notebook/folium/tri_tide_movie.ipynb

tri_tide_movie-signell.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#  Plotting predicted tides\n\n\nMust call with higher `NotebookApp.iopub_data_rate_limit` to display the animations.\n\nE.g: `jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000 tri_tide_movie-signell.ipynb`"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import netCDF4\n\n\n# Versions >1.2.4 are failing with\n# http://nbviewer.jupyter.org/gist/anonymous/410bf777e19ac8ca14bb06a24b03745f\n# on Python 3.\nprint(netCDF4.__version__)",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1.2.4\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from netCDF4 import Dataset\n\n\nurl = 'http://geoport.whoi.edu/thredds/dodsC/usgs/vault0/models/tides/vdatum_gulf_of_maine/adcirc54_38_orig.nc'\n\nnc = Dataset(url)\n\nprint(nc.variables.keys())",
      "execution_count": 2,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "odict_keys(['tidenames', 'tidefreqs', 'ele', 'lon', 'lat', 'depth', 'depth-averaged', 'u_amp', 'v_amp', 'u_phase', 'v_phase'])\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "units = {\n    'knots': 1.9438,\n    'm/s': 1.0\n}\nconsts = ['STEADY', 'M2', 'S2', 'N2', 'K1', 'O1', 'P1', 'M4', 'M6']\n\n# Vineyard sound 2.\nbbox = [-70.7234, -70.4532, 41.4258, 41.5643]\n\nhalo = 0.1\n\nax2 = [\n    bbox[0] - halo * (bbox[1] - bbox[0]),\n    bbox[1] + halo * (bbox[1] - bbox[0]),\n    bbox[2] - halo * (bbox[3] - bbox[2]),\n    bbox[3] + halo * (bbox[3] - bbox[2])\n]",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pytz\nfrom datetime import datetime\nfrom pandas import date_range\n\n\nfmt = '%d-%b-%Y %H:%M'\ntzinfo = pytz.utc\n\nstart = datetime.strptime('18-Sep-2015 05:00', fmt).replace(tzinfo=tzinfo)\nstop = datetime.strptime('19-Sep-2015 05:00', fmt).replace(tzinfo=tzinfo)\n\nglocals = date_range(start, stop, freq='1H').to_pydatetime()\n\nntimes = len(glocals)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def parse_string(name):\n    try:\n        const = ''.join(name).strip()\n    except TypeError:\n        const = b''.join(name).strip()\n        const = const.decode()\n    return const ",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = nc['tidenames'][:]\n\nnames = []\nfor name in data.tolist():\n    names.append(parse_string(name))",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from scipy.spatial import Delaunay\n\n\ndepth = nc['depth'][:]\nlatf = nc['lat'][:]\nlonf = nc['lon'][:]\nfrequency = nc['tidefreqs'][:]\n\ntrif = Delaunay(list(zip(lonf, latf))).vertices",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\n\n\ninbox = np.logical_and(\n    np.logical_and(lonf >= ax2[0], lonf <= ax2[1]),\n    np.logical_and(latf >= ax2[2], latf <= ax2[3])\n)\n\nlon = lonf[inbox]\nlat = latf[inbox]",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from utide import _ut_constants_fname, loadbunch\n\n\ncon_info = loadbunch(_ut_constants_fname)['const']",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Find the indices of the tidal constituents."
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "k = 0\nind_nc, ind_ttide = [], []\n\nconst_name = [e.strip() for e in con_info['name'].tolist()]\n\nfor name in consts:\n    try:\n        if name == 'STEADY':\n            indx = const_name.index('Z0')\n        else:\n            indx = const_name.index(name)\n        k += 1\n        ind_ttide.append(indx)\n        ind_nc.append(names.index(name))\n    except ValueError:\n        pass  # `const` not found.",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Download everything first to speed the slice below."
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "uamp = nc['u_amp'][0, ...]\nvamp = nc['v_amp'][0, ...]\n\nupha = nc['u_phase'][0, ...]\nvpha = nc['v_phase'][0, ...]",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Slice the data for plotting."
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "uamp = uamp[inbox, :][:, ind_nc]\nvamp = vamp[inbox, :][:, ind_nc]\n\nupha = upha[inbox, :][:, ind_nc]\nvpha = vpha[inbox, :][:, ind_nc]",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "freq_nc = frequency[ind_nc]\n\nfreq_ttide = con_info['freq'][ind_ttide]\n\nt_tide_names = np.array(const_name)[ind_ttide]",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "omega_ttide = 2*np.pi * freq_ttide  # Convert from radians/s to radians/hour.\n\nomega = freq_nc * 3600\n\nrllat = 55  # Reference latitude for 3rd order satellites (degrees) (55 is fine always)\n\nvscale = 10  # smaller makes arrows bigger",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from matplotlib.dates import date2num\n\n\n# Convert to Matlab datenum.\n# (Soon UTide will take python datetime objects.)\njd_start = date2num(start) + 366.1667",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from utide.harmonics import FUV\n\n\nv, u, f = FUV(\n    t=np.array([jd_start]),\n    tref=np.array([0]),\n    lind=np.array([ind_ttide]),\n    lat=rllat,\n    ngflgs=[0, 0, 0, 0]\n)",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Convert phase in radians.\nv, u, f = map(np.squeeze, (v, u, f))\nv = v * 2 * np.pi\nu = u * 2 * np.pi\n\nthours = np.array(\n    [d.total_seconds() for d in (glocals - glocals[0])]\n) / 60 / 60.",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import json\nimport mplleaflet\nimport matplotlib.pyplot as plt\n\n\nfig, ax = plt.subplots(figsize=(10, 5))\n\nU = (f * uamp * np.cos(v + thours[k] * omega + u - upha * np.pi/180)).sum(axis=1)\nV = (f * vamp * np.cos(v + thours[k] * omega + u - vpha * np.pi/180)).sum(axis=1)\n\nw = units['knots'] * (U + 1j * V)\n\nwf = np.NaN * np.ones_like(lonf, dtype=w.dtype)\nwf[inbox] = w\n\n# FIXME: Cannot use masked arrays and tricontour!\n# wf = ma.masked_invalid(wf)\n# cs = ax.tricontour(lonf, latf, trif, np.abs(wf).filled(fill_value=0))\n# fig.colorbar(cs)\nq = ax.quiver(lon, lat, U, V, scale=vscale, color='white')\nax.axis(bbox)  # Vineyard sound 2.\nmplleaflet.display(fig=fig, tiles='esri_aerial')",
      "execution_count": 18,
      "outputs": [
        {
          "data": {

I cant get it work