def load_channels(self):
"""Load all channel definitions as given in the selfuration
Channels are loaded from sections named [channels-...] or
those sections whose name is a channel name in itself
"""
channels_re = re.compile('channels[-\s]')
# parse channel grouns into individual sections
for section in filter(channels_re.match, self.sections()):
names = split_channels(self.get(section, 'channels'))
items = dict(self.nditems(section, raw=True))
items.pop('channels')
for name in names:
name = name.strip(' \n')
if not self.has_section(name):
self.add_section(name)
for key, val in items.iteritems():
if not self.has_option(name, key):
self.set(name, key, val)
# read all channels
raw = set()
trend = set()
for section in self.sections():
try:
m = Channel.MATCH.match(section).groupdict()
except AttributeError:
continue
else:
if not m['ifo']:
continue
if m['trend']:
trend.add(section)
else:
raw.add(section)
channels = ChannelList()
for group in [raw, trend]:
try:
newchannels = get_channels(group)
except httplib.HTTPException:
newchannels = []
# read custom channel definitions
for channel, section in zip(newchannels, group):
for key, val in nat_sorted(self.nditems(section),
key=lambda x: x[0]):
key = re_cchar.sub('_', key.rstrip())
if key.isdigit():
if not hasattr(channel, 'bits'):
channel.bits = []
while len(channel.bits) < int(key):
channel.bits.append(None)
if val.startswith('r"') or val.startswith('r\''):
val = eval(val)
channel.bits.append(str(val))
else:
setattr(channel, key, safe_eval(val.rstrip()))
channels.append(channel)
return channels
def load_channels(self):
"""Load all channel definitions as given in the selfuration
Channels are loaded from sections named [channels-...] or
those sections whose name is a channel name in itself
"""
channels_re = re.compile('channels[-\s]')
# parse channel grouns into individual sections
for section in filter(channels_re.match, self.sections()):
names = split_channels(self.get(section, 'channels'))
items = dict(self.nditems(section, raw=True))
items.pop('channels')
for name in names:
name = name.strip(' \n')
if not self.has_section(name):
self.add_section(name)
for key, val in items.iteritems():
if not self.has_option(name, key):
self.set(name, key, val)
# read all channels
raw = set()
trend = set()
for section in self.sections():
try:
m = Channel.MATCH.match(section).groupdict()
except AttributeError:
continue
else:
if not m['ifo']:
continue
if m['trend']:
trend.add(section)
else:
raw.add(section)
channels = ChannelList()
for group in [raw, trend]:
try:
newchannels = get_channels(group)
except httplib.HTTPException:
newchannels = []
# read custom channel definitions
for channel, section in zip(newchannels, group):
for key, val in nat_sorted(self.nditems(section),
key=lambda x: x[0]):
key = re_cchar.sub('_', key.rstrip())
if key.isdigit():
if not hasattr(channel, 'bits'):
channel.bits = []
while len(channel.bits) < int(key):
channel.bits.append(None)
if val.startswith('r"') or val.startswith('r\''):
val = eval(val)
channel.bits.append(val)
else:
setattr(channel, key, safe_eval(val.rstrip()))
channels.append(channel)
return channels
def __init__(self, channels, logger=Logger('buffer'), **kwargs):
"""Create a new `DataBuffer`
"""
if isinstance(channels, str):
channels = [channels]
self.channels = ChannelList.from_names(*channels)
self.data = self.DictClass()
self.logger = logger
def channels(self):
"""List of data-source
:class:`Channels <~gwpy.detector.channel.Channel>` for this
`DataPlot`.
:type: :class:`~gwpy.detector.channel.ChannelList`
"""
return ChannelList(get_channel(c) for c in self._channels)
def get_channels(channels, **kwargs):
"""Find (or create) multiple channels.
See Also
--------
get_channel
"""
return ChannelList(get_channel(c, **kwargs) for c in channels)
def __init__(self, channels, logger=Logger('buffer'), **kwargs):
"""Create a new `DataBuffer`
"""
if isinstance(channels, str):
channels = [channels]
self.channels = ChannelList.from_names(*channels)
self.data = self.DictClass()
self.logger = logger
def test_query_nds2(self):
try:
import nds2
except ImportError as e:
self.skipTest(str(e))
try:
from gwpy.io import kerberos
kerberos.which('kinit')
except ValueError as e:
self.skipTest(str(e))
try:
cl = ChannelList.query_nds2(self.REAL_CHANNELS,
host='nds.ligo.caltech.edu')
except IOError as e:
self.skipTest(str(e))
self.assertEqual(len(cl), 5)
def test_query_nds2(self):
try:
import nds2
except ImportError as e:
self.skipTest(str(e))
try:
from gwpy.io import kerberos
kerberos.which('kinit')
except ValueError as e:
self.skipTest(str(e))
try:
cl = ChannelList.query_nds2(self.REAL_CHANNELS,
host='nds.ligo.caltech.edu')
except IOError as e:
self.skipTest(str(e))
self.assertEqual(len(cl), 5)
def test_read_write_clf(self):
# write clf to file and read it back
try:
with NamedTemporaryFile(suffix='.ini', delete=False,
mode='w') as f:
f.write(CLF)
cl = ChannelList.read(f.name)
assert len(cl) == 4
a = cl[0]
assert a.name == 'H1:GDS-CALIB_STRAIN'
assert a.sample_rate == 16384 * units.Hz
assert a.frametype == 'H1_HOFT_C00'
assert a.frequency_range[0] == 4. * units.Hz
assert a.frequency_range[1] == float('inf') * units.Hz
assert a.safe is False
assert a.params == {
'qhigh': '150',
'safe': 'unsafe',
'fidelity': 'clean'
}
b = cl[1]
assert b.name == 'H1:ISI-GND_STS_HAM2_X_DQ'
assert b.frequency_range[0] == .1 * units.Hz
assert b.frequency_range[1] == 60. * units.Hz
c = cl[2]
assert c.name == 'H1:ISI-GND_STS_HAM2_Y_DQ'
assert c.sample_rate == 256 * units.Hz
assert c.safe is False
d = cl[3]
assert d.name == 'H1:ISI-GND_STS_HAM2_Z_DQ'
assert d.safe is True
assert d.params['fidelity'] == 'glitchy'
finally:
if os.path.isfile(f.name):
os.remove(f.name)
# write omega config again using ChannelList.write and read it back
# and check that the two lists match
try:
with NamedTemporaryFile(suffix='.ini', delete=False,
mode='w') as f2:
cl.write(f2)
cl2 = type(cl).read(f2.name)
assert cl == cl2
finally:
if os.path.isfile(f2.name):
os.remove(f2.name)
def test_read_write_clf(self):
# write clf to file and read it back
try:
with NamedTemporaryFile(suffix='.ini', delete=False,
mode='w') as f:
f.write(CLF)
cl = ChannelList.read(f.name)
assert len(cl) == 4
a = cl[0]
assert a.name == 'H1:GDS-CALIB_STRAIN'
assert a.sample_rate == 16384 * units.Hz
assert a.frametype == 'H1_HOFT_C00'
assert a.frequency_range[0] == 4. * units.Hz
assert a.frequency_range[1] == float('inf') * units.Hz
assert a.safe is False
assert a.params == {'qhigh': '150', 'safe': 'unsafe',
'fidelity': 'clean'}
b = cl[1]
assert b.name == 'H1:ISI-GND_STS_HAM2_X_DQ'
assert b.frequency_range[0] == .1 * units.Hz
assert b.frequency_range[1] == 60. * units.Hz
c = cl[2]
assert c.name == 'H1:ISI-GND_STS_HAM2_Y_DQ'
assert c.sample_rate == 256 * units.Hz
assert c.safe is False
d = cl[3]
assert d.name == 'H1:ISI-GND_STS_HAM2_Z_DQ'
assert d.safe is True
assert d.params['fidelity'] == 'glitchy'
finally:
if os.path.isfile(f.name):
os.remove(f.name)
# write omega config again using ChannelList.write and read it back
# and check that the two lists match
try:
with NamedTemporaryFile(suffix='.ini', delete=False,
mode='w') as f2:
cl.write(f2)
cl2 = type(cl).read(f2.name)
assert cl == cl2
finally:
if os.path.isfile(f2.name):
os.remove(f2.name)
def read(cls, list_file, maxchans=10):
"""
Read from a file. Returns a channel `dict`
Parameters
----------
list_file : `str`
File list to read dict from
maxchans : `int`, optional
Maximum number of channels to read.
