Date: Sun, 4 Aug 2024 17:21:47 GMT From: Po-Chuan Hsieh <sunpoet@FreeBSD.org> To: ports-committers@FreeBSD.org, dev-commits-ports-all@FreeBSD.org, dev-commits-ports-main@FreeBSD.org Subject: git: 176436adbfd6 - main - graphics/py-geosnap: Add py-geosnap 0.14.0 Message-ID: <202408041721.474HLlwZ059081@gitrepo.freebsd.org>
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The branch main has been updated by sunpoet: URL: https://cgit.FreeBSD.org/ports/commit/?id=176436adbfd6df0f67926945995abd207364762f commit 176436adbfd6df0f67926945995abd207364762f Author: Po-Chuan Hsieh <sunpoet@FreeBSD.org> AuthorDate: 2024-08-04 16:44:09 +0000 Commit: Po-Chuan Hsieh <sunpoet@FreeBSD.org> CommitDate: 2024-08-04 17:13:52 +0000 graphics/py-geosnap: Add py-geosnap 0.14.0 geosnap provides a suite of tools for exploring, modeling, and visualizing the social context and spatial extent of neighborhoods and regions over time. It brings together state-of-the-art techniques from geodemographics, regionalization, spatial data science, and segregation analysis to support social science research, public policy analysis, and urban planning. It provides a simple interface tailored to formal analysis of spatiotemporal urban data. Main Features: - fast, efficient tooling for standardizing data from multiple time periods into a shared geographic representation appropriate for spatiotemporal analysis - analytical methods for understanding sociospatial structure in neighborhoods, cities, and regions, using unsupervised ML from scikit-learn and spatial optimization from PySAL - classic and spatial analytic methods for diagnosing model fit, and locating (spatial) statistical outliers novel techniques for understanding the evolution of neighborhoods over time, including identifying hotspots of local neighborhood change, as well as modeling and simulating neighborhood conditions into the future - quick access to a large database of commonly-used neighborhood indicators from U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the cloud thanks to quilt and the highly-performant geoparquet file format. --- graphics/Makefile | 1 + graphics/py-geosnap/Makefile | 48 +++++++++++++++++++++++++++++++++++++++++++ graphics/py-geosnap/distinfo | 3 +++ graphics/py-geosnap/pkg-descr | 21 +++++++++++++++++++ 4 files changed, 73 insertions(+) diff --git a/graphics/Makefile b/graphics/Makefile index 3dbe0b7c506a..1450edc57d10 100644 --- a/graphics/Makefile +++ b/graphics/Makefile @@ -901,6 +901,7 @@ SUBDIR += py-gdal SUBDIR += py-geomdl SUBDIR += py-geopandas + SUBDIR += py-geosnap SUBDIR += py-giddy SUBDIR += py-gizeh SUBDIR += py-glcontext diff --git a/graphics/py-geosnap/Makefile b/graphics/py-geosnap/Makefile new file mode 100644 index 000000000000..38f9269bbb9f --- /dev/null +++ b/graphics/py-geosnap/Makefile @@ -0,0 +1,48 @@ +PORTNAME= geosnap +PORTVERSION= 0.14.0 +CATEGORIES= graphics python +MASTER_SITES= PYPI +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= sunpoet@FreeBSD.org +COMMENT= Geospatial Neighborhood Analysis Package +WWW= https://oturns.github.io/geosnap-guide/ \ + https://github.com/oturns/geosnap + +LICENSE= BSD3CLAUSE +LICENSE_FILE= ${WRKSRC}/LICENSE.txt + +BUILD_DEPENDS= ${PYTHON_PKGNAMEPREFIX}setuptools>=61.0:devel/py-setuptools@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}setuptools-scm>=6.2:devel/py-setuptools-scm@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}wheel>=0:devel/py-wheel@${PY_FLAVOR} +RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}contextily>=0:graphics/py-contextily@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}fsspec>=:devel/py-fsspec@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}geopandas>=0.9:graphics/py-geopandas@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}giddy>=2.2.1:graphics/py-giddy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}libpysal>=0:science/py-libpysal@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}mapclassify>=0:graphics/py-mapclassify@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}matplotlib>=0:math/py-matplotlib@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}numpy>=0,1:math/py-numpy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pandana>=0:graphics/py-pandana@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pandas>=0,1:math/py-pandas@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}platformdirs>=0:devel/py-platformdirs@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pooch>=0:devel/py-pooch@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}proplot>=0.9:graphics/py-proplot@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pyarrow>=0.14.1:databases/py-pyarrow@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}pyproj>=3:graphics/py-pyproj@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}quilt3>=3.6:www/py-quilt3@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}s3fs>=0:devel/py-s3fs@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}scikit-learn>=0:science/py-scikit-learn@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}seaborn>=0:math/py-seaborn@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}segregation>=2.1:science/py-segregation@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}spopt>=0.3.0:math/py-spopt@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}tobler>=0.8.2:science/py-tobler@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}tqdm>=0:misc/py-tqdm@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}xlrd>=0:textproc/py-xlrd@${PY_FLAVOR} + +USES= python +USE_PYTHON= autoplist concurrent pep517 + +NO_ARCH= yes + +.include <bsd.port.mk> diff --git a/graphics/py-geosnap/distinfo b/graphics/py-geosnap/distinfo new file mode 100644 index 000000000000..301a31ce5e9c --- /dev/null +++ b/graphics/py-geosnap/distinfo @@ -0,0 +1,3 @@ +TIMESTAMP = 1722711129 +SHA256 (geosnap-0.14.0.tar.gz) = 3ef75ef510934e5eb30b0e9c80cd94952a1266a05a5987ae06945781e97eac1f +SIZE (geosnap-0.14.0.tar.gz) = 29913517 diff --git a/graphics/py-geosnap/pkg-descr b/graphics/py-geosnap/pkg-descr new file mode 100644 index 000000000000..9ddb78464622 --- /dev/null +++ b/graphics/py-geosnap/pkg-descr @@ -0,0 +1,21 @@ +geosnap provides a suite of tools for exploring, modeling, and visualizing the +social context and spatial extent of neighborhoods and regions over time. It +brings together state-of-the-art techniques from geodemographics, +regionalization, spatial data science, and segregation analysis to support +social science research, public policy analysis, and urban planning. It provides +a simple interface tailored to formal analysis of spatiotemporal urban data. + +Main Features: +- fast, efficient tooling for standardizing data from multiple time periods into + a shared geographic representation appropriate for spatiotemporal analysis +- analytical methods for understanding sociospatial structure in neighborhoods, + cities, and regions, using unsupervised ML from scikit-learn and spatial + optimization from PySAL +- classic and spatial analytic methods for diagnosing model fit, and locating + (spatial) statistical outliers novel techniques for understanding the + evolution of neighborhoods over time, including identifying hotspots of local + neighborhood change, as well as modeling and simulating neighborhood + conditions into the future +- quick access to a large database of commonly-used neighborhood indicators from + U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the + cloud thanks to quilt and the highly-performant geoparquet file format.
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