{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cff183fe-2268-4845-a491-3cdc39375f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import matplotlib\n",
    "#matplotlib.use('agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import logging\n",
    "\n",
    "# Redirect warnings to log\n",
    "logging.captureWarnings(True)\n",
    "\n",
    "from metpy.plots import StationPlot, USCOUNTIES\n",
    "from metpy.units import units\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e67af499-0e77-4044-9977-1083c68b0058",
   "metadata": {},
   "source": [
    "In this notebook, we will be working with normals data from NOAA/NCEI.\n",
    "\n",
    "Data is tarred and zipped in the following location:\n",
    "https://www.ncei.noaa.gov/data/normals-monthly/1991-2020/archive/\n",
    "\n",
    "Download and unzip the data in the directory you will be working in. In this example, I have placed the temperature normals files in a temperature/ folder and the precipitation normals files in a precipitation/ folder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe9b4fb-9d3c-4a62-9340-358f400c32a0",
   "metadata": {},
   "source": [
    "First, we are going to read in the inventory of monthly stations in the temperature directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ecc69cf-7511-4960-ab67-7933a25f8e2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
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      ],
      "text/plain": [
       "          GHCN_ID      lat       lon   elev state              stnName    a  \\\n",
       "0     AQW00061705 -14.3306 -170.7136    3.7    AS     PAGO PAGO WSO AP  GSN   \n",
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       "4     CQC00914855  15.1167  145.7167   65.5    MP       SAIPAN INTL AP  NaN   \n",
       "...           ...      ...       ...    ...   ...                  ...  ...   \n",
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       "7302  VQW00011624  17.7028  -64.8056   18.6    VI     CHRISTIANSTED AP  NaN   \n",
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       "7304  WQW00041606  19.2833  166.6500    4.3    UM          WAKE ISLAND  NaN   \n",
       "\n",
       "        b        c  \n",
       "0     NaN  91765.0  \n",
       "1     NaN      NaN  \n",
       "2     NaN      NaN  \n",
       "3     NaN  91221.0  \n",
       "4     NaN  91232.0  \n",
       "...   ...      ...  \n",
       "7300  NaN      NaN  \n",
       "7301  NaN      NaN  \n",
       "7302  NaN  78551.0  \n",
       "7303  NaN  78543.0  \n",
       "7304  NaN  91246.0  \n",
       "\n",
       "[7305 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_url = 'temperature/mly_inventory.txt'\n",
    "\n",
    "# inventory is a text file, fixed width, with no header. Add in column names\n",
    "temp_stns = pd.read_fwf(inv_url, header=None, names=['GHCN_ID','lat','lon','elev','state','stnName','a','b','c'])\n",
    "\n",
    "temp_stns  ## see what's in the file, you will see 7,305 rows (one for each station)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd4d42d2-93a0-48f3-a680-cf3e269a23a3",
   "metadata": {
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   "outputs": [
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       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7305 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          GHCN_ID  month  day  hour  ANN-TAVG-NORMAL  \\\n",
       "0     CAW00064757     99   99    99             44.1   \n",
       "1     AQW00061705     99   99    99             82.3   \n",
       "2     CQC00914080     99   99    99             78.8   \n",
       "3     CQC00914801     99   99    99             80.3   \n",
       "4     CQC00914855     99   99    99             81.6   \n",
       "...           ...    ...  ...   ...              ...   \n",
       "7300  USW00096409     99   99    99             18.9   \n",
       "7301  VQC00671740     99   99    99             81.1   \n",
       "7302  VQW00011624     99   99    99             80.7   \n",
       "7303  VQW00011640     99   99    99             81.9   \n",
       "7304  WQW00041606     99   99    99             81.2   \n",
       "\n",
       "     meas_flag_ANN-TAVG-NORMAL comp_flag_ANN-TAVG-NORMAL  \\\n",
       "0                                                      R   \n",
       "1                                                      S   \n",
       "2                                                      R   \n",
       "3                                                      R   \n",
       "4                                                      R   \n",
       "...                        ...                       ...   \n",
       "7300                                                   E   \n",
       "7301                                                   P   \n",
       "7302                                                   R   \n",
       "7303                                                   R   \n",
       "7304                                                   P   \n",
       "\n",
       "      years_ANN-TAVG-NORMAL  ANN-TMIN-NORMAL meas_flag_ANN-TMIN-NORMAL  \\\n",
       "0                        16             35.1                             \n",
       "1                        28             77.9                             \n",
       "2                        19             74.3                             \n",
       "3                        19             75.2                             \n",
       "4                        14             76.8                             \n",
       "...                     ...              ...                       ...   \n",
       "7300                      3             10.3                             \n",
