Increase coverage

Author: kdorr

mplaltair/_axis.py

@@ -91,7 +91,7 @@ def _set_scale_type(channel, ax):
         if 'base' in channel.scale:
             base = channel.scale['base']
 
-        if channel.channel == 'x':
+        if channel.name == 'x':
             ax.set_xscale('log', basex=base)
             # lower limit: round down to nearest major tick (using log base change rule)
             lims['left'] = base**np.floor(np.log10(channel.data.min())/np.log10(base))

mplaltair/_data.py

@@ -6,6 +6,7 @@
 from datetime import datetime
 import numpy as np
 
+
 def _normalize_data(chart):
     """Converts the data to a Pandas dataframe
 
@@ -39,118 +40,6 @@ def _normalize_data(chart):
     chart.data = df
 
 
-# def _locate_channel_dtype(chart, channel):
-#     """Locates dtype used for each channel
-#         Parameters
-#         ----------
-#         chart
-#             The Altair chart
-#         channel
-#             The Altair channel being examined
-#
-#         Returns
-#         -------
-#         A string representing the data type from the Altair chart ('quantitative', 'ordinal', 'numeric', 'temporal')
-#         """
-#
-#     channel_val = chart.to_dict()['encoding'][channel]
-#     if channel_val.get('type'):
-#         return channel_val.get('type')
-#     else:
-#         # TODO: find some way to deal with 'value' so that, opacity, for instance, can be plotted with a value defined
-#         if channel_val.get('value'):
-#             raise NotImplementedError
-#         raise NotImplementedError
-
-
-# def _locate_channel_data(chart, channel):
-#     """Locates data used for each channel
-#
-#     Parameters
-#     ----------
-#     chart
-#         The Altair chart
-#     channel
-#         The Altair channel being examined
-#
-#     Returns
-#     -------
-#     A numpy ndarray containing the data used for the channel
-#
-#     Raises
-#     ------
-#     ValidationError
-#         Raised when the specification does not contain any data attribute
-#
-#     """
-#
-#     channel_val = chart.to_dict()['encoding'][channel]
-#     if channel_val.get('value'):
-#         data = channel_val.get('value')
-#     elif channel_val.get('aggregate'):
-#         data = _aggregate_channel()
-#     elif channel_val.get('timeUnit'):
-#         data = _handle_timeUnit()
-#     else:  # field is required if the above are not present.
-#         data = chart.data[channel_val.get('field')].values
-#
-#     return data
-#
-#
-# def _aggregate_channel():
-#     raise NotImplementedError
-#
-#
-# def _handle_timeUnit():
-#     raise NotImplementedError
-#
-#
-# def _locate_channel_scale(chart, channel):
-#     """Locates the channel's scale information.
-#
-#     Parameters
-#     ----------
-#     chart
-#         The Altair chart
-#     channel
-#         The Altair channel being examined
-#
-#     Returns
-#     -------
-#     A dictionary with the scale information
-#     """
-#     channel_val = chart.to_dict()['encoding'][channel]
-#     if channel_val.get('scale'):
-#         return channel_val.get('scale')
-#     else:
-#         return {}
-#
-#
-# def _locate_channel_axis(chart, channel):
-#     """Locates the channel's scale information.
-#
-#     Parameters
-#     ----------
-#     chart
-#         The Altair chart
-#     channel
-#         The Altair channel being examined
-#
-#     Returns
-#     -------
-#     A dictionary with the axis information
-#     """
-#     channel_val = chart.to_dict()['encoding'][channel]
-#     if channel_val.get('axis'):
-#         return channel_val.get('axis')
-#     else:
-#         return {}
-#
-#
-# def _locate_channel_field(chart, channel):
-#     return chart.to_dict()['encoding'][channel]['field']
-
-
 def _convert_to_mpl_date(data):
     """Converts datetime, datetime64, strings, and Altair DateTime objects to Matplotlib dates.
 

mplaltair/parse_chart.py

@@ -54,8 +54,6 @@ def _locate_channel_data(self, alt_chart):
         A numpy ndarray containing the data used for the channel
 
         """
-        if not alt_chart.to_dict().get('encoding'):
-            raise ValueError("Encoding is not provided with the chart specification")
 
         channel_val = alt_chart.to_dict()['encoding'][self.name]
         if channel_val.get('value'):
@@ -103,10 +101,8 @@ class ChartMetadata(object):
 
     def __init__(self, alt_chart):
 
-        if not alt_chart.to_dict()['mark']:
-            raise ValueError("Mark not provided")
         if not alt_chart.to_dict().get('encoding'):
-            raise ValueError("Ranged encoding channels like x2, y2 not allowed for Mark: {}".format(alt_chart.mark))
+            raise ValueError("Encoding is not provided with the chart specification")
 
         _normalize_data(alt_chart)
         self.data = alt_chart.data

mplaltair/tests/test_axis.py

@@ -53,7 +53,6 @@ def test_axis_set_tick_formatter_fail():
     from .._axis import _set_tick_formatter
     _, ax = plt.subplots()
     chart = ChartMetadata(alt.Chart(df_quant).mark_point().encode('a:N', 'c:O'))
-    _set_tick_formatter(chart.encoding['x'], ax)
 
 
 @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_axis')

mplaltair/tests/test_convert.py

@@ -27,7 +27,6 @@ def test_encoding_not_provided():  # TODO: move to the parse_chart tests
     chart_spec = alt.Chart(df).mark_point()
     with pytest.raises(ValueError):
         chart = ChartMetadata(chart_spec)
-        # _convert(chart)
 
 def test_invalid_encodings():
     chart_spec = alt.Chart(df).encode(x2='quant').mark_point()
@@ -39,7 +38,6 @@ def test_invalid_encodings():
 def test_invalid_temporal():  # TODO: move to parse_chart tests???
     chart = alt.Chart(df).mark_point().encode(alt.X('quant:T'))
     ChartMetadata(chart)
-    # _convert(chart)
 
 @pytest.mark.parametrize('channel', ['quant', 'ord', 'nom'])
 def test_convert_x_success(channel):
@@ -58,7 +56,6 @@ def test_convert_x_success_temporal(column):
 def test_convert_x_fail():
     with pytest.raises(KeyError):
         chart_spec = ChartMetadata(alt.Chart(df).encode(x='b:N').mark_point())
-        _convert(chart_spec)
 
 @pytest.mark.parametrize('channel', ['quant', 'ord', 'nom'])
 def test_convert_y_success(channel):
@@ -75,7 +72,6 @@ def test_convert_y_success_temporal(column):
 def test_convert_y_fail():
     with pytest.raises(KeyError):
         chart_spec = ChartMetadata(alt.Chart(df).encode(y='b:N').mark_point())
-        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=ValueError, reason="It doesn't make sense to have x2 and y2 on scatter plots")
 def test_quantitative_x2_y2():
@@ -105,10 +101,9 @@ def test_convert_color_success_temporal(column):
     mapping = _convert(chart)
     assert list(mapping['c']) == list(mdates.date2num(df[column].values))
 
-def test_convert_color_fail():  # TODO: What is this covering?
+def test_convert_color_fail():
     with pytest.raises(KeyError):
         chart_spec = ChartMetadata(alt.Chart(df).encode(color='b:N').mark_point())
-        _convert(chart_spec)
 
 @pytest.mark.parametrize('channel,type', [('quant', 'Q'), ('ord', 'O')])
 def test_convert_fill(channel, type):
@@ -128,10 +123,9 @@ def test_convert_fill_success_temporal(column):
     assert list(mapping['c']) == list(mdates.date2num(df[column].values))
 
 
-def test_convert_fill_fail():  # TODO: what is this covering?
+def test_convert_fill_fail():
     with pytest.raises(KeyError):
         chart_spec = ChartMetadata(alt.Chart(df).encode(fill='b:N').mark_point())
-        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The marker argument in scatter() cannot take arrays")
 def test_quantitative_shape():
@@ -141,13 +135,12 @@ def test_quantitative_shape():
 @pytest.mark.xfail(raises=NotImplementedError, reason="The marker argument in scatter() cannot take arrays")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_shape_fail_temporal(column):
-    chart = ChartMetadata(alt.Chart(df).mark_point().encode(alt.Shape(column)))
-    mapping = _convert(chart)
+    chart = alt.Chart(df).mark_point().encode(alt.Shape(column))
+    convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Merge: the dtype for opacity isn't assumed to be quantitative")
 def test_quantitative_opacity_value():
     chart = ChartMetadata(alt.Chart(df_quant).mark_point().encode(opacity=alt.value(.5)))
-    mapping = _convert(chart)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The alpha argument in scatter() cannot take arrays")
 def test_quantitative_opacity_array():
@@ -174,7 +167,6 @@ def test_convert_size_success_nominal():
 def test_convert_size_fail():
     with pytest.raises(KeyError):
         chart_spec = ChartMetadata(alt.Chart(df).encode(size='b:N').mark_point())
-        _convert(chart_spec)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Dates would need to be normalized for the size.")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
@@ -201,12 +193,11 @@ def test_convert_stroke_fail_temporal(column):
 def test_quantitative_x_count_y():
     df_count = pd.DataFrame({"a": [1, 1, 2, 3, 5], "b": [1.4, 1.4, 2.9, 3.18, 5.3]})
     chart = ChartMetadata(alt.Chart(df_count).mark_point().encode(alt.X('a'), alt.Y('count()')))
-    mapping = _convert(chart)
+
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="specifying timeUnit is not supported yet")
 def test_timeUnit():
     chart = ChartMetadata(alt.Chart(df).mark_point().encode(alt.X('date(combination)')))
-    _convert(chart)
 
