lucgiffon

import random
from shapely.geometry import Point


def generate_random(number: int, polygon: gpd.GeoSeries):
    """
    Select random point in polygon based on an inscribed circle of the polygon.
    
    Parameters
    ----------

lucgiffon

export TIME='TIME Command being timed: "%C"
TIME User time (seconds): %U
TIME System time (seconds): %S
TIME Elapsed (wall clock) time : %e
TIME Percent of CPU this job got: %P
TIME Average shared text size (kbytes): %X
TIME Average unshared data size (kbytes): %D
TIME Average stack size (kbytes): %p
TIME Average total size (kbytes): %K
TIME Maximum resident set size (kbytes): %M

lucgiffon

// ==UserScript==
// @name         Ads visibility on startpage
// @description  Background-color is added to ads on the startpage site so they are made more visible.
// @version      0.1
// @author       You
// @match        https://www.startpage.com/*
// @grant        GM_addStyle
// ==/UserScript==

GM_addStyle("#adBlock > * { background-color: #e6e6ff; }");

lucgiffon

// ==UserScript==
// @name         Disable comments
// @description  Make reddit comments invisible.
// @version      0.1
// @author       You
// @grant        GM_addStyle
// @match        https://*.reddit.com/*
// @match        https://*.youtube.com/*
// @match        https://www.lefigaro.fr/*
// @match        https://www.linkedin.com/*

lucgiffon

"""https://stackoverflow.com/a/43703735/4803860"""

import tensorflow as tf
with tf.device('/gpu:0'):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)

with tf.Session() as sess:
    print (sess.run(c))

lucgiffon

if [ -x /usr/games/fortune -a -x /usr/games/cowsay ]; then
        fortune -a | cowsay -f $(ls /usr/share/cowsay/cows/ | shuf -n1)
fi

lucgiffon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

lucgiffon

import os
import struct
import numpy as np

"""
Loosely inspired by http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py
which is GPL licensed.
"""

def read(dataset = "training", path = "."):

lucgiffon

'''
Implementation of Fastfood (Le, Sarlos, and Smola, ICML 2013).

Primarily by @esc (Valentin Haenel) and felixmaximilian
from https://github.com/scikit-learn/scikit-learn/pull/3665.

Modified by @dougalsutherland.

FHT implementation was "inspired by" https://github.com/nbarbey/fht.
'''

lucgiffon

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote