{ "cells": [ { "cell_type": "code", "execution_count": 69, "id": "seventh-childhood", "metadata": {}, "outputs": [], "source": [ "# This tutorial will demonstrate how to create basic plots of data and\n", "# functions.\n", "\n", "# I will first import the module numpy and 'matplotlib' which we will use\n", "# for ploting. Matplotlib provides a MATLAB-like interface. Many of the \n", "# options used for plotting in MATLAB are the same in Matplotlib.\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 70, "id": "forward-motorcycle", "metadata": {}, "outputs": [], "source": [ "# Here is a sample data set.\n", "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", "y = [1.02, 4.3, 8.6, 16, 22, 34, 45, 60.2, 80.1, 96, 125, 144]" ] }, { "cell_type": "code", "execution_count": 71, "id": "negative-footwear", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here's the simplest possible plot of y vs x.\n", "plt.plot(x, y);" ] }, { "cell_type": "code", "execution_count": 72, "id": "acoustic-avenue", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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zewXIBcqBhcAEIAWYZmZjqPoHYGQkgopIzVq+ZQ83PDOPRvWSmDJ2CB1bqtjjSVh7y7j7Q8BDR20+TNUqXkTi1LLNe7jx2apinzp2KJktGwUdSU6RTl4oIl+zbHPVir1x/boq9jimww+IyFeWFlSt2FOS6zJ17BA6tFCxxyut3EUEgCUFu7nhmbkq9gShlbuIsHjTbm56dh5NG9Zj6tghZKSq2OOdVu4itdyiTbu58dl5NGukYk8kWrmL1GILN+7i5mfnk9q4PlPGDqF984ZBR5II0cpdpJbKPaLYp6rYE45W7iK10IINuxg9cT4tU6qKPb2Zij3RaOUuUsss2LCT0RPn00rFntBU7iK1SM76ndz87HxaN0lm6tihKvYEpnIXqSU+X1+1Ym/TtAFTbh9C22YNgo4kUaSZu0gtMH/dTr773HzaNm3AlLFDaNNUxZ7oVO4iCW7e2h3c8vzntG3WgKm3DyFNxV4raCwjksDmhoo9vVkDpo5VsdcmKneRBPXZFzu45bnPade8IVPGDiGtiYq9NtFYRiQBffrFdsY8n0NGakNevH0IrZskBx1JaphW7iIJ5tP87dz6/Od0aKFir820chdJIJ/kb2fMpM/p2KIxk28fTKsUFXttFdbK3cyam9krZrbSzPLMbKiZtTCz2Wa2JvQ1NVJhReT4Pl5TtWLv1LIxL6rYa71wxzJPAm+5e3egH5AHjAPmuHsWMCd0WUSi6KM1xYyZ9DmdWzVm8m2Daalir/WqXe5m1hQ4B3gWwN1L3X03MByYFLrZJOCq8CKKyP9n9opt3DYph86tGvPi7UNU7AKEt3LvAhQDz5nZQjN7xswaA23cvRAg9DXtWHc2s7FmlmNmOcXFxWHEEKmdyioqeeTNPG7/7xy+0aYJL94+hBaN6wcdS2JEOOVeFxgI/NndBwAHOIURjLtPcPdsd89u3bp1GDFEap+te0oY9fRc/vr3tYwanMnLdw5VscvXhLO3TAFQ4O7zQpdfoarct5lZursXmlk6UBRuSBH5h4/WFHPf1EUcKqvgiWv7c9WA9kFHkhhU7ZW7u28FNplZt9CmYcAKYBYwOrRtNDAzrIQiAkBFpfNfs1dz88T5tGhcn1nfP1PFLscV7n7uPwAmm1l9YC1wC1X/YEwzszHARmBkmM8hUutt33+Y+6Yu4uP87Vw9oD0Pf6c3jerrYypyfGH9dLj7IiD7GFcNC+dxReQf5q/byQ+m5LL7YBn/OaIP12R3wMyCjiUxTv/0i8SoykpnwkdreeztVXRIbchz3zuDnu2aBh1L4oTKXSQG7T5YygMvL+bdvCIu7dOW/xzRlyYN6gUdS+KIyl0kxizetJvvTc6laF8Jv7iiJ6O/2UljGDllKneRGOHu/PdnG3j49RWkNWnAtDuGMiBTh2aS6lG5i8SAfSVljJuxlNeXFHJ+9zQev6YfzRvpQ0lSfSp3kYDlFe7le5Nz2bjzIA9e3J07zulCnToaw0h4VO4iAZr2+SZ+PnMZzRrW48XbBjO4S8ugI0mCULmLBOBQaQU/n7mMVxYUcGbXljxx7QCdMUkiSuUuUsPyi/Zz9+RcVhft455hWdw7LIskjWEkwlTuIjVo1uItjJ++hOR6SUy65QzO+YaOiCrRoXIXqQGHyyv45WsreGHuRrI7pvKHUQNIb9Yw6FiSwFTuIlG2ccdB7n4xl6Wb93DHOV144KJu1EsK9wyXIv8/lbtIFL2zfCs/enkxBjx9czbf7tkm6EhSS6jcRaKgrKKS37y1kqc/WkffjGY8NWogHVo0CjqW1CIqd5EIW7l1Lz97dRkLNuzi5qEd+dllPUiumxR0LKllVO4iEbJ1TwmPz17FKwsKSEmuy++vH8CV/doFHUtqKZW7SJj2lZTx17+v5ZmP11JZCWPO6szd53XVsWEkUCp3kWoqq6hk6vyNPPHuGnYcKOXKfu348UXdNFuXmKByFzlF7s7by7fxm7dWsnb7AQZ3bsHES3vQr0PzoKOJfCXscjezJCAH2Ozul5tZC+AloBOwHrjG3XeF+zwisWDBhl088kYeORt20TUthWdHZ3N+9zSdTENiTiRW7vcCecCXJ3ccB8xx90fNbFzo8oMReB6RwKzffoDfvL2SN5ZupVVKMr/+Th+uyc6grj6MJDEqrHI3swzgMuBXwA9Dm4cD54a+nwR8gMpd4tTOA6X8fs4aJs/bQL2kOtx3QRa3n92FxsmaaEpsC/cn9AngJ0CTI7a1cfdCAHcvNLO0Y93RzMYCYwEyMzPDjCESWSVlFUz8ZB1/fv8LDpSWc+3pmdx/QRZpTRsEHU3kpFS73M3scqDI3ReY2bmnen93nwBMAMjOzvbq5hCJpIpK59WFm/ndO6so3FPCBT3SGHdJd7qmNTnxnUViSDgr9zOBK83sUqAB0NTMXgC2mVl6aNWeDhRFIqhItH20pphfv7GSvMK99M1oxuPX9GfoaTozksSnape7u48HxgOEVu4PuPuNZvYYMBp4NPR1ZvgxRaInr3Avj7y5kg9XF5OR2pDfXz+Ay/uk6zymEtei8a7Qo8A0MxsDbARGRuE5RMJWuOcQv3tnNdNzC2jaoB7/elkPbhraUceBkYQQkXJ39w+o2isGd98BDIvE44pEw96SMv7ywRc8+/E63OH2s7tw97ldadaoXtDRRCJG+3NJrVFaXsmU+Rt5cs4adh4oZXj/djxwoQ4XIIlJ5S4J78vDBTz6Zh7rdxxkaJeW/PTSHvTJaBZ0NJGoUblLQltbvJ+HZi3nozXbyUpLYeJ3szmvmw4XIIlP5S4J6WBpOX98L5+nP1pLg7pJPHRFT24a0lGHC5BaQ+UuCcXdeWvZVn752gq27Cnh6oHtGXdJd9Ka6JOlUruo3CVhfFG8n1+ERjDd2zbhyesHcHqnFkHHEgmEyl3i3sHScv7wXj7PhEYwv7iiJzdqBCO1nMpd4pa782ZoBFO4p4QRAzMYd0l3WjdJDjqaSOBU7hKX8ouqRjAf52+nR3pT/nD9ALI1ghH5ispd4sqBw1UjmGc/XkuDehrBiByPyl3igrvzxtKtPPy6RjAiJ0PlLjFPIxiRU6dyl5h19Ajm36/sxQ2DMzWCETkJKneJOUePYP5lUAYPXqwRjMipULlLTMkv2s9Ds5bxSf4OeqY35Y+jBjCoo0YwIqdK5S4x4cDhcn7/3homfryOBvWS+I/hvbhhcEeSdDYkkWpRuUug3J3Xlxby8Gt5bN1bwshBGTx4SXdapWgEIxIOlbsEJr9oHw/NWv7VCOapGwYyqGNq0LFEEoLKXWrUrgOlfLC6iHfzinh72VYa1U/il8N7MUojGJGIqna5m1kH4L+BtkAlMMHdnzSzFsBLQCdgPXCNu+8KP6rEI3fni+L9vJtXxJy8bSzYsItKh1YpyYwanMk9w7I0ghGJgnBW7uXAj9w918yaAAvMbDbwXWCOuz9qZuOAccCD4UeVeFFaXsnn63fybt425uQVsXHnQQB6pjfl++d15fwebejbvhl1tFIXiZpql7u7FwKFoe/3mVke0B4YDpwbutkk4ANU7glv14FS3l9VxJy8Ij5cXcy+w+XUr1uHM09rydhzunB+9zTaNW8YdEyRWiMiM3cz6wQMAOYBbULFj7sXmlnace4zFhgLkJmZGYkYUoPcnfyi/cxZ+fVxS+smyVzWN53zu6dxVlYrGtXX2zoiQQj7b56ZpQDTgfvcfe/JnnjY3ScAEwCys7M93BwSfccbt/RqVzVuGdajDX00bhGJCWGVu5nVo6rYJ7v7jNDmbWaWHlq1pwNF4YaU4Jxo3DKsRxrpzTRuEYk14ewtY8CzQJ67P37EVbOA0cCjoa8zw0ooNerLccu7eUW8t/Kfxy3DerThzK4tNW4RiXHh/A09E7gJWGpmi0LbfkpVqU8zszHARmBkWAkl6tydJQV7mLloC+/mbfv6uOX8LC7okUbvdhq3iMSTcPaW+Rg43t/2YdV9XKk5hXsO8erCzczI3Ux+0X7q163DWV1bcce3qvZu0bhFJH7pd+ta5mBpOW8v38r0BZv55IvtuMPpnVJ55Oo+XNonnWYN6wUdUUQiQOVeC1RWOvPW7WR6bgFvLi3kQGkFGakN+cH5WYwY2J6OLRsHHVFEIkzlnsDWbT/AjNwCZuRuZvPuQ6Qk1+WyvumMGJjB6Z1aaIYuksBU7glmz6EyXluyhRm5m1mwYRd1DM7Kas1PLu7GhT3b0rB+UtARRaQGqNwTQHlFJR+uKWb6gs3MzttGaXklWWkpjLukO1f1b0/bZg2CjigiNUzlHsdWbNnL9NwCZi7awvb9h0ltVI9RZ2QyYmAGvds35WQ/LSwiiUflHmeK9x1m5qLNTM/dTF7hXuolGed3T2PEwAzO7ZZG/bp1go4oIjFA5R4HSsoqeDdvGzNyN/P31cVUVDr9MprxH8N7cUXfdqQ2rh90RBGJMSr3GLX/cDmLN+3m9aWFvLZ4C3tLymnbtAFjz+nCiIHt6ZrWJOiIIhLDVO4xoKLSWb1tH4s27WbRxt0s2rSbNUX7qHRoWC+Ji3u3ZcTADIae1lKnohORk6JyD8DWPSUs2rSLhaEyX7p5DwdLKwBo1rAe/Ts056LebRnQoTmnd25BSrL+mETk1Kg1ouxgaTlLCvZ8bVW+dW8JAPWSjJ7pTRk5KIP+mc3p3yGVTi0baS8XEQmbyj2CKiqrDpe7aNMuFm3azcKNu1m9rWq8ApDZohFndG5B/w7N6Z/ZnJ7pTWlQTx8qEpHIU7mHoWhvSdVo5Yjxyv7D5QA0bVCXfh2ac2HPNvTPbE6/jOa0TEkOOLGI1BYq95Pg7hTtO0x+0X5WbNkbWpXvYsueqvFK3TpGj/SmfGdA+69W5Z1bNtaxW0QkMCr3I7g7W/aUsGbbPvKL9rNm237WFO1jTdF+9pWUf3W7jNSGDOyYyq0dmjMgszm92jXTeEVEYkqtLPfKSqdg16GvinvNtv3kF1UV+oHQXisArVLq0zUthav6tyerTQpd01L4RpsmtNJ4RURiXEKXe3lFJRt3HmRN0f7QSryqzL8o3k9JWeVXt2vTNJmstCaMzO5AVpsUstKa0DUthRb65KeIxKmolbuZXQw8CSQBz7j7o9F6rtLyStbvOPC1MUr+tv2s236A0op/lHj75g3pmpbC0C4tQyvxqhLX2YdEJNFEpdzNLAl4Cvg2UAB8bmaz3H1FJJ9n2eY93Dt1Iet3HKQitL+hWdUuh1lpKZzbvTVZaU3ISkvhtLQUfRhIRGqNaLXdGUC+u68FMLOpwHAgouXeMjQTv6R3+lcz8dNap+jNTRGp9aJV7u2BTUdcLgAGH3kDMxsLjAXIzMys1pOkN2vIX2/KrmZEEZHEFa2Dfx9rB2//2gX3Ce6e7e7ZrVu3jlIMEZHaKVrlXgB0OOJyBrAlSs8lIiJHiVa5fw5kmVlnM6sPXAfMitJziYjIUaIyc3f3cjP7PvA2VbtCTnT35dF4LhER+WdR2zfQ3d8A3ojW44uIyPHpbMoiIglI5S4ikoBU7iIiCcjc/cS3inYIs2JgQ9A5TlIrYHvQIaIokV+fXlv8SuTXF85r6+jux/ygUEyUezwxsxx3T9iPxSby69Nri1+J/Pqi9do0lhERSUAqdxGRBKRyP3UTgg4QZYn8+vTa4lciv76ovDbN3EVEEpBW7iIiCUjlLiKSgFTuJ8nMOpjZ+2aWZ2bLzezeoDNFmpklmdlCM3st6CyRZGbNzewVM1sZ+vMbGnSmSDKz+0M/k8vMbIqZNQg6U3WZ2UQzKzKzZUdsa2Fms81sTehrapAZw3Gc1/dY6GdziZm9ambNI/FcKveTVw78yN17AEOAu82sZ8CZIu1eIC/oEFHwJPCWu3cH+pFAr9HM2gP3ANnu3puqo7BeF2yqsDwPXHzUtnHAHHfPAuaELser5/nn1zcb6O3ufYHVwPhIPJHK/SS5e6G754a+30dVQbQPNlXkmFkGcBnwTNBZIsnMmgLnAM8CuHupu+8ONFTk1QUamlldoBFxfGIcd/8Q2HnU5uHApND3k4CrajJTJB3r9bn7O+5eHro4l6qTG4VN5V4NZtYJGADMCzhKJD0B/ASoDDhHpHUBioHnQiOnZ8yscdChIsXdNwO/BTYChcAed38n2FQR18bdC6FqkQWkBZwnmm4F3ozEA6ncT5GZpQDTgfvcfW/QeSLBzC4Hitx9QdBZoqAuMBD4s7sPAA4Q37/Wf01o/jwc6Ay0Axqb2Y3BppLqMLOfUTX+nRyJx1O5nwIzq0dVsU929xlB54mgM4ErzWw9MBU438xeCDZSxBQABe7+5W9Zr1BV9oniAmCduxe7exkwA/hmwJkibZuZpQOEvhYFnCfizGw0cDlwg0fow0cq95NkZkbV3DbP3R8POk8kuft4d89w905UvRn3nrsnxOrP3bcCm8ysW2jTMGBFgJEibSMwxMwahX5Gh5FAbxiHzAJGh74fDcwMMEvEmdnFwIPAle5+MFKPq3I/eWcCN1G1ql0U+u/SoEPJSfkBMNnMlgD9gV8HGydyQr+RvALkAkup+jsdtx/VN7MpwGdANzMrMLMxwKPAt81sDfDt0OW4dJzX90egCTA71Ct/ichz6fADIiKJRyt3EZEEpHIXEUlAKncRkQSkchcRSUAqdxGRBKRyFxFJQCp3EZEE9H/yi/H4rbnQsgAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Of course, there are lots of options that can be used to better format\n", "# the plot. Before we get to those options, let's clarify an important\n", "# point. If we were to execute another plot command within the same Jupyter \n", "# cell, it would add to the plot that was we created just a moment ago. To \n", "# tell Python that we want a new plot to be started while still working in a\n", "# single cell, we must first use the command 'plt.figure()'.\n", "\n", "# Here are two curves on a single plot.\n", "plt.plot(x, y)\n", "plt.plot(x, 1.4*np.array(y))\n", "\n", "# Here are the same two curves on the separate plots.\n", "\n", "# First make a new figure.\n", "plt.figure()\n", "\n", "# Generate the first plot.\n", "plt.plot(x, y)\n", "\n", "# Make a new figure.\n", "plt.figure()\n", "\n", "# Generatre the second plot.\n", "plt.plot(x, 1.4*np.array(y));" ] }, { "cell_type": "code", "execution_count": 73, "id": "recognized-spirituality", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Now let's do some formating. I will not attempt to show you all of the\n", "# possible plot options -- you can check the online Python and matplotlib \n", "# documentation yourself. I'll just show you some of the basics.\n", "\n", "# You can make line plots or scatter plots. One way to make a scatter plot\n", "# is to specify the mark type after the x, y input.\n", "plt.plot(x, y, 's');" ] }, { "cell_type": "code", "execution_count": 74, "id": "frozen-training", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter plot point types:\n", "# o - circle\n", "# + - plus sign\n", "# * - star\n", "# . - point\n", "# x - cross\n", "# s - square\n", "# d - diamond\n", "# ^ - up-pointing triangle\n", "# v - down-pointing triangle\n", "# > - right-pointing triangle\n", "# < - left-pointing triangle\n", "# p - pentagram\n", "# h - hexagram\n", "\n", "# We can combine scatter plots and line plots by specifying both a mark style \n", "# and a line type.