def job_page(job):
# 需要处理的数据
city_num_list = DATA.get_the_kw_msg(lan_name=job)
# 生成图表
## 饼图
city_geo_page = Page().add(
pie_yht(attr_v1=city_num_list[:20],
chart_name=job + '前二十个城市各自占的比重',
v1_name='城市'))
## 生成柱状图
city_bar_page = Page().add(
bar_sssf(attr_v1=city_num_list[:100],
chart_name=job + '每个城市中该职位数量',
v1_name='城市'))
return render_template(
'lan.html',
title=job,
top5_city=DATA.top5_city_name(),
top5_lan=DATA.top5_lan_name(),
top5_job=DATA.top5_job_name(),
city_geo_page=city_geo_page.render_embed(),
city_bar_page=city_bar_page.render_embed(),
script_list=city_geo_page.get_js_dependencies() +
city_bar_page.get_js_dependencies(),
)
def search():
page = Page()
bar = get_bar()
page.add(bar)
keyword = request.args.get('keyword').replace(' ', '') # 获得前端传回的keyword
print(keyword)
if '：' in keyword: # 判断是否是高级搜索
key = keyword.split('：')[0] # 获取传回的高级搜索关键字，如"诗人：李白"，此时获取‘诗人’
value = keyword.split('：')[1] # 获取‘李白’
if key == '诗人' and value: # 判断高级搜索为‘诗人’，并且搜索内容不为空
poet = Poet.query.filter_by(
name=value).first() # 在数据库中查询姓名为”李白“的诗人数据
if poet: # 如果查询到
context = {
'poet': poet, # 诗人字段信息
'poem': poet.poems # 诗人的是诗的字段信息
}
return render_template('search.html',
**context,
chart=page.render_embed())
else: # 如果没有查询到
context = {
'message': '没有找到相关内容' # 返回信息
}
return render_template('search.html', **context) # 渲染页面
elif key == '诗句' and value: # 判断高级搜索为‘诗句’，并且搜索内容不为空 如”诗句：清明“
poem = Poem.query.filter(
Poem.content.contains(value)).all() # 获取全部诗句内容中包含”清明“诗句
if poem: # 如果查询到
context = {
'poem': poem # 诗的字段信息
}
return render_template(
'search.html',
**context,
chart=page.render_embed(),
host='/static/js',
script_list=page.get_js_dependencies()) # 渲染页面
else: # 如果没有查询到
context = {'message': '没有找到相关内容'}
return render_template('search.html', **context) # 渲染页面
else: # 普通搜索，直接搜索诗的名字 如：‘江南’
poem = Poem.query.filter_by(title=keyword).all() # 获取所有诗的名字为‘江南’的数据
if poem: # 如果查询的到
context = {
'poem': poem # 所有诗的名字为‘江南’字段信息
}
return render_template(
'search.html',
**context,
chart=page.render_embed(),
host='/static/js',
script_list=page.get_js_dependencies()) # 渲染页面
else: # 如果没有查询的到
context = {'message': '没有找到相关内容'}
return render_template('search.html', **context) #渲染页面
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 scatter3d():
from pyecharts import Scatter3D, Page, Bar
import random
page = Page()
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("3D柱状图示例")
bar.add('商家A', attr, v1)
bar.add('商家B', attr, v2)
page.add(bar)
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)
] for _ in range(80)]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter3D = Scatter3D("3D 散点图 demo", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
page.add(scatter3D)
return page.render_embed()
def city_page(city):
# 需要处理的数据
lan_list, job_list = DATA.get_kw_msg_of_city(city_name=city)
kw_list = DATA.get_the_city_msg(city_name=city)
kw_num = DATA.get_kw_num(city=city)
lan_num, job_num = DATA.get_lan_job_num(city=city)
# 生成数据图表
kw_bar_page = Page().add(
bar_sssf(attr_v1=kw_list, chart_name=city + '各种职位总览-柱状图',
v1_name='数量'))
## 对语言的分析柱状图与饼图
lan_bar_page = Page().add(
bar_sssf(attr_v1=lan_list, chart_name=city + '各语言数量-柱状图',
v1_name='语言'))
