Weather_viz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import fsspec\n",
    "from distributed import Client\n",
    "import hvplot.xarray\n",
    "import numpy as np\n",
    "import hvplot.xarray  # noqa: adds hvplot method to xarray objects\n",
    "import holoviews as hv\n",
    "import geoviews as gv\n",
    "from geoviews import opts\n",
    "import pandas as pd\n",
    "import panel as pn\n",
    "import holoviews as hv\n",
    "from datetime import datetime, timedelta\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "import s3fs\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"\"\"<style>\n",
    "@media (min-width: 1200px) {\n",
    "  .container {\n",
    "    width: 98%;\n",
    "  }\n",
    "}\n",
    "\n",
    "div.output_subarea{padding:.4em .4em 0 .4em;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1;max-width:100%}\n",
    ".bk-input {background-color:blue}\n",
    "</style>\n",
    "<script>\n",
    "    $('#appmode-leave').hide();                          // Hides the edit app button.\n",
    "    $('#appmode-busy').hide();                           // Hides the kernel busy indicator.\n",
    "    $('#appmode-loader').append('<h2>Loading...</h2>');  // Adds a loading message.\n",
    ";</script>\n",
    "\n",
    "\"\"\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "latlngbox = [-82, -74, 44.5, 49]\n",
    "climatic_period = ['1981-01-01', '2010-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket = 's3://cires-20-century-reanalysis-v3/zarr/single-levels-space'\n",
    "storage_options = {'endpoint_url': 'https://s3.us-east-1.wasabisys.com'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_zarr(fsspec.get_mapper(bucket,\n",
    "                                    client_kwargs=storage_options,\n",
    "                                    anon=True),\n",
    "                  consolidated=True)\n",
    "# ds = xr.open_zarr('/home/slanglois/Documents/store/final/target.zarr',\n",
    "#                   consolidated=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_date = '1979-09-25'\n",
    "start_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=2)).strftime('%Y-%m-%d')\n",
    "\n",
    "\n",
    "ds_ls_sliced = ds.sel(time=slice(start_date, end_date)).load()\n",
    "\n",
    "\n",
    "ds_sliced = ds_ls_sliced.sel(longitude=slice(latlngbox[0], latlngbox[1]),\n",
    "                             latitude=slice(latlngbox[2], latlngbox[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_values = ds_sliced['prate'].max('time')\n",
    "df_da = da_values.to_dataframe().reset_index()\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    df_da, geometry=gpd.points_from_xy(df_da.longitude, df_da.latitude))\n",
    "gdf.crs = 4326\n",
    "gdf = gdf.to_crs(3857)\n",
    "gdf['longitude'] = gdf.geometry.x\n",
    "gdf['latitude'] = gdf.geometry.y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ruled-based algorithm\n",
    "\n",
    "def preliminary_type(ds_rolling_sl,\n",
    "                     ds_rolling_pl):\n",
    "\n",
    "\n",
    "    # 1. 0.12 < Precipitation rate <= 0.5 and CAPE < 500\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate <= 0.5/3600, ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_sl.prate >0.12/3600, a)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 40, -1)\n",
    "\n",
    "    # 2. 0.12 < Precipitation rate <= 0.5 and CAPE >= 500\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate <= 0.5/3600, ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_sl.prate >0.12/3600, a)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 40, da)\n",
    "\n",
    "    # 3. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 30, da)\n",
    "\n",
    "    # 4. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 33, da)\n",
    "\n",
    "    # 5. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 10, da)\n",
    "\n",
    "    # 6. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 13, da)\n",
    "\n",
    "    # 7. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 60, da)\n",
    "\n",
    "    # 8. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 63, da)\n",
    "\n",
    "    # 9. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 60, da)\n",
    "\n",
    "    # 10. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 63, da)\n",
    "\n",
    "    # 10. Precipitation rate <0.12\n",
    "    da = xr.where(ds_rolling_sl.prate<=0.12/3600, 99, da)\n",
    "    return pd.DataFrame(da.values[::-1,:])\n",
    "\n",
    "def preliminary_type_da(ds_rolling_sl,\n",
    "                        ds_rolling_pl):\n",
    "\n",
    "\n",
    "    # 1. 0.12 < Precipitation rate <= 0.5 and CAPE < 500\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate <= 0.5/3600, ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_sl.prate >0.12/3600, a)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 40, -1)\n",
    "\n",
    "    # 2. 0.12 < Precipitation rate <= 0.5 and CAPE >= 500\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate <= 0.5/3600, ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_sl.prate >0.12/3600, a)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 40, da)\n",
    "\n",
    "    # 3. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 30, da)\n",
    "\n",
    "    # 4. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 33, da)\n",
    "\n",
    "    # 5. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 10, da)\n",
    "\n",
    "    # 6. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 13, da)\n",
    "\n",
    "    # 7. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 60, da)\n",
    "\n",
    "    # 8. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient <=9.5, 900 mb Pressure gradient >= 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500<=9.5 ,ds_rolling_pl.gradh900>=8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 63, da)\n",
    "\n",
    "    # 9. Precipitation rate > 0.5, CAPE < 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape<500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 60, da)\n",
    "\n",
    "    # 10. Precipitation rate > 0.5, CAPE >= 500, 500mb Pressure gradient >9.5, 900 mb Pressure gradient < 8\n",
    "    a = xr.ufuncs.logical_and(ds_rolling_sl.prate>0.5/3600 ,ds_rolling_sl.cape>=500)\n",
    "    b = xr.ufuncs.logical_and(ds_rolling_pl.gradh500>9.5 ,ds_rolling_pl.gradh900<8)\n",
    "    c = xr.ufuncs.logical_and(a,b)\n",
    "    da = xr.where(c, 63, da)\n",
    "\n",
    "    # 10. Precipitation rate <0.12\n",
    "    da = xr.where(ds_rolling_sl.prate<=0.12/3600, 99, da)\n",
    "    return da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find important storms\n",
    "\n",
    "\n",
    "# Compute preliminary storm type and add to cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket = 's3://cires-20-century-reanalysis-v3/zarr/pressure-levels-space'\n",
    "\n",
    "storage_options = {'endpoint_url': 'https://s3.us-east-1.wasabisys.com'}\n",
    "\n",
    "# ds_pl = xr.open_zarr('/home/slanglois/Documents/store/pressure/final/target.zarr', consolidated=True)\n",
    "\n",
    "ds_pl = xr.open_zarr(fsspec.get_mapper(bucket,\n",
    "                                    client_kwargs=storage_options,\n",
    "                                    anon=True),\n",
    "                     consolidated=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_ls_pl_sliced = ds_pl.sel(level=[500,900], time=slice(start_date, end_date)).load()\n",
    "\n",
    "level = 500\n",
    "variable = 'gradh500'\n",
    "\n",
    "da = np.sqrt(np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('longitude')) + \n",
    "             np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('latitude')))\n",
    "ds_ls_pl_sliced[variable] = da\n",
    "\n",
    "level = 900\n",
    "variable = 'gradh900'\n",
    "\n",
    "da = np.sqrt(np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('longitude')) + \n",
    "             np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('latitude')))\n",
    "ds_ls_pl_sliced[variable] = da\n",
    "\n",
    "ds_pl_sliced = ds_ls_pl_sliced.sel(longitude=slice(latlngbox[0], latlngbox[1]),\n",
    "                             latitude=slice(latlngbox[2], latlngbox[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = preliminary_type(ds_sliced.max('time'),\n",
    "                      ds_pl_sliced.max('time'))\n",
    "\n",
    "ds_sliced['prelim_type'] = preliminary_type_da(ds_sliced.max('time'),\n",
    "                                               ds_pl_sliced.max('time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
