StateTimeSeries

class gwpy.timeseries.StateTimeSeries[source]

Bases: gwpy.timeseries.core.TimeSeriesBase

Boolean array representing a good/bad state determination

Parameters:

value : array-like

input data array

t0 : LIGOTimeGPS, float, str, optional

GPS epoch associated with these data, any input parsable by to_gps is fine

dt : float, Quantity, optional, default: 1

time between successive samples (seconds), can also be given inversely via sample_rate

sample_rate : float, Quantity, optional, default: 1

the rate of samples per second (Hertz), can also be given inversely via dt

times : array-like

the complete array of GPS times accompanying the data for this series. This argument takes precedence over t0 and dt so should be given in place of these if relevant, not alongside

name : str, optional

descriptive title for this array

channel : Channel, str, optional

source data stream for these data

dtype : dtype, optional

input data type

copy : bool, optional, default: False

choose to copy the input data to new memory

subok : bool, optional, default: True

allow passing of sub-classes by the array generator

Notes

Key methods

to_dqflag([name, minlen, dtype, round, …]) Convert this series into a DataQualityFlag

Methods Summary

abs(x, /[, out, where, casting, order, …]) Calculate the absolute value element-wise.
all([axis, out, keepdims]) Returns True if all elements evaluate to True.
any([axis, out, keepdims]) Returns True if any of the elements of a evaluate to True.
append(other[, gap, inplace, pad, resize]) Connect another series onto the end of the current one.
argmax([axis, out]) Return indices of the maximum values along the given axis.
argmin([axis, out]) Return indices of the minimum values along the given axis of a.
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.
dot(b[, out]) Dot product of two arrays.
dump(file) Dump a pickle of the array to the specified file.
dumps() Returns the pickle of the array as a string.
ediff1d([to_end, to_begin])
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
fill(value) Fill the array with a scalar value.
find(channel, start, end[, frametype, pad, …]) Find and read data from frames for a channel
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_, **metadata) Construct a new TimeSeries from an nds2.buffer object
from_pycbc(pycbcseries[, copy]) Convert a pycbc.types.timeseries.TimeSeries into a TimeSeries
get(channel, start, end[, pad, dtype, …]) Get data for this channel from frames or NDS
getfield(dtype[, offset]) Returns a field of the given array as a certain type.
insert(obj, values[, axis]) Insert values along the given axis before the given indices and return a new 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 standard Python scalar and return it.
itemset(*args) Insert scalar into an array (scalar is cast to array’s dtype, if possible)
max([axis, out]) Return the maximum along a given axis.
mean([axis, dtype, out, keepdims]) Returns the average of the array elements along given axis.
median([axis]) Compute the median along the specified axis.
min([axis, out, keepdims]) Return the minimum along a given axis.
nansum([axis, out, keepdims])
newbyteorder([new_order]) Return the array with the same data viewed with a different byte order.
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]) Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array.
plot(**kwargs) Plot the data for this timeseries
prepend(other[, gap, inplace, pad, resize]) Connect another series onto the start of the current one.
prod([axis, dtype, out, keepdims]) Return the product of the array elements over the given axis
ptp([axis, out]) Peak to peak (maximum - minimum) value along a 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 TimeSeries
repeat(repeats[, axis]) Repeat elements of an array.
reshape(shape[, order]) 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, and UPDATEIFCOPY, respectively.
shift(delta) Shift this TimeSeries forward in time by delta
sort([axis, kind, order]) Sort an array, in-place.
squeeze([axis]) Remove single-dimensional entries from the shape of a.
std([axis, dtype, out, ddof, keepdims]) Returns the standard deviation of the array elements along given axis.
sum([axis, dtype, out, keepdims]) 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]) Return a new Quantity object with the specified unit.
to_dqflag([name, minlen, dtype, round, …]) Convert this series into a DataQualityFlag
to_lal(*args, **kwargs) Bogus function inherited from superclass, do not use.
to_pycbc([copy]) Convert this TimeSeries into a PyCBC
to_value([unit, equivalencies]) The numerical value, possibly in a different unit.
tobytes([order]) Construct Python bytes containing the raw data bytes in the array.
tofile(fid[, sep, format]) Write array to a file as text or binary (default).
tolist() Return the array as a (possibly nested) list.
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]) 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 TimeSeries to a file
zip() Zip the xindex and value arrays of this Series

Methods Documentation

abs(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])[source]

Calculate the absolute value element-wise.

Parameters:

x : array_like

Input array.

out : ndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

where : array_like, optional

Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

**kwargs

For other keyword-only arguments, see the ufunc docs.
Returns:

absolute : ndarray

An ndarray containing the absolute value of each element in x. For complex input, a + ib, the absolute value is

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.

Examples

>>> x = np.array([-1.2, 1.2])
>>> np.absolute(x)
array([ 1.2,  1.2])
>>> np.absolute(1.2 + 1j)
1.5620499351813308

Plot the function over [-10, 10]:

>>> import matplotlib.pyplot as plt

>>> x = np.linspace(start=-10, stop=10, num=101)
>>> plt.plot(x, np.absolute(x))
>>> plt.show()

(png)

../_images/gwpy-timeseries-StateTimeSeries-1_00_00.png

Plot the function over the complex plane:

>>> xx = x + 1j * x[:, np.newaxis]
>>> plt.imshow(np.abs(xx), extent=[-10, 10, -10, 10], cmap='gray')
>>> plt.show()

(png)

../_images/gwpy-timeseries-StateTimeSeries-1_01_00.png
all(axis=None, out=None, keepdims=False)

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

See also

numpy.all
equivalent function
any(axis=None, out=None, keepdims=False)

Returns True if any of the elements of a evaluate to True.