\n", "# line plot line types\n", "# - solid line (default)\n", "# -- dashed line\n", "# : dotted line\n", "# -. dash-dot line\n", "plt.plot(x, y, 's:');" ] }, { "cell_type": "code", "execution_count": 75, "id": "isolated-guest", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Of course, you can change the colours too.\n", "# point/line colours\n", "# y - yellow\n", "# m - magenta\n", "# c - cyan\n", "# r - red\n", "# g - green\n", "# b - blue\n", "# w - white\n", "# k - black\n", "plt.plot(x, y, 'gs:');" ] }, { "cell_type": "code", "execution_count": 76, "id": "paperback-homework", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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jSJRp5C4yDL159ChP1vUu4XvlmDFMzsoKOJFEm0buIsPMH44e5erKSsZnZPDJwkIyUzTGS0b6vyoyjPz30aNcVVlJSUYGK8rKVOxJTCN3kWHiv44c4ZoNGxiXkcErZWVMyNSa7MlMP7ZFhoE3jhw5PhXzqop9WFC5iyS5148c4ZrKSiZmZvJqWRnjVezDgqZlRJLY60eO8LHKSiZlZfHynDmMU7EPGxq5iySxLndmZGfziop92FG5iySh+s5OABaOHs2q+fMpUbEPOyp3kSSzurmZaStXsry+9/LFKWYBJ5IgqNxFksx52dl8Ztw4Lh81KugoEqCI3lA1s1HAw8BswIHPAFuBZ4BSYA9wk7s3RnIcETlZyQMl1LXWvbshfza0vUNxZja1d9UGF0ziQqQj94eA37r7ucAcoAq4B1jh7tOBFeH7IhJl7yn20fPhggfgrC+8d7sMW4MudzPLBy4DHgFw9053PwJcDywL77YMuCGyiCLyvgougfPug2M1sOsfg04jcSKSkfs0oAH4mZmtNbOHzSwHKHb3AwDhr0X9fbOZLTGzCjOraGhoiCCGyHCVAlOXwOz7oG0PrL8Duo4GHUriRCTlngbMA37q7nOBVs5gCsbdl7p7ubuXFxYWRhBDZPjZ19EBcx6EyZ+C/S/A2i9Cd1PQsSSORFLuNUCNu78Vvv8svWVfZ2bjAMJf6yOLKCJ9/f7wYeZWVEDeOVB1H2z/EXhX0LEkzgy63N29Fqg2sz9edHEhsBl4AVgc3rYYeD6ihCJy3OtHjvDRykoK09MpqPoG1P/+pH2Kc4oDSCbxJtK1Zb4IPGlmGcAu4NP0/sBYbma3AXuBGyM8hsiw5+6YGR8cOZIfnnUWnx8/npyL1gQdS+JYROXu7uuA8n4eWhjJ84rIu95uauJz27bxH+efz4TMTO6cNCnoSJIA9AlVkTg3Mi0NA450dwcdRRKIyl0kDjV2dfFQTQ0eXtVx9fz5nJeTE3QsSSBaz10kzqxqauLGTZvY39nJwlGjmJ2bi2nxLzlDGrmLxAl35yc1NVy6di0OvDF3LrNzc4OOJQlKI3eRONDU3c1nt25leUMDHx8zhsdnzmRMenrQsSSBqdxFAlbZ0sInN21i17Fj3D9tGl+bNElrsEvEVO4iAXr0wAFu376d0WlpvFxWxmVag12iROUuEpDOUIgf19RwaX4+T86aRXFGRtCRJImo3EWG2La2NsZlZJCXlsZLc+YwNj2dVE3DSJTpbBmRIXS4q4uLVq/m67t2AVCckaFil5jQyF1kCITcSTFjTHo6P5k+Xdc3lZjTyF0kxnYdO8aCNWv4zaFDANxcUsLErKyAU0my08hdJIpOumh1waVw7j0YECotDSqWDEMauYtE0fFit1SY9nmY/TfQVoOv/iwfLygINpwMKxq5i0RbzlSYfgeMnA37fgk7f6orJcmQU7mLRMm+jg4452tQchV0t8Hm70HDK0HHkmFK5S4SBe7OJzZsgOIroOYXsPcJ6G4OOpYMY5pzFxmkrlCIf96/n+bubsyMn55zDqxaDLt+qmKXwKncRQapsrWVz2/bxjP19QAsyM+nONX73VcXrZahFvG0jJmlAhXAPne/1szGAM8ApcAe4CZ3b4z0OCLx4M2jR3mzqYk7Jk1ifl4eq+bNY35e3vHHa++qDTCdyLuiMXL/MlDV5/49wAp3nw6sCN8XSWg72tq4cdMmLlm7lgerq2kJX8+0PD9fV0mSuBRRuZvZRODjwMN9Nl8PLAvfXgbcEMkxRIJ0sLOTL23fzsxVq/jNoUN8p7SULRddRG6azkWQ+Bbp39AfA18H8vpsK3b3AwDufsDMivr7RjNbAiwBmDx5coQxRKLrWE8PD9XU8P29e2nt6eEvxo3jO6WllGRmBh1NZEAGXe5mdi1Q7+6rzezyM/1+d18KLAUoLy/v/10okQA8UVvLvbt3U9PRwXUFBdw/bRozc3KCjiVyRiIZuV8KXGdmHwOygHwzewKoM7Nx4VH7OKA+GkFFhsprR48yLiODJ2bO5MNavVES1KDn3N39Xnef6O6lwCLgZXe/GXgBWBzebTHwfMQpRWKopr2dq9ev562mJgAeOvtsVs6bp2KXhBaL89zvB640s+3AleH7InGnx3tnA0empbG3o4Pq9nYAslNTdYFqSXhRecvf3V8FXg3fPgQsjMbzikTqpCV4AVKzyT7rs8yYfjNvzZtHXloaGy+8UIUuSUXnc0lSe0+xWyqM+wRMWUxbxihmZmfT2tPDqJQUFbskHZW7DA8FH4RpSyB7EjSugQ3/zJN3bg06lUjMqNwluY2YCGd/CcZcCK17YMO9cHhl0KlEYk7lLknrwepqKH8UQh2w4x9g378DoaBjiQwJlbskFXcnBKSaMTEzE+pfhl3/DF1au06GFy35K0mjraeHqyoreaC6GoCbiooorvlZv8WuJXgl2WnkLgkv5E6KGdmpqYzPyGB0n0W9tASvDFcauUvCcneera9nxttvs72tDYDHZs5kyfjxAScTCZ7KXRLSltZWPlpZyY2bN5OTkkJbSG+UivSlaRlJKC3d3fzNO+/wYE0N2Skp/MPZZ/P58eNJS9E4RaQvlbskBHfn2YYG7ti5k5qODm4tKeH+adMozsgIOppIXFK5S9zbfewYS7Zt4/eNjZTl5vLMrFlcMnJk0LFE4prKXeJeihmbWls1BSNyBlTuEpeeP3iQ5w8e5JEZM5iSlcWeD3yADJW6yIDpX4vEpXfa21nb0kJjdzeAil3kDOlfjMSFlu5u7t65kydqez90dPuECVTMn8+Y9PSAk4kkJk3LyJDq9+IZhZeTcvYXCGUUcNekSdxM79owIjJ4KncZUu8p9uzJvcvxjp5PqHk7f1hwBRfrLBiRqFC5y9BKy4cxF0HBJTD2Q9BzDLb/GPa/yMWf+GzQ6USSxqDL3cwmAY8DJfQukr3U3R8yszHAM0ApsAe4yd213uow5e60h0KMSE1lR1sbXPJc7+XuOg/DgRfhnceh60jQMUWSTiQj927gTndfY2Z5wGozewm4FVjh7veb2T3APcDdkUeVROHumBnuzkVr1nBBTg6PnHsuZ40YAbsfgSNroXkr4EFHFUlagy53dz8AHAjfbjazKmACcD1weXi3ZcCrqNyT3qGuLn596BAvHjrEO+3tvDV/PmbGjYWFTMjMBMDMoPqpgJOKDA9RmXM3s1JgLvAWUBwuftz9gJkVneJ7lgBLACZPnhyNGDKE3J2qtjZePHSI/zh0iD8cPUoIKMnI4NqCAjpCITJTUvj6Cf9vi3OKTz5bBl08QyTazD2yX43NLBd4DbjP3Z8zsyPuPqrP443uPvr9nqO8vNwrKioiyiFDY3NrK0v37+fFQ4fY1d4OwNzcXK4tKOATBQXMz8sjRacxigwJM1vt7uX9PRbRyN3M0oFfAE+6+3PhzXVmNi48ah8H1EdyDIm9fs89p3c0vfOr+3iuoYEL8/I4NyeHve3t/NP+/SwcPZqvTZrEtQUFTMzKCiC1iLyfSM6WMeARoMrdH+zz0AvAYuD+8NfnI0ooMXdSsWdPgdQs6pq30hEKceuWLXy3tJRv5uSwcPRoDn3wg+SkpgYTVkQGJJKR+6XALcAGM1sX3vaX9Jb6cjO7DdgL3BhRQhkaeTOgaGHv+ecjJvSe0bL+Dsakp7PxwguZkZ0NQHpKCloQQCT+RXK2zH8Bp5pcXTjY55WhU9PezhN1dVD+M8gphVAnNK6G6mfg0JvH95uZkxNcSBEZFH1CdRha0djI/Xv3sqKxsfdM8+5m2PoANLwKPa0BpxORaNCqkMNAyJ1XGxtp6OwEYH9HBzuOHeNbU6awY8ECWPclqP2Vil0kiajck1hnKATAjmPH+JP16/nXut43Tj9VVMTOBQv47tSpnDVixCnPMde55yKJS9MySeZIVxfPNDTweG0tEzMzeea88zgnO5tfnX8+l48aBXDSZepq76oNIKmIxJLKPQl0h0L8Z2Mjy2preeHgQTrcmZWdzU1F7344+GMFBQEmFJGhpnJPYOtbWlhWW8vP6+qo6+qiIC2NJePHs7ikhHm5ub1ruYjIsKRyj0Pv94nRP06h/Mv+/SzZto10M64tKGBxSQnXjBmja42KCKByj0v9FTtp+dRN/QrL6+u5qaiIjxcU8JPp01lUVESBrjMqIidQucer1BGQNxPS8uDga73noqflcix8Bsz4zExunzAh4JAiEq9U7nGgx53Nra2sbGriraYmmP8I5EzpvWLRsQO95Y7Dui+y+PovBB1XRBKAyj1Ajx44wL/W1VHR3ExLTw8Ao9PSoLMBDr4BzVVwtDLglCKSiFTuMdbS3U1GSgoZKSk8XVfHPbt2sfHCC8lNS2NfRwetPT3cWlLCgrw8FuTnc/aIEaR870NBxxaRBKdyH4CBnL0CvdMrVa2tvNXczFvhKZaNra28NGcOHxk9mvGZmVwyciRNPT3kpqXxrdJSvlVa2u/z6mpFIhIJlfsA9Hv2ClB37CjPHzx4vMhXNTfTHJ5eGZWWxkV5eVw/diyTwtcQvWzUKC4Lf0r0/egToyISKZX7GTGY+Elo3QONqyBjNDds3EiaGXNycriluJgF+fksyM9n+ogRutyciARG5d6Hu1Pd0cHm1lY2t7Ud/8r0O2H73wMOkz4F9St6y739AH+YO5ey3FxG6MpEIhJHhmW5u/vxj+b/0759rGxqYnNbG1VtbcfPWgEoSk9nVk4OtB9495vfvhl62o7fvXjkyCHLLSIyUAlb7gN5k7M7FGJXezs7jx3jmvDCWV/Zvp0/NDXx9vz5ACxvaGBrWxuzcnL4TEkJs3JymJWdzczsbMZmZABgz//83QP0KXYRkXgVs3I3s6uBh4BU4GF3vz+az/+eYrc0GDERsqdQlzOFRZs2sbmtja1tbXS6A9DyoQ+Rk5pKWW4uWX3WX/ntBRecdj0Wnb0iIokmJuVuZqnA/wOuBGqAVWb2grtvjvrBJt4E05b0fpoTwEOsam5mVk4O14wZc3wknhmehrl13Lj3fPtAFtrS2SsikmhiNXK/CNjh7rsAzOxp4Hog+uXevBX2PgVt70DbHmirZue32qN+GBGRRBKrcp8AVPe5XwMs6LuDmS0BlgBMnjx58Ec6ur73j4iIHBerxb/7O8Hb33PHfam7l7t7eWFhYYxiiIgMT7Eq9xpgUp/7E4H90TyALuosInJqsZqWWQVMN7OpwD5gEfC/onkAvckpInJqMSl3d+82sy8A/0nvqZCPuvumWBxLREROFrPz3N3918CvY/X8IiJyarqasohIElK5i4gkIZW7iEgSMnc//V6xDmHWALwTdI4BGgscDDpEDCXz69NrS1zJ/PoieW1T3L3fDwrFRbknEjOrcPfyoHPESjK/Pr22xJXMry9Wr03TMiIiSUjlLiKShFTuZ25p0AFiLJlfn15b4krm1xeT16Y5dxGRJKSRu4hIElK5i4gkIZX7AJnZJDN7xcyqzGyTmX056EzRZmapZrbWzP4j6CzRZGajzOxZM9sS/v93cdCZosnMvhr+O7nRzJ4ys6ygMw2WmT1qZvVmtrHPtjFm9pKZbQ9/HR1kxkic4vX9MPx3s9LMfmlmo6JxLJX7wHUDd7r7TOADwO1mNivgTNH2ZaAq6BAx8BDwW3c/F5hDEr1GM5sAfAkod/fZ9K7CuijYVBF5DLj6hG33ACvcfTqwInw/UT3Gya