FrequencySeries¶

class gwpy.frequencyseries.FrequencySeries(data, unit=None, f0=None, df=None, frequencies=None, name=None, epoch=None, channel=None, **kwargs)[source]¶

A data array holding some metadata to represent a frequency series

Parameters¶

valuearray-like: input data array
unitUnit, optional: physical unit of these data
f0float, Quantity, optional, default: 0: starting frequency for these data
dffloat, Quantity, optional, default: 1: frequency resolution for these data
frequenciesarray-like: the complete array of frequencies indexing the data. This argument takes precedence over f0 and df so should be given in place of these if relevant, not alongside
epochLIGOTimeGPS, float, str, optional: GPS epoch associated with these data, any input parsable by to_gps is fine
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:

`read`(source, args, *kwargs)	Read data into a `FrequencySeries`
`write`(target, args, *kwargs)	Write this `FrequencySeries` to a file
`plot`([xscale])	Plot the data for this series
`zpk`(zeros, poles, gain[, analog])	Filter this `FrequencySeries` by applying a zero-pole-gain filter

Attributes Summary

`T`	The transposed array.
`base`	Base object if memory is from some other object.
`cgs`	Returns a copy of the current `Quantity` instance with CGS units.
`channel`	Instrumental channel associated with these data
`ctypes`	An object to simplify the interaction of the array with the ctypes module.
`data`	Python buffer object pointing to the start of the array's data.
`df`	Frequency spacing of this `FrequencySeries`
`dtype`	Data-type of the array's elements.
`dx`	X-axis sample separation
`epoch`	GPS epoch associated with these data
`equivalencies`	A list of equivalencies that will be applied by default during unit conversions.
`f0`	Starting frequency for this `FrequencySeries`
`flags`	Information about the memory layout of the array.
`flat`	A 1-D iterator over the Quantity array.
`frequencies`	Series of frequencies for each sample
`imag`	The imaginary part of the array.
`info`	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.
`itemsize`	Length of one array element in bytes.
`name`	Name for this data set
`nbytes`	Total bytes consumed by the elements of the array.
`ndim`	Number of array dimensions.
`real`	The real part of the array.
`shape`	Tuple of array dimensions.
`si`	Returns a copy of the current `Quantity` instance with SI units.
`size`	Number of elements in the array.
`strides`	Tuple of bytes to step in each dimension when traversing an array.
`unit`	The physical unit of these data
`value`	The numerical value of this instance.
`x0`	X-axis coordinate of the first data point
`xindex`	Positions of the data on the x-axis
`xspan`	X-axis [low, high) segment encompassed by these data
`xunit`	Unit of x-axis index

