def test_spread_kwargs(self, operation):
sm = scatter_matrix(self.df,
datashade=True,
**{
operation: True,
'shape': 'circle'
})
dm = sm['a', 'b']
dm[()]
self.assertEqual(
dm.last.pipeline.operations[-1].args[0].keywords['shape'],
'circle')
def test_spread_but_no_rasterize_or_datashade(self):
with self.assertRaises(ValueError):
scatter_matrix(self.df, dynspread=True)
with self.assertRaises(ValueError):
scatter_matrix(self.df, spread=True)
with self.assertRaises(ValueError):
scatter_matrix(self.df, dynspread=True, spread=True)
def test_c(self):
df = self.df.copy(deep=True)
df['e'] = np.random.choice(list('xyz'), size=len(df))
sm = scatter_matrix(df, c='e')
self.assertIsInstance(sm['a', 'a'], NdOverlay)
diag_kdims = sm['a', 'a'].kdims
self.assertEqual(len(diag_kdims), 1)
self.assertEqual(diag_kdims[0].name, 'e')
self.assertIsInstance(sm['a', 'b'], Scatter)
offdiag_vdims = sm['a', 'b'].vdims
self.assertTrue('e' in (d.name for d in offdiag_vdims))
def test_diagonal_kwargs_mutually_exclusive(self):
with self.assertRaises(TypeError):
scatter_matrix(self.df,
diagonal_kwds=dict(a=1),
hist_kwds=dict(a=1))
with self.assertRaises(TypeError):
scatter_matrix(self.df,
diagonal_kwds=dict(a=1),
density_kwds=dict(a=1))
with self.assertRaises(TypeError):
scatter_matrix(self.df,
density_kwds=dict(a=1),
hist_kwds=dict(a=1))
def test_diagonal_kwargs(self):
sm = scatter_matrix(self.df, diagonal_kwds=dict(line_color='red'))
self.assertEqual(sm['a', 'a'].opts.get().kwargs['line_color'], 'red')
def test_offdiagonal_hexbin(self):
sm = scatter_matrix(self.df, chart='hexbin')
self.assertIsInstance(sm['a', 'b'], HexTiles)
def test_offdiagonal_bivariate(self):
sm = scatter_matrix(self.df, chart='bivariate')
self.assertIsInstance(sm['a', 'b'], Bivariate)
def test_diagonal_kde(self):
sm = scatter_matrix(self.df, diagonal='kde')
self.assertIsInstance(sm['a', 'a'], Distribution)
def test_rasterization(self, operation):
sm = scatter_matrix(self.df, **{operation: True})
dm = sm['a', 'b']
self.assertEqual(dm.callback.operation.name, operation)
dm[()]
self.assertEqual(len(dm.last.pipeline.operations), 3)
def test_diagonal_default(self):
sm = scatter_matrix(self.df)
self.assertIsInstance(sm['a', 'a'], Histogram)
def test_wrong_chart(self):
with self.assertRaises(ValueError):
scatter_matrix(self.df, chart='wrong')
def test_wrong_diagonal(self):
with self.assertRaises(ValueError):
scatter_matrix(self.df, diagonal='wrong')
def test_returns_gridmatrix(self):
sm = scatter_matrix(self.df)
self.assertIsInstance(sm, GridMatrix)
def test_spread_datashade(self, operation):
sm = scatter_matrix(self.df, datashade=True, **{operation: True})
dm = sm['a', 'b']
dm[()]
self.assertEqual(len(dm.last.pipeline.operations), 4)
def test_datashade_aggregator(self, operation):
sm = scatter_matrix(self.df, aggregator='mean', **{operation: True})
dm = sm['a', 'b']
dm[()]
self.assertEqual(dm.last.pipeline.operations[-1].aggregator, 'mean')
def test_rasterize_datashade_mutually_exclusive(self):
with self.assertRaises(ValueError):
scatter_matrix(self.df, rasterize=True, datashade=True)
# %% [markdown] {"slideshow": {"slide_type": "slide"}}
# # PyViz (holoviews)
#
# [See here for tutorial.](http://pyviz.org/tutorial/scipy18)
# %% [markdown]
# ## Scatterplot matrix
# %%
import holoviews as hv
import hvplot
import hvplot.pandas
hv.extension("bokeh") # use bokeh backend
iris = sns.load_dataset("iris")
hvplot.scatter_matrix(iris, c='species')
# %% [markdown] {"slideshow": {"slide_type": "subslide"}}
# ## Time series
# %%
# Get data
diseases = pd.read_csv("data/diseases.csv.gz")
diseases.head()
# %% [markdown] {"slideshow": {"slide_type": "subslide"}}
# ### Static plot with pandas
# %%
measles_by_year = diseases[["Year",
"measles"]].groupby("Year").aggregate(np.sum)
def test_offdiagonal_default(self):
sm = scatter_matrix(self.df)
self.assertIsInstance(sm['a', 'b'], Scatter)