def test_fits():
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
get_from_wiki('Aardvark.fit')
file_name = os.path.join('data','Aardvark.fit')
config = treecorr.read_config('Aardvark.yaml')
config['verbose'] = 1
# Just test a few random particular values
cat1 = treecorr.Catalog(file_name, config)
np.testing.assert_equal(len(cat1.ra), 390935)
np.testing.assert_equal(cat1.nobj, 390935)
np.testing.assert_almost_equal(cat1.ra[0], 56.4195 * (pi/180.))
np.testing.assert_almost_equal(cat1.ra[390934], 78.4782 * (pi/180.))
np.testing.assert_almost_equal(cat1.dec[290333], 83.1579 * (pi/180.))
np.testing.assert_almost_equal(cat1.g1[46392], 0.0005066675)
np.testing.assert_almost_equal(cat1.g2[46392], -0.0001006742)
np.testing.assert_almost_equal(cat1.k[46392], -0.0008628797)
# The catalog doesn't have x, y, or w, but test that functionality as well.
del config['ra_col']
del config['dec_col']
config['x_col'] = 'RA'
config['y_col'] = 'DEC'
config['w_col'] = 'MU'
config['flag_col'] = 'INDEX'
config['ignore_flag'] = 64
cat2 = treecorr.Catalog(file_name, config)
np.testing.assert_almost_equal(cat2.x[390934], 78.4782, decimal=4)
np.testing.assert_almost_equal(cat2.y[290333], 83.1579, decimal=4)
np.testing.assert_almost_equal(cat2.w[46392], 0.) # index = 1200379
np.testing.assert_almost_equal(cat2.w[46393], 0.9995946) # index = 1200386
# Test using a limited set of rows
config['first_row'] = 101
config['last_row'] = 50000
cat3 = treecorr.Catalog(file_name, config)
np.testing.assert_equal(len(cat3.x), 49900)
np.testing.assert_equal(cat3.ntot, 49900)
np.testing.assert_equal(cat3.nobj, sum(cat3.w != 0))
np.testing.assert_equal(cat3.sumw, sum(cat3.w))
np.testing.assert_equal(cat3.sumw, sum(cat2.w[100:50000]))
np.testing.assert_almost_equal(cat3.g1[46292], 0.0005066675)
np.testing.assert_almost_equal(cat3.g2[46292], -0.0001006742)
np.testing.assert_almost_equal(cat3.k[46292], -0.0008628797)
cat4 = treecorr.read_catalogs(config, key='file_name', is_rand=True)[0]
np.testing.assert_equal(len(cat4.x), 49900)
np.testing.assert_equal(cat4.ntot, 49900)
np.testing.assert_equal(cat4.nobj, sum(cat4.w != 0))
np.testing.assert_equal(cat4.sumw, sum(cat4.w))
np.testing.assert_equal(cat4.sumw, sum(cat2.w[100:50000]))
assert cat4.g1 is None
assert cat4.g2 is None
assert cat4.k is None
def test_list():
# Test different ways to read in a list of catalog names.
# This is based on the bug report for Issue #10.
nobj = 5000
rng = np.random.RandomState(8675309)
x_list = []
y_list = []
file_names = []
ncats = 3
for k in range(ncats):
x = rng.random_sample(nobj)
y = rng.random_sample(nobj)
file_name = os.path.join('data','test_list%d.dat'%k)
with open(file_name, 'w') as fid:
# These are intentionally in a different order from the order we parse them.
fid.write('# ra,dec,x,y,k,g1,g2,w,flag\n')
for i in range(nobj):
fid.write(('%.8f %.8f\n')%(x[i],y[i]))
x_list.append(x)
y_list.append(y)
file_names.append(file_name)
# Start with file_name being a list:
config = {
'x_col' : 1,
'y_col' : 2,
'file_name' : file_names
}
cats = treecorr.read_catalogs(config, key='file_name')
np.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
np.testing.assert_almost_equal(cats[k].x, x_list[k])
np.testing.assert_almost_equal(cats[k].y, y_list[k])
# Next check that the list can be just a string with spaces between names:
config['file_name'] = " ".join(file_names)
# Also check that it is ok to include file_list to read_catalogs.
cats = treecorr.read_catalogs(config, 'file_name', 'file_list')
np.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
np.testing.assert_almost_equal(cats[k].x, x_list[k])
np.testing.assert_almost_equal(cats[k].y, y_list[k])
# Next check that having the names in a file_list file works:
list_name = os.path.join('data','test_list.txt')
with open(list_name, 'w') as fid:
for name in file_names:
fid.write(name + '\n')
del config['file_name']
config['file_list'] = list_name
cats = treecorr.read_catalogs(config, 'file_name', 'file_list')
np.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
np.testing.assert_almost_equal(cats[k].x, x_list[k])
np.testing.assert_almost_equal(cats[k].y, y_list[k])
# Also, should be allowed to omit file_name arg:
cats = treecorr.read_catalogs(config, list_key='file_list')
np.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
np.testing.assert_almost_equal(cats[k].x, x_list[k])
np.testing.assert_almost_equal(cats[k].y, y_list[k])
def test_fits():
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
get_from_wiki('Aardvark.fit')
file_name = os.path.join('data','Aardvark.fit')
config = treecorr.read_config('Aardvark.yaml')
config['verbose'] = 1
config['kk_file_name'] = 'kk.fits'
config['gg_file_name'] = 'gg.fits'
# Just test a few random particular values
cat1 = treecorr.Catalog(file_name, config)
np.testing.assert_equal(len(cat1.ra), 390935)
np.testing.assert_equal(cat1.nobj, 390935)
np.testing.assert_almost_equal(cat1.ra[0], 56.4195 * (pi/180.))
np.testing.assert_almost_equal(cat1.ra[390934], 78.4782 * (pi/180.))
np.testing.assert_almost_equal(cat1.dec[290333], 83.1579 * (pi/180.))
