Visualize Morphospace

Plot specimens in reduced space with optional shape overlays.

Setup

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA

from ktch.datasets import load_outline_mosquito_wings
from ktch.harmonic import EllipticFourierAnalysis
from ktch.plot import explained_variance_ratio_plot, morphospace_plot

data = load_outline_mosquito_wings(as_frame=True)
coords = data.coords.to_numpy().reshape(-1, 100, 2)

efa = EllipticFourierAnalysis(n_harmonics=20)
coef = efa.fit_transform(coords)

pca = PCA(n_components=5)
scores = pca.fit_transform(coef)

df_pca = pd.DataFrame(scores, columns=[f"PC{i + 1}" for i in range(5)])
df_pca.index = data.meta.index
df_pca = df_pca.join(data.meta)

Basic scatter plot

fig, ax = plt.subplots()
sns.scatterplot(data=df_pca, x="PC1", y="PC2", hue="genus", palette="Paired", ax=ax)
ax.set_aspect("equal")

Morphospace with shape overlays

ax = morphospace_plot(
    data=df_pca,
    x="PC1", y="PC2", hue="genus",
    reducer=pca,
    descriptor=efa,
    palette="Paired",
    n_shapes=5,
    shape_scale=0.5,
)

Landmark morphospace with links

from ktch.datasets import load_landmark_mosquito_wings
from ktch.landmark import GeneralizedProcrustesAnalysis

data_lm = load_landmark_mosquito_wings(as_frame=True)
df_coords = (
    data_lm.coords.unstack()
    .swaplevel(1, 0, axis=1)
    .sort_index(axis=1)
)
df_coords.columns = [
    dim + "_" + str(idx) for idx, dim in df_coords.columns
]

gpa = GeneralizedProcrustesAnalysis().set_output(transform="pandas")
df_shapes = gpa.fit_transform(df_coords)

pca_lm = PCA(n_components=5).set_output(transform="pandas")
df_pca_lm = pca_lm.fit_transform(df_shapes)
df_pca_lm = df_pca_lm.join(data_lm.meta)

links = [
    [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
    [6, 7], [7, 8], [8, 9], [9, 10], [10, 11],
    [1, 12], [2, 12], [13, 14], [14, 3], [14, 4],
    [15, 5], [16, 6], [16, 7], [17, 8], [17, 9],
]

ax = morphospace_plot(
    data=df_pca_lm,
    x="pca0", y="pca1", hue="genus",
    reducer=pca_lm,
    n_dim=2,
    links=links,
    palette="Paired",
    n_shapes=5,
    shape_scale=1.0,
    shape_alpha=1.0,
    s=5,
)

Multiple component pairs

fig, axes = plt.subplots(2, 2, figsize=(16, 16), dpi=200)

for ax, (i, j) in zip(axes.flat[:3], [(0, 1), (1, 2), (2, 0)]):
    morphospace_plot(
        data=df_pca,
        x=f"PC{i + 1}", y=f"PC{j + 1}", hue="genus",
        reducer=pca,
        descriptor=efa,
        components=(i, j),
        palette="Paired",
        n_shapes=5,
        shape_color="gray",
        shape_scale=0.8,
        shape_alpha=0.8,
        ax=ax,
    )

explained_variance_ratio_plot(pca, ax=axes[1, 1])

<Axes: >

Compose with existing axes

Add shape overlays to a pre-existing scatter plot:

fig, ax = plt.subplots(figsize=(10, 10))
sns.scatterplot(
    data=df_pca, x="PC1", y="PC2", hue="genus", palette="Paired", ax=ax, s=80,
)
morphospace_plot(
    reducer=pca,
    descriptor=efa,
    components=(0, 1),
    ax=ax,
)

<Axes: xlabel='PC1', ylabel='PC2'>

Plot explained variance

fig, ax = plt.subplots(figsize=(8, 4))
explained_variance_ratio_plot(pca, ax=ax)

<Axes: >

See also

• Visualize Shape Variation for visualizing shape changes along component axes

• Use Non-PCA Reducers for using KernelPCA and other non-PCA reducers

• Morphometric Visualization for the reconstruction pipeline design

• Reconstruct Shapes for shape reconstruction

• Elliptic Fourier Analysis for a complete EFA workflow