lan_pie_page = Page().add(
pie_yht(attr_v1=lan_list, chart_name='每种语言占总数占比-饼状图', v1_name='语言'))
## 对职位的分析柱状图与饼图
job_bar_page = Page().add(
bar_sssf(attr_v1=job_list, chart_name=city + '各职位数量-柱状图',
v1_name='职位'))
job_pie_page = Page().add(
pie_yht(attr_v1=job_list, chart_name='每种职位占总数占比-饼状图', v1_name='职位'))
return render_template(
'city.html',
title=city,
kw_list=kw_list,
lan_num=lan_num,
job_num=job_num,
top5_city=DATA.top5_city_name(),
top5_lan=DATA.top5_lan_name(),
top5_job=DATA.top5_job_name(),
kw_bar_page=kw_bar_page.render_embed(),
lan_bar_page=lan_bar_page.render_embed(),
lan_pie_page=lan_pie_page.render_embed(),
job_bar_page=job_bar_page.render_embed(),
job_pie_page=job_pie_page.render_embed(),
script_list=kw_bar_page.get_js_dependencies() +
lan_bar_page.get_js_dependencies() +
lan_pie_page.get_js_dependencies() +
job_bar_page.get_js_dependencies() +
job_pie_page.get_js_dependencies(),
)
def recommendation():
page3 = Page()
sca = scatter()
page3.add(sca)
return render_template('recommendation.html',
myechart=page3.render_embed(),
host=DEFAULT_HOST,
script_list=page3.get_js_dependencies())
def index():
asset_count = Asset.query.count() # 总资产数
page = Page() # 生成页面对象
# 生成服务器图
webserver_pie = webserver(asset_count) # web服务器
os_pie = webos() # 操作系统
pl_pie = pl() # 开发语言
jsf_pie = jsf() # js框架
cms_pie = cms() #cms
page.add([webserver_pie, os_pie, pl_pie, jsf_pie, cms_pie])
return render_template('cms/cms_index.html', chart=page.render_embed(), host='/static/front/js', total=asset_count,
script_list=page.get_js_dependencies())
def virtualization():
page2 = Page()
data_clound = data_cloud()
page2.add(data_clound)
_bar = force()
page2.add(_bar)
map = _map()
page2.add(map)
pie = _pie()
page2.add(pie)
return render_template('virtualization.html',
myechart=page2.render_embed(),
host=DEFAULT_HOST,
script_list=page2.get_js_dependencies())
def stock():
page = Page()
if request.method == 'POST':
stock_num = request.form['stock_num']
if stock_num:
kline = k_line(stock_num)
page.add(kline)
if kline == 0:
error = "No Such Stock, Check First"
return render_template('stock.html', error=error)
normalline = nomoral_line(stock_num)
page.add(normalline)
return render_template('stock.html',
myechart=page.render_embed(),
host=DEFAULT_HOST,
script_list=page.get_js_dependencies())
else:
return render_template('stock.html')
return render_template('stock.html')
def index():
stock_basics = pd.read_hdf(app.config['FIN_TECH_HDF5'],
'stock_basics',
mode='r')
area_count = stock_basics.groupby('area')['code'].count().reset_index()
area_sum = stock_basics.groupby('area')['totalAssets'].sum().reset_index()
value_count = list(area_count['code'])
attr_count = list(area_count['area'])
value_sum = list(area_sum['totalAssets'].astype(int))
attr_sum = list(area_sum['area'])
map_count = Map("上市公司分布-公司数", width=1200, height=600)
map_count.add("",
attr_count,
value_count,
maptype='china',
is_visualmap=True,
visual_range=[0, 400],
is_map_symbol_show=False,
visual_text_color='#000')
map_sum = Map("上市公司分布-公司市值", width=1200, height=600)
map_sum.add("",
attr_sum,
value_sum,
maptype='china',
is_visualmap=True,
visual_range=[10000000, 1000000000],