np.testing.assert_almost_equal(cat1.g1[46392], 0.0005066675)
np.testing.assert_almost_equal(cat1.g2[46392], -0.0001006742)
np.testing.assert_almost_equal(cat1.k[46392], -0.0008628797)
assert_raises(ValueError, treecorr.Catalog, file_name, config, ra_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, dec_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, r_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, w_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, wpos_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, flag_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g1_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g2_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, k_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, ra_col='0')
assert_raises(ValueError, treecorr.Catalog, file_name, config, dec_col='0')
assert_raises(ValueError, treecorr.Catalog, file_name, config, x_col='x')
assert_raises(ValueError, treecorr.Catalog, file_name, config, y_col='y')
assert_raises(ValueError, treecorr.Catalog, file_name, config, z_col='z')
assert_raises(ValueError, treecorr.Catalog, file_name, config, ra_col='0', dec_col='0')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g1_col='0')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g2_col='0')
assert_raises(ValueError, treecorr.Catalog, file_name, config, k_col='0')
assert_raises(TypeError, treecorr.Catalog, file_name, config, x_units='arcmin')
assert_raises(TypeError, treecorr.Catalog, file_name, config, y_units='arcmin')
del config['ra_units']
assert_raises(TypeError, treecorr.Catalog, file_name, config)
del config['dec_units']
assert_raises(TypeError, treecorr.Catalog, file_name, config, ra_units='deg')
# The catalog doesn't have x, y, or w, but test that functionality as well.
del config['ra_col']
del config['dec_col']
config['x_col'] = 'RA'
config['y_col'] = 'DEC'
config['w_col'] = 'MU'
config['flag_col'] = 'INDEX'
config['ignore_flag'] = 64
cat2 = treecorr.Catalog(file_name, config)
np.testing.assert_almost_equal(cat2.x[390934], 78.4782, decimal=4)
np.testing.assert_almost_equal(cat2.y[290333], 83.1579, decimal=4)
np.testing.assert_almost_equal(cat2.w[46392], 0.) # index = 1200379
np.testing.assert_almost_equal(cat2.w[46393], 0.9995946) # index = 1200386
assert_raises(ValueError, treecorr.Catalog, file_name, config, x_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, y_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, z_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, ra_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, dec_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, r_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, w_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, wpos_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, flag_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g1_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g2_col='invalid')
assert_raises(ValueError, treecorr.Catalog, file_name, config, k_col='invalid')
# Test using a limited set of rows
config['first_row'] = 101
config['last_row'] = 50000
cat3 = treecorr.Catalog(file_name, config)
np.testing.assert_equal(len(cat3.x), 49900)
np.testing.assert_equal(cat3.ntot, 49900)
np.testing.assert_equal(cat3.nobj, sum(cat3.w != 0))
np.testing.assert_equal(cat3.sumw, sum(cat3.w))
np.testing.assert_equal(cat3.sumw, sum(cat2.w[100:50000]))
np.testing.assert_almost_equal(cat3.g1[46292], 0.0005066675)
np.testing.assert_almost_equal(cat3.g2[46292], -0.0001006742)
np.testing.assert_almost_equal(cat3.k[46292], -0.0008628797)
cat4 = treecorr.read_catalogs(config, key='file_name', is_rand=True)[0]
np.testing.assert_equal(len(cat4.x), 49900)
np.testing.assert_equal(cat4.ntot, 49900)
np.testing.assert_equal(cat4.nobj, sum(cat4.w != 0))
np.testing.assert_equal(cat4.sumw, sum(cat4.w))
np.testing.assert_equal(cat4.sumw, sum(cat2.w[100:50000]))
assert cat4.g1 is None
assert cat4.g2 is None
assert cat4.k is None
do_pickle(cat1)
do_pickle(cat2)
do_pickle(cat3)
do_pickle(cat4)
assert_raises(ValueError, treecorr.Catalog, file_name, config, first_row=-10)
assert_raises(ValueError, treecorr.Catalog, file_name, config, first_row=0)
assert_raises(ValueError, treecorr.Catalog, file_name, config, first_row=60000)
assert_raises(ValueError, treecorr.Catalog, file_name, config, first_row=50001)
assert_raises(TypeError, treecorr.read_catalogs, config)
assert_raises(TypeError, treecorr.read_catalogs, config, key='file_name', list_key='file_name')
# If gg output not given, it is still invalid to only have one or the other of g1,g2.
del config['gg_file_name']
assert_raises(ValueError, treecorr.Catalog, file_name, config, g1_col='0')
assert_raises(ValueError, treecorr.Catalog, file_name, config, g2_col='0')
def corr3(config, logger=None):
"""Run the full three-point correlation function code based on the parameters in the
given config dict.
The function print_corr3_params() will output information about the valid parameters
that are expected to be in the config dict.
Optionally a logger parameter maybe given, in which case it is used for logging.
If not given, the logging will be based on the verbose and log_file parameters.
:param config: The configuration dict which defines what to do.
:param logger: If desired, a logger object for logging. (default: None, in which case
one will be built according to the config dict's verbose level.)
"""
# Setup logger based on config verbose value
if logger is None:
logger = treecorr.config.setup_logger(
treecorr.config.get(config,'verbose',int,1),
config.get('log_file',None))
# Check that config doesn't have any extra parameters.
# (Such values are probably typos.)
# Also convert the given parameters to the correct type, etc.
config = treecorr.config.check_config(config, corr3_valid_params, corr3_aliases, logger)
import pprint
logger.debug('Using configuration dict:\n%s',pprint.pformat(config))
if ( 'output_dots' not in config
and config.get('log_file',None) is None
and config['verbose'] >= 2 ):
config['output_dots'] = True
# Set the number of threads
num_threads = config.get('num_threads',None)
logger.debug('From config dict, num_threads = %d',num_threads)
treecorr.set_omp_threads(num_threads, logger)
# Read in the input files. Each of these is a list.
cat1 = treecorr.read_catalogs(config, 'file_name', 'file_list', 0, logger)
if len(cat1) == 0:
raise AttributeError("Either file_name or file_list is required")
cat2 = treecorr.read_catalogs(config, 'file_name2', 'rand_file_list2', 1, logger)
cat3 = treecorr.read_catalogs(config, 'file_name3', 'rand_file_list3', 1, logger)
rand1 = treecorr.read_catalogs(config, 'rand_file_name', 'rand_file_list', 0, logger)
rand2 = treecorr.read_catalogs(config, 'rand_file_name2', 'rand_file_list2', 1, logger)
rand3 = treecorr.read_catalogs(config, 'rand_file_name3', 'rand_file_list3', 1, logger)
if len(cat2) == 0 and len(rand2) > 0:
raise AttributeError("rand_file_name2 is invalid without file_name2")
if len(cat3) == 0 and len(rand3) > 0:
raise AttributeError("rand_file_name3 is invalid without file_name3")
logger.info("Done reading input catalogs")
# Do GGG correlation function if necessary
if 'ggg_file_name' in config: #or 'm3_file_name' in config:
logger.info("Start GGG calculations...")
