small updates

gridfinder/gridfinder.py

@@ -3,23 +3,21 @@
 """
 
 import pickle
-import sys
 from heapq import heapify, heappop, heappush
 from math import sqrt
-from pathlib import Path
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Tuple
 
 import numba as nb
 import numpy as np
 import rasterio
 from affine import Affine
 
-sys.setrecursionlimit(100000)
+from gridfinder.util import Pathy
 
 
 def get_targets_costs(
-    targets_in: Union[str, Path],
-    costs_in: Union[str, Path],
+    targets_in: Pathy,
+    costs_in: Pathy,
 ) -> Tuple[np.ndarray, np.ndarray, Tuple[int, int], Affine]:
     """Load the targets and costs arrays from the given file paths.
 

gridfinder/post.py

@@ -2,7 +2,6 @@
 Post-processing for gridfinder package.
 """
 
-from pathlib import Path
 from typing import Optional, Tuple, Union
 
 import geopandas as gpd
@@ -16,9 +15,11 @@
 from shapely.geometry import LineString, Point
 from skimage.morphology import skeletonize
 
+from gridfinder.util import Pathy
+
 
 def threshold(
-    dists_in: Union[str, Path, np.ndarray], cutoff: float = 0.0
+    dists_in: Union[Pathy, np.ndarray], cutoff: float = 0.0
 ) -> Tuple[np.ndarray, Optional[Affine]]:
     """Convert distance array into binary array of connected locations.
 
@@ -51,7 +52,7 @@ def threshold(
         return guess, affine
 
 
-def thin(guess_in: Union[str, Path, np.ndarray]) -> Tuple[np.ndarray, Optional[Affine]]:
+def thin(guess_in: Union[Pathy, np.ndarray]) -> Tuple[np.ndarray, Optional[Affine]]:
     """
     Use scikit-image skeletonize to 'thin' the guess raster.
 
@@ -80,7 +81,7 @@ def thin(guess_in: Union[str, Path, np.ndarray]) -> Tuple[np.ndarray, Optional[A
         return guess_skel, affine
 
 
-def raster_to_lines(guess_skel_in: Union[str, Path]) -> gpd.GeoDataFrame:
+def raster_to_lines(guess_skel_in: Pathy) -> gpd.GeoDataFrame:
     """
     Convert thinned raster to linestring geometry.
 
@@ -150,9 +151,9 @@ def raster_to_lines(guess_skel_in: Union[str, Path]) -> gpd.GeoDataFrame:
 
 
 def accuracy(
-    grid_in: Union[str, Path],
-    guess_in: Union[str, Path],
-    aoi_in: Union[str, Path],
+    grid_in: Pathy,
+    guess_in: Pathy,
+    aoi_in: Pathy,
     buffer_amount: float = 0.01,
 ) -> Tuple[float, float]:
     """Measure accuracy against a specified grid 'truth' file.
@@ -172,7 +173,7 @@ def accuracy(
         aoi = gpd.read_file(aoi_in)
 
     grid_masked = gpd.read_file(grid_in, mask=aoi)
-    grid = gpd.sjoin(grid_masked, aoi, how="inner", op="intersects")
+    grid = gpd.sjoin(grid_masked, aoi, how="inner", predicate="intersects")
     grid = grid[grid_masked.columns]
 
     grid_buff = grid.buffer(buffer_amount)

gridfinder/prepare.py

@@ -19,13 +19,13 @@
 from rasterio.warp import Resampling, reproject
 from scipy import signal
 
-from gridfinder.util import clip_raster, save_raster
+from gridfinder.util import Pathy, clip_raster, save_raster
 
 
 def clip_rasters(
-    folder_in: Union[str, Path],
-    folder_out: Union[str, Path],
-    aoi_in: Union[str, Path],
+    folder_in: Pathy,
+    folder_out: Pathy,
+    aoi_in: Pathy,
     debug: bool = False,
 ) -> None:
     """Read continental rasters one at a time, clip to AOI and save
@@ -58,7 +58,7 @@ def clip_rasters(
 
 
 def merge_rasters(
-    folder: Union[str, Path],
+    folder: Pathy,
     percentile: int = 70,
 ) -> Tuple[np.ndarray, Optional[Affine]]:
     """Merge a set of monthly rasters keeping the nth percentile value.
@@ -119,8 +119,8 @@ def create_filter() -> np.ndarray:
 
 
 def prepare_ntl(
-    ntl_in: Union[str, Path],
-    aoi_in: Union[str, Path],
+    ntl_in: Pathy,
+    aoi_in: Pathy,
     ntl_filter: Optional[np.ndarray] = None,
     threshold: float = 0.1,
     upsample_by: int = 2,
@@ -204,9 +204,9 @@ def prepare_ntl(
 
 
 def drop_zero_pop(
-    targets_in: Union[str, Path],
-    pop_in: Union[str, Path],
-    aoi: Union[str, Path, GeoDataFrame],
+    targets_in: Pathy,
+    pop_in: Pathy,
+    aoi: Union[Pathy, GeoDataFrame],
 ) -> np.ndarray:
     """Drop electrified cells with no other evidence of human activity.
 
@@ -297,9 +297,9 @@ def add_around(blob: list, cell: Tuple[int, int]) -> list:
 
 
 def prepare_roads(
-    roads_in: Union[str, Path],
-    aoi_in: Union[str, Path, GeoDataFrame],
-    ntl_in: Union[str, Path],
+    roads_in: Pathy,
+    aoi_in: Union[Pathy, GeoDataFrame],
+    ntl_in: Pathy,
 ) -> Tuple[np.ndarray, Affine]:
     """Prepare a roads feature layer for use in algorithm.
 
@@ -327,10 +327,10 @@ def prepare_roads(
         aoi = gpd.read_file(aoi_in)
 
     roads_masked = gpd.read_file(roads_in, mask=aoi)
-    roads = gpd.sjoin(roads_masked, aoi, how="inner", op="intersects")
+    roads = gpd.sjoin(roads_masked, aoi, how="inner", predicate="intersects")
     roads = roads[roads_masked.columns]
 
-    roads["weight"] = 1
+    roads["weight"] = 1.0
     roads.loc[roads["highway"] == "motorway", "weight"] = 1 / 10
     roads.loc[roads["highway"] == "trunk", "weight"] = 1 / 9
     roads.loc[roads["highway"] == "primary", "weight"] = 1 / 8

gridfinder/util.py

@@ -15,9 +15,11 @@
 from rasterio.io import DatasetReader
 from rasterio.mask import mask
 
+Pathy = Union[str, Path]
+
 
 def save_raster(
-    path: Union[str, Path],
+    path: Pathy,
     raster: np.ndarray,
     affine: Affine,
     crs: Optional[Union[str, Proj]] = None,
@@ -57,8 +59,8 @@ def save_raster(
 
 
 def clip_raster(
-    raster: Union[str, Path, DatasetReader],
-    boundary: Union[str, Path, GeoDataFrame],
+    raster: Union[Pathy, DatasetReader],
+    boundary: Union[Pathy, GeoDataFrame],
     boundary_layer: Optional[str] = None,
 ) -> Tuple[
     np.ndarray,