def setUpClass(cls):
cls.scratch_dir = tempfile.mkdtemp(dir=ROOT, prefix="scratchSpace-")
cls.db_src_name = os.path.join(cls.scratch_dir,
'compound_cat_filter_db.txt')
if os.path.exists(cls.db_src_name):
os.unlink(cls.db_src_name)
cls.db_name = os.path.join(cls.scratch_dir,
'compound_cat_filter_db.db')
if os.path.exists(cls.db_name):
os.unlink(cls.db_name)
with open(cls.db_src_name, 'w') as output_file:
output_file.write('#a header\n')
for ii in range(10):
output_file.write('%d %d %d %d\n' %
(ii, ii + 1, ii + 2, ii + 3))
dtype = np.dtype([('id', int), ('ip1', int), ('ip2', int),
('ip3', int)])
fileDBObject(cls.db_src_name,
runtable='test',
dtype=dtype,
idColKey='id',
database=cls.db_name)
def setUpClass(cls):
cls.scratch_dir = tempfile.mkdtemp(dir=ROOT, prefix="scratchSpace-")
cls.write_star_txt()
cls.write_galaxy_txt()
cls.dbName = os.path.join(cls.scratch_dir, 'ConnectionPassingTestDB.db')
if os.path.exists(cls.dbName):
os.unlink(cls.dbName)
galDtype = np.dtype([('id', np.int),
('raJ2000', np.float), ('decJ2000', np.float),
('redshift', np.float), ('umag', np.float),
('gmag', np.float)])
starDtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('umag', np.float),
('gmag', np.float)])
fileDBObject(cls.star_txt_name,
database=cls.dbName, driver='sqlite',
runtable='stars', idColKey='id',
dtype=starDtype)
fileDBObject(cls.gal_txt_name,
database=cls.dbName, driver='sqlite',
runtable='galaxies', idColKey='id',
dtype=galDtype)
def setUpClass(cls):
cls.scratch_dir = tempfile.mkdtemp(dir=ROOT, prefix="scratchSpace-")
cls.write_star_txt()
cls.write_galaxy_txt()
cls.dbName = os.path.join(cls.scratch_dir,
'ConnectionPassingTestDB.db')
if os.path.exists(cls.dbName):
os.unlink(cls.dbName)
galDtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('redshift', np.float),
('umag', np.float), ('gmag', np.float)])
starDtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('umag', np.float),
('gmag', np.float)])
fileDBObject(cls.star_txt_name,
database=cls.dbName,
driver='sqlite',
runtable='stars',
idColKey='id',
dtype=starDtype)
fileDBObject(cls.gal_txt_name,
database=cls.dbName,
driver='sqlite',
runtable='galaxies',
idColKey='id',
dtype=galDtype)
def setUpClass(cls):
cls.obs = ObservationMetaData(
bandpassName=['u', 'g', 'r', 'i', 'z', 'y'],
m5=[22.0, 23.0, 24.0, 25.0, 26.0, 27.0])
baselineDtype = np.dtype([
(name, np.float) for name in baselineSSMCatalog.column_outputs
])
dbdtype = np.dtype([('id', np.int), ('sedFilename', str, 100),
('magNorm', np.float), ('velRA', np.float),
('velDec', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData')
inputFile = os.path.join(inputDir, 'SSMphotometryCatalog.txt')
cls.db = fileDBObject(inputFile,
runtable='test',
idColKey='id',
dtype=dbdtype)
cat = baselineSSMCatalog(cls.db, obs_metadata=cls.obs)
catName = tempfile.mktemp(prefix='IndexTestCase_setUpClass')
cat.write_catalog(catName)
cls.controlData = np.genfromtxt(catName,
dtype=baselineDtype,
delimiter=',')
os.unlink(catName)
def setUpClass(cls):
cls.obs = ObservationMetaData(
bandpassName=['u', 'g', 'r', 'i', 'z', 'y'],
m5=[22.0, 23.0, 24.0, 25.0, 26.0, 27.0])
baselineDtype = np.dtype([
(name, np.float) for name in baselineStarCatalog.column_outputs
])
dbdtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('sedFilename', str, 100),
('magNorm', np.float), ('galacticAv', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogStars.txt')
cls.db = fileDBObject(inputFile,
runtable='test',
idColKey='id',
dtype=dbdtype)
cat = baselineStarCatalog(cls.db, obs_metadata=cls.obs)
cls.catName = tempfile.mktemp(dir=ROOT,
prefix='indicesStarControlCat-',
suffix='.txt')
cat.write_catalog(cls.catName)
cls.controlData = np.genfromtxt(cls.catName,
dtype=baselineDtype,
delimiter=',')
os.unlink(cls.catName)
def setUpClass(cls):
rng = np.random.RandomState(99)
n_sne = 100
ra_list = rng.random_sample(n_sne) * 7.0 + 78.0
dec_list = rng.random_sample(n_sne) * 4.0 - 69.0
zz_list = rng.random_sample(n_sne) * 1.0 + 0.05
cls.scratchDir = tempfile.mkdtemp(dir=ROOT, prefix='scratchSpace-')
cls.input_cat_name = os.path.join(cls.scratchDir, "sne_input_cat.txt")
with open(cls.input_cat_name, "w") as output_file:
for ix in range(n_sne):
output_file.write("%d;%.12f;%.12f;%.12f;%.12f;%.12f\n"
% (ix + 1, ra_list[ix], dec_list[ix],
np.radians(ra_list[ix]), np.radians(dec_list[ix]),
zz_list[ix]))
dtype = np.dtype([('id', np.int),
('raDeg', np.float), ('decDeg', np.float),
('raJ2000', np.float), ('decJ2000', np.float),
('redshift', np.float)])
cls.db = fileDBObject(cls.input_cat_name, delimiter=';',
runtable='test', dtype=dtype,
idColKey='id')
cls.db.raColName = 'raDeg'
cls.db.decColName = 'decDeg'
cls.db.objectTypeId = 873
cls.opsimDb = os.path.join(getPackageDir("sims_data"), "OpSimData")
cls.opsimDb = os.path.join(cls.opsimDb, "opsimblitz1_1133_sqlite.db")
def setUpClass(cls):
cls.obs = ObservationMetaData(bandpassName=['u', 'g', 'r', 'i', 'z', 'y'],
m5 = [22.0, 23.0, 24.0, 25.0, 26.0, 27.0])
baselineDtype = np.dtype([(name, np.float) for name in baselineSSMCatalog.column_outputs])
dbdtype = np.dtype([
('id', np.int),
('sedFilename', str, 100),
('magNorm', np.float),
('velRA', np.float),
('velDec', np.float)
])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'testData')
inputFile = os.path.join(inputDir, 'SSMphotometryCatalog.txt')
cls.db = fileDBObject(inputFile, runtable='test',
idColKey='id', dtype=dbdtype)
cat = baselineSSMCatalog(cls.db, obs_metadata=cls.obs)
catName = tempfile.mktemp(prefix='IndexTestCase_setUpClass')
cat.write_catalog(catName)
cls.controlData = np.genfromtxt(catName, dtype=baselineDtype, delimiter=',')
os.unlink(catName)
def setUpClass(cls):
cls.obs = ObservationMetaData(bandpassName=['u', 'g', 'r', 'i', 'z', 'y'],
m5=[24.0, 25.0, 26.0, 27.0, 28.0, 29.0])
dtype = np.dtype([
('id', np.int),
('sedFilenameBulge', str, 100),
('magNormBulge', np.float),
('sedFilenameDisk', str, 100),
('magNormDisk', np.float),
('sedFilenameAgn', str, 100),
('magNormAgn', np.float),
('internalAvBulge', np.float),
('internalAvDisk', np.float),
('galacticAv', np.float),
('redshift', np.float)
])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogGalaxies.txt')
cls.db = fileDBObject(inputFile, dtype=dtype, runtable='test',
idColKey='id')
cls.db.objectTypeId = 44
cat = baselineGalaxyCatalog(cls.db, obs_metadata=cls.obs)
dtype = np.dtype([(name, np.float) for name in cat.column_outputs])
catName = tempfile.mktemp(dir=ROOT, prefix='', suffix='.txt')
cat.write_catalog(catName)
cls.controlData = np.genfromtxt(catName, dtype=dtype, delimiter=',')
os.remove(catName)
def setUpClass(cls):
cls.obs = ObservationMetaData(bandpassName=['u', 'g', 'r', 'i', 'z', 'y'],
m5 = [22.0, 23.0, 24.0, 25.0, 26.0, 27.0])
baselineDtype = np.dtype([(name, np.float) for name in baselineStarCatalog.column_outputs])
dbdtype = np.dtype([
('id', np.int),
('raJ2000', np.float),
('decJ2000', np.float),
('sedFilename', str, 100),
('magNorm', np.float),
('galacticAv', np.float)
])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogStars.txt')
cls.db = fileDBObject(inputFile, runtable='test',
idColKey='id', dtype=dbdtype)
cat = baselineStarCatalog(cls.db, obs_metadata=cls.obs)
cls.catName = tempfile.mktemp(dir=ROOT, prefix='indicesStarControlCat-', suffix='.txt')
cat.write_catalog(cls.catName)
cls.controlData = np.genfromtxt(cls.catName, dtype=baselineDtype, delimiter=',')
os.unlink(cls.catName)
def setUpClass(cls):
cls.obs = ObservationMetaData(
bandpassName=['u', 'g', 'r', 'i', 'z', 'y'],
m5=[24.0, 25.0, 26.0, 27.0, 28.0, 29.0])
dtype = np.dtype([('id', np.int), ('sedFilenameBulge', str, 100),
('magNormBulge', np.float),
('sedFilenameDisk', str, 100),
('magNormDisk', np.float),
('sedFilenameAgn', str, 100),
('magNormAgn', np.float),
('internalAvBulge', np.float),
('internalAvDisk', np.float),
('galacticAv', np.float), ('redshift', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogGalaxies.txt')
cls.db = fileDBObject(inputFile,
dtype=dtype,
runtable='test',
idColKey='id')
cls.db.objectTypeId = 44
cat = baselineGalaxyCatalog(cls.db, obs_metadata=cls.obs)
dtype = np.dtype([(name, np.float) for name in cat.column_outputs])
catName = tempfile.mktemp(dir=ROOT, prefix='', suffix='.txt')
cat.write_catalog(catName)
cls.controlData = np.genfromtxt(catName, dtype=dtype, delimiter=',')
os.remove(catName)
def create_star_cache(db=None):
"""
Read the stars from a database and cache them in
$TWINKLES_DIR/data/star_cache.db
in the table 'star_cache_table'
Params
------
db is a CatalogDBObject connecting to the database from which to read
the data. If None, this will use the CatSim class StarObj(), which
connects to the star table on fatboy.
