In [1]:

    

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import netCDF4

tidx = -1       # just get the final frame, for now.
subsample = 9  # roughly every third point in each direction (3**2 = 9)
scale = 0.1
#url = 'http://testbedapps-dev.sura.org/thredds/dodsC/alldata/Shelf_Hypoxia/tamu/roms/tamu_roms.nc'
url = 'http://tds.ve.ismar.cnr.it:8080/thredds/dodsC/ismar/model/field2/run1/Field_2km_1108_out30min_his_0724.nc'

#####################################################################################

nc = netCDF4.Dataset(url)

basemap = Basemap(llcrnrlon=12,
                  llcrnrlat=40,
                  urcrnrlon=20,
                  urcrnrlat=46,
                  projection='lcc',
                  lat_0=43.0,
                  lon_0=16.0,
                  resolution ='i',
                  area_thresh=0.)


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

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


mask = nc.variables['mask_rho'][:]
lon_rho = nc.variables['lon_rho'][:]
lat_rho = nc.variables['lat_rho'][:]
anglev = nc.variables['angle'][:]

x_rho, y_rho = basemap(lon_rho, lat_rho)

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])

# some code to plot random points.
idx, idy = np.where(mask[1:-1, 1:-1] == 1.0)
idv = np.arange(len(idx))
np.random.shuffle(idv)
Nvec = int(len(idx) / subsample)
idv = idv[:Nvec]
idx = idx[idv]
idy = idy[idv]

# <codecell>

figure = plt.figure()
ax = figure.add_subplot(111)

basemap.drawcoastlines()
basemap.fillcontinents()

q = ax.quiver( x_rho[idx, idy], y_rho[idx,idy], 
            u[idx,  idy], v[idx, idy],
            scale=1.0/scale, pivot='middle', zorder=1e35, width=0.003)

plt.quiverkey(q, 0.85, 0.07, 1.0, label=r'1 m s$^{-1}$', coordinates='figure')

plt.show()

# <codecell>


    

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