Einarbeiten
This commit is contained in:
Patrick Hangl
102
FIR_phangl.ipynb
102
FIR_phangl.ipynb
@@ -54,24 +54,26 @@
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"source": [
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"# Chirp Generator\n",
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"\n",
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"n=3000 # number of samples to use for the chirp\n",
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"fs=20000 # The sampling rate for the chrip\n",
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"f0=100# the start frequency in Hz for the chirp\n",
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"f1=1000 # the stop frequency of the chirp\n",
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"t1=n/fs # the total length of the chirp in s\n",
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"n=3000 #Sampleanzahl\n",
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"fs=20000 #Samplingrate\n",
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"f0=100 #Startfrequenz\n",
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"f1=1000 #Stopfrequenz\n",
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"t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
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"\n",
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"t_chrip = np.linspace(0, t1, n)\n",
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"# generate a chrip and scale to int16 (1 bit for sign)\n",
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"y_chrip = np.round(signal.chirp(t_chrip, f0=f0, f1=f1, t1=t1, method='linear')*(2**15-1)).astype(int)\n",
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"t_chrip = np.linspace(0, t1, n) #Array mit Anzahl der Samples anlegen für Zeitachse\n",
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"y_chrip = np.round(signal.chirp(t_chrip, f0=f0, f1=f1, t1=t1, method='linear')*(2**15-1)).astype(int) #Chirp erstellen, auf Ganzzahlen runden, auf 16 Bit Integer skalieren\n",
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"\n",
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"# Erste 4 Samples wegschneiden\n",
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"cutsamps = 45\n",
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"y_chrip = y_chrip[cutsamps:]\n",
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"t_chrip = t_chrip[cutsamps:]\n",
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"\n",
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"# Generate an array were the data is present in an interleaved format with the inverted signal \n",
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"# Doppelt so langes Signal mit abwechselnd Original- und invertierten Werten - Struktur für Symmetrie und 2-Kanal-Systeme\n",
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"y_chrip_interleaved = np.empty((2*y_chrip.size), dtype=y_chrip.dtype)pa\n",
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"y_chrip_interleaved[0::2] = y_chrip\n",
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"y_chrip_interleaved[1::2] = -1*y_chrip[::-1]\n",
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"\n",
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"# Chirp in Header-Datei schreiben, welche über PCM eingelesen werden kann\n",
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"file_str= f\"#define CHIRP_DATA_SAMPLE_RATE {int(fs)}\\n\"\\\n",
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" \"#define CHIRP_DATA_LEN\"f\" {y_chrip.size}\" \"\\n\"\\\n",
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" \"#define CHIRP_DATA_INTERLEAVED_LEN\"f\" {y_chrip_interleaved.size}\" \"\\n\"\\\n",
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@@ -81,6 +83,88 @@
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"with open(\"pcm_chirp/include/chirp_data.h\", \"w\") as f:\n",
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" f.write(file_str)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc1c61df",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ScyPyFIR Filter anlegen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dea7ae97",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Vergleich und Plot\n",
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"\n",
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"%matplotlib widget\n",
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"\n",
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"b=signal.firwin(20, 100, fs=fs)\n",
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"y_lfiltered = signal.lfilter(b, [1.0], y_chrip)\n",
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"yf=fft(y_lfiltered)\n",
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"xf = fftfreq(n, t1/n)[:n//2]\n",
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"\n",
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"cols = 1\n",
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"rows = 3\n",
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"\n",
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"fig = plt.figure(1)\n",
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"ax1 = fig.add_subplot(rows, cols, 1)\n",
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"line1, = ax1.plot([0], \".-\", label = \"chrip\")\n",
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"ax2 = fig.add_subplot(rows, cols, 2)\n",
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"line2, = ax2.plot([0], label=\"chrip filtered signal.lfilter\")\n",
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"ax3 = fig.add_subplot(rows, cols, 3)\n",
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"line3, = ax3.plot([0], label=\"own fir implementation\")\n",
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"\n",
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"def update(numtaps = 40, f_cut=100):\n",
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" # Calculate the filter coefficients for given paramters\n",
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" b=signal.firwin(numtaps, f_cut, fs=fs)\n",
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" print(f\"Filter coeffs for {numtaps} tabs and {f_cut}Hz cutoff are:\\n\", b)\n",
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" \n",
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" bits=16\n",
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" if min(b)<0:\n",
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" bits=bits-1\n",
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" \n",
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" print(f\"Filter coeffs converted to Q1.{bits}bit int are :\\n\", \n",
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" \", \".join(\n",
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" np.array(np.array(np.round(b*(2**(bits)-1)), dtype=np.int32), dtype=str)\n",
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" )\n",
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" )\n",
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"\n",
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" # plot the chirp\n",
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" line1.set_data(range(len(y_chrip)), y_chrip)\n",
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" ax1.set_xlim(0, len(y_chrip))\n",
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" ax1.set_ylim(min(y_chrip), max(y_chrip))\n",
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"\n",
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" # Apply the coefficents with scipy function\n",
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" y_lfiltered = signal.lfilter(b, [1.0], y_chrip)\n",
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" line2.set_data(range(len(y_lfiltered)), y_lfiltered)\n",
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" ax2.set_xlim(0, len(y_lfiltered))\n",
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" ax2.set_ylim(min(y_lfiltered), max(y_lfiltered))\n",
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"\n",
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" # yf=2.0/n*np.abs(fft(y_chrip[:n//2]))\n",
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" # xf = fftfreq(n, t1/n)[:n//2]\n",
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" data = simple_fir(b, y_chrip)\n",
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" line3.set_data(range(len(data)), data)\n",
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" ax3.set_xlim(0, len(data))\n",
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" ax3.set_ylim(min(data), max(data))\n",
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"\n",
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" fig.canvas.draw_idle()\n",
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" # save coefficients to file\n",
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" with open(\"pcm_data_processing/include/coefficients.h\", \"w\") as f:\n",
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" f.write(\n",
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" \"#define NUMTAPS \" + str(numtaps) + \"\\n\" +\n",
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" \"#define COEFFICIENTS {\" + \",\".join(np.array(np.array(np.round(b*(2**(bits)-1)), dtype=np.int32), dtype=str)) +\"}\" \"\\n\"\n",
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" )\n",
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"\n",
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"plt.tight_layout()\n",
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"interact(update, numtaps=(0, 100,2), f_cut=(100,5000))"
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]
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}
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],
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"metadata": {
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197
LMS_phangl.ipynb
Normal file
197
LMS_phangl.ipynb
Normal file
@@ -0,0 +1,197 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"import os\n",
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"from ipywidgets import interact\n",
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"from scipy import signal\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import soundfile as sf\n",
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"from numba import njit, jit\n",
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"\n",
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"\n",
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"# Chirp Generator\n",
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"n=2000 #Sampleanzahl\n",
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"fs=20000 #Samplingrate\n",
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"f0=100 #Startfrequenz\n",
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"f1=1000 #Stopfrequenz\n",
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"t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
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"f_disturber=2000 #Störfrequenz\n",
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"\n",
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"disturber_amplitude=0.3\n",
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"chirp_disturber_full_amp=0.6\n",
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"\n",
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"t = np.linspace(0, t1, n)\n",
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"\n",
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"# Chirp anlegen mit Amplitude\n",
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"y_signal = signal.chirp(t, f0=f0, f1=f1, t1=t1, method='linear')\n",
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"y_signal = (chirp_disturber_full_amp-disturber_amplitude)*y_signal#*(2**(15-1))\n",
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"\n",
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"# Störsinus anlegen, wird auf Chirp Signal addiert\n",
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"y_disturber_sine = np.sin(2*np.pi*f_disturber*t)\n",
