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[pre-commit.ci] pre-commit autoupdate (#767)
* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.5.0 → v0.6.9](astral-sh/ruff-pre-commit@v0.5.0...v0.6.9) * reup linting & formating * Update pyproject.toml --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: James Gaboardi <jgaboardi@gmail.com> Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>
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‎.pre-commit-config.yaml‎

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files: "libpysal\/"
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files: "libpysal\/|docs\/"
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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: "v0.5.0"
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rev: "v0.6.9"
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hooks:
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- id: ruff
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- id: ruff-format

‎docs/conf.py‎

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# -*- coding: utf-8 -*-
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#
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# libpysal documentation build configuration file, created by
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# sphinx-quickstart on Wed Jun 6 15:54:22 2018.
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#
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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#
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import sphinx_bootstrap_theme
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import libpysal
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import libpysal
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# -- General configuration ------------------------------------------------
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‎docs/user-guide/data/examples.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"from libpysal.examples import get_path \n"
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"from libpysal.examples import get_path"
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]
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},
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{
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"outputs": [],
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"source": [
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"import libpysal\n",
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"\n",
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"dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
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]
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},
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}
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],
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"source": [
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"libpysal.examples.load_example('dataset42')"
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"libpysal.examples.load_example(\"dataset42\")"
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]
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},
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{
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}
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],
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"source": [
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"balt_url = libpysal.examples.get_url('Baltimore')\n",
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"balt_url = libpysal.examples.get_url(\"Baltimore\")\n",
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"balt_url"
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]
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},
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}
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],
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"source": [
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"libpysal.examples.explain('taz')"
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"libpysal.examples.explain(\"taz\")"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"taz = libpysal.examples.load_example('taz')"
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"taz = libpysal.examples.load_example(\"taz\")"
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]
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},
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{
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}
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],
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"source": [
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"libpysal.examples.explain('Baltimore')"
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"libpysal.examples.explain(\"Baltimore\")"
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},
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{
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}
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],
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"source": [
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"balt = libpysal.examples.load_example('Baltimore')"
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"balt = libpysal.examples.load_example(\"Baltimore\")"
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]
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},
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],
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"source": [
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"from libpysal.examples import explain\n",
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"explain('Tampa1')"
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"\n",
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"explain(\"Tampa1\")"
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],
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"source": [
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"from libpysal.examples import load_example\n",
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"tampa1 = load_example('Tampa1')"
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"\n",
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"tampa1 = load_example(\"Tampa1\")"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"tampa_counties_shp = tampa1.load('tampa_counties.shp')"
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"tampa_counties_shp = tampa1.load(\"tampa_counties.shp\")"
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))"
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"tampa_df = geopandas.read_file(tampa1.get_path(\"tampa_counties.shp\"))"
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}
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"source": [
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"libpysal.examples.explain('Rio Grande do Sul')"
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"libpysal.examples.explain(\"Rio Grande do Sul\")"
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}
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"source": [
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"rio = libpysal.examples.load_example('Rio Grande do Sul')"
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"rio = libpysal.examples.load_example(\"Rio Grande do Sul\")"
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{
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}
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"source": [
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"libpysal.examples.remote_datasets.datasets # a listing of all remotes"
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"libpysal.examples.remote_datasets.datasets # a listing of all remotes"
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{

‎docs/user-guide/graph/matching-graph.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"sys.path.append(os.path.abspath('..'))\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\n",
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"\n",
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"sys.path.append(os.path.abspath(\"..\"))\n",
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"import geopandas\n",
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"import pandas"
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"points = np.row_stack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7,4)])\n",
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"points = np.row_stack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7, 4)])\n",
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"gdf = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(*points.T))"
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}
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],
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"source": [
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"f, ax =plt.subplots(1,3)\n",
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"for i,g in enumerate((g1, g2, g3)):\n",
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"f, ax = plt.subplots(1, 3)\n",
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"for i, g in enumerate((g1, g2, g3)):\n",
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" g.plot(gdf, ax=ax[i])\n",
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" ax[i].set_title(f\"k = {i+1}\")"
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"outputs": [],
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"source": [
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"import geodatasets\n",
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"\n",
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"stores = geopandas.read_file(geodatasets.get_path(\"geoda liquor_stores\")).explode(\n",
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" index_parts=False\n",
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")"
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}
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"source": [
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"f, ax =plt.subplots(1,3)\n",
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"for i,g in enumerate((g1, g5, g10)):\n",
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"f, ax = plt.subplots(1, 3)\n",
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"for i, g in enumerate((g1, g5, g10)):\n",
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" g.plot(stores, ax=ax[i], nodes=False)\n",
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" ax[i].set_title(f\"k = {(1, 5, 10)[i]}\")"
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"sources = stores.sample(100)\n",
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"sinks = stores[~stores.index.isin(sources.index)].sample(100)\n",
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"ax = sources.plot(color='red')\n",
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"ax = sources.plot(color=\"red\")\n",
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"sinks.plot(color=\"blue\", ax=ax)\n",
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"import shapely"
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"\n",
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"crosspattern_heads, crosspattern_tails, weights, mip = _spatial_matching(x=sink_coordinates, y = source_coordinates, n_matches=1, return_mip=True)"
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"crosspattern_heads, crosspattern_tails, weights, mip = _spatial_matching(\n",
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" x=sink_coordinates, y=source_coordinates, n_matches=1, return_mip=True\n",
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")"
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"lines = shapely.linestrings(\n",
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" zip(\n",
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" map(list, source_coordinates[crosspattern_heads]),\n",
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" map(list, sink_coordinates[crosspattern_tails])\n",
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" list(\n",
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" zip(\n",
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" map(list, source_coordinates[crosspattern_heads]),\n",
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" map(list, sink_coordinates[crosspattern_tails]),\n",
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" strict=False,\n",
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" )\n",
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" )\n",
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")\n",
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"ax = sources.plot(color=\"red\")\n",
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}

‎docs/user-guide/graph/w_g_migration.ipynb‎

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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"ngdf = gdf.set_index(\"NAME\")"
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}
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"g.explore(ngdf, m=m, focal='Ashe')"
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"m = ngdf.loc[g[\"Ashe\"].index].explore(color=\"#25b497\")\n",
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"ngdf.loc[[\"Ashe\"]].explore(m=m, color=\"#fa94a5\")\n",
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"g.explore(ngdf, m=m, focal=\"Ashe\")"
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},
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{

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