ggg = treecorr.GGGCorrelation(config,logger)
ggg.process(cat1,cat2,cat3)
logger.info("Done GGG calculations.")
if 'ggg_file_name' in config:
ggg.write(config['ggg_file_name'])
if 'm3_file_name' in config:
ggg.writeMapSq(config['m3_file_name'])
# Do NNN correlation function if necessary
if 'nnn_file_name' in config:
if len(rand1) == 0:
raise AttributeError("rand_file_name is required for NNN correlation")
if len(cat2) > 0 and len(rand2) == 0:
raise AttributeError("rand_file_name2 is required for NNN cross-correlation")
if len(cat3) > 0 and len(rand3) == 0:
raise AttributeError("rand_file_name3 is required for NNN cross-correlation")
if (len(cat2) > 0) != (len(cat3) > 0):
raise NotImplementedError(
"Cannot yet handle 3-point corrleations with only two catalogs. "+
"Need both cat2 and cat3.")
logger.info("Start DDD calculations...")
ddd = treecorr.NNNCorrelation(config,logger)
ddd.process(cat1,cat2,cat3)
logger.info("Done DDD calculations.")
if len(cat2) == 0:
rrr = treecorr.NNNCorrelation(config,logger)
rrr.process(rand1)
logger.info("Done RRR calculations.")
if config['nnn_statistic'] == 'compensated':
drr = treecorr.NNNCorrelation(config,logger)
drr.process(cat1,rand2,rand3)
logger.info("Done DRR calculations.")
ddr = treecorr.NNNCorrelation(config,logger)
ddr.process(cat1,cat2,rand3)
logger.info("Done DDR calculations.")
ddd.write(config['nnn_file_name'],rrr,drr,ddr)
else:
ddd.write(config['nnn_file_name'],rrr)
else:
rrr = treecorr.NNNCorrelation(config,logger)
rrr.process(rand1,rand2,rand3)
logger.info("Done RRR calculations.")
if config['nnn_statistic'] == 'compensated':
drr = treecorr.NNNCorrelation(config,logger)
drr.process(cat1,rand2,rand3)
logger.info("Done DRR calculations.")
ddr = treecorr.NNNCorrelation(config,logger)
ddr.process(cat1,cat2,rand3)
logger.info("Done DDR calculations.")
rdr = treecorr.NNNCorrelation(config,logger)
rdr.process(rand1,cat2,rand3)
logger.info("Done RDR calculations.")
rrd = treecorr.NNNCorrelation(config,logger)
rrd.process(rand1,rand2,cat3)
logger.info("Done RRD calculations.")
drd = treecorr.NNNCorrelation(config,logger)
drd.process(cat1,rand2,cat3)
logger.info("Done DRD calculations.")
rdd = treecorr.NNNCorrelation(config,logger)
rdd.process(rand1,cat2,cat3)
logger.info("Done RDD calculations.")
ddd.write(config['nnn_file_name'],rrr,drr,ddr,rdr,rrd,drd,rdd)
else:
ddd.write(config['nnn_file_name'],rrr)
# Do KKK correlation function if necessary
if 'kkk_file_name' in config:
logger.info("Start KKK calculations...")
kkk = treecorr.KKKCorrelation(config,logger)
kkk.process(cat1,cat2,cat3)
logger.info("Done KKK calculations.")
kkk.write(config['kkk_file_name'])
# Do NNG correlation function if necessary
if False:
#if 'nng_file_name' in config or 'nnm_file_name' in config:
if len(cat3) == 0:
raise AttributeError("file_name3 is required for nng correlation")
logger.info("Start NNG calculations...")
nng = treecorr.NNGCorrelation(config,logger)
nng.process(cat1,cat2,cat3)
logger.info("Done NNG calculation.")
# The default ng_statistic is compensated _iff_ rand files are given.
rrg = None
if len(rand1) == 0:
if config.get('nng_statistic',None) == 'compensated':
raise AttributeError("rand_files is required for nng_statistic = compensated")
elif config.get('nng_statistic','compensated') == 'compensated':
rrg = treecorr.NNGCorrelation(config,logger)
rrg.process(rand1,rand1,cat2)
logger.info("Done RRG calculation.")
if 'nng_file_name' in config:
nng.write(config['nng_file_name'], rrg)
if 'nnm_file_name' in config:
nng.writeNNMap(config['nnm_file_name'], rrg)
# Do NNK correlation function if necessary
if False:
#if 'nnk_file_name' in config:
if len(cat3) == 0:
raise AttributeError("file_name3 is required for nnk correlation")
logger.info("Start NNK calculations...")
nnk = treecorr.NNKCorrelation(config,logger)
nnk.process(cat1,cat2,cat3)
logger.info("Done NNK calculation.")
rrk = None
if len(rand1) == 0:
if config.get('nnk_statistic',None) == 'compensated':
raise AttributeError("rand_files is required for nnk_statistic = compensated")
elif config.get('nnk_statistic','compensated') == 'compensated':
rrk = treecorr.NNKCorrelation(config,logger)
rrk.process(rand1,rand1,cat2)
logger.info("Done RRK calculation.")
nnk.write(config['nnk_file_name'], rrk)
# Do KKG correlation function if necessary
if False:
#if 'kkg_file_name' in config:
if len(cat3) == 0:
raise AttributeError("file_name3 is required for kkg correlation")
logger.info("Start KKG calculations...")
kkg = treecorr.KKGCorrelation(config,logger)
kkg.process(cat1,cat2,cat3)
logger.info("Done KKG calculation.")
kkg.write(config['kkg_file_name'])
def corr2(config, logger=None):
"""Run the full two-point correlation function code based on the parameters in the
given config dict.
