IMERG_ITCZst_variance_hist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Fractional varaince histogram for ITCZ state plot\n",
    "\n",
    "Author: Fouzia Fahrin (ffahrin@iastate.edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from netCDF4 import Dataset\n",
    "import pandas as pd\n",
    "import netCDF4\n",
    "\n",
    "from cartopy import config\n",
    "\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.util import add_cyclic_point\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "yr0 = 2001\n",
    "yrf = 2021\n",
    "time0 = str(yr0)+'-01-01T00:00:00.000000000'\n",
    "timef = str(yrf)+'-12-31T00:00:00.000000000'\n",
    "\n",
    "mn0 = 2\n",
    "mnf = 4\n",
    "\n",
    "lats = -10\n",
    "latn = 10.\n",
    "lonw = 225#-135.\n",
    "lone = 285#-75.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "figfolder = './figures/'\n",
    "\n",
    "\n",
    "dri = '/lss/research/agon-lab/IMERG-daily/NASA_interpolated/'\n",
    "fn = 'IMERG_20000601-20210930_anom_final_stdz.nc'\n",
    "\n",
    "data = xr.open_dataset(dri+fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_all = data['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up conditional statements \n",
    "cond1 = (time_all['time.month'] >= mn0) & (time_all['time.month'] <= mnf) # season of interest\n",
    "cond2 = (time_all['time.year'] <= yrf) & (time_all['time.year'] >= yr0) # season of interest\n",
    "# now find only the times when MJO amplitude is in season of interest AND exceeds some threshold\n",
    "time_fma = time_all.where((cond1) & (cond2), drop=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "dri = '/home/ffahrin/test/IMERG/ITCZ_states/'\n",
    "fn = 'IMERG_ITCZ_st_daily_EastPac_60prc.nc' #latest ITCZ state\n",
    "\n",
    "data_ITCZ = xr.open_dataset(dri+fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "itcz_state_haffke = data_ITCZ['itcz_state'].sel(time=time_fma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now composite average precip for specific ITCZ states\n",
    "# set up conditional statements \n",
    "cond_dITCZ_haffke = (itcz_state_haffke == 1)\n",
    "cond_nITCZ_haffke = (itcz_state_haffke == 2)\n",
    "cond_sITCZ_haffke = (itcz_state_haffke == 3)\n",
    "cond_aITCZ_haffke = (itcz_state_haffke == 4)\n",
    "cond_eITCZ_haffke = (itcz_state_haffke == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_nITCZ = time_fma.where((cond_nITCZ_haffke), drop=True)\n",
    "time_dITCZ = time_fma.where((cond_dITCZ_haffke), drop=True)\n",
    "time_sITCZ = time_fma.where((cond_sITCZ_haffke), drop=True)\n",
    "time_eITCZ = time_fma.where((cond_eITCZ_haffke), drop=True)\n",
    "time_aITCZ = time_fma.where((cond_aITCZ_haffke), drop=True)\n",
    "#time_dITCZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dri = '/lss/research/agon-lab/IMERG-daily/NASA_interpolated/equ_waves/'\n",
    "fn = '1deg_IMERG_2000-2021_Kelvin_stdz.nc'\n",
    "data_kvw = xr.open_dataset(dri+fn)\n",
    "\n",
    "\n",
    "fn = '1deg_IMERG_2000-2021_IG0_stdz.nc'\n",
    "data_ig0 = xr.open_dataset(dri+fn)\n",
    "\n",
    "fn = '1deg_IMERG_2000-2021_MRG-TDtype_stdz.nc'\n",
    "data_tdt = xr.open_dataset(dri+fn)\n",
    "\n",
    "fn = '1deg_IMERG_2000-2021_ER_stdz.nc'\n",
    "data_erw = xr.open_dataset(dri+fn)\n",
    "\n",
