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       "    for (const render_item of render_items) {\n",
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       "\t  root_el.id = root_el.id + '-rendered'\n",
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       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
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       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
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       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
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       "    var Bokeh = get_bokeh(root)\n",
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       "  }\n",
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       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "  \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "</div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gp\n",
    "import thalassa\n",
    "import holoviews as hv\n",
    "import hvplot.pandas\n",
    "hv.extension(\"bokeh\")\n",
    "import pyproj\n",
    "import json\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import panel as pn\n",
    "from holoviews import opts, dim\n",
    "from holoviews import streams\n",
    "from holoviews.operation import histogram\n",
    "from holoviews.plotting.links import RangeToolLink\n",
    "from bokeh.models import HoverTool\n",
    "\n",
    "import param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_std_dev_circles(std_dev_range: np.ndarray) -> hv.Overlay:\n",
    "    std_dev_circles = []\n",
    "    for std in std_dev_range:\n",
    "        angle = np.linspace(0, np.pi/2, 100)\n",
    "        radius = np.full(100, std)\n",
    "        x = radius * np.cos(angle)\n",
    "        y = radius * np.sin(angle)\n",
    "        std_dev_circles.append(\n",
    "            hv.Curve((x, y)).opts(color='gray', line_dash='dotted', line_width=1)\n",
    "        )\n",
    "    return hv.Overlay(std_dev_circles)\n",
    "\n",
    "def create_std_ref(radius: float) -> hv.Overlay:\n",
    "    angle = np.linspace(0, np.pi/2, 100)\n",
    "    x = radius * np.cos(angle)\n",
    "    y = radius * np.sin(angle)\n",
    "    return hv.Curve((x, y)).opts(color='gray', line_dash='dashed', line_width=2) * \\\n",
    "        hv.Text(radius, 0., f'REF', halign='right', valign='bottom').opts(\n",
    "            text_font_size='10pt', text_color='gray')\n",
    "\n",
    "def create_corr_lines(corr_range: np.ndarray, std_dev_max: float) -> hv.Overlay:\n",
    "    corr_lines = []\n",
    "    for corr in corr_range:\n",
    "        theta = np.arccos(corr)\n",
    "        radius = np.linspace(0, std_dev_max, 2)\n",
    "        x = radius * np.cos(theta)\n",
    "        y = radius * np.sin(theta)\n",
    "        corr_lines.append(\n",
    "            hv.Curve((x, y)).opts(color='blue', line_dash='dashed', line_width=1) *\n",
    "            hv.Text(x[-1], y[-1], f'{corr:.2f}', halign='left', valign='bottom').opts(\n",
    "                text_font_size='10pt', text_color='blue')\n",
    "        )\n",
    "    corr_label = hv.Text( 0.75 * std_dev_max, 0.75 * std_dev_max, f'Correlation Coefficient' ).opts( text_font_size='12pt', text_color='blue', angle=-45 )\n",
    "    return hv.Overlay(corr_lines) * corr_label\n",
    "\n",
    "def create_rms_contours(standard_ref: float, std_dev_max: float, rms_range: np.ndarray, norm:bool) -> hv.Overlay:\n",
    "    rms_contours = []\n",
    "    for rms in rms_range:\n",
    "        angle = np.linspace(0, np.pi, 100)\n",
    "        x = standard_ref + rms * np.cos(angle)\n",
    "        y = rms * np.sin(angle)\n",
    "        inside_max_std = np.sqrt(x**2 + y**2) < std_dev_max\n",
    "        x[~inside_max_std] = np.nan\n",
    "        y[~inside_max_std] = np.nan\n",
    "        rms_contours.append(\n",
    "            hv.Curve((x, y)).opts(color='green', line_dash='dashed', line_width=1) *\n",
    "            hv.Text(standard_ref + rms * np.cos(2*np.pi/3), rms * np.sin(2*np.pi/3), f'{rms:.2f}', halign='left', valign='bottom').opts(\n",
    "                text_font_size='10pt', text_color='green')\n",
    "        )\n",
    "    label = \"RMS %\" if norm else \"RMS\"\n",
    "    rms_label = hv.Text( standard_ref, rms_range[1]*np.sin(np.pi/2), label, halign='left', valign='bottom' ).opts( text_font_size='11pt', text_color='green' )\n",
    "    return hv.Overlay(rms_contours) * rms_label\n",
    "\n",
    "def taylor_diagram(df: pd.DataFrame,\n",
    "                   norm: bool = True, \n",
    "                   marker: str = \"circle\", \n",
    "                   color: str = \"black\", \n",
    "                   label: str = \"Taylor Diagram\"\n",
    "    ) -> hv.Overlay:\n",
    "    theta = np.arccos(df['cr'])  # Convert Cr to radians for polar plot\n",
    "    if norm: \n",
    "        std_ref = 1\n",
    "        std_mod = df['std_mod'] / df['std_obs']\n",
    "    else: \n",
    "        if len(df) > 1:\n",
    "            raise ValueError('for not normalised Taylor diagrams, you need only 1 data point')\n",
    "        std_ref = df['std_obs'].mean()\n",
    "        std_mod = df['std_mod'].mean()\n",
    "    # \n",
    "    std_range = np.arange(0, 1.5 * std_ref, np.round(std_ref/5, 2))\n",
    "    corr_range = np.arange(0, 1, 0.1)\n",
    "    rms_range = np.arange(0, 1.5 * std_ref, np.round(std_ref/5, 2))\n",
    "\n",
    "    std_dev_overlay = create_std_dev_circles(std_range) * create_std_ref(std_ref)\n",
    "    corr_lines_overlay = create_corr_lines(corr_range, std_range.max())\n",
    "    rms_contours_overlay = create_rms_contours(std_ref, std_range.max(), rms_range, norm=norm)\n",
    "\n",
    "    x = std_mod * np.cos(theta)\n",
    "    y = std_mod * np.sin(theta)\n",
    "    df['x'] = x\n",
    "    df['y'] = y\n",
    "    df['rms_perc'] = df['rms'] / df['std_obs']\n",
    "    # hover parameters\n",
    "    tooltips = [\n",
    "        ('Corr Coef (%)', '@cr'),\n",
    "        ('RMS (m)', '@rms'),\n",
    "        ('RMS Taylor (m)', '@rms_centered'),\n",
    "        ('Std Dev Model (m)', '@std_mod'),\n",
    "        ('Std Dev Measure (m)', '@std_obs'),\n",
    "        # ('Station (m)', '@ioc_code'),\n",
    "    ]\n",
    "    if norm: \n",
    "        tooltips.append(('RMS %', '@rms_perc'))\n",
    "    hover = HoverTool(tooltips=tooltips)\n",
    "\n",
    "    # Scatter plot for models with hover tool\n",
    "    scatter_plot = hv.Points(\n",
    "        df, ['x', 'y'],['cr', 'std_mod', 'std_obs', 'rms', 'rms_centered', 'rms_perc'],#, 'ioc_code'],\n",
    "    ).opts(\n",
    "        color=color,\n",
    "        cmap='Category20',\n",
    "        # line_color='ioc_code',  \n",
    "        line_width=1,\n",
    "        marker = marker,\n",
    "        size=5,  \n",
    "        tools=[hover],\n",
    "        default_tools=[],\n",
    "        show_legend=True,\n",
    "        hover_fill_color='firebrick',\n",
    "        xlim=(0, std_range.max()*1.05),\n",
    "        ylim=(0, std_range.max()*1.05)\n",
    "    )\n",
    "\n",
    "    # Combine all the elements\n",
    "    taylor_diagram = scatter_plot * std_dev_overlay * corr_lines_overlay * rms_contours_overlay\n",
    "    return taylor_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first load local data\n",
    "IOC_CLEANUP = pd.read_csv('assets/ioc_cleanup_2023.csv')\n",
    "CUSTOM_LIST = ['viti', 'prus', 'mill', 'pkem', 'vard', 'wood', 'kahu', 'cald2', 'hmda', 'kinl', 'benc', 'dzao', \n",
    "    'lerw2', 'live', 'cres', 'hie2', 'ptmt', 'ilfa', 'wick', 'rorv', 'pwil2', 'dkwa', 'bame', 'abed', 'lime', 'nshi', \n",
    "    'newl',  'chst', 'bamf', 'kush', 'greg', 'bamd', 'honn', 'dutc',  'talc2', 'dpnc', 'quir2', 'fpnt', 'chrp', 'ishig', \n",
    "    'bgct',  'vhbc', 'thev', 'npor', 'pslu', 'cher', 'boma', 'stjo', 'djve', 'ross', 'elak', 'hana', 'saig', 'pich2', \n",
    "    'dapi', 'ptal2', 'stor', 'ande', 'aren', 'ohig3', 'newl2', 'trst', 'kusm', 'alam', 'leit', 'sado', 'asto', 'shee', \n",
    "    'malo', 'corr2', 'prin2', 'nkfa', 'heys', 'helg', 'kawa', 'atka', 'qtro2', 'dove', 'waka', 'treg', 'cuxh', 'tosa', \n",
    "    'yaku', 'brom', 'smog', 'darw', 'cwfl', 'huat', 'sprg', 'fue2', 'abur', 'guam', 'kwfl', 'pcha2', 'barn', \n",
    "    'bapj', 'amal', 'hilo', 'wpwa', 'plym', 'coru', 'gokr', 'pmur', 'whit', 'nhav', 'porp', 'kungr', 'mumb', 'stqy2', 'crom', \n",
    "    'sitk', 'stqy', 'oste', 'acnj', 'bang', 'naga', 'mare', 'fuka', 'herb', 'pagb', 'work', 'mhav', 'lajo', 'harw', \n",
    "    'omae', 'coqu2', 'holy2', 'horn', 'sdpt', 'lowe', 'naha']\n",
    "SURGE_FOLDER = './surge/'\n",
    "STATIONS_DF = IOC_CLEANUP[IOC_CLEANUP.ioc_code.isin(CUSTOM_LIST)]\n",
    "# stats data\n",
    "STATS_JSON = \"assets/stats.json\"\n",
    "with open(STATS_JSON) as f:\n",
    "    STATS = json.load(f)\n",
    "TMIN = '2023-01-01'\n",
    "TMAX = '2023-12-31'\n",
    "\n",
    "# MODELS\n",
    "VERSIONS = {\n",
    "    'v0':   'results/2D/v0.nc',        \n",
    "    'v0.2': 'results/2D/v0.2.nc',        \n",
    "    'v1.2': 'results/2D/v0.2.nc',\n",
    "    'v2.1': 'results/2D/v0.2.nc',\n",
    "    'v2.2': 'results/2D/v0.2.nc',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_param(df, x, y, z, bins = 180):\n",
    "    dfx = df.sort_values(by=[x]) #.rolling(2, center=True).mean()\n",
    "    dfy = df.sort_values(by=[y]) #.rolling(2, center=True).mean()\n",
    "    # average_param(dfx, x, y, z)\n",
    "    # print(dfx)\n",
    "    # dfx = dfx.rolling(10, center=True).mean()\n",
    "    # print((dfx[x].values, dfx[z].values))\n",
    "    dfx[x].values, dfx[z].values\n",
    "    xbins = np.linspace(-180, 180, bins)\n",
    "    ybins = np.linspace(-90, 90, int(bins/2))\n",
    "    \n",
    "    xdata = np.zeros(len(df[x], 2))\n",
    "    for bin in zip(xbins[:-1], xbins[1:]):\n",
    "        xdata[bin[0] <= df[x] < bin[1]] = df[z]\n",
    "        \n",
    "    ydata = np.digitize(df[y], ybins)\n",
    "    print(xdata, ydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "HEIGHT = 500\n",
    "WIDTH = 1000\n",
    "\n",
    "def scatter_hist(src, x, y, z, map_):\n",
    "    p = hv.Points(src, kdims=[x,y]).opts(width = WIDTH, height = HEIGHT)\n",
    "\n",
    "    # Define the histograms for x and y with dynamically computed mean z values\n",
    "    xhist, yhist = (histogram(p, num_bins=90, dimension=xy, weight_dimension=z, mean_weighted=True).opts(\n",
    "        opts.Histogram(tools=['hover'], color=dim(z).norm(), cmap='rainbow4')) for xy in [x, y])\n",
    "    \n",
    "    # Define layout composition\n",
    "    composition = ((p) << yhist << xhist).opts(\n",
    "        opts.Points(\n",
    "            show_title=False, \n",
    "            tools=['hover', 'box_select', 'tap'], \n",
    "            size=5, cmap='rainbow4',\n",
    "            color=z, colorbar=True\n",
    "        ),\n",
    "        opts.Histogram(\n",
    "            tools=['hover', 'box_select'], \n",
    "            color=z\n",
    "        ),\n",
    "    )\n",
    "    return composition\n",
    "\n",
    "def hist_(src,z):\n",
    "    e,f = np.histogram(src[z], bins=10)\n",
    "    return hv.Histogram((f,e)).opts(width = HEIGHT, height = HEIGHT)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sliders & buttons\n",
    "version_select = pn.widgets.Select(name='Version', options=list(STATS.keys()))\n",
    "param_select = pn.widgets.Select(name='Parameter', options=['rms','rms_centered','std_mod', 'std_obs', 'nsccoef2', 'lamba', 'cr'])\n",
    "# frame_slider = pn.widgets.DateSlider(name=\"Time\", value=25, start=pd.Timestamp(2023,1,1), end=len(ds.time), width=WIDTH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_166380/2462383145.py:9: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
      "  self.map = gp.read_file(gp.datasets.get_path('naturalearth_lowres'))\n",
      "/home/tomsail/miniconda3/envs/pos_test/lib/python3.11/site-packages/holoviews/operation/element.py:903: RuntimeWarning: invalid value encountered in divide\n",
      "  hist /= hist_mean\n",
      "/home/tomsail/miniconda3/envs/pos_test/lib/python3.11/site-packages/holoviews/operation/element.py:903: RuntimeWarning: invalid value encountered in divide\n",
      "  hist /= hist_mean\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching server at http://localhost:41069\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<panel.io.server.Server at 0x7455bcf27610>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='p2012', ...)\n",
      "WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='p1237', ...)\n"
     ]
    }
   ],
   "source": [
    "# Define a class to encapsulate dashboard logic and state\n",
    "class Dashboard(param.Parameterized):\n",
    "    version = param.Selector(objects=list(STATS.keys()))\n",
    "    parameter = param.Selector(objects=['rms', 'rms_centered', 'std_mod', 'std_obs', 'nsccoef2', 'lamba', 'cr'])\n",
    "    selected_station = param.Integer(default=0)\n",
    "    \n",
    "    def __init__(self, **params):\n",
    "        super().__init__(**params)\n",
    "        self.map = gp.read_file(gp.datasets.get_path('naturalearth_lowres'))\n",
    "        self.map_ = hv.EdgePaths(self.map).opts(width=WIDTH, height=HEIGHT)\n",
    "        self.df = pd.DataFrame()\n",
    "        self.ds = None\n",
    "        self.update_data()\n",
    "    \n",
    "    @param.depends('version', watch=True)\n",
    "    def update_data(self):\n",
    "        self.df = pd.DataFrame(STATS[self.version]).T.reset_index()\n",
    "        self.df.loc[self.df['nsccoef'] > 0, 'nsccoef2'] = self.df['nsccoef']\n",
    "        self.df.loc[self.df['nsccoef'] < 0, 'nsccoef2'] = 0\n",
    "\n",
    "    \n",
    "    @param.depends('version', 'parameter')\n",
    "    def view(self):\n",
    "        # Update the DataFrame based on the selected version\n",
    "        self.update_data()\n",
    "        \n",
    "        # Your plotting logic here (simplified for this example)\n",
    "        scatter_ = scatter_hist(self.df, 'obs_lon', 'obs_lat', self.parameter, self.map_)\n",
    "        \n",
    "        stream = streams.Selection1D(source=scatter_[0])\n",
    "\n",
    "        # Update the layout with new plots\n",
    "        # layout = pn.Column(scatter_, time_series.opts(shared_axes=False))\n",
    "        layout = pn.Column(scatter_)\n",
    "        return layout\n",
    "\n",
    "    @param.depends('version')\n",
    "    def taylor(self):\n",
    "        self.update_data()\n",
    "        diagram = taylor_diagram(self.df, norm=True)\n",
    "        return diagram.opts(width=HEIGHT, height=HEIGHT, shared_axes=False)\n",
    "    \n",
    "    @param.depends('version', 'parameter')\n",
    "    def hist(self):\n",
    "        # Update the DataFrame based on the selected version\n",
    "        self.update_data()    \n",
    "        hist = hist_(self.df, self.parameter)\n",
    "        return hist\n",
    "\n",
    "\n",
    "# Instantiate the dashboard and create the layout\n",
    "dashboard = Dashboard()\n",
    "layout = pn.Row(pn.Column(pn.Row(dashboard.param.version, dashboard.param.parameter), dashboard.view), pn.Column(dashboard.taylor, dashboard.hist))\n",
    "\n",
    "# Serve the Panel app\n",
    "pn.serve(layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='p2406', ...)\n",
      "WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='p3179', ...)\n"
     ]
    }
   ],
   "source": [
    "from bokeh.resources import INLINE\n",
    "layout.save('Skill.html', resources=INLINE)"
   ]
  }
 ],
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