       "7301                     12             76.8                             \n",
       "7302                     24             74.5                             \n",
       "7303                     22             76.8                             \n",
       "7304                     12             76.0                             \n",
       "\n",
       "     comp_flag_ANN-TMIN-NORMAL  years_ANN-TMIN-NORMAL  ANN-TMAX-NORMAL  \\\n",
       "0                            R                     16             53.2   \n",
       "1                            S                     28             86.7   \n",
       "2                            R                     21             83.2   \n",
       "3                            R                     19             85.4   \n",
       "4                            R                     14             86.5   \n",
       "...                        ...                    ...              ...   \n",
       "7300                         E                      3             27.5   \n",
       "7301                         R                     12             85.4   \n",
       "7302                         S                     24             87.0   \n",
       "7303                         S                     26             87.0   \n",
       "7304                         P                     12             86.5   \n",
       "\n",
       "     meas_flag_ANN-TMAX-NORMAL comp_flag_ANN-TMAX-NORMAL  \\\n",
       "0                                                      R   \n",
       "1                                                      S   \n",
       "2                                                      R   \n",
       "3                                                      R   \n",
       "4                                                      R   \n",
       "...                        ...                       ...   \n",
       "7300                                                   E   \n",
       "7301                                                   P   \n",
       "7302                                                   S   \n",
       "7303                                                   R   \n",
       "7304                                                   P   \n",
       "\n",
       "      years_ANN-TMAX-NORMAL  ANN-DUTR-NORMAL meas_flag_ANN-DUTR-NORMAL  \\\n",
       "0                        16             18.1                             \n",
       "1                        28              8.8                             \n",
       "2                        19              8.9                             \n",
       "3                        22             10.2                             \n",
       "4                        14              9.7                             \n",
       "...                     ...              ...                       ...   \n",
       "7300                      3             17.2                             \n",
       "7301                     14              8.7                             \n",
       "7302                     24             12.5                             \n",
       "7303                     22             10.2                             \n",
       "7304                     12             10.5                             \n",
       "\n",
       "     comp_flag_ANN-DUTR-NORMAL  years_ANN-DUTR-NORMAL  \n",
       "0                            R                     16  \n",
       "1                            S                     28  \n",
       "2                            R                     19  \n",
       "3                            R                     19  \n",
       "4                            R                     14  \n",
       "...                        ...                    ...  \n",
       "7300                         E                      3  \n",
       "7301                         P                     12  \n",
       "7302                         R                     24  \n",
       "7303                         R                     22  \n",
       "7304                         P                     12  \n",
       "\n",
       "[7305 rows x 20 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'temperature/ann-normal-allall.csv'\n",
    "\n",
    "norms = pd.read_csv(filename)  ## read it in\n",
    "\n",
    "norms ## see what's in the file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ca67cd-b6b9-459a-a8e4-9d3b3949790d",
   "metadata": {},
   "source": [
    "Let's merge the inventory file with the data file. This way, we have station information with the data. This will also make it easier to subset and grab the specific stations we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1f26832-05a6-4cbe-8ffb-55722f994026",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GHCN_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>elev</th>\n",
       "      <th>state</th>\n",
       "      <th>stnName</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>month</th>\n",
       "      <th>...</th>\n",
       "      <th>comp_flag_ANN-TMIN-NORMAL</th>\n",
       "      <th>years_ANN-TMIN-NORMAL</th>\n",
       "      <th>ANN-TMAX-NORMAL</th>\n",
       "      <th>meas_flag_ANN-TMAX-NORMAL</th>\n",
       "      <th>comp_flag_ANN-TMAX-NORMAL</th>\n",
       "      <th>years_ANN-TMAX-NORMAL</th>\n",
       "      <th>ANN-DUTR-NORMAL</th>\n",
       "      <th>meas_flag_ANN-DUTR-NORMAL</th>\n",
       "      <th>comp_flag_ANN-DUTR-NORMAL</th>\n",
       "      <th>years_ANN-DUTR-NORMAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AQW00061705</td>\n",
       "      <td>-14.3306</td>\n",
       "      <td>-170.7136</td>\n",
       "      <td>3.7</td>\n",
       "      <td>AS</td>\n",
       "      <td>PAGO PAGO WSO AP</td>\n",
