simplePlot_adp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple plot of data from txt file\n",
    "## txt file contains text lines and numerical data\n",
    "- Comments: # for **single line comment** and ''' lines ''' for **multilines comment**\n",
    "- **Shortcut command to run** file.ipynb in Jupyter in windows OS: Shift+Enter \n",
    "- \n",
    "- open txt file\n",
    "- create emply lists for x and y values\n",
    "- read line by line from txt file\n",
    "- if line starts with any character (here #), continue without doing anything\n",
    "- if line starts with other than #, then slpit the line by delimiter (here is a space)\n",
    "- specify which are which\n",
    "- add those values in respective parameters, if needed\n",
    "- plot interested lists\n",
    "- format plot\n",
    "- save plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt         # only one module is necessary here for this practice\n",
    "#import random as random                # if no need, comment \n",
    "#import json as json\n",
    "#import pandas as pd\n",
    "#import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open (\"exampledata.txt\",'r') as f:\n",
    "    i = 0                                       #it works even without use of i\n",
    "    X = []                                      # create blank list for x axis values\n",
    "    Y = []\n",
    "    for line in f:                              #each lin in file f\n",
    "        #print(line)\n",
    "        if line.startswith(\"#\"):                #if line start with # character, continue without doing any work\n",
    "            #print(line)\n",
    "            continue\n",
    "        else:                                   #if line start with othern than # then slpit the line by a space ' '\n",
    "            lineParts = line.split(' ')\n",
    "            #print(lineParts)\n",
    "            t = lineParts[0]                    # after slpit, 0 intexed value is time\n",
    "            #print(t)\n",
    "            x = float(lineParts[1])             # 1 intexed value is x, check w/wo float()\n",
    "            #print(x)\n",
    "            X.append(x)                         # add values of x in list X\n",
    "            #print(X)\n",
    "            y = float(lineParts[2])        \n",
    "            #print(y)\n",
    "            #r = y / x\n",
    "            #print(\"resistance = \", r,\"Ohm\")\n",
    "            Y.append(y)\n",
    "            '''print(Y)'''                       # two ways of commenting\n",
    "            #print(Y)\n",
    "            #print(i,x,y)\n",
    "            i = i + 1\n",
    "            #r = y/x\n",
    "#print(i,x,y)\n",
    "#print(X,Y)\n",
    "plt.figure(figsize = [4,4])                      #figsize(width, height) in inches\n",
    "plt.scatter(X,Y,color = 'r', marker = 'o')       # try withh plt.plot\n",
    "plt.axis([0, 20, 0, 10])                         #axis[xmin,xmax, ymin,ymax]\n",
    "#another way to set axis limit\n",
    "#plt.xlim(xmin,xmax)\n",
    "#plt.ylim(ymin,ymax)\n",
    "plt.title('Practice, NPSSC2020', fontsize=16)\n",
    "plt.xlabel('Current (A)', fontsize=14)\n",
    "plt.ylabel('Voltage (V)', fontsize=14)\n",
    "plt.tight_layout()                             # this is for margin\n",
    "plt.grid(True)                                 # comment this if u do not need grid\n",
    "plt.savefig(\"testplot.png\")                    # png, jpg, eps, pdf as u like\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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