def _render_as_image(self, file_type):
"""
This is an internal function to serve _repr_jpeg_,
_repr_png_ and _repr_svg_.
:param file_type: the parameter is mostly image file types.
"""
if CURRENT_CONFIG.jupyter_presentation != file_type:
return None
if self.renderer == constants.SVG_RENDERER:
if file_type != constants.SVG:
raise exceptions.InvalidConfiguration(
"svg renderer produces only svg image."
)
elif file_type not in [constants.JPEG, constants.PNG]:
# CANVAS_RENDERER here
raise exceptions.InvalidConfiguration(
"svg output requires svg renderer."
)
env = engine.create_default_environment(file_type)
outfile = "tmp." + file_type
content = env.render_chart_to_file(
chart=self, path=outfile, verbose=False
)
if content:
os.unlink(outfile)
return content
def render_embed(self):
"""
渲染图表的所有配置项，为 web pages 服务，不过需先提供所需要的js 依赖文件
"""
env = engine.create_default_environment(constants.DEFAULT_HTML)
html = env.render_container_and_echarts_code(self)
return Markup(html)
def _repr_html_(self):
"""
:return: html content for jupyter
"""
dependencies = self.js_dependencies
require_config = CURRENT_CONFIG.produce_require_configuration(
dependencies
)
config_items = require_config["config_items"]
libraries = require_config["libraries"]
env = engine.create_default_environment(constants.DEFAULT_HTML)
return env.render_chart_to_notebook(
charts=self, config_items=config_items, libraries=libraries
)
def render(
self,
path="render.html",
template_name="simple_chart.html",
object_name="chart",
**kwargs
):
_, ext = os.path.splitext(path)
_file_type = ext[1:]
env = engine.create_default_environment(_file_type)
env.render_chart_to_file(
chart=self,
object_name=object_name,
path=path,
template_name=template_name,
**kwargs
)
def render(self,
path="render.html",
template_name="simple_page.html",
object_name="page",
**kwargs):
_, ext = os.path.splitext(path)
_file_type = ext[1:]
if _file_type != constants.DEFAULT_HTML:
raise NotImplementedError(
"Rendering Page instance as image is not supported!")
env = engine.create_default_environment(constants.DEFAULT_HTML)
env.render_chart_to_file(chart=self,
object_name=object_name,
path=path,
template_name=template_name,
**kwargs)
def _repr_html_(self):
"""
渲染配置项并将图形显示在 notebook 中
chart/page => charts
chart.js_dependencies => require_config => config_items, libraries
:return A unicode string.
"""
if CURRENT_CONFIG.jupyter_presentation != constants.DEFAULT_HTML:
return None
require_config = CURRENT_CONFIG.produce_require_configuration(
self.js_dependencies)
config_items = require_config["config_items"]
libraries = require_config["libraries"]
env = engine.create_default_environment(constants.DEFAULT_HTML)
return env.render_chart_to_notebook(charts=(self, ),
config_items=config_items,
libraries=libraries)
def _repr_html_(self):
"""
渲染配置项并将图形显示在 notebook 中
chart/page => charts
chart.js_dependencies => require_config => config_items, libraries
:return A unicode string.
"""
if CURRENT_CONFIG.jupyter_presentation != constants.DEFAULT_HTML:
return None
require_config = CURRENT_CONFIG.produce_require_configuration(
self.js_dependencies
)
config_items = require_config["config_items"]
libraries = require_config["libraries"]
env = engine.create_default_environment(constants.DEFAULT_HTML)
return env.render_chart_to_notebook(
charts=(self,), config_items=config_items, libraries=libraries
)
def render(
self,
path="render.html",
template_name="simple_page.html",
object_name="page",
**kwargs
):
_, ext = os.path.splitext(path)
_file_type = ext[1:]
if _file_type != constants.DEFAULT_HTML:
raise NotImplementedError(
"Rendering Page instance as image is not supported!"
