import hvplot import holoviews as hv import numpy as np import pandas as pd import pytest import xarray as xr from holoviews import Store from holoviews.core.options import Options, OptionTree @pytest.fixture(scope='class') def load_pandas_accessor(): import hvplot.pandas # noqa @pytest.fixture(scope='class') def load_xarray_accessor(): import hvplot.xarray # noqa @pytest.fixture(params=['bokeh', 'matplotlib', 'plotly'], scope='class') def backend(request): backend = request.param backend_copy = Store.current_backend if backend not in Store.registry: hvplot.extension(backend, compatibility='bokeh') Store.set_current_backend(backend) store_copy = OptionTree(sorted(Store.options().items()), groups=Options._option_groups) yield backend Store.options(val=store_copy) Store._custom_options = {k: {} for k in Store._custom_options.keys()} Store.set_current_backend(backend_copy) @pytest.fixture(scope='module') def df(): return pd.DataFrame( [[1, 2, 'A', 0.1], [3, 4, 'B', 0.2], [5, 6, 'C', 0.3]], columns=['x', 'y', 'category', 'number'], ) @pytest.fixture(scope='module') def symmetric_df(): return pd.DataFrame([[1, 2, -1], [3, 4, 0], [5, 6, 1]], columns=['x', 'y', 'number']) @pytest.mark.usefixtures('load_pandas_accessor') class TestOptions: @pytest.mark.parametrize( 'backend', [ 'bokeh', pytest.param( 'matplotlib', marks=pytest.mark.xfail( reason='legend_position not supported w/ matplotlib for scatter' ), ), pytest.param( 'plotly', marks=pytest.mark.xfail( reason='legend_position not supported w/ plotly for scatter' ), ), ], indirect=True, ) def test_scatter_legend_position(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category', legend='left') opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['legend_position'] == 'left' @pytest.mark.parametrize( 'backend', [ 'bokeh', 'matplotlib', pytest.param( 'plotly', marks=pytest.mark.xfail(reason='legend_position not supported w/ plotly for hist'), ), ], indirect=True, ) def test_histogram_by_category_legend_position(self, df, backend): plot = df.hvplot.hist('y', by='category', legend='left') opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['legend_position'] == 'left' @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_logz(self, df, kind, backend): plot = df.hvplot('x', 'y', c='x', logz=True, kind=kind) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['logz'] is True @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_color_dim(self, df, kind, backend): plot = df.hvplot('x', 'y', c='number', kind=kind) opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['color'] == 'number' assert 'number' in plot.vdims @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_size_dim(self, df, kind, backend): plot = df.hvplot('x', 'y', s='number', kind=kind) opts = Store.lookup_options(backend, plot, 'style') if backend in ['bokeh', 'plotly']: param = 'size' elif backend == 'matplotlib': param = 's' assert opts.kwargs[param] == 'number' assert 'number' in plot.vdims @pytest.mark.parametrize( 'backend', [ 'bokeh', pytest.param( 'matplotlib', marks=pytest.mark.xfail(reason='cannot map a dim to alpha w/ matplotlib'), ), pytest.param( 'plotly', marks=pytest.mark.xfail(reason='cannot map a dim to alpha w/ plotly') ), ], indirect=True, ) @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_alpha_dim(self, df, kind, backend): plot = df.hvplot('x', 'y', alpha='number', kind=kind) opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['alpha'] == 'number' assert 'number' in plot.vdims # Special matplotlib code to trigger an error that happens on render if backend == 'matplotlib': mpl_renderer = hv.Store.renderers['matplotlib'] mpl_renderer.get_plot(plot) @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_marker_dim(self, df, kind, backend): plot = df.hvplot('x', 