def test_get_column_string(self):
rcParams['text.usetex'] = True
self.assertEqual(get_column_string('snr'), 'SNR')
self.assertEqual(get_column_string('reduced_chisq'),
r'Reduced $\chi^2$')
self.assertEqual(get_column_string('flow'), r'f$_{\mbox{\small low}}$')
self.assertEqual(get_column_string('end_time_ns'), r'End Time $(ns)$')
def test_get_column_string(self):
rcParams['text.usetex'] = True
self.assertEqual(get_column_string('snr'), 'SNR')
self.assertEqual(get_column_string('reduced_chisq'),
r'Reduced $\chi^2$')
self.assertEqual(get_column_string('flow'),
r'f$_{\mbox{\small low}}$')
self.assertEqual(get_column_string('end_time_ns'),
r'End Time $(ns)$')
def binned_event_rates(self, stride, column, bins, operator='>=',
start=None, end=None, timecolumn='time'):
"""Calculate an event rate `~gwpy.timeseries.TimeSeriesDict` over
a number of bins.
Parameters
----------
stride : `float`
size (seconds) of each time bin
column : `str`
name of column by which to bin.
bins : `list`
a list of `tuples <tuple>` marking containing bins, or a list of
`floats <float>` defining bin edges against which an math operation
is performed for each event.
operator : `str`, `callable`
one of:
- ``'<'``, ``'<='``, ``'>'``, ``'>='``, ``'=='``, ``'!='``,
for a standard mathematical operation,
- ``'in'`` to use the list of bins as containing bin edges, or
- a callable function that takes compares an event value
against the bin value and returns a boolean.
.. note::
If ``bins`` is given as a list of tuples, this argument
is ignored.
start : `float`, :class:`~gwpy.time.LIGOTimeGPS`, optional
GPS start epoch of rate `~gwpy.timeseries.TimeSeries`.
end : `float`, `~gwpy.time.LIGOTimeGPS`, optional
GPS end time of rate `~gwpy.timeseries.TimeSeries`.
This value will be rounded up to the nearest sample if needed.
timecolumn : `str`, optional, default: ``time``
name of time-column to use when binning events
Returns
-------
rates : :class:`~gwpy.timeseries.TimeSeriesDict`
a dict of (bin, `~gwpy.timeseries.TimeSeries`) pairs describing a
rate of events per second (Hz) for each of the bins.
"""
from gwpy.timeseries import (TimeSeries, TimeSeriesDict)
from gwpy.plotter.table import get_column_string
# get time data
times = get_table_column(self, timecolumn)
# get channel
try:
channel = self[0].channel
except (IndexError, AttributeError):
channel = None
# generate time bins
if not start:
start = times.min()
if not end:
end = times.max()
nsamp = int(ceil((end - start) / stride))
timebins = numpy.arange(nsamp + 1) * stride + start
# generate column bins
if not bins:
bins = [(-numpy.inf, numpy.inf)]
if operator == 'in' and not isinstance(bins[0], tuple):
bins2 = []
for i, bin_ in enumerate(bins[:-1]):
bins2.append((bin_, bins[i+1]))
bins = bins2
elif isinstance(operator, (unicode, str)):
op = OPERATORS[operator]
else:
op = operator
coldata = get_table_column(self, column)
colstr = get_column_string(column)
# generate one TimeSeries per bin
out = TimeSeriesDict()
for bin_ in bins:
if isinstance(bin_, tuple):
bintimes = times[(coldata >= bin_[0]) & (coldata < bin_[1])]
else:
bintimes = times[op(coldata, bin_)]
out[bin_] = TimeSeries(
numpy.histogram(bintimes, bins=timebins)[0] / float(stride),
epoch=start, sample_rate=1/float(stride), unit='Hz',
name='%s $%s$ %s' % (colstr, operator, bin_), channel=channel)
return out
def test_get_column_string(self):
with rc_context(rc={'text.usetex': True}):
assert get_column_string('snr') == 'SNR'
assert get_column_string('reduced_chisq') == r'Reduced $\chi^2$'
assert get_column_string('flow') == r'f$_{\mbox{\small low}}$'
assert get_column_string('end_time_ns') == r'End Time $(ns)$'
def draw(self):
"""Read in all necessary data, and generate the figure.
