Christian J. Tai Udovicic cjtu

Unscaling a DEM with GDAL (gdal_translate plus a VRT)

Some image data like the Moon SLDEMs from https://pgda.gsfc.nasa.gov/products/67 have scale factors applied for image compression. The slope images are supplied as Int format and the label states to multiply by scale of 0.0015 and offest of 45 to convert to slope in degrees.

Often this metadata is machine readable and applied automatically but in this case QGIS doesn't see it.

To manually attach the scale/offset with gdal we can make a VRT (a simple "virtual" image that doesn't copy the original but points to it and adds this metadata):

gdal_translate -a_scale 0.0015 -a_offset 45 SLDEM2015_64_SL_90S_90N_000_360.JP2 tmp.vrt

How to make a repo from a template with CookieCutter

Sign up for a GitHub account here
Install cookiecutter with pip/conda/apt-get instructions here
Choose a cookiecutter template (search on GitHub here)
- The one I showed was https://mgancita.github.io/cookiecutter-pypackage/ which does the automatic GitHub-Pages, testing, linting, and more
Get the link to the cookiecutter repo that ends in .git (should be in the template install instructions)
Run cookiecutter reponame.git (e.g. cookiecutter https://github.com/mgancita/cookiecutter-pypackage.git)
Follow the prompts to set the options for your new template folder (your name, the repo name, etc.)
Change directory to your new empty template (cd new-repo-name)

Trunkify

Tl;DR

Run trunkify.sh <GH_username> to rename the master branch of all repos in a directory.
You need to update the default branch for each repo on GitHub (follow urls printed out).

More instructions

First download the .trunkify.sh script and place it in a folder that has git repo(s) in it (e.g. your projects/ folder, NOT in a Git repo itself).

	channels:
	- conda-forge
	dependencies:
	- statsmodels
	- numpy
	- matplotlib

	# Built-in dependencies
	import os
	import subprocess

	# External dependencies (requires install, e.g. with conda)
	import h5py
	import numpy as np

	# Uncomment and edit this if your Davinci interpreter is not at '/usr/local/bin/davinci'
	# DAVINCI_PATH = "/path/to/bin/davinci"

	# Built-in dependencies
	import os
	import subprocess

	# External dependencies (requires install, e.g. with conda)
	import h5py
	import numpy as np

	# Uncomment and edit this if your Davinci interpreter is not at '/usr/local/bin/davinci'
	# DAVINCI_PATH = "/path/to/bin/davinci"