def geo(self, id=None):
"""
话题地理传播路径
:param id:
:return:
"""
if self.geohtml and (not id or self.id == id):
self.load(QUrl('file:///' + dirname + '/qt/buffer/geo.html'))
self.flag = 5
return
self.id = id
provinces = {}
links = []
topic = self.topic.find_one({'_id': id})
for weibo_id in topic['text_id_list']:
if MONGO_DB == 'weibo':
data = self.repost.find_one({'id': weibo_id})
if not data:
continue
uid = data['uid']
dataset = self.repost.find({'uid': uid})
for data in dataset:
if data['area']:
province = self.get_province(data['area'])
if province:
if province not in provinces:
provinces[province] = 0
provinces[province] += 1
break
else:
weibo = self.weibo.find_one({'id': weibo_id})
user = self.user.find_one({'uid': weibo['uid']})
if not user:
continue
if user['area']:
# print(user['area'])
province = self.get_province(user['area'])
# print(province)
if province:
if province not in provinces:
provinces[province] = 0
provinces[province] += 1
temp = None
for province in provinces:
if not temp:
temp = province
continue
links.append((temp, province))
temp = province
# print(provinces)
# print(links)
c = (
Geo()
.add_schema(maptype="china")
.add(
"geo",
[(key, value) for key, value in provinces.items()],
type_=ChartType.EFFECT_SCATTER,
)
.add(
"geo",
links,
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(
symbol=SymbolType.ARROW, symbol_size=6, color="blue"
),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="话题地理传播路径图")
)
)
html = c.render_embed()
html = re.sub(r'width:\d+px', 'width:100%', html)
html = re.sub(r'height:\d+px', 'height:800px', html)
with open(dirname + '/qt/buffer/geo.html', 'w') as f:
f.write(html)
self.geohtml = True
self.load(QUrl('file:///' + dirname + '/qt/buffer/geo.html'))
self.flag = 5
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
c = (
Geo()
.add_schema(maptype="china")
.add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="Geo-基本示例")
)
)
c.width = "100%"
put_html(c.render_notebook())
from pyecharts.charts import Geo
from pyecharts.globals import ChartType
from pyecharts import options as opts
# bar = Bar()
# bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
# bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件
# 也可以传入路径参数，如 bar.render("mycharts.html")
# bar.render()
citys = ['丰县', '沛县', '铜山区', '贾汪区', '鼓楼区', '泉山区', '云龙区', '邳州市', '新沂市', '睢宁县']
values = [120, 160, 180, 200, 180, 190, 130, 160, 140, 170]
geo = Geo()
geo.add_schema(maptype="徐州")
geo.add_coordinate("某地", 117.3, 34.3)
print([list(z) for z in zip(citys, values)])
geo.add("test", [list(z) for z in zip(citys, values)],
type_=ChartType.EFFECT_SCATTER)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo地图示例"))
geo.render("测试.html")
mymap.add(series_name='中国最新疫情地图', data_pair=sequence, maptype='china')
mymap.set_global_opts(
visualmap_opts=opts.VisualMapOpts(pieces=[{
"min": 1,
"max": 9,
"label": "1-9",
"color": 'lightcoral'
}, {
"min": 10,
"max": 99,
"label": "10-99",
"color": 'indianred'
}, {
"min": 100,
"max": 999,
"label": "100-999",
"color": 'brown'
}, {
"min": 1000,
"max": 9999,
"label": "1000-9999",
"color": 'firebrick'
}, {
"min": 10000,
"max": 60000,
"label": "≥10000",
"color": 'darkred'
}],
is_piecewise=True))
mymap.render('地图2.html')
from pyecharts.charts import Geo
from pyecharts import options as opts
data = [['广东', 24], ['北京', 56], ['上海', 56], ['江西', 119], ['湖南', 79],
['浙江', 23], ['江苏', 97]]
# 采用链式调用
chart = (
Geo()
# 控制地图类型、视角中心点等
.add_schema(maptype="china")
# 添加图表名称、传入数据集、选择geo图类型、调整图例等
.add("geo", data)
# 系列配置项 series_options
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 全局配置项 global_options
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
title_opts=opts.TitleOpts(title="Geo_China"),
))
chart.render('geo1.html')
def draw_chart(df,
extend_field,
save_path="",
trade_list=None,
kline_title="买卖点K线图"):
datetime_array = df.datetime.tolist()
volume_array = df.volume.tolist()
# open close low high
kline_data = df[["open", "close", "low", "high"]].values.tolist()
point_list = []
if trade_list is None:
trade_list = []
for trade in trade_list:
# 开多
if type(trade[0]) == str:
trade_datetime = trade[0]
else:
trade_datetime = trade[0].strftime("%Y-%m-%d %H:%M:%S")
if trade_datetime < datetime_array[0]:
continue
if trade[1] == "多" and trade[4] == "开":
point_list.append(
opts.MarkPointItem(
name="开多",
coord=[trade_datetime, trade[6]],
value="开多",
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a")))
# 开空
elif trade[1] == "空" and trade[4] == "开":
