Matplotlib Example Gallery

Autocorrelation Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")
plot_autocorr(data; var_names=("tau", "mu"))

gcf()

See plot_autocorr

Bayesian P-Value Posterior Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("regression1d")
plot_bpv(data)

gcf()

See plot_bpv

Bayesian P-Value with Median T Statistic Posterior Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("regression1d")
plot_bpv(data; kind="t_stat", t_stat="0.5")

gcf()

See plot_bpv

Compare Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

model_compare = compare(
    Dict(
        "Centered 8 schools" => load_arviz_data("centered_eight"),
        "Non-centered 8 schools" => load_arviz_data("non_centered_eight"),
    ),
)
plot_compare(model_compare; figsize=(12, 4))

gcf()

See compare, plot_compare

Density Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

centered_data = load_arviz_data("centered_eight")
non_centered_data = load_arviz_data("non_centered_eight")
plot_density(
    [centered_data, non_centered_data];
    data_labels=["Centered", "Non Centered"],
    var_names=["theta"],
    shade=0.1,
)

gcf()

See plot_density

Dist Plot

using Random
using Distributions
using PyPlot
using ArviZ

Random.seed!(308)

ArviZ.use_style("arviz-darkgrid")

a = rand(Poisson(4), 1000)
b = rand(Normal(0, 1), 1000)
_, ax = plt.subplots(1, 2; figsize=(10, 4))
plot_dist(a; color="C1", label="Poisson", ax=ax[1])
plot_dist(b; color="C2", label="Gaussian", ax=ax[2])

gcf()

See plot_dist

ELPD Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

d1 = load_arviz_data("centered_eight")
d2 = load_arviz_data("non_centered_eight")
plot_elpd(Dict("Centered eight" => d1, "Non centered eight" => d2); xlabels=true)

gcf()

See plot_elpd

Energy Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")
plot_energy(data; figsize=(12, 8))

gcf()

See plot_energy

ESS Evolution Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

idata = load_arviz_data("radon")
plot_ess(idata; var_names=["b"], kind="evolution")

gcf()

See plot_ess

ESS Local Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

idata = load_arviz_data("non_centered_eight")
plot_ess(idata; var_names=["mu"], kind="local", marker="_", ms=20, mew=2, rug=true)

gcf()

See plot_ess

ESS Quantile Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

idata = load_arviz_data("radon")
plot_ess(idata; var_names=["sigma"], kind="quantile", color="C4")

gcf()

See plot_ess

Forest Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

centered_data = load_arviz_data("centered_eight")
non_centered_data = load_arviz_data("non_centered_eight")
plot_forest(
    [centered_data, non_centered_data];
    model_names=["Centered", "Non Centered"],
    var_names=["mu"],
)
title("Estimated theta for eight schools model")

gcf()

See plot_forest

Ridge Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

rugby_data = load_arviz_data("rugby")
plot_forest(
    rugby_data;
    kind="ridgeplot",
    var_names=["defs"],
    linewidth=4,
    combined=true,
    ridgeplot_overlap=1.5,
    colors="blue",
    figsize=(9, 4),
)
title("Relative defensive strength\nof Six Nation rugby teams")

gcf()

See plot_forest

Plot HDI

using Random
using PyPlot
using ArviZ

Random.seed!(308)

ArviZ.use_style("arviz-darkgrid")

x_data = randn(100)
y_data = 2 .+ x_data .* 0.5
y_data_rep = 0.5 .* randn(200, 100) .+ transpose(y_data)
plot(x_data, y_data; color="C6")
plot_hdi(x_data, y_data_rep; color="k", plot_kwargs=Dict("ls" => "--"))

gcf()

See plot_hdi

Joint Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("non_centered_eight")
plot_pair(
    data;
    var_names=["theta"],
    coords=Dict("school" => ["Choate", "Phillips Andover"]),
    kind="hexbin",
    marginals=true,
    figsize=(10, 10),
)

gcf()

See plot_pair

KDE Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")

## Combine different posterior draws from different chains
obs = data.posterior_predictive["obs"].values
size_obs = size(obs)
y_hat = reshape(obs, prod(size_obs[1:2]), size_obs[3:end]...)

plot_kde(
    y_hat;
    label="Estimated Effect\n of SAT Prep",
    rug=true,
    plot_kwargs=Dict("linewidth" => 2, "color" => "black"),
    rug_kwargs=Dict("color" => "black"),
)

gcf()

See plot_kde

2d KDE

using Random
using PyPlot
using ArviZ

Random.seed!(308)

ArviZ.use_style("arviz-darkgrid")

plot_kde(rand(100), rand(100))

gcf()

See plot_kde

KDE Quantiles Plot

using Random
using Distributions
using PyPlot
using ArviZ

Random.seed!(308)

