def test_probplot_beta_dist_best_fit_x(plot_data):
fig, (ax1, ax2) = plt.subplots(nrows=2)
dist = stats.beta(3, 3)
fig = viz.probplot(
plot_data,
dist=dist,
ax=ax1,
problabel="Beta scale",
bestfit=True,
datascale="log",
probax="x",
)
ax1.set_xlim(left=0.5, right=98)
fig = viz.probplot(
plot_data,
ax=ax2,
problabel="Default (norm)",
bestfit=True,
datascale="log",
probax="x",
estimate_ci=True,
)
ax2.set_xlim(left=0.5, right=98)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_beta_dist_best_fit_y(plot_data):
fig, (ax1, ax2) = plt.subplots(ncols=2)
dist = stats.beta(3, 3)
fig = viz.probplot(plot_data, dist=dist, ax=ax1, problabel='Beta scale',
bestfit=True, datascale='log', probax='y')
ax1.set_ylim(bottom=0.5, top=98)
fig = viz.probplot(plot_data, ax=ax2, datalabel='Default (norm)',
bestfit=True, datascale='log', probax='y',
estimate_ci=True)
ax2.set_ylim(bottom=0.5, top=98)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_beta_dist_best_fit_x(plot_data):
fig, (ax1, ax2) = plt.subplots(nrows=2)
dist = stats.beta(3, 3)
fig = viz.probplot(plot_data, dist=dist, ax=ax1, problabel='Beta scale',
bestfit=True, datascale='log', probax='x')
ax1.set_xlim(left=0.5, right=98)
fig = viz.probplot(plot_data, ax=ax2, problabel='Default (norm)',
bestfit=True, datascale='log', probax='x',
estimate_ci=True)
ax2.set_xlim(left=0.5, right=98)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_qq_dist(plot_data):
fig, ax = plt.subplots()
norm = stats.norm(*stats.norm.fit(plot_data))
fig = viz.probplot(
plot_data, ax=ax, plottype="qq", dist=norm, datalabel="Test label"
)
return fig
def test_probplot_pp(plot_data):
fig, ax = plt.subplots()
scatter_kws = dict(color='b', linestyle='--', markeredgecolor='g', markerfacecolor='none')
fig = viz.probplot(plot_data, ax=ax, plottype='pp', datascale='linear',
datalabel='test x', problabel='test y',
scatter_kws=scatter_kws)
return fig
def test_probplot_prob_probax_y(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(
plot_data, ax=ax, datalabel="Test xlabel", datascale="log", probax="y"
)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_test_results():
fig, ax = plt.subplots()
data = setup_plot_data()
fig, results = viz.probplot(data, return_results=True)
nt.assert_true(isinstance(results, dict))
known_keys = sorted(['q', 'x', 'y', 'xhat', 'yhat', 'res'])
nt.assert_list_equal(sorted(list(results.keys())), known_keys)
def test_probplot_test_results(plot_data):
fig, ax = plt.subplots()
fig, results = viz.probplot(plot_data, return_best_fit_results=True)
assert isinstance(results, dict)
known_keys = sorted(["q", "x", "y", "xhat", "yhat", "res"])
assert sorted(list(results.keys())) == known_keys
return fig
def test_probplot_color_and_label(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data,
ax=ax,
color='pink',
label='A Top-Level Label')
ax.legend(loc='lower right')
return fig
def test_probplot_test_results(plot_data):
fig, ax = plt.subplots()
fig, results = viz.probplot(plot_data, return_best_fit_results=True)
assert isinstance(results, dict)
known_keys = sorted(['q', 'x', 'y', 'xhat', 'yhat', 'res'])
assert sorted(list(results.keys())) == known_keys
return fig
def test_probplot_pp_bestfit_probax_y(plot_data):
fig, ax = plt.subplots()
scatter_kws = {'marker': 's', 'color': 'red'}
line_kws = {'linestyle': '--', 'linewidth': 3}
fig = viz.probplot(plot_data, ax=ax, plottype='pp', datascale='linear', probax='y',
datalabel='test x', bestfit=True, problabel='test y',
scatter_kws=scatter_kws, line_kws=line_kws, estimate_ci=True)
return fig
def test_probplot_prob(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data,
ax=ax,
problabel='Test xlabel',
datascale='log')
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_test_results(plot_data):
fig, ax = plt.subplots()
fig, results = viz.probplot(plot_data, return_best_fit_results=True)
assert isinstance(results, dict)
known_keys = sorted(['q', 'x', 'y', 'xhat', 'yhat', 'res'])
assert sorted(list(results.keys())) == known_keys
return fig
def test_probplot_pp_bestfit_probax_y():
fig, ax = plt.subplots()
data = setup_plot_data()
scatter_kws = {'marker': 's', 'color': 'red'}
line_kws = {'linestyle': '--', 'linewidth': 3}
fig = viz.probplot(data, ax=ax, axtype='pp', otherscale='linear', probax='y',
xlabel='test x', bestfit=True, ylabel='test y',
scatter_kws=scatter_kws, line_kws=line_kws)
def test_probplot_qq(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data,
ax=ax,
plottype='qq',
datalabel='Test label',
datascale='log',
scatter_kws=dict(color='r'))
return fig
def test_probplot_qq_bestfit(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data,
ax=ax,
plottype='qq',
bestfit=True,
problabel='Test label',
datascale='log',
estimate_ci=True)
return fig
def test_probplot_prob_bestfit(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data,
ax=ax,
datalabel='Test xlabel',
bestfit=True,
datascale='log',
estimate_ci=True)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_qq(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(