point_list.append(
opts.MarkPointItem(
name="开空",
coord=[trade_datetime, trade[6]],
value="开空",
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a")))
# 平多
elif trade[1] == "多" and (trade[4] == "平" or trade[4] == "平今"
or trade[4] == "平昨"):
point_list.append(
opts.MarkPointItem(
name="平空",
coord=[trade_datetime, trade[6]],
value="平空",
itemstyle_opts=opts.ItemStyleOpts(color="#14b143")))
# 平空
elif trade[1] == "空" and (trade[4] == "平" or trade[4] == "平今"
or trade[4] == "平昨"):
point_list.append(
opts.MarkPointItem(
name="平多",
coord=[trade_datetime, trade[6]],
value="平多",
itemstyle_opts=opts.ItemStyleOpts(color="#14b143")))
kline = (Kline().add_xaxis(xaxis_data=datetime_array).add_yaxis(
series_name="Kline",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=point_list),
).set_global_opts(
title_opts=opts.TitleOpts(title=kline_title,
pos_left="2%",
pos_top="1%"),
legend_opts=opts.LegendOpts(is_show=True,
pos_top=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="88%",
range_start=0,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
bar = (Bar().add_xaxis(xaxis_data=datetime_array).add_yaxis(
series_name="Volume",
yaxis_data=volume_array,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
if len(extend_field) > 0:
line = (Line().add_xaxis(
xaxis_data=datetime_array).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category"),
legend_opts=opts.LegendOpts(is_show=True),
))
for field in extend_field:
field_value_array = df[field].tolist()
line.add_yaxis(
series_name=field,
y_axis=field_value_array,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
)
# Kline And Line
kline = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="100%",
height="760px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3.5%",
pos_right="3.5%",
height="60%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="3.5%",
pos_right="3.5%",
pos_top="70%",
height="16%"),
)
grid_chart.render(os.path.join(save_path, "KLineChart.html"))
Line3D()
.add(
"",
data,
xaxis3d_opts=opts.Axis3DOpts(data=clock, type_="value"),
yaxis3d_opts=opts.Axis3DOpts(data=week_en, type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
dimension=2,
max_=30,
min_=0,
range_color=[
"#313695",
"#4575b4",
"#74add1",
"#abd9e9",
"#e0f3f8",
"#ffffbf",
"#fee090",
"#fdae61",
"#f46d43",
"#d73027",
"#a50026",
],
)
)
.render("line3d_rectangular_projection.html")
)
def Candle_render_output(x_date, y_data, vol_data) -> Grid:
# ydata type : open close low high
# vol_data is for bar
print('Candle_render_output')
kline = (Kline().add_xaxis(xaxis_data=x_date).add_yaxis(
series_name="Dow-Jones index",
y_axis=y_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="Kline)",
subtitle="MA(5, 10, 30, 60)",
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
bar = (
Bar().add_xaxis(xaxis_data=x_date).add_yaxis(
series_name="Volume",
# yaxis_data=[
# [i, y_data[i][3], 1 if y_data[i][0] > y_data[i][1] else -1]
# for i in range(len(y_data))
# ],
yaxis_data=vol_data,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
lines = Kline_ma_line(x_date, y_data)
kline = kline.overlap(lines)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="50%"))
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
#!/usr/bin/env python
# -*-coding:utf-8 -*-
import os
import re
import linecache
import math
import time
import shutil
# import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Map
os.chdir(os.path.split(os.path.realpath(__file__))[0])
a1 = ['北京', '上海', '广东省']
a2 = [1, 2, 3]
output = (Map().add("污染指数", zip(
a1, a2), "china", is_map_symbol_show=False).set_global_opts(
title_opts=opts.TitleOpts(title="2020年4月生态环境部通报"),
visualmap_opts=opts.VisualMapOpts(min_=1,
max_=3)).render("重点城市环境空气排名2.html"))
import pyecharts
from pyecharts.charts import Bar # 柱状图
from pyecharts import options as ops
from pyecharts.globals import ThemeType # 内置主题类型
bar = (
Bar(init_opts=ops.InitOpts(theme="")).add_xaxis(
["xxx", "juice", "14", "ts", "drake",
"young thug"]).add_yaxis("商家A", [100, 20, 36, 10, 75, 90])
# 全局配置项
.set_global_opts(
# 标题配置项
title_opts=ops.TitleOpts(
# 主标题
title="hello",
subtitle="hhh"),
# 图例配置项
legend_opts=ops.LegendOpts(is_show=True),
# 工具配置项
toolbox_opts=ops.ToolboxOpts(is_show=False),
# 视觉映射配置项
visualmap_opts=ops.VisualMapOpts(is_show=True,
# type_="color"
)))
bar.render("柱状图.html")
# 使用 options 配置项，在 pyecharts 中，一切皆 Options。
# 全局配置项可通过 set_global_opts 方法设置
page.add(bar)
# 产品数量前15店铺柱状图
bar1 = (Bar().add_xaxis(
