StateTimeSeries

class gwpy.timeseries.StateTimeSeries(data, t0=None, dt=None, sample_rate=None, times=None, channel=None, name=None, **kwargs)

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, default: 1

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

sample_ratefloat, Quantity, optional, default: 1

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, default: False

choose to copy the input data to new memory

subokbool, 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.

Attributes Summary

T	View of the transposed array.
abs(x, /[, out, where, casting, order, ...])	Calculate the absolute value element-wise.
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	Container for meta information like name, description, format.
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
read
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.
write
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 Summary

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, gap, pad, resize])	Connect another series onto this 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
check_compatible(other[, casting, ...])	Check whether this Series and other are compatible.
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])
dump(file)	Not implemented, use .value.dump() instead.
dumps()	Not implemented, use .value.dumps() instead.
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 GWOSC.
fill(value)	Fill the array with a scalar value.
find(channel, start, end, *[, observatory, ...])	Find and return data for multiple channels using GWDataFind.
flatten([order])	Return a copy of the array collapsed into one dimension.
from_arrakis(series[, copy])	Construct a new series from an arrakis.Series object.
from_lal(lalts[, copy])	Generate a new TimeSeries from a LAL TimeSeries of any type.
from_nds2_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.
get(channel, start, end, *[, source])	Get data for this channel.
getfield(dtype[, offset])	Returns a field of the given array as a certain type.
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 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.
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, out, overwrite_input, keepdims])	Compute the median along the specified axis.
min([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
nonzero()	Return the indices of the elements that are non-zero.
override_unit(*args, **kwargs)	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, gap, pad, 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.
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 Quantity object with the specified unit.
to_device
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 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])	Not implemented, use .value.tostring() instead.
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, gap, pad])	Update this series by appending new data like a buffer.
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.
zip()	Zip the xindex and value arrays of this Series.

Attributes Documentation

T

View of the transposed array.

Same as self.transpose().

See also

transpose

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.T
array([[1, 3],
       [2, 4]])

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

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

Calculate the absolute value element-wise.

np.abs is a shorthand for this function.

Parameters:

xarray_like

Input array.

outndarray, 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.

wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

**kwargs

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

Returns:

absolutendarray

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

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. This is a scalar if x is a scalar.

Examples

>>> import numpy as np
>>> 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)

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)

The abs function can be used as a shorthand for np.absolute on ndarrays.

>>> x = np.array([-1.2, 1.2])
>>> abs(x)
array([1.2, 1.2])

base

Base object if memory is from some other object.

Examples

The base of an array that owns its memory is None:

>>> import numpy as np
>>> x = np.array([1,2,3,4])
>>> x.base is None
True

Slicing creates a view, whose memory is shared with x:

>>> y = x[2:]
>>> y.base is x
True

cgs

Returns a copy of the current Quantity instance with CGS units. The value of the resulting object will be scaled.

channel

Instrumental channel associated with these data.

ctypes

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

This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.

Parameters:

None

Returns:

cPython object

Possessing attributes data, shape, strides, etc.

See also

numpy.ctypeslib

Notes

Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):

_ctypes.data

A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as: self._array_interface_['data'][0].

Note that unlike data_as, a reference won’t be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.shape

(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.

_ctypes.strides

(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.

_ctypes.data_as(obj)

Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).

The returned pointer will keep a reference to the array.

_ctypes.shape_as(obj)

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).

_ctypes.strides_as(obj)

Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute.

Examples

>>> import numpy as np
>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
       [2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy._core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary

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.

Warning

Setting arr.dtype is discouraged and may be deprecated in the future. Setting will replace the dtype without modifying the memory (see also ndarray.view and ndarray.astype).

Parameters:

None

Returns:

dnumpy dtype object

See also

ndarray.astype

Cast the values contained in the array to a new data-type.

ndarray.view

Create a view of the same data but a different data-type.

numpy.dtype

Examples

>>> import numpy as np
>>> x = np.arange(4).reshape((2, 2))
>>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int64')   # may vary (OS, bitness)
>>> isinstance(x.dtype, np.dtype)
True

duration

Duration of this series in seconds.

Type:

Quantity scalar

dx

X-axis sample separation.

epoch

GPS epoch for these data.

