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

Example

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

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.

    Example:
        >>> 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()