import json import os import matplotlib.pyplot as plt import numpy as np import random import requests import matplotlib import matplotlib.font_manager as font_manager from tqdm import tqdm # Load a font from TTF file, # relative to this Python module # https://stackoverflow.com/a/69016300/315168 #https://github.com/satbyy/go-noto-universal/releases/tag/v7.0 font_path = os.path.join(os.path.dirname(__file__), 'GoNotoKurrent-Regular.ttf') assert os.path.exists(font_path) font_manager.fontManager.addfont(font_path) prop = font_manager.FontProperties(fname=font_path) # Set it as default matplotlib font matplotlib.rc('font', family='sans-serif') matplotlib.rcParams.update({ 'font.size': 26, 'font.sans-serif': prop.get_name(), }) def load_wordlist(lang): #https://github.com/frekwencja/most-common-words-multilingual os.makedirs("lists", exist_ok=True) if os.path.exists(f"lists/{lang}.json"): lines = json.load(open(f"lists/{lang}.json", "r")) else: data = requests.get(f"https://raw.githubusercontent.com/frekwencja/most-common-words-multilingual/main/data/wordfrequency.info/{lang}.txt").text # it's a file like object and works just like a file lines = data.split("\n") lines = lines[int(len(lines) / 2):] json.dump(lines, open(f"lists/{lang}.json", "w")) return lines def generate_bar_plot_configurations(n): configurations = [] for _ in range(n): num_bars = random.randint(4, 8) config = { 'num_bars': num_bars, 'orientation': random.choice(['vertical', 'horizontal']), 'values': [random.randint(30, 100) for _ in range(num_bars)], 'colors': random.sample( ['blue', 'green', 'red', 'yellow', 'black', 'orange', 'purple', 'brown'], num_bars), 'figsize': (random.uniform(5, 10), random.uniform(3, 8)) } configurations.append(config) return configurations def generate_pie_plot_configurations(n): configurations = [] for _ in range(n): num_slices = random.randint(4, 8) config = { 'num_slices': num_slices, 'values': [random.randint(10, 200) for _ in range(num_slices)], 'labels': [f'Label {i + 1}' for i in range(num_slices)], 'colors': random.sample( ['blue', 'green', 'red', 'yellow', 'cyan', 'orange', 'purple', 'brown'], num_slices), 'explode': [0.1 if random.random() > 0.8 else 0 for _ in range(num_slices)], # Randomly explode some slices 'figsize': (random.uniform(5, 10), random.uniform(5, 10)) } configurations.append(config) return configurations def create_and_save_pie_plots(configurations, language, output_dir='pie_plots'): word_list = load_wordlist(language) annotations = [] if not os.path.exists(output_dir): os.makedirs(output_dir) for i, config in tqdm(enumerate(configurations)): config["labels"] = random.sample(word_list, config["num_slices"]) plt.figure(figsize=config['figsize']) plt.pie(config['values'], labels=config['labels'], colors=config['colors'], explode=config['explode'], autopct='%1.1f%%', startangle=140) plt.tight_layout() plt.savefig(os.path.join(output_dir, f'pie_plot_{i}.png')) plt.close() half = int(config['num_slices']//2) questions_name = [ f"What is the label of the biggest slice?", f"What is the label of the smallest slice?", f"What is the label of the {config['colors'][0]} slice?", f"What is the label of the {config['colors'][-1]} slice?", f"What is the label of the {config['colors'][half]} slice?", ] answer_name = [ config["labels"][np.argmax(config['values'])], config["labels"][np.argmin(config['values'])], config["labels"][0], config["labels"][-1], config["labels"][half], ] question_ground = [ f"Is the slice with label '{config['labels'][np.argmax(config['values'])]}' the biggest?", f"Is the slice with label '{config['labels'][(np.argmax(config['values'])+half)%config['num_slices']]}' the biggest?", f"Is the slice with label '{config['labels'][np.argmin(config['values'])]}' the smallest?", f"Is the slice with label '{config['labels'][(np.argmin(config['values'])+half)%config['num_slices']]}' the smallest?", f"Is the slice with label '{config['labels'][0]}' colored in {config['colors'][0]}?", f"Is the slice with label '{config['labels'][0]}' colored in {config['colors'][0+half]}?", f"Is the slice with label '{config['labels'][half]}' colored in {config['colors'][half]}?", f"Is the slice with label '{config['labels'][half]}' colored in {config['colors'][-1]}?", ] answer_ground = [ "yes", "no", "yes", "no", "yes", "no", "yes", "no", ] annotation = { "image": f'pie_plot_{i}.png', "question_ground": question_ground, "answer_ground": answer_ground, "questions_name": questions_name, "answer_name": answer_name, } annotations.append(annotation) json.dump(annotations, open(os.path.join(output_dir, f'pie_annotations_{language}.json'), 'w', encoding='utf-8'), indent=4) def create_and_save_bar_plots(configurations, language, output_dir='bar_plots'): word_list = load_wordlist(language) annotations = [] if not os.path.exists(output_dir): os.makedirs(output_dir) for i, config in tqdm(enumerate(configurations)): config["labels"] = random.sample(word_list, config["num_bars"]) plt.figure(figsize=config['figsize']) if config['orientation'] == 'vertical': plt.bar(config['labels'], config['values'], color=config['colors']) plt.gcf().autofmt_xdate() else: plt.barh(config['labels'], config['values'], color=config['colors']) plt.tight_layout() plt.savefig(os.path.join(output_dir, f'bar_plot_{i}.png')) plt.close() half = int(config['num_bars']//2) questions_name = [ f"What is the label of the biggest bar?", f"What is the label of the smallest bar?", f"What is the label of the {config['colors'][0]} bar?", f"What is the label of the {config['colors'][-1]} bar?", f"What is the label of the {config['colors'][half]} bar?", ] answer_name = [ config["labels"][np.argmax(config['values'])], config["labels"][np.argmin(config['values'])], config["labels"][0], config["labels"][-1], config["labels"][half], ] question_ground = [ f"Is the bar with label '{config['labels'][np.argmax(config['values'])]}' the biggest?", f"Is the bar with label '{config['labels'][(np.argmax(config['values'])+half)%config['num_bars']]}' the biggest?", f"Is the bar with label '{config['labels'][np.argmin(config['values'])]}' the smallest?", f"Is the bar with label '{config['labels'][(np.argmin(config['values'])+half)%config['num_bars']]}' the smallest?", f"Is the bar with label '{config['labels'][0]}' colored in {config['colors'][0]}?", f"Is the bar with label '{config['labels'][0]}' colored in {config['colors'][0+half]}?", f"Is the bar with label '{config['labels'][half]}' colored in {config['colors'][half]}?", f"Is the bar with label '{config['labels'][half]}' colored in {config['colors'][-1]}?", ] answer_ground = [ "yes", "no", "yes", "no", "yes", "no", "yes", "no", ] annotation = { "image": f'bar_plot_{i}.png', "question_ground": question_ground, "answer_ground": answer_ground, "questions_name": questions_name, "answer_name": answer_name, } annotations.append(annotation) json.dump(annotations, open(os.path.join(output_dir, f'bar_annotations_{language}.json'), 'w', encoding='utf-8'), indent=4) if __name__ == '__main__': languages = ["en", "zu", "id", "it", "de", "th", "ar", "ko", "zh-CN", "ru", "hi"] if not os.path.exists("bar_configs.json"): bar_configs = generate_bar_plot_configurations(50) json.dump(bar_configs, open("bar_configs.json", "w")) else: bar_configs = json.load(open("bar_configs.json")) if not os.path.exists("pie_configs.json"): pie_configs = generate_pie_plot_configurations(50) json.dump(pie_configs, open("pie_configs.json", "w")) else: pie_configs = json.load(open("pie_configs.json")) for language in languages: print(language) os.makedirs(f"/media/gregor/DATA/datasets/smpqa/{language}", exist_ok=True) create_and_save_bar_plots(bar_configs, language, output_dir=f"/media/gregor/DATA/datasets/smpqa/{language}") create_and_save_pie_plots(pie_configs, language, output_dir=f"/media/gregor/DATA/datasets/smpqa/{language}")