# Source code for psi4.driver.p4util.numpy_helper

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    import sys

    import numpy as np

    from psi4 import core
    from .exceptions import ValidationError

    ### Matrix and Vector properties

    def _get_raw_views(self, copy=False):
        """
        Gets simple raw view of the passed in object.
        """
        if copy:
            return tuple([np.array(x) for x in self.array_interface()])
        else:
            return tuple(self.array_interface())

    def _find_dim(arr, ndim):
        """
        Helper function to help deal with zero or sized arrays
        """

        # Zero arrays
        if (arr is None) or (arr is False):
            return [0] * ndim

        # Make sure this is a numpy array like thing
        if not hasattr(arr, 'shape'):
            raise ValidationError("Expected numpy array, found object of type '%s'" % type(arr))

        if len(arr.shape) == ndim:
            return [arr.shape[x] for x in range(ndim)]
        else:
            raise ValidationError("Input array does not have a valid shape.")

    def array_to_matrix(self, arr, name="New Matrix", dim1=None, dim2=None):
        """
        Converts a numpy array or list of numpy arrays into a Psi4 Matrix (irreped if list).

        Parameters
        ----------
        arr : array or list of arrays
            Numpy array or list of arrays to use as the data for a new core.Matrix
        name : str
            Name to give the new core.Matrix
        dim1 : list, tuple, or core.Dimension (optional)
            If a single dense numpy array is given, a dimension can be supplied to
            apply irreps to this array. Note that this discards all extra information
            given in the matrix besides the diagonal blocks determined by the passed
            dimension.
        dim2 :
            Same as dim1 only if using a psi4.core.Dimension object.

        Returns
        -------
        matrix : :py:class:`~psi4.core.Matrix` or :py:class:`~psi4.core.Vector`
           Returns the given Psi4 object

        Notes
        -----
        This is a generalized function to convert a NumPy array to a Psi4 object

        Examples
        --------

        >>> data = np.random.rand(20)
        >>> vector = array_to_matrix(data)

        >>> irrep_data = [np.random.rand(2, 2), np.empty(shape=(0,3)), np.random.rand(4, 4)]
        >>> matrix = array_to_matrix(irrep_data)
        >>> print matrix.rowspi().to_tuple()
        (2, 0, 4)
        """

        # What type is it? MRO can help.
        arr_type = self.__mro__[0]

        # Irreped case
        if isinstance(arr, (list, tuple)):
            if (dim1 is not None) or (dim2 is not None):
                raise ValidationError("Array_to_Matrix: If passed input is list of arrays dimension cannot be specified.")

            irreps = len(arr)
            if arr_type == core.Matrix:
                sdim1 = core.Dimension(irreps)
                sdim2 = core.Dimension(irreps)

                for i in range(irreps):
                    d1, d2 = _find_dim(arr[i], 2)
                    sdim1[i] = d1
                    sdim2[i] = d2

                ret = self(name, sdim1, sdim2)

            elif arr_type == core.Vector:
                sdim1 = core.Dimension(irreps)

                for i in range(irreps):
                    d1 = _find_dim(arr[i], 1)
                    sdim1[i] = d1[0]

                ret = self(name, sdim1)
            else:
                raise ValidationError("Array_to_Matrix: type '%s' is not recognized." % str(arr_type))

            for view, vals in zip(ret.nph, arr):
                if 0 in view.shape: continue
                view[:] = vals

            return ret

        # No irreps implied by list
        else:
            if arr_type == core.Matrix:

                # Build an irreped array back out
                if dim1 is not None:
                    if dim2 is None:
                        raise ValidationError("Array_to_Matrix: If dim1 is supplied must supply dim2 also")

                    dim1 = core.Dimension.from_list(dim1)
                    dim2 = core.Dimension.from_list(dim2)

                    if dim1.n() != dim2.n():
                        raise ValidationError("Array_to_Matrix: Length of passed dim1 must equal length of dim2.")

                    ret = self(name, dim1, dim2)

                    start1 = 0
                    start2 = 0
                    for num, interface in enumerate(ret.nph):
                        d1 = dim1[num]
                        d2 = dim2[num]
                        if (d1 == 0) or (d2 == 0):
                            continue

