Tools for neuroscience experiments
Project description
toon
Description
Additional tools for neuroscience experiments, including:
- A framework for polling input devices on a separate process.
- A framework for keyframe-based animation.
- High-resolution clocks.
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 mydevice.mouse 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
:
- If there's no data for a given
read
,None
is returned. - The returned data is a copy of the local copy of the data. If you don't need copies, set
use_views=True
when instantiating theMpDevice
. - If receiving batches of data when reading from the device, you can return a list of (time, data) tuples.
- You can optionally use
device.start()
/device.stop()
instead of a context manager. - You can check for remote errors at any point using
device.check_error()
, though this automatically happens after entering the context manager and when reading. - In addition to python types/dtypes/ctypes, devices can return
ctypes.Structure
s (see input tests or the example_devices folder for examples).
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:
- Non-numeric attributes, like color strings, can also be modified in this framework (easing is ignored).
- Multiple objects can be modified simultaneously by feeding a list of objects into
player.add()
.
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
- Uses
SetPriorityClass
andSetThreadPriority
/AvSetMmMaxThreadCharacteristics
. level = 2
only seems to work if running Python as administrator.
MacOS
- Only disables/enables garbage collection; I don't have a Mac to test on.
Linux
- Sets the scheduler policy and parameters
sched_setscheduler
. - If
level == 2
, locks the calling process's virtual address space into RAM viamlockall
. - Any
level > 0
seems to fail unless the user is either superuser, or has the right capability. I've used setcap:sudo setcap cap_sys_nice=eip <path to python>
(disable by passingsudo setcap cap_sys_nice= <path>
). For memory locking, I've used Psychtoolbox's 99-psychtoolboxlimits.conf and added myself to the psychtoolbox group.
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
- In the task manager under details, right-clicking on python and mousing over "Set priority" will show the current priority level. I haven't figured out how to verify the Avrt threading parts are working.
Linux
- Check
mlockall
withcat /proc/{python pid}/status | grep VmLck
- Check priority with
top -c -p $(pgrep -d',' -f python)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for toon-0.15.8-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0534e7a0676bfa426f2e7dd30be3b5cbb2259906606e94272f82d828743d9d1c |
|
MD5 | 0ad16b0515122ad746a24e20300ba0aa |
|
BLAKE2b-256 | ce6b44510050a4d61b60cb09357ca0d32cc33814e6d568ef9b8e72091300be27 |
Hashes for toon-0.15.8-cp38-cp38-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 97b7137ba6964181cdb3b3207086a3f71e7d1b29dffad9e619160e57318a71b9 |
|
MD5 | f4c12f429332d8ad25d5e7cabe3ac501 |
|
BLAKE2b-256 | bccf7aa0b18be21bd1ab25216f5903e161bdf1c1cc9d7be78632e4313cf68187 |
Hashes for toon-0.15.8-cp38-cp38-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 17d6a42cdadd3e369a13a0b25a657ede19e1c920a4cdd3b3bb15757e30b99a64 |
|
MD5 | 37cbf0db958cdc83c080c049f637b5ce |
|
BLAKE2b-256 | f6c339df1bd6b1cdfb77a9020beaef18bd34993cc12c863d1dd33e3067a1038f |
Hashes for toon-0.15.8-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c1b404c879fc64fad9beb7c4361ab0438b45eea47b99031781cc5ae1b8557e4e |
|
MD5 | 96495cb5439e50719fe8d80331b1e1ca |
|
BLAKE2b-256 | 26164c96661ca38163245319e23bda7e6f1ebd2233b08bd9f785346a9beac1e7 |
Hashes for toon-0.15.8-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d93ce350110a7fe6e0433923a56c78672484de8cf01b7d217b90bc9cd1d60a2e |
|
MD5 | bdac2a82c7154c1853ebddf1bb3b809a |
|
BLAKE2b-256 | 316b94c8e21de0be3df63c633308ba261e689b4f047065c82ca1ef787ce66e97 |
Hashes for toon-0.15.8-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4186a295360f477690d9fa311cf80a930ed3ecff535f258b83406f9d76b1ad8c |
|
MD5 | 36442b773f0745e9cb2a511f2cace749 |
|
BLAKE2b-256 | 9742edada0c695939e4c5b41a7fc5eaa79c8ded2e4894e76a716f989acdb4277 |
Hashes for toon-0.15.8-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | da771011dfc5575cf26125f8f7507be9f989524e830010d016bfd059186ab3a6 |
|
MD5 | 120651e864170ddd75a700da834e335e |
|
BLAKE2b-256 | 657170c56d50443876f70230d322ae35b593c42b406beb00ad0d2e5e0f85cb86 |
Hashes for toon-0.15.8-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4d14ed43928534fddd3cbe186db1c8adeaca988e251e432a7099cb1ad0de05a8 |
|
MD5 | 5e2284b794e37506fdc038adfaf0ee9b |
|
BLAKE2b-256 | f9a6b3cd42c87ef091e71afae85f1927185c343511bbf01e8bbb297963f81857 |
Hashes for toon-0.15.8-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b95bd572c76ed100a102dcf5da3f6c8318e2727a8c39ed8fb5cad7543651074f |
|
MD5 | eaa9abc230f3c760ad439a38b59f5bc2 |
|
BLAKE2b-256 | f67fac273b1108ac53d4168d1c2a53155ed3bc4f26a425909207b248402f8b43 |
Hashes for toon-0.15.8-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b6a3446e65bded8ed9b0bacfcaa09f5edc19ec4ca5fc8cba6cc1dba85d49698c |
|
MD5 | 949124970642d10dcc81ef6bab38d494 |
|
BLAKE2b-256 | 046aa75d468ef612a529a59caa013987364258dbc795b24dfef418823da850ee |
Hashes for toon-0.15.8-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3d6acad323345f2c6b33f0c910df8ff37af3e636664ca178943ac9a185884d89 |
|
MD5 | 294b491e4cbe170c4379f3f9b48d1752 |
|
BLAKE2b-256 | 83b11f6e92b35048d7e93290798600b395a92b958edcee206e03c9e856690dce |
Hashes for toon-0.15.8-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 698f37eb0c453e62d9fb7b9287da628410eccdac78e35ed979d8e4a25c090e81 |
|
MD5 | 12e5e6126057c663f07f74fcd5771742 |
|
BLAKE2b-256 | c982d897ba3f746c7748b6ab2eaec2d86ea3fc6b7e6fd21c52fdada5451b71ab |