{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac92d7c1-e5af-4f1a-b3df-1fd361073edc",
   "metadata": {},
   "source": [
    "# Variations of the IP, the PG and the RMM with the MJO phase\n",
    "Here we plot variations of various parameters with the MJO. In particular, we plot the ionospheric potential (IP), the fair-weather potential gradient (PG) and the two components of the RMM index. We also plot the sunspot number, outgoing long-wave radiation (OLR) and equatorial convective avaliable potential energy (CAPE)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2dfdec-3074-422d-b615-4f0c770c671f",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26804089-74df-41d6-8c8e-b60af3173175",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data processing\n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "# plotting the data\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.path import Path\n",
    "from matplotlib.patches import PathPatch\n",
    "\n",
    "# dates\n",
    "import datetime as dt\n",
    "\n",
    "# testing correlation significance\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e68cb6-0540-4cca-b1b9-32e52321b9c4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d5a5c63-feb9-44e4-8398-3902c4e4d18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53dda0e6-8893-4ae2-82f3-c57c8550d2d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "map_contrib = np.load('%s/DAILY-IP-MAP-V4.3.npy'%path)\n",
    "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
    "# contains IP values (not normalised) depending on (d, lat, lon)\n",
    "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
    "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
    "# lat (axis 1) describes the latitude (an integer in [0, 179])\n",
    "# lon (axis 2) describes the longitude (an integer in [0, 359])\n",
    "\n",
    "map_contrib /= np.mean(np.sum(map_contrib, axis=(1, 2)))\n",
    "map_contrib *= 240e3\n",
    "# normalisation of contributions to the IP to the global mean of 240 kV\n",
    "\n",
    "ip = np.sum(map_contrib, axis=(1, 2))  # total IP values for different days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d663a94f-942b-4583-9179-08699c48bb4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_olr_source = np.load('%s/OLR_41years_NOAA.npy'%path)\n",
    "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
    "# contains OLR values depending on (d, lat, lon)\n",
    "# d (axis 0) is the number of a day starting with 0 and ending with 14976\n",
    "# every third day is taken, 0 corresponds to 1 Jan 1980 and 14976 corresponds to 31 Dec 2020\n",
    "# lat (axis 1) describes the latitude (an integer in [0, 180])\n",
    "# lon (axis 2) describes the longitude (an integer in [0, 360])\n",
    "# the data downloaded from https://psl.noaa.gov/mddb2/showDataset.html?datasetID=37\n",
    "\n",
    "olr_days_ind = np.flatnonzero(np.amin(map_olr_source, axis=(1, 2)) >= 0)\n",
    "# indices of the days for which the OLR data are available\n",
    "map_olr = np.take(map_olr_source, olr_days_ind, axis=0)\n",
    "\n",
    "olr = np.sum(\n",
    "    np.mean(\n",
    "        map_olr[:, 75:105, :], axis=2\n",
    "    ) * (\n",
    "        np.cos(np.arange(75, 105) * np.pi / 180) -\n",
    "        np.cos(np.arange(76, 106) * np.pi / 180)\n",
    "    ), axis=1\n",
    ") / np.sum(\n",
    "    np.cos(np.arange(75, 105) * np.pi / 180) -\n",
    "    np.cos(np.arange(76, 106) * np.pi / 180)\n",
    ")\n",
    "# global means of OLR values for 15°S–15°N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9ce8214-a1ea-4aae-8093-f96440e18815",
   "metadata": {},
   "outputs": [],
   "source": [
    "cape_map = np.load('%s/DAILY-CAPE-MAP-V4.3.npy'%path)\n",
    "# original data with the shape (number of days, number of latitudes, number of longitudes)\n",
    "# contains mean CAPE values in J/kg depending on (d, lat, lon)\n",
    "# d (axis 0) is the number of a day starting with 0 and ending with 4991\n",
    "# every third day is taken, 0 corresponds to 1 Jan 1980 and 4991 corresponds to 29 Dec 2020\n",
    "# lat (axis 1) describes the latitude of the cell (an integer in [0, 179])\n",
    "# lon (axis 2) describes the longitude of the cell (an integer in [0, 359])\n",
    "\n",
    "eq_cape = np.zeros((2, 4992), dtype=float)\n",
    "eq_cape[0] = np.sum(\n",
    "    np.mean(\n",
    "        cape_map[:, 75:105, :], axis=2\n",
    "    ) * (\n",
    "        np.cos(np.arange(75, 105) * np.pi / 180) -\n",
    "        np.cos(np.arange(76, 106) * np.pi / 180)\n",
    "    ), axis=1\n",
    ") / np.sum(\n",
    "    np.cos(np.arange(75, 105) * np.pi / 180) -\n",
    "    np.cos(np.arange(76, 106) * np.pi / 180)\n",
    ")\n",
    "# equatorial CAPE for different days (15°S–15°N)\n",
    "eq_cape[1] = np.sum(\n",
    "    np.mean(\n",
    "        cape_map[:, 60:120, :], axis=2\n",
    "    ) * (\n",
    "        np.cos(np.arange(60, 120) * np.pi / 180) -\n",
    "        np.cos(np.arange(61, 121) * np.pi / 180)\n",
    "    ), axis=1\n",
    ") / np.sum(\n",
    "    np.cos(np.arange(60, 120) * np.pi / 180) -\n",
    "    np.cos(np.arange(61, 121) * np.pi / 180)\n",
    ")\n",
    "# equatorial CAPE for different days (30°S–30°N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fd8402a-3763-47d5-b510-413eb03ee3aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d_start = dt.date(1980, 1, 1)  # first date\n",
    "d_end = dt.date(2021, 1, 1)  # last date\n",
    "\n",
    "pg = np.full(((d_end - d_start).days, 24), np.nan)\n",
    "\n",
    "# hourly data obtained from 10-s data\n",
    "with open('%s/vostok_hourly_from_10_s_without_calibration_and_empty.tsv'%path) as file:\n",
    "    tsv_file = csv.reader(file, delimiter='\\t')\n",
    "    next(tsv_file)  # skip the header\n",
    "    for line in tsv_file:\n",
    "        timestamp = dt.datetime.strptime(line[0], '%Y-%m-%d %H:%M:%S')\n",
    "        pg_value = float(line[1]) / 3  # hourly PG value in V/m (3 is the form factor)\n",
    "        if 0 < pg_value <= 300:\n",
    "            day = (timestamp.date() - d_start).days\n",
    "            hour = timestamp.hour\n",
    "            pg[day, hour] = pg_value\n",
    "            # negative PG values and PG values greater than 300 V/m are omitted\n",
    "\n",
    "# hourly data obtained from 5-min data\n",
    "with open('%s/vostok_hourly_from_5_min_without_calibration_and_empty.tsv'%path) as file:\n",
    "    tsv_file = csv.reader(file, delimiter='\\t')\n",
    "    next(tsv_file)  # skip the header\n",
    "    for line in tsv_file:\n",
    "        timestamp = dt.datetime.strptime(line[0], '%Y-%m-%d %H:%M:%S')\n",
    "        pg_value = float(line[1]) / 3  # hourly PG value in V/m (3 is the form factor)\n",
    "        day = (timestamp.date() - d_start).days\n",
    "        hour = timestamp.hour\n",
    "        if np.isnan(pg[day, hour]) and 0 < pg_value <= 300:\n",
    "            pg[day, hour] = pg_value\n",
    "            # negative PG values and PG values greater than 300 V/m are omitted\n",