is_map_symbol_show=False,
visual_text_color='#000')
page = Page()
page.add(map_count)
page.add(map_sum)
# grid = Grid(width="100%")
# grid.add(map_count, grid_left="60%")
# grid.add(map_sum, grid_right="60%")
return render_template(
'chart.html',
myechart=page.render_embed(),
host=app.config['REMOTE_HOST'],
# my_option=json_dumps(page.options),
script_list=page.get_js_dependencies())
def pyecharts(request):
"""dashboard view"""
# 工单数量统计
chart_dao = ChartDao()
bar1 = Bar('SQL上线工单统计(数量)', width="100%")
data = chart_dao.workflow_by_date(30)
today = date.today()
one_month_before = today - relativedelta(days=+30)
attr = chart_dao.get_date_list(one_month_before, today)
_dict = {}
for row in data['rows']:
_dict[row[0]] = row[1]
value = [_dict.get(day) if _dict.get(day) else 0 for day in attr]
bar1.add("月统计", attr, value, is_stack=False, legend_selectedmode='single')
# 工单按组统计
pie1 = Pie('SQL上线工单统计(组)', width="100%")
data = chart_dao.workflow_by_group(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1.add("月统计", attr, value, is_legend_show=False, is_label_show=True)
# 工单按人统计
bar2 = Bar('SQL上线工单统计(用户)', width="100%")
data = chart_dao.workflow_by_user(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2.add("月统计", attr, value, is_label_show=True)
# SQL语句类型统计
pie2 = Pie("SQL上线工单统计(类型)", width="100%")
data = chart_dao.sql_syntax()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2.add("", attr, value, is_label_show=True)
# SQL查询统计(每日检索行数)
line1 = Line("SQL查询统计", width="100%")
attr = chart_dao.get_date_list(one_month_before, today)
effect_data = chart_dao.querylog_effect_row_by_date(30)
effect_dict = {}
for row in effect_data['rows']:
effect_dict[row[0]] = int(row[1])
effect_value = [effect_dict.get(day) if effect_dict.get(day) else 0 for day in attr]
count_data = chart_dao.querylog_count_by_date(30)
count_dict = {}
for row in count_data['rows']:
count_dict[row[0]] = int(row[1])
count_value = [count_dict.get(day) if count_dict.get(day) else 0 for day in attr]
line1.add("检索行数", attr, effect_value, is_stack=False, legend_selectedmode='single', mark_point=["average"])
line1.add("检索次数", attr, count_value, is_stack=False, legend_selectedmode='single', is_smooth=True,
mark_line=["max", "average"])
# SQL查询统计(用户检索行数)
pie4 = Pie("SQL查询统计(用户检索行数)", width="100%")
data = chart_dao.querylog_effect_row_by_user(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie4.add("月统计", attr, value, radius=[40, 75], is_legend_show=False, is_label_show=True)
# SQL查询统计(DB检索行数)
pie5 = Pie("SQL查询统计(DB检索行数)", width="100%")
data = chart_dao.querylog_effect_row_by_db(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie5.add("月统计", attr, value, is_legend_show=False, is_label_show=True)
# 可视化展示页面
page = Page()
page.add(bar1)
page.add(pie1)
page.add(bar2)
page.add(pie2)
page.add(line1)
page.add(pie4)
page.add(pie5)
myechart = page.render_embed() # 渲染配置
host = 'https://pyecharts.github.io/assets/js' # js文件源地址
script_list = page.get_js_dependencies() # 获取依赖的js文件名称（只获取当前视图需要的js）
return render(request, "dashboard.html", {"myechart": myechart, "host": host, "script_list": script_list})
def dau():
