def dataStatistics(request):
supplierNumber = SupplierList.objects.values('supplier_class').annotate(
dcount=Count('supplier_class')).order_by('-dcount')
supplier_count = list(supplierNumber.values_list('dcount'))
supplier_count = [x[0] for x in supplier_count]
supplier_class = list(supplierNumber.values_list('supplier_class'))
supplier_class = [x[0] for x in supplier_class]
chart = Echart('评审供应商数量', 'TaTaTa')
chart.use(Bar('China', supplier_count))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=supplier_class))
supplierNumber = SupplierList.objects.values('position_abbr').annotate(
dcount=Count('position_abbr')).order_by('-dcount')
abbr_count = list(supplierNumber.values_list('dcount'))
abbr_count = [x[0] for x in abbr_count][:10]
position_abbr = list(supplierNumber.values_list('position_abbr'))
position_abbr = [x[0] for x in position_abbr][:10]
chart2 = Echart('供应商分布', 'TaTaTa')
chart2.use(Bar('China', abbr_count))
chart2.use(Legend(['GDP']))
chart2.use(Axis('category', 'bottom', data=position_abbr))
return render(request, 'dataStatistics.html', locals())
def client_x9_load_scenes_draw_bar(filelist):
pname, datas = arrage_logs(filelist)
tags = list()
json_data = list()
# 绘图对象，包含标题设置
chart = Echart(title, subtext)
# 绘图数据
for data in datas:
tags.append(data)
chart.use(Bar(data, datas[data], stack='time', label={'normal':{'show':True}}))
# x轴
chart.use(Axis('category', 'bottom', '设备', pname, axisLabel={'rotate':40,'interval':0}, axisTick={'interval':0}))
# y轴
chart.use(Axis('value', 'left', '秒'))
# 标签，与之相似的还有个叫visualmap，但未实现
chart.use(Legend(tags, top=80))
# 添加工具栏，默认在右上角
chart.use(Toolbox())
# 设置绘图区域的位置，默认上留120，下留100
chart.use(Grid())
chart.json
json_data.append(chart.get_opt())
return json_data
def test_axis():
chart = Echart('Axis', 'Proportion of Browser')
assert not chart.json['xAxis'] and not chart.json['yAxis']
chart.use(Axis('category', 'bottom', 'proportion', inverse=True))
assert chart.json['xAxis']
chart.use(Axis('category', 'left', 'proportion', inverse=True))
assert chart.json['yAxis']
map(repr, chart.x_axis)
def bar():
chart = Echart('GDP', 'This is a fake chart')
chart.use(Bar('China', [2, 3, 4, 5]))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=['Nov', 'Dec', 'Jan',
'Feb']))
return jsonify(chart.json)
def main():
urls = [
'http://www.weather.com.cn/textFC/hz.shtml']
# 'http://www.weather.com.cn/textFC/hb.shtml',
# 'http://www.weather.com.cn/textFC/db.shtml',
# 'http://www.weather.com.cn/textFC/hd.shtml',
# 'http://www.weather.com.cn/textFC/hn.shtml',
# 'http://www.weather.com.cn/textFC/xb.shtml',
# 'http://www.weather.com.cn/textFC/xn.shtml
for url in urls:
get_temperature(url)
time.sleep(2)
SORTED_TEMPERATURE_LIST = sorted(TEMPERATURE_LIST, key=lambda x: int(x["min"]), reverse=True)
TOP20_TEMPERATURE_LIST = SORTED_TEMPERATURE_LIST[0:20]
print(TOP20_TEMPERATURE_LIST)
TOP20_CITY_LIST = []
TOP20_MIN_LIST = []
for city_min in TOP20_TEMPERATURE_LIST:
TOP20_CITY_LIST.append(city_min['city'])
TOP20_MIN_LIST.append(city_min['min'])
# #获取
# 排序问题等到后续解决
echart = Echart(u'全国最高温度排名', u'知了课堂贡献')
bar = Bar(u'最高温度', TOP20_MIN_LIST)
axis = Axis('category', 'bottom', data=TOP20_CITY_LIST)
echart.use(bar)
echart.use(axis)
echart.plot()
def display_temperature():
#url各区为华北,东北,华东,华中,华南,西北,西南
urls = [
'http://www.weather.com.cn/textFC/hb.shtml',
'http://www.weather.com.cn/textFC/db.shtml',
'http://www.weather.com.cn/textFC/hd.shtml',
'http://www.weather.com.cn/textFC/hz.shtml',
'http://www.weather.com.cn/textFC/hn.shtml',
'http://www.weather.com.cn/textFC/xb.shtml',
'http://www.weather.com.cn/textFC/xn.shtml'
]
#循环传各区url参数并执行get气温函数
for url in urls:
get_temperature(url)
time.sleep(2) #每爬一个页面停顿防止过度干扰服务器不友好
#对执行完获得全国最高温进行排序
SORTED_TEMPERATURE_LIST = sorted(TEMPERATURE_LIST,
key=(lambda x: (int(x['max']))),
reverse=True)
#，页面显示不了这么多，故筛选排前20的并定义新变量赋值
TOP20_TEMPERATURE_LIST = SORTED_TEMPERATURE_LIST[0:20]
TOP20_CITY_LIST = []
OP20_MAX_LIST = []
for city_max in TOP20_TEMPERATURE_LIST:
TOP20_CITY_LIST.append(city_max['city'])
TOP20_MAX_LIST.append(city_max['max'])
#用echarts库进行可视化制图
echart = Echart(u'全国最高气温排名TOP1~20')
bar = Bar(u'最高温度', TOP20_MAX_LIST)
axis = Axis('category', 'left', data=TOP20_CITY_LIST)
echart.use(bar)
echart.use(axis)
echart.plot()
def main():
# urls=['http://www.weather.com.cn/textFC/hb.shtml',
# 'http://www.weather.com.cn/textFC/db.shtml',
# 'http://www.weather.com.cn/textFC/hd.shtml',
# 'http://www.weather.com.cn/textFC/hz.shtml',
# 'http://www.weather.com.cn/textFC/hn.shtml',
# 'http://www.weather.com.cn/textFC/xb.shtml',
# 'http://www.weather.com.cn/textFC/xn.shtml',
# ]
# for url in urls:
# get_temperature(url)
# time.sleep(2)
#
# line = json.dumps(TEMPERATUR_LIST, ensure_ascii=False)
# with open('temperature.json','w') as fp:
# fp.write(line.encode('utf-8'))
with open('temperature.json', 'r') as fp:
TEMPERATUR_LIST = json.load(fp, encoding='utf-8')
SORTED_TEMPERATURE_LIST = sorted(
TEMPERATUR_LIST, lambda x, y: cmp(int(y['max']), int(x['max'])))
TOP20_TEMPERATURE_LIST = SORTED_TEMPERATURE_LIST[0:20]
TOP20_CITY_LIST = []
TOP20_MAX_LIST = []
for city_max in TOP20_TEMPERATURE_LIST:
TOP20_CITY_LIST.append(city_max['city'])
TOP20_MAX_LIST.append(city_max['max'])
echart = Echart(u'全国最高温度排名', u'ZG爬虫')
bar = Bar(u'最高温度', TOP20_MAX_LIST)
axis = Axis('category', 'bottom', data=TOP20_CITY_LIST)
echart.use(bar)
echart.use(axis)
echart.plot()
def index(request):
# data = '我的名字叫蒋文华'
# return render(request, 'index.html', {'data': data})
if request.method == 'POST':
form = SqlForm(request.POST)
if form.is_valid():
sql = form.cleaned_data['sql']
title = form.cleaned_data['title']
x = form.cleaned_data['x_label']
y = form.cleaned_data['y_label']
x1, y1 = conn('wboss', 'postgres', '', '10.0.0.141', '5432', sql)
# y1 = [2, 3, 4, 5, 6]
# x1 = ['Nov', 'Dec', 'Jan', 'Feb', 'marth']
chart = Echart(title, '')
chart.use(Bar(y, y1))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=x1, name=x))
chart.use(Tooltip())
chart.plot()
# return HttpResponse(res)
else:
form = SqlForm()
return render(request, 'index.html', {'form': form})
def main():
# target_urls = ['http://www.weather.com.cn/textFC/hb.shtml',
# 'http://www.weather.com.cn/textFC/db.shtml',
# 'http://www.weather.com.cn/textFC/hd.shtml',
# 'http://www.weather.com.cn/textFC/hz.shtml',
# 'http://www.weather.com.cn/textFC/hn.shtml',
# 'http://www.weather.com.cn/textFC/xb.shtml',
# 'http://www.weather.com.cn/textFC/xn.shtml']
#
# all_data = get_temperature(target_urls)
# print(all_data)
# 将数据保存到文件
# with open('temperature.json', 'w', encoding='utf-8') as fw:
# fw.write(json.dumps(all_data))
# 从文件中读取数据
with open('temperature.json', 'r') as fr:
all_data = json.loads(fr.read())
# print(all_data)
# 重新构造list结构
temp_city_list = []
for data in all_data:
provence = data['provence']
city_list = data['city_list']
for city in city_list:
city['city'] = '%s-%s' % (provence, city['city'])
temp_city_list.append(city)
print(temp_city_list)
# 按照最高温度排序
sort_max_temp_list = sorted(
temp_city_list, key=lambda t: int(t.__getitem__('max_temperature')))
# 按照最低温度排序
sort_min_temp_list = sorted(
temp_city_list, key=lambda t: int(t.__getitem__('min_temperature')))
print(sort_max_temp_list)
print(sort_min_temp_list)
# 获取温度最高的10条数据
top10_max = sort_max_temp_list[-10:]
top10_max_city = []
top10_max_temp = []
print(top10_max)
for item in top10_max:
top10_max_city.append(item['city'])
top10_max_temp.append(item['max_temperature'])
# 获取温度最低的10条数据
top10_min = sort_min_temp_list[:10]
# 使用第三方Echarts库在浏览器中输出图形统计界面
echart = Echart(u'全国温度统计')
bar = Bar(u'最高温度', top10_max_temp)
axis = Axis('category', 'bottom', data=top10_max_city)
echart.use(bar)
echart.use(axis)
echart.plot()
def draw(datas, **kwargs):
title = '缺少标题'
if 'title' in kwargs:
title = kwargs['title']
subtext = ''
if 'subtext' in kwargs:
subtext = kwargs['subtext']
# 绘图对象，包含标题设置
chart = Echart(title, subtext)
# 绘图数据
tags = list()
for data in datas:
tags.append(data)
chart.use(Line(data, datas[data]))
x_name = ''
y_name = ''
x_line = []
if 'label' in kwargs:
x_name = kwargs['label'][0]
y_name = kwargs['label'][1]
x_line = kwargs['label'][2]
# x轴
chart.use(Axis('category', 'bottom', x_name, data=x_line))
# y轴
chart.use(Axis('value', 'left', y_name))
# 标签
t_top = 60
if 'top' in kwargs:
t_top = kwargs['top']
chart.use(Legend(tags, top=t_top))
# 添加工具栏
chart.use(Toolbox())
# 设置绘图区域的位置，默认上留120，下留100
chart.use(Grid())
# tooltip
chart.use(Tooltip())
# 打开浏览器并绘图
# chart.plot()
return chart.get_opt()
def bar():
# set bar color
color = dict(color='#EE5500')
chart = Echart('GDP', 'This is a fake chart')
chart.use(Bar('China', [2, 3, 4, 5], itemStyle=dict(normal=color)))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=['Nov', 'Dec', 'Jan',
'Feb']))
return jsonify(chart.json)
def xx(self, data):
_PR = PR.getInstance()
chart = Echart('GDP', 'This is a fake chart')
chart.use(Bar('China', [2, 3, 4, 5]))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=['Nov', 'Dec', 'Jan',
'Feb']))
_chart = chart.json
print(_chart)
_chart["tooltip"] = {}
_PR.setResult(_chart)
return _PR.getPRBytes()
def data_visualization(temperature_list):
top20_temperature_list = sorted(
temperature_list, lambda x, y: cmp(int(x['min']), int(y['min'])))[0:20]
city20_list = []
min20_list = []
for city_min in top20_temperature_list:
city20_list.append(city_min['city'])
min20_list.append(city_min['min'])
echart_show = Echart(u'全国最低温度排名', u'小白打酱油提供', u'这是气温排名')
bar = Bar(u'最低温度', min20_list)
axis = Axis('category', 'bottom', data=city20_list)
echart_show.use(bar)
echart_show.use(axis)
