{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# **Chapter 3**  \n",
    "**ATMOS 5340: Environmental Programming and Statistics**  \n",
    "**John Horel **\n",
    "\n",
    "Evaluation of persistence forecasts of total seasonal snow at Alta based on snow totals each month earlier during the season or from the previous season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these are python modules used in the program\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alta snowfall\n",
    "https://utahavalanchecenter.org/alta-monthly-snowfall\n",
    "\n",
    "\n",
    "Look in the `data` folder at the called `alta_snow.csv`\n",
    "\n",
    "Open the `alta_snow.csv` file see the column contents and the units.\n",
    "\n",
    "- The 0th column is the Year at Season End\n",
    "- The 1st-6th column are the total snowfall during each month from November to April (in inches)\n",
    "- The 7th column is the Nov-Apr total snowfall (inches)\n",
    "\n",
    "\n",
    "Begins in the 1946 season and ends in 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ending Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>276.860</td>\n",
       "      <td>210.820</td>\n",
       "      <td>214.630</td>\n",
       "      <td>127.00</td>\n",
       "      <td>175.260</td>\n",
       "      <td>140.970</td>\n",
       "      <td>1145.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>175.260</td>\n",
       "      <td>160.020</td>\n",
       "      <td>154.940</td>\n",
       "      <td>134.62</td>\n",
       "      <td>172.720</td>\n",
       "      <td>152.400</td>\n",
       "      <td>949.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>299.720</td>\n",
       "      <td>203.200</td>\n",
       "      <td>116.840</td>\n",
       "      <td>167.64</td>\n",
       "      <td>419.100</td>\n",
       "      <td>187.960</td>\n",
       "      <td>1394.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>180.340</td>\n",
       "      <td>406.400</td>\n",
       "      <td>335.280</td>\n",
       "      <td>147.32</td>\n",
       "      <td>246.380</td>\n",
       "      <td>12.700</td>\n",
       "      <td>1328.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>99.060</td>\n",
       "      <td>347.980</td>\n",
       "      <td>337.820</td>\n",
       "      <td>86.36</td>\n",
       "      <td>276.860</td>\n",
       "      <td>63.500</td>\n",
       "      <td>1211.580</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>38.100</td>\n",
       "      <td>109.220</td>\n",
       "      <td>130.810</td>\n",
       "      <td>167.64</td>\n",
       "      <td>179.070</td>\n",
       "      <td>106.680</td>\n",
       "      <td>731.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>116.840</td>\n",
       "      <td>144.780</td>\n",
       "      <td>215.900</td>\n",
       "      <td>309.88</td>\n",
       "      <td>254.000</td>\n",
       "      <td>161.290</td>\n",
       "      <td>1206.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>224.790</td>\n",
       "      <td>166.370</td>\n",
       "      <td>297.180</td>\n",
       "      <td>184.15</td>\n",
       "      <td>121.920</td>\n",
       "      <td>63.500</td>\n",
       "      <td>1056.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>129.540</td>\n",
       "      <td>129.540</td>\n",
       "      <td>126.492</td>\n",
       "      <td>314.96</td>\n",
       "      <td>134.620</td>\n",
       "      <td>114.300</td>\n",
       "      <td>949.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>42.926</td>\n",
       "      <td>276.098</td>\n",
       "      <td>54.102</td>\n",
       "      <td>52.07</td>\n",
       "      <td>143.002</td>\n",
       "      <td>149.098</td>\n",
       "      <td>717.296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 NOV      DEC      JAN     FEB      MAR      APR     TOTAL\n",
