Expected_Ndays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7843862d-ac17-4f2d-abfb-ac962e207777",
   "metadata": {},
   "source": [
    "# Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alert-drawing",
   "metadata": {},
   "source": [
    "This notebooks uses the calendar and probability distributions to calculate/create a table of outputs showing the expected number of \"Natural\" counts one would expect to see over three different time scales---annual, seasonal (3 months), and monthly---given a particularly chosen percentile.\n",
    "\n",
    "The resulting numbers are printed to create Supporting Information Table S2, and the counts are saved into **[Ndays_Expected.nc](./data/analysis/Ndays_Expected.nc)** for use in the attribution analyses (see XXXXXXXXX).\n",
    "\n",
    "**TABLES PRODUCED IN THIS CODE**\n",
    "* Table S2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd09bf86-89b7-4fd3-802e-dd9fd2d7d79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from calendar import monthrange\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ambient-century",
   "metadata": {},
   "source": [
    "### Definitions -- Percentiles and Time Periods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8239180b-6be7-4fd4-90ff-cc8dbdeab439",
   "metadata": {},
   "source": [
    "Time scales considered:\n",
    "* Annual (1 year: 365 days)\n",
    "* Seasonal (3 months: DJF, MAM, JJA, SON; ndays vary by months included)\n",
    "* Monthly (ndays vary by month)\n",
    "\n",
    "Percentiles explored:\n",
    "* 99.99, 99.9, 99, 98, 97.5, 97, 96.7 96, ..., 50\n",
    "* 0.01, 0.1, 1, 2, 2.5, 3, 3.33, 4, 5, ..., 49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fallen-democracy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01  0.1   1.    2.    2.5   3.    3.333 3.    4.    5.   ]\n",
      "[94.    95.    96.    96.667 97.    97.5   98.    99.    99.9   99.99 ]\n"
     ]
    }
   ],
   "source": [
    "# define the percentile grids\n",
    "midgrid=np.arange(3,96+1,1)\n",
    "lowgrid=np.asarray([0.01,0.1,1,2,2.5,3,3.333])\n",
    "highgrid=np.flip(100-lowgrid)\n",
    "# combine them into a single grid\n",
    "pgrid=np.concatenate((lowgrid,midgrid,highgrid))\n",
    "print(pgrid[:10])\n",
    "print(pgrid[-10:])\n",
    "npc=len(pgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "suffering-introduction",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the number of days in each month (ignore leap years)\n",
    "mondays=np.asarray([monthrange(2011, i)[1] for i in np.arange(12)+1])\n",
    "\n",
    "# get the number of days in a year\n",
    "ndays_yr=np.sum(mondays)\n",
    "\n",
    "# get the number of days in each season (DJF, MAM, JJA, SON), by shifting mondays 1 month\n",
    "ndays_seas=np.asarray([np.sum(np.roll(mondays,1)[i:i+3]) for i in np.arange(0,10,3)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-advisory",
   "metadata": {},
   "source": [
    "## Find the number of Expected counts in a season, by percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rotary-kelly",
   "metadata": {},
   "source": [
    "The expected number of counts for a specific period is by the definition of the percentile, where the ordinal rank ($n_{rank}$) is given by the percentile between 0 and 100, $p$, and the number of values in the ordered list ($N$):\n",
    "\n",
    "$ n_{rank} = \\frac{p}{100} \\times N$\n",
    "\n",
    "For our purposes, the number of values in our list corresponds to the number of days we are analyzing over, i.e. $N \\equiv n_{days}$:\n",
    "\n",
    "$ n_{rank,day} = \\frac{p}{100} \\times (n_{days})$\n",
    "\n",
    "So that, through the definition of the ordinal rank, the total of number of days which will exceed it ($n_{exceedances}$), based on the percentile, is given by:\n",
    "\n",
    "$n_{exceedances} = n_{days} - n_{rank,day} = n_{days} - \\frac{p}{100} \\times (n_{days}) $"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compressed-contemporary",
   "metadata": {},
   "source": [
    "#### Example\n",
    "\n",
    "Let's assume that we are searching for the threshold for the number of exceedances of the defined percentile threshold, if $p=95$ and our time period is annual ($n_{days}=365$).\n",
    "\n",
    "#### Subceedance\n",
    "\n",
    "If we are looking for the number of values _under_ the threshold, we would find the subceedance, which is equal to the number of days in our period MINUS the threshold number, i.e. \n",
    "$n_{subceedances} =  n_{days} - n_{exceedances}$ , which is identically $n_{rank,day}$.\n",
    "\n",
    "NOTE: The unit of $n_{exceedances}$/$n_{subceedances}$ always matches that of the counts in your list (annual is number of days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aerial-toner",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions to calculate the natural thresholds associated with a list of values\n",
    "def nat_exceed(pp,ndays):\n",
    "    # pp is the percentile over 0-100\n",
    "    # ndays in the number of values in your list (usually number of days)\n",
    "    nexceed=ndays-(pp/100)*ndays\n",
    "    return(nexceed)\n",
    "\n",
    "def nat_subceed(pp,ndays):\n",
    "    # pp is the percentile over 0-100\n",
    "    # ndays in the number of values in your list (usually number of days)\n",
    "    nsubceed=(pp/100)*ndays\n",
    "    return(nsubceed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "addressed-arena",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.25\n",
      "346.75\n"
     ]
    }
   ],
   "source": [
    "p1=95\n",
    "n1=nat_exceed(p1,ndays_yr)\n",
    "n2=nat_subceed(p1,ndays_yr)\n",
    "print(n1)\n",
    "print(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "productive-parallel",
   "metadata": {},
   "source": [
    "So all together, for the 95th percentile we would expect there to be exactly 18.25 days which would meet or exceed this percentile over a representative 365 day period. Meanwhile, the number of days at or below that threshold is 346.75."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convenient-screen",
   "metadata": {},
   "source": [
    "## Create the Grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "wooden-destination",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an annual grid of exceedances\n",
    "annual_exceed=np.empty((npc,),dtype='float')\n",
    "annual_exceed[:,]=nat_exceed(pgrid,ndays_yr)\n",
    "annual_subceed=np.empty((npc,),dtype='float')\n",
    "annual_subceed[:,]=nat_subceed(pgrid,ndays_yr)\n",
    "\n",
    "# create a monthly and seasonal grid\n",
    "mon_exceed=np.empty((npc,len(mondays)),dtype='float')\n",
    "mon_subceed=np.empty((npc,len(mondays)),dtype='float')\n",
    "seas_exceed=np.empty((npc,len(ndays_seas)),dtype='float')\n",
    "seas_subceed=np.empty((npc,len(ndays_seas)),dtype='float')\n",
    "for pp,p0 in enumerate(pgrid):\n",
    "    seas_exceed[pp,:]=nat_exceed(p0,ndays_seas)\n",
    "    mon_exceed[pp,:]=nat_exceed(p0,mondays)\n",
    "    seas_subceed[pp,:]=nat_subceed(p0,ndays_seas)\n",
    "    mon_subceed[pp,:]=nat_subceed(p0,mondays)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab2881bf-fbe1-44c4-b30e-895eeb55d3fa",
   "metadata": {},
   "source": [
    "For sanity, check the average of the expected number of days in a season (should be ~90 days)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "commercial-vinyl",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ndays_seas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f84740-c375-4c16-9ef4-017322b87582",
   "metadata": {},
   "source": [
    "## Quick Look at the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "radical-portsmouth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.0\n",
