In [23]:

    

%matplotlib inline
from nansat import *
n = Nansat('/files/normap/barents1km_asar_wind_20110930T191057_20110930T191354.nc')
print n


    

    




07:58:51|30|mapper_generic|__init__|Use generic mapper - OK!
WARNING:Nansat:Use generic mapper - OK!






    




=>Barents Sea Surface Winds from ASAR<=
----------------------------------------
/files/normap/barents1km_asar_wind_20110930T191057_20110930T191354.nc
----------------------------------------
Mapper: generic
----------------------------------------
Band : 1 mask
  _FillValue: 0
  grid_mapping: stereographic
  name: mask
  long_name: Status flag for wind speed retrieval
  dataType: 1
  valid_range: {0,255}
  flag_meanings: no_data clouds land nominal 
  standard_name: northward_wind status_flag
  flag_values: {0,1,2,64}
  SourceFilename: NETCDF:"/files/normap/barents1km_asar_wind_20110930T191057_20110930T191354.nc":mask
  NETCDF_DIM_time: 40814.79927083333
  SourceBand: 1
  wkv: northward_wind status_flag
Band : 2 V
  _FillValue: -10000
  grid_mapping: stereographic
  scale_factor: 0.0099999998
  colormap: jet
  short_name: V
  dataType: 6
  add_offset: 0
  long_name: Northward wind velocity
  standard_name: northward_wind
  wkv: northward_wind
  units: m s-1
  SourceFilename: NETCDF:"/files/normap/barents1km_asar_wind_20110930T191057_20110930T191354.nc":V
  NETCDF_DIM_time: 40814.79927083333
  minmax: -10 10
  SourceBand: 1
  valid_range: {-3500,3500}
  name: V
Band : 3 U
  _FillValue: -10000
  grid_mapping: stereographic
  scale_factor: 0.0099999998
  colormap: jet
  short_name: U
  dataType: 6
  add_offset: 0
  long_name: Eastward wind velocity
  standard_name: eastward_wind
  wkv: eastward_wind
  units: m s-1
  SourceFilename: NETCDF:"/files/normap/barents1km_asar_wind_20110930T191057_20110930T191354.nc":U
  NETCDF_DIM_time: 40814.79927083333
  minmax: -10 10
  SourceBand: 1
  valid_range: {-3500,3500}
  name: U
----------------------------------------
Domain:[1100 x 1340]
----------------------------------------
Projection:
PROJCS["unnamed",
    GEOGCS["WGS 84",
        DATUM["WGS_1984",
            SPHEROID["WGS 84",6378137,298.257223563],
            TOWGS84[0,0,0,0,0,0,0]],
        PRIMEM["Greenwich",0],
        UNIT["degree",0.0174532925199433]],
    PROJECTION["Stereographic"],
    PARAMETER["latitude_of_origin",74.5],
    PARAMETER["central_meridian",40],
    PARAMETER["scale_factor",1],
    PARAMETER["false_easting",0],
    PARAMETER["false_northing",0],
    UNIT["Meter",1]]
----------------------------------------
Corners (lon, lat):
	 ( 12.50,  79.32)  ( 67.50,  79.32)
	 ( 26.81,  67.96)  ( 53.19,  67.96)

In [30]:

    

n.undo(100)
n.crop(0, 0, 400, 600)
u = n['U']
v = n['V']
mask = n.vrt.dataset.GetRasterBand(1).ReadAsArray()
u[mask<64]=np.nan
v[mask<64]=np.nan


    

In [31]:

    

plt.imshow(u)


    

    
Out[31]:





<matplotlib.image.AxesImage at 0x7f0560e7a2d0>






    

In [51]:

    

nmap = Nansatmap(n, resolution='l')

nmap.pcolormesh(np.hypot(u,v), vmax=18)
nmap.add_colorbar(fontsize=16)
nmap.quiver(u, v, step=10, scale=300, width=0.002)
nmap.draw_continents()
nmap.drawmeridians(np.arange(12, 53, 5), labels=[False,False,True,False])
nmap.drawparallels(np.arange(67, 80, 3), labels=[True, False, False, False])

# set size of the figure (inches)
nmap.fig.set_figheight(20)
nmap.fig.set_figwidth(15)

# save figure to a PNG file
nmap.draw_continents()
plt.suptitle('High resolution\nwind speed and direction\nfrom ASAR and NCEP', fontsize=16)
nmap.fig.savefig('normap_asar_wind.png', bbox_inches='tight')


    

In [ ]: