diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 00000000..db558c25 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v2 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper/paper.pdf \ No newline at end of file diff --git a/.github/workflows/linux.yml b/.github/workflows/linux.yml index 1f3e371b..490fc5a8 100644 --- a/.github/workflows/linux.yml +++ b/.github/workflows/linux.yml @@ -23,7 +23,7 @@ jobs: config: ["Release"] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Timestamp # Idea from https://tinyurl.com/47wz7vpv id: timestamp @@ -34,7 +34,7 @@ jobs: - name: Configuring Cache id: cache - uses: actions/cache@v2 + uses: actions/cache@v3 with: path: | /home/runner/.cache @@ -50,7 +50,6 @@ jobs: python-version: ${{ matrix.python-version }} channels: conda-forge channel-priority: true - use-only-tar-bz2: true # IMPORTANT: This needs to be set true for caching of miniconda packages to work properly! - name: Configuring Conda Environment shell: bash -l {0} diff --git a/.github/workflows/osx.yml b/.github/workflows/osx.yml index 4afd3306..64b70f0f 100644 --- a/.github/workflows/osx.yml +++ b/.github/workflows/osx.yml @@ -23,7 +23,7 @@ jobs: config: ["Release"] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Timestamp # Idea from https://tinyurl.com/47wz7vpv id: timestamp @@ -34,7 +34,7 @@ jobs: - name: Configuring Cache id: cache - uses: actions/cache@v2 + uses: actions/cache@v3 with: path: | /Users/runner/Library/Caches/ccache @@ -50,7 +50,6 @@ jobs: python-version: ${{ matrix.python-version }} channels: conda-forge channel-priority: true - use-only-tar-bz2: true # IMPORTANT: This needs to be set true for caching of miniconda packages to work properly! - name: Configuring Conda Environment shell: bash -l {0} diff --git a/.github/workflows/windows.yml b/.github/workflows/windows.yml index 14d709dc..f91ec961 100644 --- a/.github/workflows/windows.yml +++ b/.github/workflows/windows.yml @@ -24,7 +24,7 @@ jobs: config: ["Release"] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Timestamp # Idea from https://tinyurl.com/47wz7vpv id: timestamp @@ -35,7 +35,7 @@ jobs: - name: Configuring Cache id: cache - uses: actions/cache@v2 + uses: actions/cache@v3 with: path: | C:\Users\runneradmin\clcache @@ -52,7 +52,6 @@ jobs: activate-environment: '' channels: conda-forge channel-priority: true - use-only-tar-bz2: true # IMPORTANT: This needs to be set true for caching of miniconda packages to work properly! - name: Configuring Conda Environment env: diff --git a/.hdoc.toml b/.hdoc.toml new file mode 100644 index 00000000..1188a255 --- /dev/null +++ b/.hdoc.toml @@ -0,0 +1,32 @@ +[project] +name = "thermofun" +version = "0.4.2" + +# Optional, adding this will enable direct links from the documentation +# to your source code. +git_repo_url = "https://github.com/thermohub/thermofun/" +git_default_branch = "master" + +[paths] +compile_commands = "build/compile_commands.json" +# output_dir = "docs/hdoc-output" + +[pages] +homepage = "docs/index.md" + +[ignore] +# Symbols from any of the following path fragments will be ignored +ignore_private_members = true +paths = [ + "/python/", + "/pytests/", + "/tests/", + "/Common/", + "OptimizationUtils.h", + "/Substances/Solvent/Reaktoro/", + "/Substances/Gases/s_solmod_.cpp", + "/Substances/Gases/s_solmod_.h", + "/Substances/Gases/s_solmod2_.cpp", + "/Substances/Gases/verror.h" + # more paths can be added as needed +] diff --git a/CMakeLists.txt b/CMakeLists.txt index 1d67a02c..bc7948e7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.9) # Set the name of the project -project(ThermoFun VERSION 0.4.0 LANGUAGES CXX) +project(ThermoFun VERSION 0.4.3 LANGUAGES CXX) # Set the cmake module path of the project set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules") diff --git a/COPYING.LESSER b/COPYING.LESSER deleted file mode 100644 index 65c5ca88..00000000 --- a/COPYING.LESSER +++ /dev/null @@ -1,165 +0,0 @@ - GNU LESSER GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - - This version of the GNU Lesser General Public License incorporates -the terms and conditions of version 3 of the GNU General Public -License, supplemented by the additional permissions listed below. - - 0. Additional Definitions. - - As used herein, "this License" refers to version 3 of the GNU Lesser -General Public License, and the "GNU GPL" refers to version 3 of the GNU -General Public License. - - "The Library" refers to a covered work governed by this License, -other than an Application or a Combined Work as defined below. - - An "Application" is any work that makes use of an interface provided -by the Library, but which is not otherwise based on the Library. -Defining a subclass of a class defined by the Library is deemed a mode -of using an interface provided by the Library. - - A "Combined Work" is a work produced by combining or linking an -Application with the Library. The particular version of the Library -with which the Combined Work was made is also called the "Linked -Version". - - The "Minimal Corresponding Source" for a Combined Work means the -Corresponding Source for the Combined Work, excluding any source code -for portions of the Combined Work that, considered in isolation, are -based on the Application, and not on the Linked Version. - - The "Corresponding Application Code" for a Combined Work means the -object code and/or source code for the Application, including any data -and utility programs needed for reproducing the Combined Work from the -Application, but excluding the System Libraries of the Combined Work. - - 1. Exception to Section 3 of the GNU GPL. - - You may convey a covered work under sections 3 and 4 of this License -without being bound by section 3 of the GNU GPL. - - 2. 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You may convey such object -code under terms of your choice, provided that, if the incorporated -material is not limited to numerical parameters, data structure -layouts and accessors, or small macros, inline functions and templates -(ten or fewer lines in length), you do both of the following: - - a) Give prominent notice with each copy of the object code that the - Library is used in it and that the Library and its use are - covered by this License. - - b) Accompany the object code with a copy of the GNU GPL and this license - document. - - 4. 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Combined Libraries. - - You may place library facilities that are a work based on the -Library side by side in a single library together with other library -facilities that are not Applications and are not covered by this -License, and convey such a combined library under terms of your -choice, if you do both of the following: - - a) Accompany the combined library with a copy of the same work based - on the Library, uncombined with any other library facilities, - conveyed under the terms of this License. - - b) Give prominent notice with the combined library that part of it - is a work based on the Library, and explaining where to find the - accompanying uncombined form of the same work. - - 6. Revised Versions of the GNU Lesser General Public License. - - The Free Software Foundation may publish revised and/or new versions -of the GNU Lesser General Public License from time to time. Such new -versions will be similar in spirit to the present version, but may -differ in detail to address new problems or concerns. - - Each version is given a distinguishing version number. If the -Library as you received it specifies that a certain numbered version -of the GNU Lesser General Public License "or any later version" -applies to it, you have the option of following the terms and -conditions either of that published version or of any later version -published by the Free Software Foundation. If the Library as you -received it does not specify a version number of the GNU Lesser -General Public License, you may choose any version of the GNU Lesser -General Public License ever published by the Free Software Foundation. - - If the Library as you received it specifies that a proxy can decide -whether future versions of the GNU Lesser General Public License shall -apply, that proxy's public statement of acceptance of any version is -permanent authorization for you to choose that version for the -Library. diff --git a/COPYING b/LICENSE similarity index 100% rename from COPYING rename to LICENSE diff --git a/README.md b/README.md index a5a8ec1a..71011a89 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,20 @@ Linux, OSX, Windows [![Build Status](https://dev.azure.com/mirondanro/thermohub/_apis/build/status/thermohub.thermofun?branchName=master)](https://dev.azure.com/mirondanro/thermohub/_build/latest?definitionId=9&branchName=master) -A code for calculating the standard state thermodynamic properties of substances and reactions at a given temperature and pressure. +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 +[![DOI](https://joss.theoj.org/papers/10.21105/joss.04624/status.svg)](https://doi.org/10.21105/joss.04624) + +- [Code documentation](https://docs.hdoc.io/dmiron/thermofun/?target=_blank) +- [Simple C++ API example](#simple-c-api-example) +- [Try ThermoFun](#try-thermofun-in-your-browser-click-launch-binder) +- [Python example](#simple-python-api-example) +- [Install using Conda](#installation-using-conda) +- [Build and install using cmake](#install-thermofun-using-cmake) +- [Build and install using cmake and conda](#compile-and-install-thermofun-using-cmake-and-conda) +- [Reporting bugs](#reporting-bugs) +- [Contributing](#contributing-with-development) ## Try ThermoFun in your browser click launch binder @@ -45,28 +58,21 @@ int main() } ``` -* Using the database client and retrieving a ThermoDataSet from the remote database +* Using the database client and retrieving a ThermoDataSet from the remote database. This example uses the [`thermohubclient`](https://github.com/thermohub/thermohubclient) ``` #!c++ int main() { - // Set the file path to the database connection and preferences file (provided in the Resources/ folder) - setDatabaseConnectionFilePath("fun-hubclient-config.json"); - // Initialize a database client object ThermoFun::DatabaseClient dbc; - // Retrieve list of records given a ThermoDataSet symbol - auto records = dbc.recordsFromThermoDataSet("psinagra07"); - // Create a ThermoFun database using the records list - ThermoFun::Database db = databaseFromRecordList(dbc, records); + ThermoFun::Database db(dbc.getDatabase('aq17')); // Initialize an batch object using the database ThermoFun::Batch batch (db); - // Optional set calculation and output preferences ThermoFun::OutputSettings op; op.isFixed = true; @@ -89,7 +95,7 @@ int main() ``` -### Simple Python API example +## Simple Python API example * Using a json database file @@ -100,7 +106,7 @@ import thermohubclient as hubclient properties = fun.ThermoPropertiesSubstance -engine = fun.ThermoEngine("Resources/Databases/aq17-thermofun.json") +engine = fun.ThermoEngine("Resources/databases/aq17-thermofun.json") prop = engine.thermoPropertiesSubstance(373.15, 100000000, "H2O@") @@ -112,7 +118,7 @@ print(prop.gibbs_energy.err) print(prop.gibbs_energy.sta) # Create the engine object using a database file in JSON -batch = fun.ThermoBatch("Resources/aq17new-format.json") +batch = fun.ThermoBatch("Resources/databases/aq17-thermofun.json") # Optional: change default units batch.setPropertiesUnits(["temperature", "pressure"],["degC","bar"]) @@ -129,26 +135,28 @@ V = batch.thermoPropertiesSubstance( 250, 1000, "H2O@", "volume").toThermoScalar 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 + ).toCSV("results.csv") ``` -* Using the database client and retrieving a ThermoDataSet from the remote database +* Using the database client and retrieving a ThermoDataSet from the remote database. This example uses the `thermohubclient`, that can be installed from conda-forge executing `conda install -c conda-forge thermohubclient` ``` #!Python -hubclient.setDatabaseConnectionFilePath("Resources/fun-hubclient-config.json") +import thermofun as fun +import thermohubclient as hubclient print("\n# Initialize a database client object\n") dbc = hubclient.DatabaseClient() -print("\n# Retrieve list of records given a ThermoDataSet symbol\n") -records = dbc.recordsFromThermoDataSet("cemdata18") +print("ThermoDataSets") +for t in dbc.availableThermoDataSets(): + print(f'{t}') +print('\n') -print("\n# Create a ThermoFun database using the records list\n") -db = hubclient.databaseFromRecordList(dbc, records) +aq17 = fun.Database(dbc.getDatabase('aq17')) print("\n# Initialize an interface object using the database\n") -batch2 = fun.ThermoBatch(db) +batch2 = fun.ThermoBatch(aq17) print("\n# Optional: set the solvent symbol used for calculating properties of aqueous species\n") batch2.setSolventSymbol("H2O@") @@ -168,7 +176,7 @@ 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]], ["Na(CO3)-", "Mg+2"], ["gibbs_energy", "entropy", +batch2.thermoPropertiesSubstance([[25,1]], ["NaCO3-", "Mg+2"], ["gibbs_energy", "entropy", "volume", "enthalpy"]).toCSV("results_dbc.csv") ``` @@ -199,7 +207,7 @@ Conda can be installed from [Miniconda](https://conda.io/miniconda.html). sudo apt-get install g++ cmake git ``` -## Download ThermoFun source code using git clone +* Download ThermoFun source code using git clone * In a terminal, at the home directory level e.g. ```@ubuntu:~$``` copy-paste and run the following code: @@ -210,11 +218,11 @@ git clone https://github.com/thermohub/thermofun.git && cd thermofun * In the terminal you should be in ```~/thermofun$```. -## (A) Build and install ThermoFun library (working with json database