plot_data,
ax=ax,
plottype="qq",
datalabel="Test label",
datascale="log",
scatter_kws=dict(color="r"),
)
return fig
def test_probplot_qq_bestfit(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(
plot_data,
ax=ax,
plottype="qq",
bestfit=True,
problabel="Test label",
datascale="log",
estimate_ci=True,
)
return fig
def test_probplot_prob_bestfit_probax_y(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(
plot_data,
ax=ax,
datalabel="Test xlabel",
bestfit=True,
datascale="log",
probax="y",
estimate_ci=True,
)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_prob_bestfit_exceedance(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(
plot_data,
ax=ax,
datalabel="Test xlabel",
bestfit=True,
datascale="log",
estimate_ci=True,
pp_kws={"exceedance": True},
)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_pp(plot_data):
fig, ax = plt.subplots()
scatter_kws = dict(color='b',
linestyle='--',
markeredgecolor='g',
markerfacecolor='none')
fig = viz.probplot(plot_data,
ax=ax,
plottype='pp',
datascale='linear',
datalabel='test x',
problabel='test y',
scatter_kws=scatter_kws)
return fig
def test_probplot_beta_dist_best_fit_y(plot_data):
fig, (ax1, ax2) = plt.subplots(ncols=2)
dist = stats.beta(3, 3)
fig = viz.probplot(plot_data,
dist=dist,
ax=ax1,
problabel='Beta scale',
bestfit=True,
datascale='log',
probax='y')
ax1.set_ylim(bottom=0.5, top=98)
fig = viz.probplot(plot_data,
ax=ax2,
datalabel='Default (norm)',
bestfit=True,
datascale='log',
probax='y',
estimate_ci=True)
ax2.set_ylim(bottom=0.5, top=98)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_pp_bestfit(plot_data):
fig, ax = plt.subplots()
scatter_kws = {'marker': 's', 'color': 'red'}
line_kws = {'linestyle': '--', 'linewidth': 3}
fig = viz.probplot(plot_data,
ax=ax,
plottype='pp',
datascale='linear',
datalabel='test x',
bestfit=True,
problabel='test y',
scatter_kws=scatter_kws,
line_kws=line_kws,
estimate_ci=True)
return fig
def test_probplot_pp(plot_data):
fig, ax = plt.subplots()
scatter_kws = dict(
color="b", linestyle="--", markeredgecolor="g", markerfacecolor="none"
)
fig = viz.probplot(
plot_data,
ax=ax,
plottype="pp",
datascale="linear",
datalabel="test x",
problabel="test y",
scatter_kws=scatter_kws,
)
return fig
def test_probplot_pp_bestfit(plot_data):
fig, ax = plt.subplots()
scatter_kws = {"marker": "s", "color": "red"}
line_kws = {"linestyle": "--", "linewidth": 3}
fig = viz.probplot(
plot_data,
ax=ax,
plottype="pp",
datascale="linear",
datalabel="test x",
bestfit=True,
problabel="test y",
scatter_kws=scatter_kws,
line_kws=line_kws,
estimate_ci=True,
)
return fig
def test_probplot_qq_bestfit_probax_y(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data, ax=ax, plottype='qq', bestfit=True, problabel='Test label',
datascale='log', probax='y', estimate_ci=True)
return fig
def test_probplot_prob_bestfit_probax_y(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data, ax=ax, datalabel='Test xlabel', bestfit=True,
datascale='log', probax='y', estimate_ci=True)
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_qq_probax_y(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data, ax=ax, plottype='qq', problabel='Test label', probax='y',
datascale='log', scatter_kws=dict(color='r'))
return fig
def test_probplot_prob_probax_y(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data, ax=ax, datalabel='Test xlabel', datascale='log', probax='y')
assert isinstance(fig, plt.Figure)
return fig
def test_probplot_color_and_label(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data, ax=ax, color="pink", label="A Top-Level Label")
ax.legend(loc="lower right")
return fig
def test_probplot_qq_bestfit_probax_y():
fig, ax = plt.subplots()
data = setup_plot_data()
fig = viz.probplot(data, ax=ax, axtype='qq', bestfit=True, ylabel='Test label', otherscale='log', probax='y')
def test_probplot_prob_bestfit_probax_y():
fig, ax = plt.subplots()
data = setup_plot_data()
fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', bestfit=True, otherscale='log', probax='y')
nt.assert_true(isinstance(fig, plt.Figure))
def test_probplot_qq_dist(plot_data):
fig, ax = plt.subplots()
norm = stats.norm(*stats.norm.fit(plot_data))
fig = viz.probplot(plot_data, ax=ax, plottype='qq', dist=norm,
datalabel='Test label')
return fig
def test_probplot_pp_probax_y():
fig, ax = plt.subplots()
data = setup_plot_data()
scatter_kws = dict(color='b', linestyle='--', markeredgecolor='g', markerfacecolor='none')
fig = viz.probplot(data, ax=ax, axtype='pp', otherscale='linear', probax='y',
xlabel='test x', ylabel='test y', scatter_kws=scatter_kws)
def test_probplot_color_and_label(plot_data):
fig, ax = plt.subplots()
fig = viz.probplot(plot_data, ax=ax, color='pink', label='A Top-Level Label')
ax.legend(loc='lower right')
return fig
def test_probplot_qq_probax_y():
fig, ax = plt.subplots()
data = setup_plot_data()
fig = viz.probplot(data, ax=ax, axtype='qq', ylabel='Test label', probax='y',
otherscale='log', scatter_kws=dict(color='r'))