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Oscar Gustafsson authoredOscar Gustafsson authored
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sfg_generators.py 7.92 KiB
"""
B-ASIC signal flow graph generators.
This module contains a number of functions generating SFGs for specific functions.
"""
from typing import Dict, Optional, Sequence, Union
import numpy as np
from b_asic.core_operations import Addition, ConstantMultiplication, Name
from b_asic.signal import Signal
from b_asic.signal_flow_graph import SFG
from b_asic.special_operations import Delay, Input, Output
from b_asic.wdf_operations import SymmetricTwoportAdaptor
def wdf_allpass(
coefficients: Sequence[float],
name: Optional[str] = None,
latency: Optional[int] = None,
latency_offsets: Optional[Dict[str, int]] = None,
execution_time: Optional[int] = None,
) -> SFG:
"""
Generate a signal flow graph of a WDF allpass section based on symmetric two-port\
adaptors.
Simplifies the SFG in case an adaptor operation is 0.
Parameters
----------
coefficients : 1D-array
Coefficients to use for the allpass section.
name : Name, optional
The name of the SFG. If None, "WDF allpass section".
latency : int, optional
Latency of the symmetric two-port adaptors.
latency_offsets : optional
Latency offsets of the symmetric two-port adaptors.
execution_time : int, optional
Execution time of the symmetric two-port adaptors.
Returns
-------
Signal flow graph
"""
np_coefficients = np.atleast_1d(np.squeeze(np.asarray(coefficients)))
order = len(np_coefficients)
if not order:
raise ValueError("Coefficients cannot be empty")
if np_coefficients.ndim != 1:
raise TypeError("coefficients must be a 1D-array")
if name is None:
name = "WDF allpass section"
input_op = Input()
output = Output()
odd_order = order % 2
if odd_order:
if np_coefficients[0]:
# First-order section
adaptor0 = SymmetricTwoportAdaptor(
np_coefficients[0],
input_op,
latency=latency,
latency_offsets=latency_offsets,
execution_time=execution_time,
)
signal_out = Signal(adaptor0.output(0))
delay = Delay(adaptor0.output(1))
Signal(delay, adaptor0.input(1))
else:
signal_out = Delay(input_op)
else:
signal_out = Signal(input_op)
# Second-order sections
sos_count = (order - 1) // 2 if odd_order else order // 2
offset1, offset2 = (1, 2) if odd_order else (0, 1)
for n in range(sos_count):
if np_coefficients[2 * n + offset1]:
adaptor1 = SymmetricTwoportAdaptor(
np_coefficients[2 * n + offset1],
signal_out,
latency=latency,
latency_offsets=latency_offsets,
execution_time=execution_time,
)
# Signal(prev_adaptor., adaptor1.input(0), name="Previous-stage to next")
delay1 = Delay(adaptor1.output(1))
else:
delay1 = Delay(signal_out)
if np_coefficients[2 * n + offset2]:
delay2 = Delay()
adaptor2 = SymmetricTwoportAdaptor(
np_coefficients[2 * n + offset2],
delay1,
delay2,
latency=latency,
latency_offsets=latency_offsets,
execution_time=execution_time,
)
Signal(adaptor2.output(0), adaptor1.input(1))
Signal(adaptor2.output(1), delay2)
signal_out = Signal(adaptor1.output(0))
else:
delay2 = Delay(delay1)
if np_coefficients[2 * n + offset1]:
Signal(delay2, adaptor1.input(1))
signal_out = Signal(adaptor1.output(0))
else:
signal_out = Signal(delay2)
output <<= signal_out
return SFG([input_op], [output], name=Name(name))
def direct_form_fir(
coefficients: Sequence[complex],
name: Optional[str] = None,
mult_properties: Optional[Union[Dict[str, int], Dict[str, Dict[str, int]]]] = None,
add_properties: Optional[Union[Dict[str, int], Dict[str, Dict[str, int]]]] = None,
) -> SFG:
r"""
Generate a signal flow graph of a direct form FIR filter.
The *coefficients* parameter is a sequence of impulse response values::
coefficients = [h0, h1, h2, ..., hN]
Leading to the transfer function:
.. math:: \sum_{i=0}^N h_iz^{-i}
Parameters
----------
coefficients : 1D-array
Coefficients to use for the FIR filter section.
name : Name, optional
The name of the SFG. If None, "Direct-form FIR filter".
mult_properties : dictionary, optional
Properties passed to :class:`~b_asic.core_operations.ConstantMultiplication`.
add_properties : dictionary, optional
Properties passed to :class:`~b_asic.core_operations.Addition`.
Returns
-------
Signal flow graph
See Also
--------
transposed_direct_form_fir
"""
np_coefficients = np.atleast_1d(np.squeeze(np.asarray(coefficients)))
taps = len(np_coefficients)
if not taps:
raise ValueError("Coefficients cannot be empty")
if np_coefficients.ndim != 1:
raise TypeError("coefficients must be a 1D-array")
if name is None:
name = "Direct-form FIR filter"
if mult_properties is None:
mult_properties = {}
if add_properties is None:
add_properties = {}
input_op = Input()
output = Output()
prev_delay = input_op
prev_add = None
for i, coefficient in enumerate(np_coefficients):
tmp_mul = ConstantMultiplication(coefficient, prev_delay, **mult_properties)
prev_add = (
tmp_mul
if prev_add is None
else Addition(tmp_mul, prev_add, **add_properties)
)
if i < taps - 1:
prev_delay = Delay(prev_delay)
output <<= prev_add
return SFG([input_op], [output], name=Name(name))
def transposed_direct_form_fir(
coefficients: Sequence[complex],
name: Optional[str] = None,
mult_properties: Optional[Union[Dict[str, int], Dict[str, Dict[str, int]]]] = None,
add_properties: Optional[Union[Dict[str, int], Dict[str, Dict[str, int]]]] = None,
) -> SFG:
r"""
Generate a signal flow graph of a transposed direct form FIR filter.
The *coefficients* parameter is a sequence of impulse response values::
coefficients = [h0, h1, h2, ..., hN]
Leading to the transfer function:
.. math:: \sum_{i=0}^N h_iz^{-i}
Parameters
----------
coefficients : 1D-array
Coefficients to use for the FIR filter section.
name : Name, optional
The name of the SFG. If None, "Transposed direct-form FIR filter".
mult_properties : dictionary, optional
Properties passed to :class:`~b_asic.core_operations.ConstantMultiplication`.
add_properties : dictionary, optional
Properties passed to :class:`~b_asic.core_operations.Addition`.
Returns
-------
Signal flow graph
See Also
--------
direct_form_fir
"""
np_coefficients = np.atleast_1d(np.squeeze(np.asarray(coefficients)))
taps = len(np_coefficients)
if not taps:
raise ValueError("Coefficients cannot be empty")
if np_coefficients.ndim != 1:
raise TypeError("coefficients must be a 1D-array")
if name is None:
name = "Transposed direct-form FIR filter"
if mult_properties is None:
mult_properties = {}
if add_properties is None:
add_properties = {}
input_op = Input()
output = Output()
prev_delay = None
for i, coefficient in enumerate(reversed(np_coefficients)):
tmp_mul = ConstantMultiplication(coefficient, input_op, **mult_properties)
tmp_add = (
tmp_mul
if prev_delay is None
else Addition(tmp_mul, prev_delay, **add_properties)
)
if i < taps - 1:
prev_delay = Delay(tmp_add)
output <<= tmp_add
return SFG([input_op], [output], name=Name(name))