    "fn = '1deg_IMERG_2000-2021_MJO_stdz.nc'\n",
    "data_mjo = xr.open_dataset(dri+fn)\n",
    "\n",
    "fn = '1deg_IMERG_2000-2021_IG1_stdz.nc'\n",
    "data_ig1 = xr.open_dataset(dri+fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_nITCZ= data_kvw['precipitationCal'].sel(time=time_nITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "kelvin_sITCZ= data_kvw['precipitationCal'].sel(time=time_sITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "kelvin_dITCZ= data_kvw['precipitationCal'].sel(time=time_dITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "kelvin_eITCZ= data_kvw['precipitationCal'].sel(time=time_eITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "ER_nITCZ= data_erw['precipitationCal'].sel(time=time_nITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "ER_sITCZ= data_erw['precipitationCal'].sel(time=time_sITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "ER_dITCZ= data_erw['precipitationCal'].sel(time=time_dITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "ER_eITCZ= data_erw['precipitationCal'].sel(time=time_eITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "IG0_nITCZ= data_ig0['precipitationCal'].sel(time=time_nITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "IG0_sITCZ= data_ig0['precipitationCal'].sel(time=time_sITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "IG0_dITCZ= data_ig0['precipitationCal'].sel(time=time_dITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "IG0_eITCZ= data_ig0['precipitationCal'].sel(time=time_eITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "IG1_nITCZ= data_ig1['precipitationCal'].sel(time=time_nITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "IG1_sITCZ= data_ig1['precipitationCal'].sel(time=time_sITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "IG1_dITCZ= data_ig1['precipitationCal'].sel(time=time_dITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "IG1_eITCZ= data_ig1['precipitationCal'].sel(time=time_eITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "MJO_nITCZ= data_mjo['precipitationCal'].sel(time=time_nITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "MJO_sITCZ= data_mjo['precipitationCal'].sel(time=time_sITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "MJO_dITCZ= data_mjo['precipitationCal'].sel(time=time_dITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "MJO_eITCZ= data_mjo['precipitationCal'].sel(time=time_eITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_nITCZ= data_tdt['precipitationCal'].sel(time=time_nITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "TD_sITCZ= data_tdt['precipitationCal'].sel(time=time_sITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "TD_dITCZ= data_tdt['precipitationCal'].sel(time=time_dITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')\n",
    "TD_eITCZ= data_tdt['precipitationCal'].sel(time=time_eITCZ,lat=slice(lats,latn),lon=slice(lonw,lone)).var(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_var_nITCZ_avg= kelvin_nITCZ.mean(dim=('lat', 'lon'))\n",
    "kelvin_var_sITCZ_avg= kelvin_sITCZ.mean(dim=('lat', 'lon'))\n",
    "kelvin_var_dITCZ_avg= kelvin_dITCZ.mean(dim=('lat', 'lon'))\n",
    "kelvin_var_eITCZ_avg= kelvin_eITCZ.mean(dim=('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "ER_var_nITCZ_avg= ER_nITCZ.mean(dim=('lat', 'lon'))\n",