"""
star_dtype = np.dtype([('simobjid', int), ('ra', float), ('decl', float),
('magNorm', float), ('mura', float),
('mudecl', float), ('parallax', float),
('ebv', float), ('vrad', float),
('varParamStr', str, 256), ('sedfilename', str, 40),
('gmag', float)])
col_names = list(star_dtype.names)
star_cache_name = os.path.join(getPackageDir('twinkles'), 'data',
'twinkles_star_cache.txt')
star_db_name = os.path.join(getPackageDir('twinkles'), 'data',
'star_cache.db')
if db is None:
db = StarObj()
result_iterator = db.query_columns(colnames=col_names,
chunk_size=100000,
obs_metadata=_obs)
with open(star_cache_name, 'w') as output_file:
output_file.write('# ')
for name in col_names:
output_file.write('%s ' % name)
output_file.write('\n')
for chunk in result_iterator:
for line in chunk:
output_file.write((
'%d;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%s;%s;%.17g\n'
% (line[1], line[2], line[3], line[4], line[5], line[6],
line[7], line[8], line[9], str(line[10]), str(line[11]),
line[12])).replace('nan',
'NULL').replace('None', 'NULL'))
if os.path.exists(star_db_name):
os.unlink(star_db_name)
dbo = fileDBObject(star_cache_name,
driver='sqlite',
runtable='star_cache_table',
database=star_db_name,
dtype=star_dtype,
delimiter=';',
idColKey='simobjid')
if os.path.exists(star_cache_name):
os.unlink(star_cache_name)
def setUpClass(cls):
cls.dbFile = os.path.join(getPackageDir('sims_catUtils'),
'tests', 'testData', 'SSMastrometryCatalog.txt')
cls.dtype = np.dtype([('id', np.int), ('raJ2000', np.float), ('decJ2000', np.float),
('velRa', np.float), ('velDec', np.float)])
cls.astDB = fileDBObject(cls.dbFile, runtable='test', dtype=cls.dtype, idColKey='id')
def setUpClass(cls):
cls.dbFile = os.path.join(getPackageDir('sims_catUtils'),
'tests', 'testData', 'SSMphotometryCatalog.txt')
cls.dtype = np.dtype([('id', np.int), ('sedFilename', str, 100), ('magNorm', np.float),
('velRa', np.float), ('velDec', np.float)])
cls.photDB = fileDBObject(cls.dbFile, runtable='test', dtype=cls.dtype, idColKey='id')
def setUpClass(cls):
cls.baseDir = tempfile.mkdtemp(dir=ROOT, prefix='scratchSpace-')
cls.textFileName = os.path.join(cls.baseDir, 'compound_obs_metadata_text_data.txt')
numpy.random.seed(42)
nSamples = 100
raList = numpy.random.random_sample(nSamples)*360.0
decList = numpy.random.random_sample(nSamples)*180.0 - 90.0
magList = numpy.random.random_sample(nSamples)*15.0 + 7.0
dtype = numpy.dtype([
('ra', numpy.float),
('dec', numpy.float),
('mag', numpy.float)
])
cls.controlArray = numpy.rec.fromrecords([(r, d, m) for r, d, m in zip(raList, decList, magList)],
dtype=dtype)
dbDtype = numpy.dtype([
('id', numpy.int),
('ra', numpy.float),
('dec', numpy.float),
('mag', numpy.float)
])
if os.path.exists(cls.textFileName):
os.unlink(cls.textFileName)
with open(cls.textFileName, 'w') as output:
output.write('# id ra dec mag\n')
for ix, (r, d, m) in enumerate(zip(raList, decList, magList)):
output.write('%d %.20f %.20f %.20f\n' % (ix, r, d, m))
cls.dbName = os.path.join(cls.baseDir, 'compound_obs_metadata_db.db')
if os.path.exists(cls.dbName):
os.unlink(cls.dbName)
fileDBObject(cls.textFileName, runtable='test',
database=cls.dbName, dtype=dbDtype,
idColKey='id')
def setUpClass(cls):
cls.scratch_dir = tempfile.mkdtemp(dir=ROOT, prefix="scratchSpace-")
cls.db_src_name = os.path.join(cls.scratch_dir, 'compound_cat_filter_db.txt')
if os.path.exists(cls.db_src_name):
os.unlink(cls.db_src_name)
cls.db_name = os.path.join(cls.scratch_dir, 'compound_cat_filter_db.db')
if os.path.exists(cls.db_name):
os.unlink(cls.db_name)
with open(cls.db_src_name, 'w') as output_file:
output_file.write('#a header\n')
for ii in range(10):
output_file.write('%d %d %d %d\n' % (ii, ii+1, ii+2, ii+3))
dtype = np.dtype([('id', int), ('ip1', int), ('ip2', int), ('ip3', int)])
fileDBObject(cls.db_src_name, runtable='test', dtype=dtype,
idColKey='id', database=cls.db_name)
def create_sn_cache(db=None):
"""
Read the supernova data from a database and cache it in the file
$TWINKLES_DIR/data/sn_cache.db
in the table 'sn_cache_table'
Param
-----
db is a CatalogDBObject from which to read the data. If None,
use the CatSim class SNDBObj, which will connect to the
TwinkSN_run3 table on fatboy.
"""
sn_dtype = np.dtype([('id', int), ('galtileid', int), ('snra', float),
('sndec', float), ('t0', float), ('x0', float),
('x1', float), ('c', float), ('redshift', float)])
col_names = list(sn_dtype.names)
sn_cache_name = os.path.join(getPackageDir('twinkles'), 'data',
'twinkles_sn_cache.txt')
if os.path.exists(sn_cache_name):
os.unlink(sn_cache_name)
sn_db_name = os.path.join(getPackageDir('twinkles'), 'data', 'sn_cache.db')
if db is None:
db = SNDBObj(table='TwinkSN_run3')
result_iterator = db.query_columns(colnames=col_names,
chunk_size=10000,
obs_metadata=_obs)
with open(sn_cache_name, 'w') as output_file:
output_file.write('# ')
for name in col_names:
output_file.write('%s ' % name)
output_file.write('\n')
for chunk in result_iterator:
for line in chunk:
output_file.write(
('%d;%ld;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g\n' %
(line[0], line[1], line[2], line[3], line[4],
line[5], line[6], line[7], line[8])).replace(
'nan', 'NULL').replace('None', 'NULL'))
if os.path.exists(sn_db_name):
os.unlink(sn_db_name)
dbo = fileDBObject(sn_cache_name,
driver='sqlite',
runtable='sn_cache_table',
database=sn_db_name,
dtype=sn_dtype,
delimiter=';',
idColKey='id')
if os.path.exists(sn_cache_name):
os.unlink(sn_cache_name)
def create_galaxy_cache(db_dir):
"""
Create an sqlite .db file in data/ containing all of the galaxies
in the Twinkles field of view.
db_dir is the directory in which we want to create the galaxy cache
"""
obs = ObservationMetaData(pointingRA=53.0091385,
pointingDec=-27.4389488,
boundType='circle',
boundLength=0.31)
db = GalaxyTileObjDegrees()
col_names = list(_galaxy_cache_dtype.names)
result_iterator = db.query_columns(colnames=col_names, chunk_size=100000,
obs_metadata=obs)
with open(_galaxy_cache_file_name, 'w') as output_file:
output_file.write('# galtileid ')
for name in col_names:
output_file.write('%s ' % name)
output_file.write('\n')
for chunk in result_iterator:
for line in chunk:
output_file.write(('%ld;%.17g;%.17g;%s;%.17g;%s;%.17g;%s;%.17g;%s;%.17g;%.17g;'
% (line[0], line[1], line[2],
line[3], line[4], line[5],
line[6], line[7], line[8],
line[9], line[10], line[11])).replace('nan', 'NULL').replace('None', 'NULL')
+ ('%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g;%.17g'
% (line[12], line[13], line[14],
line[15], line[16], line[17],
line[18], line[19], line[20],
line[21], line[22], line[23])).replace('nan', 'NULL').replace('None', 'NULL')
+ '\n')
full_db_name = os.path.join(db_dir, _galaxy_cache_db_name)
if os.path.exists(_galaxy_cache_db_name):
raise RuntimeError("Trying to create %s, but it already exists" % full_db_name)
dbo = fileDBObject(_galaxy_cache_file_name, driver='sqlite',
runtable=_galaxy_cache_table_name,
database=full_db_name,
dtype=_galaxy_cache_dtype,
delimiter=';',
idColKey='galtileid')
if os.path.exists(_galaxy_cache_file_name):
os.unlink(_galaxy_cache_file_name)
def setUpClass(cls):
cls.dbFile = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData', 'SSMphotometryCatalog.txt')
cls.dtype = np.dtype([('id', np.int), ('sedFilename', str, 100),
('magNorm', np.float), ('velRa', np.float),
('velDec', np.float)])
cls.photDB = fileDBObject(cls.dbFile,
runtable='test',
dtype=cls.dtype,
idColKey='id')
def setUpClass(cls):
cls.dbFile = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData', 'SSMastrometryCatalog.txt')
cls.dtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('velRa', np.float),
('velDec', np.float)])
cls.astDB = fileDBObject(cls.dbFile,
runtable='test',
dtype=cls.dtype,
idColKey='id')
def setUpClass(cls):
numpy.random.seed(42)
dtype = numpy.dtype([('a', numpy.float),
('b', numpy.float),
('c', numpy.float),
('d', str, 20)])
nSamples = 100
aList = numpy.random.random_sample(nSamples)*10.0
bList = numpy.random.random_sample(nSamples)*(-1.0)
cList = numpy.random.random_sample(nSamples)*10.0-5.0
ww = 'a'
dList = []
for ix in range(nSamples):
ww += 'b'
dList.append(ww)
cls.controlArray = numpy.rec.fromrecords([(aa, bb, cc, dd)
for aa, bb, cc, dd in zip(aList, bList, cList, dList)],
dtype=dtype)
cls.baseDir = tempfile.mkdtemp(dir=ROOT, prefix='scratchSpace-')
cls.textFileName = os.path.join(cls.baseDir, 'compound_test_data.txt')
if os.path.exists(cls.textFileName):
os.unlink(cls.textFileName)
with open(cls.textFileName, 'w') as output:
output.write('# id a b c d\n')
for ix, (aa, bb, cc, dd) in enumerate(zip(aList, bList, cList, dList)):
output.write('%d %e %e %e %s\n' % (ix, aa, bb, cc, dd))
cls.dbName = os.path.join(cls.baseDir, 'compoundCatalogTestDB.db')
if os.path.exists(cls.dbName):
os.unlink(cls.dbName)
cls.otherDbName = os.path.join(cls.baseDir, 'otherDb.db')
if os.path.exists(cls.otherDbName):
os.unlink(cls.otherDbName)
dtype = numpy.dtype([
('id', numpy.int),
('a', numpy.float),
('b', numpy.float),
('c', numpy.float),
('d', str, 20)
])
fileDBObject(cls.textFileName, runtable='test',
database=cls.dbName, dtype=dtype,
idColKey='id')
fileDBObject(cls.textFileName, runtable='test',
database=cls.otherDbName, dtype=dtype,
idColKey='id')
fileDBObject(cls.textFileName, runtable='otherTest',
database=cls.dbName, dtype=dtype,
idColKey='id')
def test_stars(self):
obs = ObservationMetaData(bandpassName=['c_u', 'c_g'], m5=[25.0, 26.0])
db_dtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('sedFilename', str, 100),
('magNorm', np.float), ('galacticAv', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogStars.txt')
db = fileDBObject(inputFile,
dtype=db_dtype,
runtable='test',
idColKey='id')
cat = CartoonStars(db, obs_metadata=obs)
with lsst.utils.tests.getTempFilePath('.txt') as catName:
cat.write_catalog(catName)
dtype = np.dtype([(name, np.float) for name in cat.column_outputs])
controlData = np.genfromtxt(catName, dtype=dtype, delimiter=',')
db_columns = db.query_columns([
'id', 'raJ2000', 'decJ2000', 'sedFilename', 'magNorm', 'galacticAv'
])
sedDir = os.path.join(getPackageDir('sims_sed_library'), 'starSED',
'kurucz')
for ix, line in enumerate(next(db_columns)):
spectrum = Sed()
spectrum.readSED_flambda(os.path.join(sedDir, line[3]))
fnorm = spectrum.calcFluxNorm(line[4], self.normband)
spectrum.multiplyFluxNorm(fnorm)
a_x, b_x = spectrum.setupCCM_ab()
spectrum.addDust(a_x, b_x, A_v=line[5])
umag = spectrum.calcMag(self.uband)
self.assertAlmostEqual(umag, controlData['cartoon_u'][ix], 3)
gmag = spectrum.calcMag(self.gband)
self.assertAlmostEqual(gmag, controlData['cartoon_g'][ix], 3)
umagError, gamma = calcMagError_m5(umag, self.uband, obs.m5['c_u'],
PhotometricParameters())
gmagError, gamma = calcMagError_m5(gmag, self.gband, obs.m5['c_g'],
PhotometricParameters())
self.assertAlmostEqual(umagError,
controlData['sigma_cartoon_u'][ix], 3)
self.assertAlmostEqual(gmagError,
controlData['sigma_cartoon_g'][ix], 3)
def test_ingest(self):
"""
Test that fileDBObject correctly ingests a text file containing
multiple data types.
"""
txt_file_name = os.path.join(self.scratch_dir,
"filedbojb_ingest_test.txt")
rng = np.random.RandomState(8821)
alphabet = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
n_rows = 34
n_letters = 72
f_list = rng.random_sample(n_rows)
i_list = rng.randint(0, 2**50, n_rows)
word_dex_list = rng.randint(0, len(alphabet) - 1, (n_rows, n_letters))
word_list = []
with open(txt_file_name, 'w') as output_file:
output_file.write("# a header\n")
for ix, (ff, ii,
ww) in enumerate(zip(f_list, i_list, word_dex_list)):
word = ''
for wwdex in ww:
word += alphabet[wwdex]
word_list.append(word)
self.assertEqual(len(word), n_letters)
output_file.write('%d %.13f %ld %s\n' % (ix, ff, ii, word))
dtype = np.dtype([('id', int), ('float', float), ('int', int),
('word', str, n_letters)])
db = fileDBObject(txt_file_name,
runtable='test',
dtype=dtype,
idColKey='id')
results = db.execute_arbitrary('SELECT * from test')
self.assertEqual(len(results), n_rows)
for row in results:
i_row = row[0]
self.assertAlmostEqual(f_list[i_row], row[1], 13)
self.assertEqual(i_list[i_row], row[2])
self.assertEqual(word_list[i_row], row[3])
if os.path.exists(txt_file_name):
os.unlink(txt_file_name)
def test_ingest(self):
"""
Test that fileDBObject correctly ingests a text file containing
multiple data types.
"""
txt_file_name = os.path.join(self.scratch_dir,
"filedbojb_ingest_test.txt")
rng = np.random.RandomState(8821)
alphabet = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
n_rows = 34
n_letters = 72
f_list = rng.random_sample(n_rows)
i_list = rng.randint(0, 2**50, n_rows)
word_dex_list = rng.randint(0, len(alphabet)-1, (n_rows, n_letters))
word_list = []
with open(txt_file_name, 'w') as output_file:
output_file.write("# a header\n")
for ix, (ff, ii, ww) in enumerate(zip(f_list, i_list, word_dex_list)):
word = ''
for wwdex in ww:
word += alphabet[wwdex]
word_list.append(word)
self.assertEqual(len(word), n_letters)
output_file.write('%d %.13f %ld %s\n' % (ix, ff, ii, word))
dtype = np.dtype([('id', int), ('float', float), ('int', int), ('word', str, n_letters)])
db = fileDBObject(txt_file_name, runtable='test', dtype=dtype, idColKey='id')
results = db.execute_arbitrary('SELECT * from test')
self.assertEqual(len(results), n_rows)
for row in results:
i_row = row[0]
self.assertAlmostEqual(f_list[i_row], row[1], 13)
self.assertEqual(i_list[i_row], row[2])
self.assertEqual(word_list[i_row], row[3])
if os.path.exists(txt_file_name):
os.unlink(txt_file_name)
def test_stars(self):
obs = ObservationMetaData(bandpassName=['c_u', 'c_g'], m5=[25.0, 26.0])
db_dtype = np.dtype([('id', np.int),
('raJ2000', np.float),
('decJ2000', np.float),
('sedFilename', str, 100),
('magNorm', np.float),
('galacticAv', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogStars.txt')
db = fileDBObject(inputFile, dtype=db_dtype, runtable='test', idColKey='id')
cat = CartoonStars(db, obs_metadata=obs)
with lsst.utils.tests.getTempFilePath('.txt') as catName:
cat.write_catalog(catName)
dtype = np.dtype([(name, np.float) for name in cat.column_outputs])
controlData = np.genfromtxt(catName, dtype=dtype, delimiter=',')
db_columns = db.query_columns(['id', 'raJ2000', 'decJ2000', 'sedFilename', 'magNorm', 'galacticAv'])
sedDir = os.path.join(getPackageDir('sims_sed_library'), 'starSED', 'kurucz')
for ix, line in enumerate(next(db_columns)):
spectrum = Sed()
spectrum.readSED_flambda(os.path.join(sedDir, line[3]))
fnorm = spectrum.calcFluxNorm(line[4], self.normband)
spectrum.multiplyFluxNorm(fnorm)
a_x, b_x = spectrum.setupCCM_ab()
spectrum.addDust(a_x, b_x, A_v=line[5])
umag = spectrum.calcMag(self.uband)
self.assertAlmostEqual(umag, controlData['cartoon_u'][ix], 3)
gmag = spectrum.calcMag(self.gband)
self.assertAlmostEqual(gmag, controlData['cartoon_g'][ix], 3)
umagError, gamma = calcMagError_m5(umag, self.uband, obs.m5['c_u'], PhotometricParameters())
gmagError, gamma = calcMagError_m5(gmag, self.gband, obs.m5['c_g'], PhotometricParameters())
self.assertAlmostEqual(umagError, controlData['sigma_cartoon_u'][ix], 3)
self.assertAlmostEqual(gmagError, controlData['sigma_cartoon_g'][ix], 3)
def write_star_file_db(file_name):
np.random.seed(88)
nstars = 10000
ra = np.random.random_sample(nstars) * 360.0
dec = (np.random.random_sample(nstars) - 0.5) * 180.0
umag = np.random.random_sample(nstars) * 10.0 + 15.0
gmag = np.random.random_sample(nstars) * 10.0 + 15.0
rmag = np.random.random_sample(nstars) * 10.0 + 15.0
imag = np.random.random_sample(nstars) * 10.0 + 15.0
zmag = np.random.random_sample(nstars) * 10.0 + 15.0
ymag = np.random.random_sample(nstars) * 10.0 + 15.0
with open(file_name, 'w') as output_file:
for ix, (rr, dd, um, gm, rm, im, zm, ym) in \
enumerate(zip(ra, dec, umag, gmag, rmag, imag, zmag, ymag)):
output_file.write(
'%d %.12f %.12f %.12f %.12f %.12f %.12f %.12f %.12f\n' %
(ix, rr, dd, um, gm, rm, im, zm, ym))
starDtype = np.dtype([('id', np.int), ('raJ2000', np.float),
('decJ2000', np.float), ('umag', np.float),
('gmag', np.float), ('rmag', np.float),
('imag', np.float), ('zmag', np.float),
('ymag', np.float)])
starDB = fileDBObject(file_name,
runtable='stars',
dtype=starDtype,
idColKey='id')
starDB.raColName = 'raJ2000'
starDB.decColName = 'decJ2000'
controlData = np.genfromtxt(file_name, dtype=starDtype)
return starDB, controlData
def write_star_file_db(file_name):
np.random.seed(88)
nstars = 10000
ra = np.random.random_sample(nstars)*360.0
dec = (np.random.random_sample(nstars)-0.5)*180.0
umag = np.random.random_sample(nstars)*10.0 + 15.0
gmag = np.random.random_sample(nstars)*10.0 + 15.0
rmag = np.random.random_sample(nstars)*10.0 + 15.0
imag = np.random.random_sample(nstars)*10.0 + 15.0
zmag = np.random.random_sample(nstars)*10.0 + 15.0
ymag = np.random.random_sample(nstars)*10.0 + 15.0
with open(file_name, 'w') as output_file:
for ix, (rr, dd, um, gm, rm, im, zm, ym) in \
enumerate(zip(ra, dec, umag, gmag, rmag, imag, zmag, ymag)):
output_file.write('%d %.12f %.12f %.12f %.12f %.12f %.12f %.12f %.12f\n' %
(ix, rr, dd, um, gm, rm, im, zm, ym))
starDtype = np.dtype([('id', np.int),
('raJ2000', np.float),
('decJ2000', np.float),
('umag', np.float),
('gmag', np.float),
('rmag', np.float),
('imag', np.float),
('zmag', np.float),
('ymag', np.float)])
starDB = fileDBObject(file_name, runtable='stars', dtype=starDtype, idColKey='id')
starDB.raColName = 'raJ2000'
starDB.decColName = 'decJ2000'
controlData = np.genfromtxt(file_name, dtype=starDtype)
return starDB, controlData
def test_ParametrizedLightCurve_in_catalog(self):
"""
Test the performance of applyParametrizedLightCurve()
in the context of an InstanceCatalog
"""
# Create dummy light curve parameters
lc_temp_file_name = tempfile.mktemp(prefix='test_ParametrizedLightCurve_in_catalog',
suffix='.gz')
rng = np.random.RandomState(1621145)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1)*5.0
b1_list = (rng.random_sample(n_c_1)-0.5)*2.0
c1_list = (rng.random_sample(n_c_1)-0.5)*0.1
omega1_list = rng.random_sample(n_c_1)*20.0
tau1_list = rng.random_sample(n_c_1)*100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2)*5.0
b2_list = (rng.random_sample(n_c_2)-0.5)*2.0
c2_list = (rng.random_sample(n_c_2)-0.5)*0.1
omega2_list = rng.random_sample(n_c_2)*20.0
tau2_list = rng.random_sample(n_c_2)*100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr999990000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr999990001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
# Create dummy database of astrophysical sources
db_temp_file_name = tempfile.mktemp(prefix='test_ParametrizedLightCurve_in_catalog_db',
suffix='.txt')
lc_list = [999990001, None, 999990001, 999990000]
t0_list = [1729.1, None, 2345.1, 10.9]
with open(db_temp_file_name, 'w') as out_file:
out_file.write('# a header\n')
for i_obj in range(len(lc_list)):
if lc_list[i_obj] is not None:
paramStr = '{"m":"kplr", "p":{"lc":%d, "t0":%.3f}}' % (lc_list[i_obj], t0_list[i_obj])
else:
paramStr = None
out_file.write('%d;10.0;20.0;0.01;0.01;%s\n' % (i_obj, paramStr))
dtype = np.dtype([('simobjid', int), ('ra', float), ('dec', float),
('ebv', float), ('parallax', float), ('varParamStr', str, 100)])
db = fileDBObject(db_temp_file_name, runtable='test', dtype=dtype, delimiter=';',
idColKey='simobjid')
class ParametrizedVarParamStrCat(InstanceCatalog, VariabilityStars):
column_outputs = ['simobjid', 'delta_lsst_u', 'delta_lsst_g', 'delta_lsst_r',
'delta_lsst_i', 'delta_lsst_z', 'delta_lsst_y']
default_formats = {'f':'%.15g'}
obs = ObservationMetaData(mjd=59580.0)
cat = ParametrizedVarParamStrCat(db, obs_metadata=obs)
cat.load_parametrized_light_curves(lc_temp_file_name)
cat_out_name = tempfile.mktemp(prefix='test_ParametrizedLightCurve_in_cat_out',
suffix='.txt')
cat.write_catalog(cat_out_name)
kp = ParametrizedLightCurveMixin()
cat_dtype = np.dtype([('simobjid', int), ('du', float), ('dg', float),
('dr', float), ('di', float), ('dz', float),
('dy', float)])
cat_data = np.genfromtxt(cat_out_name, dtype=cat_dtype, delimiter=', ')
for i_obj in range(len(cat_data)):
obj_id = cat_data['simobjid'][i_obj]
if lc_list[obj_id] is None:
self.assertEqual(cat_data['du'][i_obj], 0.0)
self.assertEqual(cat_data['dg'][i_obj], 0.0)
self.assertEqual(cat_data['dr'][i_obj], 0.0)
self.assertEqual(cat_data['di'][i_obj], 0.0)
self.assertEqual(cat_data['dz'][i_obj], 0.0)
self.assertEqual(cat_data['dy'][i_obj], 0.0)
else:
q_flux, d_flux = kp._calc_dflux(lc_list[obj_id], obs.mjd.TAI-t0_list[obj_id])
d_mag_true = -2.5*np.log10(1.0+d_flux/q_flux)
self.assertGreater(np.abs(d_mag_true), 0.0001)
self.assertAlmostEqual(cat_data['du'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dg'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dr'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['di'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dz'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dy'][i_obj], d_mag_true, 15)
if os.path.exists(cat_out_name):
os.unlink(cat_out_name)
if os.path.exists(db_temp_file_name):
