{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python: Plotting\n", "## Chapter 10 & 11 from the Alex DeCaria textbook\n", "\n", "Reading in and manipulating data is great, however, visualizing this data in some shape or form is often the end result for most programs! In this lecture we will learn how to plot up that data and edit different plotting elements to make our plots more visually appealing.\n", "- We learn how to make a simple 1-D line plot\n", "- Adjust different elements in our line plot \n", "- Plot data from the dateframe that we were working with last week.\n", "- Visualize data in other ways such as histograms, pie charts, and scatter plots.\n", "\n", "**Before starting:** Make sure that you open up a Jupyter notebook session using OnDemand and copy this file for your `atmos5340/module_9 subdirectory`!\n", "\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "# Basic plotting\n", "\n", "Before we can start plotting data, we must import the matplotlib plotting library, which contains classes and functions for creatig MATLAB-style plots and graphs. We will primarily be working with the `pyplot` submodule for today's lecture. In addition, lets go ahead and import NumPy and Pandas, since we will be utilizing functions from those modules as well." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from pandas.plotting import register_matplotlib_converters\n", "register_matplotlib_converters()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets create a dummy data set to initially work with before we start working with 'real' data. Using NumPy, create the following 1-D array:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.arange(0,100.5,0.5)\n", "y = 2.0*np.sqrt(x) \n", "plt.plot(x,y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What happened? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you call the function pyplot.show() is called, which can be shorthanded as `plt.show()`, no more changes can be made to our plot or its axes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "A matplotlib plot generally consists of a `figure` object and an `axes` object. In the example above, these were automatically generated for us using the `plt.plot()` function. However, if we want to have more control of our plot, such as adding muliple axes, we will want to have more control over our plotting routines. Here, we will want to store our plot as a figure object by setting setting plt.figure() to a variable:\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once a figure has been created, axes objected can be created and placed on our figure using `add_axes()` method for the figure object. Anyways more on this later, as this is used for slightly more advanced plots, which we are not quite ready for yet!