def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
self.spline = lg.loadGibbsSpline('gsp_puresubstance.mat')
self.mlout = load._stripNestingToFields(
sio.loadmat('gsp2d_out.mat')['gsp2d_out'])
self.P = np.arange(0, 3001, 200).astype(float)
self.T = np.arange(0, 401, 50).astype(float)
def test_loadgibbs_3d_ScalarGo(self):
gsp = lg.loadGibbsSpline('gsp_1scalarGo.mat')
# just do a spot check of a field in the spline since it calls load.getSplineDict which is tested elsewhere
self.assertTrue(
np.array_equal(gsp['sp']['number'], np.array([29, 20, 14])))
self.assertTrue(
np.array_equal(gsp['MW'], np.array([0.01801528, 0.05844])))
self.assertEqual(gsp['nu'], 2)
def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
self.spline = lg.loadGibbsSpline('gsp_singlesolute.mat', 'sp_NaCl')
self.mlout = load._stripNestingToFields(
sio.loadmat('gsp3d_out.mat')['gsp3d_out'])
self.P = np.arange(0.1, 8000, 200).astype(float)
self.T = np.arange(239, 501, 50).astype(float)
self.M = np.arange(0, 8, 2).astype(float)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from mpl_toolkits.mplot3d import Axes3D
from lbftd import statevars, loadGibbs as lg, evalGibbs as eg
# load and evaluate the spline
# this is a high-pressure spline that starts with IAPWS 95 below 1 GPa
# but modifies it above 1 GPa to better match DAC and shockwave data
water_spline = lg.loadGibbsSpline('water_demo_spline.mat')
P = np.linspace(0, 1500, num=200)
T = np.linspace(240, 500, num=200)
# evaluate the spline at the requested P(ressure, in MPa) and T(emperature, in K)
# requested thermodynamic state variables
# (see README for full list of available state vars or don't list any to get the full set):
# - rho: density in kg m^-3
# - Cp: isobaric specific heat in J kg^-1 K^-1
# - Kt: isothermal bulk modulus in MPa
# - alpha: thermal expansivity in K-1
tdstate = eg.evalSolutionGibbsGrid(water_spline['sp'], np.array([P, T]), 'rho',
'Cp', 'Kt', 'alpha')
# for full set of implemented statevars: eg.evalSolutionGibbsGrid(water_spline['sp'], np.array([P, T]))
# graph the output
rcParams[
'axes.labelpad'] = 10 # add some padding to prevents axis labels from covering ticks
fig = plt.figure()
pP, pT = np.meshgrid(P, T)
rho_ax = fig.add_subplot(221, projection='3d')
def test_loadgibbs_2d(self):
gsp = lg.loadGibbsSpline('gsp_puresubstance.mat')
self.assertTrue(np.array_equal(gsp['sp']['number'], np.array([50,
40])))
self.assertTrue(np.array_equal(gsp['MW'], np.array([0.01801528])))
self.assertFalse('nu' in gsp)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from mpl_toolkits.mplot3d import Axes3D # even though this is not used directly, it is required to do 3d plots
from lbftd import statevars, loadGibbs as lg, evalGibbs as eg
# load and evaluate the spline
# this is a high-pressure spline that starts with IAPWS 95 below 1 GPa
# but modifies it above 1 GPa to better match DAC and shockwave data
water_spline = lg.loadGibbsSpline('water_demo_iapws_modified.mat')
P = np.linspace(0, 2000, num=200) # pressure, in MPa
T = np.linspace(200, 1000, num=200) # temperature, in K
# requested thermodynamic state variables
# (see README for full list of available state vars or don't list any to get the full set):
# - rho: density in kg m^-3
# - Cp: isobaric specific heat in J kg^-1 K^-1
# - Kt: isothermal bulk modulus in MPa
# - alpha: thermal expansivity in K-1
tdstate = eg.evalSolutionGibbsGrid(water_spline['sp'],
np.array([P, T]),
'rho',
'Cp',
'Kt',
'alpha',
failOnExtrapolate=False)
# for full set of implemented statevars: eg.evalSolutionGibbsGrid(water_spline['sp'], np.array([P, T]))
# graph the output
rcParams[
'axes.labelpad'] = 10 # add some padding to prevents axis labels from covering ticks
def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
self.puresubstancespline = lg.loadGibbsSpline('gsp_puresubstance.mat')