from itertools import tee
import numpy as np
import scipy.interpolate as intp
from scipy.signal import savgol_filter

def get_edge_bin(array):
    """Detect the edge indcies of a binary 1-D array.

    Args:
        array (:class:`numpy.ndarray`): A list or Numpy 1d array, with binary
            (0/1) or boolean (True/False) values.

    Returns:
        list: A list containing starting and ending indices of the non-zero
            blocks.

    Examples:

        .. code-block:: python

            >>> a = [0,1,1,0,0,0,1,0,1]
            >>> get_edge_bin(a)
            [(1, 3), (6, 7), (8, 9)]
            >>> b = [True, False, True, True, False, False]
            >>> get_edge_bin(b)
            [(0, 1), (2, 4)]
    """
    array1 = np.int64(array)
    array1 = np.insert(array1, 0, 0)
    array1 = np.append(array1, 0)
    tmp = array1 - np.roll(array1, 1)
    i1_lst = np.nonzero(tmp == 1)[0] - 1
    i2_lst = np.nonzero(tmp ==-1)[0] - 1
    return list(zip(i1_lst, i2_lst))

def get_local_minima(x, window=None):
    """Get the local minima of a 1d array in a window.

    Args:
        x (:class:`numpy.ndarray`): A list or Numpy 1d array.
        window (*int* or :class:`numpy.ndarray`): An odd integer or a list of
            odd integers as the lengthes of searching window.
    Returns:
        tuple: A tuple containing:

            * **index** (:class:`numpy.ndarray`): A numpy 1d array containing 
              indices of all local minima.
            * **x[index]** (:class:`numpy.ndarray`): A numpy 1d array containing
              values of all local minima.

    """
    x = np.array(x)
    dif = np.diff(x)
    ind = dif > 0
    tmp = np.logical_xor(ind, np.roll(ind,1))
    idx = np.logical_and(tmp,ind)
    index = np.where(idx)[0]
    if window is None:
        # window is not given
        return index, x[index]
    else:
        # window is given
        if isinstance(window, int):
            # window is an integer
            window = np.repeat(window, len(x))
        elif isinstance(window, np.ndarray):
            # window is a numpy array
            #if np.issubdtype(window.dtype, int):
            if window.dtype.type in [np.int16, np.int32, np.int64]:
                pass
            else:
                # window are not integers
                print('window array are not integers')
                raise ValueError
        else:
            raise ValueError

        if 0 in window%2:
            # not all of the windows are odd
            raise ValueError

        halfwin_lst = (window-1)//2
        index_lst = []
        for i in index:
            halfwin = halfwin_lst[i]
            i1 = max(0, i-halfwin)
            i2 = min(i+halfwin+1, len(x))
            if i == x[i1:i2].argmin() + i1:
                index_lst.append(i)
        if len(index_lst)>0:
            index_lst = np.array(index_lst)
            return index_lst, x[index_lst]
        else:
            return np.array([]), np.array([])

def implete_none(lst):
    """Replace the None elemnets at the beginning and the end of list by auto
    increment integers.
    
    Convert the first and last few `None` elements to auto increment integers.
    These integers are determined by the first and last integers in the input
    array.
    While the `None` elements between two integers in the input list will
    remain.

    Args:
        lst (list): A list contaning None values.
    Returns:
        newlst (list): A list containing auto increment integers.
	
    Examples:
        .. code-block:: python

            >>> a = [None,None,3,4,None,5,6,None,None]
            >>> implete_none(a)
            [1, 2, 3, 4, None, 5, 6, 7, 8]

    """
    # filter the None values
    notnone_lst = [v for v in lst if v is not None]
    for i, v in enumerate(lst):
        if v == notnone_lst[0]:
            # first not-None element and its index
            notnone1 = i
            value1   = v
        if v == notnone_lst[-1]:
            # last not-None element and its index
            notnone2 = i
            value2   = v
    newlst = []
    for i,v in enumerate(lst):
        if i < notnone1:
            newlst.append(value1-(notnone1-i))
        elif i > notnone2:
            newlst.append(value2+(i-notnone2))
        else:
            newlst.append(v)
    return newlst


def derivative(*args, **kwargs):
    """Get the first derivative of data arrays (*x*, *y*).

    If **y** is not given, the first argument will be taken as **y**, and the
    differential of the input array will be returned.

    Args:
        x (list or :class:`numpy.ndarray`): X-values of the input array (optional).
        y (list or :class:`numpy.ndarray`): Y-values of the input array.
        points (int): Number of points used to calculate derivative
            (optional, default is 3).

