-
Notifications
You must be signed in to change notification settings - Fork 0
/
plot_class.py
235 lines (215 loc) · 9.94 KB
/
plot_class.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
import pandas as pd
from typing import List,Union,Optional
from pyecharts.charts import Bar,Map,Timeline,Pie,Geo,MapGlobe,Map3D,Page,Grid
import pyecharts.options as opts
from pyecharts.faker import Faker
import streamlit as st
#demo为累计确诊
class Visualize:
def __init__(self,name:List[str],value_df:pd.DataFrame,type:str='全球',max:int=1e3):
'''
name：城市或国家（区域）名称
value：列名为日期
type：为图表类型，从“中国”与“全球”中择其一
'''
self.name=name
self.value_df=value_df
self.type=type
if self.type=='全球':
self.region='world'
else:
self.region='china'
# self.max=self.value_df.max().max()
self.max=max
self.min=self.value_df.min().min()
self.feature='累计确诊'
def map(self,date):
value=self.value_df.loc[date]
name=list(value.index)
value=list(value)
map=(
Map(init_opts=opts.InitOpts(width='700px',height='400px'))
.add(f'{date}{self.feature}',[list(z) for z in zip(name,value)],self.region,is_map_symbol_show=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))#不展示国家或地区名称
.set_global_opts(
title_opts=opts.TitleOpts(title=f"{self.type}疫情地图"),
visualmap_opts=opts.VisualMapOpts(max_=self.max),
)
)
return map
def bar(self,date):
value=self.value_df.loc[date].sort_values(ascending=False)[:10]
name=list(value.index)
value=list(value)
bar=(
Bar(init_opts=opts.InitOpts(width='350px',height='400px'))
.add_xaxis(list(name))
.add_yaxis(
series_name=f'{date}{self.feature}',
y_axis=list(value),
is_selected=True,#选中图例
label_opts=opts.LabelOpts(is_show=False),
# is_realtime_sort=True,
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-90)),
title_opts=opts.TitleOpts(title=f'{self.type}疫情柱状图', subtitle="数据来自约翰霍普金斯大学"),
tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="shadow"),
)
)
return bar
def pie(self,date):
value=self.value_df.loc[date].sort_values(ascending=False)[:10]
name=list(value.index)
value=list(value)
pie=(
Pie(init_opts=opts.InitOpts(width='350px',height='400px'))
.add(
series_name=f'{date}{self.feature}饼状图',
data_pair=[list(z) for z in zip(name,value)],
center=["90%", "25%"],
radius="35%",
)
)
return pie
def map3D(self,date):
'''不支持TimeLine，请输入具体日期'''
pass
# for date in self.value_df.columns:
# value=list(self.value_df[date])
# map3d = (
# Map3D()
# .add_schema(
# maptype=self.region,
# itemstyle_opts=opts.ItemStyleOpts(
# color="rgb(5,101,123)",
# opacity=1,
# border_width=0.8,
# border_color="rgb(62,215,213)",
# ),
# map3d_label=opts.Map3DLabelOpts(
# is_show=False,
# formatter=JsCode("function(data){return data.name + " " + data.value[2];}"),
# ),
# emphasis_label_opts=opts.LabelOpts(
# is_show=False,
# color="#fff",
# font_size=10,
# background_color="rgba(0,23,11,0)",
# ),
# light_opts=opts.Map3DLightOpts(
# main_color="#fff",
# main_intensity=1.2,
# main_shadow_quality="high",
# is_main_shadow=False,
# main_beta=10,
# ambient_intensity=0.3,
# ),
# )
# .add(
# maptype=self.region,
# series_name=f'{self.type}{self.feature}三维分布图',
# data_pair=[[z[0],[z[1][1],z[1][0],z[2]]] for z in zip(self.name,self.location,value)],
# type_=ChartType.BAR3D,
# bar_size=1,
# shading="lambert",
# is_animation=True,
# label_opts=opts.LabelOpts(
# is_show=False,
# formatter=JsCode("function(data){return data.name + ' ' + data.value[2];}"),
# ),
# )
# .set_global_opts(title_opts=opts.TitleOpts(title="Map3D-Bar3D"))
# )
# return map3d
def mapGlobe(self,date):
