108 lines
3.5 KiB
Plaintext
108 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c3a0b4a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[0, 0, 0, 0, 0, 0, 0, 0, 0]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# FIR Filter anlegen\n",
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"\n",
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"from scipy import signal\n",
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"from scipy.fft import fft, fftfreq\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from ipywidgets import interact\n",
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"\n",
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"\n",
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"def fir_filter(taps, input): # taps, input sind 1d Eingabelisten mit Koeffizienten und Samples\n",
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" fir=[] # Ausgabeliste anlegen\n",
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" for j in range(0, len(input) - len(taps)): # Erste Samples (Koeffizientenzahl) zählen nicht zur Filterantwort\n",
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" fir_i=0\n",
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" for i in range (len(taps)): # Durch Koeffizienten durchiterieren\n",
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" taps_i = taps[i] # taps_i ist Laufvariable\n",
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" fir_i += taps_i*input[j+i] # fir_i ist Laufvariable für Filterergebnis - jeweiliger Koeffizient wird mit dem i-ten Input-Sample der reduzierten Liste j multipliziert\n",
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" fir.append(fir_i) # hänge Ergebnis an Ergebnisliste an\n",
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" return fir\n"
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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": "3f78fe4f",
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"metadata": {},
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"outputs": [],
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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",
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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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"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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"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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"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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" \"#define CHIRP_DATA {\" + \",\".join(y_chrip.astype(str)) +\"}\\n\"\\\n",
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" \"#define CHIRP_DATA_INTERLEAVED_INVERTED {\" + \",\".join(y_chrip_interleaved.astype(str)) +\"}\" \"\\n\"\n",
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"\n",
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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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"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",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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