OneDSpectrum¶
- class spectral_cube.OneDSpectrum(value, beam=None, read_beam=False, **kwargs)[source]¶
Bases:
BaseOneDSpectrum
,BeamMixinClass
Attributes Summary
View of the transposed array.
Get a pure array representation of the LDO.
Base object if memory is from some other object.
Returns a copy of the current
Quantity
instance with CGS units.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.
Data-type of the array's elements.
A list of equivalencies that will be applied by default during unit conversions.
The replacement value used by
filled_data
.Return a portion of the data array, with excluded mask values
Information about the memory layout of the array.
A 1-D iterator over the Quantity array.
The imaginary part of the array.
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.
Total bytes consumed by the elements of the array.
Number of array dimensions.
Get a pure
Quantity
representation of the LDO.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.
A
Quantity
array containing the central values of each channel along the spectral axis.Tuple of bytes to step in each dimension when traversing an array.
A
UnitBase
object representing the unit of this quantity.Return a portion of the data array, with excluded mask values
The numerical value of this instance.
Write this LowerDimensionalObject object out in the specified format.
Methods Summary
all
([axis, out, keepdims, where])Returns True if all elements evaluate to True.
any
([axis, out, keepdims, where])Returns True if any of the elements of
a
evaluate to True.argmax
([axis, out, keepdims])Return indices of the maximum values along the given axis.
argmin
([axis, out, keepdims])Return indices of the minimum values along the given axis.
argpartition
(kth[, axis, kind, order])Returns the indices that would partition this array.
argsort
([axis, kind, order])Returns the indices that would sort this array.
astype
(dtype[, order, casting, subok, copy])Copy of the array, cast to a specified type.
byteswap
([inplace])Swap the bytes of the array elements
check_jybeam_smoothing
([raise_error_jybm])This runs for spatial resolution operations (e.g.
spatial_smooth
) and either an error or warning when smoothing will affect brightness in Jy/beam operations.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.
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])dot
(b[, out])dump
(file)Not implemented, use
.value.dump()
instead.dumps
()Not implemented, use
.value.dumps()
instead.ediff1d
([to_end, to_begin])fill
(value)Fill the array with a scalar value.
filled
([fill_value])flatten
([order])Return a copy of the array collapsed into one dimension.
from_hdu
(hdu[, ext])Return a OneDSpectrum from a FITS HDU or HDU list.
getfield
(dtype[, offset])Returns a field of the given array as a certain type.
insert
(obj, values[, axis])Insert values along the given axis before the given indices and return a new
Quantity
object.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.
min
([axis, out, keepdims, initial, where])Return the minimum along a given axis.
nansum
([axis, out, keepdims, initial, where])Deprecated since version 5.3.
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.
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.
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 alln
in indices.quicklook
([filename, drawstyle])Plot the spectrum with current spectral units in the currently open figure
ravel
([order])Return a flattened array.
read
(*args, **kwargs)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.
Copy of the numpy masked_array shrink_mask method.
sort
([axis, kind, order])Sort an array in-place.
spectral_interpolate
(spectral_grid[, ...])Resample the spectrum onto a specific grid
spectral_smooth
(kernel[, convolve])Smooth the spectrum
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.
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
andaxis2
interchanged.take
(indices[, axis, out, mode])Return an array formed from the elements of
a
at the given indices.to
(unit[, equivalencies])Return a new
OneDSpectrum
of the same class 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])Not implemented, use
.value.tobytes()
instead.tofile
(fid[, sep, format])Not implemented, use
.value.tofile()
instead.tolist
()Return the array as an
a.ndim
-levels deep nested list of Python scalars.tostring
([order])Not implemented, use
.value.tostring()
instead.trace
([offset, axis1, axis2, dtype, out])Return the sum along diagonals of the array.
transpose
(*axes)Returns a view of the array with axes transposed.
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.
with_beam
(beam[, raise_error_jybm])Attach a new beam object to the OneDSpectrum.
with_fill_value
(fill_value)Create a new
OneDSpectrum
with a differentfill_value
.with_spectral_unit
(unit[, ...])Attributes Documentation
- 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])
- array¶
Get a pure array representation of the LDO. Useful when multiplying and using numpy indexing tricks.
- 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
- beam¶
- cgs¶
Returns a copy of the current
Quantity
instance with CGS units. The value of the resulting object will be scaled.
- 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
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
(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 (seec_intp
). This base-type could bectypes.c_int
,ctypes.c_long
, orctypes.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 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
- data¶
Python buffer object pointing to the start of the array’s data.
- 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 alsondarray.view
andndarray.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'>
- equivalencies¶
A list of equivalencies that will be applied by default during unit conversions.
- fill_value¶
The replacement value used by
filled_data
.fill_value is immutable; use
with_fill_value
to create a new cube with a different fill value.
