effigies

Contemporary Python Packaging

This document lays out a set of Python packaging practices. I don't claim they are best practices, but they fit my needs, and might fit yours.

Validity

This document has been superseded as of January 2023.

This was written in July 2020, superseding this gist from 2019.

emdupre

"""
Functions to identify TE-dependent and TE-independent components.
"""
import json
import logging
import pickle

from nilearn._utils import check_niimg
import numpy as np
from scipy import stats

chrisgorgo

vickyqian

import tweepy
import csv
import pandas as pd
####input your credentials here
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)

joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

cawhitworth

import random

add = lambda a,b: a+b
sub = lambda a,b: a-b
mul = lambda a,b: a*b
div = lambda a,b: a/b if a % b == 0 else 0/0

operations = [ (add, '+'),
               (sub, '-'),
               (mul, '*'),

hit9

class Color(object):
    """
     utility to return ansi colored text.
    """

    colors = {
        'black': 30,
        'red': 31,
        'green': 32,
        'yellow': 33,

fajrif

git clone <repo-address>

git tag -l

git checkout <tag-name>

git branch -D master

git checkout -b master

brantfaircloth

# assume the following directory structure where contents of doc/ 
# and source/ are already checked into repo., with the exception 
# of the _build directory (i,e. you can check in _themes or _sources 
# or whatever else).
#
# proj/
#    source/
#    doc/

# remove doc/_build/html if present

grmwld

#!/usr/bin/env python2.7

import scipy.ndimage as ndimage
import numpy

def local_maxima(array, min_distance = 1, periodic=False, edges_allowed=True): 
    """Find all local maxima of the array, separated by at least min_distance."""
    array = numpy.asarray(array)
    cval = 0 
    if periodic: