read_tro_database.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description\n",
    "This notebook decompresses the csrs-ppp outputs and reads calculated ZWD from .tro files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Directry structure\n",
    "### Before decompression\n",
    "> PPP_result_2024/\n",
    ">> rov_20240401.obs_full_output.zip\\\n",
    ">> rov_20240402.obs_full_output.zip\\\n",
    ">> rov_20240403.obs_full_output.zip\\\n",
    ">> rov_20240501.obs_full_output.zip, ...\n",
    "\n",
    "### After decompression\n",
    "> PPP_result_2024/\n",
    ">> 202404/\n",
    ">>> rov_20240401.clk\\\n",
    ">>> rov_20240401.csv\\\n",
    ">>> rov_20240401.pdf\\\n",
    ">>> rov_20240401.pos\\\n",
    ">>> rov_20240401.sum\\\n",
    ">>> rov_20240401.tro\\\n",
    ">>> rov_20240402.clk\\\n",
    ">>> rov_20240402.csv\\\n",
    ">>> rov_20240402.pdf\\\n",
    ">>> rov_20240402.pos\\\n",
    ">>> rov_20240402.sum\\\n",
    ">>> rov_20240402.tro, ...\n",
    "\n",
    ">> 202405/\n",
    ">>> rov_20240501.clk\\\n",
    ">>> rov_20240501.csv\\\n",
    ">>> rov_20240501.pdf\\\n",
    ">>> rov_20240501.pos\\\n",
    ">>> rov_20240501.sum\\\n",
    ">>> rov_20240501.tro, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decompress zip folders of csrs-ppp results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "csrsdir = '/mnt/AKLabNAS/jsugiyama/SA_GNSS_data/PPP_result_2024'\n",
    "#mdirs = [d for d in os.listdir(csrsdir) if \".zip\" in d]\n",
    "mdirs = [d for d in os.listdir(csrsdir) if \"rov_202303\" in d]\n",
    "mdirs.sort()\n",
    "# decompress zip folders\n",
    "for m in mdirs:\n",
    "    zip_path = os.path.join(csrsdir, m)\n",
    "    out_dir = os.path.join(csrsdir, m[4:10])\n",
    "    if not os.path.exists(out_dir):\n",
    "        os.makedirs(out_dir)\n",
    "    shutil.unpack_archive(zip_path, out_dir)\n",
    "    # remove the zip folder\n",
    "    os.remove(zip_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read zenith wet delay from .tro files\n",
    "This cell reads .tro files and calculates zwd per minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "csrsdir = '/mnt/AKLabNAS/jsugiyama/SA_GNSS_data/PPP_result_2024'\n",
    "mdirs = []\n",
    "start_folder = 202401\n",
    "end_folder = 202405\n",
    "for i in range(start_folder, end_folder+1):\n",
    "    mdirs.append(os.path.join(csrsdir, str(i)))\n",
    "tro_data = []\n",
    "for mdir in mdirs:\n",
    "    mpath = os.path.join(csrsdir, mdir)\n",
    "    #print(mpath)\n",
    "    trofiles = [f for f in os.listdir(mpath) if f.endswith('.tro')]\n",
    "    trofiles.sort()\n",
    "    for trofile in trofiles:\n",
    "        tropath = os.path.join(mpath, trofile)\n",
    "        #print(tropath)\n",
    "        with open(tropath, 'rt') as f:\n",
    "            lines = f.readlines()\n",
    "\n",
    "        count = 0\n",
    "\n",
    "        tro_data_minute = []\n",
    "        for line in lines:\n",
    "            if line.find('ROV_') < 0:\n",
    "                continue\n",
    "            if line.startswith('%'):\n",
    "                continue\n",
    "            linesp = line.split()\n",
    "            if len(linesp) != 9:\n",
    "                continue\n",
    "\n",
    "            count += 1\n",
    "            if count==1:\n",
    "                continue\n",
    "\n",
    "            epoch = linesp[1]\n",
    "            t = datetime.datetime.strptime(epoch[:6], '%y:%j')\n",
    "            delta = datetime.timedelta(seconds=int(epoch[7:]))\n",
    "            t = t + delta\n",
    "\n",
    "            if len(tro_data_minute)>0:\n",
    "                t0 = tro_data_minute[0][0]\n",
    "                if t0.minute != t.minute:\n",
    "                    tro_data_minute = np.array(tro_data_minute)\n",
    "                    data_mean = np.mean(tro_data_minute[:,1:], axis=0)\n",
    "                    tro_data.append([t0, data_mean])\n",
    "                    tro_data_minute = []\n",
    "            \n",
    "            ztdd = float(linesp[2])\n",
    "            ztdw = float(linesp[3])\n",
    "            ztdw_std = float(linesp[4])\n",
    "            #tgntot = float(linesp[5])\n",
    "            #tgntot_std = float(linesp[6])\n",
    "            #tgetot = float(linesp[7])\n",
    "            #tgetot_std = float(linesp[8])\n",
    "        \n",
    "            tro_data_minute.append([t, ztdd, ztdw, ztdw_std])\n",
    "        if len(tro_data_minute)>10:\n",
    "            tro_data_minute = np.array(tro_data_minute)\n",
    "            data_mean = np.mean(tro_data_minute[:,1:], axis=0)\n",
    "            tro_data.append([t0, data_mean])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_26857/151169912.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  timestamps = np.array(tro_data).T[0]\n",
      "/tmp/ipykernel_26857/151169912.py:3: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  for d in np.array(tro_data).T[1]:\n"
     ]
    }
   ],
   "source": [
    "timestamps = np.array(tro_data).T[0]\n",
    "zwd = []\n",
    "for d in np.array(tro_data).T[1]:\n",
    "    zwd.append(d[1])\n",
    "zwd = np.array(zwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(timestamps, zwd)\n",
    "plt.ylim(-10, 100)\n",
    "plt.ylabel(\"zwd (mm)\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "soenv_20220826",
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}