ThermoPyL

Tools for exploring and using the ThermoML Archive from the NIST TRC.

References

See the arXiv preprint:

Towards Automated Benchmarking of Atomistic Forcefields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive Kyle A. Beauchamp, Julie M. Behr, Ariën S. Rustenburg, Christopher I. Bayly, Kenneth Kroenlein, John D. Chodera arXiv:1506.00262

Installation:

The easiest way to install ThermoPyL is via the conda package manager, which comes with the Anaconda Scientific Python Distribution:

conda config --add channels choderalab
conda install thermopyl

Creating a local mirror of the ThermoML Archive

Create a local mirror of the ThermoML Archive:
```
thermoml-update-mirror
```
By default, the archive is placed in ~/.thermoml/. You can use the environment variable THERMOML_PATH to store its location on your disk. Re-running this command will update the local mirror with new data published in the ThermoML Archive RSS feeds.
Run thermoml-build-pandas to create a pandas version of the database, saved as an HDF5 file in the archive directory. You can restrict the data subset that will be compiled with command-line arguments. See the command-line help:
```
% thermoml-build-pandas --help
usage: thermoml-build-pandas [-h] [--journalprefix JOURNALPREFIX]
                         [--path path]
```

Build a Pandas dataset from local ThermoML Archive mirror.

optional arguments: -h, --help show this help message and exit --journalprefix JOURNALPREFIX journal prefix to use in globbing *.xml files --path path path to local ThermoML Archive mirror

3.  Use Pandas to query the experimental literature:
```python
import thermopyl
# Read ThermoML archive data into pandas dataframe
df = thermopyl.pandas_dataframe()

Datatypes are listed in columns (in addition to useful fields like components, filename, and phase):

datatypes = list(df.columns)

Unavailable data is labeled as NaN. You can use this to extract useful data by querying on data that is not NaN. For example, to extract dataframe rows with mass densities:

# Extract rows with mass densities
densities = df[np.isnan(df['Mass density, kg/m3'])==False]
# Get a list of unique components
unique_components = set(df['components'])

Updating a locally existing copy of the ThermoML Archive via the Python API

To update an existing locally saved archive via the Python API:

import thermopyl
thermopyl.update_archive()

Maintainers

Kyle A Beaucamp (MSKCC)
John D. Chodera (MSKCC)
Arien Sebastian Rustenburg (MSKCC)

choderalab / thermopyl