REF: make ipywidgets optional dependency

.github/workflows/unittests.yml

@@ -16,12 +16,7 @@ jobs:
     strategy:
       matrix:
         os: [macos-latest, ubuntu-latest, windows-latest]
-        environment-file: [ci/36-GIT.yaml, ci/36-PYPI.yaml, ci/37-GIT.yaml, ci/37-PYPI.yaml, ci/38-GIT.yaml, ci/38-PYPI.yaml]
-        exclude:
-           - environment-file: ci/36-PYPI.yaml
-             os: windows-latest
-           - environment-file: ci/36-GIT.yaml
-             os: windows-latest
+        environment-file: [ci/37-GIT.yaml, ci/37-PYPI.yaml, ci/38-GIT.yaml, ci/38-PYPI.yaml, ci/39-GIT.yaml, ci/39-PYPI.yaml]
     defaults:
        run:
          shell: bash -l {0}
@@ -59,4 +54,4 @@ jobs:
           token: ${{ secrets.CODECOV_TOKEN }}
           file: ./coverage.xml
           name: splot-codecov
- 
+

ci/37-PYPI.yaml

@@ -13,7 +13,7 @@
    - esda
    - geopandas>=0.4.0
    - giddy
-   - ipywidgets
+  #  - ipywidgets
    - libpysal
    - mapclassify
    - matplotlib

ci/36-GIT.yaml → ci/39-GIT.yaml

@@ -2,7 +2,7 @@
  channels:
    - conda-forge
  dependencies:
-   - python=3.6
+   - python=3.9
    # testing
    - codecov
    - pytest

ci/36-PYPI.yaml → ci/39-PYPI.yaml

@@ -2,7 +2,7 @@
  channels:
    - conda-forge
  dependencies:
-   - python=3.6
+   - python=3.9
    # testing
    - codecov
    - pytest

splot/_viz_giddy_mpl.py

@@ -3,8 +3,6 @@
 import matplotlib.pyplot as plt
 import matplotlib as mpl
 from esda.moran import Moran_Local
-import ipywidgets as widgets
-from ipywidgets import interact, fixed
 from giddy.directional import Rose
 
 from ._viz_utils import moran_hot_cold_spots
@@ -108,7 +106,7 @@ def dynamic_lisa_heatmap(rose, p=0.05, ax=None, **kwargs):
     >>> for year in range(1969, 2010):
     ...     income_table[str(year) + '_rel'] = (
     ...         income_table[str(year)] / income_table[str(year)].mean())
-    
+
     merge to one gdf
 
     >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name')
@@ -218,9 +216,9 @@ def dynamic_lisa_rose(rose, attribute=None, ax=None, **kwargs):
     >>> for year in range(1969, 2010):
     ...     income_table[str(year) + '_rel'] = (
     ...         income_table[str(year)] / income_table[str(year)].mean())
-    
+
     merge to one gdf
-    
+
     >>> gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name')
 
     retrieve spatial weights and data for two points in time
@@ -290,7 +288,7 @@ def dynamic_lisa_rose(rose, attribute=None, ax=None, **kwargs):
 def _add_arrow(line, position=None, direction='right', size=15, color=None):
     """
     add an arrow to a line.
-    
+
     Parameters
     ----------
     line:
@@ -404,7 +402,7 @@ def dynamic_lisa_vectors(rose, ax=None,
 
     xlim = [rose.Y.min(), rose.Y.max()]
     ylim = [rose.wY.min(), rose.wY.max()]
-    
+
     if 'c' in kwargs.keys():
         color = kwargs.pop('c', 'b')
         can_insert_colorbar = False
@@ -656,6 +654,13 @@ def dynamic_lisa_composite_explore(rose, gdf, pattern='',
     >>> # plt.show()
 
