# TODO: find way to have conditional arguments based off of what xaxis and yaxis are
# (in other words, we have an excess of variables as arguments)
my_parser = Plot3D()
args = vars(my_parser.parse_args())
assert args['yaxis'], 'No y axis chosen for plot'
assert args['xaxis'], 'No x axis chosen for plot'
my_plotter = Plotter(**args)
my_plotter.read_json()
boxFrom, boxTo = args['boxes']
mol1, mol2 = args['molecules']
kwargs = {'boxFrom': boxFrom, 'boxTo': boxTo, 'mol1': mol1, 'mol2': mol2}
data = {'x': {}, 'y': {}, 'z': {}}
for feed in args['feeds']:
file_name = ''
# determine if run has been completed
my_plotter.run = checkRun(args['type'], my_plotter.variables, feed)
my_plotter.feed = feed
for axis in ['x', 'y', 'z']:
kwargs['mol'] = args['mol%s' % axis]
kwargs['box'] = args['box%s' % axis]
file_name += 'mol%s-box%s-%s_' % (kwargs['mol'], kwargs['box'],
args['%saxis' % axis])
data[axis][feed] = my_plotter.convertChoice(
args['%saxis' % axis], **kwargs)
write(data, file_name + '.dat',
my_plotter.gen_data[my_plotter.feed][my_plotter.run]['numIndep'])
my_data.variables.append(my_data.P)
my_data.read_json()
# gather data
temperatures = []
gas_densities = {
'mean': [],
'error': []
}
liquid_densities = {
'mean': [],
'error': []
}
pressures = {'mean': [], 'error': []}
for feed, T in zip(args['feeds'], args['temperatures']):
my_data.run = checkRun(args['type'], my_data.variables, feed)
my_data.feed = feed
rhoG, drhoG, rhoL, drhoL, p, dp = getResults(my_data, args['mol'],
args['molWeight'])
temperatures.append(T)
gas_densities['mean'].append(rhoG)
gas_densities['error'].append(drhoG)
liquid_densities['mean'].append(rhoL)
liquid_densities['error'].append(drhoL)
pressures['mean'].append(p)
pressures['error'].append(dp)
# write to file
with open('vlcc.inp', 'w') as f:
f.write(str(args['feeds']) + str(args['temperatures']) + '\n')
f.write('NDATA BETA NITER\n')
rho = {}
C = {}
gen_data = {}
for file, var in zip(['rho-data.db', 'Conc-data.db', 'general-data.db'],
[rho, C, gen_data]):
with shelve.open('%s/%s' % (args['path'], file)) as db:
for feed in args['feeds']:
assert feed in db.keys(), 'Feed {} not in database'.format(
feed)
var[feed] = db[feed]
mol_data = {}
for feed in args['feeds']:
# determine if run has been completed
run = checkRun(args['type'], [C, rho], feed)
# gen data
numIndep = gen_data[feed][run]['numIndep']
# concentrations
conc, c_mol, box = getX(C[feed])
# initialize variables if needed
if c_mol not in mol_data.keys():
mol_data[c_mol] = {}
for key in ['kH', 'P']:
mol_data[c_mol][key] = {'mean': [], '95conf': [], 'feed': []}
henry_constant = mol_data[c_mol]['kH']
pressure = mol_data[c_mol]['P']
rho_mean, rho_stdev = (rho[feed][run][c_mol]['box3']['mean'],
rho[feed][run][c_mol]['box3']['stdev'])
# convert number density to pressure [kPa]
# (molec/nm**3)*(mol/molec)*(nm**3*kPa/(mol*K))*K = kPa
plt.subplots_adjust(left=0.11, right=0.97, top=0.97, bottom=0.5)
fig = plt.gcf()
fig.set_size_inches(6.5, 6.5)
fig.savefig('%s-%s.png' % (feed, run), dpi=300)
plt.show()
import math, random
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
import argparse, os, shelve
from MCFlow.runAnalyzer import checkRun
parser = argparse.ArgumentParser(
description='plot swap and swatch accptances for given feed')
parser.add_argument('-f',
'--feed',
help='feed to analyze swaps and swatches for')
parser.add_argument('-t',
'--type',
help='type of run to analyze',
default='equil-')
args = vars(parser.parse_args())
assert os.path.isfile('SWAP-data.db'), 'No SWAP data found'
with shelve.open('SWAP-data.db') as db:
assert args['feed'] in db.keys(), 'Feed not in database'
data = {args['feed']: db[args['feed']]}
my_run = checkRun(args['type'], [data], args['feed'])
plot_transfer_acceptance(data[args['feed']][my_run], args['feed'], my_run)