def boreali_processing(obj, final_path):
wavelen = [412, 443, 469, 488, 531, 547, 555, 645, 667, 678]
cpa_limits = [0.01, 2,
0.01, 1,
0.01, 1, 10]
b = Boreali('michigan', wavelen)
n = Nansat(obj)
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
n.reproject(dom)
theta = numpy.zeros_like(n[2])
custom_n = Nansat(domain=n)
band_rrs_numbers = list(map(lambda x: n._get_band_number('Rrs_' + str(x)),
wavelen))
for index in range(0, len(wavelen)):
# Преобразуем в Rrsw
rrsw = n[band_rrs_numbers[index]] / (0.52 + 1.7 * n[band_rrs_numbers[index]])
custom_n.add_band(rrsw, parameters={'name': 'Rrsw_' + str(wavelen[index]),
'units': 'sr-1',
'wavelength': wavelen[index]})
custom_n = create_mask(custom_n)
cpa = b.process(custom_n, cpa_limits, mask=custom_n['mask'], theta=theta, threads=4)
custom_n.add_band(array=cpa[0], parameters={'name': 'chl',
'long_name': 'Chlorophyl-a',
'units': 'mg m-3'})
custom_n.add_band(array=cpa[1], parameters={'name': 'tsm',
'long_name': 'Total suspended matter',
'units': 'g m-3'})
custom_n.add_band(array=cpa[2], parameters={'name': 'doc',
'long_name': 'Dissolved organic carbon',
'units': 'gC m-3'})
custom_n.add_band(array=cpa[3], parameters={'name': 'mse',
'long_name': 'Root Mean Square Error',
'units': 'sr-1'})
custom_n.add_band(array=cpa[4], parameters={'name': 'mask',
'long_name': 'L2 Boreali mask',
'units': '1'})
custom_n.export(final_path + obj.split('/')[-1] + 'cpa_deep.nc')
fig_params = {'legend': True,
'LEGEND_HEIGHT': 0.5,
'NAME_LOCATION_Y': 0,
'mask_array': cpa[4],
'mask_lut': {1: [255, 255, 255], 2: [128, 128, 128], 4: [200, 200, 255]}}
custom_n.write_figure(final_path + obj.split('/')[-1] + 'chl_deep.png', 'chl', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'tsm_deep.png', 'tsm', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'doc_deep.png', 'doc', clim=[0, .2], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'mse_deep.png', 'mse', clim=[1e-5, 1e-2], logarithm=True, **fig_params)
n.write_figure(final_path + obj.split('/')[-1] + 'rgb_deep.png',
[16, 14, 6],
clim=[[0, 0, 0], [0.006, 0.04, 0.024]],
mask_array=cpa[4],
mask_lut={2: [128, 128, 128]})
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 _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
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
def boreali_osw_processing(obj, final_path):
"""
Мой код в данной функции основан на tutorial.py который я нашел в репозитории boreali.
:param obj: путь до изображения
:param final_path: Путь для сохранения файлов
:return:
"""
wavelen = [412, 443, 469, 488, 531, 547, 555, 645, 667, 678]
cpa_limits = [0.01, 2,
0.01, 1,
0.01, 1, 10]
h = get_deph() # Глубина исследуемого района по батиметрии
b = Boreali('michigan', wavelen)
n = Nansat(obj)
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
n.reproject(dom)
theta = numpy.zeros_like(n[2])
custom_n = Nansat(domain=n)
band_rrs_numbers = list(map(lambda x: n._get_band_number('Rrs_' + str(x)),
wavelen)) # Получаем список номеров бандов в которых лежат значения Rrs
# для корректной работы складываем в custom_n значения и Rrs и Rrsw
for index in range(0, len(wavelen)):
rrsw = n[band_rrs_numbers[index]] / (0.52 + 1.7 * n[band_rrs_numbers[index]]) # Пересчитываем Rrs в Rrsw
custom_n.add_band(rrsw, parameters={'name': 'Rrsw_' + str(wavelen[index]), # Складываем в новый объект Rrsw
'units': 'sr-1',
'wavelength': wavelen[index]})
# Складываем в новый объект значения Rrs
custom_n.add_band(n[band_rrs_numbers[index]], parameters={'name': 'Rrs_' + str(wavelen[index]),
'units': 'sr-1',
'wavelength': wavelen[index]})
custom_n = create_mask(custom_n)
cpa = b.process(custom_n, cpa_limits, mask=custom_n['mask'], depth=h, theta=theta, threads=4)
custom_n.add_band(array=cpa[0], parameters={'name': 'chl',
'long_name': 'Chlorophyl-a',
'units': 'mg m-3'})
custom_n.add_band(array=cpa[1], parameters={'name': 'tsm',
'long_name': 'Total suspended matter',
'units': 'g m-3'})
custom_n.add_band(array=cpa[2], parameters={'name': 'doc',
'long_name': 'Dissolved organic carbon',
'units': 'gC m-3'})
custom_n.add_band(array=cpa[3], parameters={'name': 'mse',
'long_name': 'Root Mean Square Error',
'units': 'sr-1'})
custom_n.add_band(array=cpa[4], parameters={'name': 'mask',
'long_name': 'L2 Boreali mask',
'units': '1'})
custom_n.export(final_path + obj.split('/')[-1] + 'cpa_OSW.nc')
fig_params = {'legend': True,
'LEGEND_HEIGHT': 0.5,
'NAME_LOCATION_Y': 0,
'mask_array': cpa[4],
'mask_lut': {1: [255, 255, 255],
2: [128, 128, 128],
4: [200, 200, 255]}}
custom_n.write_figure(final_path + obj.split('/')[-1] + 'chl_OSW.png', 'chl', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'tsm_OSW.png', 'tsm', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'doc_OSW.png', 'doc', clim=[0, .2], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'mse_OSW.png', 'mse', clim=[1e-5, 1e-2],
logarithm=True, **fig_params)
n.write_figure(final_path + obj.split('/')[-1] + 'rgb_OSW.png',
[16, 14, 6],
clim=[[0, 0, 0], [0.006, 0.04, 0.024]],
mask_array=cpa[4],
mask_lut={2: [128, 128, 128]})