Default set to 10 channels.
Returns
-------
Gives a `dict`
"""
chandict = ChannelDict(list_file)
channels = ChannelList.read(list_file)
for channel in channels:
# add channel to it's sub group
try:
chandict[channel.group].append(channel)
# if key doesn't exist...add it
except KeyError:
chandict[channel.group] = []
chandict[channel.group].append(channel)
# Now remake the dict based on maxchans
chandict2 = {}
for key in chandict.keys():
nchans = 0
ngroups = (len(chandict[key]) / maxchans)
if not ((len(chandict[key]) % maxchans) == 0):
ngroups += 1
for group in range(ngroups):
newkey = '%s %d' % (key, group + 1)
chandict2[newkey] = []
totmax = min((group + 1) * maxchans, len(chandict[key]))
for ii in range(group * maxchans, totmax):
chandict2[newkey].append(chandict[key][ii])
return chandict2
def add_channels(self, *channels, **fetchargs):
"""Add one of more channels to this `DataBuffer`
Parameters
----------
*channels : `str`, `~gwpy.detector.Channel`
one or more channels to add to the buffer. Any channels that
already exist in the buffer will be ignored
**fetchargs
keyword arguments to pass to the `fetch()` method.
"""
# find new channels
channels = ChannelList.from_names(*channels)
new = []
for c in channels:
if c not in self.channels:
new.append(c)
self.channels.append(c)
self.data[c] = self.ListClass()
# fetch data for new channels
for seg in self.segments:
self.get((seg[0], seg[1]), channels=new, **fetchargs)
def add_channels(self, *channels, **fetchargs):
"""Add one of more channels to this `DataBuffer`
Parameters
----------
*channels : `str`, `~gwpy.detector.Channel`
one or more channels to add to the buffer. Any channels that
already exist in the buffer will be ignored
**fetchargs
keyword arguments to pass to the `fetch()` method.
"""
# find new channels
channels = ChannelList.from_names(*channels)
new = []
for c in channels:
if c not in self.channels:
new.append(c)
self.channels.append(c)
self.data[c] = self.ListClass()
# fetch data for new channels
for seg in self.segments:
self.get((seg[0], seg[1]), channels=new, **fetchargs)
from gwpy.detector import ChannelList chname = 'K1:PEM-IXV_GND*' chlst = ChannelList.query_nds2([chname], host='10.68.10.121', port=8088) print chlst
def from_ini(filepath, ifo=None):
"""Configure a new Monitor from an INI file
Parameters
----------
filepath : `str`
path to INI-format configuration file
ifo : `str`, optional
prefix of relevant interferometer. This is only required if
'%(ifo)s' interpolation is used in the INI file. The default
value is taken from the ``IFO`` environment variable, if found.
Returns
-------
monitor : `Monitor`
a new monitor of the type given in the INI file
Raises
------
ValueError
if the configuration file cannot be read
OR
if IFO interpolation is used, but the `ifo` is not given or found
"""
# get ifo
ifo = ifo or os.getenv('IFO', None)
# read configuration file
if isinstance(filepath, str):
filepath = filepath.split(',')
cp = ConfigParser()
readok = cp.read(filepath)
if not len(readok) == len(filepath):
failed = [cf for cf in filepath if cf not in readok]
raise ValueError("Failed to read configuration file %r" % failed[0])
# get basic params
basics = dict(cp.items('monitor', raw=True))
type_ = basics.pop('type')
channels = map(str, ChannelList.from_names(basics.pop('channels', '')))
combinations = basics.pop('combinations', None)
basics = dict((key, safe_eval(val)) for (key, val) in basics.iteritems())
# get type
monitor = get_monitor(type_)
# get plotting parameters
pparams = dict((key, safe_eval(val)) for (key, val) in cp.items('plot'))
# get channel and reference curve names
sections = cp.sections()
if not channels:
channels = [c for c in sections if c not in ['monitor', 'plot']
if c[:4] not in ['ref:', 'com:']]
references = [c for c in sections if c not in ['monitor', 'plot']
if c[:4] == 'ref:']
if combinations is None:
combinations = [c for c in sections if c not in ['monitor', 'plot']
if c[:4] == 'com:']
# get channel parameters
cparams = {}
for i, channel in enumerate(channels):
# get channel section
_params = cp.items(channel)
# interpolate ifo
if r'%(ifo)s' in channel and not ifo:
raise ValueError("Cannot interpolate IFO in channel name without "
"IFO environment variable or --ifo on command "
"line")
channels[i] = channel % {'ifo': ifo}
for param, val in _params:
val = safe_eval(val)
if param not in cparams:
cparams[param] = []
while len(cparams[param]) < len(channels):
cparams[param].append(None)
cparams[param][i] = val
# get reference parameters
# reference parameters will be sent to the monitor in a dictionary
# with the path of each reference as keys and with the name and the other
# parameters as a dictionary for each key
rparams = OrderedDict()
for reference in references:
rparamsi = OrderedDict([('name', reference[4:])])
for param, val in cp.items(reference):
val = safe_eval(val)
if param == 'path':
refpath = val
else:
rparamsi[param] = val
try:
if os.path.isdir(refpath):
# Section is a directory:
# import all references in folder (assumes 'dat' format)
for f in os.listdir(refpath):
if os.path.splitext(f)[1] in ['.txt', '.dat', '.gz']:
refpath += f
rparamsi.setdefault(
'label', os.path.basename(refpath).split('.')
