{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/rcsetup.py:342: UserWarning: axes.color_cycle is deprecated.  Use axes.prop_cycle instead. Will be removed in 2.1.0\n",
      "  warnings.warn(\"axes.color_cycle is deprecated.  Use axes.prop_cycle \"\n",
      "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "# hostogram with bins for paper\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "#matplotlib.use('macosx')  # TkAgg Force mpl to use Tk backend\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap, shiftgrid\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import netCDF4\n",
    "import sys\n",
    "import math \n",
    "import matplotlib.patches as mpatches\n",
    "# needs gridspec\n",
    "from matplotlib import gridspec\n",
    "from matplotlib.colors import ListedColormap\n",
    "import palettable \n",
    "from pandas import DataFrame \n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "from Boreal_Variables import bare_ea_east as bare\n",
    "from Boreal_Variables import savanna_ea_east as savanna\n",
    "from Boreal_Variables import forest_ea_east as forest \n",
    "\n",
    "\n",
    "savabmap = ListedColormap(palettable.colorbrewer.diverging.PuOr_5_r.mpl_colors)\n",
    "baremap = ListedColormap(palettable.colorbrewer.diverging.PuOr_5.mpl_colors)\n",
    "\n",
    "sns.set_style(style=\"white\")\n",
    "dataforr_ea_east = np.loadtxt(\"/Users/Beniamino/Documents/OneDrive/MPI-M/iPython_Notebooks/Boreal_Area/boreal_ea_east_database.txt\") # columns=[\"VCFF\", \"MAR\", \"MASM\", \"Tmin\", \"PZI\", \"FF\", \"GDD0\", \"State\", \"Elev\", \"Lon\", \"Lat\"]\n",
    "\n",
    "# conditional histograms section\n",
    "\n",
    "cmapbare = sns.light_palette(\"#7570b3\", n_colors=5, reverse=False, as_cmap=True)\n",
    "cmapsava = sns.light_palette(\"#d95f02\", n_colors=5, reverse=False, as_cmap=True)\n",
    "cmapfore = sns.light_palette(\"#1b9e77\", n_colors=5, reverse=False, as_cmap=True)\n",
    "colbare = '#7570b3'\n",
    "colsava = '#d95f02'\n",
    "colfore = '#1b9e77'\n",
    "\n",
    "\n",
    "\n",
    "del_ind = []\n",
    "for i in range(len(forest[:,5])):\n",
    "    if forest[i,5]==0: del_ind.append(i)\n",
    "for_with_fire = np.delete(forest[:], del_ind, 0)\n",
    "\n",
    "del_ind = []\n",
    "for i in range(len(bare[:,5])):\n",
    "    if bare[i,5]==0: del_ind.append(i)\n",
    "bar_with_fire = np.delete(bare[:], del_ind, 0)\n",
    "\n",
    "del_ind = []\n",
    "for i in range(len(savanna[:,5])):\n",
    "    if savanna[i,5]==0: del_ind.append(i)\n",
    "sav_with_fire = np.delete(savanna[:], del_ind, 0)\n",
    "\n",
    "del_ind = []\n",
    "for i in range(len(dataforr_ea_east[:,5])):\n",
    "    if dataforr_ea_east[i,5]==0: del_ind.append(i)\n",
    "data_with_fire = np.delete(dataforr_ea_east[:], del_ind, 0)\n",
    "\n",
    "columns_2=[\"VCFF [%]\", \"MAR - [\"+r'mm yr$^{-1}$'+\"]\", \"Mean_Spring_SM - [\"+r'mm'+\"]\", \"Mean_Tmin - [\"+r'$^\\circ$C'+\"]\",\\\n",
    "           \"PZI\", \"FF - [\"+r'fires yr$^{-1}$'+\"]\", \"GDD0 - [\"+r'$^\\circ$C yr$^{-1}$'+\"]\", \"PTD\", \"ST\", \"State\",\"Elev\", \"Lon\", \"Lat\" ]\n",
    "columns=[\"VCFF\", \"MAR\", \"Mean_Spring_SM\", \"Mean_Tmin\", \"PZI\", \"FF\", \"GDD0\", \"PTD\", \"ST\", \"State\",\"Elev\", \"Lon\", \"Lat\" ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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M4Pr163r3L1u2LMO+48ePM2nSJHr37k1ISAj29vYMHjyYp0+fFkbIQpR4J06cYM2aNaxf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\niIiIiFTGoJuIiIiISGUMuomIiIiIVPb/AyUfnvnJwtcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122729850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# In[7]:\n",
    "index = 3\n",
    "fsize=14\n",
    "sava_90 = np.percentile(savanna[:,index],90)\n",
    "sava_10 = np.percentile(savanna[:,index],10)\n",
    "\n",
    "\n",
    "\n",
    "binBoundaries = np.linspace(np.min(dataforr_ea_east[:,index]),np.max(dataforr_ea_east[:,index]),101) #\n",
    "binw = (binBoundaries[2]-binBoundaries[1])\n",
    "s_annot_location = round((sava_90)/binw)*binw+binw#+ binBoundaries[0]-binw\n",
    "s_annot_location_10 = round((sava_10)/binw)*binw+binw#+ binBoundaries[0]-2*binw\n",
    "delimiters = [-10.55258484,  -8.54070816,  -6.52883148,  -4.5169548,   -2.50507812]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "gs = gridspec.GridSpec(6, 1)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0:2,:], xticks=delimiters)\n",
    "ax.set_title('Eastern North Eurasia '+str(columns_2[index]), fontsize=fsize, y=1.06)\n",
    "\n",
    "sns.despine(left=True)\n",
    "hist_X = ax.hist([bare[:,index], savanna[:,index], forest[:,index]], label=[\"Treeless\", \"Open woodland\", \"Forest\"],\\\n",
    "             stacked=True,bins=binBoundaries, alpha=0.7,normed=False, color = ['#7570b3', '#d95f02', '#1b9e77'])#hist(bare[:,1], color = '#5673E0', normed = True)\n",
    "lin_s = np.linspace(0,0.20*np.max(hist_X[0]))\n",
    "\n",
    "# [colbare, colsava, colfore]\n",
    "mi, qua, mid, triqua, ma = 0, (np.min(hist_X[0])+np.max(hist_X[0]))/4,(np.min(hist_X[0])+np.max(hist_X[0]))/2,(np.min(hist_X[0])+np.max(hist_X[0]))/4+(np.min(hist_X[0])+np.max(hist_X[0]))/2, np.max(hist_X[0])\n",
    "ax.set_yticks([int(mi), int(qua), int(mid), int(triqua), int(ma)])\n",
    "\n",
    "plt.plot([delimiters[0] for i in lin_s ], lin_s*4.2, color = 'k')\n",
    "ax.annotate('open woodland\\n10th percentile', ha=\"center\", fontsize = fsize-2, xy=(delimiters[0], 0.92*np.max(hist_X[0])))\n",
    "plt.plot([delimiters[-1] for i in lin_s ], lin_s*3.2, color = 'k')\n",
    "ax.annotate('open woodland\\n90th percentile', ha=\"center\", fontsize = fsize-2, xy=(delimiters[-1], 0.72*np.max(hist_X[0])))\n",
    "\n",
    "plt.xticks(fontsize = fsize)\n",
    "plt.yticks(fontsize = fsize)\n",
    "plt.xlim(np.min(binBoundaries),np.max(binBoundaries))\n",
    "\n",
    "plt.legend(fontsize=14, loc=\"upper left\")\n",
    "plt.ylabel(\"No. of gridcells\", fontsize=fsize)\n",
    "\n",
    "gs.update(hspace=0.50)\n",
    "\n",
    "#dividers plot\n",
    "axt=fig.add_subplot(gs[2,:], xticks=[], ylim=(-0.4,0.2), yticks=[])\n",
    "axt.xaxis.set_label_position('top') \n",
    "axt.xaxis.tick_top()\n",
    "\n",
    "\n",
    "sns.despine(ax=axt,left=True, top=True, bottom=True)\n",
    "plt.xticks(fontsize = fsize)\n",
    "\n",
    "\n",
    "annotations = [(np.min(binBoundaries)-10.55258484)/2.,  (-10.55258484-8.54070816)/2.,  (-8.54070816-6.52883148)/2.,  (-6.52883148-4.5169548)/2.,   (-4.5169548-2.50507812)/2., (-2.50507812+np.max(binBoundaries))/2.]\n",
    "for elem in delimiters:\n",
    "    plt.plot([elem for i in lin_s ], lin_s, color = 'k', linewidth=2.)\n",
    "for elem in [np.min(binBoundaries),np.max(binBoundaries)]:\n",
    "    plt.plot([elem for i in lin_s ], lin_s, color = 'k', linewidth=4.0)\n",
    "   \n",
    "    \n",
    "for i in range(len(annotations)):\n",
    "    axt.annotate(\"bin \"+str(i+1), xy=(annotations[i], 0.1), ha=\"center\", va=\"center\", size=16)\n",
    "\n",
    "    \n",
    "\n",
    "plt.xlim(np.min(binBoundaries),np.max(binBoundaries))\n",
    "#plt.ylim(0,520)\n",
    "#plt.savefig( '/Users/Beniamino/Documents/OneDrive/MPI-M/My_Papers/First_Paper_First_Draft/bor_ea_east_MTmin_bins_with_title.pdf',dpi=300, transparent=False)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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