This attribute is stored internally by the t0 attribute

Type:

Time

equivalencies

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

flags

Information about the memory layout of the array.

Attributes:

C_CONTIGUOUS (C)

The data is in a single, C-style contiguous segment.

F_CONTIGUOUS (F)

The data is in a single, Fortran-style contiguous segment.

OWNDATA (O)

The array owns the memory it uses or borrows it from another object.

WRITEABLE (W)

The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception.

ALIGNED (A)

The data and all elements are aligned appropriately for the hardware.

WRITEBACKIFCOPY (X)

This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.

FNC

F_CONTIGUOUS and not C_CONTIGUOUS.

FORC

F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).

BEHAVED (B)

ALIGNED and WRITEABLE.

CARRAY (CA)

BEHAVED and C_CONTIGUOUS.

FARRAY (FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes

The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access.

Only the WRITEBACKIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.

The array flags cannot be set arbitrarily:

• WRITEBACKIFCOPY can only be set False.

• ALIGNED can only be set True if the data is truly aligned.

• WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays.

Even for contiguous arrays a stride for a given dimension arr.strides[dim] may be arbitrary if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.

flat

A 1-D iterator over the Quantity array.

This returns a QuantityIterator instance, which behaves the same as the flatiter instance returned by flat, and is similar to, but not a subclass of, Python’s built-in iterator object.

imag

The imaginary part of the array.

Examples

>>> import numpy as np
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0.        ,  0.70710678])
>>> x.imag.dtype
dtype('float64')

info

Container for meta information like name, description, format. This is required when the object is used as a mixin column within a table, but can be used as a general way to store meta information.

isscalar

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

Note

This is subtly different from numpy.isscalar in that numpy.isscalar returns False for a zero-dimensional array (e.g. np.array(1)), while this is True for quantities, since quantities cannot represent true numpy scalars.

itemset

itemsize

Length of one array element in bytes.

Examples

>>> import numpy as np
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16

mT

View of the matrix transposed array.

The matrix transpose is the transpose of the last two dimensions, even if the array is of higher dimension.

Added in version 2.0.

Raises:

ValueError

If the array is of dimension less than 2.

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.mT
array([[1, 3],
       [2, 4]])

>>> a = np.arange(8).reshape((2, 2, 2))
>>> a
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])
>>> a.mT
array([[[0, 2],
        [1, 3]],

       [[4, 6],
        [5, 7]]])

name

Name for this data set.

nbytes

Total bytes consumed by the elements of the array.

See also

sys.getsizeof

Memory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.

Notes

Does not include memory consumed by non-element attributes of the array object.

Examples

>>> import numpy as np
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480

ndim

Number of array dimensions.

Examples

>>> import numpy as np
>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3

newbyteorder

ptp

read

real

The real part of the array.

See also

numpy.real

equivalent function

Examples

>>> import numpy as np
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')

sample_rate

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

This attribute is stored internally by the dx attribute

Type:

Quantity scalar

shape

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Warning

Setting arr.shape is discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.

See also

numpy.shape

Equivalent getter function.

numpy.reshape

Function similar to setting shape.

ndarray.reshape

Method similar to setting shape.

Examples

>>> import numpy as np
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot reshape array of size 24 into shape (3,6)
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.

si

Returns a copy of the current Quantity instance with SI units. The value of the resulting object will be scaled.

size

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

Notes

a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.

Examples

>>> import numpy as np
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30

span

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

strides

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

The byte offset of element (i[0], i[1], ..., i[n]) in an array a is:

offset = sum(np.array(i) * a.strides)

A more detailed explanation of strides can be found in The N-dimensional array (ndarray).

Warning

Setting arr.strides is discouraged and may be deprecated in the future. numpy.lib.stride_tricks.as_strided should be preferred to create a new view of the same data in a safer way.

See also

numpy.lib.stride_tricks.as_strided

Notes

Imagine an array of 32-bit integers (each 4 bytes):

x = np.array([[0, 1, 2, 3, 4],
              [5, 6, 7, 8, 9]], dtype=np.int32)

This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4).