'y', marker='category', kind=kind) opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['marker'] == 'category' assert 'category' in plot.vdims @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_color_dim_overlay(self, df, kind, backend): plot = df.hvplot('x', 'y', c='number', by='category', kind=kind) opts = Store.lookup_options(backend, plot.last, 'style') assert opts.kwargs['color'] == 'number' assert 'number' in plot.last.vdims @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_size_dim_overlay(self, df, kind, backend): plot = df.hvplot('x', 'y', s='number', by='category', kind=kind) opts = Store.lookup_options(backend, plot.last, 'style') if backend in ['bokeh', 'plotly']: param = 'size' elif backend == 'matplotlib': param = 's' assert opts.kwargs[param] == 'number' assert 'number' in plot.last.vdims @pytest.mark.parametrize( 'backend', [ 'bokeh', 'matplotlib', pytest.param( 'plotly', marks=pytest.mark.xfail(reason='cannot map a dim to alpha w/ plotly') ), ], indirect=True, ) @pytest.mark.parametrize('kind', ['scatter', 'points']) def test_alpha_dim_overlay(self, df, kind, backend): plot = df.hvplot('x', 'y', alpha='number', by='category', kind=kind) opts = Store.lookup_options(backend, plot.last, 'style') assert opts.kwargs['alpha'] == 'number' assert 'number' in plot.last.vdims def test_hvplot_defaults(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'plot') if backend == 'bokeh': assert opts.kwargs['height'] == 300 assert opts.kwargs['width'] == 700 elif backend == 'matplotlib': assert opts.kwargs['aspect'] == pytest.approx(2.333333) assert opts.kwargs['fig_size'] == pytest.approx(233.333333) if backend == 'bokeh': assert opts.kwargs['responsive'] is False assert opts.kwargs['shared_axes'] is True # legend_position shouldn't only be for Bokeh assert opts.kwargs['legend_position'] == 'right' assert opts.kwargs['show_grid'] is False assert opts.kwargs['show_legend'] is True assert opts.kwargs['logx'] is False assert opts.kwargs['logy'] is False assert opts.kwargs.get('logz') is None @pytest.mark.parametrize( 'backend', [ 'bokeh', pytest.param( 'matplotlib', marks=pytest.mark.xfail(reason='default opts not supported w/ matplotlib'), ), pytest.param( 'plotly', marks=pytest.mark.xfail(reason='default opts not supported w/ plotly') ), ], indirect=True, ) def test_holoviews_defined_default_opts(self, df, backend): hv.opts.defaults(hv.opts.Scatter(height=400, width=900, show_grid=True)) plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'plot') # legend_position shouldn't apply only to bokeh if backend == 'bokeh': assert opts.kwargs['legend_position'] == 'right' assert opts.kwargs['show_grid'] is True assert opts.kwargs['height'] == 400 assert opts.kwargs['width'] == 900 @pytest.mark.parametrize( 'backend', [ 'bokeh', pytest.param( 'matplotlib', marks=pytest.mark.xfail(reason='default opts not supported w/ matplotlib'), ), pytest.param( 'plotly', marks=pytest.mark.xfail(reason='default opts not supported w/ plotly') ), ], indirect=True, ) def test_holoviews_defined_default_opts_overwritten_in_call(self, df, backend): hv.opts.defaults(hv.opts.Scatter(height=400, width=900, show_grid=True)) plot = df.hvplot.scatter('x', 'y', c='category', width=300, legend='left') opts = Store.lookup_options(backend, plot, 'plot') # legend_position shouldn't apply only to bokeh if backend == 'bokeh': assert opts.kwargs['legend_position'] == 'left' assert opts.kwargs['show_grid'] is True assert opts.kwargs['height'] == 400 assert opts.kwargs['width'] == 300 @pytest.mark.parametrize( 'backend', [ 'bokeh', pytest.param( 'matplotlib', marks=pytest.mark.xfail( reason='default opts not supported not supported w/ matplotlib' ), ), pytest.param( 'plotly', marks=pytest.mark.xfail( reason='default opts not supported not supported w/ plotly' ), ), ], indirect=True, ) def test_holoviews_defined_default_opts_are_not_mutable(self, df, backend): hv.opts.defaults(hv.opts.Scatter(tools=['tap'])) plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['tools'] == ['tap', 'hover'] default_opts = Store.options(backend=backend)['Scatter'].groups['plot'].options assert default_opts['tools'] == ['tap'] def test_axis_set_to_visible_by_default(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'plot') assert 'xaxis' not in opts.kwargs assert 'yaxis' not in opts.kwargs def test_axis_set_to_none(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category', xaxis=None, yaxis=None) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['xaxis'] is None assert opts.kwargs['yaxis'] is None def test_axis_set_to_false(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category', xaxis=False, yaxis=False) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['xaxis'] is None assert opts.kwargs['yaxis'] is None def test_axis_set_to_none_in_holoviews_opts_default(self, df, backend): hv.opts.defaults(hv.opts.Scatter(xaxis=None, yaxis=None)) plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['xaxis'] is None assert opts.kwargs['yaxis'] is None @pytest.mark.xfail def test_axis_set_to_none_in_holoviews_opts_default_overwrite_in_call(self, df, backend): hv.opts.defaults(hv.opts.Scatter(xaxis=None, yaxis=None)) plot = df.hvplot.scatter('x', 'y', c='category', xaxis=True, yaxis=True) opts = Store.lookup_options(backend, plot, 'plot') assert 'xaxis' not in opts.kwargs assert 'yaxis' not in opts.kwargs def test_loglog_opts(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category', loglog=True) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['logx'] is True assert opts.kwargs['logy'] is True assert opts.kwargs.get('logz') is None def test_logy_opts(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='category', logy=True) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['logx'] is False assert opts.kwargs['logy'] is True assert opts.kwargs.get('logz') is None @pytest.mark.parametrize( 'backend', [ 'bokeh', pytest.param( 'matplotlib', marks=pytest.mark.xfail(reason='default opts not supported w/ matplotlib'), ), pytest.param( 'plotly', marks=pytest.mark.xfail(reason='defaykt opts not supported w/ plotly') ), ], indirect=True, ) def test_holoviews_defined_default_opts_logx(self, df, backend): hv.opts.defaults(hv.opts.Scatter(logx=True)) plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['logx'] is True assert opts.kwargs['logy'] is False assert opts.kwargs.get('logz') is None def test_holoviews_defined_default_opts_logx_overwritten_in_call(self, df, backend): hv.opts.defaults(hv.opts.Scatter(logx=True)) plot = df.hvplot.scatter('x', 'y', c='category', logx=False) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['logx'] is False assert opts.kwargs['logy'] is False assert opts.kwargs.get('logz') is None def test_hvplot_default_cat_cmap_opts(self, df, backend): import colorcet as cc plot = df.hvplot.scatter('x', 'y', c='category') opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['cmap'] == cc.palette['glasbey_category10'] def test_hvplot_default_num_cmap_opts(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='number') opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['cmap'] == 'kbc_r' def test_cmap_opts_by_type(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='number', cmap='diverging') opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['cmap'] == 'coolwarm' def test_cmap_opts_by_name(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='number', cmap='fire') opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['cmap'] == 'fire' def test_colormap_opts_by_name(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='number', colormap='fire') opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['cmap'] == 'fire' def test_cmap_opts_as_a_list(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='number', cmap=['red', 'blue', 'green']) opts = Store.lookup_options(backend, plot, 'style') assert opts.kwargs['cmap'] == ['red', 'blue', 'green'] @pytest.mark.parametrize( ('opt', 'backend'), [ ('aspect', 'bokeh'), ('aspect', 'matplotlib'), ('aspect', 'plotly'), ('data_aspect', 'bokeh'), ('data_aspect', 'matplotlib'), pytest.param( 'data_aspect', 'plotly', marks=pytest.mark.xfail(reason='data_aspect not supported w/ plotly'), ), ], indirect=['backend'], ) def test_aspect(self, df, opt, backend): plot = df.hvplot(x='x', y='y', **{opt: 2}) opts = Store.lookup_options(backend, plot, 'plot').kwargs assert opts[opt] == 2 if backend in ['bokeh', 'matplotlib']: assert opts.get('width') is None assert opts.get('height') is None elif backend == 'matplotlib': assert opts.get('fig_size') is None @pytest.mark.parametrize( ('opt', 'backend'), [ ('aspect', 'bokeh'), ('aspect', 'matplotlib'), ('aspect', 'plotly'), ('data_aspect', 'bokeh'), ('data_aspect', 'matplotlib'), pytest.param( 'data_aspect', 'plotly', marks=pytest.mark.xfail(reason='data_aspect not supported w/ plotly'), ), ], indirect=['backend'], ) def test_aspect_and_width(self, df, opt, backend): plot = df.hvplot(x='x', y='y', width=150, **{opt: 2}) opts = hv.Store.lookup_options(backend, plot, 'plot').kwargs assert opts[opt] == 2 if backend in ['bokeh', 'plotly']: assert opts.get('width') == 150 assert opts.get('height') is None elif backend == 'matplotlib': assert opts.get('fig_size') == pytest.approx(50.0) def test_symmetric_dataframe(self, backend): df = pd.DataFrame([[1, 2, -1], [3, 4, 0], [5, 6, 1]], columns=['x', 'y', 'number']) plot = df.hvplot.scatter('x', 'y', c='number') plot_opts = Store.lookup_options(backend, plot, 'plot') assert plot_opts.kwargs['symmetric'] is True style_opts = Store.lookup_options(backend, plot, 'style') assert style_opts.kwargs['cmap'] == 'coolwarm' def test_symmetric_is_deduced_dataframe(self, symmetric_df, backend): plot = symmetric_df.hvplot.scatter('x', 'y', c='number') plot_opts = Store.lookup_options(backend, plot, 'plot') assert plot_opts.kwargs['symmetric'] is True style_opts = Store.lookup_options(backend, plot, 'style') assert style_opts.kwargs['cmap'] == 'coolwarm' def test_symmetric_from_opts(self, df, backend): plot = df.hvplot.scatter('x', 'y', c='number', symmetric=True) plot_opts = Store.lookup_options(backend, plot, 'plot') assert plot_opts.kwargs['symmetric'] is True style_opts = Store.lookup_options(backend, plot, 'style') assert style_opts.kwargs['cmap'] == 'coolwarm' def test_symmetric_from_opts_does_not_deduce(self, symmetric_df, backend): plot = symmetric_df.hvplot.scatter('x', 'y', c='number', symmetric=False) plot_opts = Store.lookup_options(backend, plot, 'plot') assert plot_opts.kwargs['symmetric'] is False style_opts = Store.lookup_options(backend, plot, 'style') assert style_opts.kwargs['cmap'] == 'kbc_r' def test_if_clim_is_set_symmetric_is_not_deduced(self, symmetric_df, backend): plot = symmetric_df.hvplot.scatter('x', 'y', c='number', clim=(-1, 1)) plot_opts = Store.lookup_options(backend, plot, 'plot') assert plot_opts.kwargs.get('symmetric') is None style_opts = Store.lookup_options(backend, plot, 'style') assert style_opts.kwargs['cmap'] == 'kbc_r' @pytest.mark.parametrize( 'backend', [ 'bokeh', 'matplotlib', pytest.param( 'plotly', marks=pytest.mark.xfail( reason='bandwidth, cut, levels not supported w/ plotly for bivariate' ), ), ], indirect=True, ) def test_bivariate_opts(self, df, backend): plot = df.hvplot.bivariate('x', 'y', bandwidth=0.2, cut=1, levels=5, filled=True) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['bandwidth'] == 0.2 assert opts.kwargs['cut'] == 1 assert opts.kwargs['levels'] == 