<jupyter_start><jupyter_text>`tueplots` provides a few prepackaged bundles that can be plugged right into matplotlib's context manager.<jupyter_code>bundles.icml2022()<jupyter_output><empty_output><jupyter_text>Compare the default plots to the context plots below.<jupyter_code>fig, ax = plt.subplots()
ax.plot([1.0, 2.0], [3.0, 4.0])
ax.set_title("Title")
ax.set_xlabel("xlabel")
ax.set_ylabel("ylabel")
plt.show()
with plt.rc_context(bundles.neurips2021()):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0])
    ax.set_title("Title")
    ax.set_xlabel("xlabel")
    ax.set_ylabel("ylabel")
    plt.show()
with plt.rc_context(bundles.jmlr2001_tex(family="serif")):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0])
    ax.set_title("Title")
    ax.set_xlabel("xlabel")
    ax.set_ylabel("ylabel")
    plt.show()<jupyter_output><empty_output><jupyter_text>To get some (subjective) default behaviour, combine the bundles with `axes.lines()` (which is highly customisable, but has some opinionated default arguments).<jupyter_code>with plt.rc_context({**bundles.neurips2021(), **axes.lines()}):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0])
    ax.set_title("Title")
    ax.set_xlabel("xlabel")
    ax.set_ylabel("ylabel")
    plt.grid()
    plt.show()
with plt.rc_context(bundles.neurips2021_tex(family="serif")):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0])
    ax.set_title("Title")
    ax.set_xlabel("xlabel $\int f(x) dx$")
    ax.set_ylabel("ylabel $x \sim \mathcal{N}(x)$")
    plt.grid()
    plt.show()
with plt.rc_context({**bundles.icml2022_tex(), **axes.lines()}):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0], label="p(x)")
    ax.set_title("Title")
    ax.set_xlabel("xlabel $\int f(x) dx$")
    ax.set_ylabel("ylabel $x \sim \mathcal{N}(x)$")
    plt.grid()
    plt.legend()
    plt.show()
with plt.rc_context(bundles.aistats2022_tex()):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0], label="p(x)")
    ax.set_title("Title")
    ax.set_xlabel("xlabel $\int f(x) dx$")
    ax.set_ylabel("ylabel $x \sim \mathcal{N}(x)$")
    plt.grid()
    plt.legend()
    plt.show()
with plt.rc_context({**bundles.aistats2022_tex(family="serif"), **axes.lines()}):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0], label="p(x)")
    ax.set_title("Title")
    ax.set_xlabel("xlabel $\int f(x) dx$")
    ax.set_ylabel("ylabel $x \sim \mathcal{N}(x)$")
    plt.grid()
    plt.legend()
    plt.show()
with plt.rc_context(bundles.beamer_moml()):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0], label="p(x)")
    ax.legend()
    ax.set_title("Title")
    ax.set_xlabel("xlabel")
    ax.set_ylabel("ylabel")
    ax.grid()
    plt.show()
with plt.rc_context(bundles.beamer_moml_dark_bg()):
    fig, ax = plt.subplots()
    ax.plot([1.0, 2.0], [3.0, 4.0])
    ax.set_title("Title")
    ax.set_xlabel("xlabel")
    ax.set_ylabel("ylabel")
    ax.grid()
    plt.show()<jupyter_output><empty_output>
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