TemporalAverage

observations.tvb_monitors.TemporalAverage(voi=None, period=4.0)

Downsample timeseries by averaging over temporal windows.

This downsampling strategy computes the mean over non-overlapping temporal windows, providing smoother output than simple subsampling. The output timestamps are centered within each averaging window.

Attributes: voi: Variable of interest (state variable index) to extract. If None, uses all variables. period: Averaging window size in milliseconds (default: 4.0)

Methods

Name	Description
prepare	Prepare complete-window averaging for block-wise execution.

prepare

observations.tvb_monitors.TemporalAverage.prepare(grid, sample, variable_names)

Prepare complete-window averaging for block-wise execution.

# TemporalAverage { #tvboptim.observations.tvb_monitors.TemporalAverage }

```python
observations.tvb_monitors.TemporalAverage(voi=None, period=4.0)
```

Downsample timeseries by averaging over temporal windows.

This downsampling strategy computes the mean over non-overlapping
temporal windows, providing smoother output than simple subsampling.
The output timestamps are centered within each averaging window.

Attributes:
    voi: Variable of interest (state variable index) to extract.
         If None, uses all variables.
    period: Averaging window size in milliseconds (default: 4.0)

## Methods

| Name | Description |
| --- | --- |
| [prepare](#tvboptim.observations.tvb_monitors.TemporalAverage.prepare) | Prepare complete-window averaging for block-wise execution. |

### prepare { #tvboptim.observations.tvb_monitors.TemporalAverage.prepare }

```python
observations.tvb_monitors.TemporalAverage.prepare(grid, sample, variable_names)
```

Prepare complete-window averaging for block-wise execution.