Releases: pysal/pysal
PySAL 2.6.0 Release Candidate 1
PySAL 2.6.0 represents 6 months of enhancements, bug-fixes, widening
of test coverage, and improved documentation. All users are
encouraged to upgrade to this version as there are numerous
optimizations as well as new features (see below) that have been
implemented.
Overall, there were 149 commits that closed 61 issues since our last release on 2021-07-31.
Highlights
New Package: pysal/momempy
Momepy is a library for quantitative analysis of urban form - urban morphometrics
Detailed Changes by Package
segregation
- #184: prepare v2.1.0
- #35: parallelization simulation based indexes and inference wrappers
- #180: use rvlib for densitycorrecteddissim
- #183: performance enhancements
spaghetti
- #653: update gitcount notebook for versioneer?
- #654: access version from pacakge in gitcount
- #652: add manual triggers for workflows
- #650: omit new versioner files from code coverage
- #651: ignore versioneer files for code coverage reporting
- #649: add deprecation warning for libpysal geometries
- #647: versioneer for version management
- #648: Versioneer
- #373: [ENH] Explore a "two workflows" style format
momepy
- #315: BUG: non-default index dropped in Blocks id series
- #316: 0.5.1 release
- #311: BUG: incorrect index in
Blocks.*_idSeries for non-default index - #314: TST: CI failure due to geopandas regression
- #313: relicense under BSD
- #305: copyright and license
- #312: REF/TST: minimise warnings
- #309: Silence warnings from geopandas 0.10
- #306: update README.md
- #303: Fix links after the transfer under pysal org
- #301: Finish transfer under PySAL org
- #299: ENH: support shapely polygon as enclosures limit
- #254: ENH: allow limit of enclosures to be shapely polygon
- #293: Import causes ImportError
- #298: ENH: catch geographic CRS in Tessellation
- #297: Tesselation fails at Generating input point array... with ValueError: need at least one array to concatenate
- #286: momepy.Tessellation returns empty rows when buildings are outside of enclosures
- #296: PERF: use dask.bag in Tessellation
- #295:
momepy.Tessellationdoesn't appear to run faster on a machine with many more cores - #292: DOC: edit/proof street network analysis notebooks
- #288: BUG: properly clip enclosures by limit
- #287: momepy.enclosures doesn't appear to observe limit parameter
- #270: Enclosure has problems with rivers
- #291: BUG: Tessellation error on non-standard enclosures
- #289: BUG: momepy.Tessellation fails with IndexError
- #290: small typo in gdf_to_nx
spopt
- #205: install pulp for docs build
- #196: rework JOSS manuscript
- #200: [WIP] JOSS paper revisions work
- #203: update the real-world facility location notebook.
- #204: Update fac-loc real world notebook
- #201: region_k_means not handling candidate move updates
- #202: Update candidates list after making a move
- #179: Update changelog tooling to report contributors
- #199: Generate Changelog notes automatically on release
- #197: access package version in tools/gitcount
- #198: Add examples section to locate module
- #190: update locate docs
- #194: switch to versioneer
- #193: Update locate docs
- #192: Reference error for Church and Murray book
- #2: facility location models in a class
- #189: Facility Location modeling solutions & CI
- #186: Add Locate Module
- #3: bug: maxp implementation is not using contiguity
- #49: spopt as solver agnostic?
- #61: solvers and solver APIs
- #18: new alternative to pulp
Contributors
Many thanks to all of the following individuals who contributed to this release:
- Eli Knaap
- Gegen07
- Germano Barcelos
- James Gaboardi
- Martin Fleischmann
- Serge Rey
pysal 2.5.0
PySAL 2.5.0 represents 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 543 commits that closed 190 issues, together with 33 pull requests since our last release on 2021-01-31.
Package Highlights
esda
This version merges two large new sets of functionalities:
- map correspondence measures in
esda.map_comparison - shape statistics in
esda.shape
segregation
Version 2.0 of the segregation package brings a new API, a massive code restructuring, and dozens of new features, enhancements, and bug fixes. For a complete overview of the new API, please see the documentation page at https://pysal.org/segregation/api. The new version does away with the distinction between spatial and aspatial segregation indices and instead partitions the functions based on single-group and multi-group measures. The spatial/aspatial distinction is echewed in version 2.0 because all aspatial indices can be generalized into spatial versions, following the logic of Reardon and O'Sullivan (see a description in this example notebook). Furthermore, "space" can be incorporated into the index calculation using either Euclidean distance or the shortest path along a travel network. With this logic, the package now offers multiscalar segregation profiles for 23 different segregation indices (a first in any software package).
tobler
Added pychnophylactic interpolation.
spaghetti
The highlights of this release include functionality to split network arcs by count, which compliments the previously available distance splitting, and a paper in the Journal of Open Source Software. Also, Python 3.6 is no longer supported.
spopt
This release includes another model to add to the suite: RandomRegions. RandomRegions, originally written by David C. Folch (@dfolch) and Serge Rey, builds regions based on an initial random seed while considering user-defined specifications such as: region count, cardinality, contiguity, and compactness (citation?). Also, we have improved the testing coverage for the models inlcuded in the initial release: AZP, Max-p-regions, Region-k-means, Skater, Spenc, and WardSpatial.
Detailed Changes by Package
libpysal
- #412: Add missing endianness in WK1 reader.
- #413: Update unittests, etc
- #415: classify repo as Python
- #389: add docs action workflow
- #411: Return a dataframe with info on available datasets
- #409: Do not fetch examples on import
- #410: Do not poll remotes on init.