       "      <td>GSN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91765.0</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>S</td>\n",
       "      <td>28</td>\n",
       "      <td>86.7</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>28</td>\n",
       "      <td>8.8</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAW00064757</td>\n",
       "      <td>44.2325</td>\n",
       "      <td>-79.7811</td>\n",
       "      <td>246.0</td>\n",
       "      <td>ON</td>\n",
       "      <td>EGBERT 1 W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>53.2</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>18.1</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CQC00914080</td>\n",
       "      <td>15.2136</td>\n",
       "      <td>145.7497</td>\n",
       "      <td>252.1</td>\n",
       "      <td>MP</td>\n",
       "      <td>CAPITOL HILL 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>21</td>\n",
       "      <td>83.2</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "      <td>8.9</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CQC00914801</td>\n",
       "      <td>14.1717</td>\n",
       "      <td>145.2428</td>\n",
       "      <td>179.2</td>\n",
       "      <td>MP</td>\n",
       "      <td>ROTA AP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91221.0</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "      <td>85.4</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>22</td>\n",
       "      <td>10.2</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CQC00914855</td>\n",
       "      <td>15.1167</td>\n",
       "      <td>145.7167</td>\n",
       "      <td>65.5</td>\n",
       "      <td>MP</td>\n",
       "      <td>SAIPAN INTL AP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91232.0</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>14</td>\n",
       "      <td>86.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>14</td>\n",
       "      <td>9.7</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7300</th>\n",
       "      <td>USW00096409</td>\n",
       "      <td>68.6483</td>\n",
       "      <td>-149.3986</td>\n",
       "      <td>750.1</td>\n",
       "      <td>AK</td>\n",
       "      <td>TOOLIK LAKE 5 ENE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>27.5</td>\n",
       "      <td></td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td></td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7301</th>\n",
       "      <td>VQC00671740</td>\n",
       "      <td>17.7469</td>\n",
       "      <td>-64.7014</td>\n",
       "      <td>9.1</td>\n",
       "      <td>VI</td>\n",
       "      <td>CHRISTIANSTED FT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>12</td>\n",
       "      <td>85.4</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>14</td>\n",
       "      <td>8.7</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302</th>\n",
       "      <td>VQW00011624</td>\n",
       "      <td>17.7028</td>\n",
       "      <td>-64.8056</td>\n",
       "      <td>18.6</td>\n",
       "      <td>VI</td>\n",
       "      <td>CHRISTIANSTED AP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78551.0</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>S</td>\n",
       "      <td>24</td>\n",
       "      <td>87.0</td>\n",
       "      <td></td>\n",
       "      <td>S</td>\n",
       "      <td>24</td>\n",
       "      <td>12.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7303</th>\n",
       "      <td>VQW00011640</td>\n",
       "      <td>18.3331</td>\n",
       "      <td>-64.9667</td>\n",
       "      <td>6.1</td>\n",
       "      <td>VI</td>\n",
       "      <td>CHARLOTTE AMALIE AP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78543.0</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>S</td>\n",
       "      <td>26</td>\n",
       "      <td>87.0</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>22</td>\n",
       "      <td>10.2</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>WQW00041606</td>\n",
       "      <td>19.2833</td>\n",
       "      <td>166.6500</td>\n",
       "      <td>4.3</td>\n",
       "      <td>UM</td>\n",
       "      <td>WAKE ISLAND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91246.0</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "      <td>86.5</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "      <td>10.5</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7305 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          GHCN_ID      lat       lon   elev state              stnName    a  \\\n",
       "0     AQW00061705 -14.3306 -170.7136    3.7    AS     PAGO PAGO WSO AP  GSN   \n",
       "1     CAW00064757  44.2325  -79.7811  246.0    ON           EGBERT 1 W  NaN   \n",
       "2     CQC00914080  15.2136  145.7497  252.1    MP       CAPITOL HILL 1  NaN   \n",
       "3     CQC00914801  14.1717  145.2428  179.2    MP              ROTA AP  NaN   \n",
       "4     CQC00914855  15.1167  145.7167   65.5    MP       SAIPAN INTL AP  NaN   \n",
       "...           ...      ...       ...    ...   ...                  ...  ...   \n",
       "7300  USW00096409  68.6483 -149.3986  750.1    AK    TOOLIK LAKE 5 ENE  NaN   \n",
       "7301  VQC00671740  17.7469  -64.7014    9.1    VI     CHRISTIANSTED FT  NaN   \n",
       "7302  VQW00011624  17.7028  -64.8056   18.6    VI     CHRISTIANSTED AP  NaN   \n",
       "7303  VQW00011640  18.3331  -64.9667    6.1    VI  CHARLOTTE AMALIE AP  NaN   \n",
       "7304  WQW00041606  19.2833  166.6500    4.3    UM          WAKE ISLAND  NaN   \n",
       "\n",
       "        b        c  month  ...  comp_flag_ANN-TMIN-NORMAL  \\\n",
       "0     NaN  91765.0     99  ...                          S   \n",
       "1     NaN      NaN     99  ...                          R   \n",
       "2     NaN      NaN     99  ...                          R   \n",
       "3     NaN  91221.0     99  ...                          R   \n",
       "4     NaN  91232.0     99  ...                          R   \n",
       "...   ...      ...    ...  ...                        ...   \n",
       "7300  NaN      NaN     99  ...                          E   \n",
       "7301  NaN      NaN     99  ...                          R   \n",
       "7302  NaN  78551.0     99  ...                          S   \n",
       "7303  NaN  78543.0     99  ...                          S   \n",
       "7304  NaN  91246.0     99  ...                          P   \n",
       "\n",
       "      years_ANN-TMIN-NORMAL  ANN-TMAX-NORMAL meas_flag_ANN-TMAX-NORMAL  \\\n",
       "0                        28             86.7                             \n",
       "1                        16             53.2                             \n",
       "2                        21             83.2                             \n",
       "3                        19             85.4                             \n",
       "4                        14             86.5                             \n",
       "...                     ...              ...                       ...   \n",
       "7300                      3             27.5                             \n",
       "7301                     12             85.4                             \n",
       "7302                     24             87.0                             \n",
       "7303                     26             87.0                             \n",
       "7304                     12             86.5                             \n",
       "\n",
       "     comp_flag_ANN-TMAX-NORMAL  years_ANN-TMAX-NORMAL  ANN-DUTR-NORMAL  \\\n",
       "0                            S                     28              8.8   \n",
       "1                            R                     16             18.1   \n",
       "2                            R                     19              8.9   \n",
       "3                            R                     22             10.2   \n",
       "4                            R                     14              9.7   \n",
       "...                        ...                    ...              ...   \n",
       "7300                         E                      3             17.2   \n",
       "7301                         P                     14              8.7   \n",
       "7302                         S                     24             12.5   \n",
       "7303                         R                     22             10.2   \n",
       "7304                         P                     12             10.5   \n",
       "\n",
       "     meas_flag_ANN-DUTR-NORMAL comp_flag_ANN-DUTR-NORMAL  \\\n",
       "0                                                      S   \n",
       "1                                                      R   \n",
       "2                                                      R   \n",
       "3                                                      R   \n",
       "4                                                      R   \n",
       "...                        ...                       ...   \n",
       "7300                                                   E   \n",
       "7301                                                   P   \n",
       "7302                                                   R   \n",
       "7303                                                   R   \n",
       "7304                                                   P   \n",
       "\n",
       "      years_ANN-DUTR-NORMAL  \n",
       "0                        28  \n",
       "1                        16  \n",
       "2                        19  \n",
       "3                        19  \n",
       "4                        14  \n",
       "...                     ...  \n",
       "7300                      3  \n",
       "7301                     12  \n",
       "7302                     24  \n",
       "7303                     22  \n",
       "7304                     12  \n",
       "\n",
       "[7305 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata = pd.merge(temp_stns,norms,on=\"GHCN_ID\")\n",
    "\n",
    "mdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48448a52-12a2-4720-b99f-009e65892f80",
   "metadata": {},
   "source": [
    "Here, we are going to select to Colorado stations only, and select the variables of interest - annual average temperature, annual maximum temperature, and annual minimum temperature. Then let's output that data to a new csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec82f241-a7e4-40cc-981d-1f7ef44627a1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-103.1417, -105.8569, -105.5183, -108.3808, -105.8914, -102.1675,\n",
       "       -102.1614, -106.8381, -105.4764, -107.6872, -104.8017, -105.5717,\n",
       "       -107.1678, -107.8989, -102.1192, -105.2667, -104.3286, -104.8361,\n",
       "       -105.2969, -106.1275, -102.2608, -104.1275, -105.7089, -102.5536,\n",
       "       -105.2256, -104.9058, -107.8914, -106.1444, -105.2783, -103.51  ,\n",
       "       -104.8319, -102.3328, -107.5569, -106.1897, -105.3847, -106.7617,\n",
       "       -107.9797, -108.7339, -104.71  , -108.595 , -107.5906, -106.9253,\n",
       "       -106.9319, -106.9772, -105.6897, -105.2003, -102.8031, -106.3078,\n",
       "       -108.0278, -105.0083, -106.0353, -108.9725, -105.2158, -108.9119,\n",
       "       -107.8564, -102.7769, -104.5997, -104.6289, -104.3994, -105.52  ,\n",
       "       -105.315 , -105.9936, -103.062 , -102.8594, -105.285 , -105.0858,\n",
       "       -105.0236, -108.0514, -103.87  , -105.8172, -108.7281, -108.9608,\n",