files) +### (A) Build and install ThermoFun library (working with json database files) 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. -### Install Dependencies (if not using Conda environment) +#### Install Dependencies (if not using Conda environment) 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$``` execute the following: @@ -223,7 +231,7 @@ The thermofun library uses nlohmann/json.hpp as thirdparty dependency to parse d sudo ./install-dependencies.sh ``` -### Compiling the C++ library +#### Compiling the C++ library In the terminal ```~/thermofun$```, execute the following commands: @@ -270,11 +278,11 @@ then execute: sudo make install ``` -## (B) Build and install ThermoFun library (working with access to the local and cloud ThemroHub database) +### (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. -### Install ThermoHubClient +#### Install ThermoHubClient Clone and install ThermoHubClient library @@ -296,7 +304,7 @@ For a global installation of the compiled library in your system, execute: sudo make install ``` -## Install ThermoFun using CMake and Conda +## Compile and install ThermoFun using CMake and Conda This procedure uses Conda for handling all the dependencies of ThermoFun and builds ThermoFun for Windows, Mac OS X, and Linux. @@ -332,3 +340,18 @@ 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.](#install-thermofun-using-cmake) + +## Reporting bugs + +To report a bug, please go to [ThermoFun's Issues](https://github.com/thermohub/thermofun/issues/new) 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](https://gitter.im/thermofun/community). + +## Contributing with development + +The [Fork & Pull Request Workflow](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) is used. Below is a summary of the necessary steps you need to take: + +1. Fork this repository +2. Clone the repository at your machine +3. Add your changes in a branch named after what's being done (`lower-case-with-hyphens`) +4. Make a pull request to `thermohub/thermofun`, targeting the `main` branch diff --git a/ThermoFun/Batch/OutputBatch.h b/ThermoFun/Batch/OutputBatch.h index 660ac3c6..16044de7 100644 --- a/ThermoFun/Batch/OutputBatch.h +++ b/ThermoFun/Batch/OutputBatch.h @@ -23,14 +23,14 @@ class Output /** * @brief toCSV write results to CSV file - * with substances/reactions on rows and proeprties on columns + * with substances/reactions on rows and properties on columns * @param filename */ auto toCSV (std::string filename) -> void; /** * @brief toCSVTransposed write results to CSV file in a special transposed format. - * with substances/reactions on columns and proeprties on rows + * with substances/reactions on columns and properties on rows * @param filename */ auto toCSVTransposed (std::string filename) -> void; diff --git a/ThermoFun/Batch/ThermoBatch.cpp b/ThermoFun/Batch/ThermoBatch.cpp index 9ae20386..55384e84 100644 --- a/ThermoFun/Batch/ThermoBatch.cpp +++ b/ThermoFun/Batch/ThermoBatch.cpp @@ -615,5 +615,24 @@ auto ThermoBatch::results() -> const vvThScalar return pimpl->results; } +auto ThermoBatch::temperatureIncrement() -> const std::map& +{ + return pimpl->Tincrement; +} + +auto ThermoBatch::pressureIncrement() -> const std::map& +{ + return pimpl->Pincrement; +} + +auto ThermoBatch::propertyUnits () -> const std::map& +{ + return pimpl->givenPropertyUnits; +} +auto ThermoBatch::propertyDigits() -> const std::map& +{ + return pimpl->givenPropertyDigits; +} + } diff --git a/ThermoFun/Batch/ThermoBatch.h b/ThermoFun/Batch/ThermoBatch.h index 89b1ac4d..22b7efee 100644 --- a/ThermoFun/Batch/ThermoBatch.h +++ b/ThermoFun/Batch/ThermoBatch.h @@ -28,7 +28,7 @@ typedef std::vector vtps; typedef std::vector vtpr; /** - * @brief The OutputOptions struct holds the options for outputing results + * @brief The OutputOptions struct holds the options for outputting results */ struct BatchPreferences { @@ -68,17 +68,17 @@ struct BatchPreferences /// /// \brief The Interface class provides an interface to C++ codes coupled to ThermoFunk library for performing /// calculations related to the standard state properties of substances and reactions at different temperatures -/// and pressures, and retriveve the results as CSV output file, 2D vector (of double or ThrmoScalar types), or +/// and pressures, and retrieve the results as CSV output file, 2D vector (of double or ThrmoScalar types), or /// single value (of double or ThermoScalar types) for one property-substance-T-P calculation. /// -/// The class provides several functions for adding the symbol of the substance or reaction (or lists of symblos) +/// The class provides several functions for adding the symbol of the substance or reaction (or lists of symbols) /// for which the calculations will be performed. /// /// The class provides several functions for adding the temperature and pressure points at which the calculations /// will be performed. /// -/// The class provides several functions for setting the unist in which the results are dysplayed and the -/// siginifincat digits (or precision) in which the values will be written in the output CSV file. +/// The class provides several functions for setting the units in which the results are displayed and the +/// siginificant digits (or precision) in which the values will be written in the output CSV file. /// /// The input temperature and pressure should be in celsius and bar /// @@ -114,7 +114,7 @@ class ThermoBatch auto setTemperatureIncrement (const double& Tmin, const double& Tmax, const double& Tstep) -> void; auto setPressureIncrement (const double& Pmin, const double& Pmax, const double& Pstep) -> void; - // claculate functions substances + // calculate functions substances auto thermoPropertiesSubstance (double T, double P, std::string symbol, std::string property) -> Output; auto thermoPropertiesSubstance (double T, double P, vstr symbols, vstr properties) -> Output; auto thermoPropertiesSubstance (vstr symbols, vstr properties) -> Output; @@ -123,7 +123,7 @@ class ThermoBatch auto thermoPropertiesSubstance (std::vector temperatures, std::vector pressures, vstr symbols, vstr properties) -> Output; auto thermoPropertiesSubstance (std::vector temperatures, std::vector pressures, vstr symbols, vstr properties, vtps vTps) -> Output; - // claculate functions reactions + // calculate functions reactions auto thermoPropertiesReaction (double T, double P, std::string symbol, std::string property) -> Output; auto thermoPropertiesReaction (double T, double P, vstr symbols, vstr properties) -> Output; auto thermoPropertiesReaction (vstr symbols, vstr properties) -> Output; @@ -135,6 +135,12 @@ class ThermoBatch auto setBatchPreferences (const BatchPreferences &value) -> void; auto setSolventSymbol (const std::string solventSymbol) ->void; + auto temperatureIncrement () -> const std::map&; + auto pressureIncrement () -> const std::map&; + + auto propertyUnits () -> const std::map&; + auto propertyDigits () -> const std::map&; + private: struct Impl; std::shared_ptr pimpl; @@ -154,7 +160,7 @@ class ThermoBatch const std::map defaultPropertyNames = { - // Substance propeties + // Substance properties {"gibbs_energy", "substance" }, {"enthalpy", "substance" }, {"entropy", "substance" }, @@ -163,7 +169,7 @@ const std::map defaultPropertyNames = {"volume", "substance" }, {"helmholtz_energy", "substance" }, {"internal_energy", "substance" }, - // Reaction propeties + // Reaction properties {"reaction_gibbs_energy", "reaction" }, {"reaction_helmholtz_energy", "reaction" }, {"reaction_internal_energy", "reaction" }, @@ -174,7 +180,7 @@ const std::map defaultPropertyNames = {"reaction_heat_capacity_cv", "reaction" }, {"logKr", "reaction" }, {"lnKr", "reaction" }, - // Solvent propeties + // Solvent properties {"density", "solvent" }, {"densityT", "solvent" }, {"densityP", "solvent" }, diff --git a/ThermoFun/Database.h b/ThermoFun/Database.h index 2f7034b8..77205275 100644 --- a/ThermoFun/Database.h +++ b/ThermoFun/Database.h @@ -43,7 +43,7 @@ class Database explicit Database(std::string filename); /** - * @brief Database constructs a database instace from a vector of records in json format + * @brief Database constructs a database instance from a vector of records in json format * Records with the same symbol will be overwritten! * @param jsonRecords vector of records in JSON string format * @param _label, optional, (element, substance, reactions), @@ -68,7 +68,7 @@ class Database * @brief appendData append records to the database from a vector of JSON strings * Records with the same symbol will be overwritten! * @param jsonRecords vector of records in JSON string format - * @param _label, oprional, (element, substance, reactions), + * @param _label, optional, (element, substance, reactions), * used when the vector of records are of one type and do not contain themselves the key "_label" */ auto appendData(std::vector jsonRecords, std::string _label) -> void; diff --git a/ThermoFun/Substance.h b/ThermoFun/Substance.h index 9dac85fa..00c1c9c5 100644 --- a/ThermoFun/Substance.h +++ b/ThermoFun/Substance.h @@ -17,10 +17,10 @@ struct ThermoParametersSubstance; class Substance { public: - /// Constrcut a default Substance instance + /// Construct a default Substance instance Substance(); - /// Constrcut a Substance instance from a JSON string + /// Construct a Substance instance from a JSON string Substance(std::string jsonSubstance); /// Construct a copy of an Substance instance diff --git a/ThermoFun/Substances/Gases/GasCORK.cpp b/ThermoFun/Substances/Gases/GasCORK.cpp index d12075bd..58d50bb7 100644 --- a/ThermoFun/Substances/Gases/GasCORK.cpp +++ b/ThermoFun/Substances/Gases/GasCORK.cpp @@ -1,3 +1,4 @@ +#include #include "Substances/Gases/GasCORK.h" #include "Substances/Gases/s_solmod_.h" #include "Substance.h" @@ -18,13 +19,13 @@ auto thermoPropertiesGasCORK(Reaktoro_::Temperature TK, Reaktoro_::Pressure Pbar solmod::TCORKcalc myCORK( 1, Pbar.val, (TK.val), Eos_Code ); // modified 05.11.2010 (TW) double TClow = subst.thermoParameters().temperature_intervals[0][0]; - double * CPg = new double[7]; + std::array CPg; for (unsigned int i = 0; i < 7; i++) { CPg[i] = subst.thermoParameters().critical_parameters[i]; } - myCORK.CORKCalcFugPure( (TClow/*+273.15*/), (CPg), FugProps ); + myCORK.CORKCalcFugPure( (TClow/*+273.15*/), CPg.data(), FugProps ); // increment thermodynamic properties tps.gibbs_energy += R_CONSTANT * (TK) * log( FugProps[0] ); // from ideal gas at 1 bar and givent T to pure real gas/fluid at T and P diff --git a/ThermoFun/ThermoEngine.h b/ThermoFun/ThermoEngine.h index 38410451..6f644df8 100644 --- a/ThermoFun/ThermoEngine.h +++ b/ThermoFun/ThermoEngine.h @@ -22,7 +22,7 @@ struct ElectroPropertiesSolvent; struct PropertiesSolvent; /** - * @brief The Thermo class mainly calculates the thermodynamic properties of the substances + * @brief The ThermoEngine class calculates the thermodynamic properties of the substances * from the internal database. It also calculates the electro-chemical and other physico-chemical * properties of the solvent */ diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..27f5e030 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,68 @@ + + +# [ThermoFun](https://thermohub.org/thermofun/thermofun/) + +#### Details on the API functionality can be found by [searching](https://docs.hdoc.io/dmiron/thermofun/search.html) the documentation. The naming is the same in C++ and Python. + +Using ThermoFun is as simple as loading a [database](https://github.com/thermohub/thermofun-jupyter/blob/master/how-to-use-a-database.ipynb) initializing a [ThermoEngine](https://github.com/thermohub/thermofun-jupyter/blob/master/how-to-calculate-properties-thermoengine.ipynb)/[ThermoBatch](https://github.com/thermohub/thermofun-jupyter/blob/master/how-to-do-batch-calculations.ipynb) and calling the function to [calculate the properties](https://github.com/thermohub/thermofun-jupyter/blob/master/how-to-calculate-properties-substance-reaction.ipynb) for given substance(s)/reaction(s) at given T-P condition(s). + +```python + import thermofun as fun + database = fun.Database('aq17-thermofun.json') + engine = fun.ThermoEngine(database) + # T(K) P(Pa) symbol + Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, 'Ca+2') + print(f'G0 {Ca_ion.gibbs_energy.val} J/mol') + # T(K) P(Pa) reaction equation + calcite_dissolution = engine.thermoPropertiesReaction(348.15, 1e5, 'Calcite = Ca+2 + CO3-2') + print(f'drS0 of (Cal = Ca+2 + CO3-2) is {calcite_dissolution.reaction_entropy.val}') + print(f'drG0 of (Cal = Ca+2 + CO3-2) is {calcite_dissolution.reaction_gibbs_energy.val}') + print(f'logK0 of (Cal = Ca+2 + CO3-2) is {calcite_dissolution.log_equilibrium_constant.val}') +``` + +In Python using the `help` function can provide hints on the usability. + +```python +help(fun.ThermoEngine) + +Help on class ThermoEngine in module thermofun.PyThermoFun: + +class ThermoEngine(pybind11_builtins.pybind11_object) + | Contains methods for calculating the thermodynamic properties of the substances and reactions + ... +``` + +Try the ThermoFun examples in your browser: + +[![Launch ThermoFun tutorials](https://img.shields.io/badge/launch-ThermoFun%20tutorials-brightgreen?style=for-the-badge&logo=jupyter)](https://mybinder.org/v2/gh/thermohub/thermofun-jupyter/master?urlpath=lab/) + +## The main ThermoFun classes + +* [Database](https://docs.hdoc.io/dmiron/thermofun/r820E4166D6856B34.html): The Database class stores maps of elements, substances and reactions. A database instance can be used to create a ThermoEngine instance which can be further used to calculate the standard thermodynamic properties of substances and reactions at T and P + +[![Launch example on initializing a database from file](https://img.shields.io/badge/launch-Database%20example-brightgreen?style=for-the-badge&logo=jupyter)](https://mybinder.org/v2/gh/thermohub/thermofun-jupyter/master?urlpath=lab/tree/how-to-use-a-database.ipynb) + +* [ThermoEngine](https://docs.hdoc.io/dmiron/thermofun/r15CBE4920E8D3EF1.html): The main ThermoFun class for doing calculations. It calculates the thermodynamic properties of the substances from the database. It also calculates the electro-chemical and other physico-chemical properties of the solvent. + +[![Launch example on using ThermoEngine to calculate thermodynamic properties](https://img.shields.io/badge/launch-ThermoEngine%20example-brightgreen?style=for-the-badge&logo=jupyter)](https://mybinder.org/v2/gh/thermohub/thermofun-jupyter/master?urlpath=lab/tree/how-to-calculate-properties-thermoengine.ipynb) + +* [ThermoBatch](https://docs.hdoc.io/dmiron/thermofun/r88DCC0D87A971DFF.html): The Batch class is useful for doing batch calculations for a given list of substances, reactions, properties, and temperature and pressure grid. 