(It's not a file, do this before using)
- Have D3.js v4 implement in your project first.
- npm install --save d3-sankey
npm install --save @types/d3-sankey
(It's not a file, do this before using)
npm install --save @types/d3-sankey
# Copyright (c) Meta Platforms, Inc. All Rights Reserved | |
import math | |
import os | |
import signal | |
import sys | |
import traceback | |
from pathlib import Path | |
from random import randint | |
import datetime |
# Copyright (c) Meta Platforms, Inc. All Rights Reserved | |
import math | |
import numpy as np | |
import scipy | |
import torch.multiprocessing | |
from pathlib import Path | |
import torch | |
import hydra |
See how a minor change to your commit message style can make a difference.
Tip
Have a look at git-conventional-commits , a CLI util to ensure these conventions and generate verion and changelogs
<?php | |
/** | |
* Add rest api endpoint for category listing | |
*/ | |
/** | |
* Class Category_List_Rest | |
*/ | |
class Category_List_Rest extends WP_REST_Controller { | |
/** |
For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.
Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon
with HyperThreading enabled, but it can work without problem on slower machines.
You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.
Paprika doesn't have their API documented, so this is me reverse-engineering it from an Android device
#!/usr/bin/env bash | |
# Abort sign off on any error | |
set -e | |
# Start the benchmark timer | |
SECONDS=0 | |
# Repository introspection | |
OWNER=$(gh repo view --json owner --jq .owner.login) |
The Gilbert–Johnson–Keerthi (GJK) distance algorithm is a method of determining the minimum distance between two convex sets. The algorithm's stability, speed which operates in near-constant time, and small storage footprint make it popular for realtime collision detection.
Unlike many other distance algorithms, it has no requirments on geometry data to be stored in any specific format, but instead relies solely on a support function to iteratively generate closer simplices to the correct answer using the Minkowski sum (CSO) of two convex shapes.