diff --git a/bilby/gw/detector.py b/bilby/gw/detector.py
deleted file mode 100644
index febb6e41528cb0c5001c5e3d9c0eb8e166b45eb8..0000000000000000000000000000000000000000
--- a/bilby/gw/detector.py
+++ /dev/null
@@ -1,2597 +0,0 @@
-from __future__ import division, print_function, absolute_import
-
-import os
-import sys
-
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.signal.windows import tukey
-from scipy.interpolate import interp1d
-
-from . import utils as gwutils
-from ..core import utils
-from ..core.utils import logger
-from ..core.series import CoupledTimeAndFrequencySeries
-from .calibration import Recalibrate
-from . import conversion
-
-try:
- import gwpy
- import gwpy.signal
-except ImportError:
- logger.warning("You do not have gwpy installed currently. You will "
- " not be able to use some of the prebuilt functions.")
-
-try:
- import lal
- import lalsimulation as lalsim
-except ImportError:
- logger.warning("You do not have lalsuite installed currently. You will"
- " not be able to use some of the prebuilt functions.")
-
-
-class InterferometerList(list):
- """ A list of Interferometer objects """
-
- def __init__(self, interferometers):
- """ Instantiate a InterferometerList
-
- The InterferometerList is a list of Interferometer objects, each
- object has the data used in evaluating the likelihood
-
- Parameters
- ----------
- interferometers: iterable
- The list of interferometers
- """
-
- list.__init__(self)
- if type(interferometers) == str:
- raise TypeError("Input must not be a string")
- for ifo in interferometers:
- if type(ifo) == str:
- ifo = get_empty_interferometer(ifo)
- if type(ifo) not in [Interferometer, TriangularInterferometer]:
- raise TypeError("Input list of interferometers are not all Interferometer objects")
- else:
- self.append(ifo)
- self._check_interferometers()
-
- def _check_interferometers(self):
- """ Check certain aspects of the set are the same """
- consistent_attributes = ['duration', 'start_time', 'sampling_frequency']
- for attribute in consistent_attributes:
- x = [getattr(interferometer.strain_data, attribute)
- for interferometer in self]
- if not all(y == x[0] for y in x):
- raise ValueError("The {} of all interferometers are not the same".format(attribute))
-
- def set_strain_data_from_power_spectral_densities(self, sampling_frequency, duration, start_time=0):
- """ Set the `Interferometer.strain_data` from the power spectral densities of the detectors
-
- This uses the `interferometer.power_spectral_density` object to set
- the `strain_data` to a noise realization. See
- `bilby.gw.detector.InterferometerStrainData` for further information.
-
- Parameters
- ----------
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
-
- """
- for interferometer in self:
- interferometer.set_strain_data_from_power_spectral_density(sampling_frequency=sampling_frequency,
- duration=duration,
- start_time=start_time)
-
- def set_strain_data_from_zero_noise(self, sampling_frequency, duration, start_time=0):
- """ Set the `Interferometer.strain_data` from the power spectral densities of the detectors
-
- This uses the `interferometer.power_spectral_density` object to set
- the `strain_data` to zero noise. See
- `bilby.gw.detector.InterferometerStrainData` for further information.
-
- Parameters
- ----------
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
-
- """
- for interferometer in self:
- interferometer.set_strain_data_from_zero_noise(sampling_frequency=sampling_frequency,
- duration=duration,
- start_time=start_time)
-
- def inject_signal(self, parameters=None, injection_polarizations=None, waveform_generator=None):
- """ Inject a signal into noise in each of the three detectors.
-
- Parameters
- ----------
- parameters: dict
- Parameters of the injection.
- injection_polarizations: dict
- Polarizations of waveform to inject, output of
- `waveform_generator.frequency_domain_strain()`. If
- `waveform_generator` is also given, the injection_polarizations will
- be calculated directly and this argument can be ignored.
- waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
- A WaveformGenerator instance using the source model to inject. If
- `injection_polarizations` is given, this will be ignored.
-
- Note
- ----------
- if your signal takes a substantial amount of time to generate, or
- you experience buggy behaviour. It is preferable to provide the
- injection_polarizations directly.
-
- Returns
- -------
- injection_polarizations: dict
-
- """
- if injection_polarizations is None:
- if waveform_generator is not None:
- injection_polarizations = \
- waveform_generator.frequency_domain_strain(parameters)
- else:
- raise ValueError(
- "inject_signal needs one of waveform_generator or "
- "injection_polarizations.")
-
- all_injection_polarizations = list()
- for interferometer in self:
- all_injection_polarizations.append(
- interferometer.inject_signal(parameters=parameters, injection_polarizations=injection_polarizations))
-
- return all_injection_polarizations
-
- def save_data(self, outdir, label=None):
- """ Creates a save file for the data in plain text format
-
- Parameters
- ----------
- outdir: str
- The output directory in which the data is supposed to be saved
- label: str
- The string labelling the data
- """
- for interferometer in self:
- interferometer.save_data(outdir=outdir, label=label)
-
- def plot_data(self, signal=None, outdir='.', label=None):
- if utils.command_line_args.test:
- return
-
- for interferometer in self:
- interferometer.plot_data(signal=signal, outdir=outdir, label=label)
-
- @property
- def number_of_interferometers(self):
- return len(self)
-
- @property
- def duration(self):
- return self[0].strain_data.duration
-
- @property
- def start_time(self):
- return self[0].strain_data.start_time
-
- @property
- def sampling_frequency(self):
- return self[0].strain_data.sampling_frequency
-
- @property
- def frequency_array(self):
- return self[0].strain_data.frequency_array
-
- def append(self, interferometer):
- if isinstance(interferometer, InterferometerList):
- super(InterferometerList, self).extend(interferometer)
- else:
- super(InterferometerList, self).append(interferometer)
- self._check_interferometers()
-
- def extend(self, interferometers):
- super(InterferometerList, self).extend(interferometers)
- self._check_interferometers()
-
- def insert(self, index, interferometer):
- super(InterferometerList, self).insert(index, interferometer)
- self._check_interferometers()
-
- @property
- def meta_data(self):
- """ Dictionary of the per-interferometer meta_data """
- return {interferometer.name: interferometer.meta_data
- for interferometer in self}
-
- @staticmethod
- def _hdf5_filename_from_outdir_label(outdir, label):
- return os.path.join(outdir, label + '.h5')
-
- def to_hdf5(self, outdir='outdir', label='ifo_list'):
- """ Saves the object to a hdf5 file
-
- Parameters
- ----------
- outdir: str, optional
- Output directory name of the file
- label: str, optional
- Output file name, is 'ifo_list' if not given otherwise. A list of
- the included interferometers will be appended.
- """
- import deepdish
- if sys.version_info[0] < 3:
- raise NotImplementedError('Pickling of InterferometerList is not supported in Python 2.'
- 'Use Python 3 instead.')
- label = label + '_' + ''.join(ifo.name for ifo in self)
- utils.check_directory_exists_and_if_not_mkdir(outdir)
- deepdish.io.save(self._hdf5_filename_from_outdir_label(outdir, label), self)
-
- @classmethod
- def from_hdf5(cls, filename=None):
- """ Loads in an InterferometerList object from an hdf5 file
-
- Parameters
- ----------
- filename: str
- If given, try to load from this filename
-
- """
- import deepdish
- if sys.version_info[0] < 3:
- raise NotImplementedError('Pickling of InterferometerList is not supported in Python 2.'
- 'Use Python 3 instead.')
- res = deepdish.io.load(filename)
- if res.__class__ == list:
- res = cls(res)
- if res.__class__ != cls:
- raise TypeError('The loaded object is not a InterferometerList')
- return res
-
-
-class InterferometerStrainData(object):
- """ Strain data for an interferometer """
-
- def __init__(self, minimum_frequency=0, maximum_frequency=np.inf,
- roll_off=0.2):
- """ Initiate an InterferometerStrainData object
-
- The initialised object contains no data, this should be added using one
- of the `set_from..` methods.
-
- Parameters
- ----------
- minimum_frequency: float
- Minimum frequency to analyse for detector. Default is 0.
- maximum_frequency: float
- Maximum frequency to analyse for detector. Default is infinity.
- roll_off: float
- The roll-off (in seconds) used in the Tukey window, default=0.2s.
- This corresponds to alpha * duration / 2 for scipy tukey window.
-
- """
- self.minimum_frequency = minimum_frequency
- self.maximum_frequency = maximum_frequency
- self.roll_off = roll_off
- self.window_factor = 1
-
- self._times_and_frequencies = CoupledTimeAndFrequencySeries()
- # self._set_time_and_frequency_array_parameters(None, None, None)
-
- self._frequency_domain_strain = None
- self._frequency_array = None
- self._time_domain_strain = None
- self._time_array = None
- self._channel = None
-
- def __eq__(self, other):
- if self.minimum_frequency == other.minimum_frequency \
- and self.maximum_frequency == other.maximum_frequency \
- and self.roll_off == other.roll_off \
- and self.window_factor == other.window_factor \
- and self.sampling_frequency == other.sampling_frequency \
- and self.duration == other.duration \
- and self.start_time == other.start_time \
- and np.array_equal(self.time_array, other.time_array) \
- and np.array_equal(self.frequency_array, other.frequency_array) \
- and np.array_equal(self.frequency_domain_strain, other.frequency_domain_strain) \
- and np.array_equal(self.time_domain_strain, other.time_domain_strain):
- return True
- return False
-
- def time_within_data(self, time):
- """ Check if time is within the data span
-
- Parameters
- ----------
- time: float
- The time to check
-
- Returns
- -------
- bool:
- A boolean stating whether the time is inside or outside the span
-
- """
- if time < self.start_time:
- logger.debug("Time is before the start_time")
- return False
- elif time > self.start_time + self.duration:
- logger.debug("Time is after the start_time + duration")
- return False
- else:
- return True
-
- @property
- def minimum_frequency(self):
- return self.__minimum_frequency
-
- @minimum_frequency.setter
- def minimum_frequency(self, minimum_frequency):
- self.__minimum_frequency = minimum_frequency
-
- @property
- def maximum_frequency(self):
- """ Force the maximum frequency be less than the Nyquist frequency """
- if self.sampling_frequency is not None:
- if 2 * self.__maximum_frequency > self.sampling_frequency:
- self.__maximum_frequency = self.sampling_frequency / 2.
- return self.__maximum_frequency
-
- @maximum_frequency.setter
- def maximum_frequency(self, maximum_frequency):
- self.__maximum_frequency = maximum_frequency
-
- @property
- def frequency_mask(self):
- """Masking array for limiting the frequency band.
-
- Returns
- -------
- array_like: An array of boolean values
- """
- try:
- return self._frequency_mask
- except AttributeError:
- frequency_array = self._times_and_frequencies.frequency_array
- mask = ((frequency_array >= self.minimum_frequency) &
- (frequency_array <= self.maximum_frequency))
- self._frequency_mask = mask
- return self._frequency_mask
-
- @property
- def alpha(self):
- return 2 * self.roll_off / self.duration
-
- def time_domain_window(self, roll_off=None, alpha=None):
- """
- Window function to apply to time domain data before FFTing.
-
- This defines self.window_factor as the power loss due to the windowing.
- See https://dcc.ligo.org/DocDB/0027/T040089/000/T040089-00.pdf
-
- Parameters
- ----------
- roll_off: float
- Rise time of window in seconds
- alpha: float
- Parameter to pass to tukey window, how much of segment falls
- into windowed part
-
- Return
- ------
- window: array
- Window function over time array
- """
- if roll_off is not None:
- self.roll_off = roll_off
- elif alpha is not None:
- self.roll_off = alpha * self.duration / 2
- window = tukey(len(self._time_domain_strain), alpha=self.alpha)
- self.window_factor = np.mean(window ** 2)
- return window
-
- @property
- def time_domain_strain(self):
- """ The time domain strain, in units of strain """
- if self._time_domain_strain is not None:
- return self._time_domain_strain
- elif self._frequency_domain_strain is not None:
- self._time_domain_strain = utils.infft(
- self.frequency_domain_strain, self.sampling_frequency)
- return self._time_domain_strain
-
- else:
- raise ValueError("time domain strain data not yet set")
-
- @property
- def frequency_domain_strain(self):
- """ Returns the frequency domain strain
-
- This is the frequency domain strain normalised to units of
- strain / Hz, obtained by a one-sided Fourier transform of the
- time domain data, divided by the sampling frequency.
- """
- if self._frequency_domain_strain is not None:
- return self._frequency_domain_strain * self.frequency_mask
- elif self._time_domain_strain is not None:
- logger.info("Generating frequency domain strain from given time "
- "domain strain.")
- logger.info("Applying a tukey window with alpha={}, roll off={}".format(
- self.alpha, self.roll_off))
- # self.low_pass_filter()
- window = self.time_domain_window()
- self._frequency_domain_strain, self.frequency_array = utils.nfft(
- self._time_domain_strain * window, self.sampling_frequency)
- return self._frequency_domain_strain * self.frequency_mask
- else:
- raise ValueError("frequency domain strain data not yet set")
-
- @frequency_domain_strain.setter
- def frequency_domain_strain(self, frequency_domain_strain):
- if not len(self.frequency_array) == len(frequency_domain_strain):
- raise ValueError("The frequency_array and the set strain have different lengths")
- self._frequency_domain_strain = frequency_domain_strain
-
- def to_gwpy_timeseries(self):
- """
- Output the time series strain data as a :class:`gwpy.timeseries.TimeSeries`.
- """
-
- return gwpy.timeseries.TimeSeries(self.time_domain_strain,
- sample_rate=self.sampling_frequency,
- t0=self.start_time,
- channel=self.channel)
-
- def to_pycbc_timeseries(self):
- """
- Output the time series strain data as a :class:`pycbc.types.timeseries.TimeSeries`.
- """
-
- try:
- import pycbc
- except ImportError:
- raise ImportError("Cannot output strain data as PyCBC TimeSeries")
-
- return pycbc.types.timeseries.TimeSeries(self.time_domain_strain,
- delta_t=(1. / self.sampling_frequency),
- epoch=lal.LIGOTimeGPS(self.start_time))
-
- def to_lal_timeseries(self):
- """
- Output the time series strain data as a LAL TimeSeries object.
- """
-
- laldata = lal.CreateREAL8TimeSeries("",
- lal.LIGOTimeGPS(self.start_time),
- 0., (1. / self.sampling_frequency),
- lal.SecondUnit,
- len(self.time_domain_strain))
- laldata.data.data[:] = self.time_domain_strain
-
- return laldata
-
- def to_gwpy_frequencyseries(self):
- """
- Output the frequency series strain data as a :class:`gwpy.frequencyseries.FrequencySeries`.
- """
-
- return gwpy.frequencyseries.FrequencySeries(self.frequency_domain_strain,
- frequencies=self.frequency_array,
- epoch=self.start_time,
- channel=self.channel)
-
- def to_pycbc_frequencyseries(self):
- """
- Output the frequency series strain data as a :class:`pycbc.types.frequencyseries.FrequencySeries`.
- """
-
- try:
- import pycbc
- except ImportError:
- raise ImportError("Cannot output strain data as PyCBC FrequencySeries")
-
- return pycbc.types.frequencyseries.FrequencySeries(self.frequency_domain_strain,
- delta_f=(self.frequency_array[1] - self.frequency_array[0]),
- epoch=lal.LIGOTimeGPS(self.start_time))
-
- def to_lal_frequencyseries(self):
- """
- Output the frequency series strain data as a LAL FrequencySeries object.
- """
-
- laldata = lal.CreateCOMPLEX16FrequencySeries("",
- lal.LIGOTimeGPS(self.start_time),
- self.frequency_array[0],
- (self.frequency_array[1] - self.frequency_array[0]),
- lal.SecondUnit,
- len(self.frequency_domain_strain))
- laldata.data.data[:] = self.frequency_domain_strain
-
- return laldata
-
- def add_to_frequency_domain_strain(self, x):
- """Deprecated"""
- self._frequency_domain_strain += x
-
- def low_pass_filter(self, filter_freq=None):
- """ Low pass filter the data """
-
- if filter_freq is None:
- logger.debug(
- "Setting low pass filter_freq using given maximum frequency")
- filter_freq = self.maximum_frequency
-
- if 2 * filter_freq >= self.sampling_frequency:
- logger.info(
- "Low pass filter frequency of {}Hz requested, this is equal"
- " or greater than the Nyquist frequency so no filter applied"
- .format(filter_freq))
- return
-
- logger.debug("Applying low pass filter with filter frequency {}".format(filter_freq))
- bp = gwpy.signal.filter_design.lowpass(
- filter_freq, self.sampling_frequency)
- strain = gwpy.timeseries.TimeSeries(
- self.time_domain_strain, sample_rate=self.sampling_frequency)
- strain = strain.filter(bp, filtfilt=True)
- self._time_domain_strain = strain.value
-
- def create_power_spectral_density(
- self, fft_length, overlap=0, name='unknown', outdir=None,
- analysis_segment_start_time=None):
- """ Use the time domain strain to generate a power spectral density
-
- This create a Tukey-windowed power spectral density and writes it to a
- PSD file.
-
- Parameters
- ----------
- fft_length: float
- Duration of the analysis segment.
- overlap: float
- Number of seconds of overlap between FFTs.
- name: str
- The name of the detector, used in storing the PSD. Defaults to
- "unknown".
- outdir: str
- The output directory to write the PSD file too. If not given,
- the PSD will not be written to file.
- analysis_segment_start_time: float
- The start time of the analysis segment, if given, this data will
- be removed before creating the PSD.
-
- Returns
- -------
- frequency_array, psd : array_like
- The frequencies and power spectral density array
-
- """
-
- data = self.time_domain_strain
-
- if analysis_segment_start_time is not None:
- analysis_segment_end_time = analysis_segment_start_time + fft_length
- inside = (analysis_segment_start_time > self.time_array[0] +
- analysis_segment_end_time < self.time_array[-1])
- if inside:
- logger.info("Removing analysis segment data from the PSD data")
- idxs = (
- (self.time_array < analysis_segment_start_time) +
- (self.time_array > analysis_segment_end_time))
- data = data[idxs]
-
- # WARNING this line can cause issues if the data is non-contiguous
- strain = gwpy.timeseries.TimeSeries(data=data, sample_rate=self.sampling_frequency)
- psd_alpha = 2 * self.roll_off / fft_length
- logger.info(
- "Tukey window PSD data with alpha={}, roll off={}".format(
- psd_alpha, self.roll_off))
- psd = strain.psd(
- fftlength=fft_length, overlap=overlap, window=('tukey', psd_alpha))
-
- if outdir:
- psd_file = '{}/{}_PSD_{}_{}.txt'.format(outdir, name, self.start_time, self.duration)
- with open('{}'.format(psd_file), 'w+') as opened_file:
- for f, p in zip(psd.frequencies.value, psd.value):
- opened_file.write('{} {}\n'.format(f, p))
-
- return psd.frequencies.value, psd.value
-
- def _infer_time_domain_dependence(
- self, start_time, sampling_frequency, duration, time_array):
- """ Helper function to figure out if the time_array, or
- sampling_frequency and duration where given
- """
- self._infer_dependence(domain='time', array=time_array, duration=duration,
- sampling_frequency=sampling_frequency, start_time=start_time)
-
- def _infer_frequency_domain_dependence(
- self, start_time, sampling_frequency, duration, frequency_array):
- """ Helper function to figure out if the frequency_array, or
- sampling_frequency and duration where given
- """
-
- self._infer_dependence(domain='frequency', array=frequency_array,
- duration=duration, sampling_frequency=sampling_frequency, start_time=start_time)
-
- def _infer_dependence(self, domain, array, duration, sampling_frequency, start_time):
- if (sampling_frequency is not None) and (duration is not None):
- if array is not None:
- raise ValueError(
- "You have given the sampling_frequency, duration, and "
- "an array")
- pass
- elif array is not None:
- if domain == 'time':
- self.time_array = array
- elif domain == 'frequency':
- self.frequency_array = array
- return
- elif sampling_frequency is None or duration is None:
- raise ValueError(
- "You must provide both sampling_frequency and duration")
- else:
- raise ValueError(
- "Insufficient information given to set arrays")
- self._set_time_and_frequency_array_parameters(duration=duration,
- sampling_frequency=sampling_frequency,
- start_time=start_time)
-
- def set_from_time_domain_strain(
- self, time_domain_strain, sampling_frequency=None, duration=None,
- start_time=0, time_array=None):
- """ Set the strain data from a time domain strain array
-
- This sets the time_domain_strain attribute, the frequency_domain_strain
- is automatically calculated after a low-pass filter and Tukey window
- is applied.
-
- Parameters
- ----------
- time_domain_strain: array_like
- An array of the time domain strain.
- sampling_frequency: float
- The sampling frequency (in Hz).
- duration: float
- The data duration (in s).
- start_time: float
- The GPS start-time of the data.
- time_array: array_like
- The array of times, if sampling_frequency and duration not
- given.
-
- """
- self._infer_time_domain_dependence(start_time=start_time,
- sampling_frequency=sampling_frequency,
- duration=duration,
- time_array=time_array)
-
- logger.debug('Setting data using provided time_domain_strain')
- if np.shape(time_domain_strain) == np.shape(self.time_array):
- self._time_domain_strain = time_domain_strain
- self._frequency_domain_strain = None
- else:
- raise ValueError("Data times do not match time array")
-
- def set_from_gwpy_timeseries(self, time_series):
- """ Set the strain data from a gwpy TimeSeries
-
- This sets the time_domain_strain attribute, the frequency_domain_strain
- is automatically calculated after a low-pass filter and Tukey window
- is applied.
-
- Parameters
- ----------
- time_series: gwpy.timeseries.timeseries.TimeSeries
-
- """
- logger.debug('Setting data using provided gwpy TimeSeries object')
- if type(time_series) != gwpy.timeseries.TimeSeries:
- raise ValueError("Input time_series is not a gwpy TimeSeries")
- self._set_time_and_frequency_array_parameters(duration=time_series.duration.value,
- sampling_frequency=time_series.sample_rate.value,
- start_time=time_series.epoch.value)
- self._time_domain_strain = time_series.value
- self._frequency_domain_strain = None
- self._channel = time_series.channel
-
- @property
- def channel(self):
- return self._channel
-
- def set_from_open_data(
- self, name, start_time, duration=4, outdir='outdir', cache=True,
- **kwargs):
- """ Set the strain data from open LOSC data
-
- This sets the time_domain_strain attribute, the frequency_domain_strain
- is automatically calculated after a low-pass filter and Tukey window
- is applied.
-
- Parameters
- ----------
- name: str
- Detector name, e.g., 'H1'.
