"""
DC2 Object Catalog Reader
"""
import os
import re
import warnings
import itertools
import shutil
import numpy as np
import pandas as pd
import yaml
from GCR import BaseGenericCatalog
from .dc2_dm_catalog import DC2DMTractCatalog
from .dc2_dm_catalog import convert_flux_to_mag, convert_flux_to_nanoJansky, convert_nanoJansky_to_mag, convert_flux_err_to_mag_err
__all__ = ['DC2ObjectCatalog', 'DC2ObjectParquetCatalog']
FILE_DIR = os.path.dirname(os.path.abspath(__file__))
FILE_PATTERN = r'(?:merged|object)_tract_\d+\.hdf5$'
GROUP_PATTERN = r'(?:coadd|object)_\d+_\d\d$'
SCHEMA_FILENAME = 'schema.yaml'
META_PATH = os.path.join(FILE_DIR, 'catalog_configs/_dc2_object_meta.yaml')
def convert_dm_ref_zp_flux_to_mag(flux, dm_ref_zp=27):
"""Convert the listed DM coadd-reported flux values to AB mag
Eventually this function should be a no-op. But presently
The processing of Run 1.1, 1.2 to date (2019-02-17) have
calibrated flux values with respect to a reference ZP=27 mag
The reference catalog is on an AB system.
Re-check dm_ref_zp if calibration is updated.
Eventually we will get nJy from the final calibrated DRP processing.
"""
flux_nJ = convert_dm_ref_zp_flux_to_nanoJansky(flux, dm_ref_zp=dm_ref_zp)
mag_AB = convert_nanoJansky_to_mag(flux_nJ)
return mag_AB
def convert_dm_ref_zp_flux_to_nanoJansky(flux, dm_ref_zp=27):
"""Convert the listed DM coadd-reported flux values to nanoJansky.
Eventually this function should be a no-op. But presently
The processing of Run 1.1, 1.2 to date (2019-02-17) have
calibrated flux values with respect to a reference ZP=27 mag
The reference catalog is on an AB system.
Re-check dm_ref_zp if calibration is updated.
Eventually we will get nJy from the final calibrated DRP processing.
"""
AB_mag_zp_wrt_Jansky = 8.90 # Definition of AB
AB_mag_zp_wrt_nanoJansky = 2.5 * 9 + AB_mag_zp_wrt_Jansky # 9 is from nano=10**(-9)
calibrated_flux_to_nanoJansky = 10**((AB_mag_zp_wrt_nanoJansky - dm_ref_zp)/2.5)
return calibrated_flux_to_nanoJansky * flux
def create_basic_flag_mask(*flags):
"""Generate a mask for a set of flags
For each item the mask will be true if and only if all flags are false
Args:
*flags (ndarray): Variable number of arrays with booleans or equivalent
Returns:
The combined mask array
"""
out = np.ones(len(flags[0]), np.bool)
for flag in flags:
out &= (~flag)
return out
class TableWrapper():
"""Wrapper class for pandas HDF5 storer
Provides a unified API to access both fixed and table formats.
Takes a file_handle to the HDF5 file
An HDF group key
And a schema to specify dtypes and default values for missing columns.
"""
_default_values = {'i': -1, 'b': False, 'U': ''}
def __init__(self, file_handle, key, schema=None):
if not file_handle.is_open:
raise ValueError('file handle has been closed!')
self.storer = file_handle.get_storer(key)
self.is_table = self.storer.is_table
if not self.is_table and not self.storer.format_type == 'fixed':
raise ValueError('storer format type not supported!')
self._schema = {} if schema is None else dict(schema)
self._native_schema = None
self._len = None
self._cache = None
self._constant_arrays = dict()
@property
def native_schema(self):
"""Get the native schema from either 'fixed' or 'table' formatted HDF5 files."""
