minke package¶

Subpackages¶

Submodules¶

minke.asimov module¶

minke.bilby module¶

This module contains code to allow Minke to interact with the bilby parameter estimation package.

minke.cli module¶

minke.detector module¶

Logic for handling detectors in heron, largely wrapping functions from lalsimulation.

class minke.detector.AdvancedLIGO[source]¶

Bases: Detector

Methods

antenna_response(ra, dec, psi, time)	Get the antenna responses for a given detector.
geocentre_delay(ra, dec, times)	Calculate the delay in arrival time for a signal at this detector relative to the centre of the Earth.

class minke.detector.AdvancedLIGOHanford[source]¶

Bases: Detector

Methods

antenna_response(ra, dec, psi, time)	Get the antenna responses for a given detector.
geocentre_delay(ra, dec, times)	Calculate the delay in arrival time for a signal at this detector relative to the centre of the Earth.

abbreviation = 'H1'¶

class minke.detector.AdvancedLIGOLivingston[source]¶

Bases: Detector

Methods

antenna_response(ra, dec, psi, time)	Get the antenna responses for a given detector.
geocentre_delay(ra, dec, times)	Calculate the delay in arrival time for a signal at this detector relative to the centre of the Earth.

abbreviation = 'L1'¶

class minke.detector.AdvancedVirgo[source]¶

Bases: Detector

Methods

antenna_response(ra, dec, psi, time)	Get the antenna responses for a given detector.
geocentre_delay(ra, dec, times)	Calculate the delay in arrival time for a signal at this detector relative to the centre of the Earth.

abbreviation = 'V1'¶

class minke.detector.AntennaResponse(plus, cross)¶

Bases: tuple

Methods

count(value, /)	Return number of occurrences of value.
index(value[, start, stop])	Return first index of value.

cross¶

Alias for field number 1

plus¶

Alias for field number 0

class minke.detector.Detector[source]¶

Bases: DetectorBase

Methods

antenna_response(ra, dec, psi, time)	Get the antenna responses for a given detector.
geocentre_delay(ra, dec, times)	Calculate the delay in arrival time for a signal at this detector relative to the centre of the Earth.

antenna_response(ra: float, dec: float, psi: float, time)[source]¶

Get the antenna responses for a given detector.

Parameters:

rafloat

The right-ascension of the source, in radians.

decfloat

The declination of the source, in radians.

psifloat

The polarisation angle, in radians.

timefloat, or array of floats

The GPS time, or an array of GPS times, at which the response should be evaluated.

Returns:

plusfloat, or array

The ‘plus’ component of the response function.

crossfloat, or array

The ‘cross’ component of the response function.

geocentre_delay(ra: float, dec: float, times)[source]¶

Calculate the delay in arrival time for a signal at this detector relative to the centre of the Earth.

Parameters:

rafloat

The right-ascension of the source, in radians.

decfloat

The declination of the source, in radians.

timesfloat, or array of floats

The GPS time, or an array of GPS times, at which the response should be evaluated.

Returns:

float

The time in seconds by which the response is delayed.

class minke.detector.DetectorBase[source]¶

Bases: object

minke.distribution module¶

minke.injection module¶

minke.mdctools module¶

minke.noise module¶

minke.sources module¶

minke.types module¶

class minke.types.PSD(data, unit=None, f0=None, df=None, frequencies=None, name=None, epoch=None, channel=None, **kwargs)[source]¶

Bases: FrequencySeries

Attributes:

T

View of the transposed array.

base

Base object if memory is from some other object.

cgs

Returns a copy of the current Quantity instance with CGS units.

channel

Instrumental channel associated with these data

ctypes

An object to simplify the interaction of the array with the ctypes module.

data

Python buffer object pointing to the start of the array’s data.

device

df

Frequency spacing of this FrequencySeries

dtype

Data-type of the array’s elements.

dx

X-axis sample separation

epoch

GPS epoch associated with these data

equivalencies

A list of equivalencies that will be applied by default during unit conversions.

f0

Starting frequency for this FrequencySeries

flags

Information about the memory layout of the array.

flat

A 1-D iterator over the Quantity array.

frequencies

Series of frequencies for each sample

imag

The imaginary part of the array.

info

isscalar

True if the value of this quantity is a scalar, or False if it is an array-like object.

itemset

itemsize

Length of one array element in bytes.

mT

View of the matrix transposed array.

name

Name for this data set

nbytes

Total bytes consumed by the elements of the array.

ndim

Number of array dimensions.

newbyteorder

ptp

real

The real part of the array.

shape

Tuple of array dimensions.

si

Returns a copy of the current Quantity instance with SI units.

size

Number of elements in the array.

strides

Tuple of bytes to step in each dimension when traversing an array.

unit

The physical unit of these data

value

The numerical value of this instance.

x0

X-axis coordinate of the first data point

xindex

Positions of the data on the x-axis

xspan

X-axis [low, high) segment encompassed by these data

xunit

Unit of x-axis index

Methods

abs(x, /[, out, where, casting, order, ...])	Calculate the absolute value element-wise.
all([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
any([axis, out, keepdims, where])	Returns True if any of the elements of a evaluate to True.
append(other[, inplace, pad, gap, resize])	Connect another series onto the end of the current one.
argmax([axis, out, keepdims])	Return indices of the maximum values along the given axis.
argmin([axis, out, keepdims])	Return indices of the minimum values along the given axis.
argpartition(kth[, axis, kind, order])	Returns the indices that would partition this array.
argsort([axis, kind, order])	Returns the indices that would sort this array.
astype(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
byteswap([inplace])	Swap the bytes of the array elements
choose(choices[, out, mode])	Use an index array to construct a new array from a set of choices.
clip([min, max, out])	Return an array whose values are limited to [min, max].
compress(condition[, axis, out])	Return selected slices of this array along given axis.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
copy([order])	Return a copy of the array.
crop([start, end, copy])	Crop this series to the given x-axis extent.
cumprod([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
cumsum([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
decompose([bases])	Generates a new Quantity with the units decomposed.
diagonal([offset, axis1, axis2])	Return specified diagonals.
diff([n, axis])	Calculate the n-th order discrete difference along given axis.
dump(file)	Not implemented, use .value.dump() instead.
dumps()	Returns the pickle of the array as a string.
fill(value)	Fill the array with a scalar value.
filter(*filt, **kwargs)	Apply a filter to this FrequencySeries.
flatten([order])	Return a copy of the array collapsed into one dimension.
from_lal(lalfs[, copy])	Generate a new FrequencySeries from a LAL FrequencySeries of any type.
from_pycbc(fs[, copy])	Convert a pycbc.types.frequencyseries.FrequencySeries into a FrequencySeries
getfield(dtype[, offset])	Returns a field of the given array as a certain type.
ifft()	Compute the one-dimensional discrete inverse Fourier transform of this FrequencySeries.
inject(other)	Add two compatible Series along their shared x-axis values.
insert(obj, values[, axis])	Insert values along the given axis before the given indices and return a new ~astropy.units.Quantity object.
interpolate(df)	Interpolate this FrequencySeries to a new resolution.
is_compatible(other)	Check whether this series and other have compatible metadata
is_contiguous(other[, tol])	Check whether other is contiguous with self.
item(*args)	Copy an element of an array to a scalar Quantity and return it.
max([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
mean([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
median([axis])	Compute the median along the specified axis.
min([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
nansum([axis, out, keepdims, initial, where])
nonzero()	Return the indices of the elements that are non-zero.
override_unit(unit[, parse_strict])	Forcefully reset the unit of these data
pad(pad_width, **kwargs)	Pad this series to a new size
partition(kth[, axis, kind, order])	Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array.
plot([xscale])	Plot the data for this series
prepend(other[, inplace, pad, gap, resize])	Connect another series onto the start of the current one.
prod([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
put(indices, values[, mode])	Set a.flat[n] = values[n] for all n in indices.
ravel([order])	Return a flattened array.
read(source, *args, **kwargs)	Read data into a FrequencySeries
repeat(repeats[, axis])	Repeat elements of an array.
reshape(shape, /, *[, order, copy])	Returns an array containing the same data with a new shape.
resize(new_shape[, refcheck])	Change shape and size of array in-place.
round([decimals, out])	Return a with each element rounded to the given number of decimals.
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
setfield(val, dtype[, offset])	Put a value into a specified place in a field defined by a data-type.
setflags([write, align, uic])	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
shift(delta)	Shift this Series forward on the X-axis by delta
sort([axis, kind, order])	Sort an array in-place.
squeeze([axis])	Remove axes of length one from a.
std([axis, dtype, out, ddof, keepdims, where])	Returns the standard deviation of the array elements along given axis.
step(**kwargs)	Create a step plot of this series
sum([axis, dtype, out, keepdims, initial, where])	Return the sum of the array elements over the given axis.
swapaxes(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
take(indices[, axis, out, mode])	Return an array formed from the elements of a at the given indices.
to(unit[, equivalencies, copy])	Return a new ~astropy.units.Quantity object with the specified unit.
to_lal()	Convert this FrequencySeries into a LAL FrequencySeries.
to_pycbc([copy])	Convert this FrequencySeries into a ~pycbc.types.frequencyseries.FrequencySeries
to_string([unit, precision, format, subfmt])	Generate a string representation of the quantity and its unit.
to_value([unit, equivalencies])	The numerical value, possibly in a different unit.
tobytes([order])	Not implemented, use .value.tobytes() instead.
tofile(fid[, sep, format])	Not implemented, use .value.tofile() instead.
tolist()	Return the array as an a.ndim-levels deep nested list of Python scalars.
tostring([order])	Construct Python bytes containing the raw data bytes in the array.
trace([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
transpose(*axes)	Returns a view of the array with axes transposed.
update(other[, inplace])	Update this series by appending new data from an other and dropping the same amount of data off the start.
value_at(x)	Return the value of this Series at the given xindex value
var([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.
view([dtype][, type])	New view of array with the same data.
write(target, *args, **kwargs)	Write this FrequencySeries to a file
zip()	Zip the xindex and value arrays of this Series
zpk(zeros, poles, gain[, analog])	Filter this FrequencySeries by applying a zero-pole-gain filter

dot
ediff1d
filterba
info
to_device

class minke.types.TimeSeries(data, unit=None, t0=None, dt=None, sample_rate=None, times=None, channel=None, name=None, **kwargs)[source]¶

Bases: TimeSeries

Overload the GWPy timeseries so that additional methods can be defined upon it.

Attributes:

T

View of the transposed array.

base

Base object if memory is from some other object.

cgs

Returns a copy of the current Quantity instance with CGS units.

channel

Instrumental channel associated with these data

ctypes

An object to simplify the interaction of the array with the ctypes module.

data

Python buffer object pointing to the start of the array’s data.

device

dt

X-axis sample separation

dtype

Data-type of the array’s elements.

duration

Duration of this series in seconds

dx

X-axis sample separation

epoch

GPS epoch for these data.

equivalencies

A list of equivalencies that will be applied by default during unit conversions.

flags

Information about the memory layout of the array.

flat

A 1-D iterator over the Quantity array.

imag

The imaginary part of the array.

info

isscalar

True if the value of this quantity is a scalar, or False if it is an array-like object.

itemset

itemsize

Length of one array element in bytes.

mT

View of the matrix transposed array.

name

Name for this data set

nbytes

Total bytes consumed by the elements of the array.

ndim

Number of array dimensions.

newbyteorder

ptp

real

The real part of the array.

sample_rate

Data rate for this TimeSeries in samples per second (Hertz).

shape

Tuple of array dimensions.

si

Returns a copy of the current Quantity instance with SI units.

size

Number of elements in the array.

span

X-axis [low, high) segment encompassed by these data

strides

Tuple of bytes to step in each dimension when traversing an array.

t0

X-axis coordinate of the first data point

times

Positions of the data on the x-axis

unit

The physical unit of these data

value

The numerical value of this instance.

x0

X-axis coordinate of the first data point

xindex

Positions of the data on the x-axis

xspan

X-axis [low, high) segment encompassed by these data

xunit

Unit of x-axis index

Methods

DictClass	alias of TimeSeriesDict
abs(x, /[, out, where, casting, order, ...])	Calculate the absolute value element-wise.
all([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
any([axis, out, keepdims, where])	Returns True if any of the elements of a evaluate to True.
append(other[, inplace, pad, gap, resize])	Connect another series onto the end of the current one.
argmax([axis, out, keepdims])	Return indices of the maximum values along the given axis.
argmin([axis, out, keepdims])	Return indices of the minimum values along the given axis.
argpartition(kth[, axis, kind, order])	Returns the indices that would partition this array.
argsort([axis, kind, order])	Returns the indices that would sort this array.
asd([fftlength, overlap, window, method])	Calculate the ASD FrequencySeries of this TimeSeries
astype(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
auto_coherence(dt[, fftlength, overlap, window])	Calculate the frequency-coherence between this TimeSeries and a time-shifted copy of itself.
average_fft([fftlength, overlap, window])	Compute the averaged one-dimensional DFT of this TimeSeries.
bandpass(flow, fhigh[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a band-pass filter.
byteswap([inplace])	Swap the bytes of the array elements
choose(choices[, out, mode])	Use an index array to construct a new array from a set of choices.
clip([min, max, out])	Return an array whose values are limited to [min, max].
coherence(other[, fftlength, overlap, window])	Calculate the frequency-coherence between this TimeSeries and another.
coherence_spectrogram(other, stride[, ...])	Calculate the coherence spectrogram between this TimeSeries and other.
compress(condition[, axis, out])	Return selected slices of this array along given axis.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
convolve(fir[, window])	Convolve this TimeSeries with an FIR filter using the
copy([order])	Return a copy of the array.
correlate(mfilter[, window, detrend, ...])	Cross-correlate this TimeSeries with another signal
crop([start, end, copy])	Crop this series to the given x-axis extent.
csd(other[, fftlength, overlap, window])	Calculate the CSD FrequencySeries for two TimeSeries
csd_spectrogram(other, stride[, fftlength, ...])	Calculate the cross spectral density spectrogram of this
cumprod([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
cumsum([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
decompose([bases])	Generates a new Quantity with the units decomposed.
demodulate(f[, stride, exp, deg])	Compute the average magnitude and phase of this TimeSeries once per stride at a given frequency
detrend([detrend])	Remove the trend from this TimeSeries
diagonal([offset, axis1, axis2])	Return specified diagonals.
diff([n, axis])	Calculate the n-th order discrete difference along given axis.
dump(file)	Not implemented, use .value.dump() instead.
dumps()	Returns the pickle of the array as a string.
fetch(channel, start, end[, host, port, ...])	Fetch data from NDS
fetch_open_data(ifo, start, end[, ...])	Fetch open-access data from the LIGO Open Science Center
fft([nfft])	Compute the one-dimensional discrete Fourier transform of this TimeSeries.
fftgram(fftlength[, overlap, window])	Calculate the Fourier-gram of this TimeSeries.
fill(value)	Fill the array with a scalar value.
filter(*filt, **kwargs)	Filter this TimeSeries with an IIR or FIR filter
find(channel, start, end[, frametype, pad, ...])	Find and read data from frames for a channel
find_gates([tzero, whiten, threshold, ...])	Identify points that should be gates using a provided threshold and clustered within a provided time window.
flatten([order])	Return a copy of the array collapsed into one dimension.
from_lal(lalts[, copy])	Generate a new TimeSeries from a LAL TimeSeries of any type.
from_nds2_buffer(buffer_[, scaled, copy])	Construct a new series from an nds2.buffer object
from_pycbc(pycbcseries[, copy])	Convert a pycbc.types.timeseries.TimeSeries into a TimeSeries
gate([tzero, tpad, whiten, threshold, ...])	Removes high amplitude peaks from data using inverse Planck window.
get(channel, start, end[, pad, scaled, ...])	Get data for this channel from frames or NDS
getfield(dtype[, offset])	Returns a field of the given array as a certain type.
heterodyne(phase[, stride, singlesided])	Compute the average magnitude and phase of this TimeSeries once per stride after heterodyning with a given phase series
highpass(frequency[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a high-pass filter.
inject(other)	Add two compatible Series along their shared x-axis values.
insert(obj, values[, axis])	Insert values along the given axis before the given indices and return a new ~astropy.units.Quantity object.
is_compatible(other)	Check whether this series and other have compatible metadata
is_contiguous(other[, tol])	Check whether other is contiguous with self.
item(*args)	Copy an element of an array to a scalar Quantity and return it.
lowpass(frequency[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a Butterworth low-pass filter.
mask([deadtime, flag, query_open_data, ...])	Mask away portions of this TimeSeries that fall within a given list of time segments
max([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
mean([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
median([axis])	Compute the median along the specified axis.
min([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
nansum([axis, out, keepdims, initial, where])
nonzero()	Return the indices of the elements that are non-zero.