/vJWC2u18AbAPujcaBVO4D5O4H3H1N+HYzvQUxIdhU0WNmE4GPAw8HnSWazCwfuAx4BMDdO939SKChoi8NGGFmaUA2Ub4wzlBy99eBwydsvh5YFr69DLhhKDNFU3+vz91/5+7d4bsr6b24UcRU7oNgZqXAXOCtgKNE04+BrwOhgHNE2zSgAfhZeMrpYTPLCTpUtLj7PuABYC9wADjq7r8LNlXUFbv7AegdZAFFAeeJpc8Av4nGE6ncz5CZ5QK/AL7i7k1B54kGM7sWqHf31UFniYE0YB7wU3efC7SS2L/Wv0d4/vl6YCowHsgxs5uDTSWDYWbfoHf698loPJ/K/QyYWTq9xf6kuz8XdJ4ouhS4zsz2AE8DHzGzJ4KNFDU1QI27//G3rGfpLftkcQWw290b3L0LeA64JOBM0VZnZuMAwl/rA84TdWa2GLgW+DOP0oePVO4DZGZG77xtlbs/GHSeaHL3e919oruX0vtm3MvunhSjP3evBarNbEZ400Jgc4CRom0v8AEzyw7/HV1IEr1hHPYCsDh8ezHwfIBZos7MrgbuBq5z97ZoPa/KfeAuBW6hd1S7LvznY0GHkgH5IvCkmVUCZcDfBhsnesK/kTwLrAE20PtvOmE/qm9mTwFvAjPMrMbMbgPuB640s+3AleH7CekUr+8nQB7wUrhX/ikqx9LyAyIiyUcjdxGRJKRyFxFJQip3EZEkpHIXEUlCKncRkSSkchcRSUIqdxGRJPT/AQLSWkDRirzhAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# If you wanted to have your line and point to be different colours (for some\n", "# reason), one way to it is by putting multiple plots into the same plt.plot()\n", "# command.\n", "plt.plot(x, y, 'gs', x, y, 'c-.')\n", "\n", "# Of course, you could also do this using separate plot statements.\n", "# If you wanted to have your line and point to be different colours (for some\n", "# reason), one way to it is by putting multiple plots into the same plt.plot()\n", "# command.\n", "plt.figure()\n", "plt.plot(x, y, 'gs')\n", "plt.plot(x, y, 'c-.');" ] }, { "cell_type": "code", "execution_count": 77, "id": "generous-arrival", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We can use the 'linewidth' option to adjust the thickness of the line\n", "# used to draw the point and we can use 'markersize' to adjust the overall\n", "# size of the point. I'll also include a 'fillstyle' option to make the point \n", "# hollow inside.\n", "plt.plot(x, y, 'ks', linewidth = 2.5, markersize = 12, fillstyle = 'none');" ] }, { "cell_type": "code", "execution_count": 78, "id": "informal-right", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# The other fillstyle options available are:\n", "# 'none'\n", "# 'top'\n", "# 'bottom'\n", "# 'right'\n", "# 'left'\n", "# 'full'\n", "\n", "# Here's another example of multiple plots in a single plot command. \n", "# Notice that I converted the y list to an array in the last two plots to \n", "# facilitate mathematical operations on the y-data. Evidentally, it's possible\n", "# to mix lists and arrays when plotting.\n", "\n", "# I've also included x- and y-axis labels, a title, and a legend. I don't know\n", "# why, but for some reason, the way to control the legend font is not the same\n", "# as it is for the axes labels and title. \n", "plt.plot(x, y, 'ks', x, np.array(y)**1.1 + 20, 'r--', x, np.array(y)**1.2 + 40, 'g*')\n", "plt.xlabel('x-axis label', fontsize = 18, fontname = 'Times', color = 'r')\n", "plt.ylabel('y-axis label', fontsize = 18, fontname = 'Palatino', fontweight = 'bold')\n", "plt.title('Title of the plot', fontsize = 22, fontname = 'Arial', style = 'italic')\n", "plt.legend(('First', 'Second', 'Third'), shadow = 'true', loc = 'upper left',\\\n", " fontsize = 16, prop={'family': 'Consolas', 'size': 16});" ] }, { "cell_type": "code", "execution_count": 79, "id": "quiet-planner", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We can format the tick labels too!\n", "plt.plot(x, y, 'ks', x, np.array(y)**1.1 + 20, 'r--', x, np.array(y)**1.2 + 40, 'g*')\n", "plt.xlabel('x-axis label', fontsize = 18, fontname = 'Times', color = 'r')\n", "plt.ylabel('y-axis label', fontsize = 18, fontname = 'Palatino', fontweight = 'bold')\n", "plt.title('Title of the plot', fontsize = 22, fontname = 'Arial', style = 'italic')\n", "plt.legend(('First', 'Second', 'Third'), shadow = 'true', loc = 'upper left',\\\n", " fontsize = 16, prop={'family': 'Consolas', 'size': 16})\n", "plt.xticks(fontsize = 26, fontname = 'Helvetica')\n", "plt.yticks(fontsize = 16, fontname = 'Times', color = 'b');" ] }, { "cell_type": "code", "execution_count": 80, "id": "endangered-burner", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Finally, you can also set the ranges of the x- and y-axes.\n", "# Use plt.axis((xmin, xmax, ymin, ymax)) to do this.\n", "plt.plot(x, y, 'ks', x, np.array(y)**1.1 + 20, 'r--', x, np.array(y)**1.2 + 40, 'g*')\n", "plt.xlabel('x-axis label', fontsize = 18, fontname = 'Times', color = 'r')\n", "plt.ylabel('y-axis label', fontsize = 18, fontname = 'Palatino', fontweight = 'bold')\n", "plt.title('Title of the plot', fontsize = 22, fontname = 'Arial', style = 'italic')\n", "plt.legend(('First', 'Second', 'Third'), shadow = 'true', loc = 'upper left',\\\n", " fontsize = 16, prop={'family': 'Consolas', 'size': 16})\n", "plt.xticks(fontsize = 26, fontname = 'Helvetica')\n", "plt.yticks(fontsize = 16, fontname = 'Times', color = 'b')\n", "plt.axis((0, 15, 0, 500));" ] }, { "cell_type": "code", "execution_count": 81, "id": "forty-morrison", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# So far, everything has been done for a simple x vs y plot of data.\n", "# In physics will often want to add error bars to our plots. Below are\n", "# some error bar values and a plot with error bars. Instead of 'plot', we\n", "# use 'errorbar'. I also threw in another option to colour the inside of\n", "# the marker pink. Notice that, for some reason, we have to add the 'fmt' \n", "# (format) syntax when using errorbar().\n", "dy = [.3, .3, .5, 2, 2, 2, 4, 4, 6, 10, 20, 20];\n", "plt.errorbar(x, y, yerr = dy, fmt = 'ko', markerfacecolor = 'pink',\\\n", " fillstyle = 'top', capsize = 5, markersize= 8)\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.axis((0, 13, 0, 170));" ] }, { "cell_type": "code", "execution_count": 82, "id": "standing-regression", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# I can't resist showing one more option. Suppose you wanted to add text\n", "# to you plot, maybe somewhere where there's a lot of white space. You can\n", "# use text(x-coordinate, y-coordinate, 'text') to do just that.\n", "plt.errorbar(x, y, yerr = dy, fmt = 'ko', markerfacecolor = 'pink',\\\n", " fillstyle = 'top', capsize = 5, markersize= 8)\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.axis((0, 13, 0, 170))\n", "plt.text(2, 125, 'some text', fontsize = 18,fontname = 'Times');" ] }, { "cell_type": "code", "execution_count": 83, "id": "indian-township", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# What if the text you wanted to add is some complicated symbollic\n", "# expression? Those of you that a familiar with entering in equations in\n", "# LaTeX will love this. Python has a LaTeX interpreter. Examine the last line\n", "# in the block of code below to see how to use it. Note that you can use\n", "# the LaTaX interpreter in axis labels and plot titles too. The 'r' for the $..