Contour Plot¶

General¶

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s also import numpy to create a random dataset.

import numpy as np

Let’s generate a dataset using the following data.

N = 100 # Number of records

data = vo.VastFrame({
    "x": np.random.normal(5, 1, N),
    "y": np.random.normal(8, 1.5, N),
})

Let’s define the function that we will utilize to create the contour plot. This function will play a crucial role in generating the color map.

def f(x, y):
    return x ** 2 - y + 1

In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.

Note

To select the desired plotting library, we simply need to use the set_option() function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.

Please click on the tabs to view the various graphics generated by the different plotting libraries.

Plotly

We can switch to using the plotly module.

vo.set_option("plotting_lib", "plotly")

vastorbit’s contour plot feature offers a concise and insightful visualization of the relationships between two continuous variables on the X and Y axes and a function of these two variables. This relationship is vividly portrayed through contour lines or color gradients, simplifying the exploration of complex datasets and enhancing data analysis capabilities.

data.contour(columns = ["x", "y"], func = f)

Matplotlib

We can switch to using the matplotlib module.

vo.set_option("plotting_lib", "matplotlib")

data.contour(columns = ["x", "y"], func = f)

Note

Machine learning models, particularly regression and classification models with two predictors, can benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios.

Chart Customization¶

vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. This customization extends to essential elements such as color schemes, text labels, and plot sizes, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit’s customization options enable you to craft graphics that suit your exact needs.

Important

Different customization parameters are available for Plotly and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib.

Colors¶

Plotly

Custom CMAP

data.contour(columns = ["x", "y"], func = f, colorscale = [[0, "white"], [1, "red"]])

Matplolib

Custom CMAP

data.contour(columns = ["x", "y"], func = f, cmap = "Reds")

Size¶

Plotly

Custom Width and Height

data.contour(columns = ["x", "y"], func = f, width = 300, height = 300)

Matplolib

Custom Width and Height

data.contour(columns = ["x", "y"], func = f, width = 6, height = 3)

Text¶

Plotly

Custom Title

data.contour(columns = ["x", "y"], func = f).update_layout(title_text = "Custom Title")

Custom Axis Titles

data.contour(columns = ["x", "y"], func = f, yaxis_title = "Custom Y-Axis Title")

Matplolib

Custom Title Text

data.contour(columns = ["x", "y"], func = f).set_title("Custom Title")

Custom Axis Titles

data.contour(columns = ["x", "y"], func = f).set_xlabel("Custom X Axis")