triplets 0.1.0rc2

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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:

# Core (python_lxml_pandas engine, no extra deps)
pip install triplets

# With pyarrow (enables python_lxml_arrow + cython_pugixml_arrow engines, ~12x faster)
pip install triplets[arrow]

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	Install	Speed
python_lxml_pandas	pip install triplets	1x baseline, always works
python_lxml_arrow	pip install triplets[arrow]	~1x, better interop
cython_pugixml_arrow	pip install triplets[arrow] (included in wheels)	12x faster

The cython_pugixml_arrow engine is a compiled C++ extension included in published wheels. It requires pyarrow at runtime, so install with triplets[arrow] to enable it.

The cython engine is pre-built in published wheels — no compilation needed.

Polars

import polars
import triplets

data = polars.read_rdf(["grid_EQ.xml", "data.zip"])   # returns polars DataFrame

data.triplets.types_dict()
data.triplets.type_tableview("ACLineSegment")
data.triplets.filter_triplets(KEY="Type", VALUE=".*Generator.*", regex=True)
data.triplets.export_to_csv(export_to_memory=True)
data.triplets.export_to_nquads("/tmp/output.nq")

DuckDB

import duckdb
import triplets

data = duckdb.connect()                              # in-memory
data = duckdb.connect("grid.duckdb")                 # persistent (no re-parsing next session)

data.read_rdf(["grid_EQ.xml", "data.zip"])           # parse via Arrow (zero-copy into DuckDB)
data.types_dict()                                     # → dict
data.type_tableview("ACLineSegment").df()             # → pandas DataFrame
data.type_tableview("ACLineSegment").pl()             # → polars DataFrame
data.filter_triplets(KEY="Type", VALUE=".*Sub.*", regex=True).df()
data.filter_by_type("Terminal").df()
data.references_to("some-uuid").df()
data.export_to_nquads("/tmp/output.nq")

# Direct SQL (full DuckDB SQL on the triplets table)
data.sql("SELECT VALUE, COUNT(*) FROM triplets WHERE KEY = 'Type' GROUP BY VALUE").df()

Accessor namespace

All engines share the same df.triplets.* accessor:

data.triplets.type_tableview("ACLineSegment")
data.triplets.types_dict()
data.triplets.filter_triplets(KEY="Type")
data.triplets.export_to_excel(export_to_memory=True)
data.triplets.export_to_nquads("/tmp/output.nq")

CLI tools

cim-spreadsheet -i model.xml -o output.xlsx
cim-diff original.xml modified.xml

Performance (RealGrid, 1.14M rows)

Operation	pandas	polars	DuckDB
Parse (cython engine)	128ms	156ms	283ms
type_tableview	72ms	21ms	53ms
filter_by_type	103ms	9ms	50ms
types_dict	21ms	11ms	18ms

The old rdf_parser.py functions still work but emit deprecation warnings.