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0)\",\"palette\":[\"#e5e5e5\",\"#e5e5e5\",\"#00b0ff\",\"#00b0ff\",\"#00b4ff\",\"#00b4ff\",\"#00b8ff\",\"#00bcff\",\"#00bcff\",\"#00c0ff\",\"#00c0ff\",\"#00c4ff\",\"#00c8ff\",\"#00c8ff\",\"#00ccff\",\"#00ccff\",\"#00d0ff\",\"#00d4ff\",\"#00d4ff\",\"#00d8ff\",\"#00d8ff\",\"#00dcfe\",\"#00dcfe\",\"#00e0fa\",\"#00e4f7\",\"#00e4f7\",\"#02e8f4\",\"#02e8f4\",\"#05ecf1\",\"#08f0ed\",\"#08f0ed\",\"#0cf4ea\",\"#0cf4ea\",\"#0ff8e7\",\"#12fce4\",\"#12fce4\",\"#15ffe1\",\"#15ffe1\",\"#18ffdd\",\"#18ffdd\",\"#1cffda\",\"#1fffd7\",\"#1fffd7\",\"#22ffd4\",\"#22ffd4\",\"#25ffd0\",\"#29ffcd\",\"#29ffcd\",\"#2cffca\",\"#2cffca\",\"#2fffc7\",\"#32ffc3\",\"#32ffc3\",\"#36ffc0\",\"#36ffc0\",\"#39ffbd\",\"#39ffbd\",\"#3cffba\",\"#3fffb7\",\"#3fffb7\",\"#42ffb3\",\"#42ffb3\",\"#46ffb0\",\"#49ffad\",\"#49ffad\",\"#4cffaa\",\"#4cffaa\",\"#4fffa6\",\"#53ffa3\",\"#53ffa3\",\"#56ffa0\",\"#56ffa0\",\"#59ff9d\",\"#59ff9d\",\"#5cff9a\",\"#5fff96\",\"#5fff96\",\"#63ff93\",\"#63ff93\",\"#66ff90\",\"#69ff8d\",\"#69ff8d\",\"#6cff89\",\"#6cff89\",\"#70ff86\",\"#73ff83\",\"#73ff83\",\"#76ff80\",\"#76ff80\",\"#79ff7d\",\"#79ff7d\",\"#7cff79\",\"#80ff76\",\"#80ff76\",\"#83ff73\",\"#83ff73\",\"#86ff70\",\"#89ff6c\",\"#89ff6c\",\"#8dff69\",\"#8dff69\",\"#90ff66\",\"#93ff63\",\"#93ff63\",\"#96ff5f\",\"#96ff5f\",\"#9aff5c\",\"#9aff5c\",\"#9dff59\",\"#a0ff56\",\"#a0ff56\",\"#a3ff53\",\"#a3ff53\",\"#a6ff4f\",\"#aaff4c\",\"#aaff4c\",\"#adff49\",\"#adff49\",\"#b0ff46\",\"#b3ff42\",\"#b3ff42\",\"#b7ff3f\",\"#b7ff3f\",\"#baff3c\",\"#baff3c\",\"#bdff39\",\"#c0ff36\",\"#c0ff36\",\"#c3ff32\",\"#c3ff32\",\"#c7ff2f\",\"#caff2c\",\"#caff2c\",\"#cdff29\",\"#cdff29\",\"#d0ff25\",\"#d4ff22\",\"#d4ff22\",\"#d7ff1f\",\"#d7ff1f\",\"#daff1c\",\"#daff1c\",\"#ddff18\",\"#e0ff15\",\"#e0ff15\",\"#e4ff12\",\"#e4ff12\",\"#e7ff0f\",\"#eaff0c\",\"#eaff0c\",\"#edff08\",\"#edff08\",\"#f1fc05\",\"#f4f802\",\"#f4f802\",\"#f7f400\",\"#f7f400\",\"#faf000\",\"#faf000\",\"#feed00\",\"#ffe900\",\"#ffe900\",\"#ffe500\",\"#ffe500\",\"#ffe200\",\"#ffde00\",\"#ffde00\",\"#ffda00\",\"#ffda00\",\"#ffd700\",\"#ffd300\",\"#ffd300\",\"#ffcf00\",\"#ffcf00\",\"#ffcb00\",\"#ffcb00\",\"#ffc800\",\"#ffc400\",\"#ffc400\",\"#ffc000\",\"#ffc000\",\"#ffbd00\",\"#ffb900\",\"#ffb900\",\"#ffb500\",\"#ffb500\",\"#ffb100\",\"#ffb100\",\"#ffae00\",\"#ffaa00\",\"#ffaa00\",\"#ffa600\",\"#ffa600\",\"#ffa300\",\"#ff9f00\",\"#ff9f00\",\"#ff9b00\",\"#ff9b00\",\"#ff9800\",\"#ff9400\",\"#ff9400\",\"#ff9000\",\"#ff9000\",\"#ff8c00\",\"#ff8900\",\"#ff8900\",\"#ff8500\",\"#ff8500\",\"#ff8100\",\"#ff8100\",\"#ff7e00\",\"#ff7a00\",\"#ff7a00\",\"#ff7600\",\"#ff7600\",\"#ff7300\",\"#ff6f00\",\"#ff6f00\",\"#ff6b00\",\"#ff6b00\",\"#ff6700\",\"#ff6400\",\"#ff6400\",\"#ff6000\",\"#ff6000\",\"#ff5c00\",\"#ff5c00\",\"#ff5900\",\"#ff5500\",\"#ff5500\",\"#ff5100\",\"#ff5100\",\"#ff4d00\",\"#ff4a00\",\"#ff4a00\",\"#ff4600\",\"#ff4600\",\"#ff4200\",\"#ff3f00\",\"#ff3f00\",\"#ff3b00\",\"#ff3b00\",\"#ff3700\",\"#ff3700\",\"#ff3400\",\"#ff3000\",\"#ff3000\",\"#ff2c00\",\"#ff2c00\",\"#ff2800\",\"#ff2500\",\"#ff2500\",\"#ff2100\",\"#ff2100\",\"#ff1d00\",\"#ff1d00\"]},\"id\":\"20474\",\"type\":\"LinearColorMapper\"},{\"attributes\":{\"end\":-3378145.504096434,\"min_interval\":5,\"reset_end\":-3378145.504096434,\"reset_start\":-13097139.133308055,\"start\":-13097139.133308055,\"tags\":[[[\"x\",\"x\",null]]]},\"id\":\"20751\",\"type\":\"Range1d\"},{\"attributes\":{\"background\":\"black\",\"margin\":[0,0,0,0],\"max_height\":25,\"max_width\":50,\"options\":[\"10\",\"13\",\"30\",\"33\",\"40\",\"43\",\"60\",\"63\",\"99\"],\"sizing_mode\":\"stretch_both\",\"value\":\"99\"},\"id\":\"19178\",\"type\":\"Select\"},{\"attributes\":{\"background\":\"black\",\"margin\":[0,0,0,0],\"max_height\":25,\"max_width\":50,\"options\":[\"10\",\"13\",\"30\",\"33\",\"40\",\"43\",\"60\",\"63\",\"99\"],\"sizing_mode\":\"stretch_both\",\"value\":\"99\"},\"id\":\"19208\",\"type\":\"Select\"},{\"attributes\":{\"dimension\":\"lat\"},\"id\":\"19468\",\"type\":\"MercatorTicker\"},{\"attributes\":{\"active\":[0],\"labels\":[\"Display numbers\"],\"margin\":[5,10,5,10]},\"id\":\"19174\",\"type\":\"CheckboxGroup\"},{\"attributes\":{\"attribution\":\"&copy; <a href=\\\"http://downloads.esri.com/ArcGISOnline/docs/tou_summary.pdf\\\">Esri</a>, Earthstar