fractional_variance_plot.ipynb

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   "source": [
    "## Fractional variance plot\n",
    "\n",
    "Created by: Fouzia Fahrin (ffahrin@iastate.edu)"
   ]
  },
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    "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",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.util import add_cyclic_point\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib as mpl\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import tol_colors as tc"
   ]
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   "source": [
    "figfolder = './figures/'"
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    "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)#, engine='netcdf4')\n",
    "time_all = data['time']"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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    "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_ER_stdz.nc'\n",
    "data_erw = 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_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)"
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    "lats = -15\n",
    "latn = 15.\n",
    "lonw = 225 #75-135\n",
    "lone = 285\n",
    "\n",
    "mn0 = 2\n",
    "mnf = 4\n",
    "yr0 = 2001\n",
    "yrf = 2020\n",
    "time0 = str(yr0)+'-01-01T00:00:00.000000000'\n",
    "timef = str(yrf)+'-12-31T00:00:00.000000000'"
   ]
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   "cell_type": "code",
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   "source": [
    "time_all1 = data['time']"
   ]
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   "source": [
    "# set up conditional statements \n",
    "cond1_fma = (time_all1['time.month'] >= mn0) & (time_all1['time.month'] <= mnf) # season of interest\n",
    "cond2_fma = (time_all1['time.year'] <= yrf) & (time_all1['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_all1.where((cond1_fma)& (cond2_fma), drop=True) "
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    "mn0 = 5\n",
    "mnf = 7\n",
    "yr0 = 2001\n",
    "yrf = 2020\n",
    "time0 = str(yr0)+'-01-01T00:00:00.000000000'\n",
    "timef = str(yrf)+'-12-31T00:00:00.000000000'"
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   "source": [
    "cond1_MJJ = (time_all['time.month'] >= mn0) & (time_all['time.month'] <= mnf) # season of interest\n",
    "cond2_MJJ = (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_MJJ = time_all.where((cond1_MJJ)& (cond2_MJJ), drop=True) "
   ]
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   "source": [
    "mn0 = 8\n",
    "mnf = 10\n",
    "yr0 = 2001\n",
    "yrf = 2020\n",
    "time0 = str(yr0)+'-01-01T00:00:00.000000000'\n",
    "timef = str(yrf)+'-12-31T00:00:00.000000000'"
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   "execution_count": 11,
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    "cond1_ASO = (time_all['time.month'] >= mn0) & (time_all['time.month'] <= mnf) # season of interest\n",
    "cond2_ASO = (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_ASO = time_all.where((cond1_ASO)& (cond2_ASO), drop=True) "
   ]
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   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "yr0 = 2001\n",
    "yrf = 2020\n",
    "time0 = str(yr0)+'-01-01T00:00:00.000000000'\n",
    "timef = str(yrf)+'-12-31T00:00:00.000000000'\n",
    "\n",
    "mn0 = 11\n",
    "mnf = 1"
   ]
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   "id": "0dfe10fa-9590-4fb2-ba2c-62284c0cc370",
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       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 1871)&gt;\n",
       "array([&#x27;2000-11-01T00:00:00.000000000&#x27;, &#x27;2000-11-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-11-03T00:00:00.000000000&#x27;, ...,\n",
       "       &#x27;2021-01-29T00:00:00.000000000&#x27;, &#x27;2021-01-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-11-01 2000-11-02 ... 2021-01-31\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    axis:           T</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1871</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d98e2c0f-9f83-4c4c-afee-69b5add982bf' class='xr-array-in' type='checkbox' checked><label for='section-d98e2c0f-9f83-4c4c-afee-69b5add982bf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2000-11-01 2000-11-02 2000-11-03 ... 2021-01-29 2021-01-30 2021-01-31</span></div><div class='xr-array-data'><pre>array([&#x27;2000-11-01T00:00:00.000000000&#x27;, &#x27;2000-11-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-11-03T00:00:00.000000000&#x27;, ...,\n",
       "       &#x27;2021-01-29T00:00:00.000000000&#x27;, &#x27;2021-01-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-92a357d4-d253-4bce-a428-16b09594a80b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92a357d4-d253-4bce-a428-16b09594a80b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-11-01 ... 2021-01-31</div><input id='attrs-e9970e6c-99c1-42b5-b5ce-a3595c385e80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9970e6c-99c1-42b5-b5ce-a3595c385e80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2e58be7-0774-4714-bd9b-bf87b3aa0ae8' class='xr-var-data-in' type='checkbox'><label for='data-a2e58be7-0774-4714-bd9b-bf87b3aa0ae8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-11-01T00:00:00.000000000&#x27;, &#x27;2000-11-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-11-03T00:00:00.000000000&#x27;, ..., &#x27;2021-01-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-30T00:00:00.000000000&#x27;, &#x27;2021-01-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0cc1783a-d051-41b5-8418-63fdf2e11b06' class='xr-section-summary-in' type='checkbox'  ><label for='section-0cc1783a-d051-41b5-8418-63fdf2e11b06' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3829444e-98c4-419a-82f6-4791c2fa0c47' class='xr-index-data-in' type='checkbox'/><label for='index-3829444e-98c4-419a-82f6-4791c2fa0c47' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-11-01&#x27;, &#x27;2000-11-02&#x27;, &#x27;2000-11-03&#x27;, &#x27;2000-11-04&#x27;,\n",
       "               &#x27;2000-11-05&#x27;, &#x27;2000-11-06&#x27;, &#x27;2000-11-07&#x27;, &#x27;2000-11-08&#x27;,\n",
       "               &#x27;2000-11-09&#x27;, &#x27;2000-11-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-01-22&#x27;, &#x27;2021-01-23&#x27;, &#x27;2021-01-24&#x27;, &#x27;2021-01-25&#x27;,\n",
       "               &#x27;2021-01-26&#x27;, &#x27;2021-01-27&#x27;, &#x27;2021-01-28&#x27;, &#x27;2021-01-29&#x27;,\n",
       "               &#x27;2021-01-30&#x27;, &#x27;2021-01-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1871, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ad3d281-7917-434e-a93f-0fe89db270ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ad3d281-7917-434e-a93f-0fe89db270ad' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 1871)>\n",
       "array(['2000-11-01T00:00:00.000000000', '2000-11-02T00:00:00.000000000',\n",
       "       '2000-11-03T00:00:00.000000000', ...,\n",
       "       '2021-01-29T00:00:00.000000000', '2021-01-30T00:00:00.000000000',\n",
       "       '2021-01-31T00:00:00.000000000'], dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-11-01 2000-11-02 ... 2021-01-31\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    axis:           T"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up conditional statements \n",
    "\n",
    "if mn0 > mnf:\n",
    "    cond1 = (time_all['time.month'] >= mn0) & (time_all['time.year'] <= yrf-1) # season of interest\n",
    "    cond2 = (time_all['time.month'] <= mnf) & (time_all['time.year'] >= yr0) # season of interest\n",
    "    time_NDJ = time_all.where((cond1) | (cond2), drop=True) \n",
    "elif mn0 < mnf:\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",
    "    time_NDJ = time_all.where((cond1) & (cond2), drop=True) \n",
    "else:\n",
    "    cond1 = (time_all['time.month'] == mn0) # season of interest\n",
    "    cond2 = (time_all['time.year'] <= yrf) & (time_all['time.year'] >= yr0) # season of interest\n",
    "    time_NDJ = time_all.where((cond1) & (cond2), drop=True) \n",
    "\n",
    "time_NDJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24bfc5e9-f75b-4884-bd25-b442e989bf90",
   "metadata": {},
   "outputs": [],
   "source": [
    "lats_eq = -15\n",
    "latn_eq = 15\n",
    "lonw_eq = 225\n",
    "lone_eq = 285"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f7b823a-d4b7-4d1d-afd8-2feee164e14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_fma = data_kvw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "kelvin_MJJ = data_kvw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "kelvin_ASO = data_kvw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "kelvin_NDJ = data_kvw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e952788-8fa2-4a46-8f80-f60e096e2f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "ER_fma = data_erw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_MJJ = data_erw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_ASO = data_erw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_NDJ = data_erw['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5229ec43-9c43-4d41-b152-73bd24c046b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "IG0_fma = data_ig0['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_MJJ = data_ig0['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_ASO = data_ig0['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_NDJ = data_ig0['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb29cb3b-c2cb-4caa-9956-b64c82fde5b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "IG1_fma = data_ig1['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_MJJ = data_ig1['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_ASO = data_ig1['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_NDJ = data_ig1['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d22f7aad-f984-451b-9268-aed1b3385ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "MJO_fma = data_mjo['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_MJJ = data_mjo['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_ASO = data_mjo['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_NDJ = data_mjo['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a90caac-4049-4e48-afcf-03ce768c8f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_fma = data_tdt['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_MJJ = data_tdt['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_ASO = data_tdt['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_NDJ = data_tdt['precipitationCal'].sel(lat=slice(lats_eq,latn_eq),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e18bd401-7109-45e0-af5b-f1f06fa6b738",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_kelvin_fma = kelvin_fma*100\n",
    "per_kelvin_MJJ = kelvin_MJJ*100\n",
    "per_kelvin_ASO = kelvin_ASO*100\n",
    "per_kelvin_NDJ = kelvin_NDJ*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8a4f0c3-a7cd-438f-b900-0f4369d9de55",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_ER_fma = ER_fma*100\n",
    "per_ER_MJJ = ER_MJJ*100\n",
    "per_ER_ASO = ER_ASO*100\n",
    "per_ER_NDJ = ER_NDJ*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "41a840c7-10d4-4fbd-be97-8ce5d44f6a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_IG0_fma = IG0_fma*100\n",
    "per_IG0_MJJ = IG0_MJJ*100\n",
    "per_IG0_ASO = IG0_ASO*100\n",
    "per_IG0_NDJ = IG0_NDJ*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4bd4b756-3367-47b4-bd73-c6e3dd5ef652",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_IG1_fma = IG1_fma*100\n",
    "per_IG1_MJJ = IG1_MJJ*100\n",
    "per_IG1_ASO = IG1_ASO*100\n",
    "per_IG1_NDJ = IG1_NDJ*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "49f692f4-d2ce-49af-9d44-0b572cf4a366",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_MJO_fma = MJO_fma*100\n",
    "per_MJO_MJJ = MJO_MJJ*100\n",
    "per_MJO_ASO = MJO_ASO*100\n",
    "per_MJO_NDJ = MJO_NDJ*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58752a76-1438-41a9-9d58-3c7436613896",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_TD_fma = TD_fma*100\n",
    "per_TD_MJJ = TD_MJJ*100\n",
    "per_TD_ASO = TD_ASO*100\n",
