Module rpps.dobject
Containers for data to ensure we can track where it came from
Functions
def ensure_bit(dobj)
-
Ensure DataObject is using bits
def ensure_byte(dobj)
-
Ensure DataObject is using bytes
Classes
class BitObject (data=None, meta=None)
-
Parent DataObject class
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class BitObject(DataObject): type = Type.BIT data = np.array([], dtype=bool) def __str__(self): return f"{super().__str__()}:{self.bin}" @property def bin(self): return self.data.astype(int)
Ancestors
Subclasses
Class variables
var data
var type
Instance variables
prop bin
-
Expand source code
@property def bin(self): return self.data.astype(int)
Inherited members
class ByteObject (data=None, meta=None)
-
Parent DataObject class
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class ByteObject(DataObject): type = Type.BYTE data = np.array([], dtype=np.uint8) def __str__(self): return f"{super().__str__()}:{self.hex}" @property def hex(self): return self.data.tobytes().hex()
Ancestors
Subclasses
Class variables
var data
var type
Instance variables
prop hex
-
Expand source code
@property def hex(self): return self.data.tobytes().hex()
Inherited members
class CodingData (data=None, meta=None)
-
Data post-coding
Expand source code
class CodingData(BitObject): """Data post-coding"""
Ancestors
Inherited members
class DataObject (data=None, meta=None)
-
Parent DataObject class
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class DataObject: """Parent DataObject class""" type = Type.NONE data = [] meta = None def __init__(self, data=None, meta=None): if not isinstance(meta, Meta): meta = Meta() self.convert(data) self.meta = meta def __str__(self): return f"{self.name()}:{self.type}:{len(self)}" def __repr__(self): return str(self) def __len__(self): return len(self.data) def __enter__(self, *args, **kwargs): return self.data.__enter__(*args, **kwargs) # type: ignore def __exit__(self, *args, **kwargs): return self.data.__exit__(*args, **kwargs) # type: ignore def __iter__(self, *args, **kwargs): return self.data.__iter__(*args, **kwargs) def __getitem__(self, *args, **kwargs): return self.data.__getitem__(*args, **kwargs) def name(self): """Get class name""" return type(self).__name__ def append(self, data): self.data = np.append(self.data, data) def to_bits(self): if isinstance(self.data, np.ndarray): if self.type == Type.BIT: return self.data elif self.type == Type.BYTE: return np.unpackbits(self.data) raise NotImplementedError(f"Cannot convert {self.type} to bits") def to_bytes(self): if isinstance(self.data, np.ndarray): if self.type == Type.BYTE: return self.data if self.type == Type.BIT: return np.packbits(self.data) raise NotImplementedError(f"Cannot convert {self.type} to bytes") def convert(self, other): """Convert other to self""" if issubclass(type(other), DataObject): if self.type == Type.BIT: self.data = other.to_bits() return elif self.type == Type.BYTE: self.data = other.to_bytes() return raise NotImplementedError(f"Cannot convert {other.type} to {self.type}") else: if other is None: if self.type == Type.BYTE: self.data = np.array([], dtype=np.uint8) return elif self.type == Type.BIT: self.data = np.array([], dtype=bool) return elif isinstance(other, bytes): if self.type == Type.BYTE: self.data = np.frombuffer(other, dtype=np.uint8) return elif self.type == Type.BIT: self.data = np.unpackbits(np.array(other)) return elif isinstance(other, np.ndarray): if self.type == Type.SYM: self.data = other return if other.dtype == np.uint8: if self.type == Type.BYTE: self.data = np.copy(other) return elif self.type == Type.BIT: self.data = np.unpackbits(other) return elif other.dtype == bool: if self.type == Type.BYTE: self.data = np.packbits(other) return elif self.type == Type.BIT: self.data = np.copy(other) return raise NotImplementedError(f"Cannot convert {type(other)} {other.shape} {other.dtype} to {self.type}") raise NotImplementedError(f"Cannot convert {type(other)} to {self.type}")
Subclasses
Class variables
var data
var meta
var type
Methods
def append(self, data)
def convert(self, other)
-
Convert other to self
def name(self)
-
Get class name
def to_bits(self)
def to_bytes(self)
class ModData (data=None, meta=None)
-
Data returned from mod.demodulate
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class ModData(BitObject): """Data returned from mod.demodulate""" soft = SoftDecision() _data = None @property def hard(self): return self.soft.hard() @property def data(self): if self._data is not None: return self._data return self.soft.bits @data.setter def data(self, value): self._data = value
Ancestors
Class variables
var soft
Instance variables
prop data
-
ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)
An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)
Arrays should be constructed using
array
,zeros
orempty
(refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)
) for instantiating an array.For more information, refer to the
numpy
module and examine the methods and attributes of an array.Parameters
(for the new method; see Notes below)
shape
:tuple
ofints
- Shape of created array.
