Trends-class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6533af15-0b31-489e-80f6-94d8ef65b9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import xarray as xa\n",
    "import cartopy\n",
    "from cartopy import crs as ccrs\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm, colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import matplotlib as mpl\n",
    "import scipy.stats\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import norm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f4a4be9-b3f0-49fc-9397-bf60f479618c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub_id:  1\n",
      "39 21.363590636973544\n",
      "sub_id:  2\n",
      "38 7.98353434112839\n",
      "sub_id:  3\n",
      "37 7.6309751208430425\n",
      "sub_id:  4\n",
      "77 6.2968948209451305\n",
      "sub_id:  5\n",
      "118 6.469523059079158\n",
      "sub_id:  6\n",
      "124 10.647874758983226\n",
      "sub_id:  7\n",
      "153 17.252742222601025\n"
     ]
    }
   ],
   "source": [
    "def is_monsoon_precip(month):\n",
    "    return (month <= 9) & (month >= 6)\n",
    "data = xa.open_dataarray('/tempest/duan0000/exprecip/cpc-global/NAM_sub_precip')\n",
    "monsoon_precip = data.sel(time=(is_monsoon_precip(data.time.dt.month)))\n",
    "monsoon_precip = monsoon_precip.sel(time=(monsoon_precip.time.dt.year < 2019))\n",
    "ext_days = {}\n",
    "for sub_id in range(1, 8):\n",
    "    print(\"sub_id: \", sub_id)\n",
    "    precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "    precip_data = precip.data\n",
    "    q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "    q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "    ext_time = precip.where(precip > q2, drop=True).time.data\n",
    "    print(len(ext_time), q2)\n",
    "    ext_days[sub_id] = ext_time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f03761b-4cff-424c-b9fe-d2e3aab0062b",
   "metadata": {},
   "source": [
    "# Number of Events"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87c5f2f-5211-4ca6-beb7-ed3a0b144537",
   "metadata": {},
   "source": [
    "* Events, consecutive days are counted as one event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd2b9a82-bbef-4224-a747-4f2a9fa644e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mk_score(x, n):\n",
    "    s = 0\n",
    "    demo = np.ones(n)\n",
    "    for k in range(n-1):\n",
    "        s = s+np.sum(demo[k+1:n][x[k+1:n] > x[k]])-np.sum(demo[k+1:n][x[k+1:n] < x[k]])\n",
    "    return s\n",
    "\n",
    "def variance_s(x, n):\n",
    "    unique_x = np.unique(x)\n",
    "    g = len(unique_x)\n",
    "    if n==g:\n",
    "        var_s = (n*(n-1)*(2*n+5))/18\n",
    "    else:\n",
    "        tp = np.zeros(unique_x.shape)\n",
    "        demo = np.ones(n)\n",
    "        for i in range(g):\n",
    "            tp[i] = np.sum(demo[x==unique_x[i]])\n",
    "        var_s = (n*(n-1)*(2*n+5) - np.sum(tp*(tp-1)*(2*tp+5)))/18\n",
    "    return var_s\n",
    "\n",
    "def z_score(s, var_s):\n",
    "    if s > 0:\n",
    "        z = (s - 1)/np.sqrt(var_s)\n",
    "    elif s == 0:\n",
    "        z = 0\n",
    "    elif s < 0:\n",
    "        z = (s + 1)/np.sqrt(var_s)\n",
    "    return z\n",
    "\n",
    "def p_value(z, alpha):\n",
    "    # two tail test\n",
    "    p = 2*(1-norm.cdf(abs(z)))  \n",
    "    h = abs(z) > norm.ppf(1-alpha/2)\n",
    "\n",
    "    if (z < 0) and h:\n",
    "        trend = 'decreasing'\n",
    "    elif (z > 0) and h:\n",
    "        trend = 'increasing'\n",
    "    else:\n",
    "        trend = 'no trend'\n",
    "    \n",
    "    return p, h, trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "033c7c63-327a-4d82-8af1-3aa4c5c8e6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sens_slope(x):\n",
    "    n = len(x)\n",
    "    slope = np.nanmedian(sens_estimator(x))\n",
    "    intercept = np.nanmedian(x) - np.median(np.arange(n)[~np.isnan(x.flatten())]) * slope  # or median(x) - (n-1)/2 *slope\n",
    "    \n",
    "    return slope, intercept\n",
    "def sens_estimator(x):\n",
    "    idx = 0\n",
    "    n = len(x)\n",
    "    print(n)\n",
    "    d = np.ones(int(n*(n-1)/2))\n",
    "\n",
    "    for i in range(n-1):\n",
    "        j = np.arange(i+1,n) # all the combination of two points selected from original series. \n",
    "        d[idx : idx + len(j)] = (x[j] - x[i]) / (j - i)\n",
    "        idx = idx + len(j)\n",
    "        # print(i, idx)\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba64dd90-03e0-4668-a573-8217dee6e693",
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_end_time(sub_id):\n",
    "    start = []\n",
    "    end = []\n",
    "    ext_times = ext_days[sub_id]\n",
    "    length = len(ext_times)\n",
    "    i = 0\n",
    "    while i <= length - 1:\n",
    "        j = 1\n",
    "        while (j + i <= length - 1) and ((ext_times[i + j] - ext_times[i + j - 1]) / np.timedelta64(1, \"D\") == 1):\n",
    "            j += 1\n",
    "        start.append(ext_times[i] - np.timedelta64(12, \"h\"))\n",
    "        end.append(ext_times[i + j - 1] + np.timedelta64(12, \"h\"))\n",
    "        i += j\n",
    "    return start, end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fddb0bc1-2841-42d7-ae8f-2e0d383b4e40",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Find categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60a265b1-d85f-460f-9948-9af62b29b563",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_categories_events(label, end):\n",
    "    years = np.arange(1979, 2019)\n",
    "    cate_end = []\n",
    "    for i in range(len(end)):\n",
    "        if label[i]>0:\n",
    "            cate_end.append(end[i])\n",
    "    cate_year = [pd.to_datetime(t).year for t in cate_end]\n",
    "    print(cate_year)\n",
    "    n_epes = []\n",
    "    for year in years:\n",
    "        n_epes.append(cate_year.count(year))\n",
    "    return n_epes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2778439-7fbd-4c2f-b3a7-49b913f95b0e",
   "metadata": {},
   "source": [
    "# Sub1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2d142c4-823d-4fa0-a7fd-ff910659396c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "40\n",
      "0.03787812392593071 True increasing\n",
      "0.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub_id = 1\n",
    "n_epe = []\n",
    "epe_tutt = []\n",
    "epe_mcs = []\n",
    "epe_tc = []\n",
    "epe_front = []\n",
    "epe_surge = []\n",
    "epe_midtro = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    n_epe.append(time_end.count(year))\n",
    "\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "plt.plot(n_epe)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e4ec5d2-9fc1-4f6f-a7bb-838ff2d8278d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.113\n",
      "Model:                            OLS   Adj. R-squared:                  0.090\n",
      "Method:                 Least Squares   F-statistic:                     4.851\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):             0.0338\n",
      "Time:                        10:32:52   Log-Likelihood:                -41.444\n",
      "No. Observations:                  40   AIC:                             86.89\n",
      "Df Residuals:                      38   BIC:                             90.27\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.3634      0.217      1.673      0.102      -0.076       0.803\n",
      "x1             0.0211      0.010      2.203      0.034       0.002       0.041\n",
      "==============================================================================\n",
      "Omnibus:                        2.852   Durbin-Watson:                   2.366\n",
      "Prob(Omnibus):                  0.240   Jarque-Bera (JB):                2.009\n",
      "Skew:                           0.363   Prob(JB):                        0.366\n",
      "Kurtosis:                       2.176   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bedd9ee-4603-4577-977e-5dc343ef1b24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02110694]\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(np.arange(n).reshape(-1, 1), n_epe)\n",
    "print(model.coef_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ffa683-c4b3-4671-bbdd-77ffeb428bad",
   "metadata": {},
   "source": [
    "## categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a17b5fb0-6e62-49ee-a0e8-9015e95f2fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_id = 1\n",
    "surge_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_surgeWINDOW_induced_flag.npy\")\n",
    "# tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced_flag.npy\")\n",
    "tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced-10degree.npy\")\n",
    "# tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_induced_flag.npy\")\n",
    "tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_corrected_flag.npy\")\n",
    "MCS_induced_flag = np.load(\"ERA5/PAU/MCS_\" + str(sub_id) + \".npy\")\n",
    "MCS_induced_flag[MCS_induced_flag < 0] = 0\n",
    "midtro_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_midTropo.npy\")\n",
    "front_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_front.npy\")\n",
    "start, end = start_end_time(sub_id)\n",
    "all_time = ext_days[sub_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09c68645-064b-4e67-a61b-6521b28c0f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1982, 1984, 1993, 1995, 1997, 2001, 2003, 2003, 2006, 2006, 2007, 2008, 2009, 2010, 2012, 2014, 2014, 2015, 2016, 2017]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "40\n",
      "0.010267104695776741 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "# TC\n",
    "n_epe = find_categories_events(tc_induced_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2f92f63-9311-4e3f-bcbc-29fa177e749b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1991, 1995, 1997, 1998, 2003, 2006, 2012, 2014, 2015, 2016]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "40\n",
      "0.05473961622978374 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(tutt_induced_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05ac9cea-3590-4896-a280-f8d414942d81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1982, 1997, 2001]\n"
     ]
    },
    {
     "data": {
      "image/png": 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iFhL0BdO0IbP2hgS9Vky+pK/s6QmZWvBIotofLPbQWy1jfy9+T1PRXCToC6ZpQ7ZuWDdgncjzJV1Fhp6+rutJxEKCvuzmV5lZLVm0FjpsiaDrehKRkKAv8dBjP0ATcegtaD8H0/LU6HXoS/av60nEovGC3s+TzWmIXCv6eSyXyIK+bP+qQxexaLygL/XQVeVSS5ZZLnvtFp2WRbVcOq35ZbAgy0XERYK+JJvb7yhDryPLvqQhrqgu+zIB9akVcWm8oPeH44U3f1pmprLFerHsSxomohpztcWVXyZKEEQkGi/oKzMqDZFrx0pR7cYT1d5g9b6VIIhYNF7QjwbHyqh2jKNVGXqVlste+6RLlhChabSgj8dOfzheWLYIKjOrI8tKUSFtQxenyURv1fWkEZ+ISKMF/YXjxZ1tUtSGrn7k8bFjTv2vyr8XotGCPp3Vt/gwyEOvF8PRmOHIK6s0yVNhMxw5w5Ha0InwSNCZv5BSSswHaCI8y5p+p1TtoYMWfBNxaLagL+lsk7Kvh1i14mQ98iVf0vsRH3T3Biv8e01WExFptKDnzeaUTdWH/iDHc5FuvCVsJ5bLcgsPpnEKEZJGC/qyVmUph6pyqRXTc7pY0A+7nagZ+qrrCZShizg0W9BPmhEsPgwxh+ciPKuWr4Vp6aC7B923u68smVQbOhGTRgv6UQ4PfVJmpuFxXTgaHAMrzmnyXlq2Goo8ZbBpXGmcQoSk0YKe10MfjMYcq8ysFvTzVC7txentOV06d4mH3lWVi4hHowU9V9li+hArcDYn4tDL9VA0ju2xzvXU00NREYFmC/qKZgQQv8ONCEteDz27beh9y0MXVdFsQV/jBtQQuR5M69BXlw7Gs1yWJQjqUyvikUvQzewOM/uCmV00s/uXbPd9ZjYys78eLsR49IcjzOBMJ8fNrxuwFvRziGosHzuff5/Woet6EuFZKehm1gbeC9wJ3A7cY2a3L9juXwAfCR1kLNJGCGa2cJu0pFGzRetBHsvl4KTSJOw5PcrzZaIEQUQkT4b+GuCiuz/t7gPgYeDuOdv9XeCDwHMB44tKbzhaOgEF4GBvMklEHno96A1HdNstOgt6ekK1D0U77RbddryZqqLZ5BH0G4FnMr9fSl47wcxuBH4UeGDZHzKze83sgplduHz58rqxBmfVJBBQmVndmKylsvyyjvVcJE8ZLMD+XksJgohCHkGf50fMTrH7eeBd7r70KnX3B939nLufO3v2bM4Q47Gq9yRoiFw3VrWAg0yGHuuhaI79S9BFDBYvOjHlEnBz5vebgGdntjkHPJx40dcBd5nZsbv/lxBBxmLV2tUQryJCxGHV8rUQ70s6j3+fvq8EQcQgj6A/CtxmZrcCfwi8CXhzdgN3vzX92cw+AHx428UcVi91Ciozqxt5bLQq69DT93U9iRisFHR3Pzaz+5hUr7SBh9z9STN7e/L+Ut98m+kPR1xz2F26jerQ68Wq9nMwKVM1C1862B+sLoMFtaET8ciToePu54HzM6/NFXJ3//HNwyqH3nDEDXmzOVkutSDPcxEzi2J7pHbPsjJYSCwXXU8iAo2fKboqm9trt9hrm4bINSGPhw5xfOwq9y0ENF3QB+OVfieoDV2d6A1HS9vPpcQ4p0c5nsnAZH0gCbqIQaMFvb9GRiXPsx70c1guEMfHzuPfQ3I9KUEQEWisoKfdZVbNFAW1oasTa53TCHXoup5ElTRW0IcjZzT2XBmV2tDVh7w+dozSwTwlkyAPXcSjsYKeCnSuG1AZVS0Yj53+MN9zkYmohm0y0RuOc3+Z9IdjxuOwPU2FaK6g55zVl24jD3376R/nm3oPcXzsdfx7mMYrRCgk6EsaIaRoiFwP8jSYSIkx6spTBgtaTkLEo7mCvsbNv6/FlGrBOqOuqj30dHshQtJcQc+57gakloua+m47qS2Wpw69SstlX0syi0g0VtDzrl2dbqNsavvpDSZfuvksl/BNJiaWSz4LD6bxChGKxgp63rWr022OBsexQxIbsu6D7uOxMzgOI6qD4zHHY8+9b5DlIsLTXEFPbqY8E0EOVGZWC/K0gEs56HZOfSbcvlevdxerBZ4QjRf0vHXoAC8EyuZEHNaqcgm8LPK6Fh6oykWEp7mCXuDmV0a13fSGE1ssX4aeNC4JJKpTCy+Hh36SocvGE2FprqCvMzyXoNeCtR6KBj6n6/r3oIeiIjzNFfR06n8nf5mZhsjbzbp16NnPhNq36tBFlTRW0PvDEWc6LVqt5d1lQG3o6sK0Dj1/6WCoWvT+WhPVJvHpehKhaayg552mDcqo6kJvMKJl0G2v42OHrnJZfU112y1aphGfCE9zBT3nrD4I/wBNxCFvT0+o1kOP1dNUiOYKes51s2Hqi6oN3Xazzqgr9Dk9WmM5ZtCSzCIOjRX0fs6FlEAeel3o5+zpCZklbEPXoa/xhaI2dCI0jRX0vK3KAA4DzyoUcVjvnIatXEr/zjr71/UkQtNYQT8aFHgoqoxqqzla47lIWq4avGwxRxksTK4pWXgiNI0V9N4aw/O0zEwZ1XaTdz1ygFbLONMJt+Jib40yWIizHrsQjRX0/hoPRdMyM3no201/jYeiMPG7Q9ahr71vXU8iMI0V9HWqXE7KzDRE3mrWKUWFsOvcr3M9nexb15MITHMFvUBGpSHydlNIVAN1ouoNx5V9mQiR0lhB7w/Huf1WkOdZB/rDUa72cyn7AbPkdZ7JwGR9IFkuIjSNFPTj0ZjBaP2MSjfgdrO25RJQVNf272W5iAjkEnQzu8PMvmBmF83s/jnv/y0zeyL599tm9orwoYajnzSqyLN2dcqkDZ1uwG3F3QtZLqFaCx4NjgtZLu7qgiXCsVLRzKwNvBe4E7gduMfMbp/Z7IvAn3f3lwM/CzwYOtCQTJsRrG4XlqKMarsZjMaMPf9MTUifiwT00Nfc99gncQsRijwp6muAi+7+tLsPgIeBu7MbuPtvu/vXk18/CdwUNsywrNOtKEVlZttNoXMa0EZbpww23TdospoISx5BvxF4JvP7peS1RfwE8F/nvWFm95rZBTO7cPny5fxRBmadlfFSVJWw3ayzfG1KyFFXEf8eNFlNhCWPoM+b+jbX+DOzv8BE0N817313f9Ddz7n7ubNnz+aPMjDTm38ND12CvtUUHXUFrUNf88sElKGLsOQxkS8BN2d+vwl4dnYjM3s58H7gTnf/apjw4tBbc6lTmJSZqQfk9rJOC7iUkKWo6yw7kO47/ZwQociToj4K3GZmt5pZF3gT8Eh2AzN7CfAh4O+4+1PhwwxLv6DlIg99e1l3+VqYnNPB8ZjReLNKk9HYGRyvWQYbePleISBHhu7ux2Z2H/ARoA085O5Pmtnbk/cfAH4K+A7gF5NuMcfufi5e2JtR2G9NyszydMQR5ZKOntYT1Wlvz6vO5K94mqVf0MIDNOoTQcl1Fbv7eeD8zGsPZH5+G/C2sKHFo6jfOho7w5HT7UjQt42iD7rTz24i6JvuW4hQNHKmaJEbUJ7ndlPkQfd+oAeTRZ7JHGhJZhGBRgp6Eb81dIcbEZZ+gclioTpRpZ8/XGeiWrKt2tCJkDRS0Ndt6AsaIm876RT+Ih56qAy9iIceaukBIaChgt4bjthrG3vt8ofnIg7pFP4qbLQiJZPTBEEPRUU4minoay51CprZt+2k5+VMp0ClSSBBX+fLJI1T15MISSMFfd11N2B6s6pueDvpD0fs7+Xv6QmZWvANR11T/z7/NdVqGft7LV1PIiiNFPR1p2mDpmpvO+uupQLVZujp9rqeREiaKehFbn6VmW01666FDlsi6LqeRECaKehrrrsBqkPfdnprtp8DTrYPVodeYP+6nkRIGino8tB3j/4Glsum57TI2kDp9qpDFyFppKAX8tC7ad2wbsBtpIjlstdu0WnZxuf0aDCi01qvDBZkuYjwNFLQjwbrC/p+Rw9Ft5ki5xTCrIleJEFI960EQYSkkYJeZHiuMrPtpoiNBmGWRa5y30JkaaSgFxmeg4bI28wmWXKIh6JVjQ6EyNJcQS9yA6pueGspUooKYb6kN0oQdD2JgDRO0Mdjpz8cr122CCoz22aKlKJC2oZus/VUekWvJ434RGAaJ+gvHK+/iFOKPM/tpb/BqCvE1P9C11NX15MIS+MEfTqrb/3/dXno28lwNGY48sKiWlmVy16b4cgZjrTioghDcwW9ogdoIjxFJ/akn6nSQwdNVhPhaJ6gF2hukRLCbxXhOVmPvMCX9H6AB5NFlmOGzNIDEnQRiMYJepoNrdMuLOWw26anDjNbR38w+ZI9LCCqh4Esl8MCXyZpvGn8QmxK4wQ9nZmnOvTd4WiYtJ+rYR06TOMXYlMaJ+hFusOnhBiei/D0NviSTksH3b3Qvt29cMmk1tgXoWmeoG/goU/KzDQ83jaK9PRMSUU1LWddl03KYLUkswhN4wR904qIwWjMscrMtor+JpVLSflq0Sx5OjooUAbbVZWLCEvjBH2jssX0IVbBbE7EoTfYYLLYhpUmIa6nnh6KikA0T9A38VsDdbgRYSnaAg42tz1C2D2yXEQomifoAW5ADZG3i2kderHZvxDCcimSIKhPrQhL4wS9PxxhBmc6G9z8ugG3iv4mpagb+tib+fdpHbquJxGGxgl6bzDicK+Nma392UNZLlvJJpbLYSAPvcjEIiUIIjSNE/SjggspwdSmUduw7eJoMKLbbtFZs6cnbH5OjzYog+20W3TbLV1PIhi57gAzu8PMvmBmF83s/jnvm5n9QvL+E2b2qvChhqFfcN0NUJnZttIfjtgvUDYImz8X2aQMFlBbQxGUlXeBmbWB9wJ3ArcD95jZ7TOb3Qnclvy7F3hf4DiDUXRlPNAQeVspOvUeMmWLmz4U3WD/svBEKPKsUPUa4KK7Pw1gZg8DdwOfy2xzN/ArPpk//Ukzu8bMbnD3L4cO+Leeusw/+/DnVm+4gGe/0eNlZ68u9NlU0H/m15/k33z0qcIxiLD832/2efHV3UKfTc/puz/6FL/0iS+u/flv9IYA7HeKJwnnP/NlPvWlrxf6vKgnf/P7buZtP/Cy4H83j6DfCDyT+f0S8Noc29wInBJ0M7uXSQbPS17yknVjBeDqMx1uu76YIAPcdv3VvPH26wt99sZrD3jLn30pX3n+hcL7F+G57fqred2fvK7QZ7/9YI97X/8yLn39qPD+b7r2kGsO9wp99t7XfxefuHi58L5FPbnu6jNR/m4eQZ9XDjK7klGebXD3B4EHAc6dO1doNaRXv/RaXv3SVxf56Ma0W8Y/vft7K9m3iIOZ8Q/v+p7K9v/m176EN7+2WHIjxCx5niRdAm7O/H4T8GyBbYQQQkQkj6A/CtxmZreaWRd4E/DIzDaPAG9Jql2+H/hmDP9cCCHEYlZaLu5+bGb3AR8B2sBD7v6kmb09ef8B4DxwF3AROALeGi9kIYQQ88jVh83dzzMR7exrD2R+duAdYUMTQgixDo2bKSqEELuKBF0IIXYECboQQuwIEnQhhNgRrGi38413bHYZ+IOCH78O+ErAcEKi2IqxzbHBdsen2IpR19he6u5n571RmaBvgpldcPdzVccxD8VWjG2ODbY7PsVWjF2MTZaLEELsCBJ0IYTYEeoq6A9WHcASFFsxtjk22O74FFsxdi62WnroQgghrqSuGboQQogZJOhCCLEj1E7QVzWsrhIz+30z+4yZPW5mFyqO5SEze87MPpt57cVm9lEz+93kv9duUWw/bWZ/mBy7x83sropiu9nM/oeZfd7MnjSzdyavV37slsRW+bEzs30z+99m9ukktp9JXt+G47YotsqPWybGtpn9jpl9OPm90HGrlYeeNKx+Cngjk6YajwL3uHvxJqMBMbPfB865e+WTFczs9cDzTHq9fm/y2r8Evubu/zz5MrzW3d+1JbH9NPC8u/+rsuOZie0G4AZ3/5SZvQh4DPirwI9T8bFbEtvfoOJjZ2YGXOXuz5vZHvAJ4J3AX6P647YotjvYgmsOwMz+HnAO+DZ3/5Gi92rdMvSThtXuPgDShtViBnf/OPC1mZfvBn45+fmXmYhB6SyIbStw9y+7+6eSn78FfJ5Jf9zKj92S2CrHJzyf/LqX/HO247gtim0rMLObgB8G3p95udBxq5ugL2pGvS048N/M7LGkIfa2cX3aSSr573dWHM8s95nZE4klU4kdlMXMbgH+DPC/2LJjNxMbbMGxS2yDx4HngI+6+9YctwWxwRYcN+Dngb8PjDOvFTpudRP0XM2oK+R17v4q4E7gHYm1IPLxPuC7gFcCXwbeXWUwZnY18EHgJ939/1UZyyxzYtuKY+fuI3d/JZOewq8xs63pqL4gtsqPm5n9CPCcuz8W4u/VTdC3uhm1uz+b/Pc54D8zsYi2iT9KfNjUj32u4nhOcPc/Sm66MfDvqPDYJT7rB4H/6O4fSl7eimM3L7ZtOnZJPN8AfpOJR70Vxy0lG9uWHLfXAX8lef72MPAXzew/UPC41U3Q8zSsrgQzuyp5UIWZXQX8IPDZ5Z8qnUeAH0t+/jHg1yqM5RTpxZvwo1R07JIHaL8EfN7d/3XmrcqP3aLYtuHYmdlZM7sm+fkA+MvA/2E7jtvc2LbhuLn7P3D3m9z9FiZ69jF3/9sUPW7uXqt/TJpRPwX8HvCPqo4nE9fLgE8n/56sOjbgV5kMI4dMRjY/AXwH8N+B303+++Itiu3fA58Bnkgu5hsqiu3PMbHxngAeT/7dtQ3HbklslR874OXA7yQxfBb4qeT1bThui2Kr/LjNxPkG4MObHLdalS0KIYRYTN0sFyGEEAuQoAshxI4gQRdCiB1Bgi6EEDuCBF0IIXYECboQQuwIEnQhhNgR/j8qQoEJ1vvBewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "40\n",
      "0.44115502039856547 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(midtro_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db2ef931-9177-4fed-a790-c6e97832ccbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2004, 2006, 2006, 2012, 2012, 2014, 2015, 2017, 2018]\n"
     ]
    },
    {
     "data": {
      "image/png": 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hllmeJ9OHA1DCFBKndzdKWKbncY6EpF5C2BVTHD0RJQF8AcCDzHxNbWfmy/V/swCeAXCvGa+3l8jmi12n3CkaqXfiBFxNI4W253EOIpuv1VsIQiv6dvREdAuArwH4ADO/ots+SkRh9TuAdwJombkjtKZYruD6eqn32G1UhCm8QC96A3qmIiFUqoxr6/LkJrSmY9IuET0F4O0AxokoDeB3AQwDADM/BuBTAA4C+JN6ali5nmETB/BMfdsQgL9g5m9a8B4GlmyXylLNiDCFN1BPXGaM86RBvVlhb9HR0TPzwx32fwjAh1psvwhgbucZglG2y+J7e6RXwhQyo3c3vQjL6InriqZOTkdNs0sYHKQy1sVsx257m6UpYQqJ0bubTK7YtbCMnilZixE6II7exajc6F4dPSDCFF5gOVdoZEj1wvhYAETSwE5ojzh6F5PJFxDw+7B/ZLjna8QjIels6HKyuQLiPbS4UAz5fRgfC0qKpdAWcfQuJpsrYjIS7Lr/iZ54JIhlTYQp3EwvwjLNxCNBCd0IbRFH72L6KZZSxCMhEaZwMUpYpt9xnoqEJHQjtEUcvYtZzhV6TrlTiDCFu1HCMv2EboBaiqWEboR2iKN3MSp00w9x6VfuanoVlmkmHg7h2noJpXLVDLOEAUMcvUtZK5axViybELqpORDpbuhO+q2KVcQbfellnIWdiKN3KdkepeWaUQ5EhCnciQqp9e3oo6IRLLRHHL1L6VVCsBkRpnA3Ga0mLDM+1r2wjJ7tTqUyzsJOxNG7lGyfVbF64pGgOHqX0o+wjJ5GiE7GWWiBOHqXYlbsVl1jWR7pXUkm338KLQAcGA1g2E8SuhFaIo7epSznChgN+DEW7Nh3riMiTOFelrVC3+swQK2v0WRYxllojTh6l5LNFfvOrVYoYYqqCFO4jpqwjHnjLPUSQivE0buUTJ+NrvTE68IUV0WYwlX0KyzTTFxEwoU2iKN3KbXYbf+P9IAuxVLit65iJd+fhGAztRCdjLGwk46OnoieIKIsEbWUAaQanyaiC0SUIqJ7dPvuJ6Lz9X2Pmmn4IMPMtUZXpoVupDrWjZi54K6uky+WsV6UvkbCzRiZ0T8J4P5d9j8A4Fj95xSAzwMAEfkBfK6+/ziAh4noeD/G7hVWN7ZQKldNDN0o7ViZ7bmJfoVlmtmujpVxFm7GiJTgd4joyC6HPAjgi1zrg/t9IooR0SEARwBcqEsKgoierh/7ct9W28S3z2exkiviX/7zGVtf16xqScXEWBBEkmPtNsye0TeUprQCjo6PmnJNozzzT2m8cDZj6jVnDozgkw/c2Vebbiv5wt9exIs/vWHqNSOhYfy39yZNvSZgwNEbYBrAJd3f6fq2Vtvf3O4iRHQKtScC3HLLLSaY1T+f+etXcSG7hvfNJ2y92baVpcyJ3YowhTvJ5Ip9C8voaYiEO5B588ffehXa5pZp9+xaoYxvnFnG+998C249aO+XlhFK5Sr++/PnEQkN4cBof1XNemIj5l1LjxmOvpUH5F22t4SZHwfwOADMz887ngdYrlRx9nIOxXIVP722gSM2zpDMrIpVSHWs+8jkCn0Ly+jZDtHZO86rGyW8fn0Dn7j/Tnzk7bebcs0zSxre/ZnvIpXWXOnoX8nkUSpX8Z/fcwLvTh522pyOmJF1kwagj20kAFzeZbsneCWzhmK95etCetXW11Yf1H5bFOuJh6U61m2YISyjZyw4hJGAH8uaveOcSmsAgGQiato13zgVRmDIh5TNnz2jNN7zdMxZQwxihqN/FsCv17Nv7gOgMfMVAD8AcIyIjhJRAMBD9WM9weLSKgCACFisD6pdZPIF7B8ZRnDIb9o141GpmnQbNUdv3pc5EdWUpmwO3Swu1T4fJ6fNc/TDfh+OH4o0HKrbWFxaRWxkGDMH9jltiiE6hm6I6CkAbwcwTkRpAL8LYBgAmPkxAM8BeBeACwA2APxGfV+ZiD4K4HkAfgBPMPNZC96DJSykNYRDQ7h9Ysz2m21ZM69aUqEXpggMSfmEG8jkivj5YxOmXnMyYv9azMKlVRwdH0V0nzlrDYpkIoqvvphGpcrw+9y1ILtwScPsdNS1C8XNGMm6ebjDfgbwSJt9z6H2ReA5UulVzE5HcUc8jC+fvmTrzZY1qdGVHjVzXFkrYjrmjVnIIKOEZaZMqpVQxCMh/NPrq6ZesxOptIY333bA9OsmEzF88e9/iosrazgWD5t+/V4pbFVwPpPHR+40Zz3CDmRq14LCVgXnl/NIJmJIJqLYKFXw45U1217f7Ed6YFuYQkrk3YFZwjLNTEVCWM4VUJt/WU82V8ByroBZE8M2ChXzd1v45uUrOVSqjFkT1ySsRhx9C360nMdWhZFMRBs328KlVVteu1JlrJjY6EohwhTuYlsr1txxnoyEUCpXoW1umXrddignPDcTM/3at0+MYSTgd92CbKruC+YSMUft6AZx9C1YrN9YyUQUt42PYTTgbyw4Wc3VtSKqbG5qJeBc6p3QmkaxlOmhG3uroFNLGnwEnDgcMf3afh/h5OEoUjZ99oySWtIwEQ6a/jRmJeLoW7CQ1nBgNIDp2D74fIST01Es2PT4aHa1pGL/SE2YQlIs3YFV46yuZ1cVdCq9imOTYYwEzCjJ2UkyEcXLl3PYqlQtuX4vpNIa5hLeWYgFxNG3ZDGtIakbyLmZGM5dyaFUtv5m2+5/Yu5swecTYQo3kckVTROW0TNlYwM7Zm58VqwiORNDsVzFK5m8Za/RDWvFMn68soakh8I2gDj6HWyUyng1m0dSt7g0Ox1FyaabzSxR8FaIMIV7MLtYSjERrjc2s8HRL61u4tp6yVpHP+2uBdkzSxqY4amFWEAc/Q7OXs6hyrjpG1stuthxs2VzBfgIGB8zP/4Xj4Skg6VLsMrRh4b9iI0M2zLOi42K2Jhlr3HrwRFEQkOucfRqYThpQZaRlYijb0Jl1+hnKTMH9iE2MmzL6n8mV8BEOGhJzn48EkJG0itdgZnCMs2oFEurWUhrGPYT7jxkXY47ESGZiLkm8yaV1jAd24eDFkzErEQcfROptIapSKjRCRCo3WyzNi3IZnLmp1YqRJjCHTSEZSwa50mbxOBT6VXcORUxtVVHK5KJKM4v51HYqlj6OkZIpTXMzXhrNg+Io9/B4lLrxaW5RAyvZKy/2ax6pAdEmMItNIRlrBrncNDy0E21ylhc0myJVScTUZSrjHNXcpa/1m6oLp2zHmlkpkccvQ5tcws/ubre0tHPJqKoVBlnL1t7s1lRFasQSUF3YLawTDPxSAgra0VUqtZVx752bR35Qhlztjj6GADnF2QbxWEeW4gFxNHfxJml9otLakF20cJYYbFcwY2NLdOrJRXi6N2BVSm0ing0hEqVcW3Nulm9KiC0Y3Z7KBrC+FjAcUev3vMJjy3EAuLob0L1nW/VtyMeCWIiHLT0ZrNCcESPVMe6A6uKpRTxsPXVsQuXNISGfbgjPmbZayjcsiC7cGkVt1nQpdMOxNHrWExruOXACPa3kAYjIswlrC3HtqosXqGEKSTF0llU5pOZwjJ67HhyW1xaxYnDUQz57XEhyUQUF1bWHE0kaLd+5wXE0etIpXdfXJqdjuHHK2tYs+hms/qRnogQtyn1TmiPFcIyeqxug1CuVHFmKWdJx8p2JBNRMG+HV+0mmy/gilbArMcqYhXi6OtcXStiaXVz14WW5Iy1N1tjRm9RjB6ofYlIGwRnsTK1EgDGxwLwkXXVsT9eWcfmVsXWNEO1FmBXc8FmUpe8uxALGHT0RHQ/EZ0nogtE9GiL/b9NRC/Vf84QUYWIDtT3vUZEi/V9p81+A2ZhpMpvuxx71RIbMrkCAkM+xEasiwFKdazz1ETBrXP0Q34fxsesS7HcXsuKWXL9VkyEgzgcDdnWXLAZ1aXzuAVdOu2go6MnIj+AzwF4AMBxAA8T0XH9Mcz8h8x8NzPfDeCTAP6Gma/rDnlHff+8eaaby0J6FUS7614eHAtiOrbPsptNpVZa2RWv5ujtE6YQdpLJFTBlcYvbuIXasan0KsaCQ7htfNSS67cjmYhZmvW2G6n0Ku6IW9el02qMzOjvBXCBmS8ycwnA0wAe3OX4hwE8ZYZxdrKY1nD7xFjHboJzM1HLxMIzuaKlYRug5gCKNgpTCDdjlbBMM/FIyDI1scW0hpPTEfhs1nGdTUTx2rUNaBv23ruqS6edaxJmY8TRTwO4pPs7Xd+2AyIaAXA/gK/qNjOAF4joRSI61e5FiOgUEZ0motMrKysGzDIPZsZCWjPUqGh2OobXr2/gxnrJdDsyFmjFNmO3MIVwM9fqwjJWhm6A+lqMBRXQpXIV567kHVFXajQXXFq19XUbXTotUNGyCyOOvtXXdrvn/l8F8HdNYZu3MPM9qIV+HiGit7Y6kZkfZ+Z5Zp6fmJgwYJZ5LOcKuLpWNJQ6pRZjrFgUymh2OHopmnISlQkzZcM4X18voVg2t2XH+eU8SpWqI216Zx1qWdxYvxvwGX0awIzu7wSAy22OfQhNYRtmvlz/NwvgGdRCQa5iob6ibiR16oRFC7JrxTLWSxXL5clUaEhSLJ3B6hRaRaOvkclPbmoh1okZfXRkGEcOjtheOGVHl06rMeLofwDgGBEdJaIAas782eaDiCgK4G0Avq7bNkpEYfU7gHcCOGOG4WayuLQKv48M6V5G9w3jtvFR02cVVldLKiYj9glTCDuxa5zV9bMmL8gupjXsHxlGYv8+U69rlNqCrL0z+lR6FXcdsr5Lp5V0dPTMXAbwUQDPAzgH4MvMfJaIPkxEH9Yd+msAXmDmdd22OIDvEtECgH8E8FfM/E3zzDeHVFrDHfEwQsPGBnI2EbXM0VtVLamwU5hC2IkSljnYovraTLZDdObP6GcTMcf0UpOJKC5rBazY1IG10aXTw2EbADCUK8TMzwF4rmnbY01/PwngyaZtFwHM9WWhxTAzUmkND5ycMnxOMhHD11+6jKyJ+dAZm2K3QC18IzF6Z1iuC8tY3TrAirWYzVIFr2bX8MvH46Zds1tUncvi0ip+4U7r7dju0hmz/LWsZM9Xxr5+fQPa5lZXcmhq0dbMWb2aeVmdjQHUeumIo3cGq6tiFftHhhHw+0xdi3n5ioZKlR2d3Z44HIGPttfVrKbRpdOjFbGKPe/oFxoVscYHUt1sZi4KZXIFjAWHOubxm4EdwhRCazK5AiYtrpUAan2NJiNBUxdjlXOdczDNcDQ4hDdMjtnWCkF16Tw2aX2XTivZ845+Mb2KwJAPd8SNr6iPBIZwRzxsaifLWlm8PTqUdghTCK2xUlimGVUFbRaLSxomw0Fbnkh2Y3a61rLYjupuu7t0WoW3rTeBhbSGuw5FEBjq7r9idrq2IGvWzZbJFW2JzwO11LtKlXFtXWb1dqKEZewcZzNDNwvp1a5CnFYxNxPF1bUSLlssdK+6dHq1NbGePe3oK1XG2SWtp450yZkYrq+XsLS6aYotVmrFNtNYqNPE0duJ1cIyzcQjIdNCN/nCFi6utJbZtBu1RmB13xvVpdMN77lf9rSjv7iyhvVSpafFpaSJVXrMjGyuaGvoBpDqWLtROe12jvNasWyKfsLiUvdrWVZx16EIhnxkeSdLVRzmhqeYftnTjr4h9tvD4tKdh8IY9pMpjv7GxhZKlaqNj/R1R29Rd0OhNcv1J6gpixTEmombWBxnpI23XYSG/bjzUNjywqlUehXh4BCOHrS3S6cV7HFHv4qRgB+3T3S/oh4c8uPOqYgpmTd2VUsqlDBFxuIYp3AzdgjL6DFTaSqV1pDYvw8HLC70MoodC7K1Lp1R27t0WsHedvRLGk4ejsLf40AmE1EsLmmo9pm9su3o7Xmkt1qYQmhNJl9AwG+tsIyeRhsEE8Y5tbTqqqKhuUQUuUIZP722Ycn1VZfOpI0qWlayZx39VqWKly/3t6KeTESRL5Tx2rX1zgfvgvog2pFfrbBSmEJojVqHsat9gFlrMdfXS7h0fdNVRUPKlgWLFmRVl86kjSpaVrJnHf355TyK5f7arap4Zb9x+mWb+tzoiUdkRm83y1rBtnUYABgLDmE04O97nFONRUn3OPo74mEEh3yWxekXXPie+2HPOnqVRdDP4+ixyTGEhn19O/pMroADowFbu+OZXUwjdMYOYZlmzGh3oZzpbjKbdjPs9+H44Yhlvemd7tJpNnvW0afSq4iEhnDrwZGerzHk9+HE4WjfC7KZXBGTYftm84B1whRCe+xMoVWY0cBuIa3htolRREL2rC0YZS4Rw5nLmiUV3qo4zKkunWazhx29ZspAJhNRnL2cQ7lS7fkamVzBtpQ7hVr4tavd615H5bPbGboB6iG6PtdiFpdWXamuNDsdxUapgh+vrJl6XdWlc1DCNsAedfSFrQrOL+dNWVxKJqLY3KrgQh83WyZXsC3lTiFFU/Zidwqtoha6KfachpjJFZDJFV2RP9/MXD0jZuHSqqnXVV063fiee8WQoyei+4noPBFdIKJHW+x/OxFpRPRS/edTRs91gnNXcihXuafWB830uyBbrlRxda1oW2qlwiphCqE1dgnLNBMPh1AqV7G6sdXT+dtFhe6b3d42PobRgN/0TpaqS+eemtETkR/A51AT9z4O4GEiOt7i0L9l5rvrP7/X5bm2om5eIxqxnTh6cBTh4FDPcfpr6yVU2Z4+9HpkRm8vdve5UfRbBZ1K12Q2jx9yn9Pz+Qgnp6Omt0JYXNIQjzjfpdNMjMzo7wVwgZkvMnMJwNMAHjR4/X7OtYxUWsP4WACHTYiLq5ut1zSvZc0+ZSk9SphCZvT2sOxU6Kb+BNHrOKfSGo5NjmFfwJ16qXMzMZy7kkOp3PsaWTNu6dJpJkYc/TSAS7q/0/VtzfwMES0Q0TeI6ESX54KIThHRaSI6vbKyYsCs3kmZvKKeTERx7kq+p5vNqditEqaQGb092Ckso2e7U2n341yT2Vx1dQhjdjqKUrmKVzJ5U67X6NLpwsXnfjDi6Ft5w+aVnR8CuJWZ5wB8BsBfdnFubSPz48w8z8zzExMTBszqjbViGRdW1kyVQ0smYihVqvjRcq7rczN59Uhvb+y29pqSS28XTqRWAttrAr2Mc/rGJm5sdCezaTdzJhUtKhpdOh1U0bICI44+DWBG93cCwGX9AcycY+a1+u/PARgmonEj59rN2SUNzOYuLvWjIZvNFeD3EQ6OOeHoZUZvF05kVgG15nv7R4Z7itGnepDZtJuZA/sQGxk2TdZThWCd1MW1AiOO/gcAjhHRUSIKAHgIwLP6A4hoiupxECK6t37da0bOtZvGQqyJPSwS+/dhf48327JWwMRYsOfGav0wGQ5JjN4mlh2olVDEI6FGi+RuSKVXEfD78MYp4zKbdkNEDbU3M0ilNcwccE+XTrPo6OiZuQzgowCeB3AOwJeZ+SwRfZiIPlw/7L0AzhDRAoBPA3iIa7Q814o3YpTUkobD0RAmTKxEJSIkE7GebrZM3v7USsVU1DxhCqE9dgvLNBOPhBqiJ92QSmu481DY1tYcvZBMRHE+k0dhq/8q74X06sA0MtNjKI+emZ9j5juY+XZm/i/1bY8x82P13z/LzCeYeY6Z72Pm7+12rpOk0quWdOFLJqJ4NbuGzVJ3N1s2V7A9tVJhpjCF0J7VurCME6EboLcQXbXKOLOkuTpso0gmYjVZ0Mvdr5Hpub5eQvrGpifec7fsqcpYbWMLP722YcnikrrZXr7S3ax+OWdvR0M9yvFI+MZaVGqlk6GblXyxq54wP7m2jnyx7OqFWIVakO1XQzY1QNKBzewpR59aWgVgzeKSuqaqqjNCYauC1Y0tx0I38agUTdmB3cIyzcQjIVQZuLpm/Avdja2J2xGPBDERDvYdp19MayACTk5HTLLMPewtR6+yCCyIwcUjIcQjwa7KsVVDMedCN+Lo7cAJYRk9vYxzKq1h37Afb+hBZtNuiAhziShSfbZCWEhruG18FGGXdek0gz3m6Fdx5OAIohZJuc1Ox7pSvHGqWEphljCFsDtO9blR9FIdm0prOHE4giG/N1zE7HQMP15Z6yuxYHFp8CpiFd4YRZNIpTVT+tu0Yy4RxcWVdeQKxhpINWK3DvbUEElB61l2QFhGT7ci4eVKFWcva55yesmZKJiBMz3O6re7dLo/VNULe8bRZ/MFXNEKpnSsbIfK5jF6s2VyzlXFKuKRUE/l8YJxnBCW0TM+FoSPjGdXvZpdQ2Gr6imnp1oW9Fo4pVode+k9d8OecfR2VLx127I4mysgMORDdJ9zMUEzhCmE3ck6ICGox+8jTISNp1h6aSFWcXAsiOnYvp4XZBeXNNd26TSDPePoU40VdesG8sBoADMH9hnuZLmcKyAeCToqV1brd9O7MIXQmUx9nJ1EjbMRUmkN4eAQjhwctdgqc0kmeq+QXXB5l85+2UOOfhVvmBjDqMXdA5NdLMhmHMyhV8Qj/QlTCLtTrlSxki+6YpyNz+g1zCai8DnQlqMfkokYXr++gRvrpa7OY2Ysplcb+fiDyJ5w9MyMxSV7FpeSiSjSNzZx3cDNViuLd94BAL0LUwi745SwTDNGq2OL5Qp+tJzz1EKsQq2/das41ejS6UIVLbPYE47+slbA1bWSLTHH2YTxRSGnOhrq6VeYQtgdp1NoFfFwCDc2tlAs796i40dX8tiqsKfi84oTPS7IWllf4xb2hKNftHFxSS32dorT5wtbWC9VMBV1PnYLSNGUVTilINaMGudshy90VXTkRUcf3TeM28ZHu47Te6FLZ7/sCUe/kNYw5CPcdcj60uZwaBi3TYx21LHMOKQh2kxDmEJSLC3BSWEZPUbbXaQureLAaADTsX12mGU6sz0syKbSGu46FEZgaHDd4eC+Mx2p9CreOBVGaNieFfW5RKzj46PKaXaqLF7RjzCF0JlsrgAfwRFhGT1GQ3SpdK1jpZOZYP2QTMSwnCsYrhnY7tIZs9Ywhxl4R1/TvbR3IGeno8jmi7vOnpRjdXqmV7NBBEisIpMrYCLsjLCMnu1Ope3vyY1SGa9m857WS+1W7e3i1VqXTital7uJgXf0r13bQL5QtjXmqGQKVbVdK5Tij9OhG2WDxOitYTnnfGolAMRGhhEY8u06zmcv51Blb7fpPXE4Ah/BcIOzxXpH20FOrQQMOnoiup+IzhPRBSJ6tMX+9xNRqv7zPSKa0+17jYgWieglIjptpvFGcKLK7/ihKPw+2jXNK5MrIBwcsjyv3wiiHWsdTgrL6CGijuPsBY3YTowEhnBsMmw482bhUq1L5+0T3ioO65aOjp6I/AA+B+ABAMcBPExEx5sO+wmAtzFzEsDvA3i8af87mPluZp43weauSKU1BId8uCNu34r6voAfxybHdl2QzeYLjnUzbKYXYQrBGG6oilXEO2gEp9KrmIqEXPHF1A/JRBSLac1QtffikoaT097p0tkrRt7dvQAuMPNFZi4BeBrAg/oDmPl7zHyj/uf3ASTMNbN3FtMajh+OYNjmgZxLxLCYXm17sy1rzolFNzNZF6a41oUwhdCZwlYFNza2XBG6ATp3Kl1Me0M6sBPJmRiurZewtLq563Fe7NLZK0a83zSAS7q/0/Vt7fhNAN/Q/c0AXiCiF4noVLuTiOgUEZ0motMrKysGzOpMpco4c1lzJP42m4jixsYW0jda32yZXNHxYinFVJdtbAVjOC0s08xunUq1zS1cvLo+GI5+2tiCrBe7dPaKEUffKl2g5TSViN6BmqP/hG7zW5j5HtRCP48Q0VtbncvMjzPzPDPPT0xMGDCrMz9eWcNGqWJpx8p2qC+XVn1vmLkeunGLA5DqWCtwS1WsIh4JYr1UaSnOcaZRKBWz2SrzufNQGMN+6ujoB1kjthkjjj4NYEb3dwLA5eaDiCgJ4AsAHmTma2o7M1+u/5sF8AxqoSBbUFkvcw70sHjjVBgBv69lheyNjS1sVdg9sVupjrUEN+gN6NltnFM2tPG2i+CQH3dORTouyC6kNYRDQ7j1wIg9hjmIEUf/AwDHiOgoEQUAPATgWf0BRHQLgK8B+AAzv6LbPkpEYfU7gHcCOGOW8Z1IpTWMBvw4Om6/7mVgyIe7DoVbzujdUhavUMIU4ujNxQ0KYnoajr5F+CaVXsUtB0awfzRgt1mWkExEsbikobpLgoFak/Bal85e6OjombkM4KMAngdwDsCXmfksEX2YiD5cP+xTAA4C+JOmNMo4gO8S0QKAfwTwV8z8TdPfRRtSSxpOTkcdK1aZTURxZim342ZTC2JuCd10K0whGMMNwjJ6GiG6FguyqjXxoJBMRJEvlPHatfWW+1WXztkBbmSmx1ASNzM/B+C5pm2P6X7/EIAPtTjvIoC55u12UCpXce5yDh/82VudeHkAtdjfn3//dVy8uo43TG4/VWQbsVt3PNIDUh1rBRkXCMvomWyEbm4e52trRSytbjr6WTEbFXdfXNJw28TOJ3rVpdNKaVE3MbDJo69k8ihVqo4utMw1brbVm7arD5rTfW70TIalOtZs3JRZBQBjwSGMBYd2jLOqIh2k2e2xyTGEhn1YuNR6QVbF7wfpKWY3BtbRL7hA9/L2iVHsG/bvuNmWcwUcHA24qlveVFRCN2aTyRUaXSPdQqvq2NSlmszmIDm9Ib8PJw5Hd0yyFKm0hoMe7tLZLe7xNCazmNYQGxnGLQ6uqA/5fTg5HdnRCsEtZfF6jApTCMZxg7BMM61CdItLq7h9YgxjLmjHYSbJ+hpZuVLdsc/rXTq7ZWAd/UJaw+y08wM5Ox3D2cvaTTdbJld0VXweMC5MIRhjrVjGeqniynHWz+iZGQtpzdMdK9uRTESxuVXBhZW1m7arLp2zeyB/XjGQjr6wVcErmbwrKt7mZqIobFXxSmb7Zlt2gSh4M6rvTlb60ptCI4XWZaGbyUgQ2Vyx0ZpjOVfASr7ois+K2aj1uebCKdWlc68sxAID6ujPXs6hUmVXVLw1pAXrscJypYqra86LgjejHJJqnyz0h1uEZZqZioRQqlRxY2MLgK5QygWfFbM5enAU4eDQjsIpVUg5SGsSnRhIR+9Ea+J2HDk4inBoqNHJ8upaCczuSq0EjAlTCMZxk7CMnubq2FR6FX4f4cRh62U27cbnI5ycju6oTl9c0nAoGnLdl7CVDKSjX0xrmAgHXREe8fkIs7qbrdH/xGU3WUOYQkI3puAWTeBmtvsaKUev4Y64fTKbdpNMRHHuSh6l8vYaWaq+freXGEhHv5BeRdIFC7GKZCKGHy3nUCxXtsviXRa7bQhTiEi4KSxr7hGW0aOf0TMzFpe0gY5VJxMxlCpVnF/OA6h16fzJ1XXMzcScNcxmBs7R5wuq3WrMaVMaJBNRbFUYP7qS347duuyRHugsTCEYx03CMnomwtudSi9d38TqxtZAx6pV+FbV1ZxZGpzmbd0wcI7+zFIOzO6Izyu2BYtXkckV4fcRDo66zwl0EqYQjFNLoXXXUxtQ6+x4YDSATK7QcH6DrJea2L8P+0eGG6HTQZBL7IWBc/Qqu8VNAzkd24eDowGk0hoyuQImxoKONVrbDZV6J/RPrc+N+xw9AEyGg8jkilhc0hCwWWbTbogIyUSs8aWWSq/i1oMjiI0MRpdOowyco19IazXHOuaeGTMRYTYRRSqtYdmFZfGKqUgIa8VyS2EKwTjMjKxLZ/RAbX0okytg4dIq7joUcVUrDitIJqJ4NbuGzVJlTy7EAgPo6N2qe5lMxPBqNo/Xrq0jHnbPl5AeESAxhxsbWyhVqq5LrVTEwyFc0TZxZsAXYhXJRAyVKuM7r65gaXVzoENV7RgoR39jvYTXr2+4aiFWkZyOosrApeubrp3pTTal3gm94TYJwWbikSCurpWw7pDMpt2oid+X/uF1AHurUEoxUI4+teTehRa9TW5LrVRMyYzeFJbd7uh1999eSDOMR0KIR4L4zisrIAJO7oEvt2YMOXoiup+IzhPRBSJ6tMV+IqJP1/eniOgeo+eayWJ9wcWNAzkZCTUc6aRLQzfthCmE7nCjsIweVaw3EvDj9haiHIOI6rU/iF06jdDR0RORH8DnADwA4DiAh4noeNNhDwA4Vv85BeDzXZxrGgtpDUfHR10j3daMmtW7dabXTphC6A71RTnh0i90df+dPOyczKbdqLUINz7t24GRr7Z7AVyoywKCiJ4G8CCAl3XHPAjgi1xrifd9IooR0SEARwycaxqLaQ1vvu2AFZc2hWQiihdezrjW0QO1OP1XXkzju69eddoUz7KyVsSB0QCCQ+5sK6CeNPZSrFq910Fsx2wEI45+GsAl3d9pAG82cMy0wXMBAER0CrWnAdxyyy0GzLqZUrmKnzs2jp97w3jX59rFe//ZDNaKlZv0Y93GR952O759Puu0GZ7mWHwM9x5x74RjIhzEx3/pGH517rDTptjGfbcdxL/5+aN49x56z3qMOPpWz3Zs8Bgj59Y2Mj8O4HEAmJ+fb3nMbgSGfPgf73NEh9wwU9EQHn3gTqfN2JX3zc/gffMzTpshWAgR4eO/dIfTZthKaNiP3/kVy6LGrseIo08D0H/yEwAuGzwmYOBcQRAEwUKMZN38AMAxIjpKRAEADwF4tumYZwH8ej375j4AGjNfMXiuIAiCYCEdZ/TMXCaijwJ4HoAfwBPMfJaIPlzf/xiA5wC8C8AFABsAfmO3cy15J4IgCEJLSGlHuon5+Xk+ffq002YIgiB4BiJ6kZnnW+0bqMpYQRAEYSfi6AVBEAYccfSCIAgDjjh6QRCEAceVi7FEtALgpz2ePg7AzfX7brcPEBvNwO32Ae630e32Ae6y8VZmnmi1w5WOvh+I6HS7lWc34Hb7ALHRDNxuH+B+G91uH+ANGwEJ3QiCIAw84ugFQRAGnEF09I87bUAH3G4fIDaagdvtA9xvo9vtA7xh4+DF6AVBEISbGcQZvSAIgqBDHL0gCMKAMzCO3k4R8l4gohki+jYRnSOis0T0MadtagUR+Ynon4jo/zptSyvqMpVfIaIf1f8vf8Zpm5ohon9fH+MzRPQUETmqHUlETxBRlojO6LYdIKJvEdGr9X/3u9DGP6yPc4qIniGimIMmtrRRt+8/EBETkSsl7gbC0dstQt4jZQC/xcx3AbgPwCMutBEAPgbgnNNG7ML/AvBNZr4TwBxcZisRTQP4dwDmmfkkau25H3LWKjwJ4P6mbY8C+GtmPgbgr+t/O8mT2GnjtwCcZOYkgFcAfNJuo5p4EjttBBHNAPhlAK/bbZBRBsLRQydgzswlAEqE3DUw8xVm/mH99zxqDmraWatuhogSAH4FwBectqUVRBQB8FYAfwoAzFxi5lVHjWrNEIB9RDQEYAQOq6ox83cAXG/a/CCAP6v//mcA/oWdNjXTykZmfoGZy/U/v4+aQp1jtPl/BIA/BvAf0UYm1Q0MiqNvJ07uSojoCIA3AfgHh01p5n+idsNWHbajHbcBWAHwv+vhpS8Q0ajTRulh5iUA/wO12d0V1NTWXnDWqpbE6ypwqP876bA9nfjXAL7htBHNENF7ACwx84LTtuzGoDh6wyLkTkNEYwC+CuDjzJxz2h4FEb0bQJaZX3Tall0YAnAPgM8z85sArMP5kMNN1GPdDwI4CuAwgFEi+lfOWuVtiOh3UAt9fslpW/QQ0QiA3wHwKadt6cSgOHojAuaOQ0TDqDn5LzHz15y2p4m3AHgPEb2GWujrF4joz501aQdpAGlmVk9CX0HN8buJXwLwE2ZeYeYtAF8D8LMO29SKDBEdAoD6v1mH7WkJEX0QwLsBvJ/dV/RzO2pf6Av1z00CwA+JaMpRq1owKI7e9SLkRESoxZbPMfMfOW1PM8z8SWZOMPMR1P7//h8zu2omyszLAC4R0Rvrm34RwMsOmtSK1wHcR0Qj9TH/RbhswbjOswA+WP/9gwC+7qAtLSGi+wF8AsB7mHnDaXuaYeZFZp5k5iP1z00awD31+9RVDISjry/YKBHycwC+7EIR8rcA+ABqM+WX6j/vctooD/JvAXyJiFIA7gbwX50152bqTxtfAfBDAIuofcYcLZMnoqcA/D2ANxJRmoh+E8AfAPhlInoVtYyRP3ChjZ8FEAbwrfrn5TEX2ugJpAWCIAjCgDMQM3pBEAShPeLoBUEQBhxx9IIgCAOOOHpBEIQBRxy9IAjCgCOOXhAEYcARRy8IgjDg/H+CWQL5kBDB8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "16\n",
      "0.35811775361995113 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(MCS_induced_flag, end)\n",
    "n_epe = n_epe[2003-1979:]\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37aa9d0d-853f-4f00-9235-cd8d3c7767c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2015]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaWklEQVR4nO3dcYyb933f8feX5B0p3ZFnR7rjJZIcuYWaxiiS1rm62YpuWdO0clpEHbBhdrMmyxoYBuIuGzY0Lop1f3QYOmTtuiJuBCF13aJBjCJ1WzdQ6wTdmvzRerCcJU5kz67grPbFOfIkO0dKJ/LuyO/+IJ8TxeMdH5IP+ZDU5wUIPpKPeF9I1kc/ffn7PV9zd0REZPIl4i5ARESioUAXEZkSCnQRkSmhQBcRmRIKdBGRKZGK6xsfPXrUT548Gde3FxGZSM8+++xld1/s9FpsgX7y5EkuXLgQ17cXEZlIZvb3+72mlouIyJRQoIuITAkFuojIlFCgi4hMCQW6iMiU6BroZvaomRXN7Jv7vG5m9ttmdsnMnjOzu6MvU0REugmzQn8MOH3A6/cCp5o/HgA+PXhZIiLSq66B7u5fAV4/4JIzwB94w9PAbWb25qgKFJHp9fTLV3hxrRx3GVMjih76MeDVlserzef2MLMHzOyCmV1YX1+P4FuLyCT7pc8/x2988cW4y5gaUQS6dXiu49QMdz/n7ivuvrK42PHkqojcItydtY0Ka6VK3KVMjSgCfRU40fL4OPBaBO8rIlPsjc1ttmp1Cgr0yEQR6E8CH2rudnk3sOHu34ngfUVkigVBvl6uUqtrFGYUut6cy8w+B7wHOGpmq8B/AmYA3P0scB54P3AJ2AQ+MqxiRWR6BIFed7hytcpSLhNzRZOva6C7+/1dXnfgY5FVJCK3hNZWy1qpokCPgE6KikgsCqVqx6+lfwp0EYlFoVQhmbDdr2VwCnQRiUWhVOF7F+dImAI9KrFNLBKRW1uhVOUttx1i4/q2Aj0iWqGLSCwKpQr5bIZ8LqMeekQU6CIycju1OpevVsnn0ixlM1qhR0SBLiIjd/nqFnWHpVyGfC6tQI+IAl1ERi4I8OVchuVchjc2t6nu1GKuavIp0EVk5IJAz+caPXSAovroA1Ogi8jI3Qj0NEu59E3PSf8U6CIycoVSlWTCODKf3l2ha6fL4BToIjJyhVKFxfk0yYSxvBvoWqEPSoEuIiNXKDe2LALcdniG2WSCQlmBPigFuoiMXGHjxt0VzYylXJrChgJ9UAp0ERm5Qrmy22qBxvZF9dAHp0AXkZGqbNf47ub2bssFGtsX1XIZnAJdREYq2G/eOtBCLZdoKNBFZKSClXi+JdDzuQzXtmpcre7EVdZUUKCLyEi1HvsPaOtiNBToIjJSwYefrT10nRaNhgJdREaqUKowm0qwcGhm97m8VuiRUKCLyEgVSo0ti2a2+5yO/0dDgS4iI1UoVW5qtwDMp1PMp1NaoQ9IgS4iI1UsVW/ashhYyqV1C90BKdBFZGTcnbXmLNF2+WyGNa3QB6JAF5GRuVrdYXOrxvJCes9rywuaLTooBbqIjMyNLYv7t1zcfdRlTQ0FuoiMTLACX9qn5bJVq/PG5vaoy5oaCnQRGZndU6ILHQJde9EHpkAXkZEJWi5L2U49dJ0WHZQCXURGplCqkE2nmEun9rwWtGG0dbF/oQLdzE6b2YtmdsnMHu7w+oKZ/bmZfd3MLprZR6IvVUQmXaFU2b1vS7vgeW1d7F/XQDezJPAIcC9wF3C/md3VdtnHgOfd/Z3Ae4DfMLPZiGsVkQlXKFU69s8B0qkkb5qbVctlAGFW6PcAl9z9ZXffAh4HzrRd40DWGjdnmAdeB3RjYxG5SaFU7XioKLCUTet+LgMIE+jHgFdbHq82n2v1KeDtwGvAN4CPu3u9/Y3M7AEzu2BmF9bX1/ssWUQmUb3uFMuVjsf+A/mcDhcNIkygW4fn2nf+/xTwNeAtwA8CnzKz3J6f5H7O3VfcfWVxcbHHUkVkkr2xucV2zVnep4cOjXukK9D7FybQV4ETLY+P01iJt/oI8IQ3XAK+BXx/NCWKyDQ46JRoYDmX4fLVKju1Pf/AlxDCBPozwCkzu7P5Qed9wJNt17wCvBfAzPLA24CXoyxURCZbMEv0oJbLUi5D3eHKta1RlTVV9m4GbePuO2b2EPAUkAQedfeLZvZg8/WzwK8Bj5nZN2i0aD7h7peHWLeITJjCRjAc+qCWSyPs1zYqB67kpbOugQ7g7ueB823PnW35+jXgJ6MtTUSmyY1Togd9KKrTooPQSVERGYlCucKRuVlmU/vHznJwP5eyti72Q4EuIiNRLB28ZRHgyHyahDWuld4p0EVkJNZKlQO3LAIkE8ZiNs3ahgK9Hwp0ERmJQqka6oPO5VxGLZc+KdBFZOh2anUuX+08HLrdUi6jlkufFOgiMnTrV6u4H7xlMZDPpXXHxT4p0EVk6IIti8shVuj5bIbvbm5T2a4Nu6ypo0AXkaEL9pWH6aHnm7fXXVcfvWcKdBEZuqAnvt9wi1aaLdo/BbqIDN1aqUIyYRyZC9dDD36O9EaBLiJDVyhVWcqmSSY63Y37ZrunRTXoomcKdBEZukKIU6KBhUMzzKYS2rrYBwW6iAxdoVQhn+3ebgEwM21d7JMCXUSGrlCq7jscupN8VqPo+qFAF5GhqmzX2Li+3dP9zfMLGYrqofdMgS4iQ1XcvQ96uJYLaIXeLwW6iAzVWg+HigL5XJprWzXKle1hlTWVFOgiMlTBSrunHrq2LvZFgS4iQ7V77P+A0XPtgkDX1sXeKNBFZKgKpQrpVILcoVAjjAGdFu2XAl1EhirYsmjW/ZRoQC2X/ijQRWSoGoeKwrdbAObSKbLplHa69EiBLiJDVSxXQ91lsd1SLk2xrEDvhQJdRIbG3VnbqPS0ZTGQz2U0LLpHCnQRGZpydYfr27VQk4ra5XMZ9dB7pEAXkaHpZbBFu3wuQ7Fcwd2jLmtqKdBFZGiCFXZ/LZc02zXnjU2dFg1LgS4iQxP0wPttubS+h3SnQBeRoSmUB2m5pG96D+lOgS4iQ1MsVclmUhyeDX9KNKDj/70LFehmdtrMXjSzS2b28D7XvMfMvmZmF83sy9GWKSKTqN8tiwCLzdvtrm1op0tYXf/aNLMk8AjwPmAVeMbMnnT351uuuQ34HeC0u79iZktDqldEJkihXOmrfw6QTiV509ysWi49CLNCvwe45O4vu/sW8Dhwpu2anwOecPdXANy9GG2ZIjKJiqX+TokG8rmMWi49CBPox4BXWx6vNp9r9X3A7Wb212b2rJl9qNMbmdkDZnbBzC6sr6/3V7GITIR63SmW+2+5QOODUR0uCi9MoHe6RVr7Tv8U8C7gp4GfAv6jmX3fnp/kfs7dV9x9ZXFxsediRWRyvL65xXbN+265QOMe6rqFbnhhPnpeBU60PD4OvNbhmsvufg24ZmZfAd4JvBRJlSIycXYHWwzUcklz+WqVnVqdVFKb8roJ8yv0DHDKzO40s1ngPuDJtmv+DPgxM0uZ2WHgR4AXoi1VRCbJ7nDoQVboCxnc4fLVrajKmmpdV+juvmNmDwFPAUngUXe/aGYPNl8/6+4vmNlfAs8BdeAz7v7NYRYuIuOtn+HQ7YL7qK+VKj3NJL1Vhdrt7+7ngfNtz51te/xJ4JPRlSYikyxouSxlB9vl0vpecjA1pURkKAqlKkfnZ5kZoPedX2j8ZaCti+Eo0EVkKIqlCks9jp5rd2QuTTJh2roYkgJdRIZirVQZaIcLQDJhLM6ntXUxJAW6iAxFoVSN5IPMxuEiBXoYCnQRidx2rc6Va9WBWy4QHP9XyyUMBbqIRO7y1Srug21ZDORzGd2gKyQFuohEbndS0cJgPXRotFy+u7lNZbs28HtNOwW6iEQu2JUSRctlaXfQhdou3SjQRSRyxfLgp0QDwc291HbpToEuIpFb26iQShhH5mYHfi8Niw5PgS4ikSuUqixl0yQSne6+3ZvdYdHautiVAl1EIlcsVwa6y2KrhUMzpFMJimX10LtRoItI5AoRnBINmFlj66JW6F0p0EUkcmsb/Q+H7iSfS6uHHoICXUQidX2rRqmyE1nLBRpbF9Vy6U6BLiKRinLLYmC52XJxbx9nLK0U6CISqaA1ElUPPXivza0a5epOZO85jRToIhKpQrM1Em0PPTgtqj76QRToIhKpIHQj7aFng1F06qMfRIEuIpEqlCpkZhLkMqFGFocS3FddWxcPpkAXkUitlaos5zKYDX5KNBAMmtbkooMp0EUkUoVSdKdEA3PpFNl0Sndc7EKBLiKRKpYqkW5ZDOQXdFq0GwW6iETG3RvDobPRbVkM5HMaFt2NAl1EIlOq7FDZrkcyHLpdPqvZot0o0EUkMsPYshhoHP+vUK/rtOh+FOgiEplgn/gwWi7LuTTbNeeNza3I33taKNBFJDJBj3soLZdgcpH66PtSoItIZIJdKFEMh26nYdHdKdBFJDLFUoVcJsWh2WTk763Tot0p0EUkMoVSdSh70AEW54PZolqh7ydUoJvZaTN70cwumdnDB1z3w2ZWM7N/Fl2JIjIp1kqVofTPAWZTCY7MzaqHfoCugW5mSeAR4F7gLuB+M7trn+v+K/BU1EWKyGQolipD6Z8HlnIZ3UL3AGFW6PcAl9z9ZXffAh4HznS47heBPwaKEdYnIhOiXneK5Wqkgy3aLefSFMoK9P2ECfRjwKstj1ebz+0ys2PAPwXOHvRGZvaAmV0wswvr6+u91ioiY+zKtS126j60Hjo0ti6ubaiHvp8wgd7pHpjtR7V+C/iEu9cOeiN3P+fuK+6+sri4GLJEEZkEwe6TYQb6Ui7DlWtVtmv1oX2PSRbmDvSrwImWx8eB19quWQEeb97/+CjwfjPbcfc/jaJIERl/N4ZDD6/lks+lcYfLV6u8eeHQ0L7PpAoT6M8Ap8zsTuDbwH3Az7Ve4O53Bl+b2WPAFxTmIreW3WP/Q1yhB3NKCyUFeiddWy7uvgM8RGP3ygvAH7n7RTN70MweHHaBIjIZ1jYqmMHiEO7jEtg9/r+hD0Y7CTX0z93PA+fbnuv4Aai7/6vByxKRSVMsVzgyl2YmObzzikvNdk5RO1060klREYlE45To8FbnAEfn0iQTpuP/+1Cgi0gk1jaGM3quVSJhLGXT2rq4DwW6iESiWB5+oMONQReylwJdRAa2Xatz+erW0Fsu0BieoZZLZwp0ERnYenn4WxYDywsZ3XFxHwp0ERnY7qSiEQR6Ppdh4/o2le0DD6bfkhToIjKwG8Ohh99yWcoG90VX26WdAl1EBjaKU6KBfMtpUbmZAl1EBlYoVZhJGm86PDv076VRdPtToIvIwNaagy0SiU43Z41WPqtA348CXUQGVixVR9I/B8gdSpFOJRToHSjQRWRghVJld+U8bGamrYv7UKCLyMCGORy6k3w2o2HRHSjQRWQgm1s7lCs7I2u5QGN7pIZF76VAF5GBFIMtiyNquUBj62KhVMW9fRrmrU2BLiIDGcUs0XbLuQzXt2uUqzsj+56TQIEuIgPZPfa/MNqWC0BBk4tuokAXkYEELZelEa7QdVq0MwW6iAykUKpwaCZJNh1qomUkbgyL1gq9lQJdRAYSbFk0G/4p0UDQctHWxZsp0EVkIMVSdfcOiKNyeDZFNpPS1sU2CnQRGUhhRKPn2gVbF+UGBbqI9M3dG8f+R3ioKLCcy1DQbNGbKNBFpG+l6ztUtuuxrNCXcmltW2yjQBeRvgUr5LhaLsVylXpdp0UDCnQR6Vscp0QD+Wyanbrz+ubWyL/3uFKgi0jf1jaCQI+hh968u+Oa2i67FOgi0rdieXSzRNsFJ1OL+mB0lwJdRPpWKFVYODRDZiY58u+t4/97KdBFpG9xbVkEdg8z6fj/DaEC3cxOm9mLZnbJzB7u8PoHzey55o+/MbN3Rl+qiIybtVI1lnYLwEwywdH5WQV6i66BbmZJ4BHgXuAu4H4zu6vtsm8B/9jd3wH8GnAu6kJFZPwUS/GcEg0sZXVatFWYFfo9wCV3f9ndt4DHgTOtF7j737j7G82HTwPHoy1TRMZNve4Uy9XYWi7Q2F2jFfoNYQL9GPBqy+PV5nP7+QXgLzq9YGYPmNkFM7uwvr4evkoRGTtXrm1Rq3usK/TlBa3QW4UJ9E73xOx4NMvM/gmNQP9Ep9fd/Zy7r7j7yuLiYvgqRWTsxHmoKLCUzXDlWpXtWj22GsZJmEBfBU60PD4OvNZ+kZm9A/gMcMbdr0RTnoiMq3EI9Hwugzusl7VKh3CB/gxwyszuNLNZ4D7gydYLzOwO4Ang5939pejLFJFxE7Q64u6hN2pRHx2g68wod98xs4eAp4Ak8Ki7XzSzB5uvnwV+FTgC/E5zasmOu68Mr2wRidtaqYIZLM7HGegaRdcq1BBAdz8PnG977mzL1x8FPhptaSIyzoqlCkfn06SS8Z1P1GnRm+mkqIj0Jc5TooEjc7OkEqYVepMCXUT6UihVyWfj+0AUIJEwlrJprdCbFOgi0pdCqUJ+Id5Ah8ZdF7VCb1Cgi0jPtnbqXLm2FfsKHXRatJUCXUR6tn41/i2LgbxW6LsU6CLSs91JRWPQcsnnMpQqO1zfqsVdSuwU6CLSs2JwSnQsWi7aix5QoItIz24c+x+HlotOiwYU6CLSs0K5ykzSuP3wbNylsBys0HU/FwW6iPSusFFhKZshkeh0M9bRCoZFFza0Qlegi0jPCuX4T4kGcpkUmZmEWi4o0EWkD4UYZ4m2M7PG1kW1XBToItK7wka8s0Tb5XMZtVxQoItIj65VdyhXd8Yv0MsKdAW6iPSkWB6fU6KBfLZx/N+943TMW4YCXUR6Mg6j59rlcxkq23VKlZ24S4mVAl1EejKWgb6g06KgQBeRHo3TKdFAPqvToqBAF5EeFUpVDs8mmU+HmmA5EhpF16BAF5GeNEbPZWgOhB8LukFXgwJdRHoyDrNE2x2aTZLLpBTocRcgIpNlnE6JttKgCwW6iPTA3XdbLuOmEejqoYuIhLJxfZvqTn2MA10rdBGRUIIV8Lj10KFRU7FcpV6/dU+LKtBFJLRxPFQUyOcy1OrOlWtbcZcSGwW6iIRWGKNZou00ik6BLiI9CMJyaSxbLtqLrkAXkdAKpSq3HZ4hM5OMu5Q9dFpUgS4iPSiUKmPZbgFYzKYx0wpdRCSUQqmye2fDcTOTTHBkLq1A78bMTpvZi2Z2ycwe7vC6mdlvN19/zszujr5UEYlboVTdvbPhOMrnFOgHMrMk8AhwL3AXcL+Z3dV22b3AqeaPB4BPR1yniMSsVnfWr47nsf/ArX5aNMz9L+8BLrn7ywBm9jhwBni+5ZozwB94Y/7T02Z2m5m92d2/E3XBX35pnf/8hee7Xygikaq5U6v7WB4qCuRzab7y0jrv+80vx13Kgf7FD5/goz/2PZG/b5hAPwa82vJ4FfiRENccA24KdDN7gMYKnjvuuKPXWgGYT6c4lZ/v6+eKyGDecWyBH397Pu4y9vXPV05Qur6DM96nRY/OD+cvxTCB3ummx+2/WmGuwd3PAecAVlZW+voVf9dbb+ddb31XPz9VRKbc3Xfczt0fvD3uMmIT5kPRVeBEy+PjwGt9XCMiIkMUJtCfAU6Z2Z1mNgvcBzzZds2TwIeau13eDWwMo38uIiL769pycfcdM3sIeApIAo+6+0Uze7D5+lngPPB+4BKwCXxkeCWLiEgnoaa8uvt5GqHd+tzZlq8d+Fi0pYmISC90UlREZEoo0EVEpoQCXURkSijQRUSmhDU+z4zhG5utA3/f508/ClyOsJxhUI2DG/f6YPxrHPf6YPxrHLf63urui51eiC3QB2FmF9x9Je46DqIaBzfu9cH41zju9cH41zju9bVSy0VEZEoo0EVEpsSkBvq5uAsIQTUObtzrg/Gvcdzrg/Gvcdzr2zWRPXQREdlrUlfoIiLSRoEuIjIlJi7Quw2sjpuZnTCz/2VmL5jZRTP7eNw1dWJmSTP7P2b2hbhr6aQ5xvDzZvZ/m7+W/yDumlqZ2b9r/v5+08w+Z2axD9o0s0fNrGhm32x57k1m9iUz+7vmf2Od/rBPjZ9s/j4/Z2Z/Yma3jVN9La/9BzNzMzsaR21hTFSghxxYHbcd4N+7+9uBdwMfG8MaAT4OvBB3EQf4H8Bfuvv3A+9kjGo1s2PAvwFW3P0HaNxW+r54qwLgMeB023MPA3/l7qeAv2o+jtNj7K3xS8APuPs7gJeAXx51US0eY299mNkJ4H3AK6MuqBcTFei0DKx29y0gGFg9Ntz9O+7+1ebXZRpBdCzeqm5mZseBnwY+E3ctnZhZDvhHwO8CuPuWu3831qL2SgGHzCwFHGYMJnS5+1eA19uePgP8fvPr3wd+dpQ1tetUo7t/0d13mg+fpjHxLBb7/BoC/Hfgl+gwWnOcTFqg7zeMeiyZ2Ungh4D/HXMp7X6Lxv+c9Zjr2M/3AOvA7zXbQp8xs7m4iwq4+7eB/0ZjtfYdGhO6vhhvVfvKB9PDmv9dirmebv418BdxF9HKzD4AfNvdvx53Ld1MWqCHGkY9DsxsHvhj4N+6eynuegJm9jNA0d2fjbuWA6SAu4FPu/sPAdeIv1Wwq9mHPgPcCbwFmDOzfxlvVZPPzH6FRsvys3HXEjCzw8CvAL8ady1hTFqgT8QwajOboRHmn3X3J+Kup82PAh8ws/9Ho2X142b2h/GWtMcqsOruwb9sPk8j4MfFTwDfcvd1d98GngD+Ycw17adgZm8GaP63GHM9HZnZh4GfAT7o43U45ntp/MX99eafmePAV81sOdaq9jFpgR5mYHWszMxo9H5fcPffjLuedu7+y+5+3N1P0vj1+5/uPlarS3dfA141s7c1n3ov8HyMJbV7BXi3mR1u/n6/lzH60LbNk8CHm19/GPizGGvpyMxOA58APuDum3HX08rdv+HuS+5+svlnZhW4u/n/6NiZqEBvfnASDKx+Afgjd78Yb1V7/Cjw8zRWvl9r/nh/3EVNoF8EPmtmzwE/CPyXeMu5ofkvh88DXwW+QePPUezHw83sc8DfAm8zs1Uz+wXg14H3mdnf0dil8etjWOOngCzwpeafl7MHvsno65sYOvovIjIlJmqFLiIi+1Ogi4hMCQW6iMiUUKCLiEwJBbqIyJRQoIuITAkFuojIlPj/SMUOa1QuODkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "16\n",
      "0.385546631571102 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(front_flag, end)\n",
    "n_epe = n_epe[2003-1979:]\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b57aa57e-8192-4b6d-8aa0-9373c9481aef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cecfee69-7ef2-4362-9eae-b5df7761cd24",
   "metadata": {},
   "source": [
    "# Sub2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0afb5be9-1aff-4392-8ba1-93ab3e07d4b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "40\n",
      "0.14411966888671857 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "sub_id = 2\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    n_epe.append(time_end.count(year))\n",
    "print(np.sum(n_epe))\n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "174da059-d264-4d88-85f2-ee816b1650f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.031\n",
      "Model:                            OLS   Adj. R-squared:                  0.006\n",
      "Method:                 Least Squares   F-statistic:                     1.222\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.276\n",
      "Time:                        10:32:53   Log-Likelihood:                -57.088\n",
      "No. Observations:                  40   AIC:                             118.2\n",
      "Df Residuals:                      38   BIC:                             121.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.0305      0.321      3.209      0.003       0.380       1.681\n",
      "x1            -0.0157      0.014     -1.106      0.276      -0.044       0.013\n",
      "==============================================================================\n",
      "Omnibus:                        9.738   Durbin-Watson:                   1.720\n",
      "Prob(Omnibus):                  0.008   Jarque-Bera (JB):                9.683\n",
      "Skew:                           1.198   Prob(JB):                      0.00790\n",
      "Kurtosis:                       3.268   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de6e9a5-bc8d-4790-a0cd-b9551e6a4d56",
   "metadata": {},
   "source": [
    "# Sub3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f397ce69-71d1-4c94-a107-e66329ad7562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29   37   29\n",
      "40\n",
      "0.1386633187830426 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "sub_id = 3\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    n_epe.append(time_end.count(year))\n",
    "print(np.sum(n_epe), ' ', len(ext_days[sub_id]), ' ', len(end))\n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8550e5d-3ddf-4867-9379-0bfdd2fc4b84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.025\n",
      "Model:                            OLS   Adj. R-squared:                 -0.001\n",
      "Method:                 Least Squares   F-statistic:                    0.9667\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.332\n",
      "Time:                        10:32:54   Log-Likelihood:                -57.219\n",
      "No. Observations:                  40   AIC:                             118.4\n",
      "Df Residuals:                      38   BIC:                             121.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.9976      0.322      3.097      0.004       0.345       1.650\n",
      "x1            -0.0140      0.014     -0.983      0.332      -0.043       0.015\n",
      "==============================================================================\n",
      "Omnibus:                       19.070   Durbin-Watson:                   1.904\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               24.036\n",
      "Skew:                           1.554   Prob(JB):                     6.03e-06\n",
      "Kurtosis:                       5.183   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edbef3a9-1cd4-486a-b7f4-db16c7dc1938",
   "metadata": {},
   "source": [
    "# Sub4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1743472c-6b6f-4aca-9307-8efc07c8cdb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64   77 64\n",
      "40\n",
      "0.12148974988719896 False no trend\n",
      "0.0 1.5\n"
     ]
    }
   ],
   "source": [
    "sub_id = 4\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    n_epe.append(time_end.count(year))\n",
    "print(np.sum(n_epe), ' ', len(ext_days[sub_id]), len(end))\n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "445acfeb-c9c5-4033-859e-526a6fedae3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.097\n",
      "Model:                            OLS   Adj. R-squared:                  0.073\n",
      "Method:                 Least Squares   F-statistic:                     4.079\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):             0.0505\n",
      "Time:                        10:32:54   Log-Likelihood:                -66.914\n",
      "No. Observations:                  40   AIC:                             137.8\n",
      "Df Residuals:                      38   BIC:                             141.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.8866      0.411      2.160      0.037       0.056       1.718\n",
      "x1             0.0366      0.018      2.020      0.051    -8.7e-05       0.073\n",
      "==============================================================================\n",
      "Omnibus:                        6.964   Durbin-Watson:                   1.850\n",
      "Prob(Omnibus):                  0.031   Jarque-Bera (JB):                5.742\n",
      "Skew:                           0.878   Prob(JB):                       0.0567\n",
      "Kurtosis:                       3.599   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21af63bb-acba-4117-986d-523519379f61",
   "metadata": {},
   "source": [
    "# Sub5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "17316867-edba-41f4-8d1d-5c94af401a42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86   118   86\n",
      "40\n",
      "0.39205415845336544 False no trend\n",
      "0.0 2.0\n"
     ]
    }
   ],
   "source": [
    "sub_id = 5\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    n_epe.append(time_end.count(year))\n",
    "print(np.sum(n_epe), ' ', len(ext_days[sub_id]), ' ', len(end))\n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0826d740-cb00-48bb-8cd1-cfe97e0f321c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.050\n",
      "Model:                            OLS   Adj. R-squared:                  0.025\n",
      "Method:                 Least Squares   F-statistic:                     1.986\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.167\n",
      "Time:                        10:32:55   Log-Likelihood:                -72.634\n",
      "No. Observations:                  40   AIC:                             149.3\n",
      "Df Residuals:                      38   BIC:                             152.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.5756      0.474      3.327      0.002       0.617       2.534\n",
      "x1             0.0295      0.021      1.409      0.167      -0.013       0.072\n",
      "==============================================================================\n",
      "Omnibus:                        8.365   Durbin-Watson:                   2.047\n",
      "Prob(Omnibus):                  0.015   Jarque-Bera (JB):                7.175\n",
      "Skew:                           0.938   Prob(JB):                       0.0277\n",
      "Kurtosis:                       3.887   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81ed5fa-dbee-4691-b391-2e6961d423a7",
   "metadata": {},
   "source": [
    "# Sub6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d6a7444b-a4a7-4ce3-9deb-cdecad390d57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105   124 105\n",
      "40\n",
      "0.043526836964626225 True increasing\n",
      "0.05 1.025\n"
     ]
    }
   ],
   "source": [
    "sub_id = 6\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    n_epe.append(time_end.count(year))\n",
    "print(np.sum(n_epe), ' ', len(ext_days[sub_id]), len(end))\n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f6e7289-abe5-4efc-a6a2-6943c50fd404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.029\n",
      "Model:                            OLS   Adj. R-squared:                  0.003\n",
      "Method:                 Least Squares   F-statistic:                     1.128\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.295\n",
      "Time:                        10:32:55   Log-Likelihood:                -83.312\n",
      "No. Observations:                  40   AIC:                             170.6\n",
      "Df Residuals:                      38   BIC:                             174.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          2.0598      0.619      3.330      0.002       0.808       3.312\n",
      "x1             0.0290      0.027      1.062      0.295      -0.026       0.084\n",
      "==============================================================================\n",
      "Omnibus:                       14.714   Durbin-Watson:                   1.982\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               16.137\n",
      "Skew:                           1.283   Prob(JB):                     0.000313\n",
      "Kurtosis:                       4.761   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b08e6ce-83ab-4bd7-8008-d5a3e64d360b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02898687]\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(np.arange(n).reshape(-1, 1), n_epe)\n",
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae41635-1422-430e-91e5-bf09fcd7ffb7",
   "metadata": {},
   "source": [
    "## categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3999af8-a6ec-473e-b566-3aa296a6568d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_id = 6\n",
    "surge_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_surgeWINDOW_induced_flag.npy\")\n",
    "# tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced_flag.npy\")\n",
    "tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced-10degree.npy\")\n",
    "# tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_induced_flag.npy\")\n",
    "tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_corrected_flag.npy\")\n",
    "MCS_induced_flag = np.load(\"ERA5/PAU/MCS_\" + str(sub_id) + \".npy\")\n",
    "MCS_induced_flag[MCS_induced_flag < 0] = 0\n",
    "midtro_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_midTropo.npy\")\n",
    "front_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_front.npy\")\n",
    "start, end = start_end_time(sub_id)\n",
    "all_time = ext_days[sub_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "355482e2-40e2-4bce-aade-48d447545ead",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1984, 1992, 1996, 1998, 2001, 2003, 2003, 2006, 2006, 2007, 2008, 2009, 2010, 2013, 2014, 2014, 2015, 2016, 2016, 2017]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "40\n",
      "0.0012961453340483153 True increasing\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "# TC\n",
    "n_epe = find_categories_events(tc_induced_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2ffe17ae-43c4-43fb-ac41-b867a3789634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1979, 1979, 1979, 1980, 1983, 1984, 1984, 1986, 1986, 1986, 1987, 1990, 1990, 1990, 1990, 1991, 1993, 1993, 1995, 1995, 1996, 1997, 1998, 1998, 2002, 2003, 2003, 2006, 2010, 2010, 2011, 2012, 2012, 2013, 2013, 2015, 2015, 2015, 2015, 2016, 2016, 2017, 2017, 2018, 2018, 2018]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46\n",
      "40\n",
      "0.37733892148535086 False no trend\n",
      "0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(tutt_induced_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b65b175-dc39-4946-8ba3-266ae351eccb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1979, 1979, 1979, 1979, 1980, 1981, 1983, 1983, 1983, 1983, 1983, 1984, 1984, 1984, 1985, 1985, 1986, 1986, 1986, 1986, 1987, 1989, 1990, 1990, 1990, 1990, 1990, 1990, 1992, 1993, 1993, 1994, 1995, 1995, 1996, 1996, 1998, 1998, 2001, 2002, 2003, 2003, 2003, 2003, 2004, 2004, 2004, 2005, 2006, 2006, 2006, 2006, 2007, 2008, 2008, 2009, 2010, 2010, 2010, 2010, 2012, 2013, 2013, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2017, 2017, 2017, 2018, 2018, 2018]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81\n",
      "40\n",
      "0.2881051849009637 False no trend\n",
      "0.0 2.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(surge_induced_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6270943-e02d-4a23-9805-4339ced054e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1983, 1985, 1992, 1998, 2004, 2004]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vGekwoZ8BTghEpptQrW91kHDVwTxkeCSlFr0unovlMPU5ir01po2gw6ybYmNCPwNqW2327Fpg71JlqtHfia0zXLW2PHivvOBuWletLc/dDazWCG6+l+9d8j725qCN4OjcimXLuik0JvQzoLbVZm13lbWVamqhbrZ7tDp93rK+e/BeecGtin3L+u7B63mhtnWGvUsV1vfs8v53st1GcIxHXynT6vTp90cuTjdyjAn9DKg32qwuTyf0zt9/8yW7B++VF9zTzZvWV6hvtQkyceeD+laH1ZUqa8vpf6cXGpfvP9a6CXPsz3T9X+VrzB4T+hlQ22qztlJldbmaWqidGL55fWXwXnmhttVmz+ICl+xZpNtXTp3pZh1SYmpbbdaWq6yuVL2fjHUe/aTJWLDmI0XFhH4G1Ld2MKIPjz+wbwUR/zM80lBvhDfAlWrw/Rx9tnqjHYzoV6rep1c68R67MtaEvtCY0O8QVeXEVpu1lUow+muksyic0O/fs4u9S5W587LHURvcACsAc/XZTrgR/XKVVqdPo+3v00iS9MrFqvWNLTIm9Duk2elxpttnbWUX+1aqdHrpLAon9PvC0WOePPraVjv8XLuA+RnRqyr1cIJ9X/g04rOl1kxh3VgZhGJiQr9DnACsrVRYXU5vUdQbbUoCFy1WprJ+fKa+1R5MaILfYhnl1Jku3b4OPHrwe8HXII/erBtjBCb0O8QJgPPoIZ2gOXujVJJgMtdjQUlLbeDRB9bNvDytuBt14NEHsfvs0ydaGRumXlrf2GJiQr9DnACcNemYQhRq4ajXvYfPgpIGtz5gdbnK7l0LVMpCbU5uYjt9SrvQNDs9KmWhUh7957xUCRZTmUdfTCaWKTbGUwsrV66tVFkolcJtyQXNpfG593D55iJxnRjnhxOD61JBREJb6syEs/xgW+h3DZ7SfJ5IbnZ6Y3PoYTsjxzz6YmIj+h3iRH1tpcrabmfdJBc0l4Lo3qPb10Fp33mmPrguwUTs6nJ1/kb0y1UuWqxQLonfI/oJjcHBPPqik0joReQWEXlJRI6IyN0x+39NRJ4Jv54XkZ6IrIX7XhGR58J9h2f9AbKmvrU9mbpSLVMtl1KO6DsD62YebIKkbFtalfDf+ckocnGurlTCuZOK15ZaszO+uxREhN6sm0IyUehFpAzcA9wKXAvcISLXRo9R1d9S1etV9XrgI8CXVbUWOeTmcP/G7EL3g1pjezJVRFhdSV7YTFXDEf22GLr3nHcGE5rhzWseVpg6alsdKmVh967A2Qwmyf2NPcmIfjGcjLURfTFJMqK/ATiiqi+raht4CLhtzPF3AA/OIrh5oB6ZTIXQokgo1CebXXp9PUsM3XvOO9s+d2hLpbguWeNWOrt5klXP016TePTVcomSmEdfVJII/eXAq5Hvj4bbzkFEloFbgM9FNivwmIg8LSIHR/0QETkoIodF5PDm5maCsPzgxNa2xw6wb3dyUXDCty/09vfNwcRfUmpbbcol4aLF7aeV1xsduj3/i2qd8zv1XOhbnckjehGx5iMFJonQx6V/jFrj/1PAV4ZsmxtV9e0E1s+HReTdcSeq6n2quqGqG+vr6wnC8oN6JGsG0j3m12LsDfee805gaQUeN2yP7F9v+j8hG50gBwalLXwliUcPQeaNWTfFJInQHwWujHx/BXBsxLG3M2TbqOqx8N/jwMMEVlBucMWvHGly4etD9sZgMtdjUUmKsz8c83QTG7bj1par1Bsdb2u5J/HoIShjbCP6YpJE6J8CrhGRq0WkSiDmjwwfJCIXA+8B/iSybUVE9rjXwPuA52cRuA/0+0q90RlMpkIwOn+jmcyicILuBDHtZK7P1GLE0m33nVpj6CltpUqvr5zyNO211elP9OjB2gkWmYlCr6pd4C7gUeBF4LOq+oKI3Ckid0YO/RngMVXdimy7FHhCRL4O/C3wZ6r6xdmFny0nWx16fR3kikMwOleFNxJYFMMTlsFr/zsaJaE2ZGlNUx4iC7q9Pq83OkO/E1d9088FX81Oj+UE1o21EywuiVbGquoh4NDQtnuHvn8AeGBo28vAdTuK0GOiS+UdUUHbt3tX7HmO+labXQuls/5I11Yq3othEuqN9mABGcxP6qibQxi++YK/tXqa7WQevVk3xcVWxu6A+pD1AulGrq4zVbTcwWroB88zA0srcl32LoeFzTy/iUULmjm2bSf/fi/9viZKr4RgMtbSK4uJCf0OiJY/cAxWtyYY/bles1HyUKr4VCtcHxC5LouVMivVspdiGSVa/sAxqL7p4e/F9YBNMhlrHn1xMaHfAcOrPyE6op8saMP52u69kk7m+spw+QOH72mKcHb5A4fPttN205HJf8om9MXFhH4HuIVN+3bHjP6SjOiHMlOi7zXP9o0r6jb8tLJvper9YrDB7zQywb5UKbNroeTlk1aSfrGOxWqZZnt+BxDG9JjQ74B6I5hMjT4271oos3vXAidOJ/Po98WM6N17zyvuaSYqljAf9W5cfG5OAYK0V19Xx7rJ1aTplebRFxMT+h0QN5kKwah+klB3en1OtrqxHr1773lle0LzbOtmbdlPsYxS2+qwUi2fI5y+3qRaCfrFOpx1k6Z5vZEPTOh3wPDqT0cSQXu94SZyh3zsHJQqjnbdijIvHv2wnQb+dv9KY90sVcv0+kp7jud/jOkwod8BtUb7LH/ekUTQajFpfLDt0fsoKklx6wOGR5lrK1Ua7Z7X9kGcnQb+lip21k2SBVPu99Eyn75wmNDvgNoORvRxaXyw7Q3XEnj8vnJihKU1D7bUcOkGx5qnE8luRJ80jz56jlEcTOh3QC0mPRKS5cK7Ef/a0BOBm8yd9xF93HVZnYN6N8OlGxxrK1VOtbp0PLM93Ig+qUcPJvRFxIR+Sjq9PqdiJlMhsGMmWRSjRvTB+fNd2KzWGH0DBL8zikZ59Kuexp4qvdLaCRYWE/opqY9YFBRsmywKtUEa3wjrZ47z6EdOUofXytcRfavTo9Huxd+kBpPkfv1eUo3ozbopLCb0U7Jd0OzcwmVJLIraVps9uxaoLpz7KwisHz8rJSYhbsUvbF8rX4U+rpqoY9XTCpapPHo3GWtCXzhM6KdkO2tmzIh+zOhvlEUQvGfVu5FjUsZZWhcvVRDxN3V0uONXlCS/0yxodXqIwK6YAcMwS2bdFBYT+impxxQ0c7ht40Z/oyZyYT4WFo1i1CQzQLkk7F2qeDvRXB+R/x/d5lvsrrvUcIZTHEvV4M/drJvikUjoReQWEXlJRI6IyN0x+28SkTdE5Jnw66NJz51XBouCxo7+RovCcF/SKKsrVZqd3lyOvAY3wJjrAn4/rcT1F3D4upCtmaAxuMMmY4vLxMYjIlIG7gHeS9A/9ikReURVvzF06F+r6gemPHfuiKtb7nAWxbgJ1drpNv/40oti9+2LTOYuVZdmEO2FY5ylBXhbMwaiPXzPnXeplEvsWVzwLvaktejB0iuLTJIR/Q3AEVV9WVXbwEPAbQnffyfnek1tq82exQUq5XMvobMoxo3+ghTEeDF0Nw/fRCUJ4yY0IRgZ+/q5alttRIIbdRw+9gpoJWwjCLBcDcZ1JvTFI4nQXw68Gvn+aLhtmHeKyNdF5Asi8qMpz0VEDorIYRE5vLm5mSCsbBnnscN4UWi2e7Q6/diRozvX/Yx5Y1SdG4evNWMgiH3vUoVyKd7vXvOwVk8jYRtB2J6wNeumeCQR+rj/9cPl774G/LCqXgd8EvjjFOcGG1XvU9UNVd1YX19PEFa2xHWHijJO6Ec15nDMc6niuGYsUVwVSB8rKNa3OiMzocDPSfJmO7l1UyoJi5WSpVcWkCRCfxS4MvL9FcCx6AGqelJVT4evDwEVEdmf5Nx5ZdKIPuj9OkLoT48Xw7ke0Y+xtCAQy25fOXWme4Ejm8yo8gcOH0sVt1JMxoJ1mSoqSYT+KeAaEblaRKrA7cAj0QNE5IckzO8SkRvC9z2R5Nx5ZVQ9F0eyEX38+RcvVSh5nG8+jnHZRJAsIykrksTum+2UJusGQqE366ZwTBR6Ve0CdwGPAi8Cn1XVF0TkThG5Mzzs54DnReTrwCeA2zUg9tzz8UEuJKo6cvWnw5UqjrMoxmXsQDiZu+xntcRJjKro6dheY+DfZ5v4O12u0ur0abT9eRppdpJ79BC2E7QRfeGYmF4JAzvm0NC2eyOvPwV8Kum5806z0+NMtz9W0PatVOn0AoviosWzvXg30o+re+5YXZ7cpcpHalttfuiixZH7Vz0d0atqbA/fKPsilprLYMmaZruf2KMHaydYVGxl7BSMW1jjGLfApt5oUxLOuQFE8TGVLwmTxHLN01LFp8506fZ1okcPfpVBMI/eSIIJ/RRslz+IT48M9o0WtBOhvVEakcbnzvdJUJIyqkSxw5VG8O1pZXux1DjbKay+6UnsqhpaN8n/jJeqZRrm0RcOE/opcDVsxo7ox5QqnjTqDd57/jz6RrtLqzPe0lqplqmWS959thMJhH67KqkfFSw7PaXX11Q2kk3GFhMT+ilw4j120nEgCueOyiel8bn3HjWZ6ytJLC0R8bKxyqQJcog+pfnxpOUEO5VHXzWPvoiY0E9BbUzlSoezKOJGf5PS+Nx79/rKyZY/GR6TSGJpgSuD4IdYOsZ1/HJctBismvXlJjXoLmUevTEBE/opqG+1KZdk7GSqsyhGjegnWTe+Vkscx6QVvw4fSwkMntLGxF4qCavL/pRZ3m4jmPzPeNGsm0JiQj8FtUab1eXK2MnUURZFv6/UG53JYuieCDwRlSRMKn/gWPNwhWltq0O1XGL3rvF+9+qyP7GnaSPoCKwbvxqcG+cfE/opqJ0evyjIsbp87krKU60uvb5OFkPn8Z/2Q1SSkGRC0+33bTK2tnWG1ZXKxAYeqx7FnqaNoGOpUqbd69PtmdgXCRP6KaiNaQMYJW7kOqn8QfTc6PHzQBJLC4Ib4BvNjldiU9vqJLp5r3k0om9N6dEDtLr+XHvj/GNCPwX1BFkzEL/oaVK9doevK0jHkcTSgu3P/nrTnwnZJBPkEFhqvswvDKyblCUQoucaxcCEfgrGNfaOElcEK6nQDyZzPRGVJNQn1Llx+HgTS7K2AcIRfaNDv5992uu0WTeApVgWDBP6lLjJ1HF1ahxxFkXSCUsR8XLSchxJsong7JoxvlBrtJP9TsO011MepL1uZ90kF3rXjcpWxxYLE/qUnGx1gsnUhCN6VXgjYlEk9eghEBWfxHASSRaCQXSFqR+frdvr83ojoUcfZkud8GB17FRZN9Y3tpCY0KckyepPR1y9m/pWm10LpUR9PtdWKt6IYRLqjfYgLXQcvk00u7mCRB59uBjMB59+mhG9y9Axj75YmNCnJEn5A0ec0Lua55PS+NzPqDf8mbAcx2B9QCKPPrhJ+mJLJSl/4BhX2uJCMyiBsJAujx7Moy8aJvQpcX/g+yYs84f43q9JJyyDnzE/1s1gfUACsdy1UGb3rgUvxBKS9Qdw+HSTanV67FooTcxyimLWTTFJJPQicouIvCQiR0Tk7pj9Py8iz4ZfXxWR6yL7XhGR50TkGRE5PMvgs8DVrhm3VN4RVwRrUhnfKKsrwWRux6N881EkqegZZXWl4k0VyFrCCXLwq0NW2u5SEBF6s24KxcT6piJSBu4B3kvQ7PspEXlEVb8ROey7wHtUtS4itwL3AT8e2X+zqv5ghnFnRpKCZo7B6G9oRH/F6nKinzXIN290WN8z+QkiS9JYWhBYIDVPbKk0E+RLlTK7Fkp+ePTtdE1HABbDujg2oi8WSUb0NwBHVPVlVW0DDwG3RQ9Q1a+qaj389kngitmG6Q/1RjCZmuQPzFkUJyJlDGpbydL4IN768ZU0lhaEPXU9GBXDtg2zd3ny04iIeGOppW0MDpZHX1SSCP3lwKuR74+G20bxy8AXIt8r8JiIPC0iB0edJCIHReSwiBze3NxMEFY21FJMpkIwqndC3en1OdnqJh/1ephvPortCc1k1s3ash9iCcFNaqVaTlwzxpebVKvTS1XnBizrpqgkaU0Tp2ixywJF5GYCoX9XZPONqnpMRC4BviQi31TVx895Q9X7CCwfNjY2sl92OIL6VnKPHc4WtHrCMr6Dcz1cQTqKNPaHO86XJ5WkaaGOuBXPWTCNR18pl6iUhYaN6AtFkhH9UeDKyPdXAMeGDxKRtwG/C9ymqifcdlU9Fv57HHiYwAqaW06kFPrViKC5xhxJMlPAr4m/SdS2kltaEFyDRrvnhYVwIuFCL8eqJ08jjXYv0XqMYaydYPFIIvRPAdeIyNUiUgVuBx6JHiAiVwGfBz6oqt+KbF8RkT3uNfA+4PlZBZ8F9Uby9Eg4e0SfpItRFOcZz8WIPqWl5ZMtlbTOjSOuWF0WNNvprRuwdoJFZKJ1o6pdEbkLeBQoA/er6gsicme4/17go8A+4HfCP/Suqm4AlwIPh9sWgM+o6hfPyye5QNTSWjcrMdZNQptgkG/ugU0wibSWVrQMwj/au3S+wkpEbavNNZfsTnz82kqVU60unV6fSjm7pSitKSZjwdoJFpFE7eNV9RBwaGjbvZHXHwI+FHPey8B1w9vnlU6vz6lWN7V14yyKEylH9OBnN6Y40qwPANi325+MoqQlih2D6puNNpfsWTxfYU1kmqwbsHaCRcRWxqZgu69oOqF2526n8aUTFV/yzceRZsUv+FPYrNXp0Wj30v1OB/18s/29NNvpJ2MhsG5sRF8sTOhTkNZjh7MFrbbVZs+uBaoLyS/72rI/K0jHkXaS2hePPml/gCirnlSwbHX603n0FfPoi4YJfQoGS+UTpkdCNEWyk7hhSZQgZ9vvEb2ztNKM6C9eqiCS/URzmvIHjujvNCu6vT7tXt88eiMRJvQpqKcof+DYTpE8k3oiF/xaWDSKtJPMAOWSsHepkvlEcz1l/n/02Cxjdz1fl6rp/4QXq+bRFw0T+hSkXRQUPbYeWjephX53lWan5/Uf5uAGmGJUDG6iOdunlamsm+Xt32lWTNN0xBFYN/4XyjNmhwl9Cmqn0z/mO4ui1uiknrCESP1zD7JTRnEiRUXPKGsr1cx97mmEvlIusWdxIdMnrdag6UiixLmzWK6WabSzb4VoXDhM6FNQb7TZs7iQKnfaWRT1rXaYgphODH1spD3MNJYWhI1VMh7R17faiAQ35DRkvWiqscMRvXn0xcKEPgXTWC8QiPWx15u0Ov3Uk7G+ZKeMY2BpTWHdZP2kUmu02btUoZyieQe47l8ZWjeDEf0UHn1o3fT73paUMmaMCX0K0i6scexbqXJk8/TgdRqiefi+kqYVXxS3GEw1O8Gpb3Wm/p1mefMdtBGcsgQCwJmu+fRFwYQ+BbWUxa8cq8tV/l+tMXidhjVPFhaNo7aV3tKCQOi7feXUmez84p08pWVppw08+imtG7DmI0XChD4FtZTFrxxrK1XcoDWtqFy0VKEk/gv9VGLpbmKns/tstSkmyMFNJGf3NLJt3ZjQG5MxoU+Iqu5o9Bf3OgnlkrDX81z6tBU9HT7ko6et0eNYXa5yptvPTCx3kl65WLXmI0XDhD4hzU6PM93+VIIW9eXTevQAq8sVrz36NO0Ro2SdUaSqqUsUO/ZlPEnenIF1Y2UQioMJfULcH/R0Qh2cUxK4aDFdGl/wM3f5PaKfU7E8daZLt687vEllkx7qRHrRrBsjASb0CUnbHSqKswZWl6uUUqbxBT+zknm++Timtj8yzigaZAtNZTsFN+ysbKcdrYw166ZwmNAnxK3gTLvgCbYFbZqbRPAzq962E2y0u8H6gCnEcqVaplouZfbZBv0BdjKRnNHK3kanR6UsUzU+cTeHhgl9YUj0v0REbhGRl0TkiIjcHbNfROQT4f5nReTtSc+dFwa16KcZ/YXnTJOa6X5mvZFtvvkotksIpL8Bikj4tJLxiH4HT2m1jJ60pm0jCNsjevPoi8NEoReRMnAPcCtwLXCHiFw7dNitwDXh10Hg0ynOnQvcH/S+lV2pz3VVHacZObrzen3lZMu/+iTb5Q/SXxd3XlZiuZN5l4sWg9W0Wd2kpm0jCObRF5EkFZFuAI6EbQERkYeA24BvRI65DfgDDYacT4rIXhG5DDiQ4NyZ8VOffOK8jVJqW23KJWHPYvoiUs6iSFv0y+FuED99z1dYmMLjP5+4x//V5Wk/W4WvHPkB7/34l2cZViLqYeeuvVPEXioJq8sV/vDJv+fRF74369Am8r2TrameLmFb6D/+pW9x/xPfnWVYxg5ZXa7y2TvfOfP3TaJalwOvRr4/Cvx4gmMuT3guACJykOBpgKuuuipBWOfy5vUV2r3zt6z72ssummoyVUT49Z/8Ea6/cu9UP/ddb9nPz/7Y5bS6fo7A3vWW/bz18ounOvcX33mAi5f+YcYRJefAvhV270p/8wa46+a38Lev1GYcUTKuuXQ3N75l/1TnXrS0wL9+95t4td6YcVTGTpkmKy8JMsn3FZF/DvxE2AAcEfkgcIOq/tvIMX8G/DdVfSL8/s+B/wC8adK5cWxsbOjhw4en/1SGYRgFQ0SeVtWNuH1JhjJHgSsj318BHEt4TDXBuYZhGMZ5JEnWzVPANSJytYhUgduBR4aOeQT4hTD75h3AG6r6WsJzDcMwjPPIxBG9qnZF5C7gUaAM3K+qL4jIneH+e4FDwPuBI0AD+KVx556XT2IYhmHEMtGjzwLz6A3DMNIxzqO3lbGGYRg5x4TeMAwj55jQG4Zh5BwTesMwjJzj5WSsiGwCfz/l6fuBH8wwnFlisU2HxTYdFtt0zGtsP6yq63E7vBT6nSAih0fNPGeNxTYdFtt0WGzTkcfYzLoxDMPIOSb0hmEYOSePQn9f1gGMwWKbDottOiy26chdbLnz6A3DMIyzyeOI3jAMw4hgQm8YhpFzciP0PjchF5FXROQ5EXlGRDKv1iYi94vIcRF5PrJtTUS+JCLfDv9d9Si23xCRfwiv3zMi8v4M4rpSRP5SRF4UkRdE5FfC7ZlftzGx+XDdFkXkb0Xk62Fs/znc7sN1GxVb5tctEmNZRP5ORP40/H6q65YLjz5sQv4t4L0ETVCeAu5Q1fPSmzYtIvIKsKGqXizCEJF3A6cJ+vy+Ndz2P4Caqv5meKNcVdX/6ElsvwGcVtX/eaHjicR1GXCZqn5NRPYATwM/DfwrMr5uY2L7F2R/3QRYUdXTIlIBngB+BfhZsr9uo2K7hYyvm0NE/h2wAVykqh+Y9u80LyP6QQNzVW0Drgm5EYOqPg4MNzu9Dfj98PXvEwjFBWdEbJmjqq+p6tfC16eAFwl6Imd+3cbEljkacDr8thJ+KX5ct1GxeYGIXAH8JPC7kc1TXbe8CP2o5uS+oMBjIvK0BE3QfeTSsCsY4b+XZBzPMHeJyLOhtZOJreQQkQPAjwF/g2fXbSg28OC6hfbDM8Bx4Euq6s11GxEbeHDdgN8m6L3dj2yb6rrlReglZps3d2bgRlV9O3Ar8OHQnjCS82ngzcD1wGvA/8oqEBHZDXwO+FVVPZlVHHHExObFdVPVnqpeT9Az+gYReWsWccQxIrbMr5uIfAA4rqpPz+L98iL0SRqYZ4aqHgv/PQ48TGA1+cb3Q6/Xeb7HM45ngKp+P/yD7AP/m4yuX+jjfg74I1X9fLjZi+sWF5sv182hqq8Df0XggXtx3RzR2Dy5bjcC/yyc33sI+Cci8n+Y8rrlRei9bUIuIivhBBkisgK8D3h+/FmZ8Ajwi+HrXwT+JMNYzsL9xw75GTK4fuHE3e8BL6rqxyO7Mr9uo2Lz5Lqti8je8PUS8E+Bb+LHdYuNzYfrpqofUdUrVPUAgZ79har+S6a9bqqaiy+C5uTfAr4D/HrW8UTiehPw9fDrBR9iAx4keCTtEDwN/TKwD/hz4Nvhv2sexfaHwHPAs+F/9MsyiOtdBHbgs8Az4df7fbhuY2Lz4bq9Dfi7MIbngY+G2324bqNiy/y6DcV5E/CnO7luuUivNAzDMEaTF+vGMAzDGIEJvWEYRs4xoTcMw8g5JvSGYRg5x4TeMAwj55jQG4Zh5BwTesMwjJzz/wFBQBfftQLftwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "40\n",
      "0.25296610456745805 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(midtro_flag, end)\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2bf2319d-9b84-4557-9995-03a83e3cf332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2002, 2003, 2004, 2004, 2004, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2010, 2010, 2011, 2012, 2012, 2013, 2013, 2014, 2014, 2015, 2015, 2015, 2015, 2016, 2016, 2017, 2017, 2017, 2017, 2018, 2018]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "16\n",
      "0.037813874720570295 False no trend\n",
      "0.09090909090909091 1.3181818181818181\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(MCS_induced_flag, end)\n",
    "n_epe = n_epe[2003-1979:]\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2f27e64-da92-46d8-a134-162811e890b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2004, 2004, 2004, 2006, 2006, 2006, 2007, 2010, 2010, 2012, 2014, 2014, 2015, 2015, 2016, 2017, 2018]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mzWvbqNabODw/jTc2trFVrWNmani/HCfTbApcWivhfbdo341yXzKGLz6Zxvcv5PDx22/S/PGdhCP6t1Y3Rz9wjIZ8hpVcxmlZlCSiQQgBvLbOpQVJSvWJXzq5CIDLLqPw5vVtVOrN1rpDLbnj8H6Eg1NcdtEARwh6Jl/GrM+DgG/4rCwWUvxcmk39mnKaTYHsENuU9iIelp4uLFqSVFYVdPUAjs8Yhkc+Z/Lfl5a4XYQP3xbD91/Oolpvav74TsIRgp4tjJ79xmZ9qDcFNra0HaZoZ71URb0ptCm5yF70LIuWJL1Wwv4ZL95xZB+I+MVuFORzFtehhg4oL7aFSh3PXFrX5fGdgiMEfXVz9APHnVV0+tXRR1091w1p0pVeY9GSpLJFxCNB+L1uHNo/zc/NCKTXigj5PQgHp3R5/PfeEobf6+Kyy5g4QtAz+dHLGXLiUs86erYwfg96O/EIr6NrJ71WQkJ955KIBPndywiksiXEI8GRvPoHYXrKjfcfj+Dscoa9iMbA9oIuhFBLLqOJZWv8X8cMfZxD224k2KSrRb5cQ65QaZUK4uEgLq2VdD0TsSPpNX1aFttZSsZwZbOMl650DqIzg2J7Qb+2VUOtIUYuZ0RVO1sjSi6RMaxz24lHgiiqJl1Op1X7VQ/z4pEAtmsNXDVwccmkUyjXkMlXWuczevHh26JwEbjsMga2F3Q5FDRq9ut1uxAOTum6ii5bKCMcnILXrc2vQ2ZSF7ns0hokku128rnhAaPBuaSeOSR0FvSFoA//4Oh+FvQxsL2gZzSoT0dn/a1pUz3QaqhIsrOOjg//UrkiPC7CkXnFfyTBXUBDI89j9C65AErZZflqHivXeNn5KNhe0LMadJDEQj6da+jajP1LpEkXH4wqL2pHFmZa734isz7M+jzc6TIE6VxJc1OuXpw+yWZd42B7QZcHjqP4uEgW5/y6l1y0aFmUuFzU2l7kdNK5EuLhncySiJ+bYUnnSjiisSlXL+KRIBKRAM6ez+r+s+yI7QU9UyhjITA11paa6Kwf66UKag3tp9hqjSbWitWxXnC6EY8EkV5zdoYuTbk6a7/c1jkcqVxRt4GibiwlF/F0eh2b2zXDfqZdsL2gKx4p44llLOSHENClayRbGM3atx+JSAAr17ZRrjU0fdxJQppyddZ+E5EArm6WUTJgV+yk01BNufQ+EG1nKRlDvSnw/QucpQ+L7QVdOXAcr5yxOKdf66KWU6LtxCNs0tXyH+mSoQM73RtMb66oplxGZujvOLwP4aCPu11GwPaCPspy6E5kOUSPOvo4Xu17IfuuU1nnilav7oz2VX3M3uhpytULl4tw+mQUT17IsVnXkNha0OuNJtaKFcTGLGfIDpSsDr7oo25T6sdO66JzRSuVU0y59gdu9B85ujDDJl0DIp8jPWxz9+L0ScWs6+k0m3UNg60Ffa1YhRDjlzMWAlPwuEiXzUWr+TI8LsL8jLamR9Kky8lZaLrHYZ7f68bh/TOOfm4GJZVTTLkWAvqYcvXifcfDmPa6uewyJLYWdNk7Pm7JxeUiRGd9upRcMvkyorM+uFzamx4lokFH91un9zjM49bFwUjnSkhE9TPl6oXf68b7j4dx9jybdQ2DrQVdHjhqUc6Ihvy6lFyy+crYXTi9iIeda9LVacrVSTystHWySdfepHLFG/r4jWQpGcNVNusaClsLeuvAUYMOkljIp1uXixabirohTbqyDjTpatV+ewh6IhpAudZkk649KJRryBb0N+XqxYdPxuAi4AkuuwyMrQU9k6/A7SIsBLQQdL9uNXStWxYlTu7mkF4tvcRIZp3s6dKbfi+KejMfmMKpo/NcRx8CWwv6ar6MSNAHtwb16VjIj3y5ju2qdoM6W9U6CuW6fiUXaUTlwFpxeu1GU65OElHuAuqHnDQ2cqiok9PJKM5fzeONDTbrGgRbC3omXx67ZVGiR+ui3IKkV8llMeTHzJTbkaKVyt5oytVJJMgmXf1I50pwu8gQU65eLCUXAQBnz3OWPgi2FvRsvoKYRksjZFlEy7JLqwtHJ0F3uQg3hwOOzdD3OswjIsSj7OmyF6lcEYf3TxtiytWLm8MB3BINsqAPSF9BJ6IvE1GWiF7scf8HiWiTiJ5TPz6nfZijodSntRFLmUVnNDxg1Gvsv51EJOi4DL3RFHhtbatVVulFIsyti3uRzpVMq5+3s5SM4Zn0Bpt1DcAgGfpXANzf55ofCCHuUD9+Z/ywxqdca2Bzu6aZWO4si9a+5KJXDR1Q6uhvXneWSdfKtS1UG00k+rTbxdmkqyeNpkB6rWRah0s7bNY1OH0FXQjxFIANA2LRFCmWWmXoIb8Hfq9L09bFTL6Maa8bIb9Hs8fsRJp0OcmIamdcvU+GziZdPblyXXGqNNKUqxd3HNqHyKyP2xcHQKsa+t1E9DwRfZuI3tLrIiJ6kIjOEdG5XC6n0Y/ujhar59ohIqV1UcNpUdmyqOcUXsKB6+h2DKX6ZejObevsh5Fr5/rRbtZVqTvnneYoaCHozwI4KoS4HcAfAPjLXhcKIR4TQpwSQpyKRCIa/OjejLscuhuxkF/TDF3PKVHJzdJ10UGi1cuUq5OjCzNwkTPbOvshnxMrlFwAxayrWKnj6fTEFQsMZWxBF0LkhRBF9fPHAXiJKDx2ZGPSGvvXWNC1rKFnCvpNiUpmpjw4uG/aUQej6VxxoMzS73Xj0P4ZRz03g5LOFTE37TXclKsX771FMeviXaN7M7agE9EiqTUDIrpTfUzTPS+zhQp8HhdC09rVp2OzyrJoLbxRhBDqcmj9Olwk8YizWhdTucEP85z23AyKsnYuYLgpVy/8Xjc+cCubdfVjkLbFrwP4EYATRLRCRL9CRA8R0UPqJT8P4EUieh7A5wF8QljgGVfE0q/pP8hYyI9yrYl8efyuiPx2HZV6U7ce9HYUk66iI/4jbG7XsFasDFz7TUSCuMQmXbvoXK5tBZaSi7i6WcaLb7JZVy/6pq9CiE/2uf8RAI9oFpFGZHTwSJFTp9l8GXPT3rEeSx7a6l1DBxQb3VK1gWyhYsgLiJmkW2vnBhOjeEQx6bqyuY1D+82biLQS0pSrX5eQ0dx7WxQuAs4sr+Jth+bMDseS2HZSVA/xklOnWvii61Hj74WTjKjSQx7myUzeSV1A/Wg9hxbL0OcDUzh1bJ7bF/fAloIuhFAzdI0FXX28VQ0ORne6cPSvoctMK+WAfutUbm9Trk54Vd9upCnXLRbL0AFg6WQML68W2KyrB7YU9EKljq1qQ/uSixz/10DQpUe51suhuyFNupySoe9lytVJJOjDrN/DB6NtpLKqKde8BQU9GQPAZl29sKWgZ3UyvZqeUqY6tWhdzOTLCPk9mJ7S3/iISDHpcoKzYHptsJZFCREhHgm2slJGeQ4P75/GlMd68nAsHMDxaJA90ntgvd+YBmQ0HvtvRxku0qaGrsVqvEFxgkmXNOUadhgmEQ4glbX/i92gpLLWMOXqxVIyhmcubWBzi826OrGpoOtnS6uM/2tQQ88b23HiBJOuQU25OklEg1jNs0kXoLwoXlq3hilXL5aSMTSaAt9js65d2FLQV3W0pdVqWjSbLxtSP5ckHGDS1fIfGfIwL67aI9j5uRkUacpl5Qz9dtWsi8suu7GloGfzFcz6PZiZ0t7FMBbyIVuojDWI0mwKZAsVLM7p3+Ei2VlHZ9+yy6jtdokom3RJLg7Zx28G0qzr+xeybNbVgS0FXY+WRUks5Ee9KbBeqo78GGulChpNYWzJJWz/futUroj5wFRfU65O2KRrh53F0NYtuQBK2aVUbbBZVwe2FPRVHaZEJVq0LrYWWxhYcpmecuPgvmlbZ6GpXKlVPhkGn0cx6bLzczMoKdWUa94iply9eE8ijJkpN84sr5odiqWwpaBndTxwlC8U4yyLNmL1XDfiEXuvXBtnZVrC5s/NoKQtZsrVC7/XjQ8cj+DsctYRHkWDYjtBV+rT+pZcgPHG/+X3Gtm2COy0LtrxP4A05Rq1OyPOJl0ArLNHdBCWkjGs5sv42ZubZodiGWwn6Ne2qqg1RMt3RWsisz4Q7Yzuj8JqvgwiIBw0PkMvVRua9NFbjWFNuTpJRIItky6nIk25rNyy2M6OWRd3u0hsJ+irOvagA4DX7cJCwDdWySWbL2Mh4Bt4PF0rdoyo7FcrTo15mBd34Kq+TnYORCcjQ9+vmnWxoO9gO0FvLYfWsZwRC/nGLLmUDW1ZlNi5dTGtmnIdHtCUq5ME7xdt2yM6GRk6ANyXZLOudmwn6HpOiUrG3S2ayVcQM7DDRdIy6bJhFprOlXB0CFOuTsLBKcz6PY7P0K1qytULadbFWbqC7QRdllwiOtanlQx9vC4XIxZbdKIYUQVsmYUqK9NGLxVIky47PjeDksoVcWR+xpKmXL04uhDArTE265JMzm9uQDL5CsLBKV3/UcZCfqwVq6g1mkN/b7XexHqpanjLoiQeDtouC603mri8PrwpVydOb11Mj9jHbzZLyRh+/NoGrm+NPuxnF2wn6EZ4pMhyTq4wfB09V1RbFk1aBZeIBHFlcxvbVfuMTK9c21ZMucY8zEtEFJOuogNNuqQpl7RBmCSWkots1qViO0HPFPSbEpXIxx/FdXFnU5E5gh6PBGxn0iW9zMc9zJPff8mBWfqb1xRTrknM0N9+cA5RNusCYENBX93U35ZWPv4orovye6JmlVxke56NFjpIL/Nxd2DKGrydnptBSckXxQnM0F0uwodPxvDkhZzjzbpsJei1RhPrJeMEfZTWRSOXQ3djZ2G0fbLQ9NpoplydtEy6HLCqrxP5d57EDB1Q2hdL1QZ+lFo3OxRTsZWgrxUrEEL/csb8zBQ8Lhqp0yVTqMDrJuyfMcf8SJp02SkLHdWUqxOfx43D8zOOWKbdSXqtNBGmXL24O7GgmnU5u+xiK0HfqU/rW85wuQjRWd9INfTMpnJo63KZZ35kt9bFdG64PaJ7EQ87s9NFeQ6tb8rVC7/XjXtujeDs+Yyj/Xj6CjoRfZmIskT0Yo/7iYg+T0QXiegFInqn9mEOhp67RDuJzflbU6nDkCmUTaufSxKRIC7lSrYw6drcqmGtWNXMfyThUJOuVK5k6aUWg7CUjCGTrzjarGuQDP0rAO7f4/6PADiufjwI4AvjhzUa0l/FEEGfHW1aNJOvmFY/lyRsZNLVOszTKkN3oElXvlxDrlCZGA+XXnzoRBRuFzm67NJ3R5sQ4ikiOrbHJQ8A+KpQ0r2niWgfER0QQlzVKshByeTLcLsICwbUAWMhH/4utTb092U2y3jfLWEdIhocmYldzBYNt/DVmtbaOc0ydOl3U8Kh/aP5wozKX/z9CkqVBn7hrqOG/lytn0Oz2B+Ywqmj+/G1Zy7j+ZXrZoezJx97+wH8k3cd0fxxtVi6eRDAG21fr6i37RJ0InoQShaPI0e0/8usblYQnfUZUp+OhvwolOvYqtYH3l1aqtRRqNRNL7m85aYQXAQ8nV7H+46b++IyLqlcEV736KZcncTbHCnvuTWiyWMOQrMp8LvffhmlSgP/+NQh+Dxuw352egJNuXrxq/fE8ch3L1p+OKxaH37KfBC0EPRu6tm1ACmEeAzAYwBw6tQpzYuUei626GSx1YtewbHwYE9jVp0sNcOYq519Mzu2o7/xcydMjWVc0qr/iFZWxNKky+hD45+9udkqgT2d3jD0xSSVK06cKVcv7r0thntvi5kdhmlo8b9gBcDhtq8PAbiiweMOTUbHXaKdjLJbtNWDboEyx33JGC5kCnh9fbJtR9MaH+YRkbrZydhOl7PnM3AR4Pe6cNbgGnA6V5o4Uy6mO1r8Br8F4FNqt8tdADbNqJ8DStuiURn6KOP/Zu0S7UbLdvT85B4g1RtNvLau/co0M3avnlnO4F3H5pU9meczhnYgKWvnJj87ZwZrW/w6gB8BOEFEK0T0K0T0EBE9pF7yOIA0gIsA/ieAf6VbtHuwXW0gX64bJ+hzOyWXQcm0xv7Nz9CPLgRwPBqc6K3pK9e2UWsIzQ/zjDbpemNjCy+vFrCUjGEpGcPVzTJefDNvyM+WplyT3rLIKAzS5fLJPvcLAA9rFtGIGNmyCACzPg+mve4hSy4VzEy5MevT4uhifJaSMXzxqTSub1Wxz6TJ1XHY2bCjrRi1m3S97dCcpo/djSfUEst9yUUE/R51T+aqIT97kk25mN3Ypmi2M1RkTDmDiJRFF0NY6Co1fr9lpvGWkjE0mgLffXkybUfTY+4R7YXR6+jOLK/iRGwWRxZmMB+Ywqmj8y2R15vWi+IEmnIxu7GNoOu9HLob0ZAfmc3haujRWfPr55LbD+1DdNaHsxNaR5emXFq/uziimnQZsUz7+lYVP3ntGk4no63bTiejhu3JlILOGbo9sI2gZ00Q9MWQH5nCcCUXs3zQuzHptqOprD6HeUaadH3vQhaNpsBScrF1m/zciBfa9FoJ+2Ym15SLuRHbCHomX4bf60LIb1x9Wu4WHaQjQQiBTL5siZbFdqTt6A8n0HY0vVYc2wO9F4lI0BAb3TPLGURnfXj7wZ16+c3hAG6JGrMnM5UtIh6eXFMu5kZsI+iravZr5D/MWMiPcq2J/Hb/bojN7Roq9aalSi7A5NqOSlOuRFSfUkE8HMCltZKuJl2VegNPXsjhdDK2a7p5KRnDM5c2sLlV0+3nA0qGPukeLswOthF0eeBoJK3hogHKLkY6QQ6D3+tWep+XJ8t2VJpy6ZahR4Oo1Jt487p+Jl0/TK2jVG20ZgLakQfWeu7JlKZc3LJoH2wj6FkzBX2A1kUrTYl2spSMIVuo4IUJsh1tbdjRaSBGHhKmdayjn1nOIDDlxnsSC7vuu+PQPoSD+u7JtIspF7ODLQRdqU9XEDO4nCFbJAexoW1NiZrs49KNe29TbEeNHjkfh/RaSVNTrk5k1qpXHb3ZFDi7nMEHbo10NeJyuQinT0bx5Cv6HVjLvxuXXOyDLQQ9X65ju9aYiAzdbKfFbkjb0Umqo2ttytVJODiFkN+j26q+F97cRLZQ6VpukSwlYyhW6ng6vaFLDOk1acplrE0wox+2EPRWy6LB5Qy/1425ae+Agl7B3LQXfq9xtqjDsDRhZl2pnL6HeUSEuI4mXWeXM3C7CPfeFu15zXtvCWPa69bNniGdK+Eom3LZClv8JlsHjiZ0kMjWxX5k8mXTNxXthcwUn5gAb5d6o4nLBviPJCJB3aZFFTOu/XsORfm9bnzg1jDOLmd1MetK5YpcP7cZthB0M6ZEJbGQf7AaeqFiyXKL5OhCALfGjOl9Hpc3VFMuvR0C45EAMvmK5iZdr69v4UKmcMMwUS+WkotYzZc135PZaAq8trbFHS42wxaCnjFR0KMD7hbNGGjtOypLyRjOXb6Ga6Wq2aHsiRzJNyJDb/95WiHfBS2d7L+I4d7boqpZl7YvtCvXtlBtNNk212bYQtCz+TJCfg+mp4yvTy/O+ZAtVPbs4W40BXJF85dD92Mpuah777MWpAxamSYfX+s6+pnlTMuMqx/SrEtrQd9pWeQM3U7YQtDN9EiJhfxoNAXW98hq10sVNJrCEost9uLtB+cQndW391kL0rkSFnQw5epED5Oua6UqfvLaxp7dLZ0sJWOam3XpZT3MmIstBH3VhKEiSXS2f+tiZlOpsVthscVeuFyE08kYnnwlh3LNumZdyto5/UsFPo8bR+ZnkNIwQ//ehSyaAkMLOqBt2SWVY1MuO2ILQTdjSlQiJz/3FHQTa/zDspSMYavawI8sbNaVyhUNyyzjGne6nFnOIBby4W0HB19ecSwst0tpJ+jpXJEtc23IxAt6symQLVRMK2cMMi0qvV6sXkMHgLvjilmXUQsWhuX6VhXrpaph7XaJiHYmXeVaA0++ksPpk7vNuPqxlIzhx69t4PqWNgfWevfxM+Yw8YK+Xqqi3hSmZb/hoA9E/TL0CoiU6UOr4/e6cc+tEXznvDXNumT5Qy9Trk7iEe1Mun6UWsdWDzOufmhp1rW5XcNakU257MjEC/pOOcOcDN3rdmEhsPdwUWazjHDQB49OY+paY2WzrrTBK9O0XEf3hGrGdXcXM65+3H5oHyKzPpxdHl/Q0wZ1CTHGMxkKswdGL4fuxuJcH0EvlC3f4dKONOvSa+R8HFqmXPunDfl5cY1aF5tNgbPnM7jnRHczrn5Is67vX8iObdbFLYv2ZeIF3Qo+47HZvadFM3nr96C3s29mCu86Zk2zrlS2iKMLAcPe7SwEtDHpen7lOnJ9zLj6saRulxr3wDq9VoTHRTg6QB88M1lMvKCvbpZBBERM3AQUDe09LZrJly3fstjJUnIRr2SKuLyu/17NYUivlQztziAiJKJBpLLjPQ9nzytmXB860duMqx/vSYQ12S6VypZ0dapkzGPif6PZQhkLAZ+p/zhjIR/WS1VU681d91XqDWyUqpb0Qd8LOZZupSzdKFOuTuLh4NgZ+pnlDO48Nj/WMFRru9SYB9bpNTblsisDqSAR3U9EF4joIhF9tsv9HySiTSJ6Tv34nPahdkeZEjW3Pi3LKbni7rJLrqDctjg3OTV0QJmSPBGbtZSgG2XK1Ukiqph0Fcqj7fe8vF7CK5niWOUWyVIyhky+MrJZlzTl4pZFe9JX0InIDeAPAXwEQBLAJ4ko2eXSHwgh7lA/fkfjOHtixi7RTvZadCFr65NWcgEU8fjJaxuWMevaWTtnfIYOAJdGXEcnXxS1EPQPqWZdZ8+P9kIrTbk4Q7cng2TodwK4KIRICyGqAL4B4AF9wxocRdDNzX6lLW5ms5ugW3f1XD+WkjE0BfDdl61h1iXLHoZn6GN2ujyxnMFti7OarMubD0zh1LHRzbrk34EzdHsyiKAfBPBG29cr6m2d3E1EzxPRt4noLd0eiIgeJKJzRHQul8uNEO6N1BpNrBWrpmfoi3tm6Ob2yY/D2w7OIRayjlmXUaZcnRxZmIHbRSP1om+Uqjg3pBlXP+4bw6wrZZD1MGMOgwh6txnlzhOZZwEcFULcDuAPAPxltwcSQjwmhDglhDgViUSGCrQbsj5ttqDvn5mC103IFHbX0DP5CrxumkgTJKX3OYanXrWGWZeRHi7t+DxuHN4/PVKG/r2Xhzfj6sfOdqnhX2jZlMveDCLoKwAOt319CMCV9guEEHkhRFH9/HEAXiIKaxZlD1Ytkv26XNRz0UU2X0Z01g+i4bw7rIKVzLqMclnsxqjr6M4sZ7AY8g9lxtWPne1Sww9+mfWiyBjDIIL+EwDHiehmIpoC8AkA32q/gIgWSVUsIrpTfVzdFSBrIRfDaI/doqsWqPGPw92JBQQsYNZltClXJ/ERTLrKtQaeejWH08mo5i/oyoH1taHNutI5Y/v4GWPpK+hCiDqATwP4GwDnAfypEOIlInqIiB5SL/t5AC8S0fMAPg/gE0KPrbYdWGFKVLLYY7doJl9uWexOIj6PG/ecGL/3eVxSJh/mJUYw6fphag1b1QZOD7BqblhOnxzerEuachnlg8MYz0B96EKIx4UQtwohEkKI/6Te9qgQ4lH180eEEG8RQtwuhLhLCPFDPYOWZPJleFyEeYMPyboR6zEtms1XWkswJpWlZAy5QgXPr1w3LQazD/PiI5h0nVnOIOjzjGTG1Y/bD+0bertUaxcrZ+i2ZaInRVfzZURnfUN7S+tBNORDoVzHVnVnQ3ypUkehUrfEO4hx+NAJadZlXtklnTPWlKuTYU26FDOuLO65dTQzrn64XIQPn4zhyQu5gc26Wi2LnKHblokW9Gy+gphFyhk7rYs7ZZdJbllsZ9/MFO4co/dZC9I5Y025OlkITGFu2jtwhv6cBmZc/bhPNev64YAH1qmcYsp1RIN+eMaaTLSgZ/JlywzsdJsWleI+SU6LvVhKxvBqtojXRpyWHJeUySvTiAjxSGDgDP3s8vhmXP24O7EwlFlXOsemXHZnon+zVuog2VlFtyPo0qt9Esf+O5GZ5qgj5+NQazTx+saW6aWCYVoXzyxn8O6b5zE349UtHrld6uzyYAfWqVyRB4pszsQK+la1jkK5bpmSS7RLhr66aY+SCwAcnp/BbYuzprQvvrGxhVpDmH6YF48EkC30N+l6ba2EV7NFXbpbOjl9Utku1c+sq9EUuLy+xVuKbM7ECnpWtixapOQy6/NgZsrdUUOvIDDlxqxfvyzNSJaSMZx7bQMbBpt1WeUwb1CTLi3NuPqxs11q7xdaacrFQ0X2ZmIFPWOhoSJAqbF2ti4qq+esEZ8WmGXW1TLlMmgxdC9uiSrZbb+yyxkNzbj6sT8whVNH+2+X2mn75AzdzkysoFtl7L+d6Kyv9c4BUMf+LRTfuLzt4BwWQ36cNbjsksoqplx61qMH4ch8AG4X7XkwulGq4tzlDdxnQHYuWUrGcCFTwOvrvc26eI+oM5hYQW+VXCxSQweUdwur7TV0C3i1awkR4XQyarhZV3rNGv4jUx4XjszP7Jmhf7dlxrVoWFz3qT/riT28XVK5IvazKZftmVhBz+TLmPa6MevzmB1Ki8U5peQihIAQYuKWQw/CUnIRW9UGfphaM+xnpkw05eokHt67dfHM8ioOzPnx1oMhw2KS26X26kBSnkPzXxQZfZlYQZcti1ZyMYzO+lCpN5HfrmNzu4ZqvWmLlsV27orPI+jzGDZkdK1UxUapaokMHVAOZi+tldDo0iZYrjXw1CtrOH0yZvi/y35mXelciTtcHMDECno2X7FcOUPGs5ovW7LGrwU+j9r7fD5riFmXPBC1UoZeqTdxpYtJ199dXMN2rYHTBtbPJaeTillXtwNracrFGbr9mVhBt2IHiXRVzOTLtpoS7USadT1ngFmX2S6Lnexl0iXNuO6KzxsdFt5+cK6nWRebcjmHiRR0pT5tnSlRieyJVwTdWm2VWiLNuozodpGmXIdMMuXqRJYtUh119JYZ1wl9zLj64XIRTidjePKV3QfWKYv08TP6M5GCnt+uo1xrWk4sZYtitlBpLd+IzFrrRUcL5ma8ePfNxph1pUw25epkXjXpSndk6M+tXMdasWJou2InvbZLpdmUyzFY43/JkGQK1sx+/V435qa9WN1Uauj7Zrzwe43P1ozAKLOudK5oqcM8IkIiEthVcjmznIHHRfigjmZc/XiPul3qTEe3SypXxJEFNuVyAhP5G7ZyOWNRnRa1Y8tiO9KnRM8svdZo4vL6luUO8+KR4K7WxTPLGbw7Po+5afOGn1rbpTrMupS1c9Z6Dhl9mEhBt7LpVTTkQ0YtuditZbEdadalp6C/sbGFelNY5kBU0mnSdWmthIsGmXH1Q5p1vaCaddXVF8VE1Drvchj9mEhBzxass0u0k1jIj4xaconZsH7ezn3JGM5d1s+sK9UaV7eWGMkXGJmln1EnNI0w4+rHjlmXEtPKtW3FlIszdEcwkYKeyZcxN23N+nQs5EOuWEGuYL0+ea1ZSi7qatYlDx6tJkaypi975M8sZ3DyQAiH9pt/6LhvZgrvOrZj1mW1Pn5GXyZW0K1YbgGUGnqjKdAU1vKZ0YO3HgzhwJy/lQ1qTTpXQjhovilXJ9KkK5UtYb1YwU8vX7NEdi5ZSi7ilUwRl9dLSGWt1cfP6MtECvqqBadEJe11c7uXXIgIp0/G8NQra7qYdSlr56wnRNKkK71WbJlxmdmu2ImM5cxyBuk1xZRrP5tyOYKJFPSshV0M2+OyaoxaspSMYbvWwN9d1N6sK71WsuxhXkLdL3pmOYMDc3685SbjzLj60X5gncqVODt3EBMn6M2mQLZQsXTJpfW5zUsuAPBuncy6pCmXFTN0QG1dXCvhB6+aY8bVj9MnY/jJaxs4fyXP9XMHMZCgE9H9RHSBiC4S0We73E9E9Hn1/heI6J3ah6qwVqqg0RSWzX7DwSkQAS4CFhzwNrfV+6yxWZfVD/Pi4QCq9Sa2aw1L1c8lcrtUoVK3XB8/ox99BZ2I3AD+EMBHACQBfJKIkh2XfQTAcfXjQQBf0DjOFq3FFhYVdI/bhXDQh3DQZ5lxdb25LxnDWlFbsy6rmXJ1In1RZn0e3BVfMDma3bzt4FzrXaxVn0NGewbZDnEngItCiDQAENE3ADwAYLntmgcAfFUIIQA8TUT7iOiAEOKq1gFbeUpUEgv5QLDWW3A9+eCJKDwuwq/+759in0aTkhulqqVMuTqRzoX3nIhgymO9F26XSzmw/tozr1v2XQ6jPYMI+kEAb7R9vQLg3QNccxDADYJORA9CyeBx5MiRYWMFAMxNe3H/WxZx0z7rCvqnP3Tc7BAMZW7ai89+5DY8+/o1TR/39kP7LPsuZz4whd+471bce5v1yi2Sf/H+OPbNeHHzAgu6UxhE0Lulmp3F0kGugRDiMQCPAcCpU6dGKrieOjaPU8eM95sehvvfatw+Savwz98fNzsEQyEifPpea79wHwsH8O9+7jazw2AMZJD0ZwXA4bavDwG4MsI1DMMwjI4MIug/AXCciG4moikAnwDwrY5rvgXgU2q3y10ANvWonzMMwzC96VtyEULUiejTAP4GgBvAl4UQLxHRQ+r9jwJ4HMBHAVwEsAXgl/ULmWEYhunGIDV0CCEehyLa7bc92va5APCwtqExDMMww2DNFgKGYRhmaFjQGYZhbAILOsMwjE1gQWcYhrEJpJxnmvCDiXIALo/47WEA2vu1agvHOD5Wjw+wfoxWjw+wfoxWi++oECLS7Q7TBH0ciOicEOKU2XHsBcc4PlaPD7B+jFaPD7B+jFaPrx0uuTAMw9gEFnSGYRibMKmC/pjZAQwAxzg+Vo8PsH6MVo8PsH6MVo+vxUTW0BmGYZjdTGqGzjAMw3TAgs4wDGMTJk7Q+y2sNhsiOkxE3yOi80T0EhF9xuyYukFEbiL6eyL6K7Nj6Ya6xvDPiOhl9bm82+yY2iGif6v+fl8koq8TkekrtIjoy0SUJaIX226bJ6IzRPSq+ud+C8b4X9Xf8wtE9BdEtM9K8bXd9xtEJIgobEZsgzBRgj7gwmqzqQP4dSHESQB3AXjYgjECwGcAnDc7iD34fQD/TwhxG4DbYaFYiegggH8D4JQQ4q1QbKU/YW5UAICvALi/47bPAviOEOI4gO+oX5vJV7A7xjMA3iqEeDuAVwD8ptFBtfEV7I4PRHQYwBKA140OaBgmStDRtrBaCFEFIBdWWwYhxFUhxLPq5wUoQnTQ3KhuhIgOAfiHAP7I7Fi6QUQhAB8A8CUAEEJUhRDXTQ1qNx4A00TkATADC2zoEkI8BWCj4+YHAPyx+vkfA/hHRsbUSbcYhRBPCCHq6pdPQ9l4Zgo9nkMA+O8A/j26rNa0EpMm6L2WUVsSIjoG4B0AnjE5lE7+B5R/nE2T4+hFHEAOwP9Sy0J/RESW2XQshHgTwH+Dkq1dhbKh6wlzo+pJTG4PU/+MmhxPP/4ZgG+bHUQ7RPRxAG8KIZ43O5Z+TJqgD7SM2goQURDAnwP4NSFE3ux4JET0MQBZIcRPzY5lDzwA3gngC0KIdwAowfxSQQu1Dv0AgJsB3AQgQES/YG5Ukw8R/TaUkuXXzI5FQkQzAH4bwOfMjmUQJk3QJ2IZNRF5oYj514QQ3zQ7ng7eC+DjRPQalJLVvUT0f8wNaRcrAFaEEPKdzZ9BEXircBrAJSFETghRA/BNAO8xOaZeZIjoAACof2ZNjqcrRPRLAD4G4J8Kaw3HJKC8cD+v/p85BOBZIlo0NaoeTJqgD7Kw2lSIiKDUfs8LIX7P7Hg6EUL8phDikBDiGJTn77tCCEtll0KIVQBvENEJ9aYPA1g2MaROXgdwFxHNqL/vD8NCh7YdfAvAL6mf/xKA/2tiLF0hovsB/AcAHxdCbJkdTztCiJ8JIaJCiGPq/5kVAO9U/41ajokSdPXgRC6sPg/gT4UQL5kb1S7eC+AXoWS+z6kfHzU7qAnkXwP4GhG9AOAOAP/Z3HB2UN85/BmAZwH8DMr/I9PHw4no6wB+BOAEEa0Q0a8A+F0AS0T0KpQujd+1YIyPAJgFcEb9//Long9ifHwTA4/+MwzD2ISJytAZhmGY3rCgMwzD2AQWdIZhGJvAgs4wDGMTWNAZhmFsAgs6wzCMTWBBZxiGsQn/H64OS91zZsETAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "16\n",
      "0.7757557340067254 False no trend\n",
      "0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "n_epe = find_categories_events(front_flag, end)\n",
    "n_epe = n_epe[2003-1979:]\n",
    "plt.plot(n_epe)\n",
    "plt.show()\n",
    "n = len(n_epe)\n",
    "print(np.sum(n_epe))\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dba8d22-1473-4bea-8773-366fc6cc4104",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "afddbc7f-f23e-4ad4-bded-1e795061c50d",
   "metadata": {},
   "source": [
    "# Sub7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fab56b05-d4c6-41af-a2d7-9c597314ae89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136   153   136\n",
      "40\n",
      "0.33991708243329377 False no trend\n",
      "0.0 3.0\n"
     ]
    }
   ],
   "source": [
    "sub_id = 7\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    start, end = start_end_time(sub_id)\n",
    "    time_end = [pd.to_datetime(t).year for t in end]\n",
    "    n_epe.append(time_end.count(year))\n",
    "print(np.sum(n_epe), ' ', len(ext_days[sub_id]), ' ', len(end))\n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "29edee06-0ecc-4ceb-bb8d-0f78f33f24b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.059\n",
      "Model:                            OLS   Adj. R-squared:                  0.035\n",
      "Method:                 Least Squares   F-statistic:                     2.394\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.130\n",
      "Time:                        10:32:56   Log-Likelihood:                -86.866\n",
      "No. Observations:                  40   AIC:                             177.7\n",
      "Df Residuals:                      38   BIC:                             181.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.3000      0.676      6.361      0.000       2.932       5.668\n",
      "x1            -0.0462      0.030     -1.547      0.130      -0.107       0.014\n",
      "==============================================================================\n",
      "Omnibus:                        0.667   Durbin-Watson:                   1.217\n",
      "Prob(Omnibus):                  0.716   Jarque-Bera (JB):                0.742\n",
      "Skew:                           0.149   Prob(JB):                        0.690\n",
      "Kurtosis:                       2.403   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b56036-45c6-44c9-9387-c38b4c1a955f",
   "metadata": {},
   "source": [
    "# Precipitation Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "251050d4-ef0b-4637-90fd-4a8534e4a667",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_categories_precip(label, end, sub_id):\n",
    "    ext_time = ext_days[sub_id]\n",
    "    precip_year = np.zeros(40)\n",
    "    for time in ext_time:\n",
    "        year = pd.to_datetime(time).year\n",
    "        current_time = time+np.timedelta64(12, 'h')\n",
    "        while current_time not in end:\n",
    "            current_time = current_time + np.timedelta64(1, 'D')\n",
    "        ind = end.index(current_time)\n",
    "        if label[ind]>0:\n",
    "            # print('here')\n",
    "            precip_year[year-1979]+=monsoon_precip.sel(sub_id=sub_id).sel(time=time)\n",
    "\n",
    "    return precip_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5819ee2c-39fa-4d9e-a9d4-b0d4a1f427db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
      " 1. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n",
      " 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0.\n",
      " 1. 1. 0. 0. 0. 1. 1. 0. 1.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc70666b790>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc_induced_flag = np.load(\"ERA5/PAU/\" + str(6) + \"_tc_induced_flag.npy\")\n",
    "print(tc_induced_flag)\n",
    "start, end = start_end_time(sub_id=6)\n",
    "precip_year = find_categories_precip(tc_induced_flag, end, sub_id=6)\n",
    "plt.plot(precip_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30de57dc-553e-444c-8ce4-be28f056db18",
   "metadata": {},
   "source": [
    "# Sub1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "41738572-6945-41c0-a43d-8aa7f56fd23c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.02262536212899602 True increasing\n",
      "0.8703217656121727 8.199267140507988\n"
     ]
    }
   ],
   "source": [
    "sub_id = 1\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0bc44016-caf5-4aba-83c1-13ada5b92502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.122\n",
      "Model:                            OLS   Adj. R-squared:                  0.099\n",
      "Method:                 Least Squares   F-statistic:                     5.271\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):             0.0273\n",
      "Time:                        10:32:57   Log-Likelihood:                -203.59\n",
      "No. Observations:                  40   AIC:                             411.2\n",
      "Df Residuals:                      38   BIC:                             414.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         11.1036     12.512      0.887      0.380     -14.225      36.432\n",
      "x1             1.2676      0.552      2.296      0.027       0.150       2.385\n",
      "==============================================================================\n",
      "Omnibus:                       11.521   Durbin-Watson:                   2.439\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               11.137\n",
      "Skew:                           1.198   Prob(JB):                      0.00382\n",
      "Kurtosis:                       3.972   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "389270fb-a116-406e-b5df-8adf32d6cc1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc706617ed0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_epe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ecd435-0613-424c-be36-232350ec5ce2",
   "metadata": {},
   "source": [
    "## categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f068583f-a256-40c6-9962-33bc4d399af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_id = 1\n",
    "surge_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_surgeWINDOW_induced_flag.npy\")\n",
    "# tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced_flag.npy\")\n",
    "tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced-10degree.npy\")\n",
    "# tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_induced_flag.npy\")\n",
    "tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_corrected_flag.npy\")\n",
    "MCS_induced_flag = np.load(\"ERA5/PAU/MCS_\" + str(sub_id) + \".npy\")\n",
    "MCS_induced_flag[MCS_induced_flag < 0] = 0\n",
    "midtro_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_midTropo.npy\")\n",
    "front_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_front.npy\")\n",
    "start, end = start_end_time(sub_id)\n",
    "all_time = ext_days[sub_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b80da84f-0a06-4d83-b37f-ce0a417bf57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.004287725722426972 True increasing\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(tc_induced_flag, end, sub_id=1)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83048344-c9a4-4228-8251-d22ad407b300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.0246941553494886 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(tutt_induced_flag, end, sub_id=1)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "34329c62-40d7-4caf-9aa3-77d17b010032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.42663740516278725 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(midtro_flag, end, sub_id=1)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ef4e12e8-1693-46e2-940d-521f1265b25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.017852008555653942 False no trend\n",
      "0.8066560925324975 8.767680559843075\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(surge_induced_flag, end, sub_id=1)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b95d3953-7257-4f78-bd0c-a08cbf042ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0.1777369996906002 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(MCS_induced_flag, end, sub_id=1)\n",
    "precip_year = precip_year[2003-1979:]\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "74962145-4527-496a-a13b-27de17ea5e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0.385546631571102 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(front_flag, end, sub_id=1)\n",
    "precip_year = precip_year[2003-1979:]\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f47efba-342c-47fc-ad91-9de53c955870",
   "metadata": {},
   "source": [
    "# Sub2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0850daa-b639-423f-a0f9-3ee9bafc3461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.07921221657854227 False no trend\n",
      "0.0 0.0\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.069\n",
      "Model:                            OLS   Adj. R-squared:                  0.045\n",
      "Method:                 Least Squares   F-statistic:                     2.820\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.101\n",
      "Time:                        10:32:59   Log-Likelihood:                -172.01\n",
      "No. Observations:                  40   AIC:                             348.0\n",
      "Df Residuals:                      38   BIC:                             351.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         19.1215      5.680      3.366      0.002       7.622      30.621\n",
      "x1            -0.4209      0.251     -1.679      0.101      -0.928       0.087\n",
      "==============================================================================\n",
      "Omnibus:                       24.070   Durbin-Watson:                   1.964\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               36.143\n",
      "Skew:                           1.818   Prob(JB):                     1.42e-08\n",
      "Kurtosis:                       5.910   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "sub_id = 2\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308b644b-7040-471f-9a83-5cc6ce794ac8",
   "metadata": {},
   "source": [
    "# Sub3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "48b89661-3d5e-4bc9-9289-f70e8fdf5a02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.16087806893207057 False no trend\n",
      "0.0 0.0\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.067\n",
      "Model:                            OLS   Adj. R-squared:                  0.043\n",
      "Method:                 Least Squares   F-statistic:                     2.733\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.107\n",
      "Time:                        10:32:59   Log-Likelihood:                -164.03\n",
      "No. Observations:                  40   AIC:                             332.1\n",
      "Df Residuals:                      38   BIC:                             335.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         16.6401      4.653      3.576      0.001       7.221      26.059\n",
      "x1            -0.3395      0.205     -1.653      0.107      -0.755       0.076\n",
      "==============================================================================\n",
      "Omnibus:                       19.102   Durbin-Watson:                   1.438\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.917\n",
      "Skew:                           1.572   Prob(JB):                     6.40e-06\n",
      "Kurtosis:                       5.112   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "sub_id = 3\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b487c19-a59c-44de-ad32-3791b548cbab",
   "metadata": {},
   "source": [
    "# Sub4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "35ad7f87-8a20-43dd-bf46-641ab728924e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.14927118153783514 False no trend\n",
      "0.2412777143351921 9.044585832944767\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.102\n",
      "Model:                            OLS   Adj. R-squared:                  0.078\n",
      "Method:                 Least Squares   F-statistic:                     4.295\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):             0.0451\n",
      "Time:                        10:32:59   Log-Likelihood:                -163.98\n",
      "No. Observations:                  40   AIC:                             332.0\n",
      "Df Residuals:                      38   BIC:                             335.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          8.1539      4.648      1.754      0.087      -1.255      17.563\n",
      "x1             0.4251      0.205      2.072      0.045       0.010       0.840\n",
      "==============================================================================\n",
      "Omnibus:                        8.273   Durbin-Watson:                   1.600\n",
      "Prob(Omnibus):                  0.016   Jarque-Bera (JB):                7.236\n",
      "Skew:                           0.995   Prob(JB):                       0.0268\n",
      "Kurtosis:                       3.620   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "sub_id = 4\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "14b04ca6-949d-42f5-830c-44f410dda112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6.,  8., 10.,  4.,  4.,  2.,  3.,  1.,  1.,  1.]),\n",
       " array([-4.5991    ,  1.91165735,  8.42241471, 14.93317206, 21.44392941,\n",
       "        27.95468676, 34.46544412, 40.97620147, 47.48695882, 53.99771618,\n",
       "        60.50847353]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = np.array(n_epe)-0.4251*np.arange(n)+8.1539\n",
    "plt.hist(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cbb2c34f-4c82-4aab-b999-2a4a999785ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc70694ba90>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_epe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5337db67-e8fe-4bff-9382-97b59cc5901b",
   "metadata": {},
   "source": [
    "# Sub5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2bade759-6e8a-4a15-b831-2bd08da03496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.9257196644596948 False no trend\n",
      "-0.018335334972307868 21.82985987541472\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.019\n",
      "Model:                            OLS   Adj. R-squared:                 -0.007\n",
      "Method:                 Least Squares   F-statistic:                    0.7370\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.396\n",
      "Time:                        10:33:00   Log-Likelihood:                -176.81\n",
      "No. Observations:                  40   AIC:                             357.6\n",
      "Df Residuals:                      38   BIC:                             361.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         20.7553      6.405      3.240      0.002       7.789      33.722\n",
      "x1             0.2427      0.283      0.858      0.396      -0.330       0.815\n",
      "==============================================================================\n",
      "Omnibus:                        8.269   Durbin-Watson:                   2.379\n",
      "Prob(Omnibus):                  0.016   Jarque-Bera (JB):                7.419\n",
      "Skew:                           1.029   Prob(JB):                       0.0245\n",
      "Kurtosis:                       3.469   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "sub_id = 5\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3fe470-4933-489d-8aab-e85aa89be31f",
   "metadata": {},
   "source": [
    "# Sub6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aba9f22b-b13e-40b3-8816-60b83aad75f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.017954626824179698 True increasing\n",
      "1.0317938286683521 11.807173366586134\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.044\n",
      "Model:                            OLS   Adj. R-squared:                  0.019\n",
      "Method:                 Least Squares   F-statistic:                     1.750\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.194\n",
      "Time:                        10:33:00   Log-Likelihood:                -203.32\n",
      "No. Observations:                  40   AIC:                             410.6\n",
      "Df Residuals:                      38   BIC:                             414.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         31.5785     12.425      2.542      0.015       6.426      56.731\n",
      "x1             0.7252      0.548      1.323      0.194      -0.385       1.835\n",
      "==============================================================================\n",
      "Omnibus:                       18.280   Durbin-Watson:                   1.973\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               22.647\n",
      "Skew:                           1.491   Prob(JB):                     1.21e-05\n",
      "Kurtosis:                       5.168   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "sub_id = 6\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "82d26eef-cdac-43fc-9f9b-013f01c1d010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc70688cb90>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_epe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a05399-913f-4753-ae6f-90a0ba941412",
   "metadata": {},
   "source": [
    "## categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3e1326a7-572f-45ba-9e82-5c466659ac40",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_id = 6\n",
    "surge_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_surgeWINDOW_induced_flag.npy\")\n",
    "# tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced_flag.npy\")\n",
    "tutt_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_TUTT_induced-10degree.npy\")\n",
    "# tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_induced_flag.npy\")\n",
    "tc_induced_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_tc_corrected_flag.npy\")\n",
    "MCS_induced_flag = np.load(\"ERA5/PAU/MCS_\" + str(sub_id) + \".npy\")\n",
    "MCS_induced_flag[MCS_induced_flag < 0] = 0\n",
    "midtro_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_midTropo.npy\")\n",
    "front_flag = np.load(\"ERA5/PAU/\" + str(sub_id) + \"_front.npy\")\n",
    "start, end = start_end_time(sub_id)\n",
    "all_time = ext_days[sub_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "46f899f8-14be-493e-aa1a-beaba5107251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KmC5UEPJ7EPJ7uz6v11i1crWOQqX9ZPsJPQJn5n2fXIaNVGIKgkNQFgqgFY8MuiOh6oFy31y87eNuTCXczpd7FvEAzZPp218kd6sw20fg1TojXx59sy8RcEFwCMOOwFUPlBNz+yNwwJ19wY10IgSASKD7Jqbaf+hkoQDOKKcXARcEh6BF4M0CPliBeGMjh2TYj5lYsO3jR6bCuHW3AObO5eZOI12oINkjAwUAvB5C2O/tWInZLQJXGS5O6EgoAi4IDkGLwJstlMFG4NfWc7hvLtbRxz0yFUGpWkcqWxroOuxE64PSW8ABbSMz18ED77YZmpAIXBCEZmq6p9ocgQ+ylJ6Z8cZGFidm2/vfgDtzwY1aKIDmg3eKwFWvk9ZSekAsFEEQWlBi3RyB58pV1Lt0y+uHzVwZ2/n2GSgKN/YFTxsYp6boNlYt0yUC3xXw0XckFAEXBAegNs3ien5yPOgDs9ZdbxCoHiidMlAA4PCku4p5ipUaCpVazzJ6Rbexau16gSvU95cIXBAEALtl880WSvNxu7m20b4HSjMhvxcz8aBrLJRMl43HdnQbq6byyYO+/fnk0YAXXg+JgAuCoKEyTpotlObjdvPGehbxkA+z8fYZKIojLuoLrhpMGd7EDPg6pxF2qMIEtH7pybBfslAEQdAYdgSuDXHonIGicFMxz24nQhObmF0slG6boRMO6YciAi4IDkD1/t4v4IMRiWsbua7+t+LIZASr6SKqtXrP544aFREbqcQEug82TrcZ5tCMUxpaiYALggPItslCaT5uJ1u5ErZ2ynvmYHbiyFQYtTpjNV20fR12o+ZUGk4jDGgeeLtCpV4ReDLsd8RYNRFwQXAArRaK6kqYGYCAX21sYBqLwAF3ZKI0im8MWyg+1BkoVvZ/uug0zEHhlKEOIuCC4AAyxQoC3t0ueoPcxNxtYmUkAndPMU+6UIGHdlMxe9FtrJrWC7yLB+6QoQ4i4ILgAJobWQFAyO+Bz0MDsVCurWcRC/ownwj1fO5CMgSvh1yRibKd10TX4zHW4rXTWLVanZEtVrsKuLJQBlVoZRQRcEFwALkWASeigZXTv7GewwkDGSgA4PN6sJAMuSYCN5pCCOxG4K2phOqc97JQ6qyNwRslIuCC4ACae4ErBtUT/OpGzpB9onBLW9ntQgVJg1WYwO5Unta+3kam+qjHRr2RKQIuCA4gW6wi1uLdxoL29wS/u1PGZq6Ek12aWLVyz1QEb7vAQknny4YzUIDOg40bZfShzl66UxpaiYALggNo9cCBwQx1UBuYnYY4tOPIVBibuRIKDphA041tsxaK7oG3FvN0G+agEAEXBKFBJwvF7sn0RppYtaIyUW453Ac304kQ6DxWzUg6ompoNepy+p4CTkQhIvoaEX2DiC4T0S/ox6eI6EUiuqrfTg5+uYIwnrSLwBODiMDXc4gGvFhM9s5AURx2QV/wep3Nb2KqLJRyBwEfkwi8BOAxZn4EwKMAPkBE3wngGQAXmPkkgAv6fUEQTFKvM3Ll6j7PdRBj1a5uZA1noCjc0Bc8W6yC2XgnQmDXA+8UgXcrpXeNgLNGTr/r1/8xgCcBnNePnwfw1CAWKAjjTq6siU87CyVXal/qbZW3Ujs4PmPc/waAmVgQIb/H0Zkou42sjGehBHwe+L20b6xaplCBz0OIBPa3klWE/B4EvB5sj3iogyEPnIi8RPQqgA0ALzLzVwHMMfMqAOi3sx1e+zQRXSSii6lUyqZlC8L40FpGr4iHtFLvHZs2D5kZm7kyZhLdW8i2QkQ4POnsroRKSM1YKIAWhbeOVVN9ULp9SiEiJCOj74diSMCZucbMjwI4DOA9RHTG6Bsw87PMfJaZz87MzFhcpiCML629wBV2l9NnClWUa/WOU+i74fS+4Fs7moBPRk0KeJue4L3K6BVJB3QkNJWFwszbAL4C4AMA1oloAQD02w27FycIB4FuEXjz4/2SymnT5Q9ZEXCH9wXfzGo/20zM+OYs0L4neKZHGb3CCUMdjGShzBDRhP51GMD7AbwO4HkA5/SnnQPw3IDWKAhjTW5IAr7Zj4BPRpAtVpF2wBSadjQuTnHjHjjQfqxar1ayCicMdTASgS8A+Csieg3AS9A88BcAfBrAE0R0FcAT+n1BEEySGZKFogR8pscYtXY0MlEcGoVvZsuIBryIBIx1IlS0s1AyBgXcCRZKz5+WmV8D8O42x7cAPD6IRQnCQWJYFoqyGQ7FzEWpwN4J9WeWkrasx05SuZKlC1M06EVKPy8KbRpP7wuBE6bySCWmIIyYoQl4rgyvhzBpItVO4fS+4JvZkiVrKBrcG4Ezs6kIPFusojbClrIi4IIwYrLFCrweQti/N+94EBbKVDRguF92M8mwH4mQz7GZKJYj8MDeNMJ8uYZqnY154JHRdyQUAReEEaPK6FvzjqMBLzxk7yamlShV4eRMFKs/mzbYeDcLpVGFaTACb37NKBABF4QRozWy2u+5EpHeUtYegUjlypb8b4VT+4KXqjVs5ysWI3AvyrU6ylVtLqaRToQK9ZxRzsYUAReEEZMtVhEPthcMbaiDfZuYVop4FEemwrh1t2Brab8dbOW0Ih6rETiAho2i0iTNWCgSgQuuI52v4Dt+8S/w1Te3Rr0U19OuE6EiHvLZMpmemZHKlXDIQpSqODIVQala35e1MWr6SY9sHatmpBOhQiwUwbVc38xhPVPCq+9sj3oprifTphe4ImHTWLVsqYpytd6fhaJnotx0mI2S6iM9snWsmrpYdutEqEiIgAtuZT1dBACsZYojXon7yRb3t5JV2DWVZzcH3HoEfr8+BOLKaqbv9dhJfxH43rFqliLw/Og6EoqA98ln//EmPva//mHUyxg6SrjXRcD7JlfqbqG0VgpaYbMPn1ixkAxhMuLH5RVnCXiqj4tT61i1dKECov05+e0I+rwI+70SgbuZr711B1976w6KFWfPC7QbJeBraRHwfmBmXcC7bWL2LxD9RKkKIsLpxSQur6b7Xo+dbObKiId8CPk79+/uRKsHnilUEAv6DOfKj7qcXgS8T5SAbWSctbEzaJSFsn7Afm67yZdrqNUZsQ4RX0y3UPrN/OinkVUzpxcTeGMth0qt3tf3sZNU1loRD9AUgZd3BdzMZPtRdyQUAe+T1YxWmbaadmaF2qBotlDqIywldjudyugV8ZAP1TqjWOlPMDezJXgImIpa38QEgAcXEyjX6ri6nuv95CGR6qNAqXWsmtFOhIpkRCJw11KvM9bTWmRz0DbzVORdrXOjmb5gnk7DHBR2ldOncmVMRQPwWiijb+b0otbI6vJt59gom/1E4A0LZdcDNxuBi4C7lDv5Msr6R8nVA+QFMzPWM0UcntRajMpGpnUyPSJwlZ3Sby54v2X0incdiiLs9+LybedsZKZy1guUwn6tXUHDQilWDKUQKpLh0Y5VEwHvg9XtXeE6SJt52VIV+XINjxyeAHCwfna7UZF1tzTC5udZxS4B93oIpxbi+KZDBLxYqSFbrFqOwIloT09wsxH4RNgvpfRuRfneHgJubx8cD1xtYD5yRPs4vZ4VAbfKrgfey0LpLwJPZUt9FfE0c3oxiW+uZhyx99FPEY8i0jRWLV2oIBkxF4Hny7WRbeqKgPeB8r0fmE8cKA9c/axnFpPw0K6gC+YxsonZ/DwraNPo7YnAAS0TJVeq4m0HVGTakR4ZDfqQK1dRqtZQrNQNDXNQJEfcD0UEvA9W00X4vYTTi4kD5YEry2RpMoxDseCBunjZzTA2MXfKmjD10welmd2NzNHbKP0U8SiiAR92SlVkCtpF0uwmJoCRpRKKgPfBWrqIuUQIixNhbOZKjZaU447atJxLhDCfDGFNcsEtky1W4SGtrWk77IjA7Sijb+a++Rh8HnJEJoqqMO0vAtcsFDO9wBWjbmglAt4Hq+kCFpIhLCRDYAY2DogXvJYpYiLiR8jvxVwiJBZKH+RKVcSC+4c5KGIBH4i0jWOr7Bbx2OOBB31enJiNOSoCn472F4HnStW+BHxUmSg9BZyIjhDRXxHRFSK6TESf1I9PEdGLRHRVv50c/HKdxVq6iPlkGPPJUOP+QWAtXcJ8QvuZ5xMhsVD6oFsnQgDweAixQH9DHezwiVs5vZh0hIBv5kqYiPgR8FmPRaNBbayamWEOit2hDqOphTDyU1cB/FtmPgXgOwH8JBE9COAZABeY+SSAC/r9AwMzYzVd1CNwLR/6oPjg6xnNOgKA+WQI6ULlwPWCsYtuvcAVsT47EqaUzWCThQJoG5mbuRI2RnzxTvU5pAJQg41rjSjaVBqhPiA67VQPnJlXmfnr+tdZAFcALAF4EsB5/WnnATw1oDU6ku18BaVqXRPwiQMWgWeKjQhcCflB+dntptM4tWa0lrJ9RODZEsiGMvpmTi8mAIx+I9OO7JpowIt8uWqqlaxCZaykC/ZMTTKLqc8dRHQMwLsBfBXAHDOvAprIA5jt8JqniegiEV1MpVJ9Ltc5qGh7IRlCPOhDNOA9EBF4pVbHZq6EueSuhQIcvFYCdqFF4N0Fo9+xapu5EiYjAfi89m15PdgQcOMbmcVKzZbWuM1YnUbfjGah1BqZJGYqMX1eD2JBn/M3MYkoBuDzAD7FzIYvu8z8LDOfZeazMzMzVtboSNb0JlbzyTCISM/GGP9inlS2BOZd4Z5Pan88Uk5vDSMWSr9DHbQo1b7oG9AuKkenI6Yi8Gc+/xr++a//o63r2MzaEIHr/VBW00WE/V7Tfnoy7B+ZB24oY52I/NDE+3eY+Qv64XUiWmDmVSJaALAxqEU6keYIXLsNH4gIXAm1Em6xUPrDmIXix43NHcvvsZkr25ZC2MzpxQSWDQ53yBYr+JPlNdSZUatz3021AK2D4E65ZksEDmjV1GbsE8Uo+6EYyUIhAJ8BcIWZf7npoecBnNO/PgfgOfuX51xWt4vweqjxhzGfDB0IEWvOAQc0cYkGvGKhWICZDVoofW5i2hCltuP0YhJv38k3sje68RdX1lGq1lGpsW1tJ+xKj1Q9wVfTBSTCxqswFaPsSGjks8J7AfwYgMeI6FX934cAfBrAE0R0FcAT+v0Dw2q6iLl4sBFJLCRD2MiWUHVQo/tBoC5SykIBgLlkSCwUCxQrdVTrPCQLxX4BVz64kcZWL3xjFSrV/eaWPSX4dqVHqgh8dbtoOQJ3bCUmM/8tMxMzP8zMj+r/vszMW8z8ODOf1G/vDGPBTmEtU2jkfwNaBF6rc6MybFxZy5Tg9xImI7tRz1z8YHz6sJteZfSKRMiPcq1uKVUzX9Y6Rx6K2+uBA8YzUdL5Cv7magoffmgBAHBjy7od1Iwq4ulfwDUPPFuqWhLwiREOdZBKTItoOeDhxn3lhY/7ZJ71TBGz8dCemYHzyZCMVrOAqq7s1Typn3L6zWz/w4w7MRsPYSYe7JmJ8meX11CpMX7iu+9F0OfBTbsE3Kb8dmWhAOaqMBVOt1CEFphZr8JsisATB6OYp/XnBjQ/fCPrntFqG5kifuYPXrU9pc0svToRKtTjVtabUjbDAAQc0KLwXhbKl167jXumInjkcBJHpyO4YZOFkrIpv11ZKIC5FMLGa8J+lKrWPiH1iwi4BTJF7WPpQpOQ7Ubg4y3g601FPIr5RBCVGuNO3h320YXXN/CFV1bw0o3Run5GLZR40HpHwkGU0TdzejGBqxu5juK1lSvh769v4SMPL4CIcGw6alsEvpkrYcqG/HZloQDmingUEyNsKSsCboHGRl6TgE9E/Aj6PFgbYwuFmbHWVEavcFsvmGsb2kDe6xujHcyrIvBYsHcpffPzzWDXNPpOnF5MolZnvLGebfv4nyyvoVZn/MAjiwCAY4eiuLmVt+XTWj/T6JtpjsCtbmICIuCuQfnczRE4EWEhGRrrCFyNUlM54Aol6G7JRFECfm3kAq4icKMeuIUIXPfAp20u5FH02sj80jdu4/hMFA/MxwEAR6cjKFXrtqSd2pVdE/HvRuBWPXBABNw17Ebg4T3Hxz0XXLWN7RiBi4Cbotc4NYXyZa0MNlbd+vw2ltE3c2QygnjQ13Yjcz1TxNdu3MEPPLLYaJd7bDoKwJ5MFLsicJ/Xg5BfOz/9ROCjSCUUAbfAaroIImC25Zdn3KsxlUC3euAzsaBrRqvly1WsbBdABFxPjVbAMwYtlL6yUAaUA67weAinFhNtI/AvX1oFM/CRhxcbx45ORwD0nwu+OybOnk8W6v/Akgce1jsSSgTuDtbSRczEgvuimnm9oMUt2Rhmaef9A1oE45bRam+mtMjv2++ZxN18BVu50aU/ZosVxIK+nmXlSlysWCh2DjPuxOnFBF5fzaLW8nv/pW/cxgPzcZyYjTWOLSTDCHg9fUfguVIVxUrdts3ZSMC6gIuF4jJWM0UsTIT3HV9MhlCtMzZ3xjMnekMvnGi1UAC4ZrSairo/cGYewGhtFCONrADtAhkJeB0ZgQPaRmahUsNbm7vn8tbdPL7+9nZj81Lh9RCOTIVxc7O/CNyuIh6F2si0UkofD/kQ9ntx6+7whzyLgFtgdbuAhbYipon6uPrga+ndUWqtuGW02rWNHDwEvP/UnHZ/hDaKkUZWCqs9wQfVyKqZdhuZf/zaKgDgBx5e3Pf8Y9PRviNwVfFs18+mZpJaicA9HsL983FcWR1+b3QRcAu0K2YBxj8XfK1NDrhi2KPVNjJF5MvmI9JrGzkcnY7i6HQEkYB3IBH4Vq6EQrl3UYeRRlYKKz3BVf/tQeWAK07MxhDwefYI+AuvreKRw0nco3vezRyd1lIJma1bjYOIwP1eQrhNcGKEUwtxvL6W7etnsoIIuEmyxQqypeqeFEKF2/KhzbLeJgdcMczRasyMj/63v8Mv/em3TL/22kYOx2diICIcn4kNRMB/8H/8PX7xy1d6Pi9XMmahAFoEbrYSM5W1d5hxJ/xeD+6fizcyUW5s7uDSSnrP5mUz7zoUQaFSa6zPCnbnt8eCPiRC/o7DpXtxaiGB7Xxl6PtAIuAm2e2HvV/IpiIBBLye8Y3A00XMJdr/waiMnGFcvG7dLWAtU8TFm+YqKau1Om5s7TQ21U7Mxmwv5lnPFPH2nbyhKk+zEbjZNMJBF/E0c1rPRGFmvPDabQDAhx9eaPvco41UQuuecSpbgtezt6laP3zXyUP44EPzll9/akGzkYZto4iAm2R3kMP+TUyPhzCXDI5lNWZVH6XW0UIZYi748ooW6X1rLYtS1XjEf/NOHpUaNwT8+EwUt9NF7NjYE+XSLW1t3crLFYP2wJVPPGgLBdAEfDtfwe10ES+8toqzRyex2GajH2jKBe9rSEUJU9GALYMhAODj77kH/+mphyy//n69UOnKavuK1EEhAm6S1kk8rSwkxjMXPJUroc5ozMJsZX6I1ZjL+kf1So1xdd14BK2i7eMzmoAoIVephXaurVbnntFYplhFvEcOuCIeNN8TfJgR+IOLSQDAH72ygtfXsvuyT5pZnAjB56G+NjLtmEZvJ4mQH4cnwxKBOx1lEcx2sBK0dLrxE/B2gxyaUcI+DAG/tJLBpN5A6NKK8aG6KuPkeJOFoh23L2paXkk3mhstd1lbqVpDuVofbASue8yDKqNv5tRCHETA//zr6/AQutoRPq8HR6YifRXzbOZKODSETxZmOLWQEAF3OqvpIg7FAgj62u9Wq34ow96NHjSto9RaiQd9iAS8WEsPNhecmbG8ksb7T80hHvKZE/CNHOYSwUZp+tHpKHwesnUj89JKGt97/ywmI/6uazNaRq+Ih/woVuqomJj4tJkrIRHydfxdtZNIwId7D0WRLVbxnfdOYzbe/vdEobWVHZ8IHABOzcfx1ubOUNvKioCbZC1daLuBqZhPhlCu1nFnxx2tVY3SqQpTQUSYTwx+tNpquog7O2U8dDiJM4tJXDYh4Nc3cnuqAv1eD45OR2wT8I1sEeuZEs4sJXFmKdl14K/RXuAKK+X0m7nyUKPU07qN0in7pJljfaQSamX05aF4+2Y4tZBAndGxM+MgEAE3yWq62Bje0I5xzQVXo9Smuuz6zw0hF1xFtWeWknjocBJX1rKGolJmxvXUDk7MxPYcPzFrXyrhZV2wzywmcGYpiTfWsx2jMaO9wBXqeWZslEENM+7Ed9w7hVjQ16hy7cbR6QhypSq2LAQ6mUIV5Vp94OmRZhlFJoqRqfS/SUQbRLTcdGyKiF4koqv67eRgl+kc1jLFjhuYwPhWY7YbpdbKMLoxLq+k4fUQHlzQRLJcrRuKeNYyReRK1T0ROAAcn4nh5lbelDXRiUsraRABp5eSeGgpiWqd8a219msbTgQ+XJvh4//kHvzdM48ZmpCjMlGsDHdI5bTfMadF4PdMacVhw8xEMRKB/zaAD7QcewbABWY+CeCCfn/sKZRr2M5XuloojQh8zDYy1zPtq0+bGcZotUsraZycjSHk9+KMKuHuYlUoVJR9fHZ/BF6tsy2T0i+tpPGuQ1HEgj48tKTZCcsd5kUa7QWuUM/LmInAbezWZwSPhwyXoquuhDcs9ERJZe2ZhWk3oyipNzKV/m8AtFYlPAngvP71eQBP2bssZ6LsgW4R+KFYED4PjV0ueLcyesWgR6upDUzltR6b1sTSyEamSiFsZ6EA9jS1urySxhl9bYcnw0iG/R0zUVRRjtEZjImGhWIsAi9WasgWq0O1UMxweDICr4csRuCDHRPXDyoTZVhJDFY98DlmXgUA/Xa20xOJ6GkiukhEF1OplMW3cwar22oST2cP3OshzCXGbzLPerpzGb1i0K0E1jMlbObKeGhJi7w9HsLpxYQhAb+WyiEe8u37oz+uC3q/vcG3ciXcThcbkTcR4cxS57XlLFooOYMCrrxlp6XaKQI+D5YmwnjLwiefzezw8tvNcmo+jkyxOrS//4FvYjLzs8x8lpnPzszMDPrtBkqvIh7FuE3myRYr2GkzSq2VQY9WU2L40OFk49iZpSSurGZQ7eFhX9MzUFp7XUSDPiwmQ31H4M2bq81r61QtanQepsLsJqaTRU5xdDpiOQL3e43bNcNk2BuZVgV8nYgWAEC/3bBvSc5lrUsflGbGTcB75YArBl1Ov7yShod2/0gA4KGlJErVes+2sNc29megKI7bkImiOvGdXtq7tk7VotliBZGA1/BE9d2hDsYi8EFPo7eDY9NRvLW5Y9pu2MyWMB0Ndt1QHxWqpP71DpvXdmNVwJ8HcE7/+hyA5+xZjrNZTRcw2aEfdjMLifEq5lHFOb088JlYEDTA0WrLK2kcn4k1pqcAuxGv6kHSjnS+gs1caV8GiuL4TAzXU7m+/r8u3Urj2HRkj6et7JR2Nkq2WDUcfQOa5RD0eZA12Ldlt4zeWal2zRydjiBbrJqeJZnK2TMLcxDEQ34cmQrjm06JwIno9wD8A4D7iegWEX0CwKcBPEFEVwE8od8fe7Q+4J39b8V8MoRCpYZMwb4mSaNkzWAEPujRapdW0g1RVLzrUBSRgLfjVHRgt1S+k4CfmI0hX6715VteWknvsU8ALa2sU7VotmS8kZVC6wlu0EKxeeDBILA64HjTwQIOAKfmh1dSbyQL5ePMvMDMfmY+zMyfYeYtZn6cmU/qt+b6erqU1XT3HHCF2uRczYxHJkq3FrqtaIMd7C+n38gUsZEt4XSLSHoNbGRe39AEopuAA9YzUe7ulLGyXdh3cSGijtWiZlrJKhIhn+GWsqlsCfGgr+enxVFy7JC1AcfDmPPZD6cWErixuWNoqEe/SCWmCTpN4mllfsyqMdfSRSTDva0jQM8FH0AErvKpW0US0GyUb97O7Buqq7iWyiHg8+Dw5P7pMED/Aq7W1hqBA+hYLZoxOA+zGa2hlUEBd2Czp1YOT0ZAZC4Cr9edWUbfzKmF+NBK6kXADVKs1LC1U247C7OVhTGbzGMkB1wxnxyMhXLpVkarclxM7HvsoSVtqO6bHTYyr23kcO+haMfe0dPRACYifsvzMRsZKIv7Bfz0YqJttWi2WDGcA64wZaE4PEoFgJDfi8Vk2FQEvl2ooFZnR1tDw8xEEQE3yIZuCxiJwGfiQXhofCLw9UyxYx/wVuYTIWzn7R+tdmkljXsPRRvTw5s502WzENDHqHWwTwDN6jjRx3i15ZU07pmKIBnZL8jqE0NrtajRifTNmInAhzGN3g6OHTLXldDuWZiD4MhkBNGAdyiZKCLgBllN9y7iUfi9HszEg43CH7ezli5ivkP/81YGlQt++fb+TULF8ZkYwn5vWwEvVmp4526+Ywph8/ewOl5teSWDM0v7PxkAnatFzUzjUZjpCT6MafR2cHQ6amoyzzCHVFhFldQPIxNFBNwgRnPAFfPJ8FgMdug1Sq2VQVRjbuZKWG2qcmzF6yE8uJhoW7b+ZmoHzJ03MBUnZmPY2injrsnueOl8BW/fyXe8uLSrFq3U6ihW6qY3MY1Opi9X60gXKo4WOcWx6Qju5itIG0wldEMEDgyvpF4E3CCrPfpht7IwJuX0m7ly11FqrSiht/Pi1a7KsZUz+lDd1kZaytc2IuCA+ZL6bpurjbW1VIuaLaNXxEM+5Mu1jpu1iq0dPUqNO9sDB3YHHN+8YywKd0MEDmgCni1WcXvAGiACbpDV7QLiIZ/h4otxqcZsfPIwGIEPYrSaSsN7sM0GpuLMUhL5cg1vtnwcv76RA5GWL94Nq5koy102MBWt1aJmp/Eo1PN79UPZzDo/B1xxzOSE+lS2hIDPg4TJi9+wObWgDznuUp9gByLgBjGaA65YSIaQK1VNzzF0Guoi1KuIRzGI0WqqTWu3rA3VH6XVRrmWyuHIZKRnCuTSRBghv8e0gF9aSWNpIozJLj2wW6tFMyZbySqMtpR1Qxm94p4pPRfcoA+e0nuct/a0cRr3zw8nE0UE3CBrGWNVmIpBd+YbFmaKeIDBjFbTNgk7R7iA1iY26PPsE/DWMWqd8HgI9x6KmU4lXG5THdqKqhZVa2tE4CZK6Zuf38sHb7RbdUEEHg54MZ8ImYrAnZ7fDmi9a45ORwaeiSICbpDVdBGLJiLwxYlw43VuZi1T7DlKrRU7R6vdaVQ5drZPAK2M/9TC3s3CWp3x5uaOIQEHzDe1yhQruLGV39MdsR2qWnRZ/zhtdpyawmhHQrf4xAozXQk3c2VXXJgA4IEhDHcQATdAuapnYpgQ8MZmnssFfD3de5RaK3b6/0Y8ZsVDS8k9G5nv3MmjXK33TCFUnJiJYWW7YLgEWuV2tysuaqW5WtTsODWF0bFqm9kyogEvwgHnltE3c2w6aioCn3HB5iygbWS+tbWDfHlwPZFEwA2wkS2CuXcf8GaUZzwOEficwRxwxWwiaNtoNRVRt/ZAaceZpQRypSpu3tHEoNMYtU6cmI2BGXhz01gUri4uvSwU9ZxCpYbrqZzpcWqKhoCXukfgbiijb+bYoSg2cyXkenRarNUZd3aGO+ezH04tJMAMvNGmnbBdiIAbYK2RQmjcAw/4VGc+dxfzrBmYhdnKfCJk22i15ZU0jk5HDDXvb63INJpCqDCbiXJpJY3FZAjTBgSleSOz3yyU3hG4O6owFcemVVOr7jbKnR0tpdUtF6dTQ9jIFAE3gNFJPK0sJN2fC25klFordtpHy7fThuwTALhvLo6Ad3cj8/pGDjPxoOHJLccOReAhGK7IXL6dNvTJANitFl2+nUa2VEXQ50HAZ+7Pz7CFMuRhxv2icsF7DThuFPG45OJ0eDKMWNCH10XAR8uaySIehdtzwRuj1EwKuMoF38j297Nv58t4506hZwaKwu/14IGFeEPAr6VyOD7TPf+7maDPi6PTUUOZKLlSFW9t7hiyT4C91aJaGb35cWAhvxcBr8dQGqGbIvDGhPoeEXhjc9YlEbjHQ/pG5uAyUUTADbCaLiIa8JpO+3J7BL5uooFXM7sReH+54Mv6JqFRkQQ0q2J5JY16nRtzMM1wfCZqyEK5vJIGs7m1qU3WdKFiuRClV0OrSq2Ou3l3lNErokFt2HQvC8VtETgAPLAQx5W1wZXUi4AbYC1TwHwyZLp4YD4ZQrpQGegu9CAxOguzlZm4Nlqt31RCVaZuJMtD8dBSEpliFV9/+y6yxarhDBTF8dkYbmzmew5JXm4zA7MXpxcTyJdreO1W2vQGpiIe8nWtxLzj8Gn0nTg2HemZieKmAiWFKqlfGVBjOxFwA2hVmMY3MBULLh/s0LCOTAq4Xx+t1u9szEsraRye7F7l2Iryy7/4ygoA4MRs3NR7npiJoVyr45273f/gllfSmEsEMRs3fm5UvvituwVLFgrQuyf4bpTqHg8c0HxwIxF42O9t21LYqez2Bh+MjSICboDVbfOZGAAwn9BE360+uNkOjM3M21DMY6TKsZX75mPwewl/fGkVgPEMFIXRTJR28zl7fm+9WhQwn0Ko6GWhuDFKBbQIfD1T6vpp1emzMNtx/1wcRIPLRBEB70G1VsdG1lwfFIXbI/D1jPFRaq3M9VlOny5UcHOrc5vWTgR9Xtw/H8d2voJY0Gc6h/24AQHfKVVxPZUzvTZVLQrA1ET6ZmLBXgLunkZWzahMlLfvdLZRUi7LrgE0f//oVASvrzlQwInoA0T0LSK6RkTP2LUoJ5HKlVBni1Foox+KO3PBtUEO5n9uoP/Rape7zJnshbJRjs9ETe9bJEJ+zCWCXQVc6/NsrDq0FRW1D8pCcVsZveKYgVTCzayzZ2F2QusN7jALhYi8AP47gA8CeBDAx4noQbsW5hSs5oADWtrXZMTv6gjcaB/wVvodrWamyrEVJfpGKzBbOT7TvamVKhTq1QOlHbsCPhgLxY0+MQAcPdS7mCflsvRIxQPzCdwYUEl9P//L7wFwjZnfBAAi+n0ATwL4ph0La+a/XriK579x2+5va4gdvbxX+dlmmU+G8dyrt/G1t+7YuayhcGNrBz/47iVLr1WZKx/61f/XcZhwNzayJSxNhDFlYgNToUTSrP+tODEbw+9+9W088ct/3fbxlO7Fms3OAXYvLlYFPBHyIVuqdlzbeqboikEOrSRCfkxHA/i1v76Oz718q+1z7uy4Y0xcK6cW4mAGvrWWxbvvmbT1e/cj4EsA3mm6fwvAd7Q+iYieBvA0ANxzzz2W3mgmHsTJOWt/jHbwWDRg+f3/1T+7F392ec3mFQ2H++bi+NjZI5Ze+8/um8EPvXsJxaq1CPzkXAzfe/+spdeeXkzgX3/PcTz5qLWLz8fOHtHLttvn7p6ci+F9J2csfe/75+P4N4+dwPefnrf0+g89vIA3N3e6ru27La5t1Hzq/SfxD29udXz8gYUEPvLwwhBXZA9nlpL4vgfn4PPYv+VIVhPMieiHAXw/M/+Efv/HALyHmX+602vOnj3LFy9etPR+giAIBxUiepmZz7Ye7+eScAtAc3h2GMBofA5BEIQDSD8C/hKAk0T0LiIKAPgRAM/bsyxBEAShF5Y9cGauEtFPAfgzAF4Av8nMl21bmSAIgtCVvnKNmPnLAL5s01oEQRAEE0glpiAIgksRARcEQXApIuCCIAguRQRcEATBpVgu5LH0ZkQpADctvvwQgE0bl2MnsjZryNqsIWuzhpvXdpSZ95XYDlXA+4GILrarRHICsjZryNqsIWuzxjiuTSwUQRAElyICLgiC4FLcJODPjnoBXZC1WUPWZg1ZmzXGbm2u8cAFQRCEvbgpAhcEQRCaEAEXBEFwKa4QcCcPTyaiG0R0iYheJaKRTqsgot8kog0iWm46NkVELxLRVf3W3plO/a3t54loRT93rxLRh0a0tiNE9FdEdIWILhPRJ/XjIz93XdY28nNHRCEi+hoRfUNf2y/ox51w3jqtbeTnTV+Hl4heIaIX9PuWzpnjPXB9ePIbAJ6ANkTiJQAfZ2bbZ29agYhuADjLzCMvECCi9wHIAfjfzHxGP/ZLAO4w86f1i98kM/87h6zt5wHkmPk/D3s9LWtbALDAzF8nojiAlwE8BeBfYMTnrsvaPoYRnzsiIgBRZs4RkR/A3wL4JIAfwujPW6e1fQDO+J37GQBnASSY+SNW/07dEIE3hiczcxmAGp4stMDMfwOgdXrykwDO61+fh/bHP3Q6rM0RMPMqM39d/zoL4Aq0ma8jP3dd1jZyWCOn3/Xr/xjOOG+d1jZyiOgwgA8D+I2mw5bOmRsEvN3wZEf8AuswgD8nopf1Ac5OY46ZVwFNDABYmxQ8OH6KiF7TLZaR2DvNENExAO8G8FU47Ny1rA1wwLnTrYBXAWwAeJGZHXPeOqwNGP15+xUAPwug3nTM0jlzg4BTm2OOuJLqvJeZvw3ABwH8pG4VCMb4NQDHATwKYBXAfxnlYogoBuDzAD7FzJlRrqWVNmtzxLlj5hozPwptJu57iOjMKNbRjg5rG+l5I6KPANhg5pft+H5uEHBHD09m5tv67QaAL0KzfJzEuu6jKj91Y8TracDM6/ofWR3Ar2OE5073ST8P4HeY+Qv6YUecu3Zrc9K509ezDeAr0DxmR5w3RfPaHHDe3gvgo/re2e8DeIyIPguL58wNAu7Y4clEFNU3lkBEUQDfB2C5+6uGzvMAzulfnwPw3AjXsgf1C6vzgxjRudM3vD4D4Aoz/3LTQyM/d53W5oRzR0QzRDShfx0G8H4Ar8MZ563t2kZ93pj555j5MDMfg6Zlf8nMPwqr54yZHf8PwIegZaJcB/DvR72epnXdC+Ab+r/Lo14bgN+D9rGwAu2TyycATAO4AOCqfjvloLX9HwCXALym/wIvjGht3wXNlnsNwKv6vw854dx1WdvIzx2AhwG8oq9hGcB/1I874bx1WtvIz1vTGr8HwAv9nDPHpxEKgiAI7XGDhSIIgiC0QQRcEATBpYiAC4IguBQRcEEQBJciAi4IguBSRMAFQRBcigi4IAiCS/n/SMTZbG+XIasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.002104804670950866 True increasing\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(tc_induced_flag, end, sub_id=6)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d1684633-fcc2-4fe4-832a-ca2a8176af4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.43302571403565326 False no trend\n",
      "0.0 14.617186746560037\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(tutt_induced_flag, end, sub_id=6)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8d28a936-da4e-40a9-aeac-50bcd2a018bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.1119858434398513 False no trend\n",
      "0.7093293624097816 12.222807757451513\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(surge_induced_flag, end, sub_id=6)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d15b7c81-d3da-492e-bbc1-4e9f59611684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.22147170645229464 False no trend\n",
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(midtro_flag, end, sub_id=6)\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "47075dd8-e16e-4b6f-8836-b54b538e24c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0.042763425833193924 False no trend\n",
      "1.7811888036998522 10.566814003296065\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(MCS_induced_flag, end, sub_id=6)\n",
    "precip_year = precip_year[2003-1979:]\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "88d1ce10-ead0-4e56-8773-56df7ad9a739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0.403869739467297 False no trend\n",
      "0.9733904435072926 13.341326158455242\n"
     ]
    }
   ],
   "source": [
    "precip_year = find_categories_precip(front_flag, end, sub_id=6)\n",
    "precip_year = precip_year[2003-1979:]\n",
    "plt.plot(precip_year)\n",
    "plt.show()\n",
    "\n",
    "n = len(precip_year)\n",
    "precip_year = np.array(precip_year)\n",
    "s = mk_score(precip_year, n)\n",
    "var_s = variance_s(precip_year, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05/6)\n",
    "slope, intercept = sens_slope(precip_year)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a23af2c-3644-411a-b0ad-bd9082fddc9f",
   "metadata": {},
   "source": [
    "# Sub7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c2191f67-4c2d-431d-a662-60a0365dc01a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "0.13577683650623618 False no trend\n",
      "-1.35819106853177 121.87959361954304\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.068\n",
      "Model:                            OLS   Adj. R-squared:                  0.044\n",
      "Method:                 Least Squares   F-statistic:                     2.777\n",
      "Date:                Sat, 18 Jun 2022   Prob (F-statistic):              0.104\n",
      "Time:                        10:33:02   Log-Likelihood:                -218.38\n",
      "No. Observations:                  40   AIC:                             440.8\n",
      "Df Residuals:                      38   BIC:                             444.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        121.8830     18.109      6.731      0.000      85.224     158.542\n",
      "x1            -1.3317      0.799     -1.667      0.104      -2.949       0.286\n",
      "==============================================================================\n",
      "Omnibus:                        0.186   Durbin-Watson:                   1.569\n",
      "Prob(Omnibus):                  0.911   Jarque-Bera (JB):                0.117\n",
      "Skew:                           0.117   Prob(JB):                        0.943\n",
      "Kurtosis:                       2.875   Cond. No.                         44.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "sub_id = 7\n",
    "n_epe = []\n",
    "precip = monsoon_precip.sel(sub_id=sub_id)\n",
    "precip_data = precip.data\n",
    "q1 = np.quantile(precip_data[precip_data > 1], 0.05)\n",
    "q2 = np.quantile(precip_data[precip_data > 1], 0.95)\n",
    "\n",
    "for year in range(1979, 2019):\n",
    "    year_precip = precip.sel(time=precip.time.dt.year==year)\n",
    "    ext_time = year_precip.where(year_precip > q2, drop=True).time.data\n",
    "    ext_precip = year_precip.sel(time=ext_time)\n",
    "    # print(len(ext_time), q2)\n",
    "    n_epe.append(np.sum(ext_precip.data))\n",
    "    \n",
    "n = len(n_epe)\n",
    "n_epe = np.array(n_epe)\n",
    "s = mk_score(n_epe, n)\n",
    "var_s = variance_s(n_epe, n)\n",
    "Tau = s/(.5*n*(n-1))\n",
    "\n",
    "z = z_score(s, var_s)\n",
    "p, h, trend = p_value(z, alpha=0.05)\n",
    "slope, intercept = sens_slope(n_epe)\n",
    "print(p, h, trend)\n",
    "print(slope, intercept)\n",
    "\n",
    "X = np.arange(n).reshape(-1, 1)\n",
    "y = n_epe\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1190e450-2ec1-4cde-83b0-61f31eed58de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc70639b9d0>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_epe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c2bbe5b-9d5b-4b63-8d3f-5343c8992fd1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "NERSC Python",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}