jdherman

==============Run Study Start============
Set parameter Username
Set parameter LicenseID to value 2734542
Academic license - for non-commercial use only - expires 2026-11-07
Cycle 1 in 1921/10/31 Skipped. (gentables)
Cycle 2 in 1921/10/31 Skipped. (swpforecast)
Variables in dvarMap not in varDoubleMap: 
[c_ntoma]
Variables in varDoubleMap not in dVarMap: 

jdherman

month, cycle, variable, CBC, Gurobi
1921_10 c14 c_mcd002 413.67056 10808.279287776533
1921_10 c14 c_mcd005 423.99056 10818.599287776533
1921_10 c14 c_mcd009 290.63034 10685.239075815958
1921_10 c14 c_mcd014 290.63034 10685.239075815958
1921_10 c14 c_mcd018 290.63034 10685.239075815958
1921_10 c14 c_mcd021 290.63034 10685.239075815958
1921_10 c14 c_mcd028 295.23552 10689.844247024068
1921_10 c14 c_mcd032 254.10301 10648.711742729001
1921_10 c14 c_mcd033 254.10301 10648.711742729001

jdherman

month cycle variable CBC Gurobi
1921_10 c24 r_11_sa4_dwrps2 42.334787 42.33478692379354
1921_10 c25 r_11_sa4_dwrps2 42.334787 42.33478692379354 
1921_10 c26 r_11_sa4_dwrps2 42.334787 42.33478692379354
1921_10 c27 r_11_sa4_dwrps2 42.334787 42.33478692379354
1921_10 c28 r_11_sa4_dwrps2 42.334787 42.33478692379353
1921_10 c29 r_11_sa4_dwrps2 42.334787 42.33478692379354
1921_10 c30 r_11_sa4_dwrps2 42.334787 42.33478692379354
1921_10 c31 r_11_sa4_dwrps2 42.334787 42.33478692379354
1921_10 c32 r_11_sa4_dwrps2 42.334787 42.33478692379354

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