Benny Istanto bennyistanto

Custom xkcd-style map for Indonesia

Label setting for capital, country name and the ocean, and the red-dot is manually set, the value is calculate from top-left main map area.

You can paste the below code into an online Python compiler like https://python-fiddle.com/ and grab the result instantly.

xkcd style for country map

This is example of Python script that could generate a xkcd-style for Country Map.

Example:

xkcd style for LSEQM illustration

This is example of Python script that could generate a xkcd graph-style to illustrate concept of Linear Scaling and Empirical Quantile Mapping (LSEQM) method for daily precipitation bias correction.

You can paste the below code into an online Python compiler like https://python-fiddle.com/ and grab the result instantly.

version a

Skip PEARSON fitting on `climate-indices` python package

I have been using climate-indices python packages years ago, and the tool developed by James Adam is awesome, it help us to calculate various climate indices. There are two indices that I frequently produced using this tool: SPI and SPEI.

I also wrote a guideline on how to calculate these two indices using this package https://bennyistanto.github.io/spi/.

When I wrote this guideline, I never have any problem during the indices calculation. Then I noticed some people contacted me to ask about some error related to Pearson fitted also in the Github repository there are lot of [Issues](https://github.com/monocongo/climate_indices/issues?q=is%3A

Install CUDA and cuDNN using Conda

Tested on:

Windows 11 Pro for Workstations and WSL2 Debian 12
Processor: Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz 2.00 GHz (2 processors)
Installed RAM: 384 GB
VGA: NVIDIA Quadro P2000 5GB

	import xarray as xr
	import os
	import glob
	from tqdm import tqdm

	# Define the input and output directories
	input_dir = "/mnt/d/temp/imerg/data/nc4_final"
	output_dir = "/mnt/d/temp/imerg/data/fdd_1day"

	# Check if input folder exists

	<?xml version="1.0" encoding="utf-8"?>
	<!-- Modified by: Benny Istanto/KLI/G2501222008, [email protected] -->
	<!-- English version available on: https://gist.github.com/bennyistanto/ef6b1a2bb1ed303a7832f789ac7e7a70 -->
	<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only" default-locale="en-US">
	<!-- changed default-locale to en-US -->
	<info>
	<title>Institut Pertanian Bogor: Pedoman Penulisan Karya Ilmiah Edisi ke-4 (English)</title>
	<!-- updated title -->
	<title-short>IPB PPKI 4</title-short>
	<id>http://www.zotero.org/styles/institut-pertanian-bogor</id>

	# -- coding: utf-8 --
	"""
	NAME
	13_mxd13q1_viproducts_annual.py
	Generate derivative product from Vegetation Indices
	DESCRIPTION
	Input data for this script will use MXD13Q1 annual data generate by 06_mxd13q1_8day2annual.py
	This script can do calculation for ratio, difference, standardize anomaly
	and vegetation condition index.
	The calculation required timeseries VI and the long-term statistics (min, mean, max, std)

	# -- coding: utf-8 --
	"""
	NAME
	12_mxd13q1_viproducts_quarter.py
	Generate derivative product from Vegetation Indices
	DESCRIPTION
	Input data for this script will use MXD13Q1 quarterly data generate by 05_mxd13q1_8day2quarter.py
	from NASA. This script can do calculation for ratio, difference, standardize anomaly
	and vegetation condition index.
	The calculation required timeseries VI and the long-term statistics (min, mean, max, std)