_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3 values | text stringlengths 75 19.8k | language stringclasses 1 value | meta_information dict |
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q32000 | BaseInferenceFile.copy_info | train | def copy_info(self, other, ignore=None):
"""Copies "info" from this file to the other.
"Info" is defined all groups that are not the samples group.
Parameters
----------
other : output file
The output file. Must be an hdf file.
ignore : (list of) str
Don't copy the given groups.
"""
logging.info("Copying info")
# copy non-samples/stats data
if ignore is None:
ignore = []
if isinstance(ignore, (str, unicode)):
ignore = [ignore]
ignore = set(ignore + [self.samples_group])
copy_groups = set(self.keys()) - ignore
for key in copy_groups:
super(BaseInferenceFile, self).copy(key, other) | python | {
"resource": ""
} |
q32001 | BaseInferenceFile.copy_samples | train | def copy_samples(self, other, parameters=None, parameter_names=None,
read_args=None, write_args=None):
"""Should copy samples to the other files.
Parameters
----------
other : InferenceFile
An open inference file to write to.
parameters : list of str, optional
List of parameters to copy. If None, will copy all parameters.
parameter_names : dict, optional
Rename one or more parameters to the given name. The dictionary
should map parameter -> parameter name. If None, will just use the
original parameter names.
read_args : dict, optional
Arguments to pass to ``read_samples``.
write_args : dict, optional
Arguments to pass to ``write_samples``.
"""
# select the samples to copy
logging.info("Reading samples to copy")
if parameters is None:
parameters = self.variable_params
# if list of desired parameters is different, rename
if set(parameters) != set(self.variable_params):
other.attrs['variable_params'] = parameters
samples = self.read_samples(parameters, **read_args)
logging.info("Copying {} samples".format(samples.size))
# if different parameter names are desired, get them from the samples
if parameter_names:
arrs = {pname: samples[p] for p, pname in parameter_names.items()}
arrs.update({p: samples[p] for p in parameters if
p not in parameter_names})
samples = FieldArray.from_kwargs(**arrs)
other.attrs['variable_params'] = samples.fieldnames
logging.info("Writing samples")
other.write_samples(other, samples, **write_args) | python | {
"resource": ""
} |
q32002 | BaseInferenceFile.copy | train | def copy(self, other, ignore=None, parameters=None, parameter_names=None,
read_args=None, write_args=None):
"""Copies metadata, info, and samples in this file to another file.
Parameters
----------
other : str or InferenceFile
The file to write to. May be either a string giving a filename,
or an open hdf file. If the former, the file will be opened with
the write attribute (note that if a file already exists with that
name, it will be deleted).
ignore : (list of) strings
Don't copy the given groups. If the samples group is included, no
samples will be copied.
parameters : list of str, optional
List of parameters in the samples group to copy. If None, will copy
all parameters.
parameter_names : dict, optional
Rename one or more parameters to the given name. The dictionary
should map parameter -> parameter name. If None, will just use the
original parameter names.
read_args : dict, optional
Arguments to pass to ``read_samples``.
write_args : dict, optional
Arguments to pass to ``write_samples``.
Returns
-------
InferenceFile
The open file handler to other.
"""
if not isinstance(other, h5py.File):
# check that we're not trying to overwrite this file
if other == self.name:
raise IOError("destination is the same as this file")
other = self.__class__(other, 'w')
# metadata
self.copy_metadata(other)
# info
if ignore is None:
ignore = []
if isinstance(ignore, (str, unicode)):
ignore = [ignore]
self.copy_info(other, ignore=ignore)
# samples
if self.samples_group not in ignore:
self.copy_samples(other, parameters=parameters,
parameter_names=parameter_names,
read_args=read_args,
write_args=write_args)
# if any down selection was done, re-set the default
# thin-start/interval/end
p = self[self.samples_group].keys()[0]
my_shape = self[self.samples_group][p].shape
p = other[other.samples_group].keys()[0]
other_shape = other[other.samples_group][p].shape
if my_shape != other_shape:
other.attrs['thin_start'] = 0
other.attrs['thin_interval'] = 1
other.attrs['thin_end'] = None
return other | python | {
"resource": ""
} |
q32003 | BaseInferenceFile.write_kwargs_to_attrs | train | def write_kwargs_to_attrs(cls, attrs, **kwargs):
"""Writes the given keywords to the given ``attrs``.
If any keyword argument points to a dict, the keyword will point to a
list of the dict's keys. Each key is then written to the attrs with its
corresponding value.
Parameters
----------
attrs : an HDF attrs
The ``attrs`` of an hdf file or a group in an hdf file.
\**kwargs :
The keywords to write.
"""
for arg, val in kwargs.items():
if val is None:
val = str(None)
if isinstance(val, dict):
attrs[arg] = val.keys()
# just call self again with the dict as kwargs
cls.write_kwargs_to_attrs(attrs, **val)
else:
attrs[arg] = val | python | {
"resource": ""
} |
q32004 | ceilpow2 | train | def ceilpow2(n):
"""convenience function to determine a power-of-2 upper frequency limit"""
signif,exponent = frexp(n)
if (signif < 0):
return 1;
if (signif == 0.5):
exponent -= 1;
return (1) << exponent; | python | {
"resource": ""
} |
q32005 | coalign_waveforms | train | def coalign_waveforms(h1, h2, psd=None,
low_frequency_cutoff=None,
high_frequency_cutoff=None,
resize=True):
""" Return two time series which are aligned in time and phase.
The alignment is only to the nearest sample point and all changes to the
phase are made to the first input waveform. Waveforms should not be split
accross the vector boundary. If it is, please use roll or cyclic time shift
to ensure that the entire signal is contiguous in the time series.
Parameters
----------
h1: pycbc.types.TimeSeries
The first waveform to align.
h2: pycbc.types.TimeSeries
The second waveform to align.
psd: {None, pycbc.types.FrequencySeries}
A psd to weight the alignment
low_frequency_cutoff: {None, float}
The low frequency cutoff to weight the matching in Hz.
high_frequency_cutoff: {None, float}
The high frequency cutoff to weight the matching in Hz.
resize: Optional, {True, boolean}
If true, the vectors will be resized to match each other. If false,
they must be the same length and even in length
Returns
-------
h1: pycbc.types.TimeSeries
The shifted waveform to align with h2
h2: pycbc.type.TimeSeries
The resized (if necessary) waveform to align with h1.
"""
from pycbc.filter import matched_filter
mlen = ceilpow2(max(len(h1), len(h2)))
h1 = h1.copy()
h2 = h2.copy()
if resize:
h1.resize(mlen)
h2.resize(mlen)
elif len(h1) != len(h2) or len(h2) % 2 != 0:
raise ValueError("Time series must be the same size and even if you do "
"not allow resizing")
snr = matched_filter(h1, h2, psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
_, l = snr.abs_max_loc()
rotation = snr[l] / abs(snr[l])
h1 = (h1.to_frequencyseries() * rotation).to_timeseries()
h1.roll(l)
h1 = TimeSeries(h1, delta_t=h2.delta_t, epoch=h2.start_time)
return h1, h2 | python | {
"resource": ""
} |
q32006 | phase_from_frequencyseries | train | def phase_from_frequencyseries(htilde, remove_start_phase=True):
"""Returns the phase from the given frequency-domain waveform. This assumes
that the waveform has been sampled finely enough that the phase cannot
change by more than pi radians between each step.
Parameters
----------
htilde : FrequencySeries
The waveform to get the phase for; must be a complex frequency series.
remove_start_phase : {True, bool}
Subtract the initial phase before returning.
Returns
-------
FrequencySeries
The phase of the waveform as a function of frequency.
"""
p = numpy.unwrap(numpy.angle(htilde.data)).astype(
real_same_precision_as(htilde))
if remove_start_phase:
p += -p[0]
return FrequencySeries(p, delta_f=htilde.delta_f, epoch=htilde.epoch,
copy=False) | python | {
"resource": ""
} |
q32007 | amplitude_from_frequencyseries | train | def amplitude_from_frequencyseries(htilde):
"""Returns the amplitude of the given frequency-domain waveform as a
FrequencySeries.
Parameters
----------
htilde : FrequencySeries
The waveform to get the amplitude of.
Returns
-------
FrequencySeries
The amplitude of the waveform as a function of frequency.
"""
amp = abs(htilde.data).astype(real_same_precision_as(htilde))
return FrequencySeries(amp, delta_f=htilde.delta_f, epoch=htilde.epoch,
copy=False) | python | {
"resource": ""
} |
q32008 | time_from_frequencyseries | train | def time_from_frequencyseries(htilde, sample_frequencies=None,
discont_threshold=0.99*numpy.pi):
"""Computes time as a function of frequency from the given
frequency-domain waveform. This assumes the stationary phase
approximation. Any frequencies lower than the first non-zero value in
htilde are assigned the time at the first non-zero value. Times for any
frequencies above the next-to-last non-zero value in htilde will be
assigned the time of the next-to-last non-zero value.
.. note::
Some waveform models (e.g., `SEOBNRv2_ROM_DoubleSpin`) can have
discontinuities in the phase towards the end of the waveform due to
numerical error. We therefore exclude any points that occur after a
discontinuity in the phase, as the time estimate becomes untrustworthy
beyond that point. What determines a discontinuity in the phase is set
by the `discont_threshold`. To turn this feature off, just set
`discont_threshold` to a value larger than pi (due to the unwrapping
of the phase, no two points can have a difference > pi).
Parameters
----------
htilde : FrequencySeries
The waveform to get the time evolution of; must be complex.
sample_frequencies : {None, array}
The frequencies at which the waveform is sampled. If None, will
retrieve from ``htilde.sample_frequencies``.
discont_threshold : {0.99*pi, float}
If the difference in the phase changes by more than this threshold,
it is considered to be a discontinuity. Default is 0.99*pi.
Returns
-------
FrequencySeries
The time evolution of the waveform as a function of frequency.
"""
if sample_frequencies is None:
sample_frequencies = htilde.sample_frequencies.numpy()
phase = phase_from_frequencyseries(htilde).data
dphi = numpy.diff(phase)
time = -dphi / (2.*numpy.pi*numpy.diff(sample_frequencies))
nzidx = numpy.nonzero(abs(htilde.data))[0]
kmin, kmax = nzidx[0], nzidx[-2]
# exclude everything after a discontinuity
discont_idx = numpy.where(abs(dphi[kmin:]) >= discont_threshold)[0]
if discont_idx.size != 0:
kmax = min(kmax, kmin + discont_idx[0]-1)
time[:kmin] = time[kmin]
time[kmax:] = time[kmax]
return FrequencySeries(time.astype(real_same_precision_as(htilde)),
delta_f=htilde.delta_f, epoch=htilde.epoch,
copy=False) | python | {
"resource": ""
} |
q32009 | phase_from_polarizations | train | def phase_from_polarizations(h_plus, h_cross, remove_start_phase=True):
"""Return gravitational wave phase
Return the gravitation-wave phase from the h_plus and h_cross
polarizations of the waveform. The returned phase is always
positive and increasing with an initial phase of 0.
Parameters
----------
h_plus : TimeSeries
An PyCBC TmeSeries vector that contains the plus polarization of the
gravitational waveform.
h_cross : TimeSeries
A PyCBC TmeSeries vector that contains the cross polarization of the
gravitational waveform.
Returns
-------
GWPhase : TimeSeries
A TimeSeries containing the gravitational wave phase.
Examples
--------s
>>> from pycbc.waveform import get_td_waveform, phase_from_polarizations
>>> hp, hc = get_td_waveform(approximant="TaylorT4", mass1=10, mass2=10,
f_lower=30, delta_t=1.0/4096)
>>> phase = phase_from_polarizations(hp, hc)
"""
p = numpy.unwrap(numpy.arctan2(h_cross.data, h_plus.data)).astype(
real_same_precision_as(h_plus))
if remove_start_phase:
p += -p[0]
return TimeSeries(p, delta_t=h_plus.delta_t, epoch=h_plus.start_time,
copy=False) | python | {
"resource": ""
} |
q32010 | amplitude_from_polarizations | train | def amplitude_from_polarizations(h_plus, h_cross):
"""Return gravitational wave amplitude
Return the gravitation-wave amplitude from the h_plus and h_cross
polarizations of the waveform.
Parameters
----------
h_plus : TimeSeries
An PyCBC TmeSeries vector that contains the plus polarization of the
gravitational waveform.
h_cross : TimeSeries
A PyCBC TmeSeries vector that contains the cross polarization of the
gravitational waveform.
Returns
-------
GWAmplitude : TimeSeries
A TimeSeries containing the gravitational wave amplitude.
Examples
--------
>>> from pycbc.waveform import get_td_waveform, phase_from_polarizations
>>> hp, hc = get_td_waveform(approximant="TaylorT4", mass1=10, mass2=10,
f_lower=30, delta_t=1.0/4096)
>>> amp = amplitude_from_polarizations(hp, hc)
"""
amp = (h_plus.squared_norm() + h_cross.squared_norm()) ** (0.5)
return TimeSeries(amp, delta_t=h_plus.delta_t, epoch=h_plus.start_time) | python | {
"resource": ""
} |
q32011 | frequency_from_polarizations | train | def frequency_from_polarizations(h_plus, h_cross):
"""Return gravitational wave frequency
Return the gravitation-wave frequency as a function of time
from the h_plus and h_cross polarizations of the waveform.
It is 1 bin shorter than the input vectors and the sample times
are advanced half a bin.
Parameters
----------
h_plus : TimeSeries
A PyCBC TimeSeries vector that contains the plus polarization of the
gravitational waveform.
h_cross : TimeSeries
A PyCBC TimeSeries vector that contains the cross polarization of the
gravitational waveform.
Returns
-------
GWFrequency : TimeSeries
A TimeSeries containing the gravitational wave frequency as a function
of time.
