#!/usr/bin/env python
"""
name: coastal_tools
authors: Steven Brus
last modified: 07/09/2018
"""
from __future__ import absolute_import, division, print_function, \
unicode_literals
import numpy as np
import pyflann
from skimage import measure
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from scipy import spatial, io
import timeit
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import mpas_tools.mesh.creation.mesh_definition_tools as mdt
from mpas_tools.mesh.creation.util import lonlat2xyz
# Constants
km = 1000.0
deg2rad = np.pi / 180.0
rad2deg = 180.0 / np.pi
call_count = 0
##########################################################################
# Bounding box declarations (for coastal refinements)
##########################################################################
# ---------------
# Region boxes
# ---------------
# Bays and estuaries
Delaware_Bay = {"include": [np.array([-75.61903, -74.22, 37.8, 40.312747])],
"exclude": []}
Galveston_Bay = {"include": [np.array([-95.45, -94.4, 29, 30])],
"exclude": []}
# Regions
Delaware_Region = {"include": [np.array([-77, -69.8, 35.5, 41])],
"exclude": []}
# Coastlines
US_East_Coast = {"include": [np.array([-81.7, -62.3, 25.1, 44.50])], # East FL to Bay of Fundy
"exclude": [np.array([-66.0, -64.0, 31.5, 33.0]), # Bermuda
np.array([-79.75, -70.0, 20.0, 28.5]), # Bahamas
np.array([-65.15, -62.43, 43.0, 45.55]), # Gulf of St. Lawence
np.array([-66.65, -62.43, 43.0, 45.0])]} # ''
US_Gulf_Coast = {"include": [np.array([-98.0, -80.0, 24.0, 31.0]), # West FL to NE Mexico
np.array([-98.5, -95.5, 20.0, 24.0]), # East Mexico
np.array([-91.0, -86.0, 20.0, 22.0]) # Yucatan
],
"exclude": []}
Caribbean = {"include": [np.array([-89.85, -59.73, 9.35, 20.86]),
],
"exclude": []}
US_West_Coast = {"include": [np.array([-127.0, -116.0, 32.5, 49.0]), # California
np.array([-117.5, -108.0, 22.8, 32.5]) # Baja and West Mexico
],
"exclude": [np.array([-116.5, -115.0, 32.8, 33.8]), # Salton Sea
np.array([-120.5, -116.5, 35.5, 40.5]), # Lake Tahoe, etc.
np.array([[-111.89, 21.24], # Baja
[-107.17, 22.48],
[-113.94, 30.77],
[-119.44, 33.09]])]}
Hawaii = {"include": [np.array([-161.0, -154.0, 18.0, 23.0])],
"exclude": []}
Alaska = {"include": [np.array([-170.0, -141.0, 49.0, 63.0]),
np.array([-141.0, -129.5, 49.0, 61.0]), # Southeast
np.array([-129.5, -121.0, 49.0, 55.0]) # Connects AK to CA
],
"exclude": [np.array([-171.0, -151.79, 49.54, 58.83])]} # Aleutian Islands
Bering_Sea_E = {"include": [np.array([-180.0, -168.5, 56.00, 64.0])],
"exclude": []}
Bering_Sea_W = {"include": [np.array([161.90, 180.0, 57.85, 66.63])],
"exclude": []}
Aleutian_Islands_E = {"include": [np.array([-180.0, -151.79, 49.54, 58.83])],
"exclude": [np.array([-173.16, -164.37, 55.81, 57.55])]}
Aleutian_Islands_W = {"include": [np.array([164.9, 180.0, 50.77, 56.31])],
"exclude": [np.array([178.5, 179.5, 51.25, 51.75])]}
Greenland = {"include":[np.array([-81.5,-12.5,60,85])],
"exclude":[np.array([[-87.6,58.7],
[-51.9,56.6],
[-68.9,75.5],
[-107.0,73.3]]),
np.array([[-119.0,74.5],
[-92.7,75.9],
[-52.84,83.25],
[-100.8,84.0]]),
np.array([[-101.3,68.5],
[-82.4,72.3],
[-68.7,81.24],
[-117.29,77.75]]),
np.array([-25.0,-10.0,62.5,67.5])]}
Atlantic = {"include": [np.array([-78.5, -77.5, 23.5, 25.25])], # Bahamas, use with large transition start to fill Atlantic
"exclude": []}
# Combined coastlines
CONUS = {"include": [], "exclude": []}
CONUS["include"].extend(US_East_Coast["include"])
CONUS["include"].extend(US_Gulf_Coast["include"])
CONUS["include"].extend(US_West_Coast["include"])
CONUS["exclude"].extend(US_East_Coast["exclude"])
CONUS["exclude"].extend(US_West_Coast["exclude"])
Continental_US = {"include": [], "exclude": []}
Continental_US["include"].extend(CONUS["include"])
Continental_US["include"].extend(Alaska["include"])
Continental_US["exclude"].extend(CONUS["exclude"])
# ----------------
# Plotting boxes
# ----------------
Western_Atlantic = np.array([-98.186645, -59.832744, 7.791301, 45.942453])
Contiguous_US = np.array([-132.0, -59.832744, 7.791301, 51.0])
North_America = np.array([-175.0, -60.0, 7.5, 72.0])
Delaware = np.array([-77, -69.8, 35.5, 41])
Entire_Globe = np.array([-180, 180, -90, 90])
# -----------------
# Restrict Boxes
# -----------------
Empty = {"include": [],
"exclude": []}
Delaware_restrict = {"include": [np.array([[-75.853, 39.732],
[-74.939, 36.678],
[-71.519, 40.156],
[-75.153, 40.077]]),
np.array([[-76.024, 37.188],
[-75.214, 36.756],
[-74.512, 37.925],
[-75.274, 38.318]])],
"exclude": []}
Gulf_restrict = {"include": [np.array([[-85.04, 13.80],
[-76.90, 16.60],
[-86.24, 36.80],
[-105.55, 22.63]])],
"exclude": []}
Caribbean_restrict = {"include": [np.array([[-76.39, 4.55],
[-53.22, 4.29],
[-53.22, 38.94],
[-94.99, 18.47]])],
"exclude": [np.array([[-80.72, 1.66],
[-73.70, 3.03],
[-78.94, 9.33],
[-84.98, 7.67]]),
np.array([[-100.18, 13.76],
[-82.93, 6.51],
[-85.08, 13.74],
[-95.86, 18.04]])]}
East_Coast_restrict = {"include": [],
"exclude": [np.array([[-72.0, 46.69],
[-61.74, 45.48],
[-44.07, 49.49],
[-63.47, 53.76]])]}
Bering_Sea_restrict = {"include": [],
"exclude": [np.array([[143.46, 51.79],
[155.65, 50.13],
[166.23, 63.78],
[148.63, 62.30]]),
np.array([[154.36, 68.22],
[-173.80, 65.94],
[-161.81, 72.02],
[163.64, 73.70]])]}
Atlantic_restrict = {"include": [np.array([[-86.39, 13.67],
[-24.44, 21.32],
[-50.09, 55.98],
[-105.58, 23.61]]),
np.array([[-76.39, 4.55],
[-30.74, -2.66],
[-30.83, 43.95],
[-94.99, 18.47]])],
"exclude": [np.array([[-80.72, 1.66],
[-73.70, 3.03],
[-78.94, 9.33],
[-84.98, 7.67]]),
np.array([[-100.18, 13.76],
[-82.93, 6.51],
[-85.08, 13.74],
[-95.86, 18.04]])]}
##########################################################################
# User-defined inputs
##########################################################################
default_params = {
# Path to bathymetry data and name of file
"nc_file": "./earth_relief_15s.nc",
"nc_vars": ["lon","lat","z"],
# Bounding box of coastal refinement region
"region_box": Continental_US,
"origin": np.array([-100, 40]),
"restrict_box": Empty,
# Coastline extraction parameters
"z_contour": 0.0,
"n_longest": 10,
"smooth_coastline": 0,
"point_list": None,
# Global mesh parameters
"grd_box": Entire_Globe,
"ddeg": .1,
# 'EC' (defaults to 60to30), 'QU' (uses dx_max_global), 'RRS' (uses dx_max_global and dx_min_global)
"mesh_type": 'EC',
"dx_max_global": 30.0 * km,
"dx_min_global": 10.0 * km,
# Coastal mesh parameters
"dx_min_coastal": 10.0 * km,
"trans_width": 600.0 * km,
"trans_start": 400.0 * km,
# Bounding box of plotting region
"plot_box": North_America,
# Options
"nn_search": "flann",
"plot_option": True
}
##########################################################################
# Functions
##########################################################################
[docs]def coastal_refined_mesh(params, cell_width=None, lon_grd=None, lat_grd=None):
# {{{
"""
Optionally create a background field of cell widths, then add a region of
refined resolution to the cell widths.
