{ "cells": [ { "cell_type": "markdown", "id": "cd4214fd-61b8-4458-a497-67bc6abddc2c", "metadata": {}, "source": [ "# Digitalisierung\n", "\n", "::::::{margin}\n", ":::::{grid}\n", "::::{grid-item-card}\n", ":class-header: bg-light\n", "Was ist der Unterschied zwischen analogen und digitalen Daten? (Digi4All)\n", "\n", "\n", "::::\n", ":::::\n", "::::::\n", "\n", "Digitalisierung bezieht sich auf die Darstellung von Daten in einer Form, die von Computern gespeichert, verarbeitet und übertragen werden kann. Im Gegensatz zu analogen Messgeräten, bei denen die Darstellung lückenlos und daher unendlich genau ist, werden Daten in der digitalen Darstellung diskret und unter Verwendung eines begrenzten Zeichenvorrats dargestellt. Bei der Umwandlung von analogen in digitale Werte kann es daher zu Informationsverlusten kommen.\n", "\n", "Viele Messungen, wie beispielsweise Spannungs- und Strommessungen, werden am Ende mittels Analog-Digital-Umsetzer (ADU) digitalisiert, da sie anders vom Computer nicht weiter verarbeitet werden können. Daher möchten wir hier das Grundprinzip des ADUs erläutern.\n", "\n", "## Kennlinie\n", "\n", "Im Zuge der Digitalisierung wird der unendliche Wertebereich einer analogen Größe, wie z.B. einer elektrischen Spannung, auf einen endlichen, diskreten **Wertebereich** abgebildet:\n", "\n", "$$W_B = (2^n-1) \\cdot 1\\,\\mathrm{LSB}$$\n", "\n", "![Kennlinie bei der Digitalisierung unter Verwendung eines 3-Bit-ADU](draw/digi_3bit.jpg)\n", "\n", "Die kontinuierliche analoge Spannung wird in viele kleine Stufen unterteilt. Die gestufte **(Treppen-)Kurve** in der obigen Abbildung zeigt die reale Übertragungskurve eines 3-Bit-Analog-Digital-Umsetzers (ADU). Das digitale Ausgangssignal bleibt konstant bei einem festen Wert, solange sich das analoge Eingangssignal innerhalb eines Inkrements, also 1 **LSB (least significant bit)**, verändert. Wenn sich das Eingangssignal nur minimal ändert, z.B. von 1,1V auf 1,9V, ändert sich der digitale Ausgangswert nicht und bleibt konstant auf *001*.\n", "\n", "## Auflösung\n", "\n", "Es ist offensichtlich, dass bei der Digitalisierung Informationen verloren gehen. Die analogen Spannungswerte sind beliebig genau und haben unendlich viele Nachkommastellen, werden jedoch nur auf einen begrenzten, diskreten Wertebereich mit einer begrenzten Anzahl von Stufen projiziert. Je feiner diese Stufen sind (also je mehr Bits verwendet werden), desto besser ist die **Auflösung**. Erst wenn ein Grenzwert überschritten wird, wird die nächste *Stufe* erreicht, und der Computer zeigt einen neuen Spannungswert an. Die Zwischenschritte werden jedoch nicht angezeigt.\n", "\n", "Es ist wichtig zu beachten, dass es in der Praxis nicht möglich oder sinnvoll ist, eine beliebig hohe Auflösung zu verwenden. Der technische Aufwand steigt mit der Anzahl der Bits erheblich an. Daher ist es wichtig, vor der Messaufgabe zu überlegen, welche Auflösung benötigt wird, insbesondere wenn die Auflösung durch andere Parameter begrenzt ist, wie z.B. die Genauigkeit der Referenzmessung und Kalibrierung." ] }, { "cell_type": "code", "execution_count": 9, "id": "c4d50f54-c84e-4d8a-9904-fde3a070673d", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "plt.rcParams['font.size'] = 13; # Schriftgröße\n", "\n", "\n", "# Initialize the figure and axis\n", "fig, ax = plt.subplots(figsize=(7, 4))\n", "f = 1\n", "# Create a function to update the plot for each frame of the animation\n", "def animate(bit):\n", " ax.clear() # Clear the previous frame\n", " \n", " # Set the signal frequency and time axis\n", "\n", " t = np.linspace(0, 1, 150)\n", " \n", " # Generate the analog signal\n", " analoges_signal = np.sin(2 * np.pi * f * t)\n", " \n", " # Digitalisierung des analogen Signals\n", " quantisierungs_bits = bit # Anzahl der Quantisierungsbits\n", " digitalisiertes_signal = np.round(analoges_signal * (2**(bit-1)))\n", " max_amplitude = 2**(bit-1) # Maximaler Amplitudenwert\n", " digitalisiertes_signal = digitalisiertes_signal / max_amplitude\n", " \n", " # Initialisierung des Plots\n", " ax.plot(t, analoges_signal, label='Analoges Signal', alpha=0.3, lw = '3')\n", " ax.stem(t, digitalisiertes_signal, 'tab:red', markerfmt='ro', basefmt=\" \", linefmt='r-', label = '%d Bit Digitalwandler'%(bit))\n", "\n", " # Set plot labels and legend\n", " #ax.set_title('Analog zeitdiskret: Abgetastete Sinuswelle')\n", " ax.set_xlabel('Zeit (s)')\n", " ax.set_ylabel('Amplitude')\n", " ax.set_xlim([0, 1])\n", " ax.set_ylim([-1.1, 1.1])\n", " ax.grid(True)\n", " ax.legend(loc='upper right')\n", "\n", "# Create an animation by varying fs from 1 to 10 with a step of 1\n", "ani = FuncAnimation(fig, animate, frames=range(1, 17), repeat=False)\n", "\n", "# Display the animation\n", "plt.tight_layout()\n", "plt.close()\n", "HTML(ani.to_jshtml())" ] }, { "cell_type": "markdown", "id": "7bf444e1-67c0-465b-a7d9-97e8f13889e7", "metadata": {}, "source": [ "## Quantisierungsabweichung\n", "\n", "Dadurch dass der Wertebereich begrenzt ist, ergeben sich absolute Messabweichungen beim Digitalisierungsprozess. Dies wird auch **absoluter Digitalisierungsfehler** genannt. \n", "Aus der Kennlinie kann man diesen Fehler ablesen, er ergibt sich aus dem Digitalwert und dem idealisierten Kennlinienwert. Im *worst case* beträgt die maximale Abweichung:\n", "\n", "$$A = 1\\,\\mathrm{LSB}$$\n", "\n", "Die relative Messabweichung bezogen auf den Wertebereich ist dadurch:\n", "\n", "$$A_\\mathrm{rel} = \\frac{A}{W_B} = \\frac{1\\,\\mathrm{LSB}}{(2^n-1)\\cdot 1\\,\\mathrm{LSB}} \\approx \\frac{1}{2^n}$$\n", "\n", "Für einen A-D-Wandler mit einer Auflösung von 10 Bit ist die relative Abweichung infolge der Quantisierung anzugeben: \n", "\n", "$$A_\\mathrm{rel} = \\frac{1}{2^{10}} = 0,00097 = 0,001 = 0,1\\% $$" ] }, { "cell_type": "code", "execution_count": 2, "id": "7d1b3a41-e076-4ae9-9d47-76685e3b019c", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rel. Abweichung: \t\t\t 0.09775171065493646 %\n", "rel. Abweichung (Näherungsformel): \t 0.09765625 %\n" ] } ], "source": [ "n_bits = 10\n", "print('rel. Abweichung: \\t\\t\\t', 1/(2**n_bits-1)*100, '%')\n", "print('rel. Abweichung (Näherungsformel): \\t', 1/2**n_bits*100, '%')" ] }, { "cell_type": "markdown", "id": "adac0d92-cebf-43a0-894d-b75cf786590d", "metadata": {}, "source": [ "```{warning} \n", "Für kleine Spannungen, die den Wertebereich des ADUs nicht ausnutzen, sollte zuerst eine Verstärkerstufe so vorgeschaltet werden, dass der komplette diskrete Wertebereich ausgenutzt wird. Befindet sich die analoge Messgröße nur innerhalb eines LSB's, wäre dies sehr ungünstig.