def write_summary_table(blocks, base=os.path.curdir):
"""Write a summary table in variou formats for users to download
Parameters
----------
blocks : `dict` of `OmegaChannel`
the channel blocks scanned in the analysis
base : `str`
the path for the `<base>` tag to link in the `<head>`
"""
# record summary data for each channel
channel, time, freq, Q, energy, snr = ([], [], [], [], [], [])
for block in blocks.values():
for chan in block['channels']:
channel.append(chan.name)
time.append(chan.t)
freq.append(chan.f)
Q.append(chan.Q)
energy.append(chan.energy)
snr.append(chan.snr)
# store in a table
data = Table([channel, time, freq, Q, energy, snr],
names=('Channel', 'Central Time', 'Central Frequency (Hz)',
'Quality Factor', 'Normalized Energy', 'SNR'))
# write in several formats
datadir = os.path.join(base, 'data')
fname = os.path.join(datadir, 'summary')
data.write(fname + '.txt', format='ascii', overwrite=True)
data.write(fname + '.csv', format='csv', overwrite=True)
data.write(fname + '.tex', format='latex', overwrite=True)
def write_summary_table(blocks, correlated, base=os.path.curdir):
"""Write a summary table in various formats for users to download
Parameters
----------
blocks : `dict` of `OmegaChannel`
the channel blocks scanned in the analysis
correlated : `bool`
Boolean switch to determine if cross-correlation is included
base : `str`
the path for the `<base>` tag to link in the `<head>`
"""
# record summary data for each channel
channel, time, freq, Q, energy, snr = ([], [], [], [], [], [])
if correlated:
corr, stdev, delay = ([], [], [])
for block in blocks.values():
for chan in block['channels']:
channel.append(chan.name)
time.append(chan.t)
freq.append(chan.f)
Q.append(chan.Q)
energy.append(chan.energy)
snr.append(chan.snr)
if correlated:
corr.append(chan.corr)
stdev.append(chan.stdev)
delay.append(chan.delay)
# store in a table
if correlated:
data = Table([channel, time, freq, Q, energy, snr, corr, stdev, delay],
names=('Channel', 'Central Time',
'Central Frequency (Hz)', 'Q', 'Energy', 'SNR',
'Correlation', 'Standard Deviation', 'Delay (ms)'))
else:
data = Table([channel, time, freq, Q, energy, snr], names=(
'Channel', 'Central Time', 'Central Frequency (Hz)', 'Q', 'Energy',
'SNR'))
# write in several formats
datadir = os.path.join(base, 'data')
fname = os.path.join(datadir, 'summary')
data.write(fname + '.txt', format='ascii', overwrite=True)
data.write(fname + '.csv', format='csv', overwrite=True)
data.write(fname + '.tex', format='latex', overwrite=True)
def _load_channel_record(summary, use_checkpoint=True, correlate=True):
"""Load a previous Omega scan from its last saved checkpoint
"""
if not (use_checkpoint and os.path.exists(summary)):
return ([], [])
LOGGER.debug('Checkpointing from {}'.format(os.path.abspath(summary)))
record = Table.read(summary)
if correlate and ('Standard Deviation' not in record.colnames):
raise KeyError('Cross-correlation is not available from this record, '
'consider running without correlation or starting from '
'scratch with --disable-checkpoint')
completed = list(record['Channel'])
return (record, completed)
def write_summary_table(blocks, correlated, base=os.path.curdir):
"""Write a summary table in various formats for users to download
Parameters
----------
blocks : `dict` of `OmegaChannel`
the channel blocks scanned in the analysis
correlated : `bool`
Boolean switch to determine if cross-correlation is included
base : `str`
the path for the `<base>` tag to link in the `<head>`
"""
# record summary data for each channel
channel, time, freq, Q, energy, snr = ([], [], [], [], [], [])
if correlated:
corr, delay = ([], [])
for block in blocks.values():
for chan in block['channels']:
channel.append(chan.name)
time.append(chan.t)
freq.append(chan.f)
Q.append(chan.Q)
energy.append(chan.energy)
snr.append(chan.snr)
if correlated:
corr.append(chan.corr)
delay.append(chan.delay)
# store in a table
if correlated:
data = Table([channel, time, freq, Q, energy, snr, corr, delay],
names=('Channel', 'Central Time',
'Central Frequency (Hz)', 'Q', 'Energy', 'SNR',
'Correlation', 'Delay (ms)'))
else:
data = Table([channel, time, freq, Q, energy, snr], names=(
'Channel', 'Central Time', 'Central Frequency (Hz)', 'Q', 'Energy',
'SNR'))
# write in several formats
datadir = os.path.join(base, 'data')
fname = os.path.join(datadir, 'summary')
data.write(fname + '.txt', format='ascii', overwrite=True)
data.write(fname + '.csv', format='csv', overwrite=True)
data.write(fname + '.tex', format='latex', overwrite=True)
always-plot = True
plot-time-durations = 4
channels = X1:TEST-STRAIN
"""
CONFIG_FILE = StringIO(CONFIGURATION)
CP = config.OmegaConfigParser(ifo='X1')
CP.read_file(CONFIG_FILE)
BLOCKS = CP.get_channel_blocks()
PRIMARY = BLOCKS['primary']
GW = BLOCKS['GW']
ROWS = [[0]] * 8
COLS = ('Q', 'Energy', 'SNR', 'Central Time', 'Central Frequency (Hz)',
'Correlation', 'Standard Deviation', 'Delay (ms)')
TABLE = Table(ROWS, names=COLS)
# -- test utilities -----------------------------------------------------------
def test_get_default_configuration():
cfile = config.get_default_configuration(ifo='X1', gpstime=1126259462)
assert cfile == [os.path.expanduser(
'~detchar/etc/omega/ER8/X-X1_R-selected.ini')]
nfile = config.get_default_configuration(ifo='Network', gpstime=1187008882)
assert nfile == [os.path.expanduser('~detchar/etc/omega/O2/Network.ini')]
def test_get_fancyplots():
fp = config.get_fancyplots(
channel='X1:TEST-STRAIN', plottype='test-plot', duration=4)
def xml_to_dataframe(prior_file, reference_frequency):
table = Table.read(prior_file, format="ligolw", tablename="sim_inspiral")
injection_values = {
"mass_1": [],
"mass_2": [],
"luminosity_distance": [],
"psi": [],
"phase": [],
"geocent_time": [],
"ra": [],
"dec": [],
"theta_jn": [],
"a_1": [],
"a_2": [],
"tilt_1": [],
"tilt_2": [],
"phi_12": [],
"phi_jl": [],
}
for row in table:
injection_values["mass_1"].append(float(row["mass1"]))
injection_values["mass_2"].append(float(row["mass2"]))
injection_values["luminosity_distance"].append(float(row["distance"]))
injection_values["psi"].append(float(row["polarization"]))
injection_values["phase"].append(float(row["coa_phase"]))
injection_values["geocent_time"].append(float(row["geocent_end_time"]))
injection_values["ra"].append(float(row["longitude"]))
injection_values["dec"].append(float(row["latitude"]))
args_list = [
float(arg) for arg in [
row["inclination"],
row["spin1x"],
row["spin1y"],
row["spin1z"],
row["spin2x"],
row["spin2y"],
row["spin2z"],
row["mass1"],
row["mass2"],
reference_frequency,
row["coa_phase"],
]
]
(
theta_jn,
phi_jl,
tilt_1,
tilt_2,
phi_12,
a_1,
a_2,
) = lalsim.SimInspiralTransformPrecessingWvf2PE(*args_list)
injection_values["theta_jn"].append(theta_jn)
injection_values["phi_jl"].append(phi_jl)
injection_values["tilt_1"].append(tilt_1)
injection_values["tilt_2"].append(tilt_2)
injection_values["phi_12"].append(phi_12)
injection_values["a_1"].append(a_1)
injection_values["a_2"].append(a_2)
injection_values = pd.DataFrame.from_dict(injection_values)
return injection_values