2 Year Sounding Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sounding LCL Analysis\n",
    "\n",
    "For funzies I wanted to see how long it would take for a job like this to run and see what the best way to deal with the results is. Also maybe find some fun results when looking at several year's worth of data at a given site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import metpy.calc as mpcalc\n",
    "from metpy.io import get_upper_air_data\n",
    "from metpy.plots import SkewT\n",
    "from metpy.units import units\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sounding_parameters(ds):\n",
    "    date = ds.name.to_pydatetime()\n",
    "    station = 'OUN'\n",
    "    try:\n",
    "        dataset = get_upper_air_data(date, station)\n",
    "    except:\n",
    "        return (np.nan, np.nan, np.nan, np.nan, np.nan, np.nan)\n",
    "    \n",
    "    p = dataset.variables['pressure'][:]\n",
    "    T = dataset.variables['temperature'][:]\n",
    "    Td = dataset.variables['dewpoint'][:]\n",
    "    u = dataset.variables['u_wind'][:]\n",
    "    v = dataset.variables['v_wind'][:]\n",
    "    \n",
    "    try:\n",
    "        lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])\n",
    "    except:\n",
    "        lcl_pressure, lcl_temperature = np.nan * p.units, np.nan * T.units\n",
    "   \n",
    "    try:\n",
    "        lfc_pressure, lfc_temperature = mpcalc.lfc(p, T, Td)\n",
    "    except:\n",
    "        lfc_pressure, lfc_temperature = np.nan * p.units, np.nan * T.units\n",
    "\n",
    "        \n",
    "    return (lcl_pressure.m, lcl_temperature.m, lfc_pressure.m, lfc_temperature.m, T[0].m, Td[0].m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = datetime(2015, 1, 1, 0)\n",
    "end_date = datetime(2017, 1, 1, 0)\n",
    "index = pd.date_range(start_date, end_date, freq='D')\n",
    "columns = ['LCL Pressure', 'LCL Temperature', 'LFC Pressure', 'LFC Temperature', 'sfc Temperature', 'sfc Dewpoint']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(index=index, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnleeman/gitrepos/MetPy/metpy/calc/thermo.py:190: RuntimeWarning: invalid value encountered in absolute\n",
      "  if np.abs(new_p - p).max() < eps:\n",
      "/Users/johnleeman/anaconda/envs/metpydev/lib/python3.6/site-packages/pint/quantity.py:1236: RuntimeWarning: invalid value encountered in absolute\n",
      "  out = uf(*mobjs)\n"
     ]
    }
   ],
   "source": [
    "df = df.apply(get_sounding_parameters, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LCL Pressure</th>\n",
       "      <th>LCL Temperature</th>\n",
       "      <th>LFC Pressure</th>\n",
       "      <th>LFC Temperature</th>\n",
       "      <th>sfc Temperature</th>\n",
       "      <th>sfc Dewpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
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       "      <th>2015-01-02</th>\n",
       "      <td>968.853713</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2015-01-03</th>\n",
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       "      <th>2015-01-04</th>\n",
       "      <td>955.195090</td>\n",
       "      <td>3.581610</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>4.8</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>835.620477</td>\n",
       "      <td>-14.828926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.7</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>816.136082</td>\n",
       "      <td>-9.393748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2015-01-07</th>\n",
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       "      <td>-8.403980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2015-01-10</th>\n",
       "      <td>772.035172</td>\n",
       "      <td>-19.486589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2015-01-11</th>\n",
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       "      <td>-15.866314</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2015-01-12</th>\n",
       "      <td>976.489875</td>\n",
       "      <td>2.278404</td>\n",
       "      <td>944.439414</td>\n",
       "      <td>0.610824</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>924.231298</td>\n",
       "      <td>-5.409026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>845.328497</td>\n",