- start_time: float
- Start GPS time of segment.
- duration: float, optional
- The total time (in seconds) to analyse. Defaults to 4s.
- outdir: str
- Directory where the psd files are saved
- cache: bool, optional
- Whether or not to store/use the acquired data.
- **kwargs:
- All keyword arguments are passed to
- `gwpy.timeseries.TimeSeries.fetch_open_data()`.
-
- """
-
- timeseries = gwutils.get_open_strain_data(
- name, start_time, start_time + duration, outdir=outdir, cache=cache,
- **kwargs)
-
- self.set_from_gwpy_timeseries(timeseries)
-
- def set_from_csv(self, filename):
- """ Set the strain data from a csv file
-
- Parameters
- ----------
- filename: str
- The path to the file to read in
-
- """
- timeseries = gwpy.timeseries.TimeSeries.read(filename, format='csv')
- self.set_from_gwpy_timeseries(timeseries)
-
- def set_from_frequency_domain_strain(
- self, frequency_domain_strain, sampling_frequency=None,
- duration=None, start_time=0, frequency_array=None):
- """ Set the `frequency_domain_strain` from a numpy array
-
- Parameters
- ----------
- frequency_domain_strain: array_like
- The data to set.
- sampling_frequency: float
- The sampling frequency (in Hz).
- duration: float
- The data duration (in s).
- start_time: float
- The GPS start-time of the data.
- frequency_array: array_like
- The array of frequencies, if sampling_frequency and duration not
- given.
-
- """
-
- self._infer_frequency_domain_dependence(start_time=start_time,
- sampling_frequency=sampling_frequency,
- duration=duration,
- frequency_array=frequency_array)
-
- logger.debug('Setting data using provided frequency_domain_strain')
- if np.shape(frequency_domain_strain) == np.shape(self.frequency_array):
- self._frequency_domain_strain = frequency_domain_strain
- self.window_factor = 1
- else:
- raise ValueError("Data frequencies do not match frequency_array")
-
- def set_from_power_spectral_density(
- self, power_spectral_density, sampling_frequency, duration,
- start_time=0):
- """ Set the `frequency_domain_strain` by generating a noise realisation
-
- Parameters
- ----------
- power_spectral_density: bilby.gw.detector.PowerSpectralDensity
- A PowerSpectralDensity object used to generate the data
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
-
- """
-
- self._set_time_and_frequency_array_parameters(duration=duration,
- sampling_frequency=sampling_frequency,
- start_time=start_time)
-
- logger.debug(
- 'Setting data using noise realization from provided'
- 'power_spectal_density')
- frequency_domain_strain, frequency_array = \
- power_spectral_density.get_noise_realisation(
- self.sampling_frequency, self.duration)
-
- if np.array_equal(frequency_array, self.frequency_array):
- self._frequency_domain_strain = frequency_domain_strain
- else:
- raise ValueError("Data frequencies do not match frequency_array")
-
- def set_from_zero_noise(self, sampling_frequency, duration, start_time=0):
- """ Set the `frequency_domain_strain` to zero noise
-
- Parameters
- ----------
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
-
- """
-
- self._set_time_and_frequency_array_parameters(duration=duration,
- sampling_frequency=sampling_frequency,
- start_time=start_time)
-
- logger.debug('Setting zero noise data')
- self._frequency_domain_strain = np.zeros_like(self.frequency_array,
- dtype=np.complex)
-
- def set_from_frame_file(
- self, frame_file, sampling_frequency, duration, start_time=0,
- channel=None, buffer_time=1):
- """ Set the `frequency_domain_strain` from a frame fiile
-
- Parameters
- ----------
- frame_file: str
- File from which to load data.
- channel: str
- Channel to read from frame.
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
- buffer_time: float
- Read in data with `start_time-buffer_time` and
- `start_time+duration+buffer_time`
-
- """
-
- self._set_time_and_frequency_array_parameters(duration=duration,
- sampling_frequency=sampling_frequency,
- start_time=start_time)
-
- logger.info('Reading data from frame file {}'.format(frame_file))
- strain = gwutils.read_frame_file(
- frame_file, start_time=start_time, end_time=start_time + duration,
- buffer_time=buffer_time, channel=channel,
- resample=sampling_frequency)
-
- self.set_from_gwpy_timeseries(strain)
-
- def _set_time_and_frequency_array_parameters(self, duration, sampling_frequency, start_time):
- self._times_and_frequencies = CoupledTimeAndFrequencySeries(duration=duration,
- sampling_frequency=sampling_frequency,
- start_time=start_time)
-
- @property
- def sampling_frequency(self):
- return self._times_and_frequencies.sampling_frequency
-
- @sampling_frequency.setter
- def sampling_frequency(self, sampling_frequency):
- self._times_and_frequencies.sampling_frequency = sampling_frequency
-
- @property
- def duration(self):
- return self._times_and_frequencies.duration
-
- @duration.setter
- def duration(self, duration):
- self._times_and_frequencies.duration = duration
-
- @property
- def start_time(self):
- return self._times_and_frequencies.start_time
-
- @start_time.setter
- def start_time(self, start_time):
- self._times_and_frequencies.start_time = start_time
-
- @property
- def frequency_array(self):
- """ Frequencies of the data in Hz """
- return self._times_and_frequencies.frequency_array
-
- @frequency_array.setter
- def frequency_array(self, frequency_array):
- self._times_and_frequencies.frequency_array = frequency_array
-
- @property
- def time_array(self):
- """ Time of the data in seconds """
- return self._times_and_frequencies.time_array
-
- @time_array.setter
- def time_array(self, time_array):
- self._times_and_frequencies.time_array = time_array
-
-
-class Interferometer(object):
- """Class for the Interferometer """
-
- def __init__(self, name, power_spectral_density, minimum_frequency, maximum_frequency,
- length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth,
- xarm_tilt=0., yarm_tilt=0., calibration_model=Recalibrate()):
- """
- Instantiate an Interferometer object.
-
- Parameters
- ----------
- name: str
- Interferometer name, e.g., H1.
- power_spectral_density: PowerSpectralDensity
- Power spectral density determining the sensitivity of the detector.
- minimum_frequency: float
- Minimum frequency to analyse for detector.
- maximum_frequency: float
- Maximum frequency to analyse for detector.
- length: float
- Length of the interferometer in km.
- latitude: float
- Latitude North in degrees (South is negative).
- longitude: float
- Longitude East in degrees (West is negative).
- elevation: float
- Height above surface in metres.
- xarm_azimuth: float
- Orientation of the x arm in degrees North of East.
- yarm_azimuth: float
- Orientation of the y arm in degrees North of East.
- xarm_tilt: float, optional
- Tilt of the x arm in radians above the horizontal defined by
- ellipsoid earth model in LIGO-T980044-08.
- yarm_tilt: float, optional
- Tilt of the y arm in radians above the horizontal.
- calibration_model: Recalibration
- Calibration model, this applies the calibration correction to the
- template, the default model applies no correction.
- """
- self.__x_updated = False
- self.__y_updated = False
- self.__vertex_updated = False
- self.__detector_tensor_updated = False
-
- self.name = name
- self.length = length
- self.latitude = latitude
- self.longitude = longitude
- self.elevation = elevation
- self.xarm_azimuth = xarm_azimuth
- self.yarm_azimuth = yarm_azimuth
- self.xarm_tilt = xarm_tilt
- self.yarm_tilt = yarm_tilt
- self.power_spectral_density = power_spectral_density
- self.calibration_model = calibration_model
- self._strain_data = InterferometerStrainData(
- minimum_frequency=minimum_frequency,
- maximum_frequency=maximum_frequency)
- self.meta_data = dict()
-
- def __eq__(self, other):
- if self.name == other.name and \
- self.length == other.length and \
- self.latitude == other.latitude and \
- self.longitude == other.longitude and \
- self.elevation == other.elevation and \
- self.xarm_azimuth == other.xarm_azimuth and \
- self.xarm_tilt == other.xarm_tilt and \
- self.yarm_azimuth == other.yarm_azimuth and \
- self.yarm_tilt == other.yarm_tilt and \
- self.power_spectral_density.__eq__(other.power_spectral_density) and \
- self.calibration_model == other.calibration_model and \
- self.strain_data == other.strain_data:
- return True
- return False
-
- def __repr__(self):
- return self.__class__.__name__ + '(name=\'{}\', power_spectral_density={}, minimum_frequency={}, ' \
- 'maximum_frequency={}, length={}, latitude={}, longitude={}, elevation={}, ' \
- 'xarm_azimuth={}, yarm_azimuth={}, xarm_tilt={}, yarm_tilt={})' \
- .format(self.name, self.power_spectral_density, float(self.minimum_frequency),
- float(self.maximum_frequency), float(self.length), float(self.latitude), float(self.longitude),
- float(self.elevation), float(self.xarm_azimuth), float(self.yarm_azimuth), float(self.xarm_tilt),
- float(self.yarm_tilt))
-
- @property
- def minimum_frequency(self):
- return self.strain_data.minimum_frequency
-
- @minimum_frequency.setter
- def minimum_frequency(self, minimum_frequency):
- self._strain_data.minimum_frequency = minimum_frequency
-
- @property
- def maximum_frequency(self):
- return self.strain_data.maximum_frequency
-
- @maximum_frequency.setter
- def maximum_frequency(self, maximum_frequency):
- self._strain_data.maximum_frequency = maximum_frequency
-
- @property
- def strain_data(self):
- """ A bilby.gw.detector.InterferometerStrainData instance """
- return self._strain_data
-
- @strain_data.setter
- def strain_data(self, strain_data):
- """ Set the strain_data
-
- This sets the Interferometer.strain_data equal to the provided
- strain_data. This will override the minimum_frequency and
- maximum_frequency of the provided strain_data object with those of
- the Interferometer object.
- """
- strain_data.minimum_frequency = self.minimum_frequency
- strain_data.maximum_frequency = self.maximum_frequency
-
- self._strain_data = strain_data
-
- def set_strain_data_from_frequency_domain_strain(
- self, frequency_domain_strain, sampling_frequency=None,
- duration=None, start_time=0, frequency_array=None):
- """ Set the `Interferometer.strain_data` from a numpy array
-
- Parameters
- ----------
- frequency_domain_strain: array_like
- The data to set.
- sampling_frequency: float
- The sampling frequency (in Hz).
- duration: float
- The data duration (in s).
- start_time: float
- The GPS start-time of the data.
- frequency_array: array_like
- The array of frequencies, if sampling_frequency and duration not
- given.
-
- """
- self.strain_data.set_from_frequency_domain_strain(
- frequency_domain_strain=frequency_domain_strain,
- sampling_frequency=sampling_frequency, duration=duration,
- start_time=start_time, frequency_array=frequency_array)
-
- def set_strain_data_from_power_spectral_density(
- self, sampling_frequency, duration, start_time=0):
- """ Set the `Interferometer.strain_data` from a power spectal density
-
- This uses the `interferometer.power_spectral_density` object to set
- the `strain_data` to a noise realization. See
- `bilby.gw.detector.InterferometerStrainData` for further information.
-
- Parameters
- ----------
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
-
- """
- self.strain_data.set_from_power_spectral_density(
- self.power_spectral_density, sampling_frequency=sampling_frequency,
- duration=duration, start_time=start_time)
-
- def set_strain_data_from_frame_file(
- self, frame_file, sampling_frequency, duration, start_time=0,
- channel=None, buffer_time=1):
- """ Set the `Interferometer.strain_data` from a frame file
-
- Parameters
- ----------
- frame_file: str
- File from which to load data.
- channel: str
- Channel to read from frame.
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
- buffer_time: float
- Read in data with `start_time-buffer_time` and
- `start_time+duration+buffer_time`
-
- """
- self.strain_data.set_from_frame_file(
- frame_file=frame_file, sampling_frequency=sampling_frequency,
- duration=duration, start_time=start_time,
- channel=channel, buffer_time=buffer_time)
-
- def set_strain_data_from_csv(self, filename):
- """ Set the `Interferometer.strain_data` from a csv file
-
- Parameters
- ----------
- filename: str
- The path to the file to read in
-
- """
- self.strain_data.set_from_csv(filename)
-
- def set_strain_data_from_zero_noise(
- self, sampling_frequency, duration, start_time=0):
- """ Set the `Interferometer.strain_data` to zero noise
-
- Parameters
- ----------
- sampling_frequency: float
- The sampling frequency (in Hz)
- duration: float
- The data duration (in s)
- start_time: float
- The GPS start-time of the data
-
- """
-
- self.strain_data.set_from_zero_noise(
- sampling_frequency=sampling_frequency, duration=duration,
- start_time=start_time)
-
- @property
- def latitude(self):
- """ Saves latitude in rad internally. Updates related quantities if set to a different value.
-
- Returns
- -------
- float: The latitude position of the detector in degree
- """
- return self.__latitude * 180 / np.pi
-
- @latitude.setter
- def latitude(self, latitude):
- self.__latitude = latitude * np.pi / 180
- self.__x_updated = False
- self.__y_updated = False
- self.__vertex_updated = False
-
- @property
- def longitude(self):
- """ Saves longitude in rad internally. Updates related quantities if set to a different value.
-
- Returns
- -------
- float: The longitude position of the detector in degree
- """
- return self.__longitude * 180 / np.pi
-
- @longitude.setter
- def longitude(self, longitude):
- self.__longitude = longitude * np.pi / 180
- self.__x_updated = False
- self.__y_updated = False
- self.__vertex_updated = False
-
- @property
- def elevation(self):
- """ Updates related quantities if set to a different values.
-
- Returns
- -------
- float: The height about the surface in meters
- """
- return self.__elevation
-
- @elevation.setter
- def elevation(self, elevation):
- self.__elevation = elevation
- self.__vertex_updated = False
-
- @property
- def xarm_azimuth(self):
- """ Saves the x-arm azimuth in rad internally. Updates related quantities if set to a different values.
-
- Returns
- -------
- float: The x-arm azimuth in degrees.
-
- """
- return self.__xarm_azimuth * 180 / np.pi
-
- @xarm_azimuth.setter
- def xarm_azimuth(self, xarm_azimuth):
- self.__xarm_azimuth = xarm_azimuth * np.pi / 180
- self.__x_updated = False
-
- @property
- def yarm_azimuth(self):
- """ Saves the y-arm azimuth in rad internally. Updates related quantities if set to a different values.
-
- Returns
- -------
- float: The y-arm azimuth in degrees.
-
- """
- return self.__yarm_azimuth * 180 / np.pi
-
- @yarm_azimuth.setter
- def yarm_azimuth(self, yarm_azimuth):
- self.__yarm_azimuth = yarm_azimuth * np.pi / 180
- self.__y_updated = False
-
- @property
- def xarm_tilt(self):
- """ Updates related quantities if set to a different values.
-
- Returns
- -------
- float: The x-arm tilt in radians.
-
- """
- return self.__xarm_tilt
-
- @xarm_tilt.setter
- def xarm_tilt(self, xarm_tilt):
- self.__xarm_tilt = xarm_tilt
- self.__x_updated = False
-
- @property
- def yarm_tilt(self):
- """ Updates related quantities if set to a different values.
-
- Returns
- -------
- float: The y-arm tilt in radians.
-
- """
- return self.__yarm_tilt
-
- @yarm_tilt.setter
- def yarm_tilt(self, yarm_tilt):
- self.__yarm_tilt = yarm_tilt
- self.__y_updated = False
-
- @property
- def vertex(self):
- """ Position of the IFO vertex in geocentric coordinates in meters.
-
- Is automatically updated if related quantities are modified.
-
- Returns
- -------
- array_like: A 3D array representation of the vertex
- """
- if not self.__vertex_updated:
- self.__vertex = gwutils.get_vertex_position_geocentric(self.__latitude, self.__longitude,
- self.elevation)
- self.__vertex_updated = True
- return self.__vertex
-
- @property
- def x(self):
- """ A unit vector along the x-arm
-
- Is automatically updated if related quantities are modified.
-
- Returns
- -------
- array_like: A 3D array representation of a unit vector along the x-arm
-
- """
- if not self.__x_updated:
- self.__x = self.unit_vector_along_arm('x')
- self.__x_updated = True
- self.__detector_tensor_updated = False
- return self.__x
-
- @property
- def y(self):
- """ A unit vector along the y-arm
-
- Is automatically updated if related quantities are modified.
-
- Returns
- -------
- array_like: A 3D array representation of a unit vector along the y-arm
-
- """
- if not self.__y_updated:
- self.__y = self.unit_vector_along_arm('y')
- self.__y_updated = True
- self.__detector_tensor_updated = False
- return self.__y
-
- @property
- def detector_tensor(self):
- """
- Calculate the detector tensor from the unit vectors along each arm of the detector.
-
- See Eq. B6 of arXiv:gr-qc/0008066
-
- Is automatically updated if related quantities are modified.
-
- Returns
- -------
- array_like: A 3x3 array representation of the detector tensor
-
- """
- if not self.__x_updated or not self.__y_updated:
- _, _ = self.x, self.y # noqa
- if not self.__detector_tensor_updated:
- self.__detector_tensor = 0.5 * (np.einsum('i,j->ij', self.x, self.x) - np.einsum('i,j->ij', self.y, self.y))
- self.__detector_tensor_updated = True
- return self.__detector_tensor
-
- def antenna_response(self, ra, dec, time, psi, mode):
- """
- Calculate the antenna response function for a given sky location
-
- See Nishizawa et al. (2009) arXiv:0903.0528 for definitions of the polarisation tensors.
- [u, v, w] represent the Earth-frame
- [m, n, omega] represent the wave-frame
- Note: there is a typo in the definition of the wave-frame in Nishizawa et al.
-
- Parameters
- -------
- ra: float
- right ascension in radians
- dec: float
- declination in radians
- time: float
- geocentric GPS time
- psi: float
- binary polarisation angle counter-clockwise about the direction of propagation
- mode: str
- polarisation mode (e.g. 'plus', 'cross')
-
- Returns
- -------
- array_like: A 3x3 array representation of the antenna response for the specified mode
-
- """
- polarization_tensor = gwutils.get_polarization_tensor(ra, dec, time, psi, mode)
- return np.einsum('ij,ij->', self.detector_tensor, polarization_tensor)
-
- def get_detector_response(self, waveform_polarizations, parameters):
- """ Get the detector response for a particular waveform
-
- Parameters
- -------
- waveform_polarizations: dict
- polarizations of the waveform
- parameters: dict
- parameters describing position and time of arrival of the signal
-
- Returns
- -------
- array_like: A 3x3 array representation of the detector response (signal observed in the interferometer)
- """
- signal = {}
- for mode in waveform_polarizations.keys():
- det_response = self.antenna_response(
- parameters['ra'],
- parameters['dec'],
- parameters['geocent_time'],
- parameters['psi'], mode)
-
- signal[mode] = waveform_polarizations[mode] * det_response
- signal_ifo = sum(signal.values())
-
- signal_ifo *= self.strain_data.frequency_mask
-
- time_shift = self.time_delay_from_geocenter(
- parameters['ra'], parameters['dec'], parameters['geocent_time'])
- dt = parameters['geocent_time'] + time_shift - self.strain_data.start_time
-
- signal_ifo = signal_ifo * np.exp(
- -1j * 2 * np.pi * dt * self.frequency_array)
-
- signal_ifo *= self.calibration_model.get_calibration_factor(
- self.frequency_array, prefix='recalib_{}_'.format(self.name), **parameters)
-
- return signal_ifo
-
- def inject_signal(self, parameters=None, injection_polarizations=None,
- waveform_generator=None):
- """ Inject a signal into noise
-
- Parameters
- ----------
- parameters: dict
- Parameters of the injection.
- injection_polarizations: dict
- Polarizations of waveform to inject, output of
- `waveform_generator.frequency_domain_strain()`. If
- `waveform_generator` is also given, the injection_polarizations will
- be calculated directly and this argument can be ignored.
- waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
- A WaveformGenerator instance using the source model to inject. If
- `injection_polarizations` is given, this will be ignored.
-
- Note
- -------
- if your signal takes a substantial amount of time to generate, or
- you experience buggy behaviour. It is preferable to provide the
- injection_polarizations directly.
-
- Returns
- -------
- injection_polarizations: dict
-
- """
-
- if injection_polarizations is None:
- if waveform_generator is not None:
- injection_polarizations = \
- waveform_generator.frequency_domain_strain(parameters)
- else:
- raise ValueError(
- "inject_signal needs one of waveform_generator or "
- "injection_polarizations.")
-
- if injection_polarizations is None:
- raise ValueError(
- 'Trying to inject signal which is None. The most likely cause'
- ' is that waveform_generator.frequency_domain_strain returned'
- ' None. This can be caused if, e.g., mass_2 > mass_1.')
-
- if not self.strain_data.time_within_data(parameters['geocent_time']):
- logger.warning(
- 'Injecting signal outside segment, start_time={}, merger time={}.'
- .format(self.strain_data.start_time, parameters['geocent_time']))
-
- signal_ifo = self.get_detector_response(injection_polarizations, parameters)
- if np.shape(self.frequency_domain_strain).__eq__(np.shape(signal_ifo)):
- self.strain_data.frequency_domain_strain = \
- signal_ifo + self.strain_data.frequency_domain_strain
- else:
- logger.info('Injecting into zero noise.')
- self.set_strain_data_from_frequency_domain_strain(
- signal_ifo,
- sampling_frequency=self.strain_data.sampling_frequency,
- duration=self.strain_data.duration,
- start_time=self.strain_data.start_time)
-
- self.meta_data['optimal_SNR'] = (
- np.sqrt(self.optimal_snr_squared(signal=signal_ifo)).real)
- self.meta_data['matched_filter_SNR'] = (
- self.matched_filter_snr(signal=signal_ifo))
- self.meta_data['parameters'] = parameters
-
- logger.info("Injected signal in {}:".format(self.name))
- logger.info(" optimal SNR = {:.2f}".format(self.meta_data['optimal_SNR']))
- logger.info(" matched filter SNR = {:.2f}".format(self.meta_data['matched_filter_SNR']))
- for key in parameters:
- logger.info(' {} = {}'.format(key, parameters[key]))
-
- return injection_polarizations
-
- def unit_vector_along_arm(self, arm):
- """
- Calculate the unit vector pointing along the specified arm in cartesian Earth-based coordinates.
-
- See Eqs. B14-B17 in arXiv:gr-qc/0008066
-
- Parameters
- -------
- arm: str
- 'x' or 'y' (arm of the detector)
-
- Returns
- -------
- array_like: 3D unit vector along arm in cartesian Earth-based coordinates
-
- Raises
- -------
- ValueError: If arm is neither 'x' nor 'y'
-
- """
- if arm == 'x':
- return self.__calculate_arm(self.__xarm_tilt, self.__xarm_azimuth)
- elif arm == 'y':
- return self.__calculate_arm(self.__yarm_tilt, self.__yarm_azimuth)
- else:
- raise ValueError("Arm must either be 'x' or 'y'.")
