def generate_genes():
"""
Generates genes for a new individual to be placed within a population.
:return:
new individual: list
list of WRF model physics parameters
"""
new_individual = wrfparams.flexible_generate()
return new_individual
def seed_initial_population(input_csv):
"""
Reads the input csv file, which contains the dates and/or parameter combinations
that you would like to see in the initial population. The CSV file should be
formatted as follows (i.e., this function looks for these column names):
Line 1: start_date, mp_physics, ra_lw_physics, ra_sw_physics, sf_surface_physics,
bl_pbl_physics, cu_physics
Line 2: Feb 28 2011, 1, 24, 99, 4, 11, 2
Line 3: Aug 31 2011, 28, 4, 5, 2, 10, 93
Line 4: Dec 10 2011, 7, 24, 3, 4, 9, 4
Line 5: Jul 22 2011, 11, 24, 2, 1, 4, 10
Line 6: May 23 2011, 1, 4, 5, 2, 10, 93
Line 7: Mar 31 2011, 28, 24, 99, 4, 11, 2
.
.
.
:param input_csv: string
defines the file name from which dates/parameters will be read.
:return intial population: list of Chromosome instances
made from the information specified in the input file.
"""
initial_population = []
try:
with open(input_csv, newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
start_date = row['start_date']
param_ids = wrfparams.flexible_generate(generate_params=False,
mp=int(row['mp_physics']), lw=int(row['ra_lw_physics']),
sw=int(row['ra_sw_physics']), lsm=int(row['sf_surface_physics']),
pbl=int(row['bl_pbl_physics']), cu=int(row['cu_physics']))
start_date, end_date = simplega.generate_random_dates(input_start_date=start_date)
initial_population.append(Chromosome(param_ids, start_date, end_date))
except IOError:
print(f'IOEror in seed_initial_population: {input_csv} does not exist.')
return initial_population
return initial_population
def test_update_sim():
"""Checks to make sure that entries in the database can be updated."""
# Generate a random set of parameters, a random start date, and a Chromosome
r_start_date, r_end_date = sga.generate_random_dates()
r_param_ids = wp.flexible_generate()
individual = sga.Chromosome(r_param_ids,
fitness=100,
start_date=r_start_date,
end_date=r_end_date)
# Put individual in the database
db_conn = conn_to_db('optwrf_repeat.db')
owp.insert_sim(individual, db_conn)
print_database(db_conn)
# Generate a new random start date and a Chromosome
r_start_date, r_end_date = sga.generate_random_dates()
individual = sga.Chromosome(r_param_ids,
fitness=50,
start_date=r_start_date,
end_date=r_end_date)
# Update the individual in the database database
owp.update_sim(individual, db_conn)
print_database(db_conn)
else:
setup_yaml = 'dirpath.yml'
# Set the number of domains to user specification, or default to a single domain.
if args.d is not None and args.d > 0:
n_domains = int(args.d)
else:
n_domains = 3
# Format the forecast start/end and determine the total time.
start_date = args.s
end_date = args.e
# Generate a parameter combination of the 6 core parameters if the user has specified this option.
# Otherwise, use specified input parameters and use defaults for the remaining paramters.
param_ids = flexible_generate(generate_params, mp, lw, sw, lsm, pbl, cu,
in_yaml)
wrf_sim = WRFModel(param_ids,
start_date,
end_date,
bc_data=bc_data,
n_domains=n_domains,
setup_yaml=setup_yaml)
# Next, get boundary condition data for the simulation
# ERA is the only supported data type right now.
vtable_sfx = wrf_sim.get_bc_data()
# Setup the working directory to run the simulation
wrf_sim.wrfdir_setup(vtable_sfx)