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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
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train | wait | Decorator for adding wait animation to long running
functions.
Args:
animation (str, tuple): String reference to animation or tuple
with custom animation.
speed (float): Number of seconds each cycle of animation.
Examples:
>>> @animation.wait('bar')
>>> def long... | animation/decorators.py | def wait(animation='elipses', text='', speed=0.2):
"""
Decorator for adding wait animation to long running
functions.
Args:
animation (str, tuple): String reference to animation or tuple
with custom animation.
speed (float): Number of seconds each cycle of animation.
Ex... | def wait(animation='elipses', text='', speed=0.2):
"""
Decorator for adding wait animation to long running
functions.
Args:
animation (str, tuple): String reference to animation or tuple
with custom animation.
speed (float): Number of seconds each cycle of animation.
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train | simple_wait | Decorator for adding simple text wait animation to
long running functions.
Examples:
>>> @animation.simple_wait
>>> def long_running_function():
>>> ... 5 seconds later ...
>>> return | animation/decorators.py | def simple_wait(func):
"""
Decorator for adding simple text wait animation to
long running functions.
Examples:
>>> @animation.simple_wait
>>> def long_running_function():
>>> ... 5 seconds later ...
>>> return
"""
@wraps(func)
def wrapper(*args, **kw... | def simple_wait(func):
"""
Decorator for adding simple text wait animation to
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>>> @animation.simple_wait
>>> def long_running_function():
>>> ... 5 seconds later ...
>>> return
"""
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train | Wait.start | Start animation thread. | animation/decorators.py | def start(self):
"""
Start animation thread.
"""
self.thread = threading.Thread(target=self._animate)
self.thread.start()
return | def start(self):
"""
Start animation thread.
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train | Wait.stop | Stop animation thread. | animation/decorators.py | def stop(self):
"""
Stop animation thread.
"""
time.sleep(self.speed)
self._count = -9999
sys.stdout.write(self.reverser + '\r\033[K\033[A')
sys.stdout.flush()
return | def stop(self):
"""
Stop animation thread.
"""
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train | merge | Merge several timeseries
Arguments:
tup: sequence of Timeseries, with the same shape except for axis 0
Returns:
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"""Merge several timeseries
Arguments:
tup: sequence of Timeseries, with the same shape except for axis 0
Returns:
Resulting merged timeseries which can have duplicate time points.
"""
if not all(tuple(ts.shape[1:] == tup[0].shape[1:] for ts in tup[1:])):
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"""Merge several timeseries
Arguments:
tup: sequence of Timeseries, with the same shape except for axis 0
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train | Timeseries.add_analyses | Dynamically add new analysis methods to the Timeseries class.
Args:
source: Can be a function, module or the filename of a python file.
If a filename or a module is given, then all functions defined
inside not starting with _ will be added as methods.
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"""Dynamically add new analysis methods to the Timeseries class.
Args:
source: Can be a function, module or the filename of a python file.
If a filename or a module is given, then all functions defined
inside not starting with _ will be a... | def add_analyses(cls, source):
"""Dynamically add new analysis methods to the Timeseries class.
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train | Timeseries.absolute | Calculate the absolute value element-wise.
Returns:
absolute (Timeseries):
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"""Calculate the absolute value element-wise.
Returns:
absolute (Timeseries):
Absolute value. For complex input (a + b*j) gives sqrt(a**a + b**2)
"""
return Timeseries(np.absolute(self), self.tspan, self.labels) | def absolute(self):
"""Calculate the absolute value element-wise.
Returns:
absolute (Timeseries):
Absolute value. For complex input (a + b*j) gives sqrt(a**a + b**2)
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train | Timeseries.angle | Return the angle of the complex argument.
Args:
deg (bool, optional):
Return angle in degrees if True, radians if False (default).
Returns:
angle (Timeseries):
The counterclockwise angle from the positive real axis on
the complex plane, with dtyp... | nsim/timeseries.py | def angle(self, deg=False):
"""Return the angle of the complex argument.
Args:
deg (bool, optional):
Return angle in degrees if True, radians if False (default).
Returns:
angle (Timeseries):
The counterclockwise angle from the positive real axis on
... | def angle(self, deg=False):
"""Return the angle of the complex argument.
Args:
deg (bool, optional):
Return angle in degrees if True, radians if False (default).
Returns:
angle (Timeseries):
The counterclockwise angle from the positive real axis on
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train | Timeseries.swapaxes | Interchange two axes of a Timeseries. | nsim/timeseries.py | def swapaxes(self, axis1, axis2):
"""Interchange two axes of a Timeseries."""
if self.ndim <=1 or axis1 == axis2:
return self
ar = np.asarray(self).swapaxes(axis1, axis2)
if axis1 != 0 and axis2 != 0:
# then axis 0 is unaffected by the swap
labels = se... | def swapaxes(self, axis1, axis2):
"""Interchange two axes of a Timeseries."""
if self.ndim <=1 or axis1 == axis2:
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ar = np.asarray(self).swapaxes(axis1, axis2)
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train | Timeseries.transpose | Permute the dimensions of a Timeseries. | nsim/timeseries.py | def transpose(self, *axes):
"""Permute the dimensions of a Timeseries."""
if self.ndim <= 1:
return self
ar = np.asarray(self).transpose(*axes)
if axes[0] != 0:
# then axis 0 is unaffected by the transposition
newlabels = [self.labels[ax] for ax in axe... | def transpose(self, *axes):
"""Permute the dimensions of a Timeseries."""
