{
    "info": {
        "author": "Alexander Forrence",
        "author_email": "aforren1@jhu.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "toon\n====\n\n[![image](https://img.shields.io/pypi/v/toon.svg)](https://pypi.python.org/pypi/toon)\n[![image](https://img.shields.io/pypi/l/toon.svg)](https://raw.githubusercontent.com/aforren1/toon/master/LICENSE.txt)\n[![image](https://img.shields.io/travis/aforren1/toon.svg)](https://travis-ci.org/aforren1/toon)\n[![image](https://img.shields.io/appveyor/ci/aforren1/toon.svg)](https://ci.appveyor.com/project/aforren1/toon)\n[![image](https://img.shields.io/coveralls/aforren1/toon.svg)](https://coveralls.io/github/aforren1/toon)\n\nDescription\n-----------\n\nAdditional tools for neuroscience experiments, including:\n\n-   A framework for polling input devices on a separate process.\n-   A framework for keyframe-based animation.\n\nEverything should work on Windows/Mac/Linux.\n\nSee [requirements.txt](https://github.com/aforren1/toon/blob/master/requirements.txt) for dependencies.\n\nInstall\n-------\n\nCurrent release:\n\n```pip install toon```\n\nDevelopment version:\n\n```pip install git+https://github.com/aforren1/toon```\n\nFor full install (including device and demo dependencies):\n\n```pip install toon[full]```\n\nSee [setup.py](https://github.com/aforren1/toon/blob/master/setup.py) for a list of those dependencies, as well as device-specific subdivisions.\n\nSee the [demos/](https://github.com/aforren1/toon/tree/master/demos) folder for usage examples (note: some require [psychopy](https://github.com/psychopy/psychopy)).\n\nOverview\n---------\n\n### Input\n\n`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.\n\nWe 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.py](https://github.com/aforren1/toon/blob/master/demos/bench.py) for an example of measuring user-side read performance.\n\nTypical use looks like this:\n\n```python\nfrom toon.input import MpDevice\nfrom toon.input.mouse import Mouse\nfrom timeit import default_timer\n\ndevice = MpDevice(Mouse())\n\nwith device:\n    t1 = default_timer() + 10\n    while default_timer() < t1:\n        data = device.read()\n        # alternatively, unpack\n        # clicks, pos, scroll = device.read()\n        if data.pos is not None:\n            # N-D array of data (0th dim is time)\n            print(data.pos)\n            # time is 1D array of timestamps\n            print(data.pos.time)\n            print(data.pos[-1].time) # most recent timestamp\n```\n\nCreating a custom device is relatively straightforward, though there are a few boxes to check.\n\n```python\nfrom toon.input import BaseDevice, make_obs\nfrom ctypes import c_double\n\n# Obs is a class that manages observations\nclass MyDevice(BaseDevice):\n    # optional: give a hint for the buffer size (we'll allocate 1s worth of this)\n    sampling_frequency = 500\n\n    # required: each data source gets its own Obs\n    # can have multiple Obs per device\n    # this can either be introduced at the class level, or during __init__\n    # ctype can be a python type, numpy dtype, or ctype\n    Pos = make_obs('Pos', shape=(3,), ctype=float)\n    RotMat = make_obs('RotMat', (3, 3), c_double) # 2D data\n\n    # optional. Do not start device communication here, wait until `enter`\n    def __init__(self):\n        pass\n\n    ## Use `enter` and `exit`, rather than `__enter__` and `__exit__`\n    # optional: configure the device, start communicating\n    def enter(self):\n        pass\n\n    # optional: clean up resources, close device\n    def exit(self, *args):\n        pass\n\n    # required\n    def read(self):\n        # See demos/ for examples of sharing a time source between the processes\n        time = self.clock()\n        # store new data with a timestamp\n        pos = self.Pos(time, (1, 2, 3))\n        rotmat = self.RotMat(time, [[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n        # can also be explicit, i.e. `self.Returns(pos=pos, rotmat=rotmat)`\n        return pos, rotmat\n```\n\nThis device can then be passed to a `toon.input.MpDevice`, which preallocates the shared memory and handles other details.