StateTimeSeries#

Bases: TimeSeriesBase

Boolean array representing a good/bad state determination.

Parameters:

valuearray_like: Input data array.
t0LIGOTimeGPS, float, str, optional: GPS epoch associated with these data, any input parsable by to_gps is fine.
dtfloat, Quantity, optional: Time between successive samples (seconds), can also be given inversely via sample_rate.
sample_ratefloat, Quantity, optional: The rate of samples per second (Hertz), can also be given inversely via dt.
timesarray-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.
namestr, optional: Descriptive title for this array.
channelChannel, str, optional: Source data stream for these data.
dtypedtype, optional: Input data type.
copybool, optional: Choose to copy the input data to new memory.
subokbool, optional: Allow passing of sub-classes by the array generator.

Notes

Key methods

to_dqflag([name, minlen, dtype, round, ...])

Convert this series into a DataQualityFlag.

Attributes Summary

unit

The unit of this StateTimeSeries.

Methods Summary

`all`([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
`diff`([n, axis])	Return the difference between successive samples.
`from_nds2_buffer`(buffer, **metadata)	Create a `StateTimeSeries` from an NDS2 buffer.
`override_unit`(unit[, parse_strict])	Override the unit of this `StateTimeSeries`.
`to_dqflag`([name, minlen, dtype, round, ...])	Convert this series into a `DataQualityFlag`.
`to_lal`()	Bogus function inherited from superclass, do not use.
`tolist`()	Return the array as an `a.ndim`-levels deep nested list of Python scalars.

Attributes Documentation

unit[source]#: The unit of this StateTimeSeries.

Methods Documentation

all( axis=None, out=None, *, keepdims=<no value>, where=<no value>, )#

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

See also

numpy.all: equivalent function

diff(n: int = 1, axis: int = -1) → Self[source]#

Return the difference between successive samples.

The difference is defined as the exclusive-or of the previous and current samples.

Parameters:

nint, optional: Number of times to apply the difference operation, defaults to 1, i.e. the first difference.
axisint, optional: Axis along which to compute the difference, defaults to the last axis, i.e. the time axis.

Returns:

newStateTimeSeries: A new StateTimeSeries containing the difference of the current series, with the same metadata as the original.

classmethod from_nds2_buffer(buffer: nds2.buffer, **metadata) → Self[source]#: Create a StateTimeSeries from an NDS2 buffer.

override_unit( unit: UnitLike, parse_strict: str = 'raise', ) → NoReturn[source]#: Override the unit of this StateTimeSeries. UNSUPPORTED DO NOT USE.

to_dqflag( name: str | None = None, minlen: int = 1, dtype: type | None = None, *, round: bool = False, label: str | None = None, description: str | None = None, ) → DataQualityFlag[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:

name: `str`, optional: Name of the segment.
minlenint, optional: Minimum number of consecutive True values to identify as a Segment. This is useful to ignore single bit flips, for example.
dtypetype, 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.
roundbool, optional: Choose to round each Segment to its inclusive integer boundaries.
labelstr, optional: The label for the output flag.
descriptionstr, optional: The description for the output flag.

Returns:

dqflagDataQualityFlag: 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() → NoReturn[source]#: Bogus function inherited from superclass, do not use.

tolist()#

Return the array as an a.ndim-levels deep nested list of Python scalars.

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

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

Parameters:

none

Returns:

yobject, or list of object, or list of list of object, or …: The possibly nested list of array elements.

Notes

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

Examples

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:

>>> import numpy as np
>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[np.uint32(1), np.uint32(2)]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>

Additionally, for a 2D array, tolist applies recursively:

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

The base case for this recursion is a 0D array:

>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
  ...
TypeError: iteration over a 0-d array
>>> a.tolist()
1