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train | Timeseries.reshape | If axis 0 is unaffected by the reshape, then returns a Timeseries,
otherwise returns an ndarray. Preserves labels of axis j only if all
axes<=j are unaffected by the reshape.
See ``numpy.ndarray.reshape()`` for more information | nsim/timeseries.py | def reshape(self, newshape, order='C'):
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otherwise returns an ndarray. Preserves labels of axis j only if all
axes<=j are unaffected by the reshape.
See ``numpy.ndarray.reshape()`` for more information
"""
... | def reshape(self, newshape, order='C'):
"""If axis 0 is unaffected by the reshape, then returns a Timeseries,
otherwise returns an ndarray. Preserves labels of axis j only if all
axes<=j are unaffected by the reshape.
See ``numpy.ndarray.reshape()`` for more information
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train | Timeseries.merge | Merge another timeseries with this one
Arguments:
ts (Timeseries): The two timeseries being merged must have the
same shape except for axis 0.
Returns:
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"""Merge another timeseries with this one
Arguments:
ts (Timeseries): The two timeseries being merged must have the
same shape except for axis 0.
Returns:
Resulting merged timeseries which can have duplicate time points.
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"""Merge another timeseries with this one
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ts (Timeseries): The two timeseries being merged must have the
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Returns:
Resulting merged timeseries which can have duplicate time points.
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train | Timeseries.expand_dims | Insert a new axis, at a given position in the array shape
Args:
axis (int): Position (amongst axes) where new axis is to be inserted. | nsim/timeseries.py | def expand_dims(self, axis):
"""Insert a new axis, at a given position in the array shape
Args:
axis (int): Position (amongst axes) where new axis is to be inserted.
"""
if axis == -1:
axis = self.ndim
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if axis == 0:... | def expand_dims(self, axis):
"""Insert a new axis, at a given position in the array shape
Args:
axis (int): Position (amongst axes) where new axis is to be inserted.
"""
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train | Timeseries.concatenate | Join a sequence of Timeseries to this one
Args:
tup (sequence of Timeseries): timeseries to be joined with this one.
They must have the same shape as this Timeseries, except in the
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axis (int, optional): The axis along which timeseri... | nsim/timeseries.py | def concatenate(self, tup, axis=0):
"""Join a sequence of Timeseries to this one
Args:
tup (sequence of Timeseries): timeseries to be joined with this one.
They must have the same shape as this Timeseries, except in the
dimension corresponding to `axis`.
axis... | def concatenate(self, tup, axis=0):
"""Join a sequence of Timeseries to this one
Args:
tup (sequence of Timeseries): timeseries to be joined with this one.
They must have the same shape as this Timeseries, except in the
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train | Timeseries.split | Split a timeseries into multiple sub-timeseries | nsim/timeseries.py | def split(self, indices_or_sections, axis=0):
"""Split a timeseries into multiple sub-timeseries"""
if not isinstance(indices_or_sections, numbers.Integral):
raise Error('splitting by array of indices is not yet implemented')
n = indices_or_sections
if self.shape[axis] % n !=... | def split(self, indices_or_sections, axis=0):
"""Split a timeseries into multiple sub-timeseries"""
if not isinstance(indices_or_sections, numbers.Integral):
raise Error('splitting by array of indices is not yet implemented')
n = indices_or_sections
if self.shape[axis] % n !=... | [
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train | plot | Plot a distributed timeseries
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title (str, optional)
points (int, optional): Limit the number of time points plotted.
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Args:
dts (DistTimeseries)
title (str, optional)
points (int, optional): Limit the number of time points plotted.
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dts: DistTimeseries with axis 2 ranging over separate instances of an
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"""Plot a polar histogram of a phase variable's probability distribution
Args:
dts: DistTimeseries with axis 2 ranging over separate instances of an
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times ... | def phase_histogram(dts, times=None, nbins=30, colormap=mpl.cm.Blues):
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dts: DistTimeseries with axis 2 ranging over separate instances of an
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train | psd | plot Welch estimate of power spectral density, using nperseg samples per
segment, with noverlap samples overlap and Hamming window. | nsim/analyses1/freq.py | def psd(ts, nperseg=1500, noverlap=1200, plot=True):
"""plot Welch estimate of power spectral density, using nperseg samples per
segment, with noverlap samples overlap and Hamming window."""
ts = ts.squeeze()
if ts.ndim is 1:
ts = ts.reshape((-1, 1))
fs = (len(ts) - 1.0) / (ts.tspan[-1] - ts... | def psd(ts, nperseg=1500, noverlap=1200, plot=True):
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train | lowpass | forward-backward butterworth low-pass filter | nsim/analyses1/freq.py | def lowpass(ts, cutoff_hz, order=3):
"""forward-backward butterworth low-pass filter"""
orig_ndim = ts.ndim
if ts.ndim is 1:
ts = ts[:, np.newaxis]
channels = ts.shape[1]
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"""forward-backward butterworth low-pass filter"""
orig_ndim = ts.ndim
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ts = ts[:, np.newaxis]
channels = ts.shape[1]
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train | bandpass | forward-backward butterworth band-pass filter | nsim/analyses1/freq.py | def bandpass(ts, low_hz, high_hz, order=3):
"""forward-backward butterworth band-pass filter"""
orig_ndim = ts.ndim
if ts.ndim is 1:
ts = ts[:, np.newaxis]
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fs = (len(ts) - 1.0) / (ts.tspan[-1] - ts.tspan[0])
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low = low_hz/nyq
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"""forward-backward butterworth band-pass filter"""
orig_ndim = ts.ndim
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ts = ts[:, np.newaxis]
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train | notch | notch filter to remove remove a particular frequency
Adapted from code by Sturla Molden | nsim/analyses1/freq.py | def notch(ts, freq_hz, bandwidth_hz=1.0):
"""notch filter to remove remove a particular frequency
Adapted from code by Sturla Molden
"""
orig_ndim = ts.ndim
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ts = ts[:, np.newaxis]
channels = ts.shape[1]
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nyq = 0.... | def notch(ts, freq_hz, bandwidth_hz=1.0):
"""notch filter to remove remove a particular frequency
Adapted from code by Sturla Molden
"""
orig_ndim = ts.ndim
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train | hilbert | Analytic signal, using the Hilbert transform | nsim/analyses1/freq.py | def hilbert(ts):
"""Analytic signal, using the Hilbert transform"""
output = signal.hilbert(signal.detrend(ts, axis=0), axis=0)
return Timeseries(output, ts.tspan, labels=ts.labels) | def hilbert(ts):
"""Analytic signal, using the Hilbert transform"""
output = signal.hilbert(signal.detrend(ts, axis=0), axis=0)
return Timeseries(output, ts.tspan, labels=ts.labels) | [
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train | hilbert_amplitude | Amplitude of the analytic signal, using the Hilbert transform | nsim/analyses1/freq.py | def hilbert_amplitude(ts):
"""Amplitude of the analytic signal, using the Hilbert transform"""
output = np.abs(signal.hilbert(signal.detrend(ts, axis=0), axis=0))
return Timeseries(output, ts.tspan, labels=ts.labels) | def hilbert_amplitude(ts):
"""Amplitude of the analytic signal, using the Hilbert transform"""
output = np.abs(signal.hilbert(signal.detrend(ts, axis=0), axis=0))
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train | hilbert_phase | Phase of the analytic signal, using the Hilbert transform | nsim/analyses1/freq.py | def hilbert_phase(ts):
"""Phase of the analytic signal, using the Hilbert transform"""
output = np.angle(signal.hilbert(signal.detrend(ts, axis=0), axis=0))
return Timeseries(output, ts.tspan, labels=ts.labels) | def hilbert_phase(ts):
"""Phase of the analytic signal, using the Hilbert transform"""
output = np.angle(signal.hilbert(signal.detrend(ts, axis=0), axis=0))
return Timeseries(output, ts.tspan, labels=ts.labels) | [
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train | cwt | Continuous wavelet transform
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Args:
ts: Timeseries of m variables, shape (n, m). Assumed constant timestep.
freqs: list of frequencies (in Hz) to use for the tranform.
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"""Continuous wavelet transform
Note the full results can use a huge amount of memory at 64-bit precision
Args:
ts: Timeseries of m variables, shape (n, m). Assumed constant timestep.
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"""Continuous wavelet transform
Note the full results can use a huge amount of memory at 64-bit precision
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train | cwt_distributed | Continuous wavelet transform using distributed computation.
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Args:
ts: Timeseries of m variables, shape (n, m). Assumed constant timestep.
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"""Continuous wavelet transform using distributed computation.
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Note: this function requires an IPython cluster to be started first.
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train | _plot_cwt | Plot time resolved power spectral density from cwt results
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ts: the original Timeseries
coefs: continuous wavelet transform coefficients as calculated by cwt()
freqs: list of frequencies (in Hz) corresponding to coefs.
tsize, fsize: size of the plot (time axis and frequency axis, in pi... | nsim/analyses1/freq.py | def _plot_cwt(ts, coefs, freqs, tsize=1024, fsize=512):
"""Plot time resolved power spectral density from cwt results
Args:
ts: the original Timeseries
coefs: continuous wavelet transform coefficients as calculated by cwt()
freqs: list of frequencies (in Hz) corresponding to coefs.
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"""Plot time resolved power spectral density from cwt results
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ts: the original Timeseries
coefs: continuous wavelet transform coefficients as calculated by cwt()
freqs: list of frequencies (in Hz) corresponding to coefs.
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train | first_return_times | For an ensemble of time series, return the set of all time intervals
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If c is not given, the default is the mean across all times and across all
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Args:
dts (DistTimeseries)
c (float): Option... | nsim/analysesN/misc.py | def first_return_times(dts, c=None, d=0.0):
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If c is not given, the default is the mean across all times and across all
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... | def first_return_times(dts, c=None, d=0.0):
"""For an ensemble of time series, return the set of all time intervals
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"""Example variability function.
Gives two continuous, time-resolved measures of the variability of a
time series, ranging between -1 and 1.
The two measures are based on variance of the centroid frequency and
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train | epochs | Identify "stationary" epochs within a time series, based on a
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Epochs are defined to contain the points of minimal variability, and to
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Args:
ts Timeseries of m variables, shape (n, m).