       "Ending Year                                                               \n",
       "1946         276.860  210.820  214.630  127.00  175.260  140.970  1145.540\n",
       "1947         175.260  160.020  154.940  134.62  172.720  152.400   949.960\n",
       "1948         299.720  203.200  116.840  167.64  419.100  187.960  1394.460\n",
       "1949         180.340  406.400  335.280  147.32  246.380   12.700  1328.420\n",
       "1950          99.060  347.980  337.820   86.36  276.860   63.500  1211.580\n",
       "...              ...      ...      ...     ...      ...      ...       ...\n",
       "2018          38.100  109.220  130.810  167.64  179.070  106.680   731.520\n",
       "2019         116.840  144.780  215.900  309.88  254.000  161.290  1206.500\n",
       "2020         224.790  166.370  297.180  184.15  121.920   63.500  1056.640\n",
       "2021         129.540  129.540  126.492  314.96  134.620  114.300   949.960\n",
       "2022          42.926  276.098   54.102   52.07  143.002  149.098   717.296\n",
       "\n",
       "[77 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read the Alta data directly into a pandas datafram. values in inches\n",
    "snow_df= pd.read_csv('../data/alta_snow.csv', delimiter=',',index_col=0,header=0)\n",
    "#display(snow_df)\n",
    "#convert snow amounts to cm. \n",
    "snow_df = snow_df*(2.54)\n",
    "display(snow_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create bar plot time series of Alta seasonal total snowfall\n",
    "#create a fig of Alta snowfall time series\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(10,3))\n",
    "years = snow_df.index.values\n",
    "ax1.bar(years,snow_df['TOTAL'],color='green')\n",
    "ax1.set(xlim=(1946,2022),ylim=(600,2000))\n",
    "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (cm)\")\n",
    "#create a list for the times for tick marks on the x axis. This will stop at 2020 (not 2030)\n",
    "decade_ticks = np.arange(1950,2030,10)\n",
    "ax1.set(xticks=decade_ticks)\n",
    "ax1.set(title=\"Alta Snowfall: 1946-2022\")\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "\n",
    "#save the figure to \n",
    "plt.savefig('alta_snowfall.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 NOV      DEC      JAN       FEB       MAR       APR     TOTAL\n",
      "Ending Year                                                                   \n",
      "1946         276.860  487.680  702.310   829.310  1004.570  1145.540  2291.080\n",
      "1947         175.260  335.280  490.220   624.840   797.560   949.960  1899.920\n",
      "1948         299.720  502.920  619.760   787.400  1206.500  1394.460  2788.920\n",
      "1949         180.340  586.740  922.020  1069.340  1315.720  1328.420  2656.840\n",
      "1950          99.060  447.040  784.860   871.220  1148.080  1211.580  2423.160\n",
      "...              ...      ...      ...       ...       ...       ...       ...\n",
      "2018          38.100  147.320  278.130   445.770   624.840   731.520  1463.040\n",
      "2019         116.840  261.620  477.520   787.400  1041.400  1202.690  2409.190\n",
      "2020         224.790  391.160  688.340   872.490   994.410  1057.910  2114.550\n",
      "2021         129.540  259.080  385.572   700.532   835.152   949.452  1899.412\n",
      "2022          42.926  319.024  373.126   425.196   568.198   717.296  1434.592\n",
      "\n",
      "[77 rows x 7 columns]\n",
      "                 NOV      DEC      JAN       FEB       MAR       APR     TOTAL\n",
      "Ending Year                                                                   \n",
      "1946         276.860  487.680  702.310   829.310  1004.570  1145.540  1145.540\n",
      "1947         175.260  335.280  490.220   624.840   797.560   949.960   949.960\n",
      "1948         299.720  502.920  619.760   787.400  1206.500  1394.460  1394.460\n",
