from huggingface_hub import snapshot_download import os import numpy as np import pandas as pd import matplotlib.pyplot as plt from datetime import datetime from scipy.ndimage import gaussian_filter1d snapshot_download("Weyaxi/followers-leaderboard", local_dir="followers-leaderboard", repo_type="dataset", max_workers=32) global_dataset_dfs = {} def gr_plot_follower_comparison(author_names, dataset_path="followers-leaderboard", smooth_sigma=1.5): if isinstance(author_names, str): author_names = [author_names] # Load or retrieve cached DataFrames if dataset_path not in global_dataset_dfs: dfs = {} for root, _, files in os.walk(dataset_path): if "data.csv" in files: file_path = os.path.join(root, "data.csv") folder_name = os.path.basename(root) date_str = '-'.join(folder_name.split('-')[:3]).split()[0] try: date = datetime.strptime(date_str, "%d-%m-%Y") df = pd.read_csv(file_path) dfs[date] = df except (ValueError, pd.errors.ParserError): continue global_dataset_dfs[dataset_path] = dfs else: dfs = global_dataset_dfs[dataset_path] data = [] for author in author_names: date_count = {} for date, df in dfs.items(): if author in df['Author'].values: count = df.loc[df['Author'] == author, 'Number of Followers'].iloc[0] date_count[date] = count if date_count: dates, counts = zip(*sorted(date_count.items())) counts = np.array(counts, dtype=np.float32) # Apply Gaussian smoothing if requested if smooth_sigma > 0 and len(counts) > 1: counts = gaussian_filter1d(counts, sigma=smooth_sigma) for d, c in zip(dates, counts): data.append({"x": d, "y": c, "author": author}) if not data: return pd.DataFrame(columns=["x", "y", "author"]) return pd.DataFrame(data)