[olivier-lacroix]

Taking assign_coords for a spin, I realised it was way too slow. this PR fixes it. Sorry for not catching this sooner...

Note

The following content was generated by AI.

What

Model.assign_coords (from #955) relabelled containers by rebuilding a master -> new dict per container and mapping each container's labels through Index.map. On a tz-aware DatetimeIndex — the rolling-horizon case the API is built for — that map falls into pandas' object-to-datetime inference (maybe_infer_to_datetimelike -> objects_to_datetime64), costing milliseconds per container; the dict was also rebuilt once per container.

Two changes, both inside the #955 code:

1. Positional relabel. A full-length container (one carrying the master's labels) maps straight to the new values; a subset container's labels are located with master.get_indexer(index) and new.take(positions). get_indexer also replaces the separate index.isin(master).all() guard, folding the membership check and the mapping into one C-level lookup.
2. One Dataset construction. assign_coords_multiindex_safe writes the new index into the coordinate's original position and builds the dataset once, instead of Dataset.assign_coords(...) followed by a second Dataset(...) to restore the coordinate order that assign_coords breaks.

Benchmark

30 containers (15 variables + 15 CSR constraints), 4380 tz-aware labels, Model(sparse=True), v1 semantics:

	Model.assign_coords
before	816.7 ms
after	2.9 ms

Reproduction

import time
import pandas as pd
import linopy
from linopy import Model

linopy.options["semantics"] = "v1"
idx = pd.date_range("2024-01-01", periods=4380, freq="30min", tz="Australia/Melbourne", name="time")
new = pd.date_range("2024-04-01", periods=4380, freq="30min", tz="Australia/Melbourne", name="time")

m = Model(sparse=True)
vs = [m.add_variables(coords=[idx], name=f"x{i}") for i in range(15)]
for i, v in enumerate(vs):
    m.add_constraints(v >= 0, name=f"c{i}")

best = 1e9
for _ in range(3):
    t0 = time.perf_counter()
    m.assign_coords(time=new)
    best = min(best, time.perf_counter() - t0)
print(f"{best * 1000:.1f} ms")

Checklist

• AI-generated content is marked (see AGENTS.md).
• Code changes are sufficiently documented; i.e. new functions contain docstrings and further explanations may be given in doc.
• Unit tests for new features were added (if applicable).
• A note for the release notes doc/release_notes.rst of the upcoming release is included.
• I consent to the release of this PR's code under the MIT license.

[codspeed]

Merging this PR will not alter performance

✅ 181 untouched benchmarks
⏩ 181 skipped benchmarks¹

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_{Comparing olivier-lacroix:perf/assign-coords-relabel (663a15c) with master (ae80bc8)}

Footnotes

1. 181 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