-
- def __calculate_arm(self, arm_tilt, arm_azimuth):
- e_long = np.array([-np.sin(self.__longitude), np.cos(self.__longitude), 0])
- e_lat = np.array([-np.sin(self.__latitude) * np.cos(self.__longitude),
- -np.sin(self.__latitude) * np.sin(self.__longitude), np.cos(self.__latitude)])
- e_h = np.array([np.cos(self.__latitude) * np.cos(self.__longitude),
- np.cos(self.__latitude) * np.sin(self.__longitude), np.sin(self.__latitude)])
-
- return (np.cos(arm_tilt) * np.cos(arm_azimuth) * e_long +
- np.cos(arm_tilt) * np.sin(arm_azimuth) * e_lat +
- np.sin(arm_tilt) * e_h)
-
- @property
- def amplitude_spectral_density_array(self):
- """ Returns the amplitude spectral density (ASD) given we know a power spectral denstiy (PSD)
-
- Returns
- -------
- array_like: An array representation of the ASD
-
- """
- return self.power_spectral_density_array ** 0.5
-
- @property
- def power_spectral_density_array(self):
- """ Returns the power spectral density (PSD)
-
- This accounts for whether the data in the interferometer has been windowed.
-
- Returns
- -------
- array_like: An array representation of the PSD
-
- """
- return (self.power_spectral_density.power_spectral_density_interpolated(self.frequency_array) *
- self.strain_data.window_factor)
-
- @property
- def frequency_array(self):
- return self.strain_data.frequency_array
-
- @property
- def frequency_mask(self):
- return self.strain_data.frequency_mask
-
- @property
- def frequency_domain_strain(self):
- """ The frequency domain strain in units of strain / Hz """
- return self.strain_data.frequency_domain_strain
-
- @property
- def time_domain_strain(self):
- """ The time domain strain in units of s """
- return self.strain_data.time_domain_strain
-
- @property
- def time_array(self):
- return self.strain_data.time_array
-
- def time_delay_from_geocenter(self, ra, dec, time):
- """
- Calculate the time delay from the geocenter for the interferometer.
-
- Use the time delay function from utils.
-
- Parameters
- -------
- ra: float
- right ascension of source in radians
- dec: float
- declination of source in radians
- time: float
- GPS time
-
- Returns
- -------
- float: The time delay from geocenter in seconds
- """
- return gwutils.time_delay_geocentric(self.vertex, np.array([0, 0, 0]), ra, dec, time)
-
- def vertex_position_geocentric(self):
- """
- Calculate the position of the IFO vertex in geocentric coordinates in meters.
-
- Based on arXiv:gr-qc/0008066 Eqs. B11-B13 except for the typo in the definition of the local radius.
- See Section 2.1 of LIGO-T980044-10 for the correct expression
-
- Returns
- -------
- array_like: A 3D array representation of the vertex
- """
- return gwutils.get_vertex_position_geocentric(self.__latitude, self.__longitude, self.__elevation)
-
- def optimal_snr_squared(self, signal):
- """
-
- Parameters
- ----------
- signal: array_like
- Array containing the signal
-
- Returns
- -------
- float: The optimal signal to noise ratio possible squared
- """
- return gwutils.optimal_snr_squared(
- signal=signal[self.frequency_mask],
- power_spectral_density=self.power_spectral_density_array[self.frequency_mask],
- duration=self.strain_data.duration)
-
- def inner_product(self, signal):
- """
-
- Parameters
- ----------
- signal: array_like
- Array containing the signal
-
- Returns
- -------
- float: The optimal signal to noise ratio possible squared
- """
- return gwutils.noise_weighted_inner_product(
- aa=signal[self.frequency_mask],
- bb=self.frequency_domain_strain[self.frequency_mask],
- power_spectral_density=self.power_spectral_density_array[self.frequency_mask],
- duration=self.strain_data.duration)
-
- def matched_filter_snr(self, signal):
- """
-
- Parameters
- ----------
- signal: array_like
- Array containing the signal
-
- Returns
- -------
- float: The matched filter signal to noise ratio squared
-
- """
- return gwutils.matched_filter_snr(
- signal=signal[self.frequency_mask],
- frequency_domain_strain=self.frequency_domain_strain[self.frequency_mask],
- power_spectral_density=self.power_spectral_density_array[self.frequency_mask],
- duration=self.strain_data.duration)
-
- @property
- def whitened_frequency_domain_strain(self):
- """ Calculates the whitened data by dividing data by the amplitude spectral density
-
- Returns
- -------
- array_like: The whitened data
- """
- return self.strain_data.frequency_domain_strain / self.amplitude_spectral_density_array
-
- def save_data(self, outdir, label=None):
- """ Creates a save file for the data in plain text format
-
- Parameters
- ----------
- outdir: str
- The output directory in which the data is supposed to be saved
- label: str
- The name of the output files
- """
-
- if label is None:
- filename_psd = '{}/{}_psd.dat'.format(outdir, self.name)
- filename_data = '{}/{}_frequency_domain_data.dat'.format(outdir, self.name)
- else:
- filename_psd = '{}/{}_{}_psd.dat'.format(outdir, self.name, label)
- filename_data = '{}/{}_{}_frequency_domain_data.dat'.format(outdir, self.name, label)
- np.savetxt(filename_data,
- np.array(
- [self.frequency_array,
- self.frequency_domain_strain.real,
- self.frequency_domain_strain.imag]).T,
- header='f real_h(f) imag_h(f)')
- np.savetxt(filename_psd,
- np.array(
- [self.frequency_array,
- self.amplitude_spectral_density_array]).T,
- header='f h(f)')
-
- def plot_data(self, signal=None, outdir='.', label=None):
- if utils.command_line_args.test:
- return
-
- fig, ax = plt.subplots()
- df = self.frequency_array[1] - self.frequency_array[0]
- asd = gwutils.asd_from_freq_series(
- freq_data=self.frequency_domain_strain, df=df)
-
- ax.loglog(self.frequency_array[self.frequency_mask],
- asd[self.frequency_mask],
- color='C0', label=self.name)
- ax.loglog(self.frequency_array[self.frequency_mask],
- self.amplitude_spectral_density_array[self.frequency_mask],
- color='C1', lw=1.0, label=self.name + ' ASD')
- if signal is not None:
- signal_asd = gwutils.asd_from_freq_series(
- freq_data=signal, df=df)
-
- ax.loglog(self.frequency_array[self.frequency_mask],
- signal_asd[self.frequency_mask],
- color='C2',
- label='Signal')
- ax.grid(True)
- ax.set_ylabel(r'Strain [strain/$\sqrt{\rm Hz}$]')
- ax.set_xlabel(r'Frequency [Hz]')
- ax.legend(loc='best')
- fig.tight_layout()
- if label is None:
- fig.savefig(
- '{}/{}_frequency_domain_data.png'.format(outdir, self.name))
- else:
- fig.savefig(
- '{}/{}_{}_frequency_domain_data.png'.format(
- outdir, self.name, label))
- plt.close(fig)
-
- def plot_time_domain_data(
- self, outdir='.', label=None, bandpass_frequencies=(50, 250),
- notches=None, start_end=None, t0=None):
- """ Plots the strain data in the time domain
-
- Parameters
- ----------
- outdir, label: str
- Used in setting the saved filename.
- bandpass: tuple, optional
- A tuple of the (low, high) frequencies to use when bandpassing the
- data, if None no bandpass is applied.
- notches: list, optional
- A list of frequencies specifying any lines to notch
- start_end: tuple
- A tuple of the (start, end) range of GPS times to plot
- t0: float
- If given, the reference time to subtract from the time series before
- plotting.
-
- """
-
- # We use the gwpy timeseries to perform bandpass and notching
- if notches is None:
- notches = list()
- timeseries = gwpy.timeseries.TimeSeries(
- data=self.time_domain_strain, times=self.time_array)
- zpks = []
- if bandpass_frequencies is not None:
- zpks.append(gwpy.signal.filter_design.bandpass(
- bandpass_frequencies[0], bandpass_frequencies[1],
- self.strain_data.sampling_frequency))
- if notches is not None:
- for line in notches:
- zpks.append(gwpy.signal.filter_design.notch(
- line, self.strain_data.sampling_frequency))
- if len(zpks) > 0:
- zpk = gwpy.signal.filter_design.concatenate_zpks(*zpks)
- strain = timeseries.filter(zpk, filtfilt=True)
- else:
- strain = timeseries
-
- fig, ax = plt.subplots()
-
- if t0:
- x = self.time_array - t0
- xlabel = 'GPS time [s] - {}'.format(t0)
- else:
- x = self.time_array
- xlabel = 'GPS time [s]'
-
- ax.plot(x, strain)
- ax.set_xlabel(xlabel)
- ax.set_ylabel('Strain')
-
- if start_end is not None:
- ax.set_xlim(*start_end)
-
- fig.tight_layout()
-
- if label is None:
- fig.savefig(
- '{}/{}_time_domain_data.png'.format(outdir, self.name))
- else:
- fig.savefig(
- '{}/{}_{}_time_domain_data.png'.format(outdir, self.name, label))
- plt.close(fig)
-
- @staticmethod
- def _hdf5_filename_from_outdir_label(outdir, label):
- return os.path.join(outdir, label + '.h5')
-
- def to_hdf5(self, outdir='outdir', label=None):
- """ Save the object to a hdf5 file
-
- Attributes
- ----------
- outdir: str, optional
- Output directory name of the file, defaults to 'outdir'.
- label: str, optional
- Output file name, is self.name if not given otherwise.
- """
- import deepdish
- if sys.version_info[0] < 3:
- raise NotImplementedError('Pickling of Interferometer is not supported in Python 2.'
- 'Use Python 3 instead.')
- if label is None:
- label = self.name
- utils.check_directory_exists_and_if_not_mkdir('outdir')
- filename = self._hdf5_filename_from_outdir_label(outdir, label)
- deepdish.io.save(filename, self)
-
- @classmethod
- def from_hdf5(cls, filename=None):
- """ Loads in an Interferometer object from an hdf5 file
-
- Parameters
- ----------
- filename: str
- If given, try to load from this filename
-
- """
- import deepdish
- if sys.version_info[0] < 3:
- raise NotImplementedError('Pickling of Interferometer is not supported in Python 2.'
- 'Use Python 3 instead.')
-
- res = deepdish.io.load(filename)
- if res.__class__ != cls:
- raise TypeError('The loaded object is not an Interferometer')
- return res
-
-
-class TriangularInterferometer(InterferometerList):
-
- def __init__(self, name, power_spectral_density, minimum_frequency, maximum_frequency,
- length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth,
- xarm_tilt=0., yarm_tilt=0.):
- InterferometerList.__init__(self, [])
- self.name = name
- # for attr in ['power_spectral_density', 'minimum_frequency', 'maximum_frequency']:
- if isinstance(power_spectral_density, PowerSpectralDensity):
- power_spectral_density = [power_spectral_density] * 3
- if isinstance(minimum_frequency, float) or isinstance(minimum_frequency, int):
- minimum_frequency = [minimum_frequency] * 3
- if isinstance(maximum_frequency, float) or isinstance(maximum_frequency, int):
- maximum_frequency = [maximum_frequency] * 3
-
- for ii in range(3):
- self.append(Interferometer(
- '{}{}'.format(name, ii + 1), power_spectral_density[ii], minimum_frequency[ii], maximum_frequency[ii],
- length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth, xarm_tilt, yarm_tilt))
-
- xarm_azimuth += 240
- yarm_azimuth += 240
-
- latitude += np.arctan(length * np.sin(xarm_azimuth * np.pi / 180) * 1e3 / utils.radius_of_earth)
- longitude += np.arctan(length * np.cos(xarm_azimuth * np.pi / 180) * 1e3 / utils.radius_of_earth)
-
-
-class PowerSpectralDensity(object):
-
- def __init__(self, frequency_array=None, psd_array=None, asd_array=None,
- psd_file=None, asd_file=None):
- """
- Instantiate a new PowerSpectralDensity object.
-
- Example
- -------
- Using the `from` method directly (here `psd_file` is a string
- containing the path to the file to load):
- >>> power_spectral_density = PowerSpectralDensity.from_power_spectral_density_file(psd_file)
-
- Alternatively (and equivalently) setting the psd_file directly:
- >>> power_spectral_density = PowerSpectralDensity(psd_file=psd_file)
-
- Attributes
- ----------
- asd_array: array_like
- Array representation of the ASD
- asd_file: str
- Name of the ASD file
- frequency_array: array_like
- Array containing the frequencies of the ASD/PSD values
- psd_array: array_like
- Array representation of the PSD
- psd_file: str
- Name of the PSD file
- power_spectral_density_interpolated: scipy.interpolated.interp1d
- Interpolated function of the PSD
-
- """
- self.frequency_array = np.array(frequency_array)
- if psd_array is not None:
- self.psd_array = psd_array
- if asd_array is not None:
- self.asd_array = asd_array
- self.psd_file = psd_file
- self.asd_file = asd_file
-
- def __eq__(self, other):
- if self.psd_file == other.psd_file \
- and self.asd_file == other.asd_file \
- and np.array_equal(self.frequency_array, other.frequency_array) \
- and np.array_equal(self.psd_array, other.psd_array) \
- and np.array_equal(self.asd_array, other.asd_array):
- return True
- return False
-
- def __repr__(self):
- if self.asd_file is not None or self.psd_file is not None:
- return self.__class__.__name__ + '(psd_file=\'{}\', asd_file=\'{}\')' \
- .format(self.psd_file, self.asd_file)
- else:
- return self.__class__.__name__ + '(frequency_array={}, psd_array={}, asd_array={})' \
- .format(self.frequency_array, self.psd_array, self.asd_array)
-
- @staticmethod
- def from_amplitude_spectral_density_file(asd_file):
- """ Set the amplitude spectral density from a given file
-
- Parameters
- ----------
- asd_file: str
- File containing amplitude spectral density, format 'f h_f'
-
- """
- return PowerSpectralDensity(asd_file=asd_file)
-
- @staticmethod
- def from_power_spectral_density_file(psd_file):
- """ Set the power spectral density from a given file
-
- Parameters
- ----------
- psd_file: str, optional
- File containing power spectral density, format 'f h_f'
-
- """
- return PowerSpectralDensity(psd_file=psd_file)
-
- @staticmethod
- def from_frame_file(frame_file, psd_start_time, psd_duration,
- fft_length=4, sampling_frequency=4096, roll_off=0.2,
- overlap=0, channel=None, name=None, outdir=None,
- analysis_segment_start_time=None):
- """ Generate power spectral density from a frame file
-
- Parameters
- ----------
- frame_file: str, optional
- Frame file to read data from.
- psd_start_time: float
- Beginning of segment to analyse.
- psd_duration: float, optional
- Duration of data (in seconds) to generate PSD from.
- fft_length: float, optional
- Number of seconds in a single fft.
- sampling_frequency: float, optional
- Sampling frequency for time series.
- This is twice the maximum frequency.
- roll_off: float, optional
- Rise time in seconds of tukey window.
- overlap: float,
- Number of seconds of overlap between FFTs.
- channel: str, optional
- Name of channel to use to generate PSD.
- name, outdir: str, optional
- Name (and outdir) of the detector for which a PSD is to be
- generated.
- analysis_segment_start_time: float, optional
- The start time of the analysis segment, if given, this data will
- be removed before creating the PSD.
-
- """
- strain = InterferometerStrainData(roll_off=roll_off)
- strain.set_from_frame_file(
- frame_file, start_time=psd_start_time, duration=psd_duration,
- channel=channel, sampling_frequency=sampling_frequency)
- frequency_array, psd_array = strain.create_power_spectral_density(
- fft_length=fft_length, name=name, outdir=outdir, overlap=overlap,
- analysis_segment_start_time=analysis_segment_start_time)
- return PowerSpectralDensity(frequency_array=frequency_array, psd_array=psd_array)
-
- @staticmethod
- def from_amplitude_spectral_density_array(frequency_array, asd_array):
- return PowerSpectralDensity(frequency_array=frequency_array, asd_array=asd_array)
-
- @staticmethod
- def from_power_spectral_density_array(frequency_array, psd_array):
- return PowerSpectralDensity(frequency_array=frequency_array, psd_array=psd_array)
-
- @staticmethod
- def from_aligo():
- logger.info("No power spectral density provided, using aLIGO,"
- "zero detuning, high power.")
- return PowerSpectralDensity.from_power_spectral_density_file(psd_file='aLIGO_ZERO_DET_high_P_psd.txt')
-
- @property
- def psd_array(self):
- return self.__psd_array
-
- @psd_array.setter
- def psd_array(self, psd_array):
- self.__check_frequency_array_matches_density_array(psd_array)
- self.__psd_array = np.array(psd_array)
- self.__asd_array = psd_array ** 0.5
- self.__interpolate_power_spectral_density()
-
- @property
- def asd_array(self):
- return self.__asd_array
-
- @asd_array.setter
- def asd_array(self, asd_array):
- self.__check_frequency_array_matches_density_array(asd_array)
- self.__asd_array = np.array(asd_array)
- self.__psd_array = asd_array ** 2
- self.__interpolate_power_spectral_density()
-
- def __check_frequency_array_matches_density_array(self, density_array):
- if len(self.frequency_array) != len(density_array):
- raise ValueError('Provided spectral density does not match frequency array. Not updating.\n'
- 'Length spectral density {}\n Length frequency array {}\n'
- .format(density_array, self.frequency_array))
-
- def __interpolate_power_spectral_density(self):
- """Interpolate the loaded power spectral density so it can be resampled
- for arbitrary frequency arrays.
- """
- self.__power_spectral_density_interpolated = interp1d(self.frequency_array,
- self.psd_array,
- bounds_error=False,
- fill_value=np.inf)
-
- @property
- def power_spectral_density_interpolated(self):
- return self.__power_spectral_density_interpolated
-
- @property
- def asd_file(self):
- return self._asd_file
-
- @asd_file.setter
- def asd_file(self, asd_file):
- asd_file = self.__validate_file_name(file=asd_file)
- self._asd_file = asd_file
- if asd_file is not None:
- self.__import_amplitude_spectral_density()
- self.__check_file_was_asd_file()
-
- def __check_file_was_asd_file(self):
- if min(self.asd_array) < 1e-30:
- logger.warning("You specified an amplitude spectral density file.")
- logger.warning("{} WARNING {}".format("*" * 30, "*" * 30))
- logger.warning("The minimum of the provided curve is {:.2e}.".format(min(self.asd_array)))
- logger.warning("You may have intended to provide this as a power spectral density.")
-
- @property
- def psd_file(self):
- return self._psd_file
-
- @psd_file.setter
- def psd_file(self, psd_file):
- psd_file = self.__validate_file_name(file=psd_file)
- self._psd_file = psd_file
- if psd_file is not None:
- self.__import_power_spectral_density()
- self.__check_file_was_psd_file()
-
- def __check_file_was_psd_file(self):
- if min(self.psd_array) > 1e-30:
- logger.warning("You specified a power spectral density file.")
- logger.warning("{} WARNING {}".format("*" * 30, "*" * 30))
- logger.warning("The minimum of the provided curve is {:.2e}.".format(min(self.psd_array)))
- logger.warning("You may have intended to provide this as an amplitude spectral density.")
-
- @staticmethod
- def __validate_file_name(file):
- """
- Test if the file contains a path (i.e., contains '/').
- If not assume the file is in the default directory.
- """
- if file is not None and '/' not in file:
- file = os.path.join(os.path.dirname(__file__), 'noise_curves', file)
- return file
-
- def __import_amplitude_spectral_density(self):
- """ Automagically load an amplitude spectral density curve """
- self.frequency_array, self.asd_array = np.genfromtxt(self.asd_file).T
-
- def __import_power_spectral_density(self):
- """ Automagically load a power spectral density curve """
- self.frequency_array, self.psd_array = np.genfromtxt(self.psd_file).T
-
- def get_noise_realisation(self, sampling_frequency, duration):
- """
- Generate frequency Gaussian noise scaled to the power spectral density.
-
- Parameters
- -------
- sampling_frequency: float
- sampling frequency of noise
- duration: float
- duration of noise
-
- Returns
- -------
- array_like: frequency domain strain of this noise realisation
- array_like: frequencies related to the frequency domain strain
-
- """
- white_noise, frequencies = utils.create_white_noise(sampling_frequency, duration)
- frequency_domain_strain = self.__power_spectral_density_interpolated(frequencies) ** 0.5 * white_noise
- out_of_bounds = (frequencies < min(self.frequency_array)) | (frequencies > max(self.frequency_array))
- frequency_domain_strain[out_of_bounds] = 0 * (1 + 1j)
- return frequency_domain_strain, frequencies
-
-
-def get_empty_interferometer(name):
- """
- Get an interferometer with standard parameters for known detectors.
-
- These objects do not have any noise instantiated.
-
- The available instruments are:
- H1, L1, V1, GEO600, CE
-
- Detector positions taken from:
- L1/H1: LIGO-T980044-10
- V1/GEO600: arXiv:gr-qc/0008066 [45]
- CE: located at the site of H1
-
- Detector sensitivities:
- H1/L1/V1: https://dcc.ligo.org/LIGO-P1200087-v42/public
- GEO600: http://www.geo600.org/1032083/GEO600_Sensitivity_Curves
- CE: https://dcc.ligo.org/LIGO-P1600143/public
-
-
- Parameters
- ----------
- name: str
- Interferometer identifier.
-
- Returns
- -------
- interferometer: Interferometer
- Interferometer instance
- """
- filename = os.path.join(os.path.dirname(__file__), 'detectors', '{}.interferometer'.format(name))
- try:
- interferometer = load_interferometer(filename)
- return interferometer
- except OSError:
- raise ValueError('Interferometer {} not implemented'.format(name))
-
-
-def load_interferometer(filename):
- """Load an interferometer from a file."""