       "      <td>-9.796580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-7.8</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>828.995032</td>\n",
       "      <td>-10.385187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>770.706014</td>\n",
       "      <td>-5.837417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-17</th>\n",
       "      <td>756.085276</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-18</th>\n",
       "      <td>745.204612</td>\n",
       "      <td>-5.047392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-9.678409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.2</td>\n",
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       "      <td>840.542873</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2015-01-22</th>\n",
       "      <td>855.600890</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.8</td>\n",
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       "      <th>2015-01-23</th>\n",
       "      <td>928.682551</td>\n",
       "      <td>1.462024</td>\n",
       "      <td>871.096964</td>\n",
       "      <td>-1.766822</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.3</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-24</th>\n",
       "      <td>814.432281</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2015-01-25</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2015-01-26</th>\n",
       "      <td>766.001807</td>\n",
       "      <td>-5.837417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>714.150271</td>\n",
       "      <td>-1.912755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4</td>\n",
       "      <td>2.4</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>715.445918</td>\n",
       "      <td>-0.936808</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.4</td>\n",
       "      <td>3.4</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>755.162018</td>\n",
       "      <td>3.969422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.6</td>\n",
       "      <td>7.6</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>848.715420</td>\n",
       "      <td>-2.679613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>-0.6</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-03</th>\n",
       "      <td>880.858084</td>\n",
       "      <td>5.271303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.8</td>\n",
       "      <td>6.8</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-04</th>\n",
       "      <td>971.533230</td>\n",
       "      <td>6.433445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.5</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>864.231529</td>\n",
       "      <td>1.491617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.2</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-06</th>\n",
       "      <td>926.079735</td>\n",
       "      <td>5.306377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5.9</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-07</th>\n",
       "      <td>812.636889</td>\n",
       "      <td>-7.815716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-08</th>\n",
       "      <td>951.453608</td>\n",
       "      <td>-1.146029</td>\n",
       "      <td>947.655943</td>\n",
       "      <td>-1.357235</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.7</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-09</th>\n",
       "      <td>811.657627</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-10</th>\n",
       "      <td>832.577939</td>\n",
       "      <td>-11.965124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>865.005655</td>\n",
       "      <td>-2.074662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-12</th>\n",
       "      <td>908.110412</td>\n",
       "      <td>4.439745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5.4</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-13</th>\n",
       "      <td>822.459586</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-14</th>\n",
       "      <td>853.083658</td>\n",
       "      <td>-7.226720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-5.4</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-15</th>\n",
       "      <td>882.724142</td>\n",