... | nsim/analysesN/epochs.py | def epochs(ts, variability=None, threshold=0.0, minlength=1.0, plot=True):
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train | epochs_joint | Identify epochs within a multivariate time series where at least a
certain proportion of channels are "stationary", based on a previously
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(Note: This requires an IPython cluster to be started first,
e.g. on a workstation type 'ipcluster start')
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"""Identify epochs within a multivariate time series where at least a
certain proportion of channels are "stationary", based on a previously
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(Note: This req... | def epochs_joint(ts, variability=None, threshold=0.0, minlength=1.0,
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train | periods | For an ensemble of oscillators, return the set of periods lengths of
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An individual oscillation is defined to start and end when the phase
passes phi (by default zero) after completing a full cycle.
If the timeseries of an oscillator phase begins (or en... | nsim/analysesN/phase.py | def periods(dts, phi=0.0):
"""For an ensemble of oscillators, return the set of periods lengths of
all successive oscillations of all oscillators.
An individual oscillation is defined to start and end when the phase
passes phi (by default zero) after completing a full cycle.
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train | circmean | Circular mean phase | nsim/analysesN/phase.py | def circmean(dts, axis=2):
"""Circular mean phase"""
return np.exp(1.0j * dts).mean(axis=axis).angle() | def circmean(dts, axis=2):
"""Circular mean phase"""
return np.exp(1.0j * dts).mean(axis=axis).angle() | [
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train | order_param | Order parameter of phase synchronization | nsim/analysesN/phase.py | def order_param(dts, axis=2):
"""Order parameter of phase synchronization"""
return np.abs(np.exp(1.0j * dts).mean(axis=axis)) | def order_param(dts, axis=2):
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train | circstd | Circular standard deviation | nsim/analysesN/phase.py | def circstd(dts, axis=2):
"""Circular standard deviation"""
R = np.abs(np.exp(1.0j * dts).mean(axis=axis))
return np.sqrt(-2.0 * np.log(R)) | def circstd(dts, axis=2):
"""Circular standard deviation"""
R = np.abs(np.exp(1.0j * dts).mean(axis=axis))
return np.sqrt(-2.0 * np.log(R)) | [
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train | JansenRit.f | Aburn2012 equations right hand side, noise free term
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scalar time
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v: (8,) array
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scalar time
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scalar time
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train | bin_power | Compute power in each frequency bin specified by Band from FFT result of
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Note
-----
A real signal can be synthesized, thus not real.
Parameters
-----------
Band
list
boundary frequencies (in Hz) of bins. They can be unequal bins, e.g.
... | nsim/analyses1/pyeeg.py | def bin_power(X, Band, Fs):
"""Compute power in each frequency bin specified by Band from FFT result of
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Note
-----
A real signal can be synthesized, thus not real.
Parameters
-----------
Band
list
boundary frequencies (in Hz) of bins... | def bin_power(X, Band, Fs):
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Note
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A real signal can be synthesized, thus not real.
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Band
list
boundary frequencies (in Hz) of bins... | [
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train | pfd | Compute Petrosian Fractal Dimension of a time series from either two
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2. D, the first order differential sequence of X (if D is provided,
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1. X, the time series of type list (default)
2. D, the first order differential sequence of X (if D is provided,
recommended to speed up)
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train | hfd | Compute Hjorth Fractal Dimension of a time series X, kmax
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""" Compute Hjorth Fractal Dimension of a time series X, kmax
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train | hjorth | Compute Hjorth mobility and complexity of a time series from either two
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cases below:
1. X, the time series of type list (default)
2. D, a first order differential sequence of X (if D is provided,
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train | spectral_entropy | Compute spectral entropy of a time series from either two cases below:
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1. X, the time series (default)
2. Power_Ratio, a list of normalized signal power in a set of frequency
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train | svd_entropy | Compute SVD Entropy from either two cases below:
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If W is None, the function will do as follows to prepare singular spectrum:
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train | ap_entropy | Computer approximate entropy (ApEN) of series X, specified by M and R.
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embedding matrix Em, of dimension (N-M+1)-by-M, such that the i-th row of
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embedding matrix Em, of dimension (N-M+1)-by-M, such that the i-th row of
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Suppose given time series is X = [x(1), x(2), ... , x(N)]. We first build
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train | samp_entropy | Computer sample entropy (SampEn) of series X, specified by M and R.
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Suppose given time series is X = [x(1), x(2), ... , x(N)]. We first build
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train | dfa | Compute Detrended Fluctuation Analysis from a time series X and length of
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The integrated signal Y = [y(1), y(2), ..., y(N)] is obtained as follows
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"""Compute Detrended Fluctuation Analysis from a time series X and length of
boxes L.
The first step to compute DFA is to integrate the signal. Let original
series be X= [x(1), x(2), ..., x(N)].
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The first step to compute DFA is to integrate the signal. Let original
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train | permutation_entropy | Compute Permutation Entropy of a given time series x, specified by
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Parameters
----------
x
list
a time series
n
integer
Permutation order
tau
integer
Embedding lag
Returns
----------
P... | nsim/analyses1/pyeeg.py | def permutation_entropy(x, n, tau):
"""Compute Permutation Entropy of a given time series x, specified by
permutation order n and embedding lag tau.