if self._native_schema is None:
self._native_schema = {}
if self.is_table:
for i in itertools.count():
try:
dtype = getattr(self.storer.table.attrs, 'values_block_{}_dtype'.format(i))
except AttributeError:
break
for col in getattr(self.storer.table.attrs, 'values_block_{}_kind'.format(i)):
self._native_schema[col] = {'dtype': dtype}
else:
for i in range(self.storer.nblocks):
dtype = getattr(self.storer.group, 'block{}_values'.format(i)).dtype.name
for col in getattr(self.storer.group, 'block{}_items'.format(i)):
self._native_schema[col.decode()] = {'dtype': dtype}
return self._native_schema
@property
def columns(self):
"""Get columns from either 'fixed' or 'table' formatted HDF5 files."""
return set(self.native_schema)
def __len__(self):
if self._len is None:
if self.is_table:
self._len = self.storer.nrows
else:
self._len = self.storer.group.axis1.nrows
return self._len
def __contains__(self, item):
return item in self.native_schema
def __getitem__(self, key):
"""Return the values of the column specified by 'key'
Uses cached values, if available.
"""
if self._cache is None:
self._cache = self.storer.read()
try:
return self._cache[key].values
except KeyError:
return self._get_constant_array(key)
get = __getitem__
@classmethod
def _get_default_value(cls, dtype, key=None): # pylint: disable=W0613
return cls._default_values.get(np.dtype(dtype).kind, np.nan)
def _get_constant_array(self, key):
"""
Get a constant array for a column; `key` should be the column name.
Find dtype and default value in `self._schema`.
If not found, default to np.float64 and np.nan.
"""
schema_this = self._schema.get(key, {})
dtype = schema_this.get('dtype', np.float64)
default = schema_this.get(
'default',
self._get_default_value(dtype, key)
)
return self._generate_constant_array(dtype=dtype, value=default)
def _generate_constant_array(self, dtype, value):
"""
Actually generate a constant array according to `dtype` and `value`
"""
dtype = np.dtype(dtype)
# here `key` is used to cache the constant array
# has nothing to do with column name
key = (dtype.str, value)
if key not in self._constant_arrays:
self._constant_arrays[key] = np.asarray(np.repeat(value, len(self)), dtype=dtype)
self._constant_arrays[key].setflags(write=False)
return self._constant_arrays[key]
def clear_cache(self):
"""
clear cached data
"""
self._native_schema = self._len = self._cache = None
self._constant_arrays.clear()
class ObjectTableWrapper(TableWrapper):
"""Same as TableWrapper but add tract and patch info"""
def __init__(self, file_handle, key, schema=None):
key_items = key.split('_')
self.tract = int(key_items[1])
self.patch = ','.join(key_items[2])
super(ObjectTableWrapper, self).__init__(file_handle, key, schema)
# Add the schema info for tract, path
# These values will be read by `get_constant_array`
self._schema['tract'] = {'dtype': 'int64', 'default': self.tract}
self._schema['patch'] = {'dtype': '<U', 'default': self.patch}
@classmethod
def _get_default_value(cls, dtype, key=None):
if np.dtype(dtype).kind == 'b' and key and (
key.endswith('_flag_bad') or key.endswith('_flag_noGoodPixels')):
return True
return super()._get_default_value(dtype, key)
@property
def tract_and_patch(self):
"""Return a dict of the tract and patch info."""
return {'tract': self.tract, 'patch': self.patch}
class DC2ObjectCatalog(BaseGenericCatalog):
r"""DC2 Object Catalog reader
Parameters
----------
base_dir (str): Directory of data files being served, required
filename_pattern (str): The optional regex pattern of served data files
groupname_pattern (str): The optional regex pattern of groups in data files
schema_filename (str): The optional location of the schema file
Relative to base_dir, unless specified as absolute path.
pixel_scale (float): scale to convert pixel to arcsec (default: 0.2)
use_cache (bool): Whether or not to cache read data in memory
is_dpdd (bool): Whether or not to the files are already in DPDD-format
Attributes
----------
base_dir (str): The directory of data files being served
available_tracts (list): Sorted list of available tracts
available_tracts_and_patches (list): Available tracts and patches as dict objects
Notes
-----
The initialization sets the version of the catalog based on the existence
of certain columns and sets a version accordingly.
This version setting should be improved and standardized as we work towardj
providing the version in the catalog files in the scripts in `DC2-production`.