       "      <td>-5.241629</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-3.8</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-16</th>\n",
       "      <td>971.855403</td>\n",
       "      <td>-1.627308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-1.5</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-17</th>\n",
       "      <td>928.302439</td>\n",
       "      <td>12.419845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.9</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-18</th>\n",
       "      <td>925.953450</td>\n",
       "      <td>-11.858245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.9</td>\n",
       "      <td>-11.2</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-19</th>\n",
       "      <td>820.994210</td>\n",
       "      <td>-20.935243</td>\n",
       "      <td>960.848243</td>\n",
       "      <td>-9.345474</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-18.7</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-20</th>\n",
       "      <td>735.407234</td>\n",
       "      <td>-22.269533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-18.8</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>756.898251</td>\n",
       "      <td>-9.364841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-22</th>\n",
       "      <td>755.779956</td>\n",
       "      <td>-5.638360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.8</td>\n",
       "      <td>-2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>858.267069</td>\n",
       "      <td>-4.653763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-24</th>\n",
       "      <td>918.322956</td>\n",
       "      <td>4.967512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>975.000000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>833.964762</td>\n",
       "      <td>4.633779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.8</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>713.732255</td>\n",
       "      <td>-8.745727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>757.120818</td>\n",
       "      <td>-6.030594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4</td>\n",
       "      <td>-2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>765.693049</td>\n",
       "      <td>-3.285119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>716.807544</td>\n",
       "      <td>-15.385708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>-11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>764.139733</td>\n",
       "      <td>-6.426444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.8</td>\n",
       "      <td>-3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>784.165885</td>\n",
       "      <td>-3.676952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.2</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LCL Pressure  LCL Temperature  LFC Pressure  LFC Temperature  \\\n",
       "2015-01-01    901.034822       -12.288410           NaN              NaN   \n",
       "2015-01-02    968.853713        -0.970307           NaN              NaN   \n",
       "2015-01-03    978.000000         2.400000           NaN              NaN   \n",
       "2015-01-04    955.195090         3.581610           NaN              NaN   \n",
       "2015-01-05    835.620477       -14.828926           NaN              NaN   \n",
       "2015-01-06    816.136082        -9.393748           NaN              NaN   \n",
       "2015-01-07    807.685878        -8.403980           NaN              NaN   \n",
       "2015-01-08    843.111902       -18.976874           NaN              NaN   \n",
       "2015-01-09    788.614942       -14.895150           NaN              NaN   \n",
       "2015-01-10    772.035172       -19.486589           NaN              NaN   \n",
       "2015-01-11    777.092831       -15.866314           NaN              NaN   \n",
       "2015-01-12    976.489875         2.278404    944.439414         0.610824   \n",
       "2015-01-13    924.231298        -5.409026           NaN              NaN   \n",
       "2015-01-14    845.328497        -9.796580           NaN              NaN   \n",
       "2015-01-15    828.995032       -10.385187           NaN              NaN   \n",
       "2015-01-16    770.706014        -5.837417           NaN              NaN   \n",
