James Adams monocongo

Automated Terraform Deployments

Requirements

AWS Account
AWS API User API Key/Secret
Bitbucket Account

Setup

Create or update an existing Bitbucket repo
Open https://bitbucket.org/USERNAME/REPO/addon/pipelines/deployments

Calculate SPI using monthly rainfall data in GeoTIFF format

These last few months, I have tried a lot of difference formulation to calculate Standardized Precipitation Index (SPI) based on rainfall data in netCDF format, check below files as a background:

SPI using IMERG netCDF (notebook)
SPI using CHIRPS netCDF (how-to guideline)

The reason why I use rainfall in netCDF format in above files because the software to calculate SPI: climate-indices python package will only accept single netCDF as input, and the SPI script will read the netCDF input file based on time dimension.

Converting raster files into netCDF is easy using GDAL or other GIS software, but to make the time dimension enabled n

	# United States Postal Service (USPS) abbreviations.
	abbreviations = [
	# https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States#States.
	"AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DE", "FL", "GA", "HI", "IA",
	"ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN", "MO",
	"MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", "OH", "OK",
	"OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VA", "VT", "WA", "WI",
	"WV", "WY",
	# https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States#Federal_district.
	"DC",

	sudo apt-get update
	sudo apt-get upgrade

	# Added by me
	sudo apt-get install freeglut3 freeglut3-dev libtbb-dev libqt4-dev
	# Copied from pyimagesearch.com
	sudo apt-get install build-essential cmake git pkg-config
	sudo apt-get install libjpeg8-dev libtiff4-dev libjasper-dev libpng12-dev
	sudo apt-get install libgtk2.0-dev
	sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev

	from pyspark.mllib.stat import Statistics
	import pandas as pd

	# result can be used w/ seaborn's heatmap
	def compute_correlation_matrix(df, method='pearson'):
	# wrapper around
	# https://forums.databricks.com/questions/3092/how-to-calculate-correlation-matrix-with-all-colum.html
	df_rdd = df.rdd.map(lambda row: row[0:])
	corr_mat = Statistics.corr(df_rdd, method=method)
	corr_mat_df = pd.DataFrame(corr_mat,

	'''
	Using OpenCV takes a mp4 video and produces a number of images.

	Requirements
	----
	You require OpenCV 3.2 to be installed.

	Run
	----
	Open the main.py and edit the path to the video. Then run:

	import sys
	import logging
	from typing import Optional, Dict

	from colorama import Fore, Back, Style


	class ColoredFormatter(logging.Formatter):
	"""Colored log formatter."""

	#!/usr/bin/env bash

	#
	# Licensed to the Apache Software Foundation (ASF) under one or more
	# contributor license agreements. See the NOTICE file distributed with
	# this work for additional information regarding copyright ownership.
	# The ASF licenses this file to You under the Apache License, Version 2.0
	# (the "License"); you may not use this file except in compliance with
	# the License. You may obtain a copy of the License at
	#

	# Copyright 2024 Gordon D. Thompson, [email protected]
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,