def classify(self):
new_hash = self.get_hash_from_string(self.name)
if self._check_regenerate(new_hash):
mkdirs(self.output_dir)
input = self.get_fit_dependency()
fitres_file = os.path.join(input["fitres_dirs"][self.index],
input["fitopt_map"][self.fitopt])
self.logger.debug(f"Looking for {fitres_file}")
if not os.path.exists(fitres_file):
self.logger.error(
f"FITRES file could not be found at {fitres_file}, classifer has nothing to work with"
)
self.passed = False
return False
df = pd.read_csv(fitres_file, delim_whitespace=True, comment="#")
df = df[[
"CID", "FITPROB"
]].rename(columns={"FITPROB": self.get_prob_column_name()})
self.logger.info(f"Saving probabilities to {self.output_file}")
df.to_csv(self.output_file, index=False, float_format="%0.4f")
chown_dir(self.output_dir)
with open(self.done_file, "w") as f:
f.write("SUCCESS")
self.save_new_hash(new_hash)
self.passed = True
return True
def classify(self, force_refresh, command):
format_dict = {
"job_name": self.job_base_name,
"conda_env": self.conda_env,
"path_to_classifier": self.path_to_classifier,
"command_opts": command,
"done_file": self.done_file,
}
slurm_script = self.slurm.format(**format_dict)
old_hash = self.get_old_hash()
new_hash = self.get_hash_from_string(slurm_script)
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
slurm_output_file = self.output_dir + "/job.slurm"
with open(slurm_output_file, "w") as f:
f.write(slurm_script)
self.save_new_hash(new_hash)
self.logger.info(f"Submitting batch job {slurm_output_file}")
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
self.should_be_done()
return True
def write_input(self, force_refresh):
self.bias_cor_fits = ",".join([m.output["fitres_file"] for m in self.merged_iasim])
self.cc_prior_fits = ",".join([m.output["fitres_file"] for m in self.merged_ccsim])
self.data = [m.output["fitres_dir"] for m in self.merged_data]
self.set_property("simfile_biascor", self.bias_cor_fits)
self.set_property("simfile_ccprior", self.cc_prior_fits)
self.set_property("varname_pIa", self.probability_column_name)
final_output = "\n".join(self.base)
new_hash = self.get_hash_from_string(final_output)
old_hash = self.get_old_hash()
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating results")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
with open(self.config_path, "w") as f:
f.writelines(final_output)
self.logger.info(f"Input file written to {self.config_path}")
self.save_new_hash(new_hash)
return True
else:
self.logger.debug("Hash check passed, not rerunning")
return False
def _run(self, force_refresh):
command_string = self.clump_command.format(genversion=self.genversion,
data_path=self.data_path)
format_dict = {
"job_name": self.job_name,
"log_file": self.logfile,
"path_to_task": self.path_to_task,
"done_file": self.done_file
}
final_slurm = self.slurm.format(**format_dict)
new_hash = self.get_hash_from_string(command_string + final_slurm)
old_hash = self.get_old_hash()
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating and launching task")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.save_new_hash(new_hash)
slurm_output_file = os.path.join(self.output_dir, "slurm.job")
clump_file = os.path.join(self.output_dir, "clump.nml")
with open(slurm_output_file, "w") as f:
f.write(final_slurm)
with open(clump_file, "w") as f:
f.write(command_string)
self.logger.info(f"Submitting batch job for data prep")
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
else:
self.should_be_done()
self.logger.info("Hash check passed, not rerunning")
return True
def write_nml(self, force_refresh):
# Parse config, first SNLCINP and then FITINP
for key, value in self.config.get("SNLCINP", {}).items():
self.set_snlcinp(key, value)
for key, value in self.config.get("FITINP", {}).items():
self.set_fitinp(key, value)
for key, value in self.options.items():
self.set_property(key,
value,
assignment=": ",
section_end="&SNLCINP")
if self.sim_task.output["ranseed_change"]:
self.set_property("VERSION",
self.sim_version + "-0*",
assignment=": ",
section_end="&SNLCINP")
else:
self.set_property("VERSION",
self.sim_version,
assignment=": ",
section_end="&SNLCINP")
self.set_property("OUTDIR",
self.lc_output_dir,
assignment=": ",
section_end="&SNLCINP")
self.set_property("DONE_STAMP",
"FINISHED.DONE",
assignment=": ",
section_end="&SNLCINP")
if isinstance(self.sim_task, DataPrep):
self.set_snlcinp("PRIVATE_DATA_PATH",
f"'{self.sim_task.output['data_path']}'")
self.set_snlcinp("VERSION_PHOTOMETRY",
f"'{self.sim_task.output['genversion']}'")
# We want to do our hashing check here
string_to_hash = self.fitopts + self.base
new_hash = self.get_hash_from_string("".join(string_to_hash))
old_hash = self.get_old_hash()
regenerate = force_refresh or (old_hash is None
or old_hash != new_hash)
if regenerate:
self.logger.info(f"Running Light curve fit. Removing output_dir")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
# Write main file
with open(self.config_path, "w") as f:
f.writelines(map(lambda s: s + "\n", string_to_hash))
self.logger.info(f"NML file written to {self.config_path}")
self.save_new_hash(new_hash)
chown_dir(self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
return regenerate, new_hash
def classify(self):
new_hash = self.get_hash_from_string(self.name +
f"{self.prob_ia}_{self.prob_cc}")
if self._check_regenerate(new_hash):
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
try:
name = self.get_prob_column_name()
cid = "CID"
s = self.get_simulation_dependency()
df = None
phot_dir = s.output["photometry_dirs"][self.index]
headers = [
os.path.join(phot_dir, a) for a in os.listdir(phot_dir)
if "HEAD" in a
]
if not headers:
Task.fail_config(
f"No HEAD fits files found in {phot_dir}!")
else:
types = self.get_simulation_dependency(
).output["types_dict"]
self.logger.debug(f"Input types are {types}")
for h in headers:
with fits.open(h) as hdul:
data = hdul[1].data
snid = np.array(data.field("SNID"))
sntype = np.array(data.field("SNTYPE")).astype(
np.int64)
is_ia = np.isin(sntype, types["IA"])
prob = (is_ia * self.prob_ia) + (~is_ia *
self.prob_cc)
dataframe = pd.DataFrame({cid: snid, name: prob})
dataframe[cid] = dataframe[cid].apply(str)
dataframe[cid] = dataframe[cid].str.strip()
if df is None:
df = dataframe
else:
df = pd.concat([df, dataframe])
df.drop_duplicates(subset=cid, inplace=True)
self.logger.info(f"Saving probabilities to {self.output_file}")
df.to_csv(self.output_file, index=False, float_format="%0.4f")
chown_dir(self.output_dir)
with open(self.done_file, "w") as f:
f.write("SUCCESS")
self.save_new_hash(new_hash)
except Exception as e:
self.logger.exception(e, exc_info=True)
self.passed = False
with open(self.done_file, "w") as f:
f.write("FAILED")
return False
else:
self.should_be_done()
self.passed = True
return True
def get_ini_file(self):
mkdirs(self.chain_dir)
directory = self.create_cov_dep.output["ini_dir"]
self.logger.debug(f"Directory: {directory}")
input_files = []
for file in self.ini_files:
path = os.path.join(directory, file)
self.logger.debug(f"Path: {path}")
if not os.path.exists(path):
self.logger.error(
f"Cannot find the file {path}, make sure you specified a correct INI string matching an existing template"
)
return None
self.logger.debug(f"Reading in {path} to format")
with open(path) as f:
input_files.append(f.read().format(
**{
"path_to_cosmomc": self.path_to_cosmomc,
"ini_dir": self.create_cov_dep.output["ini_dir"],
"root_dir": self.chain_dir
}))
self.logger.debug(f"Input Files: {input_files}")
return input_files
def classify(self, force_refresh):
new_hash = self.check_regenerate(force_refresh)
if new_hash:
mkdirs(self.output_dir)
input = self.get_fit_dependency()
fitres_file = input["fitres_file"]
self.logger.debug(f"Looking for {fitres_file}")
if not os.path.exists(fitres_file):
self.logger.error(
f"FITRES file could not be found at {fitres_file}, classifer has nothing to work with"
)
self.passed = False
return False
df = pd.read_csv(fitres_file,
sep='\s+',
comment="#",
compression="infer")
df = df[[
"CID", "FITPROB"
]].rename(columns={"FITPROB": self.get_prob_column_name()})
self.logger.info(f"Saving probabilities to {self.output_file}")
df.to_csv(self.output_file, index=False, float_format="%0.4f")
chown_dir(self.output_dir)
with open(self.done_file, "w") as f:
f.write("SUCCESS")
self.save_new_hash(new_hash)
self.passed = True
return True
def classify(self, command):
self.setup()
if self.batch_file is None:
if self.gpu:
self.sbatch_header = self.sbatch_gpu_header
else:
self.sbatch_header = self.sbatch_cpu_header
else:
with open(self.batch_file, 'r') as f:
self.sbatch_header = f.read()
self.sbatch_header = self.clean_header(self.sbatch_header)
header_dict = {
"REPLACE_NAME": self.job_base_name,
"REPLACE_LOGFILE": "output.log",
"REPLACE_WALLTIME": "00:55:00",
"REPLACE_MEM": "8GB",
"APPEND": ["#SBATCH --ntasks=1", "#SBATCH --cpus-per-task=4"]
}
header_dict = merge_dict(header_dict, self.batch_replace)
self.update_header(header_dict)
setup_dict = {
"job_name": self.job_base_name,
"conda_env": self.conda_env,
"path_to_classifier": self.path_to_classifier,
"command_opts": command
}
format_dict = {
"done_file":
self.done_file,
"sbatch_header":
self.sbatch_header,
"task_setup":
self.update_setup(setup_dict, self.task_setup['nearest_neighbour'])
}
slurm_script = self.slurm.format(**format_dict)
new_hash = self.get_hash_from_string(slurm_script)
if self._check_regenerate(new_hash):
self.logger.debug("Regenerating")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
slurm_output_file = self.output_dir + "/job.slurm"
with open(slurm_output_file, "w") as f:
f.write(slurm_script)
self.save_new_hash(new_hash)
self.logger.info(f"Submitting batch job {slurm_output_file}")
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
self.should_be_done()
return True
def write_nml(self, force_refresh):
self.logger.debug(f"Loading fitopts file from {self.fitopts_file}")
with open(self.fitopts_file, "r") as f:
self.fitopts = list(f.read().splitlines())
self.logger.info(
f"Loaded {len(self.fitopts)} fitopts file from {self.fitopts_file}"
)
# Parse config, first SNLCINP and then FITINP
for key, value in self.config.get("SNLCINP", {}).items():
self.set_snlcinp(key, value)
for key, value in self.config.get("FITINP", {}).items():
self.set_fitinp(key, value)
self.set_property(
"VERSION",
self.sim_version + "*",
assignment=": ",
section_end="&SNLCINP") # TODO FIX THIS, DOUBLE VERSION KEY
self.set_property("OUTDIR",
self.lc_output_dir,
assignment=": ",
section_end="&SNLCINP")
if isinstance(self.sim_task, DataPrep):
self.set_snlcinp("PRIVATE_DATA_PATH",
f"'{self.sim_task.output['data_path']}'")
self.set_snlcinp("VERSION_PHOTOMETRY",
f"'{self.sim_task.output['genversion']}'")
# We want to do our hashing check here
string_to_hash = self.fitopts + self.base
# with open(os.path.abspath(inspect.stack()[0][1]), "r") as f:
# string_to_hash += f.read()
new_hash = self.get_hash_from_string("".join(string_to_hash))
old_hash = self.get_old_hash()
regenerate = force_refresh or (old_hash is None
or old_hash != new_hash)
if regenerate:
self.logger.info(f"Running Light curve fit. Removing output_dir")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
# Write main file
with open(self.config_path, "w") as f:
f.writelines(map(lambda s: s + '\n', string_to_hash))
self.logger.info(f"NML file written to {self.config_path}")
self.save_new_hash(new_hash)
chown_dir(self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
return regenerate, new_hash
def predict(self, force_refresh):
train_info = self.get_fit_dependency()
model = self.options.get("MODEL")
assert model is not None, "If TRAIN is not specified, you have to point to a model to use"
for t in self.dependencies:
if model == t.name:
self.logger.debug(
f"Found task dependency {t.name} with model file {t.output['model_filename']}"
)
model = t.output["model_filename"]
model_path = get_output_loc(model)
self.logger.debug(f"Looking for model in {model_path}")
if not os.path.exists(model_path):
self.logger.error(f"Cannot find {model_path}")
return False
old_hash = self.get_old_hash()
new_hash = self.get_hash_from_string(self.name + model_path)
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating")
if os.path.exists(self.output_dir):
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.save_new_hash(new_hash)
job_name = 'nearnbr_apply.exe'
inArgs = f'-inFile_data {train_info["fitres_file"]} -inFile_MLpar {model_path}'
outArgs = f'-outFile {self.outfile_predict} -varName_prob {self.get_prob_column_name()}'
cmd_job = ('%s %s %s' % (job_name, inArgs, outArgs))
self.logger.debug(f"Executing command {cmd_job}")
with open(self.logging_file, "w") as f:
val = subprocess.run(cmd_job.split(" "),
stdout=f,
stderr=subprocess.STDOUT,
cwd=self.output_dir)
with open(self.done_file, "w") as f:
if val.returncode == 0:
f.write("SUCCESS")
else:
f.write("FAILURE")
else:
self.logger.debug("Not regenerating")
return True
def write_nml(self):
# Parse config, first SNLCINP and then FITINP
for key, value in self.config.get("SNLCINP", {}).items():
self.set_snlcinp(key, value)
for key, value in self.config.get("FITINP", {}).items():
self.set_fitinp(key, value)
for key, value in self.options.items():
#print(key,value)
self.yaml["CONFIG"][key] = value
self.compute_fitopts()
if self.sim_task.output["ranseed_change"]:
self.yaml["CONFIG"]["VERSION"] = [self.sim_version + "-0*"]
else:
self.yaml["CONFIG"]["VERSION"] = [self.sim_version]
self.yaml["CONFIG"]["OUTDIR"] = self.lc_output_dir
# self.yaml["CONFIG"]["DONE_STAMP"] = "ALL.DONE"
if isinstance(self.sim_task, DataPrep):
data_path = self.sim_task.output["data_path"]
if "SNDATA_ROOT/lcmerge" not in data_path:
self.set_snlcinp("PRIVATE_DATA_PATH", f"'{self.sim_task.output['data_path']}'")
self.set_snlcinp("VERSION_PHOTOMETRY", f"'{self.sim_task.output['genversion']}'")
# We want to do our hashing check here
string_to_hash = self.get_output_string()
new_hash = self.get_hash_from_string(string_to_hash)
regenerate = self._check_regenerate(new_hash)
if regenerate:
self.logger.info(f"Running Light curve fit. Removing output_dir")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
# Write main file
# Write the primary input file
self.write_output_file(self.config_path)
self.logger.info(f"NML file written to {self.config_path}")
self.save_new_hash(new_hash)
chown_dir(self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
return regenerate, new_hash
def _run(self):
self.yaml["CONFIG"]["WFITOPT"] = self.wfitopts
self.yaml["CONFIG"]["INPDIR"] = self.create_cov_dirs
self.yaml["CONFIG"]["OUTDIR"] = os.path.join(self.output_dir, "output")
# Pass all OPTS keys through to the yaml dictionary
for k, v in self.options.items():
# Clobber WFITOPTS to WFITOPT
if k == "WFITOPTS":
k = "WFITOPT"
self.yaml["CONFIG"][k] = v
final_output_for_hash = self.get_output_string()
new_hash = self.get_hash_from_string(final_output_for_hash)
if self._check_regenerate(new_hash):
self.logger.debug("Regenerating and launching task")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.save_new_hash(new_hash)
with open(self.input_file, "w") as f:
f.write(self.get_output_string())
cmd = ["submit_batch_jobs.sh", os.path.basename(self.input_file)]
self.logger.debug(
f"Submitting wfit job: {' '.join(cmd)} in cwd: {self.output_dir}"
)
self.logger.debug(f"Logging to {self.logfile}")
with open(self.logfile, 'w') as f:
subprocess.run(' '.join(cmd),
stdout=f,
stderr=subprocess.STDOUT,
cwd=self.output_dir,
shell=True)
chown_dir(self.output_dir)
else:
self.should_be_done()
self.logger.info("Has check passed, not rerunning")
return True
def classify(self):
mkdirs(self.output_dir)
fitres = f"{self.fit_dir}/FITOPT000.FITRES.gz"
self.logger.debug(f"Looking for {fitres}")
if not os.path.exists(fitres):
self.logger.error(
f"FITRES file could not be found at {fitres}, classifer has nothing to work with"
)
return False
data = pd.read_csv(fitres, sep='\s+', comment="#", compression="infer")
ids = data["CID"].values
probability = np.random.uniform(size=ids.size)
combined = np.vstack((ids, probability)).T
output_file = self.output_dir + "/prob.txt"
self.logger.info(f"Saving probabilities to {output_file}")
np.savetxt(output_file, combined)
chown_dir(self.output_dir)
return True # change to hash
def _run(self, force_refresh):
command = self.cmd_prefix + [
self.lc_fit["fitres_file"], self.agg["merge_key_filename"]
] + self.cmd_suffix
old_hash = self.get_old_hash()
new_hash = self.get_hash_from_string(" ".join(command))
if force_refresh or new_hash != old_hash:
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.logger.debug("Regenerating, running combine_fitres")
self.save_new_hash(new_hash)
with open(self.logfile, "w") as f:
subprocess.run(command,
stdout=f,
stderr=subprocess.STDOUT,
cwd=self.output_dir)
else:
self.logger.debug("Not regnerating")
return True
def run(args):
# Load YAML config file
yaml_path = os.path.abspath(os.path.expandvars(args.yaml))
assert os.path.exists(yaml_path), f"File {yaml_path} cannot be found."
