).suffix.lstrip('.')
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            path = str(path)
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            path = rna.path.resolve(path)
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        else:
            extension = kwargs.pop('extension', 'npz')
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        try:
            load_method = getattr(cls, '_load_{e}'.format(e=extension))
        except:
            raise NotImplementedError("Can not find load method for extension: "
                                      "{extension}.".format(**locals()))
        return load_method(path, *args, **kwargs)

    def _save_npz(self, path, **kwargs):
        """
        Args:
            path (open file or str/unicode): destination to save file to.
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        Examples:
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            Build some dummies:
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            >>> import tfields
            >>> from tempfile import NamedTemporaryFile
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            >>> out_file = NamedTemporaryFile(suffix='.npz')
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            >>> p = tfields.Points3D([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]],
            ...                      name='my_points')
            >>> scalars = tfields.Tensors([0, 1, 2], name=42)
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            >>> 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])]
            >>> m = tfields.TensorMaps(vectors, scalars,
            ...                        maps=maps)

            Simply give the file name to save
            >>> p.save(out_file.name)
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            >>> _ = out_file.seek(0)  # this is only necessary in the test
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            >>> p1 = tfields.Points3D.load(out_file.name)
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            >>> assert p.equal(p1)
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            >>> assert p.coord_sys == p1.coord_sys
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            The fully nested structure of a TensorMaps object is reconstructed
            >>> out_file_maps = NamedTemporaryFile(suffix='.npz')
            >>> m.save(out_file_maps.name)
            >>> _ = out_file_maps.seek(0)
            >>> m1 = tfields.TensorMaps.load(out_file_maps.name)
            >>> assert m.equal(m1)
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            >>> assert m.maps[0].dtype == m1.maps[0].dtype
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            Names are preserved
            >>> assert p.name == 'my_points'
            >>> m.names
            [42]

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        """
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        content_dict = self._as_dict()
        np.savez(path, **content_dict)
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    @classmethod
    def _load_npz(cls, path, **load_kwargs):
        """
        Factory method
        Given a path to a npz file, construct the object
        """
        np_file = np.load(path, **load_kwargs)
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        return cls._from_dict(**np_file)

    def _as_dict(self):
        """
        Recursively walk trough all __slots__ and describe all elements
        """
        d = {}
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        d['bulk'] = self.bulk
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        d['bulk_type'] = self.__class__.__name__
        for attr in self._iter_slots():
            value = getattr(self, attr)
            if isinstance(value, list):
                if len(value) == 0:
                    d[attr] = None
                if all([isinstance(part, AbstractNdarray) for part in value]):
                    for i, part in enumerate(value):
                        part_dict = part._as_dict()
                        for part_attr, part_value in part_dict.items():
                            d["{attr}::{i}::{part_attr}".format(**locals())] = part_value
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                    continue
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            if isinstance(value, AbstractNdarray):
                value = value._as_dict()
            d[attr] = value
        return d

    @classmethod
    def _from_dict(cls, **d):
        """
        Opposite of _as_dict
        """
        list_dict = {}
        kwargs = {}
        '''
        De-Flatten the first layer of lists
        '''
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        for key in sorted(list(d)):
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            if '::' in key:
                splits = key.split('::')
                attr, _, end = key.partition('::')
                if attr not in list_dict:
                    list_dict[attr] = {}

                index, _, end = end.partition('::')
                if not index.isdigit():
                    raise ValueError("None digit index given")
                index = int(index)
                if index not in list_dict[attr]:
                    list_dict[attr][index] = {}
                list_dict[attr][index][end] = d[key]
            else:
                kwargs[key] = d[key]

        '''
        Build the lists (recursively)
        '''
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        for key in list(list_dict):
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            sub_dict = list_dict[key]
            list_dict[key] = []
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            for index in sorted(list(sub_dict)):
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                bulk_type = sub_dict[index].get('bulk_type').tolist()
                if isinstance(bulk_type, bytes):
                    # asthonishingly, this is not necessary under linux. Found under nt. ???
                    bulk_type = bulk_type.decode('UTF-8')
                bulk_type = getattr(tfields, bulk_type)
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                list_dict[key].append(bulk_type._from_dict(**sub_dict[index]))

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        with cls._bypass_setter('fields'):
            '''
            Build the normal way
            '''
            bulk = kwargs.pop('bulk')
            bulk_type = kwargs.pop('bulk_type')
            obj = cls.__new__(cls, bulk, **kwargs)

            '''
            Set list attributes
            '''
            for attr, list_value in list_dict.items():
                setattr(obj, attr, list_value)
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        return obj
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class Tensors(AbstractNdarray):
    """
    Set of tensors with the same basis.
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    TODO:
        all slot args should be protected -> _base
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    Args:
        tensors: np.ndarray or AbstractNdarray subclass
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        **kwargs:
            name: optional - custom name, can be anything
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    Examples:
        >>> import numpy as np
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        >>> import tfields
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        Initialize a scalar range
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        >>> scalars = tfields.Tensors([0, 1, 2])
        >>> scalars.rank == 0
        True

        Initialize vectors
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        >>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
        >>> vectors.rank == 1
        True
        >>> vectors.dim == 3
        True
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        >>> assert vectors.coord_sys == 'cartesian'
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        Initialize the Levi-Zivita Tensor
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        >>> 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]]])
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        >>> matrices.shape == (3, 3, 3)
        True
        >>> matrices.rank == 2
        True
        >>> matrices.dim == 3
        True

        Initializing in different start coordinate system
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        >>> cyl = tfields.Tensors([[5, np.arctan(4. / 3.), 42]], coord_sys='cylinder')
        >>> assert cyl.coord_sys == 'cylinder'
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        >>> cyl.transform('cartesian')
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        >>> assert cyl.coord_sys == 'cartesian'
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        >>> 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
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        >>> with vectors.tmp_transform('cylinder'):
        ...     vect_cyl = tfields.Tensors(vectors)
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        ...     assert vect_cyl.coord_sys == vectors.coord_sys
        >>> assert vect_cyl.coord_sys == 'cylinder'
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        You can demand a special dimension.
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        >>> _ = 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
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        >>> _ = 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)

    """
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    __slots__ = ['coord_sys', 'name']
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    __slot_defaults__ = ['cartesian']
    __slot_setters__ = [tfields.bases.get_coord_system_name]

    def __new__(cls, tensors, **kwargs):
        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
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            coord_sys = kwargs.pop('coord_sys', tensors.coord_sys)
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            tensors = tensors.copy()
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            tensors.transform(coord_sys)
            kwargs['coord_sys'] = coord_sys
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            kwargs['name'] = kwargs.pop('name', tensors.name)
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            if dtype is None:
                dtype = tensors.dtype
        else:
            if dtype is None:
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                if hasattr(tensors, 'dtype'):
                    dtype = tensors.dtype
                else:
                    dtype = np.float64
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        ''' demand iterable structure '''
        try:
            len(tensors)
        except TypeError as err:
            raise TypeError("Iterable structure necessary."
                            " Got {tensors}"
                            .format(**locals()))

        ''' 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
            else:
                raise ValueError("Empty tensors need dimension "
                                 "parameter 'dim'.")

        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:
                raise ValueError("Dimensions are inconstistent. "
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                                 "Manifold dimension is {obj.dim}. "
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                                 "Found dimensions {found} in {obj}."
                                 .format(found=obj.shape[1:], **locals()))
        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 '''
        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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    def __iter__(self):
        """
        Forwarding iterations to the bulk array. Otherwise __getitem__ would
        kick in and slow down imensely.
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        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)

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    @classmethod
    def merged(cls, *objects, **kwargs):
        """
        Factory method
        Merges all tensor inputs to one tensor

        Examples:
            >>> import numpy as np
            >>> import tfields
            >>> import tfields.bases

