We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
update path handling in examples/preprocessing/otp.py
all of our `data_path` functions now return `pathlib.Path` objects, which allows things like the following:

https://github.com/mne-tools/mne-python/blob/37a9e5587443109515a85cdc4396af4e014ecf82/examples/datasets/brainstorm_data.py#L32-L35

Compare this to the old pattern (using `os.path.join`, often imported as `op.join`):

```py
raw_path = os.path.join(data_path, 'MEG', 'bst_raw',
                        'subj001_somatosensory_20111109_01_AUX-f.ds')
```

or the *really* old pattern (using the `+` operator on plain strings):

```py
raw_path = (data_path + '/MEG/bst_raw/' +
            'subj001_somatosensory_20111109_01_AUX-f.ds')
```

Update the paths in the file mentioned in the issue title to use the new pathlib-style, which is considered the modern, best-practice way to handle file paths.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/preprocessing/otp.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 """
3 .. _ex-otp:
4 
5 ===========================================================
6 Plot sensor denoising using oversampled temporal projection
7 ===========================================================
8 
9 This demonstrates denoising using the OTP algorithm :footcite:`LarsonTaulu2018`
10 on data with with sensor artifacts (flux jumps) and random noise.
11 """
12 # Author: Eric Larson <larson.eric.d@gmail.com>
13 #
14 # License: BSD-3-Clause
15 
16 # %%
17 
18 import os.path as op
19 import mne
20 import numpy as np
21 
22 from mne import find_events, fit_dipole
23 from mne.datasets.brainstorm import bst_phantom_elekta
24 from mne.io import read_raw_fif
25 
26 print(__doc__)
27 
28 # %%
29 # Plot the phantom data, lowpassed to get rid of high-frequency artifacts.
30 # We also crop to a single 10-second segment for speed.
31 # Notice that there are two large flux jumps on channel 1522 that could
32 # spread to other channels when performing subsequent spatial operations
33 # (e.g., Maxwell filtering, SSP, or ICA).
34 
35 dipole_number = 1
36 data_path = bst_phantom_elekta.data_path()
37 raw = read_raw_fif(
38     op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif'))
39 raw.crop(40., 50.).load_data()
40 order = list(range(160, 170))
41 raw.copy().filter(0., 40.).plot(order=order, n_channels=10)
42 
43 # %%
44 # Now we can clean the data with OTP, lowpass, and plot. The flux jumps have
45 # been suppressed alongside the random sensor noise.
46 
47 raw_clean = mne.preprocessing.oversampled_temporal_projection(raw)
48 raw_clean.filter(0., 40.)
49 raw_clean.plot(order=order, n_channels=10)
50 
51 
52 # %%
53 # We can also look at the effect on single-trial phantom localization.
54 # See the :ref:`tut-brainstorm-elekta-phantom`
55 # for more information. Here we use a version that does single-trial
56 # localization across the 17 trials are in our 10-second window:
57 
58 def compute_bias(raw):
59     events = find_events(raw, 'STI201', verbose=False)
60     events = events[1:]  # first one has an artifact
61     tmin, tmax = -0.2, 0.1
62     epochs = mne.Epochs(raw, events, dipole_number, tmin, tmax,
63                         baseline=(None, -0.01), preload=True, verbose=False)
64     sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None,
65                                    verbose=False)
66     cov = mne.compute_covariance(epochs, tmax=0, method='oas',
67                                  rank=None, verbose=False)
68     idx = epochs.time_as_index(0.036)[0]
69     data = epochs.get_data()[:, :, idx].T
70     evoked = mne.EvokedArray(data, epochs.info, tmin=0.)
71     dip = fit_dipole(evoked, cov, sphere, n_jobs=None, verbose=False)[0]
72     actual_pos = mne.dipole.get_phantom_dipoles()[0][dipole_number - 1]
73     misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1)
74     return misses
75 
76 
77 bias = compute_bias(raw)
78 print('Raw bias: %0.1fmm (worst: %0.1fmm)'
79       % (np.mean(bias), np.max(bias)))
80 bias_clean = compute_bias(raw_clean)
81 print('OTP bias: %0.1fmm (worst: %0.1fmm)'
82       % (np.mean(bias_clean), np.max(bias_clean),))
83 
84 # %%
85 # References
86 # ----------
87 # .. footbibliography::
88
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/preprocessing/otp.py b/examples/preprocessing/otp.py
--- a/examples/preprocessing/otp.py
+++ b/examples/preprocessing/otp.py
@@ -15,7 +15,6 @@
 
 # %%
 
-import os.path as op
 import mne
 import numpy as np
 
@@ -34,8 +33,7 @@
 
 dipole_number = 1
 data_path = bst_phantom_elekta.data_path()
-raw = read_raw_fif(
-    op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif'))
+raw = read_raw_fif(data_path / 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')
 raw.crop(40., 50.).load_data()
 order = list(range(160, 170))
 raw.copy().filter(0., 40.).plot(order=order, n_channels=10)

{"golden_diff": "diff --git a/examples/preprocessing/otp.py b/examples/preprocessing/otp.py\n--- a/examples/preprocessing/otp.py\n+++ b/examples/preprocessing/otp.py\n@@ -15,7 +15,6 @@\n \n # %%\n \n-import os.path as op\n import mne\n import numpy as np\n \n@@ -34,8 +33,7 @@\n \n dipole_number = 1\n data_path = bst_phantom_elekta.data_path()\n-raw = read_raw_fif(\n-    op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif'))\n+raw = read_raw_fif(data_path / 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')\n raw.crop(40., 50.).load_data()\n order = list(range(160, 170))\n raw.copy().filter(0., 40.).plot(order=order, n_channels=10)\n", "issue": "update path handling in examples/preprocessing/otp.py\nall of our `data_path` functions now return `pathlib.Path` objects, which allows things like the following:\r\n\r\nhttps://github.com/mne-tools/mne-python/blob/37a9e5587443109515a85cdc4396af4e014ecf82/examples/datasets/brainstorm_data.py#L32-L35\r\n\r\nCompare this to the old pattern (using `os.path.join`, often imported as `op.join`):\r\n\r\n```py\r\nraw_path = os.path.join(data_path, 'MEG', 'bst_raw',\r\n                        'subj001_somatosensory_20111109_01_AUX-f.ds')\r\n```\r\n\r\nor the *really* old pattern (using the `+` operator on plain strings):\r\n\r\n```py\r\nraw_path = (data_path + '/MEG/bst_raw/' +\r\n            'subj001_somatosensory_20111109_01_AUX-f.ds')\r\n```\r\n\r\nUpdate the paths in the file mentioned in the issue title to use the new pathlib-style, which is considered the modern, best-practice way to handle file paths.\r\n\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"\n.. _ex-otp:\n\n===========================================================\nPlot sensor denoising using oversampled temporal projection\n===========================================================\n\nThis demonstrates denoising using the OTP algorithm :footcite:`LarsonTaulu2018`\non data with with sensor artifacts (flux jumps) and random noise.\n\"\"\"\n# Author: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD-3-Clause\n\n# %%\n\nimport os.path as op\nimport mne\nimport numpy as np\n\nfrom mne import find_events, fit_dipole\nfrom mne.datasets.brainstorm import bst_phantom_elekta\nfrom mne.io import read_raw_fif\n\nprint(__doc__)\n\n# %%\n# Plot the phantom data, lowpassed to get rid of high-frequency artifacts.\n# We also crop to a single 10-second segment for speed.\n# Notice that there are two large flux jumps on channel 1522 that could\n# spread to other channels when performing subsequent spatial operations\n# (e.g., Maxwell filtering, SSP, or ICA).\n\ndipole_number = 1\ndata_path = bst_phantom_elekta.data_path()\nraw = read_raw_fif(\n    op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif'))\nraw.crop(40., 50.).load_data()\norder = list(range(160, 170))\nraw.copy().filter(0., 40.).plot(order=order, n_channels=10)\n\n# %%\n# Now we can clean the data with OTP, lowpass, and plot. The flux jumps have\n# been suppressed alongside the random sensor noise.\n\nraw_clean = mne.preprocessing.oversampled_temporal_projection(raw)\nraw_clean.filter(0., 40.)\nraw_clean.plot(order=order, n_channels=10)\n\n\n# %%\n# We can also look at the effect on single-trial phantom localization.\n# See the :ref:`tut-brainstorm-elekta-phantom`\n# for more information. Here we use a version that does single-trial\n# localization across the 17 trials are in our 10-second window:\n\ndef compute_bias(raw):\n    events = find_events(raw, 'STI201', verbose=False)\n    events = events[1:]  # first one has an artifact\n    tmin, tmax = -0.2, 0.1\n    epochs = mne.Epochs(raw, events, dipole_number, tmin, tmax,\n                        baseline=(None, -0.01), preload=True, verbose=False)\n    sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None,\n                                   verbose=False)\n    cov = mne.compute_covariance(epochs, tmax=0, method='oas',\n                                 rank=None, verbose=False)\n    idx = epochs.time_as_index(0.036)[0]\n    data = epochs.get_data()[:, :, idx].T\n    evoked = mne.EvokedArray(data, epochs.info, tmin=0.)\n    dip = fit_dipole(evoked, cov, sphere, n_jobs=None, verbose=False)[0]\n    actual_pos = mne.dipole.get_phantom_dipoles()[0][dipole_number - 1]\n    misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1)\n    return misses\n\n\nbias = compute_bias(raw)\nprint('Raw bias: %0.1fmm (worst: %0.1fmm)'\n      % (np.mean(bias), np.max(bias)))\nbias_clean = compute_bias(raw_clean)\nprint('OTP bias: %0.1fmm (worst: %0.1fmm)'\n      % (np.mean(bias_clean), np.max(bias_clean),))\n\n# %%\n# References\n# ----------\n# .. footbibliography::\n", "path": "examples/preprocessing/otp.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"\n.. _ex-otp:\n\n===========================================================\nPlot sensor denoising using oversampled temporal projection\n===========================================================\n\nThis demonstrates denoising using the OTP algorithm :footcite:`LarsonTaulu2018`\non data with with sensor artifacts (flux jumps) and random noise.\n\"\"\"\n# Author: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD-3-Clause\n\n# %%\n\nimport mne\nimport numpy as np\n\nfrom mne import find_events, fit_dipole\nfrom mne.datasets.brainstorm import bst_phantom_elekta\nfrom mne.io import read_raw_fif\n\nprint(__doc__)\n\n# %%\n# Plot the phantom data, lowpassed to get rid of high-frequency artifacts.\n# We also crop to a single 10-second segment for speed.\n# Notice that there are two large flux jumps on channel 1522 that could\n# spread to other channels when performing subsequent spatial operations\n# (e.g., Maxwell filtering, SSP, or ICA).\n\ndipole_number = 1\ndata_path = bst_phantom_elekta.data_path()\nraw = read_raw_fif(data_path / 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')\nraw.crop(40., 50.).load_data()\norder = list(range(160, 170))\nraw.copy().filter(0., 40.).plot(order=order, n_channels=10)\n\n# %%\n# Now we can clean the data with OTP, lowpass, and plot. The flux jumps have\n# been suppressed alongside the random sensor noise.\n\nraw_clean = mne.preprocessing.oversampled_temporal_projection(raw)\nraw_clean.filter(0., 40.)\nraw_clean.plot(order=order, n_channels=10)\n\n\n# %%\n# We can also look at the effect on single-trial phantom localization.\n# See the :ref:`tut-brainstorm-elekta-phantom`\n# for more information. Here we use a version that does single-trial\n# localization across the 17 trials are in our 10-second window:\n\ndef compute_bias(raw):\n    events = find_events(raw, 'STI201', verbose=False)\n    events = events[1:]  # first one has an artifact\n    tmin, tmax = -0.2, 0.1\n    epochs = mne.Epochs(raw, events, dipole_number, tmin, tmax,\n                        baseline=(None, -0.01), preload=True, verbose=False)\n    sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None,\n                                   verbose=False)\n    cov = mne.compute_covariance(epochs, tmax=0, method='oas',\n                                 rank=None, verbose=False)\n    idx = epochs.time_as_index(0.036)[0]\n    data = epochs.get_data()[:, :, idx].T\n    evoked = mne.EvokedArray(data, epochs.info, tmin=0.)\n    dip = fit_dipole(evoked, cov, sphere, n_jobs=None, verbose=False)[0]\n    actual_pos = mne.dipole.get_phantom_dipoles()[0][dipole_number - 1]\n    misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1)\n    return misses\n\n\nbias = compute_bias(raw)\nprint('Raw bias: %0.1fmm (worst: %0.1fmm)'\n      % (np.mean(bias), np.max(bias)))\nbias_clean = compute_bias(raw_clean)\nprint('OTP bias: %0.1fmm (worst: %0.1fmm)'\n      % (np.mean(bias_clean), np.max(bias_clean),))\n\n# %%\n# References\n# ----------\n# .. footbibliography::\n", "path": "examples/preprocessing/otp.py"}]}