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contour

simple_contour(fun, min_x=-1, max_x=1, min_y=-1, max_y=1, min_z=None, max_z=None, n_samples=100, n_levels=30)

Simple contour plot

Parameters:

Name Type Description Default
fun _type_

description

required
min_x int

description. Defaults to -1.

-1
max_x int

description. Defaults to 1.

1
min_y int

description. Defaults to -1.

-1
max_y int

description. Defaults to 1.

1
min_z int

description. Defaults to 0.

None
max_z int

description. Defaults to 1.

None
n_samples int

description. Defaults to 100.

100
n_levels int

description. Defaults to 5.

30

Examples:

>>> import matplotlib.pyplot as plt
    import numpy as np
    from spotpython.fun.objectivefunctions import analytical
    fun = analytical().fun_branin
    simple_contour(fun=fun, n_levels=30, min_x=-5, max_x=10, min_y=0, max_y=15)
Source code in spotpython/plot/contour.py
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def simple_contour(
    fun,
    min_x=-1,
    max_x=1,
    min_y=-1,
    max_y=1,
    min_z=None,
    max_z=None,
    n_samples=100,
    n_levels=30,
):
    """
    Simple contour plot

    Args:
        fun (_type_): _description_
        min_x (int, optional): _description_. Defaults to -1.
        max_x (int, optional): _description_. Defaults to 1.
        min_y (int, optional): _description_. Defaults to -1.
        max_y (int, optional): _description_. Defaults to 1.
        min_z (int, optional): _description_. Defaults to 0.
        max_z (int, optional): _description_. Defaults to 1.
        n_samples (int, optional): _description_. Defaults to 100.
        n_levels (int, optional): _description_. Defaults to 5.

    Examples:
        >>> import matplotlib.pyplot as plt
            import numpy as np
            from spotpython.fun.objectivefunctions import analytical
            fun = analytical().fun_branin
            simple_contour(fun=fun, n_levels=30, min_x=-5, max_x=10, min_y=0, max_y=15)

    """
    XX, YY = np.meshgrid(np.linspace(min_x, max_x, n_samples), np.linspace(min_y, max_y, n_samples))
    zz = np.array([fun(np.array([xi, yi]).reshape(-1, 2)) for xi, yi in zip(np.ravel(XX), np.ravel(YY))]).reshape(n_samples, n_samples)
    fig, ax = plt.subplots(figsize=(5, 2.7), layout="constrained")
    if min_z is None:
        min_z = np.min(zz)
    if max_z is None:
        max_z = np.max(zz)
    plt.contourf(
        XX,
        YY,
        zz,
        levels=np.linspace(min_z, max_z, n_levels),
        zorder=1,
        cmap="jet",
        vmin=min_z,
        vmax=max_z,
    )
    plt.colorbar()