From a numpy file

Use this path for pre-computed feature arrays stored as .npy.

Expected shape: (n_samples, n_variables) or (n_variables, n_samples). The longer dimension is assumed to be n_samples.

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Steps

Tip

Install EthoGraph if you haven’t already, then launch via shortcut or: conda activate ethograph && ethograph launch In the I/O widget, click Create with own data — the wizard opens.

1. Under Single trial, select: 4) Generate from npy file

2. Click Next — the dialog opens

3. Set Npy file (.npy)

4. Set Data sampling rate (Hz)

5. Optionally set Video file — frame rate is auto-detected

6. Set Output path for the generated session.nc

7. Click Generate .nc file

8. The I/O widget auto-populates -> click Load

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Dialog fields

Field	Notes
Npy file	2D array — shape (n_samples, n_vars) or (n_vars, n_samples)
Data sampling rate	Hz
Video file	Optional
Output path

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Adding named variables

The dialog creates generic variable names (var_0, var_1, …). To give columns meaningful names, create the dataset via a short script instead:

import numpy as np
import xarray as xr

data = np.load("features.npy")   # shape: (n_samples, n_vars)
sr = 1000.0

ds = xr.Dataset({
    "emg": xr.DataArray(
        data,
        dims=["time", "channels"],
        coords={
            "time": np.arange(data.shape[0]) / sr,
            "channels": ["biceps", "triceps"],
        },
    )
})

ds.to_netcdf("session.nc")

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Data requirements

Attribute	Value
attrs["fps"]	Not required unless video is also loaded

For the full xarray.Dataset structure see Data Format Requirements.