plot_ts

plotly.time_series.plot_ts(
    data: np.ndarray,
    x: np.ndarray | None = None,
    names: list[str] | None = None,
    show: bool = False,
    use_resampler: bool = True,
)

Plot one or multiple time series with optional resampling.

Creates a multi-row subplot figure with one trace per row. Optionally uses plotly-resampler for efficient rendering of large time series data.

Parameters

Name	Type	Description	Default
data	np.ndarray	Data array with shape (n_samples,) for single time series or (n_samples, n_features) for multiple time series.	required
x	np.ndarray or None	Array of x-axis values with shape (n_samples,). If None, uses np.arange(data.shape[0]).	`None`
names	list of str or None	Labels for each time series trace. If None, generates default names like ‘y0’, ‘y1’, etc.	`None`
show	bool	If True, display the figure immediately.	`False`
use_resampler	bool	If True and plotly-resampler is installed, use FigureResampler for efficient large dataset rendering. If False or resampler not available, uses standard plotly Figure.	`True`

Returns

Name	Type	Description
	plotly.graph_objects.Figure	Figure object (FigureResampler if available and use_resampler=True, otherwise standard Figure).

Raises

Name	Type	Description
	DataShapeError	If data has more than 2 dimensions.

Notes

If use_resampler=True but plotly-resampler is not installed, a UserWarning will be issued and standard plotly Figure will be used instead.

Examples

>>> import numpy as np
>>> # Single time series
>>> data = np.random.randn(1000)
>>> fig = plot_ts(data)

>>> # Multiple time series with custom x-axis
>>> data = np.random.randn(1000, 3)
>>> x = np.linspace(0, 10, 1000)
>>> fig = plot_ts(data, x=x, names=['Signal A', 'Signal B', 'Signal C'])

>>> # Large dataset with resampling
>>> large_data = np.random.randn(1_000_000, 2)
>>> fig = plot_ts(large_data, use_resampler=True)

Notes

When plotly-resampler is installed and use_resampler=True, the function creates a FigureResampler which dynamically downsamples data during pan/zoom operations for improved performance with large datasets.

Without plotly-resampler, all data points are rendered which may cause performance issues with datasets larger than ~100k points.

# plot_ts { #mdu.plotly.time_series.plot_ts } ```python plotly.time_series.plot_ts( data: np.ndarray, x: np.ndarray | None = None, names: list[str] | None = None, show: bool = False, use_resampler: bool = True, ) ``` Plot one or multiple time series with optional resampling. Creates a multi-row subplot figure with one trace per row. Optionally uses plotly-resampler for efficient rendering of large time series data. ## Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |---------------|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------| | data | np.ndarray | Data array with shape (n_samples,) for single time series or (n_samples, n_features) for multiple time series. | _required_ | | x | np.ndarray or None | Array of x-axis values with shape (n_samples,). If None, uses np.arange(data.shape[0]). | `None` | | names | list of str or None | Labels for each time series trace. If None, generates default names like 'y0', 'y1', etc. | `None` | | show | bool | If True, display the figure immediately. | `False` | | use_resampler | bool | If True and plotly-resampler is installed, use FigureResampler for efficient large dataset rendering. If False or resampler not available, uses standard plotly Figure. | `True` | ## Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|-----------------------------|-------------------------------------------------------------------------------------------------| | | plotly.graph_objects.Figure | Figure object (FigureResampler if available and use_resampler=True, otherwise standard Figure). | ## Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|----------------|-------------------------------------| | | DataShapeError | If data has more than 2 dimensions. | ## Notes {.doc-section .doc-section-notes} If use_resampler=True but plotly-resampler is not installed, a UserWarning will be issued and standard plotly Figure will be used instead. ## Examples {.doc-section .doc-section-examples} ```python >>> import numpy as np >>> # Single time series >>> data = np.random.randn(1000) >>> fig = plot_ts(data) ``` ```python >>> # Multiple time series with custom x-axis >>> data = np.random.randn(1000, 3) >>> x = np.linspace(0, 10, 1000) >>> fig = plot_ts(data, x=x, names=['Signal A', 'Signal B', 'Signal C']) ``` ```python >>> # Large dataset with resampling >>> large_data = np.random.randn(1_000_000, 2) >>> fig = plot_ts(large_data, use_resampler=True) ``` ## Notes {.doc-section .doc-section-notes} When plotly-resampler is installed and use_resampler=True, the function creates a FigureResampler which dynamically downsamples data during pan/zoom operations for improved performance with large datasets. Without plotly-resampler, all data points are rendered which may cause performance issues with datasets larger than ~100k points.