       "       -103.4944, -105.6997, -109.0528, -107.3172, -106.0056, -108.5247,\n",
       "       -108.4664, -105.8322, -105.8667, -105.6786, -105.5119, -104.6992,\n",
       "       -106.3333, -105.3503, -105.3925, -106.9672, -107.2547, -107.095 ,\n",
       "       -105.9986, -102.1317, -102.3411, -105.6319, -103.4897, -102.2939,\n",
       "       -107.5333, -102.6805, -102.9297, -102.2653, -103.5428, -105.0953,\n",
       "       -103.3219, -103.2986, -102.7997, -102.7233, -106.3681, -103.5439,\n",
       "       -103.4767, -107.3147, -107.3036, -105.4706, -105.1206, -102.6306,\n",
       "       -103.2153, -107.6592, -102.9906, -103.4986, -105.0775, -105.085 ,\n",
       "       -105.94  , -108.3161, -105.0656, -108.605 , -103.8456, -108.0947,\n",
       "       -107.9058, -106.7422, -108.4892, -106.1867, -107.8792, -104.8683,\n",
       "       -104.8675, -103.8461, -103.8678, -109.0133, -105.0133, -108.2864,\n",
       "       -108.285 , -103.7322, -103.7058, -107.6686, -107.6725, -108.3506,\n",
       "       -107.6225, -108.9494, -104.7503, -108.0514, -104.7178, -105.2353,\n",
       "       -108.7728, -107.7583, -107.7261, -107.2667, -103.6947, -105.0703,\n",
       "       -104.0939, -105.7106, -104.9742, -104.9483, -106.1444, -106.0158,\n",
       "       -105.4244, -107.1097, -106.4236, -102.5161, -102.8678, -103.2922,\n",
       "       -105.2353, -102.2967, -102.2169, -107.2267, -107.6633, -106.4475,\n",
       "       -106.8233, -103.2083, -102.6003, -105.1208, -106.3714, -104.0567,\n",
       "       -106.6086, -107.8736, -104.4869, -104.5567, -106.3506, -108.7422,\n",
       "       -106.3539, -107.5811, -107.5833, -105.2186, -106.2792, -104.7956,\n",
       "       -102.2986, -105.2103, -106.0317, -105.4661, -104.5214, -105.1169,\n",
       "       -103.0192, -106.2039, -103.5678, -102.2069, -106.9108, -108.755 ,\n",
       "       -108.6636, -102.7217, -102.6881, -107.7264, -104.6575, -102.2842,\n",
       "       -102.6139, -107.6928, -108.5036, -103.8225, -105.8614, -104.8694,\n",
       "       -108.54  , -103.5122, -104.3378, -103.2167, -107.5211, -107.7597,\n",
       "       -106.3169, -103.7158, -107.8975, -104.6883, -104.4983, -104.8489,\n",
       "       -108.6275, -106.8683, -107.8853, -104.7553, -105.5408, -108.9678])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdata = mdata[mdata.state == \"CO\"]\n",
    "\n",
    "df_ann_out = mdata[['GHCN_ID','lat','lon','elev','stnName','ANN-TAVG-NORMAL','ANN-TMAX-NORMAL','ANN-TMIN-NORMAL']]\n",
    "\n",
    "df_ann_out.to_csv('CO_normals/annual_temperature_normals.csv')\n",
    "\n",
    "mdata['lon'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2219195f-b3bc-4990-ae4e-f8d74ad37505",
   "metadata": {},
   "source": [
    "Now let's plot annual average temperature on a map!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9497a7e8-b69a-4607-bc4f-d747a9a510db",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    # Set up the map projection\n",
    "    proj = ccrs.LambertConformal(central_longitude=-105, central_latitude=37.5,\n",
    "                                 standard_parallels=[37.5])\n",
    "\n",
    "    # Use the cartopy map projection to transform station locations to the map \n",
    "    point_locs = proj.transform_points(ccrs.PlateCarree(), mdata['lon'].values, mdata['lat'].values)\n",
    "    # then refine the number of stations plotted by setting a 15km radius\n",
    "\n",
    "    # Change the DPI of the resulting figure. Higher DPI drastically improves the\n",
    "    # look of the text rendering.\n",
    "    plt.rcParams['savefig.dpi'] = 255\n",
    "\n",
    "    # Create the figure and an axes set to the projection.\n",
    "    fig = plt.figure(figsize=(9,6))\n",
    "    #add_metpy_logo(fig, 1775, 170, size='large')\n",
    "    ax = fig.add_subplot(1, 1, 1, projection=proj)\n",
    "\n",
    "    # Add some various map elements to the plot to make it recognizable.\n",
    "    ax.add_feature(cfeature.LAND.with_scale('10m'))\n",
    "    ax.add_feature(USCOUNTIES.with_scale('5m'), edgecolor=\"gray\", linewidth=0.4)\n",
    "    ax.add_feature(cfeature.STATES.with_scale('10m'))\n",
    "    ax.add_feature(cfeature.BORDERS.with_scale('10m'))\n",
    "\n",
    "    ax.set_extent((-109.5,-101.5,36.4,41.5))\n",
    "\n",
    "    # Start the station plot by specifying the axes to draw on, as well as the\n",
    "    # lon/lat of the stations (with transform). We also the fontsize to 12 pt.\n",
    "    stationplot = StationPlot(ax, mdata['lon'].values, mdata['lat'].values, clip_on=True,\n",
    "                              transform=ccrs.PlateCarree(),fontsize=7)\n",
    "\n",
    "\n",
    "    dotcolor = \"maroon\"\n",
    "    cmap = 'RdYlBu_r'\n",
    "    formatter='4.1f'\n",
    "    units = 'temperature (°F)'\n",
    "\n",
    "    # Plot the precip above the station point\n",
    "#    stationplot.plot_parameter('N', mdata['ANN-TAVG-NORMAL'], color=dotcolor, \n",
    "#                               formatter=formatter,fontweight='bold',fontsize=6.5)\n",
    "    ## add colored dots at station location\n",
    "    sc = plt.scatter(mdata['lon'],mdata['lat'],linestyle='None',\n",
    "             c=mdata['ANN-TAVG-NORMAL'],cmap=cmap,\n",
    "                edgecolors='black',linewidths=0.5,\n",
    "             transform=ccrs.PlateCarree())\n",
    "    \n",
    "    cb = plt.colorbar(sc, ax=ax,orientation='vertical', extend='max', aspect=30, shrink=0.9, pad=0.01,\n",
    "                  extendrect='True')\n",
    "    cb.ax.tick_params(labelsize=7.5)\n",
    "    cb.set_label(units, fontsize=7.5)\n",
    "    \n",
    "    plt.title('Annual Average Temperature, 1991-2020', fontsize=9.25)\n",
    "\n",
    "    outfile = 'images/ann_ave_t.png'\n",
