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
def create_charts(data):
# data字典格式(三个表的情况下)：
# {'charcloud':[str:表一的前描述,str:表一的后描述,数据1,数据2,...,数据n],'...':[...]}
html = ''
page = Page()
style = Style(width=900, height=600)
# 本页面包含：1：所有字的词云charcloud(两个数据chars,values)、
# 表一：
# 获取表一的数据
html_before = data['charcloud'][0]
html_after = data['charcloud'][1]
chars = data['charcloud'][2]
values = data['charcloud'][3]
wordcloud = WordCloud("唐诗用字云图", **style.init_style)
wordcloud.add("字云",
chars,
values,
word_size_range=[10, 100],
shape='pentagon')
java_script = wordcloud.render_embed()
html += html_before + java_script + html_after
page.add(wordcloud)
# 表二：
html_before = data['chartop10'][0]
html_after = data['chartop10'][1]
chars = data['chartop10'][2]
values = data['chartop10'][3]
bar = Bar("唐诗高频十字", **style.init_style)
bar.add("柱状图", chars, values)
java_script = bar.render_embed()
html += html_before + java_script + html_after
page.add(bar)
# 表三:
html_before = data['frequency×'][0]
html_after = data['frequency×'][1]
keys = data['frequency×'][2]
values = data['frequency×'][3]
line = Line("唐诗字频-字数", **style.init_style)
line.add("字频--字数",
keys,
values,
is_smooth=True,
is_fill=True,
area_opacity=0.2,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[0, 60],
xaxis_interval=1,
yaxis_formatter="字",
xaxis_name="频次",
yaxis_name="字数",
xaxis_name_pos="end",
yaxis_name_pos="end",
is_more_utils=True)
java_script = line.render_embed()
html += html_before + java_script + html_after
page.add(line)
# 最后
script = page.get_js_dependencies()
return html, script
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 create_charts(data):
style = Style(width=900, height=900)
page = Page()
html=''
charts = GeoLines("丝绸之路", **style.init_style)
data_eastnorth = [
['西安市', '彬县'],
['彬县', '泾川县'],
['泾川县', '固原市'],
['固原市', '靖远县'],
['靖远县', '武威市'],
['武威市', '张掖市']
]
charts.add("东段北线", data_eastnorth, **style_geo)
data_eastmid = [
['西安市', '彬县'],
['彬县', '泾川县'],
['泾川县', '平凉市'],
['平凉市', '会宁县'],
['会宁县', '兰州市'],
['兰州市', '武威市'],
['武威市', '张掖市']
]
charts.add("东段中线", data_eastmid, **style_geo)
data_eastsouth = [
['西安市', '凤翔县'],
['凤翔县', '天水市'],
['天水市', '陇西县'],
['陇西县', '临夏市'],
['临夏市', '乐都县'],
['乐都县', '西宁市'],
['西宁市', '张掖市']
]
charts.add("东段南线", data_eastsouth, **style_geo)
data_hexizoulang = [
['张掖', '酒泉'],
['酒泉', '玉门'],
['玉门', '瓜州']
]
charts.add("河西走廊", data_hexizoulang, geo_cities_coords={
'张掖': [100.45, 38.93],
'酒泉': [98.500685, 39.740023],
'玉门': [97.055736, 40.296519],
'瓜州': [95.793335, 40.530274]
}, **style_geo)
data_westnorth = [
['瓜州', '哈密'],
['哈密', '伊川'],
['伊川', '吉木萨尔'],
['吉木萨尔', '乌鲁木齐'],
['乌鲁木齐', '伊宁'],
['伊宁', '碎叶']
]
charts.add("中段北道", data_westnorth, geo_cities_coords={
'瓜州': [95.793335, 40.530274],
'哈密': [93.52294, 42.833441],
'伊川': [91.435282, 43.430981],
'吉木萨尔': [89.181374, 44.004888],
'乌鲁木齐': [87.62214, 43.831179],
'伊宁': [81.283092, 43.916546],
'碎叶': [75.283092, 43.831179]
}, **style_geo)
data_westmid = [
['瓜州', '敦煌'],
['敦煌', '玉门关'],
['玉门关', '高昌'],
['高昌', '焉耆'],
['焉耆', '库车'],
['库车', '阿克苏'],
['阿克苏', '喀什'],
['喀什', '大宛']
]
charts.add("中段中道",data_westmid, geo_cities_coords={
'瓜州': [95.793335, 40.530274],
'敦煌': [94.6806, 40.148308],
'玉门关': [93.871678, 40.360147],
'高昌': [89.192459, 42.948971],
'焉耆': [86.588696, 42.065708],
'库车': [82.969034, 41.723448],
'阿克苏': [80.258894, 41.175899],
'喀什': [75.975982,39.542678],
'大宛': [70.258894, 41.175899]
}, **style_geo)
data_westsouth = [
['瓜州', '敦煌'],
['敦煌', '阳关'],
['阳关', '若羌县'],
['若羌县', '且丰县'],
['且丰县', '和田'],
['和田', '莎车'],
['莎车', '葱岭']
]
charts.add("中段南道", data_westsouth, geo_cities_coords={
'瓜州': [95.793335, 40.530274],
'敦煌': [94.6806, 40.148308],
'阳关': [94.060858, 39.940913],
'若羌县': [89.060858, 39.940913],
'且丰县': [86.560858, 38.540919],
'和田': [79.933681, 37.121827],
'莎车': [77.249562, 38.422871],
'葱岭': [72.249562, 37.921827]
}, **style_geo)
page.add(charts)
html=charts.render_embed()
script = page.get_js_dependencies()
return html, script
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 create_charts(data):
# data字典格式(三个表的情况下)：
# {'nouns':[str:表一的前描述,str:表一的后描述,数据1,数据2,...,数据n],'...':[...]}
html = ''
page = Page()
style = Style(width=900, height=600)
# 本页面包含：1：noun(两个数据noun,values)、
# 表一：
# 获取表一的数据
html_before = data['nounfre'][0]
html_after = data['nounfre'][1]
keys = data['nounfre'][2]
values = data['nounfre'][3]
line = Line("唐诗词频--名词", **style.init_style)
line.add("词频--名词",
keys,
values,
is_smooth=True,
is_fill=True,
area_opacity=0.2,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[0, 60],
xaxis_interval=1,
xaxis_name="名词",
yaxis_name="词频",
xaxis_name_pos="end",
yaxis_name_pos="end",
is_more_utils=True)
java_script = line.render_embed()
html += html_before + java_script + html_after
page.add(line)
#表二
html_before = data['geofre'][0]
html_after = data['geofre'][1]
keys = data['geofre'][2]
values = data['geofre'][3]
line = Line("唐诗词频--地理名词", **style.init_style)
line.add("词频--地理名词",
keys,
values,
is_smooth=True,
is_fill=True,
area_opacity=0.2,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[0, 60],
xaxis_interval=1,
xaxis_name="地理名词",
yaxis_name="词频",
xaxis_name_pos="end",
yaxis_name_pos="end",
is_more_utils=True)
java_script = line.render_embed()
html += html_before + java_script + html_after
page.add(line)
# 表三
html_before = data['timefre'][0]
html_after = data['timefre'][1]
keys = data['timefre'][2]
values = data['timefre'][3]
line = Line("唐诗词频--地理名词", **style.init_style)
line.add("词频--时间名词",
keys,
values,
is_smooth=True,
is_fill=True,
area_opacity=0.2,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[0, 60],
xaxis_interval=1,
xaxis_name="时间名词",
yaxis_name="词频",
xaxis_name_pos="end",
yaxis_name_pos="end",
is_more_utils=True)
java_script = line.render_embed()
html += html_before + java_script + html_after
page.add(line)
# 表四
html_before = data['verbfre'][0]
html_after = data['verbfre'][1]
keys = data['verbfre'][2]
values = data['verbfre'][3]
line = Line("唐诗词频--动词", **style.init_style)
line.add("词频--动词",
keys,
values,
is_smooth=True,
is_fill=True,
area_opacity=0.2,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[0, 60],
xaxis_interval=1,
xaxis_name="动词",
yaxis_name="词频",
xaxis_name_pos="end",
yaxis_name_pos="end",
is_more_utils=True)
java_script = line.render_embed()
html += html_before + java_script + html_after
page.add(line)
# 表五
html_before = data['adjfre'][0]
html_after = data['adjfre'][1]
keys = data['adjfre'][2]
values = data['adjfre'][3]
line = Line("唐诗词频--xrc", **style.init_style)
line.add("词频--形容词",
keys,
values,
is_smooth=True,
is_fill=True,
area_opacity=0.2,
is_datazoom_show=True,
datazoom_type="both",
datazoom_range=[0, 60],
xaxis_interval=1,
xaxis_name="形容词",
yaxis_name="词频",
xaxis_name_pos="end",
yaxis_name_pos="end",
is_more_utils=True)
java_script = line.render_embed()
html += html_before + java_script + html_after
page.add(line)
script = page.get_js_dependencies()
return html, script
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 create_charts(data):
page = Page()
style = Style(width=900, height=900)
html = ''
html_before = data['full'][0]
html_after = data['full'][1]
nodes_full = data['full'][2]
links_full = data['full'][3]
category=data['full'][4]
chart = Graph("全唐诗人关系图", **style.init_style)
chart.add("", nodes_full, links_full,category, line_color='#aaa', label_pos="center",
is_label_show=True, graph_repulsion=1000, is_legend_show=True,
line_curve=0.4, label_text_color="#000", graph_layout='circular',
label_emphasis_textcolor='#000')
java_script = chart.render_embed()
html += html_before + java_script + html_after
page.add(chart)
# style = Style(width=900, height=600)
html_before = data['early'][0]
html_after = data['early'][1]
nodes_early = data['early'][2]
links_early = data['early'][3]
chart = Graph("初唐诗人关系图", **style.init_style)
chart.add("", nodes_early, links_early, line_color='#aaa', label_pos="center", is_label_show=True,
graph_repulsion=1000, is_legend_show=False, line_curve=0.2, label_text_color=None)
java_script = chart.render_embed()
html += html_before + java_script + html_after
page.add(chart)
html_before = data['middle'][0]
html_after = data['middle'][1]
nodes_middle = data['middle'][2]
links_middle = data['middle'][3]
chart = Graph("中唐诗人关系图", **style.init_style)
chart.add("", nodes_middle, links_middle, line_color='#aaa', label_pos="center", is_label_show=True,
graph_repulsion=1000, is_legend_show=False, line_curve=0.2, label_text_color=None)
java_script = chart.render_embed()
html += html_before + java_script + html_after
page.add(chart)
html_before = data['high'][0]
html_after = data['high'][1]
nodes_high = data['high'][2]
links_high = data['high'][3]
chart = Graph("盛唐诗人关系图", **style.init_style)
chart.add("", nodes_high, links_high, line_color='#aaa', label_pos="center", is_label_show=True,
graph_repulsion=1000, is_legend_show=False, line_curve=0.2, label_text_color=None)
java_script = chart.render_embed()
html += html_before + java_script + html_after
page.add(chart)
html_before = data['late'][0]
html_after = data['late'][1]
nodes_late = data['late'][2]
links_late = data['late'][3]
chart = Graph("晚唐诗人关系图", **style.init_style)
chart.add("", nodes_late, links_late, line_color='#aaa', label_pos="center", is_label_show=True,
graph_repulsion=1000, is_legend_show=False, line_curve=0.2, label_text_color=None)
java_script = chart.render_embed()
html += html_before + java_script + html_after
page.add(chart)
# 最后
script = page.get_js_dependencies()
return html, script
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
})
