Simple ArXiv ML

Demonstrating some arrow and font styles that can be used with the ArXiv ML data map

ArXiv ML Landscape, A data map of papers from the Machine Learning section of ArXiv
import datamapplot
import numpy as np
import requests
import PIL
import matplotlib.pyplot as plt
import colorcet

plt.rcParams['savefig.bbox'] = 'tight'

arxivml_data_map = np.load("arxiv_ml_data_map.npz")["arr_0"]
arxivml_labels = np.load("arxiv_ml_simplified_cluster_labels.npz", allow_pickle=True)["arr_0"]

arxiv_logo_response = requests.get(
    "https://upload.wikimedia.org/wikipedia/commons/7/7a/ArXiv_logo_2022.png",
    stream=True,
    headers={'User-Agent': 'My User Agent 1.0'}
)
arxiv_logo = np.asarray(PIL.Image.open(arxiv_logo_response.raw).convert("RGBA"))

fig, ax = datamapplot.create_plot(
    arxivml_data_map,
    arxivml_labels,
    title="ArXiv ML Landscape",
    sub_title="A data map of papers from the Machine Learning section of ArXiv",
    logo=arxiv_logo,
    font_family="Montserrat Thin",
    label_font_size=9.5,
    label_margin_factor=1.0,
    arrowprops={
        "arrowstyle": "wedge", "connectionstyle": "arc3,rad=0.3", "linewidth": 0.25, "ec": "#4f3e2d"
    },
    title_keywords={"fontfamily":"Montserrat Thin", "fontweight":"black", "fontsize":36},
    sub_title_keywords={"fontfamily":"Montserrat Thin", "fontsize":22},
    glow_keywords={"n_levels":16, "kernel":"exponential", "kernel_bandwidth":0.2},
    cmap=colorcet.cm.CET_L9,
)
fig.savefig("plot_simple_arxiv.png", bbox_inches="tight")
plt.show()

Total running time of the script: (0 minutes 10.870 seconds)

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