def testColumnNames(self):
"""
Test the method that returns the names of columns in a table
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
names = dbobj.get_column_names('doubleTable')
self.assertEqual(len(names), 3)
self.assertIn('id', names)
self.assertIn('sqrt', names)
self.assertIn('log', names)
names = dbobj.get_column_names('intTable')
self.assertEqual(len(names), 3)
self.assertIn('id', names)
self.assertIn('twice', names)
self.assertIn('thrice', names)
names = dbobj.get_column_names()
keys = ['doubleTable', 'intTable', 'junkTable']
for kk in names:
self.assertIn(kk, keys)
self.assertEqual(len(names['doubleTable']), 3)
self.assertEqual(len(names['intTable']), 3)
self.assertIn('id', names['doubleTable'])
self.assertIn('sqrt', names['doubleTable'])
self.assertIn('log', names['doubleTable'])
self.assertIn('id', names['intTable'])
self.assertIn('twice', names['intTable'])
self.assertIn('thrice', names['intTable'])
def testColumnNames(self):
"""
Test the method that returns the names of columns in a table
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
names = dbobj.get_column_names('doubleTable')
self.assertEqual(len(names), 3)
self.assertIn('id', names)
self.assertIn('sqrt', names)
self.assertIn('log', names)
names = dbobj.get_column_names('intTable')
self.assertEqual(len(names), 3)
self.assertIn('id', names)
self.assertIn('twice', names)
self.assertIn('thrice', names)
names = dbobj.get_column_names()
keys = ['doubleTable', 'intTable', 'junkTable']
for kk in names:
self.assertIn(kk, keys)
self.assertEqual(len(names['doubleTable']), 3)
self.assertEqual(len(names['intTable']), 3)
self.assertIn('id', names['doubleTable'])
self.assertIn('sqrt', names['doubleTable'])
self.assertIn('log', names['doubleTable'])
self.assertIn('id', names['intTable'])
self.assertIn('twice', names['intTable'])
self.assertIn('thrice', names['intTable'])
class dbInterface(object):
def __init__(self, database, host, port, driver):
self._dbo = DBObject(database=database, host=host, port=port,
driver=driver)
def forcedSourceFromId(self, objectId):
dtype = np.dtype([('object_id', np.int),
('ccd_visit_id', np.int),
('psf_flux', np.float),
('psf_flux_err', np.float),
('flags', np.int)])
query = """select objectId, ccdVisitId, psFlux, psFlux_Sigma, flags
from ForcedSource
where objectId = %i""" % objectId
results = self._dbo.execute_arbitrary(query, dtype=dtype)
return results
def visitFromCcdVisitId(self, visitId):
dtype = np.dtype([('visit_id', np.int),
('filter', str, 300),
('obs_start', str, 300)])
query = """select visitId, filterName, obsStart
from CcdVisit
where ccdVisitId = %i""" % visitId
results = self._dbo.execute_arbitrary(query, dtype=dtype)
return results
def objectFromId(self, objectId):
dtype = np.dtype([('object_id', np.int),
('parent_object_id', np.int),
('ra', np.float),
('dec', np.float)])
query = """select objectId, parentObjectId, psRa, psDecl
from Object
where objectId = %i""" % objectId
results = self._dbo.execute_arbitrary(query, dtype=dtype)
return results
def objectFromRaDec(self, ra, dec, tol):
dtype = np.dtype([('object_id', np.int),
('parent_object_id', np.int),
('ra', np.float),
('dec', np.float)])
query = """select objectId, parentObjectId, psRa, psDecl
from Object
where (psRa > %f) and (psRa < %f)
and (psDecl > %f) and (psDecl < %f)""" % (ra - tol,
ra + tol,
dec - tol,
dec + tol)
results = self._dbo.execute_arbitrary(query, dtype=dtype)
return results
def testTableNames(self):
"""
Test the method that returns the names of tables in a database
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
names = dbobj.get_table_names()
self.assertEqual(len(names), 3)
self.assertIn('doubleTable', names)
self.assertIn('intTable', names)
def testTableNames(self):
"""
Test the method that returns the names of tables in a database
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
names = dbobj.get_table_names()
self.assertEqual(len(names), 3)
self.assertIn('doubleTable', names)
self.assertIn('intTable', names)
def testMinMax(self):
"""
Test queries on SQL functions by using the MIN and MAX functions
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT MAX(thrice), MIN(thrice) FROM intTable'
results = dbobj.execute_arbitrary(query)
self.assertEqual(results[0][0], 594)
self.assertEqual(results[0][1], 0)
dtype = [('MAXthrice', int), ('MINthrice', int)]
self.assertEqual(results.dtype, dtype)
def testMinMax(self):
"""
Test queries on SQL functions by using the MIN and MAX functions
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT MAX(thrice), MIN(thrice) FROM intTable'
results = dbobj.execute_arbitrary(query)
self.assertEqual(results[0][0], 594)
self.assertEqual(results[0][1], 0)
dtype = [('MAXthrice', int), ('MINthrice', int)]
self.assertEqual(results.dtype, dtype)
def testReadOnlyFilter(self):
"""
Test that the filters we placed on queries made with execute_aribtrary()
work
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
controlQuery = 'SELECT doubleTable.id, intTable.id, doubleTable.log, intTable.thrice '
controlQuery += 'FROM doubleTable, intTable WHERE doubleTable.id = intTable.id'
dbobj.execute_arbitrary(controlQuery)
# make sure that execute_arbitrary only accepts strings
query = ['a', 'list']
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
# check that our filter catches different capitalization permutations of the
# verboten commands
query = 'DROP TABLE junkTable'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'DELETE FROM junkTable WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'UPDATE junkTable SET sqrt=0.0, log=0.0 WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'INSERT INTO junkTable VALUES (9999, 1.0, 1.0)'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'Drop Table junkTable'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'Delete FROM junkTable WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'Update junkTable SET sqrt=0.0, log=0.0 WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'Insert INTO junkTable VALUES (9999, 1.0, 1.0)'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'dRoP TaBlE junkTable'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'dElEtE FROM junkTable WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'uPdAtE junkTable SET sqrt=0.0, log=0.0 WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'iNsErT INTO junkTable VALUES (9999, 1.0, 1.0)'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
def testReadOnlyFilter(self):
"""
Test that the filters we placed on queries made with execute_aribtrary()
work
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
controlQuery = 'SELECT doubleTable.id, intTable.id, doubleTable.log, intTable.thrice '
controlQuery += 'FROM doubleTable, intTable WHERE doubleTable.id = intTable.id'
dbobj.execute_arbitrary(controlQuery)
# make sure that execute_arbitrary only accepts strings
query = ['a', 'list']
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
# check that our filter catches different capitalization permutations of the
# verboten commands
query = 'DROP TABLE junkTable'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'DELETE FROM junkTable WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'UPDATE junkTable SET sqrt=0.0, log=0.0 WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'INSERT INTO junkTable VALUES (9999, 1.0, 1.0)'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query.lower())
query = 'Drop Table junkTable'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'Delete FROM junkTable WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'Update junkTable SET sqrt=0.0, log=0.0 WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'Insert INTO junkTable VALUES (9999, 1.0, 1.0)'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'dRoP TaBlE junkTable'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'dElEtE FROM junkTable WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'uPdAtE junkTable SET sqrt=0.0, log=0.0 WHERE id=4'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
query = 'iNsErT INTO junkTable VALUES (9999, 1.0, 1.0)'
self.assertRaises(RuntimeError, dbobj.execute_arbitrary, query)
def testSingleTableQuery(self):
"""
Test a query on a single table (using chunk iterator)
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT id, sqrt FROM doubleTable'
results = dbobj.get_chunk_iterator(query)
dtype = [('id', int),
('sqrt', float)]
i = 1
for chunk in results:
for row in chunk:
self.assertEqual(row[0], i)
self.assertAlmostEqual(row[1], np.sqrt(i))
self.assertEqual(dtype, row.dtype)
i += 1
self.assertEqual(i, 201)
def testSingleTableQuery(self):
"""
Test a query on a single table (using chunk iterator)
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT id, sqrt FROM doubleTable'
results = dbobj.get_chunk_iterator(query)
dtype = [('id', int),
('sqrt', float)]
i = 1
for chunk in results:
for row in chunk:
self.assertEqual(row[0], i)
self.assertAlmostEqual(row[1], np.sqrt(i))
self.assertEqual(dtype, row.dtype)
i += 1
self.assertEqual(i, 201)
def testPassingConnection(self):
"""
Repeat the test from testJoin, but with a DBObject whose connection was passed
directly from another DBObject, to make sure that passing a connection works
"""
dbobj_base = DBObject(driver=self.driver, database=self.db_name)
dbobj = DBObject(connection=dbobj_base.connection)
query = 'SELECT doubleTable.id, intTable.id, doubleTable.log, intTable.thrice '
query += 'FROM doubleTable, intTable WHERE doubleTable.id = intTable.id'
results = dbobj.get_chunk_iterator(query, chunk_size=10)
dtype = [
('id', int),
('id_1', int),
('log', float),
('thrice', int)]
i = 0
for chunk in results:
if i < 90:
self.assertEqual(len(chunk), 10)
for row in chunk:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
self.assertEqual(dtype, row.dtype)
i += 1
self.assertEqual(i, 99)
# make sure that we found all the matches whe should have
results = dbobj.execute_arbitrary(query)
self.assertEqual(dtype, results.dtype)
i = 0
for row in results:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
i += 1
self.assertEqual(i, 99)
def testPassingConnection(self):
"""
Repeat the test from testJoin, but with a DBObject whose connection was passed
directly from another DBObject, to make sure that passing a connection works
"""
dbobj_base = DBObject(driver=self.driver, database=self.db_name)
dbobj = DBObject(connection=dbobj_base.connection)
query = 'SELECT doubleTable.id, intTable.id, doubleTable.log, intTable.thrice '
query += 'FROM doubleTable, intTable WHERE doubleTable.id = intTable.id'
results = dbobj.get_chunk_iterator(query, chunk_size=10)
dtype = [
('id', int),
('id_1', int),
('log', float),
('thrice', int)]
i = 0
for chunk in results:
if i < 90:
self.assertEqual(len(chunk), 10)
for row in chunk:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
self.assertEqual(dtype, row.dtype)
i += 1
self.assertEqual(i, 99)
# make sure that we found all the matches whe should have
results = dbobj.execute_arbitrary(query)
self.assertEqual(dtype, results.dtype)
i = 0
for row in results:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
i += 1
self.assertEqual(i, 99)
def testDtype(self):
"""
Test that passing dtype to a query works
(also test q query on a single table using .execute_arbitrary() directly
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT id, log FROM doubleTable'
dtype = [('id', int), ('log', float)]
results = dbobj.execute_arbitrary(query, dtype = dtype)
self.assertEqual(results.dtype, dtype)
for xx in results:
self.assertAlmostEqual(np.log(xx[0]), xx[1], 6)
self.assertEqual(len(results), 200)
results = dbobj.get_chunk_iterator(query, chunk_size=10, dtype=dtype)
next(results)
for chunk in results:
self.assertEqual(chunk.dtype, dtype)
def testDtype(self):
"""
Test that passing dtype to a query works
(also test q query on a single table using .execute_arbitrary() directly
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT id, log FROM doubleTable'
dtype = [('id', int), ('log', float)]
results = dbobj.execute_arbitrary(query, dtype = dtype)
self.assertEqual(results.dtype, dtype)
for xx in results:
self.assertAlmostEqual(np.log(xx[0]), xx[1], 6)
self.assertEqual(len(results), 200)
results = dbobj.get_chunk_iterator(query, chunk_size=10, dtype=dtype)
next(results)
for chunk in results:
self.assertEqual(chunk.dtype, dtype)
def testJoin(self):
"""
Test a join
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT doubleTable.id, intTable.id, doubleTable.log, intTable.thrice '
query += 'FROM doubleTable, intTable WHERE doubleTable.id = intTable.id'
results = dbobj.get_chunk_iterator(query, chunk_size=10)
dtype = [
('id', int),
('id_1', int),
('log', float),
('thrice', int)]
i = 0
for chunk in results:
if i < 90:
self.assertEqual(len(chunk), 10)
for row in chunk:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
self.assertEqual(dtype, row.dtype)
i += 1
self.assertEqual(i, 99)
# make sure that we found all the matches whe should have
results = dbobj.execute_arbitrary(query)
self.assertEqual(dtype, results.dtype)
i = 0
for row in results:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
i += 1
self.assertEqual(i, 99)
def testJoin(self):
"""
Test a join
"""
dbobj = DBObject(driver=self.driver, database=self.db_name)
query = 'SELECT doubleTable.id, intTable.id, doubleTable.log, intTable.thrice '
query += 'FROM doubleTable, intTable WHERE doubleTable.id = intTable.id'
results = dbobj.get_chunk_iterator(query, chunk_size=10)
dtype = [
('id', int),
('id_1', int),
('log', float),
('thrice', int)]
i = 0
for chunk in results:
if i < 90:
self.assertEqual(len(chunk), 10)
for row in chunk:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
self.assertEqual(dtype, row.dtype)
i += 1
self.assertEqual(i, 99)
# make sure that we found all the matches whe should have
results = dbobj.execute_arbitrary(query)
self.assertEqual(dtype, results.dtype)
i = 0
for row in results:
self.assertEqual(2*(i+1), row[0])
self.assertEqual(row[0], row[1])
self.assertAlmostEqual(np.log(row[0]), row[2], 6)
self.assertEqual(3*row[0], row[3])
i += 1
self.assertEqual(i, 99)
def testValidationErrors(self):
""" Test that appropriate errors and warnings are thrown when connecting
"""
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
DBObject('sqlite:///' + self.db_name)
assert len(w) == 1
# missing database
self.assertRaises(AttributeError, DBObject, driver=self.driver)
# missing driver
self.assertRaises(AttributeError, DBObject, database=self.db_name)
# missing host
self.assertRaises(AttributeError, DBObject, driver='mssql+pymssql')
# missing port
self.assertRaises(AttributeError, DBObject, driver='mssql+pymssql', host='localhost')
def get_table_mins(table_tag, dmag_dict,_out_dir):
db = DBObject(database='LSSTCATSIM', host='fatboy.phys.washington.edu',
port=1433, driver='mssql+pymssql')
query = 'SELECT '
query += 'htmid, simobjid, varParamStr '
query += 'FROM stars_obafgk_part_%s' % table_tag
dtype = np.dtype([('htmid', int), ('simobjid', int),
('varParamStr', str, 200)])
data_iter = get_arbitrary_chunk_iterator(query, dtype=dtype, chunk_size=10000)
with open(os.path.join(_out_dir,'dmag_%s.txt' % table_tag), 'w') as out_file:
for chunk in data_iter:
for star in chunk:
param_dict = json.loads(star['varParamStr'])
lc_id = param_dict['p']['lc']
out_file.write('%d %d %d\n' % (star['htmid'], star['simobjid'], dmag_dict[lc_id]))
def __init__(self, database=None, driver='sqlite', host=None, port=None):
"""
Constructor for the class
Parameters
----------
database : string
absolute path to the output of the OpSim database
driver : string, optional, defaults to 'sqlite'
driver/dialect for the SQL database
host : hostName, optional, defaults to None,
hostName, None is good for a local database
port : hostName, optional, defaults to None,
port, None is good for a local database
Returns
------
Instance of the ObserverMetaDataGenerator class
..notes : For testing purposes a small OpSim database is available at
`os.path.join(getPackageDir('sims_data'), 'OpSimData/opsimblitz1_1133_sqlite.db')`
"""
self._opsim_version = None
self.driver = driver
self.host = host
self.port = port
self.database = database
self._seeing_column = 'FWHMeff'
if self.database is None:
return
if not os.path.isfile(self.database):
raise RuntimeError('%s is not a file' % self.database)
self.opsimdb = DBObject(driver=self.driver, database=self.database,
host=self.host, port=self.port)
# 27 January 2016
# Detect whether the OpSim db you are connecting to uses 'finSeeing'
# as its seeing column (deprecated), or FWHMeff, which is the modern
# standard
list_of_tables = self.opsimdb.get_table_names()
if 'Summary' in list_of_tables:
self._opsim_version = 3
else:
self._opsim_version = 4
self._summary_columns = self.opsimdb.get_column_names(self.table_name)
self._set_seeing_column(self._summary_columns)
# Set up self.dtype containg the dtype of the recarray we expect back from the SQL query.
# Also setup baseQuery which is just the SELECT clause of the SQL query
#
# self.active_columns will be a list containing the subset of OpSim database columns
# (specified in self.user_interface_to_opsim) that actually exist in this opsim database
dtypeList = []
self.baseQuery = 'SELECT'
self.active_columns = []
self._queried_columns = [] # This will be a list of all of the
# OpSim columns queried
# Note: here we will refer to the
# columns by their names in OpSim
for column in self.user_interface_to_opsim:
rec = self.user_interface_to_opsim[column]
if rec[0] in self._summary_columns:
self.active_columns.append(column)
dtypeList.append((rec[0], rec[2]))
if self.baseQuery != 'SELECT':
self.baseQuery += ','
self.baseQuery += ' ' + rec[0]
self._queried_columns.append(rec[0])
# Now loop over self._summary_columns, adding any columns
# to the query that have not already been included therein.
# Since we do not have explicit information about the
# data types of these columns, we will assume they are floats.
for column in self._summary_columns:
if column not in self._queried_columns:
self.baseQuery += ', ' + column
dtypeList.append((column, float))
self.dtype = np.dtype(dtypeList)
def testDetectDtype(self):
"""
Test that DBObject.execute_arbitrary can correctly detect the dtypes
of the rows it is returning
"""
db_name = os.path.join(self.scratch_dir, 'testDBObject_dtype_DB.db')
if os.path.exists(db_name):
os.unlink(db_name)
conn = sqlite3.connect(db_name)
c = conn.cursor()
try:
c.execute('''CREATE TABLE testTable (id int, val real, sentence int)''')
conn.commit()
except:
raise RuntimeError("Error creating database.")
for ii in range(10):
cmd = '''INSERT INTO testTable VALUES (%d, %.5f, %s)''' % (ii, 5.234*ii, "'this, has; punctuation'")
c.execute(cmd)
conn.commit()
conn.close()
db = DBObject(database=db_name, driver='sqlite')
query = 'SELECT id, val, sentence FROM testTable WHERE id%2 = 0'
results = db.execute_arbitrary(query)
np.testing.assert_array_equal(results['id'], np.arange(0,9,2,dtype=int))
np.testing.assert_array_almost_equal(results['val'], 5.234*np.arange(0,9,2), decimal=5)
for sentence in results['sentence']:
self.assertEqual(sentence, 'this, has; punctuation')
self.assertEqual(str(results.dtype['id']), 'int64')
self.assertEqual(str(results.dtype['val']), 'float64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
self.assertEqual(len(results.dtype), 3)
# now test that it works when getting a ChunkIterator
chunk_iter = db.get_arbitrary_chunk_iterator(query, chunk_size=3)
ct = 0
for chunk in chunk_iter:
self.assertEqual(str(chunk.dtype['id']), 'int64')
self.assertEqual(str(chunk.dtype['val']), 'float64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
self.assertEqual(len(chunk.dtype), 3)
for line in chunk:
ct += 1
self.assertEqual(line['sentence'], 'this, has; punctuation')
self.assertAlmostEqual(line['val'], line['id']*5.234, 5)
self.assertEqual(line['id']%2, 0)
self.assertEqual(ct, 5)
# test that doing a different query does not spoil dtype detection
query = 'SELECT id, sentence FROM testTable WHERE id%2 = 0'
results = db.execute_arbitrary(query)
self.assertGreater(len(results), 0)
self.assertEqual(len(results.dtype.names), 2)
self.assertEqual(str(results.dtype['id']), 'int64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
query = 'SELECT id, val, sentence FROM testTable WHERE id%2 = 0'
chunk_iter = db.get_arbitrary_chunk_iterator(query, chunk_size=3)
ct = 0
for chunk in chunk_iter:
self.assertEqual(str(chunk.dtype['id']), 'int64')
self.assertEqual(str(chunk.dtype['val']), 'float64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
self.assertEqual(len(chunk.dtype), 3)
for line in chunk:
ct += 1
self.assertEqual(line['sentence'], 'this, has; punctuation')
self.assertAlmostEqual(line['val'], line['id']*5.234, 5)
self.assertEqual(line['id']%2, 0)
self.assertEqual(ct, 5)
if os.path.exists(db_name):
os.unlink(db_name)
"""
This script will produce delta_magnitude scatter plots of simulated flares
to compare with Figures 10 and 14 of Chang et al. 2015 (ApJ 814, 35)
"""
from __future__ import with_statement
from lsst.sims.catalogs.db import DBObject
import numpy as np
rng = np.random.RandomState(813)
_au_to_parsec = 1.0/206265.0
table = 'stars_mlt_part_1180'
db = DBObject(database='LSSTCATSIM', host='fatboy.phys.washington.edu',
port=1433, driver='mssql+pymssql')
query = 'SELECT TOP 100 gal_l, gal_b, parallax, '
query += 'sdssr, sdssi, sdssz, '
query += 'umag, gmag, rmag, imag, zmag, ymag '
query += 'FROM %s' % table
dtype = np.dtype([('lon', float), ('lat', float), ('parallax', float),
('sdssr', float), ('sdssi', float), ('sdssz', float),
('umag', float), ('gmag', float), ('rmag', float),
('imag', float), ('zmag', float), ('ymag', float)])
data = db.execute_arbitrary(query, dtype=dtype)
# parallax will be in milli arcsec
# magnorm = -2.5*log10(flux_scale)-18.402732642
def __init__(self, database=None, driver='sqlite', host=None, port=None):
"""
Constructor for the class
Parameters
----------
database : string
absolute path to the output of the OpSim database
driver : string, optional, defaults to 'sqlite'
driver/dialect for the SQL database
host : hostName, optional, defaults to None,
hostName, None is good for a local database
port : hostName, optional, defaults to None,
port, None is good for a local database
Returns
------
Instance of the ObserverMetaDataGenerator class
..notes : For testing purposes a small OpSim database is available at
`os.path.join(getPackageDir('sims_data'), 'OpSimData/opsimblitz1_1133_sqlite.db')`
"""
self._opsim_version = None
self.driver = driver
self.host = host
self.port = port
self.database = database
self._seeing_column = 'FWHMeff'
if self.database is None:
return
if not os.path.isfile(self.database):
raise RuntimeError('%s is not a file' % self.database)
self.opsimdb = DBObject(driver=self.driver,
database=self.database,
host=self.host,
port=self.port)
# 27 January 2016
# Detect whether the OpSim db you are connecting to uses 'finSeeing'
# as its seeing column (deprecated), or FWHMeff, which is the modern
# standard
list_of_tables = self.opsimdb.get_table_names()
if 'Summary' in list_of_tables:
self._opsim_version = 3
else:
self._opsim_version = 4
self._summary_columns = self.opsimdb.get_column_names(self.table_name)
self._set_seeing_column(self._summary_columns)
# Set up self.dtype containg the dtype of the recarray we expect back from the SQL query.
# Also setup baseQuery which is just the SELECT clause of the SQL query
#
# self.active_columns will be a list containing the subset of OpSim database columns
# (specified in self.user_interface_to_opsim) that actually exist in this opsim database
dtypeList = []
self.baseQuery = 'SELECT'
self.active_columns = []
self._queried_columns = [] # This will be a list of all of the
# OpSim columns queried
# Note: here we will refer to the
# columns by their names in OpSim
for column in self.user_interface_to_opsim:
rec = self.user_interface_to_opsim[column]
if rec[0] in self._summary_columns:
self.active_columns.append(column)
dtypeList.append((rec[0], rec[2]))
if self.baseQuery != 'SELECT':
self.baseQuery += ','
self.baseQuery += ' ' + rec[0]
self._queried_columns.append(rec[0])
# Now loop over self._summary_columns, adding any columns
# to the query that have not already been included therein.
# Since we do not have explicit information about the
# data types of these columns, we will assume they are floats.
for column in self._summary_columns:
if column not in self._queried_columns:
self.baseQuery += ', ' + column
dtypeList.append((column, float))
self.dtype = np.dtype(dtypeList)
def get_pointing_htmid(pointing_dir,
opsim_db_name,
ra_colname='descDitheredRA',
dec_colname='descDitheredDec',
rottel_colname='descDitheredRotTelPos'):
"""
For a list of OpSim pointings, find dicts mapping those pointings to:
- The trixels filling the pointings
- The MJDs of the pointings
- The telescope filters of the pointings
Parameters
----------
pointing_dir contains a series of files that are two columns: obshistid, mjd.
The files must each have 'visits' in their name. These specify the pointings
for which we are assembling data. See:
https://github.com/LSSTDESC/DC2_Repo/tree/master/data/Run1.1
for an example.
opsim_db_name is the path to the OpSim database from which to take those pointings
ra_colname is the column used for RA of the pointing (default:
descDitheredRA)
dec_colname is the column used for the Dec of the pointing (default:
descDitheredDec)
rottel_colname is the column used for the rotTelPos of the pointing
(default: desckDitheredRotTelPos')
Returns
-------
htmid_bound_dict -- a dict keyed on ObsHistID. Values are the list of trixels filling
the OpSim pointing, as returned by lsst.sims.utils.HalfSpace.findAllTrixels
mjd_dict -- a dict keyed on ObsHistID. Values are the MJD(TAI) of the OpSim pointings.
filter_dict -- a dict keyed on ObsHistID. Values are the 'ugrizy' filter of the
OpSim pointings.
obsmd_dict -- a dict keyed on ObsHistID. The values are ObservationMetaData with the
RA, Dec, MJD, and rotSkyPos of the pointings (for use in focal plane geometry calculations)
"""
radius = 2.0 # field of view of a pointing in degrees
if not os.path.isfile(opsim_db_name):
raise RuntimeError("%s is not a valid file name" % opsim_db_name)
if not os.path.isdir(pointing_dir):
raise RuntimeError("%s is not a valid dir name" % pointing_dir)
dtype = np.dtype([('obshistid', int), ('mjd', float)])
obs_data = None
for file_name in os.listdir(pointing_dir):
if 'visits' in file_name:
full_name = os.path.join(pointing_dir, file_name)
data = np.genfromtxt(full_name, dtype=dtype)
if obs_data is None:
obs_data = data['obshistid']
else:
obs_data = np.concatenate((obs_data, data['obshistid']),
axis=0)
obs_data = np.sort(obs_data)
db = DBObject(opsim_db_name, driver='sqlite')
dtype = np.dtype([('obshistid', int), ('mjd', float), ('band', str, 1),
('ra', float), ('dec', float), ('rotTelPos', float)])
htmid_bound_dict = {}
mjd_dict = {}
filter_dict = {}
obsmd_dict = {}
d_obs = len(obs_data) // 5
for i_start in range(0, len(obs_data), d_obs):
i_end = i_start + d_obs
if len(obs_data) - i_start < d_obs:
i_end = len(obs_data)
subset = obs_data[i_start:i_end]
query = 'SELECT obsHistId, expMJD, filter,'
query += ' %s, %s, %s FROM Summary' % (ra_colname, dec_colname,
rottel_colname)
query += ' WHERE obsHistID BETWEEN %d and %e' % (subset.min(),
subset.max())
query += ' GROUP BY obsHistID'
results = db.execute_arbitrary(query, dtype=dtype)
for ii in range(len(results)):
obshistid = results['obshistid'][ii]
if obshistid not in obs_data:
continue
hs = halfSpaceFromRaDec(np.degrees(results['ra'][ii]),
np.degrees(results['dec'][ii]), radius)
trixel_bounds = hs.findAllTrixels(_truth_trixel_level)
htmid_bound_dict[obshistid] = trixel_bounds
mjd_dict[obshistid] = results['mjd'][ii]
filter_dict[obshistid] = results['band'][ii]
obs_md = ObservationMetaData(
pointingRA=np.degrees(results['ra'][ii]),
pointingDec=np.degrees(results['dec'][ii]),
mjd=results['mjd'][ii])
rotsky_rad = _getRotSkyPos(results['ra'][ii], results['dec'][ii],
obs_md, results['rotTelPos'][ii])
obsmd_dict[obshistid] = ObservationMetaData(
pointingRA=np.degrees(results['ra'][ii]),
pointingDec=np.degrees(results['dec'][ii]),
mjd=results['mjd'][ii],
rotSkyPos=np.degrees(rotsky_rad))
assert len(obs_data) == len(htmid_bound_dict)
return htmid_bound_dict, mjd_dict, filter_dict, obsmd_dict
from lsst.sims.catalogs.db import DBObject
if args.use_tunnel:
from lsst.sims.catUtils.baseCatalogModel import BaseCatalogConfig
config = BaseCatalogConfig
host = config.host
port = config.port
database = config.database
driver = config.driver
else:
host = 'fatboy.phys.washington.edu'
port = 1433
database = 'LSSTCATSIM'
driver = 'mssql+pymssql'
db = DBObject(database=database, host=host, port=port, driver=driver)
if args.limit is None:
query = 'SELECT '
else:
query = 'SELECT TOP %d ' % args.limit
query += 'htmid, simobjid, gal_l, gal_b, parallax, sdssr, sdssi, sdssz '
query += 'FROM %s ' % args.table
from mdwarf_utils import activity_type_from_color_z
from mdwarf_utils import xyz_from_lon_lat_px
import os
import numpy as np
import time
parser.add_argument('--partition', type=str, default=None,
help='htmid tag of stars_partition_* table to run')
parser.add_argument('--outdir', type=str, default=None,
help='dir to write output to')
parser.add_argument('--n_procs', type=int, default=20)
args = parser.parse_args()
assert args.partition is not None
assert args.outdir is not None
assert os.path.isdir(args.outdir)
try:
db = DBObject(database='LSST',
port=1433,
host='epyc.astro.washington.edu',
driver='mssql+pymssql')
except:
db = DBObject(database='LSST',
port=51432,
host='localhost',
driver='mssql+pymssql')
table_name = 'stars_partition_%s' % args.partition
out_name = os.path.join(args.outdir,'isvar_lookup_%s.txt' % args.partition)
if os.path.isfile(out_name):
os.unlink(out_name)
#raise RuntimeError("\n%s\nexists\n" % out_name)
query = "SELECT "
# Hide imports here so documentation builds
from lsst.sims.catalogs.db import DBObject
from lsst.sims.utils import halfSpaceFromRaDec
from lsst.sims.utils import levelFromHtmid
from lsst.sims.utils import angularSeparation, raDec2Hpid
#from lsst.sims.catalogs.generation.db import CatalogDBObject
# Import the bits needed to get the catalog to work
#from lsst.sims.catUtils.baseCatalogModels import *
#from lsst.sims.catUtils.exampleCatalogDefinitions import *
# connect to fatboy
gaia_db = DBObject(database='LSSTCATSIM', host='fatboy.phys.washington.edu',
port=1433, driver='mssql+pymssql')
# get all of the column names for the gaia table in a list
gaia_columns = gaia_db.get_column_names(tableName='gaia_2016')
# Set up healpy map and ra, dec centers
nside = 64
# Set the min to 15 since we saturate there. CatSim max is 28
mag_max = 15.2
bins = np.arange(0., mag_max, .2)
starDensity = np.zeros((hp.nside2npix(nside), np.size(bins)-1), dtype=float)
overMaxMask = np.zeros(hp.nside2npix(nside), dtype=bool)
lat, ra = hp.pix2ang(nside, np.arange(0, hp.nside2npix(nside)))
dec = np.pi/2.-lat
ra = np.degrees(ra)
def test_alert_data_generation(self):
dmag_cutoff = 0.005
mag_name_to_int = {'u': 0, 'g': 1, 'r': 2, 'i': 3, 'z' : 4, 'y': 5}
_max_var_param_str = self.max_str_len
class StarAlertTestDBObj(StellarAlertDBObjMixin, CatalogDBObject):
objid = 'star_alert'
tableid = 'stars'
idColKey = 'simobjid'