- parameters = dict()
- with open(filename, 'r') as parameter_file:
- lines = parameter_file.readlines()
- for line in lines:
- if line[0] == '#':
- continue
- split_line = line.split('=')
- key = split_line[0].strip()
- value = eval('='.join(split_line[1:]))
- parameters[key] = value
- if 'shape' not in parameters.keys():
- interferometer = Interferometer(**parameters)
- logger.debug('Assuming L shape for {}'.format('name'))
- elif parameters['shape'].lower() in ['l', 'ligo']:
- parameters.pop('shape')
- interferometer = Interferometer(**parameters)
- elif parameters['shape'].lower() in ['triangular', 'triangle']:
- parameters.pop('shape')
- interferometer = TriangularInterferometer(**parameters)
- else:
- raise IOError("{} could not be loaded. Invalid parameter 'shape'.".format(filename))
- return interferometer
-
-
-def get_interferometer_with_open_data(
- name, trigger_time, duration=4, start_time=None, roll_off=0.2,
- psd_offset=-1024, psd_duration=100, cache=True, outdir='outdir',
- label=None, plot=True, filter_freq=None, **kwargs):
- """
- Helper function to obtain an Interferometer instance with appropriate
- PSD and data, given an center_time.
-
- Parameters
- ----------
-
- name: str
- Detector name, e.g., 'H1'.
- trigger_time: float
- Trigger GPS time.
- duration: float, optional
- The total time (in seconds) to analyse. Defaults to 4s.
- start_time: float, optional
- Beginning of the segment, if None, the trigger is placed 2s before the end
- of the segment.
- roll_off: float
- The roll-off (in seconds) used in the Tukey window.
- psd_offset, psd_duration: float
- The power spectral density (psd) is estimated using data from
- `center_time+psd_offset` to `center_time+psd_offset + psd_duration`.
- cache: bool, optional
- Whether or not to store the acquired data
- outdir: str
- Directory where the psd files are saved
- label: str
- If given, an identifying label used in generating file names.
- plot: bool
- If true, create an ASD + strain plot
- filter_freq: float
- Low pass filter frequency
- **kwargs:
- All keyword arguments are passed to
- `gwpy.timeseries.TimeSeries.fetch_open_data()`.
-
- Returns
- -------
- bilby.gw.detector.Interferometer: An Interferometer instance with a PSD and frequency-domain strain data.
-
- """
-
- logger.warning(
- "Parameter estimation for real interferometer data in bilby is in "
- "alpha testing at the moment: the routines for windowing and filtering"
- " have not been reviewed.")
-
- utils.check_directory_exists_and_if_not_mkdir(outdir)
-
- if start_time is None:
- start_time = trigger_time + 2 - duration
-
- strain = InterferometerStrainData(roll_off=roll_off)
- strain.set_from_open_data(
- name=name, start_time=start_time, duration=duration,
- outdir=outdir, cache=cache, **kwargs)
- strain.low_pass_filter(filter_freq)
-
- strain_psd = InterferometerStrainData(roll_off=roll_off)
- strain_psd.set_from_open_data(
- name=name, start_time=start_time + duration + psd_offset,
- duration=psd_duration, outdir=outdir, cache=cache, **kwargs)
- # Low pass filter
- strain_psd.low_pass_filter(filter_freq)
- # Create and save PSDs
- psd_frequencies, psd_array = strain_psd.create_power_spectral_density(
- name=name, outdir=outdir, fft_length=strain.duration)
-
- interferometer = get_empty_interferometer(name)
- interferometer.power_spectral_density = PowerSpectralDensity(
- psd_array=psd_array, frequency_array=psd_frequencies)
- interferometer.strain_data = strain
-
- if plot:
- interferometer.plot_data(outdir=outdir, label=label)
-
- return interferometer
-
-
-def get_interferometer_with_fake_noise_and_injection(
- name, injection_parameters, injection_polarizations=None,
- waveform_generator=None, sampling_frequency=4096, duration=4,
- start_time=None, outdir='outdir', label=None, plot=True, save=True,
- zero_noise=False):
- """
- Helper function to obtain an Interferometer instance with appropriate
- power spectral density and data, given an center_time.
-
- Note: by default this generates an Interferometer with a power spectral
- density based on advanced LIGO.
-
- Parameters
- ----------
- name: str
- Detector name, e.g., 'H1'.
- injection_parameters: dict
- injection parameters, needed for sky position and timing
- injection_polarizations: dict
- Polarizations of waveform to inject, output of
- `waveform_generator.frequency_domain_strain()`. If
- `waveform_generator` is also given, the injection_polarizations will
- be calculated directly and this argument can be ignored.
- waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
- A WaveformGenerator instance using the source model to inject. If
- `injection_polarizations` is given, this will be ignored.
- sampling_frequency: float
- sampling frequency for data, should match injection signal
- duration: float
- length of data, should be the same as used for signal generation
- start_time: float
- Beginning of data segment, if None, injection is placed 2s before
- end of segment.
- outdir: str
- directory in which to store output
- label: str
- If given, an identifying label used in generating file names.
- plot: bool
- If true, create an ASD + strain plot
- save: bool
- If true, save frequency domain data and PSD to file
- zero_noise: bool
- If true, set noise to zero.
-
- Returns
- -------
- bilby.gw.detector.Interferometer: An Interferometer instance with a PSD and frequency-domain strain data.
-
- """
-
- utils.check_directory_exists_and_if_not_mkdir(outdir)
-
- if start_time is None:
- start_time = injection_parameters['geocent_time'] + 2 - duration
-
- interferometer = get_empty_interferometer(name)
- interferometer.power_spectral_density = PowerSpectralDensity.from_aligo()
- if zero_noise:
- interferometer.set_strain_data_from_zero_noise(
- sampling_frequency=sampling_frequency, duration=duration,
- start_time=start_time)
- else:
- interferometer.set_strain_data_from_power_spectral_density(
- sampling_frequency=sampling_frequency, duration=duration,
- start_time=start_time)
-
- injection_polarizations = interferometer.inject_signal(
- parameters=injection_parameters,
- injection_polarizations=injection_polarizations,
- waveform_generator=waveform_generator)
-
- signal = interferometer.get_detector_response(
- injection_polarizations, injection_parameters)
-
- if plot:
- interferometer.plot_data(signal=signal, outdir=outdir, label=label)
-
- if save:
- interferometer.save_data(outdir, label=label)
-
- return interferometer
-
-
-def get_event_data(
- event, interferometer_names=None, duration=4, roll_off=0.2,
- psd_offset=-1024, psd_duration=100, cache=True, outdir='outdir',
- label=None, plot=True, filter_freq=None, **kwargs):
- """
- Get open data for a specified event.
-
- Parameters
- ----------
- event: str
- Event descriptor, this can deal with some prefixes, e.g., '150914',
- 'GW150914', 'LVT151012'
- interferometer_names: list, optional
- List of interferometer identifiers, e.g., 'H1'.
- If None will look for data in 'H1', 'V1', 'L1'
- duration: float
- Time duration to search for.
- roll_off: float
- The roll-off (in seconds) used in the Tukey window.
- psd_offset, psd_duration: float
- The power spectral density (psd) is estimated using data from
- `center_time+psd_offset` to `center_time+psd_offset + psd_duration`.
- cache: bool
- Whether or not to store the acquired data.
- outdir: str
- Directory where the psd files are saved
- label: str
- If given, an identifying label used in generating file names.
- plot: bool
- If true, create an ASD + strain plot
- filter_freq: float
- Low pass filter frequency
- **kwargs:
- All keyword arguments are passed to
- `gwpy.timeseries.TimeSeries.fetch_open_data()`.
-
- Return
- ------
- list: A list of bilby.gw.detector.Interferometer objects
- """
- event_time = gwutils.get_event_time(event)
-
- interferometers = []
-
- if interferometer_names is None:
- interferometer_names = ['H1', 'L1', 'V1']
-
- for name in interferometer_names:
- try:
- interferometers.append(get_interferometer_with_open_data(
- name, trigger_time=event_time, duration=duration, roll_off=roll_off,
- psd_offset=psd_offset, psd_duration=psd_duration, cache=cache,
- outdir=outdir, label=label, plot=plot, filter_freq=filter_freq,
- **kwargs))
- except ValueError as e:
- logger.debug("Error raised {}".format(e))
- logger.warning('No data found for {}.'.format(name))
-
- return InterferometerList(interferometers)
-
-
-def load_data_from_cache_file(
- cache_file, start_time, segment_duration, psd_duration, psd_start_time,
- channel_name=None, sampling_frequency=4096, roll_off=0.2,
- overlap=0, outdir=None):
- """ Helper routine to generate an interferometer from a cache file
-
- Parameters
- ----------
- cache_file: str
- Path to the location of the cache file
- start_time, psd_start_time: float
- GPS start time of the segment and data stretch used for the PSD
- segment_duration, psd_duration: float
- Segment duration and duration of data to use to generate the PSD (in
- seconds).
- roll_off: float, optional
- Rise time in seconds of tukey window.
- overlap: float,
- Number of seconds of overlap between FFTs.
- channel_name: str
- Channel name
- sampling_frequency: int
- Sampling frequency
- outdir: str, optional
- The output directory in which the data is saved
-
- Returns
- -------
- ifo: bilby.gw.detector.Interferometer
- An initialised interferometer object with strain data set to the
- appropriate data in the cache file and a PSD.
- """
-
- data_set = False
- psd_set = False
-
- with open(cache_file, 'r') as ff:
- for line in ff:
- cache = lal.utils.cache.CacheEntry(line)
- data_in_cache = (
- (cache.segment[0].gpsSeconds < start_time) &
- (cache.segment[1].gpsSeconds > start_time + segment_duration))
- psd_in_cache = (
- (cache.segment[0].gpsSeconds < psd_start_time) &
- (cache.segment[1].gpsSeconds > psd_start_time + psd_duration))
- ifo = get_empty_interferometer(
- "{}1".format(cache.observatory))
- if not data_set & data_in_cache:
- ifo.set_strain_data_from_frame_file(
- frame_file=cache.path,
- sampling_frequency=sampling_frequency,
- duration=segment_duration,
- start_time=start_time,
- channel=channel_name, buffer_time=0)
- data_set = True
- if not psd_set & psd_in_cache:
- ifo.power_spectral_density = \
- PowerSpectralDensity.from_frame_file(
- cache.path,
- psd_start_time=psd_start_time,
- psd_duration=psd_duration,
- fft_length=segment_duration,
- sampling_frequency=sampling_frequency,
- roll_off=roll_off,
- overlap=overlap,
- channel=channel_name,
- name=cache.observatory,
- outdir=outdir,
- analysis_segment_start_time=start_time)
- psd_set = True
- if data_set and psd_set:
- return ifo
- elif not data_set:
- raise ValueError('Data not loaded for {}'.format(ifo.name))
- elif not psd_set:
- raise ValueError('PSD not created for {}'.format(ifo.name))
-
-
-def get_safe_signal_duration(mass_1, mass_2, a_1, a_2, tilt_1, tilt_2, flow=10,
- **kwargs):
- """ Calculate the safe signal duration, given the parameters
-
- Parameters
- ----------
- mass_1, mass_2, a_1, a_2, tilt_1, tilt_2: float
- The signal parameters
- flow: float
- The lower frequency cutoff from which to calculate the signal duration
-
- Returns
- -------
- safe_signal_duration: float
- The shortest safe signal duration (i.e., the signal duration rounded up
- to the nearest power of 2)
-
- """
- chirp_time = lalsim.SimInspiralChirpTimeBound(
- flow, mass_1 * lal.MSUN_SI, mass_2 * lal.MSUN_SI,
- a_1 * np.cos(tilt_1), a_2 * np.cos(tilt_2))
- return max(2**(int(np.log2(chirp_time)) + 1), 4)
-
-
-def inject_signal_into_gwpy_timeseries(
- data, waveform_generator, parameters, det, outdir=None, label=None):
- """ Inject a signal into a gwpy timeseries
-
- Parameters
- ----------
- data: gwpy.timeseries.TimeSeries
- The time-series data into which we want to inject the signal
- waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
- An initialised waveform_generator
- parameters: dict
- A dictionary of the signal-parameters to inject
- ifo: bilby.gw.detector.Interferometer
- The interferometer for which the data refers too
- outdir, label: str
- If given, the outdir and label used to generate a plot
-
- Returns
- -------
- data_and_signal: gwpy.timeseries.TimeSeries
- The data with the time-domain signal added
- meta_data: dict
- A dictionary of meta data about the injection
-
- """
- ifo = get_empty_interferometer(det)
- ifo.strain_data.set_from_gwpy_timeseries(data)
-
- parameters_check, _ = conversion.convert_to_lal_binary_black_hole_parameters(parameters)
- safe_time = get_safe_signal_duration(**parameters_check)
- if data.duration.value < safe_time:
- ValueError(
- "Injecting a signal with safe-duration {} longer than the data {}"
- .format(safe_time, data.duration.value))
-
- waveform_polarizations = waveform_generator.time_domain_strain(parameters)
-
- signal = np.zeros(len(data))
-
- for mode in waveform_polarizations.keys():
- det_response = ifo.antenna_response(
- parameters['ra'], parameters['dec'], parameters['geocent_time'],
- parameters['psi'], mode)
- signal += waveform_polarizations[mode] * det_response
- time_shift = ifo.time_delay_from_geocenter(
- parameters['ra'], parameters['dec'], parameters['geocent_time'])
-
- dt = parameters['geocent_time'] + time_shift - data.times[0].value
- n_roll = dt * data.sample_rate.value
- n_roll = int(np.round(n_roll))
- signal_shifted = gwpy.timeseries.TimeSeries(
- data=np.roll(signal, n_roll), times=data.times, unit=data.unit)
-
- signal_and_data = data.inject(signal_shifted)
-
- if outdir is not None and label is not None:
- fig = gwpy.plot.Plot(signal_shifted)
- fig.savefig('{}/{}_{}_time_domain_injected_signal'.format(
- outdir, ifo.name, label))
-
- meta_data = dict()
- frequency_domain_signal, _ = utils.nfft(signal, waveform_generator.sampling_frequency)
- meta_data['optimal_SNR'] = (
- np.sqrt(ifo.optimal_snr_squared(signal=frequency_domain_signal)).real)
- meta_data['matched_filter_SNR'] = (
- ifo.matched_filter_snr(signal=frequency_domain_signal))
- meta_data['parameters'] = parameters
-
- logger.info("Injected signal in {}:".format(ifo.name))
- logger.info(" optimal SNR = {:.2f}".format(meta_data['optimal_SNR']))
- logger.info(" matched filter SNR = {:.2f}".format(meta_data['matched_filter_SNR']))
- for key in parameters:
- logger.info(' {} = {}'.format(key, parameters[key]))
-
- return signal_and_data, meta_data
diff --git a/bilby/gw/detector/__init__.py b/bilby/gw/detector/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e057ac6ca7ac0fdf6b06d23056711abec1d0851
--- /dev/null
+++ b/bilby/gw/detector/__init__.py
@@ -0,0 +1,273 @@
+from bilby.gw import conversion
+from .interferometer import *
+from .networks import *
+from .psd import *
+from .strain_data import *
+
+try:
+ import lal
+ import lalsimulation as lalsim
+except ImportError:
+ logger.warning("You do not have lalsuite installed currently. You will"
+ " not be able to use some of the prebuilt functions.")
+
+
+def get_safe_signal_duration(mass_1, mass_2, a_1, a_2, tilt_1, tilt_2, flow=10):
+ """ Calculate the safe signal duration, given the parameters
+
+ Parameters
+ ----------
+ mass_1, mass_2, a_1, a_2, tilt_1, tilt_2: float
+ The signal parameters
+ flow: float
+ The lower frequency cutoff from which to calculate the signal duration
+
+ Returns
+ -------
+ safe_signal_duration: float
+ The shortest safe signal duration (i.e., the signal duration rounded up
+ to the nearest power of 2)
+
+ """
+ chirp_time = lalsim.SimInspiralChirpTimeBound(
+ flow, mass_1 * lal.MSUN_SI, mass_2 * lal.MSUN_SI,
+ a_1 * np.cos(tilt_1), a_2 * np.cos(tilt_2))
+ return max(2**(int(np.log2(chirp_time)) + 1), 4)
+
+
+def inject_signal_into_gwpy_timeseries(
+ data, waveform_generator, parameters, det, outdir=None, label=None):
+ """ Inject a signal into a gwpy timeseries
+
+ Parameters
+ ----------
+ data: gwpy.timeseries.TimeSeries
+ The time-series data into which we want to inject the signal
+ waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
+ An initialised waveform_generator
+ parameters: dict
+ A dictionary of the signal-parameters to inject
+ ifo: bilby.gw.detector.Interferometer
+ The interferometer for which the data refers too
+ outdir, label: str
+ If given, the outdir and label used to generate a plot
+
+ Returns
+ -------
+ data_and_signal: gwpy.timeseries.TimeSeries
+ The data with the time-domain signal added
+ meta_data: dict
+ A dictionary of meta data about the injection
+
+ """
+ ifo = get_empty_interferometer(det)
+ ifo.strain_data.set_from_gwpy_timeseries(data)
+
+ parameters_check, _ = conversion.convert_to_lal_binary_black_hole_parameters(parameters)
+ safe_time = get_safe_signal_duration(**parameters_check)
+ if data.duration.value < safe_time:
+ ValueError(
+ "Injecting a signal with safe-duration {} longer than the data {}"
+ .format(safe_time, data.duration.value))
+
+ waveform_polarizations = waveform_generator.time_domain_strain(parameters)
+
+ signal = np.zeros(len(data))
+
+ for mode in waveform_polarizations.keys():
+ det_response = ifo.antenna_response(
+ parameters['ra'], parameters['dec'], parameters['geocent_time'],
+ parameters['psi'], mode)
+ signal += waveform_polarizations[mode] * det_response
+ time_shift = ifo.time_delay_from_geocenter(
+ parameters['ra'], parameters['dec'], parameters['geocent_time'])
+
+ dt = parameters['geocent_time'] + time_shift - data.times[0].value
+ n_roll = dt * data.sample_rate.value
+ n_roll = int(np.round(n_roll))
+ signal_shifted = gwpy.timeseries.TimeSeries(
+ data=np.roll(signal, n_roll), times=data.times, unit=data.unit)
+
+ signal_and_data = data.inject(signal_shifted)
+
+ if outdir is not None and label is not None:
+ fig = gwpy.plot.Plot(signal_shifted)
+ fig.savefig('{}/{}_{}_time_domain_injected_signal'.format(
+ outdir, ifo.name, label))
+
+ meta_data = dict()
+ frequency_domain_signal, _ = utils.nfft(signal, waveform_generator.sampling_frequency)
+ meta_data['optimal_SNR'] = (
+ np.sqrt(ifo.optimal_snr_squared(signal=frequency_domain_signal)).real)
+ meta_data['matched_filter_SNR'] = (
+ ifo.matched_filter_snr(signal=frequency_domain_signal))
+ meta_data['parameters'] = parameters
+
+ logger.info("Injected signal in {}:".format(ifo.name))
+ logger.info(" optimal SNR = {:.2f}".format(meta_data['optimal_SNR']))
+ logger.info(" matched filter SNR = {:.2f}".format(meta_data['matched_filter_SNR']))
+ for key in parameters:
+ logger.info(' {} = {}'.format(key, parameters[key]))
+
+ return signal_and_data, meta_data
+
+
+def get_interferometer_with_fake_noise_and_injection(
+ name, injection_parameters, injection_polarizations=None,
+ waveform_generator=None, sampling_frequency=4096, duration=4,
+ start_time=None, outdir='outdir', label=None, plot=True, save=True,
+ zero_noise=False):
+ """
+ Helper function to obtain an Interferometer instance with appropriate
+ power spectral density and data, given an center_time.
+
+ Note: by default this generates an Interferometer with a power spectral
+ density based on advanced LIGO.
+
+ Parameters
+ ----------
+ name: str
+ Detector name, e.g., 'H1'.
+ injection_parameters: dict
+ injection parameters, needed for sky position and timing
+ injection_polarizations: dict
+ Polarizations of waveform to inject, output of
+ `waveform_generator.frequency_domain_strain()`. If
+ `waveform_generator` is also given, the injection_polarizations will
+ be calculated directly and this argument can be ignored.
+ waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
+ A WaveformGenerator instance using the source model to inject. If
+ `injection_polarizations` is given, this will be ignored.
+ sampling_frequency: float
+ sampling frequency for data, should match injection signal
+ duration: float
+ length of data, should be the same as used for signal generation
+ start_time: float
+ Beginning of data segment, if None, injection is placed 2s before
+ end of segment.
+ outdir: str
+ directory in which to store output
+ label: str
+ If given, an identifying label used in generating file names.
+ plot: bool
+ If true, create an ASD + strain plot
+ save: bool
+ If true, save frequency domain data and PSD to file
+ zero_noise: bool
+ If true, set noise to zero.
+
+ Returns
+ -------
+ bilby.gw.detector.Interferometer: An Interferometer instance with a PSD and frequency-domain strain data.
+
+ """
+
+ utils.check_directory_exists_and_if_not_mkdir(outdir)
+
+ if start_time is None:
+ start_time = injection_parameters['geocent_time'] + 2 - duration
+
+ interferometer = get_empty_interferometer(name)
+ interferometer.power_spectral_density = PowerSpectralDensity.from_aligo()
+ if zero_noise:
+ interferometer.set_strain_data_from_zero_noise(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=start_time)
+ else:
+ interferometer.set_strain_data_from_power_spectral_density(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=start_time)
+
+ injection_polarizations = interferometer.inject_signal(
+ parameters=injection_parameters,
+ injection_polarizations=injection_polarizations,
+ waveform_generator=waveform_generator)
+
+ signal = interferometer.get_detector_response(
+ injection_polarizations, injection_parameters)
+
+ if plot:
+ interferometer.plot_data(signal=signal, outdir=outdir, label=label)
+
+ if save:
+ interferometer.save_data(outdir, label=label)
+
+ return interferometer
+
+
+def load_data_from_cache_file(
+ cache_file, start_time, segment_duration, psd_duration, psd_start_time,
+ channel_name=None, sampling_frequency=4096, roll_off=0.2,
+ overlap=0, outdir=None):
+ """ Helper routine to generate an interferometer from a cache file
+
+ Parameters
+ ----------
+ cache_file: str
+ Path to the location of the cache file
+ start_time, psd_start_time: float
+ GPS start time of the segment and data stretch used for the PSD
+ segment_duration, psd_duration: float
+ Segment duration and duration of data to use to generate the PSD (in
+ seconds).
+ roll_off: float, optional
+ Rise time in seconds of tukey window.
+ overlap: float,
+ Number of seconds of overlap between FFTs.
+ channel_name: str
+ Channel name
+ sampling_frequency: int
+ Sampling frequency
+ outdir: str, optional
+ The output directory in which the data is saved
+
+ Returns
+ -------
+ ifo: bilby.gw.detector.Interferometer
+ An initialised interferometer object with strain data set to the
+ appropriate data in the cache file and a PSD.
