Scripts for obtaining and comparing ESI (evaporative stress index) data from https://climateserv.servirglobal.net
SCRIPT: get_ESI_select_pt.py
OUTPUT: poly_ESI_df.csv
DESCRIPTION: This retrieves ESI data from the https://climateserv.servirglobal.net/ website for specific point locations. User can select type of ESI data (4wk or 12wk), start and end dates, polygon size around the point location.
SCRIPT: get_ESI_tif.py
OUTPUT: ESI_tif.zip
DESCRIPTION: This retrieves ESI .tif files from the https://climateserv.servirglobal.net/ website. User enters minimum and maximum decimal degree values for longitude and latitude to create a bounding box. To clip these to a more specific boundary using a .shp or .GeoJSON file, see script: ESI_tif_clip.py. When I first used climatserv, I could input a geometry. However, when I tried later, it gave me an error message about the geometry being too big. Hence, I use a simple bounding box in this script.
SCRIPT: ESI_tif_clip.py
OUTPUT: _CLIP.tif (for each input tif file)
DESCRIPTION: Clips all .tif files in the input directory to the geometry in the input shapefile.
SCRIPT: tif2select_pt
OUTPUT: ESI_tif2select_pt.csv
DESCRIPTION: Uses output .tif files from ESI_tif_clip.py to extract ESI values from specific sites as referenced from the input meta .csv file. Input metadata file must contain columns: longitude, latitude, stationTriplet. Longitude and latitude are in decimal degrees. The stationTriplet column is just a column of names for the stations - point location names.
SCRIPT: merge_esi_csv.py
OUTPUT: master_ESI_CLIP_xyz.csv
DESCRIPTION: Not a stand-alone script. See flowchart for preceeding tif2xyz.sh script which produces _CLIP.csv output files. These files contain xyz data for all pixel coordinates of the .tif files produced by the ESI_tif_clip.py script. xyz data is: long. and lat. for centroids (center of pixel boxes) and their corresponding ESI values.
SCRIPT: ESI_output_comparison.py
OUTPUT: comparison_output.txt
DESCRIPTION: Compare the output from our 2 methods of acquiring ESI data (polygon vs tif/row/col method.)
Compare values across the 8 closest pixels (user can change that value).
To get the closest pixels, I used centroids (or middle of the pixel) long/lats.
NOTE TO SELF: I should change the distance comparison using the haversine method!!!
NOTE: This is still in development for stand-alone. I may change output type as well....more to come.
climateSERV_env.yml
If you are not familiar with environments, you should get started. Here's a nice website: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
conda env create -f climateSERV_env.yml
conda activate climateSERV_env
SCAN_AL_metadata.csv - metadata file containing the longitude, latitude, and station names (stationTriplet)