    "ER_var_sITCZ_avg= ER_sITCZ.mean(dim=('lat', 'lon'))\n",
    "ER_var_dITCZ_avg= ER_dITCZ.mean(dim=('lat', 'lon'))\n",
    "ER_var_eITCZ_avg= ER_eITCZ.mean(dim=('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "IG0_var_nITCZ_avg= IG0_nITCZ.mean(dim=('lat', 'lon'))\n",
    "IG0_var_sITCZ_avg= IG0_sITCZ.mean(dim=('lat', 'lon'))\n",
    "IG0_var_dITCZ_avg= IG0_dITCZ.mean(dim=('lat', 'lon'))\n",
    "IG0_var_eITCZ_avg= IG0_eITCZ.mean(dim=('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "IG1_var_nITCZ_avg= IG1_nITCZ.mean(dim=('lat', 'lon'))\n",
    "IG1_var_sITCZ_avg= IG1_sITCZ.mean(dim=('lat', 'lon'))\n",
    "IG1_var_dITCZ_avg= IG1_dITCZ.mean(dim=('lat', 'lon'))\n",
    "IG1_var_eITCZ_avg= IG1_eITCZ.mean(dim=('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "MJO_var_nITCZ_avg= MJO_nITCZ.mean(dim=('lat', 'lon'))\n",
    "MJO_var_sITCZ_avg= MJO_sITCZ.mean(dim=('lat', 'lon'))\n",
    "MJO_var_dITCZ_avg= MJO_dITCZ.mean(dim=('lat', 'lon'))\n",
    "MJO_var_eITCZ_avg= MJO_eITCZ.mean(dim=('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_var_nITCZ_avg= TD_nITCZ.mean(dim=('lat', 'lon'))\n",
    "TD_var_sITCZ_avg= TD_sITCZ.mean(dim=('lat', 'lon'))\n",
    "TD_var_dITCZ_avg= TD_dITCZ.mean(dim=('lat', 'lon'))\n",
    "TD_var_eITCZ_avg= TD_eITCZ.mean(dim=('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_kelvin_nITCZ= (kelvin_var_nITCZ_avg)*100\n",
    "var_kelvin_sITCZ= (kelvin_var_sITCZ_avg)*100\n",
    "var_kelvin_dITCZ= (kelvin_var_dITCZ_avg)*100\n",
    "var_kelvin_eITCZ= (kelvin_var_eITCZ_avg)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_ER_nITCZ= (ER_var_nITCZ_avg)*100\n",
    "var_ER_sITCZ= (ER_var_sITCZ_avg)*100\n",
    "var_ER_dITCZ= (ER_var_dITCZ_avg)*100\n",
    "var_ER_eITCZ= (ER_var_eITCZ_avg)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_IG0_nITCZ= (IG0_var_nITCZ_avg)*100\n",
    "var_IG0_sITCZ= (IG0_var_sITCZ_avg)*100\n",
    "var_IG0_dITCZ= (IG0_var_dITCZ_avg)*100\n",
    "var_IG0_eITCZ= (IG0_var_eITCZ_avg)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_IG1_nITCZ= (IG1_var_nITCZ_avg)*100\n",
    "var_IG1_sITCZ= (IG1_var_sITCZ_avg)*100\n",
    "var_IG1_dITCZ= (IG1_var_dITCZ_avg)*100\n",
    "var_IG1_eITCZ= (IG1_var_eITCZ_avg)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_MJO_nITCZ= (MJO_var_nITCZ_avg)*100\n",
    "var_MJO_sITCZ= (MJO_var_sITCZ_avg)*100\n",
    "var_MJO_dITCZ= (MJO_var_dITCZ_avg)*100\n",
    "var_MJO_eITCZ= (MJO_var_eITCZ_avg)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_TD_nITCZ= (TD_var_nITCZ_avg)*100\n",
    "var_TD_sITCZ= (TD_var_sITCZ_avg)*100\n",
    "var_TD_dITCZ= (TD_var_dITCZ_avg)*100\n",
    "var_TD_eITCZ= (TD_var_eITCZ_avg)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.19967083632946\n",
      "5.377929657697678\n",
      "4.451173916459084\n",
      "5.885845422744751\n"
     ]
    }
   ],
   "source": [
    "print(var_IG1_nITCZ.values)\n",
    "print(var_IG1_sITCZ.values)\n",
    "print(var_IG1_dITCZ.values)\n",
    "print(var_IG1_eITCZ.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_itcz= np.array([var_kelvin_nITCZ.values,var_kelvin_sITCZ.values,var_kelvin_dITCZ.values,var_kelvin_eITCZ.values])\n",
    "IG0_itcz = np.array([var_IG0_nITCZ.values,var_IG0_sITCZ.values,var_IG0_dITCZ.values,var_IG0_eITCZ.values])\n",
    "TD_itcz = np.array([var_TD_nITCZ.values,var_TD_sITCZ.values,var_TD_dITCZ.values,var_TD_eITCZ.values])\n",