Parameters
----------
params : dict
A dictionary of parameters determining how the mesh is constructed.
See ``mpas_tools.ocean.coastal_tools.default_params``.
cell_width : ndarray, optional
A 2D array of cell widths in meters. If none is provided, one a base
``cell_width`` field constructed using parameter values from ``params``
to call ``create_background_mesh``.
lon_grd : ndarray, optional
A 1D array of longitudes in degrees in the range from -180 to 180
lat_grd : ndarray, optional
A 1D array of latitudes in degrees in the range from -90 to 90
Returns
-------
cell_width : ndarray
A 2D array of cell widths in meters.
lon_grd : ndarray
A 1D array of longitudes in degrees in the range from -180 to 180
lat_grd : ndarray
A 1D array of latitudes in degrees in the range from -90 to 90
"""
coastal_refined_mesh.counter += 1
call_count = coastal_refined_mesh.counter
if cell_width is None:
# Create the background cell width array
lon_grd, lat_grd, cell_width = create_background_mesh(
params["grd_box"],
params["ddeg"],
params["mesh_type"],
params["dx_min_global"],
params["dx_max_global"],
params["plot_option"],
params["plot_box"],
call_count)
# Get coastlines from bathy/topo data set
coastlines = extract_coastlines(
params["nc_file"],
params["nc_vars"],
params["region_box"],
params["z_contour"],
params["n_longest"],
params["point_list"],
params["plot_option"],
params["plot_box"],
call_count)
# Compute distance from background grid points to coastline
D = distance_to_coast(
coastlines,
lon_grd,
lat_grd,
params["nn_search"],
params["smooth_coastline"],
params["plot_option"],
params["plot_box"],
call_count)
# Blend coastline and background resolution, save cell_width array as .mat file
cell_width = compute_cell_width(
D,
cell_width,
lon_grd,
lat_grd,
params["dx_min_coastal"],
params["trans_start"],
params["trans_width"],
params["restrict_box"],
params["plot_option"],
params["plot_box"],
coastlines,
call_count)
# Save matfile
# save_matfile(cell_width/km,lon_grd,lat_grd)
print("")
return (cell_width, lon_grd, lat_grd)
# }}}
coastal_refined_mesh.counter = 0
##############################################################
[docs]def create_background_mesh(grd_box, ddeg, mesh_type, dx_min, dx_max, # {{{
plot_option=False, plot_box=[], call=None):
"""
Create a background field of cell widths
Parameters
----------
grd_box : list of float
A list of 4 floats defining the bounds (min lon, max lon, min lat, max
lat) of the grid
ddeg : float
The resolution of the mesh in degrees
mesh_type : {'QU', 'EC', 'RRS'}
The type of mesh: quasi-uniform (QU), Eddy-closure (EC) or Rossby-radius
scaling (RRS)
dx_min : float
The resolution in meters of a QU mesh or the minimum resolution of of
an RRS mesh. This parameter is ignored for EC meshes and the default
function arguments to ``EC_CellWidthVsLat()`` are used instead.
dx_max : float
The maximum resolution in meters of of an RRS mesh. This parameter is
ignored for QU meshes and EC meshes. For EC meshes, the default
function arguments are used instead.
plot_option : bool, optional
Whether to plot the resulting cell width and save it to files
named ``bckgrnd_grid_cell_width_vs_lat###.png`` and
``bckgnd_grid_cell_width###.png``, where ``###`` is given by
``call`` and is meant to indicate how many times this function has been
called during mesh creation.
plot_box : list of float, optional
The extent of the plot if ``plot_option=True``
call : int, optional
The number of times the function has been called, used to give the
plot a unique name.
Returns
-------
cell_width : ndarray
A 2D array of cell widths in meters.
lon_grd : ndarray
A 1D array of longitudes in degrees in the range from -180 to 180
lat_grd : ndarray
A 1D array of latitudes in degrees in the range from -90 to 90
"""
print("Create background mesh")
print("------------------------")
# Create cell width background grid
ny_grd = int((grd_box[3]-grd_box[2])/ddeg) + 1
nx_grd = int((grd_box[1]-grd_box[0])/ddeg) + 1
lat_grd = grd_box[2] + ddeg*np.arange(ny_grd)
lon_grd = grd_box[0] + ddeg*np.arange(nx_grd)
print(" Background grid dimensions:", ny_grd, nx_grd)
# Assign background grid cell width values
if mesh_type == 'QU':
cell_width_lat = dx_max / km * np.ones(lat_grd.size)
elif mesh_type == 'EC':
cell_width_lat = mdt.EC_CellWidthVsLat(lat_grd)
elif mesh_type == 'RRS':
cell_width_lat = mdt.RRS_CellWidthVsLat(lat_grd, dx_max / km, dx_min / km)
else:
raise ValueError('Unknown mesh_type {}'.format(mesh_type))
cell_width = np.tile(cell_width_lat, (nx_grd, 1)).T * km
# Plot background cell width
if plot_option:
print(" Plotting background cell width")
plt.figure()
plt.plot(lat_grd, cell_width_lat)
plt.savefig('bckgrnd_grid_cell_width_vs_lat' + str(call) + '.png')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
plt.contourf(lon_grd, lat_grd, cell_width, transform=ccrs.PlateCarree())
plot_coarse_coast(ax, plot_box)
plt.colorbar()
plt.savefig(
'bckgnd_grid_cell_width' +
str(call) +
'.png',
bbox_inches='tight')
plt.close()
print(" Done")
return (lon_grd, lat_grd, cell_width) # }}}
##############################################################
##############################################################
[docs]def distance_to_coast(coastlines, lon_grd, lat_grd, nn_search, smooth_window,
plot_option=False, plot_box=[], call=None):
# {{{
"""
Extracts a set of coastline contours
Parameters
----------
coastlines : ndarray
An n x 2 array of (longitude, latitude) points along the coastline
contours returned from ``extract_coastlines()``
lon_grd : ndarray
A 1D array of longitudes in degrees in the range from -180 to 180
lat_grd : ndarray
A 1D array of latitudes in degrees in the range from -90 to 90
nn_search : {'kdtree', 'flann'}
The algorithm to use for the nearest neightbor search. 'flann' is
strongly recommended, as it is faster and more memory efficient in our
testing.
smooth_window : int
The number of adjacent coastline points to average together to smooth
the coastal contours. Use ``0`` to indicate no smoothing.
plot_option : bool, optional
Whether to plot the resulting coastline points and the plot to a file
named ``bathy_coastlines###.png``, where ``###`` is given by
``call`` and is meant to indicate how many times this function has been
called during mesh creation.
plot_box : list of float, optional
The extent of the plot if ``plot_option=True``
call : int, optional
The number of times the function has been called, used to give the
plot a unique name.
Returns
-------
D : ndarray
A len(lat_grd) x len(lon_grd) array of distances in meters on the
lon/lat grid to the closest point in the (smoothed) coastline contour.