Methods Summary

`abs`(x, /[, out, where, casting, order, ...])	Calculate the absolute value element-wise.
`all`([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
`any`([axis, out, keepdims, where])	Returns True if any of the elements of `a` evaluate to True.
`append`(other[, inplace, pad, gap, resize])	Connect another series onto the end of the current one.
`argmax`([axis, out, keepdims])	Return indices of the maximum values along the given axis.
`argmin`([axis, out, keepdims])	Return indices of the minimum values along the given axis.
`argpartition`(kth[, axis, kind, order])	Returns the indices that would partition this array.
`argsort`([axis, kind, order])	Returns the indices that would sort this array.
`astype`(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
`byteswap`([inplace])	Swap the bytes of the array elements
`choose`(choices[, out, mode])	Use an index array to construct a new array from a set of choices.
`clip`([min, max, out])	Return an array whose values are limited to `[min, max]`.
`compress`(condition[, axis, out])	Return selected slices of this array along given axis.
`conj`()	Complex-conjugate all elements.
`conjugate`()	Return the complex conjugate, element-wise.
`copy`([order])	Return a copy of the array.
`crop`([start, end, copy])	Crop this series to the given x-axis extent.
`cumprod`([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
`cumsum`([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
`decompose`([bases])	Generates a new `Quantity` with the units decomposed.
`diagonal`([offset, axis1, axis2])	Return specified diagonals.
`diff`([n, axis])	Calculate the n-th order discrete difference along given axis.
`dot`(b[, out])
`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])
`fill`(value)	Fill the array with a scalar value.
`filter`(filt, *kwargs)	Apply a filter to this `FrequencySeries`.
`filterba`(args, *kwargs)
`flatten`([order])	Return a copy of the array collapsed into one dimension.
`from_lal`(lalfs[, copy])	Generate a new `FrequencySeries` from a LAL `FrequencySeries` of any type.
`from_pycbc`(fs[, copy])	Convert a `pycbc.types.frequencyseries.FrequencySeries` into a `FrequencySeries`
`getfield`(dtype[, offset])	Returns a field of the given array as a certain type.
`ifft`()	Compute the one-dimensional discrete inverse Fourier transform of this `FrequencySeries`.
`inject`(other)	Add two compatible `Series` along their shared x-axis values.
`insert`(obj, values[, axis])	Insert values along the given axis before the given indices and return a new `Quantity` object.
`interpolate`(df)	Interpolate this `FrequencySeries` to a new resolution.
`is_compatible`(other)	Check whether this series and other have compatible metadata
`is_contiguous`(other[, tol])	Check whether other is contiguous with self.
`item`(*args)	Copy an element of an array to a scalar Quantity and return it.
`itemset`(*args)	Insert scalar into an array (scalar is cast to array's dtype, if possible)
`max`([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
`mean`([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
`median`([axis])	Compute the median along the specified axis.
`min`([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
`nansum`([axis, out, keepdims, initial, where])
`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 the value of the element in kth position is in the position it would be in a sorted array.
`plot`([xscale])	Plot the data for this series
`prepend`(other[, inplace, pad, gap, resize])	Connect another series onto the start of the current one.
`prod`([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
`ptp`([axis, out, keepdims])	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 `FrequencySeries`
`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, 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_lal`()	Convert this `FrequencySeries` into a LAL FrequencySeries.
`to_pycbc`([copy])	Convert this `FrequencySeries` into a `FrequencySeries`
`to_string`([unit, precision, format, subfmt])	Generate a string representation of the quantity and its unit.
`to_value`([unit, equivalencies])	The numerical value, possibly in a different unit.
`tobytes`([order])	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 an `a.ndim`-levels deep nested list of Python scalars.
`tostring`([order])	Construct Python bytes containing the raw data bytes in the array.
`trace`([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
`transpose`(*axes)	Returns a view of the array with axes transposed.
`update`(other[, inplace])	Update this series by appending new data from an other and dropping the same amount of data off the start.
`value_at`(x)	Return the value of this `Series` at the given `xindex` value
`var`([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.
`view`([dtype][, type])	New view of array with the same data.
`write`(target, args, *kwargs)	Write this `FrequencySeries` to a file
`zip`()	Zip the `xindex` and `value` arrays of this `Series`
`zpk`(zeros, poles, gain[, analog])	Filter this `FrequencySeries` by applying a zero-pole-gain filter

Attributes Documentation

T¶

The transposed array.

Same as self.transpose().

See also

transpose

Examples

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

base¶

Base object if memory is from some other object.

Examples

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

>>> 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

Type¶: Channel

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 will not 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 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.

df¶

Frequency spacing of this FrequencySeries

Type¶: Quantity scalar

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

>>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>

dx¶

X-axis sample separation

Type¶: Quantity scalar

epoch¶

GPS epoch associated with these data

Type¶: Time

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

f0¶

Starting frequency for this FrequencySeries

Type¶: Quantity scalar

flags¶

Information about the memory layout of the array.

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.

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.

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.

frequencies¶: Series of frequencies for each sample

imag¶

The imaginary part of the array.

Examples

>>> 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.

itemsize¶

Length of one array element in bytes.

Examples

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

name¶

Name for this data set

Type¶: str

nbytes¶

Total bytes consumed by the elements of the array.

Notes

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

Examples

>>> 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

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

real¶

The real part of the array.

See also

numpy.real: equivalent function

Examples

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

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

>>> 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: total size of new array must be unchanged
>>> 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

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

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 “ndarray.rst” file in the NumPy reference guide.

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

>>> y = np.reshape(np.arange(2*3*4), (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]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17

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

unit¶

The physical unit of these data

Type¶: UnitBase

value¶

The numerical value of this instance.

See also

to_value: Get the numerical value in a given unit.

x0¶

X-axis coordinate of the first data point

Type¶: Quantity scalar

xindex¶

Positions of the data on the x-axis

Type¶: Quantity array

xspan¶

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

Type¶: Segment

xunit¶

Unit of x-axis index

Type¶: Unit

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.

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 $\sqrt{ a^2 + b^2 }$ . This is a scalar if x is a scalar.

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-frequencyseries-FrequencySeries-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-frequencyseries-FrequencySeries-1_01_00.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])

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, inplace=True, pad=None, gap=None, resize=True)[source]¶

Connect another series onto the end of the current one.

Parameters¶

otherSeries

another series of the same type to connect to this one

inplacebool, optional

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

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!

padfloat, optional

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

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

resizebool, optional

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, default: True.

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.

New in version 1.8.0.

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).

Notes

Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not.

Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted.

Examples

>>> 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

>>> 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.newbyteorder().byteswap() produces an array with the same values: but different representation in memory

>>> A = np.array([1, 2, 3])
>>> 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.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> 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)

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')[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 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

>>> 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¶

startfloat, optional: lower limit of x-axis to crop to, defaults to current x0
endfloat, optional: upper limit of x-axis to crop to, defaults to current series end
copybool, optional, default: False: copy the input data to fresh memory, otherwise return a view

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=[])¶

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)[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¶

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)¶

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

Parameters¶

filestr or Path: A string naming the dump file.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

dumps()[source]¶

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

Parameters¶

None

ediff1d(to_end=None, to_begin=None)¶

fill(value)¶

Fill the array with a scalar value.

Parameters¶

valuescalar: 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.])

filter(*filt, **kwargs)[source]¶

Apply a filter to this FrequencySeries.

Parameters¶

*filtfilter arguments

1, 2, 3, or 4 arguments defining the filter to be applied,

an Nx1 ndarray of FIR coefficients

an Nx6 ndarray of SOS coefficients

(numerator, denominator) polynomials

(zeros, poles, gain)

(A, B, C, D) ‘state-space’ representation

analogbool, optional

if True, filter definition will be converted from Hertz to Z-domain digital representation, default: False

inplacebool, optional

if True, this array will be overwritten with the filtered version, default: False

Returns¶

resultFrequencySeries: the filtered version of the input FrequencySeries, if inplace=True was given, this is just a reference to the modified input array

Raises¶

ValueError: if filt arguments cannot be interpreted properly

filterba(*args, **kwargs)[source]¶

flatten(order='C')[source]¶

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’}, 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¶

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_lal(lalfs, copy=True)[source]¶: Generate a new FrequencySeries from a LAL FrequencySeries of any type.

classmethod from_pycbc(fs, copy=True)[source]¶

Convert a pycbc.types.frequencyseries.FrequencySeries into a FrequencySeries

Parameters¶

fspycbc.types.frequencyseries.FrequencySeries: the input PyCBC FrequencySeries array
copybool, optional, default: True: if True, copy these data to a new array

Returns¶

spectrumFrequencySeries: a GWpy version of the input frequency series

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

>>> 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.]])

ifft()[source]¶

Compute the one-dimensional discrete inverse Fourier transform of this FrequencySeries.

Returns¶

outTimeSeries: the normalised, real-valued TimeSeries.

See also

numpy.fft.irfft: The inverse (real) FFT function

Notes

This method applies the necessary normalisation such that the condition holds:

>>> timeseries = TimeSeries([1.0, 0.0, -1.0, 0.0], sample_rate=1.0)
>>> timeseries.fft().ifft() == timeseries

inject(other)[source]¶

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>

interpolate(df)[source]¶

Interpolate this FrequencySeries to a new resolution.

Parameters¶

dffloat: desired frequency resolution of the interpolated FrequencySeries, in Hz

Returns¶

outFrequencySeries: the interpolated version of the input FrequencySeries

See also

numpy.interp: for the underlying 1-D linear interpolation scheme

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¶

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 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 scalar Quantity and return it.

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

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¶

*argsArguments: 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

>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
       [1, 0, 6],
       [1, 0, 9]])

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, **kwargs)[source]¶

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 is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0.
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.

New in version 1.9.0.

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

>>> 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, 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

nansum(axis=None, out=None, keepdims=False, *, initial=None, where=True)¶

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_orderstring, 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
{‘<’, ‘little’} - little endian
{‘>’, ‘big’} - big endian
{‘=’, ‘native’} - native order, equivalent to sys.byteorder
{‘|’, ‘I’} - ignore (no change to byte order)

The default value (‘S’) results in swapping the current byte order.