Refer to numpy.any for full documentation.

See also

numpy.any
equivalent function
append(other, gap='raise', inplace=True, pad=0, resize=True)[source]

Connect another series onto the end of the current one.

Parameters:

other : Series

another series of the same type to connect to this one

gap : str, optional, default: 'raise'

action to perform if there’s a gap between the other series and this one. One of

  • 'raise' - raise an Exception
    • 'ignore' - remove gap and join data
  • 'pad' - pad gap with zeros

inplace : bool, optional, default: True

perform operation in-place, modifying current Series, otherwise copy data and return new Series

Warning

inplace append bypasses the reference check in numpy.ndarray.resize, so be carefully to only use this for arrays that haven’t been sharing their memory!

pad : float, optional, default: 0.0

value with which to pad discontiguous series

resize : bool, optional, default: True

resize this array to accommodate new data, otherwise shift the old data to the left (potentially falling off the start) and put the new data in at the end
Returns:

series : Series

a new series containing joined data sets
argmax(axis=None, out=None)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

See also

numpy.argmax
equivalent function
argmin(axis=None, out=None)

Return indices of the minimum values along the given axis of a.

Refer to numpy.argmin for detailed documentation.

See also

numpy.argmin
equivalent function
argpartition(kth, axis=-1, kind='introselect', order=None)

Returns the indices that would partition this array.

Refer to numpy.argpartition for full documentation.

New in version 1.8.0.

See also

numpy.argpartition
equivalent function
argsort(axis=-1, kind='quicksort', order=None)

Returns the indices that would sort this array.

Refer to numpy.argsort for full documentation.

See also

numpy.argsort
equivalent function
astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array, cast to a specified type.

Parameters:

dtype : str or dtype

Typecode or data-type to which the array is cast.

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional

Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

casting : {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.

  • ‘no’ means the data types should not be cast at all.
  • ‘equiv’ means only byte-order changes are allowed.
  • ‘safe’ means only casts which can preserve values are allowed.
  • ‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.
  • ‘unsafe’ means any data conversions may be done.

subok : bool, optional

If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.

copy : bool, optional

By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.
Returns:

arr_t : ndarray

Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.
Raises:

ComplexWarning

When casting from complex to float or int. To avoid this, one should use a.real.astype(t).

Notes

Starting in NumPy 1.9, astype method now returns an error if the string dtype to cast to is not long enough in ‘safe’ casting mode to hold the max value of integer/float array that is being casted. Previously the casting was allowed even if the result was truncated.

Examples

>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. ,  2. ,  2.5])

>>> x.astype(int)
array([1, 2, 2])
byteswap(inplace)

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place.

Parameters:

inplace : bool, optional

If True, swap bytes in-place, default is False.
Returns:

out : ndarray

The byteswapped array. If inplace is True, this is a view to self.

Examples

>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(True)
array([  256,     1, 13090], dtype=int16)
>>> map(hex, A)
['0x100', '0x1', '0x3322']

Arrays of strings are not swapped

>>> A = np.array(['ceg', 'fac'])
>>> A.byteswap()
array(['ceg', 'fac'],
      dtype='|S3')
choose(choices, out=None, mode='raise')

Use an index array to construct a new array from a set of choices.

Refer to numpy.choose for full documentation.

See also

numpy.choose
equivalent function
clip(min=None, max=None, out=None)

Return an array whose values are limited to [min, max]. One of max or min must be given.

Refer to numpy.clip for full documentation.

See also

numpy.clip
equivalent function
compress(condition, axis=None, out=None)

Return selected slices of this array along given axis.

Refer to numpy.compress for full documentation.

See also

numpy.compress
equivalent function
conj()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate
equivalent function
conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate
equivalent function
copy(order='C')[source]

Return a copy of the array.

Parameters:

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional

Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.)

See also

numpy.copy, numpy.copyto

Examples

>>> x = np.array([[1,2,3],[4,5,6]], order='F')

>>> y = x.copy()

>>> x.fill(0)

>>> x
array([[0, 0, 0],
       [0, 0, 0]])

>>> y
array([[1, 2, 3],
       [4, 5, 6]])

>>> y.flags['C_CONTIGUOUS']
True
crop(start=None, end=None, copy=False)[source]

Crop this series to the given x-axis extent.

Parameters:

start : float, optional

lower limit of x-axis to crop to, defaults to current x0

end : float, optional

upper limit of x-axis to crop to, defaults to current series end

copy : bool, optional, default: False

copy the input data to fresh memory, otherwise return a view
Returns:

series : Series

A new series with a sub-set of the input data

Notes

If either start or end are outside of the original Series span, warnings will be printed and the limits will be restricted to the xspan

cumprod(axis=None, dtype=None, out=None)

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

See also

numpy.cumprod
equivalent function
cumsum(axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

See also

numpy.cumsum
equivalent function
decompose(bases=[])

Generates a new Quantity with the units decomposed. Decomposed units have only irreducible units in them (see astropy.units.UnitBase.decompose).

Parameters:

bases : sequence of UnitBase, optional

The bases to decompose into. When not provided, decomposes down to any irreducible units. When provided, the decomposed result will only contain the given units. This will raises a UnitsError if it’s not possible to do so.
Returns:

newq : Quantity

A new object equal to this quantity with units decomposed.
diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

See also

numpy.diagonal
equivalent function
diff(n=1, axis=-1)[source]

Calculate the n-th order discrete difference along given axis.

The first order difference is given by out[n] = a[n+1] - a[n] along the given axis, higher order differences are calculated by using diff recursively.