[0].replace('_', r' '))
rparams[refpath] = rparamsi
else:
rparamsi.setdefault(
'label', os.path.basename(refpath).split('.')[0]
.replace('_', r' '))
rparams[refpath] = rparamsi
except NameError:
raise ValueError('Cannot load reference {0} plot if no '
'parameter "path" is defined'
.format(reference))
# get combination parameters # IN PROGRESS
combparams = OrderedDict()
for comb in combinations:
# get combination section
_params = cp.items(comb)
deflabel = comb[4:]
combparamsi = OrderedDict([('label', deflabel)])
for param, val in _params:
val = safe_eval(val)
combparamsi[param] = val
combparams[deflabel] = combparamsi
params = dict(basics.items() + pparams.items() + cparams.items())
if rparams:
params['reference'] = rparams
if combparams:
params['combination'] = combparams
return monitor(*channels, **params)
def test_find(self):
cl = ChannelList.from_names(*self.NAMES)
self.assertEqual(cl.find(self.NAMES[2]), 2)
self.assertRaises(ValueError, cl.find, 'blah')
def channels(self):
"""List of channels for this plot
:type: `~gwpy.detector.ChannelList`
"""
return ChannelList(map(Channel, self._channels))
def test_find(self):
cl = ChannelList.from_names(*self.NAMES)
self.assertEqual(cl.find(self.NAMES[2]), 2)
self.assertRaises(ValueError, cl.find, 'blah')
def test_from_names(self):
cl = ChannelList.from_names(*self.NAMES)
self.assertListEqual(cl, map(Channel, self.NAMES))
cl2 = ChannelList.from_names(','.join(self.NAMES))
self.assertListEqual(cl, cl2)
def create(self):
return ChannelList(self.channels)
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GWSumm. If not, see <http://www.gnu.org/licenses/>.
"""Set of global memory variables for GWSumm package
"""
import time
from gwpy.time import tconvert
from gwpy.segments import DataQualityDict
from gwpy.detector import ChannelList
CHANNELS = ChannelList()
STATES = {}
DATA = {}
SPECTROGRAMS = {}
SPECTRUM = {}
SEGMENTS = DataQualityDict()
TRIGGERS = {}
VERBOSE = False
PROFILE = False
START = time.time()
# run time variables
MODE = 4
WRITTEN_PLOTS = []
def from_ini(filepath, ifo=None):
"""Configure a new Monitor from an INI file
Parameters
----------
filepath : `str`
path to INI-format configuration file
ifo : `str`, optional
prefix of relevant interferometer. This is only required if
'%(ifo)s' interpolation is used in the INI file. The default
value is taken from the ``IFO`` environment variable, if found.
Returns
-------
monitor : `Monitor`
a new monitor of the type given in the INI file
Raises
------
ValueError
if the configuration file cannot be read
OR
if IFO interpolation is used, but the `ifo` is not given or found
"""
# get ifo
ifo = ifo or os.getenv('IFO', None)
# read configuration file
if isinstance(filepath, str):
filepath = filepath.split(',')
cp = ConfigParser()
readok = cp.read(filepath)
if not len(readok) == len(filepath):
failed = [cf for cf in filepath if cf not in readok]
raise ValueError("Failed to read configuration file %r" % failed[0])
# get basic params
basics = dict(cp.items('monitor', raw=True))
type_ = basics.pop('type')
channels = map(str, ChannelList.from_names(basics.pop('channels', '')))
combinations = basics.pop('combinations', None)
basics = dict((key, safe_eval(val)) for (key, val) in basics.iteritems())
# get type
monitor = get_monitor(type_)
# get plotting parameters
pparams = dict((key, safe_eval(val)) for (key, val) in cp.items('plot'))
# get channel and reference curve names
sections = cp.sections()
if not channels:
channels = [
c for c in sections if c not in ['monitor', 'plot']
if c[:4] not in ['ref:', 'com:']
]
references = [
c for c in sections if c not in ['monitor', 'plot'] if c[:4] == 'ref:'
]
if combinations is None:
combinations = [
c for c in sections if c not in ['monitor', 'plot']
if c[:4] == 'com:'
]
# get channel parameters
cparams = {}
for i, channel in enumerate(channels):
# get channel section
_params = cp.items(channel)
# interpolate ifo
if r'%(ifo)s' in channel and not ifo:
raise ValueError("Cannot interpolate IFO in channel name without "
"IFO environment variable or --ifo on command "
"line")
channels[i] = channel % {'ifo': ifo}
for param, val in _params:
val = safe_eval(val)
if param not in cparams:
cparams[param] = []
while len(cparams[param]) < len(channels):
cparams[param].append(None)
cparams[param][i] = val
# get reference parameters
# reference parameters will be sent to the monitor in a dictionary
# with the path of each reference as keys and with the name and the other
# parameters as a dictionary for each key
rparams = OrderedDict()
for reference in references:
rparamsi = OrderedDict([('name', reference[4:])])
for param, val in cp.items(reference):
val = safe_eval(val)
if param == 'path':
refpath = val
else:
rparamsi[param] = val
try:
if os.path.isdir(refpath):
# Section is a directory:
# import all references in folder (assumes 'dat' format)
for f in os.listdir(refpath):
if os.path.splitext(f)[1] in ['.txt', '.dat', '.gz']:
refpath += f
rparamsi.setdefault(
'label',
os.path.basename(refpath).split('.')
