How-to use the Data Management Framework#

The Data Management Framework (DMF) is used to organize data within IDAES. It is often not visible, but does have an API and command-line tool (‘dmf’) to view and edit its information.


Create initial configuration (bootstrap)#

The DMF uses a file in the user’s home directory called, by default, “.dmf” to give the location of the currently active workpace (and, potentially, other things in the future). To create this file and initialize it with the default workspace for the current IDAES installation, you can use either a command-line or Python call. You only need to do this once per home-directory (which for most people means once, period).


dmf setup

API call:

from idaes.core.dmf import create_configuration
result = create_configuration()
# result will be the location of the configuration file, as a
# Python pathlib.Path object.

Tabular Data#

The DMF has built-in support for a set of data tables (in CSV or Excel) associated with a reference such as a journal publication. This page documents how to use existing tables and how to add your own tables for others to use.

Find and use existing data#

Use the idaes.core.datasets module. You can list all available datasets with available().

from idaes.core import datasets
# returns a dictionary keyed by name with namedtuple value, like:
# {'Pitzer': AvailableResult(Class=<class 'idaes.core.datasets.Pitzer'>,
#                           description='Pitzer(1984) publication and related tables.')}

You can either use the class directly by its name, or pull it out of the available() result. In either case, instantiate the class to pull its data from the DMF:

# the next 2 lines are equivalent
pz = datasets.Pitzer()
pz = datasets.available()["Pitzer"].Class()

Once you have the dataset, you can see which tables are present with the list_tables() method.

# sample output:
# ['Standard G', 'Standard S', ..., 'Specific Enthalpy']

Then to retrieve and use a given table, use get_table() with the name of the desired table:

t = pz.get_table('Standard G')
# is a Pandas dataframe
# t.description has a (text) description of the table contents
# t.units and t.units_dict give the units by column index and name

Create and add new data#

To create your own tables, you need to create a new directory and put two things in it: a configuration file called dataset.json, and data files in Excel or CSV format.

An excerpt of a dataset.json is shown below:

    "name": "ThermoStuff:1999",
    "text": {
        "file": "Thermodynamic Properties.pdf",
        "title": "Thermodynamic Properties of Some Stuff",
        "date": "1999",
        "authors": "Joe Bazooka, Carl Froffenheffer, Andrew Lee",
        "venue": "Journal of Interesting Observations",
        "doi": ""
    "tables": [
        "name": "Standard G",
        "description": "Standard Gibbs energies, according to some guy I know",
        "datafile": "gibbs_std.csv"


Make sure you pick a unique string for the name field, since this will be the key by which this publication and associated data are found. Using the same name for different publications will result in one overwriting the other and other bad behavior.

The dataset.json above referred to one file that had the text of the publication and one file with comma-separated values of the data. Copy or move all these files into the same directory, let’s call it DataDir, whose contents will now be:

  • dataset.json

  • Thermodynamic Properties.pdf

  • gibbs_std.csv

Then you can load this directory of data into the DMF with the following command-line:

dmf load --global DataDir

The --global option means “use the default global DMF workspace instead of any current workspace”. If you choose to use your own workspace instead, you’ll have to pass it in explicitly later, e.g., to the subclass of Publication that you create below.

The data file format is a header row plus data, with the only “special” aspect being that if the name of a column in the header ends with some text in square brackets, that text is assumed to be the units for the values. For example:

Temperature [K], Pressure [Pa], Value
100, 90, 12.34

In this table, the units “K” and “Pa” would be parsed out of the first two columns, and the units of the third column would be empty.

Finally, you can make the dataset accessible as a class by subclassing from Publication and invoking the superclass with the name of the dataset. The key part to get right here is that the name used in the class constructor must match the name field from the dataset.json configuration file. For example, with the configuration given above:

from idaes.core.dmf.datasets import Publication

class ThermoStuff(Publication):
    def __init__(self, **kwargs):
        super().__init__("ThermoStuff:1999", **kwargs)

General API usage#

Create a DMF resource#

from idaes.core.dmf.resource import Resource

r = Resource()
r.v["version_info"]["version"] = test_version
r.v["collaborators"] = [
    {"name": "Clark Kent", "email": ""},
    {"name": "Superman", "email": ""},
        "isbn": "978-0201616224",
        "source": 'Hunt, A. and Thomas, D., "The Pragmatic Programmer", '
        "Addison-Wesley, 1999",
        "date": "1999-01-01",
        "type": "function",
        "name": "test_resource_full",
        "desc": "The test function",
        "location": "",
        "version": test_version,
r.v["datafiles"].append({"path": "/etc/passwd"})
r.v["aliases"] = ["test_resource_full"]
r.v["tags"] = ["test", "resource"] = {"arbitrary": {"values": [1, 2, 3]}}
return r