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Remove varname from legend in plotppc #1559

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Feb 14, 2021
Merged

Remove varname from legend in plotppc #1559

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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
### Maintenance and fixes
* Updated `from_cmdstan` and `from_numpyro` converter to follow schema convention ([1541](https://github.com/arviz-devs/arviz/pull/1541) and [1525](https://github.com/arviz-devs/arviz/pull/1525))
* Fix calculation of mode as point estimate ([1552](https://github.com/arviz-devs/arviz/pull/1552))
* Remove variable name from legend in posterior predictive plot ([1559](https://github.com/arviz-devs/arviz/pull/1559))

### Deprecation
* Removed Geweke diagnostic ([1545](https://github.com/arviz-devs/arviz/pull/1545))
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26 changes: 11 additions & 15 deletions arviz/plots/backends/matplotlib/ppcplot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,14 +124,12 @@ def plot_ppc(
plot_kwargs = {"color": color, "alpha": alpha, "linewidth": 0.5 * linewidth}
if dtype == "i":
plot_kwargs["drawstyle"] = "steps-pre"
ax_i.plot(
[], color=color, label="{} predictive {}".format(group.capitalize(), pp_var_name)
)
ax_i.plot([], color=color, label="{} predictive".format(group.capitalize()))
if observed:
if dtype == "f":
plot_kde(
obs_vals,
label="Observed {}".format(var_name),
label="Observed",
plot_kwargs={"color": "k", "linewidth": linewidth, "zorder": 3},
fill_kwargs={"alpha": 0},
ax=ax_i,
Expand All @@ -144,7 +142,7 @@ def plot_ppc(
ax_i.plot(
bin_edges,
hist,
label="Observed {}".format(var_name),
label="Observed",
color="k",
linewidth=linewidth,
zorder=3,
Expand Down Expand Up @@ -179,7 +177,7 @@ def plot_ppc(
ax_i.plot(x_s, y_s, **plot_kwargs)

if mean:
label = "{} predictive mean {}".format(group.capitalize(), pp_var_name)
label = "{} predictive mean".format(group.capitalize())
if dtype == "f":
rep = len(pp_densities)
len_density = len(pp_densities[0])
Expand Down Expand Up @@ -224,7 +222,7 @@ def plot_ppc(
*_empirical_cdf(obs_vals),
color="k",
linewidth=linewidth,
label="Observed {}".format(var_name),
label="Observed",
drawstyle=drawstyle,
zorder=3
)
Expand Down Expand Up @@ -253,15 +251,15 @@ def plot_ppc(
drawstyle=drawstyle,
linewidth=linewidth
)
ax_i.plot([], color=color, label="Posterior predictive {}".format(pp_var_name))
ax_i.plot([], color=color, label="Posterior predictive")
if mean:
ax_i.plot(
*_empirical_cdf(pp_vals.flatten()),
color=color,
linestyle="--",
linewidth=linewidth * 1.5,
drawstyle=drawstyle,
label="Posterior predictive mean {}".format(pp_var_name)
label="Posterior predictive mean"
)
ax_i.set_yticks([0, 0.5, 1])

Expand All @@ -276,7 +274,7 @@ def plot_ppc(
"linewidth": linewidth * 1.5,
"zorder": 3,
},
label="Posterior predictive mean {}".format(pp_var_name),
label="Posterior predictive mean",
ax=ax_i,
legend=legend,
)
Expand All @@ -290,7 +288,7 @@ def plot_ppc(
hist,
color=color,
linewidth=linewidth * 1.5,
label="Posterior predictive mean {}".format(pp_var_name),
label="Posterior predictive mean",
zorder=3,
linestyle="--",
drawstyle="steps-pre",
Expand All @@ -316,7 +314,7 @@ def plot_ppc(
color="k",
markersize=markersize,
alpha=alpha,
label="Observed {}".format(var_name),
label="Observed",
zorder=4,
)

Expand All @@ -340,9 +338,7 @@ def plot_ppc(
vals, yvals, "o", zorder=2, color=color, markersize=markersize, alpha=alpha
)

ax_i.plot(
[], color=color, marker="o", label="Posterior predictive {}".format(pp_var_name)
)
ax_i.plot([], color=color, marker="o", label="Posterior predictive")

ax_i.set_yticks([])

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8 changes: 8 additions & 0 deletions arviz/tests/base_tests/test_plots_matplotlib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -794,6 +794,14 @@ def test_plot_ppc_bad_ax(models, fig_ax):
plot_ppc(models.model_1, ax=ax2)


def test_plot_legend(models):
axes = plot_ppc(models.model_1)
legend_texts = axes.get_legend().get_texts()
result = [i.get_text() for i in legend_texts]
expected = ["Posterior predictive", "Observed", "Posterior predictive mean"]
assert result == expected


@pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"]))
def test_plot_violin(models, var_names):
axes = plot_violin(models.model_1, var_names=var_names)
Expand Down