            Use of most frequent coordinate system
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            >>> vec_a = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
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            >>> vec_b = tfields.Tensors([[5, 4, 1]], coord_sys=tfields.bases.cylinder)
            >>> vec_c = tfields.Tensors([[4, 2, 3]], coord_sys=tfields.bases.cylinder)
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            >>> merge = tfields.Tensors.merged(vec_a, vec_b, vec_c, [[2, 0, 1]])
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            >>> assert merge.coord_sys == 'cylinder'
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            >>> assert merge.equal([[0, 0, 0],
            ...                     [0, 0, 1],
            ...                     [1, -np.pi / 2, 0],
            ...                     [5, 4, 1],
            ...                     [4, 2, 3],
            ...                     [2, 0, 1]])

            Merge also shifts the maps to still refer to the same tensors
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            >>> tm_a = tfields.TensorMaps(merge, maps=[[[0, 1, 2]]])
            >>> tm_b = tm_a.copy()
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            >>> assert tm_a.coord_sys == 'cylinder'
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            >>> tm_merge = tfields.TensorMaps.merged(tm_a, tm_b)
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            >>> assert tm_merge.coord_sys == 'cylinder'
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            >>> assert tm_merge.maps[0].equal([[0, 1, 2],
            ...                               list(range(len(merge),
            ...                                          len(merge) + 3,
            ...                                          1))])
            
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            >>> obj_list = [tfields.Tensors([[1, 2, 3]], coord_sys=tfields.bases.CYLINDER),
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            ...             tfields.Tensors([[3] * 3]),
            ...             tfields.Tensors([[5, 1, 3]])]
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            >>> merge2 = tfields.Tensors.merged(*obj_list, coord_sys=tfields.bases.CARTESIAN)
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            >>> assert merge2.equal([[-0.41614684, 0.90929743, 3.],
            ...                      [3, 3, 3], [5, 1, 3]], atol=1e-8)
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        """

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        ''' get most frequent coord_sys or predefined coord_sys '''
        coord_sys = kwargs.get('coord_sys', None)
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        dimension = kwargs.get('dim', None)
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        if coord_sys is None:
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            bases = []
            for t in objects:
                try:
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                    bases.append(t.coord_sys)
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                except AttributeError:
                    pass
            if bases:
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                # get most frequent coord_sys
                coord_sys = sorted(bases, key=Counter(bases).get, reverse=True)[0]
                kwargs['coord_sys'] = coord_sys
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            else:
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                default = cls.__slot_defaults__[cls.__slots__.index('coord_sys')]
                kwargs['coord_sys'] = default
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        ''' transform all raw inputs to cls type with correct coord_sys. Also
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        automatically make a copy of those instances that are of the correct
        type already.'''
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        objects = [cls.__new__(cls, t, **kwargs) for t in objects]
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        ''' 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:
            raise TypeError("Tensors must have the same dimension for merging.")

        ''' merge all objects '''
        remainingObjects = objects[1:] or []
        tensors = objects[0]

        for i, obj in enumerate(remainingObjects):
            tensors = np.append(tensors, obj, axis=0)

        if len(tensors) == 0 and dimension is None:
            for obj in objects:
                kwargs['dim'] = dim(obj)

        return cls.__new__(cls, tensors, **kwargs)

    @classmethod
    def grid(cls, *base_vectors, **kwargs):
        """
        Args:
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            *base_vectors (Iterable): base coordinates. The amount of base
                vectors defines the dimension

            **kwargs:
                iter_order (list): order in which the iteration will be done.
                    Frequency rises with position in list. default is [0, 1, 2]
                    iteration will be done like::
                          
