Command Wrappers

pyvartools exposes every vartools command as a typed Python class under pyvartools.commands. Each command is categorised the same way as in the CLI reference.

Browse the per-category pages in the sidebar, or use the alphabetical Command Index to jump straight to a specific command.

Three common import idioms:

import pyvartools as vt

# 1. Top-level one-liner — accepts path / DataFrame / LightCurve / array
result = vt.LS("EXAMPLES/2", 1.0, 2.0, 0.01)

# 2. Chaining API
lc = vt.LightCurve.from_file("EXAMPLES/2")
result = lc.LS(1.0, 2.0, 0.01).rms()

# 3. Pipeline — the canonical form, underlies both above
from pyvartools import commands as cmd
pipe = vt.Pipeline().clip(5.0).LS(0.1, 10.0, 0.1)
result = pipe.run_file("EXAMPLES/2")

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Auxiliary output files

Many commands can write auxiliary files (periodograms, model curves, coefficient tables, etc.) to disk. Such parameters are generally named save_* and accept multiple modes, controlled by passing a bool, a path string, or an Output object.

The four modes

Value	Mode	Written to disk?	Captured in Python?
False (default)	4 — suppress	no	no
True	1 — temp, capture	temp dir (auto-deleted)	yes — result.files["key"]
"/path/to/dir"	3 — disk only	that directory	no
Output("/path/to/dir", capture=True)	2 — disk + capture	that directory	yes — result.files["key"]

Output class

from pyvartools import Output

Output(path=None, capture=True)

Parameter	Type	Description
path	str or None	Directory to write the file to. None means use a pipeline-managed temp directory.
capture	bool	Whether to read the file into Python and include it in result.files. Default True.

Examples

import pyvartools as vt
from pyvartools import commands as cmd, Output

lc = vt.LightCurve.from_file("EXAMPLES/2")

# Mode 1 (default True): temp dir, captured into result.files
result = vt.Pipeline().LS(0.1, 10.0, 1e-3, save_periodogram=True).run(lc)
pgram = result.files["LS_periodogram_0"]   # pd.DataFrame

# Mode 3: written to EXAMPLES/OUTDIR1, NOT in result.files
result = vt.Pipeline().LS(0.1, 10.0, 1e-3, save_periodogram="EXAMPLES/OUTDIR1").run(lc)
# EXAMPLES/OUTDIR1/stdin.ls written; result.files has no "LS_periodogram_0"

# Mode 2: written to EXAMPLES/OUTDIR1 AND captured
result = (vt.Pipeline()
        .LS(0.1, 10.0, 1e-3,
           save_periodogram=Output("EXAMPLES/OUTDIR1", capture=True))).run(lc)
pgram = result.files["LS_periodogram_0"]   # captured from EXAMPLES/OUTDIR1/stdin.ls

# Mode 4 (default False): nothing written, nothing captured
result = vt.Pipeline().LS(0.1, 10.0, 1e-3, save_periodogram=False).run(lc)
# result.files has no "LS_periodogram_0"

Output file keys

Captured files appear in result.files under a key of the form "{CommandName}_{logical_name}_{idx}", where idx is the 0-based index of the command in the pipeline. For example, the first LS command's periodogram is "LS_periodogram_0", and the second is "LS_periodogram_1".

In a batch run (run_batch / run_filelist) each key maps to a list of DataFrames — one per light curve.

Note on autocorrelation

autocorrelation is a special case: the output file is always written to disk regardless of the save_result setting. Passing save_result=False suppresses Python capture but the file is still written to a temp directory (and discarded after the run).

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Base class

All wrappers inherit from VartoolsCommand:

from pyvartools._command import VartoolsCommand

Custom commands can be written by subclassing VartoolsCommand and implementing _to_cli_args() → list[str]. Override _output_file_specs() to declare output files that should be captured by the Pipeline.

class MyCommand(VartoolsCommand):
    _vt_name = "mycommand"

    def __init__(self, param: float):
        self.param = param

    def _to_cli_args(self):
        return ["-mycommand", str(self.param)]