shop_name_group['shop_name'].values.tolist()[:15]).add_yaxis(
'产品数量前15店铺',
shop_name_group['title'].values.tolist()[:15]).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=30))))
page.add(bar1)
# 销量前15店铺饼状图
pie = (Pie().add('',
sell_data.values.tolist()[:15]).set_global_opts(
title_opts=opts.TitleOpts(title='销量前15店铺',
pos_left='center'),
legend_opts=opts.LegendOpts(is_show=False)))
page.add(pie)
# 店铺位置分布图
shop_map = (Map().add(
'', location_data.values.tolist(), 'china').set_global_opts(
title_opts=opts.TitleOpts(title='店铺位置分布图'),
visualmap_opts=opts.VisualMapOpts(
max_=max(location_data['shop_name'].values.tolist()),
min_=min(location_data['shop_name'].values.tolist()),
is_show=True)))
page.add(shop_map)
page.render()
def kline_profession_example() -> Grid:
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
x_data = ["2017-7-{}".format(i + 1) for i in range(31)]
kline = (Kline().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Dow-Jones index",
y_axis=data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="上证指数",
subtitle="MA5",
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5, d=data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
bar = (Bar().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Volume",
yaxis_data=[[i, data[i][3], 1 if data[i][0] > data[i][1] else -1]
for i in range(len(data))],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
from pyecharts.charts import MapGlobe, Grid
from pyecharts.globals import ThemeType
high = max([x for _, x in POPULATION[1:]])
low = min([x for _, x in POPULATION[1:]])
m = (
MapGlobe(init_opts=opts.InitOpts(
width="1920px",
height="900px",
page_title="全球互联网网络地图",
theme=ThemeType.WALDEN)).add_schema().add(
maptype="world",
series_name="World Population",
data_pair=POPULATION[1:],
is_map_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
# title_opts=opts.TitleOpts(title="全球互联网网络AS号分布图"),
visualmap_opts=opts.VisualMapOpts(
min_=low,
max_=high,
range_text=["max", "min"],
is_calculable=True,
# is_piecewise=True,
orient="horizontal",
pos_right="20%",
range_color=["lightskyblue", "yellow", "orangered"],
)))
m.render("geo_global_3D.html")
import pyecharts.options as opts
from pyecharts.charts import MapGlobe
from pyecharts.faker import POPULATION
data = [x for _, x in POPULATION[1:]]
low, high = min(data), max(data)
c = (MapGlobe().add_schema().add(
maptype="world",
series_name="World Population",
data_pair=POPULATION[1:],
is_map_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(visualmap_opts=opts.VisualMapOpts(
min_=low,
max_=high,
range_text=["max", "min"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
)).render("map_globe.html"))
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True, # 设置是否为分段显示
# 自定义的每一段的范围，以及每一段的文字，以及每一段的特别的样式。例如：
pieces=[
{
"min": 4000,
"label": '>4000人',
"color": "#eb2f06"
},
{
"min": 2000,
"max": 4000,
"label": '2001-4000人',
"color": "#FF3030"
}, # 不指定 max，表示 max 为无限大（Infinity）。
{
"min": 1000,
"max": 2000,
"label": '1001-2000人',
"color": "#FF4500"
},
{
"min": 500,
"max": 1000,
"label": '501-1000人',
"color": "#FF7F50"
},
{
"min": 100,
"max": 500,
"label": '100-500人',
"color": "#FFA500"
},
{
"min": 1,
"max": 100,
"label": '1-99人',
"color": "#FFDEAD"
},
],
# 两端的文本，如['High', 'Low']。
range_text=['高', '低'],
),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pos_top=50,
pos_right=10,
pieces=[{
'min':
0,
'max':
50,
'color':
'#93CE07'
}, {
'min':
50,
'max':
100,
'color':
'#FBDB0F'
}, {
'min':
100,
'max':
150,
'color':
'#FC7D02'
}, {
'min':
150,
'max':
200,
'color':
'#FD0100'
}, {
'min':
200,
'max':
300,
'color':
'#AA069F'
}, {
'min':
300,
'color':
'#AC3B2A'
}]),
def MultipleLineCharts(datas):
line = Line(init_opts=opts.InitOpts(width="100%"))
line.add_xaxis(xaxis_data=[item[0] for item in datas[1][0][1]]) # 设置x轴数据
for data in datas[1]:
line.add_yaxis(
series_name=data[0],
y_axis=[item[1] for item in data[1]],
yaxis_index=0,
is_smooth=True,
is_symbol_show=False,
is_connect_nones=True,
) # 设置y轴数据
line.set_global_opts(
title_opts=opts.TitleOpts(title=datas['title']),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
datazoom_opts=[
opts.DataZoomOpts(yaxis_index=0, range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside", yaxis_index=0),
],
visualmap_opts=opts.VisualMapOpts(
pos_top="10",
pos_right="10",
is_piecewise=True,
pieces=[
{
"gt": 0,
"lte": 25,
"color": "#096"
},
{
"gt": 25,
"lte": 50,
"color": "#ffde33"
},
{
"gt": 50,
"lte": 75,
