triplets

Pre-release

This release is a pre-release and may not be stable for production use.

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

Upgrading from 0.0.x? See docs/migration_0.0_to_0.1.md.

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]

Install extras by feature:

Extra	Enables
`arrow`	compiled Arrow parser engines (~12x faster parsing)
`polars`	polars DataFrames (`polars.read_rdf`, `.triplets` namespace)
`duckdb`	DuckDB connections (`con.read_rdf`, SQL over triplets)
`sparql`	SPARQL queries (rdflib reference engine)
`oxigraph`	recommended pip performance path — embedded Rust SPARQL engine (auto-preferred over rdflib)
`validation`	SHACL validation (pyshacl reference engine)
`excel` / `networkx` / `visualization`	Excel export / graph export / drawing

The embedded qlever SPARQL engine (fastest) ships in no wheel — it is a local source build, see docs/building.md.

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.tableview_by_type("ACLineSegment")

Export:

from triplets.export_schema import schemas
from triplets.export import ExportType

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

Export schemas are versioned and shipped per profile. Alongside the CGMES bundles (schemas.ENTSOE_CGMES_2_4_15_552_ED1, schemas.ENTSOE_CGMES_3_0_0_552_ED1, …) the versioned NC (Network Code) profiles are available as schemas.ENTSOE_NC_2_4_1_552_ED1 / schemas.ENTSOE_NC_2_4_1_552_ED2. The _ED1 / _ED2 suffix selects the serialization edition; profile resolution is schema-driven, so the right profile section is matched from the instance header.

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.get_types_count()
data.triplets.tableview_by_type("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")
data = polars.read_nquads("/tmp/output.nq")           # round-trips N-Quads back to triplets

read_nquads is registered as pandas.read_nquads / polars.read_nquads and is also exposed top-level as triplets.read_nquads.

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.get_types_count()                                     # → dict
data.tableview_by_type("ACLineSegment").df()             # → pandas DataFrame
data.tableview_by_type("ACLineSegment").pl()             # → polars DataFrame
data.filter_triplets(KEY="Type", VALUE=".*Sub.*", regex=True).df()
data.filter_triplets_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()

# The same tools are also on the `.triplets` namespace (parity with pandas/polars)
data.triplets.tableview_by_type("ACLineSegment").df()
data.triplets.get_types_count()

SPARQL queries

SPARQL 1.1 over the loaded data — SELECT → DataFrame, ASK → bool, CONSTRUCT → triplet DataFrame. Works on pandas, polars and DuckDB inputs:

PREFIXES = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX cim: <http://iec.ch/TC57/CIM100#>
"""
names = data.sparql.query(PREFIXES + "SELECT ?s ?name WHERE { ?s cim:IdentifiedObject.name ?name }")

Three engines behind one API (auto picks the fastest available):

Engine	Install	Role
`qlever`	local source build (docs/building.md)	fastest — embedded C++, persistent on-disk index
`oxigraph`	`pip install triplets[oxigraph]`	embedded Rust — ~3x faster import, 2–5x faster queries than rdflib
`rdflib`	`pip install triplets[sparql]`	pure-Python reference

Details and measured numbers: docs/sparql.md.

SHACL validation

Validate against SHACL shape files; the result is a violations DataFrame (empty = conforms) with the same shape across all engines:

from triplets.export_schema import schemas

violations = data.shacl.validate("shapes.ttl", rdf_map=schemas.ENTSOE_CGMES_3_0_0_552_ED1)

# slower optional context pass: source file, object type/name,
# shape sh:name/sh:description, schema attribute/class definitions
violations = data.shacl.validate(shapes, context=True)

# SARIF 2.1.0 for GitHub / SonarQube / any SARIF viewer — grouped by default
# (one result per rule with occurrenceCount + sample instances)
violations.shacl.to_sarif(path="report.sarif")

# standard SHACL sh:ValidationReport (Turtle) for any SHACL-aware tooling
violations.shacl.to_shacl_report(path="report.ttl")

Engines: polars (auto, real profiles in ~2 s) → pandas → pyshacl (reference); duckdb for larger-than-memory data. sh:sparql constraints ride the SPARQL engine above (minutes → milliseconds with oxigraph/qlever). Details: docs/validation.md.

Accessor namespace

pandas and polars DataFrames use df.triplets.*; a DuckDB connection uses con.triplets.*. The same method names are available on both (DuckDB returns relations — add .df() or .pl() when needed):

# pandas / polars
df.triplets.tableview_by_type("ACLineSegment")
df.triplets.export_to_nquads("/tmp/output.nq")

# DuckDB
con.triplets.tableview_by_type("ACLineSegment").df()
con.triplets.get_types_count()

Root-level methods (df.type_tableview(...), con.filter_triplets(...)) still work for backwards compatibility.

Cache lifecycle

Engines keep internal state (compiled shapes, SPARQL indexes) cached across calls. Reset it explicitly with triplets.clear_caches(), or scope it to a block so it is cleared on exit:

import triplets

with triplets.cache_scope():
    ...            # caches populated here are dropped when the block exits

triplets.clear_caches()   # or clear everything manually

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
tableview_by_type	72ms	21ms	53ms
filter_triplets_by_type	103ms	9ms	50ms
get_types_count	21ms	11ms	18ms

The old rdf_parser.py functions still work but emit deprecation warnings. See docs/migration_0.0_to_0.1.md for renames and breaking changes.

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