Sebastian Berg seberg

seberg / new-fancy-indexing.rst

Last active August 29, 2015 14:24

NEP: New ways to index NumPy arrays

New ways to index NumPy arrays

seberg / object_einsum.py

Last active March 28, 2021 10:39

	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...

seberg / vectorized_percentile.py

Last active March 18, 2019 21:53

Vectorized version of percentile

	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.

seberg / rolling_window.py

Created October 10, 2012 14:38

Multidimensional rolling_window for numpy

	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

seberg / correlate_with_einsum+stride_tricks.py

Created August 23, 2012 00:07

Numpy tricks I stumbled upon

	"""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()

seberg / stride_tricks.py

Created August 22, 2012 22:39

numy stride tricks based functions for changing the shape and axis order of arrays.

	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
	----------