Settings: System Preferences » Keyboard » Key Repeat/Delay Until Repeat
Use the commands below to increase the key repeat rate on macOS beyond the possible settings via the user interface. The changes aren't applied until you restart your computer.
import json | |
import pyarrow as pa | |
import pyarrow.parquet as pq | |
import pandas as pd | |
import numpy as np | |
def main(): | |
# Create random point data within bbox | |
size = 1_000_000 |
Settings: System Preferences » Keyboard » Key Repeat/Delay Until Repeat
Use the commands below to increase the key repeat rate on macOS beyond the possible settings via the user interface. The changes aren't applied until you restart your computer.
zoom_level | tolerance |
---|
0 | 78271.516953125
1 | 39135.7584765625
2 | 19567.8792382812
3 | 9783.93961914062
4 | 4891.96980957031
5 | 2445.98490478516
6 | 1222.99245239258
7 | 611.496226196289
import rasterio | |
import click | |
@click.command() | |
@click.argument("rgba_path") | |
@click.argument("out_path") | |
@click.option("--internal-mask/--external-mask", default=True) | |
@click.option("--ycbcr", default=False, is_flag=True) | |
@click.option("--use-blocks/--no-use-blocks", default=True) |
With the release of Node 6.0.0, the surface of code that needs transpilation to use ES6 features has been reduced very dramatically.
This is what my current workflow looks like to set up a minimalistic and fast microservice using micro and async
+ await
.
# Parses maps.stamen.com log files | |
# 99% of the code comes from http://pymotw.com/2/multiprocessing/mapreduce.html | |
# Another useful article can be found here, http://effbot.org/zone/wide-finder.htm | |
# Per file processing time could be improved with threads I imagine | |
# roughly 2k-3k files totaling ~ 150mb takes about 30min | |
""" | |
Usage: | |
1. Download log files from s3 into local dated directories with your weapon of choice. |
These instructions will get you up and running with Deis and CoreOS in a AWS VPC. There already exists a CloudFormation script to get up and running in AWS. But if you want to get down and dirty, this document will help walk you through the steps.
I tried to build these instructions for both the AWS web console and equivilent AWS CLI. If you find errors, please feel free to comment so I can update them. You can also find me in the #deis IRC channel on Freenode if you have questions.
Since we will be running CoreOS and Deis inside a VPC we need some AWS setup first. We need a single subnet VPC, some security groups, and instances of CoreOS.
A lightweight node port of websocketd, originally written in go.
node-websocketd --port=8080 ./count.sh
var perlin = require('perlin').noise.perlin3 | |
var fill = require('ndarray-fill') | |
var zeros = require('zeros') | |
var scale = 0.075 | |
var threshold = 0.125 | |
// Untested in 3D, but "theoretically" this should | |
// work. Using the equivalent 2d getter with the | |
// continuous-box2d demo works well. |
#!/usr/bin/env python | |
''' Convert a series of PNG images to a sparse bundle disk image. | |
Image file names are passed in as command line arguments. | |
Example: | |
ls *.png | xargs deimgify.py | |
Output: | |
Goodtimes.sparsebundle/Info.bckup |