116 lines
3.3 KiB
Python
Executable File
116 lines
3.3 KiB
Python
Executable File
#! /usr/bin/env python3
|
|
|
|
import csv
|
|
import sys
|
|
import numpy as np
|
|
from scipy import signal
|
|
import matplotlib.pyplot as plt
|
|
from scipy.io import wavfile
|
|
from scipy.signal import butter, lfilter
|
|
from math import log10
|
|
import pyqtgraph as pg
|
|
from PyQt5 import QtWidgets
|
|
|
|
tones = {}
|
|
with open('tones.csv', newline='') as csvfile:
|
|
reader = csv.DictReader(csvfile)
|
|
for row in reader:
|
|
tones[row['designator']] = float(row['frequency'])
|
|
|
|
|
|
# Shamelessly lifted from
|
|
# https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass
|
|
def butter_bandpass(lowcut, highcut, fs, order=5):
|
|
nyq = 0.5 * fs
|
|
low = lowcut / nyq
|
|
high = highcut / nyq
|
|
b, a = butter(order, [low, high], btype='band')
|
|
return b, a
|
|
|
|
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
|
|
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
|
|
y = lfilter(b, a, data)
|
|
return y
|
|
|
|
# tone synthesis
|
|
def note(freq, cycles, amp=32767.0, rate=44100):
|
|
len = cycles * (1.0/rate)
|
|
t = np.linspace(0, len, int(len * rate))
|
|
if freq == 0:
|
|
data = np.zeros(int(len * rate))
|
|
else:
|
|
data = np.sin(2 * np.pi * freq * t) * amp
|
|
return data.astype(int)
|
|
|
|
def decimate_from_sample_rate(sample_rate):
|
|
if sample_rate == 44100:
|
|
return 4 # rate = 11025, Fmax = 5512.5 Hz
|
|
elif sample_rate == 48000:
|
|
return 5 # rate = 9600, Fmax = 4800 Hz
|
|
elif sample_rate == 22050:
|
|
return 2 # rate = 11025, Fmax = 5512.5 Hz
|
|
elif sample_rate == 11025:
|
|
return 1 # rate = 11025, Fmax = 5512.5 Hz
|
|
else:
|
|
raise ValueError("Sample rate not supported")
|
|
|
|
|
|
# analyze wav file
|
|
if __name__ == '__main__':
|
|
FLT_LEN = 2000 # Samples
|
|
|
|
file_name = sys.argv[1]
|
|
sample_rate, data = wavfile.read(file_name)
|
|
|
|
print(f"{file_name}: {len(data)} samples @ {sample_rate} Hz")
|
|
|
|
decimate = decimate_from_sample_rate(sample_rate)
|
|
if decimate > 1:
|
|
data = signal.decimate(data, decimate)
|
|
sample_rate = sample_rate / decimate
|
|
|
|
print(f'Length after decimation: {len(data)} samples')
|
|
|
|
data = butter_bandpass_filter(data, 270, 1700, sample_rate, order=8)
|
|
|
|
pure_signals = {tone:note(freq, FLT_LEN, rate=sample_rate) for tone,freq in tones.items()}
|
|
correlations = {tone:np.abs(signal.correlate(data, pure, mode='same')) for tone,pure in pure_signals.items()}
|
|
|
|
|
|
app = QtWidgets.QApplication([])
|
|
win = pg.GraphicsLayoutWidget(show=True, title="SELCAL Tone Correlation")
|
|
|
|
plot = win.addPlot(title=file_name)
|
|
plot.addLegend()
|
|
|
|
color_map = pg.colormap.get('CET-C6s')
|
|
colors = color_map.getLookupTable(nPts=len(tones))
|
|
|
|
for (tone, correlation), color in zip(correlations.items(), colors):
|
|
plot.plot(correlation, pen=pg.mkPen(color=color), fillLevel=0, name=tone)
|
|
|
|
plot.setLabel('left', 'Signal Correlation')
|
|
plot.setLabel('bottom', 'Time (samples)')
|
|
|
|
plot.showGrid(x=True, y=True)
|
|
|
|
app.exec_()
|
|
|
|
# matplotlib is too slow
|
|
'''
|
|
fig, ax = plt.subplots()
|
|
|
|
# Step 4: Plot the data
|
|
for tone,correlation in correlations.items():
|
|
ax.plot(correlation, label=tone)
|
|
|
|
# Step 5: Customize the plot
|
|
ax.set_title('Multiple Lines Sharing the Same X Data')
|
|
ax.set_xlabel('Time (samples)')
|
|
ax.set_ylabel('Signal Correlation')
|
|
ax.legend() # Add a legend
|
|
ax.grid(True) # Add a grid
|
|
|
|
# Step 6: Display the plot
|
|
plt.show()
|
|
''' |