5 assert opts.kwargs['filled'] is True def test_kde_opts(self, df, backend): plot = df.hvplot.kde('x', bandwidth=0.2, cut=1, filled=True) opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['bandwidth'] == 0.2 assert opts.kwargs['cut'] == 1 assert opts.kwargs['filled'] is True def test_bgcolor(self, df, backend): plot = df.hvplot.scatter('x', 'y', bgcolor='black') opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['bgcolor'] == 'black' @pytest.fixture(scope='module') def da(): return xr.DataArray( data=np.arange(16).reshape((2, 2, 2, 2)), coords={'time': [0, 1], 'y': [0, 1], 'x': [0, 1], 'band': [0, 1]}, dims=['time', 'y', 'x', 'band'], name='test', ) @pytest.fixture(scope='module') def da2(): return xr.DataArray( data=np.arange(27).reshape((3, 3, 3)), coords={'y': [0, 1, 2], 'x': [0, 1, 2]}, dims=['y', 'x', 'other'], name='test2', ) @pytest.fixture(scope='module') def ds1(da): return xr.Dataset(dict(foo=da)) @pytest.fixture(scope='module') def ds2(da, da2): return xr.Dataset(dict(foo=da, bar=da2)) @pytest.mark.usefixtures('load_xarray_accessor') class TestXarrayTitle: def test_dataarray_2d_with_title(self, da, backend): da_sel = da.sel(time=0, band=0) plot = da_sel.hvplot() # Image plot opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'time = 0, band = 0' def test_dataarray_1d_with_title(self, da, backend): da_sel = da.sel(time=0, band=0, x=0) plot = da_sel.hvplot() # Line plot opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'time = 0, x = 0, band = 0' def test_dataarray_1d_and_by_with_title(self, da, backend): da_sel = da.sel(time=0, band=0, x=[0, 1]) plot = da_sel.hvplot(by='x') # Line plot with hue/by opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'time = 0, band = 0' def test_override_title(self, da, backend): da_sel = da.sel(time=0, band=0) plot = da_sel.hvplot(title='title') # Image plot opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'title' def test_dataarray_4d_line_no_title(self, da, backend): plot = da.hvplot.line(dynamic=False) # Line plot with widgets opts = Store.lookup_options(backend, plot.last, 'plot') assert 'title' not in opts.kwargs def test_dataarray_3d_histogram_with_title(self, da, backend): da_sel = da.sel(time=0) plot = da_sel.hvplot() # Histogram and no widgets opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'time = 0' def test_dataset_empty_raises(self, ds1, backend): with pytest.raises(ValueError, match='empty xarray.Dataset'): ds1.drop_vars('foo').hvplot() def test_dataset_one_var_behaves_like_dataarray(self, ds1, backend): ds_sel = ds1.sel(time=0, band=0) plot = ds_sel.hvplot() # Image plot opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'time = 0, band = 0' def test_dataset_scatter_with_title(self, ds2, backend): ds_sel = ds2.sel(time=0, band=0, x=0, y=0) plot = ds_sel.hvplot.scatter(x='foo', y='bar') # Image plot opts = Store.lookup_options(backend, plot, 'plot') assert opts.kwargs['title'] == 'time = 0, y = 0, x = 0, band = 0' @pytest.mark.usefixtures('load_xarray_accessor') class TestXarrayCticks: def test_cticks(self, da2): plot = da2.isel(other=0).hvplot(cticks=[5, 10]) handles = hv.renderer('bokeh').get_plot(plot).handles assert handles['colorbar'].ticker.ticks == [5, 10] def test_subcoordinate_y_bool(load_pandas_accessor): df = pd.DataFrame(np.random.random((10, 3)), columns=list('ABC')) plot = df.hvplot.line(subcoordinate_y=True) opts = Store.lookup_options('bokeh', plot, 'plot') assert opts.kwargs['subcoordinate_y'] is True def test_subcoordinate_y_dict(load_pandas_accessor): df = pd.DataFrame(np.random.random((10, 3)), columns=list('ABC')) plot = df.hvplot.line(subcoordinate_y={'subcoordinate_scale': 2}) opts = Store.lookup_options('bokeh', plot, 'plot') assert opts.kwargs['subcoordinate_y'] is True assert opts.kwargs['subcoordinate_scale'] == 2