CanSen saves the solution information to a binary save file in a standard format called HDF5. Many programming and scripting languages have interfaces for HDF5 files, including C++, MATLAB, Fortran 90 and Python. Notably, these are all of the interfaces that Cantera supports. The Python interface will be demonstrated in this tutorial, but the structure of the data and thus the main content of this tutorial will remain the same for all of the interfaces.

There are several Python interfaces for HDF5, but the one we will be using is called PyTables. The documentation for PyTables can be found on their GitHub page.

Note that on the following lines, the >>> indicates that you should type the text at a Python prompt, not including the >>>. First, we will import the necessary libraries:

>>> import tables
>>> import cantera as ct

If either of these don’t work, make sure that PyTables and Cantera are both properly installed.

To print information about the save file, just type the name of its variable

>>> save_file

The data is saved in the save file with the Table format. Each Row in the Table represents one time step. Each Row further consists of a number of Columns where the data is stored. The Columns can be of arbitrary shape - thus, the entire 2-D sensitivity array is saved in one Column on each time step (i.e. in each row).

The format of the save file is hierarchical. The Table with each time step is stored in a Group, which is stored in the Root. It can be thought of as nested directories, with the Root as the top directory, then the Group, then the Table, like so:


To access the information in the Table, it should be stored in a variable for quick access. The name of the Group in the save files from CanSen is reactor.

>>> table = save_file.root.reactor

The Table can now be used like any other class instance. In particular, the Table class defines a number of useful functions and attributes, such as nrows, which prints the number of rows in the Table.

>>> table.nrows

PyTables provides a method to iterate over the rows in a table automatically, called iterrows. Here we introduce one way to access information in a particular Column in the Table, by using natural name indexing. In this case, we print the value of the time at each time step.

>>> for row in table.iterrows():

Note that numerical indexing is also supported. The following is equivalent to the above:

>>> for row in table.iterrows():

The information stored by CanSen is written into case-sensitive columns named:

  1. time
  2. temperature
  3. pressure
  4. volume
  5. massfractions
  6. sensitivity

Columns 0-3 have a single value in each row. Column 4 (massfractions) contains a vector with length of the number of species in the mechanism. Column 5 is optional and included only if the user requested sensitivity analysis during the simulation. The dimensions of Column 5 are (n_vars, n_sensitivity_params).

In addition to the method of iterating through Rows, entire Columns can be accessed and stored in variables. First, all of the Columns can be stored in a variable.

>>> all_cols = table.cols

In this method, different Columns are accessed by their numerical index. The first index to all_cols gives the row and the second index gives the column number. Remembering that Python is zero-based, to access the mass fractions on the 4th time step, do

>>> mass_fracs_4 = all_cols[3][4]

Individual Columns can be stored in variables as well. This is done by the natural naming scheme.

>>> all_mass_fracs = table.cols.massfractions

This stores an instance of the Column class in all_mass_fracs. It may be more useful to store the data in a particular column in a variable. To do that, get a slice of the column by using the index and the colon operator. For instance, to store all of the mass fraction data in a variable

>>> all_mass_fracs = table.cols.massfractions[:]

Or, to store the fifth through tenth time steps

>>> mass_fracs_5_10 = table.cols.massfractions[4:9]

Or, to store every other time step from the sixth through the 20th

>>> mass_fracs_alt = table.cols.massfractions[5:19:2]

Once the data has been extracted from the save file, we need to actually be able to do something with it. Fortunately, Cantera offers a simple way to do this, by initializing a Solution to the desired conditions.

>>> gas = ct.Solution('mech.xml')
>>> for row in table.iterrows():
        gas.TPY = row['temperature'], row['pressure'], row['massfractions']

This will print the creation rates of each species at each time step. Any method or parameter supported by the Solution class can be used to retrieve data at any given time step.

Further information about the PyTables package can be found at and information about Cantera can be found at