Bunch of Fixes

.github/workflows/codecov.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: [3.9]
+        python: [3.12]
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python }}
@@ -31,8 +31,9 @@ jobs:
       run: |
         NUMBA_DISABLE_JIT=1 pytest --cov=./ --cov-report=xml --ignore ./xrspatial/tests/test_polygonize.py
     - name: Upload coverage to Codecov
-      uses: codecov/codecov-action@v2
+      uses: codecov/codecov-action@v3
       with:
+        token: ${{ secrets.CODECOV_TOKEN }}
         env_vars: OS,PYTHON
         fail_ci_if_error: true
         verbose: true

.github/workflows/test.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         os: ['ubuntu-latest', 'macos-latest', 'windows-latest']
-        python: [3.8, 3.9, '3.10', 3.11]
+        python: ['3.10', 3.11, 3.12]
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python }}

README.md

@@ -13,10 +13,8 @@
   <td>Downloads</td>
   <td>
     <div>
-    <a href="https://xarray-spatial.org/getting_started/installation.html">
     <img src="https://img.shields.io/pypi/dm/xarray-spatial?label=PyPI"
          alt="PyPI downloads per month" />
-    </a>
     </div>
   </td>
 </tr>
@@ -123,9 +121,6 @@ In all the above, the command will download and store the files into your curren
 `xarray-spatial` does not depend on GDAL / GEOS, which makes it fully extensible in Python but does limit the breadth of operations that can be covered.  xarray-spatial is meant to include the core raster-analysis functions needed for GIS developers / analysts, implemented independently of the non-Python geo stack.
 
 
-Our documentation is still under construction, but [docs can be found here](https://xarray-spatial.org/).
-
-
 #### Raster-huh?
 
 Rasters are regularly gridded datasets like GeoTIFFs, JPGs, and PNGs.
@@ -283,4 +278,4 @@ With the introduction of projects like Numba, Python gained new ways to provide
 #### Citation
 Cite our code:
 
-`makepath/xarray-spatial, https://github.com/makepath/xarray-spatial, ©2020-2023.`
+`makepath/xarray-spatial, https://github.com/makepath/xarray-spatial, ©2020-2024.`

requirements.txt

@@ -1,4 +1,5 @@
 datashader
+dask[dataframe]
 noise >=1.2.2
 numba
 xarray

xrspatial/experimental/polygonize.py

@@ -39,6 +39,33 @@
 _visited_dtype = np.uint8
 
 
+def generated_jit(function=None, cache=False,
+                  pipeline_class=None, **options):
+    """
+    This decorator allows flexible type-based compilation
+    of a jitted function.  It works as `@jit`, except that the decorated
+    function is called at compile-time with the *types* of the arguments
+    and should return an implementation function for those types.
+    """
+    from numba.extending import overload
+    jit_options = dict()
+    if pipeline_class is not None:
+        jit_options['pipeline_class'] = pipeline_class
+    jit_options['cache'] = cache
+    jit_options |= options
+
+    if function is not None:
+        overload(function, jit_options=jit_options,
+                    strict=False)(function)
+        return function
+    else:
+        def wrapper(func):
+            overload(func, jit_options=jit_options,
+                        strict=False)(func)
+            return func
+        return wrapper
+
+
 class Turn(Enum):
     Left = -1
     Straight = 0
@@ -183,7 +210,7 @@ def _follow(
 # Generator of numba-compatible comparison functions for values.
 # If both values are integers use a fast equality operator, otherwise use a
 # slower floating-point comparison like numpy.isclose.
-@nb.generated_jit(nogil=True, nopython=True)
+@generated_jit(nogil=True, nopython=True)
 def _is_close(
     reference: Union[int, float],
     value: Union[int, float],