# 构造Request和访问协议是否是https
appDAUQuest = aop.api.UmengUappGetActiveUsersRequest()
appNEWQuest = aop.api.UmengUappGetNewUsersRequest()
#计算时间
now_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() + timedelta(days=-30)).strftime('%Y-%m-%d')
#创建字典
iosDAU = {}
andDAU = {}
allDAU = {}
iosNEW = {}
andNEW = {}
allNEW = {}
#IOS DAU
respIOSDAU = appDAUQuest.get_response(None,
appkey="5c76338bb465f50bc6001045",
startDate=start_date,
endDate=now_date,
periodType="daily")
for k in respIOSDAU['activeUserInfo']:
iosDAU[k['date']] = k['value']
#IOS NEW
respIOSNEW = appNEWQuest.get_response(None,
appkey="5c76338bb465f50bc6001045",
startDate=start_date,
endDate=now_date,
periodType="daily")
for k in respIOSNEW['newUserInfo']:
iosNEW[k['date']] = k['value']
#Android DAU
respANDDAU = appDAUQuest.get_response(None,
appkey="5c73a5e9f1f55699ab0000ef",
startDate=start_date,
endDate=now_date,
periodType="daily")
for k in respANDDAU['activeUserInfo']:
andDAU[k['date']] = k['value']
#Android NEW
respANDNEW = appNEWQuest.get_response(None,
appkey="5c73a5e9f1f55699ab0000ef",
startDate=start_date,
endDate=now_date,
periodType="daily")
for k in respANDNEW['newUserInfo']:
andNEW[k['date']] = k['value']
x, y = Counter(iosDAU), Counter(andDAU)
allDAU = dict(x + y)
x, y = Counter(iosNEW), Counter(andNEW)
allNEW = dict(x + y)
page = Page()
#处理渲染
columns = list(allDAU.keys())
#设置DAU数据
data1 = list(iosDAU.values())
data2 = list(andDAU.values())
data3 = list(allDAU.values())
#设置NEW数据
data4 = list(iosNEW.values())
data5 = list(andNEW.values())
data6 = list(allNEW.values())
#设置柱状图的主标题与副标题
bar = Bar("DAU", "完美世界电竞App最近一个月的DAU")
#添加柱状图的数据及配置项
bar.add("IOS", columns, data1, is_datazoom_show=True)
bar.add("Android", columns, data2, is_datazoom_show=True)
bar.add("全部", columns, data3, is_datazoom_show=True)
line = Line("Line Chart Demo")
line.add("全部", columns, data3, is_smooth=True)
overlap = Overlap(height=500, width=1200)
overlap.add(bar)
overlap.add(line)
#设置柱状图的主标题与副标题
bar = Bar("NEW USER", "完美世界电竞App最近一个月的新增用户")
#添加柱状图的数据及配置项
bar.add("IOS", columns, data4, is_datazoom_show=True)
bar.add("Android", columns, data5, is_datazoom_show=True)
bar.add("全部", columns, data6, is_datazoom_show=True)
line = Line("Line Chart Demo")
line.add("全部", columns, data6, is_smooth=True)
overlap2 = Overlap(height=500, width=1200)
overlap2.add(bar)
overlap2.add(line)
page.add(overlap)
page.add(overlap2)
#生成本地文件（默认为.html文件）
return render_template('bar.html',
myechart=page.render_embed(),
script_list=page.get_js_dependencies())
def home():
"""
主页信息
"""
# 将要显示的数据
print('test')
city_num_list = CITY_NUM_LIST[:100]
kw_num_city_list = KW_NUM_OF_ALL_CITY_LIST[:100]
lan_num_list = LAN_NUM_LIST
job_num_list = JOB_NUM_LIST
all_num = ALL_NUM
# 生成相应的图表
top_10_city_page = Page()
top_10_city_page.add(
bar_ssdd(
attr_v1_v2=kw_num_city_list[:10],
chart_name='前十城市中语言数与职位数对比-折线图',
v1_name='语言数量',
v2_name='职位数量',
))
top_10_city_map_page = Page()
top_10_city_map_page.add(
geo_qgtd(attr_v1=city_num_list[:10],
chart_name='前十城市地理位置',
v1_name='城市'))
top_100_city_page = Page()
top_100_city_page.add(
bar_sssf(attr_v1=city_num_list,
chart_name='前百城市职位数量-柱状图',
v1_name='职位数'))
top_100_city_map_page = Page()
