import matplotlib
from plot_data import *
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

matplotlib.use("Qt5Agg")
# 解决中文乱码
plt.rcParams["font.sans-serif"] = ["simsun"]
plt.rcParams["font.family"] = "sans-serif"
# plt.rcParams["font.weight"] = "bold"
# 解决负号无法显示的问题
plt.rcParams["axes.unicode_minus"] = False
plt.rcParams["savefig.dpi"] = 1200  # 图片像素
# plt.savefig("port.png", dpi=600, bbox_inches="tight")
fontsize = 12
################################################
witdh_of_bar=0.3
color=plt.cm.BuPu(np.linspace(152/255, 251/255,152/255))
percent1 = data_150m塔高_不同地线保护角[:, 1] / data_150m塔高_不同地线保护角[:, 0]
# percent1 = data_66m串长_不同塔高[:, 1] / data_66m串长_不同塔高[:, 0]
# percent2 = data_68m串长_不同塔高[:, 1] / data_68m串长_不同塔高[:, 0]
fig, ax = plt.subplots()
x = np.arange(len(category_names_150m塔高_不同地线保护角))  # the label locations
p1 = ax.bar(category_names_150m塔高_不同地线保护角, percent1, witdh_of_bar, label="绕击/反击跳闸率比值",color=color,hatch='-')
# p1 = ax.bar(x - 0.3 / 2, percent1, 0.3, label="6.6m绝缘距离")
# p2 = ax.bar(x + 0.3 / 2, percent2, 0.3, label="6.8m绝缘距离")
ax.xaxis.set_major_locator(mticker.FixedLocator(x))
ax.set_xticklabels(category_names_150m塔高_不同地线保护角)
ax.set_ylabel("比值", fontsize=fontsize)
ax.set_xlabel("地线保护角(°)", fontsize=fontsize)
# ax.set_xlabel("接地电阻(Ω)", fontsize=fontsize)
plt.xticks(fontsize=fontsize)
plt.yticks(fontsize=fontsize)
ax.bar_label(p1, padding=0, fontsize=fontsize)
# ax.bar_label(p2, padding=0, fontsize=fontsize)
ax.legend(fontsize=fontsize)

fig.tight_layout()
plt.show()


# results = {
#     "100m": 100 * data[0, :] / np.sum(data[0, :]),
#     "110m": data[1, :] / np.sum(data[1, :]),
#     "120m": data[2, :] / np.sum(data[2, :]),
#     "130m": data[3, :] / np.sum(data[3, :]),
#     "140m": data[4, :] / np.sum(data[4, :]),
#     "150m": data[5, :] / np.sum(data[5, :]),
# }


# def survey(results, category_names):
#     """
#     Parameters
#     ----------
#     results : dict
#         A mapping from question labels to a list of answers per category.
#         It is assumed all lists contain the same number of entries and that
#         it matches the length of *category_names*.
#     category_names : list of str
#         The category labels.
#     """
#     labels = list(results.keys())
#     data = np.array(list(results.values()))
#     data_cum = data.cumsum(axis=1)
#     category_colors = plt.get_cmap("RdYlGn")(np.linspace(0.15, 0.85, data.shape[1]))
#
#     fig, ax = plt.subplots(figsize=(9.2, 5))
#     ax.invert_yaxis()
#     ax.xaxis.set_visible(False)
#     ax.set_xlim(0, np.sum(data, axis=1).max())
#
#     for i, (colname, color) in enumerate(zip(category_names, category_colors)):
#         widths = data[:, i]
#         starts = data_cum[:, i] - widths
#         rects = ax.barh(
#             labels, widths, left=starts, height=0.5, label=colname, color=color
#         )
#
#         r, g, b, _ = color
#         text_color = "white" if r * g * b < 0.5 else "darkgrey"
#         ax.bar_label(rects, label_type="center", color=text_color)
#     ax.legend(
#         ncol=len(category_names),
#         bbox_to_anchor=(0, 1),
#         loc="lower left",
#         fontsize="small",
#     )
#
#     return fig, ax

# percent=data/np.sum(data,axis=1)[:,None]*100
# percent = data[:, 1] / data[:, 0]
# plt.bar(category_names, percent, 0.3, label="黑")
# # plt.bar(category_names, percent[:,0], 0.2, label="r")
#
# # plt.bar(category_names, [0.014094 / 100, 0.025094 / 100], 0.2, label="h")
# plt.legend()
# # survey(results, category_names)
# plt.show()