def plot(self,
panels: List[str] = ['original', 'pointwise', 'cumulative'],
figsize: Tuple[int] = (10, 7),
show: bool = True) -> None:
"""
Plots the graphic with results associated to Causal Impact.
Args
----
panels: List[str]
Which graphics to plot. 'original' plots the original data, forecasts means
and credible intervals related to the fitted model.
'pointwise' plots the point wise differences between observed data and
predictions. Finally, 'cumulative' is a cumulative summation over real
data and its forecasts.
figsize: Tuple[int]
Sets the width and height of the figure to plot.
show: bool
If `True` then plots the figure by running `plt.plot()`.
If `False` then nothing will be plotted which allows for accessing and
manipulating the figure and axis of the plot, i.e., the figure can be saved
and the styling can be modified. To get the axis, just run:
`import matplotlib.pyplot as plt; ax = plt.gca()` or the figure:
`fig = plt.gcf()`. Defaults to `True`.
"""
plotter.plot(self.inferences,
self.pre_data,
self.post_data,
panels=panels,
figsize=figsize,
show=show)
def test_plot_original_panel_gap_data_show_is_false(rand_data,
pre_int_gap_period,
post_int_gap_period,
inferences, monkeypatch):
plot_mock = mock.Mock()
pre_data = rand_data.loc[pre_int_gap_period[0]:pre_int_gap_period[1]]
post_data = rand_data.loc[post_int_gap_period[0]:post_int_gap_period[1]]
pre_post_index = pre_data.index.union(post_data.index)
monkeypatch.setattr("causalimpact.plot.get_plotter", plot_mock)
plotter.plot(inferences,
pre_data,
post_data,
panels=["original"],
show=False)
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
plot_mock.return_value.subplot.assert_any_call(1, 1, 1)
ax_mock = plot_mock.return_value.subplot.return_value
ax_args = ax_mock.plot.call_args_list
assert_array_equal(pre_post_index, ax_args[0][0][0])
assert_array_equal(pd.concat([pre_data.iloc[:, 0], post_data.iloc[:, 0]]),
ax_args[0][0][1])
assert ax_args[0][0][2] == "k"
assert ax_args[0][1] == {"label": "y"}
assert_array_equal(pre_post_index[1:], ax_args[1][0][0])
assert_array_equal(inferences["complete_preds_means"].iloc[1:],
ax_args[1][0][1])
assert ax_args[1][1] == {
"color": "orangered",
"ls": "dashed",
"label": "Predicted"
}
ax_mock.axvline.assert_called_with(pre_int_gap_period[1],
c="gray",
linestyle="--")
ax_args = ax_mock.fill_between.call_args_list[0]
assert_array_equal(ax_args[0][0], pre_post_index[1:])
assert_array_equal(ax_args[0][1],
inferences["complete_preds_lower"].iloc[1:])
assert_array_equal(ax_args[0][2],
inferences["complete_preds_upper"].iloc[1:])
assert ax_args[1] == {"color": (1.0, 0.4981, 0.0549), "alpha": 0.4}
ax_mock.grid.assert_called_with(True, color="gainsboro")
ax_mock.legend.assert_called()
# If `show == False` then `plt.show()` should not have been called
plot_mock.return_value.show.assert_not_called()
def test_plot_raises_wrong_input_panel(rand_data, pre_int_period,
post_int_period, inferences,
monkeypatch):
plot_mock = mock.Mock()
pre_data = rand_data.loc[pre_int_period[0]:pre_int_period[1]]
post_data = rand_data.loc[post_int_period[0]:post_int_period[1]]
monkeypatch.setattr('causalimpact.plot.get_plotter', plot_mock)
plotter.plot(inferences, pre_data, post_data, panels=['cumulative'])
with pytest.raises(ValueError) as excinfo:
plotter.plot(inferences, rand_data, rand_data, panels=['test'])
assert str(
excinfo.value) == ('"test" is not a valid panel. Valid panels are: '
'"original", "pointwise", "cumulative".')
def test_plot_original_panel_date_index_no_freq(date_rand_data, pre_str_period,
post_str_period, inferences,
monkeypatch):
dd = date_rand_data.copy()
dd.drop(dd.index[10:20], inplace=True)
plot_mock = mock.Mock()
pre_data = dd.loc[pre_str_period[0]:pre_str_period[1]]
post_data = dd.loc[post_str_period[0]:post_str_period[1]]
pre_post_index = pre_data.index.union(post_data.index)
monkeypatch.setattr('causalimpact.plot.get_plotter', plot_mock)
plotter.plot(inferences, pre_data, post_data, panels=['original'])
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
plot_mock.return_value.subplot.assert_any_call(1, 1, 1)
ax_mock = plot_mock.return_value.subplot.return_value
ax_args = ax_mock.plot.call_args_list
assert_array_equal(pre_post_index, ax_args[0][0][0])
assert_array_equal(pd.concat([pre_data.iloc[:, 0], post_data.iloc[:, 0]]),
ax_args[0][0][1])
assert ax_args[0][0][2] == 'k'
assert ax_args[0][1] == {'label': 'y'}
assert_array_equal(pre_post_index[1:], ax_args[1][0][0])
assert_array_equal(inferences['complete_preds_means'].iloc[1:],
ax_args[1][0][1])
assert ax_args[1][1] == {
'color': 'orangered',
'ls': 'dashed',
'label': 'Predicted'
}
idx_value = pre_post_index.get_loc(pre_str_period[1])
ax_mock.axvline.assert_called_with(pre_post_index[idx_value],
c='gray',
linestyle='--')
ax_args = ax_mock.fill_between.call_args_list[0]
assert_array_equal(ax_args[0][0], pre_post_index[1:])
assert_array_equal(ax_args[0][1],