echart_show.plot()
def main():
urls = [
'http://www.weather.com.cn/textFC/hb.shtml',
'http://www.weather.com.cn/textFC/db.shtml',
'http://www.weather.com.cn/textFC/hd.shtml',
'http://www.weather.com.cn/textFC/hz.shtml',
'http://www.weather.com.cn/textFC/hn.shtml',
'http://www.weather.com.cn/textFC/xb.shtml',
'http://www.weather.com.cn/textFC/xn.shtml',
]
for url in urls:
get_temperature(url)
time.sleep(2)
echarts = Echart(u'温度排名')
bar_x = Bar(u'最低温度', MIN_LIST)
bar_y = Bar(u'最高温度', MAX_LIST)
axis = Axis('category', 'bottom', data=CITY_LIST)
echarts.use(bar_x)
echarts.use(bar_y)
echarts.use(axis)
echarts.plot()
def main():
urls=['http://www.weather.com.cn/textFC/hb.shtml',
'http://www.weather.com.cn/textFC/db.shtml',
'http://www.weather.com.cn/textFC/hd.shtml']
for url in urls:
get_temperature(url)
#top10_max=max_list[0:10]
#top10_cit=city_list[0:10]
sorted_temprature_list=sorted(temperature_list,lambda x,y:cmp(int(x['max']),int(y['max'])))
top10_temprature_list=sorted_temprature_list[0:8]
top10_city_list=[]
top10_max_list=[]
for city_max in top10_temprature_list:
top10_city_list.append(city_max['city'])
top10_max_list.append(city_max['max'])
print city_max['city'],city_max['max']
echart=Echart(u'全国最高温度',u'3.19号')
bar=Bar(u'最高温度',top10_max_list)
axis=Axis('category','bottom',data=top10_city_list)
echart.use(bar)
echart.use(axis)
echart.plot()
def main():
urls = ['http://www.weather.com.cn/textFC/hb.shtml',
'http://www.weather.com.cn/textFC/db.shtml',
'http://www.weather.com.cn/textFC/hd.shtml',
'http://www.weather.com.cn/textFC/hz.shtml',
'http://www.weather.com.cn/textFC/hn.shtml',
'http://www.weather.com.cn/textFC/xb.shtml',
'http://www.weather.com.cn/textFC/xn.shtml']
for url in urls:
get_temperature(url)
time.sleep(2)
TOP20_MIN_LIST = MINTEMP_LIST[0:20]
TOP20_CITY_LIST = CITY_LIST[0:20]
print(TOP20_CITY_LIST)
echart = Echart('全国最低温度统计', 'Author: AlexZ33')
bar = Bar('最低温度', TOP20_MIN_LIST)
axis = Axis('category', 'bottom', data = TOP20_CITY_LIST)
echart.use(bar)
echart.use(axis)
# echart.use(Legend(['℃']))
echart.plot()
import json
from echarts import Echart, Legend, Bar, Axis
with open(u'温度数据.json', 'r') as fr:
data = json.load(fr, encoding='utf-8')
city_list = []
min_list = []
sorted_list = sorted(data,
lambda x, y: cmp(int(x['C_min']), int(y['C_min'])),
reverse=True)
for item in sorted_list[:20]:
city_list.append(item[u'城市'])
min_list.append(item['C_min'])
#print item[u'省份'], item[u'城市'],item['C_min']
chart = Echart(u'全国TOP20温度', u'全国温度最高的前20个市')
chart.use(Bar(u'temp', min_list))
chart.use(Axis('category', 'bottom', 'map', city_list))
chart.plot()
# line = line + 1
# if line==32:
# html.write('data: %s\n' % valueList)
# if line==26:
# html.write('data: [')
# for str in nameList:
# html.write('\"%s\",' % str.encode('utf-8'))
# html.write(']\n')
# else:
# html.write(s)
# 用echart画图
chart = Echart(u'WeChat 好友省份分布图', 'data from xuqidong')
chart.use(Bar('China', valueList))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'left', data=nameList))
chart.plot()
# 获取好友列表
# friends = itchat.get_friends(update=True)[0:]
# 初始化计数器，有男有女，当然，有些人是不填的
male = female = other = 0
# 遍历这个列表，列表里第一位是自己，所以从"自己"之后开始计算
# 1表示男性，2女性
for i in friends[1:]:
sex = i["Sex"]
if sex == 1:
male += 1
elif sex == 2:
female += 1
else:
con = sqlite3.connect("database.sqlite", isolation_level=None)
cur = con.cursor()
age_set = {}
name = '%iniesta%'
#today = date.today()
cur.execute(
'select short_passing,long_passing,strength,vision,dribbling,balance,positioning from Player inner join Player_Attributes on Player.player_api_id = Player_Attributes.player_api_id where player_name like ? group by player_name',
(name, ))
i = list(cur.fetchone())
iniesta_attrib = {
"short_passing": i[0],
"long_passing": i[1],
"strength": i[2],
"vision": i[3],
"dribbling": i[4],
"balance": i[5],
"positioning": i[6]
}
#labels = ["short_passing","long_passing","strength","vision","dribbling","balance","positioning"]
p_val = list(iniesta_attrib.values())
p_key = list(iniesta_attrib.keys())
print p_val
xv = plt.DataFrame({'val': p_val, 'keyt': p_key})
chart = Echart('Andres Iniesta', 'Mid Fielder Attributes')
#chart.use(Bar('Out of 100', p_val))
chart.use(Line('Out of 100', p_val))
chart.use(Legend(['Andres Iniesta']))
chart.use(Axis('category', 'bottom', data=p_key))
chart.plot()
def dataStatistics(request):
chart = Echart('GDP', 'This is a fake chart')
chart.use(Bar('China', [2, 3, 4, 5]))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=['Nov', 'Dec', 'Jan', 'Feb']))
return render(request, 'dataStatistics.html', locals())
'xn.shtml'
]
for area in area_list:
city_temp(url + area)
time.sleep(20)
line = json.dumps(city_tem_list, ensure_ascii=False)
with open('tem.json', 'w') as fp:
fp.write(line.encode('utf-8'))