                    for v0 in base_vectors[iter_order[0]]:
                        for v1 in base_vectors[iter_order[1]]:
                            for v2 in base_vectors[iter_order[2]]:
                                coords0.append(locals()['v%i' % iter_order[0]])
                                coords1.append(locals()['v%i' % iter_order[1]])
                                coords2.append(locals()['v%i' % iter_order[2]])
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        Examples:
            Initilaize using the mgrid notation
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            >>> import tfields
            >>> mgrid = tfields.Tensors.grid((0, 1, 2j), (3, 4, 2j), (6, 7, 2j))
            >>> mgrid.equal([[0, 3, 6],
            ...              [0, 3, 7],
            ...              [0, 4, 6],
            ...              [0, 4, 7],
            ...              [1, 3, 6],
            ...              [1, 3, 7],
            ...              [1, 4, 6],
            ...              [1, 4, 7]])
            True
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            Lists or arrays are accepted also.
            Furthermore, the iteration order can be changed
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            >>> lins = tfields.Tensors.grid(np.linspace(3, 4, 2), np.linspace(0, 1, 2),
            ...                             np.linspace(6, 7, 2), iter_order=[1, 0, 2])
            >>> lins.equal([[3, 0, 6],
            ...             [3, 0, 7],
            ...             [4, 0, 6],
            ...             [4, 0, 7],
            ...             [3, 1, 6],
            ...             [3, 1, 7],
            ...             [4, 1, 6],
            ...             [4, 1, 7]])
            True
            >>> lins2 = tfields.Tensors.grid(np.linspace(0, 1, 2),
            ...                              np.linspace(3, 4, 2),
            ...                              np.linspace(6, 7, 2),
            ...                              iter_order=[2, 0, 1])
            >>> lins2.equal([[0, 3, 6],
            ...              [0, 4, 6],
            ...              [1, 3, 6],
            ...              [1, 4, 6],
            ...              [0, 3, 7],
            ...              [0, 4, 7],
            ...              [1, 3, 7],
            ...              [1, 4, 7]])
            True

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            When given the coord_sys argument, the grid is performed in the
            given coorinate system:
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            >>> lins3 = tfields.Tensors.grid(np.linspace(4, 9, 2),
            ...                              np.linspace(np.pi/2, np.pi/2, 1),
            ...                              np.linspace(4, 4, 1),
            ...                              iter_order=[2, 0, 1],
            ...                              coord_sys=tfields.bases.CYLINDER)
            >>> assert lins3.coord_sys == 'cylinder'
            >>> lins3.transform('cartesian')
            >>> assert np.array_equal(lins3[:, 1], [4, 9])

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        """
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        cls_kwargs = {attr: kwargs.pop(attr) for attr in list(kwargs) if attr in cls.__slots__}
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        inst = cls.__new__(cls,
                           tfields.lib.grid.igrid(*base_vectors, **kwargs),
                           **cls_kwargs)
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        return inst

    @property
    def rank(self):
        """
        Tensor rank
        """
        return rank(self)

    @property
    def dim(self):
        """
        Manifold dimension
        """
        return dim(self)

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    def transform(self, coord_sys):
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        """
        Args:
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            coord_sys (str)
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        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
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            >>> assert t[0, 0] == 3

            phi
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            >>> assert t[1, 1] == 0.
            >>> assert t[2, 1] == 0.

            theta is 0 at (0, 0, 1) and pi / 2 at (0, 0, -1)
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            >>> 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
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            >>> assert t[4, 0] == 0.
            >>> assert t[4, 2] == 0.


            CARTESIAN to CYLINDER
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            >>> 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')
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            >>> assert t_cyl.coord_sys == 'cylinder'
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            R
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            >>> 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
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            >>> 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
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            >>> assert t_cyl[0, 2] == 42
            >>> assert t_cyl[2, 2] == -1

            >>> t_cyl.transform('cartesian')
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            >>> assert t_cyl.coord_sys == 'cartesian'
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            >>> assert t_cyl[0, 0] == 3

        """
        #           scalars                 empty             already there
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        if self.rank == 0 or self.shape[0] == 0 or self.coord_sys == coord_sys:
            self.coord_sys = coord_sys
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            return

