Projection¶

class spectral_cube.Projection(value, unit=None, dtype=None, copy=True, wcs=None, meta=None, mask=None, header=None, beam=None, fill_value=nan, read_beam=False, wcs_tolerance=0.0)[source]¶

Bases: LowerDimensionalObject, SpatialCoordMixinClass, MaskableArrayMixinClass, BeamMixinClass

Attributes Summary

`T`	View of the transposed array.
`array`	Get a pure array representation of the LDO.
`base`	Base object if memory is from some other object.
`beam`
`cgs`	Returns a copy of the current `Quantity` instance with CGS units.
`ctypes`	An object to simplify the interaction of the array with the ctypes module.
`data`	Python buffer object pointing to the start of the array's data.
`device`
`dtype`	Data-type of the array's elements.
`equivalencies`	A list of equivalencies that will be applied by default during unit conversions.
`fill_value`	The replacement value used by `filled_data`.
`filled_data`	Return a portion of the data array, with excluded mask values
`flags`	Information about the memory layout of the array.
`flat`	A 1-D iterator over the Quantity array.
`hdu`
`header`
`imag`	The imaginary part of the array.
`info`	Container for meta information like name, description, format.
`isscalar`	True if the `value` of this quantity is a scalar, or False if it is an array-like object.
`itemsize`	Length of one array element in bytes.
`latitude_extrema`
`longitude_extrema`
`mT`	View of the matrix transposed array.
`mask`
`meta`
`nbytes`	Total bytes consumed by the elements of the array.
`ndim`	Number of array dimensions.
`pixels_per_beam`
`quantity`	Get a pure `Quantity` representation of the LDO.
`real`	The real part of the array.
`shape`	Tuple of array dimensions.
`si`	Returns a copy of the current `Quantity` instance with SI units.
`size`	Number of elements in the array.
`spatial_coordinate_map`
`strides`	Tuple of bytes to step in each dimension when traversing an array.
`unit`	A `UnitBase` object representing the unit of this quantity.
`unitless_filled_data`	Return a portion of the data array, with excluded mask values
`value`	The numerical value of this instance.
`wcs`
`world`	Return a list of the world coordinates in a cube, projection, or a view
`world_extrema`
`write`	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, stable])	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.
`conjugate`()	Return the complex conjugate, element-wise.
`convolve_to`(beam[, convolve])	Convolve the image to a specified beam.
`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`(other, /[, out])	Refer to `numpy.dot()` for full documentation.
`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.
`flattened_world`([view])	Retrieve the world coordinates corresponding to the extracted flattened version of the cube
`from_hdu`(hdu[, ext])	Return a projection from a FITS HDU.
`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.
`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.
`nonzero`()	Return the indices of the elements that are non-zero.
`partition`(kth[, axis, kind, order])	Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array.
`prod`([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
`put`(indices, values[, mode])	Set `a.flat[n] = values[n]` for all `n` in indices.
`quicklook`([filename, use_aplpy, aplpy_kwargs])	Use APLpy to make a quick-look image of the projection.
`ravel`([order])	Return a flattened array.
`read`(args, *kwargs)
`repeat`(repeats[, axis])	Repeat elements of an array.
`reproject`(header[, order])	Reproject the image into a new header.
`reshape`(a.reshape)	Returns an array containing the same data with a new shape.
`resize`(a.resize)	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.
`shrink_mask`()	Copy of the numpy masked_array shrink_mask method.
`sort`([axis, kind, order, stable])	Sort an array in-place.
`squeeze`([axis])	Remove axes of length one from `a`.
`std`([axis, dtype, out, ddof, keepdims, ...])	Returns the standard deviation of the array elements along given axis.
`subimage`([xlo, xhi, ylo, yhi])	Extract a region spatially.
`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, freq])	Return a new `Projection` of the same class with the specified unit.
`to_device`(device, /, *[, stream])	For Array API compatibility.
`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, ...])	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 Projection.
`with_fill_value`(fill_value)	Create a new `Projection` or `Slice` with a different `fill_value`.
`world_spines`()	Returns a list of 1D arrays, for the world coordinates along each pixel axis.

