def load_pairs(self):
r"""load the set of map/noise pairs specified by keys handed to the
database. This sets up operations on the quadratic product
Q = map1^T noise_inv1 B noise_inv2 map2
"""
par = self.params
(self.pairlist,
pairdict) = dp.cross_maps(par['map1'],
par['map2'],
par['noise_inv1'],
par['noise_inv2'],
noise_inv_suffix=";noise_weight",
verbose=False,
tack_on=self.tack_on_input,
db_to_use=self.datapath_db)
for pairitem in self.pairlist:
pdict = pairdict[pairitem]
print "-" * 80
dp.print_dictionary(
pdict,
sys.stdout,
key_list=['map1', 'noise_inv1', 'map2', 'noise_inv2'])
map1 = algebra.make_vect(algebra.load(pdict['map1']))
map2 = algebra.make_vect(algebra.load(pdict['map2']))
if par['simfile'] is not None:
print "adding %s with multiplier %s" % (par['simfile'],
par['sim_multiplier'])
sim = algebra.make_vect(algebra.load(par['simfile']))
sim *= par['sim_multiplier']
print sim.shape, map1.shape
else:
sim = algebra.zeros_like(map1)
noise_inv1 = algebra.make_vect(algebra.load(pdict['noise_inv1']))
noise_inv2 = algebra.make_vect(algebra.load(pdict['noise_inv2']))
pair = map_pair.MapPair(map1 + sim, map2 + sim, noise_inv1,
noise_inv2, self.freq_list)
pair.set_names(pdict['tag1'], pdict['tag2'])
pair.params = self.params
self.pairs[pairitem] = pair
if par['subtract_inputmap_from_sim'] or \
par['subtract_sim_from_inputmap']:
if par['subtract_inputmap_from_sim']:
pair_parallel_track = map_pair.MapPair(
map1, map2, noise_inv1, noise_inv2, self.freq_list)
if par['subtract_sim_from_inputmap']:
pair_parallel_track = map_pair.MapPair(
sim, sim, noise_inv1, noise_inv2, self.freq_list)
pair_parallel_track.set_names(pdict['tag1'], pdict['tag2'])
pair_parallel_track.params = self.params
self.pairs_parallel_track[pairitem] = pair_parallel_track
def load_pairs(self):
r"""load the set of map/noise pairs specified by keys handed to the
database. This sets up operations on the quadratic product
Q = map1^T noise_inv1 B noise_inv2 map2
"""
par = self.params
(self.pairlist, pairdict) = dp.cross_maps(par['map1'], par['map2'],
par['noise_inv1'],
par['noise_inv2'],
noise_inv_suffix=";noise_weight",
verbose=False,
tack_on=self.tack_on_input,
db_to_use=self.datapath_db)
for pairitem in self.pairlist:
pdict = pairdict[pairitem]
print "-" * 80
dp.print_dictionary(pdict, sys.stdout,
key_list=['map1', 'noise_inv1',
'map2', 'noise_inv2'])
map1 = algebra.make_vect(algebra.load(pdict['map1']))
map2 = algebra.make_vect(algebra.load(pdict['map2']))
if par['simfile'] is not None:
print "adding %s with multiplier %s" % (par['simfile'],
par['sim_multiplier'])
sim = algebra.make_vect(algebra.load(par['simfile']))
sim *= par['sim_multiplier']
print sim.shape, map1.shape
else:
sim = algebra.zeros_like(map1)
noise_inv1 = algebra.make_vect(algebra.load(pdict['noise_inv1']))
noise_inv2 = algebra.make_vect(algebra.load(pdict['noise_inv2']))
pair = map_pair.MapPair(map1 + sim, map2 + sim,
noise_inv1, noise_inv2,
self.freq_list)
pair.set_names(pdict['tag1'], pdict['tag2'])
pair.params = self.params
self.pairs[pairitem] = pair
if par['subtract_inputmap_from_sim'] or \
par['subtract_sim_from_inputmap']:
if par['subtract_inputmap_from_sim']:
pair_parallel_track = map_pair.MapPair(map1, map2,
noise_inv1, noise_inv2,
self.freq_list)
if par['subtract_sim_from_inputmap']:
pair_parallel_track = map_pair.MapPair(sim, sim,
noise_inv1, noise_inv2,
self.freq_list)
pair_parallel_track.set_names(pdict['tag1'], pdict['tag2'])
pair_parallel_track.params = self.params
