# example showing how to use Line Integral Convolution to visualize a vector
# flow field (from Hurricane Earl).  Produces something akin to streamlines.
# Requires vectorplot scikit (http://scikits.appspot.com/vectorplot).
from netCDF4 import Dataset as NetCDFFile
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
try:
    from scikits.vectorplot import lic_internal
except ImportError:
    raise  ImportError('need vectorplot scikit for this example')

# H*wind data from http://www.aoml.noaa.gov/hrd/data_sub/wind.html
ncfile = NetCDFFile('rita.nc')
udat = ncfile.variables['sfc_u'][0,:,:]
vdat = ncfile.variables['sfc_v'][0,:,:]
lons1 = ncfile.variables['longitude'][:]
lats1 = ncfile.variables['latitude'][:]
lat0 = lats1[len(lats1)/2]; lon0 = lons1[len(lons1)/2]
lons, lats = np.meshgrid(lons1,lats1)
ncfile.close()


fig = plt.figure(figsize=(8,8))
m = Basemap(projection='cyl',llcrnrlat=lats1[0],llcrnrlon=lons1[0],urcrnrlat=lats1[-1],urcrnrlon=lons1[-1],resolution='i')
kernellen=31
texture = np.random.rand(udat.shape[0],udat.shape[1]).astype(np.float32)
kernel = np.sin(np.arange(kernellen)*np.pi/kernellen).astype(np.float32)
image = lic_internal.line_integral_convolution(udat.astype(np.float32),\
        vdat.astype(np.float32), texture, kernel)
im = m.imshow(image,plt.cm.gist_stern)
m.drawcoastlines()
m.drawmeridians(np.arange(-120,-60,2),labels=[0,0,0,1])
m.drawparallels(np.arange(0,30,2),labels=[1,0,0,0])
plt.title('Hurricane Rita flow field visualized with Line Integral Convolution',\
        fontsize=13)
plt.show()

# example showing how to use Line Integral Convolution to visualize a vector
# flow field (from Hurricane Earl).  Produces something akin to streamlines.
# Requires vectorplot scikit (http://scikits.appspot.com/vectorplot).
from netCDF4 import Dataset as NetCDFFile
from mpl_toolkits.basemap import Basemap, interp
import numpy as np
import matplotlib.pyplot as plt
try:
    from scikits.vectorplot import lic_internal
except ImportError:
    raise  ImportError('need vectorplot scikit for this example')

# H*wind data from http://www.aoml.noaa.gov/hrd/data_sub/wind.html
ncfile = NetCDFFile('rita.nc')
udat = ncfile.variables['sfc_u'][0,:,:]
vdat = ncfile.variables['sfc_v'][0,:,:]
lons1 = ncfile.variables['longitude'][:]
lats1 = ncfile.variables['latitude'][:]
lat0 = lats1[len(lats1)/2]; lon0 = lons1[len(lons1)/2]
lons, lats = np.meshgrid(lons1,lats1)
ncfile.close()

# downsample to finer grid.
nlats = 2*udat.shape[0]; nlons = 2*udat.shape[1]
lons = np.linspace(lons1[0],lons1[-1],nlons)
lats = np.linspace(lats1[0],lats1[-1],nlats)
lons, lats = np.meshgrid(lons, lats)
udat = interp(udat,lons1,lats1,lons,lats,order=3)
vdat = interp(vdat,lons1,lats1,lons,lats,order=3)


fig = plt.figure(figsize=(8,8))
m = Basemap(projection='cyl',llcrnrlat=lats1[0],llcrnrlon=lons1[0],urcrnrlat=lats1[-1],urcrnrlon=lons1[-1],resolution='i')
kernellen=31
texture = np.random.rand(udat.shape[0],udat.shape[1]).astype(np.float32)
kernel = np.sin(np.arange(kernellen)*np.pi/kernellen).astype(np.float32)
image = lic_internal.line_integral_convolution(udat.astype(np.float32),\
        vdat.astype(np.float32), texture, kernel)
im = m.imshow(image,plt.cm.gist_stern)
m.drawcoastlines()
m.drawmeridians(np.arange(-120,-60,2),labels=[0,0,0,1])
m.drawparallels(np.arange(0,30,2),labels=[1,0,0,0])
plt.title('Hurricane Rita flow field visualized with Line Integral Convolution',\
        fontsize=13)
plt.show()

interpolate to finer grid for nicer looking plot