\n", "```" ] }, { "cell_type": "markdown", "id": "0f61a45a-f23d-4937-8006-4a7a3331e42d", "metadata": {}, "source": [ "## Nyquist-Shannon-Abtasttheorem\n", "\n", "```{epigraph}\n", "Ein auf $f_\\mathrm{max}$ bandbegrenztes Signal kann exakt rekonstruiert werden, wenn es mit einer Frequenz $> 2\\cdot f_\\mathrm{max}$ abgetastet wurde.\n", "\n", "-- Wikipedia\n", "```\n", "\n", "::::::{margin}\n", ":::::{grid}\n", "::::{grid-item-card}\n", ":class-header: bg-light\n", "Nyquist Shannon Theorem (Elektrotechnik in 5 Minuten by Alexander Stöger)\n", "\n", "\n", "::::\n", ":::::\n", "::::::\n", "\n", "Das Nyquist-Shannon-Abtasttheorem, auch als Nyquist-Kriterium bekannt, ist ein grundlegendes Prinzip in der Signalverarbeitung. Es besagt, dass ein bandbegrenztes Signal, das auf eine maximale Frequenz von $f_\\mathrm{max}$ beschränkt ist, genau dann exakt rekonstruiert werden kann, wenn es mit einer Abtastfrequenz $f_\\mathrm{ab}$ abgetastet wird, die größer ist als das Doppelte der maximalen Signal-Frequenz.\n", "\n", "$$f_\\mathrm{ab} > 2\\cdot f_\\mathrm{max}$$\n", "\n", "Angenommen, wir haben ein bandbegrenztes Signal, das beispielsweise nur mit einer Bandbreite von $f_\\mathrm{max} = 1\\,\\mathrm{kHz}$ übertragen werden kann. Das bedeutet, dass wir mit einem verfügbaren Messgerät nur eine Bandbreite von 1 kHz erfassen können, und das Signal ist dementsprechend bandbegrenzt. Die höchste Frequenzkomponente dieses Signals beträgt 1 kHz. Gemäß dem Nyquist-Shannon-Abtasttheorem muss dieses Signal mit mindestens 2 kHz abgetastet werden. \n", "\n", "Die Abtastdauer $T_\\mathrm{ab}$, die den Zeitraum zwischen aufeinanderfolgenden Messungen angibt, ergibt sich aus der Formel \n", "\n", "$$T_\\mathrm{ab} = \\frac{1}{f_\\mathrm{ab}}$$\n", "\n", "Im vorliegenden Beispiel mit $f_\\mathrm{ab} = 2\\,\\mathrm{kHz}$ beträgt die Abtastdauer $0{,}5\\,\\mathrm{ms}$. Das bedeutet, dass das Signal alle 0,5 ms abgetastet wird, um alle relevanten Informationen zu erfassen. \n", "Für $f_\\mathrm{max} = 5\\,\\mathrm{Hz}$ beträgt die Abtastfrequenz 10 Hz und die Abtastdauer entsprechend 0,1 s, bzw. 10 Abtastzeitpunkte pro Sekunde (siehe folgendes Diagramm).\n", "\n", "Verletzt man das Nyquist-Shannon-Abtasttheorem, indem man das Signal mit einer zu niedrigen Abtastfrequenz abtastet, kommt es zu Informationsverlusten und zu sogenanntem Aliasing. Aliasing äußert sich darin, dass höherfrequente Signalanteile im abgetasteten Signal fälschlicherweise als niederfrequente Signalanteile interpretiert werden, was zu Verzerrungen führt.\n", "\n", "In der Praxis bedeutet das Theorem, dass bei der Erfassung von Signalen sicherzustellen ist, dass die Abtastfrequenz ausreichend hoch ist, um das Signal korrekt wiederherstellen zu können. Beachtet man diese Grundregel, können hochwertige Abtastungen und Digitalisierungen von Signalen erreicht werden, ohne wichtige Informationen zu verlieren." ] }, { "cell_type": "code", "execution_count": 7, "id": "330f99ce-b0ab-42f7-a0a6-dae4cca859c0", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "plt.rcParams['font.size'] = 13; # Schriftgröße\n", "\n", "\n", "# Initialize the figure and axis\n", "fig, ax = plt.subplots(figsize=(7, 4))\n", "f = 5\n", "# Create a function to update the plot for each frame of the animation\n", "def animate(fs):\n", " ax.clear() # Clear the previous frame\n", " \n", " # Set the signal frequency and time