[0].replace('_', r' '))
rparams[refpath] = rparamsi
else:
rparamsi.setdefault(
'label',
os.path.basename(refpath).split('.')[0].replace(
'_', r' '))
rparams[refpath] = rparamsi
except NameError:
raise ValueError(
'Cannot load reference {0} plot if no '
'parameter "path" is defined'.format(reference))
# get combination parameters # IN PROGRESS
combparams = OrderedDict()
for comb in combinations:
# get combination section
_params = cp.items(comb)
deflabel = comb[4:]
combparamsi = OrderedDict([('label', deflabel)])
for param, val in _params:
val = safe_eval(val)
combparamsi[param] = val
combparams[deflabel] = combparamsi
params = dict(basics.items() + pparams.items() + cparams.items())
if rparams:
params['reference'] = rparams
if combparams:
params['combination'] = combparams
return monitor(*channels, **params)
def main(args=None):
"""Run the lasso command-line interface
"""
# declare global variables
# this is needed for multiprocessing utilities
global auxdata, cluster_threshold, cmap, colors, counter, gpsstub
global line_size_aux, line_size_primary, max_correlated_channels
global nonzerocoef, nonzerodata, p1, primary, primary_mean, primary_std
global primaryts, range_is_primary, re_delim, start, target, times
global threshold, trend_type, xlim
parser = create_parser()
args = parser.parse_args(args=args)
# get run params
start = int(args.gpsstart)
end = int(args.gpsend)
pad = args.filter_padding
# set pertinent global variables
cluster_threshold = args.cluster_coefficient
line_size_aux = args.line_size_aux
line_size_primary = args.line_size_primary
threshold = args.threshold
trend_type = args.trend_type
# let's go
LOGGER.info('{} Lasso correlations {}-{}'.format(args.ifo, start, end))
# get primary channel frametype
primary = args.primary_channel.format(ifo=args.ifo)
range_is_primary = 'EFFECTIVE_RANGE_MPC' in args.primary_channel
if args.primary_cache is not None:
LOGGER.info("Using custom primary cache file")
elif args.primary_frametype is None:
try:
args.primary_frametype = DEFAULT_FRAMETYPE[
args.primary_channel.split(':')[1]].format(ifo=args.ifo)
except KeyError as exc:
raise type(exc)("Could not determine primary channel's frametype, "
"please specify with --primary-frametype")
# create output directory
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
os.chdir(args.output_dir)
# multiprocessing for plots
nprocplot = (args.nproc_plot or args.nproc) if USETEX else 1
# bandpass primary
if args.band_pass:
try:
flower, fupper = args.band_pass
except TypeError:
flower, fupper = None
LOGGER.info("-- Loading primary channel data")
bandts = get_data(primary,
start - pad,
end + pad,
verbose='Reading primary:'.rjust(30),
frametype=args.primary_frametype,
source=args.primary_cache,
nproc=args.nproc)
if flower < 0 or fupper >= float((bandts.sample_rate / 2.).value):
raise ValueError(
"bandpass frequency is out of range for this "
"channel, band (Hz): {0}, sample rate: {1}".format(
args.band_pass, bandts.sample_rate))
# get darm BLRMS
LOGGER.debug("-- Filtering data")
if trend_type == 'minute':
stride = 60
else:
stride = 1
if flower:
darmbl = (bandts.highpass(
flower / 2., fstop=flower / 4., filtfilt=False,
ftype='butter').notch(60, filtfilt=False).bandpass(
flower,
fupper,
fstop=[flower / 2., fupper * 1.5],
filtfilt=False,
ftype='butter').crop(start, end))
darmblrms = darmbl.rms(stride)
darmblrms.name = '%s %s-%s Hz BLRMS' % (primary, flower, fupper)
else:
darmbl = bandts.notch(60).crop(start, end)
darmblrms = darmbl.rms(stride)
darmblrms.name = '%s RMS' % primary
primaryts = darmblrms
bandts_asd = bandts.asd(4, 2, method='median')
darmbl_asd = darmbl.asd(4, 2, method='median')
spectrum_plots = gwplot.make_spectrum_plots(start, end, flower, fupper,
args.primary_channel,
bandts_asd, darmbl_asd)
spectrum_plot_zoomed_out = spectrum_plots[0]
spectrum_plot_zoomed_in = spectrum_plots[1]
else:
# load primary channel data
LOGGER.info("-- Loading primary channel data")
primaryts = get_data(primary,
start,
end,
frametype=args.primary_frametype,
source=args.primary_cache,
verbose='Reading:'.rjust(30),
nproc=args.nproc).crop(start, end)
if args.remove_outliers:
LOGGER.debug("-- Removing outliers above %f sigma" %
args.remove_outliers)
gwlasso.remove_outliers(primaryts, args.remove_outliers)
elif args.remove_outliers_pf:
LOGGER.debug("-- Removing outliers in the bottom {} percent "
"of data".format(args.remove_outliers_pf))
gwlasso.remove_outliers(primaryts,
args.remove_outliers_pf,
method='pf')
start = int(primaryts.span()[0])
end = int(primaryts.span()[1])
primary_mean = numpy.mean(primaryts.value)
primary_std = numpy.std(primaryts.value)
# get aux data
LOGGER.info("-- Loading auxiliary channel data")
if args.channel_file is None:
host, port = io_nds2.host_resolution_order(args.ifo)[0]
channels = ChannelList.query_nds2('*.mean',
host=host,
port=port,
type='m-trend')
else:
with open(args.channel_file, 'r') as f:
channels = [name.rstrip('\n') for name in f]
nchan = len(channels)
LOGGER.debug("Identified %d channels" % nchan)
if trend_type == 'minute':
frametype = '%s_M' % args.ifo # for minute trends
else:
frametype = '%s_T' % args.ifo # for second trends
# read aux channels
auxdata = get_data(channels,
start,
end,
verbose='Reading:'.rjust(30),
frametype=frametype,
nproc=args.nproc,
pad=0).crop(start, end)
# -- removes flat data to be re-introdused later
LOGGER.info('-- Pre-processing auxiliary channel data')
auxdata = gwlasso.remove_flat(auxdata)
flatable = Table(data=(list(set(channels) - set(auxdata.keys())), ),
names=('Channels', ))
LOGGER.debug('Removed {0} channels with flat data'.format(len(flatable)))
LOGGER.debug('{0} channels remaining'.format(len(auxdata)))
# -- remove bad data
LOGGER.info("Removing any channels with bad data...")
nbefore = len(auxdata)
auxdata = gwlasso.remove_bad(auxdata)
nafter = len(auxdata)
LOGGER.debug('Removed {0} channels with bad data'.format(nbefore - nafter))
LOGGER.debug('{0} channels remaining'.format(nafter))
data = numpy.array([scale(ts.value) for ts in auxdata.values()]).T