)
env = engine.create_default_environment(constants.DEFAULT_HTML)
env.render_chart_to_file(
chart=self,
object_name=object_name,
path=path,
template_name=template_name,
**kwargs
)
from pyecharts import Bar
from pyecharts.engine import create_default_environment
title = 'bar chart'
index = pd.date_range('7/11/2018', periods=6, freq='M')
df1 = pd.DataFrame(np.random.randn(6), index=index)
df2 = pd.DataFrame(np.random.randn(6), index=index)
print(index)
print(df1.values)
dtvalue1 = [i[0] for i in df1.values]
dtvalue2 = [i[0] for i in df2.values]
print(df1.index)
bar = Bar(title, 'Profit and loss situation')
#提供更多使用工具 is_more_utils=True
#is_convert x轴和y轴转换
bar.add('Profit',
index,
dtvalue1,
is_more_utils=True,
is_legend_show=True,
is_convert=True,
is_stack=True)
bar.add('loss', index, dtvalue1, is_convert=True, is_stack=True)
env = create_default_environment('png')
bar.render(path=r'E:\python_project\pyecharts\ex2.html')
def drawing(chart, path, filet_type='html'):
env = create_default_environment(filet_type)
# create_default_environment(filet_type)
# file_type: 'html', 'svg', 'png', 'jpeg', 'gif' or 'pdf'
env.render_chart_to_file(chart, path=path)
def create_charts():
page = Page()
#饼形图
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
chart = Pie("饼图示例1")
chart.add("", attr, v1, is_label_show=True)
#chart.render()
page.add(chart)
#1.第一个示例
bar = Bar("我的第一个图表1", "这里是副标题")
#主要方法，用于添加图表的数据和设置各种配置项
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], \
[5, 20, 36, 10, 75, 90])
# 该行只为了打印配置项，方便调试时使用
bar.print_echarts_options()
# 生成本地 HTML 文件
#默认将会在根目录下生成一个 render.html 的文件，支持 path 参数，设置文件保存位置，
# 如 render(r"e:\my_first_chart.html")，文件用浏览器打开。
bar.render(r'我的第一个图表.html')
#使用Page类，将图表加入到对象中
page.add(bar)
#2.如果想要提供更多实用工具按钮，请在 add() 中设置 is_more_utils 为 True
bar = Bar("我的第一个图表2", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90],
is_more_utils=True)
#bar.render()
page.add(bar)
#3.使用主题
bar = Bar("我的第一个图表3", "这里是副标题")
bar.use_theme('dark')
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], \
[5, 20, 36, 10, 75, 90])
#bar.render()
page.add(bar)
#4.使用 pyecharts-snapshot 插件--直接保存为图片，
# 文件结尾可以为 svg/jpeg/png/pdf/gif。
# 请注意，svg 文件需要你在初始化 bar 的时候设置 renderer='svg'。
bar = Bar("我的第一个图表4", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90])
bar.render(path=r'我的第一个图表.gif')
#page.add(bar)
#5.多次显示图表
bar = Bar("我的第一个图表5", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90])
line = Line("我的第一个图表", "这里是副标题")
line.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"],
[5, 20, 36, 10, 75, 90])
env = create_default_environment("html")
# 为渲染创建一个默认配置环境
# create_default_environment(filet_ype)
# file_type: 'html', 'svg', 'png', 'jpeg', 'gif' or 'pdf'
env.render_chart_to_file(bar, path='bar.html')
env.render_chart_to_file(line, path='line.html')
return page
from pyecharts import Bar, Line
from pyecharts.engine import create_default_environment
bar = Bar("我的第一个图表", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])
line = Line("我的第一个图表", "这里是副标题")
line.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])
env = create_default_environment("html")
env.render_container_and_echarts_code(line)
env.render_container_and_echarts_code(bar)
print(env.render_container_and_echarts_code(bar))
window['myChart_'+]
%hist
print(df[['area', 'city']])
#城市分布
city_count = df.groupby('city')['city'].count().sort_values(ascending=False)
print(city_count)
#柱状图分析
bar = Bar("城市分布")
bar.add(
"城市",
city_count.index[:30], #取前三十个数据
city_count.values[:30], #取前三十个数据
is_label_show=True,
xaxis_interval=0,
xaxis_rotate=-45)
