pass distance matrix to seaborn clustermap

Plot a matrix dataset as a hierarchically-clustered heatmap.,Distance metric to use for the data. See scipy.spatial.distance.pdist() documentation for more options. To use different metrics (or methods) for rows and columns, you may construct each linkage matrix yourself and provide them as {row,col}_linkage.,Linkage method to use for calculating clusters. See scipy.cluster.hierarchy.linkage() documentation for more information.,Precomputed linkage matrix for the rows or columns. See scipy.cluster.hierarchy.linkage() for specific formats.

>>>
import seaborn as sns;
sns.set_theme(color_codes = True) >>>
   iris = sns.load_dataset("iris") >>>
   species = iris.pop("species") >>>
   g = sns.clustermap(iris)

>>> g = sns.clustermap(iris,
   ...figsize = (7, 5),
   ...row_cluster = False,
   ...dendrogram_ratio = (.1, .2),
   ...cbar_pos = (0, .2, .03, .4))

>>> lut = dict(zip(species.unique(), "rbg")) >>>
   row_colors = species.map(lut) >>>
   g = sns.clustermap(iris, row_colors = row_colors)

>>> g = sns.clustermap(iris, cmap = "mako", vmin = 0, vmax = 10)

>>> g = sns.clustermap(iris, metric = "correlation")

>>> g = sns.clustermap(iris, method = "single")

I want to pass my own distance matrix (row anycodings_seaborn linkages) to seaborn clustermap.,Is there an option where it purely uses my anycodings_seaborn distances and creates the linkages?,My distance matrix is already based on a anycodings_seaborn certain metric and method, why would I want anycodings_seaborn to recalculate this in scipy hierarchy anycodings_seaborn linkage ?,Use Distance Matrix in anycodings_seaborn scipy.cluster.hierarchy.linkage()?

#
import packages
from scipy.cluster
import hierarchy
import scipy.spatial.distance as ssd
import seaborn as sns

# define distance array as in linked answer
distArray = ssd.squareform(distMatrix)

# define linkage object
distLinkage = hierarchy.linkage(distArray)

# make clustermap
sns.clustermap(distMatrix, row_linkage = distLinkage, col_linkage = distLinkage)

See squareform for information on how to calculate the index of this entry or to convert the condensed distance matrix to a redundant square matrix.,converts between condensed distance matrices and square distance matrices.,Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors u and v which disagree. To save memory, the matrix X can be of type boolean.,Y = pdist(X, 'hamming') Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors u and v which disagree. To save memory, the matrix X can be of type boolean.

dm = pdist(X, lambda u, v: np.sqrt(((u - v) ** 2).sum()))

dm = pdist(X, sokalsneath)

dm = pdist(X, 'sokalsneath')