# -- perform lasso regression -------------------
# create model
LOGGER.info('-- Fitting data to target')
target = scale(primaryts.value)
model = gwlasso.fit(data, target, alpha=args.alpha)
LOGGER.info('Alpha: {}'.format(model.alpha))
# restructure results for convenience
allresults = Table(data=(list(auxdata.keys()), model.coef_,
numpy.abs(model.coef_)),
names=('Channel', 'Lasso coefficient', 'rank'))
allresults.sort('rank')
allresults.reverse()
useful = allresults['rank'] > 0
allresults.remove_column('rank')
results = allresults[useful] # non-zero coefficient
zeroed = allresults[numpy.invert(useful)] # zero coefficient
# extract data for useful channels
nonzerodata = {name: auxdata[name] for name in results['Channel']}
nonzerocoef = {name: coeff for name, coeff in results.as_array()}
# print results
LOGGER.info('Found {} channels with |Lasso coefficient| >= {}:\n\n'.format(
len(results), threshold))
print(results)
print('\n\n')
# convert to pandas
set_option('max_colwidth', -1)
df = results.to_pandas()
df.index += 1
# write results to files
gpsstub = '%d-%d' % (start, end - start)
resultsfile = '%s-LASSO_RESULTS-%s.csv' % (args.ifo, gpsstub)
results.write(resultsfile, format='csv', overwrite=True)
zerofile = '%s-ZERO_COEFFICIENT_CHANNELS-%s.csv' % (args.ifo, gpsstub)
zeroed.write(zerofile, format='csv', overwrite=True)
flatfile = '%s-FLAT_CHANNELS-%s.csv' % (args.ifo, gpsstub)
flatable.write(flatfile, format='csv', overwrite=True)
# -- generate lasso plots
modelFit = model.predict(data)
re_delim = re.compile(r'[:_-]')
p1 = (.1, .15, .9, .9) # global plot defaults for plot1, lasso model
times = primaryts.times.value
xlim = primaryts.span
cmap = get_cmap('tab20')
colors = [cmap(i) for i in numpy.linspace(0, 1, len(nonzerodata) + 1)]
plot = Plot(figsize=(12, 4))
plot.subplots_adjust(*p1)
ax = plot.gca(xscale='auto-gps', epoch=start, xlim=xlim)
ax.plot(times,
_descaler(target),
label=texify(primary),
color='black',
linewidth=line_size_primary)
ax.plot(times,
_descaler(modelFit),
label='Lasso model',
linewidth=line_size_aux)
if range_is_primary:
ax.set_ylabel('Sensitive range [Mpc]')
ax.set_title('Lasso Model of Range')
else:
ax.set_ylabel('Primary Channel Units')
ax.set_title('Lasso Model of Primary Channel')
ax.legend(loc='best')
plot1 = gwplot.save_figure(plot,
'%s-LASSO_MODEL-%s.png' % (args.ifo, gpsstub),
bbox_inches='tight')
# summed contributions
plot = Plot(figsize=(12, 4))
plot.subplots_adjust(*p1)
ax = plot.gca(xscale='auto-gps', epoch=start, xlim=xlim)
ax.plot(times,
_descaler(target),
label=texify(primary),
color='black',
linewidth=line_size_primary)
summed = 0
for i, name in enumerate(results['Channel']):
summed += scale(nonzerodata[name].value) * nonzerocoef[name]
if i:
label = 'Channels 1-{0}'.format(i + 1)
else:
label = 'Channel 1'
ax.plot(times,
_descaler(summed),
label=label,
color=colors[i],
linewidth=line_size_aux)
if range_is_primary:
ax.set_ylabel('Sensitive range [Mpc]')
else:
ax.set_ylabel('Primary Channel Units')
ax.set_title('Summations of Channel Contributions to Model')
ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5))
plot2 = gwplot.save_figure(plot,
'%s-LASSO_CHANNEL_SUMMATION-%s.png' %
(args.ifo, gpsstub),
bbox_inches='tight')
# individual contributions
plot = Plot(figsize=(12, 4))
plot.subplots_adjust(*p1)
ax = plot.gca(xscale='auto-gps', epoch=start, xlim=xlim)
ax.plot(times,
_descaler(target),
label=texify(primary),
color='black',
linewidth=line_size_primary)
for i, name in enumerate(results['Channel']):
this = _descaler(scale(nonzerodata[name].value) * nonzerocoef[name])
if i:
label = 'Channels 1-{0}'.format(i + 1)
else:
label = 'Channel 1'
ax.plot(times,
this,
label=texify(name),
color=colors[i],
linewidth=line_size_aux)
if range_is_primary:
ax.set_ylabel('Sensitive range [Mpc]')
else:
ax.set_ylabel('Primary Channel Units')
ax.set_title('Individual Channel Contributions to Model')
ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5))
plot3 = gwplot.save_figure(plot,
'%s-LASSO_CHANNEL_CONTRIBUTIONS-%s.png' %
(args.ifo, gpsstub),
bbox_inches='tight')
# -- process aux channels, making plots
LOGGER.info("-- Processing channels")
counter = multiprocessing.Value('i', 0)
# process channels
pool = multiprocessing.Pool(nprocplot)
results = pool.map(_process_channel, enumerate(list(nonzerodata.items())))
results = sorted(results, key=lambda x: abs(x[1]), reverse=True)
# generate clustered time series plots
counter = multiprocessing.Value('i', 0)
max_correlated_channels = 20
if args.no_cluster is False:
LOGGER.info("-- Generating clusters")
pool = multiprocessing.Pool(nprocplot)
clusters = pool.map(_generate_cluster, enumerate(results))
channelsfile = '%s-CHANNELS-%s.csv' % (args.ifo, gpsstub)
numpy.savetxt(channelsfile, channels, delimiter=',', fmt='%s')
# write html
trange = '%d-%d' % (start, end)
title = '%s Lasso Correlation: %s' % (args.ifo, trange)
if args.band_pass:
links = [trange
] + [(s, '#%s' % s.lower())
for s in ['Parameters', 'Spectra', 'Model', 'Results']]
else:
links = [trange] + [(s, '#%s' % s.lower())
for s in ['Parameters', 'Model', 'Results']]
(brand, class_) = htmlio.get_brand(args.ifo, 'Lasso', start)
navbar = htmlio.navbar(links, class_=class_, brand=brand)
page = htmlio.new_bootstrap_page(title='%s Lasso | %s' %
(args.ifo, trange),
navbar=navbar)
page.h1(title, class_='pb-2 mt-3 mb-2 border-bottom')
# -- summary table
content = [
('Primary channel', markup.oneliner.code(primary)),
('Primary frametype', markup.oneliner.code(args.primary_frametype)
or '-'),
('Primary cache file', markup.oneliner.code(args.primary_cache)
or '-'), ('Outlier threshold', '%s sigma' % args.remove_outliers),
('Lasso coefficient threshold', str(threshold)),
('Cluster coefficient threshold', str(args.cluster_coefficient)),
('Non-zero coefficients', str(numpy.count_nonzero(model.coef_))),
('α (model)', '%.4f' % model.alpha)
]
if args.band_pass:
content.insert(
2, ('Primary bandpass', '{0}-{1} Hz'.format(flower, fupper)))
page.h2('Parameters', class_='mt-4 mb-4', id_='parameters')
page.div(class_='row')
page.div(class_='col-md-9 col-sm-12')
page.add(htmlio.parameter_table(content, start=start, end=end))
page.div.close() # col-md-9 col-sm-12
# -- download button
files = [('%s analyzed channels (CSV)' % nchan, channelsfile),
('%s flat channels (CSV)' % len(flatable), flatfile),
('%s zeroed channels (CSV)' % len(zeroed), zerofile)]
page.div(class_='col-md-3 col-sm-12')
page.add(
htmlio.download_btn(files,
label='Channel information',
btnclass='btn btn-%s dropdown-toggle' %
args.ifo.lower()))
page.div.close() # col-md-3 col-sm-12
page.div.close() # rowa
# -- command-line
page.h5('Command-line:')
page.add(htmlio.get_command_line(about=False, prog=PROG))
if args.band_pass:
page.h2('Primary channel spectra', class_='mt-4', id_='spectra')
page.div(class_='card border-light card-body shadow-sm')
page.div(class_='row')
page.div(class_='col-md-6')
spectra_img1 = htmlio.FancyPlot(spectrum_plot_zoomed_out)
page.add(htmlio.fancybox_img(spectra_img1))
page.div.close() # col-md-6
page.div(class_='col-md-6')
spectra_img2 = htmlio.FancyPlot(spectrum_plot_zoomed_in)
page.add(htmlio.fancybox_img(spectra_img2))
page.div.close() # col-md-6
page.div.close() # row
page.div.close() # card border-light card-body shadow-sm
# -- model information
page.h2('Model information', class_='mt-4', id_='model')
page.div(class_='card card-%s card-body shadow-sm' % args.ifo.lower())
page.div(class_='row')
page.div(class_='col-md-8 offset-md-2', id_='results-table')
page.p('Below are the top {} mean minute-trend channels, ranked by '
'Lasso correlation with the primary.'.format(df.shape[0]))
page.add(
df.to_html(classes=('table', 'table-sm', 'table-hover'),
formatters={
'Lasso coefficient': lambda x: "%.4f" % x,
'Channel':
lambda x: str(htmlio.cis_link(x.split('.')[0])),
'__index__': lambda x: str(x)
},
escape=False,
border=0).replace(' style="text-align: right;"', ''))
page.div.close() # col-md-10 offset-md-1
page.div.close() # row
page.div(class_='row', id_='primary-lasso')
page.div(class_='col-md-8 offset-md-2')
img1 = htmlio.FancyPlot(plot1)
page.add(htmlio.fancybox_img(img1)) # primary lasso plot
page.div.close() # col-md-8 offset-md-2
page.div.close() # primary-lasso
page.div(class_='row', id_='channel-summation')
img2 = htmlio.FancyPlot(plot2)
page.div(class_='col-md-8 offset-md-2')
page.add(htmlio.fancybox_img(img2))
page.div.close() # col-md-8 offset-md-2
page.div.close() # channel-summation
page.div(class_='row', id_='channels-and-primary')
img3 = htmlio.FancyPlot(plot3)
page.div(class_='col-md-8 offset-md-2')
page.add(htmlio.fancybox_img(img3))
page.div.close() # col-md-8 offset-md-2
page.div.close() # channels-and-primary
page.div.close() # card card-<ifo> card-body shadow-sm
# -- results
page.h2('Top channels', class_='mt-4', id_='results')
page.div(id_='results')
# for each aux channel, create information container and put plots in it
for i, (ch, lassocoef, plot4, plot5, plot6, ts) in enumerate(results):
# set container color/context based on lasso coefficient
if lassocoef == 0:
break
elif abs(lassocoef) < threshold:
h = '%s [lasso coefficient = %.4f] (Below threshold)' % (ch,
lassocoef)
else:
h = '%s [lasso coefficient = %.4f]' % (ch, lassocoef)
if ((lassocoef is None) or (lassocoef == 0)
or (abs(lassocoef) < threshold)):
card = 'card border-light mb-1 shadow-sm'
card_header = 'card-header bg-light'
elif abs(lassocoef) >= .5:
card = 'card border-danger mb-1 shadow-sm'
card_header = 'card-header text-white bg-danger'
elif abs(lassocoef) >= .2:
card = 'card border-warning mb-1 shadow-sm'
card_header = 'card-header text-white bg-warning'
else:
card = 'card border-info mb-1 shadow-sm'
card_header = 'card-header text-white bg-info'
page.div(class_=card)
# heading
page.div(class_=card_header)
page.a(h,
class_='collapsed card-link cis-link',
href='#channel%d' % i,
**{'data-toggle': 'collapse'})
page.div.close() # card-header
# body
page.div(id_='channel%d' % i,
class_='collapse',
**{'data-parent': '#results'})
page.div(class_='card-body')
if lassocoef is None:
page.p('The amplitude data for this channel is flat (does not '
'change) within the chosen time period.')
elif abs(lassocoef) < threshold:
page.p('Lasso coefficient below the threshold of %g.' %
(threshold))
else:
for image in [plot4, plot5, plot6]:
img = htmlio.FancyPlot(image)
page.div(class_='row')
page.div(class_='col-md-8 offset-md-2')
page.add(htmlio.fancybox_img(img))
page.div.close() # col-md-8 offset-md-2
page.div.close() # row
page.add('<hr class="row-divider">')
if args.no_cluster is False:
if clusters[i][0] is None:
page.p("<font size='3'><br />No channels were highly "
"correlated with this channel.</font>")
else:
page.div(class_='row', id_='clusters')
page.div(class_='col-md-12')
cimg = htmlio.FancyPlot(clusters[i][0])
page.add(htmlio.fancybox_img(cimg))
page.div.close() # col-md-12
page.div.close() # clusters
if clusters[i][1] is not None:
corr_link = markup.oneliner.a(
'Export {} channels (CSV)'.format(
max_correlated_channels),
href=clusters[i][1],
download=clusters[i][1],
)
page.button(
corr_link,
class_='btn btn-%s' % args.ifo.lower(),
)
page.div.close() # card-body
page.div.close() # collapse
page.div.close() # card
page.div.close() # results
htmlio.close_page(page, 'index.html') # save and close
LOGGER.info("-- Process Completed")
def test_from_names(self):
cl = ChannelList.from_names(*self.NAMES)
self.assertListEqual(cl, map(Channel, self.NAMES))
cl2 = ChannelList.from_names(','.join(self.NAMES))
self.assertListEqual(cl, cl2)
def allchannels(self):
"""List of all unique channels for this plot
"""
chans = set([re.split(r'[#@]', str(c), 1)[0] for c in self._channels])
return ChannelList(map(Channel, chans))
def main(args=None):
"""Run the old lasso command-line interface
"""
parser = create_parser()
args = parser.parse_args(args=args)
start = int(args.gpsstart)
end = int(args.gpsend)
pad = args.filter_padding
try:
flower, fupper = args.band_pass
except TypeError:
flower, fupper = None
LOGGER.info('{} Slow Correlation {}-{}'.format(args.ifo, start, end))
if args.primary_channel == '{ifo}:GDS-CALIB_STRAIN':
args.primary_frametype = '%s_HOFT_C00' % args.ifo
primary = args.primary_channel.format(ifo=args.ifo)
rangechannel = args.range_channel.format(ifo=args.ifo)
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
os.chdir(args.output_dir)
nprocplot = args.nproc_plot or args.nproc
# load data
LOGGER.info("-- Loading range data")
rangets = get_data(
rangechannel, start, end, frametype=args.range_frametype,
verbose=True, nproc=args.nproc)
if args.trend_type == 'minute':
dstart, dend = rangets.span
else:
dstart = start
dend = end
LOGGER.info("-- Loading h(t) data")
darmts = get_data(primary, dstart-pad, dend+pad, verbose=True,
frametype=args.primary_frametype, nproc=args.nproc)
# get darm BLRMS
LOGGER.debug("-- Filtering h(t) data")
if args.trend_type == 'minute':
stride = 60
else:
stride = 1
if flower:
darmblrms = (
darmts.highpass(flower/2., fstop=flower/4.,
filtfilt=False, ftype='butter')
.notch(60, filtfilt=False)
.bandpass(flower, fupper, fstop=[flower/2., fupper*1.5],
filtfilt=False, ftype='butter')
.crop(dstart, dend).rms(stride))
darmblrms.name = '%s %s-%s Hz BLRMS' % (primary, flower, fupper)
else:
darmblrms = darmts.notch(60).crop(dstart, dend).rms(stride)
darmblrms.name = '%s RMS' % primary
if args.remove_outliers:
LOGGER.debug(
"-- Removing outliers above %f sigma" % args.remove_outliers)
gwlasso.remove_outliers(darmblrms, args.remove_outliers)
gwlasso.remove_outliers(rangets, args.remove_outliers)
if args.trend_type == 'minute':
# calculate the r value between the DARM BLRMS and the Range timeseries
corr_p = numpy.corrcoef(rangets.value, darmblrms.value)[0, 1]
# calculate the ρ value between the DARM BLRMS and the Range timeseries
corr_s = spearmanr(rangets.value, darmblrms.value)[0]
else:
# for second trends, set correlation to 0 since sample rates differ
corr_p = 0
corr_s = 0
# create scaled versions of data to compare to each other
LOGGER.debug("-- Creating scaled data")
rangescaled = rangets.detrend()
rangerms = numpy.sqrt(sum(rangescaled**2.0)/len(rangescaled))
darmscaled = darmblrms.detrend()
darmrms = numpy.sqrt(sum(darmscaled**2.0)/len(darmscaled))
# create scaled darm using the rms(range) and the rms(darm)
if args.trend_type == 'minute':
darmscaled *= (-rangerms / darmrms)
# get aux data
LOGGER.info("-- Loading auxiliary channel data")
host, port = io_nds2.host_resolution_order(args.ifo)[0]
if args.channel_file is None:
channels = ChannelList.query_nds2('*.mean', host=host, port=port,
type='m-trend')
else:
with open(args.channel_file, 'r') as f:
channels = f.read().rstrip('\n').split('\n')
nchan = len(channels)
LOGGER.debug("Identified %d channels" % nchan)
if args.trend_type == 'minute':
frametype = '%s_M' % args.ifo # for minute trends
else:
frametype = '%s_T' % args.ifo # for second trends
auxdata = get_data(
list(map(str, channels)), dstart, dend, verbose=True,
pad=0, frametype=frametype, nproc=args.nproc)
gpsstub = '%d-%d' % (start, end-start)
re_delim = re.compile('[:_-]')
LOGGER.info("-- Processing channels")
counter = multiprocessing.Value('i', 0)
p1 = (.1, .1, .9, .95)
p2 = (.1, .15, .9, .9)
def process_channel(input_,):
chan, ts = input_
flat = ts.value.min() == ts.value.max()
if flat:
corr1 = None
corr2 = None
corr1s = None
corr2s = None
plot1 = None
plot2 = None
plot3 = None
else:
corr1 = numpy.corrcoef(ts.value, darmblrms.value)[0, 1]
corr1s = spearmanr(ts.value, darmblrms.value)[0]
if args.trend_type == 'minute':
corr2 = numpy.corrcoef(ts.value, rangets.value)[0, 1]
corr2s = spearmanr(ts.value, rangets.value)[0]
else:
corr2 = 0.0
corr2s = 0.0
# if all corralations are below threshold it does not plot
if((abs(corr1) < args.threshold)
and (abs(corr1s) < args.threshold)
and (abs(corr2) < args.threshold)
and (abs(corr2s) < args.threshold)):
plot1 = None
plot2 = None
plot3 = None
return (chan, corr1, corr2, plot1,
plot2, plot3, corr1s, corr2s)
plot = Plot(darmblrms, ts, rangets,
xscale="auto-gps", separate=True,
figsize=(12, 12))
plot.subplots_adjust(*p1)
plot.axes[0].set_ylabel('$h(t)$ BLRMS [strain]')
plot.axes[1].set_ylabel('Channel units')
plot.axes[2].set_ylabel('Sensitive range [Mpc]')
for ax in plot.axes:
ax.legend(loc='best')
ax.set_xlim(start, end)
ax.set_epoch(start)
channelstub = re_delim.sub('_', str(chan)).replace('_', '-', 1)
plot1 = '%s_TRENDS-%s.png' % (channelstub, gpsstub)
try:
plot.save(plot1)
except (IOError, IndexError):
plot.save(plot1)
except RuntimeError as e:
if 'latex' in str(e).lower():
plot.save(plot1)
else:
raise
plot.close()
# plot auto-scaled verions
tsscaled = ts.detrend()
tsrms = numpy.sqrt(sum(tsscaled**2.0)/len(tsscaled))
if args.trend_type == 'minute':
tsscaled *= (rangerms / tsrms)
if corr1 > 0:
tsscaled *= -1
else:
tsscaled *= (darmrms / tsrms)
if corr1 < 0:
tsscaled *= -1
plot = Plot(darmscaled, rangescaled, tsscaled,
xscale="auto-gps", figsize=[12, 6])
plot.subplots_adjust(*p2)
ax = plot.gca()
ax.set_xlim(start, end)
ax.set_epoch(start)
ax.set_ylabel('Scaled amplitude [arbitrary units]')
ax.legend(loc='best')
plot2 = '%s_COMPARISON-%s.png' % (channelstub, gpsstub)
try:
plot.save(plot2)
except (IOError, IndexError):
plot.save(plot2)
except RuntimeError as e:
if 'latex' in str(e).lower():
plot.save(plot2)
else:
raise
plot.close()
# plot scatter plots
rangeColor = 'red'
darmblrmsColor = 'blue'
tsCopy = ts.reshape(-1, 1)
rangetsCopy = rangets.reshape(-1, 1)
darmblrmsCopy = darmblrms.reshape(-1, 1)
darmblrmsReg = linear_model.LinearRegression()
darmblrmsReg.fit(tsCopy, darmblrmsCopy)
darmblrmsFit = darmblrmsReg.predict(tsCopy)
rangeReg = linear_model.LinearRegression()
rangeReg.fit(tsCopy, rangetsCopy)
rangeFit = rangeReg.predict(tsCopy)
fig = Plot(figsize=(12, 6))
fig.subplots_adjust(*p2)
ax = fig.add_subplot(121)
ax.set_xlabel('Channel units')
ax.set_ylabel('Sensitive range [Mpc]')
yrange = abs(max(darmblrms.value) - min(darmblrms.value))
upperLim = max(darmblrms.value) + .1 * yrange
lowerLim = min(darmblrms.value) - .1 * yrange
ax.set_ylim(lowerLim, upperLim)
ax.text(.9, .1, 'r = ' + str('{0:.2}'.format(corr1)),
