def plot_s1a_example(fsize='small'):
test_data.get_sentinel1a(fsize=fsize)
#w = SARWind(test_data.sentinel1a[fsize])
w = BayesianWind(test_data.sentinel1a[fsize])
cc = w.get_corners()
lonmin = np.int(np.floor(np.min(cc[0])*100))/100.
lonmax = np.int(np.ceil(np.max(cc[0])*100))/100.
latmin = np.int(np.floor(np.min(cc[1])*100))/100.
latmax = np.int(np.ceil(np.max(cc[1])*100))/100.
w.reproject( Domain(NSR().wkt, ext='-lle %s %s %s %s -ts %s %s' %(lonmin,
latmin, lonmax, latmax, (lonmax-lonmin)*110., (latmax-latmin)*110.) ) )
#u = w['U']
#v = w['V']
windspeed = w['bspeed_modcmod']
winddir = w['bdir_modcmod']
u = -windspeed*np.sin((180.0 - winddir)*np.pi/180.0)
v = windspeed*np.cos((180.0 - winddir)*np.pi/180.0)
nmap = Nansatmap(w, resolution='h')
nmap.pcolormesh(np.hypot(u,v), vmax=18)
nmap.add_colorbar(fontsize=8)
nmap.quiver(u, v, step=20)#, scale=1, width=0.001)
nmap.draw_continents()
nmap.drawgrid()
#nmap.drawmeridians(np.arange(lonmin, lonmax, 5), labels=[False, False,
# True, False])
#nmap.drawparallels(np.arange(latmin, latmax, 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 Sentinel-1A and ' \
# 'NCEP\n%s' %w.time_coverage_start.isoformat(),
# fontsize=8
#)
#nmap.fig.savefig('s1a_wind_%s.png'%fsize, dpi=150, bbox_inches='tight')
nmap.fig.savefig('s1a_bwind_%s.png'%fsize, dpi=150, bbox_inches='tight')
def mean_gc_geostrophic(datetime_start=timezone.datetime(2010,1,1,
tzinfo=timezone.utc), datetime_end=timezone.datetime(2010,2,1,
tzinfo=timezone.utc), domain=Domain(NSR().wkt,
'-te 10 -44 40 -30 -tr 0.05 0.05')):
#gc_datasets = Dataset.objects.filter(entry_title__contains='globcurrent',
# time_coverage_start__range=[datetime_start,
# datetime_end])
shapeD = domain.shape()
U = np.zeros((shapeD[0], shapeD[1], 1))
fn = 'http://tds0.ifremer.fr/thredds/dodsC/CLS-L4-CURGEO_0M-ALT_OI_025-V02.0_FULL_TIME_SERIE'
dt = datetime_start
while dt <= datetime_end:
expFn = '/vagrant/shared/test_data/globcurrent/eastward_geostrophic_current_velocity_%d-%02d-%02d.nc'%(dt.year, dt.month, dt.day)
#n = Nansat(
# fn, date='%d-%02d-%02d'%(dt.year, dt.month, dt.day),
# bands=['eastward_geostrophic_current_velocity'])
#n.export(expFn)
n = Nansat(expFn, mapper='generic')
n.reproject(domain, addmask=False)
u = n['eastward_geostrophic_current_velocity']
# OK:
#plt.imshow(u)
#plt.colorbar()
#plt.show()
if np.sum(np.isnan(u))==u.size:
continue
else:
U = np.append(U, np.expand_dims(u, axis=2), axis=2)
dt = dt + timezone.timedelta(days=1)
meanU = np.nanmean(U, axis=2)
nu = Nansat(array=meanU, domain=domain)
nmap=Nansatmap(nu, resolution='h')
nmap.pcolormesh(nu[1], vmin=-1.5, vmax=1.5, cmap='jet') #bwr
nmap.add_colorbar()
nmap.draw_continents()
nmap.fig.savefig('/vagrant/shared/u_gc.png', bbox_inches='tight')
n.reproject(d)
w = Nansat(os.path.join(home,
'conferences/ESABigData2014/demo_data/gfs20110222/gfs.t00z.master.grbf03'))
w.reproject(d)
L_560 = n['L_560']
L_560[L_560>90] = np.nan
u = w['U']
v = w['V']
nMap = Nansatmap(n, resolution='f')
nMap.pcolormesh(L_560, vmin=20, vmax=90)
nMap.quiver(u,v,quivectors=20)
nMap.add_colorbar()
nMap.drawgrid()
nMap.quiver(u,v,quivectors=20)
plt.suptitle('TOA radiance from MERIS image and wind direction, 2011.02.22')
nMap.save('20110222_L_560.png')
def save_wind_map_image(
self,
fileName,
scale=None,
numArrowsRange=10,
landmask=True,
colorbar=True,
cbar_fontsize=6,
drawgrid=True,
title=None,
title_fontsize=10,
edgecolor=None,
quiverScaleCriteria=None,
tight=True,
**kwargs
):
pcolormeshArgs = {"vmin": 0, "vmax": 20}
for iKey in pcolormeshArgs.keys():
if iKey in kwargs.keys():
pcolormeshArgs[iKey] = kwargs.pop(iKey)
quiverArgs = {
"X": None,
"Y": None,
"U": None,
"label": None,
"labelpos": "E",
"coordinates": "figure",
"fontproperties": None,
"width": None,
}
popKeys = []
for iKey in quiverArgs:
if "quiver_" + iKey in kwargs.keys():
quiverArgs[iKey] = kwargs.pop("quiver_" + iKey)
else:
popKeys.append(iKey)
for key in popKeys:
quiverArgs.pop(key)
nMap = Nansatmap(self, resolution="l", figsize=(5, 8))
# use wind direction "to" for calculating u and v
winddirection = np.mod(self["winddirection"] + 180, 360)
windspeed = self["windspeed"]
windspeedPcolor = np.copy(windspeed)
# if data has non-value (dark blue), replace to Nan
if edgecolor is not None:
# Replace the edge color (dark blue) to white
windspeedPcolor[windspeedPcolor == edgecolor] = np.NaN
# Put colormesh
nMap.pcolormesh(windspeedPcolor, **pcolormeshArgs)
# apply landmask to windspeeds
windspeed = np.ma.masked_where(self.watermask()[1] == 2, windspeed)
# specify the number of quiver
quiPixelSpacing = int(np.round(windspeed.shape[1] / numArrowsRange))
numQuiX = int(self.vrt.dataset.RasterXSize / quiPixelSpacing)
numQuiY = int(self.vrt.dataset.RasterYSize / quiPixelSpacing)
# compute maximum wind speed on the sea
maxSpeed = max(windspeed[windspeed.mask == False])
# compute a scale for quiver lenght
scale = None
if quiverScaleCriteria is not None:
for iKey in quiverScaleCriteria.keys():
if eval(iKey % maxSpeed):
scale = quiverScaleCriteria[iKey]
# Draw quivers
Ux = np.sin(np.radians(winddirection)) * windspeed
Vx = np.cos(np.radians(winddirection)) * windspeed
nMap.quiver(Ux, Vx, scale=scale, quivectors=(numQuiY, numQuiX), **quiverArgs)
if colorbar:
nMap.add_colorbar(fontsize=cbar_fontsize)
if drawgrid:
nMap.drawgrid()
if title is not None:
plt.title(title, fontsize=title_fontsize)
if tight:
nMap.fig.tight_layout()
nMap.save(fileName, landmask=landmask, **kwargs)
def save_wind_map_image(self,
fileName,
scale=None,
numArrowsRange=10,
landmask=True,
colorbar=True,
cbar_fontsize=6,
drawgrid=True,
title=None,
title_fontsize=10,
edgecolor=None,
quiverScaleCriteria=None,
tight=True,
**kwargs):
pcolormeshArgs = {'vmin': 0, 'vmax': 20}
for iKey in pcolormeshArgs.keys():
if iKey in kwargs.keys():
pcolormeshArgs[iKey] = kwargs.pop(iKey)
quiverArgs = {
'X': None,
'Y': None,
'U': None,
'label': None,
'labelpos': 'E',
'coordinates': 'figure',
'fontproperties': None,
'width': None
}
popKeys = []
for iKey in quiverArgs:
if 'quiver_' + iKey in kwargs.keys():
quiverArgs[iKey] = kwargs.pop('quiver_' + iKey)
else:
popKeys.append(iKey)
for key in popKeys:
quiverArgs.pop(key)
nMap = Nansatmap(self, resolution='l', figsize=(5, 8))
# use wind direction "to" for calculating u and v
winddirection = np.mod(self['winddirection'] + 180, 360)
windspeed = self['windspeed']
windspeedPcolor = np.copy(windspeed)
# if data has non-value (dark blue), replace to Nan
if edgecolor is not None:
# Replace the edge color (dark blue) to white
windspeedPcolor[windspeedPcolor == edgecolor] = np.NaN
# Put colormesh
nMap.pcolormesh(windspeedPcolor, **pcolormeshArgs)
# apply landmask to windspeeds
windspeed = np.ma.masked_where(
self.watermask(tps=True)[1] == 2, windspeed)
# specify the number of quiver
quiPixelSpacing = int(np.round(windspeed.shape[1] / numArrowsRange))
numQuiX = int(self.vrt.dataset.RasterXSize / quiPixelSpacing)
numQuiY = int(self.vrt.dataset.RasterYSize / quiPixelSpacing)
# compute maximum wind speed on the sea
maxSpeed = max(windspeed[windspeed.mask == False])
# compute a scale for quiver lenght
scale = None
if quiverScaleCriteria is not None:
for iKey in quiverScaleCriteria.keys():
if eval(iKey % maxSpeed):
scale = quiverScaleCriteria[iKey]
# Draw quivers
Ux = np.sin(np.radians(winddirection)) * windspeed
Vx = np.cos(np.radians(winddirection)) * windspeed
nMap.quiver(Ux,
Vx,
scale=scale,
quivectors=(numQuiY, numQuiX),
**quiverArgs)
if colorbar:
nMap.add_colorbar(fontsize=cbar_fontsize)
if drawgrid:
nMap.drawgrid()
if title is not None:
plt.title(title, fontsize=title_fontsize)
if tight:
nMap.fig.tight_layout()
nMap.save(fileName, landmask=landmask, **kwargs)
def calc_mean_doppler(datetime_start=timezone.datetime(2010,1,1,
tzinfo=timezone.utc), datetime_end=timezone.datetime(2010,2,1,
tzinfo=timezone.utc), domain=Domain(NSR().wkt,
'-te 10 -44 40 -30 -tr 0.05 0.05')):
geometry = WKTReader().read(domain.get_border_wkt(nPoints=1000))
ds = Dataset.objects.filter(entry_title__contains='Doppler',
time_coverage_start__range=[datetime_start, datetime_end],
geographic_location__geometry__intersects=geometry)
Va = np.zeros(domain.shape())
Vd = np.zeros(domain.shape())
ca = np.zeros(domain.shape())
cd = np.zeros(domain.shape())
sa = np.zeros(domain.shape())
sd = np.zeros(domain.shape())
sum_var_inv_a = np.zeros(domain.shape())
sum_var_inv_d = np.zeros(domain.shape())
for dd in ds:
uris = dd.dataseturi_set.filter(uri__endswith='nc')
for uri in uris:
dop = Doppler(uri.uri)
# Consider skipping swath 1 and possibly 2...
dop.reproject(domain)
# TODO: HARDCODING - MUST BE IMPROVED
satpass = dop.get_metadata(key='Originating file').split('/')[6]
if satpass=='ascending':
try:
v_ai = dop['Ur']
v_ai[np.abs(v_ai)>3] = np.nan
except:
# subswath doesn't cover the given domain
continue
# uncertainty:
# 5 Hz - TODO: estimate this correctly...
sigma_ai = -np.pi*np.ones(dop.shape())*5./(112*np.sin(dop['incidence_angle']*np.pi/180.))
alpha_i = -dop['sensor_azimuth']*np.pi/180.
Va = np.nansum(np.append(np.expand_dims(Va, 2),
np.expand_dims(v_ai/np.square(sigma_ai), 2), axis=2),
axis=2)
ca = np.nansum(np.append(np.expand_dims(ca, 2),
np.expand_dims(np.cos(alpha_i)/np.square(sigma_ai), 2),
axis=2), axis=2)
sa = np.nansum(np.append(np.expand_dims(sa, 2),
np.expand_dims(np.sin(alpha_i)/np.square(sigma_ai), 2),
axis=2), axis=2)
sum_var_inv_a =np.nansum(np.append(np.expand_dims(sum_var_inv_a, 2),
np.expand_dims(1./np.square(sigma_ai), 2), axis=2),
axis=2)
else:
try:
v_dj = -dop['Ur']
v_dj[np.abs(v_dj)>3] = np.nan
except:
# subswath doesn't cover the given domain
continue
# 5 Hz - TODO: estimate this correctly...
sigma_dj = -np.pi*np.ones(dop.shape())*5./(112*np.sin(dop['incidence_angle']*np.pi/180.))
delta_j = (dop['sensor_azimuth']-180.)*np.pi/180.
Vd = np.nansum(np.append(np.expand_dims(Vd, 2),
np.expand_dims(v_dj/np.square(sigma_dj), 2), axis=2),
axis=2)
cd = np.nansum(np.append(np.expand_dims(cd, 2),
np.expand_dims(np.cos(delta_j)/np.square(sigma_dj), 2),
axis=2), axis=2)
sd = np.nansum(np.append(np.expand_dims(sd, 2),
np.expand_dims(np.sin(delta_j)/np.square(sigma_dj), 2),
axis=2), axis=2)
sum_var_inv_d = np.nansum(np.append(
np.expand_dims(sum_var_inv_d, 2), np.expand_dims(
1./np.square(sigma_ai), 2), axis=2), axis=2)
u = (Va*sd + Vd*sa)/(sa*cd + sd*ca)
v = (Va*cd - Vd*ca)/(sa*cd + sd*ca)
sigma_u = np.sqrt(np.square(sd)*sum_var_inv_a +
np.square(sa)*sum_var_inv_d) / (sa*cd + sd*ca)
sigma_v = np.sqrt(np.square(cd)*sum_var_inv_a +
np.square(ca)*sum_var_inv_d) / (sa*cd + sd*ca)
nu = Nansat(array=u, domain=domain)
nmap=Nansatmap(nu, resolution='h')
nmap.pcolormesh(nu[1], vmin=-1.5, vmax=1.5, cmap='bwr')
nmap.add_colorbar()
nmap.draw_continents()
nmap.fig.savefig('/vagrant/shared/unwasc.png', bbox_inches='tight')