def test_model_obj():
"""
Test the object behavior for the `Model` object
Test the instantiation and attribute data for the `Model` object of
the `single_bubble_model` module.
Notes
-----
This test function only tests instantiation from a netCDF file of ambient
CTD data and does not test any of the object methods. Instantiation
from simulation data and testing of the object methods is done in the
remaining test functions.
See Also
--------
test_simulation
"""
# Get the ambient profile data
profile = get_profile()
# Initialize a Model object
sbm = single_bubble_model.Model(profile)
# Check the model attributes
assert_approx_equal(sbm.p.rho_r, 1031.035855535142, significant=6)
(T, S, P) = profile.get_values(1000.,
['temperature', 'salinity', 'pressure'])
(Tp, Sp,
Pp) = sbm.profile.get_values(1000.,
['temperature', 'salinity', 'pressure'])
assert Tp == T
assert Sp == S
assert Pp == P
from tamoc import dbm
from tamoc import seawater
from tamoc import single_bubble_model
from tamoc import dispersed_phases
import numpy as np
if __name__ == '__main__':
# Open an ambient profile object from the netCDF dataset
nc = '../../test/output/test_bm54.nc'
bm54 = ambient.Profile(nc, chem_names='all')
bm54.close_nc()
# Initialize a single_bubble_model.Model object with this data
sbm = single_bubble_model.Model(bm54)
# Create a light gas bubble to track
composition = ['methane', 'ethane', 'propane', 'oxygen']
bub = dbm.FluidParticle(composition, fp_type=0.)
# Set the mole fractions of each component at release.
mol_frac = np.array([0.95, 0.03, 0.02, 0.])
# Specify the remaining particle initial conditions
de = 0.005
z0 = 1000.
T0 = 273.15 + 30.
fdis = 1.e-15
# Also, use the hydrate model from Jun et al. (2015) to set the
def test_simulation():
"""
Test the output from the `Model.simulate` method
Test the output of the `Model.simulate` method of the
`single_bubble_model` module. These tests include a single component
bubble, multi-component bubbles and drops, and multi-component fluid
particles with gas stripping from the ambient dissolved chemicals.
"""
# Get the ambient profile data
profile = get_profile()
# Initialize a Model object
sbm = single_bubble_model.Model(profile)
# Set up the initial conditions
composition = ['methane', 'ethane', 'propane', 'oxygen']
bub = dbm.FluidParticle(composition)
mol_frac = np.array([0.90, 0.07, 0.03, 0.0])
de = 0.005
z0 = 1000.
T0 = 273.15 + 30.
# Run the simulation
sbm.simulate(bub,
np.array([0., 0., z0]),
de,
mol_frac,
T0,
K_T=1,
fdis=1e-8,
delta_t=10.)
# Check the solution
assert sbm.y.shape[0] == 1117
assert sbm.y.shape[1] == 8
assert sbm.t.shape[0] == 1117
assert_approx_equal(sbm.t[-1], 11016.038751523512, significant=6)
assert_array_almost_equal(sbm.y[-1, :],
np.array([
0.00000000e+00, 0.00000000e+00,
3.36934635e+02, 4.31711152e-14,
6.66318106e-15, -2.91389824e-13,
-1.08618680e-15, -1.37972400e-07
]),
decimal=6)
# Write the output files
sbm.save_sim('./output/sbm_data.nc', './test_BM54.nc',
'Results of ./test_sbm.py script')
sbm.save_txt('./output/sbm_data', './test_BM54.nc',
'Results of ./test_sbm.py script')
# Reload the simulation
sbm_f = single_bubble_model.Model(simfile='./output/sbm_data.nc')
# Check that the attributes are loaded correctly
assert sbm_f.y[0, 0] == sbm.y[0, 0] # x0
assert sbm_f.y[0, 1] == sbm.y[0, 1] # y0
assert sbm_f.y[0, 2] == sbm.y[0, 2] # z0
assert_array_almost_equal(sbm_f.particle.m0, sbm.particle.m0, decimal=6)
assert sbm_f.particle.T0 == sbm.particle.T0
print sbm_f.particle.K_T, sbm.particle.K_T
assert sbm_f.particle.K == sbm.particle.K
assert sbm_f.particle.K_T == sbm.particle.K_T
assert sbm_f.particle.fdis == sbm.particle.fdis
assert sbm_f.K_T0 == sbm.K_T0
assert sbm_f.delta_t == sbm.delta_t
(T, S, P) = profile.get_values(1000.,
['temperature', 'salinity', 'pressure'])
(Tp, Sp,
Pp) = sbm_f.profile.get_values(1000.,
['temperature', 'salinity', 'pressure'])
assert Tp == T
assert Sp == S
assert Pp == P
# Check that the results are still correct
assert sbm.y.shape[0] == 1117
assert sbm.y.shape[1] == 8
assert sbm.t.shape[0] == 1117
assert_approx_equal(sbm.t[-1], 11016.038751523512, significant=6)
assert_array_almost_equal(sbm.y[-1, :],
np.array([
0.00000000e+00, 0.00000000e+00,
3.36934635e+02, 4.31711152e-14,
6.66318106e-15, -2.91389824e-13,
-1.08618680e-15, -1.37972400e-07
]),
decimal=6)
# Load the data in the txt file and check the solution
data = np.loadtxt('./output/sbm_data.txt')
assert sbm.y.shape[0] == 1117
assert sbm.y.shape[1] == 8
assert sbm.t.shape[0] == 1117
assert_approx_equal(sbm.t[-1], 11016.038751523512, significant=6)
assert_array_almost_equal(sbm.y[-1, :],
np.array([
0.00000000e+00, 0.00000000e+00,
3.36934635e+02, 4.31711152e-14,
6.66318106e-15, -2.91389824e-13,
-1.08618680e-15, -1.37972400e-07
]),
decimal=6)
# Create an inert particle that is compressible
oil = dbm.InsolubleParticle(True, True, rho_p=840.)
mol_frac = np.array([1.])
# Specify the remaining particle initial conditions
de = 0.03
z0 = 1000.
T0 = 273.15 + 30.
# Simulate the trajectory through the water column and plot the results
sbm.simulate(oil,
np.array([0., 0., z0]),
de,
mol_frac,
T0,
K_T=1,
delta_t=10.)
ans = np.array([
0.00000000e+00, 0.00000000e+00, 1.16067764e-01, 1.26136097e-02,
7.57374681e+03
])
for i in range(5):
assert_approx_equal(sbm.y[-1, i], ans[i], significant=6)
from tamoc import ambient
from tamoc import dbm
from tamoc import seawater
from tamoc import single_bubble_model
import numpy as np
if __name__ == '__main__':
# Create a single_bubble_model.Model object from the bubble.py results
# stored on the hard drive. Note that the netCDF file is self-documenting
# and actually points to the ambient CTD profile data. If that file
# does not exist, the post_processing method cannot be applied as it
# reads from the ambient data.
sbm = single_bubble_model.Model(simfile='./bubble.nc')
# Echo the results to the screen:
print 'The results of ./bin/bubble.py have been loaded into memory'
print ' len(t) : %d' % sbm.t.shape[0]
print ' shape(y) : %d, %d' % (sbm.y.shape[0], sbm.y.shape[1])
print ' composition : %s, ' % sbm.particle.composition
# You can re-run the simulation with different parameters by calling
# the simulate method.
print '\nRe-running simulation with de = 0.01 m:'
z0 = sbm.y[0,2]
yk = sbm.particle.particle.mol_frac(sbm.particle.m0)
T0 = sbm.particle.T0
K = sbm.particle.K
fdis = sbm.particle.fdis