if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("model_name", type=str, help="The model name.")
parsed_args = parser.parse_args()
model_name = parsed_args.model_name
# Load best sample
grid = ParameterGrid(model_name)
# Make directory for output
directory = os.path.join(grid.directory, "faux")
os.makedirs(directory, exist_ok=True)
# Calculate metrics
for i in range(NITERS):
filename = os.path.join(directory, f"metrics_{i}.csv")
if os.path.isfile(filename):
continue
grid.logger.debug(f"Iteration {i+1} of {NITERS}.")
dff = grid.make_faux_observations()
grid.evaluate(dfo=dff,
filename=filename,
nproc=NPROC,
metrics_ignore=METRICS_IGNORE)
""" Code to evaluate a grid, replacing whatever metrics have already been calculated """
from udgsizes.fitting.grid import ParameterGrid
if __name__ == "__main__":
pop_name = "blue"
metrics_ignore = ["kstest_2d"] # Skip because it takes ages
nproc = 4
p = ParameterGrid(pop_name)
p.evaluate(metrics_ignore=metrics_ignore, nproc=nproc)
model_name = "blue_sedgwick_shen_highkink"
model_type = "udgsizes.model.sm_size.Model"
config = get_config()
config["grid"][model_type]["parameters"]["rec_phys_offset"]["alpha"][
"max"] = 0.6
config["grid"][model_type]["parameters"]["rec_phys_offset"]["alpha"][
"step"] = 0.05
config["grid"][model_type]["parameters"]["logmstar"]["a"]["min"] = -1.50
config["grid"][model_type]["parameters"]["logmstar"]["a"]["max"] = -1.45
config["grid"][model_type]["parameters"]["logmstar"]["a"]["step"] = 0.05
metrics_ignore = ["kstest_2d"] # Takes too long for whole grid
n_samples = 500
burnin = 250
grid = ParameterGrid(model_name, config=config)
if CHECK_INITIAL_VALUES:
grid.check_initial_values()
if SAMPLE:
grid.sample(overwrite=True, n_samples=n_samples, burnin=burnin)
grid.evaluate(metrics_ignore=metrics_ignore)
dfm = grid.load_metrics()
if MAKEPLOTS:
grid.summary_plot()
plt.show(block=False)