"""
# pylint: disable=too-many-instance-attributes
_native_filter_quantities = {'tract', 'patch'}
def _subclass_init(self, **kwargs):
self.base_dir = kwargs['base_dir']
self._filename_re = re.compile(kwargs.get('filename_pattern', FILE_PATTERN))
self._groupname_re = re.compile(kwargs.get('groupname_pattern', GROUP_PATTERN))
_schema_filename = kwargs.get('schema_filename', SCHEMA_FILENAME)
# If _schema_filename is an absolute path, os.path.join will just return _schema_filename
self._schema_path = os.path.join(self.base_dir, _schema_filename)
self.pixel_scale = float(kwargs.get('pixel_scale', 0.2))
self.use_cache = bool(kwargs.get('use_cache', True))
if not os.path.isdir(self.base_dir):
raise ValueError('`base_dir` {} is not a valid directory'.format(self.base_dir))
self._schema = None
if self._schema_path and os.path.isfile(self._schema_path):
self._schema = self._generate_schema_from_yaml(self._schema_path)
self._file_handles = dict()
self._datasets = self._generate_datasets() # uses self._schema when available
if not self._datasets:
err_msg = 'No catalogs were found in `base_dir` {}'
raise RuntimeError(err_msg.format(self.base_dir))
if not self._schema:
warnings.warn('Falling back to reading all datafiles for column names')
self._schema = self._generate_schema_from_datafiles(self._datasets)
if kwargs.get('is_dpdd'):
self._quantity_modifiers = {col: None for col in self._schema}
bands = [col[0] for col in self._schema if len(col) == 5 and col.startswith('mag_')]
else:
# A slightly crude way of checking for version of schema to have modelfit mag
# A future improvement will be to explicitly store version information in the datasets
# and just rely on that versioning.
has_modelfit_mag = any(col.endswith('_modelfit_mag') for col in self._schema)
has_modelfit_flux = any('_modelfit_CModel_' in col for col in self._schema)
has_modelfit_flag = any(col.endswith('_modelfit_CModel_flag') for col in self._schema)
if not (has_modelfit_mag or has_modelfit_flux):
warnings.warn("No modelfit infomation is available in the columns.")
if any(col.endswith('_fluxSigma') for col in self._schema):
dm_schema_version = 1
elif any(col.endswith('_fluxErr') for col in self._schema):
dm_schema_version = 2
elif any(col == 'base_Blendedness_abs_instFlux' for col in self._schema):
dm_schema_version = 3
else:
dm_schema_version = 4
bands = [col[0] for col in self._schema if len(col) == 5 and col.endswith('_mag')]
self._quantity_modifiers = self._generate_modifiers(
self.pixel_scale, bands, has_modelfit_mag, has_modelfit_flux, has_modelfit_flag, dm_schema_version)
self._quantity_info_dict = self._generate_info_dict(META_PATH, bands)
self._len = None
def __del__(self):
self.close_all_file_handles()
@staticmethod
def _generate_modifiers(pixel_scale=0.2, bands='ugrizy',
has_modelfit_mag=True, has_modelfit_flux=True, has_modelfit_flag=True,
dm_schema_version=4):
"""Creates a dictionary relating native and homogenized column names
Args:
pixel_scale (float): Scale of pixels in coadd images
bands (list): List of photometric bands as strings
has_modelfit_mag (bool): Whether or not pre-calculated model fit magnitudes are present
dm_schema_version (int): DM schema version (1, 2, 3, 4)
Returns:
A dictionary of the form {<homogenized name>: <native name>, ...}
"""
if dm_schema_version not in (1, 2, 3, 4):
raise ValueError('Only supports dm_schema_version == 1, 2, 3, 4')
FLUX = 'flux' if dm_schema_version <= 2 else 'instFlux'
ERR = 'Sigma' if dm_schema_version <= 1 else 'Err'
BLENDEDNESS_SUFFIX = '_%s' % FLUX if dm_schema_version <= 3 else ''
modifiers = {
'objectId': 'id',
'parentObjectId': 'parent',
'ra': (np.rad2deg, 'coord_ra'),
'dec': (np.rad2deg, 'coord_dec'),
'x': 'base_SdssCentroid_x',
'y': 'base_SdssCentroid_y',
'xErr': 'base_SdssCentroid_x{}'.format(ERR),
'yErr': 'base_SdssCentroid_y{}'.format(ERR),
'xy_flag': 'base_SdssCentroid_flag',
'psNdata': 'base_PsfFlux_area',
'extendedness': 'base_ClassificationExtendedness_value',
'blendedness': 'base_Blendedness_abs{}'.format(BLENDEDNESS_SUFFIX),
}
not_good_flags = (
'base_PixelFlags_flag_edge',
'base_PixelFlags_flag_interpolatedCenter',
'base_PixelFlags_flag_saturatedCenter',
'base_PixelFlags_flag_crCenter',
'base_PixelFlags_flag_bad',
'base_PixelFlags_flag_suspectCenter',
'base_PixelFlags_flag_clipped',
)
modifiers['good'] = (create_basic_flag_mask,) + not_good_flags
modifiers['clean'] = (
create_basic_flag_mask,
'deblend_skipped',
) + not_good_flags
# cross-band average, second moment values
modifiers['I_flag'] = 'ext_shapeHSM_HsmSourceMoments_flag'
for ax in ['xx', 'yy', 'xy']:
modifiers['I{}'.format(ax)] = 'ext_shapeHSM_HsmSourceMoments_{}'.format(ax)
modifiers['I{}PSF'.format(ax)] = 'base_SdssShape_psf_{}'.format(ax)
for band in bands:
modifiers['mag_{}'.format(band)] = '{}_mag'.format(band)
modifiers['magerr_{}'.format(band)] = '{}_mag_err'.format(band)
modifiers['psFlux_{}'.format(band)] = (convert_dm_ref_zp_flux_to_nanoJansky,
'{}_base_PsfFlux_{}'.format(band, FLUX))
modifiers['psFlux_flag_{}'.format(band)] = '{}_base_PsfFlux_flag'.format(band)
modifiers['psFluxErr_{}'.format(band)] = (convert_dm_ref_zp_flux_to_nanoJansky,
'{}_base_PsfFlux_{}{}'.format(band, FLUX, ERR))
modifiers['I_flag_{}'.format(band)] = '{}_base_SdssShape_flag'.format(band)
for ax in ['xx', 'yy', 'xy']:
modifiers['I{}_{}'.format(ax, band)] = '{}_base_SdssShape_{}'.format(band, ax)
modifiers['I{}PSF_{}'.format(ax, band)] = '{}_base_SdssShape_psf_{}'.format(band, ax)
modifiers['psf_fwhm_{}'.format(band)] = (
lambda xx, yy, xy: pixel_scale * 2.355 * (xx * yy - xy * xy) ** 0.25,
'{}_base_SdssShape_psf_xx'.format(band),
'{}_base_SdssShape_psf_yy'.format(band),
'{}_base_SdssShape_psf_xy'.format(band),
)
if has_modelfit_flux:
# The zp=27.0 is based on the default calibration for the coadds
# as specified in the DM code. It's correct for Run 1.1p.
modifiers['cModelFlux_{}'.format(band)] = (convert_dm_ref_zp_flux_to_nanoJansky,
'{}_modelfit_CModel_{}'.format(band, FLUX))
modifiers['cModelFluxErr_{}'.format(band)] = (convert_dm_ref_zp_flux_to_nanoJansky,
'{}_modelfit_CModel_{}{}'.format(band, FLUX, ERR))
if has_modelfit_flag:
modifiers['cModelFlux_flag_{}'.format(band)] = '{}_modelfit_CModel_flag'.format(band)
if not has_modelfit_mag:
modifiers['mag_{}_cModel'.format(band)] = (convert_dm_ref_zp_flux_to_mag,
'{}_modelfit_CModel_{}'.format(band, FLUX))
modifiers['magerr_{}_cModel'.format(band)] = (convert_flux_err_to_mag_err,
'{}_modelfit_CModel_{}'.format(band, FLUX),
'{}_modelfit_CModel_{}{}'.format(band, FLUX, ERR))
modifiers['snr_{}_cModel'.format(band)] = (
np.divide,
'{}_modelfit_CModel_{}'.format(band, FLUX),
'{}_modelfit_CModel_{}{}'.format(band, FLUX, ERR),
)
if has_modelfit_mag:
modifiers['mag_{}_cModel'.format(band)] = '{}_modelfit_mag'.format(band)
modifiers['magerr_{}_cModel'.format(band)] = '{}_modelfit_mag_err'.format(band)
modifiers['snr_{}_cModel'.format(band)] = '{}_modelfit_SNR'.format(band)
return modifiers
@staticmethod
def _generate_info_dict(meta_path, bands='ugrizy'):
"""Creates a 2d dictionary with information for each homogenized quantity
Separate entries for each band are created for any key in the yaml
dictionary at meta_path containing the substring '<band>'.
Args:
meta_path (path): Path of yaml config file with object meta data
bands (list): List of photometric bands as strings
Returns:
Dictionary of the form
{<homonogized value (str)>: {<meta value (str)>: <meta data>}, ...}
"""
with open(meta_path, 'r') as ofile:
base_dict = yaml.safe_load(ofile)
info_dict = dict()
for quantity, info_list in base_dict.items():
quantity_info = dict(
description=info_list[0],
unit=info_list[1],
in_GCRbase=info_list[2],
in_DPDD=info_list[3]
)
if '<band>' in quantity:
for band in bands:
band_quantity = quantity.replace('<band>', band)
band_quantity_info = quantity_info.copy()
band_quantity_info['description'] = band_quantity_info['description'].replace('`<band>`', '{} band'.format(band))
info_dict[band_quantity] = band_quantity_info
else:
info_dict[quantity] = quantity_info
return info_dict
def _get_quantity_info_dict(self, quantity, default=None):
"""Return a dictionary with descriptive information for a quantity
Returned information includes a quantity description, quantity units, whether
the quantity is defined in the DPDD, and if the quantity is available in GCRbase.
Args:
quantity (str): The quantity to return information for
default (object): Value to return if no information is available (default None)
Returns:
A dictionary with information about the provided quantity
"""
return self._quantity_info_dict.get(quantity, default)
def _generate_datasets(self):
"""Return viable data sets from all files in self.base_dir
Returns:
A list of ObjectTableWrapper(<file path>, <key>) objects
for all files and keys
"""
datasets = list()
for fname in sorted(os.listdir(self.base_dir)):
if not self._filename_re.match(fname):
continue
file_path = os.path.join(self.base_dir, fname)
try:
fh = self._open_hdf5(file_path)
except (IOError, OSError) as e:
warnings.warn('Cannot access {}; skipped'.format(file_path))
print(e)
continue
for key in fh:
if self._groupname_re.match(key.lstrip('/')):
datasets.append(ObjectTableWrapper(fh, key, self._schema))
continue
warn_msg = 'incorrect group name "{}" in {}; skipped this group'
warnings.warn(warn_msg.format(os.path.basename(file_path), key))
return datasets
@staticmethod
def _generate_schema_from_yaml(schema_path):
"""Return a dictionary of columns based on schema in YAML file
Args:
schema_path (string): <file path> to schema file.
Returns:
The columns defined in the schema.
A dictionary of {<column_name>: {'dtype': ..., 'default': ...}, ...}
Warns:
If one or more column names are repeated.
"""
schema = None
try:
with open(schema_path, 'r') as schema_stream:
schema = yaml.safe_load(schema_stream)
except (IOError, OSError, yaml.YAMLError):
pass
if schema is None:
warn_msg = 'No schema found or loaded in schema file {}'
warnings.warn(warn_msg.format(schema_path))
return schema
@staticmethod
def _generate_schema_from_datafiles(datasets):
"""Return the native schema for given datasets
Args:
datasets (list): A list of tuples (<file path>, <key>)
Returns:
A dict of schema ({col_name: {'dtype': dtype}}) found in all data sets
"""
schema = {}
for dataset in datasets:
schema.update(dataset.native_schema)
return schema
def generate_schema_yaml(self, overwrite=False):
"""
Generate the schema from the datafiles and write as a yaml file.
This function write the schema yaml file to the schema location specified for the catalog.
One needs to set `overwrite=True` to overwrite an existing schema file.
"""
if self._schema_path and os.path.isfile(self._schema_path):
if not overwrite:
raise RuntimeError('Schema file `{}` already exists! Set `overwrite=True` to overwrite.'.format(self._schema_path))
warnings.warn('Overwriting schema file `{0}`, which is backed up at `{0}.bak`'.format(self._schema_path))
shutil.copyfile(self._schema_path, self._schema_path + '.bak')
schema = self._generate_schema_from_datafiles(self._datasets)
for col, schema_this in schema.items():
if np.dtype(schema_this['dtype']).kind == 'b' and (
col.endswith('_flag_bad') or col.endswith('_flag_noGoodPixels')):
schema_this['default'] = True
with open(self._schema_path, 'w') as schema_stream:
yaml.dump(schema, schema_stream)
@property
def available_tracts_and_patches(self):
"""Return a list of available tracts and patches as dict objects
Returns:
A list of dictionaries of the form:
[{"tract": <tract (int)>, "patch": <patch (str)>}, ...]
"""
return [dataset.tract_and_patch for dataset in self._datasets]
@property
def available_tracts(self):
"""Returns a sorted list of available tracts
Returns:
A sorted list of available tracts as integers
"""
return sorted(set(dataset.tract for dataset in self._datasets))
def clear_cache(self):
"""Empty the catalog reader cache and frees up memory allocation"""
for dataset in self._datasets:
dataset.clear_cache()
def _open_hdf5(self, file_path):
"""Return the file handle of an HDF5 file as an pd.HDFStore object
Cache and return the file handle for the HDF5 file at <file_path>
Args:
file_path (str): The path of the desired file
Return:
The cached file handle
"""
if (file_path not in self._file_handles or
not self._file_handles[file_path].is_open):
self._file_handles[file_path] = pd.HDFStore(file_path, 'r')
return self._file_handles[file_path]
def close_all_file_handles(self):
"""Clear all cached file handles"""
if isinstance(getattr(self, "_file_handles", None), dict):
for fh in self._file_handles.values():
fh.close()
self._file_handles.clear()
def _generate_native_quantity_list(self):
"""Return a set of native quantity names as strings"""
return set(self._schema).union(self._native_filter_quantities)
def _iter_native_dataset(self, native_filters=None):
# pylint: disable=C0330
for dataset in self._datasets:
if (native_filters is None or
native_filters.check_scalar(dataset.tract_and_patch)):
yield dataset.get
if not self.use_cache:
dataset.clear_cache()
def __len__(self):
if self._len is None:
# pylint: disable=attribute-defined-outside-init
self._len = sum(len(dataset) for dataset in self._datasets)
return self._len
class DC2ObjectParquetCatalog(DC2DMTractCatalog):
r"""DC2 Object (Parquet) Catalog reader
Parameters
----------
base_dir (str): Directory of data files being served, required
filename_pattern (str): The optional regex pattern of served data files
is_dpdd (bool): File are already in DPDD-format. No translation.
Attributes
----------
base_dir (str): The directory of data files being served
"""
def _subclass_init(self, **kwargs):
self.FILE_PATTERN = r'object_tract_\d+\.parquet$'
self.META_PATH = META_PATH
self._default_pixel_scale = 0.2
self.pixel_scale = float(kwargs.get('pixel_scale', self._default_pixel_scale))
super()._subclass_init(**kwargs)
def _detect_available_bands(self):
return [col.rpartition('_')[0] for col in self._columns if col.endswith('_FLUXMAG0')]
