jdherman

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

filename = 'vic_error_9p_1K.txt'

# Set up the map
# lat_ts is the latitude of true scale.
# resolution = 'c' means use crude resolution coastlines.

jdherman

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.cm as cm
from mpl_toolkits.basemap import Basemap, shiftgrid
from matplotlib.backends import backend_agg as agg # raster backend

filename = 'vic_error_9p_1K.txt'

# Set up the color data

jdherman

clc; clear all;

domain = -0.5:0.005:0.5;
[X,Y] = meshgrid(domain,domain);
Z = zeros(length(domain), length(domain));
cmap = hot;
cmap = flipud(cmap(:, [3 2 1]));
outfile = 'aaa.gif';
set(gcf,'renderer','zbuffer');
t_vals = [0:4:200];

jdherman

function [C] = C_analytical(x,y,t)
  
  mdot = 1; % kg/s
  D = 10^-3;
  L = 1;
  
  outer_term = mdot./(4*pi*t*sqrt(D*D));
  exp_sum = 0;
  
  for m = -20:20

jdherman

// Optional settings:
// BORG_Debug_on();
// BORG_Algorithm_ms_max_time(0.008);
// BORG_Algorithm_output_aerovis();

char runtime[256];
char outputFilename[256];
FILE* outputFile = NULL;

BORG_Algorithm_ms_startup(&argc, &argv);

jdherman

clc; clear all;

% Plot the feasible space for the Masonry LP
% max Z = 140*x1 + 160*x2
% subject to:
% 2*x1 + 4*x2 <= 28
% 5*x1 + 5*x2 <= 50
% x1 <= 8
% x2 <= 6
% x1, x2 >= 0

jdherman

clc; clear all;

% Solve the Masonry LP
% max Z = 140*x1 + 160*x2
% subject to:
% 2*x1 + 4*x2 <= 28
% 5*x1 + 5*x2 <= 50
% x1 <= 8
% x2 <= 6
% x1, x2 >= 0

jdherman

#!/bin/bash

# This submission will submit 200 jobs in "globs", 
# Where one glob will not begin until the previous one finishes without errors

FILENUMS=$(seq 0 199)
MAX_SIMULT=7 # max number of simultaneous jobs
GLOBNUM=0
GLOBNUM_PREV=0
DEPEND=""

jdherman

from __future__ import division
from netCDF4 import Dataset
import numpy as np
import os

PATH = '.'
LL = np.loadtxt('%s/global_soils_default.txt' % PATH);
LL = LL[:,2:4]
OBS = np.loadtxt('%s/VIC_GRDC_Monthly_Climatology.txt' % PATH, delimiter=',', skiprows=1)

jdherman

from __future__ import division
from netCDF4 import Dataset
import numpy as np
import os
import math

PATH = '.'
LL = np.loadtxt('%s/global_soils_default.txt' % PATH);
LL = LL[:,2:4]
OBS = np.loadtxt('%s/VIC_GRDC_Monthly_Climatology.txt' % PATH, delimiter=',', skiprows=1)