    "MJO_itcz = np.array([var_MJO_nITCZ.values,var_MJO_sITCZ.values,var_MJO_dITCZ.values,var_MJO_eITCZ.values])\n",
    "IG1_itcz = np.array([var_IG1_nITCZ.values,var_IG1_sITCZ.values,var_IG1_dITCZ.values,var_IG1_eITCZ.values])\n",
    "ER_itcz = np.array([var_ER_nITCZ.values,var_ER_sITCZ.values,var_ER_dITCZ.values,var_ER_eITCZ.values])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_fma = data_kvw['precipitationCal'].sel(lat=slice(lats,latn),lon=slice(lonw,lone), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_fma = data_erw['precipitationCal'].sel(lat=slice(lats,latn),lon=slice(lonw,lone), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_fma = data_ig0['precipitationCal'].sel(lat=slice(lats,latn),lon=slice(lonw,lone), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_fma = data_ig1['precipitationCal'].sel(lat=slice(lats,latn),lon=slice(lonw,lone), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_fma = data_mjo['precipitationCal'].sel(lat=slice(lats,latn),lon=slice(lonw,lone), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_fma = data_tdt['precipitationCal'].sel(lat=slice(lats,latn),lon=slice(lonw,lone), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_kelvin = (kelvin_fma)*100\n",
    "pr_ER = (ER_fma)*100\n",
    "pr_IG0 = (IG0_fma)*100\n",
    "pr_IG1 = (IG1_fma)*100\n",
    "pr_MJO = (MJO_fma)*100\n",
    "pr_TD = (TD_fma)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "nITCZ_prc = np.array([var_TD_nITCZ.values,var_kelvin_nITCZ.values,var_IG0_nITCZ.values,var_ER_nITCZ.values,var_MJO_nITCZ.values,var_IG1_nITCZ.values])\n",
    "sITCZ_prc = np.array([var_TD_sITCZ.values,var_kelvin_sITCZ.values,var_IG0_sITCZ.values,var_ER_sITCZ.values,var_MJO_sITCZ.values,var_IG1_sITCZ.values])\n",
    "dITCZ_prc = np.array([var_TD_dITCZ.values,var_kelvin_dITCZ.values,var_IG0_dITCZ.values,var_ER_dITCZ.values,var_MJO_dITCZ.values,var_IG1_dITCZ.values])\n",
    "eITCZ_prc = np.array([var_TD_eITCZ.values,var_kelvin_eITCZ.values,var_IG0_eITCZ.values,var_ER_eITCZ.values,var_MJO_eITCZ.values,var_IG1_eITCZ.values])\n",
    "total_prc= np.array([pr_TD.values, pr_kelvin.values,pr_IG0.values,pr_ER.values,pr_MJO.values,pr_IG1.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prc = [list(nITCZ_prc.flatten()),\n",
    "list(sITCZ_prc.flatten()),\n",
    "list(dITCZ_prc.flatten()),list(eITCZ_prc.flatten()), list(total_prc.flatten())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "itcz = ['nITCZ ('+str(np.round(sum(nITCZ_prc), decimals=1))+'%)', 'sITCZ ('+str(np.round(sum(sITCZ_prc), decimals=1))+'%)','dITCZ ('+str(np.round(sum(dITCZ_prc), decimals=1))+'%)', 'eITCZ ('+str(np.round(sum(eITCZ_prc), decimals=1))+'%)']\n",
    "kelvin_itcz= np.array([var_kelvin_nITCZ.values,var_kelvin_sITCZ.values,var_kelvin_dITCZ.values,var_kelvin_eITCZ.values])#,per_kelvin.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "col = ['#f6eff7','#d0d1e6','#a6bddb','#67a9cf','#1c9099','#016c59']   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 5000x5000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "npan = 2\n",
    "wid=.16\n",
    "w=0.4\n",
    "fs=50\n",
    "# all of the sizes are double the size they'll be in the paper \n",
    "fig = plt.figure(figsize=(50,50))\n",
    "\n",
    "for j in range(0,npan,1):\n",