    Returns:
        :class:`numpy.ndarray`: Derivative of the input array.
    """
    if len(args) == 1:
        y = np.array(args[0], dtype=np.float64)
        x = np.arange(y.size)
    elif len(args) == 2:
        x = np.array(args[0], dtype=np.float64)
        y = np.array(args[1], dtype=np.float64)
    else:
        raise ValueError
    npts = x.size
    points = kwargs.pop('points', 3)
    if points == 3:
        der = (np.roll(y,-1) - np.roll(y,1))/(np.roll(x,-1) - np.roll(x,1))
        a = np.array([-3., 4., -1.])
        der[0]  = (a*y[0:3]).sum() / (a*x[0:3]).sum()
        der[-1] = (-a[::-1]*y[-3:]).sum() / (-a[::-1]*x[-3:]).sum()
        return der
    else:
        raise ValueError

def pairwise(array):
    """Return pairwises of an iterable arrary.

    Args:
        array (list or :class:`numpy.ndarray`): The input iterable array.
    Returns:
        :class:`zip`: zip objects.
    """
    a, b = tee(array)
    next(b, None)
    return zip(a, b)

def smooth(array, points, deg):
    """Smooth an array.

    Args:
        array (:class:`numpy.ndarray`): Input array.
        points (int): Points of smoothing.
        deg (int): Degree of smoothing.

    Returns:
        :class:`numpy.ndarray`: smoothed array

    """
    n = array.size
    if points == 5:
        if deg == 2:
            w_2 = np.array([31.,  9., -3., -5.,  3.])/35.
            w_1 = np.array([ 9., 13., 12.,  6., -5.])/35.
            w_0 = np.array([-3., 12., 17., 12., -3.])/35.
        elif deg == 3:
            w_2 = np.array([69.,  4., -6.,  4., -1.])/70.
            w_1 = np.array([ 2., 27., 12., -8.,  2.])/35.
            w_0 = np.array([-3., 12., 17., 12., -3.])/35.

        a = np.zeros((n, n))
        a[0, 0:5] = w_2
        a[1, 0:5] = w_1
        for i in np.arange(2, n-2):
            a[i, i-2:i+3] = w_0
        a[-2, -5:] = w_1[::-1]
        a[-1, -5:] = w_2[::-1]

    result = np.matrix(a)*np.matrix(array.reshape(-1,1))
    return np.array(result)[:,0]


def iterative_savgol_filter(y, winlen=5, order=3, maxiter=10,
        upper_clip=None, lower_clip=None):
    """Smooth the input array with Savitzky-Golay filter with lower and/or
    upper clippings.

    Args:
        y (:class:`numpy.ndarray`): Input array.
        winlen (int): Window length of Savitzky-Golay filter.
        order (int): Order of Savitzky-Gaoly filter.
        maxiter (int): Maximum number of iterations.
        lower_clip (float): Lower sigma-clipping value.
        upper_clip (float): Upper sigma-clipping value.

    Returns:
        tuple: A tuple containing:

            * **ysmooth** (:class:`numpy.ndarray`) – Smoothed y values.
            * **yres** (:class:`numpy.ndarray`) – Residuals of y values.
            * **mask** (:class:`numpy.ndarray`) – Mask of y values.
            * **std** (float) – Standard deviation.
    """
    x = np.arange(y.size)
    mask = np.ones_like(y, dtype=np.bool)

    for ite in range(maxiter):

        # fill masked values in y using interpolation
        f = intp.InterpolatedUnivariateSpline(x[mask], y[mask], k=3)
        ysmooth = savgol_filter(f(x), window_length=winlen, polyorder=order)
        yres = y - ysmooth
        std = yres[mask].std()

        # generate new mask
        # make a copy of existing mask
        new_mask = mask * np.ones_like(mask, dtype=np.bool)
        # give new mask with lower and upper clipping value
        if lower_clip is not None:
            new_mask *= (yres > -lower_clip * std)
        if upper_clip is not None:
            new_mask *= (yres < upper_clip * std)

        if new_mask.sum() == mask.sum():
            break
        mask = new_mask

    return ysmooth, yres, mask, std

numpy.array numpy.ones_like numpy.matrix numpy.zeros numpy.roll numpy.logical_and numpy.diff numpy.nonzero numpy.where numpy.arange numpy.append numpy.int64 numpy.insert