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"y_disturber_sine = y_disturber_sine * disturber_amplitude #* (2**(15-1))\n",
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"y_signal_disturber_sine = y_signal + y_disturber_sine\n",
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"\n",
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"# Störrauschen anlegen, wird auf Chirp Signal addiert\n",
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"y_disturber_noise = np.random.normal(0, 0.2, n)\n",
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"y_disturber_noise = y_disturber_noise * disturber_amplitude #* (2**(15-1))\n",
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"y_signal_disturber_noise = y_signal + y_disturber_noise\n",
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"\n",
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"def load_wav(filename):\n",
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" y, fs = sf.read(filename, dtype='float32')\n",
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" return fs, y.T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[WinError 3] The system cannot find the path specified: './lpdsp32/cSensorSignalProcessing/test/testdata/input'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[3], line 29\u001b[0m\n\u001b[0;32m 27\u001b[0m wav_folder_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./lpdsp32/cSensorSignalProcessing/test/testdata/input\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# Get a list of all files in the folder\u001b[39;00m\n\u001b[1;32m---> 29\u001b[0m file_names \u001b[38;5;241m=\u001b[39m [f \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwav_folder_path\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m f\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.wav\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n\u001b[0;32m 30\u001b[0m data_sel\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchirp_sine\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchirp_noise\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m file_names\n\u001b[0;32m 32\u001b[0m \u001b[38;5;66;03m# setup the plot\u001b[39;00m\n",
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"\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: './lpdsp32/cSensorSignalProcessing/test/testdata/input'"
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]
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}
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],
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"source": [
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"%matplotlib widget\n",
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"\n",
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"@njit\n",
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"def lms_fir(data, ref_noise, N_coeffs, mu, scale_bits=31, adaption_step = 1, fix_point=False):\n",
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" coeffs_matrix = np.zeros((len(data), N_coeffs), dtype=np.float32) #2d Koeffizientenmatrix anlegen, Zeilen->Samples, Spalten -> Koeffizienten für Plot abspeichern\n",
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" out=np.zeros(data.shape[0], dtype=np.float32) #1d Ausgabematrix anlegen, selbes Format wie Input-Daten\n",
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" coeffs = np.zeros(N_coeffs, dtype=np.float32) #1d Filtermatrix anlegen, Länge ist Anzahl der Koeffizienten\n",
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" tap_buffer = np.zeros(N_coeffs, dtype=np.float32) #1d Tap Buffer Matrix anlegen - warum?\n",
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" \n",
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" # Interate over the data\n",
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" for j in range(0, len(data) - len(coeffs)): \n",
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" acc=0\n",
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" for i in range(N_coeffs): #iterate over the coefficients to calculate the filter and get the canceller\n",
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" noise=ref_noise[j+i]\n",
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" tap_buffer[i] = noise\n",
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" acc+=coeffs[i] * noise\n",
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" out[j] = data[j]-acc #Ausgabesignal = Eingangssignal - gefiltertes Störsignal?\n",
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" prod = mu*out[j]\n",
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" if (j % adaption_step) == 0:\n",
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" for i in range(N_coeffs):\n",
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" coeffs[i] += prod*tap_buffer[i] \n",
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" coeffs_matrix[j, :] = coeffs[:] #Koeffizienten in Matrix abspeichern für Plot\n",
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" return out, coeffs_matrix\n",
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"\n",
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"\n",
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"# load the wav files names\n",
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"wav_folder_path = \"./lpdsp32/cSensorSignalProcessing/test/testdata/input\"\n",
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"# Get a list of all files in the folder\n",
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"file_names = [f for f in os.listdir(wav_folder_path) if f.endswith(\".wav\")]\n",
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"data_sel=['chirp_sine', 'chirp_noise'] + file_names\n",
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"\n",
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"# setup the plot\n",
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"cols = 1\n",
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"rows = 4\n",
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"fig=plt.figure(figsize=(11, 6))\n",
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"plot1 = plt.subplot2grid( (rows, cols), (0,0), 1)\n",
