def test_get_time(self):
n1 = Nansat(self.test_file_gcps, logLevel=40)
t = n1.get_time()
self.assertEqual(len(t), len(n1.bands()))
self.assertEqual(type(t[0]), datetime.datetime)
def test_get_time(self):
n1 = Nansat(self.test_file_gcps, logLevel=40)
t = n1.get_time()
self.assertEqual(len(t), len(n1.bands()))
self.assertEqual(type(t[0]), datetime.datetime)
class SARWind(Nansat, object):
'''
A class for calculating wind speed from SAR images using CMOD
'''
def __init__(self, sar_image, winddir=None, pixelsize=500):
'''
Parameters
-----------
sar_image : string or Nansat object
SAR image filename (original, raw file)
winddir : int, string, Nansat, None
Auxiliary wind field information needed to calculate
SAR wind (must be or have wind direction)
'''
if isinstance(sar_image, str) or isinstance(sar_image, unicode):
super(SARWind, self).__init__(sar_image)
elif isinstance(sar_image, Nansat):
super(SARWind, self).__init__(domain=sar_image)
self.vrt = sar_image.vrt
# Check that this is a SAR image with VV pol NRCS
try:
self.sigma0_bandNo = self._get_band_number(
{'standard_name':
'surface_backwards_scattering_coefficient_of_radar_wave',
'polarization': 'VV'})
except:
raise TypeError(self.fileName +
' does not have SAR NRCS in VV polarization')
self.SAR_image_time = self.get_time(
self.sigma0_bandNo).replace(tzinfo=None)
if pixelsize != 'fullres':
print 'Resizing SAR image to ' + str(pixelsize) + ' m pixel size'
self.resize(pixelsize=pixelsize)
self.winddir = winddir
if winddir is not None:
self.calculate_wind()
def calculate_wind(self, winddir=None, storeModelSpeed=True):
# Calculate wind speed from SAR sigma0 in VV polarization
if winddir:
self.winddir = winddir
if self.winddir is None or self.winddir == 'online':
self.winddir = 'ncep_wind_online' # default source
if isinstance(self.winddir, int):
# Constant wind direction is input
print 'Using constant wind (from) direction: ' + str(self.winddir) + \
' degrees clockwise from North'
winddirArray = np.ones(self.shape())*self.winddir
winddir_time = None
storeModelSpeed = False # Not relevant if direction given as number
else:
# Nansat readable file
if isinstance(self.winddir, str):
try:
self.winddir = Nansat(self.winddir)
except:
try:
self.winddir = Nansat(self.winddir +
datetime.strftime(
self.SAR_image_time, ':%Y%m%d%H%M'))
except:
pass
if not isinstance(self.winddir, Nansat):
raise ValueError('Wind direction not available')
winddir_time = self.winddir.get_time()[0]
# Bi-linear interpolation onto SAR image
self.winddir.reproject(self, eResampleAlg=1)
# Check time difference between SAR image and wind direction object
timediff = self.SAR_image_time - winddir_time
hoursDiff = np.abs(timediff.total_seconds()/3600.)
print 'Time difference between SAR image and wind direction: ' \
+ '%.2f' % hoursDiff + ' hours'
print 'SAR image time: ' + str(self.SAR_image_time)
print 'Wind dir time: ' + str(winddir_time)
if hoursDiff > 3:
print '#########################################'
print 'WARNING: time difference exceeds 3 hours!'
print '#########################################'
wind_u_bandNo = self.winddir._get_band_number({
'standard_name': 'eastward_wind',
})
wind_v_bandNo = self.winddir._get_band_number({
'standard_name': 'northward_wind',
})
# Get wind direction
u_array = self.winddir[wind_u_bandNo]
v_array = self.winddir[wind_v_bandNo]
winddirArray = np.degrees(
np.arctan2(-u_array, -v_array)) # 0 from North, 90 from East
# Calculate SAR wind with CMOD
# TODO:
# - add other CMOD versions than CMOD5
print 'Calculating SAR wind with CMOD...'
startTime = datetime.now()
windspeed = cmod5n_inverse(self[self.sigma0_bandNo],
np.mod(winddirArray - self['SAR_look_direction'], 360),
self['incidence_angle'])
print 'Calculation time: ' + str(datetime.now() - startTime)
windspeed[np.where(np.isnan(windspeed))] = np.nan
windspeed[np.where(np.isinf(windspeed))] = np.nan
# Add wind speed and direction as bands
# TODO: make it possible to update existing bands... See
# https://github.com/nansencenter/nansat/issues/58
self.add_band(array=windspeed, parameters={
'wkv': 'wind_speed',
'name': 'windspeed',
'time': self.get_time(self.sigma0_bandNo),
'winddir_time': winddir_time
})
self.add_band(array=winddirArray, parameters={
'wkv': 'wind_from_direction',
'name': 'winddirection',
'time': winddir_time
})
if storeModelSpeed:
self.add_band(array=self.winddir['windspeed'], parameters={
'wkv': 'wind_speed',
'name': 'model_windspeed',
'time': winddir_time,
})
# TODO: Replace U and V bands with pixelfunctions
u = -windspeed*np.sin((180.0 - winddirArray)*np.pi/180.0)
v = windspeed*np.cos((180.0 - winddirArray)*np.pi/180.0)
self.add_band(array=u, parameters={
'wkv': 'eastward_wind',
})
self.add_band(array=v, parameters={
'wkv': 'northward_wind',
})
def _get_masked_windspeed(self, landmask=True, icemask=True):
try:
sar_windspeed = self['windspeed']
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) first.')
sar_windspeed[sar_windspeed<0] = 0
palette = jet
if landmask:
try: # Land mask
sar_windspeed = np.ma.masked_where(
self.watermask()[1]==2, sar_windspeed)
palette.set_bad([.3, .3, .3], 1.0) # Land is masked (bad)
except:
print 'Land mask not available'
if icemask:
try: # Ice mask
try: # first try local file
ice = Nansat('metno_local_hires_seaice_' +
self.SAR_image_time.strftime('%Y%m%d'),
mapperName='metno_local_hires_seaice')
except: # otherwise Thredds
ice = Nansat('metno_hires_seaice:' +
self.SAR_image_time.strftime('%Y%m%d'))
ice.reproject(self)
iceBandNo = ice._get_band_number(
{'standard_name': 'sea_ice_area_fraction'})
sar_windspeed[ice[iceBandNo]>0] = -1
palette.set_under('w', 1.0) # Ice is 'under' (-1)
except:
print 'Ice mask not available'
return sar_windspeed, palette
def write_geotiff(self, filename, landmask=True, icemask=True):
sar_windspeed, palette = self._get_masked_windspeed(landmask, icemask)
nansat_geotiff = Nansat(array=sar_windspeed, domain=self,
parameters = {'name': 'masked_windspeed',
'minmax': '0 20'})
nansat_geotiff.write_geotiffimage(filename)
def plot(self, filename=None, numVectorsX = 16, show=True,
landmask=True, icemask=True, flip=True, maskWindAbove=35):
''' Basic plotting function showing CMOD wind speed
overlaid vectors in SAR image projection'''
try:
sar_windspeed, palette = self._get_masked_windspeed(landmask, icemask)
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) before plotting.')
sar_windspeed[sar_windspeed>maskWindAbove] = np.nan
winddirReductionFactor = np.round(
self.vrt.dataset.RasterXSize/numVectorsX)
# model_winddir is direction from which wind is blowing
winddir_relative_up = 360 - self['winddirection'] + \
self.azimuth_up()
indX = range(0, self.vrt.dataset.RasterXSize, winddirReductionFactor)
indY = range(0, self.vrt.dataset.RasterYSize, winddirReductionFactor)
X, Y = np.meshgrid(indX, indY)
try: # scaling of wind vector length, if model wind is available
model_windspeed = self['model_windspeed']
model_windspeed = model_windspeed[Y, X]
except:
model_windspeed = 8*np.ones(X.shape)
Ux = np.sin(np.radians(winddir_relative_up[Y, X]))*model_windspeed
Vx = np.cos(np.radians(winddir_relative_up[Y, X]))*model_windspeed
# Make sure North is up, and east is right
if flip == True:
lon, lat = self.get_corners()
if lat[0] < lat[1]:
sar_windspeed = np.flipud(sar_windspeed)
Ux = -np.flipud(Ux)
Vx = -np.flipud(Vx)
if lon[0] > lon[2]:
sar_windspeed = np.fliplr(sar_windspeed)
Ux = np.fliplr(Ux)
Vx = np.fliplr(Vx)
# Plotting
figSize = sar_windspeed.shape
legendPixels = 60.0
legendPadPixels = 5.0
legendFraction = legendPixels/figSize[0]
legendPadFraction = legendPadPixels/figSize[0]
dpi=100.0
fig = plt.figure()
fig.set_size_inches((figSize[1]/dpi, (figSize[0]/dpi)*
(1+legendFraction+legendPadFraction)))
ax = fig.add_axes([0,0,1,1+legendFraction])
ax.set_axis_off()
plt.imshow(sar_windspeed, cmap=palette, interpolation='nearest')
plt.clim([0, 20])
cbar = plt.colorbar(orientation='horizontal', shrink=.80,
aspect=40,
fraction=legendFraction, pad=legendPadFraction)
cbar.ax.set_ylabel('[m/s]', rotation=0)
cbar.ax.yaxis.set_label_position('right')
ax.quiver(X, Y, Ux, Vx, angles='xy', width=0.004,
scale=200, scale_units='width',
color=[.0, .0, .0], headaxislength=4)
if filename is not None:
fig.savefig(filename, pad_inches=0, dpi=dpi)
if show:
plt.show()
return fig
# Open an input file
# Create a Nansat object <n> for futher high-level operations
n = Nansat(iFileName)
# Open an input file, specify which Mapper to use, set logging level
n = Nansat(iFileName, mapperName='generic', logLevel=10)
# list bands and georeference of the object
print 'Raw Nansat:', n, '\n'
# get dictionary with metadata from all bands
print 'Bands:', n.bands(), '\n'
# get time of the image aquisition
print 'Time:', n.get_time()[0], '\n'
# set GlobalMetadata
n.set_metadata(key='GlobalKey', value='GlobalVal')
# get Global Metadata
print 'Global Metadata:', n.get_metadata(), '\n'
# set BandMetadata to the 1st band
n.set_metadata(key='BandKey', value='BandVal', bandID=1)
# get 1st Band Metadata
print '1st Band Metadata:', n.get_metadata(bandID=1), '\n'
# add a band from file (copy the 2nd band to the end (4th band)
n.add_band(fileName=n.fileName, bandID=2)
# add a band from numpy array (copy the 1st band to the end (5th band))
n.add_band(array=n[1], parameters={'name': 'Name1',
print 'Raw Nansat:', n
# get dictionary with metadata from all bands
print 'Bands:', n.bands()
# get size of the object (Y and X dimensions, to follow Numpy style)
print 'Shape:', n.shape()
# get list with coordinates of the object corners
print 'Corners:', n.get_corners()
# get lists with coordinates of the object borders
print 'Border:', n.get_border()
# get time of the image aquisition
print 'Time:', n.get_time()[0]
# Get band data and do some operations
# 1. Get data from 1st band as numpy array
# 2. Plot the array (pyplot image is save to a PNG file)
# 3. Save as Matlab file
a = n[1]
plt.imshow(a);plt.colorbar();plt.savefig(oFileName + '_imshow.png');plt.close()
savemat(oFileName + '.mat', {'band_1': a})
# make simple indexed image from 1st band with default colormap
n.write_figure(oFileName + '.png')
# make RGB image from bands 1,2,3 with brightness correction
n.write_figure(oFileName + '_rgb.png', bands=[1,2,3], clim='hist', ratio=0.9)
class SARWind(Nansat, object):
'''
A class for calculating wind speed from SAR images using CMOD
'''
def __init__(self, sar_image, winddir=None, pixelsize=500):
'''
Parameters
-----------
sar_image : string or Nansat object
SAR image filename (original, raw file)
winddir : int, string, Nansat, None
Auxiliary wind field information needed to calculate