Parameters:

n : int, optional

The number of times values are differenced.

axis : int, optional

The axis along which the difference is taken, default is the last axis.
Returns:

diff : Series

The n order differences. The shape of the output is the same as the input, except along axis where the dimension is smaller by n.

See also

numpy.diff
for documentation on the underlying method
dot(b, out=None)

Dot product of two arrays.

Refer to numpy.dot for full documentation.

See also

numpy.dot
equivalent function

Examples

>>> a = np.eye(2)
>>> b = np.ones((2, 2)) * 2
>>> a.dot(b)
array([[ 2.,  2.],
       [ 2.,  2.]])

This array method can be conveniently chained:

>>> a.dot(b).dot(b)
array([[ 8.,  8.],
       [ 8.,  8.]])
dump(file)

Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.

Parameters:

file : str

A string naming the dump file.
dumps()[source]

Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.

Parameters:
None
ediff1d(to_end=None, to_begin=None)
fetch(channel, start, end, host=None, port=None, verbose=False, connection=None, verify=False, pad=None, allow_tape=None, type=None, dtype=None)[source]

Fetch data from NDS

Parameters:

channel : str, Channel

the data channel for which to query

start : LIGOTimeGPS, float, str

GPS start time of required data, any input parseable by to_gps is fine

end : LIGOTimeGPS, float, str

GPS end time of required data, any input parseable by to_gps is fine

host : str, optional

URL of NDS server to use, if blank will try any server (in a relatively sensible order) to get the data

port : int, optional

port number for NDS server query, must be given with host

verify : bool, optional, default: False

check channels exist in database before asking for data

connection : nds2.connection, optional

open NDS connection to use

verbose : bool, optional

print verbose output about NDS progress, useful for debugging

type : int, optional

NDS2 channel type integer

dtype : type, numpy.dtype, str, optional

identifier for desired output data type
fetch_open_data(ifo, start, end, sample_rate=4096, tag=None, version=None, format=None, host='https://losc.ligo.org', verbose=False, cache=None, **kwargs)[source]

Fetch open-access data from the LIGO Open Science Center

Parameters:

ifo : str

the two-character prefix of the IFO in which you are interested, e.g. 'L1'

start : LIGOTimeGPS, float, str, optional

GPS start time of required data, defaults to start of data found; any input parseable by to_gps is fine

end : LIGOTimeGPS, float, str, optional

GPS end time of required data, defaults to end of data found; any input parseable by to_gps is fine

sample_rate : float, optional,

the sample rate of desired data; most data are stored by LOSC at 4096 Hz, however there may be event-related data releases with a 16384 Hz rate, default: 4096

tag : str, optional

file tag, e.g. 'CLN' to select cleaned data, or 'C00' for ‘raw’ calibrated data.

version : int, optional

version of files to download, defaults to highest discovered version

format : str, optional

the data format to download and parse, defaults to the most efficient option based on third-party libraries available; one of:

host : str, optional

HTTP host name of LOSC server to access

verbose : bool, optional, default: False

print verbose output while fetching data

cache : bool, optional

save/read a local copy of the remote URL, default: False; useful if the same remote data are to be accessed multiple times. Set GWPY_CACHE=1 in the environment to auto-cache.

**kwargs

any other keyword arguments are passed to the TimeSeries.read method that parses the file that was downloaded

Notes

StateVector data are not available in txt.gz format.

Examples

>>> from gwpy.timeseries import (TimeSeries, StateVector)
>>> print(TimeSeries.fetch_open_data('H1', 1126259446, 1126259478))
TimeSeries([  2.17704028e-19,  2.08763900e-19,  2.39681183e-19,
            ...,   3.55365541e-20,  6.33533516e-20,
              7.58121195e-20]
           unit: Unit(dimensionless),
           t0: 1126259446.0 s,
           dt: 0.000244140625 s,
           name: Strain,
           channel: None)
>>> print(StateVector.fetch_open_data('H1', 1126259446, 1126259478))
StateVector([127,127,127,127,127,127,127,127,127,127,127,127,
             127,127,127,127,127,127,127,127,127,127,127,127,
             127,127,127,127,127,127,127,127]
            unit: Unit(dimensionless),
            t0: 1126259446.0 s,
            dt: 1.0 s,
            name: Data quality,
            channel: None,
            bits: Bits(0: data present
                       1: passes cbc CAT1 test
                       2: passes cbc CAT2 test
                       3: passes cbc CAT3 test
                       4: passes burst CAT1 test
                       5: passes burst CAT2 test
                       6: passes burst CAT3 test,
                       channel=None,
                       epoch=1126259446.0))

For the StateVector, the naming of the bits will be format-dependent, because they are recorded differently by LOSC in different formats.

For events published in O2 and later, LOSC typically provides multiple data sets containing the original ('C00') and cleaned ('CLN') data. To select both data sets and plot a comparison, for example:

>>> orig = TimeSeries.fetch_open_data('H1', 1187008870, 1187008896,
...                                   tag='C00')
>>> cln = TimeSeries.fetch_open_data('H1', 1187008870, 1187008896,
...                                  tag='CLN')
>>> origasd = orig.asd(fftlength=4, overlap=2)
>>> clnasd = cln.asd(fftlength=4, overlap=2)
>>> plot = origasd.plot(label='Un-cleaned')
>>> ax = plot.gca()
>>> ax.plot(clnasd, label='Cleaned')
>>> ax.set_xlim(10, 1400)
>>> ax.set_ylim(1e-24, 1e-20)
>>> ax.legend()
>>> plot.show()

(png)

../_images/gwpy-timeseries-StateTimeSeries-2.png
fill(value)

Fill the array with a scalar value.

