Filters

Butterworth

stride.utils.filters.bandpass_filter_butterworth(data, f_min, f_max, padding=0, order=8, zero_phase=True, adjoint=False, axis=-1, **kwargs)

Apply a Butterworth bandpass filter using cascaded second-order sections.

Parameters:

• data (2-dimensional array) – Data to apply the filter to, with shape (number_of_traces, number_of_timesteps)

• f_min (float) – Minimum frequency of the filter, dimensionless

• f_max (float) – Maximum frequency of the filter, dimensionless

• padding (int, optional) – Padding to apply before AND after the traces to compensate for the filtering, defaults to 0.

• order (int, optional) – Order of the filter, defaults to 8.

• zero_phase (bool, optional) – Whether the filter should be zero phase, defaults to True.

• adjoint (bool, optional) – Whether to run the adjoint of the filter, defaults to False.

• axis (int, optional) – Axis on which to perform the filtering, defaults to -1

Returns:

Data after filtering, with shape (…, number_of_timesteps+2*padding)

Return type:

n-dimensional array

stride.utils.filters.lowpass_filter_butterworth(data, f_max, padding=0, order=8, zero_phase=True, adjoint=False, axis=-1, **kwargs)

Apply a Butterworth lowpass filter using cascaded second-order sections.

Parameters:

• data (2-dimensional array) – Data to apply the filter to, with shape (number_of_traces, number_of_timesteps)

• f_max (float) – Maximum frequency of the filter, dimensionless

• padding (int, optional) – Padding to apply before AND after the traces to compensate for the filtering, defaults to 0.

• order (int, optional) – Order of the filter, defaults to 8.

• zero_phase (bool, optional) – Whether the filter should be zero phase, defaults to True.

• adjoint (bool, optional) – Whether to run the adjoint of the filter, defaults to False.

• axis (int, optional) – Axis on which to perform the filtering, defaults to -1

Returns:

Data after filtering, with shape (…, number_of_timesteps+2*padding)

Return type:

n-dimensional array

stride.utils.filters.highpass_filter_butterworth(data, f_min, padding=0, order=8, zero_phase=True, adjoint=False, axis=-1, **kwargs)

Apply a Butterworth highpass filter using cascaded second-order sections.

Parameters:

• data (2-dimensional array) – Data to apply the filter to, with shape (number_of_traces, number_of_timesteps)

• f_min (float) – Minimum frequency of the filter, dimensionless

• padding (int, optional) – Padding to apply before AND after the traces to compensate for the filtering, defaults to 0.

• order (int, optional) – Order of the filter, defaults to 8.

• zero_phase (bool, optional) – Whether the filter should be zero phase, defaults to True.

• adjoint (bool, optional) – Whether to run the adjoint of the filter, defaults to False.

• axis (int, optional) – Axis on which to perform the filtering, defaults to -1

Returns:

Data after filtering, with shape (…, number_of_timesteps+2*padding)

Return type:

n-dimensional array

FIR

stride.utils.filters.bandpass_filter_fir(data, f_min, f_max, padding=0, attenuation=30, zero_phase=True, adjoint=False, axis=-1, **kwargs)

Apply a FIR bandpass filter designed using a kaiser window.

Parameters:

• data (2-dimensional array) – Data to apply the filter to, with shape (number_of_traces, number_of_timesteps)

• f_min (float) – Minimum frequency of the filter, dimensionless

• f_max (float) – Minimum frequency of the filter, dimensionless

• padding (int, optional) – Padding to apply before AND after the traces to compensate for the filtering, defaults to 0.

• attenuation (float, optional) – Attenuation of the reject band in dB, defaults to 30.

• zero_phase (bool, optional) – Whether the filter should be zero phase, defaults to True.

• adjoint (bool, optional) – Whether to run the adjoint of the filter, defaults to False.

• axis (int, optional) – Axis on which to perform the filtering, defaults to -1

Returns:

Data after filtering, with shape (…, number_of_timesteps+2*padding)

Return type:

n-dimensional array

stride.utils.filters.lowpass_filter_fir(data, f_max, padding=0, attenuation=30, zero_phase=True, adjoint=False, axis=-1, **kwargs)

Apply a FIR lowpass filter designed using a kaiser window.

Parameters:

• data (2-dimensional array) – Data to apply the filter to, with shape (number_of_traces, number_of_timesteps)

• f_max (float) – Maximum frequency of the filter, dimensionless

• padding (int, optional) – Padding to apply before AND after the traces to compensate for the filtering, defaults to 0.

• attenuation (float, optional) – Attenuation of the reject band in dB, defaults to 30.

• zero_phase (bool, optional) – Whether the filter should be zero phase, defaults to True.

• adjoint (bool, optional) – Whether to run the adjoint of the filter, defaults to False.

• axis (int, optional) – Axis on which to perform the filtering, defaults to -1

Returns:

Data after filtering, with shape (…, number_of_timesteps+2*padding)

Return type:

n-dimensional array

stride.utils.filters.highpass_filter_fir(data, f_min, padding=0, attenuation=30, zero_phase=True, adjoint=False, axis=-1, **kwargs)

Apply a FIR highpass filter designed using a kaiser window.

Parameters:

• data (2-dimensional array) – Data to apply the filter to, with shape (number_of_traces, number_of_timesteps)

• f_min (float) – Minimum frequency of the filter, dimensionless

• padding (int, optional) – Padding to apply before AND after the traces to compensate for the filtering, defaults to 0.

• attenuation (float, optional) – Attenuation of the reject band in dB, defaults to 30.

• zero_phase (bool, optional) – Whether the filter should be zero phase, defaults to True.

• adjoint (bool, optional) – Whether to run the adjoint of the filter, defaults to False.

• axis (int, optional) – Axis on which to perform the filtering, defaults to -1

Returns:

Data after filtering, with shape (…, number_of_timesteps)

Return type:

n-dimensional array