raColName = 'ra'
decColName = 'dec'
objectTypeId = 0
columns = [('raJ2000', 'ra*0.01745329252'),
('decJ2000', 'dec*0.01745329252'),
('parallax', 'px*0.01745329252/3600.0'),
('properMotionRa', 'pmra*0.01745329252/3600.0'),
('properMotionDec', 'pmdec*0.01745329252/3600.0'),
('radialVelocity', 'vrad'),
('variabilityParameters', 'varParamStr', str, _max_var_param_str)]
class TestAlertsVarCatMixin(object):
@register_method('alert_test')
def applyAlertTest(self, valid_dexes, params, expmjd, variability_cache=None):
if len(params) == 0:
return np.array([[], [], [], [], [], []])
if isinstance(expmjd, numbers.Number):
dmags_out = np.zeros((6, self.num_variable_obj(params)))
else:
dmags_out = np.zeros((6, self.num_variable_obj(params), len(expmjd)))
for i_star in range(self.num_variable_obj(params)):
if params['amp'][i_star] is not None:
dmags = params['amp'][i_star]*np.cos(params['per'][i_star]*expmjd)
for i_filter in range(6):
dmags_out[i_filter][i_star] = dmags
return dmags_out
class TestAlertsVarCat(TestAlertsVarCatMixin, AlertStellarVariabilityCatalog):
pass
class TestAlertsTruthCat(TestAlertsVarCatMixin, CameraCoordsLSST, AstrometryStars,
Variability, InstanceCatalog):
column_outputs = ['uniqueId', 'chipName', 'dmagAlert', 'magAlert']
@compound('delta_umag', 'delta_gmag', 'delta_rmag',
'delta_imag', 'delta_zmag', 'delta_ymag')
def get_TruthVariability(self):
return self.applyVariability(self.column_by_name('varParamStr'))
@cached
def get_dmagAlert(self):
return self.column_by_name('delta_%smag' % self.obs_metadata.bandpass)
@cached
def get_magAlert(self):
return self.column_by_name('%smag' % self.obs_metadata.bandpass) + \
self.column_by_name('dmagAlert')
star_db = StarAlertTestDBObj(database=self.star_db_name, driver='sqlite')
# assemble the true light curves for each object; we need to figure out
# if their np.max(dMag) ever goes over dmag_cutoff; then we will know if
# we are supposed to simulate them
true_lc_dict = {}
true_lc_obshistid_dict = {}
is_visible_dict = {}
obs_dict = {}
max_obshistid = -1
n_total_observations = 0
for obs in self.obs_list:
obs_dict[obs.OpsimMetaData['obsHistID']] = obs
obshistid = obs.OpsimMetaData['obsHistID']
if obshistid > max_obshistid:
max_obshistid = obshistid
cat = TestAlertsTruthCat(star_db, obs_metadata=obs)
for line in cat.iter_catalog():
if line[1] is None:
continue
n_total_observations += 1
if line[0] not in true_lc_dict:
true_lc_dict[line[0]] = {}
true_lc_obshistid_dict[line[0]] = []
true_lc_dict[line[0]][obshistid] = line[2]
true_lc_obshistid_dict[line[0]].append(obshistid)
if line[0] not in is_visible_dict:
is_visible_dict[line[0]] = False
if line[3] <= self.obs_mag_cutoff[mag_name_to_int[obs.bandpass]]:
is_visible_dict[line[0]] = True
obshistid_bits = int(np.ceil(np.log(max_obshistid)/np.log(2)))
skipped_due_to_mag = 0
objects_to_simulate = []
obshistid_unqid_set = set()
for obj_id in true_lc_dict:
dmag_max = -1.0
for obshistid in true_lc_dict[obj_id]:
if np.abs(true_lc_dict[obj_id][obshistid]) > dmag_max:
dmag_max = np.abs(true_lc_dict[obj_id][obshistid])
if dmag_max >= dmag_cutoff:
if not is_visible_dict[obj_id]:
skipped_due_to_mag += 1
continue
objects_to_simulate.append(obj_id)
for obshistid in true_lc_obshistid_dict[obj_id]:
obshistid_unqid_set.add((obj_id << obshistid_bits) + obshistid)
self.assertGreater(len(objects_to_simulate), 10)
self.assertGreater(skipped_due_to_mag, 0)
log_file_name = tempfile.mktemp(dir=self.output_dir, suffix='log.txt')
alert_gen = AlertDataGenerator(testing=True)
alert_gen.subdivide_obs(self.obs_list, htmid_level=6)
for htmid in alert_gen.htmid_list:
alert_gen.alert_data_from_htmid(htmid, star_db,
photometry_class=TestAlertsVarCat,
output_prefix='alert_test',
output_dir=self.output_dir,
dmag_cutoff=dmag_cutoff,
log_file_name=log_file_name)
dummy_sed = Sed()
bp_dict = BandpassDict.loadTotalBandpassesFromFiles()
phot_params = PhotometricParameters()
# First, verify that the contents of the sqlite files are all correct
n_tot_simulated = 0
alert_query = 'SELECT alert.uniqueId, alert.obshistId, meta.TAI, '
alert_query += 'meta.band, quiescent.flux, alert.dflux, '
alert_query += 'quiescent.snr, alert.snr, '
alert_query += 'alert.ra, alert.dec, alert.chipNum, '
alert_query += 'alert.xPix, alert.yPix, ast.pmRA, ast.pmDec, '
alert_query += 'ast.parallax '
alert_query += 'FROM alert_data AS alert '
alert_query += 'INNER JOIN metadata AS meta ON meta.obshistId=alert.obshistId '
alert_query += 'INNER JOIN quiescent_flux AS quiescent '
alert_query += 'ON quiescent.uniqueId=alert.uniqueId '
alert_query += 'AND quiescent.band=meta.band '
alert_query += 'INNER JOIN baseline_astrometry AS ast '
alert_query += 'ON ast.uniqueId=alert.uniqueId'
alert_dtype = np.dtype([('uniqueId', int), ('obshistId', int),
('TAI', float), ('band', int),
('q_flux', float), ('dflux', float),
('q_snr', float), ('tot_snr', float),
('ra', float), ('dec', float),
('chipNum', int), ('xPix', float), ('yPix', float),
('pmRA', float), ('pmDec', float), ('parallax', float)])
sqlite_file_list = os.listdir(self.output_dir)
n_tot_simulated = 0
obshistid_unqid_simulated_set = set()
for file_name in sqlite_file_list:
if not file_name.endswith('db'):
continue
full_name = os.path.join(self.output_dir, file_name)
self.assertTrue(os.path.exists(full_name))
alert_db = DBObject(full_name, driver='sqlite')
alert_data = alert_db.execute_arbitrary(alert_query, dtype=alert_dtype)
if len(alert_data) == 0:
continue
mjd_list = ModifiedJulianDate.get_list(TAI=alert_data['TAI'])
for i_obj in range(len(alert_data)):
n_tot_simulated += 1
obshistid_unqid_simulated_set.add((alert_data['uniqueId'][i_obj] << obshistid_bits) +
alert_data['obshistId'][i_obj])
unq = alert_data['uniqueId'][i_obj]
obj_dex = (unq//1024)-1
self.assertAlmostEqual(self.pmra_truth[obj_dex], 0.001*alert_data['pmRA'][i_obj], 4)
self.assertAlmostEqual(self.pmdec_truth[obj_dex], 0.001*alert_data['pmDec'][i_obj], 4)
self.assertAlmostEqual(self.px_truth[obj_dex], 0.001*alert_data['parallax'][i_obj], 4)
ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex],
self.px_truth[obj_dex], self.vrad_truth[obj_dex],
mjd=mjd_list[i_obj])
distance = angularSeparation(ra_truth, dec_truth,
alert_data['ra'][i_obj], alert_data['dec'][i_obj])
distance_arcsec = 3600.0*distance
msg = '\ntruth: %e %e\nalert: %e %e\n' % (ra_truth, dec_truth,
alert_data['ra'][i_obj],
alert_data['dec'][i_obj])
self.assertLess(distance_arcsec, 0.0005, msg=msg)
obs = obs_dict[alert_data['obshistId'][i_obj]]
chipname = chipNameFromRaDecLSST(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
pm_ra=self.pmra_truth[obj_dex],
pm_dec=self.pmdec_truth[obj_dex],
parallax=self.px_truth[obj_dex],
v_rad=self.vrad_truth[obj_dex],
obs_metadata=obs,
band=obs.bandpass)
chipnum = int(chipname.replace('R', '').replace('S', '').
replace(' ', '').replace(';', '').replace(',', '').
replace(':', ''))
self.assertEqual(chipnum, alert_data['chipNum'][i_obj])
xpix, ypix = pixelCoordsFromRaDecLSST(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
pm_ra=self.pmra_truth[obj_dex],
pm_dec=self.pmdec_truth[obj_dex],
parallax=self.px_truth[obj_dex],
v_rad=self.vrad_truth[obj_dex],
obs_metadata=obs,
band=obs.bandpass)
self.assertAlmostEqual(alert_data['xPix'][i_obj], xpix, 4)
self.assertAlmostEqual(alert_data['yPix'][i_obj], ypix, 4)
dmag_sim = -2.5*np.log10(1.0+alert_data['dflux'][i_obj]/alert_data['q_flux'][i_obj])
self.assertAlmostEqual(true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]],
dmag_sim, 3)
mag_name = ('u', 'g', 'r', 'i', 'z', 'y')[alert_data['band'][i_obj]]
m5 = obs.m5[mag_name]
q_mag = dummy_sed.magFromFlux(alert_data['q_flux'][i_obj])
self.assertAlmostEqual(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex],
q_mag, 4)
snr, gamma = calcSNR_m5(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex],
bp_dict[mag_name],
self.obs_mag_cutoff[alert_data['band'][i_obj]],
phot_params)
self.assertAlmostEqual(snr/alert_data['q_snr'][i_obj], 1.0, 4)
tot_mag = self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex] + \
true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]]
snr, gamma = calcSNR_m5(tot_mag, bp_dict[mag_name],
m5, phot_params)
self.assertAlmostEqual(snr/alert_data['tot_snr'][i_obj], 1.0, 4)
for val in obshistid_unqid_set:
self.assertIn(val, obshistid_unqid_simulated_set)
self.assertEqual(len(obshistid_unqid_set), len(obshistid_unqid_simulated_set))
astrometry_query = 'SELECT uniqueId, ra, dec, TAI '
astrometry_query += 'FROM baseline_astrometry'
astrometry_dtype = np.dtype([('uniqueId', int),
('ra', float),
('dec', float),
('TAI', float)])
tai_list = []
for obs in self.obs_list:
tai_list.append(obs.mjd.TAI)
tai_list = np.array(tai_list)
n_tot_ast_simulated = 0
for file_name in sqlite_file_list:
if not file_name.endswith('db'):
continue
full_name = os.path.join(self.output_dir, file_name)
self.assertTrue(os.path.exists(full_name))
alert_db = DBObject(full_name, driver='sqlite')
astrometry_data = alert_db.execute_arbitrary(astrometry_query, dtype=astrometry_dtype)
if len(astrometry_data) == 0:
continue
mjd_list = ModifiedJulianDate.get_list(TAI=astrometry_data['TAI'])
for i_obj in range(len(astrometry_data)):
n_tot_ast_simulated += 1
obj_dex = (astrometry_data['uniqueId'][i_obj]//1024) - 1
ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex],
self.px_truth[obj_dex], self.vrad_truth[obj_dex],
mjd=mjd_list[i_obj])
distance = angularSeparation(ra_truth, dec_truth,
astrometry_data['ra'][i_obj],
astrometry_data['dec'][i_obj])
self.assertLess(3600.0*distance, 0.0005)
del alert_gen
gc.collect()
self.assertGreater(n_tot_simulated, 10)
self.assertGreater(len(obshistid_unqid_simulated_set), 10)
self.assertLess(len(obshistid_unqid_simulated_set), n_total_observations)
self.assertGreater(n_tot_ast_simulated, 0)
def testDetectDtype(self):
"""
Test that DBObject.execute_arbitrary can correctly detect the dtypes
of the rows it is returning
"""
db_name = os.path.join(self.scratch_dir, 'testDBObject_dtype_DB.db')
if os.path.exists(db_name):
os.unlink(db_name)
conn = sqlite3.connect(db_name)
c = conn.cursor()
try:
c.execute('''CREATE TABLE testTable (id int, val real, sentence int)''')
conn.commit()
except:
raise RuntimeError("Error creating database.")
for ii in range(10):
cmd = '''INSERT INTO testTable VALUES (%d, %.5f, %s)''' % (ii, 5.234*ii, "'this, has; punctuation'")
c.execute(cmd)
conn.commit()
conn.close()
db = DBObject(database=db_name, driver='sqlite')
query = 'SELECT id, val, sentence FROM testTable WHERE id%2 = 0'
results = db.execute_arbitrary(query)
np.testing.assert_array_equal(results['id'], np.arange(0,9,2,dtype=int))
np.testing.assert_array_almost_equal(results['val'], 5.234*np.arange(0,9,2), decimal=5)
for sentence in results['sentence']:
self.assertEqual(sentence, 'this, has; punctuation')
self.assertEqual(str(results.dtype['id']), 'int64')
self.assertEqual(str(results.dtype['val']), 'float64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
self.assertEqual(len(results.dtype), 3)
# now test that it works when getting a ChunkIterator
chunk_iter = db.get_arbitrary_chunk_iterator(query, chunk_size=3)
ct = 0
for chunk in chunk_iter:
self.assertEqual(str(chunk.dtype['id']), 'int64')
self.assertEqual(str(chunk.dtype['val']), 'float64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
self.assertEqual(len(chunk.dtype), 3)
for line in chunk:
ct += 1
self.assertEqual(line['sentence'], 'this, has; punctuation')
self.assertAlmostEqual(line['val'], line['id']*5.234, 5)
self.assertEqual(line['id']%2, 0)
self.assertEqual(ct, 5)
# test that doing a different query does not spoil dtype detection
query = 'SELECT id, sentence FROM testTable WHERE id%2 = 0'
results = db.execute_arbitrary(query)
self.assertGreater(len(results), 0)
self.assertEqual(len(results.dtype.names), 2)
self.assertEqual(str(results.dtype['id']), 'int64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
query = 'SELECT id, val, sentence FROM testTable WHERE id%2 = 0'
chunk_iter = db.get_arbitrary_chunk_iterator(query, chunk_size=3)
ct = 0
for chunk in chunk_iter:
self.assertEqual(str(chunk.dtype['id']), 'int64')
self.assertEqual(str(chunk.dtype['val']), 'float64')
if sys.version_info.major == 2:
self.assertEqual(str(results.dtype['sentence']), '|S22')
else:
self.assertEqual(str(results.dtype['sentence']), '<U22')
self.assertEqual(len(chunk.dtype), 3)
for line in chunk:
ct += 1
self.assertEqual(line['sentence'], 'this, has; punctuation')
self.assertAlmostEqual(line['val'], line['id']*5.234, 5)
self.assertEqual(line['id']%2, 0)
self.assertEqual(ct, 5)
if os.path.exists(db_name):
os.unlink(db_name)
def write_alerts(self, obshistid, data_dir, prefix_list,
htmid_list, out_dir, out_prefix,
dmag_cutoff, lock=None, log_file_name=None):
"""
Write the alerts for an obsHistId to a properly formatted avro file.
Parameters
----------
obshistid is the integer uniquely identifying the OpSim pointing
being simulated
data_dir is the directory containing the sqlite files created by
the AlertDataGenerator
prefix_list is a list of prefixes for those sqlite files.
htmid_list is the list of htmids identifying the trixels that overlap
this obshistid's field of view. For each htmid in htmid_list and each
prefix in prefix_list, this method will process the files
data_dir/prefix_htmid_sqlite.db
searching for alerts that correspond to this obshistid
out_dir is the directory to which the avro files should be written
out_prefix is the prefix of the avro file names
dmag_cutoff is the minimum delta magnitude needed to trigger an alert
lock is an optional multiprocessing.Lock() for use when running many
instances of this method. It prevents multiple processes from writing to
the logfile or stdout at once.
log_file_name is the name of an optional text file to which progress is
written.
"""
out_name = os.path.join(out_dir, '%s_%d.avro' % (out_prefix, obshistid))
if os.path.exists(out_name):
os.unlink(out_name)
with DataFileWriter(open(out_name, "wb"),
DatumWriter(), self._alert_schema) as data_writer:
diasource_query = 'SELECT alert.uniqueId, alert.xPix, alert.yPix, '
diasource_query += 'alert.chipNum, alert.dflux, alert.snr, alert.ra, alert.dec, '
diasource_query += 'meta.band, meta.TAI, quiescent.flux, quiescent.snr '
diasource_query += 'FROM alert_data as alert '
diasource_query += 'INNER JOIN metadata AS meta ON alert.obshistId=meta.obshistId '
diasource_query += 'INNER JOIN quiescent_flux AS quiescent ON quiescent.uniqueId=alert.uniqueID '
diasource_query += 'AND quiescent.band=meta.band '
diasource_query += 'WHERE alert.obshistId=%d ' % obshistid
diasource_query += 'ORDER BY alert.uniqueId'
diasource_dtype = np.dtype([('uniqueId', int), ('xPix', float), ('yPix', float),
('chipNum', int), ('dflux', float), ('tot_snr', float),
('ra', float), ('dec', float), ('band', int), ('TAI', float),
('quiescent_flux', float), ('quiescent_snr', float)])
diaobject_query = 'SELECT uniqueId, ra, dec, TAI, pmRA, pmDec, parallax '
diaobject_query += 'FROM baseline_astrometry'
diaobject_dtype = np.dtype([('uniqueId', int), ('ra', float), ('dec', float),
('TAI', float), ('pmRA', float), ('pmDec', float),
('parallax', float)])
t_start = time.time()
alert_ct = 0
for htmid in htmid_list:
for prefix in prefix_list:
db_name = os.path.join(data_dir, '%s_%d_sqlite.db' % (prefix, htmid))
if not os.path.exists(db_name):
warnings.warn('%s does not exist' % db_name)
continue
db_obj = DBObject(db_name, driver='sqlite')
diaobject_data = db_obj.execute_arbitrary(diaobject_query,
dtype=diaobject_dtype)
diaobject_dict = self._create_objects(diaobject_data)
diasource_data = db_obj.execute_arbitrary(diasource_query,
dtype=diasource_dtype)
dmag = 2.5*np.log10(1.0+diasource_data['dflux']/diasource_data['quiescent_flux'])
valid_alerts = np.where(np.abs(dmag) >= dmag_cutoff)
diasource_data = diasource_data[valid_alerts]
avro_diasource_list = self._create_sources(obshistid, diasource_data)
for i_source in range(len(avro_diasource_list)):
alert_ct += 1
unq = diasource_data[i_source]['uniqueId']
diaobject = diaobject_dict[unq]
diasource = avro_diasource_list[i_source]
avro_alert = {}
avro_alert['alertId'] = np.long((obshistid << 20) + alert_ct)
avro_alert['l1dbId'] = np.long(unq)
avro_alert['diaSource'] = diasource
avro_alert['diaObject'] = diaobject
data_writer.append(avro_alert)
if lock is not None:
lock.acquire()
elapsed = (time.time()-t_start)/3600.0
msg = 'finished obshistid %d; %d alerts in %.2e hrs' % (obshistid, alert_ct, elapsed)
print(msg)
if log_file_name is not None:
with open(log_file_name, 'a') as out_file:
out_file.write(msg)
out_file.write('\n')
if lock is not None:
lock.release()
class ObservationMetaDataGenerator(object):
"""
A class that allows the user to generate instantiations of
`lsst.sims.utils.ObservationMetaData` corresponding to OpSim pointings.
The functionality includes:
- getOpSimRecords : obtain OpSim records matching the intersection of user
specified ranges on each column in the OpSim output database. The
records are in the form of a `numpy.recarray`
- ObservationMetaDataFromPointing : convert an OpSim record for a single
OpSim Pointing to an instance of ObservationMetaData usable by catsim
and PhoSim Instance Catalogs.
- getObservationMetaData : Obtain a list of ObservationMetaData instances
corresponding to OpSim pointings matching the intersection of user
specified ranges on each column in the OpSim output database.
The major method is ObservationMetaDataGenerator.getObservationMetaData()
which accepts bounds on columns of the opsim summary table and returns
a list of ObservationMetaData instantiations that fall within those
bounds.
"""
def _set_seeing_column(self, input_summary_columns):
"""
input_summary_columns is a list of columns in the OpSim database schema
This method sets the member variable self._seeing_column to a string
denoting the name of the seeing column in the OpSimDatabase. It also
sets self._user_interface_to_opsim['seeing'] to the correct value.
"""
if 'FWHMeff' in input_summary_columns:
self._seeing_column = 'FWHMeff'
else:
self._seeing_column = 'finSeeing'
self._user_interface_to_opsim['seeing'] = (self._seeing_column, None, float)
def __init__(self, database=None, driver='sqlite', host=None, port=None):
"""
Constructor for the class
Parameters
----------
database : string
absolute path to the output of the OpSim database
driver : string, optional, defaults to 'sqlite'
driver/dialect for the SQL database
host : hostName, optional, defaults to None,
hostName, None is good for a local database
port : hostName, optional, defaults to None,
port, None is good for a local database
Returns
------
Instance of the ObserverMetaDataGenerator class
..notes : For testing purposes a small OpSim database is available at
`os.path.join(getPackageDir('sims_data'), 'OpSimData/opsimblitz1_1133_sqlite.db')`
"""
self.driver = driver
self.host = host
self.port = port
self.database = database
self._seeing_column = 'FWHMeff'
# a dict keyed on the user interface names of the OpSimdata columns
# (i.e. the args to getObservationMetaData). Returns a tuple that is the
# (name of data column in OpSim, transformation to go from user interface to OpSim units,
# dtype in OpSim)
#
# Note: this dict will contain entries for every column (except propID) in the OpSim
# summary table, not just those the ObservationMetaDataGenerator is designed to query
# on. The idea is that ObservationMetaData generated by this class will carry around
# records of the values of all of the associated OpSim Summary columns so that users
# can pass those values on to PhoSim/other tools and thier own discretion.
self._user_interface_to_opsim = {'obsHistID': ('obsHistID', None, np.int64),
'expDate': ('expDate', None, int),
'fieldRA': ('fieldRA', np.radians, float),
'fieldDec': ('fieldDec', np.radians, float),
'moonRA': ('moonRA', np.radians, float),
'moonDec': ('moonDec', np.radians, float),
'rotSkyPos': ('rotSkyPos', np.radians, float),
'telescopeFilter':
('filter', lambda x: '\'{}\''.format(x), (str, 1)),
'rawSeeing': ('rawSeeing', None, float),
'sunAlt': ('sunAlt', np.radians, float),
'moonAlt': ('moonAlt', np.radians, float),
'dist2Moon': ('dist2Moon', np.radians, float),
'moonPhase': ('moonPhase', None, float),
'expMJD': ('expMJD', None, float),
'altitude': ('altitude', np.radians, float),
'azimuth': ('azimuth', np.radians, float),
'visitExpTime': ('visitExpTime', None, float),
'airmass': ('airmass', None, float),
'm5': ('fiveSigmaDepth', None, float),
'skyBrightness': ('filtSkyBrightness', None, float),
'sessionID': ('sessionID', None, int),
'fieldID': ('fieldID', None, int),
'night': ('night', None, int),
'visitTime': ('visitTime', None, float),
'finRank': ('finRank', None, float),
'FWHMgeom': ('FWHMgeom', None, float),
# do not include FWHMeff; that is detected by
# self._set_seeing_column()
'transparency': ('transparency', None, float),
'vSkyBright': ('vSkyBright', None, float),
'rotTelPos': ('rotTelPos', None, float),
'lst': ('lst', None, float),
'solarElong': ('solarElong', None, float),
'moonAz': ('moonAz', None, float),
'sunAz': ('sunAz', None, float),
'phaseAngle': ('phaseAngle', None, float),
'rScatter': ('rScatter', None, float),
'mieScatter': ('mieScatter', None, float),
'moonBright': ('moonBright', None, float),
'darkBright': ('darkBright', None, float),
'wind': ('wind', None, float),
'humidity': ('humidity', None, float),
'slewDist': ('slewDist', None, float),
'slewTime': ('slewTime', None, float),
'ditheredRA': ('ditheredRA', None, float),
'ditheredDec': ('ditheredDec', None, float)}
if self.database is None:
return
if not os.path.exists(self.database):
raise RuntimeError('%s does not exist' % self.database)
self.opsimdb = DBObject(driver=self.driver, database=self.database,
host=self.host, port=self.port)
# 27 January 2016
# Detect whether the OpSim db you are connecting to uses 'finSeeing'
# as its seeing column (deprecated), or FWHMeff, which is the modern
# standard
self._summary_columns = self.opsimdb.get_column_names('Summary')
self._set_seeing_column(self._summary_columns)
# Set up self.dtype containg the dtype of the recarray we expect back from the SQL query.
# Also setup baseQuery which is just the SELECT clause of the SQL query
#
# self.active_columns will be a list containing the subset of OpSim database columns
# (specified in self._user_interface_to_opsim) that actually exist in this opsim database
dtypeList = []
self.baseQuery = 'SELECT'
self.active_columns = []
self._queried_columns = [] # This will be a list of all of the
# OpSim columns queried
# Note: here we will refer to the
# columns by their names in OpSim
for column in self._user_interface_to_opsim:
rec = self._user_interface_to_opsim[column]
if rec[0] in self._summary_columns:
self.active_columns.append(column)
dtypeList.append((rec[0], rec[2]))
if self.baseQuery != 'SELECT':
self.baseQuery += ','
self.baseQuery += ' ' + rec[0]
self._queried_columns.append(rec[0])
# Now loop over self._summary_columns, adding any columns
# to the query that have not already been included therein.
# Since we do not have explicit information about the
# data types of these columns, we will assume they are floats.
for column in self._summary_columns:
if column not in self._queried_columns:
self.baseQuery += ', ' + column
dtypeList.append((column, float))
self.dtype = np.dtype(dtypeList)
def getOpSimRecords(self, obsHistID=None, expDate=None, night=None, fieldRA=None,
fieldDec=None, moonRA=None, moonDec=None,
rotSkyPos=None, telescopeFilter=None, rawSeeing=None,
seeing=None, sunAlt=None, moonAlt=None, dist2Moon=None,
moonPhase=None, expMJD=None, altitude=None,
azimuth=None, visitExpTime=None, airmass=None,
skyBrightness=None, m5=None, boundType='circle',
boundLength=1.75, limit=None):
"""
This method will query the summary table in the `self.opsimdb` database
according to constraints specified in the input ranges and return a
`numpy.recarray` containing the records that match those constraints. If limit
is used, the first N records will be returned in the list.
Parameters
----------
obsHistID, expDate, night, fieldRA, fieldDec, moonRa, moonDec, rotSkyPos,
telescopeFilter, rawSeeing, seeing, sunAlt, moonAlt, dist2Moon,
moonPhase, expMJD, altitude, azimuth, visitExpTime, airmass,
skyBrightness, m5 : tuples of length 2, optional, defaults to None
each of these variables represent a single column (perhaps through
an alias) in the OpSim database, and potentially in a different unit.
if not None, the variable self.columnMapping is used to constrain
the corresponding column in the OpSim database to the ranges (inclusive)
specified in the tuples, after a unit transformation if necessary.
The ranges must be specified in the tuple in degrees for all angles in this
(moonRa, moonDec, rotSkyPos, sunAlt, moonAlt, dist2Moon, altitude,
azimuth). The times in (expMJD, are in units of MJD). visitExpTime has
units of seconds since the start of the survey. moonPhase is a number
from 0., to 100.
boundType : `sims.utils.ObservationMetaData.boundType`, optional, defaults to 'circle'
{'circle', 'box'} denoting the shape of the pointing. Further
documentation `sims.catalogs.generation.db.spatialBounds.py``
boundLength : float, optional, defaults to 0.1
sets `sims.utils.ObservationMetaData.boundLenght`
limit : integer, optional, defaults to None
if not None, denotes max number of records returned by the query
Returns
-------
`numpy.recarray` with OpSim records. The column names may be obtained as
res.dtype.names
.. notes:: The `limit` argument should only be used if a small example
is required. The angle ranges in the argument should be specified in degrees.
"""
self._set_seeing_column(self._summary_columns)
query = self.baseQuery + ' FROM SUMMARY'
nConstraints = 0 # the number of constraints in this query
for column in self._user_interface_to_opsim:
transform = self._user_interface_to_opsim[column]
# this try/except block is because there will be columns in the OpSim Summary
# table (and thus in self._user_interface_to_opsim) which the
# ObservationMetaDataGenerator is not designed to query on
try:
value = eval(column)
except:
value = None
if value is not None:
if column not in self.active_columns:
raise RuntimeError("You have asked ObservationMetaDataGenerator to SELECT pointings on "
"%s; that column does not exist in your OpSim database" % column)
if nConstraints > 0:
query += ' AND'
else:
query += ' WHERE '
if isinstance(value, tuple):
if len(value) > 2:
raise RuntimeError('Cannot pass a tuple longer than 2 elements ' +
'to getObservationMetaData: %s is len %d'
% (column, len(value)))
# perform any necessary coordinate transformations
if transform[1] is not None:
vmin = transform[1](value[0])
vmax = transform[1](value[1])
else:
vmin = value[0]
vmax = value[1]
query += ' %s >= %s AND %s <= %s' % \
(transform[0], vmin, transform[0], vmax)
else:
# perform any necessary coordinate transformations
if transform[1] is not None:
vv = transform[1](value)
else:
vv = value
query += ' %s == %s' % (transform[0], vv)
nConstraints += 1
query += ' GROUP BY expMJD ORDER BY expMJD'
if limit is not None:
query += ' LIMIT %d' % limit
if nConstraints == 0 and limit is None:
raise RuntimeError('You did not specify any contraints on your query;' +
' you will just return ObservationMetaData for all poitnings')
results = self.opsimdb.execute_arbitrary(query, dtype=self.dtype)
return results
def ObservationMetaDataFromPointing(self, OpSimPointingRecord, OpSimColumns=None,
boundLength=1.75, boundType='circle'):
"""
Return instance of ObservationMetaData for an OpSim Pointing record
from OpSim.
Parameters
----------
OpSimPointingRecord : Dictionary, mandatory
Dictionary of values with keys corresponding to certain columns of
the Summary table in the OpSim database. The minimal list of keys
required for catsim to work is 'fiveSigmaDepth',
'filtSkyBrightness', and at least one of ('finSeeing', 'FWHMeff').
More keys defined in columnMap may be necessary for PhoSim to work.
OpSimColumns : tuple of strings, optional, defaults to None
The columns corresponding to the OpSim records. If None, attempts
to obtain these from the OpSimRecord as OpSimRecord.dtype.names
boundType : {'circle', 'box'}, optional, defaults to 'circle'
Shape of the observation
boundLength : scalar float, optional, defaults to 1.75
'characteristic size' of observation field, in units of degrees.
