pygwb.omega_spectra.OmegaSpectrogram
- class pygwb.omega_spectra.OmegaSpectrogram(data, **kwargs)[source]
Bases:
Spectrogram
Subclass of gwpy’s Spectrogram class.
See also
gwpy.spectrogram.Spectrogram
More information here.
- __call__(*args, **kwargs)
Call self as a function.
Methods
__init__
(data[, alpha, fref, h0])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.
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.
crop_frequencies
([low, high, copy])Crop this Spectrogram to the specified frequencies
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 the given filter to this Spectrogram.
flatten
([order])Return a copy of the array collapsed into one dimension.
from_spectra
(*spectra, **kwargs)Build a new Spectrogram from a list of spectra.
getfield
(dtype[, offset])Returns a field of the given array as a certain type.
imshow
(**kwargs)inject
(other)Add two compatible Series along their shared x-axis values.
insert
(obj, values[, axis])Insert values along the given axis before the given indices and return a new ~astropy.units.Quantity object.
is_compatible
(other)Check whether this series and other have compatible metadata
is_contiguous
(other[, tol])Check whether other is contiguous with self.
item
(*args)Copy an element of an array to a scalar Quantity and return it.
itemset
(*args)Insert scalar into an array (scalar is cast to array's dtype, if possible)
load_from_pickle
(filename)Load spectrogram object from pickle file.
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.
pcolormesh
(**kwargs)percentile
(percentile)Calculate a given spectral percentile for this Spectrogram.
plot
([method, figsize, xscale])Plot the data for this Spectrogram
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.ratio
(operand)Calculate the ratio of this Spectrogram against a reference
ravel
([order])Return a flattened array.
read
(source, *args, **kwargs)Read data into a Spectrogram.
repeat
(repeats[, axis])Repeat elements of an array.
reset_h0
(new_h0)Reset the hubble parameter h0.
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.
reweight
(*[, new_alpha, new_fref])Reweight the spectrogram by a new spectral index alpha, and/or refer to a new reference frequency.
round
([decimals, out])Return a with each element rounded to the given number of decimals.
save_to_pickle
(filename)Save spectrogram object to pickle file.
searchsorted
(v[, side, sorter])Find indices where elements of v should be inserted in a to maintain order.
setfield
(val, dtype[, offset])Put a value into a specified place in a field defined by a data-type.
setflags
([write, align, uic])Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
shift
(delta)Shift this Series forward on the X-axis by
delta
sort
([axis, kind, order])Sort an array in-place.
squeeze
([axis])Remove axes of length one from a.
std
([axis, dtype, out, ddof, keepdims, where])Returns the standard deviation of the array elements along given axis.
step
(**kwargs)Create a step plot of this series
sum
([axis, dtype, out, keepdims, initial, where])Return the sum of the array elements over the given axis.
swapaxes
(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.
take
(indices[, axis, out, mode])Return an array formed from the elements of a at the given indices.
to
(unit[, equivalencies, copy])Return a new ~astropy.units.Quantity object with the specified unit.
to_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, y)Return the value of this Series at the given (x, y) coordinates
var
([axis, dtype, out, ddof, keepdims, where])Returns the variance of the array elements, along given axis.
variance
([bins, low, high, nbins, log, ...])Calculate the SpectralVariance of this Spectrogram.
view
([dtype][, type])New view of array with the same data.
write
(target, *args, **kwargs)Write this Spectrogram to a file.
zip
()Zip the xindex and value arrays of this Series
zpk
(zeros, poles, gain[, analog])Filter this Spectrogram by applying a zero-pole-gain filter
Attributes
View of the transposed array.
Spectral index alpha.
Frequency band described by this Spectrogram
Base object if memory is from some other object.
Returns a copy of the current Quantity instance with CGS units.
Instrumental channel associated with these data
An object to simplify the interaction of the array with the ctypes module.
Python buffer object pointing to the start of the array's data.
Frequency spacing of this Spectrogram
Time-spacing for this Spectrogram
Data-type of the array's elements.
X-axis sample separation
Y-axis sample separation
Starting GPS epoch for this Spectrogram
A list of equivalencies that will be applied by default during unit conversions.
Starting frequency for this Spectrogram
Information about the memory layout of the array.
A 1-D iterator over the Quantity array.
Reference frequency.
Series of frequencies for this Spectrogram
Hubble parameter h0.
The imaginary part of the array.
info
Container for meta information like name, description, format.
True if the value of this quantity is a scalar, or False if it is an array-like object.
Length of one array element in bytes.
Name for this data set
Total bytes consumed by the elements of the array.
Number of array dimensions.
The real part of the array.
Tuple of array dimensions.
Returns a copy of the current Quantity instance with SI units.
Number of elements in the array.
GPS [start, stop) span for this Spectrogram
Tuple of bytes to step in each dimension when traversing an array.