    "    plt.savefig(outfile, bbox_inches='tight', facecolor='white', transparent=False)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    plt.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30581878-e2b1-453a-8d6e-1ff1ead107e0",
   "metadata": {
    "tags": []
   },
   "source": [
    "We're going to grab more annual normals data, make some maps, and put in csv files.\n",
    "\n",
    "The following annual files are available:\n",
    "ann-normal-allall.csv (opened above)\n",
    "\n",
    "ann-cldd-normal.csv (cooling degree days)\n",
    "ann-grdd-normal.csv (growing degree days)\n",
    "ann-htdd-normal.csv (heating degree days)\n",
    "\n",
    "ann-tmax-avgnds.csv (average number of days maximum temperature threshold)\n",
    "Inside the file are the following variables:\n",
    "ANN-TMAX-AVGNDS-GRTH032 (number of days greater than 32°)\n",
    "ANN-TMAX-AVGNDS-GRTH040 (number of days greater than 40°)\n",
    "ANN-TMAX-AVGNDS-GRTH050 (number of days greater than 50°)\n",
    "ANN-TMAX-AVGNDS-GRTH060 (number of days greater than 60°)\n",
    "ANN-TMAX-AVGNDS-GRTH070 (number of days greater than 70°)\n",
    "ANN-TMAX-AVGNDS-GRTH080 (number of days greater than 80°)\n",
    "ANN-TMAX-AVGNDS-GRTH090 (number of days greater than 90°)\n",
    "ANN-TMAX-AVGNDS-GRTH100 (number of days greater than 100°)\n",
    "ANN-TMAX-AVGNDS-LSTH032 (number of days less than 32°)\n",
    "\n",
    "ann-tmin-avgnds.csv (average number of days minimum temperature threshold)\n",
    "Inside the file are the following variables:\n",
    "ANN-TMIN-AVGNDS-LSTH000 (number of days less than 0°)\n",
    "ANN-TMIN-AVGNDS-LSTH010 (number of days less than 10°)\n",
    "ANN-TMIN-AVGNDS-LSTH020 (number of days less than 20°)\n",
    "ANN-TMIN-AVGNDS-LSTH032 (number of days less than 32°)\n",
    "ANN-TMIN-AVGNDS-LSTH040 (number of days less than 40°)\n",
    "ANN-TMIN-AVGNDS-LSTH050 (number of days less than 50°)\n",
    "ANN-TMIN-AVGNDS-LSTH060 (number of days less than 60°)\n",
    "ANN-TMIN-AVGNDS-LSTH070 (number of days less than 70°)\n",
    "\n",
    "ann-tmin-prbfst-t??.csv\n",
    "Probability dates of first frost at 16,20,24,28,32, and 36 temperature thresholds.\n",
    "\n",
    "ann-tmin-prbgsl-t??.csv\n",
    "Probability of growing season lengths between temperature thresholds.\n",
    "\n",
    "ann-tmin-prblst-t??.csv\n",
    "Probability dates of last frost at temperature thresholds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "179dcacb-4013-41cc-af8a-b3a5d246f6ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'temperature/ann-tmax-avgnds.csv'\n",
    "\n",
    "norms = pd.read_csv(filename)  ## read it in\n",
    "\n",
    "norms ## see what's in the file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c038408-de2e-475e-8b44-99b48b9f59f4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "source": [
    "Now let's pull some monthly normals, put data in csv files and plot on maps.\n",
    "\n",
    "Here's the monthly files we'll be working with:\n",
    "mly-normal-allall.csv\n",
    "contains:\n",
    "MLY-TAVG-NORMAL\n",
    "MLY-TMAX-NORMAL\n",
    "MLY-TMIN-NORMAL\n",
    "MLY-DUTR-NORMAL\n",
    "\n",
    "mly-tmax-avgnds.csv\n",
    "mly-tmin-avgnds.csv\n",
    "These both contain the same thresholds as the annual files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a604b975-2cb9-4c56-b527-af713e050b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GHCN_ID</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>MLY-TAVG-NORMAL</th>\n",
       "      <th>meas_flag_MLY-TAVG-NORMAL</th>\n",
       "      <th>comp_flag_MLY-TAVG-NORMAL</th>\n",
       "      <th>years_MLY-TAVG-NORMAL</th>\n",
       "      <th>MLY-TMAX-NORMAL</th>\n",
       "      <th>meas_flag_MLY-TMAX-NORMAL</th>\n",
       "      <th>comp_flag_MLY-TMAX-NORMAL</th>\n",
       "      <th>years_MLY-TMAX-NORMAL</th>\n",
       "      <th>MLY-TMIN-NORMAL</th>\n",
       "      <th>meas_flag_MLY-TMIN-NORMAL</th>\n",
       "      <th>comp_flag_MLY-TMIN-NORMAL</th>\n",
       "      <th>years_MLY-TMIN-NORMAL</th>\n",
       "      <th>MLY-DUTR-NORMAL</th>\n",
       "      <th>meas_flag_MLY-DUTR-NORMAL</th>\n",
       "      <th>comp_flag_MLY-DUTR-NORMAL</th>\n",
       "      <th>years_MLY-DUTR-NORMAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAW00064757</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>19.1</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>26.6</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>11.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>15.1</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAW00064757</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>20.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>28.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>12.6</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>15.8</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAW00064757</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>29.3</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>37.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>21.1</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>16.4</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAW00064757</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>41.4</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>51.2</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>31.5</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>19.7</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAW00064757</td>\n",
       "      <td>5</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>53.9</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>64.9</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>42.9</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>22.0</td>\n",
