jdherman

from random import random

# function to optimize: takes in a list of decision variables, returns an objective value
# this is the Rosenbrock function: http://en.wikipedia.org/wiki/Rosenbrock_function
# the global minimum is located at x = (1,1) where f(x) = 0
def my_function(x):
  return (1-x[0])**2 + 100*(x[1] - x[0]**2)**2

# function to perform (a very crude, stupid) optimization
# bounds = lower and upper bounds for each decision variable (2D list)

jdherman

#!/bin/bash
bsondump database.bson > database.json

# replace ObjectId("50f330869178b31dde000001")
# with just the string

sed -re 's/ObjectId\((.*)\)/\1/g' database.json > test.json

# then in python

jdherman

// utility functions for boost matrices/vectors

#include <fstream>

namespace ublas = boost::numeric::ublas;
using namespace std;

// create matrix:
// ublas::matrix<double> my_matrix(num_rows, num_columns);
// loadtxt("/path/to/file", my_matrix);

jdherman

#!/bin/bash

declare -a arr=("professional" "phd" "presentations" "proposals")

for d in "${arr[@]}"
do
  rsync -raz --progress --exclude=".git*" --delete ./"$d"/* Dropbox/jdh/"$d"
done

chown -R Administrators Dropbox/jdh/*

jdherman

from SALib.sample import saltelli
from string import Template

# Generate samples for sensitivity analysis, and put them into an input file template
# This will generate N(2D+2) copies of the template with the values inserted
# The use case is for older models (like F77) where the input file something like below.
# Put dollar-sign variables where you want to replace with numbers, like so:
#
#C  IWQVLIM = option for velocity limitation of macroalgae growth
#C          = 0, macroalgae growth is not limited by stream velocity

jdherman

from __future__ import division
import numpy as np
from scipy.optimize import brentq as root
from borg import *

nvars = 100 # decision variables: pollution for each timestep
nobjs = 4
nconstr = 1
nsamples = 100 # 100 scenarios of natural inflows
b = 0.42 # decay rate for P in lake (0.42 = irreversible)

jdherman

import glob

kw = ['climate', 'decision theory', 'dynamic', 'economics', 'game theory', 'heuristic', 'hydrology',
      'integer', 'linear', 'model', 'nonlinear', 'policy', 'probability', 'statistics', 
      'stochastic','quality']

files = glob.glob('*.txt')
courses_including = {}
avg_count = {}

jdherman

from __future__ import division
from osgeo import gdal
from geopy.geocoders import Nominatim

def get_value_at_point(rasterfile, pos):
  gdata = gdal.Open(rasterfile)
  gt = gdata.GetGeoTransform()
  data = gdata.ReadAsArray().astype(np.float)
  gdata = None
  x = int((pos[0] - gt[0])/gt[1])

jdherman

from osgeo import gdal
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.basemap import Basemap

# Plotting 2070 projected August (8) precip from worldclim
gdata = gdal.Open("D:/jon/datasets/worldclim/future/pr/gf45pr70_usa/gf45pr708.tif")
geo = gdata.GetGeoTransform()
data = gdata.ReadAsArray()

jdherman

#!/bin/bash

DIR=current
TYPE=tmean
RES=5m
pwd

for MONTH in {1..12}
do
  gdalwarp -cutline states_shape/states.shp \