Metadata-Version: 2.1
Name: mdata
Version: 0.2.0
Summary: 
License: MIT
Keywords: data format,machine data
Author: Leah Tacke genannt Unterberg
Author-email: leah.tgu@pads.rwth-aachen.de
Requires-Python: >=3.11,<3.13
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: immutabledict
Requires-Dist: lets-plot
Requires-Dist: lttb
Requires-Dist: matplotlib
Requires-Dist: nitime
Requires-Dist: numpy
Requires-Dist: pandas[compression,computation,excel,hdf5,mysql,output-formatting,performance,plot,postgresql,sql-other] (>=2.0,<3.0)
Requires-Dist: plotly (>=5.18,<6.0)
Requires-Dist: pm4py (>=2.7,<3.0)
Requires-Dist: pyyaml
Requires-Dist: ruptures
Requires-Dist: seaborn
Requires-Dist: sktime[annotation] (>=0.25,<0.26)
Requires-Dist: statsmodels
Requires-Dist: tsfresh
Description-Content-Type: text/markdown

This is the official machine data format package.

It supports csv importing, exporting and format compliance checking.
Machine data files can be parsed into a python object that provides typed views on the contained measurement and event timeseries.

Some examples:

`validation.py` in `files/input/extension_testing` 
```python
from mdata.core import as_v2
from mdata.io import write_machine_data_v2, read_machine_data_zip, write_machine_data_zip
from mdata.io.util import HeaderFileFormats, mk_canon_filenames_v2

# md = read_machine_data_v2(mk_canon_filenames_v2(header_format=HeaderFileFormats.YAML))
md = read_machine_data_zip('md.zip', header_format=HeaderFileFormats.YAML)
write_machine_data_zip('md_test.zip', md, header_format=HeaderFileFormats.YAML)
write_machine_data_v2(mk_canon_filenames_v2('test/', header_format=HeaderFileFormats.YAML), md, header_format=HeaderFileFormats.YAML)

as_v2(md)

print(md.event_specs)
print(md.measurement_specs)
print(md.segment_specs)
print(md.segment_data_specs)
print()
print(md.observation_index)
print(md.segments.df)
```