      "30.416666666666668\n",
      "3.65\n",
      "93.0\n",
      "2.1291666666666647\n",
      "95.0\n",
      "1.520833333333334\n",
      "96.667\n",
      "1.0137874999999996\n"
     ]
    }
   ],
   "source": [
    "print(pgrid[92])\n",
    "print(np.mean(mondays))\n",
    "print(np.mean(mon_exceed[92,:]))\n",
    "\n",
    "print(pgrid[97])\n",
    "print(np.mean(mon_exceed[97,:]))\n",
    "\n",
    "print(pgrid[99])\n",
    "print(np.mean(mon_exceed[99,:]))\n",
    "\n",
    "print(pgrid[101])\n",
    "print(np.mean(mon_exceed[101,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "official-number",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.0\n",
      "91.25\n",
      "10.950000000000003\n",
      "93.0\n",
      "6.387499999999996\n",
      "95.0\n",
      "4.5625000000000036\n",
      "96.667\n",
      "3.0413625000000017\n"
     ]
    }
   ],
   "source": [
    "print(pgrid[92])\n",
    "print(np.mean(ndays_seas))\n",
    "print(np.mean(seas_exceed[92,:]))\n",
    "\n",
    "print(pgrid[97])\n",
    "print(np.mean(seas_exceed[97,:]))\n",
    "\n",
    "print(pgrid[99])\n",
    "print(np.mean(seas_exceed[99,:]))\n",
    "\n",
    "print(pgrid[101])\n",
    "print(np.mean(seas_exceed[101,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "unlikely-seeker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.0\n",
      "365.0\n",
      "43.80000000000001\n",
      "93.0\n",
      "25.549999999999955\n",
      "95.0\n",
      "18.25\n",
      "96.667\n",
      "12.165449999999964\n"
     ]
    }
   ],
   "source": [
    "print(pgrid[92])\n",
    "print(np.mean(365))\n",
    "print(np.mean(annual_exceed[92]))\n",
    "\n",
    "print(pgrid[97])\n",
    "print(np.mean(annual_exceed[97]))\n",
    "\n",
    "print(pgrid[99])\n",
    "print(np.mean(annual_exceed[99]))\n",
    "\n",
    "print(pgrid[101])\n",
    "print(np.mean(annual_exceed[101]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f232fd-b73c-4bd6-95ed-2d07aa8fd236",
   "metadata": {},
   "source": [
    "# Create/Show Table S2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2227c0-7ddb-45c9-a713-c9cea8ff2ac9",
   "metadata": {},
   "source": [
    "#### Aggregate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3192488d-defc-46b6-8ca2-0b91cda08fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "q90_dict={\n",
    "    '30 (Month)': [np.mean(mon_exceed[92])],\n",
    "    '91 (Season)': [np.mean(seas_exceed[92])],\n",
    "    '365 (Year)': [np.mean(annual_exceed[92])],\n",
    "         }\n",
    "q90df=pd.DataFrame(q90_dict,index={'qi=0.90'})\n",
    "q95_dict={\n",
    "    '30 (Month)': np.mean(mon_exceed[97]),\n",
    "    '91 (Season)': np.mean(seas_exceed[97]),\n",
    "    '365 (Year)': np.mean(annual_exceed[97]),\n",
    "         }\n",
    "q95df=pd.DataFrame(q95_dict,index={'qi=0.95'})\n",
    "q975_dict={\n",
    "    '30 (Month)': np.mean(mon_exceed[99]),\n",
    "    '91 (Season)': np.mean(seas_exceed[99]),\n",
    "    '365 (Year)': np.mean(annual_exceed[99]),\n",
    "         }\n",
    "q975df=pd.DataFrame(q975_dict,index={'qi=0.975'})\n",
    "q99_dict={\n",
    "    '30 (Month)': np.mean(mon_exceed[101]),\n",
    "    '91 (Season)': np.mean(seas_exceed[101]),\n",
    "    '365 (Year)': np.mean(annual_exceed[101]),\n",
    "         }\n",
    "q99df=pd.DataFrame(q99_dict,index={'qi=0.99'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c2ab42-4b68-4122-bd7c-3c6f6195eac6",
   "metadata": {},
   "source": [
    "#### Print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbcf40ec-6257-4efd-ae9c-fde87c540cb3",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>30 (Month)</th>\n",
       "      <th>91 (Season)</th>\n",
       "      <th>365 (Year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>qi=0.90</th>\n",
       "      <td>3.65</td>\n",
       "      <td>10.95</td>\n",