@staticmethod
def _generate_modifiers(dm_schema_version=4, bands='ugrizy', pixel_scale=0.2, **kwargs): # pylint: disable=arguments-differ
"""Creates a dictionary relating native and homogenized column names
Args:
pixel_scale (float): Scale of pixels in coadd images
bands (list): List of photometric bands as strings
Returns:
A dictionary of the form {<homogenized name>: <native name>, ...}
"""
FLUX = 'flux' if dm_schema_version <= 2 else 'instFlux'
ERR = 'Sigma' if dm_schema_version <= 1 else 'Err'
modifiers = {
'objectId': 'id',
'parentObjectId': 'parent',
'ra': (np.rad2deg, 'coord_ra'),
'dec': (np.rad2deg, 'coord_dec'),
'x': 'base_SdssCentroid_x',
'y': 'base_SdssCentroid_y',
'xErr': f'base_SdssCentroid_x{ERR}',
'yErr': f'base_SdssCentroid_y{ERR}',
'xy_flag': 'base_SdssCentroid_flag',
'psNdata': 'base_PsfFlux_area',
'extendedness': 'base_ClassificationExtendedness_value',
'blendedness': 'base_Blendedness_abs',
}
not_good_flags = (
'base_PixelFlags_flag_edge',
'base_PixelFlags_flag_interpolatedCenter',
'base_PixelFlags_flag_saturatedCenter',
'base_PixelFlags_flag_crCenter',
'base_PixelFlags_flag_bad',
'base_PixelFlags_flag_suspectCenter',
'base_PixelFlags_flag_clipped',
)
modifiers['good'] = (create_basic_flag_mask,) + not_good_flags
modifiers['clean'] = (
create_basic_flag_mask,
'deblend_skipped',
) + not_good_flags
# cross-band average, second moment values
modifiers['I_flag'] = 'ext_shapeHSM_HsmSourceMoments_flag'
for ax in ['xx', 'yy', 'xy']:
modifiers[f'I{ax}'] = f'ext_shapeHSM_HsmSourceMoments_{ax}'
modifiers[f'I{ax}PSF'] = f'base_SdssShape_psf_{ax}'
for band in bands:
modifiers[f'psFlux_{band}'] = (convert_flux_to_nanoJansky,
f'{band}_slot_PsfFlux_{FLUX}',
f'{band}_FLUXMAG0')
modifiers[f'psFlux_flag_{band}'] = f'{band}_slot_PsfFlux_flag'
modifiers[f'psFluxErr_{band}'] = (convert_flux_to_nanoJansky,
f'{band}_slot_PsfFlux_{FLUX}{ERR}',
f'{band}_FLUXMAG0')
modifiers[f'mag_{band}'] = (convert_flux_to_mag,
f'{band}_slot_PsfFlux_{FLUX}',
f'{band}_FLUXMAG0')
modifiers[f'magerr_{band}'] = (convert_flux_err_to_mag_err,
f'{band}_slot_PsfFlux_{FLUX}',
f'{band}_slot_PsfFlux_{FLUX}{ERR}')
modifiers[f'cModelFlux_{band}'] = (convert_flux_to_nanoJansky,
f'{band}_modelfit_CModel_{FLUX}',
f'{band}_FLUXMAG0')
modifiers[f'cModelFluxErr_{band}'] = (convert_flux_to_nanoJansky,
f'{band}_modelfit_CModel_{FLUX}{ERR}',
f'{band}_FLUXMAG0')
modifiers[f'cModelFlux_flag_{band}'] = f'{band}_modelfit_CModel_flag'
modifiers[f'mag_{band}_cModel'] = (convert_flux_to_mag,
f'{band}_modelfit_CModel_{FLUX}',
f'{band}_FLUXMAG0')
modifiers[f'magerr_{band}_cModel'] = (convert_flux_err_to_mag_err,
f'{band}_modelfit_CModel_{FLUX}',
f'{band}_modelfit_CModel_{FLUX}{ERR}')
modifiers[f'snr_{band}_cModel'] = (np.divide,
f'{band}_modelfit_CModel_{FLUX}',
f'{band}_modelfit_CModel_{FLUX}{ERR}')
# Per-band shape information
modifiers[f'I_flag_{band}'] = f'{band}_base_SdssShape_flag'
for ax in ['xx', 'yy', 'xy']:
modifiers[f'I{ax}_{band}'] = f'{band}_base_SdssShape_{ax}'
modifiers[f'I{ax}PSF_{band}'] = f'{band}_base_SdssShape_psf_{ax}'
modifiers[f'psf_fwhm_{band}'] = (
lambda xx, yy, xy: pixel_scale * 2.355 * (xx * yy - xy * xy) ** 0.25,
f'{band}_base_SdssShape_psf_xx',
f'{band}_base_SdssShape_psf_yy',
f'{band}_base_SdssShape_psf_xy')
return modifiers