'''只支持全球地图，请输入具体日期'''
# mapGlobe不能和其他的图一起放在同一个网页内。。。。
if self.type=='全球':
tl=Timeline()
# for date in self.value_df.columns:
value=list(self.value_df.loc[date])
map_globe=(
MapGlobe()
.add_schema()
.add(
maptype=self.region,
series_name=f'{self.type}{self.feature}全球分布图',
data_pair=[list(z) for z in zip(list(self.name),value)],
is_map_symbol_show=False,#展示每个国家上的圆圈
label_opts=opts.LabelOpts(is_show=False)#不展示国家或地区名称
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
min_=self.min,
max_=self.max,
range_text=["max", "min"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
)
)
)
return map_globe
else:
return None
def __call__(self,date,dynamic=False):
page=Page(layout=Page.DraggablePageLayout, page_title="疫情数据动态展示")
if dynamic:
tl1=Timeline();tl2=Timeline()
for date in self.value_df.index:
# map=self.map(date)
# tl1.add(map,f'{date}{self.feature}平面图')
# tl1.add_schema(is_auto_play=True, play_interval=10,is_loop_play=False)
bar=self.bar(date)
pie=self.pie(date)
tl2.add(bar.overlap(pie),f'{date}{self.feature}柱形图与饼状图')
tl2.add_schema(is_auto_play=True, play_interval=10,is_loop_play=False)
grid=(
Grid(init_opts=opts.InitOpts(width="1200px", height="800px"))
# .add(map_globe,grid_opts=opts.GridOpts(pos_right="58%"), is_control_axis_index=True)
# .add(map,grid_opts=opts.GridOpts(pos_top="50%", pos_bottom="100%"), is_control_axis_index=True)
.add(tl2,grid_opts=opts.GridOpts(pos_top="20%", pos_bottom="0%"), is_control_axis_index=True)
)
return grid
else:
# map_globe=self.mapGlobe(date)
map=self.map(date)
bar=self.bar(date)
pie=self.pie(date)
grid=(
Grid(init_opts=opts.InitOpts(width="1200px", height="800px"))
# .add(map_globe,grid_opts=opts.GridOpts(pos_right="58%"), is_control_axis_index=True)
.add(bar.overlap(pie),grid_opts=opts.GridOpts(pos_top="0%", pos_bottom="50%"), is_control_axis_index=True)
# .add(map,grid_opts=opts.GridOpts(pos_top="50%", pos_bottom="100%"))
)
return grid
if __name__=='__main__':
raw=pd.read_csv('demo.csv')
raw.index=pd.DatetimeIndex(raw['Unnamed: 0'])
del raw['Unnamed: 0']
raw.index.rename('date',inplace=True)
st.set_page_config(page_title="毕业论文——葛新杰",layout="centered")
#
name_map_world={'US':'United States'}
name_map_cn={'Heilongjiang': '黑龙江省', 'Jilin': '吉林省', 'Liaoning': '辽宁省', 'Beijing': '北京市',
'Tianjin': '天津市', 'Hebei': '河北省', 'Shanxi': '山西省', 'Inner Mongolia': '内蒙古自治区',
'Shanghai': '上海市', 'Jiangsu': '江苏省', 'Shandong': '山东省', 'Zhejiang': '浙江省',
'Anhui': '安徽省', 'Jiangxi': '江西省', 'Fujian': '福建省', 'Guangdong': '广东省',
'Macau': '澳门特别行政区', 'Taiwan': '台湾省', 'Hong Kong': '香港特别行政区', 'Tibet': '西藏自治区',
'Guangxi': '广西省', 'Hainan': '海南省', 'Henan': '河南省', 'Hubei': '湖北省', 'Hunan': '湖南省',
'Shaanxi': '陕西省', 'Xinjiang': '新疆自治区', 'Ningxia': '宁夏自治区', 'Gansu': '甘肃省',
'Qinghai': '青海省', 'Chongqing': '重庆市', 'Sichuan': '四川省', 'Guizhou': '贵州省', 'Yunnan': '云南省',}
raw.rename(columns=name_map_cn,inplace=True)
del raw['Unknown']
# 全球数据
world=Visualize(name=raw.columns,value_df=raw,type='中国',max=1e4)
tl=world('2022/4/30')
tl.render('world figure.html')
# # 中国数据
# data=data.loc[data['Country/Region']=='China']
# data.dropna(inplace=True)
# name=data['Province/State'];name.replace(name_map_cn,inplace=True)
# value_df=data.iloc[:,4:]
# world=Visualize(name=name,value_df=value_df,type='中国',max=1e4,location=data[['Lat','Long']].to_numpy().tolist())
# tl=world.bar('4/30/22')
# tl.render('china figure.html')