       "2015-01-17    756.085276        -5.050019           NaN              NaN   \n",
       "2015-01-18    745.204612        -5.047392           NaN              NaN   \n",
       "2015-01-19    670.067404        -9.678409           NaN              NaN   \n",
       "2015-01-20    638.871079       -11.771805           NaN              NaN   \n",
       "2015-01-21    840.542873         1.076075           NaN              NaN   \n",
       "2015-01-22    855.600890        -2.080949           NaN              NaN   \n",
       "2015-01-23    928.682551         1.462024    871.096964        -1.766822   \n",
       "2015-01-24    814.432281        -5.448801           NaN              NaN   \n",
       "2015-01-25    776.588546        -2.301852           NaN              NaN   \n",
       "2015-01-26    766.001807        -5.837417           NaN              NaN   \n",
       "2015-01-27    714.150271        -1.912755           NaN              NaN   \n",
       "2015-01-28    715.445918        -0.936808           NaN              NaN   \n",
       "2015-01-29    755.162018         3.969422           NaN              NaN   \n",
       "2015-01-30    848.715420        -2.679613           NaN              NaN   \n",
       "...                  ...              ...           ...              ...   \n",
       "2016-12-03    880.858084         5.271303           NaN              NaN   \n",
       "2016-12-04    971.533230         6.433445           NaN              NaN   \n",
       "2016-12-05    864.231529         1.491617           NaN              NaN   \n",
       "2016-12-06    926.079735         5.306377           NaN              NaN   \n",
       "2016-12-07    812.636889        -7.815716           NaN              NaN   \n",
       "2016-12-08    951.453608        -1.146029    947.655943        -1.357235   \n",
       "2016-12-09    811.657627       -15.793387           NaN              NaN   \n",
       "2016-12-10    832.577939       -11.965124           NaN              NaN   \n",
       "2016-12-11    865.005655        -2.074662           NaN              NaN   \n",
       "2016-12-12    908.110412         4.439745           NaN              NaN   \n",
       "2016-12-13    822.459586        -5.448301           NaN              NaN   \n",
       "2016-12-14    853.083658        -7.226720           NaN              NaN   \n",
       "2016-12-15    882.724142        -5.241629           NaN              NaN   \n",
       "2016-12-16    971.855403        -1.627308           NaN              NaN   \n",
       "2016-12-17    928.302439        12.419845           NaN              NaN   \n",
       "2016-12-18    925.953450       -11.858245           NaN              NaN   \n",
       "2016-12-19    820.994210       -20.935243    960.848243        -9.345474   \n",
       "2016-12-20    735.407234       -22.269533           NaN              NaN   \n",
       "2016-12-21    756.898251        -9.364841           NaN              NaN   \n",
       "2016-12-22    755.779956        -5.638360           NaN              NaN   \n",
       "2016-12-23    858.267069        -4.653763           NaN              NaN   \n",
       "2016-12-24    918.322956         4.967512           NaN              NaN   \n",
       "2016-12-25    975.000000        12.400000           NaN              NaN   \n",
       "2016-12-26    833.964762         4.633779           NaN              NaN   \n",
       "2016-12-27    713.732255        -8.745727           NaN              NaN   \n",
       "2016-12-28    757.120818        -6.030594           NaN              NaN   \n",
       "2016-12-29    765.693049        -3.285119           NaN              NaN   \n",
       "2016-12-30    716.807544       -15.385708           NaN              NaN   \n",
       "2016-12-31    764.139733        -6.426444           NaN              NaN   \n",
       "2017-01-01    784.165885        -3.676952           NaN              NaN   \n",
       "\n",
       "            sfc Temperature  sfc Dewpoint  \n",
       "2015-01-01             -5.1         -11.1  \n",
       "2015-01-02              0.0          -0.8  \n",
       "2015-01-03              2.4           2.4  \n",
       "2015-01-04              4.8           3.8  \n",
       "2015-01-05             -1.7         -12.7  \n",