    "            # 5-min data are used only if there are no 10-s data available\n",
    "\n",
    "diff = (np.amax(pg, axis=1) - np.amin(pg, axis=1)) / \\\n",
    "       np.maximum(np.mean(pg, axis=1), 1)\n",
    "# the elementwise maximum in the denominator is taken in order to avoid division by zero\n",
    "\n",
    "fw_days_ind = np.flatnonzero(diff <= 1.5)\n",
    "# indices of fair-weather days according to the criteria used\n",
    "pg_fw = np.take(np.mean(pg, axis=1), fw_days_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dabe9bd5-2e61-48ff-b63f-cd2d0010326c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sn = np.full(((d_end - d_start).days), np.nan)  # sunspot number\n",
    "\n",
    "with open('%s/sunspot_number_data.csv'%path) as file:\n",
    "    # the file downloaded from https://www.sidc.be/silso/datafiles\n",
    "    csv_file = csv.reader(file, delimiter=';')\n",
    "    for line in csv_file:\n",
    "        timestamp = dt.date(*list(map(int, line[:3])))\n",
    "        day = (timestamp - d_start).days\n",
    "        if day >= 0 and line[4] != -1 and timestamp.year < 2021:\n",
    "            sn[day] = int(line[4])\n",
    "            # only the available data for 1980–2020 are used\n",
    "\n",
    "sn_days_ind = np.flatnonzero(np.logical_not(np.isnan(sn)))\n",
    "# indices of the days for which the sunspot number is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c45b721f-6f09-4c8e-85ed-528bc15e02d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert sn.shape == sn_days_ind.shape\n",
    "# all days are available for the sunspot number in 1980–2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c61cb9c0-5ead-4abf-a725-726b35ea055f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sn_fw = np.take(sn, fw_days_ind)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa57840a-f79c-4c0a-9727-ee3233bd0594",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Averaging over MJO phases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9d4adde-6492-40ee-b11b-5c47cc0e9dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmm_source = np.genfromtxt('%s/rmm.txt'%path)\n",
    "\n",
    "\n",
    "def phase_separation(rmm, val):\n",
    "    \"\"\"\n",
    "    Compute average values of a variable for each MJO phase.\n",
    "    :param rmm: an array of RMM values, the shape: (number of days, 2)\n",
    "    :param val: an array of values, the shape: (number of days)\n",
    "    :return: average values for MJO phases, the shape: (8)\n",
    "    :return: average square values for MJO phases, the shape: (8)\n",
    "    :return: number of days for each phase, the shape: (8)\n",
    "    \"\"\"\n",
    "\n",
    "    assert rmm.shape[0] == len(val)\n",
    "\n",
    "    angle_rmm = np.arctan2(rmm[:, 1], rmm[:, 0])  # phase angles of RMM values\n",
    "    phase_rmm = np.floor((angle_rmm / np.pi + 1) * 4).astype(int)  # phase numbers\n",
    "\n",
    "    phase_avg_val = np.zeros((8), dtype=float)\n",
    "    phase_avg_sqr = np.zeros((8), dtype=float)\n",
    "    counter = np.zeros((8), dtype=int)\n",
    "\n",
    "    for i in range(len(phase_rmm)):  # summing over each phase\n",
    "        counter[phase_rmm[i]] += 1\n",
    "        phase_avg_val[phase_rmm[i]] += val[i]\n",
    "        phase_avg_sqr[phase_rmm[i]] += val[i]**2\n",
    "\n",
    "    phase_avg_val /= counter\n",
    "    phase_avg_sqr /= counter\n",
    "    # averaging over each phase\n",
    "\n",
    "    return phase_avg_val, phase_avg_sqr, counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f99af7a-43bf-4bd9-8535-1374a4534dc7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# phase separation for the RMM1 and RMM2\n",
    "rmm = rmm_source[:, [3, 4]]  # RMM1 and RMM2\n",
    "\n",
    "phase_avg_rmm1 = phase_separation(rmm, rmm[:, 0])[0]\n",
    "phase_avg_rmm2 = phase_separation(rmm, rmm[:, 1])[0]\n",
    "\n",
    "\n",
    "# phase separation for the IP for the whole period of 1980—2020\n",
    "rmm = rmm_source[::3, [3, 4]]  # RMM1 and RMM2\n",
    "# the array should look like the IP data (with every third day taken)\n",
    "\n",
    "phase_avg_ip, phase_avg_sqr_ip, counter_ip = phase_separation(rmm, ip)\n",
    "\n",
    "\n",
    "# phase separation for the IP for 2006–2020\n",
    "# (when the PG data are available)\n",
    "d_start = dt.date(1980, 1, 1)  # first date (for the IP)\n",
    "d_pg = dt.date(2006, 1, 1)  # first date for the PG\n",
    "ip_pg = ip[((d_pg-d_start).days + 2)//3:]\n",
    "# the first index corresponds to the first day in 2006 for which\n",
    "# the IP data are available\n",
    "rmm = rmm_source[((d_pg-d_start).days + 2)//3*3::3, [3, 4]]  # RMM1 and RMM2\n",
    "# the array should look like the IP data\n",
    "# (with every third day taken, starting from 2006)\n",
    "\n",
    "phase_avg_ip_pg, phase_avg_sqr_ip_pg, counter_ip_pg = phase_separation(rmm, ip_pg)\n",
    "\n",
    "\n",
    "# phase separation for the PG\n",
    "rmm = rmm_source[np.ix_(fw_days_ind, [3, 4])]  # RMM1 and RMM2\n",
    "# the array should look like the PG data\n",
    "# (with only fair-weather days retained)\n",
    "\n",
    "phase_avg_pg, phase_avg_sqr_pg, counter_pg = phase_separation(rmm, pg_fw)\n",
    "\n",
    "\n",
    "# phase separation for the sunspot number for 2006–2020\n",
    "# (when the PG data are available)\n",
    "rmm = rmm_source[np.ix_(fw_days_ind, [3, 4])]  # RMM1 and RMM2\n",
    "# the array should look like the PG data\n",
    "# (with only fair-weather days retained)\n",
    "\n",
    "phase_avg_sn, phase_avg_sqr_sn, counter_sn = phase_separation(rmm, sn_fw)\n",
    "\n",
    "\n",
    "# phase separation for OLR for 1980—2020\n",
    "rmm = rmm_source[np.ix_(olr_days_ind, [3, 4])]  # RMM1 and RMM2\n",
    "# the array should look like the OLR data\n",
    "\n",
    "phase_avg_olr, phase_avg_sqr_olr, counter_olr = phase_separation(rmm, olr)\n",
    "\n",
    "\n",
    "# phase separation for the equatorial CAPE for 1980—2020\n",
    "rmm = rmm_source[::3, [3, 4]]  # RMM1 and RMM2\n",
    "# the array should look like the IP data (with every third day taken)\n",
    "\n",
    "phase_avg_cape = np.zeros((2, 8), dtype=float)\n",
    "phase_avg_sqr_cape = np.zeros((2, 8), dtype=float)\n",
    "counter_cape = np.zeros((2, 8), dtype=int)\n",
    "\n",
    "for j in range(2):\n",
    "    phase_avg_cape[j], phase_avg_sqr_cape[j], counter_cape[j] = \\\n",
    "        phase_separation(rmm, eq_cape[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c323b584-c1f9-4299-9fba-14009f743ad6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Averaging of partial data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "befd58ab-d5e8-4ebd-88e5-6e909da21585",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# phase separation for the IP for 1981–1990, 1991–2000, 2001–2010, 2011–2020\n",
    "phase_avg_ip_part = np.zeros((4, 8), dtype=float)\n",
    "phase_avg_sqr_ip_part = np.zeros((4, 8), dtype=float)\n",
    "counter_ip_part = np.zeros((4, 8), dtype=int)\n",
    "for i in range(4):\n",
    "    d_start = dt.date(1980, 1, 1)  # first date (for the whole IP dataset)\n",
    "    d_first = dt.date(1981 + 10*i, 1, 1)  # first date\n",
    "    d_last = dt.date(1991 + 10*i, 1, 1)  # last date\n",
    "    idx_first = ((d_first-d_start).days + 2)//3\n",
    "    idx_last = ((d_last-d_start).days + 2)//3\n",
    "    ip_part = ip[idx_first:idx_last]\n",
    "    rmm = rmm_source[idx_first*3:idx_last*3:3, [3, 4]]\n",
    "    # the array should look like the IP data\n",
    "\n",
    "    phase_avg_ip_part[i], phase_avg_sqr_ip_part[i], counter_ip_part[i] = \\\n",
    "        phase_separation(rmm, ip_part)\n",
    "\n",
    "\n",
    "# phase separation for the PG for 2006–2015 and 2011–2020\n",
    "phase_avg_pg_part = np.zeros((2, 8), dtype=float)\n",
    "phase_avg_sqr_pg_part = np.zeros((2, 8), dtype=float)\n",
    "counter_pg_part = np.zeros((2, 8), dtype=int)\n",
    "for i in range(2):\n",
    "    d_start = dt.date(1980, 1, 1)  # first date (for the whole IP dataset)\n",
    "    if i == 0:\n",
    "        d_first = dt.date(2006, 1, 1)  # first date\n",
    "        d_last = dt.date(2016, 1, 1)  # last date\n",
    "    else:\n",
    "        d_first = dt.date(2011, 1, 1)  # first date\n",
    "        d_last = dt.date(2021, 1, 1)  # last date\n",
    "    idx_first = (d_first-d_start).days\n",
    "    idx_last = (d_last-d_start).days\n",
    "    fw_idx_first = np.sum(np.where(fw_days_ind < idx_first, 1, 0))\n",
    "    fw_idx_last = np.sum(np.where(fw_days_ind < idx_last, 1, 0))\n",
    "    pg_fw_part = pg_fw[fw_idx_first:fw_idx_last]\n",
    "    rmm = rmm_source[np.ix_(fw_days_ind[fw_idx_first:fw_idx_last], [3, 4])]\n",
    "    # the array should look like the PG data\n",
    "\n",
    "    phase_avg_pg_part[i], phase_avg_sqr_pg_part[i], counter_pg_part[i] = \\\n",
    "        phase_separation(rmm, pg_fw_part)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de93dfa2-280f-4b7e-98b8-7da504bf7cec",
   "metadata": {},
   "source": [
    "## Estimating standard errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e2b96da-d6db-429d-9f8c-8bc844718c9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def std_error(avg_val, avg_sqr, counter):\n",
    "    \"\"\"\n",
    "    Estimate the standard error from the average value\n",
    "    and the average value of the square.\n",
    "    :param avg_val: the average value\n",
    "    :param avg_sqr: the average square value\n",
    "    :param counter: the size of the sample\n",
    "    :return: the standard error\n",
    "    \"\"\"\n",
    "\n",
    "    return np.sqrt((avg_sqr - avg_val**2) / (counter - 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9634e8-fe13-4295-b76b-e1f00be37561",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Estimating correlation coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cbfb26db-80ae-4bd7-bb7e-8f16015adf32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient between the IP and RMM1: 0.33046323421940305.\n",
      "Correlation coefficient between the IP and RMM2: -0.9300119158570614.\n"
     ]
    }
   ],
   "source": [
    "print('Correlation coefficient between the IP and RMM1: '\n",
    "      f'{np.corrcoef(phase_avg_ip, phase_avg_rmm1)[0, 1]}.')\n",
    "print('Correlation coefficient between the IP and RMM2: '\n",
    "      f'{np.corrcoef(phase_avg_ip, phase_avg_rmm2)[0, 1]}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9648327-d770-4534-823b-733ffe35b5e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient between the PG and RMM1: -0.6786301924889234.\n",
      "Correlation coefficient between the PG and RMM2: -0.6688733027123144.\n"
     ]
    }
   ],
   "source": [
    "print('Correlation coefficient between the PG and RMM1: '\n",
    "      f'{np.corrcoef(phase_avg_pg, phase_avg_rmm1)[0, 1]}.')\n",
    "print('Correlation coefficient between the PG and RMM2: '\n",
    "      f'{np.corrcoef(phase_avg_pg, phase_avg_rmm2)[0, 1]}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "814e19ad-7a71-4aef-95c9-6a471db693ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient between OLR and RMM1: -0.4962456609250617.\n",
      "Correlation coefficient between OLR and RMM2: 0.8422317866250696.\n"
     ]
    }
   ],
   "source": [
    "print('Correlation coefficient between OLR and RMM1: '\n",
    "      f'{np.corrcoef(phase_avg_olr, phase_avg_rmm1)[0, 1]}.')\n",
    "print('Correlation coefficient between OLR and RMM2: '\n",
    "      f'{np.corrcoef(phase_avg_olr, phase_avg_rmm2)[0, 1]}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ef049d7-9d36-461f-a837-01c00423836b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient between the IP and the PG: 0.5036181549956392.\n",
      "Correlation coefficient between the IP and the shifted PG: 0.9019059036035604.\n"
     ]
    }
   ],
   "source": [
    "print('Correlation coefficient between the IP and the PG: '\n",
    "      f'{np.corrcoef(phase_avg_ip_pg, phase_avg_pg)[0, 1]}.')\n",
    "print('Correlation coefficient between the IP and the shifted PG: '\n",
    "      f'{np.corrcoef(phase_avg_ip_pg, np.roll(phase_avg_pg, 1))[0, 1]}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a046c8d5-6f7c-45ba-82c2-fca9d4456c57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significance level: 0.01.\n",
      "Number of points: 8.\n",
      "Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 3.707428021324907.\n",
      "Threshold correlation coefficient: 0.834341625597055.\n"
     ]
    }
   ],
   "source": [
    "P = 8  # the number of points\n",
    "a = 0.01  # significance level\n",
    "\n",
    "q = st.t.ppf(1 - a / 2, P - 2)\n",
    "r = q / np.sqrt(q**2 + P - 2)\n",
    "\n",
    "print(f'Significance level: {a}.')\n",
    "print(f'Number of points: {P}.')\n",
    "print(f'Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): {q}.')\n",
    "print(f'Threshold correlation coefficient: {r}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d9108a37-3893-439d-a59a-c645e6ee653b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum correlation coefficient between the IP and RMM is 0.987443102662834 at the angle 290°.\n",
      "Maximum correlation coefficient between the PG and RMM is 0.9520895875523926 at the angle 225°.\n",
      "Minimum correlation coefficient between OLR and RMM is -0.9781956110458861 at the angle 300°.\n"
     ]
    }
   ],
   "source": [
    "ip_rmm_corr = np.corrcoef(phase_avg_ip[np.newaxis, :],\n",
    "                          phase_avg_rmm1[np.newaxis, :] *\n",
    "                          np.cos(np.reshape(np.arange(360), (360, 1)) * np.pi/180) +\n",
    "                          phase_avg_rmm2[np.newaxis, :] *\n",
    "                          np.sin(np.reshape(np.arange(360), (360, 1)) * np.pi/180))[0, 1:]\n",
    "pg_rmm_corr = np.corrcoef(phase_avg_pg[np.newaxis, :],\n",
    "                          phase_avg_rmm1[np.newaxis, :] *\n",
    "                          np.cos(np.reshape(np.arange(360), (360, 1)) * np.pi/180) +\n",
    "                          phase_avg_rmm2[np.newaxis, :] *\n",
    "                          np.sin(np.reshape(np.arange(360), (360, 1)) * np.pi/180))[0, 1:]\n",
    "olr_rmm_corr = np.corrcoef(phase_avg_olr[np.newaxis, :],\n",
    "                           phase_avg_rmm1[np.newaxis, :] *\n",
    "                           np.cos(np.reshape(np.arange(360), (360, 1)) * np.pi/180) +\n",
    "                           phase_avg_rmm2[np.newaxis, :] *\n",
    "                           np.sin(np.reshape(np.arange(360), (360, 1)) * np.pi/180))[0, 1:]\n",
    "ip_rmm_angle = np.argmax(ip_rmm_corr)\n",
    "pg_rmm_angle = np.argmax(pg_rmm_corr)\n",
    "olr_rmm_angle = np.argmin(olr_rmm_corr)\n",
    "print('Maximum correlation coefficient between the IP and RMM is '\n",
    "      f'{ip_rmm_corr[ip_rmm_angle]} at the angle {ip_rmm_angle}°.')\n",
    "print('Maximum correlation coefficient between the PG and RMM is '\n",
    "      f'{pg_rmm_corr[pg_rmm_angle]} at the angle {pg_rmm_angle}°.')\n",
    "print('Minimum correlation coefficient between OLR and RMM is '\n",
    "      f'{olr_rmm_corr[olr_rmm_angle]} at the angle {olr_rmm_angle}°.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ab8aa1-635b-4e52-ab8e-a14b4c2f29c1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92ca748a-eec7-4731-8d67-2c3d138cc0c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q+KMrn3PHoXdFiYmJsnjxYufrV155Rfr27SsiIsXFxTJ58mRp3769RERESFRUlADy3XffiYjI559/LkOHDpWmTZtK//795dNPPxURkSFDhkhoaKhERUU5h9DQUFmxYsV58z9+/LhERETIsmXLpKioSDIzM+Wqq66SBQsW1Ho5PckTYXngwAF57LHHZM6cOeftSHVVn6As53A4ZP369TJt2jR55pln3LrMdgxJEXvvI1Xp3Lmz/Pvf/671croalLY5mSMiI6yuoSYrVqwgLS2NtWvXkpCQQE5ODtHR0eVBT+/evUlLS6OoqIjnn3+e4cOHk5mZSVxcHMnJySQnJ9c4jx9++IGgoCBGjhwJQNu2bbnjjjtIT09n3LhxHl2+2nDnCZ6TJ0/y17/+lfDwcB599FEiIiLcWGn9GWPo168f/fr14+DBg8yePZvOnTszcuRIgoKC6tyunU7cuIs39pGqGGOc8/AE//jreElubi6NGjWiefPm5OfnM23aNOe4wsJCli9fTk5ODiEhIURGRjo3/nvuuYdFixaxadMmRITTp0+zevVqcnNzz5tHp06dEBFWrFiBw+Hg6NGjvPHGG3Tv3t1ry+mq+l46VFxczAsvvMDcuXMZN24cjzzyiO1CsrK2bdvy1FNP0b17dx5++GE2bdpUp3b8MSTBO/vIgQMH+OSTTygsLKSgoIC//OUv/Pzzz/Tt29djy+U/fyEvGDlyJPHx8cTGxtKlSxf69OlzzvjXXnuNhIQEIiMjWbRoEcuXLwegV69eLF68mPHjxxMdHU3Hjh1ZunRplfOIjIzk7bffZt68eURHR9OzZ0+6du1KSkqKpxevTuoalt9//z0TJkzg6quvZvbs2cTExHiwSvf75S9/ybx58/jqq69ITU2t1bL7a0iCd/aR3Nxcxo4dS3R0NLGxsfzf//0f7733Hs2bN/fcgrlyfG7nwR2/Qan6q81vlitXrpTp06fL2bNnPV6XN7aPw4cPy8MPP+z8va06dv1NMlDh4m+U/vW/M2UZV75ZlpSU8PTTT5OXl8fMmTNtcYG3O7Ru3Zo5c+awadMm/vWvf11wOn/+Junv9K+l3Ka6sDx+/DgPPfQQN954I3fccYdFFXqOMYYHH3wQh8PB/PnzzzuxoCHp2/QvptyqqrDcsmULs2fPZtasWXTr1s3iCj1r+PDh9OrVi+TkZIqKigANSX9gm8uDlP+oGJa33HILn3/+OXPnzg2YkOjbty8tW7bk4Ycf5vbbbyc9PV1D0sdpUCqPiImJ4Te/+Q0pKSmsXbs24EKiY8eO3HbbbUyYMIHVq1cH3PL7G/3rKY/YuXMn69evZ+XKlUyZMsVWXXR5w/bt21mzZg0fffQRs2bNIj8/3+qSVD1oUCq3O3ToEEuXLmXmzJm0aNHCp/qzdIeKv0lGRkaSkpJCcnIyJSUlVpem6kiDUrlVXl4eTz755Dm/yfla57/1UdWJm9atW3P33Xfz5JNPWlydqisNSuU2DoeDlJQUZsyYQWho6DnjAiEsqzu7fdlll9GjRw/efPNNi6pT9aFBqdzm5Zdf5q677qJFixZVjvfnsHTlEqChQ4eyd+9evv76ay9Xp+pLg1K5xeHDhzl48CC9e/eudjp/DMvaXCc5adIkFixYQEFBgZeqU+6gQanq5MEHH2TJkiXO5888++yzTJw40aXP+lNY1vZi8uDgYB566CEWLVrkheqUu2hQqjrZuXMn999/P1deeSX33nsv1113Xa36pPSHsKzrHTcdOnQgNzeXn376yYPVKXfSoFR1Vv6UwxdffJHU1FRef/31WnWe6sthWd/bEh944AGef/55D1SmPEGDUtVJ5UDcvHkzd999N8OHD69VO74Ylu64dzsqKoq4uDh27tzp5uqUJ2hQKrcIDQ1l4MCBLFmypNaf9aWwdGcHF2PGjOHll1/26CMMlHtoUKo6qbhzt2nThscee4yVK1cSGRlZp/Z8ISzd3QtQcHAwgwcPZvXq1W6oTnmSBqWqk7NnzwLQvXt30tLSmDp1KsaYerVp57D0VFdpSUlJ/Oc//9FvlTanQanq5MCBA9x444188skn9OrVy23t2jEsPd2f5PXXX8+GDRvc3q5yHw1KVScffvghq1evrtdjai/ETmHpjU53Bw8eTHp6ukfaVu6hQanqpHPnzh5t3w5h6a2eyYODgwkPDyc7O9tj81D1o0GpbMvKsPT24xuGDRvG22+/7fH5qLpxeQswxkRf4P227itHqXNZEZZWPOOmU6dO7N271yvzUrVX41ZgjOlkjPkaOG6MOWSMqXxF8VeeKU2pUt4MSysfBBYVFUVOTo5X56lc48qWMB94E2gO3A88a4yZUmF8/a4JUcoF3ghLq5+WePPNN7Nq1Sqvz1fVzJWtoTcwU0ROisi7QB/g98aYJzxamVKVeDIsrQ5JKO3c95tvvrFk3qp6rmwRDiCi/IWIHAQGAEOMMX/1UF1KVckTYWmHkATqfcG+8hxXtopPgVsrviEix4DrKf122cQDdSl1Qe4MS7uEZDljjN6lY0OubBmPArsqvyki2cCvgD+4uSalauSOsLRbSAK0atWKo0ePWl2GqqTGrUNE9orIFxcYlysir7q/LKVqVp+wtGNIAnTr1o1du877XqIs5srlQS/XNHijUKWqUpewtGtIAnTt2lX7qLQhV7aS0cDVwDHg0AUGpSxTm7C0c0gCREdH662MNuTKlnIrpReVjwQuAtJF5LGKg0crVMoFroSl3UNS2Zcrv1GmichvgS7ADkovON9jjEkxxjR1VyHGmMFl7X5X6YJ2pVxSXVj6Ukjavb5A5PJfpOyC8/8FkoA0IBW43B1FGGOCgAXAEEoDeYQxpos72laBpaqw9KWQVPbk0lZjjGlgjBlijPkHsAdoBvxKRD5yUx1XAt+JyA8iUgj8A/iNm9pWAaY8LCdNmkpmZqaGpKq34JomMMbMBYYDO4FXgdEiUuDmOmKBzAqvDwJXVapjAKV3BJ2jb9++bi5F+YNiRzD7gm7ju+Yfc73J5fHHH7e6JJd99JG7vn8od6kxKIGHgO8pvY1xHDCu8q1WItLf/aWdS0QygIzK76emps5ITU319OyVD8kvcDBlwTEKTuRzOtTBe4Sw7aGHiI6Ksro0l+k27R0zZ850aTpXgtIbd94cAuIqvG6LXnak6qA8JL/68SwzxrRk979/4n7Tit/Mepz01JkeeXSF8n81BqWILPNCHV8AlxhjLqY0IO8A7vTCfJUfqRiSj425iMQrmnDk340YKLl80awZD0yZwl+fesr2Yan3etuPK79RjqxpmvrexigixcaY8cAaIAh4WUR216dNFViqCkmAvUQyr+FBehZFwHXXMXnyZJ5++mlbh6UGpf24cui9FPgOOErVnfQKpSd56kVE0gF9FJ2qk8PHijlwtOickAQooQFdu/Vm7JffsHD3bv7zpz/ZOiw1JO3J1R7OmwC5wPPAQBG5tsLg8RM5StWkY1xDlj/e5pyQdOoxgJmOQzQNDuLTY8csf7pjdY4cOULr1q2tLkNV4sqdOQ8B8cBC4H+AfcaYxcaYfp4uTqnaCAu9wOYc9wua9R3K1yNuZeq119riUbgXsmvXLrp162Z1GaoSl67AFZESEVktIrcDvwBOAhnGmOs8Wp1S7mAM3DCSFhf/92Yvu4blzp07ueyyy6wuQ1VSm8fVRhlj7gXeo7SjjFnANg/VpZTH2TEss7OziY6u8snQykKu9Ef5a2PMm8A3lN7b/aiIXCIiM0XkpMcrVKqC/AIHu74/67b27BiWyn5c+UaZBnQFXgeygCRjzOMVB49WqFSZ/AIHUxccY/LzWeTklbitXbuEZUlJid6PblOu/FVeBT6jtC/KuCqGth6rTqky5SG5+8ezTPp9c6LCg9zavh3CcuPGjVx55ZWWzFtVz5Wz3qNF5A9VDcA8KjzKVilPqBiSla+TrI/Xd+zg13//u/PaRavDcu3atQwcONDr81U1c+U3yibGmFnGmJXGmGeNMZHGmPbGmHeATyg9HFfKI9wekgX58OFy2LebopISVn37LSsqPKPGqrAUEQoLC2nUqJHX5qlc58qh9wLg15Q+DmIg8BbwMbAbuFhE7vdceSqQlTiEaQvd/E0yOAS++D/Y8j6jevbkl61bM2ntWvIKC52TWBGWmzZtonfv3l6Zl6o9V4IyCRgkIpOBGyl9lvedIpIiIj97tDoV0IIaGJKuDnPr4TbBIdC1L3z9GQ0KC5g/eDCHc3N5esOGcybzdliuWrWKG2+80ePzUXXjSlCGi0gWgIgcBPJEZL1ny1J29+2333plPkOuDndfSJbrPgCKC+HrjfRt144RXbsyZ+NG9lV6+qG3wvL06dOEhIToYbeNuRKUwcaY64wx1xtjrgeo+Lr8PRVY1qxZw48//mh1GXUT1xmiW8H2jwF4euBADDDpgw/Om9QbYfnmm28ybNgwj7St3MOVoMwCXgaWlA3HK71+yWPVKdsaPXo0y5Z5o6tSDzAGuifCjzsh52fioqKY0q8fn2Rmcjw//7zJPRmWJSUl7Nixgy5d9Fl6dubK5UEJInJxNUN7bxSq7CUiIoKgoCCyKx2u+ozuiYDAznUAPHrNNewZP57mTao+zPdUWC5btozf/e53bmtPeYbeBqDqbMyYMSxcuLDe7eQXOHj8pZ85lFXkhqpc1Lw1XHUTXFR6v0RoSAjhDRtW+xF3h2Vubi579+7liiuuqHdbyrM0KFWdxcbGEhUVxc4K1yHWVnnP5Ou25bPviBeDEmDI3fCL2t0J486w/Nvf/saf/vSnerWhvEODUtXLfffdx+LFiykpqf2915Uf39C3h5vPbnuIO8Jy3759NGzYkDZt2ri5OuUJGpSqXoKCgvjDH/7ASy/V7pzehZ5x4yvqG5YLFy5k3LhxHqhMeYIGpaq3yy+/nGPHjnHw4EGXpvf1kCxX17D89NNP6dmzJ00ucOJI2Y8GpXKLhx56iHnz5rk07Xuf5tk+JLNOn+bG5ctJ37u32ulqG5Y5OTn84x//4I477nBXqcoLNCiVW4SFhTFo0CD+9a9/1Tjt/1wXwcJJrWwbkgBNGzfmuxMneHjNGopq+P3V1bAUEVJTU5kxY4b2O+lj9K+l3CYpKYk9e/awffv2aqczxtCpXfWX4nhVSQls+w98t835VsOgIJ5NSmLP8eMs+OKLGptwJSyff/557rzzTpo3b+6uypWXaFAqt5o6dSpLly7l8OHDVpfiugYNYN2/4JO3z3n7pksuIalDB1IzMjh2+nSNzVQXlqtXryYqKkp7CPJRGpTKrRo0aMATTzzBE088QW5urtXluMZ5S+MuyPm5wtuGeUlJ5BUW8thHH7nUVFVhuWXLFnbt2sXIkSM9Ur7yPA1K5XZNmjQhNTWVRydP5+cTPvLAru79AYEdH5/z9qUxMYy/8kpe3LKFbUePutRUxbD86quv+Oc//8mkSZM8ULTyFg1K5RFhEc0h7mFGp2yjoKDA6nJq1qw1xP2iNCjLHg1RbkZiIs2bNGHNd9+53FxMTAzDhg1jxIgRTJo0CWOMuytWXqRBqdyu/DrJ7w4Jf7ilHY8++iinTp2yuqya9RgAxzLhyA/nvB0dGso399/P5H79XG5qzZo1bNiwgffff5/p06fro3B9nAalcqvKF5P/dlA7pk+fTnJyMl9//bXV5VXvsr4QFHze4TdwwV6FqrJkyRIOHTpESkoKLVu2tPzpjqr+NCiV21zojpuYmBiee+453nnnHd58802Lq6xGaDj8Mgki63b5jsPhYPbs2cTGxjJmzBjn+1Y/3VHVnwZlJQMGDKj1fcuq5tsSg4KCmDZtGiEhIcycOZOiIi/3FOSqG++Ga35T64+dOXOGiRMnMnToUAYPHnzeeH8Ky0DcRwIyKBMSEggNDSU8PJyWLVsyevRoW228jz32GN26dSM4OJjU1FSry3GJAUKCqfG2xFtuuYURI0bw4IMPctTFs8h2t27dOqZMmcLEiRPp3r37BafzpbC0+z5S7uOPP8YYQ0pKikfnE5BBCbBy5Ury8vLYunUrmzdvZvbs2VaX5NSxY0eeeeYZbrrpJqtLcVlo4wbMeaCFS7cldurUiWeeeYa//vWvvPbaazgcDi9U6H4nT54kOTmZzMxMnnvuOZe6TPOlsLTzPgJQVFTEhAkTuOqqqzw+r4ANynKxsbEMGTKEXbt2Od/bv38/ffv2JSIigkGDBvHzz/+9CHnYsGG0atWKqKgo+vfvz+7du53j0tPT6dKlCxEREcTGxjJnzhznuFWrVtGzZ0+aNm3KNddcw44dOy5Y06hRoxgyZAgRERFuXlrPqs0lMGFhYTz55JNccsklPPTQQ3z66acerMy9iouLGTN/Pr987jkemDCBu+66q1bL7kthCfbcRwDmzp3LoEGD+MUvfuHGpa1awAdlZmYm6enpXH755c73VqxYwSuvvEJWVhaFhYXn/DGHDBnC3r17ycrK4oorruCuu+5yjvvjH//ICy+8QG5uLrt27eL660sfUPnll18yZswYXnjhBY4fP869997L0KFDOXv2rPcW1Kb69OnDvHnz2LdvHxMnTmTbtm1Wl3RBIsKaNWuYOHEiF198MT82aMCaI0fq1JYvhaUd95H9+/fz8ssvM336dA8tdSUi4tPDjBkzpLbi4+MlLCxMoqKipF27djJ27FjJz88XEZHExESZNWuWc9oFCxZIUlJSle2cPHlSAMnOzhYRkbi4OFm0aJHk5OScM919990nKSkp57zXqVMnycjIqLbOu+66S+qyfL6qsLBQXnnlFXnkkUdk8+bN4nA46t2mO9bfsWPHZP78+fLoo4/KqlWrxOFwSInDIVcuXiyt58yR3LNn69x2VlaWjBs3TnJzc+tdpzvZfR8ZOnSo/OMf/xARkVGjRklycnKdlhNIFRdyxvJvlMaYYcaY3cYYhzGml7fm++6775Kdnc3+/ftZuHAhoaGhznGtWrVy/rtJkybO/+OXlJQwZcoUOnToQGRkJAkJCQDOw4633nqL9PR04uPjSUxMZOPGjUDp//3mzp1L06ZNnUNmZqZvdRxRJr/AwZsfnsLhkJonrqWQkBBGjx7Nn//8Z7799lumTZvGE088wY4dOxBx//yqU1JSwsZ/vsKSqeN55ZVXuPXWW52/GxtjaGAM8wcP5kheHn9ev77O87HzN0u77iMrV64kNzeX22+/3VOLfp5gr83pwnYB/wO8YHUhNVmxYgVpaWmsXbuWhIQEcnJyiI6Odu7EvXv3Ji0tjaKiIp5//nmGDx9OZmYmcXFxJCcnk5ycbPES1E/FS4C6d2xE5/hGHplPSEgII0aMYMSIEZw6dYrVq1fz97//nbCwMG6++WZ69OjhkVsCy5+x/d5775Gbm8vkmBz6xAZh7p9Y2nFGJX3atuV33bszd+NG/njFFbSPjq7TfCuG5dNPP014eHh9F8Uy3thHPvzwQzZv3uwM65ycHIKCgti5cydpaWkeWS7Lg1JEvobanQiwSm5uLo0aNaJ58+bk5+czbdo057jCwkLefPNNbr75ZqKiooiMjHR2znrPPfdw6623MnDgQK688kry8/PJyMigf//+VZ6wKSoqoqSkBIfDQXFxMQUFBYSEhBAUFOS1Za2s8nWSngrJyiIjI52hmZeXx+rVq/nnP/9JcHAwzZo1o1u3bnTr1o2YmJhabUMlJSX8+OOP7N69m927d3PmzBkaNGhAt27dGDt2LNHR0bB5DaxaVHpLY5sOVbbz1K9+xdtff82jH3zAW8OH13k5/SUsvbGPzJo1iylTpjhfT5gwgTZt2vDYY495bLksD0pfMnLkSNasWUNsbCzNmjVj1qxZ/O///q9z/Guvvcb48eMpKSmhc+fOLF++HIBevXqxePFixo8fz969ewkNDaVfv37079+/yvncc889LFu2zPn6iSee4JVXXmH06NEeXb4LscszbsLDw7n99tudh1zHjx9n586dvPHGG85DO2MMIuIMzYyMjPOuRXU4HDRo0ID27dtz2WWXMWjQoHMOK50u6wvvvQTbMy4YlLGRkUzr148XtmzheH5+rW51rMwfwtIb+0hERMQ54RkaGkpYWBjNmjXz2HIZb/z2Y4xZC7SqYlSyiKSVTZMBTBSRzRdoYwAwoPL7o0aNmrF06VI3Vaoqs0tI1lVqamr9Ltp/4xk48BU8/FLpfeBVKCguxiFCk5CQus+ngmPHjpGamuqzYelLjDEzRSS1pum8cjJHRAaKSNcqBpd/UBCRDBFJrTyU/1isPGPuihM+G5Ju0SMRTufA99suOEnj4GC3hSTY+wRPoLL8rLeytz8ObcrMewI0JAE6XgGhEbD9/B6FPEnD0l4sD0pjzK3GmIPA1cBqY8waq2tS/9XmomD69gjQkAQIDoGe10Nw8Hkd+nqahqV9WB6UIvKOiLQVkUYi0lJEkqyuSalzJI2GWydUeYmQp2lY2oPlQamUPyq/o8MdNCytp0GpACjxwJ02gSrr9GmSXn+d9L173damhqW1NCgV+QUOHn4ui9Wf6A7oDk0bN+ZATg4PrVlDYUmJ29rVsLSOBmWAK79OcvcPZwkP1c3BHRoGBTEvKYm9J07wt02b3Nq2hqU1dM8IYL5+MbmdDbnkEm685BIeX7eOn9wcaBqW3qdBGaA0JOvo1An4ofoOZcs9O2gQ+UVFpPznP24vQ8PSuzQoA5CGZD383xL411woKa5x0s4XXcQDV17Jki+/5Ms6dvBbHQ1L79GgDED7jhTxw6FCDcm66J4I+aeqvaWxoscSE4kJC2PtDz94pBwNS+/QoAxAXS5uxPLH22hI1kXHy2t1S2PTxo3ZM348j/bt67GSNCw9T4MyQEWFW9e3pU8LDoGu/WDP51Bw2qWPNG3c2MNFaVh6mgalUrXVYwAUF8JXG62u5Bwalp6jQalUbcVeAs1al3boazMalp6hQenH8gscfHug0Ooy/I8x0OM6KDwDRfZbvxqW7qdB6afKLwF69K9Z5OU7rC7H/1z7P3DvXAhpWKePe/rJAhqW7qVB6YcqXif50J3NCG+if2a3a1D3k2FLtm7lhtdew6Fh6TN0D/IzFUMyZcxFDNBLgGyncXAwH/74I8u2bfP4vDQs3UOD0o9oSPqGO7t1o0/btkz98ENOnT3r8flpWNafBqWfKCkRpmpI+gRjDPMHD+an06d5cv16r8xTw7J+NCj9RFCQ4drLQzUkfcSVsbGM6tGDeZ99xncnTnhlnhqWdadB6Uduuz5SQ9IKxUVwqPa9mf/5V7+iYVAQE99/3wNFVU3Dsm40KJWqrw+Xw8vJcMa1WxrLtY6IIPnaa9n+008cz8/3UHHn07CsPQ1Kpeqra18oKYKvPq31Rx/q04evxo2jeRPvHgloWNaOBqVS9dWmIzSPhR0Ztf5oo+BgQkNC3F+TCzQsXadB6WPyCxw8tew4x7Jr7jhWeYkx0CMR9n8FJ7OsrqZWNCxdo0HpQ8qvk1z7xWm+yyyyuhxVUbfE0v/udK2fSjvRsKyZBqWPqPz4hqu7hVpdkqoougXEX1baoa+Hb030BA3L6mlQ+gB9xo2P6DEA8nPg1PF6NSMiHu80oyoalhemQWlzGpI+pHsiPPIyRF1U5yZ+ysvj+ldfZeW337qxMNdpWFZNg9Lm3snI1ZD0FcEhpUM9NAsN5ae8PB5es4azxdacsNOwPJ8Gpc3dMSiS+Q+31JAMECFBQcxLSuL7kyeZv2mTZXVoWJ5Lg9LmghoYLmvfyOoylBcldezIrzt1Yta6dRy1MKQ0LP9Lg1IpG5o7aBBni4uZ9uGHltahYVlKg1IpG7qkeXMmXHUVS7dtY/Phw5bWomGpQWkbZwocFBb53vV3qhonf6rXx1P696dleDjr9u93U0F1F+hhqUFpA+WXAKUuPmbJ9XPKAza/D/Pvq9ctjVGNG7Nn/HgevvpqNxZWd4EclhqUFssvcDB1wTF2/3iWpD7hGGOsLkm5Q4eepf/dUb9bGiMb2etEXqCGpQalhSqGpF4n6WectzR+5JO3NFYnEMPS8qA0xvzFGPONMWaHMeYdY0xTq2vyBg3JANBjAJw4Uqfez+0u0MLS8qAEPgC6ikh34FtgqsX1eJyGZIDocjUEN4TtGVZX4hGBFJaWB6WIvC8i5fdqfQa0tbIebyhxQHGJaEj6u8Zh0Lk37NpQ+lwdN7HTCb9ACUvLg7KSMcB7VY0wxgwwxqRWHvbt2+fdCt0gokkD/jZRb0sMCD0GQMFpOPydW5p7ccsWBixbRonD4Zb23CEQwtIrQWmMWWuM2VXF8JsK0yQDxcDyqtoQkQwRSa08JCQkeGMR3K5BAz27HRA69ISHXoR2l7qluchGjVi3fz+vbNvmlvbcxd/D0itBKSIDRaRrFUMagDFmNHAzcJfY6bhCqfoKCobI5m5r7vbLLqNvXBzJ//kPOQUFbmvXHfw5LC0/9DbGDAYmAUNFxHvP7FTKBxlj2DBmDD9NnEhU48ZWl3Mefw1Ly4MSeB6IAD4wxmwzxiyyuiB3yS9wsHJ9rq1+fK9owIABvPTSS1aX4Rd0Xf6XP4al5UEpIh1FJE5EepYN91ldkzuU35Y4/42T/HDIugeBJSQkEBoaSnh4OC1btmT06NG22XizsrIYMWIEbdq0ISoqir59+7LJwj4Ya2LndQlw3XXXERMTQ2RkJD169CAtLc2yWvwtLC0PSn9U+fENHdo2tLSelStXkpeXx9atW9m8eTOzZ8+2tJ5yeXl59O7dmy1btnDixAlGjRrFTTfdZOsdy67rEmD+/PkcOXKEU6dO8eKLL/K73/2OI0eOWFaPP4WlBqWb2fkZN7GxsQwZMoRdu3Y539u/fz99+/YlIiKCQYMG8fPPPzvHDRs2jFatWhEVFUX//v3ZvXu3c1x6ejpdunQhIiKC2NhY5syZ4xy3atUqevbsSdOmTbnmmmvYsWNHlfW0b9+ehx9+mNatWxMUFMT/+3//j8LCQvbs2eOBpXcvu61LgO7duxMcHAyU/pZZVFREZmamOxe71vwmLMuf+Oarw4wZM8QuTp8pkT/NOSq/un+/ZGw5bXU5IiISHx8vH3zwgYiIHDhwQLp06SIpKSkiIpKYmCjt27eXPXv2SH5+viQmJsrkyZOdn12yZImcOnVKCgoKZMKECdKjRw/nuFatWsm6detEROTEiROyZcsWERHZunWrxMTEyGeffSbFxcWydOlSiY+Pl4KCghpr/fLLL6VRo0aSnZ3trsUXd24fvrAub7rpJmnUqJEAkpSUJCUlJW5b/vrIysqScePGSW5urtWlnANIFRdyxvKgq+9gp6Cc/kKWrUJSpHTnDgsLk6ioKGnXrp2MHTtW8vPzRaR05541a5Zz2gULFkhSUlKV7Zw8eVIAZ4jFxcXJokWLJCcn55zp7rvvPmd4lOvUqZNkZGRUW2dOTo507dpVnnzyyVovY3XcHZS+sC4LCwslPT1d5s6dW+tl9CQ7hqWrQamH3m40+uYoZtxtr8NtgHfffZfs7Gz279/PwoULCQ0NdY5r1aqV899NmjRxHh6VlJQwZcoUOnToQGRkJOUX9pcfTr711lukp6cTHx9PYmIiGzduBEoPP+fOnUvTpk2dQ2ZmJoer6aX7zJkz/PrXv6ZPnz5MnWrvW/3tvi4BQkJCGDJkCO+//z7//ve/3bn49eLLh+EalG50cZuGXNvTXiFZVytWrCAtLY21a9eSk5ND+a2iUnapU+/evUlLSyMrK4tbbrmF4cOHAxAXF0dycjLZ2dnOIT8/nxEjRlQ5n7Nnz3LLLbfQtm1bXnjhBa8sm7d5a11WVlxczPfff++RZaorXw1LDUpVpdzcXBo1akTz5s3Jz89n2rRpznGFhYUsX76cnJwcQkJCiIyMpEGD0k3pnnvuYdGiRWzatAkR4fTp06xevZrc3Nzz5lFUVMRtt91GaGgoy5Ytc7bhb7yxLr/55hvee+89zpw5Q1FREa+//jrr1q0jMTHRa8vpKl8MS//cMlW9jRw5kvj4eGJjY+nSpQt9+vQ5Z/xrr71GQkICkZGRLFq0iOXLS2/R79WrF4sXL2b8+PFER0fTsWNHli5dWuU8Pv30U1atWsX7779P06ZNCQ8PJzw8nPXr13t68bzKG+tSREhNTaVFixbExMQwf/583njjDa644gpPL16d+FxYuvJDpp0HK07mlJQ4vD5PVTd2Otmnzmf1CR70ZI5n5Bc4eGR+FhlbTltdilI+z1e+WWpQ1kL5xeQ7vz+rDwFTyk18ISw1KF1k5ztulKro+xMnaDR7NqPffdfqUlxm97DUoHSBhqTyJR2aNeOhPn1Ytn07nx86ZHU5LrNzWGpQ1kBDUvmi5GuvJTYigk8tvte7tuwalhqUNfhmfyF7DxRqSCqfEtGoEXvGj+fBSpci+QI7hqUGZQ2u6NyY5Y+30ZBUnuNwgKPE7c2GNbS2e7/6sFtYalC6oFlUkNUlKH91/AjMvw/2bLa6EtuxU1hqUCplpaYtSp/5vSPD6kpsyS5hqUGplJWCgqDbtaXfKPPPv4db2SMsNSjL5Bc42HfEumfbqADWPREcxbD7E4/PyiH2fNBdTawOSw1K/nsJ0CPzf+LMWYfV5ahA07o9xMTBjo89Opv//eIL+r38MiUO39zGrQzLgA/KitdJPjC8GaGNAn6VKG8zBnoMgMxv4ITnHgbWvEkTNh48yEtbt3psHp5mVVgGdCroxeTKNrr1BwzsWOexWQzr0oVr27Uj5aOPyC4o8Nh8PM2KsAzYoNSQVLYSdRHcmQx9fu2xWRhjmD94MMfz83n8Y88e5nuat8MyIIOysEg0JJX9dPolNPbstnh569bcfcUV/O3zz/mmwuN0fZE3wzIgg7JhiKHXpY01JFVAmn399TQJCeHhNWusLqXevBWWARmUACNvjNKQVAGpRVgY0/v354eTJzmen291OfXmjbAM2KBUKpA9cNVV7Bg7luZN/OPLgqfDUoNSqQAUEhREwyD/6sPAk2GpQamU8hueCku/Dsr8Agdzlx8nJ8/9XVgp5TEF+bBsBmx+3+pKfJInwtJvg7L8Osn3Np7mm/2FVpejlOsahULeSdj+kdWV+Cx3h6VfBmXli8mvuizU6pKUcl3FWxqPe+6WxsocZc+w9hfuDEu/C0q940b5Bectjd65g+ZoXh7XLFnCW19/7ZX5eYu7wtKvglJDUvmNqIvg4m6lQemFb3kxTZpwpriYie+/z5ki/+pu0B1h6VdB+Y8PTmlIKv/RIxFOHoXMPR6fVVCDBjyXlMT+nBye3bjR4/PztvqGpeVBaYyZZYzZYYzZZox53xjTpq5t/X5IFHMntNCQVP7h0qshuKHXTupcd/HF/PbSS3lywwYOnTrllXl6U33C0vKgBP4iIt1FpCewCphe14ZCgg09LmnstsKUslSjULjlAY/2KFTZX264gRKHgykffui1eXpTXcPS8qAUkYr/6woD/Oe0m1L11bUvxLT12uwujo5m4jXX8PqOHWw6eNBr8/WmimHpKmOHywGMMU8AI4Ec4DoROVbFNAOAAVV8PAnwxW5QEoB9FtdQWwlozd6SgFV1h4U1ZODAXqxZ8zkFBcW1+GQCvreue4vITTVN5JWgNMasBVpVMSpZRNIqTDcVaCwiM2rRdqqIpNa/Su/yxbq1Zu/xxbr9ueZgL9SCiAx0cdLlQDrgclAqpZSnWf4bpTHmkgovfwN8Y1UtSilVFa98o6zBU8aYzoAD2A/cZ3E9Sil1DsuDUkR+a3UNSilVHcsPvd0gw+oC6ijD6gLqIMPqAuogw+oC6ijD6gLqIMPqAuogw5WJbHF5kFJK2Zk/fKNUSimP0qBUSqka+EVQurNjDW8xxvzFGPNNWd3vGGOaWl2TK4wxw4wxu40xDmNML6vrqY4xZrAxZo8x5jtjzBSr63GFMeZlY0yWMWaX1bW4yhgTZ4z5yBjzVdm2McHqmmpijGlsjPncGLO9rOaZ1U7vD79RGmMiy+8ZN8Y8AHQREVtfZmSMGQT8R0SKjTFPA4iI6zefWsQYcymll3K9AEwUkc0Wl1QlY0wQ8C1wA3AQ+AIYISJfWVpYDYwx/YE84FUR6Wp1Pa4wxrQGWovIVmNMBLAFuMXO69oYY4AwEckzxoQAG4AJIvJZVdP7xTdKX+xYQ0TeF5Hy+2g/A7zX80E9iMjXIuL5DhLr70rgOxH5QUQKgX9QekODrYnIOuCE1XXUhogcEZGtZf/OBb4GYq2tqnpSqrz7oJCy4YK54RdBCaUdaxhjMoG7qEdXbRYZA7xndRF+JhbIrPD6IDbfef2BMSYBuBzYZHEpNTLGBBljtgFZwAcicsGafSYojTFrjTG7qhh+AyAiySISR+n94uOtrbZUTTWXTZMMFFNaty24UrdSlRljwoG3gAcrHeXZkoiUlPWD2xa40hhzwZ86LL8zx1W+2LFGTTUbY0YDNwO/Ehv9WFyLdW1nh4C4Cq/blr2nPKDsd763gOUi8rbV9dSGiGQbYz4CBgNVnkTzmW+U1fHFjjWMMYOBScBQEcm3uh4/9AVwiTHmYmNMQ+AO4N8W1+SXyk6MLAG+FpFnra7HFcaYmPIrTYwxoZSe9LtgbvjLWe+3gHM61hARW397MMZ8BzQCjpe99Zndz9QDGGNuBf4GxADZwDYRSbK0qAswxtwIPAcEAS+LyBPWVlQzY8zfKe2g+iLgJ2CGiCyxtKgaGGP6AeuBnZTugwDTRCTduqqqZ4zpDiyjdNtoAPxTRB6/4PT+EJRKKeVJfnHorZRSnqRBqZRSNdCgVEqpGmhQKqVUDTQolVKqBhqUSilVAw1KZVvGmH3GmDPGmDxjzFFjzNKy2+Qo+7dUvq3SGDOv7P3RZa9Hl72eV2m635S9v7TCey+WdcvmKP+8UqBBqezv1yISDvSktLOFqRXGfQuMLH9hjAkGhgPfV2rje2B42fhyo8o+X9F2YByw1S2VK7+hQal8gogcBdZQGpjlVgL9jDHRZa8HAzuAo5U+fpTSu0aSAIwxzYBrqHRLo4gsEJEPgQJ31698mwal8gnGmLbAEOC7Cm8XAGmU3scNpd8uX71AE6/y32+fd5R97qz7K1X+SINS2d27xphcSvuWzOL8XqFeBUaWdXCQCLx7gXbeAQYYY6KoPlCVOo8GpbK7W0QkgtKOIn5BaWcRTiKygdIOOpKBVSJypqpGyt5fDaQAzUXkE08WrfyLBqXyCSLyMbAUmFPF6NeBR6j5W+KrZdO97tbilN/zmY57laK0y7R9xpgeld7/K6XdfK2r4fMfU9rv4JdVjSzrt7IBYIAQY0xjoFBEHFVNrwKHfqNUPkNEjlH6rXB6pfdPiMiHNfUSX/ZAqQ9F5EIP73ofOEPpGfEXy/7dv/6VK1+n/VEqpVQN9BulUkrVQINSKaVqoEGplFI10KBUSqkaaFAqpVQNNCiVUqoGGpRKKVUDDUqllKqBBqVSStXg/wNZDMHvHK7pwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 5),\n",
    "                       constrained_layout=False)\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(0.5)\n",
    "ax.tick_params(length=6, width=0.5)\n",
    "\n",
    "ax.set_xlim(-3., 3.)\n",
    "ax.set_xticks(np.arange(-3, 4))\n",
    "ax.set_xticklabels(f'−{-x:d}' if x < 0 else f'{x:d}'\n",
    "                   for x in np.arange(-3, 4))\n",
    "ax.set_xlabel('RMM1', fontsize='large')\n",
    "ax.set_ylim(-3., 3.)\n",
    "ax.set_yticks(np.arange(-3, 4))\n",
    "ax.set_yticklabels(f'−{-y:d}' if y < 0 else f'{y:d}'\n",
    "                   for y in np.arange(-3, 4))\n",
    "ax.set_ylabel('RMM2', fontsize='large')\n",
    "\n",
    "ax.set_aspect(1)\n",
    "\n",
    "ax.axhline(color='0.', linewidth=0.5)\n",
    "ax.axvline(color='0.', linewidth=0.5)\n",
    "ax.plot([0, 3], [0, 3], linewidth=0.5, color='0.')\n",
    "ax.plot([-3, 3], [3, -3], linewidth=0.5, color='0.')\n",
    "\n",
    "ax.plot([0, -3 / np.tan(np.pi * ip_rmm_angle/180)], [0, -3], linewidth=1.5,\n",
    "        linestyle=(0, (5, 5)), color='coral', clip_on=False, zorder = 4)\n",
    "ax.plot([0, -3 / np.tan(np.pi * pg_rmm_angle/180)], [0, -3], linewidth=1.5,\n",
    "        linestyle=(0, (5, 5)), color='royalblue', clip_on=False, zorder = 4)\n",
    "ax.plot([0, -3 / np.tan(np.pi * olr_rmm_angle/180)], [0, -3], linewidth=1.5,\n",
    "        linestyle=(0, (5, 5)), color='teal', clip_on=False, zorder = 4)\n",
    "\n",
    "for i in range(8):\n",
    "    ax.text(0.5 + 0.4 * np.cos(np.pi * (1 + i/4 + 1/8)),\n",
    "            0.5 + 0.4 * np.sin(np.pi * (1 + i/4 + 1/8)),\n",
    "            f'Phase {i + 1}', fontsize='large', ha='center', va='center',\n",
    "            transform=ax.transAxes, zorder=5,\n",
    "            bbox=dict(edgecolor='1.', facecolor='1.', boxstyle='square, pad=0.'))\n",
    "\n",
    "ang = np.pi * 7/8\n",
    "d = 0.1\n",
    "coef = 1.5\n",
    "th = np.arcsin(coef * d / 2)\n",
    "P = Path.arc(-ang * 180/np.pi, ang * 180/np.pi)\n",
    "ax.add_patch(PathPatch(P, fill=False, linewidth=0.5))\n",
    "ax.arrow(np.cos(ang) + d * np.sin(ang - th),\n",
    "         np.sin(ang) - d * np.cos(ang - th),\n",
    "         -d * np.sin(ang - th),\n",
    "         d * np.cos(ang - th),\n",
    "         linewidth=0.5, color='0.', head_width=coef*d,\n",
    "         head_length=coef*d, length_includes_head=True)\n",
    "\n",
    "fig.savefig('figures/rmm_diagram.eps', bbox_inches='tight')\n",
    "fig.savefig('figures/rmm_diagram.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "306b70f0-69aa-4f0a-9b7a-7cceacd0b57e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 7),\n",
    "                       constrained_layout=False)\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        for axis in ['top', 'bottom', 'left', 'right']:\n",
    "            ax[i, j].spines[axis].set_linewidth(0.5)\n",
    "        ax[i, j].tick_params(length=6, width=0.5)\n",
    "\n",
    "        ax[i, j].set_xlim(0.5, 8.5)\n",
    "        ax[i, j].set_xticks(np.arange(1, 9))\n",
    "        ax[i, j].set_xticklabels(np.arange(1, 9).astype(int),\n",
    "                                 fontsize='large')\n",
    "\n",
    "        if i == 1:\n",
    "            ax[i, j].set_xlabel('MJO phase', fontsize='large')\n",
    "\n",
    "        ax[i, j].grid(color='0.8', linewidth=0.5, axis='y')\n",
    "\n",
    "ax1 = ax[1, 1].twinx()\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax1.spines[axis].set_linewidth(0.5)\n",
    "ax1.tick_params(length=6, width=0.5)\n",
    "ax1.grid(color='0.8', linewidth=0.5, axis='y')\n",
    "\n",
    "ax[0, 0].set_ylim(220, 260)\n",
    "ax[0, 0].set_yticks(np.arange(220, 261, 10))\n",
    "ax[0, 0].set_yticklabels([f'{y:d}' for y in np.arange(220, 261, 10)],\n",
    "                         fontsize='large')\n",
    "ax[0, 0].set_ylabel('Ionospheric potential, kV', fontsize='large')\n",
    "\n",
    "ax[0, 1].set_ylim(-2, 2)\n",
    "ax[0, 1].set_yticks(np.arange(-2, 3, 1))\n",
    "ax[0, 1].set_yticklabels([f'−{-y:d}' if y < 0 else f'{y:d}'\n",
    "                          for y in np.arange(-2, 3, 1)],\n",
    "                         fontsize='large')\n",
    "ax[0, 1].set_ylabel('RMM1 and RMM2', fontsize='large')\n",
    "\n",
    "ax[1, 0].set_ylim(120, 160)\n",
    "ax[1, 0].set_yticks(np.arange(120, 161, 10))\n",
    "ax[1, 0].set_yticklabels([f'{y:d}' for y in np.arange(120, 161, 10)],\n",
    "                         fontsize='large')\n",
    "ax[1, 0].set_ylabel('Potential gradient, V/m', fontsize='large')\n",
    "\n",
    "ax[1, 1].set_ylim(220, 270)\n",
    "ax[1, 1].set_yticks(np.arange(220, 271, 10))\n",
    "ax[1, 1].set_yticklabels([f'{y:d}' for y in np.arange(220, 271, 10)],\n",
    "                         fontsize='large')\n",
    "ax[1, 1].set_ylabel('Ionospheric potential, kV', fontsize='large')\n",
    "ax1.set_ylim(120, 170)\n",
    "ax1.set_yticks(np.arange(120, 171, 10))\n",
    "ax1.set_yticklabels([f'{y:d}' for y in np.arange(120, 171, 10)],\n",
    "                    fontsize='large')\n",
    "ax1.set_ylabel('Potential gradient, V/m', fontsize='large',\n",
    "               rotation=270, va='bottom')\n",
    "\n",
    "for j in range(2):\n",
    "    fig.align_ylabels([ax[i, j] for i in range(2)])\n",
    "\n",
    "for j in range(2):\n",
    "    ax[0, j].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
    "                  transform=ax[0, j].transAxes, fontsize='large')\n",
    "    ax[1, j].text(0.02, 0.98, '2006–2020', ha='left', va='top',\n",
    "                  transform=ax[1, j].transAxes, fontsize='large')\n",
    "\n",
    "ax[0, 0].bar(np.arange(1, 9), phase_avg_ip / 1e3,\n",
    "             yerr=std_error(phase_avg_ip, phase_avg_sqr_ip, counter_ip) / 1e3,\n",
    "             width=0.6, color='coral')\n",
    "\n",
    "ax[0, 1].bar(np.arange(1, 9) - 0.2, phase_avg_rmm1,\n",
    "             width=0.4, color='peru', label='RMM1')\n",
    "ax[0, 1].bar(np.arange(1, 9) + 0.2, phase_avg_rmm2,\n",
    "             width=0.4, color='darkgreen', label='RMM2')\n",
    "\n",
    "ax[1, 0].bar(np.arange(1, 9), phase_avg_pg,\n",
    "             yerr=std_error(phase_avg_pg, phase_avg_sqr_pg, counter_pg),\n",
    "             width=0.6, color='royalblue')\n",
    "\n",
    "ax[1, 1].bar(np.arange(1, 9) - 0.2, phase_avg_ip_pg / 1e3,\n",
    "             yerr=std_error(phase_avg_ip_pg, phase_avg_sqr_ip_pg, counter_ip_pg) / 1e3,\n",
    "             width=0.4, color='coral', label='Ionosph. potential')\n",
    "ax1.bar(np.arange(1, 9) + 0.2, phase_avg_pg,\n",
    "        yerr=std_error(phase_avg_pg, phase_avg_sqr_pg, counter_pg),\n",
    "        width=0.4, color='royalblue', label='Potential gradient')\n",
    "\n",
    "leg = ax[0, 1].legend(fontsize='large', framealpha=1, edgecolor='0.',\n",
    "                      ncol=2, columnspacing=1., loc='lower right', handlelength=1.5)\n",
    "leg.get_frame().set_linewidth(0.5)\n",
    "\n",
    "handles, labels = [(a + b) for a, b in\n",
    "                   zip(ax[1, 1].get_legend_handles_labels(),\n",
    "                       ax1.get_legend_handles_labels())]\n",
    "leg = ax1.legend(handles, labels,\n",
    "                 fontsize='large', framealpha=1, edgecolor='0.', handlelength=1.5)\n",
    "leg.get_frame().set_linewidth(0.5)\n",
    "\n",
    "for p in range(1, 9):\n",
    "    ax[0, 0].annotate(str(counter_ip[p-1]), xy=(p, 221),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='0.')\n",
    "    ax[1, 0].annotate(str(counter_pg[p-1]), xy=(p, 121),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='1.')\n",
    "    ax[1, 1].annotate(str(counter_ip_pg[p-1]), xy=(p - 0.2, 221),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='medium', color='0.')\n",
    "    ax1.annotate(str(counter_pg[p-1]), xy=(p + 0.2, 121),\n",
    "                 ha='center', va='bottom', rotation=90,\n",
    "                 fontsize='medium', color='1.')\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax[i, j].text(-0.25, 1.05, chr(ord('a') + 2*i + j), fontsize='x-large',\n",
    "                      fontweight='semibold', ha='left', va='bottom',\n",
    "                      transform=ax[i, j].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.25, wspace=0.3)\n",
    "\n",
    "fig.savefig('figures/ip_pg_rmm_variation.eps', bbox_inches='tight')\n",
    "fig.savefig('figures/ip_pg_rmm_variation.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c169197e-495c-475c-9a97-3bc3ca7494b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Largest positive IP deviation from the long-term mean: 12251.515085229592.\n",
      "Largest negative IP deviation from the long-term mean: -11782.171890650236.\n"
     ]
    }
   ],
   "source": [
    "print('Largest positive IP deviation from the long-term mean: '\n",
    "      f'{np.amax(phase_avg_ip) - 240e3}.')\n",
    "print('Largest negative IP deviation from the long-term mean: '\n",
    "      f'{np.amin(phase_avg_ip) - 240e3}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63796d95-d604-49d4-b4de-1174787e526b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x756 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10, 10.5),\n",
    "                       constrained_layout=False)\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(2):\n",
    "        for axis in ['top', 'bottom', 'left', 'right']:\n",
    "            ax[i, j].spines[axis].set_linewidth(0.5)\n",
    "        ax[i, j].tick_params(length=6, width=0.5)\n",
    "\n",
    "        ax[i, j].set_xlim(0.5, 8.5)\n",
    "        ax[i, j].set_xticks(np.arange(1, 9))\n",
    "        ax[i, j].set_xticklabels(np.arange(1, 9).astype(int), fontsize='large')\n",
    "\n",
    "        if i == 2:\n",
    "            ax[i, j].set_xlabel('MJO phase', fontsize='large')\n",
    "\n",
    "        ax[i, j].grid(color='0.8', linewidth=0.5, axis='y')\n",
    "\n",
    "ax[0, 0].set_ylim(210, 260)\n",
    "ax[0, 0].set_yticks(np.arange(210, 261, 10))\n",
    "ax[0, 0].set_yticklabels([f'{y:d}' for y in np.arange(210, 261, 10)],\n",
    "                         fontsize='large')\n",
    "\n",
    "ax[0, 1].set_ylim(200, 250)\n",
    "ax[0, 1].set_yticks(np.arange(200, 251, 10))\n",
    "ax[0, 1].set_yticklabels([f'{y:d}' for y in np.arange(200, 251, 10)],\n",
    "                         fontsize='large')\n",
    "\n",
    "ax[1, 0].set_ylim(220, 270)\n",
    "ax[1, 0].set_yticks(np.arange(220, 271, 10))\n",
    "ax[1, 0].set_yticklabels([f'{y:d}' for y in np.arange(220, 271, 10)],\n",
    "                         fontsize='large')\n",
    "\n",
    "ax[1, 1].set_ylim(220, 270)\n",
    "ax[1, 1].set_yticks(np.arange(220, 271, 10))\n",
    "ax[1, 1].set_yticklabels([f'{y:d}' for y in np.arange(220, 271, 10)],\n",
    "                         fontsize='large')\n",
    "\n",
    "ax[2, 0].set_ylim(120, 170)\n",
    "ax[2, 0].set_yticks(np.arange(120, 171, 10))\n",
    "ax[2, 0].set_yticklabels([f'{y:d}' for y in np.arange(120, 171, 10)],\n",
    "                         fontsize='large')\n",
    "\n",
    "ax[2, 1].set_ylim(110, 160)\n",
    "ax[2, 1].set_yticks(np.arange(110, 161, 10))\n",
    "ax[2, 1].set_yticklabels([f'{y:d}' for y in np.arange(110, 161, 10)],\n",
    "                         fontsize='large')\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i, 0].set_ylabel('Ionospheric potential, kV', fontsize='large')\n",
    "ax[2, 0].set_ylabel('Potential gradient, V/m', fontsize='large')\n",
    "\n",
    "for j in range(2):\n",
    "    fig.align_ylabels([ax[i, j] for i in range(3)])\n",
    "\n",
    "for k in range(4):\n",
