Plot

gl.Plot is the main widget. It renders a single Gladly plot inside a Jupyter cell.

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Constructor

plot = gl.Plot(config, data)

Parameter	Type	Description
config	dict	Gladly plot configuration. Passed through to JS verbatim — any valid Gladly config works.
data	Data \| DataGroup \| pd.DataFrame \| dict	The data to plot. Auto-wrapped if not already a Data or DataGroup.

The widget is displayed when the Plot object is returned from or assigned in a notebook cell (standard IPython / Jupyter display protocol).

Config dict

The config dict is the same structure as Gladly’s JS plot.update({ config }) argument. All keys are optional:

config = {
    # List of layers. Each item is a one-key dict: { layer_type_name: params }.
    "layers": [
        {"points":    {"xData": "input.x", "yData": "input.y"}},
        {"lines":     {"xData": "input.x", "yData": "input.y"}},
        {"bars":      {"xData": "input.x", "yData": "input.y"}},
        {"histogram": {"data":  "input.y", "bins": 100}},
        {"tile":      {"url":   "https://tile.openstreetmap.org/{z}/{x}/{y}.png"}},
    ],

    # Named transforms applied before layers.
    "transforms": [
        {"name": "h", "transform": {"HistogramData": {"input": "input.y", "bins": 50}}}
    ],

    # Axis configuration. Spatial axes: xaxis_bottom, xaxis_top, yaxis_left, yaxis_right.
    # Color/filter axes use the quantity kind name as the key.
    "axes": {
        "xaxis_bottom": {"label": "Time (s)", "min": 0.0, "max": 100.0, "scale": "linear"},
        "yaxis_left":   {"label": "Depth (m)", "scale": "log"},
        "amplitude":    {"colorscale": "viridis", "colorbar": "vertical"},
    },

    # Floating colorbar widgets.
    "colorbars": [
        {"xAxis": "frequency", "yAxis": "amplitude", "colorscale": "plasma"}
    ],
}

For the full list of layer types and their parameters, see the Gladly layer type docs.

Column references

Columns are referenced in the config as "groupname.colname":

• Single Data / DataFrame → group name is always "input" (e.g. "input.time")
• DataGroup → use the name you gave each source (e.g. "raw.time", "filtered.time")

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plot.update(config=None, data=None)

Update the config and/or data after the plot has been created. Mirrors JS plot.update({ config, data }).

# Update config only (e.g. change axis labels)
plot.update(config={"layers": [...], "axes": {...}})

# Update data only (re-streams all columns)
plot.update(data=new_df)

# Update both
plot.update(config=new_config, data=new_df)

Config updates are applied immediately via the anywidget trait sync. Data updates re-register all columns with the kernel HTTP server and update the column metadata.

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plot.axis

Attribute-access proxy that returns Axis proxy objects by name. Each name maps to a Gladly axis ID.

# Spatial axes
x = plot.axis.xaxis_bottom
x = plot.axis.xaxis_top
y = plot.axis.yaxis_left
y = plot.axis.yaxis_right

# Color or filter axis (use the quantity kind name)
c = plot.axis.amplitude

The same Axis object is returned on every access (cached). This means identity is stable, which is required for link_axes to store links correctly.

See Linking for how to use axes.

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plot.get_config(timeout=5.0) → dict

Request the current plot configuration from the browser, including live axis domains (current zoom/pan state). Blocks the Python kernel until the JS responds or the timeout expires.

config = plot.get_config()
print(config["axes"]["xaxis_bottom"])  # {'min': 3.2, 'max': 7.8, ...}

Returns None if the widget has not been displayed or the timeout expires.

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plot.schema(timeout=5.0) → dict

Request the JSON Schema for the plot configuration from the browser. The schema is data-dependent (it enumerates available column names as enum values for data attribute fields). Blocks until the JS responds.

import json
schema = plot.schema()
print(json.dumps(schema, indent=2))

Returns None if the widget has not been displayed or the timeout expires.

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await plot.async_get_config(timeout=5.0) → dict

Async variant of get_config(). Use in async notebook cells (JupyterLab supports await at the top level):

config = await plot.async_get_config()

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await plot.async_schema(timeout=5.0) → dict

Async variant of schema().

schema = await plot.async_schema()

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Traits (advanced)

The following anywidget traits are synced between Python and JavaScript. Direct access is rarely needed.

Trait	Type	Description
config	dict	The current plot config. Setting this re-renders the plot.
_widget_id	str	UUID identifying this widget’s data on the kernel HTTP server.
_kernel_port	int	Port of the kernel HTTP server.
_meta	dict	Column metadata (length, quantityKind, min, max) per column.
_is_group	bool	Whether the data is a DataGroup.
_registrations	str	JS code for module-level registrations, applied before rendering.
_links	list	Active axis link records used for cross-widget linking.