Source code for torcheeg.transforms.numpy.spectrum
from typing import Dict, Union
import io
import pywt
import numpy as np
import matplotlib.pyplot as plt
from ..base_transform import EEGTransform
[docs]class CWTSpectrum(EEGTransform):
r'''
A transform method to convert EEG signals of each channel into spectrograms using wavelet transform.
.. code-block:: python
from torcheeg import transforms
t = transforms.CWTSpectrum()
t(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 128, 1000)
Part of the existing work uses :obj:`Resize` to warp the output spectrum to a specified size suitable for CNN processing.
.. code-block:: python
from torcheeg import transforms
t = Compose([
CWTSpectrum(),
ToTensor(),
Resize([260, 260])
])
t(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 128, 1000)
When contourf is set to True, a spectrogram of filled contours will be generated for each channel and converted to np.ndarray and returned. This option is usually used for single-channel analysis or visualization of a single channel.
.. code-block:: python
from torcheeg import transforms
t = CWTSpectrum(contourf=True)
t(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 480, 640, 4)
Args:
sampling_rate (int): The sampling period for the frequencies output in Hz. (default: :obj:`128`)
wavelet (str): Wavelet to use. Options include: cgau1, cgau2, cgau3, cgau4, cgau5, cgau6, cgau7, cgau8, cmor, fbsp, gaus1, gaus2 , gaus3, gaus4, gaus5, gaus6, gaus7, gaus8, mexh, morl, shan. (default: :obj:`'morl'`)
total_scale: (int): The total wavelet scales to use. (default: :obj:`128`)
contourf: (bool): Whether to output the np.ndarray corresponding to the image with content of filled contours. (default: :obj:`False`)
apply_to_baseline: (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (default: :obj:`False`)
.. automethod:: __call__
'''
def __init__(self,
sampling_rate: int = 250,
wavelet: str = 'morl',
total_scale: int = 128,
contourf: bool = False,
apply_to_baseline: bool = False):
super(CWTSpectrum, self).__init__(apply_to_baseline=apply_to_baseline)
self.sampling_rate = sampling_rate
self.wavelet = wavelet
self.total_scale = total_scale
self.contourf = contourf
fc = pywt.central_frequency(wavelet)
cparam = 2 * fc * total_scale
self.scales = cparam / np.arange(1, self.total_scale + 1)
[docs] def __call__(self,
*args,
eeg: np.ndarray,
baseline: Union[np.ndarray, None] = None,
**kwargs) -> Dict[str, np.ndarray]:
r'''
Args:
eeg (np.ndarray): The input EEG signals in shape of [number of electrodes, number of data points].
baseline (np.ndarray, optional) : The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.
Returns:
np.ndarray[number of electrodes, ...]: The spectrograms based on the wavelet transform for all electrodes. If contourf=False, the output shape is [number of electrodes, total_scale, number of data points]. Otherwise, the output shape is [number of electrodes, height of image, width of image of image, 4], where 4 represents the four channels of the image colors.
'''
return super().__call__(*args, eeg=eeg, baseline=baseline, **kwargs)
def apply(self, eeg: np.ndarray, **kwargs) -> np.ndarray:
channel_list = []
for channel in eeg:
channel_list.append(self.opt(channel))
channel_list = np.array(channel_list)
return np.array(channel_list)
def opt(self, eeg: np.ndarray, **kwargs) -> np.ndarray:
t = np.arange(0,
len(eeg) / self.sampling_rate, 1.0 / self.sampling_rate)
[cwtmatr, frequencies] = pywt.cwt(eeg, self.scales, self.wavelet,
1.0 / self.sampling_rate)
if self.contourf:
fig = plt.figure()
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.subplots_adjust(top=1,
bottom=0,
left=0,
right=1,
hspace=0,
wspace=0)
plt.margins(0, 0)
plt.contourf(t, frequencies, abs(cwtmatr))
with io.BytesIO() as buf:
fig.savefig(buf, format='raw')
buf.seek(0)
img_cwtmatr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
w, h = fig.canvas.get_width_height()
img_cwtmatr = img_cwtmatr.reshape((int(h), int(w), -1))
return img_cwtmatr
return cwtmatr
@property
def repr_body(self) -> Dict:
return dict(
super().repr_body, **{
'sampling_rate': self.sampling_rate,
'wavelet': self.wavelet,
'total_scale': self.total_scale,
'contourf': self.contourf
})
[docs]class DWTDecomposition(EEGTransform):
r'''
Splitting the EEG signal from each electrode into two functions using wavelet decomposition.
.. code-block:: python
from torcheeg import transforms
t = transforms.DWTDecomposition()
t(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 500)
Args:
apply_to_baseline: (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (default: :obj:`False`)
.. automethod:: __call__
'''
def __init__(self, apply_to_baseline: bool = False):
super(DWTDecomposition,
self).__init__(apply_to_baseline=apply_to_baseline)
[docs] def __call__(self,
*args,
eeg: np.ndarray,
baseline: Union[np.ndarray, None] = None,
**kwargs) -> Dict[str, np.ndarray]:
r'''
Args:
eeg (np.ndarray): The input EEG signals in shape of [number of electrodes, number of data points].
baseline (np.ndarray, optional) : The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.
Returns:
np.ndarray[number of electrodes, 2, number of data points / 2]: EEG signal after wavelet decomposition, where 2 corresponds to the two functions of the wavelet decomposition, and number of data points / 2 represents the length of each component
'''
return super().__call__(*args, eeg=eeg, baseline=baseline, **kwargs)
def apply(self, eeg: np.ndarray, **kwargs) -> np.ndarray:
return np.stack(pywt.dwt(eeg, 'haar'), axis=0)