os.unlink(db_temp_file_name)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
def test_ParametrizedLightCurve_in_catalog(self):
"""
Test the performance of applyParametrizedLightCurve()
in the context of an InstanceCatalog
"""
# Create dummy light curve parameters
lc_temp_file_name = tempfile.mktemp(
prefix='test_ParametrizedLightCurve_in_catalog', suffix='.gz')
rng = np.random.RandomState(1621145)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1) * 5.0
b1_list = (rng.random_sample(n_c_1) - 0.5) * 2.0
c1_list = (rng.random_sample(n_c_1) - 0.5) * 0.1
omega1_list = rng.random_sample(n_c_1) * 20.0
tau1_list = rng.random_sample(n_c_1) * 100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2) * 5.0
b2_list = (rng.random_sample(n_c_2) - 0.5) * 2.0
c2_list = (rng.random_sample(n_c_2) - 0.5) * 0.1
omega2_list = rng.random_sample(n_c_2) * 20.0
tau2_list = rng.random_sample(n_c_2) * 100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr999990000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr999990001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
# Create dummy database of astrophysical sources
db_temp_file_name = tempfile.mktemp(
prefix='test_ParametrizedLightCurve_in_catalog_db', suffix='.txt')
lc_list = [999990001, None, 999990001, 999990000]
t0_list = [1729.1, None, 2345.1, 10.9]
with open(db_temp_file_name, 'w') as out_file:
out_file.write('# a header\n')
for i_obj in range(len(lc_list)):
if lc_list[i_obj] is not None:
paramStr = '{"m":"kplr", "p":{"lc":%d, "t0":%.3f}}' % (
lc_list[i_obj], t0_list[i_obj])
else:
paramStr = None
out_file.write('%d;10.0;20.0;0.01;0.01;%s\n' %
(i_obj, paramStr))
dtype = np.dtype([('simobjid', int), ('ra', float), ('dec', float),
('ebv', float), ('parallax', float),
('varParamStr', str, 100)])
db = fileDBObject(db_temp_file_name,
runtable='test',
dtype=dtype,
delimiter=';',
idColKey='simobjid')
class ParametrizedVarParamStrCat(InstanceCatalog, VariabilityStars):
column_outputs = [
'simobjid', 'delta_lsst_u', 'delta_lsst_g', 'delta_lsst_r',
'delta_lsst_i', 'delta_lsst_z', 'delta_lsst_y'
]
default_formats = {'f': '%.15g'}
obs = ObservationMetaData(mjd=59580.0)
cat = ParametrizedVarParamStrCat(db, obs_metadata=obs)
cat.load_parametrized_light_curves(lc_temp_file_name)
cat_out_name = tempfile.mktemp(
prefix='test_ParametrizedLightCurve_in_cat_out', suffix='.txt')
cat.write_catalog(cat_out_name)
kp = ParametrizedLightCurveMixin()
cat_dtype = np.dtype([('simobjid', int), ('du', float), ('dg', float),
('dr', float), ('di', float), ('dz', float),
('dy', float)])
cat_data = np.genfromtxt(cat_out_name, dtype=cat_dtype, delimiter=', ')
for i_obj in range(len(cat_data)):
obj_id = cat_data['simobjid'][i_obj]
if lc_list[obj_id] is None:
self.assertEqual(cat_data['du'][i_obj], 0.0)
self.assertEqual(cat_data['dg'][i_obj], 0.0)
self.assertEqual(cat_data['dr'][i_obj], 0.0)
self.assertEqual(cat_data['di'][i_obj], 0.0)
self.assertEqual(cat_data['dz'][i_obj], 0.0)
self.assertEqual(cat_data['dy'][i_obj], 0.0)
else:
q_flux, d_flux = kp._calc_dflux(lc_list[obj_id],
obs.mjd.TAI - t0_list[obj_id])
d_mag_true = -2.5 * np.log10(1.0 + d_flux / q_flux)
self.assertGreater(np.abs(d_mag_true), 0.0001)
self.assertAlmostEqual(cat_data['du'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dg'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dr'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['di'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dz'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dy'][i_obj], d_mag_true, 15)
if os.path.exists(cat_out_name):
os.unlink(cat_out_name)
if os.path.exists(db_temp_file_name):
os.unlink(db_temp_file_name)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
def test_different_cameras(self):
rng = np.random.RandomState(6512)
pointing_ra = 15.0
pointing_dec = 13.0
n_obj = 100
ra_list = pointing_ra + 2.0*rng.random_sample(n_obj)
dec_list = pointing_dec + 2.0*rng.random_sample(n_obj)
px_list = radiansFromArcsec(0.005)*rng.random_sample(n_obj)
px_list += radiansFromArcsec(0.001)
mura_list = radiansFromArcsec(0.005)*rng.random_sample(n_obj)
mudec_list = radiansFromArcsec(0.005)*rng.random_sample(n_obj)
vrad_list = 100.0*rng.random_sample(n_obj)
with lsst.utils.tests.getTempFilePath('.txt') as db_text_file:
with open(db_text_file, 'w') as out_file:
for ix, (rdeg, ddeg, rrad, drad, px, mura, mudec, vrad) in \
enumerate(zip(ra_list, dec_list,
np.radians(ra_list), np.radians(dec_list),
px_list, mura_list, mudec_list, vrad_list)):
out_file.write('%d %e %e %e %e %e %e %e %e\n' % (ix, rdeg, ddeg,
rrad, drad,
px,
mura, mudec,
vrad))
dtype = np.dtype([('id', int), ('raDeg', float), ('decDeg', float),
('raJ2000', float), ('decJ2000', float),
('parallax', float),
('properMotionRa', float), ('properMotionDec', float),
('radialVelocity', float)])
db = fileDBObject(db_text_file, dtype=dtype, idColKey='id')
db.raColName = 'raDeg'
db.decColName = 'decDeg'
class CameraCoordsCatalog(AstrometryStars, CameraCoords,
InstanceCatalog):
camera = LsstSimMapper().camera
column_outputs = ['id', 'chipName']
class CameraCoordsLSSTCatalog(AstrometryStars, CameraCoordsLSST,
InstanceCatalog):
column_outputs = ['id', 'chipName']
obs = ObservationMetaData(pointingRA=pointing_ra,
pointingDec=pointing_dec,
boundLength=1.75,
boundType='circle',
rotSkyPos=23.0,
mjd=59580.0)
control_cat = CameraCoordsCatalog(db, obs_metadata=obs)
test_cat = CameraCoordsLSSTCatalog(db, obs_metadata=obs)
control_line_list = []
none_chips = 0
for line in control_cat.iter_catalog():
if line[1] is None:
none_chips += 1
control_line_list.append(line)
self.assertGreater(len(control_line_list), 0)
self.assertLess(none_chips, len(control_line_list)/2)
line_ct = 0
for line in test_cat.iter_catalog():
line_ct += 1
self.assertIn(line, control_line_list)
self.assertEqual(line_ct, len(control_line_list))
def setUpClass(cls):
"""
Create a fake catalog of RR Lyrae stars. Store it in cls.stellar_db
"""
cls.scratchDir = tempfile.mkdtemp(dir=ROOT, prefix='StellarLigghtCurveTest-')
rng = np.random.RandomState(88)
n_stars = 10000
sed_dir = os.path.join(getPackageDir("sims_sed_library"))
sed_dir = os.path.join(sed_dir, "starSED", "kurucz")
list_of_seds = os.listdir(sed_dir)
lc_dir = os.path.join(getPackageDir("sims_sed_library"), "rrly_lc")
lc_dir = os.path.join(lc_dir, "RRab")
list_of_lc = ['rrly_lc/RRab/%s' % ww for ww in os.listdir(lc_dir) if "per.txt" in ww]
cls.dtype = np.dtype([('id', np.int),
('raDeg', np.float),
('decDeg', np.float),
('raJ2000', np.float),
('decJ2000', np.float),
('magNorm', np.float),
('galacticAv', np.float),
('sedFilename', str, 300),
('varParamStr', str, 300),
('parallax', np.float),
('ebv', np.float)])
# write the catalog as a text file to be ingested with fileDBObject
cls.txt_name = os.path.join(cls.scratchDir, "stellar_lc_catalog.txt")
with open(cls.txt_name, "w") as output_file:
sed_dex = rng.randint(0, len(list_of_seds), size=n_stars)
lc_dex = rng.randint(0, len(list_of_lc), size=n_stars)
mjd0 = rng.random_sample(n_stars)*10000.0+40000.0
raList = rng.random_sample(n_stars)*360.0
decList = -90.0 + rng.random_sample(n_stars)*120.0
magNormList = rng.random_sample(n_stars)*3.0+14.0
AvList = rng.random_sample(n_stars)*0.2+0.1
pxList = rng.random_sample(n_stars)*0.1
for ix in range(n_stars):
varparams = {'varMethodName': 'applyRRly',
'pars': {'tStartMjd': mjd0[ix],
'filename': list_of_lc[lc_dex[ix]]}}
varparamstr = json.dumps(varparams)
output_file.write("%d;%lf;%lf;%lf;%lf;%lf;%lf;%s;%s;%lf;%lf\n"
% (ix, raList[ix], decList[ix],
np.radians(raList[ix]),
np.radians(decList[ix]),
magNormList[ix], AvList[ix],
list_of_seds[sed_dex[ix]],
varparamstr,pxList[ix],
AvList[ix]/3.1))
cls.stellar_db = fileDBObject(cls.txt_name, delimiter=';',
runtable='test', dtype=cls.dtype,
idColKey='id')
cls.stellar_db.raColName = 'raDeg'
cls.stellar_db.decColName = 'decDeg'
cls.stellar_db.objectTypeId = 32
cls.opsimDb = os.path.join(getPackageDir("sims_data"), "OpSimData")
cls.opsimDb = os.path.join(cls.opsimDb, "opsimblitz1_1133_sqlite.db")
def setUpClass(cls):
"""
Create a fake catalog of RR Lyrae stars and MLT dwarves with flaring
light curves. Store it in cls.stellar_db
"""
cls.scratchDir = tempfile.mkdtemp(
dir=ROOT, prefix='FastStellar_stellar_lc_gen_case-')
cls.raRange = (78.0, 85.0)
cls.decRange = (-69.0, -65.0)
rng = np.random.RandomState(88)
cls.n_stars = 20
sed_dir = os.path.join(getPackageDir("sims_sed_library"))
sed_dir = os.path.join(sed_dir, "starSED", "kurucz")
list_of_seds = os.listdir(sed_dir)
lc_dir = os.path.join(getPackageDir("sims_sed_library"), "rrly_lc")
lc_dir = os.path.join(lc_dir, "RRab")
list_of_rrly_lc = [
'rrly_lc/RRab/%s' % ww for ww in os.listdir(lc_dir)
if "per.txt" in ww
]
cls.mlt_lc_file_name = os.path.join(cls.scratchDir,
"fast_lc_mlt_file.npz")
if os.path.exists(cls.mlt_lc_file_name):
os.unlink(cls.mlt_lc_file_name)
mlt_lc_files = {}
mlt_lc_files['lc_1_time'] = np.arange(0.0, 3652.51, 0.1)
mlt_lc_files['lc_1_g'] = 2.2e32 * np.power(
np.cos(mlt_lc_files['lc_1_time'] / 100.0 - 5.0), 2)
mlt_lc_files['lc_1_r'] = 1.3e32 * (
1.0 + np.sin(mlt_lc_files['lc_1_time'] / 100.0 - 3.0))
mlt_lc_files['lc_2_time'] = np.arange(0.0, 3652.51, 0.1)
mlt_lc_files['lc_2_g'] = 5.1e33 * (
1.0 + np.cos(mlt_lc_files['lc_2_time'] / 300.0 - 10.0))
mlt_lc_files['lc_2_r'] = 4.3e32 * (
1.0 + np.sin(mlt_lc_files['lc_2_time'] / 50.0 - 71.0))
with open(cls.mlt_lc_file_name, 'wb') as file_handle:
np.savez(file_handle, **mlt_lc_files)
cls.dtype = np.dtype([('id', np.int), ('raDeg', np.float),
('decDeg', np.float), ('raJ2000', np.float),
('decJ2000', np.float), ('magNorm', np.float),
('galacticAv', np.float),
('sedFilename', str, 300),
('varParamStr', str, 300),
('parallax', np.float), ('ebv', np.float)])
# write the catalog as a text file to be ingested with fileDBObject
cls.txt_name = os.path.join(cls.scratchDir,
"fast_stellar_lc_catalog.txt")
with open(cls.txt_name, "w") as output_file:
output_file.write('# a silly header\n')
sed_dex = rng.randint(0, len(list_of_seds), size=cls.n_stars // 2)