"color": "#ff9933"
},
{
"gt": 75,
"color": "#cc0033"
},
],
out_of_range={"color": "#999"},
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
name_location="start",
min_=0,
max_=100,
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_inside=False),
),
)
line.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[
{
"yAxis": 25
},
{
"yAxis": 50
},
{
"yAxis": 75
},
],
label_opts=opts.LabelOpts(position="end"),
))
# line.render()#渲染图表，默认文件名为render.html
return line.render_embed()
def to_map_city(self, city, variate, province, update_time):
#pices定义数据分段
pieces = [
{
"max": 59999,
"min": 10000,
"label": ">10000",
"color": "#990033"
},
{
"max": 9999,
"min": 5000,
"label": "5000-9999",
"color": "#CC0033"
},
{
"max": 4999,
"min": 1000,
"label": "1000-4999",
"color": "#FF0033"
},
{
"max": 999,
"min": 100,
"label": "100-999",
"color": "#FF6633"
},
{
"max": 99,
"min": 50,
"label": "50-99",
"color": "#FF9900"
},
{
"max": 49,
"min": 10,
"label": "10-49",
"color": "#FFCC66"
},
{
"max": 9,
"min": 1,
"label": "1-9",
"color": "#FFFFCC"
},
{
"max": 0,
"min": 0,
"label": "0",
"color": "#FFFFFF"
},
]
c = (
# 设置地图大小
Map(init_opts=opts.InitOpts(width='1000px', height='880px')
).add("累计确诊人数", [list(z) for z in zip(city, variate)],
province,
is_map_symbol_show=False)
#设置不显示市级名称
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置全局变量
.set_global_opts(
title_opts=opts.TitleOpts(title="%s地区疫情地图分布" % (province),
subtitle='截止%s %s省疫情分布情况' %
(update_time, province),
pos_left="center",
pos_top="10px"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
max_=200,
is_piecewise=True,
pieces=pieces,
),
).render("./map/china/{}疫情地图.html".format(province)))
def draw_charts(echarts_data):
kline_data = echarts_data["data"]
date = echarts_data["date"]
kline = (
Kline().add_xaxis(xaxis_data=date).add_yaxis(
series_name="Dow-Jones index",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
# 其中inside是支持鼠标缩放
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4],
range_start=50,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1, 2, 3, 4],
type_="slider",
range_start=50,
range_end=100,
),
],
# # 不知道为什么要分开三个写，可能是想通过xaxis_index分别配置
# datazoom_opts=[
# opts.DataZoomOpts(
# is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
# ),
# opts.DataZoomOpts(
# is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
# ),
# opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
# ],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
title_opts=opts.TitleOpts(title="my_echart"),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
# 所有图的axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MA5",
y_axis=echarts_data['ma5'],
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 去掉折线上的小圆点
).add_yaxis(
series_name="MA20",
y_axis=echarts_data['ma20'],
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 去掉折线上的小圆点
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
# Kline And Line
overlap_kline_line = kline.overlap(line)
bar = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="Volume",
y_axis=echarts_data['volume'],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
#
# # 变色
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MACD",
y_axis=echarts_data['macd'],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
macd_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="DIFF",
y_axis=echarts_data['macd_diff'],
# xaxis_index=2,
# yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=echarts_data["macd_dea"],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
)
# .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
# MACD BAR And MACD Line
bar_macd_line = bar_2.overlap(macd_line)
# KDJ
kdj_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="K",
y_axis=echarts_data['kdj_k'],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="D",
y_axis=echarts_data["kdj_d"],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="J",
y_axis=echarts_data["kdj_j"],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
# markline_opts=opts.MarkLineOpts(
# data=[100]
# ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
), ))
######################
## kdj的macd
# Bar-2 (Overlap Bar + Line)
macd_kdj_bar = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MACD_KDJ",
y_axis=echarts_data['kdj_macd'],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
macd_kdj_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="KDJ_DIFF",
y_axis=echarts_data['kdj_macd_diff'],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="KDJ_DEA",