Examples
--------
>>> from pycbc.waveform import get_td_waveform, phase_from_polarizations
>>> hp, hc = get_td_waveform(approximant="TaylorT4", mass1=10, mass2=10,
f_lower=30, delta_t=1.0/4096)
>>> freq = frequency_from_polarizations(hp, hc)
"""
phase = phase_from_polarizations(h_plus, h_cross)
freq = numpy.diff(phase) / ( 2 * lal.PI * phase.delta_t )
start_time = phase.start_time + phase.delta_t / 2
return TimeSeries(freq.astype(real_same_precision_as(h_plus)),
delta_t=phase.delta_t, epoch=start_time) | python | {
"resource": ""
} |
q32012 | taper_timeseries | train | def taper_timeseries(tsdata, tapermethod=None, return_lal=False):
"""
Taper either or both ends of a time series using wrapped
LALSimulation functions
Parameters
----------
tsdata : TimeSeries
Series to be tapered, dtype must be either float32 or float64
tapermethod : string
Should be one of ('TAPER_NONE', 'TAPER_START', 'TAPER_END',
'TAPER_STARTEND', 'start', 'end', 'startend') - NB 'TAPER_NONE' will
not change the series!
return_lal : Boolean
If True, return a wrapped LAL time series object, else return a
PyCBC time series.
"""
if tapermethod is None:
raise ValueError("Must specify a tapering method (function was called"
"with tapermethod=None)")
if tapermethod not in taper_map.keys():
raise ValueError("Unknown tapering method %s, valid methods are %s" % \
(tapermethod, ", ".join(taper_map.keys())))
if tsdata.dtype not in (float32, float64):
raise TypeError("Strain dtype must be float32 or float64, not "
+ str(tsdata.dtype))
taper_func = taper_func_map[tsdata.dtype]
# make a LAL TimeSeries to pass to the LALSim function
ts_lal = tsdata.astype(tsdata.dtype).lal()
if taper_map[tapermethod] is not None:
taper_func(ts_lal.data, taper_map[tapermethod])
if return_lal:
return ts_lal
else:
return TimeSeries(ts_lal.data.data[:], delta_t=ts_lal.deltaT,
epoch=ts_lal.epoch) | python | {
"resource": ""
} |
q32013 | apply_fd_time_shift | train | def apply_fd_time_shift(htilde, shifttime, kmin=0, fseries=None, copy=True):
"""Shifts a frequency domain waveform in time. The shift applied is
shiftime - htilde.epoch.
Parameters
----------
htilde : FrequencySeries
The waveform frequency series.
shifttime : float
The time to shift the frequency series to.
kmin : {0, int}
The starting index of htilde to apply the time shift. Default is 0.
fseries : {None, numpy array}
The frequencies of each element in htilde. This is only needed if htilde is not
sampled at equal frequency steps.
copy : {True, bool}
Make a copy of htilde before applying the time shift. If False, the time
shift will be applied to htilde's data.
Returns
-------
FrequencySeries
A frequency series with the waveform shifted to the new time. If makecopy
is True, will be a new frequency series; if makecopy is False, will be
the same as htilde.
"""
dt = float(shifttime - htilde.epoch)
if dt == 0.:
# no shift to apply, just copy if desired
if copy:
htilde = 1. * htilde
elif isinstance(htilde, FrequencySeries):
# FrequencySeries means equally sampled in frequency, use faster shifting
htilde = apply_fseries_time_shift(htilde, dt, kmin=kmin, copy=copy)
else:
if fseries is None:
fseries = htilde.sample_frequencies.numpy()
shift = Array(numpy.exp(-2j*numpy.pi*dt*fseries),
dtype=complex_same_precision_as(htilde))
if copy:
htilde = 1. * htilde
htilde *= shift
return htilde | python | {
"resource": ""
} |
q32014 | td_taper | train | def td_taper(out, start, end, beta=8, side='left'):
"""Applies a taper to the given TimeSeries.
A half-kaiser window is used for the roll-off.
Parameters
----------
out : TimeSeries
The ``TimeSeries`` to taper.
start : float
The time (in s) to start the taper window.
end : float
The time (in s) to end the taper window.
beta : int, optional
The beta parameter to use for the Kaiser window. See
``scipy.signal.kaiser`` for details. Default is 8.
side : {'left', 'right'}
The side to apply the taper to. If ``'left'`` (``'right'``), the taper
will roll up (down) between ``start`` and ``end``, with all values
before ``start`` (after ``end``) set to zero. Default is ``'left'``.
Returns
-------
TimeSeries
The tapered time series.
"""
out = out.copy()
width = end - start
winlen = 2 * int(width / out.delta_t)
window = Array(signal.get_window(('kaiser', beta), winlen))
xmin = int((start - out.start_time) / out.delta_t)
xmax = xmin + winlen//2
if side == 'left':
out[xmin:xmax] *= window[:winlen//2]
if xmin > 0:
out[:xmin].clear()
elif side == 'right':
out[xmin:xmax] *= window[winlen//2:]
if xmax < len(out):
out[xmax:].clear()
else:
raise ValueError("unrecognized side argument {}".format(side))
return out | python | {
"resource": ""
} |
q32015 | fd_taper | train | def fd_taper(out, start, end, beta=8, side='left'):
"""Applies a taper to the given FrequencySeries.
A half-kaiser window is used for the roll-off.
Parameters
----------
out : FrequencySeries
The ``FrequencySeries`` to taper.
start : float
The frequency (in Hz) to start the taper window.
end : float
The frequency (in Hz) to end the taper window.
beta : int, optional
The beta parameter to use for the Kaiser window. See
``scipy.signal.kaiser`` for details. Default is 8.
side : {'left', 'right'}
The side to apply the taper to. If ``'left'`` (``'right'``), the taper
will roll up (down) between ``start`` and ``end``, with all values
before ``start`` (after ``end``) set to zero. Default is ``'left'``.
Returns
-------
FrequencySeries
The tapered frequency series.
"""
out = out.copy()
width = end - start
winlen = 2 * int(width / out.delta_f)
window = Array(signal.get_window(('kaiser', beta), winlen))
kmin = int(start / out.delta_f)
kmax = kmin + winlen//2
if side == 'left':
out[kmin:kmax] *= window[:winlen//2]
out[:kmin] *= 0.
elif side == 'right':
out[kmin:kmax] *= window[winlen//2:]
out[kmax:] *= 0.
else:
raise ValueError("unrecognized side argument {}".format(side))
return out | python | {
"resource": ""
} |
q32016 | fd_to_td | train | def fd_to_td(htilde, delta_t=None, left_window=None, right_window=None,
left_beta=8, right_beta=8):
"""Converts a FD waveform to TD.
A window can optionally be applied using ``fd_taper`` to the left or right
side of the waveform before being converted to the time domain.
Parameters
----------
htilde : FrequencySeries
The waveform to convert.
delta_t : float, optional
Make the returned time series have the given ``delta_t``.
left_window : tuple of float, optional
A tuple giving the start and end frequency of the FD taper to apply
on the left side. If None, no taper will be applied on the left.
right_window : tuple of float, optional
A tuple giving the start and end frequency of the FD taper to apply
on the right side. If None, no taper will be applied on the right.
left_beta : int, optional
The beta parameter to use for the left taper. See ``fd_taper`` for
details. Default is 8.
right_beta : int, optional
The beta parameter to use for the right taper. Default is 8.
Returns
-------
TimeSeries
The time-series representation of ``htilde``.
"""
if left_window is not None:
start, end = left_window
htilde = fd_taper(htilde, start, end, side='left', beta=left_beta)
if right_window is not None:
start, end = right_window
htilde = fd_taper(htilde, start, end, side='right', beta=right_beta)
return htilde.to_timeseries(delta_t=delta_t) | python | {
"resource": ""
} |
q32017 | ParameterList.docstr | train | def docstr(self, prefix='', include_label=True):
"""Returns the ``docstr`` of each parameter joined together."""
return '\n'.join([x.docstr(prefix, include_label) for x in self]) | python | {
"resource": ""
} |
q32018 | hist_overflow | train | def hist_overflow(val, val_max, **kwds):
""" Make a histogram with an overflow bar above val_max """
import pylab, numpy
overflow = len(val[val>=val_max])
pylab.hist(val[val<val_max], **kwds)
if 'color' in kwds:
color = kwds['color']
else:
color = None
if overflow > 0:
rect = pylab.bar(val_max+0.05, overflow, .5, color=color)[0]
pylab.text(rect.get_x(),
1.10*rect.get_height(), '%s+' % val_max) | python | {
"resource": ""
} |
q32019 | setup_psd_workflow | train | def setup_psd_workflow(workflow, science_segs, datafind_outs,
output_dir=None, tags=None):
'''
Setup static psd section of CBC workflow. At present this only supports pregenerated
psd files, in the future these could be created within the workflow.
Parameters
----------
workflow: pycbc.workflow.core.Workflow
An instanced class that manages the constructed workflow.
science_segs : Keyed dictionary of glue.segmentlist objects
scienceSegs[ifo] holds the science segments to be analysed for each
ifo.
datafind_outs : pycbc.workflow.core.FileList
The file list containing the datafind files.
output_dir : path string
The directory where data products will be placed.
tags : list of strings
If given these tags are used to uniquely name and identify output files
that would be produced in multiple calls to this function.
Returns
--------
psd_files : pycbc.workflow.core.FileList
The FileList holding the psd files, 0 or 1 per ifo
'''
if tags is None:
tags = []
logging.info("Entering static psd module.")
make_analysis_dir(output_dir)
cp = workflow.cp
# Parse for options in ini file.
try:
psdMethod = cp.get_opt_tags("workflow-psd", "psd-method",
tags)
except:
# Predefined PSD sare optional, just return an empty list if not
# provided.
return FileList([])
if psdMethod == "PREGENERATED_FILE":
logging.info("Setting psd from pre-generated file(s).")
psd_files = setup_psd_pregenerated(workflow, tags=tags)
else:
errMsg = "PSD method not recognized. Only "
errMsg += "PREGENERATED_FILE is currently supported."
raise ValueError(errMsg)
logging.info("Leaving psd module.")
return psd_files | python | {
"resource": ""
} |
q32020 | generate_inverse_mapping | train | def generate_inverse_mapping(order):
"""Genereate a lambda entry -> PN order map.
This function will generate the opposite of generate mapping. So where
generate_mapping gives dict[key] = item this will give
dict[item] = key. Valid PN orders are:
{}
Parameters
----------
order : string
A string containing a PN order. Valid values are given above.
Returns
--------
mapping : dictionary
An inverse mapping between the active Lambda terms and index in the
metric
"""
mapping = generate_mapping(order)
inv_mapping = {}
for key,value in mapping.items():
inv_mapping[value] = key
return inv_mapping | python | {
"resource": ""
} |
q32021 | ethinca_order_from_string | train | def ethinca_order_from_string(order):
"""
Returns the integer giving twice the post-Newtonian order
used by the ethinca calculation. Currently valid only for TaylorF2 metric
Parameters
----------
order : string
Returns
-------
int
"""
if order in get_ethinca_orders().keys():
return get_ethinca_orders()[order]
else: raise ValueError("Order "+str(order)+" is not valid for ethinca"
"calculation! Valid orders: "+
str(get_ethinca_orders().keys())) | python | {
"resource": ""
} |
q32022 | compile | train | def compile(source, name):
""" Compile the string source code into a shared object linked against
the static version of cufft for callback support.
"""
cache = os.path.join(pycbc._cache_dir_path, name)
hash_file = cache + ".hash"
lib_file = cache + ".so"
obj_file = cache + ".o"
try:
if int(open(hash_file, "r").read()) == hash(source):
return lib_file
raise ValueError
except:
pass
src_file = cache + ".cu"
fsrc = open(src_file, "w")
fsrc.write(source)
fsrc.close()
cmd = ["nvcc", "-ccbin", "g++", "-dc", "-m64",
"--compiler-options", "'-fPIC'",
"-o", obj_file,
"-c", src_file]
print(" ".join(cmd))
subprocess.check_call(cmd)
cmd = ["nvcc", "-shared", "-ccbin", "g++", "-m64",
"-o", lib_file, obj_file, "-lcufft_static", "-lculibos"]
print(" ".join(cmd))
subprocess.check_call(cmd)
hash_file = cache + ".hash"
fhash = open(hash_file, "w")
fhash.write(str(hash(source)))
return lib_file | python | {
"resource": ""
} |
q32023 | get_fn_plan | train | def get_fn_plan(callback=None, out_callback=None, name='pycbc_cufft', parameters=None):
""" Get the IFFT execute and plan functions
"""
if parameters is None:
parameters = []
source = fftsrc.render(input_callback=callback, output_callback=out_callback, parameters=parameters)
path = compile(source, name)
lib = ctypes.cdll.LoadLibrary(path)
fn = lib.execute
fn.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
plan = lib.create_plan
plan.restype = ctypes.c_void_p
plan.argyptes = [ctypes.c_uint]
return fn, plan | python | {
"resource": ""
} |
q32024 | determine_eigen_directions | train | def determine_eigen_directions(metricParams, preserveMoments=False,
vary_fmax=False, vary_density=None):
"""
This function will calculate the coordinate transfomations that are needed
to rotate from a coordinate system described by the various Lambda
components in the frequency expansion, to a coordinate system where the
metric is Cartesian.
Parameters
-----------
metricParams : metricParameters instance
Structure holding all the options for construction of the metric.
preserveMoments : boolean, optional (default False)
Currently only used for debugging.
If this is given then if the moments structure is already set
within metricParams then they will not be recalculated.
vary_fmax : boolean, optional (default False)
If set to False the metric and rotations are calculated once, for the
full range of frequency [f_low,f_upper).
If set to True the metric and rotations are calculated multiple times,
for frequency ranges [f_low,f_low + i*vary_density), where i starts at
1 and runs up until f_low + (i+1)*vary_density > f_upper.
Thus values greater than f_upper are *not* computed.