+ """
+
+ data_set = False
+ psd_set = False
+
+ with open(cache_file, 'r') as ff:
+ for line in ff:
+ cache = lal.utils.cache.CacheEntry(line)
+ data_in_cache = (
+ (cache.segment[0].gpsSeconds < start_time) &
+ (cache.segment[1].gpsSeconds > start_time + segment_duration))
+ psd_in_cache = (
+ (cache.segment[0].gpsSeconds < psd_start_time) &
+ (cache.segment[1].gpsSeconds > psd_start_time + psd_duration))
+ ifo = get_empty_interferometer(
+ "{}1".format(cache.observatory))
+ if not data_set & data_in_cache:
+ ifo.set_strain_data_from_frame_file(
+ frame_file=cache.path,
+ sampling_frequency=sampling_frequency,
+ duration=segment_duration,
+ start_time=start_time,
+ channel=channel_name, buffer_time=0)
+ data_set = True
+ if not psd_set & psd_in_cache:
+ ifo.power_spectral_density = \
+ PowerSpectralDensity.from_frame_file(
+ cache.path,
+ psd_start_time=psd_start_time,
+ psd_duration=psd_duration,
+ fft_length=segment_duration,
+ sampling_frequency=sampling_frequency,
+ roll_off=roll_off,
+ overlap=overlap,
+ channel=channel_name,
+ name=cache.observatory,
+ outdir=outdir,
+ analysis_segment_start_time=start_time)
+ psd_set = True
+ if data_set and psd_set:
+ return ifo
+ elif not data_set:
+ raise ValueError('Data not loaded for {}'.format(ifo.name))
+ elif not psd_set:
+ raise ValueError('PSD not created for {}'.format(ifo.name))
diff --git a/bilby/gw/detectors/CE.interferometer b/bilby/gw/detector/detectors/CE.interferometer
similarity index 100%
rename from bilby/gw/detectors/CE.interferometer
rename to bilby/gw/detector/detectors/CE.interferometer
diff --git a/bilby/gw/detectors/ET.interferometer b/bilby/gw/detector/detectors/ET.interferometer
similarity index 100%
rename from bilby/gw/detectors/ET.interferometer
rename to bilby/gw/detector/detectors/ET.interferometer
diff --git a/bilby/gw/detectors/GEO600.interferometer b/bilby/gw/detector/detectors/GEO600.interferometer
similarity index 100%
rename from bilby/gw/detectors/GEO600.interferometer
rename to bilby/gw/detector/detectors/GEO600.interferometer
diff --git a/bilby/gw/detectors/H1.interferometer b/bilby/gw/detector/detectors/H1.interferometer
similarity index 100%
rename from bilby/gw/detectors/H1.interferometer
rename to bilby/gw/detector/detectors/H1.interferometer
diff --git a/bilby/gw/detectors/K1.interferometer b/bilby/gw/detector/detectors/K1.interferometer
similarity index 100%
rename from bilby/gw/detectors/K1.interferometer
rename to bilby/gw/detector/detectors/K1.interferometer
diff --git a/bilby/gw/detectors/L1.interferometer b/bilby/gw/detector/detectors/L1.interferometer
similarity index 100%
rename from bilby/gw/detectors/L1.interferometer
rename to bilby/gw/detector/detectors/L1.interferometer
diff --git a/bilby/gw/detectors/V1.interferometer b/bilby/gw/detector/detectors/V1.interferometer
similarity index 100%
rename from bilby/gw/detectors/V1.interferometer
rename to bilby/gw/detector/detectors/V1.interferometer
diff --git a/bilby/gw/detector/interferometer.py b/bilby/gw/detector/interferometer.py
new file mode 100644
index 0000000000000000000000000000000000000000..271090c5e88f63f075e627ab6a266374b217324f
--- /dev/null
+++ b/bilby/gw/detector/interferometer.py
@@ -0,0 +1,933 @@
+import os
+import sys
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+from bilby.core import utils
+from bilby.core.utils import logger
+from bilby.gw import utils as gwutils
+from bilby.gw.calibration import Recalibrate
+from .strain_data import InterferometerStrainData
+
+try:
+ import gwpy
+ import gwpy.signal
+except ImportError:
+ logger.warning("You do not have gwpy installed currently. You will "
+ " not be able to use some of the prebuilt functions.")
+
+
+class Interferometer(object):
+ """Class for the Interferometer """
+
+ def __init__(self, name, power_spectral_density, minimum_frequency, maximum_frequency,
+ length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth,
+ xarm_tilt=0., yarm_tilt=0., calibration_model=Recalibrate()):
+ """
+ Instantiate an Interferometer object.
+
+ Parameters
+ ----------
+ name: str
+ Interferometer name, e.g., H1.
+ power_spectral_density: bilby.gw.detector.PowerSpectralDensity
+ Power spectral density determining the sensitivity of the detector.
+ minimum_frequency: float
+ Minimum frequency to analyse for detector.
+ maximum_frequency: float
+ Maximum frequency to analyse for detector.
+ length: float
+ Length of the interferometer in km.
+ latitude: float
+ Latitude North in degrees (South is negative).
+ longitude: float
+ Longitude East in degrees (West is negative).
+ elevation: float
+ Height above surface in metres.
+ xarm_azimuth: float
+ Orientation of the x arm in degrees North of East.
+ yarm_azimuth: float
+ Orientation of the y arm in degrees North of East.
+ xarm_tilt: float, optional
+ Tilt of the x arm in radians above the horizontal defined by
+ ellipsoid earth model in LIGO-T980044-08.
+ yarm_tilt: float, optional
+ Tilt of the y arm in radians above the horizontal.
+ calibration_model: Recalibration
+ Calibration model, this applies the calibration correction to the
+ template, the default model applies no correction.
+ """
+ self.__x_updated = False
+ self.__y_updated = False
+ self.__vertex_updated = False
+ self.__detector_tensor_updated = False
+
+ self.name = name
+ self.length = length
+ self.latitude = latitude
+ self.longitude = longitude
+ self.elevation = elevation
+ self.xarm_azimuth = xarm_azimuth
+ self.yarm_azimuth = yarm_azimuth
+ self.xarm_tilt = xarm_tilt
+ self.yarm_tilt = yarm_tilt
+ self.power_spectral_density = power_spectral_density
+ self.calibration_model = calibration_model
+ self._strain_data = InterferometerStrainData(
+ minimum_frequency=minimum_frequency,
+ maximum_frequency=maximum_frequency)
+ self.meta_data = dict()
+
+ def __eq__(self, other):
+ if self.name == other.name and \
+ self.length == other.length and \
+ self.latitude == other.latitude and \
+ self.longitude == other.longitude and \
+ self.elevation == other.elevation and \
+ self.xarm_azimuth == other.xarm_azimuth and \
+ self.xarm_tilt == other.xarm_tilt and \
+ self.yarm_azimuth == other.yarm_azimuth and \
+ self.yarm_tilt == other.yarm_tilt and \
+ self.power_spectral_density.__eq__(other.power_spectral_density) and \
+ self.calibration_model == other.calibration_model and \
+ self.strain_data == other.strain_data:
+ return True
+ return False
+
+ def __repr__(self):
+ return self.__class__.__name__ + '(name=\'{}\', power_spectral_density={}, minimum_frequency={}, ' \
+ 'maximum_frequency={}, length={}, latitude={}, longitude={}, elevation={}, ' \
+ 'xarm_azimuth={}, yarm_azimuth={}, xarm_tilt={}, yarm_tilt={})' \
+ .format(self.name, self.power_spectral_density, float(self.minimum_frequency),
+ float(self.maximum_frequency), float(self.length), float(self.latitude), float(self.longitude),
+ float(self.elevation), float(self.xarm_azimuth), float(self.yarm_azimuth), float(self.xarm_tilt),
+ float(self.yarm_tilt))
+
+ @property
+ def minimum_frequency(self):
+ return self.strain_data.minimum_frequency
+
+ @minimum_frequency.setter
+ def minimum_frequency(self, minimum_frequency):
+ self._strain_data.minimum_frequency = minimum_frequency
+
+ @property
+ def maximum_frequency(self):
+ return self.strain_data.maximum_frequency
+
+ @maximum_frequency.setter
+ def maximum_frequency(self, maximum_frequency):
+ self._strain_data.maximum_frequency = maximum_frequency
+
+ @property
+ def strain_data(self):
+ """ A bilby.gw.detector.InterferometerStrainData instance """
+ return self._strain_data
+
+ @strain_data.setter
+ def strain_data(self, strain_data):
+ """ Set the strain_data
+
+ This sets the Interferometer.strain_data equal to the provided
+ strain_data. This will override the minimum_frequency and
+ maximum_frequency of the provided strain_data object with those of
+ the Interferometer object.
+ """
+ strain_data.minimum_frequency = self.minimum_frequency
+ strain_data.maximum_frequency = self.maximum_frequency
+
+ self._strain_data = strain_data
+
+ def set_strain_data_from_frequency_domain_strain(
+ self, frequency_domain_strain, sampling_frequency=None,
+ duration=None, start_time=0, frequency_array=None):
+ """ Set the `Interferometer.strain_data` from a numpy array
+
+ Parameters
+ ----------
+ frequency_domain_strain: array_like
+ The data to set.
+ sampling_frequency: float
+ The sampling frequency (in Hz).
+ duration: float
+ The data duration (in s).
+ start_time: float
+ The GPS start-time of the data.
+ frequency_array: array_like
+ The array of frequencies, if sampling_frequency and duration not
+ given.
+
+ """
+ self.strain_data.set_from_frequency_domain_strain(
+ frequency_domain_strain=frequency_domain_strain,
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=start_time, frequency_array=frequency_array)
+
+ def set_strain_data_from_power_spectral_density(
+ self, sampling_frequency, duration, start_time=0):
+ """ Set the `Interferometer.strain_data` from a power spectal density
+
+ This uses the `interferometer.power_spectral_density` object to set
+ the `strain_data` to a noise realization. See
+ `bilby.gw.detector.InterferometerStrainData` for further information.
+
+ Parameters
+ ----------
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+
+ """
+ self.strain_data.set_from_power_spectral_density(
+ self.power_spectral_density, sampling_frequency=sampling_frequency,
+ duration=duration, start_time=start_time)
+
+ def set_strain_data_from_frame_file(
+ self, frame_file, sampling_frequency, duration, start_time=0,
+ channel=None, buffer_time=1):
+ """ Set the `Interferometer.strain_data` from a frame file
+
+ Parameters
+ ----------
+ frame_file: str
+ File from which to load data.
+ channel: str
+ Channel to read from frame.
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+ buffer_time: float
+ Read in data with `start_time-buffer_time` and
+ `start_time+duration+buffer_time`
+
+ """
+ self.strain_data.set_from_frame_file(
+ frame_file=frame_file, sampling_frequency=sampling_frequency,
+ duration=duration, start_time=start_time,
+ channel=channel, buffer_time=buffer_time)
+
+ def set_strain_data_from_csv(self, filename):
+ """ Set the `Interferometer.strain_data` from a csv file
+
+ Parameters
+ ----------
+ filename: str
+ The path to the file to read in
+
+ """
+ self.strain_data.set_from_csv(filename)
+
+ def set_strain_data_from_zero_noise(
+ self, sampling_frequency, duration, start_time=0):
+ """ Set the `Interferometer.strain_data` to zero noise
+
+ Parameters
+ ----------
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+
+ """
+
+ self.strain_data.set_from_zero_noise(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=start_time)
+
+ @property
+ def latitude(self):
+ """ Saves latitude in rad internally. Updates related quantities if set to a different value.
+
+ Returns
+ -------
+ float: The latitude position of the detector in degree
+ """
+ return self.__latitude * 180 / np.pi
+
+ @latitude.setter
+ def latitude(self, latitude):
+ self.__latitude = latitude * np.pi / 180
+ self.__x_updated = False
+ self.__y_updated = False
+ self.__vertex_updated = False
+
+ @property
+ def longitude(self):
+ """ Saves longitude in rad internally. Updates related quantities if set to a different value.
+
+ Returns
+ -------
+ float: The longitude position of the detector in degree
+ """
+ return self.__longitude * 180 / np.pi
+
+ @longitude.setter
+ def longitude(self, longitude):
+ self.__longitude = longitude * np.pi / 180
+ self.__x_updated = False
+ self.__y_updated = False
+ self.__vertex_updated = False
+
+ @property
+ def elevation(self):
+ """ Updates related quantities if set to a different values.
+
+ Returns
+ -------
+ float: The height about the surface in meters
+ """
+ return self.__elevation
+
+ @elevation.setter
+ def elevation(self, elevation):
+ self.__elevation = elevation
+ self.__vertex_updated = False
+
+ @property
+ def xarm_azimuth(self):
+ """ Saves the x-arm azimuth in rad internally. Updates related quantities if set to a different values.
+
+ Returns
+ -------
+ float: The x-arm azimuth in degrees.
+
+ """
+ return self.__xarm_azimuth * 180 / np.pi
+
+ @xarm_azimuth.setter
+ def xarm_azimuth(self, xarm_azimuth):
+ self.__xarm_azimuth = xarm_azimuth * np.pi / 180
+ self.__x_updated = False
+
+ @property
+ def yarm_azimuth(self):
+ """ Saves the y-arm azimuth in rad internally. Updates related quantities if set to a different values.
+
+ Returns
+ -------
+ float: The y-arm azimuth in degrees.
+
+ """
+ return self.__yarm_azimuth * 180 / np.pi
+
+ @yarm_azimuth.setter
+ def yarm_azimuth(self, yarm_azimuth):
+ self.__yarm_azimuth = yarm_azimuth * np.pi / 180
+ self.__y_updated = False
+
+ @property
+ def xarm_tilt(self):
+ """ Updates related quantities if set to a different values.
+
+ Returns
+ -------
+ float: The x-arm tilt in radians.
+
+ """
+ return self.__xarm_tilt
+
+ @xarm_tilt.setter
+ def xarm_tilt(self, xarm_tilt):
+ self.__xarm_tilt = xarm_tilt
+ self.__x_updated = False
+
+ @property
+ def yarm_tilt(self):
+ """ Updates related quantities if set to a different values.
+
+ Returns
+ -------
+ float: The y-arm tilt in radians.
+
+ """
+ return self.__yarm_tilt
+
+ @yarm_tilt.setter
+ def yarm_tilt(self, yarm_tilt):
+ self.__yarm_tilt = yarm_tilt
+ self.__y_updated = False
+
+ @property
+ def vertex(self):
+ """ Position of the IFO vertex in geocentric coordinates in meters.
+
+ Is automatically updated if related quantities are modified.
+
+ Returns
+ -------
+ array_like: A 3D array representation of the vertex
+ """
+ if not self.__vertex_updated:
+ self.__vertex = gwutils.get_vertex_position_geocentric(self.__latitude, self.__longitude,
+ self.elevation)
+ self.__vertex_updated = True
+ return self.__vertex
+
+ @property
+ def x(self):
+ """ A unit vector along the x-arm
+
+ Is automatically updated if related quantities are modified.
+
+ Returns
+ -------
+ array_like: A 3D array representation of a unit vector along the x-arm
+
+ """
+ if not self.__x_updated:
+ self.__x = self.unit_vector_along_arm('x')
+ self.__x_updated = True
+ self.__detector_tensor_updated = False
+ return self.__x
+
+ @property
+ def y(self):
+ """ A unit vector along the y-arm
+
+ Is automatically updated if related quantities are modified.
+
+ Returns
+ -------
+ array_like: A 3D array representation of a unit vector along the y-arm
+
+ """
+ if not self.__y_updated:
+ self.__y = self.unit_vector_along_arm('y')
+ self.__y_updated = True
+ self.__detector_tensor_updated = False
+ return self.__y
+
+ @property
+ def detector_tensor(self):
+ """
+ Calculate the detector tensor from the unit vectors along each arm of the detector.
+
+ See Eq. B6 of arXiv:gr-qc/0008066
+
+ Is automatically updated if related quantities are modified.
+
+ Returns
+ -------
+ array_like: A 3x3 array representation of the detector tensor
+
+ """
+ if not self.__x_updated or not self.__y_updated:
+ _, _ = self.x, self.y # noqa
+ if not self.__detector_tensor_updated:
+ self.__detector_tensor = 0.5 * (np.einsum('i,j->ij', self.x, self.x) - np.einsum('i,j->ij', self.y, self.y))
+ self.__detector_tensor_updated = True
+ return self.__detector_tensor
+
+ def antenna_response(self, ra, dec, time, psi, mode):
+ """
+ Calculate the antenna response function for a given sky location
+
+ See Nishizawa et al. (2009) arXiv:0903.0528 for definitions of the polarisation tensors.
+ [u, v, w] represent the Earth-frame
+ [m, n, omega] represent the wave-frame
+ Note: there is a typo in the definition of the wave-frame in Nishizawa et al.
+
+ Parameters
+ -------
+ ra: float
+ right ascension in radians
+ dec: float
+ declination in radians
+ time: float
+ geocentric GPS time
+ psi: float
+ binary polarisation angle counter-clockwise about the direction of propagation
+ mode: str
+ polarisation mode (e.g. 'plus', 'cross')
+
+ Returns
+ -------
+ array_like: A 3x3 array representation of the antenna response for the specified mode
+
+ """
+ polarization_tensor = gwutils.get_polarization_tensor(ra, dec, time, psi, mode)
+ return np.einsum('ij,ij->', self.detector_tensor, polarization_tensor)
+
+ def get_detector_response(self, waveform_polarizations, parameters):
+ """ Get the detector response for a particular waveform
+
+ Parameters
+ -------
+ waveform_polarizations: dict
+ polarizations of the waveform
+ parameters: dict
+ parameters describing position and time of arrival of the signal
+
+ Returns
+ -------
+ array_like: A 3x3 array representation of the detector response (signal observed in the interferometer)
+ """
+ signal = {}
+ for mode in waveform_polarizations.keys():
+ det_response = self.antenna_response(
+ parameters['ra'],
+ parameters['dec'],
+ parameters['geocent_time'],
+ parameters['psi'], mode)
+
+ signal[mode] = waveform_polarizations[mode] * det_response
+ signal_ifo = sum(signal.values())
+
+ signal_ifo *= self.strain_data.frequency_mask
+
+ time_shift = self.time_delay_from_geocenter(
+ parameters['ra'], parameters['dec'], parameters['geocent_time'])
+ dt = parameters['geocent_time'] + time_shift - self.strain_data.start_time
+
+ signal_ifo = signal_ifo * np.exp(
+ -1j * 2 * np.pi * dt * self.frequency_array)
+
+ signal_ifo *= self.calibration_model.get_calibration_factor(
+ self.frequency_array, prefix='recalib_{}_'.format(self.name), **parameters)
+
+ return signal_ifo
+
+ def inject_signal(self, parameters=None, injection_polarizations=None,
+ waveform_generator=None):
+ """ Inject a signal into noise
+
+ Parameters
+ ----------
+ parameters: dict
+ Parameters of the injection.
+ injection_polarizations: dict
+ Polarizations of waveform to inject, output of
+ `waveform_generator.frequency_domain_strain()`. If
+ `waveform_generator` is also given, the injection_polarizations will
+ be calculated directly and this argument can be ignored.
+ waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
+ A WaveformGenerator instance using the source model to inject. If
+ `injection_polarizations` is given, this will be ignored.
+
+ Note
+ -------
+ if your signal takes a substantial amount of time to generate, or
+ you experience buggy behaviour. It is preferable to provide the
+ injection_polarizations directly.
+
+ Returns
+ -------
+ injection_polarizations: dict
+
+ """
+
+ if injection_polarizations is None:
+ if waveform_generator is not None:
+ injection_polarizations = \
+ waveform_generator.frequency_domain_strain(parameters)
+ else:
+ raise ValueError(
+ "inject_signal needs one of waveform_generator or "
+ "injection_polarizations.")
+
+ if injection_polarizations is None:
+ raise ValueError(
+ 'Trying to inject signal which is None. The most likely cause'
+ ' is that waveform_generator.frequency_domain_strain returned'
+ ' None. This can be caused if, e.g., mass_2 > mass_1.')
+
+ if not self.strain_data.time_within_data(parameters['geocent_time']):
+ logger.warning(
+ 'Injecting signal outside segment, start_time={}, merger time={}.'
+ .format(self.strain_data.start_time, parameters['geocent_time']))
+
+ signal_ifo = self.get_detector_response(injection_polarizations, parameters)
+ if np.shape(self.frequency_domain_strain).__eq__(np.shape(signal_ifo)):
+ self.strain_data.frequency_domain_strain = \
+ signal_ifo + self.strain_data.frequency_domain_strain
+ else:
+ logger.info('Injecting into zero noise.')
+ self.set_strain_data_from_frequency_domain_strain(
+ signal_ifo,
+ sampling_frequency=self.strain_data.sampling_frequency,
+ duration=self.strain_data.duration,
+ start_time=self.strain_data.start_time)
+
+ self.meta_data['optimal_SNR'] = (
+ np.sqrt(self.optimal_snr_squared(signal=signal_ifo)).real)
+ self.meta_data['matched_filter_SNR'] = (
+ self.matched_filter_snr(signal=signal_ifo))
+ self.meta_data['parameters'] = parameters
+
+ logger.info("Injected signal in {}:".format(self.name))
+ logger.info(" optimal SNR = {:.2f}".format(self.meta_data['optimal_SNR']))
+ logger.info(" matched filter SNR = {:.2f}".format(self.meta_data['matched_filter_SNR']))
+ for key in parameters:
+ logger.info(' {} = {}'.format(key, parameters[key]))
+
+ return injection_polarizations
+
+ def unit_vector_along_arm(self, arm):
+ """
+ Calculate the unit vector pointing along the specified arm in cartesian Earth-based coordinates.
+
+ See Eqs. B14-B17 in arXiv:gr-qc/0008066
+
+ Parameters
+ -------
+ arm: str
+ 'x' or 'y' (arm of the detector)
+
+ Returns
+ -------
+ array_like: 3D unit vector along arm in cartesian Earth-based coordinates
+
+ Raises
+ -------
+ ValueError: If arm is neither 'x' nor 'y'
+
+ """
+ if arm == 'x':
+ return self.__calculate_arm(self.__xarm_tilt, self.__xarm_azimuth)
+ elif arm == 'y':
+ return self.__calculate_arm(self.__yarm_tilt, self.__yarm_azimuth)
+ else:
+ raise ValueError("Arm must either be 'x' or 'y'.")
