Update & test min reqs + handling upstream deprecation warnings (#169)

* resolves #162, etc.

* minimum environment testing

* add min testing to workflow

* matplotlib==2.2.4

* update min mpl vers & give envs BASE flag

* min pins based on NEP 29

* min pin geopandas 0.9.0

.github/workflows/unittests.yml

@@ -32,15 +32,16 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         environment-file:
-          - ci/38.yaml
-          - ci/39.yaml
-          - ci/310.yaml
+          - ci/38-MIN.yaml
+          - ci/38-BASE.yaml
+          - ci/39-BASE.yaml
+          - ci/310-BASE.yaml
           - ci/310-DEV.yaml
           - ci/310-DEV_shapely_dev.yaml
         include:
-          - environment-file: ci/310.yaml
+          - environment-file: ci/310-BASE.yaml
             os: macos-latest
-          - environment-file: ci/310.yaml
+          - environment-file: ci/310-BASE.yaml
             os: windows-latest
       fail-fast: false
 

README.md

@@ -22,7 +22,7 @@ If you are new to `splot` and `PySAL` you will best get started with our [docume
 
 ### Installing dependencies
 
-`splot` is compatible with `Python` 3.8+ and depends on `geopandas` 0.4.0 or later and `matplotlib` 2.2.2 or later.
+`splot` is compatible with `Python` 3.8+ and depends on `geopandas` 0.9.0 or later and `matplotlib` 3.3.3 or later.
 
 splot also uses
 

ci/310.yaml → ci/310-BASE.yaml

@@ -12,11 +12,12 @@ dependencies:
   - esda
   - geopandas
   - giddy
-  # - ipywidgets
+  - ipywidgets
   - libpysal
   - mapclassify
   - matplotlib
   - numpy
+  - packaging
   - pip
   - seaborn>=0.11.0
   - spreg

ci/310-DEV.yaml

@@ -9,10 +9,11 @@ dependencies:
   - pytest-cov
   # required
   - bokeh
-  - geopandas>=0.4.0
+  - geopandas
   - ipywidgets
   - matplotlib
   - numpy
+  - packaging
   - pip
   - seaborn>=0.11.0
   # formatting

ci/310-DEV_shapely_dev.yaml

@@ -14,8 +14,9 @@ dependencies:
   - ipywidgets
   - matplotlib
   - numpy
+  - packaging
   - pip
-  - seaborn>=0.11.0
+  - seaborn
   - shapely>=2.0b1
   # formatting
   - black

ci/38.yaml → ci/38-BASE.yaml

@@ -10,13 +10,14 @@ dependencies:
   # required
   - bokeh
   - esda
-  - geopandas>=0.4.0
+  - geopandas>=0.9.0
   - giddy
   - ipywidgets
   - libpysal
   - mapclassify
-  - matplotlib
+  - matplotlib>=3.3.3
   - numpy
+  - packaging
   - pip
   - seaborn>=0.11.0
   - spreg

ci/38-MIN.yaml

@@ -0,0 +1,27 @@
+name: test
+channels:
+  - conda-forge
+dependencies:
+  - python=3.8
+  # testing
+  - codecov
+  - pytest
+  - pytest-cov
+  # required
+  - bokeh
+  - esda
+  - geopandas==0.9.0
+  - giddy
+  - libpysal
+  - mapclassify
+  - matplotlib==3.3.3
+  - numpy
+  - packaging
+  - pip
+  - seaborn==0.11.0
+  - spreg
+  # formatting
+  - black
+  - flake8
+  - isort
+  - pyflakes

ci/39.yaml → ci/39-BASE.yaml

@@ -10,13 +10,14 @@ dependencies:
   # required
   - bokeh
   - esda
-  - geopandas>=0.4.0
+  - geopandas
   - giddy
   - ipywidgets
   - libpysal
   - mapclassify
   - matplotlib
   - numpy
+  - packaging
   - pip
   - seaborn>=0.11.0
   - spreg

doc/installation.rst

@@ -7,7 +7,7 @@ Installing dependencies
 -----------------------
 
 `splot` is compatible with Python `3.8+` and
-depends on GeoPandas 0.4.0 or later and matplotlib 2.2.2 or later.
+depends on GeoPandas 0.9.0 or later and matplotlib 3.3.3 or later.
 Please make sure that you are operating in a Python 3 environment.
 
