MAINT: bump Python, make tests robust to tile changes (#264)

* bump python

* test robust to tile changes

* update expected value

* another one

Author: martinfleis

.github/workflows/tests.yaml

@@ -20,17 +20,17 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         environment-file:
-          - ci/envs/39-minimal.yaml
           - ci/envs/310-conda-forge.yaml
           - ci/envs/311-conda-forge.yaml
           - ci/envs/312-latest-conda-forge.yaml
+          - ci/envs/313-latest-conda-forge.yaml
         include:
           - os: macos-13
-            environment-file: ci/envs/312-latest-conda-forge.yaml
+            environment-file: ci/envs/313-latest-conda-forge.yaml
           - os: macos-latest # apple silicon
-            environment-file: ci/envs/312-latest-conda-forge.yaml
+            environment-file: ci/envs/313-latest-conda-forge.yaml
           - os: windows-latest
-            environment-file: ci/envs/312-latest-conda-forge.yaml
+            environment-file: ci/envs/313-latest-conda-forge.yaml
     defaults:
       run:
         shell: bash -l {0}

README.md

@@ -1,6 +1,6 @@
 # `contextily`: context geo tiles in Python
 
-`contextily` is a small Python 3 (3.9 and above) package to retrieve tile maps from the
+`contextily` is a small Python 3 (3.10 and above) package to retrieve tile maps from the
 internet. It can add those tiles as basemap to matplotlib figures or write tile
 maps to disk into geospatial raster files. Bounding boxes can be passed in both
 WGS84 (`EPSG:4326`) and Spheric Mercator (`EPSG:3857`). See the notebook
@@ -33,7 +33,7 @@ package. This includes some popular tile maps, such as:
 
 ## Installation
 
-**Python 3 only** (3.9 and above)
+**Python 3 only** (3.10 and above)
 
 [Latest released version](https://github.com/geopandas/contextily/releases/), using pip:
 

ci/envs/39-minimal.yaml ci/envs/313-latest-conda-forge.yamlci/envs/39-minimal.yaml renamed to ci/envs/313-latest-conda-forge.yaml

@@ -2,7 +2,7 @@ name: test-environment
 channels:
   - conda-forge
 dependencies:
-  - python=3.9
+  - python=3.13
   # required
   - geopy
   - matplotlib
@@ -15,4 +15,4 @@ dependencies:
   # testing
   - pip
   - pytest
-  - pytest-cov
+  - pytest-cov

pyproject.toml

@@ -19,9 +19,13 @@ readme = "README.md"
 classifiers = [
     "License :: OSI Approved :: BSD License",
     "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Framework :: Matplotlib",
 ]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
     "geopy",
     "matplotlib",

tests/test_cx.py

@@ -39,9 +39,10 @@ def test_bounds2raster():
     )
     for i, j in zip(rtr.bounds, solu):
         assert round(i - j, TOL) == 0
-    assert img[0, 100, :].tolist() == [250, 250, 248, 255]
-    assert img[20, 120, :].tolist() == [139, 153, 164, 255]
-    assert img[200, 100, :].tolist() == [250, 250, 248, 255]
+    # Check approximate pixel values instead of exact matches for robustness
+    assert np.allclose(img[0, 100, :], [250, 250, 248, 255], atol=10)
+    assert np.allclose(img[20, 120, :], [139, 153, 164, 255], atol=10)
+    assert np.allclose(img[200, 100, :], [250, 250, 248, 255], atol=10)
     assert img[:, :, :3].sum() == pytest.approx(47622796, rel=0.1)
     assert img.sum() == pytest.approx(64334476, rel=0.1)
     assert_array_almost_equal(img[:, :, :3].mean(), 242.2220662434896, decimal=0)
@@ -105,9 +106,10 @@ def test_bounds2img(n_connections):
         )
         for i, j in zip(ext, solu):
             assert round(i - j, TOL) == 0
-        assert img[0, 100, :].tolist() == [250, 250, 248, 255]
-        assert img[20, 120, :].tolist() == [139, 153, 164, 255]
-        assert img[200, 100, :].tolist() == [250, 250, 248, 255]
+        # Check approximate pixel values instead of exact matches for robustness
+        assert np.allclose(img[0, 100, :], [250, 250, 248, 255], atol=10)
+        assert np.allclose(img[20, 120, :], [139, 153, 164, 255], atol=10)
+        assert np.allclose(img[200, 100, :], [250, 250, 248, 255], atol=10)
     elif n_connections == 0:  # no connections should raise an error
         with pytest.raises(ValueError):
             img, ext = cx.bounds2img(
@@ -138,9 +140,10 @@ def test_warp_tiles():
             ]
         ),
     )
-    assert wimg[100, 100, :].tolist() == [249, 249, 247, 255]
-    assert wimg[100, 200, :].tolist() == [250, 250, 248, 255]
-    assert wimg[20, 120, :].tolist() == [250, 250, 248, 255]
+    # Check approximate pixel values instead of exact matches for robustness
+    assert np.allclose(wimg[100, 100, :], [249, 249, 247, 255], atol=10)
+    assert np.allclose(wimg[100, 200, :], [250, 250, 248, 255], atol=10)
+    assert np.allclose(wimg[20, 120, :], [250, 250, 248, 255], atol=10)
 
 
 @pytest.mark.network
@@ -157,9 +160,10 @@ def test_warp_img_transform():
     rtr = rio.open("test.tif")
     img = np.array([band for band in rtr.read()])
     wimg, _ = cx.warp_img_transform(img, rtr.transform, rtr.crs, "epsg:4326")
-    assert wimg[:, 100, 100].tolist() == [249, 249, 247, 255]
-    assert wimg[:, 100, 200].tolist() == [250, 250, 248, 255]
-    assert wimg[:, 20, 120].tolist() == [250, 250, 248, 255]
+    # Check approximate pixel values instead of exact matches for robustness
+    assert np.allclose(wimg[:, 100, 100], [249, 249, 247, 255], atol=10)
+    assert np.allclose(wimg[:, 100, 200], [250, 250, 248, 255], atol=10)
+    assert np.allclose(wimg[:, 20, 120], [250, 250, 248, 255], atol=10)
 
 
 def test_howmany():
@@ -369,7 +373,7 @@ def test_add_basemap_query():
     ax_extent = (x1, x2, y1, y2)
     assert ax.axis() == ax_extent
 
-    assert ax.images[0].get_array().sum() == 64685390
+    assert ax.images[0].get_array().sum() == pytest.approx(64685390, rel=0.1)
     assert ax.images[0].get_array().shape == (256, 256, 4)
     assert_array_almost_equal(
         ax.images[0].get_array()[:, :, :3].mean(), 244.03656, decimal=0
@@ -451,8 +455,8 @@ def test_add_basemap_auto_zoom():
                 4852834.0517692715,
                 4891969.810251278,
             ),
-            763769618,
-            1031156498,
+            764077703,
+            1031464583,
             (1024, 1024, 4),
         ),
         # zoom_adjust and expected values where zoom_adjust == -1