with open('tem.json', 'r') as fp:
city_tem_list = json.load(fp, encoding='utf-8')
sort_max_list = sorted(city_tem_list,
lambda x, y: cmp(int(x['tem']), int(y['tem'])),
reverse=True)
top10_sort_list = sort_max_list[0:10]
top10_tem_list = []
top10_city_list = []
for top10_sort in top10_sort_list:
top10_city_list.append(top10_sort['city'])
top10_tem_list.append(top10_sort['tem'])
echart = Echart('the order of high tem', 'SSKeng study')
bar = Bar('the highest tem', top10_tem_list)
axis = Axis('category', 'bottom', data=top10_city_list)
echart.use(bar)
echart.use(axis)
echart.plot()
fp.close()
from echarts import Echart, Legend, Bar, Axis
bars = "[['2015-10-16', 18.4, 18.58, 18.33, 18.79, 67.00, 1, 0.04, 0.11, 0.09], ['2015-10-19', 18.56, 18.25, 18.19, 18.56, 55.00, 0, -0.00, 0.08, 0.09], ['2015-10-20', 18.3, 18.22, 18.05, 18.41, 37.00, 0, 0.01, 0.09, 0.09], ['2015-10-21', 18.18, 18.69, 18.02, 18.98, 89.00, 0, 0.03, 0.10, 0.08], ['2015-10-22', 18.42, 18.29, 18.22, 18.48, 43.00, 0, -0.06, 0.05, 0.08], ['2015-10-23', 18.26, 18.19, 18.08, 18.36, 46.00, 0, -0.10, 0.03, 0.09], ['2015-10-26', 18.33, 18.07, 17.98, 18.35, 65.00, 0, -0.15, 0.03, 0.10], ['2015-10-27', 18.08, 18.04, 17.88, 18.13, 37.00, 0, -0.19, 0.03, 0.12], ['2015-10-28', 17.96, 17.86, 17.82, 17.99, 35.00, 0, -0.24, 0.03, 0.15], ['2015-10-29', 17.85, 17.81, 17.8, 17.93, 27.00, 0, -0.24, 0.06, 0.18], ['2015-10-30', 17.79, 17.93, 17.78, 18.08, 43.00, 0, -0.22, 0.11, 0.22], ['2015-11-02', 17.78, 17.83, 17.78, 18.04, 27.00, 0, -0.20, 0.15, 0.25], ['2015-11-03', 17.84, 17.9, 17.84, 18.06, 34.00, 0, -0.12, 0.22, 0.28], ['2015-11-04', 17.97, 18.36, 17.85, 18.39, 62.00, 0, -0.00, 0.30, 0.30], ['2015-11-05', 18.3, 18.57, 18.18, 19.08, 177.00, 0, 0.07, 0.33, 0.30], ['2015-11-06', 18.53, 18.68, 18.3, 18.71, 95.00, 0, 0.12, 0.35, 0.29], ['2015-11-09', 18.75, 19.08, 18.75, 19.98, 202.00, 1, 0.16, 0.35, 0.27], ['2015-11-10', 18.85, 18.64, 18.56, 18.99, 85.00, 0, 0.09, 0.29, 0.25], ['2015-11-11', 18.64, 18.44, 18.31, 18.64, 50.00, 0, 0.06, 0.27, 0.23], ['2015-11-12', 18.55, 18.27, 18.17, 18.57, 43.00, 0, 0.05, 0.25, 0.23], ['2015-11-13', 18.13, 18.14, 18.09, 18.34, 35.00, 0, 0.05, 0.24, 0.22], ['2015-11-16', 18.01, 18.1, 17.93, 18.17, 34.00, 0, 0.07, 0.25, 0.21], ['2015-11-17', 18.2, 18.14, 18.08, 18.45, 58.00, 0, 0.11, 0.25, 0.20], ['2015-11-18', 18.23, 18.16, 18.0, 18.45, 47.00, 0, 0.13, 0.25, 0.19], ['2015-11-19', 18.08, 18.2, 18.05, 18.25, 32.00, 0, 0.15, 0.24, 0.17], ['2015-11-20', 18.15, 18.15, 18.11, 18.29, 36.00, 0, 0.13, 0.21, 0.15], ['2015-11-23', 18.16, 18.19, 18.12, 18.34, 47.00, 0, 0.11, 0.18, 0.13], ['2015-11-24', 18.23, 17.88, 17.7, 18.23, 62.00, 0, 0.03, 0.13, 0.11], ['2015-11-25', 17.85, 17.73, 17.56, 17.85, 66.00, 0, -0.03, 0.09, 0.11], ['2015-11-26', 17.79, 17.53, 17.5, 17.92, 63.00, 0, -0.10, 0.06, 0.11], ['2015-11-27', 17.51, 17.04, 16.9, 17.51, 67.00, 0, -0.16, 0.05, 0.13], ['2015-11-30', 17.07, 17.2, 16.98, 17.32, 55.00, 0, -0.12, 0.09, 0.15], ['2015-12-01', 17.28, 17.11, 16.91, 17.28, 39.00, 0, -0.09, 0.12, 0.16], ['2015-12-02', 17.13, 17.91, 17.05, 17.99, 102.00, 0, -0.01, 0.17, 0.18], ['2015-12-03', 17.8, 17.78, 17.61, 17.98, 71.00, 0, -0.09, 0.14, 0.18], ['2015-12-04', 17.6, 17.25, 17.13, 17.69, 51.00, 0, -0.18, 0.10, 0.19], ['2015-12-07', 17.2, 17.39, 17.15, 17.45, 43.00, 0, -0.19, 0.12, 0.22], ['2015-12-08', 17.3, 17.42, 17.18, 17.62, 45.00, 0, -0.23, 0.13, 0.24], ['2015-12-09', 17.33, 17.39, 17.32, 17.59, 44.00, 0, -0.29, 0.13, 0.28], ['2015-12-10', 17.39, 17.26, 17.21, 17.65, 44.00, 0, -0.37, 0.13, 0.32], ['2015-12-11', 17.23, 16.92, 16.66, 17.26, 114.00, 1, -0.44, 0.15, 0.37], ['2015-12-14', 16.75, 17.06, 16.5, 17.09, 94.00, 0, -0.44, 0.21, 0.44], ['2015-12-15', 17.03, 17.03, 16.9, 17.06, 46.00, 0, -0.44, 0.28, 0.50], ['2015-12-16', 17.08, 16.96, 16.87, 17.09, 30.00, 0, -0.40, 0.36, 0.56], ['2015-12-17', 17.0, 17.1, 16.95, 17.12, 50.00, 0, -0.30, 0.47, 0.62], ['2015-12-18', 17.09, 17.52, 17.04, 18.06, 156.00, 0, -0.14, 0.59, 0.66], ['2015-12-21', 17.43, 18.23, 17.35, 18.45, 152.00, 1, 0.02, 0.69, 0.68], ['2015-12-22', 18.14, 18.27, 18.06, 18.32, 94.00, 0, 0.08, 0.72, 0.68], ['2015-12-23', 18.28, 18.19, 18.17, 18.71, 108.00, 0, 0.13, 0.73, 0.67], ['2015-12-24', 18.18, 18.14, 18.01, 18.31, 37.00, 0, 0.19, 0.74, 0.65], ['2015-12-25', 18.22, 18.33, 18.2, 18.36, 48.00, 0, 0.26, 0.75, 0.62], ['2015-12-28', 18.35, 17.84, 17.8, 18.39, 48.00, 0, 0.27, 0.72, 0.59], ['2015-12-29', 17.83, 17.94, 17.71, 17.97, 36.00, 0, 0.36, 0.73, 0.55], ['2015-12-30', 17.9, 18.26, 17.55, 18.3, 71.00, 1, 0.43, 0.71, 0.50], ['2015-12-31', 18.12, 17.99, 17.91, 18.33, 72.00, 0, 0.40, 0.63, 0.43], ['2016-01-04', 17.91, 17.28, 17.16, 17.95, 37.00, 1, 0.34, 0.55, 0.38], ['2016-01-05', 17.17, 17.23, 17.0, 17.55, 51.00, 0, 0.37, 