def get_albedo(self, grid):
"""
Retrieve the Rayleigh single scattering albedo.
Parameters
----------
grid: shdom.Grid
The new grid to which the data will be resampled
Returns
-------
albedo: list of shdom.GridData
A list of GridData objects containing the single scattering albedo [0,1] on a grid.
The length of the list is the number of wavelengths.
"""
if self.num_wavelengths == 1:
albedo = shdom.GridData(grid,
data=np.full(shape=grid.shape,
fill_value=1.0,
dtype=np.float32))
else:
albedo = [
shdom.GridData(grid,
data=np.full(shape=grid.shape,
fill_value=1.0,
dtype=np.float32))
for wavelength in self._wavelength
]
return albedo
def get_extinction(self, grid):
"""
Retrieve the Rayleigh extinction profile (as a function of altitude).
Parameters
----------
grid: shdom.Grid
The new grid to which the data will be resampled
Returns
-------
extinction_profile: list of shdom.GridData
A list of GridData objects containing the extinction on a 1D grid.
The length of the list is the number of wavelengths.
"""
if self.num_wavelengths == 1:
ext_profile = core.rayleigh_extinct(
nzt=grid.nz,
zlevels=grid.z,
temp=self.temperature_profile.data,
raysfcpres=self.surface_pressure,
raylcoef=self._raylcoef)
extinction = shdom.GridData(grid, ext_profile)
else:
ext_profile = [
core.rayleigh_extinct(nzt=grid.nz,
zlevels=grid.z,
temp=self.temperature_profile.data,
raysfcpres=self.surface_pressure,
raylcoef=raylcoef)
for raylcoef in self._raylcoef
]
extinction = [shdom.GridData(grid, ext) for ext in ext_profile]
return extinction
def load_from_csv(self, path, veff=0.1):
"""
A utility function to load a microphysical medium.
Parameters
----------
path: str
Path to file.
veff: float
If effective variance is not specified in the csv file as a 6th column,
this value is used as a homogeneous value.
Default value is veff=0.1
Notes
-----
CSV format should be as follows:
# comment line (description)
nx ny nz
dz dy dz z_levels[0] z_levels[1] ... z_levels[nz-1]
ix iy iz lwc[ix, iy, iz] reff[ix, iy, iz] veff[ix, iy, iz](optional)
.
.
.
ix iy iz lwc[ix, iy, iz] reff[ix, iy, iz] veff[ix, iy, iz](optional)
"""
grid = self.load_grid(path)
data = np.genfromtxt(path, skip_header=3)
grid_index = data[:, :3].astype(int)
lwc = data[:, 3]
reff = data[:, 4]
if data.shape[1] == 6:
veff = data[:, 5]
else:
veff = veff * np.ones_like(reff)
lwc_data = np.full(shape=(grid.nx, grid.ny, grid.nz),
fill_value=np.nan)
reff_data = np.full(shape=(grid.nx, grid.ny, grid.nz),
fill_value=np.nan)
veff_data = np.full(shape=(grid.nx, grid.ny, grid.nz),
fill_value=np.nan)
lwc_data[grid_index[:, 0], grid_index[:, 1], grid_index[:, 2]] = lwc
reff_data[grid_index[:, 0], grid_index[:, 1], grid_index[:, 2]] = reff
veff_data[grid_index[:, 0], grid_index[:, 1], grid_index[:, 2]] = veff
self.set_microphysics(lwc=shdom.GridData(grid,
lwc_data).squeeze_dims(),
reff=shdom.GridData(grid,
reff_data).squeeze_dims(),
veff=shdom.GridData(grid,
veff_data).squeeze_dims())
def get_phase(self, grid):
"""
Retrieve the Rayleigh phase function.
Parameters
----------
grid: shdom.Grid
The new grid to which the data will be resampled
Returns
-------
phase: list of shdom.GridPhase
A list of GridPhase objects containing the phase function on a grid.
The length of the list is the number of wavelengths.
"""
phase = []
for wavelength in self._wavelength:
index = shdom.GridData(grid,
data=np.ones(shape=grid.shape,
dtype=np.int32))
table, table_type = core.rayleigh_phase_function(
wavelen=wavelength)
table = LegendreTable(table.astype(np.float32),
table_type.decode())
phase.append(GridPhase(table, index))
if self.num_wavelengths == 1:
phase = phase[0]
return phase
def compute_extinction(self, veff=0.1):
# extrarc grid data:
bounding_box = shdom.BoundingBox(self.bounding_box_xmin,
self.bounding_box_ymin,
self.bounding_box_xmin,
self.bounding_box_xmax,
self.bounding_box_ymax,
self.bounding_box_zmax)
grid = shdom.Grid(bounding_box=bounding_box,
nx=self.nx,
ny=self.ny,
nz=self.nz)
# self.extinction = np.zeros([self.nx,self.ny,self.nz])
veff = veff * np.ones_like(self.re)
lwc = shdom.GridData(grid, self.lwc).squeeze_dims()
reff = shdom.GridData(grid, self.re).squeeze_dims()
veff = shdom.GridData(grid, veff).squeeze_dims()
extinction = self.mie.get_extinction(lwc, reff, veff)
self.extinction = extinction.data
def get_mask(self, threshold):
"""
Get a mask based on the optical extinction.
Parameters
----------
threshold: float
A threshold which above this value it is considered a populated voxel.
Returns
-------
mask: shdom.GridData object
A boolean mask with True for dense voxels and False for optically thin regions.
"""
data = self.extinction.data > threshold
return shdom.GridData(self.grid, data)
def get_mask(self, threshold):
"""
Get a mask based on the liquid water content.
Parameters
----------
threshold: float
A threshold which above this value it is considered a populated voxel.
Returns
-------
mask: shdom.GridData object
A boolean mask with True for dense voxels and False for thin voxels.
"""
data = self.lwc.data > threshold
return shdom.GridData(self.grid, data)
def get_albedo(self, reff, veff):
"""
Interpolate the single scattering albedo over a grid.
Parameters
----------
reff: shdom.GridData
A shdom.GridData object containing the effective radii (micron) on a 3D grid.
veff: shdom.GridData
A GridData object containing effective variances on a 3D grid.
Returns
-------
albedo: shdom.GridData object
A shdom.GridData object containing the single scattering albedo unitless in range [0, 1] on a 3D grid
"""
grid = veff.grid + reff.grid
data = self._ssalb_interpolator(
(reff.resample(grid).data, veff.resample(grid).data))
albedo = shdom.GridData(grid, data)
return albedo
def get_extinction(self, lwc, reff, veff):
"""
Retrieve the extinction coefficient over a grid.
Parameters
----------
lwc: shdom.GridData
A GridData object containing liquid water content (g/m^3) on a grid.
reff: shdom.GridData
A GridData object containing effective radii (micron) on a grid.
veff: shdom.GridData
A GridData object containing effective variances on a grid.
Returns
-------
extinction: shdom.GridData object
A GridData object containing the extinction (1/km) on a grid
"""
grid = lwc.grid + veff.grid + reff.grid
data = self._ext_interpolator(
(reff.resample(grid).data, veff.resample(grid).data))
extinction = lwc.resample(grid) * shdom.GridData(grid, data)
return extinction
def get_phase(self, reff, veff, squeeze_table=True):
"""
Interpolate the phase function over a grid.
Parameters
----------
reff: shdom.GridData
A shdom.GridData object containing the effective radii (micron) on a 3D grid.
veff: shdom.GridData
A GridData object containing effective variances on a 3D grid.
squeeze_table: boolean
True: return compact table containing the range of provided reff, veff.
False will return the current table.
Returns
-------
phase: GridPhase
A GridPhase object containing the phase function legendre coeffiecients as a table.
"""
grid = veff.grid + reff.grid
index = self._legen_index_interpolator(
(reff.resample(grid).data,
veff.resample(grid).data)).astype(np.int32)
nre, nve = self.size_distribution.nretab, self.size_distribution.nvetab
# Clip table to make it compact
if squeeze_table:
max_re_idx = min(
nre - 1,
find_nearest(self.size_distribution.reff, reff.data.max()))
max_ve_idx = min(
nve - 1,
find_nearest(self.size_distribution.veff, veff.data.max()))
min_re_idx = max(
0,
find_nearest(self.size_distribution.reff,
reff.data[reff.data > 0.0].min()))
min_ve_idx = max(
0,
find_nearest(self.size_distribution.veff,
veff.data[veff.data > 0.0].min()))
legcoef = self.legcoef_2d[..., min_re_idx:max_re_idx + 1,
min_ve_idx:max_ve_idx + 1]
nre, nve = legcoef.shape[-2:]
legcoef = legcoef.reshape(self.legcoef.shape[:-1] + (-1, ),
order='F')
index[..., 0] -= min_re_idx
index[..., 1] -= min_ve_idx
else:
legcoef = self.legcoef
legen_table = shdom.LegendreTable(legcoef, self.table_type)
index = np.ravel_multi_index(np.rollaxis(index, axis=-1),
dims=(nre, nve),
order='F',
mode='clip') + 1
legen_index = shdom.GridData(grid, index.astype(np.int32))
phase = GridPhase(legen_table, legen_index)
return phase