       "2015-01-06              5.0          -7.0  \n",
       "2015-01-07              7.2          -5.8  \n",
       "2015-01-08             -5.9         -16.9  \n",
       "2015-01-09              1.8         -12.2  \n",
       "2015-01-10             -0.5         -16.5  \n",
       "2015-01-11              2.0         -13.0  \n",
       "2015-01-12              2.4           2.3  \n",
       "2015-01-13             -0.1          -4.5  \n",
       "2015-01-14              2.2          -7.8  \n",
       "2015-01-15              2.8          -8.2  \n",
       "2015-01-16             13.4          -2.6  \n",
       "2015-01-17             15.4          -1.6  \n",
       "2015-01-18             16.6          -1.4  \n",
       "2015-01-19             20.2          -4.8  \n",
       "2015-01-20             21.6          -6.4  \n",
       "2015-01-21             13.2           3.2  \n",
       "2015-01-22              8.8          -0.2  \n",
       "2015-01-23              6.2           2.3  \n",
       "2015-01-24              9.0          -3.0  \n",
       "2015-01-25             15.8           0.8  \n",
       "2015-01-26             13.4          -2.6  \n",
       "2015-01-27             23.4           2.4  \n",
       "2015-01-28             24.4           3.4  \n",
       "2015-01-29             24.6           7.6  \n",
       "2015-01-30              9.4          -0.6  \n",
       "...                     ...           ...  \n",
       "2016-12-03             13.8           6.8  \n",
       "2016-12-04              6.8           6.5  \n",
       "2016-12-05             11.2           3.2  \n",
       "2016-12-06              8.6           5.9  \n",
       "2016-12-07              6.6          -5.4  \n",
       "2016-12-08              1.4          -0.7  \n",
       "2016-12-09             -0.3         -13.3  \n",
       "2016-12-10              1.2          -9.8  \n",
       "2016-12-11              7.6          -0.4  \n",
       "2016-12-12              9.8           5.4  \n",
       "2016-12-13              7.8          -3.2  \n",
       "2016-12-14              3.6          -5.4  \n",
       "2016-12-15              3.2          -3.8  \n",
       "2016-12-16             -0.9          -1.5  \n",
       "2016-12-17             15.0          12.9  \n",
       "2016-12-18             -7.9         -11.2  \n",
       "2016-12-19             -6.7         -18.7  \n",
       "2016-12-20              0.2         -18.8  \n",
       "2016-12-21             11.0          -6.0  \n",
       "2016-12-22             14.8          -2.2  \n",
       "2016-12-23              6.2          -2.8  \n",
       "2016-12-24              9.6           5.8  \n",
       "2016-12-25             12.4          12.4  \n",
       "2016-12-26             16.8           6.8  \n",
       "2016-12-27             16.4          -4.6  \n",
       "2016-12-28             14.4          -2.6  \n",
       "2016-12-29             16.0           0.0  \n",
       "2016-12-30              9.6         -11.4  \n",
       "2016-12-31             12.8          -3.2  \n",
       "2017-01-01             13.2          -0.8  \n",
       "\n",
       "[732 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JzW+s5fFFO7l+XgGFB062OtfWsip6pMWTGh/j8h784vR+NDSbWb3Pv5h3nghE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\npDOvMmU1WKbIGU643Inch5fqjhcMzVTvURoNL+RFtpBIGQ9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GmHwXxVUUxSIOvNMD/BCY\n67R88VDjzgGMMd2A14CZIlJpMZrjRWQ0cAYwwxhzomMCKopiGwfqeQd8ztafFJFCoAaY5aCIiqI4\ngEPvdPxTL84BXnFKtkRR484mxpiO+ArBX0Xkb/7T0bZQi4qIbPV/lgKvA0e7I7GiKMniUD3fDGwW\nkS/8v1/lu511FEXxAE690/2cASwRkR3OSxobNe5sYIwx+ObPlIjIH0L+egO43P/9cuDvceLJ90/e\nxD9McxrwjfMSK4qSLE7VcxHZDmwyxgz1nzoVKHZYXEVRLOJUXQ/hR6RhSBZ0tawtjDHjgX8D/wVa\n/KdvxzdG/zIwBP8Wav5t0vYBFgE9/OGrgcOBfvh668A3dPOSiPwuVfehKEp0nKrnIlJpjBkFzAE6\nAWuBK0VkdyrvR1GUyDhc1/OATcD3RGRPau9EjTtFURRFUZSsQodlFUVRFEVRsgg17hRFURRFUbII\nNe4URVEURVGyCDXuFEVRFEVRsgg17hRFURRFUbIINe4URVEURVGyCDXuFEVRFEVRsgg17hRFURRF\nUbIINe4URVEURVGyCDXuFEVRFEVRsgg17hRFURRFUbIINe4URVEURVGyCDXuFEVRFEVRsgg17hRF\nURRFUbIINe4URVEURVGyCDXuFEVRFEVRsgg17hRFURRFUbKIjDPujDFdjDFfGmO+NsZ8a4z5tf/8\nQcaYL4wxq4wx840xndItq6IoiqIoSqrJOOMO2AucIiIjgVHAJGPMMcDvgUdE5FBgN3B1GmVUFEVR\nFEVJCxln3ImPav/Pjv5DgFOAV/3nXwDOS4N4iqIoiqIoaaVDugWwgjEmF1gMHAI8AawBKkSkyR9k\nMzA4wnXXAtcC5OfnjykoKEiNwIqiKIqiKDZYvHhxmYj0TyRsRhp3ItIMjDLG9AJeB4ZFChbhuqeB\npwHGjh0rixYtclVORVEURVEUJzDGbEg0bMYNy4YiIhXAh8AxQC9jTMBY3Q/Ymi65FEVRFEVR0kXG\nGXfGmP7+HjuMMV2BCUAJ8AFwkT/Y5cDf0yOhoiiKoihK+sjEYdlBwAv+eXc5wMsi8pYxphiYZ4z5\nLVAEPJtOIRVFURRFUdJBxhl3IrIMKIxwfi1wdOolUhRFURRF8Q4ZNyyrKIqiKIqiRMcx484Y09EY\n09up+BRFURRFUZTksWXc+bcC+7UxZjVQD+wM+e8oY8zLxpgRdoVUFEVRFEVREsPynDtjTD4+NyRj\ngNX4HAkfHBKkBDgLWAsssy6ioiiKoiiKkih2eu5ux2fYXScihwEvhf7p3yLsI3yuShRFURRFUZQU\nYMe4mwy8LyKz/b/b7AgBrMfnUFhRFEVRFEVJAXaMuyH49neNRSXQy0YaiqIoiqIoShLYMe5qgHgb\n2B4ElNtIQ1EURVEURUkCO8bdYuAMY0xepD+NMf2BScBnNtJQFEVRFEVRksCOcfcnYCDwf8aYIaF/\n+H/PBboBf7SRhqIoiqIoipIElo07EXkDeAjfath1wC8AjDHr/b9PAX4rIh/ZF1NRFEVRUkNtUREb\np11DbVFRukVRFEvYcmIsIrcA5wDvA8Z/DAQ+Bs4VkbttS6goiqIoKaTsidnUfPIJZU/Mjh9YUTyI\n7e3HROQtEZkoIt2BLiLSVUROFpE3HZBPURRFUVJKvxnTyR8/nn4zplu63u2eP+1ZVOJh2bgzxrxh\njLkj9JyINNgXSVHaL6q0FSX95BUWMmTOM+QVFiZ8TWjddbvnT3sWlXhY3n4MmAh845QgiqJ8p7Tr\nvvmG/Z+cndTLRVEUZ6gtKmLHffcDMPC2WQnVw0DdBYI9flZ6/gLGYb8Z06Omayd+pX1gZ1h2DTDY\nKUEUJdV4sZes34zp5PTqRUtFhbbKFSVNlD0xm/ply6hftizhehg6lGul5y807Xi9cnbiV9oHdoy7\n54AzjTGDnBJGUVKJF4c28goL2f/J2W3m+3jREFUUr2O13vSbMZ0uI0bQZcSIhHvHnDK47M73UxQA\nIxJpS9gELvQ5KX4WOBz4LfAVsJ0Ie8yKiOd2qRg7dqwsWrQo3WIoKSZ0yAOIO/zhFTZOu4aaTz4h\nf/x4hsx5Jt3iKEpGkEn1JpHhWKV9Y4xZLCJjEwlrZ87dDnyGnMFn5EVDbKajKI4ROi9myJxnklb4\n4XNxAnG6rZD7zZhOc2UlzZWV1BYVqfJXlARwa26aG4ZYuG5SFDvYGZb9m/94LeR7pON1mzIqimPY\nHfIIn4sTaWg3MBS0e/58y0Op4cNJeYWF5PbokdQcoFjxKUp7wO6q12j1Jla9t1rHoukmrbuKFSz3\nqInIRU4KoihOE6l1HVD2Vgn0oAW+h54PELritaWigsZt22jatYsBN86k99SpCaUTqRVvpxdCewUU\nJT61RUVs+tl0Wioqguci1ZvQuhjQM82VldQvW0ZzZSW5PXokPfUjmm6yUnd1iFfJqOFSY8z+wIvA\nPkAL8LSIPGaM6QPMBw4E1gNTRGR3uuRUvEEspZis8gsMx7bU1JCTn0+vCy8IXh8ed0Cpd584gaoF\nC6lduhSprmbHgw9RtWBhxDTD5YlkyIUr/2TmD4bHp8pfyXaslPGyJ2bTUlFBTq9eURtv0LouBub1\ndRkxgvzx42nctq2VkVfzySetDL5k65uVRp025pSMMu6AJuAXIrLEGNMdWGyMWQBcAbwnIvcbY2YB\ns4Bb0yin4gGiKcXw1nkiyi8wHBugdOPGqNeHKv7eU6eye/58djz4EDQ3R1W4VpTxjvvub/MSiXZ9\nuGGoyl/JdqyU8VCdETDCojWoAv+HX7NuytQ28TVXViYki1OjDZF0X6LGbqRw2hjMPCwbd8aYxxMM\nKiJyg9V0wiLaBmzzf68yxpTg87V3LnCSP9gLwIeocdfuiTXMEal1HovAcGxoz12gFy4evadOpWrB\nQp9y79CB7hMnRIw/9DORXseWmpqo1ydyP8mEV5RMw0oZj2dIRaqX4dcMvG1WK0NoyJxn2vSyRyK0\n0Wmnpy/afSRq7EYKp43BDERELB34hkVjHc2BT6tpxEn/QGAj0AOoCPtvd7zrx4wZI0p2UbNkiWy4\neprULFniSDgnqVmyRJaPO0aKhxbI8nHHJCTj2slTZO3kKW3Cbrh6mhQPLZC1k6dYuo903L+ieIF4\nZT/S/6HnrNSdeHEGCNTr5eOOkbWTp0jx0ALZcPU0R+4r0TCBcOG6x8u6tT0BLJJEbaREA7a5EI6I\nchwPzMTn824ucITVNGKk3Q1YDFzg/52QcQdcCywCFg0ZMsTRTFfSS6jxlKhCdCLNZBRZPBnD4wso\n+8C5ZF8w0cKFvkRUCSvZSiwDKpqOiPR/tGsC8ZfPmxezPgauX3X6JCkZPUZKjhwhJSNGSvHQAllx\nwolSV1LSxqAKlz1WYy+R+0omj0LjS7YBaVUOJTGSMe7srJb9Nsbfnxpj3gC+Bt4CYoVNCmNMR3zu\nV/4qIn/zn95hjBkkItv8O2aURpH5aeBp8DkxdkomJf1YGWp1Is3QoYqGzZupmP8y9SuW0+O00+h5\nzjmYTp2C4fMKC9nn7rvY+cijtNTW0rRrFx369m0TX2Bf2dBhpWT884Wv3gtcE6DfjOnBlbxlT8y2\n5Ouv7InZwQUjTs/D0fk9ihPEWnHefeIENk67pk0ZizSUG+2a8FXxoemE0m/GdESEuqIipLa21X/N\npaWsO+98cvLzaampocuoURHn+oXO+Y1UZ61Os4g23BptrmC8uqnTPbyD5R0qEorcmP8HHC4iox2K\nz+CbU1cuIjNDzj8I7JLvFlT0EZFbYsWlO1RkB7vnz6f0kUfpddGF7F2+IqUGQaii6zhgAOt/+COa\ndu+m44ABNG7diuncmZ4XnE/ngw7CdO5C/bffUvHyy2AMHQcPpkP//gy45WaA4Ercxh07kOrqNh71\nkzF4QlfvRZu3Y8eACsQf2APXae//mbSrgOJdYpVxK2Us/JpkGzmV771P2VNPYYyhx9lnUfn2O/T9\nybXUfPwx1e9/QHNVFQNvv43OhxwScUFDqPN0p3RcvHgD+nXAjTPpfNhhwTmBWjfTQzI7VDg+Fy70\nAB4Eah2Mbzy+HS+WAUv9x5lAX+A9YJX/s0+8uHTOXXYQOo/NLlbnizTX1cnq0ydJ8bDDZfXZP5Dq\nxYtlzQUXSvHQgtZHwTDZ9pt7pGHbtlbDF4HvdubRJXofducPhcYRbzjKKjpvp/2SqmdvZWqD0+Xe\n6pBotOtize0Llzk0bPhQarT/dDpH+iEVw7Lx8PeynQhUOxWniHyCb7uzSJzqVDpK5jDgxpnBlmUo\nVn1cJbsirOarr9h83fW07NkDQMOqVeya/ST73PGrYCvXdO/Ovvf+jvyjjiK3Vy+g7fBFwDGyE63y\nWCv+Qu8RIjtoTST+wFCxGz2ldh1NK5mLU6sy49X/RMtYpOkQgR68ZGSM5JMyktPjUB+XAX95NYsX\n0+XQQ4O6IVoeBc6HxhdwlxTwtRm4JnRIOaA7I63UjzZMbWckQKddpAY7rlCiDbV2APYHrgbG4htG\nVZQgTlbu3lOn0nvq1OAWPYE4Q33AHfTy/ITi6j5xArVLl9K4bVtC+7fuXbuWTT/5aat5NKZbt6AM\n+z85O2jgVcx/mR4TJwKR7z9RGa0SOoQE0XfXSPTZqGsExWlqi4porqyky4gRtudsOeXAPNYcvHgy\nhqYTqVEVcHocPq8tYHgG/eXV1QW3HRwy55mo6UeaJxeg48CBdBw1qtW1gbmCVQsWRt19I7whl0ij\nMVKYaHmhusM97PTcLcI3RBoN4w9zs400lCzDigPhRLC6RU/ofJOqBQuR6moaqqvZce999Jh0Ot1O\nPpnmXbvoMGAAHYcMoaWmll1znmH3S3Npqawkp1s3Ohx4ICY3l5z8/FY9bwEDL9y/VUDWRLYlc2qu\nTbT8ieULK1LvXCwj0QncbtVrr4F3CSwayB8/PuqzSXSeWywDLFDGa5cupfMhB5M3ciS9Jk+mQ79+\n5OTnU/7Xv9JcUcGAmTMj9vAl2+sXqXcs8D2vsDCqD7yBt81qtStO6IKOWI7KAzqjubLS548zwrzb\naLop0v0lolsDfkCbKysjNozj9QaGo/XUPnaMuz8Q2bhrAXYDXwIf