lectures/lecture_05/lecture_5_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I present the code to replicate statically the plot here:\n",
    "\n",
    "[http://www.swpc.noaa.gov/products/real-time-solar-wind](http://www.swpc.noaa.gov/products/real-time-solar-wind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Gather Data\n",
    "import json, urllib.request\n",
    "\n",
    "with urllib.request.urlopen('https://services.swpc.noaa.gov/products/solar-wind/plasma-2-hour.json') as url:\n",
    "    plasmadata = json.loads(url.read().decode())\n",
    "    \n",
    "with urllib.request.urlopen('https://services.swpc.noaa.gov/products/solar-wind/mag-2-hour.json') as url:\n",
    "    magdata = json.loads(url.read().decode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Organize Data\n",
    "Bz = [float(entry[3]) for entry in magdata[1:]]\n",
    "Bt = [float(entry[6]) for entry in magdata[1:]]\n",
    "density = [float(entry[1]) for entry in plasmadata[1:]]\n",
    "speed = [float(entry[2]) for entry in plasmadata[1:]]\n",
    "temperature = [float(entry[3]) for entry in plasmadata[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Create date lists.\n",
    "import datetime\n",
    "\n",
    "plasmadates = [datetime.datetime.strptime(row[0][:-4], '%Y-%m-%d %H:%M:%S') for row in plasmadata[1:]]\n",
    "magdates = [datetime.datetime.strptime(row[0][:-4], '%Y-%m-%d %H:%M:%S') for row in magdata[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c3cc7f0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Step 4: Plot one of the fields versus time.\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(plasmadates, density, 'o', color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Temperature')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1280 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extra: Recreate webpage-like plot of values (ignoring the derived calulated field of Phi)\n",
    "f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k', sharex=True)\n",
    "\n",
    "# Magnetic Field Properties\n",
    "ax1.plot(magdates, Bt, 'ko', markersize=1.5)\n",
    "ax1.plot(magdates, Bz, 'ro', markersize=1.5)\n",
    "\n",
    "# Plasma Field Properties\n",
    "ax2.plot(plasmadates, density, 'o', color='orange', markersize=1.5)\n",
    "ax3.plot(plasmadates, speed, 'o', color='mediumpurple', markersize=1.5)\n",
    "ax4.plot(plasmadates, temperature, 'o', color='lime', markersize=1.5)\n",
    "ax2.set_ylabel('Density', color='orange')\n",
    "ax3.set_ylabel('Speed', color='mediumpurple')\n",
    "ax4.set_ylabel('Temperature', color='lime')"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}