#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2017    Sining Sun

from __future__ import absolute_import

import sys, os, time
import pprint
import numpy as np
import warnings
import tensorflow as tf
import tensorflow.contrib.slim as slim

pp = pprint.PrettyPrinter()


def show_all_variables():
    model_vars = tf.trainable_variables()
    slim.model_analyzer.analyze_vars(model_vars, print_info=True)
    sys.stdout.flush()


def mkdir_p(path):
    """ Creates a path recursively without throwing an error if it already exists

    :param path: path to create
    :return: None
    """
    if not os.path.exists(path):
        os.makedirs(path)
        

def segment_axis(a, length, overlap=0, axis=None, end='cut', endvalue=0):
    """Generate a new array that chops the given array along the given axis into overlapping frames.

    example:
    >>> segment_axis(np.arange(10), 4, 2)
    array([[0, 1, 2, 3],
           [2, 3, 4, 5],
           [4, 5, 6, 7],
           [6, 7, 8, 9]])

    arguments:
    a       The array to segment
    length  The length of each frame
    overlap The number of array elements by which the frames should overlap
    axis    The axis to operate on; if None, act on the flattened array
    end     What to do with the last frame, if the array is not evenly
            divisible into pieces. Options are:

            'cut'   Simply discard the extra values
            'wrap'  Copy values from the beginning of the array
            'pad'   Pad with a constant value

    endvalue    The value to use for end='pad'

    The array is not copied unless necessary (either because it is
    unevenly strided and being flattened or because end is set to
    'pad' or 'wrap').
    """

    if axis is None:
        a = np.ravel(a)  # may copy
        axis = 0

    l = a.shape[axis]

    if overlap >= length: raise ValueError(
        "frames cannot overlap by more than 100%")
    if overlap < 0 or length <= 0: raise ValueError(
        "overlap must be nonnegative and length must be positive")

    if l < length or (l - length) % (length - overlap):
        if l > length:
            roundup = length + (1 + (l - length) // (length - overlap)) * (
                    length - overlap)
            rounddown = length + ((l - length) // (length - overlap)) * (
                    length - overlap)
        else:
            roundup = length
            rounddown = 0
        assert rounddown < l < roundup
        assert roundup == rounddown + (length - overlap) or (
                roundup == length and rounddown == 0)
        a = a.swapaxes(-1, axis)

        if end == 'cut':
            a = a[..., :rounddown]
        elif end in ['pad', 'wrap']:  # copying will be necessary
            s = list(a.shape)
            s[-1] = roundup
            b = np.empty(s, dtype=a.dtype)
            b[..., :l] = a
            if end == 'pad':
                b[..., l:] = endvalue
            elif end == 'wrap':
                b[..., l:] = a[..., :roundup - l]
            a = b

        a = a.swapaxes(-1, axis)

    l = a.shape[axis]
    if l == 0: raise ValueError(
        "Not enough data points to segment array in 'cut' mode; try 'pad' or 'wrap'")
    assert l >= length
    assert (l - length) % (length - overlap) == 0
    n = 1 + (l - length) // (length - overlap)
    s = a.strides[axis]
    newshape = a.shape[:axis] + (n, length) + a.shape[axis + 1:]
    newstrides = a.strides[:axis] + ((length - overlap) * s, s) + a.strides[axis + 1:]

    if not a.flags.contiguous:
        a = a.copy()
        newstrides = a.strides[:axis] + ((length - overlap) * s, s) + a.strides[axis + 1:]
        return np.ndarray.__new__(np.ndarray, strides=newstrides, shape=newshape, buffer=a, dtype=a.dtype)

    try:
        return np.ndarray.__new__(np.ndarray, strides=newstrides, shape=newshape, buffer=a, dtype=a.dtype)
    except TypeError or ValueError:
        warnings.warn("Problem with ndarray creation forces copy.")
        a = a.copy()
        # Shape doesn't change but strides does
        newstrides = a.strides[:axis] + ((length - overlap) * s, s) + a.strides[axis + 1:]
        return np.ndarray.__new__(np.ndarray, strides=newstrides, shape=newshape, buffer=a, dtype=a.dtype)