                        view = np.asarray(interface)
                        view[:] = arr[start1:start1 + d1, start2:start2 + d2]
                        start1 += d1
                        start2 += d2

                    return ret

                # Simple case without irreps
                else:
                    ret = self(name, arr.shape[0], arr.shape[1])
                    ret.np[:] = arr
                    return ret

            elif arr_type == core.Vector:
                # Build an irreped array back out
                if dim1 is not None:
                    if dim2 is not None:
                        raise ValidationError("Array_to_Matrix: If dim2 should not be supplied for 1D vectors.")

                    dim1 = core.Dimension.from_list(dim1)
                    ret = self(name, dim1)

                    start1 = 0
                    for num, interface in enumerate(ret.nph):
                        d1 = dim1[num]
                        if (d1 == 0):
                            continue

                        view = np.asarray(interface)
                        view[:] = arr[start1:start1 + d1]
                        start1 += d1

                    return ret

                # Simple case without irreps
                else:
                    ret = self(name, arr.shape[0])
                    ret.np[:] = arr
                    return ret

            else:
                raise ValidationError("Array_to_Matrix: type '%s' is not recognized." % str(arr_type))

    def _to_array(matrix, copy=True, dense=False):
        """
        Converts a Psi4 Matrix or Vector to a numpy array. Either copies the data or simply
        constructs a view.

        Parameters
        ----------
        matrix : :py:class:`~psi4.core.Matrix` or :py:class:`~psi4.core.Vector`
            Pointers to which Psi4 core class should be used in the construction.
        copy : bool, optional
            Copy the data if `True`, return a view otherwise
        dense : bool, optional
            Converts irreped Psi4 objects to diagonally blocked dense arrays if `True`. Returns a list of arrays otherwise.

        Returns
        -------
        array : ndarray or list of ndarray
           Returns either a list of np.array's or the base array depending on options.

        Notes
        -----
        This is a generalized function to convert a Psi4 object to a NumPy array

        Examples
        --------

        >>> data = psi4.Matrix(3, 3)
        >>> data.to_array()
        [[ 0.  0.  0.]
         [ 0.  0.  0.]
         [ 0.  0.  0.]]
        """
        if matrix.nirrep() > 1:

            # We will copy when we make a large matrix
            if dense:
                copy = False

            matrix_views = _get_raw_views(matrix, copy=copy)

            # Return the list of arrays
            if dense is False:
                return matrix_views

            # Build the dense matrix
            if isinstance(matrix, core.Vector):
                ret_type = '1D'
            elif isinstance(matrix, core.Matrix):
                ret_type = '2D'
            else:
                raise ValidationError("Array_to_Matrix: type '%s' is not recognized." % type(matrix))

            dim1 = []
            dim2 = []
            for h in matrix_views:
                # Ignore zero dim irreps
                if 0 in h.shape:
                    dim1.append(0)
                    dim2.append(0)
                else:
                    dim1.append(h.shape[0])
                    if ret_type == '2D':
                        dim2.append(h.shape[1])

            ndim1 = np.sum(dim1)
            ndim2 = np.sum(dim2)
            if ret_type == '1D':
                dense_ret = np.zeros(shape=(ndim1))
                start = 0
                for d1, arr in zip(dim1, matrix_views):
                    if d1 == 0: continue
                    dense_ret[start:start + d1] = arr
                    start += d1
            else:
                dense_ret = np.zeros(shape=(ndim1, ndim2))
                start1 = 0
                start2 = 0
                for d1, d2, arr in zip(dim1, dim2, matrix_views):
                    if (d1 == 0) or (d2 == 0): continue

                    dense_ret[start1:start1 + d1, start2:start2 + d2] = arr
                    start1 += d1
                    start2 += d2

            return dense_ret

        else:
            return _get_raw_views(matrix, copy=copy)[0]

    @property
    def _np_shape(self):
        """
        Shape of the Psi4 data object
        """
        view_data = _get_raw_views(self)
        if self.nirrep() > 1:
            return tuple(view_data[x].shape for x in range(self.nirrep()))
        else:
            return view_data[0].shape

    @property
    def _np_view(self):
        """
        View without only one irrep
        """
        if self.nirrep() > 1:
            raise ValidationError("Attempted to call .np on a Psi4 data object with multiple irreps."
                                  "Please use .nph for objects with irreps.")
        return _get_raw_views(self)[0]

    @property
    def _nph_view(self):
        """
        View with irreps.
        """
        return _get_raw_views(self)

    @property
    def _array_conversion(self):
        """
        Provides the array interface to simply classes so that np.array(core.Matrix(5, 5)) works flawlessly.
        """
        if self.nirrep() > 1:
            raise ValidationError("__array__interface__ can only be called on Psi4 data object with only one irrep!")
        else:
            return self.np.__array_interface__

    def _np_write(self, filename=None, prefix=""):
        """
        Writes the irreped matrix to a NumPy zipped file.

