Use codebase.visualize(fig) to view Plotly charts in the Codegen IDE

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Plotly Customization

Plotly offers a wide range of customization options to create professional and interactive data visualizations. This guide will walk you through some of the key features for enhancing your charts.

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Layout Customization

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Title and Axes

Customize the overall layout of your chart, including titles, axes labels, and font styles:

fig.update_layout(
    title="My Custom Chart",
    title_font=dict(size=24, color="darkblue", family="Arial"),
    xaxis_title="X-Axis Label",
    yaxis_title="Y-Axis Label",
    font=dict(size=14, family="Arial")
)

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Margins and Sizing

Adjust the margins and overall size of your plot:

fig.update_layout(
    width=800,
    height=600,
    margin=dict(l=50, r=50, t=80, b=50)
)

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Background and Grid

Modify the plot background, grid lines, and zero lines:

fig.update_layout(
    plot_bgcolor="lightgray",
    paper_bgcolor="white",
    xaxis=dict(showgrid=True, gridcolor="lightgray", zeroline=True, zerolinecolor="black"),
    yaxis=dict(showgrid=True, gridcolor="lightgray", zeroline=True, zerolinecolor="black")
)

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Trace Customization

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Colors and Styles

Customize the appearance of individual traces:

fig.update_traces(
    marker=dict(color="royalblue", size=10, line=dict(color="darkblue", width=2)),
    line=dict(color="firebrick", width=3, dash="dot"),
    opacity=0.8
)

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Hover Information

Enhance the hover information displayed when interacting with data points:

fig.update_traces(
    hoverinfo="text+x+y",
    hovertext=["Custom hover text 1", "Custom hover text 2", "Custom hover text 3"],
    hoverlabel=dict(bgcolor="white", font_size=12, font_family="Rockwell")
)

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Advanced Customization

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Subplots

Create multi-plot layouts using make_subplots:

from plotly.subplots import make_subplots

fig = make_subplots(rows=2, cols=2, subplot_titles=("Plot 1", "Plot 2", "Plot 3", "Plot 4"))

# Add traces to subplots
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Bar(x=[1, 2, 3], y=[2, 3, 1]), row=1, col=2)
fig.add_trace(go.Heatmap(z=[[1, 2], [3, 4]]), row=2, col=1)
fig.add_trace(go.Box(y=[1, 2, 3, 4, 5]), row=2, col=2)

fig.update_layout(height=800, width=800, title_text="Subplot Example")

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Animations

Create animated charts using frames:

import plotly.graph_objects as go

# Create frames for animation
frames = [go.Frame(data=[go.Scatter(x=[1, 2], y=[i, i**2])]) for i in range(10)]

fig = go.Figure(
    data=[go.Scatter(x=[1, 2], y=[0, 0])],
    layout=go.Layout(
        title="Simple Animation",
        xaxis=dict(range=[0, 3], autorange=False),
        yaxis=dict(range=[0, 20], autorange=False),
        updatemenus=[dict(
            type="buttons",
            buttons=[dict(label="Play", method="animate", args=[None])]
        )]
    ),
    frames=frames
)

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Custom Shapes and Annotations

Add shapes and annotations to your plots:

fig.update_layout(
    shapes=[
        dict(
            type="rect",
            xref="x", yref="y",
            x0=1, y0=1, x1=2, y1=3,
            line=dict(color="red", width=2),
            fillcolor="rgba(255, 0, 0, 0.2)",
        ),
    ],
    annotations=[
        dict(
            x=2.5, y=4,
            xref="x", yref="y",
            text="Important Point",
            showarrow=True,
            arrowhead=2,
            arrowsize=1,
            arrowwidth=2,
            arrowcolor="black",
        )
    ]
)

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Interactivity

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Zoom and Pan

Enable or disable zoom and pan functionality:

fig.update_layout(
    xaxis=dict(fixedrange=False),  # Enable x-axis zoom
    yaxis=dict(fixedrange=True),   # Disable y-axis zoom
    dragmode="pan"  # Set default drag mode to pan
)

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Click Events

Capture click events on your chart (requires additional JavaScript):

fig.update_layout(
    clickmode="event+select"
)

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Customizable Buttons

Add custom buttons for interactivity:

fig.update_layout(
    updatemenus=[
        dict(
            type="buttons",
            direction="left",
            buttons=[
                dict(label="Linear Scale", method="relayout", args=["yaxis.type", "linear"]),
                dict(label="Log Scale", method="relayout", args=["yaxis.type", "log"]),
            ],
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

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Theming

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Built-in Themes

Apply pre-defined themes to your charts:

import plotly.io as pio

pio.templates.default = "plotly_dark"  # Set default theme
# Other options: "plotly", "plotly_white", "simple_white", "ggplot2", "seaborn", "none"

# Or apply theme to a specific figure
fig.update_layout(template="seaborn")

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Custom Themes

Create and apply custom themes:

import plotly.graph_objects as go
import plotly.io as pio

my_theme = go.layout.Template(
    layout=go.Layout(
        font=dict(family="Arial", size=14),
        title=dict(font=dict(size=24, color="darkblue")),
        plot_bgcolor="lightgray",
        paper_bgcolor="white",
        colorway=["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"],
    )
)

pio.templates["my_custom_theme"] = my_theme
fig.update_layout(template="my_custom_theme")

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Performance Optimization

For large datasets, consider using these optimization techniques:

  • Use Scattergl instead of Scatter for improved rendering of many points.
  • Implement data decimation to reduce the number of points plotted.
  • Use webgl rendering for 3D plots.
import plotly.graph_objects as go

# Example of using Scattergl for better performance
fig = go.Figure(data=go.Scattergl(x=large_x_data, y=large_y_data, mode='markers'))

By leveraging these customization options, you can create highly tailored, interactive, and visually appealing charts with Plotly. Experiment with different combinations to find the perfect style for your data visualization needs.

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