      "1949         180.340  586.740  922.020  1069.340  1315.720  1328.420  1328.420\n",
      "1950          99.060  447.040  784.860   871.220  1148.080  1211.580  1211.580\n",
      "...              ...      ...      ...       ...       ...       ...       ...\n",
      "2018          38.100  147.320  278.130   445.770   624.840   731.520   731.520\n",
      "2019         116.840  261.620  477.520   787.400  1041.400  1202.690  1206.500\n",
      "2020         224.790  391.160  688.340   872.490   994.410  1057.910  1056.640\n",
      "2021         129.540  259.080  385.572   700.532   835.152   949.452   949.960\n",
      "2022          42.926  319.024  373.126   425.196   568.198   717.296   717.296\n",
      "\n",
      "[77 rows x 7 columns]\n",
      "['NOV', 'DEC', 'JAN', 'FEB', 'MAR', 'APR', 'TOTAL']\n"
     ]
    }
   ],
   "source": [
    "#accumulate totals each month as the season progresses\n",
    "#axis=1 means summing over columns for each year\n",
    "snow_accum = snow_df.cumsum(axis=1)\n",
    "print(snow_accum)\n",
    "#don't want to double up the totals so replace with the originals\n",
    "snow_accum['TOTAL']=snow_df['TOTAL']\n",
    "print(snow_accum)\n",
    "#get the column labels\n",
    "headers = list(snow_accum)\n",
    "print(headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NOV', 'DEC', 'JAN', 'FEB', 'MAR']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create time series of Alta accumulating snowfall each month during each season\n",
    "#create a list for the times for tick marks on the x axis. This will stop at 2020 (not 2030)\n",
    "\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(12,6))\n",
    "#the earlier bars are plotted on top of the later ones to see them all\n",
    "ax1.bar(years,snow_accum['APR'], color='b')\n",
    "ax1.bar(years,snow_accum['MAR'], color='g')\n",
    "ax1.bar(years,snow_accum['FEB'], color='olive')\n",
    "ax1.bar(years,snow_accum['JAN'], color='tan')\n",
    "ax1.bar(years,snow_accum['DEC'], color='orange')\n",
    "ax1.bar(years,snow_accum['NOV'],color='r')\n",
    "ax1.set(xlim=(1946,2022),ylim=(0,2000))\n",
    "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (cm)\")\n",
    "ax1.set(xticks=decade_ticks)\n",
    "ax1.set(title=\"Alta Snowfall: 1946-2022\")\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "#have to reverse the headers to match the order plotted\n",
    "print(headers[0:-2])\n",
    "headers_mt = headers[0:-1]\n",
    "headers_r = headers_mt[::-1]\n",
    "ax1.legend(headers_r)\n",
    "#save the figure\n",
    "plt.savefig('alta_snowfall_accum.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOV       522.986\n",
      "DEC       621.030\n",
      "JAN       507.238\n",
      "FEB       397.764\n",
      "MAR       464.820\n",
      "APR       346.202\n",
      "TOTAL    1893.316\n",
      "dtype: float64 NOV       158.750\n",
      "DEC       207.772\n",
      "JAN       218.440\n",
      "FEB       197.866\n",
      "MAR       213.360\n",
      "APR       149.860\n",
      "TOTAL    1198.880\n",
      "dtype: float64 NOV        0.000\n",
      "DEC       31.242\n",
      "JAN        2.540\n",
      "FEB       52.070\n",
      "MAR       60.960\n",
      "APR        0.000\n",
      "TOTAL    679.450\n",
      "dtype: float64\n",
      "NOV       522.986\n",
      "DEC      1144.016\n",
      "JAN      1651.254\n",
      "FEB      2049.018\n",
      "MAR      2513.838\n",
      "APR      2860.040\n",
      "TOTAL    1893.316\n",
      "dtype: float64 NOV       158.750\n",
      "DEC       366.522\n",
      "JAN       584.962\n",
      "FEB       782.828\n",
      "MAR       996.188\n",
      "APR      1146.048\n",
      "TOTAL    1198.880\n",
      "dtype: float64 NOV        0.000\n",
      "DEC       31.242\n",
      "JAN       33.782\n",
      "FEB       85.852\n",
