class InitRiseVelFromDist(InitBaseClass, Serializable):
_state = copy.deepcopy(InitBaseClass._state)
def __init__(self, distribution=None, **kwargs):
"""
Set the rise velocity parameters to be sampled from a distribution.
Use distribution to define rise_vel
:param distribution: An object capable of generating a probability
distribution.
:type distribution: Right now, we have:
- UniformDistribution
- NormalDistribution
- LogNormalDistribution
- WeibullDistribution
New distribution classes could be made. The only
requirement is they need to have a set_values()
method which accepts a NumPy array.
(presumably, this function will also modify
the array in some way)
"""
super(InitRiseVelFromDist, self).__init__(**kwargs)
if distribution:
self.dist = distribution
else:
self.dist = UniformDistribution()
def initialize(self, num_new_particles, spill, data_arrays,
substance=None):
'Update values of "rise_vel" data array for new particles'
self.dist.set_values(data_arrays['rise_vel'][-num_new_particles:])
class InitRiseVelFromDist(DistributionBase):
_state = copy.deepcopy(DistributionBase._state)
def __init__(self, distribution=None, **kwargs):
"""
Set the rise velocity parameters to be sampled from a distribution.
Use distribution to define rise_vel
:param distribution: An object capable of generating a probability
distribution.
:type distribution: Right now, we have:
- UniformDistribution
- NormalDistribution
- LogNormalDistribution
- WeibullDistribution
New distribution classes could be made. The only
requirement is they need to have a set_values()
method which accepts a NumPy array.
(presumably, this function will also modify
the array in some way)
"""
super(InitRiseVelFromDist, self).__init__(**kwargs)
if distribution:
self.distribution = distribution
else:
self.distribution = UniformDistribution()
self.array_types.add('rise_vel')
self.name = 'rise_vel'
def initialize(self,
num_new_particles,
spill,
data_arrays,
substance=None):
'Update values of "rise_vel" data array for new particles'
self.distribution.set_values(
data_arrays['rise_vel'][-num_new_particles:])
class InitRiseVelFromDropletSizeFromDist(InitBaseClass, Serializable):
_state = copy.deepcopy(InitBaseClass._state)
def __init__(self, distribution=None,
water_density=1020.0, water_viscosity=1.0e-6,
**kwargs):
"""
Set the droplet size from a distribution. Use the C++ get_rise_velocity
function exposed via cython (rise_velocity_from_drop_size) to obtain
rise_velocity from droplet size. Even though the droplet size is not
changing over time, it is still stored in data array, as it can be
useful for post-processing (called 'droplet_diameter')
Use distribution to define rise_vel
:param distribution: An object capable of generating a probability
distribution.
:type distribution: Right now, we have:
- UniformDistribution
- NormalDistribution
- LogNormalDistribution
- WeibullDistribution
New distribution classes could be made. The only
requirement is they need to have a set_values()
method which accepts a NumPy array.
(presumably, this function will also modify
the array in some way)
:param water_density: 1020.0 [kg/m3]
:type water_density: float
:param water_viscosity: 1.0e-6 [m^2/s]
:type water_viscosity: float
"""
super(InitRiseVelFromDropletSizeFromDist, self).__init__(**kwargs)
if distribution:
self.dist = distribution
else:
self.dist = UniformDistribution()
self.water_viscosity = water_viscosity
self.water_density = water_density
def initialize(self, num_new_particles, spill, data_arrays, substance):
"""
Update values of 'rise_vel' and 'droplet_diameter' data arrays for
new particles. First create a droplet_size array sampled from specified
distribution, then use the cython wrapped (C++) function to set the
'rise_vel' based on droplet size and properties like LE_density,
water density and water_viscosity:
gnome.cy_gnome.cy_rise_velocity_mover.rise_velocity_from_drop_size()
"""
drop_size = np.zeros((num_new_particles, ), dtype=np.float64)
le_density = np.zeros((num_new_particles, ), dtype=np.float64)
self.dist.set_values(drop_size)
data_arrays['droplet_diameter'][-num_new_particles:] = drop_size
le_density[:] = substance.density
# now update rise_vel with droplet size - dummy for now
rise_velocity_from_drop_size(
data_arrays['rise_vel'][-num_new_particles:],
le_density, drop_size,
self.water_viscosity, self.water_density)
class InitRiseVelFromDropletSizeFromDist(DistributionBase):
_state = copy.deepcopy(DistributionBase._state)
def __init__(self,
distribution=None,
water_density=1020.0,
water_viscosity=1.0e-6,
**kwargs):
"""
Set the droplet size from a distribution. Use the C++ get_rise_velocity
function exposed via cython (rise_velocity_from_drop_size) to obtain
rise_velocity from droplet size. Even though the droplet size is not
changing over time, it is still stored in data array, as it can be
useful for post-processing (called 'droplet_diameter')
Use distribution to define rise_vel
:param distribution: An object capable of generating a probability
distribution.
:type distribution: Right now, we have:
- UniformDistribution
- NormalDistribution
- LogNormalDistribution
- WeibullDistribution
New distribution classes could be made. The only
requirement is they need to have a set_values()
method which accepts a NumPy array.
(presumably, this function will also modify
the array in some way)
:param water_density: 1020.0 [kg/m3]
:type water_density: float
:param water_viscosity: 1.0e-6 [m^2/s]
:type water_viscosity: float
"""
super(InitRiseVelFromDropletSizeFromDist, self).__init__(**kwargs)
if distribution:
self.distribution = distribution
else:
self.distribution = UniformDistribution()
self.water_viscosity = water_viscosity
self.water_density = water_density
self.array_types.update(('rise_vel', 'droplet_diameter'))
self.name = 'rise_vel'
def initialize(self, num_new_particles, spill, data_arrays, substance):
"""
Update values of 'rise_vel' and 'droplet_diameter' data arrays for
new particles. First create a droplet_size array sampled from specified
distribution, then use the cython wrapped (C++) function to set the
'rise_vel' based on droplet size and properties like LE_density,
water density and water_viscosity:
gnome.cy_gnome.cy_rise_velocity_mover.rise_velocity_from_drop_size()
"""
drop_size = np.zeros((num_new_particles, ), dtype=np.float64)
le_density = np.zeros((num_new_particles, ), dtype=np.float64)
self.distribution.set_values(drop_size)
data_arrays['droplet_diameter'][-num_new_particles:] = drop_size
le_density[:] = substance.get_density()
# now update rise_vel with droplet size - dummy for now
rise_velocity_from_drop_size(
data_arrays['rise_vel'][-num_new_particles:], le_density,
drop_size, self.water_viscosity, self.water_density)