Examples

>>> import numpy as np
>>> y = np.reshape(np.arange(2 * 3 * 4, dtype=np.int32), (2, 3, 4))
>>> y
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],
       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]], dtype=np.int32)
>>> y.strides
(48, 16, 4)
>>> y[1, 1, 1]
np.int32(17)
>>> offset = sum(y.strides * np.array((1, 1, 1)))
>>> offset // y.itemsize
np.int64(17)

>>> x = np.reshape(np.arange(5*6*7*8, dtype=np.int32), (5, 6, 7, 8))
>>> x = x.transpose(2, 3, 1, 0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3, 5, 2, 2], dtype=np.int32)
>>> offset = sum(i * x.strides)
>>> x[3, 5, 2, 2]
np.int32(813)
>>> offset // x.itemsize
np.int64(813)

t0

X-axis coordinate of the first data point.

times

Positions of the data on the x-axis.

unit

value

The numerical value of this instance.

See also

to_value

Get the numerical value in a given unit.

write

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 Documentation

all(axis=None, out=None, keepdims=False, *, where=True)

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, *, where=True)

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: numpy.ndarray | Series, *, inplace: bool = True, gap: Literal['raise', 'ignore', 'pad'] | None = None, pad: float | None = None, resize: bool = True) → Self

Connect another series onto this one.

Parameters:

othernumpy.ndarray, Series

Another Series, or a simple data array to connect to this one.

inplacebool, optional

If True (default) perform the operation in-place, modifying current series. If False copy the data to new memory before modifying.

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!

gapstr, optional

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

• 'raise' - raise a ValueError

• 'ignore' - remove gap and join data

• 'pad' - pad gap with zeros

If pad is given and is not None, the default is gap='pad', otherwise gap='raise'.

If gap='pad' is given, the default for pad is 0.

padfloat, optional

Value with which to pad discontiguous series, by default gaps will result in a ValueError.

resizebool, optional

If True (default) resize this array to accommodate new data. If False roll the current data like a buffer to the left and insert new data at the other end.

Returns:

seriesSeries

A new series containing joined data sets.

argmax(axis=None, out=None, *, keepdims=False)

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, *, keepdims=False)

Return indices of the minimum values along the given axis.

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.

See also

numpy.argpartition

equivalent function

argsort(axis=-1, kind=None, 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:

dtypestr 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.

subokbool, optional

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

copybool, 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_tndarray

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).

Examples

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

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

byteswap(inplace=False)

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. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

Parameters:

inplacebool, optional

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

Returns:

outndarray

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

Examples

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

Arrays of byte-strings are not swapped

>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')

A.view(A.dtype.newbyteorder()).byteswap() produces an array with the same values but different representation in memory

>>> A = np.array([1, 2, 3],dtype=np.int64)
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
       0, 0], dtype=uint8)
>>> A.view(A.dtype.newbyteorder()).byteswap(inplace=True)
array([1, 2, 3], dtype='>i8')
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
       0, 3], dtype=uint8)

check_compatible(other: list | numpy.ndarray, casting: Literal['no', 'equiv', 'safe', 'same_kind', 'unsafe'] | None = 'safe', *, irregular_equal: bool = True) → None

Check whether this Series and other are compatible.

Parameters:

othernumpy.ndarray, Series

The array to compare to.

castingstr, optional

The type of casting to support when comparing dtypes.

irregular_equalbool, optional

Require irregular indices to be equal (default). If irregular_equal=False and either (or both) of the series are irregular, this method just returns without doing anything.

Raises:

ValueError

If any metadata elements aren’t compatible.

TypeError

If the dtype can’t be safely cast between the arrays.

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, **kwargs)

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')

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 numpy.copy() are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

See also

numpy.copy

Similar function with different default behavior

numpy.copyto

Notes

This function is the preferred method for creating an array copy. The function numpy.copy() is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

Examples

>>> import numpy as np
>>> 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

For arrays containing Python objects (e.g. dtype=object), the copy is a shallow one. The new array will contain the same object which may lead to surprises if that object can be modified (is mutable):

>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> b = a.copy()
>>> b[2][0] = 10
>>> a
array([1, 'm', list([10, 3, 4])], dtype=object)

To ensure all elements within an object array are copied, use copy.deepcopy:

>>> import copy
>>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
>>> c = copy.deepcopy(a)
>>> c[2][0] = 10
>>> c
array([1, 'm', list([10, 3, 4])], dtype=object)
>>> a
array([1, 'm', list([2, 3, 4])], dtype=object)

crop(start: Quantity | float | None = None, end: Quantity | float | None = None, *, copy: bool = False) → Self

Crop this series to the given x-axis extent.

Parameters:

startfloat, optional

Lower limit of x-axis to crop to, defaults to x0.

endfloat, optional

Upper limit of x-axis to crop to, defaults to series end.

copybool, optional

Copy the input data to fresh memory, otherwise return a view (default).

Returns:

seriesSeries

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: Collection[UnitBase] = ()) → Self

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

Parameters:

basessequence 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:

newqQuantity

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)

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:

nint, optional

The number of times values are differenced.

axisint, optional

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

Returns:

diffSeries

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)

dump(file)

Not implemented, use .value.dump() instead.

dumps()

Not implemented, use .value.dumps() instead.

ediff1d(to_end=None, to_begin=None)

classmethod fetch(channel: str | Channel, start: GpsLike, end: GpsLike, *, host: str | None = None, port: int | None = None, verbose: bool | str = False, connection: nds2.connection | None = None, verify: bool = False, pad: float | None = None, allow_tape: bool | None = None, scaled: bool | None = None, type: int | str | None = None, dtype: int | str | None = None)

Fetch data from NDS.

Parameters:

channelstr, Channel

The name (or representation) of the data channel to fetch.

startLIGOTimeGPS, float, str

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

endLIGOTimeGPS, float, str, optional

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

hoststr, optional

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

One of connection or host must be given.

portint, optional

Port number for NDS server query, must be given with host.

verifybool, optional

Check channels exist in database before asking for data. Default is True.

verbosebool, optional

Print verbose progress information about NDS download. If verbose is specified as a string, this defines the prefix for the progress meter.

connectionnds2.connection, optional

Open NDS connection to use. Default is to open a new connection using host and port arguments.

One of connection or host must be given.

padfloat, optional

Float value to insert between gaps. Default behaviour is to raise an exception when any gaps are found.

scaledbool, optional

Apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

allow_tapebool, optional

Allow data access from slow tapes. If host or connection is given, the default is to do whatever the server default is, otherwise servers will be searched with allow_tape=False first, then allow_tape=True if that fails.

typeint, str, optional

NDS2 channel type integer or string name to match. Default is to search for any channel type.

dtypenumpy.dtype, str, type, or dict, optional

NDS2 data type to match. Default is to search for any data type.

classmethod fetch_open_data(ifo, start, end, sample_rate=4096, version=None, format='hdf5', host='https://gwosc.org', verbose=False, cache=None, **kwargs)

Fetch open-access data from GWOSC.

Parameters:

ifostr

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

startLIGOTimeGPS, float, str, optional

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

endLIGOTimeGPS, float, str, optional

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

sample_ratefloat, optional,

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

versionint, optional

version of files to download, defaults to highest discovered version

formatstr, optional

the data format to download and parse, default: 'h5py'

• 'hdf5'

• 'gwf' - requires lalframe

hoststr, optional

HTTP host name of GWOSC server to access

verbosebool, optional, default: False

print verbose output while fetching data

cachebool, 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.

timeoutfloat, optional

the time to wait for a response from the GWOSC server.

**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 GWOSC in different formats.

fill(value)

Fill the array with a scalar value.

Parameters:

valuescalar

All elements of a will be assigned this value.

Examples

>>> import numpy as np
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])

Fill expects a scalar value and always behaves the same as assigning to a single array element. The following is a rare example where this distinction is important:

>>> a = np.array([None, None], dtype=object)
>>> a[0] = np.array(3)
>>> a
array([array(3), None], dtype=object)
>>> a.fill(np.array(3))
>>> a
array([array(3), array(3)], dtype=object)

Where other forms of assignments will unpack the array being assigned:

>>> a[...] = np.array(3)
>>> a
array([3, 3], dtype=object)

classmethod find(channel: str | Channel, start: GpsLike, end: GpsLike, *, observatory: str | None = None, frametype: str | None = None, pad: float | None = None, scaled: bool | None = None, allow_tape: bool | None = None, parallel: int = 1, verbose: bool | str = False, **readargs) → Self

Find and return data for multiple channels using GWDataFind.