notch(frequency[, type, filtfilt])	Notch out a frequency in this TimeSeries.
override_unit(unit[, parse_strict])	Forcefully reset the unit of these data
pad(pad_width, **kwargs)	Pad this series to a new size
partition(kth[, axis, kind, order])	Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array.
plot([method, figsize, xscale])	Plot the data for this timeseries
prepend(other[, inplace, pad, gap, resize])	Connect another series onto the start of the current one.
prod([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
psd([fftlength, overlap, window, method])	Calculate the PSD FrequencySeries for this TimeSeries
put(indices, values[, mode])	Set a.flat[n] = values[n] for all n in indices.
q_gram([qrange, frange, mismatch, snrthresh])	Scan a TimeSeries using the multi-Q transform and return an EventTable of the most significant tiles
q_transform([qrange, frange, gps, search, ...])	Scan a TimeSeries using the multi-Q transform and return an interpolated high-resolution spectrogram
ravel([order])	Return a flattened array.
rayleigh_spectrogram(stride[, fftlength, ...])	Calculate the Rayleigh statistic spectrogram of this TimeSeries
rayleigh_spectrum([fftlength, overlap, window])	Calculate the Rayleigh FrequencySeries for this TimeSeries.
read(source, *args, **kwargs)	Read data into a TimeSeries
repeat(repeats[, axis])	Repeat elements of an array.
resample(rate[, window, ftype, n])	Resample this Series to a new rate
reshape(shape, /, *[, order, copy])	Returns an array containing the same data with a new shape.
resize(new_shape[, refcheck])	Change shape and size of array in-place.
rms([stride])	Calculate the root-mean-square value of this TimeSeries once per stride.
round([decimals, out])	Return a with each element rounded to the given number of decimals.
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
setfield(val, dtype[, offset])	Put a value into a specified place in a field defined by a data-type.
setflags([write, align, uic])	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
shift(delta)	Shift this Series forward on the X-axis by delta
sort([axis, kind, order])	Sort an array in-place.
spectral_variance(stride[, fftlength, ...])	Calculate the SpectralVariance of this TimeSeries.
spectrogram(stride[, fftlength, overlap, ...])	Calculate the average power spectrogram of this TimeSeries using the specified average spectrum method.
spectrogram2(fftlength[, overlap, window])	Calculate the non-averaged power Spectrogram of this TimeSeries
squeeze([axis])	Remove axes of length one from a.
std([axis, dtype, out, ddof, keepdims, where])	Returns the standard deviation of the array elements along given axis.
step(**kwargs)	Create a step plot of this series
sum([axis, dtype, out, keepdims, initial, where])	Return the sum of the array elements over the given axis.
swapaxes(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
take(indices[, axis, out, mode])	Return an array formed from the elements of a at the given indices.
taper([side, duration, nsamples])	Taper the ends of this TimeSeries smoothly to zero.
to(unit[, equivalencies, copy])	Return a new ~astropy.units.Quantity object with the specified unit.
to_lal()	Convert this TimeSeries into a LAL TimeSeries.
to_pycbc([copy])	Convert this TimeSeries into a PyCBC ~pycbc.types.timeseries.TimeSeries
to_string([unit, precision, format, subfmt])	Generate a string representation of the quantity and its unit.
to_value([unit, equivalencies])	The numerical value, possibly in a different unit.
tobytes([order])	Not implemented, use .value.tobytes() instead.
tofile(fid[, sep, format])	Not implemented, use .value.tofile() instead.
tolist()	Return the array as an a.ndim-levels deep nested list of Python scalars.
tostring([order])	Construct Python bytes containing the raw data bytes in the array.
trace([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
transfer_function(other[, fftlength, ...])	Calculate the transfer function between this TimeSeries and another.
transpose(*axes)	Returns a view of the array with axes transposed.
update(other[, inplace])	Update this series by appending new data from an other and dropping the same amount of data off the start.
value_at(x)	Return the value of this Series at the given xindex value
var([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.
view([dtype][, type])	New view of array with the same data.
whiten([fftlength, overlap, method, window, ...])	Whiten this TimeSeries using inverse spectrum truncation
write(target, *args, **kwargs)	Write this TimeSeries to a file
zip()	Zip the xindex and value arrays of this Series
zpk(zeros, poles, gain[, analog])	Filter this TimeSeries by applying a zero-pole-gain filter

dot
ediff1d
info
to_device

class minke.types.Waveform(variance=None, covariance=None, *args, **kwargs)[source]¶

Bases: WaveformBase

Attributes:

T

View of the transposed array.

base

Base object if memory is from some other object.

cgs

Returns a copy of the current Quantity instance with CGS units.

channel

Instrumental channel associated with these data

ctypes

An object to simplify the interaction of the array with the ctypes module.

data

Python buffer object pointing to the start of the array’s data.

device

dt

X-axis sample separation

dtype

Data-type of the array’s elements.

duration

Duration of this series in seconds

dx

X-axis sample separation

epoch

GPS epoch for these data.

equivalencies

A list of equivalencies that will be applied by default during unit conversions.

flags

Information about the memory layout of the array.

flat

A 1-D iterator over the Quantity array.

imag

The imaginary part of the array.

info

isscalar

True if the value of this quantity is a scalar, or False if it is an array-like object.

itemset

itemsize

Length of one array element in bytes.

