{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5a1434d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os.path, os, glob, datetime\n",
    "import xarray as xr, numpy as np, pandas as pd, matplotlib.pyplot as plt\n",
    "#more imports\n",
    "import geoxarray\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "from matplotlib import dates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0897cfb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def xticksyear(base=1, month=1, day=1, tz=None, ax=None):\n",
    "    \"\"\"Make ticks on a given day of each year that is a multiple of base.\"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    years = dates.YearLocator(base=base, month=month, day=day, tz=tz)\n",
    "    ax.xaxis.set_major_locator(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cc54256b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ifile='data_precip_FLOR_histRCP45_5ens_1860-2100_Madagascar.nc'\n",
    "#ifile='data_precip_FLOR_histRCP45_5ens_1860-2100_MozMal.nc'\n",
    "\n",
    "#ifile_3d=[ifile[:-3] + '.3drun_maxJul-Jun.nc']\n",
    "\n",
    "ofile = ifile.replace('.nc', '.png').replace('data','fig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a0495e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fig_precip_FLOR_histRCP45_5ens_1860-2100_Madagascar.png'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ofile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "24dd4c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(ifile)\n",
    "daname = list(ds.data_vars)[0]\n",
    "da = ds[daname]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7eaff857",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmm = da.geo.fldmean().rolling(time=3).mean() \\\n",
    "     .sel(time=slice('1981-01-01', '2021-12-31'))\\\n",
    "     .mean('ens')*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41178e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "    dd=ax1.xaxis.set_major_locator(years)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "87e289af",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d464474b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "tzinfo argument must be None or of a tzinfo subclass, not type 'cftime._cftime.DatetimeJulian'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2228\u001b[0m                        else suppress())\n\u001b[1;32m   2229\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2230\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2232\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2778\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2779\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2780\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   2781\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   2782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                 **kwargs)\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2879\u001b[0m                 \u001b[0martists\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2881\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_title_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2883\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxison\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0minframe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2810\u001b[0m                 if (ax.xaxis.get_ticks_position() in ['top', 'unknown']\n\u001b[1;32m   2811\u001b[0m                         or ax.xaxis.get_label_position() == 'top'):\n\u001b[0;32m-> 2812\u001b[0;31m                     \u001b[0mbb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2813\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2814\u001b[0m                     \u001b[0mbb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1079\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1081\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1021\u001b[0m         \u001b[0mthe\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m  \u001b[0mReturn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mticks\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdrawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \"\"\"\n\u001b[0;32m-> 1023\u001b[0;31m         \u001b[0mmajor_locs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m         \u001b[0mmajor_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \u001b[0mmajor_ticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_majorticklocs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1253\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_majorticklocs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m         \u001b[0;34m\"\"\"Return this Axis' major tick locations in data coordinates.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_minorticklocs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1499\u001b[0m         \u001b[0;31m# if no data have been set, this will tank with a ValueError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0mdmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviewlim_to_dt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mviewlim_to_dt\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1125\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m             \u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1127\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mnum2date\u001b[0;34m(x, tz)\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtz\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \u001b[0mtz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_rc_timezone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_from_ordinalf_np_vectorized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2161\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2163\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2165\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2244\u001b[0m             \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2246\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2248\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tigress/wenchang/miniconda3/lib/python3.9/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m_from_ordinalf\u001b[0;34m(x, tz)\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtzinfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdateutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgettz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'UTC'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[0;31m# but maybe we are working in a different timezone so move.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m     \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastimezone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m     \u001b[0;31m# fix round off errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m70\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m365\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: tzinfo argument must be None or of a tzinfo subclass, not type 'cftime._cftime.DatetimeJulian'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 14400x4800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da = dmm.sel(time = slice('1980', '2020'))\n",
    "sns.set(style=\"whitegrid\")\n",
    "fig, ax1 = plt.subplots(figsize = (12,4), dpi = 1200)\n",
    "line1 = ax1.plot(da.time, da, color = 'b', linewidth = 1.5, alpha = 0.8, label = 'FLOR')\n",
    "ax1.set_xlabel('Day',color = 'k')\n",
    "ax1.set_ylabel('Precip mm/day',color = 'k')\n",
    "years = dates.YearLocator(base=1, month=1, day=1, tz=da['time'])\n",
    "ax1.xaxis.set_major_locator(years)\n",
    "#xticksyear(base=1, month=1, day=1, tz=da, ax=ax1)\n",
    "#ax1.set_xticks([f'{year}' for year in range(1980, 2021, 10)])\n",
    "#ax1.xaxis.set_major_formatter(da.time.DateFormatter('%Y')) \n",
    "#ax1.set_xlim(pd.Timestamp('1980-01-01'), pd.Timestamp('2022-01-01'))\n",
    "#plt.savefig(ofile,dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fd2454fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3cc27415",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=xr.open_dataset(ifile)\n",
    "da=ds.mean('ens')\n",
    "da_std=np.std(ds.precip, ddof=1,axis = 0) /  np.sqrt(np.size(ds.precip,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8d9a42d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 14400x6000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "fig, ax1 = plt.subplots(figsize = (12,5), dpi = 1200)\n",
    "line1 = ax1.plot(da.year, da.precip, color = '#CC4F1B', linewidth = 1.5, alpha = 0.8, label = 'FLOR')\n",
    "ax1.set_xlabel('Year',color = 'k')\n",
    "ax1.set_ylabel('Precip mm/day',color = 'k')\n",
    "ax1.set_ylim(10,40)\n",
    "#ax1.set_yticks([15,20,25,30,35,40])\n",
    "ax1.set_xlim(1981,2010)\n",
    "ax2=plt.fill_between(da.year, da.precip-da_std, da.precip+da_std\\\n",
    "                     ,linewidth = 1.5, alpha=0.5,\\\n",
    "                      facecolor='#FF9848')\n",
