swenzel/live_plot.py

## live_plot.py
# DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
# Version 2, December 2004
#
# Copyright (C) 2015 Swen Wenzel <swenzel@uos.de>
#
# Everyone is permitted to copy and distribute verbatim or modified
# copies of this license document, and changing it is allowed as long
# as the name is changed.
#
# DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
# TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
#
# 0. You just DO WHAT THE FUCK YOU WANT TO.

import multiprocessing as mp
import ctypes
import numpy as np
import nest


class LivePlot(mp.Process):
    """
    :param senders: Sender ids, smallest and largest determine y-axis range.
                    If set to `None` the axis will be automatically updated.
    :type senders: list or tuple of int
    :param title: Title used for the figure
    :type title: str
    :param interval: Timeinterval in ms between figure updates
    :type interval: int
    :param timeframe: Defines in ms how far the plot goes back in time
    :type timeframe: positive int
    :param bufsize: Defines how many spike events are buffered
    :type bufsize: number (decimals will be ignored)

    This class automatically opens a figure which is constantly updated within
    its own process. The process is directly started and therefore the object
    is ready to use after creation. Due to process start up delays the figure
    will only appear shortly after creation. If desired, it is possible to wait
    for the figure (see func:`waitForFigure`).
    Usage example:

        >>> import nest
        >>> from live_plot import LivePlot
        >>> spike_generator = nest.Create('poisson_generator')
        >>> nest.SetStatus(spike_generator, 'rate', 10.0)
        >>> spike_detector = nest.Create('spike_detector')
        >>> nest.Connect(spike_generator, spike_detector)
        >>> myLivePlot = LivePlot()
        >>> myLivePlot.waitForFigure()
        >>> # closing the window will finish the thread and stop the simulation
        >>> while myLivePlot.is_alive():
        >>>     nest.Simulate(10)
        >>>     events = nest.GetStatus(spike_detector, 'events')
        >>>     # remove events which were just read to avoid adding them twice
        >>>     nest.SetStatus(spike_detector, 'n_events', 0)
        >>>     myLivePlot.addData(events)

    """

    def __init__(self, senders=None, title=None, interval=100, timeframe=500,
                 bufsize=1e5):
        super(LivePlot, self).__init__()
        bufsize = int(bufsize)
        self._datalock = mp.Lock()
        self._stopEvent = mp.Event()
        self._readyEvent = mp.Event()
        self._times = mp.Array(ctypes.c_double, bufsize)
        self._senders = mp.Array(ctypes.c_int, bufsize)
        self._now = mp.Value(ctypes.c_double)
        self._len = mp.Value(ctypes.c_uint)
        self._interval = interval
        self._minsender = mp.Value(ctypes.c_double)
        self._maxsender = mp.Value(ctypes.c_double)

        if senders is None:
            self._autoSenders = True
            self._maxsender.value = -np.inf
            self._minsender.value = np.inf
        else:
            self._autoSenders = False
            self._maxsender.value = np.max(senders)
            self._minsender.value = np.min(senders)

        # will be used as reference timepoint for the timewindow
        self._now.value = 0

        # since the array won't be filled from the beginning, _len will keep
        # track of the amount of entries
        self._len.value = 0

        # since we have ringbuffer-like memory arrays, we have to keep track of
        # the next insert point
        self._nextInsert = 0

        if timeframe <= 0:
            raise ValueError('timeframe has to be >0!')

        self._timeframe = timeframe
        self._title = title

        self.start()

    def run(self):
        """
        This thread's main method. Even though it is not marked as private, it
        is not supposed to be called from outside!
        """
        import matplotlib.pyplot as plt
        from matplotlib import animation

        self._figure = plt.figure()

        # need to save it to local variable, otherwise it doesn't work
        # might be an issue with the garbage collector
        _ani = animation.FuncAnimation(self._figure, self._update,
                                       self._getData, interval=self._interval,
                                       init_func=self._initFigure)

        self._plt.set_xlim(-self._timeframe, 0)
        self._plt.set_ylabel('Neuron ID')
        plt.show(block=True)

    def stop(self):
        """
        Tells the thread to close the window and therefore stop the live plot.
        It is save to call this method multiple times.
        """
        if self.is_alive():
            self._stopEvent.set()
            self.join(timeout=5)

    def addData(self, events, now=None):
        """
        :param events: New data coming from
                       `nest.GetStatus(spike_detector, 'events')`
        :type events: dict or list/tuple of dicts
        :param now: Used to set to current simulation time in ms
                    If set to `None`, `nest.GetKernelStatus('time')` will be
                    used.
        :type now: int

        Adds new spike events to this livePlot's memory. Also updates the
        current simulation time and therefore shifts the spikes in the next
        frame update.