top_100_city_map_page.add(
geo_qgtd(attr_v1=city_num_list[:100],
chart_name='前百城市地理位置',
v1_name='城市'))
lan_page = Page()
lan_page.add(
pie_yht(attr_v1=lan_num_list, chart_name='各种语言占总数比值-饼图', v1_name='语言'))
job_page = Page()
job_page.add(
pie_yht(attr_v1=job_num_list, chart_name='各种职位占总数比值-饼图', v1_name='职位'))
return render_template(
'index.html',
title='首页',
all_num=all_num,
top5_city=DATA.top5_city_name(),
top5_lan=DATA.top5_lan_name(),
top5_job=DATA.top5_job_name(),
top_10_city=top_10_city_page.render_embed(),
top_10_city_map_page=top_10_city_map_page.render_embed(),
top_100_city_page=top_100_city_page.render_embed(),
top_100_city_map_page=top_100_city_map_page.render_embed(),
lan_page=lan_page.render_embed(),
job_page=job_page.render_embed(),
script_list=top_10_city_page.get_js_dependencies() +
top_100_city_page.get_js_dependencies() +
lan_page.get_js_dependencies() + job_page.get_js_dependencies() +
top_10_city_map_page.get_js_dependencies() +
top_100_city_map_page.get_js_dependencies(),
)
def events():
#构造Request和访问协议是否是https
req = aop.api.UmengUappEventListRequest()
#得到时间
get_date = request.args.get("date")
if is_valid_date(get_date) == False:
now_date = datetime.now().strftime('%Y-%m-%d')
else:
now_date = get_date
AndEvents = {}
IosEvents = {}
allEvents = {}
resp = req.get_response(None,
appkey="5c73a5e9f1f55699ab0000ef",
startDate=now_date,
endDate=now_date,
perPage=30,
page=1,
version="")
AndEvents[now_date] = {}
for k in resp['eventInfo']:
AndEvents[now_date][k['displayName']] = k['count']
resp = req.get_response(None,
appkey="5c76338bb465f50bc6001045",
startDate=now_date,
endDate=now_date,
perPage=30,
page=1,
version="")
IosEvents[now_date] = {}
for k in resp['eventInfo']:
IosEvents[now_date][k['displayName']] = k['count']
x, y = Counter(AndEvents[now_date]), Counter(IosEvents[now_date])
allEvents[now_date] = {}
allEvents[now_date] = dict(x + y)
resIosEvents = {}
resAndEvents = {}
for k in allEvents[now_date].keys():
if k not in AndEvents[now_date]:
resAndEvents[k] = 0
else:
resAndEvents[k] = AndEvents[now_date][k]
if k not in IosEvents[now_date]:
resIosEvents[k] = 0
else:
resIosEvents[k] = IosEvents[now_date][k]
page = Page()
#处理渲染
columns = list(allEvents[now_date].keys())
#设置DAU数据
data1 = list(resIosEvents.values())
data2 = list(resAndEvents.values())
data3 = list(allEvents[now_date].values())
#设置柱状图的主标题与副标题
bar = Bar("EVENTS", "完美世界电竞App的事件消息数:" + now_date)
#添加柱状图的数据及配置项
bar.add("IOS",
columns,
data1,
is_more_utils=True,
xaxis_label_textsize=10,
is_datazoom_show=True)
bar.add("Android",
columns,
data2,
is_more_utils=True,
xaxis_label_textsize=10)
bar.add("全部",
columns,
data3,
is_more_utils=True,
xaxis_label_textsize=10,
xaxis_interval=-10,
xaxis_rotate=45)
overlap = Overlap(height=600, width=1600)
overlap.add(bar)
page.add(overlap)
#生成本地文件（默认为.html文件）
return render_template('events.html',
myechart=page.render_embed(),
script_list=page.get_js_dependencies())
def pyecharts(request):
# 工单数量统计
bar1 = Bar('SQL上线工单统计(数量)', width="100%")
data = chart_dao.workflow_by_date(1)
begin_date = (date.today() - relativedelta(months=+1)).strftime("%Y-%m-%d")
attr = chart_dao.get_date_list(begin_date,
