def update_truth(filepath, hdu=2, chunksize=50000, skip=('SLOPES', 'EMLINES')):
"""Add data from columns in other HDUs of the Truth table.
Parameters
----------
filepath : :class:`str`
Full path to the data file.
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 2).
chunksize : :class:`int`, optional
If set, update database `chunksize` rows at a time (default 50000).
skip : :func:`tuple`, optional
Do not load columns with these names (default, ``('SLOPES', 'EMLINES')``)
"""
tcls = Truth
tn = tcls.__tablename__
t = tcls.__table__
if filepath.endswith('.fits'):
with fits.open(filepath) as hdulist:
data = hdulist[hdu].data
elif filepath.endswith('.ecsv'):
data = Table.read(filepath, format='ascii.ecsv')
else:
log.error("Unrecognized data file, %s!", filepath)
return
log.info("Read data from %s HDU %s", filepath, hdu)
try:
colnames = data.names
except AttributeError:
colnames = data.colnames
for col in colnames:
if data[col].dtype.kind == 'f':
bad = np.isnan(data[col])
if np.any(bad):
nbad = bad.sum()
log.warning("%d rows of bad data detected in column " +
"%s of %s.", nbad, col, filepath)
log.info("Integrity check complete on %s.", tn)
# if rowfilter is None:
# good_rows = np.ones((maxrows,), dtype=np.bool)
# else:
# good_rows = rowfilter(data[0:maxrows])
# data_list = [data[col][0:maxrows][good_rows].tolist() for col in colnames]
data_list = [data[col].tolist() for col in colnames if col not in skip]
data_names = [col.lower() for col in colnames if col not in skip]
data_names[0] = 'b_targetid'
finalrows = len(data_list[0])
log.info("Initial column conversion complete on %s.", tn)
del data
data_rows = list(zip(*data_list))
del data_list
log.info("Converted columns into rows on %s.", tn)
for k in range(finalrows//chunksize + 1):
data_chunk = [dict(zip(data_names, row))
for row in data_rows[k*chunksize:(k+1)*chunksize]]
q = t.update().where(t.c.targetid == bindparam('b_targetid'))
if len(data_chunk) > 0:
engine.execute(q, data_chunk)
log.info("Updated %d rows in %s.",
min((k+1)*chunksize, finalrows), tn)
def load_fiberassign(datapath,
maxpass=4,
hdu='FIBERASSIGN',
q3c=False,
latest_epoch=False,
last_column='NUMOBS_MORE'):
"""Load fiber assignment files into the fiberassign table.
Tile files can appear in multiple epochs, so for a given tileid, load
the tile file with the largest value of epoch. In the "real world",
a tile file appears in each epoch until it is observed, therefore
the tile file corresponding to the actual observation is the one
with the largest epoch.
Parameters
----------
datapath : :class:`str`
Full path to the directory containing tile files.
maxpass : :class:`int`, optional
Search for pass numbers up to this value (default 4).
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 'FIBERASSIGN').
q3c : :class:`bool`, optional
If set, create q3c index on the table.
latest_epoch : :class:`bool`, optional
If set, search for the latest tile file among several epochs.
last_column : :class:`str`, optional
Do not load columns past this name (default 'NUMOBS_MORE').
"""
fiberpath = os.path.join(datapath, 'fiberassign*.fits')
log.info("Using tile file search path: %s.", fiberpath)
tile_files = glob.glob(fiberpath)
if len(tile_files) == 0:
log.error("No tile files found!")
return
log.info("Found %d tile files.", len(tile_files))
#
# Find the latest epoch for every tile file.
#
latest_tiles = dict()
if latest_epoch:
tileidre = re.compile(r'/(\d+)/fiberassign/fiberassign\-(\d+)\.fits$')
for f in tile_files:
m = tileidre.search(f)
if m is None:
log.error("Could not match %s!", f)
continue
epoch, tileid = map(int, m.groups())
if tileid in latest_tiles:
if latest_tiles[tileid][0] < epoch:
latest_tiles[tileid] = (epoch, f)
else:
latest_tiles[tileid] = (epoch, f)
else:
for f in tile_files:
# fiberassign-TILEID.fits
tileid = int(
re.match('fiberassign\-(\d+)\.fits', os.path.basename(f))[1])
latest_tiles[tileid] = (0, f)
log.info("Identified %d tile files for loading.", len(latest_tiles))