axis\n", "\n", " t = np.linspace(0, 1, 1000)\n", " \n", " # Generate the analog signal\n", " analog_signal = np.sin(2 * np.pi * f * t)\n", " \n", " # Generate the sampled signal based on the current fs\n", " abtastzeitpunkte = np.arange(0, 1, 1 / fs)\n", " abgetastetes_signal = np.sin(2 * np.pi * f * abtastzeitpunkte)\n", " \n", " next_integer = round(f/fs)\n", " aliased_signal = np.sin(2 * np.pi * (next_integer*fs-f) * t)\n", "\n", " # Plot the analog signal, sampled signal, and sampling points\n", " ax.plot(t, analog_signal, label='Analoges Signal, f = %5.1f Hz' % f, alpha=0.3, lw='3')\n", " ax.stem(abtastzeitpunkte, abgetastetes_signal, 'tab:red', markerfmt='ro', basefmt=\" \", linefmt='r-', label='%d Abtastzeitpunkte'%(fs))\n", "\n", " # Set plot labels and legend\n", " #ax.set_title('Analog zeitdiskret: Abgetastete Sinuswelle')\n", " ax.set_xlabel('Zeit (s)')\n", " ax.set_ylabel('Amplitude')\n", " ax.set_xlim([0, 1])\n", " ax.set_ylim([-1.1, 1.1])\n", " ax.grid(True)\n", " ax.legend(loc='upper right')\n", "\n", "# Create an animation by varying fs from 1 to 10 with a step of 1\n", "ani = FuncAnimation(fig, animate, frames=range(4, 100), repeat=False)\n", "\n", "# Display the animation\n", "plt.tight_layout()\n", "plt.close()\n", "HTML(ani.to_jshtml())" ] }, { "cell_type": "markdown", "id": "fd3c6278-e145-4528-81bc-690ab9a267ac", "metadata": {}, "source": [ "Sollte ein Signal aus mehreren Frequenzen bestehen, so ist immer die höchste zu erwartende Frequenz im Signal als Anhaltspunkt für das Abtasttheorem zu nehmen. Die **Nyquist-(Abtast-) Frequenz** $f_\\mathrm{ab}$ wird entsprechend dieser höchsten Frequenz gewählt. \n", "\n", "Im Kapitel [Fourier-Analyse](3_FourierAnalyse.ipynb) ist gezeigt, dass bestimmte Signale wie z.B. Rechteckpulse, aber auch Einzelpulse, in der Theorie beliebig steile Flanken haben. Das heißt eine Rechteckspannung hat unendliche hohe Frequenzen, welches mit Zeitintervallen von 0 Sekunden Länge abgetastet werden müsste, um die benötigte Abtastfrequenz $2\\cdot \\infty$ zu erreichen. Dies ist durch die Digitalisierung nicht möglich und in diesem Fall ist zwangsläufig mit einem Informationsverlust zu rechnen.\n", "\n", "Andersherum gibt der vorliegende ADU vor, welche Signale und Signalfrequenzen mit diesem Gerät noch verlustfrei analysiert werden können. Hat das vorliegende Messsystem eine Abtastfrequenz (auch Sampling-Frequenz oder Bandbreite genannt) von $f_\\mathrm{ab} = 100\\,\\mathrm{MHz}$, so können nur Signale mit Frequenzanteilen bis zu $f = 50\\,\\mathrm{MHz}$ verlustfrei analysiert werden. \n", "\n", "## Aliasing\n", "\n", "::::::{margin}\n", ":::::{grid}\n", "::::{grid-item-card}\n", ":class-header: bg-light\n", "What is aliasing and the Nyquist theorem? (AwesomeAcoustics in English)\n", "\n", "\n", "::::\n", ":::::\n", "::::::\n", "\n", "Wenn die Abtastfrequenz zu klein ist, oder das Messsignal doch aus höheren Frequenzen besteht, als eigentlich erlaubt ist (z.B. ein Rechtecksignal) kommt es zum Aliasing-Effekt. Im Folgenden nehmen wir ein analoges Signal an, welches digitalisiert werden soll. Das heißt es wird zu dikreten Zeitpunkten mit zu wenig Punkten abgetastet. Werden die Abtastpunkte miteinander verbunden, so wird eine Sinusschwingung rekonstruiert, die eine falsche Frequenz besitzt. Das digitale Signal hat also eine andere Frequenz