intro/python-plotting-intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required packages\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pylab import cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use numpy.loadtxt to import our data\n",
    "wavelength, samp_1_abs, samp_2_abs = np.loadtxt('Absorbance_Data.csv', unpack=True, delimiter=',', skiprows=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and add axes object\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "# Plot and show our data\n",
    "ax.plot(wavelength, samp_1_abs)\n",
    "ax.plot(wavelength, samp_2_abs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fonts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['STIXSizeFourSym', 'DejaVu Sans Mono', 'cmr10', 'DejaVu Sans Mono', 'DejaVu Serif', 'STIXSizeFiveSym', 'STIXNonUnicode', 'STIXSizeFourSym', 'STIXSizeTwoSym', 'STIXGeneral', 'DejaVu Serif', 'cmmi10', 'DejaVu Sans Mono', 'cmtt10', 'cmex10', 'cmsy10', 'STIXGeneral', 'STIXGeneral', 'DejaVu Sans', 'STIXNonUnicode', 'STIXSizeThreeSym', 'DejaVu Serif', 'STIXSizeThreeSym', 'STIXSizeOneSym', 'STIXNonUnicode', 'STIXGeneral', 'cmss10', 'STIXSizeTwoSym', 'DejaVu Sans', 'DejaVu Sans Mono', 'cmb10', 'STIXSizeOneSym', 'STIXNonUnicode', 'DejaVu Serif', 'DejaVu Sans', 'DejaVu Serif Display', 'DejaVu Sans', 'DejaVu Sans Display', 'Didot', 'Silom', 'Luxi Mono', 'Luxi Mono', 'Raanana', 'Arial Black', '.SF NS Display Condensed', 'Bitstream Vera Serif', 'Hiragino Maru Gothic Pro', 'Chalkboard', 'Kohinoor Telugu', 'Bitstream Vera Sans Mono', 'Ayuthaya', 'Bradley Hand', 'Bitstream Vera Sans Mono', 'Courier New', 'Lao MN', 'System Font', 'Avenir Next Condensed', 'Beirut', '.SF Compact Display', 'Phosphate', 'Serto Batnan', '.SF NS Rounded', 'Snell Roundhand', 'Serto Jerusalem', 'PT Mono', '.SF NS Display Condensed', 'Luxi Mono', 'STIXSizeOneSym', 'Serto Jerusalem Outline', 'Luxi Serif', '.SF NS Symbols', '.SF NS Text Condensed', 'Estrangelo TurAbdin', 'Myriad Pro', 'Hiragino Sans', 'Georgia', 'Estrangelo Nisibin Outline', 'Al Tarikh', 'Goha-Tibeb Zemen', 'Sinhala Sangam MN', 'System Font', 'Gujarati Sangam MN', 'Hoefler Text', '.SF NS Symbols', 'Farah', 'Estrangelo Quenneshrin', 'Verdana', 'Charter', 'Luxi Sans', 'Webdings', '.Aqua Kana', 'Myriad Pro', 'Verdana', 'Herculanum', 'Marion', 'InaiMathi', 'Bitstream Vera Sans Mono', 'Luxi Sans', 'Goha-Tibeb Zemen', 'Serto Jerusalem', '.SF Compact Text', 'KufiStandardGK', '.Arabic UI Display', 'Serto Jerusalem', 'Bitstream Vera Sans Mono', 'Courier New', 'Serto Urhoy', '.SF NS Display Condensed', 'Sukhumvit Set', 'Arial Rounded MT Bold', 'Zapf Dingbats', 'Apple Braille', 'Myriad Pro', 'Comic Sans MS', '.SF NS Display Condensed', '.SF Compact Text', 'Trebuchet MS', 'Lao Sangam MN', 'STIXSizeThreeSym', 'Bitstream Vera Sans', 'Luxi Sans', 'STIXSizeThreeSym', 'STIXSizeFourSym', 'Wingdings', 'Bitstream Vera Sans', 'Arial', 'System Font', 'Diwan Kufi', 'Arial Unicode MS', 'Bitstream Vera Sans', 'STIXSizeFiveSym', 'STIXGeneral', '.SF Compact Text', 'Devanagari Sangam MN', 'Serto Mardin', 'Serto Kharput', 'Hiragino Sans', 'Baskerville', 'Apple Chancery', 'Bodoni 72 Smallcaps', 'Brush Script MT', 'Tahoma', 'Kohinoor Bangla', 'Malayalam Sangam MN', 'Kohinoor Devanagari', 'Hiragino Sans', 'Bangla Sangam MN', '.SF Compact Rounded', 'Verdana', 'Serto Mardin', 'PT Sans', '.SF Compact Display', 'Palatino', 'Estrangelo Nisibin', 'Estrangelo Talada', '.SF Compact Display', 'Telugu MN', '.SF Compact Text', 'DIN Condensed', 'Iowan Old Style', '.SF Compact Rounded', 'Hiragino Sans', 'Estrangelo Edessa', 'Mishafi Gold', 'Optima', 'Luxi Serif', 'Arial Narrow', '.SF Compact Text', 'Songti SC', 'Apple Braille', 'Arial', '.SF Compact Text', 'Hiragino Sans', 'Luxi Mono', 'Superclarendon', 'Courier New', 'Gurmukhi MN', 'Symbol', 'STIXIntegralsUpD', 'STIXSizeTwoSym', 'Tamil Sangam MN', 'System Font', '.SF NS Symbols', '.SF Compact Text', '.SF Compact Rounded', 'Sinhala MN', 'System Font', 'Helvetica Neue', 'Arial Narrow', '.SF NS Symbols', 'Myriad Pro', 'Kefa', 'Heiti TC', 'Luxi Serif', 'East Syriac Ctesiphon', 'Hoefler Text', 'Serto Malankara', 'Apple SD Gothic Neo', 'Hiragino Sans', 'Noto Nastaliq Urdu', 'Arial', 'STIXIntegralsUpSm', 'Waseem', 'SignPainter', 'STIXVariants', 'STIXGeneral', 'AppleMyungjo', '.SF Compact Display', 'Serto Batnan', 'System Font', '.SF Compact Text', 'Tahoma', 'STIXIntegralsSm', '.SF Compact Rounded', 'Luxi Sans', '.SF NS Display Condensed', '.SF NS Text Condensed', 'Estrangelo Midyat', 'Myanmar Sangam MN', 'Helvetica', '.LastResort', 'PT Serif Caption', 'Damascus', 'Hiragino Sans', 'Lucida Grande', 'Diwan Thuluth', 'Bodoni Ornaments', 'Papyrus', 'Estrangelo Antioch', 'Luxi Serif', 'STIXSizeOneSym', 'System Font', 'Bitstream Vera Serif', 'Georgia', 'New Peninim MT', 'Muna', '.SF NS Symbols', '.SF Compact Rounded', '.SF NS Display Condensed', 'System Font', '.SF Compact Text', 'Oriya MN', 'Wingdings 2', 'Apple Braille', 'Gurmukhi MT', 'Serto Urhoy', 'PT Serif', 'Courier New', 'GFS Neohellenic', 'Apple Braille', 'Plantagenet Cherokee', 'Krungthep', '.SF Compact Rounded', 'System Font', '.SF NS Symbols', '.SF Compact Text', 'East Syriac Adiabene', 'Khmer Sangam MN', 'Kokonor', 'STIXIntegralsD', 'Thonburi', 'STIXIntegralsUpSm', 'Trebuchet MS', '.SF NS Display Condensed', 'Farisi', 'Avenir', 'Hiragino Mincho ProN', '.SF Compact Display', 'Tamil MN', '.SF Compact Text', 'Hiragino Sans', 'Trattatello', 'Noteworthy', 'Gurmukhi Sangam MN', 'STIXNonUnicode', 'ITF Devanagari', 'DecoType Naskh', 'STIXSizeFourSym', 'Luminari', 'STIXNonUnicode', 'Mishafi', 'AppleGothic', 'Product Sans', 'Menlo', '.SF NS Symbols', 'Bitstream Vera Sans', 'Futura', 'Skia', '.SF Compact Rounded', 'Al Nile', 'System Font', 'Heiti TC', 'Bangla MN', 'System Font', 'System Font', 'DIN Alternate', '.SF Compact Display', 'Myriad Pro', 'Rockwell', 'Devanagari MT', 'STIXIntegralsUpD', 'American Typewriter', 'Georgia', 'STIXVariants', 'Kannada MN', 'Baghdad', 'Arial', 'Myanmar MN', 'Chalkduster', 'Shree Devanagari 714', 'STIXNonUnicode', 'Arial Narrow', 'Microsoft Sans Serif', '.SF NS Text Condensed', 'Verdana', 'STIXIntegralsSm', 'Trebuchet MS', 'Myriad Pro', 'Times New Roman', 'Arial Hebrew', 'Copperplate', '.SF NS Symbols', 'Big Caslon', '.SF Compact Display', '.SF Compact Display', 'PingFang HK', 'Telugu Sangam MN', 'Product Sans', 'Marker Felt', 'Myriad Pro', 'Sana', 'Apple Braille', '.Arabic UI Text', 'Al Bayan', '.SF Compact Display', 'Avenir Next', '.SF Compact Rounded', 'STIXIntegralsD', 'STIXSizeTwoSym', 'Mshtakan', 'Euphemia UCAS', 'Gill Sans', 'Sathu', 'Geeza Pro', 'Hiragino Sans', 'Arial Narrow', 'Myriad Pro', 'Comic Sans MS', 'Athelas', 'Malayalam MN', 'STIXIntegralsUp', 'Bodoni 72', 'Times New Roman', 'Savoye LET', 'STIXNonUnicode', 'Corsiva Hebrew', 'Hiragino Sans GB', 'Oriya Sangam MN', '.SF NS Text Condensed', 'Myriad Pro', '.SF Compact Text', 'Bodoni 72 Oldstyle', 'STIXIntegralsUp', '.SF NS Text Condensed', 'Georgia', 'Nadeem', 'Khmer MN', 'Gujarati MT', '.Keyboard', '.SF NS Display Condensed', '.SF NS Symbols', 'Times New Roman', 'Trebuchet MS', 'Myriad Pro', 'Times', 'Kannada Sangam MN', 'Wingdings 3', 'Andale Mono', 'STIXGeneral', 'Impact', 'System Font', '.SF NS Text Condensed', 'Product Sans', 'Zapfino', '.SF NS Display Condensed', 'Cochin', 'Kailasa', 'STIXGeneral', 'Apple Symbols', 'Times New Roman', 'Seravek', 'Chalkboard SE', 'System Font', '.SF Compact Rounded', '.Helvetica Neue DeskInterface', 'System Font', 'System Font', 'Hiragino Sans']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "\n",
    "# Collect all the font names available to matplotlib\n",
    "font_names = [f.name for f in fm.fontManager.ttflist]\n",
    "print(font_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Plot Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Edit the font, font size, and axes width\n",
    "mpl.rcParams['font.family'] = 'Avenir'\n",
    "plt.rcParams['font.size'] = 18\n",
    "plt.rcParams['axes.linewidth'] = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Set of Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate 2 colors from the 'Set1' colormap\n",
    "colors = cm.get_cmap('tab10', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Figure and Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure object and store it in a variable called 'fig'\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "\n",
    "# Add axes object to our figure that takes up entire figure\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Axis Panels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure object and store it in a variable called 'fig'\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "\n",
    "# Add two axes objects to create a paneled figure\n",
    "ax1 = fig.add_axes([0, 0, 1, 0.4])\n",
    "ax2 = fig.add_axes([0, 0.6, 1, 0.4])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Axis Insets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure object and store it in a variable called 'fig'\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "\n",
    "# Add two axes objects to create an inset figure\n",
    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
    "ax2 = fig.add_axes([0.5, 0.5, 0.4, 0.4])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Spines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure object and store it in a variable called 'fig'\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "\n",
    "# Add axes object to our figure that takes up entire figure\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "# Hide the top and right spines of the axis\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Edited Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to Convert Energy to Wavelength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert energy (eV) to wavelength (nm)\n",
    "def E_to_WL(E):\n",
    "    return [1240/i for i in E]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure object and store it in a variable called 'fig'\n",
    "fig = plt.figure(figsize=(3, 3))\n",
    "\n",
    "# Add axes object to our figure that takes up entire figure\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "# Create new axes object by cloning the y-axis of the first plot\n",
    "ax2 = ax.twiny()\n",
    "\n",
    "# Edit the major and minor ticks of the x and y axes\n",
    "ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in')\n",
    "ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in')\n",
    "ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')\n",
    "ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')\n",
    "\n",
    "# Edit the tick parameters of the energy x-axis\n",
    "ax2.xaxis.set_tick_params(which='major', size=10, width=2, direction='in')\n",
    "ax2.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in')\n",
    "\n",
    "# Plot the two sample absorbances, using previously generated colors\n",
    "ax.plot(wavelength, samp_1_abs, linewidth=2, color=colors(0), label='Sample 1')\n",
    "ax.plot(wavelength, samp_2_abs, linewidth=2, color=colors(1), label='Sample 2')\n",
    "\n",
    "# Edit the major and minor tick locations of x and y axes\n",
    "ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(100))\n",
    "ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(50))\n",
    "ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.5))\n",
    "ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))\n",
    "\n",
    "# Add ticks manually to energy axis\n",
    "ax2.xaxis.set_major_locator(mpl.ticker.FixedLocator(E_to_WL(np.linspace(1.5, 3.0, 4))))\n",
    "ax2.xaxis.set_minor_locator(mpl.ticker.FixedLocator(E_to_WL(np.linspace(1.4, 3.2, 19))))\n",
    "\n",
    "# Add tick labels manually to energy axis\n",
    "ax2.set_xticklabels(['1.5', '2.0', '2.5', '3.0'])\n",
    "\n",
    "# Add the x and y-axis labels\n",
    "ax.set_xlabel(r'$\\mathregular{\\lambda}$ (nm)', labelpad=10)\n",
    "ax.set_ylabel('Absorbance (O.D.)', labelpad=10)\n",
    "\n",
    "# Add energy axis label\n",
    "ax2.set_xlabel('Energy (eV)', labelpad=10)\n",
    "\n",
    "# Set the axis limits\n",
    "ax.set_xlim(370, 930)\n",
    "ax.set_ylim(-0.2, 2.2)\n",
    "\n",
    "# Set the energy axis limits\n",
    "ax2.set_xlim(370, 930)\n",
    "\n",
    "# Add legend to plot\n",
    "ax.legend(bbox_to_anchor=(1, 1), loc=1, frameon=False, fontsize=16)\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Final_Plot.png', dpi=300, transparent=False, bbox_inches='tight')\n",
    "\n",
    "# Show figure\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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}