The calculation for the full range [f_low,f_upper) is also done.
vary_density : float, optional
If vary_fmax is True, this will be used in computing the frequency
ranges as described for vary_fmax.
Returns
--------
metricParams : metricParameters instance
Structure holding all the options for construction of the metric.
**THIS FUNCTION ONLY RETURNS THE CLASS**
The following will be **added** to this structure
metricParams.evals : Dictionary of numpy.array
Each entry in the dictionary corresponds to the different frequency
ranges described in vary_fmax. If vary_fmax = False, the only entry
will be f_upper, this corresponds to integrals in [f_low,f_upper). This
entry is always present. Each other entry will use floats as keys to
the dictionary. These floats give the upper frequency cutoff when it is
varying.
Each numpy.array contains the eigenvalues which, with the eigenvectors
in evecs, are needed to rotate the
coordinate system to one in which the metric is the identity matrix.
metricParams.evecs : Dictionary of numpy.matrix
Each entry in the dictionary is as described under evals.
Each numpy.matrix contains the eigenvectors which, with the eigenvalues
in evals, are needed to rotate the
coordinate system to one in which the metric is the identity matrix.
metricParams.metric : Dictionary of numpy.matrix
Each entry in the dictionary is as described under evals.
Each numpy.matrix contains the metric of the parameter space in the
Lambda_i coordinate system.
metricParams.moments : Moments structure
See the structure documentation for a description of this. This
contains the result of all the integrals used in computing the metrics
above. It can be used for the ethinca components calculation, or other
similar calculations.
"""
evals = {}
evecs = {}
metric = {}
unmax_metric = {}
# First step is to get the moments needed to calculate the metric
if not (metricParams.moments and preserveMoments):
get_moments(metricParams, vary_fmax=vary_fmax,
vary_density=vary_density)
# What values are going to be in the moments
# J7 is the normalization factor so it *MUST* be present
list = metricParams.moments['J7'].keys()
# We start looping over every item in the list of metrics
for item in list:
# Here we convert the moments into a form easier to use here
Js = {}
for i in range(-7,18):
Js[i] = metricParams.moments['J%d'%(i)][item]
logJs = {}
for i in range(-1,18):
logJs[i] = metricParams.moments['log%d'%(i)][item]
loglogJs = {}
for i in range(-1,18):
loglogJs[i] = metricParams.moments['loglog%d'%(i)][item]
logloglogJs = {}
for i in range(-1,18):
logloglogJs[i] = metricParams.moments['logloglog%d'%(i)][item]
loglogloglogJs = {}
for i in range(-1,18):
loglogloglogJs[i] = metricParams.moments['loglogloglog%d'%(i)][item]
mapping = generate_mapping(metricParams.pnOrder)
# Calculate the metric
gs, unmax_metric_curr = calculate_metric(Js, logJs, loglogJs,
logloglogJs, loglogloglogJs, mapping)
metric[item] = numpy.matrix(gs)
unmax_metric[item] = unmax_metric_curr
# And the eigenvalues
evals[item],evecs[item] = numpy.linalg.eig(gs)
# Numerical error can lead to small negative eigenvalues.
for i in range(len(evals[item])):
if evals[item][i] < 0:
# Due to numerical imprecision the very small eigenvalues can
# be negative. Make these positive.
evals[item][i] = -evals[item][i]
if evecs[item][i,i] < 0:
# We demand a convention that all diagonal terms in the matrix
# of eigenvalues are positive.
# This is done to help visualization of the spaces (increasing
# mchirp always goes the same way)
evecs[item][:,i] = - evecs[item][:,i]
metricParams.evals = evals
metricParams.evecs = evecs
metricParams.metric = metric
metricParams.time_unprojected_metric = unmax_metric
return metricParams | python | {
"resource": ""
} |
q32025 | interpolate_psd | train | def interpolate_psd(psd_f, psd_amp, deltaF):
"""
Function to interpolate a PSD to a different value of deltaF. Uses linear
interpolation.
Parameters
----------
psd_f : numpy.array or list or similar
List of the frequencies contained within the PSD.
psd_amp : numpy.array or list or similar
List of the PSD values at the frequencies in psd_f.
deltaF : float
Value of deltaF to interpolate the PSD to.
Returns
--------
new_psd_f : numpy.array
Array of the frequencies contained within the interpolated PSD
new_psd_amp : numpy.array
Array of the interpolated PSD values at the frequencies in new_psd_f.
"""
# In some cases this will be a no-op. I thought about removing this, but
# this function can take unequally sampled PSDs and it is difficult to
# check for this. As this function runs quickly anyway (compared to the
# moment calculation) I decided to always interpolate.
new_psd_f = []
new_psd_amp = []
fcurr = psd_f[0]
for i in range(len(psd_f) - 1):
f_low = psd_f[i]
f_high = psd_f[i+1]
amp_low = psd_amp[i]
amp_high = psd_amp[i+1]
while(1):
if fcurr > f_high:
break
new_psd_f.append(fcurr)
gradient = (amp_high - amp_low) / (f_high - f_low)
fDiff = fcurr - f_low
new_psd_amp.append(amp_low + fDiff * gradient)
fcurr = fcurr + deltaF
return numpy.asarray(new_psd_f), numpy.asarray(new_psd_amp) | python | {
"resource": ""
} |
q32026 | calculate_moment | train | def calculate_moment(psd_f, psd_amp, fmin, fmax, f0, funct,
norm=None, vary_fmax=False, vary_density=None):
"""
Function for calculating one of the integrals used to construct a template
bank placement metric. The integral calculated will be
\int funct(x) * (psd_x)**(-7./3.) * delta_x / PSD(x)
where x = f / f0. The lower frequency cutoff is given by fmin, see
the parameters below for details on how the upper frequency cutoff is
chosen
Parameters
-----------
psd_f : numpy.array
numpy array holding the set of evenly spaced frequencies used in the PSD
psd_amp : numpy.array
numpy array holding the PSD values corresponding to the psd_f
frequencies
fmin : float
The lower frequency cutoff used in the calculation of the integrals
used to obtain the metric.
fmax : float
The upper frequency cutoff used in the calculation of the integrals
used to obtain the metric. This can be varied (see the vary_fmax
option below).
f0 : float
This is an arbitrary scaling factor introduced to avoid the potential
for numerical overflow when calculating this. Generally the default
value (70) is safe here. **IMPORTANT, if you want to calculate the
ethinca metric components later this MUST be set equal to f_low.**
funct : Lambda function
The function to use when computing the integral as described above.
norm : Dictionary of floats
If given then moment[f_cutoff] will be divided by norm[f_cutoff]
vary_fmax : boolean, optional (default False)
If set to False the metric and rotations are calculated once, for the
full range of frequency [f_low,f_upper).
If set to True the metric and rotations are calculated multiple times,
for frequency ranges [f_low,f_low + i*vary_density), where i starts at
1 and runs up until f_low + (i+1)*vary_density > f_upper.
Thus values greater than f_upper are *not* computed.
The calculation for the full range [f_low,f_upper) is also done.
vary_density : float, optional
If vary_fmax is True, this will be used in computing the frequency
ranges as described for vary_fmax.
Returns
--------
moment : Dictionary of floats
moment[f_cutoff] will store the value of the moment at the frequency
cutoff given by f_cutoff.
"""
# Must ensure deltaF in psd_f is constant
psd_x = psd_f / f0
deltax = psd_x[1] - psd_x[0]
mask = numpy.logical_and(psd_f > fmin, psd_f < fmax)
psdf_red = psd_f[mask]
comps_red = psd_x[mask] ** (-7./3.) * funct(psd_x[mask], f0) * deltax / \
psd_amp[mask]
moment = {}
moment[fmax] = comps_red.sum()
if norm:
moment[fmax] = moment[fmax] / norm[fmax]
if vary_fmax:
for t_fmax in numpy.arange(fmin + vary_density, fmax, vary_density):
moment[t_fmax] = comps_red[psdf_red < t_fmax].sum()
if norm:
moment[t_fmax] = moment[t_fmax] / norm[t_fmax]
return moment | python | {
"resource": ""
} |
q32027 | calculate_metric | train | def calculate_metric(Js, logJs, loglogJs, logloglogJs, loglogloglogJs, \
mapping):
"""
This function will take the various integrals calculated by get_moments and
convert this into a metric for the appropriate parameter space.
Parameters
-----------
Js : Dictionary
The list of (log^0 x) * x**(-i/3) integrals computed by get_moments()
The index is Js[i]
logJs : Dictionary
The list of (log^1 x) * x**(-i/3) integrals computed by get_moments()
The index is logJs[i]
loglogJs : Dictionary
The list of (log^2 x) * x**(-i/3) integrals computed by get_moments()
The index is loglogJs[i]
logloglogJs : Dictionary
The list of (log^3 x) * x**(-i/3) integrals computed by get_moments()
The index is logloglogJs[i]
loglogloglogJs : Dictionary
The list of (log^4 x) * x**(-i/3) integrals computed by get_moments()
The index is loglogloglogJs[i]
mapping : dictionary
Used to identify which Lambda components are active in this parameter
space and map these to entries in the metric matrix.
Returns
--------
metric : numpy.matrix
The resulting metric.
"""
# How many dimensions in the parameter space?
maxLen = len(mapping.keys())
metric = numpy.matrix(numpy.zeros(shape=(maxLen,maxLen),dtype=float))
unmax_metric = numpy.matrix(numpy.zeros(shape=(maxLen+1,maxLen+1),
dtype=float))
for i in range(16):
for j in range(16):
calculate_metric_comp(metric, unmax_metric, i, j, Js,
logJs, loglogJs, logloglogJs,
loglogloglogJs, mapping)
return metric, unmax_metric | python | {
"resource": ""
} |
q32028 | bank_bins_from_cli | train | def bank_bins_from_cli(opts):
""" Parses the CLI options related to binning templates in the bank.
Parameters
----------
opts : object
Result of parsing the CLI with OptionParser.
Results
-------
bins_idx : dict
A dict with bin names as key and an array of their indices as value.
bank : dict
A dict of the datasets from the bank file.
"""
bank = {}
fp = h5py.File(opts.bank_file)
for key in fp.keys():
bank[key] = fp[key][:]
bank["f_lower"] = float(opts.f_lower) if opts.f_lower else None
if opts.bank_bins:
bins_idx = coinc.background_bin_from_string(opts.bank_bins, bank)
else:
bins_idx = {"all" : numpy.arange(0, len(bank[fp.keys()[0]]))}
fp.close()
return bins_idx, bank | python | {
"resource": ""
} |
q32029 | get_found_param | train | def get_found_param(injfile, bankfile, trigfile, param, ifo, args=None):
"""
Translates some popular trigger parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
bankfile: hdf5 File object or None
Template bank file
trigfile: hdf5 File object or None
Single-detector trigger file
param: string
Parameter to be calculated for the recovered triggers
ifo: string or None
Standard ifo name, ex. 'L1'
args : Namespace object returned from ArgumentParser instance
Calling code command line options, used for f_lower value
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
foundtmp = injfile["found_after_vetoes/template_id"][:]
if trigfile is not None:
# get the name of the ifo in the injection file, eg "detector_1"
# and the integer from that name
ifolabel = [name for name, val in injfile.attrs.items() if \
"detector" in name and val == ifo][0]
foundtrg = injfile["found_after_vetoes/trigger_id" + ifolabel[-1]]
if bankfile is not None and param in bankfile.keys():
return bankfile[param][:][foundtmp]
elif trigfile is not None and param in trigfile[ifo].keys():
return trigfile[ifo][param][:][foundtrg]
else:
b = bankfile
return get_param(param, args, b['mass1'][:], b['mass2'][:],
b['spin1z'][:], b['spin2z'][:])[foundtmp] | python | {
"resource": ""
} |
q32030 | get_inj_param | train | def get_inj_param(injfile, param, ifo, args=None):
"""
Translates some popular injection parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
param: string
Parameter to be calculated for the injected signals
ifo: string
Standard detector name, ex. 'L1'
args: Namespace object returned from ArgumentParser instance
Calling code command line options, used for f_lower value
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
det = pycbc.detector.Detector(ifo)
inj = injfile["injections"]
if param in inj.keys():
return inj["injections/"+param]
if param == "end_time_"+ifo[0].lower():
return inj['end_time'][:] + det.time_delay_from_earth_center(
inj['longitude'][:],
inj['latitude'][:],
inj['end_time'][:])
else:
return get_param(param, args, inj['mass1'][:], inj['mass2'][:],
inj['spin1z'][:], inj['spin2z'][:]) | python | {
"resource": ""
} |
q32031 | ns_g_mass_to_ns_b_mass | train | def ns_g_mass_to_ns_b_mass(ns_g_mass, ns_sequence):
"""
Determines the baryonic mass of an NS given its gravitational
mass and an NS equilibrium sequence.
Parameters
-----------
ns_g_mass: float
NS gravitational mass (in solar masses)
ns_sequence: 3D-array
contains the sequence data in the form NS gravitational
mass (in solar masses), NS baryonic mass (in solar
masses), NS compactness (dimensionless)
Returns
----------
float
The NS baryonic mass (in solar massesr**3*(r**2*(r-6)+chi**2*(3*r+4))+chi**4*(3*r*(r-2)+chi**2))
"""
x = ns_sequence[:,0]
y = ns_sequence[:,1]
f = scipy.interpolate.interp1d(x, y)
return f(ns_g_mass) | python | {
"resource": ""
} |
q32032 | low_frequency_cutoff_from_cli | train | def low_frequency_cutoff_from_cli(opts):
"""Parses the low frequency cutoff from the given options.
Returns
-------
dict
Dictionary of instruments -> low frequency cutoff.
"""
# FIXME: this just uses the same frequency cutoff for every instrument for
# now. We should allow for different frequency cutoffs to be used; that
# will require (minor) changes to the Likelihood class
instruments = opts.instruments if opts.instruments is not None else []
return {ifo: opts.low_frequency_cutoff for ifo in instruments} | python | {
"resource": ""
} |
q32033 | data_from_cli | train | def data_from_cli(opts):
"""Loads the data needed for a model from the given
command-line options. Gates specifed on the command line are also applied.
Parameters
----------
opts : ArgumentParser parsed args
Argument options parsed from a command line string (the sort of thing
returned by `parser.parse_args`).
Returns
-------
strain_dict : dict
Dictionary of instruments -> `TimeSeries` strain.
stilde_dict : dict
Dictionary of instruments -> `FrequencySeries` strain.
psd_dict : dict
Dictionary of instruments -> `FrequencySeries` psds.
"""
# get gates to apply
gates = gates_from_cli(opts)
psd_gates = psd_gates_from_cli(opts)
# get strain time series
instruments = opts.instruments if opts.instruments is not None else []
strain_dict = strain_from_cli_multi_ifos(opts, instruments,
precision="double")
# apply gates if not waiting to overwhiten
if not opts.gate_overwhitened:
strain_dict = apply_gates_to_td(strain_dict, gates)
# get strain time series to use for PSD estimation
# if user has not given the PSD time options then use same data as analysis
if opts.psd_start_time and opts.psd_end_time:
logging.info("Will generate a different time series for PSD "
"estimation")
psd_opts = opts
psd_opts.gps_start_time = psd_opts.psd_start_time
psd_opts.gps_end_time = psd_opts.psd_end_time
psd_strain_dict = strain_from_cli_multi_ifos(psd_opts,
instruments,
precision="double")
# apply any gates
logging.info("Applying gates to PSD data")
psd_strain_dict = apply_gates_to_td(psd_strain_dict, psd_gates)
elif opts.psd_start_time or opts.psd_end_time:
raise ValueError("Must give --psd-start-time and --psd-end-time")
else:
psd_strain_dict = strain_dict
# FFT strain and save each of the length of the FFT, delta_f, and
# low frequency cutoff to a dict
stilde_dict = {}
length_dict = {}
delta_f_dict = {}
low_frequency_cutoff_dict = low_frequency_cutoff_from_cli(opts)
for ifo in instruments:
stilde_dict[ifo] = strain_dict[ifo].to_frequencyseries()
length_dict[ifo] = len(stilde_dict[ifo])
delta_f_dict[ifo] = stilde_dict[ifo].delta_f
# get PSD as frequency series
psd_dict = psd_from_cli_multi_ifos(
opts, length_dict, delta_f_dict, low_frequency_cutoff_dict,
instruments, strain_dict=psd_strain_dict, precision="double")
# apply any gates to overwhitened data, if desired
if opts.gate_overwhitened and opts.gate is not None:
logging.info("Applying gates to overwhitened data")
# overwhiten the data
for ifo in gates:
stilde_dict[ifo] /= psd_dict[ifo]
stilde_dict = apply_gates_to_fd(stilde_dict, gates)
# unwhiten the data for the model
for ifo in gates:
stilde_dict[ifo] *= psd_dict[ifo]
return strain_dict, stilde_dict, psd_dict | python | {
"resource": ""
} |
q32034 | add_plot_posterior_option_group | train | def add_plot_posterior_option_group(parser):
"""Adds the options needed to configure plots of posterior results.
Parameters
----------
parser : object
ArgumentParser instance.
"""
pgroup = parser.add_argument_group("Options for what plots to create and "
"their formats.")
pgroup.add_argument('--plot-marginal', action='store_true', default=False,
help="Plot 1D marginalized distributions on the "
"diagonal axes.")
pgroup.add_argument('--marginal-percentiles', nargs='+', default=None,
type=float,
help="Percentiles to draw lines at on the 1D "
"histograms.")
pgroup.add_argument("--plot-scatter", action='store_true', default=False,
help="Plot each sample point as a scatter plot.")
pgroup.add_argument("--plot-density", action="store_true", default=False,
help="Plot the posterior density as a color map.")
pgroup.add_argument("--plot-contours", action="store_true", default=False,
help="Draw contours showing the 50th and 90th "
"percentile confidence regions.")
pgroup.add_argument('--contour-percentiles', nargs='+', default=None,
type=float,
help="Percentiles to draw contours if different "
"than 50th and 90th.")
# add mins, maxs options
pgroup.add_argument('--mins', nargs='+', metavar='PARAM:VAL', default=[],
help="Specify minimum parameter values to plot. This "
"should be done by specifying the parameter name "
"followed by the value. Parameter names must be "
"the same as the PARAM argument in --parameters "
"(or, if no parameters are provided, the same as "
"the parameter name specified in the variable "
"args in the input file. If none provided, "
"the smallest parameter value in the posterior "
"will be used.")
pgroup.add_argument('--maxs', nargs='+', metavar='PARAM:VAL', default=[],
help="Same as mins, but for the maximum values to "
"plot.")
# add expected parameters options
pgroup.add_argument('--expected-parameters', nargs='+',
metavar='PARAM:VAL',
default=[],
help="Specify expected parameter values to plot. If "
"provided, a cross will be plotted in each axis "
"that an expected parameter is provided. "
"Parameter names must be "
"the same as the PARAM argument in --parameters "
"(or, if no parameters are provided, the same as "
"the parameter name specified in the variable "
"args in the input file.")
pgroup.add_argument('--expected-parameters-color', default='r',
help="What to color the expected-parameters cross. "
"Default is red.")
pgroup.add_argument('--plot-injection-parameters', action='store_true',
default=False,
help="Get the expected parameters from the injection "
"in the input file. There must be only a single "
"injection in the file to work. Any values "
"specified by expected-parameters will override "
"the values obtained for the injection.")
return pgroup | python | {
"resource": ""
} |
q32035 | plot_ranges_from_cli | train | def plot_ranges_from_cli(opts):
"""Parses the mins and maxs arguments from the `plot_posterior` option
group.
Parameters
----------
opts : ArgumentParser
The parsed arguments from the command line.
Returns
-------
mins : dict
Dictionary of parameter name -> specified mins. Only parameters that
were specified in the --mins option will be included; if no parameters
were provided, will return an empty dictionary.
maxs : dict
Dictionary of parameter name -> specified maxs. Only parameters that
were specified in the --mins option will be included; if no parameters
were provided, will return an empty dictionary.
"""
mins = {}
for x in opts.mins:
x = x.split(':')
if len(x) != 2:
raise ValueError("option --mins not specified correctly; see help")
mins[x[0]] = float(x[1])
maxs = {}
for x in opts.maxs:
x = x.split(':')
if len(x) != 2:
raise ValueError("option --maxs not specified correctly; see help")
maxs[x[0]] = float(x[1])
return mins, maxs | python | {
"resource": ""
} |
q32036 | add_scatter_option_group | train | def add_scatter_option_group(parser):
"""Adds the options needed to configure scatter plots.
Parameters
----------
parser : object
ArgumentParser instance.
"""
scatter_group = parser.add_argument_group("Options for configuring the "
"scatter plot.")
scatter_group.add_argument(
'--z-arg', type=str, default=None, action=ParseParametersArg,
help='What to color the scatter points by. Syntax is the same as the '
'parameters option.')
scatter_group.add_argument(
"--vmin", type=float, help="Minimum value for the colorbar.")
scatter_group.add_argument(
"--vmax", type=float, help="Maximum value for the colorbar.")
scatter_group.add_argument(
"--scatter-cmap", type=str, default='plasma',
help="Specify the colormap to use for points. Default is plasma.")
return scatter_group | python | {
"resource": ""
} |
q32037 | add_density_option_group | train | def add_density_option_group(parser):
"""Adds the options needed to configure contours and density colour map.
Parameters
----------
parser : object
ArgumentParser instance.
"""
density_group = parser.add_argument_group("Options for configuring the "
"contours and density color map")
density_group.add_argument(
"--density-cmap", type=str, default='viridis',
help="Specify the colormap to use for the density. "
"Default is viridis.")
density_group.add_argument(
"--contour-color", type=str, default=None,
help="Specify the color to use for the contour lines. Default is "
"white for density plots and black for scatter plots.")
density_group.add_argument(
'--use-kombine-kde', default=False, action="store_true",
help="Use kombine's KDE for determining contours. "
"Default is to use scipy's gaussian_kde.")
return density_group | python | {
"resource": ""
} |
q32038 | prior_from_config | train | def prior_from_config(cp, prior_section='prior'):
"""Loads a prior distribution from the given config file.
Parameters
----------
cp : pycbc.workflow.WorkflowConfigParser
The config file to read.
sections : list of str, optional
The sections to retrieve the prior from. If ``None`` (the default),
will look in sections starting with 'prior'.
Returns
-------
distributions.JointDistribution
The prior distribution.
"""
# Read variable and static parameters from the config file
variable_params, _ = distributions.read_params_from_config(
cp, prior_section=prior_section, vargs_section='variable_params',
sargs_section='static_params')
# Read constraints to apply to priors from the config file
constraints = distributions.read_constraints_from_config(cp)
# Get PyCBC distribution instances for each variable parameter in the
# config file
dists = distributions.read_distributions_from_config(cp, prior_section)
# construct class that will return draws from the prior
return distributions.JointDistribution(variable_params, *dists,
**{"constraints": constraints}) | python | {
"resource": ""
} |
q32039 | compute_search_efficiency_in_bins | train | def compute_search_efficiency_in_bins(
found, total, ndbins,
sim_to_bins_function=lambda sim: (sim.distance,)):
"""
Calculate search efficiency in the given ndbins.
The first dimension of ndbins must be bins over injected distance.
sim_to_bins_function must map an object to a tuple indexing the ndbins.
"""
bins = bin_utils.BinnedRatios(ndbins)
# increment the numerator and denominator with found / found+missed injs
[bins.incnumerator(sim_to_bins_function(sim)) for sim in found]
[bins.incdenominator(sim_to_bins_function(sim)) for sim in total]
# regularize by setting denoms to 1 to avoid nans
bins.regularize()
# efficiency array is the ratio
eff = bin_utils.BinnedArray(bin_utils.NDBins(ndbins), array=bins.ratio())
# compute binomial uncertainties in each bin
err_arr = numpy.sqrt(eff.array * (1-eff.array)/bins.denominator.array)
err = bin_utils.BinnedArray(bin_utils.NDBins(ndbins), array=err_arr)
return eff, err | python | {
"resource": ""
} |
q32040 | compute_search_volume_in_bins | train | def compute_search_volume_in_bins(found, total, ndbins, sim_to_bins_function):
"""
Calculate search sensitive volume by integrating efficiency in distance bins
No cosmological corrections are applied: flat space is assumed.
The first dimension of ndbins must be bins over injected distance.
sim_to_bins_function must maps an object to a tuple indexing the ndbins.
"""
eff, err = compute_search_efficiency_in_bins(
found, total, ndbins, sim_to_bins_function)
dx = ndbins[0].upper() - ndbins[0].lower()
r = ndbins[0].centres()
# volume and errors have one fewer dimension than the input NDBins
vol = bin_utils.BinnedArray(bin_utils.NDBins(ndbins[1:]))
errors = bin_utils.BinnedArray(bin_utils.NDBins(ndbins[1:]))
# integrate efficiency to obtain volume
vol.array = numpy.trapz(eff.array.T * 4. * numpy.pi * r**2, r, dx)
# propagate errors in eff to errors in V
errors.array = numpy.sqrt(
((4 * numpy.pi * r**2 * err.array.T * dx)**2).sum(axis=-1)
)
return vol, errors | python | {
"resource": ""
} |
q32041 | volume_to_distance_with_errors | train | def volume_to_distance_with_errors(vol, vol_err):
""" Return the distance and standard deviation upper and lower bounds
Parameters
----------
vol: float
vol_err: float
Returns
-------
dist: float
ehigh: float
elow: float
"""
dist = (vol * 3.0/4.0/numpy.pi) ** (1.0/3.0)
ehigh = ((vol + vol_err) * 3.0/4.0/numpy.pi) ** (1.0/3.0) - dist
delta = numpy.where(vol >= vol_err, vol - vol_err, 0)
elow = dist - (delta * 3.0/4.0/numpy.pi) ** (1.0/3.0)
return dist, ehigh, elow | python | {
"resource": ""
} |
q32042 | volume_montecarlo | train | def volume_montecarlo(found_d, missed_d, found_mchirp, missed_mchirp,
distribution_param, distribution, limits_param,
min_param=None, max_param=None):
"""
Compute sensitive volume and standard error via direct Monte Carlo integral
Injections should be made over a range of distances such that sensitive
volume due to signals closer than D_min is negligible, and efficiency at
distances above D_max is negligible
TODO : Replace this function by Collin's formula given in Usman et al .. ?
OR get that coded as a new function?
Parameters
-----------
found_d: numpy.ndarray
The distances of found injections
missed_d: numpy.ndarray
The distances of missed injections
found_mchirp: numpy.ndarray
Chirp mass of found injections
missed_mchirp: numpy.ndarray
Chirp mass of missed injections
distribution_param: string
Parameter D of the injections used to generate a distribution over
distance, may be 'distance', 'chirp_distance'.
distribution: string
form of the distribution over the parameter, may be
'log' (uniform in log D)
'uniform' (uniform in D)
'distancesquared' (uniform in D**2)
'volume' (uniform in D***3)
limits_param: string
Parameter Dlim specifying limits inside which injections were made
may be 'distance', 'chirp distance'
min_param: float
minimum value of Dlim at which injections were made; only used for
log distribution, then if None the minimum actually injected value
will be used
max_param: float
maximum value of Dlim out to which injections were made; if None
the maximum actually injected value will be used
Returns
--------
volume: float
Volume estimate
volume_error: float
The standard error in the volume
"""
d_power = {
'log' : 3.,
'uniform' : 2.,
'distancesquared' : 1.,
'volume' : 0.
}[distribution]
mchirp_power = {
'log' : 0.,
'uniform' : 5. / 6.,
'distancesquared' : 5. / 3.,
'volume' : 15. / 6.
