.Carpet()

A carpet plot is a specialized visualization in Plotly that shows the interaction between two or more independent variables and one or more dependent variables in a two-dimensional plot. Carpet plots distinguish themselves from other plots by enabling more accurate interpolation of data points. They are created using the .Carpet() method of the graph_objects class.

Carpet plots are particularly useful in engineering, scientific research, and data analysis where visualizing relationships between multiple variables is necessary. They can serve as a foundation for other traces like scattercarpet and contourcarpet, which can specify a carpet plot on which they lie.

Syntax

plotly.graph_objects.Carpet(a=None, aaxis=None, b=None, baxis=None, carpet=None, cheaterslope=None, color=None, x=None, y=None, ...)

Parameters:

• a: An array containing values of the first parameter value
• b: A two-dimensional array of y coordinates at each carpet point
• x: A two-dimensional array of x coordinates at each carpet point. If omitted, the plot is a cheater plot with xaxis hidden by default
• y: A two-dimensional array of y coordinates at each carpet point
• aaxis: Sets the a-axis properties (can be provided as a dictionary)
• baxis: Sets the b-axis properties (can be provided as a dictionary)
• carpet: An identifier for this carpet, so that scattercarpet and contourcarpet traces can specify which carpet plot they lie on
• cheaterslope: The shift applied to each successive row of data in creating a cheater plot (used when x is omitted)
• color: Sets the default color for all elements

Return value:

Returns a carpet object that can be added to a figure created with plotly.graph_objects.Figure().

Example 1: Carpet Plot with a and b Parameters

To create a comprehensive carpet plot, the syntax should include a and b parameter values which define the coordinate system:

import plotly.graph_objects as go

fig = go.Figure(go.Carpet(
    a = [4, 4, 4, 4.5, 4.5, 4.5, 5, 5, 5, 6, 6, 6],
    b = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
    y = [2, 3.5, 4, 3, 4.5, 5, 5.5, 6.5, 7.5, 8, 8.5, 10]
))

fig.show()

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In this example, a and b arrays are included that define the coordinate system for our carpet plot. The values in these arrays correspond to the positions of the data points, creating a grid-like structure.

Example 2: Styling Carpet Plots for Better Visualization

The carpet plot can be styled using axis properties to make it more visually appealing and easier to interpret:

import plotly.graph_objects as go

fig = go.Figure(go.Carpet(
    a = [4, 4, 4, 4.5, 4.5, 4.5, 5, 5, 5, 6, 6, 6],
    b = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
    y = [2, 3.5, 4, 3, 4.5, 5, 5.5, 6.5, 7.5, 8, 8.5, 10],
    aaxis = dict(
        tickprefix = 'a = ',
        ticksuffix = 'm',
        smoothing = 1,
        minorgridcount = 9,
    ),
    baxis = dict(
        tickprefix = 'b = ',
        ticksuffix = 'Pa',
        smoothing = 1,
        minorgridcount = 9,
    )
))

fig.update_layout(
    plot_bgcolor = 'black',
    paper_bgcolor = 'black',
    font_color = 'white'
)

fig.show()

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​This example enhances the carpet plot by adding custom styling to the a and b axes. Tick prefixes and suffixes are included to indicate units, the smoothing parameter is adjusted, and minor grid lines are added. Additionally, the background colors are modified for better contrast.

Example 3: Advanced Carpet Plot with Cheatertype and Custom Layout

For more complex data relationships, different cheatertype values can be used and the layout can be adjusted to better represent the data:

import plotly.graph_objects as go

fig = go.Figure(go.Carpet(
    a = [4, 4.5, 5, 6],
    b = [1, 2, 3],
    y = [[2, 3, 5.5, 8],
         [3.5, 4.5, 6.5, 8.5],
         [4, 5, 7.5, 10]],
    cheaterslope = -5,
    aaxis = dict(cheatertype = 'index'),
    baxis = dict(cheatertype = 'value')
))

fig.show()

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This example demonstrates using a more advanced configuration where:

1. The y parameter is provided as an array of arrays, representing a 2D grid
2. We set cheaterslope to -5, which changes how rows are shifted
3. The cheatertype property is set differently for the a and b axes

The cheatertype parameter controls how axes are constructed. When set to 'index', the axis uses the index position of values, while ‘value’ uses the actual values.

Note: The layout of cheater plots depends upon the cheaterslope and axis cheatertype parameters, which can be adjusted to achieve the desired visual representation.

Carpet plots provide a powerful way to visualize and analyze multi-dimensional data on a two-dimensional surface, especially for applications in engineering, physics, and advanced data analysis.