dtype
:data-type
, optional- Any object that can be interpreted as a numpy data type.
buffer
:object exposing buffer interface
, optional- Used to fill the array with data.
offset
:int
, optional- Offset of array data in buffer.
strides
:tuple
ofints
, optional- Strides of data in memory.
order
:{'C', 'F'}
, optional- Row-major (C-style) or column-major (Fortran-style) order.
Attributes
T
:ndarray
- Transpose of the array.
data
:buffer
- The array's elements, in memory.
dtype
:dtype object
- Describes the format of the elements in the array.
flags
:dict
- Dictionary containing information related to memory use, e.g., 'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
flat
:numpy.flatiter object
- Flattened version of the array as an iterator.
The iterator
allows assignments, e.g.,
x.flat = 3
(Seendarray.flat
for assignment examples; TODO). imag
:ndarray
- Imaginary part of the array.
real
:ndarray
- Real part of the array.
size
:int
- Number of elements in the array.
itemsize
:int
- The memory use of each array element in bytes.
nbytes
:int
- The total number of bytes required to store the array data,
i.e.,
itemsize * size
. ndim
:int
- The array's number of dimensions.
shape
:tuple
ofints
- Shape of the array.
strides
:tuple
ofints
- The step-size required to move from one element to the next in
memory. For example, a contiguous
(3, 4)
array of typeint16
in C-order has strides(8, 2)
. This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4
). ctypes
:ctypes object
- Class containing properties of the array needed for interaction with ctypes.
base
:ndarray
- If the array is a view into another array, that array is its
base
(unless that array is also a view). Thebase
array is where the array data is actually stored.
See Also
array
- Construct an array.
zeros
- Create an array, each element of which is zero.
empty
- Create an array, but leave its allocated memory unchanged (i.e., it contains "garbage").
dtype
- Create a data-type.
numpy.typing.NDArray
- An ndarray alias :term:
generic <generic type>
w.r.t. itsdtype.type <numpy.dtype.type>
.
Notes
There are two modes of creating an array using
__new__
:- If
buffer
is None, then onlyshape
,dtype
, andorder
are used. - If
buffer
is an object exposing the buffer interface, then all keywords are interpreted.
No
__init__
method is needed because the array is fully initialized after the__new__
method.Examples
These examples illustrate the low-level
ndarray
constructor. Refer to theSee Also
section above for easier ways of constructing an ndarray.First mode,
buffer
is None:>>> np.ndarray(shape=(2,2), dtype=float, order='F') array([[0.0e+000, 0.0e+000], # random [ nan, 2.5e-323]])
Second mode:
>>> np.ndarray((2,), buffer=np.array([1,2,3]), ... offset=np.int_().itemsize, ... dtype=int) # offset = 1*itemsize, i.e. skip first element array([2, 3])
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@property def data(self): if self._data is not None: return self._data return self.soft.bits
prop hard
-
Expand source code
@property def hard(self): return self.soft.hard()
Inherited members
class ScramData (data=None, meta=None)
-
Data post-scrambler
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class ScramData(BitObject): """Data post-scrambler"""
Ancestors
Inherited members
class StreamData (data=None, meta=None)
-
Raw input/output data
Expand source code
class StreamData(ByteObject): """Raw input/output data"""
Ancestors
Inherited members
class SymData (data=None, meta=None)
-
Data returned from mod.modulate
Expand source code
class SymData(SymObject): """Data returned from mod.modulate"""
Ancestors
Inherited members
class SymObject (data=None, meta=None)
-
Parent DataObject class
Expand source code
class SymObject(DataObject): type = Type.SYM def __str__(self): return f"{super().__str__()} syms"
Ancestors
Subclasses
Class variables
var type
Inherited members
class Type (*args, **kwds)
-
DataObject data data formats
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class Type(Enum): """DataObject data data formats""" BIT = 0 BYTE = 1 SYM = 2 NONE = -1
Ancestors
- enum.Enum
Class variables
var BIT
var BYTE
var NONE
var SYM