lc_dex = rng.randint(0,
len(list_of_rrly_lc),
size=cls.n_stars // 2)
mjd0 = rng.random_sample(cls.n_stars // 2) * 10000.0 + 40000.0
raList = rng.random_sample(cls.n_stars // 2) * (
cls.raRange[1] - cls.raRange[0]) + cls.raRange[0]
decList = cls.decRange[0] + rng.random_sample(
cls.n_stars // 2) * (cls.decRange[1] - cls.decRange[1])
magNormList = rng.random_sample(cls.n_stars // 2) * 3.0 + 14.0
AvList = rng.random_sample(cls.n_stars // 2) * 0.2 + 0.1
pxList = rng.random_sample(cls.n_stars // 2) * 0.1
for ix in range(cls.n_stars // 2):
varparams = {
'varMethodName': 'applyRRly',
'pars': {
'tStartMjd': mjd0[ix],
'filename': list_of_rrly_lc[lc_dex[ix]]
}
}
varparamstr = json.dumps(varparams)
output_file.write(
"%d;%lf;%lf;%lf;%lf;%lf;%lf;%s;%s;%lf;%lf\n" %
(ix, raList[ix], decList[ix], np.radians(
raList[ix]), np.radians(decList[ix]), magNormList[ix],
AvList[ix], list_of_seds[sed_dex[ix]], varparamstr,
pxList[ix], AvList[ix] / 3.1))
sed_dex = rng.randint(0, len(list_of_seds), size=cls.n_stars // 2)
lc_dex = rng.randint(1, 3, size=cls.n_stars // 2)
mjd0 = rng.random_sample(cls.n_stars // 2) * 10000.0 + 40000.0
raList = rng.random_sample(cls.n_stars // 2) * (
cls.raRange[1] - cls.raRange[0]) + cls.raRange[0]
decList = cls.decRange[0] + rng.random_sample(
cls.n_stars // 2) * (cls.decRange[1] - cls.decRange[1])
magNormList = rng.random_sample(cls.n_stars // 2) * 3.0 + 14.0
AvList = rng.random_sample(cls.n_stars // 2) * 0.2 + 0.1
pxList = rng.random_sample(cls.n_stars // 2) * 0.1
for ix in range(cls.n_stars // 2):
varparams = {
'm': 'MLT',
'p': {
'lc': 'lc_%d' % lc_dex[ix],
't0': rng.random_sample() * 1000.0
}
}
varparamstr = json.dumps(varparams)
output_file.write(
"%d;%lf;%lf;%lf;%lf;%lf;%lf;%s;%s;%lf;%lf\n" %
(ix + cls.n_stars / 2, raList[ix], decList[ix],
np.radians(raList[ix]), np.radians(decList[ix]),
magNormList[ix], AvList[ix], list_of_seds[sed_dex[ix]],
varparamstr, pxList[ix], AvList[ix] / 3.1))
cls.stellar_db = fileDBObject(cls.txt_name,
delimiter=';',
runtable='test',
dtype=cls.dtype,
idColKey='id')
cls.stellar_db.raColName = 'raDeg'
cls.stellar_db.decColName = 'decDeg'
cls.stellar_db.objectTypeId = 32
cls.opsimDb = os.path.join(getPackageDir("sims_data"), "OpSimData")
cls.opsimDb = os.path.join(cls.opsimDb, "opsimblitz1_1133_sqlite.db")
def test_InstanceCatalog_against_catalog_chunks(self):
"""
Test that we can reproduce the validated data using the
InstanceCatalog framework when the catalog must be written
in multiple chunks
"""
obs = ObservationMetaData(pointingRA=53.00913847303155535,
pointingDec=-27.43894880881512321,
rotSkyPos=256.75075318193080420,
mjd=59580.13955500000156462,
bandpassName='r',
site=Site(name="LSST",
pressure=0.0,
humidity=0.0))
data_dir = os.path.join(getPackageDir('sims_catUtils'), 'tests',
'testData')
dtype = np.dtype([('id', int), ('ra', float), ('dec', float),
('ra_deprecessed', float),
('dec_deprecessed', float), ('x_dm', float),
('y_dm', float), ('x_focal', float),
('y_focal', float), ('x_cam', float),
('y_cam', float)])
data = np.genfromtxt(os.path.join(data_dir,
'pixel_prediction_catalog.txt'),
dtype=dtype)
data_txt_file = tempfile.mktemp(dir=data_dir,
prefix='ic_validation_cat',
suffix='.txt')
cat_dtype = np.dtype([('id', int), ('raJ2000', float),
('decJ2000', float)])
with open(data_txt_file, 'w') as out_file:
out_file.write('# a header\n')
for ii, rr, dd in zip(data['id'], np.radians(data['ra']),
np.radians(data['dec'])):
out_file.write('%d %.17f %.17f\n' % (ii, rr, dd))
db = fileDBObject(data_txt_file,
idColKey='id',
dtype=cat_dtype,
delimiter=' ')
class DeprecessionTestCatalog_chunks(PhoSimCatalogPoint):
def get_uniqueId(self):
return self.column_by_name('id')
def get_properMotionRa(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_properMotionDec(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_radialVelocity(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_parallax(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_galacticAv(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_galacticRv(self):
return 3.1 * np.ones(len(self.column_by_name('raJ2000')))
def get_sedFilepath(self):
return np.array(['sed_flat.txt.gz'] *
len(self.column_by_name('raJ2000')))
def get_phoSimMagNorm(self):
return np.ones(len(self.column_by_name('raJ2000')))
cat = DeprecessionTestCatalog_chunks(db, obs_metadata=obs)
cat.phoSimHeaderMap = DefaultPhoSimHeaderMap
id_list = []
ra_dep_list = []
dec_dep_list = []
phosim_cat_name = tempfile.mktemp(dir=data_dir,
prefix='phosim_dep',
suffix='.txt')
cat.write_catalog(phosim_cat_name, chunk_size=10)
with open(phosim_cat_name, 'r') as input_file:
for line in input_file:
params = line.strip().split()
if len(params) < 3:
continue
id_list.append(int(params[1]))
ra_dep_list.append(float(params[2]))
dec_dep_list.append(float(params[3]))
id_list = np.array(id_list)
np.testing.assert_array_equal(id_list, data['id'])
ra_dep_list = np.array(ra_dep_list)
dec_dep_list = np.array(dec_dep_list)
dd = 3600.0 * angularSeparation(data['ra_deprecessed'],
data['dec_deprecessed'], ra_dep_list,
dec_dep_list)
self.assertLess(dd.max(), 1.0e-5)
if os.path.exists(data_txt_file):
os.unlink(data_txt_file)
if os.path.exists(phosim_cat_name):
os.unlink(phosim_cat_name)
def setUpClass(cls):
cls.scratch_dir = tempfile.mkdtemp(dir=ROOT, prefix="CompoundCatalogTest")
cls.table1FileName = os.path.join(cls.scratch_dir, 'compound_table1.txt')
cls.table2FileName = os.path.join(cls.scratch_dir, 'compound_table2.txt')
if os.path.exists(cls.table1FileName):
os.unlink(cls.table1FileName)
if os.path.exists(cls.table2FileName):
os.unlink(cls.table2FileName)
dtype1 = np.dtype([('ra', np.float),
('dec', np.float),
('mag', np.float),
('dmag', np.float),
('dra', np.float),
('ddec', np.float)])
dbDtype1 = np.dtype([('id', np.int),
('ra', np.float),
('dec', np.float),
('mag', np.float),
('dmag', np.float),
('dra', np.float),
('ddec', np.float)])
nPts = 100
np.random.seed(42)
raList = np.random.random_sample(nPts)*360.0
decList = np.random.random_sample(nPts)*180.0-90.0
magList = np.random.random_sample(nPts)*10.0+15.0
dmagList = np.random.random_sample(nPts)*10.0 - 5.0
draList = np.random.random_sample(nPts)*5.0 - 2.5
ddecList = np.random.random_sample(nPts)*(-2.0) - 4.0
cls.table1Control = np.rec.fromrecords([(r, d, mm, dm, dr, dd)
for r, d, mm, dm, dr, dd
in zip(raList, decList,
magList, dmagList,
draList, ddecList)],
dtype=dtype1)
with open(cls.table1FileName, 'w') as output:
output.write("# id ra dec mag dmag dra ddec\n")
for ix, (r, d, mm, dm, dr, dd) in \
enumerate(zip(raList, decList, magList, dmagList, draList, ddecList)):
output.write('%d %.12f %.12f %.12f %.12f %.12f %.12f\n'
% (ix, r, d, mm, dm, dr, dd))
dtype2 = np.dtype([('ra', np.float),
('dec', np.float),
('mag', np.float)])
dbDtype2 = np.dtype([('id', np.int),
('ra', np.float),
('dec', np.float),
('mag', np.float)])
ra2List = np.random.random_sample(nPts)*360.0
dec2List = np.random.random_sample(nPts)*180.0-90.0
mag2List = np.random.random_sample(nPts)*10+18.0
cls.table2Control = np.rec.fromrecords([(r, d, m)
for r, d, m in
zip(ra2List, dec2List, mag2List)], dtype=dtype2)
with open(cls.table2FileName, 'w') as output:
output.write('# id ra dec mag\n')
for ix, (r, d, m) in enumerate(zip(ra2List, dec2List, mag2List)):
output.write('%d %.12f %.12f %.12f\n' % (ix, r, d, m))
cls.dbName = os.path.join(cls.scratch_dir, 'compound_db.db')
if os.path.exists(cls.dbName):
os.unlink(cls.dbName)
fileDBObject(cls.table1FileName, runtable='table1',
database=cls.dbName, dtype=dbDtype1,
idColKey='id')
fileDBObject(cls.table2FileName, runtable='table2',
database=cls.dbName, dtype=dbDtype2,
idColKey='id')
def test_mixed_stars(self):
"""
Here we will test the (somewhat absurd) case of a catalog with two different bandpasses
(lsst_ and cartoon_) in order to verify that gamma values are being cached correctly
"""
lsst_u_band = Bandpass()
lsst_u_band.readThroughput(os.path.join(getPackageDir('throughputs'), 'baseline', 'total_u.dat'))
lsst_g_band = Bandpass()
lsst_g_band.readThroughput(os.path.join(getPackageDir('throughputs'), 'baseline', 'total_g.dat'))
obs = ObservationMetaData(bandpassName=['c_u', 'c_g', 'u', 'g'],
m5=[25.0, 26.0, 15.0, 16.0])
# make the difference in m5 between the two bandpass systems extreme
# so that, in the unit test, we can be sure that the correct values
# are being used for the correct getters
db_dtype = np.dtype([('id', np.int),
('raJ2000', np.float),
('decJ2000', np.float),
('sedFilename', str, 100),
('magNorm', np.float),
('galacticAv', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogStars.txt')
db = fileDBObject(inputFile, dtype=db_dtype, runtable='test', idColKey='id')
cat = CartoonStars(db, obs_metadata=obs, column_outputs=['lsst_u', 'lsst_g',
'sigma_lsst_u', 'sigma_lsst_g'])
with lsst.utils.tests.getTempFilePath('.txt') as catName:
cat.write_catalog(catName)
dtype = np.dtype([(name, np.float) for name in cat._column_outputs])
controlData = np.genfromtxt(catName, dtype=dtype, delimiter=',')
db_columns = db.query_columns(['id', 'raJ2000', 'decJ2000', 'sedFilename', 'magNorm', 'galacticAv'])
sedDir = os.path.join(getPackageDir('sims_sed_library'), 'starSED', 'kurucz')
for ix, line in enumerate(next(db_columns)):
spectrum = Sed()
spectrum.readSED_flambda(os.path.join(sedDir, line[3]))
fnorm = spectrum.calcFluxNorm(line[4], self.normband)
spectrum.multiplyFluxNorm(fnorm)
a_x, b_x = spectrum.setupCCM_ab()
spectrum.addDust(a_x, b_x, A_v=line[5])
umag = spectrum.calcMag(self.uband)
self.assertAlmostEqual(umag, controlData['cartoon_u'][ix], 3)
gmag = spectrum.calcMag(self.gband)
self.assertAlmostEqual(gmag, controlData['cartoon_g'][ix], 3)
lsst_umag = spectrum.calcMag(lsst_u_band)
self.assertAlmostEqual(lsst_umag, controlData['lsst_u'][ix], 3)
lsst_gmag = spectrum.calcMag(lsst_g_band)
self.assertAlmostEqual(lsst_gmag, controlData['lsst_g'][ix], 3)
umagError, gamma = calcMagError_m5(umag, self.uband, obs.m5['c_u'], PhotometricParameters())