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "To change the color of our line plot, we can simply note a color when executing the plt.plot() function. For example, what if we wanted a *red* line:\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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pb5KZdQF+D5zu7utq2Wd7M9vxh+fAycCcVAuVmPvkkxDuhx8Os2aFu24XLIDLLw8LoohIxtQb/GY2Avg7sK+ZlZnZ5UA/YEdgqpnNNrP+iX1bmNnExEd3B942sw+B94FX3H1SVs5C8ldZGVxxBey/P0yaBLffDosWhXH5224bdXUisVRvU8rdeybZPLCWfZcCXRPPFwHt06pO4mv16rDoycMPh1E7110Ht94Ku+0WdWUisad/Q0vj2rAh9NvfcQdUVEDPnuFGrDZtoq5MpGAo+KVxuMPLL4e1bOfPDzdcvfJK6NMXkUalYRKSfaWl4Uar7t3D63Hjwt22Cn2RSCj4JXs+/xwuuCAE/McfhymT//lPOP10jcUXiZC6eiTz1q4NF27vvz+8vu220MWz007R1iUigIJfMskdRo4MIV9WBiUlYVGU1q2jrkxEqlFXj2TGzJnwy1/C+edDURG89RaMGKHQF8lBCn5Jz5dfhrtrDz883Gn79NNhTp1jjom6MhGphbp6JDWVlWH1q7vuCith/fa3YX3b//qvqCsTkXoo+GXzvfYa9OkDn34Kp50W7r7dZ5+oqxKRBlJXjzTc0qXhTtvOncM0C6+8AhMmKPRF8oyCX+pXVQWPPgo//zmMHQt33glz5kDXrlFXJiIpUFeP1O2dd+Daa+HDD8MiKI89Fta8FZG8pRa/JLdiRZguuWNH+OorGD0aJk5U6IvEgIJffswdBg6EffeFIUPgpptg3jw46yxNsyASEw1ZiGWQmS03sznVtjU1s6lmtiDxuEstn+1iZvPNbKGZ3ZzJwiULFiwISx7+sDDKP/4Rpl3YYYeoKxORDGpIi38w0KXGtpuBae7eDpiWeP0jZtYEeJyw0Pp+QE8z2y+taiU7NmyAe++FAw8MYf/UU2H2zAMOiLoyEcmCeoPf3WcAK2ts7g4MSTwfApyR5KNHAAvdfZG7VwIjE5+TXFJaGu66veUW6NYtdOtccYUWNheJsVT/du/u7uUAicdk6+W1BL6o9rossU1ywdq14W7bDh3CSlhjx8KLL0Lz5lFXJiJZls3hnMmuBHqtO5v1BnoDtNbEXtn1+uthfp3Fi+Hqq0M3j6ZaECkYqbb4l5lZc4DE4/Ik+5QBe1R73QpYWtsXuvsAdy929+KioqIUy5I6ffttmGrhxBNh661hxgx48kmFvkiBSTX4xwO9Es97AeOS7PMB0M7M2prZ1kBJ4nMShRkzoH37EPS/+Q3Mnh2mURaRgtOQ4ZwjgL8D+5pZmZldDtwLdDazBUDnxGvMrIWZTQRw9yqgLzAZmAeMcve52TkNqdW6dXDDDdCpUxiH/+abYVK1n/wk6spEJCL19vG7e89a3joxyb5Lga7VXk8EJqZcnaTnnXfgkkvC+Pw+fcJqWNtvH3VVIhIxjdmLo/Xr4fe/D105lZUwbRr066fQFxFAk7TFz7x5YfnD2bPhyivhwQe1yLmI/Iha/HHhDv37w2GHhYXOx42DAQMU+iLyH9Tij4OKinC37fjxcPLJMHiwbsQSkVqpxZ/vpk6Fgw6CSZPgkUfg1VcV+iJSJwV/vlq/Pky5cPLJ0LQpvP9+GLapOXZEpB7q6slH//oXnHsuzJoVhmk+8IDG5YtIgyn4882YMXDppdCkSbiAe/rpUVckInlG/QL5orIydOWcfXZY9HzWLIW+iKREwZ8PFi8ON2M9+mgI/7fegjZtoq5KRPKUunpy3YQJcPHFsGlTWPD8rLOirkhE8pxa/Llq0ya46y741a9C637mTIW+iGSEWvy5aPXq0MofNy489u+vUTsikjEK/lwzfz6ccUaYUfPRR+G668J0yiIiGaLgzyUvvwwXXgjbbBNm1DzuuKgrEpEYUh9/LnCHP/4xDM9s1w5KSxX6IpI1KQe/me1rZrOr/aw2sxtq7NPJzFZV2+f29EuOmfXr4aKL4PbbQ2v/rbdAi82LSBal3NXj7vOBgwHMrAmwBBibZNe33L1bqseJtRUr4Mwz4e234U9/gltvVX++iGRdpvr4TwT+5e7/ztD3xd/8+XDaaWHu/OefD3PviIg0gkz18ZcAI2p57ygz+9DMXjWz/TN0vPw2fToceWQYtvnGGwp9EWlUaQe/mW0NnA68kOTtWcCe7t4eeAx4qY7v6W1mpWZWWlFRkW5ZuWvw4DCVcosW8N574ReAiEgjykSL/1Rglrsvq/mGu692928TzycCW5lZs2Rf4u4D3L3Y3YuLiooyUFaOcYf/+78ws2anTvC3v0HbtlFXJSIFKBPB35NaunnM7Kdm4WqlmR2RON5XGThmftm0KUyuduutYSH0V16BnXeOuioRKVBpXdw1s+2AzsBV1bZdDeDu/YFzgGvMrAr4Dihxd0/nmHmnshIuuQRGjAjh/9BDWiVLRCKVVvC7+zpg1xrb+ld73g/ol84x8tq334b586dMgXvvhd/9TsM1RSRymrIhW1atgq5d4d13YeBAuOyyqCsSEQEU/NmxciWccgrMng2jRoVWv4hIjlDwZ1pFBXTuDPPmhfVxf/WrqCsSEfkRBX8mlZfDSSfBokVhps2TT466IhGR/6Dgz5Ty8jA+f8kSePXV8FxEJAcp+DOhoiK09JcsgcmToWPHqCsSEamVgj9dX38dunQWLYKJExX6IpLzFPzpWLMGTj0V5s6F8ePh+OOjrkhEpF4K/lStWwfduoXVsl58Ebp0iboiEZEGUfCnoqoKevQIq2UNHx4WRxcRyRMK/s3lDtdeG/rzn3wSSkqirkhEZLNotrDNdffd8NRTYabNq6+OuhoRkc2m4N8cgwfD//4vXHxxWCNXRCQPKfgbasoUuPLKMF7/qac0y6aI5C0Ff0PMmRMmWttvPxg9GrbeOuqKRERSpuCvz1dfwemnw447hgu6O+0UdUUiImlJdwWuxcAaYCNQ5e7FNd434FGgK7AOuMTdZ6VzzEZVVQXnnRemYpgxA1q2jLoiEZG0ZWI45/HuvqKW904F2iV+OgBPJh7zw003wbRp4aJuh/wpW0SkLtnu6ukODPXgXWBnM2ue5WNmxogR8Oc/h3Vye/WKuhoRkYxJN/gdmGJmM82sd5L3WwJfVHtdltj2H8yst5mVmllpRUVFmmWlaf586N0bjjkGHngg2lpERDIs3eDv6O6HErp0+pjZsTXeTzbm0ZN9kbsPcPdidy8uKipKs6w0rFsXpmPYdlsYORK21M3NIhIvaQW/uy9NPC4HxgJH1NilDNij2utWwNJ0jpl1v/51GL753HO6mCsisZRy8JvZ9ma24w/PgZOBOTV2Gw9cbMGRwCp3L0+52mwbNQoGDgzTMZxyStTViIhkRTr9GLsDY8OITbYEhrv7JDO7GsDd+wMTCUM5FxKGc16aXrlZtHRpmHunQwe4886oqxERyZqUg9/dFwHtk2zvX+25A31SPUajcYfLL4fvv4ehQ9WvLyKxpoQDGDAAJk2Cfv1gn32irkZEJKs0ZcOiRXDjjdC5M1xzTdTViIhkXWEHvzv06QNbbBEu6m5R2P85RKQwFHZXz+jRoYvnkUdgjz3q319EJAYKt4m7Zg1cfz0cfDD07Rt1NSIijaZwW/y33w7l5TBmjEbxiEhBKcwW/0cfwV/+Eubj0aybIlJgCjP4b7klLKhyzz1RVyIi0ugKr49jxoywktZ990HTplFXIyLS6Aqrxe8eWvstWuiCrogUrMJq8U+YAO+8A3/9K2y3XdTViIhEonBa/Bs3hlk327WDS3N3rjgRkWwrnBb/qFFhnv2RI2GrraKuRkQkMoXR4ncPF3N/8YuwupaISAErjBb/1Knw4YcwaJDm4xGRglcYKfjAA2Ekz/nnR12JiEjk0ll6cQ8zm25m88xsrpldn2SfTma2ysxmJ35uT6/cFHzyCbz2Whi+uc02jX54EZFck05XTxXwW3eflVh7d6aZTXX3j2vs95a7d0vjOOkZMCBczL3ssshKEBHJJSm3+N293N1nJZ6vAeYBLTNVWEZ89x0MHgxnnQW77x51NSIiOSEjffxm1gY4BHgvydtHmdmHZvaqme1fx3f0NrNSMyutqKjIRFlhvv2vv4arrsrM94mIxEDawW9mOwCjgRvcfXWNt2cBe7p7e+Ax4KXavsfdB7h7sbsXFxUVpVtWMGIE7LkndOqUme8TEYmBtILfzLYihP4wdx9T8313X+3u3yaeTwS2MrNm6Ryzwb76CqZMgfPOA7NGOaSISD5IZ1SPAQOBee7+cC37/DSxH2Z2ROJ4X6V6zM0yZgxUVUFJSaMcTkQkX6QzqqcjcBHwTzObndh2K9AawN37A+cA15hZFfAdUOLunsYxG27kyDAvz8EHN8rhRETyRcrB7+5vA3X2obh7P6BfqsdI2erV8OabcNNN6uYREakhnnfuzpgRZuPs3DnqSkREck48g3/aNNh2Wzj66KgrERHJOfEM/tdeg2OOCeEvIiI/Er/gX7YszLt/4olRVyIikpPiF/zTp4fHk06Ktg4RkRwVv+D/5JMwkqd9+6grERHJSfEL/rIy+OlPtbyiiEgt4hn8rVpFXYWISM5S8IuIFBgFv4hIgYlX8K9ZA6tWKfhFROoQr+BfsiQ8KvhFRGoVr+AvKwuPCn4RkVrFM/hb5tbSvyIiuUTBLyJSYNJderGLmc03s4VmdnOS983M/pJ4/yMzOzSd49WrrAyaNdPkbCIidUhn6cUmwOPAqcB+QE8z26/GbqcC7RI/vYEnUz1eg2gop4hIvdJp8R8BLHT3Re5eCYwEutfYpzsw1IN3gZ3NrHkax6zbkiUKfhGReqQT/C2BL6q9Lkts29x9MkctfhGReqUT/MkWs625kHpD9gk7mvU2s1IzK62oqNj8ajZtgi5doGPHzf+siEgBSXmxdULrfY9qr1sBS1PYBwB3HwAMACguLk76y6FOW2wBzz672R8TESk06bT4PwDamVlbM9saKAHG19hnPHBxYnTPkcAqdy9P45giIpKmlFv87l5lZn2ByUATYJC7zzWzqxPv9wcmAl2BhcA64NL0SxYRkXSk09WDu08khHv1bf2rPXegTzrHEBGRzIrXnbsiIlIvBb+ISIFR8IuIFBgFv4hIgVHwi4gUGAsDb3KLmVUA/07x482AFRksJx/onOOv0M4XdM6ba093L2rIjjkZ/Okws1J3L466jsakc46/Qjtf0Dlnk7p6REQKjIJfRKTAxDH4B0RdQAR0zvFXaOcLOuesiV0fv4iI1C2OLX4REalDbIK/voXf48DM9jCz6WY2z8zmmtn1ie1NzWyqmS1IPO4Sda2ZZmZNzOwfZjYh8TrW52xmO5vZi2b2SeLP+6gCOOffJP6/nmNmI8xs27ids5kNMrPlZjan2rZaz9HMbklk2nwzOyVTdcQi+Bu48HscVAG/dfdfAEcCfRLneTMwzd3bAdMSr+PmemBetddxP+dHgUnu/nOgPeHcY3vOZtYS+DVQ7O4HEKZ6LyF+5zwY6FJjW9JzTPzdLgH2T3zmiUTWpS0WwU/DFn7Pe+5e7u6zEs/XEMKgJeFchyR2GwKcEU2F2WFmrYDTgKerbY7tOZvZTsCxwEAAd69092+I8TknbAn8xMy2BLYjrNYXq3N29xnAyhqbazvH7sBId1/v7p8R1jU5IhN1xCX4G3dR9xxgZm2AQ4D3gN1/WNks8bhbdJVlxZ+B3wGbqm2L8znvBVQAzyS6t542s+2J8Tm7+xLgQeBzoJywWt8UYnzO1dR2jlnLtbgEf4MXdY8DM9sBGA3c4O6ro64nm8ysG7Dc3WdGXUsj2hI4FHjS3Q8B1pL/XRx1SvRrdwfaAi2A7c3swmirilzWci0uwd/gRd3znZltRQj9Ye4+JrF5mZk1T7zfHFgeVX1Z0BE43cwWE7rwTjCz54j3OZcBZe7+XuL1i4RfBHE+55OAz9y9wt03AGOAo4n3Of+gtnPMWq7FJfgbsvB73jMzI/T7znP3h6u9NR7olXjeCxjX2LVli7vf4u6t3L0N4c/1dXe/kHif85fAF2a2b2LTicDHxPicCV08R5rZdon/z08kXMOK8zn/oLZzHA+UmNk2ZtYWaAe8n5EjunssfgiLun8K/Au4Lep6snSOxxD+qfcRMDvx0xXYlTAaYEHisWnUtWbp/DsBExLPY33OwMHadu2jAAAAaElEQVRAaeLP+iVglwI45z8AnwBzgGeBbeJ2zsAIwjWMDYQW/eV1nSNwWyLT5gOnZqoO3bkrIlJg4tLVIyIiDaTgFxEpMAp+EZECo+AXESkwCn4RkQKj4BcRKTAKfhGRAqPgFxEpMP8PK5L9y5zLYXQAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(x,y,'red')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Did you get a red line?