[Mne_analysis] mne.io.Raw filter function. Type error: ...must have a dtype of np.float64,

[Luke Bloy]

Alex is correct that the type error is coming from repeatedly calling
apply_hilbert on the same data. however you are also filtering the same
data repeatedly in a loop which is most likely not what you want.

the following code snippet will make a copy of data_EEG before at the
beginning of each loop, then filter and call apply_hilbert. It runs without
error although it throws warnings as your filters are too long for your
data.

#################################################################
# set epoching parameters
event_id = dict(note=1)
tmin, tmax = -0.1, 0
baseline = None

# get the header to extract events
# data_EEG = mne.io.read_raw_fif(raw_fname, preload=False)
# events = mne.find_events(data_EEG, stim_channel='STI 014')
events = mne.find_events(data_EEG, stim_channel='STI1')
print(events)

frequency_map = list()

for band, fmin, fmax in iter_freqs:
    this_data_eeg = data_EEG.copy()
    print("Band "+ band + " fmin: "+ str(fmin) + " fmax: " + str(fmax))
    # (re)load the data to save memory
#     raw = mne.io.read_raw_fif(raw_fname, preload=True)
    this_data_eeg.pick_types(eeg=True, eog=False, stim=True)


#     #bandpass filter and compute Hilbert
    this_data_eeg.filter(fmin, fmax, n_jobs=1,  # use more jobs to speed up.
               l_trans_bandwidth=1.0,  # make sure filter params are the
same
               h_trans_bandwidth=1.0,  # in each band and skip "auto"
option.
               fir_design='firwin')

    this_data_eeg.apply_hilbert(n_jobs=1, envelope=False)

    epochs = mne.Epochs(this_data_eeg, events, event_id, tmin, tmax,
baseline=baseline, reject=None, preload=True)
    # remove evoked response and get analytic signal (envelope)
    epochs.subtract_evoked()  # for this we need to construct new epochs.
    epochs = mne.EpochsArray(data=np.abs(epochs.get_data()), info=
epochs.info, tmin=epochs.tmin)
    # now average and move on
    frequency_map.append(((band, fmin, fmax), epochs.average()))
#################################################################

On Mon, Apr 2, 2018 at 1:40 PM Eric Larson <larson.eric.d at gmail.com> wrote:

> you need to do
>>
>> raw_hilbert = data_EEG.copy().apply_hilbert(n_jobs=1, envelope=False)
>>
>>
> I think this is a typo and Alex meant `envelope=True`, which will return
> the amplitude at each time instance.
>
> Eric
>
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