notebooks/rompy-dev_matchup_code v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dutch-offer",
   "metadata": {},
   "source": [
    "# Developing match up function v2\n",
    "\n",
    "- Move to a 1D model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "responsible-recommendation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.core.options.set_options at 0x7f2a49ae9f70>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import rompy\n",
    "from rompy import utils ## Should we import utils in __init__.py?\n",
    "from shapely.geometry import MultiPoint,Point\n",
    "%matplotlib inline\n",
    "\n",
    "xr.set_options(display_style = 'text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "light-marker",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_109801/3533311998.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  obs['TIME'] =  pd.to_datetime(obs['TIME'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 121, longitude: 160, latitude: 300)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2021-02-01 ... 2021-02-06\n",
       "  * longitude  (longitude) float32 115.0 115.0 115.0 115.0 ... 115.8 115.8 115.8\n",
       "  * latitude   (latitude) float32 -33.0 -32.99 -32.99 ... -31.51 -31.51 -31.5\n",
       "Data variables:\n",
       "    depth      (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    ubot       (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    hs         (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    hswe       (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    theta0     (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    tps        (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    tm01       (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    xwnd       (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "    ywnd       (time, latitude, longitude) float32 dask.array&lt;chunksize=(24, 300, 160), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:                     CF-1.5\n",
       "    History:                         Created with agioncmd version 1.5\n",
       "    Directional_convention:          nautical\n",
       "    project:                         SWAN\n",
       "    run:                             \n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 121, longitude: 160, latitude: 300)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2021-02-01 ... 2021-02-06\n",
       "  * longitude  (longitude) float32 115.0 115.0 115.0 115.0 ... 115.8 115.8 115.8\n",
       "  * latitude   (latitude) float32 -33.0 -32.99 -32.99 ... -31.51 -31.51 -31.5\n",
       "Data variables:\n",
       "    depth      (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    ubot       (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    hs         (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    hswe       (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    theta0     (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    tps        (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    tm01       (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    xwnd       (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "    ywnd       (time, latitude, longitude) float32 dask.array<chunksize=(24, 300, 160), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:                     CF-1.5\n",
       "    History:                         Created with agioncmd version 1.5\n",
       "    Directional_convention:          nautical\n",
       "    project:                         SWAN\n",
       "    run:                             \n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = rompy.cat\n",
    "model_ds = cat.csiro.swan.swan_perth_fc.map(fcdate='2021-02').to_dask()\n",
    "\n",
    "x = model_ds.longitude.values\n",
    "y = model_ds.latitude.values \n",
    "xx,yy = np.meshgrid(x,y)\n",
    "points = MultiPoint(list(map(Point,zip(xx.ravel(),yy.ravel())))) \n",
    "geom = points.convex_hull.buffer(0.01).simplify(tolerance=0.01)\n",
    "df=cat.aodn.nrt_wave_buoys(startdt='2021-02',enddt='2021-04',geom=geom.to_wkt()).read()\n",
    "obs = df[['TIME','LATITUDE','LONGITUDE','WHTH']]\n",
    "obs['TIME'] =  pd.to_datetime(obs['TIME'])\n",
    "model_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "multiple-reset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>WHTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-01 00:00:00</td>\n",
       "      <td>-32.10</td>\n",
       "      <td>115.40</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-01 15:00:00</td>\n",
       "      <td>-32.10</td>\n",
       "      <td>115.40</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-01 18:00:00</td>\n",
       "      <td>-32.10</td>\n",
       "      <td>115.40</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-02 06:00:00</td>\n",
       "      <td>-32.10</td>\n",
       "      <td>115.40</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-02 09:00:00</td>\n",
