Running pyFastChem

In addition to the C++ executable, we provide several Python scripts that can run the FastChem code through its Python interface pyFastChem. The sample scripts can be found within the python/ folder. These sample scripts show different use cases and can be used as a basis for your own FastChem Python scripts.

Provided Python examples

Currently, we provide the following examples:
fastchem.py

Runs a simple FastChem calculation on a temperature-pressure structure defined within the script, writes output files, and creates a plot with selected species.

fastchem_c_o.py

Runs a FastChem calculation on a temperature-pressure structure defined within the script and for a range of different C/O ratios. It will write output files, and create a plot with selected species.

fastchem_metallicity.py

Runs a FastChem calculation on a temperature-pressure structure defined within the script and for a range of different metallicity factors. It will write output files, and create a plot with selected species.

fastchem_cond.py

Runs a FastChem calculation on a read-in temperature-pressure structure of a brown dwarf with equilibrium condensation turned on. The rainout approximation can optionally be turned on as well. It will write output files, and create a plot with selected species.

fastchem_cond_disk.py

Runs a FastChem calculation for the temperature-pressure structure of the midplane of a protoplanetary disk with equilibrium condensation. It will write output files, and create a plot with selected species. Warning: Due to the very low temperatures in the outer part of the disk, this calculation might take quite some time depending on your computer.

Note that the scripts should be executed from within the Python folder since all file paths in the scripts are given relative to this directory. These files can be used as templates to create your own special Python scripts to run pyFastChem. The following section provides some details on the steps required to run FastChem from within Python. A more detailed overview of all the methods and variables available within pyFastChem can be found here.

Detailed steps for running pyFastChem

As a first step, we need to import the pyFastChem module:
import pyfastchem
This will import the module compiled by PyBind11. Next, we have to create a FastChem object (here named fastchem) with a corresponding constructor pyfastchem.FastChem provided by pyFastChem. There are three different possible versions of the constructor. F

If we’re only interested in a pure gas phase chemistry calculation, we can use

fastchem = pyfastchem.FastChem('input/element_abundances/asplund_2009.dat',
                               'input/logK/logK.dat',
                               1)
This constructor requires three different arguments: the location of the element abundance file, the location of the file with the equilibrium constants, and the verbose level.
For a FastChem object that also includes condensed species, we have:
fastchem = pyfastchem.FastChem('input/element_abundances/asplund_2009.dat',
                               'input/logK/logK.dat',
                               'input/logK/logK_condensates.dat',
                               1)

where the additional argument is the location of the input file with the equilibrium constants of the condensed species. It is also possible to use 'none' for this argument:

fastchem = pyfastchem.FastChem('input/element_abundances/asplund_2009.dat',
                               'input/logK/logK.dat',
                               'none',
                                1)
in which case no condensate data will be read in and this constructor behaves like the first one by only allowing the calculation of the pure gas phase composition.
Alternatively, a FastChem object can also be created via
fastchem = pyfastchem.FastChem('input/parameters_py.dat', 1)
where the first argument is the location of the parameter file and the second one the initial verbose level. The latter one will later be replaced by the corresponding value read in from the parameter file. The structure of this parameter file is discussed here.
Creating a FastChem object with the first two methods will set internal parameters to their default values. The maximum number of chemistry iterations will be 3000, the number of Newton, bisection and Nelder-Mead method iterations is 3000, and the accuracy of Newton’s method and the convergence criterion for the chemistry iterations is set to \(10^{-5}\). The requirement for element conservation is set to \(10^{-4}\) by default. All of these values can, however, be adjusted during runtime by using the methods listed here.
Next, we need to create the input and output structures used by pyFastChem:
input_data = pyfastchem.FastChemInput()
output_data = pyfastchem.FastChemOutput()
Details on these structures can be found here. The input structure contains the temperature (in K) and pressure (in bar) arrays that the chemistry should be calculated for. They can be set, for example, by:
input_data.temperature = temperature
input_data.pressure = pressure
where temperature and pressure are standard Python lists or NumPy arrays. Both arrays need to have the same length. The input structure also contains two boolean flags that enable the calculation of the condensed phase:
input_data.equilibrium_condensation
input_data.rainout_condensation
Setting the first flag to True will calculate the chemical composition assuming equilibrium condensation for each temperature-pressure point of the input structure separately. Setting the rainout condensation flag to True enables the calculation using the rainout approximation. Details on this can be found in Kitzmann, Stock & Patzer (2023). Note that if the rainout flag is set to True, the value of the equilibrium condensation flag is ignored. By default, both flags are set to False.
With the input structure properly set up, we can now run the actual FastChem calculation by calling the calcDensities method:
fastchem_flag = fastchem.calcDensities(input_data, output_data)
This method returns an integer flag that describes the overall outcome of the calculation. A description of the different flags can be found here. After calling the calcDensity method, the output structure will be filled with the corresponding output data. For example,
output_data.number_densities will contain the number densities of the chemical species. This is a 2D list, where the first dimension refers to the temperature and pressure input arrays and the second dimension refers to the different chemical species. The list can be easily converted into a NumPy array via:
number_densities = np.array(output_data.number_densities)
The Python directory of the FastChem repository also contains functions that save the output into files, identical to those from the C++ version. They can be called by:
saveChemistryOutput(output_dir + '/chemistry.dat',
                    temperature,
                    pressure,
                    output_data.total_element_density,
                    output_data.mean_molecular_weight,
                    output_data.number_densities,
                    fastchem)

