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class
xpipeline.core.xtimeseries.XTimeSeries[source]¶ Bases:
gwpy.timeseries.timeseries.TimeSeriesDict-
asd(self, fftlength, **kwargs)[source]¶ Obtain the asd of items in this dict.
- Parameters
fftlength : dict, float
either a dict of (channel, float) pairs for key-wise asd calc, or a single float/int to compute as of all items.
**kwargs
other keyword arguments to pass to each item’s asd method.
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fftgram(self, fftlength)[source]¶ Obtain the spectrograms of items in this dict.
- Parameters
fftlength : dict, float
either a dict of (channel, float) pairs for key-wise asd calc, or a single float/int to computer as of all items.
**kwargs
other keyword arguments to pass to each item’s asd method.
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classmethod
generate_data(event_time, block_time, channel_names, sample_frequency, verbose=False)[source]¶ Obtain data for either on source, off source, or injections.
This uses the gwpy TimeSeriesDict.get method
- Parameters
event_time : (`float)
trigger time of event to be processing
block_time : (int)
length of data to be processed
channel_names (`list`) :
required data channels.
sample_frequency (`int`):
sample rate of the data desired
verbose : bool, optional
print verbose output about NDS progress.
Returns:
TimeSeriesDict :
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classmethod
generate_noise_from_file(file_name, event_time, block_time, channel_names, sample_frequency, verbose=False)[source]¶ Obtain data for either on source, off source, or injections.
This uses the gwpy TimeSeriesDict.get method
- Parameters
event_time : (`float)
trigger time of event to be processing
block_time : (int)
length of data to be processed
channel_names (`list`) :
required data channels.
sample_frequency (`int`):
sample rate of the data desired
verbose : bool, optional
print verbose output about NDS progress.
Returns:
TimeSeriesDict :
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highpass(self, frequency, corruption=2)[source]¶ Design a high-pass filter.
- Parameters
fhigh : float
high-pass corner frequency
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inject(self, injection_data, **kwargs)[source]¶ Take an injection time series and inject into XTimeSeries
This is essentially a wrapper arounf the very useful gwpy.timeseries.TimeSeries.inject method
- Parameters
injection_data : XTimeSeries,
A XtimeSeries containing the same keys as the XTimeSeries you are injecting into i.e. if you are injecting into an XTimeSeries with ‘H1’, ‘L1’ and ‘V1’ data then your injeciton data should have the same keys
**kwargs
other keyword arguments to pass to each item’s asd method.
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classmethod
retrieve_data(event_time, block_time, channel_names, sample_frequency, **kwargs)[source]¶ Obtain data for either on source, off source, or injections.
This uses the gwpy TimeSeriesDict.get method
- Parameters
event_time : (`float)
trigger time of event to be processing
block_time : (int)
length of data to be processed
channel_names (`list`) :
required data channels.
sample_frequency (`int`):
sample rate of the data desired
frame_types : dict, optional
key channel and value frametype in whcih the channel is stored
verbose : bool, optional
print verbose output about NDS progress.
Returns:
TimeSeriesDict :
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spectrogram(self, fftlength)[source]¶ Obtain the spectrograms of items in this dict.
- Parameters
fftlength : dict, float
either a dict of (channel, float) pairs for key-wise asd calc, or a single float/int to computer as of all items.
**kwargs
other keyword arguments to pass to each item’s asd method.
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