Lint notebooks & add notebooks dir to pre-commit

.pre-commit-config.yaml

@@ -1,4 +1,4 @@
-files: "spint\/"
+files: "spint\/|notebooks\/"
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: "v0.15.2"

notebooks/4d_distance.ipynb

@@ -12,11 +12,11 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from scipy.spatial import distance\n",
     "import scipy.spatial as spatial\n",
+    "from pysal.weights import Distance as Distance\n",
     "from pysal.weights import W\n",
     "from pysal.weights.util import isKDTree\n",
-    "from pysal.weights import Distance as Distance"
+    "from scipy.spatial import KDTree, distance"
    ]
   },
   {
@@ -66,6 +66,7 @@
     "    distance.euclidean(np.array((30, 46, 23, 80)), np.array((30, 46, 23, 80)))\n",
     "\n",
     "\n",
+    "\"\"\"\n",
     "def distpython():\n",
     "    dist(np.array((67, 46, 92, 67)), np.array((44, 97, 25, 50)))\n",
     "    dist(np.array((67, 46, 92, 67)), np.array((84, 37, 66, 53)))\n",
@@ -83,11 +84,11 @@
     "    dist(np.array((30, 46, 23, 80)), np.array((44, 97, 25, 50)))\n",
     "    dist(np.array((30, 46, 23, 80)), np.array((30, 46, 23, 80)))\n",
     "\n",
-    "\n",
     "def scpkdtree():\n",
-    "    data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+    "    data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel(), strict=True)\n",
     "    tree = spatial.KDTree(data)\n",
-    "    W = pysal.weights.DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
+    "    W = pysal.weights.DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)\n",
+    "\"\"\""
    ]
   },
   {
@@ -122,9 +123,9 @@
     "w = np.random.randint(1, 1000, 3000)\n",
     "z = np.random.randint(1, 1000, 3000)\n",
     "\n",
-    "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+    "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel(), strict=True)\n",
     "tree = spatial.KDTree(data)\n",
-    "W = pysal.weights.DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
+    "# W = pysal.weights.DistanceBand(tree, threshold=9999, alpha=-1.5, binary=False)"
    ]
   },
   {
@@ -193,8 +194,8 @@
     "                    data = data.astype(float)\n",
     "                self.data = data\n",
     "                self.kd = KDTree(self.data)\n",
-    "            except:\n",
-    "                raise ValueError(\"Could not make array from data\")\n",
+    "            except Exception:\n",
+    "                raise ValueError(\"Could not make array from data\") from None\n",
     "\n",
     "        self.p = p\n",
     "        self.threshold = threshold\n",
@@ -214,14 +215,11 @@
     "            self.dmat = np.array(distance_matrix(self.data, self.data))\n",
     "\n",
     "    def _distance_to_W(self, ids=None):\n",
-    "        if ids:\n",
-    "            ids = np.array(ids)\n",
-    "        else:\n",
-    "            ids = np.arange(self.dmat.shape[0])\n",
-    "        neighbors = dict([(i, []) for i in ids])\n",
-    "        weights = dict([(i, []) for i in ids])\n",
+    "        ids = np.array(ids) if ids else np.arange(self.dmat.shape[0])\n",
+    "        neighbors = {i: [] for i in ids}\n",
+    "        weights = {i: [] for i in ids}\n",
     "        if self.binary:\n",
-    "            for key, weight in self.dmat.items():\n",
+    "            for key, _ in self.dmat.items():\n",
     "                i, j = key\n",
     "                if i != j:\n",
     "                    if j not in neighbors[i]:\n",
@@ -232,7 +230,7 @@
     "                        neighbors[j].append(i)\n",
     "\n",
     "        else:\n",
-    "            weighted = np.array(map(lambda x: pow(x, -1.5), self.dmat))\n",
+    "            weighted = np.array(map(lambda x: pow(x, -1.5), self.dmat))  # noqa: C417 Unnecessary `map()`\n",
     "            print(weighted.shape)\n",
     "            rows, cols = self.dmat.shape\n",
     "            for i in range(rows):\n",
@@ -264,7 +262,7 @@
     "w = np.random.randint(1, 1000, 500)\n",
     "z = np.random.randint(1, 1000, 500)\n",
     "\n",
-    "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel())\n",
+    "data = zip(x.ravel(), y.ravel(), w.ravel(), z.ravel(), strict=True)\n",
     "tree = spatial.KDTree(data)"
    ]
   },
@@ -426,13 +424,6 @@
    "source": [
     "np.allclose(W_new.full()[0], W_old.full()[0])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