date.today().strftime("%Y-%m-%d"))
dict = {}
for row in data['rows']:
dict[row[0]] = row[1]
value = [dict.get(day) if dict.get(day) else 0 for day in attr]
bar1.add("月统计", attr, value, is_stack=False, legend_selectedmode='single')
# 工单按组统计
pie1 = Pie('SQL上线工单统计(组)', width="100%")
data = chart_dao.workflow_by_group(1)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1.add("月统计", attr, value, is_legend_show=False, is_label_show=True)
# 工单按人统计
bar2 = Bar('SQL上线工单统计(用户)', width="100%")
data = chart_dao.workflow_by_user(1)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2.add("月统计", attr, value, is_label_show=True)
# SQL语句类型统计
pie2 = Pie("SQL上线工单统计(类型)", width="100%")
data = chart_dao.sql_syntax()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2.add("", attr, value, is_label_show=True)
# SQL执行情况统计
pie3 = Pie("SQL上线工单统计(Inception)", width="100%")
data = InceptionDao().statistic()
attr = data['column_list']
if data['column_list']:
value = [int(row) for row in data['rows'][0]]
else:
value = []
pie3.add("", attr, value, is_legend_show=False, is_label_show=True)
# SQL查询统计(每日检索行数)
line1 = Line("SQL查询统计", width="100%")
begin_date = (date.today() - relativedelta(months=+1)).strftime("%Y-%m-%d")
attr = chart_dao.get_date_list(begin_date,
date.today().strftime("%Y-%m-%d"))
effect_data = chart_dao.querylog_effect_row_by_date(1)
effect_dict = {}
for row in effect_data['rows']:
effect_dict[row[0]] = int(row[1])
effect_value = [
effect_dict.get(day) if effect_dict.get(day) else 0 for day in attr
]
count_data = chart_dao.querylog_count_by_date(1)
count_dict = {}
for row in count_data['rows']:
count_dict[row[0]] = int(row[1])
count_value = [
count_dict.get(day) if count_dict.get(day) else 0 for day in attr
]
line1.add("检索行数",
attr,
effect_value,
is_stack=False,
legend_selectedmode='single',
mark_point=["average"])
line1.add("检索次数",
attr,
count_value,
is_stack=False,
legend_selectedmode='single',
is_smooth=True,
mark_line=["max", "average"])
# SQL查询统计(用户检索行数)
pie4 = Pie("SQL查询统计(用户检索行数)", width="100%")
data = chart_dao.querylog_effect_row_by_user(1)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie4.add("月统计",
attr,
value,
radius=[40, 75],
is_legend_show=False,
is_label_show=True)
# SQL查询统计(DB检索行数)
pie5 = Pie("SQL查询统计(DB检索行数)", width="100%")
data = chart_dao.querylog_effect_row_by_db(1)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie5.add("月统计", attr, value, is_legend_show=False, is_label_show=True)
# 可视化展示页面
page = Page()
page.add(bar1)
page.add(pie1)
page.add(bar2)
page.add(pie2)
page.add(pie3)
page.add(line1)
page.add(pie4)
page.add(pie5)
myechart = page.render_embed() # 渲染配置
host = 'https://pyecharts.github.io/assets/js' # js文件源地址
script_list = page.get_js_dependencies() # 获取依赖的js文件名称（只获取当前视图需要的js）
return render(request, "dashboard.html", {
"myechart": myechart,
"host": host,
"script_list": script_list
})
def scatter3d():
from pyecharts import Scatter3D, Page, Map, Geo, Style, GeoLines
import random
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color='#404a59')
style_geo = style.add(
is_label_show=True,
line_curve=0.2,
line_opacity=0.6,
legend_text_color="#eee",