The function print_corr2_params() will output information about the valid parameters
that are expected to be in the config dict.
Optionally a logger parameter maybe given, in which case it is used for logging.
If not given, the logging will be based on the verbose and log_file parameters.
:param config: The configuration dict which defines what to do.
:param logger: If desired, a logger object for logging. (default: None, in which case
one will be built according to the config dict's verbose level.)
"""
# Setup logger based on config verbose value
if logger is None:
logger = treecorr.config.setup_logger(
treecorr.config.get(config,'verbose',int,1),
config.get('log_file',None))
# Check that config doesn't have any extra parameters.
# (Such values are probably typos.)
# Also convert the given parameters to the correct type, etc.
config = treecorr.config.check_config(config, corr2_valid_params, corr2_aliases, logger)
import pprint
logger.debug('Using configuration dict:\n%s',pprint.pformat(config))
if ( 'output_dots' not in config
and config.get('log_file',None) is None
and config['verbose'] >= 2 ):
config['output_dots'] = True
# Set the number of threads
num_threads = config.get('num_threads',None)
logger.debug('From config dict, num_threads = %s',num_threads)
treecorr.set_omp_threads(num_threads, logger)
# Read in the input files. Each of these is a list.
cat1 = treecorr.read_catalogs(config, 'file_name', 'file_list', 0, logger)
if len(cat1) == 0:
raise AttributeError("Either file_name or file_list is required")
cat2 = treecorr.read_catalogs(config, 'file_name2', 'file_list2', 1, logger)
rand1 = treecorr.read_catalogs(config, 'rand_file_name', 'rand_file_list', 0, logger)
rand2 = treecorr.read_catalogs(config, 'rand_file_name2', 'rand_file_list2', 1, logger)
if len(cat2) == 0 and len(rand2) > 0:
raise AttributeError("rand_file_name2 is invalid without file_name2")
logger.info("Done reading input catalogs")
# Do GG correlation function if necessary
if 'gg_file_name' in config or 'm2_file_name' in config:
logger.warning("Performing GG calculations...")
gg = treecorr.GGCorrelation(config,logger)
gg.process(cat1,cat2)
logger.info("Done GG calculations.")
if 'gg_file_name' in config:
gg.write(config['gg_file_name'])
logger.warning("Wrote GG correlation to %s",config['gg_file_name'])
if 'm2_file_name' in config:
gg.writeMapSq(config['m2_file_name'], m2_uform=config['m2_uform'])
logger.warning("Wrote Mapsq values to %s",config['m2_file_name'])
# Do NG correlation function if necessary
if 'ng_file_name' in config or 'nm_file_name' in config or 'norm_file_name' in config:
if len(cat2) == 0:
raise AttributeError("file_name2 is required for ng correlation")
logger.warning("Performing NG calculations...")
ng = treecorr.NGCorrelation(config,logger)
ng.process(cat1,cat2)
logger.info("Done NG calculation.")
# The default ng_statistic is compensated _iff_ rand files are given.
rg = None
if len(rand1) == 0:
if config.get('ng_statistic',None) == 'compensated':
raise AttributeError("rand_files is required for ng_statistic = compensated")
elif config.get('ng_statistic','compensated') == 'compensated':
rg = treecorr.NGCorrelation(config,logger)
rg.process(rand1,cat2)
logger.info("Done RG calculation.")
if 'ng_file_name' in config:
ng.write(config['ng_file_name'], rg)
logger.warning("Wrote NG correlation to %s",config['ng_file_name'])
if 'nm_file_name' in config:
ng.writeNMap(config['nm_file_name'], rg, m2_uform=config['m2_uform'])
logger.warning("Wrote NMap values to %s",config['nm_file_name'])
if 'norm_file_name' in config:
gg = treecorr.GGCorrelation(config,logger)
gg.process(cat2)
logger.info("Done GG calculation for norm")
dd = treecorr.NNCorrelation(config,logger)
dd.process(cat1)
logger.info("Done DD calculation for norm")
rr = treecorr.NNCorrelation(config,logger)
rr.process(rand1)
logger.info("Done RR calculation for norm")
dr = None
if config['nn_statistic'] == 'compensated':
dr = treecorr.NNCorrelation(config,logger)
dr.process(cat1,rand1)
logger.info("Done DR calculation for norm")
ng.writeNorm(config['norm_file_name'],gg,dd,rr,dr,rg,m2_uform=config['m2_uform'])
logger.warning("Wrote Norm values to %s",config['norm_file_name'])
# Do NN correlation function if necessary
if 'nn_file_name' in config:
if len(rand1) == 0:
raise AttributeError("rand_file_name is required for NN correlation")
if len(cat2) > 0 and len(rand2) == 0:
raise AttributeError("rand_file_name2 is required for NN cross-correlation")
logger.warning("Performing DD calculations...")
dd = treecorr.NNCorrelation(config,logger)
dd.process(cat1,cat2)
logger.info("Done DD calculations.")
dr = None
rd = None
if len(cat2) == 0:
logger.warning("Performing RR calculations...")
rr = treecorr.NNCorrelation(config,logger)
rr.process(rand1)
logger.info("Done RR calculations.")
if config['nn_statistic'] == 'compensated':
logger.warning("Performing DR calculations...")
dr = treecorr.NNCorrelation(config,logger)
dr.process(cat1,rand1)
logger.info("Done DR calculations.")
else:
logger.warning("Performing RR calculations...")
rr = treecorr.NNCorrelation(config,logger)
rr.process(rand1,rand2)
logger.info("Done RR calculations.")
if config['nn_statistic'] == 'compensated':
logger.warning("Performing DR calculations...")
dr = treecorr.NNCorrelation(config,logger)
dr.process(cat1,rand2)
logger.info("Done DR calculations.")
rd = treecorr.NNCorrelation(config,logger)
rd.process(rand1,cat2)
logger.info("Done RD calculations.")
dd.write(config['nn_file_name'],rr,dr,rd)
logger.warning("Wrote NN correlation to %s",config['nn_file_name'])
# Do KK correlation function if necessary
if 'kk_file_name' in config:
logger.warning("Performing KK calculations...")