als das analoge. Es besteht keine Möglichkeit, die Originalschwingung aus diesem Datenset von Messpunkten vollständig zu rekonstruieren. Das analoge Signal muss mit einer höheren Abtastfrequenz neu aufgenommen werden. " ] }, { "cell_type": "code", "execution_count": 8, "id": "48f94274-0aa0-44a2-ae70-04166e5eecf7", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "plt.rcParams['font.size'] = 13; # Schriftgröße\n", "\n", "\n", "# Initialize the figure and axis\n", "fig, ax = plt.subplots(figsize=(7, 4))\n", "f = 10\n", "# Create a function to update the plot for each frame of the animation\n", "def animate(fs):\n", " ax.clear() # Clear the previous frame\n", " \n", " # Set the signal frequency and time axis\n", "\n", " t = np.linspace(0, 1, 1000)\n", " \n", " # Generate the analog signal\n", " analog_signal = np.sin(2 * np.pi * f * t)\n", " \n", " # Generate the sampled signal based on the current fs\n", " abtastzeitpunkte = np.arange(0, 1, 1 / fs)\n", " abgetastetes_signal = np.sin(2 * np.pi * f * abtastzeitpunkte)\n", " \n", " next_integer = round(f/fs)\n", " aliased_signal = np.sin(2 * np.pi * (next_integer*fs-f) * t)\n", "\n", " # Plot the analog signal, sampled signal, and sampling points\n", " ax.plot(t, analog_signal, label='Analoges Signal, f = %5.1f Hz' % f, alpha=0.3, lw='3')\n", " ax.stem(abtastzeitpunkte, abgetastetes_signal, 'tab:red', markerfmt='ro', basefmt=\" \", linefmt='r-', label='Abtastzeitpunkte')\n", " ax.plot(t, -aliased_signal, label='Digitales Signal, f = %5.1f Hz'%(np.abs((next_integer*fs-f))), color='black', lw=2)\n", "\n", " # Set plot labels and legend\n", " #ax.set_title('Analog zeitdiskret: Abgetastete Sinuswelle')\n", " ax.set_xlabel('Zeit (s)')\n", " ax.set_ylabel('Amplitude')\n", " ax.set_xlim([0, 1])\n", " ax.set_ylim([-1.1, 1.1])\n", " ax.grid(True)\n", " ax.legend(loc='upper right')\n", "\n", "# Create an animation by varying fs from 1 to 10 with a step of 1\n", "ani = FuncAnimation(fig, animate, frames=range(1, 2*f+1), repeat=False)\n", "\n", "# Display the animation\n", "plt.tight_layout()\n", "plt.close()\n", "HTML(ani.to_jshtml())" ] }, { "cell_type": "markdown", "id": "6a83fc10-dd62-4f55-804a-267904e02a33", "metadata": {}, "source": [ "Die rekonstruierte Frequenz ist bei einer zu niederigen Abtastfrequenz nicht identisch mit der Originalfrequenz. Stattdessen folgt sie einer Dreiecks-Funktion.Im folgenden ist die Kennlinie der digitalen Frequenzmessung gegenüber der Frequenz des analogen Signals aufgetragen. Idealerweise hat man eine ideale Kennlinie, die zu jeder analogen Frequenz den identischen Frequenzwert für das digitale Signal liefert. In der Realität gilt dies jedoch nur in einem bestimmten Bereich.\n", "\n", "* Ist die Signalfrequenz $f$ unterhalb von $f_\\mathrm{ab}/2$ folgt die rekonstruierte Frequenz der analogen.\n", "* Ist die Signalfrequenz $f$ oberhalb von $f_\\mathrm{ab}/2$ fällt die rekonstruierte Frequenz plötzlich wieder ab, obwohl die analoge Frequenz weiter steigt.