self.pairs_parallel_track[pairitem] = pair_parallel_track
def load_pairs(self, regenerate=True):
r"""load the set of map/noise pairs specified by keys handed to the
database. This sets up operations on the quadratic product
Q = map1^T noise_inv1 B noise_inv2 map2
"""
par = self.params
(self.pairlist, pairdict) = dp.cross_maps(par['map1'],
par['map2'],
par['noise_inv1'],
par['noise_inv2'],
verbose=False)
for pairitem in self.pairlist:
pdict = pairdict[pairitem]
print "-" * 80
dp.print_dictionary(
pdict,
sys.stdout,
key_list=['map1', 'noise_inv1', 'map2', 'noise_inv2'])
map1 = algebra.make_vect(algebra.load(pdict['map1']))
map2 = algebra.make_vect(algebra.load(pdict['map2']))
sim = algebra.make_vect(algebra.load(par['simfile']))
if not par['no_weights']:
noise_inv1 = self.process_noise_inv(pdict['noise_inv1'],
regenerate=regenerate)
noise_inv2 = self.process_noise_inv(pdict['noise_inv2'],
regenerate=regenerate)
else:
noise_inv1 = algebra.ones_like(map1)
noise_inv2 = algebra.ones_like(map2)
pair = map_pair.MapPair(map1 + sim, map2 + sim, noise_inv1,
noise_inv2, self.freq_list)
pair.set_names(pdict['tag1'], pdict['tag2'])
pair.lags = self.lags
pair.params = self.params
self.pairs[pairitem] = pair
pair_nosim = map_pair.MapPair(map1, map2, noise_inv1, noise_inv2,
self.freq_list)
pair_nosim.set_names(pdict['tag1'], pdict['tag2'])
pair_nosim.lags = self.lags
pair_nosim.params = self.params
self.pairs_nosim[pairitem] = pair_nosim
def gather_batch_data_run():
datapath_db = data_paths.DataPath()
outpath = datapath_db.fetch("quadratic_batch_data")
print "reading from to: " + outpath
funcname = "correlate.batch_quadratic.call_xspec_run"
caller = batch_handler.MemoizeBatch(funcname, outpath,
verbose=True)
for mode_num in range(0,55,5):
map1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
map2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
pwr_1d = []
pwr_2d = []
for item in pairdict.keys():
pairrun = pairdict[item]
print pairrun['tag1']
print pairrun['map1']
print pairrun['noise_inv1']
print pairdict[item].keys()
pwr2d_run, pwr1d_run = caller.execute(pairrun['map1'],
pairrun['map2'],
pairrun['noise_inv1'],
pairrun['noise_inv2'])
pwr_2d.append(pwr2d_run)
pwr_1d.append(pwr1d_run)
pe.summarize_1d_agg_pwrspec(pwr_1d,
"pwr_data_%dmodes_1d.dat" % mode_num)
pe.summarize_2d_agg_pwrspec(pwr_2d,
"pwr_data_%dmodes_2d.dat" % mode_num)
def gather_batch_data_run():
datapath_db = data_paths.DataPath()
outpath = datapath_db.fetch("quadratic_batch_data")
print "reading from to: " + outpath
funcname = "correlate.batch_quadratic.call_xspec_run"
caller = batch_handler.MemoizeBatch(funcname, outpath, verbose=True)
for mode_num in range(0, 55, 5):
map1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
map2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
pwr_1d = []
pwr_2d = []
for item in pairdict.keys():
pairrun = pairdict[item]
print pairrun['tag1']
print pairrun['map1']
print pairrun['noise_inv1']
print pairdict[item].keys()
pwr2d_run, pwr1d_run = caller.execute(pairrun['map1'],
pairrun['map2'],
pairrun['noise_inv1'],
pairrun['noise_inv2'])
pwr_2d.append(pwr2d_run)
pwr_1d.append(pwr1d_run)
pe.summarize_1d_agg_pwrspec(pwr_1d,
"pwr_data_%dmodes_1d.dat" % mode_num)
pe.summarize_2d_agg_pwrspec(pwr_2d,
"pwr_data_%dmodes_2d.dat" % mode_num)
def generate_ini_batch(self, map1_key, map2_key, noise1_key, noise2_key):
r""" produce a batch of ini files which can be fed to PBS or run
independently on prawn. The correlations are built from all pairs and
then labelled by their database keys, e.g.