y_axis=echarts_data["kdj_macd_dea"],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
)
# .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
# MACD BAR And MACD Line
bar_macd_kdj_line = macd_kdj_bar.overlap(macd_kdj_line)
######################
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(kline_data))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="30%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="35%",
height="10%"),
)
# 加上MACD
grid_chart.add(
bar_macd_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="45%",
height="10%",
),
)
# 加上KDJ
grid_chart.add(
kdj_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="55%",
height="10%",
),
)
# 加上KDJ的MACD
grid_chart.add(
bar_macd_kdj_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="65%",
height="10%",
),
)
grid_chart.render("300368.html")
def to_map_world(self, country, variate, confirmed_glo, update_time):
pieces = [
{
"max": 9999999,
"min": 1000000,
"label": ">1000000",
"color": "#990033"
},
{
"max": 999999,
"min": 100000,
"label": "100000-999999",
"color": "#CC0033"
},
{
"max": 99999,
"min": 10000,
"label": "10000-99999",
"color": "#FF0033"
},
{
"max": 9999,
"min": 1000,
"label": "1000-9999",
"color": "#FF6633"
},
{
"max": 999,
"min": 100,
"label": "100-999",
"color": "#FF9900"
},
{
"max": 99,
"min": 10,
"label": "10-99",
"color": "#FFCC66"
},
{
"max": 9,
"min": 1,
"label": "1-9",
"color": "#FFFFCC"
},
{
"max": 0,
"min": 0,
"label": "0",
"color": "#FFFFFF"
},
]
c = (
# 设置地图大小
Map(init_opts=opts.InitOpts(width='1000px', height='880px')
).add("累计确诊人数", [list(z) for z in zip(country, variate)],
"world",
is_map_symbol_show=False)
#设置不显示国家名称
.set_series_opts(label_opts=opts.LabelOpts(is_show=False)
).set_global_opts(
title_opts=opts.TitleOpts(
title="世界疫情地图分布",
subtitle='截止{0}世界已确诊人数{1}'.format(
update_time, confirmed_glo),
pos_left="center",
pos_top="10px"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
max_=200,
is_piecewise=True,
pieces=pieces,
),
).render("./map/世界疫情地图.html"))
from pyecharts.charts import Map
from pyecharts import options as opts
from pyecharts.faker import Faker
(Map(init_opts=opts.InitOpts(width='900px', height='600px')).add(
series_name='商家A',
data_pair=[list(z) for z in zip(Faker.guangdong_city, Faker.values())],
maptype='广东').set_global_opts(title_opts=opts.TitleOpts(title="Map-广东地图"),
visualmap_opts=opts.VisualMapOpts()).render(
'./picture/my_guangdong.html'))
]
data.append(temp)
calend = (
charts.Calendar()
# range_决定了时间尺度
.add(series_name='',
yaxis_data=data,
calendar_opts=opts.CalendarOpts(range_='2018'))
# .add(series_name='', yaxis_data=data, calendar_opts=opts.CalendarOpts(range_=['2018-1', '2018-7'])) # range_决定了时间尺度
.set_global_opts(
title_opts=opts.TitleOpts(title='Calendar-2018年微信步数'),
visualmap_opts=opts.VisualMapOpts(
max_=25000,
min_=100,
range_size=[10, 20], # 不知道干嘛的
orient='horizontal',
is_calculable=True,
is_piecewise=True, # 颜色条是否分成pieces:True:pices, False:一根长条
pos_top='230px',
pos_left='100px',
),
))
# calend.render('calendar_echats.html')
page.add(calend)
from pyecharts import options as opts
from pyecharts.globals import SymbolType
words = [
("Sam S Club", 10000),
("Macys", 6181),
("Amy Schumer", 4386),
("Jurassic World", 4055),
def map_visualmap():
c = (Map().add("男", [list(z) for z in zip(attr_1, value_1)], "china").add(
"女", [list(z) for z in zip(attr_2, value_2)], "china").set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap（连续型）"),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True)))
return c
"""
begin = datetime.date(2017, 1, 1)
end = datetime.date(2017, 12, 31)
data = [
[str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)]
for i in range((end - begin).days + 1)
]
(
Calendar(init_opts=opts.InitOpts(width="1600px", height="1000px"))
.add(
series_name="",
yaxis_data=data,
calendar_opts=opts.CalendarOpts(
pos_top="120",
pos_left="30",
pos_right="30",
range_="2017",
yearlabel_opts=opts.LabelOpts(is_show=False),
),
)
.set_global_opts(
title_opts=opts.TitleOpts(pos_top="30", pos_left="center", title="2017年步数情况"),
visualmap_opts=opts.VisualMapOpts(
max_=20000, min_=500, orient="horizontal", is_piecewise=False
),
)
.render("calendar_heatmap.html")
)
def Fang_Scatter(
filepath, searchType
): #在当前路径生成一个散点分布图render.html,散点图纵坐标为price/unitPrice,横坐标为想要查询的字段searchType,需要传入数据路径和查询字段
oridata = pd.read_csv(filepath)
if (searchType not in oridata.columns):
print('不存在该字段! No Such attribution!')