    "#plt.savefig(ofile,dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7dc1103",
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 0.25\n",
    "\n",
    "x = np.arange(1980,2019,1)\n",
    "\n",
    "x1 = np.arange(1980,2020,1)\n",
    "\n",
    "xforlabel = np.arange(1980,2020,2)\n",
    "\n",
    "labels = [str(i) for i in list(np.arange(1980,2020,2))]\n",
    "\n",
    "\n",
    "ts_mean = np.zeros(len(x1))+TS_ann.mean()\n",
    "\n",
    "hc_mean = np.zeros(len(x1))+HC_ann.mean()\n",
    "\n",
    "sd_mean = np.zeros(len(x1))+seed_ann.mean()\n",
    "\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize = (12,3), dpi = 1200)\n",
    "\n",
    "\n",
    "# TS and Hurricane numbers (lines)\n",
    "\n",
    "line1 = ax1.plot(x, TS_ann, color = 'k', linewidth = 1.5, \n",
    "\n",
    "                 alpha = 0.8, label = 'Tropical Storm $(\\geq 34\\,knot)$')\n",
    "\n",
    "line2 = ax1.plot(x, HC_ann, color = 'tab:red',  linewidth = 1.5,\n",
    "\n",
    "                 alpha = 0.5, label = 'Hurricane $(\\geq 64\\,knot)$')\n",
    "\n",
    "ax1.set_xlabel('Year',color = 'k')\n",
    "\n",
    "ax1.set_ylabel('Annual Mean frequency',color = 'k')\n",
    "\n",
    "ax1.set_ylim(0,30)\n",
    "\n",
    "ax1.set_yticks([0,5,10,15,20,25,30])\n",
    "\n",
    "ax1.set_xlim(1980,2019)\n",
    "\n",
    "ax1.set_xticks(xforlabel)\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='k')\n",
    "\n",
    "ax1.grid(axis='both', linestyle='--',alpha = 0.5)\n",
    "\n",
    "\n",
    "# mark the active / non-acitve years by dots\n",
    "\n",
    "plt.scatter(x[N_act_flag], TS_ann[N_act_flag], marker = 'o',c='',edgecolors='r')\n",
    "\n",
    "plt.scatter(x[N_inact_flag], TS_ann[N_inact_flag], marker = 'o',c='',edgecolors='b')\n",
    "\n",
    "line1_mean = ax1.plot(x1, ts_mean, linestyle='--', color='k', alpha = 0.5, linewidth = 1.5)\n",
    "\n",
    "para1= np.polyfit(x, TS_ann, 30)\n",
    "\n",
    "f1 = np.poly1d(para1)\n",
    "\n",
    "ax1.plot(x, f1(x),color='grey',linewidth = 3,alpha = 0.6)\n",
    "\n",
    "\n",
    "plt.scatter(x[Nhc_act_flag], HC_ann[Nhc_act_flag], marker = '.',c='',edgecolors='tab:red')\n",
    "\n",
    "plt.scatter(x[Nhc_inact_flag], HC_ann[Nhc_inact_flag], marker = '.',c='',edgecolors='tab:blue')\n",
    "\n",
    "line2_mean = ax1.plot(x1, hc_mean, linestyle='--', color='tab:red', alpha = 0.3, linewidth = 1.5)\n",
    "\n",
    "para2= np.polyfit(x, HC_ann, 30)\n",
    "\n",
    "f2 = np.poly1d(para2)\n",
    "\n",
    "ax1.plot(x, f2(x),color='tab:red',linewidth = 3,alpha = 0.2)\n",
    "\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "\n",
    "# seed numbers (bars)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "line3 = ax2.bar(x[2:], seed_ann, color = 'tab:blue',  linewidth = 1.5,\n",
    "\n",
    "                alpha = 0.2, label = 'Seed')\n",
    "\n",
    "line3_mean = ax2.plot(x1, sd_mean, linestyle='--', color='tab:blue', alpha = 0.3, linewidth = 1.5)\n",
    "\n",
    "para3= np.polyfit(x[2:], seed_ann, 30)\n",
    "\n",
    "f3 = np.poly1d(para3)\n",
    "\n",
    "ax2.plot(x[2:], f3(x[2:]),color='tab:blue',linewidth = 3,alpha = 0.2)\n",
    "\n",
    "ax2.set_ylabel('Annual Mean seed frequency',color = 'tab:blue')\n",
    "\n",
    "\n",
    "ax2.tick_params(axis='y', which='both', color='tab:blue')\n",
    "\n",
    "ax2.set_ylim(60,180)\n",
    "\n",
    "ax2.set_yticks([60,80,100,120,140,160,180])\n",
    "\n",
    "ax2.set_yticklabels([60,80,100,120,140,160,180], color='tab:blue')\n",
    "\n",
    "ax2.grid(False)\n",
    "\n",
    "plt.legend(loc = 'upper right')\n",
    "\n",
    "# plt.savefig(fpathfig + 'interannual_NA_1.png', dpi=1200, bbox_inches = 'tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3532e539",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.cos(x/6*np.pi)    \n",
    "error = np.random.rand(len(y)) * 0.5\n",
    "y += np.random.normal(0, 0.1, size=y.shape)\n",
    "pl.plot(x, y, 'k', color='#1B2ACC')\n",
    "pl.fill_between(x, y-error, y+error,\n",
    "    alpha=0.2, edgecolor='#1B2ACC', facecolor='#089FFF',\n",
    "    linewidth=4, linestyle='dashdot', antialiased=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5942e7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "da = dmm.sel(time = slice('1990', '2000'))\n",
    "da['time'] = da.time.dt.year\n",
    "da.time"
   ]
  }
 ],
 "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": "3.9.1"
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}