verticalalignment='bottom', horizontalalignment='right',
transform=ax.transAxes, color='black', size=20,
bbox=dict(boxstyle='square', facecolor='white', alpha=.75,
edgecolor='black'))
fig.add_scatter(ts, darmblrms, color=darmblrmsColor)
fig.add_line(ts, darmblrmsFit, color='black')
ax = fig.add_subplot(122)
ax.set_xlabel('Channel units')
ax.set_ylabel('$h(t)$ BLRMS [strain]')
ax.text(.9, .1, 'r = ' + str('{0:.2}'.format(corr2)),
verticalalignment='bottom', horizontalalignment='right',
transform=ax.transAxes, color='black', size=20,
bbox=dict(boxstyle='square', facecolor='white', alpha=.75,
edgecolor='black'))
fig.add_scatter(ts, rangets, color=rangeColor)
fig.add_line(ts, rangeFit, color='black')
plot3 = '%s_SCATTER-%s.png' % (channelstub, gpsstub)
try:
fig.save(plot3)
except (IOError, IndexError):
fig.save(plot3)
except RuntimeError as e:
if 'latex' in str(e).lower():
fig.save(plot3)
else:
raise
plt.close(fig)
# increment counter and print status
with counter.get_lock():
counter.value += 1
pc = 100 * counter.value / nchan
LOGGER.debug("Completed [%d/%d] %3d%% %-50s"
% (counter.value, nchan, pc, '(%s)' % str(chan)))
sys.stdout.flush()
return chan, corr1, corr2, plot1, plot2, plot3, corr1s, corr2s
pool = multiprocessing.Pool(nprocplot)
results = pool.map(process_channel, list(auxdata.items()))
results.sort(key=lambda x: (x[1] is not None and max(abs(x[1]), abs(x[2]),
abs(x[6]), abs(x[7])) or 0, x[0]), reverse=True)
with open('results.txt', 'w') as f:
for ch, corr1, corr2, _, _, _, corr1s, corr2s in results:
print('%s %s %s %s %s' % (
ch, corr1, corr2, corr1s, corr2s), file=f)
# -- write html
trange = '%d-%d' % (start, end)
title = '%s Slow Correlations: %s' % (args.ifo, trange)
links = [trange] + [(s, '#%s' % s.lower())
for s in ['Parameters', 'Results']]
(brand, class_) = htmlio.get_brand(args.ifo, 'Correlations', start)
navbar = htmlio.navbar(links, class_=class_, brand=brand)
page = htmlio.new_bootstrap_page(title=title, navbar=navbar)
# header
if flower:
pstr = ('<code>%s</code> (band-limited %s-%s Hz)'
% (primary, flower, fupper))
else:
pstr = primary
if args.trend_type == 'minute':
pstr += ' and <code>%s</code>' % rangechannel
page.div(class_='pb-2 mt-3 mb-2 border-bottom')
page.h1(title)
page.p("This analysis searched %d channels for linear correlations with %s"
% (nchan, pstr))
page.div.close()
# run parameters
contents = [
('Primary channel',
'{} ({})'.format(
primary, args.primary_frametype.format(ifo=args.ifo))),
('Range channel',
'{} ({})'.format(rangechannel, args.range_frametype or '-')),
('Band-pass', '{}-{}'.format(flower, fupper))]
page.add(htmlio.parameter_table(contents, start=start, end=end))
# results
page.h2('Results', class_='mt-4', id_='results')
r_blrms = "<i>r<sub>blrms</sub> </i>"
r_range = "<i>r<sub>range</sub> </i>"
r = "<i>r</i>"
rho_blrms = "<i>ρ<sub>blrms</sub> </i>"
rho_range = "<i>ρ<sub>range</sub> </i>"
rho = "<i>ρ</i>"
Pearson_wikilink = htmlio.markup.oneliner.a(
"Pearson's correlation coefficient",
href="https://en.wikipedia.org/wiki/"
"Pearson_product-moment_correlation_coefficient",
rel="external")
numpylink = htmlio.markup.oneliner.a(
"<code>numpy.corrcoef</code>",
href="http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/"
"numpy.corrcoef.html",
rel="external")
Spearman_wikilink = htmlio.markup.oneliner.a(
"Spearman's correlation coefficient",
href="https://en.wikipedia.org/wiki/"
"Spearman%27s_rank_correlation_coefficient",
rel="external")
scipylink = htmlio.markup.oneliner.a(
"<code>scipy.stats.spearmanr</code>",
href="http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/"
"scipy.stats.spearmanr.html",
rel="external")
page.p("In the results below, all %s values are calculated as"
" the square of %s using %s and all %s values are calculated"
" as the square of %s using %s."
% (r, Pearson_wikilink, numpylink, rho,
Spearman_wikilink, scipylink))
if args.trend_type == 'minute':
page.p("%s and %s are reported for <code>%s</code>."
" %s and %s are reported for <code>%s</code>."
" The %s between these two channels is %.2f."
" The %s between these two channels is %.2f."
% (r_blrms, rho_blrms, primary, r_range, rho_range,
rangechannel, r, corr_p, rho, corr_s))
page.div(id_='accordion')
for i, (ch, corr1, corr2, plot1, plot2, plot3,
corr1s, corr2s) in enumerate(results):
if corr1 is None:
h = '%s [flat]' % ch
elif plot1 is None:
h = ('%s [%s = %.2f, %s = %.2f] [%s = %.2f, %s = %.2f]'
' [below threshold]'
% (ch, r_blrms, corr1, r_range, corr2, rho_blrms,
corr1s, rho_range, corr2s))
elif args.trend_type == 'minute':
h = ('%s [%s = %.2f, %s = %.2f] [%s = %.2f, %s = %.2f]'
% (ch, r_blrms, corr1, r_range, corr2, rho_blrms,
corr1s, rho_range, corr2s))
else:
h = '%s [%s = %.2f]' % (ch, r_blrms, corr1)
if (corr1 is None) or (corr1 == 0) or (plot1 is None):
context = 'bg-light'
elif((numpy.absolute(corr1) >= .6) or (numpy.absolute(corr1s) >= .6)
or (numpy.absolute(corr2) >= .6)
or (numpy.absolute(corr2s) >= .6)):
context = 'text-white bg-danger'
elif((numpy.absolute(corr1) >= .4) or (numpy.absolute(corr1s) >= .4)
or (numpy.absolute(corr2) >= .4)
or (numpy.absolute(corr2s) >= .4)):
context = 'text-white bg-warning'
else:
context = 'text-white bg-info'
page.div(class_='card %s' % context)
# heading
page.div(class_='card-header')
page.a(h, class_='collapsed card-link cis-link', href='#channel%d' % i,
**{'data-toggle': 'collapse'})
page.div.close() # card-header
# body
page.div(id_='channel%d' % i, class_='collapse',
**{'data-parent': '#accordion'})
page.div(class_='card-body')
if corr1 is None:
page.p("The amplitude data for this channel is flat"
" (does not change) for the chosen time period.")
elif plot1 is None:
page.p("Niether r nor rho are above the threshold of %.2f."
% (args.threshold))
else:
for p in (plot1, plot2, plot3):
img = htmlio.FancyPlot(p)
page.add(htmlio.fancybox_img(img))
page.div.close() # card-body
page.div.close() # collapse
page.div.close() # card
page.div.close() # accordion
htmlio.close_page(page, 'index.html') # save and close
LOGGER.info("-- Process Completed")