    "per_TD_NDJ = TD_NDJ*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc31e738-d636-411b-9e54-3c3aaf27d99d",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_fma = np.round((per_kelvin_fma+per_ER_fma+per_IG0_fma+per_IG1_fma+per_MJO_fma+per_TD_fma), decimals=1)\n",
    "total_MJJ = np.round((per_kelvin_MJJ+per_ER_MJJ+per_IG0_MJJ+per_IG1_MJJ+per_MJO_MJJ+per_TD_MJJ), decimals=1)\n",
    "total_ASO = np.round((per_kelvin_ASO+per_ER_ASO+per_IG0_ASO+per_IG1_ASO+per_MJO_ASO+per_TD_ASO), decimals=1)\n",
    "total_NDJ = np.round((per_kelvin_NDJ+per_ER_NDJ+per_IG0_NDJ+per_IG1_NDJ+per_MJO_NDJ+per_TD_NDJ), decimals=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e54d4ca-29c2-48ed-a7b3-5e1b661fbde1",
   "metadata": {},
   "outputs": [],
   "source": [
    "MJJ_prc = np.array([per_TD_MJJ.values, per_kelvin_MJJ.values,per_IG0_MJJ.values, per_ER_MJJ.values,   per_MJO_MJJ.values,per_IG1_MJJ.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bc01f0ec-0df3-4039-9943-1ea144d089d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fma_prc = np.array([per_TD_fma.values,per_kelvin_fma.values,per_IG0_fma.values, per_ER_fma.values,  per_MJO_fma.values, per_IG1_fma.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a0dd1614-1df5-41f2-87d2-4d2b1409a74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ASO_prc = np.array([per_TD_ASO.values,per_kelvin_ASO.values, per_IG0_ASO.values,per_ER_ASO.values,  per_MJO_ASO.values,  per_IG1_ASO.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d5e50947-1374-4dd1-a684-eb09c972f7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "NDJ_prc = np.array([per_TD_NDJ.values,per_kelvin_NDJ.values,per_IG0_NDJ.values, per_ER_NDJ.values,  per_MJO_NDJ.values,  per_IG1_NDJ.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "381bc927-46e0-4dfc-b1bd-2b5f18c3d22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "title = ['EQ-15$^{\\circ}$S','EQ-15$^{\\circ}$N','15$^{\\circ}$N-15$^{\\circ}$S']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9a435e72-98f3-4fd6-8757-6ca0f6911be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = ['EQ-15S','EQ-15N','15N-15S']\n",
    "fs =20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "21153200-37c4-47bd-886d-f2fb82f33748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), tight_layout='true')\n",
    "plotdata = pd.DataFrame({\n",
    "    'FMA ('+str(total_fma.values)+'%)':fma_prc,\n",
    "    'MJJ ('+str(total_MJJ.values)+'%)': MJJ_prc, \n",
    "    'ASO ('+str(total_ASO.values)+'%)':ASO_prc,\n",
    "    'NDJ ('+str(total_NDJ.values)+'%)':NDJ_prc,\n",
    "    }, \n",
    "    index=[\"MRG-TD type\",\"Kelvin\", \"IG0\",\"ER\", \"MJO\",\"IG1\"]\n",
    ")\n",
    "plotdata.plot(kind=\"bar\",color=['#2166AC', '#4393C3', '#92C5DE', '#D1E5F0'], figsize = (18, 8),width=0.8,edgecolor= \"k\")\n",
    "#plotdata.plot(kind=\"bar\",color=['#2166AC', '#D1E5F0'],figsize = (25, 12), width=0.4,edgecolor= \"k\")\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(axis='both', which='major', length=10, width=1, direction='out', labelsize=fs)\n",
    "plt.xticks(rotation=0, horizontalalignment=\"center\", fontsize=fs)\n",
    "plt.yticks(np.arange(0, 26, 2),rotation=0, fontsize=fs)\n",
    "plt.legend(loc=\"upper right\", fontsize =fs)#,ncol=len(plotdata.columns))\n",
    "#plt.title( number[1] + title[1],fontsize=ls, loc=\"left\") # change with base point\n",
    "plt.title(title[2],fontsize=20, loc=\"left\", fontweight=\"bold\")\n",
    "    \n",
    "#plt.xlabel(\"Waves\", fontsize=15)\n",
    "plt.ylabel(\"Percent Variance (%)\",fontsize=fs)#, fontweight=\"bold\")\n",
    "plt.savefig(figfolder+name[2]+'_histogram.pdf', bbox_inches=\"tight\",transparent=False)#dpi=300) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fc82ee36-ce3d-4f08-8811-fc9cd44928ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "lats_eq_NH = 0.\n",
    "latn_eq_NH = 10.\n",
    "lonw_eq = 225\n",
    "lone_eq = 285"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc8e5570-2f97-486d-87cc-67c0b75fc710",
   "metadata": {},
   "outputs": [],
   "source": [
    "lats_eq_SH = -10.\n",
