Notebook

Check Tide Map¶

Check if a given point is within a tide model domain

OTIS format tidal solutions provided by Ohio State University and ESR

http://volkov.oce.orst.edu/tides/region.html
https://www.esr.org/research/polar-tide-models/list-of-polar-tide-models/
ftp://ftp.esr.org/pub/datasets/tmd/

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

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¶

convert_ll_xy.py: convert lat/lon points to and from projected coordinates
model.py: retrieves tide model parameters for named tide models
read_tide_model.py: extract tidal harmonic constants from OTIS tide models
read_netcdf_model.py: extract tidal harmonic constants from netcdf models
read_GOT_model.py: extract tidal harmonic constants from GSFC GOT models
read_FES_model.py: extract tidal harmonic constants from FES tide models
convert_ll_xy.py: converts lat/lon points to and from projected coordinates

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

Load modules¶

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from __future__ import print_function

import os
import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widgets
import IPython.display

import pyTMD.time
import pyTMD.model
import pyTMD.tools
import pyTMD.read_tide_model
import pyTMD.read_netcdf_model
import pyTMD.read_GOT_model
import pyTMD.read_FES_model
import pyTMD.convert_ll_xy
# autoreload
%load_ext autoreload
%autoreload 2

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

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# 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

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# get model parameters
model = pyTMD.model(TMDwidgets.directory.value,
    format=TMDwidgets.atlas.value,
    compressed=TMDwidgets.compress.value
   ).elevation(TMDwidgets.model.value)
    
# read tidal constants and interpolate to grid points
if model.format in ('OTIS','ATLAS','ESR'):
    # if reading a single OTIS solution
    xi,yi,hz,mz,iob,dt = pyTMD.read_tide_model.read_tide_grid(model.grid_file)
elif (model.format == 'netcdf'):
    # if reading a netCDF OTIS atlas solution
    xi,yi,hz = pyTMD.read_netcdf_model.read_netcdf_grid(model.grid_file,
        compressed=model.compressed, type='z')
    # invert bathymetry mask
    mz = np.invert(hz.mask)
elif (model.format == 'GOT'):
    # if reading a NASA GOT solution
    hc,xi,yi,c = pyTMD.read_GOT_model.read_GOT_grid(model.model_file[0],
        compressed=model.compressed)
    # invert tidal constituent mask
    mz = np.invert(hc.mask)
elif (model.format == 'FES'):
    # if reading a FES netCDF solution
    hc,xi,yi = pyTMD.read_FES_model.read_netcdf_file(model.model_file[0],
        compressed=model.compressed, type='z', version=model.version)
    # invert tidal constituent mask
    mz = np.invert(hc.mask)

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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','ESR'):
        # if reading a single OTIS solution
        xi,yi,hz,mz,iob,dt = pyTMD.read_tide_model.read_tide_grid(model.grid_file)
        # convert coordinate systems of input latitude and longitude
        x,y = pyTMD.convert_ll_xy(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 == '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 == 'GOT'):
        # 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'):
        # 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)

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%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)

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