Pytry is a light-weight Python package for defining something that you want to try, some parameters you'd like to vary while you try it, and some measurement that you want to do each time you try it.

For example, suppose you have some complicated simulation you want to run and measure how well it performs. You might write code that looks like this:

model = create_my_large_model(a=3, b=7)
data = model.run()
average_value = numpy.mean(data)
final_value = data[-1]

But now you want to explore what happens as you adjust parameter values. Sometimes you just want to do this by hand, changing a and b and seeing what happens. Sometimes you want to do this more rigorously, by automatically varying the parameter values over some range. And you probably what to record those results of the individual runs.

Pytry lets you slightly reorganize the code to look like this:

class MyModel(pytry.Trial):
    def params(self):
        self.param('the first parameter', a=3)
        self.param('the second parameter', b=7)
    def evaluate(self, p):
        model = create_my_large_model(a=p.a, b=p.b)
        data = model.run()
        return dict(
            average_value=numpy.mean(data),
            final_value=data[-1],
            )

Now you can run the Trial in one of two ways. First, you can use pytry itself from the command line. Just do:

pytry filename.py --a 5 --b 8

from the command line and your model will be run with your new parameter settings. Second, you can run it from a separate script:

for a in [3, 5, 7, 9]:
    for b in [3, 5, 7, 9]:
        MyModel().run(a=a, b=b)

This will run the model 16 times with all combinations of parameter settings

When you use pytry from the command line, you can give the --help argument to see the various options. This will include the defined parameters of the model. For example, for the model above you will see:

usage: pytry [-h] [--a <X>] [--b <X>] [--seed <X>] [--data_dir <X>]
             [--data_filename <X>] [--verbose <X>]
             FILE

pytry: Run Trials with parameters

positional arguments:
  FILE                 .py file containing Trial object

optional arguments:
  -h, --help           show this help message and exit
  --a <X>              the first parameter (default=3)
  --b <X>              the second parameter (default=7)
  --seed <X>           random number seed (default=1)
  --data_dir <X>       data directory (default='data')
  --data_filename <X>  filename for data (default=None)
  --verbose <X>        print progress information (default=True)

The parameters a and b are determined from the Trial you defined, but the other parameters are automatically generated. They are:

• seed, which sets the random number seeds for the standard random random number generator, as well as the numpy.random random number generator
• data_dir, which indicates the directory to store the results of the trial in.
• data_filename, the filename to use for this trial. If it is not set, it defaults to <ModelName>#<timestamp>-<UUID>.
• verbose, whether information will be printed to the screen

All of these parameters are also available when running from a script. For example, you can do:

MyModel().run(a=3, b=5, seed=2, verbose=False)

Each trial is saved as a separate file. By default, it uses a simple text format, including both the parameter settings and the evaluation results. For example, the above model will create files like this:

a = 3
b = 7
seed = 1

average_value = 3.4240799645298909
final_value = 7.8862242385055321

You can change the save format using the data_format option. The currently known formats are txt (the default text format) and npz (the numpy savez format).

You can also load all the data from all the trials in a directory:

data = pytry.read('data')

This will give you a list of dictionaries, with each dictionary containing the contents of one trial. Since this is the same format used by common data management packages such as pandas, you can do this to get it into a pandas DataFrame:

df = pandas.DataFrame(pytry.read('data'))

You may want to generate plots in addition to the raw results from running the trial. To do this, use pytry.PlotTrial rather than pytry.Trial. This adds three new parameters:

  --plt                display plot (default=False)
  --plt_save           save plot (default=False)
  --plt_overlay        show overlay on figures (default=False)

• plt, whether to show the plot on the screen
• plt_save, whether those plots should be saved to a file
• plt_overlay, whether the plots should have the parameter values and results written on them as well

When using the pytry.PlotTrial, the evaluate function will get a plt argument passed into it. This will be the matplotlib.pyplot object (or None if neither the plt nor plt_save parameters are set).

class MyModel(pytry.PlotTrial):
    def params(self):
        self.param('the first parameter', a=3)
        self.param('the second parameter', b=7)
    def evaluate(self, p, plt):
        model = create_my_large_model(a=p.a, b=p.b)
        data = model.run()

        if plt is not None:
            plt.plot(data)

        return dict(
            average_value=numpy.mean(data),
            final_value=data[-1],
            )

If plt is set, this plot will be shown on the screen. If plt_save is set, this plot will be saved as a .png file in the data directory. These parameters are dependent on matplotlib being installed.

By subclassing pytry.Trial you can make customized Trials for different circumstances. pytry has one of these built-in that is suitable for use with nengo, a package for producing large-scale neural simulations.

The pytry.NengoTrial class introduces new parameters and separates evaluate into two stages (making the model and running the model). This works as follows:

class NengoSimple(pytry.NengoTrial):
    def params(self):
        self.param('number of neurons', N=100)
        self.param('number of dimensions', D=2)

    # define the model
    def model(self, p):
        model = nengo.Network()
        with model:
            stim = nengo.Node([0]*p.D)
            ens = nengo.Ensemble(n_neurons=p.N, dimensions=p.D)
            nengo.Connection(stim, ens)
            self.probe = nengo.Probe(ens, synapse=0.01)
        return model

    # run the model and evaluate the results
    def evaluate(self, p, sim, plt):
        sim.run(1)

        if plt is not None:
            plt.plot(sim.trange(), sim.data[self.probe])

        rmse = np.sqrt(np.mean((sim.data[self.probe])**2))
        return dict(rmse=rmse)

This also adds three new parameters (in addition to the three introduced by pytry.PlotTrial:

  --backend <X>        nengo backend to use (default='nengo')
  --dt <X>             nengo timestep (default=0.001)
  --gui                run in nengo GUI (default=False)

• backend allows you to run the neural model using different implementations of the neural simulator (such as nengo_ocl or nengo_spinnaker)
• dt specifies the time step of the simulation
• gui does not run the simulation. Instead, it creates the model and then starts the nengo_gui interactive simulation environment, allowing you to directly interact with the model.