legend_pos="right",
geo_effect_symbol="plane",
geo_effect_symbolsize=15,
label_color=['#a6c84c', '#ffa022', '#46bee9'],
label_pos="right",
label_formatter="{b}",
label_text_color="#eee",
)
page = Page()
'''
Scatter3D
'''
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)
] for _ in range(80)]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter3D = Scatter3D("3D scattering plot demo", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
page.add(scatter3D)
'''
Geo
'''
data = [('汕头市', 50), ('杭州市', 500)]
chart = Geo("地理坐标系", **style.init_style)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
maptype="china",
is_visualmap=True,
is_legend_show=False,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right')
page.add(chart)
data = [('北京市', 50), ('上海市', 500), ('伦敦市', 100)]
chart = Geo("地理坐标系", **style.init_style)
'''
自定义城市
'''
chart.add_coordinate('伦敦市', 0.1, 51.30)
attr, value = chart.cast(data)
chart.add("",
attr,
value,
maptype="world",
is_visualmap=True,
is_legend_show=False,
tooltip_formatter='{b}',
label_emphasis_textsize=15,
label_emphasis_pos='right')
page.add(chart)
'''
GeoLines
'''
data_guangzhou = [["广州", "上海"], ["广州", "北京"], ["广州", "南京"], ["广州", "重庆"],
["广州", "兰州"], ["广州", "杭州"]]
charts = GeoLines("GeoLines-默认示例", **style.init_style)
charts.add("从广州出发", data_guangzhou, **style_geo)
page.add(charts)
'''
Map
'''
value = [155, 10, 66, 78]
attr = ["福建", "山东", "北京", "上海"]
chart = Map("全国地图", **style.init_style)
chart.add("", attr, value, maptype='china', is_label_show=True)
page.add(chart)
value = [155, 66, 78]
attr = ["宁波市", "温州市", "杭州市"]
chart = Map("地图", **style.init_style)
'''
is_map_symbol_show=False不带标记点
'''
chart.add("",
attr,
value,
maptype='浙江',
is_label_show=True,
is_visualmap=True,
visual_range=[min(value), max(value)],
is_map_symbol_show=False,
visual_text_color='#000')
page.add(chart)
return page.render_embed()
def LineAndBox_plot(self, id, geneName, list_name_mouse, list_values_mouse,
others_type, list_others_data, list_special_striatum,
list_special_cortex, max):
grid = Page()
if others_type != 0:
"""
第一张图
"""
overlap = Overlap()
line_special = Line()
# title="\tSpatio-temporal Human Developmental Brain Expression (BrainSpan)",
# title_pos="left")
i = 0
es = Scatter(geneName)
attr = ['Early fetal\n8-9PCW', 'Early fetal\n10-12PCW', 'Early mid-fetal\n13PCW-15PCW',
'Early mid-fetal\n16PCW-18PCW', \
'Late mid-fetal\n19PCW-23PCW', 'Late fetal\n24PCW-37PCW',
'Neonatal and early infancy\n0M(birth)-5M', \
'Late infancy\n6M-11M', 'Early childhood\n1Y-5Y', 'Middle and late childhood\n6Y-11Y', \
'Adolescence\n12Y-19Y', 'Young adulthood\n20Y-39Y', 'Middle adulthood\n40Y']
"""
画多数值类型的拟合曲线
"""
"""
1.求和 ；2.取平均值；3.画smooth
"""
"""
1-1 striatum
"""
list_linshi_striatum_ave = [0 for i in range(13)]
list_linshi_striatum_num = [0 for i in range(13)]
for i in range(13):
if list_special_striatum[i] != []:
for item in list_special_striatum[i]:
list_linshi_striatum_ave[i] += item
list_linshi_striatum_num[i] += 1
if list_special_striatum[i] == []:
list_linshi_striatum_ave[i] = ''
for i in range(13):
if list_linshi_striatum_ave[i] != '':
list_linshi_striatum_ave[i] = list_linshi_striatum_ave[
