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wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1958.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1959.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1960.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1961.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1962.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1963.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1964.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_ppt_1965.nc
wget -c http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/d

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"""
This code uses the rasterio library to open the two input rasters and the scipy library
to perform the focal regression using the generic_filter function. The size parameter 
specifies the size of the neighborhood around each pixel to use for the regression, 
in this case a 3x3 window. The extra_arguments parameter is used to pass the second raster 
to the np.polyfit function, which fits a polynomial of degree 1 (a linear regression) to 
the two rasters. The result is a 3D array with the slope and intercept values for each pixel. 
The code then extracts the slope and intercept arrays from the result and saves them to file 
using rasterio.

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# -*- coding: utf-8 -*-
"""
NAME
    clip_netcdf_with_shp.py
    Batch clip  NetCDF in a folder with a shapefile
DESCRIPTION
    Input data for this script will be NetCDF files and a shapefile in a folder
    This script can do batch clipping of NetCDF files, and save it in seperate folder
REQUIREMENT
    It required glob, xarray, rasterio and shapely module. So it will work on any machine environment

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# -*- coding: utf-8 -*-
"""
NAME
    taylor_diagram_outmetrics.py
    Generate Taylor Diagram from all metrics output
DESCRIPTION
    Input data for this script will use metric.csv generated by imerg_cpc_biascorrection.py.
REQUIREMENT
    It required numpy, pandas, metpy, matplotlib and xarray module. So it will work on any machine environment
EXAMPLES

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import pandas as pd

# Load the data
min_df = pd.read_csv("min.csv")
mean_df = pd.read_csv("mean.csv")
max_df = pd.read_csv("max.csv")

# Melt the data frames to long format
min_long = pd.melt(min_df, id_vars=["lon", "lat", "orig_id"], var_name="date", value_name="min")
mean_long = pd.melt(mean_df, id_vars=["lon", "lat", "orig_id"], var_name="date", value_name="mean")

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import pandas as pd

# Load the data from the output CSV file
df = pd.read_csv("output.csv")

# Pivot the data frame to wide format for the min variable
min_wide = df.pivot_table(index=["lon", "lat", "orig_id", "year", "month"], columns="variable", values="value")

# Reset the index to obtain a flat data frame
min_wide = min_wide.reset_index()

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# -*- coding: utf-8 -*-
"""
NAME
    imerg_cpc_biascorrection.py
DESCRIPTION
    Bias correction using various methods:
    i. Scale, 
    ii. Distribution, 
    iii. Delta, 
    iv. the Least Squares Composite differencing (LSC)

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# -*- coding: utf-8 -*-
"""
NAME
    ensemble_corrected_data.py
DESCRIPTION
    Ensemble bias correction output using various methods:
    i. Decision Tree
    ii. Bayesian
    iii. Random Forest
    iv. Neural Network

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# -*- coding: utf-8 -*-
"""
NAME
    02_mxd13q1_stats_8day.py
    MXD13Q1 8-days statistics data, long-term average, max, min and stdev
DESCRIPTION
    Input data for this script will use MXD13Q1 8-days data generate from GEE or downloaded from NASA
    This script can do 8-days statistics calculation (AVERAGE, MAXIMUM, MINIMUM and STD)
REQUIREMENT
    ArcGIS must installed before using this script, as it required arcpy module.

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# -*- coding: utf-8 -*-
"""
NAME
    spei_monthly_blend.py
    Global TerraClimate's SPEI-based dry/wet indicators blend.
DESCRIPTION
    Input data for this script will use TerraClimate SPEI monthly data in GeoTIFF format
    This experimental SPEI blends integrate several SPEI scales into a single product.
    The combines 3-, 6-, 9-, 12-, and 24-month SPEI to estimate the overall dry/wet conditions.
METHOD