Input formats¶

Keep signals as 1D float arrays (raw time series) in any supported format (see Batch processing). No need to normalize or preprocess them. The CLI also accepts 2D arrays (precomputed spectrograms) directly. The app scans a signal directory for .npy files (default data/input, configurable in the Analyze tab).

Bringing your own data takes two lines:

import numpy as np

signal = ...  # any 1D float array: tokamak diagnostic, hydrophone, etc.
np.save("shots/myshot.npy", signal)

tokeye run shots/myshot.npy --output-dir results

No data yet? tokeye example writes a synthetic demo signal (tokeye_example_sr200000.npy) you can run immediately, and the web app has a matching "Load Example Signal" button.

For headless / scripted use (no browser needed), run inference directly (tokeye, or python -m tokeye when the script is not on your PATH). For example:

tokeye run "files/*.npy" --output-dir results

INPUT arguments can be files (.npy, .npz, .wav, .flac, .ogg, .csv, .txt, .mat, .h5), directories (every supported file inside except .csv/.txt), or quoted glob patterns. Glob patterns match supported files only. Outputs are named after each input's file stem, so inputs that share a stem, ignoring case (shot.npy and shot.wav, or Shot.npy and shot.npy), are rejected; run them into separate --output-dirs. The sampling rate is read from the file when it records one (or from a _sr<fs> filename suffix); --fs sets it explicitly. In .npz, .mat and .h5/.hdf5 files, TokEye reads the array named data, signal, x, spectrogram, values or y, or else the only numeric array. When several qualify, it stops and lists them; --key NAME picks one. A suffix of whole HDF5 path components, such as tree/pointname/data, selects the same array in every file. Quote the key in the shell when it contains a backslash. A 2-column .csv/.txt is read as (time, value), with time in seconds unless its header says ms or us. Each input is interpreted by its shape: - 1D array — a raw time series. TokEye computes its STFT spectrogram using the flags below before running inference. Frames are centred on samples 0, hop, 2·hop, … (the model's training framing), so N samples give 1 + N // hop columns (for the default, even, n_fft) and column j is at j·hop/fs s. - 2D array — a precomputed spectrogram, fed to the model directly. Its columns are assumed to follow the same rule.