Topkappy - Reference manual¶

Kappa Models¶

class topkappy.KappaModel.KappaModel(agents, rules, initial_quantities, snapshot_times=None, plots=(), plot_time_step=0.1, duration=None, stop_condition=None)[source]¶

Class to represent Kappa models and create corresponding Kappa scripts.

See the README example for a quick intro.

Parameters

agents: List of all KappaAgent in the model.
rules: List of all KappaRule in the model.
initial_quantities: Either a dict {agent_name: initial_quantity} or a single integer if all agents start with the same initial quantity. Note that the more initial agents, the less noisy the simulation results/
duration: Virtual duration of the simulation, in seconds/
snapshot_times: A dict {snapshot_name: time}, e.g. {‘end’: 10}. The snapshot can then be accessed as simulation_results[‘snapshots’][‘end’].
plots: List of Kappa expressions to plot. Each list element can be either a string (a Kappa expression like |A(.)|) or a KappaSiteState like [KappaSiteState('B', 'b', '.'), KappaSiteState('C', 'c', '.')].
plot_time_step: Time interval between two points when gathering data for plotting.
stop_condition: String representing a Kappa stopping condition. If none is provided, the simulation stops after the provided duration is reached.

Methods

`get_simulation_results`()	Run a simulation of the model and return results as a dict.
`set_parameters`([duration, stop_condition, …])	(Re-)set some parameters of the model.

class topkappy.KappaClasses.KappaAgent(name, sites)[source]¶

Class to represent a Kappa agent.

Parameters

name: Agent name, e.g. ‘A’.
sites: List of sites, e.g. [‘a1’, ‘a2’].

class topkappy.KappaClasses.KappaSiteState(agent, site, state='.')[source]¶

Class to represent a Kappa site state like ‘A(x[.])’.

Parameters

agent: A KappaAgent object.
site: Name of the site. Should be a name in agent.sites.
state: The site state, e.g. ‘.’, ‘1’, 2, etc.

class topkappy.KappaClasses.KappaRule(name, reactants, sense, products, rate)[source]¶

Class to represent a Kappa rule. Possibly very incomplete.

Parameters

name: Name of the rule.
reactants: List of KappaSiteState that are consumed by the rule.
sense: String arrow like ‘->’ or ‘<->’.
products: List of KappaSiteState that are produced by the rule.
rate: Value indicating the frequency at which the rule happens. (or pair of values if sense is ‘<->’.

class topkappy.FormattedKappaError.FormattedKappaError[source]¶

Class to raise and pretty-print Kappa script syntax errors.

When Kappa complains that the script doesn’t make sense (through kappy), you can catch the error, pass it to FormattedKappaError.from_kappa_error() and reraise it. The resulting error will nicely print the errors in context.

Examples

>>> kappa_client = kappy.KappaStd()
>>> kappa_client.add_model_string(model_string)
>>> try:
>>>     kappa_client.project_parse()
>>> except kappy.KappaError as kappa_error:
>>>     raise FormattedKappaError.from_kappa_error(kappa_error,
>>>                                                model_string)

Attributes

args

Methods

`formatted_string`(error_items, model_string)	Create a FormattedKappaError from a Kappy project parsing error.
`from_kappa_error`(kappa_error, model_string)	Create a FormattedKappaError from a Kappy project parsing error.
`with_traceback`	Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

Result analysis¶

topkappy.agents_graphs.plot_snapshot_agents(agents, with_ports=True, columns='auto', ax_inches=3, layout_method='FR', freq_cutoff=0)[source]¶

Plot graph representations of all complexes in a multi-ax figure.

Parameters

agents: Snapshot agents as provided in the result of get_simulation_results() by simulation_results['snapshots']['SNAP_NAME']['snapshot_agents'].
with_ports: If true, ports (= agents binding sites) will be represented on the figure.
columns: How many columns (= plots by lines) in the figure ? Keep to “auto” for a more-or-less squared figure.
ax_inches: Size of one figure in inches.
layout_method: Networkx layout method for the graph drawing. Either ‘FR’ (fruchterman_reingold), ‘spectral’ or ‘spring’.
freq_cutoff: All complexes with a number of occurences lower than frew_cutoff in the snapshot will be ignored.

Returns

fig, axes: Matplotlib “figure” and “axes” list of the multi-ax figure generated.

topkappy.plot_simulation_time_series.plot_simulation_time_series(plots_data, ax=None)[source]¶

Plot the time series data from a KappaModel simulation.

Examples

>>> model = KappaModel(...)
>>> simulation_results = model.get_simulation_results()
>>> ax = plot_simulation_time_series(simulation_results['plots'])
>>> ax.figure.savefig('basic_example_time_series.png')