def test_timeline_label_color():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add(
"bar",
attr,
[randint(10, 50) for _ in range(6)],
label_color=["red", "#213", "black"],
)
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
content = timeline._repr_html_()
assert '"color": [' in content
assert "red" in content
assert "#213" in content
assert "black" in content
def creatTimeLine(charts, timepoints, speed=500, showTimeLine=True):
tt = Timeline(timeline_play_interval=speed, is_timeline_show=showTimeLine)
cnt = 0
for chart in charts:
tt.add(chart, time_point=timepoints[cnt])
cnt += 1
return tt
def spot_line(cnt,name):
follow_spot=df[df['channel_id']==cnt]
follow_spot=follow_spot.groupby(by=['spot_id','date'],as_index=False).sum()[['spot_id','date','cost_r']]
spt=follow_spot[follow_spot['date'].apply(lambda x:str(x))==end_time.replace('-', '')].sort_values(by='cost_r').tail(7)['spot_id']
top_spt=follow_spot[follow_spot['spot_id'].isin(spt)] #TOP广告位
lists=[]
for i,j in itertools.product(top_spt['spot_id'].drop_duplicates(),df['date'].drop_duplicates()):
lists.append([i,j])
lists=pd.DataFrame(lists,columns=['spot_id','date'])
top_spt=pd.merge(lists,top_spt,how='left',on=['spot_id','date'])
top_spt=top_spt.fillna(0)
top_spt=pd.merge(top_spt,meta_spot,how='left',left_on='spot_id',right_on='spotid')
top_spt.loc[top_spt['name'].isnull(),'name']=top_spt[top_spt['name'].isnull()]['spot_id']
line = Line(name+'TOP广告位消耗')
for i in top_spt['name'].drop_duplicates():
line.add(i,top_spt[top_spt['name']==i]['date'].apply(lambda x:str(x)),top_spt[top_spt['name']==i]['cost_r'].apply(lambda x:int(x)),legend_pos="84%",mark_point=['max'])
# grid= Grid(width=1000,height=350)
# grid.add(line,grid_right="25%")
# page.add_chart(grid)
''' 涨跌幅TOP广告位 '''
lists=[]
for i,j in itertools.product(follow_spot['spot_id'].drop_duplicates(),df['date'].drop_duplicates()):
lists.append([i,j])
lists=pd.DataFrame(lists,columns=['spot_id','date'])
follow_spt=pd.merge(lists,follow_spot,how='left',on=['spot_id','date'])#笛卡尔积
follow_spt=follow_spt.fillna(0)
diff_spt=follow_spt.groupby(by='spot_id',as_index=False).diff()['cost_r'].rename('rise')
diff_spt=pd.concat([follow_spt,diff_spt],axis=1)
#涨幅TOP 7
spt=diff_spt[diff_spt['date'].apply(lambda x:str(x))==end_time.replace('-', '')].sort_values(by='rise').tail(7)['spot_id']
rise_spt=diff_spt[diff_spt['spot_id'].isin(spt)] #TOP广告位
rise_spt=pd.merge(rise_spt,meta_spot,how='left',left_on='spot_id',right_on='spotid')
rise_spt.loc[rise_spt['name'].isnull(),'name']=rise_spt[rise_spt['name'].isnull()]['spot_id']
line1 = Line(name+'涨幅TOP广告位',width=1000,height=450)
for i in rise_spt['name'].drop_duplicates():
line1.add(i,rise_spt[rise_spt['name']==i]['date'].apply(lambda x:str(x)),rise_spt[rise_spt['name']==i]['cost_r'].apply(lambda x:int(x)),legend_pos="84%")
#跌幅TOP 7
spt=diff_spt[diff_spt['date'].apply(lambda x:str(x))==end_time.replace('-', '')].sort_values(by='rise').head(7)['spot_id']
fall_spt=diff_spt[diff_spt['spot_id'].isin(spt)] #TOP广告位
fall_spt=pd.merge(fall_spt,meta_spot,how='left',left_on='spot_id',right_on='spotid')
fall_spt.loc[fall_spt['name'].isnull(),'name']=fall_spt[fall_spt['name'].isnull()]['spot_id']
line2 = Line(name+'跌幅TOP广告位',width=1000,height=450)
for i in fall_spt['name'].drop_duplicates():
line2.add(i,fall_spt[fall_spt['name']==i]['date'].apply(lambda x:str(x)),fall_spt[fall_spt['name']==i]['cost_r'].apply(lambda x:int(x)),legend_pos="84%")
#画图
grid= Grid(width=900,height=350)
timeline = Timeline(timeline_bottom=0)
timeline.add(line, name+'TOP消耗')
timeline.add(line1, name+'涨幅TOP消耗')
timeline.add(line2, name+'跌幅TOP消耗')
grid.add(timeline)
# grid= Grid(width=1200,height=350)
# grid.add(line,grid_right="55%")
# grid.add(line1,grid_left="55%")
page.add_chart(grid)
class MultiPlot:
def __init__(self):
self.data = ''
self.timeLine = Timeline()
self.page = Page()
self.script_list = Bar().get_js_dependencies()
def multi_plot(self, dat):
self.data = dat
try:
for m_title, tpv1 in self.data.items():
self.timeLine = Timeline(is_auto_play=False, timeline_bottom=0)
attr = tpv1['index']
for m_time in list(tpv1.columns):
if m_time != 'index':
tp_bar = Bar(m_time)
tpv1 = tpv1.sort_values(by=m_time ,ascending=False)
tp_bar.add(m_title, list(tpv1['index']),
list(tpv1[m_time]),
xaxis_interval=0,
xaxis_rotate=30,
yaxis_rotate=30,)
self.timeLine.add(tp_bar, m_time)
self.page.add(self.timeLine)
return self.page.render_embed()
except Exception as e:
print('multi_plot error with {}'.format(str(e)))
return None
def gen_zwyx_type(zwlb):
qs = ZpZwyxByTypeModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zwyx_type/{zwlb}.html'
else:
path = 'zwyx_type.html'
# 当月职位月薪与公司性质
df = read_frame(qs.all())
if len(df) > 0:
page = Page()
Grid_chart1 = Timeline(width=1500, height=450, timeline_bottom=0)
Grid_chart2 = Timeline(width=1500, height=450, timeline_bottom=0)
df_group = df.groupby(['year', 'month'])
for name, group in df_group:
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('type').apply(get_echarts_all_by_zwyx_value,
'type')
# 薪资
Overlap_chart = Overlap(width=800, height=450)
bar_chart = Bar(f'{zwlb}职位月薪与公司性质')
bar_chart.add('最低薪资',
df_new['type'].tolist(),
df_new['min_zwyx'].tolist(),
is_label_show=True,
is_more_utils=True)
bar_chart.add('最高薪资',
df_new['type'].tolist(),
df_new['max_zwyx'].tolist(),
is_label_show=True,
is_more_utils=True)
line_chart = Line()
line_chart.add("平均薪资",
df_new['type'].tolist(),
[(a + b) / 2
for a, b in zip(df_new['min_zwyx'].tolist(),
df_new['max_zwyx'].tolist())],
is_label_show=True)
Overlap_chart.add(bar_chart)
Overlap_chart.add(line_chart)
Grid_chart1.add(Overlap_chart, f'{year}年{month}月')
# 职位量
chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500)
chart3.add('职位量'.format(zwlb),
df_new['type'].tolist(),
df_new['count'].tolist(),
is_label_show=True,
is_stack=True,
center=[25, 50])
chart3.add('招聘人数'.format(zwlb),
df_new['type'].tolist(),
df_new['zprs'].tolist(),
is_label_show=True,
is_stack=True,
center=[75, 50])
Grid_chart2.add(chart3, f'{year}年{month}月')
page.add(Grid_chart1)
page.add(Grid_chart2)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def gen_gwzz_word(zwlb):
qs = ZpWordByZwlbModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zp_word/{zwlb}.html'
else:
path = 'zp_word.html'
df = read_frame(qs.all())
if len(df) > 0:
page = Page()
Grid_chart1 = Timeline(width=1500, height=800, timeline_bottom=0)
df_group = df.groupby(['year', 'month'])
for name, group in df_group:
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('word').apply(get_echarts_all_by_value,
'word')
chart = WordCloud(f'{zwlb}岗位需求词云', width=1500)
shape_list = [
None, 'circle', 'cardioid', 'diamond', 'triangle-forward',
'triangle', 'pentagon', 'star'
]
chart.add("",
df_new['word'].tolist(),
df_new['count'].tolist(),
word_size_range=[30, 100],
rotate_step=66,
shape=shape_list[random.randint(0,
len(shape_list) - 1)])
Grid_chart1.add(chart, f'{year}年{month}月')
page.add(Grid_chart1)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def test_timeline_label_color():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add(
"bar",
attr,
[randint(10, 50) for _ in range(6)],
label_color=["red", "#213", "black"],
)
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
content = timeline._repr_html_()
assert '"color": [' in content
assert "red" in content
assert "#213" in content
assert "black" in content
def gen_zwyx_dd(zwlb):
qs = ZpZwByAreaModel.objects
if zwlb:
qs = qs.filter(zwlb=zwlb)
path = f'zwyx_dd/{zwlb}.html'
else:
path = 'zwyx_dd.html'
page = Page()
df = read_frame(qs.all())
if len(df) > 0:
df_group = df.groupby(['year', 'month'])
time_line_chart1 = Timeline(width=1500,
height=450,
is_auto_play=False,
timeline_bottom=0)
time_line_chart2 = Timeline(width=1500,
height=450,
is_auto_play=False,
timeline_bottom=0)
for name, group in df_group:
# 地图 平均薪资
month = group['month'].tolist()[0]
year = group['year'].tolist()[0]
df_new = group.groupby('province').apply(
get_echarts_all_by_zwyx_value, 'province')
data = [
(a, (b + c) / 2)
for a, b, c in zip(df_new['province'].tolist(
), df_new['max_zwyx'].tolist(), df_new['min_zwyx'].tolist())
]
chart = Map(f'{zwlb}平均职位月薪与地点', width=1500, height=450)
attr, value = chart.cast(data)
chart.add(f'平均薪资',
attr,
value,
wmaptype='china',
is_label_show=True,
is_visualmap=True,
visual_range=[int(min(value)),
int(max(value))],
visual_pos='right',
visual_top='top')
time_line_chart1.add(chart, f'{year}年{month}月')
# 本月职位量Top20
chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500)
chart3.add('职位量',
df_new['province'].tolist(),
df_new['count'].tolist(),
center=[25, 50],
is_label_show=True)
chart3.add('招聘人数',
df_new['province'].tolist(),
df_new['zprs'].tolist(),
center=[75, 50],
is_label_show=True)
time_line_chart2.add(chart3, f'{year}年{month}月')
page.add(time_line_chart1)
page.add(time_line_chart2)
page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path)))
def test_timeline_map():
timeline = Timeline(timeline_bottom=0)
value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = ["福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test1")
value = [155, 10, 66, 78, 33]
attr = ["福建", "山东", "北京", "上海", "甘肃"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test2")
assert len(timeline._option.get("baseOption").get("series")) == 2
timeline.render()
def ShowNumWithYear(df):
# show bus num in different year at different cities
years = list(set(df['year']))
years.sort()
cities = []
values = []
total_num = 0
geos = [] # store the geo every year
timeline = Timeline(width=1500,height=800,is_auto_play=True, timeline_bottom=-10,timeline_symbol_size=20,\
timeline_play_interval = 800,timeline_left=20,timeline_right=100 , is_timeline_show = False )
for index in range(len(years)):
df_temp = df[df['year'] == years[index]]
cities = cities + list(df_temp['city'])
values = values + list(df_temp['num'])
total_num = sum(values)
geos.append(Geo( str(years[index]) + " , Fist level title" , title_top = "10%" , title_text_size=50 , subtitle = "second level title" , \
subtitle_text_size = 23 , subtitle_color="white", \
title_color="red", title_pos="center", width=1200, height=600, \
background_color='#404a59'))
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geos[index].add("数量", cities, values, type="effectScatter", maptype='china' , is_random=True, effect_scale=3, is_selected = True,is_toolbox_show = True ,is_more_utils =True,\
visual_text_color="#fff", symbol_size=10, is_label_show = True , legend_orient = 'left' ,is_legend_show = False, legend_top = 'bottom' , label_formatter = '{b}' , \
is_visualmap=True, is_roam=True , label_text_color="#00FF00" , is_piecewise=True, label_text_size = 7,visual_range=[1, 300] , \
geo_cities_coords = {'柯桥': [120.443 , 30.0822] ,} , \
pieces=[
{"min":0.1, "max": 500 , "label": "0-500"},
{"min": 500, "max": 1000 , "label": "501-1000"},
{"min": 1001, "max": 2000 , "label": "1001-2000"},
{"min":2001, "max": 5000, "label": "2001-5000"},
{"min":5001, "max": 100000, "label": ">5000"}, ] )
geos[index].show_config()
geos[index].render("数量.html")
# 时间轴定义
timeline.add(geos[index], years[index])
timeline.render('redult.html')
def test_timeline_map():
timeline = Timeline(timeline_bottom=0)
value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = ["福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test1")
value = [155, 10, 66, 78, 33]
attr = ["福建", "山东", "北京", "上海", "甘肃"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
visual_top="30%",
)
timeline.add(map, "test2")
assert len(timeline.options.get("baseOption").get("series")) == 2
timeline.render()
def get_pic():
df2 = shops(mob_site)[1]
a = order(df2, order_countJA, order_countHA, '')
sz = order(df2, order_countJ, order_countH, shop_order, 4)[0]
gz = order(df2, order_countJ, order_countH, shop_order, 42)[0]
hz = order(df2, order_countJ, order_countH, shop_order, 53)[0]
hk = order(df2, order_countJ, order_countH, shop_order, 85)[0]
sh = order(df2, order_countJ, order_countH, shop_order, 11)[0]
cs = order(df2, order_countJ, order_countH, shop_order, 205)[0]
fz = order(df2, order_countJ, order_countH, shop_order, 201)[0]
bar_a = out_bar(a, "全国")
bar_sz = out_bar(sz, "深圳")
bar_gz = out_bar(gz, "广州")
bar_hz = out_bar(hz, "杭州")
bar_hk = out_bar(hk, "海口")
bar_sh = out_bar(sh, "上海")
bar_cs = out_bar(cs, "长沙")
bar_fz = out_bar(fz, "福州")
timeline = Timeline(is_auto_play=False, timeline_bottom=0, width=1100)
timeline.add(bar_a, '全国')
timeline.add(bar_sz, '深圳')
timeline.add(bar_gz, '广州')
timeline.add(bar_hz, '杭州')
timeline.add(bar_hk, '海口')
timeline.add(bar_sh, '上海')
timeline.add(bar_cs, '长沙')
timeline.add(bar_fz, '福州')
return timeline
def _initial(self, speed):
timeline = Timeline(width=self.width,
height=self.height,
timeline_play_interval=speed)
index = 0
for bar in self.bars:
timeline.add(bar, time_point=self.timePoints[index])
index += 1
return timeline
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
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for name, c in df.groupby('COMMUNITY_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("各月份社区事件数", **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)
chart = Timeline(is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT)
for month, group in df.groupby('MONTH'):
COMMUNITY_NAME = group.COMMUNITY_NAME.value_counts()
chart_1 = Bar("各月份社区事件数", **style.init_style)
chart_1.add("",
COMMUNITY_NAME.index,
COMMUNITY_NAME.values,
mark_point=["max", "min"],
mark_line=["average"],
is_stack=True)
chart.add(chart_1, month)
page.add(chart)
return page
def show_ruanke_best(begin_year,end_year):
'''
@author: Hong wentao
用于展示每一个学科的学校的排名可视化，使用的是软科最好大学排名的数据
'''
path=data_path+"ruanke_best_subject/"
ruanke_html_path=html_path+"ruanke/"
isExists=os.path.exists(ruanke_html_path)
if not isExists:
os.makedirs(ruanke_html_path)
first_names=os.listdir(path)
for first_name in first_names:
first_data_path=path+first_name+"/"
first_html_path=ruanke_html_path+first_name+"/"
isExists=os.path.exists(first_html_path)
if not isExists:
os.makedirs(first_html_path)
second_names=os.listdir(first_data_path)
for second_name in second_names:
second_data_path=first_data_path+second_name+"/"
second_html_path=first_html_path+second_name+"/"
isExists=os.path.exists(second_html_path)
if not isExists:
os.makedirs(second_html_path)
data=deal_ruanke.every_subject_ruanke(first_name,second_name)
timeline = Timeline(is_auto_play=False, timeline_bottom=0,width=stand_width)
for i in range(begin_year,end_year+1):
bar = Bar(second_name+str(i)+"年大学排名数据")
try:
want_data=data[i]
nan='空白区域说明该学校当年无该数据'
final_data=[]
have_name=[]
before_median=[]
for x in want_data:
have_name.append(x[2])
for name in university_name:
if name not in have_name:
final_data.append("nan")
else:
for x in want_data:
if x[2]==name:
final_data.append(x[0])
before_median.append(x[0])
break;
a=np.array(before_median).astype("int")
median=np.median(a).astype("int")
bar.add("排名 "+nan, university_name,final_data, is_label_show=True,is_stack=True,xaxis_interval=0,xaxis_name_size=15,xaxis_rotate=0,yaxis_name_rotate=0,xaxis_name_pos="end", xaxis_name="学校名",yaxis_name="排名",yaxis_name_gap=10,yaxis_name_pos="end",mark_point=["max","min"],mark_line_raw=[{"yAxis": median,"name":"中位数","lineStyle": {
"color": 'black'
}}])
timeline.add(bar, str(i)+'年')
timeline.render(second_html_path+second_name+".html")
except Exception:
print(first_name+" "+second_name+str(i)+"没有")
continue
timeline.render(second_html_path+second_name+".html")
def test_null_in_timeline_options():
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._option["series"][0]["symbol"] = NULL
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, "2012 年")
assert isinstance(timeline._option["options"][0]["series"][0]["symbol"],
JsValue)
def gauge_timeline(data_array):
chart1 = Timeline(**timeline_Style)
array_gauge = []
for i in range(0, 13):
array_gauge.append(data_array[i]['heat'].sum())
maxgauge = max(array_gauge)
array_gauge = soomthByStage(array_gauge, 5)
for i in range(0, 60):
gauge = Gauge("观看人数趋势图 ", title_pos='center')
gauge.add("", "占比", int(array_gauge[i] / maxgauge * 100))
chart1.add(gauge, String_filename_1[i])
return chart1
def show_map_school(begin_year, end_year):
'''
用地图展示各学校的排名，这样可以观察地域与学校排名的关系
'''
datas = deal_ruanke.ranking_ruanke(begin_year, end_year)
university_name = (list)(datas.keys())
ranking_datas = (list)(datas.values())
timeline = Timeline(is_auto_play=False,
timeline_bottom=0,
width=stand_width,
height=500)
for i in range(begin_year, end_year + 1):
data = []
dict = {}
nan = ''
for j in range(0, len(university_name)):
line = []
line.append(university_name[j])
if (ranking_datas[j][i - begin_year] == "nan"):
continue
line.append(ranking_datas[j][i - begin_year])
line_tuple = (tuple)(line)
data.append(line_tuple)
dict[university_name[j]] = ranking_datas[j][i - begin_year]
if (i == 2016):
nan = "(云南大学，青海大学，西藏大学未纳入排名)"
geo = Geo(str(i) + "年度全国大学排名" + nan,
"全国大学排名",
title_color="#000",
title_pos="center",
width=1600,
height=400,
background_color='#DCDCDC')
attr, value = geo.cast(data)
geo.add("",
attr,
value,
visual_range=[0, 600],
maptype='china',
visual_text_color="#000",
symbol_size=20,
is_visualmap=True,
is_roam=False,
visual_top="center",
geo_normal_color="#404a59",
visual_range_text=["high", "low"],
visual_range_color=['#50a3ba', '#faef61', '#d94e5d'],
label_formatter="{c0}",
tooltip_formatter="{b}: [经度(E)，纬度(N)，排名] {c}名")
geo
timeline.add(geo, str(i) + '年')
return timeline
def time_line():
timeline = Timeline(page_title="Time Line")
stack_bar, line = StackBar.stack_bar()
tree_chart = TreeChart.tree_chart()
stack_bar_chart = StackBarChart.stack_bar_chart2()
stack_line = StackChart.starck_line_chart()
timeline.add(stack_line, time_point=7)
timeline.add(stack_bar_chart, time_point=8)
timeline.add(tree_chart, time_point=6)
timeline.add(stack_bar,time_point=2)
timeline.add(line, time_point=3)
timeline.render("timeline.html")
def geographical_location_GDP_ratio_distribution():
data = pd.read_excel("./data/car_sales_data.xlsx", sheet_name=u"人均GDP", encoding='utf-8')
data.columns = [str(col).split('-')[0] for col in data.columns]
float_cols = [str(year) for year in range(1998, 2018)]
not_replace = [
u'池州市', u'宣城市', u'眉山市', u'达州市', u'庆阳市',
u'广安市', u'贺州市', u'来宾市', u'崇左市', u'临沧市',
u'固原市', u'中卫市', u'丽江市'
]
data['city_name'] = data['city_name'].apply(lambda xx: xx.replace(u'市', '') if xx not in not_replace else xx)
city_none = [u'黔西南布依族苗族自治州', u'乌兰察布', u'巴音郭楞蒙古自治州（库尔勒）']
data = data[~data['city_name'].isin(city_none)]
data[float_cols] = data[float_cols].fillna(0).applymap(lambda xx: round(xx, 2) if xx else 0)
cols_show = ['city_name'] + float_cols
data = data[cols_show]
data = calc_ratio_percent(data)
data[range(1999, 2018)] = data[range(1999, 2018)].applymap(lambda xx: round(xx * 100, 2))
data.to_excel('./car_sales_visualization/GDP_ratio_distribution.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1999, 2018):
dataset = [(city, sales) for city, sales in data[['city_name'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100.0, 100.0],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/GDP_ratio_distribution.html')
def show_total_ruanke(begin_year, end_year):
'''
1.软科总体排名
@author: Hong wentao
最终的可视化结果呈现出的是2016年至今这14所学校在软科最好大学排名中的排名名次
'''
datas = deal_ruanke.ranking_ruanke(begin_year, end_year)
university_name = (list)(datas.keys())
ranking_datas = (list)(datas.values())
timeline = Timeline(is_auto_play=False,
timeline_bottom=0,
width=stand_width)
for i in range(0, end_year - begin_year + 1):
bar = Bar("软科" + str(2016 + i) + "年大学排名数据")
want_data = []
nan = ''
before_median = []
for x in ranking_datas:
want_data.append(x[i])
if x[i] == "nan":
nan = "（如果某学校当年无数据，则该区域为为空白）"
else:
before_median.append(x[i])
a = np.array(before_median).astype("int")
median = np.median(a).astype("int")
bar.add("排名 " + nan,
university_name,
want_data,
is_label_show=True,
is_stack=True,
xaxis_interval=0,
xaxis_name_size=15,
xaxis_rotate=0,
yaxis_name_rotate=0,
xaxis_name_pos="end",
xaxis_name="学校名",
yaxis_name="排名",
yaxis_name_gap=10,
yaxis_name_pos="end",
mark_point=["max", "min"],
mark_line_raw=[{
"yAxis": median,
"name": "中位数",
"lineStyle": {
"color": 'black'
}
}])
timeline.add(bar, str(2016 + i) + ' 年')
return timeline
def geographical_location_ratio_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
data = calc_ratio_percent(data)
data[range(1998, 2019)] = data[range(1998, 2019)].applymap(lambda xx: round(xx * 100, 4))
cols_show = ['city'] + range(1998, 2019)
data = data[cols_show]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
data.to_excel('./car_sales_visualization/car_sales_num_ratio.xlsx')
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[['city'] + [year]].values]
geo = Geo(
"%s - Car Sales Num Ratio Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2480,
height=1330,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
type="effectScatter",
is_visualmap=True,
maptype='china',
visual_range=[-100, 100],
visual_text_color="black",
effect_scale=5,
symbol_size=5
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_ratio.html')
def get_html_project_report(project_name, type=0):
mylogger = common.LogOutput.Log().get_logger()
mylogger.info(
"------------------------------------------------------------------")
mylogger.info("当前运行文件：{}".format(__file__))
try:
project_run_num = get_project_run_num(project_name, type)
# num = [successNum, failedNum, noRunNum]
num = [project_run_num[0], project_run_num[1], project_run_num[2]]
attr = ["成功用例", "失败用例", "不执行用例"]
bar = Bar(project_name, "用例执行结果")
bar.add("", attr, num, is_more_utils=True)
bar.show_config()
pie_1 = Pie(project_name, "用例执行结果")
pie_1.add(project_name,
attr,
num,
is_label_show=True,
radius=[30, 55],
rosetype='radius')
timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar, '树形图')
timeline.add(pie_1, '饼形图')
PATH = lambda P: os.path.abspath(
os.path.join(os.path.dirname(__file__), P))
html_dirname = PATH(
"../report") + "\\" + project_name + "\\" + "htmlReport\\"
# 判断是否存在case目录，没有，则创建
if not os.path.exists(html_dirname):
os.mkdir(html_dirname)
mylogger.info("生成用例目录路径：{}".format(html_dirname))
if type == 0:
html_report_name = time.strftime('%Y-%m-%d %H-%M-%S') + '.html'
elif type == 1:
html_report_name = 'settingReport' + time.strftime(
'%Y-%m-%d %H-%M-%S') + '.html'
htmlReportPath = html_dirname + html_report_name
timeline.render(htmlReportPath)
return 'success'
except TypeError as e:
return 'caseEmpty', e
def geographical_location_amount_distribution():
data = pd.read_csv("./data/car_sales_volume_data.csv", encoding='utf-8')
data = data[data['city'].str.len() > 1]
city_none = [
u'三沙', u'中卫', u'临沧', u'丽江',
u'克孜勒苏柯尔克孜自治州', u'其它',
u'固原', u'新疆自治区直辖', u'普洱',
u'河南省省直辖', u'海南省省直辖', u'湖北省省直辖'
]
data = data[~data['city'].isin(city_none)]
int_cols = [str(col) for col in range(1998, 2019)]
data[int_cols] = data[int_cols].applymap(lambda xx: float(xx))
timeline = Timeline(is_auto_play=False, timeline_bottom=1250, width=2480, height=1330)
for year in range(1998, 2019):
dataset = [(city, sales) for city, sales in data[('city %s' % year).split()].values]
print dataset
geo = Geo(
"%s - Car Sales Num Amount Distribution" % year,
"",
title_pos="center",
title_color="black",
width=2700,
height=1340,
background_color='#ffffff'
)
attr, value = geo.cast(dataset)
geo.add(
"",
attr,
value,
maptype='china',
visual_range=[0, 300000],
visual_text_color="black",
type="heatmap",
is_visualmap=True,
effect_scale=5,
symbol_size=5,
)
timeline.add(geo, year)
timeline.render('./car_sales_visualization/car_sales_num_amount.html')
def bar_map():
kwargs = dict(
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='right',
legend_top='center',
legend_selectedmode='single',
legend_text_size=18,
label_text_size=18,
label_emphasis_textsize=18,
xaxis_name_size=18,
xaxis_label_textsize=18,
xaxis_margin=5,
xaxis_line_width=2,
yaxis_name_size=18,
yaxis_label_textsize=18,
bar_category_gap=25,
yaxis_line_width=2,
is_splitline_show=False,
is_xaxis_boundarygap=True,
is_toolbox_show=False,
)
timeline = Timeline(timeline_bottom=0,
width=1400,
height=700,
timeline_symbol_size=10)
for year in value_base.current_years:
bar = Bar('村镇对比图',
title_pos='center',
title_text_size=30,
title_top=10)
for index in value_base.current_indexs:
data = []
for i in value_base.current_areas:
for veti_data in value_base.veti_data_dict[year]:
if veti_data[0] == i:
data.append(
veti_data[value_base.index_num_dict[index]])
bar.add(index, value_base.current_areas, data, **kwargs)
timeline.add(bar, year)
result = str(value_base.output_dir + u"村镇对比.html")
timeline.render(result)
def radar_map():
kwargs = dict(label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='right',
legend_top='center',
legend_text_size=18,
label_text_size=18,
label_emphasis_textsize=18,
xaxis_name_size=18,
xaxis_label_textsize=18,
xaxis_margin=5,
xaxis_line_width=2,
yaxis_name_size=18,
yaxis_label_textsize=18,
yaxis_line_width=2,
is_splitline_show=False,
is_xaxis_boundarygap=True,
is_toolbox_show=False,
line_width=3)
timeline = Timeline(timeline_bottom=0,
width=1400,
height=700,
timeline_symbol_size=10)
for year in value_base.current_years:
radar_map = Radar('雷达图',
title_pos='center',
title_text_size=30,
title_top=10)
for area in value_base.current_areas:
data = [[]]
for index in value_base.current_indexs:
for veti_data in value_base.veti_data_dict[year]:
if veti_data[0] == area:
data[0].append(
round(veti_data[value_base.index_num_dict[index]],
2))
radar_map.set_radar_component(schema=value_base.radar_schema)
radar_map.add(area, data, **kwargs)
timeline.add(radar_map, year)
result = str(value_base.output_dir + u"雷达图.html")
timeline.render(result)
def draw_pie_all_cost():
_all = Account.objects.filter( # 让其显示最近六个月的数据
spent_date__gte=datetime(now().year,
now().month, 1) -
relativedelta(
months=+5) # dateutil.relativedelata.relativedelta()偏移选择日期
).values("spent_date", "reason").annotate(Sum("cost"))
d = dict((i, list(j)) for i, j in groupby(
_all, key=lambda x: (x['spent_date'].year, x['spent_date'].month)))
timeline = Timeline(is_auto_play=False,
timeline_bottom=0,
width=800,
height=500)
# TODO:最大问题在于，如果有未显示的字段，会导致饼图颜色分类的不一致
# TODO: 开始展示折线图，然后点击折线图可以跳转
CHOICE = dict(Account.TAGS_CHOICE) # 名字
for k, v in d.items():
dict_cost = {}
for _ in v:
r = CHOICE[_['reason']] # 转换之后的*消费理由*
c = _['cost__sum'] # 花费
if r in dict_cost:
dict_cost[r] += c
else:
dict_cost[r] = c
cost_pie = Pie(
"{}年{}月".format(*k), "总开销{}元".format(sum(dict_cost.values()),
width=800,
height=100))
cost_pie.add("{}-{}".format(*k),
dict_cost.keys(),
dict_cost.values(),
radius=[30, 75],
is_label_show=True)
timeline.add(cost_pie, "{}-{}".format(*k))
return timeline
def test_timeline_different_legend():
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季a", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季a", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季a", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季a", 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b", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季b", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季b", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季b", 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')
content = timeline._repr_html_()
assert "\\u6625\\u5b63a" in content # 春季 a
assert "\\u6625\\u5b63b" in content # 春季 b
def dashboard_loan_amount():
'''添加放款金额轮播图表'''
# 生成图表实例
timeline = Timeline(is_auto_play=True, timeline_bottom=0, width="100%")
# 使用暗色主题
timeline.use_theme('dark')
# 生成每个月份的图表
for month in months:
# X轴label
n_day_in_month = pd.Period(month+"-01").days_in_month
attr = list(range(1,n_day_in_month+1))
# 图表数据
v1 = list(np.random.randint(2000, 10000, n_day_in_month) / 10)
# 生成图表实例
chart = Line("%s月放款金额走势图" %month,"当月放款总金额为%.2f万" %np.sum(v1), background_color=BACKGROUND_COLOR, width="100%")
# 使用暗色主题
chart.use_theme('dark')
# 载入默认设置并差异化更新
chart_kwargs = DEFAULT_KWARGS
chart_kwargs.update({
"xaxis_interval":0,
"mark_line": ["average"],
"is_smooth": True,
"is_fill": True,
"area_opacity": 0.3,
})
# 添加数据
chart.add("放款金额", attr, v1, **chart_kwargs)
timeline.add(chart,month)
return timeline
def plot_map(df):
# maptype='china' 只显示全国直辖市和省级
# 数据只能是省名和直辖市的名称
# province_distribution = {'青岛': 22, '龙口': 37.56, '河北': 21, '辽宁': 12, '江西': 6, '上海': 20, '安徽': 10, '江苏': 16, '湖南': 9, '浙江': 13, '海南': 2, '广东': 22, '湖北': 8, '黑龙江': 11, '澳门': 1, '陕西': 11, '四川': 7, '内蒙古': 3, '重庆': 3, '云南': 6, '贵州': 2, '吉林': 3, '山西': 12, '山东': 11, '福建': 4, '青海': 1, '舵主科技，质量保证': 1, '天津': 1, '其他': 1}
# provice=list(province_distribution.keys())
# values=list(province_distribution.values())
years = list(df.index)
geos = []
timeline = Timeline(width=1700,height=900,is_auto_play=True, timeline_bottom=-10,timeline_symbol_size=20,timeline_play_interval=400,timeline_left=20,timeline_right=100 , \
is_timeline_show = False )
for index in range(len(years)):
cities = list(df.columns)
cities.remove('total')
values = list(df.loc[years[index], :])
total_num = values[-1]
del (values[-1])
# print(cities)
# print(values)
geos.append(Geo( str(int(total_num)), title_top="10%" , title_text_size=50 , subtitle = years[index] +" , subtitle", \
subtitle_text_size = 23 , subtitle_color="white", \
title_color="red", title_pos="center", width=1200, height=600, \
background_color='#404a59'))
# type="effectScatter", is_random=True, effect_scale=5 使点具有发散性
geos[index].add("title level1", cities, values, type="effectScatter", maptype='china' , is_random=True, effect_scale=3, is_selected = True,is_toolbox_show = True ,is_more_utils =True,\
visual_text_color="#fff", symbol_size=10, is_label_show = True , legend_orient = 'left' ,is_legend_show = False, legend_top = 'bottom' , label_formatter = '{b}' , \
is_visualmap=True, is_roam=True , label_text_color="#00FF00" , is_piecewise=True, label_text_size = 7,visual_range=[1, 300] , \
geo_cities_coords = {'赣江': [115.934192 , 28.826235] , '红河州' : [103.381549,23.369996] , '蒙自' : [103.371546,23.40208] , '海安' : [120.469259,32.544553] , \
'济阳' : [117.023094,36.965519] , '库车' : [82.970183,41.733785] , '文山-砚山' : [104.334442,23.621612] , '文安':[116.455985,38.891083] , '罗平':[104.309188,24.890519] , \
'宣城' : [118.762662,30.957007] , '古田' : [118.747401,26.596702] , '泗阳':[118.699691,33.723524] , } , \
pieces=[
{"min":0.1, "max": 50 , "label": "0-50"},
{"min": 51, "max": 100 , "label": "51-100"},
{"min": 101, "max": 200 , "label": "101-200"},
{"min":201, "max": 500, "label": "201-500"},
{"min":500, "max": 2900, "label": ">500"}, ] )
geos[index].show_config()
geos[index].render("xxxx售出数量.html")
# 时间轴定义
timeline.add(geos[index], years[index])
timeline.render('final_graph.html')
def test_timeline_pie():
style = Style()
pie_style = style.add(is_label_show=True,
radius=[30, 55],
rosetype="radius")
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True,
timeline_bottom=0,
width=1200,
height=600)
timeline.add(pie_1, "2012 年")
timeline.add(pie_2, "2013 年")
timeline.add(pie_3, "2014 年")
timeline.add(pie_4, "2015 年")
timeline.add(pie_5, "2016 年")
assert len(timeline._option.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(
is_label_show=True,
radius=[30, 55],
rosetype="radius"
)
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True, timeline_bottom=0,
width=1200, height=600)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
timeline.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(
is_label_show=True,
radius=[30, 55],
rosetype="radius"
)
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True, timeline_bottom=0,
width=1200, height=600)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
def test_timeline_pie():
style = Style()
pie_style = style.add(
is_label_show=True, radius=[30, 55], rosetype="radius"
)
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(
is_auto_play=True, timeline_bottom=0, width=1200, height=600
)
timeline.add(pie_1, "2012 年")
timeline.add(pie_2, "2013 年")
timeline.add(pie_3, "2014 年")
timeline.add(pie_4, "2015 年")
timeline.add(pie_5, "2016 年")
assert len(timeline.options.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_different_legend():
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季a", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季a", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季a", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季a", 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b", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季b", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季b", CLOTHES, [randint(10, 100) for _ in range(6)])
bar_2.add(
"冬季b",
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")
content = timeline._repr_html_()
assert "\\u6625\\u5b63a" in content # 春季 a
assert "\\u6625\\u5b63b" in content # 春季 b
def test_timeline_bar_line():
attr = ["{}月".format(i) for i in range(1, 7)]
bar = Bar("1 月份数据", "数据纯属虚构")
bar.add("bar", attr, [randint(10, 50) for _ in range(6)])
line = Line()
line.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_0 = Overlap()
overlap_0.add(bar)
overlap_0.add(line)
bar_1 = Bar("2 月份数据", "数据纯属虚构")
bar_1.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_1 = Line()
line_1.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_1 = Overlap()
overlap_1.add(bar_1)
overlap_1.add(line_1)
bar_2 = Bar("3 月份数据", "数据纯属虚构")
bar_2.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_2 = Line()
line_2.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_2 = Overlap()
overlap_2.add(bar_2)
overlap_2.add(line_2)
bar_3 = Bar("4 月份数据", "数据纯属虚构")
bar_3.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_3 = Line()
line_3.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_3 = Overlap()
overlap_3.add(bar_3)
overlap_3.add(line_3)
bar_4 = Bar("5 月份数据", "数据纯属虚构")
bar_4.add("bar", attr, [randint(10, 50) for _ in range(6)])
line_4 = Line()
line_4.add("line", attr, [randint(50, 80) for _ in range(6)])
overlap_4 = Overlap()
overlap_4.add(bar_4)
overlap_4.add(line_4)
timeline = Timeline(timeline_bottom=0)
timeline.add(overlap_0, "1 月")
timeline.add(overlap_1, "2 月")
timeline.add(overlap_2, "3 月")
timeline.add(overlap_3, "4 月")
timeline.add(overlap_4, "5 月")
timeline.render()
def test_timeline_bar():
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")
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")
assert len(timeline.options.get("baseOption").get("series")) == 0
timeline.render()
def test_timeline_bar():
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")
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')
timeline.render()
def graphpage(items,startdate,enddate,option,width1, height1): #labels:复权ork线or分笔 option:hfq, qfq or 15, 30, D, etc
page = Page()
for i in items:#generate numbers of graphs according to numbers of queries in treewidget
j = re.split("-",i)
if len(j)==3:
a = generateline(j[1],j[2],startdate,enddate,option)#stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
continue
time = [d[0] for d in a]#get time from returned dictionary
if j[2]!="Kline":
if len(a[0])==4 and a[0][2]=="bar": #for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(j[0] + "-" + j[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(j[0] + "-" + j[2], time, form, yaxis_min = "dataMin",yaxis_max = "dataMax",is_datazoom_show = True, datazoom_type = "slider")
overlap.add(bar)
line = Line(j[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in a]
line.add(j[0] + "price", time, price, yaxis_min = "dataMin",yaxis_max = "dataMax", is_datazoom_show = True, datazoom_type = "slider",
yaxis_type="value")
overlap.add(line,yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0])==5 and a[0][3]=="pie":
overlap = Overlap()
timeline = Timeline(is_auto_play=False, timeline_bottom=0) #zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in a]
valuearray = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(j[0]+"-"+"前十股东".decode("utf-8"),width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(j[0]+"-"+"前十股东".decode("utf-8"), names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter = "{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
return
#need more statement
else:
form = [e[1] for e in a]#for not分笔 data
line = Line(j[0] + "-" + j[2], width=width1*10/11, height=(height1*10/11)/len(items))
line.add(j[0] + "-" + j[2], time, form, is_datazoom_show=True, datazoom_type="slider",yaxis_min="dataMin",yaxis_max="dataMax")
page.add(line)
else:
overlap = Overlap()#for k线
close = zip(*a)[2]
candle = [[x[1], x[2], x[3], x[4]] for x in a]
candlestick = Kline(j[0] + "-" + j[2], width=width1*10/11, height = (height1*10/11) / len(items))
candlestick.add(j[0], time, candle, is_datazoom_show=True, datazoom_type="slider",yaxis_interval = 1)
overlap.add(candlestick)
if len(close)>10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(j[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close)>20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(j[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close)>30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(j[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:
for k in range(1, len(j)/3):#if graphs are combined
j[3*k-1] = re.sub("\n&","",j[3*k-1])
sizearray=[]
#if j[1] != "Candlestick"
layout = Overlap()
for i in xrange(0, len(j),3):
array = j[i:i +3]
b = generateline(array[1],array[2],startdate,enddate,option)
if b is None:
continue
btime = [d[0] for d in b]
if array[2] != "Kline":
if len(b[0])==4 and b[0][2]=="bar":
form = [e[1] for e in b]
bar = Bar(array[0] + "-" + array[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(array[0] + "-" + array[2], btime, form, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_max="dataMax")
layout.add(bar)
line = Line(array[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in b]
line.add(array[0] + "price", btime, price, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_type="value")
layout.add(line, yaxis_index=1, is_add_yaxis=True)
else:
line = Line(array[0] + "-" + array[2],width=width1*10/11, height=(height1*10/11) / len(items))
line.add(array[0]+"-"+array[2], btime, b, is_datazoom_show=True, yaxis_max = "dataMax", yaxis_min = "dataMin",datazoom_type="slider")
layout.add(line)
else:
candle = [[x[1], x[2], x[3], x[4]] for x in b]
candlestick = Kline(array[0] + "-" + array[1], width=width1*10/11,
height=(height1*10/11) / len(items))
candlestick.add(array[0], btime, candle, is_datazoom_show=True, datazoom_type=["slider"])
#if i == 0:
close = zip(*b)[2]
if len(close)>10:
ma10 = calculateMa(close, 10)
line4 = Line(title_color="#C0C0C0")
line4.add(array[0] + "-" + "MA10", btime, ma10)
layout.add(line4)
if len(close)>20:
ma20 = calculateMa(close, 20)
line5 = Line(title_color="#C0C0C0")
line5.add(array[0] + "-" + "MA20", btime, ma20)
layout.add(line5)
if len(close)>30:
ma30 = calculateMa(close, 30)
line6 = Line(title_color="#C0C0C0")
line6.add(array[0] + "-" + "MA30", btime, ma30)
layout.add(line6)
layout.add(candlestick)
page.add(layout)
page.render()
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()
def graphpage(labels,mode_combo,startdate,enddate,optInterval,width1, height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/","-")#convert to tushare readable date
enddate = enddate.replace("/","-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2]!="分笔" and label1[2]!="季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1], start=startdate, end=enddate, autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1], start=startdate, end=enddate, ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc() # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum() #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist() for index, row in re_array.iterrows()) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval, width=width1*10/11, height = (height1*10/11) / len(labels))
kline.add(label1[0], time, data_li, is_datazoom_show=True, datazoom_type="slider", yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:#When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = close
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume':#volume
list_aft = array['volume'].tolist()
else:#amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2], time, list_aft, is_datazoom_show=True, yaxis_max="dataMax",
yaxis_min="dataMin", datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2]=="分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2], time, form, is_datazoom_show=True, datazoom_type="slider", yaxis_min="dataMin", yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price", time, price, yaxis_min="dataMin", yaxis_max="dataMax", is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2]=="季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in range(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东", width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东", names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
