Emily Soth emlys

Nested ARGS_SPEC data types examples and specification

leaving out all other arg properties besides "type" to save space

"landcover_snapshot_csv": {
    "type": "csv",
    "structure": {
        "columns": {

Here are some examples of how to use pygeoprocessing for reclassification. Note: tested with pygeoprocessing 2.3.2.

pygeoprocessing provides the reclassify_raster function which can handle basic cases. See the docstring for details.

import pygeoprocessing

	#!/bin/bash
	# Setup for a new EC2 instance for testing Dockstore

	# Install Dockstore (from https://staging.dockstore.org/quick-start)---------------------------------------------------------

	# Install Java 11:
	sudo apt-get update
	sudo apt install -y openjdk-11-jdk

	#Install the dockstore command-line program and add it to the path.

	import numpy
	import multiprocessing


	array_in = numpy.empty(1, dtype='datetime64,f4')
	print(array_in.dtype['f0'].metadata) # prints None

	# if you use queue.Queue instead, this doesn't happen
	q = multiprocessing.Queue()
	q.put(array_in)

	from natcap.invest import carbon


	def format_spec(name, spec):
	"""Format an arg spec or subsection of an arg spec into text.

	Works for the entire args spec, or any nested dictionary within it (individual args or parts of args).
	This way, the generated content can be combined into the existing user's guide
	at whatever level makes sense for each situation.

	"""
	Script to wrap multi-line text blocks in dictionaries.
	Made this for InVEST model ARGS_SPECs "about" properties since I'm
	moving things around and it's tedious to wrap them all by hand.
	This could be generalized to any multi-line text block or an entire file.
	"""
	import ast
	import importlib
	import textwrap

	#!/bin/sh

	# post-commit hook to keep one branch updated with all the changes from another
	# so that the target branch always has a superset of the changes in the source branch
	# do this by rebasing target off of source after each commit to source

	SOURCE_BRANCH=example/generate-docs
	BRANCH_TO_REBASE=task/31/models-A-D

	# get the current branch in the format "* branch_name"

	import pygeoprocessing
	import numpy
	from osgeo import gdal, osr

	srs = osr.SpatialReference()
	srs.ImportFromEPSG(32731) # WGS84/UTM zone 31s
	projection_wkt = srs.ExportToWkt()
	arr = numpy.array([[0, 1, -1]], dtype=numpy.int16)
	base_nodata = 0
	target_datatype = gdal.GDT_Int16