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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import netCDF4
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tidx = -1 # just get the final frame, for now.
scale = 0.03
isub = 3
url = 'http://geoport.whoi.edu/thredds/dodsC/examples/bora_feb.nc'
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def shrink(a,b):
"""Return array shrunk to fit a specified shape by triming or averaging.
a = shrink(array, shape)
array is an numpy ndarray, and shape is a tuple (e.g., from
array.shape). a is the input array shrunk such that its maximum
dimensions are given by shape. If shape has more dimensions than
array, the last dimensions of shape are fit.
as, bs = shrink(a, b)
If the second argument is also an array, both a and b are shrunk to
the dimensions of each other. The input arrays must have the same
number of dimensions, and the resulting arrays will have the same
shape.
Example
-------
>>> shrink(rand(10, 10), (5, 9, 18)).shape
(9, 10)
>>> map(shape, shrink(rand(10, 10, 10), rand(5, 9, 18)))
[(5, 9, 10), (5, 9, 10)]
"""
if isinstance(b, np.ndarray):
if not len(a.shape) == len(b.shape):
raise Exception, \
'input arrays must have the same number of dimensions'
a = shrink(a,b.shape)
b = shrink(b,a.shape)
return (a, b)
if isinstance(b, int):
b = (b,)
if len(a.shape) == 1: # 1D array is a special case
dim = b[-1]
while a.shape[0] > dim: # only shrink a
if (dim - a.shape[0]) >= 2: # trim off edges evenly
a = a[1:-1]
else: # or average adjacent cells
a = 0.5*(a[1:] + a[:-1])
else:
for dim_idx in range(-(len(a.shape)),0):
dim = b[dim_idx]
a = a.swapaxes(0,dim_idx) # put working dim first
while a.shape[0] > dim: # only shrink a
if (a.shape[0] - dim) >= 2: # trim off edges evenly
a = a[1:-1,:]
if (a.shape[0] - dim) == 1: # or average adjacent cells
a = 0.5*(a[1:,:] + a[:-1,:])
a = a.swapaxes(0,dim_idx) # swap working dim back
return a
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def rot2d(x, y, ang):
'''rotate vectors by geometric angle'''
xr = x*np.cos(ang) - y*np.sin(ang)
yr = x*np.sin(ang) + y*np.cos(ang)
return xr, yr
In [5]:
nc = netCDF4.Dataset(url)
mask = nc.variables['mask_rho'][:]
lon_rho = nc.variables['lon_rho'][:]
lat_rho = nc.variables['lat_rho'][:]
anglev = nc.variables['angle'][:]
u = nc.variables['u'][tidx, -1, :, :]
v = nc.variables['v'][tidx, -1, :, :]
u = shrink(u, mask[1:-1, 1:-1].shape)
v = shrink(v, mask[1:-1, 1:-1].shape)
u, v = rot2d(u, v, anglev[1:-1, 1:-1])
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lon_c = lon_rho[1:-1, 1:-1]
lat_c = lat_rho[1:-1, 1:-1]
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import cartopy.crs as ccrs
from cartopy.feature import NaturalEarthFeature, COLORS
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
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LAND = NaturalEarthFeature('physical', 'land', '10m', edgecolor='face',
facecolor=COLORS['land'])
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fig, ax = plt.subplots(figsize=(12,12),
subplot_kw=dict(projection=ccrs.PlateCarree()))
ax.set_extent([lon_c.min(), lon_c.max(), lat_c.min(), lat_c.max()])
ax.add_feature(LAND)
ax.coastlines(resolution='10m')
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
#kw = dict(scale=20, headwidth=2)
#n = 5
#q = ax.quiver(lon.points[::n, ::n], lat.points[::n, ::n],
# u[::n, ::n], v[::n, ::n], color='black', **kw)
legend_vel=1.0
Q = ax.quiver( lon_c[::isub,::isub], lat_c[::isub,::isub], u[::isub,::isub], v[::isub,::isub],
scale=1.0/scale, pivot='middle', zorder=1e35, width=0.003)
legend_str='%3.1f m/s' % legend_vel
qk = ax.quiverkey(Q,0.92,0.88,legend_vel,legend_str,labelpos='W')
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