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# -*- coding: utf-8 -*-
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import matplotlib
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matplotlib.use("agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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from datetime import datetime
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from matplotlib.font_manager import FontProperties
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# # 数据
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# data = {
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# "停电用户\n（万户）": {"昨天": 200.87, "今天": 132.59},
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# "过载配变\n（台）": {"昨天": 126, "今天": 119},
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# "95598供电类投诉\n（条）": {"昨天": 18, "今天": 12},
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# "涉电力供应类舆情风险信息\n（条）": {"昨天": 79, "今天": 40}
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# }
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def plot_electricity_comparison(year, month, day, data):
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year = int(year)
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month = int(month)
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day = int(day)
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# # 设置中文字体
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plt.rcParams["font.sans-serif"] = [
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"Microsoft YaHei"
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] # 字体设置，用来正常显示中文标签
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plt.rcParams["axes.unicode_minus"] = False # 用来正常显示负号
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# # 创建一个大图形，1行4列的子图布局
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# fig, axs = plt.subplots(1, 4, figsize=(8, 4)) # 1行4列的子图布局
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# 定义横轴标签
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categories = ["昨天", "今天"]
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x = np.arange(len(categories))
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# 计算变化百分比
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def calculate_change_percentage(yesterday, today):
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return ((today - yesterday) / yesterday) * 100
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# 检查数据完整性并过滤掉不完整的数据
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valid_data = {}
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for title, values in data.items():
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if "昨天" in values and "今天" in values:
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if values["昨天"] is not None and values["今天"] is not None:
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valid_data[title] = values
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# 如果没有有效的数据，返回 None 或其他指示
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if not valid_data:
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return None # 没有有效的数据，不生成图片
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# 根据有效数据的数量动态创建子图布局
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num_valid_data = len(valid_data)
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fig, axs = plt.subplots(
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1, num_valid_data, figsize=(2 * num_valid_data, 4)
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) # 动态调整布局
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# 如果只有一个子图，axs 是一个单个的 Axes 对象而不是数组，需要将其转换为列表
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if num_valid_data == 1:
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axs = [axs]
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# 绘制每个子图
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for i, (title, values) in enumerate(valid_data.items()):
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ax = axs[i] # 获取当前子图
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y = list(values.values())
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# 将蓝色柱子改为暗蓝色
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bars = ax.bar(x, y, color=["#1E3A8A", "#FF8C00"], width=0.6)
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# 设置子图标题和坐标轴标签
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ax.set_title(
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title, fontsize=12, fontweight="bold", color="#00008B"
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) # 设置标题字体加粗深蓝色
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# 设置坐标轴标签字体加粗深蓝色
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ax.set_xticks(x)
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ax.set_xticklabels(categories, fontsize=10, fontweight="bold", color="#00008B")
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# 动态设置纵坐标范围
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max_y = max(y) * 1.2 # 增加20%的范围
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ax.set_ylim(0, max_y)
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# 隐藏纵轴刻度线
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ax.tick_params(axis="y", length=0)
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ax.tick_params(axis="x", length=0)
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# 添加自定义的淡颜色细长分割线
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for y_tick in ax.get_yticks():
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ax.axhline(y=y_tick, color="#87CEEB", linestyle="--", alpha=0.3)
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# 设置刻度标签字体加粗深蓝色
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ax.tick_params(axis="y", labelsize=12, labelcolor="#00008B")
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# 添加数据标签
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for bar in bars:
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height = bar.get_height()
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# 根据柱子颜色设置数据标签颜色
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if bar == bars[0]:
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color = "#1E3A8A" # 暗蓝色
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else:
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color = "#FF8C00" # 暗橙色
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ax.text(
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bar.get_x() + bar.get_width() / 2,
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height,
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f"{height}",
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ha="center",
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va="bottom",
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fontsize=10,
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fontweight="bold",
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color=color,
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)
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# 添加变化百分比和箭头
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change_percent = calculate_change_percentage(y[0], y[1])
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# 根据变化百分比设置符号和颜色
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if change_percent < 0:
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symbol = "\u25bc" # 倒三角
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color = "#006400" # 深绿色
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# 调整箭头起始点和终点位置：从柱子的边角开始指向边角
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bar0_height = bars[0].get_height()
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bar1_height = bars[1].get_height()
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ax.annotate(
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"",
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xy=(x[1] - bars[1].get_width() / 2+0.1, bar1_height),
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#xy=(x[1], bar1_height),