      "MAR      146.812\n",
      "APR      146.812\n",
      "TOTAL    679.450\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#years with the max and min totals\n",
    "#In what year did the max values happen and what were the max values?\n",
    "#print(snow_df.idxmax(),snow_df.max())\n",
    "#print(snow_df.idxmin(),snow_df.min())\n",
    "\n",
    "#compute the max, median, and min snow for each month\n",
    "snow_max = snow_df.max(axis=0)\n",
    "snow_med = snow_df.median(axis=0)\n",
    "snow_min = snow_df.min(axis=0)\n",
    "print(snow_max,snow_med,snow_min)\n",
    "\n",
    "#accumulate totals as if the season progressed with the least, median and max amounts each month\n",
    "snow_max_accum = snow_max.cumsum()\n",
    "snow_med_accum = snow_med.cumsum()\n",
    "snow_min_accum = snow_min.cumsum()\n",
    "#don't want to double up the totals so replace with the originals\n",
    "snow_max_accum['TOTAL']=snow_max['TOTAL']\n",
    "snow_med_accum['TOTAL']=snow_med['TOTAL']\n",
    "snow_min_accum['TOTAL']=snow_min['TOTAL']\n",
    "print(snow_max_accum,snow_med_accum,snow_min_accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              NOV         DEC          JAN          FEB          MAR  \\\n",
      "count   77.000000   77.000000    77.000000    77.000000    77.000000   \n",
      "mean   170.084338  398.214273   631.378026   840.819169  1060.606688   \n",
      "std     94.717482  160.733232   205.931445   226.120544   244.573630   \n",
      "min      0.000000   77.470000   205.740000   392.430000   560.070000   \n",
      "33%    119.989600  317.621920   556.666400   722.579200   933.754800   \n",
      "50%    158.750000  383.540000   601.980000   829.310000  1041.400000   \n",
      "66%    190.906400  448.259200   701.040000   919.601920  1150.315200   \n",
      "max    522.986000  985.520000  1217.676000  1360.678000  1688.084000   \n",
      "\n",
      "               APR        TOTAL  \n",
      "count    77.000000    77.000000  \n",
      "mean   1227.078948  1227.123481  \n",
      "std     273.405113   273.420076  \n",
      "min     679.450000   679.450000  \n",
      "33%    1060.653200  1059.484800  \n",
      "50%    1198.880000  1198.880000  \n",
      "66%    1383.792000  1383.792000  \n",
      "max    1893.062000  1893.316000  \n"
     ]
    }
   ],
   "source": [
    "#get some additional stats for each month\n",
    "#tercile values and median\n",
    "stats = snow_accum.describe(percentiles=[.33,.50,.66])\n",
    "print(stats)\n",
    "#print(snow_accum['NOV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOV [[14.  8.  4.]\n",
      " [10.  8.  7.]\n",
      " [ 2.  9. 15.]]\n",
      "DEC [[16.  8.  2.]\n",
      " [ 9. 11.  5.]\n",
      " [ 1.  6. 19.]]\n",
      "JAN [[20.  5.  1.]\n",
      " [ 6. 12.  8.]\n",
      " [ 0.  8. 17.]]\n",
      "FEB [[22.  3.  1.]\n",
      " [ 4. 16.  5.]\n",
      " [ 0.  6. 20.]]\n",
      "MAR [[23.  3.  0.]\n",
      " [ 3. 19.  3.]\n",
      " [ 0.  3. 23.]]\n",
      "APR [[26.  0.  0.]\n",
      " [ 0. 25.  0.]\n",
      " [ 0.  0. 26.]]\n",
      "TOTAL [[26.  0.  0.]\n",
      " [ 0. 25.  0.]\n",
      " [ 0.  0. 26.]]\n",
      "recognize the ordering. 2 years where TOTAL was low and NOV was high 2.0\n"
     ]
    }
   ],
   "source": [
    "#count the cases for each month (3rd index in ctr array; iterating over columns) \n",
    "# for the predictor/forecast-1st index in ctr array (prior month category of low, mid, or hi precip)\n",
    "# and for the predictand/observed- 2nd index in ctr array (seasonal total falling in low, mid, or hi terciles)\n",
    "# 3rd index is the month\n",
    "ctr = np.zeros((3,3,7))\n",
    "ih = 0\n",
    "for h in headers: \n",
    "        ctr[0,0,ih] = sum((snow_accum[h]<stats[h][\"33%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]))\n",
    "        ctr[1,0,ih] = sum((snow_accum[h]>=stats[h][\"33%\"]) & (snow_accum[h]<=stats[h][\"66%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]))\n",
    "        ctr[2,0,ih] = sum((snow_accum[h]>stats[h][\"66%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]))\n",
    "        ctr[0,1,ih] = sum((snow_accum[h]<stats[h][\"33%\"]) & (snow_accum[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]))\n",
    "        ctr[1,1,ih] = sum((snow_accum[h]>=stats[h][\"33%\"]) & (snow_accum[h]<=stats[h][\"66%\"]) & (snow_accum[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]))\n",
    "        ctr[2,1,ih] = sum((snow_accum[h]>stats[h][\"66%\"]) & (snow_accum[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]))\n",
    "        ctr[0,2,ih] = sum((snow_accum[h]<stats[h][\"33%\"]) & (snow_accum[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]))\n",
    "        ctr[1,2,ih] = sum((snow_accum[h]>=stats[h][\"33%\"]) & (snow_accum[h]<=stats[h][\"66%\"]) & (snow_accum[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]))\n",
    "        ctr[2,2,ih] = sum((snow_accum[h]>stats[h][\"66%\"]) & (snow_accum[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]))\n",
    "        print(h,ctr[:,:,ih])\n",
    "        ih=ih+1\n",
    "\n",
    "print(\"recognize the ordering. 2 years where TOTAL was low and NOV was high\",ctr[2,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ending Year\n",
      "1946    False\n",
      "1947    False\n",
      "1948    False\n",
      "1949    False\n",
      "1950    False\n",
      "        ...  \n",
      "2018     True\n",
      "2019    False\n",
      "2020    False\n",
      "2021    False\n",
      "2022     True\n",
      "Length: 77, dtype: bool\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'max_year' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [9], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(predictor_snow)\n\u001b[1;32m      4\u001b[0m fig,(ax1) \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m----> 5\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(predictor_snow,color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m'\u001b[39m,label\u001b[38;5;241m=\u001b[39m\u001b[43mmax_year\u001b[49m)\n\u001b[1;32m      7\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset(xlabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYear\u001b[39m\u001b[38;5;124m\"\u001b[39m,ylabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSnowfall (cm)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAlta Snowfall\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'max_year' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " #given that the season total is below normal, in what years was Nov below normal?\n",
    "predictor_snow =(snow_accum[\"NOV\"]<stats[\"NOV\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"])\n",
    "print(predictor_snow)\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(10,6))\n",
    "ax1.plot(predictor_snow,color='green',label=max_year)\n",
    "\n",
    "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (cm)\")\n",
    "ax1.set(title=\"Alta Snowfall\")\n",
    "#add grids to the plot\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "#save the figure to \n",
    "plt.savefig('alta_snowfall_below_normal_given_below_normal.png')\n",
    "\n",
    "print(stats[\"NOV\"][\"33%\"]/2.54)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print out totals relative to the Below normal threshold\n",
    "low_years= snow_accum[\"TOTAL\"]-stats[\"TOTAL\"][\"33%\"]\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(10,6))\n",
    "ax1.plot(low_years,color='green',label=max_year)\n",
    "\n",
    "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (cm)\")\n",
    "ax1.set(title=\"Alta Snowfall\")\n",
    "#add grids to the plot\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "ax1.legend()\n",
    "#save the figure to \n",
