%global _empty_manifest_terminate_build 0
Name: python-toon
Version: 0.15.9
Release: 1
Summary: Tools for neuroscience experiments
License: MIT
URL: https://github.com/aforren1/toon
Source0: https://mirrors.aliyun.com/pypi/web/packages/73/38/a467485209fbc25f1b37d6fa3a80848244d68321892bf8c210e3a3955fb7/toon-0.15.9.zip
Requires: python3-numpy
Requires: python3-psutil
%description
# toon
[](https://pypi.python.org/pypi/toon)
[](https://raw.githubusercontent.com/aforren1/toon/master/LICENSE.txt)
## 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/](https://github.com/aforren1/toon/tree/master/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](https://github.com/aforren1/toon/blob/master/demos/bench_plot.py) for an example of measuring user-side read performance.
Typical use looks like this:
```python
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.
```python
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 the `MpDevice`.
- 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](https://github.com/aforren1/toon/tree/master/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](https://magnum.graphics/) framework, though lacking some of the features (e.g. Track extrapolation, proper handling of time scaling).
Example:
```python
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:
```python
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](http://psychtoolbox.org/docs/Priority)). General usage is:
```python
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` and `SetThreadPriority`/`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 via `mlockall`.
- 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 passing `sudo setcap cap_sys_nice= <path>`). For memory locking, I've used Psychtoolbox's [99-psychtoolboxlimits.conf](https://github.com/Psychtoolbox-3/Psychtoolbox-3/blob/master/Psychtoolbox/PsychBasic/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](http://psychtoolbox.org/docs/Priority).
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` with `cat /proc/{python pid}/status | grep VmLck`
- Check priority with `top -c -p $(pgrep -d',' -f python)`
%package -n python3-toon
Summary: Tools for neuroscience experiments
Provides: python-toon
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-toon
# toon
[](https://pypi.python.org/pypi/toon)
[](https://raw.githubusercontent.com/aforren1/toon/master/LICENSE.txt)
## 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/](https://github.com/aforren1/toon/tree/master/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](https://github.com/aforren1/toon/blob/master/demos/bench_plot.py) for an example of measuring user-side read performance.
Typical use looks like this:
```python
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.
```python
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 the `MpDevice`.
- 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](https://github.com/aforren1/toon/tree/master/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](https://magnum.graphics/) framework, though lacking some of the features (e.g. Track extrapolation, proper handling of time scaling).
Example:
```python
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:
```python
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](http://psychtoolbox.org/docs/Priority)). General usage is:
```python
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` and `SetThreadPriority`/`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 via `mlockall`.
- 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 passing `sudo setcap cap_sys_nice= <path>`). For memory locking, I've used Psychtoolbox's [99-psychtoolboxlimits.conf](https://github.com/Psychtoolbox-3/Psychtoolbox-3/blob/master/Psychtoolbox/PsychBasic/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](http://psychtoolbox.org/docs/Priority).
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` with `cat /proc/{python pid}/status | grep VmLck`
- Check priority with `top -c -p $(pgrep -d',' -f python)`
%package help
Summary: Development documents and examples for toon
Provides: python3-toon-doc
%description help
# toon
[](https://pypi.python.org/pypi/toon)
[](https://raw.githubusercontent.com/aforren1/toon/master/LICENSE.txt)
## 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/](https://github.com/aforren1/toon/tree/master/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](https://github.com/aforren1/toon/blob/master/demos/bench_plot.py) for an example of measuring user-side read performance.
Typical use looks like this:
```python
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.
```python
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 the `MpDevice`.
- 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](https://github.com/aforren1/toon/tree/master/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](https://magnum.graphics/) framework, though lacking some of the features (e.g. Track extrapolation, proper handling of time scaling).
Example:
```python
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:
```python
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](http://psychtoolbox.org/docs/Priority)). General usage is:
```python
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` and `SetThreadPriority`/`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 via `mlockall`.
- 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 passing `sudo setcap cap_sys_nice= <path>`). For memory locking, I've used Psychtoolbox's [99-psychtoolboxlimits.conf](https://github.com/Psychtoolbox-3/Psychtoolbox-3/blob/master/Psychtoolbox/PsychBasic/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](http://psychtoolbox.org/docs/Priority).
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` with `cat /proc/{python pid}/status | grep VmLck`
- Check priority with `top -c -p $(pgrep -d',' -f python)`
%prep
%autosetup -n toon-0.15.9
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-toon -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.15.9-1
- Package Spec generated