    "    ax[k // 2, k % 2].text(0.02, 0.98, f'{1981 + 10*k}–{1990 + 10*k}',\n",
    "                           ha='left', va='top',\n",
    "                           transform=ax[k // 2, k % 2].transAxes,\n",
    "                           fontsize='large')\n",
    "ax[2, 0].text(0.02, 0.98, '2006–2015', ha='left', va='top',\n",
    "              transform=ax[2, 0].transAxes, fontsize='large')\n",
    "ax[2, 1].text(0.02, 0.98, '2011–2020', ha='left', va='top',\n",
    "              transform=ax[2, 1].transAxes, fontsize='large')\n",
    "\n",
    "for k in range(4):\n",
    "    ax[k // 2, k % 2].bar(np.arange(1, 9), phase_avg_ip_part[k] / 1e3,\n",
    "                          yerr=std_error(phase_avg_ip_part[k],\n",
    "                                         phase_avg_sqr_ip_part[k],\n",
    "                                         counter_ip_part[k]) / 1e3,\n",
    "                          width=0.6, color='coral')\n",
    "\n",
    "for j in range(2):\n",
    "    ax[2, j].bar(np.arange(1, 9), phase_avg_pg_part[j],\n",
    "                 yerr=std_error(phase_avg_pg_part[j],\n",
    "                                phase_avg_sqr_pg_part[j],\n",
    "                                counter_pg_part[j]),\n",
    "                 width=0.6, color='royalblue')\n",
    "\n",
    "for p in range(1, 9):\n",
    "    ax[0, 0].annotate(str(counter_ip_part[0, p-1]), xy=(p, 211),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='0.')\n",
    "    ax[0, 1].annotate(str(counter_ip_part[1, p-1]), xy=(p, 201),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='0.')\n",
    "    ax[1, 0].annotate(str(counter_ip_part[2, p-1]), xy=(p, 221),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='0.')\n",
    "    ax[1, 1].annotate(str(counter_ip_part[3, p-1]), xy=(p, 221),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='0.')\n",
    "    ax[2, 0].annotate(str(counter_pg_part[0, p-1]), xy=(p, 121),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='1.')\n",
    "    ax[2, 1].annotate(str(counter_pg_part[1, p-1]), xy=(p, 111),\n",
    "                      ha='center', va='bottom', rotation=90,\n",
    "                      fontsize='large', color='1.')\n",
    "\n",
    "for i in range(3):\n",
    "    ax[i, 0].text(-0.25, 1.05, chr(ord('a') + 2*i), fontsize='x-large',\n",
    "                  fontweight='semibold', ha='left', va='bottom',\n",
    "                  transform=ax[i, 0].transAxes)\n",
    "    ax[i, 1].text(-0.18, 1.05, chr(ord('a') + 2*i + 1), fontsize='x-large',\n",
    "                  fontweight='semibold', ha='left', va='bottom',\n",
    "                  transform=ax[i, 1].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.25, wspace=0.25)\n",
    "\n",
    "fig.savefig('figures/ip_pg_variation_partial.eps', bbox_inches='tight')\n",
    "fig.savefig('figures/ip_pg_variation_partial.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70eebfb7-2a9e-4a2d-9012-09627684ec85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 7),\n",
    "                       constrained_layout=False)\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        for axis in ['top', 'bottom', 'left', 'right']:\n",
    "            ax[i, j].spines[axis].set_linewidth(0.5)\n",
    "        ax[i, j].tick_params(length=6, width=0.5)\n",
    "\n",
    "        ax[i, j].set_xlim(0.5, 8.5)\n",
    "        ax[i, j].set_xticks(np.arange(1, 9))\n",
    "        ax[i, j].set_xticklabels(np.arange(1, 9).astype(int),\n",
    "                                 fontsize='large')\n",
    "\n",
    "        if i == 1:\n",
    "            ax[i, j].set_xlabel('MJO phase', fontsize='large')\n",
    "\n",
    "        ax[i, j].grid(color='0.8', linewidth=0.5, axis='y')\n",
    "\n",
    "ax[0, 0].set_ylim(249, 252)\n",
    "ax[0, 0].set_yticks(np.arange(249, 253, 1))\n",
    "ax[0, 0].set_yticklabels([f'{y:d}' for y in np.arange(249, 253, 1)],\n",
    "                         fontsize='large')\n",
    "ax[0, 0].set_ylabel('Mean OLR for 15° S–15° N, W/m²', fontsize='large')\n",
    "# thin spaces after '°'\n",
    "\n",
    "ax[0, 1].set_ylim(30, 50)\n",
    "ax[0, 1].set_yticks(np.arange(30, 51, 5))\n",
    "ax[0, 1].set_yticklabels([f'{y:d}' for y in np.arange(30, 51, 5)],\n",
    "                         fontsize='large')\n",
    "ax[0, 1].set_ylabel('Average daily sunspot number', fontsize='large')\n",
    "\n",
    "ax[1, 0].set_ylim(650, 710)\n",
    "ax[1, 0].set_yticks(np.arange(650, 711, 10))\n",
    "ax[1, 0].set_yticklabels([f'{y:d}' for y in np.arange(650, 711, 10)],\n",
    "                         fontsize='large')\n",
    "ax[1, 0].set_ylabel('Mean CAPE for 15 °S–15° N, J/kg', fontsize='large')\n",
    "# thin spaces after '°'\n",
    "\n",
    "ax[1, 1].set_ylim(470, 510)\n",
    "ax[1, 1].set_yticks(np.arange(470, 511, 10))\n",
    "ax[1, 1].set_yticklabels([f'{y:d}' for y in np.arange(470, 511, 10)],\n",
    "                         fontsize='large')\n",
    "ax[1, 1].set_ylabel('Mean CAPE for 30 °S–30° N, J/kg', fontsize='large')\n",
    "# thin spaces after '°'\n",
    "\n",
    "for j in range(2):\n",
    "    fig.align_ylabels([ax[i, j] for i in range(2)])\n",
    "\n",
    "ax[0, 0].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
    "              transform=ax[0, 0].transAxes, fontsize='large')\n",
    "ax[0, 1].text(0.02, 0.98, '2006–2020', ha='left', va='top',\n",
    "              transform=ax[0, 1].transAxes, fontsize='large')\n",
    "for j in range(2):\n",
    "    ax[1, j].text(0.02, 0.98, '1980–2020', ha='left', va='top',\n",
    "                  transform=ax[1, j].transAxes, fontsize='large')\n",
    "\n",
    "ax[0, 0].bar(np.arange(1, 9), phase_avg_olr,\n",
    "             yerr=std_error(phase_avg_olr, phase_avg_sqr_olr, counter_olr),\n",
    "             width=0.6, color='teal')\n",
    "\n",
    "ax[0, 1].bar(np.arange(1, 9), phase_avg_sn,\n",
    "             yerr=std_error(phase_avg_sn, phase_avg_sqr_sn, counter_sn),\n",
    "             width=0.6, color='darkgoldenrod')\n",
    "\n",
    "ax[1, 0].bar(np.arange(1, 9), phase_avg_cape[0],\n",
    "             yerr=std_error(phase_avg_cape[0], phase_avg_sqr_cape[0], counter_cape[0]),\n",
    "             width=0.6, color='orangered')\n",
    "\n",
    "ax[1, 1].bar(np.arange(1, 9), phase_avg_cape[1],\n",
    "             yerr=std_error(phase_avg_cape[1], phase_avg_sqr_cape[1], counter_cape[1]),\n",
    "             width=0.6, color='orangered')\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax[i, j].text(-0.25, 1.05, chr(ord('a') + 2*i + j), fontsize='x-large',\n",
    "                      fontweight='semibold', ha='left', va='bottom',\n",
    "                      transform=ax[i, j].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.25, wspace=0.3)\n",
    "\n",
    "fig.savefig('figures/olr_sn_cape_variation.eps', bbox_inches='tight')\n",
    "fig.savefig('figures/olr_sn_cape_variation.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b848fba-d5b0-432b-9121-4eaab5cad412",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}