+
+ def __calculate_arm(self, arm_tilt, arm_azimuth):
+ e_long = np.array([-np.sin(self.__longitude), np.cos(self.__longitude), 0])
+ e_lat = np.array([-np.sin(self.__latitude) * np.cos(self.__longitude),
+ -np.sin(self.__latitude) * np.sin(self.__longitude), np.cos(self.__latitude)])
+ e_h = np.array([np.cos(self.__latitude) * np.cos(self.__longitude),
+ np.cos(self.__latitude) * np.sin(self.__longitude), np.sin(self.__latitude)])
+
+ return (np.cos(arm_tilt) * np.cos(arm_azimuth) * e_long +
+ np.cos(arm_tilt) * np.sin(arm_azimuth) * e_lat +
+ np.sin(arm_tilt) * e_h)
+
+ @property
+ def amplitude_spectral_density_array(self):
+ """ Returns the amplitude spectral density (ASD) given we know a power spectral denstiy (PSD)
+
+ Returns
+ -------
+ array_like: An array representation of the ASD
+
+ """
+ return self.power_spectral_density_array ** 0.5
+
+ @property
+ def power_spectral_density_array(self):
+ """ Returns the power spectral density (PSD)
+
+ This accounts for whether the data in the interferometer has been windowed.
+
+ Returns
+ -------
+ array_like: An array representation of the PSD
+
+ """
+ return (self.power_spectral_density.power_spectral_density_interpolated(self.frequency_array) *
+ self.strain_data.window_factor)
+
+ @property
+ def frequency_array(self):
+ return self.strain_data.frequency_array
+
+ @property
+ def frequency_mask(self):
+ return self.strain_data.frequency_mask
+
+ @property
+ def frequency_domain_strain(self):
+ """ The frequency domain strain in units of strain / Hz """
+ return self.strain_data.frequency_domain_strain
+
+ @property
+ def time_domain_strain(self):
+ """ The time domain strain in units of s """
+ return self.strain_data.time_domain_strain
+
+ @property
+ def time_array(self):
+ return self.strain_data.time_array
+
+ def time_delay_from_geocenter(self, ra, dec, time):
+ """
+ Calculate the time delay from the geocenter for the interferometer.
+
+ Use the time delay function from utils.
+
+ Parameters
+ -------
+ ra: float
+ right ascension of source in radians
+ dec: float
+ declination of source in radians
+ time: float
+ GPS time
+
+ Returns
+ -------
+ float: The time delay from geocenter in seconds
+ """
+ return gwutils.time_delay_geocentric(self.vertex, np.array([0, 0, 0]), ra, dec, time)
+
+ def vertex_position_geocentric(self):
+ """
+ Calculate the position of the IFO vertex in geocentric coordinates in meters.
+
+ Based on arXiv:gr-qc/0008066 Eqs. B11-B13 except for the typo in the definition of the local radius.
+ See Section 2.1 of LIGO-T980044-10 for the correct expression
+
+ Returns
+ -------
+ array_like: A 3D array representation of the vertex
+ """
+ return gwutils.get_vertex_position_geocentric(self.__latitude, self.__longitude, self.__elevation)
+
+ def optimal_snr_squared(self, signal):
+ """
+
+ Parameters
+ ----------
+ signal: array_like
+ Array containing the signal
+
+ Returns
+ -------
+ float: The optimal signal to noise ratio possible squared
+ """
+ return gwutils.optimal_snr_squared(
+ signal=signal[self.frequency_mask],
+ power_spectral_density=self.power_spectral_density_array[self.frequency_mask],
+ duration=self.strain_data.duration)
+
+ def inner_product(self, signal):
+ """
+
+ Parameters
+ ----------
+ signal: array_like
+ Array containing the signal
+
+ Returns
+ -------
+ float: The optimal signal to noise ratio possible squared
+ """
+ return gwutils.noise_weighted_inner_product(
+ aa=signal[self.frequency_mask],
+ bb=self.frequency_domain_strain[self.frequency_mask],
+ power_spectral_density=self.power_spectral_density_array[self.frequency_mask],
+ duration=self.strain_data.duration)
+
+ def matched_filter_snr(self, signal):
+ """
+
+ Parameters
+ ----------
+ signal: array_like
+ Array containing the signal
+
+ Returns
+ -------
+ float: The matched filter signal to noise ratio squared
+
+ """
+ return gwutils.matched_filter_snr(
+ signal=signal[self.frequency_mask],
+ frequency_domain_strain=self.frequency_domain_strain[self.frequency_mask],
+ power_spectral_density=self.power_spectral_density_array[self.frequency_mask],
+ duration=self.strain_data.duration)
+
+ @property
+ def whitened_frequency_domain_strain(self):
+ """ Calculates the whitened data by dividing data by the amplitude spectral density
+
+ Returns
+ -------
+ array_like: The whitened data
+ """
+ return self.strain_data.frequency_domain_strain / self.amplitude_spectral_density_array
+
+ def save_data(self, outdir, label=None):
+ """ Creates a save file for the data in plain text format
+
+ Parameters
+ ----------
+ outdir: str
+ The output directory in which the data is supposed to be saved
+ label: str
+ The name of the output files
+ """
+
+ if label is None:
+ filename_psd = '{}/{}_psd.dat'.format(outdir, self.name)
+ filename_data = '{}/{}_frequency_domain_data.dat'.format(outdir, self.name)
+ else:
+ filename_psd = '{}/{}_{}_psd.dat'.format(outdir, self.name, label)
+ filename_data = '{}/{}_{}_frequency_domain_data.dat'.format(outdir, self.name, label)
+ np.savetxt(filename_data,
+ np.array(
+ [self.frequency_array,
+ self.frequency_domain_strain.real,
+ self.frequency_domain_strain.imag]).T,
+ header='f real_h(f) imag_h(f)')
+ np.savetxt(filename_psd,
+ np.array(
+ [self.frequency_array,
+ self.amplitude_spectral_density_array]).T,
+ header='f h(f)')
+
+ def plot_data(self, signal=None, outdir='.', label=None):
+ if utils.command_line_args.test:
+ return
+
+ fig, ax = plt.subplots()
+ df = self.frequency_array[1] - self.frequency_array[0]
+ asd = gwutils.asd_from_freq_series(
+ freq_data=self.frequency_domain_strain, df=df)
+
+ ax.loglog(self.frequency_array[self.frequency_mask],
+ asd[self.frequency_mask],
+ color='C0', label=self.name)
+ ax.loglog(self.frequency_array[self.frequency_mask],
+ self.amplitude_spectral_density_array[self.frequency_mask],
+ color='C1', lw=1.0, label=self.name + ' ASD')
+ if signal is not None:
+ signal_asd = gwutils.asd_from_freq_series(
+ freq_data=signal, df=df)
+
+ ax.loglog(self.frequency_array[self.frequency_mask],
+ signal_asd[self.frequency_mask],
+ color='C2',
+ label='Signal')
+ ax.grid(True)
+ ax.set_ylabel(r'Strain [strain/$\sqrt{\rm Hz}$]')
+ ax.set_xlabel(r'Frequency [Hz]')
+ ax.legend(loc='best')
+ fig.tight_layout()
+ if label is None:
+ fig.savefig(
+ '{}/{}_frequency_domain_data.png'.format(outdir, self.name))
+ else:
+ fig.savefig(
+ '{}/{}_{}_frequency_domain_data.png'.format(
+ outdir, self.name, label))
+ plt.close(fig)
+
+ def plot_time_domain_data(
+ self, outdir='.', label=None, bandpass_frequencies=(50, 250),
+ notches=None, start_end=None, t0=None):
+ """ Plots the strain data in the time domain
+
+ Parameters
+ ----------
+ outdir, label: str
+ Used in setting the saved filename.
+ bandpass: tuple, optional
+ A tuple of the (low, high) frequencies to use when bandpassing the
+ data, if None no bandpass is applied.
+ notches: list, optional
+ A list of frequencies specifying any lines to notch
+ start_end: tuple
+ A tuple of the (start, end) range of GPS times to plot
+ t0: float
+ If given, the reference time to subtract from the time series before
+ plotting.
+
+ """
+
+ # We use the gwpy timeseries to perform bandpass and notching
+ if notches is None:
+ notches = list()
+ timeseries = gwpy.timeseries.TimeSeries(
+ data=self.time_domain_strain, times=self.time_array)
+ zpks = []
+ if bandpass_frequencies is not None:
+ zpks.append(gwpy.signal.filter_design.bandpass(
+ bandpass_frequencies[0], bandpass_frequencies[1],
+ self.strain_data.sampling_frequency))
+ if notches is not None:
+ for line in notches:
+ zpks.append(gwpy.signal.filter_design.notch(
+ line, self.strain_data.sampling_frequency))
+ if len(zpks) > 0:
+ zpk = gwpy.signal.filter_design.concatenate_zpks(*zpks)
+ strain = timeseries.filter(zpk, filtfilt=True)
+ else:
+ strain = timeseries
+
+ fig, ax = plt.subplots()
+
+ if t0:
+ x = self.time_array - t0
+ xlabel = 'GPS time [s] - {}'.format(t0)
+ else:
+ x = self.time_array
+ xlabel = 'GPS time [s]'
+
+ ax.plot(x, strain)
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel('Strain')
+
+ if start_end is not None:
+ ax.set_xlim(*start_end)
+
+ fig.tight_layout()
+
+ if label is None:
+ fig.savefig(
+ '{}/{}_time_domain_data.png'.format(outdir, self.name))
+ else:
+ fig.savefig(
+ '{}/{}_{}_time_domain_data.png'.format(outdir, self.name, label))
+ plt.close(fig)
+
+ @staticmethod
+ def _hdf5_filename_from_outdir_label(outdir, label):
+ return os.path.join(outdir, label + '.h5')
+
+ def to_hdf5(self, outdir='outdir', label=None):
+ """ Save the object to a hdf5 file
+
+ Attributes
+ ----------
+ outdir: str, optional
+ Output directory name of the file, defaults to 'outdir'.
+ label: str, optional
+ Output file name, is self.name if not given otherwise.
+ """
+ import deepdish
+ if sys.version_info[0] < 3:
+ raise NotImplementedError('Pickling of Interferometer is not supported in Python 2.'
+ 'Use Python 3 instead.')
+ if label is None:
+ label = self.name
+ utils.check_directory_exists_and_if_not_mkdir('outdir')
+ filename = self._hdf5_filename_from_outdir_label(outdir, label)
+ deepdish.io.save(filename, self)
+
+ @classmethod
+ def from_hdf5(cls, filename=None):
+ """ Loads in an Interferometer object from an hdf5 file
+
+ Parameters
+ ----------
+ filename: str
+ If given, try to load from this filename
+
+ """
+ import deepdish
+ if sys.version_info[0] < 3:
+ raise NotImplementedError('Pickling of Interferometer is not supported in Python 2.'
+ 'Use Python 3 instead.')
+
+ res = deepdish.io.load(filename)
+ if res.__class__ != cls:
+ raise TypeError('The loaded object is not an Interferometer')
+ return res
diff --git a/bilby/gw/detector/networks.py b/bilby/gw/detector/networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee91fe80ad069b15e0b3879b85ad9e6e6da5d969
--- /dev/null
+++ b/bilby/gw/detector/networks.py
@@ -0,0 +1,472 @@
+import os
+import sys
+
+import numpy as np
+
+from bilby.core import utils
+from bilby.core.utils import logger
+from .interferometer import Interferometer
+from .psd import PowerSpectralDensity
+from .strain_data import InterferometerStrainData
+
+
+class InterferometerList(list):
+ """ A list of Interferometer objects """
+
+ def __init__(self, interferometers):
+ """ Instantiate a InterferometerList
+
+ The InterferometerList is a list of Interferometer objects, each
+ object has the data used in evaluating the likelihood
+
+ Parameters
+ ----------
+ interferometers: iterable
+ The list of interferometers
+ """
+
+ list.__init__(self)
+ if type(interferometers) == str:
+ raise TypeError("Input must not be a string")
+ for ifo in interferometers:
+ if type(ifo) == str:
+ ifo = get_empty_interferometer(ifo)
+ if type(ifo) not in [Interferometer, TriangularInterferometer]:
+ raise TypeError("Input list of interferometers are not all Interferometer objects")
+ else:
+ self.append(ifo)
+ self._check_interferometers()
+
+ def _check_interferometers(self):
+ """ Check certain aspects of the set are the same """
+ consistent_attributes = ['duration', 'start_time', 'sampling_frequency']
+ for attribute in consistent_attributes:
+ x = [getattr(interferometer.strain_data, attribute)
+ for interferometer in self]
+ if not all(y == x[0] for y in x):
+ raise ValueError("The {} of all interferometers are not the same".format(attribute))
+
+ def set_strain_data_from_power_spectral_densities(self, sampling_frequency, duration, start_time=0):
+ """ Set the `Interferometer.strain_data` from the power spectral densities of the detectors
+
+ This uses the `interferometer.power_spectral_density` object to set
+ the `strain_data` to a noise realization. See
+ `bilby.gw.detector.InterferometerStrainData` for further information.
+
+ Parameters
+ ----------
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+
+ """
+ for interferometer in self:
+ interferometer.set_strain_data_from_power_spectral_density(sampling_frequency=sampling_frequency,
+ duration=duration,
+ start_time=start_time)
+
+ def set_strain_data_from_zero_noise(self, sampling_frequency, duration, start_time=0):
+ """ Set the `Interferometer.strain_data` from the power spectral densities of the detectors
+
+ This uses the `interferometer.power_spectral_density` object to set
+ the `strain_data` to zero noise. See
+ `bilby.gw.detector.InterferometerStrainData` for further information.
+
+ Parameters
+ ----------
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+
+ """
+ for interferometer in self:
+ interferometer.set_strain_data_from_zero_noise(sampling_frequency=sampling_frequency,
+ duration=duration,
+ start_time=start_time)
+
+ def inject_signal(self, parameters=None, injection_polarizations=None, waveform_generator=None):
+ """ Inject a signal into noise in each of the three detectors.
+
+ Parameters
+ ----------
+ parameters: dict
+ Parameters of the injection.
+ injection_polarizations: dict
+ Polarizations of waveform to inject, output of
+ `waveform_generator.frequency_domain_strain()`. If
+ `waveform_generator` is also given, the injection_polarizations will
+ be calculated directly and this argument can be ignored.
+ waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
+ A WaveformGenerator instance using the source model to inject. If
+ `injection_polarizations` is given, this will be ignored.
+
+ Note
+ ----------
+ if your signal takes a substantial amount of time to generate, or
+ you experience buggy behaviour. It is preferable to provide the
+ injection_polarizations directly.
+
+ Returns
+ -------
+ injection_polarizations: dict
+
+ """
+ if injection_polarizations is None:
+ if waveform_generator is not None:
+ injection_polarizations = \
+ waveform_generator.frequency_domain_strain(parameters)
+ else:
+ raise ValueError(
+ "inject_signal needs one of waveform_generator or "
+ "injection_polarizations.")
+
+ all_injection_polarizations = list()
+ for interferometer in self:
+ all_injection_polarizations.append(
+ interferometer.inject_signal(parameters=parameters, injection_polarizations=injection_polarizations))
+
+ return all_injection_polarizations
+
+ def save_data(self, outdir, label=None):
+ """ Creates a save file for the data in plain text format
+
+ Parameters
+ ----------
+ outdir: str
+ The output directory in which the data is supposed to be saved
+ label: str
+ The string labelling the data
+ """
+ for interferometer in self:
+ interferometer.save_data(outdir=outdir, label=label)
+
+ def plot_data(self, signal=None, outdir='.', label=None):
+ if utils.command_line_args.test:
+ return
+
+ for interferometer in self:
+ interferometer.plot_data(signal=signal, outdir=outdir, label=label)
+
+ @property
+ def number_of_interferometers(self):
+ return len(self)
+
+ @property
+ def duration(self):
+ return self[0].strain_data.duration
+
+ @property
+ def start_time(self):
+ return self[0].strain_data.start_time
+
+ @property
+ def sampling_frequency(self):
+ return self[0].strain_data.sampling_frequency
+
+ @property
+ def frequency_array(self):
+ return self[0].strain_data.frequency_array
+
+ def append(self, interferometer):
+ if isinstance(interferometer, InterferometerList):
+ super(InterferometerList, self).extend(interferometer)
+ else:
+ super(InterferometerList, self).append(interferometer)
+ self._check_interferometers()
+
+ def extend(self, interferometers):
+ super(InterferometerList, self).extend(interferometers)
+ self._check_interferometers()
+
+ def insert(self, index, interferometer):
+ super(InterferometerList, self).insert(index, interferometer)
+ self._check_interferometers()
+
+ @property
+ def meta_data(self):
+ """ Dictionary of the per-interferometer meta_data """
+ return {interferometer.name: interferometer.meta_data
+ for interferometer in self}
+
+ @staticmethod
+ def _hdf5_filename_from_outdir_label(outdir, label):
+ return os.path.join(outdir, label + '.h5')
+
+ def to_hdf5(self, outdir='outdir', label='ifo_list'):
+ """ Saves the object to a hdf5 file
+
+ Parameters
+ ----------
+ outdir: str, optional
+ Output directory name of the file
+ label: str, optional
+ Output file name, is 'ifo_list' if not given otherwise. A list of
+ the included interferometers will be appended.
+ """
+ import deepdish
+ if sys.version_info[0] < 3:
+ raise NotImplementedError('Pickling of InterferometerList is not supported in Python 2.'
+ 'Use Python 3 instead.')
+ label = label + '_' + ''.join(ifo.name for ifo in self)
+ utils.check_directory_exists_and_if_not_mkdir(outdir)
+ deepdish.io.save(self._hdf5_filename_from_outdir_label(outdir, label), self)
+
+ @classmethod
+ def from_hdf5(cls, filename=None):
+ """ Loads in an InterferometerList object from an hdf5 file
+
+ Parameters
+ ----------
+ filename: str
+ If given, try to load from this filename
+
+ """
+ import deepdish
+ if sys.version_info[0] < 3:
+ raise NotImplementedError('Pickling of InterferometerList is not supported in Python 2.'
+ 'Use Python 3 instead.')
+ res = deepdish.io.load(filename)
+ if res.__class__ == list:
+ res = cls(res)
+ if res.__class__ != cls:
+ raise TypeError('The loaded object is not a InterferometerList')
+ return res
+
+
+class TriangularInterferometer(InterferometerList):
+
+ def __init__(self, name, power_spectral_density, minimum_frequency, maximum_frequency,
+ length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth,
+ xarm_tilt=0., yarm_tilt=0.):
+ InterferometerList.__init__(self, [])
+ self.name = name
+ # for attr in ['power_spectral_density', 'minimum_frequency', 'maximum_frequency']:
+ if isinstance(power_spectral_density, PowerSpectralDensity):
+ power_spectral_density = [power_spectral_density] * 3
+ if isinstance(minimum_frequency, float) or isinstance(minimum_frequency, int):
+ minimum_frequency = [minimum_frequency] * 3
+ if isinstance(maximum_frequency, float) or isinstance(maximum_frequency, int):
+ maximum_frequency = [maximum_frequency] * 3
+
+ for ii in range(3):
+ self.append(Interferometer(
+ '{}{}'.format(name, ii + 1), power_spectral_density[ii], minimum_frequency[ii], maximum_frequency[ii],
+ length, latitude, longitude, elevation, xarm_azimuth, yarm_azimuth, xarm_tilt, yarm_tilt))
+
+ xarm_azimuth += 240
+ yarm_azimuth += 240
+
+ latitude += np.arctan(length * np.sin(xarm_azimuth * np.pi / 180) * 1e3 / utils.radius_of_earth)
+ longitude += np.arctan(length * np.cos(xarm_azimuth * np.pi / 180) * 1e3 / utils.radius_of_earth)
+
+
+def get_event_data(
+ event, interferometer_names=None, duration=4, roll_off=0.2,
+ psd_offset=-1024, psd_duration=100, cache=True, outdir='outdir',
+ label=None, plot=True, filter_freq=None, **kwargs):
+ """
+ Get open data for a specified event.
+
+ Parameters
+ ----------
+ event: str
+ Event descriptor, this can deal with some prefixes, e.g., '150914',
+ 'GW150914', 'LVT151012'
+ interferometer_names: list, optional
+ List of interferometer identifiers, e.g., 'H1'.
+ If None will look for data in 'H1', 'V1', 'L1'
+ duration: float
+ Time duration to search for.
+ roll_off: float
+ The roll-off (in seconds) used in the Tukey window.
+ psd_offset, psd_duration: float
+ The power spectral density (psd) is estimated using data from
+ `center_time+psd_offset` to `center_time+psd_offset + psd_duration`.
+ cache: bool
+ Whether or not to store the acquired data.
+ outdir: str
+ Directory where the psd files are saved
+ label: str
+ If given, an identifying label used in generating file names.
+ plot: bool
+ If true, create an ASD + strain plot
+ filter_freq: float
+ Low pass filter frequency
+ **kwargs:
+ All keyword arguments are passed to
+ `gwpy.timeseries.TimeSeries.fetch_open_data()`.
+
+ Return
+ ------
+ list: A list of bilby.gw.detector.Interferometer objects
+ """
+ event_time = utils.get_event_time(event)
+
+ interferometers = []
+
+ if interferometer_names is None:
+ interferometer_names = ['H1', 'L1', 'V1']
+
+ for name in interferometer_names:
+ try:
+ interferometers.append(get_interferometer_with_open_data(
+ name, trigger_time=event_time, duration=duration, roll_off=roll_off,
+ psd_offset=psd_offset, psd_duration=psd_duration, cache=cache,
+ outdir=outdir, label=label, plot=plot, filter_freq=filter_freq,
+ **kwargs))
+ except ValueError as e:
+ logger.debug("Error raised {}".format(e))
+ logger.warning('No data found for {}.'.format(name))
+
+ return InterferometerList(interferometers)
+
+
+def get_empty_interferometer(name):
+ """
+ Get an interferometer with standard parameters for known detectors.
+
+ These objects do not have any noise instantiated.
+
+ The available instruments are:
+ H1, L1, V1, GEO600, CE
+
+ Detector positions taken from:
+ L1/H1: LIGO-T980044-10
+ V1/GEO600: arXiv:gr-qc/0008066 [45]
+ CE: located at the site of H1
+
+ Detector sensitivities:
+ H1/L1/V1: https://dcc.ligo.org/LIGO-P1200087-v42/public
+ GEO600: http://www.geo600.org/1032083/GEO600_Sensitivity_Curves
+ CE: https://dcc.ligo.org/LIGO-P1600143/public
+
+
+ Parameters
+ ----------
+ name: str
+ Interferometer identifier.