Geographics\",\"url\":\"https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{Z}/{Y}/{X}.jpg\"},\"id\":\"19486\",\"type\":\"WMTSTileSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.65},\"fill_color\":{\"field\":\"gradh900\",\"transform\":{\"id\":\"20266\"}},\"line_alpha\":{\"value\":0},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"20270\",\"type\":\"MultiPolygons\"},{\"attributes\":{\"dimension\":\"lat\"},\"id\":\"20629\",\"type\":\"MercatorTickFormatter\"},{\"attributes\":{\"bar_color\":\"success\",\"margin\":[5,10,5,10],\"min_width\":200,\"value\":0,\"width\":200},\"id\":\"19172\",\"type\":\"panel.models.widgets.Progress\"},{\"attributes\":{},\"id\":\"20830\",\"type\":\"BasicTicker\"},{\"attributes\":{\"background\":\"black\",\"margin\":[0,0,0,0],\"max_height\":25,\"max_width\":50,\"options\":[\"10\",\"13\",\"30\",\"33\",\"40\",\"43\",\"60\",\"63\",\"99\"],\"sizing_mode\":\"stretch_both\",\"value\":\"99\"},\"id\":\"19205\",\"type\":\"Select\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"field\":\"prate\",\"transform\":{\"id\":\"19493\"}},\"line_alpha\":{\"value\":0.2},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"19499\",\"type\":\"MultiPolygons\"},{\"attributes\":{\"data_source\":{\"id\":\"19494\"},\"glyph\":{\"id\":\"19497\"},\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"19499\"},\"nonselection_glyph\":{\"id\":\"19498\"},\"selection_glyph\":null,\"view\":{\"id\":\"19501\"}},\"id\":\"19500\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis\":{\"id\":\"20602\"},\"grid_line_color\":null,\"ticker\":null},\"id\":\"20605\",\"type\":\"Grid\"},{\"attributes\":{\"children\":[{\"id\":\"19177\"}],\"margin\":[0,0,0,0],\"name\":\"Column89915\",\"sizing_mode\":\"stretch_width\"},\"id\":\"19176\",\"type\":\"Column\"},{\"attributes\":{\"align\":null,\"below\":[{\"id\":\"20602\"}],\"center\":[{\"id\":\"20605\"},{\"id\":\"20609\"}],\"frame_height\":250,\"frame_width\":525,\"left\":[{\"id\":\"20606\"}],\"margin\":null,\"match_aspect\":true,\"min_border_bottom\":10,\"min_border_left\":10,\"min_border_right\":10,\"min_border_top\":10,\"plot_height\":250,\"plot_width\":525,\"renderers\":[{\"id\":\"20649\"},{\"id\":\"20660\"},{\"id\":\"20673\"}],\"right\":[{\"id\":\"20663\"}],\"sizing_mode\":\"fixed\",\"title\":{\"id\":\"20594\"},\"toolbar\":{\"id\":\"20616\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"20583\"},\"x_scale\":{\"id\":\"20598\"},\"y_range\":{\"id\":\"20584\"},\"y_scale\":{\"id\":\"20600\"}},\"id\":\"20593\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"source\":{\"id\":\"19494\"}},\"id\":\"19501\",\"type\":\"CDSView\"},{\"attributes\":{\"labels\":[\"Shared axis\"],\"margin\":[5,10,5,10]},\"id\":\"19175\",\"type\":\"CheckboxGroup\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.65},\"fill_color\":{\"field\":\"prate\",\"transform\":{\"id\":\"19493\"}},\"line_alpha\":{\"value\":0},\"xs\":{\"field\":\"xs\"},\"ys\":{\"field\":\"ys\"}},\"id\":\"19497\",\"type\":\"MultiPolygons\"},{\"attributes\":{},\"id\":\"19534\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"dimension\":\"lon\"},\"id\":\"19853\",\"type\":\"MercatorTicker\"}],\"root_ids\":[\"19167\",\"21365\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.1\"}};\n",
       "  var render_items = [{\"docid\":\"dfe16c48-4117-4af0-9f9f-8fa61a3e9e5d\",\"root_ids\":[\"19167\"],\"roots\":{\"19167\":\"e8e8ee0e-5a7c-41ae-9e85-21ff3b1ad241\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] Row\n",
       "        [0] Markdown(str)\n",
       "        [1] Column\n",
       "            [0] Select(name='# de tempête', options=['Temps sec : 1979-09-23 -...], value='Temps sec : 1...)\n",
       "            [1] Progress(value=0, width=200)\n",
       "            [2] Row\n",
       "                [0] Checkbox(name='Display numbers', value=True)\n",
       "                [1] Checkbox(name='Shared axis')\n",
       "        [2] Column(sizing_mode='stretch_width')\n",
       "            [0] GridSpec(height=100, sizing_mode='stretch_both')\n",
       "                [0] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [1] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [2] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [3] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [4] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [5] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [6] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [7] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [8] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [9] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [10] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [11] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [12] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [13] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [14] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [15] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [16] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [17] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [18] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [19] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [20] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [21] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [22] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [23] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [24] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [25] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [26] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [27] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [28] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [29] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [30] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [31] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [32] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [33] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [34] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [35] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [36] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [37] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [38] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [39] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [40] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [41] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [42] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [43] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "                [44] Select(background='black', margin=(0, 0, 0, 0), max_height=25, max_width=50, options=[10, 13, 30, 33, ...], sizing_mode='stretch_both', value=99.0)\n",
       "    [1] Row\n",
       "        [0] HoloViews(Overlay)\n",
       "        [1] HoloViews(Overlay)\n",
       "        [2] HoloViews(Overlay)\n",
       "    [2] Row\n",
       "        [0] HoloViews(Overlay)\n",
       "        [1] HoloViews(Overlay)\n",
       "        [2] HoloViews(Overlay)\n",
       "    [3] Row\n",
       "        [0] HoloViews(Overlay)\n",
       "        [1] HoloViews(Overlay)\n",
       "        [2] HoloViews(Overlay)\n",
       "    [4] Row\n",
       "        [0] HoloViews(Overlay)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "19167"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_code = [10, 13, 30, 33, 40, 43, 60, 63, 99]\n",
    "gspec = pn.GridSpec(sizing_mode='stretch_both', height=100)\n",
    "\n",
    "\n",
    "fs = s3fs.S3FileSystem(anon=True,\n",
    "                       client_kwargs= {'endpoint_url': 'https://s3.us-east-2.wasabisys.com'})\n",
    "\n",
    "for x in range(0,5):\n",
    "    for y in range(0,9):\n",
    "        gspec[x, y] = pn.widgets.Select(value=10, options=numerical_code, max_height=25, max_width=50,\n",
    "                                        margin=(0,0,0,0), background='black')\n",
    "        \n",
    "# look for a storm type calculated by meteorologist\n",
    "filename = 's3://analytics-store/manual_storm_types/{}.csv'.format(end_date)\n",
    "if fs.exists(filename):\n",
    "    with fs.open(filename) as f:\n",
    "        df = pd.read_csv(f)\n",
    "        for i in range(0,5):\n",
    "            for j in range(0,9):\n",
    "                gspec[i, j].value = df.iloc[i,j]\n",
    "\n",
    "# if empty, plot preliminary storm type\n",
    "else:\n",
    "    df = preliminary_type(ds_sliced.max('time'),\n",
    "                          ds_pl_sliced.max('time'))\n",
    "    for i in range(0,5):\n",
    "        for j in range(0,9):\n",
    "            gspec[i, j].value = df.iloc[i,j]   \n",
    "\n",
    "def plot_variable_ls(ds, ds_ls, variable, gdf, title, numbers, shared):\n",
    "    da_values = ds[variable]\n",
    "    \n",
    "        \n",
    "    if variable == 'prelim_type':\n",
    "        da_values_ls = ds_ls['tcdc']\n",
    "    else:\n",
    "        da_values_ls = ds_ls[variable]\n",
    "        \n",
    "    precise = False\n",
