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docs: expand legacy cli and api pages
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# Legacy CLI workflow: `batdetect2 detect`
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# CLI workflow: `batdetect2 detect`
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This page documents the previous CLI workflow based on `batdetect2 detect`.
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```{warning}
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This is legacy documentation.
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This is documentation for a previous version of batdetect2.
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For new workflows, use `batdetect2 process directory` instead.
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If you are migrating, start with {doc}`migration-guide`.
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```
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## Legacy command shape
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## Processing a folder of audio files
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```bash
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batdetect2 detect AUDIO_DIR ANN_DIR DETECTION_THRESHOLD
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```
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Common legacy options included:
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- `--cnn_features`
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- `--spec_features`
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- `--time_expansion_factor`
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- `--save_preds_if_empty`
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- `--model_path`
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## Current replacement
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The closest current CLI entry point is:
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Example:
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```bash
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batdetect2 process directory \
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path/to/model.ckpt \
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path/to/audio_dir \
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path/to/outputs
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batdetect2 detect example_data/audio/ example_data/anns/ 0.3
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```
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This command scans a directory of audio files, runs the BatDetect2 detector on
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each file, and writes BatDetect2-style outputs into `ANN_DIR`.
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Those outputs usually include one JSON file and one CSV file per recording, and
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can optionally include extra feature CSVs.
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`AUDIO_DIR` is the folder containing the input `.wav` files.
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`ANN_DIR` is the folder where model outputs are written.
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`DETECTION_THRESHOLD` controls which detections are kept.
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Predictions below this score are discarded.
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Smaller values keep more detections, but usually also increase mistakes.
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Common options:
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- `--cnn_features` Write extra CNN feature CSV files for each recording.
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- `--spec_features` Extract and write traditional acoustic spectrogram feature
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CSV files.
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These are saved as `*_spec_features.csv` files.
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- `--time_expansion_factor` Set the time expansion factor used for all files in
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the run.
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- `--save_preds_if_empty` Save output files even when no detections are found.
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- `--model_path` Use a specific checkpoint instead of the included default
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model.
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If omitted, the command uses the default model trained on UK data.
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## Related pages
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- Migration guide:
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@ -3,37 +3,52 @@
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This page documents the previous Python API workflow based on `batdetect2.api`.
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```{warning}
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This is legacy documentation.
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For new workflows, use `batdetect2.api_v2.BatDetect2API`.
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This is documentation for a previous version of batdetect2.
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For new workflows, use `batdetect2.BatDetect2API`.
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If you are migrating, start with {doc}`migration-guide`.
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```
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## Legacy entry points
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## Using BatDetect2 in Python
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Common legacy functions included:
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If you prefer to process data inside a Python script, you can use the `batdetect2.api` module.
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- `process_file`
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- `process_audio`
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- `process_spectrogram`
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- `load_audio`
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- `generate_spectrogram`
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- `postprocess`
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This interface gives you a simple entry point for running the built-in BatDetect2 model and also exposes the default model and default configuration more directly than the current API.
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The legacy API also exposed the default model and default config more directly.
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You can process a whole file in one step, or load audio, generate a spectrogram, and work with lower-level functions yourself.
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## Current replacement
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Common functions:
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The current Python path is:
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- `process_file` Load an audio file, run the model, and return BatDetect2-style results for that recording.
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- `process_audio` Run inference on an audio array that is already loaded in memory.
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- `process_spectrogram` Run inference starting from a spectrogram tensor instead of raw audio.
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- `load_audio` Load and resample audio using the legacy preprocessing path.
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- `generate_spectrogram` Convert audio into the spectrogram representation expected by the model.
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- `postprocess` Convert raw model outputs into detections and extracted features.
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Typical usage:
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```python
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from pathlib import Path
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import batdetect2.api as api
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from batdetect2.api_v2 import BatDetect2API
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AUDIO_FILE = "example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav"
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api = BatDetect2API.from_checkpoint(Path("path/to/model.ckpt"))
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prediction = api.process_file(Path("path/to/audio.wav"))
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# Process a whole file
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results = api.process_file(AUDIO_FILE)
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annotations = results["pred_dict"]["annotation"]
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# Or, load audio and compute spectrograms
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audio = api.load_audio(AUDIO_FILE)
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spec = api.generate_spectrogram(audio)
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# And process the audio or the spectrogram with the model
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detections, features, spec = api.process_audio(audio)
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detections, features = api.process_spectrogram(spec)
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# Integrate the detections or extracted features into your own analysis
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```
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This interface is most useful when you want to work directly with detections, features, spectrograms, or intermediate arrays inside your own code.
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## Related pages
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- Migration guide: {doc}`migration-guide`
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