fill-nan

name	fill_nan
description	Replace non-finite values (NaN, Inf) with a specified constant
layer	L3
group	channel
metadata	{"tags":["operator","nan","missing","cleanup","repair"],"modalities":["eeg","seeg","ecog","meg","spike","fnirs"],"step_string":"fill_nan","analysis_goal_allowed":["classification","source_localization","feature_extraction","clinical_screening","exploratory","generic","connectivity","phase_amplitude_coupling","online_inference"],"analysis_goal_forbidden":[]}

Fill NaN

Function

Replaces all non-finite values (NaN, +Inf, -Inf) in the data array with a specified constant value. A data cleaning step for corrupted samples.

Parameter Format

fill_nan:{value}

Examples:

• fill_nan:0 — Replace with zero (default)
• fill_nan:-1 — Replace with -1

Default (no parameter): fills with 0.

Parameters

Parameter	Type	Default	Description
value	float	0.0	Replacement value for non-finite samples

When to Use

• Data contains NaN values from hardware glitches, corrupted samples, or failed sensor readings
• After loading data that has missing value markers
• Before operations that cannot handle NaN (most numpy operations propagate NaN)
• fNIRS data with dropout periods

When NOT to Use

• Data is clean (no NaN/Inf present) — this step is a no-op in that case
• When NaN represents meaningful gaps that should be handled differently (e.g., epoch rejection)
• After scaling if zero-fill would create outliers (use the scaled mean instead)

Constraints

• Simple replacement — does not interpolate from neighbors
• Logs a warning with count of replaced values
• If many values are non-finite (> 10%), consider the data quality and whether processing is meaningful

Ordering

• Apply EARLY — before any filtering or mathematical operations
• NaN propagates through all subsequent steps if not handled
• Typically first or second step if NaN is detected during inspection

Reference Code

Implementation

import numpy as np

fill_val = 0.0
data = np.nan_to_num(data, nan=fill_val, posinf=fill_val, neginf=fill_val)

With Logging

import numpy as np

mask = ~np.isfinite(data)
n_bad = np.count_nonzero(mask)
if n_bad > 0:
    print(f"Replacing {n_bad} non-finite values with 0")
    data = np.nan_to_num(data, nan=0.0, posinf=0.0, neginf=0.0)

Key API

• np.nan_to_num(data, nan=val, posinf=val, neginf=val)
• np.isfinite(data) — mask for detecting non-finite values