GPS time of first time bin
Series of GPS times for each sample
The physical unit of these data
The numerical value of this instance.
X-axis coordinate of the first data point
Positions of the data on the x-axis
X-axis [low, high) segment encompassed by these data
Unit of x-axis index
Y-axis coordinate of the first data point
Positions of the data on the y-axis
Y-axis [low, high) segment encompassed by these data
Unit of Y-axis index
- property T
View of the transposed array.
Same as
self.transpose()
.See also
Examples
>>> 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, extobj])
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()
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()
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
- property alpha
Spectral index alpha.
- 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)
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'
. Ifgap='pad'
is given, the default forpad
is0
.- 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])
- property band
Frequency band described by this Spectrogram
- 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
- 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 isFalse
.
- 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 valuesbut 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)
- property cgs
Returns a copy of the current Quantity instance with CGS units. The value of the resulting object will be scaled.
- property channel
Instrumental channel associated with these data
- Type:
~gwpy.detector.Channel
- 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
>>> 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)
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
orend
are outside of the original Series span, warnings will be printed and the limits will be restricted to thexspan
.
- crop_frequencies(low=None, high=None, copy=False)
Crop this Spectrogram to the specified frequencies
- Parameters:
- lowfloat
lower frequency bound for cropped Spectrogram
- highfloat
upper frequency bound for cropped Spectrogram
- copybool
if False return a view of the original data, otherwise create a fresh memory copy
- Returns:
- specSpectrogram
A new Spectrogram with a subset of data from the frequency axis
- 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 likectypes.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
A ctypes array of length self.ndim where the basetype is the C-integer corresponding to
dtype('p')
on this platform (see ~numpy.ctypeslib.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.- Type:
(c_intp*self.ndim)
- _ctypes.strides
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.
- Type:
(c_intp*self.ndim)
- _ctypes.data_as(obj)
Return the data pointer cast to a particular c-types object. For example, calling
self._as_parameter_
is equivalent toself.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
- 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
- data
Python buffer object pointing to the start of the array’s data.
- 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 ~astropy.units.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 ~astropy.units.UnitsError if it’s not possible to do so.
- Returns:
- newq~astropy.units.Quantity
A new object equal to this quantity with units decomposed.
- property df
Frequency spacing of this Spectrogram
- Type:
~astropy.units.Quantity in Hertz
- 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
- property dt
Time-spacing for this Spectrogram
- Type:
~astropy.units.Quantity in seconds
- dtype
Data-type of the array’s elements.
Warning
Setting
arr.dtype
is discouraged and may be deprecated in the future. Setting will replace thedtype
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'>
- 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()
Returns the pickle of the array as a string. pickle.loads will convert the string back to an array.
- Parameters:
- None
- property dx
X-axis sample separation
- Type:
~astropy.units.Quantity scalar
- property dy
Y-axis sample separation
- Type:
~astropy.units.Quantity scalar
- property epoch
Starting GPS epoch for this Spectrogram
- Type:
~gwpy.segments.Segment
- property equivalencies
A list of equivalencies that will be applied by default during unit conversions.
- property f0
Starting frequency for this Spectrogram
- Type:
~astropy.units.Quantity in Hertz
- 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.])
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)
- filter(*filt, **kwargs)
Apply the given filter to this Spectrogram.
- Parameters:
- *filtfilter arguments
1, 2, 3, or 4 arguments defining the filter to be applied,
an
Nx1
~numpy.ndarray of FIR coefficientsan
Nx6
~numpy.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:
- resultSpectrogram
the filtered version of the input Spectrogram, if
inplace=True
was given, this is just a reference to the modified input array
- Raises:
- ValueError
if
filt
arguments cannot be interpreted properly
- 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 ina.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 ifarr.shape[dim] == 1
or the array has no elements. It does not generally hold thatself.strides[-1] == self.itemsize
for C-style contiguous arrays orself.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.
- property flat
A 1-D iterator over the Quantity array.
This returns a
QuantityIterator
instance, which behaves the same as the ~numpy.flatiter instance returned by ~numpy.ndarray.flat, and is similar to, but not a subclass of, Python’s built-in iterator object.
- flatten(order='C')
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 ~astropy.units.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:
- y~astropy.units.Quantity
A copy of the input array, flattened to one dimension.
Examples
>>> a = Array([[1,2], [3,4]], unit='m', name='Test') >>> a.flatten() <Quantity [1., 2., 3., 4.] m>
- property fref
Reference frequency.
- property frequencies
Series of frequencies for this Spectrogram
- classmethod from_spectra(*spectra, **kwargs)
Build a new Spectrogram from a list of spectra.