    "latn_eq_SH = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8af52219-c643-4ecf-aea5-f3b371352519",
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_fma_NH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))#.\n",
    "kelvin_MJJ_NH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))#\n",
    "kelvin_ASO_NH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))#.\n",
    "kelvin_NDJ_NH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))#."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70fc4a03-94d2-4d93-8d27-5c78b414438c",
   "metadata": {},
   "outputs": [],
   "source": [
    "kelvin_fma_SH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "kelvin_MJJ_SH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "kelvin_ASO_SH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "kelvin_NDJ_SH = data_kvw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "47dbbfe8-6e5f-4813-a030-6a92a90b04e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ER_fma_NH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_MJJ_NH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_ASO_NH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_NDJ_NH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "32349f59-3447-4535-beb7-797f327ebe5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ER_fma_SH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_MJJ_SH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_ASO_SH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "ER_NDJ_SH = data_erw['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "853c8f91-c981-4fdf-8054-4a65743c5499",
   "metadata": {},
   "outputs": [],
   "source": [
    "IG0_fma_NH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_MJJ_NH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_ASO_NH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_NDJ_NH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0223851d-b75d-49c7-9d50-20f6b6635b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "IG0_fma_SH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_MJJ_SH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_ASO_SH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG0_NDJ_SH = data_ig0['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "544b0e5d-e4cd-4e84-9977-529569d39a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "IG1_fma_NH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_MJJ_NH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_ASO_NH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_NDJ_NH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "35ec2b07-177d-4463-8b5f-f5749166a782",
   "metadata": {},
   "outputs": [],
   "source": [
    "IG1_fma_SH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_MJJ_SH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_ASO_SH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "IG1_NDJ_SH = data_ig1['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "809f042b-6b67-4261-8f16-4cc9009e73fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "MJO_fma_NH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_MJJ_NH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_ASO_NH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_NDJ_NH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8ae83a83-afb0-481b-9a40-86bd0a180772",
   "metadata": {},
   "outputs": [],
   "source": [
    "MJO_fma_SH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_MJJ_SH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_ASO_SH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "MJO_NDJ_SH = data_mjo['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d402196f-5bab-4853-81af-405fe52f3c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_fma_NH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_MJJ_NH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_ASO_NH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_NDJ_NH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_NH,latn_eq_NH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c56c1a08-e547-4032-ba1d-0c0c5b89a49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_fma_SH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_fma).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_MJJ_SH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_MJJ).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_ASO_SH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_ASO).var(dim=('time')).mean(dim=('lat','lon'))\n",
    "TD_NDJ_SH = data_tdt['precipitationCal'].sel(lat=slice(lats_eq_SH,latn_eq_SH),lon=slice(lonw_eq,lone_eq), time=time_NDJ).var(dim=('time')).mean(dim=('lat','lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "be63a1ab-2792-4036-8aec-40d7cf7da1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_kelvin_fma_NH = kelvin_fma_NH*100\n",
    "per_kelvin_MJJ_NH = kelvin_MJJ_NH*100\n",
    "per_kelvin_ASO_NH = kelvin_ASO_NH*100\n",
    "per_kelvin_NDJ_NH = kelvin_NDJ_NH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5d6bc1b6-6bbb-4022-b602-04ae03a2d99d",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_kelvin_fma_SH = kelvin_fma_SH*100\n",
    "per_kelvin_MJJ_SH = kelvin_MJJ_SH*100\n",
    "per_kelvin_ASO_SH = kelvin_ASO_SH*100\n",
    "per_kelvin_NDJ_SH = kelvin_NDJ_SH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "59203309-355d-44da-9db9-d78dfbeaf292",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_ER_fma_NH = ER_fma_NH*100\n",
    "per_ER_MJJ_NH = ER_MJJ_NH*100\n",
    "per_ER_ASO_NH = ER_ASO_NH*100\n",
    "per_ER_NDJ_NH = ER_NDJ_NH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "92860d1e-0353-48c6-8750-cbde1683dc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_ER_fma_SH = ER_fma_SH*100\n",
    "per_ER_MJJ_SH = ER_MJJ_SH*100\n",
    "per_ER_ASO_SH = ER_ASO_SH*100\n",
    "per_ER_NDJ_SH = ER_NDJ_SH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c1982757-6560-4c52-93e4-f68d6a8c9079",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_IG0_fma_NH = IG0_fma_NH*100\n",
    "per_IG0_MJJ_NH = IG0_MJJ_NH*100\n",
    "per_IG0_ASO_NH = IG0_ASO_NH*100\n",
    "per_IG0_NDJ_NH = IG0_NDJ_NH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81c65ec6-92aa-47a4-b44d-a16f87f89631",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_IG0_fma_SH = IG0_fma_SH*100\n",
    "per_IG0_MJJ_SH = IG0_MJJ_SH*100\n",
    "per_IG0_ASO_SH = IG0_ASO_SH*100\n",
    "per_IG0_NDJ_SH = IG0_NDJ_SH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c2d5f0e5-6f9d-4371-9848-d5a455aa4646",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_IG1_fma_NH = IG1_fma_NH*100\n",
    "per_IG1_MJJ_NH = IG1_MJJ_NH*100\n",
    "per_IG1_ASO_NH = IG1_ASO_NH*100\n",
    "per_IG1_NDJ_NH = IG1_NDJ_NH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "06385cac-6d0d-4669-9160-049da5a411cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_IG1_fma_SH = IG1_fma_SH*100\n",
    "per_IG1_MJJ_SH = IG1_MJJ_SH*100\n",
    "per_IG1_ASO_SH = IG1_ASO_SH*100\n",
    "per_IG1_NDJ_SH = IG1_NDJ_SH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3df4406a-4528-40fe-81ee-39ab0f2eaf58",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_MJO_fma_NH = MJO_fma_NH*100\n",
    "per_MJO_MJJ_NH = MJO_MJJ_NH*100\n",
    "per_MJO_ASO_NH = MJO_ASO_NH*100\n",
    "per_MJO_NDJ_NH = MJO_NDJ_NH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5224504a-a463-492e-9620-a3e7919d90e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_MJO_fma_SH = MJO_fma_SH*100\n",
    "per_MJO_MJJ_SH = MJO_MJJ_SH*100\n",
    "per_MJO_ASO_SH = MJO_ASO_SH*100\n",
    "per_MJO_NDJ_SH = MJO_NDJ_SH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eb85ae95-c56b-4e43-82e1-2437e348b56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_TD_fma_NH = TD_fma_NH*100\n",
    "per_TD_MJJ_NH = TD_MJJ_NH*100\n",
    "per_TD_ASO_NH = TD_ASO_NH*100\n",
    "per_TD_NDJ_NH = TD_NDJ_NH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2dcca3c3-85b4-4fee-85cd-a68da6d9542b",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_TD_fma_SH = TD_fma_SH*100\n",
    "per_TD_MJJ_SH = TD_MJJ_SH*100\n",
    "per_TD_ASO_SH = TD_ASO_SH*100\n",
    "per_TD_NDJ_SH = TD_NDJ_SH*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "28cef260-cadb-4795-81a2-1a262ebab67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_fma_NH = np.round((per_kelvin_fma_NH+per_ER_fma_NH+per_IG0_fma_NH+per_IG1_fma_NH+per_MJO_fma_NH+per_TD_fma_NH), decimals=1)\n",
    "total_MJJ_NH = np.round((per_kelvin_MJJ_NH+per_ER_MJJ_NH+per_IG0_MJJ_NH+per_IG1_MJJ_NH+per_MJO_MJJ_NH+per_TD_MJJ_NH), decimals=1)\n",
    "total_ASO_NH = np.round((per_kelvin_ASO_NH+per_ER_ASO_NH+per_IG0_ASO_NH+per_IG1_ASO_NH+per_MJO_ASO_NH+per_TD_ASO_NH), decimals=1)\n",
    "total_NDJ_NH = np.round((per_kelvin_NDJ_NH+per_ER_NDJ_NH+per_IG0_NDJ_NH+per_IG1_NDJ_NH+per_MJO_NDJ_NH+per_TD_NDJ_NH), decimals=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8cc49512-411a-4482-80e6-90bc108e7a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_fma_SH = np.round((per_kelvin_fma_SH+per_ER_fma_SH+per_IG0_fma_SH+per_IG1_fma_SH+per_MJO_fma_SH+per_TD_fma_SH), decimals=1)\n",
    "total_MJJ_SH = np.round((per_kelvin_MJJ_SH+per_ER_MJJ_SH+per_IG0_MJJ_SH+per_IG1_MJJ_SH+per_MJO_MJJ_SH+per_TD_MJJ_SH), decimals=1)\n",
    "total_ASO_SH = np.round((per_kelvin_ASO_SH+per_ER_ASO_SH+per_IG0_ASO_SH+per_IG1_ASO_SH+per_MJO_ASO_SH+per_TD_ASO_SH), decimals=1)\n",
    "total_NDJ_SH = np.round((per_kelvin_NDJ_SH+per_ER_NDJ_SH+per_IG0_NDJ_SH+per_IG1_NDJ_SH+per_MJO_NDJ_SH+per_TD_NDJ_SH), decimals=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c5d0778e-6f6b-45a1-99b5-cbafd3108e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "fma_prc_NH = np.array([per_TD_fma_NH.values,per_kelvin_fma_NH.values,per_IG0_fma_NH.values, per_ER_fma_NH.values, per_MJO_fma_NH.values,per_IG1_fma_NH.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3d4cc2c5-787f-4274-b0b8-bb0b4484fb3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fma_prc_SH = np.array([per_TD_fma_SH.values, per_kelvin_fma_SH.values,per_IG0_fma_SH.values, per_ER_fma_SH.values, per_MJO_fma_SH.values, per_IG1_fma_SH.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3883f72a-176e-4b19-a008-75d9cc1a8c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [list(fma_prc.flatten()),\n",
    "list(MJJ_prc.flatten()),\n",
    "list(ASO_prc.flatten()),\n",
    "list(NDJ_prc.flatten())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d9aa2cb9-bd93-461c-b639-346bd09c8a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_eq= [list(fma_prc_NH.flatten()),\n",
    "list(fma_prc_SH.flatten())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1201f40a-54be-4bb4-889b-45c3d4d873bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "label=['FMA ('+str(total_fma.values)+'%)','MJJ ('+str(total_MJJ.values)+'%)', 'ASO ('+str(total_ASO.values)+'%)','NDJ ('+str(total_NDJ.values)+'%)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b2ffe6c0-ffb2-433c-8d1d-3680593288cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls=20\n",
    "plotdata = pd.DataFrame({\n",
    "    'EQ-10$^{\\circ}$N ('+str(total_fma_NH.values)+'%)':fma_prc_NH,\n",
    "    'EQ-10$^{\\circ}$S ('+str(total_fma_SH.values)+'%)': fma_prc_SH, \n",
    "    \n",
    "    }, \n",
    "    index=[\"MRG-TD type\",\"Kelvin\", \"IG0\", \"ER\", \"MJO\", \"IG1\"]\n",
    ")\n",
    "plotdata.plot(kind=\"bar\",color=['#BB5566', '#004488'],figsize = (18, 8),edgecolor= \"k\",width=0.4)#, '#228833', '#CCBB44', '#EE6677'], figsize = (25, 12))\n",
    "#plotdata.plot(kind=\"bar\",color=['#4477AA', '#CCBB44', '#EE6677'], figsize = (25, 12))\n",
    "\n",
    "\n",
    "plt.minorticks_off()\n",
    "plt.tick_params(axis='both', which='major', length=10, width=1, direction='out', labelsize=ls)#, right='false', top='false')\n",
    "plt.xticks(rotation=0, horizontalalignment=\"center\", fontsize=ls)\n",
    "plt.yticks(np.arange(0, 44, 4),rotation=0, fontsize=ls)\n",
    "plt.title(\"FMA (NH vs. SH)\",fontsize=20,loc=\"left\", fontweight=\"bold\")\n",
    "plt.legend(loc=\"upper right\", fontsize =ls)\n",
    "#plt.xlabel(\"Waves\", fontsize=15)\n",
    "plt.ylabel(\"Percent Variance (%)\",fontsize=ls)#,fontweight=\"bold\")#,labelpad=15)\n",
    "plt.savefig(figfolder+'Histogram_Feb-Apr.pdf',bbox_inches=\"tight\",transparent=False)# dpi=300)             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c372752e-4f47-45b1-a666-7aae01c7863b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}