$\n", "# is necessary.\n", "plt.errorbar(x, y, yerr = dy, fmt = 'ko', markerfacecolor = 'pink',\\\n", " fillstyle = 'top', capsize = 5, markersize= 8)\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.axis((0, 13, 0, 170))\n", "plt.text(2, 125, r'$p_n(\\mathbf{k})=\\frac{e^{-n\\hbar\\omega(k)/k_\\mathrm{B}T}}{Z(\\mathbf{k})}$',\\\n", " fontsize = 18);" ] }, { "cell_type": "code", "execution_count": 84, "id": "christian-blink", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# One thing that matplotlib does very nicely is export figures to a variety\n", "# of file formats. Here, for example, are png and jpg exports. \n", "\n", "# For this to work, you need to generate the plot in the same cell that the\n", "# 'plt.savefig()' commands are used.\n", "plt.errorbar(x, y, yerr = dy, fmt = 'ko', markerfacecolor = 'pink',\\\n", " fillstyle = 'top', capsize = 5, markersize= 8)\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.axis((0, 13, 0, 170))\n", "plt.text(2, 125, r'$p_n(\\mathbf{k})=\\frac{e^{-n\\hbar\\omega(k)/k_\\mathrm{B}T}}{Z(\\mathbf{k})}$',\\\n", " fontsize = 18)\n", "\n", "plt.savefig('Jupyter test_plot.png', format='png');\n", "plt.savefig('Jupyter test_plot.jpg', format='jpg');" ] }, { "cell_type": "code", "execution_count": 85, "id": "massive-speaking", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# I generaly don't recommend using png or jpg for figure files. They look bad \n", "# after zooming in just a little bit. It is far better to use vector graphics.\n", "# I always cringe when I see posters around the campus with low-resolution\n", "# figures that have been blown up and look blurry as hell. I use Inkscape \n", "# to create figures with vector graphics. Below is a svg, pdf, and eps export\n", "# of the last plot created. The eps files are espcially useful for including\n", "# in LaTeX documents.\n", "\n", "# Generate the plot.\n", "plt.errorbar(x, y, yerr = dy, fmt = 'ko', markerfacecolor = 'pink',\\\n", " fillstyle = 'top', capsize = 5, markersize= 8)\n", "plt.xlabel('x-axis label', fontsize = 14, fontname = 'Times')\n", "plt.ylabel('y-axis label', fontsize = 14, fontname = 'Times')\n", "plt.axis((0, 13, 0, 170))\n", "plt.text(2, 125, r'$p_n(\\mathbf{k})=\\frac{e^{-n\\hbar\\omega(k)/k_\\mathrm{B}T}}{Z(\\mathbf{k})}$',\\\n", " fontsize = 18)\n", "\n", "# Save the figures in the desired format.\n", "plt.savefig('Jupyter test_plot.svg', format='svg')\n", "plt.savefig('Jupyter test_plot.pdf', format='pdf')\n", "plt.savefig('Jupyter test_plot.eps', format='eps')\n", "\n", "# Note that the image files displayed in the html output of this script\n", "# are png files. They will not look good if you zoom in. I have included\n", "# copies of the svg, pdf, and eps files generated on the Python tutorial\n", "# website. You can compare the quality of these images with the corresponding \n", "# png and jpg files (also posted on the website)." ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }