mne_raw_to_polars

mne.mne2dataframe.mne_raw_to_polars(raw: mne.io.BaseRaw)

Convert MNE Raw object to Polars DataFrame.

Converts continuous EEG/MEG data from MNE format to a long-format Polars DataFrame. Data is automatically scaled from Volts to microvolts (µV).

Parameters

Name	Type	Description	Default
raw	mne.io.BaseRaw	MNE Raw object (e.g., mne.io.Raw, mne.io.RawArray) containing continuous data.	required

Returns

Name	Type	Description
	pl.DataFrame	Polars DataFrame with columns: - Channel columns: One column per channel with data in µV - time: Time in seconds from recording start - sample_idx: Sequential sample index (0 to n_times-1)

Examples

>>> import mne
>>> from mdu.mne.mne2dataframe import mne_raw_to_polars
>>> # Create sample raw data
>>> info = mne.create_info(['Ch1', 'Ch2'], sfreq=100, ch_types='eeg')
>>> data = np.random.randn(2, 1000) * 1e-6  # 2 channels, 1000 timepoints
>>> raw = mne.io.RawArray(data, info)
>>> df = mne_raw_to_polars(raw)
>>> df.shape
(1000, 4)  # 1000 timepoints, with Ch1, Ch2, time, sample_idx

See Also

mne_epochs_to_polars : Convert epoched data to DataFrame

# mne_raw_to_polars { #mdu.mne.mne2dataframe.mne_raw_to_polars }

```python
mne.mne2dataframe.mne_raw_to_polars(raw: mne.io.BaseRaw)
```

Convert MNE Raw object to Polars DataFrame.

Converts continuous EEG/MEG data from MNE format to a long-format Polars DataFrame.
Data is automatically scaled from Volts to microvolts (µV).

## Parameters {.doc-section .doc-section-parameters}

| Name   | Type           | Description                                                                    | Default    |
|--------|----------------|--------------------------------------------------------------------------------|------------|
| raw    | mne.io.BaseRaw | MNE Raw object (e.g., mne.io.Raw, mne.io.RawArray) containing continuous data. | _required_ |

## Returns {.doc-section .doc-section-returns}

| Name   | Type         | Description                                                                                                                                                                                  |
|--------|--------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|        | pl.DataFrame | Polars DataFrame with columns: - Channel columns: One column per channel with data in µV - time: Time in seconds from recording start - sample_idx: Sequential sample index (0 to n_times-1) |

## Examples {.doc-section .doc-section-examples}

```python
>>> import mne
>>> from mdu.mne.mne2dataframe import mne_raw_to_polars
>>> # Create sample raw data
>>> info = mne.create_info(['Ch1', 'Ch2'], sfreq=100, ch_types='eeg')
>>> data = np.random.randn(2, 1000) * 1e-6  # 2 channels, 1000 timepoints
>>> raw = mne.io.RawArray(data, info)
>>> df = mne_raw_to_polars(raw)
>>> df.shape
(1000, 4)  # 1000 timepoints, with Ch1, Ch2, time, sample_idx
```

## See Also {.doc-section .doc-section-see-also}

mne_epochs_to_polars : Convert epoched data to DataFrame