class sdr.Decimator(sdr.FIR)

Implements a polyphase decimating FIR filter.

Notes

The polyphase decimating filter is equivalent to first filtering the input signal \(x[n]\) with the prototype FIR filter with feedforward coefficients \(h_{i}\) and then decimating the filtered signal by \(r\) (by discarding \(r-1\) samples between each sample).

Instead, the polyphase decimating filter first decomposes the prototype FIR filter into \(r\) polyphase filters with feedforward coefficients \(h_{i, j}\). The polyphase filters are then applied to the commutated input signal \(x[n]\) in parallel. The outputs of the polyphase filters are then summed. This prevents the need to compute outputs that will be discarded, as is done in the first case.

Polyphase 2x Decimating FIR Filter Block Diagram
                          +------------------------+
 ..., x[4], x[2], x[0] -->| h[0], h[2], h[4], h[6] |--+
                          +------------------------+  |
                                                      @--> ..., y[1], y[0]
                          +------------------------+  |
 ..., x[3], x[1], 0    -->| h[1], h[3], h[5], 0    |--+
                          +------------------------+

 Input Commutator                                      Output Summation
 (bottom-to-top)

 x[n] = Input signal with sample rate fs
 y[n] = Output signal with sample rate fs/r
 h[n] = Prototype FIR filter
 @ = Adder

The polyphase feedforward taps \(h_{i, j}\) are related to the prototype feedforward taps \(h_i\) by

\[h_{i, j} = h_{i + j r} .\]

Examples

Create an input signal to interpolate.

In [1]: x = np.cos(np.pi / 64 * np.arange(280))

Create a polyphase filter that decimates by 7 using the Kaiser window method.

In [2]: fir = sdr.Decimator(7); fir
Out[2]: sdr.Decimator(7, 'kaiser', streaming=False)

In [3]: y = fir(x)

In [4]: plt.figure(figsize=(8, 4)); \
   ...: sdr.plot.time_domain(x, marker=".", label="Input"); \
   ...: sdr.plot.time_domain(y, sample_rate=1/fir.rate, marker="o", label="Decimated"); \
   ...: plt.title("Decimation by 7 with the Kaiser window method"); \
   ...: plt.tight_layout();
   ...:
../../_images/sdr_Decimator_1.png

Create a streaming polyphase filter that decimates by 7 using the Kaiser window method. This filter preserves state between calls.

In [5]: fir = sdr.Decimator(7, streaming=True); fir
Out[5]: sdr.Decimator(7, 'kaiser', streaming=True)

In [6]: y1 = fir(x[0:70]); \
   ...: y2 = fir(x[70:140]); \
   ...: y3 = fir(x[140:210]); \
   ...: y4 = fir(x[210:280]); \
   ...: y5 = fir.flush()
   ...: 

In [7]: plt.figure(figsize=(8, 4)); \
   ...: sdr.plot.time_domain(x, marker=".", label="Input"); \
   ...: sdr.plot.time_domain(y1, sample_rate=1/fir.rate, offset=-fir.delay*fir.rate + 0, marker="o", label="Decimated $y_1[n]$"); \
   ...: sdr.plot.time_domain(y2, sample_rate=1/fir.rate, offset=-fir.delay*fir.rate + 70, marker="o", label="Decimated $y_2[n]$"); \
   ...: sdr.plot.time_domain(y3, sample_rate=1/fir.rate, offset=-fir.delay*fir.rate + 140, marker="o", label="Decimated $y_3[n]$"); \
   ...: sdr.plot.time_domain(y4, sample_rate=1/fir.rate, offset=-fir.delay*fir.rate + 210, marker="o", label="Decimated $y_4[n]$"); \
   ...: sdr.plot.time_domain(y5, sample_rate=1/fir.rate, offset=-fir.delay*fir.rate + 280, marker="o", label="Decimated $y_5[n]$"); \
   ...: plt.title("Streaming decimation by 7 with the Kaiser window method"); \
   ...: plt.tight_layout();
   ...:
../../_images/sdr_Decimator_2.png

Constructors

Decimator(rate: int, taps: 'kaiser' | ArrayLike = 'kaiser', ...)

Creates a polyphase FIR decimating filter.

Special methods

__call__(x: ArrayLike, mode: 'rate' | 'full' = 'rate') → NDArray

Filters and decimates the input signal \(x[n]\) with the polyphase FIR filter.

__len__() → int

Returns the filter length \(N + 1\).

String representation

__repr__() → str

Returns a code-styled string representation of the object.

__str__() → str

Returns a human-readable string representation of the object.

Streaming mode only

reset()

Resets the filter state. Only useful when using streaming mode.

flush() → NDArray

Flushes the filter state by passing zeros through the filter. Only useful when using streaming mode.

property streaming : bool

Indicates whether the filter is in streaming mode.

property state : NDArray

The filter state consisting of the previous \(N\) inputs.

Methods

impulse_response(N: int | None = None) → NDArray

Returns the impulse response \(h[n]\) of the FIR filter. The impulse response \(h[n]\) is the filter output when the input is an impulse \(\delta[n]\).

step_response(N: int | None = None) → NDArray

Returns the step response \(s[n]\) of the FIR filter. The step response \(s[n]\) is the filter output when the input is a unit step \(u[n]\).

frequency_response(...) → tuple[NDArray, NDArray]

Returns the frequency response \(H(\omega)\) of the FIR filter.

frequency_response_log(...) → tuple[NDArray, NDArray]

Returns the frequency response \(H(\omega)\) of the FIR filter on a logarithmic frequency axis.

Properties

property rate : int

The decimation rate \(r\).

property method : 'kaiser' | 'custom'

The method used to design the multirate filter.

property taps : NDArray

The prototype feedforward taps \(h_i\).

property polyphase_taps : NDArray

The polyphase feedforward taps \(h_{i, j}\).

property delay : int

The delay of FIR filter in samples. The delay indicates the output sample index that corresponds to the first input sample.

property order : int

The order of the FIR filter \(N\).