env = create_default_environment('html')
env.render_chart_to_file(bar, path='bar.html')
# #组合成元祖，方便后续地图可视化传入数据
# city_data=list(zip(city_count.index,city_count.values))
# print(city_data)
#
# #地理坐标系Geo
# geo=Geo('城市分布情况','data from SinaNews',title_color='#fff',title_pos='center',
# width=800,height=600,background_color='#404a59')
# attr,value=geo.cast(city_data)
# geo.add(
# '',
# attr,
# value,
# visual_range=[0,360],
line_opacity="80%",
xaxis_label_textsize=18,
yaxis_label_textsize=18,
xaxis_name_size=18,
yaxis_name_size=18,
xaxis_name_gap=30,
yaxis_name_gap=80,
xaxis_name_pos="center",
yaxis_name_pos="middle",
xaxis_line_width=2,
yaxis_line_width=2,
xaxis_splitline_show=False,
yaxis_splitline_show=False,
is_splitline_show=False,
symbol_size=15,
is_datazoom_show=args.zoom,
)
for key in xdata:
scatter.add(key,
xdata[key],
ydata[key],
xaxis_max=args.xmax,
yaxis_max=args.ymax,
xaxis_name=xaxis_name,
yaxis_name=yaxis_name,
**s_style)
grid.add(scatter, grid_bottom="20%")
env = create_default_environment(args.type)
env.render_chart_to_file(grid, path=args.outfile)
import json
from pyecharts import Line, Bar, Pie, EffectScatter
from pyecharts.engine import create_default_environment
#from snapshot_selenium import snapshot as driver
#from pyecharts.render import make_snapshot
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
line = Line('折线图')
line.add('商家A', attr, v1, mark_point=['max'])
line.add('商家B', attr, v2, mark_point=['min'], is_smooth=True)
#line.show_config()
env = create_default_environment("png")
env.render_chart_to_file(line, path='line.png')
#line.render(path='./test.html')
def analyse(calculate_word_cloud=True):
"""
分析 data.db -> user 的用户数据。
:param calculate_word_cloud: 是否计算词云，这个比较花费时间。
:return:
"""
conn = sqlite3.connect('data.db')
cursor = conn.cursor()
cursor.execute('SELECT * FROM user')
data = cursor.fetchall()
# 为渲染创建一个默认配置环境
# create_default_environment(filet_ype)
# file_type: 'html', 'svg', 'png', 'jpeg', 'gif' or 'pdf'
env = create_default_environment("html")
# 1. gender
gender_dict = {'male': 0, 'female': 0, 'unknown': 0}
for x in data:
if x[3] == 'male':
gender_dict['male'] += 1
elif x[3] == 'female':
gender_dict['female'] += 1
else:
gender_dict['unknown'] += 1
print(
"There are {} male users, {} female users, and {} users gender unknown."
.format(gender_dict['male'], gender_dict['female'],
gender_dict['unknown']))
attr1 = ['Male', 'Female', 'unknown']
v1 = [gender_dict['male'], gender_dict['female'], gender_dict['unknown']]
gender = Pie("性别")
gender.add("", attr1, v1, is_label_show=True)
env.render_chart_to_file(gender, path='./html_files/gender.html')
print(
'-----------------------------------------------------------------------------------'
)
# 2. 地区
region_dict = dict()
for x in data:
if x[6] in region_dict.keys():
region_dict[x[6]] += 1
else:
region_dict[x[6]] = 1
region_list = [(k, v) for k, v in region_dict.items()]
region_list = sorted(region_list, key=lambda x: x[1], reverse=True)
print('Top30 region count: ')
for i in range(30):
if region_list[i][0]:
print(region_list[i][0], end=',')
print(region_list[i][1])
else:
print('None', end=',')
print(region_list[i][1])
style = Style(width=1100, height=600)
# 1是中国数据，2是世界数据
china_data_dict = dict()
v1 = list()
v2 = list()
a1 = list()
a2 = list()
# 国家名字典
country_name_dict = {
'西班牙': 'Spain',
'英国': 'United Kingdom',
'美国': 'United States',
'俄罗斯': 'Russia',
'意大利': 'Italy',
'加拿大': 'Canada',
'澳大利亚': 'Australia',
'日本': 'Japan',
'法国': 'France',
'韩国': 'Korea',
'巴西': 'Brazil',
'新加坡': 'Singapore',