Parameters:

value : scalar

All elements of a will be assigned this value.

Examples

>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([ 1.,  1.])
find(channel, start, end, frametype=None, pad=None, dtype=None, nproc=1, verbose=False, **readargs)[source]

Find and read data from frames for a channel

Parameters:

channel : str, Channel

the name of the channel to read, or a Channel object.

start : LIGOTimeGPS, float, str

GPS start time of required data, any input parseable by to_gps is fine

end : LIGOTimeGPS, float, str

GPS end time of required data, any input parseable by to_gps is fine

frametype : str, optional

name of frametype in which this channel is stored, will search for containing frame types if necessary

pad : float, optional

value with which to fill gaps in the source data, only used if gap is not given, or gap='pad' is given

nproc : int, optional, default: 1

number of parallel processes to use, serial process by default.

dtype : numpy.dtype, str, type, or dict

numeric data type for returned data, e.g. numpy.float, or dict of (channel, dtype) pairs

allow_tape : bool, optional, default: True

allow reading from frame files on (slow) magnetic tape

verbose : bool, optional

print verbose output about NDS progress.

**readargs

any other keyword arguments to be passed to read()
flatten(order='C')

Return a copy of the array collapsed into one dimension.

Parameters:

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional

‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.
Returns:

y : ndarray

A copy of the input array, flattened to one dimension.

See also

ravel
Return a flattened array.
flat
A 1-D flat iterator over the array.

Examples

>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
from_lal(lalts, copy=True)[source]

Generate a new TimeSeries from a LAL TimeSeries of any type.

from_nds2_buffer(buffer_, **metadata)[source]

Construct a new TimeSeries from an nds2.buffer object

Parameters:

buffer_ : nds2.buffer

the input NDS2-client buffer to read

**metadata

any other metadata keyword arguments to pass to the TimeSeries constructor
Returns:

timeseries : TimeSeries

a new TimeSeries containing the data from the nds2.buffer, and the appropriate metadata

Notes

This classmethod requires the nds2-client package

from_pycbc(pycbcseries, copy=True)[source]

Convert a pycbc.types.timeseries.TimeSeries into a TimeSeries

Parameters:

pycbcseries : pycbc.types.timeseries.TimeSeries

the input PyCBC TimeSeries array

copy : bool, optional, default: True

if True, copy these data to a new array
Returns:

timeseries : TimeSeries

a GWpy version of the input timeseries
get(channel, start, end, pad=None, dtype=None, verbose=False, allow_tape=None, **kwargs)[source]

Get data for this channel from frames or NDS

This method dynamically accesses either frames on disk, or a remote NDS2 server to find and return data for the given interval

Parameters:

channel : str, Channel

the name of the channel to read, or a Channel object.

start : LIGOTimeGPS, float, str

GPS start time of required data, any input parseable by to_gps is fine

end : LIGOTimeGPS, float, str

GPS end time of required data, any input parseable by to_gps is fine

pad : float, optional

value with which to fill gaps in the source data, default to ‘don’t fill gaps’

dtype : numpy.dtype, str, type, or dict

numeric data type for returned data, e.g. numpy.float, or dict of (channel, dtype) pairs

nproc : int, optional, default: 1

number of parallel processes to use, serial process by default.

allow_tape : bool, optional, default: None

allow the use of frames that are held on tape, default is None to attempt to allow the TimeSeries.fetch method to intelligently select a server that doesn’t use tapes for data storage (doesn’t always work), but to eventually allow retrieving data from tape if required

verbose : bool, optional

print verbose output about NDS progress.

**kwargs

other keyword arguments to pass to either find() (for direct GWF file access) or fetch() for remote NDS2 access

See also

TimeSeries.fetch
for grabbing data from a remote NDS2 server
TimeSeries.find
for discovering and reading data from local GWF files
getfield(dtype, offset=0)

Returns a field of the given array as a certain type.

A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.

Parameters:

dtype : str or dtype

The data type of the view. The dtype size of the view can not be larger than that of the array itself.

offset : int

Number of bytes to skip before beginning the element view.

Examples

>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[ 1.+1.j,  0.+0.j],
       [ 0.+0.j,  2.+4.j]])
>>> x.getfield(np.float64)
array([[ 1.,  0.],
       [ 0.,  2.]])

By choosing an offset of 8 bytes we can select the complex part of the array for our view:

>>> x.getfield(np.float64, offset=8)
array([[ 1.,  0.],
   [ 0.,  4.]])
insert(obj, values, axis=None)

Insert values along the given axis before the given indices and return a new Quantity object.

This is a thin wrapper around the numpy.insert function.

Parameters:

obj : int, slice or sequence of ints

Object that defines the index or indices before which values is inserted.

values : array-like

Values to insert. If the type of values is different from that of quantity, values is converted to the matching type. values should be shaped so that it can be broadcast appropriately The unit of values must be consistent with this quantity.

axis : int, optional

Axis along which to insert values. If axis is None then the quantity array is flattened before insertion.
Returns:

out : Quantity

A copy of quantity with values inserted. Note that the insertion does not occur in-place: a new quantity array is returned.

Examples

>>> import astropy.units as u
>>> q = [1, 2] * u.m
>>> q.insert(0, 50 * u.cm)
<Quantity [ 0.5,  1.,  2.] m>

>>> q = [[1, 2], [3, 4]] * u.m
>>> q.insert(1, [10, 20] * u.m, axis=0)
<Quantity [[  1.,  2.],
           [ 10., 20.],
           [  3.,  4.]] m>

>>> q.insert(1, 10 * u.m, axis=1)
<Quantity [[  1., 10.,  2.],
           [  3., 10.,  4.]] m>
is_compatible(other)[source]

Check whether this series and other have compatible metadata

This method tests that the sample size, and the unit match.

is_contiguous(other, tol=3.814697265625e-06)[source]

Check whether other is contiguous with self.

