def load_injections(config=None, logger=None):
"""
"""
if config is None:
config = get_config()
if logger is None:
logger = get_logger()
path = os.path.join(config['directories']['data'], 'input', 'injections')
filename = os.path.join(path, config['injections']['catalogue_filename'])
df = pd.read_csv(filename)
logger.debug(f"Loaded {df.shape[0]} injections from file.")
return df
def load_gama_specobj(config=None, logger=None):
"""
"""
if config is None:
config = get_config()
if logger is None:
logger = get_logger()
filename = os.path.join(config['directories']['data'], 'input',
'gama_specobj.csv')
df = pd.read_csv(filename)
df["ra"] = df["RA"]
df["dec"] = df["DEC"]
logger.debug(f"Loaded {df.shape[0]} GAMA objects.")
return df
def __init__(self,
config=None,
logger=None,
zmin=0.0001,
zmax=2,
z_samples=500):
"""
"""
self.logger = get_logger() if logger is None else logger
self.config = get_config() if config is None else config
self._cosmo = self.config["cosmology"]
self._zmin = zmin
self._zmax = zmax
zz = np.linspace(self._zmin, self._zmax, z_samples)
self._cosmo_interps = self._make_cosmo_interps(zz)
def load_recovery_efficiency(config=None, logger=None):
"""
"""
if config is None:
config = get_config()
if logger is None:
logger = get_logger()
dirname = os.path.join(config["directories"]["data"], "input", "injections")
filename = config["injections"]["receff_filename"]
logger.debug(f"Loading interpolated recover efficiency: {filename}.")
with open(os.path.join(dirname, filename), "rb") as f:
interp = pickle.load(f)
fn = partial(recovery_efficiency, interp=interp)
return fn
def load_sample(config=None, logger=None, select=True):
"""
"""
if config is None:
config = get_config()
if logger is None:
logger = get_logger()
filename = os.path.join(config['directories']['data'], 'input',
'lsbgs_public.csv')
df = pd.read_csv(filename)
if select:
cond = select_samples(uae=df['mueff_av'].values,
rec=df['rec_arcsec'].values)
cond &= df["g_r"] < GR_MAX
# cond &= df["is_red"].values == 0
df = df[cond].reset_index(drop=True)
logger.debug(f"Loaded {df.shape[0]} LSBGs from file.")
return df
import os
from udgsizes.core import get_config, get_logger
from udgsizes.obs.sample import load_sample, load_gama_specobj
from udgsizes.utils import xmatch
if __name__ == "__main__":
logger = get_logger()
radius = 3. / 3600
df = load_sample(select=False)
dfg = load_gama_specobj()
dfm = xmatch.match_dataframe(df, dfg, radius=radius)
logger.info(f"Matched {dfm.shape[0]} sources.")
datadir = get_config()["directories"]["data"]
dfm.to_csv(os.path.join(datadir, "input", "lsbgs_gama_xmatch.csv"))