Ternary Plots
- ternary_distance_clusters(feature1_dispersion: ndarray[Any, dtype[float64]], feature1_name: str, feature2_dispersion: ndarray[Any, dtype[float64]], feature2_name: str, feature3_dispersion: ndarray[Any, dtype[float64]], feature3_name: str, density_estimation: Union[Literal['histogram'], Literal['gaussian_kde']], bins: Optional[int] = None, contour_lines: int = 4, figsize: int = 4, clusters: Optional[ndarray[Any, dtype[_ScalarType_co]]] = None, min_cluster_size=30, mpl_params: dict = {'alpha': 0.3, 's': 1})
- Parameters
density_estimation (Union[Literal['histogram'], ~typing.Literal['gaussian_kde']]) – Controls the method by which density of the input space is estimated.
bins (Optional[int]) – How many bins to use for the histogram in each dimension when estimating the gradient. If fewer bins are chosen, the coloring will be more homogeneous and change gradually. If more bins are chosen, the coloring will vary more.
contour_lines (int) – How many contour lines to draw.
figsize (int) – Passed to matplotlib.pyplot.subplots.
clusters (Optional[ndarray[Any, dtype[_ScalarType_co]]]) – Labels for clusters, should be the same length as the distance matrices featurei_dispersion
min_cluster_size – Ignore clusters below the threshold size (density plots are somewhat useless when there are very few observations)
mpl_params (dict) – Passed to matplotlib.
feature1_name (str) –
feature2_name (str) –
feature3_name (str) –