Notebook/FigS6_YRB_HW_nonstationary_fitting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The nonstationary fitting code is adapted from https://github.com/clairbarnes/wwa "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import scipy\n",
    "from scipy.stats import norm, gamma, lognorm, genextreme, genextreme as gev\n",
    "from scipy.optimize import minimize\n",
    "from xclim.indices.stats import fit\n",
    "\n",
    "import random\n",
    "import sys; sys.path.append('scripts/')\n",
    "from nonstationary_fitting import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_gmst = pd.read_csv('data/era5_global_mean_surf_temp.csv',index_col=0)\n",
    "era5_gmst['gmst'] = era5_gmst['gmst'].rolling(5).mean()\n",
    "era5_gmst_1979_2022 = era5_gmst[era5_gmst['time']>1978]\n",
    "era5_gmst_1979_2022 = era5_gmst_1979_2022['gmst']\n",
    "era5_gmst_1979_2022.index = range(1979,2023)\n",
    "\n",
    "era5_gmst_1979_2022 = era5_gmst_1979_2022 - 273.15\n",
    "era5_gmst_1979_2022 = era5_gmst_1979_2022 - era5_gmst_1979_2022[era5_gmst_1979_2022.index<2011].mean()\n",
    "\n",
    "era5_gmst_1979_2021 = era5_gmst_1979_2022[era5_gmst_1979_2022.index<2022]\n",
    "era5_gmst_1979_2021 = era5_gmst_1979_2021 - 273.15\n",
    "era5_gmst_1979_2021 = era5_gmst_1979_2021 - era5_gmst_1979_2021[era5_gmst_1979_2021.index<2011].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "YRB_SAT_1979_2022 = pd.read_csv('data/YRB_averaged_SAT_1979_2022.csv',index_col=0)\n",
    "YRB_SAT_1979_2022['t2m'] = YRB_SAT_1979_2022['t2m'] - 273.15\n",
    "YRB_SAT_1979_2021 = YRB_SAT_1979_2022[:-1]\n",
    "YRB_HWD_1979_2022 = pd.read_csv('data/YRB_averaged_HWD_1979_2022.csv',index_col=0)\n",
    "YRB_HWD_1979_2021 = YRB_HWD_1979_2022[:-1]\n",
    "YRB_HWL_1979_2022 = pd.read_csv('data/YRB_averaged_HWL_1979_2022.csv',index_col=0)\n",
    "YRB_HWL_1979_2021 = YRB_HWL_1979_2022[:-1]\n",
    "YRB_HWM_1979_2022 = pd.read_csv('data/YRB_averaged_HWM_1979_2022.csv',index_col=0)\n",
    "YRB_HWM_1979_2021 = YRB_HWM_1979_2022[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "YRB_HW_data_1979_2021 = pd.DataFrame(\n",
    "    {\n",
    "    'SAT':YRB_SAT_1979_2021['t2m'].tolist(),\n",
    "    'HWD':YRB_HWD_1979_2021['t2m'].tolist(),\n",
    "    'HWL':YRB_HWL_1979_2021['t2m'].tolist(),\n",
    "    'HWM':YRB_HWM_1979_2021['t2m'].tolist(),\n",
    "    'GMST':era5_gmst_1979_2021.tolist()})\n",
    "YRB_HW_data_1979_2022 = pd.DataFrame(\n",
    "    {\n",
    "    'SAT':YRB_SAT_1979_2022['t2m'].tolist(),\n",
    "    'HWD':YRB_HWD_1979_2022['t2m'].tolist(),\n",
    "    'HWL':YRB_HWL_1979_2022['t2m'].tolist(),\n",
    "    'HWM':YRB_HWM_1979_2022['t2m'].tolist(),\n",
    "    'GMST':era5_gmst_1979_2022.tolist()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot Figure 2 for SAT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.21877450883753807, 23.15137739949357, 0.44306322049553826)\n",
      "AIC value:61.11870692092705\n"
     ]
    }
   ],
   "source": [
    "x = YRB_SAT_1979_2021['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.09599736575816467, 23.14372055281912, 0.45375238697719456)\n",
      "AIC value:70.35544672219207\n"
     ]
    }
   ],
   "source": [
    "x = YRB_SAT_1979_2022['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[23.05798513,  0.34040654,  1.23575688,  0.21566048],\n",
      "       [23.05796767,  0.34040927,  1.23570215,  0.21567708],\n",
      "       [23.05798057,  0.34043141,  1.23582691,  0.21574382],\n",
      "       [23.05795708,  0.34044466,  1.2356929 ,  0.21569836],\n",
      "       [23.05800688,  0.34042347,  1.23572999,  0.21573716]]), array([16.07126921, 16.0712693 , 16.07126934, 16.07126959, 16.07126963]))\n",
      "           fun: 16.071269205456424\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 237\n",
      "           nit: 141\n",
      "          pars: {'mu': 23.057985132706797, 'sigma': 0.3404065388479822, 'alpha': 1.2357568769917124, 'shape': 0.21566047878525715}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([23.05798513,  0.34040654,  1.23575688,  0.21566048])\n",
      "AIC value:40.14253841091285\n"
     ]
    }
   ],
   "source": [
    "# calculate nonstationary fitting parameters\n",
    "# sigma and shape are the constant parameters\n",
    "# mu and alpha is two new parameters to generate the time-varing location parameter, i.e., loc = mu + alpha * covariate\n",
    "params_1979_2021_mdl_SAT_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='SAT')\n",
    "params_1979_2021_SAT_shift = params_1979_2021_mdl_SAT_shift['results']\n",
    "print(params_1979_2021_SAT_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_SAT_shift['pars'])+2*params_1979_2021_SAT_shift['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[23.05081844,  0.35450154,  1.35334223,  0.16998365],\n",
      "       [23.05084818,  0.35449069,  1.35331706,  0.16995589],\n",
      "       [23.05082971,  0.35450735,  1.35336115,  0.1700101 ],\n",
      "       [23.05081186,  0.35449833,  1.35344014,  0.16997228],\n",
      "       [23.05082233,  0.3545091 ,  1.35332034,  0.16995477]]), array([19.44466393, 19.44466396, 19.44466397, 19.44466403, 19.44466404]))\n",
      "           fun: 19.44466393203229\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 245\n",
      "           nit: 144\n",
      "          pars: {'mu': 23.05081844330312, 'sigma': 0.35450153881622903, 'alpha': 1.3533422293793305, 'shape': 0.16998365257869602}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([23.05081844,  0.35450154,  1.35334223,  0.16998365])\n",
      "AIC value:46.88932786406458\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_SAT_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='SAT')\n",
    "params_1979_2022_SAT_shift = params_1979_2022_mdl_SAT_shift['results']\n",
    "print(params_1979_2022_SAT_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_SAT_shift['pars'])+2*params_1979_2022_SAT_shift['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[23.02750637,  0.43967335,  0.68771661,  0.39043154],\n",
      "       [23.02750977,  0.43968292,  0.68774883,  0.3904584 ],\n",
      "       [23.02750756,  0.43968872,  0.68767241,  0.39043021],\n",
      "       [23.0275131 ,  0.43966933,  0.68764526,  0.39042382],\n",
      "       [23.02752608,  0.43967013,  0.68766361,  0.39041548]]), array([25.21189118, 25.21189119, 25.21189121, 25.21189121, 25.21189121]))\n",
      "           fun: 25.21189117776631\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 259\n",
      "           nit: 157\n",
      "          pars: {'mu': 23.027506367397542, 'sigma': 0.439673352790811, 'alpha': 0.6877166140868969, 'shape': 0.3904315440644089}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([23.02750637,  0.43967335,  0.68771661,  0.39043154])\n",
      "AIC value:58.42378235553262\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_SAT_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='SAT')\n",
    "params_1979_2021_SAT_scale = params_1979_2021_mdl_SAT_scale['results']\n",
    "print(params_1979_2021_SAT_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_SAT_scale['pars'])+2*params_1979_2021_SAT_scale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[23.01598471,  0.47123701,  0.89520699,  0.43196819],\n",
      "       [23.01599915,  0.47120484,  0.89518099,  0.43195116],\n",
      "       [23.01598152,  0.4711969 ,  0.89511541,  0.4319064 ],\n",
      "       [23.01597038,  0.47122669,  0.89529446,  0.43198726],\n",
      "       [23.01599695,  0.47120628,  0.89515666,  0.43190413]]), array([28.16618179, 28.16618183, 28.16618184, 28.16618195, 28.16618203]))\n",
      "           fun: 28.166181788719097\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 239\n",
      "           nit: 138\n",
      "          pars: {'mu': 23.015984711055683, 'sigma': 0.47123701234426196, 'alpha': 0.895206986355533, 'shape': 0.43196818960804545}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([23.01598471,  0.47123701,  0.89520699,  0.43196819])\n",
      "AIC value:64.3323635774382\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_SAT_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='SAT')\n",
    "params_1979_2022_SAT_scale = params_1979_2022_mdl_SAT_scale['results']\n",
    "print(params_1979_2022_SAT_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_SAT_scale['pars'])+2*params_1979_2022_SAT_scale['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting tpye: shiftscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[23.05911487,  0.33699029,  1.22900738,  0.2184255 ,  0.03160104],\n",
      "       [23.05909865,  0.33695315,  1.22907324,  0.21835165,  0.0316404 ],\n",
      "       [23.05910505,  0.33696519,  1.22909539,  0.2184132 ,  0.03160709],\n",
      "       [23.0591213 ,  0.33695598,  1.22895487,  0.2184123 ,  0.03166558],\n",
      "       [23.05907102,  0.33695977,  1.22903724,  0.21835386,  0.03159735],\n",
      "       [23.05910624,  0.33694667,  1.22903619,  0.21835025,  0.03160041]]), array([16.05337427, 16.05337429, 16.0533743 , 16.05337447, 16.05337448,\n",
      "       16.05337456]))\n",
      "           fun: 16.053374267143056\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 473\n",
      "           nit: 291\n",
      "          pars: {'mu': 23.059114868009033, 'sigma': 0.3369902918710691, 'alpha': 1.2290073769837548, 'shape': 0.21842550051597304, 'beta': 0.03160104473318074}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([23.05911487,  0.33699029,  1.22900738,  0.2184255 ,  0.03160104])\n",
      "AIC value:42.10674853428611\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_SAT_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='SAT')\n",
    "params_1979_2021_SAT_shiftscale = params_1979_2021_mdl_SAT_shiftscale['results']\n",
    "print(params_1979_2021_SAT_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_SAT_shiftscale['pars'])+2*params_1979_2021_SAT_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[23.06062112,  0.33796579,  1.31407703,  0.19887948,  0.14796504],\n",
      "       [23.0605925 ,  0.3379454 ,  1.314036  ,  0.19882562,  0.14797325],\n",
      "       [23.06060546,  0.33796579,  1.31399095,  0.19886657,  0.14801012],\n",
      "       [23.06061222,  0.33796657,  1.31415034,  0.19889742,  0.14799363],\n",
      "       [23.0606183 ,  0.33794411,  1.3140372 ,  0.19884567,  0.14801574],\n",
      "       [23.06059955,  0.33796736,  1.31408845,  0.19882846,  0.14796805]]), array([18.90042108, 18.90042112, 18.90042122, 18.90042124, 18.90042129,\n",
      "       18.90042132]))\n",
      "           fun: 18.900421084655704\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 386\n",
      "           nit: 238\n",
      "          pars: {'mu': 23.06062112406321, 'sigma': 0.33796579288761197, 'alpha': 1.31407703373845, 'shape': 0.19887948016665113, 'beta': 0.1479650392143052}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([23.06062112,  0.33796579,  1.31407703,  0.19887948,  0.14796504])\n",
      "AIC value:47.80084216931141\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_SAT_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='SAT')\n",
    "params_1979_2022_SAT_shiftscale = params_1979_2022_mdl_SAT_shiftscale['results']\n",
    "print(params_1979_2022_SAT_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_SAT_shiftscale['pars'])+2*params_1979_2022_SAT_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/Documents/paper/HW_track/scripts/nonstationary_fitting.py:250: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return 1/ep\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rlplot(params_1979_2021_mdl, era5_gmst_1979_2022.tolist()[-1], era5_gmst_1979_2022.tolist()[-1] - 1.2, YRB_SAT_1979_2022['t2m'][2022], lower = False, ax = None, ci_nsamp = 10, legend = True, seed = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n"
     ]
    }
   ],
   "source": [
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/Documents/paper/HW_track/scripts/nonstationary_fitting.py:250: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return 1/ep\n",
      "/var/folders/gl/305lw9j910n_yd3gdvyjpyz80000gn/T/ipykernel_52777/687510739.py:97: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 2022 YRB SAT is 24.99\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 877.04\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 113.24\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_locs = [\n",
    "    [0.05,0.56,0.9,0.38],\n",
    "    [0.05,0.05,0.9,0.38],\n",
    "]\n",
    "\n",
    "fig = plt.figure(figsize = (7,7))\n",
    "ax1 = fig.add_axes(axes_locs[0])\n",
    "ax1.scatter(era5_gmst_1979_2021.values,YRB_SAT_1979_2021['t2m'],marker='o',color='blue',alpha=0.6,s=80)\n",
    "x1 = [era5_gmst_1979_2021.min(),era5_gmst_1979_2021.max()]\n",
    "mu1 = params_1979_2021['pars']['mu']\n",
    "alpha1 = params_1979_2021['pars']['alpha']\n",
    "alpha1, mu1 = np.polyfit(YRB_HW_data_1979_2021.GMST, YRB_HW_data_1979_2021.SAT, 1)\n",
    "y1 = [mu1+x1[0]*alpha1,mu1+x1[1]*alpha1]\n",
    "l1 = ax1.plot(x1,y1,c='blue',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha1,2)) + '*X+' + str(round(mu1,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.05, y = 0.85, s = s, fontsize=14, color = 'b',transform=ax1.transAxes)\n",
    "\n",
    "star = ax1.scatter(era5_gmst_1979_2022.values[-1],YRB_SAT_1979_2022['t2m'][2022],marker='*',color='red',alpha=0.8,s=200)\n",
    "x2 = [era5_gmst_1979_2022.min(),era5_gmst_1979_2022.max()]\n",
    "mu2 = params_1979_2022['pars']['mu']\n",
    "alpha2 = params_1979_2022['pars']['alpha']\n",
    "alpha2, mu2 = np.polyfit(YRB_HW_data_1979_2022.GMST, YRB_HW_data_1979_2022.SAT, 1)\n",
    "y2 = [mu2+x2[0]*alpha2,mu2+x2[1]*alpha2]\n",
    "l2 = ax1.plot(x2,y2,c='red',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha2,2)) + '*X+' + str(round(mu2,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.05, y = 0.75, s = s, fontsize=14, color = 'r',transform=ax1.transAxes)\n",
    "\n",
    "ax1.set_ylabel('YRB averaged SAT (°C)',fontdict={'fontsize':15})\n",
    "ax1.set_xlabel('Global mean surface temperature anomalies (°C)',fontdict={'fontsize':15})\n",
    "ax1.set_ylim([21.5,25.3])\n",
    "ax1.legend(\n",
    "    [star,l1[0],l2[0]], ['2022 event','2022 excluded','2022 included'],fancybox = True,frameon=True, loc='lower right', #bbox_to_anchor=[0.65,0.35], \n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "############# ax2\n",
    "ax2 = fig.add_axes(axes_locs[1])\n",
    "# z for return period\n",
    "z1 = []\n",
    "for i in range(len(YRB_SAT_1979_2022['t2m'].tolist())):\n",
    "    z1.append(return_period(mdl = params_1979_2021_mdl,event_value = YRB_SAT_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "z2 = []\n",
    "for i in range(len(YRB_SAT_1979_2022['t2m'].tolist())):\n",
    "    z2.append(return_period(mdl = params_1979_2022_mdl,event_value = YRB_SAT_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "\n",
    "y_fit_1 = np.linspace(23, 25.1, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(23, 26, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "l1 = ax2.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax2.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "# ax2.scatter(z1,YRB_SAT_1979_2022['t2m'],marker='x',color='blue',alpha=0.6,s=80)\n",
    "# ax2.scatter(z2,YRB_SAT_1979_2022['t2m'],marker='+',color='red',alpha=0.6,s=120)\n",
    "\n",
    "YRB_SAT_1979_2022_shifted_to_2022 = YRB_SAT_1979_2022['t2m'] + (era5_gmst_1979_2022[2022] - era5_gmst_1979_2022) * alpha1\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_SAT_1979_2022_shifted_to_2022)+1)/float(len(YRB_SAT_1979_2022_shifted_to_2022) + 1))\n",
    "ax2.scatter(empirical_x[:-1],YRB_SAT_1979_2022_shifted_to_2022.sort_values()[:-1],marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax2.scatter(empirical_x[-1],YRB_SAT_1979_2022_shifted_to_2022.sort_values()[2022],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "# empirical_x = 1 / (1 - np.arange(1,len(YRB_SAT_1979_2022['t2m'])+1)/float(len(YRB_SAT_1979_2022['t2m']) + 1))\n",
    "# ax2.scatter(empirical_x,YRB_SAT_1979_2022['t2m'].sort_values(),marker='x',color='blue',alpha=0.6,s=80)\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_ylabel('YRB averaged SAT (°C)',fontdict={'fontsize':15})\n",
    "ax2.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax2.set_ylim([22.5,26])\n",
    "ax2.set_xlim([0.8,10**3])\n",
    "l3 = ax2.axhline(y=YRB_SAT_1979_2022_shifted_to_2022.sort_values()[2022],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "def add_panel_label(ax, label, x = -0.1, y = 1):\n",
    "    ax.text(x, y, label, transform=ax.transAxes,\n",
    "      fontsize=20, fontweight='bold', va='top', ha='left')\n",
    "add_panel_label(ax1, 'a', x=0, y = 1.11)\n",
    "add_panel_label(ax2, 'b', x=0, y = 1.11)\n",
    "\n",
    "ax2.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 SAT','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl,event_value = YRB_SAT_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl,event_value = YRB_SAT_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB SAT is {:.2f}'.format(YRB_SAT_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/Fig2_GEV_fitting.pdf\",bbox_inches='tight')\n",
    "fig.savefig('Figures/Fig2_GEV_fitting.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot Figure S2 for HWD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.06999494283626981, 8.386236201236471, 4.094973809883337)\n",
      "AIC value:266.8622549664996\n"
     ]
    }
   ],
   "source": [
    "x = YRB_HWD_1979_2021['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.19118264219689318, 8.339841118226971, 4.202668526831746)\n",
      "AIC value:280.91484157405375\n"
     ]
    }
   ],
   "source": [
    "x = YRB_HWD_1979_2022['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 7.84656638,  3.2010728 , 14.49398373,  0.07247644],\n",
      "       [ 7.84655712,  3.20107189, 14.4940683 ,  0.07247634],\n",
      "       [ 7.84656108,  3.20107114, 14.49402177,  0.07247855],\n",
      "       [ 7.84654452,  3.20105755, 14.49401915,  0.07247446],\n",
      "       [ 7.8465708 ,  3.20106061, 14.49403154,  0.07247653]]), array([115.93016633, 115.93016633, 115.93016633, 115.93016633,\n",
      "       115.93016633]))\n",
      "           fun: 115.93016633027659\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 288\n",
      "           nit: 162\n",
      "          pars: {'mu': 7.846566381065604, 'sigma': 3.201072795592532, 'alpha': 14.493983734879135, 'shape': 0.0724764427472755}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 7.84656638,  3.2010728 , 14.49398373,  0.07247644])\n",
      "AIC value:239.86033266055318\n"
     ]
    }
   ],
   "source": [
    "# calculate nonstationary fitting parameters\n",
    "# sigma and shape are the constant parameters\n",
    "# mu and alpha is two new parameters to generate the time-varing location parameter, i.e., loc = mu + alpha * covariate\n",
    "params_1979_2021_mdl_HWD_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWD')\n",
    "params_1979_2021_HWD_shift = params_1979_2021_mdl_HWD_shift['results']\n",
    "print(params_1979_2021_HWD_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWD_shift['pars'])+2*params_1979_2021_HWD_shift['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 7.84695447,  3.38986129, 14.46298954, -0.03531049],\n",
      "       [ 7.84696837,  3.38985929, 14.46294028, -0.03530994],\n",
      "       [ 7.84696017,  3.38985615, 14.46304184, -0.0353086 ],\n",
      "       [ 7.84697099,  3.38986723, 14.46295002, -0.03530741],\n",
      "       [ 7.84694754,  3.38984539, 14.46297245, -0.03531079]]), array([123.75553, 123.75553, 123.75553, 123.75553, 123.75553]))\n",
      "           fun: 123.75552999767783\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 341\n",
      "           nit: 201\n",
      "          pars: {'mu': 7.846954468543578, 'sigma': 3.3898612875256986, 'alpha': 14.462989544715889, 'shape': -0.03531049033411009}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 7.84695447,  3.38986129, 14.46298954, -0.03531049])\n",
      "AIC value:255.51105999535565\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWD_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWD')\n",
    "params_1979_2022_HWD_shift = params_1979_2022_mdl_HWD_shift['results']\n",
    "print(params_1979_2022_HWD_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWD_shift['pars'])+2*params_1979_2022_HWD_shift['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[7.18561378, 4.16308667, 5.92169392, 0.20071849],\n",
      "       [7.18564738, 4.16310444, 5.92163705, 0.20072137],\n",
      "       [7.18561668, 4.16308205, 5.92162358, 0.20071382],\n",
      "       [7.18562031, 4.16309931, 5.92168307, 0.20071705],\n",
      "       [7.1856601 , 4.16308878, 5.92165186, 0.2007173 ]]), array([128.08339458, 128.08339458, 128.08339458, 128.08339458,\n",
      "       128.08339458]))\n",
      "           fun: 128.08339457742932\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 301\n",
      "           nit: 175\n",
      "          pars: {'mu': 7.185613776500993, 'sigma': 4.163086673135728, 'alpha': 5.921693923483028, 'shape': 0.20071848882275606}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([7.18561378, 4.16308667, 5.92169392, 0.20071849])\n",
      "AIC value:264.16678915485863\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_HWD_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWD')\n",
    "params_1979_2021_HWD_scale = params_1979_2021_mdl_HWD_scale['results']\n",
    "print(params_1979_2021_HWD_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWD_scale['pars'])+2*params_1979_2021_HWD_scale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[7.04465434, 4.20475706, 6.16403931, 0.10923274],\n",
      "       [7.04467433, 4.20474281, 6.16396456, 0.10922879],\n",
      "       [7.04466322, 4.20475232, 6.16401615, 0.10922958],\n",
      "       [7.04467969, 4.20475444, 6.16397473, 0.10923005],\n",
      "       [7.04468578, 4.20475859, 6.164008  , 0.1092319 ]]), array([135.20140172, 135.20140172, 135.20140172, 135.20140172,\n",
      "       135.20140172]))\n",
      "           fun: 135.2014017157979\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 350\n",
      "           nit: 203\n",
      "          pars: {'mu': 7.044654341592692, 'sigma': 4.204757057209117, 'alpha': 6.164039313597268, 'shape': 0.10923274239183313}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([7.04465434, 4.20475706, 6.16403931, 0.10923274])\n",
      "AIC value:278.4028034315958\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWD_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWD')\n",
    "params_1979_2022_HWD_scale = params_1979_2022_mdl_HWD_scale['results']\n",
    "print(params_1979_2022_HWD_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWD_scale['pars'])+2*params_1979_2022_HWD_scale['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shiftscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 7.86718186,  2.74951327, 13.54242034,  0.03909603,  3.14612376],\n",
      "       [ 7.8672061 ,  2.74951189, 13.5423827 ,  0.03909503,  3.14622289],\n",
      "       [ 7.86719045,  2.74949516, 13.54237083,  0.03909183,  3.14621142],\n",
      "       [ 7.86718029,  2.74951042, 13.54243463,  0.03909646,  3.14619381],\n",
      "       [ 7.86718992,  2.74949183, 13.54245166,  0.03909134,  3.14613971],\n",
      "       [ 7.86722366,  2.7495119 , 13.5424414 ,  0.03909876,  3.14608609]]), array([114.22011154, 114.22011154, 114.22011154, 114.22011154,\n",
      "       114.22011154, 114.22011154]))\n",
      "           fun: 114.22011153785392\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 751\n",
      "           nit: 469\n",
      "          pars: {'mu': 7.8671818647485505, 'sigma': 2.7495132652913385, 'alpha': 13.542420336816956, 'shape': 0.03909603395199366, 'beta': 3.1461237556278854}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 7.86718186,  2.74951327, 13.54242034,  0.03909603,  3.14612376])\n",
      "AIC value:238.44022307570785\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_HWD_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWD')\n",
    "params_1979_2021_HWD_shiftscale = params_1979_2021_mdl_HWD_shiftscale['results']\n",
    "print(params_1979_2021_HWD_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWD_shiftscale['pars'])+2*params_1979_2021_HWD_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 7.8902419 ,  2.79942087, 13.86265657, -0.02114448,  4.11486536],\n",
      "       [ 7.89024478,  2.79942583, 13.86258455, -0.02114258,  4.11483715],\n",
      "       [ 7.89022862,  2.79942563, 13.86257504, -0.02114249,  4.11489334],\n",
      "       [ 7.89023739,  2.7994316 , 13.86266386, -0.02114036,  4.11483688],\n",
      "       [ 7.89024625,  2.79943035, 13.86269411, -0.02114082,  4.11490249],\n",
      "       [ 7.89024087,  2.79943681, 13.86270269, -0.02114785,  4.1148951 ]]), array([120.26190108, 120.26190108, 120.26190108, 120.26190108,\n",
      "       120.26190108, 120.26190108]))\n",
      "           fun: 120.26190108325262\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 638\n",
      "           nit: 396\n",
      "          pars: {'mu': 7.89024190440286, 'sigma': 2.7994208682906945, 'alpha': 13.862656574278018, 'shape': -0.02114447972691503, 'beta': 4.114865364017096}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 7.8902419 ,  2.79942087, 13.86265657, -0.02114448,  4.11486536])\n",
      "AIC value:250.52380216650525\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWD_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWD')\n",
    "params_1979_2022_HWD_shiftscale = params_1979_2022_mdl_HWD_shiftscale['results']\n",
    "print(params_1979_2022_HWD_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWD_shiftscale['pars'])+2*params_1979_2022_HWD_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rlplot(params_1979_2021_mdl, era5_gmst_1979_2022.tolist()[-1], era5_gmst_1979_2022.tolist()[-1] - 1.2, YRB_HWD_1979_2022['t2m'][2022], lower = False, ax = None, ci_nsamp = 10, legend = True, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n"
     ]
    }
   ],
   "source": [
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 2022 YRB HWD is 37.38\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 8003.27\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 282.22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gl/305lw9j910n_yd3gdvyjpyz80000gn/T/ipykernel_52777/725902344.py:92: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_locs = [\n",
    "    [0.05,0.56,0.9,0.38],\n",
    "    [0.05,0.05,0.9,0.38],\n",
    "]\n",
    "\n",
    "fig = plt.figure(figsize = (7,7))\n",
    "ax1 = fig.add_axes(axes_locs[0])\n",
    "ax1.scatter(era5_gmst_1979_2021.values,YRB_HWD_1979_2021['t2m'],marker='o',color='blue',alpha=0.6,s=80)\n",
    "x1 = [era5_gmst_1979_2021.min(),era5_gmst_1979_2021.max()]\n",
    "mu1 = params_1979_2021['pars']['mu']\n",
    "alpha1 = params_1979_2021['pars']['alpha']\n",
    "alpha1, mu1 = np.polyfit(YRB_HW_data_1979_2021.GMST, YRB_HW_data_1979_2021.HWD, 1)\n",
    "y1 = [mu1+x1[0]*alpha1,mu1+x1[1]*alpha1]\n",
    "l1 = ax1.plot(x1,y1,c='blue',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha1,2)) + '*X+' + str(round(mu1,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.4, y = 0.85, s = s, fontsize=14, color = 'b',transform=ax1.transAxes)\n",
    "\n",
    "star = ax1.scatter(era5_gmst_1979_2022.values[-1],YRB_HWD_1979_2022['t2m'][2022],marker='*',color='red',alpha=0.8,s=200)\n",
    "x2 = [era5_gmst_1979_2022.min(),era5_gmst_1979_2022.max()]\n",
    "mu2 = params_1979_2022['pars']['mu']\n",
    "alpha2 = params_1979_2022['pars']['alpha']\n",
    "alpha2, mu2 = np.polyfit(YRB_HW_data_1979_2022.GMST, YRB_HW_data_1979_2022.HWD, 1)\n",
    "y2 = [mu2+x2[0]*alpha2,mu2+x2[1]*alpha2]\n",
    "l2 = ax1.plot(x2,y2,c='red',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha2,2)) + '*X+' + str(round(mu2,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.4, y = 0.75, s = s, fontsize=14, color = 'r',transform=ax1.transAxes)\n",
    "\n",
    "ax1.set_ylabel('YRB averaged HWD (day)',fontdict={'fontsize':15})\n",
    "ax1.set_xlabel('Global mean surface temperature anomalies (°C)',fontdict={'fontsize':15})\n",
    "# ax1.set_ylim([21.5,25.3])\n",
    "ax1.legend(\n",
    "    [star,l1[0],l2[0]], ['2022 event','2022 excluded','2022 included'],fancybox = True,frameon=True, loc='upper left', #bbox_to_anchor=[0.65,0.35], \n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "############# ax2\n",
    "ax2 = fig.add_axes(axes_locs[1])\n",
    "# z for return period\n",
    "z1 = []\n",
    "for i in range(len(YRB_HWD_1979_2022['t2m'].tolist())):\n",
    "    z1.append(return_period(mdl = params_1979_2021_mdl,event_value = YRB_HWD_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "z2 = []\n",
    "for i in range(len(YRB_HWD_1979_2022['t2m'].tolist())):\n",
    "    z2.append(return_period(mdl = params_1979_2022_mdl,event_value = YRB_HWD_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "\n",
    "y_fit_1 = np.linspace(10, 60, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(10, 60, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "l1 = ax2.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax2.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "YRB_HWD_1979_2022_shifted_to_2022 = YRB_HWD_1979_2022['t2m'] + (era5_gmst_1979_2022[2022] - era5_gmst_1979_2022) * alpha1\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_HWD_1979_2022_shifted_to_2022)+1)/float(len(YRB_HWD_1979_2022_shifted_to_2022) + 1))\n",
    "ax2.scatter(empirical_x[:-1],YRB_HWD_1979_2022_shifted_to_2022.sort_values()[:-1],marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax2.scatter(empirical_x[-1],YRB_HWD_1979_2022_shifted_to_2022.sort_values()[2022],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_ylabel('YRB averaged HWD (day)',fontdict={'fontsize':15})\n",
    "ax2.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax2.set_ylim([0,60])\n",
    "ax2.set_xlim([0.8,10**4])\n",
    "l3 = ax2.axhline(y=YRB_HWD_1979_2022_shifted_to_2022.sort_values()[2022],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "def add_panel_label(ax, label, x = -0.1, y = 1):\n",
    "    ax.text(x, y, label, transform=ax.transAxes,\n",
    "      fontsize=20, fontweight='bold', va='top', ha='left')\n",
    "add_panel_label(ax1, 'a', x=0, y = 1.11)\n",
    "add_panel_label(ax2, 'b', x=0, y = 1.11)\n",
    "\n",
    "ax2.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 HWD','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl,event_value = YRB_HWD_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl,event_value = YRB_HWD_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB HWD is {:.2f}'.format(YRB_HWD_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/FigS2_GEV_fitting_for_HWD.pdf\",bbox_inches='tight')\n",
    "fig.savefig('Figures/FigS2_GEV_fitting_for_HWD.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot Figure S3 for HWL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.026983435214398936, 3.3321281581961815, 1.3200238826979676)\n",
      "AIC value:166.93615409099365\n"
     ]
    }
   ],
   "source": [
    "x = YRB_HWL_1979_2021['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.15992193473618155, 3.3171393124141115, 1.3826225450568481)\n",
      "AIC value:181.25940497154534\n"
     ]
    }
   ],
   "source": [
    "x = YRB_HWL_1979_2022['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 3.08420146,  0.95268812,  4.1399999 , -0.04354328],\n",
      "       [ 3.08420685,  0.95267128,  4.13992732, -0.04355414],\n",
      "       [ 3.08419028,  0.95267872,  4.14006538, -0.04353984],\n",
      "       [ 3.08421648,  0.95268542,  4.14000419, -0.04353812],\n",
      "       [ 3.08421069,  0.95267235,  4.13991095, -0.04354001]]), array([66.78125879, 66.7812588 , 66.7812588 , 66.7812588 , 66.7812588 ]))\n",
      "           fun: 66.78125879079093\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 297\n",
      "           nit: 179\n",
      "          pars: {'mu': 3.0842014600982157, 'sigma': 0.9526881202774333, 'alpha': 4.139999902124957, 'shape': -0.043543277830914995}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 3.08420146,  0.95268812,  4.1399999 , -0.04354328])\n",
      "AIC value:141.56251758158186\n"
     ]
    }
   ],
   "source": [
    "# calculate nonstationary fitting parameters\n",
    "# sigma and shape are the constant parameters\n",
    "# mu and alpha is two new parameters to generate the time-varing location parameter, i.e., loc = mu + alpha * covariate\n",
    "params_1979_2021_mdl_HWL_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWL')\n",
    "params_1979_2021_HWL_shift = params_1979_2021_mdl_HWL_shift['results']\n",
    "print(params_1979_2021_HWL_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWL_shift['pars'])+2*params_1979_2021_HWL_shift['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 3.0832649 ,  1.01786623,  4.16471463, -0.15854143],\n",
      "       [ 3.08323951,  1.01789294,  4.16479356, -0.15854101],\n",
      "       [ 3.08325484,  1.01787508,  4.16467046, -0.15855262],\n",
      "       [ 3.08328124,  1.017893  ,  4.16472464, -0.15853886],\n",
      "       [ 3.08325735,  1.01788095,  4.16472858, -0.15855196]]), array([74.11601171, 74.11601172, 74.11601172, 74.11601173, 74.11601174]))\n",
      "           fun: 74.11601171211255\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 280\n",
      "           nit: 161\n",
      "          pars: {'mu': 3.083264896264389, 'sigma': 1.0178662310416546, 'alpha': 4.164714625327454, 'shape': -0.15854142826007583}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 3.0832649 ,  1.01786623,  4.16471463, -0.15854143])\n",
      "AIC value:156.2320234242251\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWL_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWL')\n",
    "params_1979_2022_HWL_shift = params_1979_2022_mdl_HWL_shift['results']\n",
    "print(params_1979_2022_HWL_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWL_shift['pars'])+2*params_1979_2022_HWL_shift['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[2.94926711, 1.28918948, 1.78869279, 0.1594269 ],\n",
      "       [2.94927707, 1.28918495, 1.78872303, 0.15943393],\n",
      "       [2.94925745, 1.28917837, 1.78871173, 0.15942172],\n",
      "       [2.94930832, 1.28918736, 1.78860303, 0.15942227],\n",
      "       [2.94926554, 1.28917874, 1.78864596, 0.15942891]]), array([78.80961925, 78.80961925, 78.80961925, 78.80961925, 78.80961925]))\n",
      "           fun: 78.80961924838125\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 275\n",
      "           nit: 165\n",
      "          pars: {'mu': 2.949267109948781, 'sigma': 1.2891894845481302, 'alpha': 1.7886927890999293, 'shape': 0.15942689886407907}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([2.94926711, 1.28918948, 1.78869279, 0.1594269 ])\n",
      "AIC value:165.6192384967625\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_HWL_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWL')\n",
    "params_1979_2021_HWL_scale = params_1979_2021_mdl_HWL_scale['results']\n",
    "print(params_1979_2021_HWL_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWL_scale['pars'])+2*params_1979_2021_HWL_scale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[2.8922071 , 1.31337116, 1.89945516, 0.04293243],\n",
      "       [2.89222638, 1.31336855, 1.89938004, 0.04292429],\n",
      "       [2.89224265, 1.31338738, 1.89938406, 0.04292678],\n",
      "       [2.8922258 , 1.31338533, 1.89942334, 0.04292429],\n",
      "       [2.8922175 , 1.31337388, 1.89937094, 0.04292779]]), array([85.94168979, 85.94168979, 85.94168979, 85.94168979, 85.9416898 ]))\n",
      "           fun: 85.94168979069666\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 285\n",
      "           nit: 166\n",
      "          pars: {'mu': 2.892207103686096, 'sigma': 1.313371156558031, 'alpha': 1.8994551616800048, 'shape': 0.04293242857596674}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([2.8922071 , 1.31337116, 1.89945516, 0.04293243])\n",
      "AIC value:179.8833795813933\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWL_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWL')\n",
    "params_1979_2022_HWL_scale = params_1979_2022_mdl_HWL_scale['results']\n",
    "print(params_1979_2022_HWL_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWL_scale['pars'])+2*params_1979_2022_HWL_scale['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shiftscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   direc: array([[ 1.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
      "       [ 0.        ,  0.        ,  0.        ,  0.        ,  1.        ],\n",
      "       [ 0.        ,  0.        ,  1.        ,  0.        ,  0.        ],\n",
      "       [ 0.        ,  0.        ,  0.        ,  1.        ,  0.        ],\n",
      "       [-0.05156823, -0.12471287, -0.01874349, -0.02118287,  0.27688872]])\n",
      "     fun: 66.43008377483498\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 244\n",
      "     nit: 4\n",
      "    pars: {'mu': 3.0850747204044846, 'sigma': 0.907774748542336, 'alpha': 4.197291840578779, 'shape': -0.026266524189742135, 'beta': 0.4512780944150625}\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 3.08507472,  0.90777475,  4.19729184, -0.02626652,  0.45127809])\n",
      "AIC value:142.86016754966997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/optimize/_optimize.py:2322: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/optimize/_optimize.py:2323: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  p = (x - v) * tmp2 - (x - w) * tmp1\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_HWL_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWL',solver='Powell')\n",
    "params_1979_2021_HWL_shiftscale = params_1979_2021_mdl_HWL_shiftscale['results']\n",
    "print(params_1979_2021_HWL_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWL_shiftscale['pars'])+2*params_1979_2021_HWL_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     fun: 77.42795899571354\n",
      "     jac: array([-0.22175755, 12.95110516,  0.13335892, -1.98365768,  0.93173896])\n",
      " message: 'Linear search failed'\n",
      "    nfev: 432\n",
      "     nit: 1\n",
      "    pars: {'mu': 3.301005420515073, 'sigma': 1.3875672929450493, 'alpha': 4.737508613597873, 'shape': -0.15826187144342713, 'beta': 1.5987469513062842}\n",
      "  status: 4\n",
      " success: False\n",
      "       x: array([ 3.30100542,  1.38756729,  4.73750861, -0.15826187,  1.59874695])\n",
      "AIC value:164.85591799142708\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWL_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWL',solver='TNC')\n",
    "params_1979_2022_HWL_shiftscale = params_1979_2022_mdl_HWL_shiftscale['results']\n",
    "print(params_1979_2022_HWL_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWL_shiftscale['pars'])+2*params_1979_2022_HWL_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rlplot(params_1979_2021_mdl_HWL_shift, era5_gmst_1979_2022.tolist()[-1], era5_gmst_1979_2022.tolist()[-1] - 1.2, YRB_HWL_1979_2022['t2m'][2022], lower = False, ax = None, ci_nsamp = 10, legend = True, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl_HWL_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl_HWL_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 2022 YRB HWL is 14.96\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 3892.25\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 303.78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gl/305lw9j910n_yd3gdvyjpyz80000gn/T/ipykernel_79357/3501213962.py:92: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_locs = [\n",
    "    [0.05,0.56,0.9,0.38],\n",
    "    [0.05,0.05,0.9,0.38],\n",
    "]\n",
    "\n",
    "fig = plt.figure(figsize = (7,7))\n",
    "ax1 = fig.add_axes(axes_locs[0])\n",
    "ax1.scatter(era5_gmst_1979_2021.values,YRB_HWL_1979_2021['t2m'],marker='o',color='blue',alpha=0.6,s=80)\n",
    "x1 = [era5_gmst_1979_2021.min(),era5_gmst_1979_2021.max()]\n",
    "mu1 = params_1979_2021_HWL_shift['pars']['mu']\n",
    "alpha1 = params_1979_2021_HWL_shift['pars']['alpha']\n",
    "alpha1, mu1 = np.polyfit(YRB_HW_data_1979_2021.GMST, YRB_HW_data_1979_2021.HWL, 1)\n",
    "y1 = [mu1+x1[0]*alpha1,mu1+x1[1]*alpha1]\n",
    "l1 = ax1.plot(x1,y1,c='blue',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha1,2)) + '*X+' + str(round(mu1,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.4, y = 0.85, s = s, fontsize=14, color = 'b',transform=ax1.transAxes)\n",
    "\n",
    "star = ax1.scatter(era5_gmst_1979_2022.values[-1],YRB_HWL_1979_2022['t2m'][2022],marker='*',color='red',alpha=0.8,s=200)\n",
    "x2 = [era5_gmst_1979_2022.min(),era5_gmst_1979_2022.max()]\n",
    "mu2 = params_1979_2022_HWL_shift['pars']['mu']\n",
    "alpha2 = params_1979_2022_HWL_shift['pars']['alpha']\n",
    "alpha2, mu2 = np.polyfit(YRB_HW_data_1979_2022.GMST, YRB_HW_data_1979_2022.HWL, 1)\n",
    "y2 = [mu2+x2[0]*alpha2,mu2+x2[1]*alpha2]\n",
    "l2 = ax1.plot(x2,y2,c='red',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha2,2)) + '*X+' + str(round(mu2,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.4, y = 0.75, s = s, fontsize=14, color = 'r',transform=ax1.transAxes)\n",
    "\n",
    "ax1.set_ylabel('YRB averaged HWL (day)',fontdict={'fontsize':15})\n",
    "ax1.set_xlabel('Global mean surface temperature anomalies (°C)',fontdict={'fontsize':15})\n",
    "# ax1.set_ylim([21.5,25.3])\n",
    "ax1.legend(\n",
    "    [star,l1[0],l2[0]], ['2022 event','2022 excluded','2022 included'],fancybox = True,frameon=True, loc='upper left', #bbox_to_anchor=[0.65,0.35], \n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "############# ax2\n",
    "ax2 = fig.add_axes(axes_locs[1])\n",
    "# z for return period\n",
    "z1 = []\n",
    "for i in range(len(YRB_HWL_1979_2022['t2m'].tolist())):\n",
    "    z1.append(return_period(mdl = params_1979_2021_mdl_HWL_shift,event_value = YRB_HWL_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "z2 = []\n",
    "for i in range(len(YRB_HWL_1979_2022['t2m'].tolist())):\n",
    "    z2.append(return_period(mdl = params_1979_2022_mdl_HWL_shift,event_value = YRB_HWL_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "\n",
    "y_fit_1 = np.linspace(3, 16, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl_HWL_shift,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(3, 16, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl_HWL_shift,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "l1 = ax2.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax2.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "YRB_HWL_1979_2022_shifted_to_2022 = YRB_HWL_1979_2022['t2m'] + (era5_gmst_1979_2022[2022] - era5_gmst_1979_2022) * alpha1\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_HWL_1979_2022_shifted_to_2022)+1)/float(len(YRB_HWL_1979_2022_shifted_to_2022) + 1))\n",
    "ax2.scatter(empirical_x[:-1],YRB_HWL_1979_2022_shifted_to_2022.sort_values()[:-1],marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax2.scatter(empirical_x[-1],YRB_HWL_1979_2022_shifted_to_2022.sort_values()[2022],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_ylabel('YRB averaged HWL (day)',fontdict={'fontsize':15})\n",
    "ax2.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax2.set_ylim([0,16])\n",
    "ax2.set_xlim([0.8,10**4])\n",
    "l3 = ax2.axhline(y=YRB_HWL_1979_2022_shifted_to_2022.sort_values()[2022],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "def add_panel_label(ax, label, x = -0.1, y = 1):\n",
    "    ax.text(x, y, label, transform=ax.transAxes,\n",
    "      fontsize=20, fontweight='bold', va='top', ha='left')\n",
    "add_panel_label(ax1, 'a', x=0, y = 1.11)\n",
    "add_panel_label(ax2, 'b', x=0, y = 1.11)\n",
    "\n",
    "ax2.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 HWL','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl_HWL_shift,event_value = YRB_HWL_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl_HWL_shift,event_value = YRB_HWL_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB HWL is {:.2f}'.format(YRB_HWL_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/FigS2_GEV_fitting_for_HWL.pdf\",bbox_inches='tight')\n",
    "fig.savefig('Figures/FigS2_GEV_fitting_for_HWL.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot Figure S4 for HWM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.19941235719792383, 0.6497721741412517, 0.12963272015052096)\n",
      "AIC value:-43.53005530764301\n"
     ]
    }
   ],
   "source": [
    "x = YRB_HWM_1979_2021['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.020860886592828566, 0.642201661100125, 0.1329372476638357)\n",
      "AIC value:-32.26659433082783\n"
     ]
    }
   ],
   "source": [
    "x = YRB_HWM_1979_2022['t2m'].tolist()\n",
    "pars = gev.fit(x)\n",
    "print(pars)\n",
    "neg_loglike = -gev.logpdf(x, *pars).sum()\n",
    "print(\"AIC value:\" + str(2*len(pars)+2*neg_loglike))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[0.62137156, 0.10186382, 0.31663369, 0.13333933],\n",
      "       [0.6213849 , 0.10186644, 0.31662556, 0.13340555],\n",
      "       [0.62137988, 0.1018593 , 0.31661511, 0.13334173],\n",
      "       [0.62138081, 0.10185938, 0.31664373, 0.13340258],\n",
      "       [0.62137834, 0.10186507, 0.31667354, 0.13335661]]), array([-33.49173881, -33.49173875, -33.49173874, -33.49173867,\n",
      "       -33.49173854]))\n",
      "           fun: -33.491738808909204\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 194\n",
      "           nit: 107\n",
      "          pars: {'mu': 0.6213715622946966, 'sigma': 0.10186381628657958, 'alpha': 0.3166336889325252, 'shape': 0.13333932905892798}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([0.62137156, 0.10186382, 0.31663369, 0.13333933])\n",
      "AIC value:-58.98347761781841\n"
     ]
    }
   ],
   "source": [
    "# calculate nonstationary fitting parameters\n",
    "# sigma and shape are the constant parameters\n",
    "# mu and alpha is two new parameters to generate the time-varing location parameter, i.e., loc = mu + alpha * covariate\n",
    "params_1979_2021_mdl_HWM_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWM')\n",
    "params_1979_2021_HWM_shift = params_1979_2021_mdl_HWM_shift['results']\n",
    "print(params_1979_2021_HWM_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWM_shift['pars'])+2*params_1979_2021_HWM_shift['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 0.61561346,  0.10456986,  0.33078518, -0.05041803],\n",
      "       [ 0.61561356,  0.10457444,  0.33080474, -0.05048188],\n",
      "       [ 0.61560838,  0.10456432,  0.33076625, -0.05050376],\n",
      "       [ 0.61560824,  0.10457913,  0.33077752, -0.05043949],\n",
      "       [ 0.61562444,  0.10457532,  0.3307284 , -0.05045334]]), array([-28.81151187, -28.81151187, -28.81151176, -28.81151151,\n",
      "       -28.81151149]))\n",
      "           fun: -28.81151186814016\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 250\n",
      "           nit: 147\n",
      "          pars: {'mu': 0.6156134617274125, 'sigma': 0.10456986030601433, 'alpha': 0.3307851841586313, 'shape': -0.0504180302027316}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 0.61561346,  0.10456986,  0.33078518, -0.05041803])\n",
      "AIC value:-49.62302373628032\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWM_shift = ns_fit(dist=gev,fittype=\"shift\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWM')\n",
    "params_1979_2022_HWM_shift = params_1979_2022_mdl_HWM_shift['results']\n",
    "print(params_1979_2022_HWM_shift)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWM_shift['pars'])+2*params_1979_2022_HWM_shift['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[0.63798151, 0.12883024, 0.06474936, 0.27232981],\n",
      "       [0.63797127, 0.1288336 , 0.06479996, 0.27237393],\n",
      "       [0.63798575, 0.12882221, 0.06474835, 0.27225072],\n",
      "       [0.63796273, 0.12882806, 0.06476279, 0.27227965],\n",
      "       [0.63797116, 0.12882347, 0.06479123, 0.27239814]]), array([-25.21362364, -25.21362355, -25.2136235 , -25.2136234 ,\n",
      "       -25.21362327]))\n",
      "           fun: -25.2136236421086\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 176\n",
      "           nit: 102\n",
      "          pars: {'mu': 0.6379815081361615, 'sigma': 0.12883024356548867, 'alpha': 0.06474935838862916, 'shape': 0.2723298101004379}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([0.63798151, 0.12883024, 0.06474936, 0.27232981])\n",
      "AIC value:-42.4272472842172\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_HWM_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWM')\n",
    "params_1979_2021_HWM_scale = params_1979_2021_mdl_HWM_scale['results']\n",
    "print(params_1979_2021_HWM_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWM_scale['pars'])+2*params_1979_2021_HWM_scale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[0.62692269, 0.13046945, 0.07716139, 0.07318026],\n",
      "       [0.62693338, 0.13048073, 0.0771601 , 0.07325271],\n",
      "       [0.6269518 , 0.130477  , 0.07710461, 0.07319041],\n",
      "       [0.62694283, 0.13048072, 0.07706192, 0.07310991],\n",
      "       [0.62692018, 0.13047339, 0.0771251 , 0.07321877]]), array([-19.56356365, -19.56356343, -19.56356333, -19.56356291,\n",
      "       -19.56356286]))\n",
      "           fun: -19.56356364629155\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 194\n",
      "           nit: 113\n",
      "          pars: {'mu': 0.6269226882274863, 'sigma': 0.13046945276535427, 'alpha': 0.07716138576902197, 'shape': 0.0731802612586951}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([0.62692269, 0.13046945, 0.07716139, 0.07318026])\n",
      "AIC value:-31.127127292583097\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWM_scale = ns_fit(dist=gev,fittype=\"scale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWM')\n",
    "params_1979_2022_HWM_scale = params_1979_2022_mdl_HWM_scale['results']\n",
    "print(params_1979_2022_HWM_scale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWM_scale['pars'])+2*params_1979_2022_HWM_scale['fun']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fitting type: shiftscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 0.62144995,  0.10230319,  0.31635437,  0.13419058, -0.00361897],\n",
      "       [ 0.62145664,  0.10229914,  0.31642035,  0.1342874 , -0.00357247],\n",
      "       [ 0.62144976,  0.10229353,  0.31636057,  0.13410754, -0.00357049],\n",
      "       [ 0.62146015,  0.10231031,  0.31638273,  0.13419037, -0.00361062],\n",
      "       [ 0.62143793,  0.1023004 ,  0.3163754 ,  0.13409722, -0.00362102],\n",
      "       [ 0.62144779,  0.10230989,  0.31635653,  0.13427897, -0.00358069]]), array([-33.49424729, -33.49424718, -33.49424715, -33.49424699,\n",
      "       -33.49424688, -33.49424678]))\n",
      "           fun: -33.49424729445716\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 322\n",
      "           nit: 206\n",
      "          pars: {'mu': 0.6214499545143253, 'sigma': 0.1023031884331665, 'alpha': 0.3163543749576293, 'shape': 0.13419058462907893, 'beta': -0.003618965721047618}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 0.62144995,  0.10230319,  0.31635437,  0.13419058, -0.00361897])\n",
      "AIC value:-56.98849458891432\n"
     ]
    }
   ],
   "source": [
    "params_1979_2021_mdl_HWM_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2021,cov_name='GMST',var_name='HWM')\n",
    "params_1979_2021_HWM_shiftscale = params_1979_2021_mdl_HWM_shiftscale['results']\n",
    "print(params_1979_2021_HWM_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2021_HWM_shiftscale['pars'])+2*params_1979_2021_HWM_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[ 0.61569318,  0.09918067,  0.35139086, -0.00819175,  0.05926865],\n",
      "       [ 0.61569739,  0.0991847 ,  0.35135431, -0.00819767,  0.05926447],\n",
      "       [ 0.61569691,  0.09917731,  0.35135486, -0.00822972,  0.05924697],\n",
      "       [ 0.61569043,  0.09918154,  0.35136099, -0.00824821,  0.05922393],\n",
      "       [ 0.61569989,  0.09918319,  0.35137681, -0.00815115,  0.05924093],\n",
      "       [ 0.61569784,  0.09917798,  0.35137351, -0.00826203,  0.05924838]]), array([-29.41744127, -29.41744119, -29.41744119, -29.41744118,\n",
      "       -29.41744116, -29.41744105]))\n",
      "           fun: -29.417441265937207\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 419\n",
      "           nit: 262\n",
      "          pars: {'mu': 0.6156931845914541, 'sigma': 0.09918067219950892, 'alpha': 0.3513908575444817, 'shape': -0.00819174997528376, 'beta': 0.05926864718914414}\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([ 0.61569318,  0.09918067,  0.35139086, -0.00819175,  0.05926865])\n",
      "AIC value:-48.83488253187441\n"
     ]
    }
   ],
   "source": [
    "# including the 2022 event\n",
    "params_1979_2022_mdl_HWM_shiftscale = ns_fit(dist=gev,fittype=\"shiftscale\",data=YRB_HW_data_1979_2022,cov_name='GMST',var_name='HWM')\n",
    "params_1979_2022_HWM_shiftscale = params_1979_2022_mdl_HWM_shiftscale['results']\n",
    "print(params_1979_2022_HWM_shiftscale)\n",
    "print(\"AIC value:\" + str(2*len(params_1979_2022_HWM_shiftscale['pars'])+2*params_1979_2022_HWM_shiftscale['fun']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n",
      "/Users/zeqinhuang/Documents/paper/HW_track/scripts/nonstationary_fitting.py:250: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return 1/ep\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rlplot(params_1979_2021_mdl_HWM_shift, era5_gmst_1979_2022.tolist()[-1], era5_gmst_1979_2022.tolist()[-1] - 1.2, YRB_HWM_1979_2022['t2m'][2022], lower = False, ax = None, ci_nsamp = 10, legend = True, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 12, 43, 45]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([2,3,43,12,45],reverse=lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n"
     ]
    }
   ],
   "source": [
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl_HWM_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl_HWM_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/Documents/paper/HW_track/scripts/nonstationary_fitting.py:250: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return 1/ep\n",
      "/var/folders/gl/305lw9j910n_yd3gdvyjpyz80000gn/T/ipykernel_79357/443961628.py:92: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 2022 YRB HWM is 1.48\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 7022377.73\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 254.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_locs = [\n",
    "    [0.05,0.56,0.9,0.38],\n",
    "    [0.05,0.05,0.9,0.38],\n",
    "]\n",
    "\n",
    "fig = plt.figure(figsize = (7,7))\n",
    "ax1 = fig.add_axes(axes_locs[0])\n",
    "ax1.scatter(era5_gmst_1979_2021.values,YRB_HWM_1979_2021['t2m'],marker='o',color='blue',alpha=0.6,s=80)\n",
    "x1 = [era5_gmst_1979_2021.min(),era5_gmst_1979_2021.max()]\n",
    "mu1 = params_1979_2021_HWM_shift['pars']['mu']\n",
    "alpha1 = params_1979_2021_HWM_shift['pars']['alpha']\n",
    "alpha1, mu1 = np.polyfit(YRB_HW_data_1979_2021.GMST, YRB_HW_data_1979_2021.HWM, 1)\n",
    "y1 = [mu1+x1[0]*alpha1,mu1+x1[1]*alpha1]\n",
    "l1 = ax1.plot(x1,y1,c='blue',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha1,2)) + '*X+' + str(round(mu1,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.4, y = 0.85, s = s, fontsize=14, color = 'b',transform=ax1.transAxes)\n",
    "\n",
    "star = ax1.scatter(era5_gmst_1979_2022.values[-1],YRB_HWM_1979_2022['t2m'][2022],marker='*',color='red',alpha=0.8,s=200)\n",
    "x2 = [era5_gmst_1979_2022.min(),era5_gmst_1979_2022.max()]\n",
    "mu2 = params_1979_2022_HWM_shift['pars']['mu']\n",
    "alpha2 = params_1979_2022_HWM_shift['pars']['alpha']\n",
    "alpha2, mu2 = np.polyfit(YRB_HW_data_1979_2022.GMST, YRB_HW_data_1979_2022.HWM, 1)\n",
    "y2 = [mu2+x2[0]*alpha2,mu2+x2[1]*alpha2]\n",
    "l2 = ax1.plot(x2,y2,c='red',linewidth=3)\n",
    "s = 'Y=' + str(round(alpha2,2)) + '*X+' + str(round(mu2,2)) + ' (P<0.0001)'\n",
    "ax1.text(x = 0.4, y = 0.75, s = s, fontsize=14, color = 'r',transform=ax1.transAxes)\n",
    "\n",
    "ax1.set_ylabel('YRB averaged HWM (day)',fontdict={'fontsize':15})\n",
    "ax1.set_xlabel('Global mean surface temperature anomalies (°C)',fontdict={'fontsize':15})\n",
    "# ax1.set_ylim([21.5,25.3])\n",
    "ax1.legend(\n",
    "    [star,l1[0],l2[0]], ['2022 event','2022 excluded','2022 included'],fancybox = True,frameon=True, loc='upper left', #bbox_to_anchor=[0.65,0.35], \n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "############# ax2\n",
    "ax2 = fig.add_axes(axes_locs[1])\n",
    "# z for return period\n",
    "z1 = []\n",
    "for i in range(len(YRB_HWM_1979_2022['t2m'].tolist())):\n",
    "    z1.append(return_period(mdl = params_1979_2021_mdl_HWM_shift,event_value = YRB_HWM_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "z2 = []\n",
    "for i in range(len(YRB_HWM_1979_2022['t2m'].tolist())):\n",
    "    z2.append(return_period(mdl = params_1979_2022_mdl_HWM_shift,event_value = YRB_HWM_1979_2022['t2m'].tolist()[i],covariate=era5_gmst_1979_2022.tolist()[-1]))\n",
    "\n",
    "y_fit_1 = np.linspace(0.6, 2, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl_HWM_shift,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(0.6, 2, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl_HWM_shift,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "l1 = ax2.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax2.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "YRB_HWM_1979_2022_shifted_to_2022 = YRB_HWM_1979_2022['t2m'] + (era5_gmst_1979_2022[2022] - era5_gmst_1979_2022) * alpha1\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_HWM_1979_2022_shifted_to_2022)+1)/float(len(YRB_HWM_1979_2022_shifted_to_2022) + 1))\n",
    "ax2.scatter(empirical_x[:-1],YRB_HWM_1979_2022_shifted_to_2022.sort_values()[:-1],marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax2.scatter(empirical_x[-1],YRB_HWM_1979_2022_shifted_to_2022.sort_values()[2022],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_ylabel('YRB averaged HWM (day)',fontdict={'fontsize':15})\n",
    "ax2.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax2.set_ylim([0,2])\n",
    "ax2.set_xlim([0.8,10**4])\n",
    "l3 = ax2.axhline(y=YRB_HWM_1979_2022_shifted_to_2022.sort_values()[2022],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "def add_panel_label(ax, label, x = -0.1, y = 1):\n",
    "    ax.text(x, y, label, transform=ax.transAxes,\n",
    "      fontsize=20, fontweight='bold', va='top', ha='left')\n",
    "add_panel_label(ax1, 'a', x=0, y = 1.11)\n",
    "add_panel_label(ax2, 'b', x=0, y = 1.11)\n",
    "\n",
    "ax2.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 HWM','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl_HWM_shift,event_value = YRB_HWM_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl_HWM_shift,event_value = YRB_HWM_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB HWM is {:.2f}'.format(YRB_HWM_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/FigS2_GEV_fitting_for_HWM.pdf\",bbox_inches='tight')\n",
    "fig.savefig('Figures/FigS2_GEV_fitting_for_HWM.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot Figure S5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_distn_infrastructure.py:1945: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  x = np.asarray((x - loc)/scale, dtype=dtyp)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_distn_infrastructure.py:1945: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.asarray((x - loc)/scale, dtype=dtyp)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_distn_infrastructure.py:1945: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  x = np.asarray((x - loc)/scale, dtype=dtyp)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_distn_infrastructure.py:1945: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.asarray((x - loc)/scale, dtype=dtyp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 2022 YRB HWD is 37.38\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 189.72\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 53.49\n",
