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import json
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import random
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import requests
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import matplotlib
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import matplotlib.font_manager as font_manager
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from tqdm import tqdm
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font_path = os.path.join(os.path.dirname(__file__), 'GoNotoKurrent-Regular.ttf')
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assert os.path.exists(font_path)
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font_manager.fontManager.addfont(font_path)
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prop = font_manager.FontProperties(fname=font_path)
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matplotlib.rc('font', family='sans-serif')
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matplotlib.rcParams.update({
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'font.size': 26,
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'font.sans-serif': prop.get_name(),
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})
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def load_wordlist(lang):
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os.makedirs("lists", exist_ok=True)
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if os.path.exists(f"lists/{lang}.json"):
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lines = json.load(open(f"lists/{lang}.json", "r"))
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else:
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data = requests.get(f"https://raw.githubusercontent.com/frekwencja/most-common-words-multilingual/main/data/wordfrequency.info/{lang}.txt").text
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lines = data.split("\n")
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lines = lines[int(len(lines) / 2):]
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json.dump(lines, open(f"lists/{lang}.json", "w"))
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return lines
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def generate_bar_plot_configurations(n):
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configurations = []
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for _ in range(n):
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num_bars = random.randint(4, 8)
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config = {
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'num_bars': num_bars,
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'orientation': random.choice(['vertical', 'horizontal']),
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'values': [random.randint(30, 100) for _ in range(num_bars)],
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'colors': random.sample(
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['blue', 'green', 'red', 'yellow', 'black', 'orange', 'purple', 'brown'],
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num_bars),
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'figsize': (random.uniform(5, 10), random.uniform(3, 8))
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}
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configurations.append(config)
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return configurations
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def generate_pie_plot_configurations(n):
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configurations = []
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for _ in range(n):
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num_slices = random.randint(4, 8)
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config = {
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'num_slices': num_slices,
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'values': [random.randint(10, 200) for _ in range(num_slices)],
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'labels': [f'Label {i + 1}' for i in range(num_slices)],
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'colors': random.sample(
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['blue', 'green', 'red', 'yellow', 'cyan', 'orange', 'purple', 'brown'],
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num_slices),
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'explode': [0.1 if random.random() > 0.8 else 0 for _ in range(num_slices)],
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'figsize': (random.uniform(5, 10), random.uniform(5, 10))
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}
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configurations.append(config)
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return configurations
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def create_and_save_pie_plots(configurations, language, output_dir='pie_plots'):
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word_list = load_wordlist(language)
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annotations = []
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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for i, config in tqdm(enumerate(configurations)):
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config["labels"] = random.sample(word_list, config["num_slices"])
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plt.figure(figsize=config['figsize'])
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plt.pie(config['values'], labels=config['labels'], colors=config['colors'], explode=config['explode'],
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autopct='%1.1f%%', startangle=140)
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, f'pie_plot_{i}.png'))
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plt.close()
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half = int(config['num_slices']//2)
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questions_name = [
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f"What is the label of the biggest slice?",
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f"What is the label of the smallest slice?",
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f"What is the label of the {config['colors'][0]} slice?",
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f"What is the label of the {config['colors'][-1]} slice?",
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f"What is the label of the {config['colors'][half]} slice?",
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]
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answer_name = [
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config["labels"][np.argmax(config['values'])],
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config["labels"][np.argmin(config['values'])],
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config["labels"][0],
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config["labels"][-1],
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config["labels"][half],
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]
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question_ground = [
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f"Is the slice with label '{config['labels'][np.argmax(config['values'])]}' the biggest?",
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f"Is the slice with label '{config['labels'][(np.argmax(config['values'])+half)%config['num_slices']]}' the biggest?",
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f"Is the slice with label '{config['labels'][np.argmin(config['values'])]}' the smallest?",
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f"Is the slice with label '{config['labels'][(np.argmin(config['values'])+half)%config['num_slices']]}' the smallest?",
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f"Is the slice with label '{config['labels'][0]}' colored in {config['colors'][0]}?",
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f"Is the slice with label '{config['labels'][0]}' colored in {config['colors'][0+half]}?",
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f"Is the slice with label '{config['labels'][half]}' colored in {config['colors'][half]}?",
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f"Is the slice with label '{config['labels'][half]}' colored in {config['colors'][-1]}?",
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]
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answer_ground = [
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"yes", "no",
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"yes", "no",
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"yes", "no",
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"yes", "no",
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]
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annotation = {
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"image": f'pie_plot_{i}.png',
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"question_ground": question_ground,
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"answer_ground": answer_ground,
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"questions_name": questions_name,
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"answer_name": answer_name,
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}
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annotations.append(annotation)
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json.dump(annotations, open(os.path.join(output_dir, f'pie_annotations_{language}.json'), 'w', encoding='utf-8'), indent=4)
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def create_and_save_bar_plots(configurations, language, output_dir='bar_plots'):
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word_list = load_wordlist(language)
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annotations = []
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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for i, config in tqdm(enumerate(configurations)):
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config["labels"] = random.sample(word_list, config["num_bars"])
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plt.figure(figsize=config['figsize'])
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if config['orientation'] == 'vertical':
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plt.bar(config['labels'], config['values'], color=config['colors'])
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plt.gcf().autofmt_xdate()
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else:
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plt.barh(config['labels'], config['values'], color=config['colors'])
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, f'bar_plot_{i}.png'))
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plt.close()
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half = int(config['num_bars']//2)
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questions_name = [
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f"What is the label of the biggest bar?",
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f"What is the label of the smallest bar?",
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f"What is the label of the {config['colors'][0]} bar?",
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f"What is the label of the {config['colors'][-1]} bar?",
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f"What is the label of the {config['colors'][half]} bar?",
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]
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answer_name = [
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config["labels"][np.argmax(config['values'])],
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config["labels"][np.argmin(config['values'])],
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config["labels"][0],
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config["labels"][-1],
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config["labels"][half],
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]
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question_ground = [
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f"Is the bar with label '{config['labels'][np.argmax(config['values'])]}' the biggest?",
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f"Is the bar with label '{config['labels'][(np.argmax(config['values'])+half)%config['num_bars']]}' the biggest?",
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f"Is the bar with label '{config['labels'][np.argmin(config['values'])]}' the smallest?",
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f"Is the bar with label '{config['labels'][(np.argmin(config['values'])+half)%config['num_bars']]}' the smallest?",
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f"Is the bar with label '{config['labels'][0]}' colored in {config['colors'][0]}?",
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f"Is the bar with label '{config['labels'][0]}' colored in {config['colors'][0+half]}?",
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f"Is the bar with label '{config['labels'][half]}' colored in {config['colors'][half]}?",
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f"Is the bar with label '{config['labels'][half]}' colored in {config['colors'][-1]}?",
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]
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answer_ground = [
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"yes", "no",
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"yes", "no",
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"yes", "no",
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"yes", "no",
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]
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annotation = {
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"image": f'bar_plot_{i}.png',
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"question_ground": question_ground,
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"answer_ground": answer_ground,
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"questions_name": questions_name,
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"answer_name": answer_name,
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}
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annotations.append(annotation)
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json.dump(annotations, open(os.path.join(output_dir, f'bar_annotations_{language}.json'), 'w', encoding='utf-8'), indent=4)
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if __name__ == '__main__':
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languages = ["en", "zu", "id", "it", "de", "th", "ar", "ko", "zh-CN", "ru", "hi"]
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if not os.path.exists("bar_configs.json"):
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bar_configs = generate_bar_plot_configurations(50)
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json.dump(bar_configs, open("bar_configs.json", "w"))
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else:
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bar_configs = json.load(open("bar_configs.json"))
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if not os.path.exists("pie_configs.json"):
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pie_configs = generate_pie_plot_configurations(50)
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json.dump(pie_configs, open("pie_configs.json", "w"))
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else:
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pie_configs = json.load(open("pie_configs.json"))
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for language in languages:
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print(language)
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os.makedirs(f"/media/gregor/DATA/datasets/smpqa/{language}", exist_ok=True)
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create_and_save_bar_plots(bar_configs, language, output_dir=f"/media/gregor/DATA/datasets/smpqa/{language}")
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create_and_save_pie_plots(pie_configs, language, output_dir=f"/media/gregor/DATA/datasets/smpqa/{language}")
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