     """
+    try:
+        from ipywidgets import interact, fixed
+    except (ImportError, ModuleNotFoundError):
+        raise ImportError(
+            "`ipywidgets` package is required to use dynamic_lisa_composite_explore."
+            "You can install it using `conda install ipywidgets` or `pip install ipywidgets`."
+        )
     coldict = {col: col for col in gdf.columns if
                col.endswith(pattern)}
     interact(_dynamic_lisa_widget_update,

splot/tests/test_viz_bokeh.py

@@ -7,58 +7,68 @@
 from libpysal import examples
 import geopandas as gpd
 import esda
+import pytest
 
-from splot._bk import (plot_choropleth, lisa_cluster,
-                       moran_scatterplot, plot_local_autocorrelation)
+from splot._bk import (
+    plot_choropleth,
+    lisa_cluster,
+    moran_scatterplot,
+    plot_local_autocorrelation,
+)
 
 
+@pytest.mark.skip(reason="to be deprecated")
 def test_plot_choropleth():
-    link = examples.get_path('columbus.shp')
+    link = examples.get_path("columbus.shp")
     df = gpd.read_file(link)
 
     w = Queen.from_dataframe(df)
-    w.transform = 'r'
+    w.transform = "r"
 
     TOOLS = "tap,help"
-    fig = plot_choropleth(df, 'HOVAL', title='columbus',
-                          reverse_colors=True, tools=TOOLS)
+    fig = plot_choropleth(
+        df, "HOVAL", title="columbus", reverse_colors=True, tools=TOOLS
+    )
 
 
+@pytest.mark.skip(reason="to be deprecated")
 def test_lisa_cluster():
-    link = examples.get_path('columbus.shp')
+    link = examples.get_path("columbus.shp")
     df = gpd.read_file(link)
 
-    y = df['HOVAL'].values
+    y = df["HOVAL"].values
     w = Queen.from_dataframe(df)
-    w.transform = 'r'
+    w.transform = "r"
 
     moran_loc = esda.moran.Moran_Local(y, w)
 
     TOOLS = "tap,reset,help"
     fig = lisa_cluster(moran_loc, df, p=0.05, tools=TOOLS)
 
 
+@pytest.mark.skip(reason="to be deprecated")
 def test_moran_scatterplot():
-    link = examples.get_path('columbus.shp')
+    link = examples.get_path("columbus.shp")
     df = gpd.read_file(link)
 
-    y = df['HOVAL'].values
+    y = df["HOVAL"].values
     w = Queen.from_dataframe(df)
-    w.transform = 'r'
+    w.transform = "r"
 
     moran_loc = esda.moran.Moran_Local(y, w)
 
     fig = moran_scatterplot(moran_loc, p=0.05)
 
 
+@pytest.mark.skip(reason="to be deprecated")
 def test_plot_local_autocorrelation():
-    link = examples.get_path('columbus.shp')
+    link = examples.get_path("columbus.shp")
     df = gpd.read_file(link)
 
-    y = df['HOVAL'].values
+    y = df["HOVAL"].values
     w = Queen.from_dataframe(df)
-    w.transform = 'r'
+    w.transform = "r"
 
     moran_loc = esda.moran.Moran_Local(y, w)
 
-    fig = plot_local_autocorrelation(moran_loc, df, 'HOVAL')
+    fig = plot_local_autocorrelation(moran_loc, df, "HOVAL")

splot/tests/test_viz_giddy_mpl.py

@@ -5,77 +5,99 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from pytest import raises
+import pytest
 
-from giddy.directional import Rose
-from splot.giddy import (dynamic_lisa_heatmap,
-                         dynamic_lisa_rose,
-                         dynamic_lisa_vectors,
-                         dynamic_lisa_composite)
+try:
+    import ipywidgets
+
+    HAS_IPYWIDGETS = True
+except (ImportError, ModuleNotFoundError):
+    HAS_IPYWIDGETS = False
 
 
 def _data_generation():
+    from giddy.directional import Rose
+
     # get csv and shp
-    shp_link = examples.get_path('us48.shp')
+    shp_link = examples.get_path("us48.shp")
     df = gpd.read_file(shp_link)
     income_table = pd.read_csv(examples.get_path("usjoin.csv"))
     # calculate relative values
     for year in range(1969, 2010):
-        income_table[str(year) + '_rel'] = (
-            income_table[str(year)] / income_table[str(year)].mean())
+        income_table[str(year) + "_rel"] = (
+            income_table[str(year)] / income_table[str(year)].mean()
+        )
     # merge
-    gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name')
+    gdf = df.merge(income_table, left_on="STATE_NAME", right_on="Name")
     # retrieve spatial weights and data for two points in time
     w = Queen.from_dataframe(gdf)
-    w.transform = 'r'
-    y1 = gdf['1969_rel'].values
-    y2 = gdf['2000_rel'].values
-    #calculate rose Object
+    w.transform = "r"
+    y1 = gdf["1969_rel"].values
+    y2 = gdf["2000_rel"].values
+    # calculate rose Object
     Y = np.array([y1, y2]).T
     rose = Rose(Y, w, k=5)
     return gdf, y1, rose
 
 
 def test_dynamic_lisa_heatmap():
+    from splot.giddy import dynamic_lisa_heatmap
+
     _, _, rose = _data_generation()
     fig, _ = dynamic_lisa_heatmap(rose)
     plt.close(fig)
-    
-    fig2, _ = dynamic_lisa_heatmap(rose, cmap='GnBu')
+
+    fig2, _ = dynamic_lisa_heatmap(rose, cmap="GnBu")
     plt.close(fig2)
 
 
 def test_dynamic_lisa_rose():
+    from splot.giddy import dynamic_lisa_rose
+
     _, y1, rose = _data_generation()
     fig1, _ = dynamic_lisa_rose(rose)
     plt.close(fig1)
 
     fig2, _ = dynamic_lisa_rose(rose, attribute=y1)
     plt.close(fig2)
-    
-    fig3, _ = dynamic_lisa_rose(rose, c='r')
+
+    fig3, _ = dynamic_lisa_rose(rose, c="r")
     plt.close(fig3)
-
-    raises(ValueError, dynamic_lisa_rose,
-                  rose, attribute=y1, color='blue')
+
+    raises(ValueError, dynamic_lisa_rose, rose, attribute=y1, color="blue")
 
 
 def test_dynamic_lisa_vectors():
+    from splot.giddy import dynamic_lisa_vectors
+
     _, _, rose = _data_generation()
     fig1, _ = dynamic_lisa_vectors(rose)
     plt.close(fig1)
-    
+
     fig2, _ = dynamic_lisa_vectors(rose, arrows=False)
     plt.close(fig2)
-    
-    fig3, _ = dynamic_lisa_vectors(rose, c='r')
+
+    fig3, _ = dynamic_lisa_vectors(rose, c="r")
     plt.close(fig3)
-    
-    fig4, axs = plt.subplots(1,3)
-    dynamic_lisa_vectors(rose, ax=axs[0], color='r')
+
+    fig4, axs = plt.subplots(1, 3)
+    dynamic_lisa_vectors(rose, ax=axs[0], color="r")
     plt.close(fig4)
 
 
 def test_dynamic_lisa_composite():
+    from splot.giddy import dynamic_lisa_composite
+
     gdf, _, rose = _data_generation()
     fig, _ = dynamic_lisa_composite(rose, gdf)
     plt.close(fig)
+
+
+@pytest.mark.skipif(HAS_IPYWIDGETS, reason="ipywidgets available")
+def test_import_ipywidgets_error():
+    with pytest.raises(ImportError, match="`ipywidgets` package is required"):
+        from splot.giddy import dynamic_lisa_composite_explore
+
+        gdf, _, rose = _data_generation()
+
+        dynamic_lisa_composite_explore(rose, gdf)