# Importing Eye tracking data with MNE-Python

This tutorial will guide you through the process of importing and
visualizing eye-tracking data using MNE-Python and `almirah`.

## Setup

First, we'll import the necessary libraries and set the log level for
MNE.

```python
import mne
import matplotlib.pyplot as plt

from almirah import Layout

mne.set_log_level(False)
```

## Loading the Data

Next, we'll set up the layout to access the eye-tracking data.

```python
lay = Layout(root="/path/to/data", specification_name="bids")
lay
```

This should output:

    <Layout root: '/path/to/data'>

We can query the layout to find all eye-tracking files with the `.asc` extension:

```python
files = lay.query(datatype="eyetrack", extension=".asc")
len(files)
```

This gives the total number of eye-tracking files:

    3632

## Querying a Specific File

To query a specific file, we can filter by subject, datatype, task, and extension:

```python
file = lay.query(subject="D0019", datatype="eyetrack", task="FIX", extension=".asc")[0]

print(file.rel_path)
```

This should output the relative path of the file:

    sub-D0019/ses-111/eyetrack/sub-D0019_ses-111_task-FIX_run-01_eyetrack.asc

## Reading and Plotting the Data

We use MNE to read the eye-tracking data file and create annotations for blinks:

```python
raw = mne.io.read_raw_eyelink(file.path, create_annotations=["blinks"])
custom_scalings = dict(eyegaze=1e3)
raw.pick(picks="eyetrack").plot(scalings=custom_scalings)
plt.close()
```
    
![png](../images/eyetrack/eye-position-plot.png)

## Inspecting the Data

We can inspect the metadata and channels in the raw object:

```python
raw
```

<details open>
  <summary><strong>General</strong></summary>
  **Measurement date:** January 01, 2009 00:03:33 GMT  
  **Experimenter:** Unknown  
  **Participant:** Unknown  
</details>

<details open>
  <summary><strong>Channels</strong></summary>
  **Digitized points:** Not available  
  **Good channels:** 2 Eye-tracking (Gaze position), 1 Eye-tracking (Pupil size)  
  **Bad channels:** None  
  **EOG channels:** Not available  
  **ECG channels:** Not available  
</details>

<details open>
  <summary><strong>Data</strong></summary>
  **Sampling frequency:** 1000.00 Hz  
  **Highpass:** 0.00 Hz  
  **Lowpass:** 500.00 Hz  
  **Filenames:** sub-D0019_ses-111_task-FIX_run-01_eyetrack.asc  
  **Duration:** 00:01:11 (HH:MM:SS)  
</details>

We can also list the channel names:

```python
raw.ch_names
```

This should output:

    ['xpos_left', 'ypos_left', 'pupil_left']

And inspect the data for a specific channel, such as `xpos_left`:

```python
raw["xpos_left"]
```

This gives the data array and the corresponding time points:

    (array([[510.2, 510.1, 509.9, ..., 454.4, 454.8, 455.5]]),
     array([0.0000e+00, 1.0000e-03, 2.0000e-03, ..., 7.0556e+01, 7.0557e+01,
            7.0558e+01]))

This concludes the tutorial. You've learned how to import, query, and
visualize eye-tracking data using MNE-Python and `almirah`.