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"J/(mol*K)" + ], + "values": [ + 29.345325469971 + ] + }, + "sm_volume": { + "errors": [ + 0 + ], + "units": [ + "J/bar" + ], + "values": [ + 2478.9699707031 + ] + }, + "symbol": "O2" + } + ], + "thermodataset": [ + "aq17" + ] +} diff --git a/paper/compile_paper.sh b/paper/compile_paper.sh new file mode 100755 index 00000000..0b90f447 --- /dev/null +++ b/paper/compile_paper.sh @@ -0,0 +1,5 @@ +docker run --rm \ + --volume $PWD:/data \ + --user $(id -u):$(id -g) \ + --env JOURNAL=joss \ + openjournals/inara diff --git a/paper/examples.ipynb b/paper/examples.ipynb new file mode 100644 index 00000000..9d55ad33 --- /dev/null +++ b/paper/examples.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e3da8efc", + "metadata": {}, + "outputs": [], + "source": [ + "import thermofun as fun" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "13b94f94", + "metadata": {}, + "outputs": [], + "source": [ + "database = fun.Database('aq17-thermofun.json')\n", + "engine = fun.ThermoEngine(database)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f0c85f6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G0 -545301.2878298083 J/mol\n" + ] + } + ], + "source": [ + "# T(K) P(Pa) symbol\n", + "Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, 'Ca+2')\n", + "print(f'G0 {Ca_ion.gibbs_energy.val} J/mol')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a36c08e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drS of (Cal = Ca+2 + CO3-2) is -259.12288450418015\n", + "drG of (Cal = Ca+2 + CO3-2) is 59914.092580924975\n", + "logK of (Cal = Ca+2 + CO3-2) is -8.988976334909019\n" + ] + } + ], + "source": [ + "# T(K) P(Pa) reaction equation\n", + "R = engine.thermoPropertiesReaction(348.15, 1e5, 'Calcite = Ca+2 + CO3-2')\n", + "print(f'drS of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}')\n", + "print(f'drG of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}')\n", + "print(f'logK of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "717d85a2", + "metadata": {}, + "outputs": [], + "source": [ + "batch = fun.ThermoBatch(database)\n", + "batch.setPropertiesUnits(['temperature', 'pressure'],['degC','bar'])\n", + "batch.setPressureIncrement(0,0,0)\n", + "batch.setTemperatureIncrement(0,300, 5)\n", + "substances = ['Na+', 'Mg+2', 'Ca+2', 'SiO2@']\n", + "properties = ['heat_capacity_cp','entropy', 'volume']\n", + "batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7721f824", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/paper/figure1.png b/paper/figure1.png new file mode 100644 index 00000000..76cef883 Binary files /dev/null and b/paper/figure1.png differ diff --git a/paper/functions.py b/paper/functions.py new file mode 100644 index 00000000..9211a520 --- /dev/null +++ b/paper/functions.py @@ -0,0 +1,87 @@ +import pandas as pd +import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +from itertools import cycle +from collections import OrderedDict + + +def plot_substances_properties_vs_temperature(results_csv_file, substances, lables, pressure=0, property=''): + + # plot settings + mpl.rcParams['lines.linewidth']=2 + mpl.rcParams['axes.labelsize']=20 + mpl.rcParams['axes.linewidth']=2 + mpl.rcParams['font.size']=18 + mpl.rcParams['figure.figsize']=[9,9] + mpl.rcParams['figure.subplot.left'] = 0.2 + + +# fig = plt.figure() + + plt.rc('grid', linestyle="--", color='gray') + plt.grid(True) + markers = [m for m in Line2D.markers] + data = pd.read_csv(results_csv_file) + if pressure != 0.0: + data = data.loc[data.iloc[:, 2] == pressure] + c_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color']) + for s, substance in enumerate(substances): + data_select = data.loc[data['Symbol']==substance] + c = next(c_cycle) + m_cycle = cycle(markers) + next(m_cycle) + next(m_cycle) + for column in data_select.columns[3:]: # loop over properties + plt.plot(data_select.iloc[:, 1], data_select[column], color=c, + marker=next(m_cycle), label=lables[s], markersize=12, markeredgecolor="w") + handles, labels = plt.gca().get_legend_handles_labels() + by_label = OrderedDict(zip(labels, handles)) + #legend1 = plt.legend(by_label.values(), by_label.keys(), loc=9, bbox_to_anchor=(1.15, 1)) #, loc=legend_loc) + plt.legend() #, loc=legend_loc) + + #plt.gca().add_artist(legend1) + plt.xlabel("Temperature [$^\circ$C]") + plt.ylabel(property) + + return plt + +def plot_properties_vs_temperature(results_csv_file, substances, pressure=0): + +# plot settings + mpl.rcParams['lines.linewidth']=2 + mpl.rcParams['axes.labelsize']=20 + mpl.rcParams['axes.linewidth']=2 + mpl.rcParams['font.size']=18 + mpl.rcParams['figure.figsize']=[9,9] + +# fig = plt.figure() + + plt.rc('grid', linestyle="--", color='gray') + plt.grid(True) + markers = [m for m in Line2D.markers] + data = pd.read_csv(results_csv_file) + if pressure != 0.0: + data = data.loc[data.iloc[:, 2] == pressure] + c_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color']) + for s, substance in enumerate(substances): + data_select = data.loc[data['Symbol']==substance] + c = next(c_cycle) + m_cycle = cycle(markers) + next(m_cycle) + next(m_cycle) + for column in data_select.columns[3:]: # loop over properties + plt.plot(data_select.iloc[:, 1], data_select[column], color=c, + marker=next(m_cycle), label=column + ' '+ substance, markersize=12, markeredgecolor="w") + handles, labels = plt.gca().get_legend_handles_labels() + by_label = OrderedDict(zip(labels, handles)) + legend1 = plt.legend(by_label.values(), by_label.keys(), loc=9, bbox_to_anchor=(1.15, 1)) #, loc=legend_loc) + lines = plt.gca().get_lines() + + plt.gca().add_artist(legend1) + plt.xlabel("Temperature ($^\circ$C)") + plt.ylabel("ThermoProp") + + return plt + + diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..7452acec --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,259 @@ +@misc{Leal:2015, + author = {Leal, Allan M.M.}, + howpublished = {\texttt{https://reaktoro.org}}, + title = {{Reaktoro: An open-source unified framework for modeling chemically reactive systems}}, + url = {https://reaktoro.org}, + year = {2015} +} +@misc{Leal:2018, + author = {Leal, Allan M.M.}, + howpublished = {\texttt{https://autodiff.github.io}}, + title = {{{autodiff, a modern, fast and expressive {C++} library for automatic differentiatio}}n}, + url = {https://autodiff.github.io}, + year = {2018} +} +@article{Bell:Deiters:Leal:2022, + abstract = {This work uses advanced numerical techniques (complex differentiation and automatic differentiation) to efficiently and accurately compute all the required thermodynamic properties of an equation of state without any analytical derivatives─particularly without any handwritten derivatives. It avoids the tedious and error-prone process of symbolic differentiation, thus allowing for more rapid development of new thermodynamic models. The technique presented here was tested with several equations of state (van der Waals, Peng-Robinson, Soave-Redlich-Kwong, PC-SAFT, and cubic-plus-association) and high-accuracy multifluid models. A minimal set of algorithms (critical locus tracing and vapor-liquid equilibrium tracing) were implemented in an extensible and concise open-source C++ library: teqp (for Templated EQuation of state Package). This work demonstrates that highly complicated equations of state can be implemented faster yet with minimal computational overhead and negligible loss in numerical precision compared with the traditional approach that relies on analytical derivatives. We believe that the approach outlined in this work has the potential to establish a new computational standard when implementing computer codes for thermodynamic models.}, + author = {Bell, Ian H. and Deiters, Ulrich K. and Leal, Allan M.M.}, + doi = {10.1021/acs.iecr.2c00237}, + file = {:C:/Users/Allan/Documents/Mendeley Desktop/Bell, Deiters, Leal - 2022 - Implementing an Equation of State without Derivatives teqp.pdf:BINARY}, + issn = {15205045}, + journal = {Industrial and Engineering Chemistry Research}, + number = {17}, + pages = {6010--6027}, + title = {{Implementing an Equation of State without Derivatives: teqp}}, + volume = {61}, + year = {2022} +} +@article{Kulik2013, + abstract = {Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on the operator-splitting approach, where the solver of chemical equilibria is called by the mass transport part for each control volume whose composition, temperature, or pressure has changed. Modeling of complex natural systems requires consideration of multiphase-multicomponent geochemical models that include nonideal solutions (aqueous electrolytes, fluids, gases, solid solutions, and melts). Direct Gibbs energy minimization (GEM) methods have numerous advantages for the realistic geochemical modeling of such fluid-rock systems. Substantial improvements and extensions to the revised GEM interior point method algorithm based on Karpov's convex programming approach are described, as implemented in the GEMS3K C/C++ code, which is also the numerical kernel of GEM-Selektor v.3 package (http://gems.web.psi.ch). GEMS3K is presented in the context of the essential criteria of chemical plausibility, robustness of results, mass balance accuracy, numerical stability, speed, and portability to high-performance computing systems. The stand-alone GEMS3K code can treat very complex chemical systems with many nonideal solution phases accurately. It is fast, delivering chemically plausible and accurate results with the same or better mass balance precision as that of conventional speciation codes. GEMS3K is already used in several coupled RMT codes (e.g., OpenGeoSys-GEMS) capable of high-performance computing. © 2012 Springer Science+Business Media B.V.}, + author = {Dmitrii A. Kulik and Thomas Wagner and Svitlana V. Dmytrieva and Georg Kosakowski and Ferdinand F. Hingerl and Konstantin V. Chudnenko and Urs R. Berner}, + doi = {10.1007/s10596-012-9310-6}, + issn = {14200597}, + issue = {1}, + journal = {Computational Geosciences}, + keywords = {Fluid-rock interaction,Geochemical modeling,Gibbs energy minimization,Nonideal systems,Reactive mass transport}, + month = {8}, + pages = {1-24}, + publisher = {Springer Netherlands}, + title = {GEM-Selektor geochemical modeling package: Revised algorithm and GEMS3K numerical kernel for coupled simulation codes}, + volume = {17}, + url = {http://link.springer.com/10.1007/s10596-012-9310-6}, + year = {2013}, +} +@article{Miron2019, + abstract = {

Thermodynamic properties of aqueous species are essential for modeling of fluid-rock interaction processes. The Helgeson-Kirkham-Flowers (HKF) model is widely used for calculating standard state thermodynamic properties of ions and complexes over a wide range of temperatures and pressures. To do this, the HKF model requires thermodynamic and electrostatic models of water solvent. In this study, we investigate and quantify the impact of choosing different models for calculating water solvent volumetric and dielectric properties, on the properties of aqueous species calculated using the HKF model. We identify temperature and pressure conditions at which the choice of different models can have a considerable effect on the properties of aqueous species and on fluid mineral equilibrium calculations. The investigated temperature and pressure intervals are 25–1000°C and 1–5 kbar, representative of upper to middle crustal levels, and of interest for modeling ore-forming processes. The thermodynamic and electrostatic models for water solvent considered are: Haar, Gallagher and Kell (1984), Wagner and Pruß (2002), and Zhang and Duan (2005), to calculate water volumetric properties, and Johnson and Norton (1991), Fernandez and others (1997), and Sverjensky and others (2014), to calculate water dielectric properties. We observe only small discrepancies in the calculated standard partial molal properties of aqueous species resulting from using different water thermodynamic models. However, large differences in the properties of charged species can be observed at higher temperatures (above 500°C) as a result of using different electrostatic models. Depending on the aqueous speciation and the reactions that control the chemical composition, the observed differences can vary. The discrepancy between various electrostatic models is attributed to the scarcity of experimental data at high temperatures. These discrepancies restrict the reliability of the geochemical modeling of hydrothermal and ore formation processes, and the retrieval of thermodynamic parameters from experimental data at elevated temperatures and pressures.