with open(yaml_path, "r") as f:
config = yaml.safe_load(f)
overwrites = config.get("GLOBAL")
if config.get("GLOBALS") is not None:
logging.warning(
"Your config file has a GLOBALS section in it. If you're trying to overwrite cfg.yml, rename this to GLOBAL"
)
global_config = get_config(initial_path=args.config, overwrites=overwrites)
config_filename = os.path.basename(args.yaml).split(".")[0].upper()
output_dir = get_output_dir()
logging_folder = os.path.abspath(os.path.join(output_dir, config_filename))
if not args.check:
mkdirs(logging_folder)
message_store, logging_filename = setup_logging(config_filename,
logging_folder, args)
for i, d in enumerate(global_config["DATA_DIRS"]):
logging.debug(f"Data directory {i + 1} set as {d}")
assert d is not None, "Data directory is none, which means it failed to resolve. Check the error message above for why."
manager = Manager(config_filename, yaml_path, config, message_store)
if args.start is not None:
args.refresh = True
manager.set_start(args.start)
manager.set_finish(args.finish)
manager.set_force_refresh(args.refresh)
manager.execute(args.check)
chown_file(logging_filename)
return manager
def _run(self, force_refresh):
sys_scale = self.calculate_input()
format_dict = {
"job_name": self.job_name,
"log_file": self.logfile,
"done_file": self.done_file,
"path_to_code": self.path_to_code,
"input_file": self.input_file,
}
final_slurm = self.slurm.format(**format_dict)
new_hash = self.get_hash_from_string("\n".join(self.base + sys_scale) +
final_slurm)
old_hash = self.get_old_hash()
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating and launching task")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
mkdirs(self.config_dir)
self.save_new_hash(new_hash)
# Write sys scales and the main input file
with open(self.sys_file_out, "w") as f:
f.write("\n".join(sys_scale))
with open(self.input_file, "w") as f:
f.write("\n".join(self.base))
# Write out slurm job script
slurm_output_file = os.path.join(self.output_dir, "slurm.job")
with open(slurm_output_file, "w") as f:
f.write(final_slurm)
self.logger.info(f"Submitting batch job for data prep")
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
else:
self.should_be_done()
self.logger.info("Hash check passed, not rerunning")
return True
def _run(self):
self.output["fitopt_map"] = self.lc_fit["fitopt_map"]
self.output["fitopt_index"] = self.lc_fit["fitopt_index"]
self.output["fitres_file"] = self.lc_fit["fitres_file"]
self.output["SURVEY"] = self.lc_fit["SURVEY"]
self.output["SURVEY_ID"] = self.lc_fit["SURVEY_ID"]
fitres_files, symlink_files = [], []
for index, (fitres_dir, outdir) in enumerate(
zip(self.lc_fit["fitres_dirs"], self.fitres_outdirs)):
files = os.listdir(fitres_dir)
fitres_files += [
(fitres_dir, outdir, f, index, self.lc_fit["name"])
for f in files if "FITRES" in f
and not os.path.islink(os.path.join(fitres_dir, f))
]
symlink_files += [(fitres_dir, outdir, f, index,
self.lc_fit["name"]) for f in files
if "FITRES" in f
and os.path.islink(os.path.join(fitres_dir, f))]
new_hash = self.get_hash_from_string(" ".join([
a + b + c + f"{d}" + e
for a, b, c, d, e in (fitres_files + symlink_files)
]))
if self._check_regenerate(new_hash):
shutil.rmtree(self.output_dir, ignore_errors=True)
self.logger.debug("Regenerating, running combine_fitres")
try:
for fitres_dir in self.fitres_outdirs:
self.logger.debug(f"Creating directory {fitres_dir}")
mkdirs(fitres_dir)
for f in fitres_files:
if f[1] == fitres_dir:
self.add_to_fitres(os.path.join(f[0], f[2]),
f[1],
f[4],
index=f[3])
for s in symlink_files:
if s[1] == fitres_dir:
self.logger.debug(
f"Creating symlink for {os.path.join(s[1], s[2])} to {os.path.join(s[1], 'FITOPT000.FITRES.gz')}"
)
os.symlink(
os.path.join(s[1], "FITOPT000.FITRES.gz"),
os.path.join(s[1], s[2]))
self.logger.debug(f"Copying MERGE.LOG")
filenames = ["MERGE.LOG", "SUBMIT.INFO"]
for f in filenames:
original = os.path.join(self.lc_fit["lc_output_dir"],
f)
moved = os.path.join(self.suboutput_dir, f)
if not os.path.exists(moved):
self.logger.debug(
f"Copying file {f} into output directory")
shutil.copy(original, moved)
self.save_new_hash(new_hash)
with open(self.done_file, "w") as f:
f.write("SUCCESS\n")
except Exception as e:
self.logger.error("Error running merger!")
self.logger.error(f"Check log at {self.logfile}")
self.logger.exception(e, exc_info=True)
return False
else:
self.should_be_done()
self.logger.info("Hash check passed, not rerunning")
return True
def _run(self, force_refresh):
if self.static:
self.logger.info(
"CMB only constraints detected, copying static files")
cosmomc_static_loc = get_data_loc(self.static_path +
self.ini_prefix)
if cosmomc_static_loc is None:
self.logger.error(
"Seems like we can't find the static chains...")
return False
else:
new_hash = self.get_hash_from_string(cosmomc_static_loc)
old_hash = self.get_old_hash()
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating and copying static chains")
shutil.rmtree(self.chain_dir, ignore_errors=True)
shutil.copytree(cosmomc_static_loc, self.chain_dir)
for done_file in self.done_files:
df = os.path.join(self.output_dir, done_file)
with open(df, "w") as f:
f.write("SUCCESS")
self.save_new_hash(new_hash)
else:
self.should_be_done()
self.logger.info("Hash check passed, not rerunning")
else:
ini_filecontents = self.get_ini_file()
if ini_filecontents is None:
return False
format_dict = {
"job_name": self.job_name,
"log_file": self.logfile,
"done_files": " ".join(self.done_files),
"path_to_cosmomc": self.path_to_cosmomc,
"output_dir": self.output_dir,
"ini_files": " ".join(self.ini_files),
"num_jobs": len(self.ini_files),
"num_walkers": self.num_walkers,
}
final_slurm = self.slurm.format(**format_dict)
new_hash = self.get_hash_from_string(final_slurm +
" ".join(ini_filecontents))
old_hash = self.get_old_hash()
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating and launching task")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.save_new_hash(new_hash)
slurm_output_file = os.path.join(self.output_dir, "slurm.job")
with open(slurm_output_file, "w") as f:
f.write(final_slurm)
for file, content in zip(self.ini_files, ini_filecontents):
filepath = os.path.join(self.output_dir, file)
with open(filepath, "w") as f:
f.write(content)
mkdirs(self.chain_dir)
needed_dirs = [
"data", "paramnames", "camb", "batch1", "batch2", "batch3"
]
for d in needed_dirs:
self.logger.debug(f"Creating symlink to {d} dir")
original_data_dir = os.path.join(self.path_to_cosmomc, d)
new_data_dir = os.path.join(self.output_dir, d)
os.symlink(original_data_dir,
new_data_dir,
target_is_directory=True)
self.logger.info(f"Submitting batch job for data prep")
subprocess.run(["sbatch", slurm_output_file],
cwd=self.output_dir)
else:
self.should_be_done()
self.logger.info("Hash check passed, not rerunning")
return True
def execute(self, check_config):
self.logger.info(f"Executing pipeline for prefix {self.prefix}")
self.logger.info(f"Output will be located in {self.output_dir}")
if check_config:
self.logger.info("Only verifying config, not launching anything")
mkdirs(self.output_dir)
c = self.run_config
self.tasks = self.get_tasks(c)
if check_config:
self.logger.notice("Config verified, exiting")
return
self.num_jobs_queue = 0
self.num_jobs_queue_gpu = 0
running_tasks = []
done_tasks = []
failed_tasks = []
blocked_tasks = []
squeue = None
start_sleep_time = self.global_config["OUTPUT"]["ping_frequency"]
max_sleep_time = self.global_config["OUTPUT"]["max_ping_frequency"]
current_sleep_time = start_sleep_time
config_file_output = os.path.join(self.output_dir,
os.path.basename(self.filename_path))
if not check_config and self.filename_path != config_file_output:
self.logger.info(
f"Saving parsed config file from {self.filename_path} to {config_file_output}"
)
shutil.copy(self.filename_path, config_file_output)
chown_file(config_file_output)
# Welcome to the primary loop
while self.tasks or running_tasks:
small_wait = False
# Check status of current jobs
for t in running_tasks:
try:
completed = self.check_task_completion(
t, blocked_tasks, done_tasks, failed_tasks,
running_tasks, squeue)
small_wait = small_wait or completed
except Exception as e:
self.logger.exception(e, exc_info=True)
self.fail_task(t, running_tasks, failed_tasks,
blocked_tasks)
# Submit new jobs if needed
while self.num_jobs_queue < self.max_jobs:
t = self.get_task_to_run(self.tasks, done_tasks)
if t is not None:
self.logger.info("")
self.tasks.remove(t)
self.logger.notice(f"LAUNCHING: {t}")
try:
started = t.run(self.get_force_refresh(t))
except Exception as e:
self.logger.exception(e, exc_info=True)
started = False
if started:
if t.gpu:
self.num_jobs_queue_gpu += t.num_jobs
else:
self.num_jobs_queue += t.num_jobs
self.logger.notice(
f"LAUNCHED: {t} with total {self.num_jobs_queue} jobs"
)
running_tasks.append(t)
completed = self.check_task_completion(
t, blocked_tasks, done_tasks, failed_tasks,
running_tasks, squeue)
small_wait = small_wait or completed
else:
self.logger.error(f"FAILED TO LAUNCH: {t}")
self.fail_task(t, running_tasks, failed_tasks,
blocked_tasks)
small_wait = True
else:
break
# Check quickly if we've added a new job, etc, in case of immediate failure
if small_wait:
self.log_status(self.tasks, running_tasks, done_tasks,
failed_tasks, blocked_tasks)
current_sleep_time = start_sleep_time
time.sleep(0.1)
squeue = None
else:
time.sleep(current_sleep_time)
current_sleep_time *= 2
if current_sleep_time > max_sleep_time:
current_sleep_time = max_sleep_time
squeue = [
i.strip() for i in subprocess.check_output(
f"squeue -h -u $USER -o '%.200j'",
shell=True,
text=True).splitlines()
]
n = len(squeue)
if n == 0 or n > self.max_jobs:
self.logger.debug(
f"Squeue is reporting {n} jobs in the queue... this is either 0 or toeing the line as to too many"
)
self.log_finals(done_tasks, failed_tasks, blocked_tasks)
def write_input(self):
# Load previous hash here if it exists
old_hash = None
hash_file = f"{self.output_dir}/hash.txt"
if os.path.exists(hash_file):
with open(hash_file, "r") as f:
old_hash = f.read().strip()
self.logger.debug(f"Previous result found, hash is {old_hash}")
# Put config in a temp directory
temp_dir_obj = tempfile.TemporaryDirectory()
temp_dir = temp_dir_obj.name
# Copy the base files across
for f in self.base_ia:
shutil.copy(self.data_dir + f, temp_dir)
for f in self.base_cc:
shutil.copy(self.data_dir + f, temp_dir)
# Copy the include input file if there is one
input_copied = []
fs = self.base_ia + self.base_cc
for ff in fs:
if ff not in input_copied:
input_copied.append(ff)
with open(self.data_dir + ff, "r") as f:
for line in f.readlines():
line = line.strip()
if line.startswith("INPUT_FILE_INCLUDE"):
include_file = line.split(":")[-1].strip()
self.logger.debug(f"Copying included file {include_file}")
shutil.copy(self.data_dir + include_file, temp_dir)
# Write the primary input file
main_input_file = f"{temp_dir}/{self.genversion}.input"
with open(main_input_file, "w") as f:
f.writelines(map(lambda s: s + '\n', self.base))
self.logger.info(f"Input file written to {main_input_file}")
# Remove any duplicates and order the output files
output_files = [f"{temp_dir}/{a}" for a in sorted(os.listdir(temp_dir))]
self.logger.debug(f"{len(output_files)} files used to create simulation. Hashing them.")
# Also add this file to the hash, so if the code changes we also regenerate. Smart.
output_files.append(os.path.abspath(inspect.stack()[0][1]))
# Get current hash
string_to_hash = ""
for file in output_files:
with open(file, "r") as f:
string_to_hash += f.read()
new_hash = get_hash(string_to_hash)
self.logger.debug(f"Current hash set to {new_hash}")
regenerate = old_hash is None or old_hash != new_hash
if regenerate:
self.logger.info(f"Running simulation, hash check failed")
# Clean output dir. God I feel dangerous doing this, so hopefully unnecessary check
if "//" not in self.output_dir and "Pippin" in self.output_dir:
self.logger.debug(f"Cleaning output directory {self.output_dir}")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.logger.debug(f"Copying from {temp_dir} to {self.output_dir}")
copytree(temp_dir, self.output_dir)
with open(hash_file, "w") as f:
f.write(str(new_hash))
self.logger.debug(f"New hash saved to {hash_file}")
self.hash_file = hash_file
chown_dir(self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
temp_dir_obj.cleanup()
return regenerate, new_hash
def _run(self, force_refresh):
new_hash = self.check_regenerate(force_refresh)
if new_hash:
mkdirs(self.output_dir)
prediction_files = [
d.output["predictions_filename"] for d in self.classifiers
]
df = None
for f in prediction_files:
dataframe = self.load_prediction_file(f)
dataframe = dataframe.rename(
columns={dataframe.columns[0]: self.id})
if df is None:
df = dataframe
self.logger.debug(
f"Merging on column {self.id} for file {f}")
else:
self.logger.debug(
f"Merging on column {self.id} for file {f}")
df = pd.merge(
df, dataframe, on=self.id, how="outer"
) # Inner join atm, should I make this outer?
if self.include_type:
self.logger.info("Finding original types")
s = self.get_underlying_sim_task()
type_df = None
phot_dir = s.output["photometry_dir"]
headers = [
os.path.join(phot_dir, a) for a in os.listdir(phot_dir)
if "HEAD" in a
]
if not headers:
self.logger.error(
f"Not HEAD fits files found in {phot_dir}!")
else:
for h in headers:
with fits.open(h) as hdul:
data = hdul[1].data
snid = np.array(data.field("SNID")).astype(
np.int64)
sntype = np.array(data.field("SNTYPE")).astype(
np.int64)
dataframe = pd.DataFrame({
self.id: snid,
self.type_name: sntype
})
if type_df is None:
type_df = dataframe
else:
type_df = pd.concat([type_df, dataframe])
df = pd.merge(df, type_df, on=self.id)
if self.plot:
self._plot(df)
self.logger.info(
f"Merged into dataframe of {df.shape[0]} rows, with columns {list(df.columns)}"
)
df.to_csv(self.output_df, index=False, float_format="%0.4f")
self.save_key_format(df)
self.logger.debug(f"Saving merged dataframe to {self.output_df}")
self.save_new_hash(new_hash)
self.output["merge_predictions_filename"] = self.output_df
self.output["merge_key_filename"] = self.output_df_key
self.output["sn_column_name"] = self.id
if self.include_type:
self.output["sn_type_name"] = self.type_name
self.passed = True
return True
def run(args):
if args is None:
return None
init()
# Load YAML config file
yaml_path = os.path.abspath(os.path.expandvars(args.yaml))
assert os.path.exists(yaml_path), f"File {yaml_path} cannot be found."
config_raw, config = load_yaml(yaml_path)
#with open(yaml_path, "r") as f:
# config = yaml.safe_load(f)
overwrites = config.get("GLOBAL")
if config.get("GLOBALS") is not None:
logging.warning(
"Your config file has a GLOBALS section in it. If you're trying to overwrite cfg.yml, rename this to GLOBAL"
)
cfg = None
if config.get("GLOBAL"):
cfg = config.get("GLOBAL").get("CFG_PATH")
if cfg is None:
cfg = args.config
global_config = get_config(initial_path=cfg, overwrites=overwrites)
config_filename = os.path.basename(args.yaml).split(".")[0].upper()
output_dir = get_output_dir()
logging_folder = os.path.abspath(os.path.join(output_dir, config_filename))
if not args.check:
mkdirs(logging_folder)
if os.path.exists(logging_folder):
chown_dir(logging_folder, walk=args.permission)
if args.permission:
return
message_store, logging_filename = setup_logging(config_filename,
logging_folder, args)
for i, d in enumerate(global_config["DATA_DIRS"]):
logging.debug(f"Data directory {i + 1} set as {d}")
assert d is not None, "Data directory is none, which means it failed to resolve. Check the error message above for why."
logging.info(
f"Running on: {os.environ.get('HOSTNAME', '$HOSTNAME not set')} login node."
)
manager = Manager(config_filename, yaml_path, config_raw, config,
message_store)
# Gracefully hand Ctrl-c
def handler(signum, frame):
logging.error("Ctrl-c was pressed.")
logging.warning(
"All remaining tasks will be killed and their hash reset")
manager.kill_remaining_tasks()
exit(1)
signal.signal(signal.SIGINT, handler)
if args.start is not None:
args.refresh = True
manager.set_start(args.start)
manager.set_finish(args.finish)
manager.set_force_refresh(args.refresh)
manager.set_force_ignore_stage(args.ignore)
manager.execute(args.check, args.compress, args.uncompress)
chown_file(logging_filename)
return manager
def prepare_train_job(self, force_refresh):
self.logger.debug("Preparing NML file for Nearest Neighbour training")
fit_output = self.get_fit_dependency()
genversion = fit_output["genversion"]
fitres_dir = fit_output["fitres_dir"]
fitres_file = fit_output["fitres_file"]
nml_file_orig = fit_output["nml_file"]
# Put config in a temp directory
temp_dir_obj = tempfile.TemporaryDirectory()
temp_dir = temp_dir_obj.name
outfile_train = f'{self.name}_train.out'
nml_file_train1 = f'{temp_dir}/{genversion}-2.nml'
nml_file_train2 = f'{self.output_dir}/{genversion}-2.nml'
train_info_local = {
"outfile_NNtrain": outfile_train,
"nml_file_NNtrain": nml_file_train2,
}
# construct sed to copy original NMLFILE and to
# + replace OUTDIR:
# + include ROOTFILE_OUT (to store histograms for NN train)
# + include DONE stamp for Sam/pippen
# + run afterburner to process ROOT file and get NN_trainPar;
# copy NN_trainPar up to where pippin can find it
#
# TODO: Check with Rick if the FITOPT000.ROOT is needed / should be hardcoded
afterBurn = f'nearnbr_maxFoM.exe FITOPT000.ROOT -truetype 1 -outfile {outfile_train} ; cp {outfile_train} {self.outfile_train}'
sedstr = 'sed'
sedstr += (r" -e '/OUTDIR:/a\OUTDIR: %s' " % self.splitfit_output_dir)
sedstr += r" -e '/OUTDIR:/d'"
sedstr += r" -e '/DONE_STAMP:/d'"
sedstr += r" -e '/SNTABLE_LIST/a\ ROOTFILE_OUT = \"bla.root\"'"
sedstr += r" -e '/_OUT/d '"
sedstr += (r" -e '/VERSION:/a\VERSION_AFTERBURNER: %s'" % afterBurn)
sedstr += (r" -e '/VERSION:/a\DONE_STAMP: %s'" % self.done_file)
sed_command = ("%s %s > %s" % (sedstr, nml_file_orig, nml_file_train1))
# use system call to apply sed command
# self.logger.debug(f"Running sed command {sed_command}")
subprocess.run(sed_command,
stderr=subprocess.STDOUT,
cwd=temp_dir,
shell=True)
# make sure that the new NML file is really there
if not os.path.isfile(nml_file_train1):
self.logger.error(
f"Unable to create {nml_file_train1} with sed command {sed_command}"
)