Parameters:

other : Series, numpy.ndarray

another series of the same type to test for contiguity

tol : float, optional

the numerical tolerance of the test
Returns:

1

if other is contiguous with this series, i.e. would attach seamlessly onto the end

-1

if other is anti-contiguous with this seires, i.e. would attach seamlessly onto the start

0

if other is completely dis-contiguous with thie series

Notes

if a raw numpy.ndarray is passed as other, with no metadata, then the contiguity check will always pass

item(*args)

Copy an element of an array to a standard Python scalar and return it.

Parameters:

*args : Arguments (variable number and type)

  • none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.
  • int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.
  • tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.
Returns:

z : Standard Python scalar object

A copy of the specified element of the array as a suitable Python scalar

Notes

When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.

item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.

Examples

>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
       [2, 8, 3],
       [8, 5, 3]])
>>> x.item(3)
2
>>> x.item(7)
5
>>> x.item((0, 1))
1
>>> x.item((2, 2))
3
itemset(*args)

Insert scalar into an array (scalar is cast to array’s dtype, if possible)

There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a.

Parameters:

*args : Arguments

If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple.

Notes

Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute look-up at each loop iteration.

Examples

>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
       [2, 8, 3],
       [8, 5, 3]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[3, 1, 7],
       [2, 0, 3],
       [8, 5, 9]])
max(axis=None, out=None)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

See also

numpy.amax
equivalent function
mean(axis=None, dtype=None, out=None, keepdims=False)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

See also

numpy.mean
equivalent function
median(axis=None, **kwargs)[source]

Compute the median along the specified axis.

Returns the median of the array elements.

Parameters:

a : array_like

Input array or object that can be converted to an array.

axis : {int, sequence of int, None}, optional

Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

overwrite_input : bool, optional

If True, then allow use of memory of input array a for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. If overwrite_input is True and a is not already an ndarray, an error will be raised.

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.

New in version 1.9.0.
Returns:

median : ndarray

A new array holding the result. If the input contains integers or floats smaller than float64, then the output data-type is np.float64. Otherwise, the data-type of the output is the same as that of the input. If out is specified, that array is returned instead.

See also

mean, percentile

Notes

Given a vector V of length N, the median of V is the middle value of a sorted copy of V, V_sorted - i e., V_sorted[(N-1)/2], when N is odd, and the average of the two middle values of V_sorted when N is even.

Examples

>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> np.median(a)
3.5
>>> np.median(a, axis=0)
array([ 6.5,  4.5,  2.5])
>>> np.median(a, axis=1)
array([ 7.,  2.])
>>> m = np.median(a, axis=0)
>>> out = np.zeros_like(m)
>>> np.median(a, axis=0, out=m)
array([ 6.5,  4.5,  2.5])
>>> m
array([ 6.5,  4.5,  2.5])
>>> b = a.copy()
>>> np.median(b, axis=1, overwrite_input=True)
array([ 7.,  2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.median(b, axis=None, overwrite_input=True)
3.5
>>> assert not np.all(a==b)
min(axis=None, out=None, keepdims=False)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

See also

numpy.amin
equivalent function
nansum(axis=None, out=None, keepdims=False)
newbyteorder(new_order='S')

Return the array with the same data viewed with a different byte order.

Equivalent to:

arr.view(arr.dtype.newbytorder(new_order))

Changes are also made in all fields and sub-arrays of the array data type.

Parameters:

new_order : string, optional

Byte order to force; a value from the byte order specifications below. new_order codes can be any of:

  • ‘S’ - swap dtype from current to opposite endian
  • {‘<’, ‘L’} - little endian
  • {‘>’, ‘B’} - big endian
  • {‘=’, ‘N’} - native order
  • {‘|’, ‘I’} - ignore (no change to byte order)

The default value (‘S’) results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of ‘B’ or ‘b’ or ‘biggish’ are valid to specify big-endian.
Returns:

new_arr : array

New array object with the dtype reflecting given change to the byte order.
nonzero()

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

See also

numpy.nonzero
equivalent function
override_unit(unit, parse_strict='raise')[source]

Forcefully reset the unit of these data

Use of this method is discouraged in favour of to(), which performs accurate conversions from one unit to another. The method should really only be used when the original unit of the array is plain wrong.

Parameters:

unit : Unit, str

the unit to force onto this array

parse_strict : str, optional

how to handle errors in the unit parsing, default is to raise the underlying exception from astropy.units
Raises:

ValueError

if a str cannot be parsed as a valid unit
pad(pad_width, **kwargs)[source]

Pad this series to a new size

Parameters:

pad_width : int, pair of ints

number of samples by which to pad each end of the array. Single int to pad both ends by the same amount, or (before, after) tuple to give uneven padding

**kwargs

see numpy.pad() for kwarg documentation
Returns:

series : Series

the padded version of the input

See also

numpy.pad
for details on the underlying functionality
partition(kth, axis=-1, kind='introselect', order=None)

Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.

New in version 1.8.0.

Parameters:

kth : int or sequence of ints

Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

axis : int, optional

Axis along which to sort. Default is -1, which means sort along the last axis.

kind : {‘introselect’}, optional

Selection algorithm. Default is ‘introselect’.

order : str or list of str, optional

When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

See also

numpy.partition
Return a parititioned copy of an array.
argpartition
Indirect partition.
sort
Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])

>>> a.partition((1, 3))
array([1, 2, 3, 4])
plot(**kwargs)[source]

Plot the data for this timeseries

All keywords are passed to TimeSeriesPlot

Returns:

plot : TimeSeriesPlot

the newly created figure, with populated Axes.