ArviZ.use_style("arviz-darkgrid")

dist = rand(Beta(rand(Uniform(0.5, 10)), 5), 1000)
plot_kde(dist; quantiles=[0.25, 0.5, 0.75])

gcf()

See plot_kde

Pareto Shape Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

idata = load_arviz_data("radon")
loo_data = loo(idata; pointwise=true)
plot_khat(loo_data; show_bins=true)

gcf()

See loo, plot_khat

LOO-PIT ECDF Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

idata = load_arviz_data("radon")

plot_loo_pit(idata; y="y", ecdf=true, color="maroon")

gcf()

See psislw, plot_loo_pit

LOO-PIT Overlay Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

idata = load_arviz_data("non_centered_eight")
plot_loo_pit(; idata=idata, y="obs", color="indigo")

gcf()

See plot_loo_pit

Quantile Monte Carlo Standard Error Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")
plot_mcse(data; var_names=["tau", "mu"], rug=true, extra_methods=true)

gcf()

See plot_mcse

Quantile MCSE Errobar Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("radon")
plot_mcse(data; var_names=["sigma_a"], color="C4", errorbar=true)

gcf()

See plot_mcse

Pair Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

centered = load_arviz_data("centered_eight")
coords = Dict("school" => ["Choate", "Deerfield"])
plot_pair(
    centered; var_names=["theta", "mu", "tau"], coords=coords, divergences=true, textsize=22
)

gcf()

See plot_pair

Hexbin Pair Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

centered = load_arviz_data("centered_eight")
coords = Dict("school" => ["Choate", "Deerfield"])
plot_pair(
    centered;
    var_names=["theta", "mu", "tau"],
    kind="hexbin",
    coords=coords,
    colorbar=true,
    divergences=true,
)

gcf()

See plot_pair

KDE Pair Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

centered = load_arviz_data("centered_eight")
coords = Dict("school" => ["Choate", "Deerfield"])
plot_pair(
    centered;
    var_names=["theta", "mu", "tau"],
    kind="kde",
    coords=coords,
    divergences=true,
    textsize=22,
)

gcf()

See plot_pair

Point Estimate Pair Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

centered = load_arviz_data("centered_eight")
coords = Dict("school" => ["Choate", "Deerfield"])
plot_pair(
    centered;
    var_names=["mu", "theta"],
    kind=["scatter", "kde"],
    kde_kwargs=Dict("fill_last" => false),
    marginals=true,
    coords=coords,
    point_estimate="median",
    figsize=(10, 8),
)

gcf()

See plot_pair

Parallel Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")
ax = plot_parallel(data; var_names=["theta", "tau", "mu"])
ax.set_xticklabels(ax.get_xticklabels(); rotation=70)
draw()

gcf()

See plot_parallel

Posterior Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")
coords = Dict("school" => ["Choate"])
plot_posterior(data; var_names=["mu", "theta"], coords=coords, rope=(-1, 1))

gcf()

See plot_posterior

Posterior Predictive Check Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("non_centered_eight")
plot_ppc(data; data_pairs=Dict("obs" => "obs"), alpha=0.03, figsize=(12, 6), textsize=14)

gcf()

See plot_ppc

Posterior Predictive Check Cumulative Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("non_centered_eight")
plot_ppc(data; alpha=0.3, kind="cumulative", figsize=(12, 6), textsize=14)

gcf()

See plot_ppc

Rank Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("centered_eight")
plot_rank(data; var_names=("tau", "mu"))

gcf()

See plot_rank

Separation Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("classification10d")
plot_separation(data; y="outcome", y_hat="outcome", figsize=(8, 1))

gcf()

See plot_separation

Trace Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("non_centered_eight")
plot_trace(data; var_names=("tau", "mu"))

gcf()

See plot_trace

Violin Plot

using PyPlot
using ArviZ

ArviZ.use_style("arviz-darkgrid")

data = load_arviz_data("non_centered_eight")
plot_violin(data; var_names=["mu", "tau"])

gcf()

See plot_violin

Styles

using PyPlot
using PyCall
using Distributions
using ArviZ

x = range(0, 1; length=100)
dist = pdf.(Beta(2, 5), x)

style_list = [
    "default",
    ["default", "arviz-colors"],
    "arviz-darkgrid",
    "arviz-whitegrid",
    "arviz-white",
]

fig = figure(; figsize=(12, 12))
for (idx, style) in enumerate(style_list)
    @pywith plt.style.context(style) begin
        ax = fig.add_subplot(3, 2, idx; label=idx)
        for i in 0:9
            ax.plot(x, dist .- i, "C$i"; label="C$i")
        end
        ax.set_title(style)
        ax.set_xlabel("x")
        ax.set_ylabel("f(x)"; rotation=0, labelpad=15)
        ax.legend(; bbox_to_anchor=(1, 1))
        draw()
    end
end
tight_layout()

gcf()