Add coverage tests

Author: kdorr

.coveragerc

@@ -2,6 +2,7 @@
 branch = true
 source =
     mplaltair
+omit = *tests*
 
 [report]
 exclude_lines =

mplaltair/__init__.py

@@ -1,35 +1,22 @@
 import matplotlib
 import altair
 
+from ._convert import _convert
 
-# TODO rename this?
-def convert(encoding, *, figure=None):
+def convert(chart):
     """Convert an altair encoding to a Matplotlib figure
 
 
     Parameters
     ----------
-    encoding
-        The Altair encoding of the plot.
-
-    figure : matplotib.figure.Figure, optional
-        # TODO: generalize this to 'thing that supports gridspec slicing?
+    chart
+        The Altair chart object generated by Altair
 
     Returns
     -------
-    figure : matplotlib.figure.Figure
-        The Figure with all artists in it (ready to be saved or shown)
-
-    mapping : dict
-        Mapping from parts of the encoding to the Matplotlib artists.  This is
-        for later customization.
-
+    mapping: dict
+        Mapping from parts of the encoding to the Matplotlib artists.
+        This is for later customization
 
     """
-    if figure is None:
-        from matplotlib import pyplot as plt
-        figure = plt.figure()
-
-    mapping = {}
-
-    return figure, mapping
+    return _convert(chart)

mplaltair/_convert.py

@@ -40,7 +40,7 @@ def _process_color(dtype, data):
         raise NotImplementedError
     elif dtype == 'ordinal':
         return ('c', data)
-    elif dtype == 'temporal':
+    else:  # temporal
         return ('c', data)
 
 
@@ -93,7 +93,7 @@ def _process_stroke(dtype, data):
     'stroke': _process_stroke,
 }
 
-def convert(chart):
+def _convert(chart):
     """Convert an altair encoding to a Matplotlib figure
 
 
@@ -123,7 +123,7 @@ def convert(chart):
         if dtype == 'temporal':
             try:
                 data = mdates.date2num(data)  # Convert dates to Matplotlib dates
-            except ValueError:
+            except AttributeError:
                 raise
         mapping[_mappings[channel](dtype, data)[0]] = _mappings[channel](dtype, data)[1]
 

mplaltair/tests/test_convert.py

@@ -5,7 +5,7 @@
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 
-from .._convert import convert
+from mplaltair import convert
 
 
 df = pd.DataFrame({
@@ -32,6 +32,11 @@ def test_invalid_encodings():
     with pytest.raises(ValueError):
         convert(chart_spec)
 
+@pytest.mark.xfail(raises=AttributeError)
+def test_invalid_temporal():
+    chart = alt.Chart(df).mark_point().encode(alt.X('quant:T'))
+    convert(chart)
+
 @pytest.mark.parametrize('channel', ['quant', 'ord', 'nom'])
 def test_convert_x_success(channel):
     chart_spec = alt.Chart(df).encode(x=channel).mark_point()
@@ -77,9 +82,9 @@ def test_convert_x2_y2_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.X2(column), alt.Y2(column))
     convert(chart)
 
-@pytest.mark.parametrize('channel', ['quant', 'ord'])
-def test_convert_color_success(channel):
-    chart_spec = alt.Chart(df).encode(color=channel).mark_point()
+@pytest.mark.parametrize('channel,dtype', [('quant','quantitative'), ('ord','ordinal')])
+def test_convert_color_success(channel, dtype):
+    chart_spec = alt.Chart(df).encode(color=alt.Color(field=channel, type=dtype)).mark_point()
     mapping = convert(chart_spec)
     assert list(mapping['c']) == list(df[channel].values)
 
@@ -151,9 +156,9 @@ def test_convert_opacity_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Opacity(column))
     convert(chart)
 
-@pytest.mark.parametrize('channel', ['quant', 'ord'])
-def test_convert_size_success(channel):
-    chart_spec = alt.Chart(df).encode(size=channel).mark_point()
+@pytest.mark.parametrize('channel,dtype', [('quant','quantitative'), ('ord', 'ordinal')])
+def test_convert_size_success(channel, dtype):
+    chart_spec = alt.Chart(df).encode(size=alt.Size(field=channel, type=dtype)).mark_point()
     mapping = convert(chart_spec)
     assert list(mapping['s']) == list(df[channel].values)