0.51, 0.33], ['2016-01-06', 17.2, 17.31, 17.06, 17.33, 31.00, 0, 0.37, 0.46, 0.28], ['2016-01-07', 17.15, 16.67, 16.51, 17.15, 19.00, 0, 0.30, 0.37, 0.22], ['2016-01-08', 16.8, 16.81, 16.61, 17.06, 60.00, 0, 0.29, 0.32, 0.18], ['2016-01-11', 16.68, 16.04, 16.0, 16.68, 65.00, 0, 0.20, 0.24, 0.14], ['2016-01-12', 16.03, 15.98, 15.88, 16.25, 46.00, 0, 0.20, 0.21, 0.11], ['2016-01-13', 16.21, 15.87, 15.78, 16.21, 57.00, 0, 0.20, 0.18, 0.08], ['2016-01-14', 15.55, 15.89, 15.52, 15.96, 42.00, 0, 0.20, 0.16, 0.05], ['2016-01-15', 15.87, 15.48, 15.45, 15.92, 34.00, 1, 0.17, 0.11, 0.02], ['2016-01-18', 15.39, 15.42, 15.36, 15.7, 26.00, 0, 0.21, 0.10, -0.00], ['2016-01-19', 15.58, 15.71, 15.35, 15.77, 38.00, 0, 0.25, 0.09, -0.03], ['2016-01-20', 15.56, 15.52, 15.24, 15.68, 38.00, 0, 0.23, 0.05, -0.07], ['2016-01-21', 15.41, 15.3, 15.28, 15.68, 35.00, 0, 0.21, 0.00, -0.10], ['2016-01-22', 15.48, 15.28, 15.13, 15.49, 30.00, 0, 0.21, -0.02, -0.13], ['2016-01-25', 15.29, 15.48, 15.2, 15.49, 21.00, 0, 0.20, -0.06, -0.16], ['2016-01-26', 15.33, 14.86, 14.78, 15.39, 30.00, 0, 0.12, -0.13, -0.19], ['2016-01-27', 14.96, 15.0, 14.84, 15.22, 51.00, 0, 0.13, -0.14, -0.20], ['2016-01-28', 14.96, 14.72, 14.62, 15.06, 25.00, 0, 0.10, -0.17, -0.22], ['2016-01-29', 14.75, 14.99, 14.62, 15.08, 36.00, 0, 0.13, -0.17, -0.24], ['2016-02-01', 14.98, 14.72, 14.48, 15.18, 27.00, 0, 0.10, -0.21, -0.26], ['2016-02-02', 14.65, 14.85, 14.65, 14.95, 18.00, 0, 0.11, -0.21, -0.27], ['2016-02-03', 14.72, 14.67, 14.55, 14.8, 23.00, 0, 0.10, -0.24, -0.29], ['2016-02-04', 14.79, 14.88, 14.69, 14.93, 22.00, 0, 0.13, -0.24, -0.30], ['2016-02-05', 14.9, 14.86, 14.78, 14.93, 16.00, 0, 0.12, -0.26, -0.32], ['2016-02-15', 14.5, 14.66, 14.47, 14.82, 19.00, 0, 0.11, -0.28, -0.34], ['2016-02-16', 14.77, 14.94, 14.72, 15.05, 26.00, 0, 0.14, -0.28, -0.35], ['2016-02-17', 14.95, 15.03, 14.88, 15.07, 38.00, 0, 0.12, -0.31, -0.37], ['2016-02-18', 14.95, 14.9, 14.87, 15.06, 28.00, 0, 0.07, -0.35, -0.39], ['2016-02-19', 14.9, 14.75, 14.68, 14.94, 22.00, 0, 0.03, -0.38, -0.40], ['2016-02-22', 14.88, 15.01, 14.79, 15.11, 38.00, 1, 0.01, -0.40, -0.40], ['2016-02-23', 15.01, 14.83, 14.72, 15.01, 24.00, 0, -0.09, -0.45, -0.40], ['2016-02-24', 14.75, 14.81, 14.67, 14.87, 21.00, 0, -0.17, -0.48, -0.39], ['2016-02-25', 14.81, 14.25, 14.21, 14.81, 51.00, 1, -0.27, -0.50, -0.37], ['2016-02-26', 14.35, 14.45, 14.28, 14.57, 28.00, 0, -0.26, -0.46, -0.33], ['2016-02-29', 14.43, 14.56, 14.04, 14.6, 48.00, 0, -0.25, -0.41, -0.29], ['2016-03-01', 14.56, 14.65, 14.36, 14.78, 32.00, 0, -0.21, -0.36, -0.25], ['2016-03-02', 14.79, 14.96, 14.72, 14.97, 60.00, 0, -0.13, -0.29, -0.22], ['2016-03-03', 14.95, 15.15, 14.91, 15.19, 53.00, 1, -0.05, -0.23, -0.21], ['2016-03-04', 15.14, 15.97, 15.02, 16.02, 164.00, 1, 0.06, -0.17, -0.20], ['2016-03-07', 15.9, 15.78, 15.65, 16.0, 41.00, 0, 0.04, -0.19, -0.21], ['2016-03-08', 15.78, 15.96, 15.21, 15.99, 45.00, 0, 0.05, -0.19, -0.21], ['2016-03-09', 15.73, 16.05, 15.41, 16.08, 74.00, 0, 0.03, -0.20, -0.22], ['2016-03-10', 15.82, 15.66, 15.65, 15.98, 19.00, 0, -0.02, -0.23, -0.22], ['2016-03-11', 15.59, 15.76, 15.42, 15.78, 32.00, 0, 0.01, -0.22, -0.22], ['2016-03-14', 15.78, 15.72, 15.65, 16.04, 31.00, 0, 0.03, -0.20, -0.22], ['2016-03-15', 15.81, 15.86, 15.6, 15.99, 35.00, 0, 0.10, -0.18, -0.23], ['2016-03-16', 15.88, 16.42, 15.79, 16.45, 123.00, 0, 0.17, -0.16, -0.24], ['2016-03-17', 16.39, 16.23, 16.11, 16.4, 46.00, 0, 0.14, -0.20, -0.26], ['2016-03-18', 16.39, 16.17, 16.04, 16.4, 59.00, 0, 0.13, -0.22, -0.28], ['2016-03-21', 16.21, 16.22, 16.11, 16.44, 50.00, 0, 0.12, -0.24, -0.30], ['2016-03-22', 16.27, 16.19, 16.16, 16.42, 33.00, 0, 0.10, -0.27, -0.32], ['2016-03-23', 16.26, 16.18, 16.18, 16.29, 19.00, 0, 0.08, -0.30, -0.33], ['2016-03-24', 16.18, 16.11, 16.01, 16.23, 23.00, 0, 0.04, -0.33, -0.35], ['2016-03-25', 16.12, 16.13, 16.1, 16.2, 15.00, 0, 0.00, -0.35, -0.35], ['2016-03-28', 16.15, 15.85, 15.81, 16.2, 22.00, 0, -0.06, -0.38, -0.35], ['2016-03-29', 15.9, 15.79, 15.76, 16.05, 19.00, 0, -0.06, -0.37, -0.34], ['2016-03-30', 15.79, 16.24, 15.78, 16.3, 29.00, 0, -0.03, -0.35, -0.33], ['2016-03-31', 16.3, 16.09, 16.02, 16.35, 25.00, 0, -0.07, -0.37, -0.33], ['2016-04-01', 16.18, 16.27, 15.98, 16.3, 38.00, 0, -0.08, -0.36, -0.32], ['2016-04-05', 16.13, 16.34, 16.07, 16.37, 39.00, 0, -0.13, -0.37, -0.31], ['2016-04-06', 16.21, 16.26, 16.19, 16.35, 30.00, 0, -0.20, -0.39, -0.29], ['2016-04-07', 16.32, 16.1, 16.05, 16.35, 29.00, 1, -0.26, -0.39, -0.26], ['2016-04-08', 16.0, 16.16, 15.98, 16.21, 22.00, 0, -0.28, -0.37, -0.23], ['2016-04-11', 16.16, 16.31, 16.15, 16.57, 31.00, 0, -0.30, -0.33, -0.19], ['2016-04-12', 16.41, 16.29, 16.12, 16.41, 17.00, 0, -0.31, -0.30, -0.14], ['2016-04-13', 16.39, 16.48, 16.0, 16.68, 40.00, 0, -0.30, -0.25, -0.10], ['2016-04-14', 16.5, 16.46, 16.37, 16.68, 22.00, 0, -0.27, -0.19, -0.06], ['2016-04-15', 16.56, 16.93, 16.46, 17.04, 58.00, 0, -0.20, -0.12, -0.02], ['2016-04-18', 16.76, 17.06, 16.72, 17.27, 50.00, 0, -0.16, -0.07, 0.01], ['2016-04-19', 17.21, 17.11, 17.02, 17.23, 30.00, 0, -0.12, -0.02, 0.03], ['2016-04-20', 17.11, 17.33, 16.8, 17.36, 78.00, 0, -0.04, 0.03, 0.05], ['2016-04-21', 17.27, 17.69, 17.17, 17.93, 79.00, 0, 0.05, 0.08, 0.06], ['2016-04-22', 17.6, 17.87, 17.56, 18.02, 55.00, 0, 0.09, 0.10, 0.05], ['2016-04-25', 17.75, 17.9, 17.41, 17.96, 39.00, 1, 0.11, 0.09, 0.04], ['2016-04-26', 17.81, 17.91, 17.6, 17.95, 39.00, 0, 0.12, 0.08, 0.02], ['2016-04-27', 17.9, 17.88, 17.81, 17.95, 25.00, 0, 0.12, 0.06, 0.00], ['2016-04-28', 17.93, 17.88, 17.67, 17.93, 28.00, 0, 0.11, 0.04, -0.01], ['2016-04-29', 17.87, 17.75, 17.73, 17.92, 19.00, 0, 0.08, 0.01, -0.03], ['2016-05-03', 17.79, 17.7, 17.56, 17.85, 35.00, 0, 0.05, -0.01, -0.04], ['2016-05-04', 17.7, 17.65, 17.59, 17.71, 24.00, 0, 0.02, -0.04, -0.05], ['2016-05-05', 17.65, 17.62, 17.46, 17.7, 20.00, 0, -0.03, -0.06, -0.05], ['2016-05-06', 17.62, 17.32, 17.3, 17.65, 29.00, 0, -0.10, -0.09, -0.05], ['2016-05-09', 17.33, 17.3, 17.21, 17.45, 23.00, 0, -0.13, -0.10, -0.03], ['2016-05-10', 17.11, 17.04, 16.98, 17.41, 28.00, 0, -0.15, -0.09, -0.01], ['2016-05-11', 17.06, 17.15, 17.06, 17.32, 20.00, 0, -0.12, -0.05, 0.01], ['2016-05-12', 17.02, 17.46, 17.02, 17.58, 26.00, 0, -0.07, -0.01, 0.03], ['2016-05-13', 17.41, 17.57, 17.34, 17.62, 23.00, 0, -0.06, 0.01, 0.03], ['2016-05-16', 17.55, 17.5, 17.48, 17.64, 37.00, 0, -0.06, 0.01, 0.04], ['2016-05-17', 17.49, 17.48, 17.39, 17.53, 13.00, 0, -0.03, 0.03, 0.05], ['2016-05-18', 17.41, 17.82, 17.39, 17.87, 46.00, 0, 0.01, 0.06, 0.06], ['2016-05-19', 17.74, 17.81, 17.67, 17.86, 17.00, 0, -0.01, 0.05, 0.05], ['2016-05-20', 17.76, 17.88, 17.7, 17.93, 14.00, 0, -0.03, 0.04, 0.06], ['2016-05-23', 17.88, 17.52, 17.48, 17.97, 16.00, 0, -0.09, 0.02, 0.06], ['2016-05-24', 17.51, 17.33, 17.32, 17.51, 8.00, 0, -0.09, 0.03, 0.07], ['2016-05-25', 17.59, 17.55, 17.44, 17.59, 10.00, 0, -0.03, 0.07, 0.08], ['2016-05-26', 17.5, 17.69, 17.5, 17.8, 12.00, 0, 0.00, 0.09, 0.09], ['2016-05-27', 17.77, 17.66, 17.62, 17.77, 7.00, 0, 0.03, 0.10, 0.09], ['2016-05-30', 17.75, 17.84, 17.62, 17.87, 20.00, 0, 0.08, 0.12, 0.08], ['2016-05-31', 17.88, 18.0, 17.81, 18.03, 41.00, 0, 0.10, 0.12, 0.07], ['2016-06-01', 18.09, 17.83, 17.73, 18.09, 22.00, 0, 0.08, 0.10, 0.06], ['2016-06-02', 17.82, 17.73, 17.66, 17.88, 10.00, 0, 0.07, 0.08, 0.05], ['2016-06-03', 17.8, 17.78, 17.71, 17.83, 9.00, 0, 0.08, 0.08, 0.04], ['2016-06-06', 17.73, 17.64, 17.56, 17.83, 12.00, 0, 0.07, 0.06, 0.03], ['2016-06-07', 17.76, 17.8, 17.59, 17.87, 11.00, 0, 0.08, 0.06, 0.02], ['2016-06-08', 17.75, 17.77, 17.65, 17.84, 9.00, 0, 0.04, 0.03, 0.01], ['2016-06-13', 17.58, 17.32, 17.29, 17.79, 16.00, 0, -0.02, -0.01, 0.00], ['2016-06-14', 17.33, 17.38, 17.29, 17.5, 10.00, 0, -0.01, 0.00, 0.00], ['2016-06-15', 17.25, 17.39, 17.25, 17.46, 18.00, 0, 0.00, 0.01, 0.00], ['2016-06-16', 17.26, 17.4, 17.26, 17.46, 22.00, 0, 0.01, 0.01, 0.00], ['2016-06-17', 17.38, 17.5, 17.37, 17.67, 13.00, 0, 0.03, 0.02, 0.00], ['2016-06-20', 17.62, 17.51, 17.42, 17.63, 15.00, 0, 0.03, 0.01, -0.00], ['2016-06-21', 17.53, 17.54, 17.5, 17.7, 11.00, 0, 0.02, 0.00, -0.01], ['2016-06-22', 17.5, 17.5, 17.46, 17.6, 10.00, 0, -0.01, -0.01, -0.01], ['2016-06-23', 17.52, 17.26, 17.24, 17.53, 16.00, 0, -0.04, -0.03, -0.01], ['2016-06-24', 17.26, 17.25, 17.18, 17.38, 60.00, 0, -0.03, -0.02, -0.00], ['2016-06-27', 17.25, 17.28, 17.18, 17.33, 19.00, 0, -0.01, -0.00, 0.00], ['2016-06-28', 17.25, 17.29, 17.21, 17.32, 13.00, 0, 0.02, 0.01, 0.00], ['2016-06-29', 17.31, 17.45, 17.27, 17.49, 21.00, 0, 0.07, 0.04, 0.00], ['2016-06-30', 17.47, 17.5, 17.39, 17.55, 17.00, 0, 0.11, 0.04, -0.01], ['2016-07-01', 17.5, 17.63, 17.49, 17.66, 10.00, 0, 0.14, 0.05, -0.03], ['2016-07-04', 17.63, 17.72, 17.63, 17.92, 17.00, 0, 0.16, 0.03, -0.05], ['2016-07-05', 17.79, 17.56, 17.45, 17.79, 18.00, 0, 0.14, 0.00, -0.07], ['2016-07-06', 17.53, 17.42, 17.31, 17.54, 20.00, 0, 0.14, -0.02, -0.09], ['2016-07-07', 17.41, 17.51, 17.35, 17.52, 15.00, 0, 0.16, -0.03, -0.11], ['2016-07-08', 17.5, 17.39, 17.35, 17.51, 15.00, 0, 0.16, -0.05, -0.13], ['2016-07-11', 17.49, 17.48, 17.4, 17.55, 16.00, 0, 0.17, -0.07, -0.15], ['2016-07-12', 17.48, 17.71, 17.46, 17.75, 25.00, 0, 0.16, -0.10, -0.18], ['2016-07-13', 17.13, 17.05, 17.02, 17.39, 28.00, 0, 0.07, -0.17, -0.20], ['2016-07-14', 17.07, 17.09, 17.0, 17.16, 12.00, 0, 0.08, -0.17, -0.21], ['2016-07-15', 17.08, 17.14, 17.08, 17.17, 11.00, 0, 0.09, -0.18, -0.22], ['2016-07-18', 17.15, 17.26, 17.13, 17.49, 24.00, 0, 0.10, -0.19, -0.23], ['2016-07-19', 17.26, 17.12, 17.09, 17.33, 13.00, 0, 0.07, -0.21, -0.25], ['2016-07-20', 17.1, 17.07, 17.02, 17.14, 11.00, 0, 0.06, -0.23, -0.26], ['2016-07-21', 17.07, 17.24, 17.07, 17.27, 14.00, 0, 0.07, -0.23, -0.27], ['2016-07-22', 17.25, 17.08, 17.03, 17.25, 10.00, 0, 0.04, -0.26, -0.28], ['2016-07-25', 17.09, 17.12, 17.01, 17.18, 8.00, 0, 0.04, -0.26, -0.28], ['2016-07-26', 17.05, 17.17, 17.05, 17.2, 11.00, 0, 0.04, -0.27, -0.29], ['2016-07-27', 17.2, 17.37, 16.89, 17.38, 32.00, 0, 0.02, -0.28, -0.29], ['2016-07-28', 17.19, 17.14, 17.09, 17.29, 19.00, 0, -0.04, -0.32, -0.30], ['2016-07-29', 17.15, 17.16, 17.04, 17.23, 12.00, 0, -0.08, -0.33, -0.29], ['2016-08-01', 17.15, 17.18, 17.1, 17.24, 19.00, 0, -0.13, -0.34, -0.28], ['2016-08-02', 17.21, 17.15, 17.12, 17.25, 9.00, 0, -0.19, -0.36, -0.26], ['2016-08-03', 17.08, 17.07, 17.01, 17.16, 9.00, 0, -0.25, -0.36, -0.24], ['2016-08-04', 17.11, 17.06, 16.98, 17.12, 11.00, 1, -0.29, -0.35, -0.20], ['2016-08-05', 17.06, 17.1, 17.05, 17.15, 16.00, 0, -0.33, -0.32, -0.16], ['2016-08-08', 17.14, 17.13, 17.07, 17.15, 13.00, 0, -0.35, -0.29, -0.11], ['2016-08-09', 17.13, 17.17, 17.1, 17.2, 25.00, 0, -0.35, -0.24, -0.06], ['2016-08-10', 17.17, 17.28, 17.15, 17.29, 18.00, 0, -0.31, -0.17, -0.01], ['2016-08-11', 17.3, 17.45, 17.26, 17.87, 31.00, 0, -0.24, -0.09, 0.03], ['2016-08-12', 17.51, 17.99, 17.47, 18.0, 44.00, 0, -0.14, -0.00, 0.07], ['2016-08-15', 18.1, 18.42, 18.02, 18.99, 81.00, 0, -0.09, 0.04, 0.09], ['2016-08-16', 18.64, 18.31, 18.12, 18.87, 60.00, 0, -0.10, 0.05, 0.10], ['2016-08-17', 18.43, 18.4, 18.31, 18.68, 21.00, 0, -0.08, 0.08, 0.11], ['2016-08-18', 18.33, 18.23, 18.13, 18.65, 32.00, 0, -0.07, 0.09, 0.13], ['2016-08-19', 18.34, 18.62, 18.31, 18.75, 39.00, 0, 0.00, 0.14, 0.14], ['2016-08-22', 18.62, 18.69, 18.51, 18.8, 20.00, 0, 0.01, 0.14, 0.13], ['2016-08-23', 18.61, 18.66, 18.52, 19.0, 28.00, 0, 0.01, 0.14, 0.13], ['2016-08-24', 18.66, 18.62, 18.43, 18.7, 19.00, 0, 0.00, 0.13, 0.13], ['2016-08-25', 18.57, 18.51, 18.19, 18.64, 19.00, 0, -0.00, 0.13, 0.13], ['2016-08-26', 18.49, 18.55, 18.44, 18.6, 16.00, 0, 0.01, 0.13, 0.13], ['2016-08-29', 18.46, 18.27, 18.03, 18.48, 20.00, 0, 0.01, 0.13, 0.13], ['2016-08-30', 18.24, 18.44, 18.23, 18.52, 19.00, 0, 0.07, 0.17, 0.13], ['2016-08-31', 18.36, 18.63, 18.36, 18.76, 15.00, 0, 0.13, 0.18, 0.12], ['2016-09-01', 18.6, 18.62, 18.55, 18.78, 15.00, 0, 0.16, 0.18, 0.10], ['2016-09-02', 18.52, 18.68, 18.48, 18.72, 17.00, 0, 0.19, 0.17, 0.08], ['2016-09-05', 18.68, 18.75, 18.57, 18.82, 19.00, 0, 0.20, 0.15, 0.05], ['2016-09-06', 18.75, 18.51, 18.43, 18.78, 17.00, 0, 0.18, 0.11, 0.02], ['2016-09-07', 18.51, 18.56, 18.4, 18.62, 17.00, 0, 0.17, 0.08, -0.00], ['2016-09-08', 18.58, 18.53, 18.48, 18.63, 8.00, 0, 0.13, 0.04, -0.03], ['2016-09-09', 18.52, 18.33, 18.31, 18.57, 8.00, 0, 0.06, -0.02, -0.05], ['2016-09-12', 18.16, 17.9, 17.81, 18.18, 28.00, 0, -0.02, -0.07, -0.06], ['2016-09-13', 17.91, 17.91, 17.9, 18.08, 13.00, 0, -0.05, -0.08, -0.05], ['2016-09-14', 17.99, 17.54, 17.48, 17.99, 22.00, 0, -0.09, -0.09, -0.05], ['2016-09-19', 17.55, 17.81, 17.55, 17.88, 16.00, 0, -0.06, -0.06, -0.03], ['2016-09-20', 17.8, 17.74, 17.67, 17.85, 10.00, 0, -0.06, -0.05, -0.02], ['2016-09-21', 17.75, 17.88, 17.75, 17.95, 7.00, 0, -0.03, -0.03, -0.02], ['2016-09-22', 17.99, 17.97, 17.88, 18.17, 12.00, 0, -0.02, -0.02, -0.01], ['2016-09-23', 17.99, 17.98, 17.93, 18.09, 13.00, 0, -0.01, -0.01, -0.01], ['2016-09-26', 17.91, 18.0, 17.85, 18.09, 14.00, 0, -0.00, -0.01, -0.01], ['2016-09-27', 17.97, 18.07, 17.94, 18.1, 10.00, 0, 0.00, -0.01, -0.01], ['2016-09-28', 18.06, 17.89, 17.83, 18.06, 10.00, 0, -0.00, -0.01, -0.01], ['2016-09-29', 17.96, 18.0, 17.92, 18.07, 10.00, 0, 0.03, 0.01, -0.01], ['2016-09-30', 17.96, 18.0, 17.95, 18.1, 8.00, 0, 0.06, 0.02, -0.01], ['2016-10-10', 18.03, 18.3, 18.03, 18.38, 19.00, 0, 0.11, 0.04, -0.02], ['2016-10-11', 18.33, 18.33, 18.26, 18.49, 12.00, 0, 0.10, 0.02, -0.04], ['2016-10-12', 18.28, 18.15, 18.1, 18.31, 10.00, 0, 0.07, -0.02, -0.05], ['2016-10-13', 18.15, 18.09, 18.05, 18.21, 10.00, 0, 0.06, -0.03, -0.06], ['2016-10-14', 18.1, 18.1, 18.0, 18.15, 12.00, 0, 0.04, -0.05, -0.07], ['2016-10-17', 18.07, 17.86, 17.83, 18.1, 12.00, 0, 0.01, -0.07, -0.08], ['2016-10-18', 17.86, 17.93, 17.84, 17.99, 14.00, 0, 0.03, -0.07, -0.08], ['2016-10-19', 17.93, 17.88, 17.83, 18.05, 11.00, 0, 0.03, -0.07, -0.08], ['2016-10-20', 17.9, 17.89, 17.83, 17.98, 12.00, 0, 0.05, -0.06, -0.09], ['2016-10-21', 17.91, 17.91, 17.82, 17.93, 12.00, 0, 0.07, -0.06, -0.09], ['2016-10-24', 17.93, 18.31, 17.86, 18.42, 29.00, 0, 0.11, -0.05, -0.10], ['2016-10-25', 18.31, 18.13, 18.09, 18.46, 19.00, 0, 0.06, -0.09, -0.12], ['2016-10-26', 18.12, 17.97, 17.95, 