Bibliographic record
Abstract
Overall, there were 463 commits that closed 259 issues since our last release on 2024-01-31. Changes by Package libpysal v4.12.0 #757: W to g guide #756: exposing mapping_distance in travel builder #233: [WIP] stop sorting ids by default #299: [WIP] remove Ids attribute #505: New changelog conventions #365: start on perimeter weighting with just pygeos #356: [WIP] initial draft of network weights #626: allow ids in graph when passing sparse #151: network weights #755: add graph from travel network #369: refactor examples #490: Logic for pushing tags to trigger release needs to be revisited #375: pygeos-based contiguity #733: W to g guide #754: DOC: move Graph from experimental to stable API #740: ENH: add xarray interface to Graph #753: make asymmetry computation in Graph summary optional #739: fix higher_order contiguity including lower order #738: BUG: higher_order(lower_order=True) not working in edge cases #742: ENH: add Graph.summary with s0, s1, s2 and similar properties to Graph #752: ENH: add index_pairs to Graph #746: BUG: fix categorical lag for custom index #743: BUG: fix handling of non-isolate self-weights of 0 and order preservation #751: adjust CI env name & add Graph to README #750: update README with mention of Graph #749: update CI env naming conventions #534: Weights sprint planning #444: Does release GHA work? #315: scikit-geometry? #747: ensure that Graph.repr fits to 80 characters #745: added examples to graph #744: test against intel & apple silicon #573: BUG: relative neighbourhood depends on the order of observations #736: [pre-commit.ci] pre-commit autoupdate #737: ENH: use representative_point instead of centroid in Graph plotting #735: Centroid or representative point in graph plotting #698: ENH: Graph IO to classic weights file formats #732: CI: do not xfail momepy in reverse checks #729: Use importlib to implement simport #731: CI: ignore spvcm, add osmnx to env, xfail stuff out of control in reverse dep testing #726: Use fixtures for test data that uses the network #728: DOC: Fix a couple Sphinx warnings #727: DOC: Remove unused mkdocs-jupyter dependency #725: COMPAT: ensure argsort output has a stable order in numpy 2 #724: [CI] precision failure on ubuntu-latest, ci/312-no-optional.yaml [2024-06-19] #723: COMPAT: fix numpy 2.0 incompatibility #722: Failure due to numpy 2.0 deprecation #721: inserted chicagoSDOH as sample data #720: ENH: pass kwargs to buffer in fuzzy_contiguity #695: import libpysal stuck on loading remote examples #718: Add timeout in request to handle off-line use cases in examples #719: COMPAT: remove typing from Graph.describe #716: Categorical spatial lag using the Graph #711: ENH: implement Graph.repr #717: ENH: include Graph.describe() to describe neighbourhood values #529: [DO NOT MERGE] Change to the new sparse arrays and see what breaks #715: PERF: sorting-related improvements in Graph #714: Refactor handling of coincident points in triangulation #713: Co-location issues with Graph triangulation builders #694: ENH: add Graph.build_h3 #667: Build Graph from H3 #710: PERF: don't build coincident lookup if not needed #709: BUG: misaligned weights in the gabriel graph #708: add back numba in Py312 tests #590: check when numba is ready for Python 3.12 #703: DOC: add examples to build_contiguity and build_triangulation #697: REF: minor performance improvements in Graph #704: Added examples to from_dicts and build_knn. #705: DOC: Add examples to build_distance_band #702: DOC: add examples to build_block_contiguity #701: DOC: add example to Graph.to_W #700: DOC: clarify the requirements of the canonical sorting of Graph index #687: Ensure Graph.sparse is robust enough #696: [pre-commit.ci] pre-commit autoupdate #693: Work around GEOS issue in voronoi_frames #691: REF: refactor Graph.to_W to avoid perf bottleneck #672: Graph.to_W is slow #692: COMPAT: compatibility with pandas 3 #678: ENH: geometry agnostic Voronoi based on shapely #665: Interest in Optimal Spatial Matching? #685: REF: remove usage of deprecated cascaded_union #686: TST: resolve FutureWarnings in graph apply tests #684: ImportError: cannot import name 'np' from 'libpysal.common' #683: Bump codecov/codecov-action from 3 to 4 pointpats v2.5.0 #140: update min supported Python and testing workflow #141: bump & sync min reqs [2024-06] #139: minimum support Python [2024-06] #138: fix typo in random.normal function #133: REF: reimplement mrr on top of shapely #136: localknox #137: Typo in random.normal method that results in errors #134: keep members of local knox hotspots spaghetti v1.7.6 #771: #770 -- doctests after full tests #770: doctests as separate action or workflow #769: update docstring tests -- numpy-2.0 failures #767: current CI failures [2024-06-19] #768: fix doctests in CI -- numpy 2.0 #765: [pre-commit.ci] pre-commit autoupdate #764: gpd & shp as hard reqs -- no optional testing -- #763 #763: 310-no-optional CI failures [2024-03-16] #762: 312-dev CI failures [2024-03-16] #761: Bump codecov/codecov-action from 3 to 4 momepy v0.7.2 #625: DOC: update user guide to avoid MultiIndex #631: DOC: update rest of the guide #628: DOC: User guide fixes for elements examples #626: REF: remove result_index attribute from describe_agg #627: REF: do not return building_id from generate_blocks #606: BUG: verify handling of MultiIndex #622: ENH: either support MultiIndex or raise an error when one is given #624: DOC: update docstrings to match numpy2 outputs #623: TYP: add type hints to the graph module #579: API: distinction between libpysal and networkx graphs #621: BUG: fix a case when there's only a single building to be passed to voronoi_frames #620: ENH: add mean_deviation #619: API: deprecate legacy API in favour of Graph-based functional implementation #612: API: deprecate legacy API #618: minor type hinting fix #617: API: allow silencing of FutureWarnings from legacy API #616: DOC: expose get_nearest_node, fix fmt #615: DOC: remaining examples in the new API #610: DOC: add examples #614: update precommit to ruff docs dir #613: DOC: ruff user guide #611: DOC: add first batch of examples + testing #609: Faster node density #608: BUG: fix describe_ function when count is not present #543: GHA: switch to autogenerated release notes #582: BUG/ENH: support custom enclosure index is in tessellation and GeoDataFrame as enclosure input #592: DOC: add citation.cff #544: DOC: update dev installation instructions #576: functional node_density implementation #575: functional reached calculations #572: functional courtyards calculation #570: ENH: describe as a replacement of AverageCharacter #566: ENH: street_alignment and get_nearest_street #559: ENH: refactor tessellation #557: ENH: add neighbors #556: ENH: mean_interbuilding_distance and building_adjacency #555: ENH: neighbor_distance using Graph and new API #554: ENH: add alignment to the new API #553: ENH: add orientation and shared_walls functional versions #600: ENH: add get_nearest_node #593: Street profile #590: ENH: adaptive buffer as a tessellation limit #589: Functional percentiles #588: Functional distribution #587: Functional dimension #586: Functional density #584: _describe API refactoring #583: Functional arearatio #581: Functional diversity #580: Functional blocks #607: Update release.yml - troubleshoot release action failure #604: gha for release notes - #543 #396: ENH: helper functions for geometry-based network simplification #461: TestDistribution.test_MeanInterbuildingDistance failure on dev #304: Add GitHub Action to build and push container #537: BUG: tessellation may produce overlapping polygons #261: EHN: Add SkyViewFactor #603: DOC: execute notebooks and ensure they're tested #264: ENH: Simple Building Volume Density #465: ENH: helper functions part 1 #562: test efficiency of graphblas in straightness #602: API: return morphological tessellation as a GeoDataFrame #601: DOC: create usable env on RTD, update clustering #599: DOC: User guide refresh #598: DOC: note on a precision issue in enclosed_tessellation #597: DOC: rework API docs around the new functional stuff #596: COMPAT: numpy 2.0 compatibility #585: Functional count #594: DEP: bump libpysal minimum to 4.11 #406: API: deprecation decorators for transition to functional API #317: Add citation.cff #310: ENH: refactor get_network_id based on sindex.nearest_all #359: Add osmnx_like keyword to gdf_to_nx #478: Enclosure function does not work correctly #525: Networkx deprecations #497: Resolve geopandas deprecations #577: more complete linting & formatting - docs & benchmarks #578: linting & formatting for benchmarks/* and docs/* spreg v1.5.0 #138: Minor adjustments to printouts and spatial impacts #136: Version 1.5 #137: Update unittests.yml -- manual trigger #130: Bump actions/github-script from 6 to 7 #131: Bump conda-incubator/setup-miniconda from 2 to 3 #133: Bump actions/cache from 3 to 4 #134: Bump codecov/codecov-action from 3 to 4 mapclassify v2.7.0 #211: WIP classify to rgba #216: plot histogram with class bins #221: [pre-commit.ci] pre-commit autoupdate #215: Add monthly downloads badge to README #217: doctest failures [2024-06-23] #220: CI: test against Python 3.12 #219: CI: doctest only on ubuntu latest #218: CI: ensure 3.9 envs are compatible #214: [CI] failing dev from libpysal.graph --> numpy.float_ #210: COMPAT: make greedy compatible with future #209: 311-dev CI failures [2024-04-01] #208: [pre-commit.ci] pre-commit autoupdate #207: 311-dev CI failures [2024-03-16] #206: Bump codecov/codecov-action from 3 to 4 <
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.283 | 0.375 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".