        Can return the packed data for saving many matrices into the same file.
        """

        ret = {}
        ret[prefix + "Irreps"] = self.nirrep()
        ret[prefix + "Name"] = self.name
        for h, v in enumerate(self.nph):
            # If returning arrays to user, we want to return copies (snapshot), not
            # views of the core.Matrix's memory.
            if filename is None and not v.flags['OWNDATA']:
                v = np.copy(v)
            ret[prefix + "IrrepData" + str(h)] = v

        if isinstance(self, core.Matrix):
            ret[prefix + "Dim1"] = self.rowdim().to_tuple()
            ret[prefix + "Dim2"] = self.coldim().to_tuple()
        if isinstance(self, core.Vector):
            ret[prefix + "Dim"] = [self.dim(x) for x in range(self.nirrep())]

        if filename is None:
            return ret

        np.savez(filename, **ret)

    def _np_read(self, filename, prefix=""):
        """
        Reads the data from a NumPy compress file.
        """

        if isinstance(filename, np.lib.npyio.NpzFile):
            data = filename
        elif isinstance(filename, str):
            if not filename.endswith('.npz'):
                filename = filename + '.npz'

            data = np.load(filename)
        else:
            raise Exception("Filename not understood: %s" % filename)

        ret_data = []

        if ((prefix + "Irreps") not in data.keys()) or ((prefix + "Name") not in data.keys()):
            raise ValidationError("File %s does not appear to be a numpyz save" % filename)

        for h in range(data[prefix + "Irreps"]):
            ret_data.append(data[prefix + "IrrepData" + str(h)])

        arr_type = self.__mro__[0]
        if arr_type == core.Matrix:
            dim1 = core.Dimension.from_list(data[prefix + "Dim1"])
            dim2 = core.Dimension.from_list(data[prefix + "Dim2"])
            ret = self(str(data[prefix + "Name"]), dim1, dim2)
        elif arr_type == core.Vector:
            dim1 = core.Dimension.from_list(data[prefix + "Dim"])
            ret = self(str(data[prefix + "Name"]), dim1)

        for h in range(data[prefix + "Irreps"]):
            ret.nph[h][:] = ret_data[h]

        return ret

    def _to_serial(data):
        """
        Converts an object with a .nph accessor to a serialized dictionary
        """

        json_data = {}
        json_data["shape"] = []
        json_data["data"] = []

        for view in data.nph:
            json_data["shape"].append(view.shape)
            json_data["data"].append(view.tostring())

        if len(json_data["shape"][0]) == 1:
            json_data["type"] = "vector"
        elif len(json_data["shape"][0]) == 2:
            json_data["type"] = "matrix"
        else:
            raise ValidationError("_to_json is only used for vector and matrix objects.")

        return json_data

    def _from_serial(self, json_data):
        """
        Converts serialized data to the correct Psi4 data type
        """

        if json_data["type"] == "vector":
            dim1 = core.Dimension.from_list([x[0] for x in json_data["shape"]])
            ret = self("Vector from JSON", dim1)
        elif json_data["type"] == "matrix":
            dim1 = core.Dimension.from_list([x[0] for x in json_data["shape"]])
            dim2 = core.Dimension.from_list([x[1] for x in json_data["shape"]])
            ret = self("Matrix from JSON", dim1, dim2)
        else:
            raise ValidationError("_from_json did not recognize type option of %s." % str(json_data["type"]))

        for n in range(len(ret.nph)):
            ret.nph[n].flat[:] = np.frombuffer(json_data["data"][n], dtype=np.double)

        return ret

    def _chain_dot(*args, **kwargs):
        """
        Chains dot products together from a series of Psi4 Matrix classes.

        By default there is no transposes, an optional vector of booleans can be passed in.
        """

        trans = kwargs.pop("trans", None)
        if trans is None:
            trans = [False for x in range(len(args))]
        else:
            if len(trans) != len(args):
                raise ValidationError(
                    "Chain dot: The length of the transpose arguements is not equal to the length of args.")