return None
# check that expected FITRES ref file is really there.
if not os.path.exists(fitres_file):
self.logger.error(
'Cannot find expected FITRES file at {fitres_path}')
return None
# open NML file in append mode and tack on NNINP namelist
with open(nml_file_train1, 'a') as f:
f.write("\n# NNINP below added by prepare_NNtrainJob\n")
f.write("\n&NNINP \n")
f.write(" NEARNBR_TRAINFILE_PATH = '%s' \n" % fitres_dir)
f.write(" NEARNBR_TRAINFILE_LIST = '%s' \n" %
os.path.basename(fitres_file))
f.write(" NEARNBR_SEPMAX_VARDEF = '%s' \n" % self.nn_options)
f.write(" NEARNBR_TRUETYPE_VARNAME = 'SIM_TYPE_INDEX' \n")
f.write(" NEARNBR_TRAIN_ODDEVEN = T \n")
f.write("\n&END\n")
input_files = [nml_file_train1]
old_hash = self.get_old_hash()
new_hash = self.get_hash_from_files(input_files)
if force_refresh or new_hash != old_hash:
self.logger.debug("Regenerating")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.logger.debug(f"Copying from {temp_dir} to {self.output_dir}")
copytree(temp_dir, self.output_dir)
self.save_new_hash(new_hash)
return new_hash, train_info_local
else:
self.logger.debug("Not regenerating")
return None, train_info_local
"--finish",
help="Stage to finish at (it runs this stage too)",
default=None)
parser.add_argument("-r",
"--refresh",
help="Refresh all tasks, do not use hash",
action="store_true")
args = parser.parse_args()
level = logging.DEBUG if args.verbose else logging.INFO
# Get base filename
config_filename = os.path.basename(args.config).split(".")[0].upper()
logging_folder = os.path.abspath(
f"{get_config()['OUTPUT']['output_dir']}/{config_filename}")
mkdirs(logging_folder)
logging_filename = f"{logging_folder}/{config_filename}.log"
message_store = MessageStore()
NOTICE_LEVELV_NUM = 25
logging.addLevelName(NOTICE_LEVELV_NUM, "NOTICE")
def notice(self, message, *args, **kws):
if self.isEnabledFor(NOTICE_LEVELV_NUM):
self._log(NOTICE_LEVELV_NUM, message, args, **kws)
logging.Logger.notice = notice
fmt = "[%(levelname)8s |%(filename)21s:%(lineno)3d] %(message)s" if args.verbose else "%(message)s"
logging.basicConfig(level=level,
format=fmt,
handlers=[
def _run(self):
# Get the m0diff files for everything
for b in self.biascor_deps:
for m in b.output["m0dif_dirs"]:
self.logger.info(f"Looking at M0diff dir {m}")
sim_number = 1
if os.path.basename(m).isdigit():
sim_number = int(os.path.basename(m))
files = [
f for f in sorted(os.listdir(m))
if f.endswith(".M0DIF") or f.endswith(".M0DIF.gz")
]
for f in files:
muopt_num = int(f.split("MUOPT")[-1].split(".")[0])
fitopt_num = int(f.split("FITOPT")[-1].split("_")[0])
if muopt_num == 0:
muopt = "DEFAULT"
else:
muopt = b.output["muopts"][muopt_num -
1] # Because 0 is default
if fitopt_num == 0:
fitopt = "DEFAULT"
else:
fitopt = b.output["fitopt_index"][fitopt_num]
self.biascor_m0diffs.append(
(b.name, sim_number, muopt, muopt_num, fitopt,
fitopt_num, os.path.join(m, f)))
data_fitres_files = [
os.path.join(l.output["fitres_dirs"][0],
l.output["fitopt_map"]["DEFAULT"])
for l in self.lcfit_deps if l.output["is_data"]
]
data_fitres_output = [
d.split("/")[-4] + ".csv.gz" for d in data_fitres_files
]
sim_fitres_files = [
os.path.join(l.output["fitres_dirs"][0],
l.output["fitopt_map"]["DEFAULT"])
for l in self.lcfit_deps if not l.output["is_data"]
]
sim_fitres_output = [
d.split("/")[-4] + ".csv.gz" for d in sim_fitres_files
]
types = list(
set([
a for l in self.lcfit_deps
for a in l.sim_task.output["types_dict"]["IA"]
]))
input_yml_file = "input.yml"
output_dict = {
"COSMOMC": {
"INPUT_FILES": self.cosmomc_input_files,
"PARSED_FILES": self.cosmomc_output_files,
"PARSED_COVOPTS": self.cosmomc_covopts,
"PARAMS": self.params,
"SHIFT": self.options.get("SHIFT", False),
"PRIOR": self.options.get("PRIOR"),
"NAMES": self.names,
"CONTOUR_COVOPTS": self.covopts,
"SINGULAR_BLIND": self.singular_blind,
},
"BIASCOR": {
"WFIT_SUMMARY_INPUT": self.wsummary_files,
"WFIT_SUMMARY_OUTPUT": "all_biascor.csv",
"FITRES_INPUT": self.biascor_fitres_input_files,
"FITRES_PROB_COLS": self.biascor_prob_col_names,
"FITRES_PARSED": self.biascor_fitres_output_files,
"M0DIFF_INPUTS": self.biascor_m0diffs,
"M0DIFF_PARSED": self.biascor_m0diff_output,
"FITRES_COMBINED": self.biascor_fitres_combined,
},
"OUTPUT_NAME": self.name,
"BLIND": self.blind_params,
"LCFIT": {
"DATA_FITRES_INPUT": data_fitres_files,
"SIM_FITRES_INPUT": sim_fitres_files,
"DATA_FITRES_PARSED": data_fitres_output,
"SIM_FITRES_PARSED": sim_fitres_output,
"IA_TYPES": types,
},
}
if self.batch_file is None:
if self.gpu:
self.sbatch_header = self.sbatch_gpu_header
else:
self.sbatch_header = self.sbatch_cpu_header
else:
with open(self.batch_file, 'r') as f:
self.sbatch_header = f.read()
self.sbatch_header = self.clean_header(self.sbatch_header)
header_dict = {
"REPLACE_NAME": self.job_name,
"REPLACE_WALLTIME": "1:00:00",
"REPLACE_LOGFILE": self.logfile,
"REPLACE_MEM": "20GB",
"APPEND": ["#SBATCH --ntasks=1", "#SBATCH --cpus-per-task=1"]
}
header_dict = merge_dict(header_dict, self.batch_replace)
self.update_header(header_dict)
setup_dict = {"output_dir": self.output_dir}
format_dict = {
"sbatch_header": self.sbatch_header,
"task_setup": self.update_setup(setup_dict,
self.task_setup['analyse']),
"input_yml": input_yml_file
}
final_slurm = self.get_slurm_raw().format(**format_dict)
new_hash = self.get_hash_from_string(final_slurm +
json.dumps(output_dict))
if self._check_regenerate(new_hash):
self.logger.debug("Regenerating and launching task")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.save_new_hash(new_hash)
for c in self.path_to_codes:
shutil.copy(c, self.output_dir)
input_yml_path = os.path.join(self.output_dir, input_yml_file)
with open(input_yml_path, "w") as f:
json.dump(output_dict, f, indent=2)
self.logger.debug(
f"Input yml file written out to {input_yml_path}")
slurm_output_file = os.path.join(self.output_dir, "slurm.job")
with open(slurm_output_file, "w") as f:
f.write(final_slurm)
self.logger.info(f"Submitting batch job for analyse chains")
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
return True
def classify(self, training):
model = self.options.get("MODEL")
model_path = ""
if not training:
assert model is not None, "If TRAIN is not specified, you have to point to a model to use"
if not os.path.exists(get_output_loc(model)):
for t in self.dependencies:
if model == t.name:
self.logger.debug(
f"Found task dependency {t.name} with model file {t.output['model_filename']}"
)
model = t.output["model_filename"]
model_path = get_output_loc(model)
self.logger.debug(f"Looking for model in {model_path}")
assert os.path.exists(model_path), f"Cannot find {model_path}"
types = self.get_types()
if types is None:
types = OrderedDict({
"1": "Ia",
"0": "unknown",
"2": "SNIax",
"3": "SNIa-pec",
"20": "SNIIP",
"21": "SNIIL",
"22": "SNIIn",
"29": "SNII",
"32": "SNIb",
"33": "SNIc",
"39": "SNIbc",
"41": "SLSN-I",
"42": "SLSN-II",
"43": "SLSN-R",
"80": "AGN",
"81": "galaxy",
"98": "None",
"99": "pending",
"101": "Ia",
"120": "SNII",
"130": "SNIbc",
})
else:
has_ia = False
has_cc = False
self.logger.debug(f"Input types set to {types}")
for key, value in types.items():
if value.upper() == "IA":
has_ia = True
elif value.upper() in ["II", "IBC"]:
has_cc = True
if not has_ia:
self.logger.debug("No Ia type found, injecting type")
types[1] = "Ia"
types = dict(
sorted(types.items(),
key=lambda x: -1 if x[0] == 1 else x[0]))
self.logger.debug(f"Inject types with Ias are {types}")
if not has_cc:
self.logger.debug("No cc type found, injecting type")
types[29] = "II"
str_types = json.dumps(types)
self.logger.debug(f"Types set to {str_types}")
sim_dep = self.get_simulation_dependency()
light_curve_dir = sim_dep.output["photometry_dirs"][self.index]
self.raw_dir = light_curve_dir
fit = self.get_fit_dependency()
fit_dir = f"" if fit is None else f"--fits_dir {fit['fitres_dirs'][self.index]}"
cyclic = "--cyclic" if self.variant in ["vanilla", "variational"
] and self.cyclic else ""
batch_size = f"--batch_size {self.batch_size}"
num_layers = f"--num_layers {self.num_layers}"
hidden_dim = f"--hidden_dim {self.hidden_dim}"
variant = f"--model {self.variant}"
if self.variant == "bayesian":
variant += " --num_inference_samples 20"
clump = sim_dep.output.get("clump_file")
if clump is None:
clump_txt = ""
else:
clump_txt = f"--photo_window_files {clump}"
if self.batch_file is None:
if self.gpu:
self.sbatch_header = self.sbatch_gpu_header
else:
self.sbatch_header = self.sbatch_cpu_header
else:
with open(self.batch_file, 'r') as f:
self.sbatch_header = f.read()
self.sbatch_header = self.clean_header(self.sbatch_header)
if self.has_yml:
self.update_yml()
setup_file = "supernnova_yml"
else:
setup_file = "supernnova"
header_dict = {
"REPLACE_NAME": self.job_base_name,
"REPLACE_WALLTIME": "23:00:00",
"REPLACE_LOGFILE": "output.log",
"REPLACE_MEM": "32GB",
"APPEND": ["#SBATCH --ntasks=1", "#SBATCH --cpus-per-task=1"]
}
header_dict = merge_dict(header_dict, self.batch_replace)
self.update_header(header_dict)
setup_dict = {
"conda_env":
self.conda_env,
"dump_dir":
self.dump_dir,
"photometry_dir":
light_curve_dir,
"fit_dir":
fit_dir,
"path_to_classifier":
self.path_to_classifier,
"job_name":
self.job_base_name,
"command":
"--train_rnn" if training else "--validate_rnn",
"sntypes":
str_types,
"variant":
variant,
"cyclic":
cyclic,
"model":
"" if training else f"--model_files {model_path}",
"phot":
"",
"test_or_train":
"" if training else "--data_testing",
"redshift":
"--redshift " + self.redshift,
"norm":
"--norm " + self.norm,
"done_file":
self.done_file,
"clump":
clump_txt,
"done_file2":
self.done_file2,
"partition":
"gpu2" if self.gpu else "broadwl",
"gres":
"#SBATCH --gres=gpu:1" if self.gpu else "",
"cuda":
"--use_cuda" if self.gpu else "",
"clean_command":
f"rm -rf {self.dump_dir}/processed" if self.clean else "",
"seed":
f"--seed {self.seed}" if self.seed else "",
"batch_size":
batch_size,
"num_layers":
num_layers,
"hidden_dim":
hidden_dim,
"data_yml":
self.output_data_yml,
"classification_yml":
self.output_classification_yml,
"classification_command":
"train_rnn" if training else "validate_rnn"
}
format_dict = {
"sbatch_header":
self.sbatch_header,
"task_setup":
self.update_setup(setup_dict, self.task_setup[setup_file])
}
slurm_output_file = self.output_dir + "/job.slurm"
self.logger.info(
f"Running SuperNNova, slurm job outputting to {slurm_output_file}")
slurm_text = self.slurm.format(**format_dict)
new_hash = self.get_hash_from_string(slurm_text)
if not self._check_regenerate(new_hash):
self.should_be_done()
else:
self.logger.info("Rerunning. Cleaning output_dir")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
if self.has_yml:
with open(self.output_data_yml, 'w') as f:
f.write(self.data_yml)
with open(self.output_classification_yml, 'w') as f:
f.write(self.classification_yml)
self.save_new_hash(new_hash)
with open(slurm_output_file, "w") as f:
f.write(slurm_text)
self.logger.info(
f"Submitting batch job to {'train' if training else 'predict using'} SuperNNova"
)
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
return True
def write_input(self, force_refresh):
self.set_property("GENVERSION",
self.genversion,
assignment=": ",
section_end="ENDLIST_GENVERSION")
for k in self.config.keys():
if k.upper() != "GLOBAL":
run_config = self.config[k]
run_config_keys = list(run_config.keys())
assert "BASE" in run_config_keys, "You must specify a base file for each option"
for key in run_config_keys:
if key.upper() in self.reserved_keywords:
continue
base_file = run_config["BASE"]
match = base_file.split(".")[0]
self.set_property(f"GENOPT({match})",
f"{key} {run_config[key]}",
section_end="ENDLIST_GENVERSION")
for key in self.config.get("GLOBAL", []):
if key.upper() == "BASE":
continue
self.set_property(key, self.config['GLOBAL'][key])
if key == "RANSEED_CHANGE":
self.delete_property("RANSEED_REPEAT")
elif key == "RANSEED_REPEAT":
self.delete_property("RANSEED_CHANGE")
self.set_property("SIMGEN_INFILE_Ia",
" ".join(self.base_ia) if self.base_ia else None)
self.set_property("SIMGEN_INFILE_NONIa",
" ".join(self.base_cc) if self.base_cc else None)
self.set_property("GENPREFIX", self.genversion)
# Put config in a temp directory
temp_dir_obj = tempfile.TemporaryDirectory()
temp_dir = temp_dir_obj.name
# Copy the base files across
for f in self.base_ia:
shutil.copy(self.data_dir + f, temp_dir)
for f in self.base_cc:
shutil.copy(self.data_dir + f, temp_dir)
# Copy the include input file if there is one
input_copied = []
fs = self.base_ia + self.base_cc
for ff in fs:
if ff not in input_copied:
input_copied.append(ff)
with open(self.data_dir + ff, "r") as f:
for line in f.readlines():
line = line.strip()
if line.startswith("INPUT_FILE_INCLUDE"):
include_file = line.split(":")[-1].strip()
self.logger.debug(
f"Copying included file {include_file}")
shutil.copy(self.data_dir + include_file, temp_dir)
# Write the primary input file
main_input_file = f"{temp_dir}/{self.genversion}.input"
with open(main_input_file, "w") as f:
f.writelines(map(lambda s: s + '\n', self.base))
self.logger.info(f"Input file written to {main_input_file}")
# Remove any duplicates and order the output files
output_files = [
f"{temp_dir}/{a}" for a in sorted(os.listdir(temp_dir))
]
self.logger.debug(
f"{len(output_files)} files used to create simulation. Hashing them."
