def bar_base(x, y, xline, yline):
scatter = Scatter().add_xaxis(x).add_yaxis(
'真实值', y).set_global_opts(title_opts=opts.TitleOpts(title="相关线"))
xline = xline.reshape(-1)
line = Line().add_xaxis(xaxis_data=xline.tolist()).add_yaxis(
series_name="拟合线", y_axis=yline.tolist())
scatter.overlap(line)
return scatter
def plt(key):
"""连接hbase"""
connection = happybase.Connection(host="localhost", port=9090)
"""打开传输"""
connection.open()
"""连接表"""
basicFeaturesTable = happybase.Table('basicFeaturesTable', connection)
socialAttributesTable = happybase.Table('socialAttributesTable',
connection)
consumptionFeaturesTable = happybase.Table('consumptionFeaturesTable',
connection)
internetFeaturesTable = happybase.Table('internetFeaturesTable',
connection)
consumptionCharacteristicsTable = happybase.Table(
'consumptionCharacteristicsTable', connection)
internetBehaviorTable = happybase.Table('internetBehaviorTable',
connection)
"""获取信息"""
try:
key_name = basicFeaturesTable.row(key, columns=["name"])
except:
"""连接hbase"""
connection = happybase.Connection(host="localhost", port=9090)
"""打开传输"""
connection.open()
"""连接表"""
basicFeaturesTable = happybase.Table('basicFeaturesTable', connection)
socialAttributesTable = happybase.Table('socialAttributesTable',
connection)
consumptionFeaturesTable = happybase.Table('consumptionFeaturesTable',
connection)
internetFeaturesTable = happybase.Table('internetFeaturesTable',
connection)
consumptionCharacteristicsTable = happybase.Table(
'consumptionCharacteristicsTable', connection)
internetBehaviorTable = happybase.Table('internetBehaviorTable',
connection)
key_name = basicFeaturesTable.row(key, columns=["name"])
key_name = list(key_name.values())
key_name = key_name[0].decode()
try:
sInfo = socialAttributesTable.row(key,
columns=["incLevel", "eduLevel"])
cInfo = consumptionFeaturesTable.row(key,
columns=["CMLevel", "CTLevel"])
iInfo = internetFeaturesTable.row(key, columns=["intLevel"])
consumptionCharacteristicsInfo = consumptionCharacteristicsTable.row(
key, columns=["commodity", "price", "date"])
internetBehaviorInfo = internetBehaviorTable.row \
(key, columns=
["date", "news", "communications", "entertainment", "domersticServices", "busApp", "toolUse"]
)
except:
"""连接hbase"""
connection = happybase.Connection(host="localhost", port=9090)
"""打开传输"""
connection.open()
"""连接表"""
basicFeaturesTable = happybase.Table('basicFeaturesTable', connection)
socialAttributesTable = happybase.Table('socialAttributesTable',
connection)
consumptionFeaturesTable = happybase.Table('consumptionFeaturesTable',
connection)
internetFeaturesTable = happybase.Table('internetFeaturesTable',
connection)
consumptionCharacteristicsTable = happybase.Table(
'consumptionCharacteristicsTable', connection)
internetBehaviorTable = happybase.Table('internetBehaviorTable',
connection)
sInfo = socialAttributesTable.row(key,
columns=["incLevel", "eduLevel"])
cInfo = consumptionFeaturesTable.row(key,
columns=["CMLevel", "CTLevel"])
iInfo = internetFeaturesTable.row(key, columns=["intLevel"])
consumptionCharacteristicsInfo = consumptionCharacteristicsTable.row(
key, columns=["commodity", "price", "date"])
internetBehaviorInfo = internetBehaviorTable.row \
(key, columns=
["date", "news", "communications", "entertainment", "domersticServices", "busApp", "toolUse"]
)
"""消费情况图"""
consumptionCharacteristicsValues = list(
consumptionCharacteristicsInfo.values())
consumptionCharacteristicsValues = list(
map(lambda x: x.decode(), consumptionCharacteristicsValues))
consumptionCharacteristicsValues[
int(len(consumptionCharacteristicsValues) / 3):-int(len(consumptionCharacteristicsValues) / 3)] \
= list(map(int, consumptionCharacteristicsValues[int(len(consumptionCharacteristicsValues) / 3):
-int(len(consumptionCharacteristicsValues) / 3)]))
consumptionCharacteristicsValues[-int(len(consumptionCharacteristicsValues) / 3):] \
= list(map(int, consumptionCharacteristicsValues[-int(len(consumptionCharacteristicsValues) / 3):]))
consumptionCharacteristicsData = []
consumptionCharacteristicsData.append(
consumptionCharacteristicsValues[:int(
len(consumptionCharacteristicsValues) / 3)])
consumptionCharacteristicsData.append(consumptionCharacteristicsValues[
int(len(consumptionCharacteristicsValues) /
3):-int(len(consumptionCharacteristicsValues) / 3)])
consumptionCharacteristicsData.append(consumptionCharacteristicsValues[
-int(len(consumptionCharacteristicsValues) / 3):])
consumptionCharacteristicsData = pd.DataFrame(
consumptionCharacteristicsData).T
consumptionCharacteristicsData.columns = ["commodity", "date", "price"]
consumptionCharacteristicsData.sort_values(by="date", inplace=True)
consumptionCharacteristicsData_y = consumptionCharacteristicsData.loc[:,
"date"].tolist(
)
consumptionCharacteristicsData_y = list(
map(str, consumptionCharacteristicsData_y))
consumptionCharacteristicsData_x = consumptionCharacteristicsData.loc[:,
"price"].tolist(
)
consumptionCharacteristicsData_commodity = consumptionCharacteristicsData.loc[:,
"commodity"].tolist(
)
quzhong_consumptionCharacteristicsData_y = list(
set(consumptionCharacteristicsData_y))
if len(quzhong_consumptionCharacteristicsData_y) != len(
consumptionCharacteristicsData_y):
for i in range(len(quzhong_consumptionCharacteristicsData_y)):
count_consumptionCharacteristicsData_y = consumptionCharacteristicsData_y. \
count(quzhong_consumptionCharacteristicsData_y[i])
if count_consumptionCharacteristicsData_y != 1:
index = consumptionCharacteristicsData_y.index(
quzhong_consumptionCharacteristicsData_y[i])
consumptionCharacteristicsData_y = consumptionCharacteristicsData_y[:index + 1] + \
consumptionCharacteristicsData_y[
index + count_consumptionCharacteristicsData_y:]
consumptionCharacteristicsData_x[index] = sum(
consumptionCharacteristicsData_x[
index:index + count_consumptionCharacteristicsData_y])
consumptionCharacteristicsData_x = consumptionCharacteristicsData_x[:index + 1] + \
consumptionCharacteristicsData_x[
index + count_consumptionCharacteristicsData_y:]
consumptionCharacteristicsData_commodity[index] = \
",".join(
consumptionCharacteristicsData_commodity[index:index + count_consumptionCharacteristicsData_y])
consumptionCharacteristicsData_commodity \
= consumptionCharacteristicsData_commodity[:index + 1] + \
consumptionCharacteristicsData_commodity[index + count_consumptionCharacteristicsData_y:]
consumptionCharacteristicsData_xx = \
[list(z) for z in zip(consumptionCharacteristicsData_x, consumptionCharacteristicsData_commodity)]
scatter = Scatter(init_opts=opts.InitOpts(width="850px", height="380px"))
scatter.add_xaxis(consumptionCharacteristicsData_y)
scatter.add_yaxis("", consumptionCharacteristicsData_xx, color="red")
scatter.set_global_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item",
axis_pointer_type="cross",
formatter=JsCode(
"function (params) {return '消费日期：' + params.name + ' <br/>消费金额：' + params.value[1] + '元 <br/>消费产品：' + params.value[2];}"
)),
yaxis_opts=opts.AxisOpts(
name="消费金额",
type_="value",
name_textstyle_opts=opts.TextStyleOpts(color="white"), ###########
axislabel_opts=opts.LabelOpts(formatter="{value} 元",
border_color="white",
color="white"), ########
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(
name="消费日期",
type_="category",
name_textstyle_opts=opts.TextStyleOpts(color="white"), ###########
axislabel_opts=opts.LabelOpts(border_color="white",
color="white"), ##############
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
legend_opts=opts.LegendOpts(is_show=False))
scatter.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
line = Line(init_opts=opts.InitOpts(width="850px", height="380px"))
line.add_xaxis(consumptionCharacteristicsData_y)
line.add_yaxis(series_name="",
y_axis=consumptionCharacteristicsData_x,
color="red")
scatter.overlap(line)
scatter.render("./static/html/pictureConsumptionCharacteristics.html")
"""互联网行为图"""
internetBehaviorInfoKeys = list(internetBehaviorInfo.keys())
internetBehaviorInfoKeys = list(
map(lambda x: x.decode(), internetBehaviorInfoKeys))
internetBehaviorInfoKeys = list(
map(lambda x: x[:x.index(':')], internetBehaviorInfoKeys))
internetBehaviorInfoValues = list(internetBehaviorInfo.values())
internetBehaviorInfoValues = list(
map(lambda x: x.decode(), internetBehaviorInfoValues))
internetBehaviorInfoValues = list(map(int, internetBehaviorInfoValues))
internetBehaviorData = []
splitNum = int(len(internetBehaviorInfoValues) / 7)
internetBehaviorData.append(internetBehaviorInfoValues[:splitNum])
internetBehaviorData.append(internetBehaviorInfoValues[splitNum:2 *
splitNum])
internetBehaviorData.append(internetBehaviorInfoValues[2 * splitNum:3 *
splitNum])
internetBehaviorData.append(internetBehaviorInfoValues[3 * splitNum:4 *
splitNum])
internetBehaviorData.append(internetBehaviorInfoValues[4 * splitNum:5 *
splitNum])
internetBehaviorData.append(internetBehaviorInfoValues[5 * splitNum:6 *
splitNum])
internetBehaviorData.append(internetBehaviorInfoValues[6 * splitNum:7 *
splitNum])
internetBehaviorData = pd.DataFrame(internetBehaviorData).T
internetBehaviorData.columns = [
internetBehaviorInfoKeys[0], internetBehaviorInfoKeys[splitNum],
internetBehaviorInfoKeys[2 * splitNum],
internetBehaviorInfoKeys[3 * splitNum],
internetBehaviorInfoKeys[4 * splitNum],
internetBehaviorInfoKeys[5 * splitNum],
internetBehaviorInfoKeys[6 * splitNum]
]
internetBehaviorData.sort_values(by="date", inplace=True)
internetBehaviorData_y = internetBehaviorData.loc[:, "date"].tolist()
internetBehaviorData_y = list(map(str, internetBehaviorData_y))
bar = Bar(init_opts=opts.InitOpts(width="800px", height="380px"))
bar.add_xaxis(internetBehaviorData_y)
bar.add_yaxis(series_name="新闻资讯",
yaxis_data=internetBehaviorData.loc[:, "news"].tolist(),
stack="stack")
bar.add_yaxis(
series_name="通信交流",
yaxis_data=internetBehaviorData.loc[:, "communications"].tolist(),
stack="stack")
bar.add_yaxis(
series_name="娱乐休闲",
yaxis_data=internetBehaviorData.loc[:, "entertainment"].tolist(),
stack="stack")
bar.add_yaxis(
series_name="生活服务",
yaxis_data=internetBehaviorData.loc[:, "domersticServices"].tolist(),
stack="stack")
bar.add_yaxis(series_name="商务应用",
yaxis_data=internetBehaviorData.loc[:, "busApp"].tolist(),
stack="stack")
bar.add_yaxis(series_name="工具使用",
yaxis_data=internetBehaviorData.loc[:, "toolUse"].tolist(),
stack="stack")
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
bar.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
name="日期",
name_textstyle_opts=opts.TextStyleOpts(color="white"), ###########
type_="category",
axislabel_opts=opts.LabelOpts(border_color="white",
color="white"), #############
axispointer_opts=opts.AxisPointerOpts(is_show=True),
),
yaxis_opts=opts.AxisOpts(
name="时间",
name_textstyle_opts=opts.TextStyleOpts(color="white"),
type_="value",
min_=0,
max_=24,
interval=4,
axislabel_opts=opts.LabelOpts(formatter="{value} 小时",
border_color="white",
color="white"), ###########
axistick_opts=opts.AxisTickOpts(is_show=True),
),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
legend_opts=opts.LegendOpts(is_show=True,
textstyle_opts=opts.TextStyleOpts(
border_color="white", color="wihte"),
orient='horizontal'))
bar.render("./static/html/pictureInternetBehavior.html")
"""个人特征图"""
sValue = list(sInfo.values())
sValue = list(map(lambda x: x.decode(), sValue))
cValue = list(cInfo.values())
cValue = list(map(lambda x: x.decode(), cValue))
iValue = list(iInfo.values())
iValue = list(map(lambda x: x.decode(), iValue))
value = sValue + cValue + iValue
value = list(map(int, value))
value = [value[1], value[2], value[3], value[0], value[4]]
value = [value]
rader = Radar(init_opts=opts.InitOpts(width="350px", height="350px"))
rader.add_schema(schema=[
opts.RadarIndicatorItem(name="收入指数", max_=5),
opts.RadarIndicatorItem(name="消费金\n额指数", max_=5),
opts.RadarIndicatorItem(name="消费次数指数", max_=5),
opts.RadarIndicatorItem(name="学历指数", max_=5),
opts.RadarIndicatorItem(name="网络依\n赖指数", max_=5),
],
shape='polygon')
rader.add(series_name=key_name, data=value)
rader.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
rader.render("./static/html/pictureCompositiveInfo.html")
