r"""
Objects for storing and producing objective values for comparing experimental data
to EOS predictions.
"""
import numpy as np
import logging
from despasito.thermodynamics import thermo
from despasito.parameter_fitting import fit_functions as ff
from despasito.parameter_fitting.interface import ExpDataTemplate
import despasito.fundamental_constants as constants
import despasito.utils.general_toolbox as gtb
logger = logging.getLogger(__name__)
##################################################################
# #
# Liquid Density #
# #
##################################################################
[docs]
class Data(ExpDataTemplate):
r"""
Object for liquid density data. This data is evaluated with "liquid_properties".
This object is initiated in :func:`~despasito.parameter_fitting.fit` with the
keyword, ``exp_data[*]["data_class_type"]="liquid_density"``.
The data could be evaluated with
:func:`~despasito.thermodynamics.calculation_types.liquid_properties`
Parameters
----------
data_dict : dict
Dictionary of exp data of type liquid density.
* calculation_type (str) - Optional, default='liquid_properties'
* MultiprocessingObject (obj) - Optional, Initiated
:class:`~despasito.utils.parallelization.MultiprocessingJob`
* eos_obj (obj) - Equation of state object
* T (list) - [K] List of temperature values for calculation
* P (list) - [Pa] List of pressure values for calculation
* xi (list) - List of liquid mole fractions used in liquid_properties
calculations
* rhol (list) - [mol/:math:`m^3`] Evaluated liquid density values
* weights (dict) - A dictionary where each key is a system constraint
(e.g. T or xi) which is also a header used in an optional exp. data file.
The value associated with a header can be a list as long as the number of
data points to multiply by the objective value associated with each point,
or a float to multiply the objective value of this data set.
* objective_method (str) - The 'method' keyword in function
despasito.parameter_fitting.fit_functions.obj_function_form.
* density_opts (dict) - Optional,
default={"min_density_fraction":(1.0 / 60000.0), "density_increment":10.0,
"max_volume_increment":1.0E-4}, Dictionary of options used in calculating
pressure vs. mole fraction curves.
* kwargs for
:func:`~despasito.parameter_fitting.fit_functions.obj_function_form`
Attributes
----------
name : str
Data type, in this case liquid_density
Eos : obj
Equation of state object
weights : dict, Optional, default: {"some_property": 1.0 ...}
Dictionary with keys corresponding to those in thermodict, with weighting
factor or vector for each system property used in fitting
obj_opts : dict
Keywords to compute the objective function with
:func:`~despasito.parameter_fitting.fit_functions.obj_function_form`.
npoints : int
Number of sets of system conditions this object computes
result_keys : list
Thermodynamic property names used in calculation of objective function. In in
this case: ["rhol", "phil"]
thermodict : dict
Dictionary of inputs needed for thermodynamic calculations
- calculation_type (str) default=liquid_properties
- density_opts (dict) default={"min_density_fraction":(1.0 / 300000.0),
"density_increment":10.0, "max_volume_increment":1.0E-4}
"""
def __init__(self, data_dict):
data_dict = data_dict.copy()
super().__init__(data_dict)
self.name = "liquid_density"
tmp = {
"min_density_fraction": (1.0 / 300000.0),
"density_increment": 10.0,
"max_volume_increment": 1.0e-4,
}
if "density_opts" in self.thermodict:
tmp.update(self.thermodict["density_opts"])
self.thermodict["density_opts"] = tmp
if self.thermodict["calculation_type"] is None:
self.thermodict["calculation_type"] = "liquid_properties"
if "xi" in data_dict:
self.thermodict["xilist"] = data_dict["xi"]
del data_dict["xi"]
if "T" in data_dict:
self.thermodict["Tlist"] = data_dict["T"]
del data_dict["T"]
if "P" in data_dict:
self.thermodict["Plist"] = data_dict["P"]
del data_dict["P"]
self.thermodict.update(data_dict)
thermo_keys = ["Plist", "xilist", "Tlist"]
self.result_keys = ["rhol", "phil"]
key_list = list(set(thermo_keys + self.result_keys))
self.thermodict.update(gtb.check_length_dict(self.thermodict, key_list))
self.npoints = np.size(self.thermodict["Tlist"])
if "xilist" not in self.thermodict and self.Eos.number_of_components > 1:
raise ValueError("Ambiguous mixture composition. Define xi")
thermo_defaults = [
constants.standard_pressure,
np.array([[1.0] for x in range(self.npoints)]),
constants.standard_temperature,
]
self.thermodict.update(gtb.set_defaults(self.thermodict, thermo_keys, thermo_defaults, lx=self.npoints))
self.weights.update(gtb.check_length_dict(self.weights, self.result_keys, lx=self.npoints))
self.weights.update(gtb.set_defaults(self.weights, self.result_keys, 1.0))
if "Tlist" not in self.thermodict and "rhol" not in self.thermodict:
raise ImportError("Given liquid property data, values for T, xi, and rhol should have" " been provided.")
logger.info(
"Data type 'liquid_properties' initiated with calculation_type, {}, and "
"data types: {}.\nWeight data by: {}".format(
self.thermodict["calculation_type"],
", ".join(self.result_keys),
self.weights,
)
)
def _thermo_wrapper(self):
"""
Generate thermodynamic predictions from Eos object
Returns
-------
phase_list : float
A list of the predicted thermodynamic values estimated from thermo
calculation. This list can be composed of lists or floats
"""
# Remove results
opts = self.thermodict.copy()
tmp = self.result_keys + ["name", "parameters_guess"]
for key in tmp:
if key in opts:
del opts[key]
try:
output_dict = thermo(self.Eos, **opts)
output = [output_dict["rhol"]]
except Exception:
raise ValueError("Calculation of calc_liquid_density failed")
return output
[docs]
def objective(self):
"""
Generate objective function value from this dataset
Returns
-------
obj_val : float
A value for the objective function
"""
phase_list = self._thermo_wrapper()
# Reformat array of results
phase_list, len_list = ff.reformat_output(phase_list)
phase_list = np.transpose(np.array(phase_list))
# objective function
obj_value = ff.obj_function_form(
phase_list, self.thermodict["rhol"], weights=self.weights["rhol"], **self.obj_opts
)
logger.info("Obj. breakdown for {}: rhol {}".format(self.name, obj_value))
if np.isnan(obj_value) or obj_value == 0.0:
obj_value = np.inf
return obj_value