Attributes Documentation

T¶

View of the transposed array.

Same as self.transpose().

See also

transpose

Examples

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

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

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:

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

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

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

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

numpy.ctypeslib

Notes

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

_ctypes.data

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

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

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

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

_ctypes.data_as(obj)

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

The returned pointer will keep a reference to the array.

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

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

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

Examples

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

data¶: Python buffer object pointing to the start of the array’s data.

device¶

dtype¶

Data-type of the array’s elements.

Warning

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

Parameters:

None

Returns:

dnumpy dtype object

See also

ndarray.astype: Cast the values contained in the array to a new data-type.
ndarray.view: Create a view of the same data but a different data-type.
numpy.dtype

Examples

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

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.

Attributes:

C_CONTIGUOUS (C): The data is in a single, C-style contiguous segment.
F_CONTIGUOUS (F): The data is in a single, Fortran-style contiguous segment.
OWNDATA (O): The array owns the memory it uses or borrows it from another object.
WRITEABLE (W): The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception.
ALIGNED (A): The data and all elements are aligned appropriately for the hardware.
WRITEBACKIFCOPY (X): This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.
FNC: F_CONTIGUOUS and not C_CONTIGUOUS.
FORC: F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
BEHAVED (B): ALIGNED and WRITEABLE.
CARRAY (CA): BEHAVED and C_CONTIGUOUS.
FARRAY (FA): BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes

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

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

The array flags cannot be set arbitrarily:

WRITEBACKIFCOPY can only be set False.
ALIGNED can only be set True if the data is truly aligned.
WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

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

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

flat¶

A 1-D iterator over the Quantity array.

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

hdu¶

header¶

imag¶

The imaginary part of the array.

Examples

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

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

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

Note

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

itemsize¶

Length of one array element in bytes.

Examples

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

latitude_extrema¶

longitude_extrema¶

mT¶

View of the matrix transposed array.

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

Added in version 2.0.

Raises:

ValueError: If the array is of dimension less than 2.

Examples

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

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

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

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

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

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

ndim¶

Number of array dimensions.

Examples

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

pixels_per_beam¶

quantity¶: Get a pure Quantity representation of the LDO.

real¶

The real part of the array.

See also

numpy.real: equivalent function

Examples

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

shape¶

Tuple of array dimensions.

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

Warning

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

See also

numpy.shape: Equivalent getter function.
numpy.reshape: Function similar to setting shape.
ndarray.reshape: Method similar to setting shape.

Examples

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

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

size¶

Number of elements in the array.

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

Notes

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

Examples

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

spatial_coordinate_map¶

strides¶

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

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

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

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

Warning

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

See also

numpy.lib.stride_tricks.as_strided

Notes

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

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

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

Examples

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

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

unit¶: A UnitBase object representing the unit of this quantity.

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¶

world¶

Return a list of the world coordinates in a cube, projection, or a view of it.

SpatialCoordMixinClass.world is called with bracket notation, like a NumPy array:
c.world[0:3, :, :]

Returns:

[v, y, x]list of NumPy arrays: The 3 world coordinates at each pixel in the view. For a 2D image, the output is [y, x].

Notes

Supports efficient Numpy slice notation, like world[0:3, :, 2:4]

Examples

Extract the first 3 velocity channels of the cube:

>>> v, y, x = c.world[0:3]

Extract all the world coordinates:

>>> v, y, x = c.world[:, :, :]

Extract every other pixel along all axes:

>>> v, y, x = c.world[::2, ::2, ::2]

Extract all the world coordinates for a 2D image:

>>> y, x = c.world[:, :]

world_extrema¶

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

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=<no value>, where=<no value>)¶

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

See also

numpy.all: equivalent function

any(axis=None, out=None, *, keepdims=<no value>, where=<no value>)¶

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.

