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aforren1 / toon

TOOls for experiments in psychophysics and Neuroscience
MIT License
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toon

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Description

Additional tools for neuroscience experiments, including:

Everything should work on Windows/Mac/Linux.

Install

Current release:

pip install toon

Development version:

pip install -i https://test.pypi.org/simple/ toon --pre

Or for the latest commit (requires compilation):

pip install git+https://github.com/aforren1/toon

See the demos/ folder for usage examples (note: some require additional packages).

Overview

Input

toon provides a framework for polling from input devices, including common peripherals like mice and keyboards, with the flexibility to handle less-common devices like eyetrackers, motion trackers, and custom devices (see toon/input/ for examples). The goal is to make it easier to use a wide variety of devices, including those with sampling rates >1kHz, with minimal performance impact on the main process.

We use the built-in multiprocessing module to control a separate process that hosts the device, and, in concert with numpy, to move data to the main process via shared memory. It seems that under typical conditions, we can expect single read() operations to take less than 500 microseconds (and more often < 100 us). See demos/bench_plot.py for an example of measuring user-side read performance.

Typical use looks like this:

from toon.input import MpDevice
from mymouse import Mouse
from timeit import default_timer

device = MpDevice(Mouse())

with device:
    t1 = default_timer() + 10
    while default_timer() < t1:
        res = device.read()
        # alternatively, unpack immediately
        # time, data = device.read()
        if res:
            time, data = res # unpack (or access via res.time, res.data)
            # N-D array of data (0th dim is time)
            print(data)
            # 1D array of times
            print(time)

Creating a custom device is relatively straightforward, though there are a few boxes to check.

from ctypes import c_double

class MyDevice(BaseDevice):
    # optional: give a hint for the buffer size (we'll allocate 1 sec worth of this)
    sampling_frequency = 500

    # this can either be introduced at the class level, or during __init__
    shape = (3, 3)
    # ctype can be a python type, numpy dtype, or ctype
    # including ctypes.Structures
    ctype = c_double

    # optional. Do not start device communication here, wait until `enter`
    def __init__(self):
        pass

    ## Use `enter` and `exit`, rather than `__enter__` and `__exit__`
    # optional: configure the device, start communicating
    def enter(self):
        pass

    # optional: clean up resources, close device
    def exit(self):
        pass

    # required
    def read(self):
        # See demos/ for examples of sharing a time source between the processes
        time = self.clock()
        # store new data with a timestamp
        data = get_data()
        return time, data

This device can then be passed to a toon.input.MpDevice, which preallocates the shared memory and handles other details.

A few things to be aware of for data returned by MpDevice:

Animation

This is still a work in progress, though I think it has some utility as-is. It's a port of the animation component in the Magnum framework, though lacking some of the features (e.g. Track extrapolation, proper handling of time scaling).

Example:

from math import sin, pi

from time import sleep
from timeit import default_timer
import matplotlib.pyplot as plt
from toon.anim import Track, Player
# see toon/anim/easing.py for all available easings
from toon.anim.easing import LINEAR, ELASTIC_IN

class Circle(object):
    x = 0
    y = 0

circle = Circle()
# list of (time, value)
keyframes = [(0.2, -0.5), (0.5, 0), (3, 0.5)]
x_track = Track(keyframes, easing=LINEAR)

# we can reuse keyframes
y_track = Track(keyframes, easing=ELASTIC_IN)

player = Player(repeats=3)

# directly modify an attribute
player.add(x_track, 'x', obj=circle)

def y_cb(val, obj):
    obj.y = val

# modify via callback
player.add(y_track, y_cb, obj=circle)

t0 = default_timer()
player.start(t0)
vals = []
times = []
while player.is_playing:
    t = default_timer()
    player.advance(t)
    times.append(t)
    vals.append([circle.x, circle.y])
    # sleep(1/60)

plt.plot(times, vals)
plt.show()

Other notes:

Utilities

The util module includes high-resolution clocks/timers via QueryPerformanceCounter/Frequency on Windows, mach_absolute_time on MacOS, and clock_gettime(CLOCK_MONOTONIC) on Linux. The class is called MonoClock, and an instantiation called mono_clock is created upon import. Usage:

from toon.util import mono_clock, MonoClock

clk = mono_clock # re-use pre-instantiated clock
clk2 = MonoClock(relative=False) # time relative to whenever the system's clock started

t0 = clk.get_time()

Another utility currently included is a priority function, which tries to improve the determinism of the calling script. This is derived from Psychtoolbox's Priority (doc here). General usage is:

from toon.util import priority

if not priority(1):
    raise RuntimeError('Failed to raise priority.')

# ...do stuff...

priority(0)

The input should be a 0 (no priority/cancel), 1 (higher priority), or 2 (realtime). If the requested level is rejected, the function will return False. The exact implementational details depend on the host operating system. All implementations disable garbage collection.

Windows

MacOS

Linux

Your mileage may vary on whether these actually improve latency/determinism. When in doubt, measure! Read the warnings here.

Notes about checking whether parts are working:

Windows

Linux