#
# Read the identified tile files.
#
data_index = None
for tileid in latest_tiles:
epoch, f = latest_tiles[tileid]
with fits.open(f) as hdulist:
data = hdulist[hdu].data
log.info("Read data from %s HDU %s", f, hdu)
for col in data.names[:data_index]:
if data[col].dtype.kind == 'f':
bad = np.isnan(data[col])
if np.any(bad):
nbad = bad.sum()
log.warning(
"%d rows of bad data detected in column " +
"%s of %s.", nbad, col, f)
#
# This replacement may be deprecated in the future.
#
if col in ('TARGET_RA', 'TARGET_DEC', 'FIBERASSIGN_X',
'FIBERASSIGN_Y'):
data[col][bad] = -9999.0
assert not np.any(np.isnan(data[col]))
assert np.all(np.isfinite(data[col]))
n_rows = len(data)
if data_index is None:
data_index = data.names.index(last_column) + 1
data_list = ([[tileid] * n_rows] +
[data[col].tolist() for col in data.names[:data_index]])
data_names = ['tileid'
] + [col.lower() for col in data.names[:data_index]]
log.info("Initial column conversion complete on tileid = %d.", tileid)
data_rows = list(zip(*data_list))
log.info("Converted columns into rows on tileid = %d.", tileid)
dbSession.bulk_insert_mappings(
FiberAssign, [dict(zip(data_names, row)) for row in data_rows])
log.info("Inserted %d rows in %s for tileid = %d.", n_rows,
FiberAssign.__tablename__, tileid)
dbSession.commit()
if q3c:
q3c_index('fiberassign', ra='target_ra')
return
def load_zbest(datapath=None, hdu='ZBEST', q3c=False):
"""Load zbest files into the zcat table.
This function is deprecated since there should now be a single
redshift catalog file.
Parameters
----------
datapath : :class:`str`
Full path to the directory containing zbest files.
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 'ZBEST').
q3c : :class:`bool`, optional
If set, create q3c index on the table.
"""
if datapath is None:
datapath = specprod_root()
zbestpath = os.path.join(datapath, 'spectra-64', '*', '*',
'zbest-64-*.fits')
log.info("Using zbest file search path: %s.", zbestpath)
zbest_files = glob.glob(zbestpath)
if len(zbest_files) == 0:
log.error("No zbest files found!")
return
log.info("Found %d zbest files.", len(zbest_files))
#
# Read the identified zbest files.
#
for f in zbest_files:
brickname = os.path.basename(os.path.dirname(f))
with fits.open(f) as hdulist:
data = hdulist[hdu].data
log.info("Read data from %s HDU %s.", f, hdu)
good_targetids = ((data['TARGETID'] != 0) & (data['TARGETID'] != -1))
#
# If there are too many targetids, the in_ clause will blow up.
# Disabling this test, and crossing fingers.
#
# q = dbSession.query(ZCat).filter(ZCat.targetid.in_(data['TARGETID'].tolist())).all()
# if len(q) != 0:
# log.warning("Duplicate TARGETID found in %s.", f)
# for z in q:
# log.warning("Duplicate TARGETID = %d.", z.targetid)
# good_targetids = good_targetids & (data['TARGETID'] != z.targetid)
data_list = [data[col][good_targetids].tolist() for col in data.names]
data_names = [col.lower() for col in data.names]
log.info("Initial column conversion complete on brick = %s.",
brickname)
#
# Expand COEFF
#
col = 'COEFF'
expand = (
'coeff_0',
'coeff_1',
'coeff_2',
'coeff_3',
'coeff_4',
'coeff_5',
'coeff_6',
'coeff_7',
'coeff_8',
'coeff_9',
)
i = data_names.index(col.lower())
del data_names[i]
del data_list[i]
for j, n in enumerate(expand):
log.debug("Expanding column %d of %s (at index %d) to %s.", j, col,
i, n)
data_names.insert(i + j, n)
data_list.insert(i + j, data[col][:, j].tolist())
log.debug(data_names)
#
# zbest files don't contain the same columns as zcatalog.
#
for col in ZCat.__table__.columns:
if col.name not in data_names:
data_names.append(col.name)
data_list.append([0] * len(data_list[0]))
data_rows = list(zip(*data_list))
log.info("Converted columns into rows on brick = %s.", brickname)
try:
dbSession.bulk_insert_mappings(
ZCat, [dict(zip(data_names, row)) for row in data_rows])
except IntegrityError as e:
log.error("Integrity Error detected!")
