ThermoML Data Sets

The ThermoMLDataSet object offers an API for extracting physical properties from the NIST ThermoML Archive, both directly from the archive itself or from files stored in the IUPAC- standard ThermoML format.

The API only supports extracting those properties which have been registered with the frameworks plug-in system, and does not currently load the full set of metadata available in the archive files.

Note

If the metadata you require is not currently exposed, please open an issue on the GitHub issue tracker to request it.

Currently the framework has built-in support for extracting:

where here both the ThermoML property name (as defined by the IUPAC XML schema) and the built-in framework class are listed.

Registering Properties

Properties to be extracted from ThermoML archives must have a corresponding class representation to be loading into. This class representation must both:

As an example, a class representation of the ThermoML ‘Mass density, kg/m3’ property could be defined and registered with the plug-in system using:

@thermoml_property("Mass density, kg/m3", supported_phases=PropertyPhase.Liquid)
class Density(PhysicalProperty):
    """A class representation of a mass density property"""

The thermoml_property() decorator takes in the name of the ThermoML property (as defined by the IUPAC schema) as well as the phases which the framework will be able to estimate this property in.

Multiple ThermoML properties can be mapped onto a single class using the flexible register_thermoml_property() function. For example, the ‘Specific volume, m3/kg’ property (which is simply the reciprocal of mass density) may be mapped onto the Density object by providing a conversion_function:

def specific_volume_to_mass_density(specific_volume):
    """Converts a specific volume measurement into a mass
    density.

    Parameters
    ----------
    specific_volume: ThermoMLProperty
        The specific volume measurement to convert.
    """
    mass_density = Density()

    mass_density.value = 1.0 / specific_volume.value

    if mass_density.uncertainty is not None:
        mass_density.uncertainty = 1.0 / mass_density.uncertainty

    mass_density.phase = specific_volume.phase

    mass_density.thermodynamic_state = specific_volume.thermodynamic_state
    mass_density.substance = specific_volume.substance

    return mass_density

# Register the ThermoML property using the conversion function.
register_thermoml_property(
    thermoml_string="Specific volume, m3/kg",
    supported_phases=PropertyPhase.Liquid,
    property_class=Density,
    conversion_function=specific_volume_to_mass_density
)

Converting the different density derivatives into a single density class removes the need to produce many very similar class representations of density measurements, and allows a single calculation schema to be defined for all variants.

Loading Data Sets

Data sets are most easily loaded using their digital object identifiers (DOI). For example, to retrieve the ThermoML data set that accompanies this paper, we can simply use the DOI 10.1016/j.jct.2005.03.012:

data_set = ThermoMLDataset.from_doi('10.1016/j.jct.2005.03.012')

Data can be pulled from multiple sources at once by specifying multiple identifiers:

identifiers = ['10.1021/acs.jced.5b00365', '10.1021/acs.jced.5b00474']
dataset = ThermoMLDataset.from_doi(*identifiers)

Entire archives of properties can be downloaded directly from the ThermoML website and parsed by the framework. For example, to create a data set object containing all of the measurements recorded from the International Journal of Thermophysics:

# Download the archive of all properties from the IJT journal.
import requests
request = requests.get("https://trc.nist.gov/ThermoML/IJT.tgz", stream=True)

# Make sure the request went ok.
assert request

# Unzip the files into a new 'ijt_files' directory.
import io, tarfile
tar_file = tarfile.open(fileobj=io.BytesIO(request.content))
tar_file.extractall("ijt_files")

# Get the names of the extracted files
import glob
file_names = glob.glob("ijt_files/*.xml")

# Create the data set object
from propertyestimator.datasets.thermoml import ThermoMLDataSet
data_set = ThermoMLDataSet.from_file(*file_names)

# Save the data set to a JSON object
data_set.json(file_path="ijt.json", format=True)