3D_FVCOM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da10ceff-0484-4060-afa8-23dc03a7d720",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from cmocean import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import mpl_toolkits\n",
    "import numpy as np\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "import matplotlib.tri as Tri\n",
    "\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import matplotlib as mpl\n",
    "\n",
    "from pylab import *\n",
    "import cartopy as cart\n",
    "\n",
    "import xarray as xr\n",
    "from netCDF4 import Dataset\n",
    "from dateutil import parser\n",
    "from datetime import datetime,timedelta\n",
    "from os import listdir\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f35f180-565a-4967-a2fc-a3e742a9f6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fvcom_netcdf(dataset):\n",
    "    # Dropping the problematic variables\n",
    "    drop_variables = ['siglay','siglev']\n",
    "    ds = xr.open_dataset(dataset,drop_variables=drop_variables,decode_times=False)\n",
    "\n",
    "    # convert lon/c, lat/c to coordinates\n",
    "    ds = ds.assign_coords({var:ds[var] for var in ['lon','lat','lonc','latc']})\n",
    "\n",
    "\n",
    "    #Solving the problem with siglay and siglev. We need to workaround using netCDF4 and renaming the coordinates.\n",
    "    # load data with netCDF4\n",
    "    nc = Dataset(dataset)\n",
    "    # load the problematic coordinates\n",
    "    coords = {name:nc[name] for name in drop_variables}\n",
    "\n",
    "    # function to extract ncattrs from `Dataset()`\n",
    "    get_attrs = lambda name: {attr:coords[name].getncattr(attr) for attr in coords[name].ncattrs()}\n",
    "    # function to convert from `Dataset()` to `xr.DataArray()`\n",
    "    nc2xr = lambda name: xr.DataArray(coords[name],attrs=get_attrs(name),name=f'{name}_coord',dims=(f'{name}','node'))\n",
    "\n",
    "    # apply `nc2xr()` and merge `xr.DataArray()` objects\n",
    "    coords = xr.merge([nc2xr(name) for name in coords.keys()])\n",
    "\n",
    "    # reassign to the main `xr.Dataset()`\n",
    "    ds = ds.assign_coords(coords)\n",
    "\n",
    "\n",
    "    #We now can assign the z coordinate for the data.\n",
    "    z = (ds.siglay_coord*ds.h)\n",
    "    z.attrs = dict(long_name='nodal z-coordinate',units='meters')\n",
    "    ds = ds.assign_coords(z=z)\n",
    "\n",
    "\n",
    "    #The time coordinate still needs some fixing. We will parse it and reassign to the dataset.\n",
    "    # the first day\n",
    "    dt0 = parser.parse(ds.time.attrs['units'].replace('days since ',''))\n",
    "    # parse dates summing days to the origin\n",
    "    ds = ds.assign(time=[dt0+timedelta(seconds=day*86400) for day in ds.time.values])\n",
    "    \n",
    "    return(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b992cbd-9a60-401a-92b4-13b58afa67c3",
   "metadata": {},
   "source": [
    "## User inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c10fdf33-16bd-4ec8-93df-f750d4fd0345",
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = [0,10,19]\n",
    "time = [1,2,3,4,5,6,7,8,9]\n",
    "variable = 'currents'#'currents'#'salinity'  #temp, u, v\n",
    "directory = r'C:\\Users\\Felicio.Cassalho\\skill_asess_results_sfbofs\\nowcast_model_20221202' #where the data to be concatenated is stored\n",
    "file = r'\\nos.sfbofs.fields.n003.20221202.t15z.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e2aca3b-c3d7-4bfa-ba6f-59b16a3f2b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "FVCOM = fvcom_netcdf(directory+file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e6d3d9-ece2-4347-b71b-bed6cf899799",
   "metadata": {},
   "source": [
    "### Triangulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7368a1ea-5726-461d-b101-e734149f4b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = FVCOM['lon'], FVCOM['lat']\n",
    "triangles = FVCOM['nv'].transpose() - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc49f26-46f0-496f-88c0-5cc8cc4e77b5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 3D Plots for Vectorial and Scalar Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b7be17f-5a8b-4cff-b82a-5ed1265162ed",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 27\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;66;03m# make frames\u001b[39;00m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m time:\n\u001b[1;32m---> 27\u001b[0m     frame \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: [], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(\u001b[43mFVCOM\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTimes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m]\u001b[49m)\u001b[38;5;241m.\u001b[39msplit()[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m1\u001b[39m][:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m7\u001b[39m]}   \n\u001b[0;32m     28\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m layers:\n\u001b[0;32m     29\u001b[0m         frame_dict \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     30\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: np\u001b[38;5;241m.\u001b[39marray(x),\n\u001b[0;32m     31\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: np\u001b[38;5;241m.\u001b[39marray(y),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     41\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopacity\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m     42\u001b[0m         }\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\dataarray.py:818\u001b[0m, in \u001b[0;36mDataArray.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    815\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_coord(key)\n\u001b[0;32m    816\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    817\u001b[0m     \u001b[38;5;66;03m# xarray-style array indexing\u001b[39;00m\n\u001b[1;32m--> 818\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_item_key_to_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\dataarray.py:1397\u001b[0m, in \u001b[0;36mDataArray.isel\u001b[1;34m(self, indexers, drop, missing_dims, **indexers_kwargs)\u001b[0m\n\u001b[0;32m   1392\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n\u001b[0;32m   1394\u001b[0m \u001b[38;5;66;03m# Much faster algorithm for when all indexers are ints, slices, one-dimensional\u001b[39;00m\n\u001b[0;32m   1395\u001b[0m \u001b[38;5;66;03m# lists, or zero or one-dimensional np.ndarray's\u001b[39;00m\n\u001b[1;32m-> 1397\u001b[0m variable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing_dims\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1398\u001b[0m indexes, index_variables \u001b[38;5;241m=\u001b[39m isel_indexes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxindexes, indexers)\n\u001b[0;32m   1400\u001b[0m coords \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\variable.py:1322\u001b[0m, in \u001b[0;36mVariable.isel\u001b[1;34m(self, indexers, missing_dims, **indexers_kwargs)\u001b[0m\n\u001b[0;32m   1319\u001b[0m indexers \u001b[38;5;241m=\u001b[39m drop_dims_from_indexers(indexers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims, missing_dims)\n\u001b[0;32m   1321\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(indexers\u001b[38;5;241m.\u001b[39mget(dim, \u001b[38;5;28mslice\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m dim \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims)\n\u001b[1;32m-> 1322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\variable.py:870\u001b[0m, in \u001b[0;36mVariable.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    857\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return a new Variable object whose contents are consistent with\u001b[39;00m\n\u001b[0;32m    858\u001b[0m \u001b[38;5;124;03mgetting the provided key from the underlying data.\u001b[39;00m\n\u001b[0;32m    859\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    867\u001b[0m \u001b[38;5;124;03marray `x.values` directly.\u001b[39;00m\n\u001b[0;32m    868\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    869\u001b[0m dims, indexer, new_order \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_broadcast_indexes(key)\n\u001b[1;32m--> 870\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mas_indexable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_order:\n\u001b[0;32m    872\u001b[0m     data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmoveaxis(data, \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(new_order)), new_order)\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\indexing.py:657\u001b[0m, in \u001b[0;36mMemoryCachedArray.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m--> 657\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(_wrap_numpy_scalars(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m))\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\indexing.py:626\u001b[0m, in \u001b[0;36mCopyOnWriteArray.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m--> 626\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(_wrap_numpy_scalars(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m))\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\indexing.py:533\u001b[0m, in \u001b[0;36mLazilyIndexedArray.__getitem__\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m    531\u001b[0m     array \u001b[38;5;241m=\u001b[39m LazilyVectorizedIndexedArray(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marray, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkey)\n\u001b[0;32m    532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m array[indexer]\n\u001b[1;32m--> 533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marray, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_updated_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\indexing.py:505\u001b[0m, in \u001b[0;36mLazilyIndexedArray._updated_key\u001b[1;34m(self, new_key)\u001b[0m\n\u001b[0;32m    503\u001b[0m         full_key\u001b[38;5;241m.\u001b[39mappend(k)\n\u001b[0;32m    504\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 505\u001b[0m         full_key\u001b[38;5;241m.\u001b[39mappend(\u001b[43m_index_indexer_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miter_new_key\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    506\u001b[0m full_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(full_key)\n\u001b[0;32m    508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(k, integer_types \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mslice\u001b[39m,)) \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m full_key):\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\pyfvcom\\lib\\site-packages\\xarray\\core\\indexing.py:278\u001b[0m, in \u001b[0;36m_index_indexer_1d\u001b[1;34m(old_indexer, applied_indexer, size)\u001b[0m\n\u001b[0;32m    276\u001b[0m         indexer \u001b[38;5;241m=\u001b[39m slice_slice(old_indexer, applied_indexer, size)\n\u001b[0;32m    277\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 278\u001b[0m         indexer \u001b[38;5;241m=\u001b[39m \u001b[43m_expand_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mold_indexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mapplied_indexer\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    279\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    280\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m old_indexer[applied_indexer]\n",