log.error(e)
dbSession.rollback()
else:
log.info("Inserted %d rows in %s for brick = %s.", len(data_rows),
ZCat.__tablename__, brickname)
dbSession.commit()
if q3c:
q3c_index('zcat')
return
def load_file(filepath,
tcls,
hdu=1,
expand=None,
convert=None,
index=None,
rowfilter=None,
q3c=False,
chunksize=50000,
maxrows=0):
"""Load a data file into the database, assuming that column names map
to database column names with no surprises.
Parameters
----------
filepath : :class:`str`
Full path to the data file.
tcls : :class:`sqlalchemy.ext.declarative.api.DeclarativeMeta`
The table to load, represented by its class.
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 1).
expand : :class:`dict`, optional
If set, map FITS column names to one or more alternative column names.
convert : :class:`dict`, optional
If set, convert the data for a named (database) column using the
supplied function.
index : :class:`str`, optional
If set, add a column that just counts the number of rows.
rowfilter : callable, optional
If set, apply this filter to the rows to be loaded. The function
should return :class:`bool`, with ``True`` meaning a good row.
q3c : :class:`bool`, optional
If set, create q3c index on the table.
chunksize : :class:`int`, optional
If set, load database `chunksize` rows at a time (default 50000).
maxrows : :class:`int`, optional
If set, stop loading after `maxrows` are loaded. Alteratively,
set `maxrows` to zero (0) to load all rows.
"""
tn = tcls.__tablename__
if filepath.endswith('.fits'):
with fits.open(filepath) as hdulist:
data = hdulist[hdu].data
elif filepath.endswith('.ecsv'):
data = Table.read(filepath, format='ascii.ecsv')
else:
log.error("Unrecognized data file, %s!", filepath)
return
if maxrows == 0:
maxrows = len(data)
log.info("Read data from %s HDU %s", filepath, hdu)
try:
colnames = data.names
except AttributeError:
colnames = data.colnames
for col in colnames:
if data[col].dtype.kind == 'f':
bad = np.isnan(data[col][0:maxrows])
if np.any(bad):
nbad = bad.sum()
log.warning(
"%d rows of bad data detected in column " + "%s of %s.",
nbad, col, filepath)
#
# Temporary workaround for bad flux values, see
# https://github.com/desihub/desitarget/issues/397
#
if col in ('FLUX_R', 'FIBERFLUX_R', 'FIBERTOTFLUX_R'):
data[col][0:maxrows][bad] = -9999.0
log.info("Integrity check complete on %s.", tn)
if rowfilter is None:
good_rows = np.ones((maxrows, ), dtype=np.bool)
else:
good_rows = rowfilter(data[0:maxrows])
data_list = [data[col][0:maxrows][good_rows].tolist() for col in colnames]
data_names = [col.lower() for col in colnames]
finalrows = len(data_list[0])
log.info("Initial column conversion complete on %s.", tn)
if expand is not None:
for col in expand:
i = data_names.index(col.lower())
if isinstance(expand[col], str):
#
# Just rename a column.
#
log.debug("Renaming column %s (at index %d) to %s.",
data_names[i], i, expand[col])
data_names[i] = expand[col]
else:
