Add support for different encoding in ANT neuro CNT format reader

mne/io/ant/ant.py

@@ -80,6 +80,8 @@ class RawANT(BaseRaw):
             Note that the impedance annotation will likely have a duration of ``0``.
             If the measurement marks a discontinuity, the duration should be modified to
             cover the discontinuity in its entirety.
+    encoding : str
+        Encoding to use for :class:`str` in the CNT file. Defaults to ``'latin-1'``.
     %(preload)s
     %(verbose)s
     """
@@ -93,6 +95,7 @@ def __init__(
         bipolars: list[str] | tuple[str, ...] | None,
         impedance_annotation: str,
         *,
+        encoding: str = "latin-1",
         preload: bool | NDArray,
         verbose=None,
     ) -> None:
@@ -102,7 +105,7 @@ def __init__(
                 "Missing optional dependency 'antio'. Use pip or conda to install "
                 "'antio'."
             )
-        check_version("antio", "0.3.0")
+        check_version("antio", "0.5.0")
 
         from antio import read_cnt
         from antio.parser import (
@@ -122,8 +125,7 @@ def __init__(
             raise ValueError("The impedance annotation cannot be an empty string.")
         cnt = read_cnt(fname)
         # parse channels, sampling frequency, and create info
-        ch_info = read_info(cnt)  # load in 2 lines for compat with antio 0.2 and 0.3
-        ch_names, ch_units, ch_refs = ch_info[0], ch_info[1], ch_info[2]
+        ch_names, ch_units, ch_refs, _, _ = read_info(cnt, encoding=encoding)
         ch_types = _parse_ch_types(ch_names, eog, misc, ch_refs)
         if bipolars is not None:  # handle bipolar channels
             bipolars_idx = _handle_bipolar_channels(ch_names, ch_refs, bipolars)
@@ -139,9 +141,9 @@ def __init__(
             ch_names, sfreq=cnt.get_sample_frequency(), ch_types=ch_types
         )
         info.set_meas_date(read_meas_date(cnt))
-        make, model, serial, site = read_device_info(cnt)
+        make, model, serial, site = read_device_info(cnt, encoding=encoding)
         info["device_info"] = dict(type=make, model=model, serial=serial, site=site)
-        his_id, name, sex, birthday = read_subject_info(cnt)
+        his_id, name, sex, birthday = read_subject_info(cnt, encoding=encoding)
         info["subject_info"] = dict(
             his_id=his_id,
             first_name=name,
@@ -315,6 +317,7 @@ def read_raw_ant(
     bipolars=None,
     impedance_annotation="impedance",
     *,
+    encoding: str = "latin-1",
     preload=False,
     verbose=None,
 ) -> RawANT:
@@ -324,13 +327,18 @@ def read_raw_ant(
     raw : instance of RawANT
         A Raw object containing ANT data.
         See :class:`mne.io.Raw` for documentation of attributes and methods.
+
+    Notes
+    -----
+    .. versionadded:: 1.9
     """
     return RawANT(
         fname,
         eog=eog,
         misc=misc,
         bipolars=bipolars,
         impedance_annotation=impedance_annotation,
+        encoding=encoding,
         preload=preload,
         verbose=verbose,
     )

tools/circleci_dependencies.sh

@@ -1,19 +1,14 @@
 #!/bin/bash -ef
 
 python -m pip install --upgrade "pip!=20.3.0" build
-# This can be removed once dipy > 1.9.0 is released
-python -m pip install --upgrade --progress-bar off \
-    numpy scipy h5py
-python -m pip install --pre --progress-bar off \
-    --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple" \
-    "dipy>1.9"
 python -m pip install --upgrade --progress-bar off \
     --only-binary "numpy,dipy,scipy,matplotlib,pandas,statsmodels" \
     -ve .[full,test,doc] "numpy>=2" \
     "git+https://github.com/pyvista/pyvista.git" \
     "git+https://github.com/sphinx-gallery/sphinx-gallery.git" \
     "git+https://github.com/mne-tools/mne-bids.git" \
     \
+    "openmeeg<2.5.13" \
     alphaCSC autoreject bycycle conpy emd fooof meggie \
     mne-ari mne-bids-pipeline mne-faster mne-features \
     mne-icalabel mne-lsl mne-microstates mne-nirs mne-rsa \

tutorials/intro/70_report.py

@@ -7,11 +7,10 @@
 
 :class:`mne.Report` is a way to create interactive HTML summaries of your data.
 These reports can show many different visualizations for one or multiple participants.
-A common use case is creating diagnostic summaries to check data
-quality at different stages in the processing pipeline. The report can show
-things like plots of data before and after each preprocessing step, epoch
-rejection statistics, MRI slices with overlaid BEM shells, all the way up to
-plots of estimated cortical activity.
+A common use case is creating diagnostic summaries to check data quality at different
+stages in the processing pipeline. The report can show things like plots of data before
+and after each preprocessing step, epoch rejection statistics, MRI slices with overlaid
+BEM shells, all the way up to plots of estimated cortical activity.
 
 Compared to a Jupyter notebook, :class:`mne.Report` is easier to deploy, as the
 HTML pages it generates are self-contained and do not require a running Python