draw.py

# -*- encoding: utf-8 -*-
# -----------------------------------------------------------------------------
# @File Name  : draw.py
# @Time       : 2020/11/19 上午10:51
# @Author     : X. Peng
# @Email      : acepengxiong@163.com
# @Software   : PyCharm
# -----------------------------------------------------------------------------
import base64
from urllib import parse

from io import BytesIO

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import ticker
from matplotlib.ticker import FuncFormatter
from matplotlib.font_manager import FontProperties


matplotlib.use('Agg')
# 中文字体初始化
plt.rcParams['font.sans-serif']=['Heiti TC']


def to_percent(temp, position):
    return '%.2f' % temp + '%'


def draw_month_return_chart(xlabels, product_list, cumulative):
    """月度回报表现图"""

    # plt.title('Scores by group and gender')
    # plt.ylabel('Scores')
    figsize = (24, 12)
    # 标签文字大小
    fontsize = 15
    # 初始化
    fig = plt.figure(figsize=figsize)
    ax1 = fig.add_subplot(111)
    ax2 = ax1.twinx()
    max_x_count = max([x['data'].size for x in product_list])
    loc = np.arange(max_x_count)  # the x locations for the groups
    width = 0.35  # the width of the bars: can also be len(x) sequence
    color_list = ['#B0B0B0', '#6C71AA', '#E1BC95', '#F9DBB8']

    # 坐标轴
    ax1.tick_params(labelsize=fontsize)
    ax2.tick_params(labelsize=fontsize)

    # 坐标轴颜色
    ax2.tick_params(axis='y', colors='#D40000')
    ax1.set_xticks(loc)
    ax1.set_xticklabels(xlabels)
    # ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
    ax2.yaxis.set_major_formatter(FuncFormatter(to_percent))
    # temp_rate = np.zeros(max_x_count)
    # for i in range(len(product_list)):
    #     temp_rate += product_list[i]['data']
    # max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
    # ax2.set_ylim(0, max_rate + 15)

    # 柱状图
    prod_legend = []
    for i in range(len(product_list)):
        ax = None
        bottom = np.zeros(max_x_count)
        if i == 0:
            ax = ax1.bar(loc, product_list[i]['data'], width, color=color_list[i], alpha=0.8)
        else:
            for j in range(i):
                bottom = bottom + product_list[j]['data']
            if i < len(color_list):
                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, color=color_list[i], alpha=0.8)
            else:
                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, alpha=0.8)
        for a, b in zip(range(len(xlabels)), product_list[0]['data']):
            if b > 0:
                ax1.text(a, b*1.08, '%.2f万' % b, ha='center', va='bottom', fontsize=fontsize)
            elif b < 0:
                ax1.text(a, b*0.92, '%.2f万' % b, ha='center', va='top', fontsize=fontsize)
        prod_legend.append(ax[0])
    ax1.legend(prod_legend, [prod['name'] for prod in product_list], loc='upper left', fontsize=fontsize)

    # 画折线图
    ax2.plot(loc, cumulative['data'], color='#D40000', marker='.', linewidth=3, label=cumulative['name'])
    # 添加数字标签
    for a, b in zip(range(len(xlabels)), cumulative['data']):
        ax2.text(a*1.05, b*1.05, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize, color='#D40000')
    ax2.legend(loc='upper center', fontsize=fontsize)

    # plt.show()
    imgdata = BytesIO()
    fig.savefig(imgdata, format='png', bbox_inches='tight')
    imgdata.seek(0)  # rewind the data
    month_return_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
    return month_return_img


def draw_contribution_chart(xlabels, product_list, cumulative):
    """贡献分解图"""

    # plt.title('Scores by group and gender')
    # plt.ylabel('Scores')
    figsize = (25, 12)
    # 标签文字大小
    fontsize = 22
    # 初始化
    fig = plt.figure(figsize=figsize)
    ax1 = fig.add_subplot(111)
    ax2 = ax1.twiny()
    max_x_count = max([x['data'].size for x in product_list])
    loc = np.arange(max_x_count)  # the x locations for the groups
    width = 0.35  # the width of the bars: can also be len(x) sequence
    color_list = ['#333333', '#928C8C', '#AFAFAF', '#D56666', '#DE7A7A',
                  '#ED9494', '#F4A9A9', '#FFC8C8', '#DEA27A', '#EFAF85',
                  '#FBBF98', '#FFD2B5', '#E1C277', '#EBCD85', '#FEDF96']