'马来西亚': 'Malaysia',
'泰国': 'Thailand',
'中国': 'China',
'其他': 'Unknown Country'
}
for item in region_list:
if item[0]:
x = item[0].split()
if x[0] != '海外':
if x[0] in china_data_dict.keys():
china_data_dict[x[0]] += item[1]
else:
china_data_dict[x[0]] = item[1]
else:
if x[1] != '其他':
a2.append(country_name_dict.get(x[1], x[1]))
v2.append(item[1])
for k, v in china_data_dict.items():
a1.append(k)
v1.append(v)
# 加上中国的数据
# a2.append('China')
# v2.append(sum(v1))
# 归一化到 0 - 100
def normalize(l):
m = min(l)
l = [x - m for x in l]
m = max(l)
l = [x / m * 100 for x in l]
return l
v1 = normalize(v1)
v2 = normalize(v2)
# 输出html
chart = Map("国内懂球帝分布", **style.init_style)
chart.add("",
a1,
v1,
maptype='china',
is_visualmap=True,
visual_text_color='#000')
env.render_chart_to_file(chart,
path='./html_files/china_distribution.html')
# 输出html
chart = Map("海外懂球帝分布", **style.init_style)
chart.add("",
a2,
v2,
maptype='world',
is_visualmap=True,
visual_text_color='#000')
env.render_chart_to_file(chart,
path='./html_files/world_distribution.html')
print(
'-----------------------------------------------------------------------------------'
)
# 3. 联赛
process_league_data('国家队', cursor, data, env)
process_league_data('中超', cursor, data, env)
process_league_data('英超', cursor, data, env)
process_league_data('西甲', cursor, data, env)
process_league_data('意甲', cursor, data, env)
process_league_data('德甲', cursor, data, env)
process_league_data('法甲', cursor, data, env)
process_league_data('国内', cursor, data, env)
process_league_data('俱乐部', cursor, data, env, figure_type='bar', limit=20)
print(
'-----------------------------------------------------------------------------------'
)
# 4. name word cloud
if calculate_word_cloud:
name_list = [x[2] for x in data]
jieba.load_userdict('dict.txt')
cut_name_list = [jieba.cut(x, cut_all=False) for x in name_list]
# cut_name_list = [jieba.cut_for_search(x) for x in name_list]
#加载停用词表
stop = [line.strip() for line in open('stop_words.txt').readlines()]
word_dict = dict()
for cut_name in cut_name_list:
for word in cut_name:
if word not in stop:
if word in word_dict.keys():
word_dict[word] += 1
else:
word_dict[word] = 1
value_list = [(k, v) for k, v in word_dict.items()]
value_list = sorted(value_list, key=lambda x: x[1], reverse=True)
# 去掉单字
attr = [x[0] for x in value_list if len(x[0]) > 1]
value = [x[1] for x in value_list if len(x[0]) > 1]
# 取 top 100
attr = attr[:100]
value = value[:100]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", attr, value, word_size_range=[20, 100])
env.render_chart_to_file(wordcloud,
path='./html_files/word_cloud.html')
print(
'-----------------------------------------------------------------------------------'
)
# 5. 加入时间
join_time = [int(x[8][6:-1]) for x in data if x[8][6:-1]]
join_time_dict = dict()
for x in join_time:
if x in join_time_dict.keys():
join_time_dict[x] += 1
else:
join_time_dict[x] = 1
join_time_list = [(k, v) for k, v in join_time_dict.items()]
join_time_list = sorted(join_time_list, key=lambda x: x[0])
attr = [x[0] for x in join_time_list]
v = [x[1] for x in join_time_list]
t = datetime.datetime(2018, 5, 12)
attr = [t - datetime.timedelta(days=x) for x in attr]
attr = [f'{x.year}-{x.month}-{x.day}' for x in attr]
bar = Bar(f"加入时间")
bar.add("", attr, v, is_stack=True)
env.render_chart_to_file(bar, path='./html_files/join_time.html')
print(
'-----------------------------------------------------------------------------------'
)
# 6. post times
post_time = [int(x[10]) for x in data if x[10] and int(x[10]) >= 0]
post_time_dict = dict()
for x in post_time:
if x in post_time_dict.keys():
post_time_dict[x] += 1
else:
post_time_dict[x] = 1
post_time_list = [(k, v) for k, v in post_time_dict.items()]
post_time_list = sorted(post_time_list, key=lambda x: x[0])
attr = [x[0] for x in post_time_list]
v = [x[1] for x in post_time_list]
bar = Bar(f"Post times")
bar.add("", attr, v, is_stack=True)
env.render_chart_to_file(bar, path='./html_files/post_times.html')
print(
'-----------------------------------------------------------------------------------'
)
# 6. reply times
reply_time = [int(x[11]) for x in data if x[11] and int(x[11]) >= 0]
reply_time_dict = dict()
for x in reply_time:
if x in reply_time_dict.keys():
reply_time_dict[x] += 1
else:
reply_time_dict[x] = 1
reply_time_list = [(k, v) for k, v in reply_time_dict.items()]
reply_time_list = sorted(reply_time_list, key=lambda x: x[0])
attr = [x[0] for x in reply_time_list]
v = [x[1] for x in reply_time_list]
bar = Bar(f"Reply times")
bar.add("", attr, v, is_stack=True)
env.render_chart_to_file(bar, path='./html_files/reply_times.html')
print(
'-----------------------------------------------------------------------------------'
)
cursor.close()
conn.commit()
conn.close()
def report(request, year, user):
year = year or datetime.datetime.now().year
year = int(year)
# 使用info存储整年和每月的数据
info = []
# 从 0 月（一整年） 到 12 月的数据
for month in range(13):
info.append(get_report_data(user, year, month))
months = ['全年'] + [str(x) + "月" for x in range(1, 13)]
charts = []
# 收治情况
charts.append(Bar("%d年收治" % (year), "每月收治人数"))
charts[-1].add("收治人数", months[1:],
[monthData['entry__sum'] for monthData in info[1:]])
# 床位日
charts.append(Bar("%d年床位日" % (year), "每月床位日"))
charts[-1].add("床位日", months[1:],
[monthData['total__sum'] for monthData in info[1:]])
# 收治率(大概率)
charts.append(Bar("%d年" % (year), "每月统计率%"))
charts[-1].add("ApacheII评分≥15分收治率", months,
[monthData['apacheII15Rate'] for monthData in info])
charts[-1].add("预计病死率", months,
[monthData['apacheIIDeathRate'] for monthData in info])
charts[-1].add("标化病死率", months,
[monthData['standDeathRate'] for monthData in info])
# 收治率
charts.append(Bar("%d年" % (year), "每月统计率%"))
charts[-1].add("治疗性抗生素标本送检率", months,
[monthData['sampleRate'] for monthData in info])
charts[-1].add("深静脉血栓预防率", months,
[monthData['preventDVTRate'] for monthData in info])
charts[-1].add("3hBundle完成率", months,
[monthData['bundle3Rate'] for monthData in info])
charts[-1].add("6hBundle完成率", months,
[monthData['bundle6Rate'] for monthData in info])
# 收治率（小概率）
charts.append(Bar("%d年" % (year), "每月统计率%"))
charts[-1].add(
"非计划气管插管拨管率", months,
[monthData['unplannedExtubationRate'] for monthData in info])
charts[-1].add("48h再插管率", months,
[monthData['reintubationRate'] for monthData in info])
charts[-1].add("非计划术后入ICU率", months,
[monthData['unplannedShiftRate'] for monthData in info])
charts[-1].add("48小时后重返ICU率", months,
[monthData['revertRate'] for monthData in info])
# 三管监测（小概率）
charts.append(Bar("%d年" % (year), "每月统计率‰"))
charts[-1].add("VAP发生率", months,
[monthData['VAPRate'] for monthData in info])
charts[-1].add("CRBSI发生率", months,
[monthData['CRBSIRate'] for monthData in info])
charts[-1].add("CAUTI发生率", months,
[monthData['CAUTIRate'] for monthData in info])
# 为渲染创建一个默认配置环境
env = create_default_environment("html")
echarts = []
for chart in charts:
echarts.append(env.render_container_and_echarts_code(chart=chart))
template = "icu/tools/qualityControl/report.html"
return render(request, template, {
'user': user,
'info': info,
'echarts': echarts
})