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        tfields.bases.transform(self, self.coord_sys, coord_sys)
        # self[:] = tfields.bases.transform(self, self.coord_sys, coord_sys)
        self.coord_sys = coord_sys
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    @contextmanager
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    def tmp_transform(self, coord_sys):
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        """
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        Temporarily change the coord_sys to another coord_sys and change it back at exit
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        This method is for cleaner code only.
        No speed improvements go with this.
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        Args:
            see transform
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        Examples:
            >>> import tfields
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            >>> p = tfields.Tensors([[1,2,3]], coord_sys=tfields.bases.SPHERICAL)
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            >>> with p.tmp_transform(tfields.bases.CYLINDER):
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            ...     assert p.coord_sys == tfields.bases.CYLINDER
            >>> assert p.coord_sys == tfields.bases.SPHERICAL
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        """
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        baseBefore = self.coord_sys
        if baseBefore == coord_sys:
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            yield
        else:
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            self.transform(coord_sys)
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            yield

            self.transform(baseBefore)

    def mirror(self, coordinate, condition=None):
        """
        Reflect/Mirror the entries meeting <condition> at <coordinate> = 0
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        Args:
            coordinate (int): coordinate index
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        Examples:
            >>> import tfields
            >>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
            >>> p.mirror(1)
            >>> assert p.equal([[1, -2, 3], [4, -5,  6], [1, -2, -6]])

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            multiple coordinates can be mirrored at the same time
            i.e. a point mirrorion would be
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            >>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
            >>> p.mirror([0,2])
            >>> assert p.equal([[-1, 2, -3], [-4, 5, -6], [-1, 2., 6.]])

            You can give a condition as mask or as str.
            The mirroring will only be applied to the points meeting the condition.
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            >>> import sympy
            >>> x, y, z = sympy.symbols('x y z')
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            >>> p.mirror([0, 2], y > 3)
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            >>> p.equal([[-1, 2, -3], [4, 5, 6], [-1, 2, 6]])
            True

        """
        if condition is None:
            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:
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                self.mirror(c, condition=condition)
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        elif isinstance(coordinate, int):
            self[:, coordinate][condition] *= -1
        else:
            raise TypeError()

    def to_segment(self, segment, num_segments, coordinate,
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                   periodicity=2 * np.pi, offset=0.,
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                   coord_sys=None):
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        """
        For circular (close into themself after
        <periodicity>) coordinates at index <coordinate> assume
        <num_segments> segments and transform all values to
        segment number <segment>
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        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
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            coord_sys (str or sympy.CoordinateSystem): in which coord sys the
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                transformation should be done
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        Examples:
            >>> import tfields
            >>> import numpy as np
            >>> pStart = tfields.Points3D([[6, 2 * np.pi, 1],
            ...                            [6, 2 * np.pi / 5 * 3, 1]],
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            ...                           coord_sys='cylinder')
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            >>> 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))

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        if coord_sys is None:
            coord_sys = self.coord_sys
        with self.tmp_transform(coord_sys):
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            # map all values to first segment
            self[:, coordinate] = \
                (self[:, coordinate] - offset) % (periodicity / num_segments) + \
                offset + segment * periodicity / num_segments

    def equal(self, other,
              rtol=None, atol=None, equal_nan=False,
              return_bool=True):
        """
        Evaluate, whether the instance has the same content as other.
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        Args:
            optional:
                rtol (float)
                atol (float)
                equal_nan (bool)
            see numpy.isclose
        """
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        if issubclass(type(other), Tensors) and self.coord_sys != other.coord_sys:
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            other = other.copy()
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            other.transform(self.coord_sys)
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        x, y = np.asarray(self), np.asarray(other)
        if rtol is None and atol is None:
            mask = (x == y)
            if equal_nan:
                both_nan = np.isnan(x) & np.isnan(y)
                mask[both_nan] = both_nan[both_nan]
        else:
            if rtol is None:
                rtol = 0.
            if atol is None:
                atol = 0.
            mask = np.isclose(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan)
        if return_bool:
            return bool(np.all(mask))
        return mask

    def contains(self, other, **kwargs):
        """
        Inspired by a speed argument @
        stackoverflow.com/questions/14766194/testing-whether-a-numpy-array-contains-a-given-row
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        Examples:
            >>> import tfields
            >>> p = tfields.Tensors([[1,2,3], [4,5,6], [6,7,8]])
            >>> p.contains([4,5,6])
            True

        """
        return any(self.equal(other, return_bool=False).all(1))