\n", "* Bei noch höheren Frequenzen $f$ oberhalb von $f_\\mathrm{ab}$ steigt die digitale Frequenz wieder an, ist aber deutlich unterhalb der Frequenz, wo sie eigentlich sein sollte. \n", "* Bei genau $f = f_\\mathrm{ab}$ beträgt die rekonstruierte Frequenz 0 Hz." ] }, { "cell_type": "code", "execution_count": 5, "id": "118fb990-102a-486f-b8fc-3396543364ee", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import signal # Importiere das Modul signal aus scipy\n", "\n", "# Abtastparameter\n", "fs = 1000 # Abtastfrequenz in Hz\n", "t = np.linspace(0, 3, int(fs), endpoint=False) # Zeitachse für 1 Sekunde\n", "\n", "# Erzeugen des Dreiecksignals\n", "frequency = 1 # Frequenz des Dreiecksignals in Hz\n", "amplitude = 1.0 # Amplitude des Dreiecksignals\n", "triangle_signal = 0.25 *( signal.sawtooth(2 * np.pi * frequency * t, width=0.5) +1)\n", "\n", "# Plot der mehreren Schwingungen des Dreiecksignals\n", "plt.figure(figsize=(10, 4))\n", "plt.plot(1000*t, 1000*triangle_signal, lw=2, color = 'tab:blue')\n", "plt.plot(1000*t, 1000*t, lw=2, color = 'tab:red', ls = ':')\n", "plt.title('Abtastfrequenz: 1kHz')\n", "\n", "plt.xlabel('Analoge Frequenz (Hz)')\n", "plt.ylabel('Rekonstruierte Frequenz (Hz)')\n", "plt.ylim([0,1000])\n", "plt.grid(True)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "c35fb8a9-7ea6-4dd7-bbe8-3a6133ef5722", "metadata": {}, "source": [ "Das heißt zu hohe Frequenzen im Messsignal, die das Nyquist-Shannon-Theorem verletzen, generieren neue Signalfrequenzen bei kleineren Werten, die eigentlich gar nicht dar sein sollten. Dies kann man am besten im Frequenzraum mittels Fourier-Transformation beobachten. \n", "\n", "Im Folgenden untersuchen wir zwei Sinussignale\n", "\n", "$$u_1(t) = 4 \\cdot \\sin(2\\pi \\cdot 5\\,\\mathrm{Hz} \\cdot t)$$\n", "\n", "$$u_2(t) = 2 \\cdot \\sin(2\\pi \\cdot 50\\,\\mathrm{Hz} \\cdot t)$$\n", "\n", "die wir per Addition zu einem Signal kombinieren:\n", "\n", "$$u(t) = u_1(t) + u_2(t)$$\n", "\n", "und mit zwei verschiedenen Abtastfrequenzen digitalisieren. \n", "\n", "Mittels [Fourier-Transformation](3_FourierAnalyse) kann man sich die Leistung bei den Frequenzen berechnen lassen, die im Signal enthalten sind. Diese Darstellung ist im Folgenden rechts gezeigt: " ] }, { "cell_type": "code", "execution_count": 6, "id": "7f1f37dd-a34a-4350-8afd-752333ec0d28", "metadata": { "tags": [ "hide-input", "thebe-init" ] }, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.fft import rfft, rfftfreq\n", "from scipy.io.wavfile import read #import the required function from the module\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "# MatplotLib Settings:\n", "plt.style.use('default') # Matplotlib Style wählen\n", "#plt.xkcd()\n", "plt.rcParams['font.size'] = 10; # Schriftgröße \n", "\n", "Fs = 150.0; # sampling rate\n", "Ts = 1.0/Fs; # sampling interval\n", "t = np.arange(0,1,Ts) # time vector\n", "ff = 5; # frequency of the signal\n", "a1 = 4.\n", "a2 = 2.\n", "y = a1 * np.sin(2 * np.pi * ff * t) + a2 * np.sin(10 * 2 * np.pi * ff * t)\n", "\n", "y_normalized = np.int16((y / y.max()) * 32767)\n", "yf = rfft(y_normalized)/5e6\n", "xf = rfftfreq(len(y), 1 / Fs)\n", "\n", "plt.figure(figsize=(8,3)) # Plot-Größe\n", "plt.subplot(1,2,1)\n", "plt.plot(t,y, 'tab:blue')\n", "plt.xlabel('Zeit (s)')\n", "plt.ylabel('Amplitude')\n", "plt.ylim(-5,5)\n", "plt.subplot(1,2,2)\n", "plt.plot(xf,abs(yf),'tab:red') # plotting the spectrum\n", "plt.xlabel('Frequenz (Hz)')\n", "plt.ylabel('Leistung')\n", "plt.ylim(-0.01,0.37)\n", "plt.xlim(-0.1,80)\n", "plt.suptitle(r'Abtastfrequenz: %5.1f Hz (Original-Signal)' %(Fs))\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Sampling frequenz: nur