For boundType='circle', this is a radius, for boundType='box', this
is a size of the box
"""
pointing = OpSimPointingRecord
pointing_column_names = pointing.dtype.names
# Decide what is the name of the column in the OpSim database
# corresponding to the Seeing. For older OpSim outputs, this is
# 'finSeeing'. For later OpSim outputs this is 'FWHMeff'
if OpSimColumns is None:
OpSimColumns = pointing_column_names
self._set_seeing_column(OpSimColumns)
# check to make sure the OpSim pointings being supplied contain
# the minimum required information
for required_column in ('fieldRA', 'fieldDec', 'expMJD', 'filter'):
if required_column not in OpSimColumns:
raise RuntimeError("ObservationMetaDataGenerator requires that the database of "
"pointings include the coluns:\nfieldRA (in radians)"
"\nfieldDec (in radians)\nexpMJD\nfilter")
# construct a raw dict of all of the OpSim columns associated with this pointing
raw_dict = dict([(col, pointing[col]) for col in pointing_column_names])
obs = ObservationMetaData(pointingRA=np.degrees(pointing['fieldRA']),
pointingDec=np.degrees(pointing['fieldDec']),
mjd=pointing['expMJD'],
bandpassName=pointing['filter'],
boundType=boundType,
boundLength=boundLength)
if 'fiveSigmaDepth' in pointing_column_names:
obs.m5 = pointing['fiveSigmaDepth']
if 'filtSkyBrightness' in pointing_column_names:
obs.skyBrightness = pointing['filtSkyBrightness']
if self._seeing_column in pointing_column_names:
obs.seeing = pointing[self._seeing_column]
if 'rotSkyPos' in pointing_column_names:
obs.rotSkyPos = np.degrees(pointing['rotSkyPos'])
obs.OpsimMetaData = raw_dict
return obs
def ObservationMetaDataFromPointingArray(self, OpSimPointingRecords,
OpSimColumns=None,
boundLength=1.75,
boundType='circle'):
"""
Static method to get a list of instances of ObservationMetaData
corresponding to the records in `numpy.recarray`, where it uses
the dtypes of the recArray for ObservationMetaData attributes that
require the dtype.
Parameters
----------
OpSimPointingRecords : `numpy.recarray` of OpSim Records
OpSimColumns : a tuple of strings, optional, defaults to None
tuple of column Names of the data in the `numpy.recarray`. If
None, these names are extracted from the recarray.
boundType : {'circle' or 'box'}
denotes the shape of the pointing
boundLength : float, optional, defaults to 1.75
the bound length of the Pointing in units of degrees. For boundType
'box', this is the length of the side of the square box. For boundType
'circle' this is the radius.
"""
if OpSimColumns is None:
OpSimColumns = OpSimPointingRecords.dtype.names
out = list(self.ObservationMetaDataFromPointing(OpSimPointingRecord,
OpSimColumns=OpSimColumns,
boundLength=boundLength,
boundType=boundType)
for OpSimPointingRecord in OpSimPointingRecords)
return out
def getObservationMetaData(self, obsHistID=None, expDate=None, night=None, fieldRA=None, fieldDec=None,
moonRA=None, moonDec=None, rotSkyPos=None, telescopeFilter=None,
rawSeeing=None, seeing=None, sunAlt=None, moonAlt=None, dist2Moon=None,
moonPhase=None, expMJD=None, altitude=None, azimuth=None,
visitExpTime=None, airmass=None, skyBrightness=None,
m5=None, boundType='circle', boundLength=1.75, limit=None):
"""
This method will query the OpSim database summary table according to user-specified
constraints and return a list of of ObservationMetaData instantiations consistent
with those constraints.
@param [in] limit is an integer denoting the maximum number of ObservationMetaData to
be returned
@param [in] boundType is the boundType of the ObservationMetaData to be returned
(see documentation in sims_catalogs_generation/../db/spatialBounds.py for more
details)
@param [in] boundLength is the boundLength of the ObservationMetaData to be
returned (in degrees; see documentation in
sims_catalogs_generation/../db/spatialBounds.py for more details)
All other input parameters are constraints to be placed on the SQL query of the
opsim output db. These contraints can either be tuples of the form (min, max)
or an exact value the user wants returned. Note: min and max are inclusive
bounds.
Parameters that can be constrained are:
@param [in] fieldRA in degrees
@param [in] fieldDec in degrees
@param [in] altitude in degrees
@param [in] azimuth in degrees
@param [in] moonRA in degrees
@param [in] moonDec in degrees
@param [in] moonAlt in degrees
@param [in] moonPhase (a value from 1 to 100 indicating how much of the moon is illuminated)
@param [in] dist2Moon the distance between the telescope pointing and the moon in degrees
@param [in] sunAlt in degrees
@param [in[ rotSkyPos (the angle of the sky with respect to the camera coordinate system) in degrees
@param [in] telescopeFilter a string that is one of u,g,r,i,z,y
@param [in] airmass
@param [in] rawSeeing (this is an idealized seeing at zenith at 500nm in arcseconds)
@param [in] seeing (this is the OpSim column 'FWHMeff' or 'finSeeing' [deprecated] in arcseconds)
@param [in] visitExpTime the exposure time in seconds
@param [in] obsHistID the integer used by OpSim to label pointings
@param [in] expDate is the date of the exposure (units????)
@param [in] expMJD is the MJD of the exposure
@param [in] night is the night (an int starting at zero) on which the observation took place
@param [in] m5 is the five sigma depth of the observation
@param [in] skyBrightness
"""
OpSimPointingRecords = self.getOpSimRecords(obsHistID=obsHistID,
expDate=expDate,
night=night,
fieldRA=fieldRA,
fieldDec=fieldDec,
moonRA=moonRA,
moonDec=moonDec,
rotSkyPos=rotSkyPos,
telescopeFilter=telescopeFilter,
rawSeeing=rawSeeing,
seeing=seeing,
sunAlt=sunAlt,
moonAlt=moonAlt,
dist2Moon=dist2Moon,
moonPhase=moonPhase,
expMJD=expMJD,
altitude=altitude,
azimuth=azimuth,
visitExpTime=visitExpTime,
airmass=airmass,
skyBrightness=skyBrightness,
m5=m5, boundType=boundType,
boundLength=boundLength,
limit=limit)
output = self.ObservationMetaDataFromPointingArray(OpSimPointingRecords,
OpSimColumns=None,
boundType=boundType,
boundLength=boundLength)
return output
def __init__(self, database=None, driver='sqlite', host=None, port=None):
"""
Constructor for the class
Parameters
----------
database : string
absolute path to the output of the OpSim database
driver : string, optional, defaults to 'sqlite'
driver/dialect for the SQL database
host : hostName, optional, defaults to None,
hostName, None is good for a local database
port : hostName, optional, defaults to None,
port, None is good for a local database
Returns
------
Instance of the ObserverMetaDataGenerator class
..notes : For testing purposes a small OpSim database is available at
`os.path.join(getPackageDir('sims_data'), 'OpSimData/opsimblitz1_1133_sqlite.db')`
"""
self.driver = driver
self.host = host
self.port = port
self.database = database
self._seeing_column = 'FWHMeff'
# a dict keyed on the user interface names of the OpSimdata columns
# (i.e. the args to getObservationMetaData). Returns a tuple that is the
# (name of data column in OpSim, transformation to go from user interface to OpSim units,
# dtype in OpSim)
#
# Note: this dict will contain entries for every column (except propID) in the OpSim
# summary table, not just those the ObservationMetaDataGenerator is designed to query
# on. The idea is that ObservationMetaData generated by this class will carry around
# records of the values of all of the associated OpSim Summary columns so that users
# can pass those values on to PhoSim/other tools and thier own discretion.
self._user_interface_to_opsim = {'obsHistID': ('obsHistID', None, np.int64),
'expDate': ('expDate', None, int),
'fieldRA': ('fieldRA', np.radians, float),
'fieldDec': ('fieldDec', np.radians, float),
'moonRA': ('moonRA', np.radians, float),
'moonDec': ('moonDec', np.radians, float),
'rotSkyPos': ('rotSkyPos', np.radians, float),
'telescopeFilter':
('filter', lambda x: '\'{}\''.format(x), (str, 1)),
'rawSeeing': ('rawSeeing', None, float),
'sunAlt': ('sunAlt', np.radians, float),
'moonAlt': ('moonAlt', np.radians, float),
'dist2Moon': ('dist2Moon', np.radians, float),
'moonPhase': ('moonPhase', None, float),
'expMJD': ('expMJD', None, float),
'altitude': ('altitude', np.radians, float),
'azimuth': ('azimuth', np.radians, float),
'visitExpTime': ('visitExpTime', None, float),
'airmass': ('airmass', None, float),
'm5': ('fiveSigmaDepth', None, float),
'skyBrightness': ('filtSkyBrightness', None, float),
'sessionID': ('sessionID', None, int),
'fieldID': ('fieldID', None, int),
'night': ('night', None, int),
'visitTime': ('visitTime', None, float),
'finRank': ('finRank', None, float),
'FWHMgeom': ('FWHMgeom', None, float),
# do not include FWHMeff; that is detected by
# self._set_seeing_column()
'transparency': ('transparency', None, float),
'vSkyBright': ('vSkyBright', None, float),
'rotTelPos': ('rotTelPos', None, float),
'lst': ('lst', None, float),
'solarElong': ('solarElong', None, float),
'moonAz': ('moonAz', None, float),
'sunAz': ('sunAz', None, float),
'phaseAngle': ('phaseAngle', None, float),
'rScatter': ('rScatter', None, float),
'mieScatter': ('mieScatter', None, float),
'moonBright': ('moonBright', None, float),
'darkBright': ('darkBright', None, float),
'wind': ('wind', None, float),
'humidity': ('humidity', None, float),
'slewDist': ('slewDist', None, float),
'slewTime': ('slewTime', None, float),
'ditheredRA': ('ditheredRA', None, float),
'ditheredDec': ('ditheredDec', None, float)}
if self.database is None:
return
if not os.path.exists(self.database):
raise RuntimeError('%s does not exist' % self.database)
self.opsimdb = DBObject(driver=self.driver, database=self.database,
host=self.host, port=self.port)
# 27 January 2016
# Detect whether the OpSim db you are connecting to uses 'finSeeing'
# as its seeing column (deprecated), or FWHMeff, which is the modern
# standard
self._summary_columns = self.opsimdb.get_column_names('Summary')
self._set_seeing_column(self._summary_columns)
# Set up self.dtype containg the dtype of the recarray we expect back from the SQL query.
# Also setup baseQuery which is just the SELECT clause of the SQL query
#
# self.active_columns will be a list containing the subset of OpSim database columns
# (specified in self._user_interface_to_opsim) that actually exist in this opsim database
dtypeList = []
self.baseQuery = 'SELECT'
self.active_columns = []
self._queried_columns = [] # This will be a list of all of the
# OpSim columns queried
# Note: here we will refer to the
# columns by their names in OpSim
for column in self._user_interface_to_opsim:
rec = self._user_interface_to_opsim[column]
if rec[0] in self._summary_columns:
self.active_columns.append(column)
dtypeList.append((rec[0], rec[2]))
if self.baseQuery != 'SELECT':
self.baseQuery += ','
self.baseQuery += ' ' + rec[0]
self._queried_columns.append(rec[0])
# Now loop over self._summary_columns, adding any columns
# to the query that have not already been included therein.
# Since we do not have explicit information about the
# data types of these columns, we will assume they are floats.
for column in self._summary_columns:
if column not in self._queried_columns:
self.baseQuery += ', ' + column
dtypeList.append((column, float))
self.dtype = np.dtype(dtypeList)
class ObservationMetaDataGenerator(object):
"""
A class that allows the user to generate instantiations of
`lsst.sims.utils.ObservationMetaData` corresponding to OpSim pointings.
The functionality includes:
- getOpSimRecords : obtain OpSim records matching the intersection of user
specified ranges on each column in the OpSim output database. The
records are in the form of a `numpy.recarray`
- ObservationMetaDataFromPointing : convert an OpSim record for a single
OpSim Pointing to an instance of ObservationMetaData usable by catsim
and PhoSim Instance Catalogs.
- getObservationMetaData : Obtain a list of ObservationMetaData instances
corresponding to OpSim pointings matching the intersection of user
specified ranges on each column in the OpSim output database.
The major method is ObservationMetaDataGenerator.getObservationMetaData()
which accepts bounds on columns of the opsim summary table and returns
a list of ObservationMetaData instantiations that fall within those
bounds.
"""
@property
def table_name(self):
"""
Return the name of the table in the OpSim database that we are querying
"""
return 'Summary'
def _make_opsim_v3_interface(self):
# a dict keyed on the user interface names of the ObservationMetaData columns
# (i.e. the args to getObservationMetaData). Returns a tuple that is the
# (name of data column in OpSim, transformation to go from user interface to OpSim units,
# dtype in OpSim)
#
# Note: this dict will contain entries for every column (except propID) in the OpSim
# summary table, not just those the ObservationMetaDataGenerator is designed to query
# on. The idea is that ObservationMetaData generated by this class will carry around
# records of the values of all of the associated OpSim Summary columns so that users
# can pass those values on to PhoSim/other tools and thier own discretion.
interface_dict = {'obsHistID': ('obsHistID', None, np.int64),
'expDate': ('expDate', None, int),
'fieldRA': ('fieldRA', np.radians, float),
'fieldDec': ('fieldDec', np.radians, float),
'moonRA': ('moonRA', np.radians, float),
'moonDec': ('moonDec', np.radians, float),
'rotSkyPos': ('rotSkyPos', np.radians, float),
'telescopeFilter':
('filter', lambda x: '\'{}\''.format(x), (str, 1)),
'rawSeeing': ('rawSeeing', None, float),
'sunAlt': ('sunAlt', np.radians, float),
'moonAlt': ('moonAlt', np.radians, float),
'dist2Moon': ('dist2Moon', np.radians, float),
'moonPhase': ('moonPhase', None, float),
'expMJD': ('expMJD', None, float),
'altitude': ('altitude', np.radians, float),
'azimuth': ('azimuth', np.radians, float),
'visitExpTime': ('visitExpTime', None, float),
'airmass': ('airmass', None, float),
'm5': ('fiveSigmaDepth', None, float),
'skyBrightness': ('filtSkyBrightness', None, float),
'sessionID': ('sessionID', None, int),
'fieldID': ('fieldID', None, int),
'night': ('night', None, int),
'visitTime': ('visitTime', None, float),
'finRank': ('finRank', None, float),
'FWHMgeom': ('FWHMgeom', None, float),
# do not include FWHMeff; that is detected by
# self._set_seeing_column()
'transparency': ('transparency', None, float),
'vSkyBright': ('vSkyBright', None, float),
'rotTelPos': ('rotTelPos', None, float),
'lst': ('lst', None, float),
'solarElong': ('solarElong', None, float),
'moonAz': ('moonAz', None, float),
'sunAz': ('sunAz', None, float),
'phaseAngle': ('phaseAngle', None, float),
'rScatter': ('rScatter', None, float),
'mieScatter': ('mieScatter', None, float),
'moonBright': ('moonBright', None, float),
'darkBright': ('darkBright', None, float),
'wind': ('wind', None, float),
'humidity': ('humidity', None, float),
'slewDist': ('slewDist', None, float),
'slewTime': ('slewTime', None, float),
'ditheredRA': ('ditheredRA', None, float),
'ditheredDec': ('ditheredDec', None, float)}
return interface_dict
def _make_opsim_v4_interface(self):
interface_dict = {'obsHistID': ('observationId', None, np.int64),
'fieldRA': ('fieldRA', None, float),
'fieldDec': ('fieldDec', None, float),
'moonRA': ('moonRA', None, float),
'moonDec': ('moonDec', None, float),
'rotSkyPos': ('rotSkyPos', None, float),
'telescopeFilter':
('filter', lambda x: '\'{}\''.format(x), (str, 1)),
'sunAlt': ('sunAlt', None, float),
'moonAlt': ('moonAlt', None, float),
'moonPhase': ('moonPhase', None, float),
'expMJD': ('observationStartMJD', None, float),
'altitude': ('altitude', None, float),
'azimuth': ('azimuth', None, float),
'visitExpTime': ('visitExposureTime', None, float),
'airmass': ('airmass', None, float),
'm5': ('fiveSigmaDepth', None, float),
'skyBrightness': ('skyBrightness', None, float),
'fieldID': ('fieldId', None, int),
'night': ('night', None, int),
'visitTime': ('visitTime', None, float),
'FWHMgeom': ('seeingFWHMgeom', None, float),
# do not include FWHMeff; that is detected by
# self._set_seeing_column()
'rotTelPos': ('rotTelPos', None, float),
'lst': ('observationStartLST', None, float),
'solarElong': ('solarElong', None, float),
'moonAz': ('moonAz', None, float),
'sunAz': ('sunAz', None, float),
'slewDist': ('slewDistance', None, float),
'slewTime': ('slewTime', None, float)}
return interface_dict
def _set_seeing_column(self, input_summary_columns):
"""
input_summary_columns is a list of columns in the OpSim database schema
This method sets the member variable self._seeing_column to a string
denoting the name of the seeing column in the OpSimDatabase. It also
sets self.user_interface_to_opsim['seeing'] to the correct value.
