Source code for brian2.monitors.ratemonitor

Module defining `PopulationRateMonitor`.
import numpy as np

from brian2.core.variables import Variables
from import CodeRunner, Group
from brian2.units.allunits import hertz, second
from brian2.units.fundamentalunits import Quantity, check_units
from brian2.utils.logger import get_logger

__all__ = ["PopulationRateMonitor"]

logger = get_logger(__name__)

[docs]class PopulationRateMonitor(Group, CodeRunner): """ Record instantaneous firing rates, averaged across neurons from a `NeuronGroup` or other spike source. Parameters ---------- source : (`NeuronGroup`, `SpikeSource`) The source of spikes to record. name : str, optional A unique name for the object, otherwise will use ``'_ratemonitor_0'``, etc. codeobj_class : class, optional The `CodeObject` class to run code with. dtype : dtype, optional The dtype to use to store the ``rate`` variable. Defaults to `~numpy.float64`, i.e. double precision. Notes ----- Currently, this monitor can only monitor the instantaneous firing rates at each time step of the source clock. Any binning/smoothing of the firing rates has to be done manually afterwards. """ invalidates_magic_network = False add_to_magic_network = True def __init__( self, source, name="ratemonitor*", codeobj_class=None, dtype=np.float64 ): #: The group we are recording from self.source = source self.codeobj_class = codeobj_class CodeRunner.__init__( self, group=self, code="", template="ratemonitor", clock=source.clock, when="end", order=0, name=name, ) self.add_dependency(source) self.variables = Variables(self) # Handle subgroups correctly start = getattr(source, "start", 0) stop = getattr(source, "stop", len(source)) self.variables.add_constant("_source_start", start) self.variables.add_constant("_source_stop", stop) self.variables.add_reference("_spikespace", source) self.variables.add_dynamic_array( "rate", size=0, dimensions=hertz.dim, read_only=True, dtype=dtype ) self.variables.add_dynamic_array( "t", size=0, dimensions=second.dim, read_only=True, dtype=self._clock.variables["t"].dtype, ) self.variables.add_reference("_num_source_neurons", source, "N") self.variables.add_array( "N", dtype=np.int32, size=1, scalar=True, read_only=True ) self.variables.create_clock_variables(self._clock, prefix="_clock_") self._enable_group_attributes()
[docs] def resize(self, new_size): # Note that this does not set N, this has to be done in the template # since we use a restricted pointer to access it (which promises that # we only change the value through this pointer) self.variables["rate"].resize(new_size) self.variables["t"].resize(new_size)
[docs] def reinit(self): """ Clears all recorded rates """ raise NotImplementedError()
@check_units(width=second) def smooth_rate(self, window="gaussian", width=None): """ smooth_rate(self, window='gaussian', width=None) Return a smooth version of the population rate. Parameters ---------- window : str, ndarray The window to use for smoothing. Can be a string to chose a predefined window(``'flat'`` for a rectangular, and ``'gaussian'`` for a Gaussian-shaped window). In this case the width of the window is determined by the ``width`` argument. Note that for the Gaussian window, the ``width`` parameter specifies the standard deviation of the Gaussian, the width of the actual window is ``4*width + dt`` (rounded to the nearest dt). For the flat window, the width is rounded to the nearest odd multiple of dt to avoid shifting the rate in time. Alternatively, an arbitrary window can be given as a numpy array (with an odd number of elements). In this case, the width in units of time depends on the ``dt`` of the simulation, and no ``width`` argument can be specified. The given window will be automatically normalized to a sum of 1. width : `Quantity`, optional The width of the ``window`` in seconds (for a predefined window). Returns ------- rate : `Quantity` The population rate in Hz, smoothed with the given window. Note that the rates are smoothed and not re-binned, i.e. the length of the returned array is the same as the length of the ``rate`` attribute and can be plotted against the `PopulationRateMonitor` 's ``t`` attribute. """ if width is None and isinstance(window, str): raise TypeError("Need a width when using a predefined window.") if width is not None and not isinstance(window, str): raise TypeError("Can only specify a width for a predefined window") if isinstance(window, str): if window == "gaussian": width_dt = int(np.round(2 * width / self.clock.dt)) # Rounding only for the size of the window, not for the standard # deviation of the Gaussian window = np.exp( -np.arange(-width_dt, width_dt + 1) ** 2 * 1.0 / (2 * (width / self.clock.dt) ** 2) ) elif window == "flat": width_dt = int(width / 2 / self.clock.dt) * 2 + 1 used_width = width_dt * self.clock.dt if abs(used_width - width) > 1e-6 * self.clock.dt: f"width adjusted from {width} to {used_width}", "adjusted_width", once=True, ) window = np.ones(width_dt) else: raise NotImplementedError(f'Unknown pre-defined window "{window}"') else: try: window = np.asarray(window) except TypeError: raise TypeError(f"Cannot use a window of type {type(window)}") if window.ndim != 1: raise TypeError("The provided window has to be one-dimensional.") if len(window) % 2 != 1: raise TypeError("The window has to have an odd number of values.") return Quantity( np.convolve(self.rate_, window * 1.0 / sum(window), mode="same"), dim=hertz.dim, ) def __repr__(self): classname = self.__class__.__name__ return f"<{classname}, recording {}>"