def getdelay(self, dataobj, wvl=np.pi * 4., writeStations=True):
'''Write delay to a matrix / HDF5 object or a file directly.
Bilinear Interpolation is used.
Args:
* dataobj : Final output. (str or HDF5 or np.array)
If str, output is written to file.
Kwargs:
* wvl : Wavelength in meters.
4*pi (by default) --> output results in delay in meters.
0.056 --> output results in delay in radians for C-band SAR.
.. note::
If dataobj is string, output is written to the file.
If np.array or HDF5 object, it should be of size (ny,nx).
'''
# To know the type of incidence (float, array)
if isinstance(self.inc, (int, float, np.float32, np.float64)):
cinc = np.cos(self.inc * np.pi / 180.)
incFileFlag = 'number'
else:
incFileFlag = 'array'
# Get some info from the dictionary
minAltp = self.dict['minAltP']
# Check output and open file if necessary
outFile = isinstance(dataobj, str)
if outFile:
fout = open(dataobj, 'wb')
dout = np.zeros((self.ny, self.nx))
else:
assert dataobj.shape == (
self.ny, self.nx), 'PyAPS: Not a valid data object.'
dout = dataobj
#######################################################################################
# BILINEAR INTERPOLATION
# Create the 1d interpolator to interpolate delays in altitude direction
if self.verb:
print('PROGRESS: FINE INTERPOLATION OF HEIGHT LEVELS')
intp_1d = si.interp1d(self.hgt, self.Delfn, kind='cubic', axis=-1)
# Interpolate the delay function every meter, for each station
self.dem[np.isnan(self.dem)] = minAltp
self.dem[self.dem < minAltp] = minAltp
minH = np.max([np.nanmin(self.dem * self.mask), self.hgt.min()])
maxH = int(np.nanmax(self.dem * self.mask)) + 100.
kh = np.arange(minH, maxH)
self.Delfn_1m = intp_1d(kh)
self.alti = kh
# no reshape
Lonu = self.lonlist[0, :]
Latu = self.latlist[:, 0]
# Create the cube interpolator for the bilinear method, to interpolate delays into a grid (x,y,z)
if self.verb:
print('PROGRESS: CREATE THE BILINEAR INTERPOLATION FUNCTION')
# Define a linear interpolating function on the 3D grid: ((x, y, z), data)
# We do the weird trick of [::-1,:,:] because Latu has to be in increasing order
# for the RegularGridInterpolator method of scipy.interpolate
linearint = si.RegularGridInterpolator((Latu[::-1], Lonu, kh),
self.Delfn_1m[::-1, :, :],
method='linear',
bounds_error=False,
fill_value=0.0)
# Show progress bar
if self.verb:
toto = utils.ProgressBar(maxValue=self.ny)
print('PROGRESS: MAPPING THE DELAY')
# Loop on the lines
for m in range(self.ny):
# Update progress bar
if self.verb:
toto.update(m + 1, every=5)
# Get latitude and longitude arrays
lati = self.lat[m, :] * self.mask[m, :]
loni = self.lon[m, :] * self.mask[m, :]
# Remove negative values
loni[loni < 0.] += 360.
# Remove NaN values
ii = np.where(np.isnan(lati))
jj = np.where(np.isnan(loni))
xx = np.union1d(ii, jj)
lati[xx] = 0.0
loni[xx] = 0.0
# Get incidence if file provided
if incFileFlag == 'array':
cinc = np.cos(self.mask[m, :] * self.inc[m, :] * np.pi / 180.)
# Make the bilinear interpolation
D = self.dem[m, :]
val = linearint(np.vstack(
(lati, loni, D)).T) * np.pi * 4.0 / (cinc * wvl)
val[xx] = np.nan
# Write outfile
if outFile:
resy = val.astype(np.float32)
resy.tofile(fout)
else:
dataobj[m, :] = val
if self.verb:
toto.close()
if outFile:
fout.close()
# All done
return
def merisfactor(self, dataobj, inc=0.0, wvl=4 * np.pi):
'''
Write pi-factor from Li et al 2012 to a matrix / HDF5 object or a file directly.
Args:
* dataobj (str or HDF5 or np.array): Final output. If str, output is written to file.
Kwargs:
* inc (np.float): Incidence angle in degrees. Default is vertical.
* wvl (np.float): Wavelength in meters. Default output results in delay in meters.
.. note::
If dataobj is string, output is written to the file.
If np.array or HDF5 object, it should be of size (ny,nx).
'''
minAltp = self.dict['minAltP']
# Incidence
cinc = np.cos(inc * np.pi / 180.0)
# Compute the two integrals
WonT = self.Vi / self.Ti
WonT2 = WonT / self.Ti
S1 = intg.cumtrapz(WonT, x=self.hgt, axis=-1)
val = 2 * S1[:, -1] - S1[:, -2]
val = val[:, None]
S1 = np.concatenate((S1, val), axis=-1)
del WonT
S2 = intg.cumtrapz(WonT2, x=self.hgt, axis=-1)
val = 2 * S2[:, -1] - S2[:, -2]
val = val[:, None]
S2 = np.concatenate((S2, val), axis=-1)
del WonT2
Tm = S1 / S2
self.Tm = Tm
# Reading in the DEM
if self.verb:
print('PROGRESS: READING DEM')
fin = open(self.hfile, 'rb')
if self.fmt in ('HGT'):
dem = np.fromfile(file=fin,
dtype=self.demtype,
count=self.nx * self.ny).reshape(
self.ny, self.nx)
elif self.fmt in ('RMG'):
dem = np.fromfile(file=fin,
dtype=self.demtype,
count=2 * self.nx * self.ny).reshape(
self.ny, 2 * self.nx)
dem = dem[:, self.nx:]
dem = np.round(dem).astype(np.int)
fin.close()
# check output, and open file if necessary
outFile = isinstance(dataobj, str)
if outFile:
fout = open(dataobj, 'wb')
dout = np.zeros((self.ny, self.nx))
else:
assert ((dataobj.shape[0] == self.ny) &
(dataobj.shape[1]
== self.nx)), 'PyAPS: Not a valid data object.'
dout = dataobj
# Create the lon/lat arrays
laty = np.linspace(self.maxlat - self.bufspc,
self.minlat + self.bufspc, self.ny)
lonx = np.linspace(self.minlon + self.bufspc,
self.maxlon - self.bufspc, self.nx)
# Create the 1d interpolator
if self.verb:
print('PROGRESS: FINE INTERPOLATION OF HEIGHT LEVELS')
intp_1d = si.interp1d(self.hgt, Tm, kind='cubic', axis=1)
# Interpolate the Tm variable every meter
dem[dem < minAltp] = minAltp
minH = dem.min()
maxH = dem.max() + 1
kh = np.arange(minH, maxH)
Tm_1m = intp_1d(kh)
self.alti = kh
# Reshape Tm
Lonu = np.unique(self.lonlist)
Latu = np.unique(self.latlist)
nLon = len(Lonu)
nLat = len(Latu)
Tm_1m = np.reshape(Tm_1m.T, (len(kh), nLat, nLon))
self.Tm_1m = Tm_1m
# Create the cube interpolator for the bilinear method
if self.verb:
print('PROGRESS: CREATE THE BILINEAR INTERPOLATION FUNCTION')
bilicube = processor.Bilinear2DInterpolator(Lonu,
Latu,
Tm_1m,
cube=True)
# Get the values from the dictionnary
k1 = self.dict['k1']
k2 = self.dict['k2']
k3 = self.dict['k3']
mmO = self.dict['mmO']
mmH = self.dict['mmH']
mma = self.dict['mma']
w = (2 * mmH + mmO) / mma
Rv = self.dict['Rv']
Rho = self.dict['Rho']
# Loop on the lines
if self.verb:
toto = utils.ProgressBar(maxValue=self.ny)
print('PROGRESS: MAPPING THE DELAY')
for m in range(self.ny):
if self.verb:
toto.update(m, every=5)
# Get Lon/Lat
loni = lonx
lati = laty[m] * np.ones((loni.shape))
# Make the bilinear interpolation
D = dem[m, :] - minH
val = bilicube(loni, lati, D)
val = 0.000001 * Rho * Rv * (k3 / val + k2 -
w * k1) * np.pi * 4.0 / (cinc * wvl)
if outFile:
resy = val.astype(np.float32)
resy.tofile(fout)
else:
dataobj[m, :] = val
if self.verb:
toto.close()
# Close if outfile
if outFile:
fout.close()
def getdelay(self,
dout=None,
outFile=None,
wvl=np.pi * 4.,
writeStations=True):
'''Get the 2D matrix of tropospheric delay with bilinear interpolation.
Kwargs:
* dout : 2D np.ndarray, output delay matrix
* outFile : str, file path of output delay matrix
* wvl : Wavelength in meters.
4*pi (by default) --> output results in delay in meters.
0.056 --> output results in delay in radians for C-band SAR.
Returns:
* dout : 2D np.ndarray in size of (ny, nx) in float32.
'''
# To know the type of incidence (float, array)
if isinstance(self.inc, (int, float, np.float32, np.float64)):
cinc = np.cos(self.inc * np.pi / 180.)
incFileFlag = 'number'
else:
incFileFlag = 'array'
# Get some info from the dictionary
minAltp = self.dict['minAltP']
# initiate output
dout = np.zeros(
(self.ny, self.nx), dtype=np.float32) if dout is None else dout
fout = open(outFile, 'wb') if outFile else None
#######################################################################################
# BILINEAR INTERPOLATION
# Create the 1d interpolator to interpolate delays in altitude direction
if self.verb:
print('PROGRESS: FINE INTERPOLATION OF HEIGHT LEVELS')
intp_1d = si.interp1d(self.hgt, self.Delfn, kind='cubic', axis=-1)
# Interpolate the delay function every meter, for each station
self.dem[np.isnan(self.dem)] = minAltp
self.dem[self.dem < minAltp] = minAltp
minH = np.max([np.nanmin(self.dem * self.mask), self.hgt.min()])
maxH = int(np.nanmax(self.dem * self.mask)) + 100.
kh = np.arange(minH, maxH)
self.Delfn_1m = intp_1d(kh)
self.alti = kh
# no reshape
Lonu = self.lonlist[0, :]
Latu = self.latlist[:, 0]
# Create the cube interpolator for the bilinear method, to interpolate delays into a grid (x,y,z)
if self.verb:
print('PROGRESS: CREATE THE BILINEAR INTERPOLATION FUNCTION')
# Define a linear interpolating function on the 3D grid: ((x, y, z), data)
# We do the weird trick of [::-1,:,:] because Latu has to be in increasing order
# for the RegularGridInterpolator method of scipy.interpolate
linearint = si.RegularGridInterpolator((Latu[::-1], Lonu, kh),
self.Delfn_1m[::-1, :, :],
method='linear',
bounds_error=False,
fill_value=0.0)
# Show progress bar
if self.verb:
toto = utils.ProgressBar(maxValue=self.ny)
print('PROGRESS: MAPPING THE DELAY')
# Loop on the lines
for m in range(self.ny):
# Update progress bar
if self.verb:
toto.update(m + 1, every=5)
# Get latitude and longitude arrays
lati = self.lat[m, :] * self.mask[m, :]
loni = self.lon[m, :] * self.mask[m, :]
# Remove negative values
if self.grib in ('ERA5', 'ERAINT', 'HRES'):
loni[loni > 180.] -= 360.
else:
loni[loni < 0.] += 360.
# Remove NaN values
ii = np.where(np.isnan(lati))
jj = np.where(np.isnan(loni))
xx = np.union1d(ii, jj)
lati[xx] = 0.0
loni[xx] = 0.0
# Get incidence if file provided
if incFileFlag == 'array':
cinc = np.cos(self.mask[m, :] * self.inc[m, :] * np.pi / 180.)
# Make the bilinear interpolation
D = self.dem[m, :]
val = linearint(np.vstack(
(lati, loni, D)).T) * np.pi * 4.0 / (cinc * wvl)
val[xx] = np.nan
# save output
dout[m, :] = val
if outFile:
val.astype(np.float32).tofile(fout)
if self.verb:
toto.close()
if outFile:
fout.close()
return dout