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#xytext=((x[0] + bars[0].get_width() / 2) + 0.05, bar0_height * 0.95),
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xytext=((x[0] + bars[0].get_width() / 2), bar0_height),
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arrowprops=dict(
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arrowstyle="-|>",
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mutation_scale=20, # 箭头大小
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connectionstyle="arc3,rad=-0.4", # 调整为负值，箭头凸起
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color="#FFD580",
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linewidth=3,
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capstyle='round',
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joinstyle='round'
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),
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) # 浅橙色箭头，加粗
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# 在子图中间显示变化百分比
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ax.text(
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0.5,
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0.9,
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f"{symbol}{abs(change_percent):.2f}%",
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ha="center",
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va="center",
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transform=ax.transAxes,
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fontsize=12,
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fontweight="bold",
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color=color,
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)
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elif change_percent > 0:
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symbol = "\u25b2" # 正三角
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color = "#FF0000" # 红色
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# 调整箭头起始点和终点位置：从柱子的边角开始指向边角
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bar0_height = bars[0].get_height()
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bar1_height = bars[1].get_height()
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ax.annotate(
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"",
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#xy=(x[1] - bars[1].get_width() / 2, bar1_height),
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xy=(x[1] - bars[1].get_width() / 2, bar1_height),
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#xytext=((x[0] + bars[0].get_width() / 2) + 0.05, bar0_height),
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xytext=((x[0] + bars[0].get_width() / 2), bar0_height),
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arrowprops=dict(
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arrowstyle="-|>",
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mutation_scale=20, # 箭头大小
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connectionstyle="arc3,rad=0.4", # 调整为负值，箭头凸起
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color="#FFD580",
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linewidth=3,
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),
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) # 浅橙色箭头，加粗
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# 在子图中间显示变化百分比
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ax.text(
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0.5,
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0.9,
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f"{symbol}{abs(change_percent):.2f}%",
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ha="center",
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va="center",
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transform=ax.transAxes,
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fontsize=12,
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fontweight="bold",
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color=color,
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)
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else:
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symbol = ""
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color = "#FFA500" # 橙色
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# 调整箭头起始点和终点位置：从柱子的边角开始指向边角
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bar0_height = bars[0].get_height()
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bar1_height = bars[1].get_height()
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ax.annotate(
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"",
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xy=(x[1] - bars[1].get_width() / 2+0.1, bar1_height),
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xytext=((x[0] + bars[0].get_width() / 2), bar0_height),
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arrowprops=dict(
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arrowstyle="-|>",
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mutation_scale=20, # 箭头大小
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connectionstyle="arc3,rad=0", # 调整为负值，箭头凸起
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color="#FFD580",
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linewidth=3,
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),
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) # 浅橙色箭头，加粗
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# 在子图中间显示变化百分比
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ax.text(
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0.5,
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0.9,
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f"持平",
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ha="center",
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va="center",
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transform=ax.transAxes,
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fontsize=12,
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fontweight="bold",
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color=color,
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)
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# 调整子图间距
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plt.subplots_adjust(wspace=0) # 进一步减小子图之间的水平间距
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plt.tight_layout(rect=[0, 0, 1, 0.95]) # 调整整体布局
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# 获取当前脚本的绝对路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(os.path.dirname(current_dir))
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# 创建 temp_picture 目录
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temp_picture_dir = os.path.join(project_root, "temp_picture")
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if not os.path.exists(temp_picture_dir):
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os.makedirs(temp_picture_dir)
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# 按年月创建子目录
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month_dir = os.path.join(temp_picture_dir, f"{year}{month:02d}")
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if not os.path.exists(month_dir):
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os.makedirs(month_dir)
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# 保存图形到指定目录
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file_name = f"电力供应数据变化对比{year}{month:02d}{day:02d}.png"
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file_path = os.path.join(month_dir, file_name)
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plt.savefig(file_path, dpi=1200, bbox_inches="tight")
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return file_path
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# # 显示图形
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# plt.show()
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# plot_electricity_comparison(data)
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