Nicolas Borla
/
BBR_1Ebene
BBR 1 Ebene
Diff: mbed-os/tools/dev/dsp_fir.py
- Revision:
- 0:fbdae7e6d805
diff -r 000000000000 -r fbdae7e6d805 mbed-os/tools/dev/dsp_fir.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mbed-os/tools/dev/dsp_fir.py Mon May 14 11:29:06 2018 +0000 @@ -0,0 +1,89 @@ +""" +mbed SDK +Copyright (c) 2011-2013 ARM Limited + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from numpy import sin, arange, pi +from scipy.signal import lfilter, firwin +from pylab import figure, plot, grid, show + +#------------------------------------------------ +# Create a signal for demonstration. +#------------------------------------------------ +# 320 samples of (1000Hz + 15000 Hz) at 48 kHz +sample_rate = 48000. +nsamples = 320 + +F_1KHz = 1000. +A_1KHz = 1.0 + +F_15KHz = 15000. +A_15KHz = 0.5 + +t = arange(nsamples) / sample_rate +signal = A_1KHz * sin(2*pi*F_1KHz*t) + A_15KHz*sin(2*pi*F_15KHz*t) + +#------------------------------------------------ +# Create a FIR filter and apply it to signal. +#------------------------------------------------ +# The Nyquist rate of the signal. +nyq_rate = sample_rate / 2. + +# The cutoff frequency of the filter: 6KHz +cutoff_hz = 6000.0 + +# Length of the filter (number of coefficients, i.e. the filter order + 1) +numtaps = 29 + +# Use firwin to create a lowpass FIR filter +fir_coeff = firwin(numtaps, cutoff_hz/nyq_rate) + +# Use lfilter to filter the signal with the FIR filter +filtered_signal = lfilter(fir_coeff, 1.0, signal) + +#------------------------------------------------ +# Plot the original and filtered signals. +#------------------------------------------------ + +# The first N-1 samples are "corrupted" by the initial conditions +warmup = numtaps - 1 + +# The phase delay of the filtered signal +delay = (warmup / 2) / sample_rate + +figure(1) +# Plot the original signal +plot(t, signal) + +# Plot the filtered signal, shifted to compensate for the phase delay +plot(t-delay, filtered_signal, 'r-') + +# Plot just the "good" part of the filtered signal. The first N-1 +# samples are "corrupted" by the initial conditions. +plot(t[warmup:]-delay, filtered_signal[warmup:], 'g', linewidth=4) + +grid(True) + +show() + +#------------------------------------------------ +# Print values +#------------------------------------------------ +def print_values(label, values): + var = "float32_t %s[%d]" % (label, len(values)) + print "%-30s = {%s}" % (var, ', '.join(["%+.10f" % x for x in values])) + +print_values('signal', signal) +print_values('fir_coeff', fir_coeff) +print_values('filtered_signal', filtered_signal)