#
# Assume this is an expansion of an array-valued column
# into individual columns.
#
del data_names[i]
del data_list[i]
for j, n in enumerate(expand[col]):
log.debug("Expanding column %d of %s (at index %d) to %s.",
j, col, i, n)
data_names.insert(i + j, n)
data_list.insert(i + j, data[col][:, j].tolist())
log.debug(data_names)
log.info("Column expansion complete on %s.", tn)
del data
if convert is not None:
for col in convert:
i = data_names.index(col)
data_list[i] = [convert[col](x) for x in data_list[i]]
log.info("Column conversion complete on %s.", tn)
if index is not None:
data_list.insert(0, list(range(1, finalrows + 1)))
data_names.insert(0, index)
log.info("Added index column '%s'.", index)
data_rows = list(zip(*data_list))
del data_list
log.info("Converted columns into rows on %s.", tn)
for k in range(finalrows // chunksize + 1):
data_chunk = [
dict(zip(data_names, row))
for row in data_rows[k * chunksize:(k + 1) * chunksize]
]
if len(data_chunk) > 0:
engine.execute(tcls.__table__.insert(), data_chunk)
log.info("Inserted %d rows in %s.",
min((k + 1) * chunksize, finalrows), tn)
# for k in range(finalrows//chunksize + 1):
# data_insert = [dict([(col, data_list[i].pop(0))
# for i, col in enumerate(data_names)])
# for j in range(chunksize)]
# session.bulk_insert_mappings(tcls, data_insert)
# log.info("Inserted %d rows in %s..",
# min((k+1)*chunksize, finalrows), tn)
# session.commit()
# dbSession.commit()
if q3c:
q3c_index(tn)
return
def load_fiberassign(datapath, maxpass=4, hdu='FIBERASSIGN', q3c=False,
latest_epoch=False, last_column='SUBPRIORITY'):
"""Load fiber assignment files into the fiberassign table.
Tile files can appear in multiple epochs, so for a given tileid, load
the tile file with the largest value of epoch. In the "real world",
a tile file appears in each epoch until it is observed, therefore
the tile file corresponding to the actual observation is the one
with the largest epoch.
Parameters
----------
datapath : :class:`str`
Full path to the directory containing tile files.
maxpass : :class:`int`, optional
Search for pass numbers up to this value (default 4).
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 'FIBERASSIGN').
q3c : :class:`bool`, optional
If set, create q3c index on the table.
latest_epoch : :class:`bool`, optional
If set, search for the latest tile file among several epochs.
last_column : :class:`str`, optional
Do not load columns past this name (default 'BRICKNAME').
"""
fiberpath = os.path.join(datapath, 'tile*.fits')
log.info("Using tile file search path: %s.", fiberpath)
tile_files = glob.glob(fiberpath)
if len(tile_files) == 0:
log.error("No tile files found!")
return
log.info("Found %d tile files.", len(tile_files))
#
# Find the latest epoch for every tile file.
#
latest_tiles = dict()
if latest_epoch:
tileidre = re.compile(r'/(\d+)/fiberassign/tile-(\d+)\.fits$')
for f in tile_files:
m = tileidre.search(f)
if m is None:
log.error("Could not match %s!", f)
continue
epoch, tileid = map(int, m.groups())
if tileid in latest_tiles:
if latest_tiles[tileid][0] < epoch:
latest_tiles[tileid] = (epoch, f)
else:
latest_tiles[tileid] = (epoch, f)
else:
for f in tile_files:
# tile_TILEID.fits or tile-TILEID.fits
tileid = int(re.match('tile[\-_](\d+)\.fits',
os.path.basename(f))[1])
latest_tiles[tileid] = (0, f)
log.info("Identified %d tile files for loading.", len(latest_tiles))
#
# Read the identified tile files.
#
data_index = None
for tileid in latest_tiles:
epoch, f = latest_tiles[tileid]
with fits.open(f) as hdulist:
data = hdulist[hdu].data
log.info("Read data from %s HDU %s", f, hdu)
for col in data.names[:data_index]:
if data[col].dtype.kind == 'f':
bad = np.isnan(data[col])
if np.any(bad):
nbad = bad.sum()
log.warning("%d rows of bad data detected in column " +
"%s of %s.", nbad, col, f)
#
# This replacement may be deprecated in the future.
#
if col in ('TARGET_RA', 'TARGET_DEC', 'DESIGN_X', 'DESIGN_Y'):
data[col][bad] = -9999.0
assert not np.any(np.isnan(data[col]))
assert np.all(np.isfinite(data[col]))
n_rows = len(data)
if data_index is None:
data_index = data.names.index(last_column) + 1
data_list = ([[tileid]*n_rows] +
[data[col].tolist() for col in data.names[:data_index]])
data_names = ['tileid'] + [col.lower() for col in data.names[:data_index]]
log.info("Initial column conversion complete on tileid = %d.", tileid)
data_rows = list(zip(*data_list))
log.info("Converted columns into rows on tileid = %d.", tileid)
dbSession.bulk_insert_mappings(FiberAssign, [dict(zip(data_names, row))
for row in data_rows])
log.info("Inserted %d rows in %s for tileid = %d.",
n_rows, FiberAssign.__tablename__, tileid)
dbSession.commit()
if q3c:
q3c_index('fiberassign', ra='target_ra')
return
def load_zbest(datapath=None, hdu='ZBEST', q3c=False):
"""Load zbest files into the zcat table.
This function is deprecated since there should now be a single
redshift catalog file.
Parameters
----------
datapath : :class:`str`
Full path to the directory containing zbest files.
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 'ZBEST').
q3c : :class:`bool`, optional
If set, create q3c index on the table.
"""
if datapath is None:
datapath = specprod_root()
zbestpath = os.path.join(datapath, 'spectra-64', '*', '*', 'zbest-64-*.fits')
log.info("Using zbest file search path: %s.", zbestpath)
zbest_files = glob.glob(zbestpath)
if len(zbest_files) == 0:
log.error("No zbest files found!")
return
log.info("Found %d zbest files.", len(zbest_files))
#
# Read the identified zbest files.
#
for f in zbest_files:
brickname = os.path.basename(os.path.dirname(f))
with fits.open(f) as hdulist:
data = hdulist[hdu].data
log.info("Read data from %s HDU %s.", f, hdu)
good_targetids = ((data['TARGETID'] != 0) & (data['TARGETID'] != -1))
#
# If there are too many targetids, the in_ clause will blow up.
# Disabling this test, and crossing fingers.
#
# q = dbSession.query(ZCat).filter(ZCat.targetid.in_(data['TARGETID'].tolist())).all()
# if len(q) != 0:
# log.warning("Duplicate TARGETID found in %s.", f)
# for z in q:
# log.warning("Duplicate TARGETID = %d.", z.targetid)
# good_targetids = good_targetids & (data['TARGETID'] != z.targetid)
data_list = [data[col][good_targetids].tolist()
for col in data.names]
data_names = [col.lower() for col in data.names]
log.info("Initial column conversion complete on brick = %s.", brickname)
#
# Expand COEFF
#
col = 'COEFF'
expand = ('coeff_0', 'coeff_1', 'coeff_2', 'coeff_3', 'coeff_4',
'coeff_5', 'coeff_6', 'coeff_7', 'coeff_8', 'coeff_9',)
i = data_names.index(col.lower())
del data_names[i]
del data_list[i]
for j, n in enumerate(expand):
log.debug("Expanding column %d of %s (at index %d) to %s.", j, col, i, n)
data_names.insert(i + j, n)
data_list.insert(i + j, data[col][:, j].tolist())
log.debug(data_names)
#
# zbest files don't contain the same columns as zcatalog.
#
for col in ZCat.__table__.columns:
if col.name not in data_names:
data_names.append(col.name)
data_list.append([0]*len(data_list[0]))
data_rows = list(zip(*data_list))
log.info("Converted columns into rows on brick = %s.", brickname)
try:
dbSession.bulk_insert_mappings(ZCat, [dict(zip(data_names, row))
for row in data_rows])
except IntegrityError as e:
log.error("Integrity Error detected!")
log.error(e)
dbSession.rollback()
else:
log.info("Inserted %d rows in %s for brick = %s.",
len(data_rows), ZCat.__tablename__, brickname)
dbSession.commit()
if q3c:
q3c_index('zcat')
return
def load_file(filepath, tcls, hdu=1, expand=None, convert=None, index=None,
rowfilter=None, q3c=False, chunksize=50000, maxrows=0):
"""Load a data file into the database, assuming that column names map
to database column names with no surprises.
