"""
Simple ArXiv ML
---------------

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