def make_snowpack(thickness, microstructure_model, density,
interface=None,
surface=None,
substrate=None,
atmosphere=None,
**kwargs):
"""
build a multi-layered snowpack. Each parameter can be an array, list or a constant value.
:param thickness: thicknesses of the layers in meter (from top to bottom). The last layer thickness can be "numpy.inf"
for a semi-infinite layer.
:param microstructure_model: microstructure_model to use (e.g. sticky_hard_spheres or independent_sphere or exponential).
:param surface: type of surface interface, flat/fresnel is the default. If surface and interface are both set,
the interface must be a constant refering to all the "internal" interfaces.
:param interface: type of interface, flat/fresnel is the default. It is usually a string for the interfaces
without parameters (e.g. Flat or Transparent) or is created with :py:func:`~smrt.core.interface.make_interface` in more complex cases.
Interface can be a constant or a list. In the latter case, its length must be the same as the number of layers,
and interface[0] refers to the surface interface.
:param density: densities of the layers.
:param substrate: set the substrate of the snowpack. Another way to add a substrate is to use the + operator
(e.g. snowpack + substrate).
:param **kwargs: All the other parameters (temperature, microstructure parameters, emmodel, etc.) are given as optional arguments
(e.g. temperature=[270, 250]).
They are passed for each layer to the function :py:func:`~smrt.inputs.make_medium.make_snow_layer`.
Thus, the documentation of this function is the reference. It describes precisely the available parameters.
The microstructure parameter(s) depend on the microstructure_model used and is documented in each microstructure_model module.
e.g.::
sp = make_snowpack([1, 10], "exponential", density=[200,300], temperature=[240, 250], corr_length=[0.2e-3, 0.3e-3])
"""
sp = Snowpack(substrate=substrate, atmosphere=atmosphere)
if not isinstance(thickness, collections.abc.Iterable):
raise SMRTError("The thickness argument must be iterable, that is, a list of numbers, numpy array or pandas Series or DataFrame.")
lib.check_argument_size(density, len(thickness), "density")
lib.check_argument_size(kwargs, len(thickness))
if surface is not None and lib.is_sequence(interface):
raise SMRTError("Setting both 'surface' and 'interface' arguments is ambiguous when inteface is a list or any sequence.")
for i, dz in enumerate(thickness):
layer = make_snow_layer(dz, lib.get(microstructure_model, i, "microstructure_model"),
density=lib.get(density, i, "density"),
**lib.get(kwargs, i))
# add the interface
linterface = lib.get(interface, i, "interface") if (i > 0) or (surface is None) else surface
sp.append(layer, interface=make_interface(linterface))
return sp
def make_snowpack(thickness,
microstructure_model,
density,
interface=None,
substrate=None,
**kwargs):
"""
build a multi-layered snowpack. Each parameter can be an array, list or a constant value.
:param thickness: thicknesses of the layers in meter (from top to bottom). The last layer thickness can be "numpy.inf" for a semi-infinite layer.
:param microstructure_model: microstructure_model to use (e.g. sticky_hard_spheres or independent_sphere or exponential).
:param interface: type of interface, flat/fresnel is the default.
:param density: densities of the layers.
:param substrate: set the substrate of the snowpack. Another way to add a substrate is to use the + operator (e.g. snowpack + substrate).
All the other parameters (temperature, microstructure parameters, emmodel, etc, etc) are given as optional arguments (e.g. temperature=[270, 250]).
They are passed for each layer to the function :py:func:`~smrt.inputs.make_medium.make_snow_layer`. Thus, the documentation of this function is the reference. It describes precisely the available parameters.
The microstructure parameter(s) depend on the microstructure_model used and is documented in each microstructure_model module.
e.g.::
sp = make_snowpack([1, 10], "exponential", density=[200,300], temperature=[240, 250], corr_length=[0.2e-3, 0.3e-3])
"""
sp = Snowpack(substrate=substrate)
if not isinstance(thickness, collections.Iterable):
raise SMRTError(
"The thickness argument must be iterable, that is, a list of numbers, numpy array or pandas Series or DataFrame."
