Signal¶
- class Signal[source]¶
Bases:
ABC,HDFSerializableAbstract base class for all signal models in HermesPy.
- static Create(samples, sampling_rate=1.0, carrier_frequency=0.0, noise_power=0.0, delay=0.0, offsets=None)[source]¶
Creates a signal model instance given signal samples. Subclasses of Signal should reroute the given arguments to init and return the result.
- Parameters:
samples (np.ndarray | Sequence[np.ndarray]) –
- Single or a sequence of 2D matricies with shapes MxT_i, where
M - number of streams, T_i - number of samples in the matrix i.
Note that M for each entry of the sequence must be the same. SignalBlock and Sequence[SignalBlock] can also be passed here.
sampling_rate (float) – Sampling rate of the signal in Hz.
offsets (List[int]) – Integer offsets of the samples if given in a sequence. Ignored if samples is not a Sequence of np.ndarray. len(offsets) must be equal to len(samples). Offset number i must be greater then offset i-1 + samples[i-1].shape[1].
- Return type:
- Returns
- signal (Signal):
SparseSignal if samples argument is a list of np.ndarrays. DenseSignal otherwise.
- static Empty(sampling_rate, num_streams=0, num_samples=0, **kwargs)[source]¶
Creates an empty signal model instance. Initializes it with the given arguments. If both num_streams and num_samples are not 0, then initilizes samples with np.empty.
- Return type:
- append_samples(signal)[source]¶
Append samples in time-domain to the signal model.
- Parameters:
signal (Signal) – The signal to be appended.
- Raises:
ValueError – If the number of streams don’t align.
- Return type:
- append_streams(signal)[source]¶
Append streams to the signal model.
- Parameters:
signal (Signal) – The signal to be appended.
- Raises:
ValueError – If the number of samples don’t align.
- Return type:
- copy()[source]¶
Copy this signal model to a new object.
- Returns:
A copy of this signal model.
- Return type:
- classmethod from_interleaved(interleaved_samples, scale=True, **kwargs)[source]¶
Initialize a signal model from interleaved samples.
- from_ndarray(samples)[source]¶
Create a new signal using parameters from this signal. Equivalent to create(samples, **self.kwargs).
- resample(sampling_rate, aliasing_filter=True)[source]¶
Resample the modeled signal to a different sampling rate.
- Parameters:
- Returns:
The resampled signal model.
- Return type:
- Raises:
ValueError – If sampling_rate is smaller or equal to zero.
- set_samples(samples, offsets=None)[source]¶
Sets given samples into this dense signal model. Validates given samples and optional offsets, writes them into _blocks attribute and resambles, if needed.
- Return type:
- superimpose(added_signal, resample=True, aliasing_filter=True, stream_indices=None)[source]¶
Superimpose an additive signal model to this model.
Internally re-samples added_signal to this model’s sampling rate, if required. Mixes added_signal according to the carrier-frequency distance.
- Parameters:
- Raises:
ValueError – If added_signal contains a different number of streams than this signal model.
RuntimeError – If resampling is required but not allowd.
NotImplementedError – If the delays if this signal and added_signal differ.
- Return type:
- to_dense()[source]¶
Concatenate all the blocks in the signal into one block. Accounts for offsets. Warning - if offset values are to big, memory overflow is possible.
- Returns:
Dense form for this signal
- Return type:
signal (DenseSignal)
- to_interleaved(data_type=<class 'numpy.int16'>, scale=True)[source]¶
Convert the complex-valued floating-point model samples to interleaved integers.
- Parameters:
data_type (optional) – Numpy resulting data type.
scale (bool, optional) – Scale the floating point values to stretch over the whole range of integers.
- Returns:
Numpy array of interleaved samples. Will contain double the samples in time-domain.
- Return type:
samples (np.ndarray)
- property carrier_frequency: float¶
The center frequency of the modeled signal in the radio-frequency transmit band.
- Returns:
The carrier frequency in Hz.
- Return type:
- property duration: float¶
Signal model duration in time-domain.
- Returns:
Duration in seconds.
- Return type:
- property energy: ndarray¶
Compute the energy of the modeled signal.
Returns: The energy of each modeled stream within a numpy vector.
- property eye: _EyeVisualization¶
Visualize the eye diagram of the signal model.
- property frequencies: ndarray¶
The signal model’s discrete sample points in frequcy domain.
Returns: Numpy vector of frequency bins.
- property kwargs: dict¶
Returns: {“sampling_rate”: self.sampling_rate, “carrier_frequency”: self.carrier_frequency, “delay”: self.delay, “noise_power”: self.noise_power}
- property noise_power: float¶
Noise power of the superimposed noise signal.
- Returns:
Noise power.
- Raises:
ValueError – If the noise power is smaller than zero.
- property num_samples: int¶
The number of samples within this signal model.
- Returns:
The number of samples.
- Return type:
- property num_streams: int¶
The number of streams within this signal model.
- Returns:
The number of streams.
- Return type:
- property plot: _SamplesVisualization¶
Visualize the samples of the signal model.
- property power: ndarray¶
Compute the power of the modeled signal.
Returns: The power of each modeled stream within a numpy vector.
- property sampling_rate: float¶
The rate at which the modeled signal was sampled.
- Returns:
The sampling rate in Hz.
- Return type: