PlotResults.ipynb
85 lines
| 2.4 KiB
| text/plain
|
TextLexer
Jean-christophe Pellion
|
r676 | { | ||
| "cells": [ | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [ | ||||
| "#%matplotlib qt\n", | ||||
| "%matplotlib notebook\n", | ||||
| "import matplotlib.pyplot as plt\n", | ||||
| "#plt.rcParams[\"figure.figsize\"] = [12,12]\n", | ||||
| "import numpy as np\n", | ||||
| "import pandas as pds" | ||||
| ] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [ | ||||
| "def try_plot(df,ax,left,right):\n", | ||||
| " try:\n", | ||||
| " df[(df.index >= left) & (df.index <= right)].plot(ax=ax,subplots=True,legend=False)\n", | ||||
| " except:\n", | ||||
| " pass\n", | ||||
| " \n", | ||||
| "def make_plots(path=\"./\",left=50e-3,right=100e-3):\n", | ||||
| " inputSig = pds.read_csv(path+\"/input.txt\",delim_whitespace=True,header=None,names=[\"BIAS1\",\"BIAS2\",\"BIAS3\",\"BIAS4\",\"BIAS5\",\"B1\",\"B2\",\"B3\"])\n", | ||||
| " inputSig.index.name=\"TSTAMP\"\n", | ||||
| " inputSig.index*=1./98304.\n", | ||||
| " fXSig=[]\n", | ||||
| " G=[0.89,0.87,0.89]\n", | ||||
| " [fXSig.append(pds.read_csv(\n", | ||||
| " path+\"./output_f{0}.txt\".format(F),index_col=0,delim_whitespace=True,header=None,\n", | ||||
| " names=[\"TSTAMP\",\"BIAS1\",\"BIAS4\",\"BIAS5\",\"B1\",\"B2\",\"B3\"])) for F in range(3) ]\n", | ||||
| " for F in range(3):\n", | ||||
| " if len(fXSig[F].index):\n", | ||||
| " fXSig[F].index*=1e-9\n", | ||||
| " fXSig[F]/=G[F]\n", | ||||
| " axes=inputSig[(inputSig.index >= left) & (inputSig.index <= right)].filter([\"BIAS1\",\"BIAS4\",\"BIAS5\",\"B1\",\"B2\",\"B3\"]).plot(subplots=True,layout=(3,2)) \n", | ||||
| " [ try_plot(df,axes,left,right) for df in fXSig ]\n", | ||||
| " return inputSig,fXSig\n", | ||||
| " " | ||||
| ] | ||||
| }, | ||||
| { | ||||
| "cell_type": "code", | ||||
| "execution_count": null, | ||||
| "metadata": { | ||||
| "collapsed": false | ||||
| }, | ||||
| "outputs": [], | ||||
| "source": [ | ||||
| "inputSig,fXSig=make_plots(left=0)" | ||||
| ] | ||||
| } | ||||
| ], | ||||
| "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": "3.5.2" | ||||
| } | ||||
| }, | ||||
| "nbformat": 4, | ||||
| "nbformat_minor": 1 | ||||
| } | ||||