saveCondOutput(output_dir + '/condensates.dat',
               temperature,
               pressure,
               output_data.element_cond_degree,
               output_data.number_densities_cond,
               fastchem)

saveMonitorOutput(output_dir + '/monitor.dat',
                  temperature,
                  pressure,
                  output_data.element_conserved,
                  output_data.fastchem_flag,
                  output_data.nb_chemistry_iterations,
                  output_data.total_element_density,
                  output_data.mean_molecular_weight,
                  fastchem)
A more detailed description of the output functions can be found in the next section.

Output functions of pyFastChem

The Python directory includes several scripts that can save the FastChem chemistry and monitor output in either text or binary data files. All these functions are located within the file save_output.py. Examples of their usage can be found in the three Python scripts discussed above.

Chemistry output scripts

save_output.py contains two functions for the general chemistry output. The first, saveChemistryOutput, saves the results in a text file that is identical to the one of the C++ version. If the chemistry is calculated for a larger number of pressure and temperature points, the output can become quite large. Saving these results into a simple text file can, therefore, take a very long time - in extreme cases even longer than the calculation itself.

Therefore, we provide an alternative function saveChemistryOutputPandas that saves the output in a pandas DataFrame format into a pickle file. Since this is a binary format, saving a large output is substantially faster than the corresponding ASCII text version.

The function for saving the output as a normal text file is
saveChemistryOutput(file_path,
                    temperature,
                    pressure,
                    total_element_density,
                    mean_molecular_weight,
                    number_densities,
                    fastchem,
                    output_species=None,
                    additional_columns=None,
                    additional_columns_desc=None)

with the following arguments:

file_path

Contains the path to the output file as a str variable.

temperature, pressure

Arrays of float values with the temperature and pressure structure the chemistry has been calculated for.

total_element_density

float array of the total number density of all atoms \(j\), i.e. \(n_\mathrm{tot} = \sum_j \left( n_j + \sum_i \nu_{ij} n_i + \sum_c \nu_{cj} n_c \right)\), summed over their atomic number densities, as well as the ones contained in all other molecules/ions \(j\) and condensate species \(c\). This quantity is usually only a diagnostic output and not relevant for other calculations. The dimension of the array is equal to that of the temperature and pressure vectors.

mean_molecular_weight

float array of the computed mean molecular weight. The dimension of the array is equal to that of the temperature and pressure vectors.

number_densities

Two-dimensional float array of the number densities. The first dimension of the array refers to the temperature and pressure input arrays, while the second dimension describes the different chemical species.

fastchem

An object of the pyFastChem class that has been used to calculate the chemistry.

output_species=None

Optional parameter. Is an array of str values that contains the chemical symbols of species the chemistry output file should be saved for. Without this optional parameter, the output function will by default save all species. The symbols have to match the ones used in the FastChem input file for the equilibrium constants. For the standard files supplied with FastChem, the Hill notation, therefore, needs to be used here.

additional_columns=None, additional_columns_desc=None

Optional parameters. Sometimes, FastChem calculations are not iterated only over temperature or pressure but also other variables, such as the metallicity or C/O ratio. The output function therefore contains these optional parameters that allow to print additional columns in the output file. The first parameter additional_columns is an \(N\times N_\mathrm{tp}\)-dimensional array of float values, where the first dimension refers to the number of additional columns and the second dimension has to be equal to the dimensions of the temperature and pressure arrays.