See also

matplotlib.pyplot.figure
for documentation of keyword arguments used to create the figure
matplotlib.figure.Figure.add_subplot
for documentation of keyword arguments used to create the axes
matplotlib.axes.Axes.plot
for documentation of keyword arguments used in rendering the data
prepend(other, gap='raise', inplace=True, pad=0, resize=True)[source]

Connect another series onto the start of the current one.

Parameters:

other : Series

another series of the same type as this one

gap : str, optional, default: 'raise'

action to perform if there’s a gap between the other series and this one. One of

  • 'raise' - raise an Exception
  • 'ignore' - remove gap and join data
  • 'pad' - pad gap with zeros

inplace : bool, optional, default: True

perform operation in-place, modifying current series, otherwise copy data and return new series

Warning

inplace prepend bypasses the reference check in numpy.ndarray.resize, so be carefully to only use this for arrays that haven’t been sharing their memory!

pad : float, optional, default: 0.0

value with which to pad discontiguous Series

resize : bool, optional, default: True
Returns:

series : TimeSeries

time-series containing joined data sets
prod(axis=None, dtype=None, out=None, keepdims=False)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

See also

numpy.prod
equivalent function
ptp(axis=None, out=None)

Peak to peak (maximum - minimum) value along a given axis.

Refer to numpy.ptp for full documentation.

See also

numpy.ptp
equivalent function
put(indices, values, mode='raise')

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

See also

numpy.put
equivalent function
ravel([order])

Return a flattened array.

Refer to numpy.ravel for full documentation.

See also

numpy.ravel
equivalent function
ndarray.flat
a flat iterator on the array.
read(source, *args, **kwargs)[source]

Read data into a TimeSeries

Arguments and keywords depend on the output format, see the online documentation for full details for each format, the parameters below are common to most formats.

Parameters:

source : str, Cache

source of data, any of the following:

  • str path of single data file
  • str path of LAL-format cache file
  • Cache describing one or more data files,

name : str, Channel

the name of the channel to read, or a Channel object.

start : LIGOTimeGPS, float, str, optional

GPS start time of required data, defaults to start of data found; any input parseable by to_gps is fine

end : LIGOTimeGPS, float, str, optional

GPS end time of required data, defaults to end of data found; any input parseable by to_gps is fine

format : str, optional

source format identifier. If not given, the format will be detected if possible. See below for list of acceptable formats.

nproc : int, optional

number of parallel processes to use, serial process by default.

Note

Parallel frame reading, via the nproc keyword argument, is only available when giving a Cache of frames, or using the format='cache' keyword argument.

gap : str, optional

how to handle gaps in the cache, one of

  • ‘ignore’: do nothing, let the undelying reader method handle it
  • ‘warn’: do nothing except print a warning to the screen
  • ‘raise’: raise an exception upon finding a gap (default)
  • ‘pad’: insert a value to fill the gaps

pad : float, optional

value with which to fill gaps in the source data, only used if gap is not given, or gap='pad' is given

Notes

The available built-in formats are:

Format Read Write Auto-identify Deprecated
ascii.losc Yes No No  
csv Yes Yes Yes  
framecpp Yes Yes No  
gwf Yes Yes Yes  
gwf.framecpp Yes Yes No  
gwf.lalframe Yes Yes No  
hdf5 Yes Yes Yes  
hdf5.losc Yes No No  
lalframe Yes Yes No  
txt Yes Yes Yes  
wav Yes No No  
losc Yes No No Yes

Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.

repeat(repeats, axis=None)

Repeat elements of an array.

Refer to numpy.repeat for full documentation.

See also

numpy.repeat
equivalent function
reshape(shape, order='C')

Returns an array containing the same data with a new shape.

Refer to numpy.reshape for full documentation.

See also

numpy.reshape
equivalent function
resize(new_shape, refcheck=True)

Change shape and size of array in-place.

Parameters:

new_shape : tuple of ints, or n ints

Shape of resized array.

refcheck : bool, optional

If False, reference count will not be checked. Default is True.
Returns:
None
Raises:

ValueError

If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.

SystemError

If the order keyword argument is specified. This behaviour is a bug in NumPy.

See also

resize
Return a new array with the specified shape.

Notes

This reallocates space for the data area if necessary.

Only contiguous arrays (data elements consecutive in memory) can be resized.

The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.

Examples

Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:

>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
       [1]])

>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
       [2]])

Enlarging an array: as above, but missing entries are filled with zeros:

>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
       [3, 0, 0]])

Referencing an array prevents resizing…

>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that has been referenced ...

Unless refcheck is False:

>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
round(decimals=0, out=None)

Return a with each element rounded to the given number of decimals.

Refer to numpy.around for full documentation.

See also

numpy.around
equivalent function
searchsorted(v, side='left', sorter=None)

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

See also

numpy.searchsorted
equivalent function
setfield(val, dtype, offset=0)

Put a value into a specified place in a field defined by a data-type.

Place val into a’s field defined by dtype and beginning offset bytes into the field.

Parameters:

val : object

Value to be placed in field.

dtype : dtype object

Data-type of the field in which to place val.

offset : int, optional

The number of bytes into the field at which to place val.
Returns:
None

See also

getfield

Examples

>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
       [3, 3, 3],
       [3, 3, 3]])
>>> x
array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
       [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
       [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])
setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.

These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)

Parameters:

write : bool, optional

Describes whether or not a can be written to.

align : bool, optional

Describes whether or not a is aligned properly for its type.

uic : bool, optional

Describes whether or not a is a copy of another “base” array.

