energy-data-trans/utils/draw/draw_file.py

# import matplotlib
#
# from utils.file.trans_methods import create_file_path
#
# matplotlib.use('Agg')
# matplotlib.rcParams['font.family'] = 'SimHei'
# matplotlib.rcParams['font.sans-serif'] = ['SimHei']
# matplotlib.rcParams['axes.unicode_minus'] = False
# from matplotlib import pyplot as plt
#
#
# def scatter(title, x_label, y_label, x_values, y_values, color=None, col_map=dict(), size=10,
#             save_file_path=''):
#     if save_file_path:
#         create_file_path(save_file_path, True)
#     else:
#         save_file_path = title + '.png'
#
#     plt.figure(figsize=(8, 6))
#     plt.title(title, fontsize=16)
#     plt.xlabel(x_label, fontsize=14)
#     plt.ylabel(y_label, fontsize=14)
#     if color is not None:
#         plt.scatter(x_values, y_values, s=size, c=color)
#         if col_map:
#             patches = [plt.Rectangle((0, 0), 1, 1, fc=c) for c in col_map.values()]
#             plt.legend(patches, list(col_map.keys()))
#     else:
#         plt.scatter(x_values, y_values, s=size)
#
#     plt.savefig(save_file_path)
#     plt.close()
#
#
# if __name__ == '__main__':
#     import pandas as pd
#     import numpy as np
#     from matplotlib import pyplot as plt
#
#     df = pd.read_csv(r"/home/wzl/test_data/2024_10_17_14_54_46_200k_Root.csv")
#     df.reset_index(inplace=True, drop=True)
#     df.columns = ['data']
#
#     # Calculate the moving average with a window of 3 (1 before, 1 after)
#     window_size = 20
#     moving_avg = df['data'].rolling(window=window_size).mean()
#     df['moving_avg'] = moving_avg
#     # Calculate the percentage difference
#     percentage_diff = abs((df['data'] - moving_avg) / moving_avg) * 100
#     df['percentage_diff'] = percentage_diff
#     # Flag values that differ by more than threshold
#     threshold = 3
#     df['is_anomaly'] = percentage_diff < threshold
#
#     avg = df['data'].mean()
#     df['avg']=df['data'] > avg
#
#
#     difference_ratio = df.iloc[window_size:]
#     difference_ratio.reset_index(inplace=True)
#     # 创建图形和轴对象
#     plt.figure(figsize=(10, 6))
#     colors = np.where((difference_ratio['is_anomaly'] == True) & (difference_ratio['avg'] == True), 'r', np.where((difference_ratio['is_anomaly'] == False) & (difference_ratio['avg'] == False), 'g', 'b'))
#
#     datas = difference_ratio['data'].values
#     # for i in range(len(datas)):
#     #     plt.plot(i, datas[i], marker='o', color=colors[i])
#
#     plt.figure(figsize=(10, 6))
#     plt.scatter([i for i in range(len(datas))], datas,  c=colors)
#
#     # 添加标题和标签
#     plt.title('Difference Ratio of Each Data Point to Its Previous 10 Data Points Mean')
#     plt.xlabel('Index')
#     plt.ylabel('Difference Ratio')
#
#     # 显示网格
#     plt.grid(True)
#
#     # 显示图形
#     plt.show()