mappingvermont

mappingvermont

mappingvermont

bash one-liners

get list of GLAD geostores in the last 30 days
gcloud logging read "(resource.type="container" AND resource.labels.cluster_name="wri-prod" AND resource.labels.namespace_id="default" AND resource.labels.container_name="forest-change" AND textPayload:(geostore))" --freshness 30d | grep glad | grep -v .py | grep -o 'geostore=.*' | cut -f2- -d= | cut -d'&' -f1 | sort | uniq

now need to do the same except for download from that dataset - download/3bec70f3-136b-4046-80c6-2a05afd741c6

gcloud logging read "(resource.type="container" AND resource.labels.cluster_name="wri-prod" AND resource.labels.namespace_id="default" AND logName="projects/resource-watch/logs/document" AND jsonPayload.src.func = "toSQLMiddleware" AND ("geostore" AND "lat"))" --freshness 30d | grep geostore

# gdal_translate

mappingvermont

mappingvermont

mappingvermont

mappingvermont

import json


def main():

    tile_list = ['000E_00N_010E_10N', '000E_10N_010E_20N', '000E_10S_010E_00N', '000E_20N_010E_30N', '010E_00N_020E_10N', '010E_10N_020E_20N', '010E_10S_020E_00N', '010E_20N_020E_30N', '010E_20S_020E_10S', '010E_30S_020E_20S', '010W_00N_000E_10N', '010W_10N_000E_20N', '010W_20N_000E_30N', '020E_00N_030E_10N', '020E_10N_030E_20N', '020E_10S_030E_00N', '020E_20N_030E_30N', '020E_20S_030E_10S', '020E_30S_030E_20S', '020W_00N_010W_10N', '020W_10N_010W_20N', '020W_20N_010W_30N', '030E_00N_040E_10N', '030E_10N_040E_20N', '030E_10S_040E_00N', '030E_20N_040E_30N', '030E_20S_040E_10S', '030E_30S_040E_20S', '030W_10N_020W_20N', '040E_00N_050E_10N', '040E_10N_050E_20N', '040E_10S_050E_00N', '040E_20N_050E_30N', '040E_20S_050E_10S', '040E_30S_050E_20S', '050E_00N_060E_10N', '050E_10N_060E_20N', '050E_10S_060E_00N', '050E_20N_060E_30N', '050E_20S_060E_10S', '050E_30S_060E_20S']

    out_gj = {"type":"FeatureCollection","features":[]}

mappingvermont

import subprocess

import xarray as xr


# convert input tifs to netcdf
subprocess.check_call(['gdal_translate', '-of', 'NetCDF', 'biomass.tif', 'biomass.nc'])
subprocess.check_call(['gdal_translate', '-of', 'NetCDF', 'extent.tif', 'extent.nc'])

# rename bands to match data

mappingvermont

import numpy as np
import rasterio



# open loss and grab metadata about the tif,
# like it's location / projection / cellsize
with rasterio.open('loss.tif') as loss_src:
    kwargs = loss_src.meta

mappingvermont

import os
import errno
import sys

import pandas as pd

input_csv = sys.argv[1]
adm_level = sys.argv[2]
csv_name = os.path.splitext(os.path.basename(input_csv))[0]