| # -*- coding: utf-8 -*- | |
| """ | |
| NAME | |
| modis_viproducts.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) |
| # -*- coding: utf-8 -*- | |
| """ | |
| NAME | |
| spatiotemporal_regression_arcpy.py | |
| DESCRIPTION | |
| Perform a focal linear regression between two sets of timeseries raster | |
| REQUIREMENT | |
| ArcGIS must installed before using this script, as it required arcpy module. | |
| EXAMPLES | |
| \\arcgispro-py3\\python spatiotemporal_regression_arcpy.py |
| # -*- coding: utf-8 -*- | |
| """ | |
| NAME | |
| spatiotemporal_regression.py | |
| DESCRIPTION | |
| Perform a focal linear regression between two sets of timeseries raster | |
| REQUIREMENT | |
| It required os, numpy, scipy, rasterio and sklearn module. | |
| So it will work on any machine environment. | |
| HOW-TO USE |
This section will explain on how to install the Weather Research and Forecasting (WRF) Model inside Windows Subsystem for Linux (WSL) 2. This step-by-step guide was tested using Windows 11 with WSL2 - Debian 12 enabled running on ThinkStation P720.
--
Benny Istanto, Climate Geographer
GOST/DECSC/DECDG, The World Bank
| #!/usr/bin/python | |
| """ | |
| NAME | |
| csv_concat.py | |
| Concatenate csv files by column, and use the date on filename as column name | |
| DESCRIPTION | |
| Input data for this script will use any csv, which must have lon, lat and value (no header) | |
| All files required to follow the naming convention "_yyyymmdd.csv" | |
| USAGE | |
| python csv_concat.py <csv_dir> |
| # -*- coding: utf-8 -*- | |
| """ | |
| NAME | |
| 03_mxd13q1_fillnullwithstats.py | |
| Filling null on MXD13Q1 data with long-term mean | |
| DESCRIPTION | |
| Input data for this script will use MXD13Q1 16-days data generate from GEE or downloaded from NASA | |
| This script can do filling null on MXD13Q1 data with long-term mean | |
| REQUIREMENT | |
| ArcGIS must installed before using this script, as it required arcpy module. |
PyCPT is a Python interface and enhancement for the command line version of the International Research Institute for Climate and Society's Climate Predictability Tool (CPT), for seasonal and sub-seasonal skill assessment and forecast experiments.
This notes is describing on how to use PyCPT s2sv1.92 and seav1.92 for Subseasonal and Seasonal Forecasting in Indonesia
This section will explain on how to install the PyCPT inside Windows Subsystem for Linux (WSL) 2. This step-by-step guide was tested using Windows 11 with WSL2 - Ubuntu 22 enabled running on Thinkpad T480 2019, i7-8650U 1.9GHz, 64 GB 2400 MHz DDR4.
| # -*- coding: utf-8 -*- | |
| """ | |
| NAME | |
| precip_improvement_plots.py | |
| Generate plots on improvements daily rainfall after the bias correction | |
| DESCRIPTION | |
| Input data for this script will use the gauge and satellite-based precipitation | |
| estimates data, and corrected precipitation from bias-correction process. | |
| REQUIREMENT | |
| It required numpy, pandas, scipy, matplotlib, seaborn and xarray module. |