    "    \n",
    "    if variable == 'prate':\n",
    "        da_values = da_values*3600\n",
    "        da_values_ls = da_values_ls*3600\n",
    "        precise = True\n",
    "    elif variable =='tmax' or variable=='tmin':\n",
    "        da_values = da_values - 273.15\n",
    "        da_values_ls = da_values_ls - 273.15\n",
    "    \n",
    "\n",
    "    # \n",
    "    df_da = da_values.to_dataframe().reset_index()\n",
    "    \n",
    "    if precise==True:\n",
    "        labels = hv.Labels({('x', 'y'): gdf[['longitude','latitude']],\n",
    "                            'text': ['{0:.1f}'.format(i) for i in df_da[variable]]},\n",
    "                                                   ['x', 'y'], 'text') \n",
    "    else:\n",
    "        labels = hv.Labels({('x', 'y'): gdf[['longitude','latitude']],\n",
    "                            'text': ['{0:.0f}'.format(i) for i in df_da[variable]]},\n",
    "                                                   ['x', 'y'], 'text') \n",
    "    clim = (da_values_ls.min().values, da_values_ls.max().values)\n",
    "    # \n",
    "    jet = cm.get_cmap('jet', 256)\n",
    "    newcolors = jet(np.linspace(0, 1, 256))\n",
    "    newcolors[75, :] = [0.9,0.9,0.9,0]\n",
    "#     newcolors[0, :] = [0.9,0.9,0.9,0]\n",
    "    cmap = ListedColormap(newcolors[75:225, :])\n",
    "#     cmap = ListedColormap(newcolors)\n",
    "    \n",
    "    grid_plot = da_values.hvplot(x='longitude', y='latitude', \n",
    "                                 cmap=cmap,\n",
    "                                 title=title,\n",
    "                                 clim=clim,\n",
    "                                 alpha=0.9,\n",
    "                                 geo=True)\n",
    "\n",
    "    shared_axes = shared.value\n",
    "    display_number = numbers.value\n",
    "    \n",
    "    xlim=(-84, -72)\n",
    "    ylim=(44,50)\n",
    "    \n",
    "    if variable == 'hgt':\n",
    "        display_number = False\n",
    "        xlim=(-98, -50)\n",
    "        ylim=(39,64)\n",
    "    \n",
    "    alpha = 0.65\n",
    "    if variable == 'prelim_type':\n",
    "        alpha = 0\n",
    "    contourf_plot = da_values_ls.hvplot.contourf(levels=10,\n",
    "                                                 geo=True,\n",
    "                                                 hover=True,\n",
    "                                                 clim=clim,\n",
    "                                                 alpha=alpha,\n",
    "                                                 cmap=cmap,\n",
    "                                                 xlim=xlim,\n",
    "                                                 ylim=ylim,\n",
    "                                                 width=525,\n",
    "                                                 height=250,\n",
    "                                                 tiles='ESRI')\n",
    "    # \n",
    "#     contour_plot = da_values_ls.hvplot.contour(levels=20,\n",
    "#                                                geo=True,\n",
    "#                                                clim=clim,\n",
    "#                                                xlim=(-84, -72),\n",
    "#                                                ylim=(44,50),\n",
    "#                                                alpha=1,\n",
    "#                                                cmap='isolum')\n",
    "    \n",
    "    # \n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "    if display_number is True:\n",
    "        overlay =  (contourf_plot*grid_plot*labels).opts(active_tools=['wheel_zoom','pan'],\n",
    "                                                         toolbar = None,\n",
    "                                                         shared_axes=shared_axes)\n",
    "    else:\n",
    "        overlay =  (contourf_plot*grid_plot).opts(active_tools=['wheel_zoom','pan'],\n",
    "                                                  toolbar = None,\n",
    "                                                  shared_axes=shared_axes)\n",
    "    \n",
    "        \n",
    "    overlay.opts(\n",
    "        opts.Labels(text_font_size='8.5pt', text_font_style='bold'))\n",
    "    return overlay\n",
    "\n",
    "# Replace by important storms\n",
    "options=['Temps sec : 1979-09-23 - 1979-09-25',\n",