- Parameters:
- *spectra
any number of ~gwpy.frequencyseries.FrequencySeries series
- dtfloat, ~astropy.units.Quantity, optional
stride between given spectra
- Returns:
- Spectrogram
a new Spectrogram from a vertical stacking of the spectra The new object takes the metadata from the first given ~gwpy.frequencyseries.FrequencySeries if not given explicitly
Notes
Each ~gwpy.frequencyseries.FrequencySeries passed to this constructor must be the same length.
- 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.]])
- property h0
Hubble parameter h0. Default is pygwb.constants.h0 = 0.6766.
- 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')
- inject(other)
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()
andplanck()
for more information.
- insert(obj, values, axis=None)
Insert values along the given axis before the given indices and return a new ~astropy.units.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 ofvalues
must be consistent with this quantity.- axisint, optional
Axis along which to insert
values
. Ifaxis
is None then the quantity array is flattened before insertion.
- Returns:
- out~astropy.units.Quantity
A copy of quantity with
values
inserted. Note that the insertion does not occur in-place: a new quantity array is returned.
Examples
>>> import astropy.units as u >>> q = [1, 2] * u.m >>> q.insert(0, 50 * u.cm) <Quantity [ 0.5, 1., 2.] m>
>>> q = [[1, 2], [3, 4]] * u.m >>> q.insert(1, [10, 20] * u.m, axis=0) <Quantity [[ 1., 2.], [ 10., 20.], [ 3., 4.]] m>
>>> q.insert(1, 10 * u.m, axis=1) <Quantity [[ 1., 10., 2.], [ 3., 10., 4.]] m>
- is_compatible(other)
Check whether this series and other have compatible metadata
This method tests that the sample size <Series.dx>, and the ~Series.unit match.
- is_contiguous(other, tol=3.814697265625e-06)
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
- property 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.
- 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 thana[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]])
- 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
- classmethod load_from_pickle(filename)[source]
Load spectrogram object from pickle file.
- Parameters:
filename (
str
) – Filename (inclusive of path) to load the pickled spectrogram from.- Returns:
- Spectrogram
OmegaSpectrogram
The spectrogram you wanted to read from
filename
.
- Spectrogram
- 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)
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 isnp.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
Notes
Given a vector
V
of lengthN
, the median ofV
is the middle value of a sorted copy ofV
,V_sorted
- i e.,V_sorted[(N-1)/2]
, whenN
is odd, and the average of the two middle values ofV_sorted
whenN
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
- property 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
- 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')
Forcefully reset the unit of these data
Use of this method is discouraged in favour of to(), which performs accurate conversions from one unit to another. The method should really only be used when the original unit of the array is plain wrong.
- Parameters:
- unit~astropy.units.Unit, 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)
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])
- percentile(percentile)
Calculate a given spectral percentile for this Spectrogram.
- Parameters:
- percentilefloat
percentile (0 - 100) of the bins to compute
- Returns:
- spectrum~gwpy.frequencyseries.FrequencySeries
the given percentile FrequencySeries calculated from this SpectralVaraicence
- plot(method='pcolormesh', figsize=(12, 6), xscale='auto-gps', **kwargs)
Plot the data for this Spectrogram
- Parameters:
- methodstr, optional
which plotting method to use to render this spectrogram, either
'pcolormesh'
(default) or'imshow'
- figsizetuple of float, optional
(width, height)
(inches) of the output figure- xscalestr, optional
the X-axis scale
- **kwargs
all keyword arguments are passed along to underlying functions, see below for references
- Returns:
- plot~gwpy.plot.Plot
the Plot containing the data
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
gwpy.plot.Axes.imshow
gwpy.plot.Axes.pcolormesh
for documentation of keyword arguments used in rendering the Spectrogram data
- prepend(other, inplace=True, pad=None, gap=None, resize=True)
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
- ratio(operand)
Calculate the ratio of this Spectrogram against a reference
- Parameters:
- operandstr, FrequencySeries, Quantity
a ~gwpy.frequencyseries.FrequencySeries or ~astropy.units.Quantity to weight against, or one of
'mean'
: weight against the mean of each spectrum in this Spectrogram'median'
: weight against the median of each spectrum in this Spectrogram
- Returns:
- spectrogramSpectrogram
a new Spectrogram
- Raises:
- ValueError
if
operand
is given as a str that isn’t supported
- 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 Spectrogram. Same usage as read method of gwpy.spectrogram.Spectrogram.
- Parameters:
source (
str
) – Source file path.- Returns:
- Data:
gwpy.spectrogram.Spectrogram
The read in spectrogram from the source.
- The available built-in formats are:
- Data:
- 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')
- repeat(repeats, axis=None)
Repeat elements of an array.
Refer to numpy.repeat for full documentation.
See also
numpy.repeat
equivalent function
- reset_h0(new_h0)[source]
Reset the hubble parameter h0. Expected values range between 0.5 and 1.
- Parameters:
new_h0 (
float
) – New h0 to set the spectrum at.
