def whichphase(PT, path=defpath):
""" Determines the most likely phase of water at each pressure/temperature
:param PT: the pressure (MPa) and temperature (K) conditions at which the phase should be determined --
the specified units are required, as conversions are built into several calculations.
This parameter can have one of the following formats:
- a 1-dimensional numpy array of tuples with one or more scattered (P,T) tuples, e.g.
PT = np.empty((3,), np.object)
PT[0] = (441.0858, 313.95)
PT[1] = (478.7415, 313.96)
PT[2] = (444.8285, 313.78)
- a numpy array with 2 nested numpy arrays, the first with pressures and the second
with temperatures -- each inner array must be sorted from low to high values
a grid will be constructed from the P and T arrays such that each row of the output
will correspond to a pressure and each column to a temperature, e.g.
P = np.arange(0.1, 1000.2, 10)
T = np.arange(240, 501, 2)
PT = np.array([P, T])
:param path: an optional path to the SeaFreeze_Gibbs.mat file --
default value assumes the spline distributed along with the project
:return: A numpy.ndarray the same size as PT, with the phase of each pressure/temperature represented by
an integer, as shown in phasenum2phase
"""
isscatter = _is_scatter(PT)
phase_sp = {
v.phase_num: load.loadSpline(path, v.sp_name)
for v in phases.values() if not np.isnan(v.phase_num)
}
ptsh = ((PT.size, ) if isscatter else
(PT[0].size, PT[1].size)) # reference shape based on PT
comp = np.full(ptsh + (max_phase_num + 1, ), np.nan) # comparison matrix
for p in phase_sp.keys():
sl = tuple(repeat(slice(None), 1 if isscatter else 2)) + (
p, ) # slice for this phase
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sp = phase_sp[p]
tdvs = _get_tdvs(sp, PT, isscatter, 'G').G
# wipe out G for PT values that fall outside the knot sequence
if isscatter:
extrap = [(pt[iP] < sp['knots'][iP].min()) +
(pt[iP] > sp['knots'][iP].max()) +
(pt[iT] < sp['knots'][iT].min()) +
(pt[iT] > sp['knots'][iT].max()) for pt in PT]
else:
pt = np.logical_or(PT[iP] < sp['knots'][iP].min(),
PT[iP] > sp['knots'][iP].max())
tt = np.logical_or(PT[iT] < sp['knots'][iT].min(),
PT[iT] > sp['knots'][iT].max())
extrap = np.logical_or(*np.meshgrid(pt, tt, indexing='ij'))
tdvs[extrap] = np.nan
comp[sl] = tdvs
# output for all-nan slices should be nan
all_nan_sl = np.all(
np.isnan(comp),
-1) # find slices where all values are nan along the innermost axis
out = np.full(ptsh, np.nan) # initialize output to nan
out[~all_nan_sl] = np.nanargmin(comp[~all_nan_sl],
-1) # find min values for other slices
return out
def seafreeze(PT, phase, path=defpath):
""" Calculates thermodynamic quantities for H2O water or ice polymorphs Ih, III, V, and VI for all phases
(see lbftd documentation for full list)
for solid phases only:
- Vp (compressional wave velocity, in m/s)
- Vs (shear wave velocity, in m/s)
- shear (shear modulus, in MPa)
Requires the SeaFreeze_Gibbs.mat library containing the Gibbs LBF parametrization (installed with this module).
NOTE: The authors recommend the use of 'water1' for any application in the 200-355 K range and up to 2300 MPa.
The ice Gibbs parametrizations are optimized for use with the 'water1' phase for phase equilibrium calculations.
Using other water parametrizations will lead to incorrect melting curves -- 'water2' and 'water_IAPWS95'
parametrizations are provided for HP extension up to 100 GPa and comparison only.
:param PT: the pressure (MPa) and temperature (K) conditions at which the thermodynamic quantities should be
calculated -- the specified units are required, as conversions are built into several calculations.
This parameter can have one of the following formats:
- a 1-dimensional numpy array of tuples with one or more scattered (P,T) tuples, e.g.
PT = np.empty((3,), np.object)
PT[0] = (441.0858, 313.95)
PT[1] = (478.7415, 313.96)
PT[2] = (444.8285, 313.78)
- a numpy array with 2 nested numpy arrays, the first with pressures and the second
with temperatures -- each inner array must be sorted from low to high values
a grid will be constructed from the P and T arrays such that each row of the output
will correspond to a pressure and each column to a temperature, e.g.
P = np.arange(0.1, 1000.2, 10)
T = np.arange(240, 501, 2)
PT = np.array([P, T])
:param phase: one of the keys of the phases dict, indicating the phase of H2O to be evaluated
:param path: an optional path to the SeaFreeze_Gibbs.mat file
default value assumes the spline distributed along with the project
:return: object containing the calculated thermodynamic quantities (as named properties), as well as
a PTM property (a copy of PT)
"""
try:
phasedesc = phases[phase]
except KeyError:
raise ValueError(
'The specified phase is not recognized. Supported phases are ' +
', '.join(phases.keys()) + '.')
sp = load.loadSpline(path, phasedesc.sp_name)
# calc density and isentropic bulk modulus
isscatter = _is_scatter(PT)
tdvs = _get_tdvs(sp, PT, isscatter)
if phasedesc.shear_mod_parms:
smg = _get_shear_mod_GPa(phasedesc.shear_mod_parms, tdvs.rho,
_get_T(PT, isscatter))
tdvs.shear = 1e3 * smg # convert to MPa for consistency with other measures
tdvs.Vp = _get_Vp(smg, tdvs.rho, tdvs.Ks)
tdvs.Vs = _get_Vs(smg, tdvs.rho)
return tdvs
def test2dsplinev73(self):
sp = load.loadSpline('spline2d_v73.mat')
self.check2dspline(sp)
def test1dsplinev73(self):
sp = load.loadSpline('spline1d_v73.mat')
self.check1dspline(sp)
def test3dsplinev73(self):
sp = load.loadSpline('spline3d_v73.mat')
self.check3dspline(sp)
def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
self.spline = load.loadSpline('spline3d_v7.mat')
self.mlout = sio.loadmat('spline3d_out.mat')['sp3d_out']
def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
self.spline = load.loadSpline('spline1d_v7.mat')
# TODO: shouldn't have to squeeze
self.mlout = sio.loadmat('spline1d_out.mat')['sp1d_out'].squeeze()