mT

View of the matrix transposed array.

name

Name for this data set

nbytes

Total bytes consumed by the elements of the array.

ndim

Number of array dimensions.

newbyteorder

ptp

real

The real part of the array.

sample_rate

Data rate for this TimeSeries in samples per second (Hertz).

shape

Tuple of array dimensions.

si

Returns a copy of the current Quantity instance with SI units.

size

Number of elements in the array.

span

X-axis [low, high) segment encompassed by these data

strides

Tuple of bytes to step in each dimension when traversing an array.

t0

X-axis coordinate of the first data point

times

Positions of the data on the x-axis

unit

The physical unit of these data

value

The numerical value of this instance.

x0

X-axis coordinate of the first data point

xindex

Positions of the data on the x-axis

xspan

X-axis [low, high) segment encompassed by these data

xunit

Unit of x-axis index

Methods

DictClass	alias of TimeSeriesDict
abs(x, /[, out, where, casting, order, ...])	Calculate the absolute value element-wise.
align(waveform_b)	Align this waveform with another one by altering the phase.
all([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
any([axis, out, keepdims, where])	Returns True if any of the elements of a evaluate to True.
append(other[, inplace, pad, gap, resize])	Connect another series onto the end of the current one.
argmax([axis, out, keepdims])	Return indices of the maximum values along the given axis.
argmin([axis, out, keepdims])	Return indices of the minimum values along the given axis.
argpartition(kth[, axis, kind, order])	Returns the indices that would partition this array.
argsort([axis, kind, order])	Returns the indices that would sort this array.
asd([fftlength, overlap, window, method])	Calculate the ASD FrequencySeries of this TimeSeries
astype(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
auto_coherence(dt[, fftlength, overlap, window])	Calculate the frequency-coherence between this TimeSeries and a time-shifted copy of itself.
average_fft([fftlength, overlap, window])	Compute the averaged one-dimensional DFT of this TimeSeries.
bandpass(flow, fhigh[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a band-pass filter.
byteswap([inplace])	Swap the bytes of the array elements
choose(choices[, out, mode])	Use an index array to construct a new array from a set of choices.
clip([min, max, out])	Return an array whose values are limited to [min, max].
coherence(other[, fftlength, overlap, window])	Calculate the frequency-coherence between this TimeSeries and another.
coherence_spectrogram(other, stride[, ...])	Calculate the coherence spectrogram between this TimeSeries and other.
compress(condition[, axis, out])	Return selected slices of this array along given axis.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
convolve(fir[, window])	Convolve this TimeSeries with an FIR filter using the
copy([order])	Return a copy of the array.
correlate(mfilter[, window, detrend, ...])	Cross-correlate this TimeSeries with another signal
crop([start, end, copy])	Crop this series to the given x-axis extent.
csd(other[, fftlength, overlap, window])	Calculate the CSD FrequencySeries for two TimeSeries
csd_spectrogram(other, stride[, fftlength, ...])	Calculate the cross spectral density spectrogram of this
cumprod([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
cumsum([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
decompose([bases])	Generates a new Quantity with the units decomposed.
demodulate(f[, stride, exp, deg])	Compute the average magnitude and phase of this TimeSeries once per stride at a given frequency
detrend([detrend])	Remove the trend from this TimeSeries
diagonal([offset, axis1, axis2])	Return specified diagonals.
diff([n, axis])	Calculate the n-th order discrete difference along given axis.
dump(file)	Not implemented, use .value.dump() instead.
dumps()	Returns the pickle of the array as a string.
fetch(channel, start, end[, host, port, ...])	Fetch data from NDS
fetch_open_data(ifo, start, end[, ...])	Fetch open-access data from the LIGO Open Science Center
fft([nfft])	Compute the one-dimensional discrete Fourier transform of this TimeSeries.
fftgram(fftlength[, overlap, window])	Calculate the Fourier-gram of this TimeSeries.
fill(value)	Fill the array with a scalar value.
filter(*filt, **kwargs)	Filter this TimeSeries with an IIR or FIR filter
find(channel, start, end[, frametype, pad, ...])	Find and read data from frames for a channel
find_gates([tzero, whiten, threshold, ...])	Identify points that should be gates using a provided threshold and clustered within a provided time window.
flatten([order])	Return a copy of the array collapsed into one dimension.
from_lal(lalts[, copy])	Generate a new TimeSeries from a LAL TimeSeries of any type.
from_nds2_buffer(buffer_[, scaled, copy])	Construct a new series from an nds2.buffer object
from_pycbc(pycbcseries[, copy])	Convert a pycbc.types.timeseries.TimeSeries into a TimeSeries
gate([tzero, tpad, whiten, threshold, ...])	Removes high amplitude peaks from data using inverse Planck window.
get(channel, start, end[, pad, scaled, ...])	Get data for this channel from frames or NDS
getfield(dtype[, offset])	Returns a field of the given array as a certain type.
heterodyne(phase[, stride, singlesided])	Compute the average magnitude and phase of this TimeSeries once per stride after heterodyning with a given phase series
highpass(frequency[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a high-pass filter.
inject(other)	Add two compatible Series along their shared x-axis values.
insert(obj, values[, axis])	Insert values along the given axis before the given indices and return a new ~astropy.units.Quantity object.
is_compatible(other)	Check whether this series and other have compatible metadata
is_contiguous(other[, tol])	Check whether other is contiguous with self.
item(*args)	Copy an element of an array to a scalar Quantity and return it.
lowpass(frequency[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a Butterworth low-pass filter.
mask([deadtime, flag, query_open_data, ...])	Mask away portions of this TimeSeries that fall within a given list of time segments
max([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
mean([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
median([axis])	Compute the median along the specified axis.
min([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
nansum([axis, out, keepdims, initial, where])
nonzero()	Return the indices of the elements that are non-zero.
notch(frequency[, type, filtfilt])	Notch out a frequency in this TimeSeries.
override_unit(unit[, parse_strict])	Forcefully reset the unit of these data
pad(pad_width, **kwargs)	Pad this series to a new size
partition(kth[, axis, kind, order])	Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array.
plot([method, figsize, xscale])	Plot the data for this timeseries
prepend(other[, inplace, pad, gap, resize])	Connect another series onto the start of the current one.
prod([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
psd([fftlength, overlap, window, method])	Calculate the PSD FrequencySeries for this TimeSeries
put(indices, values[, mode])	Set a.flat[n] = values[n] for all n in indices.
q_gram([qrange, frange, mismatch, snrthresh])	Scan a TimeSeries using the multi-Q transform and return an EventTable of the most significant tiles
q_transform([qrange, frange, gps, search, ...])	Scan a TimeSeries using the multi-Q transform and return an interpolated high-resolution spectrogram
ravel([order])	Return a flattened array.
rayleigh_spectrogram(stride[, fftlength, ...])	Calculate the Rayleigh statistic spectrogram of this TimeSeries
rayleigh_spectrum([fftlength, overlap, window])	Calculate the Rayleigh FrequencySeries for this TimeSeries.
read(source, *args, **kwargs)	Read data into a TimeSeries
repeat(repeats[, axis])	Repeat elements of an array.
resample(rate[, window, ftype, n])	Resample this Series to a new rate
reshape(shape, /, *[, order, copy])	Returns an array containing the same data with a new shape.
resize(new_shape[, refcheck])	Change shape and size of array in-place.
rms([stride])	Calculate the root-mean-square value of this TimeSeries once per stride.
round([decimals, out])	Return a with each element rounded to the given number of decimals.
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
setfield(val, dtype[, offset])	Put a value into a specified place in a field defined by a data-type.
setflags([write, align, uic])	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
shift(delta)	Shift this Series forward on the X-axis by delta
sort([axis, kind, order])	Sort an array in-place.
spectral_variance(stride[, fftlength, ...])	Calculate the SpectralVariance of this TimeSeries.
spectrogram(stride[, fftlength, overlap, ...])	Calculate the average power spectrogram of this TimeSeries using the specified average spectrum method.
spectrogram2(fftlength[, overlap, window])	Calculate the non-averaged power Spectrogram of this TimeSeries
squeeze([axis])	Remove axes of length one from a.
std([axis, dtype, out, ddof, keepdims, where])	Returns the standard deviation of the array elements along given axis.
step(**kwargs)	Create a step plot of this series
sum([axis, dtype, out, keepdims, initial, where])	Return the sum of the array elements over the given axis.
swapaxes(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
take(indices[, axis, out, mode])	Return an array formed from the elements of a at the given indices.
taper([side, duration, nsamples])	Taper the ends of this TimeSeries smoothly to zero.
to(unit[, equivalencies, copy])	Return a new ~astropy.units.Quantity object with the specified unit.
to_lal()	Convert this TimeSeries into a LAL TimeSeries.
to_pycbc([copy])	Convert this TimeSeries into a PyCBC ~pycbc.types.timeseries.TimeSeries
to_string([unit, precision, format, subfmt])	Generate a string representation of the quantity and its unit.
to_value([unit, equivalencies])	The numerical value, possibly in a different unit.
tobytes([order])	Not implemented, use .value.tobytes() instead.
tofile(fid[, sep, format])	Not implemented, use .value.tofile() instead.
tolist()	Return the array as an a.ndim-levels deep nested list of Python scalars.
tostring([order])	Construct Python bytes containing the raw data bytes in the array.
trace([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
transfer_function(other[, fftlength, ...])	Calculate the transfer function between this TimeSeries and another.
transpose(*axes)	Returns a view of the array with axes transposed.
update(other[, inplace])	Update this series by appending new data from an other and dropping the same amount of data off the start.
value_at(x)	Return the value of this Series at the given xindex value
var([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.
view([dtype][, type])	New view of array with the same data.
whiten([fftlength, overlap, method, window, ...])	Whiten this TimeSeries using inverse spectrum truncation
write(target, *args, **kwargs)	Write this TimeSeries to a file
zip()	Zip the xindex and value arrays of this Series
zpk(zeros, poles, gain[, analog])	Filter this TimeSeries by applying a zero-pole-gain filter

