James Adams monocongo

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

	import json

	# try to load the entire file as a single JSON object
	file_path = '/home/james/tmp/api_data.json'
	try:
	with open(file_path, 'r') as f:
	data = json.load(f)
	print("The file is a single JSON object.")
	except json.JSONDecodeError:
	print("The file is not a single JSON object.")

	# Copyright 2023 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,

	#!/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
	#

	import pandas as pd
	from sbpy.data import Names

	# function to create the new packed format column
	def pack_designation(
	row: pd.Series,
	) -> pd.Series:
	return pd.Series(
	data=[Names.to_packed(row.pdes)],
	index=['Principal_desig'],

	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,

	import os
	directory = "/home/james/images"
	extension = ".jpg"
	prefix = "prefix_"
	for filename in os.listdir(directory):
	if not filename.endswith(extension):
	continue
	new_filename = prefix + filename
	old_file_path = os.path.join(directory, filename)
	new_file_path = os.path.join(directory, new_filename)