plot.contour

plot.contour

Functions

Name	Description
contourf_plot	Creates contour plots (single or faceted) using matplotlib.
mo_generate_plot_grid	Generate a grid of input variables and apply objective functions.
plotModel	Generate 2D contour and optionally 3D surface plots for a model’s predictions.
simple_contour	Simple contour plot

contourf_plot

plot.contour.contourf_plot(
    data,
    x_col,
    y_col,
    z_col,
    facet_col=None,
    aspect=1,
    as_table=True,
    figsize=(4, 4),
    levels=10,
    cmap='viridis',
    show_contour_lines=True,
    contour_line_color='black',
    contour_line_width=0.5,
    colorbar_orientation='vertical',
    wspace=0.4,
    hspace=0.4,
    highlight_point=None,
    highlight_color='red',
    highlight_marker='x',
    highlight_size=100,
)

Creates contour plots (single or faceted) using matplotlib.

Parameters

Name	Type	Description	Default
data	pd.DataFrame	Data for plotting.	required
x_col	str	Column name for x-axis.	required
y_col	str	Column name for y-axis.	required
z_col	str	Column name for z-axis (values).	required
facet_col	str	Column name for faceting.	None
aspect	float	Aspect ratio.	1
as_table	bool	If True, arranges facets in a table.	True
figsize	tuple	Figure size per plot key (if not faceted) or base.	(4, 4)
levels	int	Number of contour levels.	10
cmap	str	Colormap.	'viridis'
show_contour_lines	bool	Whether to show contour lines.	True
contour_line_color	str	Color of contour lines.	'black'
contour_line_width	float	Width of contour lines.	0.5
colorbar_orientation	str	‘vertical’ or ‘horizontal’.	'vertical'
wspace	float	Width reserved for space between subplots.	0.4
hspace	float	Height reserved for space between subplots.	0.4
highlight_point	dict - like	Point to highlight. Must contain x_col, y_col keys. If faceted, must contain facet_col key.	None
highlight_color	str	Color of the highlight point.	'red'
highlight_marker	str	Marker style for the highlight point.	'x'
highlight_size	int	Size of the highlight point.	100

mo_generate_plot_grid

plot.contour.mo_generate_plot_grid(variables, resolutions, functions)

Generate a grid of input variables and apply objective functions.

Parameters

Name	Type	Description	Default
variables	dict	A dictionary where keys are variable names (e.g., “time”, “temperature”) and values are tuples of (min_value, max_value).	required
resolutions	dict	A dictionary where keys are variable names and values are the number of points.	required
functions	dict	A dictionary where keys are function names and values are callable functions.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: A DataFrame containing the grid and the results of the objective functions.

plotModel

plot.contour.plotModel(
    model,
    lower,
    upper,
    i=0,
    j=1,
    min_z=None,
    max_z=None,
    var_type=None,
    var_name=None,
    show=True,
    filename=None,
    n_grid=50,
    contour_levels=10,
    dpi=200,
    title='',
    figsize=(12, 6),
    use_min=False,
    use_max=False,
    aspect_equal=True,
    legend_fontsize=12,
    cmap='viridis',
    X_points=None,
    y_points=None,
    plot_points=True,
    points_color='white',
    points_size=30,
    point_color_below='blue',
    point_color_above='red',
    atol=1e-06,
    plot_3d=False,
)

Generate 2D contour and optionally 3D surface plots for a model’s predictions.

Parameters

Name	Type	Description	Default
model	object	A model with a predict method.	required
lower	array_like	Lower bounds for each dimension.	required
upper	array_like	Upper bounds for each dimension.	required
i	int	Index for the x-axis dimension.	0
j	int	Index for the y-axis dimension.	1
min_z	float	Min value for color scaling. Defaults to None.	None
max_z	float	Max value for color scaling. Defaults to None.	None
var_type	list	Variable types for each dimension. Defaults to None.	None
var_name	list	Variable names for labeling axes. Defaults to None.	None
show	bool	Whether to display the plot. Defaults to True.	True
filename	str	File path to save the figure. Defaults to None.	None
n_grid	int	Resolution for each axis. Defaults to 50.	50
contour_levels	int	Number of contour levels. Defaults to 10.	10
dpi	int	DPI for saving. Defaults to 200.	200
title	str	Title for the figure. Defaults to ““.	''
figsize	tuple	Figure size. Defaults to (12, 6).	(12, 6)
use_min	bool	If True, leftover dims are set to lower bounds.	False
use_max	bool	If True, leftover dims are set to upper bounds.	False
aspect_equal	bool	Whether axes have equal scaling. Defaults to True.	True
legend_fontsize	int	Font size for labels and legends. Defaults to 12.	12
cmap	str	Colormap. Defaults to “viridis”.	'viridis'
X_points	ndarray	Original data points. Shape: (N, D).	None
y_points	ndarray	Original target values. Shape: (N,).	None
plot_points	bool	Whether to plot X_points. Defaults to True.	True
points_color	str	Fallback color for data points. Defaults to “white”.	'white'
points_size	int	Marker size for data points. Defaults to 30.	30
point_color_below	str	Color if actual z < predicted z. Defaults to “blue”.	'blue'
point_color_above	str	Color if actual z >= predicted z. Defaults to “red”.	'red'
atol	float	Absolute tolerance for comparing actual and predicted z-values. Defaults to 1e-6.	1e-06
plot_3d	bool	Whether to plot the 3D surface plot. Defaults to False.	False

Returns

Name	Type	Description
	(fig, (ax_contour, ax_surface))	Figure and axes for the contour and surface plots.

simple_contour

plot.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

Returns

Name	Type	Description
	None	None

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)