See also

numpy.argpartition: equivalent function

argsort(axis=-1, kind=None, order=None, *, stable=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’, ‘same_value’, ‘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.
‘same_value’ means any data conversions may be done, but the values must not change, including rounding of floats or overflow of ints

Added in version 2.4: Support for 'same_value' was added.

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).
ValueError: When casting using 'same_value' and the values change or would overflow

Examples

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

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

>>> x.astype(int, casting="same_value")
Traceback (most recent call last):
...
ValueError: could not cast 'same_value' double to long

>>> x[:2].astype(int, casting="same_value")
array([1, 2])

byteswap(inplace=False)¶

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

Parameters:

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

Returns:

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

Examples

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

Arrays of byte-strings are not swapped

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

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

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

check_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 and with_beams methods, including 1D spectra with Jy/beam units.

Parameters:

raise_error_jybmbool, optional: Raises a BeamUnitsError when True (default). When False, it triggers a BeamWarning.

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=<no value>, max=<no value>, 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

convolve_to(beam, convolve=<function convolve_fft>, **kwargs)[source]¶

Convolve the image to a specified beam.

Parameters:

beamradio_beam.Beam: The beam to convolve to
convolvefunction: The astropy convolution function to use, either astropy.convolution.convolve or astropy.convolution.convolve_fft

Returns:

projProjection: A Projection convolved to the given beam object.

copy(order='C')¶

Return a copy of the array.

Parameters:

order{‘C’, ‘F’, ‘A’, ‘K’}, optional: Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and numpy.copy() are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

See also

numpy.copy: Similar function with different default behavior
numpy.copyto

Notes

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

Examples

>>> import numpy as np
>>> x = np.array([[1,2,3],[4,5,6]], order='F')

>>> y = x.copy()

>>> x.fill(0)

>>> x
array([[0, 0, 0],
       [0, 0, 0]])

>>> y
array([[1, 2, 3],
       [4, 5, 6]])

>>> y.flags['C_CONTIGUOUS']
True

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

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

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

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

cumprod(axis=None, dtype=None, out=None)¶

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

See also

numpy.cumprod: equivalent function

cumsum(axis=None, dtype=None, out=None)¶

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

See also

numpy.cumsum: equivalent function

decompose(bases: Collection[UnitBase] = ()) → Self¶

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

Parameters:

basessequence of UnitBase, optional: The bases to decompose into. When not provided, decomposes down to any irreducible units. When provided, the decomposed result will only contain the given units. This will raises a UnitsError if it’s not possible to do so.

Returns:

newqQuantity: A new object equal to this quantity with units decomposed.

diagonal(offset=0, axis1=0, axis2=1)¶

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

See also

numpy.diagonal: equivalent function

diff(n=1, axis=-1)¶

dot(other, /, out=None)¶

Refer to numpy.dot() for full documentation.

See also

numpy.dot: equivalent function

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

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

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

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

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

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

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 flatten a in the order the elements occur in memory. The default is ‘C’.

Returns:

yndarray: A copy of the input array, flattened to one dimension.

See also

ravel: Return a flattened array.
flat: A 1-D flat iterator over the array.

Examples

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

flattened_world(view=())¶: Retrieve the world coordinates corresponding to the extracted flattened version of the cube

static from_hdu(hdu, ext=0)[source]¶

Return a projection from a FITS HDU.

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

>>> import numpy as np
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j,  0.+0.j],
       [0.+0.j,  2.+4.j]])
>>> x.getfield(np.float64)
array([[1.,  0.],
       [0.,  2.]])

By choosing an offset of 8 bytes we can select the complex part of the array for our view:

>>> x.getfield(np.float64, offset=8)
array([[1.,  0.],
       [0.,  4.]])

insert(obj, values, axis=None)¶

Insert values along the given axis before the given indices and return a new Quantity object.

This is a thin wrapper around the numpy.insert function.