       "      <td></td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87655</th>\n",
       "      <td>WQW00041606</td>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>83.7</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>14</td>\n",
       "      <td>89.4</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>14</td>\n",
       "      <td>78.0</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>14</td>\n",
       "      <td>11.4</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87656</th>\n",
       "      <td>WQW00041606</td>\n",
       "      <td>9</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>83.4</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "      <td>89.1</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "      <td>77.7</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "      <td>11.3</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87657</th>\n",
       "      <td>WQW00041606</td>\n",
       "      <td>10</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>82.8</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>88.1</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>77.4</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>10.7</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87658</th>\n",
       "      <td>WQW00041606</td>\n",
       "      <td>11</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>81.3</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>86.0</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>9.5</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87659</th>\n",
       "      <td>WQW00041606</td>\n",
       "      <td>12</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>79.9</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>84.4</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>75.3</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>P</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87660 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           GHCN_ID  month  day  hour  MLY-TAVG-NORMAL  \\\n",
       "0      CAW00064757      1   99    99             19.1   \n",
       "1      CAW00064757      2   99    99             20.5   \n",
       "2      CAW00064757      3   99    99             29.3   \n",
       "3      CAW00064757      4   99    99             41.4   \n",
       "4      CAW00064757      5   99    99             53.9   \n",
       "...            ...    ...  ...   ...              ...   \n",
       "87655  WQW00041606      8   99    99             83.7   \n",
       "87656  WQW00041606      9   99    99             83.4   \n",
       "87657  WQW00041606     10   99    99             82.8   \n",
       "87658  WQW00041606     11   99    99             81.3   \n",
       "87659  WQW00041606     12   99    99             79.9   \n",
       "\n",
       "      meas_flag_MLY-TAVG-NORMAL comp_flag_MLY-TAVG-NORMAL  \\\n",
       "0                                                       R   \n",
       "1                                                       R   \n",
       "2                                                       R   \n",
       "3                                                       R   \n",
       "4                                                       R   \n",
       "...                         ...                       ...   \n",
       "87655                                                   P   \n",
       "87656                                                   P   \n",
       "87657                                                   P   \n",
       "87658                                                   P   \n",
       "87659                                                   P   \n",
       "\n",
       "       years_MLY-TAVG-NORMAL  MLY-TMAX-NORMAL meas_flag_MLY-TMAX-NORMAL  \\\n",
       "0                         16             26.6                             \n",
       "1                         16             28.5                             \n",
       "2                         16             37.5                             \n",
       "3                         16             51.2                             \n",
       "4                         16             64.9                             \n",
       "...                      ...              ...                       ...   \n",
       "87655                     14             89.4                             \n",
       "87656                     12             89.1                             \n",
       "87657                     13             88.1                             \n",
       "87658                     13             86.0                             \n",
       "87659                     13             84.4                             \n",
       "\n",
       "      comp_flag_MLY-TMAX-NORMAL  years_MLY-TMAX-NORMAL  MLY-TMIN-NORMAL  \\\n",
       "0                             R                     16             11.5   \n",
       "1                             R                     16             12.6   \n",
       "2                             R                     16             21.1   \n",
       "3                             R                     16             31.5   \n",
       "4                             R                     16             42.9   \n",
       "...                         ...                    ...              ...   \n",
       "87655                         P                     14             78.0   \n",
       "87656                         P                     12             77.7   \n",
       "87657                         P                     13             77.4   \n",
       "87658                         P                     13             76.5   \n",
       "87659                         P                     13             75.3   \n",
