array - Converting sequences¶
This module provides various functions and classes to access sequences and
buffer-style objects in different ways. It also provides conversion routines
to improve the interoperability of sequences with
ctypes data types.
Providing read-write access for sequential data¶
Two classes allow you to access sequential data in different ways. The
CTypesView provides byte-wise access to iterable objects and allows
you to convert the object representation to matching byte-widths for
ctypes or other modules.
Depending on the the underlying object and the chosen size of each particular
item of the object, the
CTypesView allows you to operate directly
on different representations of the object’s contents.
>>> text = bytearray("Hello, I am a simple ASCII string!") >>> ctview = CTypesView(text, itemsize=1) >>> ctview.view = 0x61 >>> print(text) aello, I am a simple ASCII string!" >>> ctview.to_uint16() = 0x6554 >>> print(text) aello,Te am a simple ASCII string!"
The snippet above provides a single-byte sized view on a
object. Afterwards, the first item of the view is changed, which causes a
change on the
bytearray(), on the first item as well, since both, the
CTypesView and the
bytearray() provide a byte-wise access to
>>> text = bytearray("Hello, I am a simple ASCII string!") >>> ctview = CTypesView(text, itemsize=2) >>> ctview.view = 0x61 >>> print(text) aello, I am a simple ASCII string!" >>> ctview.to_uint16() = 0x6554 >>> print(text) aello,Te am a simple ASCII string!"
If the encapsuled object does not provide a (writeable)
interface, but is iterable, the
CTypesView will create an
internal copy of the object data using Python’s
array module and
perform all operations on that copy.
>>> mylist = [18, 52, 86, 120, 154, 188, 222, 240] >>> ctview = CTypesView(mylist, itemsize=1, docopy=True) >>> print(ctview.object) array('B', [18, 52, 86, 120, 154, 188, 222, 240]) >>> ctview.view = 0xFF >>> print(mylist) [18, 52, 86, 120, 154, 188, 222, 240] >>> print(ctview.object) array('B', [18, 52, 86, 255, 154, 188, 222, 240])
As for directly accessible objects, you can define your own itemsize to be used. If the iterable does not provide a direct byte access to their contents, this won’t have any effect except for resizing the item widths.
>>> mylist = [18, 52, 86, 120, 154, 188, 222, 240] >>> ctview = CTypesView(mylist, itemsize=4, docopy=True) >>> print(ctview.object) array('I', [18L, 52L, 86L, 120L, 154L, 188L, 222L, 240L])
Accessing data over multiple dimensions¶
The second class,
MemoryView provides an interface to access
data over multiple dimensions. You can layout and access a simple
byte stream over e.g. two or more axes, providing a greater flexibility
for functional operations and complex data.
Let’s assume, we are reading image data from a file stream into some buffer object and want to access and manipulate the image data. Images feature two axes, one being the width, the other being the height, defining a rectangular graphics area.
When we read all data from the file, we have an one-dimensional view of the
image graphics. The
MemoryView allows us to define a
two-dimensional view over the image graphics, so that we can operate on
both, rows and columns of the image.
>>> imagedata = bytearray("some 1-byte graphics data") >>> view = MemoryView(imagedata, 1, (5, 5)) >>> print(view) [[s, o, m, e, ], [1, -, b, y, t], [e, , g, r, a], [p, h, i, c, s], [ , d, a, t, a]] >>> for row in view: ... print(row) ... [s, o, m, e, ] [1, -, b, y, t] [e, , g, r, a] [p, h, i, c, s] [ , d, a, t, a] >>> for row in view: ... row = "X" ... print row ... [s, X, m, e, ] [1, X, b, y, t] [e, X, g, r, a] [p, X, i, c, s] [ , X, a, t, a] >>> print(imagedata) sXme 1XbyteXgrapXics Xata
>>> firstrow = view >>> type(firstrow) <class 'mule.array.MemoryView'> >>> type(firstrow) <type 'bytearray'>
>>> view.ndim 2 >>> view.strides (5, 5)
MemoryView.strides, which have to be passed on creating a
MemoryView, define the layout of the data over different
dimensions. In the example above, we created a 5x5 two-dimensional view
to the image graphics.
>>> twobytes = MemoryView(imagedata, 2, (5, 1)) >>> print(twobytes) [[sX, me, 1, Xb, yt], [eX, gr, ap, Xi, cs]]
CTypesView(obj : iterable[, itemsize=1[, docopy=False[, objsize=None]]])¶
A proxy class for byte-wise accessible data types to be used in ctypes bindings. The CTypesView provides a read-write access to arbitrary objects that are iterable.
In case the object does not provide a
buffer()interface for direct access, the CTypesView can copy the object’s contents into an internal buffer, from which data can be retrieved, once the necessary operations have been performed.
Depending on the item type stored in the iterable object, you might need to provide a certain itemsize, which denotes the size per item in bytes. The objsize argument might be necessary of iterables, for which len() does not return the correct amount of objects or is not implemented.
Returns the length of the encapsuled object in bytes.
Indicates, if changes on the CTypesView data effect the encapsuled object directly. if not, this means that the object was copied internally and needs to be updated by the user code outside of the CTypesView.
The encapsuled object.
Provides a read-write aware view of the encapsuled object data that is suitable for usage from
to_bytes() → ctypes.POINTER¶
Returns a byte representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()points to an array of
to_uint16() → ctypes.POINTER¶
Returns a 16-bit representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()points to an array of
to_uint32() → ctypes.POINTER¶
Returns a 32-bit representation of the encapsuled object. The return value allows a direct read-write access to the object data, if it is not copied. The
ctypes.POINTER()points to an array of
MemoryView(source : object, itemsize : int, strides : tuple[, getfunc=None[, setfunc=None[, srcsize=None]]])¶
MemoryViewprovides a read-write access to arbitrary data objects, which can be indexed.
itemsize denotes the size of a single item. strides defines the dimensions and the length (n items * itemsize) for each dimension. getfunc and setfunc are optional parameters to provide specialised read and write access to the underlying source. srcsize can be used to provide the correct source size, if
len(source)does not return the absolute size of the source object in all dimensions.
The MemoryView is a pure Python-based implementation and makes heavy use of recursion for multi-dimensional access. If you aim for speed on accessing a n-dimensional object, you want to consider using a specialised library such as numpy. If you need n-dimensional access support, where such a library is not supported, or if you need to provide access to objects, which do not fulfill the requirements of that particular libray,
MemoryViewcan act as solid fallback solution.
The size of a single item in bytes.
The size in bytes of the underlying source object.
The underlying data source.
to_ctypes(dataseq : iterable, dtype[, mcount=0]) → array, int¶
Converts an arbitrary sequence to a ctypes array of the specified dtype and returns the ctypes array and amount of items as two-value tuple.
TypeError, if one or more elements in the passed sequence do not match the passed dtype.
to_list(dataseq : iterable) → list¶
Converts a ctypes array to a list.
to_tuple(dataseq : iterable) → tuple¶
Converts a ctypes array to a tuple.