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    def indices(self, tensor, rtol=None, atol=None):
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        """
        Returns:
            list of int: indices of tensor occuring
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        Examples:
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            Rank 1 Tensors
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            >>> import tfields
            >>> p = tfields.Tensors([[1,2,3], [4,5,6], [6,7,8], [4,5,6],
            ...                      [4.1, 5, 6]])
            >>> p.indices([4,5,6])
            array([1, 3])
            >>> p.indices([4,5,6.1], rtol=1e-5, atol=1e-1)
            array([1, 3, 4])

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            Rank 0 Tensors
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            >>> p = tfields.Tensors([2, 3, 6, 3.01])
            >>> p.indices(3)
            array([1])
            >>> p.indices(3, rtol=1e-5, atol=1e-1)
            array([1, 3])

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        """
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        x, y = np.asarray(self), np.asarray(tensor)
        if rtol is None and atol is None:
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            equal_method = np.equal
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        else:
            equal_method = lambda a, b: np.isclose(a, b, rtol=rtol, atol=atol)
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        # inspired by https://stackoverflow.com/questions/19228295/find-ordered-vector-in-numpy-array
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        if self.rank == 0:
            indices = np.where(equal_method((x-y), 0))[0]
        elif self.rank == 1:
            indices = np.where(np.all(equal_method((x-y), 0), axis=1))[0]
        else:
            raise NotImplementedError()
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        return indices

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    def index(self, tensor, **kwargs):
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        """
        Args:
            tensor
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        Returns:
            int: index of tensor occuring
        """
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        indices = self.indices(tensor, **kwargs)
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        if not indices:
            return None
        if len(indices) == 1:
            return indices[0]
        raise ValueError("Multiple occurences of value {}"
                         .format(tensor))

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    def moment(self, moment, weights=None):
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        """
        Returns:
            Moments of the distribution.
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        Args:
            moment (int): n-th moment
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        Examples:
            >>> import tfields
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            >>> import numpy as np
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            Skalars
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            >>> t = tfields.Tensors(range(1, 6))
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            >>> assert t.moment(1) == 0
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            >>> assert t.moment(1, weights=[-2, -1, 20, 1, 2]) == 0.5
            >>> assert t.moment(2, weights=[0.25, 1, 17.5, 1, 0.25]) == 0.2

            Vectors
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            >>> t = tfields.Tensors(list(zip(range(1, 6), range(1, 6))))
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            >>> assert Tensors([0.5, 0.5]).equal(t.moment(1, weights=[-2, -1, 20, 1, 2]))
            >>> assert Tensors([1. , 0.5]).equal(
            ...     t.moment(1, weights=list(zip([-2, -1, 10, 1, 2],
            ...                                  [-2, -1, 20, 1, 2]))))
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        """
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        array = tfields.lib.stats.moment(self, moment, weights=weights)
        if self.rank == 0:  # scalar
            array = [array]
        return Tensors(array, coord_sys=self.coord_sys)
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    def closest(self, other, **kwargs):
        """
        Args:
            other (Tensors): closest points to what? -> other
            **kwargs: forwarded to scipy.spatial.cKDTree.query
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        Returns:
            array shape(len(self)): Indices of other points that are closest to own points
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        Examples:
            >>> import tfields
            >>> m = tfields.Tensors([[1,0,0], [0,1,0], [1,1,0], [0,0,1],
            ...                      [1,0,1]])
            >>> p = tfields.Tensors([[1.1,1,0], [0,0.1,1], [1,0,1.1]])
            >>> p.closest(m)
            array([2, 3, 4])

        """
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        with other.tmp_transform(self.coord_sys):
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            # balanced_tree option gives huge speedup!
            kd_tree = sp.spatial.cKDTree(other, 1000,
                                         balanced_tree=False)
            res = kd_tree.query(self, **kwargs)
            array = res[1]

        return array

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    def evalf(self, expression=None, coord_sys=None):
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        """
        Args:
            expression (sympy logical expression)
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            coord_sys (str): coord_sys to evalfuate the expression in.
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        Returns:
            np.ndarray: mask of dtype bool with lenght of number of points in self.
                 This array is True, where expression evalfuates True.
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        Examples:
            >>> import tfields
            >>> import numpy
            >>> 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]])
            >>> np.array_equal(p.evalf(x > 0),
            ...                [True, True, True, False, True, False])
            True
            >>> np.array_equal(p.evalf(x >= 0),
            ...                [True, True, True, False, True, True])
            True