    "plt.savefig('alta_snowfall_below_normal.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  predictor-persistence from total observed during previous year. \n",
    "#shift the values in the dataframe one year forward\n",
    "snow_shift=snow_accum.shift(periods=1)\n",
    "#print(snow_shift)\n",
    "# starting from what tercle the previous year was in and seeing if those conditions persisted to the next year\n",
    "ctr_pers = np.zeros((3,3))\n",
    "ctr_pers[0,0] = sum((snow_shift[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]))\n",
    "ctr_pers[1,0] = sum((snow_shift[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_shift[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]))\n",
    "ctr_pers[2,0] = sum((snow_shift[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]) & (snow_accum[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]))\n",
    "ctr_pers[0,1] = sum((snow_shift[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]))\n",
    "ctr_pers[1,1] = sum((snow_shift[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_shift[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"])& (snow_accum[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]))\n",
    "ctr_pers[2,1] = sum((snow_shift[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]) & (snow_accum[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]))\n",
    "ctr_pers[0,2] = sum((snow_shift[\"TOTAL\"]<stats[\"TOTAL\"][\"33%\"]) & (snow_accum[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]))\n",
    "ctr_pers[1,2] = sum((snow_shift[\"TOTAL\"]>=stats[\"TOTAL\"][\"33%\"]) & (snow_shift[\"TOTAL\"]<=stats[\"TOTAL\"][\"66%\"]) & (snow_accum[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]))\n",
    "ctr_pers[2,2] = sum((snow_shift[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]) & (snow_accum[\"TOTAL\"]>stats[\"TOTAL\"][\"66%\"]))\n",
    "print(ctr_pers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1995 1893.316\n",
      "2015 679.45\n",
      "NOV      6\n",
      "DEC      6\n",
      "JAN      6\n",
      "FEB      6\n",
      "MAR      6\n",
      "APR      6\n",
      "TOTAL    6\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#In what year did the max/min seasonal total values happen and what were the values?\n",
    "max_year = snow_df.idxmax()[\"TOTAL\"]\n",
    "min_year = snow_df.idxmin()[\"TOTAL\"]\n",
    "print(max_year,snow_df.max()[\"TOTAL\"])\n",
    "print(min_year,snow_df.min()[\"TOTAL\"])\n",
    "#accumulated values during the years with the max and min total\n",
    "max_year_vals = (snow_accum.loc[snow_accum.idxmax()[\"TOTAL\"]])\n",
    "min_year_vals = (snow_accum.loc[snow_accum.idxmin()[\"TOTAL\"]])\n",
    "#print(max_year_vals,min_year_vals,rec_year_vals)\n",
    "#get the values from the most recent year\n",
    "recent_year_vals = snow_accum.values[-1]\n",
    "recent_year = snow_accum['TOTAL'].index[-1]\n",
    "#print(rec_year,rec_year_vals)\n",
    "\n",
    "# determine the median values later in the season when Nov is in each tercile\n",
    "thresh_low = stats[\"NOV\"][\"33%\"]\n",
    "median_low_nov = snow_accum[snow_accum[\"NOV\"] < thresh_low].median()\n",
    "thresh_mid = stats[\"NOV\"][\"66%\"]\n",
    "median_mid_nov = snow_accum[((snow_accum[\"NOV\"] >= thresh_low) & (snow_accum[\"NOV\"] <= thresh_mid))] .median()\n",
    "median_hi_nov = snow_accum[snow_accum[\"NOV\"] > thresh_mid].median()\n",
    "#print(median_low_nov,median_mid_nov,median_hi_nov)\n",
    "\n",
    "#new one 2022 given Nov 2022 ~90 in, so group the cases between 85 and 90 inches\n",
    "median_2023 = snow_accum[((snow_accum[\"NOV\"] >= 215) & (snow_accum[\"NOV\"] <= 240))] .median()\n",
    "\n",
    "count_2023 = snow_accum[((snow_accum[\"NOV\"] >= 215) & (snow_accum[\"NOV\"] <= 240))] .count()\n",
