From ffff3810b67dd53f1c4791feb5f3d00dd835599c Mon Sep 17 00:00:00 2001
From: Scott Huberty <scott.huberty@mail.mcgill.ca>
Date: Sat, 20 Jan 2024 14:10:08 -0800
Subject: [PATCH] FIX: Ruff flakes

---
 eoglearn/io/eegeyenet.py     |  4 +---
 eoglearn/io/tests/test_io.py |  1 -
 eoglearn/models/model.py     | 27 ++++++++++++---------------
 3 files changed, 13 insertions(+), 19 deletions(-)

diff --git a/eoglearn/io/eegeyenet.py b/eoglearn/io/eegeyenet.py
index 8c6fbe0..54b65dd 100644
--- a/eoglearn/io/eegeyenet.py
+++ b/eoglearn/io/eegeyenet.py
@@ -1,9 +1,7 @@
 from importlib import import_module
 
-import numpy as np
-
 import mne
-from mne._fiff.constants import FIFF
+import numpy as np
 
 
 def _check_pymatreader_installed():
diff --git a/eoglearn/io/tests/test_io.py b/eoglearn/io/tests/test_io.py
index 5745df7..f967263 100644
--- a/eoglearn/io/tests/test_io.py
+++ b/eoglearn/io/tests/test_io.py
@@ -1,4 +1,3 @@
-import numpy as np
 
 from mne._fiff.constants import FIFF
 
diff --git a/eoglearn/models/model.py b/eoglearn/models/model.py
index 0f28a2f..0f2a0b1 100644
--- a/eoglearn/models/model.py
+++ b/eoglearn/models/model.py
@@ -6,7 +6,6 @@
 import matplotlib.pyplot as plt
 import mne
 import numpy as np
-import mne
 from mne.utils import logger
 from sklearn.preprocessing import StandardScaler
 from tensorflow.keras.layers import LSTM
@@ -19,28 +18,26 @@
 class EOGDenoiser:
     """Use simultaneous EEG and Eyetracking to Denoise EOG from the EEG data.
 
-        Parameters
-        ----------
+    Parameters
+    ----------
         raw : mne.io.Raw
             An instance of ``mne.io.Raw``, with EEG, eyegaze, and pupil channels.
         downsample : int
             The factor by which to downsample the EEG and eyetracking data. EEG channels
-            will be low-pass filtered before downsampling using ``mne.io.filter.resample``.
-            Eyetracking channels will be decimated without any filtering. resampling and
-            decimating will be done on copies of the data, so the original input data will
-            be preserved.
+            will be low-pass filtered before downsampling using
+            ``mne.io.filter.resample``. Eyetracking channels will be decimated without
+            any filtering. resampling and decimating will be done on copies of the data,
+            so the original input data will be preserved.
         n_units : int
             The number of units to pass into the initial LSTM layer. Defaults to 50.
         n_times : int
-            The number of timepoints to pass into the LSTM model at once. Defaults to 100.
+            The number of timepoints to pass into the LSTM model at once. Defaults to
+            100.
         noise_picks: list | None
             Channels that contain the noise channels.
-    <<<<<<< HEAD
 
-    =======
-    >>>>>>> lina/main
-        Attributes
-        ----------
+    Attributes
+    ----------
         raw : mne.io.Raw
             The original input ``mne.io.Raw`` instance.
         downsample : int
@@ -72,8 +69,8 @@ class EOGDenoiser:
             i.e. ``(n_samples, n_meeg_channels)`` of the input :class:`~mne.io.Raw`
             object.
 
-        Notes
-        -----
+    Notes
+    -----
         See the MNE-Python tutorial on aligning EEG and eyetracking data for information
         on how to create a raw object with both EEG and eyetracking channels.
     """