From 4a98d4162317f741c376ce4ebad5df01df6a2618 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mario=20H=C3=BCttel?= Date: Tue, 24 Aug 2021 21:48:04 +0200 Subject: [PATCH] Add sample data and jupyter notebook containing filter analysis --- .../Analog_Measurement_Analysis.ipynb | 9 +- measurement-data/FilterAnalysis.ipynb | 475 ++++ measurement-data/pt1000_hf_2kOhm_v1.3.dat | 2001 +++++++++++++++++ measurement-data/pt1000_hf_changing.dat | 2001 +++++++++++++++++ 4 files changed, 4485 insertions(+), 1 deletion(-) create mode 100644 measurement-data/FilterAnalysis.ipynb create mode 100644 measurement-data/pt1000_hf_2kOhm_v1.3.dat create mode 100644 measurement-data/pt1000_hf_changing.dat diff --git a/measurement-data/Analog_Measurement_Analysis.ipynb b/measurement-data/Analog_Measurement_Analysis.ipynb index 0bab877..cf1cd78 100644 --- a/measurement-data/Analog_Measurement_Analysis.ipynb +++ b/measurement-data/Analog_Measurement_Analysis.ipynb @@ -422,6 +422,13 @@ "print(calc_temp(2000))\n", "print(calc_temp(2000+adc_min_res))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -440,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/measurement-data/FilterAnalysis.ipynb b/measurement-data/FilterAnalysis.ipynb new file mode 100644 index 0000000..a158de5 --- /dev/null +++ b/measurement-data/FilterAnalysis.ipynb @@ -0,0 +1,475 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "7c270395", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy import signal\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from scipy.fft import fft, fftfreq" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b09956cf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "348b4663", + "metadata": {}, + "source": [ + "## Filter comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eef8fc32", + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.01\n", + "mavg_b = [alpha]\n", + "mavg_a = [1, -(1-alpha)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06cc8d91", + "metadata": {}, + "outputs": [], + "source": [ + "# Sinc filter\n", + "b_sinc = [\n", + " 0.013166773445594984,\n", + " 0.015510026576574206,\n", + " 0.017943762856303655,\n", + " 0.020436419999452039,\n", + " 0.022953654956480787,\n", + " 0.025459023223972144,\n", + " 0.027914730737546672,\n", + " 0.030282439536854874,\n", + " 0.032524106530629059,\n", + " 0.034602833419516990,\n", + " 0.036483705201686277,\n", + " 0.038134594717421900,\n", + " 0.039526911389630152,\n", + " 0.040636273671486346,\n", + " 0.041443086683647982,\n", + " 0.041933009054862178,\n", + " 0.042097295996679322,\n", + " 0.041933009054862178,\n", + " 0.041443086683647982,\n", + " 0.040636273671486346,\n", + " 0.039526911389630152,\n", + " 0.038134594717421900,\n", + " 0.036483705201686277,\n", + " 0.034602833419516990,\n", + " 0.032524106530629059,\n", + " 0.030282439536854874,\n", + " 0.027914730737546672,\n", + " 0.025459023223972144,\n", + " 0.022953654956480787,\n", + " 0.020436419999452039,\n", + " 0.017943762856303655,\n", + " 0.015510026576574206,\n", + " 0.013166773445594984,\n", + "]\n", + "b_sinc = [\n", + " -0.000005301919181359,\n", + " -0.000020372384569462,\n", + " -0.000043393375246200,\n", + " -0.000071643508460196,\n", + " -0.000101272447699964,\n", + " -0.000127045709202358,\n", + " -0.000142084479366378,\n", + " -0.000137630058679112,\n", + " -0.000102865422361326,\n", + " -0.000024826753571648,\n", + " 0.000111564545505229,\n", + " 0.000323323938002668,\n", + " 0.000629062120588115,\n", + " 0.001048471626315150,\n", + " 0.001601644955843253,\n", + " 0.002308239744151386,\n", + " 0.003186518744650963,\n", + " 0.004252303894058680,\n", + " 0.005517893567100900,\n", + " 0.006990999568786174,\n", + " 0.008673764799041549,\n", + " 0.010561923390649710,\n", + " 0.012644162210467481,\n", + " 0.014901735907231652,\n", + " 0.017308377414832196,\n", + " 0.019830532444994709,\n", + " 0.022427930710902887,\n", + " 0.025054489273884574,\n", + " 0.027659525485854094,\n", + " 0.030189239563895277,\n", + " 0.032588410933658420,\n", + " 0.034802239101869442,\n", + " 0.036778249821351520,\n", + " 0.038468181363671021,\n", + " 0.039829764251821352,\n", + " 0.040828311002284692,\n", + " 0.041438040179150003,\n", + " 0.041643070995549647,\n", + " 0.041438040179150003,\n", + " 0.040828311002284692,\n", + " 0.039829764251821366,\n", + " 0.038468181363671021,\n", + " 0.036778249821351520,\n", + " 0.034802239101869456,\n", + " 0.032588410933658420,\n", + " 0.030189239563895291,\n", + " 0.027659525485854094,\n", + " 0.025054489273884584,\n", + " 0.022427930710902898,\n", + " 0.019830532444994709,\n", + " 0.017308377414832207,\n", + " 0.014901735907231647,\n", + " 0.012644162210467488,\n", + " 0.010561923390649715,\n", + " 0.008673764799041547,\n", + " 0.006990999568786178,\n", + " 0.005517893567100902,\n", + " 0.004252303894058680,\n", + " 0.003186518744650965,\n", + " 0.002308239744151388,\n", + " 0.001601644955843254,\n", + " 0.001048471626315152,\n", + " 0.000629062120588114,\n", + " 0.000323323938002668,\n", + " 0.000111564545505229,\n", + " -0.000024826753571648,\n", + " -0.000102865422361327,\n", + " -0.000137630058679112,\n", + " -0.000142084479366378,\n", + " -0.000127045709202359,\n", + " -0.000101272447699963,\n", + " -0.000071643508460196,\n", + " -0.000043393375246200,\n", + " -0.000020372384569461,\n", + " -0.000005301919181359,\n", + "]\n", + "b_sinc = np.around([k * 56536 for k in b_sinc])/65536\n", + "\n", + "\n", + "def combined_sinc_mavg(alpha = 0.4):\n", + " b1 = [alpha]\n", + " b2 = b_sinc\n", + " a1 = [1, -(1-alpha)]\n", + " a2 = [1]\n", + " \n", + " b = np.convolve(b1, b2)\n", + " a = np.convolve(a1, a2)\n", + " return b,a\n", + "\n", + "def iir_notch(freq, r, fsa):\n", + " b = [1, -2*np.cos(freq/fsa*2*np.pi), 1]\n", + " a = [1, -2*r*np.cos(freq/fsa*2*np.pi), r*r]\n", + " return b,a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "299fc59e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0af3fa7b", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_transfer_func(b, a, fsa = 1):\n", + " omega, vals = signal.freqz(b, a, worN = 1024)\n", + " plt.plot(omega/(2*np.pi)*fsa, abs(vals))\n", + " \n", + "sample_rate = 1e3/6\n", + "plt.figure(figsize=(16,8))\n", + "plt.yscale('log')\n", + "plot_transfer_func(mavg_b, mavg_a, sample_rate)\n", + "plt.ylabel('|H(w)| [dB]')\n", + "plt.xlabel('Frequency [Hz]')\n", + "\n", + "plot_transfer_func(b_sinc, [1], sample_rate)\n", + "\n", + "# Combined