Parameters and options

(We use parameter and option synonymously.)

Standard simulations in TeNPy can be defined by just set of options collected in a dictionary (possibly containing other parameter dictionaries). It can be convenient to represent these options in a [yaml] file, say parameters.yml, which might look like this:

output_filename : params_output.h5
overwrite_output : True
model_class :  SpinChain
model_params :
    L : 14
    bc_MPS : finite

    method : lat_product_state
    product_state : [[up], [down]]

algorithm_class: TwoSiteDMRG
        chi_max: 120
        svd_min: 1e.-8
    max_sweeps: 10
    mixer : True

Note that the default values and even the allowed/used option names often depend on other parameters. For example, the model_class parameter above given to a Simulation selects a model class, and diffent model classes might have completely different parameters. This gives you freedom to define your own parameters when you implement a model, but it also makes it a little bit harder to keep track of allowed values.

In the TeNPy documentation, we use the Options sections of doc-strings to define parameters that are read out. Each documented parameter is attributed to one set of parameters, called “config”, and managed in a Config class at runtime. The above example represents the config for a Simulation, with the model_params representing the config given as options to the model for initialization. Sometimes, there is also a structure of one config including the parameters from another one: For example, the generic parameters for time evolution algorithms, TimeEvolutionAlgorithm are included into the TEBDEngine config, similarly to the sub-classing used.

During runtime, the Config class logs the first use of any parameter (with DEBUG log-level, if the default is used, and with INFO log-level, if it is non-default). Moreover, the default is saved into the parameter dictionary. Hence, it will contain the full set of all used parameters, default and non-default, at the end of a simulation, e.g., in the sim_params of the results returned by

You can find a list of all the different configs in the Config Index, and a list of all parameters in Config-Options Index.

If you add extra options to your configuration that TeNPy doesn’t read out by the end of the simulation, it will issue a warning. Getting such a warnings is an indicator for a typo in your configuration, or an option being in the wrong config dictionary.