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
import deepdish as dd

from . import utils as gwutils
from ..core import utils
from ..core.utils import logger
from ..core.series import CoupledTimeAndFrequencySeries
from .calibration import Recalibrate

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 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 spectal 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 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.
        """
        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)
        dd.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

        """
        if sys.version_info[0] < 3:
            raise NotImplementedError('Pickling of InterferometerList is not supported in Python 2.'
                                      'Use Python 3 instead.')
        res = dd.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

    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
        """
        return ((self.frequency_array >= self.minimum_frequency) &
                (self.frequency_array <= self.maximum_frequency))

    @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 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

        """

        if analysis_segment_start_time is not None:
            logger.info("Removing analysis segment data from the PSD data")
            analysis_segment_end_time = analysis_segment_start_time + fft_length
            idxs = (
                (self.time_array < analysis_segment_start_time) +
                (self.time_array > analysis_segment_end_time))
            data = self.time_domain_strain[idxs]
        else:
            data = self.time_domain_strain

        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

    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')
        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,
            power_spectral_density=self.power_spectral_density_array,
            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, bb=self.frequency_domain_strain,
            power_spectral_density=self.power_spectral_density_array,
            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, frequency_domain_strain=self.frequency_domain_strain,
            power_spectral_density=self.power_spectral_density_array,
            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
        """
        np.savetxt('{}/{}_frequency_domain_data.dat'.format(outdir, self.name),
                   np.array(
                       [self.frequency_array,
                        self.frequency_domain_strain.real,
                        self.frequency_domain_strain.imag]).T,
                   header='f real_h(f) imag_h(f)')
        if label is None:
            filename = '{}/{}_psd.dat'.format(outdir, self.name)
        else:
            filename = '{}/{}_{}_psd.dat'.format(outdir, self.name, label)
        np.savetxt(filename,
                   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()
        ax.loglog(self.frequency_array,
                  gwutils.asd_from_freq_series(freq_data=self.frequency_domain_strain,
                                               df=(self.frequency_array[1] - self.frequency_array[0])),
                  color='C0', label=self.name)
        ax.loglog(self.frequency_array,
                  self.amplitude_spectral_density_array,
                  color='C1', lw=0.5, label=self.name + ' ASD')
        if signal is not None:
            ax.loglog(self.frequency_array,
                      gwutils.asd_from_freq_series(freq_data=signal,
                                                   df=(self.frequency_array[1] - self.frequency_array[0])),
                      color='C2',
                      label='Signal')
        ax.grid('on')
        ax.set_ylabel(r'strain [strain/$\sqrt{\rm Hz}$]')
        ax.set_xlabel(r'frequency [Hz]')
        ax.set_xlim(20, 2000)
        ax.legend(loc='best')
        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))

    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))

    @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.
        """
        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)
        dd.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

        """
        if sys.version_info[0] < 3:
            raise NotImplementedError('Pickling of Interferometer is not supported in Python 2.'
                                      'Use Python 3 instead.')

        res = dd.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))