        .. warning::
           Duplicate events are not filtered out! So don't forget to call
           `nest.SetStatus(spike_detector, 'n_events', 0)`

        """
        if not isinstance(events, (dict, list, tuple)):
            raise TypeError("events must be dict or list of dicts, " +
                            "not {}".format(type(events)))

        if isinstance(events, (list, tuple)):
            for e in events:
                self.addData(e)
            return

        senders = events['senders']
        times = events['times']

        assert len(times) == len(senders), ('times and senders have to be ' +
                                            'the same size!')

        times = np.array(times, dtype=np.float64)
        senders = np.array(senders, dtype=np.int32)
        with self._datalock:
            myTimes = np.frombuffer(self._times.get_obj(),
                                    dtype=ctypes.c_double)
            mySenders = np.frombuffer(self._senders.get_obj(),
                                      dtype=ctypes.c_uint)
            if now is None:
                self._now.value = nest.GetKernelStatus('time')
            else:
                self._now.value = now

            if len(times) == 0:
                return

            # compute indices which start at the the next insert position
            idx = np.arange(self._nextInsert, times.size+self._nextInsert,
                            dtype=np.int32)
            # wrap indices around the size
            idx %= myTimes.size
            # update next insert position
            self._nextInsert = idx[-1]+1

            # since our buffer is not full from the beginning we have to
            # continually increase self._len
            if self._len.value != myTimes.size:
                self._len.value = min(myTimes.size, self._len.value+len(times))

            # insert new values at their appropriate positions
            myTimes[idx] = times
            mySenders[idx] = senders

            # update minsender and maxsender if necessary
            if self._autoSenders:
                minsender = np.min(senders)
                maxsender = np.max(senders)
                if self._minsender.value > minsender:
                    self._minsender.value = minsender
                if self._maxsender.value < maxsender:
                    self._maxsender.value = maxsender

    def waitForFigure(self):
        self._readyEvent.wait()

    def _initFigure(self):
        if self._title is not None:
            self._figure.suptitle(self._title)
        self._figure.show()
        self._plt = self._figure.add_subplot(111)
        self._scat = self._plt.scatter([], [], marker='|')
        self._readyEvent.set()

    def _update(self, data):
        """
        Updates the figure using `data` coming from :func:`_getData`.
        """
        with self._datalock:
            minsender = self._minsender.value
            maxsender = self._maxsender.value

        if data == 'close':
            import matplotlib.pyplot as plt
            plt.close()
        else:
            curtime, data = data
            if len(data) > 0:
                self._scat.set_offsets(data)
                # try not to have events on the edges of the plot
                margin = 0.1*(maxsender-minsender)
                self._plt.set_ylim(minsender-margin, maxsender+margin)
            self._plt.set_xlabel('time since: {} ms'.format(curtime))

    def _getData(self):
        """
        Data 'generator' for matplotlib's animation.
        """
        myTimes = np.frombuffer(self._times.get_obj(), dtype=ctypes.c_double)
        mySenders = np.frombuffer(self._senders.get_obj(), dtype=ctypes.c_uint)
        while not self._stopEvent.is_set():
            with self._datalock:
                curtime = self._now.value

                # inFrame is a boolean mask initialized to all False
                # so it can be used together with the next step to
                # hide all invalid values
                inFrame = np.zeros_like(myTimes, dtype=np.bool)

                # from the valid values (i.e. the ones until _len) filter out
                # those which are too old
                myLen = self._len.value
                inFrame[:myLen] = myTimes[:myLen] >= curtime-self._timeframe

                if np.any(inFrame):
                    data = list(zip(myTimes[inFrame]-curtime,
                                    mySenders[inFrame]))
                else:
                    data = [[], []]

            yield curtime, data
        yield 'close'


def main():
    import time
    plot = LivePlot(title='Random example data')
    plot.waitForFigure()
    now = 0
    timedif = 50
    nEvents = 100
    for _ in range(100000):
        if not plot.is_alive():
            break
        senders = np.random.randint(100, size=nEvents)
        times = now + np.random.rand(nEvents)*timedif
        times.sort()
        now += timedif
        plot.addData({'times': times, 'senders': senders}, now)
        time.sleep(timedif/1000.)
    plot.stop()


if __name__ == "__main__":
    main()
	# DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
	# Version 2, December 2004
	#
	# Copyright (C) 2015 Swen Wenzel <swenzel@uos.de>
	#
	# Everyone is permitted to copy and distribute verbatim or modified
	# copies of this license document, and changing it is allowed as long
	# as the name is changed.
	#
	# DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
	# TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
	#
	# 0. You just DO WHAT THE FUCK YOU WANT TO.

	import multiprocessing as mp
	import ctypes
	import numpy as np
	import nest


	class LivePlot(mp.Process):
	"""
	:param senders: Sender ids, smallest and largest determine y-axis range.
	If set to `None` the axis will be automatically updated.
	:type senders: list or tuple of int
	:param title: Title used for the figure
	:type title: str
	:param interval: Timeinterval in ms between figure updates
	:type interval: int
	:param timeframe: Defines in ms how far the plot goes back in time
	:type timeframe: positive int
	:param bufsize: Defines how many spike events are buffered
	:type bufsize: number (decimals will be ignored)

	This class automatically opens a figure which is constantly updated within
	its own process. The process is directly started and therefore the object
	is ready to use after creation. Due to process start up delays the figure
	will only appear shortly after creation. If desired, it is possible to wait
	for the figure (see func:`waitForFigure`).
	Usage example:

	>>> import nest
	>>> from live_plot import LivePlot
	>>> spike_generator = nest.Create('poisson_generator')
	>>> nest.SetStatus(spike_generator, 'rate', 10.0)
	>>> spike_detector = nest.Create('spike_detector')
	>>> nest.Connect(spike_generator, spike_detector)
	>>> myLivePlot = LivePlot()
	>>> myLivePlot.waitForFigure()
	>>> # closing the window will finish the thread and stop the simulation
	>>> while myLivePlot.is_alive():
	>>> nest.Simulate(10)
	>>> events = nest.GetStatus(spike_detector, 'events')
	>>> # remove events which were just read to avoid adding them twice
	>>> nest.SetStatus(spike_detector, 'n_events', 0)
	>>> myLivePlot.addData(events)

	"""

	def __init__(self, senders=None, title=None, interval=100, timeframe=500,
	bufsize=1e5):
	super(LivePlot, self).__init__()
	bufsize = int(bufsize)
	self._datalock = mp.Lock()
	self._stopEvent = mp.Event()
	self._readyEvent = mp.Event()
	self._times = mp.Array(ctypes.c_double, bufsize)
	self._senders = mp.Array(ctypes.c_int, bufsize)
	self._now = mp.Value(ctypes.c_double)
	self._len = mp.Value(ctypes.c_uint)
	self._interval = interval
	self._minsender = mp.Value(ctypes.c_double)
	self._maxsender = mp.Value(ctypes.c_double)

	if senders is None:
	self._autoSenders = True
	self._maxsender.value = -np.inf
	self._minsender.value = np.inf
	else:
	self._autoSenders = False
	self._maxsender.value = np.max(senders)
	self._minsender.value = np.min(senders)

	# will be used as reference timepoint for the timewindow
	self._now.value = 0

	# since the array won't be filled from the beginning, _len will keep
	# track of the amount of entries
	self._len.value = 0

	# since we have ringbuffer-like memory arrays, we have to keep track of
	# the next insert point
	self._nextInsert = 0

	if timeframe <= 0:
	raise ValueError('timeframe has to be >0!')

	self._timeframe = timeframe
	self._title = title

	self.start()

	def run(self):
	"""
	This thread's main method. Even though it is not marked as private, it
	is not supposed to be called from outside!
	"""
	import matplotlib.pyplot as plt
	from matplotlib import animation

	self._figure = plt.figure()

	# need to save it to local variable, otherwise it doesn't work
	# might be an issue with the garbage collector
	_ani = animation.FuncAnimation(self._figure, self._update,
	self._getData, interval=self._interval,
	init_func=self._initFigure)

	self._plt.set_xlim(-self._timeframe, 0)
	self._plt.set_ylabel('Neuron ID')
	plt.show(block=True)

	def stop(self):
	"""
	Tells the thread to close the window and therefore stop the live plot.
	It is save to call this method multiple times.
	"""
	if self.is_alive():
	self._stopEvent.set()
	self.join(timeout=5)

	def addData(self, events, now=None):
	"""
	:param events: New data coming from
	`nest.GetStatus(spike_detector, 'events')`
	:type events: dict or list/tuple of dicts
	:param now: Used to set to current simulation time in ms
	If set to `None`, `nest.GetKernelStatus('time')` will be
	used.
	:type now: int

	Adds new spike events to this livePlot's memory. Also updates the
	current simulation time and therefore shifts the spikes in the next
	frame update.

