def test_plotly_main_plot(self): if PLOTLY_FOUND: # switch to plotly plotting.backend('plotly') self.assertTrue(isinstance(self.V.plot(show=False), go.Figure)) plotting.backend('matplotlib')
def _plot_plotly(func_args, plot_args): # set matplotlib backend backend('plotly') # get the variogram variogram = func_args['variogram'] plot_type = func_args.get('plot_type', 'plot') return __plot(variogram, plot_type, **plot_args)
def test_change_plotting_backend(): """ Set the correct backend and check """ # change to plotly backend('plotly') assert backend() == 'plotly' # change back backend('matplotlib')
def pair_field(self, ax=None, cmap="gist_rainbow", points='all', add_points=True, alpha=0.3, **kwargs): # pragma: no cover """ Plot a pair field. Plot a network graph for all point pairs that fulfill the direction filter and lie within each others search area. Parameters ---------- ax : matplotlib.Subplot A matplotlib Axes object to plot the pair field onto. If ``None``, a new new matplotlib figure will be created. cmap : string Any color-map name that is supported by matplotlib points : 'all', int, list If not ``'all'``, only the given coordinate (int) or list of coordinates (list) will be plotted. Recommended, if the input data is quite large. add_points : bool If True (default) The coordinates will be added as black points. alpha : float Alpha value for the colors to make overlapping vertices visualize better. Defaults to ``0.3``. """ # get the backend used_backend = plotting.backend() if used_backend == 'matplotlib': return plotting.matplotlib_pair_field(self, ax=ax, cmap=cmap, points=points, add_points=add_points, alpha=alpha, **kwargs) elif used_backend == 'plotly': return plotting.plotly_pair_field(self, fig=ax, points=points, add_points=add_points, alpha=alpha, **kwargs)
def test_backend_no_args(): """ The default backend should be 'matplotlib' """ assert backend() == 'matplotlib'
def test_raise_value_error(): """ Raise a value error by setting the wrong backend """ with pytest.raises(ValueError): backend('not-a-backend')
# ------------- # # 4.3.1 :func:`Variogram.plot <skgstat.Variogram.plot>` # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # The :func:`Variogram.plot <skgstat.Variogram.plot>` is the main plotting # function in SciKit-GStat. # Before you use the variogram for further geostatistical methods, like kriging, # or further analysis, make sure, that a suitable model was found and fitted # to the experimental data. Further, you have to make sure that the statistical # foundation of this estimation is sound, the lag classes are well designed and # backed by a suiatable amount of data. # Otherwise, any other geostatistical analysis or method will have to fail, # no matter how nice the results might look like. # from skgstat.plotting import backend backend('plotly') # %% # Plotly # """""" fig = V.plot(show=False) fig # %% # A useful argument for ``plot`` is the ``ax``, this takes a # ``matplotlib.AxesSubplot`` for the ``'matplotlib'`` backend and a # ``plotly.Figure`` for the ``'plotly'`` backend. # You need to supply the correct amount of subplots (two). For convenience, # the histogram in the upper subplot can be disabled. fig = make_subplots(rows=1, cols=1) fig.update_layout(width=800,