# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import scipy.ndimage
import scipy.signal
[docs]
def signal_resample(
signal,
desired_length=None,
sampling_rate=None,
desired_sampling_rate=None,
method="interpolation",
):
"""**Resample a continuous signal to a different length or sampling rate**
Up- or down-sample a signal. The user can specify either a desired length for the vector, or
input the original sampling rate and the desired sampling rate.
Parameters
----------
signal : Union[list, np.array, pd.Series]
The signal (i.e., a time series) in the form of a vector of values.
desired_length : int
The desired length of the signal.
sampling_rate : int
The original sampling frequency (in Hz, i.e., samples/second).
desired_sampling_rate : int
The desired (output) sampling frequency (in Hz, i.e., samples/second).
method : str
Can be ``"interpolation"`` (see ``scipy.ndimage.zoom()``), ``"numpy"`` for numpy's
interpolation (see ``np.interp()``),``"pandas"`` for Pandas' time series resampling,
``"poly"`` (see ``scipy.signal.resample_poly()``) or ``"FFT"`` (see
``scipy.signal.resample()``) for the Fourier method. ``"FFT"`` is the most accurate
(if the signal is periodic), but becomes exponentially slower as the signal length
increases. In contrast, ``"interpolation"`` is the fastest, followed by ``"numpy"``,
``"poly"`` and ``"pandas"``.
Returns
-------
array
Vector containing resampled signal values.
See Also
--------
signal_interpolate
Examples
--------
**Example 1**: Downsampling
.. ipython:: python
import numpy as np
import pandas as pd
import neurokit2 as nk
signal = nk.signal_simulate(duration=1, sampling_rate=500, frequency=3)
# Downsample
data = {}
for m in ["interpolation", "FFT", "poly", "numpy", "pandas"]:
data[m] = nk.signal_resample(signal, sampling_rate=500, desired_sampling_rate=30, method=m)
@savefig p_signal_resample1.png scale=100%
nk.signal_plot([data[m] for m in data.keys()])
@suppress
plt.close()
**Example 2**: Upsampling
.. ipython:: python
:verbatim:
signal = nk.signal_simulate(duration=1, sampling_rate=30, frequency=3)
# Upsample
data = {}
for m in ["interpolation", "FFT", "poly", "numpy", "pandas"]:
data[m] = nk.signal_resample(signal, sampling_rate=30, desired_sampling_rate=500, method=m)
@savefig p_signal_resample2.png scale=100%
nk.signal_plot([data[m] for m in data.keys()], labels=list(data.keys()))
@suppress
plt.close()
**Example 3**: Benchmark
.. ipython:: python
:verbatim:
signal = nk.signal_simulate(duration=1, sampling_rate=1000, frequency=3)
# Timing benchmarks
%timeit nk.signal_resample(signal, method="interpolation",
sampling_rate=1000, desired_sampling_rate=500)
%timeit nk.signal_resample(signal, method="FFT",
sampling_rate=1000, desired_sampling_rate=500)
%timeit nk.signal_resample(signal, method="poly",
sampling_rate=1000, desired_sampling_rate=500)
%timeit nk.signal_resample(signal, method="numpy",
sampling_rate=1000, desired_sampling_rate=500)
%timeit nk.signal_resample(signal, method="pandas",
sampling_rate=1000, desired_sampling_rate=500)
"""
if desired_length is None:
desired_length = int(np.round(len(signal) * desired_sampling_rate / sampling_rate))
# Sanity checks
if len(signal) == desired_length:
return signal
# Resample
if method.lower() == "fft":
resampled = _resample_fft(signal, desired_length)
elif method.lower() == "poly":
resampled = _resample_poly(signal, desired_length)
elif method.lower() == "numpy":
resampled = _resample_numpy(signal, desired_length)
elif method.lower() == "pandas":
resampled = _resample_pandas(signal, desired_length)
else:
resampled = _resample_interpolation(signal, desired_length)
return resampled
# =============================================================================
# Methods
# =============================================================================
def _resample_numpy(signal, desired_length):
resampled_signal = np.interp(
np.linspace(0.0, 1.0, desired_length, endpoint=False), # where to interpolate
np.linspace(0.0, 1.0, len(signal), endpoint=False), # known positions
signal, # known data points
)
return resampled_signal
def _resample_interpolation(signal, desired_length):
resampled_signal = scipy.ndimage.zoom(signal, desired_length / len(signal))
return resampled_signal
def _resample_fft(signal, desired_length):
resampled_signal = scipy.signal.resample(signal, desired_length)
return resampled_signal
def _resample_poly(signal, desired_length):
resampled_signal = scipy.signal.resample_poly(signal, desired_length, len(signal))
return resampled_signal
def _resample_pandas(signal, desired_length):
# Convert to Time Series
index = pd.date_range("20131212", freq="ms", periods=len(signal))
resampled_signal = pd.Series(signal, index=index)
# Create resampling factor
resampling_factor = str(np.round(1 / (desired_length / len(signal)), 6)) + "ms"
# Resample
resampled_signal = resampled_signal.resample(resampling_factor).bfill().values
# Sanitize
resampled_signal = _resample_sanitize(resampled_signal, desired_length)
return resampled_signal
# =============================================================================
# Internals
# =============================================================================
def _resample_sanitize(resampled_signal, desired_length):
# Adjust extremities
diff = len(resampled_signal) - desired_length
if diff < 0:
resampled_signal = np.concatenate(
[resampled_signal, np.full(np.abs(diff), resampled_signal[-1])]
)
elif diff > 0:
resampled_signal = resampled_signal[0:desired_length]
return resampled_signal