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"line1 = plot1.plot([0], label=\"Chirp with noise\")\n",
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"plot2 = plt.subplot2grid((rows,cols),(1,0), sharex=plot1)\n",
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"line2 = plot2.plot([0], label=\"noise\")\n",
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"plot3 = plt.subplot2grid((rows, cols), (2,0), sharex=plot1)\n",
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"line3 = plot3.plot([0], label=\"output\")\n",
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"plot4 = plt.subplot2grid((rows, cols), (3,0), sharex=plot1)\n",
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"line4 = plot4.plot([0], label=\"filter coefficients\")\n",
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"\n",
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"\n",
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"def update(\n",
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" data_sel, num_coeff = 128, mu=0.01, \n",
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" adaption_step=1\n",
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" ):\n",
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" start0 = time.time()\n",
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" global y_signal_disturber, y_disturber\n",
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"\n",
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" if data_sel == \"chirp_sine\":\n",
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" y_s_d = y_signal_disturber_sine.astype(np.float32)\n",
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" y_d = y_disturber_sine.astype(np.float32)\n",
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" elif data_sel == \"chirp_noise\":\n",
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" y_s_d = y_signal_disturber_noise.astype(np.float32)\n",
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" y_d = y_disturber_noise.astype(np.float32)\n",
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" else:\n",
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" fs, data = load_wav(f\"{wav_folder_path}/{data_sel}\")\n",
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" y_s_d = data[1]\n",
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" y_d = data[0]\n",
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"\n",
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" # plot the chirp with noise\n",
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" line1[0].set_data(range(len(y_s_d)), y_s_d)\n",
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" plot1.set_xlim(0, len(y_s_d))\n",
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" plot1.set_ylim(min(y_s_d), max(y_s_d))\n",
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"\n",
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" # Plot the noise\n",
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" data = y_d\n",
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" line2[0].set_data(range(len(data)), data)\n",
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" plot2.set_xlim(0, len(data))\n",
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" plot2.set_ylim(min(data), max(data))\n",
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"\n",
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" #Plot the result\n",
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" start1 = time.time()\n",
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" data, coeffs_matrix = lms_fir(y_s_d, y_d, num_coeff, mu, \n",
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" #scale_bits=scale_bits, fix_point=fix_point\n",
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" adaption_step=adaption_step, \n",
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" )\n",
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" end1 = time.time()\n",
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" print(\"Elapsed (for fir_lms) = %s\" % round(end1 - start1, 3))\n",
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" line3[0].set_data(range(len(data)), data)\n",
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" plot3.set_xlim(0, len(data))\n",
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" plot3.set_ylim(np.min(data), np.max(data))\n",
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"\n",
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" #plot the coefficients progress\n",
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" plot4.clear()\n",
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" plot4.set_title(f\"mu={round(mu,4)}, N_coeffs={num_coeff}\")\n",
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" for i in range(coeffs_matrix.shape[1]):\n",
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" data = coeffs_matrix[:, i]\n",
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" plot4.plot(range(data.size), data, label=\"coeff {}\".format(i))\n",
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" plot4.set_xlim(0, data.size)\n",
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" plot4.autoscale(axis='y')\n",
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" plot4.legend(bbox_to_anchor=(1,1), loc=\"upper left\")\n",
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" fig.canvas.draw_idle()\n",
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" #plt.tight_layout()\n",
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" end0 = time.time()\n",
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" print(\"Elapsed (for update function) = %s\" % round(end0 - start0, 3))\n",
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"\n",
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"interact(update, data_sel=data_sel ,num_coeff=(0, 256, 2), mu=(0.001, 0.1, 0.001), \n",
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" #fix_point=False, scale_bits=(1, 31, 1), \n",
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" adaption_step=(1 , 128 ,1)\n",
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" )\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user