This method uses gwdatafind to discover the URLs that provide the requested data, then reads those files using TimeSeriesDict.read().

Parameters:

channelstr

Name of data channel to find.

startLIGOTimeGPS, float, str

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

endLIGOTimeGPS, float, str

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

observatorystr, optional

The observatory to use when searching for data. Default is to use the observatory from the channel name prefix, but this should be specified when searching for data in a multi-observatory dataset (e.g. observatory='HLV').

frametypestr, optional

Name of frametype (dataset) in which this channel is stored. Default is to search all available datasets for a match, which can be very slow.

frametype_matchstr, optional

Regular expression to use for frametype matching.

padfloat, optional

Value with which to fill gaps in the source data, by default gaps will result in a ValueError.

scaledbool, optional

Apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

parallelint, optional

Number of parallel processes to use.

allow_tapebool, optional

Allow reading from frame files on (slow) magnetic tape.

verbosebool, optional

Print verbose output about read progress, if verbose is specified as a string, this defines the prefix for the progress meter.

readargs

Any other keyword arguments to be passed to read().

flatten(order: str = 'C') → Quantity

Return a copy of the array collapsed into one dimension.

Any index information is removed as part of the flattening, and the result is returned as a Quantity array.

Parameters:

order{‘C’, ‘F’, ‘A’, ‘K’}

‘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:

yQuantity

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 = Array([[1,2], [3,4]], unit='m', name='Test')
>>> a.flatten()
<Quantity [1., 2., 3., 4.] m>

classmethod from_arrakis(series: arrakis.Series, copy: bool = True, **metadata)

Construct a new series from an arrakis.Series object.

Parameters:

seriesarrakis.Series

The input Arrakis data series to read.

copybool, optional

If True, copy the contained data array to new to a new array.

**metadata

Any other metadata keyword arguments to pass to the TimeSeries constructor.

Returns:

timeseriesTimeSeries

A new TimeSeries containing the data from the arrakis.Series and the appropriate metadata.

classmethod from_lal(lalts, copy=True)

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

classmethod from_nds2_buffer(scaled=None, copy=True, **metadata)

Construct a new series from an nds2.buffer object.

Requires: NDS2

Parameters:

buffer_nds2.buffer

the input NDS2-client buffer to read

scaledbool, optional

apply slope and bias calibration to ADC data, for non-ADC data this option has no effect

copybool, optional

if True, copy the contained data array to new to a new array

**metadata

any other metadata keyword arguments to pass to the TimeSeries constructor

Returns:

timeseriesTimeSeries

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

classmethod from_pycbc(pycbcseries, copy=True)

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

Parameters:

pycbcseriespycbc.types.timeseries.TimeSeries

the input PyCBC TimeSeries array

copybool, optional, default: True

if True, copy these data to a new array

Returns:

timeseriesTimeSeries

a GWpy version of the input timeseries

classmethod get(channel: str | Channel, start: GpsLike, end: GpsLike, *, source: str | None = None, **kwargs) → Self

Get data for this channel.

This method attemps to get data any way it can, potentially iterating over multiple available data sources.

Parameters:

channelstr, Channel

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

startLIGOTimeGPS, float, str

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

endLIGOTimeGPS, float, str

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

sourcestr

The data source to use. Give one of

“files”

Use gwdatafind to find the paths of local files and then read them.

“nds2”

Use NDS2.

frametypestr

Name of frametype in which this channel is stored, by default will search for all required frame types.

padfloat

Value with which to fill gaps in the source data, by default gaps will result in a ValueError.

scaledbool

apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

nprocint, default: 1

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

allow_tapebool, default: None

Allow the use of data files 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.

verbosebool

Print verbose output about data access progress. If verbose is specified as a string, this defines the prefix for the progress meter.

kwargs

Other keyword arguments to pass to the data access function for each data source.

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:

dtypestr or dtype

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

offsetint

Number of bytes to skip before beginning the element view.

Examples

>>> import numpy as np
>>> 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.]])

inject(other: Series) → Self

Add two compatible Series along their shared x-axis values.

Parameters:

otherSeries

A Series whose xindex intersects with self.xindex.

Returns:

outSeries

The sum of self and other along their shared x-axis values.