    # 坐标轴
    ax1.tick_params(labelsize=fontsize)
    ax1.set_xticks(loc)
    ax1.set_xticklabels(xlabels)
    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
    ax1.grid(axis='y')
    # temp_rate = np.zeros(max_x_count)
    # for i in range(len(product_list)):
    #     temp_rate += product_list[i]['data']
    # max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
    ax2.set_xticks([])
    # ax2.set_ylim(0, max_rate + 10)

    # 堆叠柱状图
    prod_legend = []
    for i in range(len(product_list)):
        ax = None
        bottom = np.zeros(max_x_count)
        if i == 0:
            ax = ax1.bar(loc, product_list[i]['data'], width, color=color_list[i], alpha=0.8)
        else:
            for j in range(i):
                bottom = bottom + product_list[j]['data']
            if i < len(color_list):
                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, color=color_list[i], alpha=0.8)
            else:
                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, alpha=0.8)
        prod_legend.append(ax[0])
    ax1.legend(prod_legend, [prod['name'] for prod in product_list], bbox_to_anchor=(0.9, -0.1), ncol=4, fontsize=fontsize)

    # 画折线图
    ax2.plot(loc, cumulative['data'], color='#B40A15', marker='', linewidth=3, label=cumulative['name'])
    # 添加数字标签
    for a, b in zip(range(len(xlabels)), cumulative['data']):
        ax2.text(a*1.1, b *1.05, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize, color='#B40A15')
    ax2.legend(loc='upper left', fontsize=fontsize)

    imgdata = BytesIO()
    fig.savefig(imgdata, format='png', bbox_inches='tight')
    imgdata.seek(0)  # rewind the data
    month_return_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
    return month_return_img

# def draw_contribution_chart(xlabels, product_list, cumulative):
#     """贡献分解图"""
#
#     # plt.title('Scores by group and gender')
#     # plt.ylabel('Scores')
#     figsize = (25, 12)
#     # 标签文字大小
#     fontsize = 22
#     # 初始化
#     fig = plt.figure(figsize=figsize)
#     ax1 = fig.add_subplot()
#     ax2 = ax1.twiny()
#     max_x_count = max([x['data'].size for x in product_list])
#     loc = np.arange(max_x_count)  # the x locations for the groups
#     width = 0.35  # the width of the bars: can also be len(x) sequence
#     color_list = ['#222A77', '#6C71AA', '#E1BC95', '#F9DBB8']
#
#     # 坐标轴
#     ax1.tick_params(labelsize=fontsize)
#     ax1.set_xticks(loc)
#     ax1.set_xticklabels(xlabels)
#     ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
#     # temp_rate = np.zeros(max_x_count)
#     # for i in range(len(product_list)):
#     #     temp_rate += product_list[i]['data']
#     # max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
#     ax2.set_xticks([])
#     # ax2.set_ylim(0, max_rate + 10)
#
#     # 堆叠柱状图
#     prod_legend = []
#     for i in range(len(product_list)):
#         ax = None
#         for j in range(len(product_list[i]['data'])):
#             product_list[i]['bottom'] = product_list[i].get('bottom', 0)
#             product_list[i]['bottom_neg'] = product_list[i].get('bottom_neg', 0)
#             if j > 0:
#                 product_list[i]['bottom'] += product_list[i].get('bottom', 0)
#                 product_list[i]['bottom_neg'] += product_list[i].get('bottom_neg', 0)
#             if i < len(color_list):
#                 for x in loc:
#                     if product_list[i]['data'][x] >= 0:
#                         ax = ax1.bar(x, product_list[i]['data'][x], width, bottom=product_list[i]['bottom'], color=color_list[i], alpha=0.8)
#                     else:
#                         ax = ax1.bar(x, product_list[i]['data'][x], width, bottom=product_list[i]['bottom_neg'],
#                                      color=color_list[i], alpha=0.8)
#             else:
#                 for x in loc:
#                     if product_list[i]['data'][x] >= 0:
#                         ax = ax1.bar(x, product_list[i]['data'][x], width, bottom=product_list[i]['bottom'], alpha=0.8)
#                     else:
#                         ax = ax1.bar(x, product_list[i]['data'][x], width, bottom=product_list[i]['bottom_neg'], alpha=0.8)
#         prod_legend.append(ax[0])
#     # ax1.legend(prod_legend, [prod['name'] for prod in product_list], bbox_to_anchor=(0.9, -0.1), ncol=4, fontsize=fontsize)
#
#     # 画折线图
#     ax2.plot(loc, cumulative['data'], color='#C6A774', marker='', linewidth=3, label=cumulative['name'])
#     ax2.legend(loc='upper left', fontsize=fontsize)
#
#     plt.show()
#     # imgdata = BytesIO()
#     # fig.savefig(imgdata, format='png', bbox_inches='tight')
#     # imgdata.seek(0)  # rewind the data
#     # month_return_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
#     # return month_return_img


def draw_comment_chart(xlabels, source_prod, target_prod):
    """个基点评图"""
    figsize = (20, 12)
    # 标签文字大小
    fontsize = 22
    # 初始化
    fig = plt.figure(figsize=figsize)
    ax1 = fig.add_subplot()
    ax2 = ax1.twiny()
    # ax = plt.gca()  # gca:get current axis得到当前轴
    # ax.spines['bottom'].set_position(('data', 0))  # data表示通过值来设置x轴的位置，将x轴绑定在y=0的位置
    product_list = [source_prod, target_prod]
    max_x_count = max([x['data'].size for x in product_list])
    loc = np.arange(max_x_count)  # the x locations for the groups

    # 坐标轴
    ax1.tick_params(labelsize=fontsize)
    ax2.tick_params(labelsize=fontsize)
    ax1.set_xticks(loc)
    ax1.set_xticklabels(xlabels)
    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
    max_rate = np.max(np.hstack((source_prod['data'], target_prod['data'])))
    ax2.set_xticks([])

    # 个基折线图
    ax1.plot(loc, source_prod['data'], color='#C6A774', marker='', linewidth=3, label=source_prod['name'])
    ax1.legend(loc='upper left', fontsize=fontsize)

    # 指数折线图
    ax2.plot(loc, target_prod['data'], color='black', marker='', linewidth=3, label=target_prod['name'])
    ax2.legend(loc='upper center', fontsize=fontsize)

    plt.show()


def draw_old_combination_chart(xlabels, origin_combination, index):
    """旧组合对比图"""
    figsize = (20, 12)
    # 标签文字大小
    fontsize = 22
    # 初始化
    fig = plt.figure(figsize=figsize)
    ax1 = fig.add_subplot(111)
    ax3 = ax1.twiny()
    # ax = plt.gca()  # gca:get current axis得到当前轴
    # ax.spines['bottom'].set_position(('data', 0))  # data表示通过值来设置x轴的位置，将x轴绑定在y=0的位置
    product_list = [origin_combination, index]
    max_x_count = max([x['data'].size for x in product_list])
    loc = np.arange(max_x_count)  # the x locations for the groups

    # 坐标轴
    ax1.tick_params(labelsize=fontsize)
    ax3.tick_params(labelsize=fontsize)
    ax1.set_xticks(loc)
    ax1.set_xticklabels(xlabels)
    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
    ax3.set_xticks([])

    # 新组合折线图
    ax1.plot(loc, origin_combination['data'], color='#C6A774', marker='', linewidth=3, label=origin_combination['name'])
    ax1.legend(loc='upper left', fontsize=fontsize)

    # 指数折线图
    ax3.plot(loc, index['data'], color='black', marker='', linewidth=3, label=index['name'])
    ax3.legend(loc='upper right', fontsize=fontsize)

    # plt.show()
    imgdata = BytesIO()
    fig.savefig(imgdata, format='png', bbox_inches='tight')
    imgdata.seek(0)  # rewind the data
    return_compare_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
    return return_compare_img


def draw_combination_chart(xlabels, new_combination, origin_combination, index):
    """新组合对比图"""
    figsize = (20, 12)
    # 标签文字大小
    fontsize = 22
    # 初始化
    fig = plt.figure(figsize=figsize)
    ax1 = fig.add_subplot(111)
    ax2 = ax1.twiny()
    ax3 = ax1.twiny()
    # ax = plt.gca()  # gca:get current axis得到当前轴
    # ax.spines['bottom'].set_position(('data', 0))  # data表示通过值来设置x轴的位置，将x轴绑定在y=0的位置
    product_list = [origin_combination, new_combination, index]
    max_x_count = max([x['data'].size for x in product_list])
    loc = np.arange(max_x_count)  # the x locations for the groups

    # 坐标轴
    ax1.tick_params(labelsize=fontsize)
    ax2.tick_params(labelsize=fontsize)
    ax3.tick_params(labelsize=fontsize)
    ax1.set_xticks(loc)
    ax1.set_xticklabels(xlabels)
    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
    ax2.set_xticks([])
    ax3.set_xticks([])