gmagError, gamma = calcMagError_m5(gmag, self.gband, obs.m5['c_g'], PhotometricParameters())
self.assertAlmostEqual(umagError, controlData['sigma_cartoon_u'][ix], 3)
self.assertAlmostEqual(gmagError, controlData['sigma_cartoon_g'][ix], 3)
lsst_umagError, gamma = calcMagError_m5(lsst_umag, lsst_u_band,
obs.m5['u'], PhotometricParameters())
lsst_gmagError, gamma = calcMagError_m5(lsst_gmag, lsst_g_band,
obs.m5['g'], PhotometricParameters())
self.assertAlmostEqual(lsst_umagError, controlData['sigma_lsst_u'][ix], 3)
self.assertAlmostEqual(lsst_gmagError, controlData['sigma_lsst_g'][ix], 3)
self.assertGreater(np.abs(lsst_umagError-umagError), 0.01)
self.assertGreater(np.abs(lsst_gmagError-gmagError), 0.01)
def test_InstanceCatalog_against_catalog_chunks(self):
"""
Test that we can reproduce the validated data using the
InstanceCatalog framework when the catalog must be written
in multiple chunks
"""
obs = ObservationMetaData(pointingRA=53.00913847303155535,
pointingDec=-27.43894880881512321,
rotSkyPos=256.75075318193080420,
mjd=59580.13955500000156462,
bandpassName='r',
site=Site(name="LSST", pressure=0.0,
humidity=0.0))
data_dir = os.path.join(getPackageDir('sims_catUtils'),'tests',
'testData')
dtype = np.dtype([('id', int), ('ra', float), ('dec', float),
('ra_deprecessed', float), ('dec_deprecessed', float),
('x_dm', float), ('y_dm', float),
('x_focal', float), ('y_focal', float),
('x_cam', float), ('y_cam', float)])
data = np.genfromtxt(os.path.join(data_dir,
'pixel_prediction_catalog.txt'),
dtype=dtype)
data_txt_file = tempfile.mktemp(dir=data_dir,
prefix='ic_validation_cat',
suffix='.txt')
cat_dtype = np.dtype([('id', int),
('raJ2000', float), ('decJ2000', float)])
with open(data_txt_file, 'w') as out_file:
out_file.write('# a header\n')
for ii, rr, dd in zip(data['id'],
np.radians(data['ra']),
np.radians(data['dec'])):
out_file.write('%d %.17f %.17f\n' % (ii, rr, dd))
db = fileDBObject(data_txt_file, idColKey='id', dtype=cat_dtype,
delimiter=' ')
class DeprecessionTestCatalog_chunks(PhoSimCatalogPoint):
def get_uniqueId(self):
return self.column_by_name('id')
def get_properMotionRa(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_properMotionDec(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_radialVelocity(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_parallax(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_galacticAv(self):
return np.zeros(len(self.column_by_name('raJ2000')))
def get_galacticRv(self):
return 3.1*np.ones(len(self.column_by_name('raJ2000')))
def get_sedFilepath(self):
return np.array(['sed_flat.txt.gz']*len(self.column_by_name('raJ2000')))
def get_phoSimMagNorm(self):
return np.ones(len(self.column_by_name('raJ2000')))
cat = DeprecessionTestCatalog_chunks(db, obs_metadata=obs)
cat.phoSimHeaderMap = DefaultPhoSimHeaderMap
id_list = []
ra_dep_list = []
dec_dep_list= []
phosim_cat_name = tempfile.mktemp(dir=data_dir,
prefix='phosim_dep',
suffix='.txt')
cat.write_catalog(phosim_cat_name, chunk_size=10)
with open(phosim_cat_name, 'r') as input_file:
for line in input_file:
params = line.strip().split()
if len(params) < 3:
continue
id_list.append(int(params[1]))
ra_dep_list.append(float(params[2]))
dec_dep_list.append(float(params[3]))
id_list = np.array(id_list)
np.testing.assert_array_equal(id_list, data['id'])
ra_dep_list = np.array(ra_dep_list)
dec_dep_list = np.array(dec_dep_list)
dd = 3600.0*angularSeparation(data['ra_deprecessed'], data['dec_deprecessed'],
ra_dep_list, dec_dep_list)
self.assertLess(dd.max(), 1.0e-5)
if os.path.exists(data_txt_file):
os.unlink(data_txt_file)
if os.path.exists(phosim_cat_name):
os.unlink(phosim_cat_name)
def testGalSimPhoSimCat(self):
"""
Run a GalSimPhoSim catalog on some data. Then, generate an ordinary PhoSim catalog using
the same data. Verify that the two resulting PhoSim catalogs are identical.
"""
galsim_cat_name = os.path.join(self.dataDir, 'galSimPhoSim_galsim_cat.txt')
phosim_cat_name = os.path.join(self.dataDir, 'galSimPhoSim_phosim_cat.txt')
galsim_image_root = os.path.join(self.dataDir, 'galSimPhoSim_images')
db = fileDBObject(self.bulge_name, dtype=self.dtype, runtable='test_bulges', idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 55
gs_cat = GalSimPhoSimGalaxies(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.PSF = SNRdocumentPSF()
gs_cat.phoSimHeaderMap = {}
gs_cat.write_catalog(galsim_cat_name)
gs_cat_0 = gs_cat
ps_cat = PhoSimCatalogSersic2D(db, obs_metadata=self.obs)
ps_cat.phoSimHeaderMap = {}
ps_cat.write_catalog(phosim_cat_name)
db = fileDBObject(self.disk_name, dtype=self.dtype, runtable='test_disks', idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 155
gs_cat = GalSimPhoSimGalaxies(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.copyGalSimInterpreter(gs_cat_0)
gs_cat.write_catalog(galsim_cat_name, write_header=False, write_mode='a')
gs_cat_0 = gs_cat
ps_cat = PhoSimCatalogSersic2D(db, obs_metadata=self.obs)
ps_cat.write_catalog(phosim_cat_name, write_header=False, write_mode='a')
db = fileDBObject(self.agn_name, dtype=self.dtype, runtable='test_agn', idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 255
gs_cat = GalSimPhoSimAgn(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.copyGalSimInterpreter(gs_cat_0)
gs_cat.write_catalog(galsim_cat_name, write_header=False, write_mode='a')
gs_cat_0 = gs_cat
ps_cat = PhoSimCatalogZPoint(db, obs_metadata=self.obs)
ps_cat.write_catalog(phosim_cat_name, write_header=False, write_mode='a')
db = fileDBObject(self.star_name, dtype=self.dtype, runtable='test_agn', idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 255
gs_cat = GalSimPhoSimStars(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.copyGalSimInterpreter(gs_cat_0)
gs_cat.write_catalog(galsim_cat_name, write_header=False, write_mode='a')
ps_cat = PhoSimCatalogPoint(db, obs_metadata=self.obs)
ps_cat.write_catalog(phosim_cat_name, write_header=False, write_mode='a')
written_files = gs_cat.write_images(nameRoot=galsim_image_root)
self.assertGreater(len(written_files), 0)
for name in written_files:
os.unlink(name)
with open(galsim_cat_name, 'r') as galsim_input:
with open(phosim_cat_name, 'r') as phosim_input:
galsim_lines = galsim_input.readlines()
phosim_lines = phosim_input.readlines()
self.assertEqual(len(galsim_lines), len(phosim_lines))
self.assertEqual(len(galsim_lines), 4*self.n_objects+7)
for line in galsim_lines:
self.assertIn(line, phosim_lines)
for line in phosim_lines:
self.assertIn(line, galsim_lines)
if os.path.exists(galsim_cat_name):
os.unlink(galsim_cat_name)
if os.path.exists(phosim_cat_name):
os.unlink(phosim_cat_name)
def test_mixed_stars(self):
"""
Here we will test the (somewhat absurd) case of a catalog with two different bandpasses
(lsst_ and cartoon_) in order to verify that gamma values are being cached correctly
"""
lsst_u_band = Bandpass()
lsst_u_band.readThroughput(os.path.join(getPackageDir('throughputs'), 'baseline', 'total_u.dat'))
lsst_g_band = Bandpass()
lsst_g_band.readThroughput(os.path.join(getPackageDir('throughputs'), 'baseline', 'total_g.dat'))
obs = ObservationMetaData(bandpassName=['c_u', 'c_g', 'u', 'g'],
m5=[25.0, 26.0, 15.0, 16.0])
# make the difference in m5 between the two bandpass systems extreme
# so that, in the unit test, we can be sure that the correct values
# are being used for the correct getters
db_dtype = np.dtype([('id', np.int),
('raJ2000', np.float),
('decJ2000', np.float),
('sedFilename', str, 100),
('magNorm', np.float),
('galacticAv', np.float)])
inputDir = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'testData')
inputFile = os.path.join(inputDir, 'IndicesTestCatalogStars.txt')
db = fileDBObject(inputFile, dtype=db_dtype, runtable='test', idColKey='id')
catName = os.path.join(getPackageDir('sims_catUtils'), 'tests', 'scratchSpace', 'cartoonStarCat.txt')
cat = CartoonStars(db, obs_metadata=obs, column_outputs=['lsst_u', 'lsst_g',
'sigma_lsst_u', 'sigma_lsst_g'])
cat.write_catalog(catName)
dtype = np.dtype([(name, np.float) for name in cat._column_outputs])
controlData = np.genfromtxt(catName, dtype=dtype, delimiter=',')
if os.path.exists(catName):
os.unlink(catName)
db_columns = db.query_columns(['id', 'raJ2000', 'decJ2000', 'sedFilename', 'magNorm', 'galacticAv'])
sedDir = os.path.join(getPackageDir('sims_sed_library'), 'starSED', 'kurucz')
for ix, line in enumerate(next(db_columns)):
spectrum = Sed()
spectrum.readSED_flambda(os.path.join(sedDir, line[3]))
fnorm = spectrum.calcFluxNorm(line[4], self.normband)
spectrum.multiplyFluxNorm(fnorm)
a_x, b_x = spectrum.setupCCMab()
spectrum.addCCMDust(a_x, b_x, A_v=line[5])
umag = spectrum.calcMag(self.uband)
self.assertAlmostEqual(umag, controlData['cartoon_u'][ix], 3)
gmag = spectrum.calcMag(self.gband)
self.assertAlmostEqual(gmag, controlData['cartoon_g'][ix], 3)
lsst_umag = spectrum.calcMag(lsst_u_band)
self.assertAlmostEqual(lsst_umag, controlData['lsst_u'][ix], 3)
lsst_gmag = spectrum.calcMag(lsst_g_band)
self.assertAlmostEqual(lsst_gmag, controlData['lsst_g'][ix], 3)
umagError, gamma = calcMagError_m5(umag, self.uband, obs.m5['c_u'], PhotometricParameters())
gmagError, gamma = calcMagError_m5(gmag, self.gband, obs.m5['c_g'], PhotometricParameters())
self.assertAlmostEqual(umagError, controlData['sigma_cartoon_u'][ix], 3)
self.assertAlmostEqual(gmagError, controlData['sigma_cartoon_g'][ix], 3)
lsst_umagError, gamma = calcMagError_m5(lsst_umag, lsst_u_band,
obs.m5['u'], PhotometricParameters())
lsst_gmagError, gamma = calcMagError_m5(lsst_gmag, lsst_g_band,
obs.m5['g'], PhotometricParameters())
self.assertAlmostEqual(lsst_umagError, controlData['sigma_lsst_u'][ix], 3)
self.assertAlmostEqual(lsst_gmagError, controlData['sigma_lsst_g'][ix], 3)
self.assertGreater(np.abs(lsst_umagError-umagError), 0.01)
self.assertGreater(np.abs(lsst_gmagError-gmagError), 0.01)
def testGalSimPhoSimCat(self):
"""
Run a GalSimPhoSim catalog on some data. Then, generate an ordinary PhoSim catalog using
the same data. Verify that the two resulting PhoSim catalogs are identical.
"""
galsim_cat_name = os.path.join(self.dataDir,
'galSimPhoSim_galsim_cat.txt')
phosim_cat_name = os.path.join(self.dataDir,
'galSimPhoSim_phosim_cat.txt')
galsim_image_root = os.path.join(self.dataDir, 'galSimPhoSim_images')
db = fileDBObject(self.bulge_name,
dtype=self.dtype,
runtable='test_bulges',
idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 55
gs_cat = GalSimPhoSimGalaxies(db, obs_metadata=self.obs)
gs_cat.camera_wrapper = GalSimCameraWrapper(self.camera)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.PSF = SNRdocumentPSF()
gs_cat.phoSimHeaderMap = {}
gs_cat.write_catalog(galsim_cat_name)
gs_cat_0 = gs_cat
ps_cat = PhoSimCatalogSersic2D(db, obs_metadata=self.obs)
ps_cat.phoSimHeaderMap = {}
ps_cat.write_catalog(phosim_cat_name)
db = fileDBObject(self.disk_name,
dtype=self.dtype,
runtable='test_disks',
idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 155
gs_cat = GalSimPhoSimGalaxies(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.copyGalSimInterpreter(gs_cat_0)
gs_cat.write_catalog(galsim_cat_name,
write_header=False,
write_mode='a')
gs_cat_0 = gs_cat
ps_cat = PhoSimCatalogSersic2D(db, obs_metadata=self.obs)
ps_cat.write_catalog(phosim_cat_name,
write_header=False,
write_mode='a')
db = fileDBObject(self.agn_name,
dtype=self.dtype,
runtable='test_agn',
idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 255
gs_cat = GalSimPhoSimAgn(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.copyGalSimInterpreter(gs_cat_0)
gs_cat.write_catalog(galsim_cat_name,
write_header=False,
write_mode='a')
gs_cat_0 = gs_cat
ps_cat = PhoSimCatalogZPoint(db, obs_metadata=self.obs)
ps_cat.write_catalog(phosim_cat_name,
write_header=False,
write_mode='a')
db = fileDBObject(self.star_name,
dtype=self.dtype,
runtable='test_agn',
idColKey='id')
db.raColName = 'ra_deg'
db.decColName = 'dec_deg'
db.objectTypeId = 255
gs_cat = GalSimPhoSimStars(db, obs_metadata=self.obs)
gs_cat.bandpassNames = self.obs.bandpass
gs_cat.copyGalSimInterpreter(gs_cat_0)
gs_cat.write_catalog(galsim_cat_name,
write_header=False,
write_mode='a')
ps_cat = PhoSimCatalogPoint(db, obs_metadata=self.obs)
ps_cat.write_catalog(phosim_cat_name,
write_header=False,
write_mode='a')
written_files = gs_cat.write_images(nameRoot=galsim_image_root)
self.assertGreater(len(written_files), 0)
for name in written_files:
os.unlink(name)
with open(galsim_cat_name, 'r') as galsim_input:
with open(phosim_cat_name, 'r') as phosim_input:
galsim_lines = galsim_input.readlines()
phosim_lines = phosim_input.readlines()
self.assertEqual(len(galsim_lines), len(phosim_lines))
self.assertEqual(len(galsim_lines), 4 * self.n_objects + 7)
for line in galsim_lines:
self.assertIn(line, phosim_lines)
for line in phosim_lines:
self.assertIn(line, galsim_lines)
if os.path.exists(galsim_cat_name):
os.unlink(galsim_cat_name)
if os.path.exists(phosim_cat_name):
os.unlink(phosim_cat_name)
def test_different_cameras(self):
rng = np.random.RandomState(6512)
pointing_ra = 15.0
pointing_dec = 13.0
n_obj = 100
ra_list = pointing_ra + 2.0 * rng.random_sample(n_obj)
dec_list = pointing_dec + 2.0 * rng.random_sample(n_obj)
px_list = radiansFromArcsec(0.005) * rng.random_sample(n_obj)
px_list += radiansFromArcsec(0.001)
mura_list = radiansFromArcsec(0.005) * rng.random_sample(n_obj)
mudec_list = radiansFromArcsec(0.005) * rng.random_sample(n_obj)
vrad_list = 100.0 * rng.random_sample(n_obj)
with lsst.utils.tests.getTempFilePath('.txt') as db_text_file:
with open(db_text_file, 'w') as out_file:
for ix, (rdeg, ddeg, rrad, drad, px, mura, mudec, vrad) in \
enumerate(zip(ra_list, dec_list,
np.radians(ra_list), np.radians(dec_list),
px_list, mura_list, mudec_list, vrad_list)):
out_file.write(
'%d %e %e %e %e %e %e %e %e\n' %
(ix, rdeg, ddeg, rrad, drad, px, mura, mudec, vrad))
dtype = np.dtype([('id', int), ('raDeg', float), ('decDeg', float),
('raJ2000', float), ('decJ2000', float),
('parallax', float), ('properMotionRa', float),
('properMotionDec', float),
('radialVelocity', float)])
db = fileDBObject(db_text_file, dtype=dtype, idColKey='id')
db.raColName = 'raDeg'
db.decColName = 'decDeg'
class CameraCoordsCatalog(AstrometryStars, CameraCoords,
InstanceCatalog):
camera = LsstSimMapper().camera
column_outputs = ['id', 'chipName']
class CameraCoordsLSSTCatalog(AstrometryStars, CameraCoordsLSST,
InstanceCatalog):
column_outputs = ['id', 'chipName']
obs = ObservationMetaData(pointingRA=pointing_ra,
pointingDec=pointing_dec,
boundLength=1.75,
boundType='circle',
rotSkyPos=23.0,
mjd=59580.0)
control_cat = CameraCoordsCatalog(db, obs_metadata=obs)
test_cat = CameraCoordsLSSTCatalog(db, obs_metadata=obs)
control_line_list = []
none_chips = 0
for line in control_cat.iter_catalog():
if line[1] is None:
none_chips += 1
control_line_list.append(line)
self.assertGreater(len(control_line_list), 0)
self.assertLess(none_chips, len(control_line_list) / 2)
line_ct = 0
for line in test_cat.iter_catalog():
line_ct += 1
self.assertIn(line, control_line_list)
self.assertEqual(line_ct, len(control_line_list))
def setUpClass(cls):
rng = np.random.RandomState(119)
n_galaxies = 20
sed_dir = os.path.join(getPackageDir("sims_sed_library"), "galaxySED")
list_of_seds = os.listdir(sed_dir)
disk_sed_dexes = rng.randint(0, len(list_of_seds), size=n_galaxies)
bulge_sed_dexes = rng.randint(0, len(list_of_seds), size=n_galaxies)
avBulge = rng.random_sample(n_galaxies) * 0.3 + 0.1
avDisk = rng.random_sample(n_galaxies) * 0.3 + 0.1
mjdList = rng.random_sample(n_galaxies) * 10.0 + 49330.0
redshiftList = rng.random_sample(n_galaxies) * 1.5 + 0.01
tauList = rng.random_sample(n_galaxies) * 1.0 + 1.0
sfuList = rng.random_sample(n_galaxies) * 2.0 + 1.0
sfgList = rng.random_sample(n_galaxies) * 2.0 + 1.0
sfrList = rng.random_sample(n_galaxies) * 2.0 + 1.0
sfiList = rng.random_sample(n_galaxies) * 2.0 + 1.0
sfzList = rng.random_sample(n_galaxies) * 2.0 + 1.0
sfyList = rng.random_sample(n_galaxies) * 2.0 + 1.0
raList = rng.random_sample(n_galaxies) * 7.0 + 78.0
decList = rng.random_sample(n_galaxies) * 4.0 - 69.0
normDisk = rng.random_sample(n_galaxies) * 5.0 + 20.0
normBulge = rng.random_sample(n_galaxies) * 5.0 + 20.0
normAgn = rng.random_sample(n_galaxies) * 5.0 + 20.0
with lsst.utils.tests.getTempFilePath('.txt') as txt_cat_name:
with open(txt_cat_name, "w") as output_file:
for ix in range(n_galaxies):
varParam = {
'varMethodName': 'applyAgn',
'pars': {
'agn_tau': tauList[ix],
'agn_sfu': sfuList[ix],
'agn_sfg': sfgList[ix],
'agn_sfr': sfrList[ix],
'agn_sfi': sfiList[ix],
'agn_sfz': sfzList[ix],
'agn_sfy': sfyList[ix],
't0_mjd': mjdList[ix],
'seed': rng.randint(0, 200000)
}
}
paramStr = json.dumps(varParam)
output_file.write(
"%d;%f;%f;" % (ix, raList[ix], decList[ix]) +
"%f;%f;" %
(np.radians(raList[ix]), np.radians(decList[ix])) +
"%f;" % (redshiftList[ix]) + "%s;%f;%f;" %
(list_of_seds[disk_sed_dexes[ix]], avDisk[ix],
normDisk[ix]) + "%s;%f;%f;" %
(list_of_seds[bulge_sed_dexes[ix]], avBulge[ix],
normBulge[ix]) + "agn.spec;%s;%f\n" %
(paramStr, normAgn[ix]))
dtype = np.dtype([('galid', np.int), ('raDeg', np.float),
('decDeg', np.float), ('raJ2000', np.float),
('decJ2000', np.float), ('redshift', np.float),
('sedFilenameDisk', str, 300),
('internalAvDisk', np.float),
('magNormDisk', np.float),
('sedFilenameBulge', str, 300),
('internalAvBulge', np.float),
('magNormBulge', np.float),
('sedFilenameAgn', str, 300),
('varParamStr', str, 600),
('magNormAgn', np.float)])
cls.agn_db = fileDBObject(txt_cat_name,
delimiter=';',
runtable='test',
dtype=dtype,
idColKey='galid')
cls.agn_db.raColName = 'raDeg'
cls.agn_db.decColName = 'decDeg'
cls.agn_db.objectTypeId = 112
# what follows is a hack to deal with the fact thar
# our varParamStr values are longer than 256 characters
# which is the default maximum length that a
# CatalogDBObject expects a string to be
#
cls.agn_db.dbTypeMap['STRING'] = (str, 600)