"""
if 'FWHMeff' in input_summary_columns:
self._seeing_column = 'FWHMeff'
elif 'seeingFwhmEff' in input_summary_columns:
self._seeing_column = 'seeingFwhmEff'
else:
self._seeing_column = 'finSeeing'
self.user_interface_to_opsim['seeing'] = (self._seeing_column, None, float)
@property
def opsim_version(self):
return self._opsim_version
@property
def user_interface_to_opsim(self):
if not hasattr(self, '_user_interface_to_opsim'):
if self.opsim_version == 3:
self._user_interface_to_opsim = self._make_opsim_v3_interface()
elif self.opsim_version == 4:
self._user_interface_to_opsim = self._make_opsim_v4_interface()
else:
raise RuntimeError("Unsure how to handle opsim_version ",self.opsim_version)
return self._user_interface_to_opsim
@property
def table_name(self):
if self.opsim_version == 3:
return 'Summary'
elif self.opsim_version == 4:
return 'SummaryAllProps'
raise RuntimeError("Unsure how to handle opsim_version ",self.opsim_version)
def __init__(self, database=None, driver='sqlite', host=None, port=None):
"""
Constructor for the class
Parameters
----------
database : string
absolute path to the output of the OpSim database
driver : string, optional, defaults to 'sqlite'
driver/dialect for the SQL database
host : hostName, optional, defaults to None,
hostName, None is good for a local database
port : hostName, optional, defaults to None,
port, None is good for a local database
Returns
------
Instance of the ObserverMetaDataGenerator class
..notes : For testing purposes a small OpSim database is available at
`os.path.join(getPackageDir('sims_data'), 'OpSimData/opsimblitz1_1133_sqlite.db')`
"""
self._opsim_version = None
self.driver = driver
self.host = host
self.port = port
self.database = database
self._seeing_column = 'FWHMeff'
if self.database is None:
return
if not os.path.isfile(self.database):
raise RuntimeError('%s is not a file' % self.database)
self.opsimdb = DBObject(driver=self.driver, database=self.database,
host=self.host, port=self.port)
# 27 January 2016
# Detect whether the OpSim db you are connecting to uses 'finSeeing'
# as its seeing column (deprecated), or FWHMeff, which is the modern
# standard
list_of_tables = self.opsimdb.get_table_names()
if 'Summary' in list_of_tables:
self._opsim_version = 3
else:
self._opsim_version = 4
self._summary_columns = self.opsimdb.get_column_names(self.table_name)
self._set_seeing_column(self._summary_columns)
# Set up self.dtype containg the dtype of the recarray we expect back from the SQL query.
# Also setup baseQuery which is just the SELECT clause of the SQL query
#
# self.active_columns will be a list containing the subset of OpSim database columns
# (specified in self.user_interface_to_opsim) that actually exist in this opsim database
dtypeList = []
self.baseQuery = 'SELECT'
self.active_columns = []
self._queried_columns = [] # This will be a list of all of the
# OpSim columns queried
# Note: here we will refer to the
# columns by their names in OpSim
for column in self.user_interface_to_opsim:
rec = self.user_interface_to_opsim[column]
if rec[0] in self._summary_columns:
self.active_columns.append(column)
dtypeList.append((rec[0], rec[2]))
if self.baseQuery != 'SELECT':
self.baseQuery += ','
self.baseQuery += ' ' + rec[0]
self._queried_columns.append(rec[0])
# Now loop over self._summary_columns, adding any columns
# to the query that have not already been included therein.
# Since we do not have explicit information about the
# data types of these columns, we will assume they are floats.
for column in self._summary_columns:
if column not in self._queried_columns:
self.baseQuery += ', ' + column
dtypeList.append((column, float))
self.dtype = np.dtype(dtypeList)
def getOpSimRecords(self, obsHistID=None, expDate=None, night=None, fieldRA=None,
fieldDec=None, moonRA=None, moonDec=None,
rotSkyPos=None, telescopeFilter=None, rawSeeing=None,
seeing=None, sunAlt=None, moonAlt=None, dist2Moon=None,
moonPhase=None, expMJD=None, altitude=None,
azimuth=None, visitExpTime=None, airmass=None,
skyBrightness=None, m5=None, boundType='circle',
boundLength=1.75, limit=None):
"""
This method will query the summary table in the `self.opsimdb` database
according to constraints specified in the input ranges and return a
`numpy.recarray` containing the records that match those constraints. If limit
is used, the first N records will be returned in the list.
Parameters
----------
obsHistID, expDate, night, fieldRA, fieldDec, moonRa, moonDec, rotSkyPos,
telescopeFilter, rawSeeing, seeing, sunAlt, moonAlt, dist2Moon,
moonPhase, expMJD, altitude, azimuth, visitExpTime, airmass,
skyBrightness, m5 : tuples of length 2, optional, defaults to None
each of these variables represent a single column (perhaps through
an alias) in the OpSim database, and potentially in a different unit.
if not None, the variable self.columnMapping is used to constrain
the corresponding column in the OpSim database to the ranges (inclusive)
specified in the tuples, after a unit transformation if necessary.
The ranges must be specified in the tuple in degrees for all angles in this
(moonRa, moonDec, rotSkyPos, sunAlt, moonAlt, dist2Moon, altitude,
azimuth). The times in (expMJD, are in units of MJD). visitExpTime has
units of seconds since the start of the survey. moonPhase is a number
from 0., to 100.
boundType : `sims.utils.ObservationMetaData.boundType`, optional, defaults to 'circle'
{'circle', 'box'} denoting the shape of the pointing. Further
documentation `sims.catalogs.generation.db.spatialBounds.py``
boundLength : float, optional, defaults to 0.1
sets `sims.utils.ObservationMetaData.boundLenght`
limit : integer, optional, defaults to None
if not None, denotes max number of records returned by the query
Returns
-------
`numpy.recarray` with OpSim records. The column names may be obtained as
res.dtype.names
.. notes:: The `limit` argument should only be used if a small example
is required. The angle ranges in the argument should be specified in degrees.
"""
self._set_seeing_column(self._summary_columns)
query = self.baseQuery + ' FROM %s' % self.table_name
nConstraints = 0 # the number of constraints in this query
for column in self.user_interface_to_opsim:
transform = self.user_interface_to_opsim[column]
# this try/except block is because there will be columns in the OpSim Summary
# table (and thus in self.user_interface_to_opsim) which the
# ObservationMetaDataGenerator is not designed to query on
try:
value = eval(column)
except:
value = None
if value is not None:
if column not in self.active_columns:
raise RuntimeError("You have asked ObservationMetaDataGenerator to SELECT pointings on "
"%s; that column does not exist in your OpSim database" % column)
if nConstraints > 0:
query += ' AND'
else:
query += ' WHERE '
if isinstance(value, tuple):
if len(value) > 2:
raise RuntimeError('Cannot pass a tuple longer than 2 elements ' +
'to getObservationMetaData: %s is len %d'
% (column, len(value)))
# perform any necessary coordinate transformations
if transform[1] is not None:
vmin = transform[1](value[0])
vmax = transform[1](value[1])
else:
vmin = value[0]
vmax = value[1]
query += ' %s >= %s AND %s <= %s' % \
(transform[0], vmin, transform[0], vmax)
else:
# perform any necessary coordinate transformations
if transform[1] is not None:
vv = transform[1](value)
else:
vv = value
if isinstance(vv, numbers.Number):
tol = np.abs(vv)*1.0e-10
if tol == 0.0:
tol = 1.0e-10
query += ' %s < %.12e AND %s > %.12e' % (transform[0],
vv+tol,
transform[0],
vv-tol)
else:
query += ' %s == %s' % (transform[0], vv)
nConstraints += 1
mjd_name = self.user_interface_to_opsim['expMJD'][0]
query += ' GROUP BY %s ORDER BY %s' % (mjd_name, mjd_name)
if limit is not None:
query += ' LIMIT %d' % limit
if nConstraints == 0 and limit is None:
raise RuntimeError('You did not specify any contraints on your query;' +
' you will just return ObservationMetaData for all poitnings')
results = self.opsimdb.execute_arbitrary(query, dtype=self.dtype)
return results
def ObservationMetaDataFromPointing(self, OpSimPointingRecord, OpSimColumns=None,
boundLength=1.75, boundType='circle'):
"""
Return instance of ObservationMetaData for an OpSim Pointing record
from OpSim.
Parameters
----------
OpSimPointingRecord : Dictionary, mandatory
Dictionary of values with keys corresponding to certain columns of
the Summary table in the OpSim database. The minimal list of keys
required for catsim to work is 'fiveSigmaDepth',
'filtSkyBrightness', and at least one of ('finSeeing', 'FWHMeff').
More keys defined in columnMap may be necessary for PhoSim to work.
OpSimColumns : tuple of strings, optional, defaults to None
The columns corresponding to the OpSim records. If None, attempts
to obtain these from the OpSimRecord as OpSimRecord.dtype.names
boundType : {'circle', 'box'}, optional, defaults to 'circle'
Shape of the observation
boundLength : scalar float, optional, defaults to 1.75
'characteristic size' of observation field, in units of degrees.
For boundType='circle', this is a radius, for boundType='box', this
is a size of the box
"""
pointing = OpSimPointingRecord
pointing_column_names = pointing.dtype.names
# Decide what is the name of the column in the OpSim database
# corresponding to the Seeing. For older OpSim outputs, this is
# 'finSeeing'. For later OpSim outputs this is 'FWHMeff'
if OpSimColumns is None:
OpSimColumns = pointing_column_names
self._set_seeing_column(OpSimColumns)
# get the names of the columns that contain the minimal schema
# for an ObservationMetaData
mjd_name = self.user_interface_to_opsim['expMJD'][0]
ra_name = self.user_interface_to_opsim['fieldRA'][0]
dec_name = self.user_interface_to_opsim['fieldDec'][0]
filter_name = self.user_interface_to_opsim['telescopeFilter'][0]
# check to see if angles are in degrees or radians
if self.user_interface_to_opsim['fieldRA'][1] is None:
in_degrees = True
else:
in_degrees = False
# check to make sure the OpSim pointings being supplied contain
# the minimum required information
for required_column in (ra_name, dec_name, mjd_name, filter_name):
if required_column not in OpSimColumns:
raise RuntimeError("ObservationMetaDataGenerator requires that the database of "
"pointings include data for:\nfieldRA"
"\nfieldDec\nexpMJD\nfilter")
# construct a raw dict of all of the OpSim columns associated with this pointing
raw_dict = dict([(col, pointing[col]) for col in pointing_column_names])
raw_dict['opsim_version'] = self.opsim_version
if in_degrees:
ra_val = pointing[ra_name]
dec_val = pointing[dec_name]
else:
ra_val = np.degrees(pointing[ra_name])
dec_val = np.degrees(pointing[dec_name])
mjd_val = pointing[mjd_name]
filter_val = pointing[filter_name]
obs = ObservationMetaData(pointingRA=ra_val,
pointingDec=dec_val,
mjd=mjd_val,
bandpassName=filter_val,
boundType=boundType,
boundLength=boundLength)
m5_name = self.user_interface_to_opsim['m5'][0]
rotSky_name = self.user_interface_to_opsim['rotSkyPos'][0]
if m5_name in pointing_column_names:
obs.m5 = pointing[m5_name]
if 'filtSkyBrightness' in pointing_column_names:
obs.skyBrightness = pointing['filtSkyBrightness']
if self._seeing_column in pointing_column_names:
obs.seeing = pointing[self._seeing_column]
if rotSky_name in pointing_column_names:
if in_degrees:
obs.rotSkyPos = pointing[rotSky_name]
else:
obs.rotSkyPos = np.degrees(pointing[rotSky_name])
obs.OpsimMetaData = raw_dict
return obs
def ObservationMetaDataFromPointingArray(self, OpSimPointingRecords,
OpSimColumns=None,
boundLength=1.75,
boundType='circle'):
"""
Static method to get a list of instances of ObservationMetaData
corresponding to the records in `numpy.recarray`, where it uses
the dtypes of the recArray for ObservationMetaData attributes that
require the dtype.
Parameters
----------
OpSimPointingRecords : `numpy.recarray` of OpSim Records
OpSimColumns : a tuple of strings, optional, defaults to None
tuple of column Names of the data in the `numpy.recarray`. If
None, these names are extracted from the recarray.
boundType : {'circle' or 'box'}
denotes the shape of the pointing
boundLength : float, optional, defaults to 1.75
the bound length of the Pointing in units of degrees. For boundType
'box', this is the length of the side of the square box. For boundType
'circle' this is the radius.
"""
if OpSimColumns is None:
OpSimColumns = OpSimPointingRecords.dtype.names
if self.opsim_version is None:
if 'obsHistID' in OpSimColumns:
self._opsim_version = 3
elif 'observationId' in OpSimColumns:
self._opsim_version = 4
else:
raise RuntimeError("Unable to determine which OpSim version your "
"OpSimPointingRecords correspond to; make sure "
"obsHistID (v3) or observationId (v4) are in the "
"records.")
out = list(self.ObservationMetaDataFromPointing(OpSimPointingRecord,
OpSimColumns=OpSimColumns,
boundLength=boundLength,
boundType=boundType)
for OpSimPointingRecord in OpSimPointingRecords)
return out
def getObservationMetaData(self, obsHistID=None, expDate=None, night=None, fieldRA=None, fieldDec=None,
moonRA=None, moonDec=None, rotSkyPos=None, telescopeFilter=None,
rawSeeing=None, seeing=None, sunAlt=None, moonAlt=None, dist2Moon=None,
moonPhase=None, expMJD=None, altitude=None, azimuth=None,
visitExpTime=None, airmass=None, skyBrightness=None,
m5=None, boundType='circle', boundLength=1.75, limit=None):
"""
This method will query the OpSim database summary table according to user-specified
constraints and return a list of of ObservationMetaData instantiations consistent
with those constraints.
@param [in] limit is an integer denoting the maximum number of ObservationMetaData to
be returned
@param [in] boundType is the boundType of the ObservationMetaData to be returned
(see documentation in sims_catalogs_generation/../db/spatialBounds.py for more
details)
@param [in] boundLength is the boundLength of the ObservationMetaData to be
returned (in degrees; see documentation in
sims_catalogs_generation/../db/spatialBounds.py for more details)
All other input parameters are constraints to be placed on the SQL query of the
opsim output db. These contraints can either be tuples of the form (min, max)
or an exact value the user wants returned. Note: min and max are inclusive
bounds.
