def phase_by_time(sig,
fs,
f_range=None,
hilbert_increase_n=False,
remove_edges=True,
**filter_kwargs):
"""Compute the instantaneous phase of a time series.
Parameters
----------
sig : 1d array
Time series.
fs : float
Sampling rate, in Hz.
f_range : tuple of float or None, optional default: None
Filter range, in Hz, as (low, high). If None, no filtering is applied.
hilbert_increase_n : bool, optional, default: False
If True, zero pad the signal's length to the next power of 2 for the Hilbert transform.
This is because ``scipy.signal.hilbert`` can be very slow for some lengths of x.
remove_edges : bool, optional, default: True
If True, replace samples that are within half of the filter's length to the edge with nan.
This removes edge artifacts from the filtered signal. Only used if `f_range` is defined.
**filter_kwargs
Keyword parameters to pass to `filter_signal`.
Returns
-------
pha : 1d array
Instantaneous phase time series.
Examples
--------
Compute the instantaneous phase, for the alpha range:
>>> from neurodsp.sim import sim_combined
>>> sig = sim_combined(n_seconds=10, fs=500,
... components={'sim_powerlaw': {}, 'sim_oscillation': {'freq': 10}})
>>> pha = phase_by_time(sig, fs=500, f_range=(8, 12))
"""
if f_range:
sig, filter_kernel = filter_signal(sig,
fs,
infer_passtype(f_range),
f_range=f_range,
remove_edges=False,
return_filter=True,
**filter_kwargs)
pha = np.angle(robust_hilbert(sig, increase_n=hilbert_increase_n))
if f_range and remove_edges:
pha = remove_filter_edges(pha, len(filter_kernel))
return pha
def amp_by_time(sig, fs, f_range=None, remove_edges=True, **filter_kwargs):
"""Compute the instantaneous amplitude of a time series.
Parameters
----------
sig : 1d array
Time series.
fs : float
Sampling rate, in Hz.
f_range : tuple of float or None, optional default: None
Filter range, in Hz, as (low, high). If None, no filtering is applied.
remove_edges : bool, optional, default: True
If True, replace samples that are within half of the filter's length to the edge with nan.
This removes edge artifacts from the filtered signal. Only used if `f_range` is defined.
**filter_kwargs
Keyword parameters to pass to `filter_signal`.
Returns
-------
amp : 1d array
Instantaneous amplitude time series.
Examples
--------
Compute the instantaneous amplitude, for the alpha range:
>>> from neurodsp.sim import sim_combined
>>> sig = sim_combined(n_seconds=10, fs=500,
... components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}})
>>> amp = amp_by_time(sig, fs=500, f_range=(8, 12))
"""
if f_range:
sig, filter_kernel = filter_signal(sig,
fs,
infer_passtype(f_range),
f_range=f_range,
remove_edges=False,
return_filter=True,
**filter_kwargs)
amp = np.abs(robust_hilbert(sig))
if f_range and remove_edges:
amp = remove_filter_edges(amp, len(filter_kernel))
return amp
def amp_by_time(sig,
fs,
f_range=None,
hilbert_increase_n=False,
remove_edges=True,
**filter_kwargs):
"""Compute the instantaneous amplitude of a time series.
Parameters
----------
sig : 1d array
Time series.
fs : float
Sampling rate, in Hz.
f_range : tuple of float or None, optional default: None
Filter range, in Hz, as (low, high). If None, no filtering is applied.
hilbert_increase_n : bool, optional, default: False
If True, zero pad the signal to length the next power of 2 when doing the Hilbert transform.
This is because :func:`scipy.signal.hilbert` can be very slow for some lengths of sig.
remove_edges : bool, optional, default: True
If True, replace samples that are within half of the filters length to the edge with np.nan.
This removes edge artifacts from the filtered signal. Only used if `f_range` is defined.
**filter_kwargs
Keyword parameters to pass to `filter_signal`.
Returns
-------
amp : 1d array
Instantaneous amplitude time series.
"""
if f_range:
sig, filter_kernel = filter_signal(sig,
fs,
infer_passtype(f_range),
f_range=f_range,
remove_edges=False,
return_filter=True,
**filter_kwargs)
amp = np.abs(robust_hilbert(sig, increase_n=hilbert_increase_n))
if f_range and remove_edges:
amp = remove_filter_edges(amp, len(filter_kernel))
return amp
def amp_by_time(sig,
fs,
f_range,
hilbert_increase_n=False,
remove_edges=True,
**filter_kwargs):
"""Calculate the amplitude time series.
Parameters
----------
sig : 1d array
Time series.
fs : float
Sampling rate, in Hz.
f_range : tuple of float
The frequency filtering range, in Hz, as (low, high).
hilbert_increase_n : bool, optional, default: False
If True, zeropad the signal to length the next power of 2 when doing the hilbert transform.
This is because scipy.signal.hilbert can be very slow for some lengths of sig.
remove_edges : bool, optional, default: True
If True, replace the samples that are within half a kernel's length to
the signal edge with np.nan.
**filter_kwargs
Keyword parameters to pass to `filter_signal`.
Returns
-------
amp : 1d array
Time series of amplitude.
"""
sig_filt, kernel = filter_signal(sig,
fs,
infer_passtype(f_range),
f_range=f_range,
remove_edges=False,
return_filter=True,
**filter_kwargs)
amp = np.abs(robust_hilbert(sig_filt, increase_n=hilbert_increase_n))
if remove_edges:
amp = remove_filter_edges(amp, len(kernel))
return amp
def filter_signal_fir(sig, fs, pass_type, f_range, n_cycles=3, n_seconds=None, remove_edges=True,
print_transitions=False, plot_properties=False, return_filter=False):
"""Apply an FIR filter to a signal.
Parameters
----------
sig : array
Time series to be filtered.
fs : float
Sampling rate, in Hz.
pass_type : {'bandpass', 'bandstop', 'lowpass', 'highpass'}
Which kind of filter to apply:
* 'bandpass': apply a bandpass filter
* 'bandstop': apply a bandstop (notch) filter
* 'lowpass': apply a lowpass filter
* 'highpass' : apply a highpass filter
f_range : tuple of (float, float) or float
Cutoff frequency(ies) used for filter, specified as f_lo & f_hi.
For 'bandpass' & 'bandstop', must be a tuple.
For 'lowpass' or 'highpass', can be a float that specifies pass frequency, or can be
a tuple and is assumed to be (None, f_hi) for 'lowpass', and (f_lo, None) for 'highpass'.
n_cycles : float, optional, default: 3
Length of filter, in number of cycles, defined at the 'f_lo' frequency.
This parameter is overwritten by `n_seconds`, if provided.
n_seconds : float, optional
Length of filter, in seconds. This parameter overwrites `n_cycles`.
remove_edges : bool, optional
If True, replace samples within half the kernel length to be np.nan.
print_transitions : bool, optional, default: False
If True, print out the transition and pass bandwidths.
plot_properties : bool, optional, default: False
If True, plot the properties of the filter, including frequency response and/or kernel.
return_filter : bool, optional, default: False
If True, return the filter coefficients of the FIR filter.
Returns
-------
sig_filt : array
Filtered time series.
filter_coefs : 1d array
Filter coefficients of the FIR filter. Only returned if `return_filter` is True.
Examples
--------
Apply a band pass FIR filter to a simulated signal:
>>> from neurodsp.sim import sim_combined
>>> sig = sim_combined(n_seconds=10, fs=500,
... components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}})
>>> filt_sig = filter_signal_fir(sig, fs=500, pass_type='bandpass', f_range=(1, 25))
Apply a high pass FIR filter to a signal, with a specified number of cycles:
>>> sig = sim_combined(n_seconds=10, fs=500,
... components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}})
>>> filt_sig = filter_signal_fir(sig, fs=500, pass_type='highpass', f_range=(2, None), n_cycles=5)
"""
# Design filter & check that the length is okay with signal
filter_coefs = design_fir_filter(fs, pass_type, f_range, n_cycles, n_seconds)
check_filter_length(sig.shape[-1], len(filter_coefs))
# Check filter properties: compute transition bandwidth & run checks
check_filter_properties(filter_coefs, 1, fs, pass_type, f_range, verbose=print_transitions)
# Remove any NaN on the edges of 'sig'
sig, sig_nans = remove_nans(sig)
# Apply filter
sig_filt = apply_fir_filter(sig, filter_coefs)
# Remove edge artifacts
if remove_edges:
sig_filt = remove_filter_edges(sig_filt, len(filter_coefs))
# Add NaN back on the edges of 'sig', if there were any at the beginning
sig_filt = restore_nans(sig_filt, sig_nans)
# Plot filter properties, if specified
if plot_properties:
f_db, db = compute_frequency_response(filter_coefs, 1, fs)
plot_filter_properties(f_db, db, fs, filter_coefs)
if return_filter:
return sig_filt, filter_coefs
else:
return sig_filt