}, + author = {G. D. Miron and Allan M. M. Leal and Alina Yapparova}, + doi = {10.1155/2019/5750390}, + issn = {1468-8115}, + journal = {Geofluids}, + month = {2}, + pages = {1-24}, + publisher = {Hindawi}, + title = {Thermodynamic Properties of Aqueous Species Calculated Using the HKF Model: How Do Different Thermodynamic and Electrostatic Models for Solvent Water Affect Calculated Aqueous Properties?}, + volume = {2019}, + url = {https://www.hindawi.com/journals/geofluids/2019/5750390/}, + year = {2019}, +} +@article{Miron2020, + abstract = {Most of the available thermodynamic data concerning radioactive waste disposal are restricted to values of reaction equilibrium constants (logK298°) at 25 °C and 1 bar. Simple estimation methods such as isocoulombic reactions can be used for extrapolating the properties of reactions involving aqueous species and minerals to elevated temperatures. The aim of this study was to validate the applicability of various alternative isocoulombic reactions to estimate logKT° values of aqueous complexation reactions for lanthanides and actinides to elevated temperatures while taking advantage of new additional literature data, and to identify criteria for choosing the “best” reactions. For each chemical species of interest, a systematic approach using dedicated software and database allowed us to identify the isocoulombic reactions and types of extrapolation that yield the best estimates of standard thermodynamic properties at elevated temperatures, when very limited or no experimental data are available. We have tested aqueous complexation reactions for selected lanthanides and actinides of different valences with chloride, fluoride, sulfate, carbonate, nitrate, phosphate and silicate ligands. “Model” complexation reactions, having known temperature trends, were systematically combined with complex formation reactions of interest whose temperature trends are unknown, into many alternative isocoulombic reactions. For each ion, we investigated which of the generated isocoulombic reactions provide the best estimates for logeKT° of the reaction of interest at elevated temperatures in order to compile the guidelines for choosing the optimal ones, then applying these guidelines to “prediction” subsets. In most cases, knowing only logeKT° at 25 °C (for the reaction of interest), it was possible to obtain rather accurate estimates of logeKT° values at elevated temperatures using isocoulombic reactions that exchange ions with similar charge and hydration properties (hydrated ionic radius and structure of the hydration shell) and known logmKT° of model reactions. These ions and their complexes interact with the solvent in comparable ways, so that their similar heat capacity and entropy effects largely cancel out on both sides of an “optimal” isocoulombic reaction.}, + author = {G. D. Miron and Dmitrii A. Kulik and Tres Thoenen}, + doi = {10.1016/j.gca.2020.07.020}, + issn = {00167037}, + journal = {Geochimica et Cosmochimica Acta}, + keywords = {Actinides,Aqueous complexation,Isocoulombic reactions,Lanthanides,Temperature extrapolations}, + month = {10}, + pages = {119-142}, + publisher = {Elsevier Ltd}, + title = {Generating isocoulombic reactions as a tool for systematic evaluation of temperature trends of thermodynamic properties: Application to aquocomplexes of lanthanides and actinides}, + volume = {286}, + year = {2020}, +} +@article{Leal2017, + abstract = {

We present an overview of novel numerical methods for chemical equilibrium and kinetic calculations for complex non-ideal multiphase systems. The methods we present for equilibrium calculations are based either on Gibbs energy minimization (GEM) calculations or on solving the system of extended law of mass-action (xLMA) equations. In both methods, no

}, + author = {Allan M. M. Leal and Dmitrii A. Kulik and William R. Smith and Martin O. Saar}, + doi = {10.1515/pac-2016-1107}, + issn = {1365-3075}, + issue = {5}, + journal = {Pure and Applied Chemistry}, + keywords = {17,ISSP,chemical equilibrium,chemical kinetics,geochemical modeling,numerical methods,reactive transport}, + month = {1}, + pages = {597-643}, + publisher = {De Gruyter}, + title = {An overview of computational methods for chemical equilibrium and kinetic calculations for geochemical and reactive transport modeling}, + volume = {89}, + url = {http://www.degruyter.com/view/j/pac.2017.89.issue-5/pac-2016-1107/pac-2016-1107.xml}, + year = {2017}, +} +@article{Leal2016, + abstract = {We present an extended law of mass-action (xLMA) method for multiphase equilibrium calculations and apply it in the context of reactive transport modeling. This extended LMA formulation differs from its conventional counterpart in that (i) it is directly derived from the Gibbs energy minimization (GEM) problem (i.e., the fundamental problem that describes the state of equilibrium of a chemical system under constant temperature and pressure); and (ii) it extends the conventional mass-action equations with Lagrange multipliers from the Gibbs energy minimization problem, which can be interpreted as stability indices of the chemical species. Accounting for these multipliers enables the method to determine all stable phases without presuming their types (e.g., aqueous, gaseous) or their presence in the equilibrium state. Therefore, the here proposed xLMA method inherits traits of Gibbs energy minimization algorithms that allow it to naturally detect the phases present in equilibrium, which can be single-component phases (e.g., pure solids or liquids) or non-ideal multi-component phases (e.g., aqueous, melts, gaseous, solid solutions, adsorption, or ion exchange). Moreover, our xLMA method requires no technique that tentatively adds or removes reactions based on phase stability indices (e.g., saturation indices for minerals), since the extended mass-action equations are valid even when their corresponding reactions involve unstable species. We successfully apply the proposed method to a reactive transport modeling problem in which we use PHREEQC and GEMS as alternative backends for the calculation of thermodynamic properties such as equilibrium constants of reactions, standard chemical potentials of species, and activity coefficients. Our tests show that our algorithm is efficient and robust for demanding applications, such as reactive transport modeling, where it converges within 1–3 iterations in most cases. The proposed xLMA method is implemented in + , a unified open-source framework for modeling chemically reactive systems.}, + author = {Allan M.M. Leal and Dmitrii A. Kulik and Georg Kosakowski and Martin O. Saar}, + doi = {10.1016/j.advwatres.2016.08.008}, + issn = {03091708}, + journal = {Advances in Water Resources}, + keywords = {Equilibrium,Gibbs energy minimization,LMA,Multiphase,Reactive transport,Speciation}, + month = {10}, + pages = {405-422}, + publisher = {Elsevier Ltd}, + title = {Computational methods for reactive transport modeling: An extended law of mass-action, xLMA, method for multiphase equilibrium calculations}, + volume = {96}, + year = {2016}, +} +@article{Miron2016, + abstract = {? 2016 Elsevier Ltd.A large amount of critically evaluated experimental data on mineral solubility, covering the entire Na-K-Al-Si-O-H-Cl system over wide ranges in temperature and pressure, was used to simultaneously refine the standard state Gibbs energies of aqueous ions and complexes in the framework of the revised Helgeson-Kirkham-Flowers equation of state. The thermodynamic properties of the solubility-controlling minerals were adopted from the internally consistent dataset of Holland and Powell (2002; Thermocalc dataset ds55). The global optimization of Gibbs energies of aqueous species, performed with the GEMSFITS code (Miron et al., 2015), was set up in such a way that the association equilibria for ion pairs and complexes, independently derived from conductance and potentiometric data, are always maintained. This was achieved by introducing reaction constraints into the parameter optimization that adjust Gibbs energies of complexes by their respective Gibbs energy effects of reaction, whenever the Gibbs energies of reactant species (ions) are changed. The optimized thermodynamic dataset is reported with confidence intervals for all parameters evaluated by Monte Carlo trial calculations. The new thermodynamic dataset is shown to reproduce all available fluid-mineral phase equilibria and mineral solubility data with good accuracy and precision over wide ranges in temperature (25-800 ?C), pressure (1 bar to 5 kbar) and composition (salt concentrations up to 5 molal). The global data optimization process adopted in this study can be readily repeated any time when extensions to new chemical elements and species are needed, when new experimental data become available, or when a different aqueous activity model or equation of state should be used. This work serves as a proof of concept that our optimization strategy is feasible and successful in generating a thermodynamic dataset reproducing all fluid-mineral and aqueous speciation equilibria in the Na-K-Al-Si-O-H-Cl system within their experimental uncertainties. The new dataset resolves the long-standing discrepancies between thermodynamic data of minerals and those of aqueous ions and complexes, by achieving an astonishing degree of consistency between a large number of fluid-mineral equilibrium data. All of this at the expense of changing the standard state properties of aqueous species, mainly the Gibbs energy of formation. Using the same strategy, the core dataset for the system Na-K-Al-Si-O-H-Cl can be extended with additional rock-forming elements such as Ca, Mg, Fe, Mn, Ti, S, C, B. In future, the standard-state properties of minerals and aqueous species should be simultaneously optimized, to create the next-generation of fully internally consistent data for fluid-mineral equilibria. Although we employ the widely used HKF equations for this study, the same computational approach can be readily applied to any other speciation-based equation of state for multicomponent aqueous solutions.}, + author = {G.D. Miron and T. Wagner and D.A. Kulik and C.A. Heinrich}, + doi = {10.1016/j.gca.2016.04.026}, + issn = {00167037}, + journal = {Geochimica et Cosmochimica Acta}, + keywords = {[Aqueous species, Fluid-rock interaction, Hydrothe}, + title = {Internally consistent thermodynamic data for aqueous species in the system Na-K-Al-Si-O-H-Cl}, + volume = {187}, + year = {2016}, +} +@article{Miron2017, + abstract = {This study presents an internally consistent thermodynamic dataset for aqueous species in the system Ca-Mg-Na-K-Al-Si-O-H-C-Cl, obtained by adding species of calcium, magnesium and carbon to the core system Na-K-Al-Si-O-H-Cl (Miron and others, 2016). Critically evaluated experimental data on mineral solubility (Ca and Mg hydroxides, Ca and Mg silicates, anorthite, Ca and Mg carbonates) in water and aqueous electrolyte solutions over wide ranges in temperature and pressure were added to the database of experimental data. The complete experimental dataset was then used to simultaneously refine the standard state Gibbs energies of all aqueous ions and complexes in the framework of the revised Helgeson-Kirkham-Flowers (HKF) equation of state. The thermodynamic properties of the solubility-controlling minerals were accepted from the internally consistent dataset of Holland and Powell (1998; updated Thermocalc dataset ds55). The association equilibria of important hydroxide, chloride, carbonate and silicate complexes were critically reviewed, and their standard state properties and HKF parameters were independently derived from conductance, potentiometric and, in a few cases, solubility measurements. In a global optimization of standard Gibbs energies of aqueous species, performed with the GEMSFITS code (Miron and others, 2015), the association equilibria for aqueous complexes were always maintained. The new thermodynamic dataset reproduces all available fluidmineral phase equilibria and mineral solubility data in the system Ca-Mg-Na-K-Al-Si-OH-C-Cl with good accuracy over wide ranges in temperature (25-800 °C), pressure (1 bar - 5 kbar) and composition (salt concentrations up to 5 molal). This makes it possible to perform geochemical and reactive transport modeling of processes in natural and engineered georeservoirs over wide ranges of conditions with an unprecedented level of accuracy and reliability and to address processes of fluid flow and fluid-rock interaction in the Earth's crust from a new perspective. Using the same strategy as applied in the present study, the internally consistent thermodynamic dataset can be further extended with additional major and trace elements, and the data refinement process can be repeated when new experimental data or next-generation equation of state or activity models for aqueous solutions become available.}, + author = {G.D. Miron and T. Wagner and D.A. Kulik and B. Lothenbach}, + doi = {10.2475/07.2017.01}, + issn = {00029599}, + issue = {7}, + journal = {American Journal of Science}, + keywords = {Aqueous species,Experimental data evaluation,Fluid-rock interaction,Hydrothermal geochemical modeling,Optimization,Thermodynamic database}, + pages = {755-806}, + title = {An internally consistent thermodynamic dataset for aqueous species in the system Ca-Mg-Na-K-Al-Si-O-H-C-Cl to 800 °c and 5 kbar}, + volume = {317}, + year = {2017}, +} +@article{JOHNSON1992, +title = {SUPCRT92: A software package for calculating the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0 to 1000°C}, +journal = {Computers & Geosciences}, +volume = {18}, +number = {7}, +pages = {899-947}, +year = {1992}, +issn = {0098-3004}, +doi = {10.1016/0098-3004(92)90029-Q}, +url = {https://www.sciencedirect.com/science/article/pii/009830049290029Q}, +author = {James W. Johnson and Eric H. Oelkers and Harold C. Helgeson}, +keywords = {SUPCRT92, Equations of state, Standard molal thermodynamic properties, Chemical equilibrium, Minerals, Gases, Aqueous species, HO, Thermodynamics, Geochemistry}, +abstract = {Recent advances in theoretical geochemistry permit calculation of the standard molal thermodynamic properties of a wide variety of minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0 to 1000°C. The SUPCRT92 software package facilitates practical application of these recent theories, equations, and data to define equilibrium constraints on geochemical processes in a wide variety of geologic systems. The SUPCRT92 package is composed of three interactive FORTRAN 77 programs, SUPCRT92, MPRONS92, and CPRONS92, and a sequential-access thermodynamic database, SPRONS92.DAT. The SUPCRT92 program reads or permits user-generation of its two input files, CON and RXN, retrieves data from the direct-access equivalent of SPRONS92.DAT, calculates the standard molal Gibbs free energy, enthalpy, entropy, heat capacity, and volume of each reaction specified on the RXN file through a range of conditions specified on the CON file, and writes the calculated reaction properties to the output TAB file and, optionally, to PLT files that facilitate their graphical depiction. Calculations can be performed along the liquid side of the H2O vaporization boundary by specifying either temperature (T) or pressure (P), and in the single-phase regions of fluid H2O by specifying either T and P, T and H2O density, T and log K, or P and log K. SPRONS92.DAT, which contains standard molal thermodynamic properties at 25°C and 1 bar, equation-of-state parameters, heat capacity coefficients, and phase transition data for approximately 500 minerals, gases, and aqueous species, can be augmented or otherwise modified using MPRONS92, and converted to its direct-access equivalent using CPRONS92.} +} +@article{ZIMMER2016, +title = {SUPCRTBL: A revised and extended thermodynamic dataset and software package of SUPCRT92}, +journal = {Computers & Geosciences}, +volume = {90}, +pages = {97-111}, +year = {2016}, +issn = {0098-3004}, +doi = {10.1016/j.cageo.2016.02.013}, +url = {https://www.sciencedirect.com/science/article/pii/S0098300416300371}, +author = {Kurt Zimmer and Yilun Zhang and Peng Lu and Yanyan Chen and Guanru Zhang and Mehmet Dalkilic and Chen Zhu}, +keywords = {SUPCRT92, Arsenic, Geological carbon sequestration, Thermodynamic properties, Geochemical modeling, Chemical equilibrium}, +abstract = {The computer-enabled thermodynamic database associated with SUPCRT92 (Johnson et al., 1992) enables the calculation of the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions for a wide range of temperatures and pressures. However, new data on the thermodynamic properties of both aqueous species and minerals have become available since the database’s initial release in 1992 and its subsequent updates. In light of these developments, we have expanded SUPCRT92’s thermodynamic dataset and have modified the accompanying computer code for thermodynamic calculations by using newly available properties. The modifications in our new version include: (1) updating the standard state thermodynamic properties for mineral end-members with properties from Holland and Powell (2011) to improve the study of metamorphic petrology and economic geology; (2) adding As-acid, As-metal aqueous species, and As-bearing minerals to improve the study of environmental geology; (3) updating properties for Al-bearing species, SiO2°(aq) and HSiO3-, boehmite, gibbsite, and dawsonite for modeling geological carbon sequestration. The new thermodynamic dataset and the modified SUPCRT92 program were implemented in a software package called SUPCRTBL, which is available online at www.indiana.edu/~hydrogeo/supcrtbl.html.} +} +@article{AWOLAYO2022, +title = {PyGeochemCalc: A Python package for geochemical thermodynamic calculations from ambient to deep Earth conditions}, +journal = {Chemical Geology}, +volume = {606}, +pages = {120984}, +year = {2022}, +issn = {0009-2541}, +doi = {10.1016/j.chemgeo.2022.120984}, +url = {https://www.sciencedirect.com/science/article/pii/S0009254122002789}, +author = {Adedapo N. Awolayo and Benjamin M. Tutolo}, +keywords = {Helgeson-Kirkham-Flowers equation of state, LogK-density extrapolation, Solid-solutions, IAPWS95 equation of state, DEW model, Thermodynamic properties, Variable-chemistry clays, Geochemical modeling, Thermodynamic database}, +abstract = {Over the past half century, techniques for evaluating the thermodynamics of water-rock interactions from ambient to deep Earth conditions have advanced incredibly and in myriad directions. As these tools for analyzing the thermodynamic states of geochemical species as a function of temperature, pressure, and composition have multiplied, so too have the possibilities for tracing water-rock interaction from ambient to deep conditions on Earth and beyond. Yet, the aqueous geochemical community still lacks a centralized platform for incorporating this constantly updating thermodynamic data into aqueous geochemical models. Here, we introduce PyGeochemCalc (PyGCC), a community-driven, open-source Python package that meets this need by providing a consolidated set of functions for calculating the thermodynamic properties of gas, aqueous, and mineral (including solid solutions and variable-formula clays) species, as well as reactions amongst these species, over a broad range of temperature and pressure conditions. The PyGCC package utilizes the revised Helgeson-Kirkham-Flowers (HKF) equation of state, and newly proposed density-based extrapolations based upon it, to calculate the thermodynamic properties of aqueous species; a choice of equations of state and electrostatic models (including the Deep Earth Water (DEW) model) to calculate thermodynamic and dielectric properties of water; and heat capacity functions to calculate thermodynamic properties of minerals and gases. Additionally, PyGCC integrates these functions to generate thermodynamic databases for various geochemical programs, including the Geochemist's Workbench (GWB), EQ3/6, TOUGHREACT, and PFLOTRAN, with straightforward possibilities for extension to other simulators. The various functions in the package can also be modularly utilized, and introduced into other modeling tools, as desired. In this paper, we detail the capabilities of PyGCC and the equations it relies on for calculating thermodynamic properties of water, aqueous species, and gases. Although the fundamental thermodynamic data necessary for state-of-the-science PyGCC calculations will necessarily evolve as our collective geochemical knowledge base expands, PyGCC's open source, community-driven design will allow for users to keep pace via rapid implementation of these advancements in this modern geochemical tool.} +} +@ARTICLE{Dick2019, +AUTHOR={Dick, Jeffrey M.}, +TITLE={CHNOSZ: Thermodynamic Calculations and Diagrams for Geochemistry}, +JOURNAL={Frontiers in Earth Science}, +VOLUME={7}, +YEAR={2019}, +URL={https://www.frontiersin.org/articles/10.3389/feart.2019.00180}, +DOI={10.3389/feart.2019.00180}, +ISSN={2296-6463}, +ABSTRACT={Thermodynamic calculations are an essential tool for many areas of geochemistry. Thermodynamics provides a framework for the quantitative description and prediction of the solubilities and relative stabilities of different minerals, metal transport in hydrothermal fluids, and geobiochemical reactions that drive microbial metabolism and contribute to the compositional variation of proteins. Accessible and up-to-date software and databases are important for the development and reproducible application of thermodynamic models. CHNOSZ is a free package for R that has been frequently updated since its first release in 2008. The package provides an integrated set of functions to calculate the standard molal thermodynamic properties and chemical affinities of reactions. It uses the graphical capabilities of R to produce high-quality chemical activity diagrams for aqueous species and predominance diagrams including Eh-pH (Pourbaix) and logfO2 -T diagrams. The extensive database utilizes the well-known revised Helgeson-Kirkham-Flowers (HKF) equations for aqueous species. Recent additions to the database include the Berman equations for minerals, the Deep Earth Water model, which extends the applicability of the HKF equations to higher pressures, and the Akinfiev-Diamond model for aqueous nonelectrolytes. The package comes with many types of documentation, including technical help pages with short code examples, longer code demos, and in-depth vignettes combining code, text and graphics, giving users a wide array of starting points for their own research. This paper provides a concise overview of the package and illustrates the new features using examples selected from the literature. Although the package does not provide a complete chemical speciation model, numerous examples from the package demonstrate the ease of reproducing selected published calculations and sometimes identifying issues with existing datasets and models.} +} +@inproceedings{Bastrakov2019, +author = {Bastrakov, Evgeniy and Dick, Jeffrey}, +year = {2019}, +month = {08}, +pages = {}, +title = {GeoTPD — an interactive online tool for geochemical modelling for the broad geological community. Goldschmidt Abstracts, 2019} +} +@misc{Chan2021, + doi = {10.48550/ARXIV.2105.14096}, + url = {https://arxiv.org/abs/2105.14096}, + author = {Chan, Andrew and Daswani, Mohit Melwani and Vance, Steven}, + keywords = {Geophysics (physics.geo-ph), FOS: Physical sciences, FOS: Physical sciences}, + title = {DEWPython: A Python Implementation of the Deep Earth Water Model and Application to Ocean Worlds}, + publisher = {arXiv}, + year = {2021}, + copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International} +} +@software{megawatsoft, title={Thermodynamic properties of fluids}, year={2022}, url={https://www.megawatsoft.com/}, author = {megawatsoft}, journal={Thermodynamic Properties of Fluids}} +@Article{Huber2022, +author={Huber, Marcia L. +and Lemmon, Eric W. +and Bell, Ian H. +and McLinden, Mark O.}, +title={The NIST REFPROP Database for Highly Accurate Properties of Industrially Important Fluids}, +journal={Industrial {\&} Engineering Chemistry Research}, +year={2022}, +month={Oct}, +day={26}, +publisher={American Chemical Society}, +volume={61}, +number={42}, +pages={15449-15472}, +issn={0888-5885}, +doi={10.1021/acs.iecr.2c01427}, +url={https://doi.org/10.1021/acs.iecr.2c01427} +} +@article{Bell2014, + author = {Bell, Ian H. and Wronski, Jorrit and Quoilin, Sylvain and Lemort, Vincent}, + title = {Pure and Pseudo-pure Fluid Thermophysical Property Evaluation and + the Open-Source Thermophysical Property Library CoolProp}, + journal = {Industrial \& Engineering Chemistry Research}, + volume = {53}, + number = {6}, + pages = {2498--2508}, + year = {2014}, + doi = {10.1021/ie4033999}, + URL = {http://pubs.acs.org/doi/abs/10.1021/ie4033999}, + eprint = {http://pubs.acs.org/doi/pdf/10.1021/ie4033999} + } +@article{Martin2022, doi = {10.21105/joss.04757}, url = {https://doi.org/10.21105/joss.04757}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {79}, pages = {4757}, author = {Christopher Martin and Joseph Ranalli and Jacob Moore}, title = {PYroMat: A Python package for thermodynamic properties}, journal = {Journal of Open Source Software} } +@software{bell, +author={Caleb Bell and Contributors}, +year = {2021}, +title={Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL)}, +url = {https://github.com/CalebBell/thermo} +} +@software{cantera, + author = "David G. Goodwin and Harry K. Moffat and Ingmar Schoegl and Raymond L. + Speth and Bryan W. Weber", + title = "Cantera: An Object-oriented Software Toolkit for Chemical + Kinetics, Thermodynamics, and Transport Processes", + year = 2022, + note = "Version 2.6.0", + howpublished = "\url{https://www.cantera.org}", + doi = {10.5281/zenodo.6387882} +} +@software{thermoengine, + author = {Thermoengine, Code Contributors To}, + title = {{ThermoEngine: Software for Model Building and + Computational Thermodynamics Supporting + Applications in the Earth Sciences}}, + month = may, + year = 2022, + publisher = {Zenodo}, + version = {1.0.0}, + doi = {10.5281/zenodo.6527840}, + url = {https://doi.org/10.5281/zenodo.6527840} +} + + + + diff --git a/paper/paper.ipynb b/paper/paper.ipynb new file mode 100644 index 00000000..b848523d --- /dev/null +++ b/paper/paper.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "18f66811-a4c2-4a8a-8817-6b6a57ab1c8b", + "metadata": {}, + "outputs": [], + "source": [ + "import thermofun as fun\n", + "import thermohubclient as client\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8ce44077-ed07-45fa-8db8-80ba23725e43", + "metadata": {}, + "outputs": [], + "source": [ + "#dbc = client.DatabaseClient()\n", + "#dbc.saveDatabase('aq17')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c6e0d78d-57f8-4c87-9471-35d340237c45", + "metadata": {}, + "outputs": [], + "source": [ + "batch = fun.ThermoBatch('aq17-thermofun.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7be922d2-689a-488a-9477-4a220ad4961c", + "metadata": {}, + "outputs": [], + "source": [ + "batch.setPropertiesUnits([\"temperature\", \"pressure\"],[\"degC\",\"bar\"])\n", + "batch.setPropertiesDigits([\"heat_capacity_cp\",\"entropy\", \"volume\", \"temperature\", \"pressure\", \"logKr\"], [4, 4, 4, 0, 2, 3])\n", + "substances = [\"Na+\", \"Ca+2\", \"SiO2@\", \"CO3-2\", 'OH-']\n", + "lables = [\"Na$^{+}$\", \"Ca$^{2+}$\", \"SiO$_{2(aq)}$\", \"CO$_{3}^{2-}$\", 'OH$^-$']\n", + "properties = [\"heat_capacity_cp\"]\n", + "batch.setPressureIncrement(0,0,0)\n", + "batch.setTemperatureIncrement(0,250, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "1c87fe09-31b0-4396-a070-533628fb85cd", + "metadata": {}, + "outputs": [], + "source": [ + "batch.thermoPropertiesSubstance(substances, properties).toCSV(\"results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "537f8e81-0b21-43d8-8c3d-c2cdbe65d2a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from functions import plot_substances_properties_vs_temperature\n", + "ax1 = plot_substances_properties_vs_temperature('results.csv', substances, lables, 0, 'C$_{p}^{\\circ}$ [J$\\cdot$K$^{-1}] $')\n", + "plt.savefig(fname='figure_Cp.png', format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "33fd844e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "batch.thermoPropertiesSubstance(substances, [\"entropy\"]).toCSV(\"results_S.csv\")\n", + "from functions import plot_substances_properties_vs_temperature\n", + "plt = plot_substances_properties_vs_temperature('results_S.csv', substances, lables, 0, 'S$_{abs}$ [J$\\cdot$K$^{-1}] $')\n", + "plt.savefig(fname='figure_s.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "20c670f6", + "metadata": {}, + "outputs": [], + "source": [ + "reactions = [\"Calcite = Ca+2 + CO3-2\", \"H2O@ = H+ + OH-\", \"NaCl@ = Na+ + Cl-\", \"Al+3 + H2O@ = AlOH+2 + H+\"]\n", + "lables = [\"Calcite $\\leftrightharpoons$ Ca$^{2+}$ + CO$_{3}^{2-}$\", \"H$_2$O$_{(l)}$ $\\leftrightharpoons$ H$^+$ + OH$^-$\", \"NaCl$_{(aq)}$ $\\leftrightharpoons$ Na$^+$ + Cl$^-$\", \"Al$^{3+}$ + H$_2$O$_{(l)}$ $\\leftrightharpoons$ AlOH$^{2+}$ + H$^+$\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c0913bce", + "metadata": {}, + "outputs": [], + "source": [ + "batch.thermoPropertiesReaction(reactions, [\"logKr\"]).toCSV(\"results_r.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4755b86c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from functions import plot_substances_properties_vs_temperature\n", + "plt = plot_substances_properties_vs_temperature('results_r.csv', reactions, lables, 0, 'log$_{10}K^{\\circ}$')\n", + "plt.savefig(fname='figure_logK.png', format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "01a05af7-3544-41bf-8364-f317c6c00f94", + "metadata": {}, + "outputs": [], + "source": [ + "engine = fun.ThermoEngine('aq17-thermofun.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c70d6799-a5e7-424c-88c4-b7cef0c2d999", + "metadata": {}, + "outputs": [], + "source": [ + "T = np.arange(273.15, 1000+273.15, 5)\n", + "P = np.arange(50e5, 3000e5, 60e5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71b33bb4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "072a2a7c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "64de663f", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "Cordi1 = [[engine.propertiesSolvent(t, p ,\"H2O@\").density.val for t in T] for p in P]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "43329ed1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.imshow(Cordi1, interpolation='quadric', origin='lower', aspect='auto', cmap='plasma', extent=[0,1000,50,3000])\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d5b6b8fb", + "metadata": {}, + "outputs": [], + "source": [ + "eps = [[engine.electroPropertiesSolvent(t, p ,\"H2O@\").epsilon.val for t in T] for p in P]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f9d2a475", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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M. Leal + affiliation: 2 + - name: S.V. Dmytrieva + affiliation: 3 + - name: Dmitrii A. Kulik + affiliation: 1 +affiliations: + - index: 1 + name: Laboratory for Waste Management LES, Paul Scherrer Institut, 5232 Villigen, Switzerland + - index: 2 + name: Geothermal Energy and Geofluids Group, Institute of Geophysics, ETH Zurich, Switzerland + - index: 3 + name: Cosylab Switzerland GmbH, Badenerstrasse 13, CH–5200 Brugg, Switzerland +date: "26 June 2022" +bibliography: paper.bib +--- + +# Summary + +[ThermoFun](https://thermohub.org/thermofun) is an open source library that facilitates the calculation and retrieval of standard thermodynamic properties of chemical substances, compounds, and reactions among them at a range of temperatures and pressures. The library is developed in C++ for performance, but it also has a Python API for broader and more convenient usage. It employs a variety of thermodynamic models and equations of state for solid, aqueous, surface, gaseous, and molten substances, and their reactions with input properties and parameters from various thermodynamic datasets that are collected and curated in the in [ThermoHub](https://thermohub.org/thermohub) database. The library can be used as a standalone code for searching and tabulating thermodynamic properties or linked to other modeling codes that require standard thermodynamic data as input. It offers the flexibility to use different thermodynamic datasets, including custom datasets and datasets retrieved from the online ThermoHub database, and to choose the most suitable models for different classes of substances necessary in various modeling applications. It can serve as a common source of thermodynamic models for standard properties of substances and reactions that can be easily integrated and combined, significantly improving the modeling capabilities for diverse (geo)chemical systems and over wide ranges of conditions. + +# Statement of need + +Thermodynamic modeling is a powerful tool that allows for the investigation of equilibrium speciation in chemical systems under conditions and time scales that cannot be reached in laboratory settings. This type of calculation requires the standard thermodynamic properties of all involved substances (species) or reactions at a specific temperature (T) and pressure (P) as input. Equations of state are used to compute these properties, known as standard thermodynamic models. However, existing thermodynamic modeling codes and standalone codes may not be directly compatible, making it difficult to retrieve properties calculated with different methods and use them in different codes, or to compare and tabulate thermodynamic data from different sources. The ThermoFun library simplifies this process by facilitating the computation of standard thermodynamic properties of substances and reactions (see \autoref{fig:properties}), and providing fast and simple access to many thermodynamic datasets, methods, and equations of state. This allows for more efficient use of these tools in modeling codes, or for the optimization and fitting of model parameters, evaluation of the models' performance, range of applicability, and accuracy. Additionally, ThermoFun is extendable with new models and has remote access to a curated collection of thermodynamic datasets on ThermoHub, making it a useful tool for various studies on hydrothermal processes, cementitious materials, nuclear engineering, waste incineration, radioactive and chemical management and disposal, and other (geo)chemical environments. + +![Example of standard thermodynamic properties of substances and reactions calculated using ThermoFun as a function of temperature along the saturated water vapor pressure curve (input parameters from `aq17` thermodynamic dataset [@Miron2016;@Miron2017]).\label{fig:properties}](figure1.png) + +The ThermoFun library can be linked to any C++ or Python geochemical equilibrium speciation, parameter optimization, or reactive mass-transport code as a source of standard thermodynamic properties of substances and/or reactions. As a standalone code, ThermoFun can be used simply for searching and tabulating thermodynamic properties of substances and reactions. For example, it has been used to calculate the standard properties of reactants and reactions at elevated temperatures when evaluating the performance of isocoulombic reactions, in order to extrapolate the properties of aqueous complexation reactions involving lanthanides and actinides to elevated temperatures [@Miron2020]. The library was also applied to evaluate the effect of using different equations of state for water-solvent on the calculated properties of dissolved aqueous species [@Miron2019]. +ThermoFun has already been adopted as a source of thermodynamic data in the [GEMS](https://gems.web.psi.ch/) equilibrium speciation codes [@Kulik2013] and the [Reaktoro](https://reaktoro.org) chemical modeling framework [@Leal:2015]. + +# Benefits of using ThermoFun + +* It simplifies the calculation of standard thermodynamic properties of substances and reactions at a range of temperatures and pressures. +* It includes a variety of equations of state and other thermodynamic models that can be used to calculate standard properties under different conditions, eliminating the need for the user to implement and test these models separately in their own code or spreadsheets. +* It uses input properties and parameters from various thermodynamic datasets that are consistently kept in the online ThermoHub database, reducing the need to collect all necessary thermodynamic data from literature or to write complex scripts to import the data from different formats. +* It uses automatic differentiation to provide temperature and pressure derivatives of the calculated properties. `autodiff` C++ library [@Leal:2018] will be used in ThermoFun for even more capable autodiff capabilities, as it has been used in both Reaktoro [@Leal:2015] and TEQP [@Bell:Deiters:Leal:2022]. +* The thermodynamic models in ThermoFun are implemented and tested based on their original publications, and the thermodynamic data in ThermoHub is curated and documented by experts, saving time and effort and enabling more efficient use of geochemical code tools for scientific problems. +* It serves as a common source of thermodynamic models for standard properties of substances and reactions that can be easily integrated and combined, significantly improving modeling capabilities for a wide range of (geo)chemical systems and conditions. + +Jupyter notebooks with examples on how to use `ThermoFun` can be found in the [thermofun-jupyter](https://github.com/thermohub/thermofun-jupyter) repository. More complex Python or C++ routines that use `ThermoFun` can be coded. For example, `ThermoFun` can be coupled to a phase diagram plotter, a chemical equilibrium solver, or a parameter optimization routine for fitting the standard properties and model parameters of substances and reactions and their temperature and pressure dependence. + +# Codes with similar functionalities + +ThermoFun is an open source library with several [benefits](#benefits-of-using-thermofun) that serves as a common source of ever-expanding thermodynamic models for standard properties of substances and reactions. If ThermoFun does not meet your needs, there are other alternatives offering related functionality, potentially using different methods, and sometimes embedded in larger applications. Below the (not exhaustive) list of codes that share some functionality with ThermoFun: + +| Name | Language | Reference | Source | +|-------|-----------|------------|-------| +| Cantera | C++, Python, Matlab | [@cantera] | [Open](https://github.com/Cantera/cantera) | +| CHNOSZ | R, Python | [@Dick2019] | [Open](https://chnosz.net/) | +| coolprop | C++ | [@Bell2014] | [Open](https://github.com/CoolProp/CoolProp)| +| DEWPython | Python | [@Chan2021] | [Open](https://github.com/chandr3w/DEWPython) | +| GEM-Selektor | C++ | [@Kulik2013] | [Open](https://gems.web.psi.ch/GEMS3) | +| GeoTPD | R-Shiny | [@Bastrakov2019] | [Closed](https://geoscienceaustralia.shinyapps.io/GeoTPD/) | +| PyGCC | Python | [@AWOLAYO2022] | [Open](https://bitbucket.org/Tutolo-RTG/pygcc/src/master/) | +| pyromat | Python | [@Martin2022] | [Open](https://github.com/chmarti1/PYroMat)| +| Reaktoro | C++, Python | [@Leal2017] | [Open](https://github.com/reaktoro) | +| REFPPROP | FORTRAN | [@Huber2022] | [Closed](https://www.nist.gov/srd/refprop) | +| SteamTables | C++, Web | [@megawatsoft] | [Closed](https://www.steamtablesonline.com/steam97web.aspx) | +| SUPCRT92 | Fortran | [@JOHNSON1992] | [Open](https://github.com/justinnhli/supcrt92) | +| SUPCRTBL | Fortran | [@ZIMMER2016] | [Closed](https://models.earth.indiana.edu/supcrtbl.php) | +| TEQP | C++, Python | [@Bell:Deiters:Leal:2022] | [Open](https://github.com/usnistgov/teqp) | +| Thermo | Python | [@bell] | [Open](https://github.com/CalebBell/thermo)| +| ThermoEngine | C++, Python | [@thermoengine] | [Open](https://gitlab.com/ENKI-portal/ThermoEngine) | + + +# Acknowledgements + +Support to G. D. Miron by the German Federal Ministry for Education and Research (BMBF), ThermAc project (02NUK039A) is gratefully acknowledged. D. A. Kulik and G. D. Miron are grateful for the financial support provided by NAGRA. A.M.M. Leal is grateful for the financial support of the Swiss National Science Foundation (SNSF) through the Ambizione grant PZ00P2-179967. + +# References diff --git a/paper/suppl.md b/paper/suppl.md new file mode 100644 index 00000000..371f1940 --- /dev/null +++ b/paper/suppl.md @@ -0,0 +1,92 @@ +--- +title: 'ThermoFun: A C++/Python library for computing standard thermodynamic properties of substances and reactions across wide ranges of temperatures and pressures' +tags: + - C++ + - Python + - thermodynamics + - standard state thermodynamic properties + - equations of state + - materials +authors: + - name: George Dan Miron^[corresponding author] + affiliation: 1 + orcid: 0000-0002-6923-7300 + - name: Allan M. M. Leal + affiliation: 2 + - name: S.V. Dmytrieva + affiliation: 3 + - name: Dmitrii A. Kulik + affiliation: 1 +affiliations: + - index: 1 + name: Laboratory for Waste Management LES, Paul Scherrer Institut, 5232 Villigen, Switzerland + - index: 2 + name: Geothermal Energy and Geofluids Group, Institute of Geophysics, ETH Zurich, Switzerland + - index: 3 + name: Cosylab Switzerland GmbH, Badenerstrasse 13, CH–5200 Brugg, Switzerland +date: "26 June 2022" +bibliography: paper.bib +--- + +The stability of a substance depends on its standard Gibbs energy function G$^{\circ}{T}$ (which represents the formation of the substance from chemical elements in their standard states), or on the equilibrium constant K$^{\circ}{T}$ of the reaction that forms it from other substances (master species reactants). In thermodynamic databases, standard Gibbs energy and equilibrium constant values are often only available at reference temperature 298.15 K (25 $^{\circ}$C). Other standard properties such as absolute entropy S$^{\circ}{298}$, enthalpy H$^{\circ}{298}$, heat capacity C${p,298}^{\circ}$, and the temperature-dependent function C${p,T}^{\circ}$ = f(T) are needed to evaluate G$^{\circ}{T}$ or K$^{\circ}{T}$. The standard molar volume V$^{\circ}{298}$ is the minimum data required to account for the effect of pressure on G$^{\circ}{T}$. In chemical equilibrium problems where other conditions are imposed (e.g., internal energy and volume instead of temperature and pressure), additional standard thermodynamic properties may be required (e.g., standard internal energies and standard volumes of the species). Therefore, modeling chemically reactive multiphase systems is only possible if standard thermodynamic properties of species or reactions can be computed [@Leal2017]. +These properties are computed using equations of state that relate standard thermodynamic properties to temperature and pressure. Different models have been developed to evaluate standard thermodynamic properties of substances or reactions over a wide range of temperature and pressure conditions. These models may have complex mathematical formulations, a large number of input parameters, and may require iterative numerical procedures, or a combination of several methods to calculate the properties of a particular type of substance.The quality of thermodynamic modeling results is strongly influenced by the predictive capability of the model and the availability, accuracy, precision, and consistency of the input parameters in the thermodynamic datasets. +ThermoFun facilitates calculation of standard thermodynamic properties of substances and reactions at a range of temperatures and pressures using a variety of equations of state and other thermodynamic models with input properties and parameters from various thermodynamic datasets that are consistently kept in the online ThermoHub database + +# Features + +ThermoFun is initialized with a thermodynamic dataset, which is a database consisting of collections of records for chemical elements, substances (species), and optionally reactions between substances, needed to calculate equilibria in chemical systems. These records contain various data such as symbol, atomic mass, name, chemical formula, standard thermodynamic properties, equation of state (EoS) parameters, etc. for species; and stoichiometry coefficients of involved species, standard thermodynamic effects, and optionally model parameters coefficients, etc. for reactions. Dedicated research and development efforts are devoted to compiling and critically selecting these properties and parameters from literature or fitting them to experimental data. Thermodynamic datasets used in ThermoFun can be read from local files or retrieved via remote access to the ThermoHub online database (and saved locally). The ThermoHub database contains a collection of widely used thermodynamic of [thermodynamic datasets](https://thermohub.org/thermohub/thermohub/#thermodatasets) that are used in various modeling applications. + +The online ThermoHub database is actively being extended with existing and newly updated and developed datasets prepared by thermodynamic database experts, which are automatically made available for calculations with ThermoFun. In general, calculations must be done using one internally consistent thermodynamic dataset, but it is also possible to use custom datasets, combine them, and append additional data from user-maintained files. Using the [ThermoHubClient](https://thermohub.org/thermohub/thermohubclient/) utility code, thermodynamic datasets can be retrieved into a simplified ThermoFun format and saved to text files that can be edited and extended. + +The ThermoFun `ThermoEngine` class contains functions that return the standard state thermodynamic properties of a substance or a reaction at a given temperature and pressure. For water solvent, additional properties such as water density and dielectric constant can also be retrieved. There is also the option for low-level fine-grained access up to individual methods. + +The `ThermoBatch` class can be used to run batch calculations for a list of substances, reactions, and temperature and pressure ranges. `ThermoBatch` can also be used to output the results in tabulated comma-separated values (CSV) or other data formats. + +Reaction properties can be calculated for any `reaction` record present in the thermodynamic dataset, with the extrapolation to the given temperature and pressure done using the model code and its input parameters available in the record. Alternatively, the user can provide strings of reaction equations written using symbols of substances available in the thermodynamic dataset (e.g., Calcite $\leftrightharpoons$ Ca$^{2+}$ + CO$_{3}^{2-}$). In this case, the standard properties of reactions are calculated from the properties of reactants, each of which is first computed for the T, P of interest using the models and parameters defined in their respective `substance` records. Another option is to set a reaction with known properties to define the properties of a given substance. In this case, the standard properties of the reaction-defined substance at T, P are retrieved from the standard properties of the reaction calculated at T, P, along with the standard properties of the other reactants (substances) that are involved in the reaction. Thus, one thermodynamic dataset can contain a combination of substances, reaction-dependent substances, and reactions, whose consistent standard properties at T, P of interest are computed recursively. + +ThermoFun takes advantage of automatic differentiation for efficient and convenient calculation of derivatives. This feature allows ThermoFun to produce not only the standard thermodynamic properties of species and reactions, but also their temperature and pressure derivatives. This is necessary, for example, when chemical equilibrium problems where temperature and/or pressure are unknown, and Newton method is used to calculate these variables together with the amounts of species in equilibrium, as is done in Reaktoro [@Leal:2015]. Without temperature and pressure derivatives of standard Gibbs energies, the algorithm would not be fast and robust for these specific calculations. We plan to use the `autodiff` C++ library [@Leal:2018] in ThermoFun for even more capable autodiff capabilities, as it has been used in both Reaktoro [@Leal:2015] and TEQP [@Bell:Deiters:Leal:2022]. + +# Basic examples + +Loading the `aq17` thermodynamic dataset [@Miron2016;@Miron2017] and calculating standard properties of a substance (calcium aqueous ion $Ca^{2+}$). Output values are rounded for