- 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 toa.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]])
- reweight(*, new_alpha=None, new_fref=None)[source]
Reweight the spectrogram by a new spectral index alpha, and/or refer to a new reference frequency.
- Parameters:
new_alpha (
float
) – New spectral index.new_fref (
float
) – New reference frequency.
- 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
- save_to_pickle(filename)[source]
Save spectrogram object to pickle file.
- Parameters:
filename (
str
) – Filename (inclusive of path) to save the pickled spectrogram to.
- 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
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
- 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.
- shift(delta)
Shift this Series forward on the X-axis by
delta
This modifies the series in-place.
- Parameters:
- deltafloat, ~astropy.units.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
- property 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 ofnp.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
- 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')])
- property span
GPS [start, stop) span for this Spectrogram
- Type:
~gwpy.segments.Segment
- 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)
Create a step plot of this series
- 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
- 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
- property t0
GPS time of first time bin
- Type:
~astropy.units.Quantity in seconds
- 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
- property times
Series of GPS times for each sample
- to(unit, equivalencies=[], copy=True)
Return a new ~astropy.units.Quantity object with the specified unit.
- Parameters:
- unitunit-like
An object that represents the unit to convert to. Must be an ~astropy.units.UnitBase object or a string parseable by the ~astropy.units package.
- equivalencieslist of tuple
A list of equivalence pairs to try if the units are not directly convertible. See astropy:unit_equivalencies. If not provided or
[]
, class default equivalencies will be used (none for ~astropy.units.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_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 itemlatex_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'
andformat='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 astropy:unit_equivalencies). If not provided or
[]
, class default equivalencies will be used (none for ~astropy.units.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.
See also
frombuffer
Inverse of this operation, construct a 1-dimensional array from Python bytes.
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 supportfileno()
(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 ~numpy.ndarray.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 aslist(a)
, except thattolist
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')
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.
See also
frombuffer
Inverse of this operation, construct a 1-dimensional array from Python bytes.
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.
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
>>> 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])
- property unit
The physical unit of these data
- Type:
~astropy.units.UnitBase
- update(other, inplace=True)
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 ~Series.append with resize=False.
- property value
The numerical value of this instance.
See also
to_value
Get the numerical value in a given unit.
- value_at(x, y)
Return the value of this Series at the given (x, y) coordinates
- Parameters:
- xfloat, ~astropy.units.Quantity
the xindex value at which to search
- xfloat, ~astropy.units.Quantity
the yindex value at which to search
- Returns:
- z~astropy.units.Quantity
the value of this Series at the given coordinates
- 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
- variance(bins=None, low=None, high=None, nbins=500, log=False, norm=False, density=False)
Calculate the SpectralVariance of this Spectrogram.
- Parameters:
- bins~numpy.ndarray, optional, default None
array of histogram bin edges, including the rightmost edge
- lowfloat, optional, default: None
left edge of lowest amplitude bin, only read if
bins
is not given- highfloat, optional, default: None
right edge of highest amplitude bin, only read if
bins
is not given- nbinsint, optional, default: 500
number of bins to generate, only read if
bins
is not given- logbool, optional, default: False
calculate amplitude bins over a logarithmic scale, only read if
bins
is not given- normbool, optional, default: False
normalise bin counts to a unit sum
- densitybool, optional, default: False
normalise bin counts to a unit integral
- Returns:
- specvarSpectralVariance
2D-array of spectral frequency-amplitude counts
See also
numpy.histogram
for details on specifying bins and weights
- 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 invokesdtype(None)
which is an alias fordtype('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)
ora.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)
ora.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)
, ifsome_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 ofa
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 Spectrogram to a file. Same usage as write method of
gwpy.spectrogram.Spectrogram
.- Parameters:
target (
str
) – Target file pathare (The available built-in formats) –
============= (====== ==== =====) –
Auto-identify (Format Read Write) –
============= – hdf5 Yes Yes No
============= –
- property x0
X-axis coordinate of the first data point
- Type:
~astropy.units.Quantity scalar
- property xindex
Positions of the data on the x-axis
- Type:
~astropy.units.Quantity array
- property xspan
X-axis [low, high) segment encompassed by these data
- Type:
~gwpy.segments.Segment
- property xunit
Unit of x-axis index
- Type:
~astropy.units.Unit
- property y0
Y-axis coordinate of the first data point
- Type:
~astropy.units.Quantity scalar
- property yindex
Positions of the data on the y-axis
- Type:
~astropy.units.Quantity array
- property yspan
Y-axis [low, high) segment encompassed by these data
- Type:
~gwpy.segments.Segment
- property yunit
Unit of Y-axis index
- Type:
~astropy.units.Unit
- zip()
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)
Filter this Spectrogram 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:
- specgramSpectrogram
the frequency-domain filtered version of the input data
See also
Spectrogram.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)