Refactor bids layout

pyproject.toml

@@ -2,7 +2,8 @@
 name = "eeg_research"
 version = "0.1.0"
 description = "All tools and pipelines developed for eeg research"
-authors = ["Samuel Louviot <[email protected]>"]
+authors = ["Dr. Samuel Louviot <[email protected]>",
+"Dr. Alp Erkent <[email protected]>"]
 license = "LGPL-3.0"
 readme = "README.md"
 packages = [{include = "eeg_research", from = "src"}]

src/eeg_research/io/__init__.py

@@ -0,0 +1,2 @@
+from .xdf import read_raw_xdf, RawXDF
+__all__ = ['read_raw_xdf', 'RawXDF']

src/eeg_research/io/xdf.py

@@ -0,0 +1,78 @@
+from pathlib import Path
+import numpy as np
+import mne
+import pyxdf
+
+def _parse_channel_names(eeg_stream: dict) -> dict:
+    chan_dict = eeg_stream['info']['desc'][0]['channels'][0]['channel']
+    ch_names = list()
+    ch_types = list()
+    for chan in chan_dict:
+        ch_names.append(chan['label'][0])
+        if chan['type'][0].lower() == 'marker':
+            ch_types.append('stim')
+        else:
+            ch_types.append(chan['type'][0].lower())
+
+    parsed_chan_info = {
+        "ch_names": ch_names,
+        "ch_types": ch_types,
+    }
+
+    return parsed_chan_info
+
+#def _convert_signal(eeg_stream:dict) -> np.ndarray:
+#    units = {
+#        "microvolts": 10e-6,
+#        "millivolts": 10e-3,
+#        "volts": 1,
+#    }
+#    unit_matrix = list()
+#    chan_dict = eeg_stream['info']['desc'][0]['channels'][0]['channel']
+#    signals = eeg_stream['time_series'].T
+#    unit_matrix = np.array([[units.get(chan['unit'][0].lower(),1) 
+#                   for chan in chan_dict]]).T
+#    return np.multiply(signals,unit_matrix)
+
+class RawXDF(mne.io.BaseRaw):
+    """Raw object from XDF file."""
+
+    def __init__(self, 
+                 input_fname,
+                 preload=False,
+                 *,
+                 verbose=None,
+            ):
+
+        eeg, _= pyxdf.load_xdf(input_fname, select_streams=5)
+        sfreq = float(eeg[0]['info']['nominal_srate'][0])
+        info = mne.create_info(**_parse_channel_names(eeg[0]), sfreq=sfreq)
+        last_samps = int(eeg[0]['footer']['info']['sample_count'][0])-1
+        chan_dict = eeg[0]['info']['desc'][0]['channels'][0]['channel']
+        orig_units = {chan['label'][0]: "uv" if chan['unit'][0] == "microvolts"
+                      else "n/a" for chan in chan_dict}
+        super().__init__(
+            info,
+            preload,
+            filenames=[input_fname],
+            last_samps=last_samps,
+            orig_format="int",
+            orig_units=orig_units,
+            verbose=verbose,
+        )
+        self._data = eeg[0]['time_series'].T
+
+def read_raw_xdf(filename, **kwargs):
+    """Read XDF file.
+
+    Args:
+        filename : str | Path
+            Path to XDF file.
+        **kwargs : dict
+            Additional keyword arguments passed to RawXDF.
+
+    Returns
+        raw : RawXDF
+            Raw object containing XDF data.
+    """
+    return RawXDF(filename, **kwargs)