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Merged
drammock merged 2 commits into from
Jul 29, 2025
Merged

MAINT: Fix CircleCI #13351

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bc59541
MAINT: Fix CircleCI
larsoner Jul 25, 2025
a50aad0
ENH: Remove cell
larsoner Jul 25, 2025
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42 changes: 30 additions & 12 deletions mne/utils/misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -358,12 +358,27 @@ def _file_like(obj):
return all(callable(getattr(obj, name, None)) for name in ("read", "seek"))


def _fullname(obj):
def _fullname(obj, *, referent=None):
klass = obj.__class__
module = klass.__module__
if module == "builtins":
return klass.__qualname__
return module + "." + klass.__qualname__
name = klass.__qualname__
if module != "builtins":
name = f"{module}.{name}"
if referent is not None:
if isinstance(obj, list | tuple):
for ii, item in enumerate(obj):
if item is referent:
name += f"[{ii}]"
break
elif isinstance(obj, dict):
for key, value in obj.items():
if key is referent:
name += "-key"
break
if value is referent:
name += f"[{key!r}]"
break
return name


def _assert_no_instances(cls, when=""):
Expand All @@ -372,7 +387,7 @@ def _assert_no_instances(cls, when=""):
ref = list()
gc.collect()
objs = gc.get_objects()
for obj in objs:
for obj in objs: # e.g., vtkPolyData, Brain, Plotter, etc.
try:
check = isinstance(obj, cls)
except Exception: # such as a weakref
Expand All @@ -382,30 +397,33 @@ def _assert_no_instances(cls, when=""):
ref.append(f"Brain._cleaned = {getattr(obj, '_cleaned', None)}")
rr = gc.get_referrers(obj)
count = 0
for r in rr:
for r in rr: # e.g., list, dict, etc. that holds the reference to obj
if (
r is not objs
and r is not globals()
and r is not locals()
and not inspect.isframe(r)
):
name = _fullname(r, referent=obj)
if isinstance(r, list | dict | tuple):
rep = f"len={len(r)}"
r_ = gc.get_referrers(r)
types = (_fullname(x) for x in r_)
types = "/".join(sorted(set(x for x in types if x is not None)))
rep += f", {len(r_)} referrers: {types}"
types = list()
for x in r_:
types.append(_fullname(x, referent=r))
types = " / ".join(sorted(types))
rep += f" | {len(r_)} referrers: {types}"
del r_
else:
rep = repr(r)[:100].replace("\n", " ")
rep = "repr="
rep += repr(r)[:100].replace("\n", " ")
# If it's a __closure__, get more information
if rep.startswith("<cell at "):
try:
rep += f" ({repr(r.cell_contents)[:100]})"
except Exception:
pass
name = _fullname(r)
ref.append(f"{name}: {rep}")
ref.append(f"{name} with {rep}")
count += 1
del r
del rr
Expand Down
15 changes: 0 additions & 15 deletions tutorials/preprocessing/70_fnirs_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,6 @@
raw_intensity = mne.io.read_raw_nirx(fnirs_cw_amplitude_dir, verbose=True)
raw_intensity.load_data()


# %%
# Providing more meaningful annotation information
# ------------------------------------------------
Expand All @@ -48,7 +47,6 @@
unwanted = np.nonzero(raw_intensity.annotations.description == "15.0")
raw_intensity.annotations.delete(unwanted)


# %%
# Viewing location of sensors over brain surface
# ----------------------------------------------
Expand Down Expand Up @@ -89,7 +87,6 @@
n_channels=len(raw_intensity.ch_names), duration=500, show_scrollbars=False
)


# %%
# Converting from raw intensity to optical density
# ------------------------------------------------
Expand All @@ -99,7 +96,6 @@
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_od.plot(n_channels=len(raw_od.ch_names), duration=500, show_scrollbars=False)


# %%
# Evaluating the quality of the data
# ----------------------------------
Expand All @@ -118,14 +114,12 @@
ax.hist(sci)
ax.set(xlabel="Scalp Coupling Index", ylabel="Count", xlim=[0, 1])


# %%
# In this example we will mark all channels with a SCI less than 0.5 as bad
# (this dataset is quite clean, so no channels are marked as bad).

raw_od.info["bads"] = list(compress(raw_od.ch_names, sci < 0.5))


# %%
# At this stage it is appropriate to inspect your data
# (for instructions on how to use the interactive data visualisation tool
Expand All @@ -134,7 +128,6 @@
# If your data contains lots of artifacts you may decide to apply
# artifact reduction techniques as described in :ref:`ex-fnirs-artifacts`.


# %%
# Converting from optical density to haemoglobin
# ----------------------------------------------
Expand All @@ -145,7 +138,6 @@
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
raw_haemo.plot(n_channels=len(raw_haemo.ch_names), duration=500, show_scrollbars=False)


# %%
# Removing heart rate from signal
# -------------------------------
Expand Down Expand Up @@ -178,7 +170,6 @@
events, event_dict = mne.events_from_annotations(raw_haemo)
fig = mne.viz.plot_events(events, event_id=event_dict, sfreq=raw_haemo.info["sfreq"])


# %%
# Next we define the range of our epochs, the rejection criteria,
# baseline correction, and extract the epochs. We visualize the log of which
Expand All @@ -203,7 +194,6 @@
)
epochs.plot_drop_log()


# %%
# View consistency of responses across trials
# -------------------------------------------
Expand All @@ -221,7 +211,6 @@
ts_args=dict(ylim=dict(hbo=[-15, 15], hbr=[-15, 15])),
)


# %%
# We can also view the epoched data for the control condition and observe
# that it does not show the expected morphology.
Expand All @@ -233,7 +222,6 @@
ts_args=dict(ylim=dict(hbo=[-15, 15], hbr=[-15, 15])),
)


# %%
# View consistency of responses across channels
# ---------------------------------------------
Expand All @@ -250,7 +238,6 @@
for ax in axes[:, column]:
ax.set_title(f"{condition}: {ax.get_title()}")


# %%
# Plot standard fNIRS response image
# ----------------------------------
Expand All @@ -277,7 +264,6 @@
evoked_dict, combine="mean", ci=0.95, colors=color_dict, styles=styles_dict
)


# %%
# View topographic representation of activity
# -------------------------------------------
Expand All @@ -290,7 +276,6 @@
times=times, topomap_args=topomap_args
)


# %%
# Compare tapping of left and right hands
# ---------------------------------------
Expand Down
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