 splot also uses

requirements.txt

@@ -1,9 +1,10 @@
 esda
-geopandas>=0.4.0
+geopandas>=0.9.0
 giddy
 libpysal
 mapclassify
-matplotlib
+matplotlib>=3.3.3
 numpy
+packaging
 seaborn>=0.11.0
 spreg

splot/_viz_esda_mpl.py

@@ -143,8 +143,8 @@ def moran_scatterplot(
     elif isinstance(moran, Moran_BV):
         if p is not None:
             warnings.warn(
-                "`p` is only used for plotting `esda.moran.Moran_Local`\n"
-                "or `Moran_Local_BV` objects"
+                "`p` is only used for plotting `esda.moran.Moran_Local` "
+                "or `Moran_Local_BV` objects."
             )
         fig, ax = _moran_bv_scatterplot(
             moran_bv=moran,

splot/_viz_utils.py

@@ -1,8 +1,17 @@
 import mapclassify as classify
+import matplotlib
 import matplotlib as mpl
-import matplotlib.cm as cm
-import matplotlib.pyplot as plt
 import numpy as np
+from packaging.version import Version
+
+# isolate MPL version - GH#162
+MPL_36 = Version(matplotlib.__version__) >= Version("3.6")
+if MPL_36:
+    from matplotlib import colormaps as cm
+else:
+    import matplotlib.cm as cm
+    import matplotlib.pyplot as plt
+
 
 """
 Utility functions for lightweight visualizations in splot
@@ -301,9 +310,18 @@ def shift_colormap(  # noqa E302
         cdict["blue"].append((si, b, b))
         cdict["alpha"].append((si, a, a))
 
+    """
     new_cmap = mpl.colors.LinearSegmentedColormap(name, cdict)
     plt.register_cmap(cmap=new_cmap)
     return new_cmap
+    """
+
+    new_cmap = mpl.colors.LinearSegmentedColormap(name, cdict)
+    if MPL_36:
+        cm.register(new_cmap)
+    else:
+        plt.register_cmap(cmap=new_cmap)
+    return new_cmap
 
 
 # Utility #2 - truncate colorcap in order to grab only positive or negative portion
@@ -329,6 +347,7 @@ def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
     -------
     new_cmap : A new colormap that has been shifted.
     """
+
     if isinstance(cmap, str):
         cmap = cm.get_cmap(cmap)
 

splot/_viz_value_by_alpha_mpl.py

@@ -1,12 +1,21 @@
 import collections.abc
 
-import matplotlib.cm as cm
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib import colors, patches
+from packaging.version import Version
 
 from ._viz_utils import _classifiers, format_legend
 
+# isolate MPL version - GH#162
+MPL_36 = Version(matplotlib.__version__) >= Version("3.6")
+if MPL_36:
+    from matplotlib import colormaps as cm
+else:
+    import matplotlib.cm as cm
+
+
 """
 Creating Maps with splot
 * Value-by-Alpha maps

splot/tests/test_viz_esda_mpl.py

@@ -2,10 +2,10 @@
 import libpysal as lp
 import matplotlib.pyplot as plt
 import numpy as np
+import pytest
 from esda.moran import Moran, Moran_BV, Moran_BV_matrix, Moran_Local, Moran_Local_BV
 from libpysal import examples
 from libpysal.weights.contiguity import Queen
-from pytest import raises, warns
 
 from splot._viz_esda_mpl import (
     _moran_bv_scatterplot,
@@ -51,18 +51,25 @@ def test_moran_scatterplot():
     y = gdf["Donatns"].values
     w = Queen.from_dataframe(gdf)
     w.transform = "r"
+
     # Calculate `esda.moran` Objects
     moran = Moran(y, w)
     moran_bv = Moran_BV(y, x, w)
     moran_loc = Moran_Local(y, w)
     moran_loc_bv = Moran_Local_BV(y, x, w)
+
     # try with p value so points are colored or warnings apply
-    fig, _ = moran_scatterplot(moran, p=0.05, aspect_equal=False)
-    plt.close(fig)
+    with pytest.warns(UserWarning, match="`p` is only used for plotting"):
+        fig, _ = moran_scatterplot(moran, p=0.05, aspect_equal=False)
+        plt.close(fig)
+
     fig, _ = moran_scatterplot(moran_loc, p=0.05)
     plt.close(fig)
-    fig, _ = moran_scatterplot(moran_bv, p=0.05)
-    plt.close(fig)
+
+    with pytest.warns(UserWarning, match="`p` is only used for plotting"):
+        fig, _ = moran_scatterplot(moran_bv, p=0.05)
+        plt.close(fig)
+
     fig, _ = moran_scatterplot(moran_loc_bv, p=0.05)
     plt.close(fig)
 
@@ -211,8 +218,8 @@ def test_moran_loc_scatterplot():
     )
     plt.close(fig)
 
-    raises(ValueError, _moran_loc_scatterplot, moran_bv, p=0.5)
-    warns(
+    pytest.raises(ValueError, _moran_loc_scatterplot, moran_bv, p=0.5)
+    pytest.warns(
         UserWarning,
         _moran_loc_scatterplot,
         moran_loc,
@@ -253,30 +260,32 @@ def test_plot_local_autocorrelation():
     plt.close(fig)
 
     # also test with quadrant and mask
-    fig, _ = plot_local_autocorrelation(
-        moran_loc,
-        df,
-        "HOVAL",
-        p=0.05,
-        region_column="POLYID",
-        aspect_equal=False,
-        mask=["1", "2", "3"],
-        quadrant=1,
-    )
-    plt.close(fig)
+    with pytest.warns(UserWarning, match="Values in `mask` are not the same dtype"):
+        fig, _ = plot_local_autocorrelation(
+            moran_loc,
+            df,
+            "HOVAL",
+            p=0.05,
+            region_column="POLYID",
+            aspect_equal=False,
+            mask=["1", "2", "3"],
+            quadrant=1,
+        )
+        plt.close(fig)
 
     # also test with quadrant and mask
-    raises(
-        ValueError,
-        plot_local_autocorrelation,
-        moran_loc,
-        df,
-        "HOVAL",
-        p=0.05,
-        region_column="POLYID",
-        mask=["100", "200", "300"],
-        quadrant=1,
-    )
+    with pytest.warns(UserWarning, match="Values in `mask` are not the same dtype"):
+        pytest.raises(
+            ValueError,
+            plot_local_autocorrelation,
+            moran_loc,
+            df,
+            "HOVAL",
+            p=0.05,
+            region_column="POLYID",
+            mask=["100", "200", "300"],
+            quadrant=1,
+        )
 
 
 def test_moran_loc_bv_scatterplot():
@@ -296,8 +305,8 @@ def test_moran_loc_bv_scatterplot():
     fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv, p=0.05, aspect_equal=False)
     plt.close(fig)
 
-    raises(ValueError, _moran_loc_bv_scatterplot, moran_loc, p=0.5)
-    warns(
+    pytest.raises(ValueError, _moran_loc_bv_scatterplot, moran_loc, p=0.5)
+    pytest.warns(
         UserWarning,
         _moran_loc_bv_scatterplot,
         moran_loc_bv,

splot/tests/test_viz_giddy_mpl.py

@@ -5,7 +5,6 @@
 import pytest
 from libpysal import examples
 from libpysal.weights.contiguity import Queen
-from pytest import raises
 
 try:
     import ipywidgets  # noqa F401
@@ -64,7 +63,7 @@ def test_dynamic_lisa_rose():
     fig3, _ = dynamic_lisa_rose(rose, c="r")
     plt.close(fig3)
 
-    raises(ValueError, dynamic_lisa_rose, rose, attribute=y1, color="blue")
+    pytest.raises(ValueError, dynamic_lisa_rose, rose, attribute=y1, color="blue")
 
 
 def test_dynamic_lisa_vectors():