Pictogram-based figure in Python - see https://www.luerig.net/posts/interactive-figures/

isopods_figure_pictograms.py

## load modules
import os
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnnotationBbox, OffsetImage

## set wd 
os.chdir(r"D:\workspace\figures\interactive_figures")

## load data (see gist interactive_figure_data_collection.py for how it was collected)
df_data = pd.read_csv(r"data\isopods_traits.csv")

## add regression line (optional)
fit = np.polyfit(df_data["log_length"], df_data["pigmentation"], 1, full=True)
slope, intercept = fit[0][0], fit[0][1]
line_x = np.linspace(df_data["log_length"].min(), df_data["log_length"].max(), 100)
line_y = slope * line_x + intercept

## create scatter plot
fig, ax = plt.subplots(figsize=(5, 5))  
plt.tight_layout(pad=3)
ax.set_xlabel("Length (mm, log)")
ax.set_ylabel("Pigmentation (0-1)")
scatter = ax.scatter(df_data['log_length'], df_data['pigmentation'], s=5)
ax.plot(line_x, line_y, color='red', linewidth=2)

## add pictograms
for idx, row in df_data.iterrows():
    if os.path.isfile(row["filepath"]):
        image = cv2.imread(row["filepath"], cv2.IMREAD_UNCHANGED)
        image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
        ab = AnnotationBbox(OffsetImage(image, zoom=0.2), 
                            (row["log_length"], row["pigmentation"]), frameon=False)
        ax.add_artist(ab)

## show / save
fig.show()
figure_path = r"figures\figure_isopods_pictograms.jpg"
fig.savefig(figure_path, dpi=500)