Linux, OSX, Windows
A code for calculating the standard state thermodynamic properties of substances and reactions at a given temperature and pressure.
If you use it in your work please cite the JOSS publication
- Code documentation
- Simple C++ API example
- Try ThermoFun
- Python example
- Install using Conda
- Build and install using cmake
- Build and install using cmake and conda
- Reporting bugs
- Contributing
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- Using a json database file
#!c++
int main()
{
// Create the batch object using a database file in JSON
ThermoFun::ThermoBatch batch("Resources/Databases/aq17-thermofun.json");
// Optional: set units, default units are in SI
batch.setPropertiesUnits({"temperature", "pressure"},{"degC","bar"});
// Optional: change default significant digits
batch.setPropertiesDigits({"gibbs_energy","entropy", "volume", "enthalpy", "temperature", "pressure"}, {0, 1, 2, 0, 0, 0});
// Retrieve the entropy of H2O
double H2Oentropy = batch.thermoPropertiesSubstance( 300, 2000, "H2O@", "entropy").toDouble();
// Retrieve the derivative of G with respect to T
double H2OdGdT = batch.thermoPropertiesSubstance( 300, 2000, "H2O", "entropy").toThermoScalar().ddt;
// Write results to a comma separate files for a list of T-P pairs, substances, and properties
batch.thermoPropertiesSubstance({{25, 1},{40, 1},{70, 100},{90, 100},{100, 100}}, // list of T-P pairs
{"Al+3", "OH-", "SiO2@"}, // list of substance symbols
{"gibbs_energy","entropy", "volume", "enthalpy"} // list of properties
).toCSV("results.csv"); // output
return 0;
}
- Using the database client and retrieving a ThermoDataSet from the remote database. This example uses the
thermohubclient
#!c++
int main()
{
// Initialize a database client object
ThermoFun::DatabaseClient dbc;
// Create a ThermoFun database using the records list
ThermoFun::Database db(dbc.getDatabase('aq17'));
// Initialize an batch object using the database
ThermoFun::ThermoBatch batch (db);
// Optional set calculation and output preferences
ThermoFun::OutputSettings op;
op.isFixed = true;
op.outputSolventProperties = true;
op.reactionPropertiesFromReactants = false;
op.substancePropertiesFromReaction = false;
batch.setOutputSettings(op);
// Optional set units and significant digits
batch.setPropertiesUnits({"temperature", "pressure"},{"degC","bar"});
batch.setPropertiesDigits({ "reaction_gibbs_energy","reaction_entropy", "reaction_volume",
"reaction_enthalpy","logKr", "temperature", "pressure"}, {0, 4, 4, 4, 4, 0, 0});
batch.thermoPropertiesReaction({{25,1}}, {"AmSO4+", "MgSiO3@"}, {"reaction_gibbs_energy", "reaction_entropy",
"reaction_volume", "reaction_enthalpy", "logKr"}).toCSV("results.csv");
batch.thermoPropertiesReaction({0,20,50,75},{0,0,0,0},{"AmSO4+", "MgSiO3@"}, {"reaction_gibbs_energy", "reaction_entropy",
"reaction_volume", "reaction_enthalpy", "logKr"}).toCSV("results.csv");
}
- Using a json database file
#!Python
import thermofun as fun
import thermohubclient as hubclient
properties = fun.ThermoPropertiesSubstance
engine = fun.ThermoEngine("Resources/databases/aq17-thermofun.json")
prop = engine.thermoPropertiesSubstance(373.15, 100000000, "H2O@")
print(prop.gibbs_energy.val)
print(prop.gibbs_energy.ddt)
print(prop.entropy.val)
print(prop.gibbs_energy.ddp)
print(prop.gibbs_energy.err)
print(prop.gibbs_energy.sta)
# Create the engine object using a database file in JSON
batch = fun.ThermoBatch("Resources/databases/aq17-thermofun.json")
# Optional: change default units
batch.setPropertiesUnits(["temperature", "pressure"],["degC","bar"])
# Optional: change default significant digits
batch.setPropertiesDigits(["gibbs_energy","entropy", "volume", "enthalpy", "temperature", "pressure"], [0, 1, 2, 0, 0, 0])
H2Oentropy = batch.thermoPropertiesSubstance( 300, 2000, "H2O@", "entropy").toDouble()
print(H2Oentropy)
V = batch.thermoPropertiesSubstance( 250, 1000, "H2O@", "volume").toThermoScalar()
# Write results to a comma separate files for a list of T-P pairs, substances, and properties
batch.thermoPropertiesSubstance( [[25, 1],[40, 1],[70, 100],[90, 100],[100, 100]], # // list of T-P pairs
["Al+3", "OH-", "SiO2@"], # // list of substance symbols
["gibbs_energy","entropy", "volume", "enthalpy"] # // list of properties
).toCSV("results.csv")
- Using the database client and retrieving a ThermoDataSet from the remote database. This example uses the
thermohubclient, that can be installed from conda-forge executingconda install -c conda-forge thermohubclient
#!Python
import thermofun as fun
import thermohubclient as hubclient
print("\n# Initialize a database client object\n")
dbc = hubclient.DatabaseClient()
print("ThermoDataSets")
for t in dbc.availableThermoDataSets():
print(f'{t}')
print('\n')
aq17 = fun.Database(dbc.getDatabase('aq17'))
print("\n# Initialize an interface object using the database\n")
batch2 = fun.ThermoBatch(aq17)
print("\n# Optional: set the solvent symbol used for calculating properties of aqueous species\n")