            And combination
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            >>> np.array_equal(p.evalf((x > 0) & (y < 3)),
            ...                [True, False, True, False, True, False])
            True

            Or combination
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            >>> np.array_equal(p.evalf((x > 0) | (y > 3)),
            ...                [True, True, True, False, True, False])
            True

        """
        coords = sympy.symbols('x y z')
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        with self.tmp_transform(coord_sys or self.coord_sys):
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            mask = tfields.evalf(np.array(self), expression, coords=coords)
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        return mask

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    def cut(self, expression, coord_sys=None):
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        """
        Default cut method for Points3D. Works on a copy.
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        Args:
            expression (sympy logical expression): logical expression which will be evalfuated.
                             use symbols x, y and z
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            coord_sys (str): coord_sys to evalfuate the expression in.
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        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
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            >>> p.cut((x > 0) & (z < 0)).equal([[1, 2, -6], [1, 0, -1]])
            True

        Returns:
            copy of self with cut applied

        """
        if len(self) == 0:
            return self.copy()
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        mask = self.evalf(expression, coord_sys=coord_sys or self.coord_sys)
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        mask.astype(bool)
        inst = self[mask].copy()
        return inst

    def distances(self, other, **kwargs):
        """
        Args:
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            other(Iterable)
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            **kwargs:
                ... is forwarded to sp.spatial.distance.cdist
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        Examples:
            >>> import tfields
            >>> p = tfields.Tensors.grid((0, 2, 3j),
            ...                          (0, 2, 3j),
            ...                          (0, 0, 1j))
            >>> p[4,2] = 1
            >>> p.distances(p)[0,0]
            0.0
            >>> p.distances(p)[5,1]
            1.4142135623730951
            >>> p.distances([[0,1,2]])[-1][0] == 3
            True

        """
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        if issubclass(type(other), Tensors) and self.coord_sys != other.coord_sys:
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            other = other.copy()
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            other.transform(self.coord_sys)
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        return sp.spatial.distance.cdist(self, other, **kwargs)

    def min_dists(self, other=None, **kwargs):
        """
        Args:
            other(array | None): if None: closest distance to self
            **kwargs:
                memory_saving (bool): for very large array comparisons
                    default False
                ... rest is forwarded to sp.spatial.distance.cdist

        Returns:
            np.array: minimal distances of self to other

        Examples:
            >>> import tfields
            >>> import numpy as np
            >>> p = tfields.Tensors.grid((0, 2, 3),
            ...                          (0, 2, 3),
            ...                          (0, 0, 1))
            >>> p[4,2] = 1
            >>> dMin = p.min_dists()
            >>> expected = [1] * 9
            >>> expected[4] = np.sqrt(2)
            >>> np.array_equal(dMin, expected)
            True

            >>> dMin2 = p.min_dists(memory_saving=True)
            >>> bool((dMin2 == dMin).all())
            True

        """
        memory_saving = kwargs.pop('memory_saving', False)

        if other is None:
            other = self
        else:
            raise NotImplementedError("Should be easy but make shure not to remove diagonal")

        try:
            if memory_saving:
                raise MemoryError()
            d = self.distances(other, **kwargs)
            return d[d > 0].reshape(d.shape[0], - 1).min(axis=1)
        except MemoryError:
            min_dists = np.empty(self.shape[0])
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            for i, point in enumerate(np.array(other)):
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                d = self.distances([point], **kwargs)
                min_dists[i] = d[d > 0].reshape(-1).min()
            return min_dists

    def epsilon_neighbourhood(self, epsilon):
        """
        Returns:
            indices for those sets of points that lie within epsilon around the other
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        Examples:
            Create mesh grid with one extra point that will have 8 neighbours
            within epsilon
            >>> import tfields
            >>> p = tfields.Tensors.grid((0, 1, 2j),
            ...                          (0, 1, 2j),
            ...                          (0, 1, 2j))
            >>> p = tfields.Tensors.merged(p, [[0.5, 0.5, 0.5]])
            >>> [len(en) for en in p.epsilon_neighbourhood(0.9)]
            [2, 2, 2, 2, 2, 2, 2, 2, 9]