[map1_key]_A_with_B_x_[map2_key]_B_with_A.ini
the noise_inv keys are not annotated in the filename but on can read
off the files in the .ini file
"""
(pairlist, pairdict) = \
dp.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
for pairname in pairlist:
ininame = pairname + ".ini"
inifile = open(self.ini_root + ininame, 'w')
print ininame
fileinfo = pairdict[pairname]
inifile.write("import os\nimport scipy as sp\n\n")
inifile.write("fs_output_root = '%s'\n" % \
self.params['output_root'])
inifile.write("fs_output_filetag = '%s'\n" % pairname)
inifile.write("fs_map1 = '%s'\n" % fileinfo['map1'])
inifile.write("fs_noise_inv1 = '%s'\n" % fileinfo['noise_inv1'])
inifile.write("fs_map2 = '%s'\n" % fileinfo['map2'])
inifile.write("fs_noise_inv2 = '%s'\n" % fileinfo['noise_inv2'])
inifile.write("fs_lags = %s\n" % self.lags)
inifile.write("fs_freq_list = range(%d)\n" % self.nfreq_bin)
cutlist = []
for fbin in range(self.nfreq_bin):
if fbin not in self.freq_list:
cutlist.append(fbin)
inifile.write("cutlist = %s\n" % repr(cutlist))
comp = '[x for x in fs_freq_list if x not in cutlist]'
inifile.write("fs_freq_list = tuple(%s)\n" % comp)
inifile.close()
def load_ext_pairs(self, index, map1name, map2name, noise1name,
noise2name):
r"""Load the external datasets (which improve cleaning)
"""
par = self.params
(self.pairlist_ext,
pairdict) = dp.cross_maps(map1name,
map2name,
noise1name,
noise2name,
noise_inv_suffix=";noise_weight",
verbose=False,
db_to_use=self.datapath_db)
# probably not wanted for external maps:
# tack_on=self.tack_on_input,
self.pairs_ext[index] = {}
for pairitem in self.pairlist_ext:
pdict = pairdict[pairitem]
print "-" * 80
print "loading ext %s pair %s" % (index, pairitem)
dp.print_dictionary(
pdict,
sys.stdout,
key_list=['map1', 'noise_inv1', 'map2', 'noise_inv2'])
map1 = algebra.make_vect(algebra.load(pdict['map1']))
map2 = algebra.make_vect(algebra.load(pdict['map2']))
noise_inv1 = algebra.make_vect(algebra.load(pdict['noise_inv1']))
noise_inv2 = algebra.make_vect(algebra.load(pdict['noise_inv2']))
pair = map_pair.MapPair(map1,
map2,
noise_inv1,
noise_inv2,
self.freq_list,
conv_factor=self.conv_factor)
pair.set_names(pdict['tag1'], pdict['tag2'])
pair.params = self.params
self.pairs_ext[index][pairitem] = pair
def generate_ini_batch(self, map1_key, map2_key, noise1_key, noise2_key):
r""" produce a batch of ini files which can be fed to PBS or run
independently on prawn. The correlations are built from all pairs and
then labelled by their database keys, e.g.
