def search():
page = Pie()
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 pie_two_html(self, chart_id, title, sub_title1, sub_title2, key1, key2, val1, val2):
# 实例化饼状图
pie = Pie(init_opts=opts.InitOpts(width="100%", height="300px"))
pie.set_global_opts(
title_opts=opts.TitleOpts(
title="{}-{}".format(self.dt, title),
title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=14),
pos_left="center"
),
legend_opts=opts.LegendOpts(is_show=False),
)
pie.chart_id = chart_id
# print("key1: %s" % key1)
# print("val1: %s" % val1)
pie.add(
sub_title1,
[list(z1) for z1 in zip(key1, val1)],
center=[300, 160],
radius=[30, 75],
)
pie.add(
sub_title2,
[list(z2) for z2 in zip(key2, val2)],
center=[1100, 160],
radius=[30, 75],
)
return pie.render_embed()
def MutiplePieCharts(datas):
height_i = '450'
if len(datas) > 3:
height_i = height_i + (350 * (len(datas) // 3))
height = str(height_i) + 'px'
pie = Pie(init_opts=opts.InitOpts(width="100%", height=height))
left_i = 1
right_i = 1
for data in datas:
right = str(300 * right_i) + 'px'
if left_i == 1:
left = '20%'
left_i = 2
elif left_i == 2:
left = '50%'
left_i = 3
else:
left = '80%'
right_i += 1
left_i = 1
pie.add(
data[0],
data[1],
center=[left, right],
radius=120,
)
pie.set_global_opts(title_opts=opts.TitleOpts(title="磁盘使用率"), )
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"), )
return pie.render_embed()
def data_visualized(user_id):
project = Project.query.filter_by(user_id=user_id).first()
duration = (project.expected_end_date - project.start_date).days
tasks = Task.query.filter_by(project_id=project.project_id).all()
task_name = []
task_duration = []
for task in tasks:
task_name.append("task " + str(task.task_id))
# start_date = datetime.datetime(task.start_date)
# end_date = datetime.datetime(task.end_date)
interval = (task.end_date - task.start_date).days
duration -= interval
task_duration.append(interval)
task_name.append('Unused time')
task_duration.append(duration)
time_usage_chart = Pie({
'width': '400px',
'height': '400px',
'chart_id': project.project_name + '_usage_chart'
})
# time_usage_chart.add_xaxis(task_name)
# time_usage_chart.add_yaxis('duration', task_duration)
time_usage_chart.add(
project.project_name,
[list(z) for z in zip(task_name, task_duration)],
radius=["30%", "55%"],
)
return render_template('data_visualized.html',
time_usage=time_usage_chart.render_embed())
def generate_pie_html(dict_rows, key, value):
data = []
for row in dict_rows:
data.append([row[key], round(row[value], 2)])
pie = Pie(init_opts=opts.InitOpts(theme=ThemeType.SHINE))
pie.add("", data)
# pie.set_global_opts(title_opts=opts.TitleOpts(title="我的摊大饼策略分布"))
pie.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
pie_html = pie.render_embed('template.html', env)
return pie_html
def sentiment_pie(comments_data):
postive_count = 0
negative_count = 0
for item in comments_data:
if item.sentiment_score > 0.5:
postive_count += 1
else:
negative_count += 1
pie = Pie()
pie.add("", [["positve", postive_count], ["negative", negative_count]])
return pie.render_embed()
def product_chart():
view_num = []
for p in Product.objects.order_by('-view_num')[:30]:
view_num.append([p.title[:20], p.view_num])
view_num_pie = Pie(init_opts=opts.InitOpts(js_host='../static/js/', width='1100px')
).add('', view_num).set_global_opts(
title_opts=opts.TitleOpts(title="查看数"),
legend_opts=opts.LegendOpts(
orient="vertical", pos_top="15%", pos_left="100px", type_='scroll'
),
)
return view_num_pie.render_embed()
def category_chart():
category_num = []
for c in Category.objects.all():
category_num.append([c.name, c.product_number])
category_num_pie = Pie(init_opts=opts.InitOpts(js_host='../static/js/', width='1100px')
).add('', category_num).set_global_opts(
title_opts=opts.TitleOpts(title="类别商品数量"),
legend_opts=opts.LegendOpts(
orient="vertical", pos_top="15%", pos_left="100px", type_='scroll'
),
)
return category_num_pie.render_embed()
def admin_control():
'''绘制 电影相关标签饼图'''
# 获取电影标签
tags = Tag.query.all()
tag_id = [i.id for i in tags]
# print(tag_id)
tag_name = [i.name for i in tags]
# print(tag_name)
t_movie = []
for i in tag_id:
# 根据 获取所有电影的标签数量
try:
movies_count = Movie.query.filter_by(tag_id=i).count()
t_movie.append(movies_count)
except Exception as e:
t_movie.append(0)
pie = Pie()
pie.add("标签-电影数据统计", [list(z) for z in zip(tag_name, t_movie)])
return pie.render_embed()
def pie_area2classes(data):
# TODO: Remove fake data
pie = Pie(init_opts=opts.InitOpts(width="2400px", height="1200px"))
root = os.path.join('static', 'spindle')
for area, defects in data.items():
y, x = area.split('_')
if 'A' in y:
cols = list(
set([
filename.split('_')[0] for filename in sorted(
os.listdir(os.path.join(root, 'C8C80')))
]))
else:
cols = list(
set([
filename.split('_')[0] for filename in sorted(
os.listdir(os.path.join(root, 'C9C78')))
]))
pie.add(
series_name=area,
data_pair=[[k, v] for k, v in defects.items()],
center=[
"{:.2f}%".format(90 / len(rows) * rows.index(int(x)) + 10),
"{:.2f}%".format(90 / len(cols) * cols.index(y) + 10),
],
radius=[50, 80],
label_opts=opts.LabelOpts(False),
)
pie.set_global_opts(
title_opts=opts.TitleOpts(title="Area -> Classes 各區域類別分佈"),
legend_opts=opts.LegendOpts(
type_="scroll",
pos_top="5%", # Label y軸位置
pos_left="left", # Label x軸位置
orient="vertical" # Label 垂直顯示
),
)
return pie.render_embed()
def pie_two_html(self, chart_id, title, sub_title1, sub_title2, key1, key2,
val1, val2):
# 实例化饼状图
pie = Pie(self.init_opts)
# 绑定id
pie.chart_id = chart_id
# 绑定属性和值
pie.add(sub_title1, [list(v) for v in zip(key1, val1)],
center=["25%", "50%"],
radius=["30%", "75%"],
rosetype="area",
label_opts=options.LabelOpts(is_show=True))
pie.add(sub_title2, [list(v) for v in zip(key2, val2)],
center=["75%", "50%"],
radius=["30%", "75%"],
rosetype="area",
label_opts=options.LabelOpts(is_show=True))
# 全局参数
pie.set_global_opts(
title_opts=self.title_opts(title), # 标题选项
legend_opts=options.LegendOpts(is_show=False), # 不显示图例组件
)
# 返回图表html代码
return pie.render_embed()
def pyecharts(request):
"""dashboard view"""
# 工单数量统计
chart_dao = ChartDao()
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 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar1.add_xaxis(attr)
bar1.add_yaxis("", value)
# 工单按组统计
data = chart_dao.workflow_by_group(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie1.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie1.add("", [list(z) for z in zip(attr, value)])
# 工单按人统计
data = chart_dao.workflow_by_user(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar2.add_xaxis(attr)
bar2.add_yaxis("", value)
# SQL语句类型统计
data = chart_dao.syntax_type()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2 = Pie()
pie2.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(类型)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie2.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie2.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(每日检索行数)
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 = Line(init_opts=opts.InitOpts(width='600', height='380px'))
line1.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(selected_mode='single'))
line1.add_xaxis(attr)
line1.add_yaxis("检索行数",
effect_value,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="average")]))
line1.add_yaxis("检索次数",
count_value,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max"),
opts.MarkLineItem(type_="average")
]))
# SQL查询统计(用户检索行数)
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 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie4.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie4.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie4.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(DB检索行数)
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 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie5.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie5.set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}", position="left"))
pie5.add("", [list(z) for z in zip(attr, value)])
# 可视化展示页面
chart = {
"bar1": bar1.render_embed(),
"pie1": pie1.render_embed(),
"bar2": bar2.render_embed(),
"pie2": pie2.render_embed(),
"line1": line1.render_embed(),
"pie4": pie4.render_embed(),
"pie5": pie5.render_embed(),
}
# 获取统计数据
dashboard_count_stats = {
"sql_wf_cnt": SqlWorkflow.objects.count(),
"query_wf_cnt": QueryPrivilegesApply.objects.count(),
"user_cnt": Users.objects.count(),
"ins_cnt": Instance.objects.count()
}
return render(request, "dashboard.html", {
"chart": chart,
"count_stats": dashboard_count_stats
})
def analyze(request):
try:
if request.session['username']==None:
return HttpResponseRedirect('/login/')
except KeyError:
return HttpResponseRedirect('/login/')
r={}
r['username'] = request.session['username']
taskid = request.GET.get('taskid')
finshed = DoubanSubject.objects.filter(taskid=taskid)
typelist=[]
l=[]
for i in finshed:
m=i.type.split(' ')
for k in m:
if k not in typelist and k!='' :
typelist.append(k)
l.append(1)
elif k!='':
l[typelist.index(k)]=l[typelist.index(k)]+1
bar=Bar()
bar.add_xaxis(typelist)
print(typelist)
print(l)
bar.add_yaxis("数量",l)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="类型数量"),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
)
r['myechart'] = bar.render_embed()
data_pair = [list(z) for z in zip(typelist, l)]
data_pair.sort(key=lambda x: x[1])
pie=Pie(init_opts=opts.InitOpts(width="1600px", height="800px", bg_color="#DDDD77"))
pie.add(
series_name="类型占比",
data_pair=data_pair,
rosetype="radius",
radius="55%",
center=["50%", "50%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
)
pie.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
)
r['mypie']=pie.render_embed()
namelist=[]
scorelist=[]
for i in finshed:
score=i.score
name=i.name
namelist.append(name)
scorelist.append(score)
bar=Bar()
bar.add_xaxis(namelist)
print(typelist)
print(l)
bar.add_yaxis("评分",scorelist)
bar.set_global_opts(
title_opts=opts.TitleOpts(title="评分"),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
)
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值"),
opts.MarkLineItem(type_="average", name="平均值"),
]
),
)
r['score'] = bar.render_embed()
return render(request,'analyze.html',r)
