192 lines
6.5 KiB
Plaintext
192 lines
6.5 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 #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) #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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"# 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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" \"#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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"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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"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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