cls.agn_db.columns = None
cls.agn_db._make_default_columns()
cls.agn_db._make_column_map()
cls.agn_db._make_type_map()
cls.opsimDb = os.path.join(getPackageDir("sims_data"), "OpSimData")
cls.opsimDb = os.path.join(cls.opsimDb, "opsimblitz1_1133_sqlite.db")
def setUpClass(cls):
rng = np.random.RandomState(119)
cls.txt_cat_name = tempfile.mktemp(prefix='agn_lc_cat', suffix='.txt', dir=ROOT)
n_galaxies = 20
sed_dir = os.path.join(getPackageDir("sims_sed_library"), "galaxySED")
list_of_seds = os.listdir(sed_dir)
disk_sed_dexes = rng.randint(0, len(list_of_seds), size=n_galaxies)
bulge_sed_dexes = rng.randint(0, len(list_of_seds), size=n_galaxies)
avBulge = rng.random_sample(n_galaxies)*0.3+0.1
avDisk = rng.random_sample(n_galaxies)*0.3+0.1
mjdList = rng.random_sample(n_galaxies)*10.0+49330.0
redshiftList = rng.random_sample(n_galaxies)*1.5+0.01
tauList = rng.random_sample(n_galaxies)*1.0+1.0
sfuList = rng.random_sample(n_galaxies)*2.0+1.0
sfgList = rng.random_sample(n_galaxies)*2.0+1.0
sfrList = rng.random_sample(n_galaxies)*2.0+1.0
sfiList = rng.random_sample(n_galaxies)*2.0+1.0
sfzList = rng.random_sample(n_galaxies)*2.0+1.0
sfyList = rng.random_sample(n_galaxies)*2.0+1.0
raList = rng.random_sample(n_galaxies)*7.0+78.0
decList = rng.random_sample(n_galaxies)*4.0-69.0
normDisk = rng.random_sample(n_galaxies)*5.0+20.0
normBulge = rng.random_sample(n_galaxies)*5.0+20.0
normAgn = rng.random_sample(n_galaxies)*5.0+20.0
with open(cls.txt_cat_name, "w") as output_file:
for ix in range(n_galaxies):
varParam = {'varMethodName': 'applyAgn',
'pars': {'agn_tau': tauList[ix], 'agn_sfu': sfuList[ix],
'agn_sfg': sfgList[ix], 'agn_sfr': sfrList[ix],
'agn_sfi': sfiList[ix], 'agn_sfz': sfzList[ix],
'agn_sfy': sfyList[ix], 't0_mjd': mjdList[ix],
'seed': rng.randint(0, 200000)}}
paramStr = json.dumps(varParam)
output_file.write("%d;%f;%f;" % (ix, raList[ix], decList[ix])
+ "%f;%f;" % (np.radians(raList[ix]), np.radians(decList[ix]))
+ "%f;" % (redshiftList[ix])
+ "%s;%f;%f;" % (list_of_seds[disk_sed_dexes[ix]],
avDisk[ix], normDisk[ix])
+ "%s;%f;%f;" % (list_of_seds[bulge_sed_dexes[ix]],
avBulge[ix], normBulge[ix])
+ "agn.spec;%s;%f\n" % (paramStr, normAgn[ix]))
dtype = np.dtype([
('galid', np.int),
('raDeg', np.float), ('decDeg', np.float),
('raJ2000', np.float), ('decJ2000', np.float),
('redshift', np.float),
('sedFilenameDisk', str, 300), ('internalAvDisk', np.float),
('magNormDisk', np.float),
('sedFilenameBulge', str, 300), ('internalAvBulge', np.float),
('magNormBulge', np.float),
('sedFilenameAgn', str, 300), ('varParamStr', str, 600),
('magNormAgn', np.float)
])
cls.agn_db = fileDBObject(cls.txt_cat_name, delimiter=';',
runtable='test', dtype=dtype,
idColKey='galid')
cls.agn_db.raColName = 'raDeg'
cls.agn_db.decColName = 'decDeg'
cls.agn_db.objectTypeId = 112
# what follows is a hack to deal with the fact thar
# our varParamStr values are longer than 256 characters
# which is the default maximum length that a
# CatalogDBObject expects a string to be
#
cls.agn_db.dbTypeMap['STRING'] = (str, 600)
cls.agn_db.columns = None
cls.agn_db._make_default_columns()
cls.agn_db._make_column_map()
cls.agn_db._make_type_map()
cls.opsimDb = os.path.join(getPackageDir("sims_data"), "OpSimData")
cls.opsimDb = os.path.join(cls.opsimDb, "opsimblitz1_1133_sqlite.db")
def setUpClass(cls):
cls.baseDir = SCRATCH_DIR
cls.table1FileName = os.path.join(cls.baseDir, 'compound_table1.txt')
cls.table2FileName = os.path.join(cls.baseDir, 'compound_table2.txt')
if os.path.exists(cls.table1FileName):
os.unlink(cls.table1FileName)
if os.path.exists(cls.table2FileName):
os.unlink(cls.table2FileName)
dtype1 = np.dtype([('ra', np.float), ('dec', np.float),
('mag', np.float), ('dmag', np.float),
('dra', np.float), ('ddec', np.float)])
dbDtype1 = np.dtype([('id', np.int), ('ra', np.float),
('dec', np.float), ('mag', np.float),
('dmag', np.float), ('dra', np.float),
('ddec', np.float)])
nPts = 100
np.random.seed(42)
raList = np.random.random_sample(nPts) * 360.0
decList = np.random.random_sample(nPts) * 180.0 - 90.0
magList = np.random.random_sample(nPts) * 10.0 + 15.0
dmagList = np.random.random_sample(nPts) * 10.0 - 5.0
draList = np.random.random_sample(nPts) * 5.0 - 2.5
ddecList = np.random.random_sample(nPts) * (-2.0) - 4.0
cls.table1Control = np.rec.fromrecords(
[(r, d, mm, dm, dr, dd) for r, d, mm, dm, dr, dd in zip(
raList, decList, magList, dmagList, draList, ddecList)],
dtype=dtype1)
with open(cls.table1FileName, 'w') as output:
output.write("# id ra dec mag dmag dra ddec\n")
for ix, (r, d, mm, dm, dr, dd) in \
enumerate(zip(raList, decList, magList, dmagList, draList, ddecList)):
output.write('%d %.12f %.12f %.12f %.12f %.12f %.12f\n' %
(ix, r, d, mm, dm, dr, dd))
dtype2 = np.dtype([('ra', np.float), ('dec', np.float),
('mag', np.float)])
dbDtype2 = np.dtype([('id', np.int), ('ra', np.float),
('dec', np.float), ('mag', np.float)])
ra2List = np.random.random_sample(nPts) * 360.0
dec2List = np.random.random_sample(nPts) * 180.0 - 90.0
mag2List = np.random.random_sample(nPts) * 10 + 18.0
cls.table2Control = np.rec.fromrecords(
[(r, d, m) for r, d, m in zip(ra2List, dec2List, mag2List)],
dtype=dtype2)
with open(cls.table2FileName, 'w') as output:
output.write('# id ra dec mag\n')
for ix, (r, d, m) in enumerate(zip(ra2List, dec2List, mag2List)):
output.write('%d %.12f %.12f %.12f\n' % (ix, r, d, m))
cls.dbName = cartoonDBbase().database
if os.path.exists(cls.dbName):
os.unlink(cls.dbName)
fileDBObject(cls.table1FileName,
runtable='table1',
database=cls.dbName,
dtype=dbDtype1,
idColKey='id')
fileDBObject(cls.table2FileName,
runtable='table2',
database=cls.dbName,
dtype=dbDtype2,
idColKey='id')
def setUpClass(cls):
"""
Create a fake catalog of RR Lyrae stars. Store it in cls.stellar_db
"""
cls.scratchDir = tempfile.mkdtemp(dir=ROOT,
prefix='StellarLigghtCurveTest-')
rng = np.random.RandomState(88)
n_stars = 10000
sed_dir = os.path.join(getPackageDir("sims_sed_library"))
sed_dir = os.path.join(sed_dir, "starSED", "kurucz")
list_of_seds = os.listdir(sed_dir)
lc_dir = os.path.join(getPackageDir("sims_sed_library"), "rrly_lc")
lc_dir = os.path.join(lc_dir, "RRab")
list_of_lc = [
'rrly_lc/RRab/%s' % ww for ww in os.listdir(lc_dir)
if "per.txt" in ww
]
cls.dtype = np.dtype([('id', np.int), ('raDeg', np.float),
('decDeg', np.float), ('raJ2000', np.float),
('decJ2000', np.float), ('magNorm', np.float),
('galacticAv', np.float),
('sedFilename', str, 300),
('varParamStr', str, 300),
('parallax', np.float), ('ebv', np.float)])
# write the catalog as a text file to be ingested with fileDBObject
cls.txt_name = os.path.join(cls.scratchDir, "stellar_lc_catalog.txt")
with open(cls.txt_name, "w") as output_file:
sed_dex = rng.randint(0, len(list_of_seds), size=n_stars)
lc_dex = rng.randint(0, len(list_of_lc), size=n_stars)
mjd0 = rng.random_sample(n_stars) * 10000.0 + 40000.0
raList = rng.random_sample(n_stars) * 360.0
decList = -90.0 + rng.random_sample(n_stars) * 120.0
magNormList = rng.random_sample(n_stars) * 3.0 + 14.0
AvList = rng.random_sample(n_stars) * 0.2 + 0.1
pxList = rng.random_sample(n_stars) * 0.1
for ix in range(n_stars):
varparams = {
'varMethodName': 'applyRRly',
'pars': {
'tStartMjd': mjd0[ix],
'filename': list_of_lc[lc_dex[ix]]
}
}
varparamstr = json.dumps(varparams)
output_file.write(
"%d;%lf;%lf;%lf;%lf;%lf;%lf;%s;%s;%lf;%lf\n" %
(ix, raList[ix], decList[ix], np.radians(
raList[ix]), np.radians(decList[ix]), magNormList[ix],
AvList[ix], list_of_seds[sed_dex[ix]], varparamstr,
pxList[ix], AvList[ix] / 3.1))
cls.stellar_db = fileDBObject(cls.txt_name,
delimiter=';',
runtable='test',
dtype=cls.dtype,
idColKey='id')
cls.stellar_db.raColName = 'raDeg'
cls.stellar_db.decColName = 'decDeg'
cls.stellar_db.objectTypeId = 32
cls.opsimDb = os.path.join(getPackageDir("sims_data"), "OpSimData")
cls.opsimDb = os.path.join(cls.opsimDb, "opsimblitz1_1133_sqlite.db")