Parameters
----------
x
list
a time series
n
integer
Permutation order
tau
integer
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"""Compute Permutation Entropy of a given time series x, specified by
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Parameters
----------
x
list
a time series
n
integer
Permutation order
tau
integer
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train | information_based_similarity | Calculates the information based similarity of two time series x
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Parameters
----------
x
list
a time series
y
list
a time series
n
integer
word order
Returns
----------
IBS
float
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"""Calculates the information based similarity of two time series x
and y.
Parameters
----------
x
list
a time series
y
list
a time series
n
integer
word order
Returns
----------
... | def information_based_similarity(x, y, n):
"""Calculates the information based similarity of two time series x
and y.
Parameters
----------
x
list
a time series
y
list
a time series
n
integer
word order
Returns
----------
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train | LLE | Calculate largest Lyauponov exponent of a given time series x using
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Parameters
----------
x
list
a time series
n
integer
embedding dimension
tau
integer
Embedding lag
fs
integer
Sampling frequency
... | nsim/analyses1/pyeeg.py | def LLE(x, tau, n, T, fs):
"""Calculate largest Lyauponov exponent of a given time series x using
Rosenstein algorithm.
Parameters
----------
x
list
a time series
n
integer
embedding dimension
tau
integer
Embedding lag
fs
in... | def LLE(x, tau, n, T, fs):
"""Calculate largest Lyauponov exponent of a given time series x using
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Parameters
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x
list
a time series
n
integer
embedding dimension
tau
integer
Embedding lag
fs
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train | mod2pi | For a timeseries where all variables represent phases (in radians),
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"""For a timeseries where all variables represent phases (in radians),
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return np.pi - np.mod(np.pi - ts, 2*np.pi) | def mod2pi(ts):
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train | phase_crossings | For a single variable timeseries representing the phase of an oscillator,
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(Thus if noise causes the phase to wander back and forth across angle phi
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"""For a single variable timeseries representing the phase of an oscillator,
find the times at which the phase crosses angle phi,
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train | periods | For a single variable timeseries representing the phase of an oscillator,
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"""For a single variable timeseries representing the phase of an oscillator,
measure the period of each successive oscillation.
An individual oscillation is defined to start and end when the phase
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train | circmean | Circular mean phase | nsim/analyses1/phase.py | def circmean(ts, axis=2):
"""Circular mean phase"""
return np.exp(1.0j * ts).mean(axis=axis).angle() | def circmean(ts, axis=2):
"""Circular mean phase"""
return np.exp(1.0j * ts).mean(axis=axis).angle() | [
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train | order_param | Order parameter of phase synchronization | nsim/analyses1/phase.py | def order_param(ts, axis=2):
"""Order parameter of phase synchronization"""
return np.abs(np.exp(1.0j * ts).mean(axis=axis)) | def order_param(ts, axis=2):
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train | cwtmorlet | complex morlet wavelet function compatible with scipy.signal.cwt
Parameters: points: int
Number of points in `vector`.
width: scalar
Width parameter of wavelet.
Equals (sample rate / fundamental frequency of wavelet)
Returns: `vector`: ... | nsim/analyses1/_cwtmorlet.py | def cwtmorlet(points, width):
"""complex morlet wavelet function compatible with scipy.signal.cwt
Parameters: points: int
Number of points in `vector`.
width: scalar
Width parameter of wavelet.
Equals (sample rate / fundamental frequenc... | def cwtmorlet(points, width):
"""complex morlet wavelet function compatible with scipy.signal.cwt
Parameters: points: int
Number of points in `vector`.
width: scalar
Width parameter of wavelet.
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Performs a continuous wavelet transform on `data`,
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"""
Continuous wavelet transform.
Performs a continuous wavelet transform on `data`,
using the `wavelet` function. A CWT performs a convolution
with `data` using the `wavelet` function, which is characterized
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P... | def roughcwt(data, wavelet, widths):
"""
Continuous wavelet transform.
Performs a continuous wavelet transform on `data`,
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train | variability_fp | Example variability function.
Gives two continuous, time-resolved measures of the variability of a
time series, ranging between -1 and 1.
The two measures are based on variance of the centroid frequency and
variance of the height of the spectral peak, respectively.
(Centroid frequency meaning the ... | nsim/analyses1/epochs.py | def variability_fp(ts, freqs=None, ncycles=6, plot=True):
"""Example variability function.
Gives two continuous, time-resolved measures of the variability of a
time series, ranging between -1 and 1.
The two measures are based on variance of the centroid frequency and
variance of the height of the ... | def variability_fp(ts, freqs=None, ncycles=6, plot=True):
"""Example variability function.
Gives two continuous, time-resolved measures of the variability of a
time series, ranging between -1 and 1.