)
# Get current hash
new_hash = self.get_hash_from_files(output_files)
old_hash = self.get_old_hash()
regenerate = force_refresh or (old_hash is None
or old_hash != new_hash)
if regenerate:
self.logger.info(f"Running simulation")
# Clean output dir. God I feel dangerous doing this, so hopefully unnecessary check
if "//" not in self.output_dir and len(self.output_dir) > 30:
self.logger.debug(
f"Cleaning output directory {self.output_dir}")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.logger.debug(
f"Copying from {temp_dir} to {self.output_dir}")
copytree(temp_dir, self.output_dir)
self.save_new_hash(new_hash)
else:
self.logger.error(
f"Seems to be an issue with the output dir path: {self.output_dir}"
)
chown_dir(self.output_dir)
else:
self.logger.info("Hash check passed, not rerunning")
temp_dir_obj.cleanup()
return regenerate, new_hash
def execute(self, check_config, compress_output, uncompress_output):
self.logger.info(f"Executing pipeline for prefix {self.prefix}")
self.logger.info(f"Output will be located in {self.output_dir}")
if check_config:
self.logger.info("Only verifying config, not launching anything")
assert not (
compress_output and uncompress_output
), "-C / --compress and -U / --uncompress are mutually exclusive"
# Whilst compressing is being debugged, false by default
self.compress = False
if compress_output:
self.compress = True
self.logger.info("Compressing output")
if uncompress_output:
self.compress = False
self.logger.info("Uncompressing output")
mkdirs(self.output_dir)
c = self.run_config
self.tasks = self.get_tasks(c)
self.num_jobs_queue = 0
self.num_jobs_queue_gpu = 0
squeue = None
if check_config:
if compress_output:
self.compress_all()
if uncompress_output:
self.uncompress_all()
self.logger.notice("Config verified, exiting")
return
self.print_dashboard()
start_sleep_time = self.global_config["OUTPUT"]["ping_frequency"]
max_sleep_time = self.global_config["OUTPUT"]["max_ping_frequency"]
current_sleep_time = start_sleep_time
config_file_output = os.path.join(self.output_dir,
os.path.basename(self.filename_path))
if not check_config and self.filename_path != config_file_output:
self.logger.info(
f"Saving processed and parsed config file to {config_file_output}"
)
with open(config_file_output, 'w') as f:
f.write(self.file_raw)
#shutil.copy(self.filename_path, config_file_output)
chown_file(config_file_output)
# Welcome to the primary loop
while self.tasks or self.running:
small_wait = False
# Check status of current jobs
for t in self.running:
try:
completed = self.check_task_completion(t, squeue)
small_wait = small_wait or completed
except Exception as e:
self.logger.exception(e, exc_info=True)
self.fail_task(t)
# Submit new jobs if needed
while self.num_jobs_queue < self.max_jobs:
t = self.get_task_to_run()
if t is not None:
self.logger.info("")
self.tasks.remove(t)
self.logger.notice(f"LAUNCHING: {t}")
try:
t.set_force_refresh(self.get_force_refresh(t))
t.set_force_ignore(self.get_force_ignore(t))
t.set_sbatch_cpu_header(self.sbatch_cpu_header)
t.set_sbatch_gpu_header(self.sbatch_gpu_header)
t.set_setup(self.task_setup)
started = t.run()
except Exception as e:
self.logger.exception(e, exc_info=True)
started = False
if started:
if t.gpu:
self.num_jobs_queue_gpu += t.num_jobs
message = (
f"LAUNCHED: {t} with {t.num_jobs} GPU NUM_JOBS. Total GPU NUM_JOBS now {self.num_jobs_queue_gpu}/{self.max_jobs_in_queue_gpu}"
)
else:
self.num_jobs_queue += t.num_jobs
message = f"LAUNCHED: {t} with {t.num_jobs} NUM_JOBS. Total NUM_JOBS now {self.num_jobs_queue}/{self.max_jobs_in_queue}"
self.logger.notice(message)
self.running.append(t)
completed = False
try:
completed = self.check_task_completion(t, squeue)
except Exception as e:
self.logger.exception(e, exc_info=True)
self.fail_task(t)
small_wait = small_wait or completed
else:
self.logger.error(f"FAILED TO LAUNCH: {t}")
self.fail_task(t)
small_wait = True
else:
break
# Check quickly if we've added a new job, etc, in case of immediate failure
if small_wait:
self.log_status()
current_sleep_time = start_sleep_time
time.sleep(0.1)
squeue = None
else:
time.sleep(current_sleep_time)
current_sleep_time *= 2
if current_sleep_time > max_sleep_time:
current_sleep_time = max_sleep_time
p = subprocess.run(f"squeue -h -u $USER -o '%.j'",
shell=True,
text=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
if (p.returncode != 0) or (p.stderr != ""):
self.logger.error(
f"Command '{p.args}' failed with exit status '{p.returncode}' and error '{p.stderr.strip()}'"
)
else:
squeue = [i.strip() for i in p.stdout.splitlines()]
n = len(squeue)
if n == 0 or n > self.max_jobs:
self.logger.debug(
f"Squeue is reporting {n} NUM_JOBS in the queue... this is either 0 or toeing the line as to too many"
)
self.log_finals()
def classify(self, training, force_refresh):
model = self.options.get("MODEL")
model_path = ""
if not training:
assert model is not None, "If TRAIN is not specified, you have to point to a model to use"
if not os.path.exists(get_output_loc(model)):
for t in self.dependencies:
if model == t.name:
self.logger.debug(
f"Found task dependency {t.name} with model file {t.output['model_filename']}"
)
model = t.output["model_filename"]
model_path = get_output_loc(model)
self.logger.debug(f"Looking for model in {model_path}")
assert os.path.exists(model_path), f"Cannot find {model_path}"
types = self.get_types()
if types is None:
types = OrderedDict({
"1": "Ia",
"0": "unknown",
"2": "SNIax",
"3": "SNIa-pec",
"20": "SNIIP",
"21": "SNIIL",
"22": "SNIIn",
"29": "SNII",
"32": "SNIb",
"33": "SNIc",
"39": "SNIbc",
"41": "SLSN-I",
"42": "SLSN-II",
"43": "SLSN-R",
"80": "AGN",
"81": "galaxy",
"98": "None",
"99": "pending",
"101": "Ia",
"120": "SNII",
"130": "SNIbc",
})
else:
has_ia = False
has_cc = False
self.logger.debug(f"Input types set to {types}")
for key, value in types.items():
if value.upper() == "IA":
has_ia = True
elif value.upper() in ["II", "IBC"]:
has_cc = True
if not has_ia:
self.logger.debug("No Ia type found, injecting type")
types.update({"1": "Ia"})
types.move_to_end("1", last=False)
if not has_cc:
self.logger.debug("No cc type found, injecting type")
types.update({"29": "II"})
str_types = json.dumps(types)
self.logger.debug(f"Types set to {str_types}")
sim_dep = self.get_simulation_dependency()
light_curve_dir = sim_dep.output["photometry_dirs"][self.index]
fit = self.get_fit_dependency()
fit_dir = f"" if fit is None else f"--fits_dir {fit['fitres_dirs'][self.index]}"
cyclic = "--cyclic" if self.variant in ["vanilla", "variational"
] else ""
variant = f"--model {self.variant}"
if self.variant == "bayesian":
variant += " --num_inference_samples 20"
clump = sim_dep.output.get("clump_file")
if clump is None:
clump_txt = ""
else:
clump_txt = f"--photo_window_files {clump}"
format_dict = {
"conda_env": self.conda_env,
"dump_dir": self.dump_dir,
"photometry_dir": light_curve_dir,
"fit_dir": fit_dir,
"path_to_classifier": self.path_to_classifier,
"job_name": self.job_base_name,
"command": "--train_rnn" if training else "--validate_rnn",
"sntypes": str_types,
"variant": variant,
"cyclic": cyclic,
"model": "" if training else f"--model_files {model_path}",
"phot": "",
"test_or_train": "" if training else "--data_testing",
"redshift": "--redshift " + self.redshift,
"norm": "--norm " + self.norm,
"done_file": self.done_file,
"clump": clump_txt,
"done_file2": self.done_file2,
}
slurm_output_file = self.output_dir + "/job.slurm"
self.logger.info(
f"Running SuperNNova, slurm job outputting to {slurm_output_file}")
slurm_text = self.slurm.format(**format_dict)
old_hash = self.get_old_hash()
new_hash = self.get_hash_from_string(slurm_text)
if not force_refresh and new_hash == old_hash:
self.logger.info("Hash check passed, not rerunning")
self.should_be_done()
else:
self.logger.info("Rerunning. Cleaning output_dir")
shutil.rmtree(self.output_dir, ignore_errors=True)
mkdirs(self.output_dir)
self.save_new_hash(new_hash)
with open(slurm_output_file, "w") as f:
f.write(slurm_text)
self.logger.info(
f"Submitting batch job to {'train' if training else 'predict using'} SuperNNova"
)
subprocess.run(["sbatch", slurm_output_file], cwd=self.output_dir)
return True