+MeJFQVwb1VruMIIdSQaS5bQ\nFWgBBSVNTexdvZrSB/9L6YMPBcN3OuggQGhYv4GuY8bQtHMnA265hR6nnhI1jUgvguAWYP6hktJH\nHo1q3MVbJZco8ZRvgO4TJ1D3zTd0nzghrjNTN4Zm3W7VJxO/vmBSSyIv/URWqIY/t0hb+9UWFSHV\n1dR/vYz6/35D+QsvgjHk9u1Lc1kZ3U4+GWlpweTktJEh0XIRq3cs9Hs0YzGvsLBVj35gODiwmj5a\n2nmFhcEda6p69IhazvMKCxl07+/I6dqVlpoaRCC3W36b+0ykIReaZrTVvAH9E37/oWmFr0SOFFZJ\nkEQn52XboQsq0kP5vHmyfNwxUj5vXtywbk+ujuY7atMNM6Vk+JGy+/XXpewvz0nF//1f0C9V8dAC\nqXzvfUf8OZXPmyclY8bK6jPPiulUNZZD0WTyKBGZQydzR8qbWBOxnSAVz9zJ/FLcId6igViLGsKf\nW/jv5vp6WTH+BCkZVSiV778v9StXSsWbb8n6yy6X5WOPkp1//rO0tLRElS2ZchGvvCWrDxP15RlI\nt3rRImnZuzeiPLtffU2KhxZIyfAjpWRUoZSMKpSqTz6xdJ+J3GvAMfPayVPa/BdrUYfyHaTIifFo\nYJ84YQYCo62m4eahxl16iOcQNJrH9lRV8j3/fFeKhxbIziefan3+X/+SFcceJ1vuuCOqvFawokBD\nFbyTL5pQQzKSh/xIjlW9qICdksur9+cGXrtXO4Z1vHJaNmeOFA8tkOpPP7WUZqKr0RMh2dX+ycTf\nWF4uq087XXa9+L/Bc+svv0KKhxbImvMvkOVjxkrJyFGy5vwLZOWJ35eSkaNk47U/sXwv8Yhl3Hmt\n/HmVVBl3zcBdccL8Cpe2H7N7qHGXHqIp3mjGhJVdJ+woik3XXSerJ02S9VdelRJFk6ys4QavXaUY\nrSfOind/r5ApcnoJr+VZsuU6EYOrfN48WTtlqpSMGCkbrrnGdpqRSDYfk+m5s8K6H10sq04+RVoa\nGqS5ulpWnnzyd66Zjhge1CXFQwtk5cmnSPHwI6Vh+/Y28TiRN3biUOPPR6qMu5YEjLs71LhTYmHV\np1MicVp5UbU0Ncn6Sy/z1IsuFKeUXEtLi5Q995ysOn2Sb2hmzFhZecqpsnzsUbJ60hmy5fbbZffr\nr0tLc3PwmkRfROlWxOlOPxNxspHgdNyJEKvOB/4rPvwI3+fwI6Vhxw5X5EhX2Yvm427NeedL8dAC\nWT3pDFlz9g+kuGCY7H7lFalcuFAq3/+g1RB3xZtvSknhaFl74UWt6r1I6o3/8PvxWuMjXSRj3Lnm\n587PYKDK5TSUDCZ82X04VnyeWdkCJ3RCb4+zzqR+5crgRGIvkUx+xJr4bYyh/ptvady0CTp0QKqr\naaquho4d6dC3L1ULFrLntb9R/cGHDH74IUyHDlQtWBicHB5tAQik302K+slLHrt5FuuZp6I8xKrz\noQsbADr07UvHAQOSij/eIorQ/9NR9qItftpbUoLp1ImGdevofNhhDH7kD/SYNCl4XfeTTwII1ue9\na9exa/Zsdj7+OANmfuc71M62Yk74HNVtzZInKePOGHNT2KnjIpwDyAWGAJcCX1mUTWkHuPEithJn\nuB+qRIwYrxBtdWC0vWUDDLz9Nmq/+IKmnTsxXbuCMQy693f0nDQJEaH8L89R+uCDbO/Tm33uuith\nBetVRayrX90jnnEV7T+niFXnA24/ttx4E03btzPwjl8lHX88AzVRA9bpMpiI/8oe555Dbteu5Pbt\n69MLe/ZEdCNTW1RE+V//CsCuZ+bQe+pUOg4aBNjT01aM+0j7+WqDLUkS7eKT74Zim8M+Yx07geOS\nSSNVhw7LpgYvDpHF2qbHiXlsqSba6sBIQ93h99ZUUSFVH30UdXXgjocekuKhBVL27F/cv5EEsfp8\ndGinfbN2yhRZe+FFlq5NZDFSImXS6TKYTHyh83UjXRP4v2TsUVJcMEx2PPyHNnFYqXt28k7rbGtw\ncVj2B/5PA7wBvATMjRCuGdgFfC0iDUmmoWQokVql6R6ii0QkmcJbpm7K6nTrPbyVG9qKj+VMdMic\nZ9i7di3lL/4vOd27t3FU3G/GdPrfeCMNmzdT+sADmM6dyDvqKDofckhE/1+pwmqZ8mqPouI+DevX\nU//1MvrfeKOl6+P1XCXas+V0GQz1S5lo2qEOoKPJtmvOs+yeN4/eP5xKx333DYaxUvfi5U2sOLXO\nWicp405E3g58N8a8Bvxf6DmlfROpkiZTOaMZPW4ZQ+lSGE4bvKHbFcXbci383uM5Kh4y5xn2ve8+\nNu7cyY57futLb9w4hjw7B9PB7Sm7bQlsUdXp4IPjOmQOR+fieQc7dTqRa1tqa8nJywv+3j1vPnTo\nQK8Lzrclt12cLoOJzoMNTbu2qIiqBQtjytbh5l+y7oILWXfBhfSafBHS0IDp2JG+038GOKc7Y9Vn\nnUZhD8vNbxGZLCKvOimMkrlE2xcydG/C2qKimHEEjIqyJ2YndN4qASUWS2EE9qqNJ7MV+s2YTv74\n8WkxLsPvPZIs4edyunThgOeeY/Djj9Fv+s+o/eILtv/ud4GpGgnhVH4Gduxo2rUruN+mklkEtiC0\nWqfj6YPNN97EitFjWHnMseyeO5f6khLKX3iB7hMm0KF//6gyuVXf3SS8rka6j/BziejTTgceyAFz\n59J56FB2PTOH8hdeZNecZ5Hauri6Mxli1Wen9X67I9Hx22w7dM6dsyTiisCqc9B0zIFz0olqqkiV\nH6ntDzwgxUMLZPu990WdqxceXywHpsnIEOqzLJPmRSrfYdc5eaxysuv//b+gH7flRx39nU+3oQVS\n/Z8v4soUq75nwlzcSPcRfi7Z+2hpaJDmvXtlxfjxsmFaW/+AdohVnzMhv1MNqfBz50uHvsDvgaX4\nFk9URjj22EnDrUONO2dJt58rp7EjcyZOAk5G5paWFtn2299J8dAC2fbb30ljeXnc+BIx7jIx35Tk\ncVMfrDjmWN+igKPHSfWiRbLnH/+Q8rlzperfn8S8LhGZMqF8xlssZoeds2dL8dACqV+1yq6YikVS\nYtzh21psHb5VsWv8n1uAzXy3WrYY+MpqGm4eaty1P1JlZGaLMRvrPlpaWmTrXXcHncKWPv5Hadiy\nRZrr6yNem0iPm9V8y8T89jKZWk/qli+X4qEFsuq0012Rvb2Xs8bycikZMVK23DpL9q5fH3P/XSu0\n9/xNhFQZd0/gWxV7jv93cMcK4FDgA6AI6G41DTcPNe7aB9G214oWRvmOeD0VLc3NUv3FF8H9KouH\nFsjGn/7MVpyRiPd8MqFHJZNIpJfVi+lsueUWKSkcLU0VFY7Ep7Rl6513Bev6put/Li0NDY7FbXe4\nPpxs1OvJGHd2/BmcASwUkTcizONbBZwH9Af+x0YaiodIxaTjZNJIJGzopNxoCxniTdz1+mRrt+SL\nt/DD5OSQf/TRkJvrO5GbS/UHH7Dm7B9EndBtZTFJvOeTzgUqSmIkUkbtlOPGrVvZ8/Y79J48mdye\nPR2XTfHR/8aZDLzjDvpeM42qf/2LjVdeRdXChUhjo+24+82YjunWjZaKCnbcd7/t+Nr9goxErcDw\nA6gHHgz53QjcGxbmWWCd1TTcPLTnLnnc6HWxk4ZTk6DjzReM5vTTK6S756pmyRIpGTO21f6day64\n0DH5vNQC95IsTuPmYpVIZcDJ/UO333uvFB8xXBq2bHFENiU+5S+/LCtPONFX74cdLmVznk3ouua9\ne4NDus21tbJx+gzZfPPNUr9qlay5aLJjvblOTvPwSr0nRcOyO4E/hP1+PizMg0Ct1TTcPNS4Sx4r\nBTxZxZnMThGpqHDJDhWkQwm4mWY8RRf4vvrMs6R4aIGsOOlkKR52uJQMP1LqSkqihk+3krRKNhsC\nbg7H2m1kRQtb/dVXUj53nhQXDJMN067ROZsppqWxUSrff19WTzrDN1Q788aY4au/+EKWH3W0rJ0y\nRao+/ti3MKtgmJSMGCnFQwtk9aRJsubc82Tt5Clpex6JrDhOF8kYd8YXPnmMMYuBtSIy2f/7feBg\n4DAR2RsSpoeIHGopERcZO3asLFq0KN1iZD2hjiiBpJxSbpx2DTWffEL++PFp2zEiWUeaqZI5VUS6\nn9BzADWffEKXESPI7dHju/1sc3LoNXkyg379P3HjyySy2bFqwAl2lxEjojrBtoMTeRe+b3KHffel\naetWcvLy6HzEEdR99VXGlq1MZsPlV1D7xRcAHDh/Hl1Hjmz1v4iwa84cyh7/I+TkYDp1oqWqCoCe\n555D/xtvpPKdf1D25JNIYyNSX9/qOaay3kVKyyv13hizWETGJhQ4USsw/MDXK7cT6OD/fRW+RRWf\nA3cD7+FbcPF7q2m4eWjPXeqJ1fpJpivc6Za2kwstsq0XINJQXbSeuJolS2Tt5CmydvIUWX/FlbLi\nmGOl+ssvI66atZo/2Za/XsLt1cpO9H4E4lh56gQpOXKEFA8tkM03/UIatmyxVTYyuVx5QfaaJUtk\n/eVXSEnhaNlyyy1t/t/55FO+BVfTZ0jT7t3SsGVLcGFG3fLl38Xz1VdSXDBMVp50cqv78UrPWboh\nRcOyw4B7gMH+3wZ4hu/coLTg238232oaUdL9C1AKfBNyrg+wAFjl/+wdLx417uxTPm+3Dj/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MvHHaMK3eOkc2PvuuJiWV44WjZMuyblaYcTLR+yYePzePW9paFBVn7/JNlw5VWW40mkAav6wD5W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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dbb0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 30\n",
    "import matplotlib.dates as mdates\n",
    "years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "yearsFmt = mdates.DateFormatter('%Y')\n",
    "# Setup figure and axes\n",
    "# Generally plots is ~1.33x width to height (10,7.5 or 12,9)\n",
    "fig = plt.figure(figsize=(10,7.5))\n",
    "ax1 = plt.subplot(211)\n",
    "ax2 = plt.subplot(212)\n",
    "\n",
    "# Set labels and tick sizes\n",
    "ax2.set_xlabel(r'Date', fontsize=20)\n",
    "ax1.set_ylabel(r'LCL Pressure', fontsize=20)\n",
    "ax2.set_ylabel(r'LCL Temperature', fontsize=20)\n",
    "# Turns off chart clutter\n",
    "\n",
    "# Turn off top and right tick marks \n",
    "ax1.get_xaxis().tick_bottom()  \n",
    "ax1.get_yaxis().tick_left()  \n",
    "\n",
    "ax2.get_xaxis().tick_bottom()  \n",
    "ax2.get_yaxis().tick_left()      \n",
    "\n",
    "# Plotting\n",
    "ax1.scatter(df.index, df['LCL Pressure'], s =2)\n",
    "ax2.scatter(df.index, df['LCL Temperature'], s =2, color='tab:red')\n",
    "ax1.plot(df.index, np.convolve(df['LCL Pressure'], np.ones((N,))/N, mode='same'))\n",
    "ax2.plot(df.index, np.convolve(df['LCL Temperature'], np.ones((N,))/N, mode='same'), color='tab:red')\n",
    "\n",
    "ax1.set_ylim(1000, 600)\n",
    "ax2.set_ylim(-30, 30)\n",
    "\n",
    "ax1.xaxis.set_major_locator(years)\n",
    "ax1.xaxis.set_major_formatter(yearsFmt)\n",
    "ax1.xaxis.set_minor_locator(months)\n",
    "ax2.xaxis.set_major_locator(years)\n",
    "ax2.xaxis.set_major_formatter(yearsFmt)\n",
    "ax2.xaxis.set_minor_locator(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jTr+0da6r9UmtHzXnL7DcSZPN+nwu/THBbVv+X5GZyc5MmMjOTJhoVzbSbfkBsJ8JsI+U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\n6YNPQABhs2YSPncOLfv20VZtbTPBu9AWFwO4ZWsVnN8ildNXHXn2u8t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GQW0BlU2VDpeX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\niuK7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dba64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 30\n",
    "import matplotlib.dates as mdates\n",
    "years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "yearsFmt = mdates.DateFormatter('%Y')\n",
    "# Setup figure and axes\n",
    "# Generally plots is ~1.33x width to height (10,7.5 or 12,9)\n",
    "fig = plt.figure(figsize=(10,7.5))\n",
    "ax1 = plt.subplot(111)\n",
    "\n",
    "# Set labels and tick sizes\n",
    "ax1.set_xlabel(r'Date', fontsize=20)\n",
    "ax1.set_ylabel(r'Surface', fontsize=20)\n",
    "\n",
    "# Turns off chart clutter\n",
    "\n",
    "# Turn off top and right tick marks \n",
    "ax1.get_xaxis().tick_bottom()  \n",
    "ax1.get_yaxis().tick_left()    \n",
    "\n",
    "# Plotting\n",
    "ax1.scatter(df.index, df['sfc Temperature'], s =2, color='tab:red')\n",
    "ax1.scatter(df.index, df['sfc Dewpoint'], s =2, color='tab:green')\n",
    "ax1.plot(df.index, np.convolve(df['sfc Temperature'], np.ones((N,))/N, mode='same'), color='tab:red')\n",
    "ax1.plot(df.index, np.convolve(df['sfc Dewpoint'], np.ones((N,))/N, mode='same'), color='tab:green')\n",
    "\n",
    "ax1.set_ylim(-20,40)\n",
    "\n",
    "ax1.xaxis.set_major_locator(years)\n",
    "ax1.xaxis.set_major_formatter(yearsFmt)\n",
    "ax1.xaxis.set_minor_locator(months)\n",
    "ax2.xaxis.set_major_locator(years)\n",
    "ax2.xaxis.set_major_formatter(yearsFmt)\n",
    "ax2.xaxis.set_minor_locator(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:metpydev]",
   "language": "python",
   "name": "conda-env-metpydev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}