Time Series¶
Filters¶
Apodization Window¶
@author: kkappler
Module to manage windowing prior to FFT. Intended to support most apodization windows available via scipy.signal.get_window()
The Taper Config has 2 possible forms: 1. Standard form for accessing scipy.signal: [“taper_family”, “num_samples_window”, “additional_args”] 2. User-defined : for defining custom tapers
Example 1 : Standard form “taper_family” = “hamming” “num_samples_window” = 128 “additional_args” = {}
Example 2 : Standard form “taper_family” = “kaiser” “num_samples_window” = 64 “additional_args” = {“beta”:8}
Examples 3 : User Defined 2. user-defined: [“array”] In this case num_samples_window is defined by the array. “array” = [1, 2, 3, 4, 5, 4, 3, 2, 1] If “array” is non-empty then assume the user-defined case.
It is a little bit unsatisfying that the args need to be ordered for scipy.signal.get_window(). Probably use OrderedDict() for any windows that have more than one additional args.
For example “taper_family” = ‘general_gaussian’ “additional_args” = OrderedDict(“power”:1.5, “sigma”:7)
- class aurora.time_series.apodization_window.ApodizationWindow(**kwargs)[source]¶
Bases:
object
Instantiate an apodization window object. Example usages: apod_window = ApodizationWindow() taper=ApodizationWindow(taper_family=’hanning’, num_samples_window=55 )
Window factors S1, S2, CG, ENBW are modelled after Heinzel et al. p12-14 [1] Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows. G. Heinzel, A. Roudiger and R. Schilling, Max-Planck Institut fur Gravitationsphysik (Albert-Einstein-Institut) Teilinstitut Hannover February 15, 2002 See Also [2] Harris FJ. On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE. 1978 Jan;66(1):51-83.
Nomenclature from Heinzel et al. ENBW: Effective Noise BandWidth, see Equation (22) NENBW Normalized Equivalent Noise BandWidth, see Equation (21)
- Parameters:
taper_family (string) – Specify the taper type - boxcar, kaiser, hanning, etc
num_samples_window (int) – The number of samples in the taper
taper (numpy array) – The actual window coefficients themselves. This can be passed if a particular custom window is desired.
additional_args (dictionary) – These are any additional requirements scipy needs in order to generate the window.
- Attributes:
S1
sum of the window coefficients
S2
sum of squares of the window coefficients
- apodization_factor
coherent_gain
DC gain of the window normalized by window length
nenbw
NENBW Normalized Equivalent Noise BandWidth, see Equation (21) in
- num_samples_window
summary
Returns
- taper
Methods
enbw
(fs)Notes that unlike NENBW, CG, S1, S2, this is not a pure property of the window -- but instead this is a property of the window combined with the sample rate.
make
()this is just a wrapper call to scipy.signal Note: see scipy.signal.get_window for a description of what is expected in args[1:].
This is just a test to verify some algebra Claim: The lsd_calibration factors A (1./coherent_gain)*np.sqrt((2*dt)/(nenbw*N)) and B np.sqrt(2/(sample_rate*self.S2))
- property S1¶
sum of the window coefficients
- property S2¶
sum of squares of the window coefficients
- property apodization_factor¶
- property coherent_gain¶
DC gain of the window normalized by window length
- enbw(fs)[source]¶
Notes that unlike NENBW, CG, S1, S2, this is not a pure property of the window – but instead this is a property of the window combined with the sample rate. :param fs: :type fs: sampling frequency (1/dt)
- make()[source]¶
this is just a wrapper call to scipy.signal Note: see scipy.signal.get_window for a description of what is expected in args[1:]. http://docs.scipy.org/doc/scipy/reference/ generated/scipy.signal.get_window.html
note: this is just repackaging the args so that scipy.signal.get_window() accepts all cases.
- property nenbw¶
NENBW Normalized Equivalent Noise BandWidth, see Equation (21) in Heinzel et al 2002
- property num_samples_window¶
- property summary¶
returns: out_str – String comprised of the taper_family, number_of_samples, and True/False if self.taper is not None :rtype: str
- property taper¶
- test_linear_spectral_density_factor()[source]¶
This is just a test to verify some algebra Claim: The lsd_calibration factors A (1./coherent_gain)*np.sqrt((2*dt)/(nenbw*N)) and B np.sqrt(2/(sample_rate*self.S2))
are identical.