 # Plots
 
@@ -279,7 +270,6 @@ def test_line_color(self):
         )
         fig, _ = convert(chart)
         return fig
-        # plt.show()
 
     @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
     @pytest.mark.parametrize('o', ['d:Q', 'c:O', 'dates:T'])
@@ -291,7 +281,6 @@ def test_line_opacity(self, o):
         )
         fig, ax = convert(chart)
         return fig
-        # plt.show()
 
     @pytest.mark.mpl_image_compare(baseline_dir='baseline_images/test_convert')
     @pytest.mark.parametrize('c,o', [('d:Q', 'd:Q'), ('c:N', 'c:O'), ('dates:T', 'dates:T')])

mplaltair/tests/test_data.py

@@ -17,6 +17,7 @@
     "quantitative": [1.1, 2.1, 3.1, 4.1, 5.1]
 })
 
+
 def test_data_list():
     chart = alt.Chart(pd.DataFrame({'a': [1], 'b': [2], 'c': [3]})).mark_point()
     _normalize_data(chart)
@@ -27,83 +28,6 @@ def test_data_url():
     _normalize_data(chart)
     assert type(chart.data) == pd.DataFrame
 
-# _locate_channel_data() tests
-
-@pytest.mark.parametrize("column, dtype", [
-    ('a', 'quantitative'), ('b', 'quantitative'), ('c', 'quantitative'), ('combination', 'temporal')
-])
-def test_data_field_quantitative(column, dtype):
-    chart = alt.Chart(df).mark_point().encode(alt.X(field=column, type=dtype))
-    for channel in chart.to_dict()['encoding']:
-        data = _data._locate_channel_data(chart, channel)
-        assert list(data) == list(df[column].values)
-
-
-@pytest.mark.parametrize("column", ['a', 'b', 'c'])
-def test_data_shorthand_quantitative(column):
-    chart = alt.Chart(df).mark_point().encode(alt.X(column))
-    for channel in chart.to_dict()['encoding']:
-        data = _data._locate_channel_data(chart, channel)
-    assert list(data) == list(df[column].values)
-
-
-def test_data_shorthand_temporal():
-    chart = alt.Chart(df).mark_point().encode(alt.X('combination'))
-    for channel in chart.to_dict()['encoding']:
-        data = _data._locate_channel_data(chart, channel)
-    assert list(data) == list(df['combination'].values)
-
-
-def test_data_value_quantitative():
-    chart = alt.Chart(df).mark_point().encode(opacity=alt.value(0.5))
-    for channel in chart.to_dict()['encoding']:
-        data = _data._locate_channel_data(chart, channel)
-    assert list(data) == list(_data._convert_to_mpl_date(df['combination'].values))
-
-
-@pytest.mark.parametrize("column", ['a', 'b', 'c'])
-def test_data_aggregate_quantitative_fail(column):
-    """"'Passes' if it raises a NotImplementedError"""
-    chart = alt.Chart(df).mark_point().encode(alt.X(field=column, type='quantitative', aggregate='average'))
-    for channel in chart.to_dict()['encoding']:
-        with pytest.raises(NotImplementedError):
-            data = _data._locate_channel_data(chart, channel)
-
-
-def test_data_timeUnit_shorthand_temporal_fail():
-    chart = alt.Chart(df).mark_point().encode(alt.X('month(combination):T'))
-    for channel in chart.to_dict()['encoding']:
-        with pytest.raises(NotImplementedError):
-            data = _data._locate_channel_data(chart, channel)
-
-
-def test_data_timeUnit_field_temporal_fail():
-    """"'Passes' if it raises a NotImplementedError"""
-    chart = alt.Chart(df).mark_point().encode(alt.X(field='combination', type='temporal', timeUnit='month'))
-    for channel in chart.to_dict()['encoding']:
-        with pytest.raises(NotImplementedError):
-            data = _data._locate_channel_data(chart, channel)
-
-
-# _locate_channel_dtype() tests
-
-@pytest.mark.parametrize('column, expected', [
-    ('a:Q', 'quantitative'), ('nom:N', 'nominal'), ('ord:O', 'ordinal'), ('combination:T', 'temporal')
-])
-def test_data_dtype(column, expected):
-    chart = alt.Chart(df).mark_point().encode(alt.X(column))
-    for channel in chart.to_dict()['encoding']:
-        dtype = _data._locate_channel_dtype(chart, channel)
-    assert dtype == expected
-
-
-def test_data_dtype_fail():
-    """"'Passes' if it raises a NotImplementedError"""
-    chart = alt.Chart(df).mark_point().encode(opacity=alt.value(.5))
-    for channel in chart.to_dict()['encoding']:
-        with pytest.raises(NotImplementedError):
-            dtype = _data._locate_channel_dtype(chart, channel)
-
 # test date conversion:
 
 df_nonstandard = pd.DataFrame({