shapely v2; rtree syntax; temp ops.substring fix

cityseer/algos/centrality.py

@@ -587,9 +587,7 @@ def local_segment_centrality(
                         # i.e. exit impedance minus half segment impedance
                         ang = m_simpl_dist - seg_ang / 2
                     # 2) f is the predecessor for e
-                    elif (
-                        m_nd_idx == src_idx or preds[n_nd_idx] == m_nd_idx  # pylint: disable=consider-using-in
-                    ):  # pylint: disable=consider-using-in
+                    elif m_nd_idx == src_idx or preds[n_nd_idx] == m_nd_idx:  # pylint: disable=consider-using-in
                         e = short_dist[m_nd_idx]
                         f = short_dist[n_nd_idx]
                         ang = n_simpl_dist - seg_ang / 2  # per above

cityseer/algos/common.py

@@ -1,5 +1,3 @@
-# pylint: disable=duplicate-code
-
 from __future__ import annotations
 
 import numpy as np

cityseer/algos/data.py

@@ -562,9 +562,7 @@ def mixed_uses(
     qs: npt.NDArray[np.float32] = np.array([], dtype=np.float32),
     mixed_use_hill_keys: npt.NDArray[np.int_] = np.array([], dtype=np.int_),
     mixed_use_other_keys: npt.NDArray[np.int_] = np.array([], dtype=np.int_),
-    cl_disparity_wt_matrix: npt.NDArray[np.float32] = np.array(
-        np.full((0, 0), np.nan), dtype=np.float32
-    ),  # pylint: disable=line-too-long
+    cl_disparity_wt_matrix: npt.NDArray[np.float32] = np.array(np.full((0, 0), np.nan), dtype=np.float32),
     jitter_scale: np.float32 = np.float32(0.0),
     angular: bool = False,
     progress_proxy: Optional[Any] = None,
@@ -960,7 +958,6 @@ def aggregate_stats(
                     stats_count_wt[num_idx, d_idx, netw_src_idx],
                 )
 
-    # pylint: disable=duplicate-code
     return (
         stats_sum,
         stats_sum_wt,

cityseer/cctypes.py

@@ -9,7 +9,7 @@
     int, float, list[Union[int, float]], tuple[Union[int, float]], npt.NDArray[Union[np.int_, np.float32]]
 ]
 BetasType = Union[float, list[float], tuple[float], npt.NDArray[np.float32]]
-QsType = Union[  # pylint: disable=invalid-name
+QsType = Union[
     int,
     float,
     Union[list[int], list[float]],

cityseer/metrics/layers.py

@@ -265,7 +265,6 @@ def compute_accessibilities(
     # determine max impedance weights
     max_curve_wts = networks.clip_weights_curve(_distances, _betas, spatial_tolerance)
     # call the underlying function
-    # pylint: disable=duplicate-code
     accessibility_data, accessibility_data_wt = data.accessibility(
         network_structure.nodes.xs,
         network_structure.nodes.ys,
@@ -524,25 +523,23 @@ def compute_mixed_uses(
     # extrapolate the requested mixed use measures
     mu_hill_keys: list[int] = []
     mu_other_keys: list[int] = []
-    if mixed_use_keys is not None:
-        for mu in mixed_use_keys:
-            if mu not in mixed_uses_options:
-                raise ValueError(f'Invalid mixed-use option: {mu}. Must be one of {", ".join(mixed_uses_options)}.')
-            idx = mixed_uses_options.index(mu)
-            if idx < 4:
-                mu_hill_keys.append(idx)
-            else:
-                mu_other_keys.append(idx - 4)
-        if not config.QUIET_MODE:
-            logger.info(f'Computing mixed-use measures: {", ".join(mixed_use_keys)}')
+    for mu in mixed_use_keys:
+        if mu not in mixed_uses_options:
+            raise ValueError(f'Invalid mixed-use option: {mu}. Must be one of {", ".join(mixed_uses_options)}.')
+        idx = mixed_uses_options.index(mu)
+        if idx < 4:
+            mu_hill_keys.append(idx)
+        else:
+            mu_other_keys.append(idx - 4)
+    if not config.QUIET_MODE:
+        logger.info(f'Computing mixed-use measures: {", ".join(mixed_use_keys)}')
     if not config.QUIET_MODE:
         progress_proxy = ProgressBar(update_interval=0.25, notebook=False, total=network_structure.nodes.count)
     else:
         progress_proxy = None
     # determine max impedance weights
     max_curve_wts = networks.clip_weights_curve(_distances, _betas, spatial_tolerance)
     # call the underlying function
-    # pylint: disable=duplicate-code
     mixed_use_hill_data, mixed_use_other_data = data.mixed_uses(
         network_structure.nodes.xs,
         network_structure.nodes.ys,
@@ -941,7 +938,6 @@ def compute_stats(
         progress_proxy = ProgressBar(update_interval=0.25, notebook=False, total=network_structure.nodes.count)
     else:
         progress_proxy = None
-    # pylint: disable=duplicate-code
     (
         stats_sum,
         stats_sum_wt,

cityseer/metrics/networks.py

@@ -79,8 +79,6 @@
 
 def _cast_beta(beta: cctypes.BetasType) -> npt.NDArray[np.float32]:
     """Type checks and casts beta parameter to a numpy array of beta."""
-    if beta is None:
-        raise TypeError("Expected beta but encountered None value.")
     if not isinstance(beta, (list, tuple, np.ndarray)):
         beta = [beta]
     if len(beta) == 0:
@@ -181,8 +179,6 @@ def _cast_distance(
     distance: cctypes.DistancesType,
 ) -> npt.NDArray[np.int_]:
     """Type checks and casts distance parameter to a numpy array of distance."""
-    if distance is None:
-        raise TypeError("Expected distance but encountered None value.")
     # cast to list form
     if isinstance(distance, (int, float)):
         distance = [distance]
@@ -483,17 +479,26 @@ def node_centrality(
     | node_harmonic         | $$\sum_{j\neq{i}}^{n}\frac{1}{Z_{(i,j)}}$$ | Harmonic closeness is an appropriate form
     of closeness centrality for localised implementations constrained by the threshold $d_{max}$. |
     | node_beta             | $$\sum_{j\neq{i}}^{n}\exp(-\beta\cdot d[i,j])$$ | Also known as the gravity index.
-    This is a spatial impedance metric differentiated from other closeness centralities by the use of an explicit $\beta$ parameter, which can be used to model the decay in walking tolerance as distances increase. |  # pylint: disable=line-too-long
-    | node_betweenness      | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. |  # pylint: disable=line-too-long
-    | node_betweenness_beta | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}\exp(-\beta\cdot d[j,k])$$ | Applies a spatial impedance decay function to betweenness centrality. $d$ represents the full distance from any $j$ to $k$ node pair passing through node $i$. |  # pylint: disable=line-too-long
+    This is a spatial impedance metric differentiated from other closeness centralities by the use of an
+    explicit $\beta$ parameter, which can be used to model the decay in walking tolerance as distances
+    increase. |
+    | node_betweenness      | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all
+    shortest-paths traversing each node $i$. |
+    | node_betweenness_beta | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}\exp(-\beta\cdot d[j,k])$$ | Applies a
+    spatial impedance decay function to betweenness centrality. $d$ represents the full distance from
+    any $j$ to $k$ node pair passing through node $i$. |
 
     The following keys use the simplest-path (shortest-angular-path) heuristic, and are available when the `angular`
     parameter is explicitly set to `True`:
 
     | key                      | formula | notes |
     | ------------------------ | :-----: | ----- |
-    | node_harmonic_angular    | $$\sum_{j\neq{i}}^{n}\frac{1}{Z_{(i,j)}}$$ | The simplest-path implementation of harmonic closeness uses angular-distances for the impedance parameter. Angular-distances are normalised by 180 and added to 1 to avoid division by zero: ${Z = 1 + (angularchange/180)}$. |  # pylint: disable=line-too-long
-    | node_betweenness_angular | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | The simplest-path version of betweenness centrality. This is distinguished from the shortest-path version by use of a simplest-path heuristic (shortest angular distance). |  # pylint: disable=line-too-long
+    | node_harmonic_angular    | $$\sum_{j\neq{i}}^{n}\frac{1}{Z_{(i,j)}}$$ | The simplest-path implementation of
+    harmonic closeness uses angular-distances for the impedance parameter. Angular-distances are normalised by 180 and
+    added to 1 to avoid division by zero: ${Z = 1 + (angularchange/180)}$. |
+    | node_betweenness_angular | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | The simplest-path version of
+    betweenness centrality. This is distinguished from the shortest-path version by use of a simplest-path heuristic
+    (shortest angular distance). |
 
     """
     network_structure.validate()
@@ -502,7 +507,6 @@ def node_centrality(
     # typos are caught below
     if not angular:
         heuristic = "shortest (non-angular)"
-        # pylint: disable=duplicate-code
         options: tuple[str, ...] = (
             "node_density",
             "node_farness",
@@ -515,7 +519,7 @@ def node_centrality(
     else:
         heuristic = "simplest (angular)"
         options = ("node_harmonic_angular", "node_betweenness_angular")
-    if measures is None:
+    if not measures:
         raise ValueError("Please select at least one measure to compute.")
     prep_measure_keys: list[str] = []
     for measure in measures:
@@ -653,7 +657,7 @@ def segment_centrality(
     else:
         heuristic = "simplest (angular)"
         options = ("segment_harmonic_hybrid", "segment_betweeness_hybrid")
-    if measures is None:
+    if not measures:
         raise ValueError("Please select at least one measure to compute.")
     prep_measure_keys: list[str] = []
     for measure in measures:

cityseer/metrics/observe.py

@@ -150,7 +150,7 @@ def _recurse_edges(
     nested_edge_data: graphs.EdgeData = _nx_multigraph[_a_nd_key][_b_nd_key][_edge_idx]
     if _method not in nested_edge_data:
         raise ValueError(f"Missing target key of {_method}")
-    nested_match_targets: list[str] = nested_edge_data[_method]
+    nested_match_targets: list[str | None] = nested_edge_data[_method]
     nested_match_targets = [nmt.lower() for nmt in nested_match_targets if nmt is not None]
     # bail if this edge info (e.g. street name) doesn't intersect with the previous
     if not _match_target.lower() in nested_match_targets:
@@ -235,7 +235,7 @@ def street_continuity(
         if method not in edge_data:
             raise ValueError(f"Could not find {method} in edge data for edge: {a_nd_key} - {b_nd_key} idx: {edge_idx}")
         # get target values, e.g. street names
-        match_targets: list[str] = edge_data[method]
+        match_targets: list[str | None] = edge_data[method]
         match_targets = [mt.lower() for mt in match_targets if mt is not None]
         # can be multiple values, e.g. route numbers per edge, so explore individually
         for match_target in match_targets:

cityseer/structures.py

@@ -416,7 +416,7 @@ def set_data_point(self, data_idx: int, data_x: np.float32, data_y: np.float32,
             raise ValueError(f'Expecting int for "data_id" but encountered {type(data_id)}')
         self.xs[data_idx] = data_x
         self.ys[data_idx] = data_y
-        if data_id is not None:
+        if data_id:
             self.data_id[data_idx] = data_id
 
     def x_y(self, data_idx: int) -> npt.NDArray[np.float32]: