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# -*- 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.pyplot as plt
from matplotlib import ticker
from matplotlib.ticker import FuncFormatter
from matplotlib.font_manager import FontProperties
# 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 = ['#222A77', '#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='#C6A774')
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+0.2, '%.0f万' % b, ha='center', va='bottom', fontsize=fontsize)
elif b < 0:
ax1.text(a, b-0.5, '%.0f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='#C6A774', 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, b + 0.1, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize)
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()
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
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='#C6A774', marker='', linewidth=3, label=cumulative['name'])
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_combination_chart(xlabels, new_combination, origin_combination, index):
"""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()
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=origin_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=new_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()
def draw_correlation_chart():
"""相关性分析图"""
pass
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)