From 87d6be9047bbd5b234e22eafd79ab7695814fb41 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Richard=20H=C3=B6chenberger?= Date: Fri, 3 Nov 2023 14:05:04 +0100 Subject: [PATCH 01/20] Try to fix ICA Report (#12167) --- doc/changes/devel.rst | 2 +- mne/report/report.py | 8 +++++--- mne/report/tests/test_report.py | 1 + 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index 57a9b1fcb41..fce4656104a 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -64,7 +64,7 @@ Bugs - Fix bug with :meth:`mne.viz.Brain.get_view` where calling :meth:`~mne.viz.Brain.show_view` with returned parameters would change the view (:gh:`12000` by `Eric Larson`_) - Fix bug with :meth:`mne.viz.Brain.show_view` where ``distance=None`` would change the view distance (:gh:`12000` by `Eric Larson`_) - Fix bug with :meth:`~mne.viz.Brain.add_annotation` when reading an annotation from a file with both hemispheres shown (:gh:`11946` by `Marijn van Vliet`_) -- Fix bug with reported component number and errant reporting of PCA explained variance as ICA explained variance in :meth:`mne.Report.add_ica` (:gh:`12155` by `Eric Larson`_) +- Fix bug with reported component number and errant reporting of PCA explained variance as ICA explained variance in :meth:`mne.Report.add_ica` (:gh:`12155`, :gh:`12167` by `Eric Larson`_ and `Richard Höchenberger`_) - Fix bug with axis clip box boundaries in :func:`mne.viz.plot_evoked_topo` and related functions (:gh:`11999` by `Eric Larson`_) - Fix bug with ``subject_info`` when loading data from and exporting to EDF file (:gh:`11952` by `Paul Roujansky`_) - Fix bug where :class:`mne.Info` HTML representations listed all channel counts instead of good channel counts under the heading "Good channels" (:gh:`12145` by `Eric Larson`_) diff --git a/mne/report/report.py b/mne/report/report.py index a99414697d9..18ca4be3768 100644 --- a/mne/report/report.py +++ b/mne/report/report.py @@ -587,9 +587,6 @@ def _plot_ica_properties_as_arrays(*, ica, inst, picks, n_jobs): """ import matplotlib.pyplot as plt - if picks is None: - picks = list(range(ica.n_components_)) - def _plot_one_ica_property(*, ica, inst, pick): figs = ica.plot_properties(inst=inst, picks=pick, show=False) assert len(figs) == 1 @@ -1666,9 +1663,14 @@ def _add_ica_properties( ) return + if picks is None: + picks = list(range(ica.n_components_)) + figs = _plot_ica_properties_as_arrays( ica=ica, inst=inst, picks=picks, n_jobs=n_jobs ) + assert len(figs) == len(picks) + captions = [] for idx in range(len(figs)): caption = f"ICA component {picks[idx]}." diff --git a/mne/report/tests/test_report.py b/mne/report/tests/test_report.py index 9f8760dfbb6..67b065ad4fe 100644 --- a/mne/report/tests/test_report.py +++ b/mne/report/tests/test_report.py @@ -984,6 +984,7 @@ def test_manual_report_2d(tmp_path, invisible_fig): inst=epochs_baseline, picks=[0], ) + r.add_ica(ica=ica, title="my ica with picks=None", inst=epochs_baseline, picks=None) r.add_covariance(cov=cov, info=raw_fname, title="my cov") r.add_forward( forward=fwd_fname, From 3bc18ffec21c2d0293d6af6452948d6f56f73b5d Mon Sep 17 00:00:00 2001 From: Clemens Brunner Date: Fri, 3 Nov 2023 15:47:45 +0100 Subject: [PATCH 02/20] Fix inferring fiducials from EEGLAB (#12165) --- doc/changes/devel.rst | 1 + mne/io/eeglab/eeglab.py | 26 ++++++++++++++++---------- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index fce4656104a..fcb51b8b7c3 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -83,6 +83,7 @@ Bugs - Fix bug with :func:`~mne.viz.plot_raw` where changing ``MNE_BROWSER_BACKEND`` via :func:`~mne.set_config` would have no effect within a Python session (:gh:`12078` by `Santeri Ruuskanen`_) - Improve handling of ``method`` argument in the channel interpolation function to support :class:`str` and raise helpful error messages (:gh:`12113` by `Mathieu Scheltienne`_) - Fix combination of ``DIN`` event channels into a single synthetic trigger channel ``STI 014`` by the MFF reader of :func:`mne.io.read_raw_egi` (:gh:`12122` by `Mathieu Scheltienne`_) +- Fix bug with :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab` where automatic fiducial detection would fail for certain files (:gh:`12165` by `Clemens Brunner`_) API changes ~~~~~~~~~~~ diff --git a/mne/io/eeglab/eeglab.py b/mne/io/eeglab/eeglab.py index 9e08807db49..6cf92bfd7bf 100644 --- a/mne/io/eeglab/eeglab.py +++ b/mne/io/eeglab/eeglab.py @@ -165,17 +165,23 @@ def _get_montage_information(eeg, get_pos, *, montage_units): ) lpa, rpa, nasion = None, None, None - if hasattr(eeg, "chaninfo") and len(eeg.chaninfo.get("nodatchans", [])): - for item in list(zip(*eeg.chaninfo["nodatchans"].values())): - d = dict(zip(eeg.chaninfo["nodatchans"].keys(), item)) - if d.get("type", None) != "FID": + if hasattr(eeg, "chaninfo") and isinstance(eeg.chaninfo["nodatchans"], dict): + nodatchans = eeg.chaninfo["nodatchans"] + types = nodatchans.get("type", []) + descriptions = nodatchans.get("description", []) + xs = nodatchans.get("X", []) + ys = nodatchans.get("Y", []) + zs = nodatchans.get("Z", []) + + for type_, description, x, y, z in zip(types, descriptions, xs, ys, zs): + if type_ != "FID": continue - elif d.get("description", None) == "Nasion": - nasion = np.array([d["X"], d["Y"], d["Z"]]) - elif d.get("description", None) == "Right periauricular point": - rpa = np.array([d["X"], d["Y"], d["Z"]]) - elif d.get("description", None) == "Left periauricular point": - lpa = np.array([d["X"], d["Y"], d["Z"]]) + if description == "Nasion": + nasion = np.array([x, y, z]) + elif description == "Right periauricular point": + rpa = np.array([x, y, z]) + elif description == "Left periauricular point": + lpa = np.array([x, y, z]) # Always check this even if it's not used _check_option("montage_units", montage_units, ("m", "dm", "cm", "mm", "auto")) From fcb59266b7616dd99908c6dac0e2295722b0c567 Mon Sep 17 00:00:00 2001 From: Jacob Woessner Date: Fri, 3 Nov 2023 10:00:00 -0500 Subject: [PATCH 03/20] [DOC] Add documentation for setting montage order (#12160) --- doc/changes/devel.rst | 2 +- mne/_fiff/meas_info.py | 6 ++++++ mne/_fiff/reference.py | 12 +++++++++++- 3 files changed, 18 insertions(+), 2 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index fcb51b8b7c3..84d24f284df 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -45,7 +45,7 @@ Enhancements - Add :class:`mne.viz.ui_events.UIEvent` linking for interactive colorbars, allowing users to link figures and change the colormap and limits interactively. This supports :func:`~mne.viz.plot_evoked_topomap`, :func:`~mne.viz.plot_ica_components`, :func:`~mne.viz.plot_tfr_topomap`, :func:`~mne.viz.plot_projs_topomap`, :meth:`~mne.Evoked.plot_image`, and :meth:`~mne.Epochs.plot_image` (:gh:`12057` by `Santeri Ruuskanen`_) - :func:`~mne.epochs.make_metadata` now accepts ``tmin=None`` and ``tmax=None``, which will bound the time window used for metadata generation by event names (instead of a fixed time). That way, you can now for example generate metadata spanning from one cue or fixation cross to the next, even if trial durations vary throughout the recording (:gh:`12118` by `Richard Höchenberger`_) - Add support for passing multiple labels to :func:`mne.minimum_norm.source_induced_power` (:gh:`12026` by `Erica Peterson`_, `Eric Larson`_, and `Daniel McCloy`_ ) - +- Added documentation to :meth:`mne.io.Raw.set_montage` and :func:`mne.add_reference_channels` to specify that montages should be set after adding reference channels (:gh:`12160` by `Jacob Woessner`_) Bugs ~~~~ diff --git a/mne/_fiff/meas_info.py b/mne/_fiff/meas_info.py index d7f94461a4d..5d67b77470c 100644 --- a/mne/_fiff/meas_info.py +++ b/mne/_fiff/meas_info.py @@ -410,6 +410,12 @@ def set_montage( a montage. Other channel types (e.g., MEG channels) should have their positions defined properly using their data reading functions. + .. warning:: + Applying a montage will only set locations of channels that exist + at the time it is applied. This means when + :ref:`re-referencing ` + make sure to apply the montage only after calling + :func:`mne.add_reference_channels` """ # How to set up a montage to old named fif file (walk through example) # https://gist.github.com/massich/f6a9f4799f1fbeb8f5e8f8bc7b07d3df diff --git a/mne/_fiff/reference.py b/mne/_fiff/reference.py index 01d6e6e1230..0062bc4f40f 100644 --- a/mne/_fiff/reference.py +++ b/mne/_fiff/reference.py @@ -175,6 +175,14 @@ def add_reference_channels(inst, ref_channels, copy=True): ------- inst : instance of Raw | Epochs | Evoked Data with added EEG reference channels. + + Notes + ----- + .. warning:: + When :ref:`re-referencing `, + make sure to apply the montage using :meth:`mne.io.Raw.set_montage` + only after calling this function. Applying a montage will only set + locations of channels that exist at the time it is applied. """ from ..epochs import BaseEpochs from ..evoked import Evoked @@ -239,7 +247,9 @@ def add_reference_channels(inst, ref_channels, copy=True): ref_dig_array = np.full(12, np.nan) logger.info( "Location for this channel is unknown; consider calling " - "set_montage() again if needed." + "set_montage() after adding new reference channels if needed. " + "Applying a montage will only set locations of channels that " + "exist at the time it is applied." ) for ch in ref_channels: From f02e5576df7cfcee3fa435cafe66602e65ff76ec Mon Sep 17 00:00:00 2001 From: Rasmus Aagaard Date: Fri, 3 Nov 2023 19:24:24 +0100 Subject: [PATCH 04/20] Changed casting rule in np.clip to allow reading of raw GDF files (#12168) Co-authored-by: roraa --- doc/changes/devel.rst | 1 + doc/changes/names.inc | 2 ++ mne/io/edf/edf.py | 4 ++-- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index 84d24f284df..306f0689f4e 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -49,6 +49,7 @@ Enhancements Bugs ~~~~ +- Fix bug where :func:`mne.io.read_raw_gdf` would fail due to improper usage of ``np.clip`` (:gh:`12168` by :newcontrib:`Rasmus Aagaard`) - Fix bugs with :func:`mne.preprocessing.realign_raw` where the start of ``other`` was incorrectly cropped; and onsets and durations in ``other.annotations`` were left unsynced with the resampled data (:gh:`11950` by :newcontrib:`Qian Chu`) - Fix bug where ``encoding`` argument was ignored when reading annotations from an EDF file (:gh:`11958` by :newcontrib:`Andrew Gilbert`) - Mark tests ``test_adjacency_matches_ft`` and ``test_fetch_uncompressed_file`` as network tests (:gh:`12041` by :newcontrib:`Maksym Balatsko`) diff --git a/doc/changes/names.inc b/doc/changes/names.inc index 82910422954..925e38bfd22 100644 --- a/doc/changes/names.inc +++ b/doc/changes/names.inc @@ -442,6 +442,8 @@ .. _ramonapariciog: https://github.com/ramonapariciog +.. _Rasmus Aagaard: https://github.com/rasgaard + .. _Rasmus Zetter: https://people.aalto.fi/rasmus.zetter .. _Reza Nasri: https://github.com/rznas diff --git a/mne/io/edf/edf.py b/mne/io/edf/edf.py index e507a651676..d27aabae8a5 100644 --- a/mne/io/edf/edf.py +++ b/mne/io/edf/edf.py @@ -1443,7 +1443,7 @@ def _read_gdf_header(fname, exclude, include=None): else: chn = np.zeros(n_events, dtype=np.uint32) dur = np.ones(n_events, dtype=np.uint32) - np.clip(dur, 1, np.inf, out=dur) + np.maximum(dur, 1, out=dur) events = [n_events, pos, typ, chn, dur] edf_info["event_sfreq"] = event_sr @@ -1878,7 +1878,7 @@ def read_raw_gdf( input_fname = os.path.abspath(input_fname) ext = os.path.splitext(input_fname)[1][1:].lower() if ext != "gdf": - raise NotImplementedError(f"Only BDF files are supported, got {ext}.") + raise NotImplementedError(f"Only GDF files are supported, got {ext}.") return RawGDF( input_fname=input_fname, eog=eog, From ec87fd8976a82ef7541c3114cd7955083eb278d6 Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Fri, 3 Nov 2023 16:17:23 -0400 Subject: [PATCH 05/20] FIX: Fix bug with coreg scalars (#12164) --- doc/changes/devel.rst | 2 +- mne/viz/backends/_pyvista.py | 15 ++++++++++++--- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index 306f0689f4e..8d02c056445 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -71,7 +71,7 @@ Bugs - Fix bug where :class:`mne.Info` HTML representations listed all channel counts instead of good channel counts under the heading "Good channels" (:gh:`12145` by `Eric Larson`_) - Fix rendering glitches when plotting Neuromag/TRIUX sensors in :func:`mne.viz.plot_alignment` and related functions (:gh:`12098` by `Eric Larson`_) - Fix bug with delayed checking of :class:`info["bads"] ` (:gh:`12038` by `Eric Larson`_) -- Fix bug with :ref:`mne coreg` where points inside the head surface were not shown (:gh:`12147` by `Eric Larson`_) +- Fix bug with :ref:`mne coreg` where points inside the head surface were not shown (:gh:`12147`, :gh:`12164` by `Eric Larson`_) - Fix bug with :func:`mne.viz.plot_alignment` where ``sensor_colors`` were not handled properly on a per-channel-type basis (:gh:`12067` by `Eric Larson`_) - Fix handling of channel information in annotations when loading data from and exporting to EDF file (:gh:`11960` :gh:`12017` :gh:`12044` by `Paul Roujansky`_) - Add missing ``overwrite`` and ``verbose`` parameters to :meth:`Transform.save() ` (:gh:`12004` by `Marijn van Vliet`_) diff --git a/mne/viz/backends/_pyvista.py b/mne/viz/backends/_pyvista.py index b09314462dc..5cb89179ef5 100644 --- a/mne/viz/backends/_pyvista.py +++ b/mne/viz/backends/_pyvista.py @@ -655,6 +655,9 @@ def quiver3d( clim=None, ): _check_option("mode", mode, ALLOWED_QUIVER_MODES) + _validate_type(scale_mode, str, "scale_mode") + scale_map = dict(none=False, scalar="scalars", vector="vec") + _check_option("scale_mode", scale_mode, list(scale_map)) with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) factor = scale @@ -667,7 +670,10 @@ def quiver3d( grid = UnstructuredGrid(*args) if scalars is None: scalars = np.ones((n_points,)) - grid.point_data["scalars"] = np.array(scalars) + mesh_scalars = None + else: + mesh_scalars = "scalars" + grid.point_data["scalars"] = np.array(scalars, float) grid.point_data["vec"] = vectors if mode == "2darrow": return _arrow_glyph(grid, factor), grid @@ -715,14 +721,17 @@ def quiver3d( glyph.Update() geom = glyph.GetOutput() mesh = grid.glyph( - orient="vec", scale=scale_mode == "vector", factor=factor, geom=geom + orient="vec", + scale=scale_map[scale_mode], + factor=factor, + geom=geom, ) actor = _add_mesh( self.plotter, mesh=mesh, color=color, opacity=opacity, - scalars=None, + scalars=mesh_scalars, colormap=colormap, show_scalar_bar=False, backface_culling=backface_culling, From b988e67d9916ed651c381b867e8bf10c3a470905 Mon Sep 17 00:00:00 2001 From: Scott Huberty <52462026+scott-huberty@users.noreply.github.com> Date: Mon, 6 Nov 2023 05:25:13 -0500 Subject: [PATCH 06/20] FIX: skip empty lines in read_raw_eyelink (#12172) --- mne/io/eyelink/_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mne/io/eyelink/_utils.py b/mne/io/eyelink/_utils.py index 65641e9cc43..3cbd06c52a6 100644 --- a/mne/io/eyelink/_utils.py +++ b/mne/io/eyelink/_utils.py @@ -122,6 +122,8 @@ def _parse_recording_blocks(fname): is_recording_block = True if is_recording_block: tokens = line.split() + if not tokens: + continue # skip empty lines if tokens[0][0].isnumeric(): # Samples data_dict["sample_lines"].append(tokens) elif tokens[0] in data_dict["event_lines"].keys(): From 58afb0fb73e55296f21f6c68a643f2ea98f77483 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Mon, 6 Nov 2023 15:22:29 +0100 Subject: [PATCH 07/20] fix docstring in 60_sleep.py (#12171) --- tutorials/clinical/60_sleep.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/clinical/60_sleep.py b/tutorials/clinical/60_sleep.py index a878b7747e7..eefeda02070 100644 --- a/tutorials/clinical/60_sleep.py +++ b/tutorials/clinical/60_sleep.py @@ -249,7 +249,7 @@ def eeg_power_band(epochs): Returns ------- - X : numpy array of shape [n_samples, 5] + X : numpy array of shape [n_samples, 5 * n_channels] Transformed data. """ # specific frequency bands From 55b0184b94ec1c3d6b2559eeed383e760220f516 Mon Sep 17 00:00:00 2001 From: Daniel McCloy Date: Mon, 6 Nov 2023 09:49:23 -0600 Subject: [PATCH 08/20] OpenSSF (#12175) --- tools/dev/Makefile | 15 +++++++++++++-- ...ed-bug-reports.jq => unacknowledged-issues.jq} | 4 ++-- 2 files changed, 15 insertions(+), 4 deletions(-) rename tools/dev/{unacknowledged-bug-reports.jq => unacknowledged-issues.jq} (91%) diff --git a/tools/dev/Makefile b/tools/dev/Makefile index 2e9f9ad14f3..e2f61735a7e 100644 --- a/tools/dev/Makefile +++ b/tools/dev/Makefile @@ -3,8 +3,13 @@ dev-activity: check_steering_committee.py python check_steering_committee.py -unacknowledged-bugs: unacknowledged-bug-reports.jq bug-reports-12-to-2-months-old.json - @jq -f unacknowledged-bug-reports.jq bug-reports-12-to-2-months-old.json +unacknowledged-bug-reports: unacknowledged-issues.jq bug-reports-12-to-2-months-old.json + @echo "MUST acknowledge bug reports (OpenSSF criterion: 50%)" + @jq -f unacknowledged-issues.jq bug-reports-12-to-2-months-old.json + +unacknowledged-feature-requests: unacknowledged-issues.jq feature-requests-12-to-2-months-old.json + @echo "SHOULD acknowledge feature requests (OpenSSF criterion: 50%)" + @jq -f unacknowledged-issues.jq feature-requests-12-to-2-months-old.json bug-reports-12-to-2-months-old.json: @echo "Querying GitHub REST API..." @@ -12,5 +17,11 @@ bug-reports-12-to-2-months-old.json: -l bug \ --json state,number,title,createdAt,comments > bug-reports-12-to-2-months-old.json +feature-requests-12-to-2-months-old.json: + @echo "Querying GitHub REST API..." + @gh issue list \ + -l enh \ + --json state,number,title,createdAt,comments > feature-requests-12-to-2-months-old.json + clean: @rm bug-reports-12-to-2-months-old.json || true diff --git a/tools/dev/unacknowledged-bug-reports.jq b/tools/dev/unacknowledged-issues.jq similarity index 91% rename from tools/dev/unacknowledged-bug-reports.jq rename to tools/dev/unacknowledged-issues.jq index 431d92be144..b91f5fc2e78 100644 --- a/tools/dev/unacknowledged-bug-reports.jq +++ b/tools/dev/unacknowledged-issues.jq @@ -1,4 +1,4 @@ -# Processor for `gh issue list` output that displays unacknowledged bug reports +# Processor for `gh issue list` output that displays unacknowledged issues # that are 2-12 months old. The date range is specific to OpenSSF best practices. # `now` is in seconds since the unix epoch @@ -28,5 +28,5 @@ map( "range": make_pretty_date_range, "has_dev_comments": map(select(.devComments > 0)) | length, "no_dev_comments": map(select(.devComments == 0) and .state == "OPEN") | length, - "unaddressed_bug_reports": map(select(.devComments == 0) | make_issue_url), + "unaddressed": map(select(.devComments == 0) | make_issue_url), } From 8ea98e2dd542735e2ae950e5daf2bdcc363f90bd Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Mon, 6 Nov 2023 11:30:47 -0500 Subject: [PATCH 09/20] MAINT: Add branch coverage (#12174) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f28204f5bca..a57b18928d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -128,7 +128,7 @@ ignore-decorators = [ [tool.pytest.ini_options] addopts = """--durations=20 --doctest-modules -ra --cov-report= --tb=short \ - --doctest-ignore-import-errors --junit-xml=junit-results.xml \ + --cov-branch --doctest-ignore-import-errors --junit-xml=junit-results.xml \ --ignore=doc --ignore=logo --ignore=examples --ignore=tutorials \ --ignore=mne/gui/_*.py --ignore=mne/icons --ignore=tools \ --ignore=mne/report/js_and_css \ From 8af12aae0ec3f1e73a19ce9be5739922712be299 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 6 Nov 2023 14:51:50 -0500 Subject: [PATCH 10/20] [pre-commit.ci] pre-commit autoupdate (#12177) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6ff357ca832..c41418ed4b4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: # Ruff mne - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.1.4 hooks: - id: ruff name: ruff mne @@ -16,7 +16,7 @@ repos: # Ruff tutorials and examples - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.1.4 hooks: - id: ruff name: ruff tutorials and examples From 7b6b79583b5e738de08828670222e690c65085f1 Mon Sep 17 00:00:00 2001 From: Clemens Brunner Date: Tue, 7 Nov 2023 15:26:11 +0100 Subject: [PATCH 11/20] Tweak README.rst (#12166) --- README.rst | 196 +++++++++++++++++++++++++++++------------------------ 1 file changed, 108 insertions(+), 88 deletions(-) diff --git a/README.rst b/README.rst index a3d35deb76a..fdc620acd9f 100644 --- a/README.rst +++ b/README.rst @@ -1,87 +1,67 @@ .. -*- mode: rst -*- -|PyPI|_ |conda-forge|_ |Zenodo|_ |Discourse|_ |Codecov|_ |Bandit|_ |OpenSSF|_ - |MNE|_ -.. |PyPI| image:: https://img.shields.io/pypi/dm/mne.svg?label=PyPI -.. _PyPI: https://pypi.org/project/mne/ - -.. |conda-forge| image:: https://img.shields.io/conda/dn/conda-forge/mne.svg?label=Conda -.. _conda-forge: https://anaconda.org/conda-forge/mne - -.