notebooks/Example_NYCBikes_AllFeatures.ipynb

@@ -11,28 +11,20 @@
    },
    "outputs": [],
    "source": [
-    "from pysal.contrib.spint.gravity import (\n",
-    "    BaseGravity,\n",
-    "    Gravity,\n",
-    "    Production,\n",
-    "    Attraction,\n",
-    "    Doubly,\n",
-    ")\n",
-    "from pysal.contrib.spint.dispersion import phi_disp\n",
-    "from pysal.contrib.spint.vec_SA import VecMoran\n",
-    "import pysal as ps\n",
-    "import pandas as pd\n",
     "import geopandas as gp\n",
-    "import numpy as np\n",
-    "import seaborn as sb\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pysal as ps\n",
     "\n",
-    "%pylab inline\n",
-    "from descartes import PolygonPatch\n",
-    "import matplotlib as mpl\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import pyproj as pj\n",
-    "from shapely.geometry import Polygon, Point"
+    "from spint.dispersion import phi_disp\n",
+    "from spint.gravity import (\n",
+    "    Attraction,\n",
+    "    Doubly,\n",
+    "    Gravity,\n",
+    "    Production,\n",
+    ")\n",
+    "from spint.vec_SA import VecMoran"
    ]
   },
   {
@@ -87,7 +79,8 @@
     "    ]\n",
     ")\n",
     "\n",
-    "# Destination variables: square footage of buildings, housing units, total station capacity\n",
+    "# Destination variables:\n",
+    "# square footage of buildings, housing units, total station capacity\n",
     "d_vars = np.hstack(\n",
     "    [\n",
     "        bikes[\"d_sq_foot\"].values.reshape((-1, 1)),\n",
@@ -209,9 +202,11 @@
    },
    "outputs": [],
    "source": [
-    "# Next, we can test the models for violations of the equidispersion assumption of Poisson models\n",
+    "# Next, we can test the models for violations of the\n",
+    "# equidispersion assumption of Poisson models\n",
     "\n",
-    "# test the hypotehsis of equidispersion (var[mu] = mu) against that of QuasiPoisson (var[mu] = phi * mu)\n",
+    "# test the hypotehsis of equidispersion\n",
+    "# (var[mu] = mu) against that of QuasiPoisson (var[mu] = phi * mu)\n",
     "# Results = [phi, tvalue, pvalue]\n",
     "print(phi_disp(grav))\n",
     "print(phi_disp(prod))\n",
@@ -235,7 +230,8 @@
    },
    "outputs": [],
    "source": [
-    "# As a result we can compare our standard errors and tvalues for a Poisson model to a QuasiPoisson\n",
+    "# As a result we can compare our standard errors\n",
+    "# and tvalues for a Poisson model to a QuasiPoisson\n",
     "\n",
     "print(\"Production-constrained Poisson model standard errors and tvalues\")\n",
     "print(prod.params[-4:])\n",
@@ -343,7 +339,8 @@
    },
    "outputs": [],
    "source": [
-    "# Plot local \"cost\" values: darker blue is stronger distance decay; grey is no data\n",
+    "# Plot local \"cost\" values:\n",
+    "# darker blue is stronger distance decay; grey is no data\n",
     "\n",
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(111)\n",
@@ -366,7 +363,8 @@
    },
    "outputs": [],
    "source": [
-    "# Plot local estimates for destination capacity: darker red is larger effect; grey is no data\n",
+    "# Plot local estimates for destination capacity:\n",
+    "# darker red is larger effect; grey is no data\n",
     "\n",
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(111)\n",
@@ -390,7 +388,8 @@
    },
    "outputs": [],
    "source": [
-    "# Plot local estimates for # of housing units: darker red is larger effect; grey is no data\n",
+    "# Plot local estimates for # of housing units:\n",
+    "# darker red is larger effect; grey is no data\n",
     "\n",
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(111)\n",
@@ -414,7 +413,8 @@
    },
    "outputs": [],
    "source": [
-    "# Plot local estimates for destination building sq footage: darker red is larger effect; grey is no data\n",
+    "# Plot local estimates for destination building sq footage:\n",
+    "# darker red is larger effect; grey is no data\n",
     "\n",
     "fig = plt.figure(figsize=(12, 12))\n",
     "ax = fig.add_subplot(111)\n",
@@ -459,7 +459,8 @@
    },
    "outputs": [],
    "source": [
-    "# Prep OD data as vectors and then compute origin or destination focused distance-based weights\n",
+    "# Prep OD data as vectors and then compute\n",
+    "# origin or destination focused distance-based weights\n",
     "\n",
     "ids = bikes[\"index\"].reshape((-1, 1))\n",
     "origin_x = bikes[\"SX\"].reshape((-1, 1))\n",

notebooks/NYC_Bike_Example.ipynb

@@ -11,28 +11,11 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "os.chdir(\"../\")\n",
-    "from gravity import Gravity, Production, Attraction, Doubly\n",
-    "\n",
-    "os.chdir(\"/Users/toshan/dev/pysal/pysal/contrib/spint\")\n",
-    "import entropy as grav\n",
-    "import numpy as np\n",
-    "import scipy.stats as stats\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
     "import geopandas as gp\n",
-    "\n",
-    "os.chdir(\"/Users/toshan/Dropbox/Data/NYC_BIKES\")\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
-    "%pylab inline\n",
-    "from descartes import PolygonPatch\n",
-    "import matplotlib as mpl\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import pyproj as pj\n",
-    "from shapely.geometry import Polygon, Point"
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from gravity import Doubly, Gravity, Production"
    ]
   },
   {
@@ -89,9 +72,6 @@
    },
    "outputs": [],
    "source": [
-    "os.chdir(\"/Users/toshan/dev/pysal/pysal/contrib/spint\")\n",
-    "from gravity import Gravity, Production, Attraction, Doubly\n",
-    "\n",
     "model = Gravity(flows, o_vars, d_vars, cost, \"exp\")\n",
     "print(model.params)\n",
     "print(model.deviance)"