    "                               'Tempête #1 : 1979-09-18 - 1979-09-20']\n",
    "\n",
    "dates = '1979-09-23 - 1979-09-25'\n",
    "\n",
    "x = pn.widgets.Select(value=options[0], \n",
    "                      options=options, \n",
    "                      name='# de tempête')\n",
    "progress = pn.widgets.Progress(name='Progress', value=0, width=200)\n",
    "\n",
    "numbers = pn.widgets.Checkbox(name='Display numbers', value=True)\n",
    "shared = pn.widgets.Checkbox(name='Shared axis', value=False)\n",
    "\n",
    "layout = pn.Column(\n",
    "            pn.Row('## Weather Vizualisation (72h window)', pn.Column(x, progress, pn.Row(numbers, shared)),\n",
    "                   pn.Column(gspec, sizing_mode='stretch_width')),\n",
    "                pn.Row(\n",
    "                plot_variable_ls(ds_sliced.sum('time'), ds_ls_sliced.max('time'), 'apcp', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : APCP (mm)', numbers, shared),\n",
    "                plot_variable_ls(ds_sliced.max('time'), ds_ls_sliced.max('time'), 'prate', gdf,\n",
    "                                 '{} : maximum precipitation rate (mm/h)'.format(dates), numbers, shared),\n",
    "                plot_variable_ls(ds_sliced.max('time'), ds_ls_sliced.max('time'), 'pr_wtr', gdf,\n",
    "                                 '{} : maximum precipitable water (mm)'.format(dates), numbers, shared)\n",
    "                ),\n",
    "            pn.Row(\n",
    "                plot_variable_ls(ds_sliced.max('time'), ds_ls_sliced.max('time'), 'cape', gdf, \n",
    "                                 '{} : maximum CAPE (J/kg)'.format(dates), numbers, shared),\n",
    "                plot_variable_ls(ds_pl_sliced.max('time'), ds_ls_pl_sliced.max('time'), 'gradh500', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : maximum 500-mb gradients (m/°)', numbers, shared)\n",
    "                ,\n",
    "                plot_variable_ls(ds_pl_sliced.max('time'), ds_ls_pl_sliced.max('time'), 'gradh900', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : maximum 900-mb gradients (m/°)', numbers, shared)\n",
    "                ),\n",
    "                pn.Row(\n",
    "                plot_variable_ls(ds_sliced, ds_ls_sliced.max('time'), 'prelim_type', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : preliminary storm type', numbers, shared),\n",
    "                plot_variable_ls(ds_pl_sliced.max('time').sel(level=500), ds_ls_pl_sliced.max('time').sel(level=500), 'hgt', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : maximum 500-mb height (m)', numbers, shared)\n",
    "                ,\n",
    "                plot_variable_ls(ds_pl_sliced.max('time').sel(level=900), ds_ls_pl_sliced.max('time').sel(level=900), 'hgt', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : maximum 900-mb height (m)', numbers, shared)\n",
    "                ),\n",
    "                pn.Row(\n",
    "                plot_variable_ls(ds_sliced.max('time'), ds_ls_sliced.max('time'), 'tcdc', gdf,\n",
    "                                 '1979-09-13 - 1979-09-15 : maximum cloud cover (%)', numbers, shared),\n",
    "                \n",
    "                )\n",
    "            \n",
    "            )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def update(event):\n",
    "    end_date = x.value.split(' - ')[-1]\n",
    "    start_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=2)).strftime('%Y-%m-%d')\n",
    "    progress.value = 5\n",
    "    ds_ls_sliced = ds.sel(time=slice(start_date,end_date)).load()\n",
    "    progress.value = 15\n",
    "    ds_ls_pl_sliced = ds_pl.sel(level=[500,900], time=slice(start_date,end_date)).load()\n",
    "    progress.value = 25\n",
    "    ###\n",
    "    ds_sliced = ds_ls_sliced.sel(longitude=slice(latlngbox[0], latlngbox[1]),\n",
    "                                 latitude=slice(latlngbox[2], latlngbox[3]))\n",
    "    level = 500\n",
    "    variable = 'gradh500'\n",
    "    da = np.sqrt(np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('longitude')) + \n",
    "                 np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('latitude')))\n",
    "    ds_ls_pl_sliced[variable] = da\n",
    "\n",
    "    level = 900\n",
    "    variable = 'gradh900'\n",
    "    da = np.sqrt(np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('longitude')) + \n",