jedes n-te Element als Stichprobe aus dem Array rauspicken\n", "n = 2\n", "Fs_n = Fs/n\n", "y_n = y[::n]\n", "t_n = t[::n]\n", "\n", "y_normalized = np.int16((y_n / y_n.max()) * 32767)\n", "yf = rfft(y_normalized)/5e6\n", "xf = rfftfreq(len(y_n), 1 / Fs_n)\n", "\n", "plt.figure(figsize=(8,3)) # Plot-Größe\n", "plt.subplot(1,2,1)\n", "plt.plot(t_n,y_n, 'tab:blue')\n", "plt.xlabel('Zeit (s)')\n", "plt.ylabel('Amplitude')\n", "plt.ylim(-5,5)\n", "plt.subplot(1,2,2)\n", "plt.plot(xf,abs(yf),'tab:red') # plotting the spectrum\n", "plt.annotate('nicht im Originalsignal enthalten!', xy=(25, 0.1),\n", " xytext=(20, 0.25), color = 'tab:red',\n", " arrowprops=dict(arrowstyle=\"->\",\n", " connectionstyle=\"arc3\", color = 'tab:red')\n", " )\n", "plt.xlabel('Frequenz (Hz)')\n", "plt.ylabel('Leistung')\n", "plt.ylim(-0.01,0.37)\n", "plt.xlim(-0.1,80)\n", "plt.suptitle(r'Abtastfrequenz: %5.1f Hz' %(Fs_n))\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "75040807-3b0a-418d-b7b9-0fa8041bd8ff", "metadata": {}, "source": [ "In den Diagrammen sieht man, dass bei der Wahl einer zu kleinen Abtastfrequenz ein Signal bei 25Hz auftritt, was im Originalsignal eigentlich gar nicht enthalten ist. Dies liegt daran, dass die gewählte Abtastfrequenz von 75Hz das Shannon-Nyquist-Theorem für das Signal bei ursprünglich 50Hz (100Hz Sampling benötigt) nicht erfüllt. \n", "Diese Frequenz wird zur halben Frequenz *runter-ge-aliased*. Das Signal bei 5Hz erfüllt das Theorem und wird nicht verändert. \n", "Außerdem erkennt man, dass die Signale nur noch die halbe Leistung aufweisen. Dies liegt an der Reduzierung der Abtastfrequenz um den Faktor 2." ] }, { "cell_type": "markdown", "id": "ee14d46d-b798-4e19-b32b-564d04ef4e3b", "metadata": {}, "source": [ "Zur Vermeidung von Alisaing werden Filter verwendet, um hohe Frequenzen im Signal vor der Digitalisierung rauszufiltern, damit diese keine *Dreckeffekte* bei der Messung hinzufügen, die eigentlich gar nicht da sind. Bei diesen sogenannten **Anti-Aliasing-Filtern** handelt es sich um steile Tiefpass-Filter höherer Ordnung, um eine möglichst große Unterdrückung bei hohen Frequenzen zu erreichen. Das eigentliche, zu digitalisierende Signal sollte in seinem Frequenzverlauf jedoch möglichst nicht verändert werden durch den *Anti-Aliasing-Filter*. Daher muss man bei dem Filterdesign sehr vorsichtig sein. " ] }, { "cell_type": "markdown", "id": "40a1d347-2dac-4ba5-b37a-4a162ddc1aa2", "metadata": {}, "source": [ "## Vorteile\n", "\n", "Digitalisierte Messsignale haben den Vorteil, dass die Genauigkeit auch über große Distanzen erhalten bleiben und sie somit gut übertragen werden können. Außerdem ist die Weiterverarbeitung und Datenanalyse einfacher. \n", "Bei analogen Messsignalen hat man das Problem, dass äußere Störungen das Messsignal stark verändern können, wie z.B. Drift- oder Rauscheigenschaften von elektronischen Komponenten. \n", "Viele empfinden analoge Messwerte jedoch angenehmer und übersichtlicher, als Reihen von Zahlen auf dem Computer. \n", "Der wichtigste Vorteil von analogen Messwerten ist jedoch die große Bandbreite und großer Wertebereich und die simultane Verarbeitung. Gerade bei dynamischen Systemen kann dies ein großer Vorteil sein, wenn man sich schnell verändernde zeitliche Messgrößen hat. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }