{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5cf2b3d-0f34-4d13-9ba3-dd2e3fb66337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIg. 6: Plot 2-D histograms for a single station\n",
    "\n",
    "from docx import Document\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from itertools import chain\n",
    "from matplotlib.colors import LinearSegmentedColormap, Normalize\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "import cmocean\n",
    "\n",
    "y_labs = ['T (˚C)', 'p (hPa)', 'V (m/s)', 'q (g/kg)', 'WDA (degree)', 'Rain (mm/3hrs)'] \n",
    "x_labs = \"Local Time\"\n",
    "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']\n",
    "\n",
    "cmap = LinearSegmentedColormap.from_list(\"white_to_dark\", [\"white\", \"black\"])\n",
    "norm = BoundaryNorm([0, 1, np.inf], ncolors=cmap.N, clip=True)\n",
    "\n",
    "count_std = np.zeros(6) \n",
    "\n",
    "y1 = [20, 1000,  0, 13,   0,  0]\n",
    "y2 = [25, 1021, 10, 25, 360, 30]\n",
    "\n",
    "vsh = ['T', 'p', 'V', 'q', 'WDA', 'R3h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51ce5ba3-dcbb-4870-a512-b2c338c0e4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot doifference in histograms\n",
    "\n",
    "vmin = 0\n",
    "vmax = 30\n",
    "\n",
    "sid = 96839 #Semarang \n",
    "\n",
    "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(8, 10), layout=\"constrained\")\n",
    "axs_flat = axs.flatten()\n",
    "axs[0, 0].set_xlim(0, 24)\n",
    "axs[0, 0].set_xticks(np.arange(0, 25, 3))\n",
    "\n",
    "HC = np.zeros((24, 24))\n",
    "HCR = np.zeros((8, 24))\n",
    "WDA = np.zeros((24, 24))\n",
    "\n",
    "# loop through variables\n",
    "nv = 0\n",
    "for v in vsh:\n",
    "    \n",
    "    data_in_path = f\"/Users/cdz/Desktop/Khafid_etal/Manuscript/2Dhist_stations_Data/hist2d_data_{sid}_{v}.npz\"\n",
    "    \n",
    "    data = np.load(data_in_path)\n",
    "\n",
    "    if v == 'R3h':\n",
    "        HR = data['H']\n",
    "        PR = 8*100*HR/HR.sum()\n",
    "    if v!= 'R3h':\n",
    "        H = data['H']\n",
    "        P = 24*100*H/H.sum()\n",
    "   \n",
    "       \n",
    "    ylimit1 = y1[nv]\n",
    "    ylimit2 = y2[nv]\n",
    "    dy = (ylimit2-ylimit1)/25\n",
    "    \n",
    "    ybins = np.arange(ylimit1, ylimit2, dy)\n",
    "    xbins = np.arange(0, 25, 1)     # hourly bins \n",
    "    \n",
    "    mv = nv \n",
    "    row = mv // 2  # number of columns = 2\n",
    "    col = mv % 2    \n",
    "    axs[row, col].set_xlim(0, 24)\n",
    "    axs[row, col].set_xticks(np.arange(0, 25, 3))\n",
    "    \n",
    "    xedges = data[\"xedges\"]\n",
    "    yedges = data[\"yedges\"]\n",
    "    \n",
    "    if v == 'R3h':\n",
    "        xbins = np.arange(0, 25, 3)     # 3 hourly bins \n",
    "    #        axs[row, col].imshow(HCR.T, origin=\"lower\", aspect=\"auto\", cmap=cmap, extent=[0, 24, 0, HCR.shape[0]])  \n",
    "        axs[row, col].pcolormesh(xbins, ybins, PR.T, cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "    if v!= 'R3h':\n",
    "    #        axs[row, col].imshow(HC.T, origin=\"lower\", aspect=\"auto\", cmap=cmap)  \n",
    "        im = axs[row, col].pcolormesh(xbins, ybins, P.T, cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "    \n",
    "        \n",
    "    #    plt.colorbar(label=\"Counts\")\n",
    "    axs[row, col].set_ylabel(y_labs[nv])\n",
    "    axs[row, col].set_xlabel(x_labs)\n",
    "    axs[row,col].set_title(labs[nv], fontsize=10, loc='left')\n",
    "    \n",
    "    #    print(nv, mv, xlimit1, xlimit2, x_labs[nv])\n",
    "    \n",
    "    H = 0\n",
    "    nv = nv + 1\n",
    "\n",
    "cbar = fig.colorbar(im, ax=axs, orientation='horizontal', location='bottom',\n",
    "                    pad=0.02, shrink=0.6, aspect=40)\n",
    "cbar.set_label('%', fontsize=12)\n",
    "\n",
    "fig_path = f\"/Users/cdz/Desktop/Khafid_etal/Manuscript/Fig6.png\"\n",
    "fig.savefig(fig_path)   \n",
    "plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b591d73-48c8-47c8-a240-f6b31fc37678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55109.0 58196.0 -3087.0\n"
     ]
    }
   ],
   "source": [
    "print(H1.sum(), H2.sum(), H1.sum()- H2.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffe99be5-daad-4bb0-a337-fc7e447f5f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting cmocean\n",
      "  Downloading cmocean-4.0.3-py3-none-any.whl.metadata (4.2 kB)\n",
      "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.11/site-packages (from cmocean) (3.8.0)\n",
      "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.11/site-packages (from cmocean) (1.26.4)\n",
      "Requirement already satisfied: packaging in /opt/anaconda3/lib/python3.11/site-packages (from cmocean) (23.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (1.4.4)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib->cmocean) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib->cmocean) (1.16.0)\n",
      "Downloading cmocean-4.0.3-py3-none-any.whl (421 kB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m421.9/421.9 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: cmocean\n",
      "Successfully installed cmocean-4.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install cmocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0428e5a-dceb-48e3-bfe0-7ea6ce20bf07",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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