- #400: Fixed
index2dacausing inverted output - #399: Raster weights
w2dafailing on 3.6 - #408: Correct way to compute spatial weights in libpysal
- #407: Bump actions/upload-release-asset from 1 to 1.0.2
- #405: Bump conda-incubator/setup-miniconda from 2 to 2.1.1
- #404: Bump actions/setup-python from 2 to 2.2.2
- #406: Bump actions/cache from 2 to 2.1.5
- #403: Bump actions/checkout from 2 to 2.3.4
- #401: Dimension issues in DistanceBand weights
- #341: Use a labelled sparse xarray.DataArray as the core representation for weights
- #397: Draft of adjacency table as df
- #398: Expand weights benchmark
- #396: Draft of adjacency table as df
- #395: xarray geograph implementation
- #392: Add start of workbench notebook
- #390: Bump actions/cache from v2 to v2.1.4
- #388: update dev docs link
- #387: make build + make sync @docsrc
- #386: bump version
- #385: Adding raster interface to docs API + notebooks
- #384: [Doc]: Update raster example notebook and docstrings
access
esda
- #188: add pygeos-tolerant fails for the module
- #133: add the start on new calculation method for getisord statistics
- #121: Gi rewrite
- #86: Incorrect behavior for Moran_Local and self-neighbors
- #186: update codecov action to v2
- #180: Add map correspondence measure from Nowosad and Stepinski
- #185: [PERF] List comprehension to array operation in local multivariate Geary
- #184: Update Spatial Autocorrelation for Areal Unit Data
- #172: Merge ljwolf/shapestats into esda
- #182: adjust setup in conf.py
- #170: [DOC] Updating read the docs api for local join counts, local geary
- #179: Bump sphinxcontrib-bibtex from 1.0.0 to 2.3.0
- #178: Bump sphinxcontrib-bibtex from 1.0.0 to 2.2.1
- #176: Bump actions/checkout from 2 to 2.3.4
- #175: Bump codecov/codecov-action from 1 to 1.5.0
- #177: Bump actions/setup-python from 2 to 2.2.2
- #173: Bump actions/create-release from 1 to 1.1.4
- #174: Bump actions/upload-release-asset from 1 to 1.0.2
- #169: Bump sphinxcontrib-bibtex from 1.0.0 to 2.2.0
- #168: add tests for parallel crand
giddy
inequality
pointpats
segregation
- #166: remove mamba reference
- #169: error in AbsoluteClustering with geographic coordinate system
- #171: add deprecation layer for 2.0
- #104: [ENH] extend segregation profile function to accept more spatial indices
- #167: Dissimilarity Index API typo
- #165: remove travis-ci badge
- #4: consider refactor to scikit-style mixins
- #161: 2.0 refactor
- #168: Segregation measure (aspatial/spatial) with groupby?
- #163: Example notebooks missing scaramento2.shp
spaghetti
- #639: prepare for v1.6.2 release
- #637: update conf.py link in tutorials
- #617: update action for GH releases
- #636: Version bump & rerun notebooks
- #635: bump to v1.6.0.post5 - another try
- #634: Try new release action (softprops/action-gh-release)
- #633: removing descartes requirement
- #632: remove descartes dependency
- #573: Blacken code in docs
- #631: doc updates for v1.6.0 release
- #597: Update copyright year
- #630: split arc by count functionality
- #494: network segmentation by count
- #522: rtree dependen...
Contributors
Assets 2
pysal 2.4.0
PySAL 2.4.0 represents 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. Three Google Summer of Code projects made substantial contributions to this release (see below). All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 1155 commits that closed 352 issues, together with 121 pull requests since our last release on 2020-07-29.
Highlights
New Package: pysal/spopt
Provides methods for solving optimization problems with spatial data. Currently, regionalization methods are supported with planned support for facility location and transportation-oriented modeling.
The regionalization models implemented include:
- Max-p-regions: It involves the aggregation or clustering of a set of small areas into the maximum number of homogeneous and spatially contiguous regions such that the value of a regional attribute is higher than a predefined threshold. The number of regions will be endogenized in order to satisfy the threshold. (Duque, Anselin, and Rey, 2012 and Wei, Rey, and Knaap, 2020)
- Automatic Zoning Procedure (AZP) algorithm: It can work with any type of objective function that is sensitive to the aggregation of data for a larger number of zones into a pre-specified smaller number of regions (Openshaw, 1977
and Openshaw and Rao, 1995) - Region-K-means: It is K-means clustering for regions with the constraint that each cluster forms a spatially connected component.
- Skater: It is a constrained spatial regionalization algorithm based on spanning tree pruning. Specifically, the number of edges is prespecified to be cut in a continuous tree to group spatial units into contiguous regions. (AssunCao et al., 2006)
- Spenc: spatially-encouraged spectral clustering is an approach to balance spatial and feature coherence using kernel combination in spectral clustering.([Wolf, 2020] (https://osf.io/yzt2p/))
- WardSpatial: It is an Agglomerative Clustering using Ward linkage with a spatial connectivity constraint. Basically, it is a "bottom-up" approach: each observation starts in its own cluster, and pairs of clusters are chosen to merge at each step in order to minimize the variance of the clusters. (sklearn.cluster.AgglomerativeClustering)
Enhancements to existing packages
libpysal
Thanks to the Google Summer of Code 2020 led by Mragank Shekhar (@MgeeeeK), we have built an interface with xarray that allows you to create spatial weights matrices from xarray.DataArray objects. This means it is possible to build a weights matrix from raster data. We are using numba and multi-core where possible so the implementation is performant and scalable within memory limits. Current weights supported include:
- libpysal.weights.Rook.from_xarray
- libpysal.weights.Queen.from_xarray
In addition, the following methods have been added and are exposed to end users:
- [RECOMMENDED] da2WSP, to build a thin weights matrix (WSP) from a xarray.DataArray da2W, to build a weights matrix from a xarray.DataArray
- [RECOMMENDED] wsp2da, to reconstruct a xarray.DataArray object from a thin weights matrix and a pandas.Series (e.g. with mapclassify or esda.Moran_Local outcomes
- w2da, to reconstruct a xarray.DataArray object from a weights matrix and a pandas.Series (e.g. with mapclassify or esda.Moran_Local outcomes testDataArray, to generate a toy xarray.DataArray object for testing purposes.
esda
- Integration of the work of @jeffcsauer's 2020 GSOC project. This adds tons of new statistical estimators, such as:
- the local heteroskedasticity estimator,
esda.LOSH - local geary and multivariate geary estimators,
esda.Geary_Localandesda.Geary_Local_MV - local join counts in univariate, bivariate, and multivariate flavors,
esda.Join_Counts_Local,esda.Join_Counts_Local_BV, andesda.Join_Counts_Local_MV.
- the local heteroskedasticity estimator,
- "analytical" moments for Moran's I for replication/comparison to R. Forms are provided in Sokal 1998.
spreg
Thanks to the Google Summer of Code 2020 led by paboloestrada, we have added the following functionality:
- Panel_RE_Lag: random effects panel estimation with spatial lagged dependent variable.
- Panel_RE_Error: random effects panel estimation with spatial error interaction.
- Lagrange Multiplier test: classic and robust version.
- Hausman test.
spaghetti
The highlights of this release include a bug fix for how network segments were being split (raised in #526) and additions to several notebooks (spatial network segmentation, caveats, and network-constrained spatial autocorrelation). Also, spaghetti is now tested against Python 3.9.
tobler
- interpolation to hexagons using h3f
- more efficient unary_union in h3f
segregation
- segregation measures on network distances
mapclassify
- streamlined api
Detailed Changes by Package
libpysal
- #385: Adding raster interface to docs API + notebooks
- #384: [Doc]: Update raster example notebook and docstrings
- #383: [WIP]: id_order as a property of WSP class
- #343: [WIP]: Optimized raster-based weights builder
- #382: remove dup matplotlib in environment.yml
- #366: fix typo
- #376: Bump PYPI version to 4.3.1
- #381: adjust duplicated rst link in README
- #380: rst link & syntax issue in README
- #379: Correct readme.rst
- #378: Troubleshoot release GHA
- #377: bump to v4.3.2 for release action
- #374: Adding deps for examples.
- #373: update unittests.yml
- #372: release action is broken due to gh deprecation of env var
- #371: Fix release action
- #370: Bump actions/setup-python from v2.1.4 to v2.2.0
- #364:
examples.explainreturning 404 - #368: This address a move in remote datasets
- #335: ENH: use query_bulk in fuzzy_contiguity
- #334: BUG: fuzzy_contiguity picks more neighbours than it should
- #367: ENH: use active geometry in from_dataframe
- #342: standardizing libpysal/io docs & ibpysal/cg docs
- #363: Testing matrix
- #359: Bump codecov/codecov-action from v1.0.14 to v1.0.15
- #358: Bump actions/checkout from v2.3.3 to v2.3.4
- #355: Add codecov.yml for customized reports/testing
- #357: Add codecov.yml
- #286: Blacken codebase
- #291: [WIP] DEV: blacken codebase
- #354: Bump codecov/codecov-action from v1.0.13 to v1.0.14
- #257: street network-based weights
- #351: Bump actions/setup-python from v2.1.3 to v2.1.4
- #279: GHA for PyPI release
- #324: Update PR template
- #347: ENH: include lower order contiguities in higher_order_sp
- #313: higher_order weights of <= k
- #348: Bump actions/setup-python from v2.1.2 to v2.1.3
- #345: Add xarray to requirements_plus_conda.txt
- #346: Bump actions/checkout from v2.3.2 to v2.3.3
- [#344:](https://github.co...
pysal 2.4.0-rc2
PySAL 2.4.0-rc2 reflects 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 1065 commits that closed 309 issues, together with 117 pull requests since our last release on 2020-07-29.
Major Highlights of PySAL 2.4.0
Entirely New Packages
In this release, the PySAL family has expanded to include:
pysal/spopt
Providing methods for solving optimization problems with spatial data.
Changes by Package
libpysal
- #381: adjust duplicated rst link in README
- #380: rst link & syntax issue in README
- #379: Correct readme.rst
- #378: Troubleshoot release GHA
- #377: bump to v4.3.2 for release action
- #374: Adding deps for examples.
- #373: update unittests.yml
- #372: release action is broken due to gh deprecation of env var
- #371: Fix release action
- #370: Bump actions/setup-python from v2.1.4 to v2.2.0
- #364:
examples.explainreturning 404 - #368: This address a move in remote datasets
- #335: ENH: use query_bulk in fuzzy_contiguity
- #334: BUG: fuzzy_contiguity picks more neighbours than it should
- #367: ENH: use active geometry in from_dataframe
- #342: standardizing libpysal/io docs & ibpysal/cg docs
- #363: Testing matrix
- #359: Bump codecov/codecov-action from v1.0.14 to v1.0.15
- #358: Bump actions/checkout from v2.3.3 to v2.3.4
- #355: Add codecov.yml for customized reports/testing
- #357: Add codecov.yml
- #286: Blacken codebase
- #291: [WIP] DEV: blacken codebase
- #354: Bump codecov/codecov-action from v1.0.13 to v1.0.14
- #257: street network-based weights
- #351: Bump actions/setup-python from v2.1.3 to v2.1.4
- #279: GHA for PyPI release
- #324: Update PR template
- #347: ENH: include lower order contiguities in higher_order_sp
- #313: higher_order weights of <= k
- #348: Bump actions/setup-python from v2.1.2 to v2.1.3
- #345: Add xarray to requirements_plus_conda.txt
- #346: Bump actions/checkout from v2.3.2 to v2.3.3
- #344: Bump actions/create-release from v1.1.3 to v1.1.4
- #318: [WIP] : Base Raster Interface
- #339: Bump codecov/codecov-action from v1.0.12 to v1.0.13
- #338: Update actions/checkout requirement to v2.3.2
- #336: Bump actions/create-release from v1.1.2 to v1.1.3
- #337: Bump actions/setup-python from v2.0.1 to v2.1.2
- #326: Updating PULL_REQUEST_TEMPLATE.md
- #298: Add github action for releasing
- #316: adding release_and_publish.yml
- #333: Rook contiguity weights fails when polygons border at two points
access
esda
- Integration of the work of @jeffcsauer's 2020 GSOC project. This adds tons of new statistical estimators, such as:
- the local heteroskedasticity estimator,
esda.LOSH - local geary and multivariate geary estimators,
esda.Geary_Localandesda.Geary_Local_MV - local join counts in univariate, bivariate, and multivariate flavors,
esda.Join_Counts_Local,esda.Join_Counts_Local_BV, andesda.Join_Counts_Local_MV.
- the local heteroskedasticity estimator,
- "analytical" moments for Moran's I for replication/comparison to R. Forms are provided in Sokal 1998.
- #160: bump version for release
- #157: Use tags now in gitcount
- #139: [ENH][DOC] local join count and LOSH statistics
- #143: document minimum numba version
- #159: Moments for Moran's I_i following Sokal 1998
- #145: [ENH][DOC] local Geary statistics
- #158: Bump actions/setup-python from v1 to v2.2.1
- #156: Bump to 2.3.5
- #155: Bump version
- #154: Prepping for 2.3.2 release.
- #142: Issue with Numba < 0.47
- #146: parallel_crand_ ValueError
- #147: fix concatenation for random statistics
- #153: Unexpected LISA results for polygons contained by another polygon
- #152: Dropping 3.6 from testing.
- #151: CI: update conda setup
- #150: Bump codecov/codecov-action from v1.0.14 to v1.0.15
- #149: Bump codecov/codecov-action from v1.0.13 to v1.0.14
- #144: Bump codecov/codecov-action from v1.0.12 to v1.0.13
- #141: Spatial cross-correlations?
giddy
inequality
pointpats
segregation
- #159: bump ver
- #79: add network, util, and profile functions to readthedocs
- #158: update docs
- #153: update docstring for decomposition plotting
- #157: Update release action for deprecation of set env
- #156: update github actions
- #155: bump to Version 1.4
- #154: add docstring to decomp plotting
- #152: more flexible decomp plotting
- #151: move testing to gha
- #148: col names hardcoded in _generate_counterfactual
- #150: updates docs link
- #149: Website is not rendering correctly
spaghetti
The highlights of this release include a bug fix for how network segments were being split (raised in #526) and additions to several notebooks (spatial network segmentation, caveats, and network-constrained spatial autocorrelation). Also, spaghetti is now tested against Python 3.9.
- #572: bump 1.5.5 --> v1.5.6 after syntax error
- #571: Prep 1.5.4 rel
- #569: new documentation build GHA schema
- #568: keep docs/notebooks directory
- #556: Revert "Python 3.9 testing for CI"
- #567: documentation maintenance and notebook generation
- #566: update docs build process
- #558: Is sphinx_gallery...
pysal 2.4.0-rc1
PySAL 2.4.0-rc1 reflects 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 980 commits that closed 277 issues, together with 95 pull requests since our last release on 2020-07-29.
Major Highlights of PySAL 2.4.0
Entirely New Packages
In this release, the PySAL family has expanded to include:
pysal/spopt
Providing methods for solving optimization problems with spatial data.
Changes by Package
libpysal
- #381: adjust duplicated rst link in README
- #380: rst link & syntax issue in README
- #379: Correct readme.rst
- #378: Troubleshoot release GHA
- #377: bump to v4.3.2 for release action
- #374: Adding deps for examples.
- #373: update unittests.yml
- #372: release action is broken due to gh deprecation of env var
- #371: Fix release action
- #370: Bump actions/setup-python from v2.1.4 to v2.2.0
- #364:
examples.explainreturning 404 - #368: This address a move in remote datasets
- #335: ENH: use query_bulk in fuzzy_contiguity
- #334: BUG: fuzzy_contiguity picks more neighbours than it should
- #367: ENH: use active geometry in from_dataframe
- #342: standardizing libpysal/io docs & ibpysal/cg docs
- #363: Testing matrix
- #359: Bump codecov/codecov-action from v1.0.14 to v1.0.15
- #358: Bump actions/checkout from v2.3.3 to v2.3.4
- #355: Add codecov.yml for customized reports/testing
- #357: Add codecov.yml
- #286: Blacken codebase
- #291: [WIP] DEV: blacken codebase
- #354: Bump codecov/codecov-action from v1.0.13 to v1.0.14
- #257: street network-based weights
- #351: Bump actions/setup-python from v2.1.3 to v2.1.4
- #279: GHA for PyPI release
- #324: Update PR template
- #347: ENH: include lower order contiguities in higher_order_sp
- #313: higher_order weights of <= k
- #348: Bump actions/setup-python from v2.1.2 to v2.1.3
- #345: Add xarray to requirements_plus_conda.txt
- #346: Bump actions/checkout from v2.3.2 to v2.3.3
- #344: Bump actions/create-release from v1.1.3 to v1.1.4
- #318: [WIP] : Base Raster Interface
- #339: Bump codecov/codecov-action from v1.0.12 to v1.0.13
- #338: Update actions/checkout requirement to v2.3.2
- #336: Bump actions/create-release from v1.1.2 to v1.1.3
- #337: Bump actions/setup-python from v2.0.1 to v2.1.2
- #326: Updating PULL_REQUEST_TEMPLATE.md
- #298: Add github action for releasing
- #316: adding release_and_publish.yml
- #333: Rook contiguity weights fails when polygons border at two points
access
esda
- #156: Bump to 2.3.5
- #155: Bump version
- #154: Prepping for 2.3.2 release.
- #142: Issue with Numba < 0.47
- #146: parallel_crand_ ValueError
- #147: fix concatenation for random statistics
- #153: Unexpected LISA results for polygons contained by another polygon
- #152: Dropping 3.6 from testing.
- #151: CI: update conda setup
- #150: Bump codecov/codecov-action from v1.0.14 to v1.0.15
- #149: Bump codecov/codecov-action from v1.0.13 to v1.0.14
- #144: Bump codecov/codecov-action from v1.0.12 to v1.0.13
- #141: Spatial cross-correlations?
giddy
inequality
pointpats
segregation
- #159: bump ver
- #79: add network, util, and profile functions to readthedocs
- #158: update docs
- #153: update docstring for decomposition plotting
- #157: Update release action for deprecation of set env
- #156: update github actions
- #155: bump to Version 1.4
- #154: add docstring to decomp plotting
- #152: more flexible decomp plotting
- #151: move testing to gha
- #148: col names hardcoded in _generate_counterfactual
- #150: updates docs link
- #149: Website is not rendering correctly
spaghetti
- #572: bump 1.5.5 --> v1.5.6 after syntax error
- #571: Prep 1.5.4 rel
- #569: new documentation build GHA schema
- #568: keep docs/notebooks directory
- #556: Revert "Python 3.9 testing for CI"
- #567: documentation maintenance and notebook generation
- #566: update docs build process
- #558: Is sphinx_gallery needed for docs?
- #565: remove sphinx_gallery from reqs
- #564: Update unittests.yml (GitHub Action)
- #557: revisit #555 (adding Python 3.9 testing)
- #555: Python 3.9 testing for CI
- #552: update GHA workflows and drop Py3.6 Windows testing
- #551: Bump codecov/codecov-action from v1.0.14 to v1.0.15
- #548: Request inclusion for GitHub Discussions.
- #546: Bump actions/checkout from v2.3.3 to v2.3.4
- #101: Network K Functionality
- #543: changed binder's link to reflect the new name of master branches from…
- #544: Update environment.yml
- #437: [JOSS] time to start thinking about a JOSS paper
- #542: minor edits and updated figure references
- #541: preparing JOSS paper for submission
- #540: Libpysal req
- #539: forgot to sync docs for v1.5.1 release
- #538: Version bump for bug fix (#535)
- #535: Vertex/Arc ID sorting bug in Network.split_arcs()
- #537: Bump codecov/codecov-action from v1.0.13 to v1.0.14
- [#526...
pysal 2.3.0
PySAL 2.3.0 represents 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 1343 commits that closed 273 issues, together with 166 pull requests since our last release on 2020-02-09.
Major Highlights of PySAL 2.3.0
Entirely New Packages
In this release, the PySAL family has expanded to include:
pysal/access
Providing classical and novel measures of spatial accessibility to services.
- We implement classic spatial access models, allowing easy comparison of methodologies and assumptions.
- We support spatial access research by providing pre-computed travel time matrices and share code for computing new matrices at scale.
- We also developed a simple web app that runs the package on Amazon Web Services, allowing users to explore results without installing the package. We think this is a fun new strategy for trying new analysis methods, and hope that it will make the package more accessible to professionals.
This access models implemented include:
- Floating Catchment Areas (FCA): For each provider, this is the ratio of providers to clients within a given travel time to the provider (Huff 1963, Joseph and Bantock 1982, and Luo 2004).
- Two-Stage FCAs (2SFCA): Calculated in two steps for a given travel time to the provider: 1) for each provider, the provider-to-client ratio is generated, 2) for each point of origin, these ratios are then summed (Luo and Wang 2002, and Wang and Luo 2005).
- Enhanced 2SFCA (E2SFCA): 2SFCA but with less weight to providers that are still within the travel threshold but at larger distances from the point of origin (Luo and Qi 2009).
- Three-Stage FCA (3SFCA): adds distance-based allocation function to E2SFCA (Wan, Zou, and Sternberg, 2012).
- Rational Agent Access Model (RAAM) (Saxon and Snow 2019).
- Access Score: This is a weighted sum of access components like distance to provider and relative importance of provider type (Isard 1960).
The package is implemented as a single class with a number of helper functions. According to PySAL tradition, we have also developed a broad set of tutorials and examples.
Enhancements to Existing Packages
pysal/esda
- Highly performant multi-core, numba-based conditional permutation inference for local autocorrelation statistics (#116)
- Adbscan: an extension of the original DBSCAN algorithm that creates an ensemble of solutions generated by running DBSCAN on a random subset and "extending" the solution to the rest of the sample through nearest-neighbor regression (see Arribas-Bel, Garcia-Lopez & Viladecans-Marsal, 2020 for more details). (#120)
pysal/spaghetti
- Improved support for native PySAL geometry objects
- Longest and largest network component identification
- Minimum/maximum spanning trees
- Network component extraction
- Refactored K function
- Regular lattice generator
- Shortest path creation and extraction
- Testing improvements — Python 3.6, 3.7, 3.8 on Linux, macOS, and Windows
- Tutorial improvements and additions
pysal/mapclassify
- Toplogical coloring to ensure no two adjacent polygons share the same color.
- Pooled classification allows for the use of the same class intervals across maps.
pysal/spreg
- New documentation website.
pysal/libpysal
pysal/giddy
- Testing improvements: GitHub Actions for Continuous Integration — Python 3.6, 3.7, 3.8 on Linux, macOS, and Windows
pysal/splot
splot 1.1.3was published asLumnitz et al., (2020). splot - visual analytics for spatial statistics. Journal of Open Source Software, 5(47), 1882, https://doi.org/10.21105/joss.01882featuring functionality, implementation and it's roadmap forward.
Detailed Changes by Package
Overall, there were 1403 commits that closed 273 issues, together with 166 pull requests since our last release on 2020-02-09.
libpysal
- #296: 4.3 Release
- #294: Standardize libpysal/examples/*.py docstrings
- #295: Fetch
- #273: Mac builds seem to take longer — bump up timeout
- #281: Voronoi_frames function causes jupyter notebook kernel to die
- #280: ENH: allow specific buffer in fuzzy_contiguity
- #278: Return alpha option & use pygeos for alphashaping if available
- #276: add weights writing as a method on weights.
- #277: Docs ci badge
- #259: [rough edge] libpysal.examples w/o internet?
- #275: removing six from ci
- #274: Handle connection errors for remote datasets
- #264: GH-263: Don't implicitly import examples when importing base library
- #263: examples directory prevents installing with pyInstaller
- #254: Error in the internal hack for the Arc_KDTree class inheritance and the KDTree function
- #271: GitHub Actions failures
- #255: Bugfix
- #270: dropping nose in ci/36.yml
- #268: Follow-up To Do for GH Actions
- #269: Polish up GitHub Action residuals
- #267: Initializing complete Github Actions CI
- #266: TEST: turning off 3.6 on github actions
- #256: fix for issue #153
- #262: Cleaning up weights/weights.py docs
- #265: DOC: Udpdating citations, minor description editing
- #261: Unused code in weights.from_networkx()?
- #9: redirect pysal/#934 to libpysal
- #35: defaulting to using the dataframe index as the id set
- #23: Handling coincident points in KNN
- #67: MGWR_Georgia_example.ipynb fails due to different sample data shapes
- #47: Kernel docstring does not mention unique Gaussian kernel behavior
- #69: MGWR_Georgia_example.ipynb missing pickle import statement
- #99: weights.Voronoi is a function, not a class.
- #121: some weights util functions are lost in ini.py
- #150: [ENH][WIP] Adding a
rasterWto extractWfrom raster and align values - #123: Current weight plot method is time consuming for a large data ...
pysal 2.3.0rc1
PySAL 2.3.0 represents 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 1343 commits that closed 273 issues, together with 166 pull requests since our last release on 2020-02-09.
Major Highlights of PySAL 2.3.0
Entirely New Packages
In this release, the PySAL family has expanded to include:
pysal/access
Providing classical and novel measures of spatial accessibility to services.
- We implement classic spatial access models, allowing easy comparison of methodologies and assumptions.
- We support spatial access research by providing pre-computed travel time matrices and share code for computing new matrices at scale.
- We also developed a simple web app that runs the package on Amazon Web Services, allowing users to explore results without installing the package. We think this is a fun new strategy for trying new analysis methods, and hope that it will make the package more accessible to professionals.
This access models implemented include:
- Floating Catchment Areas (FCA): For each provider, this is the ratio of providers to clients within a given travel time to the provider (Huff 1963, Joseph and Bantock 1982, and Luo 2004).
- Two-Stage FCAs (2SFCA): Calculated in two steps for a given travel time to the provider: 1) for each provider, the provider-to-client ratio is generated, 2) for each point of origin, these ratios are then summed (Luo and Wang 2002, and Wang and Luo 2005).
- Enhanced 2SFCA (E2SFCA): 2SFCA but with less weight to providers that are still within the travel threshold but at larger distances from the point of origin (Luo and Qi 2009).