}[distribution]
# establish maximum physical distance: first for chirp distance distribution
if limits_param == 'chirp_distance':
mchirp_standard_bns = 1.4 * 2.**(-1. / 5.)
all_mchirp = numpy.concatenate((found_mchirp, missed_mchirp))
max_mchirp = all_mchirp.max()
if max_param is not None:
# use largest actually injected mchirp for conversion
max_distance = max_param * \
(max_mchirp / mchirp_standard_bns)**(5. / 6.)
else:
max_distance = max(found_d.max(), missed_d.max())
elif limits_param == 'distance':
if max_param is not None:
max_distance = max_param
else:
# if no max distance given, use max distance actually injected
max_distance = max(found_d.max(), missed_d.max())
else:
raise NotImplementedError("%s is not a recognized parameter"
% limits_param)
# volume of sphere
montecarlo_vtot = (4. / 3.) * numpy.pi * max_distance**3.
# arrays of weights for the MC integral
if distribution_param == 'distance':
found_weights = found_d ** d_power
missed_weights = missed_d ** d_power
elif distribution_param == 'chirp_distance':
# weight by a power of mchirp to rescale injection density to the
# target mass distribution
found_weights = found_d ** d_power * \
found_mchirp ** mchirp_power
missed_weights = missed_d ** d_power * \
missed_mchirp ** mchirp_power
else:
raise NotImplementedError("%s is not a recognized distance parameter"
% distribution_param)
all_weights = numpy.concatenate((found_weights, missed_weights))
# measured weighted efficiency is w_i for a found inj and 0 for missed
# MC integral is volume of sphere * (sum of found weights)/(sum of all weights)
# over injections covering the sphere
mc_weight_samples = numpy.concatenate((found_weights, 0 * missed_weights))
mc_sum = sum(mc_weight_samples)
if limits_param == 'distance':
mc_norm = sum(all_weights)
elif limits_param == 'chirp_distance':
# if injections are made up to a maximum chirp distance, account for
# extra missed injections that would occur when injecting up to
# maximum physical distance : this works out to a 'chirp volume' factor
mc_norm = sum(all_weights * (max_mchirp / all_mchirp) ** (5. / 2.))
# take out a constant factor
mc_prefactor = montecarlo_vtot / mc_norm
# count the samples
if limits_param == 'distance':
Ninj = len(mc_weight_samples)
elif limits_param == 'chirp_distance':
# find the total expected number after extending from maximum chirp
# dist up to maximum physical distance
if distribution == 'log':
# only need minimum distance in this one case
if min_param is not None:
min_distance = min_param * \
(numpy.min(all_mchirp) / mchirp_standard_bns) ** (5. / 6.)
else:
min_distance = min(numpy.min(found_d), numpy.min(missed_d))
logrange = numpy.log(max_distance / min_distance)
Ninj = len(mc_weight_samples) + (5. / 6.) * \
sum(numpy.log(max_mchirp / all_mchirp) / logrange)
else:
Ninj = sum((max_mchirp / all_mchirp) ** mchirp_power)
# sample variance of efficiency: mean of the square - square of the mean
mc_sample_variance = sum(mc_weight_samples ** 2.) / Ninj - \
(mc_sum / Ninj) ** 2.
# return MC integral and its standard deviation; variance of mc_sum scales
# relative to sample variance by Ninj (Bienayme' rule)
vol = mc_prefactor * mc_sum
vol_err = mc_prefactor * (Ninj * mc_sample_variance) ** 0.5
return vol, vol_err | python | {
"resource": ""
} |
q32043 | volume_binned_pylal | train | def volume_binned_pylal(f_dist, m_dist, bins=15):
""" Compute the sensitive volume using a distance binned efficiency estimate
Parameters
-----------
f_dist: numpy.ndarray
The distances of found injections
m_dist: numpy.ndarray
The distances of missed injections
Returns
--------
volume: float
Volume estimate
volume_error: float
The standard error in the volume
"""
def sims_to_bin(sim):
return (sim, 0)
total = numpy.concatenate([f_dist, m_dist])
ndbins = bin_utils.NDBins([bin_utils.LinearBins(min(total), max(total), bins),
bin_utils.LinearBins(0., 1, 1)])
vol, verr = compute_search_volume_in_bins(f_dist, total, ndbins, sims_to_bin)
return vol.array[0], verr.array[0] | python | {
"resource": ""
} |
q32044 | volume_shell | train | def volume_shell(f_dist, m_dist):
""" Compute the sensitive volume using sum over spherical shells.
Parameters
-----------
f_dist: numpy.ndarray
The distances of found injections
m_dist: numpy.ndarray
The distances of missed injections
Returns
--------
volume: float
Volume estimate
volume_error: float
The standard error in the volume
"""
f_dist.sort()
m_dist.sort()
distances = numpy.concatenate([f_dist, m_dist])
dist_sorting = distances.argsort()
distances = distances[dist_sorting]
low = 0
vol = 0
vol_err = 0
for i in range(len(distances)):
if i == len(distances) - 1:
break
high = (distances[i+1] + distances[i]) / 2
bin_width = high - low
if dist_sorting[i] < len(f_dist):
vol += 4 * numpy.pi * distances[i]**2.0 * bin_width
vol_err += (4 * numpy.pi * distances[i]**2.0 * bin_width)**2.0
low = high
vol_err = vol_err ** 0.5
return vol, vol_err | python | {
"resource": ""
} |
q32045 | raw_samples_to_dict | train | def raw_samples_to_dict(sampler, raw_samples):
"""Convenience function for converting ND array to a dict of samples.
The samples are assumed to have dimension
``[sampler.base_shape x] niterations x len(sampler.sampling_params)``.
Parameters
----------
sampler : sampler instance
An instance of an MCMC sampler.
raw_samples : array
The array of samples to convert.
Returns
-------
dict :
A dictionary mapping the raw samples to the variable params. If the
sampling params are not the same as the variable params, they will
also be included. Each array will have shape
``[sampler.base_shape x] niterations``.
"""
sampling_params = sampler.sampling_params
# convert to dictionary
samples = {param: raw_samples[..., ii] for
ii, param in enumerate(sampling_params)}
# apply boundary conditions
samples = sampler.model.prior_distribution.apply_boundary_conditions(
**samples)
# apply transforms to go to model's variable params space
if sampler.model.sampling_transforms is not None:
samples = sampler.model.sampling_transforms.apply(
samples, inverse=True)
return samples | python | {
"resource": ""
} |
q32046 | blob_data_to_dict | train | def blob_data_to_dict(stat_names, blobs):
"""Converts list of "blobs" to a dictionary of model stats.
Samplers like ``emcee`` store the extra tuple returned by ``CallModel`` to
a list called blobs. This is a list of lists of tuples with shape
niterations x nwalkers x nstats, where nstats is the number of stats
returned by the model's ``default_stats``. This converts that list to a
dictionary of arrays keyed by the stat names.
Parameters
----------
stat_names : list of str
The list of the stat names.
blobs : list of list of tuples
The data to convert.
Returns
-------
dict :
A dictionary mapping the model's ``default_stats`` to arrays of values.
Each array will have shape ``nwalkers x niterations``.
"""
# get the dtypes of each of the stats; we'll just take this from the
# first iteration and walker
dtypes = [type(val) for val in blobs[0][0]]
assert len(stat_names) == len(dtypes), (
"number of stat names must match length of tuples in the blobs")
# convert to an array; to ensure that we get the dtypes correct, we'll
# cast to a structured array
raw_stats = numpy.array(blobs, dtype=zip(stat_names, dtypes))
# transpose so that it has shape nwalkers x niterations
raw_stats = raw_stats.transpose()
# now return as a dictionary
return {stat: raw_stats[stat] for stat in stat_names} | python | {
"resource": ""
} |
q32047 | get_optional_arg_from_config | train | def get_optional_arg_from_config(cp, section, arg, dtype=str):
"""Convenience function to retrieve an optional argument from a config
file.
Parameters
----------
cp : ConfigParser
Open config parser to retrieve the argument from.
section : str
Name of the section to retrieve from.
arg : str
Name of the argument to retrieve.
dtype : datatype, optional
Cast the retrieved value (if it exists) to the given datatype. Default
is ``str``.
Returns
-------
val : None or str
If the argument is present, the value. Otherwise, None.
"""
if cp.has_option(section, arg):
val = dtype(cp.get(section, arg))
else:
val = None
return val | python | {
"resource": ""
} |
q32048 | BaseMCMC.niterations | train | def niterations(self):
"""The current number of iterations."""
itercounter = self._itercounter
if itercounter is None:
itercounter = 0
lastclear = self._lastclear
if lastclear is None:
lastclear = 0
return itercounter + lastclear | python | {
"resource": ""
} |
q32049 | BaseMCMC.get_thin_interval | train | def get_thin_interval(self):
"""Gets the thin interval to use.
If ``max_samples_per_chain`` is set, this will figure out what thin
interval is needed to satisfy that criteria. In that case, the thin
interval used must be a multiple of the currently used thin interval.
"""
if self.max_samples_per_chain is not None:
# the extra factor of 2 is to account for the fact that the thin
# interval will need to be at least twice as large as a previously
# used interval
thinfactor = 2 * self.niterations // self.max_samples_per_chain
# make the new interval is a multiple of the previous, to ensure
# that any samples currently on disk can be thinned accordingly
thin_interval = (thinfactor // self.thin_interval) * \
self.thin_interval
# make sure it's at least 1
thin_interval = max(thin_interval, 1)
else:
thin_interval = self.thin_interval
return thin_interval | python | {
"resource": ""
} |
q32050 | BaseMCMC.pos | train | def pos(self):
"""A dictionary of the current walker positions.
If the sampler hasn't been run yet, returns p0.
"""
pos = self._pos
if pos is None:
return self.p0
# convert to dict
pos = {param: self._pos[..., k]
for (k, param) in enumerate(self.sampling_params)}
return pos | python | {
"resource": ""
} |
q32051 | BaseMCMC.p0 | train | def p0(self):
"""A dictionary of the initial position of the walkers.
This is set by using ``set_p0``. If not set yet, a ``ValueError`` is
raised when the attribute is accessed.
"""
if self._p0 is None:
raise ValueError("initial positions not set; run set_p0")
# convert to dict
p0 = {param: self._p0[..., k]
for (k, param) in enumerate(self.sampling_params)}
return p0 | python | {
"resource": ""
} |
q32052 | BaseMCMC.set_p0 | train | def set_p0(self, samples_file=None, prior=None):
"""Sets the initial position of the walkers.
Parameters
----------
samples_file : InferenceFile, optional
If provided, use the last iteration in the given file for the
starting positions.
prior : JointDistribution, optional
Use the given prior to set the initial positions rather than
``model``'s prior.
Returns
-------
p0 : dict
A dictionary maping sampling params to the starting positions.
"""
# if samples are given then use those as initial positions
if samples_file is not None:
with self.io(samples_file, 'r') as fp:
samples = fp.read_samples(self.variable_params,
iteration=-1, flatten=False)
# remove the (length 1) niterations dimension
samples = samples[..., 0]
# make sure we have the same shape
assert samples.shape == self.base_shape, (
"samples in file {} have shape {}, but I have shape {}".
format(samples_file, samples.shape, self.base_shape))
# transform to sampling parameter space
if self.model.sampling_transforms is not None:
samples = self.model.sampling_transforms.apply(samples)
# draw random samples if samples are not provided
else:
nsamples = numpy.prod(self.base_shape)
samples = self.model.prior_rvs(size=nsamples, prior=prior).reshape(
self.base_shape)
# store as ND array with shape [base_shape] x nparams
ndim = len(self.variable_params)
p0 = numpy.ones(list(self.base_shape)+[ndim])
for i, param in enumerate(self.sampling_params):
p0[..., i] = samples[param]
self._p0 = p0
return self.p0 | python | {
"resource": ""
} |
q32053 | BaseMCMC.set_initial_conditions | train | def set_initial_conditions(self, initial_distribution=None,
samples_file=None):
"""Sets the initial starting point for the MCMC.
If a starting samples file is provided, will also load the random
state from it.
"""
self.set_p0(samples_file=samples_file, prior=initial_distribution)
# if a samples file was provided, use it to set the state of the
# sampler
if samples_file is not None:
self.set_state_from_file(samples_file) | python | {
"resource": ""
} |
q32054 | BaseMCMC.run | train | def run(self):
"""Runs the sampler."""
if self.target_eff_nsamples and self.checkpoint_interval is None:
raise ValueError("A checkpoint interval must be set if "
"targetting an effective number of samples")
# get the starting number of samples:
# "nsamples" keeps track of the number of samples we've obtained (if
# target_eff_nsamples is not None, this is the effective number of
# samples; otherwise, this is the total number of samples).
# _lastclear is the number of iterations that the file already
# contains (either due to sampler burn-in, or a previous checkpoint)
if self.new_checkpoint:
self._lastclear = 0
else:
with self.io(self.checkpoint_file, "r") as fp:
self._lastclear = fp.niterations
if self.target_eff_nsamples is not None:
target_nsamples = self.target_eff_nsamples
with self.io(self.checkpoint_file, "r") as fp:
nsamples = fp.effective_nsamples
elif self.target_niterations is not None:
# the number of samples is the number of iterations times the
# number of walkers
target_nsamples = self.nwalkers * self.target_niterations
nsamples = self._lastclear * self.nwalkers
else:
raise ValueError("must set either target_eff_nsamples or "
"target_niterations; see set_target")
self._itercounter = 0
# figure out the interval to use
iterinterval = self.checkpoint_interval
if iterinterval is None:
iterinterval = self.target_niterations
# run sampler until we have the desired number of samples
while nsamples < target_nsamples:
# adjust the interval if we would go past the number of iterations
if self.target_niterations is not None and (
self.niterations + iterinterval > self.target_niterations):
iterinterval = self.target_niterations - self.niterations
# run sampler and set initial values to None so that sampler
# picks up from where it left off next call
logging.info("Running sampler for {} to {} iterations".format(
self.niterations, self.niterations + iterinterval))
# run the underlying sampler for the desired interval
self.run_mcmc(iterinterval)
# update the itercounter
self._itercounter = self._itercounter + iterinterval
# dump the current results
self.checkpoint()
# update nsamples for next loop
if self.target_eff_nsamples is not None:
nsamples = self.effective_nsamples
logging.info("Have {} effective samples post burn in".format(
nsamples))
else:
nsamples += iterinterval * self.nwalkers | python | {
"resource": ""
} |
q32055 | BaseMCMC.effective_nsamples | train | def effective_nsamples(self):
"""The effective number of samples post burn-in that the sampler has
acquired so far."""
try:
act = numpy.array(list(self.acts.values())).max()
except (AttributeError, TypeError):
act = numpy.inf
if self.burn_in is None:
nperwalker = max(int(self.niterations // act), 1)
elif self.burn_in.is_burned_in:
nperwalker = int(
(self.niterations - self.burn_in.burn_in_iteration) // act)
# after burn in, we always have atleast 1 sample per walker
nperwalker = max(nperwalker, 1)
else:
nperwalker = 0
return self.nwalkers * nperwalker | python | {
"resource": ""
} |
q32056 | BaseMCMC.checkpoint | train | def checkpoint(self):
"""Dumps current samples to the checkpoint file."""