       "\n",
       "      meas_flag_MLY-TMIN-NORMAL comp_flag_MLY-TMIN-NORMAL  \\\n",
       "0                                                       R   \n",
       "1                                                       R   \n",
       "2                                                       R   \n",
       "3                                                       R   \n",
       "4                                                       R   \n",
       "...                         ...                       ...   \n",
       "87655                                                   P   \n",
       "87656                                                   P   \n",
       "87657                                                   P   \n",
       "87658                                                   P   \n",
       "87659                                                   P   \n",
       "\n",
       "       years_MLY-TMIN-NORMAL  MLY-DUTR-NORMAL meas_flag_MLY-DUTR-NORMAL  \\\n",
       "0                         16             15.1                             \n",
       "1                         16             15.8                             \n",
       "2                         16             16.4                             \n",
       "3                         16             19.7                             \n",
       "4                         16             22.0                             \n",
       "...                      ...              ...                       ...   \n",
       "87655                     14             11.4                             \n",
       "87656                     12             11.3                             \n",
       "87657                     13             10.7                             \n",
       "87658                     13              9.5                             \n",
       "87659                     13              9.0                             \n",
       "\n",
       "      comp_flag_MLY-DUTR-NORMAL  years_MLY-DUTR-NORMAL  \n",
       "0                             R                     16  \n",
       "1                             R                     16  \n",
       "2                             R                     16  \n",
       "3                             R                     16  \n",
       "4                             R                     16  \n",
       "...                         ...                    ...  \n",
       "87655                         P                     14  \n",
       "87656                         P                     12  \n",
       "87657                         P                     13  \n",
       "87658                         P                     13  \n",
       "87659                         P                     13  \n",
       "\n",
       "[87660 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'temperature/mly-normal-allall.csv'\n",
    "\n",
    "norms = pd.read_csv(filename)  ## read it in\n",
    "\n",
    "norms ## see what's in the file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c82784-a7d3-430b-98fb-3c5c49a98bc6",
   "metadata": {},
   "source": [
    "Looking at this file, you'll notice a similar format as the annual file, but with an important difference. Each station now takes up 12 rows, one for each month of the year. (7305 stations x 12 months gives you 87,660 rows)\n",
    "\n",
    "We will merge the data with the station inventory again, select just Colorado stations. And then we will separate out each month as a column. We can then output a file for each variable with a new station on each row and the months as columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "008fe36a-1c3f-44e3-803a-1f1664c6e683",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mdata = pd.merge(temp_stns,norms,on='GHCN_ID')\n",
    "mdata = mdata[mdata.state == \"CO\"]\n",
    "\n",
    "mdata1 = mdata[['GHCN_ID','lat','lon','elev','stnName','month','MLY-TAVG-NORMAL','MLY-TMAX-NORMAL','MLY-TMIN-NORMAL']]\n",
    "\n",
    "## we'll start with January to set up the new data frames for each variable.\n",
    "df_temp = mdata1[mdata1.month == 1]\n",
    "\n",
    "mly_tmax = df_temp[['GHCN_ID','lat','lon','elev','stnName','MLY-TMAX-NORMAL']]\n",
    "mly_tmax.rename(columns = {'MLY-TMAX-NORMAL':'Jan'}, inplace = True)\n",
    "\n",
    "mly_tmin = df_temp[['GHCN_ID','lat','lon','elev','stnName','MLY-TMIN-NORMAL']]\n",
    "mly_tmin.rename(columns = {'MLY-TMIN-NORMAL':'Jan'}, inplace = True)\n",
    "\n",
    "mly_tavg = df_temp[['GHCN_ID','lat','lon','elev','stnName','MLY-TAVG-NORMAL']]\n",
    "mly_tavg.rename(columns = {'MLY-TAVG-NORMAL':'Jan'}, inplace = True)\n",
    "\n",
    "## set up column names for the remaining months\n",
    "strmo = ['Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
    "\n",
    "## grab each month and add it to the data frame for each variable\n",
    "for x in range(11):\n",
    "    mo = x+2\n",
    "    df_temp = mdata1[mdata1.month == mo]\n",
    "    max_temp = df_temp['MLY-TMAX-NORMAL'].values\n",
    "    mly_tmax[strmo[x]] = max_temp\n",
    "\n",
    "    min_temp = df_temp['MLY-TMIN-NORMAL'].values\n",
    "    mly_tmin[strmo[x]] = min_temp\n",
    "\n",
    "    avg_temp = df_temp['MLY-TAVG-NORMAL'].values\n",
    "    mly_tavg[strmo[x]] = avg_temp\n",
    "\n",
    "## output to csv files for each variable\n",
    "mly_tmax.to_csv(\"CO_normals/monthly_tmax_normals.csv\")\n",
    "mly_tmin.to_csv(\"CO_normals/monthly_tmin_normals.csv\")\n",
    "mly_tavg.to_csv(\"CO_normals/monthly_tavg_normals.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91597451-1189-4971-ac65-2cc6b2032d6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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