def hist_order_base():
# 数据查询
dataX, dataY = order_sum_query()
# 对象声明
# 订单数据
c = (Line(init_opts=opts.InitOpts(
theme=ThemeType.LIGHT)).add_xaxis(dataX).add_yaxis(
"订单量", dataY, is_smooth=True).set_global_opts(
title_opts=opts.TitleOpts(title="日订单量历史数据趋势图"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
))
return c
def line_areastyle_boundary_gap() -> Line:
a = (Line().add_xaxis(x1).add_yaxis("拉美", 拉美GDP, is_smooth=True).add_yaxis(
"美国", 美国GDP, is_smooth=True).add_yaxis(
"世界", 世界GDP, color="#5793f3", is_smooth=True).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="1971-1990GDP增长率"),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=opts.DataZoomOpts(),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
))
return a
def books_pubdate_scatter(id, num) -> Scatter:
(time,times,num) = books_pubdate(id, num)
if time == 0:
return 0
x_data = list(map(str, times.keys()))
y_data = list(map(str, times.values()))
c = (
Scatter() # init_opts=opts.InitOpts(width="1600px", height="1000px")
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="图书数",
y_axis=y_data,
symbol_size=20,
label_opts=opts.LabelOpts(is_show=False),
)
.set_series_opts()
.set_global_opts(
title_opts=opts.TitleOpts(title="图书发行趋势", subtitle="数据量：" + str(num),),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(is_show=True),
name="图书发行时间"
),
yaxis_opts=opts.AxisOpts(
# type_="value",
name="图书数量",
name_gap=50,
name_rotate=90,
name_location="center",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
tooltip_opts=opts.TooltipOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(type_="size", max_=5, min_=1),
toolbox_opts=opts.ToolboxOpts(is_show=True,pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"dataView": {},
"restore": {},
"saveAsImage": {},
},
),
)
.dump_options_with_quotes()
)
return c
def drawtable(request):
json_data = request.body.decode('utf-8')
info = json.loads(json_data)
table_name = info.get('table_name', '').strip()
# line_path = 'mycharts/render/tabledata/charts_%s.html' % table_name.split('.')[1]
# 部署到apache服务器后需要使用绝对路径
line_path = os.path.join(
app_dir, 'render/tabledata/charts_%s.html' % table_name.split('.')[1])
if not os.path.exists(
line_path) or time.time() - os.path.getmtime(line_path) > 86400:
table = get_object_or_404(TableData, table_name=table_name)
line_name = table.table_name
table_data = table.table_data
x_data = table_data.get("total_diff_rate").keys()
y_data = table_data.get("total_diff_rate").values()
line = (charts.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),
),
title_opts=opts.TitleOpts(
title="差异比",
subtitle="单位1")).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name=line_name,
y_axis=y_data,
symbol="circle",
itemstyle_opts=opts.ItemStyleOpts(border_color='#fff'),
markpoint_opts=opts.MarkPointOpts(
label_opts=opts.LabelOpts(color='#fff'),
data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值'),
opts.MarkLineItem(type_="average", name="平均值")
]),
markline_opts=opts.MarkLineItem(type_='average'),
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
is_connect_nones=True))
line.render(line_path)
chart = open(line_path).read()
return HttpResponse(chart)
def describeLineArea(ctime, trans, comment, like):
c = (Line().add_xaxis(ctime).add_yaxis(
"转发量", trans,
is_smooth=True).add_yaxis("评论量", comment, is_smooth=True).add_yaxis(
"点赞量", like, is_smooth=True).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="实时趋势图"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line")))
return c
def drawLine(self):
df = pd.read_csv('energySpider/homec(1).csv')
df["时间"] = pd.to_datetime(df['时间'], unit='s')
df_time = df["时间"].astype(str)
df = df["房屋整体能耗 [kW]"]
df = df.tail(15)
df_time = df_time.tail(15)
print(df_time)
# 绘制折线图
difference = df
heming = df_time
x_data = heming.values
y_data = difference.values
subdue = (Line().set_global_opts(
title_opts=opts.TitleOpts(
title="总能耗折线图",
title_textstyle_opts=opts.TextStyleOpts(color="black")),
legend_opts=opts.LegendOpts(textstyle_opts=opts.TextStyleOpts(
color="black")),
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(rotate=30, color="black"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black"))),
yaxis_opts=opts.AxisOpts(
type_="value",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")),
axislabel_opts=opts.LabelOpts(color="black"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="能耗(kW)",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
color='#9AECDB',
symbol_size=5,
is_smooth=False,
is_hover_animation=True))
return subdue
def draw_pic(profitArray):
x_data = [x['time'] for x in profitArray]
y_axis = [x['profit_rate'] for x in profitArray]
(Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="收益率",
stack="总量",
y_axis=y_axis,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="策略收益率图(海龟策略+多因子)"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("策略收益率(海龟策略+多因子).html"))
def analysis_struct():
# 处理数据
x_data = pdata['年份'].map(lambda x: "%d" % x).tolist()
y_data1 = pdata['0-14岁人口(万人)'].map(lambda x: "%.2f" % x).tolist()
y_data2 = pdata['15-64岁人口(万人)'].map(lambda x: "%.2f" % x).tolist()
y_data3 = pdata['65岁及以上人口(万人)'].map(lambda x: "%.2f" % x).tolist()
# 人口结构折线图
line = Line()
line.add_xaxis(x_data)
line.add_yaxis('0-14岁人口',
y_data1,
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('15-64岁人口',
y_data2,
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('65岁及以上人口',
y_data3,
label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(
title_opts=opts.TitleOpts(title="人口结构",
pos_bottom="bottom",
pos_left="center"),
xaxis_opts=opts.AxisOpts(
name='年份',
name_location='end',
type_="category",
# axislabel_opts=opts.LabelOpts(is_show=True, color="#000", interval=0, rotate=90),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="shadow", label=opts.LabelOpts(is_show=True))),
# y轴相关选项设置
yaxis_opts=opts.AxisOpts(name='人口数（万人）',
type_="value",
position="left",
axislabel_opts=opts.LabelOpts(is_show=True)),
legend_opts=opts.LegendOpts(is_show=True))
# 渲染图像，将多个图像显示在一个html中
# DraggablePageLayout表示可拖拽
page = Page(layout=Page.DraggablePageLayout)
page.add(line)
page.render('population_struct.html')
def kdj(self, echarts_data):
# KDJ
kdj_line = (
Line()
.add_xaxis(xaxis_data=echarts_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,
),
# title_opts=opts.TitleOpts(subtitle="KDJ"),
title_opts=opts.TitleOpts(subtitle="KDJ", pos_top="62%"), # 标题
legend_opts=opts.LegendOpts(pos_top="58%"), # 图例位置
)
)
return kdj_line
def bottom_all(width,height,title,date,view,follower,likes,video_count):
c = (
Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK,width=width,height=height))
.add_xaxis(date)
.add_yaxis(
series_name="被关注数",
stack="总量",
y_axis=follower,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="点赞数",
stack="总量",
y_axis=likes,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="视频总数",
stack="总量",
y_axis=video_count,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="播放总数",
stack="总量",
y_axis=view,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
)
return c
def conf_new_base() -> Line:
# 静态数据
# df = pd.read_excel("data/linedata.xlsx",
# usecols=[1],
# names=None) # 读取项目名称列,不要列名
# df_li = df.values.tolist()
# dataY1 = []
# dataY2 = []
# dataX = []
# for s_li in df_li:
# dataY1.append(s_li[0])
# dataY2.append(s_li[0])
# dataX.append(s_li[0])
# print(result)
dataY1 = [
77, 259, 769, 1737, 2590, 3887, 3399, 2478, 5090, 2048, 394, 648, 406,
427, 125, 143, 40, 15
]
dataY2 = [
27, 680, 3806, 4148, 4562, 3971, 4214, 3536, 2450, 1563, 1277, 882,
439, 248, 129, 102, 60, 33
]
dataX = [
'2020.01.21', '2020.01.24', '2020.01.27', '2020.01.30', '2020.02.02',
'2020.02.05', '2020.02.08', '2020.02.11', '2020.02.14', '2020.02.17',
'2020.02.20', '2020.02.23', '2020.02.26', '2020.02.29', '2020.03.03',
'2020.03.06', '2020.03.09', '2020.03.11'
]
c = (Line(init_opts=opts.InitOpts(
theme=ThemeType.LIGHT)).add_xaxis(dataX).add_yaxis(
"新增确诊", dataY1, is_smooth=True).add_yaxis(
"新增疑似", dataY2, is_smooth=True).set_global_opts(
title_opts=opts.TitleOpts(title="全国疫情新增确诊/疑似趋势图"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category",
boundary_gap=False),
))
return c
def draw_degree_histogram():
degree_histogram = nx.degree_histogram(G)
c = (Line().add_xaxis([x for x in range(len(degree_histogram))]).add_yaxis(
"Number of authors", degree_histogram, is_smooth=True).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="Maximum"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="Degree Histogram"),
datazoom_opts=opts.DataZoomOpts(),
xaxis_opts=opts.AxisOpts(
name="Degree",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
).render("Degree_Histogram.html"))
def get_line(data_x, data_y, line_name=""):
line = Line()
line.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis"),
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),
),
)
line.add_xaxis(xaxis_data=data_x)
line.add_yaxis(series_name=line_name,
y_axis=data_y,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True)
return line
def line_center(width,height,title,date,view):
c = (
Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK,width=width,height=height))
.add_xaxis(date)
.add_yaxis("哔哩哔哩", view)
# .add_yaxis("YouTube", [3,2,55,4,5])
.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
)
return c
def set_lagou_education(education):
"""
绘制拉勾网经验学历要求图,采用象形图
:return:
"""
experienced = ['{}+{}'.format(i.split('/')[1], i.split('/')[0]).replace('经验', '') for i in
[e[0] for e in education]]
counts = [i[1] for i in education]
pictorialbar = (
PictorialBar(init_opts=opts.InitOpts(width='700px', height='500px'))
.add_xaxis(experienced)
.add_yaxis("最新拉勾网20000+职位数据的学历经验门槛",
counts,
symbol=SymbolType.DIAMOND,
symbol_repeat=True,
symbol_repeat_direction='end',
is_symbol_clip=True,
color='#DEB887',
)
.reversal_axis() # 翻转
.set_global_opts(title_opts=opts.TitleOpts(title='拉勾网30000+招聘的企业的工作经验+学历门槛'),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(margin=-2)
),
legend_opts=opts.LegendOpts(pos_left='right')
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False,
position='right'),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name='最大值',
type_='max',
# value_index=0,
),
opts.MarkPointItem(name='最小值',
type_='min',
),
]
),
)
)
return pictorialbar
def draw_degree():
degree = nx.degree(G)
degree_sort = sorted(degree, key=lambda x: x[1], reverse=True)
e0 = [
opts.BarItem(name=G.nodes[degree_sort[0][0]], value=degree_sort[0][1])
]
e1 = [
opts.BarItem(name=G.nodes[x[0]]['name'], value=x[1])
for x in degree_sort if G.nodes[x[0]]['E_numbers'] == 1
][:50]
e2 = [
opts.BarItem(name=G.nodes[x[0]]['name'], value=x[1])
for x in degree_sort if G.nodes[x[0]]['E_numbers'] == 2
][:50]
xaxis = [x + 1 for x in range(50)]
c = (Bar().add_xaxis(xaxis).add_yaxis(
"Erdos_number is 0",
e0,
category_gap=0,
itemstyle_opts=opts.ItemStyleOpts(color='#d48265'),
gap="0%").add_yaxis(
"Erdos_number is 1",
e1,
category_gap=0,
itemstyle_opts=opts.ItemStyleOpts(color='#749f83'),
gap="0%").add_yaxis(
"Erdos_number is 2", e2, category_gap=0,
gap="0%").set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisOpts(interval=1),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="Average"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="Top 50 degree authors"),
datazoom_opts=opts.DataZoomOpts(),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).render("bar_stack1.html"))
def mouth_article(biz):
begin_date = (datetime.datetime.now() -
relativedelta(months=+1)).strftime("%Y-%m-%d")
date_list = getBetweenDay(begin_date=begin_date)
data_list = [
WechatArticleList.query.filter_by(__biz=biz).filter(
and_(
extract(
'year',
WechatArticleList.publish_time) == str(date).split('-')[0],
extract(
'month',
WechatArticleList.publish_time) == str(date).split('-')[1],
extract(
'day',
WechatArticleList.publish_time) == str(date).split('-')[2],
)).count() for date in date_list
]
c = (Line().add_xaxis(xaxis_data=date_list).add_yaxis(
"文章数",
data_list,
is_smooth=True,
color='#66CDAA',
label_opts=opts.LabelOpts(is_show=False)).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
).set_global_opts(
title_opts=opts.TitleOpts(title="月发布文章统计",
pos_left='43%',
pos_top='8%'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
legend_opts=opts.LegendOpts(is_show=False),
))
return c
def show_scatter():
scatter = (
Scatter(init_opts=opts.InitOpts(width="1600px", height="1000px")).
add_xaxis(xaxis_data=co_friend_count).add_yaxis(
series_name="Covid",
y_axis=covid_polarity,
symbol="triangle",
symbol_size=15,
label_opts=opts.LabelOpts(is_show=False),
).add_xaxis(xaxis_data=cr_friend_count).add_yaxis(
series_name="Crime",
y_axis=crime_polarity,
symbol="circle",
symbol_size=15,
label_opts=opts.LabelOpts(is_show=False),
).set_series_opts().set_global_opts(
title_opts=opts.TitleOpts(
title="Twitter Friend Count VS Polarity"),
xaxis_opts=opts.AxisOpts(
name='Friend Count',
name_location='middle',
name_gap=20,
type_="value",
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
name='Polarity',
name_location='middle',
name_gap=20,
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
# tooltip_opts=opts.TooltipOpts(is_show=False),
toolbox_opts=opts.ToolboxOpts(),
visualmap_opts=opts.VisualMapOpts(
type_="color",
max_=1,
min_=-1,
range_text=['Positive', 'Negative'],
),
))
return scatter
def pictorialbar_base() -> PictorialBar:
c = (PictorialBar().add_xaxis(location).add_yaxis(
"",
values,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=18,
symbol_repeat="fixed",
symbol_offset=[0, 0],
is_symbol_clip=True,
symbol=SymbolType.ROUND_RECT,
).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title="PictorialBar-各省份人口数量（虚假数据）"),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
opacity=0)),
),
))
return c
def net_io_line() -> Line:
net_io()
c = (Line().add_xaxis(net_io_dict['net_io_time']).add_yaxis(
"发送字节数", net_io_dict['net_io_sent'], is_smooth=True).add_yaxis(
"接收字节数", net_io_dict['net_io_recv'],
is_smooth=True).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="网卡IO", pos_left="center"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
yaxis_opts=opts.AxisOpts(type_="value", name='B/2S'),
legend_opts=opts.LegendOpts(pos_left="left"),
))
return c
def draw(self, indexs, values, filename):
c = (PictorialBar().add_xaxis(indexs).add_yaxis(
"",
values,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=18,
symbol_repeat="fixed",
symbol_offset=[0, 0],
is_symbol_clip=True,
symbol=SymbolType.ROUND_RECT,
).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title="报道香港新闻热门博主前十名"),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(opacity=0)),
),
))
c.render(filename)
def line_areastyle_boundary_gap() -> Line:
c = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), is_smooth=True)
.add_yaxis("商家B", Faker.values(), is_smooth=True)
.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Line-面积图（紧贴 Y 轴）"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
)
return c
def draw_two(stock_profit, profit):
x_data = [x['time'] for x in profit]
# 策略收益
y_axis = [x['profit_rate'] for x in profit]
# 湾区涨幅
y_stock_axis = [x['profit_rate'] for x in stock_profit]
c = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(
"策略收益", y_axis, is_smooth=True).add_yaxis(
"大湾区指数", y_stock_axis, is_smooth=True).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="湾区指数和策略对比图(最大夏普率)"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
).render("../第二题/湾区指数和策略对比图(最大夏普率).html"))
def pictorialbar_custom_symbol() -> PictorialBar:
c = (PictorialBar().add_xaxis(location).add_yaxis(
"",
values,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=22,
symbol_repeat="fixed",
symbol_offset=[0, -5],
is_symbol_clip=True,
symbol=symbols["boy"],
).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title="PictorialBar-自定义 Symbol"),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
opacity=0)),
),
))
return c
def show_showtime():
data=select_data()
# 折线图初始化
line = Line(init_opts=opts.InitOpts(page_title="豆瓣电影-中外电影上映年份分布图"))
# 横坐标
line.add_xaxis(data[0])
# 纵坐标
line.add_yaxis("全球", data[1], color="#2f4554")
line.add_yaxis("中国", data[2], color="#749f83")
line.add_yaxis("美国", data[3], color="#c23531")
line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
# 标记最大值
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
]
)
)
# 全局配置
line.set_global_opts(
# 标题
title_opts=opts.TitleOpts(
title="豆瓣电影-中外电影上映年份分布图", pos_left='center'),
# 标签
legend_opts=opts.LegendOpts(
pos_left="620px", pos_top="28px"),
# 水平滚动条
datazoom_opts=opts.DataZoomOpts(),
# 纵坐标设置
yaxis_opts=opts.AxisOpts(
# 显示坐标轴刻度
axistick_opts=opts.AxisTickOpts(is_show=True),
# 显示分割线
splitline_opts=opts.SplitLineOpts(is_show=True)
)
)
# 生成HTML
html="pages/iframes/showtime.html"
line.render("./templates/"+html)
return html
def article_day_num():
today = datetime.datetime.strptime('2019-12-31', '%Y-%m-%d')
date_list = [(today - datetime.timedelta(days=i)).strftime('%Y-%m-%d')
for i in range(365)]
date_list.reverse()
data_list = [
WechatArticle.query.filter(
and_(
extract('year',
WechatArticle.publish_time) == str(date).split('-')[0],
extract('month',
WechatArticle.publish_time) == str(date).split('-')[1],
extract('day',
WechatArticle.publish_time) == str(date).split('-')[2],
)).count() for date in date_list
]
c = (Line({
"width": "1500px",
"height": "700px"
}).add_xaxis(xaxis_data=date_list).add_yaxis(
"文章数(篇)",
data_list,
is_smooth=True,
color='#66CDAA',
label_opts=opts.LabelOpts(is_show=False)).set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5), ).set_global_opts(
title_opts=opts.TitleOpts(title="2019年每天收录文章统计",
pos_left='43%',
pos_top='8%'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
legend_opts=opts.LegendOpts(is_show=False),
))
c.render(path='./static/year/article_day_num.html')
print('生成每天收录文章统计')
return c
def place_top(self):
print('正在生成销售地Top10...')
group_count = self.collection.aggregate([{
'$group': {
'_id': '$店铺地址',
'count': {
'$sum': 1
}
}
}])
addr = []
count = []
sorted_list = sorted([list(item.values()) for item in group_count],
key=lambda x: x[-1],
reverse=True)
for item in sorted_list[:10][::-1]:
addr.append(item[0])
count.append(item[1])
c = (PictorialBar().add_xaxis(addr).add_yaxis(
'',
count,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=16,
symbol_repeat='fixed',
symbol_offset=[0, 0],
is_symbol_clip=True,
symbol=SymbolType.ROUND_RECT,
).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title='销售地Top10'),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(opacity=0)),
),
).render("销售地Top10.html"))
print('生成完成')
return c
def line_areastyle_boundary_gap() -> Line:
c = (
Line()
.add_xaxis(["2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017"])
.add_yaxis("70+ years old",七十岁以上, is_smooth=True)
.add_yaxis("15-49 years old",十五至四十九岁, is_smooth=True)
.add_yaxis("50-69 years old",五十至六十九岁, is_smooth=True)
.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="按年龄划分的吸烟死亡人数"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
)
return render_template('nl.html')
def plot_line(dfs):
x_data = dfs['品牌']
y_data = dfs['Chg']
line = 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="Chg",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),)
line.render("basic_line_chart.html")
return line.render_embed()
def drawPictorialBar(location, values, name):
c = (
PictorialBar().add_xaxis(location).add_yaxis(
"",
values,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=22,
symbol_repeat="fixed",
symbol_offset=[0, -5],
is_symbol_clip=True,
# symbol='image://https://github.githubassets.com/images/spinners/octocat-spinner-32.gif'
symbol='image://http://weizhendong.top/images/1.png').
reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title=name),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(opacity=0)),
),
).render("{0}.html".format(name)))
