ansys.dyna.core.lib.mixins.curve_plotting.CurvePlottingMixin#

class ansys.dyna.core.lib.mixins.curve_plotting.CurvePlottingMixin#

Mixin that provides plotting capabilities for DefineCurve classes.

This mixin adds a .plot() method to curve classes, enabling 2D curve visualization using matplotlib. The method automatically applies scale factors and offsets (sfa, sfo, offa, offo) to the curve data.

Attributes expected from the host class:: curves: pd.DataFrame with ‘a1’ (abscissa) and ‘o1’ (ordinate) columns sfa: float - scale factor for abscissa sfo: float - scale factor for ordinate offa: float - offset for abscissa offo: float - offset for ordinate lcid: int - load curve ID (for plot title) title: str - optional title text

plot(apply_transforms: bool = True, title: str | None = None, xlabel: str | None = None, ylabel: str | None = None, show: bool = True, ax: Any | None = None, **kwargs) → Any#

Plot the curve using matplotlib.

Parameters:

apply_transformsbool, default=True: If True, apply scale factors (sfa, sfo) and offsets (offa, offo) to the curve data. If False, plot raw data from the curves table.
titlestr, optional: Plot title. If not provided, uses the curve’s lcid and title attribute.
xlabelstr, optional: X-axis label. Defaults to “Abscissa” (or “Time” if transforms applied).
ylabelstr, optional: Y-axis label. Defaults to “Ordinate” (or “Value” if transforms applied).
showbool, default=True: If True, display the plot immediately using plt.show(). If False, return the axes object for further customization.
axmatplotlib.axes.Axes, optional: Matplotlib axes to plot on. If None, creates a new figure and axes.
**kwargs: Additional keyword arguments passed to matplotlib’s plot() function (e.g., color, linewidth, linestyle, marker, etc.)

Returns:

matplotlib.axes.Axes: The axes object containing the plot. Can be used for further customization.

Raises:

ImportError: If matplotlib is not installed.
ValueError: If the curve data is empty or invalid.

Examples

>>> curve = DefineCurve(lcid=1)
>>> curve.curves = pd.DataFrame({"a1": [0, 1, 2], "o1": [0, 1, 4]})
>>> curve.plot()  # Plot with default settings

>>> curve.plot(apply_transforms=False, color='red', linewidth=2)  # Customize plot

>>> fig, ax = plt.subplots()
>>> curve.plot(ax=ax, show=False)  # Plot on existing axes
>>> ax.grid(True)
>>> plt.show()

get_curve_data(apply_transforms: bool = True) → Tuple[numpy.ndarray, numpy.ndarray]#

Get the curve data as numpy arrays.

Parameters:

apply_transformsbool, default=True: If True, apply scale factors (sfa, sfo) and offsets (offa, offo) to the curve data. If False, return raw data from the curves table.

Returns:

tuple of (x_data, y_data): Two numpy arrays containing the abscissa and ordinate values.

Raises:

AttributeError: If the keyword does not have curve data.
ValueError: If the curve data is empty or invalid.

Examples

>>> curve = DefineCurve(lcid=1)
>>> curve.curves = pd.DataFrame({"a1": [0, 1, 2], "o1": [0, 1, 4]})
>>> x, y = curve.get_curve_data()
>>> print(x)  # [0. 1. 2.]
>>> print(y)  # [0. 1. 4.]