       "      <td>43.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "         30 (Month)  91 (Season)  365 (Year)\n",
       "qi=0.90        3.65        10.95        43.8"
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>qi=0.95</th>\n",
       "      <td>2.129167</td>\n",
       "      <td>6.3875</td>\n",
       "      <td>25.55</td>\n",
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       "         30 (Month)  91 (Season)  365 (Year)\n",
       "qi=0.95    2.129167       6.3875       25.55"
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       "      <th>qi=0.975</th>\n",
       "      <td>1.520833</td>\n",
       "      <td>4.5625</td>\n",
       "      <td>18.25</td>\n",
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       "          30 (Month)  91 (Season)  365 (Year)\n",
       "qi=0.975    1.520833       4.5625       18.25"
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       "      <th>qi=0.99</th>\n",
       "      <td>1.013787</td>\n",
       "      <td>3.041363</td>\n",
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       "         30 (Month)  91 (Season)  365 (Year)\n",
       "qi=0.99    1.013787     3.041363    12.16545"
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   ],
   "source": [
    "display(q90df)\n",
    "display(q95df)\n",
    "display(q975df)\n",
    "display(q99df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc01b80-2325-4edc-8902-fb86ea1270b4",
   "metadata": {},
   "source": [
    "# Save out the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c4f1e5-d097-4a8b-9854-ce2c88e28615",
   "metadata": {},
   "source": [
    "We will store the result in an xarray dataset and then save out the data to a netcdf that can be read throughout the attribution analysis codebase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3278ab98-61f7-4ea5-90e6-699f054b0445",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define monthly period labels\n",
    "seaslabels=['DJF','MAM','JJA','SON']\n",
    "monlabels=['J','F','M','A','M','J','J','A','S','O','N','D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "active-immunology",
   "metadata": {},
   "outputs": [],
   "source": [
    "dso=xr.Dataset(\n",
    "    {\n",
    "        'AnnualExceedance': (['pp'], annual_exceed),\n",
    "        'AnnualSubceedance': (['pp'], annual_subceed),\n",
    "        'SeasonalExceedance': (['pp','seas'], seas_exceed),\n",
    "        'SeasonalSubceedance': (['pp','seas'], seas_subceed),\n",
    "        'MonthlyExceedance': (['pp','mon'], mon_exceed),\n",
    "        'MonthlySubceedance': (['pp','mon'], mon_subceed),\n",
    "    },\n",
    "    coords={\n",
    "        'pp': (['pp'], pgrid.squeeze()),\n",
    "        'seas': (['seas'], seaslabels),\n",
    "        'mon': (['mon'], monlabels)\n",
    "    },\n",
    "    attrs={\n",
    "        'Dataset Unit': 'Number of Days Equal to or Exceeding OR Subceeding the percentile'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "amended-spice",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save out the result\n",
    "savepath='./data/analysis/'\n",
    "savename='Ndays_Expected.nc'\n",
    "# remove the file if it already exists\n",
    "if os.path.exists(savepath+savename):\n",
    "    os.remove(savepath+savename)\n",
    "# the save the result\n",
    "dso.to_netcdf(savepath+savename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89977565-5a7e-4a70-8c29-e43ac59d624a",
   "metadata": {},
   "source": [
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-conference",
   "metadata": {},
   "source": [
    "# Plot the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5c7072-68da-463c-a5ff-218d9b55307f",
   "metadata": {},
   "source": [
    "The following analyses are for illustrative/intuition-building purposes only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genuine-cooking",