"""
# get rate arguments
stride = self.pargs.pop('stride')
if self.column:
cname = get_column_string(self.column)
bins = self.pargs.pop('bins')
operator = self.pargs.pop('operator', '>=')
try:
opstr = LATEX_OPERATOR[operator]
except KeyError:
opstr = str(operator)
else:
bins = ['_']
# work out labels
labels = self.pargs.pop('labels', None)
if isinstance(labels, (unicode, str)):
labels = labels.split(',')
elif labels is None and self.column and len(self.channels) > 1:
labels = []
for channel, bin in [(c, b) for c in self.channels for b in bins]:
labels.append(r' '.join([channel, '$%s$' % opstr,
str(b)]))
self.pargs.setdefault('legend-title', cname)
elif labels is None and self.column:
labels = [r' '.join(['$%s$' % opstr, str(b)]) for b in bins]
self.pargs.setdefault('legend-title', cname)
elif labels is None:
labels = self.channels
self.pargs['labels'] = map(lambda s: str(s).strip('\n '), labels)
# get time column
try:
tcol = self.pargs.pop('timecolumn')
except KeyError:
if self.etg in ['pycbc_live']:
tcol = 'end_time'
else:
tcol = 'time'
# generate data
keys = []
for channel in self.channels:
if self.state and not self.all_data:
valid = self.state.active
else:
valid = SegmentList([self.span])
if '#' in str(channel) or '@' in str(channel):
key = '%s,%s' % (str(channel), state and str(state) or 'All')
else:
key = str(channel)
table_ = get_triggers(key, self.etg, valid, query=False)
if self.column:
rates = binned_event_rates(
table_, stride, self.column, bins, operator, self.start,
self.end, timecolumn=tcol).values()
else:
rates = [event_rate(table_, stride, self.start, self.end,
timecolumn=tcol)]
for bin, rate in zip(bins, rates):
rate.channel = channel
keys.append('%s_%s_EVENT_RATE_%s_%s'
% (str(channel), str(self.etg),
str(self.column), bin))
if keys[-1] not in globalv.DATA:
add_timeseries(rate, keys[-1])
# reset channel lists and generate time-series plot
channels = self.channels
outputfile = self.outputfile
self.channels = keys
out = super(TriggerRateDataPlot, self).draw(outputfile=outputfile)
self.channels = channels
return out
def get_column_label(column):
try:
return COLUMN_LABEL.get(column)
except KeyError:
return get_column_string(column)
def process(self):
"""Read in all necessary data, and generate the figure.
"""
# get rate arguments
stride = self.pargs.pop('stride')
if self.column:
cname = get_column_string(self.column)
bins = self.pargs.pop('bins')
operator = self.pargs.pop('operator', '>=')
try:
opstr = LATEX_OPERATOR[operator]
except KeyError:
opstr = str(operator)
else:
bins = ['_']
# work out labels
labels = self.pargs.pop('labels', None)
if isinstance(labels, (unicode, str)):
labels = labels.split(',')
elif labels is None and self.column and len(self.channels) > 1:
labels = []
for channel, bin in [(c, b) for c in self.channels for b in bins]:
labels.append(r' '.join(
[channel, cname, '$%s$' % opstr,
str(b)]))
elif labels is None and self.column:
labels = [r' '.join([cname, '$%s$' % opstr, str(b)]) for b in bins]
elif labels is None:
labels = self.channels
self.pargs['labels'] = map(lambda s: str(s).strip('\n '), labels)
# generate data
keys = []
for channel in self.channels:
if self.state and not self.all_data:
valid = self.state.active
else:
valid = SegmentList([self.span])
if '#' in str(channel) or '@' in str(channel):
key = '%s,%s' % (str(channel), state and str(state) or 'All')
else:
key = str(channel)
table_ = get_triggers(key, self.etg, valid, query=False)
if self.column:
rates = binned_event_rates(table_, stride, self.column, bins,
operator, self.start,
self.end).values()
else:
rates = [event_rate(table_, stride, self.start, self.end)]
for bin, rate in zip(bins, rates):
rate.channel = channel
keys.append(
'%s_%s_EVENT_RATE_%s_%s' %
(str(channel), str(self.etg), str(self.column), bin))
if keys[-1] not in globalv.DATA:
add_timeseries(rate, keys[-1])
# reset channel lists and generate time-series plot
channels = self.channels
outputfile = self.outputfile
self.channels = keys
out = super(TriggerRateDataPlot, self).process(outputfile=outputfile)
self.channels = channels
return out
def binned_event_rates(self,
stride,
column,
bins,
operator='>=',
start=None,
end=None,
timecolumn='time'):
"""Calculate an event rate `~gwpy.timeseries.TimeSeriesDict` over
a number of bins.