return
#字段的单位
unit = {}
#非数值数据列表
x_1 = []
x_1.append('direction')
x_1.append('rentType')
x_1.append('houseType')
unit['direction'] = '方向'
unit['rentType'] = '整租'
unit['houseType'] = '户型'
#不干净的数值型数据
x_2 = []
#租房和二手房有不同的价格字段
if ('price' in oridata.columns):
x_2.append('price')
else:
x_2.append('unitPrice')
x_2.append('area')
x_2.append('viewNum')
x_2.append('floor')
x_2.append('buildTime')
unit['area'] = '平米'
unit['viewNum'] = '人'
unit['floor'] = '层'
unit['price'] = '元/月'
unit['unitPrice'] = '元/m2'
unit['buildTime'] = '年'
for i in x_2:
if (i in oridata.columns):
oridata[i] = Colum2value(oridata[i])
#xflag标记是否找到查询字段,=1说明在x_1里,为非数值字段,=2在x_2里
xflag = 0
#查找searchType
for i in x_1:
if i == searchType:
xflag = 1
break
if xflag == 0:
for i in x_2:
if i == searchType:
xflag = 2
break
if xflag == 0:
print('没有找到该字段! No Such attribution!')
return
x = oridata[searchType]
y = oridata[x_2[0]]
# %%
# pyecharts绘图
c = (Scatter().add_xaxis(x).add_yaxis(
"price", y, symbol_size=10, label_opts=opts.LabelOpts(is_show=False)))
if xflag == 1: #横坐标是类目数据时,改变X轴类型
c.set_global_opts(
title_opts=opts.TitleOpts(title=searchType + " with Price"),
xaxis_opts=opts.AxisOpts(name=searchType + '/' + unit[searchType],
type_="category"),
yaxis_opts=opts.AxisOpts(name=x_2[0] + '/' + unit[x_2[0]]),
visualmap_opts=opts.VisualMapOpts(max_=60000),
datazoom_opts=opts.DataZoomOpts(range_start=0),
)
else:
c.set_global_opts(
title_opts=opts.TitleOpts(title=searchType + " with Price"),
xaxis_opts=opts.AxisOpts(name=searchType + '/' + unit[searchType]),
yaxis_opts=opts.AxisOpts(name=x_2[0] + '/' + unit[x_2[0]]),
visualmap_opts=opts.VisualMapOpts(max_=100000, min_=10000),
datazoom_opts=opts.DataZoomOpts(range_start=0),
)
c.render()
'江苏': 271,
'湖南': 521,
'浙江': 724,
'海南': 2,
'广东': 725,
'湖北': 11177,
'黑龙江': 121,
'澳门': 1,
'陕西': 128,
'四川': 254,
'内蒙古': 3,
'重庆': 312,
'云南': 6,
'贵州': 2,
'吉林': 3,
'山西': 12,
'山东': 259,
'福建': 179,
'青海': 1,
'天津': 1,
'其他': 1
}
provice = list(province_distribution.keys())
values = list(province_distribution.values())
c = (Map().add("确诊人数", [list(z) for z in zip(provice, values)],
"china").set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="全国实时疫情分布地图"),
visualmap_opts=opts.VisualMapOpts(max_=1000),
))
def map_visualmap(sequence, date) -> Map:
c = Map()
c.add(date, sequence, maptype="china")
c.set_global_opts(title_opts=opts.TitleOpts(title="CBIC：全国各省现存确诊_至4月8号",subtitle="数据来源于丁香园"),legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,\
pieces=pieces_list))
# c = (
# Map()
# .add(date, sequence, maptype="china")
# .set_global_opts(
# title_opts=opts.TitleOpts(title="全国疫情动态地图_确诊累计人数_截至至3月14号"),
# visualmap_opts=opts.VisualMapOpts(max_=1400)
# )
# )
return c
map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map.render('世界疫情（累计确诊）.html')
"""
map1 = Map().add(series_name="世界疫情分布",
data_pair=data_list_new,
maptype="world",
name_map=nameMap,
is_map_symbol_show=False
)
map1.set_global_opts(title_opts=opts.TitleOpts(title="世界疫情（新增确诊）"),
visualmap_opts=opts.VisualMapOpts(max_=58000,is_piecewise=True))
map1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map1.render('世界疫情（新增确诊）.html')
"""
map2 = Map().add(series_name="世界疫情分布",
data_pair=data_list_dead,
maptype="world",
name_map=nameMap,
is_map_symbol_show=False
)
map2.set_global_opts(title_opts=opts.TitleOpts(title="世界疫情（死亡）"),
visualmap_opts=opts.VisualMapOpts(max_=140000,is_piecewise=True))
map2.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
def candlechart(self,str_tscode=None,subset='masterCandle',dat_cursor=None,MA=[5,10,21], renderfile=False) -> Grid:
str_tscode = DataAgent.formatCode(str_tscode)
int_chartsize = 500
if str_tscode == False or str_tscode is None:
print('invalid sec code provided...')
return False
dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)) if dat_cursor is None else dat_cursor
closestTradeDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
tpl_dateindex = tuple(str(i) for i in self.da.df_calTbl.loc[self.da.df_calTbl['cal_date'] <= closestTradeDate]['cal_date'][-int_chartsize:] )
# 连接储存对象
if subset=='masterCandle':
data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')
else:
data_store = libs.tryOpenH5('{}{}.dat'.format(cfg.H5_FILE_PATH,subset),mode='r')
# 初始化交易日表格，作为标准交易日序列join个股交易数据
df_toPlot = data_store['masterCandle'].loc[data_store['masterCandle']['ts_code']==str_tscode]
df_dateindex = pd.DataFrame({'trade_caldate': tpl_dateindex}).astype(int)
data_store.close()
try:
str_secname = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0]
str_sectype = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['sec_type'].values[0]
except Exception as e:
return libs.log_csv(str_cat='WARNING',str_op='candlechart',str_desc='security code {} does not exist in basic info table...'.format(str_tscode))
str_plotname = '{} - {}'.format(str_tscode,str_secname)
# 交易日时间轴标准化，补全停牌日,当日live数据等k线
'''------ 获取实时交易数据部分--------------------'''
int_liveDate = self.da.int_findClosestTradeDate(datetime.strftime(datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)),'%Y%m%d') )
if int_liveDate<=closestTradeDate and int_liveDate>df_toPlot['trade_date'].max():
df_liveQuote = self.da.query('quote_now',[self.da.formatCode(str_tscode,False),])
if len(df_liveQuote)>0:
df_liveQuote.rename(columns={'open':'adj_open',
'high':'adj_high',
'low':'adj_low',
'price':'adj_close',
'volume':'vol',
'amount':'amount'}, inplace=True)
df_liveQuote=df_liveQuote.astype(dtype= {'adj_open':'float','adj_high':'float','adj_low':'float','adj_close':'float','vol':'float','amount':'float'})
df_liveQuote['trade_date'] = int_liveDate
df_liveQuote['ts_code'] = str_tscode
df_liveQuote['vol'] = df_liveQuote['vol']/100 if str_sectype==1 or str_tscode[:3]=='399' else df_liveQuote['vol']
df_liveQuote['amount'] = df_liveQuote['amount']/1000
df_toPlot = df_toPlot.append(df_liveQuote[['ts_code','trade_date','adj_open','adj_high','adj_low','adj_close','vol','amount']],sort=False)
'''------ 结束 获取实时交易数据部分--------------------'''
df_toPlot = pd.merge(df_dateindex, df_toPlot, left_on='trade_caldate', right_on='trade_date', how='left')
df_toPlot['close'].fillna(method='ffill',inplace=True)
df_toPlot['ts_code'].fillna(method='ffill',inplace=True)
df_toPlot['klineSML'].fillna('NNNN',inplace=True)
df_toPlot['adj_close'].fillna(method='ffill',inplace=True)
df_toPlot['vol'].fillna(value=0,inplace=True)
df_toPlot['amount'].fillna(value=0,inplace=True)
df_toPlot['open'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['high'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['low'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['adj_open'].fillna(df_toPlot['adj_close'],inplace=True)
df_toPlot['adj_high'].fillna(df_toPlot['adj_close'],inplace=True)
df_toPlot['adj_low'].fillna(df_toPlot['adj_close'],inplace=True)
self.kchartdf = df_toPlot #输出至可访问对象属性中
'''-----------------画图部分---------------------'''
lst_ohlcv=df_toPlot.loc[:,['adj_open','adj_close','adj_low','adj_high']].values.tolist()
lst_vol=list(df_toPlot['vol'].values)
lst_amount=list(df_toPlot['amount'].values)
lst_peaks=list(abs(df_toPlot['peaks'].values))
# lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['pivots'].values)]
lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['valid_pivots'].values)]
# lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['pivots'].values)]
lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['valid_pivots'].values)]
lst_xaxis=list(df_toPlot['trade_caldate'].astype(str))
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
kline = (Kline()
.add_xaxis(lst_xaxis)
.add_yaxis(series_name=str_secname, y_axis=lst_ohlcv,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min",value_dim="close"),
opts.MarkPointItem(type_="max",value_dim="close")],
symbol_size = [20, 20], #表示标记宽为 20，高为 10
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#ec0000",
border_color0="#00da3c",
),)
.set_global_opts(
title_opts=opts.TitleOpts(
title=str_plotname,
subtitle='MA='+str(MA),
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='10%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0,1,2],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0,1,2],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",font_size=10),
),
# 阴量绿阳量红
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=list(map(lambda x: x+len(MA),[4,5])), #动态算出vol和amt柱状图的series index
is_piecewise=True,
pieces=[
{"value": 1, "color": "#ec0000"},
{"value": -1, "color": "#00da3c"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
trendline = (
Line()
.add_xaxis(lst_xaxis)
.add_yaxis('高低点', lst_peaks,
itemstyle_opts=opts.ItemStyleOpts(color="green"))
)
for i in MA:
if i is not None:
trendline.add_yaxis(
series_name='MA'+str(i),
y_axis=calculate_ma(day_count=i, d=lst_ohlcv),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
keyPoints = (
EffectScatter()
.add_xaxis(lst_xaxis)
.add_yaxis("末跌高", lst_pivotdown,symbol=SymbolType.ARROW,symbol_rotate=180,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="purple"))
.add_yaxis("末升低", lst_pivotup,symbol=SymbolType.ARROW,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="blue"))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
vol_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易量",
yaxis_data=[
[i, lst_vol[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_vol))
],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# .add_yaxis("交易量", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
amnt_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易额",
yaxis_data=[
[i, lst_amount[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_amount))
],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=2,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# .add_yaxis("交易额", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
priceChart = kline.overlap(trendline).overlap(keyPoints)
gridChart = Grid()
gridChart.add(
priceChart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_bottom='40%'),
)
gridChart.add(
vol_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="60%", height='15%'),
)
gridChart.add(
amnt_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%"),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'kline')
gridChart.render(fname) if renderfile else None
self.kchart = gridChart # 将结果输出到analytics对象属性中用于function以外的调用
return self
def map(self, area=None):
"""
话题地理分布
:param area: 限制区域，默认为中国
:return:
"""
if self.maparea:
if not area or self.maparea == area:
self.load(QUrl('file:///' + dirname + '/qt/buffer/map.html'))
self.flag = 4
return
if not area:
area = 'china'
self.maparea = area
topicset = self.topic.find()
temp = {}
for topic in topicset:
for item in topic['entity']['Ns']:
if self.maparea == 'china':
city = self.get_province(''.join(item[0]))
else:
city = self.get_city(''.join(item[0]), self.maparea)
if city:
if city not in temp:
temp[city] = 0
temp[city] += item[1]
data = []
for key, value in temp.items():
data.append([key, value])
if self.maparea == 'china':
temparea = '中国'
else:
temparea = self.maparea
max_value = 100
if temp.values():
max_value = max(temp.values())
map = (
Map()
.add("话题分布", data, self.maparea)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="话题分布-" + temparea),
visualmap_opts=opts.VisualMapOpts(max_=max_value),
)
)
html = map.render_embed()
html_id = re.search('div id="(.*?)"', html).group(1)
html = re.sub(r'width:\d+px', 'width:100%', html)
# JavaScript代码注入，实现点击地图获取点击区域
js = '''<script>
new QWebChannel(qt.webChannelTransport, function (channel) {
window.map = channel.objects.map;
});
chart_html_id.on('click', function(params){
area = params.name;
map.map_click(area);
});
document.onclick = function(e){
ctx = e.target.getContext('2d')
data = ctx.getImageData(e.pageX,e.pageY,1,1).data
if (data[3] == 0) {
map.map_click('china')
}
}
</script>'''
qwebchanneljs = '<script type="text/javascript" src="qwebchannel.js"></script>'
html = re.sub('</head>', qwebchanneljs + '</head>', html)
html = re.sub('</body>', js + '</body>', html)
html = re.sub('html_id', html_id, html)
with open(dirname + '/qt/buffer/map.html', 'w') as f:
f.write(html)
self.load(QUrl('file:///' + dirname + '/qt/buffer/map.html'))
self.flag = 4