   "metadata": {},
   "source": [
    "### Annual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "announced-dynamics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(pgrid,annual_exceed,lw=3,label='exceed')\n",
    "plt.plot(pgrid,annual_subceed,lw=3,label='subceed')\n",
    "plt.xlim([100,0])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.ylabel('Associated Day Threshold',fontsize=16)\n",
    "plt.title('Number of Days Annually expected to Exceed each Percentile',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(pgrid,annual_exceed,lw=3,label='exceed')\n",
    "plt.plot(pgrid,annual_subceed,lw=3,label='subceed')\n",
    "plt.xlim([100,95])\n",
    "plt.ylim([0,20])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.ylabel('Associated Day Threshold',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.hlines(1,0,100,color='r',linestyle='--')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(pgrid,annual_exceed,lw=3,label='exceed')\n",
    "plt.plot(pgrid,annual_subceed,lw=3,label='subceed')\n",
    "plt.xlim([5,0])\n",
    "plt.ylim([0,20])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.hlines(1,0,100,color='r',linestyle='--',label='1 day per year')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collected-administrator",
   "metadata": {},
   "source": [
    "### Seasonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "antique-fitting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,5))\n",
    "clevels=np.arange(0,55,5)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "c1=plt.contourf(pgrid,np.arange(4),seas_exceed.T,levels=clevels)\n",
    "plt.contour(pgrid,np.arange(4),seas_exceed.T,colors='r',linewidths=2,linestyles='--',levels=[0.99,1.01])\n",
    "plt.yticks(np.arange(4),seaslabels)\n",
    "plt.colorbar(c1)\n",
    "plt.xlim([100,50])\n",
    "plt.ylim([3,0])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.ylabel('Seasons',fontsize=16)\n",
    "plt.title('Seasonal Exceedances (ndays)',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "c2=plt.contourf(pgrid,np.arange(4),seas_subceed.T,levels=clevels)\n",
    "plt.contour(pgrid,np.arange(4),seas_subceed.T,colors='r',linewidths=2,linestyles='--',levels=[0.99,1.01])\n",
    "plt.yticks(np.arange(4),seaslabels)\n",
    "plt.colorbar(c2)\n",
    "plt.xlim([50,0])\n",
    "plt.ylim([3,0])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.ylabel('Seasons',fontsize=16)\n",
    "plt.title('Seasonal Subceedances (ndays)',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "noble-protest",
   "metadata": {},
   "source": [
    "### Monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "comfortable-image",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,5))\n",
    "clevels=np.arange(0,16+1,2)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "c1=plt.contourf(pgrid,np.arange(12),mon_exceed.T,levels=clevels)\n",
    "plt.contour(pgrid,np.arange(12),mon_exceed.T,colors='r',linewidths=2,linestyles='--',levels=[0.99,1.01])\n",
    "plt.yticks(np.arange(12),monlabels)\n",
    "plt.colorbar(c1)\n",
    "plt.xlim([100,50])\n",
    "plt.ylim([11,0])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.ylabel('Seasons',fontsize=16)\n",
    "plt.title('Monthly Exceedances (ndays)',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "c2=plt.contourf(pgrid,np.arange(12),mon_subceed.T,levels=clevels)\n",
    "plt.contour(pgrid,np.arange(12),mon_subceed.T,colors='r',linewidths=2,linestyles='--',levels=[0.99,1.01])\n",
    "plt.yticks(np.arange(12),monlabels)\n",
    "plt.colorbar(c2)\n",
    "plt.xlim([50,0])\n",
    "plt.ylim([11,0])\n",
    "plt.xlabel('Percentile',fontsize=16)\n",
    "plt.ylabel('Seasons',fontsize=16)\n",
    "plt.title('Monthly Subceedances (ndays)',fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "infrared-formation",
   "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}