The two measures are based on variance of the centroid frequency and
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train | _rescale | Shift and rescale array ar to the interval [-1, 1] | nsim/analyses1/epochs.py | def _rescale(ar):
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max = np.nanmax(ar)
min = np.nanmin(ar)
midpoint = (max + min) / 2.0
return 2.0 * (ar - midpoint) / (max - min) | def _rescale(ar):
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max = np.nanmax(ar)
min = np.nanmin(ar)
midpoint = (max + min) / 2.0
return 2.0 * (ar - midpoint) / (max - min) | [
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train | _get_color_list | Get cycle of colors in a way compatible with all matplotlib versions | nsim/analyses1/epochs.py | def _get_color_list():
"""Get cycle of colors in a way compatible with all matplotlib versions"""
if 'axes.prop_cycle' in plt.rcParams:
return [p['color'] for p in list(plt.rcParams['axes.prop_cycle'])]
else:
return plt.rcParams['axes.color_cycle'] | def _get_color_list():
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train | _plot_variability | Plot the timeseries and variability. Optionally plot epochs. | nsim/analyses1/epochs.py | def _plot_variability(ts, variability, threshold=None, epochs=None):
"""Plot the timeseries and variability. Optionally plot epochs."""
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
import matplotlib.pyplot as plt
if variability.ndim is 1:
variability = variab... | def _plot_variability(ts, variability, threshold=None, epochs=None):
"""Plot the timeseries and variability. Optionally plot epochs."""
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
import matplotlib.pyplot as plt
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train | epochs | Identify "stationary" epochs within a time series, based on a
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Epochs are defined to contain the points of minimal variability, and to
extend as wide as possible with variability not exceeding the threshold.
Args:
ts Timeseries of m variables, shape (n, m).
... | nsim/analyses1/epochs.py | def epochs(ts, variability=None, threshold=0.0, minlength=1.0, plot=True):
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Epochs are defined to contain the points of minimal variability, and to
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train | epochs_distributed | Same as `epochs()`, but computes channels in parallel for speed.
(Note: This requires an IPython cluster to be started first,
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Identify "stationary" epochs within a time series, based on a
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Epochs are defined t... | nsim/analyses1/epochs.py | def epochs_distributed(ts, variability=None, threshold=0.0, minlength=1.0,
plot=True):
"""Same as `epochs()`, but computes channels in parallel for speed.
(Note: This requires an IPython cluster to be started first,
e.g. on a workstation type 'ipcluster start')
Identify... | def epochs_distributed(ts, variability=None, threshold=0.0, minlength=1.0,
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"""Same as `epochs()`, but computes channels in parallel for speed.
(Note: This requires an IPython cluster to be started first,
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train | epochs_joint | Identify epochs within a multivariate time series where at least a
certain proportion of channels are "stationary", based on a previously
computed variability measure.
(Note: This requires an IPython cluster to be started first,
e.g. on a workstation type 'ipcluster start')
Args:
ts Tim... | nsim/analyses1/epochs.py | def epochs_joint(ts, variability=None, threshold=0.0, minlength=1.0,
proportion=0.75, plot=True):
"""Identify epochs within a multivariate time series where at least a
certain proportion of channels are "stationary", based on a previously
computed variability measure.
(Note: This req... | def epochs_joint(ts, variability=None, threshold=0.0, minlength=1.0,
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"""Identify epochs within a multivariate time series where at least a
certain proportion of channels are "stationary", based on a previously
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train | plot | Plot a Timeseries
Args:
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title str
show bool whether to display the figure or just return a figure object | nsim/analyses1/plots.py | def plot(ts, title=None, show=True):
"""Plot a Timeseries
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ts Timeseries
title str
show bool whether to display the figure or just return a figure object
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ts Timeseries
title str
show bool whether to display the figure or just return a figure object
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train | _remove_pi_crossings | For each variable in the Timeseries, checks whether it represents
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train | timeseries_from_mat | load a multi-channel Timeseries from a MATLAB .mat file
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varname (str): variable name. only needed if there is more than one
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Args:
filename (str): .mat file to load
varname (str): variable name. only needed if there is more than one
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train | save_mat | save a Timeseries to a MATLAB .mat file
Args:
ts (Timeseries): the timeseries to save
filename (str): .mat filename to save to | nsim/readfile.py | def save_mat(ts, filename):
"""save a Timeseries to a MATLAB .mat file
Args:
ts (Timeseries): the timeseries to save
filename (str): .mat filename to save to
"""
import scipy.io as sio
tspan = ts.tspan
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mat_dict = {'data': np.asar... | def save_mat(ts, filename):
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Args:
ts (Timeseries): the timeseries to save
filename (str): .mat filename to save to
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train | timeseries_from_file | Load a multi-channel Timeseries from any file type supported by `biosig`
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Full list is here: http://pub.ist.ac.at/~schloegl/biosig/TESTED
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"""Load a multi-channel Timeseries from any file type supported by `biosig`
Supported file formats include EDF/EDF+, BDF/BDF+, EEG, CNT and GDF.
Full list is here: http://pub.ist.ac.at/~schloegl/biosig/TESTED
For EDF, EDF+, BDF and BDF+ files, we will use python-edf... | def timeseries_from_file(filename):
"""Load a multi-channel Timeseries from any file type supported by `biosig`
Supported file formats include EDF/EDF+, BDF/BDF+, EEG, CNT and GDF.
Full list is here: http://pub.ist.ac.at/~schloegl/biosig/TESTED
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train | _load_edflib | load a multi-channel Timeseries from an EDF (European Data Format) file
or EDF+ file, using edflib.