kk = treecorr.KKCorrelation(config,logger)
kk.process(cat1,cat2)
logger.info("Done KK calculations.")
kk.write(config['kk_file_name'])
logger.warning("Wrote KK correlation to %s",config['kk_file_name'])
# Do NG correlation function if necessary
if 'nk_file_name' in config:
if len(cat2) == 0:
raise AttributeError("file_name2 is required for nk correlation")
logger.warning("Performing NK calculations...")
nk = treecorr.NKCorrelation(config,logger)
nk.process(cat1,cat2)
logger.info("Done NK calculation.")
rk = None
if len(rand1) == 0:
if config.get('nk_statistic',None) == 'compensated':
raise AttributeError("rand_files is required for nk_statistic = compensated")
elif config.get('nk_statistic','compensated') == 'compensated':
rk = treecorr.NKCorrelation(config,logger)
rk.process(rand1,cat2)
logger.info("Done RK calculation.")
nk.write(config['nk_file_name'], rk)
logger.warning("Wrote NK correlation to %s",config['nk_file_name'])
# Do KG correlation function if necessary
if 'kg_file_name' in config:
if len(cat2) == 0:
raise AttributeError("file_name2 is required for kg correlation")
logger.warning("Performing KG calculations...")
kg = treecorr.KGCorrelation(config,logger)
kg.process(cat1,cat2)
logger.info("Done KG calculation.")
kg.write(config['kg_file_name'])
logger.warning("Wrote KG correlation to %s",config['kg_file_name'])
def test_list():
# Test different ways to read in a list of catalog names.
# This is based on the bug report for Issue #10.
nobj = 5000
numpy.random.seed(8675309)
x_list = []
y_list = []
file_names = []
ncats = 3
for k in range(ncats):
x = numpy.random.random_sample(nobj)
y = numpy.random.random_sample(nobj)
file_name = os.path.join('data','test_list%d.dat'%k)
with open(file_name, 'w') as fid:
# These are intentionally in a different order from the order we parse them.
fid.write('# ra,dec,x,y,k,g1,g2,w,flag\n')
for i in range(nobj):
fid.write(('%.8f %.8f\n')%(x[i],y[i]))
x_list.append(x)
y_list.append(y)
file_names.append(file_name)
# Start with file_name being a list:
config = {
'x_col' : 1,
'y_col' : 2,
'file_name' : file_names
}
cats = treecorr.read_catalogs(config, key='file_name')
numpy.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
numpy.testing.assert_almost_equal(cats[k].x, x_list[k])
numpy.testing.assert_almost_equal(cats[k].y, y_list[k])
# Next check that the list can be just a string with spaces between names:
config['file_name'] = " ".join(file_names)
# Also check that it is ok to include file_list to read_catalogs.
cats = treecorr.read_catalogs(config, 'file_name', 'file_list')
numpy.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
numpy.testing.assert_almost_equal(cats[k].x, x_list[k])
numpy.testing.assert_almost_equal(cats[k].y, y_list[k])
# Next check that having the names in a file_list file works:
list_name = os.path.join('data','test_list.txt')
with open(list_name, 'w') as fid:
for name in file_names:
fid.write(name + '\n')
del config['file_name']
config['file_list'] = list_name
cats = treecorr.read_catalogs(config, 'file_name', 'file_list')
numpy.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
numpy.testing.assert_almost_equal(cats[k].x, x_list[k])
numpy.testing.assert_almost_equal(cats[k].y, y_list[k])
# Also, should be allowed to omit file_name arg:
cats = treecorr.read_catalogs(config, list_key='file_list')
numpy.testing.assert_equal(len(cats), ncats)
for k in range(ncats):
numpy.testing.assert_almost_equal(cats[k].x, x_list[k])
numpy.testing.assert_almost_equal(cats[k].y, y_list[k])
def corr3(config, logger=None):
"""Run the full three-point correlation function code based on the parameters in the
given config dict.
The function print_corr3_params() will output information about the valid parameters
that are expected to be in the config dict.
Optionally a logger parameter maybe given, in which case it is used for logging.
If not given, the logging will be based on the verbose and log_file parameters.
:param config: The configuration dict which defines what to do.
:param logger: If desired, a logger object for logging. (default: None, in which case
one will be built according to the config dict's verbose level.)
"""
# Setup logger based on config verbose value
if logger is None:
logger = treecorr.config.setup_logger(
treecorr.config.get(config, 'verbose', int, 1),
config.get('log_file', None))
# Check that config doesn't have any extra parameters.
# (Such values are probably typos.)
# Also convert the given parameters to the correct type, etc.
config = treecorr.config.check_config(config, corr3_valid_params,
corr3_aliases, logger)
import pprint
logger.debug('Using configuration dict:\n%s', pprint.pformat(config))
if ('output_dots' not in config and config.get('log_file', None) is None
and config['verbose'] >= 2):
config['output_dots'] = True
# Set the number of threads
num_threads = config.get('num_threads', None)
logger.debug('From config dict, num_threads = %s', num_threads)
treecorr.set_omp_threads(num_threads, logger)
# Read in the input files. Each of these is a list.
cat1 = treecorr.read_catalogs(config, 'file_name', 'file_list', 0, logger)
if len(cat1) == 0:
raise AttributeError("Either file_name or file_list is required")
cat2 = treecorr.read_catalogs(config, 'file_name2', 'rand_file_list2', 1,
logger)
cat3 = treecorr.read_catalogs(config, 'file_name3', 'rand_file_list3', 1,
logger)
rand1 = treecorr.read_catalogs(config, 'rand_file_name', 'rand_file_list',
0, logger)
rand2 = treecorr.read_catalogs(config, 'rand_file_name2',
'rand_file_list2', 1, logger)
rand3 = treecorr.read_catalogs(config, 'rand_file_name3',
'rand_file_list3', 1, logger)
if len(cat2) == 0 and len(rand2) > 0:
raise AttributeError("rand_file_name2 is invalid without file_name2")
if len(cat3) == 0 and len(rand3) > 0:
raise AttributeError("rand_file_name3 is invalid without file_name3")
logger.info("Done reading input catalogs")
# Do GGG correlation function if necessary
if 'ggg_file_name' in config: #or 'm3_file_name' in config:
logger.info("Start GGG calculations...")
ggg = treecorr.GGGCorrelation(config, logger)
ggg.process(cat1, cat2, cat3)
logger.info("Done GGG calculations.")
if 'ggg_file_name' in config:
ggg.write(config['ggg_file_name'])
if 'm3_file_name' in config:
ggg.writeMapSq(config['m3_file_name'])
# Do NNN correlation function if necessary
if 'nnn_file_name' in config:
if len(rand1) == 0:
raise AttributeError(
"rand_file_name is required for NNN correlation")
if len(cat2) > 0 and len(rand2) == 0:
raise AttributeError(
"rand_file_name2 is required for NNN cross-correlation")
if len(cat3) > 0 and len(rand3) == 0:
raise AttributeError(
"rand_file_name3 is required for NNN cross-correlation")
if (len(cat2) > 0) != (len(cat3) > 0):
raise NotImplementedError(
"Cannot yet handle 3-point corrleations with only two catalogs. "
+ "Need both cat2 and cat3.")
logger.info("Start DDD calculations...")
ddd = treecorr.NNNCorrelation(config, logger)
ddd.process(cat1, cat2, cat3)
logger.info("Done DDD calculations.")
if len(cat2) == 0:
rrr = treecorr.NNNCorrelation(config, logger)
rrr.process(rand1)
logger.info("Done RRR calculations.")
# For the next step, just make cat2 = cat3 = cat1 and rand2 = rand3 = rand1.
cat2 = cat3 = cat1
rand2 = rand3 = rand1
else:
rrr = treecorr.NNNCorrelation(config, logger)
rrr.process(rand1, rand2, rand3)
logger.info("Done RRR calculations.")
if config['nnn_statistic'] == 'compensated':
drr = treecorr.NNNCorrelation(config, logger)
drr.process(cat1, rand2, rand3)
logger.info("Done DRR calculations.")
ddr = treecorr.NNNCorrelation(config, logger)
ddr.process(cat1, cat2, rand3)
logger.info("Done DDR calculations.")
rdr = treecorr.NNNCorrelation(config, logger)
rdr.process(rand1, cat2, rand3)
logger.info("Done RDR calculations.")
rrd = treecorr.NNNCorrelation(config, logger)
rrd.process(rand1, rand2, cat3)
logger.info("Done RRD calculations.")
drd = treecorr.NNNCorrelation(config, logger)
drd.process(cat1, rand2, cat3)
logger.info("Done DRD calculations.")
rdd = treecorr.NNNCorrelation(config, logger)
rdd.process(rand1, cat2, cat3)
logger.info("Done RDD calculations.")
ddd.write(config['nnn_file_name'], rrr, drr, rdr, rrd, ddr, drd,
rdd)
else:
ddd.write(config['nnn_file_name'], rrr)
# Do KKK correlation function if necessary
if 'kkk_file_name' in config:
logger.info("Start KKK calculations...")
kkk = treecorr.KKKCorrelation(config, logger)
kkk.process(cat1, cat2, cat3)
logger.info("Done KKK calculations.")
kkk.write(config['kkk_file_name'])
# Do NNG correlation function if necessary
if False:
#if 'nng_file_name' in config or 'nnm_file_name' in config:
if len(cat3) == 0:
raise AttributeError("file_name3 is required for nng correlation")
logger.info("Start NNG calculations...")
nng = treecorr.NNGCorrelation(config, logger)
nng.process(cat1, cat2, cat3)
logger.info("Done NNG calculation.")
# The default ng_statistic is compensated _iff_ rand files are given.
rrg = None
if len(rand1) == 0:
if config.get('nng_statistic', None) == 'compensated':
raise AttributeError(
"rand_files is required for nng_statistic = compensated")
elif config.get('nng_statistic', 'compensated') == 'compensated':
rrg = treecorr.NNGCorrelation(config, logger)
rrg.process(rand1, rand1, cat2)
logger.info("Done RRG calculation.")
if 'nng_file_name' in config:
nng.write(config['nng_file_name'], rrg)
if 'nnm_file_name' in config:
nng.writeNNMap(config['nnm_file_name'], rrg)
# Do NNK correlation function if necessary
if False:
#if 'nnk_file_name' in config:
if len(cat3) == 0:
raise AttributeError("file_name3 is required for nnk correlation")
logger.info("Start NNK calculations...")
nnk = treecorr.NNKCorrelation(config, logger)
nnk.process(cat1, cat2, cat3)
logger.info("Done NNK calculation.")
rrk = None
if len(rand1) == 0:
if config.get('nnk_statistic', None) == 'compensated':
raise AttributeError(
"rand_files is required for nnk_statistic = compensated")
elif config.get('nnk_statistic', 'compensated') == 'compensated':
rrk = treecorr.NNKCorrelation(config, logger)
rrk.process(rand1, rand1, cat2)
logger.info("Done RRK calculation.")
nnk.write(config['nnk_file_name'], rrk)
# Do KKG correlation function if necessary
if False:
#if 'kkg_file_name' in config:
if len(cat3) == 0:
raise AttributeError("file_name3 is required for kkg correlation")
logger.info("Start KKG calculations...")
kkg = treecorr.KKGCorrelation(config, logger)
kkg.process(cat1, cat2, cat3)
logger.info("Done KKG calculation.")
kkg.write(config['kkg_file_name'])
def corr3(config, logger=None):
"""Run the full three-point correlation function code based on the parameters in the
given config dict.
The function `print_corr3_params` will output information about the valid parameters
that are expected to be in the config dict.
Optionally a logger parameter maybe given, in which case it is used for logging.
If not given, the logging will be based on the verbose and log_file parameters.
:param config: The configuration dict which defines what to do.
:param logger: If desired, a logger object for logging. (default: None, in which case
one will be built according to the config dict's verbose level.)