+
+ Returns
+ -------
+ interferometer: Interferometer
+ Interferometer instance
+ """
+ filename = os.path.join(os.path.dirname(__file__), 'detectors', '{}.interferometer'.format(name))
+ try:
+ return load_interferometer(filename)
+ except OSError:
+ raise ValueError('Interferometer {} not implemented'.format(name))
+
+
+def load_interferometer(filename):
+ """Load an interferometer from a file."""
+ parameters = dict()
+ with open(filename, 'r') as parameter_file:
+ lines = parameter_file.readlines()
+ for line in lines:
+ if line[0] == '#':
+ continue
+ split_line = line.split('=')
+ key = split_line[0].strip()
+ value = eval('='.join(split_line[1:]))
+ parameters[key] = value
+ if 'shape' not in parameters.keys():
+ ifo = Interferometer(**parameters)
+ logger.debug('Assuming L shape for {}'.format('name'))
+ elif parameters['shape'].lower() in ['l', 'ligo']:
+ parameters.pop('shape')
+ ifo = Interferometer(**parameters)
+ elif parameters['shape'].lower() in ['triangular', 'triangle']:
+ parameters.pop('shape')
+ ifo = TriangularInterferometer(**parameters)
+ else:
+ raise IOError("{} could not be loaded. Invalid parameter 'shape'.".format(filename))
+ return ifo
+
+
+def get_interferometer_with_open_data(
+ name, trigger_time, duration=4, start_time=None, roll_off=0.2,
+ psd_offset=-1024, psd_duration=100, cache=True, outdir='outdir',
+ label=None, plot=True, filter_freq=None, **kwargs):
+ """
+ Helper function to obtain an Interferometer instance with appropriate
+ PSD and data, given an center_time.
+
+ Parameters
+ ----------
+
+ name: str
+ Detector name, e.g., 'H1'.
+ trigger_time: float
+ Trigger GPS time.
+ duration: float, optional
+ The total time (in seconds) to analyse. Defaults to 4s.
+ start_time: float, optional
+ Beginning of the segment, if None, the trigger is placed 2s before the end
+ of the segment.
+ roll_off: float
+ The roll-off (in seconds) used in the Tukey window.
+ psd_offset, psd_duration: float
+ The power spectral density (psd) is estimated using data from
+ `center_time+psd_offset` to `center_time+psd_offset + psd_duration`.
+ cache: bool, optional
+ Whether or not to store the acquired data
+ outdir: str
+ Directory where the psd files are saved
+ label: str
+ If given, an identifying label used in generating file names.
+ plot: bool
+ If true, create an ASD + strain plot
+ filter_freq: float
+ Low pass filter frequency
+ **kwargs:
+ All keyword arguments are passed to
+ `gwpy.timeseries.TimeSeries.fetch_open_data()`.
+
+ Returns
+ -------
+ bilby.gw.detector.Interferometer: An Interferometer instance with a PSD and frequency-domain strain data.
+
+ """
+
+ logger.warning(
+ "Parameter estimation for real interferometer data in bilby is in "
+ "alpha testing at the moment: the routines for windowing and filtering"
+ " have not been reviewed.")
+
+ utils.check_directory_exists_and_if_not_mkdir(outdir)
+
+ if start_time is None:
+ start_time = trigger_time + 2 - duration
+
+ strain = InterferometerStrainData(roll_off=roll_off)
+ strain.set_from_open_data(
+ name=name, start_time=start_time, duration=duration,
+ outdir=outdir, cache=cache, **kwargs)
+ strain.low_pass_filter(filter_freq)
+
+ strain_psd = InterferometerStrainData(roll_off=roll_off)
+ strain_psd.set_from_open_data(
+ name=name, start_time=start_time + duration + psd_offset,
+ duration=psd_duration, outdir=outdir, cache=cache, **kwargs)
+ # Low pass filter
+ strain_psd.low_pass_filter(filter_freq)
+ # Create and save PSDs
+ psd_frequencies, psd_array = strain_psd.create_power_spectral_density(
+ name=name, outdir=outdir, fft_length=strain.duration)
+
+ interferometer = get_empty_interferometer(name)
+ interferometer.power_spectral_density = PowerSpectralDensity(
+ psd_array=psd_array, frequency_array=psd_frequencies)
+ interferometer.strain_data = strain
+
+ if plot:
+ interferometer.plot_data(outdir=outdir, label=label)
+
+ return interferometer
diff --git a/bilby/gw/noise_curves/AdV_asd.txt b/bilby/gw/detector/noise_curves/AdV_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/AdV_asd.txt
rename to bilby/gw/detector/noise_curves/AdV_asd.txt
diff --git a/bilby/gw/noise_curves/AdV_psd.txt b/bilby/gw/detector/noise_curves/AdV_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/AdV_psd.txt
rename to bilby/gw/detector/noise_curves/AdV_psd.txt
diff --git a/bilby/gw/noise_curves/Aplus_asd.txt b/bilby/gw/detector/noise_curves/Aplus_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/Aplus_asd.txt
rename to bilby/gw/detector/noise_curves/Aplus_asd.txt
diff --git a/bilby/gw/noise_curves/CE_asd.txt b/bilby/gw/detector/noise_curves/CE_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/CE_asd.txt
rename to bilby/gw/detector/noise_curves/CE_asd.txt
diff --git a/bilby/gw/noise_curves/CE_psd.txt b/bilby/gw/detector/noise_curves/CE_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/CE_psd.txt
rename to bilby/gw/detector/noise_curves/CE_psd.txt
diff --git a/bilby/gw/noise_curves/CE_wb_asd.txt b/bilby/gw/detector/noise_curves/CE_wb_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/CE_wb_asd.txt
rename to bilby/gw/detector/noise_curves/CE_wb_asd.txt
diff --git a/bilby/gw/noise_curves/CE_wb_psd.txt b/bilby/gw/detector/noise_curves/CE_wb_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/CE_wb_psd.txt
rename to bilby/gw/detector/noise_curves/CE_wb_psd.txt
diff --git a/bilby/gw/noise_curves/ET_B_asd.txt b/bilby/gw/detector/noise_curves/ET_B_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/ET_B_asd.txt
rename to bilby/gw/detector/noise_curves/ET_B_asd.txt
diff --git a/bilby/gw/noise_curves/ET_B_psd.txt b/bilby/gw/detector/noise_curves/ET_B_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/ET_B_psd.txt
rename to bilby/gw/detector/noise_curves/ET_B_psd.txt
diff --git a/bilby/gw/noise_curves/ET_D_asd.txt b/bilby/gw/detector/noise_curves/ET_D_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/ET_D_asd.txt
rename to bilby/gw/detector/noise_curves/ET_D_asd.txt
diff --git a/bilby/gw/noise_curves/ET_D_psd.txt b/bilby/gw/detector/noise_curves/ET_D_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/ET_D_psd.txt
rename to bilby/gw/detector/noise_curves/ET_D_psd.txt
diff --git a/bilby/gw/noise_curves/GEO600_S6e_asd.txt b/bilby/gw/detector/noise_curves/GEO600_S6e_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/GEO600_S6e_asd.txt
rename to bilby/gw/detector/noise_curves/GEO600_S6e_asd.txt
diff --git a/bilby/gw/noise_curves/KAGRA_design_asd.txt b/bilby/gw/detector/noise_curves/KAGRA_design_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/KAGRA_design_asd.txt
rename to bilby/gw/detector/noise_curves/KAGRA_design_asd.txt
diff --git a/bilby/gw/noise_curves/KAGRA_design_psd.txt b/bilby/gw/detector/noise_curves/KAGRA_design_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/KAGRA_design_psd.txt
rename to bilby/gw/detector/noise_curves/KAGRA_design_psd.txt
diff --git a/bilby/gw/noise_curves/LIGO_srd_asd.txt b/bilby/gw/detector/noise_curves/LIGO_srd_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/LIGO_srd_asd.txt
rename to bilby/gw/detector/noise_curves/LIGO_srd_asd.txt
diff --git a/bilby/gw/noise_curves/LIGO_srd_psd.txt b/bilby/gw/detector/noise_curves/LIGO_srd_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/LIGO_srd_psd.txt
rename to bilby/gw/detector/noise_curves/LIGO_srd_psd.txt
diff --git a/bilby/gw/noise_curves/README.md b/bilby/gw/detector/noise_curves/README.md
similarity index 100%
rename from bilby/gw/noise_curves/README.md
rename to bilby/gw/detector/noise_curves/README.md
diff --git a/bilby/gw/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt b/bilby/gw/detector/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_ZERO_DET_high_P_asd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt b/bilby/gw/detector/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_early_asd.txt b/bilby/gw/detector/noise_curves/aLIGO_early_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_early_asd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_early_asd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_early_high_asd.txt b/bilby/gw/detector/noise_curves/aLIGO_early_high_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_early_high_asd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_early_high_asd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_early_high_psd.txt b/bilby/gw/detector/noise_curves/aLIGO_early_high_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_early_high_psd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_early_high_psd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_early_psd.txt b/bilby/gw/detector/noise_curves/aLIGO_early_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_early_psd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_early_psd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_late_asd.txt b/bilby/gw/detector/noise_curves/aLIGO_late_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_late_asd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_late_asd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_late_psd.txt b/bilby/gw/detector/noise_curves/aLIGO_late_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_late_psd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_late_psd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_mid_asd.txt b/bilby/gw/detector/noise_curves/aLIGO_mid_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_mid_asd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_mid_asd.txt
diff --git a/bilby/gw/noise_curves/aLIGO_mid_psd.txt b/bilby/gw/detector/noise_curves/aLIGO_mid_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/aLIGO_mid_psd.txt
rename to bilby/gw/detector/noise_curves/aLIGO_mid_psd.txt
diff --git a/bilby/gw/detector/noise_curves/aplus.txt b/bilby/gw/detector/noise_curves/aplus.txt
new file mode 100644
index 0000000000000000000000000000000000000000..38f0712c2097a080ab39633c662055f769c141e4
--- /dev/null
+++ b/bilby/gw/detector/noise_curves/aplus.txt
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diff --git a/bilby/gw/noise_curves/highf_psd.txt b/bilby/gw/detector/noise_curves/highf_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/highf_psd.txt
rename to bilby/gw/detector/noise_curves/highf_psd.txt
diff --git a/bilby/gw/noise_curves/lisa_asd.txt b/bilby/gw/detector/noise_curves/lisa_asd.txt
similarity index 100%
rename from bilby/gw/noise_curves/lisa_asd.txt
rename to bilby/gw/detector/noise_curves/lisa_asd.txt
diff --git a/bilby/gw/noise_curves/lisa_psd.txt b/bilby/gw/detector/noise_curves/lisa_psd.txt
similarity index 100%
rename from bilby/gw/noise_curves/lisa_psd.txt
rename to bilby/gw/detector/noise_curves/lisa_psd.txt
diff --git a/bilby/gw/detector/psd.py b/bilby/gw/detector/psd.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d30d6a49e5d1b587f35514bdad3e3d7eb74991
--- /dev/null
+++ b/bilby/gw/detector/psd.py
@@ -0,0 +1,267 @@
+import os
+
+import numpy as np
+from scipy.interpolate import interp1d
+
+from bilby.core import utils
+from bilby.core.utils import logger
+from .strain_data import InterferometerStrainData
+
+
+class PowerSpectralDensity(object):
+
+ def __init__(self, frequency_array=None, psd_array=None, asd_array=None,
+ psd_file=None, asd_file=None):
+ """
+ Instantiate a new PowerSpectralDensity object.
+
+ Example
+ -------
+ Using the `from` method directly (here `psd_file` is a string
+ containing the path to the file to load):
+ >>> power_spectral_density = PowerSpectralDensity.from_power_spectral_density_file(psd_file)
+
+ Alternatively (and equivalently) setting the psd_file directly:
+ >>> power_spectral_density = PowerSpectralDensity(psd_file=psd_file)
+
+ Attributes
+ ----------
+ asd_array: array_like
+ Array representation of the ASD
+ asd_file: str
+ Name of the ASD file
+ frequency_array: array_like
+ Array containing the frequencies of the ASD/PSD values
+ psd_array: array_like
+ Array representation of the PSD
+ psd_file: str
+ Name of the PSD file
+ power_spectral_density_interpolated: scipy.interpolated.interp1d
+ Interpolated function of the PSD
+
+ """
+ self.frequency_array = np.array(frequency_array)
+ if psd_array is not None:
+ self.psd_array = psd_array
+ if asd_array is not None:
+ self.asd_array = asd_array
+ self.psd_file = psd_file
+ self.asd_file = asd_file
+
+ def __eq__(self, other):
+ if self.psd_file == other.psd_file \
+ and self.asd_file == other.asd_file \
+ and np.array_equal(self.frequency_array, other.frequency_array) \
+ and np.array_equal(self.psd_array, other.psd_array) \
+ and np.array_equal(self.asd_array, other.asd_array):
+ return True
+ return False
+
+ def __repr__(self):
+ if self.asd_file is not None or self.psd_file is not None:
+ return self.__class__.__name__ + '(psd_file=\'{}\', asd_file=\'{}\')' \
+ .format(self.psd_file, self.asd_file)
+ else:
+ return self.__class__.__name__ + '(frequency_array={}, psd_array={}, asd_array={})' \
+ .format(self.frequency_array, self.psd_array, self.asd_array)
+
+ @staticmethod
+ def from_amplitude_spectral_density_file(asd_file):
+ """ Set the amplitude spectral density from a given file
+
+ Parameters
+ ----------
+ asd_file: str
+ File containing amplitude spectral density, format 'f h_f'
+
+ """
+ return PowerSpectralDensity(asd_file=asd_file)
+
+ @staticmethod
+ def from_power_spectral_density_file(psd_file):
+ """ Set the power spectral density from a given file
+
+ Parameters
+ ----------
+ psd_file: str, optional
+ File containing power spectral density, format 'f h_f'
+
+ """
+ return PowerSpectralDensity(psd_file=psd_file)
+
+ @staticmethod
+ def from_frame_file(frame_file, psd_start_time, psd_duration,
+ fft_length=4, sampling_frequency=4096, roll_off=0.2,
+ overlap=0, channel=None, name=None, outdir=None,
+ analysis_segment_start_time=None):
+ """ Generate power spectral density from a frame file
+
+ Parameters
+ ----------
+ frame_file: str, optional
+ Frame file to read data from.
+ psd_start_time: float
+ Beginning of segment to analyse.
+ psd_duration: float, optional
+ Duration of data (in seconds) to generate PSD from.
+ fft_length: float, optional
+ Number of seconds in a single fft.
+ sampling_frequency: float, optional
+ Sampling frequency for time series.
+ This is twice the maximum frequency.
+ roll_off: float, optional
+ Rise time in seconds of tukey window.
+ overlap: float,
+ Number of seconds of overlap between FFTs.
+ channel: str, optional
+ Name of channel to use to generate PSD.
+ name, outdir: str, optional
+ Name (and outdir) of the detector for which a PSD is to be
+ generated.
+ analysis_segment_start_time: float, optional
+ The start time of the analysis segment, if given, this data will
+ be removed before creating the PSD.
+
+ """
+ strain = InterferometerStrainData(roll_off=roll_off)
+ strain.set_from_frame_file(
+ frame_file, start_time=psd_start_time, duration=psd_duration,
+ channel=channel, sampling_frequency=sampling_frequency)
+ frequency_array, psd_array = strain.create_power_spectral_density(
+ fft_length=fft_length, name=name, outdir=outdir, overlap=overlap,
+ analysis_segment_start_time=analysis_segment_start_time)
+ return PowerSpectralDensity(frequency_array=frequency_array, psd_array=psd_array)
+
+ @staticmethod
+ def from_amplitude_spectral_density_array(frequency_array, asd_array):
+ return PowerSpectralDensity(frequency_array=frequency_array, asd_array=asd_array)
+
+ @staticmethod
+ def from_power_spectral_density_array(frequency_array, psd_array):
+ return PowerSpectralDensity(frequency_array=frequency_array, psd_array=psd_array)
+
+ @staticmethod
+ def from_aligo():
+ logger.info("No power spectral density provided, using aLIGO,"
+ "zero detuning, high power.")
+ return PowerSpectralDensity.from_power_spectral_density_file(psd_file='aLIGO_ZERO_DET_high_P_psd.txt')
+
+ @property
+ def psd_array(self):
+ return self.__psd_array
+
+ @psd_array.setter
+ def psd_array(self, psd_array):
+ self.__check_frequency_array_matches_density_array(psd_array)
+ self.__psd_array = np.array(psd_array)
+ self.__asd_array = psd_array ** 0.5
+ self.__interpolate_power_spectral_density()
+
+ @property
+ def asd_array(self):
+ return self.__asd_array
+
+ @asd_array.setter
+ def asd_array(self, asd_array):
+ self.__check_frequency_array_matches_density_array(asd_array)
+ self.__asd_array = np.array(asd_array)
+ self.__psd_array = asd_array ** 2
+ self.__interpolate_power_spectral_density()
+
+ def __check_frequency_array_matches_density_array(self, density_array):
+ if len(self.frequency_array) != len(density_array):
+ raise ValueError('Provided spectral density does not match frequency array. Not updating.\n'
+ 'Length spectral density {}\n Length frequency array {}\n'
+ .format(density_array, self.frequency_array))
+
+ def __interpolate_power_spectral_density(self):
+ """Interpolate the loaded power spectral density so it can be resampled
+ for arbitrary frequency arrays.
+ """
+ self.__power_spectral_density_interpolated = interp1d(self.frequency_array,
+ self.psd_array,
+ bounds_error=False,
+ fill_value=np.inf)
+
+ @property
+ def power_spectral_density_interpolated(self):
+ return self.__power_spectral_density_interpolated
+
+ @property
+ def asd_file(self):
+ return self._asd_file
+
+ @asd_file.setter
+ def asd_file(self, asd_file):
+ asd_file = self.__validate_file_name(file=asd_file)
+ self._asd_file = asd_file
+ if asd_file is not None:
+ self.__import_amplitude_spectral_density()
+ self.__check_file_was_asd_file()
+
+ def __check_file_was_asd_file(self):
+ if min(self.asd_array) < 1e-30:
+ logger.warning("You specified an amplitude spectral density file.")
+ logger.warning("{} WARNING {}".format("*" * 30, "*" * 30))
+ logger.warning("The minimum of the provided curve is {:.2e}.".format(min(self.asd_array)))
+ logger.warning("You may have intended to provide this as a power spectral density.")
+
+ @property
+ def psd_file(self):
+ return self._psd_file
+
+ @psd_file.setter
+ def psd_file(self, psd_file):
+ psd_file = self.__validate_file_name(file=psd_file)
+ self._psd_file = psd_file
+ if psd_file is not None:
+ self.__import_power_spectral_density()
+ self.__check_file_was_psd_file()
+
+ def __check_file_was_psd_file(self):
+ if min(self.psd_array) > 1e-30:
+ logger.warning("You specified a power spectral density file.")
+ logger.warning("{} WARNING {}".format("*" * 30, "*" * 30))
+ logger.warning("The minimum of the provided curve is {:.2e}.".format(min(self.psd_array)))
+ logger.warning("You may have intended to provide this as an amplitude spectral density.")
+
+ @staticmethod
+ def __validate_file_name(file):
+ """
+ Test if the file contains a path (i.e., contains '/').
+ If not assume the file is in the default directory.
+ """
+ if file is not None and '/' not in file:
+ file = os.path.join(os.path.dirname(__file__), 'noise_curves', file)
+ return file
+
+ def __import_amplitude_spectral_density(self):
+ """ Automagically load an amplitude spectral density curve """
+ self.frequency_array, self.asd_array = np.genfromtxt(self.asd_file).T
+
+ def __import_power_spectral_density(self):
+ """ Automagically load a power spectral density curve """
+ self.frequency_array, self.psd_array = np.genfromtxt(self.psd_file).T
+
+ def get_noise_realisation(self, sampling_frequency, duration):
+ """
+ Generate frequency Gaussian noise scaled to the power spectral density.
+
+ Parameters
+ -------
+ sampling_frequency: float
+ sampling frequency of noise
+ duration: float
+ duration of noise
+
+ Returns
+ -------
+ array_like: frequency domain strain of this noise realisation
+ array_like: frequencies related to the frequency domain strain
+
+ """
+ white_noise, frequencies = utils.create_white_noise(sampling_frequency, duration)
+ frequency_domain_strain = self.__power_spectral_density_interpolated(frequencies) ** 0.5 * white_noise
+ out_of_bounds = (frequencies < min(self.frequency_array)) | (frequencies > max(self.frequency_array))
+ frequency_domain_strain[out_of_bounds] = 0 * (1 + 1j)
+ return frequency_domain_strain, frequencies
diff --git a/bilby/gw/detector/strain_data.py b/bilby/gw/detector/strain_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..e68083d8490e6bcea6f9f7212f05a15f7546e0e0
--- /dev/null
+++ b/bilby/gw/detector/strain_data.py
@@ -0,0 +1,688 @@
+import numpy as np
+from scipy.signal.windows import tukey
+
+from bilby.core import utils
+from bilby.core.series import CoupledTimeAndFrequencySeries
+from bilby.core.utils import logger
+from bilby.gw import utils as gwutils
+
+try:
+ import gwpy
+ import gwpy.signal
+except ImportError:
+ logger.warning("You do not have gwpy installed currently. You will "
+ " not be able to use some of the prebuilt functions.")
+
+try:
+ import lal
+except ImportError:
+ logger.warning("You do not have lalsuite installed currently. You will"
+ " not be able to use some of the prebuilt functions.")
+
+
+class InterferometerStrainData(object):
+ """ Strain data for an interferometer """
+
+ def __init__(self, minimum_frequency=0, maximum_frequency=np.inf,
+ roll_off=0.2):
+ """ Initiate an InterferometerStrainData object
+
+ The initialised object contains no data, this should be added using one
+ of the `set_from..` methods.
+
+ Parameters
+ ----------
+ minimum_frequency: float
+ Minimum frequency to analyse for detector. Default is 0.
+ maximum_frequency: float
+ Maximum frequency to analyse for detector. Default is infinity.
+ roll_off: float
+ The roll-off (in seconds) used in the Tukey window, default=0.2s.
+ This corresponds to alpha * duration / 2 for scipy tukey window.