filter\n", + "b,a = combined_sinc_mavg(alpha = 1)\n", + "b_notch, a_notch = iir_notch(50, 0.875, sample_rate)\n", + "b = np.convolve(b, b_notch)\n", + "a = np.convolve(a, a_notch)\n", + "plot_transfer_func(b,a, sample_rate)\n", + "plt.grid()\n", + "\n", + "plt.legend(['MAVG a = 0.01', 'SINC', 'SINC+MAVG'])\n", + "plt.xlim(0, 70)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c9b87b82", + "metadata": {}, + "source": [ + "# Notch filters (Not used for implementation)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c360b127", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(16,8))\n", + "plt.yscale('log')\n", + "a = [1]\n", + "b = [1, -2*np.cos(50/sample_rate*2*np.pi), 1]\n", + "plot_transfer_func(b,a, sample_rate)\n", + "\n", + "b = [1, -2*np.cos(60/sample_rate*2*np.pi), 1]\n", + "plot_transfer_func(b,a, sample_rate)\n", + "\n", + "r = 0.875\n", + "b,a = iir_notch(50, r, sample_rate)\n", + "plot_transfer_func(b,a, sample_rate)\n", + "\n", + "b,a = iir_notch(60, r, sample_rate)\n", + "plot_transfer_func(b,a, sample_rate)\n", + "plt.xlim(40,70)\n", + "plt.legend(['FIR 50 Hz Notch', 'FIR 60 Hz', 'IIR 50Hz', 'IIR 60Hz'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "891bf909", + "metadata": {}, + "source": [ + "# Singal testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29277520", + "metadata": {}, + "outputs": [], + "source": [ + "N = 30000\n", + "time = np.linspace(0, 1/sample_rate*N, N, endpoint=False)\n", + "sig = 10*signal.sawtooth(2*np.pi*0.2*time)+10 #+ 0.5 * np.sin(2*np.pi*50*time)+0.8 * np.sin(2*np.pi*80*time)\n", + "\n", + "plt.figure(figsize=(16,8))\n", + "\n", + "spur_len = 3\n", + "\n", + "sig[1000:1000+spur_len] = sig[1000]+5\n", + "sig[2000:2000+spur_len] = sig[2000]-6\n", + "sig[3000:3000+spur_len] = sig[3000]+2\n", + "sig[4000:4000+spur_len] = sig[4000]+8\n", + "\n", + "\n", + "plt.plot(time, sig)\n", + "\n", + "\n", + "# Apply the combined filter:\n", + "b,a = combined_sinc_mavg(alpha = 1)\n", + "\n", + "bn,an = iir_notch(50, 0.875, sample_rate)\n", + "a = np.convolve(a, an)\n", + "b = np.convolve(b, bn)\n", + "\n", + "#b,a = mavg_b, mavg_a\n", + "\n", + "w,h = signal.freqz(b,a)\n", + "gain_corr = round(1/abs(h[0])*65536)/65536\n", + "\n", + "sig_f = signal.lfilter(b, a, sig) * gain_corr\n", + "\n", + "\n", + "print('Gain correction:', gain_corr)\n", + "\n", + "plt.plot(time, sig_f)\n", + "plt.xlim(5,10)\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(16,8))\n", + "plt.plot(time, abs(sig-sig_f))\n", + "plt.xlim(5,10)\n", + "plt.ylim(0,2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13758640", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "window = signal.windows.hamming(N)\n", + "\n", + "h_sig = fft(sig*window)\n", + "f_sig = fftfreq(N, 1/sample_rate)[:N//2]\n", + "\n", + "h_sig_f = fft(sig_f*window)\n", + "f_sig_f = fftfreq(N, 1/sample_rate)[:N//2]\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "plt.yscale('log')\n", + "plt.plot(f_sig, 2.0/N*abs(h_sig[0:N//2]))\n", + "plt.plot(f_sig_f, 2.0/N*abs(h_sig_f[0:N//2]))\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "id": "f4b81600", + "metadata": {}, + "source": [ + "# PT1000 HF