two decimal places. + +```python + import thermofun as fun + database = fun.Database('aq17-thermofun.json') + engine = fun.ThermoEngine(database) + # T(K) P(Pa) symbol + Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, 'Ca+2') + print(f'G0 {Ca_ion.gibbs_energy.val} J/mol') +``` + + `G0 -545301.29 J/mol` + +Calculating the properties of a reaction given as a reaction equation (calcite dissolution): + +```python + # T(K) P(Pa) reaction equation + R = engine.thermoPropertiesReaction(348.15, 1e5, 'Calcite = Ca+2 + CO3-2') + print(f'drS0 of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}') + print(f'drG0 of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}') + print(f'logK0 of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}') +``` + +``` +drS0 of (Cal = Ca+2 + CO3-2) is -259.12 +drG0 of (Cal = Ca+2 + CO3-2) is 59914.09 +logK0 of (Cal = Ca+2 + CO3-2) is -8.99 +``` + +Using the batch class to do sequential calculations and output the results to a CSV file: + +```python + batch = fun.ThermoBatch(database) + batch.setPropertiesUnits(['temperature', 'pressure'],['degC','bar']) + batch.setPressureIncrement(0,0,0) + batch.setTemperatureIncrement(0,300, 5) + substances = ['Na+', 'Mg+2', 'Ca+2', 'SiO2@'] + properties = ['heat_capacity_cp','entropy', 'volume'] + batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv') +``` + +# References diff --git a/python/pyThermoFun/Batch/pyOutputBatch.cpp b/python/pyThermoFun/Batch/pyOutputBatch.cpp index 0f9ff711..f7d1bebd 100644 --- a/python/pyThermoFun/Batch/pyOutputBatch.cpp +++ b/python/pyThermoFun/Batch/pyOutputBatch.cpp @@ -35,14 +35,14 @@ namespace ThermoFun { void exportOutput(py::module& m) { - py::class_(m, "Output") + py::class_(m, "Output", "Type for outputting results to different formats") .def(py::init()) - .def("toCSV", &Output::toCSV) - .def("toCSVTransposed", &Output::toCSVTransposed) - .def("toCSVPropertyGrid", &Output::toCSVPropertyGrid) + .def("toCSV", &Output::toCSV, "write results to CSV file with substances/reactions on rows and properties on columns") + .def("toCSVTransposed", &Output::toCSVTransposed, "write results to CSV file in a special transposed format with substances/reactions on columns and properties on rows") + .def("toCSVPropertyGrid", &Output::toCSVPropertyGrid, "write results to CSV file in a property grid format") .def("toDouble", &Output::toDouble) .def("toThermoScalar", &Output::toThermoScalar) - .def("to2DVectorDouble", &Output::to2DVectorDouble) + .def("to2DVectorDouble", &Output::to2DVectorDouble, "returns a list of results") ; } } diff --git a/python/pyThermoFun/Batch/pyThermoBatch.cpp b/python/pyThermoFun/Batch/pyThermoBatch.cpp index bf48722f..03dac323 100644 --- a/python/pyThermoFun/Batch/pyThermoBatch.cpp +++ b/python/pyThermoFun/Batch/pyThermoBatch.cpp @@ -35,88 +35,90 @@ namespace py = pybind11; #include #include -namespace ThermoFun { - -void exportThermoBatch(py::module& m) +namespace ThermoFun { - auto thermoPropertiesSubstance1 = static_cast - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesSubstance2 = static_cast, std::vector)> - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesSubstance3 = static_cast, std::vector)> - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesSubstance4 = static_cast>, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesSubstance5 = static_cast>, std::vector, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesSubstance6 = static_cast, std::vector, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesSubstance7 = static_cast, std::vector, std::vector, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesSubstance); - auto thermoPropertiesReaction1 = static_cast - (&ThermoBatch::thermoPropertiesReaction); - auto thermoPropertiesReaction2 = static_cast, std::vector)> - (&ThermoBatch::thermoPropertiesReaction); - auto thermoPropertiesReaction3 = static_cast, std::vector)> - (&ThermoBatch::thermoPropertiesReaction); - auto thermoPropertiesReaction4 = static_cast>, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesReaction); - auto thermoPropertiesReaction5 = static_cast>, std::vector, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesReaction); - auto thermoPropertiesReaction6 = static_cast, std::vector, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesReaction); - auto thermoPropertiesReaction7 = static_cast, std::vector, std::vector, std::vector, std::vector)> - (&ThermoBatch::thermoPropertiesReaction); + void exportThermoBatch(py::module &m) + { + auto thermoPropertiesSubstance1 = static_cast(&ThermoBatch::thermoPropertiesSubstance); + auto thermoPropertiesSubstance2 = static_cast, std::vector)>(&ThermoBatch::thermoPropertiesSubstance); + auto thermoPropertiesSubstance3 = static_cast, std::vector)>(&ThermoBatch::thermoPropertiesSubstance); + auto thermoPropertiesSubstance4 = static_cast>, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesSubstance); + auto thermoPropertiesSubstance5 = static_cast>, std::vector, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesSubstance); + auto thermoPropertiesSubstance6 = static_cast, std::vector, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesSubstance); + auto thermoPropertiesSubstance7 = static_cast, std::vector, std::vector, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesSubstance); - py::class_(m, "ThermoBatch") - .def(py::init()) - .def(py::init()) - .def(py::init()) - .def("setUnits", &ThermoBatch::setUnits) - .def("setDigits", &ThermoBatch::setDigits) - .def("setPropertiesUnits", &ThermoBatch::setPropertiesUnits) - .def("setPropertiesDigits", &ThermoBatch::setPropertiesDigits) - .def("setPropertyUnit", &ThermoBatch::setPropertyUnit) - .def("setPropertyDigit", &ThermoBatch::setPropertyDigit) - .def("setPropertyUnitDigit", &ThermoBatch::setPropertyUnitDigit) - .def("setTemperatureIncrement", &ThermoBatch::setTemperatureIncrement) - .def("setPressureIncrement", &ThermoBatch::setPressureIncrement) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance1) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance2) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance3) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance4) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance5) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance6) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance7) - .def("thermoPropertiesReaction", thermoPropertiesReaction1) - .def("thermoPropertiesReaction", thermoPropertiesReaction2) - .def("thermoPropertiesReaction", thermoPropertiesReaction3) - .def("thermoPropertiesReaction", thermoPropertiesReaction4) - .def("thermoPropertiesReaction", thermoPropertiesReaction5) - .def("thermoPropertiesReaction", thermoPropertiesReaction6) - .def("thermoPropertiesReaction", thermoPropertiesReaction7) - .def("setBatchPreferences", &ThermoBatch::setBatchPreferences) - .def("setSolventSymbol", &ThermoBatch::setSolventSymbol) - ; -} + auto thermoPropertiesReaction1 = static_cast(&ThermoBatch::thermoPropertiesReaction); + auto thermoPropertiesReaction2 = static_cast, std::vector)>(&ThermoBatch::thermoPropertiesReaction); + auto thermoPropertiesReaction3 = static_cast, std::vector)>(&ThermoBatch::thermoPropertiesReaction); + auto thermoPropertiesReaction4 = static_cast>, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesReaction); + auto thermoPropertiesReaction5 = static_cast>, std::vector, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesReaction); + auto thermoPropertiesReaction6 = static_cast, std::vector, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesReaction); + auto thermoPropertiesReaction7 = static_cast, std::vector, std::vector, std::vector, std::vector)>(&ThermoBatch::thermoPropertiesReaction); -void exportBatchPreferences(py::module& m) -{ - py::class_(m, "BatchPreferences") - .def(py::init<>()) - .def_readwrite("isFixed", &BatchPreferences::isFixed) - .def_readwrite("isFloating", &BatchPreferences::isFloating) - .def_readwrite("isScientific", &BatchPreferences::isScientific) - .def_readwrite("separator", &BatchPreferences::separator) - .def_readwrite("fileName", &BatchPreferences::fileName) - .def_readwrite("solventFileName", &BatchPreferences::solventFileName) - .def_readwrite("outputSolventProperties", &BatchPreferences::outputSolventProperties) - .def_readwrite("substancePropertiesFromReaction", &BatchPreferences::substancePropertiesFromReaction) - .def_readwrite("reactionPropertiesFromReactants", &BatchPreferences::reactionPropertiesFromReactants) - .def_readwrite("loopOverTPpairsFirst", &BatchPreferences::loopOverTPpairsFirst) - .def_readwrite("loopTemperatureThenPressure", &BatchPreferences::loopTemperatureThenPressure) - ; -} + py::class_(m, "ThermoBatch", "Useful for performing batch calculations for multiple substance/reaction records for multiple T-P") + .def(py::init()) + .def(py::init()) + .def(py::init()) + // .def("setUnits", &ThermoBatch::setUnits) + // .def("setDigits", &ThermoBatch::setDigits) + .def("setPropertiesUnits", &ThermoBatch::setPropertiesUnits) + .def("setPropertiesDigits", &ThermoBatch::setPropertiesDigits) + .def("setPropertyUnit", &ThermoBatch::setPropertyUnit) + .def("setPropertyDigit", &ThermoBatch::setPropertyDigit) + .def("setPropertyUnitDigit", &ThermoBatch::setPropertyUnitDigit) + .def("setTemperatureIncrement", &ThermoBatch::setTemperatureIncrement) + .def("setPressureIncrement", &ThermoBatch::setPressureIncrement) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance1, "calculate for given property, substance symbol, temperature and pressure", py::arg("temperature"), py::arg("pressure"), py::arg("symbol"), py::arg("property")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance2, "calculate for given properties, substance symbols, temperature and pressure", py::arg("temperature"), py::arg("pressure"), py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance3, "calculate for given properties, substance symbols, temperatures and pressures are set with increment functions", py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance4, "calculate for given properties, substance symbols, and temperature and pressure pairs", py::arg("TP_pairs"), py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance5, "calculate for given properties, substance symbols, and temperature and pressure pairs", py::arg("TP_pairs"), py::arg("symbols"), py::arg("properties"), py::arg("provided_results")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance6, "calculate for given properties, substance symbols, temperatures and pressures", py::arg("temperatures"), py::arg("pressures"), py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance7, "calculate for given properties, substance symbols, temperatures and pressures", py::arg("temperatures"), py::arg("pressures"), py::arg("symbols"), py::arg("properties"), py::arg("provided_results")) + .def("thermoPropertiesReaction", thermoPropertiesReaction1, "calculate for given property, reaction symbol, temperature and pressure", py::arg("temperature"), py::arg("pressure"), py::arg("symbol"), py::arg("property")) + .def("thermoPropertiesReaction", thermoPropertiesReaction2, "calculate for given properties, reaction symbols, temperature and pressure", py::arg("temperature"), py::arg("pressure"), py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesReaction", thermoPropertiesReaction3, "calculate for given properties, reaction symbols, temperatures and pressures are set with increment functions", py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesReaction", thermoPropertiesReaction4, "calculate for given properties, reaction symbols, and temperature and pressure pairs", py::arg("TP_pairs"), py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesReaction", thermoPropertiesReaction5, "calculate for given properties, reaction symbols, and temperature and pressure pairs", py::arg("TP_pairs"), py::arg("symbols"), py::arg("properties"), py::arg("provided_results")) + .def("thermoPropertiesReaction", thermoPropertiesReaction6, "calculate for given properties, reaction symbols, temperatures and pressures", py::arg("temperatures"), py::arg("pressures"), py::arg("symbols"), py::arg("properties")) + .def("thermoPropertiesReaction", thermoPropertiesReaction7, "calculate for given properties, reaction symbols, temperatures and pressures", py::arg("temperatures"), py::arg("pressures"), py::arg("symbols"), py::arg("properties"), py::arg("provided_results")) + .def("setBatchPreferences", &ThermoBatch::setBatchPreferences) + .def("setSolventSymbol", &ThermoBatch::setSolventSymbol) + .def("temperatureIncrement", &ThermoBatch::temperatureIncrement, "returns the default temperature increment") + .def("pressureIncrement", &ThermoBatch::pressureIncrement, "returns the default pressure increment") + .def("propertyUnits", &ThermoBatch::propertyUnits, "returns the default properties units") + .def("propertyDigits", &ThermoBatch::propertyDigits, "returns the default properties number of round digits"); + } + + // void exportDefault(py::module& m) + //{ + // py::class_(m, "defs", "fdefs") + // .def_property_readonly("defaultPropertyNames", defaultPropertyNames) + // .def_property_readonly("defaultSolventPropertyUnits", defaultSolventPropertyUnits) + // .def_property_readonly("defaultSolventPropertyDigits", defaultSolventPropertyDigits) + // .def_property_readonly("defaultPropertyUnits", defaultPropertyUnits) + // .def_property_readonly("defaultPropertyDigits", defaultPropertyDigits) + // .def_property_readonly("defaultTemperatureIncrement", defaultTemperatureIncrement) + // .def_property_readonly("defaultPressureIncrement", defaultPressureIncrement) + // ; + // } + + void exportBatchPreferences(py::module &m) + { + py::class_(m, "BatchPreferences", "holds the options for outputting results") + .def(py::init<>()) + .def_readwrite("isFixed", &BatchPreferences::isFixed, "default=True, using fixed-point notation: the value is represented with exactly as many digits in the decimal part as specified by the precision field") + .def_readwrite("isFloating", &BatchPreferences::isFloating, "default=False, using floating point notation") + .def_readwrite("isScientific", &BatchPreferences::isScientific, "default=False, if True write values in scientific notation") + .def_readwrite("separator", &BatchPreferences::separator, "default=, separator used in output csv file") + .def_readwrite("fileName", &BatchPreferences::fileName, "output file name") + .def_readwrite("solventFileName", &BatchPreferences::solventFileName, "output file for solvent properties") + .def_readwrite("outputSolventProperties", &BatchPreferences::outputSolventProperties, "default=False, output calculated solvent properties to file") + .def_readwrite("substancePropertiesFromReaction", &BatchPreferences::substancePropertiesFromReaction, "default=False, calculate substance properties from reaction, if provided in the substance record with kez \"reaction\"") + .def_readwrite("reactionPropertiesFromReactants", &BatchPreferences::reactionPropertiesFromReactants, "default=False, calculate reaction properties from reactants") + .def_readwrite("loopOverTPpairsFirst", &BatchPreferences::loopOverTPpairsFirst, "default=True, when calculating properties the loop is going for T-P points first and then for substances/reactions list") + .def_readwrite("loopTemperatureThenPressure", &BatchPreferences::loopTemperatureThenPressure, "default=True, when calculating properties the loop is going for T and the P"); + } } diff --git a/python/pyThermoFun/PyThermoFun.cpp b/python/pyThermoFun/PyThermoFun.cpp index 8bd96240..b4373c8f 100644 --- a/python/pyThermoFun/PyThermoFun.cpp +++ b/python/pyThermoFun/PyThermoFun.cpp @@ -45,10 +45,10 @@ PYBIND11_MODULE(PyThermoFun, m) exportReaction(m); exportElement(m); - m.def("availableSubstanceTPMethods", availableSubstanceTPMethods, "list of JSONs templates of avialable substance methods."); - m.def("availableReactionTPMethods", availableReactionTPMethods, "list of JSONs templates of avialable reaction methods."); + m.def("availableSubstanceTPMethods", availableSubstanceTPMethods, "list of JSONs templates of available substance methods."); + m.def("availableReactionTPMethods", availableReactionTPMethods, "list of JSONs templates of available reaction