#
#------------------------------------------------------------------------------
# Copyright (c) 2013-2014, Christian Therien
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#------------------------------------------------------------------------------
#
# data_format.py - This file is part of the PySptools package.
#

"""
convert2d, convert3d, normalize functions
"""

from __future__ import division

import numpy as np


# not up-to-date with the version 0.05 and following
# deprecated 2013-10-13
# commented 2014-04-27
##def convert2d_signal_first(M):
##    """
##    Converts a 3D data cube (p x m x n) to a 2D matrix of points (p x N)
##    where N = mn
##
##    Usage
##      convert2d_signal_first(M)
##    Inputs
##      M - 3D HSI cube (p x m x n), M is a numpy array
##    Outputs
##      2D data matrix (p x N), a numpy array
##    """
##
##    import pysptools.util.err as err
##    if M.ndim != 3:
##        err._raise(err.e_array_not_3dim,
##                    m1='convert2d_signalfirst() function', m2='M', m3=M.ndim)
##
##    numBands, h, w = M.shape
##
##    return np.reshape(M, (numBands, w*h))
##


def convert2d(M):
    """
    Converts a 3D data cube (m x n x p) to a 2D matrix of points
    where N = m*n.

    Parameters:
        M: `numpy array`
            A HSI cube (m x n x p).

    Returns: `numpy array`
        2D data matrix (N x p)
    """

    if M.ndim != 3:
        raise RuntimeError('in formating.convert2d,  M have {0} dimension(s), expected 3 dimensions'.format(M.ndim))

    h, w, numBands = M.shape

    return np.reshape(M, (w*h, numBands))


# deprecated
# commented 2014-04-27
##def convert2d(M, sigLast=True):
##    """
##    Converts a 3D data cube (m x n x p) to a 2D matrix of points
##    where N = m*n.
##
##    Parameters:
##        M: `numpy array`
##            A HSI cube (m x n x p).
##
##        siglast: `boolean [default True]`
##            Determine if output is to be (p x N) or (N x p).
##
##    Returns: `numpy array`
##            * 2D data matrix (p x N) if sigLast=False.
##            * 2D data matrix (N x p) if sigLast=True.
##
##    Notes:
##        The sigLast=False parameter is deprecated. It give a Matlab flavor
##        where the signals are in column order. For numpy and the SPy libraries,
##        this organization is not the best.
##    """
##
##    if M.ndim != 3:
##        raise RuntimeError('in formating.convert2d,  M have {0} dimension(s), expected 3 dimensions'.format(M.ndim))
##
##    h, w, numBands = M.shape
##
##    if sigLast == False:
##        return np.reshape(M, (w*h, numBands), order='F').transpose()
##
##    if sigLast == True:
##        return np.reshape(M, (w*h, numBands))


def convert3d(N, h, w, sigLast=True):
    """
    Converts a 1D (N) or 2D matrix (p x N) or (N x p) to a 3D
    data cube (m x n x p) where N = m * n

    Parameters:
        N: `numpy array`
            1D (N) or 2D data matrix (p x N) or (N x p)

        h: `integer`
            Height axis length (or y axis) of the cube.

        w: `integer`
            Width axis length (or x axis) of the cube.

        siglast: `True [default False]`
            Determine if input N is (p x N) or (N x p).

    Returns: `numpy array`
            A 3D data cube (m x n x p)
    """

    if M.ndim > 2:
        raise RuntimeError('in formating.convert2d,  M have {0} dimension(s), expected 1 or 2 dimensions'.format(M.ndim))

    N = np.array(N)

    if sigLast == False:
        if N.ndim == 1:
            return np.reshape(N, (h, w), order='F')
        else:
            numBands, n = N.shape
            return np.reshape(N.transpose(), (h, w, numBands), order='F')

    if sigLast == True:
        if N.ndim == 1:
            return np.reshape(N, (h, w))
        else:
            numBands, n = N.shape
            return np.reshape(N.transpose(), (h, w, numBands), order='F')


def normalize(M):
    """
    Normalizes M to be in range [0, 1].

    Parameters:
      M: `numpy array`
          1D, 2D or 3D data.

    Returns: `numpy array`
          Normalized data.
    """

    minVal = np.min(M)
    maxVal = np.max(M)

    Mn = M - minVal;

    if maxVal == minVal:
        return np.zeros(M.shape);
    else:
        return Mn / (maxVal-minVal)