Args:
filename: EDF+ file
Returns:
Timeseries | nsim/readfile.py | def _load_edflib(filename):
"""load a multi-channel Timeseries from an EDF (European Data Format) file
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Args:
filename: EDF+ file
Returns:
Timeseries
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Args:
filename: EDF+ file
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Timeseries
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train | annotations_from_file | Get a list of event annotations from an EDF (European Data Format file
or EDF+ file, using edflib.
Args:
filename: EDF+ file
Returns:
list: annotation events, each in the form [start_time, duration, text] | nsim/readfile.py | def annotations_from_file(filename):
"""Get a list of event annotations from an EDF (European Data Format file
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Args:
filename: EDF+ file
Returns:
list: annotation events, each in the form [start_time, duration, text]
"""
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e = edflib.EdfR... | def annotations_from_file(filename):
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filename: EDF+ file
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train | _ufunc_wrap | After using the superclass __numpy_ufunc__ to route ufunc computations
on the array data, convert any resulting ndarray, RemoteArray and DistArray
instances into Timeseries, RemoteTimeseries and DistTimeseries instances
if appropriate | nsim/nsim.py | def _ufunc_wrap(out_arr, ufunc, method, i, inputs, **kwargs):
"""After using the superclass __numpy_ufunc__ to route ufunc computations
on the array data, convert any resulting ndarray, RemoteArray and DistArray
instances into Timeseries, RemoteTimeseries and DistTimeseries instances
if appropriate"""
... | def _ufunc_wrap(out_arr, ufunc, method, i, inputs, **kwargs):
"""After using the superclass __numpy_ufunc__ to route ufunc computations
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instances into Timeseries, RemoteTimeseries and DistTimeseries instances
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train | _rts_from_ra | construct a RemoteTimeseries from a RemoteArray | nsim/nsim.py | def _rts_from_ra(ra, tspan, labels, block=True):
"""construct a RemoteTimeseries from a RemoteArray"""
def _convert(a, tspan, labels):
from nsim import Timeseries
return Timeseries(a, tspan, labels)
return distob.call(
_convert, ra, tspan, labels, prefer_local=False, block=block) | def _rts_from_ra(ra, tspan, labels, block=True):
"""construct a RemoteTimeseries from a RemoteArray"""
def _convert(a, tspan, labels):
from nsim import Timeseries
return Timeseries(a, tspan, labels)
return distob.call(
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train | _dts_from_da | construct a DistTimeseries from a DistArray | nsim/nsim.py | def _dts_from_da(da, tspan, labels):
"""construct a DistTimeseries from a DistArray"""
sublabels = labels[:]
new_subarrays = []
for i, ra in enumerate(da._subarrays):
if isinstance(ra, RemoteTimeseries):
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train | newsim | Make a simulation of the system defined by functions f and G.
dy = f(y,t)dt + G(y,t).dW with initial condition y0
This helper function is for convenience, making it easy to define
one-off simulations interactively in ipython.
Args:
f: callable(y, t) (defined in global scope) returning (n,) arra... | nsim/nsim.py | def newsim(f, G, y0, name='NewModel', modelType=ItoModel, T=60.0, dt=0.005, repeat=1, identical=True):
"""Make a simulation of the system defined by functions f and G.
dy = f(y,t)dt + G(y,t).dW with initial condition y0
This helper function is for convenience, making it easy to define
one-off simulati... | def newsim(f, G, y0, name='NewModel', modelType=ItoModel, T=60.0, dt=0.005, repeat=1, identical=True):
"""Make a simulation of the system defined by functions f and G.
dy = f(y,t)dt + G(y,t).dW with initial condition y0
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train | newmodel | Use the functions f and G to define a new Model class for simulations.
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It will take functions f and G from global scope and make a new Model class
out of them. It will automatically gather any globals used in the definition
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train | __clone_function | Make a new version of a function that has its own independent copy
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All other attributes are assigned from the original function.
Args:
f: the function to clone
name (str): the name for the new function (if None, keep the same name)
... | nsim/nsim.py | def __clone_function(f, name=None):
"""Make a new version of a function that has its own independent copy
of any globals that it uses directly, and has its own name.
All other attributes are assigned from the original function.
Args:
f: the function to clone
name (str): the name for the ... | def __clone_function(f, name=None):
"""Make a new version of a function that has its own independent copy
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f: the function to clone
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train | DistTimeseries.expand_dims | Insert a new axis, at a given position in the array shape
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train | DistTimeseries.absolute | Calculate the absolute value element-wise.
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train | DistTimeseries.angle | Return the angle of a complex Timeseries
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Return angle in degrees if True, radians if False (default).
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deg (bool, optional):
Return angle in degrees if True, radians if False (default).
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angle (Timeseries):
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... | def angle(self, deg=False):
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train | NetworkModel.coupling | How to couple the output of one subsystem to the input of another.
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"""How to couple the output of one subsystem to the input of another.
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train | NetworkModel._reshape_timeseries | Introduce a new axis 2 that ranges across nodes of the network | nsim/nsim.py | def _reshape_timeseries(self, ts):
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train | NetworkModel._reshape_output | Introduce a new axis 2 that ranges across nodes of the network | nsim/nsim.py | def _reshape_output(self, ts):
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train | Simulation.timeseries | Simulated time series | nsim/nsim.py | def timeseries(self):
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train | Simulation.output | Simulated model output | nsim/nsim.py | def output(self):
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train | MultipleSim.output | Rank 3 array representing output time series. Axis 0 is time,
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train | DistSim._tosubs | Maps a list of integer indices to sub-indices.