        """
        indices = np.arange(self.shape[0])
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        dists = self.distances(self)  # this takes long
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        distsInEpsilon = dists <= epsilon
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        indices = [indices[die] for die in distsInEpsilon]  # this takes long
        return indices
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    def _weights(self, weights, rigid=True):
        """
        transformer method for weights inputs.
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        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
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        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

    def cov_eig(self, weights=None):
        """
        Calculate the covariance eigenvectors with lenghts of eigenvalues
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        Args:
            weights (np.array | int | None): index to scalars to weight with
        """
        # weights = self.getNormedWeightedAreas(weights=weights)
        weights = self._weights(weights)
        cov = np.cov(self.T,
                     ddof=0,
                     aweights=weights)
        # calculate eigenvalues and eigenvectors of covariance
        evalfs, evecs = np.linalg.eigh(cov)
        idx = evalfs.argsort()[::-1]
        evalfs = evalfs[idx]
        evecs = evecs[:, idx]
        e = np.concatenate((evecs, evalfs.reshape(1, 3)))
        return e.T.reshape(12, )

    def main_axes(self, weights=None):
        """
        Returns:
            Main Axes eigen-vectors
        """
        # weights = self.getNormedWeightedAreas(weights=weights)
        weights = self._weights(weights)
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        mean = np.array(self).mean(axis=0)
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        relative_coords = self - mean
        cov = np.cov(relative_coords.T,
                     ddof=0,
                     aweights=weights)
        # calculate eigenvalues and eigenvectors of covariance
        evalfs, evecs = np.linalg.eigh(cov)
        return (evecs * evalfs.T).T

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    def plot(self, **kwargs):
        """
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        Forwarding to rna.plotting.plot_array
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        """
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        artist = rna.plotting.plot_array(self, **kwargs)
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        return artist

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def as_tensors_list(tensors_list):
    """
    Setter for TensorFields.fields
    Copies input
    Examples:
        >>> import tfields
        >>> import numpy as np
        >>> 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]]),
        ...         tfields.TensorFields([[1], [2]], [-42, -21])]
        >>> mesh = tfields.TensorMaps(vectors, scalars,
        ...                           maps=maps)
        >>> mesh.maps[0].fields = [[42, 21]]
        >>> assert len(mesh.maps[0].fields) == 1
        >>> assert mesh.maps[0].fields[0].equal([42, 21])
    
    """
    if tensors_list is not None:
        new_list = []
        for tensors in tensors_list:
            tensors_list = Tensors(tensors)
            new_list.append(tensors_list)
        tensors_list = new_list
    return tensors_list


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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:
        >>> from tfields import Tensors, TensorFields
        >>> scalars = Tensors([0, 1, 2])
        >>> vectors = Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
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        >>> scalar_field = TensorFields(vectors, scalars)
        >>> scalar_field.rank
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        1
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        >>> scalar_field.fields[0].rank
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        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
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        >>> empty_field = TensorFields([], dim=3)
        >>> assert empty_field.shape == (0, 3)
        >>> assert empty_field.fields == []

        Directly initializing with lists or arrays
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        >>> vec_field_raw = tfields.TensorFields([[0, 1, 2], [3, 4, 5]],
        ...                                       [1, 6], [2, 7])
        >>> assert len(vec_field_raw.fields) == 2

        Copying
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        >>> cp = TensorFields(vectorField)
        >>> assert vectorField.equal(cp)

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        Copying takes care of coord_sys
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        >>> cp.transform(tfields.bases.CYLINDER)
        >>> cp_cyl = TensorFields(cp)
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        >>> assert cp_cyl.coord_sys == tfields.bases.CYLINDER
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        Copying with changing type