       "      <td>-32.10</td>\n",
       "      <td>115.40</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2021-03-25 06:00:00</td>\n",
       "      <td>-32.45</td>\n",
       "      <td>115.57</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2021-03-25 12:00:00</td>\n",
       "      <td>-32.45</td>\n",
       "      <td>115.57</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2021-03-25 15:00:00</td>\n",
       "      <td>-32.45</td>\n",
       "      <td>115.57</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2021-03-29 06:00:00</td>\n",
       "      <td>-32.45</td>\n",
       "      <td>115.57</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2021-03-30 21:00:00</td>\n",
       "      <td>-32.45</td>\n",
       "      <td>115.57</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   TIME  LATITUDE  LONGITUDE  WHTH\n",
       "0   2021-02-01 00:00:00    -32.10     115.40  1.91\n",
       "1   2021-02-01 15:00:00    -32.10     115.40  1.44\n",
       "2   2021-02-01 18:00:00    -32.10     115.40  1.43\n",
       "3   2021-02-02 06:00:00    -32.10     115.40  1.32\n",
       "4   2021-02-02 09:00:00    -32.10     115.40  1.36\n",
       "..                  ...       ...        ...   ...\n",
       "393 2021-03-25 06:00:00    -32.45     115.57  0.92\n",
       "394 2021-03-25 12:00:00    -32.45     115.57  1.02\n",
       "395 2021-03-25 15:00:00    -32.45     115.57  1.08\n",
       "396 2021-03-29 06:00:00    -32.45     115.57  1.26\n",
       "397 2021-03-30 21:00:00    -32.45     115.57  1.43\n",
       "\n",
       "[398 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "figured-melbourne",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (observation: 38)\n",
       "Coordinates:\n",
       "    time             (observation) datetime64[ns] 2021-02-01 ... 2021-02-05T2...\n",
       "    longitude        (observation) float32 115.4 115.4 115.4 ... 115.6 115.6\n",
       "    latitude         (observation) float32 -32.1 -32.1 -32.1 ... -32.45 -32.45\n",
       "Dimensions without coordinates: observation\n",
       "Data variables:\n",
       "    meas_time        (observation) datetime64[ns] 2021-02-01 ... 2021-02-05T2...\n",
       "    meas_longitude   (observation) float64 115.4 115.4 115.4 ... 115.6 115.6\n",
       "    meas_latitude    (observation) float64 -32.1 -32.1 -32.1 ... -32.45 -32.45\n",
       "    meas_whth        (observation) float64 1.91 1.44 1.43 1.32 ... 1.3 1.26 1.31\n",
       "    model_hs         (observation) float32 1.922 1.919 1.83 ... 1.469 1.547\n",
       "    model_time       (observation) datetime64[ns] 2021-02-01 ... 2021-02-05T2...\n",
       "    model_longitude  (observation) float32 115.4 115.4 115.4 ... 115.6 115.6\n",
       "    model_latitude   (observation) float32 -32.1 -32.1 -32.1 ... -32.45 -32.45\n",
       "    dist             (observation) float64 0.003217 0.003217 ... 0.002341\n",
       "Attributes:\n",
       "    grid:     regular</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:          (observation: 38)\n",
       "Coordinates:\n",
       "    time             (observation) datetime64[ns] 2021-02-01 ... 2021-02-05T2...\n",
       "    longitude        (observation) float32 115.4 115.4 115.4 ... 115.6 115.6\n",
       "    latitude         (observation) float32 -32.1 -32.1 -32.1 ... -32.45 -32.45\n",
       "Dimensions without coordinates: observation\n",
       "Data variables:\n",
       "    meas_time        (observation) datetime64[ns] 2021-02-01 ... 2021-02-05T2...\n",
       "    meas_longitude   (observation) float64 115.4 115.4 115.4 ... 115.6 115.6\n",
       "    meas_latitude    (observation) float64 -32.1 -32.1 -32.1 ... -32.45 -32.45\n",
       "    meas_whth        (observation) float64 1.91 1.44 1.43 1.32 ... 1.3 1.26 1.31\n",
       "    model_hs         (observation) float32 1.922 1.919 1.83 ... 1.469 1.547\n",
       "    model_time       (observation) datetime64[ns] 2021-02-01 ... 2021-02-05T2...\n",
       "    model_longitude  (observation) float32 115.4 115.4 115.4 ... 115.6 115.6\n",
       "    model_latitude   (observation) float32 -32.1 -32.1 -32.1 ... -32.45 -32.45\n",
       "    dist             (observation) float64 0.003217 0.003217 ... 0.002341\n",
       "Attributes:\n",
       "    grid:     regular"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_ds = rompy.utils.find_matchup_data(obs,model_ds,{'WHTH':'hs'},time_thresh=None,KDtree_kwargs={})\n",
    "out_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "basic-saskatchewan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hs')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = plt.subplots(figsize=(12,8))\n",
    "\n",
    "ax.scatter(out_ds['model_hs'],out_ds['meas_whth'])\n",
    "ax.plot([0,3],[0,3],ls='--',c='#252525')\n",
    "ax.set_ylim(0,2.5)\n",
    "ax.set_xlim(0,2.5)\n",
    "ax.set_xlabel('Model')\n",
    "ax.set_ylabel('Measured')\n",
    "ax.set_title('Hs')"
   ]
  }
 ],
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