# thin and write new samples
for fn in [self.checkpoint_file, self.backup_file]:
with self.io(fn, "a") as fp:
# write the current number of iterations
fp.write_niterations(self.niterations)
thin_interval = self.get_thin_interval()
# thin samples on disk if it changed
if thin_interval > 1:
# if this is the first time writing, set the file's
# thinned_by
if fp.last_iteration() == 0:
fp.thinned_by = thin_interval
else:
# check if we need to thin the current samples on disk
thin_by = thin_interval // fp.thinned_by
if thin_by > 1:
logging.info("Thinning samples in %s by a factor "
"of %i", fn, int(thin_by))
fp.thin(thin_by)
fp_lastiter = fp.last_iteration()
logging.info("Writing samples to %s with thin interval %i", fn,
thin_interval)
self.write_results(fn)
# see if we had anything to write after thinning; if not, don't try
# to compute anything
with self.io(self.checkpoint_file, "r") as fp:
nsamples_written = fp.last_iteration() - fp_lastiter
if nsamples_written == 0:
logging.info("No samples written due to thinning")
else:
# check for burn in, compute the acls
self.acls = None
if self.burn_in is not None:
logging.info("Updating burn in")
self.burn_in.evaluate(self.checkpoint_file)
burn_in_index = self.burn_in.burn_in_index
logging.info("Is burned in: %r", self.burn_in.is_burned_in)
if self.burn_in.is_burned_in:
logging.info("Burn-in iteration: %i",
int(self.burn_in.burn_in_iteration))
else:
burn_in_index = 0
# Compute acls; the burn_in test may have calculated an acl and
# saved it, in which case we don't need to do it again.
if self.acls is None:
logging.info("Computing acls")
self.acls = self.compute_acl(self.checkpoint_file,
start_index=burn_in_index)
logging.info("ACT: %s", str(numpy.array(self.acts.values()).max()))
# write
for fn in [self.checkpoint_file, self.backup_file]:
with self.io(fn, "a") as fp:
if self.burn_in is not None:
fp.write_burn_in(self.burn_in)
if self.acls is not None:
fp.write_acls(self.acls)
# write effective number of samples
fp.write_effective_nsamples(self.effective_nsamples)
# check validity
logging.info("Validating checkpoint and backup files")
checkpoint_valid = validate_checkpoint_files(
self.checkpoint_file, self.backup_file)
if not checkpoint_valid:
raise IOError("error writing to checkpoint file")
elif self.checkpoint_signal:
# kill myself with the specified signal
logging.info("Exiting with SIG{}".format(self.checkpoint_signal))
kill_cmd="os.kill(os.getpid(), signal.SIG{})".format(
self.checkpoint_signal)
exec(kill_cmd)
# clear the in-memory chain to save memory
logging.info("Clearing samples from memory")
self.clear_samples() | python | {
"resource": ""
} |
q32057 | BaseMCMC.set_target_from_config | train | def set_target_from_config(self, cp, section):
"""Sets the target using the given config file.
This looks for ``niterations`` to set the ``target_niterations``, and
``effective-nsamples`` to set the ``target_eff_nsamples``.
Parameters
----------
cp : ConfigParser
Open config parser to retrieve the argument from.
section : str
Name of the section to retrieve from.
"""
if cp.has_option(section, "niterations"):
niterations = int(cp.get(section, "niterations"))
else:
niterations = None
if cp.has_option(section, "effective-nsamples"):
nsamples = int(cp.get(section, "effective-nsamples"))
else:
nsamples = None
self.set_target(niterations=niterations, eff_nsamples=nsamples) | python | {
"resource": ""
} |
q32058 | BaseMCMC.set_burn_in_from_config | train | def set_burn_in_from_config(self, cp):
"""Sets the burn in class from the given config file.
If no burn-in section exists in the file, then this just set the
burn-in class to None.
"""
try:
bit = self.burn_in_class.from_config(cp, self)
except ConfigParser.Error:
bit = None
self.set_burn_in(bit) | python | {
"resource": ""
} |
q32059 | BaseMCMC.set_thin_interval_from_config | train | def set_thin_interval_from_config(self, cp, section):
"""Sets thinning options from the given config file.
"""
if cp.has_option(section, "thin-interval"):
thin_interval = int(cp.get(section, "thin-interval"))
logging.info("Will thin samples using interval %i", thin_interval)
else:
thin_interval = None
if cp.has_option(section, "max-samples-per-chain"):
max_samps_per_chain = int(cp.get(section, "max-samples-per-chain"))
logging.info("Setting max samples per chain to %i",
max_samps_per_chain)
else:
max_samps_per_chain = None
# check for consistency
if thin_interval is not None and max_samps_per_chain is not None:
raise ValueError("provide either thin-interval or "
"max-samples-per-chain, not both")
# check that the thin interval is < then the checkpoint interval
if thin_interval is not None and self.checkpoint_interval is not None \
and thin_interval >= self.checkpoint_interval:
raise ValueError("thin interval must be less than the checkpoint "
"interval")
self.thin_interval = thin_interval
self.max_samples_per_chain = max_samps_per_chain | python | {
"resource": ""
} |
q32060 | BaseMCMC.acts | train | def acts(self):
"""The autocorrelation times of each parameter.
The autocorrelation time is defined as the ACL times the
``thin_interval``. It gives the number of iterations between
independent samples.
"""
if self.acls is None:
return None
return {p: acl * self.get_thin_interval()
for (p, acl) in self.acls.items()} | python | {
"resource": ""
} |
q32061 | MCMCAutocorrSupport.compute_acl | train | def compute_acl(cls, filename, start_index=None, end_index=None,
min_nsamples=10):
"""Computes the autocorrleation length for all model params in the
given file.
Parameter values are averaged over all walkers at each iteration.
The ACL is then calculated over the averaged chain. If an ACL cannot
be calculated because there are not enough samples, it will be set
to ``inf``.
Parameters
-----------
filename : str
Name of a samples file to compute ACLs for.
start_index : int, optional
The start index to compute the acl from. If None, will try to use
the number of burn-in iterations in the file; otherwise, will start
at the first sample.
end_index : int, optional
The end index to compute the acl to. If None, will go to the end
of the current iteration.
min_nsamples : int, optional
Require a minimum number of samples to compute an ACL. If the
number of samples per walker is less than this, will just set to
``inf``. Default is 10.
Returns
-------
dict
A dictionary giving the ACL for each parameter.
"""
acls = {}
with cls._io(filename, 'r') as fp:
for param in fp.variable_params:
samples = fp.read_raw_samples(
param, thin_start=start_index, thin_interval=1,
thin_end=end_index, flatten=False)[param]
samples = samples.mean(axis=0)
# if < min number of samples, just set to inf
if samples.size < min_nsamples:
acl = numpy.inf
else:
acl = autocorrelation.calculate_acl(samples)
if acl <= 0:
acl = numpy.inf
acls[param] = acl
return acls | python | {
"resource": ""
} |
q32062 | LiveSingleFarThreshold.check | train | def check(self, triggers, data_reader):
""" Look for a single detector trigger that passes the thresholds in
the current data.
"""
if len(triggers['snr']) == 0:
return None
i = triggers['snr'].argmax()
# This uses the pycbc live convention of chisq always meaning the
# reduced chisq.
rchisq = triggers['chisq'][i]
nsnr = ranking.newsnr(triggers['snr'][i], rchisq)
dur = triggers['template_duration'][i]
if nsnr > self.newsnr_threshold and \
rchisq < self.reduced_chisq_threshold and \
dur > self.duration_threshold:
fake_coinc = {'foreground/%s/%s' % (self.ifo, k): triggers[k][i]
for k in triggers}
fake_coinc['foreground/stat'] = nsnr
fake_coinc['foreground/ifar'] = self.fixed_ifar
fake_coinc['HWINJ'] = data_reader.near_hwinj()
return fake_coinc
return None | python | {
"resource": ""
} |
q32063 | InferenceTXTFile.write | train | def write(cls, output_file, samples, labels, delimiter=None):
""" Writes a text file with samples.
Parameters
-----------
output_file : str
The path of the file to write.
samples : FieldArray
Samples to write to file.
labels : list
A list of strings to include as header in TXT file.
delimiter : str
Delimiter to use in TXT file.
"""
delimiter = delimiter if delimiter is not None else cls.delimiter
header = delimiter.join(labels)
numpy.savetxt(output_file, samples,
comments=cls.comments, header=header,
delimiter=delimiter) | python | {
"resource": ""
} |
q32064 | get_topclasses | train | def get_topclasses(cls):
"""Gets the base classes that are in pycbc."""
bases = [c for c in inspect.getmro(cls)
if c.__module__.startswith('pycbc') and c != cls]
return ', '.join(['{}.{}'.format(c.__module__, c.__name__) for c in bases]) | python | {
"resource": ""
} |
q32065 | int_gps_time_to_str | train | def int_gps_time_to_str(t):
"""Takes an integer GPS time, either given as int or lal.LIGOTimeGPS, and
converts it to a string. If a LIGOTimeGPS with nonzero decimal part is
given, raises a ValueError."""
if isinstance(t, int):
return str(t)
elif isinstance(t, float):
# Wouldn't this just work generically?
int_t = int(t)
if abs(t - int_t) > 0.:
raise ValueError('Need an integer GPS time, got %s' % str(t))
return str(int_t)
elif isinstance(t, lal.LIGOTimeGPS):
if t.gpsNanoSeconds == 0:
return str(t.gpsSeconds)
else:
raise ValueError('Need an integer GPS time, got %s' % str(t))
else:
err_msg = "Didn't understand input type {}".format(type(t))
raise ValueError(err_msg) | python | {
"resource": ""
} |
q32066 | select_tmpltbank_class | train | def select_tmpltbank_class(curr_exe):
""" This function returns a class that is appropriate for setting up
template bank jobs within workflow.
Parameters
----------
curr_exe : string
The name of the executable to be used for generating template banks.
Returns
--------
exe_class : Sub-class of pycbc.workflow.core.Executable that holds utility
functions appropriate for the given executable. Instances of the class
('jobs') **must** have methods
* job.create_node()
and
* job.get_valid_times(ifo, )
"""
exe_to_class_map = {
'pycbc_geom_nonspinbank' : PyCBCTmpltbankExecutable,
'pycbc_aligned_stoch_bank': PyCBCTmpltbankExecutable
}
try:
return exe_to_class_map[curr_exe]
except KeyError:
raise NotImplementedError(
"No job class exists for executable %s, exiting" % curr_exe) | python | {
"resource": ""
} |
q32067 | select_matchedfilter_class | train | def select_matchedfilter_class(curr_exe):
""" This function returns a class that is appropriate for setting up
matched-filtering jobs within workflow.
Parameters
----------
curr_exe : string
The name of the matched filter executable to be used.
Returns
--------
exe_class : Sub-class of pycbc.workflow.core.Executable that holds utility
functions appropriate for the given executable. Instances of the class
('jobs') **must** have methods
* job.create_node()
and
* job.get_valid_times(ifo, )
"""
exe_to_class_map = {
'pycbc_inspiral' : PyCBCInspiralExecutable,
'pycbc_inspiral_skymax' : PyCBCInspiralExecutable,
'pycbc_multi_inspiral' : PyCBCMultiInspiralExecutable,
}
try:
return exe_to_class_map[curr_exe]
except KeyError:
# also conceivable to introduce a default class??
raise NotImplementedError(
"No job class exists for executable %s, exiting" % curr_exe) | python | {
"resource": ""
} |
q32068 | select_generic_executable | train | def select_generic_executable(workflow, exe_tag):
""" Returns a class that is appropriate for setting up jobs to run executables
having specific tags in the workflow config.
Executables should not be "specialized" jobs fitting into one of the
select_XXX_class functions above, i.e. not a matched filter or template
bank job, which require extra setup.
Parameters
----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance.
exe_tag : string
The name of the config section storing options for this executable and
the option giving the executable path in the [executables] section.
