diff options
| -rwxr-xr-x | plot.py | 38 | ||||
| -rw-r--r-- | src/main.rs | 14 |
2 files changed, 44 insertions, 8 deletions
@@ -1,6 +1,26 @@ #!/usr/bin/env python +import sys import matplotlib.pyplot as plt import numpy as np +from scipy.signal import butter, lfilter, freqz +import matplotlib.pyplot as plt + +samp_rate = 10000 +input_rate = 1000 +assert(samp_rate % input_rate == 0) +rf_to_baseband_sample_ratio = samp_rate // input_rate; + +L = 200 + +def butter_lowpass(cutoff, fs, order=5): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + b, a = butter(order, normal_cutoff, btype='low', analog=False) + return b, a + +def butter_lowpass_filter(data, cutoff, fs, order=6): + b, a = butter_lowpass(cutoff, fs, order=order) + return lfilter(b, a, data) debug_pd = np.loadtxt("debug-pd.csv", delimiter=",") sample, slope, pd, pdslope = np.split(debug_pd, 4, 1) @@ -11,12 +31,6 @@ dds_ix, dds_phase, dds_phase_delta, dds_phase_idx_i, dds_phase_idx_q = np.split( out = np.fromfile("debug-out.i8", dtype="i1") out_r, out_g = np.split(np.reshape(out, newshape=(out.shape[0]//2, 2)), 2, 1) -samp_rate = 10000 -input_rate = 1000 -assert(samp_rate % input_rate == 0) -rf_to_baseband_sample_ratio = samp_rate // input_rate; - -L = 200 L_out = L * rf_to_baseband_sample_ratio @@ -52,4 +66,16 @@ plt.title("output") plt.plot(out_r[0:L_out]) plt.plot(out_g[0:L_out]) +plt.figure() +plt.subplot(2, 1, 1) +plt.title("out sum") +out_sum = out_r[0:L_out].astype(float) + out_g[0:L_out].astype(float) + +plt.plot(out_sum) + +plt.subplot(2, 1, 2) +plt.title("out sum filt") + +plt.plot(butter_lowpass_filter(out_sum, cutoff=10, fs=2000)) + plt.show() diff --git a/src/main.rs b/src/main.rs index e35062e..8519158 100644 --- a/src/main.rs +++ b/src/main.rs @@ -270,10 +270,20 @@ fn main() { } } - // Downmix stereo to mono, apply (v/2 + i16::MAX/2), and normalise to [-1.0; 1.0] + // Downmix stereo to mono, apply (v/2 + i16::MAX/2), + // normalise to [0.0; 1.0], + // take the arcsine to compensate for the phasing, + // and divide by PI/2 to normalise to [0.0; 1.0] + use std::f32::consts::FRAC_2_PI; let buf: Vec<f32> = buf .chunks_exact(2) - .map(|v| ((v[0]/2 + v[1]/2)/2 + i16::MAX/2) as f32 / 32768.0) + .map(|v| { + let samp = ((v[0]/2 + v[1]/2)/2 + i16::MAX/2) as f32 / 32768.0; + if samp < -1.0 || samp > 1.0 { + panic!("sample normalistion broken") + } + f32::asin(samp) * FRAC_2_PI + }) .collect(); if let Some(w) = &mut in_debug { |