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import numpy as np
from collections import defaultdict as _dd

def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------

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"""Impressive little thing how einsum + stride_tricks can beat numpys build in C functions
for correlate (for large data). (ok depending on the implementation of np.correlate, the
comparison is not fair, but still rather impressive that you can get comparable speeds)
"""

import numpy as np
import stride_tricks as st # stride_tricks.py gist

a = np.random.random((100,100,100)).ravel()
stamp = np.random.random((3,3,3)).ravel()

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def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------
    array : array_like
        Array to which the rolling window is applied.
    window : int or tuple

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import numpy as np
from numpy import asarray, add, rollaxis, sort, arange

def percentile(a, q, limit=None, interpolation='linear', axis=None,
               out=None, overwrite_input=False):
    """
    Compute the qth percentile of the data along the specified axis.

    Returns the qth percentile of the array elements.

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import numpy as np

def object_einsum(string, *arrays):
    """Simplified object einsum, not as much error checking
    
    does not support "..." or list input and will see "...", etc. as three times
    an axes identifier, tries normal einsum first!
    
    NOTE: This is untested, and not fast, but object type is
    never really fast anyway...

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New ways to index NumPy arrays

Author:	Sebastian Berg
Contact:	[email protected]
Date:	2015-07-14
Status:	draft

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#
# A Map reduce made from ufuncs and with nditer, a C-Api version
# would be very similar (but cleaner in some cases). This still creates
# some unnecessary temporaries, which may or may not slow down things a
# a bit for no reason
#
# WARNING : Mostly untested and I am not sure I would use it except to
# get the idea of how to implement a specialized version maybe.

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"""
Simply run the script to try to guess some information about how numpy
is linked.

If there is something odd going on, run/import the script after
your real import of numpy.

All versions tested on Linux, MKL is confusing me a bit since both lower and
upper case versions exist.

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Numpy Promotion Table

This table includes the promotion rules for the basic numeric types. The only suprising rule is that 8-byte integers are considered to cast "safely" to f8 (and complex), which also shows up in these promotion rules (If there was an int128, it would be allowed to safely cast to float128!):

	i1	u1	i2	u2	i4	u4	i8	u8	f2	f4	f8	f16	c8	c16	c32

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import operator

def normalize_index(index, ndim):
    """Does basic index normalization like numpy, disregards advanced indexing.
    
    Parameters
    ----------
    index : tuple
        Indexing tuple to normalize
    ndim : int