def add_product(ops_list, reduce_list, cleaner_list, grid_data,
product_metadata):
product_type = product_metadata[PRODUCT_TYPE]
#allocate space for the gridded result and initialize with max int
metadata = {'units': 'm', 'long_name': 'maximum on shore oil thickness'}
reduce_array = grid_data.allocate(product_name,
DT.SPRECISION,
metadata=metadata)
# Make a temporary array that will be used in the operation
map_array = reduce_array.copy()
# Calculate cell diagonal:
cell_diagonal = grid_data._grid.cell_diagonal
coroutine = max_shore_grid_thickness_op(map_array, cell_diagonal)
# Append the operation coroutine to the list
ops_list.append(coroutine)
# use the array allocated in grid data as the result in the reduce method
coroutine = max_reduce(reduce_array)
# append the reduce coroutine and the argument it will be passed to the reduce list
reduce_list.append((coroutine, map_array))
# Now add a reset value for the map array
cleaner_list.append((map_array, DT.SPRECISION(0.0)))
def add_product(ops_list, reduce_list, cleaner_list, grid_data,
product_metadata):
product_type = product_metadata[PRODUCT_TYPE]
config = get_config()
# This method now uses a quadratic index space
cell_depth_range = config[
product_type].products.max_concentration.cell_depth_range
#allocate space for the gridded result
metadata = {'units': 'g/cm^3', 'long_name': 'Maximum Oil Concentration'}
reduce_array = grid_data.allocate(product_name,
DT.SPRECISION,
metadata=metadata)
# Calculate cell area:
cell_area = grid_data._grid.cell_area # m**2
# Make a temporary array that will be used in the operation
map_array = reduce_array.copy()
coroutine = max_concentration_op(map_array, cell_depth_range, cell_area)
# Append the operation coroutine to the list
ops_list.append(coroutine)
coroutine = max_reduce(reduce_array)
reduce_list.append((coroutine, map_array))
# Now add a reset value for the map array
cleaner_list.append((map_array, DT.SPRECISION(0.0)))
def make_particles(self, iteration=0):
particles = numpy.zeros((self.blocksize, ), dtype=DT.IDEAL_PARTICLE)
# Grrr - no way to pass the buffer to put random numbers in ?
particles['loc'] = util.random_sample(particles['loc'].shape,
dtype=DT.PRECISION)
particles['prev_loc'] = util.random_sample(particles['loc'].shape,
dtype=DT.PRECISION)
particles[
'lifetime'][:] = ( # Cheat and make time a function of block number
DT.SPRECISION(iteration))
mass_offset = DT.SPRECISION(0.9)
mass_scale = DT.SPRECISION(.1)
mass_time_param = DT.SPRECISION(1) / numpy.log10(
DT.SPRECISION(iteration + 2))
# 1/log10(i+2) * (rand[0.9 - 1.0) ) )
particles['mass'] = (
mass_time_param *
(util.random_sample(*particles['mass'].shape) * mass_scale +
mass_offset))
dens_offset = DT.SPRECISION(0.9)
dens_scale = DT.SPRECISION(.1)
# dens = rand[0.9 - 1.0)
particles['density'] = (
numpy.random.rand(*particles['density'].shape) * dens_scale +
dens_offset)
return particles
def add_product(ops_list, reduce_list, cleaner_list, grid_data,
product_metadata):
product_type = product_metadata[PRODUCT_TYPE]
#allocate space for the gridded result
metadata = {
'units': 'm',
'long_name': 'Maximum of spillet thickness',
'fill_value': DT.SPRECISION(0.0)
}
reduce_array = grid_data.allocate(product_name,
DT.SPRECISION,
metadata=metadata)
# Calculate cell area:
cell_area = grid_data._grid.cell_area
# Make a temporary array that will be used in the operation
map_array = reduce_array.copy()
#config = get_config()
#advective_dispersion = config[product_type].products.max_of_spillet_thickness.get('advective_dispersion',numpy.nan)
#spillet_dispersion = advective_dispersion * 0.2 # m^2/s
#coroutine = thickest_spillet_op(map_array, spillet_dispersion)
coroutine = thickest_spillet_op(map_array)
# Append the operation coroutine to the list
ops_list.append(coroutine)
coroutine = max_reduce(reduce_array)
reduce_list.append((coroutine, map_array))
# Now add a reset value for the map array
cleaner_list.append((map_array, DT.SPRECISION(0.0)))
def add_product(ops_list, reduce_list, cleaner_list, grid_data,
product_metadata):
product_type = product_metadata[PRODUCT_TYPE]
#allocate space for the gridded result
metadata = {'units': 'm^3', 'long_name': 'Aggregate Oil Volume'}
reduce_array = grid_data.allocate(product_name,
DT.SPRECISION,
metadata=metadata)
# Make a temporary array that will be used in the operation
map_array = reduce_array.copy()
coroutine = oil_volume_op(map_array)
# Append the operation coroutine to the list
ops_list.append(coroutine)
coroutine = max_reduce(reduce_array)
reduce_list.append((coroutine, map_array))
# Now add a reset value for the map array
cleaner_list.append((map_array, DT.SPRECISION(0.0)))