\n\nA few things to be aware of for data returned by `MpDevice`:\n\n - If a device only has a single `Obs`, `MpDevice` returns a single `TsArray` (a numpy array with a `time` attribute). Otherwise, `MpDevice` returns a named tuple of observations, where the names are alphabetically-sorted, lowercased versions of the pre-defined `Obs`. \n - If the data returned by a single read is scalar (e.g. a 1D force sensor), `MpDevice` will drop the 1st dimension.\n - If there's no data for a given observation, `None` is returned. The named tuple has a method for checking all members at once (`data.any()`).\n\n\nOther notes:\n  - The returned data is a *view* of the local copy of the data. `toon.input.TsArray`s and device `Returns` have a `copy` method, which may be useful if e.g. appending to a list for later concatenation.\n  - Re: concatenation, there is a `stack` function available via `from toon.input import stack`, which is like numpy's `vstack` but keeps the time attribute intact. It also dispatches appropriately for either `TsArray`s or `Returns`.\n  - If receiving batches of data when reading from the device, you can return a list of `Returns` (see `tests/input/mockdevices.py` for an example).\n  - You can optionally use `device.start()`/`device.stop()` instead of a context manager.\n  - 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.\n  - In addition to python types/dtypes/ctypes, `Obs` can use `ctypes.Structure`s (see input tests or the [cyberglove](https://github.com/aforren1/toon/blob/master/toon/input/cyberglove.py) for examples).\n\n### Animation\n\nThis 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).\n\nExample:\n\n```python\nfrom math import sin, pi\n\nfrom time import sleep\nfrom timeit import default_timer\nimport matplotlib.pyplot as plt\nfrom toon.anim import Track, Player\n# see toon/anim/easing.py for all available easings\nfrom toon.anim.easing import linear\n\nclass Circle(object):\n    x = 0\n    y = 0\n\ncircle = Circle()\n# list of (time, value)\nkeyframes = [(0.2, -0.5), (0.5, 0), (3, 0.5)]\nx_track = Track(keyframes, easing=linear)\n\n# currently, easings can be any function that takes a single\n# positional argument as input (time normalized to [0, 1]) and returns\n# a scalar (probably float), generally having a lower asymptote\n# of 0 and upper asymptote of 1, which is used as the current time\n# for purposes of interpolation\ndef elastic_in(x):\n    return pow(2.0, 10.0 * (x - 1.0)) * sin(13.0 * pihalf * x)\n\n# we can reuse keyframes\ny_track = Track(keyframes, easing=elastic_in)\n\nplayer = Player(repeats=3)\n\n# directly modify an attribute\nplayer.add(x_track, 'x', obj=circle)\n\ndef y_cb(val, obj):\n    obj.y = val\n\n# modify via callback\nplayer.add(y_track, y_cb, obj=circle)\n\nt0 = default_timer()\nplayer.start(t0)\nvals = []\nwhile player.is_playing:\n    player.advance(default_timer())\n    vals.append([circle.x, circle.y])\n    sleep(1/60)\n\nplt.plot(vals)\nplt.show()\n```\n\nOther notes:\n  - Non-numeric attributes, like color strings, can also be modified in this framework (easing is ignored).\n  - The `Timeline` class (in `toon.anim`) can be used to get the time between frames, or the time since some origin time, taken at `timeline.start()`.\n  - The `Player` can also be used as a mixin, in which case the `obj` argument can be omitted from `player.add()` (see the [demos/](https://github.com/aforren1/toon/tree/master/demos) folder for examples).\n  - Multiple objects can be modified simultaneously by feeding a list of objects into `player.add()`.\n\n\n",
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