- Three-Stage FCA (3SFCA): adds distance-based allocation function to E2SFCA (Wan, Zou, and Sternberg, 2012).
- Rational Agent Access Model (RAAM) (Saxon and Snow 2019).
- Access Score: This is a weighted sum of access components like distance to provider and relative importance of provider type (Isard 1960).
The package is implemented as a single class with a number of helper functions. According to PySAL tradition, we have also developed a broad set of tutorials and examples.
Enhancements to Existing Packages
pysal/esda
- Highly performant multi-core, numba-based conditional permutation inference for local autocorrelation statistics (#116)
- Adbscan: an extension of the original DBSCAN algorithm that creates an ensemble of solutions generated by running DBSCAN on a random subset and "extending" the solution to the rest of the sample through nearest-neighbor regression (see Arribas-Bel, Garcia-Lopez & Viladecans-Marsal, 2020 for more details). (#120)
pysal/spaghetti
- Improved support for native PySAL geometry objects
- Longest and largest network component identification
- Minimum/maximum spanning trees
- Network component extraction
- Refactored K function
- Regular lattice generator
- Shortest path creation and extraction
- Testing improvements — Python 3.6, 3.7, 3.8 on Linux, macOS, and Windows
- Tutorial improvements and additions
pysal/mapclassify
- Toplogical coloring to ensure no two adjacent polygons share the same color.
- Pooled classification allows for the use of the same class intervals across maps.
pysal/spreg
- New documentation website.
pysal/libpysal
pysal/giddy
- Testing improvements: GitHub Actions for Continuous Integration — Python 3.6, 3.7, 3.8 on Linux, macOS, and Windows
pysal/splot
splot 1.1.3was published asLumnitz et al., (2020). splot - visual analytics for spatial statistics. Journal of Open Source Software, 5(47), 1882, https://doi.org/10.21105/joss.01882featuring functionality, implementation and it's roadmap forward.
Detailed Changes by Package
libpysal
- #296: 4.3 Release
- #294: Standardize libpysal/examples/*.py docstrings
- #295: Fetch
- #273: Mac builds seem to take longer — bump up timeout
- #281: Voronoi_frames function causes jupyter notebook kernel to die
- #280: ENH: allow specific buffer in fuzzy_contiguity
- #278: Return alpha option & use pygeos for alphashaping if available
- #276: add weights writing as a method on weights.
- #277: Docs ci badge
- #259: [rough edge] libpysal.examples w/o internet?
- #275: removing six from ci
- #274: Handle connection errors for remote datasets
- #264: GH-263: Don't implicitly import examples when importing base library
- #263: examples directory prevents installing with pyInstaller
- #254: Error in the internal hack for the Arc_KDTree class inheritance and the KDTree function
- #271: GitHub Actions failures
- #255: Bugfix
- #270: dropping nose in ci/36.yml
- #268: Follow-up To Do for GH Actions
- #269: Polish up GitHub Action residuals
- #267: Initializing complete Github Actions CI
- #266: TEST: turning off 3.6 on github actions
- #256: fix for issue #153
- #262: Cleaning up weights/weights.py docs
- #265: DOC: Udpdating citations, minor description editing
- #261: Unused code in weights.from_networkx()?
- #9: redirect pysal/#934 to libpysal
- #35: defaulting to using the dataframe index as the id set
- #23: Handling coincident points in KNN
- #67: MGWR_Georgia_example.ipynb fails due to different sample data shapes
- #47: Kernel docstring does not mention unique Gaussian kernel behavior
- #69: MGWR_Georgia_example.ipynb missing pickle import statement
- #99: weights.Voronoi is a function, not a class.
- #121: some weights util functions are lost in ini.py
- #150: [ENH][WIP] Adding a
rasterWto extractWfrom raster and align values - #123: Current weight plot method is time consuming for a large data set
- #151: network kernel weights
- #208: Weight ...
PySAL v2.2.0
PySAL v<2.2.0>, 2020-02-13
PySAL 2.2.0 represents 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
Overall, there were 1122 commits that closed 307 issues, together with 192 pull requests since our last release on 2019-07-29.
Major Highlights of PySAL 2.2.0
Meta-package loses some serious weight
PySAL is no longer a source release meta-package, but instead specifies the packages in the PySAL ecosystem as dependencies. This means that installing the pysal meta-package will ensure that all the ecosystem packages are installed.
Entirely New Packages
In this release, the PySAL family has expanded to include:
toblerAreal interpolation and dasymetric mapping.
Enhancements to Existing Packages
- Refactoring of pysal/libpysal to use remote example data sets which reduces the footprint of the source install from 25mb to under 3mb. Remote examples can now be examined with interactive maps if being run in Jupyter Notebooks.
- pysal/mapclassify: Choropleth legend formatting has been enhanced to permit more granular control of class labels.
pysal/spaghetti: This release includes a major overhaul to the API documentation and notebooks/tutorials.pysal/splot: This release includes additional documentation, in-depth tutorials and pointers how to solve common installation issues.splothas been submitted and will soon be available as an article in the journal of open source software.pysal/giddy: Two enhancements: (1) a new sequence module consisting of several alignment-based sequence analysis methods (example notebook) (2) extend Markov methods to deal with non-ergodic Markov chains. This release also includes a bug fix for the estimation of kendall's Tau and its spatial extension when ties are present.
Changes by Package
libpysal
- #176: Fetch
- #238: REL: version bump for bug fix release
- #236: test_map breakage due to pandas 1.0 deprecation of ufunc.outer
- #237: BUG: ufunc.outer deprecated
- #230: raise warning when islands are used in to_adjlist
- #113: Some example datasets are missing documentation
- #229: DOC: Cleaning up docs and docsr for tutorial
- #165:
to_adjlist(remove_symmetric=True)fails on string-indexed weights. - #204: AttributeError: 'Queen' object has no attribute 'silent_island_warning'
- #226: 4.2.1
- #228: Revert "4.2.1"
- #227: 4.2.1
- #225: DOC: images for notebooks
- #224: 4.2.1
- #223: 4.2.1
- #221: duplicate pypi package badge
- #222: 4.2.1
- #220: REL: 4.2.1
- #214: libpysal 4.2.0 won't import on Windows
- #215: libpysal 4.2.0 Windows import issue
- #212: Constructing contiguity spatial weights using from_dataframe and from_shapefile could give different results
- #213: fix bug 212
- #216: alpha_shapes docs not rendering
- #217: corrected docstrings in cg.alpha_shapes.py
- #211: Updating requirements
- #174: Big tarball
- #209: DOC: math rendering in sphinx, and members included for W
- #125: metadata for examples
- #210: (docs) automatically generate docstrings for class members
- #207: (docs) keep file .nojekyll in docs when syncing between docs/ and docsrc/_build/html/
- #206: (bug) replace silent_island_warning with silence_warnings for weights
- #205: Documentation does not work
- #203: updating cg.standalone.distance_matrix docs
- #195: error message in cg.standalone.distance_matrix()
- #202: improved docs in io.util.shapefile
- #201: [ENH] moving jit import to common.py / improve documentation
- #199: rearrange shapely import in cg.alpha_shapes
- #200: fix quasi-redundant import of shapely
- #196: Remove more relics (from pre-reorg PySAL)
- #198: [BUG] correcting shapely import bug
- #197: [BUG] alpha_shapes/shapely import error
- #129: decorating functions with requires()
- #192: removing iteritems decorator
- #193: [WIP] removing unused relics
- #194: README.txt refers to pre-reorg PySAL
- #147: remove
distribute_setup.py? - #128: requires() decorator for libpysal.cg.alpha_shapes
- #191: necessity of libpysal.common.iteritems()?
- #189: Voronoi results in weights of different shape than input points
- #190: BUG: alpha_shape_auto can fail to contain all points in the set.
- #186: Cast arrays as lists (Issue 185)
- #185: WSP(sparse).to_W() has
arrays in weights,neighbors dictionaries, rather than lists. - #188: BUG: Update for geopandas use of GeometryArray
- #187: Updated documentation error (link incorrectly specified) in README.rst
- #182: Docs: badges for pypi
- #178: development guidelines link failure
- #181: DOCS: moving off rtd
- #180: REL 4.1.1 bf release
- #179: BUG: Updating manifest for additional requirements files
- #169: libpysal 4.1.0 is not released on pypi or conda-forge
- #131: addressing DeprecationWarning: fromstring()
- #132: addressing DeprecationWarning: fromstring()
- #175: ENH: fromstring has been deprecated
- #172: Ci
esda
- #102: REL: 2.2.1 bf release
- #101: Fixes for pandas deprecation and 3.8 verbosity
- #98: ENH: adjust tests for new libpysal.examples
- #97: update docs
- #96: DOC: changelog update
- #95: ENH: bumping version and handle array creation error in join counts
- #87: development guidelines link in README.md and README.rst
- #92:
PYSAL_PYPIis not defined in.travis.yml - #93: addressing #92 -- .travis.yml issue
- #88: resolving dev guidelines link
- #80: Update officially supported Python versions
- #74: ENH - Join count tails
- #83: DOC: Have notebooks show output in the src
- #82: no output in documentation notebooks
giddy
Release Notes for PySAL 2.1.0
PySAL 2.1.0 represents 6 months of enhancements, bug-fixes, widening of test coverage, and improved documentation. All users are encouraged to upgrade to this version as there are numerous optimizations as well as new features (see below) that have been implemented.
From this version forward, PySAL supports Python 3 only and is tested on Python 3.6 and 3.7. Please make sure that you are operating in a Python 3 environment.
Major Highlights of PySAL 2.1.0
Entirely New Packages
This release we have added the following new package(s) to the PySAL stack:
segregation. Segregation Analytics with PySAL.
This is an extensive package that deals with segregation measurements with several frameworks ranging from inference, decomposition, street network based measures, and a multiscalar segregation profiles on urban street networks. With a consistent and well defined api following the pep8 standards, users of segregation (v1.1.1) can:
Calculate over 40 segregation measures from simple to state-of-the art, including:
- aspatial segregation indices
- spatial segregation indices
- local segregation indices
Test whether segregation estimates are statistically significant:
Decompose
segregation comparisons into
- differences arising from spatial structure
- differences arising from demographic structure
Significant Enhancements to Existing Packages
- Addition of two rank-based Markov methods to pysal/giddy: Full Rank Markov and Geographic Rank Markov, both of which avoid the potentially arbitrary selection of the discretization scheme when a discrete Markov chain is applied to continuous variables (e.g. incomes). An example notebook applying these two methods to reveal interesting spatiotemporal patterns of income distribution dynamics in the United States 1929-2009 is given here.
- A new function fdr in pysal/esda which account for multiple testing in local statistics by controlling the False Discovery Rate (FDR). The function takes the p-values for all of n local statistics and the significance level as the inputs, and returns the p-value cutoff which would be used for deciding whether to reject the null for each local test.
- The S-MAUP test was added to
esda. This test statistic measures the sensitivity of statistical results to re-aggregation. Contributed by Juan Carlos Duque (@JuancaDuque), the statistic is defined in this PLOS paper. - The Lee & Local Lee statistics were added to
esda. These statistics characterize the structure of local autocorrelation using Pearson's r. The statistic is defined in Lee (2001). - The
notebooksproject, which provides a centralised online site with example notebooks from all the federated packages has undergone several back-end improvements and (partially) supports now interactive notebooks through Binder. The project is available at http://pysal.org/notebooks - Computational improvements to
mgwrpackage includes memory optimization and parallelization. The update allows GWR and Multi-scale GWR model to be calibrated faster and on much larger datasets. Additionally, there are API changes to the kernel functions inmgwr. (#55)
Changes by Package
Overall, there were 992 commits that closed 261 issues, together with 173 pull requests since our last release on 2019-01-30.
libpysal
- #158: Allow for **kwargs any time there's a weights construction
- #134: Some functions do not support silence_warnings=True
- #159: REL: update changelog
- #157: MAINT: bumping version for a release
- #122: update interactive examples in inline docstrings
- #156: BUG: fix for scipy bump #154
- #155: Revert "bump supported Python versions and correct lat2SW doctest"
- #154: bump supported Python versions and correct lat2SW doctest
- #141: WIP debugging travis failure
- #152: replace deprecated "fromstring" with "frombytes"
- #48: doctests on weights are failing across the board
- #149: Use Unix line-endings for all files.
- #148: Remove unnecessary executable bits.
