def cnplot(cn, new=True, axes=None, kdata=None):
"""绘制系统有效条件
:param ConditionBase cn: 系统有效条件
:param new: 仅在未指定axes的情况下生效，当为True时，创建新的窗口对象并在其中进行绘制
:param axes: 指定在那个轴对象中进行绘制
:param KData kdata: 指定的KData，如该值为None，则认为该系统有效条件已经
指定了交易对象，否则，使用该参数作为交易对象
"""
if kdata is None:
kdata = cn.getTO()
else:
cn.setTO(kdata)
refdates = kdata.getDatetimeList()
if kdata.getQuery().kType == KQuery.DAY:
x_list = [d.date() for d in refdates]
else:
x_list = [d.datetime() for d in refdates]
axes2 = None
if axes is None:
if new:
axes, axes2 = create_figure(2)
kplot(kdata, axes=axes)
else:
axes = gca()
max_value = max(HIGH(kdata))
line = Line()
y1 = [max_value if cn.isValid(d) else 0 for d in refdates]
y2 = [0 if cn.isValid(d) else max_value for d in refdates]
line.add("",
x_list,
y1,
is_step=True,
is_fill=True,
yaxis_max=max_value,
is_symbol_show=False,
line_opacity=0,
label_color='#CD0000',
area_color='#CD0000',
area_opacity=0.2)
line.add("",
x_list,
y2,
is_step=True,
is_fill=True,
yaxis_max=max_value,
is_symbol_show=False,
line_opacity=0,
label_color='#0000FF',
area_color='#0000FF',
area_opacity=0.2)
gcf().set_xaxis(x_list)
if axes2 is not None:
axes2.add(line)
gcf().add_axis(axes2)
else:
axes.add(line)
gcf().add_axis(axes)
return gcf()
list_pre2.append(df_plot.iloc[j + 1, j])
list_pre3.append(df_plot.iloc[j + 2, j])
list_pre4.append(df_plot.iloc[j + 3, j])
date_all = df.timestamp[df.shape[0] - months + 3:]
sales_real = df.sales[df.shape[0] - months + 3:]
date_pre = df_plot.columns
sales1 = list_pre1
sales2 = list_pre2
sales3 = list_pre3
sales4 = list_pre4
line = Line("空调销售预测",
title_pos='right',
width=1400,
height=700,
title_color='red',
title_text_size=10)
line.add("实际销量结果", date_all, sales_real, line_color='black', line_width=2)
line.add("第一次预测结果", date_pre, sales1, line_type='dashed', line_color='green')
line.add("第二次预测结果", date_pre, sales2, line_type='dashed', line_color='green')
line.add("第三次预测结果", date_pre, sales3, line_type='dashed', line_color='green')
line.add("第四次预测结果", date_pre, sales4, line_type='dashed', line_color='green')
#line.add("python版arima预测结果",date_pre,sales_arima_python,line_type='dashed',line_color='red',line_width=2)
#line.add("商家B", attr, v2, is_smooth=True,mark_line=["max", "average"])
line.render('../data/python_ets.html')
'''
results1 = holtWinters(tsA, 12, 4, 4, mtype = 'additive')
results2 = holtWinters(tsA, 12, 4, 4, mtype = 'multiplicative')
def hpa():
try:
td = time.strftime("%Y-%m-%d", time.localtime())
valus = []
db_k8s_deploy = db_op.k8s_deploy
db_project = db_op.project_list
Key = 'op_k8s_ingress_log'
keys = tables = ('name', 'deployment', '最大副本', '最小副本', '当前副本', 'CPU阀值',
'CPU当前值', 'QPS当前值', '管理')
v1 = client.AutoscalingV1Api()
ret = v1.list_horizontal_pod_autoscaler_for_all_namespaces()
for i in ret.items:
try:
rps = 0
RPS = []
project = db_k8s_deploy.query.with_entities(
db_k8s_deploy.project).filter(
db_k8s_deploy.deployment ==
i.spec.scale_target_ref.name).limit(1).all()
if project:
domains = db_project.query.with_entities(
db_project.domain).filter(
db_project.project == project[0][0]).limit(
1).all()
if domains[0][0]:
for domain in domains[0][0].split(','):
vals = RC.hgetall('%s_%s_%s' % (Key, domain, td))
vals = sorted(vals.items(),
key=lambda item: item[0])
if vals:
RPS.append(int(int(vals[-1][-1]) / 60))
if RPS:
rps = RPS[0]
if len(RPS) > 1:
rps = reduce(lambda x, y: x + y, RPS)
valus.append([
i.metadata.name, i.spec.scale_target_ref.name,
i.spec.max_replicas, i.spec.min_replicas,
i.status.current_replicas, '{0}%'.format(
i.spec.target_cpu_utilization_percentage),
'{0}%'.format(
i.status.current_cpu_utilization_percentage), rps
])
except Exception as e:
logging.error(e)
td = time.strftime('%Y-%m-%d', time.localtime())
Key = 'op_hpa_chart_%s_%s' % (g.context, td)
infos = RC.hgetall(Key)
infos = sorted(infos.items(), key=lambda item: item[0].split('_')[-1])
line = Line('HPA动态伸缩实时状态',
width='110%',
height='250px',
title_pos='8%',
title_text_size=14)
for project in valus:
attr = []
vals = []
for info in infos:
if project[0] in info[0]:
attr.append(str(info[0].split('_')[-1]))
vals.append(int(info[1]))
line.add(
project[0],
attr,
vals,
is_toolbox_show=False,
is_smooth=True,
mark_point=["max", "min"],
mark_point_symbolsize=60,
legend_pos='40%',
is_datazoom_show=True,
datazoom_range=[v for v in range(100, 10)],
datazoom_type='both',
)
except Exception as e:
logging.error(e)
return render_template('k8s-resource.html',
valus=valus,
tables=tables,
keys=keys,
line=line,
resource='HPA')
def __init__(self):
self.kline = Kline()
self.line = Line()
self.empty_line = Line()
from pyecharts import Line
attr = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
line = Line('折线面积示例图')
line.add(
'商家A',
attr,
v1,
is_fill=True,
line_opacity=0.2, #线条不透明度
area_opacity=0.4,
symbol=None)
line.add(
'商家B',
attr,
v2,
is_fill=True,
line_color='#000', #黑色
area_opacity=0.3, #填充不透明度
is_smooth=True)
line.render('./picture6.html')
from pyecharts import Line, configure
configure(output_image=True)
line = Line("折线图", background_color="white", title_text_size=25)
attr = ['惠州', '东莞', '广州', '深圳', '佛山', '江门', '珠海']
v1 = [23, 45, 68, 58, 32, 28, 36]
v2 = [12, 22, 34, 29, 16, 14, 18]
line.add('举例数字1', attr, v1, mark_line=['average'], is_label_show=True)
#is_smooth:是否平滑处理，可以看到举例数字2在设了is_smooth = True之后，线条变为平滑曲线。
line.add('举例数字2', attr, v2, is_label_show=True, is_smooth=True)
#is_fill实现面积图:
#area_opacity：填充面积的透明度；area_color：填充面积的颜色；symbol = None:去掉线上的点。
line.add('举例数字1', attr, v1, is_fill=True, area_opacity=0.4)
line.add('举例数字2', attr, v2, is_fill=True, is_smooth=True, area_opacity=0.4)
line.render()
#encoding:utf-8
# 作者：孙亚楠
# 日期：2020/3/21 0021 18:41
# 工具：PyCharm
# Python版本：3.7.3
#此python文件完成功能：
from pyecharts import Grid,Line,Bar
# //设置行名
columns = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
# //设置数据
data1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
data2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
# //设置折线图标题位置
line = Line("折线图","一年的降水量与蒸发量",title_top="45%")
line.add("降水量", columns, data1, is_label_show=True)
line.add("蒸发量", columns, data2, is_label_show=True)
grid = Grid()
# //设置柱状图的主标题与副标题
bar = Bar("柱状图", "一年的降水量与蒸发量")
# //添加柱状图的数据及配置项
bar.add("降水量", columns, data1, mark_line=["average"], mark_point=["max", "min"])
bar.add("蒸发量", columns, data2, mark_line=["average"], mark_point=["max", "min"])
# //设置两个图表的相对位置
grid.add(bar, grid_bottom="60%")
grid.add(line, grid_top="60%")
grid.render()
from pyecharts import Line
attr = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
line = Line('折线示例图')
line.add('商家A',
attr,
v1,
mark_point=['average', 'max', 'min'],
is_fill=True,
mark_point_textcolor='#40ff27',
line_opacity=0.2) #数字越小颜色越浅
line.add('商家B',
attr,
v2,
mark_point=['average', 'max', 'min'],
is_smooth=True,
is_fill=True,
mark_point_symbol='arrow',
mark_point_symbolsize=40)
line.render('./Line-graph.html')
data1,
mark_line=["average"],
mark_point=["max", "min"])
bar.add("蒸发量",
columns,
data2,
mark_line=["average"],
mark_point=["max", "min"])
# 生成本地文件（默认为.html文件）
bar.render()
# 折线图
cities = ["合肥", "芜湖", "南京", "北京", "天津", "马鞍山", "杭州", "扬州", "苏州", "亳州"]
data3 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0]
# data2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8]
line = Line("气温变化折线图", '2018-4-16', width=1200, height=600)
line.add("最高气温",
cities,
data3,
mark_point=['average'],
is_datazoom_show=False,
is_label_show=True)
# line.add("最低气温", cities, data2, mark_line=['average'], is_smooth=True)
# line.render('Line-High-Low.html')
line.render(path='折线图.gif')
# 仪表盘图
gu = Gauge("仪表盘图")
gu.add("指标", "达标", 85)
gu.render("Guage-eg.html")
def plot(df):
kl = df.copy()
kl['MID'] = (kl['HIGH'] + kl['LOW']) / 2
kl['AG13'] = kl['MID'].rolling(window=13).mean()
kl['AG8'] = kl['MID'].rolling(window=8).mean()
kl['AG5'] = kl['MID'].rolling(window=5).mean()
kl['SMA5'] = kl['MID'].rolling(window=5).mean()
kl['SMA34'] = kl['MID'].rolling(window=34).mean()
kl['AO'] = kl['SMA5'] - kl['SMA34']
kl['AC'] = (kl['AO'] -
kl['AO'].rolling(window=5).mean()).rolling(window=5).mean()
kl = kl[20:]
for i in range(2, len(kl['MID'])):
kl.ix[i, 'AG13'] = (kl.ix[i - 1, 'AG13'] * 12 +
(kl.ix[i, 'HIGH'] + kl.ix[i, 'LOW']) / 2) / 13
kl.ix[i, 'AG8'] = (kl.ix[i - 1, 'AG8'] * 7 +
(kl.ix[i, 'HIGH'] + kl.ix[i, 'LOW']) / 2) / 8
kl.ix[i, 'AG5'] = (kl.ix[i - 1, 'AG5'] * 4 +
(kl.ix[i, 'HIGH'] + kl.ix[i, 'LOW']) / 2) / 5
kl['AG13'] = kl['AG13'].shift(8)
kl['AG8'] = kl['AG8'].shift(5)
kl['AG5'] = kl['AG5'].shift(3)
kl = kl.where(kl.notnull(), 0)
kl['GTUP'] = abs(kl['AG13'] - kl['AG8'])
kl['GTDOWN'] = abs(kl['AG8'] - kl['AG5'])
kl['MUP'] = 0
kl['MDOWN'] = 0
markd = []
for i in range(2, len(kl['MID']) - 2):
if kl.ix[i, 'HIGH'] == max(kl.ix[i - 2, 'HIGH'], kl.ix[i - 1, 'HIGH'],
kl.ix[i, 'HIGH'], kl.ix[i + 1, 'HIGH'],
kl.ix[i + 2, 'HIGH']):
#kl.ix[i,'MUP']=1
markd.append({
"coord": [kl.index[i], kl.ix[i, 'HIGH']],
"name": "1"
})
if kl.ix[i, 'LOW'] == min(kl.ix[i - 2, 'LOW'], kl.ix[i - 1, 'LOW'],
kl.ix[i, 'LOW'], kl.ix[i + 1, 'LOW'],
kl.ix[i + 2, 'LOW']):
markd.append({
"coord": [kl.index[i], kl.ix[i, 'LOW']],
"name": "2"
})
page = Page(page_title='AC')
kline = Kline()
kline.add('',
list(kl.index), [
list(kl[['OPEN', 'CLOSE', 'LOW', 'HIGH']].iloc[i, ].values)
for i in range(len(kl))
],
is_datazoom_show=True,
datazoom_xaxis_index=[0, 1],
datazoom_type="both",
is_xaxislabel_align=True,
tooltip_axispointer_type="cross",
mark_point=markd,
mark_point_symbol='circle',
mark_point_symbolsize=10)
line = Line(' ')
line.add('JAW',
list(kl.index),
list(kl['AG13']),
line_color=['#0000ff'],
label_color=['#0000ff'])
line.add('TEETH',
list(kl.index),
list(kl['AG8']),
line_color=['#ff0000'],
label_color=['#ff0000'])
line.add('LIPS',
list(kl.index),
list(kl['AG5']),
line_color=['#00ff00'],
label_color=['#00ff00'])
overlap1 = Overlap()
overlap1.add(kline)
overlap1.add(line)
#gator
# bar1 = Bar(' ')
# bar2 = Bar(' ')
# up = list(kl['GTUP'])
# down = list(kl['GTDOWN'])
# redup = []
# greenup = []
# reddown = []
# greendown = []
# for i in range(len(up)):
# if (i == 0):
# greenup.append(up[i])
# redup.append(0)
# greendown.append(-down[i])
# reddown.append(0)
# continue
# if (up[i] > up[i - 1]):
# greenup.append(up[i])
# redup.append(0)
# else:
# greenup.append(0)
# redup.append(up[i])
# if (down[i] > down[i - 1]):
# greendown.append(-down[i])
# reddown.append(0)
# else:
# greendown.append(0)
# reddown.append(-down[i])
#
# # bar.add('MACDhist',klt,macd[2].tolist(),is_datazoom_show=True,legend_top='65%')
# bar1.add('GTREDUP', list(kl.index), redup, legend_top='65%', label_color=['#ff0000'])
# bar2.add('GTREDDOWN', list(kl.index), reddown, legend_top='65%', label_color=['#00ff00'])
# bar1.add('GTGREENUP', list(kl.index), greenup, legend_top='65%', label_color=['#ff0000'])
# bar2.add('GTGREENDOWN', list(kl.index), greendown, legend_top='65%', label_color=['#00ff00'])
bar1 = Bar(' ')
bar2 = Bar(' ')
ac = list(kl['AC'])
acr = []
acg = []
for i in range(len(ac)):
if (i == 0):
acr.append(ac[i])
acg.append(0)
continue
if ac[i] > ac[i - 1]:
acr.append(0)
acg.append(ac[i])
else:
acr.append(ac[i])
acg.append(0)
bar1.add('ACR',
list(kl.index),
acr,
legend_top='65%',
label_color=['#ff0000'])
bar2.add('ACG',
list(kl.index),
acg,
legend_top='65%',
label_color=['#00ff00'])
overlap2 = Overlap()
overlap2.add(bar1)
overlap2.add(bar2)
grid = Grid(width=1920, height=950)
grid.add(overlap1, grid_bottom='40%')
grid.add(overlap2, grid_top='70%')
# page.add(line)
# page.add(overlap)
page.add(grid)
path = os.path.abspath('.')
page.render(path + '\\plot\\AC.html')
async def process(resample='1w', window_size=7*52, min_timetomarket=None, test=False, where='ALL'):
rsts = await util.fetch(URL, resample=resample, window_size=window_size,
min_timetomarket=min_timetomarket, test=test, where=where)
c = defaultdict(Counter)
for rst in rsts:
for key, value in rst['result'].items():
c[key].update(**value)
df = None
for key, value in c.items():
tmp = pd.DataFrame.from_dict(value, 'index', columns=[key])
if df is None:
df = tmp
else:
df = pd.concat([df, tmp], axis=1, sort=True)
df.index = pd.DatetimeIndex(df.index)
df = df.sort_index()
ds = pd.date_range(min(df.index), max(df.index), freq=resample)
df = df.reindex(ds,
copy=False, fill_value=0)
# print(df)
# x = df.plot()
# plt.show()
df = df.fillna(value=0)
line1 = Line()
line1.add('is_rolling_max', df.index, df['is_rolling_max'])
line2 = Line()
line2.add('is_rolling_min', df.index, df['is_rolling_min'])
overlap = Overlap(
)
overlap.add(line1)
overlap.add(line2) # , yaxis_index=1, is_add_yaxis=True
util.render(overlap, path="render.html",)
line1 = Line()
line1.add('ismax', df.index, df['ismax'])
line2 = Line()
line2.add('ismin', df.index, df['ismin'])
overlap = Overlap(
)
overlap.add(line1)
overlap.add(line2)
util.render(overlap, path="render2.html",)
# overlap.render(path="render2.html",)
for c in df.columns:
df[c] = pd.to_numeric(df[c])
df = df.resample('1m').sum()
market_size = await util.get_marketsize(where=where)
market_size = pd.DataFrame.from_dict(market_size)
market_size.index = pd.DatetimeIndex(market_size.index)
df['marketsize'] = market_size
df['ismin'] = df['ismin'] / df['marketsize']
df['ismax'] = df['ismax'] / df['marketsize']
line1 = Line()
line1.add('ismax', df.index, df['ismax'])
line2 = Line()
line2.add('ismin', df.index, df['ismin'])
overlap = Overlap(
)
overlap.add(line1)
overlap.add(line2)
util.render(overlap, path="render3.html",)
return df
def test_page_grid_timeline_overlap():
# Grid
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", CLOTHES, v1, is_stack=True)
bar.add("商家B", CLOTHES, v2, is_stack=True, legend_pos="80%")
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add(
"es",
[11, 11, 15, 13, 12, 13, 10],
[1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%",
)
grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")
# Timeline
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5 = Bar("2016 年销量", "数据纯属虚构", height=720, width=1200)
bar_5.add("春季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_5.add(
"冬季",
CLOTHES,
[randint(10, 100) for _ in range(6)],
is_legend_show=True,
)
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
timeline.add(bar_2, "2013 年")
timeline.add(bar_3, "2014 年")
timeline.add(bar_4, "2015 年")
timeline.add(bar_5, "2016 年")
# Overlap
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar(height=720, width=1200)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
overlap = Overlap()
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page = Page()
page.add(grid)
page.add(timeline)
page.add(overlap)
page.render()
citys = ['beijing', 'shanghai', 'guangzhou', 'shenzhen']
v = []
for i in range(4):
filename = 'air_' + citys[i] + '_2018.csv'
df = pd.read_csv(filename, header=None, names=["Date", "Quality_grade", "AQI", "AQI_rank", "PM"])
dom = df[['Date', 'PM']]
list1 = []
for j in dom['Date']:
time = j.split('-')[1]
list1.append(time)
df['month'] = list1
month_message = df.groupby(['month'])
month_com = month_message['PM'].agg(['mean'])
month_com.reset_index(inplace=True)
month_com_last = month_com.sort_index()
v1 = np.array(month_com_last['mean'])
v1 = ["{}".format(int(i)) for i in v1]
v.append(v1)
attr = ["{}".format(str(i) + '月') for i in range(1, 12)]
line = Line("2018年北上广深PM2.5全年走势图", title_pos='center', title_top='0', width=800, height=400)
line.add("北京", attr, v[0], line_color='red', legend_top='8%')
line.add("上海", attr, v[1], line_color='purple', legend_top='8%')
line.add("广州", attr, v[2], line_color='blue', legend_top='8%')
line.add("深圳", attr, v[3], line_color='orange', legend_top='8%')
line.render("2018年北上广深PM2.5全年走势图.html")
# 定义k线图的提示框的显示函数
def show_kline_data(params, pos):
param = params[0]
if param.data[4]:
return "date = " + param.name + "<br/>" + "open = " + param.data[
1] + "<br/>" + "close = " + param.data[
2] + "<br/>" + "high = " + param.data[
3] + "<br/>" + "low = " + param.data[4] + "<br/> "
else:
return "date = " + param.name + "<br/>" + "cci = " + param.value + "<br/>"
# 绘制cci
cci_line = Line()
cci_line.add(
"cci",
x_axis=data['date'],
y_axis=cci,
is_datazoom_show=True,
datazoom_xaxis_index=[0, 1],
tooltip_tragger='axis',
is_toolbox_show=True,
yaxis_force_interval=100,
legend_top="70%",
legend_orient='vertical',
legend_pos='right',
yaxis_pos='left',
is_xaxislabel_align=True,
tooltip_formatter=show_kline_data,
def score_draw(csv_file):
page = Page(csv_file+":评论等级分析")
score, date, val, score_list = [], [], [], []
result = {}
path = os.path.abspath(os.curdir)
csv_file = path + "\\" + csv_file + ".csv"
csv_file = csv_file.replace('\\', '\\\\')
d = pd.read_csv(csv_file, engine='python', encoding='utf-8')[['score', 'date']].dropna() # 读取CSV转为dataframe格式，并丢弃评论为空的记录
for indexs in d.index: # 一种遍历df行的方法（下面还有第二种，iterrows）
score_list.append(tuple(d.loc[indexs].values[:])) # 目前只找到转换为tuple然后统计相同元素个数的方法
print("有效评分总数量为：",len(score_list), " 条")
for i in set(list(score_list)):
result[i] = score_list.count(i) # dict类型
info = []
for key in result:
score= key[0]
date = key[1]
val = result[key]
info.append([score, date, val])
info_new = DataFrame(info) # 将字典转换成为数据框
info_new.columns = ['score', 'date', 'votes']
info_new.sort_values('date', inplace=True) # 按日期升序排列df，便于找最早date和最晚data，方便后面插值
print("first df", info_new)
# 以下代码用于插入空缺的数据，每个日期的评分类型应该有5中，依次遍历判断是否存在，若不存在则往新的df中插入新数值
mark = 0
creat_df = pd.DataFrame(columns = ['score', 'date', 'votes']) # 创建空的dataframe
for i in list(info_new['date']):
location = info_new[(info_new.date==i)&(info_new.score=="力荐")].index.tolist()
if location == []:
creat_df.loc[mark] = ["力荐", i, 0]
mark += 1
location = info_new[(info_new.date==i)&(info_new.score=="推荐")].index.tolist()
if location == []:
creat_df.loc[mark] = ["推荐", i, 0]
mark += 1
location = info_new[(info_new.date==i)&(info_new.score=="还行")].index.tolist()
if location == []:
creat_df.loc[mark] = ["还行", i, 0]
mark += 1
location = info_new[(info_new.date==i)&(info_new.score=="较差")].index.tolist()
if location == []:
creat_df.loc[mark] = ["较差", i, 0]
mark += 1
location = info_new[(info_new.date==i)&(info_new.score=="很差")].index.tolist()
if location == []:
creat_df.loc[mark] = ["很差", i, 0]
mark += 1
info_new = info_new.append(creat_df.drop_duplicates(), ignore_index=True)
score_list = []
info_new.sort_values('date', inplace=True) # 按日期升序排列df，便于找最早date和最晚data，方便后面插值
print(info_new)
for index, row in info_new.iterrows(): # 第二种遍历df的方法
score_list.append([row['date'], row['votes'], row['score']])
tr = ThemeRiver()
tr.add(['力荐', '推荐', '还行', '较差', '很差'], score_list, is_label_show=True, is_more_utils=True)
page.add_chart(tr)
attr, v1, v2, v3, v4, v5 = [], [], [], [], [], []
attr = list(sorted(set(info_new['date'])))
bar = Bar()
for i in attr:
v1.append(int(info_new[(info_new['date']==i)&(info_new['score']=="力荐")]['votes']))
v2.append(int(info_new[(info_new['date']==i)&(info_new['score']=="推荐")]['votes']))
v3.append(int(info_new[(info_new['date']==i)&(info_new['score']=="还行")]['votes']))
v4.append(int(info_new[(info_new['date']==i)&(info_new['score']=="较差")]['votes']))
v5.append(int(info_new[(info_new['date']==i)&(info_new['score']=="很差")]['votes']))
bar.add("力荐", attr, v1, is_stack=True)
bar.add("推荐", attr, v2, is_stack=True)
bar.add("还行", attr, v3, is_stack=True)
bar.add("较差", attr, v4, is_stack=True)
bar.add("很差", attr, v5, is_stack=True, is_convert=True, mark_line=["average"], is_more_utils=True)
page.add_chart(bar)
line = Line()
line.add("力荐", attr, v1, is_stack=True)
line.add("推荐", attr, v2, is_stack=True)
line.add("还行", attr, v3, is_stack=True)
line.add("较差", attr, v4, is_stack=True)
line.add("很差", attr, v5, is_stack=True, is_convert=False, mark_line=["average"], is_more_utils=True)
page.add_chart(line)
page.render(csv_file[:-4] + "_日投票量分析汇总.html")
from pyecharts import Line, Page,Gauge
line = Line("本周气温")
attr = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
line.add(
"最高气温",
attr,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
attr,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
gauge = Gauge("湿度计量表")
gauge.add(
"最佳湿度",
"实际湿度",
66.66,
angle_range=[180, 0],
scale_range=[0, 100],
is_legend_show=True,
)
page = Page()
page.add(line)
page.add(gauge)
page.render()
def interface_report():
try:
Bars = []
searchs = []
db_project = db_op.project_list
db_idcs = db_idc.idc_id
db_servers = db_idc.idc_servers
db_project_third = db_op.project_third
db_third = db_idc.third_resource
Infos = defaultdict()
values = OrderedDict()
dt = datetime.datetime.now()
#获取域名列表
hosts = RC_CLUSTER.smembers(
'api_domain_lists_%s' %
time.strftime('%Y-%m-%d', time.localtime()))
hosts = [host for host in hosts]
if hosts:
searchs = json.dumps([{
"id": str(i),
"text": str(host)
} for i, host in enumerate(hosts)])
#获取域名和接口信息
if 'business_bigdata_select_host' in request.cookies and 'business_bigdata_select_uri' in request.cookies:
host = request.cookies['business_bigdata_select_host']
uri = urllib.parse.unquote(
request.cookies['business_bigdata_select_uri'])
#获取接口相关的资产信息
projects = db_project.query.with_entities(
db_project.ip, db_project.ssh_port,
db_project.resource).filter(
db_project.domain.like('%{0},%'.format(host))).all()
if projects:
idc_lists = db_idcs.query.with_entities(
db_idcs.id, db_idcs.aid).all()
idc_lists = {idcs[0]: idcs[-1] for idcs in idc_lists}
third_lists = db_third.query.with_entities(
db_third.id, db_third.resource_type).all()
third_lists = {third[0]: third[-1] for third in third_lists}
ips = [val[0] for val in projects]
resource = projects[0][-1]
idc_ids = db_servers.query.with_entities(
db_servers.idc_id, func.count(db_servers.idc_id)).filter(
db_servers.ip.in_(tuple(ips))).group_by(
db_servers.idc_id).all()
if idc_ids:
idc_ids = [list(ids) for ids in idc_ids]
for ids in idc_ids:
ids[0] = idc_lists[ids[0]]
RC.delete('idc_incrs')
for ids in idc_ids:
RC.hincrby('idc_incrs', ids[0], ids[1])
idc_ids = RC.hgetall('idc_incrs')
idc_attr = [ids for ids in idc_ids]
idc_vals = [int(idc_ids[ids]) for ids in idc_ids]
pie = Pie("%s台%s服务器机房分布" % (len(set(projects)), resource),
width='100%',
height='270px',
title_pos='center',
title_text_size=14)
pie.add("",
idc_attr,
idc_vals,
is_label_show=True,
is_toolbox_show=False,
is_legend_show=False,
legend_orient='vertical',
legend_pos='left',
radius=[0, 65],
is_random=True)
Infos['pie'] = pie
project = db_project.query.with_entities(
db_project.project).filter(
db_project.domain.like('%{0},%'.format(host))).all()
#获取接口相关的应用服务资源
if project:
project = project[0][0]
third_vals = db_project_third.query.with_entities(
db_project_third.third_id,
func.count(db_project_third.third_id)).filter(
db_project_third.project == project).group_by(
db_project_third.third_id).all()
if third_vals:
third_vals = [list(val) for val in third_vals]
for val in third_vals:
val[0] = third_lists[val[0]]
RC.delete('third_incrs')
for val in third_vals:
RC.hincrby('third_incrs', val[0], val[1])
third_vals = RC.hgetall('third_incrs')
third_vals[resource] = len(projects)
third_attr = [val for val in third_vals]
third_counts = [int(third_vals[val]) for val in third_vals]
pie_third = Pie("应用服务资源分布",
width='100%',
height='270px',
title_pos='center',
title_text_size=14)
pie_third.add(
"",
third_attr,
third_counts,
is_label_show=True,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
radius=[55, 65],
is_random=True,
is_legend_show=False,
)
Infos['pie_third'] = pie_third
#接口性能数据获取
year = time.strftime('%Y', time.localtime())
for i in range(2, 9):
ot = dt - datetime.timedelta(days=i - 1)
nt = dt - datetime.timedelta(days=i - 2)
ot = ot.strftime('%Y-%m-%dT00:00:00Z')
nt = nt.strftime('%Y-%m-%dT00:00:00Z')
try:
pv_sum = 0.0
cmd = 'select sum(mean_pv) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
pv_sum = infos['sum']
except Exception as e:
logging.error(e)
try:
values['avg_resp'] = 0.0
cmd = 'select mean(mean_avg_resp) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s'and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
values['avg_resp:%s' %
ot.split('T')[0]] = '%.2f' % infos['mean']
except Exception as e:
logging.error(e)
try:
s4 = 0.0
cmd = 'select sum(mean_status_4xx) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
s4 = infos['sum']
s5 = 0.0
cmd = 'select sum(mean_status_5xx) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
s5 = infos['sum']
if pv_sum > 0:
values['status_sum:%s' % ot.split('T')[0]] = float(
'%.2f' % (float(s4 + s5) / pv_sum * 100))
except Exception as e:
logging.error(e)
values['pv_sum:%s' % ot.split('T')[0]] = int(pv_sum) * 60
#接口性能展示
if values:
#pv总数
bar_pv = Bar("PV访问量",
width='105%',
height='270px',
title_pos='center',
title_text_size=14)
vals = [(val.split(':')[-1], values[val]) for val in values
if 'pv_sum:' in val]
if vals:
bar_vals = [val[0] for val in vals]
bar_counts = [val[-1] for val in vals]
bar_pv.add('',
bar_vals,
bar_counts,
is_label_show=True,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
xaxis_interval=0,
is_random=True)
Bars.append(bar_pv)
#平均响应时间
line_resp = Line("平均响应时间",
width='105%',
height='270px',
title_pos='center',
title_text_size=14)
vals = [(val.split(':')[-1], values[val]) for val in values
if 'avg_resp:' in val]
if vals:
line_vals = [val[0] for val in vals]
line_counts = [val[-1] for val in vals]
line_resp.add('',
line_vals,
line_counts,
is_label_show=True,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
xaxis_interval=0,
yaxis_formatter='s',
is_random=True,
is_smooth=False,
mark_point=["min", "max"])
Bars.append(line_resp)
#错误状态码占比
line_status = Line("错误码占比",
width='105%',
height='270px',
title_pos='center',
title_text_size=14)
vals = [(val.split(':')[-1], values[val]) for val in values
if 'status_sum:' in val]
if vals:
line_vals = [val[0] for val in vals]
line_counts = [val[-1] for val in vals]
line_status.add('',
line_vals,
line_counts,
is_fill=True,
is_label_show=True,
is_smooth=False,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
xaxis_interval=0,
yaxis_formatter='%',
is_random=True,
mark_line=["max", "min"])
Bars.append(line_status)
except Exception as e:
logging.error(e)
return render_template('interface_report.html',
Bars=Bars,
Infos=Infos,
searchs=searchs)
def draw():
# 热力图
tomato_com = pd.read_csv('西虹市首富.csv')
grouped = tomato_com.groupby(['city'])
grouped_pct = grouped['score']
# tip_pct列
city_com = grouped_pct.agg(['mean', 'count'])
city_com.reset_index(inplace=True)
city_com['mean'] = round(city_com['mean'], 2)
# data = [(city_com['city'][i], city_com['count'][i])
# for i in range(0, city_com.shape[0])]
# geo = Geo(
# '《西虹市首富》全国热力图',
# title_color="#fff",
# title_pos="center",
# width=1200,
# height=600,
# background_color='#404a59')
# attr, value = geo.cast(data)
# geo.add(
# "",
# attr,
# value,
# type="heatmap",
# visual_range=[0, 200],
# visual_text_color="#fff",
# symbol_size=10,
# is_visualmap=True,
# is_roam=False)
# geo.render('西虹市首富全国热力图.html')
# 折线和柱状图
city_main = city_com.sort_values('count', ascending=False)[0:20]
attr = city_main['city']
v1 = city_main['count']
v2 = city_main['mean']
line = Line("主要城市评分")
line.add(
"城市",
attr,
v2,
is_stack=True,
xaxis_rotate=30,
yaxis_min=4.2,
mark_point=['min', 'max'],
xaxis_interval=0,
line_color='lightblue',
line_width=4,
mark_point_textcolor='black',
mark_point_color='lightblue',
is_splitline_show=False)
bar = Bar("主要城市评论数")
bar.add(
"城市",
attr,
v1,
is_stack=True,
xaxis_rotate=30,
yaxis_min=4.2,
xaxis_interval=0,
is_splitline_show=False)
overlap = Overlap()
# 默认不新增 x y 轴，并且 x y 轴的索引都为 0
overlap.add(bar)
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
overlap.render('主要城市评论数_平均分.html')
def test_more_charts():
page = Page()
# line
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie])
# kline
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
kline = Kline("K 线图示例")
kline.add(
"日K",
["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True,
)
page.add(kline)
# radar
schema = [
("销售", 6500),
("管理", 16000),
("信息技术", 30000),
("客服", 38000),
("研发", 52000),
("市场", 25000),
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add(
"实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode="single",
)
page.add(radar)
# scatter3d
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100),
] for _ in range(80)]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3D)
# wordcloud
name = [
"Sam S Club",
"Macys",
"Amy Schumer",
"Jurassic World",
"Charter Communications",
"Chick Fil A",
"Planet Fitness",
"Pitch Perfect",
"Express",
"Home",
"Johnny Depp",
"Lena Dunham",
"Lewis Hamilton",
"KXAN",
"Mary Ellen Mark",
"Farrah Abraham",
"Rita Ora",
"Serena Williams",
"NCAA baseball tournament",
"Point Break",
]
value = [
10000,
6181,
4386,
4055,
2467,
2244,
1898,
1484,
1112,
965,
847,
582,
555,
550,
462,
366,
360,
282,
273,
265,
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
assert len(page) == 7
assert isinstance(page[0], Line)
assert ("echarts" in page.js_dependencies) or ("echarts.min"
in page.js_dependencies)
page.render()
def create_charts():
page = Page()
style = Style(width=WIDTH, height=HEIGHT)
df = pd.read_csv('./data_cleaned.csv')
df['CREATE_TIME'] = pd.to_datetime(df['CREATE_TIME'])
df['MONTH'] = 0
months = []
for i in df.CREATE_TIME:
month = i.strftime("%Y-%m")
months.append(month)
df.MONTH = months
EVENT_SRC_NAME = df.EVENT_SRC_NAME.value_counts()
chart = Bar("投诉渠道种类", **style.init_style)
chart.add("",
EVENT_SRC_NAME.index,
EVENT_SRC_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True)
page.add(chart)
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for month, group in df.groupby('MONTH'):
EVENT_SRC_NAME = group.EVENT_SRC_NAME.value_counts()
chart_1 = Bar("投诉渠道事件数", **style.init_style)
chart_1.add("",
EVENT_SRC_NAME.index,
EVENT_SRC_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True)
chart.add(chart_1, month)
page.add(chart)
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for name, c in df.groupby('EVENT_SRC_NAME'):
month_count = defaultdict(int)
for month, group in c.groupby('MONTH'):
month_count[month] = len(group)
m_s = sorted(list(month_count.keys()))
m_l = [month_count[i] for i in m_s]
chart_1 = Line("各月份投诉渠道数", **style.init_style)
chart_1.add(
"事件数",
m_s,
m_l,
mark_point=["max", "min"],
is_more_utils=True,
mark_line=["average"],
is_smooth=True,
)
chart.add(chart_1, name)
page.add(chart)
return page
import numpy as np
import pandas as pd
from pyecharts import Line
df = pd.read_csv('air_tianjin_2017.csv',
header=None,
names=["Date", "Quality_grade", "AQI", "AQI_rank", "PM"])
dom = df[['Date', 'PM']]
list1 = []
for j in dom['Date']:
time = j.split('-')[1]
list1.append(time)
df['month'] = list1
month_message = df.groupby(['month'])
month_com = month_message['PM'].agg(['mean'])
month_com.reset_index(inplace=True)
month_com_last = month_com.sort_index()
attr = ["{}".format(str(i) + '月') for i in range(1, 13)]
v1 = np.array(month_com_last['mean'])
v1 = ["{}".format(int(i)) for i in v1]
line = Line("2017年天津月均PM2.5走势图",
title_pos='center',
title_top='18',
width=800,
height=400)
line.add("", attr, v1, mark_point=["max", "min"])
line.render("2017年天津月均PM2.5走势图.html")
from pyecharts import Line
line = Line("折线图--面积实例", 'hello')
#data
attr = ["{}".format(n) for n in range(1, 7)]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
# 填充 不透明性 填充颜色 节点标注
line.add("one",
attr,
v1,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
area_color='yellow',
is_label_show=True)
#平滑曲线
line.add("two",
attr,
v2,
is_fill=True,
line_opacity=0.8,
area_opacity=0.2,
area_color='green',
is_smooth=True)
line.render(r'C:\Users\Administrator\Desktop\数据可视化\pyecharts\html\12.html')
import numpy as np
data = pd.read_csv(r'E:\vscode_code\GitHub项目\爬虫+数据分析\猫眼\maoyan.csv',
encoding='utf-8')
#print(data['star'].mean())
data['year'] = data['pub_time'].str.split('-').str[1]
data['month'] = data['pub_time'].str.split('-').str[1]
#print(data.head())
year = data.groupby('year')['year'].count()
month = data.groupby('month')['month'].count()
from pyecharts import Line
attr = list(year.index)
v = list(year)
line = Line("电影年份分布情况")
line.add("", attr, v, mark_point=["average"])
line.render(r'E:\vscode_code\GitHub项目\爬虫+数据分析\猫眼\电影年份分布情况.html')
from pyecharts import Bar
attr = list(month.index)
v = list(month)
bar = Bar("")
bar.add("", attr, v)
bar.render(r'E:\vscode_code\GitHub项目\爬虫+数据分析\猫眼\电影月份分布情况.html')
def get_country(i):
country = i.split('(')
if len(country) == 1:
def createReport(self, dev):
lisMem = pick.readInfo(AppPerCon.info_path + self.dev + '_' +
self.pack + '_' + self.flag + "_mem.pickle")
lisCpu = pick.readInfo(AppPerCon.info_path + self.dev + '_' +
self.pack + '_' + self.flag + "_cpu.pickle")
lisFps = pick.readInfo(AppPerCon.info_path + self.dev + '_' +
self.pack + '_' + self.flag + "_fps.pickle")
lisDevinfo = pick.readInfo(AppPerCon.info_path + "info.pickle")
print("lisDevinfo: {}, dev: {}".format(lisDevinfo, dev))
# pix = lisDevinfo[0][dev]['header']['pix']
# net = lisDevinfo[0][dev]['header']['net']
# name = lisDevinfo[0][dev]['header']['phone_name']
# rom = lisDevinfo[0][dev]['header']['rom']
# devinfo = "设备信息-分辨率：" + pix + "\\"\
# +"网络：" + net + "\\"\
# +"设备名："+ name + "\\"\
# +"内存容量："+ str(rom)+"MB"
v1 = [i for i in lisCpu if type(i) == str]
v2 = [i for i in lisCpu if type(i) != str]
v3 = [i for i in lisMem if type(i) == str]
v4 = [i for i in lisMem if type(i) != str]
v5 = [i for i in lisFps if type(i) == str]
v6 = [i for i in lisFps if type(i) != str]
page = Page(self.reportName)
attr = v1
bar = Bar()
bar.add("ROKI_bar", attr, v2)
line = Line(self.reportName + "-" + "CPU占用", [],
width=1200,
height=400)
# line.add("ROKI_line", attr, v2, is_stack=True, is_label_show=True,
# is_smooth=False ,is_more_utils =True,is_datazoom_show=False, yaxis_formatter="%",
# mark_point=["max", "min"], mark_line=["average"])
overlap = Overlap(self.reportName + "-" + "CPU占用",
width=1200,
height=400)
# overlap.add(line)
overlap.add(bar)
page.add(overlap)
attr1 = v3
line1 = Line(self.reportName + "-" + "MEM消耗", width=1200, height=400)
line1.add("ROKI_line",
attr1,
v4,
is_stack=True,
is_label_show=True,
is_smooth=False,
is_more_utils=True,
is_datazoom_show=False,
yaxis_formatter="MB",
mark_point=["max", "min"],
mark_line=["average"])
bar1 = Bar()
bar1.add("ROKI_bar", attr1, v4)
overlap1 = Overlap(width=1200, height=400)
overlap1.add(line1)
overlap1.add(bar1)
page.add(overlap1)
attr2 = v5
line2 = Line(self.reportName + "-" + "FPS帧率", width=1200, height=400)
line2.add("ROKI_line",
attr2,
v6,
is_stack=True,
is_label_show=True,
is_smooth=False,
is_more_utils=True,
is_datazoom_show=False,
yaxis_formatter="fps",
mark_point=["max", "min"],
mark_line=["average"])
bar2 = Bar()
bar2.add("ROKI_bar", attr2, v6)
overlap2 = Overlap(width=1200, height=400)
overlap2.add(line2)
overlap2.add(bar2)
page.add(overlap2)
page.render(AppPerCon.report_path + self.dev + "_" + self.pack + "_" +
self.flag + "_" + "report.html")
df1 = pd.read_csv(fn)
df1['datetime'] = df1['date'] + ' ' + df1['time']
#print(df1.head())
dt_list = list(df1['datetime'])
#print(dt_list)
k_plot_value = df1.apply(lambda record: [record['open'], record['close'], record['low'], record['high']], axis=1).tolist()
#print(k_plot_value)
kline = Kline("K 线图示例")
kline.add("日K", dt_list, k_plot_value, is_datazoom_show=True, )
#kline.render()
df_p = pd.read_csv('points1.csv')
line = Line()
line.add('test',df_p.datetime,df_p.value, line_type='dashed')
# #line.render()
#2019-08-14,21:04:00,5546
#2019-08-14,22:22:00,5530
#2019-08-14,22:50:00,5536.
overlap = Overlap()
overlap.add(kline)
overlap.add(line)
overlap.render()
# if __name__ == '__main__':
# pass
# Line + Kline
from pyecharts import Grid, Line, Kline
attr_2 = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
v3 = [11, 11, 15, 13, 12, 13, 10]
v4 = [1, -2, 2, 5, 3, 2, 0]
line = Line('折线图示例')
line.add('最高气温', attr_2, v3, mark_point=['max', 'min'], mark_line=['average'])
line.add('最低气温',
attr_2,
v4,
mark_point=['max', 'min'],
mark_line=['average'],
legend_pos='20%')
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
def overlap_charts():
page = Page()
chart_init = {
"width": WIDTH,
"height": HEIGHT,
}
attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("折线图-柱状图叠加", **chart_init)
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)
chart = Overlap()
chart.add(bar)
chart.add(line)
page.add(chart)
v1 = [10, 20, 30, 40, 50, 60]
v2 = [30, 30, 30, 30, 30, 30]
v3 = [50, 50, 50, 50, 50, 50]
v4 = [10, 10, 10, 10, 10, 10]
es = EffectScatter("散点图-动态散点图叠加", **chart_init)
es.add("es", v1, v2)
scatter = Scatter()
scatter.add("scatter", v1, v3)
es_1 = EffectScatter()
es_1.add("es_1", v1, v4, symbol='pin', effect_scale=5)
chart = Overlap()
chart.add(es)
chart.add(scatter)
chart.add(es_1)
page.add(chart)
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
attr = ["2017/7/{}".format(i + 1) for i in range(31)]
kline = Kline("K 线图-折线图叠加", **chart_init)
kline.add("日K", attr, v1)
line_1 = Line()
line_1.add("line-1", attr, [random.randint(2400, 2500) for _ in range(31)])
line_2 = Line()
line_2.add("line-2", attr, [random.randint(2400, 2500) for _ in range(31)])
chart = Overlap()
chart.add(kline)
chart.add(line_1)
chart.add(line_2)
page.add(chart)
attr = ["{}月".format(i) for i in range(1, 13)]
v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar("多 Y 轴叠加", **chart_init)
bar.add("蒸发量", attr, v1)
bar.add("降水量", attr, v2, yaxis_formatter=" ml", yaxis_max=250)
line = Line()
line.add("平均温度", attr, v3, yaxis_formatter=" °C")
chart = Overlap()
chart.add(bar)
chart.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(chart)
return page
vixDate = datetime.strptime(vixDate, '%Y/%m/%d')
T_near = (near - vixDate).days / 365.0
T_next = (nexts - vixDate).days / 365.0
# the forward index prices
nearPriceDiff = getStrikeMinCallMinusPutClosePrice(optionsNearTerm)
nextPriceDiff = getStrikeMinCallMinusPutClosePrice(optionsNextTerm)
near_F = nearPriceDiff[0] + np.exp(T_near * R_near) * nearPriceDiff[1]
next_F = nextPriceDiff[0] + np.exp(T_next * R_next) * nextPriceDiff[1]
# 计算不同到期日期权对于VIX的贡献
near_sigma = calSigmaSquare(optionsNearTerm, near_F, R_near, T_near)
next_sigma = calSigmaSquare(optionsNextTerm, next_F, R_next, T_next)
# 利用两个不同到期日的期权对VIX的贡献sig1和sig2，
# 已经相应的期权剩余到期时间T1和T2；
# 差值得到并返回VIX指数(%)
w = (T_next - 30.0 / 365.0) / (T_next - T_near)
vix = T_near * w * near_sigma + T_next * (1 - w) * next_sigma
return 100 * np.sqrt(abs(vix) * 365.0 / 30.0)
ivix = []
for day in tradeday['DateTime']:
ivix.append(calDayVIX(day))
from pyecharts import Line
attr = true_ivix[u'日期'].tolist()
line = Line(u"中国波指")
line.add("中证指数发布", attr, true_ivix[u'收盘价(元)'].tolist(), mark_point=["max"])
line.add("手动计算", attr, ivix, mark_line=["max", 'average'])
line.render()
print(earlest_msg_node)
latest_reply = msg_list[len(msg_list) - 1][0]
latest_reply_msg = msg_list[len(msg_list) - 1][1]
content_match = regex.search(latest_reply_msg[1])
if content_match:
latest_time_node = "最晚一条发言：%s %s，来自：%s群" % (latest_reply_msg[0], latest_reply, content_match.group(1))
print(latest_time_node)
latest_msg_node = '"%s" 说 「%s」' % (content_match.group(2), content_match.group(3))
print(latest_msg_node)
page = Page()
# line
item_name_list, item_num_list = counter2list(dict2sorted_by_key(date_msg_counter))
line = Line("群心情走势图", "截至日期：%s" % item_name_list[len(item_name_list) - 1], title_text_size=30,
subtitle_text_size=18, title_pos='center')
line.add("", item_name_list, item_num_list, mark_point=["max"], legend_pos='65%',
xaxis_interval=2, xaxis_rotate=27, xaxis_label_textsize=20, yaxis_label_textsize=20, yaxis_name_pos='end',
yaxis_pos="%50", is_label_show=True)
page.add(line)
# pie
attr = ["总发言数", "日均发言", "发言最多", "发言最少"]
v1 = [len(msg_list), math.floor(len(msg_list) / len(date_msg_counter)), most_msg_count[1],
dict_sorted_by_value[0][1]]
pie = Pie("群聊数据统计", title_pos='center')
pie.add("", attr, v1, radius=[40, 75], label_text_color=None, is_label_show=True, legend_orient='vertical',
legend_pos='left')
page.add(pie)
# bar
regr=linear_model.LinearRegression()
regr.fit(x,y)
print('Intercept:{}'.format(regr.intercept_))
print('Coeffecien:{}'.format(regr.coef_))
plt.plot(x,regr.predict(x),linewidth=10,color='blue')
'''
#散点图绘制
plt.scatter(x,y,color='black')
plt.xlabel('专业课成绩')
plt.ylabel('数学成绩')
#考生编号转换列为索引
computer_top_ten.set_index('考生编号')
#print(computer_top_ten)
line1=Line("计算机前十名")
i=computer_top_ten['考生编号']
j=computer_top_ten['总分']
attr1=list(map(str,i))
v=list(j)
line1.add("",attr1,v,is_smooth=True,mark_line=["max","average"])
page.add(line1)
'''
绘制成绩分布直方图
'''
computer.set_index('考生编号')