The second optional parameter additional_columns_desc contains an array of str values with the header descriptions of the additional columns. The dimension has to be equal to number of additional columns. If this is not the case, or if the parameter is missing entirely, the columns will be labelled unk instead.

All of these function arguments, except for the optional parameters, are contained within the input and output structures of pyFastChem, discussed here.
Saving the chemistry output with the panda DataFrame format in a pickle file is possible via the function:
saveChemistryOutputPandas(file_path,
                          temperature,
                          pressure,
                          total_element_density,
                          mean_molecular_weight,
                          number_densities,
                          fastchem,
                          output_species=None,
                          additional_columns=None,
                          additional_columns_desc=None)

All arguments are identical to those of the previous ASCII output function. The saved panda DataFrame contains the same columns and headers as the simple text output.

Condensate output script

The two condensate output scripts are almost identical to the gas phase chemistry one described above. The first script saves the output into a simple text file:
saveCondOutput(file_path,
               temperature,
               pressure,
               element_cond_degree,
               number_densities,
               fastchem,
               output_species=None,
               additional_columns=None,
               additional_columns_desc=None)
It has the following arguments:
file_path

Contains the path to the output file as a str variable.

temperature, pressure

Arrays of float values with the temperature and pressure structure the chemistry has been calculated for.

element_cond_degree

Two-dimensional float array of the degrees of condensation for all elements. The first dimension of the array refers to the temperature and pressure input arrays, while the second dimension describes the different elements.

number_densities

Two-dimensional float array of the (fictitious) condensate number densities. The first dimension of the array refers to the temperature and pressure input arrays, while the second dimension describes the different condensate species.

fastchem

An object of the pyFastChem class that has been used to calculate the chemistry.

output_species=None

Optional parameter. Is an array of str values that contains the chemical symbols of condensates the chemistry output file should be saved for. Without this optional parameter, the output function will by default save all species. The symbols have to match the ones used in the FastChem input file for the equilibrium constants.

additional_columns=None, additional_columns_desc=None

Optional parameters. Sometimes, FastChem calculations are not iterated only over temperature or pressure but also other variables, such as the metallicity or C/O ratio. The output function therefore contains these optional parameters that allow to print additional columns in the output file. The first parameter additional_columns is an \(N\times N_\mathrm{tp}\)-dimensional array of float values, where the first dimension refers to the number of additional columns and the second dimension has to be equal to the dimensions of the temperature and pressure arrays.

The second optional parameter additional_columns_desc contains an array of str values with the header descriptions of the additional columns. The dimension has to be equal to number of additional columns. If this is not the case, or if the parameter is missing entirely, the columns will be labelled unk instead.

All of these function arguments, except for the optional parameters, are contained within the input and output structures of pyFastChem, discussed here.
Saving the condensate output with the panda DataFrame format in a pickle file is possible via the function:
saveCondOutputPandas(file_path,
                     temperature,
                     pressure,
                     element_cond_degree,
                     number_densities_cond,
                     fastchem,
                     output_species=None,
                     additional_columns=None,
                     additional_columns_desc=None)

All arguments are identical to those of the previous ASCII output function. The saved panda DataFrame contains the same columns and headers as the simple text output.

Monitor output scripts

save_output.py also contains two functions for the FastChem monitor output. The first, savMonitorOutput, saves the debug output in a text file that is identical to the one of the C++ version. Just like for the chemistry output, saving the results for a large number of calculations can be quite slow. Therefore, we also provide an alternative function saveMonitorOutputPandas that saves the output as a pandas DataFrame format into a pickle file.