Note sqrt(2*dt)==sqrt(2*sample_rate) so we can cancel these terms and A=B IFF
(1./coherent_gain) * np.sqrt(1/(nenbw*N)) == 1/np.sqrt(S2) which I show in githib aurora issue #3 via . (CG**2) * NENBW *N = S2
Decorators¶
- aurora.time_series.decorators.can_use_xr_dataarray(func)[source]¶
Intended as a decorator. Most of the windowed time series methods are intended to work with xarray.Dataset class. But I would like to be able to pass them xarray.DataArray objects. This class casts a DataArray to a Dataset, runs it through func and casts back to a DataArray.
A similar decorator could be written for numpy arrays. :param func:
Frequency Band Helpers¶
- aurora.time_series.frequency_band_helpers.adjust_band_for_coherence_sorting(frequency_band, spectrogram, rule='min3')[source]¶
- Parameters:
frequency_band –
spectrogram (Spectrogram) –
rule –
- aurora.time_series.frequency_band_helpers.check_time_axes_synched(X, Y)[source]¶
Utility function for checking that time axes agree
- Parameters:
X (xarray) –
Y (xarray) –
- aurora.time_series.frequency_band_helpers.extract_band(frequency_band, fft_obj, channels=[], epsilon=1e-07)[source]¶
Stand alone method that operates on an xr.DataArray, and is wrapped with Spectrogram
Note #1: 20230902 drop=True does not play nice with h5py and Dataset, results in a type error. File “stringsource”, line 2, in h5py.h5r.Reference.__reduce_cython__ TypeError: no default __reduce__ due to non-trivial __cinit__ However, it works OK with DataArray, so maybe use data array in general
- Parameters:
frequency_band (mt_metadata.transfer_functions.processing.aurora.band.Band) – Specifies interval corresponding to a frequency band
fft_obj (xarray.core.dataset.Dataset) – To be replaced with an fft_obj() class in future
epsilon (float) – Use this when you are worried about missing a frequency due to round off error. This is in general not needed if we use a df/2 pad around true harmonics.
- Returns:
band – The frequencies within the band passed into this function
- Return type:
xr.DataArray
- aurora.time_series.frequency_band_helpers.get_band_for_tf_estimate(band, dec_level_config, local_stft_obj, remote_stft_obj)[source]¶
Returns spectrograms X, Y, RR for harmonics within the given band
- Parameters:
band (mt_metadata.transfer_functions.processing.aurora.FrequencyBands) – object with lower_bound and upper_bound to tell stft object which subarray to return
config (mt_metadata.transfer_functions.processing.aurora.decimation_level.DecimationLevel) – information about the input and output channels needed for TF estimation problem setup
local_stft_obj (xarray.core.dataset.Dataset or None) – Time series of Fourier coefficients for the station whose TF is to be estimated
remote_stft_obj (xarray.core.dataset.Dataset or None) – Time series of Fourier coefficients for the remote reference station
- Returns:
X, Y, RR – data structures as local_stft_object and remote_stft_object, but restricted only to input_channels, output_channels, reference_channels and also the frequency axes are restricted to being within the frequency band given as an input argument.
- Return type:
xarray.core.dataset.Dataset or None
Time Axis Helpers¶
- aurora.time_series.time_axis_helpers.test_generate_time_axis(t0, n_samples, sample_rate)[source]¶
Two obvious ways to generate an axis of timestamps here. One method is slow and more precise, the other is fast but drops some nanoseconds due to integer roundoff error.
To see this, consider the example of say 3Hz, we are 333333333ns between samples, which drops 1ns per second if we scale a nanoseconds=np.arange(N) The issue here is that the nanoseconds granularity forces a roundoff error
Probably will use logic like: | if there_are_integer_ns_per_sample: | time_stamps = do_it_the_fast_way() | else: | time_stamps = do_it_the_slow_way() | return time_stamps
- Parameters:
t0 (_type_) – _description_
n_samples (_type_) – _description_
sample_rate (_type_) – _description_
Window Helpers¶
Notes in google doc: https://docs.google.com/document/d/1CsRhSLXsRG8HQxM4lKNqVj-V9KA9iUQAvCOtouVzFs0/edit?usp=sharing
- aurora.time_series.window_helpers.apply_fft_to_windowed_array(windowed_array)[source]¶
This will operate row-wise as well :param windowed_array:
- aurora.time_series.window_helpers.available_number_of_windows_in_array(n_samples_array, n_samples_window, n_advance)[source]¶
- Parameters:
- Returns:
available_number_of_strides – The number of windows the time series will yield
- Return type:
- aurora.time_series.window_helpers.check_all_sliding_window_functions_are_equivalent()[source]¶
simple sanity check that runs each sliding window function on a small array and confirms the results are numerically identical. Note that striding window will return int types where others return float.
- aurora.time_series.window_helpers.sliding_window_crude(data, num_samples_window, num_samples_advance, num_windows=None)[source]¶
- Parameters:
data (np.ndarray) – The time series data to be windowed
num_samples_window (int) – The length of the window (in samples)
num_samples_advance (int) – The number of samples the window advances at each step
num_windows (int) – The number of windows to “take”. Must be less or equal to the number of available windows.
- Returns:
output_array – The windowed time series
- Return type:
- aurora.time_series.window_helpers.sliding_window_numba(data, num_samples_window, num_samples_advance, num_windows)[source]¶
- Parameters:
- Returns:
output_array – The windowed time series
- Return type:
- aurora.time_series.window_helpers.striding_window(data, num_samples_window, num_samples_advance, num_windows=None)[source]¶
Applies a striding window to an array. We use 1D arrays here. Note that this method is extendable to N-dimensional arrays as was once shown at http://www.johnvinyard.com/blog/?p=268
Karl has an implementation of this code but chose to restict to 1D here. This is becuase of several warnings encountered, on the notes of stride_tricks.py, as well as for example here: https://stackoverflow.com/questions/4936620/using-strides-for-an-efficient-moving-average-filter
While we can possibly setup Aurora so that no copies of the strided window are made downstream, we cannot guarantee that another user may not add methods that require copies. For robustness we will use 1d implementation only for now.
Another clean example of this method can be found in the razorback codes from brgm.
result is 2d: result[i] is the i-th window
>>> sliding_window(np.arange(15), 4, 3, 2) array([[0, 1, 2], [2, 3, 4], [4, 5, 6], [6, 7, 8]])
- Parameters:
data (np.ndarray) – The time series data to be windowed
num_samples_window (int) – The length of the window (in samples)
num_samples_advance (int) – The number of samples the window advances at each step
num_windows (int) – The number of windows to “take”. Must be less or equal to the number of available windows.
- Returns:
strided_window – The windowed time series
- Return type:
Windowed Time Series¶
- class aurora.time_series.windowed_time_series.WindowedTimeSeries[source]¶
Bases:
object
Time series that has been chopped into (possibly) overlapping windows.
This is a place where we can put methods that operate on these sorts of objects.
The assumption is that we take xarrays keyed by “channel”
- Specific methods:
Demean Detrend Prewhiten stft invert_prewhitening
probably make these @staticmethod s so we import WindowedTimeSeries and then call the static methods
Methods
apply_stft
([data, sample_rate, ...])Only supports xr.Dataset at this point
staticmethod(function) -> method
delay_correction
(dataset, run_obj)- param dataset:
detrend
([data, detrend_axis, detrend_type, ...])Notes: overwrite data=True probably best for most applications but be careful
- static apply_stft(data=None, sample_rate=None, detrend_type=None, spectral_density_calibration=1.0, fft_axis=None)[source]¶
Only supports xr.Dataset at this point
- Parameters:
data –
sample_rate –
detrend_type –
- apply_taper()¶
staticmethod(function) -> method
Convert a function to be a static method.
A static method does not receive an implicit first argument. To declare a static method, use this idiom:
- class C:
@staticmethod def f(arg1, arg2, …):
…
It can be called either on the class (e.g. C.f()) or on an instance (e.g. C().f()). Both the class and the instance are ignored, and neither is passed implicitly as the first argument to the method.
Static methods in Python are similar to those found in Java or C++. For a more advanced concept, see the classmethod builtin.
- static detrend(data=None, detrend_axis=None, detrend_type=None, inplace=True)[source]¶
- Notes: overwrite data=True probably best for most applications but be careful
with that. Do we want to avoid this in general? Could we be possibly overwriting stuff on MTH5 in future? Also, is overwrite even working how I think it is here? Overwrite_data not working right in scipy.signal, dont use it for now
- Parameters:
data (xarray Dataset) –
detrend_axis (string) –
detrend_type (string) – “linear” or “constant” This argument is provided to scipy.signal.detrend
- aurora.time_series.windowed_time_series.get_time_coordinate_axis(dataset)[source]¶
It is common to pass an argument to scipy.signal methods axis=int where that integer specifies along which dimension we are applying the operator. This method helps ensure that we have the correct axis. :param dataset: :type dataset: xarray.Dataset
- aurora.time_series.windowed_time_series.validate_coordinate_ordering_time_domain(dataset)[source]¶
Check that the data dimensions are what you expect. This may evolve, but for now, just want to make sure that we are operating along the correct axes when we demean, detrend, taper, etc. :param dataset: :type dataset: xarray.Dataset
Windowing Scheme¶
The windowing scheme defines the chunking and chopping of the time series for the Short Time Fourier Transform. Often referred to as a “sliding window” or a “striding window”. Iin its most basic form it is a taper with a rule to say how far to advance at each stride (or step).
