##############################################################################
# MDTraj: A Python Library for Loading, Saving, and Manipulating
#         Molecular Dynamics Trajectories.
# Copyright 2012-2013 Stanford University and the Authors
#
# Authors: Robert McGibbon
# Contributors:
#
# MDTraj is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 2.1
# of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with MDTraj. If not, see <http://www.gnu.org/licenses/>.
##############################################################################


##############################################################################
# imports
##############################################################################

from __future__ import print_function, division
import warnings
import numbers
import numpy as np
import collections
from mdtraj.utils.six.moves import zip_longest

##############################################################################
# functions / classes
##############################################################################


class TypeCastPerformanceWarning(RuntimeWarning):
    pass


def ensure_type(val, dtype, ndim, name, length=None, can_be_none=False, shape=None,
    warn_on_cast=True, add_newaxis_on_deficient_ndim=False):
    """Typecheck the size, shape and dtype of a numpy array, with optional
    casting.

    Parameters
    ----------
    val : {np.ndaraay, None}
        The array to check
    dtype : {nd.dtype, str}
        The dtype you'd like the array to have
    ndim : int
        The number of dimensions you'd like the array to have
    name : str
        name of the array. This is used when throwing exceptions, so that
        we can describe to the user which array is messed up.
    length : int, optional
        How long should the array be?
    can_be_none : bool
        Is ``val == None`` acceptable?
    shape : tuple, optional
        What should be shape of the array be? If the provided tuple has
        Nones in it, those will be semantically interpreted as matching
        any length in that dimension. So, for example, using the shape
        spec ``(None, None, 3)`` will ensure that the last dimension is of
        length three without constraining the first two dimensions
    warn_on_cast : bool, default=True
        Raise a warning when the dtypes don't match and a cast is done.
    add_newaxis_on_deficient_ndim : bool, default=True
        Add a new axis to the beginining of the array if the number of
        dimensions is deficient by one compared to your specification. For
        instance, if you're trying to get out an array of ``ndim == 3``,
        but the user provides an array of ``shape == (10, 10)``, a new axis will
        be created with length 1 in front, so that the return value is of
        shape ``(1, 10, 10)``.

    Notes
    -----
    The returned value will always be C-contiguous.

    Returns
    -------
    typechecked_val : np.ndarray, None
        If `val=None` and `can_be_none=True`, then this will return None.
        Otherwise, it will return val (or a copy of val). If the dtype wasn't right,
        it'll be casted to the right shape. If the array was not C-contiguous, it'll
        be copied as well.

    """
    if can_be_none and val is None:
        return None

    if not isinstance(val, np.ndarray):
        if isinstance(val, collections.Iterable):
            # If they give us an iterator, let's try...
            if isinstance(val, collections.Sequence):
                # sequences are easy. these are like lists and stuff
                val = np.array(val, dtype=dtype)
            else:
                # this is a generator...
                val = np.array(list(val), dtype=dtype)
        elif np.isscalar(val) and add_newaxis_on_deficient_ndim and ndim == 1:
            # special case: if the user is looking for a 1d array, and
            # they request newaxis upconversion, and provided a scalar
            # then we should reshape the scalar to be a 1d length-1 array
            val = np.array([val])
        else:
            raise TypeError(("%s must be numpy array. "
                " You supplied type %s" % (name, type(val))))

    if warn_on_cast and val.dtype != dtype:
        warnings.warn("Casting %s dtype=%s to %s " % (name, val.dtype, dtype),
            TypeCastPerformanceWarning)

    if not val.ndim == ndim:
        if add_newaxis_on_deficient_ndim and val.ndim + 1 == ndim:
            val = val[np.newaxis, ...]
        else:
            raise ValueError(("%s must be ndim %s. "
                "You supplied %s" % (name, ndim, val.ndim)))

    val = np.ascontiguousarray(val, dtype=dtype)

    if length is not None and len(val) != length:
        raise ValueError(("%s must be length %s. "
            "You supplied %s" % (name, length, len(val))))

    if shape is not None:
        # the shape specified given by the user can look like (None, None 3)
        # which indicates that ANY length is accepted in dimension 0 or
        # dimension 1
        sentenel = object()
        error = ValueError(("%s must be shape %s. You supplied  "
                "%s" % (name, str(shape).replace('None', 'Any'), val.shape)))
        for a, b in zip_longest(val.shape, shape, fillvalue=sentenel):
            if a is sentenel or b is sentenel:
                # if the sentenel was reached, it means that the ndim didn't
                # match or something. this really shouldn't happen
                raise error
            if b is None:
                # if the user's shape spec has a None in it, it matches anything
                continue
            if a != b:
                # check for equality
                raise error

    return val


def cast_indices(indices):
    """Check that ``indices`` are appropriate for indexing an array

    Parameters
    ----------
    indices : {None, array_like, slice}
        If indices is None or slice, it'll just pass through. Otherwise, it'll
        be converted to a numpy array and checked to make sure it contains
        unique integers.

    Returns
    -------
    value : {slice, np.ndarray}
        Either a slice or an array of integers, depending on the input type
    """
    if indices is None or isinstance(indices, slice):
        return indices

    if not len(indices) == len(set(indices)):
        raise ValueError("indices must be unique.")
        
    out = np.asarray(indices)
    if not issubclass(out.dtype.type, np.integer):
        raise ValueError('indices must be of an integer type. %s is not an integer type' % out.dtype)

    return out


def check_random_state(seed):
    """Turn seed into a np.random.RandomState instance

    Parameters
    ----------
    seed : {None, int, RandomState}
        Seed for a random number generator

    Returns
    -------
    randomstate : RandomState
        If seed is None, return the RandomState singleton used by np.random.
        If seed is an int, return a new RandomState instance seeded with seed.
        If seed is already a RandomState instance, return it.
        Otherwise raise ValueError.
    """
    # This code is direcly from the scikit-learn project (sklearn/utils/validation.py)
    # Authors: Olivier Grisel and Gael Varoquaux and others (please update me)
    # License: BSD 3 clause

    if seed is None or seed is np.random:
        return np.random.mtrand._rand
    if isinstance(seed, (numbers.Integral, np.integer)):
        return np.random.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return seed
    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                     ' instance' % seed)