Returns
--------
exe_class : Sub-class of pycbc.workflow.core.Executable that holds utility
functions appropriate for the given executable. Instances of the class
('jobs') **must** have a method job.create_node()
"""
exe_path = workflow.cp.get("executables", exe_tag)
exe_name = os.path.basename(exe_path)
exe_to_class_map = {
'ligolw_add' : LigolwAddExecutable,
'ligolw_cbc_sstinca' : LigolwSSthincaExecutable,
'pycbc_sqlite_simplify' : PycbcSqliteSimplifyExecutable,
'ligolw_cbc_cluster_coincs': SQLInOutExecutable,
'ligolw_cbc_repop_coinc' : SQLInOutExecutable,
'repop_coinc_expfit' : SQLInOutExecutable,
'ligolw_cbc_dbinjfind' : SQLInOutExecutable,
'lalapps_inspinj' : LalappsInspinjExecutable,
'pycbc_dark_vs_bright_injections' : PycbcDarkVsBrightInjectionsExecutable,
'pycbc_timeslides' : PycbcTimeslidesExecutable,
'pycbc_compute_durations' : ComputeDurationsExecutable,
'pycbc_calculate_far' : PycbcCalculateFarExecutable,
"pycbc_run_sqlite" : SQLInOutExecutable,
# FIXME: We may end up with more than one class for using ligolw_sqlite
# How to deal with this?
"ligolw_sqlite" : ExtractToXMLExecutable,
"pycbc_inspinjfind" : InspinjfindExecutable,
"pycbc_pickle_horizon_distances" : PycbcPickleHorizonDistsExecutable,
"pycbc_combine_likelihood" : PycbcCombineLikelihoodExecutable,
"pycbc_gen_ranking_data" : PycbcGenerateRankingDataExecutable,
"pycbc_calculate_likelihood" : PycbcCalculateLikelihoodExecutable,
"gstlal_inspiral_marginalize_likelihood" : GstlalMarginalizeLikelihoodExecutable,
"pycbc_compute_far_from_snr_chisq_histograms" : GstlalFarfromsnrchisqhistExecutable,
"gstlal_inspiral_plot_sensitivity" : GstlalPlotSensitivity,
"gstlal_inspiral_plot_background" : GstlalPlotBackground,
"gstlal_inspiral_plotsummary" : GstlalPlotSummary,
"gstlal_inspiral_summary_page" : GstlalSummaryPage,
"pycbc_condition_strain" : PycbcConditionStrainExecutable
}
try:
return exe_to_class_map[exe_name]
except KeyError:
# Should we try some sort of default class??
raise NotImplementedError(
"No job class exists for executable %s, exiting" % exe_name) | python | {
"resource": ""
} |
q32069 | multi_ifo_coherent_job_setup | train | def multi_ifo_coherent_job_setup(workflow, out_files, curr_exe_job,
science_segs, datafind_outs, output_dir,
parents=None, slide_dict=None, tags=None):
"""
Method for setting up coherent inspiral jobs.
"""
if tags is None:
tags = []
data_seg, job_valid_seg = curr_exe_job.get_valid_times()
curr_out_files = FileList([])
if 'IPN' in datafind_outs[-1].description \
and 'bank_veto_bank' in datafind_outs[-2].description:
ipn_sky_points = datafind_outs[-1]
bank_veto = datafind_outs[-2]
frame_files = datafind_outs[:-2]
else:
ipn_sky_points = None
bank_veto = datafind_outs[-1]
frame_files = datafind_outs[:-1]
split_bank_counter = 0
if curr_exe_job.injection_file is None:
for split_bank in parents:
tag = list(tags)
tag.append(split_bank.tag_str)
node = curr_exe_job.create_node(data_seg, job_valid_seg,
parent=split_bank, dfParents=frame_files,
bankVetoBank=bank_veto, ipn_file=ipn_sky_points,
slide=slide_dict, tags=tag)
workflow.add_node(node)
split_bank_counter += 1
curr_out_files.extend(node.output_files)
else:
for inj_file in curr_exe_job.injection_file:
for split_bank in parents:
tag = list(tags)
tag.append(inj_file.tag_str)
tag.append(split_bank.tag_str)
node = curr_exe_job.create_node(data_seg, job_valid_seg,
parent=split_bank, inj_file=inj_file, tags=tag,
dfParents=frame_files, bankVetoBank=bank_veto,
ipn_file=ipn_sky_points)
workflow.add_node(node)
split_bank_counter += 1
curr_out_files.extend(node.output_files)
# FIXME: Here we remove PSD files if they are coming
# through. This should be done in a better way. On
# to-do list.
curr_out_files = [i for i in curr_out_files if 'PSD_FILE'\
not in i.tags]
out_files += curr_out_files
return out_files | python | {
"resource": ""
} |
q32070 | JobSegmenter.pick_tile_size | train | def pick_tile_size(self, seg_size, data_lengths, valid_chunks, valid_lengths):
""" Choose job tiles size based on science segment length """
if len(valid_lengths) == 1:
return data_lengths[0], valid_chunks[0], valid_lengths[0]
else:
# Pick the tile size that is closest to 1/3 of the science segment
target_size = seg_size / 3
pick, pick_diff = 0, abs(valid_lengths[0] - target_size)
for i, size in enumerate(valid_lengths):
if abs(size - target_size) < pick_diff:
pick, pick_diff = i, abs(size - target_size)
return data_lengths[pick], valid_chunks[pick], valid_lengths[pick] | python | {
"resource": ""
} |
q32071 | JobSegmenter.get_valid_times_for_job | train | def get_valid_times_for_job(self, num_job, allow_overlap=True):
""" Get the times for which this job is valid. """
if self.compatibility_mode:
return self.get_valid_times_for_job_legacy(num_job)
else:
return self.get_valid_times_for_job_workflow(num_job,
allow_overlap=allow_overlap) | python | {
"resource": ""
} |
q32072 | JobSegmenter.get_valid_times_for_job_workflow | train | def get_valid_times_for_job_workflow(self, num_job, allow_overlap=True):
""" Get the times for which the job num_job will be valid, using workflow's
method.
"""
# small factor of 0.0001 to avoid float round offs causing us to
# miss a second at end of segments.
shift_dur = self.curr_seg[0] + int(self.job_time_shift * num_job\
+ 0.0001)
job_valid_seg = self.valid_chunk.shift(shift_dur)
# If we need to recalculate the valid times to avoid overlap
if not allow_overlap:
data_per_job = (self.curr_seg_length - self.data_loss) / \
float(self.num_jobs)
lower_boundary = num_job*data_per_job + \
self.valid_chunk[0] + self.curr_seg[0]
upper_boundary = data_per_job + lower_boundary
# NOTE: Convert to int after calculating both boundaries
# small factor of 0.0001 to avoid float round offs causing us to
# miss a second at end of segments.
lower_boundary = int(lower_boundary)
upper_boundary = int(upper_boundary + 0.0001)
if lower_boundary < job_valid_seg[0] or \
upper_boundary > job_valid_seg[1]:
err_msg = ("Workflow is attempting to generate output "
"from a job at times where it is not valid.")
raise ValueError(err_msg)
job_valid_seg = segments.segment([lower_boundary,
upper_boundary])
return job_valid_seg | python | {
"resource": ""
} |
q32073 | JobSegmenter.get_valid_times_for_job_legacy | train | def get_valid_times_for_job_legacy(self, num_job):
""" Get the times for which the job num_job will be valid, using the method
use in inspiral hipe.
"""
# All of this should be integers, so no rounding factors needed.
shift_dur = self.curr_seg[0] + int(self.job_time_shift * num_job)
job_valid_seg = self.valid_chunk.shift(shift_dur)
# If this is the last job, push the end back
if num_job == (self.num_jobs - 1):
dataPushBack = self.data_length - self.valid_chunk[1]
job_valid_seg = segments.segment(job_valid_seg[0],
self.curr_seg[1] - dataPushBack)
return job_valid_seg | python | {
"resource": ""
} |
q32074 | JobSegmenter.get_data_times_for_job | train | def get_data_times_for_job(self, num_job):
""" Get the data that this job will read in. """
if self.compatibility_mode:
job_data_seg = self.get_data_times_for_job_legacy(num_job)
else:
job_data_seg = self.get_data_times_for_job_workflow(num_job)
# Sanity check that all data is used
if num_job == 0:
if job_data_seg[0] != self.curr_seg[0]:
err= "Job is not using data from the start of the "
err += "science segment. It should be using all data."
raise ValueError(err)
if num_job == (self.num_jobs - 1):
if job_data_seg[1] != self.curr_seg[1]:
err = "Job is not using data from the end of the "
err += "science segment. It should be using all data."
raise ValueError(err)
if hasattr(self.exe_class, 'zero_pad_data_extend'):
job_data_seg = self.exe_class.zero_pad_data_extend(job_data_seg,
self.curr_seg)
return job_data_seg | python | {
"resource": ""
} |
q32075 | PyCBCInspiralExecutable.get_valid_times | train | def get_valid_times(self):
""" Determine possible dimensions of needed input and valid output
"""
if self.cp.has_option('workflow-matchedfilter',
'min-analysis-segments'):
min_analysis_segs = int(self.cp.get('workflow-matchedfilter',
'min-analysis-segments'))
else:
min_analysis_segs = 0
if self.cp.has_option('workflow-matchedfilter',
'max-analysis-segments'):
max_analysis_segs = int(self.cp.get('workflow-matchedfilter',
'max-analysis-segments'))
else:
# Choose ridiculously large default value
max_analysis_segs = 1000
if self.cp.has_option('workflow-matchedfilter', 'min-analysis-length'):
min_analysis_length = int(self.cp.get('workflow-matchedfilter',
'min-analysis-length'))
else:
min_analysis_length = 0
if self.cp.has_option('workflow-matchedfilter', 'max-analysis-length'):
max_analysis_length = int(self.cp.get('workflow-matchedfilter',
'max-analysis-length'))
else:
# Choose a ridiculously large default value
max_analysis_length = 100000
segment_length = int(self.get_opt('segment-length'))
pad_data = 0
if self.has_opt('pad-data'):
pad_data += int(self.get_opt('pad-data'))
# NOTE: Currently the tapered data is ignored as it is short and
# will lie within the segment start/end pad. This means that
# the tapered data *will* be used for PSD estimation (but this
# effect should be small). It will also be in the data segments
# used for SNR generation (when in the middle of a data segment
# where zero-padding is not being used) but the templates should
# not be long enough to use this data assuming segment start/end
# pad take normal values. When using zero-padding this data will
# be used for SNR generation.
#if self.has_opt('taper-data'):
# pad_data += int(self.get_opt( 'taper-data' ))
if self.has_opt('allow-zero-padding'):
self.zero_padding=True
else:
self.zero_padding=False
start_pad = int(self.get_opt( 'segment-start-pad'))
end_pad = int(self.get_opt('segment-end-pad'))
seg_ranges = range(min_analysis_segs, max_analysis_segs + 1)
data_lengths = []
valid_regions = []
for nsegs in seg_ranges:
analysis_length = (segment_length - start_pad - end_pad) * nsegs
if not self.zero_padding:
data_length = analysis_length + pad_data * 2 \
+ start_pad + end_pad
start = pad_data + start_pad
end = data_length - pad_data - end_pad
else:
data_length = analysis_length + pad_data * 2
start = pad_data
end = data_length - pad_data
if data_length > max_analysis_length: continue
if data_length < min_analysis_length: continue
data_lengths += [data_length]
valid_regions += [segments.segment(start, end)]
# If min_analysis_length is given, ensure that it is added as an option
# for job analysis length.
if min_analysis_length:
data_length = min_analysis_length
if not self.zero_padding:
start = pad_data + start_pad
end = data_length - pad_data - end_pad
else:
start = pad_data
end = data_length - pad_data
if end > start:
data_lengths += [data_length]
valid_regions += [segments.segment(start, end)]
return data_lengths, valid_regions | python | {
"resource": ""
} |
q32076 | PyCBCTmpltbankExecutable.create_nodata_node | train | def create_nodata_node(self, valid_seg, tags=None):
""" A simplified version of create_node that creates a node that does not
need to read in data.
Parameters
-----------
valid_seg : glue.segment
The segment over which to declare the node valid. Usually this
would be the duration of the analysis.
Returns
--------
node : pycbc.workflow.core.Node
The instance corresponding to the created node.
"""
if tags is None:
tags = []
node = Node(self)
# Set the output file
# Add the PSD file if needed
if self.write_psd:
node.new_output_file_opt(valid_seg, '.txt', '--psd-output',
tags=tags+['PSD_FILE'], store_file=self.retain_files)
node.new_output_file_opt(valid_seg, '.xml.gz', '--output-file',
store_file=self.retain_files)
if self.psd_files is not None:
should_add = False
# If any of the ifos for this job are in the set
# of ifos for which a static psd was provided.
for ifo in self.ifo_list:
for psd_file in self.psd_files:
if ifo in psd_file.ifo_list:
should_add = True
if should_add:
node.add_input_opt('--psd-file', psd_file)
return node | python | {
"resource": ""
} |
q32077 | PycbcSplitBankExecutable.create_node | train | def create_node(self, bank, tags=None):
"""
Set up a CondorDagmanNode class to run splitbank code
Parameters
----------
bank : pycbc.workflow.core.File
The File containing the template bank to be split
Returns
--------
node : pycbc.workflow.core.Node
The node to run the job
"""
if tags is None:
tags = []
node = Node(self)
node.add_input_opt('--bank-file', bank)
# Get the output (taken from inspiral.py)
out_files = FileList([])
for i in range( 0, self.num_banks):
curr_tag = 'bank%d' %(i)
# FIXME: What should the tags actually be? The job.tags values are
# currently ignored.
curr_tags = bank.tags + [curr_tag] + tags
job_tag = bank.description + "_" + self.name.upper()
out_file = File(bank.ifo_list, job_tag, bank.segment,
extension=self.extension, directory=self.out_dir,
tags=curr_tags, store_file=self.retain_files)
out_files.append(out_file)
node.add_output_list_opt('--output-filenames', out_files)
return node | python | {
"resource": ""
} |
q32078 | PycbcCreateInjectionsExecutable.create_node | train | def create_node(self, config_file=None, seed=None, tags=None):
""" Set up a CondorDagmanNode class to run ``pycbc_create_injections``.