Notes

Array flags provide information about how the memory area used for the array is to be interpreted. There are 6 Boolean flags in use, only three of which can be changed by the user: UPDATEIFCOPY, WRITEABLE, and ALIGNED.

WRITEABLE (W) the data area can be written to;

ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);

UPDATEIFCOPY (U) this array is a copy of some other array (referenced by .base). When this array is deallocated, the base array will be updated with the contents of this array.

All flags can be accessed using their first (upper case) letter as well as the full name.

Examples

>>> y
array([[3, 1, 7],
       [2, 0, 0],
       [8, 5, 9]])
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set UPDATEIFCOPY flag to True
shift(delta)[source]

Shift this TimeSeries forward in time by delta

This modifies the series in-place.

Parameters:

delta : float, Quantity, str

The amount by which to shift (in seconds if float), give a negative value to shift backwards in time

Examples

>>> from gwpy.timeseries import TimeSeries
>>> a = TimeSeries([1, 2, 3, 4, 5], t0=0, dt=1)
>>> print(a.t0)
0.0 s
>>> a.shift(5)
>>> print(a.t0)
5.0 s
>>> a.shift('-1 hour')
-3595.0 s
sort(axis=-1, kind='quicksort', order=None)

Sort an array, in-place.

Parameters:

axis : int, optional

Axis along which to sort. Default is -1, which means sort along the last axis.

kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional

Sorting algorithm. Default is ‘quicksort’.

order : str or list of str, optional

When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

See also

numpy.sort
Return a sorted copy of an array.
argsort
Indirect sort.
lexsort
Indirect stable sort on multiple keys.
searchsorted
Find elements in sorted array.
partition
Partial sort.

Notes

See sort for notes on the different sorting algorithms.

Examples

>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
       [1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
       [1, 4]])

Use the order keyword to specify a field to use when sorting a structured array:

>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([('c', 1), ('a', 2)],
      dtype=[('x', '|S1'), ('y', '<i4')])
squeeze(axis=None)

Remove single-dimensional entries from the shape of a.

Refer to numpy.squeeze for full documentation.

See also

numpy.squeeze
equivalent function
std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

See also

numpy.std
equivalent function
sum(axis=None, dtype=None, out=None, keepdims=False)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

See also

numpy.sum
equivalent function
swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

See also

numpy.swapaxes
equivalent function
take(indices, axis=None, out=None, mode='raise')

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

See also

numpy.take
equivalent function
to(unit, equivalencies=[])

Return a new Quantity object with the specified unit.

Parameters:

unit : UnitBase instance, str

An object that represents the unit to convert to. Must be an UnitBase object or a string parseable by the units package.

equivalencies : list of equivalence pairs, optional

A list of equivalence pairs to try if the units are not directly convertible. See Equivalencies. If not provided or [], class default equivalencies will be used (none for Quantity, but may be set for subclasses) If None, no equivalencies will be applied at all, not even any set globally or within a context.

See also

to_value
get the numerical value in a given unit.
to_dqflag(name=None, minlen=1, dtype=None, round=False, label=None, description=None)[source]

Convert this series into a DataQualityFlag

Each contiguous set of True values are grouped as a Segment running from the GPS time the first found True, to the GPS time of the next False (or the end of the series)

Parameters:

minlen : int, optional, default: 1

minimum number of consecutive True values to identify as a Segment. This is useful to ignore single bit flips, for example.

dtype : type, callable

output segment entry type, can pass either a type for simple casting, or a callable function that accepts a float and returns another numeric type, defaults to the dtype of the time index

round : bool, optional, default: False

choose to round each Segment to its inclusive integer boundaries
Returns:

dqflag : DataQualityFlag

a segment representation of this StateTimeSeries, the span defines the known segments, while the contiguous True sets defined each of the active segments
to_lal(*args, **kwargs)[source]

Bogus function inherited from superclass, do not use.

to_pycbc(copy=True)[source]

Convert this TimeSeries into a PyCBC TimeSeries

Parameters:

copy : bool, optional, default: True

if True, copy these data to a new array
Returns:

timeseries : TimeSeries

a PyCBC representation of this TimeSeries
to_value(unit=None, equivalencies=[])

The numerical value, possibly in a different unit.

Parameters:

unit : UnitBase instance or str, optional

The unit in which the value should be given. If not given or None, use the current unit.

equivalencies : list of equivalence pairs, optional

A list of equivalence pairs to try if the units are not directly convertible (see Equivalencies). If not provided or [], class default equivalencies will be used (none for Quantity, but may be set for subclasses). If None, no equivalencies will be applied at all, not even any set globally or within a context.
Returns:

value : ndarray or scalar

The value in the units specified. For arrays, this will be a view of the data if no unit conversion was necessary.

See also

to
Get a new instance in a different unit.
tobytes(order='C')

Construct Python bytes containing the raw data bytes in the array.

Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’, or ‘Any’ order (the default is ‘C’-order). ‘Any’ order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means ‘Fortran’ order.

New in version 1.9.0.

Parameters:

order : {‘C’, ‘F’, None}, optional

Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.
Returns:

s : bytes

Python bytes exhibiting a copy of a’s raw data.

Examples

>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'
tofile(fid, sep="", format="%s")

Write array to a file as text or binary (default).

Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().

Parameters:

fid : file or str

An open file object, or a string containing a filename.

sep : str

Separator between array items for text output. If “” (empty), a binary file is written, equivalent to file.write(a.tobytes()).

format : str

Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.

Notes

This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.

tolist()

Return the array as a (possibly nested) list.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible Python type.