inferences['complete_preds_lower'].iloc[1:])
assert_array_equal(ax_args[0][2],
inferences['complete_preds_upper'].iloc[1:])
assert ax_args[1] == {'color': (1.0, 0.4981, 0.0549), 'alpha': 0.4}
ax_mock.grid.assert_called_with(True, color='gainsboro')
ax_mock.legend.assert_called()
plot_mock.return_value.show.assert_called_once()
def test_plot_cumulative_panel_gap_date_index(date_rand_data,
pre_str_gap_period,
post_str_gap_period, inferences,
monkeypatch):
plot_mock = mock.Mock()
pre_data = date_rand_data.loc[pre_str_gap_period[0]:pre_str_gap_period[1]]
post_data = date_rand_data.loc[
post_str_gap_period[0]:post_str_gap_period[1]]
pre_post_index = pre_data.index.union(post_data.index)
monkeypatch.setattr('causalimpact.plot.get_plotter', plot_mock)
plotter.plot(inferences, pre_data, post_data, panels=['cumulative'])
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
plot_mock.return_value.subplot.assert_any_call(1, 1, 1)
ax_mock = plot_mock.return_value.subplot.return_value
ax_args = ax_mock.plot.call_args_list
assert_array_equal(pre_post_index[1:], ax_args[0][0][0])
assert_array_equal(inferences['post_cum_effects_means'][1:],
ax_args[0][0][1])
assert ax_args[0][1] == {
'label': 'Cumulative Effect',
'ls': 'dashed',
'color': 'orangered'
}
idx_value = pre_post_index.get_loc(pre_str_gap_period[1])
ax_mock.axvline.assert_called_with(pre_post_index[idx_value],
c='gray',
linestyle='--')
ax_args = ax_mock.fill_between.call_args_list[0]
assert_array_equal(ax_args[0][0], pre_post_index[1:])
assert_array_equal(ax_args[0][1],
inferences['post_cum_effects_lower'].iloc[1:])
assert_array_equal(ax_args[0][2],
inferences['post_cum_effects_upper'].iloc[1:])
assert ax_args[1] == {'color': (1.0, 0.4981, 0.0549), 'alpha': 0.4}
ax_mock.grid.assert_called_with(True, color='gainsboro')
ax_mock.legend.assert_called()
plot_mock.return_value.show.assert_called_once()
def plot(
self,
panels: List[str] = ['original', 'pointwise', 'cumulative'],
figsize: Tuple[int] = (10, 7)
) -> None:
"""
Plots the graphic with results associated to Causal Impact.
Args
----
panels: List[str]
Which graphics to plot. 'original' plots the original data, forecasts means
and credible intervals related to the fitted model.
'pointwise' plots the point wise differences between observed data and
predictions. Finally, 'cumulative' is a cumulative summation over real
data and its forecasts.
figsize: Tuple[int]
Sets the width and height of the figure to plot.
"""
plotter.plot(self.inferences,
self.pre_data,
self.post_data,
panels=panels,
figsize=figsize)
def test_plot_multi_panels(rand_data, pre_int_period, post_int_period,
inferences, monkeypatch):
plot_mock = mock.Mock()
pre_data = rand_data.loc[pre_int_period[0]:pre_int_period[1]]
post_data = rand_data.loc[post_int_period[0]:post_int_period[1]]
monkeypatch.setattr('causalimpact.plot.get_plotter', plot_mock)
plotter.plot(inferences,
pre_data,
post_data,
panels=['original', 'pointwise'])
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
ax_mock = plot_mock.return_value.subplot.return_value
assert ax_mock.plot.call_count == 3
assert ax_mock.axvline.call_count == 2
assert ax_mock.grid.call_count == 2
assert ax_mock.legend.call_count == 2
assert ax_mock.fill_between.call_count == 2
assert ax_mock.axhline.call_count == 1
assert plot_mock.return_value.setp.call_count == 1
plot_mock.reset_mock()
ax_mock.reset_mock()
plotter.plot(inferences,
pre_data,
post_data,
panels=['original', 'cumulative'])
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
ax_mock = plot_mock.return_value.subplot.return_value
assert ax_mock.plot.call_count == 3
assert ax_mock.axvline.call_count == 2
assert ax_mock.grid.call_count == 2
assert ax_mock.legend.call_count == 2
assert ax_mock.fill_between.call_count == 2
assert ax_mock.axhline.call_count == 1
assert plot_mock.return_value.setp.call_count == 1
plot_mock.reset_mock()
ax_mock.reset_mock()
plotter.plot(inferences,
pre_data,
post_data,
panels=['original', 'pointwise', 'cumulative'])
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
ax_mock = plot_mock.return_value.subplot.return_value
assert ax_mock.plot.call_count == 4
assert ax_mock.axvline.call_count == 3
assert ax_mock.grid.call_count == 3
assert ax_mock.legend.call_count == 3
assert ax_mock.fill_between.call_count == 3
assert ax_mock.axhline.call_count == 2
assert plot_mock.return_value.setp.call_count == 2
plot_mock.reset_mock()
ax_mock.reset_mock()
plotter.plot(inferences,
pre_data,
post_data,
panels=['pointwise', 'cumulative'])
plot_mock.assert_called_once()
plot_mock.return_value.figure.assert_called_with(figsize=(10, 7))
ax_mock = plot_mock.return_value.subplot.return_value
assert ax_mock.plot.call_count == 2
assert ax_mock.axvline.call_count == 2
assert ax_mock.grid.call_count == 2
assert ax_mock.legend.call_count == 2
assert ax_mock.fill_between.call_count == 2
assert ax_mock.axhline.call_count == 2
assert plot_mock.return_value.setp.call_count == 1
