update docstring tests -- numpy-2.0 failures (#769)

* RLS: v1.7.5.post1

* temporarily disable doctests

* update pre-commit

* update pre-commit [2]

* update doctests & ruff

* try skipping doctests for 3.10 oldest

* try skipping doctests for 3.10 oldest [2]

* try skipping doctests for 3.10 oldest [3]

.github/workflows/testing.yml

@@ -60,7 +60,15 @@
        
        - name: spatial versions
          run: 'python -c "import geopandas; geopandas.show_versions();"'
-
+
+       - name: declare doctest run
+         run: |
+           if [[ $matrix.environment-file == 'ci/310-oldest.yaml' ]]; then
+               echo "DOCTEST='--doctest-modules'" >> "$GITHUB_ENV"
+           else
+               echo "DOCTEST=''" >> "$GITHUB_ENV"
+           fi
+
        - name: run tests
          run: |
            pytest \
@@ -73,8 +81,8 @@
            --cov-append \
            --cov-report term-missing \
            --cov-report xml \
-           --doctest-modules \
-           --timeout 60
+           --timeout 60 \
+           ${{ env.DOCTEST }}
        
        - name: codecov
          uses: codecov/codecov-action@v4

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 files: "spaghetti\/"
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.3.5"
+    rev: "v0.4.9"
     hooks:
       - id: ruff
       - id: ruff-format

spaghetti/analysis.py

@@ -80,7 +80,7 @@ def validatedistribution(self):
 
         valid_distributions = ["uniform"]
         if self.distribution not in valid_distributions:
-            msg = "%s distribution not currently supported." % self.distribution
+            msg = f"{self.distribution} distribution not currently supported."
             raise RuntimeError(msg)
 
     def computeenvelope(self):

spaghetti/network.py

@@ -227,12 +227,12 @@ class Network:
 
     >>> import numpy
     >>> list(numpy.unique(ntw.network_component_labels))
-    [0]
+    [np.int32(0)]
 
     Show whether each component of the network is an isolated ring (or not).
 
     >>> ntw.network_component_is_ring
-    {0: False}
+    {np.int32(0): False}
 
     Show how many network arcs are associated with the component.
 
@@ -246,9 +246,9 @@ class Network:
     >>> ntw.graph_n_components
     1
     >>> list(numpy.unique(ntw.graph_component_labels))
-    [0]
+    [np.int32(0)]
     >>> ntw.graph_component_is_ring
-    {0: False}
+    {np.int32(0): False}
     >>> edges = len(ntw.graph_component2edge[ntw.graph_component_labels[0]])
     >>> edges
     179
@@ -1018,7 +1018,7 @@ def distancebandweights(
         There are ``8`` units with ``3`` neighbors in the ``W`` object.
 
         >>> w.histogram[-1]
-        (8, 3)
+        (np.int64(8), np.int64(3))
 
         """
 
@@ -1718,7 +1718,7 @@ def allneighbordistances(
         the distance between one observation and another will be positive value.
 
         >>> s2s_dist[0,0], s2s_dist[1,0]
-        (nan, 3105.189475447081)
+        (np.float64(nan), np.float64(3105.189475447081))
 
         If calculating a ``type-a`` to ``type-b`` distance matrix
         the distance between all observations will likely be positive
@@ -1993,7 +1993,8 @@ def nearestneighbordistances(
 
         >>> nn = ntw.nearestneighbordistances("crimes", keep_zero_dist=True)
         >>> nn[11], nn[18]
-        (([18, 19], 165.33982412719126), ([19], 0.0))
+        (([18, 19], np.float64(165.33982412719126)), ([19], np.float64(0.0)))
+
 
         This may be remedied by setting the ``keep_zero_dist`` keyword
         argument to ``False``. With this parameter set, observation ``11``
@@ -2003,13 +2004,13 @@ def nearestneighbordistances(
 
         >>> nn = ntw.nearestneighbordistances("crimes", keep_zero_dist=False)
         >>> nn[11], nn[18]
-        (([18, 19], 165.33982412719126), ([11], 165.33982412719126))
+        (([18, 19], np.float64(165.33982412719126)), ([11], np.float64(165.33982412719126)))
 
         There are valid reasons for both retaining or masking zero distance
         neighbors. When conducting analysis, thought must be given as to
         which model more accurately represents the specific scenario.
 
-        """
+        """  # noqa: E501
 
         # raise exception is the specified point pattern does not exist
         if sourcepattern not in self.pointpatterns:
@@ -2618,7 +2619,7 @@ def Moran(self, pp_name, permutations=999, graph=False):  # noqa N802
 
         >>> moran_res, _ = ntw.Moran(crimes)
         >>> round(moran_res.I, 6)
-        0.005193
+        np.float64(0.005193)
 
         Notes
         -----
@@ -2748,7 +2749,7 @@ def extract_component(net, component_id, weightings=None):
     >>> longest.network_n_components
     1
     >>> longest.network_component_lengths
-    {0: 13508.169276875526}
+    {np.int32(0): 13508.169276875526}
 
     """
 
@@ -3150,7 +3151,7 @@ def element_as_gdf(
     Calculate the total length of the network.
 
     >>> arcs_df.geometry.length.sum()
-    104414.09200823458
+    np.float64(104414.09200823458)
 
     """
 

spaghetti/util.py

@@ -64,7 +64,7 @@ def get_neighbor_distances(ntw, v0, link):
     >>> ntw = spaghetti.Network(examples.get_path("streets.shp"))
     >>> neighs = spaghetti.util.get_neighbor_distances(ntw, 0, ntw.arc_lengths)
     >>> numpy.round(neighs[1], 10)
-    102.6235345344
+    np.float64(102.6235345344)
 
     """
 
@@ -107,9 +107,9 @@ def generatetree(pred):
     >>> distance, pred = spaghetti.util.dijkstra(ntw, 0)
     >>> tree = spaghetti.util.generatetree(pred)
     >>> tree[3]
-    [23, 22, 20, 19, 170, 2, 0]
+    [np.int64(23), np.int64(22), np.int64(20), np.int64(19), np.int64(170), np.int64(2), np.int64(0)]
 
-    """
+    """  # noqa: E501
 
     # instantiate tree lookup
     tree = {}
@@ -181,7 +181,7 @@ def dijkstra(ntw, v0, initial_dist=numpy.inf):
     >>> round(distance[196], 4)
     5505.6682
     >>> pred[196]
-    133
+    np.int64(133)
 
     """
 
@@ -269,7 +269,7 @@ def dijkstra_mp(ntw_vertex):
     >>> round(distance[196], 4)
     5505.6682
     >>> pred[196]
-    133
+    np.int64(133)
 
     """
 
@@ -305,7 +305,7 @@ def squared_distance_point_link(point, link):
     >>> import spaghetti
     >>> point, link = (1,1), ((0,0), (2,0))
     >>> spaghetti.util.squared_distance_point_link(point, link)
-    (1.0, array([1., 0.]))
+    (np.float64(1.0), array([1., 0.]))
 
     """