Add sample data and jupyter notebook containing filter analysis
This commit is contained in:
parent
836c7af163
commit
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@ -422,6 +422,13 @@
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"print(calc_temp(2000))\n",
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"print(calc_temp(2000))\n",
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"print(calc_temp(2000+adc_min_res))"
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"print(calc_temp(2000+adc_min_res))"
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]
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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@ -440,7 +447,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.9.6"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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475
measurement-data/FilterAnalysis.ipynb
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475
measurement-data/FilterAnalysis.ipynb
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@ -0,0 +1,475 @@
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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": null,
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"id": "7c270395",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from scipy import signal\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from scipy.fft import fft, fftfreq"
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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": "b09956cf",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "348b4663",
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"metadata": {},
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"source": [
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"## Filter comparison"
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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": "eef8fc32",
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"metadata": {},
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"outputs": [],
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"source": [
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"alpha = 0.01\n",
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"mavg_b = [alpha]\n",
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"mavg_a = [1, -(1-alpha)]"
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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": "06cc8d91",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Sinc filter\n",
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"b_sinc = [\n",
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" 0.013166773445594984,\n",
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" 0.015510026576574206,\n",
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" 0.017943762856303655,\n",
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" 0.020436419999452039,\n",
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" 0.022953654956480787,\n",
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" 0.025459023223972144,\n",
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" 0.027914730737546672,\n",
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" 0.030282439536854874,\n",
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" 0.032524106530629059,\n",
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" 0.034602833419516990,\n",
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" 0.036483705201686277,\n",
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" 0.038134594717421900,\n",
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" 0.039526911389630152,\n",
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" 0.040636273671486346,\n",
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" 0.041443086683647982,\n",
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" 0.041933009054862178,\n",
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" 0.042097295996679322,\n",
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" 0.041933009054862178,\n",
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" 0.041443086683647982,\n",
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" 0.040636273671486346,\n",
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" 0.039526911389630152,\n",
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" 0.038134594717421900,\n",
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" 0.036483705201686277,\n",
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" 0.034602833419516990,\n",
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" 0.032524106530629059,\n",
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" 0.030282439536854874,\n",
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" 0.027914730737546672,\n",
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" 0.025459023223972144,\n",
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" 0.022953654956480787,\n",
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" 0.020436419999452039,\n",
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" 0.017943762856303655,\n",
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" 0.015510026576574206,\n",
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" 0.013166773445594984,\n",
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"]\n",
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"b_sinc = [\n",
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" -0.000005301919181359,\n",
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" -0.000020372384569462,\n",
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" -0.000043393375246200,\n",
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" -0.000071643508460196,\n",
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" -0.000101272447699964,\n",
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" -0.000127045709202358,\n",
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" -0.000142084479366378,\n",
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" -0.000137630058679112,\n",
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" -0.000102865422361326,\n",
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" -0.000024826753571648,\n",
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" 0.000111564545505229,\n",
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" 0.000323323938002668,\n",
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" 0.000629062120588115,\n",
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" 0.001048471626315150,\n",
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" 0.001601644955843253,\n",
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" 0.002308239744151386,\n",
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" 0.003186518744650963,\n",
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" 0.004252303894058680,\n",
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" 0.005517893567100900,\n",
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" 0.006990999568786174,\n",
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" 0.008673764799041549,\n",
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" 0.010561923390649710,\n",
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" 0.012644162210467481,\n",
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" 0.014901735907231652,\n",
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" 0.017308377414832196,\n",
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" 0.019830532444994709,\n",
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" 0.022427930710902887,\n",
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" 0.025054489273884574,\n",
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" 0.027659525485854094,\n",
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" 0.030189239563895277,\n",
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" 0.032588410933658420,\n",
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" 0.034802239101869442,\n",
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" 0.036778249821351520,\n",
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" 0.038468181363671021,\n",
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" 0.039829764251821352,\n",
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" 0.040828311002284692,\n",
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" 0.041438040179150003,\n",
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" 0.041643070995549647,\n",
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" 0.041438040179150003,\n",
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" 0.040828311002284692,\n",
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" 0.039829764251821366,\n",
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" 0.038468181363671021,\n",
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" 0.036778249821351520,\n",
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" 0.034802239101869456,\n",
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" 0.032588410933658420,\n",
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" 0.030189239563895291,\n",
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" 0.027659525485854094,\n",
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" 0.025054489273884584,\n",
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" 0.022427930710902898,\n",
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" 0.019830532444994709,\n",
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" 0.017308377414832207,\n",
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" 0.014901735907231647,\n",
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" 0.012644162210467488,\n",
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" 0.010561923390649715,\n",
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" 0.008673764799041547,\n",
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" 0.006990999568786178,\n",
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" 0.005517893567100902,\n",
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" 0.004252303894058680,\n",
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" 0.003186518744650965,\n",
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" 0.002308239744151388,\n",
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" 0.001601644955843254,\n",
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" 0.001048471626315152,\n",
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" 0.000629062120588114,\n",
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" 0.000323323938002668,\n",
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" 0.000111564545505229,\n",
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" -0.000024826753571648,\n",
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" -0.000102865422361327,\n",
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" -0.000137630058679112,\n",
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" -0.000142084479366378,\n",
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" -0.000127045709202359,\n",
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" -0.000101272447699963,\n",
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" -0.000071643508460196,\n",
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" -0.000043393375246200,\n",
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" -0.000020372384569461,\n",
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" -0.000005301919181359,\n",
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"]\n",
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"b_sinc = np.around([k * 56536 for k in b_sinc])/65536\n",
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"\n",
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"\n",
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"def combined_sinc_mavg(alpha = 0.4):\n",
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" b1 = [alpha]\n",
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" b2 = b_sinc\n",
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" a1 = [1, -(1-alpha)]\n",
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" a2 = [1]\n",
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" \n",
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" b = np.convolve(b1, b2)\n",
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" a = np.convolve(a1, a2)\n",
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" return b,a\n",
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"\n",
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"def iir_notch(freq, r, fsa):\n",
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" b = [1, -2*np.cos(freq/fsa*2*np.pi), 1]\n",
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" a = [1, -2*r*np.cos(freq/fsa*2*np.pi), r*r]\n",
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" return b,a"
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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": "299fc59e",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "0af3fa7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_transfer_func(b, a, fsa = 1):\n",
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" omega, vals = signal.freqz(b, a, worN = 1024)\n",
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" plt.plot(omega/(2*np.pi)*fsa, abs(vals))\n",
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" \n",
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"sample_rate = 1e3/6\n",
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"plt.figure(figsize=(16,8))\n",
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"plt.yscale('log')\n",
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"plot_transfer_func(mavg_b, mavg_a, sample_rate)\n",
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"plt.ylabel('|H(w)| [dB]')\n",
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"plt.xlabel('Frequency [Hz]')\n",
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"\n",
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"plot_transfer_func(b_sinc, [1], sample_rate)\n",
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"\n",
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"# Combined filter\n",
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"b,a = combined_sinc_mavg(alpha = 1)\n",
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"b_notch, a_notch = iir_notch(50, 0.875, sample_rate)\n",
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"b = np.convolve(b, b_notch)\n",
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"a = np.convolve(a, a_notch)\n",
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"plot_transfer_func(b,a, sample_rate)\n",
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"plt.grid()\n",
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"\n",
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"plt.legend(['MAVG a = 0.01', 'SINC', 'SINC+MAVG'])\n",
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"plt.xlim(0, 70)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c9b87b82",
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"metadata": {},
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"source": [
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"# Notch filters (Not used for implementation)"
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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": "c360b127",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(16,8))\n",
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"plt.yscale('log')\n",
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"a = [1]\n",
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"b = [1, -2*np.cos(50/sample_rate*2*np.pi), 1]\n",
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"plot_transfer_func(b,a, sample_rate)\n",
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"\n",
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"b = [1, -2*np.cos(60/sample_rate*2*np.pi), 1]\n",
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"plot_transfer_func(b,a, sample_rate)\n",
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"\n",
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"r = 0.875\n",
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"b,a = iir_notch(50, r, sample_rate)\n",
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"plot_transfer_func(b,a, sample_rate)\n",
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"\n",
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"b,a = iir_notch(60, r, sample_rate)\n",
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"plot_transfer_func(b,a, sample_rate)\n",
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"plt.xlim(40,70)\n",
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"plt.legend(['FIR 50 Hz Notch', 'FIR 60 Hz', 'IIR 50Hz', 'IIR 60Hz'])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "891bf909",
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"metadata": {},
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"source": [
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"# Singal testing"
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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": "29277520",
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"metadata": {},
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"outputs": [],
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"source": [
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"N = 30000\n",
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"time = np.linspace(0, 1/sample_rate*N, N, endpoint=False)\n",
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"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",
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"\n",
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"plt.figure(figsize=(16,8))\n",
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"\n",
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"spur_len = 3\n",
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"\n",
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"sig[1000:1000+spur_len] = sig[1000]+5\n",
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"sig[2000:2000+spur_len] = sig[2000]-6\n",
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"sig[3000:3000+spur_len] = sig[3000]+2\n",
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"sig[4000:4000+spur_len] = sig[4000]+8\n",
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"\n",
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"\n",
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"plt.plot(time, sig)\n",
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"\n",
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"\n",
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"# Apply the combined filter:\n",
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"b,a = combined_sinc_mavg(alpha = 1)\n",
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"\n",
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"bn,an = iir_notch(50, 0.875, sample_rate)\n",
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"a = np.convolve(a, an)\n",
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"b = np.convolve(b, bn)\n",
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"\n",
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"#b,a = mavg_b, mavg_a\n",
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"\n",
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"w,h = signal.freqz(b,a)\n",
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"gain_corr = round(1/abs(h[0])*65536)/65536\n",
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"\n",
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"sig_f = signal.lfilter(b, a, sig) * gain_corr\n",
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"\n",
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"\n",
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"print('Gain correction:', gain_corr)\n",
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"\n",
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"plt.plot(time, sig_f)\n",
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"plt.xlim(5,10)\n",
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"plt.show()\n",
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"\n",
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"plt.figure(figsize=(16,8))\n",
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"plt.plot(time, abs(sig-sig_f))\n",
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"plt.xlim(5,10)\n",
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"plt.ylim(0,2)\n",
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"plt.show()"
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]
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},
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{
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"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
|
||||||
|
}
|
||||||
2001
measurement-data/pt1000_hf_2kOhm_v1.3.dat
Normal file
2001
measurement-data/pt1000_hf_2kOhm_v1.3.dat
Normal file
File diff suppressed because it is too large
Load Diff
2001
measurement-data/pt1000_hf_changing.dat
Normal file
2001
measurement-data/pt1000_hf_changing.dat
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user