[map1_key]_A_with_B_x_[map2_key]_B_with_A.ini
the noise_inv keys are not annotated in the filename but on can read
off the files in the .ini file
"""
(pairlist, pairdict) = \
dp.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
for pairname in pairlist:
ininame = pairname + ".ini"
inifile = open(self.ini_root + ininame, 'w')
print ininame
fileinfo = pairdict[pairname]
inifile.write("import os\nimport scipy as sp\n\n")
inifile.write("fs_output_root = '%s'\n" % \
self.params['output_root'])
inifile.write("fs_output_filetag = '%s'\n" % pairname)
inifile.write("fs_map1 = '%s'\n" % fileinfo['map1'])
inifile.write("fs_noise_inv1 = '%s'\n" % fileinfo['noise_inv1'])
inifile.write("fs_map2 = '%s'\n" % fileinfo['map2'])
inifile.write("fs_noise_inv2 = '%s'\n" % fileinfo['noise_inv2'])
inifile.write("fs_lags = %s\n" % self.lags)
inifile.write("fs_freq_list = range(%d)\n" % self.nfreq_bin)
cutlist = []
for fbin in range(self.nfreq_bin):
if fbin not in self.freq_list:
cutlist.append(fbin)
inifile.write("cutlist = %s\n" % repr(cutlist))
comp = '[x for x in fs_freq_list if x not in cutlist]'
inifile.write("fs_freq_list = tuple(%s)\n" % comp)
inifile.close()
def load_pairs(self, regenerate=True):
r"""load the set of map/noise pairs specified by keys handed to the
database. This sets up operations on the quadratic product
Q = map1^T noise_inv1 B noise_inv2 map2
"""
par = self.params
(self.pairlist, pairdict) = dp.cross_maps(
par["map1"], par["map2"], par["noise_inv1"], par["noise_inv2"], verbose=False
)
for pairitem in self.pairlist:
pdict = pairdict[pairitem]
print "-" * 80
dp.print_dictionary(pdict, sys.stdout, key_list=["map1", "noise_inv1", "map2", "noise_inv2"])
map1 = algebra.make_vect(algebra.load(pdict["map1"]))
map2 = algebra.make_vect(algebra.load(pdict["map2"]))
sim = algebra.make_vect(algebra.load(par["simfile"]))
if not par["no_weights"]:
noise_inv1 = self.process_noise_inv(pdict["noise_inv1"], regenerate=regenerate)
noise_inv2 = self.process_noise_inv(pdict["noise_inv2"], regenerate=regenerate)
else:
noise_inv1 = algebra.ones_like(map1)
noise_inv2 = algebra.ones_like(map2)
pair = map_pair.MapPair(map1 + sim, map2 + sim, noise_inv1, noise_inv2, self.freq_list)
pair.set_names(pdict["tag1"], pdict["tag2"])
pair.lags = self.lags
pair.params = self.params
self.pairs[pairitem] = pair
pair_nosim = map_pair.MapPair(map1, map2, noise_inv1, noise_inv2, self.freq_list)
pair_nosim.set_names(pdict["tag1"], pdict["tag2"])
pair_nosim.lags = self.lags
pair_nosim.params = self.params
self.pairs_nosim[pairitem] = pair_nosim
def load_ext_pairs(self, index, map1name, map2name,
noise1name, noise2name):
r"""Load the external datasets (which improve cleaning)
"""
par = self.params
(self.pairlist_ext, pairdict) = dp.cross_maps(map1name, map2name,
noise1name, noise2name,
noise_inv_suffix=";noise_weight",
verbose=False,
db_to_use=self.datapath_db)
# probably not wanted for external maps:
# tack_on=self.tack_on_input,
self.pairs_ext[index] = {}
for pairitem in self.pairlist_ext:
pdict = pairdict[pairitem]
print "-" * 80
print "loading ext %s pair %s" % (index, pairitem)
dp.print_dictionary(pdict, sys.stdout,
key_list=['map1', 'noise_inv1',
'map2', 'noise_inv2'])
map1 = algebra.make_vect(algebra.load(pdict['map1']))
map2 = algebra.make_vect(algebra.load(pdict['map2']))
noise_inv1 = algebra.make_vect(algebra.load(pdict['noise_inv1']))
noise_inv2 = algebra.make_vect(algebra.load(pdict['noise_inv2']))
pair = map_pair.MapPair(map1, map2,
noise_inv1, noise_inv2,
self.freq_list,
conv_factor=self.conv_factor)
pair.set_names(pdict['tag1'], pdict['tag2'])
pair.params = self.params
self.pairs_ext[index][pairitem] = pair
def batch_GBTxwigglez_data_run_old():
for mode_num in range(0, 55, 5):
map1_key = "%s_cleaned_%dmode" % (mapkey, mode_num)
map2_key = "%s_cleaned_%dmode" % (mapkey, mode_num)
noise1_key = "%s_cleaned_%dmode" % (mapkey, mode_num)
noise2_key = "%s_cleaned_%dmode" % (mapkey, mode_num)
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=True)
for item in pairdict.keys():
pairrun = pairdict[item]
caller.execute(pairrun['map1'], "db:WiggleZ_15hr_binned_data",
pairrun['noise_inv1'], "db:WiggleZ_15hr_montecarlo")
def batch_data_run(sim=False, alt_sig="", alt_noise=""):
for mode_num in range(0, 55, 5):
map1_key = "%s_cleaned_%s%dmode" % (mapsim, alt_sig, mode_num)
map2_key = "%s_cleaned_%s%dmode" % (mapsim, alt_sig, mode_num)
noise1_key = "%s_cleaned_%s%dmode" % (mapsim, alt_noise, mode_num)
noise2_key = "%s_cleaned_%s%dmode" % (mapsim, alt_noise, mode_num)
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=True)
for item in pairdict.keys():
pairrun = pairdict[item]
caller.execute(pairrun['map1'], pairrun['map2'],
pairrun['noise_inv1'], pairrun['noise_inv2'])
def gather_batch_datasim_run(tag,
subtract_mean=False,
degrade_resolution=False,
unitless=True,
return_3d=False,
truncate=False,
window=None,
n_modes=None,
refinement=2,
pad=5,
order=2,
outdir="./plot_data",
alt=""):
datapath_db = data_paths.DataPath()
outpath = datapath_db.fetch("quadratic_batch_simulations")
print "reading from: " + outpath
funcname = "correlate.batch_quadratic.call_xspec_run"
caller = batch_handler.MemoizeBatch(funcname, outpath, verbose=True)
transfer_functions = {}
for mode_num in range(0, 55, 5):
mapsim = "sim_15hr"
map1_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
map2_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
noise1_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
noise2_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
pwr_1d = []
pwr_2d = []
pwr_1d_from_2d = []
for item in pairdict.keys():
pairrun = pairdict[item]
print pairrun['tag1'], pairrun['map1'], pairrun['noise_inv1']
pwr2d_run, pwr1d_run = caller.execute(
pairrun['map1'],
pairrun['map2'],
pairrun['noise_inv1'],
pairrun['noise_inv2'],
subtract_mean=subtract_mean,
degrade_resolution=degrade_resolution,
unitless=unitless,
return_3d=return_3d,
truncate=truncate,
window=window,
n_modes=n_modes,
refinement=refinement,
pad=pad,
order=order)
pwr_1d_from_2d.append(pe.convert_2d_to_1d(pwr2d_run))
pwr_2d.append(pwr2d_run)
pwr_1d.append(pwr1d_run)
if (mode_num == 0):
pwr_1d_from2d_zero_mode = copy.deepcopy(pwr_1d_from_2d)
pwr_1d_zero_mode = copy.deepcopy(pwr_1d)
pwr_2d_zero_mode = copy.deepcopy(pwr_2d)
ttag = tag + "_%dmodes_2dtrans" % mode_num
trans2d_mode = tf.calculate_2d_transfer_function(
pwr_2d, pwr_2d_zero_mode, ttag)
ttag = tag + "_%dmodes_1dtrans" % mode_num
trans1d_mode = tf.calculate_1d_transfer_function(
pwr_1d, pwr_1d_zero_mode, ttag)
ttag = tag + "_%dmodes_1dtrans_from2d" % mode_num
trans1d_mode = tf.calculate_1d_transfer_function(
pwr_1d_from_2d, pwr_1d_from2d_zero_mode, ttag)
transfer_functions[mode_num] = (trans1d_mode, trans2d_mode)
mtag = tag + "_%dmodes" % mode_num
pe.summarize_agg_pwrspec(pwr_1d,
pwr_1d_from_2d,
pwr_2d,
mtag,
outdir="./plot_data")
return transfer_functions
def load_pairs(self):
r"""load the set of map/noise pairs specified by keys handed to the
database. This sets up operations on the quadratic product
Q = map1^T noise_inv1 B noise_inv2 map2
"""
par = self.params
if not par['pairlist']:
if par['calc_diagnal']:
noise_inv_suffix = ";noise_inv"
else:
noise_inv_suffix = ";noise_weight"
(self.pairlist, pairdict) = dp.cross_maps(par['map1'], par['map2'],
par['noise_inv1'],
par['noise_inv2'],
noise_inv_suffix=noise_inv_suffix,
verbose=False,
db_to_use=self.datapath_db)
else:
self.pairlist = par['pairlist']
pairdict = par['pairdict']
for pairitem in self.pairlist:
pdict = pairdict[pairitem]
print "-" * 80
dp.print_dictionary(pdict, sys.stdout,
key_list=['map1', 'noise_inv1',
'map2', 'noise_inv2'])
# map1 & noise_inv1
map1 = algebra.make_vect(algebra.load(pdict['map1']))
if par['simfile1'] is not None:
print "adding %s with multiplier %s" % (par['simfile1'],
par['sim_multiplier'])
sim1 = algebra.make_vect(algebra.load(par['simfile1']))
sim1 *= par['sim_multiplier']
else:
sim1 = algebra.zeros_like(map1)
if not par['no_weights']:
noise_inv1 = wrap_find_weight(pdict['noise_inv1'],
regenerate=par['regenerate_noise_inv'],
calc_diagnal = par['calc_diagnal'])
else:
noise_inv1 = algebra.ones_like(map1)
# map2 & noise_inv2
#if pairitem == 'I_with_E':
if len(self.freq_list2) == 4*len(self.freq_list1):
'''For IQUV case'''
print 'Construct E map using I Q U V'
iquvdict = {}
iquvdict['imap'] = pdict['map2'].replace('_E', '_I')
iquvdict['qmap'] = pdict['map2'].replace('_E', '_Q')
iquvdict['umap'] = pdict['map2'].replace('_E', '_U')
iquvdict['vmap'] = pdict['map2'].replace('_E', '_V')
iquvdict['imap_weight'] = pdict['noise_inv2'].replace('_E', '_I')
iquvdict['qmap_weight'] = pdict['noise_inv2'].replace('_E', '_Q')
iquvdict['umap_weight'] = pdict['noise_inv2'].replace('_E', '_U')
iquvdict['vmap_weight'] = pdict['noise_inv2'].replace('_E', '_V')
map_dict = extend_iquv_map(source_dict=iquvdict)
map2 = map_dict['map']
noise_inv2 = map_dict['weight']
sim2 = copy.deepcopy(sim1)
map_dict = {}
map_dict['imap'] = sim2
map_dict['qmap'] = algebra.zeros_like(sim1)
map_dict['umap'] = algebra.zeros_like(sim1)
map_dict['vmap'] = algebra.zeros_like(sim1)
map_dict = extend_iquv_map(map_dict=map_dict)
sim2 = map_dict['map']
elif len(self.freq_list2) == 3*len(self.freq_list1):
'''For IQU case'''
print 'Construct E map using I Q U'
iquvdict = {}
iquvdict['imap'] = pdict['map2'].replace('_E', '_I')
iquvdict['qmap'] = pdict['map2'].replace('_E', '_Q')
iquvdict['umap'] = pdict['map2'].replace('_E', '_U')
iquvdict['imap_weight'] = pdict['noise_inv2'].replace('_E', '_I')
iquvdict['qmap_weight'] = pdict['noise_inv2'].replace('_E', '_Q')
iquvdict['umap_weight'] = pdict['noise_inv2'].replace('_E', '_U')
map_dict = extend_iqu_map(source_dict=iquvdict)
map2 = map_dict['map']
noise_inv2 = map_dict['weight']
sim2 = copy.deepcopy(sim1)
map_dict = {}
map_dict['imap'] = sim2
map_dict['qmap'] = algebra.zeros_like(sim1)
map_dict['umap'] = algebra.zeros_like(sim1)
map_dict = extend_iqu_map(map_dict=map_dict)
sim2 = map_dict['map']
else:
'''For common case'''
map2 = algebra.make_vect(algebra.load(pdict['map2']))
if par['simfile2'] is not None:
print "adding %s with multiplier %s" % (par['simfile2'],
par['sim_multiplier'])
sim2 = algebra.make_vect(algebra.load(par['simfile2']))
sim2 *= par['sim_multiplier']
else:
sim2 = algebra.zeros_like(map2)
if not par['no_weights']:
noise_inv2 = wrap_find_weight(pdict['noise_inv2'],
regenerate=par['regenerate_noise_inv'],
calc_diagnal = par['calc_diagnal'])
else:
noise_inv2 = algebra.ones_like(map2)
#if self.params['clip_weight_percent'] is not None:
# print "Note: your are clipping the weight maps"
# mask1 = self.define_weightmask(noise_inv1,
# percentile=self.params['clip_weight_percent'])
# mask2 = self.define_weightmask(noise_inv2,
# percentile=self.params['clip_weight_percent'])
# noise_inv1 = self.saturate_weight(noise_inv1, mask1)
# noise_inv2 = self.saturate_weight(noise_inv2, mask2)
pair = map_pair.MapPair(map1 + sim1, map2 + sim2,
noise_inv1, noise_inv2,
self.freq_list1, self.freq_list2)
pair.set_names(pdict['tag1'], pdict['tag2'])
pair.params = self.params
self.pairs[pairitem] = pair
if par['subtract_inputmap_from_sim'] or \
par['subtract_sim_from_inputmap']:
if par['subtract_inputmap_from_sim']:
pair_parallel_track = map_pair.MapPair(map1, map2,
noise_inv1, noise_inv2,
self.freq_list1, self.freq_list2)
if par['subtract_sim_from_inputmap']:
pair_parallel_track = map_pair.MapPair(sim1, sim2,
noise_inv1, noise_inv2,
self.freq_list1, self.freq_list2)
pair_parallel_track.set_names(pdict['tag1'], pdict['tag2'])
pair_parallel_track.params = self.params
self.pairs_parallel_track[pairitem] = pair_parallel_track
def gather_batch_datasim_run(tag, subtract_mean=False,
degrade_resolution=False, unitless=True,
return_3d=False, truncate=False, window=None,
n_modes=None, refinement=2, pad=5, order=2,
outdir="./plot_data", alt=""):
datapath_db = data_paths.DataPath()
outpath = datapath_db.fetch("quadratic_batch_simulations")
print "reading from: " + outpath
funcname = "correlate.batch_quadratic.call_xspec_run"
caller = batch_handler.MemoizeBatch(funcname, outpath,
verbose=True)
transfer_functions = {}
for mode_num in range(0,55,5):
mapsim = "sim_15hr"
map1_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
map2_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
noise1_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
noise2_key = "%s_cleaned_%s%dmode" % (mapsim, alt, mode_num)
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
pwr_1d = []
pwr_2d = []
pwr_1d_from_2d = []
for item in pairdict.keys():
pairrun = pairdict[item]
print pairrun['tag1'], pairrun['map1'], pairrun['noise_inv1']
pwr2d_run, pwr1d_run = caller.execute(pairrun['map1'],
pairrun['map2'],
pairrun['noise_inv1'],
pairrun['noise_inv2'],
subtract_mean=subtract_mean,
degrade_resolution=degrade_resolution,
unitless=unitless,
return_3d=return_3d,
truncate=truncate,
window=window, n_modes=n_modes,
refinement=refinement,
pad=pad, order=order)
pwr_1d_from_2d.append(pe.convert_2d_to_1d(pwr2d_run))
pwr_2d.append(pwr2d_run)
pwr_1d.append(pwr1d_run)
if (mode_num == 0):
pwr_1d_from2d_zero_mode = copy.deepcopy(pwr_1d_from_2d)
pwr_1d_zero_mode = copy.deepcopy(pwr_1d)
pwr_2d_zero_mode = copy.deepcopy(pwr_2d)
ttag = tag + "_%dmodes_2dtrans" % mode_num
trans2d_mode = tf.calculate_2d_transfer_function(
pwr_2d, pwr_2d_zero_mode,
ttag)
ttag = tag + "_%dmodes_1dtrans" % mode_num
trans1d_mode = tf.calculate_1d_transfer_function(
pwr_1d, pwr_1d_zero_mode,
ttag)
ttag = tag + "_%dmodes_1dtrans_from2d" % mode_num
trans1d_mode = tf.calculate_1d_transfer_function(
pwr_1d_from_2d, pwr_1d_from2d_zero_mode,
ttag)
transfer_functions[mode_num] = (trans1d_mode, trans2d_mode)
mtag = tag + "_%dmodes" % mode_num
pe.summarize_agg_pwrspec(pwr_1d, pwr_1d_from_2d, pwr_2d, mtag,
outdir="./plot_data")
return transfer_functions
