def _get_indices(cube, bbox):
"""
Get the 4 corner indices of a `cube` given a `bbox`.
Examples
--------
>>> import iris
>>> url = ("http://omgsrv1.meas.ncsu.edu:8080/thredds/dodsC/fmrc/sabgom/"
... "SABGOM_Forecast_Model_Run_Collection_best.ncd")
>>> cube = iris.load_cube(url, 'sea_water_potential_temperature')
>>> bbox = [-87.40, 24.25, -74.70, 36.70]
>>> idxs = _get_indices(cube, bbox)
>>> [isinstance(idx, np.integer) for idx in idxs]
[True, True, True, True]
>>> idxs
(27, 320, 164, 429)
"""
from oceans import wrap_lon180
lons = cube.coord('longitude').points
lats = cube.coord('latitude').points
lons = wrap_lon180(lons)
inregion = np.logical_and(np.logical_and(lons > bbox[0], lons < bbox[2]),
np.logical_and(lats > bbox[1], lats < bbox[3]))
region_inds = np.where(inregion)
imin, imax = _minmax(region_inds[0])
jmin, jmax = _minmax(region_inds[1])
return imin, imax + 1, jmin, jmax + 1
def find_bbox(cube, bbox):
"""
Get the four corner indices of a `cube` given a `bbox`.
Examples
--------
>>> import iris
>>> import numpy as np
>>> url = ("http://omgsrv1.meas.ncsu.edu:8080/thredds/dodsC/fmrc/sabgom/"
... "SABGOM_Forecast_Model_Run_Collection_best.ncd")
>>> cube = iris.load_cube(url, 'sea_water_potential_temperature')
>>> bbox = [-87.40, -74.70, 24.25, 36.70]
>>> idxs = find_bbox(cube, bbox)
>>> [isinstance(idx, np.integer) for idx in idxs]
[True, True, True, True]
"""
from oceans import wrap_lon180
lons = cube.coords(axis='X')[0].points
lats = cube.coords(axis='Y')[0].points
lons = wrap_lon180(lons)
inregion = np.logical_and(np.logical_and(lons > bbox[0],
lons < bbox[1]),
np.logical_and(lats > bbox[2],
lats < bbox[3]))
region_inds = np.where(inregion)
imin, imax = _minmax(region_inds[0])
jmin, jmax = _minmax(region_inds[1])
return imin, imax+1, jmin, jmax+1
def make_tree(cube):
"""Create KDTree."""
lon = cube.coord(axis='X').points
lat = cube.coord(axis='Y').points
# FIXME: Not sure if it is need when using `iris.intersect()`.
lon = wrap_lon180(lon)
# Structured models with 1D lon, lat.
if (lon.ndim == 1) and (lat.ndim == 1) and (cube.ndim == 3):
lon, lat = np.meshgrid(lon, lat)
# Unstructure are already paired!
tree = KDTree(zip(lon.ravel(), lat.ravel()))
return tree, lon, lat
def make_tree(cube):
"""Create KDTree."""
lon = cube.coord(axis='X').points
lat = cube.coord(axis='Y').points
# FIXME: Not sure if it is need when using `iris.intersect()`.
lon = wrap_lon180(lon)
# Structured models with 1D lon, lat.
if (lon.ndim == 1) and (lat.ndim == 1) and (cube.ndim == 3):
lon, lat = np.meshgrid(lon, lat)
# Unstructure are already paired!
tree = KDTree(zip(lon.ravel(), lat.ravel()))
return tree, lon, lat
def bbox_extract_2Dcoords(cube, bbox):
"""Extract a sub-set of a cube inside a lon, lat bounding box
bbox=[lon_min lon_max lat_min lat_max].
NOTE: This is a work around too subset an iris cube that has
2D lon, lat coords."""
lons = cube.coord('longitude').points
lats = cube.coord('latitude').points
lons = wrap_lon180(lons)
inregion = np.logical_and(np.logical_and(lons > bbox[0], lons < bbox[2]),
np.logical_and(lats > bbox[1], lats < bbox[3]))
region_inds = np.where(inregion)
imin, imax = minmax(region_inds[0])
jmin, jmax = minmax(region_inds[1])
return cube[..., imin:imax + 1, jmin:jmax + 1]
def bbox_extract_2Dcoords(cube, bbox):
"""Extract a sub-set of a cube inside a lon, lat bounding box
bbox=[lon_min lon_max lat_min lat_max].
NOTE: This is a work around too subset an iris cube that has
2D lon, lat coords."""
lons = cube.coord('longitude').points
lats = cube.coord('latitude').points
lons = wrap_lon180(lons)
inregion = np.logical_and(np.logical_and(lons > bbox[0],
lons < bbox[2]),
np.logical_and(lats > bbox[1],
lats < bbox[3]))
region_inds = np.where(inregion)
imin, imax = minmax(region_inds[0])
jmin, jmax = minmax(region_inds[1])
return cube[..., imin:imax+1, jmin:jmax+1]
1.25, 1.50, 1.75, 2.00, 2.50])
# Colors for Vorticity.
colorsavor500 = ('#660066', '#660099', '#6600CC', '#6600FF',
'w', '#ffE800', '#ffD800', '#ffC800', '#ffB800',
'#ffA800', '#ff9800', '#ff8800', '#ff7800', '#ff6800',
'#ff5800', '#ff5000', '#ff4000', '#ff3000')
# <codecell>
from oceans import wrap_lon180
# Subset the lats and lons from the model according to view desired.
clats1 = hght300.coord(axis='Y').points
clons1 = wrap_lon180(hght300.coord(axis='X').points)
# Make a grid of lat/lon values to use for plotting with Basemap.
clats, clons = np.meshgrid(clons1, clats1)
# <codecell>
%matplotlib inline
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.mpl import geoaxes
import cartopy.feature as cfeature
from mpl_toolkits.basemap import cm
with warnings.catch_warnings():
warnings.simplefilter("ignore") # Suppress iris warnings.
cube = quick_load_cubes(url, name_list, callback=None, strict=True)
# <codecell>
cube
# <codecell>
cube.coord(axis='X').points[:20]
# <codecell>
# Just found a bug in proc_cube(). Meanwhile lets skip the constraint step.
cube.coord(axis='X').points = wrap_lon180(cube.coord(axis='X').points)
# <codecell>
cube.coord(axis='X').points[:20]
# <codecell>
# cube = proc_cube(cube, bbox=bbox, time=(start, stop), units=units)
cube = get_surface(cube) # Get a 2D surface cube. I am working on 3D...
cube
# <codecell>
from utilities import get_nearest_water, make_tree