399 lines
9.9 KiB
Text
399 lines
9.9 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"import scipy.fftpack"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib widget"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = np.genfromtxt('data/Netzfrequenz_Sekundenwerte_2012_KW37.csv', delimiter=',')[1:,1:]"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "171a6975a39e48bcac5e1247903b70f4",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f0144563d30>]"
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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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"fig, ax = plt.subplots()\n",
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"ax.plot(data[:3600*24, 0])"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.02051102806199375"
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]
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},
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"execution_count": 5,
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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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"np.std(data[:,0])"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2b835c8fb082428cabc1ad9112286728",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"(1e-06, 0.5)"
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]
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},
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"execution_count": 6,
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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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"# Number of samplepoints\n",
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"N = len(data[:,0])\n",
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"# sample spacing\n",
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"T = 1.0\n",
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"x = np.linspace(0.0, N*T, N)\n",
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"yf = scipy.fftpack.fft(data[:,0])\n",
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"xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n",
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"\n",
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"yf = 2.0/N * np.abs(yf[:N//2])\n",
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"\n",
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"#yf = sum(yf[s::10] for s in range(10)) / 10\n",
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"#xf = sum(xf[s::10] for s in range(10)) / 10\n",
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"\n",
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"fig, ax = plt.subplots()\n",
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"ax.loglog(xf, yf)\n",
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n",
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"ax.set_xlabel('T in s')\n",
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"ax.set_ylabel('Amplitude Δf')\n",
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"ax.grid()\n",
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"ax.set_xlim([1/1000000, 0.5])"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "94ca3cb49e7d452dab8f7e2e8d632b84",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"(5e-07, 0.02)"
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]
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},
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"execution_count": 7,
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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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"# Number of samplepoints\n",
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"N = len(data[:,0])\n",
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"# sample spacing\n",
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"T = 1.0\n",
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"x = np.linspace(0.0, N*T, N)\n",
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"yf = scipy.fftpack.fft(data[:,0])\n",
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"xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n",
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"\n",
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"yf = 2.0/N * np.abs(yf[:N//2])\n",
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"\n",
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"average_from = lambda val, start, average_width: np.hstack([val[:start], [ np.mean(val[i:i+average_width]) for i in range(start, len(val), average_width) ]])\n",
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"\n",
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"average_width = 20\n",
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"average_start = 100\n",
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"yf = average_from(yf, average_start, average_width)\n",
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"xf = average_from(xf, average_start, average_width)\n",
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"yf = average_from(yf, 300, average_width)\n",
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"xf = average_from(xf, 300, average_width)\n",
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"\n",
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"fig, ax = plt.subplots()\n",
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"ax.loglog(xf, yf)\n",
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n",
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"ax.set_xlabel('T in s')\n",
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"ax.set_ylabel('Amplitude Δf')\n",
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"\n",
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"for i, t in enumerate([45, 60, 600, 1200, 1800, 3600]):\n",
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" ax.axvline(1/t, color='red', alpha=0.5)\n",
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" ax.annotate(f'{t} s', xy=(1/t, 3e-3), xytext=(-15, 0), xycoords='data', textcoords='offset pixels', rotation=90)\n",
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"#ax.text(1/60, 10,'60 s', ha='left')\n",
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"ax.grid()\n",
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"ax.set_xlim([1/60000, 0.5])\n",
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"ax.set_ylim([5e-7, 2e-2])"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "91a04300b9164bd7a9915d0028f3e563",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"ys = scipy.fftpack.fft(data[:,0])\n",
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"ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
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"s = 60\n",
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"\n",
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"ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
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"\n",
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"xs = np.linspace(0, 1.0/2.0, len(data)//2)\n",
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"#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
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"\n",
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"fig, ax = plt.subplots()\n",
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"ax.loglog(xs[s//2:-s//2+1], ys)\n",
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\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",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2811a79d4ad8487f822750e4419ccfdb",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"ys = scipy.fftpack.fft(data[:,0])\n",
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"ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
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"s = 1\n",
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"\n",
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"ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
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"\n",
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"xs = np.linspace(0, 1.0/2.0, len(data)//2)\n",
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"#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
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"\n",
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"fig, ax = plt.subplots()\n",
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"ax.loglog(xs[s//2:-s//2+1 if s > 1 else None], ys)\n",
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\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",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c678197e011e4ab4982d3e1d2a2cee9a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"ys = scipy.fftpack.fft(data[:,0])\n",
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"ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
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"s = 1\n",
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"\n",
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"ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
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"\n",
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"xs = np.linspace(0, 1.0/2.0, len(data)//2)\n",
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"\n",
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"ys *= 2*np.pi*xs\n",
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"#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
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"\n",
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"fig, ax = plt.subplots()\n",
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"ax.loglog(xs[s//2:-s//2+1 if s > 1 else None], ys)\n",
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\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",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "52bcd29a41a54ed1bf9dc63e0c9e83d8",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"ys = scipy.fftpack.fft(data[:,0])\n",
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"ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
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"s = 30\n",
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"\n",
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"ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
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"\n",
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"xs = np.linspace(0, 1.0/2.0, len(data)//2)\n",
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"\n",
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"ys *= 2*np.pi*xs[s//2:-s//2+1]\n",
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"\n",
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"#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(9,5))\n",
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"ax.loglog(xs[s//2:-s//2+1], ys)\n",
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"ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\n",
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"ax.grid()\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",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"15.923566878980893"
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]
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},
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"execution_count": 12,
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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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"1/0.0628"
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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": "labenv",
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"language": "python",
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"name": "labenv"
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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": "3.8.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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