- #144:
import pysalin libpysal/io/iohandlers/dat.py - #143: enforce strict channel in .travis.yml
- #145: continued failing doctests in libpysal.io
- #146: sphinxcontrib-napoleon is no longer necessary
- #142: pysal --> libpysal docs conv & modernizing .travis.yml
- #7: fix README for pypi
- #138: build_lattice_shapefile swapped arguments
- #124: Accidental create of branch
- #127: Travis errors on Python3.6 PYTHON_PLUS=True
- #140: [WIP] solution for Travis CI failures
- #139: Conda travis
- #111: alphashapes & n<4
- #115: [WIP] ensure safe returns for small n alphashapes
esda
- #64: DOC: style sheet update and adding Smaup to init
- #63: DOC: correcting Geary documentation
- #62: 2.1.0
- #52: (ENH) FDR-based adjustment to account for multiple testing in local statistics
- #58: [WIP] Contributing Smaup test to
esda - #53: G_Local: EG_sim and seG_sim are scalar
- #55: docs building failed
- #57: (bug) fix docs build
- #54: BUG: EG_Sim and seG_sim were incorrectly given as scalars. #53
giddy
- #97: (docs) change the css to accommodate new versions of sphinx and sphinx bootstrap theme
- #96: PyPi page configuration
- #95: pip install github master.zip of pysal dependencies
- #93: update README.md
- #94: Update readme
- #92: migrate from readthedocs configuration file v1 to v2
- #91: updating supported python versions (3.6 and 3.7) in setup.py
- #90: Move testing off of 3.5 and add 3.7
- #89: release on conda-forge
- #53: allow user specified lag and check shape
- #87: Update zenodo doi and pypi badge for version 2.1.0 (new release)
- #86: (bug) format readme.rst as long_description for pypi display
- #85: Prepare for release 2.1.0
inequality
pointpats
PySAL 2.0.0
Release Notes for PySAL 2.0
This release represents over 15 months of hard work on PySAL. It brings major transitions in the project together with many new enhancements, improved documentation, and numerous bug fixes and optimizations.
Table of Contents
Major Changes
This release, we've had a ton of activity in PySAL, much of which has been conducted in our subpackages, the independently-released components of our library. Because it is too onerous to list all the changes to the API here, please consult the Migrating to PySAL 2.0 page for a detailed discussion of all reorganization-related changes. This document will focus only on improvements to functionality, enhancements, and additional modules added to 2.0, over and above the last stable release of the 1.0 series, PySAL 1.14.4.
Entirely New Packages
For starters, we've added some entirely-new subpackages to this release candidate:
-
mgwr, the multi-scale Geographically-weighted regression package for Python.Don't worry,
mgwrfits single-scale geographically-weighted regressions, too. Geographically-weighted regression is a kind of generalized additive model that uses kernel functions in the geographic area around each observation to predict outcomes at that area more accuratly, kind of like Gaussian Process regression for geographic data. -
spvcm, for spatially-correlated multilevel models.Spatially-correlated multilevel models are models that allow for random effects of nearby areas, regions, or groups to be correlated with one another. This is a Gibbs sampling framework plus diagnostics & plotting tools for general Bayesian analysis of Gibbs samplers. The package also contains simple tools to implement new samplers on top of the infrastructure provided, which is fast, parallel, serializable, iterative-write, and interruptible.
-
spint, for estimating spatial interaction models, such as the production-constrained or consumption-constrained gravity models. -
spglm, a package for fitting sparse GLMs, focused on performance over sparse categorical data. -
splot, for spatial vizualization in Python, built on top of the excellentgeopandas. This is headed by our Google Summer of Code (2018) student, Stefanie Lumnitz, and will be ongoing throughout the release candidate maturation cycle. -
pointpats, a package for the statistical analysis of point patterns, geographical colocation, and dispersion.
Significant Enhancements to Existing Packages
We've also had a ton of activity adding new features in our submodules:
- alpha shapes, a fast implementation (numba-enabled) of the original algorithm generalizing convex hulls to allow for non-convex hulls
- Contiguity weights from voronoi tesselations/delaunay triangulation graphs for point patterns
- Fuzzy contiguity weights, for polygonal data where planarity is not strictly enforced (provided in part by
geopandas) - A family of spatially explicit indicators of mobility association including the inter- and intra-regional indicators as well as the local indicators. The inter- and intra-regional indicators provide a meso-level view on the exchange mobility pattern, shedding light on specific regions hosting frequent rank exchanges while the local indicators examine the concordance relationship between a focal spatial unit and its neighbors for revealing the role of space in shaping the exchange mobility pattern over time.
- Categorical Spatial Markov, a generalization of the Spatial_Markov class previously implemented to interrogate the role of space in shaping regional income distribution dynamics to incorporate direct variables. Here, the categorical spatial lag is adopted for formalizing the neighborhood.
- Connected Components checks for our spatial graph matrices
- Pandas Adjacency list constructors and NetworkX adapters for PySAL graph objects
- Attach islands, tooling to merge isolated observations into existing spatial graphs
- Construct Contiguity graphs from linestrings, allowing for easy and fast access to road network data in PySAL
Changes by Package
Overall, there were 1636 commits that closed 368 issues, together with 236 pull requests since our last release on 2017-11-03.
libpysal
- weights.distance.KNN.from_dataframe ignoring radius (#116)
- Always make spherical KDTrees if radius is passed (#117)
- [ENH] should
weights.util.get_ids()also accept a geodataframe? (#97) - enh: add doctests to travis (#2) (#112)
- sphinx docs need updating (#49)
- Add notebooks for subpackage contract (#108)
- Api docs complete (#110)
- Doctests and start of documentation for libpysal (#109)
- Add dependencies to requirements_plus.txt for test_db (#107)
- Weights/util/get ids gdf (#101)
- missing adjustments to lower case module names (#106)
- Rel.4.0.0 (#105)
- REL: 3.0.8 (#104)
- error importing v3.0.7 (#100)
- Lower case module names (#98)
- remove function regime_weights (#96)
- depreciating regime_weights in the new release? (#94)
- inconsistency in api? (#93)
- Ensure consistency in
from .module import *in components of libpysal (#95) - [WIP] cleanup (#88)
- docstrings for attributes are defined in properties (#87)
- docstrings in W class need editing (#64)
- version name as version (#92)
- remove
delstatements and modify alphashape all (#89) - libpysal/libpysal/cg/init.py not importing
rtree(#90) - including rtree in imports (#91)
- BUG: test_weights_IO.py is using pysal and hard-coded paths (#85)
- fix hardcoded swm test (#86)
- check for spatial index if nonplanar neighbors (#84)
- nonplanar_neighbors fails when sindex is not constructed. (#63)
- increment version number and add bugfixes, api changes (#79)
- Spherebug (#82)
- only warn once for islands/disconnected components (#83)
- only warn on disconnected compone...