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "We can also plot multiple lines/markers on a single plot just by calling the plt.plot() function multiple times before showing the figure.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# evenly sampled time at 200ms intervals\n", "t = np.arange(0., 5., 0.2)\n", "\n", "plt.plot(t, t, color=\"red\", linestyle = \"--\",linewidth=2) \n", "plt.plot(t, t**2, color=\"blue\", marker = \"o\",linewidth=0)\n", "plt.plot(t, t**3, color=\"green\", marker = \"^\",linewidth=0)\n", "plt.plot(t, t**(5/2), color=\"orange\", linestyle = \"-\",linewidth=5/2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "> I really nice quick guide on Python line and marker options can be found [here](https://towardsdatascience.com/all-your-matplotlib-questions-answered-420dd95cb4ff)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, the figure below may be useful as it identifies plotting elements and the terminology used to name them. This could be helpful, especially when googling questions:
\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "To add labels to our figure we can use the `plt.xlabel()`, `plt.ylabel()`, and `plt.title()` functions:\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = np.arange(0., 5., 0.2)\n", "\n", "plt.plot(t, t, color=\"red\", linestyle = \"--\",linewidth=2) \n", "plt.plot(t, t**2, color=\"blue\", marker = \"o\",linewidth=0)\n", "plt.plot(t, t**3, color=\"green\", marker = \"^\",linewidth=0)\n", "plt.plot(t, t**(5/2), color=\"orange\", linestyle = \"-\",linewidth=5/2)\n", "plt.xlabel('Time [seconds]')\n", "plt.ylabel('Velocity [m/s]')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "If labels are added to each plot element, we can use this to create a legend.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = np.arange(0., 5., 0.2)\n", "\n", "plt.plot(t, t, color=\"red\", linestyle = \"--\",linewidth=2,label=\"Blimp\") \n", "plt.plot(t, t**2, color=\"blue\", marker = \"o\",linewidth=0,label=\"Propeller plane\")\n", "plt.plot(t, t**3, color=\"green\", marker = \"^\",linewidth=0,label=\"737\")\n", "plt.plot(t, t**(5/2), color=\"orange\", linestyle = \"-\",linewidth=5/2,label=\"Airbus\")\n", "plt.xlabel('Time [seconds]')\n", "plt.ylabel('Velocity [m/s]')\n", "plt.title('Aircraft velocities')\n", "plt.legend(loc=\"upper left\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "Lets say we wanted to zoom in on the lower velocity values. How would we do this? Fortunately, maplotlib has a function for explicitly setting the y `ylim` and x `xlim` axis limits:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = np.arange(0., 5., 0.2)\n", "\n", "plt.plot(t, t, color=\"red\", linestyle = \"--\",linewidth=2,label=\"Blimp\") \n", "plt.plot(t, t**2, color=\"blue\", marker = \"o\",linewidth=0,label=\"Propeller plane\")\n", "plt.plot(t, t**3, color=\"green\", marker = \"^\",linewidth=0,label=\"737\")\n", "plt.plot(t, t**(5/2), color=\"orange\", linestyle = \"-\",linewidth=5/2,label=\"Airbus\")\n", "plt.xlabel('Time [seconds]')\n", "plt.ylabel('Velocity [m/s]')\n", "plt.title('Aircraft velocities')\n", "plt.legend(loc=\"upper left\")\n", "plt.ylim(0,20)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "Python's matplotlib library as has the ability to create multi-panel plots when the programmer utlizies the the `plt.subplot()` function. The first two arguments, `nrows`, `ncols`, defines the number of panels. These are usually inputed as integers. In the code snipit below, we tell matplotlib that we want 2 rows and 1 column, so a 'vertical' 2 panel plot. This function returns a figure and axes object. If you check the length of each of these objects using the `len()` function, unsuprisingly, they have a length = 2, since we wanted *two* panels as given by our arguements for the `plt.subplot()` function.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Define a function\n", "def f(t):\n", " return np.exp(-t) * np.cos(2*np.pi*t)\n", "\n", "#Create some data points...\n", "t1 = np.arange(0.0, 5.0, 0.1)\n", "t2 = np.arange(0.0, 5.0, 0.02)\n", "\n", "#Lets create a plot!\n", "fig, axs = plt.subplots(2, 1, constrained_layout=True)\n", "axs[0].plot(t1, f(t1),color=\"blue\", marker = \"o\",linewidth=0)\n", "axs[0].plot(t1, f(t1),color=\"black\", linestyle = \"-\")\n", "axs[0].set_title('subplot 1')\n", "axs[0].set_xlabel('distance (m)')\n", "axs[0].set_ylabel('Damped oscillation')\n", "fig.suptitle('This is a somewhat long figure title', fontsize=16)\n", "\n", "axs[1].plot(t2, np.cos(2*np.pi*t2),color='red',linestyle = \"--\")\n", "axs[1].set_xlabel('time (s)')\n", "axs[1].set_title('subplot 2')\n", "axs[1].set_ylabel('Undamped')\n", "plt.show()\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lets plot up some real data!\n", "\n", "Now that we can make some basic 1-D plots, lets play around with some real data now! From our last lecture, you may recall that we read in some meteorological data for WBB into a dataframe and played around with some of this data using Pandas. Now that we know how to plot data based on the example provided above, lets go back and plot up some of our meteorological data from last class!\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#Read in our csv file\n", "dat = pd.read_csv('../module_8/module8_WBB.csv',sep=',',skiprows=6,index_col=1,parse_dates=True)\n", " \n", "#Decompose wind into u- and v-components incase we do any sort of time averaging...\n", "u = -1*dat['wind_speed_set_1']*np.sin(dat['wind_direction_set_1']*(np.pi/180))\n", "v = -1*dat['wind_speed_set_1']*np.cos(dat['wind_direction_set_1']*(np.pi/180))\n", " \n", "dat['u_wind'] = u\n", "dat['v_wind'] = v " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "# Do it yourself!\n", "\n", "1) Make a plot of temperature from our pandas dataframe, which we just read in\n", "\n", "2) Can you make a panel plot with temperature and wind?\n", "\n", "3) Does the data tell you anything? Is there anything interesting about this data?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "# Other types of plots\n", "\n", "In addition to doing timeseries, matplotlib library also has the ability to make other types of plots such as histograms, pie charts, scatter plots and so on. Lets look at a few quick examples...\n", "

\n", "**Histograms:** Lets use our wind data and check out the distribution of winds during our downslope wind storm:\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Histogram of wind gusts')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "plt.hist(dat['wind_gust_set_1'],bins=20,edgecolor='black', linewidth=1,color='purple')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel('Wind speed [mph]',fontsize=14)\n", "plt.ylabel('Frequency [#]',fontsize=14)\n", "plt.title('Histogram of wind gusts',fontsize=18)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", "**Scatterplots:** What is the relationship between wind speeds and gusts? Do we see higher wind gusts when the wind is stronger? \n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 69.43)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "plt.scatter(dat['wind_speed_set_1'],dat['wind_gust_set_1'], marker='o')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel('Mean wind [mph]',fontsize=14)\n", "plt.ylabel('Wind gusts [mph]',fontsize=14)\n", "plt.xlim(0,np.max([dat['wind_gust_set_1'],dat['wind_speed_set_1']]))\n", "plt.ylim(0,np.max([dat['wind_gust_set_1'],dat['wind_speed_set_1']]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Categorial plots:** You may also want to create a plot that uses categorical variables in Matplotlib...\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 0. , 2.5, 5. , 7.5, 10. , 12.5, 15. , 17.5, 20. , 22.5]),\n", " )" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Create a dictionary of items we want to plot\n", "data = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20}\n", "names = list(data.keys())\n", "values = list(data.values())\n", " \n", "plt.bar(names, values,color=\"red\",edgecolor='black',linewidth=1)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Box and whisker plots:**\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'whiskers': [,\n", " ],\n", " 'caps': [,\n", " ],\n", " 'boxes': [],\n", " 'medians': [],\n", " 'fliers': [],\n", " 'means': []}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Fixing random state for reproducibility\n", "np.random.seed(19680801)\n", "\n", "#fake up some data\n", "spread = np.random.rand(50) * 100\n", "center = np.ones(25) * 50\n", "flier_high = np.random.rand(10) * 100 + 100\n", "flier_low = np.random.rand(10) * -100\n", "data = np.concatenate((spread, center, flier_high, flier_low))\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.set_title('Basic Plot')\n", "ax1.boxplot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Contour plots:** Finally, matplotlib can also plot maps and contour maps. More on this next week....!\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "\n", "# Want more practice!?\n", "\n", "Check out the following webpages:
\n", "https://matplotlib.org/2.0.2/users/pyplot_tutorial.html
\n", "https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python
\n", "https://pythonprogramming.net/matplotlib-python-3-basics-tutorial/
\n", "https://www.w3schools.com/PYTHON/matplotlib_intro.asp
\n", "https://www.johnny-lin.com/pyintro/ed01/free_pdfs/ch09.pdf
\n", "https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html
\n", "\n" ] }, { "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 4 }