semvec/pypi-packages huggingface dataset:
turn_by_turn 1.2.0
Read and write turn-by-turn measurement files from different particle accelerator formats.
Requires Python: >=3.10

# Turn-By-Turn

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This package provides reading functionality for turn-by-turn BPM measurements data from different particle accelerators.
It also provides writing functionality in the `LHC`'s own SDDS format, through our `sdds` package.
Files are read into a custom-made `TbtData` dataclass encompassing the relevant information.

See the [API documentation](https://pylhc.github.io/turn_by_turn/) for details.

## Installing

Installation is easily done via `pip`:

```bash
python -m pip install turn_by_turn
```

One can also install in a `conda` environment via the `conda-forge` channel with:

```bash
conda install -c conda-forge turn_by_turn
```

## Example Usage

 The package is imported as `turn_by_turn`, and exports top-level functions for reading and writing:

```python
import turn_by_turn as tbt

# Loading a file is simple and returns a custom dataclass named TbtData
data: tbt.TbtData = tbt.read("Beam2@BunchTurn@2018_12_02@20_08_49_739.sdds", datatype="lhc")

# Easily access relevant information from the loaded data: transverse data,
# number of turns, bunches and IDs of the recorded bunches
first_bunch_transverse_positions: tbt.TransverseData = data.matrices[0]

# Transverse positions are recorded as pandas DataFrames
first_bunch_x = first_bunch_transverse_positions.X.copy()
first_bunch_y = first_bunch_transverse_positions.Y.copy()

# Do any operations with these as you usually do with pandas
first_bunch_mean_x = first_bunch_x.mean()

# Average over all bunches/particles at all used BPMs from the measurement
averaged_tbt: tbt.TbtData = tbt.utils.generate_average_tbtdata(data)

# Writing out to disk (in the LHC's SDDS format) is simple too, potentially with added noise
tbt.write("path_to_output.sdds", averaged_tbt, noise=1e-5)
```

## License

This project is licensed under the `MIT License` - see the [LICENSE](LICENSE) file for details.