- filled_data¶
Return a portion of the data array, with excluded mask values replaced by
fill_value
.- Returns:
- dataQuantity
The masked data.
Notes
Supports efficient Numpy slice notation, like
filled_data[0:3, :, 2:4]
- flags¶
Information about the memory layout of the array.
Notes
The
flags
object can be accessed dictionary-like (as ina.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.
- flat¶
A 1-D iterator over the Quantity array.
This returns a
QuantityIterator
instance, which behaves the same as theflatiter
instance returned byflat
, and is similar to, but not a subclass of, Python’s built-in iterator object.
- hdu¶
- header¶
- 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 thatnumpy.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
- mask¶
- meta¶
- nbytes¶
Total bytes consumed by the elements of the array.
See also
sys.getsizeof
Memory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.
Notes
Does not include memory consumed by non-element attributes of the array object.
Examples
>>> 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
- pixels_per_beam¶
- 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. Usingndarray.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 suggestednp.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
- spectral_axis¶
A
Quantity
array containing the central values of each channel along the spectral axis.
- 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 arraya
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
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
- unitless_filled_data¶
Return a portion of the data array, with excluded mask values replaced by
fill_value
.- Returns:
- datanumpy.array
The masked data.
Notes
Supports efficient Numpy slice notation, like
unitless_filled_data[0:3, :, 2:4]
- value¶
The numerical value of this instance.
See also
to_value
Get the numerical value in a given unit.
- wcs¶
- write¶
Write this LowerDimensionalObject object out in the specified format.
This allows easily writing a dataset in many supported data formats using syntax such as:
>>> data.write('data.fits', format='fits')
Get help on the available writers for LowerDimensionalObject using the``help()`` method:
>>> LowerDimensionalObject.write.help() # Get help writing LowerDimensionalObject and list supported formats >>> LowerDimensionalObject.write.help('fits') # Get detailed help on LowerDimensionalObject FITS writer >>> LowerDimensionalObject.write.list_formats() # Print list of available formats
See also: http://docs.astropy.org/en/stable/io/unified.html
- Parameters:
- *argstuple, optional
Positional arguments passed through to data writer. If supplied the first argument is the output filename.
- formatstr
File format specifier.
- **kwargsdict, optional
Keyword arguments passed through to data writer.
Methods Documentation
- all(axis=None, out=None, keepdims=False, *, where=True)¶
Returns True if all elements evaluate to True.
Refer to
numpy.all
for full documentation.See also
numpy.all
equivalent function
- any(axis=None, out=None, keepdims=False, *, where=True)¶
Returns True if any of the elements of
a
evaluate to True.Refer to
numpy.any
for full documentation.See also
numpy.any
equivalent function
- 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
, andsubok
requirements are satisfied, the input array is returned instead of a copy.
- Returns:
- 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 isFalse
.
- Returns:
- outndarray
The byteswapped array. If
inplace
isTrue
, 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)
- check_jybeam_smoothing(raise_error_jybm=True)¶
This runs for spatial resolution operations (e.g.
spatial_smooth
) and either an error or warning when smoothing will affect brightness in Jy/beam operations.This is also true for using the
with_beam
andwith_beams
methods, including 1D spectra with Jy/beam units.- Parameters:
- raise_error_jybmbool, optional
Raises a
BeamUnitsError
when True (default). When False, it triggers aBeamWarning
.
- 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 ofa
as closely as possible. (Note that this function andnumpy.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
- 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 (seeastropy.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.
- basessequence of
- Returns:
- newq
Quantity
A new object equal to this quantity with units decomposed.
- newq
- 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)¶
- dot(b, out=None)¶
- dump(file)¶
Not implemented, use
.value.dump()
instead.
- dumps()¶
Not implemented, use
.value.dumps()
instead.
- ediff1d(to_end=None, to_begin=None)¶
- 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)
- filled(fill_value=None)¶
- flatten(order='C')¶
Return a copy of the array collapsed into one dimension.
- Parameters:
- order{‘C’, ‘F’, ‘A’, ‘K’}, optional
‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if
a
is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flattena
in the order the elements occur in memory. The default is ‘C’.
- Returns:
- yndarray
A copy of the input array, flattened to one dimension.
Examples
>>> a = np.array([[1,2], [3,4]]) >>> a.flatten() array([1, 2, 3, 4]) >>> a.flatten('F') array([1, 3, 2, 4])
- static from_hdu(hdu, ext=0)¶
Return a OneDSpectrum from a FITS HDU or HDU list.
- Parameters:
- extint
The integer index to load when given an
astropy.io.fits.HDUList
. Default is 0 (the first HDU in the list.
- 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.]])
- 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 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
Quantity
A copy of quantity with
values
inserted. Note that the insertion does not occur in-place: a new quantity array is returned.