Raises:

ValueError

If self and other have incompatible units or xindex intervals.

Notes

If other.xindex and self.xindex do not intersect, this method will return a copy of self. If the series have uniformly offset indices, this method will raise a warning.

If self.xindex is an array of timestamps, and if other.xspan is not a subset of self.xspan, then other will be cropped before being adding to self.

Users who wish to taper or window their Series should do so before passing it to this method. See TimeSeries.taper() and planck() for more information.

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:

objint, slice or sequence of int

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

valuesarray-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.

axisint, optional

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

Returns:

outQuantity

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: list | ndarray) → bool

Check whether this series and other have compatible metadata.

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

is_contiguous(other: Series | ndarray | list, tol: float = 3.814697265625e-06) → int

Check whether other is contiguous with self.

Parameters:

otherSeries, numpy.ndarray

Another series of the same type to test for contiguity.

tolfloat, 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 this series.

Notes

If other is an array that doesn’t have index information (e.g. a numpy.ndarray), this method always returns 1.

If self *or* other` have an irregular Index array (e.g. aren’t linearly sampled), this method will always return 1 if other starts after self finishes, or -1` if the inverse. If the two arrays overlap, that is bad and will raise an error.

item(*args)

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

Like item() except that it always returns a Quantity, not a Python scalar.

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

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, *, where=True)

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, out=None, overwrite_input=False, keepdims=False)

Compute the median along the specified axis.

Returns the median of the array elements.

Parameters:

aarray_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, axis=None, will compute the median along a flattened version of the array. If a sequence of axes, the array is first flattened along the given axes, then the median is computed along the resulting flattened axis.

outndarray, 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_inputbool, 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.

keepdimsbool, 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.

Returns:

medianndarray

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

>>> import numpy as np
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> np.median(a)
np.float64(3.5)
>>> np.median(a, axis=0)
array([6.5, 4.5, 2.5])
>>> np.median(a, axis=1)
array([7.,  2.])
>>> np.median(a, axis=(0, 1))
np.float64(3.5)
>>> 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)
np.float64(3.5)
>>> assert not np.all(a==b)

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

See also

numpy.amin

equivalent function

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(*args, **kwargs)

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:

unitUnit, str

the unit to force onto this array

parse_strictstr

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

See also

gwpy.detector.units.parse_unit

For details of unit string parsing.

pad(pad_width: int | tuple[int, int], **kwargs) → Self

Pad this series to a new size.

This just wraps numpy.pad and handles shifting the Index to accommodate padding on the left.

Parameters:

pad_widthint, tuple[int, int]

Number of samples by which to pad each end of the array; given a single int to pad both ends by the same amount, or a (before, after) tuple for assymetric padding.

kwargs

Other keyword arguments are passed to numpy.pad.

Returns:

seriesSeries

The padded version of the input.

See also

numpy.pad

For details on the pad function and valid keyword arguments.

partition(kth, axis=-1, kind='introselect', order=None)

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. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two partitions on the either side of the k-th element in the output array is undefined.

Parameters:

kthint 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 of 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.

Deprecated since version 1.22.0: Passing booleans as index is deprecated.

axisint, optional

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

kind{‘introselect’}, optional

Selection algorithm. Default is ‘introselect’.

orderstr 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 to 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 partitioned copy of an array.

argpartition

Indirect partition.

sort

Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

>>> import numpy as np
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4]) # may vary

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

plot(method='plot', figsize=(12, 4), xscale='auto-gps', **kwargs)

Plot the data for this timeseries.

Returns:

figureFigure

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: QuantityLike, *, inplace: bool = True, gap: Literal['raise', 'ignore', 'pad'] | None = None, pad: float | None = None, resize: bool = True) → Series

Connect another series onto the start of the current one.

Parameters:

othernumpy.ndarray, Series

The data to prepend to this series.

inplacebool, optional

If True (default) perform the operation in-place, modifying current series. If False copy the data to new memory before modifying.

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!

gapstr, optional

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

• 'raise' - raise a ValueError

• 'ignore' - remove gap and join data

• 'pad' - pad gap with zeros

If pad is given and is not None, the default is gap='pad', otherwise gap='raise'.

