68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
from functools import reduce
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from operator import add
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from math import cos, pi
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from scipy import signal
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import matplotlib.pyplot as plt
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from migen import *
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from migen.fhdl import verilog
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# A synthesizable FIR filter.
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class FIR(Module):
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def __init__(self, coef, wsize=16):
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self.coef = coef
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self.wsize = wsize
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self.i = Signal((self.wsize, True))
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self.o = Signal((self.wsize, True))
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###
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muls = []
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src = self.i
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for c in self.coef:
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sreg = Signal((self.wsize, True))
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self.sync += sreg.eq(src)
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src = sreg
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c_fp = int(c*2**(self.wsize - 1))
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muls.append(c_fp*sreg)
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sum_full = Signal((2*self.wsize-1, True))
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self.sync += sum_full.eq(reduce(add, muls))
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self.comb += self.o.eq(sum_full >> self.wsize-1)
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# A test bench for our FIR filter.
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# Generates a sine wave at the input and records the output.
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def fir_tb(dut, frequency, inputs, outputs):
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f = 2**(dut.wsize - 1)
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for cycle in range(200):
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v = 0.1*cos(2*pi*frequency*cycle)
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yield dut.i, int(f*v)
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inputs.append(v)
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outputs.append((yield dut.o)/f)
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yield
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if __name__ == "__main__":
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# Compute filter coefficients with SciPy.
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coef = signal.remez(30, [0, 0.1, 0.2, 0.4, 0.45, 0.5], [0, 1, 0])
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# Simulate for different frequencies and concatenate
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# the results.
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in_signals = []
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out_signals = []
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for frequency in [0.05, 0.1, 0.25]:
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dut = FIR(coef)
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tb = fir_tb(dut, frequency, in_signals, out_signals)
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Simulator(dut, tb).run()
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# Plot data from the input and output waveforms.
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plt.plot(in_signals)
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plt.plot(out_signals)
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plt.show()
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# Print the Verilog source for the filter.
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fir = FIR(coef)
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print(verilog.convert(fir, ios={fir.i, fir.o}))
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