def line_markpoint_custom():
x, y = Faker.choose(), Faker.values()
obj_l = Line()
obj_l.add_xaxis(x)
obj_l.add_yaxis("商家A",
y,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(name="自定义标记点", coord=[x[2], y[2]], value=y[2])]
)
)
obj_l.set_series_opts(
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkPoint（自定义）",subtitle="自定义标注"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def line_markline():
obj_l = Line()
obj_l.add_xaxis(Faker.choose())
obj_l.add_yaxis("商家A",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
# 该参数设置在这里只对商家A起作用
label_opts=opts.LabelOpts(is_show=False)
)
obj_l.add_yaxis("商家B",
Faker.values(),
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]),
)
obj_l.set_series_opts(
# 该参数设置在这里对商家A,商家B都起作用
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-MarkLine", subtitle="平均值的线"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=True,
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
)
return obj_l
def createPlotLine(df, xAxisTitle, yAxisTitle, title=None):
line = Line()
line.add_xaxis(df[xAxisTitle].tolist())
line.add_yaxis(yAxisTitle, df[yAxisTitle].tolist())
if not title:
title = ""
line.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
title_opts=opts.TitleOpts(title=title)
)
ptg75 = df[yAxisTitle].quantile(.75)
ptg50 = df[yAxisTitle].quantile(.5)
ptg25 = df[yAxisTitle].quantile(.25)
line.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(y=ptg75, name="75%"),
opts.MarkLineItem(y=ptg50, name="50%"),
opts.MarkLineItem(y=ptg25, name="25%")
]
),
)
return line
def generate_html():
xlist = []
ylist = []
with open(filename, 'r') as f:
r = csv.reader(f, dialect='excel')
content = list(r)
xlist.append(content[0][0])
for i in range(1, len(content[0])):
ylist.append([content[0][i]])
for i in range(1, len(content)):
xlist.append(content[i][0])
for j in range(1, len(content[i])):
ylist[j - 1].append(content[i][j])
line = Line()
line.add_xaxis(xlist[1:])
for i in range(len(ylist)):
line.add_yaxis(ylist[i][0], ylist[i][1:])
line.set_global_opts(title_opts=opts.TitleOpts('CPU实时监测'))
line.set_series_opts(markpoint_opts=opts.MarkPointItem(
type_=['min', 'max']))
line.set_global_opts(xaxis_opts=opts.AxisOpts(name=xlist[0]))
line.set_global_opts(yaxis_opts=opts.AxisOpts(name='percent'))
line.render(today + '_cpu.html')
def draw_balance_line(xaxis,
yaxis,
title="消费统计",
markline=None,
width=2000) -> Line:
"""
x = [月_日, 月_日, 月_日, ....]
y = [(title1, [num1, num2, num3, num4, ...]), (title2, [num1, num2, num3, num4, ...])]
:param xaxis: x轴
:param yaxis: y轴
:param title: 标题
:param markline: 标记辅助线
:param width: 宽
:return: Line
"""
line = Line()
line.add_xaxis(xaxis)
for name, axis in yaxis:
line.add_yaxis(name, axis, is_symbol_show=True)
line.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")
],
tooltip_opts=opts.TooltipOpts(
trigger='axis', axis_pointer_type='shadow'))
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if markline is not None:
line.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=markline, name='预算')]))
return line
def gen_cci(self, df1, n):
high_list = df1.apply(lambda record: float(record['high']),
axis=1).tolist()
high_list = np.array(high_list)
low_list = df1.apply(lambda record: float(record['low']),
axis=1).tolist()
low_list = np.array(low_list)
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
rsi_n = talib.CCI(high_list, low_list, close_list, n)
line = Line()
line.add_xaxis(xaxis_data=list(df1['datetime']))
line.add_yaxis(
'cci_' + str(n),
y_axis=rsi_n,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
yaxis_opts=opts.AxisOpts(min_=-150, max_=150),
# xaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True, pos_right="38%"),
)
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
return line
def report(request):
"""返回慢SQL历史趋势"""
checksum = request.GET.get('checksum')
cnt_data = ChartDao().slow_query_review_history_by_cnt(checksum)
pct_data = ChartDao().slow_query_review_history_by_pct_95_time(checksum)
cnt_x_data = [row[1] for row in cnt_data['rows']]
cnt_y_data = [int(row[0]) for row in cnt_data['rows']]
pct_y_data = [str(row[0]) for row in pct_data['rows']]
line = Line(init_opts=opts.InitOpts(width='800', height='380px'))
line.add_xaxis(cnt_x_data)
line.add_yaxis("慢查次数",
cnt_y_data,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max", name='最大值'),
opts.MarkLineItem(type_="average", name='平均值')
]))
line.add_yaxis("慢查时长(95%)",
pct_y_data,
is_smooth=True,
is_symbol_show=False)
line.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5, ))
line.set_global_opts(
title_opts=opts.TitleOpts(title='SQL历史趋势'),
legend_opts=opts.LegendOpts(selected_mode='single'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
result = {"status": 0, "msg": '', "data": line.render_embed()}
return HttpResponse(json.dumps(result), content_type='application/json')
def gen_rsi(df1):
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
rsi_5 = talib.RSI(close_list, 5)
rsi_20 = talib.RSI(close_list, 20)
line = Line()
line.add_xaxis(xaxis_data=list(df1['datetime']))
line.add_yaxis(
'rsi_5',
y_axis=rsi_5,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.add_yaxis(
'rsi_20',
y_axis=rsi_20,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
# xaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), ),
legend_opts=opts.LegendOpts(is_show=True, pos_right="50%"),
)
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
return line
def line_html(self, title, key, val, color=None):
# 实例化
line = Line(self.init_opts)
# X轴
line.add_xaxis(key)
# Y轴
line.add_yaxis(
"",
val,
markline_opts=self.markline_opts, # 标记线选项
markpoint_opts=self.markpoint_opts # 标记点选项
)
# 系列配置
line.set_series_opts(
linestyle_opts=options.LineStyleOpts(
opacity=0.2, # 透明度
), # 线条样式
areastyle_opts=options.AreaStyleOpts(
opacity=0.4, # 透明度
color=color # 颜色
) # 面积样式
)
# 全局配置
line.set_global_opts(
title_opts=self.title_opts_custom(title), # 标题选项
datazoom_opts=[options.DataZoomOpts()], # 缩放选项
yaxis_opts=options.AxisOpts(min_=0, max_=100), # Y轴范围
)
# 返回图表html代码
return line.render_embed()
def stat_area_trend():
df = load_data()
df['发证月份'] = [i[:7] for i in df['发证日期']]
summary = df.groupby('发证月份').sum()
columns_area = ['预售建筑面积', '总建筑面积', '住宅面积', '办公面积', '其他面积', '车库面积']
columns_other = ['层数', '住宅套数']
chart = Line()
chart.add_xaxis(summary.index.tolist())
for column in columns_area:
chart.add_yaxis(column,
summary[column].tolist(),
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
is_smooth=True)
chart.set_series_opts(label_opts=opts.LabelOpts(
is_show=False), ).extend_axis(yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), is_show=False))
bar = Bar()
bar.add_xaxis(summary.index.tolist())
for column in columns_other:
bar.add_yaxis(column, summary[column].tolist(), yaxis_index=1)
chart.overlap(bar)
min_date = min(df['发证日期'])
max_date = max(df['发证日期'])
chart.set_global_opts(title_opts=opts.TitleOpts(
title="长沙预售房屋面积变化趋势",
subtitle="统计日期:{}至{}，数据来源：{}".format(min_date, max_date, DATA_SOURCE)),
legend_opts=opts.LegendOpts(is_show=True),
toolbox_opts=opts.ToolboxOpts(is_show=True))
filename = os.path.join('..', 'assets',
'01_changsha_zhufangyushou_面积趋势.html')
chart.render(filename)
def gen_line(df1, symbol, price_min, price_max):
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
dt_list = list(df1['datetime'])
close_list = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list = np.array(close_list)
line1 = Line(init_opts=opts.InitOpts(height='700px', width='1300px'))
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
#min_=999,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=0,
range_end=100,
),
],
)
line1.add_xaxis(xaxis_data=dt_list)
line1.add_yaxis(
symbol,
y_axis=close_list,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def line_power():
obj_l = Line()
obj_l.add_xaxis(xaxis_data=["0","1", "2", "3", "4", "5", "6", "7", "8"])
obj_l.add_yaxis("2的指数图像", y_axis=[1, 2, 4, 8, 16, 32, 64, 128, 256],linestyle_opts=opts.LineStyleOpts(width=2), is_smooth=True)
obj_l.set_series_opts(
# 设置该参数，则x对应的y值会显示出来，该参数默认为True
label_opts=opts.LabelOpts(is_show=True),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-2的指数图像",subtitle="副标题"),
xaxis_opts=opts.AxisOpts(
name="x轴",
# 坐标轴两边留白策略，类目轴和非类目轴的设置和表现不一样,可设置x轴刻度顶格
boundary_gap=False
# 类目轴中在 boundaryGap 为 true 的时候有效，可以保证刻度线和标签对齐。
# axistick_opts=opts.AxisTickOpts(is_align_with_label=True)
),
yaxis_opts=opts.AxisOpts(
# y轴名字
name="y轴",
# 根据y轴刻度的横线
splitline_opts=opts.SplitLineOpts(is_show=True),
# 只在数值轴中（type: 'value'）有效
# is_scale = False
)
)
return obj_l
def gen_rsi(df1):
close_list = df1.apply(lambda record: float(record['close']), axis=1).tolist()
close_list = np.array(close_list)
rsi_5 = talib.RSI(close_list, 5)
rsi_20 = talib.RSI(close_list, 20)
line = Line()
line.add_xaxis( xaxis_data=list(df1['datetime']) )
line.add_yaxis( 'rsi_5',
y_axis=rsi_5,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.add_yaxis( 'rsi_20',
y_axis=rsi_20,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
# xaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False),),
)
line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
#data=[opts.MarkLineItem(y=66),opts.MarkLineItem(y=33),],
data=[opts.MarkLineItem(y=50)],
)
)
return line
def Line_charts() -> Line:
c = Line()
c.add_xaxis(xaxis_data=x)
c.add_yaxis(series_name='', y_axis=y1)
c.add_yaxis(series_name='', y_axis=y2)
# c.is_inverse()
c.set_series_opts(label_opts=opts.LabelOpts(position="right"))
c.set_global_opts(title_opts=opts.TitleOpts(title="Bar-测试渲染图片"))
return c
def line_chart():
# 折线图
line = Line()
x_y_axis = get_chart_data()
line.add_xaxis(x_y_axis[0])
for j in x_y_axis[1]:
for key, value in j.items():
line.add_yaxis(key,
value,
areastyle_opts=opts.AreaStyleOpts(opacity=1),
label_opts=opts.LabelOpts(is_show=False))
line.set_series_opts(
# linestyle_opts=opts.LineStyleOpts(
# width=5,
# opacity=1,
# type_='solid',
# )
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]), )
line.set_global_opts(
title_opts=opts.TitleOpts(
title="API耗时统计",
subtitle="生产环境",
pos_left="30%",
title_textstyle_opts=opts.TextStyleOpts(color='red'),
subtitle_textstyle_opts=opts.TextStyleOpts(color='blue')),
xaxis_opts=opts.AxisOpts(
name="运行时间",
type_="category",
boundary_gap=False,
# axislabel_opts=opts.LabelOpts(rotate=15)
),
yaxis_opts=opts.AxisOpts(name="实际的响应时间（单位：秒）",
# min_=0,
# max_=20
),
legend_opts=opts.LegendOpts(type_='scroll',
selected_mode='multiple',
pos_left='right',
pos_top='10%',
orient='vertical'),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
toolbox_opts=opts.ToolboxOpts(is_show=True, pos_left='right'),
visualmap_opts=opts.VisualMapOpts(is_show=True,
type_="size",
min_=0,
max_=20,
range_text=["最大值", "最小值"]),
datazoom_opts=[opts.DataZoomOpts(range_start=50, range_end=100)],
)
line = line.dump_options_with_quotes()
return line
def gen_line(df1, s1, price_min, price_max):
df1['datetime'] = df1['date'] + ' ' + df1['time']
# df1['datetime'] = df1['date']
dt_list1 = list(df1['datetime'])
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list1 = np.array(close_list1)
# print(close_list1)
#line1 = Line(init_opts=opts.InitOpts(width='1500px', height='600px'))
line1 = Line()
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
],
#legend_opts=opts.LegendOpts(is_show=False),
#tooltip_opts=opts.TooltipOpts( trigger="axis",axis_pointer_type="cross" ),
tooltip_opts=opts.TooltipOpts(axis_pointer_type="cross"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
),
)
line1.add_xaxis(xaxis_data=dt_list1)
line1.add_yaxis(
s1,
y_axis=close_list1,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def charthtml(FileName):
data = pd.read_csv(FileName)
# 发布时间类别
category = list(set(data["发布时间"].values.tolist()))
line = Line()
# line.add_xaxis(data['当地时间'].tolist())
for i in category:
line.add_xaxis(data[data["发布时间"] == i]['当地时间'].to_list())
line.add_yaxis(i, data[data["发布时间"] == i]['播放数'].to_list())
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
line.render('ACI单个作品实时数据.csv.html')
def line_areastyle_boundary_gap():
obj_l = Line()
obj_l.add_xaxis(Faker.choose())
obj_l.add_yaxis("商家A", Faker.values(), is_smooth=True)
obj_l.add_yaxis("商家B", Faker.values(), is_smooth=True)
obj_l.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
obj_l.set_global_opts(
title_opts=opts.TitleOpts(title="Line-面积图（紧贴 Y 轴）",subtitle="副标题"),
xaxis_opts=opts.AxisOpts(boundary_gap=False)
)
return obj_l
def echarts_lin(x_data_list, y_name, y_data_list):
# 渲染图大小
line = Line(init_opts=opts.InitOpts(width='1000px'))
# X轴数据
line.add_xaxis(x_data_list)
# Y轴名称和
for i_name, i_reslut in zip(y_name, y_data_list):
if i_name not in ('学资源', '讨论', '单题', '微课程', '一般任务', '直播课', '个性化',
'先声'):
line.add_yaxis('{}'.format(i_name), i_reslut, is_selected=True)
else:
line.add_yaxis('{}'.format(i_name), i_reslut, is_selected=False)
# 全局配置
line.set_global_opts(
# 主标题
title_opts=opts.TitleOpts(title=""),
# 缩放功能
datazoom_opts=opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=90,
range_end=100),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(axislabel_opts={
"interval": "0",
"rotate": 45
}, ),
)
# 系统配置
line.set_series_opts(
# 图中显示数值
label_opts=opts.LabelOpts(is_show=True),
# 线装图，平均线
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
# 点装图，最大值最小值
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="max", name="最大值"),
]),
)
return line
def tail_northup():
"""
北上资金跟踪
:return:
"""
df = db.read("tail_northup")
result_df = df.set_index(['day', 'name']).unstack('name')['share_ratio']
rs_df = ((result_df - result_df.shift()).fillna(0) * 100).applymap(lambda x: round(x))
selected = sorted(list(rs_df.iloc[-1].items()), key=lambda x: x[1], reverse=True)[0][0]
c = Line()
c = c.add_xaxis(rs_df.index.tolist())
for key in rs_df:
is_selected = key == selected
c = c.add_yaxis(key, rs_df[key].values.tolist(), is_smooth=True, is_selected=is_selected)
c = c.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
c = c.set_global_opts(
title_opts=opts.TitleOpts(title="主力跟踪-北上资金", subtitle=datetime.now().strftime("%Y%m%d")),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
legend_opts=opts.LegendOpts(pos_left=100, pos_top=50)
)
return c
def Line_ele(name, model_names, datas, y_name, y_max):
x = [n for n in range(len(datas[0]))]
line = Line(init_opts=opts.InitOpts(bg_color='rgba(255,250,205,0.2)',
width='1500px',
height='600px',
page_title=name))
line.add_xaxis(xaxis_data=x)
average = []
for i in range(len(datas)):
sum = 0
num = 0
y = [n[0] for n in datas[i]]
line.add_yaxis(series_name=model_names[i],
y_axis=y,
symbol="arrow",
is_symbol_show=False)
for j in datas[i]:
sum += j[0]
num = num + 1 if j[0] > 0 else num
average.append(sum / num)
print(sum, num, average[i])
line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="max", name="最大值"),
]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(name="multi", y=average[0]),
opts.MarkLineItem(name="multis", y=average[1]),
opts.MarkLineItem(name="plans", y=average[2])
]),
)
line.set_global_opts(
title_opts=opts.TitleOpts(title=name),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=30),
name='TEST_PICS'),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=30),
name=y_name,
min_=0,
max_=y_max),
legend_opts=opts.LegendOpts(textstyle_opts=opts.TextStyleOpts(
font_size=30)))
util.mkdir('./analysis')
line.render('./analysis/' + name + '.html')
def gen_line(df1):
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
dt_list1 = list(df1['datetime'])
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close1']),
axis=1).tolist()
close_list1 = np.array(close_list1)
# print(close_list1)
close_list2 = df1.apply(lambda record: float(record['close2']),
axis=1).tolist()
close_list2 = np.array(close_list2)
price_min = 9000
line1 = Line(init_opts=opts.InitOpts(width='1500px', height='600px'))
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
#max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=0,
range_end=100,
),
],
)
line1.extend_axis(yaxis=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value} °C")))
line1.add_xaxis(xaxis_data=dt_list1)
line1.add_yaxis(
'left',
y_axis=close_list1,
)
line1.add_yaxis('right', y_axis=close_list2, yaxis_index=1)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def gen_resid(dt_list, close_list):
line = Line()
line.add_xaxis(xaxis_data=dt_list)
line.add_yaxis(
'resid',
y_axis=close_list,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts( #yaxis_opts=opts.AxisOpts(min_=-150,max_=150),
# xaxis_opts=opts.AxisOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=False), ), )
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False), )
return line
def _draw_sim(data: VideoCutResult) -> Line:
x_axis = [str(i.start) for i in data.range_list]
ssim_axis = [i.ssim for i in data.range_list]
mse_axis = [i.mse for i in data.range_list]
psnr_axis = [i.psnr for i in data.range_list]
line = Line(init_opts=opts.InitOpts(bg_color=BACKGROUND_COLOR))
line.add_xaxis(x_axis)
line.add_yaxis('ssim', ssim_axis)
line.add_yaxis('mse', mse_axis)
line.add_yaxis('psnr', psnr_axis)
line.set_global_opts(
title_opts=opts.TitleOpts(title='SIM'),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(is_show=True, trigger='axis', axis_pointer_type='cross'),
)
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line
def build_line_chart(title="Line-Chart",
xaxis=list(
map(lambda x: datetime.now() + timedelta(x),
range(0, 7))),
yaxis=[("商家A", [114, 55, 27, 101, 125, 27, 105]),
("商家B", [57, 134, 137, 129, 145, 60, 49])],
position="right"):
l = Line()
l.add_xaxis(xaxis)
for y in yaxis:
l.add_yaxis(
*y,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
)
l.set_series_opts(label_opts=opts.LabelOpts(position=position))
l.set_global_opts(title_opts=opts.TitleOpts(title=title))
return l
def _draw_sim(data: VideoCutResult) -> Line:
x_axis = [str(i.start) for i in data.range_list]
ssim_axis = [i.ssim for i in data.range_list]
mse_axis = [i.mse for i in data.range_list]
psnr_axis = [i.psnr for i in data.range_list]
line = Line(init_opts=opts.InitOpts(bg_color=constants.BACKGROUND_COLOR))
line.add_xaxis(x_axis)
line.add_yaxis("ssim", ssim_axis)
line.add_yaxis("mse", mse_axis)
line.add_yaxis("psnr", psnr_axis)
line.set_global_opts(
title_opts=opts.TitleOpts(title="SIM"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
)
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line
def zhe_xian():
line = Line(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))
line.add_xaxis(data["月份"].to_list())
line.add_yaxis("盈利",
data["母婴"].to_list(),
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.2,
color='#9bf6ff'))
line.set_global_opts(title_opts=opts.TitleOpts(title="母婴:月盈利"))
line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False), #不显示数字
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="average", name="平均值"),
]),
)
line.render('../graph/5.4(母婴月盈利).html')
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 show_score():
data=select_data()
line = Line(init_opts=opts.InitOpts(page_title='豆瓣电影TOP250-中外电影评分分布'))
line.add_xaxis(data[0])
line.add_yaxis("全球", data[1], is_smooth=True, color="#749f83")
line.add_yaxis("美国", data[2], is_smooth=True, color="#c23531")
line.add_yaxis("中国", data[3], is_smooth=True, color="#61a0a8")
line.set_series_opts(
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
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"),
]
)
)
line.set_global_opts(
title_opts=opts.TitleOpts(title="豆瓣电影TOP250-中外电影评分分布", pos_left='center'),
# 水平滚动条
datazoom_opts=opts.DataZoomOpts(),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
legend_opts=opts.LegendOpts(
pos_left="620px", pos_top="28px"),
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/score.html"
line.render("./templates/"+html)
return html
def gen_line(df1, s1, price_min, price_max):
df1['datetime'] = df1['date'] + ' ' + df1['time']
# df1['datetime'] = df1['date']
dt_list1 = list(df1['datetime'])
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list1 = np.array(close_list1)
# print(close_list1)
line1 = Line(init_opts=opts.InitOpts(width='1500px', height='600px'))
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=0,
range_end=100,
),
],
)
line1.add_xaxis(xaxis_data=dt_list1)
line1.add_yaxis(
s1,
y_axis=close_list1,
#is_connect_nones=True,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