If gap='pad' is given, the default for pad is 0.

padfloat, optional

Value with which to pad discontiguous series, by default gaps will result in a ValueError.

resizebool, optional

If True (default) resize this array to accommodate new data. If False roll the current data like a buffer to the left or right (depending on prepend) and insert new data at the other end.

Returns:

seriesSeries

The modified series.

prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

See also

numpy.prod

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.

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', copy=None)

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

Refer to numpy.reshape for full documentation.

See also

numpy.reshape

equivalent function

Notes

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

resize(new_shape, refcheck=True)

Change shape and size of array in-place.

Parameters:

new_shapetuple of ints, or n ints

Shape of resized array.

refcheckbool, 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:

>>> import numpy as np

>>> 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 references or is 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:

valobject

Value to be placed in field.

dtypedtype object

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

offsetint, optional

The number of bytes into the field at which to place val.

Returns:

None

See also

getfield

Examples

>>> import numpy as np
>>> 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]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+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, WRITEBACKIFCOPY, 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 WRITEBACKIFCOPY 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:

writebool, optional

Describes whether or not a can be written to.

alignbool, optional

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

uicbool, 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 7 Boolean flags in use, only three of which can be changed by the user: WRITEBACKIFCOPY, 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);

WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.

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

Examples

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

shift(delta: QuantityLike) → None

Shift this Series forward on the X-axis by delta.

This modifies the series in-place.

Parameters:

deltafloat, Quantity, str

The amount by which to shift (in x-axis units if float), give a negative value to shift backwards in time

Examples

>>> from gwpy.types import Series
>>> a = Series([1, 2, 3, 4, 5], x0=0, dx=1, xunit='m')
>>> print(a.x0)
0.0 m
>>> a.shift(5)
>>> print(a.x0)
5.0 m
>>> a.shift('-1 km')
-995.0 m

sort(axis=-1, kind=None, order=None)

Sort an array in-place. Refer to numpy.sort for full documentation.

Parameters:

axisint, optional

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

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

Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.

orderstr 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.

numpy.argsort

Indirect sort.

numpy.lexsort

Indirect stable sort on multiple keys.

numpy.searchsorted

Find elements in sorted array.

numpy.partition

Partial sort.

Notes

See numpy.sort for notes on the different sorting algorithms.

Examples

>>> import numpy as np
>>> 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([(b'c', 1), (b'a', 2)],
      dtype=[('x', 'S1'), ('y', '<i8')])

squeeze(axis=None)

Remove axes of length one from 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, *, where=True)

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

Refer to numpy.std for full documentation.

See also

numpy.std

equivalent function

step(**kwargs) → Plot

Create a step plot of this series.

kwargs

All keyword arguments are passed to the plot() method. of this series.

See also

plot

For details of the plotting.

sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

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=[], copy=True)

Return a new Quantity object with the specified unit.

Parameters:

unitunit-like

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

equivalencieslist of tuple

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.

copybool, optional

If True (default), then the value is copied. Otherwise, a copy will only be made if necessary.

See also

to_value

get the numerical value in a given unit.

to_device()

to_dqflag(name=None, minlen=1, dtype=None, round=False, label=None, description=None)

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:

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(*args, **kwargs)

Bogus function inherited from superclass, do not use.

to_pycbc(copy=True)

Convert this TimeSeries into a PyCBC TimeSeries.

Parameters:

copybool, optional, default: True

if True, copy these data to a new array

Returns:

timeseriesTimeSeries

a PyCBC representation of this TimeSeries

to_string(unit=None, precision=None, format=None, subfmt=None, *, formatter=None)

Generate a string representation of the quantity and its unit.

The behavior of this function can be altered via the numpy.set_printoptions function and its various keywords. The exception to this is the threshold keyword, which is controlled via the [units.quantity] configuration item latex_array_threshold. This is treated separately because the numpy default of 1000 is too big for most browsers to handle.