"""
# Setup logger based on config verbose value
if logger is None:
logger = treecorr.config.setup_logger(
treecorr.config.get(config, 'verbose', int, 1),
config.get('log_file', None))
# Check that config doesn't have any extra parameters.
# (Such values are probably typos.)
# Also convert the given parameters to the correct type, etc.
config = treecorr.config.check_config(config, corr3_valid_params,
corr3_aliases, logger)
import pprint
logger.debug('Using configuration dict:\n%s', pprint.pformat(config))
if ('output_dots' not in config and config.get('log_file', None) is None
and config['verbose'] >= 2):
config['output_dots'] = True
# Set the number of threads
num_threads = config.get('num_threads', None)
logger.debug('From config dict, num_threads = %s', num_threads)
treecorr.set_omp_threads(num_threads, logger)
# Read in the input files. Each of these is a list.
cat1 = treecorr.read_catalogs(config, 'file_name', 'file_list', 0, logger)
cat2 = treecorr.read_catalogs(config, 'file_name2', 'rand_file_list2', 1,
logger)
cat3 = treecorr.read_catalogs(config, 'file_name3', 'rand_file_list3', 1,
logger)
rand1 = treecorr.read_catalogs(config, 'rand_file_name', 'rand_file_list',
0, logger)
rand2 = treecorr.read_catalogs(config, 'rand_file_name2',
'rand_file_list2', 1, logger)
rand3 = treecorr.read_catalogs(config, 'rand_file_name3',
'rand_file_list3', 1, logger)
if len(cat1) == 0:
raise TypeError("Either file_name or file_list is required")
if len(cat2) == 0: cat2 = None
if len(cat3) == 0: cat3 = None
if len(rand1) == 0: rand1 = None
if len(rand2) == 0: rand2 = None
if len(rand3) == 0: rand3 = None
if cat2 is None and rand2 is not None:
raise TypeError("rand_file_name2 is invalid without file_name2")
if cat3 is None and rand3 is not None:
raise TypeError("rand_file_name3 is invalid without file_name3")
if (cat2 is None) != (cat3 is None):
raise NotImplementedError(
"Cannot yet handle 3-point corrleations with only two catalogs. " +
"Need both cat2 and cat3.")
logger.info("Done reading input catalogs")
# Do GGG correlation function if necessary
if 'ggg_file_name' in config or 'm3_file_name' in config:
logger.warning("Performing GGG calculations...")
ggg = treecorr.GGGCorrelation(config, logger)
ggg.process(cat1, cat2, cat3)
logger.info("Done GGG calculations.")
if 'ggg_file_name' in config:
ggg.write(config['ggg_file_name'])
logger.warning("Wrote GGG correlation to %s",
config['ggg_file_name'])
if 'm3_file_name' in config:
ggg.writeMap3(config['m3_file_name'])
logger.warning("Wrote Map3 values to %s", config['m3_file_name'])
# Do NNN correlation function if necessary
if 'nnn_file_name' in config:
logger.warning("Performing DDD calculations...")
ddd = treecorr.NNNCorrelation(config, logger)
ddd.process(cat1, cat2, cat3)
logger.info("Done DDD calculations.")
drr = None
rdr = None
rrd = None
ddr = None
drd = None
rdd = None
if rand1 is None:
if rand2 is not None or rand3 is not None:
raise TypeError(
"rand_file_name is required if rand2 or rand3 is given")
logger.warning(
"No random catalogs given. Only doing ntri calculation.")
rrr = None
elif cat2 is None:
logger.warning("Performing RRR calculations...")
rrr = treecorr.NNNCorrelation(config, logger)
rrr.process(rand1)
logger.info("Done RRR calculations.")
# For the next step, just make cat2 = cat3 = cat1 and rand2 = rand3 = rand1.
cat2 = cat3 = cat1
rand2 = rand3 = rand1
else:
if rand2 is None:
raise TypeError(
"rand_file_name2 is required when file_name2 is given")
if cat3 is not None and rand3 is None:
raise TypeError(
"rand_file_name3 is required when file_name3 is given")
logger.warning("Performing RRR calculations...")
rrr = treecorr.NNNCorrelation(config, logger)
rrr.process(rand1, rand2, rand3)
logger.info("Done RRR calculations.")
if rrr is not None and config['nnn_statistic'] == 'compensated':
logger.warning("Performing DRR calculations...")
drr = treecorr.NNNCorrelation(config, logger)
drr.process(cat1, rand2, rand3)
logger.info("Done DRR calculations.")
logger.warning("Performing DDR calculations...")
ddr = treecorr.NNNCorrelation(config, logger)
ddr.process(cat1, cat2, rand3)
logger.info("Done DDR calculations.")
logger.warning("Performing RDR calculations...")
rdr = treecorr.NNNCorrelation(config, logger)
rdr.process(rand1, cat2, rand3)
logger.info("Done RDR calculations.")
logger.warning("Performing RRD calculations...")
rrd = treecorr.NNNCorrelation(config, logger)
rrd.process(rand1, rand2, cat3)
logger.info("Done RRD calculations.")
logger.warning("Performing DRD calculations...")
drd = treecorr.NNNCorrelation(config, logger)
drd.process(cat1, rand2, cat3)
logger.info("Done DRD calculations.")
logger.warning("Performing RDD calculations...")
rdd = treecorr.NNNCorrelation(config, logger)
rdd.process(rand1, cat2, cat3)
logger.info("Done RDD calculations.")
ddd.write(config['nnn_file_name'], rrr, drr, rdr, rrd, ddr, drd, rdd)
logger.warning("Wrote NNN correlation to %s", config['nnn_file_name'])
# Do KKK correlation function if necessary
if 'kkk_file_name' in config:
logger.warning("Performing KKK calculations...")
kkk = treecorr.KKKCorrelation(config, logger)
kkk.process(cat1, cat2, cat3)
logger.info("Done KKK calculations.")
kkk.write(config['kkk_file_name'])
logger.warning("Wrote KKK correlation to %s", config['kkk_file_name'])
def corr3(config, logger=None):
"""Run the full three-point correlation function code based on the parameters in the
given config dict.
The function `print_corr3_params` will output information about the valid parameters
that are expected to be in the config dict.
Optionally a logger parameter maybe given, in which case it is used for logging.
If not given, the logging will be based on the verbose and log_file parameters.