SAR wind (must be or have wind direction)
'''
if isinstance(sar_image, str) or isinstance(sar_image, unicode):
super(SARWind, self).__init__(sar_image)
elif isinstance(sar_image, Nansat):
super(SARWind, self).__init__(domain=sar_image)
self.vrt = sar_image.vrt
# Check that this is a SAR image with VV pol NRCS
try:
self.sigma0_bandNo = self._get_band_number({
'standard_name':
'surface_backwards_scattering_coefficient_of_radar_wave',
'polarization': 'VV'
})
except:
raise TypeError(self.fileName +
' does not have SAR NRCS in VV polarization')
self.SAR_image_time = self.get_time(
self.sigma0_bandNo).replace(tzinfo=None)
if pixelsize != 'fullres':
print 'Resizing SAR image to ' + str(pixelsize) + ' m pixel size'
self.resize(pixelsize=pixelsize)
self.winddir = winddir
if winddir is not None:
self.calculate_wind()
def calculate_wind(self, winddir=None, storeModelSpeed=True):
# Calculate wind speed from SAR sigma0 in VV polarization
if winddir:
self.winddir = winddir
if self.winddir is None or self.winddir == 'online':
self.winddir = 'ncep_wind_online' # default source
if isinstance(self.winddir, int):
# Constant wind direction is input
print 'Using constant wind (from) direction: ' + str(self.winddir) + \
' degrees clockwise from North'
winddirArray = np.ones(self.shape()) * self.winddir
winddir_time = None
storeModelSpeed = False # Not relevant if direction given as number
else:
# Nansat readable file
if isinstance(self.winddir, str):
try:
self.winddir = Nansat(self.winddir)
except:
try:
self.winddir = Nansat(self.winddir + datetime.strftime(
self.SAR_image_time, ':%Y%m%d%H%M'))
except:
pass
if not isinstance(self.winddir, Nansat):
raise ValueError('Wind direction not available')
winddir_time = self.winddir.get_time()[0]
# Bi-linear interpolation onto SAR image
self.winddir.reproject(self, eResampleAlg=1)
# Check time difference between SAR image and wind direction object
timediff = self.SAR_image_time - winddir_time
hoursDiff = np.abs(timediff.total_seconds() / 3600.)
print 'Time difference between SAR image and wind direction: ' \
+ '%.2f' % hoursDiff + ' hours'
print 'SAR image time: ' + str(self.SAR_image_time)
print 'Wind dir time: ' + str(winddir_time)
if hoursDiff > 3:
print '#########################################'
print 'WARNING: time difference exceeds 3 hours!'
print '#########################################'
wind_u_bandNo = self.winddir._get_band_number({
'standard_name':
'eastward_wind',
})
wind_v_bandNo = self.winddir._get_band_number({
'standard_name':
'northward_wind',
})
# Get wind direction
u_array = self.winddir[wind_u_bandNo]
v_array = self.winddir[wind_v_bandNo]
winddirArray = np.degrees(np.arctan2(
-u_array, -v_array)) # 0 from North, 90 from East
# Calculate SAR wind with CMOD
# TODO:
# - add other CMOD versions than CMOD5
print 'Calculating SAR wind with CMOD...'
startTime = datetime.now()
windspeed = cmod5n_inverse(
self[self.sigma0_bandNo],
np.mod(winddirArray - self['SAR_look_direction'], 360),
self['incidence_angle'])
print 'Calculation time: ' + str(datetime.now() - startTime)
windspeed[np.where(np.isnan(windspeed))] = np.nan
windspeed[np.where(np.isinf(windspeed))] = np.nan
# Add wind speed and direction as bands
# TODO: make it possible to update existing bands... See
# https://github.com/nansencenter/nansat/issues/58
self.add_band(array=windspeed,
parameters={
'wkv': 'wind_speed',
'name': 'windspeed',
'time': self.get_time(self.sigma0_bandNo),
'winddir_time': winddir_time
})
self.add_band(array=winddirArray,
parameters={
'wkv': 'wind_from_direction',
'name': 'winddirection',
'time': winddir_time
})
if storeModelSpeed:
self.add_band(array=self.winddir['windspeed'],
parameters={
'wkv': 'wind_speed',
'name': 'model_windspeed',
'time': winddir_time,
})
# TODO: Replace U and V bands with pixelfunctions
u = -windspeed * np.sin((180.0 - winddirArray) * np.pi / 180.0)
v = windspeed * np.cos((180.0 - winddirArray) * np.pi / 180.0)
self.add_band(array=u, parameters={
'wkv': 'eastward_wind',
})
self.add_band(array=v, parameters={
'wkv': 'northward_wind',
})
def _get_masked_windspeed(self, landmask=True, icemask=True):
try:
sar_windspeed = self['windspeed']
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) first.')
sar_windspeed[sar_windspeed < 0] = 0
palette = jet
if landmask:
try: # Land mask
sar_windspeed = np.ma.masked_where(self.watermask()[1] == 2,
sar_windspeed)
palette.set_bad([.3, .3, .3], 1.0) # Land is masked (bad)
except:
print 'Land mask not available'
if icemask:
try: # Ice mask
try: # first try local file
ice = Nansat('metno_local_hires_seaice_' +
self.SAR_image_time.strftime('%Y%m%d'),
mapperName='metno_local_hires_seaice')
except: # otherwise Thredds
ice = Nansat('metno_hires_seaice:' +
self.SAR_image_time.strftime('%Y%m%d'))
ice.reproject(self)
iceBandNo = ice._get_band_number(
{'standard_name': 'sea_ice_area_fraction'})
sar_windspeed[ice[iceBandNo] > 0] = -1
palette.set_under('w', 1.0) # Ice is 'under' (-1)
except:
print 'Ice mask not available'
return sar_windspeed, palette
def write_geotiff(self, filename, landmask=True, icemask=True):
sar_windspeed, palette = self._get_masked_windspeed(landmask, icemask)
nansat_geotiff = Nansat(array=sar_windspeed,
domain=self,
parameters={
'name': 'masked_windspeed',
'minmax': '0 20'
})
nansat_geotiff.write_geotiffimage(filename)
def plot(self,
filename=None,
numVectorsX=16,
show=True,
landmask=True,
icemask=True,
flip=True,
maskWindAbove=35):
''' Basic plotting function showing CMOD wind speed
overlaid vectors in SAR image projection'''
try:
sar_windspeed, palette = self._get_masked_windspeed(
landmask, icemask)
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) before plotting.')
sar_windspeed[sar_windspeed > maskWindAbove] = np.nan
winddirReductionFactor = np.round(self.vrt.dataset.RasterXSize /
numVectorsX)
# model_winddir is direction from which wind is blowing
winddir_relative_up = 360 - self['winddirection'] + \
self.azimuth_up()
indX = range(0, self.vrt.dataset.RasterXSize, winddirReductionFactor)
indY = range(0, self.vrt.dataset.RasterYSize, winddirReductionFactor)
X, Y = np.meshgrid(indX, indY)
try: # scaling of wind vector length, if model wind is available
model_windspeed = self['model_windspeed']
model_windspeed = model_windspeed[Y, X]
except:
model_windspeed = 8 * np.ones(X.shape)
Ux = np.sin(np.radians(winddir_relative_up[Y, X])) * model_windspeed
Vx = np.cos(np.radians(winddir_relative_up[Y, X])) * model_windspeed
# Make sure North is up, and east is right
if flip == True:
lon, lat = self.get_corners()
if lat[0] < lat[1]:
sar_windspeed = np.flipud(sar_windspeed)
Ux = -np.flipud(Ux)
Vx = -np.flipud(Vx)
if lon[0] > lon[2]:
sar_windspeed = np.fliplr(sar_windspeed)
Ux = np.fliplr(Ux)
Vx = np.fliplr(Vx)
# Plotting
figSize = sar_windspeed.shape
legendPixels = 60.0
legendPadPixels = 5.0
legendFraction = legendPixels / figSize[0]
legendPadFraction = legendPadPixels / figSize[0]
dpi = 100.0
fig = plt.figure()
fig.set_size_inches(
(figSize[1] / dpi,
(figSize[0] / dpi) * (1 + legendFraction + legendPadFraction)))
ax = fig.add_axes([0, 0, 1, 1 + legendFraction])
ax.set_axis_off()
plt.imshow(sar_windspeed, cmap=palette, interpolation='nearest')
plt.clim([0, 20])
cbar = plt.colorbar(orientation='horizontal',
shrink=.80,
aspect=40,
fraction=legendFraction,
pad=legendPadFraction)
cbar.ax.set_ylabel('[m/s]', rotation=0)
cbar.ax.yaxis.set_label_position('right')
ax.quiver(X,
Y,
Ux,
Vx,
angles='xy',
width=0.004,
scale=200,
scale_units='width',
color=[.0, .0, .0],
headaxislength=4)
if filename is not None:
fig.savefig(filename, pad_inches=0, dpi=dpi)
if show:
plt.show()
return fig
