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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts have already been removed.
The sources matching the ECG are automatically found and displayed.
Subsequently, artefact detection and rejection quality are assessed.
Finally, the impact on the evoked ERF is visualized.
Note that this example does quite a bit of processing, so even on a
fast machine it can take about a minute to complete.
Step1:  Fit ICA model using the FastICA algorithm, detect and inspect components
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Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts have already been removed.
The sources matching the ECG are automatically found and displayed.
Subsequently, artefact detection and rejection quality are assessed.
Finally, the impact on the evoked ERF is visualized.
Note that this example does quite a bit of processing, so even on a
fast machine it can take about a minute to complete.
End of explanation
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 30, method='iir')
raw.pick_types(meg=True, eeg=False, exclude='bads', stim=True)
# longer + more epochs for more artifact exposure
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id=None, tmin=-0.2, tmax=0.5)
ica = ICA(n_components=0.95, method='fastica').fit(epochs)
ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5)
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs)
ica.plot_components(ecg_inds)
Explanation: Fit ICA model using the FastICA algorithm, detect and inspect components
End of explanation
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