Parameters
----------
stride : `float`
size (seconds) of each time bin
column : `str`
name of column by which to bin.
bins : `list`
a list of `tuples <tuple>` marking containing bins, or a list of
`floats <float>` defining bin edges against which an math operation
is performed for each event.
operator : `str`, `callable`
one of:
- ``'<'``, ``'<='``, ``'>'``, ``'>='``, ``'=='``, ``'!='``,
for a standard mathematical operation,
- ``'in'`` to use the list of bins as containing bin edges, or
- a callable function that takes compares an event value
against the bin value and returns a boolean.
.. note::
If ``bins`` is given as a list of tuples, this argument
is ignored.
start : `float`, :class:`~gwpy.time.LIGOTimeGPS`, optional
GPS start epoch of rate `~gwpy.timeseries.TimeSeries`.
end : `float`, `~gwpy.time.LIGOTimeGPS`, optional
GPS end time of rate `~gwpy.timeseries.TimeSeries`.
This value will be rounded up to the nearest sample if needed.
timecolumn : `str`, optional, default: ``time``
name of time-column to use when binning events
Returns
-------
rates : :class:`~gwpy.timeseries.TimeSeriesDict`
a dict of (bin, `~gwpy.timeseries.TimeSeries`) pairs describing a
rate of events per second (Hz) for each of the bins.
"""
from gwpy.timeseries import (TimeSeries, TimeSeriesDict)
from gwpy.plotter.table import get_column_string
# get time data
times = get_table_column(self, timecolumn)
# get channel
try:
channel = self[0].channel
except (IndexError, AttributeError):
channel = None
# generate time bins
if not start:
start = times.min()
if not end:
end = times.max()
nsamp = int(ceil((end - start) / stride))
timebins = numpy.arange(nsamp + 1) * stride + start
# generate column bins
if not bins:
bins = [(-numpy.inf, numpy.inf)]
if operator == 'in' and not isinstance(bins[0], tuple):
bins2 = []
for i, bin_ in enumerate(bins[:-1]):
bins2.append((bin_, bins[i + 1]))
bins = bins2
elif isinstance(operator, (unicode, str)):
op = OPERATORS[operator]
else:
op = operator
coldata = get_table_column(self, column)
colstr = get_column_string(column)
# generate one TimeSeries per bin
out = TimeSeriesDict()
for bin_ in bins:
if isinstance(bin_, tuple):
bintimes = times[(coldata >= bin_[0]) & (coldata < bin_[1])]
else:
bintimes = times[op(coldata, bin_)]
out[bin_] = TimeSeries(numpy.histogram(bintimes, bins=timebins)[0] /
float(stride),
epoch=start,
sample_rate=1 / float(stride),
unit='Hz',
name='%s $%s$ %s' % (colstr, operator, bin_),
channel=channel)
return out
def test_get_column_string(self):
with rc_context(rc={'text.usetex': True}):
assert get_column_string('snr') == 'SNR'
assert get_column_string('reduced_chisq') == r'Reduced $\chi^2$'
assert get_column_string('flow') == r'f$_{\mbox{\small low}}$'
assert get_column_string('end_time_ns') == r'End Time $(ns)$'
def get_column_label(column):
try:
return COLUMN_LABEL.get(column)
except KeyError:
return get_column_string(column)