Returns¶

new_arrarray: 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¶

unitUnit, str: the unit to force onto this array
parse_strictstr, 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_widthint, pair of ints: 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: see numpy.pad() for kwarg documentation

Returns¶

seriesSeries: 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 the 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¶

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

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

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

plot(xscale='log', **kwargs)[source]¶

Plot the data for this series

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, inplace=True, pad=None, gap=None, resize=True)[source]¶

Connect another series onto the start of the current one.

Parameters¶

otherSeries

another series of the same type as this one

inplacebool, optional

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

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!

padfloat, optional

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

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 'pad', otherwise 'raise'.

resizebool, optional

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, default: True.

Returns¶

seriesTimeSeries: time-series containing joined data sets

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

ptp(axis=None, out=None, keepdims=False)¶

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.

classmethod read(source, *args, **kwargs)[source]¶

Read data into a FrequencySeries

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¶

sourcestr, list

Source of data, any of the following:

str path of single data file,
str path of LAL-format cache file,
list of paths.

*args

Other arguments are (in general) specific to the given format.

formatstr, optional

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

**kwargs

Other keywords are (in general) specific to the given format.

Raises¶

IndexError: if source is an empty list

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
csv	Yes	Yes	Yes
hdf5	Yes	Yes	Yes
ligolw	Yes	No	No
txt	Yes	Yes	Yes

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

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:

>>> 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

>>> 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 and 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 four 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

>>> 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)[source]¶

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.

Changed in version 1.15.0: The ‘stable’ option was added.
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

>>> 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)[source]¶: Create a step plot of this series

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_lal()[source]¶

Convert this FrequencySeries into a LAL FrequencySeries.

Returns¶

lalspecFrequencySeries: an XLAL-format FrequencySeries of a given type, e.g. REAL8FrequencySeries

Notes

Currently, this function is unable to handle unit string conversion.

to_pycbc(copy=True)[source]¶

Convert this FrequencySeries into a FrequencySeries

Parameters¶

copybool, optional, default: True: if True, copy these data to a new array

Returns¶

frequencyseriespycbc.types.frequencyseries.FrequencySeries: a PyCBC representation of this FrequencySeries

to_string(unit=None, precision=None, format=None, subfmt=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

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')¶

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 is produced in C-order by default. This behavior is controlled by the order parameter.

New in version 1.9.0.

Parameters¶

order{‘C’, ‘F’, ‘A’}, optional: Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

Returns¶

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

Examples

>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\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¶

fidfile or str or Path: An open file object, or a string containing a filename.

Changed in version 1.17.0: pathlib.Path objects are now accepted.
sepstr: Separator between array items for text output. If “” (empty), a binary file is written, equivalent to file.write(a.tobytes()).
formatstr: 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.

When fid is a file object, array contents are directly written to the file, bypassing the file object’s write method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not support fileno() (e.g., BytesIO).

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:

>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 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')[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 is produced in C-order by default. This behavior is controlled by the order parameter.

New in version 1.9.0.

Parameters¶

order{‘C’, ‘F’, ‘A’}, optional: Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

Returns¶

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

Examples

>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\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, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2-D array, this is a standard 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¶

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 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¶

outndarray: View of a, with 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

>>> 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¶

xfloat, Quantity: the xindex value at which to search

Returns¶

yQuantity: the value of this Series at the given xindex 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('float_').

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

>>> 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.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
>>> 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]
(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[:, ::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)

write(target, *args, **kwargs)[source]¶

Write this FrequencySeries to a file

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¶

targetstr: output filename
formatstr, 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
hdf5	Yes	Yes	No
txt	Yes	Yes	Yes

zip()[source]¶

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.]])

zpk(zeros, poles, gain, analog=True)[source]¶

Filter this FrequencySeries by applying a zero-pole-gain filter

Parameters¶

zerosarray-like: list of zero frequencies (in Hertz)
polesarray-like: list of pole frequencies (in Hertz)
gainfloat: DC gain of filter
analogbool, optional: type of ZPK being applied, if analog=True all parameters will be converted in the Z-domain for digital filtering

Returns¶

spectrumFrequencySeries: the frequency-domain filtered version of the input data

See also

FrequencySeries.filter: for details on how a digital ZPK-format filter is applied

Examples

To apply a zpk filter with file poles at 100 Hz, and five zeros at 1 Hz (giving an overall DC gain of 1e-10):

>>> data2 = data.zpk([100]*5, [1]*5, 1e-10)