def WorldMap():
namemap = {"阿富汗": "Afghanistan", "安哥拉": "Angola", "阿尔巴尼亚": "Albania", "阿尔及利亚": "Algeria", "阿根廷": "Argentina",
"亚美尼亚": "Armenia", "澳大利亚": "Australia", "奥地利": "Austria", "阿塞拜疆": "Azerbaijan", "巴哈马": " Bahamas ",
"孟加拉国": "Bangladesh", "比利时": " Belgium ", "贝宁": "Benin", "布基纳法索": "Burkina Faso", "布隆迪": " Burundi",
"保加利亚": "Bulgaria", "波斯尼亚和黑塞哥维那": "Bosnia and Herz", "白俄罗斯": "Belarus", "伯利兹": "Belize",
"百慕大群岛": "Bermuda", "玻利维亚": "Bolivia", "巴西": "Brazil", "文莱": "Brunei ", "不丹": "Bhutan",
"博茨瓦纳": "Botswana", "柬埔寨": "Cambodia", "喀麦隆": "Cameroon", "加拿大": "Canada",
"中非共和国": "Central African Rep.", "乍得": "Chad", "智利": "Chile", "中国": "China", "哥伦比亚": "Colombia",
"刚果（金）": "Congo", "哥斯达黎加": "Costa Rica", "科特迪瓦": "Côte d'Ivoire", "克罗地亚": "Croatia ", "古巴": "Cuba",
"塞浦路斯": "Cyprus", "捷克共和国": "CzechRep", "韩国": "Dem.Rep.Korea", "刚果（布）": "Dem. Rep. Congo",
"丹麦": "Denmark", "吉布提": "Djibouti", "多米尼加": "Dominican Rep.", "厄瓜多尔": "Ecuador", "埃及": "Egypt",
"萨尔瓦多": "ElSalvador", "赤道几内亚": "Eq.Guinea", "厄立特里亚": "Eritrea", "爱沙尼亚": "Estonia", "埃塞俄比亚": "Ethiopia",
"福克兰群岛": "FalklandIs", "斐济": "Fiji", "芬兰": "Finland", "法国": "France", "法属圭亚那": "FrenchGuiana",
"法属南部领地": "Fr.S.AntarcticLands", "加蓬": "Gabon", "冈比亚": "Gambia", "德国": "Germany", "佐治亚州": "Georgia ",
"加纳": "Ghana", "希腊": "Greece", "格陵兰": "Greenland", "危地马拉": "Guatemala", "几内亚": "Guinea",
"几内亚比绍": "Guinea-Bissau", "圭亚那": "Guyana", "海地": "Haiti", "赫德岛和麦克唐纳群岛": "HeardI.andMcDonaldIs",
"洪都拉斯": "Honduras", "匈牙利": "Hungary", "冰岛": "Iceland", "印度": "India", "印度尼西亚": "Indonesia", "伊朗": "Iran",
"伊拉克": "Iraq", "爱尔兰": "Ireland", "以色列": "Israel", "意大利": "Italy", "象牙海岸": "IvoryCoast", "牙买加": "Jamaica",
"日本": "Japan", "乔丹": "Jordan", "克什米尔": "Kashmir", "哈萨克斯坦": "Kazakhstan", "肯尼亚": "Kenya", "科索沃": "Kosovo",
"科威特": "Kuwait", "吉尔吉斯斯坦": "Kyrgyzstan", "老挝": "Lao PDR", "拉脱维亚": "Latvia", "黎巴嫩": "Lebanon",
"莱索托": "Lesotho", "利比里亚": "Liberia", "利比亚": "Libya", "立陶宛": "Lithuania", "卢森堡": "Luxembourg",
"马达加斯加": "Madagascar", "马其顿": "Macedonia", "马拉维": "Malawi", "马来西亚": "Malaysia", "马里": "Mali",
"毛里塔尼亚": "Mauritania", "墨西哥": "Mexico", "摩尔多瓦": "Moldova", "蒙古": "Mongolia", "黑山": "Montenegro",
"摩洛哥": "Morocco", "莫桑比克": "Mozambique", "缅甸": "Myanmar", "纳米比亚": "Namibia", "荷兰": "Netherlands",
"新喀里多尼亚": "New Caledonia", "新西兰": "New Zealand", "尼泊尔": "Nepal", "尼加拉瓜": "Nicaragua", "尼日尔": "Niger",
"尼日利亚": "Nigeria", "朝鲜": "Korea", "北塞浦路斯": "NorthernCyprus", "挪威": "Norway", "阿曼": "Oman",
"巴基斯坦": "Pakistan", "巴拿马": "Panama", "巴布亚新几内亚": "Papua New Guinea", "巴拉圭": "Paraguay", "秘鲁": "Peru",
"刚果": "Republi cofthe Congo", "菲律宾": "Philippines", "波兰": "Poland", "葡萄牙": "Portugal",
"波多黎各": "Puerto Rico", "卡塔尔": "Qatar", "塞尔维亚共和国": "RepublicofSerbia", "罗马尼亚": "Romania", "俄罗斯": "Russia",
"卢旺达": "Rwanda", "萨摩亚": "Samoa", "沙特阿拉伯": "Saudi Arabia", "塞内加尔": "Senegal", "塞尔维亚": "Serbia",
"塞拉利昂": "Sierra Leone", "斯洛伐克": "Slovakia", "斯洛文尼亚": "Slovenia", "所罗门群岛": "SolomonIs",
"索马里兰": "Somaliland", "索马里": "Somalia", "南非": "South Africa", "南乔治亚和南桑德威奇群岛": "S.Geo.andS.Sandw.Is",
"南苏丹": "S.Sudan", "西班牙": "Spain", "斯里兰卡": "Sri Lanka", "苏丹": "Sudan", "苏里南": "Suriname",
"斯威士兰": "Swaziland", "瑞典": "Sweden", "瑞士": "Switzerland", "叙利亚": "Syria", "塔吉克斯坦": "Tajikistan",
"坦桑尼亚": "Tanzania", "泰国": "Thailand", "东帝汶": "Timor-Leste", "多哥": "Togo",
"特立尼达和多巴哥": "TrinidadandTobago", "突尼斯": "Tunisia", "土耳其": "Turkey", "土库曼斯坦": "Turkmenistan",
"乌干达": "Uganda", "乌克兰": "Ukraine", "沙特阿拉伯": "United Arab Emirates", "大不列颠联合王国": "United Kingdom",
"坦桑尼亚联合共和国": "UnitedRepublicofTanzania", "美国": "United States", "美利坚合众国": "UnitedStatesofAmerica",
"乌拉圭": "Uruguay", "乌兹别克斯坦": "Uzbekistan", "瓦努阿图": "Vanuatu", "委内瑞拉": "Venezuela", "越南": "Vietnam",
"西岸": "WestBank", "西撒哈拉": "W.Sahara", "也门共和国": "Yemen", "赞比亚共和国": "Zambia", "津巴布韦": "Zimbabwe"}
url = 'http://api.tianapi.com/txapi/ncovabroad/index?key=0fd7df4315148dc405068de771dc279e'
data_world = requests.get(url).json()
oversea_confirm = []
for item in data_world['newslist']:
try:
country = namemap[item['provinceName']]
except:
country = 'United Kingdom'
oversea_confirm.append((country.replace('United States of America', 'United States'), item['confirmedCount']))
world_map = (
Map()
.add('累计确诊人数', oversea_confirm, 'world', is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False, color='#fff'))
.set_global_opts(
title_opts=opts.TitleOpts(title='全球疫情累计确诊人数地图'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=2700,
is_piecewise=True,
pieces=[
{"max":999999999,"min":1000000,"label":"1000000人以上",
"color":"#000000"},
{"max": 999999, "min": 100000, "label": "100000-999999人",
"color": "#8A0808"},
{"max": 99999, "min": 10000, "label": "10000-99999人",
"color": "#B40404"},
{"max": 9999, "min": 1000, "label": "1000-9999人", "color": "#DF0101"},
{"max": 999, "min": 500, "label": "500-999人", "color": "#F78181"},
{"max": 499, "min": 100, "label": "100-499人", "color": "#F5A9A9"},
{"max": 99, "min": 10, "label": "10-99人", "color": "#FFFFCC"},
])
)
)
return world_map
.add(
"",
data_pair=data,
highlight_policy="ancestor",
radius=[0, "95%"],
sort_="null",
levels=[
{},
{
"r0": "15%",
"r": "35%",
"itemStyle": {"borderWidth": 2},
"label": {"rotate": "tangential"},
},
{"r0": "35%", "r": "70%", "label": {"align": "right"}},
{
"r0": "70%",
"r": "72%",
"label": {"position": "outside", "padding": 3, "silent": False},
"itemStyle": {"borderWidth": 3},
},
],
)
.set_global_opts(title_opts=opts.TitleOpts(title="Sunburst-官方示例"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))
)
c.width = "100%"
put_html(c.render_notebook())
def render_lines(country_name):
#-------------------------------------------------------------------------------------
# 第一步：读取数据
#-------------------------------------------------------------------------------------
n = "dataSets/countrydata.csv"
data = pd.read_csv(n)
data = data[data['countryName'] == country_name]
date_list = list(data['dateId'])
date_list = list(map(lambda x: str(x), date_list))
confirm_list = list(data['confirmedCount'])
current_list = list(data['currentConfirmedCount'])
dead_list = list(data['deadCount'])
heal_list = list(data['curedCount'])
print(len(date_list))
#print(date_list) # 日期
#print(confirm_list) # 确诊数据
#print(current_list) # 疑似数据
#print(dead_list) # 死亡数据
#print(heal_list) # 治愈数据
#-------------------------------------------------------------------------------------
# 第二步：绘制折线面积图
#-------------------------------------------------------------------------------------
line = (
Line().add_xaxis(date_list)
# 平均线 最大值 最小值
.add_yaxis('确诊数据',
confirm_list,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
'现存确诊数据',
current_list,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
'死亡数据',
dead_list,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
])).add_yaxis(
'治愈数据',
heal_list,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max"),
opts.MarkPointItem(type_="min")
]))
# 隐藏数字 设置面积
.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False))
# 设置x轴标签旋转角度
.set_global_opts(xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-30)),
yaxis_opts=opts.AxisOpts(name='人数', min_=3),
title_opts=opts.TitleOpts(title='2019-nCoV' +
country_name + '疫情数据曲线图')))
return line
from pyecharts import options as opts
from pyecharts.charts import Scatter
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
c = (Scatter().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
[list(z) for z in zip(Faker.values(), Faker.choose())],
label_opts=opts.LabelOpts(formatter=JsCode(
"function(params){return params.value[1] +' : '+ params.value[2];}")),
).set_global_opts(
title_opts=opts.TitleOpts(title="Scatter-多维度数据"),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) {return params.name + ' : ' + params.value[2];}")),
visualmap_opts=opts.VisualMapOpts(type_="color",
max_=150,
min_=20,
dimension=1),
).render("Scatter_multi_dimension.html"))
def image_host_resource(doc_path, hosts_metrics, host):
options = opts.InitOpts(js_host=os.path.join(os.getcwd(),
"scripts/javascript/"),
animation_opts=opts.AnimationOpts(animation=False))
line1 = (Line(options).add_xaxis(
hosts_metrics[host['ip']]['metrics']['datetime']
).add_yaxis(
"CPU",
hosts_metrics[host['ip']]['metrics']['cpu'],
is_symbol_show=False,
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'rgb(255,158,68)'},{offset:1,color:'rgb(255,70,131)'}])"
),
opacity=0.5)
).add_yaxis(
"Memory",
hosts_metrics[host['ip']]['metrics']['memory'],
is_symbol_show=False,
is_smooth=True).set_series_opts(linestyle_opts=opts.LineStyleOpts(
width=2)).set_global_opts(
title_opts=opts.TitleOpts(
title="主机资源使用率 (%s)" % (host['ip']),
title_textstyle_opts=opts.TextStyleOpts(font_size=13),
subtitle=" 数据来源-健康度平台"),
xaxis_opts=opts.AxisOpts(type_="time"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter=utils.JsCode(
"function(val){return val + '%';}")))))
line2 = (Line(options).add_xaxis(
hosts_metrics[host['ip']]['metrics']['datetime']
).add_yaxis(
"Process",
hosts_metrics[host['ip']]['metrics']['process'],
is_symbol_show=False,
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'#fff'},{offset:1,color:'#61a0a8'}])"
),
opacity=0.5)
).set_series_opts(linestyle_opts=opts.LineStyleOpts(
width=2)).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="time",
position="top",
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(is_inverse=True),
title_opts=opts.TitleOpts(
title="应用运行数量(所有)",
pos_bottom="0%",
title_textstyle_opts=opts.TextStyleOpts(font_size=13)),
legend_opts=opts.LegendOpts(pos_bottom="0%")))
grid = (Grid(options).add(
line1,
grid_opts=opts.GridOpts(pos_top=50,
pos_left=50,
pos_right=50,
pos_bottom="50%")).add(line2,
grid_opts=opts.GridOpts(
pos_top="58%",
pos_left=50,
pos_right=50,
pos_bottom=30)))
make_snapshot(
snapshot,
grid.render(
os.path.join(doc_path,
"render_%s.html" % (host['ip'].replace(".", "_")))),
os.path.join(doc_path,
"host_res_%s.png" % (host['ip'].replace(".", "_"))),
pixel_ratio=1,
delay=1,
is_remove_html=True)
def map_without_label():
obj_map = Map()
obj_map.add("中国", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
obj_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
obj_map.set_global_opts(title_opts=opts.TitleOpts(title="Map-不显示Label",subtitle ="副标题"))
return obj_map
for i in range(2):
for j in range(5):
data_listy[i][j] = data[i][j][data_index[m]].astype(float).round(
2).tolist()
n = n + 4
m = m + 1
except KeyError:
pass
# 将数据转换为pyecharts支持的数据类型
line = (
Line().add_xaxis(data_listx[0][0]) # 设置x轴数据
.add_yaxis(
'1-1-C15',
data_listy[0][0], # 设置y轴数据并调整样式
label_opts=opts.LabelOpts(is_show=False), # 不显示标签数据
linestyle_opts=opts.LineStyleOpts(), # 设置线的样式，暂不设置
itemstyle_opts=opts.ItemStyleOpts(opacity=0), # 将标签点的透明度设置为100%
color='#000000', # 1-1-C15为纯黑 # 设置线条颜色
#is_smooth=True
).add_xaxis(data_listx[0][1]).add_yaxis(
'1-2-C15',
data_listy[0][1],
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(),
itemstyle_opts=opts.ItemStyleOpts(opacity=0),
color=' #808080', # 1-2-C15为灰色
#is_smooth=True
).add_xaxis(data_listx[0][2]).add_yaxis(
'1-3-C15',
data_listy[0][2],
def line_color_with_js_func(place="上海野生动物园") -> Line:
if place is None:
weather_data = weather_spider(weather_dict.get("上海野生动物园"))
else:
if place in weather_dict.keys():
place_weather_url = weather_dict.get(place)
weather_data = weather_spider(place_weather_url)
else:
return None
x_data = [
x.replace("".join(re.findall("（.*", x)), "")
for x in weather_data.get("days")
]
y_data = [
maxt.rstrip("℃").split("/")[0]
for maxt in weather_data.get("max_min_t")
]
y_data2 = [
mint.rstrip("℃").split("/")[1]
for mint in weather_data.get("max_min_t")
]
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#6495ED'}, {offset: 1, color: '#12406d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(
width="100%", height="100%")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True, color="white")).
add_xaxis(xaxis_data=x_data).add_yaxis(
"日最高气温",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white",
font_size=16),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# 第二
.add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="日最低气温",
y_axis=y_data2,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white",
font_size=16),
itemstyle_opts=opts.ItemStyleOpts(color="#0ff",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
))
return c
def geo():
place_avaliable = passenger_spider(None, all=True)
place_no_weather = passenger_spider_no_weather()
city = '上海'
g = Geo(init_opts=opts.InitOpts(
width="100%", height="100%", bg_color="#12406d"))
g.add_schema(maptype=city,
itemstyle_opts=opts.ItemStyleOpts(color="#135dbe",
border_color="#fff"),
zoom=1.2)
# 定义坐标对应的名称，添加到坐标库中 add_coordinate(name, lng, lat)
for k, v in jingwei_dict.items():
g.add_coordinate(k, float(v[1]), float(v[0]))