Parameters:
none
Returns:

y : list

The possibly nested list of array elements.

Notes

The array may be recreated, a = np.array(a.tolist()).

Examples

>>> a = np.array([1, 2])
>>> a.tolist()
[1, 2]
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
tostring(order='C')[source]

Construct Python bytes containing the raw data bytes in the array.

Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’, or ‘Any’ order (the default is ‘C’-order). ‘Any’ order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means ‘Fortran’ order.

This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.

Parameters:

order : {‘C’, ‘F’, None}, optional

Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.
Returns:

s : bytes

Python bytes exhibiting a copy of a’s raw data.

Examples

>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'
trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

See also

numpy.trace
equivalent function
transpose(*axes)

Returns a view of the array with axes transposed.

For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).

Parameters:

axes : None, tuple of ints, or n ints

  • None or no argument: reverses the order of the axes.
  • tuple of ints: i in the j-th place in the tuple means a’s i-th axis becomes a.transpose()’s j-th axis.
  • n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)
Returns:

out : ndarray

View of a, with axes suitably permuted.

See also

ndarray.T
Array property returning the array transposed.

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.transpose()
array([[1, 3],
       [2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
       [2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
       [2, 4]])
update(other, inplace=True)[source]

Update this series by appending new data from an other and dropping the same amount of data off the start.

This is a convenience method that just calls append with resize=False.

value_at(x)[source]

Return the value of this Series at the given xindex value

Parameters:

x : float, Quantity

the xindex value at which to search
Returns:

y : Quantity

the value of this Series at the given xindex value
var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

See also

numpy.var
equivalent function
view(dtype=None, type=None)

New view of array with the same data.

Parameters:

dtype : data-type or ndarray sub-class, optional

Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).

type : Python type, optional

Type of the returned view, e.g., ndarray or matrix. Again, the default None results in type preservation.

Notes

a.view() is used two different ways:

a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.

a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.

For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance of a (shown by print(a)). It also depends on exactly how a is stored in memory. Therefore if a is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results.

Examples

>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])

Viewing array data using a different type and dtype:

>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrixlib.defmatrix.matrix'>

Creating a view on a structured array so it can be used in calculations

>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
       [3, 4]], dtype=int8)
>>> xv.mean(0)
array([ 2.,  3.])

Making changes to the view changes the underlying array

>>> xv[0,1] = 20
>>> print(x)
[(1, 20) (3, 4)]

Using a view to convert an array to a recarray:

>>> z = x.view(np.recarray)
>>> z.a
array([1], dtype=int8)

Views share data:

>>> x[0] = (9, 10)
>>> z[0]
(9, 10)

Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortran-ordering, etc.:

>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
       [4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array.
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
       [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
write(target, *args, **kwargs)[source]

Write this TimeSeries to a file

Parameters:

target : str

path of output file

format : str, optional

output format identifier. If not given, the format will be detected if possible. See below for list of acceptable formats.

Notes

The available built-in formats are:

Format Read Write Auto-identify
csv Yes Yes Yes
framecpp Yes Yes No
gwf Yes Yes Yes
gwf.framecpp Yes Yes No
gwf.lalframe Yes Yes No
hdf5 Yes Yes Yes
lalframe Yes Yes No
txt Yes Yes Yes
wav Yes Yes No
zip()[source]

Zip the xindex and value arrays of this Series

Returns:

stacked : 2-d numpy.ndarray

The array formed by stacking the the xindex and value of this series

Examples

>>> a = Series([0, 2, 4, 6, 8], xindex=[-5, -4, -3, -2, -1])
>>> a.zip()
array([[-5.,  0.],
       [-4.,  2.],
       [-3.,  4.],
       [-2.,  6.],
       [-1.,  8.]])
DictClass[source]

alias of StateTimeSeriesDict

diff(n=1, axis=-1)[source]

Calculate the n-th order discrete difference along given axis.

The first order difference is given by out[n] = a[n+1] - a[n] along the given axis, higher order differences are calculated by using diff recursively.

Parameters:

n : int, optional

The number of times values are differenced.

axis : int, optional

The axis along which the difference is taken, default is the last axis.
Returns:

diff : Series

The n order differences. The shape of the output is the same as the input, except along axis where the dimension is smaller by n.

See also

numpy.diff
for documentation on the underlying method
override_unit(unit, parse_strict='raise')[source]

Forcefully reset the unit of these data

Use of this method is discouraged in favour of to(), which performs accurate conversions from one unit to another. The method should really only be used when the original unit of the array is plain wrong.

Parameters:

unit : Unit, str

the unit to force onto this array

parse_strict : str, optional

how to handle errors in the unit parsing, default is to raise the underlying exception from astropy.units
Raises:

ValueError

if a str cannot be parsed as a valid unit
to_dqflag(name=None, minlen=1, dtype=None, round=False, label=None, description=None)[source]

Convert this series into a DataQualityFlag

Each contiguous set of True values are grouped as a Segment running from the GPS time the first found True, to the GPS time of the next False (or the end of the series)

Parameters:

minlen : int, optional, default: 1

minimum number of consecutive True values to identify as a Segment. This is useful to ignore single bit flips, for example.

dtype : type, callable

output segment entry type, can pass either a type for simple casting, or a callable function that accepts a float and returns another numeric type, defaults to the dtype of the time index

round : bool, optional, default: False

choose to round each Segment to its inclusive integer boundaries
Returns:

dqflag : DataQualityFlag

a segment representation of this StateTimeSeries, the span defines the known segments, while the contiguous True sets defined each of the active segments
to_lal(*args, **kwargs)[source]

Bogus function inherited from superclass, do not use.