+
+ """
+ self.minimum_frequency = minimum_frequency
+ self.maximum_frequency = maximum_frequency
+ self.roll_off = roll_off
+ self.window_factor = 1
+
+ self._times_and_frequencies = CoupledTimeAndFrequencySeries()
+ # self._set_time_and_frequency_array_parameters(None, None, None)
+
+ self._frequency_domain_strain = None
+ self._frequency_array = None
+ self._time_domain_strain = None
+ self._time_array = None
+ self._channel = None
+
+ def __eq__(self, other):
+ if self.minimum_frequency == other.minimum_frequency \
+ and self.maximum_frequency == other.maximum_frequency \
+ and self.roll_off == other.roll_off \
+ and self.window_factor == other.window_factor \
+ and self.sampling_frequency == other.sampling_frequency \
+ and self.duration == other.duration \
+ and self.start_time == other.start_time \
+ and np.array_equal(self.time_array, other.time_array) \
+ and np.array_equal(self.frequency_array, other.frequency_array) \
+ and np.array_equal(self.frequency_domain_strain, other.frequency_domain_strain) \
+ and np.array_equal(self.time_domain_strain, other.time_domain_strain):
+ return True
+ return False
+
+ def time_within_data(self, time):
+ """ Check if time is within the data span
+
+ Parameters
+ ----------
+ time: float
+ The time to check
+
+ Returns
+ -------
+ bool:
+ A boolean stating whether the time is inside or outside the span
+
+ """
+ if time < self.start_time:
+ logger.debug("Time is before the start_time")
+ return False
+ elif time > self.start_time + self.duration:
+ logger.debug("Time is after the start_time + duration")
+ return False
+ else:
+ return True
+
+ @property
+ def minimum_frequency(self):
+ return self.__minimum_frequency
+
+ @minimum_frequency.setter
+ def minimum_frequency(self, minimum_frequency):
+ self.__minimum_frequency = minimum_frequency
+
+ @property
+ def maximum_frequency(self):
+ """ Force the maximum frequency be less than the Nyquist frequency """
+ if self.sampling_frequency is not None:
+ if 2 * self.__maximum_frequency > self.sampling_frequency:
+ self.__maximum_frequency = self.sampling_frequency / 2.
+ return self.__maximum_frequency
+
+ @maximum_frequency.setter
+ def maximum_frequency(self, maximum_frequency):
+ self.__maximum_frequency = maximum_frequency
+
+ @property
+ def frequency_mask(self):
+ """Masking array for limiting the frequency band.
+
+ Returns
+ -------
+ array_like: An array of boolean values
+ """
+ try:
+ return self._frequency_mask
+ except AttributeError:
+ frequency_array = self._times_and_frequencies.frequency_array
+ mask = ((frequency_array >= self.minimum_frequency) &
+ (frequency_array <= self.maximum_frequency))
+ self._frequency_mask = mask
+ return self._frequency_mask
+
+ @property
+ def alpha(self):
+ return 2 * self.roll_off / self.duration
+
+ def time_domain_window(self, roll_off=None, alpha=None):
+ """
+ Window function to apply to time domain data before FFTing.
+
+ This defines self.window_factor as the power loss due to the windowing.
+ See https://dcc.ligo.org/DocDB/0027/T040089/000/T040089-00.pdf
+
+ Parameters
+ ----------
+ roll_off: float
+ Rise time of window in seconds
+ alpha: float
+ Parameter to pass to tukey window, how much of segment falls
+ into windowed part
+
+ Return
+ ------
+ window: array
+ Window function over time array
+ """
+ if roll_off is not None:
+ self.roll_off = roll_off
+ elif alpha is not None:
+ self.roll_off = alpha * self.duration / 2
+ window = tukey(len(self._time_domain_strain), alpha=self.alpha)
+ self.window_factor = np.mean(window ** 2)
+ return window
+
+ @property
+ def time_domain_strain(self):
+ """ The time domain strain, in units of strain """
+ if self._time_domain_strain is not None:
+ return self._time_domain_strain
+ elif self._frequency_domain_strain is not None:
+ self._time_domain_strain = utils.infft(
+ self.frequency_domain_strain, self.sampling_frequency)
+ return self._time_domain_strain
+
+ else:
+ raise ValueError("time domain strain data not yet set")
+
+ @property
+ def frequency_domain_strain(self):
+ """ Returns the frequency domain strain
+
+ This is the frequency domain strain normalised to units of
+ strain / Hz, obtained by a one-sided Fourier transform of the
+ time domain data, divided by the sampling frequency.
+ """
+ if self._frequency_domain_strain is not None:
+ return self._frequency_domain_strain * self.frequency_mask
+ elif self._time_domain_strain is not None:
+ logger.info("Generating frequency domain strain from given time "
+ "domain strain.")
+ logger.info("Applying a tukey window with alpha={}, roll off={}".format(
+ self.alpha, self.roll_off))
+ # self.low_pass_filter()
+ window = self.time_domain_window()
+ self._frequency_domain_strain, self.frequency_array = utils.nfft(
+ self._time_domain_strain * window, self.sampling_frequency)
+ return self._frequency_domain_strain * self.frequency_mask
+ else:
+ raise ValueError("frequency domain strain data not yet set")
+
+ @frequency_domain_strain.setter
+ def frequency_domain_strain(self, frequency_domain_strain):
+ if not len(self.frequency_array) == len(frequency_domain_strain):
+ raise ValueError("The frequency_array and the set strain have different lengths")
+ self._frequency_domain_strain = frequency_domain_strain
+
+ def to_gwpy_timeseries(self):
+ """
+ Output the time series strain data as a :class:`gwpy.timeseries.TimeSeries`.
+ """
+
+ return gwpy.timeseries.TimeSeries(self.time_domain_strain,
+ sample_rate=self.sampling_frequency,
+ t0=self.start_time,
+ channel=self.channel)
+
+ def to_pycbc_timeseries(self):
+ """
+ Output the time series strain data as a :class:`pycbc.types.timeseries.TimeSeries`.
+ """
+
+ try:
+ import pycbc
+ except ImportError:
+ raise ImportError("Cannot output strain data as PyCBC TimeSeries")
+
+ return pycbc.types.timeseries.TimeSeries(self.time_domain_strain,
+ delta_t=(1. / self.sampling_frequency),
+ epoch=lal.LIGOTimeGPS(self.start_time))
+
+ def to_lal_timeseries(self):
+ """
+ Output the time series strain data as a LAL TimeSeries object.
+ """
+
+ laldata = lal.CreateREAL8TimeSeries("",
+ lal.LIGOTimeGPS(self.start_time),
+ 0., (1. / self.sampling_frequency),
+ lal.SecondUnit,
+ len(self.time_domain_strain))
+ laldata.data.data[:] = self.time_domain_strain
+
+ return laldata
+
+ def to_gwpy_frequencyseries(self):
+ """
+ Output the frequency series strain data as a :class:`gwpy.frequencyseries.FrequencySeries`.
+ """
+
+ return gwpy.frequencyseries.FrequencySeries(self.frequency_domain_strain,
+ frequencies=self.frequency_array,
+ epoch=self.start_time,
+ channel=self.channel)
+
+ def to_pycbc_frequencyseries(self):
+ """
+ Output the frequency series strain data as a :class:`pycbc.types.frequencyseries.FrequencySeries`.
+ """
+
+ try:
+ import pycbc
+ except ImportError:
+ raise ImportError("Cannot output strain data as PyCBC FrequencySeries")
+
+ return pycbc.types.frequencyseries.FrequencySeries(self.frequency_domain_strain,
+ delta_f=(self.frequency_array[1] - self.frequency_array[0]),
+ epoch=lal.LIGOTimeGPS(self.start_time))
+
+ def to_lal_frequencyseries(self):
+ """
+ Output the frequency series strain data as a LAL FrequencySeries object.
+ """
+
+ laldata = lal.CreateCOMPLEX16FrequencySeries("",
+ lal.LIGOTimeGPS(self.start_time),
+ self.frequency_array[0],
+ (self.frequency_array[1] - self.frequency_array[0]),
+ lal.SecondUnit,
+ len(self.frequency_domain_strain))
+ laldata.data.data[:] = self.frequency_domain_strain
+
+ return laldata
+
+ def add_to_frequency_domain_strain(self, x):
+ """Deprecated"""
+ self._frequency_domain_strain += x
+
+ def low_pass_filter(self, filter_freq=None):
+ """ Low pass filter the data """
+
+ if filter_freq is None:
+ logger.debug(
+ "Setting low pass filter_freq using given maximum frequency")
+ filter_freq = self.maximum_frequency
+
+ if 2 * filter_freq >= self.sampling_frequency:
+ logger.info(
+ "Low pass filter frequency of {}Hz requested, this is equal"
+ " or greater than the Nyquist frequency so no filter applied"
+ .format(filter_freq))
+ return
+
+ logger.debug("Applying low pass filter with filter frequency {}".format(filter_freq))
+ bp = gwpy.signal.filter_design.lowpass(
+ filter_freq, self.sampling_frequency)
+ strain = gwpy.timeseries.TimeSeries(
+ self.time_domain_strain, sample_rate=self.sampling_frequency)
+ strain = strain.filter(bp, filtfilt=True)
+ self._time_domain_strain = strain.value
+
+ def create_power_spectral_density(
+ self, fft_length, overlap=0, name='unknown', outdir=None,
+ analysis_segment_start_time=None):
+ """ Use the time domain strain to generate a power spectral density
+
+ This create a Tukey-windowed power spectral density and writes it to a
+ PSD file.
+
+ Parameters
+ ----------
+ fft_length: float
+ Duration of the analysis segment.
+ overlap: float
+ Number of seconds of overlap between FFTs.
+ name: str
+ The name of the detector, used in storing the PSD. Defaults to
+ "unknown".
+ outdir: str
+ The output directory to write the PSD file too. If not given,
+ the PSD will not be written to file.
+ analysis_segment_start_time: float
+ The start time of the analysis segment, if given, this data will
+ be removed before creating the PSD.
+
+ Returns
+ -------
+ frequency_array, psd : array_like
+ The frequencies and power spectral density array
+
+ """
+
+ data = self.time_domain_strain
+
+ if analysis_segment_start_time is not None:
+ analysis_segment_end_time = analysis_segment_start_time + fft_length
+ inside = (analysis_segment_start_time > self.time_array[0] +
+ analysis_segment_end_time < self.time_array[-1])
+ if inside:
+ logger.info("Removing analysis segment data from the PSD data")
+ idxs = (
+ (self.time_array < analysis_segment_start_time) +
+ (self.time_array > analysis_segment_end_time))
+ data = data[idxs]
+
+ # WARNING this line can cause issues if the data is non-contiguous
+ strain = gwpy.timeseries.TimeSeries(data=data, sample_rate=self.sampling_frequency)
+ psd_alpha = 2 * self.roll_off / fft_length
+ logger.info(
+ "Tukey window PSD data with alpha={}, roll off={}".format(
+ psd_alpha, self.roll_off))
+ psd = strain.psd(
+ fftlength=fft_length, overlap=overlap, window=('tukey', psd_alpha))
+
+ if outdir:
+ psd_file = '{}/{}_PSD_{}_{}.txt'.format(outdir, name, self.start_time, self.duration)
+ with open('{}'.format(psd_file), 'w+') as opened_file:
+ for f, p in zip(psd.frequencies.value, psd.value):
+ opened_file.write('{} {}\n'.format(f, p))
+
+ return psd.frequencies.value, psd.value
+
+ def _infer_time_domain_dependence(
+ self, start_time, sampling_frequency, duration, time_array):
+ """ Helper function to figure out if the time_array, or
+ sampling_frequency and duration where given
+ """
+ self._infer_dependence(domain='time', array=time_array, duration=duration,
+ sampling_frequency=sampling_frequency, start_time=start_time)
+
+ def _infer_frequency_domain_dependence(
+ self, start_time, sampling_frequency, duration, frequency_array):
+ """ Helper function to figure out if the frequency_array, or
+ sampling_frequency and duration where given
+ """
+
+ self._infer_dependence(domain='frequency', array=frequency_array,
+ duration=duration, sampling_frequency=sampling_frequency, start_time=start_time)
+
+ def _infer_dependence(self, domain, array, duration, sampling_frequency, start_time):
+ if (sampling_frequency is not None) and (duration is not None):
+ if array is not None:
+ raise ValueError(
+ "You have given the sampling_frequency, duration, and "
+ "an array")
+ pass
+ elif array is not None:
+ if domain == 'time':
+ self.time_array = array
+ elif domain == 'frequency':
+ self.frequency_array = array
+ return
+ elif sampling_frequency is None or duration is None:
+ raise ValueError(
+ "You must provide both sampling_frequency and duration")
+ else:
+ raise ValueError(
+ "Insufficient information given to set arrays")
+ self._set_time_and_frequency_array_parameters(duration=duration,
+ sampling_frequency=sampling_frequency,
+ start_time=start_time)
+
+ def set_from_time_domain_strain(
+ self, time_domain_strain, sampling_frequency=None, duration=None,
+ start_time=0, time_array=None):
+ """ Set the strain data from a time domain strain array
+
+ This sets the time_domain_strain attribute, the frequency_domain_strain
+ is automatically calculated after a low-pass filter and Tukey window
+ is applied.
+
+ Parameters
+ ----------
+ time_domain_strain: array_like
+ An array of the time domain strain.
+ sampling_frequency: float
+ The sampling frequency (in Hz).
+ duration: float
+ The data duration (in s).
+ start_time: float
+ The GPS start-time of the data.
+ time_array: array_like
+ The array of times, if sampling_frequency and duration not
+ given.
+
+ """
+ self._infer_time_domain_dependence(start_time=start_time,
+ sampling_frequency=sampling_frequency,
+ duration=duration,
+ time_array=time_array)
+
+ logger.debug('Setting data using provided time_domain_strain')
+ if np.shape(time_domain_strain) == np.shape(self.time_array):
+ self._time_domain_strain = time_domain_strain
+ self._frequency_domain_strain = None
+ else:
+ raise ValueError("Data times do not match time array")
+
+ def set_from_gwpy_timeseries(self, time_series):
+ """ Set the strain data from a gwpy TimeSeries
+
+ This sets the time_domain_strain attribute, the frequency_domain_strain
+ is automatically calculated after a low-pass filter and Tukey window
+ is applied.
+
+ Parameters
+ ----------
+ time_series: gwpy.timeseries.timeseries.TimeSeries
+
+ """
+ logger.debug('Setting data using provided gwpy TimeSeries object')
+ if type(time_series) != gwpy.timeseries.TimeSeries:
+ raise ValueError("Input time_series is not a gwpy TimeSeries")
+ self._set_time_and_frequency_array_parameters(duration=time_series.duration.value,
+ sampling_frequency=time_series.sample_rate.value,
+ start_time=time_series.epoch.value)
+ self._time_domain_strain = time_series.value
+ self._frequency_domain_strain = None
+ self._channel = time_series.channel
+
+ @property
+ def channel(self):
+ return self._channel
+
+ def set_from_open_data(
+ self, name, start_time, duration=4, outdir='outdir', cache=True,
+ **kwargs):
+ """ Set the strain data from open LOSC data
+
+ This sets the time_domain_strain attribute, the frequency_domain_strain
+ is automatically calculated after a low-pass filter and Tukey window
+ is applied.
+
+ Parameters
+ ----------
+ name: str
+ Detector name, e.g., 'H1'.
+ start_time: float
+ Start GPS time of segment.
+ duration: float, optional
+ The total time (in seconds) to analyse. Defaults to 4s.
+ outdir: str
+ Directory where the psd files are saved
+ cache: bool, optional
+ Whether or not to store/use the acquired data.
+ **kwargs:
+ All keyword arguments are passed to
+ `gwpy.timeseries.TimeSeries.fetch_open_data()`.
+
+ """
+
+ timeseries = gwutils.get_open_strain_data(
+ name, start_time, start_time + duration, outdir=outdir, cache=cache,
+ **kwargs)
+
+ self.set_from_gwpy_timeseries(timeseries)
+
+ def set_from_csv(self, filename):
+ """ Set the strain data from a csv file
+
+ Parameters
+ ----------
+ filename: str
+ The path to the file to read in
+
+ """
+ timeseries = gwpy.timeseries.TimeSeries.read(filename, format='csv')
+ self.set_from_gwpy_timeseries(timeseries)
+
+ def set_from_frequency_domain_strain(
+ self, frequency_domain_strain, sampling_frequency=None,
+ duration=None, start_time=0, frequency_array=None):
+ """ Set the `frequency_domain_strain` from a numpy array
+
+ Parameters
+ ----------
+ frequency_domain_strain: array_like
+ The data to set.
+ sampling_frequency: float
+ The sampling frequency (in Hz).
+ duration: float
+ The data duration (in s).
+ start_time: float
+ The GPS start-time of the data.
+ frequency_array: array_like
+ The array of frequencies, if sampling_frequency and duration not
+ given.
+
+ """
+
+ self._infer_frequency_domain_dependence(start_time=start_time,
+ sampling_frequency=sampling_frequency,
+ duration=duration,
+ frequency_array=frequency_array)
+
+ logger.debug('Setting data using provided frequency_domain_strain')
+ if np.shape(frequency_domain_strain) == np.shape(self.frequency_array):
+ self._frequency_domain_strain = frequency_domain_strain
+ self.window_factor = 1
+ else:
+ raise ValueError("Data frequencies do not match frequency_array")
+
+ def set_from_power_spectral_density(
+ self, power_spectral_density, sampling_frequency, duration,
+ start_time=0):
+ """ Set the `frequency_domain_strain` by generating a noise realisation
+
+ Parameters
+ ----------
+ power_spectral_density: bilby.gw.detector.PowerSpectralDensity
+ A PowerSpectralDensity object used to generate the data
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+
+ """
+
+ self._set_time_and_frequency_array_parameters(duration=duration,
+ sampling_frequency=sampling_frequency,
+ start_time=start_time)
+
+ logger.debug(
+ 'Setting data using noise realization from provided'
+ 'power_spectal_density')
+ frequency_domain_strain, frequency_array = \
+ power_spectral_density.get_noise_realisation(
+ self.sampling_frequency, self.duration)
+
+ if np.array_equal(frequency_array, self.frequency_array):
+ self._frequency_domain_strain = frequency_domain_strain
+ else:
+ raise ValueError("Data frequencies do not match frequency_array")
+
+ def set_from_zero_noise(self, sampling_frequency, duration, start_time=0):
+ """ Set the `frequency_domain_strain` to zero noise
+
+ Parameters
+ ----------
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+
+ """
+
+ self._set_time_and_frequency_array_parameters(duration=duration,
+ sampling_frequency=sampling_frequency,
+ start_time=start_time)
+
+ logger.debug('Setting zero noise data')
+ self._frequency_domain_strain = np.zeros_like(self.frequency_array,
+ dtype=np.complex)
+
+ def set_from_frame_file(
+ self, frame_file, sampling_frequency, duration, start_time=0,
+ channel=None, buffer_time=1):
+ """ Set the `frequency_domain_strain` from a frame fiile
+
+ Parameters
+ ----------
+ frame_file: str
+ File from which to load data.
+ channel: str
+ Channel to read from frame.
+ sampling_frequency: float
+ The sampling frequency (in Hz)
+ duration: float
+ The data duration (in s)
+ start_time: float
+ The GPS start-time of the data
+ buffer_time: float
+ Read in data with `start_time-buffer_time` and
+ `start_time+duration+buffer_time`
+
+ """
+
+ self._set_time_and_frequency_array_parameters(duration=duration,
+ sampling_frequency=sampling_frequency,
+ start_time=start_time)
+
+ logger.info('Reading data from frame file {}'.format(frame_file))
+ strain = gwutils.read_frame_file(
+ frame_file, start_time=start_time, end_time=start_time + duration,
+ buffer_time=buffer_time, channel=channel,
+ resample=sampling_frequency)
+
+ self.set_from_gwpy_timeseries(strain)
+
+ def _set_time_and_frequency_array_parameters(self, duration, sampling_frequency, start_time):
+ self._times_and_frequencies = CoupledTimeAndFrequencySeries(duration=duration,
+ sampling_frequency=sampling_frequency,
+ start_time=start_time)
+
+ @property
+ def sampling_frequency(self):
+ return self._times_and_frequencies.sampling_frequency
+
+ @sampling_frequency.setter
+ def sampling_frequency(self, sampling_frequency):
+ self._times_and_frequencies.sampling_frequency = sampling_frequency
+
+ @property
+ def duration(self):
+ return self._times_and_frequencies.duration
+
+ @duration.setter
+ def duration(self, duration):
+ self._times_and_frequencies.duration = duration
+
+ @property
+ def start_time(self):
+ return self._times_and_frequencies.start_time
+
+ @start_time.setter
+ def start_time(self, start_time):
+ self._times_and_frequencies.start_time = start_time
+
+ @property
+ def frequency_array(self):
+ """ Frequencies of the data in Hz """
+ return self._times_and_frequencies.frequency_array
+
+ @frequency_array.setter
+ def frequency_array(self, frequency_array):
+ self._times_and_frequencies.frequency_array = frequency_array
+
+ @property
+ def time_array(self):
+ """ Time of the data in seconds """
+ return self._times_and_frequencies.time_array
+
+ @time_array.setter
+ def time_array(self, time_array):
+ self._times_and_frequencies.time_array = time_array
diff --git a/setup.py b/setup.py
index f571a0569bbd77f92b73975f9e43594af948ac36..f1b0dac5a287c7570892e2c1e132d60666556aa2 100644
--- a/setup.py
+++ b/setup.py
@@ -70,10 +70,11 @@ setup(name='bilby',
license="MIT",
version=VERSION,
packages=['bilby', 'bilby.core', 'bilby.core.sampler',
- 'bilby.gw', 'bilby.hyper', 'cli_bilby'],
+ 'bilby.gw', 'bilby.gw.detector', 'bilby.gw.sampler',
+ 'bilby.hyper', 'cli_bilby'],
package_dir={'bilby': 'bilby'},
- package_data={'bilby.gw': ['prior_files/*', 'noise_curves/*.txt',
- 'detectors/*'],
+ package_data={'bilby.gw': ['prior_files/*'],
+ 'bilby.gw.detector': ['noise_curves/*.txt', 'detectors/*'],
'bilby': [version_file]},
install_requires=[
'future',
diff --git a/test/detector_test.py b/test/detector_test.py
index abcf5a17a53d242f14ddf95379737bd2367c9b52..e3f83434b3dc8b797d16cbf8b51e121029ec31bc 100644
--- a/test/detector_test.py
+++ b/test/detector_test.py
@@ -845,7 +845,7 @@ class TestInterferometerList(unittest.TestCase):
def test_init_with_string_list(self):
""" Merely checks if this ends up in the right bracket """
- with mock.patch('bilby.gw.detector.get_empty_interferometer') as m:
+ with mock.patch('bilby.gw.detector.networks.get_empty_interferometer') as m:
m.side_effect = TypeError
with self.assertRaises(TypeError):
bilby.gw.detector.InterferometerList(['string'])