- out
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>
- item(*args)¶
Copy an element of an array to a scalar Quantity and return it.
Like
item()
except that it always returns aQuantity
, 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 andargs
must select a single item in the arraya
.- 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 anndarray
, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when usingitemset
(anditem
) 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
- 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)¶
Deprecated since version 5.3: The nansum method is deprecated and may be removed in a future version. Use np.nansum instead.
- 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
- 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])
- 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 alln
in indices.Refer to
numpy.put
for full documentation.See also
numpy.put
equivalent function
- quicklook(filename=None, drawstyle='steps-mid', **kwargs)¶
Plot the spectrum with current spectral units in the currently open figure
kwargs are passed to
matplotlib.pyplot.plot
- Parameters:
- filenamestr or Non
Optional - the filename to save the quicklook to.
- ravel([order])¶
Return a flattened array.
Refer to
numpy.ravel
for full documentation.See also
numpy.ravel
equivalent function
ndarray.flat
a flat iterator on the array.
- read(*args, **kwargs)¶
- 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 onndarray
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.
- new_shapetuple of ints, or
- 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
intoa
’s field defined bydtype
and beginningoffset
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
- shrink_mask()¶
Copy of the numpy masked_array shrink_mask method. This is essentially a hack needed for matplotlib to show images.
- 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')])
- spectral_interpolate(spectral_grid, suppress_smooth_warning=False, fill_value=None)¶
Resample the spectrum onto a specific grid
- Parameters:
- spectral_gridarray
An array of the spectral positions to regrid onto
- suppress_smooth_warningbool
If disabled, a warning will be raised when interpolating onto a grid that does not nyquist sample the existing grid. Disable this if you have already appropriately smoothed the data.
- fill_valuefloat
Value for extrapolated spectral values that lie outside of the spectral range defined in the original data. The default is to use the nearest spectral channel in the cube.
- Returns:
- spectrumOneDSpectrum
- spectral_smooth(kernel, convolve=<function convolve>, **kwargs)¶
Smooth the spectrum
- Parameters:
- kernel
Kernel1D
A 1D kernel from astropy
- convolvefunction
The astropy convolution function to use, either
astropy.convolution.convolve
orastropy.convolution.convolve_fft
- kwargsdict
Passed to the convolve function
- kernel
- 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
- 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
andaxis2
interchanged.Refer to
numpy.swapaxes
for full documentation.See also
numpy.swapaxes
equivalent function
- take(indices, axis=None, out=None, mode='raise')¶
Return an array formed from the elements of
a
at the given indices.Refer to
numpy.take
for full documentation.See also
numpy.take
equivalent function
- to(unit, equivalencies=[])¶
Return a new
OneDSpectrum
of the same class with the specified unit. Seeastropy.units.Quantity.to
for further details.
- 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 thethreshold
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 Equivalencies). If not provided or
[]
, class default equivalencies will be used (none forQuantity
, but may be set for subclasses). IfNone
, no equivalencies will be applied at all, not even any set globally or within a context.
- Returns:
- valuendarray or scalar
The value in the units specified. For arrays, this will be a view of the data if no unit conversion was necessary.
See also
to
Get a new instance in a different unit.
- tobytes(order='C')¶
Not implemented, use
.value.tobytes()
instead.
- tofile(fid, sep='', format='%s')¶
Not implemented, use
.value.tofile()
instead.
- tolist()¶
Return the array as an
a.ndim
-levels deep nested list of Python scalars.Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the
item
function.If
a.ndim
is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.- Parameters:
- none
- Returns:
- yobject, or list of object, or list of list of object, or …
The possibly nested list of array elements.
Notes
The array may be recreated via
a = np.array(a.tolist())
, although this may sometimes lose precision.Examples
For a 1D array,
a.tolist()
is almost the same 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')¶
Not implemented, use
.value.tostring()
instead.
- trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)¶
Return the sum along diagonals of the array.
Refer to
numpy.trace
for full documentation.See also
numpy.trace
equivalent function
- transpose(*axes)¶
Returns a view of the array with axes transposed.
Refer to
numpy.transpose
for full documentation.- Parameters:
- axesNone, tuple of ints, or
n
ints None or no argument: reverses the order of the axes.
tuple of ints:
i
in thej
-th place in the tuple means that the array’si
-th axis becomes the transposed array’sj
-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).
- axesNone, tuple of ints, or
- 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])
- 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 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 thetype
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 ofndarray_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)
- with_beam(beam, raise_error_jybm=True)[source]¶
Attach a new beam object to the OneDSpectrum.
- Parameters:
- beam
Beam
A new beam object.
- beam
- with_fill_value(fill_value)¶
Create a new
OneDSpectrum
with a differentfill_value
.
- with_spectral_unit(unit, velocity_convention=None, rest_value=None)¶