jdherman

# this runs in python 3 as of Jan. 2018
import pandas as pd 
import time
import urllib
from bs4 import BeautifulSoup
import numpy as np
from io import StringIO
import matplotlib.pyplot as plt

dates = pd.date_range(start='4/20/2010', end='1/20/2018')

jdherman

jdherman

# Leaf River Mississippi, 40 years of daily data
# Precip (mm/day)  PET (mm/day)   Q (mm/day)
0.0000000e+000  4.6004000e+000  2.8995124e-001
0.0000000e+000  4.3092000e+000  2.4476064e-001
0.0000000e+000  4.3286000e+000  2.1338209e-001
0.0000000e+000  4.7777000e+000  1.9329787e-001
0.0000000e+000  2.9110000e+000  1.8325798e-001
0.0000000e+000  3.5714000e+000  1.7447252e-001
1.3518000e+000  1.4499000e+000  1.7195922e-001
0.0000000e+000  4.6986000e+000  1.6694149e-001

jdherman

import glob

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

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

jdherman

def init_plotting():
    sns.set_style('whitegrid')
    plt.rcParams['figure.figsize'] = (5, 4)
    plt.rcParams['font.size'] = 13
    plt.rcParams['font.family'] = 'OfficinaSanITCBoo'
    # plt.rcParams['font.weight'] = 'bold'
    plt.rcParams['axes.labelsize'] = 1.1*plt.rcParams['font.size']
    plt.rcParams['axes.titlesize'] = 1.1*plt.rcParams['font.size']
    plt.rcParams['legend.fontsize'] = plt.rcParams['font.size']
    plt.rcParams['xtick.labelsize'] = plt.rcParams['font.size']

jdherman

###First Clone the latest version of the model from bitbucket.

###Upload code to TACC You can use scp, rsync, even git, whatever. I use rsync:

rsync -raz . jdh33@stampede.tacc.utexas.edu:~/projects/emodps

when you're in the highest level directory of the project (the dot .) the nice thing about rsync is that it only transfers files that were added or modified relative to the destination.

jdherman

Jan 2021 - have not updated in several years. Many links are broken now, but the datasets are still out there somewhere.

HYDROLOGY

• CDEC: historical and real-time reservoir storage, inflows, outflows, stream gauges, snowpack
• USGS daily streamflow
• USGS all sites
• NRCS snotel site data
• NOAA Historical precip/temp: at individual stations
• DWR Full Natural Flows 1922-2003

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);