Check Tide Map

This (notebook) demonstrates checking if given points are within a tide model domain

OTIS format tidal solutions provided by Oregon State University and ESR

Global Tide Model (GOT) solutions provided by Richard Ray at GSFC

https://earth.gsfc.nasa.gov/geo/data/ocean-tide-models

Finite Element Solution (FES) provided by AVISO

https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes.html

Python Dependencies

Program Dependencies

crs.py: Coordinate Reference System (CRS) routines
io.model.py: retrieves tide model parameters for named tide models
io.OTIS.py: extract tidal harmonic constants from OTIS tide models
io.ATLAS.py: extract tidal harmonic constants from netcdf models
io.GOT.py: extract tidal harmonic constants from GSFC GOT models
io.FES.py: extract tidal harmonic constants from FES tide models

This notebook uses Jupyter widgets to set parameters for calculating the tidal maps.

Load modules

from __future__ import print_function

import numpy as np
import matplotlib.pyplot as plt
import IPython.display

# import tide programs
import pyTMD.crs
import pyTMD.io
import pyTMD.time
import pyTMD.tools
# autoreload
%load_ext autoreload
%autoreload 2

Set parameters for program

Model directory
Tide model

# available model list
model_list = sorted(pyTMD.io.model.ocean_elevation())
# display widgets for setting directory and model
TMDwidgets = pyTMD.tools.widgets()
TMDwidgets.model.options = model_list
TMDwidgets.model.value = 'GOT4.10'
TMDwidgets.VBox([
    TMDwidgets.directory,
    TMDwidgets.model,
    TMDwidgets.compress
])

Select location to check

# default coordinates to use
LAT,LON = (32.86710263,-117.25750387)
m = pyTMD.tools.leaflet(center=(LAT,LON), zoom=12,
    zoom_control=True, marker_control=True)
# show map
m.map

Read and create mask of valid model values

# get model parameters
model = pyTMD.io.model(TMDwidgets.directory.value,
    compressed=TMDwidgets.compress.value
   ).elevation(TMDwidgets.model.value)

# read tidal constants and interpolate to grid points
if model.format in ('OTIS','ATLAS-compact','TMD3'):
    # if reading a single OTIS solution
    xi,yi,hz,mz,iob,dt = pyTMD.io.OTIS.read_otis_grid(model.grid_file)
elif model.format in ('ATLAS-netcdf',):
    # if reading a netCDF OTIS atlas solution
    xi,yi,hz = pyTMD.io.ATLAS.read_netcdf_grid(model.grid_file,
        compressed=model.compressed, type='z')
    # invert bathymetry mask
    mz = np.invert(hz.mask)
elif (model.format == 'GOT-ascii'):
    # if reading a NASA GOT solution
    hc,xi,yi,c = pyTMD.io.GOT.read_ascii_file(model.model_file[0],
        compressed=model.compressed)
    # invert tidal constituent mask
    mz = np.invert(hc.mask)
elif (model.format == 'GOT-netcdf'):
    # if reading a NASA GOT netcdf solution
    hc,xi,yi,c = pyTMD.io.GOT.read_netcdf_file(model.model_file[0],
        compressed=model.compressed)
    # invert tidal constituent mask
    mz = np.invert(hc.mask)
elif (model.format == 'FES-netcdf'):
    # if reading a FES netCDF solution
    hc,xi,yi = pyTMD.io.FES.read_netcdf_file(model.model_file[0],
        compressed=model.compressed, type='z', version=model.version)
    # invert tidal constituent mask
    mz = np.invert(hc.mask)

def update_coordinates(sender):
    # leaflet location
    LAT,LON = np.copy(m.marker.location)
    # verify longitudes
    LON = m.wrap_longitudes(LON)
    # adjust dimensions of input coordinates to be iterable
    LON = np.atleast_1d(LON)
    LAT = np.atleast_1d(LAT)
    # read tidal constants and interpolate to grid points
    if model.format in ('OTIS','ATLAS-compact','TMD3'):
        # if reading a single OTIS solution
        xi,yi,hz,mz,iob,dt = pyTMD.io.OTIS.read_otis_grid(model.grid_file)
        # convert coordinate systems of input latitude and longitude
        x,y = pyTMD.crs().convert(np.atleast_1d(LON), np.atleast_1d(LAT),
            model.projection, 'F')
        # adjust longitudinal convention of input latitude and longitude
        # to fit tide model convention (if global)
        if (np.min(x) < np.min(xi)) & (model.projection == '4326'):
            lt0, = np.nonzero(x < 0)
            x[lt0] += 360.0
        if (np.max(x) > np.max(xi)) & (model.projection == '4326'):
            gt180, = np.nonzero(x > 180)
            x[gt180] -= 360.0
    elif (model.format == 'ATLAS-netcdf'):
        # if reading a netCDF OTIS atlas solution
        # adjust longitudinal convention of input latitude and longitude
        # to fit tide model convention
        x,y = np.copy([LON,LAT]).astype(np.float64)
        lt0, = np.nonzero(x < 0)
        x[lt0] += 360.0
    elif model.format in ('GOT-ascii', 'GOT-netcdf'):
        # if reading a NASA GOT solution
        # adjust longitudinal convention of input latitude and longitude
        # to fit tide model convention
        x,y = np.copy([LON,LAT]).astype(np.float64)
        lt0, = np.nonzero(x < 0)
        x[lt0] += 360.0
    elif (model.format == 'FES-netcdf'):
        # if reading a FES netCDF solution
        # adjust longitudinal convention of input latitude and longitude
        # to fit tide model convention
        x,y = np.copy([LON,LAT]).astype(np.float64)
        lt0, = np.nonzero(x < 0)
        x[lt0] += 360.0
    # update plot coordinates
    m.point.set_xdata(x)
    m.point.set_ydata(y)
    # refresh plot
    IPython.display.display(m.figure)

Plot mask and selected location

%matplotlib widget
# check coordinates on tide grid
m.figure,ax = plt.subplots(num=1, figsize=(8.25,5.25))
ax.imshow(mz, interpolation='nearest',
    extent=(xi.min(),xi.max(),yi.min(),yi.max()),
    origin='lower', cmap='gray')
m.point, = ax.plot([],[],'r*')
update_coordinates(None)
# no ticks on the x and y axes
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
# stronger linewidth on frame
[i.set_linewidth(2.0) for i in ax.spines.values()]
# adjust subplot within figure
m.figure.tight_layout()
IPython.display.clear_output(wait=True)
m.marker.observe(update_coordinates)