def main(argv):
if len(argv) == 2:
# this is in case hiding the base yaml is wanted
base_config = 'base.yaml'
input_yaml = argv[1]
elif len(argv) == 3:
base_config = argv[1]
input_yaml = argv[2]
else:
print(len(argv))
print("Usage: main <yaml file>")
sys.exit()
name = input_yaml.split("/")[-1].split(".")[0]
with open(input_yaml, 'r') as f:
config_dict = yaml.safe_load(f)
print(config_dict)
run_dict = config_dict
try:
run_dict['class_name'] = Estimator._find_subclass(name)
except KeyError:
raise ValueError(f"Class name {name} for PZ code is not defined")
code = Estimator._find_subclass(name)
print(f"code name: {code}")
pz = code(base_config, run_dict)
pz.inform()
outf = initialize_writeout(pz.saveloc, pz.num_rows, pz.nzbins)
for start, end, data in iter_chunk_hdf5_data(pz.testfile, pz._chunk_size,
'photometry'):
pz_dict = pz.estimate(data)
write_out_chunk(outf, pz_dict, start, end)
print("finished " + name)
finalize_writeout(outf, pz.zgrid)
print("finished")
def main(argv):
if len(argv) == 2:
# this is in case hiding the base yaml is wanted
input_yaml = argv[1]
base_config = 'base.yaml'
elif len(argv) == 3:
input_yaml = argv[1]
base_config = argv[2]
else:
print(len(argv))
print("Usage: main <config yaml file> [base config yaml]")
sys.exit()
with open(input_yaml, 'r') as f:
run_dict = yaml.safe_load(f)
name = run_dict['run_params']['class_name']
try:
Estimator._find_subclass(name)
except KeyError:
raise ValueError(f"Class name {name} for PZ code is not defined")
code = Estimator._find_subclass(name)
print(f"code name: {name}")
pz = code(base_config, run_dict)
pz.inform_dict = run_dict['run_params']['inform_options']
if pz.inform_dict['load_model']:
# note: specific options set in subclasss func def
pz.load_pretrained_model()
else:
trainfile = pz.trainfile
train_fmt = trainfile.split(".")[-1]
training_data = load_training_data(trainfile, train_fmt, pz.groupname)
pz.inform(training_data)
if 'run_name' in run_dict['run_params']:
outfile = run_dict['run_params']['run_name'] + '.hdf5'
tmpfile = "temp_" + outfile
else:
outfile = 'output.hdf5'
if pz.output_format == 'qp':
tmploc = os.path.join(pz.outpath, name, tmpfile)
outfile = run_dict['run_params']['run_name'] + "_qp.hdf5"
saveloc = os.path.join(pz.outpath, name, outfile)
if pz.output_format == 'qp':
initialize_qp_output(saveloc)
else:
outf = initialize_writeout(saveloc, pz.num_rows, pz.nzbins)
for chunk, (start, end, data) in enumerate(
iter_chunk_hdf5_data(pz.testfile, pz._chunk_size, 'photometry')):
pz_data_chunk = pz.estimate(data)
if pz.output_format == 'qp':
write_qp_output_chunk(tmploc, saveloc, pz_data_chunk, chunk)
else:
write_out_chunk(outf, pz_data_chunk, start, end)
print("writing " + name + f"[{start}:{end}]")
num_chunks = end // pz._chunk_size
if end % pz._chunk_size > 0:
num_chunks += 1
if pz.output_format == 'qp':
qp_reformat_output(tmploc, saveloc, num_chunks)
else:
finalize_writeout(outf, pz.zgrid)
print("finished")
def test_find_subclass():
_ = Estimator._find_subclass('randomPZ')