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.592483.svg -.. _Zenodo: https://doi.org/10.5281/zenodo.592483 - -.. |Discourse| image:: https://img.shields.io/discourse/status?label=Community&server=https%3A%2F%2Fmne.discourse.group%2F -.. _Discourse: https://mne.discourse.group/ - -.. |Codecov| image:: https://img.shields.io/codecov/c/github/mne-tools/mne-python?label=Coverage -.. _Codecov: https://codecov.io/gh/mne-tools/mne-python - -.. |Bandit| image:: https://img.shields.io/badge/security-bandit-yellow.svg -.. _Bandit: https://github.com/PyCQA/bandit - -.. |OpenSSF| image:: https://www.bestpractices.dev/projects/7783/badge -.. _OpenSSF: https://www.bestpractices.dev/projects/7783 - -.. |MNE| image:: https://mne.tools/stable/_static/mne_logo.svg -.. _MNE: https://mne.tools/dev/ - - MNE-Python ========== -`MNE-Python software`_ is an open-source Python package for exploring, +MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, -machine learning, and statistics. +machine learning, statistics, and more. Documentation ^^^^^^^^^^^^^ -`MNE documentation`_ for MNE-Python is available online. +`Documentation`_ for MNE-Python encompasses installation instructions, tutorials, +and examples for a wide variety of topics, contributing guidelines, and an API +reference. Forum ^^^^^^ -Our user forum is https://mne.discourse.group and is the best place to ask -questions about MNE-Python usage or about the contribution process. It also -includes job opportunities and other announcements. +The `user forum`_ is the best place to ask questions about MNE-Python usage or +the contribution process. The forum also features job opportunities and other +announcements. + +If you find a bug or have an idea for a new feature that should be added to +MNE-Python, please use the +`issue tracker `__ of +our GitHub repository. -Installing MNE-Python -^^^^^^^^^^^^^^^^^^^^^ +Installation +^^^^^^^^^^^^ -To install the latest stable version of MNE-Python, you can use pip_ in a terminal: +To install the latest stable version of MNE-Python with minimal dependencies +only, use pip_ in a terminal: .. code-block:: console $ pip install --upgrade mne -- MNE-Python 0.17 was the last release to support Python 2.7 -- MNE-Python 0.18 requires Python 3.5 or higher -- MNE-Python 0.21 requires Python 3.6 or higher -- MNE-Python 0.24 requires Python 3.7 or higher -- MNE-Python 1.4 requires Python 3.8 or higher +The current MNE-Python release requires Python 3.8 or higher. MNE-Python 0.17 +was the last release to support Python 2.7. -For more complete instructions and more advanced installation methods (e.g. for -the latest development version), see the `installation guide`_. +For more complete instructions, including our standalone installers and more +advanced installation methods, please refer to the `installation guide`_. -Get the latest code -^^^^^^^^^^^^^^^^^^^ +Get the development version +^^^^^^^^^^^^^^^^^^^^^^^^^^^ -To install the latest version of the code using pip_ open a terminal and type: +To install the latest development version of MNE-Python using pip_, open a +terminal and type: .. code-block:: console $ pip install --upgrade git+https://github.com/mne-tools/mne-python@main -To get the latest code using `git `__, open a terminal and type: +To clone the repository with `git `__, open a terminal +and type: .. code-block:: console @@ -93,55 +73,68 @@ Dependencies The minimum required dependencies to run MNE-Python are: -- Python >= 3.8 -- NumPy >= 1.21.2 -- SciPy >= 1.7.1 -- Matplotlib >= 3.5.0 -- pooch >= 1.5 -- tqdm -- Jinja2 -- decorator -- lazy_loader +- `Python `__ ≥ 3.8 +- `NumPy `__ ≥ 1.21.2 +- `SciPy `__ ≥ 1.7.1 +- `Matplotlib `__ ≥ 3.5.0 +- `Pooch `__ ≥ 1.5 +- `tqdm `__ +- `Jinja2 `__ +- `decorator `__ +- `lazy_loader `__ For full functionality, some functions require: -- Scikit-learn >= 1.0 -- joblib >= 0.15 (for parallelization control) -- mne-qt-browser >= 0.1 (for fast raw data visualization) -- Qt5 >= 5.12 via one of the following bindings (for fast raw data visualization and interactive 3D visualization): - - - PyQt6 >= 6.0 - - PySide6 >= 6.0 - - PyQt5 >= 5.12 - - PySide2 >= 5.12 +- `scikit-learn `__ ≥ 1.0 +- `Joblib `__ ≥ 0.15 (for parallelization) +- `mne-qt-browser `__ ≥ 0.1 (for fast raw data visualization) +- `Qt `__ ≥ 5.12 via one of the following bindings (for fast raw data visualization and interactive 3D visualization): + + - `PyQt6 `__ ≥ 6.0 + - `PySide6 `__ ≥ 6.0 + - `PyQt5 `__ ≥ 5.12 + - `PySide2 `__ ≥ 5.12 + +- `Numba `__ ≥ 0.54.0 +- `NiBabel `__ ≥ 3.2.1 +- `OpenMEEG `__ ≥ 2.5.6 +- `pandas `__ ≥ 1.3.2 +- `Picard `__ ≥ 0.3 +- `CuPy `__ ≥ 9.0.0 (for NVIDIA CUDA acceleration) +- `DIPY `__ ≥ 1.4.0 +- `imageio `__ ≥ 2.8.0 +- `PyVista `__ ≥ 0.32 (for 3D visualization) +- `PyVistaQt `__ ≥ 0.4 (for 3D visualization) +- `mffpy `__ ≥ 0.5.7 +- `h5py `__ +- `h5io `__ +- `pymatreader `__ + + +Contributing +^^^^^^^^^^^^ -- Numba >= 0.54.0 -- NiBabel >= 3.2.1 -- OpenMEEG >= 2.5.6 -- Pandas >= 1.3.2 -- Picard >= 0.3 -- CuPy >= 9.0.0 (for NVIDIA CUDA acceleration) -- DIPY >= 1.4.0 -- Imageio >= 2.8.0 -- PyVista >= 0.32 (for 3D visualization) -- pyvistaqt >= 0.4 (for 3D visualization) -- mffpy >= 0.5.7 -- h5py -- h5io -- pymatreader +Please see the `contributing guidelines `__ on our documentation website. -Contributing to MNE-Python -^^^^^^^^^^^^^^^^^^^^^^^^^^ -Please see the documentation on the MNE-Python homepage: +About +^^^^^ -https://mne.tools/dev/install/contributing.html ++---------+------------+----------------+ +| CI | |Codecov|_ | |Bandit|_ | ++---------+------------+----------------+ +| Package | |PyPI|_ | |conda-forge|_ | ++---------+------------+----------------+ +| Docs | |Docs|_ | |Discourse|_ | ++---------+------------+----------------+ +| Meta | |Zenodo|_ | |OpenSSF|_ | ++---------+------------+----------------+ -Licensing -^^^^^^^^^ +License +^^^^^^^ -MNE-Python is **BSD-licenced** (BSD-3-Clause): +MNE-Python is **BSD-licensed** (BSD-3-Clause): This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. @@ -177,7 +170,34 @@ MNE-Python is **BSD-licenced** (BSD-3-Clause): damage.** -.. _MNE-Python software: https://mne.tools/dev/ -.. _MNE documentation: https://mne.tools/dev/overview/index.html +.. _Documentation: https://mne.tools/dev/ +.. _user forum: https://mne.discourse.group .. _installation guide: https://mne.tools/dev/install/index.html .. _pip: https://pip.pypa.io/en/stable/ + +.. |PyPI| image:: https://img.shields.io/pypi/dm/mne.svg?label=PyPI +.. _PyPI: https://pypi.org/project/mne/ + +.. |conda-forge| image:: https://img.shields.io/conda/dn/conda-forge/mne.svg?label=Conda +.. _conda-forge: https://anaconda.org/conda-forge/mne + +.. |Docs| image:: https://img.shields.io/badge/Docs-online-green?label=Documentation +.. _Docs: https://mne.tools/dev/ + +.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.592483.svg +.. _Zenodo: https://doi.org/10.5281/zenodo.592483 + +.. |Discourse| image:: https://img.shields.io/discourse/status?label=Forum&server=https%3A%2F%2Fmne.discourse.group%2F +.. _Discourse: https://mne.discourse.group/ + +.. |Codecov| image:: https://img.shields.io/codecov/c/github/mne-tools/mne-python?label=Coverage +.. _Codecov: https://codecov.io/gh/mne-tools/mne-python + +.. |Bandit| image:: https://img.shields.io/badge/Security-Bandit-yellow.svg +.. _Bandit: https://github.com/PyCQA/bandit + +.. |OpenSSF| image:: https://www.bestpractices.dev/projects/7783/badge +.. _OpenSSF: https://www.bestpractices.dev/projects/7783 + +.. |MNE| image:: https://mne.tools/stable/_static/mne_logo.svg +.. _MNE: https://mne.tools/dev/ From cc377dabd5a4e449d2bc706470a2ed16ea2339fe Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Tue, 7 Nov 2023 11:16:53 -0500 Subject: [PATCH 12/20] ENH: Enable sensor-specific OPM coregistration in mne coreg (#11405) --- .circleci/config.yml | 19 +- doc/api/datasets.rst | 1 + doc/documentation/datasets.rst | 13 ++ examples/datasets/kernel_phantom.py | 103 +++++++++ mne/_fiff/_digitization.py | 3 + mne/bem.py | 5 +- mne/channels/channels.py | 4 + mne/coreg.py | 10 +- mne/data/helmets/Kernel_Flux.fif.gz | Bin 0 -> 493001 bytes mne/data/helmets/Kernel_Flux_ch_pos.txt | 202 ++++++++++++++++++ mne/datasets/__init__.pyi | 2 + mne/datasets/config.py | 8 + mne/datasets/phantom_kernel/__init__.py | 3 + mne/datasets/phantom_kernel/phantom_kernel.py | 32 +++ mne/datasets/utils.py | 2 +- mne/gui/_coreg.py | 59 +++-- mne/surface.py | 79 ++++++- mne/tests/test_transforms.py | 17 ++ mne/transforms.py | 52 +++++ mne/utils/config.py | 1 + mne/utils/docs.py | 26 ++- mne/viz/_3d.py | 93 ++++++-- mne/viz/_brain/_brain.py | 27 +-- mne/viz/backends/_pyvista.py | 2 + mne/viz/tests/test_3d.py | 4 +- mne/viz/utils.py | 4 +- tools/circleci_download.sh | 3 + tutorials/inverse/35_dipole_orientations.py | 4 +- .../inverse/80_brainstorm_phantom_elekta.py | 4 +- tutorials/inverse/90_phantom_4DBTi.py | 4 +- 30 files changed, 690 insertions(+), 96 deletions(-) create mode 100644 examples/datasets/kernel_phantom.py create mode 100644 mne/data/helmets/Kernel_Flux.fif.gz create mode 100644 mne/data/helmets/Kernel_Flux_ch_pos.txt create mode 100644 mne/datasets/phantom_kernel/__init__.py create mode 100644 mne/datasets/phantom_kernel/phantom_kernel.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 6f11c346045..492454e057a 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -223,13 +223,16 @@ jobs: - data-cache-fsaverage - restore_cache: keys: - - data-cache-bst-phantom-ctf + - data-cache-bst-raw - restore_cache: keys: - - data-cache-bst-raw + - data-cache-bst-phantom-ctf - restore_cache: keys: - data-cache-bst-phantom-elekta + - restore_cache: + keys: + - data-cache-bst-phantom-kernel - restore_cache: keys: - data-cache-bst-auditory @@ -368,18 +371,22 @@ jobs: key: data-cache-fsaverage paths: - ~/mne_data/MNE-fsaverage-data # (762 M) - - save_cache: - key: data-cache-bst-phantom-ctf - paths: - - ~/mne_data/MNE-brainstorm-data/bst_phantom_ctf # (177 M) - save_cache: key: data-cache-bst-raw paths: - ~/mne_data/MNE-brainstorm-data/bst_raw # (830 M) + - save_cache: + key: data-cache-bst-phantom-ctf + paths: + - ~/mne_data/MNE-brainstorm-data/bst_phantom_ctf # (177 M) - save_cache: key: data-cache-bst-phantom-elekta paths: - ~/mne_data/MNE-brainstorm-data/bst_phantom_elekta # (1.4 G) + - save_cache: + key: data-cache-bst-phantom-kernel + paths: + - ~/mne_data/MNE-phantom-kernel-data # (362 M) - save_cache: key: data-cache-bst-auditory paths: diff --git a/doc/api/datasets.rst b/doc/api/datasets.rst index b46e3e387a9..5f5e3762044 100644 --- a/doc/api/datasets.rst +++ b/doc/api/datasets.rst @@ -41,6 +41,7 @@ Datasets ucl_opm_auditory.data_path visual_92_categories.data_path phantom_4dbti.data_path + phantom_kernel.data_path refmeg_noise.data_path ssvep.data_path erp_core.data_path diff --git a/doc/documentation/datasets.rst b/doc/documentation/datasets.rst index 348da772e90..ef63a3b139e 100644 --- a/doc/documentation/datasets.rst +++ b/doc/documentation/datasets.rst @@ -293,6 +293,19 @@ the MEG center in La Timone hospital in Marseille. * :ref:`tut-phantom-4Dbti` +Kernel OPM phantom dataset +========================== +:func:`mne.datasets.phantom_kernel.data_path`. + +This dataset was obtained with a Neuromag phantom in a Kernel Flux (720-sensor) +system at ILABS at the University of Washington. Only 7 out of 42 possible modules +were active for testing purposes, yielding 121 channels of data with limited coverage +(mostly occipital and parietal). + +.. topic:: Examples + + * :ref:`ex-kernel-opm-phantom` + OPM === :func:`mne.datasets.opm.data_path` diff --git a/examples/datasets/kernel_phantom.py b/examples/datasets/kernel_phantom.py new file mode 100644 index 00000000000..51c6c847de6 --- /dev/null +++ b/examples/datasets/kernel_phantom.py @@ -0,0 +1,103 @@ +""" +.. _ex-kernel-opm-phantom: + +Kernel OPM phantom data +======================= + +In this dataset, a Neuromag phantom was placed inside the Kernel OPM helmet and +stimulated with 7 modules active (121 channels). Here we show some example traces. +""" + +import numpy as np + +import mne + +data_path = mne.datasets.phantom_kernel.data_path() +fname = data_path / "phantom_32_100nam_raw.fif" +raw = mne.io.read_raw_fif(fname).load_data() +events = mne.find_events(raw, stim_channel="STI101") + +# Bads identified by inspecting averages +raw.info["bads"] = [ + "RC2.bx.ave", + "LC3.bx.ave", + "RC2.by.7", + "RC2.bz.7", + "RC2.bx.4", + "RC2.by.4", + "LC3.bx.5", +] +# Drop the module-average channels +raw.drop_channels([ch_name for ch_name in raw.ch_names if ".ave" in ch_name]) +# Add field correction projectors +raw.add_proj(mne.preprocessing.compute_proj_hfc(raw.info, order=2)) +raw.pick("meg", exclude="bads") +raw.filter(0.5, 40) +epochs = mne.Epochs( + raw, + events, + tmin=-0.1, + tmax=0.25, + decim=5, + preload=True, + baseline=(None, 0), +) +evoked = epochs["17"].average() # a high-SNR dipole for these data +fig = evoked.plot() +t_peak = 0.016 # based on visual inspection of evoked +fig.axes[0].axvline(t_peak, color="k", ls=":", lw=3, zorder=2) + +# %% +# The data covariance has an interesting structure because of densely packed sensors: +cov = mne.compute_covariance(epochs, tmax=-0.01) +mne.viz.plot_cov(cov, raw.info) + +# %% +# So let's be careful and compute rank ahead of time and regularize: + +rank = mne.compute_rank(epochs, tol=1e-3, tol_kind="relative") +cov = mne.compute_covariance(epochs, tmax=-0.01, rank=rank, method="shrunk") +mne.viz.plot_cov(cov, raw.info) + +# %% +# Look at our alignment: + +sphere = mne.make_sphere_model(r0=(0.0, 0.0, 0.0), head_radius=0.08) +trans = mne.transforms.Transform("head", "mri", np.eye(4)) +align_kwargs = dict( + trans=trans, + bem=sphere, + surfaces={"outer_skin": 0.2}, + show_axes=True, +) +mne.viz.plot_alignment( + raw.info, + coord_frame="meg", + meg=dict(sensors=1.0, helmet=0.05), + **align_kwargs, +) + +# %% +# Let's do dipole fits, which are not great because the dev_head_t is approximate and +# the sensor coverage is sparse: + +data = list() +for ii in range(1, 33): + evoked = epochs[str(ii)][1:-1].average().crop(t_peak, t_peak) + data.append(evoked.data[:, 0]) +evoked = mne.EvokedArray(np.array(data).T, evoked.info, tmin=0.0) +del epochs +dip, residual = mne.fit_dipole(evoked, cov, sphere, n_jobs=None) +actual_pos, actual_ori = mne.dipole.get_phantom_dipoles() +actual_amp = np.ones(len(dip)) # fake amp, needed to create Dipole instance +actual_gof = np.ones(len(dip)) # fake GOF, needed to create Dipole instance +dip_true = mne.Dipole(dip.times, actual_pos, actual_amp, actual_ori, actual_gof) + +fig = mne.viz.plot_alignment( + evoked.info, coord_frame="head", meg="sensors", **align_kwargs +) +mne.viz.plot_dipole_locations( + dipoles=dip_true, mode="arrow", color=(0.0, 0.0, 0.0), fig=fig +) +mne.viz.plot_dipole_locations(dipoles=dip, mode="arrow", color=(0.2, 1.0, 0.5), fig=fig) +mne.viz.set_3d_view(figure=fig, azimuth=30, elevation=70, distance=0.4) diff --git a/mne/_fiff/_digitization.py b/mne/_fiff/_digitization.py index 932429d9a11..b8cae6f3f92 100644 --- a/mne/_fiff/_digitization.py +++ b/mne/_fiff/_digitization.py @@ -293,6 +293,7 @@ def _get_data_as_dict_from_dig(dig, exclude_ref_channel=True): # Split up the dig points by category hsp, hpi, elp = list(), list(), list() fids, dig_ch_pos_location = dict(), list() + dig = [] if dig is None else dig for d in dig: if d["kind"] == FIFF.FIFFV_POINT_CARDINAL: @@ -307,6 +308,8 @@ def _get_data_as_dict_from_dig(dig, exclude_ref_channel=True): dig_ch_pos_location.append(d["r"]) dig_coord_frames = set([d["coord_frame"] for d in dig]) + if len(dig_coord_frames) == 0: + dig_coord_frames = set([FIFF.FIFFV_COORD_HEAD]) if len(dig_coord_frames) != 1: raise RuntimeError( "Only single coordinate frame in dig is supported, " diff --git a/mne/bem.py b/mne/bem.py index e7b9704be1e..eaa5b1f2d4f 100644 --- a/mne/bem.py +++ b/mne/bem.py @@ -1751,6 +1751,7 @@ def _add_gamma_multipliers(bem): FIFF.FIFFV_BEM_SURF_ID_SKULL: "outer skull", FIFF.FIFFV_BEM_SURF_ID_HEAD: "outer skin ", FIFF.FIFFV_BEM_SURF_ID_UNKNOWN: "unknown ", + FIFF.FIFFV_MNE_SURF_MEG_HELMET: "MEG helmet ", } _sm_surf_name = { FIFF.FIFFV_BEM_SURF_ID_BRAIN: "brain", @@ -1758,6 +1759,7 @@ def _add_gamma_multipliers(bem): FIFF.FIFFV_BEM_SURF_ID_SKULL: "outer skull", FIFF.FIFFV_BEM_SURF_ID_HEAD: "outer skin ", FIFF.FIFFV_BEM_SURF_ID_UNKNOWN: "unknown ", + FIFF.FIFFV_MNE_SURF_MEG_HELMET: "helmet", } @@ -1850,7 +1852,8 @@ def _write_bem_surfaces_block(fid, surfs): """Write bem surfaces to open file handle.""" for surf in surfs: start_block(fid, FIFF.FIFFB_BEM_SURF) - write_float(fid, FIFF.FIFF_BEM_SIGMA, surf["sigma"]) + if "sigma" in surf: + write_float(fid, FIFF.FIFF_BEM_SIGMA, surf["sigma"]) write_int(fid, FIFF.FIFF_BEM_SURF_ID, surf["id"]) write_int(fid, FIFF.FIFF_MNE_COORD_FRAME, surf["coord_frame"]) write_int(fid, FIFF.FIFF_BEM_SURF_NNODE, surf["np"]) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 6e975e1cade..725aa884493 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -104,6 +104,10 @@ def _get_meg_system(info): system = "ARTEMIS123" have_helmet = False break + elif coil_type == FIFF.FIFFV_COIL_KERNEL_OPM_MAG_GEN1: + system = "Kernel_Flux" + have_helmet = True + break else: system = "unknown" have_helmet = False diff --git a/mne/coreg.py b/mne/coreg.py index c50d3d00274..7591eff1322 100644 --- a/mne/coreg.py +++ b/mne/coreg.py @@ -1550,7 +1550,9 @@ def _setup_bem(self): low_res_path = _find_head_bem(self._subject, self._subjects_dir, high_res=False) if high_res_path is None and low_res_path is None: raise RuntimeError( - "No standard head model was " f"found for subject {self._subject}" + "No standard head model was " + f"found for subject {self._subject} in " + f"{self._subjects_dir}" ) if high_res_path is not None: self._bem_high_res = _read_surface( @@ -1987,9 +1989,9 @@ def fit_fiducials( return self def 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zcKDIyyy?(!6{*M5tvGG_H?}&}686rOLBX&$sz58d#P3f|FV{k(-%=V-aGY(}k;&&y z=hy=5V$T(W#2ufGQk2{Vz74WcQuc}bs| +# +# License: BSD Style. + +from ...utils import verbose +from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc + + +@verbose +def data_path( + path=None, force_update=False, update_path=True, download=True, *, verbose=None +): # noqa: D103 + return _download_mne_dataset( + name="phantom_kernel", + processor="nested_untar", + path=path, + force_update=force_update, + update_path=update_path, + download=download, + ) + + +data_path.__doc__ = _data_path_doc.format( + name="phantom_kernel", conf="MNE_DATASETS_PHANTOM_KERNEL_PATH" +) + + +def get_version(): # noqa: D103 + return _get_version("phantom_kernel") + + +get_version.__doc__ = _version_doc.format(name="phantom_kernel") diff --git a/mne/datasets/utils.py b/mne/datasets/utils.py index 9ff4efd3400..5ee5539ce1b 100644 --- a/mne/datasets/utils.py +++ b/mne/datasets/utils.py @@ -328,7 +328,7 @@ def _download_all_example_data(verbose=True): "refmeg_noise ssvep epilepsy_ecog ucl_opm_auditory eyelink " "erp_core brainstorm.bst_raw brainstorm.bst_auditory " "brainstorm.bst_resting brainstorm.bst_phantom_ctf " - "brainstorm.bst_phantom_elekta" + "brainstorm.bst_phantom_elekta phantom_kernel" ).split(): mod = importlib.import_module(f"mne.datasets.{kind}") data_path_func = getattr(mod, "data_path") diff --git a/mne/gui/_coreg.py b/mne/gui/_coreg.py index e07848db693..2ee787c7c31 100644 --- a/mne/gui/_coreg.py +++ b/mne/gui/_coreg.py @@ -56,7 +56,7 @@ _plot_helmet, _plot_hpi_coils, _plot_mri_fiducials, - _plot_sensors, + _plot_sensors_3d, ) from ..viz.backends._utils import _qt_app_exec, _qt_safe_window from ..viz.utils import safe_event @@ -1092,11 +1092,14 @@ def _update_distance_estimation(self): ) dists = self.coreg.compute_dig_mri_distances() * 1e3 if self._hsp_weight > 0: - value += ( - "\nHSP <-> MRI (mean/min/max): " - f"{np.mean(dists):.2f} " - f"/ {np.min(dists):.2f} / {np.max(dists):.2f} mm" - ) + if len(dists) == 0: + value += "\nNo head shape points found." + else: + value += ( + "\nHSP <-> MRI (mean/min/max): " + f"{np.mean(dists):.2f} " + f"/ {np.min(dists):.2f} / {np.max(dists):.2f} mm" + ) self._forward_widget_command("fit_label", "set_value", value) def _update_parameters(self): @@ -1266,35 +1269,29 @@ def _add_channels(self): plot_types["meg"] = ["sensors"] if self._fnirs_channels: plot_types["fnirs"] = ["sources", "detectors"] - sens_actors = list() - # until opacity can be specified using a dict, we need to iterate - sensor_opacity = dict( + sensor_alpha = dict( eeg=self._defaults["sensor_opacity"], fnirs=self._defaults["sensor_opacity"], meg=0.25, ) - for ch_type, plot_type in plot_types.items(): - picks = pick_types(self._info, ref_meg=False, **{ch_type: True}) - if not (len(picks) and plot_type): - continue - logger.debug(f"Drawing {ch_type} sensors") - these_actors = _plot_sensors( - self._renderer, - self._info, - self._to_cf_t, - picks=picks, - warn_meg=False, - head_surf=self._head_geo, - units="m", - sensor_opacity=sensor_opacity[ch_type], - orient_glyphs=self._orient_glyphs, - scale_by_distance=self._scale_by_distance, - surf=self._head_geo, - check_inside=self._check_inside, - nearest=self._nearest, - **plot_types, - ) - sens_actors.extend(sum(these_actors.values(), list())) + picks = pick_types(self._info, ref_meg=False, meg=True, eeg=True, fnirs=True) + these_actors = _plot_sensors_3d( + self._renderer, + self._info, + self._to_cf_t, + picks=picks, + warn_meg=False, + head_surf=self._head_geo, + units="m", + sensor_alpha=sensor_alpha, + orient_glyphs=self._orient_glyphs, + scale_by_distance=self._scale_by_distance, + surf=self._head_geo, + check_inside=self._check_inside, + nearest=self._nearest, + **plot_types, + ) + sens_actors = sum(these_actors.values(), list()) self._update_actor("sensors", sens_actors) def _add_head_surface(self): diff --git a/mne/surface.py b/mne/surface.py index d0aac3abe0d..93d9f89caca 100644 --- a/mne/surface.py +++ b/mne/surface.py @@ -8,6 +8,7 @@ # Many of the computations in this code were derived from Matti Hämäläinen's # C code. +import json import time import warnings from collections import OrderedDict @@ -15,6 +16,7 @@ from functools import lru_cache, partial from glob import glob from os import path as op +from pathlib import Path import numpy as np from scipy.ndimage import binary_dilation @@ -28,8 +30,11 @@ from .parallel import parallel_func from .transforms import ( Transform, + _angle_between_quats, _cart_to_sph, + _fit_matched_points, _get_trans, + _MatchedDisplacementFieldInterpolator, _pol_to_cart, apply_trans, transform_surface_to, @@ -52,6 +57,9 @@ warn, ) +_helmet_path = Path(__file__).parent / "data" / "helmets" + + ############################################################################### # AUTOMATED SURFACE FINDING @@ -157,8 +165,26 @@ def _get_head_surface(subject, source, subjects_dir, on_defects, raise_error=Tru return surf +# New helmets can be written for example with: +# +# import os.path as op +# import mne +# from mne.io.constants import FIFF +# surf = mne.read_surface('kernel.obj', return_dict=True)[-1] +# surf['rr'] *= 1000 # needs to be in mm +# mne.surface.complete_surface_info(surf, copy=False, do_neighbor_tri=False) +# surf['coord_frame'] = FIFF.FIFFV_COORD_DEVICE +# surfs = mne.bem._surfaces_to_bem( +# [surf], ids=[FIFF.FIFFV_MNE_SURF_MEG_HELMET], sigmas=[1.], +# incomplete='ignore') +# del surfs[0]['sigma'] +# bem_fname = op.join(op.dirname(mne.__file__), 'data', 'helmets', +# 'kernel.fif.gz') +# mne.write_bem_surfaces(bem_fname, surfs, overwrite=True) + + @verbose -def get_meg_helmet_surf(info, trans=None, verbose=None): +def get_meg_helmet_surf(info, trans=None, *, verbose=None): """Load the MEG helmet associated with the MEG sensors. Parameters @@ -186,10 +212,11 @@ def get_meg_helmet_surf(info, trans=None, verbose=None): system, have_helmet = _get_meg_system(info) if have_helmet: logger.info("Getting helmet for system %s" % system) - fname = op.join(op.split(__file__)[0], "data", "helmets", system + ".fif.gz") + fname = _helmet_path / f"{system}.fif.gz" surf = read_bem_surfaces( fname, False, FIFF.FIFFV_MNE_SURF_MEG_HELMET, verbose=False ) + surf = _scale_helmet_to_sensors(system, surf, info) else: rr = np.array( [ @@ -229,6 +256,54 @@ def get_meg_helmet_surf(info, trans=None, verbose=None): return surf +def _scale_helmet_to_sensors(system, surf, info): + fname = _helmet_path / f"{system}_ch_pos.txt" + if not fname.is_file(): + return surf + with open(fname) as fid: + ch_pos_from = json.load(fid) + # find correspondence + fro, to = list(), list() + for key, f_ in ch_pos_from.items(): + t_ = [ch["loc"][:3] for ch in info["chs"] if ch["ch_name"].startswith(key)] + if not len(t_): + continue + fro.append(f_) + to.append(np.mean(t_, axis=0)) + if len(fro) < 4: + logger.info( + "Using untransformed helmet, not enough sensors found to deform to match " + f"acquisition based on sensor positions (got {len(fro)}, need at least 4)" + ) + return surf + fro = np.array(fro, float) + to = np.array(to, float) + delta = np.ptp(surf["rr"], axis=0) * 0.1 # 10% beyond bounds + extrema = np.array([surf["rr"].min(0) - delta, surf["rr"].max(0) + delta]) + interp = _MatchedDisplacementFieldInterpolator(fro, to, extrema=extrema) + new_rr = interp(surf["rr"]) + try: + quat, sc = _fit_matched_points(surf["rr"], new_rr) + except np.linalg.LinAlgError as exc: + logger.info( + f"Using untransformed helmet, deformation using {len(fro)} points " + f"failed ({exc})" + ) + return surf + rot = np.rad2deg(_angle_between_quats(quat[:3])) + tr = 1000 * np.linalg.norm(quat[3:]) + logger.info( + f" Deforming CAD helmet to match {len(fro)} acquisition sensor positions:" + ) + logger.info(f" 1. Affine: {rot:0.1f}°, {tr:0.1f} mm, {sc:0.2f}× scale") + deltas = interp._last_deltas * 1000 + mu, mx = np.mean(deltas), np.max(deltas) + logger.info(f" 2. Nonlinear displacement: " f"mean={mu:0.1f}, max={mx:0.1f} mm") + surf["rr"] = new_rr + complete_surface_info(surf, copy=False, verbose=False) + return surf + + def _reorder_ccw(rrs, tris): """Reorder tris of a convex hull to be wound counter-clockwise.""" # This ensures that rendering with front-/back-face culling works properly diff --git a/mne/tests/test_transforms.py b/mne/tests/test_transforms.py index a09071d571b..36c533a59a0 100644 --- a/mne/tests/test_transforms.py +++ b/mne/tests/test_transforms.py @@ -31,6 +31,7 @@ _find_vector_rotation, _fit_matched_points, _get_trans, + _MatchedDisplacementFieldInterpolator, _pol_to_cart, _quat_real, _quat_to_affine, @@ -632,3 +633,19 @@ def test_volume_registration(): ], atol=0.001, ) + + +def test_displacement_field(): + """Test that our matched point deformation works.""" + to = np.array([[5, 4, 1], [6, 1, 0], [4, -1, 1], [3, 3, 0]], float) + fro = np.array([[0, 2, 2], [2, 2, 1], [2, 0, 2], [0, 0, 1]], float) + interp = _MatchedDisplacementFieldInterpolator(fro, to) + fro_t = interp(fro) + assert_allclose(to, fro_t, atol=1e-12) + # check midpoints (should all be decent) + for a in range(len(to)): + for b in range(a + 1, len(to)): + to_ = np.mean(to[[a, b]], axis=0) + fro_ = np.mean(fro[[a, b]], axis=0) + fro_t = interp(fro_) + assert_allclose(to_, fro_t, atol=1e-12) diff --git a/mne/transforms.py b/mne/transforms.py index f8a4c813025..c25ca958cf9 100644 --- a/mne/transforms.py +++ b/mne/transforms.py @@ -2072,3 +2072,55 @@ def apply_volume_registration_points( info2.set_montage(montage2) # converts to head coordinates return info2, trans2 + + +class _MatchedDisplacementFieldInterpolator: + """Interpolate from matched points using a displacement field in ND. + + For a demo, see + https://gist.github.com/larsoner/fbe32d57996848395854d5e59dff1e10 + and related tests. + """ + + def __init__(self, fro, to, *, extrema=None): + from scipy.interpolate import LinearNDInterpolator + + fro = np.array(fro, float) + to = np.array(to, float) + assert fro.shape == to.shape + assert fro.ndim == 2 + # this restriction is only necessary because it's what + # _fit_matched_points requires + assert fro.shape[1] == 3 + + # Prealign using affine + uniform scaling + self._quat, self._scale = _fit_matched_points(fro, to, scale=True) + trans = _quat_to_affine(self._quat) + trans[:3, :3] *= self._scale + self._affine = trans + fro = apply_trans(trans, fro) + + # Add points at extrema + if extrema is None: + delta = (to.max(axis=0) - to.min(axis=0)) / 2.0 + assert (delta > 0).all() + extrema = np.array([fro.min(axis=0) - delta, fro.max(axis=0) + delta]) + assert extrema.shape == (2, 3) # min, max + self._extrema = np.array(np.meshgrid(*extrema.T)).T.reshape(-1, fro.shape[-1]) + fro_concat = np.concatenate((fro, self._extrema)) + to_concat = np.concatenate((to, self._extrema)) + + # Compute the interpolator (which internally uses Delaunay) + self._interp = LinearNDInterpolator(fro_concat, to_concat) + + def __call__(self, x): + assert x.ndim in (1, 2) and x.shape[-1] == 3 + assert np.isfinite(x).all() + singleton = x.ndim == 1 + x = apply_trans(self._affine, x) + assert np.isfinite(x).all() + out = self._interp(x) + assert np.isfinite(out).all() + self._last_deltas = np.linalg.norm(x - out, axis=1) + out = out[0] if singleton else out + return out diff --git a/mne/utils/config.py b/mne/utils/config.py index 7f6267a4e19..9497749b94d 100644 --- a/mne/utils/config.py +++ b/mne/utils/config.py @@ -164,6 +164,7 @@ def set_memmap_min_size(memmap_min_size): "MNE_DATASETS_KILOWORD_PATH": "str, path for kiloword data", "MNE_DATASETS_FIELDTRIP_CMC_PATH": "str, path for fieldtrip_cmc data", "MNE_DATASETS_PHANTOM_4DBTI_PATH": "str, path for phantom_4dbti data", + "MNE_DATASETS_PHANTOM_KERNEL_PATH": "str, path for phantom_kernel data", "MNE_DATASETS_LIMO_PATH": "str, path for limo data", "MNE_DATASETS_REFMEG_NOISE_PATH": "str, path for refmeg_noise data", "MNE_DATASETS_SSVEP_PATH": "str, path for ssvep data", diff --git a/mne/utils/docs.py b/mne/utils/docs.py index 40308a9074d..7875f83feb0 100644 --- a/mne/utils/docs.py +++ b/mne/utils/docs.py @@ -1159,7 +1159,7 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75): docdict[ "eeg" ] = """ -eeg : bool | str | list +eeg : bool | str | list | dict String options are: - "original" (default; equivalent to ``True``) @@ -1169,8 +1169,11 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75): The EEG locations projected onto the scalp, as is done in forward modeling - Can also be a list of these options, or an empty list (``[]``, - equivalent of ``False``). + Can also be a list of these options, or a dict to specify the alpha values + to use, e.g. ``dict(original=0.2, projected=0.8)``. + + .. versionchanged:: 1.6 + Added support for specifying alpha values as a dict. """ docdict[ @@ -1769,11 +1772,16 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75): docdict[ "fnirs" ] = """ -fnirs : str | list | bool | None +fnirs : str | list | dict | bool | None Can be "channels", "pairs", "detectors", and/or "sources" to show the fNIRS channel locations, optode locations, or line between source-detector pairs, or a combination like ``('pairs', 'channels')``. - True translates to ``('pairs',)``. + True translates to ``('pairs',)``. A dict can also be used to specify + alpha values (but only "channels" and "pairs" will be used), e.g. + ``dict(channels=0.2, pairs=0.7)``. + + .. versionchanged:: 1.6 + Added support for specifying alpha values as a dict. """ docdict[ @@ -2568,11 +2576,15 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75): docdict[ "meg" ] = """ -meg : str | list | bool | None +meg : str | list | dict | bool | None Can be "helmet", "sensors" or "ref" to show the MEG helmet, sensors or reference sensors respectively, or a combination like ``('helmet', 'sensors')`` (same as None, default). True translates to - ``('helmet', 'sensors', 'ref')``. + ``('helmet', 'sensors', 'ref')``. Can also be a dict to specify alpha values, + e.g. ``{"helmet": 0.1, "sensors": 0.8}``. + + .. versionchanged:: 1.6 + Added support for specifying alpha values as a dict. """ docdict[ diff --git a/mne/viz/_3d.py b/mne/viz/_3d.py index ece5798b582..0063aa37bdd 100644 --- a/mne/viz/_3d.py +++ b/mne/viz/_3d.py @@ -638,7 +638,7 @@ def plot_alignment( from ..source_space._source_space import _ensure_src from .backends.renderer import _get_renderer - meg, eeg, fnirs, warn_meg = _handle_sensor_types(meg, eeg, fnirs) + meg, eeg, fnirs, warn_meg, sensor_alpha = _handle_sensor_types(meg, eeg, fnirs) _check_option("interaction", interaction, ["trackball", "terrain"]) info = create_info(1, 1000.0, "misc") if info is None else info @@ -837,7 +837,14 @@ def plot_alignment( # plot helmet if "helmet" in meg and pick_types(info, meg=True).size > 0: - _, _, src_surf = _plot_helmet(renderer, info, to_cf_t, head_mri_t, coord_frame) + _, _, src_surf = _plot_helmet( + renderer, + info, + to_cf_t, + head_mri_t, + coord_frame, + alpha=sensor_alpha["meg_helmet"], + ) # plot surfaces if brain and "lh" not in surfs: # one layer sphere @@ -877,7 +884,7 @@ def plot_alignment( # plot sensors (NB snapshot_brain_montage relies on the last thing being # plotted being the sensors, so we need to do this after the surfaces) if picks.size > 0: - _plot_sensors( + _plot_sensors_3d( renderer, info, to_cf_t, @@ -888,6 +895,7 @@ def plot_alignment( warn_meg, head_surf, "m", + sensor_alpha=sensor_alpha, sensor_colors=sensor_colors, ) @@ -970,28 +978,56 @@ def _handle_sensor_types(meg, eeg, fnirs): if isinstance(fnirs, str): fnirs = [fnirs] + alpha_map = dict( + meg=dict(sensors="meg", helmet="meg_helmet", ref="ref_meg"), + eeg=dict(original="eeg", projected="eeg_projected"), + fnirs=dict(channels="fnirs", pairs="fnirs_pairs"), + ) + sensor_alpha = { + key: 0.25 if key == "meg_helmet" else 0.8 + for ch_dict in alpha_map.values() + for key in ch_dict.values() + } for kind, var in zip(("eeg", "meg", "fnirs"), (eeg, meg, fnirs)): - if not isinstance(var, (list, tuple)) or not all( - isinstance(x, str) for x in var - ): - raise TypeError(f"{kind} must be list or tuple of str, got {type(kind)}") + _validate_type(var, (list, tuple, dict), f"{kind}") + for ix, x in enumerate(var): + which = f"{kind} key {ix}" if isinstance(var, dict) else f"{kind}[{ix}]" + _validate_type(x, str, which) + if isinstance(var, dict) and x in alpha_map[kind]: + alpha = var[x] + _validate_type(alpha, "numeric", f"{kind}[{ix}]") + if not 0 <= alpha <= 1: + raise ValueError( + f"{kind}[{ix}] alpha value must be between 0 and 1, got {alpha}" + ) + sensor_alpha[alpha_map[kind][x]] = alpha + meg, eeg, fnirs = tuple(meg), tuple(eeg), tuple(fnirs) for xi, x in enumerate(meg): _check_option(f"meg[{xi}]", x, ("helmet", "sensors", "ref")) for xi, x in enumerate(eeg): _check_option(f"eeg[{xi}]", x, ("original", "projected")) for xi, x in enumerate(fnirs): _check_option(f"fnirs[{xi}]", x, ("channels", "pairs", "sources", "detectors")) - return meg, eeg, fnirs, warn_meg + # Add these for our True-only options, too -- eventually should support dict. + sensor_alpha.update( + seeg=0.8, + ecog=0.8, + source=sensor_alpha["fnirs"], + detector=sensor_alpha["fnirs"], + ) + return meg, eeg, fnirs, warn_meg, sensor_alpha @verbose def _ch_pos_in_coord_frame(info, to_cf_t, warn_meg=True, verbose=None): """Transform positions from head/device/mri to a coordinate frame.""" from ..forward import _create_meg_coils + from ..forward._make_forward import _read_coil_defs chs = dict(ch_pos=dict(), sources=dict(), detectors=dict()) unknown_chs = list() # prepare for chs with unknown coordinate frame type_counts = dict() + coilset = _read_coil_defs(verbose=False) for idx in range(info["nchan"]): ch_type = channel_type(info, idx) if ch_type in type_counts: @@ -1010,9 +1046,13 @@ def _ch_pos_in_coord_frame(info, to_cf_t, warn_meg=True, verbose=None): # example, a straight line / 1D geometry) this_coil = [info["chs"][idx]] try: - coil = _create_meg_coils(this_coil, acc="accurate")[0] + coil = _create_meg_coils( + this_coil, acc="accurate", coilset=coilset + )[0] except RuntimeError: # we don't have an accurate one - coil = _create_meg_coils(this_coil, acc="normal")[0] + coil = _create_meg_coils(this_coil, acc="normal", coilset=coilset)[ + 0 + ] # store verts as ch_coord ch_coord, triangles = _sensor_shape(coil) ch_coord = apply_trans(coil_trans, ch_coord) @@ -1071,14 +1111,22 @@ def _plot_head_surface( def _plot_helmet( - renderer, info, to_cf_t, head_mri_t, coord_frame, alpha=0.25, color=None + renderer, + info, + to_cf_t, + head_mri_t, + coord_frame, + *, + alpha=0.25, + scale=1.0, ): - color = DEFAULTS["coreg"]["helmet_color"] if color is None else color + color = DEFAULTS["coreg"]["helmet_color"] src_surf = get_meg_helmet_surf(info, head_mri_t) assert src_surf["coord_frame"] == FIFF.FIFFV_COORD_MRI - src_surf = transform_surface_to( - src_surf, coord_frame, [to_cf_t["mri"], to_cf_t["head"]], copy=True - ) + if to_cf_t is not None: + src_surf = transform_surface_to( + src_surf, coord_frame, [to_cf_t["mri"], to_cf_t["head"]], copy=True + ) actor, dst_surf = renderer.surface( surface=src_surf, color=color, opacity=alpha, backface_culling=False ) @@ -1404,7 +1452,7 @@ def _plot_forward(renderer, fwd, fwd_trans, fwd_scale=1, scale=1.5e-3, alpha=1): return actors -def _plot_sensors( +def _plot_sensors_3d( renderer, info, to_cf_t, @@ -1415,7 +1463,7 @@ def _plot_sensors( warn_meg, head_surf, units, - sensor_opacity=0.8, + sensor_alpha, orient_glyphs=False, scale_by_distance=False, project_points=False, @@ -1461,6 +1509,7 @@ def _plot_sensors( origin=sources[ch_name][np.newaxis] * unit_scalar, destination=detectors[ch_name][np.newaxis] * unit_scalar, radius=0.001 * unit_scalar, + opacity=sensor_alpha["fnirs_pairs"], ) actors[ch_type].append(actor) del ch_type @@ -1485,6 +1534,7 @@ def _plot_sensors( sensor_colors = dict() assert isinstance(sensor_colors, dict) for ch_type, sens_loc in locs.items(): + logger.debug(f"Drawing {ch_type} sensors") assert len(sens_loc) # should be guaranteed above colors = to_rgba_array(sensor_colors.get(ch_type, defaults[ch_type + "_color"])) _check_option( @@ -1493,6 +1543,7 @@ def _plot_sensors( (len(sens_loc), 1), ) scale = defaults[ch_type + "_scale"] * unit_scalar + this_alpha = sensor_alpha[ch_type] if isinstance(sens_loc[0], dict): # meg coil if len(colors) == 1: colors = [colors[0]] * len(sens_loc) @@ -1500,7 +1551,7 @@ def _plot_sensors( actor, _ = renderer.surface( surface=surface, color=color[:3], - opacity=sensor_opacity * color[3], + opacity=this_alpha * color[3], backface_culling=False, # visible from all sides ) actors[ch_type].append(actor) @@ -1514,7 +1565,7 @@ def _plot_sensors( loc=sens_loc[mask] * unit_scalar, color=colors[0, :3], scale=scale, - opacity=sensor_opacity * colors[0, 3], + opacity=this_alpha * colors[0, 3], orient_glyphs=orient_glyphs, scale_by_distance=scale_by_distance, project_points=project_points, @@ -1533,7 +1584,7 @@ def _plot_sensors( loc=loc * unit_scalar, color=color[:3], scale=scale, - opacity=sensor_opacity * color[3], + opacity=this_alpha * color[3], orient_glyphs=orient_glyphs, scale_by_distance=scale_by_distance, project_points=project_points, @@ -1558,7 +1609,7 @@ def _plot_sensors( color=defaults["eegp_color"], mode="cylinder", scale=defaults["eegp_scale"] * unit_scalar, - opacity=0.6, + opacity=sensor_alpha["eeg_projected"], glyph_height=defaults["eegp_height"], glyph_center=(0.0, -defaults["eegp_height"] / 2.0, 0), glyph_resolution=20, diff --git a/mne/viz/_brain/_brain.py b/mne/viz/_brain/_brain.py index 80c9d313924..6d5719186fd 100644 --- a/mne/viz/_brain/_brain.py +++ b/mne/viz/_brain/_brain.py @@ -32,7 +32,7 @@ vertex_to_mni, ) from ...defaults import DEFAULTS, _handle_default -from ...surface import _marching_cubes, _mesh_borders, get_meg_helmet_surf, mesh_edges +from ...surface import _marching_cubes, _mesh_borders, mesh_edges from ...transforms import ( Transform, _frame_to_str, @@ -62,7 +62,8 @@ _handle_sensor_types, _handle_time, _plot_forward, - _plot_sensors, + _plot_helmet, + _plot_sensors_3d, _process_clim, ) from .._3d_overlay import _LayeredMesh @@ -2791,7 +2792,7 @@ def add_sensors( from ...preprocessing.ieeg._projection import _project_sensors_onto_inflated _validate_type(info, Info, "info") - meg, eeg, fnirs, warn_meg = _handle_sensor_types(meg, eeg, fnirs) + meg, eeg, fnirs, warn_meg, sensor_alpha = _handle_sensor_types(meg, eeg, fnirs) picks = pick_types( info, meg=("sensors" in meg), @@ -2825,7 +2826,7 @@ def add_sensors( # Do the main plotting for _ in self._iter_views("vol"): if picks.size > 0: - sensors_actors = _plot_sensors( + sensors_actors = _plot_sensors_3d( self._renderer, info, to_cf_t, @@ -2836,6 +2837,7 @@ def add_sensors( warn_meg, head_surf, self._units, + sensor_alpha=sensor_alpha, sensor_colors=sensor_colors, ) # sensors_actors can still be None @@ -2844,15 +2846,14 @@ def add_sensors( self._add_actor(item, actor) if "helmet" in meg and pick_types(info, meg=True).size > 0: - surf = get_meg_helmet_surf(info, head_mri_t) - verts = surf["rr"] * (1 if self._units == "m" else 1e3) - actor, _ = self._renderer.mesh( - *verts.T, - surf["tris"], - color=DEFAULTS["coreg"]["helmet_color"], - opacity=0.25, - reset_camera=False, - render=False, + actor, _, _ = _plot_helmet( + self._renderer, + info, + to_cf_t, + head_mri_t, + "mri", + alpha=sensor_alpha["meg_helmet"], + scale=1 if self._units == "m" else 1e3, ) self._add_actor("helmet", actor) diff --git a/mne/viz/backends/_pyvista.py b/mne/viz/backends/_pyvista.py index 5cb89179ef5..1b4bfe6c37b 100644 --- a/mne/viz/backends/_pyvista.py +++ b/mne/viz/backends/_pyvista.py @@ -599,6 +599,7 @@ def tube( colormap="RdBu", normalized_colormap=False, reverse_lut=False, + opacity=None, ): with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) @@ -622,6 +623,7 @@ def tube( show_scalar_bar=False, cmap=cmap, smooth_shading=self.smooth_shading, + opacity=opacity, ) return actor, tube diff --git a/mne/viz/tests/test_3d.py b/mne/viz/tests/test_3d.py index 8b02b8e228f..2c763275805 100644 --- a/mne/viz/tests/test_3d.py +++ b/mne/viz/tests/test_3d.py @@ -277,10 +277,10 @@ def test_plot_alignment_meg(renderer, system): assert system == "KIT" this_info = read_raw_kit(sqd_fname).info - meg = ["helmet", "sensors"] + meg = {"helmet": 0.1, "sensors": 0.2} sensor_colors = "k" # should be upsampled to correct shape if system == "KIT": - meg.append("ref") + meg["ref"] = 0.3 with pytest.raises(TypeError, match="instance of dict"): plot_alignment(this_info, meg=meg, sensor_colors=sensor_colors) sensor_colors = dict(meg=sensor_colors) diff --git a/mne/viz/utils.py b/mne/viz/utils.py index e9c36281bae..cfdf5aa62a6 100644 --- a/mne/viz/utils.py +++ b/mne/viz/utils.py @@ -1166,7 +1166,7 @@ def plot_sensors( colors[pick_idx] = color_vals[ind] break title = "Sensor positions (%s)" % ch_type if title is None else title - fig = _plot_sensors( + fig = _plot_sensors_2d( pos, info, picks, @@ -1222,7 +1222,7 @@ def _close_event(event, fig): fig.lasso.disconnect() -def _plot_sensors( +def _plot_sensors_2d( pos, info, picks, diff --git a/tools/circleci_download.sh b/tools/circleci_download.sh index 9cf5124fd53..d2dff2eb499 100755 --- a/tools/circleci_download.sh +++ b/tools/circleci_download.sh @@ -58,6 +58,9 @@ else if [[ $(cat $FNAME | grep -x ".*brainstorm.*bst_phantom_elekta.*" | wc -l) -gt 0 ]]; then python -c "import mne; print(mne.datasets.brainstorm.bst_phantom_elekta.data_path(update_path=True, accept=True))"; fi; + if [[ $(cat $FNAME | grep -x ".*datasets.*phantom_kernel.*" | wc -l) -gt 0 ]]; then + python -c "import mne; print(mne.datasets.phantom_kernel.data_path(update_path=True))"; + fi; if [[ $(cat $FNAME | grep -x ".*datasets.*hcp_mmp_parcellation.*" | wc -l) -gt 0 ]]; then python -c "import mne; print(mne.datasets.sample.data_path(update_path=True))"; python -c "import mne; print(mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=mne.datasets.sample.data_path() / 'subjects', accept=True))"; diff --git a/tutorials/inverse/35_dipole_orientations.py b/tutorials/inverse/35_dipole_orientations.py index bffc5ad1fc0..26a9b5713c3 100644 --- a/tutorials/inverse/35_dipole_orientations.py +++ b/tutorials/inverse/35_dipole_orientations.py @@ -54,8 +54,8 @@ dip_times = [0] white = (1.0, 1.0, 1.0) # RGB values for a white color -actual_amp = np.ones(dip_len) # misc amp to create Dipole instance -actual_gof = np.ones(dip_len) # misc GOF to create Dipole instance +actual_amp = np.ones(dip_len) # fake amp, needed to create Dipole instance +actual_gof = np.ones(dip_len) # fake GOF, needed to create Dipole instance dipoles = mne.Dipole(dip_times, dip_pos, actual_amp, dip_ori, actual_gof) trans = mne.read_trans(trans_fname) diff --git a/tutorials/inverse/80_brainstorm_phantom_elekta.py b/tutorials/inverse/80_brainstorm_phantom_elekta.py index 2da04a19c0d..40585254aea 100644 --- a/tutorials/inverse/80_brainstorm_phantom_elekta.py +++ b/tutorials/inverse/80_brainstorm_phantom_elekta.py @@ -169,8 +169,8 @@ # Let's plot the positions and the orientations of the actual and the estimated # dipoles -actual_amp = np.ones(len(dip)) # misc amp to create Dipole instance -actual_gof = np.ones(len(dip)) # misc GOF to create Dipole instance +actual_amp = np.ones(len(dip)) # fake amp, needed to create Dipole instance +actual_gof = np.ones(len(dip)) # fake GOF, needed to create Dipole instance dip_true = mne.Dipole(dip.times, actual_pos, actual_amp, actual_ori, actual_gof) fig = mne.viz.plot_alignment( diff --git a/tutorials/inverse/90_phantom_4DBTi.py b/tutorials/inverse/90_phantom_4DBTi.py index 12b4643049f..1efc932dab5 100644 --- a/tutorials/inverse/90_phantom_4DBTi.py +++ b/tutorials/inverse/90_phantom_4DBTi.py @@ -69,8 +69,8 @@ # %% # Plot the dipoles in 3D -actual_amp = np.ones(len(dip)) # misc amp to create Dipole instance -actual_gof = np.ones(len(dip)) # misc GOF to create Dipole instance +actual_amp = np.ones(len(dip)) # fake amp, needed to create Dipole instance +actual_gof = np.ones(len(dip)) # fake GOF, needed to create Dipole instance dip = mne.Dipole(dip.times, pos, actual_amp, ori, actual_gof) dip_true = mne.Dipole(dip.times, actual_pos, actual_amp, ori, actual_gof) From b55df6397459245d280ffed9fce27ed3e808187c Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Tue, 7 Nov 2023 13:19:15 -0500 Subject: [PATCH 13/20] ENH: Improve Covariance.__repr__ (#12181) --- doc/changes/devel.rst | 1 + mne/cov.py | 12 +++++------- mne/tests/test_cov.py | 2 ++ mne/utils/__init__.pyi | 2 ++ mne/utils/numerics.py | 6 ++++++ 5 files changed, 16 insertions(+), 7 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index 8d02c056445..d11bb3ad6b5 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -38,6 +38,7 @@ Enhancements - Add support for writing forward solutions to HDF5 and convenience function :meth:`mne.Forward.save` (:gh:`12036` by `Eric Larson`_) - Refactored internals of :func:`mne.read_annotations` (:gh:`11964` by `Paul Roujansky`_) - Add support for drawing MEG sensors in :ref:`mne coreg` (:gh:`12098` by `Eric Larson`_) +- Improve string representation of :class:`mne.Covariance` (:gh:`12181` by `Eric Larson`_) - Add ``check_version=True`` to :ref:`mne sys_info` to check for a new release on GitHub (:gh:`12146` by `Eric Larson`_) - Bad channels are now colored gray in addition to being dashed when spatial colors are used in :func:`mne.viz.plot_evoked` and related functions (:gh:`12142` by `Eric Larson`_) - By default MNE-Python creates matplotlib figures with ``layout='constrained'`` rather than the default ``layout='tight'`` (:gh:`12050`, :gh:`12103` by `Mathieu Scheltienne`_ and `Eric Larson`_) diff --git a/mne/cov.py b/mne/cov.py index 376cd6a8a59..64d82492023 100644 --- a/mne/cov.py +++ b/mne/cov.py @@ -60,6 +60,7 @@ ) from .rank import compute_rank from .utils import ( + _array_repr, _check_fname, _check_on_missing, _check_option, @@ -273,13 +274,10 @@ def _get_square(self): return np.diag(self.data) if self["diag"] else self.data.copy() def __repr__(self): # noqa: D105 - if self.data.ndim == 2: - s = "size : %s x %s" % self.data.shape - else: # ndim == 1 - s = "diagonal : %s" % self.data.size - s += ", n_samples : %s" % self.nfree - s += ", data : %s" % self.data - return "" % s + s = "" + return s def __add__(self, cov): """Add Covariance taking into account number of degrees of freedom.""" diff --git a/mne/tests/test_cov.py b/mne/tests/test_cov.py index f7dd2ffce9e..dcbe1b30ad9 100644 --- a/mne/tests/test_cov.py +++ b/mne/tests/test_cov.py @@ -250,6 +250,8 @@ def test_io_cov(tmp_path): assert_equal(cov["method"], cov2["method"]) assert_equal(cov["loglik"], cov2["loglik"]) assert "Covariance" in repr(cov) + assert "range :" in repr(cov) + assert "\n" not in repr(cov) cov2 = read_cov(cov_gz_fname) assert_array_almost_equal(cov.data, cov2.data) diff --git a/mne/utils/__init__.pyi b/mne/utils/__init__.pyi index b2c611f7d1c..42694921f00 100644 --- a/mne/utils/__init__.pyi +++ b/mne/utils/__init__.pyi @@ -18,6 +18,7 @@ __all__ = [ "_apply_scaling_cov", "_arange_div", "_array_equal_nan", + "_array_repr", "_assert_no_instances", "_auto_weakref", "_build_data_frame", @@ -347,6 +348,7 @@ from .numerics import ( _apply_scaling_cov, _arange_div, _array_equal_nan, + _array_repr, _cal_to_julian, _check_dt, _compute_row_norms, diff --git a/mne/utils/numerics.py b/mne/utils/numerics.py index 44cf8210771..fa78f24bb7d 100644 --- a/mne/utils/numerics.py +++ b/mne/utils/numerics.py @@ -1152,3 +1152,9 @@ def cache_fun(*args): return cache_fun return dec + + +def _array_repr(x): + """Produce compact info about float ndarray x.""" + assert isinstance(x, np.ndarray), type(x) + return f"shape : {x.shape}, range : [{np.nanmin(x):+0.2g}, {np.nanmax(x):+0.2g}]" From ffbce015ed8e6c069e00d68114079251781f7fb8 Mon Sep 17 00:00:00 2001 From: Clemens Brunner Date: Tue, 7 Nov 2023 22:42:55 +0100 Subject: [PATCH 14/20] Tweak logo for dark mode (#12176) Co-authored-by: Eric Larson --- doc/_static/mne_logo.svg | 1571 +++++++++++------------ doc/_static/mne_logo_dark.svg | 1571 +++++++++++------------ doc/_static/mne_logo_gray.svg | 783 +++++++++++ doc/_static/mne_logo_small.svg | 12 +- doc/sphinxext/gen_commands.py | 4 +- logo/generate_mne_logos.py | 112 +- mne/icons/mne_default_icon.png | Bin 10369 -> 12710 bytes mne/icons/mne_splash.png | Bin 22283 -> 31518 bytes mne/utils/config.py | 2 +- mne/viz/backends/_utils.py | 2 +- tools/check_mne_location.py | 2 +- tools/generate_codemeta.py | 2 +- tutorials/evoked/20_visualize_evoked.py | 2 +- 13 files changed, 2356 insertions(+), 1707 deletions(-) create mode 100644 doc/_static/mne_logo_gray.svg diff --git a/doc/_static/mne_logo.svg b/doc/_static/mne_logo.svg index 470b72f0ab6..dc9ff39d029 100644 --- a/doc/_static/mne_logo.svg +++ b/doc/_static/mne_logo.svg @@ -1,16 +1,16 @@ - + - 2022-04-13T15:28:22.434101 + 2023-11-07T13:26:54.126105 image/svg+xml - Matplotlib v3.5.1, https://matplotlib.org/ + Matplotlib v3.8.0.dev1998+gc5707d9c79, https://matplotlib.org/ @@ -22,843 +22,760 @@ - + - +" clip-path="url(#p379c9ce79c)" style="fill: #ffffff"/> - + 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rI28UurqLXSf7pkMA+7P6gfunECYAD3AD3JNB/DtFXPzM55xFDLBVVUpQHeA61wbsM/6rfHlHxTS/epC9+eTWoXrD72722DzmB3/m42E8FkZgxnYMFc6D/fMzKlxHIxkjOZU+x3dnKdxDzOQZsq/Pnc2OyD8UsD9rDrh/KyIbHqt0wT/q04nMXk8FMQFGS09qQP0mtus+ax0N2L/5cnexNz8OqH334agnu6P+pJExM7lVu96YIQMjlKuSwQjl7B/rvyND3X9HBuPf2kjGrhxD9rmsjDwlyK7aSc7wrDEL2QsA9mfFAvdnsQN4QAPC32QYb1lzUgXqN7GBdaA+XG9ZmoC9eXNDdrYu9uZjyB6Rr9ZNpPLQyvzwv+uYWI/n1Aw2/x0Z9tfy35HBeJ2yZozkMH8njWZVvX+4aTdkV7vvnM2dzY7IN2B/9vhDa4H7nwVwAXhAH8Lf9O0CYVhry1opdZj+KEawDuSusQH7VW9uwN78DNlX1qHaXWPIHpvHnsWac2oGIzCzv5b/jgw2/x0ZjOdhJMM54zkKWYBWF3vbdz/Yn8nNzgYM2P/0+Kqf/22UAZkZATzwfuEUQfxNVy4olvW3rEdVAunPMlh/l787b/3xGrIbsn/20O1wUYPsM7lVH5SZs1jByakZbP47Muxf239HBuPfMmuGc3SyAC3IrnbPyZDNMIed6blvoJZ7h/vjzllgmRXA31SlG/6drl6EbOfF0lM20N0pVqh+U/a5qAjXW85Kb37A3jwN2T97nAnZZ7KrQ/bRPPYs1pxTMxjBnP1r++/IYPPfkcF4HkYynKOTBXhUDHPubDZQp4ud4V8N491ImWdjFgAP8MHeit3w39R78bKdMytW2cCWQexQ/SaGc2XAPuK95hhO6GJvXobsDPkeGcORxwwbWHNOzVD335Fh/9r+OzIYQT5rRsUchSzgDMjuLvYxGbC/8vlfLddmuLMAeODPhWSFuZ9OVmUY/0ozFz/r+a0kBgirIBWoDvCc04w1M2D/5LvC05D9u4chu0quAmhnz2LNOTWjAmC0v/13Z7D5s2aM5LB+R+zMUcgCdCC72r1mZi5gwB5dR/N5W8vYS1OZATzAD2lP7Iof1Q54xni9sADTk6QE1G9iuk6y1q8CYG/+huzzXobsUTWc9ACiANlH85iBA2vOqRls/jsy7G//nf7OWJvBnqOQ5Xns6zJnciOysyF79nNNZB3N53ItY8D9z8DfygbHihAe+H4BZK9rVTFBS2udFIH6TWzXaHW43rJW+58J2JtfpJche1QNmZ301R+4FKB+JbjhjHUZ9rd/Jf8dGRX+jkcyRnIqfQ+pZKl0sbd9dTIzcwED9sg6ms9wLTHA/VlsAB7QhfCP8pgay3otZZj+KDawDuSurQH7Ve8Vnu5iv+aTfzNoyM6byf6AfnrOqRk+htr+jGDU/s6IzGH+HlLIMmRfk5mZC8xBdoYxMQzPVHefsOfNNcD9RdAvscDhdwupBuKB6xcXy9pb1hVVAemPYoTqQP5aV4LrLeNcwN78Ir0M2aNrOOlBxJA9L6sKEGLMYIRa9rf/yf47Mhg/i0YyRnJYv4N2Z43em6hAdqWmipnM2VxAv4ud4Xkqqo7m81J7gPuzXhXDBIIrgfhnGcxbmcqGurvECtVvyj4Pu9fHgP2VZ3y9huxr6zBk581UgPrMMKAKrGHMsL/9T/bfkXHiMYxkjOQwfwftzDJkj82byczMNWDneJ66+1zSzx+BWVCZtQv+UZVB/LN6L2bG82WtVTa0zRY7VAc4zlFFuN5ytAB7863fxd68Ijxq3BSe9jCi8LDHnnV6DmOGj8H+O/0ZoSjT+uzw35HB+rnN+t0wmrM7awTKqgB2tczZXAN2jmepu8+Q/uxwZwXwAC/U/XQSK8L4V4r4Y2A9v9XEAGEVpADUb2I5pxlrpt693vxX+fIC9uZXq4u9eeTXoQa7s3KrjoxhBwKMUOjUDPvb3/48/jsyWL+HWL9/duaMZrmLvUbmTZlz2A3YY+toPv98HynDAuCB9wfODGoN468rCxruvn5Y4OjJUoLpNzFdN1nrV6F7vfmv8j1jTEzzivLJvynLHldjyM6Vx57FCI5HMkZyGDPsb3/76/rvyDj585Q9ZzRrJ2RX6ihXywQM2AsC9uf/qn+G+6sDygbHSt3wj7pycWSv7QliApnWvBRh+k2M16LhekTGKl8D9n4fjq56Q/Z9uYbs81msOadm+Bhy/ZVrt3++f5UM1u+FijlA7VExSoB9NvdkwM4C15tPhMelWmJemqoC4QENEP8oQ3nL0oboz2KE6kDuGu9cE9Xu9ebNDdibX03IznCTaMi+PlMhjxkKsOYwZlQAaPaP81eu/UT/HRkVjmEkwzlNlbvYTxoTY8CeW0NUHc1nqJYY4P5KTKNoHvVpwdVg/E1XTz7LObAsoBZEfxYrVAfOAestT7d7vXkbsI/5yHU/LKvBkL1GHjMUYM05NcP+9t/hfaL/jowKxzCSMZLD+t0zmgO4iz0yLyvTgD23BqY6AOC/K4H7s94VzQSBq3TFv1PvhcN0bixuVYbnr8QM1G/KPieG6yP+ZwH25hflU+cGLQt0Z2ZXf/BjBgO7slhBB+NxsB2D/e0/6s9W+44MNv+TM9hzAH7IrnKflZU5A9iBOciuDtizn5l++8zX8uZa+PlfkVlQmXEczbMqdsVf0cyFx3YOrc/KhrNsUgDqAM95qwjXW85K7zXHYMDe6xPhoX3DasjOk8eexQi3qmSw+a/OUF8f++d478pQ9z85gz3Ho2Ly82YygbwuduXnhYj8iBqi6gAuXwf3Dvfn4jNBsgKEv+nbSa8M5D9pFUxivQ5WiwWsVpAKTH8Uy/nPWLsKcL35awD25sk3JqZ5cdyoNR/tm1ZDdp489ixGOD2SMZJzIkSzf463/e0/639yBnuOu9hj8rIyDdhz8iNqiKpj4hp4P1Lm1cGxQXiAH8AayMeKBTxanFKE6TcxXdtZ67hzDQzYHz3rAvbmE+GRf9OY8YCSma3yAMg8xqVaDmMG4w8S9s/zV67d/nr+OzJYvw9GciqOiql8z5ORBxiwq+ZH1HDT7Kigm/7BP30z3Jm64G9SBfE3XbmwGNbZsjKlDNIfxQTVbzJcj8o4E7A3v0ivfLB999G+cTRkr5FXCXbsyjkRctm/rr9y7faP99+Rwfp9wPqdA9QdFaNyfzWTacCumR9RAxAK11/913MvTWXrgn+UOoh/1NWLkWXtLeuKqkD0RzEC9Zsy13v3uqh2rzfvFZ61AXvzifDIh/2njYqZyVV5CGQGBJVyGDPY/HdkGCTH+CvXbv94/x0ZjJ+hIxk7czwqRjMP2PsDyaOy7tNnsxnyI2oAlgP2Z80B91dihvDA55OkCOMf1XsRM50XS1sV4fk7Gap/VjW43jIM2OO8uMbXGLIbsrPkMWexAg/GDMbzoeyvXDubv3LtJ/rvyGD8DB3J2JlTcVSMyr3VTKZiF3s24FbPvykCsE9cA/HA/ZXeLTYb8K0M419p9I+A7bxZMWKAtdlihumPYjhXGWtluP7O14C9z0e/Q8OQvUYee1YVsMJ4HGzHcJK/cu29/sq1n+i/I4Px820kYySHGbAD+9ZtNEspz4B9b/ZsflQNm7vXv+pv4Ad/4580mMzeDf+obxdARSD/TtHQiPWcs4oBtipKBabfxHKeq4L1lrP22E4E7M2PZ0xM89G/iczMN2SPzWPPOhWsMK6V/eP8lWtf7a9c+4n+OzJO/75xF/tcVkbeaYBd/bkkogYgvXv9l96syc+//+OfIUwQHuCHsgby42IBi5au1GD6TUzXftYaGq5/842v24D9ikd+HYpdMhkPDwoPg+xZjAC8Sob9dfyVa+/1V67d/s5YkcMM2Xfe5yjcU92UMYfdgD0nH5CA66/0fqQME4QHPh8UO4wHrl9kBvOW1aQK0R/FBNRvylzXKnC9ZazyPQewN68IDw7gr3wzq/YAofBAyJ7F2lnICHDYYJr94/yVa+/1V67d/hwZlb5rmAF7289ZzzJg35PNkC8K11+pb4Y7G4S/SR3GP6rn4mRYe8u6qgoA/VmMQB3IX+ud66IM15u3AfuYj7vYZ/OVRsXsznTWvoyRHMYM++v4K9fe669cu/3z/VkzRnOYIbsC9FYZE3MaYFd+FrmJBbAveE6df2nquwVmgcGVYPyzRi5ulvNiaSob5O4WK1C/Kft87F4fw/VXntF+fHC9eXHcRCnf1Ko9RKg8EDI/WFfKYfyxgO0Y7J/jvdpfufbV/v5ciPevlFENsCtkzeQZsK/Pnc2OyC8M119pHrj3BLPB3m+LrA7kX2n2D4TtHFrXlQ1n2cQO0wGec1YRrLecld5rjsGAvcejRh2KN/QK0Ht3XiX4vSvHMM3+u7zZ/F27/XdmMH52jmQA9SC7AfufygDsquNhsuE6MA/YSUbD/OvRXcs64P5KnwpkBLlXTkpFKP9JO0AY47WwUixQtYoUQPqjWM5/1rpVgOvNn797vXkasH/30e0cycpWGRUzmseexZpzaob94/xde46/cu0n+u/IYPyhADBgV8kCDNhX585mR+RX6l4Pemb++VVMJjxmH03zToby8WIBkBaP1CD6o9iu5+pgvWWt9tfoXm+edQF784nw0L65dRe7fh7zg3wFULQjw/5x/q49x1+5dvvH+7NmAP1QjxmwV84yYF+bm51tuP7K5w/9fNsgHRardcW/Us9FkL3elrVDygD9WWxAHchd30pgvWWcCdebX6RXHcAeUYe72NdmKjyIGrJzwagdGfbP8V7tz1Q707rYX89/R4a72GveawD7Abtaw0lm7mw2wDEahuU5sPlc0veRMu+MGMBwBRj/rN6LiOE8WGerEjx/JUagftMpYL3l7chYd0wG7KM+ER75N3iqHSyG7HFZzB3zrMCA8TjYjkHZ37XH+DOtS6+/cu07/HdkMB4DUKuL3YD9t9zBzp0LcHSvszwDNp9hjc9wZwbxwPfFVQXyzxo9+SznyeJQdWj+Tsww/absc5OxRobrrzzj6zVgX1eH8pgapYeZauB7NGcki/V4GI+D7RiY/F17jr9rt/+oP2sGaxc7O/jefc+26zzdpHRPqpoLuHs9uo7m8w+w4qWp7CD+plOA/DtFXUhs5/UUZUNYBSnAdIDnXFYF6y1HC643X97u9ebHA9ibj34XxUk3+Apd7KN5zA/ZjHC6SsZJ/q49x5+pdqZ1sX+8/64Md7Fzw3xAZ0zM7vvDmczZ3Nlsd6/H1tF83tYSD9zfF/FarMD2ygmsDuWviAUW9ujxmlOs/2SpQPRnMV1nlcF6yzJcb56RXnH1sQD25pFbg7vY12Yasu/LOTXjJH/XHuPPtC69/sq12z/efySjUhd7xXsMwIB9RWZmLpDfvX4QXH+lH/yNf1LB8acDZ4XxN11dbIN5LjHBT0sXoj+K7ZrKXNMKXev3jFW+Buz9Phw3Wu5i35Or0PXFCr935TBmMI5WUPZ37TH+Xpccf+Xad2W4i71OzmiWAXt8ZmYuoA/Ymf4VdUAtPx+NskGxMox/VO+Jyl53y5pRBYD+LDagDuSvc5Wu9ea/0psbrjc/A/ZVdSh2sc/kqjxIsT8Asz7UM2YYsud5r/Znqp1pXXr9Xbv9d2ac3sXOfn9hwB6fmZmrDtebR41nvrvPL30eKfMqlAUGf1tUJSD/rJGTzXJerBrKhrk7xQjTb8o+D7vXRrlrvXnzw/XmacC+qo7TAPtMblXIztwxf2qG/XX8mWr3usT4K9du/2uq0sVe8f7CgL1G5k0zgN1wPbaO5vNV/TPcPxXHBH0rA/lXirhomM6fNaZsQMskZpD+KIZzVhGst5yV3muO4YTu9eZV44bLgH19pkIe88M2K6Rgg0b2j/Nnqt3rEuPv2nX8d2Ts6GKv9B25M8eAPTZvJnM2V717vcqz3t1nSLEvTVWB8cD1BasG5j9pN/hjuyaixQBSq0kFoj+K5TrIWLsKYL35nwfXm1+UT+qNzpOH9s3nKZ087F1mO7MYYUuVDPvneK/2V/Ve7c9UO9O62P+aGLvYK30Pj+YYsMfmzWTO5ip3r1eC68HPv7HA/ULgSzGDV4P5dWIBkVaeFAH6o9iu4az13LkOhuuPnnW715tPhIf2Dai72HnymB+4WXPcKZ/r79pj/E9ZF6Y17/VXrn2H/0iGATtnDrBvTj6w97iUGjxmMgHDdYZnvKg6ms/bWn7wN/Jh8bfFYgbyN/WerOw1t6xoqcPzZ7HB9Jsy19lg/ar3Ck93r1/z0b4JPQWwj2YqAP1KD/eMGfbX8Weq/ZR18Zrbf9S/ypiYSt/BwN4OdoXmh5m8mczZ3CzAnv1cw1JDRB3No7uWn6/hDGC4ApB/1swJZzgnVk1Vg+bvxArTgfxzsHttdhyv4Xqkl/RNT3gdpwH2mVyFBznmh27WHMYM++d4r/Znql3Vu9efac17/ZVr3+EPrO9iP/l7azTHgD0/byYTOLt7PTs/ooa7T8gz7/eRMu8KZoK+VxZDEcq/U8RFxHT+rDllA1oWMUP0RzGcr4y1Mlh/51sfrjevCI/8mzAD9vWZCnmsEGEkhzGDEbgw+bv2GH9V717/U9bc/t916pgY1u9fwICdIW8m03A9Lz+ihrvPfC1vroXxGe7sXfHPurqIlcD8J2VAP8brIlIMILWaVCD6o1iug6pQveWsPbYT4Xrz4+lebz75N2LKN6NK/1R318PwzqxKOYwZ9s/zZ6pd1bvXX9W7159pzSv4M46JYfw+GckYzakK2Kvfc9506miY7PyIGqLqALqugzUvTf22GMzgtecknALno8QCIq08KQL0R7Fdw1nruXMdDNcfPSO9uLrXm0/+zZjyDekJDzu7Hoh3ZjF2mFfJOMnfte/37vVX9e71Z1rzXn/l2m9iGxNz8vci4BecZufNZBqu5+RH1BBVx8w18K/WAPdvUgbyj+o9iQb0ViWpw/NnscF0IH+NDdavehuu9/vUuBlTBOwzuSoPWKwP+NVyGMEL2zGoQtNef1XvXn9V717/U9a8gj8bYG/7nJkB8HewK9yPZeQBmoBd+TkmqoaoOgLgOoA/roWfP4pjgMJVgPyzRi8EhnNi1VM2zN0lRpD+qOzzsHt9dhyvUtd68432M2BfVYcBO2cmexZrDmOG/fP8mWq397y/qvdqf+XagRpz2Ct9Jxqwa+YZruvlR9Rw0yK4/kp/dri/Owgm6HvlZKlC+VeKurCYzqE1pmw4yyR2kH4TwznLWCtlsN68DdfHfWJqYrgxOw2wz+QqPGSxPuQz5zBm2D/Hu9df1bvX397z/kznk9GfrYudFX4zd7FXBOxKzRynAXbD9aaNYP2dro+U+XbAbDD36kmuBOa/KQP8sV0XEWIAqFWlAtFvYroWqkL1lrPa/zy43vyifAzYI/LVbsoVAPtoHvODPiO0YMxQ9meq/ZR1sfe8/ynXyg5/NsDe9jkzAzBgz8iayQP2/suDm1S71xn+FXIRuP5L/65J3Az3T4vEDF17Lo6T4HyUmICktV9qAP1ZbNdv1nruXAfD9ZsfJ1xvXvk3Zix1nATYZ3LZH+x2ZbGCfMYM+8f5M9Vu73l/Ve9ef6Y13+HPBtkrfI+MZAD1ALtCo8NMXkb3uuF6bg0RcD0YrL/TnpemqnXHv1PvxWVAb1WROjh/FhtIvylznStB9ZahAdbvvnH1ssH15hPhoX2TmJWtAthH89izWHMYfyxgOwYmf6ba7T3vb+95f6bzOeKvDth3ZBiw898XZeSd1L2eDbez8wEpuP5Ke4D7N105ESpQ/lGzF6iBvRWharD8k1hB+k3Z52L3+iiD9ea9wrN253rziVH2TeJsDQbsXHms4HtXDmMGG+Bhg0eq4FTVu9ff3nu9V/sbsJ+TYcCukQWc1b2eDbez88XB+huff34+mjEB36snUBHMv1M07GE6n9ZrZQNZRrFD9EcxnL+M9VIH681/hScvXG9+PDcl6t3rs/knjIjZnceeVQVcKAM2Nn+m2lW9e/3tvde715/pWhnx7wFIFeawM36HAAbsozmjWTN5u7vXFe/ds7Nn8wEeuL6hSexzh/u3AhgB7olg/qoYYOA3rbimFI77JCkB9EcxXUdVoXrL0QPrzddwvc8n/ybJgH19pkJepQdkxgz75/mrevf629veLP7qXeynAnZmuL4zqzpcB/Yf40ymevZNs3CdsGu9Z/O5kTKKQP6mkYvnREi/W0xQ0/ouVXj+KMZrLmtdd66FwfqjZ7Sf9I3JG4/8OgzYa+QxP7gywgtD/Fh/ptrtbe8V/qdc44ABO0tGJcDOfj80mgW4e505dzYbOKprvUdrZ7hfOVhmKP+s0UU3qLdYVQGYvxMjSAfy17wSVG8ZK70N1/t9OOpRnmNowB6bx/yQzAov1CGPsj9T7fa2d7Z3r78Be67/jgyPh9l772W4Xi93Nhvg6FoXeXbNf2nq1YVSAvPPijqJBvcWkA9sM8UK0R+VfX52r9Gu4zVYj6sxsjZ3r2tmG7DnZLE+jDMeB9MxMNV+yrrY294r/A3Yc/1HMgzY+e+FPBqGN3c2u1LXelJT2M/HYCbA23NgynD+k3aBLabzrqJsyKoqBYB+E9M5zlg39W715r/mGAzXezzy68juClF6SDBgn89iBBiMGcr+rp3bW3W9VSE403r3+huwx/qPZOwA7MzQ293rf0rpvlk1F6jTtc7yr63/1beXpn4ulhXM9i5OVUA/KiawaOlICZ4/i+2az1rLCt3qzV8DrDdPw3X2OhRvnA3Y87IYu793ZNg/zl/Vu9efpW571/Hu9Tdgj/UfyTBg574PMlyvlwsYrkfXAQD/fV3L7EtTvx8gK5R/1MgiG9JbFaUMzV+JDaQDuWu8cz12HOepYL35RXrxdAKc2r0+m10ZsI/mVXpYZsxQ75JnWx+W2u19pvcp1zfQB5oM2OMzTn/BKXMOsG+ED5Bzz6x2nz6bC8zBdYP1P/UGrL/T+hnuVxdHAcw/avaEGdhbkaoGyj+JEaI/Kvtc7F4fZbDevFd4Gq5f88i/eVKcqaj0gGLAvi+HMeMkf6ba7T3nrXqd2Pu1VgJepr97Rn+gDmBnvl8YzarevX4aXM/uWq/ybHhTJ1h/p/yXpt5UFcy/0ypIZZDPq2wQyyp2gP4ohnOYsV7qUL35r/CMr9lwfV0dp3Wvz+QqPBSxgu9dOYwZ9s/x7vU/wVv1XNp73tuAPc8f4ATsrN/jO3MAje51w/VrMlyPqyMIrL9z+flYJCO87TkxVeB8jxiAoHW2lAD6TUx/N1nrt2sNFMF68+XtWm9+PHC9+eTfRBmwr8+sCNhHsliPh/E4WIBsr/dqf6baWbxV15vFm+U89nr3+jPNYWdalxH/kQwDds6cnd3ru+8h1TJnc9XhOsMzIbAcrL/Tt5emfhcjlL9p5OSeCOkt65UUwfkrMcF0IHddd66FwfqjZ7Sf4Xp0HaqjaQzY87IYAQBjxkn+9rZ3tDcLvGfyZgLsbXuOdRnxH8lgW/+2D99330jGaA7g7vXIvJnM2dxMuJ79LBZVQxJYf6f5kTLqUP5ZMxeJYb3FpCrA/JXYIPqjste9ElRvGat819TOOBKmeUX56MP1iBrUbuIVHo7Ysxi7v3dk2D/O39683iyA3d6v5Redxnj3+gM1AHul+wSgdve64fo1uWu9KQKuRz1tv1mTPTPcry6mEph/pSg4YnB/prJBbbaYIfpNDOdo9zrtOuaVx3VS13rzivLh6KTPvqnLgvtK/9TWgH1fDiNoYDoG136uNxPY9A8Dc94G7Hn+Bux8Oe5ez8+byQTOhussYB2Igeud68Hz0lSg72Sow/lPyoB6hvxNDEC1ghTg+bNYzn3G2lWA6s2fH6w3T8P1zx66N5ZZN/IKDyzsWaw5jKCB7RiYQCGLd68/S90s3ixrrerNBHmZPk8Y/Q3YeTIAd68z5M1kArpwPbvBCZAG6+/0c8mIEcaOnMzKkH5WLLDR4pIiOH8U43WdtaaG6le8o/1iazVcj68hM1/pAcKAfV8OY8ZJ/ky123vOm+U6YVkPJm8D9hj/1XAd4PsXBKwZozns3euG6++lPG89+/kL4BkHE/Vc/OZ6uNbhrgrlnzVzYRjWW4pSB+bvxAjSgdz13rkmO45z1fGwd6w3v0ivOnA9og53r9fIY354ZgQBykCJzZ+pdhZv1fX2Wu/zNmDf7w2s714H+NZ/JGNXDnv3euX7xtnMU7vWDdb/VOe1EDdSpucAFOD8syIBiOG99U5VAfk3sQL0mxjOy+41UobqzXuVr+H6NY9z4fpMtkr3+mgeexYj/N6RYf84/xO8VQGk13qf9ymAne2zyuNheDKAfd3r7PdWo1kzeTOZWXBdvWu9Elif/dcLD8qZ4V4dzn/TTnhnuD8uBsiqKHZ4/iimc5yxbupQvfmv8jVYv+6jDddn85W613dnsmexPqQzASX723ulNwtQ9nrMeasCdqbPkhF/NsDO+H23K8NwfS4rIw/QhOsG601MYP1v/N93/9PPHwvOBmhHFrIipB8VE1C0dKQEzV+J7brPWs9d62Co/ugZ7We4Hl2DGlyfyVV4WGLOMmC3v71jvVXhqdf6twzYc/zVAXul721mwG64/lo7R/rcpPyvdRnAetIYmLf6ANbf6c8O9ysnhg3KP2v2xBjYW+pSB+avxAbRb8pc651rsuM4DdajvCI8OEB/dheHSid5RiZ7VqUcNujTm8FWP0vtTOvCUjcLPPV6/JYB+7z/aXC9UkY1uK6QNZPnrvU+Gaz/1gBUf6t/8H/HRsr0nFB2OP9KkeDD8N76popw/IpYAfpNDOdl9xopQ/XmbbA+5pPftR5RhyJcn8mt3I3ECr5Hchgz7J/j3evP4s2y3l6Pcd9ebwP2vd6APmBn/K4byTBc3581k5cB11XBOjAP1xnAemK3+kv989ln/Qz33otCEdB/0k5oZ7g/LwbIqih2eP4olnOcsWa7jl0NqjffaD8+sN68DNdn89VG0lR9QDNgt3+G/wneJwBlr8dvsQD2U/7WgfMA+8nd6xUbFkazZvLURsIoj4OpAtY3QfVPynlp6ieNXFzVIP2oWECipSklaP4stms/ay13rsPqYzwRrDe/KJ+Yuk6H67P5Kg8x7A+DO7MM2OMz2OpnqZ3FmwUor/SuXnOvdw/IWAkXT/lbB3jWvG3P9R2xK6Na97rh+p86Da4brN/qSAfrv/RQz8+lRWLvnJ59GDewt9SlDMvfiQ2i35S51rvXRBWqN+8VnnW71ptPhIfh+s5chQcn5izWB1zG42ACV6q1n+B9AqxWrJkF9rJcH73evf7uXufIWH0eAG64rpAF7Ifravf6N83AdYP131oA1t/pWod7zwKzw/lXigIfBvfWVVUE5N/ECtAflX1eqgH1lqEF1ZsvZ9d686oD1yPqOGk0zEwuexcUK/jelcMGT1ZnMK0P09qz1M1yLquvBwvwNWCf9zdgj/UfyWDtXq94LzWaBRiuX5F61zoLWN8I1T8pfqRM7wlSBPTvtBPWGe7HKhu0qkkBnj+K5fxmrJs6VG/+q3wN1q/7aMP12Xx3r8fmsYLvXTkVugdVwViv/wnehtXj3izn8IQZ7EzeBuy5/oC719nv2YAz4PrJXesG65d88me4j5zoSpB+VCwA0dKWGjh/FNvfQNZa7lwHd6vfPKP9DNeja1CD6zO5Vf+ZcaUHXQP2OO/V/id4G1bv8V3pbcC+15tlvdv2XJ+vOzJYX2zKes+RkaUC10/sWjdYj62j0+vn5YXD3j09c9EY1ltVpAzL34kNot+UudZVgPo9Y5XvOWC9eUV4cIB+w/X1mQp5rA+7p2awQNle715/e895u+Y93izAl2U9VnuzrHfbnueze4c/4O515hzAcP2bTu5arwbWA3xed7j3XGDscP5ZkcDD8N7qUUVA/k2sAP0mhnOye42UoXrzNlgf88nvWo+oIxPuG7BrZp0Iv3dkGLCf6e2a93izAF+W9VjtzTLzvm1/lr+713lzgPpw3WB9NL8OWI/sev/vn17zI2V6L1I1QP9JGbDOkH9eDJBVTezg/Fks5zhj3XYduxpUb77RfnxgvXkZrs/mG67H5lV7CGUDHKsz2AANS+0neLvmPd4swJflh7SV3kzz15U/W0czGLvXK91vjOYAhuuflAXX1cE6C1SP9HkB1T9p/wz30Qu9EqifEQtItPSkBs2fxXbtZ61nBaDe/DWgevOsC9abT34dhuucmexZbGB6V4b9z/ZmAZ2K68Gyzgbs+7xZ/rVA2173s3XEv0r3eqV7mpt2nJublEbCGKyPiQWsJ0H1T/r538ll75yOgBSG9pai1EH5O7EB9Jsy13vnmuw4zpXHY7A+4hPhYbi+M9fd6zVz1CGKKnjr9WfxZllvxfVgWedVgN3Xxp9i+TGjbc9xLnf4A1z/euC+D1/GzhygNlzP6Fo3WM+tIcojEKp/Wpd7h3vvyWcH9K+0AtAY4ltAXSh+Razg/FEM5ydjnQzVX3nG1lsVrDcf3ZEws/mG67F5zA+jbJBjRwaTP1PtLHWzeLvmMV+AA7Cz/K2s9GYCvEyfqzv8mdb+vg/fPcBIxmiOR8K8lmLXujpYZ4HqQBxYH1yT8ZEyIxeQIqT/JgaQd9Mp8J9pzStJAZw/iuk6qArTW87aY1t1HCeA9eYV4aHdtT6brwTXRzMN1/flMD68s4DT1f4neLOATtd8F8vIEsXrudebZa3b9hzeO/xPnbvOep8B7IXrBuufZbA+J6Zu9YgOfuDXMe2d4T5zMVaE9dFiApDWXqnB8ndiu4Yz17UKUG8Zq3zjazdY56/DcH1NJntWlYdrNpDC5M9UO0vdLN4Mviu9GcbDtDp6ts1fi9Xeimvd693rf+JoGMbv5pGM0RzD9T+lNg4mE6wbqv/WAqj+ST//u8jZu6MjwYXhvcWgKpD8ndjg+aOy13732qh2qTdvbqje/LjAevPJh+vKI2mqw/XRPOYHUsbu9R0Zyv72nvNmgXoMviu9GcbDtO29zo9SXOte7x3+J8L1kRxmuL5zJIwKWAfG4brBep6HKFT/pMcZ7n1iB/SftBo+GehrKxvGsokZnD+K5bxVg+n3nJXe/FC9eRqsr6pDEa7P5FaF6zuzGB/iGTNYwNtqfxZvFrCnuB4MvgAHYK9+/gB3r+/2BvjgetuHZ/1HM0ZzDNd/S6lrfQasq4+BYelWJ4LqAN4e08wM9zEpg/qrYgF/lnWTCjR/FtPfUtYaVgDqzX/NcTB3qze/KJ8aYH22DsVxNApwfTSPFa6P5DBmnOTPVDtL3a55vS+gB9hZYLLiOrftObx3+LMB9grfyyMZwD64zn4PCJzVta7csc7QrS4C1T9p7wx3YP6B/wRgb1k3qYLyd2IC6DdlrvHO9dhxnCpQvXnydas3r5i6GOC68kgaBdi9O48569QMZX97z3m75jFfxa5qRcCuuM693r3+huux/qwZgOH6o3Z3rRus79+fBawnQvW3+hv/9+d/F6XKGJRo8GKAb0WqGiD/JEZ4/qjsc7F7fZSBevNe4cnZrd686oD1iDqyRsLMZKsAffYs1hzGB3mmY2CqncWbZb0ZfFd6M4BfxbVQ7F5vtfRsq+nd688G19s+POuzM4MZrlcfCaM2DiYTrDOMgSkM1T/9z48z3PsuWBVA/007oZzh/l5lA1cVsYPzR7Gc04w123XsalC9+RqsX/cxXN+d6+71+SzD79r+9p7zZvBd6a0G2KtfFwDHOrft63sDfICd7ftmV4bhepMKWAfG4fqpYL1St/pGqP5JMzPcxy78KqB+RCyw0KolJWD+Smx/F1nrWQGoN/9VvvF1M4L15hXhoQ3WM/Orw/XRPFa4PpLDmHGS/wneLHCPwXelNwP4rX5drIS9itdcr3ev/2lwfUeG4fp4lgpcV+xaPx2ss3WrR9XztC4ZM9znIcDJ0N6qI3VQ/k5sAP2mzPXeuSY7jvNUqN78uMB68zFcz+iaN1zPyTo1g8mfqXYWb5b1VvPt9a4M2FmuZcXxMMqfSacBdsbv42pwXWEkzEld68pgvVq3+iKo/kk/+Bt6o05WASWDfOtZVaH4FbGC80cxnJ/d66QM1Jv3mvoN1q965NeR0UGumOvRMPtyKgADFrDX63+CN8u5VPNV7KxW8wU4fsRo29f3NlyP9R/JMFxvGoHrKl3rBuv9YuhWF4bqn9Q63Ef/UNVA/TcxwLtHnfQDANvaV5ECNH8Wy7WQtXa7jt9QPb5Og/XYGtTg+u5Mw/WxHMYMZX+m2lnqds1jvtXBL4Ov4o8YK71Xf5ay/GuBtj2X/44Mw3WNrvWMcTAG62NiAetkUP1TPXMjZWYfpqsB+2ixgEcrR4qw/FmM13DmulaA6fcMDajePPm61ZtXDbAeUYfh+ppM9qwKD/Q7MliAYa93rz+LN8t6M/iu9GYA7F7ju6r/iLHam6l7nek7ZlcGK1zfeR/mrvU/pQjWDdXj6ogC6sBwPftnuD8qGqwY4FtZqgDHv4kRnt+Uvf6710YZqDfvFZ51u9WbT4yUu9Zn81Xg+s7O9dE8d6/b/1RvFsCn5qvYWa3mW32NmbyZ4HrbnmNddmXsgOvM90aVu9YN1q8rG6xXg+pRXfNPXj9//BEpjzHZDd0M+DmVDV+ZxQzNn8VwHjPWa9dxqwH15ssJ1ZuXwTpLfka3/G6Yb7h+bgYTWFEE1Su9XfNdDJ3VDOuw0pthjdv2tdcZ8GiYTH/Dde6udaU564pg3VC9iQ2qd/j82eE++5CsDOx7xQAErXOkBMufxfa3krWWO9dBEag37/pQvXlF+eSC9dkaFEfRGK5r5DBmKPuf4M0C+Bh8e70Z4K/aGjOsb6ujZ9v8tVjtzbLWbfuz/Ff/ywGA84f0m3bB9epd6wbr/ZoF64dD9U+KHykTBSROAvdWTSkD8k9ig+c3Za93FZh+z1jlG197dK1sYD2iHoY61LrWZ3IN1/lz1MEEk/8J3q75LsP19d5qa7zSm+WHIoDnXwpU8GeE6+5abzJYf6/MMTCG6nF1RPoAH9c2d4b7J60GSgb6ZysbzmaLFZo/i+E87V6rXces1KXePKP9uKB68zFYn83fDbozMitB7105jBksMKvXu9f/BG81315vNfir5suwvm37/LVguY4BnrWu4G+43qddXesG6++l2q3OMAKmIlSfWNefSx8cFWeVM4C8XrH9SKC4hlWkAsyfxXTNZKxhBZje/NccxwlQvXkZrM/mq3XLG67vy2HMUPY/wbs6mFSDvwzrwAJ+K6/xam+W0TDKn/8j/mwvlG37rM9gBuvAGFxXAeuKY2DUu9UN1d/raW2udbhHgYaK4H6nmGClNSZVUP5KjNdj5vruXA8D9UfP2FqZutWbT4RHLuBXHEWjANZH83ZlsUJ8Njih7H+CN4PvSm/D37W+DOvbts9fC8UfMVodPdvyfD7v8Ddcv66KXevuWP+uzG71KlCdDagDXWuzd6TMCthiiG9FqxIUvyJGcP6o7POxe312HO/KYzoJqjcvg/WoGty1Hp/HnHVqBgso6/Xu9WfxZllvBl8G+Fv93FVeY5a/adXRMEyfzyP+huvX5K71mDyD9T3ZQP4LW6M8gGVd6jPineF+VZkwzrA/XtlwVVHswPxZLOc4Y912HbsaUG++vFC9+RmsR9ZguB6fx57F2CGvDkBU4RCLd/WaDX/X+qqt70pvxdEwTJ/PTJ/9AB9c33Gf4q71JoP198ocA2OofhcbVP8b/99P//PP1IMr20zx3WIBh5au1GD5K7H9HWSt6c51WH2MKlC9eUZ6xdVnsD6fr9Ytzw68d2ZV6CzfkcECfHv9T/Bm8O31ZgCTamvs9V3r2+t9wmgYpu8VwHD9qpjh+s6udYP1zzq9Wz0CqosB9W+a63CPhBOnw3tLQxUA+TuxgfNHZa97JZjeMlZ6G6r3++SD9Yg6DNe58k6G6zsylP1P8GYBZoa/mr5q67vSm2GNAZ7udVVvwHD9qqrB9Z3g2WB9bS6QD9WjPCLAOglU/6X/4v/3+B95RsrsAmoG+/WUDWOZxAzNn8Vw3jLWy0D9nW+0n8H6qjoU57wrZbJCb+YcNvjdm8FU/wneLMCsMgBW8/X69vv2ejOscduew7vX33D9u3bAdYP1+f1GQavB+t78zBE6j2KD6k9A/Zt+/vggqz6XnAHyWdY7KQHzR7H9XVUF6fesld4aQL158kH15mWwPpuv1LU+mumxMGM5jBlM/vae82bwrQwmGa6JyuvL4uvRMHPevf6G69/lrnWD9U/KAuvqUB3g6VRPAuof9Tf+758d7pHQoTq8tyxVQP5JbPD8psy1rgLTm/+6YzFUH/GJ8NAF67P5lUfCjOZVgusjOUwQhM3/BG/FmhkAMMMae301fVlGw7D8Pa/2VofrI/cOjHCdGayPZhmsr8udAcoVoLp4l/pHfVnftSNlVoMjA33rkyrC8KtiheaPYjg/u9dpxzGfDNTvnpFeHN3qzcdgfWeu4Tp/jjL8ZvNn8WYBWwxwkgFMqvl6fft9e70Z1rhtn7/Ovd69/qfBdY+E2QPJDdbXZGZ2q1eB6oJA/Zt4ZriPiAHYfVO1HwUU1lxdCrD8WUzXRcb67Tx+NaDefGNrjq6zUrd6RB0G62szPdt9LEd99AwTxOn1ZwF9ijUzwEm1Nfb68vgyrG/bPn8ter17/Q3XP+v0rvWd42BUwLq71ftlqP6nIn6sAIB/Xh/Xz/CDn18+ek1MINJaI0VA/k6s12vWGlcB6feMVb7cXerNjweqN5+zwfpMvkfQxGWx5qjD9V5/pvVh8WZZbzU4ybAOXl9NX8W564qfbQAXXK8wEoa1a91g/bd2g3VD9TGxQHURoP5N4x3uK+GQYb4VqUpA/IpYofmjss/J7jVShunNmx+oN896UL355NaRma8AulXyGKF0lQwmkKMIqld6q8FJtXXw+mr6VofrK72Z4DrTdxXAORKmWte6wXpcHpAH1tWhuoH6Zz3UxDlSJhvGPcs/AFwT23lTlQIwfxTLec9at13HrwbUm2+0X1ydFee8nwjWZ3IV4DorWN+Vw5jBApJ7vXv9WbwZfAED4NW+V9fXa9vvWx2uM30ms6x1254LrjN2rRusN+3sxM8YA2OoPiYWqE4O1L/p5+OHa7X546NiAYoWp9QA+TsxXufVIXrL2pGhAdSbp6E6ex0G6zx5huvawEIRgK/0ZgFnhutrfZXWl+FvgwX6MlxnK71Z1rnV0qfT4HqlcTAG6zF5QM7LWU+H6kxd6klA/Zs+d7ivAkEG+dZOVQHiV8QIzR+VfS52r48yTG/eKzxj62WD6s0nwiMXrGd2y1cfQcOexQi+d2Qw+TPVzlK34a+mr9d3rW/l9V3prQrXTwPrQJ2udYP1mDzFbnVD9bpA/e/va5MzUiYbut1k8L9WLOdZReyw/Fks5zdj3XYd++pjUwDqzTPSiweqNx/dMTCz+dXB+mgec1YVgK/sf4I3A5wEroMzBoiotr5q8JfB1+u7x9tw/bXctb52nxEoa7Aelze7r6E6F1TfCNS/iXOG+y6xAENLV2qQ/JUY/w6y1nXnWijC9Ls3b5d686sF1ZtHbg1qYH0mlxl4j+aMZFUB+Ibr3N4r19uAcq2v567nry2gt74rvQ3XX0sdru/oWjdYbzJY/6wZsD7XnZ8/T91A/bP++/rYfpZBH79o1GJSBTD+TYzg/Kbs9d+9NjuOVwmoN89ov7gaWUbANJ/cOgzWufKYIb7heq4/izcLuK8MKBl8vb5e2x3einCd7XukB65XGAlTaRzMTvhcHawrQnVgHqxXgepCQP2b1nW4ZwA2Q35uZUNXdjED82exnMuMNVOH6c2fH6g3Ty6o3nzyu9Uj6sgE+7tB9+5M9izWHDZoweR/gjfDaJhWx3VlrwXD+lZd25W+Xt+1vsAZnevuWl+TwQzX2cF6xnx1RbCuDtWZutQJgPpL/XOvq9ZIGRYIaJ0lJVD+Smx/N1nruXMdFIF684324xxNw9Kt3jzOA+szuQpd66N5rHCdMeMkfxZvj4bR9c1eX4Y1qLq2bdu6voDh+iupw/XTu9bZwTqg07FuqD6mil3qi4D6N/0MPcD5ZaOWotTB+CexQfNHZa97JZDeMtYdD3uXevOL8qkD1SPqUAPrM7nsI2F2Zhmua/mf4G1AudbX6+u1ZfLt9TZc/1OnwXXWrnX2Oes7wbq71a/JUP3mIQ3Uv2msw303QDPgr6dsCMsmZmD+LIZzl7Fe6jC9+a/w5B5PY7AeV4PBenwecxZjBttoGyZ4z+Lt0TBcvn6xqdd2pe9Kb8P1P2W4Hrs9UK9r3WD9tQzV82poHvMAmxGoX7g2NEbKMAA+y3olJVD+LLa/q6y13LUOijC9+Z4B1JtXhIc2VJ/NVwP6zMC7Wo7hepw/izfL6Ac1SKkEKNXWQGltWw3XtaKDv8e315thfVsdPduu+xxeCdfVwfpIRu/2ButNCmA9o1s9cwRMNlSvBtQDu9Onrwu0kTKvP6D9AlKrkpTB+CexQfNHZa757nXZcaynAvXmxwXVm4/BulIuO1jfmVUBrp/kr+qtBCgVfbPXl2ENqq5t27auL8P6tu054Lq71mO3B3jHwewC69Xnqyt2q6tDdaaxL2RA/X96cz2/73DfDcsM+M9RVfg9ImZg/iiGc5axVruOe+WxnQbUm1+UTz5Uj6jDYJ0rb1cWK8BXht+r/VW9GQAaA0isDChXrIPX9rZtXV+G9W3br/FmGgmjDtZH9jFY3wfIT+lWN1SfExNQ3wDTr4hnpAwD0LOsK1KB5K/E9neWtZYVQHrzX3Mc7LPemYB684nwyIPqs/lKYH00013rnBnK/qreDACNASRWBpSG61pry+Krtr693h4JE+fPOA6mGliv3q2uCNUN1JuKAfU/9LTOP7++aPxyUquKlKH4N7FB85uy13znuuw41pXHww7Um1+UTx2oPluHYqe8wbpGDmMGk7+qtxpAU/P1+nptV/r6fQzj3qojYdTBOrC+a91gfXw/pREwhupjqgjUF8L0K/rd4Z4BzAz5aysbwrKJFZY/i+G8ZazVruNWg+nNlxOoNy9D9cgaTgDro5nsWRW61ndksECdXn8DtDHfHm8D4HW+Xlut8wXUf5mpR8LE+bONgzFYH4e3Kt3qJ0J1hi71qkA9ak79v/rPf9rHf/5IGQawZ1nvpALI34nt7ytrPSuA9Oa/yje+bkag3rwiPPKh+mwdnuuuncWawwSn1f3duT7mXRlSMgDKbLCstLZK5wswXH8Wy0gYg/Xv2jGixmC9SWkETBZUd5d6UwRQT+5Of6cbTL+ifOBuWbNSh+KfxAbMH5W97jvXZsexrjwedqDe/AzVV9RhsL4mjz3LI2Fy/Zm8lQBlr+8qbwZIybC+2WA5e22z13Wlb/batm3rj4RZ2bWuDtaB9cdgsF6/W91QfUzVgHoSTP+qv/B/ftKg2V/4T0qutUbZ8JVRzLD8WQznL2O9dh23GkxvvtF+XEC9+XCMosmE6rP51cH6aB4rWB/JYcxg8mfyrg7RDNfzYa0BsNb5ArTWttcXWAfX3bX+XmzjYAzW94J1pREwqlDdQP23goB6NEy/sllehzsD4LOsRykB8ldi+5vKXM8KIL35a8D05hlbq6F6bA1ZUD8D5rOD9V1Zp2awdFP3+huuj3uv8FVb32xfJQCstK69vtlr27atPRKGadb6aWAd6K/JYL1+t3oWVFfvUjdQf6+LMP2KPFLG0pI6FP8mNmj+qOy137k2O4515fGcBNSbFwdUbx7uVt+Vq9Ah7651+0d4q0G0ld7ZoJJhfbN9lQCw0rr2+mavbds2H657JEzM9j3gjHHOOitYZ5+vrgLVgXGwrgrVGbrUI4A6G0wHQoH6N6+frRDtr41Z1jplg1dWMcPyZ7Gcw91rtuu41WB6860P1JtXhEdut/psDQbr8Xm7sqoA/JP8DdfHvZVApdoanP6vApTWFdBa215fRbjONBJGvWt9xzgYd6zrgPWMbnVlqM7SpR4A1Rm70yO89na4s0A+y7pJCZK/EtvfVNZ67lyH1ceoAtSbZ6SXoXp0DaeA9dFM5nEwu3IYM5T9FeF6214L0FUFlUprq7SuPb7Z6wrUXVvA89ZnvU+D6wbrButReW3fnJnq2VDdQD3VyyNlLH6pQ/FPYgPmj8pe991rs+N4Vx4TO1BvfjyjX5pPPlSPqEMNrM/ksnet7zou1uNhgt+r/VngequlZ1stQFcVVFZd2+x1XeWbva6A3tpWh+tMI2FOA+vAnnnxBuu3LEP1b1KH6kxjX6IgeCRMn/D7WQqZ/sJ/lnlb+5QNXlnFDMufxXIOM9Zs17GrwfTmG+3H1aXefAzVZ/Org/XRPFboPZLDmMHSpd3rDXBAtLa9FqAzqNRaW6V17fHNXldAb20Z4LrqvHXlrnXGcTAG6/W71U+E6pW61BmBeqTXf/D/evyPazvcWSCfZQFagPyd2P6mstZ05zqsPkYVmN48+YB68+LonM+G+4qd8gbr/DlsGUzgHuCAaG17LUBnUFl3bbPXtcdXaV1bDddluH7z1RwJwwTWgRrjYAzWb1kjx6TTrZ4F1Qt0qZcE6gth+hV5pIzFowpA/JvYgPmjstd/99rsON6Vx8QO1JtflA8HUG8e+R3zat3qM7nsYH1nlrvWc/17uycN18d8K4PKihDY61r3mq3+MlPVrnWD9fjtgT1g3WNgXu1nqN6XPwXVaYA6Y3f6AEz/qP8H/2+gjZRZp78WeltxygatCmIG5c9iOZ8Za7br2FcfmwJMb56RXobqkTUYrMfnsYL1kRzGDKaueBa4zrImBpV9vkpr63XNv2az1xXQ61pv3msg9Sld6wbr1+Ru9bmstt85UN1APc4j0mcRTL+i1SNlLGu/lOD4O7H97WSu6c61UATpd2/O7vS7Hw9Qbz6G6kq57ljfl6MM1kf8FV9mutJbCVS2Gq7rdAicva7Z56rXV2Vde33V4Lq71vfWMuLP9AJZoN4YmJ3d6obq3zUD1Q3UYz1uigTqHTD9q/7C//FIGWuPKkDwq2KD5c/KPhcZ67PjmJVgevOM9our0VCdI393t/popsH6WA5jhupImFZLz7b5gG6ldzZcZ1jfFb7Z69q2rQfXlda1x9cjYcZ8e7c3WI/zrwLWq3arK0H1k7vUWWA4W3d6JEgHLh/fTwp8+wv/2Z55grJBqrLYIfmzmM511trtWgM1kN58V3jWBOrNJ7cORag+k6swdqYSWB/JUQbrAM9ImLa9DqDr9QXqjtjI9lWCwNnr6h+D3LU+6s0yDsZg/bNY56vv6lY3VH+tU7vUZ0G2YfpnRXbeI+ulqUyw0NKSGhj/JMa/g8z13bkeO47zVJje/KJ8OIB689CF6jP5Sh3y1brjT804oWt9pXfVrvW2rY6v0tpmr6vSNZt9XQGG6yO+quNgDNa/63SwrjIC5iCobqD+rw6A6Vf8PFLGmlMlAH5VjKD8WQznJWOdlEF6817lWxuoN68IjzOh+my2wfp8lsfBfNcJXesrvbNBZavhurLXQWlts9c1+1wBNeetG6yPeZ/QtX4aWGedr+5u9d/aDdVVu9QN1H8rCqhHwvRArx8KMPdJf2UXsFjs619FCpD8lZiuj6w13LUGq4/vNJh+96vTpR5Rh6F6nbxTx8HsyPCLTMd9e72rwnVD4JrrqnS9umu9qXrXOtM4GIP176rWrW6o/inz3C51FqBeHKZf8ePvcGcCjtZaqULxT2K9fjPXeveaqIL05h1fe2Wg3nzyofpsHYoz3Q3W9+cwZjB1rQP14TrLDxnZsJJhfVXgeva6Kv0Y5JEwN998UK34ElOD9fcyWN8D1g3VP0q6S312/6rd6YRgnh+4W/tVEXz3ihWUP4vhXGWs1Y7jXn1cCjC9eXKNfWk++lB9tga/KDU+jxWsj+QwwnuWrvW2PUd3eTb8BfIBsKKv4Xq8Z8VRO8AauG6wfpdi1zrTOBiD9WtihurAGFg3VP+uGeBaAagbpof4/VAAO8ualQogfyW2v8Gstazy0lSlGe+s42iYuuZVofpMdvUO+UpgfSTHXeu1vA3X1/kqrW32umZfs9nHD1yH6+5a79/WXetz3iPbr4brq8G6u9WbVMD6IFSX7VJnGPnCBNRZYXqAlzvcrX1ShuKfxAbMH5W95lUg+j1DB6Y3T06g3rw4utSbTx5Un81X65KvCNZHshjB+kiG4fpebyUArOartLbZ65p9zWYfv0fC9PsywHXVrnWD9SsZdTrWK3erZ3Sqn9ylHgHU2brTyWD6FT8D95OUDV/ZxAzKn8Vy7jLWTB2kN/8VntwjaSp1qUfUodgpr5LJnnXaS0wBj4SJ8FYCwGq+Smur4gnojNrp8VWC65XBOrCuZtWudYP17zJYl4HqkqNfsoE6MA/V2UA4m0+A3w8NyLOsb1IC5K/E9rd2wotTlWe9G6j3+hiqK+UarI/lnNa13rbnAOB+mammr9LaZq9rRbiePW+91RB/rVaG64pd60zjYAzW12QwQ3WgPlg/FaozAHU2CE4E06/4ucPdipE6DP8mNlj+rOz1370+yiC9eXPD9OYXV6OhOke+whiY3XmsYH0k5ySw3uvP4r1yzbNh7SpfpX8VkL2u2ddt9vWaDdc9EqaJAa57HMyfOhGsu1u9yVB93b4G6vV9Jv3uL039C/8JLchaq2zAqiJ2UP4opnOasW67jl8NpDffFZ41gXrzya1DEarP5Bqs780ZyVCG66re2fC3bZv/Q0M2BAZ0ZoNnA/vsa9YvM10D1yuD9d46WLrWmcbBVHh5qcG6ofqqTGAOqlcB6mwQXGiW+73DnQn2WWdJCYp/EuPfUOba+oWpV31XeMbWWg2oNw9D9V25zC8vHc1izVEG673+TN7ZoLJtW9dXpcO6xzd7XSv+YOGRMLXhusH6nzJY/7a9oXrL0oDqil3qBuqxHpE+0V6Dfh4pY/2pKgD8qhhB+bMYzsnudaow5/0kmN686gD1iDoM1bnyDNaviwms9/ordq23Onq21QHLPb5KP1xkr2vFHyw8EsZd6701sIyDMVh/ryrz1dlHwBiqv1fm2JdZ4MsCwqvD9IVgfv6lqX9N7n+CGGBpJSkA8ldiug6y1rACRG/+q3y5Z7szAfXmcy5Un8lWGTvDnlUBrANccJ3JWwn+KvpW7LDu8c1eV4+EiV9Td62vrYFhfXu3N1iP82/79B1DtW716lBdsUvdQD3WI9In2mvQb77DnQkiWhxSBeKfxHqdZ6/1znXxi1JfeUZ61Zzp7nnu+zIV8qqAdUC7a52pI95d6+t9VTqse3yz17XiDxbuWs+H2pXBeq+3wfprnToGxlA9dz/VsS8G8mt8mL2e/DxSpqqyQSyDWCH5s1jOVdUXpa4+LnaQfvc0UF9Vx0lQfXdmtc7408D6av9VMA3Qgr8rvRl+vFCB4NnXbvY166713K71ymAdyF9fwGA90t/d6t21bYXq7lJfv2/E/tU8In2ivQb9fmhgn3W2VOD4KzH+DWWtZ6UXpZ76klTGrnl1qK6Y7W71uRyD9Vh/lnEwrZaebbWgfTZcz16D7HWt+IPF6S8yZQC/anCdAawDK9fYYP2TDu9WN1R/o1OBOgsIrw7T3eFupUgZgl8VIyy/iWH9K74o1S9JjfQyUGfIV5nlXrEz3mA917961zrDjxjZELhtWw/Yq/xgUfFFpkrw12C935cFrLdaroPdlWC9wnz1Hd3qhurz+2V0qZ8M1CvCdBGQflUG7ixigKsKYgbkr8R0XjPWzi9J/ebLC9ObXx2gHlGHIlSfyVXojjdY791nXcbq+t21vt43G65nr0H2uiqtaeZIGHetr6lVDaz3bn8CWAf64Lq71Xty9oD1ylBdsUs9G4izAPXqMD0azD94/lABQauO1MD4OzH+fWSu7e71MEh/9o304htDY6iuA9VHM9lHzjCC9bYPF7xnGQcD5HdVr/auCIHbtjpwWeVfA6iMhMl+kWl2Z7W71vt9DdZ/S30MzOnd6obqf8pAPWf/SB+mWoj83OF+iqoA8KtiBOXPYjgn1QD6PWelNz9Mb34G6qvqUIPqM7ns3eqjWQbr+f4rwXqrpWdbHajc65sNgdu2HglzLf+6PBLmmlYAYAb4uwLuZ69r77YsLzB1x/p7FQHr26D66H6G6pz7MuzP5BHpE+210O/3S1P/wn9CQ6qKAZRWkwIgfyWma8EvSp311pntzjqKhmUETTbYV4PqM7kG65xgvTeDaRwMkA9+V3tXhesMa5AJ17OvW4+E0RgJ4671/m0VX2BqsP5eO8bAuFu9SQWqG6jrezD6RHsN+P3ucGeChxafVKH4OzFf75lr7Rel9viu8OSE6c2LA6g3j7wu9ex8hW713Xk7wDqwHkzvyGCC64pd6yu9s+E6w/pmd8N73vrV/Dy4ng2As7vWe2owWH+sY03X+kqw3utvsH5J3WDdUP23ToLqFYA6GwRnhemL5rh7pIyaqkHvETGD8kexnCu/LHXUe5Wvgfp1H0P1nbns89UBg3Umf5au9bZ9PgDPBsBAfof1Kl/PW78mj4TRGAnjrvUmhq511Tnr6mC90hgY9hEwJ0D1TCDNAMNZPBh9CPz80lRrrVTg+Csx/m1krefOtfCLUh89o/1qAfXmkVuDoXp83ghUBwzWV/mfMGt9pbfhev7aqvxrAM9b90iYaF+D9cc61nStG6y/VxWwbqieu1/mvtnZ1TwifaK9VvjBL009V8og/IoYYflNDGtf8WWpavPdWWF684ryyQfqEXWoQfWZXIP12/bnZaiC9ZXeDOutBNZX+VZcV4+EifXs2TZ7JIxS13r1l5garL/WarBeaQzMrhEwhuprMmf3d4f7Gh9mr04/A/fVYoCrCmIG5K/EdF4z1m7X8a8+NpXZ7qxjaAzU5/OrQ3WAdwxM24cLeu/KYJmz3rbnAODuWu/zzV7b7HX1SJhrUoHA2V3r2Wvauy1D1zrLnHWD9U/+53arG6rH7ae6b8T+LDUw+kR7rfBDGynDAw4tXqkB8U9ivOaz13fnmqhC9Oa9wpMTpjevGkA9og5D9c9i7lZv+/BB7x3HccKc9ZXeBuvrfN217q71yG0rzlrPXlOgNlg/pWOdDKoDpN3qFaG6gfrafRn2Z/Jg9Fnl1/G37w53NWWD2SwxQvJnsZyb3WvlF6R+8q0N05tPhIc2UJ/N3w3VRzMN1cdyDNbj/FmgvRJcZ1hfd61fU7WudRWwvio/u2vdYP0uxY7107rVWUfAGKrn7pe5b4X9mTzYvYCxv/cvai9N/SvalkQsAPQkKYDxd2K7XvyC1Aj/Vb7cM93Z5rkbqGt1x+8aAQOcDdZ3/OhxAlhf6Z0NgNu2NYG9u9avqVrXejYEPn0cTGWwrtqxbrAeu/3oPrvAuqF63H6Z+zLsz+QR6RPttQCi9/zttw53NtBoxUsZhH8T8/Wbve5+OWqvNzdIb35cML355AP1iDoM1b+LuVt9JItx1AxgsL7b22B9nW/22rpr/Zqqda17HMyq+fX5YB3g6Fhnmq+uDtVH9tkB1dk71U8A4ycD9YownRmkj/69f9Jf+D8eKbNC2ZCVWcxw/JWYzqVfjjrjv+Y4ToDpzctAfTa/OlQHanWrj+SsHgMDrH2BKVNHPwNYB7TgutL6Zq+ru9ZjPfMhsMY4mGywDvTUugasN+81XeuKYN3d6rHbA/U61VWg+mlAnQGGs0HwKB92kN55nH5pqvWn1KD4OzFe25lrW222u9pMd9Z57p7lHpdvqP5uH94s9fnqAFfHeq//yh8Esjur27Y6vtn/IiD7+N21Hpuv0rWePQ4mG6y3GvK71g3Wn2tZBtZLdKsbqmtknbpvxP7VPIByIP2qpzvcmVUFfPeIEZI/i+G8ZKyTX476zpN7DI2BelwNWTBfAaq3/ep0q4/krAbrwBkd673eButrfd21fk3uWv8ula717HEwBuv9vieAdXerf1c1qK7Sba4IxbOBOgsIZ4TpkSB9EUTv2bw2cGcAo5WlAMdfie268MtRI/xX+Z4B05tXhIc2UJ/Nz4DqgLvVd+e4Yz3O3+Ng1vpm/3CRffzuWo/NP7lrPXscjMH6bVuD9SfJg/Ud3erMgFwFjp8E4yvsH+UBxMF0ZpC+Asz/6/tDBx+teamC8G9ivlaz19wvR+315gbpzY8LpjeffKAeUYeh+pX99sDuXVlsLy4F1v84oArWAXetr/Z11/o1uWv9u9y1rtG1brB+1yqwrjxf/dRu9YpQXQmMG8bnegB8MF0BpLvDPVnZ4JVNzJD8WSznLmvNKrwgVeXlqM2zJlBvPvl1GKpf2c/d6gDfKJi2PQ/8VhwH0+ro2TYfgqt0rbcarm7nrvUrctd6rKe71nPheo9n234NXD+hY93d6mfuo5B16r4R+0d5RMB01q50BTD/r69fmmppQfF3Yr2Oq8Pze95qf5257qwvR21eHEC9eeR1qc/mK0H1tm+tbvXRnNPAeq8/C1gH8jurFX1VRsKs6FoHzoXr+SDYXetXdP2864D1Xl+D9V86Cqyf3q2uAMcN43X2B+p2piuAdHe4F1EFCH5FrKD8UQznohpAv+fogPTmWR+mN68Ij/zxM2pQHajbrb4zy2A91l9xHEzbPh+Cr/DNXt9Vx++RMHHbAfFwXaVrvcc3/8eKeLhusH6rwWA9YvvVYH0HiK4IyE+A6gbqHN3prF3pBCD9ouehwJ0BoKpKAZA/i+l8+wWpEf6rfHlhevPj6U5vPtpAHTBUX5G3K2vk3Bmsx3p7HMx6X5Wu9R5fd63H5meOhFHpWl8B1oGaXetqYL3VsWbOusH6e60G66yQ3FA9bj/VfSP2N0xf77PKr3l2yS9NrSRFGP5JzNdm5lr7Bam93n5B6phPPlCfrWMGqAOG6tF5O7rVAb6Xl45kMPlXHwfT460E1lsNV7dz1/oVqcDgal3rKj9UtPyzX2Kq+AJTVbCuDtVH9mEF8Tv3Ucg6dd+bZoE6E0xnhPLRXs1vjf7Cf87scF+harB7RMyA/Fks5ytjzSq8HLX584P05lkTpjefCI+zutTbvobqz6owBmZHhsH6Pu9suJ69vp61zg+DT5617nEwHgfTalhybF1QHVgLytXBuqE6Pxw3jL8uw/R1PtFezS9ef+E/PZv7pamnSQmKvxPjNeuXo0b5nzvTnXGeuzpQB86B6qOZOyF+lTEwOzJYRsEAnrM+6ltxHAzgrvXI7TLHwfTkV/uhAtAZB2OwfqvBYP1R6lCdNYN9n91ZM/saqI8pAjhXB+kEEP2iJ4BTZ7izqAL8vipGSP4ohnNRDZ7fc7QgevPlnedeCaYDuUAd0ILqCnmsUL3tw9NNvsPfYH2Pb/Y4mLZtXte6ClgH4mFwfpc1f9e6Clhv+XnjYAzWbzXkg3WPgdHZ3vvo7Ze5r2E6r0/zitVCiN6jesCdAZwqix2MvxLbOa/+YlTVl6I2b16Q3vx4YHrzMVDfnasA1QFesK7erT7ib7C+x/d0sA54HMwV+SWmcdsBueNgsuesG6zffA3WH2Wo7n2Y98vcdwaoV4Hp1UE6CUS/4Pm/Ov3SVBUpgvBvYr72std759r4pajvfKP9+MbPnA7U2/6G6u/ECtXbPue9GNVgfY+vEljv8V0B1lu+x8F806njYAzWzwbrii8uPWUMDCMkr5KhkpWx3+y+ykCdBaafAtKJxs7U63CPVDZ0ZRIzHH8W03nzS1Fn/A3S57zyYTpwJlCfyTVUf9yHr1t9R8Yq4Ntq6RMDAGd4UawSWAfqjYOp2WUdC/bP/hcAGi8wVQLrBN3qAAlYd7d63vasGaP7KGRl7AcYprN4NJ84RYJ0Ioj+xfOX/NLUKlIC4u/Eei1mru3ONdlxnGrz3CvDdMBAfTbfUP1PMXaS78hg6lYH1kFfVe/srvWKYL3lX/WsBYOrzVnv8TRYv6br63l8xzrNi0vdrR6z/Y4MZqheGcTP7HsyUGeB6aeAdLKxM+5wj1YF8N0jVkj+KIZzkrFOFWa6+6Wo44qA6cA8UAfO61Kfyd0J1QF3qxus1/LOButt23PnrDffU2Fw3jiYmv8CoNYLTA3WNeerrwTrbFCabXvWjJ37KO2XBdSzYXg1mB4Fq4tD9A+ef9TJDdwZQGkVKYDxV2K7Bqq/ELVl+aWovz0jveLqY+hOB2LWJwuoZ2UbqvPnGKxzeyuB9R5fz1lXgcEeB/NN0V3rKmC9bXut1myw3moI/8GgPFhnGgNzIiRnBuTVu9tHofrJQJ0FprN2pBee3X6TX5qaLVUQ/k3M11X2mu9emwoz3dlBevPjgulADaA+W4Nad/xOqA7wAu+RnB0Zp4D1Xn+D9X5fj4OJzc/sWvc4mCvZGuNgDNbzwTrLi0tPGgPDtj1rxs59MvZTA+rZMLwaTGcF6YQQ/YPvH+LucF+lbODKKGZA/iym8+eXos74a7wUtXnywXTAQD0i31D93T6cUH0kZySDCay37TleMrryBwEluJ49DqbV4K71b3LX+nd5HMyV7eLHwWSD9eYbD9dZurlPAOsnQnJmQG6o/lonA/WKMJ0ZpJOOnfFLU1WkBMQ/ifF6y15bvxj1qu8aVYbpAAdQj6jDUP2zdo63MVi/Lhb4zeStBNZ7fd21HpvvWetx27lr/Zoyu9aVwDqwDg5XB+tsUJpt+137sEN1FaAO5EH1TJjfPGb3rwnTDwHpLzxf1nlmh/ussgHtbjFC8mcxnBO/GHXUe5VvbM2MMB0wUM/Mnrkmds1Vb/vxZjGOgQEM1iO8Ddab3LV+Te5aj9tWoWt9BVjv8VUZB2Ow3rQKrJ/Uga6+PWtGRtYpXeq53fHzigDFLHU0n1gdML8dYALuDMBUXQpg/JXYzn3mOlYA6PeMVb7xtUfCdICrO735aAP12fyZ7N1QHdjXQb4zixGs7/jRgwV+9/ozgPVWR8+29cbBtBquetaCwYC71r+pWte6x8GEw3WD9YEaerdX9d6xPWsG+z5KXeqnAnXD9E9evGNnmue2+e2AX5q6R6og/JuYrx2GNa/4clS1F6OygnSAC6Y3n9w6MvN3j38BDNVncpTHwLD5rwLrQN2u9Wyw3nz5YTDgrvXI7Oi19DiYaxIZB2OwPlBD7/aq3hW2r7iPSpe6IlA3TI+t4+7FC9I3Q/Qe8XS4R4sBuDKJGY6/Esv5y1o3vxj1s6JBOsAJ05tXfnd6RB2KQB3YD9XbfrwjYEazThsDs9qfZRQMUBesAx4Hc1WZXet98DIaWp85DqZ58netG6wbrPdue4r3ju1ZM3buMwLVDdSv7DunWThbDaazgnSV2e3N922tfmkqg9Rg+DuxXkvZ6+uXol4XO0gH+GB684nw0AXqgM7ol7Yff16VbnXAYP2dDNbvUgHrgAYMBjwO5psyx8FUA+tA7jgYg3U9sG5QrrM9a8ZNlaG6IlA3TI+t4+4VJ4XZ7c03pM66He69yoayGWIF5M9iOTd+KeqYVkB0gBukN786MD2iDkWgDhiqR+S4Wz3X32D9ruxxMAbr1+RxMN+yPWf9m0TAOtAB1xngc2Wwbgiftz1rBmCgHp85pxkImt0ZH1HD3YdrdrsCRF81duaL6zxwZ4GhVaUCxV+J8drIWs9q89xXQXTgHJDevKJ8DNRHlfEyVkP1ptO61Vf7q728tNf3dLDe41ttznqPp8IPFZnjYDLnrB8M1qU61ldB9Z4aFLdl8mbcnjUD4IfqWiB+XNnd6dn5ETXcfWLE2iV/94yH6MGOfmlqr5QB+BWxXw/Z65+xPruOWQmiA2vOhWH6ujpmry+lLvXdmYbqXBmr6zdYv8tg3WA9yq9nWwWw3jxju9YzX2AqAtYBoY71yt3qqnUo18KcAeyD6jsht1qHunJ3OgtMPwGkC0D0/+n/+e7MN1ImG6iqiB2MP4vtvFZ/GSqwFqADayA6cA5Ib151xs4oAvW2L3+X+mjeaBbjCJi2D083+Q7/VWCd5ccGg/VcsA7UeoFpj6fnrMf4tW35X2BqsF4XrLPAbDbwrb79yD7uUo/ab1xZQD0bpjN1pbOC9GiIngjQe+WXpkZKDYJfEev1kb3Wu9dlNTwH1gF0YN35ij4PhunvdRpQn8k1VG9ihOqrM1ZCdcBg/XcNa8btZILgnm2rzVk3WL/iabD+PT90vE36KBjPV+fZlsm7wvYj+zB3qVcH6qrd6QwwnQmknwbRFwB0AF3ryNfhPqpsAJstVjD+SkznKmPddsDzmwzRb57Rfobpz8oC6m3/vV3quzOZoTrAB70Z/VnA+sp1MVg3WL8qv8D0myf/C0yLgXUguWPdYF1z29XbM9Uysv3IPtWguoE6Z+5sdlQNUXU0nxhFgnQViL5ivvy/8gz3XVIC4u/Eeq1krm0VeH7TynN8EkhvXlE++kAdOKdLfTTTUJ0zg2UMTKulZ1uD9btvLrD2nPU4v55tFeasn/oCU4P1a1IC6yzQmQVmq3rvyjBUn8ka1yhIPRWos8D0E0C6EkTvXMc6He49qgC/r4oVkj+K4XzsBOc37QDogB5Eb74rPGvCdCB33MtNakB9Jnc3xK8E1UdyGMG9wfpvVQXrPb4G67GeHgcT49e2rfMC0xVgHVgDlivOWK8Ov1nqqLC9obpOl7oiUDdMj63jpkjsrQLRV7yk9e4dDNwZwKmqFMD4K7Gd8wxwDuyD58Cea+VUkN78onw4YDqQD9Tb/obqnzR6nlnBOmMG0xgYwGD9dw09vvXAOuBxMFfkcTDftuMfB7PgBaYG6wbrNNuu3p6plpt6wbqhuoH66tzZ7Ij8iBqi6gC4QboSRB+o1S9N/SRVCP5NzOc8C5g/aic8B7QB+t1/hScnSL/7Gaj/3l8LqM/kGqrvzTFYj/E2WO/3ddd6rGelrnWPg7mS7XEw35QN1nu2zc4/pQ7G7Q3V60J1RaBumP5bUWg5EqQXh+gd3v/hHSlTFXb3ihmOvxIDML9pNzi/ade1qwjRmy/3LPfI+qL+HtSB+my+ofpr7To2VnhvsP5aq8B6q0MHrlcE6z357lq/kp3Tte5xMF9lsJ64LQtIZtj2FG9gzwgYbkDODdUN1PdkR+RH1ACcAdJXQPTFAL13F780dVZqQPydmED5o7Kg+U07/z52XEtq89xZu9KBWjC9eZwF1Gdyd0J1gBd4j+SshuqAwfo7uWvd42D8EtNvfvxd6x4HE7ddz7YrxsEwAGUDe646dmy/uludG5DvgeoqXeqKQN0w/beiYDo7SCd5AWqnN4BKL02tAr6vihWQPysbmN+U8cPSrmtSDaI33xWeNWE6oA/UZ/MN1d/twwnVR3KUu9Xb9hxgvXLHeq9v5viSVfkeB/Mt2y8x/ZybNg7GYP2isrfNzu/d9oQ6RrZnHAGzA5DvfEmpQpe6gfr+fCAGpjOCdAWIvgGg9+o1cD8NXq+SChR/JRZQ/qisf42x++9h9XGuPB52kA4Ypq+qQw2oA4bqCjnKYL3X22B9zNdgPdbT42DiPD0O5rOqgfWe/GxInJ2/clvVOka2Z+tWZ4Xq7lKPy5vJzMydzb6JBaafBNJFZ7ff/t+zXpqqDMCviBGSPyp7fFHGtV5hnrvKLPfIv28mmN58DNRHpADU236csHtXzsh5Uh0DAxisj/p6zrrHwXz28ziYz9l542AygbHBej54ZthWtQ7AUP36Pv2q3KWu1hWfnQ3Mw3S2rvRIUK0A0clmt9+0ZqRMdbA9I3Yo/krZoPxZWT8SVYDn94xVvvG1R3+eGKbH15CVP3NtVIXqI1m74L1yt3qvP8t89VZLj7fBusH6dxmsf1fWOJhTwToQ/wLTbECbne9t921vqH51n37tguoG6utyZ7MBDpheHaSrQPSVY2ea/y/9GI7/K0UQ/klskPxR2f+qYvfaKAP05r2mflaQDtSC6RF1ZAL93V3qAD9UZ85ZDdUB3W51gOPFpc3bYN1g/ZpOnLNusP5Z7ljn92TYlgV+s9QBrLt+R2phhPBtn97tubvUDdTX5gJ1YHoUCGaG6GoAPaDe/JemVgPdvWIG48/KBuWPqj7PXXWO+4of8BhBevPi6ZQ/EagDGl3qo3m7snZAdUC7Wx2oPwYGMFjvyTdYv5LND9bbtn6B6SdFw1V3rJ+bz7Ltam9lqL4jw13q+7MUM2/KhukG6Ve84iU+t/1RP8cD729SAuLvxATKb8pe151rsuNYVx7Pqn8FE/3ZY5i+rg41oA7UheqsnerAWVAd0ATrPaC6x3sF1AYM1hXAesvPeYGpAlhv2SkvMDVYD9w2GxRn51fftnd79REwhuqcOaNZM3kzmbO5hulxdTQfXoiuBtAD683vcL+ibDi7U4xw/JUYzolfgjouFYgOcIL05hXhoQ3TAQP1FXm7shihetuHB6yvhOqtlh7vfFBtsL6mVoP1OE+D9c8yWN/vqZTvbX9LuVu9AlRnBuqjWaN5GUD9ZJjO1JXOCtJVZrY337SxMz8U4FRNKlD8WYznOnMtd67HjuNUguhAbZDefPJhekQdakC97VsTqrN2qgPuVv9cS693Pqg2WM8F60D8eB2D9c8SAOvAgjnrBuvOV9h2pffKbnU+SG6ovjpnNEspD5gD6hVgukF6j1+8hDrmNTrcv0kVgF8RIyR/FMPaV3wJKrAOoAMaEP3uWROmN5/8OgzUa+Sxvay0bb8+g+Wlpa2WHu+689WBfLDe41sNrLf82DnrfT9U5LzA9CpYB/JeYKoA1nu2jQbrPdknv+Q0O19xW4CnW10dqjPDbuac0ayMPCAPqBumP/vEiB2iCwH0J/+vdfcDdwbAyih2MP4stvNY/SWowFqADqx9AfEpIL15ccD05qEL1AFD9egsj4C5JtUxMIDB+kgNButXsvlfYNoBo9NeYNp3XcaOg8kEtwpgfYVnNijOzlfcVrVbnQ2qt334OtXZu9QN1L8rG6hn50fVcffh7JJvfjojZ5r3Euj/Qwdeo6UGwr+J9Xxlr3PGuqwG6IAeRG++fhnqdZ+8cS+AJlCfyVWA+FVGwIxkMIH1lWNgAIP1kRoywXrPtplz1hXAetv2uHEwEnPWV3QAK4D1ip7Vt3W3+rvt+8QIvCt2qSsBdcP0eTF1yDevWLF3y999181tb/6XtGekTDaMzRIrHH8llnOUtWY74DmwFqADOhC9eUb71YLpgDZQb/vvBdwZmZWgettnfUe8wfq8t8F603VgnXtcCnPWAY+D+ezHPw7m1DnrFSF4dj7LtqvAOhMoZ4PqOzIM1eeygJwO9UygzQCx2brST5vb3nzlRs/4palKUPyVGM9f9prugufAeoAOrD3HJ4H05hXlE1PTqUB9JlslsxJUH8lhguoAz4tLe70N1ps8Duaq53ld630/UuR0rXvOesx29qy7rSJYP61bnRHcj2SM5uzOUgPq2TA7Oz+qjuYRJ3aQLgjQ//W/XDffS1OzYe1qMQLyZ7Gcg53g/KYdAB3Qg+jNd4UnH0xvXvnd6cCZQH0mdzfEHz3HrGBdvVsdMFh/pRWw+nSwDmh0rV8F6z2eHgfzWR4Hs9dTBYIzQG2GbRnAOheE75OhOl/OaBawf+yLKlBngNhsXeknjZy5+67TwvEz9We4j0oBjL8S2/nMgObAPnB+0+rrZeV5ZQfpzS/Si6M7HdAG6jP5Kl3qQD2oPpKzGqoDa8H6yjEwvf7Z8HdlHdlgvWfbamAd8EtMv3tyd60rjIMxWK+V37MtA1TvrYMFwrfte7Y1VK+QA5wD1LNhdnZ+RA13n3NAutrYmbt/l/g63D9JFYJ/Ehsgf1YWML9pNzgH9lxnq8+7yiz3qiAdiLl2VYH6bLYCVB89v1WgOqDdrQ6sg7692zOAdaWO9Z5tDdavbMcP1lu2x8G8k+es7892Pke3eu/27lbP8x/JYM8BxqB6xotJs4C6YfqjD1eXfPM6G6KvHj2Dq8C9IujuFTsYf1Y2KH9UBjS/ade1qwrQm/c5c9wrwfTmkTPyJStboUsd4IXqAN8IGOCMMTCAZ6yP+GaC9ZYfO2c9E6y3bT1n/Z0yQWelOesqwDgbVqvkAxxgnaVbXR16V4Hq7EAd2N+lrgjUq8D06iB9BZRWBegDddd4aaoaDH8nJkj+rBOg+e9Mvwz1T894RdZpmB5bg9r8dvbRL22/GlAd0O5W7/U3WL/LYN0vMP2e7Tnr72Swbs+dnr3bVgbrJ42AYYTqrOAeqA/UDdNza4io4+5zHkQXndv+P/0ncqRMFeh9Vcxw/FGZoPxZVcH5PWu1/5kvQ438W4v6ezBQ35+r0KXe9jNUv+7PA9V7t2d4cWlvHQbrBuvfZLAes10lsO7s2G2z89Wg+krvldDYUH1NxmjOzrEvBuprc2ezo2qIqqP5cIJ0JYC+Gp5PuNd9aaoKEH8lJkj+qMx/DbH7OlWf484O0YG6IL356ML02fyZbAWoPnp8O6A6wDdXvWXwgHWWbvXeWgzW64H1Hs9osA7kvcD0RLAO5L3AtBrcVshmyFcD6yd0q7NB9UqjX3Z2qasAddXu9Eowna1DvnlpQPSVAH19X/uvz5b1L01VBt9XxQrIH8UwOijrx50Kc9yVxs9E/81Xg+nNI7cGA/Vv+50N1QF3q38SQ7d6r/eKmhleDGuw/l3kYB1IfIHpCoBpsD6/nT1zwTpPB/qabnUm8M3YRV4Jqhuox2bO5s5mM+RH1HD3iRP7y09XAfTV8HzmBcf/6ucIIA5oQPFXYgDlj8r8FxE718Iz3P8UK0gHDNNZ8meuEQWovguoA4bqK7Z3t/qYr8G6wfp3z5wXmBqsO/sETyB3TBHLtm37nm15wPdp/iMZ7GNfTgDqmTC7Ulc646iZ5herFRB9JUAPgOdf9WKN13e4v5MqAP8mNkD+KIbxQZXnuKsBdGDNv0CpCtKbT4SHgXqP3KV+FyNUB7jA+spu9ZW1GKzfajgTrAPxc9YN1j+LHayv8KxUY0VPj4G5bXtdHgGj4w/U7FI/Baird6ZXBensEF0VoAeu609Z8P1NzGD8lRhg+U1Za1fpBagt40yIDsSubyWYHlGHGlAHDNUftQOqtxweMD2yPcsYmN5aDNavg+CWb7D+PbtnPXPmrCtAzBXjNtiBucKxVPQ0WL9te10s3erq0Pt0qK4A1P0S1P35ETVE1XH3ipMKRBcB6G/839ae1+H+Tmog/JOYIPmjstc4Y12UATqw7l0IzCC9+fHA9OaT250+W4OB+mcZqvd3tzOBdZZu9V7vqmC91eCO9W86Eaz3bJsF1nuy2bfLzFaB4BXBemWofoo3oz/QD9zcpZ6bN5OZmTubHVVDVB3NJ0YKEH0VQE+E5yN6D9yzoexuscLxZzGdF78EdU4r359wCkhvXlE+2t3pgB5Qb/vWhOqAR8BcleoYmF5vg/U1YB24Dtf71jV2zvpVsN6y/QLTd8p8SWQlGF2pxmxPg3UeQK3arX7qTHV3qefmzWQyZM/mR9UQUcfdhxukr4DoKwH6qpe1/s74qB8qgPtJKkD8nVjX+ZSXoLa89ceqBNFvMky/6pFfx+z1Zaj+XsxQveWc1a0OaI6BAQzW7zVc7dyuBdYBz1mP2pZ9zroCjFaoUSXbYF0TfjOBbzZ/Q3X+LMXMiGyG/Kg67j4x9ZwM0VcD9AV1z42UUYfg38QKyW9iWf/KL0JdCdABHYjePPlAevPiGTtjoL4v01C9yd3qn2WwfldFsN7j63Ew38Xete4565zZKp3omS/V7ck3WO/3PcV7xP90qF69S11xzMxsdkR+RA13nzNAuiJEXz1+pmV8rP+HBtrOiB2MvxLbumeu4c61WA3QgXUQHeAH6c0v0qsOTAd0gXrbn79LHTBUH80xWH8vg/VbDZ6zfi3f42A+qUrXeiUQrnAsqzwzwXrPtgbr/b5M3jv8V89VN1TfnzWTN5M5mzubzZAfVcdNUUhZAaIrA/QFta99aaoiCP8kNkj+KIa13r0+O+A5sBagA+vOHTNIb348ML355HanA2cBdcBQ/VGMUB3gAuss89V7vQ3W14D1tq3HwXySwviNLLC+wpN9u5Oz3bF+XWpg3d3q77XjZaXMUF1hzMxoXlbmbO5sdkR+RA03nQLSV0H04vPbAaUZ7j1iBuOvxHQOstZuFzwHdAF6846vnRWkN68on/zudEATqM9kjwJ1wFB9JuekbvVef4P1xxoM1q9l84+DyYSeHgezb7vMbIUaAXesqwFwBt9e715/Q/UaOaNZGXkzmZm5s9lRNQCcIF0BoiuOnblnLNHaDvdXUoPh78QEyR+Vvb47wTmwHp7fpAbRm2+0Hx9Ib176MB3QBOqARpc6YKi+GqoDZ4yB6d3eYN1g/YKOnLPek11lu8xshRp7tlW4Htdue3W72vBbtVv9VKhetUvdQH1PdkQ+EAPTWUG6CkQXfPnpi4xLx1Bjhvs3scLxZ7Gci93Q/KYK8Lz5nzvDnQ2kAxwwHTBQ79GuLvWWdS5UB9ytHrHtyh8zDNYN1ndvdxVkAnXmrCvA6ErH0rPtyWCdAYCr+TJ5A3xz1Vf/6DCSwZ4zmpWRN5M5mzubHZHP1pXOCtLVALrg3PZH7e9wfyUVIP5KLJD8UVnA/KZd4BzYd+2cDNGbX6wM05/3z+uOVxn7Ahiq33TSCBjA3ep/1nEVLPfUa7B+zbMOWAf456xXgswnHnPPttXAusfA8Piu9jZUPzNnNGsmbyYzM3c2G+DqSmd7IWvzipfntr/L6NKfwF0Zfn8TIxx/VjYsv2knNL+pAjxv/qt8z5nfHvV3wADTm4eB+hXtBOqAoXqvWEbA9Pqv/AFBqVsdMFgPButA4gtMTwTrmdk+5phtDdavbnsNGqjBb9VudUN1jozRHIUsxcyIbIAHpp8A0lVGzjRf/bEzYHxpqgIUfyUWUH5TBjC/aec1teN68fz22DoN02NrUALqAH+X+mgWI1QH3K3+TpXHwABrwHoHhDZYT9gO4AfrKzzZt8vMNljP8ry6nRYAV/Pt9T4NqjN3j7N3qZ8E1LNhekWQzg7RFQH6Jng+cgzXRsqoQvBPYgPkj8qE5Tdl/BCz6zpTneHOPnom8m+qCkyfrWMGpgP756gDdbvUR/czVI/3ZxgD0+owWDdYj9uuZ1uDdd3tMrMVrsWe7PxZ7Fe30wHVLKNaWEbAGKqvyakK1DPAdmZ3ehWYzgjSFSC64siZe8b60TMth/ylqcxQ/JUYQPmjsv71ws5rascxnj6/nQ2kA3HrptydDmgB9ZZpqA5wQvWRDJZudUBvDEzzjgfrK8bAAAbrkdv1bJsF1nu2PW27zOxK12JPdu45vK5sAJ5d60rfXu+V3eonQnVWcD+aM5qllDeTCRimR9fRfLghuipAJ5zb/qjxl6aqwfB3YoPkj8oe97P7xxjPcP/mywvSAS6Ynt2ZfpMiUAc0utRH83YAdWDfjwTuVn8vBrCe3a3etjVYvyLDzDO2y8xWOOZK12Kf53UZrHP4Ajzd6obqNXJGszLyZjKBOaA+C9MN0j95xUppZnvzLjG3/d+s/wDAjxQ4Z4bjj8oG5Y/K+hcMlea4q81wZwXpAE9nevOYUyZMB7SAOmCofpOh+nd5DMyYr8H6tQ0zwexVmAnwv8DUgHt+u8xsg/Ur2xqsM/i6Wz3PfySjYs5o1kzeTGZ2d3r2S1gjarj71B830zzX6NC57Y8a73B/JxUo/kpMoPxRmWN/dq9JhTnuq47hBJDevPJhOnAmUG+5/F3qo/uNQHVgPZAG+EbAjPgbrP+WwXrfOb54XJfBOpALAD1nXXe7zOwTf+RZ53l1u3pgnaU7exVYP6lbnRGqMwN1hazZzEygXgWmnwDSlea2N++12jW3vWX9Tz8G5IvFMiO/8ktQW9Zqfw2IDsT/6MXUld58YmSg3i9D9d9Sn6s+4r/6mFeNgWm1GKwD18F6q+GabzWw3rOt56zv2y4zW+GYT7wW27ZXt6sH1nt8GWoF1oF1JvDN+BLRKhkqWTN5quNeDNPf+fCOnGment3+PmNa8R3ur6QAxl+JBZbflLmO1ea5rz6ek0B68+Lqlp+F6YAuUG/Ze7vUZzKrQXVg/QgYwGD9cy0G64AMWAc8DoZ2u8xs9u0ysz0O5tt2NWG1Uq2qY2CYoP0Of9aM0RyFrJtG91QG6iww/QSQrjR2pnlLwPOOrP8dz48sDH8nNkj+KIa1zlifKi9DXfW+A2aQ3vwM019pdl1O6VIf3bcaVB/J2XEsqmNgWi0G68AasN5qOHPOOuBxMIzbZWZ7NNH8dn2e15U5YsVgnQesM4FvQ3W+nNGsmbwMoJ497iU7P6KGuw9Xl/zdj3/sTPOt8fLTgOPY0+H+Tsxw/FkMsPymU2a6qwJ0YM27DKqDdCAGpgO53ek3qQH1mdzR/QzVm07qVgfWzVdv3gbrrQYNsA7kgr1KncIaIPWs7Xq2ZQfrKzxVILjBuuZ8dTbwre4/krEzZ3dWxtiXTKBdBaYzgnSVbnTPb+/J+58+A3clIP5KTJD8UQzrWvVlqGoQHVhzLgzTPyuzO73lawH1mX0N1Zt2vHiVCayv7FZv/gbrrYZzX2Das+2JYD0zm327zGzPWf+2ncF6pGevr+J8dVXvHf6sGTtzRrOA/V3qquNeDNPf+fB3oyvOb98Fz4OO4YcC/t7ECsgf5fWqAc9vOhWiN784VYLpgIF6j3YCdaAWVB/JYYLqAM8YGMBg/V6DwfoV1RrBUWO7zGx2sN6TbbC+37PH12D9ti1HJzyT94g/awZ7DnAOUM/uTmeB6YwgnX3kzN13nQrPbwd6R8ooAPF3YgLlN530EtQd8BxYB9CBdefrBJAO1IHprY4coD6TPVOzofpdjFAdcLd6hLfBusF65HYrPA3C+bZTuBYN1rk9V/lWHwPDBr7V/VkzZrIM1NfmzmZH5EfUcPfhBOkqI2ear2e3P4nzpamMcPxZLOuWsVYV4Dmw9hyuOC/R9TJ1pQM1YDqg16EOaAB1wFDd3eox3lXB+skz1gGDddbsKtsB/HPWDda5PXt83a0+5q0Ovdn8RzJ25gBjUF0JqHtue4zYxs00r3NHzjTv9do9u/1F2txLUxXA+LNYQPlNmWu4C5zftBqgA4boAF9XOlAHpgNnAXWAv0t9NMtQ/ZpWdqsDemNggDVgfUG3OmCwftmTHXyeeCwK1yM7WO/zvOqXB5dVwLrHwKz1ZfJm9GfNGM3Z2aWuBtQ9t50PpJ8M0au8+DQ4hWuG+yuxAfJHMazdbmgO7AHnwPpzv+r8sUN0IA6kAzwwHcjtTp/NPwGoA/u61AFD9R65W/23srvVm6/BemR2z7YagLTGdpnZ7GB9hachOLcnYLA+4svkzeg/kjGSU7lLXQ2oZ3enZ+dH1XH34Rw30/z0APoOeL63t/3tZ9Jch/ujmMH4sxhA+aMyoDmwD5zfpArQm/caVQfpQB2YPluDgXp83q5j29Wt7271T9sbrEMIrAN5MLNnWw1Ayr1dZjY7WO/Zlh2sr/BUgeDZYL0yVD/Fe8R/RwZzl7qBOmfubHZEfkQNdx9OkK40t715r8PbO8H56OfOB3HOcH8lNkh+UxYsf1Q1cH7P0ZzhHg3RAU6QDvDAdCC3O312/wygDhiqz2wPGKpH+/dAdcBg/SZ3rHN7sm+Xmc0O1nuyDdbn/Sp6ulu935fJu9ffUL1PO8e+7Abqhul5NUTVcfeK08kjZ27aAdAXgPO3+nIO4jrcH8UKx5/FAMtv2g3Nb9r5g4sqQAf4ITpQF6QD+d3ps/sbqH/WjtEvo/swQnVgLVhfCdXb9vnd6q0Og/VTO9ZXeJ5YIztYB/jnrEeD9bZtLGCuBsEN1vV8mbwr+I9kANxQXalDXbErfjY7qoaoOppPjE6G6KuxNhE4HxX/DPdXYgLlN2UB85t2/0uFXdeNGkQHuEE6YJi+Yn81oA4Yqj9qB1QH3K3+SQbrTatgeSZY78mvBK0zs9m369mWfc76mrW5up3B+m5PJbDO0lXO0q3OBr2rQPWdo19UutRPBeosMP0UkL4ColcA6Ktf3vpn3sdjWtPh/ixGQP6obFh+U9Z4nwrw/CYViA7wgnSgFkyP8JgB6oBWl/po5s5Z8Ybq18XUrQ5wjIFpdbhjXQGsr/BUyK5UYxWwvsKTHayv8MwGy6eDdRZArdqtXmFuO3OXetuPO2smbyYzM3c2O6qGqDqaD1eX/N1PB6JXgucLx+j8Bu7sYPyVWGD5TZkz8Xf/awVlgA6sgegAN0gHuGA6YKBevUt9Jo8VrLONgAG4wHoPVAcM1nu369222px1Z2tvB/DDdXawvsKzUo09noDOy0sN1jm9Gf2BPZ3q7lKPy5vJnM2dzY7Ij6ghqo67V5yiga4iRC8Az9/kfdRPCmRng+SPYniJbMaYn53HvRKiAzogHeDsSgdqwXTgPKA+k8veqQ7U6VZvGQbrr+uoC9ZXbVsNrK/wVMiuVGMVsN7nedXvvHEwmbAe0AHrPb7V4TcT+D61W50dqhuor82dzY6qIaqO5hMnBZC+CqLvAOi74PnCY7k2UoYZkD+KAZY/Kms+fiV4DqwD6MC6c8TalQ5wwfQoH1WgDuzvUgfcqf6oCt3qvRkrx8AABuurt10B1nvyFYDwqdnsYB3gh+vsYH2FZ6UagTXjYLKBssE6p/cOf9YRMIbqcXkzmbO5s9kR+RE13H1iFAl6DdFv3vLw/E3elxnuDDCdDZTflP1C2Yx12QHQAUP0myJBOmCY/k6KQH0me/fxVutUB87rVgd4wHrvdVEVrAO5c9Z7tq0ErTOzFWqs0rV+4ktMq42DqQjWV/myAGqWHwMY/SuNgGGH6hlw+2SgXhWmR+NjNYi+A6DvhOeLjmfspamsgPxR2bD8UVnrVQGe37TyfJ4E0gEumA7kdqcDmkAd0OhSBwzVV3ert+05XlzaajFYH9m2Glhf4ensvdspvFS3Ste6AgjPrHEFWG/5V7fz7PaV3ixd9r3eI/5VoLpfiBqXN5PJkD2bH1VDRB2PikSv0SBdFaBXneHeMt/qhw6eM4Hym7LXaBc4v0kdoAPrXgDMDNKBejAdMFDfmWuofssZOZ5zutWBdWC98zjLgnXg3DnrKzwVsv0S053bXVOlUSsKNQK5c9azgTIDWGeB30zd6owjYAzV9wN1tcyIbIb8qDpuOg2kr4TPqwE62ctPRzXW4f4sRkj+qGxgDuyH5jftSlUF6EA8RAd4QXqkFwNMBwzUd+w3ukaG6iPHr9mt3moxWL/pZLC+Il8hW6HG08B62/bqdtwgvFoXvMF6rKeiL5P3iP/qbnVWqO4u9bi8mczZ3NnsiPyIGm5iBelKEL3C/PaWtV8fju+HGpYzgPJHZUFzYB84B/b8gLISoAMaEB3gBOmAYXpEftbceEP1x5z1UH0k55Ru9ea9ZgwMkA+LAR2w3rNtNc8Ta2QH6ys82SGzwfpnqYB1BlCt5rvS21CdI2M0RyFrJm8mczZ3Npsh/6YoVMsM0lcAaXWAXmB+OxDV4f4oNkh+UyYsvymjgl0/qCgC9JsM0vvFANMBA/Ue7QTqgKG6MlQHeMD6Kvjcu73KjPWebat5nlhj5g8+p42DqTS6JfMFpgbr9u317fXu9TdU58tRyJrNzMydzY7IB+qDdCWI7vntM5kfxTfD/ZUYYPlNWZXs/pcIygAd4IfoK/wM02NrUAPqgKH6oxihOqAN1j0G5i6DdX5onZm9okb2rnV2sL7Ck91vhafBep4vS+d39jqMeAN8YP10qK7Spa4I1KvA9FNAutLYmea9XkXmtwMrOtxvYoLkj8quKmOEz2p4DmgCdGBN3ZVBOsAzA14VqM/srwDUgVpQve3jbvU38hiYwRqqQXAVT4UaTwPrbdur29UA4QqAuedR9yoUUTjuHs8eXwZIzQK/T+pWZ4Tq7J3jBuprc2ezAS6YzvZS1rtfvBRntzf/PfA8Y3Z7y/14fHfgzgrIH8VSYdbc+x3g/KbVAB3QgugrfKNAOmCY/kondagD/F3qLctQvUcndKv3bm+wbs8q2VlgvWdb9q51dr8Vngbr+z17fBkAOIPvau8V1+29lj6xAu/VxzGSoZKlmBmRDfDAdEaQrgLRPbu9N2/pMf1sAe0soPym7BfF7gTnwB54DqwD6IAORAc4QTrAA9OB3O70iPzqY18AQ3WgH6oD0t3qAMkYmN7tDdZ18itle876p+2uySB8nx8QPw5GBVireFb37fU+rVvdUH0/4DZQ71dFmH7yC1CrzG5vWfvnt7fcj3o9UoYNkD8rG5jftBucA/vgObAWoANrj4UdpAOG6Z90IlAHDNUjcirMVQfOGAPTu73Buk5+JWDes63HwXzarga4PvEFpirAWsXTvr+1Eqwbqq/JGM1RyFLMvKkKTGftSlcYO9N8Pb+9L2+pfrajaxZYflMGNL+pEjwH1h+PAkQHOEE6UAemR9ShBtQBQ/Xf+6yH6sBZ3epAPvi9qSpY79lWJf/U7Giw3pPtrvU9fis8T3yBaUUIzgCq1XyBs8bAMI6ZYQfdp3Spz+RmA3UmmH4aSPf89itZObo8w/2T2CD5ozKB+U07wflNOwA6oAnRAX6QDvDBdMBAHTBQ/6ZdUL3tpz8CBuAC6yzz1QGD9d5t7bl/O+C8rvVMQGq/98rqWlcB1iqe1X2ZxsCcCNVHcjz6JS5vJhOYA+oM3elsMJ0dpCtC9KovQF14XD+pMJ0Blt+UAc2BfeD8JlWAftNpIB3ggulA/riXCA9D9c8ageqjead2q7cMjjEwvdsbrPNsW83TXeu7t7umSuCavUZ3rZ/nqegLrOtYZwPfbP4jGew5u7NG82YzM4E6S3c6I0xXAOmK42fuGfuUNce9Zb/UtQ73V2KC5TdlQfObqsHzXTkrIDqw5nwwdqUDPDAdMFAflaH6b+2A6sBZY2B6tzdY19y2mqdnrUd5XvWrAdfZ/YAzu9azwXI2rM4+/l5f4AywzthJXg2qn9ClnjnyhaE7nQ2ms4N0VYi+C6BnwPMFx/ZDBc6zgflNu8H5TVUAOrAOogP8IB2oC9MBfaAOGKqvyKsE1QGD9U9iAOu926tA6Ir57lrfvd01uct8n1+lrnUVCJ4Nqw3WH+vo06r16PXe4c+asTNnNCsjD8jrUq8C1BlhugJI94tQe7Ly9OI4xzvcn8UCy2/KgubA/rVQB+jAuvPFDNIBw/R3Og2ot9yaUB3gBeuLoTpgsB5Si9K2FfNVYL271j9tVwNcK3Rbu2v9LM9Vvgbr+7wZ/VkzRnNGszLyTgXqFWE6O0hXhejV4Xnw8f3QgfKbMoH5TRlrsytzNUAHdCA6wAvSAS6YHuETsdYnAXXAUP1RZLPVATKoPrK9O9bXblsxv9I4mJ5sd63P+a3wrNK1rrCGKzyVYHW2L8vLS1VfXMoIvSt1xCtk3TS658lAvTJMVwDp6gC90EtQL+b/T3Ed7o9igOU3Zf6gsDNbGaAD69aKGaQD9WA6kNudDswBdWD/2BdgL1Bv+9WC6sB53eq92/dAdcBgfWTbivkK42AA/q71vnW8ul0NcM3uB/B3rZ8K67M9e3xXQeoTwDob9GYF3js64kdydmcB5wF1lu50ljruXrFi75j/7e057nO5YfoN3JlA+U0MHfi7a9gBz4H151sFogO8IB2oBdMBXaDesmtD9RGgDvBC9ZbjbvVPYnh56Urv7G0r5p84DmaFJztozgKZmbCVHayv8FSoMdtzle8qsN4LDbPXYcT7RP9dGSM5u7Myxr5kvIQ1IjsiP6KGqDqaT5xUutFVx8/8ztkLzzNnubf8/wDAjyx07VVWHbvgObDnB5OV63gaSAe4YDpgoK4E1Nu+huotZy1UB7TBOst89ZXe2dtWzPc4mKjtrqkSdGX3O3EcjErXtopnj6/BOqc3o/9IxkjOznnqJ3SpqwN1w/QrXvFShui74HkmNA86xvcjZVhA+U3Z9ewE58C+f22gBtBvOgWkR3pVgOmAHlCfyd05+gUoBdUBd6t/VTYkZqpDBVivyM8E64DHwdiv36/Hk71rvdIPMhU9e3wZwDoL/GYC34wjYFjHzOzMAfZD9awudeXu+Iga7j4xYgbpq0C08viZe85eZc9xB4D/7HxpajYwB/ZD85sqwPOblCA6cAZIBzhgOpDbnd7ytYB629dQ/Z7DB9VH9mEC6yzwe6V39rYV8yuB9RWe7NC1Usd1FbC+wrNSjdmeq3wZwLrqfHUmaL/DfyRjJEdh9IuB+p7siPyIGu4+Z4H0VQB6B2jeCc+zwHlQ6rWXpjLA8puyoDmwf8a9OkAH1h0DM0iP9Is6PwwwHTBQ79FOoA7UgurAekjOBNUBTfjNUofB+jVlzVnv2TYLrLdta4Brdr9osN6yr27HvYYrPLM7wVVmtxusj/n2evf6M46AqdaprgLVDdTzaoiqo/nEKhrmKkL0qjPcgTBoPqN9He43ZQLzmzJeDrtrnXesrwpEB3hBOsAF04GzgfpstqH6cw4nVB/ZRxmss8Dvld7ZEDp7W89Zj/S86scNXRW6mat0rSuAcIUasz17fNXAuuqoFiZoP+LPmjGaMwKtlLrUFTvjZ7Ojaoiqo/nESQGkK0P0nfA8E5rPvBPiX13rcH8UAzAHcqD5TTt/pFAG6MC688QM0gHD9Pd1zB2PWpc6YKj+pBIjYACD9d3eStuq5HsczLft6oBrdr8ssN62rbGGKzxVxsGsgtUr4Hp1sM4GvtX9RzJGcxSguoH63vyIGu4+MWIH6Yoz3Jv/HqydAc8DoPmMfo4a0fKsjFE56gAdWHveVtTO2JUOxK6jgboeUAf4oXrL4uxW3wHimaD66u1ZvKtum+1ZqWvdLzHd61kFrvuc2POdKnetM/j2evf6M0Jv1jEzhuqxmZm5s9lRNUTV0XziwKgCSF8J0XcA9N0YOxOcT5yr/g73R2UD85uyZszv/LHCEH29r2H6tzoiXsyqN/YF0OhSB+pB9V37GKzv9/a2azwzu9Z7tnXXen2/zO7gKmu4wlOh+7/Hs8fXYH2tL5M3o/9IxmjOLrCuAtVPBeqG6Ve8dED6aoi+C2dngfNds+qf9EMDzYHcl7Pu7vTfcayrz60KSAdqw3TAQL3tW79LHdgK1QFisK4+Bmb19izeDDUz1Ouu9UjPq378sLAKGPZImH1+KzyzO8wzu9YN1s/y3uG/K8NQPSZvJjMzdzY7qoaoOppPrNjHzzTPdfC5KkBPAucP+V3HO9fh/kqZ0BzImTG/85hVIfoq7+jzXRWmA/pAve1vqP45rxZUH9mnF6oDBuvs3tnbZuef2rXetr26HTckrQTrPRJm3rMSrM/2VOpaZ+n+ZoHf6mB9R7c6+wgYFaiu2qVuoP7O51yQvgNt7wToWfB818x6vALu2cD8pszZ8pUAOqAH0YE1558RpgMG6r/3n1sLJagO8I+AAfZAcsZudeAcsL6yFm/b56nQtb7C05B03m+F52lwXeGcnNwJ7651Dl8m7wr+AHe3uqH6utzZbIb8qDruPtwwXRGk7wLoGfB8Izj/UMNH/SyFsZnQHMj58WDXiJ7Vx3YqSAcM01fWoQjUgdqd6sAeqD66D9sYmJF9DNa97bOqda33eV71qwMg2f08Emaf3wpPhRp7PFd0rTOAajVfJu8K/kA/VGMfAaMC1Q3UZz1ixDrHvfnFg1tliL4bnmeC88XH+n2kTDY0B/K67nfOt99xjGo/rqxY/6owHTBQBwzVP6kSVAe0x8Cwbc+wLUMdq/LdtX5lW24AqdDRzA7XfU7m/VZ4umvdwJ7Ju9efcb56tW51Q/V1ubPZEfkRNUTVcfeKkxJIXwnRdwL0DHiePdf9phfH/rMNqGePqtn9clh1gA6s+7GFHaQDNWE6YKA+KkP1mP1OGwPDtn1lWL5q2xVgHXDXeoZnFb8ez9Pguq9DXs/Tu9Y9v32f9w5/gLdb3VA9Lm8mMyKbIT+qjrtPnKLB7gpMrA7Rd8PzTHC+6Fj7XpqaDc2B/eAc2HfcO3KUIDrAC9KB2PPF0J3ePAzU+3P5ofroflW61Uf2Ydr+BG+lbVeA9Z58d63H+K3wdNf6Ps8qfis8Feb+AzXBenVfJu8d/ju61Q3Vc/NmMmdzZ7Mj8iNquPvEiB2kK0P0XQA9C5wzzHS/6d81WDvD/Z0yoPlNO49XGaDfdCJIB2rC9OajC9QBQ/XofUb32wHVAS7orb49i7fatpnjYHq2XQPrr25XB0AqHDM7XGf3W+GpcN1UGwejBqrVfJm8d/if3q1eGeDPZM7mzmYz5EfVcfeJA6IqIH0lfN4BmE+a536vIUx9He6PyoTmwP5u+115O0b8qEB0oD5Iv4mhOx0wUB+QO9UftAOqj+xz0vYs3mrbehzMVU8DyF1+7GB9hWcVvxWeCj/yADpd62oAnAV+q3oD7lY3VOfLnc2OyI+o4e4TA0oVQPoqCF0NoGfBc5aZ7gB+jgPnuzOVAfpNCiAdMEy/ImWgDtTvUt+93whUBzhHwIzso7w9i7fatiePg2nbXt3OADLCswpcP/GcnHjMQL2udTUAzgKoVb0Bzm51ZqiukDWTN5M5mzubHZEfUUNUHUAsTFcB6auh8y64fNI890dNHvd4h/ujMme7787e9ZJZRYgO8IN0gA+mN5/87nTgSKAOFIfqAG+3OiNUX52hCu1ZjlFlznrPtn6J6X5PdrAOnAfX2f1WeLprPcezx9dgndMbWA/Wd0BodtBtqL42dzY7Ij+ihpuYYboaSK8yy71l5YlgPE0D7gwvQ82qoQpAB9ZBdOA8kA5wwXTAQD0DqAOG6s9yt7re9mqwvHdblXEwPdu6az3Gb4Vnla71q54KP8qw+63wzOxazwbLahBczZfJG+AD68zd6pWhuoF6Xg0AL0xXAunqs9xbTo4IwDmAoeOPf2lqdXB+kzpAB9ZAdGDNNcDYld686sB0IBeot3xD9U/aBdWB9cB41z4nbc/irQTWe2rIXgMVuHciJGWH61X8VnhW+ls5uWudAVSr+TJ5A2eCdfa56obqa3Nns6NqAOKAOjNMXwFt1SH6boCeDc4TfjB4PVImu+N9NzwH9r0EVhWiA/wgHeCE6YCB+j1fC6jP7LsTqgNnd6vvyGDansW7Kljv2Xbdel3drg4IX+FZCWhWAc2G9TGe0T/ytOyr22l49vgyAPCV40gM1j9tzzkGhjlnNCsjbyZzNnc2O6qG6jBdCaRXmefesnLgOctsd+DSGsR3uD8qA5wD++A5sB6gA3oQHYgH6UBtmA5wAHXAUH3Hfobq+/ZR3p7Fe2UdFcfB9GyrAAtXeCoct8IYjiow/MRru9pIGBVP++7zNljnyFDJmsmbyZzNnc2OyGcD6swwXRGk7wDMGfCcAZxvOO5rL03NAufAXnh+kzpEB84F6c2vJkwH5IE6YKj+VbtGwFTb56TtWbyzwXpvDfkd7le30+geVQBx7HC90hqy+63wrPQvKNq29lTxXeltsK6ZMZozmqWUN5M5mzubfVMEFowA6qfBdMV57veMfRA9E55nj6l51tNa/GyF6VXh+U2rITqgBdIBbpgOGKi/riEPqANaUB3g71Yf3Y8Rku/IYNqexbu3jmy4zrAOlWDhCk+FOdeG6/X9VngqwPVqP+r1eNp33NtgnSNjZ85oVkbeTOZs7mw2UA+os8N0VZC+Cy5nAHQWcB587Nc63F8pA54DewE6oA3RAR2Q3jw5YTpQC6i3OgzVe+Rudf59TtqexTsbrDNsq9KVqQAgFeZcs4PmFZ6n+a3wjL4OW/bV7c71XOXLAsBZutbVwToz8GbOGc2ayZvJzMwFDNQ/e8VKaaZ7814PmncD9Ex4zjCq5kE/aeAc2A/PgT0AHVj/4tlVIB0wTB9V1PVsoJ6zrwJUH92PdR/GDKbtWbx7/zay4XrFrvUeXwVoZrg+77fC037zngpwXeVHvR7f7M9dFt9e75Vd6wbrPBkzWUp5M5mzucA8VK8I1NlhuuJM9+a/T1nwnAmcT6zBeIf7syrDc2A9QAf0IHrzjT8HjDAdMFB/lqH6d7FD9V1ZjBnK26u9xLS3huxts6GJAnBd4VlpFAe73wrP0/yAWi8zzQbL2Z+7Pb4rO8tXeRus62XszBnNysibyZzNrQLUGWG6Slf6Svi8CzDvBugM4Dyx4/5P4J4Bzm/aCdABfYgOnAvSgbowHQhZ/9Qu9dn9DdVj92PdhzGDafvqYL1n++yu9bYtPzRT8TyxW7iK3wpPdj+A/19Q2PO6KvsCPONgDNZr5IxmzeRlZQL5UN1A/YpXvJRB+k6YnAnPWWa8P+rCevwsAey7wTmwB57fpArRmzc/SAc4YTpA0Z0OEHSoZ++/G6oD+6B1xSzGDOXtK4+DWbvt1e00wJECIO3xPLFb+DS/FZ7+kecszx5fBgDOMg6Gac46I/SukqGSlZlpoB5Xx92LF6avgrRVIHoGQGcB55uO/fNImQxwDuyF58B6gA6sP6YVIB3g7koH+GA6YKAesf8oUAf2d6qP7ssMyHftw5jBtH2vN0PXOse211UNHClAZoC/W1hhHU/zW+F54o882Z49vtmfuyy+quNg2EbNsGbszNmdNZo3m5kJ1asB9dNg+koIuwM07wbomfCcYVTNOz2sy89yqL4bngN7ADpgiP4sw/TLOh6oA2dA9dH9mAE+6/Eob68I1nu3rzhrvcf3VBC3ogOTHQwrnOsqfj2ehuv2ZPcF1nWtM8Fvxm7yamD9hDnuM5gvu0u9IlBn7Zi/e67RauC8EypnwXMmcL5gDa6/NDUDnAP74Dmw5xhXQXTgTJAOxMF0oBZQj/BQ61IHakP1nVmMkHxHBgt0BtaB9d5aOLa9LpXuyWqd8JVGcbD7rfCs4lfpRx575kNwhq71U8B6lY51Q/WYPCAPqlcB6ifAdEWQvgsw7wboDOCcZVzNg35KzT5/1q5jWwnRAR2QDhim90gdqAOG6qv2Ywb4rMfDtP1KsA54HMx923O71ld4VhoJs8Kzit8KT8P1eb8Vnio/FPb4Kn1HADxz1ldD49P8RzJ25ihk3XQqVK8I1BVgujJI3wmTM+E5GzSfXIvrHe7P2g3PgToAHVgH0QENkA5QwnTAQP2XZoA6kAPVDeN19mHMWLk9C1jv3V4JrmfDmGzAY7ge43ma3wrPLLhe6V9Q2PO6GHwVu9bVwffpYF2lW30Un2WOfjFQf+UTB0Kjge4qSFsFomcAdBZwntx5/5MCzm/a2V2vDtGBNSAd4O5KB+rC9AifiDqyutQBQ3XVLIP1zzJYf972urKhSXatnrce47fCs4rfCk92uK5w3azw9GfkdTGA9VZHz7aa3oz+rBkqWYBmp3o2VGcZPdN8zoLpKyHsDtC8GyJnw3OGcTWvdGFdxjvcn7V7xvsOgA7oQnSAvysd4ITpAA9Qj/BQ7FLP2ldhv0qjYxgzTpizvtL7dBij4lkJrisATXa/FZ6G6/N+9qwL11Xht8H6mozRnN1Zp0F1li51ljruXnFSgumrofMuqJwFz9mg+aZ1eA3cM16QugugA+shOqAH0gFqmA4U7E6P8pkF6sBZUH1mX2ZAXm0ftu3dtf687XUxQBMVcJQJ1z2K4wy/FZ6G6/N+9sz/nqgO11W9d2UYrN+VMQJGFapXA+rs3ekroO1K8FoVorPA8+zO+2/6z8qXpu4E6IA+RAd0QDrAC9OBOBAe5ZU99gXIG/2iuK/CfpX2Ydt+JVgH1gHwld5q0OTkbvhKcH2FZxW/FZ6G6/v87Jn72as4EsZd63H+rBmjOaNZwH6wbqheo46717kwfQdw3gmTMwE6KzQPqur6SJmKAP0mVZAOSMB0gBSoM/lkd6kDeWB8Zn8VIM+exQjWd2S4a/3V9j3bGq4brsf4rfBk91vhWQWut+yr2533o0dFzx5fd63re4/4n5wxk6XUrT4L3rKA/mx2VA13nxixjqG5e+rB9OoQnQmek1TyU26Uy02rITqgB9KBc2B6pFeUT3aXOmCoXmk/1n0YM9y1/mrbPlUE1qt8Ddf3e54IXdnhOuD5/xF+FT17fA3Xx7zZusrZ/Ecy2HPcrb42dzY7qoaoOpoPL1BfAW4rgPTdED0boJNA87e68Pk599LUnQAd0IfowDqQDvDDdMBA/Zsyu9Sz968O5NmzDNa/iwWW93v3+OZDiGzf7FoN1/f6rfCs4gfkjYY58TxX9OzxrQzXWX7kZvIe8WfN2JmjAtZPheoG6le8DNN/++9TFkBnBOcz/4qnQ3fgvhueA3sAOrAeogNyIB0gh+mRfpWAOnAmVJ/ZV2E/9iy2OesAF1znAvE922pBiGzAowKNFI79xBrZ/QDD9Z1+FT17fLM/J1sN18XwvcbkXcF/JGNnjsE6Z+5sdlQNUXXcveIUDXVXAekKID0DoLPA803AfFY/oaB9F0AH9CE6oAPSAV6YHukVAdSB/C71WQ9FIK+SyQzjWY+HCaz3bq/Ytb7S29DomjI711d4VgKa7H49nobr+/zsabi+0rfXu9efDayzZozmjCAjQ/W1ubPZUTVE1dF8YnQ6TK8I0bOxNSs4DzrX70fK7ITnN+2A6IBB+rNOgOkAT4c6kAu1s/evDtVH9zt9H2Ww3ru9u9Y1favBdQWgucKT3W+Fp+H6vN8KTxUQng3X1cD6Sm82+K3uP5IxmlMZrJ8K1Q3Ur/jFyiD9u7LwNRs43zmq54N+loL1XQAdkIXoN0l0pa/wrNadflN2l/rs/orZlfertM9JYL13e8UHeiXfitBIAcIpAM1Kx8wO11d4Vvp7qejprvWzvHf4s2YA/GA9A3AbqnN0yzefOChqmF4XojPAcxJg/lVfroHrL03dCc+B9QD9poUgfVlHOnAWTAcM1KvtX73LnT3LYJ1h+55ta3fhZQK9Ht8V0EgFmFUCmobrn3Kv6cTzbE/DdcXvYibvXRnVwLq71dfmzmZH5EfUcPc5B6irg/SdSDsToLOC800/lvyUGePyKMVu9JtWgPQVvowwHagF1CM8FLvcVaD66H7MWYwvLwW4YPlKsN621wIFVX0N1+P8Vniy+63wZIfrKzwr1bjCs+LnZMu/ruw1WOnL5F3BfyQDMFiPypvJnM2dzY7Ij6ghqo67V5xUYPpqyLoLa2cBdCZ4vutfF0zqeof7s3YCdGA5RAcM0pd5Gqiv81DeXwGO796PeZ+Rv+OTutZZwPpKbyXfFSNhAMP1DM8TAanh+rzfCk+Fa2eVZzZc93cmp/cO/10ZBuu5eTOZEdkM+RE13H3iZJi+B6TvhugM8Jwdmges0R24FwTowGKIDmiB9BW+jDAd4IDhLB4nQfWMTGZIbrC+avuebTm62Vge5jPBFlAPrhs+xvit8GSH621b7vPi6zvHsypcr/ydOeJ9oj9QE6yf1K1uqP7oEyMFoL4KzFYD6VkQnRGcM/ygAOAnFLRXgejAOuC90psZpgNc3elsPidC9Zl9K+9nsM60fZ+qP8yr+UaDzJZ/XQZ7e/1WeLLD9ZZ9dTvD9Vm/FZ7Zn5WG6zwAXNV7xH9HhsH6XNZM3kzmbO5sdkR+RA1RdTQfbqCuCNN3QfQMaMwEz0mg+SW9WbfPI2U2AXRgE0QHDNJvYoXpQOzxMsBwBg8Deb79TgbrI/swwXWmB1c1AGG4nudpoLnXL/NfU7Cv4QrPU6/vHk/DdZ7vepYf6Cv4A9xw3WCdL3c2O6qGqDqaD1fX/N3PMP2VdoJkBoDODM4Xr8/PSqheAqKv9leA6UBtoB7lo9ylPru/2r7VXnYK7AHrACMo5+la7/Vn8VbzNVzP8VQAmgowkx2ur/CsVOMKz4pwvW2bd/wrfVd6M92jMPobrI9nZeTNZM7mzmZH5EfUcPeJg4KR8HMFrFyFP1eD9FMgOhs8Z/hB4YO6X5q6DaID60H66oxV3szd6YCB+koP5f0Vus5373cyWN+RwdJZrurN4JvZJdy2tWeUZyVAmgXX27Y+L7v8lDxP/iFype9Kbyb4vRqsj2T0IhRmsK6QNZM3kzmbO5vNkB9Vx90nTipAvQpMz4DHLACdHJz/T53r9QMUhOirc1Z6s3enA3WBepRPtociVJ/ZV2G/0axTx8GMZfRsy9OxxeLN4KsC13t8q3meWGPmdenzstdT4YcPoOZnZY9v9e/NXu9ef6Z7oJsqwfWdsPsksJ4N1SNqiKqj+cQpEnSqdafvgM27QXI2QGcG5wlr8xMG2yuA9NX+p8H0aL9KUD27hqz6qwN5ZrAOsIJynq711f4s3gy+2eMOqoFwFU+FGt29vs9vhafC9d3jeTpcr+7b693rz3QPBOwZCVMRrCvlzWTO5s5mR+RH1BBVx90rFoBG49SV3emrAesuuJwJ0dkAevYPCp16P1JmF0DflaUG0oF4mA4YqO/0yYbqs/ur7auwHytYH9mHrWu9ba/50MribbieCwpVPCvVmDmG40TQXOnaWeWZeU22bQ3tFb+Te713+J8O1w3WOXNns6NqiKrj7sXbpa7YnV4ZpLMAdCVwPrFmP0tBtDpIB86G6dGeLCA80ofBw53ufPtVAusj+yiD9dX+LN5V4Xrblh/qneyp0C2cNRpmhSe73wpPlb+Zit3rDN9Dit/JTN4j/r0ognUkTOVxMBmAW71bnQmqnwbU1WH6bpicDdGZ4Xny2nS/NHULRN+RswqkA2tgOhC/Jsx+huo8+yvB8dF9d46CAeqMgxnZhwl+9/oz1c4AH06H6z2+p8JHj4aJ8ax0rhWuHcP1PE8W31O8R/xXw3V3re/PysycyY3Ins2PqiGijptOBuqr4epOsJwFitngefaPCZNa2+H+rB1ZK0E6oAPTV3gaqHN7qEH1mX1377erYx3YBb35wHrb54wHVhZvw3UdCFUNPmbBdcAvNmX0W+GZ/XcY/YNPy7+6Xf53RnatK71Zat7hb7jOmTOaNZM3kzmbO5vNkB9Vx02RCDIaqBumf8rYD49ZALoSOA9cs/4O91cySP8sw/R8LwYgHuHh8TFr96sG1kf2Gcvo3Z7rgfIEcF8Vrvf42jPH06NhYjwVzkulGns8Ddft2+vb693rzwbWgfXrM5KxM2d31mheVuZs7mx2VA1RdQBxUF0BqK+EsRVBejZEZ4bn2WvzpNfAfWfXu0H6Hl9GCB7pxeJzMpQ3WH8tVrA+sg9b13qvv/LDsOF6n282fKnm6dEwMZ6VzrXCtXPyaJjsz2D7cnoD53atVx4HY7C+Nz+iBoC3S10JqK+GqzvBchYoZoPnZMC8W0/ruWakzGqIDqwF6cC5MD3ajwWER/oweKiB8Zl9FearA7yQfHyf3u15wPpqf1Vvw3UdsFXNUwFmngiaXeN3Vetez/68rO7L5A3UgOusAH80ZzQrI28mMzN3NjuqBuCcLvUV0LYKTM8AyAwQXQmcL1yv6yNldkB0QBekr/Jm92T0YgDiER6K42Nm9jVYz9qnd3u+LiTVh2HD9bW+2bUqwMwVnpkws9K5cY0xfj2e1eB6j2/25yWL70rv0+A64/3iSIZK1kxeVuZMLks+wAnVTwfqO4DzTqCcDdCZ4Xn22nzRTxmQDhimr/A0UOf2UOtWn9nXYH12n34xgW91f8P1tb7ZtdrzuxRgpgIUPrHGFZ6Z/5qibXvuZxuL70pvw3XNjJ05o1kzeWqZEdkR+UB9qK4E1FcD1hNAOhtAJ4fmX/VhPedemroDogN6IH2lt4H6Xh8GD8VO95ncqmB9ZxbjQwwT/O71Z/I2XO/zza61mmel7nWFdXSNMX6Axg8+2Z72HfNd7a0O15mhd2W4brA+pgjsFwXVTwTqVWB6BkBmgOhK4HzTer0G7hVA+mp/BZge7cnoxQDEIzxUobzBem4WY9f6jgymB2GWh2zD9T5fFc8eX4/i4PRb4VmpxhWeJ3evZ38OVfdd7W24zpGhkpWRN5MZkR2Rb6h+1S9eK4HsDoC6GyhnQ3RmgJ69Nhf1swSuK4P0lf7M3enRfkxAPcpHGarP7r97DAywF6yP7rc3q3d7vq71HRknPGQbrq/zted3ZcJMhR8qKtWoAuwVfvDJ9lzly1DrSu+V9yyG6xwZozkKWbOZmbmz2QAPWGeF6kpAvRJMzwTFbABdBJp/1Jc17Rspsxp078hQgekrPFn9mHwYPBRHyFQH66P7sXatj+Qod62z+a+C662Onm3zQYkK1DnZUwFmKsDRSjWu8FQYV9S21eg0z/7MVPRd6W24zpExmqOQNZM3kzmbO5sNzIN1tm71E6H6avBaGaazQHQ1eL5p3X7+DVsP0nfkKMH0Fb7MfobqPPtnjIEBDNZf7zeyD98DDNuDHpP/yodsNZiR7WvPa6oEMyuB5lNrzPwXFW3bWp49vkqf76u9mUbDsN0TsWbszBnNUsqbyYzIBjjA+glQXa1LvSpMzwbp7AA9e30u6ocCPmZnqAD1aM/KXlU8TgLrgAokr9O1PpLD9qDH5M/ygL1qTRhAhqFWrGcmzFTo5HaNMX49ngr/oqKip33HvU/rXmf8V5UjGaM5ClmzmZm5gMH6a59YKXWp7wCrO+FyJihmhOgi4Pyr3qzt55EyFTrf1bxP6FKP8mLpmlfe/wSwProfc9f6rhz1h7zV/itgZaujT2owoyKAUvFUgJmndnKfWCNQ619U9Hj2+GZ/vtn3t1Z997da+sR2X8SaMZqjkDWTN5M5mwvUAeus3erRIFMVqO+CzFngmAmiK8LzxesX0+H+TqojZFZ5s3syejH5GKz3SwGsj+5XCayP5LA95KnC9VZLz7b5wKGqb0XPSjBToZPbNX5W5r+oaNvW8uzxVfos7vVd6c3Uvc52X8SasTNnNCsjbyZzNtdg/dknTobqe4BzBkhmAOkKAJ1hnT6o76WpN3mEzD7fEzremXyUoTpgsL5uv5F96jxUsD3kGa7P+a70VoFPPb4qnpkwUwE0r/B0jZ+l8C8qKnrad8wX4Pn+b9uf5T+SsTNnNEspbyYTMFiPruPuxQ/VV4HZajA9Gw6zAvTsdVmhf9f65+m/9AiZ3d4nAPVILwaoHuGR1a0OGKx/3m9kH95/osqYwebP8nDN0s2n5Jtda7anAsxUAM0rPE+tUeGarOjZ45v9ubnSt9d7Zfe6+r0R4/3jSMZojkJWZiYwB9dnwToLVG8+MWKH6qpA/QSYzgbRVeH5onWMHSnjETJ6nkwgPNKLwUO1Wx3YD7p3Z7LffDN2re/IYILrAM/DNQPIqOqr4pnZvd62NWje6alybqIBu8rfo4qnfe9a+f3faunZluve6HS47q71zzJYjwWIkVDPUH0PbM4AyCwQXQWes6zXC10bKeMRMvt8WYF6pBeTTzZUB/TGwGTty961zgzxGTMM1+e9WboEVTo7e3yzaz21U1gBNCvUuMIz80cflb/x7M+NHl+lWke8Wf71WtueY012ZTDfd1eH66rjYKqB9dOgujJQ3w2Us8EwM0DPXptFWvvS1JsUYfoq7xOAeqQXA1SP8FAcA5O1706w3vbjvcmv0nVkuM7tXdU3u9ZqozhOBc2VauzxVLgmK3r2+GZ/xrH4AusAOxucZvNnzRjNUciazQS0u9YrgnXWDvq75xpV607PAsaMEF0dngeu6dhLU2/yCJn1vsx+hup/SnEMTNa+VcH6zizD9e9S/WfhDBCjqm92rZmdwm1bg+YIz0o1Ah4PE+nZ45v9eaToy9K9znZ/xAi+We+HVbJm8gCDdZbO+eYTq2igqQjVd4Hm00G6GjxnWbd/9Rq4u+t9n+8JQD3Ki8Ujs1sdMFj/vh/3TTdrDmOG4fqc70rv08FTte71FZ4qoPnU464E2LM/O5Q+j7Jr7fUFzuheZ7wHY2zSGMlQyZrJA/LgusH6s0+cFKC6OlDfDZMZYLACQGdYpyCtGSmj2vmushZsILyqj8H6zszR/dy1PpLDmMEE19v2HKBBDY4o+ap0r7f8q9udCUd93J+V+a8qVABzxc8jRd+V9wIs39UV/EcyKuaMZs3kqXatG6y/8zkXqlcD6pmQmBWiFwLnAIbWuX+kjCpMX+Wt4MkEsdl8lKH67P45UH40r2Y3CyP43pFxClxf6V0Z3CvVmgky27b8ngbs+z0N2Ot4Vvdd9SNlq6NnW03vXRnM0Jv9Xn8m79SudRawztqtbqj+6L8HNGeBYyaQrgrPk9fwDtw9RmafLytQZ/RigOqAwXr/fuNi71ofzWME3zsyRlb4lH8SrgZGqvpmw6FTobCPO87TgL2OZ3Xf7H8FdN+ew5vRnzVjZ85o1kyeIlw3WH/24e5WXwEeKwD1DJDMAtJVIDrLeg0obqSMaue7AlCP9mT0Mlif3z9v39H9+G+AmW/sGeE64O51du+qvtlwqNr89RWeBuxxnr4u63iu8mWoFeAA7OoAnM1/JGMkh/1+fzQLMFzPyI+qo3nwdqurQfWKQD0bDjND9Oy12aGnI7w2Uka5+13Fl9mPCaoD+WNgAIP1/n0N12eyWI9HGa73+rN4Vwb3DLUaZHJ7ngrt3cFex3OVL0OtgAH7Tu9dGe5en8syXN+fH1HD3YcH9P/24+6m/+29FrLuBMyZwJgRpFcB6AuPogF31e70ld7sXerRftWgOqAP1mf3d9d6bJ7hep+U4XqvP4u3fft9Ddi5PQ3Yv8uAndtzlS9DrSvvC1gAO9O9xg7/kYyRHPaGmtEsYBzcKMJ1hm7xymBdqVu9ClTPAMhMIF0VoBNXHTNSRhGor/I+AapHehmsz+/vrvX4PPYsj4aJ9T/B275NBuzcnirjhlasZfS1qXLc1c55jyeDrwH7Pm9Gf9aMnTmjWYDh+q7sqBqi6mg+cVLpVl8JY6sCdRaYrgDS+Ssc0veRMu5+1/Jk9IqA6oDB+nz26H77b87YgffOLMN1LX93xvP4rugSbvlXt7NnlJ+K56mA3Z71fmC4KftztG3L8Z3d613BfySjYg5guL4rOyI/qo7mEScFsK4O1U8C6swQnbeyGHV8rse9NPWdDNTjPQ3VP4vhX23kgvmZ3L1d67sz2bMqwHWA64GR6WGXpW6GmrO7Lw3Y63ieCthb/tXt+Neyoucq35XfU9mfo21bju+/Xu8T/UcyduaMZgH7Abvhel4Nd58YnQzWVwPonZDZMP0urmrGNfMD6KCuvTT1ndRg+krvE6A6YLDOtL/ajZnCCBr2rJWdaTe5e53fW63m7PEwQG6XcI+vPeP8VngasNuzku/KH+AZ1qHXu9ef7X6GNYM9BzBc35HNkB9Vx92HG6wbqn/L2S8mkM5TyXUlAPNZvQfu7nzX8qwK1QGDdcWbMsP1+SzDdS1/Fu/KvgbsZ3oasF/Zjn8tsz17fBk+77L/FRBgwL7Te4c/a8ZozmiW4fra3NnsqBqi6mg+54H1lTC6KlBngOn5FVyTIDgf1bqRMmpAfYUvs5+h+hqPrDnrbV+drvXRTPaRMLuyGOF620f3YfQEbzVfA3Z7Rnlmdgd7fFGcX49njy/D553S52ir4bpYADvTPQej/0gGew6w5575USrPVBG5s9lRNUTV0Xy4uujvfobqf2bsVSZQZ8bTleF5wDU2NlJGtfudvUs92i8KqgMG6/H7z+yrdUOmMIKGPWtlV9pN6g+Kqg/pBvd3GbDbM8rTgN2euz1ZfA3Y93hX8B/JGMlx9/qr/bSe5SKyI/Ijarj7xEHCSPCrBNZXA+idQN0w/a4KAD1jXNAbvQbuit3pq7zZPatC9Sgfg3WNXHbgvTOrAlxfncH0kMvizbLeKiM42rYaIM+ecX49npk/AFU7P9mePb7ZnyG9vgbse7wr+LNmjOYAe7vXlZqWZjIjsiPyI2qIquPuFSf2MTV33xpQPQuos6BrRYhOBM1nFD9SRg2or/CN9mOE6gAHEGfxyLwpOeEGsCrI3/GgwNi9xOTPVDsDvFm1HkpQKHtt7Rnn1+Op8i8sFM5PtmePb/bfe6+v0mdpry+T94n+Ixk7c6p3r6s+S85mR9UQVUfziZPB+h5wmgHUGRC2CkgvAs9H1D9SxkB9jWdlqB7lw+ChejNUvWt9d57hOk8G0wMuizeDb1UopOLZ41vt2DP/lYUKED7Zs8c3+28TqPtZyua92p/t3mk0Y2eOQvf6Sc91EdkR+VF13H1ipADWDdX7lImymUF6dXgefJ39CdwVgfoqb2aoDtQcARPlowzWZ/MN12PzmB8SGP9pMNMD7mp/lh8GGGquCoWy19aecX6Axhz2auenx7PHt+LfJqD3WbrSm+n+oML9E2sG4O71FZmzubPZEfkRNdx94sQ6pubuuQbWVoPqWUibEaZXgehZo4M+KHakjBpQX+HLCtUBLiAe5ZMN1pvHWd0Nhuu/teshocqDFdMD6AnehkJ9vtlra89YzxXXqUr3fjXPHt/sz5Ee31Wfpa2Gnm3zP6eZvE/0H8kYzdkJ2N29vj43Ij+qhog67j7cXeuqYH0XFDVQ1wTphNB8Rn0jZVYC9VX+KzxPgOqRXgxgvXnM7n9W1/pMrgJcH81z97r9mb0N2Nf5Ztdqz2uK7mJXuZZO9uzxzf4cAWp/nq70Vr5H2OHPmgHUBuyKz3ez2Qz5UXXcfXi71hXBemWozgDUlUB6MYDeqxojZRSgOmCwvs9jXqo3QIbrsXmscL3tY8Ce6c/izQCEgNwRHD2eq3yzaz3ZMxqwt+yr29mT3bPHl+HzlAGwr/Rmgfe93oz+rBlA7fnrioA9G65H1BBVR/M5C66rg/XduDYbqLPD9JMAesC5iB0pA2hBdYC7Wx0wWP/sMa/sGxC1Gy+Vm9rRvEqAnTFD2Z/Fe+WaqEGhkyFexTVVGBPTtrVnhmePb/bfPFD387TX9xTvEf8dGcyAHXAH+4rMmVyW/Iga7j5cnfR3P4P1R+1EuAbqr1UNpJOsc99IGWAd+F7pbaie68UA17O/+BXH0ShlMnffVOle35HB5M/ibcC+1je7VhXPHl+FMTEt/+p29oz07PFV+vus/Hl6incF/10ZBuxxeTOZs7mz2RH5ETXcfQzX531rgPVMqE4Cev8nZZDOtpYDqjFSBuAfAQNwwvBILwaw3jy0bzyUQHdGZsUHA9YHKvUHT0XAvtK7MhDKhm0qsFHF013sZ3r2+Gb/fQL+TN3hzXSfwOg/kjGaw9yo0vbjfx6ZzZzNnc1myI+q4+4Tq2i4qQjWq0N1FgisCNJZ1m6jdEbKrADqADdUj/YzWI+vQ/FmqzpcB7gfClgfphgfClke+FW9DYPW+WbXWtHTXez2zPD1Z+p6b6b7Eab7EOYMgPteuu2n8Uwyk5mZO5sdVUNUHc0nTgbrdcE6AxRWgekMa5WtL+eKa6SMClQHOEE4oxfLr9jZNxyKN1oqN7LuXq+RwfRA3uvP4t17hpSAEAMIUqlVxdNd7Gd69vhm/y0BtSE7C6hm+j6v4D+SARiwZ+fNZEZkM+RH1HD3iZMCXFcH66dBdWagnr02O7Rx/V8Dd0P1phO61aO8WMB688itwXB9Xabh+r4c9QdOFgi+2tswqM/39FqzPd3FXsezxzf7b6nHt/Jn6kpvpu9FpvuFEf9dGQbsuXkzmbO5s9lRNTDVcfc6F65XA+uZ4JgRqFcC6Yzr+0bxI2VWAXXgPKge6RcL6CM88r9cs3/FN1z/rl0PAwAv+B7JYcxQ9mfyrgyDsn2za614/O5i5/fs8VXx7PX152q/L5N3r3+Fe6qRDAP23LyZzNnc2eyI/Iga7j4xioR2But3nQDV2YCvKkxnW8cF6h8pAxiqR3syejF9kTF8wRuur8909/q+HMYMJn8mb4MgPd/sWrM93cWe49njq+LZ42vAvt6b6buR6Z5hh/9IhgF7bt5M5mzubDZDflQdd5/z4HoVsJ4Bk1lAsBpIZ1k3Ir0G7mpAHeCH6tF+FcF688mvI+vGKjN7980rO1xv+xmwr8xg8meqnQEEte3rAUEG3+xa3cUe69nje7Jnj6+72Hl8T/He4b8rgxmyG7Bz5s5mR9UQVUfz4YXril3rO8D6qVBdAagzrFOmJs/RzxK4bqjO6cf0JcbwxZ4N99Vu6BTgOsD/EMDYBcWYYcD+p3r/JhiAjZJvdq1Kx1+ti73H92TPHt/s8wTkXqdtWw6gzPL9yPTdW8EfMGDPzMvKnMllyY+q4+7DVc/dTwuuVwTr2bCYGahnr81qkaz92EgZQAuqr/Bl9Yu8sKqA9Yg6DNevy93r/DmMGUz+LIAd0ANB9s2vVaWLveVf3c6ekZ49vkp/T9nXads233elN9N3O9N9w4j/SAaw9j7iJvb769GsmbyZzMzc2eyI/Iga7j6c3etqI2FWw/WdsDMTHJNA3T9UCaazrvFFfQbup0P1aM94SB/lw/OrMMMX+mlwfSbX3evzWYzwe0cGk//q2tUg0ErvbN/Ta82GlypAWMWzxzf72s/uYgc4Pl/VfJm8T/QH6nWxG7CvzZ3NjsiPqCGqjrtXnJTgusF6RC6XlGE621pu0I+h+gJPg/UrHvpf5Jk3M0pwHeDvrhnNM2A/w58FsLdaerbNhzVVfavWenXLbHBpz1jPHl+Gaz/7x6C2ff46sIBwVe8R/x0Z1QD7aJZS3kxmZm5EPlsdzSdW0YDTcP1bzn6gzASC1YA609oRanykzE0G65F+UT5cvwYzfIln16B4A6fQvd7244b5jA9/IzlMAJzNv/eMZcPK1d7Zvkq19viu8MzuDlY5/xXPfY9v9vEDOj8GrfRd6c3yg0OvN6P/SEY1yG7Avi53NpshP6qOu0+cDNfrgnUWMKwC1FnWK1uT5+sacFeC6it8WcF68+L6osoG2ww1KI6k2d29DnBD79GckSzW42F7gGXzdxe7fUd9s2s1ZM/z7PH1dXpdhuxj3obsef4A7yx29n8pOpqlmBmRzZAfVcfdJ04KgF0drp8I1pmhOsP67BLBebgD91Xwe5W3gifrlwFL13rzya8jswZ3r3/br+ZNP2N3FWMGC5QA3MVu3z2eq3xXwMvs4z/Zs8c3+zz1+Bqyr/c2ZM/zB2pBdoV7+5m8mczM3NnsqBqY6rh7Ga6v1ElgnQDkvlVFoM683l/0EwaZVbrVV3hGX9QsMPvuw1NP9i/0ijdP1bvXR/OYs1gfltgeXt3FPue70lvJt2qthux5nj2+Kp6rfA3Z+31P8a7gP3KnaciulzeTOZs7mx1VA1Mdd69zAXsluG6w/ltVgDrj2i5Q/wx3g/VIP66u9ebD80WpDNdn8zO61wED9sysHZ1VbA+uqzNUu9jb9loAaNV5ZKg327ciZFdaU5Xjz17TXt+rWxqyn+Vdwd+QXQewz2Rm5s5mR+RH1BBVx90rjq0Yrj/67wGjWSCZDfwqA3W2tSTQa+CuBNVX+J4A1psXx5ckQx3ZNy0ZgH3mS9+AfT6LDU6P+O/IYHowZuliX+lt33W+2eASMGSP9uzxPf34AUP21d4sNa/2Z2wG2AHZK94Lj2Zl5M1kzubOZkfkR9QQVcfdixewG65/y9kvJhisBtWZ1k5McSNlHqUC1ptnpFfshcjSKX73ifDI/6LOvmFx97p+XhXAviODzd9d7Pu87WtwmX2uVDx7fLPXtMd31Q+aSmuw2pul5l5/tnuJkYyV9xM3Md+j7s4azcvKzMydzY6qIaqOu5cBe6x3TbjOAodVoDrLemVr0fnqHylz06lQ/e5Zt2u9+UR4cHxJZ96wqMF1QAN4784zYOfKcBf7nLdrXue50teQvZZnj2/2mvb4GrKv91YF4Ybs18R8n6qQNZM3kzmbO5vNkB9Vx93nLMCuDtdPBOvsUJ1hjXaK8Hx8B+4G6wbr13wM12fzDdjj89izGEe47MhgAuyAZhf7Sm/78vgastfy7PHNXtNeX5VrlcXX3vPeO/wBQ3aF+/2ZvJnMzNzZ7KgaoupoPjF8QwGuA+vgoOF6dDavqgJ15jXvVAPuq6D6Km92sN78Ir0M11fUYbjeuy//DTd7FiP83pGxeq1WPwyzwAjF7kr79vkqdQdnr2v2uTr9+AFD9tXeLN89TN47/AFDdvZ/RTqTl5XJkD2bH1lH8+HsYHf3+rP/Phms/1YloM64vhsUM8N9XRf8Cs/6YL15RflwfCkrw3VAD7Cr3PyyZ7HmMGYwdbEDhjMz3vbt882G7NnH3+ObXevpxw8Ysq/2ZvkcZ/Le4Q9wQnZ28O0u9rW5s9kR+RE13H0M2Od9Ddfnc7mkDtXZ1pNMfTPcDdYj/ep1rTefCI/8G4Os7nVAC7DvvgE1YN+Xw/YwzATY2/YcIKI6UFLyXfWvMgzZDdmjPXt9s6/Xtm38+erx7fVW/I5g8t7hD9SB7Oz3x6NZM3kzmZm5s9kR+RE1RNVxkwH7Cu99yoDKTCBYEaozrZ+wXgP3k8F684z0qtm13nwiPPJvCE7rXm/77r3pVchjzjo1gwmys8Dqld4sYEbJ19Ayf10rHn+PryF7fV8m715/xn+BxwjZ2z61ckazMvJmMmdzZ7Mj8iNquPvEcIYTAXuV7vWT4boSWGdZM1YFncuYkTLPUvkAYu1ab15RPobrkTUodq+3fTVueBXyWB+IGDNWd5sxdbH3+rN4u+Z+36rQMntde3yza80+/h7fqterou8p3oz+huyG7JGZs7mz2RH5ETVE1QFwA3Z3r3/K2QuYGSCxClRnWKtMkZynvpEyzzJYj/KL8uGB680n90Ykooas7nXAgJ0lr9LDEGMGUxd7256ji5DFmwX4KPkqQUuldc2uNfv4V/kqXa8sviu9WT5ze70Z/Q3ZDdkjM2dzZ7MZ8qPquOk0yK4O2A3X+cSwRjvEfh4+6BpwV/pVjxmuR9ZWDa43j9waTuten8ndnckO2HdlVckwZOf2ZgE+Sr6Glvm1Zh9/jy9Drb5meXxP8d7hP3IHt/oeo+1jyJ6VN5M5m5udPZsfWQdgwB7rvV47IWc2OGYGutlrs0rMax6o38D9ZLDePA3Xr/nU+PI3YN+Tu/PGmj2LscN8VwYTZGcCBCzehuxjviugpdIauFatWg3Z1/rae957xB/guse478N7b6mQNZM3k5mZO5sdVUNUHQAvZGdnSX96r9VJ3euMoLcaVGdc4yT9UILhP32j/Xh/EWWE/lW++A3Y9+QqdMowZ7E+1LF1mSl34Z3gXdlXCVoqra1rzb9ms6/Xlb4rvVlq7vVX/h4F6oyMYW8W2Z01mpeVOZs7mx1VQ1QdQBxkP7mLvRJgN1y/qwJYZ1tTco3PcD+xa735RXoZrq+sQxWwt/11bjQVAPtoHiv83pXD9vCrDAdO8K7sqwTZe3wZ1rZirT2+vmY1fVd6M33XMX2PAh4Zww7ZlfJmMhmyZ/Mj6wA4IbsCW7r7rtUJgJ0NBKvCdbZ1FNd34K70ocIM15tfTH1McL35RHjkdq8DmoB9Jlsps1oX+66cHRmG7Gd7V6/ZncE1gbhSrb2+K0A7wxqs9K5ec683oz9jNzvrvZ9K1kzeTGZm7mx2RH5EDYBHxcT4rtUucJoBlpmgsCJYZ1q/KnpzHfw8bKD1QWK4PuLF8Ut688kF7LNf7AbsazMrdrGPZDF2sQOG7JH+LN6KEEkJWLYariu7OzrbV6nWHl9fs5q+K71P+T4a8Xc3O3c3u9K/qp3JnM2dzY7Ij6gBOAeyq3Gxu78B+2opwXWWNWPWhvP5E3oiVH5RZP5QZgHad58Ij/wv+BMB+0xuZcA+mlfpAcuQPdefxduQ/S4DS61ae3yVau31VfoXGCu9GXxP8d7h72729TmjWUp5M5mzubPZEfkRNQCG7PO+61QVsLOAYhW4zrJe2SI9X2Mz3FW61ptntB/fFwUTXG8++V/uBux1M9kfRnY8YO16iDNkj/M/wVvNF6g7fkPJ17X6x6HVviu9WT5/mbxH/AG+e462D+d94M6c0ayZPLXMiOyI/IgaAEP2Oc91MmBfWQO3GNYoQ+zn5YKuzHBfo+iLhr27PrI+JsDO8qVuwL4vV+VmvdoDz46ckZU2ZD/bWxF6VQWWDGub7atUa69v1etW0fcU7x3+7mZfnzOapZQ3k5mZO5sdVQPACdnZ+c7dc512gNbTADs7xM1enx1iPweBepzhvkYqH2qG61d9OL7QDdj35apksmex5hiy5/qzeCsCfMNKDl+lWhl8fd2u9V3pzfI52evN6O9uds6c0ayMvJnM2dzZbIb8m6IICCtkXwUvldjZnxl7lQ2QWeFu9rqsEut6J+mHEg7/9oxXdbjevCI8OL7II77ADdj35CrcpLNnMf6zbcCQPdL/BG8G35VAx+A631ep1l5fg/a1viu9mb6bmL73AM5u9rZPHZg/mqWUN5OZmTubHVUDwAfZAV6mcvdcp2qQPRMks8LeanCddZ0JNTrDXeOXwug6Wetz9/qfUgXsWdkqmexZux46DNn1/Jlqrw68DCvX+SrVyuBb+bpd6c3ge4r3Dn9G0M58X8h8/5mVN5M5m5udPZt/kyH7iOc6rQawBux5qgDXGddVVFdmuGvA9ebJC9jZutebD8eXtwG7VpeGys0580POrpwKD7kn+Z/gbchuXyZfhlp97fb7rvRm+azs9e71Z/yhn3FsDDv8Vri3nsmbyczMnc2OyAcM2cc818mQPSKXS+pwnW09C+o3cFf60DJc7/WJqcmAfT5f7cZRJZM9y5Dd/hH+LHCHBewYVur5KtW60tfX7lpfe897j/iP3Imx/Wu6kYydOQpZipmzubPZUTUAfKCddUrA3W+NdsDYXcDUgL1JFbCzreNB+qGEw3/6cn+oMq6hAfvz/pqAfSZb6QZZIY/5ge3UB9xTIMYJ3mqgsm1vcG3ffl+G65dhHVg+j1k+L3v9mb6jgBo/9o9k7MxRyJrJm8nMzJ3NjsgH+CA7EMtcTm4M/dN/jzLgMhMYVoTrTOtXTYPXw9gM9xa4Robro14G7L/3171xUrtRNWTPyWGE7G0fLgjA5H+Ct7vZ7XuKb+Vrd6U3g+8p3jv8Ddo5c0azMvKyMmdyWfIBPtDOyDd+e66RIftMJofUADvLuikp4RxfA+5KH0wG7H0yYI+pQbGDXimTvaPHkJ0LACj7n+C9EugwwEr7rvNVqhWofe2u9Fasmcl7h79BO2fOaJZS3kwmQ/Zs/k0sz/A3MXKOu98aGbLPZHJIBbKzrBejyM/ha+C+4oSeBtebX6SXAfufHrpdCWpd7DO57OB7ZxYrZAf4Hv53ZKjCkRO8V4HKVkfPtlpAUclXqdZeX4P2MW/Fmpm8d/gbtHPmjGYp5c1kMmTP5gO1u9mbHy+L+e27DvAZsq8VOZz9nxjWikEq5+uDfqQ+iE75UI+siwWwA7k3SRE1KHaxz+QqZTI/OI1mMT7Utn24Hv6V/ZlqZ6lbDVSu9FbyVap1pa+vXy7fU7x3+DPekzADcIP2uLyZTPXsmyqDdlYe86evIftYZr4UoC3DOmVJ4fxMaHyG+6MU4HrzrP/Pppi+kBk6AbJvkgzZ12WyP8ywdrNXgOw7MlgAV68/i/dKmMMAKu1r30epXb8rvavX3Ovd68/0fQIYtLPmjGYp5c1kzubOZkfkA1zP9QAv/2h+a6QO2k+E7AoAN3uNdkvhnCxSP3BftVindK83LwP29x75dah2QSjdCLOD751ZOyA7wPlAywYWmPxP8VYDlfa176PUrt+V3tVr7vXu9Wf7vmJsAGAG4Oz3p0p5M5mzubPZEfkA1/M9wMtBml+8VsPBiqA9GyCzA93s9dkl9vOQoO/A/cTu9eYX6WXA/t4jv47TuthnclUy2bNYIXvbR/+B+SQowlI7A6RsdfRsmw8T7avpywDa2/b5a6H4GXSK94g/I2hv+/BljOYoZGXkzWTO5s5mR+QDXKNgAV4e0vzi5W723rxcscPd7PVZLfb1J9Fv4K7Svd48eT+0qwJ2gAOyZ9eg2MU+k6uUyd6V5AfZdRkn+at6G7TbV93X1/B631O8Gf1771FOvT8ZzVHImsnLypzJzc6+qSpoZ+Y1v30N2vvy8sQMeSsDduZ1J9ePxAche5e9Afs3j7O72Gfz1W58q0L20SzWbnbGTvMdGUz+TLUrgnaW9bBvfV+D9jHfld6nfDbv8K8A2p0zlzWTN5OpmjubfZNB+1W/NVJqNv0z4xzI3vI5gW/2uqwQ61qLav6lqQqAPbpGA/YrPtpd7LM1KGYrZbI/uLBC9rYP34OyOqxg8mfyZgDtLOthX03flSBSbS1YQPsp3jv8V4N2gPOHevac3VmjeVmZs7mz2RH5VZ//mxffDwB/+hq0X8/LEyv4rQbZWde5iPqAuwJcb57RfnyAHeD6kq3QxT5bgyH72kz2h5bRs1/lAZYxQ9mfqfbqgHKlt301fQG9H4yq/30weff6M323AAbtrDmjWUp5M5mzubPZEfkG7Ve91sig/WpWnljhbyXIzrrGRfUeuCt90Bmw96kKYI+oQ3VUzEy2IXtslrvZ9TNOAS0sY2MADkC50tu+670ZfjRiuI5ZPjMUr4te79X+OyD1atjO+B0/kjGao5A1k6eWGZEdkQ9wjY9hBe3NL14G7Vez8sQIgatAdsa1PUh34G7AHqOqgL35GLLP5mfdbCrdWCvkVYLsIzmMGfbP8TZoP8d3pTeDrxpoX+mtWDOTN6O/u9o5c3ZnjeZlZWbmzmbfZNB+1S9eBu1Xs/LECIMrgHbGdT1UP9Tw+u4Z7VcbsAMcYPvuk1+HKmSfyVa7uVV4UDFo58uwf463QTuf70rvyr4MoL1tn78WLDUzeff6M30HAHvuWxjvJXbmKGRl5M1kzubOZkfkRz3Vs8H2k0F7814rg/YcGbJbizT30lSVXw2j62SE7Exfoiyw/0TIPpOtlOlu9ts+nDmMGcr+TLWzgPa2vRagZLlG7HsXA2xnWQt7z3kz+q/uaq9037IzRyFrNlM1dzb7Jne1X/WLlwqf+tPfoD1D6qCdbT2tP9T70lSND7ATADtgyL6iDtUbPI+Mic8zaOeDBzsymPyZajdoH/dd6V3Zd6W3Gmhf6a14Lfd69/ozfQ+M+LurfX2OQlZG3kzmbO5sdkS+u9qv+sVLhVO9zqgP29nAsEG7tUmfgbvKBxczYAfqfmkyQPbsGgzZeTN3dbMDtUD7qRnK/gbtc96K4JPl+mComQG0tzp6ts1fi5XeTJ+nTJ/VO/zd1c6ZsztrNC8rMzN3Nvsmd7Vf9YuXCrP609+gfbcM2q3N+g3clT6smCF71S725hPhod3JPpt/0o2sAmQH9nWzA3seuFgfVBkzlP2ZwI0inFzp7ZrX+/Z6u6t9zPcU715/pu8CgLOrve3D970/mjOapXIPnZU5k5udfZNh+xWveCmxqz8z9oBTj4+5Sxm2s62ldVk/S06ewgcqaxc7YMi+qo7sbno10K6UyQ7amR/qWB+GmcCKuv8JoH2lNwtAZPBd6e2u9jHfld4s1waTN6P/6q52gPN7nz1nNEspbyZzNnc2OyI/6umfDba7q32d3NW+XwbtVqLmXpp6k8qHaPUu9ubF84VtyK55A6t0s+6xMdw5jBkn+TNBGwPEPb4rvRl8e73d1T7me4p3BX/Dds6c0SylvJnM2dzZ7Ih8d7Vf9YuXYfuVrByxAWJV2M62jtawxoD7qgvXXeyjXvlg++6TX0t2DaeNq1F5MDBo35fDmGH/9zoBtK/0VgSq1WtmuaZ9Pc/5M32uMq3LTathO+s9BnvOaJZS3kzmbO5sdkS+YftVv1ithJCG7RG5XJDYsN0i0HXgrvLrpCF7j0eMWGpRvnlTg+wzuQqgnXk+O3MOY4b9P2tVB3CrpWdbDm8WeKi4HizrzNDV3rbPX2d7z3sz+u+4r6l0nzGSo5I1mpeVmZk7m32TYftVv1gZtl/NyhETJFYF7QDXOloheg/cDdhjxAS1714RHobss/mK2bsh+2jmzm52gPfBdFcOY8aOtVIGQiygvW1vMDnq7Zp/iwG2K64zk3evP9tnK9Nn902MsJ31fkYlayZvJlM1dzb7JsP2q36xMmy/mpUjJkhs2G6R6Q7cVcbEANyQnfHLjwn6nw7ZZ/PVbpAVutmBfWNj2n4G7eoZyv69q8nSAdzrzwIPWcBh9ZpZfkBSvO5O8a7gv/Lz+ybG7+idOQpZM3kzmaq5s9k3GbZf9YuVYfvVrBwZEsfI61hWPxIfiisuP0P2qz4c9TDAftUbRaV/amrQPp/FCKirZCj7rwY1ipBW1bt6zb3eDF3tbfva55DJ+0R/w/b1OQpZM3kzmaq5s9k3GbZf9dORYXtELhckVrr+HsW2jlaoxl6a+iwFyM46KqZ5cX0BM9XjbvYzcg3a57NYcxgzTvNX7Wo/wZsFeCquB8t1zXIOT/Fe7c/4HWfYvj5HIWsmbyZTNTdKhu1X/eK1CkQatkfkckFihs8Ky3qhMeC+6g/MkH3EJ0ZM9bibXeuGOCPToJ0/hzFDvWueDdKwQKwTvBWB+ErvVV3trY6ebe2907vXn+nzezTDsH19jkLWTN5MZmZuhGbzuZBinE6G7ZZlWRt1HbgrdLEDZ0D25hXlkw+3WepQ7qY3aP8sg3buHEbIwXYMTJBGFZCd4M0CUhVHyLQ6erblWA9V79X+bLWPZBi2r88ZzRpVBkRUe76IyI7IjxJjd/vJcne7ZVkb9R64q3SxA4bs/T48X/zZN2PZNSjeBGc80Bi018xhzFD3P6WrvdefpW4Wb9W1Nmyv4b3an632kQzD9j3KaDrZmaf4nDGbHZEPeJTMdb94sfxYYr0W2/nxDw8WsX4Dd0P2KC9D9s8e7mZXvAE2aH+3X60s1gdqxgw2f9Wu9l5/e895s4BOFtjOsh6neK/23/Gd1yumz/KbqgGKnWBfaZTMqDKvD4Zrkwsn8orhXPVIrV7rmv6G9rn9G//Q/YhhhelnyclVAOxAfcjevPLh9t0nvxaD9n25KqAd4H9QOxmCj+QwwZ8R/94Mpq72tj0HHLM3r7dh+5neO/xXS/mH05tYv7t35oxKCbZn/e1k/81m5990Qnf7CinU+E4eJ2NZ1pPGXpr6SidC9ubHB9rdzR5fh0H7nlyD9vmsSjmMGWz+TLCdaW3sPefN0o0PGLZX8bb/d+1ANZXuEUZzRrNGlQEQs+7/Z8UAEXVxr2VZqnKXe1nNAfdVlwTzuJjmZ8j+3cegfTbfoP27doL2th/vQ+euLNaHaMbjYIMzLDCy159p3Vm8VdfbsJ3Xu1dMf5cV/Ee0ojHpWez3PSNSGe3CAJ97lQ2MsvNv2vG32avotVG7PtXq/abMMSqMcFh9rAzAua7WtPqBuyF7lF+Uj0F7tRoM2r9LAbSP5jE/3DJC6h0ZbMfA1NXetucBtKqwk2VNmLwN2+e043NrlTfT3/2I/4hWf64D+jDiWZVHyczI3e115DWxrLP0N/4BwPPjoTWta8BdBbID3KCdcQYbQxf53Se/DoP2fbkKsJ0dtO/M2gGSKmQod7UDXOtj73O9V8H2XqkCcbbPrZXejEDKo2TW54xKBWIwXtfflL22EfkaV4dlWa9Uocv9JoP3MvoM3Fec3tMge/OL8uGC/wyAm6WOzG6SjOzqoL3tt697jPmhlrHjvErGSbDdIH/eu1csdTPBdtU1YblO2vY8tY+I7XN9RIzjKnZLobvdsiwe7YCxJ40EYT3WStAdMHgvoD+B+6mQvXlyAe27F0cH+d0nH3Cz1HEaaJ/JrQzaR/PYsxg7zkdyGDMM22O8e/1VvXv9meru0QmwvVdMwJfpGmf0H5G72+t2t3ucjGVZK5UNlw3d98ngXVYNuHtkTKRfpJdBO2sdBu3XNXO2FGA7e1alnFMzDNtj/JlgG9Oa99XRu/31ulc+QqiuCdPfz2pVeIh0d/seMV23K6R4fLPXvuIxv5P/Rq2dyobLzNAdqPXZAtzBO1DjvukA/YSfJgXI3jxrd7M3ryiffMDNUkfmDaUaaAfc1a6YxZrDmLHjGJhgO9v6qNZu7z+18jpX7hBfKaa/zxGp1w+4u53p7+GdqsEaa73+i3+OgO7ZoHdEHiuzRszHrHidXpXhu4SuvTT1m1Z9qbibfdQryicfcLPUoQras7Krd7WP5rmDnnfdmIDsTYbtcf4rvVmg9Slgmeklqcp/Qyul/Pc/Kne3c+uEcTKWVUHM8Paqdh8DA1RmPm9Vu90f9QjfAX8HEWkOuJ/Yzd78Ir3qjY1pPhEeZ4P22Xx3tX/bjz+PFU4z5zBmGLbr+DNBTibvlddLz9arxyWxePeK6W+IUYxAX3tF88T0d8cm9b9Ta62YgehOMcDpFWI4LvZr7ATwfpMBPI36gbu72aP8eMB28+Gpx6D9LNAO1Ibt7FmVchgz1GF7rwzbY/yZAOFK75W3/0xQmel89orpb5RVrMfA/P1uWdZ3MUDUT1pV32pwW3W0DMP1ovByz78f/v/s9dqlZwB/E/N5KqLrwN3d7BFeNcfGNJ8Ij3zQHlHHaeNjgP1d7QB/p3nVLNYcxowKsF0ZQirXvtKb6UeIHjGN02B6QGO6FneI6ZrcKabrP0JVzotlKYm9A7masqA7kP9drwDeAZ71ypJB/HJ9Bu4q3ezN06C934sDcDef3I5yhhrc1X5NCl3to3nsWaw5jBknwnZVqDziv9Kb6YeCld7ubn+3vZUpj5OxVJUJU2ezGTpzAeAfxPx9nvLiVMBd7t9zcsAzy9+UGngHONYtW+9APMB/Lsn0GrirdLM3X4P2fi93tUfWoJzvrnaePPYs1hzWjF6dBtuVAady7Szeq6/3lVJ+EDvpc8CyrLNUtXs7GpyuWCdD9ytZ53a7AzrgHTB8/6ZPMP5RCud6g+7A/eRu9uYZ6WXQ/t0jH7RH1KHa1T6TndHVDmjAdmftzamSYdge679STLUzwU2mm2rldWGqnVFMnwU3nXYOLKu6GLvcDd3XQndgX7d7xneGwfu4/n76zwxrqKKrYP5ZKtfGRf0YtId6cXbbM4H25pMP25VB+2z+KSNk2r7cULpqVhUQfiJsZ5Nhfo6Y1sXd7daomP6mLItd2SMoIvKrdrmrSBW6t4x90B3I+X7K/ht/1COMVfqbNYBfr1FQT6r4S+SvBa5/4T9LYDYrbI+qLXLdWOqJWBvD9jEpwfaZa40dgI/KsH39zRzTDOtRndSRqwzzmdZd+bpnuh7Z5LWxrHEVAwaXVOmYo47kv4Fr8gz65v3WnK/oOu++66+vVbW/zsr5e/kbe4/ziv7GP7KfH38//Z9lPenzS1N7dGJHe/Pz+JhrPvpd7dk1ZGarwfZRKcB2Vgi+M2dHBhPQvMmjZGL9mcRUu3K3jnLtvWL6AWiHv1VL7kR+r5kuVPV1rdjlftpomea7rtMdWHvPtHvETMvKGzPT8jmk2vX+qFfQnWV9rRTNA3eD9givOBm089Zh2H5dhu1xedVg+46blgovSbVixQY3e8T00HLSda98zViW1cQ0gmGHZo+XDXbPKGqWO2DoHu9dZ8RMy8qda8404/2mCvD9JkP4ozV+qleMjmm+3KNjmqdh+zWffMgdUcfsNTQ7Ssew/ep+eWu8I4sZgo+o0vEwjtRQ7z5n81+pk2pXFtO6W9apGv0n+xnjCvJGRmiOZrgpYixD1BqwruTp42Wa954RM7vHzGT+/bKORbmtS/b6ROp5FA3r2lvT6u9wX/XA4a72Ga96oL35RHh4XvuolGD7jDIA/44shR8RVot93a7qtBelqsvrr6GTfiywrJU6rQt8hzLXlKHLnWm0TFSne2SXO7Cm0x2I/25c3ekOrP8+390BztLx3mrgU6Xu92e9g+6M58G6pOvA3aA90i/SiwduN58aXe0RdRi29ynjeHfDdtYO7Zms00fJMHa390q9+5zNX1m+obess7VzJMju8SOjeYoz1WdzDd1/6xTo3jzjr9nVwLo6eG+Zhu+v9NzxXvWe/Vv3O+O5sQBcPTWrRsecOD4mEmwbtvPWYdjeJ8P2WDFDcOYcxgzlGdZWvPxjwXv5YcN61p6RA/0ZI1X9d2Cvqv88vepxPWv2OLNHL2Tn38Q2Xua/+Gfo7/mdVvw9KI6Yaf57rrmM8R8M41QURp88j5/JXrNdejeixiNr0vW5w91d7ZF+kV71QHvzifAwbJ9RBmyfUXXYztoJXlGML0odkYFsrrz+cfKPTVryWJGzNXr+3eW+LndWDB3mUcfP1ukOxL9IFdAZMdN812jnOJaMl40ydL23On6L+fv/FXQ/9f67F7ozn1cRvQbuJ4P25sk5q7358cDt5sNTz+mwfVZZsF1hhvqMFI6Puevc3e1cOm3ci8rnjIK8ktasdsBH/3CQNwalRxnnKevayBxpY+j+p1ihO6AzYqb5Nq0G7y2j1qiZey4HfAf4R888yxD+mqK64hWuiRX6+xm4K4H25hvtV7+rvXlF+Ri2R9aQma8G22ekAMBHxQqn2VXlJqDKcayU18iyrkkBurJoZK1GoN0InNsJiXd3uat01WdlRmWzQHcQ1HFTNHQH4poslLrdmzf+9V6n3TPeW9ZescL3mxTuv9+Nn8lezwo6eJzNXy/+v+iENV3thu2jXlE+hu2RNRi29+7LP9olI2+XTu9uHxFnVZa1T2dM0dyn3rmkjA87rDPWrfrrNnp8M39HM2uaPc894vMj4ppiqQNo32mRfyWRc92BNTObV87E3jFjeudM78y52Yzzy5Vnib+aC8+2vhat/loCr+/u542QaX6RXobtnz0M27Nge5ZYgWqUFF7KerqqXIOrx71Y1ox64YPaA9yz1OuvpJFz4ZenzmVlAPBRGbrn1RBZB9vLVIH4F6oCa/5GqoD30+A7EyBWBvCP+gTjmdbbStOax+e/8B8J2B5dZ/SPF4bt3zwM2zPBn0fJrMnbKb+Ulfd4GOe3W1Ylre4QP/FBa8dD8y4YXk07100FumfBb0N3DujefHi73SPB+yqouQO8V+p6b3m5kJkVCD8DeHUQf5OB/PGKRw0eIRPlZ9j+2SMftqvLo2TqiP34Th8nc6p8PiyLS4w/GrA+cFbrch+VCgQ/DbpnZUfkR9QQVUfzie12P23MTPNdCw8rdr23zHywzA6AK0L4R30D8qznxbqsn++bdEihq715Grb3+xi2R9eh3N1+iirPbvf1s09eacuyrqj35YSMLzbd8ZLOkQzGtZrRzheoqqxdxvHNXO+ZLzONeInk7N961Issoz5zIq9z5peqAndoGf1ZvfrFnbteSprxAtJnkJzVTPUMdxk/+99Bd/YGtBn1QnfG83aoYoD7qhNq2D7rFeXDA9tZpA7bT+put6wTVOnz1bJYVQGI92rHMbCu0y6wHwnnvmnHDyKzWRkA/ETozpAfUcOtDgTV0nzm/yJveOxE8N6849bytT/+9V+rDPjecu/KvMdXAPA3nQji32mmK575HAtqHrgbtkf68cH2KDHBf7a12a3TPkKrn++dx1d9LS2LXauB72r/XrDY28VrIH5le84O9B0ZI+ebGbqPdLkDe/+FgBIAN3Qfz0dADc2Dp9s9oh5AC7wD67reV4P3lrFWDPC9ZefpFchlv4/6NIbGz7Z/aucIG+ZrJ2gd5oD7ybA9WqywnWndWGC7enf7jE7qbq88TsYal8/TdVWAmb06Efhan1UBiLN2ue/s1t6hndB9RIbuazJvucD4PUY2dI+oIbIOBNTSvM4C74DuuJmWcddO+N7yzgTwgCaEv8kwPlcHzKcfv4xOh+1VofajPErGsixLQ9VeIrRKjC+NtGK1+hwz/q0xvtx010tHd2SwHstNIy9W9EtN12Uqv0yV5YWqTC9VbV5xf88rXqx6+79IrXxB5Y6XQe5+yWbmCy4ZXypa4eWfr17YyrjWFrXGUKhhO6+Ya5tRle72bGlX36+qfw9WntRuFiPFeGN58vm4qtWgtNe/94xFQ4QMMUL9kzOYofvo1W7oHp+ZkXvLnlEE8J5VxN8+G3iPBobR4B3AUvBu+N6TlwuZmcGwOoR/1hUoz7T+1nb1j5RRB46zOmVuu7vb1+jUcTKWZVnWazGOHFEX26z7EVUZ+7Ijw/Pcm0bGy+wevTKTB4zdB2fNV88eMYPE/IgaImtZUU/z4hs1A6wfNwOsfdFq818/dqblrBfDi0dfQV+We8lP0L0Ke+yB7iznxQoRz+lU6W4/QWzrxlIPSx2KUpvfblmRYu3dYOwqYewCYexOXi13uX8X43gixrEv1TrQq3a67xybM9Pxp5Q5mzubfcuf259jzMytlghFj5qJqusf6IybAWp0vrec/V3ILB3eCp3Y70bTMD7DROlq17zC+bM6O9w9Sibaj7O7PUpMNTHVYlmW9U07X4S3UowvBa3QIc7Y/bxavX8TjGtUoZO+7bP+OJg70Hd2ugP7XqQ6elzA3s7zrJepAjkvNc3udo+oIbqO2Vqi6rl7xb1cFVjb9Q648/19zm/tuldkeukocyf8s75Bd/X73hGtgO6s559ZD+fhOnBXgu0rpFKnxavsa+i8rxzLOkuscG61KoBSRiC++hhGx2asVAUgvgsg96oaDN+VA4z9reyE7m2/c0bMzOQiKTsiP6KGqDqiaomsp3mtGTcDGL7f/feNZ8kC8C2bG8IDfM8Dz7rSBc92780od85P6dqfidqFqFAva3c729qx1ZMpr4Vlfdau8QCVVGXMRa8qnHfGkSa9Wj1ahnFkyo7PqSoZzrlrZK/RURInjHvJHPUSkZ2ZH1FDVB1RtQDxYxlWvWR11YtWFcfOtIx9Y0ayR3iwjVWpMNrk08galnW2pJX7uxT7r2LWuHxuLaue9s435NWuebyMc6sBznNT4YcDH8M17YDuvWJcJ0N37pzKc91nIPjYfnOZGbm37BkxAO+K4L15xYN3w/f9AH6HskEzKxyuAOMfdRXMs6y/RaPvI2XUumpPm91uWZbFLsZxGVXFOPIF6L8GWEdcrM6osE6Mo2UYx7JUmYPOPI6FOWc0a/TvRWnEDDZn3nJPHDMTUQNTHY+1zNbTvO5iHDkDaI6dAeLX9nXG/rEsDDPQ30Fflmexb9Cd8TmmR6PQneX8WKHqe2lqpNT/kCwd+VrT1imwNmP+acV1ZYYyjDOrgb2AaWXGjmua8e+mwjr1/m2MwENGkGzo3if2HGzKmnmZKtAPz2aOre2nM9sdg7m37Kz56izAO7KO2Vqi6rl74V+vGKnCd2Dt3Hdg7f3lMwzdcU/HAOEBfhB/U3Ug/05R3fFs5/Nw5QF3i0/+4+TWLCTJhH1ZXbcZuawdxlHaeXzV1/KKdoDFtg8fhN2RsWOtKgDxHetUBbr3auRcAHxAfARsMcPwUVA3ekwjWe52/5zZ9tXJnc2OyI+oIaqOqFoe62leXF3vzVMHvgN7u9+BegC+5f6prGeeT6CXkQ9dGUtz8vNj1lgbxmtlRMHr9xm4V1m0GbH/sbLXxyB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id="image73e6625c3e" transform="scale(1 -1) translate(0 -135.12)" x="0" y="-4.66125" width="360" height="135.12"/> - - - + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - + - - + + - - + diff --git a/doc/_static/mne_logo_gray.svg b/doc/_static/mne_logo_gray.svg new file mode 100644 index 00000000000..1cabd8f5932 --- /dev/null +++ b/doc/_static/mne_logo_gray.svg @@ -0,0 +1,783 @@ + + + + + + + + 2023-11-07T13:26:54.034781 + image/svg+xml + + + Matplotlib v3.8.0.dev1998+gc5707d9c79, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/_static/mne_logo_small.svg b/doc/_static/mne_logo_small.svg index 506d2c0f4c0..0f62fdf3ce5 100644 --- a/doc/_static/mne_logo_small.svg +++ b/doc/_static/mne_logo_small.svg @@ -6,11 +6,11 @@ - 2022-04-13T15:28:23.367808 + 2023-11-07T13:26:54.345596 image/svg+xml - Matplotlib v3.5.1, https://matplotlib.org/ + Matplotlib v3.8.0.dev1998+gc5707d9c79, https://matplotlib.org/ @@ -21,9 +21,9 @@ - + +iVBORw0KGgoAAAANSUhEUgAAAEIAAAAQCAYAAACiEqkUAAAAsElEQVR4nO2QW2rEMBAEa8Ri2WLXd8rJcyeH+Bnc+VCHwJ5hCoYeNZI+KiR0r4H2gK2gve86Am2BzoC9/HdODp+P0vN0fwa6vDt1FXQG62PuU2fW8cXaZvbpyac++N4ry9b42hrL2ViuxvLT0BDcQ6Ah0IAz0AM0eq++MwaqoNo7jX3u0e+mt37yH61QSABSxB8pwqQIkyJMijApwqQIkyJMijApwqQIkyJMijApwvwCdAFgHcVQk7IAAAAASUVORK5CYII=" id="imagec02f1fbe38" transform="scale(1 -1) translate(0 -12)" x="86" y="-10.75" width="49.5" height="12"/> +" clip-path="url(#pf7563a16a6)" style="fill: #808080"/> - + diff --git a/doc/sphinxext/gen_commands.py b/doc/sphinxext/gen_commands.py index 0ca15319d36..21c31d2829b 100644 --- a/doc/sphinxext/gen_commands.py +++ b/doc/sphinxext/gen_commands.py @@ -51,8 +51,8 @@ def generate_commands_rst(app=None): from sphinx.util.display import status_iterator except Exception: from sphinx.util import status_iterator - root = Path(__file__).parent.parent.parent.absolute() - out_dir = (root / "doc" / "generated").absolute() + root = Path(__file__).parents[2] + out_dir = root / "doc" / "generated" out_dir.mkdir(exist_ok=True) out_fname = out_dir / "commands.rst.new" diff --git a/logo/generate_mne_logos.py b/logo/generate_mne_logos.py index 34b77788750..419dbe4279d 100644 --- a/logo/generate_mne_logos.py +++ b/logo/generate_mne_logos.py @@ -1,23 +1,19 @@ -""" -=============================================================================== -Script 'mne logo' -=============================================================================== +"""Generate the MNE-Python logos.""" -This script makes the logo for MNE. -""" # @author: drmccloy # Created on Mon Jul 20 11:28:16 2015 # License: BSD-3-Clause -import numpy as np -import os.path as op +import pathlib + import matplotlib.pyplot as plt -from matplotlib import rcParams -from scipy.stats import multivariate_normal +import numpy as np +from matplotlib import font_manager, rcParams +from matplotlib.colors import LinearSegmentedColormap +from matplotlib.patches import Ellipse, FancyBboxPatch, PathPatch, Rectangle from matplotlib.path import Path from matplotlib.text import TextPath -from matplotlib.patches import PathPatch, Ellipse, FancyBboxPatch, Rectangle -from matplotlib.colors import LinearSegmentedColormap +from scipy.stats import multivariate_normal # manually set values dpi = 300 @@ -25,7 +21,7 @@ tagline_scale_fudge = 0.97 # to get justification right tagline_offset_fudge = np.array([0, -100.0]) -# font, etc +# font, etc (default to MNE font) rcp = { "font.sans-serif": ["Primetime"], "font.style": "normal", @@ -38,6 +34,9 @@ plt.rcdefaults() rcParams.update(rcp) +# %% +# mne_logo.svg and mne_logo_dark.svg + # initialize figure (no axes, margins, etc) fig = plt.figure(1, figsize=(5, 2.25), frameon=False, dpi=dpi) ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) @@ -108,14 +107,21 @@ im.set_clip_path(mne_clip, transform=im.get_transform()) ax.add_patch(rect) rect.set_clip_path(mne_clip, transform=im.get_transform()) -for coll in cs.collections: - coll.set_clip_path(mne_clip, transform=im.get_transform()) +cs.set_clip_path(mne_clip, transform=im.get_transform()) # get final position of clipping mask mne_corners = mne_clip.get_extents().corners() +# For this make sure that this gives something like "" +fnt = font_manager.findfont("Cooper Hewitt:style=normal:weight=book") +if "Book" not in fnt or "CooperHewitt" not in fnt: + print( + f"WARNING: Might not use correct Cooper Hewitt, got {fnt} but want " + "CooperHewitt-Book.otf or similar" + ) + # add tagline -rcParams.update({"font.sans-serif": ["Cooper Hewitt"], "font.weight": "300"}) -tag_path = TextPath((0, 0), "MEG + EEG ANALYSIS & VISUALIZATION") +with plt.rc_context({"font.sans-serif": ["Cooper Hewitt"], "font.weight": "book"}): + tag_path = TextPath((0, 0), "MEG + EEG ANALYSIS & VISUALIZATION") dims = tag_path.vertices.max(0) - tag_path.vertices.min(0) vert = tag_path.vertices - dims / 2.0 mult = tagline_scale_fudge * (plot_dims / dims).min() @@ -126,26 +132,36 @@ - tagline_offset_fudge ) tag_clip = Path(offset + vert * mult, tag_path.codes) -tag_patch = PathPatch(tag_clip, facecolor="k", edgecolor="none", zorder=10) +tag_patch = PathPatch(tag_clip, facecolor="0.6", edgecolor="none", zorder=10) ax.add_patch(tag_patch) yl = ax.get_ylim() yy = np.max([tag_clip.vertices.max(0)[-1], tag_clip.vertices.min(0)[-1]]) ax.set_ylim(np.ceil(yy), yl[-1]) # only save actual image extent plus a bit of padding -plt.draw() -static_dir = op.join(op.dirname(__file__), "..", "doc", "_static") -assert op.isdir(static_dir) -plt.savefig(op.join(static_dir, "mne_logo.svg"), transparent=True) -tag_patch.set_facecolor("w") -rect.set_facecolor("0.5") -plt.savefig(op.join(static_dir, "mne_logo_dark.svg"), transparent=True) -tag_patch.set_facecolor("k") -rect.set_facecolor("w") +fig.canvas.draw_idle() +static_dir = pathlib.Path(__file__).parents[1] / "doc" / "_static" +assert static_dir.is_dir() +kind_color = dict( + mne_logo_dark=("0.8", "0.5"), + mne_logo_gray=("0.6", "0.75"), + mne_logo=("0.3", "w"), # always last +) +for kind, (tag_color, rect_color) in kind_color.items(): + tag_patch.set_facecolor(tag_color) + rect.set_facecolor(rect_color) + fig.savefig( + static_dir / f"{kind}.svg", + transparent=True, + ) + +# %% +# mne_splash.png # modify to make the splash screen -data_dir = op.join(op.dirname(__file__), "..", "mne", "icons") -ax.patches[-1].set_facecolor("w") +data_dir = pathlib.Path(__file__).parents[1] / "mne" / "icons" +assert data_dir.is_dir() +tag_patch.set_facecolor("0.7") for coll in list(ax.collections): coll.remove() bounds = np.array( @@ -159,7 +175,10 @@ r = np.diff(bounds, axis=1).max() * 1.2 w, h = r, r * (2 / 3) box_xy = [xy[0] - w * 0.5, xy[1] - h * (2 / 5)] -ax.set_ylim(box_xy[1] + h * 1.001, box_xy[1] - h * 0.001) +ax.set( + ylim=(box_xy[1] + h * 1.001, box_xy[1] - h * 0.001), + xlim=(box_xy[0] - w * 0.001, box_xy[0] + w * 1.001), +) patch = FancyBboxPatch( box_xy, w, @@ -174,19 +193,30 @@ ) ax.add_patch(patch) fig.set_size_inches((512 / dpi, 512 * (h / w) / dpi)) -plt.savefig(op.join(data_dir, "mne_splash.png"), transparent=True) +fig.savefig( + data_dir / "mne_splash.png", + transparent=True, +) patch.remove() +# %% +# mne_default_icon.png + # modify to make an icon -ax.patches.pop(-1) # no tag line for our icon +ax.patches[-1].remove() # no tag line for our icon patch = Ellipse(xy, r, r, clip_on=False, zorder=-1, fc="k") ax.add_patch(patch) -ax.set_ylim(xy[1] + r / 1.9, xy[1] - r / 1.9) +ax.set_ylim(xy[1] + r / 1.99, xy[1] - r / 1.99) fig.set_size_inches((256 / dpi, 256 / dpi)) # Qt does not support clip paths in SVG rendering so we have to use PNG here # then use "optipng -o7" on it afterward (14% reduction in file size) -plt.savefig(op.join(data_dir, "mne_default_icon.png"), transparent=True) -plt.close() +fig.savefig( + data_dir / "mne_default_icon.png", + transparent=True, +) + +# %% +# mne_logo_small.svg # 188x45 image dpi = 96 # for SVG it's different @@ -194,13 +224,12 @@ h_px = 45 center_fudge = np.array([60, 0]) scale_fudge = 2.1 -rcParams.update({"font.sans-serif": ["Primetime"], "font.weight": "black"}) x = np.linspace(-1.0, 1.0, w_px // 2) y = np.linspace(-1.0, 1.0, h_px // 2) X, Y = np.meshgrid(x, y) # initialize figure (no axes, margins, etc) fig = plt.figure( - 1, figsize=(w_px / dpi, h_px / dpi), facecolor="k", frameon=False, dpi=dpi + 2, figsize=(w_px / dpi, h_px / dpi), facecolor="k", frameon=False, dpi=dpi ) ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) ax.set_axis_off() @@ -232,6 +261,9 @@ ypad = np.abs(np.diff([ymax, ymin])) / 20.0 ax.set_xlim(xmin - xpad, xl[1] + xpad) ax.set_ylim(ymax + ypad, ymin - ypad) -plt.draw() -plt.savefig(op.join(static_dir, "mne_logo_small.svg"), dpi=dpi, transparent=True) -plt.close() +fig.canvas.draw_idle() +fig.savefig( + static_dir / "mne_logo_small.svg", + dpi=dpi, + transparent=True, +) diff --git a/mne/icons/mne_default_icon.png b/mne/icons/mne_default_icon.png index 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b/mne/utils/config.py index 9497749b94d..453cdb5c084 100644 --- a/mne/utils/config.py +++ b/mne/utils/config.py @@ -537,7 +537,7 @@ def _get_stim_channel(stim_channel, info, raise_error=True): def _get_root_dir(): """Get as close to the repo root as possible.""" - root_dir = Path(__file__).parent.parent.expanduser().absolute() + root_dir = Path(__file__).parents[1] up_dir = root_dir.parent if (up_dir / "setup.py").is_file() and all( (up_dir / x).is_dir() for x in ("mne", "examples", "doc") diff --git a/mne/viz/backends/_utils.py b/mne/viz/backends/_utils.py index b114e99e349..fc42538335f 100644 --- a/mne/viz/backends/_utils.py +++ b/mne/viz/backends/_utils.py @@ -89,7 +89,7 @@ def _alpha_blend_background(ctable, background_color): def _qt_init_icons(): from qtpy.QtGui import QIcon - icons_path = f"{Path(__file__).parent.parent.parent}/icons" + icons_path = str(Path(__file__).parents[2] / "icons") QIcon.setThemeSearchPaths([icons_path]) return icons_path diff --git a/tools/check_mne_location.py b/tools/check_mne_location.py index 75991ac2743..8691cad7d72 100755 --- a/tools/check_mne_location.py +++ b/tools/check_mne_location.py @@ -3,7 +3,7 @@ from pathlib import Path import mne -want_mne_dir = Path(__file__).parent.parent / "mne" +want_mne_dir = Path(__file__).parents[1] / "mne" got_mne_dir = Path(mne.__file__).parent want_mne_dir = want_mne_dir.resolve() got_mne_dir = got_mne_dir.resolve() diff --git a/tools/generate_codemeta.py b/tools/generate_codemeta.py index e547261dc4d..f53d2ae856a 100644 --- a/tools/generate_codemeta.py +++ b/tools/generate_codemeta.py @@ -7,7 +7,7 @@ parser.add_argument("release_version", type=str) release_version = parser.parse_args().release_version -out_dir = Path(__file__).parent.parent +out_dir = Path(__file__).parents[1] # NOTE: ../codemeta.json and ../citation.cff should not be continuously # updated. Run this script only at release time. diff --git a/tutorials/evoked/20_visualize_evoked.py b/tutorials/evoked/20_visualize_evoked.py index e07b1e2e601..4f7e2ba4967 100644 --- a/tutorials/evoked/20_visualize_evoked.py +++ b/tutorials/evoked/20_visualize_evoked.py @@ -279,7 +279,7 @@ def custom_func(x): # compute 3D field maps without a ``trans`` file, but it will only work for # calculating the field *on the MEG helmet from the MEG sensors*. -subjects_dir = root.parent.parent / "subjects" +subjects_dir = root.parents[1] / "subjects" trans_file = root / "sample_audvis_raw-trans.fif" # %% From bb93c0a20cc936f7cdfbadc8aa5ee40a974a6e80 Mon Sep 17 00:00:00 2001 From: Clemens Brunner Date: Wed, 8 Nov 2023 14:55:43 +0100 Subject: [PATCH 15/20] Use gray logo (works in light and dark modes) (#12184) --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index fdc620acd9f..e8690281bcb 100644 --- a/README.rst +++ b/README.rst @@ -199,5 +199,5 @@ MNE-Python is **BSD-licensed** (BSD-3-Clause): .. |OpenSSF| image:: https://www.bestpractices.dev/projects/7783/badge .. _OpenSSF: https://www.bestpractices.dev/projects/7783 -.. |MNE| image:: https://mne.tools/stable/_static/mne_logo.svg +.. |MNE| image:: https://mne.tools/dev/_static/mne_logo_gray.svg .. _MNE: https://mne.tools/dev/ From 8d86df6780dd39dfcd55544ee358424a69d4b9be Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Wed, 8 Nov 2023 09:58:14 -0500 Subject: [PATCH 16/20] BUG: Fix bug with logging and n_jobs>1 (#12154) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Richard Höchenberger --- doc/changes/devel.rst | 1 + mne/parallel.py | 8 +++++++- mne/utils/tests/test_logging.py | 22 ++++++++++++++++++++++ 3 files changed, 30 insertions(+), 1 deletion(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index d11bb3ad6b5..698e033b758 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -57,6 +57,7 @@ Bugs - Fix bug with :func:`mne.channels.read_ch_adjacency` (:gh:`11608` by :newcontrib:`Ivan Zubarev`) - Fix bugs with saving splits for :class:`~mne.Epochs` (:gh:`11876` by `Dmitrii Altukhov`_) - Fix bug with multi-plot 3D rendering where only one plot was updated (:gh:`11896` by `Eric Larson`_) +- Fix bug where ``verbose`` level was not respected inside parallel jobs (:gh:`12154` by `Eric Larson`_) - Fix bug where subject birthdays were not correctly read by :func:`mne.io.read_raw_snirf` (:gh:`11912` by `Eric Larson`_) - Fix bug with :func:`mne.chpi.compute_head_pos` for CTF data where digitization points were modified in-place, producing an incorrect result during a save-load round-trip (:gh:`11934` by `Eric Larson`_) - Fix bug where non-compliant stimulus data streams were not ignored by :func:`mne.io.read_raw_snirf` (:gh:`11915` by `Johann Benerradi`_) diff --git a/mne/parallel.py b/mne/parallel.py index 2598d93ae92..bb8a14d381d 100644 --- a/mne/parallel.py +++ b/mne/parallel.py @@ -14,6 +14,7 @@ _validate_type, get_config, logger, + use_log_level, verbose, warn, ) @@ -120,7 +121,12 @@ def parallel_func( logger.debug(f"Got {n_jobs} parallel jobs after requesting {n_jobs_orig}") if max_jobs is not None: n_jobs = min(n_jobs, max(_ensure_int(max_jobs, "max_jobs"), 1)) - my_func = delayed(func) + + def run_verbose(*args, verbose=logger.level, **kwargs): + with use_log_level(verbose=verbose): + return func(*args, **kwargs) + + my_func = delayed(run_verbose) if total is not None: diff --git a/mne/utils/tests/test_logging.py b/mne/utils/tests/test_logging.py index 2b9b6a8aeee..a091bea0f83 100644 --- a/mne/utils/tests/test_logging.py +++ b/mne/utils/tests/test_logging.py @@ -1,3 +1,4 @@ +import logging import os import re import warnings @@ -8,6 +9,7 @@ from mne import Epochs, create_info, read_evokeds from mne.io import RawArray, read_raw_fif +from mne.parallel import parallel_func from mne.utils import ( _get_call_line, catch_logging, @@ -252,3 +254,23 @@ def meth_2(self, verbose=None): o.meth_2(verbose=False) log = log.getvalue() assert log == "" + + +@pytest.mark.parametrize("n_jobs", (1, 2)) +def test_verbose_threads(n_jobs): + """Test that our verbose level propagates to threads.""" + + def my_fun(): + from mne.utils import logger + + return logger.level + + with use_log_level("info"): + assert logger.level == logging.INFO + with use_log_level("warning"): + assert logger.level == logging.WARNING + parallel, p_fun, got_jobs = parallel_func(my_fun, n_jobs=n_jobs) + assert got_jobs in (1, n_jobs) # FORCE_SERIAL could be set + out = parallel(p_fun() for _ in range(5)) + want_levels = [logging.WARNING] * 5 + assert out == want_levels From 14f4f865e6fa28231962b8c7582cac5f41798f9f Mon Sep 17 00:00:00 2001 From: Mathieu Scheltienne Date: Wed, 8 Nov 2023 17:13:17 +0100 Subject: [PATCH 17/20] Add argument splash to disable splash-screen from Qt-browser (#12185) Co-authored-by: Eric Larson --- doc/changes/devel.rst | 7 ++++--- mne/epochs.py | 2 ++ mne/io/base.py | 2 ++ mne/preprocessing/ica.py | 2 ++ mne/utils/docs.py | 8 ++++++++ mne/viz/_figure.py | 2 +- mne/viz/epochs.py | 5 +++++ mne/viz/ica.py | 7 +++++++ mne/viz/raw.py | 5 +++++ 9 files changed, 36 insertions(+), 4 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index 698e033b758..2819e361a25 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -47,10 +47,11 @@ Enhancements - :func:`~mne.epochs.make_metadata` now accepts ``tmin=None`` and ``tmax=None``, which will bound the time window used for metadata generation by event names (instead of a fixed time). That way, you can now for example generate metadata spanning from one cue or fixation cross to the next, even if trial durations vary throughout the recording (:gh:`12118` by `Richard Höchenberger`_) - Add support for passing multiple labels to :func:`mne.minimum_norm.source_induced_power` (:gh:`12026` by `Erica Peterson`_, `Eric Larson`_, and `Daniel McCloy`_ ) - Added documentation to :meth:`mne.io.Raw.set_montage` and :func:`mne.add_reference_channels` to specify that montages should be set after adding reference channels (:gh:`12160` by `Jacob Woessner`_) +- Add argument ``splash`` to the function using the ``qt`` browser backend to allow enabling/disabling the splash screen (:gh:`12185` by `Mathieu Scheltienne`_) Bugs ~~~~ -- Fix bug where :func:`mne.io.read_raw_gdf` would fail due to improper usage of ``np.clip`` (:gh:`12168` by :newcontrib:`Rasmus Aagaard`) +- Fix bug where :func:`mne.io.read_raw_gdf` would fail due to improper usage of ``np.clip`` (:gh:`12168` by :newcontrib:`Rasmus Aagaard`) - Fix bugs with :func:`mne.preprocessing.realign_raw` where the start of ``other`` was incorrectly cropped; and onsets and durations in ``other.annotations`` were left unsynced with the resampled data (:gh:`11950` by :newcontrib:`Qian Chu`) - Fix bug where ``encoding`` argument was ignored when reading annotations from an EDF file (:gh:`11958` by :newcontrib:`Andrew Gilbert`) - Mark tests ``test_adjacency_matches_ft`` and ``test_fetch_uncompressed_file`` as network tests (:gh:`12041` by :newcontrib:`Maksym Balatsko`) @@ -70,7 +71,7 @@ Bugs - Fix bug with reported component number and errant reporting of PCA explained variance as ICA explained variance in :meth:`mne.Report.add_ica` (:gh:`12155`, :gh:`12167` by `Eric Larson`_ and `Richard Höchenberger`_) - Fix bug with axis clip box boundaries in :func:`mne.viz.plot_evoked_topo` and related functions (:gh:`11999` by `Eric Larson`_) - Fix bug with ``subject_info`` when loading data from and exporting to EDF file (:gh:`11952` by `Paul Roujansky`_) -- Fix bug where :class:`mne.Info` HTML representations listed all channel counts instead of good channel counts under the heading "Good channels" (:gh:`12145` by `Eric Larson`_) +- Fix bug where :class:`mne.Info` HTML representations listed all channel counts instead of good channel counts under the heading "Good channels" (:gh:`12145` by `Eric Larson`_) - Fix rendering glitches when plotting Neuromag/TRIUX sensors in :func:`mne.viz.plot_alignment` and related functions (:gh:`12098` by `Eric Larson`_) - Fix bug with delayed checking of :class:`info["bads"] ` (:gh:`12038` by `Eric Larson`_) - Fix bug with :ref:`mne coreg` where points inside the head surface were not shown (:gh:`12147`, :gh:`12164` by `Eric Larson`_) @@ -86,7 +87,7 @@ Bugs - Fix bug with :func:`~mne.viz.plot_raw` where changing ``MNE_BROWSER_BACKEND`` via :func:`~mne.set_config` would have no effect within a Python session (:gh:`12078` by `Santeri Ruuskanen`_) - Improve handling of ``method`` argument in the channel interpolation function to support :class:`str` and raise helpful error messages (:gh:`12113` by `Mathieu Scheltienne`_) - Fix combination of ``DIN`` event channels into a single synthetic trigger channel ``STI 014`` by the MFF reader of :func:`mne.io.read_raw_egi` (:gh:`12122` by `Mathieu Scheltienne`_) -- Fix bug with :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab` where automatic fiducial detection would fail for certain files (:gh:`12165` by `Clemens Brunner`_) +- Fix bug with :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab` where automatic fiducial detection would fail for certain files (:gh:`12165` by `Clemens Brunner`_) API changes ~~~~~~~~~~~ diff --git a/mne/epochs.py b/mne/epochs.py index 510161f99bc..50ecf10ee64 100644 --- a/mne/epochs.py +++ b/mne/epochs.py @@ -1299,6 +1299,7 @@ def plot( *, theme=None, overview_mode=None, + splash=True, ): return plot_epochs( self, @@ -1324,6 +1325,7 @@ def plot( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, ) @copy_function_doc_to_method_doc(plot_topo_image_epochs) diff --git a/mne/io/base.py b/mne/io/base.py index 4c1d13ffaf7..658eb0e4ea2 100644 --- a/mne/io/base.py +++ b/mne/io/base.py @@ -1801,6 +1801,7 @@ def plot( *, theme=None, overview_mode=None, + splash=True, verbose=None, ): return plot_raw( @@ -1838,6 +1839,7 @@ def plot( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, verbose=verbose, ) diff --git a/mne/preprocessing/ica.py b/mne/preprocessing/ica.py index c0139427a4a..df1952669a5 100644 --- a/mne/preprocessing/ica.py +++ b/mne/preprocessing/ica.py @@ -2552,6 +2552,7 @@ def plot_sources( *, theme=None, overview_mode=None, + splash=True, ): return plot_ica_sources( self, @@ -2569,6 +2570,7 @@ def plot_sources( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, ) @copy_function_doc_to_method_doc(plot_ica_scores) diff --git a/mne/utils/docs.py b/mne/utils/docs.py index 7875f83feb0..573066ca3c2 100644 --- a/mne/utils/docs.py +++ b/mne/utils/docs.py @@ -4201,6 +4201,14 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75): .. versionchanged:: 1.1 Added ``'eeglab'`` option. """ +docdict[ + "splash" +] = """ +splash : bool + If True (default), a splash screen is shown during the application startup. Only + applicable to the ``qt`` backend. +""" + docdict[ "split_naming" ] = """ diff --git a/mne/viz/_figure.py b/mne/viz/_figure.py index f53e079c6b5..7bd58f4ee9e 100644 --- a/mne/viz/_figure.py +++ b/mne/viz/_figure.py @@ -659,7 +659,7 @@ def _get_browser(show, block, **kwargs): figsize = kwargs.setdefault("figsize", _get_figsize_from_config()) if figsize is None or np.any(np.array(figsize) < 8): kwargs["figsize"] = (8, 8) - kwargs["splash"] = True if show else False + kwargs["splash"] = kwargs.get("splash", True) and show if kwargs.get("theme", None) is None: kwargs["theme"] = get_config("MNE_BROWSER_THEME", "auto") if kwargs.get("overview_mode", None) is None: diff --git a/mne/viz/epochs.py b/mne/viz/epochs.py index 613f3a2b62a..22d686c9b95 100644 --- a/mne/viz/epochs.py +++ b/mne/viz/epochs.py @@ -786,6 +786,7 @@ def plot_epochs( *, theme=None, overview_mode=None, + splash=True, ): """Visualize epochs. @@ -881,6 +882,9 @@ def plot_epochs( %(overview_mode)s .. versionadded:: 1.1 + %(splash)s + + .. versionadded:: 1.6 Returns ------- @@ -1086,6 +1090,7 @@ def plot_epochs( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, ) fig = _get_browser(show=show, block=block, **params) diff --git a/mne/viz/ica.py b/mne/viz/ica.py index 2b16c5f6837..24a9af42b12 100644 --- a/mne/viz/ica.py +++ b/mne/viz/ica.py @@ -55,6 +55,7 @@ def plot_ica_sources( *, theme=None, overview_mode=None, + splash=True, ): """Plot estimated latent sources given the unmixing matrix. @@ -99,6 +100,9 @@ def plot_ica_sources( %(overview_mode)s .. versionadded:: 1.1 + %(splash)s + + .. versionadded:: 1.6 Returns ------- @@ -139,6 +143,7 @@ def plot_ica_sources( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, ) elif isinstance(inst, Evoked): if start is not None or stop is not None: @@ -1266,6 +1271,7 @@ def _plot_sources( *, theme=None, overview_mode=None, + splash=True, ): """Plot the ICA components as a RawArray or EpochsArray.""" from ..epochs import BaseEpochs, EpochsArray @@ -1410,6 +1416,7 @@ def _plot_sources( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, ) if is_epo: params.update( diff --git a/mne/viz/raw.py b/mne/viz/raw.py index ec5c95f57ab..cdefc285c19 100644 --- a/mne/viz/raw.py +++ b/mne/viz/raw.py @@ -65,6 +65,7 @@ def plot_raw( *, theme=None, overview_mode=None, + splash=True, verbose=None, ): """Plot raw data. @@ -196,6 +197,9 @@ def plot_raw( %(overview_mode)s .. versionadded:: 1.1 + %(splash)s + + .. versionadded:: 1.6 %(verbose)s Returns @@ -394,6 +398,7 @@ def plot_raw( use_opengl=use_opengl, theme=theme, overview_mode=overview_mode, + splash=splash, ) fig = _get_browser(show=show, block=block, **params) From 6874483f6033743510a1ab0766c56ea007db1c20 Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Wed, 8 Nov 2023 11:38:17 -0500 Subject: [PATCH 18/20] BUG: Fix bug with spectrum warning (#12186) --- doc/changes/devel.rst | 1 + mne/time_frequency/spectrum.py | 12 +++++++----- mne/time_frequency/tests/test_spectrum.py | 13 ++++++++----- .../preprocessing/50_artifact_correction_ssp.py | 3 ++- 4 files changed, 18 insertions(+), 11 deletions(-) diff --git a/doc/changes/devel.rst b/doc/changes/devel.rst index 2819e361a25..d5089fd95ee 100644 --- a/doc/changes/devel.rst +++ b/doc/changes/devel.rst @@ -60,6 +60,7 @@ Bugs - Fix bug with multi-plot 3D rendering where only one plot was updated (:gh:`11896` by `Eric Larson`_) - Fix bug where ``verbose`` level was not respected inside parallel jobs (:gh:`12154` by `Eric Larson`_) - Fix bug where subject birthdays were not correctly read by :func:`mne.io.read_raw_snirf` (:gh:`11912` by `Eric Larson`_) +- Fix bug where warnings were emitted when computing spectra for channels marked as bad (:gh:`12186` by `Eric Larson`_) - Fix bug with :func:`mne.chpi.compute_head_pos` for CTF data where digitization points were modified in-place, producing an incorrect result during a save-load round-trip (:gh:`11934` by `Eric Larson`_) - Fix bug where non-compliant stimulus data streams were not ignored by :func:`mne.io.read_raw_snirf` (:gh:`11915` by `Johann Benerradi`_) - Fix bug with ``pca=False`` in :func:`mne.minimum_norm.compute_source_psd` (:gh:`11927` by `Alex Gramfort`_) diff --git a/mne/time_frequency/spectrum.py b/mne/time_frequency/spectrum.py index 3d6acbc3914..c39d5f8a078 100644 --- a/mne/time_frequency/spectrum.py +++ b/mne/time_frequency/spectrum.py @@ -439,15 +439,17 @@ def _check_values(self): """Check PSD results for correct shape and bad values.""" assert len(self._dims) == self._data.ndim, (self._dims, self._data.ndim) assert self._data.shape == self._shape - # negative values OK if the spectrum is really fourier coefficients - if "taper" in self._dims: - return # TODO: should this be more fine-grained (report "chan X in epoch Y")? ch_dim = self._dims.index("channel") - dims = np.arange(self._data.ndim).tolist() + dims = list(range(self._data.ndim)) dims.pop(ch_dim) # take min() across all but the channel axis - bad_value = self._data.min(axis=tuple(dims)) <= 0 + # (if the abs becomes memory intensive we could iterate over channels) + use_data = self._data + if use_data.dtype.kind == "c": + use_data = np.abs(use_data) + bad_value = use_data.min(axis=tuple(dims)) == 0 + bad_value &= ~np.isin(self.ch_names, self.info["bads"]) if bad_value.any(): chs = np.array(self.ch_names)[bad_value].tolist() s = _pl(bad_value.sum()) diff --git a/mne/time_frequency/tests/test_spectrum.py b/mne/time_frequency/tests/test_spectrum.py index 96fe89a2e6d..75768aff130 100644 --- a/mne/time_frequency/tests/test_spectrum.py +++ b/mne/time_frequency/tests/test_spectrum.py @@ -1,4 +1,3 @@ -from contextlib import nullcontext from functools import partial import numpy as np @@ -359,7 +358,6 @@ def test_spectrum_complex(method, average): assert len(epochs) == 5 assert len(epochs.times) == 2 * sfreq kwargs = dict(output="complex", method=method) - ctx = pytest.warns(UserWarning, match="Zero value") if method == "welch": kwargs["n_fft"] = sfreq want_dims = ("epoch", "channel", "freq") @@ -371,11 +369,9 @@ def test_spectrum_complex(method, average): else: assert method == "multitaper" assert not average - ctx = nullcontext() want_dims = ("epoch", "channel", "taper", "freq") want_shape = (5, 1, 7, sfreq + 1) - with ctx: - spectrum = epochs.compute_psd(**kwargs) + spectrum = epochs.compute_psd(**kwargs) idx = np.argmin(np.abs(spectrum.freqs - freq)) assert spectrum.freqs[idx] == freq assert spectrum._dims == want_dims @@ -389,6 +385,13 @@ def test_spectrum_complex(method, average): coef = coef.mean(-1) # over segments coef = coef.item() assert_allclose(np.angle(coef), phase, rtol=1e-4) + # Now test that it warns appropriately + epochs._data[0, 0, :] = 0 # actually zero for one epoch and ch + with pytest.warns(UserWarning, match="Zero value.*channel 0"): + epochs.compute_psd(**kwargs) + # But not if we mark that channel as bad + epochs.info["bads"] = epochs.ch_names[:1] + epochs.compute_psd(**kwargs) def test_spectrum_kwarg_triaging(raw): diff --git a/tutorials/preprocessing/50_artifact_correction_ssp.py b/tutorials/preprocessing/50_artifact_correction_ssp.py index 6adcacfb4e0..2d18367efe8 100644 --- a/tutorials/preprocessing/50_artifact_correction_ssp.py +++ b/tutorials/preprocessing/50_artifact_correction_ssp.py @@ -111,7 +111,8 @@ # individual spectrum for each sensor, or an average (with confidence band) # across sensors: -spectrum = empty_room_raw.compute_psd(verbose="error") # ignore zero value warning +raw.info["bads"] = ["MEG 2443"] +spectrum = empty_room_raw.compute_psd() for average in (False, True): spectrum.plot(average=average, dB=False, xscale="log", picks="data", exclude="bads") From 2bc47d078999203a23a6c78ac55952c9e699c861 Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Wed, 8 Nov 2023 14:56:12 -0500 Subject: [PATCH 19/20] BUG: Fix bug with default alpha and axes (#12187) --- mne/viz/_3d.py | 2 +- mne/viz/backends/_pyvista.py | 2 +- tutorials/forward/20_source_alignment.py | 3 +-- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/mne/viz/_3d.py b/mne/viz/_3d.py index 0063aa37bdd..41d6f0476b7 100644 --- a/mne/viz/_3d.py +++ b/mne/viz/_3d.py @@ -984,7 +984,7 @@ def _handle_sensor_types(meg, eeg, fnirs): fnirs=dict(channels="fnirs", pairs="fnirs_pairs"), ) sensor_alpha = { - key: 0.25 if key == "meg_helmet" else 0.8 + key: dict(meg_helmet=0.25, meg=0.25).get(key, 0.8) for ch_dict in alpha_map.values() for key in ch_dict.values() } diff --git a/mne/viz/backends/_pyvista.py b/mne/viz/backends/_pyvista.py index 1b4bfe6c37b..221672d9915 100644 --- a/mne/viz/backends/_pyvista.py +++ b/mne/viz/backends/_pyvista.py @@ -733,7 +733,7 @@ def quiver3d( mesh=mesh, color=color, opacity=opacity, - scalars=mesh_scalars, + scalars=mesh_scalars if colormap is not None else None, colormap=colormap, show_scalar_bar=False, backface_culling=backface_culling, diff --git a/tutorials/forward/20_source_alignment.py b/tutorials/forward/20_source_alignment.py index 292e5346415..83ad252bfda 100644 --- a/tutorials/forward/20_source_alignment.py +++ b/tutorials/forward/20_source_alignment.py @@ -66,8 +66,7 @@ # to their equivalent locations in another. The three main coordinate frames # are: # -# * :blue:`"meg"`: the coordinate frame for the physical locations of MEG -# sensors +# * :blue:`"meg"`: the coordinate frame for the physical locations of MEG sensors # * :gray:`"mri"`: the coordinate frame for MRI images, and scalp/skull/brain # surfaces derived from the MRI images # * :pink:`"head"`: the coordinate frame for digitized sensor locations and From fd53fc44915ee3bea2f18c468eece4ed84476e1d Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Wed, 8 Nov 2023 15:18:34 -0500 Subject: [PATCH 20/20] MAINT: Fix CIs (#12188) --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index ac95ac1f591..6d45a299fdc 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -106,7 +106,7 @@ jobs: - name: 'Install OSMesa VTK variant' run: | # TODO: As of 2023/02/28, notebook tests need a pinned mesalib - mamba install -c conda-forge "vtk>=9.2=*osmesa*" "vtk-base>=9.2=*osmesa*" "mesalib=23.1.4" + mamba install -c conda-forge "vtk>=9.2=*osmesa*" "vtk-base>=9.2=*osmesa*" "mesalib=23.1.4" "numpy=1.24.4" "numba=0.57.1" mamba list if: matrix.kind == 'notebook' - run: ./tools/github_actions_dependencies.sh

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