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    "<div style=\"width:1000 px\">\n",
    "\n",
    "<div style=\"float:right; width:98 px; height:98px;\">\n",
    "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidata_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n",
    "</div>\n",
    "\n",
    "<h1>Primer Exercises</h1>\n",
    "<h3>Unidata Python Workshop</h3>\n",
    "\n",
    "<div style=\"clear:both\"></div>\n",
    "</div>\n",
    "\n",
    "<hr style=\"height:2px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the function `pressure_to_height_std`, can you calculate the height of the 700 millibar level assuming a standard atmosphere?"
   ]
  },
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   "execution_count": null,
   "metadata": {},
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  {
   "cell_type": "markdown",
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   "source": [
    "What is the `windchill` when the temperature is 263K and the winds are blowing at 20 m/s? (Bonus points: find it in Fahrenheit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using only values from `metpy.constants`, what is the dry adiabatic lapse rate? ($\\Gamma_d = \\frac{g}{C_{pd}}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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    "<hr style=\"height:2px;\">"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises 2"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Heat index is only defined for temperatures >= 80F and relative humidity values >= 40%. Using the data generated below, use boolean indexing to extract the data where heat index has a valid value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our import for numpy and metpy\n",
    "import numpy as np\n",
    "from metpy.units import units\n",
    "\n",
    "# Here's the \"data\"\n",
    "np.random.seed(19990503)  # Make sure we all have the same data\n",
    "temp = (20 * np.cos(np.linspace(0, 2 * np.pi, 100)) +\n",
    "        80 + 2 * np.random.randn(100)) * units.degF\n",
    "rh = (np.abs(20 * np.cos(np.linspace(0, 4 * np.pi, 100)) +\n",
    "              50 + 5 * np.random.randn(100))) * units('percent')\n",
    "\n",
    "\n",
    "# Create a mask for the two conditions described above\n",
    "# good_heat_index = \n",
    "\n",
    "\n",
    "\n",
    "# Use this mask to grab the temperature and relative humidity values that together\n",
    "# will give good heat index values\n",
    "# temp[] ?\n",
    "\n",
    "\n",
    "# BONUS POINTS: Plot only the data where heat index is defined by\n",
    "# inverting the mask (using `~mask`) and setting invalid values to np.nan"
   ]
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