ThermoMLDataSet¶

class openff.evaluator.datasets.thermoml.ThermoMLDataSet[source]¶

A dataset of physical property measurements created from a ThermoML dataset.

Examples

For example, we can use the DOI 10.1016/j.jct.2005.03.012 as a key for retrieving the dataset from the ThermoML Archive:

>>> dataset = ThermoMLDataSet.from_doi('10.1016/j.jct.2005.03.012')

You can also specify multiple ThermoML Archive keys to create a dataset from multiple ThermoML files:

>>> thermoml_keys = ['10.1021/acs.jced.5b00365', '10.1021/acs.jced.5b00474']
>>> dataset = ThermoMLDataSet.from_doi(*thermoml_keys)

__init__()[source]¶: Constructs a new ThermoMLDataSet object.

Methods

`__init__`()	Constructs a new ThermoMLDataSet object.
`add_properties`(*physical_properties[, validate])	Adds a physical property to the data set.
`filter_by_components`(number_of_components)	Filter the data set based on the number of components present in the substance the data points were collected for.
`filter_by_elements`(*allowed_elements)	Filters out those properties which were estimated for
`filter_by_function`(filter_function)	Filter the data set using a given filter function.
`filter_by_phases`(phases)	Filter the data set based on the phase of the property (e.g liquid).
`filter_by_pressure`(min_pressure, max_pressure)	Filter the data set based on a minimum and maximum pressure.
`filter_by_property_types`(*property_types)	Filter the data set based on the type of property (e.g Density).
`filter_by_smiles`(*allowed_smiles)	Filters out those properties which were estimated for
`filter_by_temperature`(min_temperature, …)	Filter the data set based on a minimum and maximum temperature.
`filter_by_uncertainties`()	Filters out those properties which don’t have their uncertainties reported.
`from_doi`(*doi_list)	Load a ThermoML data set from a list of DOIs
`from_file`(*file_list)	Load a ThermoML data set from a list of files
`from_json`(file_path)	Create this object from a JSON file.
`from_url`(*url_list)	Load a ThermoML data set from a list of URLs
`from_xml`(xml, default_source)	Load a ThermoML data set from an xml object.
`json`([file_path, format])	Creates a JSON representation of this class.
`merge`(data_set[, validate])	Merge another data set into the current one.
`parse_json`(string_contents[, encoding])	Parses a typed json string into the corresponding class structure.
`properties_by_substance`(substance)	A generator which may be used to loop over all of the properties which were measured for a particular substance.
`properties_by_type`(property_type)	A generator which may be used to loop over all of properties of a particular type, e.g.
`to_pandas`()	Converts a PhysicalPropertyDataSet to a pandas.DataFrame object with columns of
`validate`()	Checks to ensure that all properties within the set are valid physical property object.

Attributes

`properties`	A list of all of the properties within this set.
`property_types`	The types of property within this data set.
`registered_properties`
`sources`	The sources from which the properties in this data set were gathered.
`substances`	The substances for which the properties in this data set were collected for.

classmethod from_doi(*doi_list)[source]¶

Load a ThermoML data set from a list of DOIs

Parameters: doi_list (str) – The list of DOIs to pull data from
Returns: The loaded data set.
Return type: ThermoMLDataSet

classmethod from_url(*url_list)[source]¶

Load a ThermoML data set from a list of URLs

Parameters: url_list (str) – The list of URLs to pull data from
Returns: The loaded data set.
Return type: ThermoMLDataSet

classmethod from_file(*file_list)[source]¶

Load a ThermoML data set from a list of files

Parameters: file_list (str) – The list of files to pull data from
Returns: The loaded data set.
Return type: ThermoMLDataSet

add_properties(*physical_properties, validate=True)¶

Adds a physical property to the data set.

Parameters

physical_properties (PhysicalProperty) – The physical property to add.
validate (bool) – Whether to validate the properties before adding them to the set.

filter_by_components(number_of_components)¶

Filter the data set based on the number of components present in the substance the data points were collected for.

Parameters: number_of_components (int) – The allowed number of components in the mixture.

Examples

Filter the dataset to only include pure substance properties.

>>> # Load in the data set of properties which will be used for comparisons
>>> from openff.evaluator.datasets.thermoml import ThermoMLDataSet
>>> data_set = ThermoMLDataSet.from_doi('10.1016/j.jct.2016.10.001')
>>>
>>> data_set.filter_by_components(number_of_components=1)

filter_by_elements(*allowed_elements)¶

Filters out those properties which were estimated for: compounds which contain elements outside of those defined in allowed_elements.

Parameters: allowed_elements (str) – The symbols (e.g. C, H, Cl) of the elements to retain.

filter_by_function(filter_function)¶

Filter the data set using a given filter function.

Parameters: filter_function (lambda) – The filter function.

filter_by_phases(phases)¶

Filter the data set based on the phase of the property (e.g liquid).

Parameters: phases (PropertyPhase) – The phase of property which should be retained.

Examples

Filter the dataset to only include liquid properties.

>>> # Load in the data set of properties which will be used for comparisons
>>> from openff.evaluator.datasets.thermoml import ThermoMLDataSet
>>> data_set = ThermoMLDataSet.from_doi('10.1016/j.jct.2016.10.001')
>>>
>>> from openff.evaluator.datasets import PropertyPhase
>>> data_set.filter_by_temperature(PropertyPhase.Liquid)

filter_by_pressure(min_pressure, max_pressure)¶

Filter the data set based on a minimum and maximum pressure.