      "\u001b[1;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    "# make figure\n",
    "fig_dict = {\n",
    "    \"data\": [],\n",
    "    # \"layout\": {},\n",
    "    \"frames\": []\n",
    "}\n",
    "\n",
    "if variable == 'currents':\n",
    "    # make data\n",
    "    for l in layers:\n",
    "        data_dict = {\n",
    "            \"x\": np.array(x),\n",
    "            \"y\": np.array(y),\n",
    "            \"z\": np.array(FVCOM['z'][l]),\n",
    "            \"i\": np.array(triangles[:,0]),\n",
    "            \"j\": np.array(triangles[:,1]),\n",
    "            \"k\": np.array(triangles[:,2]),\n",
    "            \"intensity\":np.array(((FVCOM['u'][0][l]**2)+(FVCOM['v'][0][l]**2))**.5),\n",
    "            \"intensitymode\":\"cell\",\n",
    "            #\"colorscale\":'rainbow',\n",
    "            \"coloraxis\":\"coloraxis\",\n",
    "            \"opacity\":1,\n",
    "        }\n",
    "        fig_dict[\"data\"].append(data_dict)\n",
    "    # make frames\n",
    "    for t in time:\n",
    "        frame = {\"data\": [], \"name\": str(FVCOM['Times'][t]).split()[3].split(\"'\")[1][:-7]}   \n",
    "        for l in layers:\n",
    "            frame_dict = {\n",
    "                \"x\": np.array(x),\n",
    "                \"y\": np.array(y),\n",
    "                \"z\": np.array(FVCOM['z'][l]),\n",
    "                \"i\": np.array(triangles[:,0]),\n",
    "                \"j\": np.array(triangles[:,1]),\n",
    "                \"k\": np.array(triangles[:,2]),\n",
    "                \"intensity\":np.array(((FVCOM['u'][t][l]**2)+(FVCOM['v'][t][l])**2)**.5),\n",
    "                \"intensitymode\":\"cell\",\n",
    "                #\"colorscale\":'rainbow',\n",
    "                \"coloraxis\":\"coloraxis\",\n",
    "                \"cmax\":1,\n",
    "                \"opacity\":1,\n",
    "            }\n",
    "\n",
    "            frame[\"data\"].append(frame_dict)\n",
    "        fig_dict[\"frames\"].append(frame)   \n",
    "else:\n",
    "    # make data\n",
    "    for l in layers:\n",
    "        data_dict = {\n",
    "            \"x\": np.array(x),\n",
    "            \"y\": np.array(y),\n",
    "            \"z\": np.array(FVCOM['z'][l]),\n",
    "            \"i\": np.array(triangles[:,0]),\n",
    "            \"j\": np.array(triangles[:,1]),\n",
    "            \"k\": np.array(triangles[:,2]),\n",
    "            \"intensity\":np.array(FVCOM[variable][0][l]),\n",
    "            #\"intensitymode\":\"cell\",\n",
    "            #\"colorscale\":'rainbow',\n",
    "            \"coloraxis\":\"coloraxis\",\n",
    "            \"opacity\":.5,\n",
    "        }\n",
    "        fig_dict[\"data\"].append(data_dict)    \n",
    "    # make frames\n",
    "    for t in time:\n",
    "        frame = {\"data\": [], \"name\": str(FVCOM['Times'][t]).split()[3].split(\"'\")[1][:-7]}   \n",
    "        for l in layers:\n",
    "            frame_dict = {\n",
    "                \"x\": np.array(x),\n",
    "                \"y\": np.array(y),\n",
    "                \"z\": np.array(FVCOM['z'][l]),\n",
    "                \"i\": np.array(triangles[:,0]),\n",
    "                \"j\": np.array(triangles[:,1]),\n",
    "                \"k\": np.array(triangles[:,2]),\n",
    "                \"intensity\":np.array(FVCOM[variable][t][l]),\n",
    "                #\"intensitymode\":\"cell\",\n",
    "                #\"colorscale\":'rainbow',\n",
    "                \"coloraxis\":\"coloraxis\",\n",
    "                \"opacity\":.5,\n",
    "            }\n",
    "\n",
    "            frame[\"data\"].append(frame_dict)\n",
    "        fig_dict[\"frames\"].append(frame)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "da078a1c-d86b-4d63-9a74-86985c4a9361",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig=go.Figure(data=[go.Mesh3d(fig_dict['data'][l]) for l in range(len(layers))],\n",
    "              layout=go.Layout(xaxis=dict(range=[0, 1], autorange=False),\n",
    "                               yaxis=dict(range=[0, 1], autorange=False),\n",
    "                               title=variable,\n",
    "                               updatemenus=[dict(type=\"buttons\",buttons=[dict(label=\"Play\",method=\"animate\",args=[None])])]),\n",
    "              frames = [go.Frame(data=[go.Mesh3d(fig_dict['frames'][t]['data'][l]) for l in range(len(layers))], name = f'fr{t}') for t in range(len(time))]\n",
    "             )\n",
    "\n",
    "\n",
    "sliders =  [dict(steps= [dict(method= 'animate',\n",
    "                              args= [[f'fr{t}'], #this steps refers to the frame of name f'fr{k}\n",
    "                                      dict(mode= 'immediate',\n",
    "                                           frame= dict( duration=300, redraw= True ),\n",
    "                                           fromcurrent=True,\n",
    "                                           transition=dict( duration= 0))],\n",
    "                                      label=str(FVCOM['Times'][t]).split()[3].split(\"'\")[1][:-7]) for t  in time], #this is the step label on the slider \n",
    "                                      minorticklen=0,\n",
    "                                      x=0,\n",
    "                                   len=1)]\n",
    "\n",
    "fig.update_layout(sliders=sliders)\n",
    "\n",
    "fig.write_html('first_figure_fvcom.html', auto_open=True)\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20022354-d470-40e1-a281-acc9e50d5e84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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