phargogh

import matplotlib.pyplot as plt
import numpy

BUFFER_DIST = 500
DISTANCE = numpy.arange(BUFFER_DIST+100)
NONE = (DISTANCE < BUFFER_DIST)
LINEAR = numpy.maximum(1 - (DISTANCE / BUFFER_DIST), 0)
EXPONENTIAL = numpy.exp(-DISTANCE / BUFFER_DIST) * NONE
OLD_HRA_EXPONENTIAL = (1 - numpy.exp((numpy.log(1e-6) / DISTANCE))) * NONE

phargogh

import logging
import os
import pprint

import numpy
import pygeoprocessing
import pygeoprocessing.routing
from natcap.invest import datastack
from natcap.invest.seasonal_water_yield import seasonal_water_yield
from osgeo import gdal

phargogh

import os
import shutil

import numpy
import pygeoprocessing
import pygeoprocessing.routing
from osgeo import gdal


def doit(dem_path, flow_dir_weights, watershed_source_vector, workspace):

phargogh

import logging
import os
import os.path

import numpy
import pygeoprocessing
from osgeo import gdal
from osgeo import osr

logging.basicConfig(level=logging.WARNING)

phargogh

This gist reflects what I understand to be the workflow for Dyna-CLUE,
in the interest of getting the core algorithm down on paper.

This is based on :
  * Verburg & Overmars (2009): https://link.springer.com/article/10.1007%2Fs10980-009-9355-7
  * The CLUE Manual: http://environmentalgeography.nl/files/data/public/cluemanual
  * The provided source code for CLUE: http://www.environmentalgeography.nl/files/data/public/dyna_clue
  * The IEEM Dyna-CLUE manual: https://publications.iadb.org/publications/english/document/The-Integrated-Economic-Environmental-Modeling-IEEM-Platform-IEEM-Platform-Technical-Guides-User-Guide-for-the-IEEM-enhanced-Land-Use-Land-Cover-Change-Model-Dyna-CLUE.pdf

phargogh

#!/bin/bash
curl https://pypi.org/pypi/numpy/json | jq '..|.filename?' | grep \.whl | grep 310

phargogh

.PHONY: all clean

WITHREADLINE := withreadline.txt
WITHOUTREADLINE := withoutreadline.txt
WITHOUTREADLINEBYLINE := withoutreadlinebyline.txt
SAMPLE := sample.txt

all: $(WITHREADLINE) $(WITHOUTREADLINE) $(WITHOUTREADLINEBYLINE)

clean:

phargogh

To use this:

1. Download all of the pygeoprocessing archives from github actions.
2. Place all downloads into a single folder.
3. Open a bash shell in this new folder that contains the archives.
4. Run the find command noted in this gist.
5. Proceed with the wheel/sdist upload via twine.

phargogh

"""Randomly generate office hours rotations for the defined dates.

This was brought up as a possible way of randomly picking office hours
assignments during the original trial run of office hours in September, 2021.
"""

import random

# Group Wednesday-to-following-Monday
# This is because of the labor day holiday on Monday the 6th that offsets the

phargogh

import time

import numpy.random
from numba import njit


@njit
def function():
    size = 10000
    arrays = [numpy.random.rand(size, size) for i in range(5)]