The function for saving the output as a normal text file is
saveMonitorOutput(file_path,
                  temperature,
                  pressure,
                  element_conserved,
                  fastchem_flags,
                  nb_iterations,
                  nb_chemistry_iterations,
                  nb_condensation_iterations,
                  total_element_density,
                  mean_molecular_weight,
                  fastchem,
                  additional_columns=None,
                  additional_columns_desc=None)

with the following arguments:

file_path

Contains the path to the output file as a str variable.

temperature, pressure

Arrays of float values with the temperature and pressure structure the chemistry has been calculated for.

element_conserved

The two-dimensional array of int numbers contains information on the state of element conservation. A value of 0 indicates that element conservation is not fulfilled, whereas a value of 1 means that the element has been conserved. The first dimension refers to the temperature-pressure grid and has the same size as the temperature and pressure vectors of the input structure. The second dimension refers to the number of elements and has a length of getElementNumber() (see here).

fastchem_flags

One-dimensional array of int numbers. Contains flags that give information on potential issues of the chemistry calculation for each temperature-pressure point. The set of potential values is stated here. A string message for each corresponding flag can also be obtained from the constant pyfastchem.FASTCHEM_MSG vector of strings, via pyfastchem.FASTCHEM_MSG[flag]. The dimension of the array is equal to that of the input temperature and pressure vectors.

nb_iterations

One-dimensional array of int numbers. Contains the number of coupled chemistry-condensation iterations that were required to solve the system for each temperature-pressure point. The dimension of the array is equal to that of the input temperature and pressure vectors.

nb_chemistry_iterations

One-dimensional array of int numbers. Contains the total number of chemistry iterations that were required to solve the system for each temperature-pressure point. The dimension of the array is equal to that of the input temperature and pressure vectors.

nb_condensation_iterations

One-dimensional array of int numbers. Contains the total number of condensation calculation iterations that were required to solve the system for each temperature-pressure point. The dimension of the array is equal to that of the input temperature and pressure vectors.

total_element_density

One-dimensional array of float numbers that contains the total number density of all atoms \(j\), i.e. \(n_\mathrm{tot} = \sum_j \left( n_j + \sum_i \nu_{ij} n_i + \sum_c \nu_{cj} n_c \right)\), summed over their atomic number densities, as well as the ones contained in all other molecules/ions \(j\) and condensate species \(c\). This quantity is usually only a diagnostic output and not relevant for other calculations. The dimension of the array is equal to that of the input temperature and pressure vectors.

mean_molecular_weight

One-dimensional array of float numbers. Contains the mean molecular weight of the mixture in units of the unified atomic mass unit. For all practical purposes, this can also be converted into units of g/mol. The dimension of the array is equal to that of the input temperature and pressure vectors.

fastchem

An object of the pyFastChem class that has been used to calculate the chemistry.

additional_columns=None, additional_columns_desc=None

Optional parameters. Sometimes, FastChem calculations are not iterated only over temperature or pressure but also other variables, such as the metallicity or C/O ratio. The output function therefore contains these optional parameters that allow to print additional columns in the output file. The first parameter additional_columns is an \(N\times N_\mathrm{tp}\)-dimensional array of float values, where the first dimension refers to the number of additional columns and the second dimension has to be equal to the dimensions of the temperature and pressure arrays.

The second optional parameter additional_columns_desc contains an array of str values with the header descriptions of the additional columns. The dimension has to be equal to number of additional columns. If this is not the case, or if the parameter is missing entirely, the columns will be labelled unk instead.

The monitor output file has the same format as the one produced by the C++ version discussed here. Saving the chemistry output with the panda DataFrame format in a pickle file is possible via the function:

saveMonitorOutputPandas(file_path,
                        temperature,
                        pressure,
                        element_conserved,
                        fastchem_flags,
                        nb_iterations,
                        nb_chemistry_iterations,
                        nb_condensation_iterations,
                        total_element_density,
                        mean_molecular_weight,
                        fastchem,
                        additional_columns=None,
                        additional_columns_desc=None)

All arguments are identical to those of the previous ASCII output function. The saved panda DataFrame contains the same columns and headers as the simple text output. The only difference between the outputs is that for the DataFrame format, the element conservation and FastChem flags are not converted to strings (i.e. to fail or ok) but rather have their original integer values that are returned by FastChem. Their values are discussed here & here.