To generate an array of data-windows from a data series we only need the two parameters window_length (L) and window_overlap (V). The parameter “window_advance” (L-V) can be used in lieu of overlap. Sliding windows are normally described terms of overlap but it is cleaner to code in terms of advance.
Choices L and V are usually made with some knowledge of time series sample rate, duration, and the frequency band of interest. In aurora because this is used to prep for STFT, L is typically a power of 2.
In general we will need one instance of this class per decimation level, but in practice often leave the windowing scheme the same for each decimation level.
This class is a key part of the “gateway” to frequency domain, so it has been given a sampling_rate attribute. While sampling rate is a property of the data, and not the windowing scheme per se, it is good for this class to be aware of the sampling rate.
Future modifications could involve: - binding this class with a time series. - Making a subclass with only L, V, and then having an extension with sample_rate
When 2D arrays are generated how should we index them? | [[ 0 1 2] | [ 2 3 4] | [ 4 5 6] | [ 6 7 8] | [ 8 9 10] | [10 11 12] | [12 13 14]]
In this example the rows are indexing the individual windows … and so they should be associated with the time of each window. We will need to set a standard for this. Obvious options are center_time of window and time_of_first sample. I prefer time_of_first sample. This can always be transformed to center time or another standard later. We can call this the “window time axis”. The columns are indexing “steps of delta-t”. The actual times are different for every row, so it would be best to use something like [0, dt, 2*dt] for that axis to keep it general. We can call this the “within-window sample time axis”
TODO: Regarding the optional time_vector input to self.apply_sliding_window() … this current implementation takes as input numpy array data. We need to also allow for an xarray to be implemented. In the simplest case we would take an xarray in and extract its “time” axis as time vector
20210529 This class is going to be modified to only accept xarray as input data. We can force any incoming numpy arrays to be either xr.DataArray or xr.Dataset. Similarly, output will be only xr.DataArray or xr.Dataset
- class aurora.time_series.windowing_scheme.WindowingScheme(**kwargs)[source]¶
Bases:
ApodizationWindow
20210415: Casting window length, overlap, advance, etc. in terms of number of samples or “points” here as this is common signal processing the nomenclature. We may provide an interface to define these things in terms of percent, duration in seconds etc. in a supporting module.
Note that sample_rate is actually a property of the data and not of the window … still not sure if we want to make sample_rate an attr here or if its better to put properties like window_duration() as a method of some composition of time series and windowing scheme.
- Attributes:
S1
sum of the window coefficients
S2
sum of squares of the window coefficients
- apodization_factor
coherent_gain
DC gain of the window normalized by window length
dt
comes from data
- duration_advance
linear_spectral_density_calibration_factor
Returns ——- float calibration_factor: Following Hienzel et al 2002, Equations 24 and 25 for Linear Spectral Density correction for a single sided spectrum.
nenbw
NENBW Normalized Equivalent Noise BandWidth, see Equation (21) in
num_samples_advance
A derived property.
- num_samples_window
summary
Returns
- taper
window_duration
units are SI seconds assuming dt is SI seconds
Methods
apply_fft
(data[, ...])- param data:
apply_sliding_window
(data[, time_vector, ...])I would like this method to support numpy arrays as well as xarrays.
apply_spectral_density_calibration
(dataset)- param dataset:
apply_taper
(data)modifies the data in place by applying a taper to each window TODO: consider adding an option to return a copy of the data without the taper applied
available_number_of_windows
(num_samples_data)- param num_samples_data:
The number of samples in the time series to be windowed by self.
cast_windowed_data_to_xarray
(windowed_array, ...)TODO?: Factor this method to a standalone function in window_helpers?
compute_window_edge_indices
(num_samples_data)This has been useful in the past but maybe not needed here
downsample_time_axis
(time_axis)- param time_axis:
This is the time axis associated with the time-series prior to
enbw
(fs)Notes that unlike NENBW, CG, S1, S2, this is not a pure property of the window -- but instead this is a property of the window combined with the sample rate.
make
()this is just a wrapper call to scipy.signal Note: see scipy.signal.get_window for a description of what is expected in args[1:].
test_linear_spectral_density_factor
()This is just a test to verify some algebra Claim: The lsd_calibration factors A (1./coherent_gain)*np.sqrt((2*dt)/(nenbw*N)) and B np.sqrt(2/(sample_rate*self.S2))
clone
frequency_axis
left_hand_window_edge_indices
- apply_fft(data, spectral_density_correction=True, detrend_type='linear')[source]¶
- Parameters:
data (xarray.core.dataset.Dataset) –
spectral_density_correction (boolean) –
detrend_type (string) –
- Return type:
spectral_ds
Assume we have already applied sliding window and taper. Things to think about: We want to assign the frequency axis during this method
- apply_sliding_window(data, time_vector=None, dt=None, return_xarray=False)[source]¶
I would like this method to support numpy arrays as well as xarrays.
- Parameters:
data (1D numpy array, xr.DataArray, xr.Dataset) – The data to break into ensembles.
time_vector (1D numpy array) – The time axis of the data.
dt (float) – The sample interval of the data (reciprocal of sample_rate)
return_xarray (boolean) – If True will return an xarray object, even if the input object was a numpy array
- Returns:
windowed_obj – Normally an object of type xarray.core.dataarray.DataArray Could be numpy array as well.
- Return type:
arraylike
- apply_taper(data)[source]¶
modifies the data in place by applying a taper to each window TODO: consider adding an option to return a copy of the data without the taper applied
- available_number_of_windows(num_samples_data)[source]¶
- Parameters:
num_samples_data (int) – The number of samples in the time series to be windowed by self.
- Returns:
number_of_windows – Count of the number of windows returned from time series of num_samples_data. Only take as many windows as available without wrapping. Start with one window for free, move forward by num_samples_advance and don’t walk over the cliff.
- Return type:
- cast_windowed_data_to_xarray(windowed_array, time_vector, dt=None)[source]¶
TODO?: Factor this method to a standalone function in window_helpers?
- Parameters:
windowed_array –
time_vector –
dt –
- compute_window_edge_indices(num_samples_data)[source]¶
This has been useful in the past but maybe not needed here
- downsample_time_axis(time_axis)[source]¶
- Parameters:
time_axis (arraylike) – This is the time axis associated with the time-series prior to the windowing operation.
- Returns:
window_time_axis – This is a time axis for the windowed data. Say that we had 1Hz data starting at t=0 and 100 samples. Then we window, with window length 10, and advance 10, the window time axis is [0, 10, 20 , … 90]. Say the same window length, but now advance is 5. Then [0, 5, 10, 15, … 90] is the result.
- Return type:
array-like
- property dt¶
comes from data
- property duration_advance¶
- property linear_spectral_density_calibration_factor¶
- Returns:
- Following Hienzel et al 2002,
Equations 24 and 25 for Linear Spectral Density correction for a single sided spectrum.
- Return type:
calibration_factor
- Return type:
- property num_samples_advance¶
A derived property. If we made this a fundamental defined property then overlap would become a derived property. Overlap is more conventional than advance in the literature however so we choose it as our property label.
- property window_duration¶
units are SI seconds assuming dt is SI seconds
- aurora.time_series.windowing_scheme.fft_xr_ds(dataset, sample_rate, detrend_type=None, prewhitening=None)[source]¶
This should call window_helpers.apply_fft_to_windowed_array or get moved to window_helpers.py
The returned harmonics do not include the Nyquist frequency. To modify this add +1 to n_fft_harmonics. Also, only 1-sided ffts are returned.
For each channel within the Dataset, fft is applied along the within-window-time axis of the associated numpy array
- Parameters:
dataset (xr.Dataset) – Data are 2D (windowed univariate time series).
sample_rate (float) –
detrend_type –
prewhitening –
- aurora.time_series.windowing_scheme.window_scheme_from_decimation(decimation)[source]¶
Helper function to workaround mt_metadata to not import form aurora
- Parameters:
decimation (mt_metadata.transfer_function.processing.aurora.decimation_level) –
.DecimationLevel –
- Return type:
windowing_scheme aurora.time_series.windowing_scheme.WindowingScheme