bennyistanto

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

bennyistanto

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

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bennyistanto

bennyistanto

# -*- 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)

bennyistanto

# -*- 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)

bennyistanto

# -*- coding: utf-8 -*-
"""
NAME
    11_mxd13q1_viproducts_monthly.py
    Generate derivative product from Vegetation Indices
DESCRIPTION
    Input data for this script will use MXD13Q1 monthly data generate by 04_mxd13q1_8day2monthly.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)

bennyistanto

# -*- coding: utf-8 -*-
"""
NAME
    10_mxd13q1_viproducts_8day.py
    Generate derivative product from Vegetation Indices
DESCRIPTION
    Input data for this script will use MXD13Q1 8-days data generate from GEE or downloaded 
    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)

bennyistanto

bennyistanto

# -*- coding: utf-8 -*-
"""
NAME
    09_mxd13q1_stats_annual.py
    MXD13Q1 annual statistics data, long-term average, max, min and stdev
DESCRIPTION
    Input data for this script will use MXD13Q1 annual data generate from modis_8day2annual.py
    This script can do annual statistics calculation (AVERAGE, MAXIMUM, MINIMUM and STD)
REQUIREMENT
    ArcGIS must installed before using this script, as it required arcpy module.

bennyistanto

# -*- coding: utf-8 -*-
"""
NAME
    07_mxd13q1_stats_monthly.py
    MXD13Q1 monthly statistics data, long-term average, max, min and stdev
DESCRIPTION
    Input data for this script will use MXD13Q1 monthly data generate from modis_8day2monthly.py
    This script can do annual statistics calculation (AVERAGE, MAXIMUM, MINIMUM and STD)
REQUIREMENT
    ArcGIS must installed before using this script, as it required arcpy module.