Parameters

min_pressure (pint.Quantity) – The minimum pressure.
max_pressure (pint.Quantity) – The maximum pressure.

Examples

Filter the dataset to only include properties measured between 70-150 kPa.

>>> # Load in the data set of properties which will be used for comparisons
>>> from openff.evaluator.datasets.thermoml import ThermoMLDataSet
>>> data_set = ThermoMLDataSet.from_doi('10.1016/j.jct.2016.10.001')
>>>
>>> from openff.evaluator import unit
>>> data_set.filter_by_temperature(min_pressure=70*unit.kilopascal, max_temperature=150*unit.kilopascal)

filter_by_property_types(*property_types)¶

Filter the data set based on the type of property (e.g Density).

Parameters: property_types (PropertyType or str) – The type of property which should be retained.

Examples

Filter the dataset to only contain densities and static dielectric constants

>>> # Load in the data set of properties which will be used for comparisons
>>> from openff.evaluator.datasets.thermoml import ThermoMLDataSet
>>> data_set = ThermoMLDataSet.from_doi('10.1016/j.jct.2016.10.001')
>>>
>>> # Filter the dataset to only include densities and dielectric constants.
>>> from openff.evaluator.properties import Density, DielectricConstant
>>> data_set.filter_by_property_types(Density, DielectricConstant)

or

>>> data_set.filter_by_property_types('Density', 'DielectricConstant')

filter_by_smiles(*allowed_smiles)¶

Filters out those properties which were estimated for: compounds which do not appear in the allowed smiles list.

Parameters: allowed_smiles (str) – The smiles identifiers of the compounds to keep after filtering.

filter_by_temperature(min_temperature, max_temperature)¶

Filter the data set based on a minimum and maximum temperature.

Parameters

min_temperature (pint.Quantity) – The minimum temperature.
max_temperature (pint.Quantity) – The maximum temperature.

Examples

Filter the dataset to only include properties measured between 130-260 K.

>>> # Load in the data set of properties which will be used for comparisons
>>> from openff.evaluator.datasets.thermoml import ThermoMLDataSet
>>> data_set = ThermoMLDataSet.from_doi('10.1016/j.jct.2016.10.001')
>>>
>>> from openff.evaluator import unit
>>> data_set.filter_by_temperature(min_temperature=130*unit.kelvin, max_temperature=260*unit.kelvin)

filter_by_uncertainties()¶: Filters out those properties which don’t have their uncertainties reported.

classmethod from_json(file_path)¶

Create this object from a JSON file.

Parameters: file_path (str) – The path to load the JSON from.
Returns: The parsed class.
Return type: cls

classmethod from_xml(xml, default_source)[source]¶

Load a ThermoML data set from an xml object.

Parameters

xml (str) – The xml string to parse.
default_source (Source) – The source to use if one cannot be parsed from the archive itself.

Returns

The loaded ThermoML data set.

Return type

ThermoMLDataSet

json(file_path=None, format=False)¶

Creates a JSON representation of this class.

Parameters

file_path (str, optional) – The (optional) file path to save the JSON file to.
format (bool) – Whether to format the JSON or not.

Returns

The JSON representation of this class.

Return type

str

merge(data_set, validate=True)¶

Merge another data set into the current one.

Parameters

data_set (PhysicalPropertyDataSet) – The secondary data set to merge into this one.
validate (bool) – Whether to validate the other data set before merging.

classmethod parse_json(string_contents, encoding='utf8')¶

Parses a typed json string into the corresponding class structure.

Parameters

string_contents (str or bytes) – The typed json string.
encoding (str) – The encoding of the string_contents.

Returns

The parsed class.

Return type

Any

property properties¶

A list of all of the properties within this set.

Type: tuple of PhysicalProperty

properties_by_substance(substance)¶

A generator which may be used to loop over all of the properties which were measured for a particular substance.

Parameters: substance (Substance) – The substance of interest.
Returns
Return type: generator of PhysicalProperty

properties_by_type(property_type)¶

A generator which may be used to loop over all of properties of a particular type, e.g. all “Density” properties.

Parameters: property_type (str or type of PhysicalProperty) – The type of property of interest. This may either be the string class name of the property or the class type.
Returns
Return type: generator of PhysicalProperty

property property_types¶

The types of property within this data set.

Type: set of str

property sources¶

The sources from which the properties in this data set were gathered.

Type: set of Source

property substances¶

The substances for which the properties in this data set were collected for.

Type: set of Substance

to_pandas()¶

Converts a PhysicalPropertyDataSet to a pandas.DataFrame object with columns of

‘Id’

‘Temperature (K)’

‘Pressure (kPa)’

‘Phase’

‘N Components’

‘Component 1’

‘Role 1’

‘Mole Fraction 1’

‘Exact Amount 1’

…

‘Component N’

‘Role N’

‘Mole Fraction N’

‘Exact Amount N’

‘<Property 1> Value (<default unit>)’

‘<Property 1> Uncertainty / (<default unit>)’

…

‘<Property N> Value / (<default unit>)’

‘<Property N> Uncertainty / (<default unit>)’

‘Source’

where ‘Component X’ is a column containing the smiles representation of component X.

Returns: The create data frame.
Return type: pandas.DataFrame

validate()¶: Checks to ensure that all properties within the set are valid physical property object.