eval_point_tide_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Compute Tidal Height Correction for a given for a given lat,lon,time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "# - Import Python  Dependencies\n",
    "from __future__ import print_function\n",
    "import os\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# - pyTMD\n",
    "from pyTMD.read_tide_model import extract_tidal_constants\n",
    "from pyTMD.infer_minor_corrections import infer_minor_corrections\n",
    "from pyTMD.predict_tide import predict_tide\n",
    "from pyTMD.read_FES_model import extract_FES_constants"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Processing Parameters"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# - Absolute path to data directory\n",
    "data_dir = os.path.join('/', 'Volumes', 'Extreme Pro')  # - NEED TO EDIT\n",
    "\n",
    "# - Output Directory\n",
    "out_dir = os.path.join(os.path.expanduser('~'), 'Desktop')  # - NEED TO EDIT\n",
    "\n",
    "# - Processing Parameters\n",
    "model = 'CATS2008'      # - CATS2008 or FES2014\n",
    "date = '2020/10/10'\n",
    "\n",
    "# - Variable to Read\n",
    "var_type = 'z'\n",
    "# - Processing Parameters\n",
    "date_str = date.split('/')\n",
    "year = int(date_str[0])\n",
    "month = int(date_str[1])\n",
    "day = int(date_str[2])\n",
    "\n",
    "# - Other Parameters\n",
    "n_sec_x_day = 24.*60.*60."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Sample Point Coordinates"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# - Sample Point Coordinates\n",
    "pt_lat = -66.68\n",
    "pt_lon = 140.05"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# - Calculate Number of days relative to Jan 1, 1992 (48622 MJD)\n",
    "# - using datetime\n",
    "t_jd_ref = datetime.datetime(year=1992, month=1, day=1, hour=0)\n",
    "t_est_tide = [datetime.datetime(year=year, month=month, day=day,\n",
    "                                hour=t) for t in range(0, 24)]\n",
    "\n",
    "# - Compute Datetime Values for the 24 Hours of the selected date.\n",
    "delta_time = [(t - t_jd_ref).total_seconds()/n_sec_x_day for t in t_est_tide]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Compute Tide Height for the seleted Date"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# - Time: 0 - Tide Elevation in meters: [0.07123420934248643]\n",
      "# - Time: 1 - Tide Elevation in meters: [-0.00802606232276636]\n",
      "# - Time: 2 - Tide Elevation in meters: [-0.05882280141999809]\n",
      "# - Time: 3 - Tide Elevation in meters: [-0.09121551532981356]\n",
      "# - Time: 4 - Tide Elevation in meters: [-0.11927247550782984]\n",
      "# - Time: 5 - Tide Elevation in meters: [-0.1560180677296059]\n",
      "# - Time: 6 - Tide Elevation in meters: [-0.20892853674420167]\n",
      "# - Time: 7 - Tide Elevation in meters: [-0.2771759888963532]\n",
      "# - Time: 8 - Tide Elevation in meters: [-0.35131948036665367]\n",
      "# - Time: 9 - Tide Elevation in meters: [-0.41547845904278713]\n",
      "# - Time: 10 - Tide Elevation in meters: [-0.4513747007152496]\n",
      "# - Time: 11 - Tide Elevation in meters: [-0.4431604119563977]\n",
      "# - Time: 12 - Tide Elevation in meters: [-0.3817754749286574]\n",
      "# - Time: 13 - Tide Elevation in meters: [-0.26772895682886516]\n",
      "# - Time: 14 - Tide Elevation in meters: [-0.11162696282062534]\n",
      "# - Time: 15 - Tide Elevation in meters: [0.06764935205671327]\n",
      "# - Time: 16 - Tide Elevation in meters: [0.24662937330145085]\n",
      "# - Time: 17 - Tide Elevation in meters: [0.4018799015178072]\n",
      "# - Time: 18 - Tide Elevation in meters: [0.5144638736995464]\n",
      "# - Time: 19 - Tide Elevation in meters: [0.5732175031508424]\n",
      "# - Time: 20 - Tide Elevation in meters: [0.5761721227843796]\n",
      "# - Time: 21 - Tide Elevation in meters: [0.5299315196113324]\n",
      "# - Time: 22 - Tide Elevation in meters: [0.44731512048143934]\n",
      "# - Time: 23 - Tide Elevation in meters: [0.34396648798715945]\n"
     ]
    }
   ],
   "source": [
    "# - Compute Tidal Correction\n",
    "int_time = datetime.datetime.now()      # - Measure Computation time\n",
    "tidal_cycle = []\n",
    "\n",
    "if model == 'CATS2008':\n",
    "    # - Define path to CATS2008 data - NEED TO EDIT\n",
    "    tide_dir = os.path.join(data_dir, 'cats2008_tmd')\n",
    "    grid_file = os.path.join(tide_dir, 'grid_CATS2008')\n",
    "    model_file = os.path.join(tide_dir, 'hf.CATS2008.out')\n",
    "    # - pyTDM parameters\n",
    "    model_format = 'OTIS'\n",
    "    epsg_code = 'CATS2008'\n",
    "    # -- read tidal constants and interpolate to grid points\n",
    "    # model_format in ('OTIS','ATLAS'):\n",
    "    amp, ph, d, c = extract_tidal_constants(pt_lon, pt_lat, grid_file,\n",
