old numpy nan

knaaptime · knaaptime · commit e77224c2c013 · 2024-11-05T11:20:20.000-08:00
diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
@@ -13,7 +13,7 @@
      strategy:
        matrix:
          os: ['ubuntu-latest']
-         environment-file: [.ci/39.yml]
+         environment-file: [ci/312.yml]
          experimental: [false]
      defaults:
        run:
diff --git a/.github/workflows/unittests.yml b/.github/workflows/unittests.yml
@@ -24,15 +24,15 @@
        matrix:
          os: [ubuntu-latest]
          environment-file:
-           - ci/310.yml
            - ci/311.yml
            - ci/312.yml
+           - ci/313.yml
          include:
-           - environment-file: ci/311.yml
+           - environment-file: ci/312.yml
              os: macos-latest
-           - environment-file: ci/311.yml
+           - environment-file: ci/312.yml
              os: macos-14  # Apple Silicon
-           - environment-file: ci/311.yml
+           - environment-file: ci/312.yml
              os: windows-latest
      defaults:
        run:
diff --git a/ci/313.yml b/ci/313.yml
@@ -2,7 +2,7 @@ name: test
 channels:
   - conda-forge
 dependencies:
-  - python=3.10
+  - python=3.13
   - deprecation
   - geopandas>=0.9
   - joblib
diff --git a/environment.yml b/environment.yml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python=3.9
+  - python>=3.12
   - deprecation
   - geopandas>=0.9
   - joblib
diff --git a/pyproject.toml b/pyproject.toml
@@ -27,7 +27,7 @@ classifiers = [
     "Intended Audience :: Science/Research",
     "Topic :: Scientific/Engineering :: GIS",
 ]
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 dependencies = [
 "deprecation",
 "geopandas>=0.9",
diff --git a/segregation/singlegroup/absolute_centralization.py b/segregation/singlegroup/absolute_centralization.py
@@ -143,8 +143,8 @@ def _absolute_centralization(
     Xi = np.cumsum(x[asc_ind]) / X
     Ai = np.cumsum(area[asc_ind]) / A
 
-    ACE = np.nansum(shift(Xi, 1, cval=np.NaN) * Ai) - np.nansum(
-        Xi * shift(Ai, 1, cval=np.NaN)
+    ACE = np.nansum(shift(Xi, 1, cval=np.nan) * Ai) - np.nansum(
+        Xi * shift(Ai, 1, cval=np.nan)
     )
 
     core_data = data[[group_pop_var, total_pop_var, data.geometry.name]]
diff --git a/segregation/singlegroup/relative_centralization.py b/segregation/singlegroup/relative_centralization.py
@@ -145,8 +145,8 @@ def _relative_centralization(
     Xi = np.cumsum(x[asc_ind]) / X
     Yi = np.cumsum(y[asc_ind]) / Y
 
-    RCE = np.nansum(shift(Xi, 1, cval=np.NaN) * Yi) - np.nansum(
-        Xi * shift(Yi, 1, cval=np.NaN)
+    RCE = np.nansum(shift(Xi, 1, cval=np.nan) * Yi) - np.nansum(
+        Xi * shift(Yi, 1, cval=np.nan)
     )
 
     core_data = data[[group_pop_var, total_pop_var, data.geometry.name]]

Original file line number	Diff line number	Diff line change
`@@ -27,7 +27,7 @@ classifiers = [`
`27`	`27`	`"Intended Audience :: Science/Research",`
`28`	`28`	`"Topic :: Scientific/Engineering :: GIS",`
`29`	`29`	`]`
`30`		`-requires-python = ">=3.8"`
	`30`	`+requires-python = ">=3.9"`
`31`	`31`	`dependencies = [`
`32`	`32`	`"deprecation",`
`33`	`33`	`"geopandas>=0.9",`
Original file line number	Diff line number	Diff line change
`@@ -143,8 +143,8 @@ def _absolute_centralization(`
`143`	`143`	`Xi = np.cumsum(x[asc_ind]) / X`
`144`	`144`	`Ai = np.cumsum(area[asc_ind]) / A`
`145`	`145`
`146`		`- ACE = np.nansum(shift(Xi, 1, cval=np.NaN) * Ai) - np.nansum(`
`147`		`- Xi * shift(Ai, 1, cval=np.NaN)`
	`146`	`+ ACE = np.nansum(shift(Xi, 1, cval=np.nan) * Ai) - np.nansum(`
	`147`	`+ Xi * shift(Ai, 1, cval=np.nan)`
`148`	`148`	`)`
`149`	`149`
`150`	`150`	`core_data = data[[group_pop_var, total_pop_var, data.geometry.name]]`
Original file line number	Diff line number	Diff line change
`@@ -145,8 +145,8 @@ def _relative_centralization(`
`145`	`145`	`Xi = np.cumsum(x[asc_ind]) / X`
`146`	`146`	`Yi = np.cumsum(y[asc_ind]) / Y`
`147`	`147`
`148`		`- RCE = np.nansum(shift(Xi, 1, cval=np.NaN) * Yi) - np.nansum(`
`149`		`- Xi * shift(Yi, 1, cval=np.NaN)`
	`148`	`+ RCE = np.nansum(shift(Xi, 1, cval=np.nan) * Yi) - np.nansum(`
	`149`	`+ Xi * shift(Yi, 1, cval=np.nan)`
`150`	`150`	`)`
`151`	`151`
`152`	`152`	`core_data = data[[group_pop_var, total_pop_var, data.geometry.name]]`