"""
print("Distance to coast")
print("-----------------")
# Remove Nan values separating coastlines
coast_pts = coastlines[np.isfinite(coastlines).all(axis=1)]
# Smooth coast points if necessary
if smooth_window > 1:
coast_pts[:, 0], coast_pts[:, 1] = smooth_coastline(
coast_pts[:, 0], coast_pts[:, 1], smooth_window)
# Convert coastline points to x,y,z and create kd-tree
npts = coast_pts.shape[0]
coast_pts_xyz = np.zeros((npts,3))
coast_pts_xyz[:, 0], coast_pts_xyz[:, 1], coast_pts_xyz[:, 2] = lonlat2xyz(coast_pts[:, 0], coast_pts[:, 1])
if nn_search == "kdtree":
tree = spatial.KDTree(coast_pts_xyz)
elif nn_search == "flann":
flann = pyflann.FLANN()
flann.build_index(
coast_pts_xyz,
algorithm='kdtree',
target_precision=1.0,
random_seed=0)
# Convert backgound grid coordinates to x,y,z and put in a nx_grd x 3 array for kd-tree query
Lon_grd, Lat_grd = np.meshgrid(lon_grd, lat_grd)
X_grd, Y_grd, Z_grd = lonlat2xyz(Lon_grd,Lat_grd)
pts = np.vstack([X_grd.ravel(), Y_grd.ravel(), Z_grd.ravel()]).T
# Find distances of background grid coordinates to the coast
print(" Finding distance")
start = timeit.default_timer()
if nn_search == "kdtree":
d, idx = tree.query(pts)
elif nn_search == "flann":
idx, d = flann.nn_index(pts, checks=2000, random_seed=0)
d = np.sqrt(d)
end = timeit.default_timer()
print(" Done")
print(" " + str(end - start) + " seconds")
# Make distance array that corresponds with cell_width array
D = np.reshape(d, Lon_grd.shape)
if plot_option:
print(" Plotting distance to coast")
# Find coordinates and data inside plotting box
lon_plot, lat_plot, D_plot = get_data_inside_box(
lon_grd, lat_grd, D, plot_box)
# Plot distance to coast
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
D_plot = D_plot / km
levels = np.linspace(np.amin(D_plot), np.amax(D_plot), 10)
plt.contourf(lon_plot, lat_plot, D_plot, levels=levels,
transform=ccrs.PlateCarree())
plot_coarse_coast(ax, plot_box)
plt.plot(coastlines[:, 0], coastlines[:, 1], color='white')
plt.grid(
xdata=lon_plot,
ydata=lat_plot,
c='k',
ls='-',
lw=0.1,
alpha=0.5)
plt.colorbar()
plt.axis('equal')
plt.savefig('distance' + str(call) + '.png', bbox_inches='tight')
plt.close()
print(" Done")
return D # }}}
##############################################################
[docs]def compute_cell_width(D, cell_width, lon, lat, dx_min, trans_start,
trans_width, restrict_box, plot_option=False,
plot_box=[], coastlines=[], call=None): # {{{
"""
Blend cell widths from the input field with the new resolution in the
refined region determined by the distance to the coastline contour.
Parameters
----------
D : ndarray
A len(lat) x len(lon) array of distances in meters on the lon/lat grid
to the closest point in the (smoothed) coastline contour returned from
``distance_to_coast()``
cell_width : ndarray
A len(lat) x len(lon) array of cell widths in meters
lon : ndarray
A 1D array of longitudes in degrees in the range from -180 to 180
lat : ndarray
A 1D array of latitudes in degrees in the range from -90 to 90
dx_min : float
The resolution in meters of the new refined region.
trans_start : float
The approximate value of ``D`` in meters at which the transition in
resolution should start
trans_width : float
The approximate width in meters over which the transition in resolution
should take place
restrict_box : dict of lists of ndarrays
A region of made up of quadrilaterals to ``include`` and ``exclude``
that defines where resolution may be altered. Outside of the
``restrict_box``, the resolution remains unchanged.
plot_option : bool, optional
Whether to plot the resulting coastline points and the plot to files
named ``cell_width###.png`` and ``trans_func###.png```, where ``###``
is given by ``call`` and is meant to indicate how many times this
function has been called during mesh creation.
plot_box : list of float, optional
The extent of the plot if ``plot_option=True``
coastlines : ndarray
An n x 2 array of (longitude, latitude) points along the coastline
contours returned from ``extract_coastlines()`` used in plotting if
``plot_option=True``
call : int, optional
The number of times the function has been called, used to give the
plot a unique name.
Returns
-------
cell_width : ndarray
A len(lat) x len(lon) array of the new cell widths in meters
"""
print("Compute cell width")
print("------------------")
# Compute cell width based on distance
print(" Computing cell width")
backgnd_weight = .5 * \
(np.tanh((D - trans_start - .5 * trans_width) / (.2 * trans_width)) + 1.0)
dist_weight = 1.0 - backgnd_weight
## Use later for depth and slope dependent resolution
##hres = np.maximum(dx_min*bathy_grd/20,dx_min)
##hres = np.minimum(hres,dx_max)
#hw = np.zeros(Lon_grd.shape) + dx_max
#hw[ocn_idx] = np.sqrt(9.81*bathy_grd[ocn_idx])*12.42*3600/25
#hs = .20*1/dbathy_grd
#h = np.fmin(hs,hw)
#h = np.fmin(h,dx_max)
#h = np.fmax(dx_min,h)
cell_width_old = np.copy(cell_width)
# Apply cell width function
if len(restrict_box["include"]) > 0:
# Only apply inside include regions
for box in restrict_box["include"]:
idx = get_indices_inside_quad(lon, lat, box)
cell_width[idx] = (dx_min*dist_weight[idx] +
np.multiply(cell_width_old[idx], backgnd_weight[idx]))
else:
# Apply everywhere
cell_width = (dx_min*dist_weight +
np.multiply(cell_width_old, backgnd_weight))
# Don't apply cell width function in exclude regions (revert to previous values)
if len(restrict_box["exclude"]) > 0:
for box in restrict_box["exclude"]:
idx = get_indices_inside_quad(lon, lat, box)
cell_width[idx] = cell_width_old[idx]
print(" Done")
if plot_option:
print(" Plotting cell width")
# Find coordinates and data inside plotting box
lon_plot, lat_plot, cell_width_plot = get_data_inside_box(
lon, lat, cell_width / km, plot_box)
# Plot cell width
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
levels = np.linspace(np.amin(cell_width_plot),
np.amax(cell_width_plot), 100)
plt.contourf(lon_plot, lat_plot, cell_width_plot, levels=levels,
transform=ccrs.PlateCarree())
plot_coarse_coast(ax, plot_box)
plt.plot(coastlines[:, 0], coastlines[:, 1], color='white')
if restrict_box:
for box in restrict_box["include"]:
plot_region_box(box, 'b')
for box in restrict_box["exclude"]:
plot_region_box(box, 'r')
plt.colorbar()
plt.axis('equal')
plt.savefig('cell_width' + str(call) + '.png', bbox_inches='tight')
plt.close()
# Plot cell width transistion functions
ts = trans_start/km
tw = trans_width/km
d = np.linspace(0,2*(ts+tw),1000)
bw = .5*(np.tanh((d-ts-.5*tw)/(.2*tw))+1)
dw = 1-bw
plt.figure()
plt.plot(d,bw)
plt.plot(d,dw)
plt.legend(('background','coastal region'))
plt.plot([ts,ts],[0.0,1.0],'k-')
plt.plot([ts+tw,ts+tw],[0.0,1.0],'k-')
plt.tight_layout()
plt.xlabel('distance (km)')
plt.ylabel('weight')
plt.savefig('trans_func'+str(call)+'.png',bbox_inches='tight')
plt.close()
print(" Done")
return cell_width # }}}
##############################################################
[docs]def save_matfile(cell_width, lon, lat):
io.savemat('cellWidthVsLatLon.mat',
mdict={
'cellWidth': cell_width,
'lon': lon,
'lat': lat})
##############################################################
[docs]def CPP_projection(lon, lat, origin):
R = 6378206.4
origin = origin * deg2rad
x = R * (lon * deg2rad - origin[0]) * np.cos(origin[1])
y = R * lat * deg2rad
return x, y
##############################################################
[docs]def smooth_coastline(x, y, window):
xs = np.copy(x)
ys = np.copy(y)
offset = (window-1)/2
for pt in range(offset-1, x.size-offset):
xs[pt] = np.mean(x[pt-offset:pt+offset])
ys[pt] = np.mean(y[pt-offset:pt+offset])
return xs, ys
##############################################################
[docs]def get_data_inside_box(lon, lat, data, box, idx=False):
# Find indicies of coordinates inside bounding box
lon_idx, = np.where((lon >= box[0]) & (lon <= box[1]))
lat_idx, = np.where((lat >= box[2]) & (lat <= box[3]))
# Get indicies inside bounding box
lon_region = lon[lon_idx]
lat_region = lat[lat_idx]
latlon_idx = np.ix_(lat_idx, lon_idx)
# Return data inside bounding box
if idx == False:
try: # Numpy indexing
z_region = data[latlon_idx]
except: # NetCDF indexing
z_region = data[lat_idx, lon_idx]
return (lon_region, lat_region, z_region)
# Return indicies of data inside bounding box
elif idx == True:
return latlon_idx
##############################################################
[docs]def get_indices_inside_quad(lon, lat, box, grid=True):
wrap = flag_wrap(box)
lon_adj = np.copy(lon)
if wrap:
idx = np.where((lon_adj >= -180.0) & (lon_adj <= -90.0))
lon_adj[idx] = lon_adj[idx] + 360.0
if grid:
# Create vectors of all coordinates
Lon_grd, Lat_grd = np.meshgrid(lon_adj, lat)
X = Lon_grd.ravel()
Y = Lat_grd.ravel()
else:
X = lon_adj
Y = lat
xb,rect = get_convex_hull_coordinates(box)
# Find indices of coordinates in convex hull of quad region
idxx = np.where((X >= xb[0]) & (X <= xb[1]))
idxy = np.where((Y >= xb[2]) & (Y <= xb[3]))
idx_ch = np.intersect1d(idxx, idxy)
idx = np.copy(idx_ch)
if rect == True:
if grid == True:
idx = np.unravel_index(idx, Lon_grd.shape)
return idx
# Initialize the local coordinate vectors to be outside unit square
R = 0.0 * X - 10.0
S = 0.0 * Y - 10.0
# Initialize the coordinate vectors for points inside convex hull of quad region
r = 0.0 * R[idx]
s = 0.0 * S[idx]
x = X[idx]
y = Y[idx]
# Map all coordinates in convex hull of quad region to unit square
# by solving inverse transformaion with Newton's method
tol = 1e-8
maxit = 25
for it in range(0, maxit):
# Compute shape fuctions
phi1 = np.multiply((1.0 - r), (1.0 - s))
phi2 = np.multiply((1.0 + r), (1.0 - s))
phi3 = np.multiply((1.0 + r), (1.0 + s))
phi4 = np.multiply((1.0 - r), (1.0 + s))
# Compute functions that are being solved
f1 = .25 * (phi1 * box[0, 0] + phi2 * box[1, 0] +
phi3 * box[2, 0] + phi4 * box[3, 0]) - x
f2 = .25 * (phi1 * box[0, 1] + phi2 * box[1, 1] +
phi3 * box[2, 1] + phi4 * box[3, 1]) - y
# Compute Jacobian
df1ds = .25 * ((r - 1.0) * box[0, 0] - (1.0 + r) * box[1, 0] + (1.0 + r) * box[2, 0] + (1.0 - r) * box[3, 0])
df1dr = .25 * ((s - 1.0) * box[0, 0] + (1.0 - s) * box[1, 0] + (1.0 + s) * box[2, 0] - (1.0 + s) * box[3, 0])
df2ds = .25 * ((r - 1.0) * box[0, 1] - (1.0 + r) * box[1, 1] + (1.0 + r) * box[2, 1] + (1.0 - r) * box[3, 1])
df2dr = .25 * ((s - 1.0) * box[0, 1] + (1.0 - s) * box[1, 1] + (1.0 + s) * box[2, 1] - (1.0 + s) * box[3, 1])
# Inverse of 2x2 matrix
det_recip = np.multiply(df1dr, df2ds) - np.multiply(df2dr, df1ds)
det_recip = 1.0 / det_recip
dr = np.multiply(det_recip, np.multiply(df2ds, f1) - np.multiply(df1ds, f2))
ds = np.multiply(det_recip, -np.multiply(df2dr, f1) + np.multiply(df1dr, f2))
# Apply Newton's method
rnew = r - dr
snew = s - ds
# Find converged values
err = R[idx] - rnew
idxr = np.where(np.absolute(err) < tol)
err = S[idx] - snew
idxs = np.where(np.absolute(err) < tol)
idx_conv = np.intersect1d(idxr, idxs)
# Update solution
R[idx] = rnew
S[idx] = snew
# Find indicies of unconverged values
idx = np.delete(idx, idx_conv)
# print("Iteration: ", it, "unconverged values: ", idx.size)
# Terminate once all values are converged
if idx.size == 0:
break
# Initialize to unconverged values for next iteration
r = R[idx]
s = S[idx]
x = X[idx]
y = Y[idx]
# Find any remaining unconverged values
if grid == True:
idx_nc = np.unravel_index(idx, Lon_grd.shape)
else:
idx_nc = np.copy(idx)
# Find indicies of coordinates inside quad region
lon_idx, = np.where((R >= -1.0) & (R <= 1.0))
lat_idx, = np.where((S >= -1.0) & (S <= 1.0))
idx = np.intersect1d(lon_idx, lat_idx)
if grid == True:
idx = np.unravel_index(idx, Lon_grd.shape)
## Plot values inside quad region
# plt.figure()
# plt.plot(X,Y,'.')
# if grid == True:
# plt.plot(Lon_grd[idx],Lat_grd[idx],'.')
# plt.plot(Lon_grd[idx_nc],Lat_grd[idx_nc],'.')
# else:
# plt.plot(lon[idx],lat[idx],'.')
# plt.plot(lon[idx_nc],lat[idx_nc],'.')
# plt.plot(box[:,0],box[:,1],'o')
# plt.savefig("restrict_box.png")
return idx
##############################################################
[docs]def get_convex_hull_coordinates(box):
wrap = flag_wrap(box)
xb = np.zeros(4)
if box.size == 4:
if wrap:
for i in range(2):
if box[i] >= -180.0 and box[i] <= -90.0:
box[i] = box[i] + 360.0
xb[0] = box[0]
xb[1] = box[1]
xb[2] = box[2]
xb[3] = box[3]
rect = True
else:
if wrap:
for i in range(4):
if box[i, 0] >= -180.0 and box[i, 0] <= -90.0:
box[i, 0] = box[i, 0] + 360.0
xb[0] = np.amin(box[:, 0])
xb[1] = np.amax(box[:, 0])
xb[2] = np.amin(box[:, 1])
xb[3] = np.amax(box[:, 1])
rect = False
return xb,rect
##############################################################
def flag_wrap(box):
wrap = False
if box.size == 4:
if box[0] > 0.0 and box[1] < 0.0:
wrap = True
else:
if np.any(box[:,0] > 0.0) and np.any(box[:,0] < 0.0):
wrap = True
return wrap
##############################################################
[docs]def plot_coarse_coast(ax, plot_box):
ax.set_extent(plot_box, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE)
##############################################################
[docs]def plot_region_box(box, color):
ls = color + '-'
if box.size == 4:
plt.plot([box[0], box[1]], [box[2], box[2]], ls)
plt.plot([box[1], box[1]], [box[2], box[3]], ls)
plt.plot([box[1], box[0]], [box[3], box[3]], ls)
plt.plot([box[0], box[0]], [box[3], box[2]], ls)
else:
plt.plot([box[0, 0], box[1, 0]], [box[0, 1], box[1, 1]], ls)
plt.plot([box[1, 0], box[2, 0]], [box[1, 1], box[2, 1]], ls)
plt.plot([box[2, 0], box[3, 0]], [box[2, 1], box[3, 1]], ls)
plt.plot([box[3, 0], box[0, 0]], [box[3, 1], box[0, 1]], ls)
##########################################################################
# Incorporate later for depth and slope dependent resolution
##########################################################################
##
## Interpolate bathymetry onto background grid
#Lon_grd = Lon_grd*deg2rad
#Lat_grd = Lat_grd*deg2rad
#bathy = inject_bathymetry.interpolate_bathymetry(data_path+nc_file,Lon_grd.ravel(),Lat_grd.ravel())
#bathy_grd = -1.0*np.reshape(bathy,(ny_grd,nx_grd))
#ocn_idx = np.where(bathy_grd > 0)
#
# if plot_option:
# plt.figure()
# levels = np.linspace(0,11000,100)
# plt.contourf(lon_grd,lat_grd,bathy_grd,levels=levels)
# plot_coarse_coast(plot_box)
# plt.colorbar()
# plt.axis('equal')
# plt.savefig('bckgnd_grid_bathy.png',bbox_inches='tight')
## Interpolate bathymetry gradient onto background grid
#dbathy = inject_bathymetry.interpolate_bathymetry(data_path+nc_file,Lon_grd.ravel(),Lat_grd.ravel(),grad=True)
#dbathy = np.reshape(dbathy,(ny_grd,nx_grd))
#dbathy_grd = np.zeros((ny_grd,nx_grd))
#dbathy_grd[ocn_idx] = dbathy[ocn_idx]
#
# if plot_option:
# plt.figure()
# plt.contourf(lon_grd,lat_grd,1/dbathy_grd)
# plot_coarse_coast(plot_box)
# plt.colorbar()
# plt.axis('equal')
# plt.savefig('bckgnd_grid_bathy_grad.png',bbox_inches='tight')