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"""Maps a list of integer indices to sub-indices.
ixlist can contain repeated indices and does not need to be sorted.
Returns pair (ss, ms) where ss is a list of subsim numbers and ms is a
list of lists of subindices m (one list for each subsim in ss).
... | def _tosubs(self, ixlist):
"""Maps a list of integer indices to sub-indices.
ixlist can contain repeated indices and does not need to be sorted.
Returns pair (ss, ms) where ss is a list of subsim numbers and ms is a
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train | DistSim.output | Rank 3 array representing output time series. Axis 0 is time,
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"""Rank 3 array representing output time series. Axis 0 is time,
axis 1 ranges across output variables of a single simulation, axis 2
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subts = [rms.output for rms in self._subsims]
distaxis = subts[0].ndim - 1
... | def output(self):
"""Rank 3 array representing output time series. Axis 0 is time,
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subts = [rms.output for rms in self._subsims]
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train | crossing_times | For a single variable timeseries, find the times at which the
value crosses ``c`` from above or below. Can optionally set a non-zero
``d`` to impose the condition that the value must wander at least ``d``
units away from ``c`` between crossings.
If the timeseries begins (or ends) exactly at ``c``, the... | nsim/analyses1/misc.py | def crossing_times(ts, c=0.0, d=0.0):
"""For a single variable timeseries, find the times at which the
value crosses ``c`` from above or below. Can optionally set a non-zero
``d`` to impose the condition that the value must wander at least ``d``
units away from ``c`` between crossings.
If the time... | def crossing_times(ts, c=0.0, d=0.0):
"""For a single variable timeseries, find the times at which the
value crosses ``c`` from above or below. Can optionally set a non-zero
``d`` to impose the condition that the value must wander at least ``d``
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train | first_return_times | For a single variable time series, first wait until the time series
attains the value c for the first time. Then record the time intervals
between successive returns to c. If c is not given, the default is the mean
of the time series.
Args:
ts: Timeseries (single variable)
c (float): ... | nsim/analyses1/misc.py | def first_return_times(ts, c=None, d=0.0):
"""For a single variable time series, first wait until the time series
attains the value c for the first time. Then record the time intervals
between successive returns to c. If c is not given, the default is the mean
of the time series.
Args:
t... | def first_return_times(ts, c=None, d=0.0):
"""For a single variable time series, first wait until the time series
attains the value c for the first time. Then record the time intervals
between successive returns to c. If c is not given, the default is the mean
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train | autocorrelation | Returns the discrete, linear convolution of a time series with itself,
optionally using unbiased normalization.
N.B. Autocorrelation estimates are necessarily inaccurate for longer lags,
as there are less pairs of points to convolve separated by that lag.
Therefore best to throw out the results excep... | nsim/analyses1/misc.py | def autocorrelation(ts, normalized=False, unbiased=False):
"""
Returns the discrete, linear convolution of a time series with itself,
optionally using unbiased normalization.
N.B. Autocorrelation estimates are necessarily inaccurate for longer lags,
as there are less pairs of points to convolve s... | def autocorrelation(ts, normalized=False, unbiased=False):
"""
Returns the discrete, linear convolution of a time series with itself,
optionally using unbiased normalization.
N.B. Autocorrelation estimates are necessarily inaccurate for longer lags,
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train | freshroastsr700.fan_speed | Verifies the value is between 1 and 9 inclusively. | freshroastsr700/__init__.py | def fan_speed(self, value):
"""Verifies the value is between 1 and 9 inclusively."""
if value not in range(1, 10):
raise exceptions.RoasterValueError
self._fan_speed.value = value | def fan_speed(self, value):
"""Verifies the value is between 1 and 9 inclusively."""
if value not in range(1, 10):
raise exceptions.RoasterValueError
self._fan_speed.value = value | [
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train | freshroastsr700.heat_setting | Verifies that the heat setting is between 0 and 3. | freshroastsr700/__init__.py | def heat_setting(self, value):
"""Verifies that the heat setting is between 0 and 3."""
if value not in range(0, 4):
raise exceptions.RoasterValueError
self._heat_setting.value = value | def heat_setting(self, value):
"""Verifies that the heat setting is between 0 and 3."""
if value not in range(0, 4):
raise exceptions.RoasterValueError
self._heat_setting.value = value | [
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train | freshroastsr700.heater_level | Verifies that the heater_level is between 0 and heater_segments.
Can only be called when freshroastsr700 object is initialized
with ext_sw_heater_drive=True. Will throw RoasterValueError
otherwise. | freshroastsr700/__init__.py | def heater_level(self, value):
"""Verifies that the heater_level is between 0 and heater_segments.
Can only be called when freshroastsr700 object is initialized
with ext_sw_heater_drive=True. Will throw RoasterValueError
otherwise."""
if self._ext_sw_heater_drive:
... | def heater_level(self, value):
"""Verifies that the heater_level is between 0 and heater_segments.
Can only be called when freshroastsr700 object is initialized
with ext_sw_heater_drive=True. Will throw RoasterValueError
otherwise."""
if self._ext_sw_heater_drive:
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