mikhailov-work

# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0

# Author: Anton Mikhailov

turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913

johnburnmurdoch

# Prepare world data

# First up, we need to load the built-up area data that we’re going to be plotting. We download this from the European Commission’s Global Human Settlement Data portal [https://ghsl.jrc.ec.europa.eu/datasets.php] — specifically using the links from this page [http://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_BUILT_LDSMT_GLOBE_R2015B/]. We want the 250m-resolution rasters for 1975 and 2015 (GHS_BUILT_LDS1975_GLOBE_R2016A_54009_250 and GHS_BUILT_LDS2014_GLOBE_R2016A_54009_250).

# Once you’ve downloaded these (they’re BIG, so might take a little while...), we can save ourselves a lot of hassle later on by re-projecting them into the same co-ordinate space as the other data we’re going to be using. Specifically we want to change their units from metres to lat/lon. We do this by:
# 1) Unzipping the archive, and then
# 2) Running the following script on the command-line:

# gdalwarp -t_srs EPSG:4326 -tr 0.01 0.01 path/to/your/built-up-area.tif path/to/your/built-up-area_reprojected.

gka

var multiCrowbar = (function() {
    /*
     * SVG Export
     * converts html labels to svg text nodes
     * will produce incorrect results when used with multi-line html texts
     *
     * Author: Gregor Aisch
     * based on https://github.com/NYTimes/svg-crowbar/blob/gh-pages/svg-crowbar-2.js
     */

jasonrdsouza

'''
This script performs efficient concatenation of files stored in S3. Given a
folder, output location, and optional suffix, all files with the given suffix
will be concatenated into one file stored in the output location.

Concatenation is performed within S3 when possible, falling back to local
operations when necessary.

Run `python combineS3Files.py -h` for more info.
'''

callumlocke

/*
  UPDATED for 2023 - Now much simpler. The old tricks are no longer needed.

  The following code makes an 800×600 canvas that is always as sharp as possible for the device.

  You still draw on it as if it's the logical size (800×600 in this case), but everything just
  looks sharper on high-DPI screens. Regular non-sharp screens are not affected.
*/

const width = 800

prophetgoddess

# The MIT License (MIT)

# Copyright (c) 2014 Lycaon (lycaon.me)

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

briantjacobs

Storytelling from Space: Tools/Resources

This list of resources is all about acquring and processing aerial imagery. It's generally broken up in three ways: how to go about this in Photoshop/GIMP, using command-line tools, or in GIS software, depending what's most comfortable to you. Often these tools can be used in conjunction with each other.

Acquiring Landsat & MODIS

Web Interface

• USGS Earth Explorer - Browser and data access (create a login)
  http://earthexplorer.usgs.gov/

• Landsat archive

dwtkns

# This is a shell function to quickly grab the data for a given Landsat 8 tile ID from Google's servers
# For example:
# l8get LC81690352014169LGN00

# The echo at the end is to remind myself of the syntax for extracting bands 8, 4, 3, and 2. (Pansharp, Red, Green, Blue)

# On OSX this would go into your ~/.bash_profile file.
# Requires gsutil from https://developers.google.com/storage/docs/gsutil_install
# Most useful in conjunction with USGS' Earth Explorer: http://earthexplorer.usgs.gov/

patriciogonzalezvivo

Please consider using http://lygia.xyz instead of copy/pasting this functions. It expand suport for voronoi, voronoise, fbm, noise, worley, noise, derivatives and much more, through simple file dependencies. Take a look to https://github.com/patriciogonzalezvivo/lygia/tree/main/generative

Generic 1,2,3 Noise

float rand(float n){return fract(sin(n) * 43758.5453123);}

float noise(float p){
	float fl = floor(p);
  float fc = fract(p);

andrewxhill

Part 2

Gilt data

The data

This dataset contains a selection of Gilt transaction data

SELECT count(*) FROM gilt_purchase_data