dot
ediff1d
info
to_device

align(waveform_b)[source]¶

Align this waveform with another one by altering the phase.

class minke.types.WaveformBase(data, unit=None, t0=None, dt=None, sample_rate=None, times=None, channel=None, name=None, **kwargs)[source]¶

Bases: TimeSeries

Attributes:

T

View of the transposed array.

base

Base object if memory is from some other object.

cgs

Returns a copy of the current Quantity instance with CGS units.

channel

Instrumental channel associated with these data

ctypes

An object to simplify the interaction of the array with the ctypes module.

data

Python buffer object pointing to the start of the array’s data.

device

dt

X-axis sample separation

dtype

Data-type of the array’s elements.

duration

Duration of this series in seconds

dx

X-axis sample separation

epoch

GPS epoch for these data.

equivalencies

A list of equivalencies that will be applied by default during unit conversions.

flags

Information about the memory layout of the array.

flat

A 1-D iterator over the Quantity array.

imag

The imaginary part of the array.

info

isscalar

True if the value of this quantity is a scalar, or False if it is an array-like object.

itemset

itemsize

Length of one array element in bytes.

mT

View of the matrix transposed array.

name

Name for this data set

nbytes

Total bytes consumed by the elements of the array.

ndim

Number of array dimensions.

newbyteorder

ptp

real

The real part of the array.

sample_rate

Data rate for this TimeSeries in samples per second (Hertz).

shape

Tuple of array dimensions.

si

Returns a copy of the current Quantity instance with SI units.

size

Number of elements in the array.

span

X-axis [low, high) segment encompassed by these data

strides

Tuple of bytes to step in each dimension when traversing an array.

t0

X-axis coordinate of the first data point

times

Positions of the data on the x-axis

unit

The physical unit of these data

value

The numerical value of this instance.

x0

X-axis coordinate of the first data point

xindex

Positions of the data on the x-axis

xspan

X-axis [low, high) segment encompassed by these data

xunit

Unit of x-axis index

Methods

DictClass	alias of TimeSeriesDict
abs(x, /[, out, where, casting, order, ...])	Calculate the absolute value element-wise.
all([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
any([axis, out, keepdims, where])	Returns True if any of the elements of a evaluate to True.
append(other[, inplace, pad, gap, resize])	Connect another series onto the end of the current one.
argmax([axis, out, keepdims])	Return indices of the maximum values along the given axis.
argmin([axis, out, keepdims])	Return indices of the minimum values along the given axis.
argpartition(kth[, axis, kind, order])	Returns the indices that would partition this array.
argsort([axis, kind, order])	Returns the indices that would sort this array.
asd([fftlength, overlap, window, method])	Calculate the ASD FrequencySeries of this TimeSeries
astype(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
auto_coherence(dt[, fftlength, overlap, window])	Calculate the frequency-coherence between this TimeSeries and a time-shifted copy of itself.
average_fft([fftlength, overlap, window])	Compute the averaged one-dimensional DFT of this TimeSeries.
bandpass(flow, fhigh[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a band-pass filter.
byteswap([inplace])	Swap the bytes of the array elements
choose(choices[, out, mode])	Use an index array to construct a new array from a set of choices.
clip([min, max, out])	Return an array whose values are limited to [min, max].
coherence(other[, fftlength, overlap, window])	Calculate the frequency-coherence between this TimeSeries and another.
coherence_spectrogram(other, stride[, ...])	Calculate the coherence spectrogram between this TimeSeries and other.
compress(condition[, axis, out])	Return selected slices of this array along given axis.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
convolve(fir[, window])	Convolve this TimeSeries with an FIR filter using the
copy([order])	Return a copy of the array.
correlate(mfilter[, window, detrend, ...])	Cross-correlate this TimeSeries with another signal
crop([start, end, copy])	Crop this series to the given x-axis extent.
csd(other[, fftlength, overlap, window])	Calculate the CSD FrequencySeries for two TimeSeries
csd_spectrogram(other, stride[, fftlength, ...])	Calculate the cross spectral density spectrogram of this
cumprod([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
cumsum([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
decompose([bases])	Generates a new Quantity with the units decomposed.
demodulate(f[, stride, exp, deg])	Compute the average magnitude and phase of this TimeSeries once per stride at a given frequency
detrend([detrend])	Remove the trend from this TimeSeries
diagonal([offset, axis1, axis2])	Return specified diagonals.
diff([n, axis])	Calculate the n-th order discrete difference along given axis.
dump(file)	Not implemented, use .value.dump() instead.
dumps()	Returns the pickle of the array as a string.
fetch(channel, start, end[, host, port, ...])	Fetch data from NDS
fetch_open_data(ifo, start, end[, ...])	Fetch open-access data from the LIGO Open Science Center
fft([nfft])	Compute the one-dimensional discrete Fourier transform of this TimeSeries.
fftgram(fftlength[, overlap, window])	Calculate the Fourier-gram of this TimeSeries.
fill(value)	Fill the array with a scalar value.
filter(*filt, **kwargs)	Filter this TimeSeries with an IIR or FIR filter
find(channel, start, end[, frametype, pad, ...])	Find and read data from frames for a channel
find_gates([tzero, whiten, threshold, ...])	Identify points that should be gates using a provided threshold and clustered within a provided time window.
flatten([order])	Return a copy of the array collapsed into one dimension.
from_lal(lalts[, copy])	Generate a new TimeSeries from a LAL TimeSeries of any type.
from_nds2_buffer(buffer_[, scaled, copy])	Construct a new series from an nds2.buffer object
from_pycbc(pycbcseries[, copy])	Convert a pycbc.types.timeseries.TimeSeries into a TimeSeries
gate([tzero, tpad, whiten, threshold, ...])	Removes high amplitude peaks from data using inverse Planck window.
get(channel, start, end[, pad, scaled, ...])	Get data for this channel from frames or NDS
getfield(dtype[, offset])	Returns a field of the given array as a certain type.
heterodyne(phase[, stride, singlesided])	Compute the average magnitude and phase of this TimeSeries once per stride after heterodyning with a given phase series
highpass(frequency[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a high-pass filter.
