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`Raw.resample` doesn't handle NaNs

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  • ¿Algo no está bien?
#14,421 4 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
45/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
data

Línea de trabajo

Start by running the reproduction script in the issue, which builds a RawArray with trailing NaNs and calls raw.resample(500). Find where Raw.resample is implemented in mne-python and check how it treats NaN samples. Done means NaN-containing channels keep NaNs only at the corresponding time points instead of turning the whole channel into NaN. The approach (interpolate, resample, restore NaNs) is the author's suggestion and still needs a maintainer decision.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

BUG
Description of the problem

Raw.resample turns the entire channel into NaN if even one sample is NaN. This can occur in real-world data, for example, in eye-tracking recordings (blinks, see #11879) or XDF recordings where streams end at different times.

Steps to reproduce
import mne
import numpy as np

sfreq = 1000
data = np.sin(2 * np.pi * 10 * np.arange(10000) / sfreq)[np.newaxis]
data[0, -10:] = np.nan
raw = mne.io.RawArray(data, mne.create_info(1, sfreq, "eeg"))

raw.resample(500)
print(np.isnan(raw.get_data()).mean())  # 1.0
Link to data

No response

Expected results

I think Raw.resample should handle NaN, for example by temporarily filling NaNs with linear interpolation, resampling, and then restoring NaNs at the corresponding locations. Unlike #11408, this approach relies on the data rather than annotations.

I'm happy to open a PR.

Actual results

The entire channel consists of NaN after resampling.

Additional information
Platform             Linux-7.2.9-arch1-1-x86_64-with-glibc2.44 (Wayland)
Python               3.12.14 (main, Aug 25 2026, 14:00:49) [Clang 22.1.3 ]
Executable           /home/clemens/Projects/mnelab/.venv/bin/python
CPU                  AMD Ryzen 7 9700X 8-Core Processor (16 cores)
Memory               60.5 GiB

Core
├☑ mne               1.13.2 (latest release)
├☑ numpy             2.5.3 (OpenBLAS 0.3.34.106.0 with 16 threads via pthreads)
├☑ scipy             1.18.1
└☑ matplotlib        3.11.2 (backend=qtagg)

Numerical (optional)
├☑ scikit-learn      1.9.0
├☑ threadpoolctl     3.6.0
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       numba, nibabel, nilearn, dipy, openmeeg, python-picard, jamica, cupy, pandas

Visualization (optional)
├☑ qtpy              2.4.3 (PySide6=6.11.2)
├☑ pyqtgraph         0.14.0
├☑ mne-qt-browser    0.7.5
└☐ unavailable       pyvista, pyvistaqt, vtk, ipympl, ipywidgets, trame, trame_client, trame_server, trame_pyvista, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ autoreject        0.4.4
├☑ edfio             0.4.17
├☑ pybv              0.8.1
└☐ unavailable       mne-bids, mne-nirs, mne-features, mne-connectivity, mne-icalabel, mne-bids-pipeline, neo, eeglabio, curryreader, mffpy, pymef, antio, defusedxml
Lenguaje dominante
Python
Estrellas
3.6k
Forks
1.6k
Merge medio
1 d 11 h
PR fusionados (30 d)
82

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