    "                                            model_file, epsg_code,\n",
    "                                            TYPE=var_type,\n",
    "                                            METHOD='spline',\n",
    "                                            GRID=model_format)\n",
    "    # -- calculate complex phase in radians for Euler's\n",
    "    cph = -1j * ph * np.pi / 180.0\n",
    "    # -- calculate constituent oscillation\n",
    "    hc = amp * np.exp(cph)\n",
    "\n",
    "    # - tidal cycle\n",
    "    tidal_cycle = []\n",
    "    for h in range(24):\n",
    "        # -- predict tidal elevations at time and infer minor corrections\n",
    "        tide_p = predict_tide(delta_time[h], hc, c, DELTAT=0,\n",
    "                              CORRECTIONS=model_format)\n",
    "        minor_p = infer_minor_corrections(delta_time[h], hc, c,\n",
    "                                          DELTAT=0,\n",
    "                                          CORRECTIONS=model_format)\n",
    "        print(f'# - Time: {h} - Tide Elevation in meters: {tide_p+minor_p}')\n",
    "        tidal_cycle.append(tide_p+minor_p)\n",
    "\n",
    "elif model == 'FES2014':\n",
    "    # - Define path to FES2014 data  - NEED TO EDIT\n",
    "    model_directory = os.path.join(data_dir, 'AVISO',\n",
    "                                   'fes2014_elevations_and_load',\n",
    "                                   'fes2014b_elevations',\n",
    "                                   'ocean_tide')\n",
    "\n",
    "    model_files = ['2n2.nc', 'eps2.nc', 'j1.nc', 'k1.nc',\n",
    "                   'k2.nc', 'l2.nc', 'la2.nc', 'm2.nc', 'm3.nc', 'm4.nc',\n",
    "                   'm6.nc', 'm8.nc', 'mf.nc', 'mks2.nc', 'mm.nc',\n",
    "                   'mn4.nc', 'ms4.nc', 'msf.nc', 'msqm.nc', 'mtm.nc',\n",
    "                   'mu2.nc', 'n2.nc', 'n4.nc', 'nu2.nc', 'o1.nc', 'p1.nc',\n",
    "                   'q1.nc', 'r2.nc', 's1.nc', 's2.nc', 's4.nc', 'sa.nc',\n",
    "                   'ssa.nc', 't2.nc']\n",
    "    model_file = [os.path.join(model_directory, x) for x in model_files]\n",
    "    model_version = 'FES2014'\n",
    "    model_format = 'FES'\n",
    "    model_type = 'z'\n",
    "    model_scale = 1.0/100.0\n",
    "\n",
    "    amp, ph = extract_FES_constants(pt_lon, pt_lat,\n",
    "                                    model_file, TYPE=model_type,\n",
    "                                    VERSION=model_version,\n",
    "                                    METHOD='spline', EXTRAPOLATE=False,\n",
    "                                    CUTOFF=None,\n",
    "                                    SCALE=model_scale, GZIP=False)\n",
    "    # -- available model constituents\n",
    "    c = ['2n2', 'eps2', 'j1', 'k1', 'k2', 'l2',\n",
    "         'lambda2', 'm2', 'm3', 'm4', 'm6', 'm8', 'mf', 'mks2', 'mm',\n",
    "         'mn4', 'ms4', 'msf', 'msqm', 'mtm', 'mu2', 'n2', 'n4', 'nu2',\n",
    "         'o1', 'p1', 'q1', 'r2', 's1', 's2', 's4', 'sa', 'ssa', 't2']\n",
    "\n",
    "    # -- calculate complex phase in radians for Euler's\n",
    "    cph = -1j * ph * np.pi / 180.0\n",
    "    # -- calculate constituent oscillation\n",
    "    hc = amp * np.exp(cph)\n",
    "\n",
    "    # - tidal cycle\n",
    "    tidal_cycle = []\n",
    "    for h in range(24):\n",
    "        # -- predict tidal elevations at time and infer minor corrections\n",
    "        tide_p = predict_tide(delta_time[h], hc, c, DELTAT=0,\n",
    "                              CORRECTIONS=model_format)\n",
    "        minor_p = infer_minor_corrections(delta_time[h], hc, c,\n",
    "                                          DELTAT=0,\n",
    "                                          CORRECTIONS=model_format)\n",
    "        print(f'# - Time: {h} - Tide Elevation in meters: '\n",
    "              f'{ tide_p+minor_p[0]}')\n",
    "        tidal_cycle.append(tide_p+minor_p)\n",
    "\n",
    "    int_time_2 = datetime.datetime.now()\n",
    "    print(f\"\\n# - Computation Time - FES2014: {int_time_2 - int_time}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# - Plot the Computed Daily tidal correction\n",
    "plt.figure()\n",
    "plt.plot(np.arange(24), tidal_cycle, color='b', label=model)\n",
    "plt.xlabel('Hr')\n",
    "plt.ylabel('Tide Height [m]')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title(date)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# - Save the Computed Daily Correction\n",
    "with open(os.path.join(out_dir, model+f'_tide_height[m]_LON={pt_lon}_LAT={pt_lat}_{month}-{day}-{year}.txt'), 'w', encoding='utf8') as w_fid:\n",
    "    print('Date'.ljust(25)+'Tide Height [m]', file=w_fid)\n",
    "    for cnt, dt in enumerate(t_est_tide):\n",
    "        print(f'{datetime.datetime.strftime(dt, \"%d/%m/%Y %H:%M:%S\"):25}{tidal_cycle[cnt][0]}', file=w_fid)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}