Parameters that can be constrained are:
@param [in] fieldRA in degrees
@param [in] fieldDec in degrees
@param [in] altitude in degrees
@param [in] azimuth in degrees
@param [in] moonRA in degrees
@param [in] moonDec in degrees
@param [in] moonAlt in degrees
@param [in] moonPhase (a value from 1 to 100 indicating how much of the moon is illuminated)
@param [in] dist2Moon the distance between the telescope pointing and the moon in degrees
@param [in] sunAlt in degrees
@param [in[ rotSkyPos (the angle of the sky with respect to the camera coordinate system) in degrees
@param [in] telescopeFilter a string that is one of u,g,r,i,z,y
@param [in] airmass
@param [in] rawSeeing (this is an idealized seeing at zenith at 500nm in arcseconds)
@param [in] seeing (this is the OpSim column 'FWHMeff' or 'finSeeing' [deprecated] in arcseconds)
@param [in] visitExpTime the exposure time in seconds
@param [in] obsHistID the integer used by OpSim to label pointings
@param [in] expDate is the date of the exposure (units????)
@param [in] expMJD is the MJD of the exposure
@param [in] night is the night (an int starting at zero) on which the observation took place
@param [in] m5 is the five sigma depth of the observation
@param [in] skyBrightness
"""
OpSimPointingRecords = self.getOpSimRecords(obsHistID=obsHistID,
expDate=expDate,
night=night,
fieldRA=fieldRA,
fieldDec=fieldDec,
moonRA=moonRA,
moonDec=moonDec,
rotSkyPos=rotSkyPos,
telescopeFilter=telescopeFilter,
rawSeeing=rawSeeing,
seeing=seeing,
sunAlt=sunAlt,
moonAlt=moonAlt,
dist2Moon=dist2Moon,
moonPhase=moonPhase,
expMJD=expMJD,
altitude=altitude,
azimuth=azimuth,
visitExpTime=visitExpTime,
airmass=airmass,
skyBrightness=skyBrightness,
m5=m5, boundType=boundType,
boundLength=boundLength,
limit=limit)
output = self.ObservationMetaDataFromPointingArray(OpSimPointingRecords,
OpSimColumns=None,
boundType=boundType,
boundLength=boundLength)
return output
def _postprocess_results(self, master_chunk):
"""
query the database specified by agn_params_db to
find the AGN varParamStr associated with each AGN
"""
if self.agn_objid is None:
gid_name = 'galaxy_id'
varpar_name = 'varParamStr'
magnorm_name = 'magNorm'
else:
gid_name = self.agn_objid + '_' + 'galaxy_id'
varpar_name = self.agn_objid + '_' + 'varParamStr'
magnorm_name = self.agn_objid + '_' + 'magNorm'
if self.agn_params_db is None:
return(master_chunk)
if not os.path.exists(self.agn_params_db):
raise RuntimeError('\n%s\n\ndoes not exist' % self.agn_params_db)
if not hasattr(self, '_agn_dbo'):
self._agn_dbo = DBObject(database=self.agn_params_db,
driver='sqlite')
self._agn_dtype = np.dtype([('galaxy_id', int),
('magNorm', float),
('varParamStr', str, 500)])
gid_arr = master_chunk[gid_name].astype(float)
gid_min = np.nanmin(gid_arr)
gid_max = np.nanmax(gid_arr)
query = 'SELECT galaxy_id, magNorm, varParamStr '
query += 'FROM agn_params '
query += 'WHERE galaxy_id BETWEEN %d AND %d ' % (gid_min, gid_max)
query += 'ORDER BY galaxy_id'
agn_data_iter = self._agn_dbo.get_arbitrary_chunk_iterator(query,
dtype=self._agn_dtype,
chunk_size=1000000)
m_sorted_dex = np.argsort(gid_arr)
m_sorted_id = gid_arr[m_sorted_dex]
for agn_chunk in agn_data_iter:
# find the indices of the elements in master_chunk
# that correspond to elements in agn_chunk
m_elements = np.in1d(m_sorted_id, agn_chunk['galaxy_id'])
m_dex = m_sorted_dex[m_elements]
# find the indices of the elements in agn_chunk
# that correspond to elements in master_chunk
a_dex = np.in1d(agn_chunk['galaxy_id'], m_sorted_id)
# make sure we have matched elements correctly
np.testing.assert_array_equal(agn_chunk['galaxy_id'][a_dex],
master_chunk[gid_name][m_dex])
if varpar_name in master_chunk.dtype.names:
master_chunk[varpar_name][m_dex] = agn_chunk['varParamStr'][a_dex]
if magnorm_name in master_chunk.dtype.names:
master_chunk[magnorm_name][m_dex] = agn_chunk['magNorm'][a_dex]
return self._final_pass(master_chunk)
from lsst.sims.catalogs.db import DBObject
db = DBObject(database='LSST',
host='localhost',
port=51432,
driver='mssql+pymssql')
import numpy as np
rng = np.random.RandomState(7153323)
dtype = np.dtype([('id', int), ('htmid', int)])
query = 'SELECT id, htmid FROM galaxy'
data_iter = db.get_arbitrary_chunk_iterator(query,
dtype=dtype,
chunk_size=10000)
with open('galaxy_sne_flag.txt', 'w') as out_file:
for chunk in data_iter:
flag_vals = rng.randint(0, 10, size=len(chunk))
for hh, ii, ff in zip(chunk['htmid'], chunk['id'], flag_vals):
out_file.write('%d;%d;%d\n' % (hh, ii, ff))
def test_alert_data_generation(self):
dmag_cutoff = 0.005
mag_name_to_int = {'u': 0, 'g': 1, 'r': 2, 'i': 3, 'z' : 4, 'y': 5}
_max_var_param_str = self.max_str_len
class StarAlertTestDBObj(StellarAlertDBObjMixin, CatalogDBObject):
objid = 'star_alert'
tableid = 'stars'
idColKey = 'simobjid'
raColName = 'ra'
decColName = 'dec'
objectTypeId = 0
columns = [('raJ2000', 'ra*0.01745329252'),
('decJ2000', 'dec*0.01745329252'),
('parallax', 'px*0.01745329252/3600.0'),
('properMotionRa', 'pmra*0.01745329252/3600.0'),
('properMotionDec', 'pmdec*0.01745329252/3600.0'),
('radialVelocity', 'vrad'),
('variabilityParameters', 'varParamStr', str, _max_var_param_str)]
class TestAlertsVarCatMixin(object):
@register_method('alert_test')
def applyAlertTest(self, valid_dexes, params, expmjd, variability_cache=None):
if len(params) == 0:
return np.array([[], [], [], [], [], []])
if isinstance(expmjd, numbers.Number):
dmags_out = np.zeros((6, self.num_variable_obj(params)))
else:
dmags_out = np.zeros((6, self.num_variable_obj(params), len(expmjd)))
for i_star in range(self.num_variable_obj(params)):
if params['amp'][i_star] is not None:
dmags = params['amp'][i_star]*np.cos(params['per'][i_star]*expmjd)
for i_filter in range(6):
dmags_out[i_filter][i_star] = dmags
return dmags_out
class TestAlertsVarCat(TestAlertsVarCatMixin, AlertStellarVariabilityCatalog):
pass
class TestAlertsTruthCat(TestAlertsVarCatMixin, CameraCoords, AstrometryStars,
Variability, InstanceCatalog):
column_outputs = ['uniqueId', 'chipName', 'dmagAlert', 'magAlert']
camera = obs_lsst_phosim.PhosimMapper().camera
@compound('delta_umag', 'delta_gmag', 'delta_rmag',
'delta_imag', 'delta_zmag', 'delta_ymag')
def get_TruthVariability(self):
return self.applyVariability(self.column_by_name('varParamStr'))
@cached
def get_dmagAlert(self):
return self.column_by_name('delta_%smag' % self.obs_metadata.bandpass)
@cached
def get_magAlert(self):
return self.column_by_name('%smag' % self.obs_metadata.bandpass) + \
self.column_by_name('dmagAlert')
star_db = StarAlertTestDBObj(database=self.star_db_name, driver='sqlite')
# assemble the true light curves for each object; we need to figure out
# if their np.max(dMag) ever goes over dmag_cutoff; then we will know if
# we are supposed to simulate them
true_lc_dict = {}
true_lc_obshistid_dict = {}
is_visible_dict = {}
obs_dict = {}
max_obshistid = -1
n_total_observations = 0
for obs in self.obs_list:
obs_dict[obs.OpsimMetaData['obsHistID']] = obs
obshistid = obs.OpsimMetaData['obsHistID']
if obshistid > max_obshistid:
max_obshistid = obshistid
cat = TestAlertsTruthCat(star_db, obs_metadata=obs)
for line in cat.iter_catalog():
if line[1] is None:
continue
n_total_observations += 1
if line[0] not in true_lc_dict:
true_lc_dict[line[0]] = {}
true_lc_obshistid_dict[line[0]] = []
true_lc_dict[line[0]][obshistid] = line[2]
true_lc_obshistid_dict[line[0]].append(obshistid)
if line[0] not in is_visible_dict:
is_visible_dict[line[0]] = False
if line[3] <= self.obs_mag_cutoff[mag_name_to_int[obs.bandpass]]:
is_visible_dict[line[0]] = True
obshistid_bits = int(np.ceil(np.log(max_obshistid)/np.log(2)))
skipped_due_to_mag = 0
objects_to_simulate = []
obshistid_unqid_set = set()
for obj_id in true_lc_dict:
dmag_max = -1.0
for obshistid in true_lc_dict[obj_id]:
if np.abs(true_lc_dict[obj_id][obshistid]) > dmag_max:
dmag_max = np.abs(true_lc_dict[obj_id][obshistid])
if dmag_max >= dmag_cutoff:
if not is_visible_dict[obj_id]:
skipped_due_to_mag += 1
continue
objects_to_simulate.append(obj_id)
for obshistid in true_lc_obshistid_dict[obj_id]:
obshistid_unqid_set.add((obj_id << obshistid_bits) + obshistid)
self.assertGreater(len(objects_to_simulate), 10)
self.assertGreater(skipped_due_to_mag, 0)
log_file_name = tempfile.mktemp(dir=self.output_dir, suffix='log.txt')
alert_gen = AlertDataGenerator(testing=True)
alert_gen.subdivide_obs(self.obs_list, htmid_level=6)
for htmid in alert_gen.htmid_list:
alert_gen.alert_data_from_htmid(htmid, star_db,
photometry_class=TestAlertsVarCat,
output_prefix='alert_test',
output_dir=self.output_dir,
dmag_cutoff=dmag_cutoff,
log_file_name=log_file_name)
dummy_sed = Sed()
bp_dict = BandpassDict.loadTotalBandpassesFromFiles()
phot_params = PhotometricParameters()
# First, verify that the contents of the sqlite files are all correct
n_tot_simulated = 0
alert_query = 'SELECT alert.uniqueId, alert.obshistId, meta.TAI, '
alert_query += 'meta.band, quiescent.flux, alert.dflux, '
alert_query += 'quiescent.snr, alert.snr, '
alert_query += 'alert.ra, alert.dec, alert.chipNum, '
alert_query += 'alert.xPix, alert.yPix, ast.pmRA, ast.pmDec, '
alert_query += 'ast.parallax '
alert_query += 'FROM alert_data AS alert '
alert_query += 'INNER JOIN metadata AS meta ON meta.obshistId=alert.obshistId '
alert_query += 'INNER JOIN quiescent_flux AS quiescent '
alert_query += 'ON quiescent.uniqueId=alert.uniqueId '
alert_query += 'AND quiescent.band=meta.band '
alert_query += 'INNER JOIN baseline_astrometry AS ast '
alert_query += 'ON ast.uniqueId=alert.uniqueId'
alert_dtype = np.dtype([('uniqueId', int), ('obshistId', int),
('TAI', float), ('band', int),
('q_flux', float), ('dflux', float),
('q_snr', float), ('tot_snr', float),
('ra', float), ('dec', float),
('chipNum', int), ('xPix', float), ('yPix', float),
('pmRA', float), ('pmDec', float), ('parallax', float)])
sqlite_file_list = os.listdir(self.output_dir)
n_tot_simulated = 0
obshistid_unqid_simulated_set = set()
for file_name in sqlite_file_list:
if not file_name.endswith('db'):
continue
full_name = os.path.join(self.output_dir, file_name)
self.assertTrue(os.path.exists(full_name))
alert_db = DBObject(full_name, driver='sqlite')
alert_data = alert_db.execute_arbitrary(alert_query, dtype=alert_dtype)
if len(alert_data) == 0:
continue
mjd_list = ModifiedJulianDate.get_list(TAI=alert_data['TAI'])
for i_obj in range(len(alert_data)):
n_tot_simulated += 1
obshistid_unqid_simulated_set.add((alert_data['uniqueId'][i_obj] << obshistid_bits) +
alert_data['obshistId'][i_obj])
unq = alert_data['uniqueId'][i_obj]
obj_dex = (unq//1024)-1
self.assertAlmostEqual(self.pmra_truth[obj_dex], 0.001*alert_data['pmRA'][i_obj], 4)
self.assertAlmostEqual(self.pmdec_truth[obj_dex], 0.001*alert_data['pmDec'][i_obj], 4)
self.assertAlmostEqual(self.px_truth[obj_dex], 0.001*alert_data['parallax'][i_obj], 4)
ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex],
self.px_truth[obj_dex], self.vrad_truth[obj_dex],
mjd=mjd_list[i_obj])
distance = angularSeparation(ra_truth, dec_truth,
alert_data['ra'][i_obj], alert_data['dec'][i_obj])
distance_arcsec = 3600.0*distance
msg = '\ntruth: %e %e\nalert: %e %e\n' % (ra_truth, dec_truth,
alert_data['ra'][i_obj],
alert_data['dec'][i_obj])
self.assertLess(distance_arcsec, 0.0005, msg=msg)
obs = obs_dict[alert_data['obshistId'][i_obj]]
chipname = chipNameFromRaDec(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
pm_ra=self.pmra_truth[obj_dex],
pm_dec=self.pmdec_truth[obj_dex],
parallax=self.px_truth[obj_dex],
v_rad=self.vrad_truth[obj_dex],
obs_metadata=obs,
camera=self.camera)
chipnum = int(chipname.replace('R', '').replace('S', '').
replace(' ', '').replace(';', '').replace(',', '').