        # Setup chain
        ret = args[0]
        if trans[0]:
            ret = ret.transpose()

        # Run through
        for n, mat in enumerate(args[1:]):
            ret = core.doublet(ret, mat, False, trans[n + 1])

        return ret

    def _irrep_access(self, *args, **kwargs):
        """
        Warns user when iterating/accessing an irreped object.
        """
        raise ValidationError("Attempted to access by index/iteration a Psi4 data object that supports multiple"
                              "irreps. Please use .np or .nph explicitly.")

    # Matrix attributes
    core.Matrix.from_array = classmethod(array_to_matrix)
    core.Matrix.from_list = classmethod(lambda self, x: array_to_matrix(self, np.array(x)))
    core.Matrix.to_array = _to_array
    core.Matrix.shape = _np_shape
    core.Matrix.np = _np_view
    core.Matrix.nph = _nph_view
    core.Matrix.__array_interface__ = _array_conversion
    core.Matrix.np_write = _np_write
    core.Matrix.np_read = classmethod(_np_read)
    core.Matrix.to_serial = _to_serial
    core.Matrix.from_serial = classmethod(_from_serial)
    core.Matrix.chain_dot = _chain_dot
    core.Matrix.__iter__ = _irrep_access
    core.Matrix.__getitem__ = _irrep_access

    # Vector attributes
    core.Vector.from_array = classmethod(array_to_matrix)
    core.Vector.from_list = classmethod(lambda self, x: array_to_matrix(self, np.array(x)))
    core.Vector.to_array = _to_array
    core.Vector.shape = _np_shape
    core.Vector.np = _np_view
    core.Vector.nph = _nph_view
    core.Vector.__array_interface__ = _array_conversion
    core.Vector.np_write = _np_write
    core.Vector.np_read = classmethod(_np_read)
    core.Vector.to_serial = _to_serial
    core.Vector.from_serial = classmethod(_from_serial)
    core.Vector.__iter__ = _irrep_access
    core.Vector.__getitem__ = _irrep_access

    ### CIVector properties

    @property
    def _civec_view(self):
        """
        Returns a view of the CIVector's buffer
        """
        return np.asarray(self)

    core.CIVector.np = _civec_view

    ### Dimension properties

    @classmethod
    def _dimension_from_list(self, dims, name="New Dimension"):
        """
        Builds a core.Dimension object from a python list or tuple. If a dimension
        object is passed a copy will be returned.
        """

        if isinstance(dims, (tuple, list, np.ndarray)):
            irreps = len(dims)
        elif isinstance(dims, core.Dimension):
            irreps = dims.n()
        else:
            raise ValidationError("Dimension from list: Type '%s' not understood" % type(dims))

        ret = core.Dimension(irreps, name)
        for i in range(irreps):
            ret[i] = dims[i]
        return ret

    def _dimension_to_tuple(dim):
        """
        Converts a core.Dimension object to a tuple.
        """

        if isinstance(dim, (tuple, list)):
            return tuple(dim)

        irreps = dim.n()
        ret = []
        for i in range(irreps):
            ret.append(dim[i])
        return tuple(ret)

    def _dimension_iter(dim):
        """
        Provides an iterator class for the Dimension object.

        Allows:
            dim = psi4.core.Dimension(...)
            list(dim)
        """

        for i in range(dim.n()):
            yield dim[i]

    # Dimension attributes
    core.Dimension.from_list = _dimension_from_list
    core.Dimension.to_tuple = _dimension_to_tuple
    core.Dimension.__iter__ = _dimension_iter

    # General functions for NumPy array manipulation
    def block_diagonal_array(*args):
        """
        Convert square NumPy array to a single block diagonal array.
        Mimic of SciPy's block_diag.
        """

        # Validate the input matrices.
        dim = 0
        for matrix in args:
            try:
                shape = matrix.shape
                dim += shape[0]
            except (AttributeError, TypeError):
                raise ValidationError("Cannot construct block diagonal from non-arrays.")
            if len(shape) != 2:
                raise ValidationError("Cannot construct block diagonal from non-2D arrays.")
            if shape[0] != shape[1]:
                raise ValidationError("Cannot construct block diagonal from non-square arrays.")

        # If this is too slow, try a sparse matrix?
        block_diag = np.zeros((dim, dim))
        start = 0
        for matrix in args:
            next_block = slice(start, start + matrix.shape[0])
            block_diag[next_block, next_block] = matrix
            start += matrix.shape[0]

        return block_diag