API documentation: | spikes | analysis | benchmark | parameters | stgen | utilities | facets |
NeuroTools.benchmark
Data
__name__ = "NeuroTools.benchmark"
Functions
get_experiment_dict(params)
Takes params = dict with vectors of all parameters values Calculates cross product of all and returns a dict with all experiments.
Classes
Benchmark
__init__(self, filename, model, experiments)
Generates the benchmark files at the specified name
A benchmark is a list of experiments to test the effect of parameter changes on a model. It is defined by: - filename is the folder where the benchmark is run - model is the Model object - params is its operating point (a dict) - experiments is the list of experimental parameters (in a dict) that change and producing the list of experiments
execute(self, experiment_list)
Execute all experiments from experiment_list.
This should be a batch of experiments to launch on one core.
experiment_filename(self, experiment="")
Returns the filename for an experiment.
This will generate one file per experiment and a default
TODO : use a more sexy filename with:
name="" for key in data_root['run'][experiment].keys(): name = name+"_"+key+"_"+str(data_root["run"][experiment][key])
get(self, key="", experiment="")
Gets dictionary at key from experiment file.
opens the main file if experiment = ""
get_figures(self)
Get figure list
list_experiments(self)
Returns the list of experiments in the benchmark
list_param(self, param)
Lists values for param in experiments = the reverse of get_experiment_dict
list_unfinished_experiments(self)
returns a list of the experiments in the benchmark that did not end
list_where(self, param, value)
Lists experiments where param is equal to value.
put(self, key, data, experiment)
Puts dict at key in experiment file.
run_simulations(self)
Runs the benchmark simulations
The strategy is applied: - only simulate what was not already simulated (did your process break?) - run the different independent simulations on different ipython1 engines and then gather all data
show_experiments(self)
Show a nice list of experiments
show_figures(self, list=[])
Show a list of figures saved as pylab objects experiments = <property object at 0x48bf80>
Model
Base class for standardized models.
A model is a system that has some parameters (params) and outputs data in a dict (out) thanks to a python call or TODO a standard unix command (cmd).
TODO: define more exactly the standard