inject(other)	Add two compatible Series along their shared x-axis values.
insert(obj, values[, axis])	Insert values along the given axis before the given indices and return a new ~astropy.units.Quantity object.
is_compatible(other)	Check whether this series and other have compatible metadata
is_contiguous(other[, tol])	Check whether other is contiguous with self.
item(*args)	Copy an element of an array to a scalar Quantity and return it.
lowpass(frequency[, gpass, gstop, fstop, ...])	Filter this TimeSeries with a Butterworth low-pass filter.
mask([deadtime, flag, query_open_data, ...])	Mask away portions of this TimeSeries that fall within a given list of time segments
max([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
mean([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
median([axis])	Compute the median along the specified axis.
min([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
nansum([axis, out, keepdims, initial, where])
nonzero()	Return the indices of the elements that are non-zero.
notch(frequency[, type, filtfilt])	Notch out a frequency in this TimeSeries.
override_unit(unit[, parse_strict])	Forcefully reset the unit of these data
pad(pad_width, **kwargs)	Pad this series to a new size
partition(kth[, axis, kind, order])	Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array.
plot([method, figsize, xscale])	Plot the data for this timeseries
prepend(other[, inplace, pad, gap, resize])	Connect another series onto the start of the current one.
prod([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
psd([fftlength, overlap, window, method])	Calculate the PSD FrequencySeries for this TimeSeries
put(indices, values[, mode])	Set a.flat[n] = values[n] for all n in indices.
q_gram([qrange, frange, mismatch, snrthresh])	Scan a TimeSeries using the multi-Q transform and return an EventTable of the most significant tiles
q_transform([qrange, frange, gps, search, ...])	Scan a TimeSeries using the multi-Q transform and return an interpolated high-resolution spectrogram
ravel([order])	Return a flattened array.
rayleigh_spectrogram(stride[, fftlength, ...])	Calculate the Rayleigh statistic spectrogram of this TimeSeries
rayleigh_spectrum([fftlength, overlap, window])	Calculate the Rayleigh FrequencySeries for this TimeSeries.
read(source, *args, **kwargs)	Read data into a TimeSeries
repeat(repeats[, axis])	Repeat elements of an array.
resample(rate[, window, ftype, n])	Resample this Series to a new rate
reshape(shape, /, *[, order, copy])	Returns an array containing the same data with a new shape.
resize(new_shape[, refcheck])	Change shape and size of array in-place.
rms([stride])	Calculate the root-mean-square value of this TimeSeries once per stride.
round([decimals, out])	Return a with each element rounded to the given number of decimals.
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
setfield(val, dtype[, offset])	Put a value into a specified place in a field defined by a data-type.
setflags([write, align, uic])	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
shift(delta)	Shift this Series forward on the X-axis by delta
sort([axis, kind, order])	Sort an array in-place.
spectral_variance(stride[, fftlength, ...])	Calculate the SpectralVariance of this TimeSeries.
spectrogram(stride[, fftlength, overlap, ...])	Calculate the average power spectrogram of this TimeSeries using the specified average spectrum method.
spectrogram2(fftlength[, overlap, window])	Calculate the non-averaged power Spectrogram of this TimeSeries
squeeze([axis])	Remove axes of length one from a.
std([axis, dtype, out, ddof, keepdims, where])	Returns the standard deviation of the array elements along given axis.
step(**kwargs)	Create a step plot of this series
sum([axis, dtype, out, keepdims, initial, where])	Return the sum of the array elements over the given axis.
swapaxes(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
take(indices[, axis, out, mode])	Return an array formed from the elements of a at the given indices.
taper([side, duration, nsamples])	Taper the ends of this TimeSeries smoothly to zero.
to(unit[, equivalencies, copy])	Return a new ~astropy.units.Quantity object with the specified unit.
to_lal()	Convert this TimeSeries into a LAL TimeSeries.
to_pycbc([copy])	Convert this TimeSeries into a PyCBC ~pycbc.types.timeseries.TimeSeries
to_string([unit, precision, format, subfmt])	Generate a string representation of the quantity and its unit.
to_value([unit, equivalencies])	The numerical value, possibly in a different unit.
tobytes([order])	Not implemented, use .value.tobytes() instead.
tofile(fid[, sep, format])	Not implemented, use .value.tofile() instead.
tolist()	Return the array as an a.ndim-levels deep nested list of Python scalars.
tostring([order])	Construct Python bytes containing the raw data bytes in the array.
trace([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
transfer_function(other[, fftlength, ...])	Calculate the transfer function between this TimeSeries and another.
transpose(*axes)	Returns a view of the array with axes transposed.
update(other[, inplace])	Update this series by appending new data from an other and dropping the same amount of data off the start.
value_at(x)	Return the value of this Series at the given xindex value
var([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.
view([dtype][, type])	New view of array with the same data.
whiten([fftlength, overlap, method, window, ...])	Whiten this TimeSeries using inverse spectrum truncation
write(target, *args, **kwargs)	Write this TimeSeries to a file
zip()	Zip the xindex and value arrays of this Series
zpk(zeros, poles, gain[, analog])	Filter this TimeSeries by applying a zero-pole-gain filter

dot
ediff1d
info
to_device

class minke.types.WaveformDict(parameters=None, **kwargs)[source]¶

Bases: object

Attributes:

hrss

parameters

Methods

project(detector[, ra, dec, psi, time, ...])

Project this waveform onto a detector.

property hrss¶

property parameters¶

project(detector, ra=None, dec=None, psi=None, time=None, iota=None, phi_0=None, **kwargs)[source]¶

Project this waveform onto a detector.

Parameters:

detectorheron.detectors.Detector

The detector onto which the waveform should be projected.

rafloat, optional

The roght ascension of the signal source

decfloat, optional

The declination of the signal source.

class minke.types.WaveformManifold[source]¶

Bases: object

Store a manifold of different waveform points.

Methods

add_waveform
array
plot

add_waveform(waveforms: WaveformDict)[source]¶

array(component='plus', parameter='m1')[source]¶

plot(component='plus', parameter='m1')[source]¶

minke.utils module¶

Various utilities and non-GPR based stuff.

minke.utils.load_yaml(filename)[source]¶

Module contents¶