JDWarner’s gists

JDWarner / dask_benchmarks.py

Created July 12, 2015 14:47

dask.array performance compared to NumPy arrays in color conversion

	import numpy as np
	import dask.array as da

	# Benchmark: NumPy with array construction included
	%timeit nparr0 = np.random.random(size=(10000, 1000)); \
	nparr1 = np.random.random(size=(10000, 1000)); \
	np.sqrt((nparr0 - nparr1) 2 + (nparr0 - nparr1) 2 + (nparr0 - nparr1) ** 2)

	# Benchmark: Dask with dask.array construction included
	%timeit x = da.random.random(size=(10000, 1000), chunks=(10000, 1000)); \

JDWarner / flood_fill.pyx

Last active October 21, 2016 22:18

Flood fill - Cython

	import numpy as np
	cimport numpy as cnp


	# Compiler directives
	@cython.cdivision(True)
	@cython.boundscheck(False)
	@cython.nonecheck(False)
	@cython.wraparound(False)
	def flood_fill(unsigned char[:, ::1] data, tuple start_coords,

JDWarner / floodfill.py

Created May 19, 2015 06:29

Flood fill - pure Python

	# Pure Python, usable speed but over 10x greater runtime than Cython version
	def fill(data, start_coords, fill_value):
	"""
	Flood fill algorithm

	Parameters
	----------
	data : (M, N) ndarray of uint8 type
	Image with flood to be filled. Modified inplace.
	start_coords : tuple

JDWarner / _jaccard.py

Last active April 20, 2024 01:38

Jaccard coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity metric, and it is a true metric. The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the modified BSD license, otherwise known as the 3-clause BSD.

	"""
	_jaccard.py : Jaccard metric for comparing set similarity.

	"""

	import numpy as np


	def jaccard(im1, im2):
	"""

JDWarner / _dice.py

Last active January 25, 2023 22:28

Dice coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity measurement (though note it is not a true metric; it does not satisfy the triangle inequality). The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the mod…

	"""
	_dice.py : Dice coefficient for comparing set similarity.

	"""

	import numpy as np


	def dice(im1, im2):
	"""

JDWarner / bezdraw.py

Created July 4, 2013 05:39 — forked from Alquimista/bezdraw.py

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

	import sys
	import math

	from PyQt4 import QtGui, QtCore


	def binomial(i, n):

JDWarner / ipyncarver.py

Created June 29, 2013 17:43 — forked from zakandrewking/ipyncarver.py

	#!/usr/bin/env python
	"""In order to use the script you need to copy your SSH key to the target server
	and also copy the server SSH public key (usually .ssh/id_rsa.pub) to .ssh/authorized_keys,
	so that the computing node can ssh passwordless to the login node"""

	from subprocess import Popen, PIPE, call
	import sys
	import webbrowser
	from getopt import getopt
	import time

JDWarner / _pad_asymmetric.py

Created May 1, 2013 05:37

Utility function for padding an array by appending `pad_amt` zeros along any `axis`. Non-symmetric. The ideas behind this snippet led to the `np.pad` improvements introduced in NumPy 1.8. This is licensed under the modified BSD.

	import numpy as np

	def _pad_asymmetric_zeros(arr, pad_amt, axis=-1):
	"""Pads `arr` by `pad_amt` along specified `axis`"""
	if axis == -1:
	axis = arr.ndim - 1

	zeroshape = tuple([x if i != axis else pad_amt
	for (i, x) in enumerate(arr.shape)])

Josh Warner JDWarner