replace(':', ''))
self.assertEqual(chipnum, alert_data['chipNum'][i_obj])
xpix, ypix = pixelCoordsFromRaDec(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
pm_ra=self.pmra_truth[obj_dex],
pm_dec=self.pmdec_truth[obj_dex],
parallax=self.px_truth[obj_dex],
v_rad=self.vrad_truth[obj_dex],
obs_metadata=obs,
camera=self.camera)
self.assertAlmostEqual(alert_data['xPix'][i_obj], xpix, 4)
self.assertAlmostEqual(alert_data['yPix'][i_obj], ypix, 4)
dmag_sim = -2.5*np.log10(1.0+alert_data['dflux'][i_obj]/alert_data['q_flux'][i_obj])
self.assertAlmostEqual(true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]],
dmag_sim, 3)
mag_name = ('u', 'g', 'r', 'i', 'z', 'y')[alert_data['band'][i_obj]]
m5 = obs.m5[mag_name]
q_mag = dummy_sed.magFromFlux(alert_data['q_flux'][i_obj])
self.assertAlmostEqual(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex],
q_mag, 4)
snr, gamma = calcSNR_m5(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex],
bp_dict[mag_name],
self.obs_mag_cutoff[alert_data['band'][i_obj]],
phot_params)
self.assertAlmostEqual(snr/alert_data['q_snr'][i_obj], 1.0, 4)
tot_mag = self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex] + \
true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]]
snr, gamma = calcSNR_m5(tot_mag, bp_dict[mag_name],
m5, phot_params)
self.assertAlmostEqual(snr/alert_data['tot_snr'][i_obj], 1.0, 4)
for val in obshistid_unqid_set:
self.assertIn(val, obshistid_unqid_simulated_set)
self.assertEqual(len(obshistid_unqid_set), len(obshistid_unqid_simulated_set))
astrometry_query = 'SELECT uniqueId, ra, dec, TAI '
astrometry_query += 'FROM baseline_astrometry'
astrometry_dtype = np.dtype([('uniqueId', int),
('ra', float),
('dec', float),
('TAI', float)])
tai_list = []
for obs in self.obs_list:
tai_list.append(obs.mjd.TAI)
tai_list = np.array(tai_list)
n_tot_ast_simulated = 0
for file_name in sqlite_file_list:
if not file_name.endswith('db'):
continue
full_name = os.path.join(self.output_dir, file_name)
self.assertTrue(os.path.exists(full_name))
alert_db = DBObject(full_name, driver='sqlite')
astrometry_data = alert_db.execute_arbitrary(astrometry_query, dtype=astrometry_dtype)
if len(astrometry_data) == 0:
continue
mjd_list = ModifiedJulianDate.get_list(TAI=astrometry_data['TAI'])
for i_obj in range(len(astrometry_data)):
n_tot_ast_simulated += 1
obj_dex = (astrometry_data['uniqueId'][i_obj]//1024) - 1
ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex],
self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex],
self.px_truth[obj_dex], self.vrad_truth[obj_dex],
mjd=mjd_list[i_obj])
distance = angularSeparation(ra_truth, dec_truth,
astrometry_data['ra'][i_obj],
astrometry_data['dec'][i_obj])
self.assertLess(3600.0*distance, 0.0005)
del alert_gen
gc.collect()
self.assertGreater(n_tot_simulated, 10)
self.assertGreater(len(obshistid_unqid_simulated_set), 10)
self.assertLess(len(obshistid_unqid_simulated_set), n_total_observations)
self.assertGreater(n_tot_ast_simulated, 0)
def __init__(self, database, host, port, driver):
self._dbo = DBObject(database=database, host=host, port=port,
driver=driver)
def write_sprinkled_lc(out_file_name, total_obs_md,
pointing_dir, opsim_db_name,
ra_colname='descDitheredRA',
dec_colname='descDitheredDec',
rottel_colname = 'descDitheredRotTelPos',
sql_file_name=None,
bp_dict=None):
"""
Create database of light curves
Note: this is still under development. It has not yet been
used for a production-level truth catalog
Parameters
----------
out_file_name is the name of the sqlite file to be written
total_obs_md is an ObservationMetaData covering the whole
survey area
pointing_dir contains a series of files that are two columns: obshistid, mjd.
The files must each have 'visits' in their name. These specify the pointings
for which we are assembling data. See:
https://github.com/LSSTDESC/DC2_Repo/tree/master/data/Run1.1
for an example.
opsim_db_name is the name of the OpSim database to be queried for pointings
ra_colname is the column used for RA of the pointing (default:
descDitheredRA)
dec_colname is the column used for the Dec of the pointing (default:
descDitheredDec)
rottel_colname is the column used for the rotTelPos of the pointing
(default: desckDitheredRotTelPos')
sql_file_name is the name of the parameter database produced by
write_sprinkled_param_db to be used
bp_dict is a BandpassDict of the telescope filters to be used
Returns
-------
None
Writes out a database to out_file_name. The tables of this database and
their columns are:
light_curves:
- uniqueId -- an int unique to all objects
- obshistid -- an int unique to all pointings
- mag -- the magnitude observed for this object at that pointing
obs_metadata:
- obshistid -- an int unique to all pointings
- mjd -- the date of the pointing
- filter -- an int corresponding to the telescope filter (0==u, 1==g..)
variables_and_transients:
- uniqueId -- an int unique to all objects
- galaxy_id -- an int indicating the host galaxy
- ra -- in degrees
- dec -- in degrees
- agn -- ==1 if object is an AGN
- sn -- ==1 if object is a supernova
"""
t0_master = time.time()
if not os.path.isfile(sql_file_name):
raise RuntimeError('%s does not exist' % sql_file_name)
sn_simulator = SneSimulator(bp_dict)
sed_dir = os.environ['SIMS_SED_LIBRARY_DIR']
create_sprinkled_sql_file(out_file_name)
t_start = time.time()
# get data about the pointings being simulated
(htmid_dict,
mjd_dict,
filter_dict,
obsmd_dict) = get_pointing_htmid(pointing_dir, opsim_db_name,
ra_colname=ra_colname,
dec_colname=dec_colname)
t_htmid_dict = time.time()-t_start
bp_to_int = {'u':0, 'g':1, 'r':2, 'i':3, 'z':4, 'y':5}
# put the data about the pointings in the obs_metadata table
with sqlite3.connect(out_file_name) as conn:
cursor = conn.cursor()
values = ((int(obs), mjd_dict[obs], bp_to_int[filter_dict[obs]])
for obs in mjd_dict)
cursor.executemany('''INSERT INTO obs_metadata VALUES (?,?,?)''', values)
cursor.execute('''CREATE INDEX obs_filter
ON obs_metadata (obshistid, filter)''')
conn.commit()
print('\ngot htmid_dict -- %d in %e seconds' % (len(htmid_dict), t_htmid_dict))
db = DBObject(sql_file_name, driver='sqlite')
# get a list of htmid corresponding to trixels in which
# variables and transients can be found
query = 'SELECT DISTINCT htmid FROM zpoint WHERE is_agn=1 OR is_sn=1'
dtype = np.dtype([('htmid', int)])
results = db.execute_arbitrary(query, dtype=dtype)
object_htmid = results['htmid']
agn_dtype = np.dtype([('uniqueId', int), ('galaxy_id', int),
('ra', float), ('dec', float),
('redshift', float), ('sed', str, 500),
('magnorm', float), ('varParamStr', str, 500),
('is_sprinkled', int)])
agn_base_query = 'SELECT uniqueId, galaxy_id, '
agn_base_query += 'raJ2000, decJ2000, '
agn_base_query += 'redshift, sedFilepath, '
agn_base_query += 'magNorm, varParamStr, is_sprinkled '
agn_base_query += 'FROM zpoint WHERE is_agn=1 '
sn_dtype = np.dtype([('uniqueId', int), ('galaxy_id', int),
('ra', float), ('dec', float),
('redshift', float), ('sn_truth_params', str, 500),
('is_sprinkled', int)])
sn_base_query = 'SELECT uniqueId, galaxy_id, '
sn_base_query += 'raJ2000, decJ2000, '
sn_base_query += 'redshift, sn_truth_params, is_sprinkled '
sn_base_query += 'FROM zpoint WHERE is_sn=1 '
filter_to_int = {'u':0, 'g':1, 'r':2, 'i':3, 'z':4, 'y':5}
n_floats = 0
with sqlite3.connect(out_file_name) as conn:
cursor = conn.cursor()
t_before_htmid = time.time()
# loop over trixels containing variables and transients, simulating
# the light curves of those objects
for htmid_dex, htmid in enumerate(object_htmid):
if htmid_dex>0:
htmid_duration = (time.time()-t_before_htmid)/3600.0
htmid_prediction = len(object_htmid)*htmid_duration/htmid_dex
print('%d htmid out of %d in %e hours; predict %e hours remaining' %
(htmid_dex, len(object_htmid), htmid_duration,htmid_prediction-htmid_duration))
mjd_arr = []
obs_arr = []
filter_arr = []
# Find only those pointings which overlap the current trixel
for obshistid in htmid_dict:
is_contained = False
for bounds in htmid_dict[obshistid]:
if htmid<=bounds[1] and htmid>=bounds[0]:
is_contained = True
break
if is_contained:
mjd_arr.append(mjd_dict[obshistid])
obs_arr.append(obshistid)
filter_arr.append(filter_to_int[filter_dict[obshistid]])
if len(mjd_arr) == 0:
continue
mjd_arr = np.array(mjd_arr)
obs_arr = np.array(obs_arr)
filter_arr = np.array(filter_arr)
sorted_dex = np.argsort(mjd_arr)
mjd_arr = mjd_arr[sorted_dex]
obs_arr = obs_arr[sorted_dex]
filter_arr = filter_arr[sorted_dex]
agn_query = agn_base_query + 'AND htmid=%d' % htmid
agn_iter = db.get_arbitrary_chunk_iterator(agn_query,
dtype=agn_dtype,
chunk_size=10000)
# put static data about the AGN (position, etc.) into the
# variables_and_transients table
for i_chunk, agn_results in enumerate(agn_iter):
values = ((int(agn_results['uniqueId'][i_obj]),
int(agn_results['galaxy_id'][i_obj]),
np.degrees(agn_results['ra'][i_obj]),
np.degrees(agn_results['dec'][i_obj]),
int(agn_results['is_sprinkled'][i_obj]),
1,0)
for i_obj in range(len(agn_results)))
cursor.executemany('''INSERT INTO variables_and_transients VALUES
(?,?,?,?,?,?,?)''', values)
agn_simulator = AgnSimulator(agn_results['redshift'])
quiescent_mag = np.zeros((len(agn_results), 6), dtype=float)
for i_obj, (sed_name, zz, mm) in enumerate(zip(agn_results['sed'],
agn_results['redshift'],
agn_results['magnorm'])):
spec = Sed()
spec.readSED_flambda(os.path.join(sed_dir, sed_name))
fnorm = getImsimFluxNorm(spec, mm)
spec.multiplyFluxNorm(fnorm)
spec.redshiftSED(zz, dimming=True)
mag_list = bp_dict.magListForSed(spec)
quiescent_mag[i_obj] = mag_list
# simulate AGN variability
dmag = agn_simulator.applyVariability(agn_results['varParamStr'],
expmjd=mjd_arr)
# loop over pointings that overlap the current trixel, writing
# out simulated photometry for each AGN observed in that pointing
for i_time, obshistid in enumerate(obs_arr):
# only include objects that were actually on a detector
are_on_chip = _actually_on_chip(np.degrees(agn_results['ra']),
np.degrees(agn_results['dec']),
obsmd_dict[obshistid])
valid_agn = np.where(are_on_chip)
if len(valid_agn[0])==0:
continue
values = ((int(agn_results['uniqueId'][i_obj]),
int(obs_arr[i_time]),
quiescent_mag[i_obj][filter_arr[i_time]]+
dmag[filter_arr[i_time]][i_obj][i_time])
for i_obj in valid_agn[0])
cursor.executemany('''INSERT INTO light_curves VALUES
(?,?,?)''', values)
conn.commit()
n_floats += len(dmag.flatten())
sn_query = sn_base_query + 'AND htmid=%d' % htmid
sn_iter = db.get_arbitrary_chunk_iterator(sn_query,
dtype=sn_dtype,
chunk_size=10000)
for sn_results in sn_iter:
t0_sne = time.time()
# write static information about SNe to the
# variables_and_transients table
values = ((int(sn_results['uniqueId'][i_obj]),
int(sn_results['galaxy_id'][i_obj]),
np.degrees(sn_results['ra'][i_obj]),
np.degrees(sn_results['dec'][i_obj]),
int(sn_results['is_sprinkled'][i_obj]),
0,1)
for i_obj in range(len(sn_results)))
cursor.executemany('''INSERT INTO variables_and_transients VALUES
(?,?,?,?,?,?,?)''', values)
conn.commit()
sn_mags = sn_simulator.calculate_sn_magnitudes(sn_results['sn_truth_params'],
mjd_arr, filter_arr)
print(' did %d sne in %e seconds' % (len(sn_results), time.time()-t0_sne))
# loop over pointings that overlap the current trixel, writing
# out simulated photometry for each SNe observed in that pointing
for i_time, obshistid in enumerate(obs_arr):
# only include objects that fell on a detector
are_on_chip = _actually_on_chip(np.degrees(sn_results['ra']),
np.degrees(sn_results['dec']),
obsmd_dict[obshistid])
valid_obj = np.where(np.logical_and(np.isfinite(sn_mags[:,i_time]),
are_on_chip))
if len(valid_obj[0]) == 0:
continue
values = ((int(sn_results['uniqueId'][i_obj]),
int(obs_arr[i_time]),
sn_mags[i_obj][i_time])
for i_obj in valid_obj[0])
cursor.executemany('''INSERT INTO light_curves VALUES (?,?,?)''', values)
conn.commit()
n_floats += len(valid_obj[0])
cursor.execute('CREATE INDEX unq_obs ON light_curves (uniqueId, obshistid)')
conn.commit()
print('n_floats %d' % n_floats)
print('in %e seconds' % (time.time()-t0_master))
bp_dict = BandpassDict.loadTotalBandpassesFromFiles()
bp = bp_dict['i']
z_grid = np.arange(0.0, 16.0, 0.01)
k_grid = np.zeros(len(z_grid), dtype=float)
for i_z, zz in enumerate(z_grid):
ss = Sed(flambda=base_sed.flambda, wavelen=base_sed.wavelen)
ss.redshiftSED(zz, dimming=True)
k = k_correction(ss, bp, zz)
k_grid[i_z] = k
cosmo = CosmologyObject()
db = DBObject(database='LSSTCATSIM',
host='fatboy.phys.washington.edu',
port=1433,
driver='mssql+pymssql')
query = 'SELECT magnorm_agn, redshift, varParamStr FROM '
query += 'galaxy WHERE varParamStr IS NOT NULL '
query += 'AND dec BETWEEN -2.5 AND 2.5 '
query += 'AND (ra<2.5 OR ra>357.5)'
dtype = np.dtype([('magnorm', float), ('redshift', float),
('varParamStr', str, 400)])
data_iter = db.get_arbitrary_chunk_iterator(query,
dtype=dtype,
chunk_size=10000)
with open('data/dc1_agn_params.txt', 'w') as out_file:
def test_catalog_db_object_cacheing(self):
"""
Test that opening multiple CatalogDBObjects that connect to the same
database only results in one connection being opened and used. We
will test this by instantiating two CatalogDBObjects and a DBObject
that connect to the same database. We will then test that the two
CatalogDBObjects' connections are identical, but that the DBObject has
its own connection.
"""
self.assertEqual(len(CatalogDBObject._connection_cache), 0)
class DbClass1(CatalogDBObject):
database = self.db_name
port = None
host = None
driver = 'sqlite'
tableid = 'test'
idColKey = 'id'
objid = 'test_db_class_1'
columns = [('identification', 'id')]
class DbClass2(CatalogDBObject):
database = self.db_name
port = None
host = None
driver = 'sqlite'
tableid = 'test'
idColKey = 'id'
objid = 'test_db_class_2'
columns = [('other', 'i1')]
db1 = DbClass1()
db2 = DbClass2()
self.assertEqual(id(db1.connection), id(db2.connection))
self.assertEqual(len(CatalogDBObject._connection_cache), 1)
db3 = DBObject(database=self.db_name,
driver='sqlite',
host=None,
port=None)
self.assertNotEqual(id(db1.connection), id(db3.connection))
self.assertEqual(len(CatalogDBObject._connection_cache), 1)
# check that if we had passed db1.connection to a DBObject,
# the connections would be identical
db4 = DBObject(connection=db1.connection)
self.assertEqual(id(db4.connection), id(db1.connection))
self.assertEqual(len(CatalogDBObject._connection_cache), 1)
# verify that db1 and db2 are both useable
results = db1.query_columns(
colnames=['id', 'i1', 'i2', 'identification'])
results = next(results)
self.assertEqual(len(results), 5)
np.testing.assert_array_equal(results['id'], list(range(5)))
np.testing.assert_array_equal(results['id'], results['identification'])
np.testing.assert_array_equal(results['id']**2, results['i1'])
np.testing.assert_array_equal(results['id'] * (-1), results['i2'])
results = db2.query_columns(colnames=['id', 'i1', 'i2', 'other'])
results = next(results)
self.assertEqual(len(results), 5)
np.testing.assert_array_equal(results['id'], list(range(5)))
np.testing.assert_array_equal(results['id']**2, results['i1'])
np.testing.assert_array_equal(results['i1'], results['other'])
np.testing.assert_array_equal(results['id'] * (-1), results['i2'])
