triplets 0.1.0rc1

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About:

• Parses CIM RDF/XML data to pandas dataframe with 4 columns [ID, KEY, VALUE, INSTANCE_ID] (triplestore like)
• The solution does not care about CIM version nor namespaces
• Input files can be xml or zip files (containing one or mutiple xml files)
• All files are parsed into one and same Pandas DataFrame, thus if you want single file or single data model, you need to filter on INSTANCE_ID column

Documentation:

https://haigutus.github.io/triplets

To get started:

python -m pip install triplets

import pandas
import triplets

path = "CGMES_v2.4.15_RealGridTestConfiguration_v2.zip"
data = pandas.read_RDF([path])

Result:

You can then query a dataframe of all same type elements and its parameters across all [EQ, SSH, TP, SV etc.] instance files, where parameters are columns and index is object ID-s

data.type_tableview("ACLineSegment")

Export:

data.export_to_cimxml(
    rdf_map=schemas.ENTSOE_CGMES_2_4_15_552_ED1,
    export_type=ExportType.XML_PER_INSTANCE_ZIP_PER_XML,
)

Look into examples folders for more

Parser engines

Three parser engines with automatic fallback (fastest available):

Engine	Requires	Speed
python_lxml_pandas	lxml + pandas (core)	1x baseline, always works
python_lxml_arrow	+ pyarrow (pip install triplets[arrow])	~1x, better interop
cython_pugixml_arrow	+ C++ build	9.8x

import pandas
import polars
import triplets  # registers pandas.read_RDF, polars.read_rdf etc.

# default (auto: best available engine)
data = pandas.read_RDF(["grid_EQ.xml", "data.zip"])

# explicit engine selection
data = pandas.read_RDF(path, engine="python_lxml_pandas")        # no pyarrow needed
data = pandas.read_RDF(path, engine="python_lxml_arrow")         # arrow intermediate
data = pandas.read_RDF(path, engine="cython_pugixml_arrow")      # fastest

# polars
data = polars.read_rdf(["grid_EQ.xml"], return_type="polars")

# return Arrow directly
table = triplets.parser.parse(path, return_type="arrow")

# direct API call (same as read_RDF)
data = triplets.parser.parse(["f.xml"], engine="python_lxml_pandas")

To build the performance engine (no system C++ deps needed):

pixi install -e build
pixi run build-cython-pugixml-arrow

See pixi.toml for build environment and tests/test_parser_backends.py for usage. See docs/parsers.md for the full call sequence and architecture.