Parameters:

unitunit-like, optional

Specifies the unit. If not provided, the unit used to initialize the quantity will be used.

precisionnumber, optional

The level of decimal precision. If None, or not provided, it will be determined from NumPy print options.

formatstr, optional

The format of the result. If not provided, an unadorned string is returned. Supported values are:

• ‘latex’: Return a LaTeX-formatted string

• ‘latex_inline’: Return a LaTeX-formatted string that uses negative exponents instead of fractions

formatterstr, callable, dict, optional

The formatter to use for the value. If a string, it should be a valid format specifier using Python’s mini-language. If a callable, it will be treated as the default formatter for all values and will overwrite default Latex formatting for exponential notation and complex numbers. If a dict, it should map a specific type to a callable to be directly passed into numpy.array2string. If not provided, the default formatter will be used.

subfmtstr, optional

Subformat of the result. For the moment, only used for format='latex' and format='latex_inline'. Supported values are:

• ‘inline’: Use $ ... $ as delimiters.

• ‘display’: Use $\displaystyle ... $ as delimiters.

Returns:

str

A string with the contents of this Quantity

to_value(unit=None, equivalencies=[])

The numerical value, possibly in a different unit.

Parameters:

unitunit-like, optional

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

equivalencieslist of tuple, 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:

valuendarray 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')

Not implemented, use .value.tobytes() instead.

tofile(fid, sep='', format='%s')

Not implemented, use .value.tofile() instead.

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 function.

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

tostring(order='C')

Not implemented, use .value.tostring() instead.

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.

Refer to numpy.transpose for full documentation.

Parameters:

axesNone, 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 that the array’s i-th axis becomes the transposed array’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:

pndarray

View of the array with its axes suitably permuted.

See also

transpose

Equivalent function.

ndarray.T

Array property returning the array transposed.

ndarray.reshape

Give a new shape to an array without changing its data.

Examples

>>> import numpy as np
>>> 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]])

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

update(other: QuantityLike, *, inplace: bool = True, gap: Literal['raise', 'ignore', 'pad'] | None = None, pad: float | None = None) → Self

Update this series by appending new data like a buffer.

Old data (at the start) are dropped to maintain a fixed size.

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

Parameters:

otherSeries, numpy.ndarray

The data to add to the end of this Series.

inplacebool

If True (default) modify the data in place. If False copy the data to new memory.

gapstr, optional

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

• 'raise' - raise a ValueError

• 'ignore' - remove gap and join data

• 'pad' - pad gap with zeros

If pad is given and is not None, the default is gap='pad', otherwise gap='raise'.

If gap='pad' is given, the default for pad is 0.

padfloat, optional

Value with which to pad discontiguous series, by default gaps will result in a ValueError.

Returns:

seriesSeries

Either the same series (if inplace=True) or a new series (if inplace=False) with other data added to the end of this ‘buffer’.

See also

append

For details of the data manipulation.

value_at(x: QuantityLike) → Quantity

Return the value of this Series at the given xindex value.

Parameters:

xfloat, Quantity

The xindex value at which to search.

Returns:

yQuantity

The value of this Series at the given xindex value.

Raises:

IndexError

If x doesn’t match an X-index value.

var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

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][, type])

New view of array with the same data.

Note

Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float64').

Parameters:

dtypedata-type or ndarray sub-class, optional

Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it 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).

typePython type, optional

Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter 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 last axis of a must be contiguous. This axis will be resized in the result.

Changed in version 1.23.0: Only the last axis needs to be contiguous. Previously, the entire array had to be C-contiguous.

Examples

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

Viewing array data using a different type and dtype:

>>> nonneg = np.dtype([("a", np.uint8), ("b", np.uint8)])
>>> y = x.view(dtype=nonneg, type=np.recarray)
>>> x["a"]
array([-1], dtype=int8)
>>> y.a
array([255], dtype=uint8)

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
>>> x
array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])

Using a view to convert an array to a recarray:

>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)

Views share data:

>>> x[0] = (9, 10)
>>> z[0]
np.record((9, 10), dtype=[('a', 'i1'), ('b', 'i1')])

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[:, ::2]
>>> y
array([[1, 3],
       [4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
    ...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
       [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])

However, views that change dtype are totally fine for arrays with a contiguous last axis, even if the rest of the axes are not C-contiguous:

>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
>>> x.transpose(1, 0, 2).view(np.int16)
array([[[ 256,  770],
        [3340, 3854]],

       [[1284, 1798],
        [4368, 4882]],

       [[2312, 2826],
        [5396, 5910]]], dtype=int16)

zip() → ndarray

Zip the xindex and value arrays of this Series.

Returns:

stacked2-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.]])