18.15, 14.00, 0, 0.02, -0.12, -0.13], ['2016-10-27', 18.06, 17.81, 17.77, 18.06, 21.00, 0, -0.01, -0.13, -0.13], ['2016-10-28', 17.8, 17.9, 17.8, 18.05, 20.00, 0, -0.01, -0.13, -0.13], ['2016-10-31', 17.87, 17.86, 17.72, 17.96, 12.00, 0, -0.02, -0.14, -0.13], ['2016-11-01', 17.87, 17.98, 17.79, 17.99, 18.00, 0, -0.03, -0.14, -0.12], ['2016-11-02', 17.86, 17.84, 17.76, 17.94, 30.00, 0, -0.06, -0.15, -0.12], ['2016-11-03', 17.83, 17.93, 17.79, 17.97, 27.00, 0, -0.07, -0.14, -0.11], ['2016-11-04', 17.9, 17.91, 17.87, 18.0, 26.00, 0, -0.09, -0.15, -0.10], ['2016-11-07', 17.91, 17.89, 17.85, 17.93, 20.00, 0, -0.11, -0.14, -0.09], ['2016-11-08', 17.92, 17.99, 17.89, 18.06, 26.00, 0, -0.12, -0.13, -0.07], ['2016-11-09', 18.0, 17.89, 17.77, 18.08, 34.00, 0, -0.15, -0.13, -0.06], ['2016-11-10', 17.95, 18.0, 17.94, 18.11, 27.00, 0, -0.15, -0.11, -0.03], ['2016-11-11', 17.95, 18.02, 17.93, 18.08, 27.00, 0, -0.17, -0.10, -0.01], ['2016-11-14', 18.0, 18.04, 17.95, 18.25, 35.00, 0, -0.18, -0.08, 0.01], ['2016-11-15', 18.1, 18.18, 18.03, 18.24, 25.00, 0, -0.18, -0.06, 0.04], ['2016-11-16', 18.23, 18.12, 18.05, 18.29, 23.00, 0, -0.21, -0.04, 0.06], ['2016-11-17', 18.11, 18.12, 18.01, 18.14, 27.00, 0, -0.21, -0.01, 0.09], ['2016-11-18', 18.12, 18.1, 18.03, 18.16, 18.00, 0, -0.19, 0.03, 0.12], ['2016-11-21', 18.08, 18.34, 18.08, 18.68, 41.00, 0, -0.13, 0.08, 0.15], ['2016-11-22', 18.37, 18.37, 18.28, 18.49, 52.00, 0, -0.09, 0.12, 0.17], ['2016-11-23', 18.4, 18.84, 18.37, 18.9, 66.00, 0, -0.02, 0.17, 0.18], ['2016-11-24', 18.77, 18.74, 18.61, 18.97, 26.00, 0, -0.02, 0.17, 0.18], ['2016-11-25', 18.8, 18.99, 18.66, 19.02, 40.00, 0, -0.01, 0.18, 0.19], ['2016-11-28', 19.1, 18.65, 18.52, 19.2, 85.00, 0, -0.06, 0.16, 0.19], ['2016-11-29', 18.65, 18.75, 18.51, 18.76, 49.00, 0, -0.06, 0.17, 0.20], ['2016-11-30', 18.76, 18.55, 18.47, 18.82, 39.00, 0, -0.08, 0.17, 0.21], ['2016-12-01', 18.55, 18.49, 18.41, 18.64, 53.00, 0, -0.06, 0.19, 0.22], ['2016-12-02', 18.53, 18.49, 18.24, 18.54, 48.00, 0, -0.02, 0.21, 0.23], ['2016-12-05', 18.39, 18.66, 18.34, 18.67, 50.00, 0, 0.03, 0.25, 0.23], ['2016-12-06', 18.66, 18.6, 18.57, 18.78, 31.00, 0, 0.08, 0.26, 0.23], ['2016-12-07', 18.65, 18.62, 18.58, 18.71, 12.00, 0, 0.15, 0.29, 0.21], ['2016-12-08', 18.67, 18.76, 18.62, 18.88, 26.00, 0, 0.25, 0.32, 0.19], ['2016-12-09', 18.76, 19.2, 18.75, 19.34, 62.00, 0, 0.34, 0.33, 0.16], ['2016-12-12', 19.16, 19.25, 18.9, 19.65, 79.00, 1, 0.34, 0.28, 0.11], ['2016-12-13', 19.09, 18.88, 18.81, 19.2, 24.00, 0, 0.27, 0.20, 0.06], ['2016-12-14', 18.8, 18.82, 18.8, 19.14, 32.00, 0, 0.23, 0.13, 0.02], ['2016-12-15', 18.73, 18.24, 18.2, 18.73, 36.00, 0, 0.13, 0.05, -0.01], ['2016-12-16', 18.24, 18.18, 18.12, 18.4, 24.00, 0, 0.10, 0.02, -0.03], ['2016-12-19', 18.15, 18.01, 17.93, 18.18, 24.00, 0, 0.06, -0.02, -0.05], ['2016-12-20', 17.99, 17.79, 17.7, 17.99, 29.00, 1, 0.02, -0.05, -0.05], ['2016-12-21', 17.83, 17.81, 17.77, 17.98, 30.00, 0, 0.00, -0.05, -0.06], ['2016-12-22', 17.85, 17.72, 17.65, 17.85, 21.00, 0, -0.03, -0.07, -0.06], ['2016-12-23', 17.77, 17.6, 17.54, 17.77, 18.00, 0, -0.04, -0.08, -0.05], ['2016-12-26', 17.56, 17.75, 17.39, 17.77, 16.00, 0, -0.04, -0.07, -0.05], ['2016-12-27', 17.73, 17.71, 17.65, 17.82, 10.00, 0, -0.06, -0.07, -0.04], ['2016-12-28', 17.72, 17.62, 17.49, 17.77, 26.00, 0, -0.09, -0.07, -0.03], ['2016-12-29', 17.6, 17.49, 17.43, 17.62, 28.00, 0, -0.09, -0.06, -0.02], ['2016-12-30', 17.53, 17.6, 17.47, 17.61, 22.00, 0, -0.05, -0.03, -0.01], ['2017-01-03', 17.6, 17.92, 17.57, 17.98, 28.00, 1, 0.00, 0.00, 0.00]]"
chart = Echart('GDP', 'This is a fake chart')
chart.use(Bar('China', [2, 3, 4, 5]))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=['Nov', 'Dec', 'Jan', 'Feb']))
chart.save('/home/fubo/', 'solr.html')
# chart.plot()
#-*- coding:utf-8 -*-
from echarts import Echart, Legend, Bar, Axis
chart = Echart('GDP', 'this is fake chart')
chart.use(Bar('china', [2, 3, 4, 5]))
chart.use(Legend(['GDP']))
chart.use(Axis('category', 'bottom', data=[11, 12, 1, 2]))
chart.plot()
from echarts import Echart, Legend, Bar, Axis
chart = Echart('GDP', 'This is a fake chart')
chart.use(Bar('China', [2, 3, 4, 5, 1, 2, 3, 4, 25, 36, 27]))
chart.use(Legend(['GDP']))
chart.use(
Axis('category',
'bottom',
data=[
'Nov', 'Dec', 'Jan', 'Feb', 'March', 'April', 'Jun', 'Jul', 'Aug',
'Sep', 'Oct'
]))
chart.use(Axis('value', 'left', data=range(0, 100, 10)))
chart.plot()
def test_axis_assert_type():
Axis('nil', 'bottom', 'proportion', inverse=True)
def test_legend_assert_position():
Axis('cetegory', 'nil', 'proportion', inverse=True)
def draw(self):
chart = Echart(self.title, self.desc)
chart.use(Bar(self.table, self.y))
chart.use(Legend(self.title))
chart.use(Axis('category', 'bottom', data=self.x))
chart.plot()