batch2.setSolventSymbol("H2O@")
print("\n# Optional set calculation and output preferences\n")
op = fun.BatchPreferences()
op.isFixed = True
op.outputSolventProperties = True
op.reactionPropertiesFromReactants = False
op.substancePropertiesFromReaction = False
batch2.setBatchPreferences(op)
print("\n# Optional set units and significant digits\n")
batch2.setPropertiesUnits(["temperature", "pressure"],["degC","bar"])
batch2.setPropertiesDigits(["gibbs_energy","entropy", "volume",
"enthalpy","logKr", "temperature", "pressure"], [0, 4, 4, 4, 4, 0, 0])
print("\n# Do calculations and write output\n")
batch2.thermoPropertiesSubstance([[25,1]], ["NaCO3-", "Mg+2"], ["gibbs_energy", "entropy",
"volume", "enthalpy"]).toCSV("results_dbc.csv")
ThermoFun can be easily installed using Conda package manager. If you have Conda installed, first add the conda-forge channel by executing
#!bash
conda config --add channels conda-forge
install ThermoFun by executing the following command:
#!bash
conda install thermofun
Conda can be installed from Miniconda.
- Make sure you have g++, cmake and git installed. If not, install them (on Ubuntu Linux):
#!bash
sudo apt-get install g++ cmake git
-
Download ThermoFun source code using git clone
-
In a terminal, at the home directory level e.g.
<user>@ubuntu:~$copy-paste and run the following code:
#!bash
git clone https://github.com/thermohub/thermofun.git && cd thermofun
- In the terminal you should be in
~/thermofun$.
This option allows the user to build thermofun library that works with a user provided thermodynamic database file in json format and has only one thirdpary library dependency. To build thermofun with access to the thermohub thermodynamic database cloud and local server see bellow.
The thermofun library uses nlohmann/json.hpp as thirdparty dependency to parse database files in json format. To install the header only json library in a terminal ~/thermofun$ execute the following:
#!bash
sudo ./install-dependencies.sh
In the terminal ~/thermofun$, execute the following commands:
#!bash
mkdir build && \
cd build && \
cmake .. && \
make
To take advantage of parallel compilation use make -j3. 3 representing the number of threads.
For a global installation of the compiled libraries in your system, execute:
#!bash
sudo make install
This will install Thermofun library and header files in the default installation directory of your system (e.g, /usr/local/ or if conda is active, in the instalation directory of the conda environment).
For a local installation, you can specify a directory path for the installed files as follows:
#!bash
cmake .. -DCMAKE_INSTALL_PREFIX=/home/username/local/
then execute:
sudo make install
To compile ThermoFun library in debug mode:
#!bash
cmake .. -DCMAKE_BUILD_TYPE=Debug
then execute:
sudo make install
(B) Build and install ThermoFun library (working with access to the local and cloud ThemroHub database)
This option builds thermofun library together with the dbclient, which provides access to the local and cloud thermohub databases, allowing specific a ThermoDataSet to be used or a selection on elements of the thermodynamic data.
Clone and install ThermoHubClient library
#!bash
git clone https://bitbucket.org/gems4/thermohubclient.git
cd thermohubclient
sudo ./install-dependencies.sh
mkdir build
cd build
cmake ..
make
For a global installation of the compiled library in your system, execute:
#!bash
sudo make install
This procedure uses Conda for handling all the dependencies of ThermoFun and builds ThermoFun for Windows, Mac OS X, and Linux.
Once you have conda installed execute:
#!bash
conda install -n base conda-devenv
This installs conda-devenv, a conda tool used to define and initialize conda environments.
Download ThermoFun from github
#!bash
git clone https://github.com/thermohub/thermofun.git && cd thermofun
In the next step we create a clean environment with all dependencies necessary to build ThermoFun, executing:
#!bash
conda devenv
In the next step we need to activate the thermofun environment
#!bash
conda activate thermofun
Remember to always activate thermofun environment whenever you use ThermoFun from C++ or Python. This is because conda will adjust some environment variables in your system.
Now we can proceed and build ThermoFun using CMake.
To report a bug, please go to ThermoFun's Issues and enter a descriptive title and write your issue with enough details. Please provide a minimum reproducible example to be more efficient in identifying the bug and fixing it.
For questions and issues don't hesitate to chat with us on Gitter.
The Fork & Pull Request Workflow is used. Below is a summary of the necessary steps you need to take:
- Fork this repository
- Clone the repository at your machine
- Add your changes in a branch named after what's being done (
lower-case-with-hyphens) - Make a pull request to
thermohub/thermofun, targeting themainbranch
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