    "print(count_2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create line plot time series of Alta accumulating snowfall each month\n",
    "\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(10,6))\n",
    "ax1.plot(max_year_vals,color='green',label=max_year)\n",
    "ax1.plot(min_year_vals,color='red',label=min_year)\n",
    "ax1.plot(recent_year_vals,color='purple',label=recent_year)\n",
    "ax1.plot(snow_med_accum,color='black',label='Median')\n",
    "ax1.plot(median_hi_nov,color='green',label='Median High Nov',linestyle='--')\n",
    "ax1.plot(median_mid_nov,color='black',label='Median Mid Nov',linestyle='--')\n",
    "ax1.plot(median_low_nov,color='red',label='Median Low Nov',linestyle='--')\n",
    "ax1.plot(median_2023,color='cyan',label='Median Near Nov 2022',linestyle='--')\n",
    "ax1.set(xlabel=\"Month\",ylabel=\"Snowfall (cm)\")\n",
    "ax1.set(title=\"Alta Snowfall\")\n",
    "#add grids to the plot\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "ax1.legend()\n",
    "#save the figure to \n",
    "plt.savefig('alta_snowfall_forecasts.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use pandas to estimate Total snow from Nov snow\n",
    "#Pearson correlation: not robust and resitant but most commonly used\n",
    "r_pc= snow_accum.corr(method='pearson')\n",
    "print(r_pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot totals\n",
    "print(snow_accum.index.name)\n",
    "fig,(ax1,ax2) = plt.subplots(2,1,figsize=(15,7.5))\n",
    "ax1.plot(snow_accum['NOV'],label='NOV')\n",
    "ax1.plot(snow_accum['TOTAL'],color='red',label='TOTAL')\n",
    "\n",
    "ax1.set_ylabel('Snowfall (cm)')\n",
    "ax1.set(xlim=(1945,2023))\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "#this will be discussed in Chapter 4\n",
    "x = snow_accum['NOV']\n",
    "y = snow_accum['TOTAL']\n",
    "\n",
    "#get means\n",
    "xm = np.mean(x)\n",
    "ym = np.mean(y)\n",
    "\n",
    "#get st devs\n",
    "xs = np.std(x)\n",
    "ys = np.std(y)\n",
    "print('xm,ym: %.1f %.1f' % (xm,ym))\n",
    "print('xs,ys: %.1f %.1f' % (xs,ys))\n",
    "print('Nov plus/minus 1 std dev: %.1f %.1f' % (xm-xs,xm+xs))\n",
    "print('Total plus/minus 1 std dev: %.1f %.1f' % (ym-ys,ym+ys))\n",
    "\n",
    "\n",
    "#get anomalies\n",
    "xprime = x - xm\n",
    "yprime = y - ym\n",
    "\n",
    "#standardize anomalies\n",
    "xstar = xprime/xs\n",
    "ystar = yprime/ys\n",
    "\n",
    "#plot anomalies\n",
    "ax2.plot(xstar,label='NOV')\n",
    "ax2.plot(ystar,label='TOTAL',color='red')\n",
    "ax2.legend()\n",
    "ax2.set(xlim=(1945,2023))\n",
    "ax2.grid()\n",
    "ax2.set_ylabel('ST ANOM')\n",
    "ax2.set_xlabel('Year')\n",
    "plt.savefig('figure_snow_time_series.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using linear algebra to estimate Season Total from Nov total\n",
    "n = len(xprime)\n",
    "covar = np.dot(xprime,yprime)\n",
    "varx = np.dot(xprime,xprime)\n",
    "vary = np.dot(yprime,yprime)\n",
    "covar = covar/n\n",
    "varx = varx/n\n",
    "vary = vary/n\n",
    "\n",
    "\n",
    "b = covar/varx\n",
    "r = covar/np.sqrt(varx*vary)\n",
    "sdx = np.sqrt(varx)\n",
    "sdy = np.sqrt(vary)#compute estimate of season totals over range of November totals\n",
    "xhat = np.linspace(-xm,600-xm,100)\n",
    "yhat = b*xhat\n",
    "print(r)\n",
    "\n",
    "XH = xm+xhat\n",
    "YH = ym+yhat\n",
    "fig,(ax1,ax2) = plt.subplots(2,1,figsize=(15,7.5))\n",
    "ax1.scatter(x,y,marker='+')\n",
    "ax1.plot(XH,YH,color='red')\n",
    "ax1.grid()\n",
    "ax1.set_xlabel('NOV')\n",
    "ax1.set_ylabel('TOTAL')\n",
    "ax2.scatter(xstar,ystar,marker='+',color='red')\n",
    "ax2.plot(xhat/xs,yhat/ys)\n",
    "ax2.grid()\n",
    "ax2.set_xlabel('NOV')\n",
    "ax2.set_ylabel('TOTAL')\n",
    "plt.savefig('figure_alta_regression.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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