methods."); m.def("availablePropertiesSubstance", availablePropertiesSubstance, "list of available properties to calculate for a substance record."); m.def("availablePropertiesReaction", availablePropertiesReaction, "list of available properties to calculate for a reaction record."); - m.def("update_loggers", &update_loggers, "update log, default parameters (False, \"thermofun.log\", 2) "); - m.def("clear_loggers", &clear_loggers,"clear log, default parameters (False, \"thermofun.log\", 2) "); + m.def("update_loggers", &update_loggers, "update log, default parameters (False, \"thermofun.log\", 2), logging levels: trace = 0, debug = 1, info = 2, warn = 3, err = 4, critical = 5, off = 6 "); + m.def("clear_loggers", &clear_loggers,"clear log, default parameters (False, \"thermofun.log\", 2), trace = 0, debug = 1, info = 2, warn = 3, err = 4, critical = 5, off = 6 "); } diff --git a/python/pyThermoFun/pyDatabase.cpp b/python/pyThermoFun/pyDatabase.cpp index 21a34bf8..6c031386 100644 --- a/python/pyThermoFun/pyDatabase.cpp +++ b/python/pyThermoFun/pyDatabase.cpp @@ -54,9 +54,9 @@ void exportDatabase(py::module& m) auto setReaction2 = static_cast(&Database::setReaction); - py::class_(m, "Database") + py::class_(m, "Database", "Stores maps of elements, substances and reactions. A database instance can be used to create a ThermoEngine instance which can be further used to calculate the standard thermodynamic properties of substances and reactions at T and P") .def(py::init<>()) - .def(py::init()) + .def(py::init(), "constructor using a JSON string with the thermofun database format") .def(py::init()) .def("appendData", appendData1, "Append records to the database from a file.") .def("appendData", appendData2, "Append records of given type (elements, substances, reactions) to the database from a list of JSON strings.") diff --git a/python/pyThermoFun/pyElement.cpp b/python/pyThermoFun/pyElement.cpp index 00676b57..aaf0a004 100644 --- a/python/pyThermoFun/pyElement.cpp +++ b/python/pyThermoFun/pyElement.cpp @@ -31,7 +31,7 @@ namespace ThermoFun { void exportElement(py::module& m) { - py::class_(m, "Element") + py::class_(m, "Element", "A type used to describe an element") .def(py::init<>()) .def(py::init()) .def(py::init()) diff --git a/python/pyThermoFun/pyReaction.cpp b/python/pyThermoFun/pyReaction.cpp index 058542b4..5e834b10 100644 --- a/python/pyThermoFun/pyReaction.cpp +++ b/python/pyThermoFun/pyReaction.cpp @@ -34,10 +34,10 @@ namespace ThermoFun { void exportReaction(py::module& m) { - py::class_(m, "Reaction") + py::class_(m, "Reaction", "A type used to describe a reaction") .def(py::init<>()) .def(py::init()) - .def(py::init()) + .def(py::init(), "Constructor using a reaction record as a JSON string") .def("setName", &Reaction::setName,"Set the name of the Reaction.") .def("setSymbol", &Reaction::setSymbol,"Set the symbol of the Reaction.") .def("setEquation", &Reaction::setEquation,"Set the equation of the Reaction") @@ -45,7 +45,7 @@ void exportReaction(py::module& m) .def("setReferenceT", &Reaction::setReferenceT,"Set reference temperature fo the standard properties of reaction (K)") .def("setReferenceP", &Reaction::setReferenceP,"Set references pressure for the standard properties of reaction (Pa)") .def("setLowerT", &Reaction::setLowerT,"Set upper temperature limit of the correction method (K)") - .def("setLowerP", &Reaction::setLowerP,"Set upper presure limit of the correction method (Pa)") + .def("setLowerP", &Reaction::setLowerP,"Set upper pressure limit of the correction method (Pa)") .def("setUpperT", &Reaction::setUpperT,"Set lower temperature limit of the correction method (K)") .def("setUpperP", &Reaction::setUpperP,"Set lower pressure limit for the correction method (Pa)") .def("setMethodGenEoS", &Reaction::setMethodGenEoS,"Set the code for general EOS calculation method") @@ -64,7 +64,7 @@ void exportReaction(py::module& m) .def("referenceT", &Reaction::referenceT,"Returns the references temperature (K)") .def("referenceP", &Reaction::referenceP,"Returns the references pressure (Pa)") .def("lowerT", &Reaction::lowerT,"Return upper temperature limit of the correction method (K)") - .def("lowerP", &Reaction::lowerP,"Return upper presure limit of the correction method (Pa)") + .def("lowerP", &Reaction::lowerP,"Return upper pressure limit of the correction method (Pa)") .def("upperT", &Reaction::upperT,"Return lower temperature limit of the correction method (K)") .def("upperP", &Reaction::upperP,"Return lower pressure limit for the correction method (Pa)") .def("methodGenEOS", &Reaction::methodGenEOS,"Return the general EOS method code") diff --git a/python/pyThermoFun/pySubstance.cpp b/python/pyThermoFun/pySubstance.cpp index 32981048..84c7c2eb 100644 --- a/python/pyThermoFun/pySubstance.cpp +++ b/python/pyThermoFun/pySubstance.cpp @@ -33,10 +33,10 @@ namespace ThermoFun { void exportSubstance(py::module& m) { - py::class_(m, "Substance") + py::class_(m, "Substance", "A type used to describe a chemical substance (species, dependent components)") .def(py::init<>()) .def(py::init()) - .def(py::init()) + .def(py::init(), "Constructor using a substance record as a JSON string") .def("setName", &Substance::setName, "Set the name of the Substance.") .def("setSymbol", &Substance::setSymbol, "Set the symbol of the Substance.") .def("setFormula", &Substance::setFormula, "Set the formula of the Substance.") @@ -48,7 +48,7 @@ void exportSubstance(py::module& m) .def("setMethod_T", &Substance::setMethod_T, "Set the code for the temperature correction method") .def("setMethod_P", &Substance::setMethod_P, "Set the code for the pressure correction method") .def("setLowerT", &Substance::setLowerT, "Set upper temperature limit of the correction method (K)") - .def("setLowerP", &Substance::setLowerP, "Set upper presure limit of the correction method (Pa)") + .def("setLowerP", &Substance::setLowerP, "Set upper pressure limit of the correction method (Pa)") .def("setUpperT", &Substance::setUpperT, "Set lower temperature limit of the correction method (K)") .def("setUpperP", &Substance::setUpperP, "Set lower pressure limit for the correction method (Pa)") .def("setSubstanceClass", &Substance::setSubstanceClass, "Set the code for the substance class") @@ -66,7 +66,7 @@ void exportSubstance(py::module& m) .def("referenceT", &Substance::referenceT, "Return the reference temperature (K)") .def("referenceP", &Substance::referenceP, "Return the reference pressure (Pa)") .def("lowerT", &Substance::lowerT, "Return upper temperature limit of the correction method (K)") - .def("lowerP", &Substance::lowerP, "Return upper presure limit of the correction method (Pa)") + .def("lowerP", &Substance::lowerP, "Return upper pressure limit of the correction method (Pa)") .def("upperT", &Substance::upperT, "Return lower temperature limit of the correction method (K)") .def("upperP", &Substance::upperP, "Return lower pressure limit for the correction method (Pa)") .def("thermoProperties", &Substance::thermoProperties, "Return the thermodynamic data of the substance.") @@ -76,7 +76,7 @@ void exportSubstance(py::module& m) .def("method_T", &Substance::method_T, "Return the temperature correction method code") .def("method_P", &Substance::method_P, "Return the pressure correction method code") .def("substanceClass", &Substance::substanceClass, "Return the class type of the substance") - .def("thermoCalculationType", &Substance::thermoCalculationType, "Return the code of the substance proeprties calculation type") + .def("thermoCalculationType", &Substance::thermoCalculationType, "Return the code of the substance properties calculation type") .def("aggregateState", &Substance::aggregateState, "Return the aggregate state of a substance") .def("charge", &Substance::charge, "Return the charge of a substance") .def("jsonString", &Substance::jsonString, "Return the record as a json string") diff --git a/python/pyThermoFun/pyThermoEngine.cpp b/python/pyThermoFun/pyThermoEngine.cpp index 137deb1e..ddd21b30 100644 --- a/python/pyThermoFun/pyThermoEngine.cpp +++ b/python/pyThermoFun/pyThermoEngine.cpp @@ -51,25 +51,25 @@ void exportThermoEngine(py::module& m) auto thermoPropertiesReaction2 = static_cast(&ThermoEngine::thermoPropertiesReaction); auto thermoPropertiesReactionFromReactants2 = static_cast(&ThermoEngine::thermoPropertiesReactionFromReactants); - py::class_(m, "ThermoEngine") + py::class_(m, "ThermoEngine", "Contains methods for calculating the thermodynamic properties of the substances and reactions") // .def(py::init<>()) - .def(py::init()) - .def(py::init()) + .def(py::init(), "Constructor using a thermofun database as a JSON string") + .def(py::init(), "Constructor using a thermofun database") .def(py::init()) - .def("appendData", appendData1, "Append records to the database from a file.") - .def("appendData", appendData2, "Append records of given type (elements, substances, reactions) to the database from a list of JSON strings.") - .def("setSolventSymbol", &ThermoEngine::setSolventSymbol) - .def("solventSymbol", &ThermoEngine::solventSymbol) - .def("thermoPropertiesSubstance", thermoPropertiesSubstance1, "Calculate the thermodynamic properties of a substance with a given symbol.") - .def("electroPropertiesSolvent", electroPropertiesSolvent1, "Calculate the electro-chemical properties of a substance solvent with a given symbol.") - .def("propertiesSolvent", propertiesSolvent1, "Calculate the properties of a substance solvent with a given symbol.") - .def("thermoPropertiesReaction", thermoPropertiesReaction1, "Calculate the thermodynamic properties of a reaction with a given symbol, or for a given reaction equation.") - .def("thermoPropertiesReactionFromReactants", thermoPropertiesReactionFromReactants1, "Calculate the thermodynamic properties of a reaction from reactants with a given symbol.") - .def("thermoPropertiesSubstance", thermoPropertiesSubstance2, "Calculate the thermodynamic properties of a given substance object.") - .def("electroPropertiesSolvent", electroPropertiesSolvent2, "Calculate the electro-chemical properties of a given substance solvent object.") - .def("propertiesSolvent", propertiesSolvent2, "Calculate the properties of a given substance solvent object.") - .def("thermoPropertiesReaction", thermoPropertiesReaction2, "Calculate the thermodynamic properties of a given reaction object.") - .def("thermoPropertiesReactionFromReactants", thermoPropertiesReactionFromReactants2, "Calculate the thermodynamic properties from the reactants of a given reaction object.") + .def("appendData", appendData1, "Append a json string record to the database from a file", py::arg("record_json_string")) + .def("appendData", appendData2, "Append records of given label (element, substance, reaction) to the database from a list of JSON strings", py::arg("records_json"), py::arg("label")) + .def("setSolventSymbol", &ThermoEngine::setSolventSymbol, "Sets the symbol of the solvent record present in the thermodynamic dataset. Will be used to calculate the solvent properties ", py::arg("symbol")) + .def("solventSymbol", &ThermoEngine::solventSymbol, "Returns the symbol of the solvent record used to calculate the solvent properties") + .def("thermoPropertiesSubstance", thermoPropertiesSubstance1, "Calculate the thermodynamic properties of a substance with a given symbol.", py::arg("temperature"), py::arg("pressure"), py::arg("symbol")) + .def("electroPropertiesSolvent", electroPropertiesSolvent1, "Calculate the electro-chemical properties of a substance solvent with a given symbol.", py::arg("temperature"), py::arg("pressure"), py::arg("symbol")) + .def("propertiesSolvent", propertiesSolvent1, "Calculate the properties of a substance solvent with a given symbol.", py::arg("temperature"), py::arg("pressure"), py::arg("symbol")) + .def("thermoPropertiesReaction", thermoPropertiesReaction1, "Calculate the thermodynamic properties of a reaction with a given symbol, or for a given reaction equation.", py::arg("temperature"), py::arg("pressure"), py::arg("symbol")) + .def("thermoPropertiesReactionFromReactants", thermoPropertiesReactionFromReactants1, "Calculate the thermodynamic properties of a reaction from reactants with a given symbol.", py::arg("temperature"), py::arg("pressure"), py::arg("symbol")) + .def("thermoPropertiesSubstance", thermoPropertiesSubstance2, "Calculate the thermodynamic properties of a given substance object.", py::arg("temperature"), py::arg("pressure"), py::arg("substance")) + .def("electroPropertiesSolvent", electroPropertiesSolvent2, "Calculate the electro-chemical properties of a given substance solvent object.", py::arg("temperature"), py::arg("pressure"), py::arg("solvent")) + .def("propertiesSolvent", propertiesSolvent2, "Calculate the properties of a given substance solvent object.", py::arg("temperature"), py::arg("pressure"), py::arg("solvent")) + .def("thermoPropertiesReaction", thermoPropertiesReaction2, "Calculate the thermodynamic properties of a given reaction object.", py::arg("temperature"), py::arg("pressure"), py::arg("reaction")) + .def("thermoPropertiesReactionFromReactants", thermoPropertiesReactionFromReactants2, "Calculate the thermodynamic properties from the reactants of a given reaction object.", py::arg("temperature"), py::arg("pressure"), py::arg("reaction")) ; } diff --git a/python/pyThermoFun/pyThermoParameters.cpp b/python/pyThermoFun/pyThermoParameters.cpp index 1091cec2..a7821789 100644 --- a/python/pyThermoFun/pyThermoParameters.cpp +++ b/python/pyThermoFun/pyThermoParameters.cpp @@ -31,7 +31,7 @@ namespace ThermoFun { void exportThermoParametersSubstance(py::module& m) { - py::class_(m, "ThermoParametersSubstance") + py::class_(m, "ThermoParametersSubstance", "Parameters of a substance record") .def(py::init<>()) .def_readwrite("isothermal_compresibility", &ThermoParametersSubstance::isothermal_compresibility) .def_readwrite("isobaric_expansivity", &ThermoParametersSubstance::isobaric_expansivity) @@ -53,7 +53,7 @@ void exportThermoParametersSubstance(py::module& m) void exportThermoParametersReaction(py::module& m) { - py::class_(m, "ThermoParametersReaction") + py::class_(m, "ThermoParametersReaction", "Parameters of a reaction record") .def(py::init<>()) .def_readwrite("temperature_intervals", &ThermoParametersReaction::temperature_intervals) .def_readwrite("pressure_intervals", &ThermoParametersReaction::pressure_intervals) diff --git a/python/pyThermoFun/pyThermoProperties.cpp b/python/pyThermoFun/pyThermoProperties.cpp index 0db02063..52de81d6 100644 --- a/python/pyThermoFun/pyThermoProperties.cpp +++ b/python/pyThermoFun/pyThermoProperties.cpp @@ -31,7 +31,7 @@ namespace ThermoFun { void exportThermoPropertiesSubstance(py::module& m) { - py::class_(m, "ThermoPropertiesSubstance") + py::class_(m, "ThermoPropertiesSubstance", "Standard molar properties of a substance") .def(py::init<>()) .def_readwrite("gibbs_energy", &ThermoPropertiesSubstance::gibbs_energy) .def_readwrite("helmholtz_energy", &ThermoPropertiesSubstance::helmholtz_energy) @@ -46,7 +46,7 @@ void exportThermoPropertiesSubstance(py::module& m) void exportThermoPropertiesReaction(py::module& m) { - py::class_(m, "ThermoPropertiesReaction") + py::class_(m, "ThermoPropertiesReaction", "Standard molar properties of a reaction") .def(py::init<>()) .def_readwrite("ln_equilibrium_constant", &ThermoPropertiesReaction::ln_equilibrium_constant) .def_readwrite("log_equilibrium_constant", &ThermoPropertiesReaction::log_equilibrium_constant) @@ -63,7 +63,7 @@ void exportThermoPropertiesReaction(py::module& m) void exportElectroPropertiesSolvent(py::module& m) { - py::class_(m, "ElectroPropertiesSolvent") + py::class_(m, "ElectroPropertiesSolvent", "Dielectric constant, its derivatives with T and P, and Born functions") .def(py::init<>()) .def_readwrite("epsilon", &ElectroPropertiesSolvent::epsilon) .def_readwrite("epsilonT", &ElectroPropertiesSolvent::epsilonT) @@ -82,7 +82,7 @@ void exportElectroPropertiesSolvent(py::module& m) void exportPropertiesSolvent(py::module& m) { - py::class_(m, "PropertiesSolvent") + py::class_(m, "PropertiesSolvent", "Physical properties of water solvent") .def(py::init<>()) .def_readwrite("speed_of_sound", &PropertiesSolvent::speed_of_sound) .def_readwrite("dynamic_viscosity", &PropertiesSolvent::dynamic_viscosity)