richpsharp

var grassland_fragmentation = function(aoi, year)
{
  var global_pasture_watch = ee.ImageCollection('projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c');
  
  var clipped_gpw = global_pasture_watch.filterDate(year + '-01-01', (year + 1) + '-01-01').first().clip(aoi);
  // Anything > 1 is grassland "i.e. rangeland"
  var grassland = clipped_gpw.gte(1);
  //original code made a 7x7 square kernel on 10m, so we're doing a 3x3 for 30m as close enough

richpsharp

"""Script to process Danny's RDS file into something that can be analyzed by the PCLD."""

import pyreadr
import geopandas as gpd

PROJECTION_EPSG = 32610
FILE_PATH = "Karp_LU_UseCase.RDS"


def main():

richpsharp

import pandas as pd

datasets={
    "PH_Top 10% of service overlap for _PH conservation": {
        'fig_title' :"Top 10% of service overlap for , PH conservation",
        'country' :"PH",
        'labels' :["1 service","2 services","3 services","4 services"],
        'visParams':{
            'min':1,
            'max':4,

richpsharp

import pandas as pd

repeating_dec_12ths = ['0833', '1666', '3333', '4166', '5833', '6666', '8333', '9166']

# 0.0833 - 1/12
# 0.1666 - 1/6
# 0.3333 - 1/3
# 0.4166 - 5/12
# 0.5833 - 7/12
# 0.6666 - 2/3

richpsharp

# run as:
# docker run --rm -it -v %CD%:/usr/local/workspace --volume=%CD%/gurobi.lic:/opt/gurobi/gurobi.lic:ro --volume=%CD%/models:/models:ro prioritizr ./run-prioritizations.R
FROM gurobi/optimizer

RUN apt-get update -qq
RUN apt-get install -y libgdal-dev
RUN apt-get install -y r-base r-base-dev
RUN apt-get install -y libudunits2-dev
RUN apt-get install -y libssl-dev

richpsharp

"""Convert isimib2 to geotiff."""
import os
import glob

import xarray as xr
from osgeo import gdal
from osgeo import osr

LOCAL_RASTER_CREATION_OPTIONS = (
    'TILED=YES', 'BIGTIFF=YES', 'COMPRESS=LZW',

richpsharp

import numpy
import scipy.optimize

evi_vs_c = [
    (0.02874, 0.95),
    (0.18186, 0.25),
    (0.3643000126, 0.13575),
    (0.33039, 0.07075),
    (0.54826, 0.035425),
    (0.65346, 0.0001),

richpsharp

vector = gdal.OpenEx(vector_path, gdal.OF_VECTOR)
layer = vector.GetLayer()
layer.SetAttributeFilter('Country="USA"') # or whatever...

feature = layer.GetNextFeature()
geom = feature.GetGeometryRef()
envelope = geom.GetEnvelope()
target_bb = [envelope[i] for i in [0, 2, 1, 3]])
geom = None
feature = None

richpsharp

import numpy
import matplotlib.pyplot

slope_in_radians_list = numpy.linspace(0.089, 0.785, 100)
beta_list = []
m_list = []
for slope_in_radians in slope_in_radians_list:
    beta = ((numpy.sin(slope_in_radians) / 0.0896) /
            (3 * numpy.sin(slope_in_radians)**0.8 + 0.56))
    m_list.append(beta/(1+beta))

richpsharp

"""InVEST Sediment Delivery Ratio (SDR) module.

The SDR method in this model is based on:
    Winchell, M. F., et al. "Extension and validation of a geographic
    information system-based method for calculating the Revised Universal
    Soil Loss Equation length-slope factor for erosion risk assessments in
    large watersheds." Journal of Soil and Water Conservation 63.3 (2008):
    105-111.
"""
import os