:param config: The configuration dict which defines what to do.
:param logger: If desired, a logger object for logging. (default: None, in which case
one will be built according to the config dict's verbose level.)
"""
# Setup logger based on config verbose value
if logger is None:
logger = treecorr.config.setup_logger(
treecorr.config.get(config,'verbose',int,1),
config.get('log_file',None))
# Check that config doesn't have any extra parameters.
# (Such values are probably typos.)
# Also convert the given parameters to the correct type, etc.
config = treecorr.config.check_config(config, corr3_valid_params, corr3_aliases, logger)
import pprint
logger.debug('Using configuration dict:\n%s',pprint.pformat(config))
if ( 'output_dots' not in config
and config.get('log_file',None) is None
and config['verbose'] >= 2 ):
config['output_dots'] = True
# Set the number of threads
num_threads = config.get('num_threads',None)
logger.debug('From config dict, num_threads = %s',num_threads)
treecorr.set_omp_threads(num_threads, logger)
# Read in the input files. Each of these is a list.
cat1 = treecorr.read_catalogs(config, 'file_name', 'file_list', 0, logger)
cat2 = treecorr.read_catalogs(config, 'file_name2', 'rand_file_list2', 1, logger)
cat3 = treecorr.read_catalogs(config, 'file_name3', 'rand_file_list3', 1, logger)
rand1 = treecorr.read_catalogs(config, 'rand_file_name', 'rand_file_list', 0, logger)
rand2 = treecorr.read_catalogs(config, 'rand_file_name2', 'rand_file_list2', 1, logger)
rand3 = treecorr.read_catalogs(config, 'rand_file_name3', 'rand_file_list3', 1, logger)
if len(cat1) == 0:
raise TypeError("Either file_name or file_list is required")
if len(cat2) == 0: cat2 = None
if len(cat3) == 0: cat3 = None
if len(rand1) == 0: rand1 = None
if len(rand2) == 0: rand2 = None
if len(rand3) == 0: rand3 = None
if cat2 is None and rand2 is not None:
raise TypeError("rand_file_name2 is invalid without file_name2")
if cat3 is None and rand3 is not None:
raise TypeError("rand_file_name3 is invalid without file_name3")
if (cat2 is None) != (cat3 is None):
raise NotImplementedError(
"Cannot yet handle 3-point corrleations with only two catalogs. "+
"Need both cat2 and cat3.")
logger.info("Done reading input catalogs")
# Do GGG correlation function if necessary
if 'ggg_file_name' in config or 'm3_file_name' in config:
logger.warning("Performing GGG calculations...")
ggg = treecorr.GGGCorrelation(config,logger)
ggg.process(cat1,cat2,cat3)
logger.info("Done GGG calculations.")
if 'ggg_file_name' in config:
ggg.write(config['ggg_file_name'])
logger.warning("Wrote GGG correlation to %s",config['ggg_file_name'])
if 'm3_file_name' in config:
ggg.writeMap3(config['m3_file_name'])
logger.warning("Wrote Map3 values to %s",config['m3_file_name'])
# Do NNN correlation function if necessary
if 'nnn_file_name' in config:
logger.warning("Performing DDD calculations...")
ddd = treecorr.NNNCorrelation(config,logger)
ddd.process(cat1,cat2,cat3)
logger.info("Done DDD calculations.")
drr = None
rdr = None
rrd = None
ddr = None
drd = None
rdd = None
if rand1 is None:
if rand2 is not None or rand3 is not None:
raise TypeError("rand_file_name is required if rand2 or rand3 is given")
logger.warning("No random catalogs given. Only doing ntri calculation.")
rrr = None
elif cat2 is None:
logger.warning("Performing RRR calculations...")
rrr = treecorr.NNNCorrelation(config,logger)
rrr.process(rand1)
logger.info("Done RRR calculations.")
# For the next step, just make cat2 = cat3 = cat1 and rand2 = rand3 = rand1.
cat2 = cat3 = cat1
rand2 = rand3 = rand1
else:
if rand2 is None:
raise TypeError("rand_file_name2 is required when file_name2 is given")
if cat3 is not None and rand3 is None:
raise TypeError("rand_file_name3 is required when file_name3 is given")
logger.warning("Performing RRR calculations...")
rrr = treecorr.NNNCorrelation(config,logger)
rrr.process(rand1,rand2,rand3)
logger.info("Done RRR calculations.")
if rrr is not None and config['nnn_statistic'] == 'compensated':
logger.warning("Performing DRR calculations...")
drr = treecorr.NNNCorrelation(config,logger)
drr.process(cat1,rand2,rand3)
logger.info("Done DRR calculations.")
logger.warning("Performing DDR calculations...")
ddr = treecorr.NNNCorrelation(config,logger)
ddr.process(cat1,cat2,rand3)
logger.info("Done DDR calculations.")
logger.warning("Performing RDR calculations...")
rdr = treecorr.NNNCorrelation(config,logger)
rdr.process(rand1,cat2,rand3)
logger.info("Done RDR calculations.")
logger.warning("Performing RRD calculations...")
rrd = treecorr.NNNCorrelation(config,logger)
rrd.process(rand1,rand2,cat3)
logger.info("Done RRD calculations.")
logger.warning("Performing DRD calculations...")
drd = treecorr.NNNCorrelation(config,logger)
drd.process(cat1,rand2,cat3)
logger.info("Done DRD calculations.")
logger.warning("Performing RDD calculations...")
rdd = treecorr.NNNCorrelation(config,logger)
rdd.process(rand1,cat2,cat3)
logger.info("Done RDD calculations.")
ddd.write(config['nnn_file_name'],rrr,drr,rdr,rrd,ddr,drd,rdd)
logger.warning("Wrote NNN correlation to %s",config['nnn_file_name'])
# Do KKK correlation function if necessary
if 'kkk_file_name' in config:
logger.warning("Performing KKK calculations...")
kkk = treecorr.KKKCorrelation(config,logger)
kkk.process(cat1,cat2,cat3)
logger.info("Done KKK calculations.")
kkk.write(config['kkk_file_name'])
logger.warning("Wrote KKK correlation to %s",config['kkk_file_name'])
