Plot ATLAS Compact

This (notebook) demonstrates plotting the global and local solutions from an ATLAS compact model

OTIS format tidal solutions provided by Oregon State University and ESR

Python Dependencies

Program Dependencies

  • io.model.py: retrieves tide model parameters for named tide models

  • io.OTIS.py: extract tidal harmonic constants from OTIS tide models

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

Load modules

from __future__ import print_function

import os
import numpy as np
import matplotlib.pyplot as plt
import pyTMD.tools
import pyTMD.io

# autoreload
%load_ext autoreload
%autoreload 2

Set parameters for program

  • Model directory

  • Tide model

# available model list
model_list = sorted(pyTMD.io.model.ATLAS_compact())
# display widgets for setting directory and model
TMDwidgets = pyTMD.tools.widgets()
TMDwidgets.model.options = model_list
TMDwidgets.model.value = 'TPXO8-atlas'
TMDwidgets.compress.value = False
TMDwidgets.VBox([
    TMDwidgets.directory,
    TMDwidgets.model,
    TMDwidgets.compress,
])

Setup tide model parameters

# get model parameters
model = pyTMD.io.model(TMDwidgets.directory.value,
    compressed=TMDwidgets.compress.value
   ).elevation(TMDwidgets.model.value)
# read each constituent
constituents,nc = pyTMD.io.OTIS.read_constituents(model.model_file)

Read ATLAS Compact model

# if reading a global solution with localized solutions
x0,y0,hz0,mz0,iob,dt,pmask,local = pyTMD.io.OTIS.read_atlas_grid(model.grid_file)
xi,yi,hz = pyTMD.io.OTIS.combine_atlas_model(x0,y0,hz0,pmask,local,variable='depth')
mz = pyTMD.io.OTIS.create_atlas_mask(x0,y0,mz0,local,variable='depth')
# resample global solution to 2 arc-minute solution
x30,y30,hz30 = pyTMD.io.OTIS.interpolate_atlas_model(x0,y0,hz0)

Plot global and local masks

%matplotlib widget
# plot the ATLAS mask
fig,ax1 = plt.subplots(num=1, figsize=(8.25,5.25), dpi=120)
ax1.imshow(mz, interpolation='nearest',
    extent=(xi.min(),xi.max(),yi.min(),yi.max()),
    vmin=0, vmax=1, origin='lower', cmap='gray_r', alpha=0.5)
ax1.imshow(pmask, interpolation='nearest',
    extent=(x0.min(),x0.max(),y0.min(),y0.max()),
    vmin=0, vmax=1, origin='lower', cmap='Purples', alpha=0.5)
# no ticks on the x and y axes
ax1.get_xaxis().set_ticks([])
ax1.get_yaxis().set_ticks([])
# stronger linewidth on frame
[i.set_linewidth(2.0) for i in ax1.spines.values()]
# adjust subplot within figure
fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98)
plt.show()

Plot differences between global and local solutions

# percent difference between grids
percent = 100.0*(hz30 - hz)/hz30
# plot the percent difference between ATLAS depth
fig,ax2 = plt.subplots(num=2, figsize=(8.25,5.25), dpi=120)
im = ax2.imshow(percent, interpolation='nearest',
    extent=(xi.min(),xi.max(),yi.min(),yi.max()),
    vmin=-40, vmax=40, origin='lower', cmap='PRGn')
# Add colorbar and adjust size
# pad = distance from main plot axis
# extend = add extension triangles to upper and lower bounds
# options: neither, both, min, max
# shrink = percent size of colorbar
# aspect = lengthXwidth aspect of colorbar
cbar = plt.colorbar(im, ax=ax2, pad=0.025, extend='both',
    extendfrac=0.0375, orientation='horizontal', shrink=0.925,
    aspect=22, drawedges=False)
# rasterized colorbar to remove lines
cbar.solids.set_rasterized(True)
# Add label to the colorbar
cbar.ax.set_title(f'{model.name} Bathymetry Differences', fontsize=13,
    rotation=0, y=-1.4, va='top')
cbar.ax.set_xlabel('%', fontsize=13, rotation=0, va='center')
cbar.ax.xaxis.set_label_coords(1.065, 0.5)
# ticks lines all the way across
cbar.ax.tick_params(which='both', width=1, length=23,
    labelsize=13, direction='in')
# no ticks on the x and y axes
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticks([])
# stronger linewidth on frame
[i.set_linewidth(2.0) for i in ax2.spines.values()]
# adjust subplot within figure
fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98)
plt.show()

# total amplitude difference for all constituents
diff = np.zeros_like(hz30)
power = np.zeros_like(hz30)
# for each constituent
for i,c in enumerate(constituents):
    # if reading a global solution with localized solutions
    z0,zlocal = pyTMD.io.OTIS.read_atlas_elevation(model.model_file,i,c)
    xi,yi,z = pyTMD.io.OTIS.combine_atlas_model(x0,y0,z0,pmask,zlocal,variable='z')
    # resample global solution to 2 arc-minute solution
    x30,y30,z30 = pyTMD.io.OTIS.interpolate_atlas_model(x0,y0,z0)
    # add to total amplitude difference
    diff += (z30.real - z.real)**2 + (z30.imag - z.imag)**2
    power += z30.real**2 + z30.imag**2

# calculate the percent difference
percent = 100.0*np.sqrt(diff/power)
# plot the percent between ATLAS tidal amplitudes
fig,ax3 = plt.subplots(num=3, figsize=(8.25,5.25), dpi=120)
im = ax3.imshow(percent, interpolation='nearest',
    extent=(xi.min(),xi.max(),yi.min(),yi.max()),
    vmin=0, vmax=100, origin='lower', cmap='BuPu')
# Add colorbar and adjust size
# pad = distance from main plot axis
# extend = add extension triangles to upper and lower bounds
# options: neither, both, min, max
# shrink = percent size of colorbar
# aspect = lengthXwidth aspect of colorbar
cbar = plt.colorbar(im, ax=ax3, pad=0.025, extend='max',
    extendfrac=0.0375, orientation='horizontal', shrink=0.925,
    aspect=22, drawedges=False)
# rasterized colorbar to remove lines
cbar.solids.set_rasterized(True)
# Add label to the colorbar
cbar.ax.set_title(f'{model.name} Tide Height Differences', fontsize=13,
    rotation=0, y=-1.4, va='top')
cbar.ax.set_xlabel('%', fontsize=13, rotation=0, va='center')
cbar.ax.xaxis.set_label_coords(1.065, 0.5)
# ticks lines all the way across
cbar.ax.tick_params(which='both', width=1, length=23,
    labelsize=13, direction='in')
# no ticks on the x and y axes
ax3.get_xaxis().set_ticks([])
ax3.get_yaxis().set_ticks([])
# stronger linewidth on frame
[i.set_linewidth(2.0) for i in ax3.spines.values()]
# adjust subplot within figure
fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98)
plt.show()