      "The 2022 YRB HWL is 14.96\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 3892.25\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 303.78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zeqinhuang/Documents/paper/HW_track/scripts/nonstationary_fitting.py:250: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return 1/ep\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n",
      "/Users/zeqinhuang/miniforge3/envs/tfMac38/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:2500: RuntimeWarning: invalid value encountered in subtract\n",
      "  -pex2+logpex2-logex2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 2022 YRB HWM is 1.48\n",
      "The return period (not include 2022 event in the fitting) of the 2022 event is 7022377.73\n",
      "The return period (include 2022 event in the fitting) of the 2022 event is 254.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_locs = [\n",
    "    [0.05,0.65,0.9,0.22],\n",
    "    [0.05,0.35,0.9,0.22],\n",
    "    [0.05,0.05,0.9,0.22],\n",
    "]\n",
    "############# ax1\n",
    "fig = plt.figure(figsize = (7,10))\n",
    "ax1 = fig.add_axes(axes_locs[0])\n",
    "\n",
    "y_fit_1 = np.linspace(10, 60, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl_HWD_shiftscale,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax1.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(10, 60, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl_HWD_shiftscale,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax1.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl_HWD_shiftscale, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl_HWD_shiftscale, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "l1 = ax1.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax1.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "YRB_HWD_1979_2022_shifted_to_2022 = stransf(mdl=params_1979_2022_mdl_HWD_shiftscale,covariate=era5_gmst_1979_2022.tolist()[-1],lower=False)\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_HWD_1979_2022_shifted_to_2022)+1)/float(len(YRB_HWD_1979_2022_shifted_to_2022) + 1))\n",
    "ax1.scatter(empirical_x[:-1],np.sort(YRB_HWD_1979_2022_shifted_to_2022[:-1]),marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax1.scatter(empirical_x[-1],YRB_HWD_1979_2022_shifted_to_2022[-1],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_ylabel('YRB averaged HWD (day)',fontdict={'fontsize':15})\n",
    "ax1.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax1.set_ylim([0,60])\n",
    "ax1.set_xlim([0.8,10**4])\n",
    "l3 = ax1.axhline(y=YRB_HWD_1979_2022_shifted_to_2022[-1],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "add_panel_label(ax1, 'a', x=0, y = 1.11)\n",
    "\n",
    "ax1.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 HWD','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl_HWD_shiftscale,event_value = YRB_HWD_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl_HWD_shiftscale,event_value = YRB_HWD_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB HWD is {:.2f}'.format(YRB_HWD_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "############# ax2\n",
    "ax2 = fig.add_axes(axes_locs[1])\n",
    "\n",
    "y_fit_1 = np.linspace(3, 16, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl_HWL_shift,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(3, 16, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl_HWL_shift,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax2.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl_HWL_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl_HWL_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "l1 = ax2.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax2.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "YRB_HWL_1979_2022_shifted_to_2022 = stransf(mdl=params_1979_2022_mdl_HWL_shift,covariate=era5_gmst_1979_2022.tolist()[-1],lower=False)\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_HWL_1979_2022_shifted_to_2022)+1)/float(len(YRB_HWL_1979_2022_shifted_to_2022) + 1))\n",
    "ax2.scatter(empirical_x[:-1],np.sort(YRB_HWL_1979_2022_shifted_to_2022[:-1]),marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax2.scatter(empirical_x[-1],YRB_HWL_1979_2022_shifted_to_2022[-1],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_ylabel('YRB averaged HWL (day)',fontdict={'fontsize':15})\n",
    "ax2.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax2.set_ylim([0,16])\n",
    "ax2.set_xlim([0.8,10**4])\n",
    "l3 = ax2.axhline(y=YRB_HWL_1979_2022_shifted_to_2022[-1],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "add_panel_label(ax2, 'b', x=0, y = 1.11)\n",
    "\n",
    "ax2.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 HWL','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl_HWL_shift,event_value = YRB_HWL_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl_HWL_shift,event_value = YRB_HWL_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB HWL is {:.2f}'.format(YRB_HWL_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "############# ax3\n",
    "ax3 = fig.add_axes(axes_locs[2])\n",
    "\n",
    "y_fit_1 = np.linspace(0.6, 2, 1000)\n",
    "x_fit_1 = return_period(mdl = params_1979_2021_mdl_HWM_shift,event_value = y_fit_1,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax3.plot(x_fit_1,y_fit_1,'-',color='blue',linewidth=2)\n",
    "\n",
    "y_fit_2 = np.linspace(0.6, 2, 1000)\n",
    "x_fit_2 = return_period(mdl = params_1979_2022_mdl_HWM_shift,event_value = y_fit_2,covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "ax3.plot(x_fit_2,y_fit_2,'-',color='red',linewidth=2)\n",
    "\n",
    "x_ci = np.array([10,20,50,100,200,500,1000,2000,5000,10000])\n",
    "rc_1 = return_ci(params_1979_2021_mdl_HWM_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "rc_2 = return_ci(params_1979_2022_mdl_HWM_shift, era5_gmst_1979_2022.tolist()[-1], lower = False, ci_nsamp = 100, seed = 1)\n",
    "l1 = ax3.plot(x_ci, rc_1.transpose(), color = \"blue\", ls = \"--\", alpha = 0.7)\n",
    "l2 = ax3.plot(x_ci, rc_2.transpose(), color = \"red\", ls = \"--\", alpha = 0.7)\n",
    "\n",
    "YRB_HWM_1979_2022_shifted_to_2022 = stransf(mdl=params_1979_2022_mdl_HWM_shift,covariate=era5_gmst_1979_2022.tolist()[-1],lower=False)\n",
    "empirical_x = 1 / (1 - np.arange(1,len(YRB_HWM_1979_2022_shifted_to_2022)+1)/float(len(YRB_HWM_1979_2022_shifted_to_2022) + 1))\n",
    "ax3.scatter(empirical_x[:-1],np.sort(YRB_HWM_1979_2022_shifted_to_2022[:-1]),marker='x',color='blue',alpha=0.6,s=80)\n",
    "ax3.scatter(empirical_x[-1],YRB_HWM_1979_2022_shifted_to_2022[-1],marker='*',color='red',alpha=0.6,s=200,zorder=10)\n",
    "\n",
    "ax3.set_xscale('log')\n",
    "ax3.set_ylabel('YRB averaged HWM (°C)',fontdict={'fontsize':15})\n",
    "ax3.set_xlabel('Return period (yr)',fontdict={'fontsize':15})\n",
    "ax3.set_ylim([0,2])\n",
    "ax3.set_xlim([0.8,10**4])\n",
    "l3 = ax3.axhline(y=YRB_HWM_1979_2022_shifted_to_2022[-1],color='magenta',linewidth=0.8)\n",
    "\n",
    "for label in (ax3.get_xticklabels() + ax3.get_yticklabels()):\n",
    "    label.set_fontname('Arial')\n",
    "    label.set_fontsize(13)\n",
    "\n",
    "add_panel_label(ax3, 'c', x=0, y = 1.11)\n",
    "\n",
    "ax3.legend(\n",
    "    [l3,l1[0],l2[0]], ['2022 HWM','CI (2022 excluded)','CI (2022 included)'],fancybox = True,frameon=True, loc='lower right',\n",
    "    ncol=1,fontsize=13)\n",
    "\n",
    "rp_2022_1 = return_period(mdl = params_1979_2021_mdl_HWM_shift,event_value = YRB_HWM_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "rp_2022_2 = return_period(mdl = params_1979_2022_mdl_HWM_shift,event_value = YRB_HWM_1979_2022['t2m'][2022],covariate=era5_gmst_1979_2022.tolist()[-1])\n",
    "print('The 2022 YRB HWM is {:.2f}'.format(YRB_HWM_1979_2022['t2m'][2022]))\n",
    "print('The return period (not include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_1))\n",
    "print('The return period (include 2022 event in the fitting) of the 2022 event is {:.2f}'.format(rp_2022_2))\n",
    "\n",
    "plt.show()\n",
    "# plt.tight_layout()\n",
    "fig.savefig(\"Figures/FigS5_GEV_fitting_for_HW_charas.pdf\",bbox_inches='tight')\n",
    "fig.savefig('Figures/FigS5_GEV_fitting_for_HW_charas.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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