    # 新组合折线图
    ax1.plot(loc, new_combination['data'], color='#C6A774', marker='', linewidth=3, label=new_combination['name'])
    ax1.legend(loc='upper left', fontsize=fontsize)

    # 原组合折线图
    ax2.plot(loc, origin_combination['data'], color='#222A77', marker='', linewidth=3, label=origin_combination['name'])
    ax2.legend(loc='upper center', fontsize=fontsize)

    # 指数折线图
    ax3.plot(loc, index['data'], color='black', marker='', linewidth=3, label=index['name'])
    ax3.legend(loc='upper right', fontsize=fontsize)

    # plt.show()
    imgdata = BytesIO()
    fig.savefig(imgdata, format='png', bbox_inches='tight')
    imgdata.seek(0)  # rewind the data
    return_compare_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
    return return_compare_img


def draw_index_combination_chart(compare_data):
    """基金和指数对比图"""
    xlabels, origin_combination, index = compare_data["xlabels"], compare_data["fund"], compare_data["index"]
    figsize = (20, 12)
    # 标签文字大小
    fontsize = 22
    # 初始化
    fig = plt.figure(figsize=figsize)
    ax1 = fig.add_subplot(111)
    ax3 = ax1.twiny()
    ax1.spines['top'].set_visible(False)
    ax1.spines['right'].set_visible(False)
    ax1.spines['bottom'].set_visible(False)
    ax1.spines['left'].set_visible(False)
    ax3.spines['top'].set_visible(False)
    ax3.spines['right'].set_visible(False)
    ax3.spines['bottom'].set_visible(False)
    ax3.spines['left'].set_visible(False)
    # ax = plt.gca()  # gca:get current axis得到当前轴
    # ax.spines['bottom'].set_position(('data', 0))  # data表示通过值来设置x轴的位置，将x轴绑定在y=0的位置
    product_list = [origin_combination, index]
    max_x_count = max([x['data'].size for x in product_list])
    loc = np.arange(max_x_count)  # the x locations for the groups

    # 坐标轴
    # ax1.tick_params(labelsize=fontsize)
    # ax3.tick_params(labelsize=fontsize)
    # ax1.set_xticks(loc)
    # ax1.set_xticklabels(xlabels)
    # ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
    # ax1.set_yticks([])
    # ax3.set_yticks([])
    # ax3.set_xticks([])
    ax1.axis('off')
    ax3.axis('off')

    # 基金折线图
    ax1.plot(loc, origin_combination['data'], color='#D40000', marker='', linewidth=3)
    # ax1.legend()

    # 指数折线图
    ax3.plot(loc, index['data'], color='grey', marker='', linewidth=3)
    # ax3.legend()

    # plt.show()
    imgdata = BytesIO()
    fig.savefig(imgdata, format='png', bbox_inches='tight')
    imgdata.seek(0)  # rewind the data
    return_compare_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
    return return_compare_img

if __name__ == '__main__':
    # xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
    # product = {'name': '月度回报率', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    # contrast = {'name': '同比上涨', 'data': np.array([10, 50, 120, 100, 36, 0, 50, 120, 100, 36, 23, 98])}
    # draw_month_return_chart(xlabels, [product], contrast)

    xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
    product1 = {'name': '塞亚成长1号', 'data': np.array([-10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product2 = {'name': '塞亚成长2号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product3 = {'name': '塞亚成长3号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product4 = {'name': '塞亚成长4号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product5 = {'name': '塞亚成长5号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product6 = {'name': '塞亚成长6号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product7 = {'name': '塞亚成长7号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product8 = {'name': '塞亚成长8号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    product_list = [product1, product2, product3, product4, product5, product6, product7, product8]
    cumulative = {'name': '总收益', 'data': np.array([10, 50, 120, 100, 36, 0, 50, 120, 100, 36, 23, 98])}
    draw_contribution_chart(xlabels, product_list, cumulative)

    # xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
    # source_prod = {'name': '远澜银杏1号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    # target_prod = {'name': '上证指数', 'data': np.array([-10, 10, 5, 55, 24, 10, 20, 8, 10, 31, 40, 32])}
    # draw_comment_chart(xlabels, source_prod, target_prod)

    # xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
    # new_combination = {'name': '新组合', 'data': np.array([20, 30, 40, 50, 60, 20, 30, 40, 50, 60, 50, 60])}
    # origin_combination = {'name': '原组合', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
    # index = {'name': '上证指数', 'data': np.array([-10, 10, 5, 55, 24, 10, 20, 8, 10, 31, 40, 32])}
    # draw_combination_chart(xlabels, new_combination, origin_combination, index)