i] / list_linshi_striatum_num[i]
line_special.add("striatum",
attr,
list_linshi_striatum_ave,
yaxis_max=math.ceil(max),
is_smooth=True,
xaxis_interval=0,
xaxis_type="category",
xaxis_rotate=-30,
xaxis_label_textsize=9,
legend_selectedmode=False,
legend_pos="top",
legend_orient="vertical")
"""
1-2 cortex
"""
list_linshi_cortex_ave = [0 for i in range(13)]
list_linshi_cortex_num = [0 for i in range(13)]
for i in range(13):
if list_special_cortex[i] != []:
for item in list_special_cortex[i]:
list_linshi_cortex_ave[i] += item
list_linshi_cortex_num[i] += 1
if list_special_cortex[i] == []:
list_linshi_cortex_ave[i] = ''
for i in range(13):
if list_linshi_cortex_ave[i] != '':
list_linshi_cortex_ave[i] = list_linshi_cortex_ave[
i] / list_linshi_cortex_num[i]
line_special.add("cortex",
attr,
list_linshi_cortex_ave,
yaxis_max=math.ceil(max),
is_smooth=True,
xaxis_interval=0,
xaxis_type="category",
xaxis_rotate=-30,
xaxis_label_textsize=9,
legend_selectedmode=False,
yaxis_name='LOG2(RPKM+1)',
yaxis_name_pos='middle',
legend_pos="10%",
legend_orient="horizontal")
overlap.add(line_special)
"""
先补齐list
"""
max_length_striatum = 0
for item in list_special_striatum:
if len(item) >= max_length_striatum:
max_length_striatum = len(item)
for i in range(13):
if len(list_special_striatum[i]) < max_length_striatum:
for j in range(max_length_striatum -
len(list_special_striatum[i])):
list_special_striatum[i].append('')
max_length_cortex = 0
for item in list_special_cortex:
if len(item) >= max_length_cortex:
max_length_cortex = len(item)
for i in range(13):
if len(list_special_cortex[i]) < max_length_cortex:
for j in range(max_length_cortex -
len(list_special_cortex[i])):
list_special_cortex[i].append('')
"""
画散点
"""
"""
striatum
"""
for i in range(max_length_striatum):
list_linshi_striatum = []
for j in range(13):
list_linshi_striatum.append(list_special_striatum[j][i])
# print(list_linshi_striatum)
es.add("striatum",
attr,
list_linshi_striatum,
xaxis_type="category",
xaxis_rotate=-30,
xaxis_interval=0,
legend_selectedmode=False,
yaxis_max=math.ceil(max),
symbol_size=3,
is_legend_show=False,
xaxis_label_textsize=9,
legend_pos="right",
legend_orient="vertical")
"""
cortex
"""
for i in range(max_length_cortex):
list_linshi_cortex = []
for j in range(13):
list_linshi_cortex.append(list_special_cortex[j][i])
# print(list_linshi_cortex)
es.add("cortex",
attr,
list_linshi_cortex,
xaxis_type="category",
xaxis_rotate=-30,
xaxis_interval=0,
legend_selectedmode=False,
yaxis_max=max,
symbol_size=3,
is_legend_show=False,
xaxis_label_textsize=9,
legend_pos="right",
legend_orient="vertical")
overlap.add(es)
"""
单数值的线
"""
k = 0
for item in others_type:
line = Line()
line.add(str(item),
attr,
list_others_data[k],
is_smooth=True,
is_symbol_show=False,
xaxis_interval=0,
legend_selectedmode=False,
xaxis_type="category",
yaxis_max=max,
xaxis_rotate=-30,
xaxis_label_textsize=9,
legend_pos="right",
legend_orient="vertical")
overlap.add(line)
k += 1
grid.add(overlap)
# grid.render()
try:
return grid.render_embed()
except:
return '<div><p>There is no corresponding data published yet,we will update it when such data available.</p></div>'
