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": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    },
    {
     "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",
    "    R = []\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,'\\t',x,'\\t',y)\n",
    "            R.append(r)\n",
    "            #print(R)\n",
    "            i = i + 1\n",
    "#print(i,x,y)\n",
    "#print(Y)\n",
    "#print(R)\n",
    "#print(len(R))\n",
    "avgR = sum(R)/len(R)\n",
    "myMean = np.mean(R)\n",
    "print(myMean)\n",
    "#print(\"resistance = \", avgR ,\"Ohm\")\n",
    "#print(X,Y)\n",
    "plt.figure(figsize = [4,4])                      #figsize(width, height) in inches\n",
    "#plt.bar(X,Y)       # ^ . try withh plt.plot\n",
    "plt.scatter(X,Y,color = 'g', marker = '^')       # ^ . try withh plt.plot\n",
    "plt.axis([0, 25, 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": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}