Parameters:

objint, slice or sequence of int: Object that defines the index or indices before which values is inserted.
valuesarray-like: Values to insert. If the type of values is different from that of quantity, values is converted to the matching type. values should be shaped so that it can be broadcast appropriately The unit of values must be consistent with this quantity.
axisint, optional: Axis along which to insert values. If axis is None then the quantity array is flattened before insertion.

Returns:

outQuantity: A copy of quantity with values inserted. Note that the insertion does not occur in-place: a new quantity array is returned.

Examples

>>> import astropy.units as u
>>> q = [1, 2] * u.m
>>> q.insert(0, 50 * u.cm)
<Quantity [ 0.5,  1.,  2.] m>

>>> q = [[1, 2], [3, 4]] * u.m
>>> q.insert(1, [10, 20] * u.m, axis=0)
<Quantity [[  1.,  2.],
           [ 10., 20.],
           [  3.,  4.]] m>

>>> q.insert(1, 10 * u.m, axis=1)
<Quantity [[  1., 10.,  2.],
           [  3., 10.,  4.]] m>

item(*args)¶

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

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

max(axis=None, out=None, *, keepdims=<no value>, initial=<no value>, where=<no value>)¶

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=<no value>, where=<no value>)¶

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=<no value>, initial=<no value>, where=<no value>)¶

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

See also

numpy.amin: equivalent function

nonzero()¶

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

See also

numpy.nonzero: equivalent function

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

Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two partitions on the either side of the k-th element in the output array is undefined.

Parameters:

kthint or sequence of ints: Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

Deprecated since version 1.22.0: Passing booleans as index is deprecated.
axisint, optional: Axis along which to sort. Default is -1, which means sort along the last axis.
kind{‘introselect’}, optional: Selection algorithm. Default is ‘introselect’.
orderstr or list of str, optional: When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

See also

numpy.partition: Return a partitioned copy of an array.
argpartition: Indirect partition.
sort: Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

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

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

prod(axis=None, dtype=None, out=None, *, keepdims=<no value>, initial=<no value>, where=<no value>)¶

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

See also

numpy.prod: equivalent function

put(indices, values, mode='raise')¶

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

See also

numpy.put: equivalent function

quicklook(filename=None, use_aplpy=True, aplpy_kwargs={})[source]¶

Use APLpy to make a quick-look image of the projection. This will make the FITSFigure attribute available.

If there are unmatched celestial axes, this will instead show an image without axis labels.

Parameters:

filenamestr or Non: Optional - the filename to save the quicklook to.

ravel(order='C')¶

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

reproject(header, order='bilinear')[source]¶

Reproject the image into a new header.

Parameters:

headerastropy.io.fits.Header

A header specifying a cube in valid WCS

orderint or str, optional

The order of the interpolation (if mode is set to 'interpolation'). This can be either one of the following strings:

‘nearest-neighbor’

‘bilinear’

‘biquadratic’

‘bicubic’

or an integer. A value of 0 indicates nearest neighbor interpolation.

reshape(shape, /, *, order='C', copy=None) a.reshape(*shape, order='C', copy=None)¶

reshape(*shape, order='C', copy=None)

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

Refer to numpy.reshape for full documentation.

See also

numpy.reshape: equivalent function

Notes

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(4, 2) is equivalent to a.reshape((4, 2)).

resize(new_shape, /, *, refcheck=True) a.resize(*new_shape, refcheck=True)¶

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. See Notes below for more explanation.

Returns:

None

Raises:

ValueError: If a does not own its own data or references or views to may exist.

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.

Reallocating arrays in-place can often lead to memory fragmentation and should be avoided. If the goal is to reclaim over-allocated memory, alternatives are to create a view or a copy of just the desired data, or using two passes to build the array: one to cheaply determine the shape and another to allocate and fill. Benchmark your use case to determine what is optimum. You may be surprised to find resize actually slows down or bloats your application.

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.

On Python 3.13 and older, the check allows objects with exactly one reference to be reallocated in-place. On Python 3.14 and newer, the array must be uniquely referenced. See [1] for more details.

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.

References

[1]

Python 3.14 What’s New, https://docs.python.org/3/whatsnew/3.14.html#whatsnew314-refcount

Examples

Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:

>>> import numpy as np

>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
       [1]])

>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
       [2]])

Enlarging an array: as above, but missing entries are filled with zeros:

>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
       [3, 0, 0]])

Referencing an array prevents resizing…

>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...

Unless refcheck is False:

>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])

round(decimals=0, out=None)¶

Return a with each element rounded to the given number of decimals.

Refer to numpy.around for full documentation.

See also

numpy.around: equivalent function

searchsorted(v, side='left', sorter=None)¶

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted.

See also

numpy.searchsorted: equivalent function

setfield(val, dtype, offset=0)¶

Put a value into a specified place in a field defined by a data-type.

Place val into a’s field defined by dtype and beginning offset bytes into the field.

Parameters:

valobject: Value to be placed in field.
dtypedtype object: Data-type of the field in which to place val.
offsetint, optional: The number of bytes into the field at which to place val.

Returns:

None

See also

getfield

Examples

>>> import numpy as np
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
       [3, 3, 3],
       [3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])

setflags(write=None, align=None, uic=None)¶

Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.

These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)

Parameters:

writebool, optional: Describes whether or not a can be written to.
alignbool, optional: Describes whether or not a is aligned properly for its type.
uicbool, optional: Describes whether or not a is a copy of another “base” array.

Notes

Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only three of which can be changed by the user: WRITEBACKIFCOPY, WRITEABLE, and ALIGNED.

WRITEABLE (W) the data area can be written to;

ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);

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

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

Examples

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

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, *, stable=None)¶

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

Parameters:

axisint, optional: Axis along which to sort. Default is -1, which means sort along the last axis.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional: Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.
orderstr or list of str, optional: When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
stablebool, optional: Sort stability. If True, the returned array will maintain the relative order of a values which compare as equal. If False or None, this is not guaranteed. Internally, this option selects kind='stable'. Default: None.

Added in version 2.0.0.

See also

numpy.sort: Return a sorted copy of an array.
numpy.argsort: Indirect sort.
numpy.lexsort: Indirect stable sort on multiple keys.
numpy.searchsorted: Find elements in sorted array.
numpy.partition: Partial sort.

Notes

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

Examples

>>> import numpy as np
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
       [1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
       [1, 4]])

Use the order keyword to specify a field to use when sorting a structured array:

>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
      dtype=[('x', 'S1'), ('y', '<i8')])

squeeze(axis=None)¶

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

See also

numpy.squeeze: equivalent function

std(axis=None, dtype=None, out=None, ddof=0, *, keepdims=<no value>, where=<no value>, mean=<no value>)¶

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

Refer to numpy.std for full documentation.

See also

numpy.std: equivalent function

subimage(xlo='min', xhi='max', ylo='min', yhi='max')[source]¶

Extract a region spatially.

When spatial WCS dimensions are given as an Quantity, the spatial coordinates of the ‘lo’ and ‘hi’ corners are solved together. This minimizes WCS variations due to the sky curvature when slicing from a large (>1 deg) image.

Parameters:

[xy]lo/[xy]hiint or astropy.units.Quantity or min/max: The endpoints to extract. If given as a quantity, will be interpreted as World coordinates. If given as a string or int, will be interpreted as pixel coordinates.

sum(axis=None, dtype=None, out=None, *, keepdims=<no value>, initial=<no value>, where=<no value>)¶

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

See also

numpy.sum: equivalent function

swapaxes(axis1, axis2, /)¶

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

See also

numpy.swapaxes: equivalent function

take(indices, axis=None, out=None, mode='raise')¶

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

See also

numpy.take: equivalent function

to(unit, equivalencies=[], freq=None)[source]¶

Return a new Projection of the same class with the specified unit.

See astropy.units.Quantity.to for further details.

to_device(device, /, *, stream=None)¶

For Array API compatibility. Since NumPy only supports CPU arrays, this method is a no-op that returns the same array.

Parameters:

device“cpu”: Must be "cpu".
streamNone, optional: Currently unsupported.

Returns:

outSelf: Returns the same array.

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

Generate a string representation of the quantity and its unit.

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

Parameters:

unitunit-like, optional

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

precisionnumber, optional

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

formatstr, optional

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

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

formatterstr, callable, dict, optional

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

subfmtstr, optional

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

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

Returns:

str: A string with the contents of this Quantity

to_value(unit=None, equivalencies=[])¶

The numerical value, possibly in a different unit.

Parameters:

unitunit-like, optional: The unit in which the value should be given. If not given or None, use the current unit.
equivalencieslist of tuple, optional: A list of equivalence pairs to try if the units are not directly convertible (see Equivalencies). If not provided or [], class default equivalencies will be used (none for Quantity, but may be set for subclasses). If None, no equivalencies will be applied at all, not even any set globally or within a context.

Returns:

valuendarray or scalar: The value in the units specified. For arrays, this will be a view of the data if no unit conversion was necessary.

See also

to: Get a new instance in a different unit.

tobytes(order='C')¶: Not implemented, use .value.tobytes() instead.

tofile(fid, sep='', format='%s')¶: Not implemented, use .value.tofile() instead.

tolist()¶

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

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

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

Parameters:

none

Returns:

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

Notes

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

Examples

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

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

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

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

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

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

tostring(order='C')¶: Not implemented, use .value.tostring() instead.

trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)¶

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

See also

numpy.trace: equivalent function

transpose(*axes)¶

Returns a view of the array with axes transposed.

Refer to numpy.transpose for full documentation.

Parameters:

axesNone, tuple of ints, or n ints

None or no argument: reverses the order of the axes.
tuple of ints: i in the j-th place in the tuple means that the array’s i-th axis becomes the transposed array’s j-th axis.
n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form).

Returns:

pndarray: View of the array with its axes suitably permuted.

See also

transpose: Equivalent function.
ndarray.T: Array property returning the array transposed.
ndarray.reshape: Give a new shape to an array without changing its data.

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.transpose()
array([[1, 3],
       [2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
       [2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
       [2, 4]])

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

var(axis=None, dtype=None, out=None, ddof=0, *, keepdims=<no value>, where=<no value>, mean=<no value>)¶

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

See also

numpy.var: equivalent function

view([dtype][, type])¶

New view of array with the same data.

Note

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

Parameters:

dtypedata-type or ndarray sub-class, optional: Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).
typePython type, optional: Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter results in type preservation.

Notes

a.view() is used two different ways:

a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.

a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.

For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the last axis of a must be contiguous. This axis will be resized in the result.

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

Examples

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

Viewing array data using a different type and dtype:

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

Creating a view on a structured array so it can be used in calculations

>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
       [3, 4]], dtype=int8)
>>> xv.mean(0)
array([2.,  3.])

Making changes to the view changes the underlying array

>>> xv[0,1] = 20
>>> x
array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])

Using a view to convert an array to a recarray:

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

Views share data:

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

Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortran-ordering, etc.:

>>> x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
>>> y = x[:, ::2]
>>> y
array([[1, 3],
       [4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
    ...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
       [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])

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

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

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

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

with_beam(beam, raise_error_jybm=True)[source]¶

Attach a new beam object to the Projection.

Parameters:

beamBeam: A new beam object.

with_fill_value(fill_value)[source]¶: Create a new Projection or Slice with a different fill_value.

world_spines()¶

Returns a list of 1D arrays, for the world coordinates along each pixel axis.

Raises error if this operation is ill-posed (e.g. rotated world coordinates, strong distortions)

This method is not currently implemented. Use world instead.

Navigation

Projection¶