for item in place_no_weather:
g.add_coordinate(item["NAME"], float(item["LOCATION_X"]),
float(item["LOCATION_Y"]))
coordinates = []
for k in jingwei_dict.keys():
data_pairs = []
data_pairs.append(k)
for p in place_avaliable:
if k == p["NAME"]:
if not p.get("MAX_NUM"):
data_pairs.append(0.0)
else:
data_pairs.append(
round(int(p["NUM"]) / int(p["MAX_NUM"]), 2))
break
if len(data_pairs) == 1:
data_pairs.append(0.0)
data_pairs = tuple(data_pairs)
coordinates.append(data_pairs)
for item in place_no_weather:
data_pairs = []
data_pairs.append(item["NAME"])
if (not item["MAX_NUM"]) or (item["MAX_NUM"] == ""):
data_pairs.append(0)
else:
data_pairs.append(round(
int(item["NUM"]) / int(item["MAX_NUM"]), 2))
data_pairs = tuple(data_pairs)
coordinates.append(data_pairs)
# 定义数据对，
# data_pair = [
# ('湖南省长沙市雨花区跳马镇仙峰岭', 25),
# ('湖南省长沙市宁乡市横市镇藕塘', 5),
# ('湖南省长沙市长沙县黄花镇新塘铺长沙黄花国际机场', 20)
# ]
data_pair = coordinates
# Geo 图类型，有 scatter, effectScatter, heatmap, lines 4 种，建议使用
# from pyecharts.globals import GeoType
# GeoType.GeoType.EFFECT_SCATTER，GeoType.HEATMAP，GeoType.LINES
# 将数据添加到地图上
g.add('', data_pair, type_=ChartType.EFFECT_SCATTER)
# 设置样式
g.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, formatter="{b}:{c}"))
# 自定义分段 color 可以用取色器取色
pieces = [
{
'max': 0.1,
'label': '10%以下',
'color': '#98F5FF'
},
{
'min': 0.1,
'max': 0.3,
'label': '10%-30%',
'color': '#0f0'
},
{
'min': 0.3,
'max': 0.5,
'label': '30%-50%',
'color': 'orange'
},
{
'min': 0.5,
'max': 0.7,
'label': '50%-70%',
'color': '#E2C568'
},
{
'min': 0.7,
'max': 0.8,
'label': '70%-80%',
'color': '#FCF84D'
},
{
'min': 0.8,
'max': 0.9,
'label': '80%-90%',
'color': '#3700A4'
},
{
'min': 0.9,
'max': 1,
'label': '90%-100%',
'color': '#DD675E'
},
{
'min': 1,
'label': '100%以上',
'color': '#f00'
} # 有下限无上限
]
# is_piecewise 是否自定义分段， 变为true 才能生效
g.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=pieces,
textstyle_opts=opts.TextStyleOpts(
color="#fff")))
return g
def global_total_map(wb):
ws_time = wb['全球疫情数据更新时间']
ws_data = wb['全球各国疫情数据']
ws_data.delete_rows(1)
country = []
surplus = []
curconfirm = []
for data in ws_data.values:
country.append(data[0])
surplus.append(data[1])
curconfirm.append(data[2])
time_global = ws_time['A2'].value
pieces = [{
'max': 0,
'min': 0,
'label': '0',
'color': '#FFFFFF'
}, {
'max': 49,
'min': 1,
'label': '1-49',
'color': '#FFE5DB'
}, {
'max': 99,
'min': 50,
'label': '50-99',
'color': '#FFC4B3'
}, {
'max': 999,
'min': 100,
'label': '100-999',
'color': '#FF9985'
}, {
'max': 9999,
'min': 1000,
'label': '1000-9999',
'color': '#F57567'
}, {
'max': 99999,
'min': 10000,
'label': '10000-99999',
'color': '#E64546'
}, {
'max': 999999,
'min': 100000,
'label': '100000-999999',
'color': '#B80909'
}, {
'max': 9999999,
'min': 1000000,
'label': '1000000-9999999',
'color': '#BA0808'
}, {
'max': 99999999,
'min': 10000000,
'label': '>=10000000',
'color': '#F00000'
}]
name_map = {
"Somalia": "索马里",
"Liechtenstein": "列支敦士登",
"Morocco": "摩洛哥",
"W. Sahara": "西撒哈拉",
"Serbia": "塞尔维亚",
"Afghanistan": "阿富汗",
"Angola": "安哥拉",
"Albania": "阿尔巴尼亚",
"Andorra": "安道尔共和国",
"United Arab Emirates": "阿拉伯联合酋长国",
"Argentina": "阿根廷",
"Armenia": "亚美尼亚",
"Australia": "澳大利亚",
"Austria": "奥地利",
"Azerbaijan": "阿塞拜疆",
"Burundi": "布隆迪",
"Belgium": "比利时",
"Benin": "贝宁",
"Burkina Faso": "布基纳法索",
"Bangladesh": "孟加拉国",
"Bulgaria": "保加利亚",
"Bahrain": "巴林",
"Bahamas": "巴哈马",
"Bosnia and Herz.": "波斯尼亚和黑塞哥维那",
"Belarus": "白俄罗斯",
"Belize": "伯利兹",
"Bermuda": "百慕大",
"Bolivia": "玻利维亚",
"Brazil": "巴西",
"Barbados": "巴巴多斯",
"Brunei": "文莱",
"Bhutan": "不丹",
"Botswana": "博茨瓦纳",
"Central African Rep.": "中非共和国",
"Canada": "加拿大",
"Switzerland": "瑞士",
"Chile": "智利",
"China": "中国",
"Côte d'Ivoire": "科特迪瓦",
"Cameroon": "喀麦隆",
"Dem. Rep. Congo": "刚果（布）",
"Congo": "刚果（金）",
"Colombia": "哥伦比亚",
"Cape Verde": "佛得角",
"Costa Rica": "哥斯达黎加",
"Cuba": "古巴",
"N. Cyprus": "北塞浦路斯",
"Cyprus": "塞浦路斯",
"Czech Rep.": "捷克",
"Germany": "德国",
"Djibouti": "吉布提",
"Denmark": "丹麦",
"Dominican Rep.": "多米尼加",
"Algeria": "阿尔及利亚",
"Ecuador": "厄瓜多尔",
"Egypt": "埃及",
"Eritrea": "厄立特里亚",
"Spain": "西班牙",
"Estonia": "爱沙尼亚",
"Ethiopia": "埃塞俄比亚",
"Finland": "芬兰",
"Fiji": "斐济",
"France": "法国",
"Gabon": "加蓬",
"United Kingdom": "英国",
"Georgia": "格鲁吉亚",
"Ghana": "加纳",
"Guinea": "几内亚",
"Gambia": "冈比亚",
"Guinea-Bissau": "几内亚比绍",
"Eq. Guinea": "赤道几内亚",
"Greece": "希腊",
"Grenada": "格林纳达",
"Greenland": "格陵兰岛",
"Guatemala": "危地马拉",
"Guam": "关岛",
"Guyana": "圭亚那合作共和国",
"Honduras": "洪都拉斯",
"Croatia": "克罗地亚",
"Haiti": "海地",
"Hungary": "匈牙利",
"Indonesia": "印度尼西亚",
"India": "印度",
"Br. Indian Ocean Ter.": "英属印度洋领土",
"Ireland": "爱尔兰",
"Iran": "伊朗",
"Iraq": "伊拉克",
"Iceland": "冰岛",
"Israel": "以色列",
"Italy": "意大利",
"Jamaica": "牙买加",
"Jordan": "约旦",
"Japan": "日本",
"Siachen Glacier": "锡亚琴冰川",
"Kazakhstan": "哈萨克斯坦",
"Kenya": "肯尼亚",
"Kyrgyzstan": "吉尔吉斯斯坦",
"Cambodia": "柬埔寨",
"Korea": "韩国",
"Kuwait": "科威特",
"Lao PDR": "老挝",
"Lebanon": "黎巴嫩",
"Liberia": "利比里亚",
"Libya": "利比亚",
"Sri Lanka": "斯里兰卡",
"Lesotho": "莱索托",
"Lithuania": "立陶宛",
"Luxembourg": "卢森堡",
"Latvia": "拉脱维亚",
"Moldova": "摩尔多瓦",
"Madagascar": "马达加斯加",
"Mexico": "墨西哥",
"Macedonia": "马其顿",
"Mali": "马里",
"Malta": "马耳他",
"Myanmar": "缅甸",
"Montenegro": "黑山",
"Mongolia": "蒙古国",
"Mozambique": "莫桑比克",
"Mauritania": "毛里塔尼亚",
"Mauritius": "毛里求斯",
"Malawi": "马拉维",
"Malaysia": "马来西亚",
"Namibia": "纳米比亚",
"New Caledonia": "新喀里多尼亚",
"Niger": "尼日尔",
"Nigeria": "尼日利亚",
"Nicaragua": "尼加拉瓜",
"Netherlands": "荷兰",
"Norway": "挪威",
"Nepal": "尼泊尔",
"New Zealand": "新西兰",
"Oman": "阿曼",
"Pakistan": "巴基斯坦",
"Panama": "巴拿马",
"Peru": "秘鲁",
"Philippines": "菲律宾",
"Papua New Guinea": "巴布亚新几内亚",
"Poland": "波兰",
"Puerto Rico": "波多黎各",
"Dem. Rep. Korea": "朝鲜",
"Portugal": "葡萄牙",
"Paraguay": "巴拉圭",
"Palestine": "巴勒斯坦",
"Qatar": "卡塔尔",
"Romania": "罗马尼亚",
"Russia": "俄罗斯",
"Rwanda": "卢旺达",
"Saudi Arabia": "沙特阿拉伯",
"Sudan": "苏丹",
"S. Sudan": "南苏丹",
"Senegal": "塞内加尔",
"Singapore": "新加坡",
"Solomon Is.": "所罗门群岛",
"Sierra Leone": "塞拉利昂",
"El Salvador": "萨尔瓦多",
"Suriname": "苏里南",
"Slovakia": "斯洛伐克",
"Slovenia": "斯洛文尼亚",
"Sweden": "瑞典",
"Swaziland": "斯威士兰",
"Seychelles": "塞舌尔",
"Syria": "叙利亚",
"Chad": "乍得",
"Togo": "多哥",
"Thailand": "泰国",
"Tajikistan": "塔吉克斯坦",
"Turkmenistan": "土库曼斯坦",
"Timor-Leste": "东帝汶",
"Tonga": "汤加",
"Trinidad and Tobago": "特立尼达和多巴哥",
"Tunisia": "突尼斯",
"Turkey": "土耳其",
"Tanzania": "坦桑尼亚",
"Uganda": "乌干达",
"Ukraine": "乌克兰",
"Uruguay": "乌拉圭",
"United States": "美国",
"Uzbekistan": "乌兹别克斯坦",
"Venezuela": "委内瑞拉",
"Vietnam": "越南",
"Vanuatu": "瓦努阿图",
"Yemen": "也门",
"South Africa": "南非",
"Zambia": "赞比亚",
"Zimbabwe": "津巴布韦",
"Aland": "奥兰群岛",
"American Samoa": "美属萨摩亚",
"Fr. S. Antarctic Lands": "南极洲",
"Antigua and Barb.": "安提瓜和巴布达",
"Comoros": "科摩罗",
"Curaçao": "库拉索岛",
"Cayman Is.": "开曼群岛",
"Dominica": "多米尼加",
"Falkland Is.": "福克兰群岛马尔维纳斯",
"Faeroe Is.": "法罗群岛",
"Micronesia": "密克罗尼西亚",
"Heard I. and McDonald Is.": "赫德岛和麦克唐纳群岛",
"Isle of Man": "曼岛",
"Jersey": "泽西岛",
"Kiribati": "基里巴斯",
"Saint Lucia": "圣卢西亚",
"N. Mariana Is.": "北马里亚纳群岛",
"Montserrat": "蒙特塞拉特",
"Niue": "纽埃",
"Palau": "帕劳",
"Fr. Polynesia": "法属波利尼西亚",
"S. Geo. and S. Sandw. Is.": "南乔治亚岛和南桑威奇群岛",
"Saint Helena": "圣赫勒拿",
"St. Pierre and Miquelon": "圣皮埃尔和密克隆群岛",
"São Tomé and Principe": "圣多美和普林西比",
"Turks and Caicos Is.": "特克斯和凯科斯群岛",
"St. Vin. and Gren.": "圣文森特和格林纳丁斯",
"U.S. Virgin Is.": "美属维尔京群岛",
"Samoa": "萨摩亚"
}
gt_map = (Map().add(
series_name='累计确诊人数',
data_pair=[list(z) for z in zip(country, curconfirm)],
maptype="world",
name_map=name_map,
is_map_symbol_show=False).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="全球疫情数据（累计确诊）",
subtitle='数据更新至：' + time_global +
'\n\n来源：百度疫情实时大数据报告'),
visualmap_opts=opts.VisualMapOpts(
max_=300, is_piecewise=True,
pieces=pieces)).dump_options_with_quotes())
gs_map = (Map().add(
series_name='现有确诊人数',
data_pair=[list(z) for z in zip(country, surplus)],
maptype="world",
name_map=name_map,
is_map_symbol_show=False).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="全球疫情数据（现有确诊）",
subtitle='数据更新至：' + time_global +
'\n\n来源：百度疫情实时大数据报告'),
visualmap_opts=opts.VisualMapOpts(
max_=300, is_piecewise=True,
pieces=pieces)).dump_options_with_quotes())
return gt_map, gs_map
)
# 3、全局配置
pie.set_global_opts(
# 标题配置项
title_opts=opts.TitleOpts(
title='Python分校人数占比饼图', # 饼图标题
subtitle='广州Python0624班级全体', # 子标题
pos_left='left', # 标题距离左侧的间距,居左显示
),
# 图例配置项
legend_opts=opts.LegendOpts(
type_='plain', # 图例类型 --普通图例
# type_='scroll', # 图例类型 --滚动图例
is_show=True, # 显示图例
pos_left='center', # 图例居中
pos_top='top'))
# 4、系列配置
pie.set_series_opts(
# 标签设置
label_opts=opts.LabelOpts(
is_show=True, # 显示标签
font_size=12, # 标签字体大小
font_style='italic', # 字体倾斜
formatter='{b}:{d}%', # 显示格式
))
# 5、生成图像
pie.render('./html/饼图绘制.html')
'South Africa': '南非',
'Zambia': '赞比亚',
'Zimbabwe': '津巴布韦',
'China': '中国'
}
map_ = Map(opts.InitOpts(width='1200px', height='600px')).add(
series_name="地区累计确诊", # 设置提示框标签
data_pair=a, # 输入数据
maptype="world", # 设置地图类型为世界地图
name_map=nameMap, # 添加映射
is_map_symbol_show=False # 不显示标记点
)
# 设置系列配置项
map_.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) # 不显示国家名称
# 设置全局配置项
map_.set_global_opts(
title_opts=opts.TitleOpts(title=""), # 设置图标题
visualmap_opts=opts.VisualMapOpts(max_=1000000, is_piecewise=True)) # 显示图例
# map_.set_global_opts(title_opts=opts.TitleOpts(title="国外疫情情况"), # 设置图标题
# visualmap_opts=opts.VisualMapOpts(pieces=[ # 自定义分组的分店和颜色
# {"min": 900000, "color": "#800000"},
# {"min": 50000, "max": 500000, "lable":'0.15~0.19', "color": "#AA0000"}, # 栗色
# {"min": 10000, "max": 50000, "color": "#CC0000"}, # 耐火砖
# {"min": 1000, "max": 10000, "color": "#FF0000"}, # 印度红
# {"min": 0, "max": 1000, "color": "#FF3333"}, # 玫瑰棕色
# {"max": 0, "color": "#FFCCCC"}, # 薄雾玫瑰
# ],
links = []
for i in groups.values:
dic = {}
dic['source'] = i[0]
dic['target'] = i[1]
dic['value'] = i[2]
links.append(dic)
pic = Sankey().add(
'', #图例名
nodes, #节点数据
links, #边和流量数据
#透明度、弯曲度、颜色
linestyle_opt=opts.LineStyleOpts(opacity=0.3, curve=0.5, color='source'),
#标签显示位置
label_opts=opts.LabelOpts(position='right'),
#节点之间距离
node_gap=30,
).set_global_opts(title_opts=opts.TitleOpts(title='幸福感指数相关性桑基图'))
pic.render('test.html')
df['survey_time'] = df['survey_time'].apply(lambda x: int(x[:4]))
def split_age(x):
if x < 20:
return 1
elif x < 40:
return 2
elif x < 60:
1、雷达图周围的图例的 textStyle 暂时无法设置背景颜色
"""
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
(Radar(init_opts=opts.InitOpts(bg_color="#CCCCCC")).add_schema(
schema=[
opts.RadarIndicatorItem(name="销售（sales）", max_=6500),
opts.RadarIndicatorItem(name="管理（Administration）", max_=16000),
opts.RadarIndicatorItem(name="信息技术（Information Technology）",
max_=30000),
opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000),
opts.RadarIndicatorItem(name="研发（Development）", max_=52000),
opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
).add(
series_name="预算分配（Allocated Budget）",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
).add(
series_name="实际开销（Actual Spending）",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="基础雷达图"),
legend_opts=opts.LegendOpts()).render("basic_radar_chart.html"))
Gallery 使用 pyecharts 1.1.0
参考地址: https://echarts.baidu.com/examples/editor.html?c=line-smooth
目前无法实现的功能:
暂无
"""
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
c = (Line().set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
))
c.width = "100%"
put_html(c.render_notebook())
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
c = (Pie().add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())]).set_global_opts(
title_opts=opts.TitleOpts(title="Pie-基本示例")).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}")))
c.width = "100%"
put_html(c.render_notebook())
p = (
Pie(init_opts=opts.InitOpts(bg_color='#031f2d', )).set_global_opts(
title_opts=opts.TitleOpts(title="%d年\n\n不同空军机型统计" % (years[i], ),
pos_left='center',
pos_top='40%',
padding=[24, 0],
title_textstyle_opts=opts.TextStyleOpts(
color='#fff',
font_size=18,
align='center')),
legend_opts=opts.LegendOpts(
type_="scroll",
pos_left="80%",
orient="vertical",
textstyle_opts=opts.TextStyleOpts(color='#fff',
font_size=16,
rich=rich),
)).
add(
"xilie1",
datalist[i],
radius=['42%', '50%'],
label_opts=opts.LabelOpts(
position="outside",
rich=rich,
formatter=
"{white|{b}} \n{hr|}\n{yellow|{c}}\n{blue|{d}%}" # 参数说明：{a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）
)))
tl.add(p, "{}年".format(years[i]))
tl.render("timeline_pie.html")
# ["Perth", 5386, 1554769, 869.4],
# ]
table.add(headers, rows)
table.set_global_opts(
title_opts=ComponentTitleOpts(
title="Statistics of the Lexicon",
subtitle="The data source and respective statistics including the \
number of word with one/two/three/four/more-than-four Chinese characters etc.")
)
# table.render_notebook()
table.render("lexicon_stat.html")
# %%
bar = Bar(init_opts=opts.InitOpts(
chart_id="id",
width="600PX",
theme=ThemeType.LIGHT,
bg_color="white"))
bar.add_xaxis([key for key, _ in data_source_word_cnt.items()])
bar.add_yaxis(None, [value for key, value in data_source_word_cnt.items()])
bar.set_global_opts(title_opts=opts.TitleOpts(title="#words of each dictionary", subtitle=None))
bar.reversal_axis()
bar.set_series_opts(label_opts=opts.LabelOpts(position="right"))
bar.render_notebook()
# %%
[1, 5, 41],
[2, 5, 110],
[3, 5, 39],
[4, 5, 40],
[5, 5, 90],
[6, 5, 50],
[7, 5, 60],
[8, 5, 146],
[9, 5, 15],
]
axisOpts = opts.AxisOpts(
type_="category",
axisline_opts=opts.AxisLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(
color='#A3C7E7',
font_size=10,
),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=True,
linestyle_opts=opts.LineStyleOpts(
is_show=True,
color='black',
width=10,
)),
)
c = (
HeatMap(init_opts=opts.InitOpts(bg_color='black', )).add_xaxis(
xdata).add_yaxis(
"月份",
ydata,
def full_chart(mydate, kdata, data_5, data_10, data_20, volume_rise, volume_drop, name):
kline = (
Kline()
.add_xaxis(mydate)
.add_yaxis("%s" % name, kdata, markpoint_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
), markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
xaxis_opts=opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="股票走势"),
datazoom_opts=[opts.DataZoomOpts(xaxis_index=[0, 1])],
toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(pos_left="20%")
)
)
line = (
Line()
.add_xaxis(mydate)
.add_yaxis("Ma5", data_5, is_smooth=True)
.add_yaxis("Ma10", data_10, is_smooth=True)
.add_yaxis("Ma20", data_20, is_smooth=True)
.set_global_opts(title_opts=opts.TitleOpts(title="移动平均线"))
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
)
bar = (
Bar()
.add_xaxis(mydate)
.add_yaxis("volume_rise", volume_rise, stack=True, color=["#ec0000"], )
.add_yaxis("volume_drop", volume_drop, stack=True, color=["#00da3c"], )
.set_global_opts(title_opts=opts.TitleOpts(),
legend_opts=opts.LegendOpts(pos_right="20%"))
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
)
)
overlap_kline_line = kline.overlap(line)
grid = Grid()
grid.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="8%", pos_top="70%", height="16%"
),
)
return grid
lista1[i] = line[0]
line = f1.readline()
i = i + 1
f1.close()
(
#颜色
Pie(init_opts=opts.InitOpts(bg_color="#2c343c")).add(
#名称
series_name="第四阶段",
data_pair=[list(z) for z in zip(lista1, x1)],
radius=["40%", "65%"],
#圆心位置
center=["50%", "50%"],
#标签
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_global_opts(
#标题
title_opts=opts.TitleOpts(
#名字
title="江苏第四阶段心态空心图",
pos_left="center",
pos_top="20",
#标题颜色
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
#没用
legend_opts=opts.LegendOpts(is_show=False),
)
#系列
.set_series_opts(
.add(map_chart, grid_opts=opts.GridOpts())
)
return grid_chart
if __name__ == '__main__':
timeline = Timeline(
init_opts=opts.InitOpts(
width="1600px", height="900px", theme=ThemeType.DARK)
)
for y in time_list:
g = get_year_chart(year=y)
timeline.add(g, time_point=str(y))
timeline.add_schema(
orient="vertical",
is_auto_play=True,
is_inverse=True,
play_interval=1200,
pos_left="null",
pos_right="5",
pos_top="20",
pos_bottom="20",
width="60",
label_opts=opts.LabelOpts(is_show=True, color="#fff"),
)
timeline.render("NCP.html")
print(MapData)
line.add_xaxis(dateId)
line.add_yaxis(
"全国新增确诊病例", #图例
all_statis, #数据
is_smooth=True, #是否平滑曲线
linestyle_opts=opts.LineStyleOpts(width=4, color='#B44038'), #线样式配置项
itemstyle_opts=opts.ItemStyleOpts(
color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis(
"湖北新增确诊病例",
hubei_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis(
"其他省份新增病例",
other_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(
title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(
def get_year_chart(year: str):
map_data = [
[[x["name"], x["value"]] for x in d["data"]] for d in MapData if d["time"] == year
][0]
min_data, max_data = (minNum, maxNum)
data_mark: List = []
i = 0
for x in time_list:
if x == year:
data_mark.append(total_num[i])
else:
data_mark.append("")
i = i + 1
map_chart = (
Map()
.add(
series_name="",
data_pair=map_data,
zoom=1,
center=[119.5, 34.5],
is_map_symbol_show=False,
itemstyle_opts={
"normal": {"areaColor": "#323c48", "borderColor": "#404a59"},
"emphasis": {
"label": {"show": Timeline},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="" +
str(year) + "全国各省份NCP实时动态(数据来源:丁香园; 数据仓库:BlankerL/DXY-2019-nCoV-Data)",
subtitle="",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"
),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""
),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="30",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
line_chart = (
Line()
.add_xaxis(time_list)
.add_yaxis("", total_num)
.add_yaxis(
"",
data_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(
title="全国各省份NCP实时动态(单位: 百人)", pos_left="72%", pos_top="5%"
)
)
)
bar_x_data = [x[0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (
Bar()
.add_xaxis(xaxis_data=bar_x_data)
.add_yaxis(
series_name="",
yaxis_data=bar_y_data,
label_opts=opts.LabelOpts(
is_show=True, position="right", formatter="{b} : {c}"
),
)
.reversal_axis()
.set_global_opts(
xaxis_opts=opts.AxisOpts(
max_=maxCount, axislabel_opts=opts.LabelOpts(is_show=False)
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="top",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
)
)
pie_data = [[x[0], x[1][0]] for x in map_data]
pie = (
Pie()
.add(
series_name="",
data_pair=pie_data,
radius=["15%", "35%"],
center=["80%", "82%"],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1, border_color="rgba(0,0,0,0.5)"
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
)
)
grid_chart = (
Grid()
.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10", pos_right="45%", pos_top="50%", pos_bottom="5"
),
)
.add(
line_chart,
grid_opts=opts.GridOpts(
pos_left="65%", pos_right="80", pos_top="10%", pos_bottom="50%"
),
)
.add(pie, grid_opts=opts.GridOpts(pos_left="45%", pos_top="60%"))
.add(map_chart, grid_opts=opts.GridOpts())
)
return grid_chart
data_lr_fb_p = pd.pivot_table(data_lr_fb,
values='IND_VAL',
index='ORG_NUM',
columns='INDIC_KEY',
aggfunc=np.sum,
fill_value=0)
data_lr_fb_p.drop(['BSBK9999'], inplace=True)
data_lr_fb_p = data_lr_fb_p.reset_index()
# print(data_lr_fb_p)
c = (Pie(init_opts=opts.InitOpts(
width='1500px',
height='760px',
)).add(
"", [
list(z)
for z in zip(data_lr_fb_p['ORG_NUM'], data_lr_fb_p['ZCFZ_A_113'])
],
radius=["1%", "60%"],
center=["35%", "50%"]).add(
"", [
list(z)
for z in zip(data_lr_fb_p['ORG_NUM'], data_lr_fb_p['ZCFZ_B_208'])
],
radius=["1%", "60%"],
center=["75%", "50%"]).set_global_opts(
title_opts=opts.TitleOpts(title="贷款、存款分布情况", subtitle="（单位：亿元）"),
legend_opts=opts.LegendOpts(
orient="vertical", pos_top="15%",
pos_left="2%")).set_series_opts(label_opts=opts.LabelOpts(
formatter="{d}%")).render("pie_base.html"))
def dray_line_piture(self,my_line_data_x,my_line_data_y):
if os.path.isfile('.\line_color.html'):
os.remove('.\line_color.html')
if(len(my_line_data_x) != len(my_line_data_y)):
print("leng not match !!!")
exit()
x_data = my_line_data_x
y_data = my_line_data_y
#print(x_data)
#print(y_data)
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True, position="top", color="white",formatter="{c}%"),
itemstyle_opts=opts.ItemStyleOpts(
color="red", border_color="#fff", border_width=3
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="Yield",
pos_left="5%",
pos_top="5%",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
#is_show=False,
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=False,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=False, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.render("line_color.html")
)
yaxis_index=0,
color="#d14a61",
).add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
).extend_axis(yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#d14a61")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#675bba")),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
)).set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
min_=0,
def draw_rose_piture(self,my_rose_data):
total_position = 0
for x in my_rose_data:
total_position = total_position + x[1]
#print(total_position)
if os.path.isfile('pie_rosetype.html'):
os.remove('.\pie_rosetype.html')
c = (
Pie()
.add(
"",
my_rose_data,
radius=["30%", "75%"],
#center=["25%", "50%"],
rosetype="radius",
#label_opts=opts.LabelOpts(is_show=False),
)
#.add(
# "",
# my_rose_data,
# radius=["30%", "75%"],
# center=["75%", "50%"],
# rosetype="area",
#)
.set_global_opts(
title_opts=opts.TitleOpts(title="Positions",pos_left="8%",pos_top="8%"),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
# 控制整体的位置
right="15%",
bottom="5%",
),
children=[
# opts.GraphicRect控制方框的显示
# 如果不需要方框，去掉该段即可
#opts.GraphicRect(
# graphic_item=opts.GraphicItem(
# z=100,
# right="center",
# bottom="middle",
# ),
# graphic_shape_opts=opts.GraphicShapeOpts(
# width=120, height=70,
# ),
# graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
# fill="#fff",
# stroke="#555",
# line_width=2,
# shadow_blur=8,
# shadow_offset_x=3,
# shadow_offset_y=3,
# shadow_color="rgba(144,238,144,0.3)",
# )
#),
# opts.GraphicText控制文字的显示
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="middle",
z=100,
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text="Total:"+str(total_position)+"%",
font="bolder 21px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#C537B6"
)
)
)
]
)
]
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b} {c}%"))
.render("pie_rosetype.html")
)
def generate_bar_html(rows, title):
x = []
y = []
for row in rows:
x.append(row[0])
y.append({'value': row[1], 'range': [row[2], row[3]]})
bar = Bar(init_opts=opts.InitOpts(height='700px',
width='1424px',
theme=ThemeType.SHINE,
chart_id="cb_tree_map"))
bar.add_xaxis(x)
bar.add_yaxis(
"个数",
y,
yaxis_index=0,
bar_width=50,
category_gap='1%',
gap='1%',
label_opts=opts.LabelOpts(
position="top",
formatter=JsCode("function(x){return x.data[0];}")),
)
bar.set_series_opts(itemstyle_opts=opts.ItemStyleOpts(color=JsCode(
"function(x){return x.data['range'][0]=='0'?'lightgray':(x.data['range'][0] < 0 ? 'lightgreen':'lightcoral')}"
)))
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="=========" + title + "=========",
subtitle_textstyle_opts=opts.TextStyleOpts(font_weight='bold',
font_size='15px'),
pos_left='center',
pos_top='-1px',
),
tooltip_opts=opts.TooltipOpts(is_show=False),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
# data=None,
# type_='category',
# name_gap=0,
# name_rotate=90,
# axislabel_opts=opts.LabelOpts(
# rotate=-60,
# ),
name='价格涨跌区间',
name_gap=35,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=True,
# symbol=['none', 'arrow']
)),
yaxis_opts=opts.AxisOpts(
type_='value',
name='个数',
# name_rotate=90,
name_gap=35,
name_location='middle',
# min_=0,
# max_=max_value,
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
# symbol=['none', 'arrow']
)),
)
bar_html = bar.render_embed('template.html', html_utils.env)
return bar_html
def radar_air_quality() -> Radar:
value_bj = [
[55, 9, 56, 0.46, 18, 6, 1],
[25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3],
[33, 7, 29, 0.33, 16, 6, 4],
[42, 24, 44, 0.76, 40, 16, 5],
[82, 58, 90, 1.77, 68, 33, 6],
[74, 49, 77, 1.46, 48, 27, 7],
[78, 55, 80, 1.29, 59, 29, 8],
[267, 216, 280, 4.8, 108, 64, 9],
[185, 127, 216, 2.52, 61, 27, 10],
[39, 19, 38, 0.57, 31, 15, 11],
[41, 11, 40, 0.43, 21, 7, 12],
]
value_sh = [
[91, 45, 125, 0.82, 34, 23, 1],
[65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3],
[109, 81, 121, 1.28, 68, 51, 4],
[106, 77, 114, 1.07, 55, 51, 5],
[109, 81, 121, 1.28, 68, 51, 6],
[106, 77, 114, 1.07, 55, 51, 7],
[89, 65, 78, 0.86, 51, 26, 8],
[53, 33, 47, 0.64, 50, 17, 9],
[80, 55, 80, 1.01, 75, 24, 10],
[117, 81, 124, 1.03, 45, 24, 11],
[99, 71, 142, 1.1, 62, 42, 12],
]
c_schema = [
{
"name": "AQI",
"max": 300,
"min": 5
},
{
"name": "PM2.5",
"max": 250,
"min": 20
},
{
"name": "PM10",
"max": 300,
"min": 5
},
{
"name": "CO",
"max": 5
},
{
"name": "NO2",
"max": 200
},
{
"name": "SO2",
"max": 100
},
]
c = (Radar().add_schema(schema=c_schema, shape="circle").add(
"北京", value_bj,
color="#f9713c").add("上海", value_sh, color="#b3e4a1").set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="Radar-空气质量")))
return c