    "    ax1 = fig.add_subplot(npan,1,j+1)\n",
    "    plt.minorticks_on()\n",
    "    ax1.tick_params(axis='both', which='major', length=10, width=1, direction='out', labelsize=fs)\n",
    "    ax1.tick_params(axis='x', labelsize=fs)\n",
    "    ax1.tick_params(axis='y', labelsize=fs)\n",
    "    if j == 0:\n",
    "        X=['MRG-TD type', 'Kelvin', 'IG0', 'ER', 'MJO','IG1'] #,'Total Variance']\n",
    "        X_axis = np.arange(len(X))\n",
    "        color = ['#f6eff7','#bdc9e1','#67a9cf','#1c9099','#016c59']  \n",
    "        plt0= plt.bar(X_axis, data_prc[0], color = color[4],label='nITCZ',width = wid,edgecolor= \"k\",linewidth=3)\n",
    "        plt1= plt.bar(X_axis+wid, data_prc[1], color = color[3], width = wid,edgecolor= \"k\",label='sITCZ',linewidth=3)\n",
    "        plt2= plt.bar(X_axis+(wid*2), data_prc[2], color = color[2], width = wid,edgecolor= \"k\",label='dITCZ',linewidth=3)\n",
    "        plt3= plt.bar(X_axis+(wid*3), data_prc[3], color = color[1], width = wid,edgecolor= \"k\",label='eITCZ',linewidth=3)\n",
    "        plt4= plt.bar(X_axis+(wid*4), data_prc[4], color = color[0], width = wid,edgecolor= \"k\",label='FMA',linewidth=3)\n",
    "        plt.xticks((X_axis+(wid*2)), X)\n",
    "\n",
    "        plt.yticks(np.arange(0, 60, 10),rotation=0, fontsize=fs)\n",
    "        plt.legend(loc=\"upper right\", fontsize =fs,ncol=7)\n",
    "        plt.ylabel(\"Percent Variance (%)\",fontsize=fs,fontweight=\"bold\")\n",
    "        plt.title('(a)Fractional Variance of CCWs',loc=\"left\",fontsize=50,fontweight=\"bold\")\n",
    "        \n",
    "    elif j == 1:\n",
    "        plt5= plt.bar(itcz, TD_itcz, color =col[5],edgecolor= \"w\",width = w , label='MRG-TD type', linewidth=3)\n",
    "        plt6= plt.bar(itcz, kelvin_itcz,bottom = TD_itcz, color= col[4],edgecolor= \"w\", width=w, label='Kelvin',linewidth=3)\n",
    "        plt7= plt.bar(itcz, IG0_itcz, bottom = TD_itcz+kelvin_itcz, color= col[3],edgecolor= \"w\", width=w,label='IG0',linewidth=3)\n",
    "        plt8= plt.bar(itcz, ER_itcz, bottom = TD_itcz+kelvin_itcz+IG0_itcz, color= col[2],edgecolor= \"w\" ,width = w,label='ER' ,linewidth=3)\n",
    "        plt9= plt.bar(itcz, MJO_itcz,bottom = TD_itcz+kelvin_itcz+IG0_itcz+ER_itcz, color= col[1],edgecolor= \"w\", width=w,label='MJO' ,linewidth=3)\n",
    "        plt10= plt.bar(itcz, IG1_itcz,bottom = kelvin_itcz+IG0_itcz+TD_itcz+ER_itcz+MJO_itcz, color= col[0],edgecolor= \"w\", width=w,label='IG1',linewidth=3)\n",
    "       \n",
    "        plt.yticks(np.arange(0, 160, 20),fontsize=fs)\n",
    "        plt.xticks(fontsize=fs)\n",
    "        plt.title('(b) Total Fractional Variance',loc=\"left\",fontsize=fs,fontweight=\"bold\") #50\n",
    "        #plt.legend(loc=\"upper left\", fontsize =10,ncol=len(plotdata.columns))\n",
    "        #plt.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\")\n",
    "        plt.ylabel(\"Percent Variance (%)\",fontsize=fs, fontweight=\"bold\")\n",
    "        plt.legend( loc=\"upper right\", ncol=7,fontsize=fs)\n",
    "        plt.minorticks_on()\n",
    "plt.savefig(figfolder+'IMERG_ITCZ_wave_var.pdf' ,bbox_inches=\"tight\",transparent=False)              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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