    "                 np.square(ds_ls_pl_sliced.sel(level=level).hgt.differentiate('latitude')))\n",
    "    ds_ls_pl_sliced[variable] = da\n",
    "    \n",
    "    ds_pl_sliced = ds_ls_pl_sliced.sel(longitude=slice(latlngbox[0], latlngbox[1]),\n",
    "                                 latitude=slice(latlngbox[2], latlngbox[3]))\n",
    "    \n",
    "    ds_sliced['prelim_type'] = preliminary_type_da(ds_sliced.max('time'),\n",
    "                                               ds_pl_sliced.max('time'))\n",
    "    \n",
    "    variables = {'apcp': ['apcp', ds_sliced.sum('time'), ds_ls_sliced.sum('time'),'APCP (mm)'],\n",
    "                 'prate': ['prate', ds_sliced.max('time'), ds_ls_sliced.max('time'),'maximum precipitation rate (mm/h)'],\n",
    "                 'pr_wtr' : ['pr_wtr', ds_sliced.max('time'), ds_ls_sliced.max('time'),'maximum precipitable water (mm)'],\n",
    "                 'cape': ['cape', ds_sliced.max('time'), ds_ls_sliced.max('time'), 'maximum CAPE (J/kg)'],\n",
    "                 'gradh500': ['gradh500', ds_pl_sliced.max('time'), ds_ls_pl_sliced.max('time'), 'maximum 500-mb gradients (m/°)'],\n",
    "                 'gradh900': ['gradh900', ds_pl_sliced.max('time'), ds_ls_pl_sliced.max('time'), 'maximum 900-mb gradients (m/°)'],\n",
    "                 'prelim_type': ['prelim_type', ds_sliced, ds_ls_sliced.max('time'), 'preliminary storm type'],\n",
    "                 'hgt': ['hgt', ds_pl_sliced.max('time').sel(level=500), ds_ls_pl_sliced.max('time').sel(level=500), 'maximum 500-mb height (m)'],\n",
    "                 'hgt2': ['hgt', ds_pl_sliced.max('time').sel(level=900), ds_ls_pl_sliced.max('time').sel(level=900), 'maximum 900-mb height (m)'],\n",
    "                 'tcdc': ['tcdc', ds_sliced.max('time'), ds_ls_sliced.max('time'), 'maximum cloud cover (%)']\n",
    "                 }\n",
    "    \n",
    "    \n",
    "    for i, (key, value) in enumerate(variables.items()):  \n",
    "        layout[int(np.ceil((i+1)/3))][i - int(np.floor(i/3))*3].object = plot_variable_ls(value[1], value[2],\n",
    "                                                 value[0], gdf, '{} - {} : {}'.format(start_date, end_date, value[3]), numbers, shared )\n",
    "        if progress.value<=95:\n",
    "            progress.value = progress.value + 5\n",
    "\n",
    "    # look for a storm type calculated by meteorologist\n",
    "    filename = 's3://analytics-store/manual_storm_types/{}.csv'.format(end_date)\n",
    "    if fs.exists(filename):\n",
    "        with fs.open(filename) as f:\n",
    "            df = pd.read_csv(f)\n",
    "            for i in range(0,5):\n",
    "                if progress.value<=94:\n",
    "                    progress.value = progress.value + 6\n",
    "                for j in range(0,9):\n",
    "                    gspec[i, j].value = df.iloc[i,j]\n",
    "                    \n",
    "                \n",
    "    # if empty, plot preliminary storm type\n",
    "    else:\n",
    "        df = preliminary_type(ds_sliced.max('time'),\n",
    "                              ds_pl_sliced.max('time'))\n",
    "        for i in range(0,5):\n",
    "            if progress.value<=94:\n",
    "                progress.value = progress.value + 6\n",
    "            for j in range(0,9):\n",
    "                gspec[i, j].value = df.iloc[i,j] \n",
    "                \n",
    "    progress.value = 0\n",
    "    \n",
    "    # TODO : Calculate preliminary storm type and replace code above\n",
    "\n",
    "\n",
    "\n",
    "def update2(event):\n",
    "    end_date = layout[0][1][0].value.split(' - ')[-1]\n",
    "    arr = np.zeros([6,10])\n",
    "    for i in range(0,5):\n",
    "        for j in range(0,9):\n",
    "            arr[i,j] = gspec[i, j].value\n",
    "    with fs.open('s3://analytics-store/manual_storm_types/{}.csv'.format(end_date), 'w') as f:\n",
    "        pd.DataFrame(arr).to_csv(f, index=False)\n",
    "\n",
    "# def update2(event):\n",
    "#     print('test')\n",
    "\n",
    "x.param.watch(update, 'value')\n",
    "numbers.param.watch(update, 'value')\n",
    "shared.param.watch(update, 'value')\n",
    "\n",
    "for i in range(0,5):\n",
    "    for j in range(0,9):\n",
    "        gspec[i, j].param.watch(update2, 'value')\n",
    "\n",
    "layout"
   ]
  }
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