Parameters
----------
filepath : :class:`str`
Full path to the data file.
tcls : :class:`sqlalchemy.ext.declarative.api.DeclarativeMeta`
The table to load, represented by its class.
hdu : :class:`int` or :class:`str`, optional
Read a data table from this HDU (default 1).
expand : :class:`dict`, optional
If set, map FITS column names to one or more alternative column names.
convert : :class:`dict`, optional
If set, convert the data for a named (database) column using the
supplied function.
index : :class:`str`, optional
If set, add a column that just counts the number of rows.
rowfilter : callable, optional
If set, apply this filter to the rows to be loaded. The function
should return :class:`bool`, with ``True`` meaning a good row.
q3c : :class:`bool`, optional
If set, create q3c index on the table.
chunksize : :class:`int`, optional
If set, load database `chunksize` rows at a time (default 50000).
maxrows : :class:`int`, optional
If set, stop loading after `maxrows` are loaded. Alteratively,
set `maxrows` to zero (0) to load all rows.
"""
tn = tcls.__tablename__
if filepath.endswith('.fits'):
with fits.open(filepath) as hdulist:
data = hdulist[hdu].data
elif filepath.endswith('.ecsv'):
data = Table.read(filepath, format='ascii.ecsv')
else:
log.error("Unrecognized data file, %s!", filepath)
return
if maxrows == 0:
maxrows = len(data)
log.info("Read data from %s HDU %s", filepath, hdu)
try:
colnames = data.names
except AttributeError:
colnames = data.colnames
for col in colnames:
if data[col].dtype.kind == 'f':
bad = np.isnan(data[col][0:maxrows])
if np.any(bad):
nbad = bad.sum()
log.warning("%d rows of bad data detected in column " +
"%s of %s.", nbad, col, filepath)
#
# Temporary workaround for bad flux values, see
# https://github.com/desihub/desitarget/issues/397
#
if col in ('FLUX_R', 'FIBERFLUX_R', 'FIBERTOTFLUX_R'):
data[col][0:maxrows][bad] = -9999.0
log.info("Integrity check complete on %s.", tn)
if rowfilter is None:
good_rows = np.ones((maxrows,), dtype=np.bool)
else:
good_rows = rowfilter(data[0:maxrows])
data_list = [data[col][0:maxrows][good_rows].tolist() for col in colnames]
data_names = [col.lower() for col in colnames]
finalrows = len(data_list[0])
log.info("Initial column conversion complete on %s.", tn)
if expand is not None:
for col in expand:
i = data_names.index(col.lower())
if isinstance(expand[col], str):
#
# Just rename a column.
#
log.debug("Renaming column %s (at index %d) to %s.", data_names[i], i, expand[col])
data_names[i] = expand[col]
else:
#
# Assume this is an expansion of an array-valued column
# into individual columns.
#
del data_names[i]
del data_list[i]
for j, n in enumerate(expand[col]):
log.debug("Expanding column %d of %s (at index %d) to %s.", j, col, i, n)
data_names.insert(i + j, n)
data_list.insert(i + j, data[col][:, j].tolist())
log.debug(data_names)
log.info("Column expansion complete on %s.", tn)
del data
if convert is not None:
for col in convert:
i = data_names.index(col)
data_list[i] = [convert[col](x) for x in data_list[i]]
log.info("Column conversion complete on %s.", tn)
if index is not None:
data_list.insert(0, list(range(1, finalrows+1)))
data_names.insert(0, index)
log.info("Added index column '%s'.", index)
data_rows = list(zip(*data_list))
del data_list
log.info("Converted columns into rows on %s.", tn)
for k in range(finalrows//chunksize + 1):
data_chunk = [dict(zip(data_names, row))
for row in data_rows[k*chunksize:(k+1)*chunksize]]
if len(data_chunk) > 0:
engine.execute(tcls.__table__.insert(), data_chunk)
log.info("Inserted %d rows in %s.",
min((k+1)*chunksize, finalrows), tn)
# for k in range(finalrows//chunksize + 1):
# data_insert = [dict([(col, data_list[i].pop(0))
# for i, col in enumerate(data_names)])
# for j in range(chunksize)]
# session.bulk_insert_mappings(tcls, data_insert)
# log.info("Inserted %d rows in %s..",
# min((k+1)*chunksize, finalrows), tn)
# session.commit()
# dbSession.commit()
if q3c:
q3c_index(tn)
return