qnm_filter package¶

Submodules¶

qnm_filter.Network module¶

Defining the core Network class.

class qnm_filter.Network.Network(**kws)[source]¶

Bases: object

Perform a ringdown filter analysis. Stores all the needed information.

Example usage:

import qnm_filter
input = dict(model_list = [(2, 2, 0, 'p')], #l, m, n, prograde/retrograde
            # trucation time (geocenter, in second)
            t_init = 1126259462.4083147+2.0*1e-3,
            # length of the analysis segment (in second)
            segment_length = 0.2,
            # sampling rate after conditioning (in Hz)
            srate = 2048,
            # sky localization
            ra = 1.95, dec = -1.27,
            # lower limit of the high-pass filter (in Hz)
            flow = 20)
fit = qnm_filter.Network(**input)
fit.import_data('H-H1_GWOSC_16KHZ_R1-1126259447-32.hdf5')
fit.detector_alignment()
fit.condition_data('original_data', **input)
fit.compute_acfs('original_data')
fit.cholesky_decomposition()
fit.first_index()
fit.add_filter(mass=68.5, chi=0.69, **input)
final_likelihood = fit.compute_likelihood(apply_filter=True)

Attributes:

original_datadict: dictionary containing unfiltered data for each detector.
filtered_datadict: dictionary containing filtered data for each detector.
acfsdict: dictionary containing autocovariance functions for each detector.
start_timesdict: dictionary containing trucation time (start time of analysis segment) for each detector, determined by specified sky location.
cholesky_Ldict: dictionary containing Cholesky factorizations of detectors.
i0_dictdict: dictionary containing the array index of the first time of the analysis segment. The computation of i0_dict needs to be after the conditioning part.
rafloat: source right ascension, in radian.
decfloat: source declination, in radian.
t_initfloat: trucation time (start time of analysis segment) at geocenter.
segment_lengthfloat: width of analysis segment

Methods

`add_filter`(**kwargs)	Apply rational filters to `Network.original_data` and store the filtered data in `Network.filtered_data`.
`cholesky_decomposition`()	Compute the Cholesky-decomposition of covariance matrix $C = L^TL$ , and the inverse of $L$ .
`compute_SNR`(data, template, ifo, optimal)	Compute matched-filter/optimal SNR.
`compute_acfs`(attr_name, **kws)	Compute ACFs with data named attr_name.
`compute_likelihood`([apply_filter])	Compute likelihood for interferometer network.
`condition_data`(attr_name, **kwargs)	Condition data for all interferometers.
`detector_alignment`()	Set the start times of analysis segment at different interferometers `Network.start_times` using sky location.
`first_index`()	Find the index of a data point that is closet to the choosen start time `Network.start_times` for each interferometer.
`import_data_array`(attr_name, data, time, ifo)	Add the inputted data to a dynamic/existing attribute.
`import_ligo_data`(filename)	Read data from disk and store data in `Network.original_data`.
`likelihood_vs_mass_spin`(M_est, chi_est, **kwargs)	Compute likelihood for the given mass and spin.
`shift_first_index`(n)	Shift the first index of the analysis segment by n.
`truncate_data`(network_data)	Select segments of the given data that are in analysis segment.

add_filter(**kwargs)[source]¶: Apply rational filters to Network.original_data and store the filtered data in Network.filtered_data.

cholesky_decomposition() → None[source]¶: Compute the Cholesky-decomposition of covariance matrix $C = L^TL$ , and the inverse of $L$ .

compute_SNR(data, template, ifo, optimal) → float[source]¶

Compute matched-filter/optimal SNR.

Parameters:

datandarray: Time-series data
templatendarray: Ringdown template
ifostring: Name of interferometer
optimal: bool: Compute optimal SNR

compute_acfs(attr_name, **kws) → None[source]¶

Compute ACFs with data named attr_name.

Parameters:

attr_namestring: Name of data for ACF estimation

compute_likelihood(apply_filter=True) → float[source]¶

Compute likelihood for interferometer network.

Returns:

likelihoodfloat: The likelihood of interferometer network

condition_data(attr_name, **kwargs) → None[source]¶

Condition data for all interferometers.

Parameters:

attr_namestring: Name of data to be conditioned

detector_alignment() → None[source]¶: Set the start times of analysis segment at different interferometers Network.start_times using sky location.

first_index()[source]¶: Find the index of a data point that is closet to the choosen start time Network.start_times for each interferometer.

import_data_array(attr_name, data, time, ifo) → None[source]¶

Add the inputted data to a dynamic/existing attribute.

Parameters:

attr_namestring: Name of the dynamic attribute
datandarray: Inputted data
timendarray: Time stamps
ifostring: Name of interferometer

import_ligo_data(filename) → None[source]¶

Read data from disk and store data in Network.original_data.

Supports only HDF5 files downloaded from https://www.gw-openscience.org.

Parameters:

filenamestring: name of file

likelihood_vs_mass_spin(M_est, chi_est, **kwargs) → float[source]¶

Compute likelihood for the given mass and spin.

Parameters:

M_estfloat: in solar mass, mass of rational filters
chi_estfloat: dimensionless spin of rational filters

Returns:

The corresponding likelihood.

property sampling_n: int¶

Number of data points in analysis segment.

Returns:

int: Length of truncated data array

shift_first_index(n)[source]¶: Shift the first index of the analysis segment by n.

truncate_data(network_data) → dict[source]¶

Select segments of the given data that are in analysis segment.

Parameters:

network_datadictionary: Network GW data to be truncated.

Returns:

datadictionary: Truncated GW data for all interferometers.

qnm_filter.bilby_helper module¶

Wrapping up some Bilby functions for convenience

qnm_filter.bilby_helper.bilby_get_strain(ifo, time_offset)[source]¶

Get strain data from Bilby’s Interferometer and store the result in Data.

Parameters:

ifobilby.gw.detector.Interferometer: An instance of bilby.gw.detector.Interferometer
time_offsetfloat: The time offset applied to data

Returns:

Data: strain data

qnm_filter.bilby_helper.bilby_injection(ifo, NR_injection_into_Bilby, **injection_parameters)[source]¶

Inject a GW strain into noise

Parameters:

ifobilby.gw.detector.Interferometer

An instance of bilby.gw.detector.Interferometer

NR_injection_into_Bilbyfunction

waveform to be injected.

