def event_temp(request):
print("query event")
print(chainIdx)
chain_keys = list(chains.keys())
chain = chains[chain_keys[int(chainIdx)]] #[{event1},{event2},{event3}]
context = {}
timelineIndex = 0
if 'timelineIndex' in request.GET and request.GET[
'timelineIndex']: #获得tl的index
context["timelineIndex"] = request.GET['timelineIndex']
timelineIndex = int(context["timelineIndex"])
if 'scrollTop' in request.GET and request.GET['scrollTop']:
context["scrollTop"] = request.GET['scrollTop']
line_data, poses, words, frame_ids, table_data = process_chain(chain)
page = Page()
print("timelineIdx:{}".format(timelineIndex))
eventmap = get_eventbmap(poses[timelineIndex])
eventmap.chart_id = "map_event"
eventline = get_eventline(line_data)
eventline.chart_id = "line_event"
wordcloudline = get_wordcloud_line(words[timelineIndex], frame_ids)
wordcloudline.chart_id = "wordcloud_line"
eventtable = get_eventtable(table_data[timelineIndex])
eventtable.chart_id = "table_event"
page.add(eventmap)
page.add(eventline)
page.add(wordcloudline)
page.add(eventtable)
page.render(path="event.html", template_name="simple_page_event.html")
#记得在以下函数中增加了模板函数
Page.save_resize_html("event.html",
cfg_file="./demo/chart_config_event.json",
dest="./demo/templates/my_event_charts.html")
#page.render("bmap.html",template="simple_page_event.html")
return render(request, "my_event_charts.html", context) #可以向模板中填充数据来显示矩形框
def multi_follower_counts(c1, c2, c3, c4, c5):
# 不同赞数区间内的粉丝数分布统计
_, y1 = data_sort(list(c1.index), list(c1))
_, y2 = data_sort(list(c2.index), list(c2))
_, y3 = data_sort(list(c3.index), list(c3))
_, y4 = data_sort(list(c4.index), list(c4))
_, y5 = data_sort(list(c5.index), list(c5))
x = [
'<50', '50-100', '100-500', '0.5k-1k', '1k-5k', '5k-10k', '1w-5w',
'5w-10w', '10w-50w', '50w-100w', '>100w'
]
line = (Line().add_xaxis(x).add_yaxis('赞同数为1k-4k', y1).add_yaxis(
'赞同数为4k-10k', y2).add_yaxis('赞同数为10k-20k', y3).add_yaxis(
'赞同数为20k-40k',
y4).add_yaxis('赞同数为40k以上', y5).extend_axis(yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts())).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(
width=2)).set_global_opts(
title_opts=opts.TitleOpts(title='赞数-粉丝数统计'),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=30),
name='粉丝数'),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts()),
tooltip_opts=opts.TooltipOpts(
trigger="axis", axis_pointer_type="cross"),
legend_opts=opts.LegendOpts(pos_left="right")))
filename = '赞数-粉丝数统计-line.html'
line.render(filename)
print('图表创建成功')
bar = (Bar().add_xaxis(x).add_yaxis('赞同数为1k-4k', y1).add_yaxis(
'赞同数为4k-10k', y2).add_yaxis('赞同数为10k-20k', y3).add_yaxis(
'赞同数为20k-40k',
y4).add_yaxis('赞同数为40k以上', y5).extend_axis(yaxis=opts.AxisOpts(
axislabel_opts=opts.LabelOpts())).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(
width=2)).set_global_opts(
title_opts=opts.TitleOpts(title='赞数-粉丝数统计'),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=30),
name='粉丝数'),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts()),
tooltip_opts=opts.TooltipOpts(
trigger="axis", axis_pointer_type="cross"),
legend_opts=opts.LegendOpts(pos_left="right")))
filename = '赞数-粉丝数统计-bar.html'
bar.render(filename)
print('图表创建成功')
page = Page(layout=Page.SimplePageLayout)
page.add(line, bar)
page.render("赞数-粉丝数统计.html")
print('图表创建成功')
def hu_run_select() -> 'html':
try:
df = pd.read_csv("GDP per person.csv")
the_region = request.form["the_region_selected"] # 年份勾选
log.info("勾选年份为{0}".format(the_region))
fig5 = bar_gdp([country_ls, data3[the_region]])
fig6 = grid_mutil_yaxis(
[country_ls, data3[the_region], data4[the_region]],
["GDP增长率(%)", "生育率(%)"])
fig7 = map_data([countries, data2[the_region]], "识字率",
"1990-2017女性识字率世界地图")
fig8 = map_data([countries, data1[the_region]], "生育率",
"1990-2017青春期女性生育率世界地图")
page = Page(layout=Page.SimplePageLayout)
page.add(fig5, fig6, fig7, fig8)
page.render("task2.html")
with open("task2.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
data_str = df.to_html()
return render_template(
'results2.html',
the_plot_all=plot_all,
the_res=data_str,
the_select_region=regions_available,
)
except Exception as ex:
log.error("页面数据请求失败: {0}".format(str(ex)))
return None
def draw_multiple_pie_02(month, day):
max_width, max_height = 1400, 3000
page = Page(layout=Page.SimplePageLayout)
h_center, v_center = 10, 40
horizontal_step, vertical_step = 350, 320
pies = []
for p in get_province_data(month, day):
title = '%s-%d例' % (p['provinceShortName'], p['confirmedCount'])
labels = [city['cityName'] for city in p['cities']]
counts = [city['confirmedCount'] for city in p['cities']]
if len(labels) == 0:
continue
pie = Pie(init_opts=opts.InitOpts(width='{}px'.format(horizontal_step),
height='{}px'.format(vertical_step)))
pie.set_global_opts(legend_opts=opts.LegendOpts(is_show=False),
title_opts=opts.TitleOpts(title))
pie.add(
title,
[list(z) for z in zip(labels, counts)],
radius=[5, 80],
# center=[h_center + 150, v_center + 110]
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"),
tooltip_opts=opts.TooltipOpts())
h_center += horizontal_step
if h_center + 200 > max_width:
h_center = 10
v_center += vertical_step
page.add(pie)
root = 'html-charts/%d%d' % (month, day)
create_dir(root)
page.render('%s/省份信息2.html' % root)
page.render('provinces.html')
def page_simple_layout(redmineObj, project_name, query_id, tracker_name):
myIssue = MyIssue(redmine)
myTracker = MyTracker(redmine)
# 数据获取
if query_id is not None:
issues = myIssue.get_issues_by_query_id(project_name, query_id)
all_issues = issues
else:
all_issues = myIssue.get_issues(project_name, None,
None) # 根据项目获取所有issue
tracker_id = myTracker.get_trackerId_by_name(
tracker_name) # 跟踪标签需要手动填，不同项目跟踪标签不同
issues = myIssue.get_issues(project_name, tracker_id,
None) # 根据tacker_id获取项目内或者自定义查询内的issue
issues_time = myIssue.stat_issue_by_createOrClose_time(
issues) # 获取issues中的创建时间和关闭时间
priority_data = myIssue.get_issues_by_priority(issues)
assign_to_data = myIssue.stat_issue_by_assignTo(issues)
tracker_bug_data = myIssue.get_issues_by_tracker(all_issues)
# 制作统计图
priority_pie = draw_pie_bug_priority(priority_data) # 按优先级统计BUG
assign_to_pie = draw_pie_bug_agent(assign_to_data) # 按指派人统计BUG
time_line = draw_line_bug_time(issues_time) # 按创建时间/关闭时间统计BUG
tracker_pie = draw_pie_bug_tracker(tracker_bug_data) # 按跟踪标签统计Issue
page = Page(layout=Page.SimplePageLayout)
page.add(tracker_pie, priority_pie, assign_to_pie, time_line)
page.render("./reports/redmine.html")
def DrawIng():
page = Page(
page_title = "coding4.county"
)
page.add(timeline_map())
page.add(timeline_bar())
page.render("4.county.html")
def draw_order_signals(trader_name, render='html'):
df_account = get_account(trader_name=trader_name)
start_timestamp = df_account['timestamp'][0]
end_timestamp = df_account['timestamp'][-1]
df_orders = get_orders(trader_name=trader_name)
grouped = df_orders.groupby('security_id')
page = Page()
for security_id, order_df in grouped:
kdata = get_kdata(security_id=security_id,
provider='netease',
start_timestamp=start_timestamp,
end_timestamp=end_timestamp)
mark_points = order_df
kline = draw_kline(df_list=[kdata],
markpoints_list=[mark_points],
render=None)
page.add(kline)
if render == 'html':
file_name = '{}_signals'.format(trader_name)
page.render(get_ui_path(file_name))
elif render == 'notebook':
page.render_notebook()
return page
def page_draggable_layout():
page = Page(layout=Page.DraggablePageLayout)
page.add(
chinamap(),
bar_sale(),
)
page.render("面板图.html")
def show_dlt(self):
red_balls = []
blue_balls = []
with open(self.input_file, 'r') as f:
for i in range(Config.PAGES * Config.ITEMS_PER_PAGE):
one_line_data = f.readline().strip()
# print(one_line_data)
red_balls.extend([
int(one_line_data[15 + (2 * i):15 + (2 * (i + 1))])
for i in range(5)
])
blue_balls.append(int(one_line_data[-4:-2]))
blue_balls.append(int(one_line_data[-2:]))
red_counter = Counter(red_balls)
blue_counter = Counter(blue_balls)
# print(red_balls)
# print(blue_balls)
# print(red_counter)
# print(blue_counter)
# print(red_counter.most_common())
# print(blue_counter.most_common())
red_dict = {}
blue_dict = {}
for i in red_counter.most_common():
red_dict['{}'.format(i[0])] = i[1]
for j in blue_counter.most_common():
blue_dict['{}'.format(j[0])] = j[1]
print(red_dict)
print(blue_dict)
red_list = sorted(red_counter.most_common(),
key=lambda number: number[0])
blue_list = sorted(blue_counter.most_common(),
key=lambda number: number[0])
print(blue_list)
print(red_list)
# 红球图表添加
red_bar = Bar()
red_x = ['{}'.format(str(x[0])) for x in red_list]
red_y = ['{}'.format(str(x[1])) for x in red_list]
red_bar.add_xaxis(red_x)
red_bar.add_yaxis('红色球出现的次数', red_y)
# 篮球图表添加
blue_bar = Bar()
blue_x = ['{}'.format(str(x[0])) for x in blue_list]
blue_y = ['{}'.format(str(x[1])) for x in blue_list]
blue_bar.add_xaxis(blue_x)
blue_bar.add_yaxis('蓝色球出现的次数',
blue_y,
itemstyle_opts=opts.ItemStyleOpts(color='blue'))
page = Page(page_title='大乐透数据分析', interval=3)
page.add(red_bar)
page.add(blue_bar)
page.render(self.output_file)
def hu_run_select() -> 'html':
the_region = request.form["the_region_selected"]
print(the_region) # 检查用户输入
dfs = df1.query("CountryName=='{}'".format(the_region))
gdp_df = getCsvData('GDP.csv') # 读取GDP.csv 数据内容
select_df = getCsvData('select_nation.csv') # 读取select_nation.csv 数据内容
fig1 = map_world_gdp(gdp_df)
fig2 = pie_base(select_df)
fig3 = bar_datazoom_select(select_df)
fig4 = line_markpoint(select_df)
page = Page(layout=Page.SimplePageLayout)
page.add(fig1, fig2, fig3, fig4)
page.render("child2.html")
with open("templates/child2.html", encoding="utf8", mode="r") as f:
plot_all = "".join(f.readlines())
data_str = dfs.to_html()
return render_template('results2.html',
the_plot_all=plot_all,
the_res=data_str,
the_select_region=regions_available,
)
def page(filename="info"):
'''
页面
'''
# test data
y_data = [{
'name': '安卓',