	.. warning::
	Duplicate events are not filtered out! So don't forget to call
	`nest.SetStatus(spike_detector, 'n_events', 0)`

	"""
	if not isinstance(events, (dict, list, tuple)):
	raise TypeError("events must be dict or list of dicts, " +
	"not {}".format(type(events)))

	if isinstance(events, (list, tuple)):
	for e in events:
	self.addData(e)
	return

	senders = events['senders']
	times = events['times']

	assert len(times) == len(senders), ('times and senders have to be ' +
	'the same size!')

	times = np.array(times, dtype=np.float64)
	senders = np.array(senders, dtype=np.int32)
	with self._datalock:
	myTimes = np.frombuffer(self._times.get_obj(),
	dtype=ctypes.c_double)
	mySenders = np.frombuffer(self._senders.get_obj(),
	dtype=ctypes.c_uint)
	if now is None:
	self._now.value = nest.GetKernelStatus('time')
	else:
	self._now.value = now

	if len(times) == 0:
	return

	# compute indices which start at the the next insert position
	idx = np.arange(self._nextInsert, times.size+self._nextInsert,
	dtype=np.int32)
	# wrap indices around the size
	idx %= myTimes.size
	# update next insert position
	self._nextInsert = idx[-1]+1

	# since our buffer is not full from the beginning we have to
	# continually increase self._len
	if self._len.value != myTimes.size:
	self._len.value = min(myTimes.size, self._len.value+len(times))

	# insert new values at their appropriate positions
	myTimes[idx] = times
	mySenders[idx] = senders

	# update minsender and maxsender if necessary
	if self._autoSenders:
	minsender = np.min(senders)
	maxsender = np.max(senders)
	if self._minsender.value > minsender:
	self._minsender.value = minsender
	if self._maxsender.value < maxsender:
	self._maxsender.value = maxsender

	def waitForFigure(self):
	self._readyEvent.wait()

	def _initFigure(self):
	if self._title is not None:
	self._figure.suptitle(self._title)
	self._figure.show()
	self._plt = self._figure.add_subplot(111)
	self._scat = self._plt.scatter([], [], marker='\|')
	self._readyEvent.set()

	def _update(self, data):
	"""
	Updates the figure using `data` coming from :func:`_getData`.
	"""
	with self._datalock:
	minsender = self._minsender.value
	maxsender = self._maxsender.value

	if data == 'close':
	import matplotlib.pyplot as plt
	plt.close()
	else:
	curtime, data = data
	if len(data) > 0:
	self._scat.set_offsets(data)
	# try not to have events on the edges of the plot
	margin = 0.1*(maxsender-minsender)
	self._plt.set_ylim(minsender-margin, maxsender+margin)
	self._plt.set_xlabel('time since: {} ms'.format(curtime))

	def _getData(self):
	"""
	Data 'generator' for matplotlib's animation.
	"""
	myTimes = np.frombuffer(self._times.get_obj(), dtype=ctypes.c_double)
	mySenders = np.frombuffer(self._senders.get_obj(), dtype=ctypes.c_uint)
	while not self._stopEvent.is_set():
	with self._datalock:
	curtime = self._now.value

	# inFrame is a boolean mask initialized to all False
	# so it can be used together with the next step to
	# hide all invalid values
	inFrame = np.zeros_like(myTimes, dtype=np.bool)

	# from the valid values (i.e. the ones until _len) filter out
	# those which are too old
	myLen = self._len.value
	inFrame[:myLen] = myTimes[:myLen] >= curtime-self._timeframe

	if np.any(inFrame):
	data = list(zip(myTimes[inFrame]-curtime,
	mySenders[inFrame]))
	else:
	data = [[], []]

	yield curtime, data
	yield 'close'


	def main():
	import time
	plot = LivePlot(title='Random example data')
	plot.waitForFigure()
	now = 0
	timedif = 50
	nEvents = 100
	for _ in range(100000):
	if not plot.is_alive():
	break
	senders = np.random.randint(100, size=nEvents)
	times = now + np.random.rand(nEvents)*timedif
	times.sort()
	now += timedif
	plot.addData({'times': times, 'senders': senders}, now)
	time.sleep(timedif/1000.)
	plot.stop()


	if __name__ == "__main__":
	main()