Example:

def NR_injection_into_Bilby(time, **waveform_kwargs):
    return {'plus': foo, 'cross': bar}

Returns:

bilby.gw.detector.Interferometer: An instance of bilby.gw.detector.Interferometer

qnm_filter.bilby_helper.set_bilby_ifo(Noise, sampling_frequency, duration, start_time, zero_noise=False, name=None, length=0, latitude=0, longitude=0, elevation=0, xarm_azimuth=0, yarm_azimuth=90)[source]¶

Construct a Bilby’s Interferometer instance given the Noise class.

Parameters:

NoiseNoise: An instance of Noise
sampling_frequencyfloat: in Hz
durationfloat: in second. The duration of the generated time series
start_timefloat: in second. The start time of the generated time series
zero_noisebool, optional: if ture, returns pure waveform strain w/o noise, by default False
namestring, optional: name of interferometer, by default None
lengthint, optional: length of interferometer, by default 0
latitudeint, optional: latitude of interferometer, by default 0
longitudeint, optional: longitude of interferometer, by default 0
elevationint, optional: elevation of interferometer, by default 0
xarm_azimuthint, optional: azimuth angle of the x-arm of interferometer, by default 0
yarm_azimuthint, optional: azimuth angle of the y-arm of interferometer, by default 90

Returns:

bilby.gw.detector.Interferometer: An instance of bilby.gw.detector.Interferometer

qnm_filter.bilby_helper.set_bilby_predefined_ifo(name, sampling_frequency, duration, start_time, zero_noise=False)[source]¶

Construct a Bilby’s Interferometer instance with its internal PSD.

Parameters:

namestring: name of interferometer, e.g., “H1” and “L1”
sampling_frequencyfloat: in Hz
durationfloat: in second. The duration of the generated time series
start_timefloat: in second. The start time of the generated time series
zero_noisebool, optional: if ture, returns pure waveform strain w/o noise, by default False

Returns:

bilby.gw.detector.Interferometer: An instance of bilby.gw.detector.Interferometer

qnm_filter.gw_data module¶

Utilities to manipulate GW data and rational filters.

class qnm_filter.gw_data.ComplexData(*args, ifo=None, **kwargs)[source]¶

Bases: DataBase

Container for complex-valued time-domain numerical-relativity waveforms.

Parameters:

ifostr: name of interferometer.

Attributes:

T: Return the transpose, which is by definition self.
array: The ExtensionArray of the data backing this Series or Index.
at: Access a single value for a row/column label pair.
attrs: Dictionary of global attributes of this dataset.
axes: Return a list of the row axis labels.
dtype: Return the dtype object of the underlying data.
dtypes: Return the dtype object of the underlying data.
empty
fft_data: FFT of the NR waveform
fft_freq: FFT angular frequency stamps.
fft_span: Span of FFT.
flags: Get the properties associated with this pandas object.
hasnans: Return True if there are any NaNs.
iat: Access a single value for a row/column pair by integer position.
iloc: Purely integer-location based indexing for selection by position.
index: The index (axis labels) of the Series.
is_monotonic_decreasing: Return boolean if values in the object are monotonically decreasing.
is_monotonic_increasing: Return boolean if values in the object are monotonically increasing.
is_unique: Return boolean if values in the object are unique.
loc: Access a group of rows and columns by label(s) or a boolean array.
name: Return the name of the Series.
nbytes: Return the number of bytes in the underlying data.
ndim: Number of dimensions of the underlying data, by definition 1.
shape: Return a tuple of the shape of the underlying data.
shifted_fft_data: Shifted FFT of the NR waveform, with the zero-frequency component being at the center.
shifted_fft_freq: Shifted FFT angular frequency stamps, with the zero-frequency component being at the center.
size: Return the number of elements in the underlying data.
time: Time stamps.
time_interval: Interval of the time stamps.
values: Return Series as ndarray or ndarray-like depending on the dtype.

Methods

`abs`()	Return a Series/DataFrame with absolute numeric value of each element.
`add`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator add).
`add_prefix`(prefix[, axis])	Prefix labels with string prefix.
`add_suffix`(suffix[, axis])	Suffix labels with string suffix.
`agg`([func, axis])	Aggregate using one or more operations over the specified axis.
`aggregate`([func, axis])	Aggregate using one or more operations over the specified axis.
`align`(other[, join, axis, level, copy, ...])	Align two objects on their axes with the specified join method.
`all`([axis, bool_only, skipna])	Return whether all elements are True, potentially over an axis.
`any`(*[, axis, bool_only, skipna])	Return whether any element is True, potentially over an axis.
`apply`(func[, convert_dtype, args])	Invoke function on values of Series.
`argmax`([axis, skipna])	Return int position of the largest value in the Series.
`argmin`([axis, skipna])	Return int position of the smallest value in the Series.
`argsort`([axis, kind, order])	Return the integer indices that would sort the Series values.
`asfreq`(freq[, method, how, normalize, ...])	Convert time series to specified frequency.
`asof`(where[, subset])	Return the last row(s) without any NaNs before where.
`astype`(dtype[, copy, errors])	Cast a pandas object to a specified dtype `dtype`.
`at_time`(time[, asof, axis])	Select values at particular time of day (e.g., 9:30AM).
`autocorr`([lag])	Compute the lag-N autocorrelation.
`backfill`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='bfill'`.
`between`(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
`between_time`(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
`bfill`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='bfill'`.
`bool`()	Return the bool of a single element Series or DataFrame.
`cat`	alias of `CategoricalAccessor`
`clip`([lower, upper, axis, inplace])	Trim values at input threshold(s).
`combine`(other, func[, fill_value])	Combine the Series with a Series or scalar according to func.
`combine_first`(other)	Update null elements with value in the same location in 'other'.
`compare`(other[, align_axis, keep_shape, ...])	Compare to another Series and show the differences.
`convert_dtypes`([infer_objects, ...])	Convert columns to the best possible dtypes using dtypes supporting `pd.NA`.
`copy`([deep])	Make a copy of this object's indices and data.
`corr`(other[, method, min_periods])	Compute correlation with other Series, excluding missing values.
`count`()	Return number of non-NA/null observations in the Series.
`cov`(other[, min_periods, ddof])	Compute covariance with Series, excluding missing values.
`cummax`([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
`cummin`([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
`cumprod`([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
`cumsum`([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
`describe`([percentiles, include, exclude])	Generate descriptive statistics.
`diff`([periods])	First discrete difference of element.
`div`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`divide`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`divmod`(other[, level, fill_value, axis])	Return Integer division and modulo of series and other, element-wise (binary operator divmod).
`dot`(other)	Compute the dot product between the Series and the columns of other.
`drop`([labels, axis, index, columns, level, ...])	Return Series with specified index labels removed.
`drop_duplicates`(*[, keep, inplace, ignore_index])	Return Series with duplicate values removed.
`droplevel`(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
`dropna`(*[, axis, inplace, how, ignore_index])	Return a new Series with missing values removed.
`duplicated`([keep])	Indicate duplicate Series values.
`eq`(other[, level, fill_value, axis])	Return Equal to of series and other, element-wise (binary operator eq).
`equals`(other)	Test whether two objects contain the same elements.
`ewm`([com, span, halflife, alpha, ...])	Provide exponentially weighted (EW) calculations.
`expanding`([min_periods, axis, method])	Provide expanding window calculations.
`explode`([ignore_index])	Transform each element of a list-like to a row.
`factorize`([sort, use_na_sentinel])	Encode the object as an enumerated type or categorical variable.
`ffill`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`fillna`([value, method, axis, inplace, ...])	Fill NA/NaN values using the specified method.
`filter`([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
`first`(offset)	Select initial periods of time series data based on a date offset.
`first_valid_index`()	Return index for first non-NA value or None, if no non-NA value is found.
`floordiv`(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator floordiv).
`ge`(other[, level, fill_value, axis])	Return Greater than or equal to of series and other, element-wise (binary operator ge).
`get`(key[, default])	Get item from object for given key (ex: DataFrame column).
`groupby`([by, axis, level, as_index, sort, ...])	Group Series using a mapper or by a Series of columns.
`gt`(other[, level, fill_value, axis])	Return Greater than of series and other, element-wise (binary operator gt).
`head`([n])	Return the first n rows.
`hist`([by, ax, grid, xlabelsize, xrot, ...])	Draw histogram of the input series using matplotlib.
`idxmax`([axis, skipna])	Return the row label of the maximum value.
`idxmin`([axis, skipna])	Return the row label of the minimum value.
`infer_objects`([copy])	Attempt to infer better dtypes for object columns.
`info`([verbose, buf, max_cols, memory_usage, ...])	Print a concise summary of a Series.
`interpolate`([method, axis, limit, inplace, ...])	Fill NaN values using an interpolation method.
`isin`(values)	Whether elements in Series are contained in values.
`isna`()	Detect missing values.
`isnull`()	Series.isnull is an alias for Series.isna.
`item`()	Return the first element of the underlying data as a Python scalar.
`items`()	Lazily iterate over (index, value) tuples.
`keys`()	Return alias for index.
`kurt`([axis, skipna, numeric_only])	Return unbiased kurtosis over requested axis.
`kurtosis`([axis, skipna, numeric_only])	Return unbiased kurtosis over requested axis.
`last`(offset)	Select final periods of time series data based on a date offset.
`last_valid_index`()	Return index for last non-NA value or None, if no non-NA value is found.
`le`(other[, level, fill_value, axis])	Return Less than or equal to of series and other, element-wise (binary operator le).
`lt`(other[, level, fill_value, axis])	Return Less than of series and other, element-wise (binary operator lt).
`map`(arg[, na_action])	Map values of Series according to an input mapping or function.
`mask`(cond[, other, inplace, axis, level])	Replace values where the condition is True.
`max`([axis, skipna, numeric_only])	Return the maximum of the values over the requested axis.
`mean`([axis, skipna, numeric_only])	Return the mean of the values over the requested axis.
`median`([axis, skipna, numeric_only])	Return the median of the values over the requested axis.
`memory_usage`([index, deep])	Return the memory usage of the Series.
`min`([axis, skipna, numeric_only])	Return the minimum of the values over the requested axis.
`mod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator mod).
`mode`([dropna])	Return the mode(s) of the Series.
`mul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator mul).
`multiply`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator mul).
`ne`(other[, level, fill_value, axis])	Return Not equal to of series and other, element-wise (binary operator ne).
`nlargest`([n, keep])	Return the largest n elements.
`notna`()	Detect existing (non-missing) values.
`notnull`()	Series.notnull is an alias for Series.notna.
`nsmallest`([n, keep])	Return the smallest n elements.
`nunique`([dropna])	Return number of unique elements in the object.
`pad`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`pad_complex_data_for_fft`(partition, len_pow)	Pad zeros on both sides for FFT
`pct_change`([periods, fill_method, limit, freq])	Percentage change between the current and a prior element.
`pipe`(func, args, *kwargs)	Apply chainable functions that expect Series or DataFrames.
`plot`	alias of `PlotAccessor`
`pop`(item)	Return item and drops from series.
`pow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator pow).
`prod`([axis, skipna, numeric_only, min_count])	Return the product of the values over the requested axis.
`product`([axis, skipna, numeric_only, min_count])	Return the product of the values over the requested axis.
`quantile`([q, interpolation])	Return value at the given quantile.
`radd`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator radd).
`rank`([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
`ravel`([order])	Return the flattened underlying data as an ndarray or ExtensionArray.
`rdiv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
`rdivmod`(other[, level, fill_value, axis])	Return Integer division and modulo of series and other, element-wise (binary operator rdivmod).
`reindex`([index, axis, method, copy, level, ...])	Conform Series to new index with optional filling logic.
`reindex_like`(other[, method, copy, limit, ...])	Return an object with matching indices as other object.
`rename`([index, axis, copy, inplace, level, ...])	Alter Series index labels or name.
`rename_axis`([mapper, index, axis, copy, inplace])	Set the name of the axis for the index or columns.
`reorder_levels`(order)	Rearrange index levels using input order.
`repeat`(repeats[, axis])	Repeat elements of a Series.
`replace`([to_replace, value, inplace, limit, ...])	Replace values given in to_replace with value.
`resample`(rule[, axis, closed, label, ...])	Resample time-series data.
`reset_index`([level, drop, name, inplace, ...])	Generate a new DataFrame or Series with the index reset.
`rfloordiv`(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator rfloordiv).
`rmod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator rmod).
`rmul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator rmul).
`rolling`(window[, min_periods, center, ...])	Provide rolling window calculations.
`round`([decimals])	Round each value in a Series to the given number of decimals.
`rpow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator rpow).
`rsub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator rsub).
`rtruediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
`sample`([n, frac, replace, weights, ...])	Return a random sample of items from an axis of object.
`searchsorted`(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
`sem`([axis, skipna, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
`set_axis`(labels, *[, axis, copy])	Assign desired index to given axis.
`set_flags`(*[, copy, allows_duplicate_labels])	Return a new object with updated flags.
`shift`([periods, freq, axis, fill_value])	Shift index by desired number of periods with an optional time freq.
`skew`([axis, skipna, numeric_only])	Return unbiased skew over requested axis.
`sort_index`(*[, axis, level, ascending, ...])	Sort Series by index labels.
`sort_values`(*[, axis, ascending, inplace, ...])	Sort by the values.
`sparse`	alias of `SparseAccessor`
`squeeze`([axis])	Squeeze 1 dimensional axis objects into scalars.
`std`([axis, skipna, ddof, numeric_only])	Return sample standard deviation over requested axis.
`str`	alias of `StringMethods`
`sub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator sub).
`subtract`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator sub).
`sum`([axis, skipna, numeric_only, min_count])	Return the sum of the values over the requested axis.
`swapaxes`(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately.
`swaplevel`([i, j, copy])	Swap levels i and j in a `MultiIndex`.
`tail`([n])	Return the last n rows.
`take`(indices[, axis])	Return the elements in the given positional indices along an axis.
`to_clipboard`([excel, sep])	Copy object to the system clipboard.
`to_csv`([path_or_buf, sep, na_rep, ...])	Write object to a comma-separated values (csv) file.
`to_dict`([into])	Convert Series to {label -> value} dict or dict-like object.
`to_excel`(excel_writer[, sheet_name, na_rep, ...])	Write object to an Excel sheet.
`to_frame`([name])	Convert Series to DataFrame.
`to_hdf`(path_or_buf, key[, mode, complevel, ...])	Write the contained data to an HDF5 file using HDFStore.
`to_json`([path_or_buf, orient, date_format, ...])	Convert the object to a JSON string.
`to_latex`([buf, columns, header, index, ...])	Render object to a LaTeX tabular, longtable, or nested table.
`to_list`()	Return a list of the values.
`to_markdown`([buf, mode, index, storage_options])	Print Series in Markdown-friendly format.
`to_numpy`([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or Index.
`to_period`([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex.
`to_pickle`(path[, compression, protocol, ...])	Pickle (serialize) object to file.
`to_sql`(name, con[, schema, if_exists, ...])	Write records stored in a DataFrame to a SQL database.
`to_string`([buf, na_rep, float_format, ...])	Render a string representation of the Series.
`to_timestamp`([freq, how, copy])	Cast to DatetimeIndex of Timestamps, at beginning of period.
`to_xarray`()	Return an xarray object from the pandas object.
`tolist`()	Return a list of the values.
`transform`(func[, axis])	Call `func` on self producing a Series with the same axis shape as self.
`transpose`(args, *kwargs)	Return the transpose, which is by definition self.
`truediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`truncate`([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
`truncate_data`([before, after, copy])	Truncate data before and after some index value
`tz_convert`(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
`tz_localize`(tz[, axis, level, copy, ...])	Localize tz-naive index of a Series or DataFrame to target time zone.
`unique`()	Return unique values of Series object.
`unstack`([level, fill_value])	Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
`update`(other)	Modify Series in place using values from passed Series.
`value_counts`([normalize, sort, ascending, ...])	Return a Series containing counts of unique values.
`var`([axis, skipna, ddof, numeric_only])	Return unbiased variance over requested axis.
`view`([dtype])	Create a new view of the Series.
`where`(cond[, other, inplace, axis, level])	Replace values where the condition is False.
`xs`(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.

dt

property fft_data¶: FFT of the NR waveform

property fft_freq¶: FFT angular frequency stamps.

pad_complex_data_for_fft(partition, len_pow)[source]¶

Pad zeros on both sides for FFT

Parameters:

partitionint: fraction of zeros to be padded on the left
len_powint: the final length of padded data is $2^{ extrm{len\_pow}}$

Returns:

ComplexData: padded data

property shifted_fft_data¶: Shifted FFT of the NR waveform, with the zero-frequency component being at the center.

property shifted_fft_freq¶: Shifted FFT angular frequency stamps, with the zero-frequency component being at the center.

truncate_data(before=None, after=None, copy=None)[source]¶

Truncate data before and after some index value

Parameters:

beforedouble, optional: truncate all data before this index value, by default None
afterdouble, optional: truncate all data after this index value, by default None
copycopy, optional: return a copy of the truncated data, by default None

Returns:

ComplexData: truncated data

class qnm_filter.gw_data.Filter(chi=None, mass=None, model_list=None)[source]¶

Bases: object

Container for rational filters.

Attributes:

chifloat: remnant dimensionless spin.
massfloat: remnant mass, in solar mass.
model_lista list of dictionaries: quasinormal modes to be filtered.

Methods

`NR_filter`(freq)	Rational filters for numerical-relativity waveforms, removing the modes stored in `Filter.model_list`.
`mass_unit`(mass)	Convert mass unit from solar mass to second.
`neg_filter`(normalized_freq, l, m, n)	The negative rational filter:
`pos_filter`(normalized_freq, l, m, n)	The positive rational filter:
`single_filter`(normalized_freq, l, m, n)	A combination of the negative and postive rational filters
`total_filter`(freq)	The total rational filter that removes the modes stored in `Filter.model_list`.

NR_filter(freq)[source]¶

Rational filters for numerical-relativity waveforms, removing the modes stored in Filter.model_list.

Parameters:

freqarray: the unit should be the same as Filter.mass

Raises:

ValueError: When Filter.mass or Filter.chi is not provided

property get_freq_list: list¶: Return a list of QNM frequencies stored in Filter.model_list.

property get_mass: float¶: Return Filter.mass.

property get_model_list: list[dict]¶: Return Filter.model_list.

property get_spin: float¶: Return Filter.chi.

static mass_unit(mass) → float[source]¶: Convert mass unit from solar mass to second.

neg_filter(normalized_freq, l, m, n)[source]¶

The negative rational filter:

$\frac{\omega+\omega_{lmn}^*}{\omega+\omega_{lmn}}$

Parameters:

normalized_freqarray: in remnant mass, frequencies that rational filters are evaluated at.
lint: angular index
mint: angular index
nint: overtone index

Returns:

array

pos_filter(normalized_freq, l, m, n)[source]¶

The positive rational filter:

$\frac{\omega-\omega_{lmn}}{\omega-\omega_{lmn}^*}$

Parameters:

normalized_freqarray: in remnant mass, frequencies that rational filters are evaluated at.
lint: angular index
mint: angular index
nint: overtone index

Returns:

array

single_filter(normalized_freq, l, m, n)[source]¶

A combination of the negative and postive rational filters

$\frac{\omega-\omega_{lmn}}{\omega-\omega_{lmn}^*}\frac{\omega+\omega_{lmn}^*}{\omega+\omega_{lmn}}$

Parameters:

normalized_freqarray: in remnant mass, frequencies that rational filters are evaluated at.
lint: angular index
mint: angular index
nint: overtone index

Returns:

array

total_filter(freq)[source]¶

The total rational filter that removes the modes stored in Filter.model_list.

Parameters:

freqarray: in Hz, frequencies that the total filter is evaluated at.

class qnm_filter.gw_data.Noise(ifo=None, **kwargs)[source]¶

Bases: object

Container for noise

Attributes:

ifostr: name of interferometer
psdData: one-sided power spectral density
asdData: amplitude spectral density
acfData: autocorrelation function
signalData: time-domain noisy signal

Methods

`from_acf`()	Compute PSD and ASD from ACF
`from_asd`()	Compute PSD and ACF from ASD
`from_psd`()	Compute ASD and ACF from PSD
`load_noise_curve`(attr_name, filename[, ifo])	Read a txt/dat file and store the data in target attribute `attr_name`.
`welch`(**kws)	Estimate PSD from data using Welch's method.

property bilby_psd¶: Construct a Bilby PowerSpectralDensity instance.

from_acf()[source]¶: Compute PSD and ASD from ACF

from_asd()[source]¶: Compute PSD and ACF from ASD

from_psd()[source]¶: Compute ASD and ACF from PSD

load_noise_curve(attr_name, filename, ifo=None)[source]¶

Read a txt/dat file and store the data in target attribute attr_name. The file should have two columns.

Parameters:

attr_namestring: name of target attribute, could be psd, asd, or acf.
filenamestring: the file name to be read.
ifostring, optional: name of interferometer, by default None

welch(**kws)[source]¶: Estimate PSD from data using Welch’s method.

class qnm_filter.gw_data.RealData(*args, ifo=None, **kwargs)[source]¶

Bases: DataBase

Container for real-valued time-domain gravitational data.

Parameters:

ifostr: name of interferometer.

Attributes:

T: Return the transpose, which is by definition self.
array: The ExtensionArray of the data backing this Series or Index.
at: Access a single value for a row/column label pair.
attrs: Dictionary of global attributes of this dataset.
axes: Return a list of the row axis labels.
dtype: Return the dtype object of the underlying data.
dtypes: Return the dtype object of the underlying data.
empty
fft_data: FFT of gravitational-wave data.
fft_freq: FFT angular frequency stamps.
fft_span: Span of FFT.
flags: Get the properties associated with this pandas object.
hasnans: Return True if there are any NaNs.
iat: Access a single value for a row/column pair by integer position.
iloc: Purely integer-location based indexing for selection by position.
index: The index (axis labels) of the Series.
is_monotonic_decreasing: Return boolean if values in the object are monotonically decreasing.
is_monotonic_increasing: Return boolean if values in the object are monotonically increasing.
is_unique: Return boolean if values in the object are unique.
loc: Access a group of rows and columns by label(s) or a boolean array.
name: Return the name of the Series.
nbytes: Return the number of bytes in the underlying data.
ndim: Number of dimensions of the underlying data, by definition 1.
shape: Return a tuple of the shape of the underlying data.
size: Return the number of elements in the underlying data.
time: Time stamps.
time_interval: Interval of the time stamps.
values: Return Series as ndarray or ndarray-like depending on the dtype.

Methods

`abs`()	Return a Series/DataFrame with absolute numeric value of each element.
`add`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator add).
`add_prefix`(prefix[, axis])	Prefix labels with string prefix.
`add_suffix`(suffix[, axis])	Suffix labels with string suffix.
`agg`([func, axis])	Aggregate using one or more operations over the specified axis.
`aggregate`([func, axis])	Aggregate using one or more operations over the specified axis.
`align`(other[, join, axis, level, copy, ...])	Align two objects on their axes with the specified join method.
`all`([axis, bool_only, skipna])	Return whether all elements are True, potentially over an axis.
`any`(*[, axis, bool_only, skipna])	Return whether any element is True, potentially over an axis.
`apply`(func[, convert_dtype, args])	Invoke function on values of Series.
`argmax`([axis, skipna])	Return int position of the largest value in the Series.
`argmin`([axis, skipna])	Return int position of the smallest value in the Series.
`argsort`([axis, kind, order])	Return the integer indices that would sort the Series values.
`asfreq`(freq[, method, how, normalize, ...])	Convert time series to specified frequency.
`asof`(where[, subset])	Return the last row(s) without any NaNs before where.
`astype`(dtype[, copy, errors])	Cast a pandas object to a specified dtype `dtype`.
`at_time`(time[, asof, axis])	Select values at particular time of day (e.g., 9:30AM).
`autocorr`([lag])	Compute the lag-N autocorrelation.
`backfill`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='bfill'`.
`between`(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
`between_time`(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
`bfill`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='bfill'`.
`bool`()	Return the bool of a single element Series or DataFrame.
`cat`	alias of `CategoricalAccessor`
`clip`([lower, upper, axis, inplace])	Trim values at input threshold(s).
`combine`(other, func[, fill_value])	Combine the Series with a Series or scalar according to func.
`combine_first`(other)	Update null elements with value in the same location in 'other'.
`compare`(other[, align_axis, keep_shape, ...])	Compare to another Series and show the differences.
`condition`([t0, srate, flow, fhigh, trim, ...])	Condition data.
`convert_dtypes`([infer_objects, ...])	Convert columns to the best possible dtypes using dtypes supporting `pd.NA`.
`copy`([deep])	Make a copy of this object's indices and data.
`corr`(other[, method, min_periods])	Compute correlation with other Series, excluding missing values.
`count`()	Return number of non-NA/null observations in the Series.
`cov`(other[, min_periods, ddof])	Compute covariance with Series, excluding missing values.
`cummax`([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
`cummin`([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
`cumprod`([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
`cumsum`([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
`describe`([percentiles, include, exclude])	Generate descriptive statistics.
`diff`([periods])	First discrete difference of element.
`div`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`divide`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`divmod`(other[, level, fill_value, axis])	Return Integer division and modulo of series and other, element-wise (binary operator divmod).
`dot`(other)	Compute the dot product between the Series and the columns of other.
`drop`([labels, axis, index, columns, level, ...])	Return Series with specified index labels removed.
`drop_duplicates`(*[, keep, inplace, ignore_index])	Return Series with duplicate values removed.
`droplevel`(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
`dropna`(*[, axis, inplace, how, ignore_index])	Return a new Series with missing values removed.
`duplicated`([keep])	Indicate duplicate Series values.
`eq`(other[, level, fill_value, axis])	Return Equal to of series and other, element-wise (binary operator eq).
`equals`(other)	Test whether two objects contain the same elements.
`ewm`([com, span, halflife, alpha, ...])	Provide exponentially weighted (EW) calculations.
`expanding`([min_periods, axis, method])	Provide expanding window calculations.
`explode`([ignore_index])	Transform each element of a list-like to a row.
`factorize`([sort, use_na_sentinel])	Encode the object as an enumerated type or categorical variable.
`ffill`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`fillna`([value, method, axis, inplace, ...])	Fill NA/NaN values using the specified method.
`filter`([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
`first`(offset)	Select initial periods of time series data based on a date offset.
`first_valid_index`()	Return index for first non-NA value or None, if no non-NA value is found.
`floordiv`(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator floordiv).
`ge`(other[, level, fill_value, axis])	Return Greater than or equal to of series and other, element-wise (binary operator ge).
`get`(key[, default])	Get item from object for given key (ex: DataFrame column).
`groupby`([by, axis, level, as_index, sort, ...])	Group Series using a mapper or by a Series of columns.
`gt`(other[, level, fill_value, axis])	Return Greater than of series and other, element-wise (binary operator gt).
`head`([n])	Return the first n rows.
`hist`([by, ax, grid, xlabelsize, xrot, ...])	Draw histogram of the input series using matplotlib.
`idxmax`([axis, skipna])	Return the row label of the maximum value.
`idxmin`([axis, skipna])	Return the row label of the minimum value.
`infer_objects`([copy])	Attempt to infer better dtypes for object columns.
`info`([verbose, buf, max_cols, memory_usage, ...])	Print a concise summary of a Series.
`interpolate`([method, axis, limit, inplace, ...])	Fill NaN values using an interpolation method.
`isin`(values)	Whether elements in Series are contained in values.
`isna`()	Detect missing values.
`isnull`()	Series.isnull is an alias for Series.isna.
`item`()	Return the first element of the underlying data as a Python scalar.
`items`()	Lazily iterate over (index, value) tuples.
`keys`()	Return alias for index.
`kurt`([axis, skipna, numeric_only])	Return unbiased kurtosis over requested axis.
`kurtosis`([axis, skipna, numeric_only])	Return unbiased kurtosis over requested axis.
`last`(offset)	Select final periods of time series data based on a date offset.
`last_valid_index`()	Return index for last non-NA value or None, if no non-NA value is found.
`le`(other[, level, fill_value, axis])	Return Less than or equal to of series and other, element-wise (binary operator le).
`lt`(other[, level, fill_value, axis])	Return Less than of series and other, element-wise (binary operator lt).
`map`(arg[, na_action])	Map values of Series according to an input mapping or function.
`mask`(cond[, other, inplace, axis, level])	Replace values where the condition is True.
`max`([axis, skipna, numeric_only])	Return the maximum of the values over the requested axis.
`mean`([axis, skipna, numeric_only])	Return the mean of the values over the requested axis.
`median`([axis, skipna, numeric_only])	Return the median of the values over the requested axis.
`memory_usage`([index, deep])	Return the memory usage of the Series.
`min`([axis, skipna, numeric_only])	Return the minimum of the values over the requested axis.
`mod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator mod).
`mode`([dropna])	Return the mode(s) of the Series.
`mul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator mul).
`multiply`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator mul).
`ne`(other[, level, fill_value, axis])	Return Not equal to of series and other, element-wise (binary operator ne).
`nlargest`([n, keep])	Return the largest n elements.
`notna`()	Detect existing (non-missing) values.
`notnull`()	Series.notnull is an alias for Series.notna.
`nsmallest`([n, keep])	Return the smallest n elements.
`nunique`([dropna])	Return number of unique elements in the object.
`pad`(*[, axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`pct_change`([periods, fill_method, limit, freq])	Percentage change between the current and a prior element.
`pipe`(func, args, *kwargs)	Apply chainable functions that expect Series or DataFrames.
`plot`	alias of `PlotAccessor`
`pop`(item)	Return item and drops from series.
`pow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator pow).
`prod`([axis, skipna, numeric_only, min_count])	Return the product of the values over the requested axis.
`product`([axis, skipna, numeric_only, min_count])	Return the product of the values over the requested axis.
`quantile`([q, interpolation])	Return value at the given quantile.
`radd`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator radd).
`rank`([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
`ravel`([order])	Return the flattened underlying data as an ndarray or ExtensionArray.
`rdiv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
`rdivmod`(other[, level, fill_value, axis])	Return Integer division and modulo of series and other, element-wise (binary operator rdivmod).
`reindex`([index, axis, method, copy, level, ...])	Conform Series to new index with optional filling logic.
`reindex_like`(other[, method, copy, limit, ...])	Return an object with matching indices as other object.
`rename`([index, axis, copy, inplace, level, ...])	Alter Series index labels or name.
`rename_axis`([mapper, index, axis, copy, inplace])	Set the name of the axis for the index or columns.
`reorder_levels`(order)	Rearrange index levels using input order.
`repeat`(repeats[, axis])	Repeat elements of a Series.
`replace`([to_replace, value, inplace, limit, ...])	Replace values given in to_replace with value.
`resample`(rule[, axis, closed, label, ...])	Resample time-series data.
`reset_index`([level, drop, name, inplace, ...])	Generate a new DataFrame or Series with the index reset.
`rfloordiv`(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator rfloordiv).
`rmod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator rmod).
`rmul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator rmul).
`rolling`(window[, min_periods, center, ...])	Provide rolling window calculations.
`round`([decimals])	Round each value in a Series to the given number of decimals.
`rpow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator rpow).
`rsub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator rsub).
`rtruediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
`sample`([n, frac, replace, weights, ...])	Return a random sample of items from an axis of object.
`searchsorted`(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
`sem`([axis, skipna, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
`set_axis`(labels, *[, axis, copy])	Assign desired index to given axis.
`set_flags`(*[, copy, allows_duplicate_labels])	Return a new object with updated flags.
`shift`([periods, freq, axis, fill_value])	Shift index by desired number of periods with an optional time freq.
`skew`([axis, skipna, numeric_only])	Return unbiased skew over requested axis.
`sort_index`(*[, axis, level, ascending, ...])	Sort Series by index labels.
`sort_values`(*[, axis, ascending, inplace, ...])	Sort by the values.
`sparse`	alias of `SparseAccessor`
`squeeze`([axis])	Squeeze 1 dimensional axis objects into scalars.
`std`([axis, skipna, ddof, numeric_only])	Return sample standard deviation over requested axis.
`str`	alias of `StringMethods`
`sub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator sub).
`subtract`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator sub).
`sum`([axis, skipna, numeric_only, min_count])	Return the sum of the values over the requested axis.
`swapaxes`(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately.
`swaplevel`([i, j, copy])	Swap levels i and j in a `MultiIndex`.
`tail`([n])	Return the last n rows.
`take`(indices[, axis])	Return the elements in the given positional indices along an axis.
`to_clipboard`([excel, sep])	Copy object to the system clipboard.
`to_csv`([path_or_buf, sep, na_rep, ...])	Write object to a comma-separated values (csv) file.
`to_dict`([into])	Convert Series to {label -> value} dict or dict-like object.
`to_excel`(excel_writer[, sheet_name, na_rep, ...])	Write object to an Excel sheet.
`to_frame`([name])	Convert Series to DataFrame.
`to_hdf`(path_or_buf, key[, mode, complevel, ...])	Write the contained data to an HDF5 file using HDFStore.
`to_json`([path_or_buf, orient, date_format, ...])	Convert the object to a JSON string.
`to_latex`([buf, columns, header, index, ...])	Render object to a LaTeX tabular, longtable, or nested table.
`to_list`()	Return a list of the values.
`to_markdown`([buf, mode, index, storage_options])	Print Series in Markdown-friendly format.
`to_numpy`([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or Index.
`to_period`([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex.
`to_pickle`(path[, compression, protocol, ...])	Pickle (serialize) object to file.
`to_sql`(name, con[, schema, if_exists, ...])	Write records stored in a DataFrame to a SQL database.
`to_string`([buf, na_rep, float_format, ...])	Render a string representation of the Series.
`to_timestamp`([freq, how, copy])	Cast to DatetimeIndex of Timestamps, at beginning of period.
`to_xarray`()	Return an xarray object from the pandas object.
`tolist`()	Return a list of the values.
`transform`(func[, axis])	Call `func` on self producing a Series with the same axis shape as self.
`transpose`(args, *kwargs)	Return the transpose, which is by definition self.
`truediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`truncate`([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
`tz_convert`(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
`tz_localize`(tz[, axis, level, copy, ...])	Localize tz-naive index of a Series or DataFrame to target time zone.
`unique`()	Return unique values of Series object.
`unstack`([level, fill_value])	Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
`update`(other)	Modify Series in place using values from passed Series.
`value_counts`([normalize, sort, ascending, ...])	Return a Series containing counts of unique values.
`var`([axis, skipna, ddof, numeric_only])	Return unbiased variance over requested axis.
`view`([dtype])	Create a new view of the Series.
`where`(cond[, other, inplace, axis, level])	Replace values where the condition is False.
`xs`(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.

dt

condition(t0=None, srate=None, flow=None, fhigh=None, trim=0.25, remove_mean=True)[source]¶

Condition data.

Credit: This function is from git@github.com:maxisi/ringdown.git.

Returns:

cond_dataData: conditioned data object.

property fft_data¶: FFT of gravitational-wave data.

property fft_freq¶: FFT angular frequency stamps.

qnm_filter.sxs_helper module¶

Wrapping up some SXS (numerical relativity) functions for convenience

class qnm_filter.sxs_helper.SXSWaveforms(**kws)[source]¶

Bases: object

Container for numerical relativity waveforms, downloaded from SXS catalog https://data.black-holes.org/waveforms/index.html

Attributes:

original_datadictionary: dictionary that stores all the GW harmonics
padded_datadictionary: dictionary that stores all the GW harmonics padded with 0 on both sides. This is mainly for FFT so the time stamp should be evenly spaced, and the total length needs to be a power of 2.
data_in_sidictionary: dictionary that stores all the GW harmonics that have SI units
filenamestring: SXS ID, in the format of SXS:BBH:XXXX
mffloat: remnant mass, in the unit of BBH’s total mass
chiffloat: remnant dimensionless spin

Methods

`add_filter`(lm, model_list)	Apply rational filters listed in model_list to the $lm$ harmonic of the NR waveform.
`get_remnant_data`([download])	Get remnant mass and dimensionless spin from SXS catalog.
`harmonics_to_polarizations`(attr_name, iota, ...)	Compute two polarizations from GW harmonics stored in attr_name
`import_sxs_data`(l, m, interpolate[, ...])	Download/Load SXS waveforms.
`pad_data`(partition, len_pow)	Pad zeros on both sides of GW harmonics `self.original_data`, the final length is $2^{\textrm{len\_pow}}$
`scale_to_si`(attr_name, mass, distance)	Convert GW waveforms stored in attr_name from numerical-relativity's units to SI units.

trunc_pad

add_filter(lm, model_list)[source]¶

Apply rational filters listed in model_list to the $lm$ harmonic of the NR waveform.

Parameters:

lmstring: the $lm$ harmonic to be filtered
model_lista list of dictionaries: quasinormal modes to be filtered.

Returns:

ComplexData: filtered harmonic

property get_bbh_m1¶: Get the mass of the first BH (at a reference time during inspiral)

property get_bbh_m2¶: Get the mass of the second BH (at a reference time during inspiral)

property get_bbh_spin1¶: Get the spin vector of the first BH (at a reference time during inspiral)

property get_bbh_spin2¶: Get the spin vector of the second BH (at a reference time during inspiral)

get_remnant_data(download=False) → None[source]¶

Get remnant mass and dimensionless spin from SXS catalog. Note only spin’s length is returned.

Parameters:

downloadbool, optional: download meta data from SXS catalog if ture, by default False

harmonics_to_polarizations(attr_name, iota, beta, model_list) → None[source]¶

Compute two polarizations from GW harmonics stored in attr_name

Parameters:

attr_namestring: the name of attribute
iotafloat: inclination angle of propagation direction, in rad.
betafloat: azimuthal angle of propagation direction, in rad.
model_lista list of dictionaries: harmonics to be added

Returns:

Dictionary: plus and cross polarizations

import_sxs_data(l, m, interpolate, extrapolation_order=2, download=False, ti=None, tf=None, delta_t=None) → None[source]¶

Download/Load SXS waveforms.

Parameters:

lint: GW harmonic index
mint: GW harmonic index
interpolatebool: interpolate to desired time stamps if ture, otherwise use sxs’s original data
extrapolation_orderint, optional: waveform extraploation order, used internally by sxs, by default 2
downloadbool, optional: download GWs from SXS catalog if ture, by default False
tifloat, optional: the initial time of desired time stamps, use the first time stamp by default. The strain’s peak is assumed to be at 0.
tffloat, optional: the final time of desired time stamps, use the last time stamp by default. The strain’s peak is assumed to be at 0.
delta_tfloat, optional: the step size of desired time stamps, use sxs’s original data by default, whose time stamps may not be even spacing

Raises:

ValueError: when the time interpolator cannot find delta_t

pad_data(partition, len_pow) → None[source]¶

Pad zeros on both sides of GW harmonics self.original_data, the final length is $2^{\textrm{len\_pow}}$

Parameters:

partitionint: fraction of zeros to be padded on the left
len_powint: the final length of padded data is $2^{\textrm{len\_pow}}$

scale_to_si(attr_name, mass, distance) → None[source]¶

Convert GW waveforms stored in attr_name from numerical-relativity’s units to SI units.

Parameters:

attr_namestring: the name of attribute
massfloat: binary’s total mass, in solar mass. Not to be confused with the remnant mass.
distancefloat: binary’s luminous intensity, in MPC.

static trunc_pad(data, before, after, partition, len_pow)[source]¶

qnm_filter.utility module¶

Useful functions for calculating and plotting data

qnm_filter.utility.evidence_parallel(self, index_spacing, num_iteration, initial_offset, M_arr, chi_arr, num_cpu=-1, apply_filter=True, verbosity=False, **kwargs)[source]¶

Compute evidence curve, which is sampled at multiples of the post-downsampling rate self.srate, therefore there is no need to recondition the data set.

Parameters:

index_spacingint: the ratio between self.srate and the evidence’s sampling rate
num_iterationint: number of sampling points for the evidence curve
initial_offsetint: the index offset of the first evidence data point with respect to self.i0_dict
M_arrarray-like: array of the values of remnant mass to calculate the likelihood function for
chi_arrarray-like: array of the values of remnant spin to calculate the likelihood function for
num_cpuint, optional: integer to be based to Parallel as n_jobs. NOTE: passing a positive integer leads to better performance than -1 but performance differs across machines, by default -1
verbositybool, optional: print more information, by default False

Returns:

Two arrays: time stamps, log-evidence

qnm_filter.utility.find_credible_region(array2d, num_cpu=-1, target_probability=0.9)[source]¶

Compute the log likelihood contour that encloses the desired probability.

Parameters:

array2dndarray: 2D array of sampling log likelihood as a function of mass and spin
num_cpuint, optional: number of CPUs used for parallelization, by default -1
target_probabilityfloat, optional: desired probability, by default 0.9

Returns:

resultfloat: the log likelihood above which has the desired probability.

Raises:

ValueError: when the target log likelihood cannot be found.

qnm_filter.utility.load_class(filename)[source]¶

Read a class object from a file

Parameters:

filenamestring: the file name to be read

Returns:

fit: class object saved in the file

qnm_filter.utility.pad_data_for_fft(data, partition, len_pow) → None[source]¶

Pad zeros on both sides of data, the final length is $2^{\textrm{len\_pow}}$

Parameters:

dataData: data to be padded
partitionint: fraction of zeros to be padded on the left
len_powint: the final length of padded data is $2^{\textrm{len\_pow}}$

Returns:

Data: padded data

qnm_filter.utility.parallel_compute(self, M_arr, chi_arr, num_cpu=-1, **kwargs)[source]¶

Parallel computation of a function that takes 2 arguments

Returns:

reshaped_resultsndarray: 2d array of the results with shape (len(x_arr), len(y_arr))

qnm_filter.utility.project_to_1d(array2d, delta_mass, delta_chi)[source]¶

Project the 2D log likelihood to 1D probability density functions, whose integrations are normalized to be 1.

Parameters:

array2dndarray: 2D array of sampling log likelihood as a function of mass and spin
delta_massfloat: step size of mass
delta_chifloat: step size of chi

Returns:

Two ndarrays: probability density functions of mass and spin, both normalized to a total probability of 1.

qnm_filter.utility.save_class(cls, filename)[source]¶

Dump a class object to a file

Parameters:

filenamestring: the file name to be dumped

qnm_filter.utility.time_shift_from_sky(ifo, ra, dec, t_init)[source]¶

Get time offset with respect to geocenter given the information

Parameters:

ifostr: name of interferometer.
rafloat: source right ascension, in radian.
decfloat: source declination, in radian.
t_initfloat: trucation time (start time of analysis segment) at geocenter.

Returns:

dt_ifofloat: the time offset

qnm_filter.utility.time_to_index(self, index_spacing, tmin, tmax)[source]¶

Estimate initial_offset and num_iteration for the evidence calculator given physical times tmin and tmax.

Parameters:

index_spacingint: the ratio between self.srate and the evidence’s sampling rate
tminfloat: the start time of the evidence curve
tmaxfloat: the end time of the evidence curve

Returns:

initial_offsetint: the index offset of the first evidence data point with respect to self.i0_dict
num_iterationint: number of sampling points for the evidence curve

qnm_filter package¶

Submodules¶

qnm_filter.Network module¶

qnm_filter.bilby_helper module¶

qnm_filter.gw_data module¶

qnm_filter.sxs_helper module¶

qnm_filter.utility module¶

Module contents¶