'values': [0.34, 0.76, 0.88, 0.99, 0.242, 0.42, 0.22]
}, {
'name': 'ios',
'values': [0.50, 0.90, 0.72, 0.34, 0.76, 0.88, 0.99]
}]
x_data = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
page = Page()
page.add(
# timeline_bar_reversal(title='渠道留存'),
liquid_data_precision("111"),
bar_datazoom_slider("222",
y_data=y_data,
x_data=x_data,
datazoom_opts=None,
toolbox_opts=None),
table_base("333"),
line_markpoint("555"),
pie_rosetype("444"),
grid_mutil_yaxis("777"),
)
page.render('{}.html'.format(filename))
def page_simple_layout():
page = Page()
page.add(
line2(),
bar1(),
gauge1(),
)
page.render("C:/Users/suqi/Desktop/allcode/page_simple_layoutnew.html")
def page_layout():
page = Page(layout=Page.DraggablePageLayout)
page.add(
map_world(),
timeline_suicides(),
bar_suicides_no(),
bar_suicides_100k(),
)
page.render("page.html")
def Gather_in_Page():
Data_List = [['项目2',25.02]]
bg= BG()
page = Page(layout=Page.DraggablePageLayout)
page.add(bg)
for NPS_List in Data_List:
gauge = NPS(NPS_List)
page.add(gauge)
page.render('NPS.html')
class Flight(object):
def __init__(self):
self.page = Page(page_title="全国航班数据可视化图表")
self.drawer = Drawing()
self.cleaner = Cleaning()
def exec(self):
today_data = self.cleaner.todayData() # 生成当天的基础数据
geo_airport_data = self.cleaner.geoAirport(
today_data) # 全国机场及出港航班数量分布地理图数据
map_airport_data = self.cleaner.mapProvince(
today_data) # 省级机场数量分布地理图数据
bar_out_port_data = self.cleaner.barOutPort(
today_data) # 机场出港航班数量柱状图数据
bar_in_port_data = self.cleaner.barInPort(today_data) # 机场进港航班数量柱状图数据
line_week_out_data = self.cleaner.lineWeekOut() # 根据周数据生成周内每天出港航班数据--
bar_company_data = self.cleaner.barCompanyOut() # 本周航空公司出港航班数据
bar_out_time_data = self.cleaner.barTime(
today_data, 'start_airport') # 根据当天的基础数据生成出港航班时间数据
bar_in_time_data = self.cleaner.barTime(
today_data, 'end_airport') # 根据当天的基础数据生成进港航班时间数据
bar_out_part_data = self.cleaner.pieTimePart(
bar_out_time_data) # 根据出港数据生成出港时段数据
bar_in_part_data = self.cleaner.pieTimePart(
bar_in_time_data) # 根据进港数据生成进港时段数据
pie_part_data = [
bar_out_part_data, bar_in_part_data, bar_out_time_data[1],
bar_in_time_data[1]
] # 将进、出港和总数添加到列表
heat_out_time_data = self.cleaner.heatOutPortTime(
today_data) # 根据周数据生成每天出港航班时间热力图数据--
heat_line_city_data = self.cleaner.geoLineCity() # 生成城市出港航班热力线图数据
# ------------------------------------------------------
self.page.add(self.drawer.geoAirport(geo_airport_data)) # 画全国机场分布与数量图
self.page.add(self.drawer.mapProvince(map_airport_data)) # 画全国机场省级分布图
self.page.add(self.drawer.barAirPort(bar_in_port_data)) # 画机场进港航班柱状图
self.page.add(self.drawer.barAirPort(bar_out_port_data)) # 画机场出港航班柱状图
self.page.add(
self.drawer.lineWeekOut(line_week_out_data)) # 画本周出港航班数量柱状图
self.page.add(
self.drawer.barCompanyOut(bar_company_data)) # 画本周航空公司出港航班柱状图
self.page.add(self.drawer.barTime(bar_in_time_data)) # 画进港时间的柱状图
self.page.add(self.drawer.barTime(bar_out_time_data)) # 画出港时间的柱状图
self.page.add(self.drawer.pieTime(pie_part_data)) # 同时画进、出港时段的玫瑰饼图
self.page.add(
self.drawer.heatOutPortTime(heat_out_time_data)) # 画周每天出港航班时间热力图
self.page.add(
self.drawer.geoLineCity(heat_line_city_data)) # 画城市出港航班热力线图
self.page.render('flights.html')
print('数据地图生成完毕，请打开浏览器查看，多谢。')
def page_layout(list, title, name):
page = Page(layout=Page.SimplePageLayout)
page.add(
death_histogram(list, title, name),
CumulativeDeaths_histogram(list, title, name),
Confirmed_histogram(list, title, name),
CumulativeConfirmed_histogram(list, title, name),
timeline(list, title, name),
)
page.render("templates/COVID/" + name + '.html')
def page_simple_layout():
page = Page(layout=Page.SimplePageLayout)
page.add(
course_selection_ranking(),
class_sum_by_subject(),
subject_pie(),
word_cloud(),
)
page.page_title = "MOOC当前开放课程可视化"
page.render("当前全部开放课程可视化.html")
def draw_multiple_map(month, day):
page = Page(layout=Page.SimplePageLayout)
size = ('1000px', '1000px')
# 全国疫情地图
labels, counts = get_province_status(month, day)
country_map = get_map(labels=labels, counts=counts,
title='全国-%s例' % get_total_statistic(month, day)['confirmedCount'], size=size, where='china',
pieces=get_default_pieces())
page.add(country_map)
page.render('全国新冠数据地理分布展示.html')
def page_draggable_layout():
page = Page(layout=Page.DraggablePageLayout)
page.add(
bar_datazoom_slider(),
line_markpoint(),
pie_rosetype(),
grid_mutil_yaxis(),
liquid_data_precision(),
table_base(),
)
page.render()
def get_all_chart():
page = Page()
page.add(get_language_proportion())
page.add(get_salary_proportion())
page.add(get_map())
#
page.add(get_education_background())
page.render("temp/all.html")
# get_education_background()
pass
def page_default_layout():
page = Page()
page.add(
bar_datazoom_slider(),
line_markpoint(),
pie_rosetype(),
grid_mutil_yaxis(),
liquid_data_precision(),
table_base(),
)
page.render("page_default_layout.html")
def plot_all():
chart1 = valuation_report()
chart2 = tail_northup()
chart3 = tail_zxqh()
page = Page()
page.add(chart1, chart2, chart3)
output_path = os.path.join(PROJECT_DIR, "utils/output/统计指标.html")
page.render(output_path)
return "utils/output/统计指标.html"
def page_draggable_layout():
page = Page(layout=Page.DraggablePageLayout)
page.add(
bar_reverse(),
gauge(),
liquid(),
rose(),
wordcloud(),
bar(),
)
page.render("page.html")
Page.save_resize_html("page.html", cfg_file="chart_config.json", dest="my_charts.html")
def draw_multiple_map(month, day):
page = Page(layout=Page.SimplePageLayout)
size = ('350px', '380px')
# 全国疫情地图
labels, counts = get_province_status(month, day)
country_map = get_map(labels=labels,
counts=counts,
title='全国-%s例' %
get_total_statistic(month, day)['confirmedCount'],
size=size,
where='china',
pieces=get_default_pieces())
page.add(country_map)
# 已知城市名称
defined_cities = [
line.strip() for line in open(
'py_echarts_city_names.txt', 'r', encoding='UTF-8').readlines()
]
# 省份疫情地图
for p in get_province_data(month, day):
title = '%s-%d例' % (p['provinceShortName'], p['confirmedCount'])
# 城市映射
labels = []
for city in p['cities']:
name = city['cityName']
map_name = get_city_map(name, defined_cities)
if not map_name:
map_name = get_city_map(name[:-1], defined_cities)
labels.append(name if not map_name else map_name)
# 数量
counts = [city['confirmedCount'] for city in p['cities']]
if len(labels) == 0:
continue
pieces = get_pieces(min(counts), max(counts))
province_map = get_map(labels=labels,
counts=counts,
title=title,
size=size,
where=p['provinceShortName'],
pieces=pieces)
page.add(province_map)
root = 'html-charts/%d%d' % (month, day)
create_dir(root)
page.render('%s/省份地图.html' % root)
def all_map():
"""
china_total_map: 中国累计确诊地图
global_total_map: 全球累计确诊地图
china_daily_map: 中国每日数据折线图
foreign_daily_map: 境外每日数据折线图
全球包含中国，境外不包含中国！
"""
page = Page(layout=Page.SimplePageLayout, page_title="TRHX丨疫情实时大数据")
page.add(china_total_map(), global_total_map(), china_daily_map(),
foreign_daily_map())
page.render('COVID-19.html')
print('国内以及境外疫情可视化图表绘制完毕！')
def charts_display(driver, movie_name):
df = preprocess_data(movie_name)
pie = rating_distribution(df)
bar, geo = city_distribution(df)
line = comment_heat_analysis(df)
word = wordcloud_analysis(df)
# 页面顺序渲染多图
title = movie_name + '影评分析-data_from_douban.html'
page = Page(page_title=title)
page.add(pie, bar, geo, line, word)
page.render(title)
# 打开生成的网页
webbrowser.open(title)
def fund_reports(request, *args, **kwargs):
bar = Bar()
bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [randrange(0, 100) for _ in range(6)])
bar.add_yaxis("商家B", [randrange(0, 100) for _ in range(6)])
bar1 = Bar()
bar1.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
bar1.add_yaxis("商家A", [randrange(0, 100) for _ in range(6)])
bar1.add_yaxis("商家B", [randrange(0, 100) for _ in range(6)])
page = Page()
page.add(bar)
page.add(bar1)
page.render('templates/test.html')
return render(request, 'test.html')
def image_info(root, path='image_info.html', show_original=False):
"""Show image visualize.
Args:
root: An image file root.
path: result save a html file.
show_original: whether show original image shape.
Returns:
A pyecharts polt object.
"""
shape = read_image(root).shape
info = PIL.Image.open(root).info
width = '' if show_original else str(360)+'px'
height = '' if show_original else str(int(360/shape[1]*shape[0]))+'px'
image_charts = image_base(root, '', '', width, height)
headers = ["Attribute", "Info"]
rows = [['path', root],
['size', str(round(os.stat(root).st_size/1024/1024, 2))+'M'],
['shape', str(read_image(root).shape)],
['jfif', info['jfif']],
['jfif_version', info['jfif_version']],
['dpi', info['dpi']],
['jfif_unit', info['jfif_unit']],
['jfif_density', info['jfif_density']]
]
table_charts = Table().add(headers, rows)
page = Page(layout=Page.SimplePageLayout).add(*[image_charts, table_charts])
return page.render(path)
def draw_account_details(trader_name, render='html'):
page = Page()
account_summary = draw_account_list([trader_name], render=None)
# klines = draw_order_signals(trader_name=trader_name, render=None)
positions = draw_positions(trader_name=trader_name, render=None)
page.add(account_summary, positions)
if render == 'html':
file_name = '{}_details'.format(trader_name)
page.render(get_ui_path(file_name))
elif render == 'notebook':
page.render_notebook()
return page
def draw_multiple_pie(month, day):
page = Page(layout=Page.SimplePageLayout)
labels, counts = get_province_status(month, day)
page.add(
get_pie(labels, counts, '全国-%s例' % get_total_statistic(month, day)['confirmedCount'], size=('360px', '330px')))
for p in get_province_data(month, day):
title = '%s-%d例' % (p['provinceShortName'], p['confirmedCount'])
labels = [city['cityName'] for city in p['cities']]
counts = [city['confirmedCount'] for city in p['cities']]
if len(labels) == 0:
continue
page.add(get_pie(labels, counts, title, size=('350px', '320px')))
root = 'html-charts/%d%d' % (month, day)
create_dir(root)
page.render('%s/省份信息.html' % root)