Parameters
----------
config_file : pycbc.workflow.core.File
A ``pycbc.workflow.core.File`` for inference configuration file
to be used with ``--config-files`` option.
seed : int
Seed to use for generating injections.
tags : list
A list of tags to include in filenames.
Returns
--------
node : pycbc.workflow.core.Node
The node to run the job.
"""
# default for tags is empty list
tags = [] if tags is None else tags
# get analysis start and end time
start_time = self.cp.get("workflow", "start-time")
end_time = self.cp.get("workflow", "end-time")
analysis_time = segments.segment(int(start_time), int(end_time))
# make node for running executable
node = Node(self)
node.add_input_opt("--config-file", config_file)
if seed:
node.add_opt("--seed", seed)
injection_file = node.new_output_file_opt(analysis_time,
".hdf", "--output-file",
tags=tags)
return node, injection_file | python | {
"resource": ""
} |
q32079 | PycbcInferenceExecutable.create_node | train | def create_node(self, channel_names, config_file, injection_file=None,
seed=None, fake_strain_seed=None, tags=None):
""" Set up a CondorDagmanNode class to run ``pycbc_inference``.
Parameters
----------
channel_names : dict
A ``dict`` of ``str`` to use for ``--channel-name`` option.
config_file : pycbc.workflow.core.File
A ``pycbc.workflow.core.File`` for inference configuration file
to be used with ``--config-files`` option.
injection_file : pycbc.workflow.core.File
A ``pycbc.workflow.core.File`` for injection file to be used
with ``--injection-file`` option.
seed : int
An ``int`` to be used with ``--seed`` option.
fake_strain_seed : dict
An ``int`` to be used with ``--fake-strain-seed`` option.
tags : list
A list of tags to include in filenames.
Returns
--------
node : pycbc.workflow.core.Node
The node to run the job.
"""
# default for tags is empty list
tags = [] if tags is None else tags
# get analysis start and end time
start_time = self.cp.get("workflow", "start-time")
end_time = self.cp.get("workflow", "end-time")
analysis_time = segments.segment(int(start_time), int(end_time))
# get multi-IFO opts
channel_names_opt = " ".join(["{}:{}".format(k, v)
for k, v in channel_names.iteritems()])
if fake_strain_seed is not None:
fake_strain_seed_opt = " ".join([
"{}:{}".format(k, v)
for k, v in fake_strain_seed.iteritems()])
# make node for running executable
node = Node(self)
node.add_opt("--instruments", " ".join(self.ifo_list))
node.add_opt("--gps-start-time", start_time)
node.add_opt("--gps-end-time", end_time)
node.add_opt("--channel-name", channel_names_opt)
node.add_input_opt("--config-file", config_file)
if fake_strain_seed is not None:
node.add_opt("--fake-strain-seed", fake_strain_seed_opt)
if injection_file:
node.add_input_opt("--injection-file", injection_file)
if seed:
node.add_opt("--seed", seed)
inference_file = node.new_output_file_opt(analysis_time,
".hdf", "--output-file",
tags=tags)
if self.cp.has_option("pegasus_profile-inference",
"condor|+CheckpointSig"):
ckpt_file_name = "{}.checkpoint".format(inference_file.name)
ckpt_file = dax.File(ckpt_file_name)
node._dax_node.uses(ckpt_file, link=dax.Link.OUTPUT,
register=False, transfer=False)
return node, inference_file | python | {
"resource": ""
} |
q32080 | ensurearray | train | def ensurearray(*args):
"""Apply numpy's broadcast rules to the given arguments.
This will ensure that all of the arguments are numpy arrays and that they
all have the same shape. See ``numpy.broadcast_arrays`` for more details.
It also returns a boolean indicating whether any of the inputs were
originally arrays.
Parameters
----------
*args :
The arguments to check.
Returns
-------
list :
A list with length ``N+1`` where ``N`` is the number of given
arguments. The first N values are the input arguments as ``ndarrays``s.
The last value is a boolean indicating whether any of the
inputs was an array.
"""
input_is_array = any(isinstance(arg, numpy.ndarray) for arg in args)
args = numpy.broadcast_arrays(*args)
args.append(input_is_array)
return args | python | {
"resource": ""
} |
q32081 | _mass2_from_mchirp_mass1 | train | def _mass2_from_mchirp_mass1(mchirp, mass1):
r"""Returns the secondary mass from the chirp mass and primary mass.
As this is a cubic equation this requires finding the roots and returning
the one that is real. Basically it can be shown that:
.. math::
m_2^3 - a(m_2 + m_1) = 0,
where
.. math::
a = \frac{\mathcal{M}^5}{m_1^3}.
This has 3 solutions but only one will be real.
"""
a = mchirp**5 / mass1**3
roots = numpy.roots([1,0,-a,-a*mass1])
# Find the real one
real_root = roots[(abs(roots - roots.real)).argmin()]
return real_root.real | python | {
"resource": ""
} |
q32082 | _mass_from_knownmass_eta | train | def _mass_from_knownmass_eta(known_mass, eta, known_is_secondary=False,
force_real=True):
r"""Returns the other component mass given one of the component masses
and the symmetric mass ratio.
This requires finding the roots of the quadratic equation:
.. math::
\eta m_2^2 + (2\eta - 1)m_1 m_2 + \eta m_1^2 = 0.
This has two solutions which correspond to :math:`m_1` being the heavier
mass or it being the lighter mass. By default, `known_mass` is assumed to
be the heavier (primary) mass, and the smaller solution is returned. Use
the `other_is_secondary` to invert.
Parameters
----------
known_mass : float
The known component mass.
eta : float
The symmetric mass ratio.
known_is_secondary : {False, bool}
Whether the known component mass is the primary or the secondary. If
True, `known_mass` is assumed to be the secondary (lighter) mass and
the larger solution is returned. Otherwise, the smaller solution is
returned. Default is False.
force_real : {True, bool}
Force the returned mass to be real.
Returns
-------
float
The other component mass.
"""
roots = numpy.roots([eta, (2*eta - 1)*known_mass, eta*known_mass**2.])
if force_real:
roots = numpy.real(roots)
if known_is_secondary:
return roots[roots.argmax()]
else:
return roots[roots.argmin()] | python | {
"resource": ""
} |
q32083 | mass2_from_mass1_eta | train | def mass2_from_mass1_eta(mass1, eta, force_real=True):
"""Returns the secondary mass from the primary mass and symmetric mass
ratio.
"""
return mass_from_knownmass_eta(mass1, eta, known_is_secondary=False,
force_real=force_real) | python | {
"resource": ""
} |
q32084 | mass1_from_mass2_eta | train | def mass1_from_mass2_eta(mass2, eta, force_real=True):
"""Returns the primary mass from the secondary mass and symmetric mass
ratio.
"""
return mass_from_knownmass_eta(mass2, eta, known_is_secondary=True,
force_real=force_real) | python | {
"resource": ""
} |
q32085 | lambda_tilde | train | def lambda_tilde(mass1, mass2, lambda1, lambda2):
""" The effective lambda parameter
The mass-weighted dominant effective lambda parameter defined in
https://journals.aps.org/prd/pdf/10.1103/PhysRevD.91.043002
"""
m1, m2, lambda1, lambda2, input_is_array = ensurearray(
mass1, mass2, lambda1, lambda2)
lsum = lambda1 + lambda2
ldiff, _ = ensurearray(lambda1 - lambda2)
mask = m1 < m2
ldiff[mask] = -ldiff[mask]
eta = eta_from_mass1_mass2(m1, m2)
p1 = (lsum) * (1 + 7. * eta - 31 * eta ** 2.0)
p2 = (1 - 4 * eta)**0.5 * (1 + 9 * eta - 11 * eta ** 2.0) * (ldiff)
return formatreturn(8.0 / 13.0 * (p1 + p2), input_is_array) | python | {
"resource": ""
} |
q32086 | chi_eff | train | def chi_eff(mass1, mass2, spin1z, spin2z):
"""Returns the effective spin from mass1, mass2, spin1z, and spin2z."""
return (spin1z * mass1 + spin2z * mass2) / (mass1 + mass2) | python | {
"resource": ""
} |
q32087 | chi_a | train | def chi_a(mass1, mass2, spin1z, spin2z):
""" Returns the aligned mass-weighted spin difference from mass1, mass2,
spin1z, and spin2z.
"""
return (spin2z * mass2 - spin1z * mass1) / (mass2 + mass1) | python | {
"resource": ""
} |
q32088 | chi_p | train | def chi_p(mass1, mass2, spin1x, spin1y, spin2x, spin2y):
"""Returns the effective precession spin from mass1, mass2, spin1x,
spin1y, spin2x, and spin2y.
"""
xi1 = secondary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y)
xi2 = primary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y)
return chi_p_from_xi1_xi2(xi1, xi2) | python | {
"resource": ""
} |
q32089 | phi_a | train | def phi_a(mass1, mass2, spin1x, spin1y, spin2x, spin2y):
""" Returns the angle between the in-plane perpendicular spins."""
phi1 = phi_from_spinx_spiny(primary_spin(mass1, mass2, spin1x, spin2x),
primary_spin(mass1, mass2, spin1y, spin2y))
phi2 = phi_from_spinx_spiny(secondary_spin(mass1, mass2, spin1x, spin2x),
secondary_spin(mass1, mass2, spin1y, spin2y))
return (phi1 - phi2) % (2 * numpy.pi) | python | {
"resource": ""
} |
q32090 | phi_s | train | def phi_s(spin1x, spin1y, spin2x, spin2y):
""" Returns the sum of the in-plane perpendicular spins."""
phi1 = phi_from_spinx_spiny(spin1x, spin1y)
phi2 = phi_from_spinx_spiny(spin2x, spin2y)
return (phi1 + phi2) % (2 * numpy.pi) | python | {
"resource": ""
} |
q32091 | chi_eff_from_spherical | train | def chi_eff_from_spherical(mass1, mass2, spin1_a, spin1_polar,
spin2_a, spin2_polar):
"""Returns the effective spin using spins in spherical coordinates."""
spin1z = spin1_a * numpy.cos(spin1_polar)
spin2z = spin2_a * numpy.cos(spin2_polar)
return chi_eff(mass1, mass2, spin1z, spin2z) | python | {
"resource": ""
} |
q32092 | chi_p_from_spherical | train | def chi_p_from_spherical(mass1, mass2, spin1_a, spin1_azimuthal, spin1_polar,
spin2_a, spin2_azimuthal, spin2_polar):
"""Returns the effective precession spin using spins in spherical
coordinates.
"""
spin1x, spin1y, _ = _spherical_to_cartesian(
spin1_a, spin1_azimuthal, spin1_polar)
spin2x, spin2y, _ = _spherical_to_cartesian(
spin2_a, spin2_azimuthal, spin2_polar)
return chi_p(mass1, mass2, spin1x, spin1y, spin2x, spin2y) | python | {
"resource": ""
} |
q32093 | primary_spin | train | def primary_spin(mass1, mass2, spin1, spin2):
"""Returns the dimensionless spin of the primary mass."""
mass1, mass2, spin1, spin2, input_is_array = ensurearray(
mass1, mass2, spin1, spin2)
sp = copy.copy(spin1)
mask = mass1 < mass2
sp[mask] = spin2[mask]
return formatreturn(sp, input_is_array) | python | {
"resource": ""
} |
q32094 | secondary_spin | train | def secondary_spin(mass1, mass2, spin1, spin2):
"""Returns the dimensionless spin of the secondary mass."""
mass1, mass2, spin1, spin2, input_is_array = ensurearray(
mass1, mass2, spin1, spin2)
ss = copy.copy(spin2)
mask = mass1 < mass2
ss[mask] = spin1[mask]
return formatreturn(ss, input_is_array) | python | {
"resource": ""
} |
q32095 | primary_xi | train | def primary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y):
"""Returns the effective precession spin argument for the larger mass.
"""
spinx = primary_spin(mass1, mass2, spin1x, spin2x)
spiny = primary_spin(mass1, mass2, spin1y, spin2y)
return chi_perp_from_spinx_spiny(spinx, spiny) | python | {
"resource": ""
} |
q32096 | secondary_xi | train | def secondary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y):
"""Returns the effective precession spin argument for the smaller mass.
"""
spinx = secondary_spin(mass1, mass2, spin1x, spin2x)
spiny = secondary_spin(mass1, mass2, spin1y, spin2y)
return xi2_from_mass1_mass2_spin2x_spin2y(mass1, mass2, spinx, spiny) | python | {
"resource": ""
} |
q32097 | xi2_from_mass1_mass2_spin2x_spin2y | train | def xi2_from_mass1_mass2_spin2x_spin2y(mass1, mass2, spin2x, spin2y):
"""Returns the effective precession spin argument for the smaller mass.
This function assumes it's given spins of the secondary mass.
"""
q = q_from_mass1_mass2(mass1, mass2)
a1 = 2 + 3 * q / 2
a2 = 2 + 3 / (2 * q)
return a1 / (q**2 * a2) * chi_perp_from_spinx_spiny(spin2x, spin2y) | python | {
"resource": ""
} |
q32098 | chi_perp_from_mass1_mass2_xi2 | train | def chi_perp_from_mass1_mass2_xi2(mass1, mass2, xi2):
"""Returns the in-plane spin from mass1, mass2, and xi2 for the
secondary mass.
"""
q = q_from_mass1_mass2(mass1, mass2)
a1 = 2 + 3 * q / 2
a2 = 2 + 3 / (2 * q)
return q**2 * a2 / a1 * xi2 | python | {
"resource": ""
} |
q32099 | chi_p_from_xi1_xi2 | train | def chi_p_from_xi1_xi2(xi1, xi2):
"""Returns effective precession spin from xi1 and xi2.
"""
xi1, xi2, input_is_array = ensurearray(xi1, xi2)
chi_p = copy.copy(xi1)
mask = xi1 < xi2
chi_p[mask] = xi2[mask]
return formatreturn(chi_p, input_is_array) | python | {
"resource": ""
} |
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