)
lib.check_argument_size(density, len(thickness), "density")
lib.check_argument_size(kwargs, len(thickness))
for i, dz in enumerate(thickness):
layer = make_snow_layer(dz,
lib.get(microstructure_model, i,
"microstructure_model"),
density=lib.get(density, i, "density"),
**lib.get(kwargs, i))
# add the interface
sp.append(layer,
interface=make_interface(lib.get(interface, i, "interface")))
return sp
def test_make_interface_noargs():
make_interface("flat")
def make_ice_column(ice_type,
thickness,
temperature,
microstructure_model,
brine_inclusion_shape='spheres',
salinity=0.,
brine_volume_fraction=None,
brine_permittivity_model=None,
ice_permittivity_model=None,
saline_ice_permittivity_model=None,
porosity=0,
density=None,
add_water_substrate=True,
interface=None,
substrate=None,
atmosphere=None,
**kwargs):
"""Build a multi-layered ice column. Each parameter can be an array, list or a constant value.
ice_type variable determines the type of ice, which has a big impact on how the medium is modelled and the parameters:
- First year ice is modelled as scattering brines embedded in a pure ice background
- Multi year ice is modelled as scattering air bubbles in a saline ice background (but brines are non-scattering in this case).
- Fresh ice is modelled as scattering air bubbles in a pure ice background (but brines are non-scattering in this case).
First-year and multi-year ice is equivalent only if scattering and porosity are nulls. It is important to understand that in multi-year ice
scattering by brine pockets is neglected because scattering is due to air bubbles and the emmodel implemented up to now are not able to deal with
three-phase media.
:param ice_type: Ice type. Options are "firstyear", "multiyear", "fresh"
:param thickness: thicknesses of the layers in meter (from top to bottom). The last layer thickness can be "numpy.inf" for a semi-infinite layer.
:param temperature: temperature of ice/water in K
:param brine_inclusion_shape: assumption for shape of brine inclusions. So far, "spheres" or "random_needles" (i.e. elongated ellipsoidal inclusions), and "mix" (a mix of the two) are implemented,
:param salinity: salinity of ice/water in kg/kg (see PSU constant in smrt module). Default is 0. If neither salinity nor brine_volume_fraction are given, the ice column is considered to consist of fresh water ice.
:param brine_volume_fraction: brine / liquid water fraction in sea ice, optional parameter, if not given brine volume fraction is calculated from temperature and salinity in ~.smrt.permittivity.brine_volume_fraction
:param density: density of ice layer in kg m :sup:`-3`
:param porosity: porosity of ice layer (0 - 1). Default is 0.
:param add_water_substrate: Adds a substrate made of water below the ice column.
Possible arguments are True (default) or False. If True looks for ice_type to determine if a saline or fresh water layer is added and/or uses the
optional arguments 'water_temperature', 'water_salinity' of the water substrate.
:param interface: type of interface, flat/fresnel is the default
:param substrate: if add_water_substrate is False, the substrate can be prescribed with this argument.
All the other optional arguments are passed for each layer to the function :py:func:`~smrt.inputs.make_medium.make_ice_layer`.
The documentation of this function describes in detail the parameters used/required depending on ice_type.
"""
# add a substrate underneath the ice (if wanted):
if add_water_substrate:
wp = water_parameters(ice_type, **kwargs)
# create a permittivity_function that depends only on frequency and temperature by setting other arguments
permittivity_model = lambda f, t: wp.water_permittivity_model(
f, t, wp.water_salinity)
substrate = Flat(temperature=wp.water_temperature,
permittivity_model=permittivity_model)
else:
substrate = substrate
sp = Snowpack(substrate=substrate, atmosphere=atmosphere)
n = len(thickness)
for name in [
"temperature", "salinity", "microstructure_model",
"brine_inclusion_shape", "brine_volume_fraction", "porosity",
"density", "brine_permittivity_model", "ice_permittivity_model",
"saline_ice_permittivity_model", "interface", "kwargs"
]:
lib.check_argument_size(locals()[name], n)
for i, dz in enumerate(thickness):
layer = make_ice_layer(
ice_type,
dz,
temperature=lib.get(temperature, i),
salinity=lib.get(salinity, i),
microstructure_model=lib.get(microstructure_model, i),
brine_inclusion_shape=lib.get(brine_inclusion_shape, i),
brine_volume_fraction=lib.get(brine_volume_fraction, i),
porosity=lib.get(porosity, i),
density=lib.get(density, i),
brine_permittivity_model=lib.get(brine_permittivity_model, i),
ice_permittivity_model=lib.get(ice_permittivity_model, i),
saline_ice_permittivity_model=lib.get(
saline_ice_permittivity_model, i),
**lib.get(kwargs, i))
sp.append(layer, interface=make_interface(lib.get(interface, i)))
return sp