Filtering" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a87370ee", + "metadata": {}, + "outputs": [], + "source": [ + "raw_data = pd.read_csv(r'pt1000_hf_2kOhm_v1.3.dat')\n", + "raw_data = pd.read_csv(r'pt1000_hf_changing.dat')\n", + "time = np.linspace(0, 2000*6e-3, 2000, endpoint=False)\n", + "\n", + "plt.figure(figsize=(22,12))\n", + "plt.plot(time, raw_data['hf_value']*2500/4096)\n", + "\n", + "alpha = 0.005\n", + "\n", + "mavg_b = [alpha]\n", + "mavg_a = [1, -(1-alpha)]\n", + "\n", + "zi = signal.lfilter_zi(mavg_b, mavg_a)\n", + "filtered, _ = signal.lfilter(mavg_b, mavg_a, raw_data['hf_value'], zi=zi*raw_data['hf_value'][0])\n", + "plt.plot(time, filtered*2500/4096)\n", + "filtered_avg_low = filtered\n", + "\n", + "alpha = 0.01\n", + "mavg_b = [alpha]\n", + "mavg_a = [1, -(1-alpha)]\n", + "\n", + "zi = signal.lfilter_zi(mavg_b, mavg_a)\n", + "filtered, _ = signal.lfilter(mavg_b, mavg_a, raw_data['hf_value'], zi=zi*raw_data['hf_value'][0])\n", + "plt.plot(time, filtered*2500/4096)\n", + "filtered_avg = filtered\n", + "\n", + "\n", + "\n", + "# Apply the combined filter:\n", + "b,a = combined_sinc_mavg(alpha = 0.08)\n", + "\n", + "bn,an = iir_notch(4, 0.75, sample_rate)\n", + "a = np.convolve(a, an)\n", + "b = np.convolve(b, bn)\n", + "\n", + "zi = signal.lfilter_zi(b, a)\n", + "filtered, _ = signal.lfilter(b, a, raw_data['hf_value'], zi=zi*raw_data['hf_value'][0])\n", + "\n", + "w,h = signal.freqz(b,a)\n", + "gain_corr = round(1/abs(h[0])*65536)/65536\n", + "\n", + "plt.plot(time, filtered*gain_corr*2500/4096)\n", + "plt.savefig('expl.pdf', format='pdf', dpi=1200)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3eae0e71", + "metadata": {}, + "source": [ + "## Spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d082b99", + "metadata": {}, + "outputs": [], + "source": [ + "window =signal.windows.hamming(len(raw_data['hf_value']))\n", + "raw_fft = fft(np.array(raw_data['hf_value'].to_list())*window)\n", + "f_raw = fftfreq(len(raw_data), 1/sample_rate)[:len(raw_data)//2]\n", + "plt.figure(figsize=(16,10));\n", + "plt.yscale('log')\n", + "plt.plot(f_raw, abs(raw_fft[:len(raw_data)//2]))\n", + "\n", + "avg_fft = fft(np.array(filtered_avg)*window)\n", + "plt.plot(f_raw, abs(avg_fft[:len(raw_data)//2]))\n", + "\n", + "avg_fft = fft(np.array(filtered_avg_low)*window)\n", + "plt.plot(f_raw, abs(avg_fft[:len(raw_data)//2]))\n", + "\n", + "sinc_fft = fft(np.array(filtered)*window)\n", + "plt.plot(f_raw, abs(sinc_fft[:len(raw_data)//2]))\n", + "plt.xscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e09d63f9", + "metadata": {}, + "outputs": [], + "source": [ + "print(np.std(filtered_avg_low/4096*2500))\n", + "print(np.std(filtered_avg/4096*2500))\n", + "print(np.std(filtered/4096*2500))\n", + "\n", + "print('Min',min(filtered_avg/4096*2500), 'Max', max(filtered_avg)/4096*2500)\n", + "print('Min',min(filtered)/4096*2500*gain_corr, 'Max', max(filtered)/4096*2500*gain_corr)" + ] + } + ], + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/measurement-data/pt1000_hf_2kOhm_v1.3.dat b/measurement-data/pt1000_hf_2kOhm_v1.3.dat new file mode 100644 index 0000000..b1986f2 --- /dev/null +++ b/measurement-data/pt1000_hf_2kOhm_v1.3.dat @@ -0,0 +1,2001 @@ 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