def test_grid_line_pie():
line = Line("折线图示例", width=1200)
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
)
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="55%")
pie.add(
"",
CLOTHES,
v1,
radius=[45, 65],
center=[65, 50],
legend_pos="80%",
legend_orient="vertical",
)
grid = Grid()
grid.add(line, grid_right="55%")
grid.add(pie, grid_left="60%")
grid.render()
def get_pie(item_name,item_name_list,item_num_list):
totle = item_num_list[0]+item_num_list[1]+item_num_list[2]
subtitle = "共有:%d个好友"%totle
pie = Pie(item_name,page_title = item_name,title_text_size=30,title_pos='center',\
subtitle = subtitle,subtitle_text_size = 25,width=800,height= 800)
pie.add("", item_name_list, item_num_list,is_label_show=True,center=[50, 45],radius=[0,50],\
legend_pos ='left',legend_orient='vertical',label_text_size=20)
out_file_name = './analyse/'+item_name+'.html'
#print(out_file_name)
pie.render(out_file_name)
def test_pie_legend():
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
pie.render()
def test_page():
page = Page()
line = Line("折线图示例")
line.chart_id = "id_my_cell_line"
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center", width="600px")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie, create_a_bar(TITLE)])
# Start render and test
html = page._repr_html_()
# Test base html structure
assert html.count("<script>") == html.count("</script>") == 2
assert html.count("<div") == html.count("</div>") == 3
assert html.count("require.config") == html.count("function(echarts)") == 1
# Test some chart attributes
json_encoded_title = json.dumps(TITLE)
assert json_encoded_title in html
assert "nbextensions/echarts" in html # default jshost
assert html.count("height:400px") == 3
assert html.count("width:600px") == 1
assert html.count("width:800px") == 2
assert html.count("id_my_cell_line") == 6
def draw_citys_pic(csv_file):
page = Page(csv_file+":评论城市分析")
info = count_city(csv_file)
geo = Geo("","Ctipsy原创",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff")
while True: # 二次筛选，和pyecharts支持的城市库进行匹配，如果报错则删除该城市对应的统计
try:
attr, val = geo.cast(info)
geo.add("", attr, val, visual_range=[0, 300], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=6, symbol_size=15, is_visualmap=True)
except ValueError as e:
e = str(e)
e = e.split("No coordinate is specified for ")[1] # 获取不支持的城市名称
info.pop(e)
else:
break
info = sorted(info.items(), key=lambda x: x[1], reverse=False) # list排序
# print(info)
info = dict(info) #list转dict
# print(info)
attr, val = [], []
for key in info:
attr.append(key)
val.append(info[key])
geo1 = Geo("", "评论城市分布", title_pos="center", width=1200, height=600,
background_color='#404a59', title_color="#fff")
geo1.add("", attr, val, visual_range=[0, 300], visual_text_color="#fff", is_geo_effect_show=False,
is_piecewise=True, visual_split_number=10, symbol_size=15, is_visualmap=True, is_more_utils=True)
#geo1.render(csv_file + "_城市dotmap.html")
page.add_chart(geo1)
geo2 = Geo("", "评论来源热力图",title_pos="center", width=1200,height=600, background_color='#404a59', title_color="#fff",)
geo2.add("", attr, val, type="heatmap", is_visualmap=True, visual_range=[0, 50],visual_text_color='#fff', is_more_utils=True)
#geo2.render(csv_file+"_城市heatmap.html") # 取CSV文件名的前8位数
page.add_chart(geo2)
bar = Bar("", "评论来源排行", title_pos="center", width=1200, height=600 )
bar.add("", attr, val, is_visualmap=True, visual_range=[0, 100], visual_text_color='#fff',mark_point=["average"],mark_line=["average"],
is_more_utils=True, is_label_show=True, is_datazoom_show=True, xaxis_rotate=45)
#bar.render(csv_file+"_城市评论bar.html") # 取CSV文件名的前8位数
page.add_chart(bar)
pie = Pie("", "评论来源饼图", title_pos="right", width=1200, height=600)
pie.add("", attr, val, radius=[20, 50], label_text_color=None, is_label_show=True, legend_orient='vertical', is_more_utils=True, legend_pos='left')
#pie.render(csv_file + "_城市评论Pie.html") # 取CSV文件名的前8位数
page.add_chart(pie)
page.render(csv_file + "_城市评论分析汇总.html")
def test_pie_type_radius():
pie = Pie("饼图示例", title_pos='center', width=1000, height=600)
pie.add("", ['A', 'B', 'C', 'D', 'E', 'F'], [335, 321, 234, 135, 251, 148],
radius=[40, 55], is_label_show=True)
pie.add("", ['H', 'I', 'J'], [335, 679, 204], radius=[0, 30],
legend_orient='vertical', legend_pos='left')
pie.render()
def test_pie_type_rose():
v1 = [11, 12, 13, 10, 10, 10]
v2 = [19, 21, 32, 20, 20, 33]
pie = Pie("饼图-玫瑰图示例", title_pos="center", width=900)
pie.add(
"商品A",
CLOTHES,
v1,
center=[25, 50],
is_random=True,
radius=[30, 75],
rosetype="radius",
)
pie.add(
"商品B",
CLOTHES,
v2,
center=[75, 50],
is_random=True,
radius=[30, 75],
rosetype="area",
is_legend_show=False,
is_label_show=True,
)
pie.render()
def test_pie_type_radius():
pie = Pie("饼图示例", title_pos="center", width=1000, height=600)
pie.add(
"",
["A", "B", "C", "D", "E", "F"],
[335, 321, 234, 135, 251, 148],
radius=[40, 55],
is_label_show=True,
)
pie.add(
"",
["H", "I", "J"],
[335, 679, 204],
radius=[0, 30],
legend_orient="vertical",
legend_pos="left",
)
pie.render()
def test_pie_multiple_movie():
pie = Pie('各类电影中"好片"所占的比例', "数据来着豆瓣", title_pos="center")
style = Style()
pie_style = style.add(
label_pos="center", is_label_show=True, label_text_color=None
)
pie.add(
"", ["剧情", ""], [25, 75], center=[10, 30], radius=[18, 24], **pie_style
)
pie.add(
"", ["奇幻", ""], [24, 76], center=[30, 30], radius=[18, 24], **pie_style
)
pie.add(
"", ["爱情", ""], [14, 86], center=[50, 30], radius=[18, 24], **pie_style
)
pie.add(
"", ["惊悚", ""], [11, 89], center=[70, 30], radius=[18, 24], **pie_style
)
pie.add(
"", ["冒险", ""], [27, 73], center=[90, 30], radius=[18, 24], **pie_style
)
pie.add(
"", ["动作", ""], [15, 85], center=[10, 70], radius=[18, 24], **pie_style
)
pie.add(
"", ["喜剧", ""], [54, 46], center=[30, 70], radius=[18, 24], **pie_style
)
pie.add(
"", ["科幻", ""], [26, 74], center=[50, 70], radius=[18, 24], **pie_style
)
pie.add(
"", ["悬疑", ""], [25, 75], center=[70, 70], radius=[18, 24], **pie_style
)
pie.add(
"",
["犯罪", ""],
[28, 72],
center=[90, 70],
radius=[18, 24],
legend_top="center",
**pie_style
)
pie.render()
def create_charts():
page = Page()
style = Style(width=WIDTH, height=HEIGHT)
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", **style.init_style)
chart.add("", attr, v1, is_label_show=True)
page.add(chart)
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-圆环图示例", title_pos='center', **style.init_style)
chart.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
page.add(chart)
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
v2 = [19, 21, 32, 20, 20, 33]
chart = Pie("饼图-玫瑰图示例", title_pos='center', **style.init_style)
chart.add("商品A",
attr,
v1,
center=[25, 50],
is_random=True,
radius=[30, 75],
rosetype='radius')
chart.add("商品B",
attr,
v2,
center=[75, 50],
is_random=True,
radius=[30, 75],
rosetype='area',
is_legend_show=False,
is_label_show=True)
page.add(chart)
chart = Pie("饼图示例", title_pos='center', **style.init_style)
chart.add("", ['A', 'B', 'C', 'D', 'E', 'F'],
[335, 321, 234, 135, 251, 148],
radius=[40, 55],
is_label_show=True)
chart.add("", ['H', 'I', 'J'], [335, 679, 204],
radius=[0, 30],
legend_orient='vertical',
legend_pos='left')
page.add(chart)
attr = ['A', 'B', 'C', 'D', 'E', 'F']
chart = Pie("饼图示例", **style.init_style)
chart.add("",
attr, [random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[25, 50],
is_random=True)
chart.add("",
attr, [random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[25, 50],
rosetype='area')
chart.add("",
attr, [random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[65, 50],
is_random=True)
chart.add("",
attr, [random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[65, 50],
rosetype='radius')
page.add(chart)
chart = Pie('各类电影中"好片"所占的比例',
"数据来着豆瓣",
title_pos='center',
**style.init_style)
chart.add(
"",
["剧情", ""],
[25, 75],
center=[10, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None,
)
chart.add("", ["奇幻", ""], [24, 76],
center=[30, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None,
legend_pos='left')
chart.add("", ["爱情", ""], [14, 86],
center=[50, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["惊悚", ""], [11, 89],
center=[70, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["冒险", ""], [27, 73],
center=[90, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["动作", ""], [15, 85],
center=[10, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["喜剧", ""], [54, 46],
center=[30, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["科幻", ""], [26, 74],
center=[50, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["悬疑", ""], [25, 75],
center=[70, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
chart.add("", ["犯罪", ""], [28, 72],
center=[90, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None,
is_legend_show=True,
legend_top="center")
page.add(chart)
return page
def fabu_type_mon(conn, width, height, zs_mj, mon):
sql_fabu_type = "select type,count,mj from kanban_fabu_type where year(date_sub(create_date,interval 1 month))='%s'" \
"and month(date_sub(create_date,interval 1 month))='%s'"%(str(mon)[:4],str(mon)[-2:])
data_fabu_type = pd.read_sql(sql_fabu_type, conn)
date_fabu_type_count = data_fabu_type.sort_values('count', ascending=False)
date_fabu_type_mj = data_fabu_type.sort_values('mj', ascending=False)
fabu_type_name1 = list(date_fabu_type_count.iloc[:, 0])
fabu_type_count1 = list(date_fabu_type_count.iloc[:, 1])
fabu_type_name2 = list(date_fabu_type_mj.iloc[:, 0])
fabu_type_mj2 = list(date_fabu_type_mj.iloc[:, 2])
if zs_mj == 'zs':
fabu_type_title1 = str(mon)[:4] + '年' + str(
mon)[-2:] + '月土流网发布土地类型宗数占比'
fabu_type_pie1 = Pie(fabu_type_title1,
title_pos='center',
width=width,
height=height)
fabu_type_pie1.add("",
fabu_type_name1,
fabu_type_count1,
rosetype='radius',
radius=[25, 50],
label_text_color=None,
is_label_show=True,
is_legend_show=False,
legend_orient='vertical',
legend_pos='left')
fabu_type_pie1.render()
return fabu_type_pie1
elif zs_mj == 'mj':
fabu_type_title2 = str(mon)[:4] + '年' + str(
mon)[-2:] + '月土流网发布土地类型面积占比'
fabu_type_pie2 = Pie(fabu_type_title2,
title_pos='center',
width=width,
height=height)
fabu_type_pie2.add("",
fabu_type_name2,
fabu_type_mj2,
rosetype='radius',
radius=[25, 50],
label_text_color=None,
is_label_show=True,
is_legend_show=False,
legend_orient='vertical',
legend_pos='left')
fabu_type_pie2.render()
return fabu_type_pie2
def analyse_touxiang_data():
# 导入腾讯优图，用来实现人脸检测等功能 https://open.youtu.qq.com
import TencentYoutuyun
from pyecharts import Pie
# 导入jieba模块，用于中文分词
import jieba
# 导入matplotlib，用于生成2D图形
import matplotlib.pyplot as plt
# 导入wordcount，用于制作词云图
from wordcloud import WordCloud, STOPWORDS
# 向腾讯优图平台申请的开发密钥，此处需要替换为自己的密钥
appid = "----"
secret_id = "----"
secret_key = "----"
userid = "----" # 你的qq号
end_point = TencentYoutuyun.conf.API_YOUTU_END_POINT # 优图开放平台
youtu = TencentYoutuyun.YouTu(appid, secret_id, secret_key, userid, end_point)
use_face_man = 0
use_face_woman = 0
not_use_face = 0
base_path = current_dir+'/headImages'
tag_tpyes = [] # 分词
for file_name in os.listdir(base_path):
result = youtu.DetectFace(os.path.join(base_path, file_name)) # 人脸检测与分析
# print(result) # 参考 https://open.youtu.qq.com/legency/#/develop/api-face-analysis-detect
# 判断是否使用人像
gender=""
age=0
beauty=0
glasses=""
if result['errorcode'] == 0: # errorcode为0表示图片中存在人像
gender = '男' if result['face'][0]['gender'] >= 50 else '女'
if gender=="男":
use_face_man+=1
else:
use_face_woman+= 1
age = result['face'][0]['age']
beauty = result['face'][0]['beauty'] # 魅力值
glasses = '不戴眼镜 ' if result['face'][0]['glasses'] == 0 else '戴眼镜'
else:
not_use_face += 1
tag_name=""
result_type = youtu.imagetag(os.path.join(base_path, file_name)) # 头像分类
if result_type['errorcode'] == 0 and len(result_type["tags"])>0:
tag_name=max(result_type["tags"], key=lambda item: item['tag_confidence']) ["tag_name"].encode('ISO-8859-1').decode("utf8")
tag_tpyes.append(tag_name)
with open(current_dir+'/header.txt', mode='a', encoding='utf-8') as f:
f.write('%s,%s,%d,%d,%s,%s\n' % (file_name[:-4], gender, age, beauty, glasses,tag_name))
attr = ['使用人脸头像-男','使用人脸头像-女', '未使用人脸头像']
value = [use_face_man,use_face_woman, not_use_face]
pie = Pie('好友头像分析', '', title_pos='center')
pie.add('', attr, value, radius=[30, 75], is_label_show=True,
is_legend_show=True, legend_top='bottom')
# pie.show_config()
pie.render(current_dir+'/好友头像分析.html')
# 设置分词
split = jieba.cut(str(tag_tpyes), cut_all=False) # False精准模式分词、True全模式分词
words = ' '.join(split) # 以空格进行拼接
# 导入背景图
bg_image = plt.imread(current_dir+'/010-wechat-bg.jpg')
# 设置词云参数，参数分别表示：画布宽高、背景颜色、背景图形状、字体、屏蔽词、最大词的字体大小
wc = WordCloud(width=1024, height=768, background_color='white', mask=bg_image, font_path='STKAITI.TTF',
max_font_size=400, random_state=50)
# 将分词后数据传入云图
wc.generate_from_text(words)
plt.imshow(wc) # 绘制图像
plt.axis('off') # 不显示坐标轴
# 保存结果到本地
wc.to_file(current_dir+'/头像类别.jpg')
# 训练数据
prediction = gmm.predict(data_new)
# print(prediction)
hero_data.insert(0, '分组', prediction)
hero_data.to_csv('hero_out.csv', index=False, sep=',', encoding='gb18030')
df = hero_data[['分组', '名称']]
grouped = df.groupby(['分组'])
k = []
for name, group in grouped:
k.append({name: list(group['名称'].values)})
kk = []
for i in k:
for k, v in i.items():
kk.append(v)
length = []
key = []
for i in kk:
key.append(str(i))
length.append(len(i))
pie = Pie('英雄完全属性分类图', title_pos='center')
pie.add("", key, length,
is_label_show=True, legend_pos="bottom", legend_orient="vertical",)
pie.render()
def graphpage(labels,mode_combo,startdate,enddate,optInterval,width1, height1):
#optInterval='D/W/M' labels
startdate = startdate.replace("/","-")#convert to tushare readable date
enddate = enddate.replace("/","-")
page = Page()
for label in labels: # generate numbers of graphs according to numbers of queries in treewidget
label1 = re.split("-", label)
overlap = Overlap()
if label1[2]!="分笔" and label1[2]!="季度饼图": #"K线" "复权" "历史分钟"
if mode_combo == "复权":
#if optInterval == "qfq" or optInterval == "hfq":#复权
array = ts.get_h_data(label1[1], start=startdate, end=enddate, autype="qfq")
array = array.sort_index()
time = array.index.format() # 日期正是index
elif mode_combo == "K线":
#elif optInterval.isalnum() :#历史K线
array = ts.get_k_data(label1[1], start=startdate, end=enddate, ktype=optInterval)
time = array['date'].tolist() # array.date
elif mode_combo == "历史分钟":
array_bfr = ts.get_tick_data(label1[1], date=startdate)
array_bfr.sort_values("time")
a = startdate + " " + array_bfr["time"]
array_bfr["time"] = a
array_bfr["time"] = pd.to_datetime(a)
array_bfr = array_bfr.set_index("time")
if label1[2] != "Volume" and label1[2] != "Amount":
price_df = array_bfr["price"].resample(optInterval).ohlc() # resample重新取样,选出ohlc=open/high/low，提取除价格之外另外4列.close,tushare是ohcl
price_df = price_df.dropna()
array = price_df
time = price_df.index.format()
elif label1[2] == "Volume":
vols = array_bfr["volume"].resample(optInterval).sum() #resample重新取样，累加间隔内数值 relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
array = vol_df
time = vol_df.index.format()
else:
amounts = array_bfr["amount"].resample(optInterval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
array = amount_df
time = amount_df.index.format()
#绘图方法
if label1[2] == 'Kline':
re_array = array[['open', 'close', 'high', 'low']]
data_li = list(row.tolist() for index, row in re_array.iterrows()) # data_list = list(re_array.as_matrix())
close = array['close'].tolist()
kline = Kline(label1[0] + "-" + optInterval, width=width1*10/11, height = (height1*10/11) / len(labels))
kline.add(label1[0], time, data_li, is_datazoom_show=True, datazoom_type="slider", yaxis_interval=1)
overlap.add(kline)
if len(close) > 10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(label1[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close) > 20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(label1[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close) > 30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(label1[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:#When label1[2]==open/close/volume
if label1[2] == 'Open':
list_aft = array['open'].tolist()
elif label1[2] == 'Close':
list_aft = close
elif label1[2] == 'High':
list_aft = array['high'].tolist()
elif label1[2] == 'Low':
list_aft = array['low'].tolist()
elif label1[2] == 'Volume':#volume
list_aft = array['volume'].tolist()
else:#amount
list_aft = array['amount'].tolist()
line = Line(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
line.add(label1[0] + "-" + label1[2], time, list_aft, is_datazoom_show=True, yaxis_max="dataMax",
yaxis_min="dataMin", datazoom_type="slider")
overlap.add(line)
page.add(overlap)
elif label1[2]=="分笔":
array = ts.get_tick_data(label1[1], date=startdate)
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = list(zip(date, amount, flag, price))
form = [e[1] for e in returnarray]
time = [d[0] for d in returnarray]
bar = Bar(label1[0] + "-" + label1[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
bar.add(label1[0] + "-" + label1[2], time, form, is_datazoom_show=True, datazoom_type="slider", yaxis_min="dataMin", yaxis_max="dataMax")
overlap.add(bar)
line = Line(label1[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(labels))
price = [e[3] for e in returnarray]
line.add(label1[0] + "price", time, price, yaxis_min="dataMin", yaxis_max="dataMax", is_datazoom_show=True,
datazoom_type="slider",
yaxis_type="value")
overlap.add(line, yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
elif label1[2]=="季度饼图":
datestr = startdate.split("-")
thisyear = datestr[0]
df2 = ts.top10_holders(code=label1[1], gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in range(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in range(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in range(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = list(zip(namearray, valuearray, quarterarray, flag, num))
timeline = Timeline(is_auto_play=False, timeline_bottom=0) # zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in returnarray]
valuearray = [d[1] for d in returnarray]
quarter = [e[2] for e in returnarray]
num = returnarray[0][4]
for x in range(0, int(num / 10)):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(label1[0] + "-" + "前十股东", width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(label1[0] + "-" + "前十股东", names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter="{b}: {c}\n{d}%")
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
page.render()
def test_timeline_pie():
style = Style()
pie_style = style.add(
is_label_show=True,
radius=[30, 55],
rosetype="radius"
)
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(is_auto_play=True, timeline_bottom=0,
width=1200, height=600)
timeline.add(pie_1, '2012 年')
timeline.add(pie_2, '2013 年')
timeline.add(pie_3, '2014 年')
timeline.add(pie_4, '2015 年')
timeline.add(pie_5, '2016 年')
timeline.render()
data3,
mark_point=['average'],
is_datazoom_show=False,
is_label_show=True)
# line.add("最低气温", cities, data2, mark_line=['average'], is_smooth=True)
# line.render('Line-High-Low.html')
line.render(path='折线图.gif')
# 仪表盘图
gu = Gauge("仪表盘图")
gu.add("指标", "达标", 85)
gu.render("Guage-eg.html")
# 导入饼图Pie
# 设置主标题与副标题，标题设置居中，设置宽度为900
pie = Pie("饼状图", "一年的降水量与蒸发量", title_pos='center', width=900)
# 加入数据，设置坐标位置为【25，50】，上方的colums选项取消显示
pie.add("降水量", columns, data1, center=[25, 50], is_legend_show=True)
# 加入数据，设置坐标位置为【75，50】，上方的colums选项取消显示，显示label标签
pie.add("蒸发量",
columns,
data2,
center=[75, 50],
is_legend_show=False,
is_label_show=True)
# 保存图表
pie.render("pie.html")
# 漏斗图+图片
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
def business(id=None):
values = defaultdict()
db_server = db_idc.idc_servers
db_idc_id = db_idc.idc_id
db_project = db_op.project_list
db_third = db_idc.third_resource
db_business = db_op.business
db_project_third = db_op.project_third
db_project_other = db_op.project_other
business_info = [('项目', '域名', '业务', '业务描述', '联系人', '联系方式')]
bar = None
busi_tables = ('日期', 'PV访问量', '平均响应时间', '4xx错误占比', '5xx错误占比')
busi_counts = OrderedDict()
try:
if id:
#获取业务信息
try:
key = 'op_business_counts_tmp'
business_id = db_business.query.with_entities(
db_business.id, db_business.business, db_business.describe,
db_business.person, db_business.contact).all()
business_id = {
id: [business, describe, person, contact]
for id, business, describe, person, contact in business_id
}
business = business_id[int(id)]
except Exception as e:
logging.error(e)
#获取项目信息
business_values = db_project.query.with_entities(
distinct(db_project.project)).filter(
db_project.business_id == int(id)).all()
other_values = db_project_other.query.with_entities(
distinct(db_project_other.project)).filter(
db_project_other.business_id == int(id)).all()
if business_values:
projects = [project[0] for project in business_values]
#合并信息
try:
for project in projects:
domain = db_project.query.with_entities(
distinct(db_project.domain)).filter(
db_project.project == project).all()
if domain:
domain = domain[0][0]
else:
domain = 'None'
project = [project, domain]
project.extend(business)
business_info.append(project)
except Exception as e:
logging.error(e)
try:
for project in projects:
#获取自有项目信息
project_val = db_project.query.with_entities(
db_project.resource,
func.count(db_project.id)).filter(
db_project.project == project).group_by(
db_project.resource).all()
if project_val:
for val in project_val:
Redis.hincrby(key, val[0], int(val[1]))
#获取第三方项目信息
project_third = db_project_third.query.with_entities(
db_project_third.third_id,
func.count(distinct(db_project_third.third_id))
).filter(db_project_third.project == project).group_by(
db_project_third.third_id).all()
if project_third:
project_third = [
list(val) for val in project_third
]
for i, val in enumerate(project_third):
vals = db_third.query.with_entities(
db_third.resource_type).filter(
db_third.id == val[0]).all()
if vals:
project_third[i][0] = vals[0][0]
for vals in project_third:
Redis.hincrby(key, str(vals[0]), int(vals[1]))
except Exception as e:
logging.error(e)
try:
apps_vals = Redis.hgetall(key)
apps_vals = sorted(apps_vals.items(),
key=lambda item: int(item[1]),
reverse=True)
pie_vals = [val[0] for val in apps_vals]
pie_counts = [val[1] for val in apps_vals]
pie = Pie(width='100%', height='100%')
pie.add("",
pie_vals,
pie_counts,
radius=[35, 75],
is_yaxislabel_align=True,
legend_orient='vertical',
legend_pos='left',
is_toolbox_show=False,
is_label_show=True,
rosetype='area')
values['app'] = pie
Redis.delete(key)
values['app_lists'] = apps_vals
except Exception as e:
logging.error(e)
#统计机房信息和服务器数量
try:
idcs = db_idc_id.query.with_entities(
db_idc_id.id, db_idc_id.aid).all()
idcs = {int(val[0]): val[1] for val in idcs}
ids = db_project_third.query.with_entities(
db_project_third.third_id).filter(
db_project_third.project.in_(
tuple(projects))).all()
third_ids = [id[0] for id in ids]
self_ips = db_project.query.with_entities(
distinct(db_project.ip)).filter(
db_project.project.in_(tuple(projects))).all()
self_ips = [ip[0] for ip in self_ips]
third_ips = db_third.query.with_entities(
distinct(db_third.ip)).filter(
db_third.id.in_(tuple(third_ids))).all()
third_ips = [ip[0] for ip in third_ips]
idc_vals = db_server.query.with_entities(
db_server.idc_id, func.count(db_server.idc_id)).filter(
db_server.ip.in_(tuple(self_ips +
third_ips))).group_by(
db_server.idc_id).all()
idc_vals = [list(val) for val in idc_vals]
for val in idc_vals:
val[0] = idcs[int(val[0])]
for val in idc_vals:
Redis.hincrby(key, val[0], val[1])
idc_vals = Redis.hgetall(key)
pie_idc = Pie(width='100%', height='100%')
except Exception as e:
logging.error(e)
if idc_vals:
try:
idc_vals = sorted(idc_vals.items(),
key=lambda item: int(item[1]),
reverse=True)
total_vals = reduce(
lambda x, y: x + y,
[int(vals[1]) for vals in idc_vals])
pie_vals = [val[0] for val in idc_vals]
pie_counts = [val[1] for val in idc_vals]
pie_idc.add("",
pie_vals,
pie_counts,
radius=[35, 75],
is_yaxislabel_align=True,
legend_orient='vertical',
legend_pos='left',
is_toolbox_show=False,
is_label_show=True,
rosetype='area')
Redis.delete(key)
idc_vals.append(['总计:', total_vals])
except Exception as e:
logging.error(e)
values['idc'] = pie_idc
values['idc_lists'] = idc_vals
#获取业务相关性能数据
try:
hosts = []
dt = datetime.datetime.now()
host_vals = [
infos[1].split(',') for infos in business_info[1:]
]
for vals in host_vals:
for val in vals:
hosts.append(val)
except Exception as e:
logging.error(e)
year = time.strftime('%Y', time.localtime())
for i in range(2, 7):
info = []
ot = dt - datetime.timedelta(days=i - 1)
nt = dt - datetime.timedelta(days=i - 2)
ot = ot.strftime('%Y-%m-%dT00:00:00Z')
nt = nt.strftime('%Y-%m-%dT00:00:00Z')
try:
pv_sum = [
0,
]
for host in hosts:
cmd = 'select sum(mean_pv) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host = '%s';" % (
ot, nt, host)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
pv_sum.append(infos['sum'] * 60)
if pv_sum:
pv_sum = reduce(lambda x, y: x + y, pv_sum)
else:
pv_sum = 0.0
info.append(int(pv_sum))
except Exception as e:
logging.error(e)
try:
avg_resp = [
0,
]
for host in hosts:
cmd = 'select mean(mean_avg_resp) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s'and host = '%s';" % (
ot, nt, host)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
avg_resp.append(infos['mean'])
if avg_resp:
avg_resp = reduce(lambda x, y: x + y, avg_resp)
else:
avg_resp = 0.0
info.append('%.3f' % avg_resp + 's')
except Exception as e:
logging.error(e)
try:
s4 = [
0,
]
for host in hosts:
cmd = 'select sum(mean_status_4xx) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host = '%s';" % (
ot, nt, host)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
s4.append(infos['sum'] * 60 / pv_sum)
if s4:
s4 = reduce(lambda x, y: x + y, s4) * 100
else:
s4 = 0.0
info.append('%.3f' % s4 + '%')
s5 = [
0,
]
for host in hosts:
cmd = 'select sum(mean_status_5xx) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host = '%s';" % (
ot, nt, host)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
s5.append(infos['sum'] * 60 / pv_sum)
if s5:
s5 = reduce(lambda x, y: x + y, s5) * 100
else:
s5 = 0.0
info.append('%.3f' % s5 + '%')
except Exception as e:
logging.error(e)
busi_counts[ot.split('T')[0]] = info
if other_values:
for val in other_values:
jar_vals = [val[0], '']
jar_vals.extend(business)
business_info.append(jar_vals)
else:
#获取业务列表
VALS = db_business.query.with_entities(
db_business.id, db_business.business,
db_business.describe).order_by(db_business.id).all()
vals = {val[0]: val for val in VALS}
busi_vals = {val[0]: val[1] for val in VALS}
#获取各业务使用web应用实例数量
web_vals = db_project.query.with_entities(
db_project.business_id,
func.count(db_project.business_id)).filter(
and_(
db_project.resource.in_(
('php', 'tomcat', 'python', 'java')),
db_project.business_id != 0)).group_by(
db_project.business_id).order_by(
desc(func.count(
db_project.business_id))).all()
pro_vals = [val[0] for val in web_vals]
jar_vals = db_project_other.query.with_entities(
db_project_other.business_id).group_by(
db_project_other.business_id).all()
jar_vals = [val[0] for val in jar_vals]
for val in jar_vals:
if val not in pro_vals:
pro_vals.append(val)
values['business'] = [vals[val] for val in pro_vals if val in vals]
project_vals = [
busi_vals[val[0]] for val in web_vals if val[0] in busi_vals
]
project_counts = [val[1] for val in web_vals]
bar = Bar(width='110%', height='250px')
bar.add("线上业务web应用实例数量",
project_vals,
project_counts,
mark_point=["max", "min"],
is_yaxislabel_align=True,
is_toolbox_show=False,
xaxis_interval=0,
xaxis_rotate=30)
except Exception as e:
flash(e)
return render_template('Message.html')
return render_template('business.html',
values=values,
id=id,
business_info=business_info,
bar=bar,
busi_counts=busi_counts,
busi_tables=busi_tables)
def create_charts(self):
page = Page()
WIDTH = 1100
HEIGHT = 550
style = Style(width=WIDTH, height=HEIGHT)
X_TIME = [
'处女座', '摩羯座', '水瓶座', '巨蟹座', '天秤座', '金牛座', '天蝎座', '射手座', '白羊座',
'双子座', '双鱼座', '狮子座'
]
Y_WEEK = [str(x) for x in range(1998, 2007)]
RANGE_COLOR = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
chart = Bar("10年内各星座住房爱好" + "\n" + "Power by 胡煜", **style.init_style)
for mystr in list(self.getdata().keys()):
chart.add(mystr,
list(self.getdata()[mystr].keys()),
list(self.getdata()[mystr].values()),
is_stack=True)
page.add(chart)
chart = Bar("10年内各星座住房爱好" + "\n" + "Power by 胡煜", **style.init_style)
for mystr in list(self.getdata().keys()):
chart.add(mystr,
list(self.getdata()[mystr].keys()),
list(self.getdata()[mystr].values()),
mark_point=["average"])
page.add(chart)
chart = Bar("10年内各星座住房爱好" + "\n" + "Power by 胡煜", **style.init_style)
for mystr in list(self.getdata().keys()):
chart.add(mystr,
list(self.getdata()[mystr].keys()),
list(self.getdata()[mystr].values()),
is_convert=True)
page.add(chart)
chart = Pie("10年内各星座住房爱好", title_pos='center', **style.init_style)
for mystr in list(self.getdata().keys()):
chart.add(mystr,
list(self.getdata()[mystr].keys()),
list(self.getdata()[mystr].values()),
is_random=True,
radius=[30, 75],
rosetype='area',
is_legend_show=False,
is_label_show=True)
page.add(chart)
data = self.gettotladata()
chart = Timeline(
is_auto_play=True,
timeline_bottom=0,
width=WIDTH,
height=HEIGHT,
)
for mystr in list(self.getdata().keys()):
your = str(mystr)
print(your)
your = Bar(mystr + "各星座住宿记录数据", "数据真实可靠")
your.add("",
list(self.getdata()[mystr].keys()),
list(self.getdata()[mystr].values()),
is_legend_show=True)
chart.add(your, str(mystr) + "年")
page.add(chart)
lastdict = {
'华东': 8347764,
'中南': 3473591,
'西北': 1109607,
'西南': 1105030,
'华北': 2744619,
'东北': 1819191
}
bar = Bar("全国人民住房信息统计")
bar.add("区域分类",
list(lastdict.keys()),
list(lastdict.values()),
is_stack=True)
page.add(bar)
# infodict = {'江苏': 2399942, '上海': 914870, '湖北': 975236, '山东': 1529117, '江西': 594503, '陕西': 572695, '四川': 665913,
# '浙江': 1338150, '北京': 494634, '广西': 193134, '海南': 48342, '辽宁': 750881, '福建': 573289, '湖南': 566187,
# '河南': 1119652, '内蒙古': 393545, '天津': 321102, '安徽': 981923, '黑龙江': 580329, '吉林': 457018, '河北': 792687,
# '广东': 562808, '重庆': 74491, '山西': 693971, '贵州': 189171, '云南': 142511, '新疆': 145680, '甘肃': 227961,
# '青海': 75836, '宁夏': 75110, '西藏': 17526, '澳门': 21805, '香港': 12984, '台湾': 2158}
# map = Map("全国各地域分布住房信息", width=1200, height=600)
# map.add("", list(infodict.keys()), list(infodict.values()), maptype='china', is_visualmap=True, visual_text_color='#000')
# page.add(map)
# chart = Bar3D("3D 柱状图-默认", **style.init_style)
# chart.add("你妹", X_TIME, Y_WEEK, [[d[0], d[1], d[2],d[3],d[4],d[5],d[6],d[7],d[8],d[9],d[10],d[11]] for d in data],
# is_visualmap=True, visual_range=[0, 70],
# visual_range_color=RANGE_COLOR,
# grid3d_width=200, grid3d_depth=100)
# page.add(chart)
return page
#! /usr/bin/env python
#coding=utf-8
from pyecharts import Pie
pie = Pie("成功率统计")
attr = ['失败','中断','通过','跳过','未知']
value= [12,22,34,29,16]
pie.add("", attr, value, is_label_show=True)
#pie.render('../html/Pie1.html')
pie.render(path='th.jpeg')
pie = Pie('饼图', background_color="white", title_text_size=25)
pie.add('',attr,v1,is_label_show = True)
#设置radius参数实现圆环图:radius = [x,y]，x是内半径，y是外半径
pie =Pie('圆环图',background_color = 'white',title_pos = 'center')
pie.add('',attr,v1,is_label_show = True,radius=[30, 75],is_legend_show = False)
pie.render()'''
#设置rosetype参数实现南丁格尔图（玫瑰图）：
#rosetype有'radius'和'area'两种模式，其中：
#radius通过半径显示数据的大小，扇区圆心角展现数据的百分比；
#area通过半径显示数据的大小，各扇区圆心角相等。
#另外还可以通过设置center参数调整圆心位置（center = [x,y]，x为横坐标，y为纵坐标）
'''configure(output_image=True)
pie =Pie('玫瑰图',background_color = 'white')
attr = ['惠州','东莞','广州','深圳','佛山','江门','珠海']
v1 = [12,22,34,29,16,14,18]
pie.add('radius',attr,v1,center = [25,50],radius=[30, 75],rosetype='radius')
pie.add('area',attr,v1,center = [75,50],radius=[30, 75],rosetype='area')
pie.render()'''
configure(output_image=True)
pie = Pie('圆环中的玫瑰图', background_color='white')
attr = ['惠州', '东莞', '广州', '深圳', '佛山', '江门', '珠海']
v1 = [12, 22, 34, 29, 16, 14, 18]
pie.add('', attr, v1, radius=[65, 75], center=[50, 50])
pie.add('', attr, v1, radius=[0, 60], center=[50, 50], rosetype='area')
pie.render()
def pie(request):
template = loader.get_template('chart/chart.html')
attr = ['菜品A', '菜品B', '菜品C', '菜品D', '菜品E', '菜品F']
pie = Pie("菜品销售占比", width=1400, height=700)
pie.add("",
attr, [random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[25, 50],
is_random=True)
pie.add("",
attr, [random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[25, 50],
rosetype='area')
pie.add("",
attr, [random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[65, 50],
is_random=True)
pie.add("",
attr, [random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[65, 50],
rosetype='radius')
context = dict(myechart=pie.render_embed(),
host=REMOTE_HOST,
script_list=pie.get_js_dependencies())
return HttpResponse(template.render(context, request))
# -*-coding:utf-8-*-
from pyecharts import Bar, Pie, Line
from HouseSpider.mysqlpipelines.sql import Sql
attr = ["工业园区", "吴中", "吴江", "高新区", "姑苏", "相城"]
min_price = []
max_price = []
# line = Line("房租分析折线图")
for i in range(6):
min_price.append(Sql.select_min_price(attr[i])) # 最小房租
max_price.append(Sql.select_max_price(attr[i])) # 最大房租
# line.add("最低房租", attr, min_price, mark_point=["average"])
# line.add("商最房租", attr, max_price, is_smooth=True, mark_line=["max", "average"])
#
# line.render()
pie = Pie("饼图示例")
pie.add("", attr, min_price, is_label_show=True)
pie.render()
def test_pie_multiple():
import random
attr = ["A", "B", "C", "D", "E", "F"]
pie = Pie("饼图示例", width=1000, height=600)
pie.add(
"",
attr,
[random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[25, 50],
is_random=True,
)
pie.add(
"",
attr,
[random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[25, 50],
rosetype="area",
)
pie.add(
"",
attr,
[random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[65, 50],
is_random=True,
)
pie.add(
"",
attr,
[random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[65, 50],
rosetype="radius",
)
pie.render()
#encoding=utf-8
from pyecharts import Pie
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
v2 = [19, 21, 32, 20, 20, 33]
pie = Pie("饼图-玫瑰图示例", title_pos='center', width=900)
pie.add("商品A",
attr,
v1,
center=[25, 50],
is_random=True,
radius=[30, 75],
rosetype='radius')
pie.add("商品B",
attr,
v2,
center=[75, 50],
is_random=True,
radius=[30, 75],
rosetype='area',
is_legend_show=False,
is_label_show=True)
pie.show_config()
pie.render()
def graphpage(items,startdate,enddate,option,width1, height1): #labels:复权ork线or分笔 option:hfq, qfq or 15, 30, D, etc
page = Page()
for i in items:#generate numbers of graphs according to numbers of queries in treewidget
j = re.split("-",i)
if len(j)==3:
a = generateline(j[1],j[2],startdate,enddate,option)#stock number, Type, startdate, enddate, 30 or 15 or days
if a is None:
continue
time = [d[0] for d in a]#get time from returned dictionary
if j[2]!="Kline":
if len(a[0])==4 and a[0][2]=="bar": #for 分笔data
overlap = Overlap()
form = [e[1] for e in a]
bar = Bar(j[0] + "-" + j[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(j[0] + "-" + j[2], time, form, yaxis_min = "dataMin",yaxis_max = "dataMax",is_datazoom_show = True, datazoom_type = "slider")
overlap.add(bar)
line = Line(j[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in a]
line.add(j[0] + "price", time, price, yaxis_min = "dataMin",yaxis_max = "dataMax", is_datazoom_show = True, datazoom_type = "slider",
yaxis_type="value")
overlap.add(line,yaxis_index=1, is_add_yaxis=True)
page.add(overlap)
if len(a[0])==5 and a[0][3]=="pie":
overlap = Overlap()
timeline = Timeline(is_auto_play=False, timeline_bottom=0) #zip(namearray,valuearray,quarter,flag,num)
namearray = [c[0] for c in a]
valuearray = [d[1] for d in a]
quarter = [e[2] for e in a]
num = a[0][4]
for x in range(0, num / 10):
list1 = valuearray[x]
names = namearray[x]
quarters = quarter[x][0]
for idx, val in enumerate(list1):
list1[idx] = float(val)
pie = Pie(j[0]+"-"+"前十股东".decode("utf-8"),width=width1 * 10 / 11, height=(height1 * 10 / 11))
pie.add(j[0]+"-"+"前十股东".decode("utf-8"), names, list1, radius=[30, 55], is_legend_show=False,
is_label_show=True, label_formatter = "{b}: {c}\n{d}%")
# print list
# print names
# print quarterarray
timeline.add(pie, quarters)
# namearray = [y for y in namearray[x]]
timeline.render()
return
#need more statement
else:
form = [e[1] for e in a]#for not分笔 data
line = Line(j[0] + "-" + j[2], width=width1*10/11, height=(height1*10/11)/len(items))
line.add(j[0] + "-" + j[2], time, form, is_datazoom_show=True, datazoom_type="slider",yaxis_min="dataMin",yaxis_max="dataMax")
page.add(line)
else:
overlap = Overlap()#for k线
close = zip(*a)[2]
candle = [[x[1], x[2], x[3], x[4]] for x in a]
candlestick = Kline(j[0] + "-" + j[2], width=width1*10/11, height = (height1*10/11) / len(items))
candlestick.add(j[0], time, candle, is_datazoom_show=True, datazoom_type="slider",yaxis_interval = 1)
overlap.add(candlestick)
if len(close)>10:
ma10 = calculateMa(close, 10)
line1 = Line(title_color="#C0C0C0")
line1.add(j[0] + "-" + "MA10", time, ma10)
overlap.add(line1)
if len(close)>20:
ma20 = calculateMa(close, 20)
line2 = Line(title_color="#C0C0C0")
line2.add(j[0] + "-" + "MA20", time, ma20)
overlap.add(line2)
if len(close)>30:
ma30 = calculateMa(close, 30)
line3 = Line(title_color="#C0C0C0")
line3.add(j[0] + "-" + "MA30", time, ma30)
overlap.add(line3)
page.add(overlap)
else:
for k in range(1, len(j)/3):#if graphs are combined
j[3*k-1] = re.sub("\n&","",j[3*k-1])
sizearray=[]
#if j[1] != "Candlestick"
layout = Overlap()
for i in xrange(0, len(j),3):
array = j[i:i +3]
b = generateline(array[1],array[2],startdate,enddate,option)
if b is None:
continue
btime = [d[0] for d in b]
if array[2] != "Kline":
if len(b[0])==4 and b[0][2]=="bar":
form = [e[1] for e in b]
bar = Bar(array[0] + "-" + array[2], width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
bar.add(array[0] + "-" + array[2], btime, form, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_max="dataMax")
layout.add(bar)
line = Line(array[0] + "price", width=width1 * 10 / 11, height=(height1 * 10 / 11) / len(items))
price = [e[3] for e in b]
line.add(array[0] + "price", btime, price, is_datazoom_show=True, datazoom_type="slider",
yaxis_min="dataMin", yaxis_type="value")
layout.add(line, yaxis_index=1, is_add_yaxis=True)
else:
line = Line(array[0] + "-" + array[2],width=width1*10/11, height=(height1*10/11) / len(items))
line.add(array[0]+"-"+array[2], btime, b, is_datazoom_show=True, yaxis_max = "dataMax", yaxis_min = "dataMin",datazoom_type="slider")
layout.add(line)
else:
candle = [[x[1], x[2], x[3], x[4]] for x in b]
candlestick = Kline(array[0] + "-" + array[1], width=width1*10/11,
height=(height1*10/11) / len(items))
candlestick.add(array[0], btime, candle, is_datazoom_show=True, datazoom_type=["slider"])
#if i == 0:
close = zip(*b)[2]
if len(close)>10:
ma10 = calculateMa(close, 10)
line4 = Line(title_color="#C0C0C0")
line4.add(array[0] + "-" + "MA10", btime, ma10)
layout.add(line4)
if len(close)>20:
ma20 = calculateMa(close, 20)
line5 = Line(title_color="#C0C0C0")
line5.add(array[0] + "-" + "MA20", btime, ma20)
layout.add(line5)
if len(close)>30:
ma30 = calculateMa(close, 30)
line6 = Line(title_color="#C0C0C0")
line6.add(array[0] + "-" + "MA30", btime, ma30)
layout.add(line6)
layout.add(candlestick)
page.add(layout)
page.render()
def draw_citys_pic(csv_file):
page = Page(csv_file + ":评论城市分析")
info = count_city(csv_file)
geo = Geo("",
"Ctipsy原创",
title_pos="center",
width=1200,
height=600,
background_color='#404a59',
title_color="#fff")
while True: # 二次筛选，和pyecharts支持的城市库进行匹配，如果报错则删除该城市对应的统计
try:
attr, val = geo.cast(info)
geo.add("",
attr,
val,
visual_range=[0, 300],
visual_text_color="#fff",
is_geo_effect_show=False,
is_piecewise=True,
visual_split_number=6,
symbol_size=15,
is_visualmap=True)
except ValueError as e:
e = str(e)
e = e.split("No coordinate is specified for ")[1] # 获取不支持的城市名称
info.pop(e)
else:
break
info = sorted(info.items(), key=lambda x: x[1], reverse=False) # list排序
# print(info)
info = dict(info) #list转dict
# print(info)
attr, val = [], []
for key in info:
attr.append(key)
val.append(info[key])
geo1 = Geo("",
"评论城市分布",
title_pos="center",
width=1200,
height=600,
background_color='#404a59',
title_color="#fff")
geo1.add("",
attr,
val,
visual_range=[0, 300],
visual_text_color="#fff",
is_geo_effect_show=False,
is_piecewise=True,
visual_split_number=10,
symbol_size=15,
is_visualmap=True,
is_more_utils=True)
#geo1.render(csv_file + "_城市dotmap.html")
page.add_chart(geo1)
geo2 = Geo(
"",
"评论来源热力图",
title_pos="center",
width=1200,
height=600,
background_color='#404a59',
title_color="#fff",
)
geo2.add("",
attr,
val,
type="heatmap",
is_visualmap=True,
visual_range=[0, 50],
visual_text_color='#fff',
is_more_utils=True)
#geo2.render(csv_file+"_城市heatmap.html") # 取CSV文件名的前8位数
page.add_chart(geo2)
bar = Bar("", "评论来源排行", title_pos="center", width=1200, height=600)
bar.add("",
attr,
val,
is_visualmap=True,
visual_range=[0, 100],
visual_text_color='#fff',
mark_point=["average"],
mark_line=["average"],
is_more_utils=True,
is_label_show=True,
is_datazoom_show=True,
xaxis_rotate=45)
#bar.render(csv_file+"_城市评论bar.html") # 取CSV文件名的前8位数
page.add_chart(bar)
pie = Pie("", "评论来源饼图", title_pos="right", width=1200, height=600)
pie.add("",
attr,
val,
radius=[20, 50],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
is_more_utils=True,
legend_pos='left')
#pie.render(csv_file + "_城市评论Pie.html") # 取CSV文件名的前8位数
page.add_chart(pie)
page.render(csv_file + "_城市评论分析汇总.html")
def test_more():
page = Page()
# line
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie])
# kline
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
kline = Kline("K 线图示例")
kline.add(
"日K",
["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True,
)
page.add(kline)
# radar
schema = [
("销售", 6500),
("管理", 16000),
("信息技术", 30000),
("客服", 38000),
("研发", 52000),
("市场", 25000),
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add(
"实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode="single",
)
page.add(radar)
# scatter3d
import random
data = [
[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100),
]
for _ in range(80)
]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3D)
# wordcloud
name = [
"Sam S Club",
"Macys",
"Amy Schumer",
"Jurassic World",
"Charter Communications",
"Chick Fil A",
"Planet Fitness",
"Pitch Perfect",
"Express",
"Home",
"Johnny Depp",
"Lena Dunham",
"Lewis Hamilton",
"KXAN",
"Mary Ellen Mark",
"Farrah Abraham",
"Rita Ora",
"Serena Williams",
"NCAA baseball tournament",
"Point Break",
]
value = [
10000,
6181,
4386,
4055,
2467,
2244,
1898,
1484,
1112,
965,
847,
582,
555,
550,
462,
366,
360,
282,
273,
265,
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
assert len(page) == 7
assert isinstance(page[0], Line)
assert (
("echarts" in page.js_dependencies)
or ("echarts.min" in page.js_dependencies)
)
page.render()
def analyze():
biglist = []
for root, dirs, files, in os.walk(
"/Users/pony/github/hades/workspace/result/"):
for file in files:
if os.path.splitext(file)[1] == '.json':
filepath = os.path.join(root, file)
biglist.append(filepath)
print("共分析%d个文件" % len(biglist))
bigresultdict = {}
for filepath in biglist:
f = open(filepath, 'r')
data = f.read()
singledict = json.loads(data)
bigresultdict[singledict.get('packageName')] = singledict
shell = analyzeshell(bigresultdict)
classify = analyzeclassify(bigresultdict)
per1 = analyzepermission(bigresultdict)
per2 = analyzepermission2(bigresultdict)
ad = analyzead(bigresultdict)
ad2 = analyzead2(bigresultdict)
nastysdk = analyzenastysdk(bigresultdict)
nastysdk2 = analyzenastysdk2(bigresultdict)
pie = Pie("分析样本中的加壳情况", title_pos='center')
bar1 = Bar("统计应用类型数量", title_pos='center')
bar2 = Bar("分析哪些应用使用权限最多", title_pos='center')
pie3 = Pie("分析哪些权限使用最频繁", title_pos='left')
pie4 = Pie("分析哪些第三方广告使用量最多", title_pos='center')
bar5 = Bar("分析哪些应用使用广告最多", title_pos='center')
pie6 = Pie("分析哪些应用使用恶意sdk最多", title_pos='center')
pie7 = Pie("分析哪些恶意sdk使用量最多", title_pos='center')
pie.add("apk数量",
shell.keys(),
shell.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
bar1.add("apk数量",
classify.keys(),
classify.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
bar2.add("权限数量",
per1.keys(),
per1.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
pie3.add("apk数量",
per2.keys(),
per2.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='bottom',
legend_orient='vertical')
pie4.add("apk数量",
ad.keys(),
ad.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
bar5.add("apk数量",
ad2.keys(),
ad2.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
pie6.add("恶意sdk数量",
nastysdk.keys(),
nastysdk.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
pie7.add("恶意sdk数量",
nastysdk2.keys(),
nastysdk2.values(),
is_label_show=True,
is_more_utils=True,
legend_pos='left',
legend_orient='vertical')
page = Page()
page.add(pie)
page.add(bar1)
page.add(bar2)
page.add(pie3)
page.add(pie4)
page.add(bar5)
page.add(pie6)
page.add(pie7)
page.render("bing2.html")
import pandas as pd
from pyecharts import Pie
df = pd.read_csv('air_tianjin_2017.csv',
header=None,
names=["Date", "Quality_grade", "AQI", "AQI_rank", "PM"])
rank_message = df.groupby(['Quality_grade'])
rank_com = rank_message['Quality_grade'].agg(['count'])
rank_com.reset_index(inplace=True)
rank_com_last = rank_com.sort_values('count', ascending=False)
attr = rank_com_last['Quality_grade']
v1 = rank_com_last['count']
pie = Pie("2017年天津全年空气质量情况", title_pos='center', title_top=0)
pie.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
legend_top="%10")
pie.render('2017年天津全年空气质量情况.html')
def weeklyDashboard():
import jinja2
loader = jinja2.FileSystemLoader(settings.BASE_DIR +
'/apps/dashboard/docs/',
encoding='utf-8')
env = jinja2.Environment(loader=loader)
template = env.get_template("week")
print('123', connect.keys())
status_json = connect.get('MANAGER_STATUS')
manager_status = json.loads(str(status_json, encoding='utf-8'))
now = datetime.datetime.now().strftime('%Y-%m-%d')
TMP = settings.DASHBOARD_ROOT + '/' + now
if not os.path.exists(TMP):
os.makedirs(TMP)
dic = {
'GROUP_COUNT':
manager_status['group_count'],
'HOST_COUNT':
manager_status['host_count'],
'ALIYUN_COUNT':
manager_status['position'][0]['value'],
'VMWARE_COUNT':
manager_status['position'][1]['value'],
'MISSION_COUNT':
OpsModels.Mission.objects.count(),
'ACTIVITY_USER':
AuthModels.ExtendUser.objects.count(),
'WEEK_START': (datetime.datetime.now() -
datetime.timedelta(days=7)).strftime("%Y年%m月%d日"),
'WEEK_END':
datetime.datetime.now().strftime('%Y年%m月%d日'),
}
systype = manager_status['systemtype']
attr = []
v1 = []
for l in systype:
attr.append(l["name"])
v1.append(l["value"])
pie = Pie("操作系统类型", title_pos='right', width=900)
pie.add("操作系统类型",
attr,
v1,
center=[50, 50],
is_random=True,
radius=[30, 75],
is_legend_show=False,
is_label_show=True)
pie.render(path=TMP + '/systype.png')
groups = manager_status['groups']
attr = []
v1 = []
for l in groups:
attr.append(l["name"])
v1.append(l["value"])
pie = Pie("接管应用与主机总数", title_pos='right', width=900)
pie.add("接管应用与主机总数",
attr,
v1,
center=[50, 50],
is_random=True,
radius=[30, 75],
is_legend_show=False,
is_label_show=True)
pie.render(path=TMP + '/group.png')
if ExpiredAliyunECS.objects.count() != 0:
ecsquery = ExpiredAliyunECS.objects.all()
dic['ECS_EXPIRED'] = ecsquery
if ExpiredAliyunRDS.objects.count() != 0:
rdsquery = ExpiredAliyunRDS.objects.all()
dic['RDS_EXPIRED'] = rdsquery
if ExpiredAliyunKVStore.objects.count() != 0:
kvsquery = ExpiredAliyunKVStore.objects.all()
dic['KVSTORE_EXPIRED'] = kvsquery
if ExpiredAliyunMongoDB.objects.count() != 0:
kvsquery = ExpiredAliyunMongoDB.objects.all()
dic['MONGODB_EXPIRED'] = kvsquery
markdown = template.render(dic)
with open(TMP + '/index.md', 'w') as f:
f.write(markdown)
# <link href="../static/github.css" rel="stylesheet">
html = '''
<html lang="zh-cn">
<head>
<meta content="text/html; charset=utf-8" http-equiv="content-type" />
<link href="../static/screen.css" rel="stylesheet">
</head>
<body>
%s
</body>
</html>
'''
import markdown2
html = html % markdown2.markdown(markdown,
extras=['tables', 'break-on-newline'])
with open(TMP + '/index.html', 'w') as f:
f.write(html)
msg = '本周平台周报地址http://deveops.8531.cn:8888/media/dashboard/' + datetime.datetime.now(
).strftime('%Y-%m-%d') + '/index.html'
smtp.sendMail('devEops平台运维周报', msg,
['*****@*****.**'
]) #,'*****@*****.**','*****@*****.**','*****@*****.**'])
def jiaoyi_liuzhuan_mon(conn, width, height, zs_mj, mon):
sql_jiaoyi_liuzhuan = "select liuzhuan,count,mj from kanban_jiaoyi_liuzhuan where year(date_sub(create_date,interval 1 month)) ='%s'" \
"and month(date_sub(create_date,interval 1 month))='%s'"%(str(mon)[:4],str(mon)[-2:])
data_jiaoyi_liuzhuan = pd.read_sql(sql_jiaoyi_liuzhuan, conn)
data_jiaoyi_liuzhuan_count = data_jiaoyi_liuzhuan.sort_values(
'count', ascending=False)
data_jiaoyi_liuzhuan_mj = data_jiaoyi_liuzhuan.sort_values('mj',
ascending=False)
jiaoyi_liuzhuan_name1 = list(data_jiaoyi_liuzhuan_count.iloc[:, 0])
jiaoyi_liuzhuan_count1 = list(data_jiaoyi_liuzhuan_count.iloc[:, 1])
jiaoyi_liuzhuan_name2 = list(data_jiaoyi_liuzhuan_mj.iloc[:, 0])
jiaoyi_liuzhuan_mj2 = list(data_jiaoyi_liuzhuan_mj.iloc[:, 2])
if zs_mj == 'zs':
jiaoyi_liuzhuan_title1 = str(mon)[:4] + '年' + str(
mon)[-2:] + '月土流网交易土地流转类型宗数占比'
jiaoyi_liuzhuan_pie1 = Pie(jiaoyi_liuzhuan_title1,
title_pos='center',
width=width,
height=height)
jiaoyi_liuzhuan_pie1.add("",
jiaoyi_liuzhuan_name1,
jiaoyi_liuzhuan_count1,
radius=[25, 50],
label_text_color=None,
is_label_show=True,
is_legend_show=False,
legend_orient='vertical',
legend_pos='left')
jiaoyi_liuzhuan_pie1.render()
return jiaoyi_liuzhuan_pie1
elif zs_mj == 'mj':
jiaoyi_liuzhuan_title2 = str(mon)[:4] + '年' + str(
mon)[-2:] + '月土流网交易土地流转类型面积占比'
jiaoyi_liuzhuan_pie2 = Pie(jiaoyi_liuzhuan_title2,
title_pos='center',
width=width,
height=height)
jiaoyi_liuzhuan_pie2.add("",
jiaoyi_liuzhuan_name2,
jiaoyi_liuzhuan_mj2,
radius=[25, 50],
label_text_color=None,
is_label_show=True,
is_legend_show=False,
legend_orient='vertical',
legend_pos='left')
jiaoyi_liuzhuan_pie2.render()
return jiaoyi_liuzhuan_pie2
def showPie(self, title, attr, value):
from pyecharts import Pie
pie = Pie(title)
pie.add("aa", attr, value, is_label_show=True)
pie.render()
bot = Bot(cache_path=True) #定义一个微信机器人
friends = bot.friends(update=False) #获取更新好友列表
male = female = other = 0
for i in friends[1:]: #[1:]是因为整个好友列表里面自己市在第一个，排除掉
sex = i.sex
if sex == 1:
male += 1
elif sex == 2:
female += 1
else:
other += 1
total = len(friends[1:]) #计算总数
#下面为分析
attr = ["男性", "女性", "其他"]
v1 = [float(male), float(female), float(other)]
pie = Pie("饼图-圆环图示例", title_pos='center')
pie.add("",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
pie.render("sex.html")
#摘抄：https://www.jianshu.com/p/c0baf3c6db15
# 分析微信好友男女比例
def DrawPie(lst):
image=Pie("", width=600, height=450)
image.add("", lst[0], lst[1], is_label_show=True)
image.render("./SE12_Cache/Pie.html")
def zd_fenbu():
# tushare
stockbasicinfo = ts.get_stock_basics()
'''代码排序'''
stockbasicinfo = stockbasicinfo.sort_index()
'''股票总数'''
stocknum = len(stockbasicinfo)
# stockcode = stockbasicinfo.index[0]
# 所有票的url拼接地址，用sina api去获取数据，因为tushare获取实时数据不稳定
urllist = []
for i in range(0, stocknum):
stockcode = stockbasicinfo.index[i]
stockcodeint = int(stockbasicinfo.index[i])
if stockcodeint >= 600000:
urllist.append('sh' + str(stockcode))
else:
urllist.append('sz' + str(stockcode))
urllen = len(urllist)
# print(urllen, urllist)
(x, y) = divmod(urllen, 9)
urlchar = ','.join(urllist)
# print(urlchar)
# 总共分成9份，因为sina每次请求不超过800
urlbase = 'http://hq.sinajs.cn/list='
url1 = urlbase + urlchar[0:9 * x - 1]
url2 = urlbase + urlchar[9 * x * 1:9 * x * 2 - 1]
url3 = urlbase + urlchar[9 * x * 2:9 * x * 3 - 1]
url4 = urlbase + urlchar[9 * x * 3:9 * x * 4 - 1]
url5 = urlbase + urlchar[9 * x * 4:9 * x * 5 - 1]
url6 = urlbase + urlchar[9 * x * 5:9 * x * 6 - 1]
url7 = urlbase + urlchar[9 * x * 6:9 * x * 7 - 1]
url8 = urlbase + urlchar[9 * x * 7:9 * x * 8 - 1]
url9 = urlbase + urlchar[9 * x * 8:]
datatemp1 = requests.get(url1)
datatemp2 = requests.get(url2)
datatemp3 = requests.get(url3)
datatemp4 = requests.get(url4)
datatemp5 = requests.get(url5)
datatemp6 = requests.get(url6)
datatemp7 = requests.get(url7)
datatemp8 = requests.get(url8)
datatemp9 = requests.get(url9)
# 获取的数据进行合并
urldata = datatemp1.text + datatemp2.text + datatemp3.text + datatemp4.text + datatemp5.text + datatemp6.text + datatemp7.text + datatemp8.text + datatemp9.text
urlsplit = re.split(r'=', urldata)
# print(len(urlsplit))
validnum = 0
validzflist = []
validyes10 = 0
validyes7 = 0
validyes5 = 0
validyes3 = 0
validyes1 = 0
validyes0 = 0
valid0 = 0
validno0 = 0
validno1 = 0
validno3 = 0
validno5 = 0
validno7 = 0
validno10 = 0
# 数据分割，提取，整理
for i in range(1, len(urlsplit)):
if len(urlsplit[i]) > 100:
urldataone = re.split(r',', urlsplit[i])
yesprice = float(urldataone[2])
nowprice = float(urldataone[3])
amount = float(urldataone[9])
buyonevol = float(urldataone[10])
sellonevol = float(urldataone[20])
if amount < 0.1:
pass
else:
if nowprice < 0.1:
pass
else:
if yesprice < 0.1:
pass
else:
zdf = int(10000 *
(nowprice / yesprice - 1) + 0.5) / 100
if abs(zdf) < 11:
validnum += 1
validzflist.append(zdf)
if zdf >= 9.8 and sellonevol < 0.1:
validyes10 += 1
if zdf >= 7 and sellonevol > 0.1:
validyes7 += 1
if 5 <= zdf < 7:
validyes5 += 1
if 3 <= zdf < 5:
validyes3 += 1
if 1 <= zdf < 3:
validyes1 += 1
if 0.0001 <= zdf < 1:
validyes0 += 1
if abs(zdf) <= 0.0001:
valid0 += 1
if -1 < zdf < -0.0001:
validno0 += 1
if -3 < zdf <= -1:
validno1 += 1
if -5 < zdf <= -3:
validno3 += 1
if -7 < zdf <= -5:
validno5 += 1
if zdf <= -7 and buyonevol > 0.1:
validno7 += 1
if zdf <= -9.8 and buyonevol < 0.1:
validno10 += 1
# 数据整理
v11 = [
0, 0, 0, 0, 0, 0, 0, validyes0, validyes1, validyes3, validyes5,
validyes7, 0
]
v22 = [
0, validno7, validno5, validno3, validno1, validno0, 0, 0, 0, 0, 0, 0,
0
]
v00 = [0, 0, 0, 0, 0, 0, valid0, 0, 0, 0, 0, 0, 0]
vzt = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, validyes10]
vdt = [validno10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
attr = [
'-10%', '>-7%', '-7~5%', '-5~3%', '-3~1%', '-1~0%', '0', '0~1%',
'1~3%', '3~5%', '5~7%', '>7%', '10%'
]
# 调用pyecharts
title = ' 涨跌分布：↑' + str(validyes0+validyes1+validyes3+validyes5+validyes7+validyes10) + \
' ↓' + str(validno0+validno1+validno3+validno5+validno7+validno10) + ' →' + str(valid0)
subtitle = ' GZH: 摸鱼大佬'
# bar
bar = Bar(title,
subtitle,
title_pos=0.1,
subtitle_text_size=15,
subtitle_color='#aa8')
# bar.use_theme("shine")
bar.add("涨", attr, v11, bar_category_gap=0, mark_point=['max', 'average'])
bar.add("", attr, vzt, bar_category_gap=0, mark_point=['max'])
bar.add("平", attr, v00, bar_category_gap=0, mark_point=['max'])
bar.add("跌", attr, v22, bar_category_gap=0, mark_point=['max', 'average'])
bar.add("", attr, vdt, bar_category_gap=0, mark_point=['max'])
# bar_render_path = save_path_zdffb[:-12] + 'bar_tozoom_' + date_zdffb + '.png'
# bar.render(path=bar_render_path)
# pie
attrpie = ['涨', '平', '跌']
vpie = [
validyes0 + validyes1 + validyes3 + validyes5 + validyes7 + validyes10,
valid0,
validno0 + validno1 + validno3 + validno5 + validno7 + validno10
]
pie = Pie("涨跌饼图", title_pos='center', width=400, height=400)
pie.add("",
attrpie,
vpie,
radius=[6, 15],
label_text_color=None,
is_label_show=True,
legend_orient='vertical',
legend_pos='left')
# pie_render_path = save_path_zdffb[:-12] + 'pie_tozoom_' + date_zdffb + '.png'
# pie.render(path=pie_render_path)
# pic_zoom(bar_render_path, save_path_zdffb, 740)
# print('zdf_distribution done: ' + save_path_zdffb)
# return bar_render_path
return bar, pie
# Author : Троцкий
# 2019-03-06 10:31
from pyecharts import Pie
import sql
attr = ['无情铁手', '宽油', '王刚', '宽水', '劝退', '宽饭', '摊主']
v1 = sql.get_data()
pie = Pie('关键词分布')
pie.add('', attr, v1, is_label_show=True)
pie.render()
def test_grid():
# grid_0
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("柱状图示例", height=720, is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%")
line = Line("折线图示例", title_top="50%")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_top="50%")
bar.grid(line.get_series(), grid_top="60%")
bar.show_config()
bar.render()
# grid_1
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200, is_grid=True)
scatter.add("散点图示例", v1, v2, grid_left="60%", legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_pos="20%")
scatter.grid(es.get_series(), grid_right="60%")
scatter.show_config()
scatter.render()
# grid_2
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("柱状图示例", height=720, width=1200, title_pos="65%", is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%", grid_left="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%", grid_left="60%", legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6,
legend_top="50%", legend_pos="20%")
bar.grid(line.get_series(), grid_bottom="60%", grid_right="60%")
bar.grid(scatter.get_series(), grid_top="60%", grid_left="60%")
bar.grid(es.get_series(), grid_top="60%", grid_right="60%")
bar.show_config()
bar.render()
# grid_3
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="65%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="45%")
pie.add("", attr, v1, radius=[30, 55], legend_pos="65%", legend_orient='vertical')
line.grid(pie.get_series(), grid_left="60%")
line.show_config()
line.render()
# grid_4
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="60%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%", grid_right="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_pos="60%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1, legend_pos="80%")
line.grid(kline.get_series(), grid_left="55%")
line.show_config()
line.render()
# grid_5
import random
x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700, is_grid=True)
heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True, visual_top="45%",
visual_text_color="#000", visual_orient='horizontal', grid_bottom="60%")
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("柱状图示例", title_top="52%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_top="50%")
heatmap.grid(bar.get_series(), grid_top="60%")
heatmap.show_config()
heatmap.render()
def myechart(request, pk):
school = get_object_or_404(Food_Struct, pk=pk)
school2 = get_object_or_404(School, pk=pk)
nutri_school = get_object_or_404(Nutri_of_Food, pk=pk)
students = school2.students
template = loader.get_template("hele/pyecharts.html")
mypie = Pie("每人日均餐标结构（单位：%）", title_pos="center")
value = [
school.rice, school.ganhuo, school.cake, school.egg, school.chicken,
school.ricenoodle, school.milk, school.beef, school.oil,
school.vegitable, school.flavor, school.pock
]
attr = [
"大米", "干货", "糕点", "鸡蛋", "鸡鸭肉", "米粉", "奶制品", "牛肉", "食用油", "蔬菜", "调味品",
"猪肉"
]
struct_value = []
for i in value:
k = roundfunc(i)
struct_value.append(k)
total = roundfunc(school.total)
mypie.add("", attr, value, is_label_show=True, is_legend_show=False)
mybar = Bar("每人日均食物摄入结构(单位：g)", title_pos="center")
nutri_value = [
nutri_school.xuqin, nutri_school.fish, nutri_school.soilbean,
nutri_school.egg, nutri_school.gushu, nutri_school.milk,
nutri_school.sault, nutri_school.vigetable, nutri_school.fruit,
nutri_school.oil
]
standart_value = [50, 50, 40, 50, 350, 200, 5, 400, 250, 30]
nutri_attr = [
"畜禽肉", "鱼虾类", "大豆坚果", "蛋类", "谷薯类", "奶制品", "食盐", "蔬菜类", "水果类", "植物油"
]
mybar.add("学生实际值",
nutri_attr,
nutri_value,
is_label_show=True,
is_legend_show=True,
legend_top="bottom")
mybar.add("标准摄入值",
nutri_attr,
standart_value,
is_label_show=True,
is_legend_show=True,
legend_top="bottom")
context = dict(
school=school,
town_list=District.objects.all(),
town=school2.district,
school_name=school.name.name,
next_school=next_school(school2.id),
school_student=students,
total=total,
nutr_total=nutri_school.total,
pie_myecharts=mypie.render_embed(),
bar_myecharts=mybar.render_embed(),
host=REMOTE_HOST,
pie_script_list=mypie.get_js_dependencies(),
bar_script_list=mybar.get_js_dependencies(),
)
return HttpResponse(template.render(context, request))
def interface_report():
try:
Bars = []
searchs = []
db_project = db_op.project_list
db_idcs = db_idc.idc_id
db_servers = db_idc.idc_servers
db_project_third = db_op.project_third
db_third = db_idc.third_resource
Infos = defaultdict()
values = OrderedDict()
dt = datetime.datetime.now()
#获取域名列表
hosts = RC_CLUSTER.smembers(
'api_domain_lists_%s' %
time.strftime('%Y-%m-%d', time.localtime()))
hosts = [host for host in hosts]
if hosts:
searchs = json.dumps([{
"id": str(i),
"text": str(host)
} for i, host in enumerate(hosts)])
#获取域名和接口信息
if 'business_bigdata_select_host' in request.cookies and 'business_bigdata_select_uri' in request.cookies:
host = request.cookies['business_bigdata_select_host']
uri = urllib.parse.unquote(
request.cookies['business_bigdata_select_uri'])
#获取接口相关的资产信息
projects = db_project.query.with_entities(
db_project.ip, db_project.ssh_port,
db_project.resource).filter(
db_project.domain.like('%{0},%'.format(host))).all()
if projects:
idc_lists = db_idcs.query.with_entities(
db_idcs.id, db_idcs.aid).all()
idc_lists = {idcs[0]: idcs[-1] for idcs in idc_lists}
third_lists = db_third.query.with_entities(
db_third.id, db_third.resource_type).all()
third_lists = {third[0]: third[-1] for third in third_lists}
ips = [val[0] for val in projects]
resource = projects[0][-1]
idc_ids = db_servers.query.with_entities(
db_servers.idc_id, func.count(db_servers.idc_id)).filter(
db_servers.ip.in_(tuple(ips))).group_by(
db_servers.idc_id).all()
if idc_ids:
idc_ids = [list(ids) for ids in idc_ids]
for ids in idc_ids:
ids[0] = idc_lists[ids[0]]
RC.delete('idc_incrs')
for ids in idc_ids:
RC.hincrby('idc_incrs', ids[0], ids[1])
idc_ids = RC.hgetall('idc_incrs')
idc_attr = [ids for ids in idc_ids]
idc_vals = [int(idc_ids[ids]) for ids in idc_ids]
pie = Pie("%s台%s服务器机房分布" % (len(set(projects)), resource),
width='100%',
height='270px',
title_pos='center',
title_text_size=14)
pie.add("",
idc_attr,
idc_vals,
is_label_show=True,
is_toolbox_show=False,
is_legend_show=False,
legend_orient='vertical',
legend_pos='left',
radius=[0, 65],
is_random=True)
Infos['pie'] = pie
project = db_project.query.with_entities(
db_project.project).filter(
db_project.domain.like('%{0},%'.format(host))).all()
#获取接口相关的应用服务资源
if project:
project = project[0][0]
third_vals = db_project_third.query.with_entities(
db_project_third.third_id,
func.count(db_project_third.third_id)).filter(
db_project_third.project == project).group_by(
db_project_third.third_id).all()
if third_vals:
third_vals = [list(val) for val in third_vals]
for val in third_vals:
val[0] = third_lists[val[0]]
RC.delete('third_incrs')
for val in third_vals:
RC.hincrby('third_incrs', val[0], val[1])
third_vals = RC.hgetall('third_incrs')
third_vals[resource] = len(projects)
third_attr = [val for val in third_vals]
third_counts = [int(third_vals[val]) for val in third_vals]
pie_third = Pie("应用服务资源分布",
width='100%',
height='270px',
title_pos='center',
title_text_size=14)
pie_third.add(
"",
third_attr,
third_counts,
is_label_show=True,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
radius=[55, 65],
is_random=True,
is_legend_show=False,
)
Infos['pie_third'] = pie_third
#接口性能数据获取
year = time.strftime('%Y', time.localtime())
for i in range(2, 9):
ot = dt - datetime.timedelta(days=i - 1)
nt = dt - datetime.timedelta(days=i - 2)
ot = ot.strftime('%Y-%m-%dT00:00:00Z')
nt = nt.strftime('%Y-%m-%dT00:00:00Z')
try:
pv_sum = 0.0
cmd = 'select sum(mean_pv) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
pv_sum = infos['sum']
except Exception as e:
logging.error(e)
try:
values['avg_resp'] = 0.0
cmd = 'select mean(mean_avg_resp) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s'and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
values['avg_resp:%s' %
ot.split('T')[0]] = '%.2f' % infos['mean']
except Exception as e:
logging.error(e)
try:
s4 = 0.0
cmd = 'select sum(mean_status_4xx) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
s4 = infos['sum']
s5 = 0.0
cmd = 'select sum(mean_status_5xx) from ' + 'analysis%s' % year + " where time >='%s' and time < '%s' and host='%s' and uri = '%s';" % (
ot, nt, host, uri)
result = Influx_cli.query(cmd)
if result:
for infos in result.get_points():
infos = infos
if infos:
s5 = infos['sum']
if pv_sum > 0:
values['status_sum:%s' % ot.split('T')[0]] = float(
'%.2f' % (float(s4 + s5) / pv_sum * 100))
except Exception as e:
logging.error(e)
values['pv_sum:%s' % ot.split('T')[0]] = int(pv_sum) * 60
#接口性能展示
if values:
#pv总数
bar_pv = Bar("PV访问量",
width='105%',
height='270px',
title_pos='center',
title_text_size=14)
vals = [(val.split(':')[-1], values[val]) for val in values
if 'pv_sum:' in val]
if vals:
bar_vals = [val[0] for val in vals]
bar_counts = [val[-1] for val in vals]
bar_pv.add('',
bar_vals,
bar_counts,
is_label_show=True,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
xaxis_interval=0,
is_random=True)
Bars.append(bar_pv)
#平均响应时间
line_resp = Line("平均响应时间",
width='105%',
height='270px',
title_pos='center',
title_text_size=14)
vals = [(val.split(':')[-1], values[val]) for val in values
if 'avg_resp:' in val]
if vals:
line_vals = [val[0] for val in vals]
line_counts = [val[-1] for val in vals]
line_resp.add('',
line_vals,
line_counts,
is_label_show=True,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
xaxis_interval=0,
yaxis_formatter='s',
is_random=True,
is_smooth=False,
mark_point=["min", "max"])
Bars.append(line_resp)
#错误状态码占比
line_status = Line("错误码占比",
width='105%',
height='270px',
title_pos='center',
title_text_size=14)
vals = [(val.split(':')[-1], values[val]) for val in values
if 'status_sum:' in val]
if vals:
line_vals = [val[0] for val in vals]
line_counts = [val[-1] for val in vals]
line_status.add('',
line_vals,
line_counts,
is_fill=True,
is_label_show=True,
is_smooth=False,
is_toolbox_show=False,
legend_orient='vertical',
legend_pos='right',
xaxis_interval=0,
yaxis_formatter='%',
is_random=True,
mark_line=["max", "min"])
Bars.append(line_status)
except Exception as e:
logging.error(e)
return render_template('interface_report.html',
Bars=Bars,
Infos=Infos,
searchs=searchs)
def test_more_charts():
page = Page()
# line
line = Line("折线图示例")
line.add(
"最高气温",
WEEK,
[11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低气温",
WEEK,
[1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
)
# pie
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos="center")
pie.add(
"",
CLOTHES,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
page.add([line, pie])
# kline
v1 = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
kline = Kline("K 线图示例")
kline.add(
"日K",
["2017/7/{}".format(i + 1) for i in range(31)],
v1,
is_datazoom_show=True,
)
page.add(kline)
# radar
schema = [
("销售", 6500),
("管理", 16000),
("信息技术", 30000),
("客服", 38000),
("研发", 52000),
("市场", 25000),
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add(
"实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode="single",
)
page.add(radar)
# scatter3d
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100),
] for _ in range(80)]
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=RANGE_COLOR)
page.add(scatter3D)
# wordcloud
name = [
"Sam S Club",
"Macys",
"Amy Schumer",
"Jurassic World",
"Charter Communications",
"Chick Fil A",
"Planet Fitness",
"Pitch Perfect",
"Express",
"Home",
"Johnny Depp",
"Lena Dunham",
"Lewis Hamilton",
"KXAN",
"Mary Ellen Mark",
"Farrah Abraham",
"Rita Ora",
"Serena Williams",
"NCAA baseball tournament",
"Point Break",
]
value = [
10000,
6181,
4386,
4055,
2467,
2244,
1898,
1484,
1112,
965,
847,
582,
555,
550,
462,
366,
360,
282,
273,
265,
]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[30, 100], rotate_step=66)
page.add(wordcloud)
# liquid
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6])
page.add(liquid)
assert len(page) == 7
assert isinstance(page[0], Line)
assert ("echarts" in page.js_dependencies) or ("echarts.min"
in page.js_dependencies)
page.render()
def test_pie_default():
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例")
pie.add("", CLOTHES, v1, is_label_show=True)
pie.render()
def test_pie_multiple():
import random
attr = ['A', 'B', 'C', 'D', 'E', 'F']
pie = Pie("饼图示例", width=1000, height=600)
pie.add("", attr, [random.randint(0, 100) for _ in range(6)],
radius=[50, 55], center=[25, 50], is_random=True)
pie.add("", attr, [random.randint(20, 100) for _ in range(6)],
radius=[0, 45], center=[25, 50], rosetype='area')
pie.add("", attr, [random.randint(0, 100) for _ in range(6)],
radius=[50, 55], center=[65, 50], is_random=True)
pie.add("", attr, [random.randint(20, 100) for _ in range(6)],
radius=[0, 45], center=[65, 50], rosetype='radius')
pie.render()
def draw_height_pie(data):
pie = Pie("妹子身高分布饼图-圆环图", title_pos='center')
pie.add("", height_interval, data, radius=[40, 75], label_text_color=None,
is_label_show=True, legend_orient='vertical', is_random=True,
legend_pos='left')
return pie
def create_simple_pie():
pie = Pie('各类电影中"好片"所占的比例', "数据来着豆瓣", title_pos='center')
pie.add(
"",
["剧情", ""],
[25, 75],
center=[10, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None,
)
pie.add("", ["奇幻", ""], [24, 76],
center=[30, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None,
legend_pos='left')
pie.add("", ["爱情", ""], [14, 86],
center=[50, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["惊悚", ""], [11, 89],
center=[70, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["冒险", ""], [27, 73],
center=[90, 30],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["动作", ""], [15, 85],
center=[10, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["喜剧", ""], [54, 46],
center=[30, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["科幻", ""], [26, 74],
center=[50, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["悬疑", ""], [25, 75],
center=[70, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None)
pie.add("", ["犯罪", ""], [28, 72],
center=[90, 70],
radius=[18, 24],
label_pos='center',
is_label_show=True,
label_text_color=None,
is_legend_show=True,
legend_top="center")
pie.renderer = 'svg'
return pie
def test_timeline_pie():
style = Style()
pie_style = style.add(
is_label_show=True, radius=[30, 55], rosetype="radius"
)
pie_1 = Pie("2012 年销量比例", "数据纯属虚构")
pie_1.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_2 = Pie("2013 年销量比例", "数据纯属虚构")
pie_2.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_3 = Pie("2014 年销量比例", "数据纯属虚构")
pie_3.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_4 = Pie("2015 年销量比例", "数据纯属虚构")
pie_4.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
pie_5 = Pie("2016 年销量比例", "数据纯属虚构")
pie_5.add("秋季", CLOTHES, [randint(10, 100) for _ in range(6)], **pie_style)
timeline = Timeline(
is_auto_play=True, timeline_bottom=0, width=1200, height=600
)
timeline.add(pie_1, "2012 年")
timeline.add(pie_2, "2013 年")
timeline.add(pie_3, "2014 年")
timeline.add(pie_4, "2015 年")
timeline.add(pie_5, "2016 年")
assert len(timeline.options.get("baseOption").get("series")) == 0
timeline.render()
return country_1
#map() 会根据提供的函数对指定序列做映射。
# 对data['pub_time']全部使用get_country函数，与apply类似，但apply一般用于数据分组后再进行函数套用
data['country'] = data['pub_time'].map(get_country)
#如果直接使用，只能对单独元素进行操作
#print(data['pub_time'][0].split('('))
#data.head()
country = data.groupby('country')['country'].count()
from pyecharts import Pie
attr = list(country.index)
v1 = list(country)
pie = Pie("", title_pos='center')
pie.add(
"",
attr,
v1,
radius=[40, 75],
label_text_color=None,
is_label_show=True,
legend_orient="vertical",
legend_pos="left",
)
pie.render(r'E:\vscode_code\GitHub项目\爬虫+数据分析\猫眼\电影国家分布情况.html')
#提取人名
str = ''
for i in range(100):
def test_pie():
# pie_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例")
pie.add("", attr, v1, is_label_show=True)
pie.show_config()
pie.render()
# pie_1
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图-圆环图示例", title_pos='center')
pie.add("", attr, v1, radius=[40, 75], label_text_color=None, is_label_show=True, legend_orient='vertical',
legend_pos='left')
pie.show_config()
pie.render()
# pie_2
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
v2 = [19, 21, 32, 20, 20, 33]
pie = Pie("饼图-玫瑰图示例", title_pos='center', width=900)
pie.add("商品A", attr, v1, center=[25, 50], is_random=True, radius=[30, 75], rosetype='radius')
pie.add("商品B", attr, v2, center=[75, 50], is_random=True, radius=[30, 75], rosetype='area', is_legend_show=False,
is_label_show=True)
pie.show_config()
pie.render()
# pie_3
pie = Pie("饼图示例", title_pos='center', width=1000, height=600)
pie.add("", ['A', 'B', 'C', 'D', 'E', 'F'], [335, 321, 234, 135, 251, 148], radius=[40, 55], is_label_show=True)
pie.add("", ['H', 'I', 'J'], [335, 679, 204], radius=[0, 30], legend_orient='vertical', legend_pos='left')
pie.show_config()
pie.render()
# pie_4
import random
attr = ['A', 'B', 'C', 'D', 'E', 'F']
pie = Pie("饼图示例", width=1000, height=600)
pie.add("", attr, [random.randint(0, 100) for _ in range(6)], radius=[50, 55], center=[25, 50], is_random=True)
pie.add("", attr, [random.randint(20, 100) for _ in range(6)], radius=[0, 45], center=[25, 50], rosetype='area')
pie.add("", attr, [random.randint(0, 100) for _ in range(6)], radius=[50, 55], center=[65, 50], is_random=True)
pie.add("", attr, [random.randint(20, 100) for _ in range(6)], radius=[0, 45], center=[65, 50], rosetype='radius')
pie.show_config()
pie.render()
# Pie_5
pie = Pie('各类电影中"好片"所占的比例', "数据来着豆瓣", title_pos='center')
pie.add("", ["剧情", ""], [25, 75], center=[10, 30], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None, )
pie.add("", ["奇幻", ""], [24, 76], center=[30, 30], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None, legend_pos='left')
pie.add("", ["爱情", ""], [14, 86], center=[50, 30], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["惊悚", ""], [11, 89], center=[70, 30], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["冒险", ""], [27, 73], center=[90, 30], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["动作", ""], [15, 85], center=[10, 70], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["喜剧", ""], [54, 46], center=[30, 70], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["科幻", ""], [26, 74], center=[50, 70], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["悬疑", ""], [25, 75], center=[70, 70], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None)
pie.add("", ["犯罪", ""], [28, 72], center=[90, 70], radius=[18, 24],
label_pos='center', is_label_show=True, label_text_color=None, is_legend_show=True, legend_top="center")
pie.show_config()
pie.render()
page = Page()
# line
item_name_list, item_num_list = counter2list(dict2sorted_by_key(date_msg_counter))
line = Line("群心情走势图", "截至日期：%s" % item_name_list[len(item_name_list) - 1], title_text_size=30,
subtitle_text_size=18, title_pos='center')
line.add("", item_name_list, item_num_list, mark_point=["max"], legend_pos='65%',
xaxis_interval=2, xaxis_rotate=27, xaxis_label_textsize=20, yaxis_label_textsize=20, yaxis_name_pos='end',
yaxis_pos="%50", is_label_show=True)
page.add(line)
# pie
attr = ["总发言数", "日均发言", "发言最多", "发言最少"]
v1 = [len(msg_list), math.floor(len(msg_list) / len(date_msg_counter)), most_msg_count[1],
dict_sorted_by_value[0][1]]
pie = Pie("群聊数据统计", title_pos='center')
pie.add("", attr, v1, radius=[40, 75], label_text_color=None, is_label_show=True, legend_orient='vertical',
legend_pos='left')
page.add(pie)
# bar
bar = Bar("群聊情感分析", title_pos='center')
item_name_list, item_num_list = emotions_count(emotions)
bar.add("", item_name_list, item_num_list, title_pos='center', is_label_show=True, xaxis_label_textsize=20,
yaxis_label_textsize=20)
page.add(bar)
# graph
nodes = [{"name": earlest_time_node, "symbolSize": 30},
{"name": latest_time_node, "symbolSize": 50},
{"name": earlest_msg_node, "symbolSize": 30},
def maps(city, job):
# # ---------------------------------------薪资频数分布图-----------------------------------
# #
# #
# # 饼图 薪资频数分布图
if city == '全国' or job == '':
a = list(JobMsg.objects.all().values_list('job_meanmoney', ))
else:
a = list(
JobMsg.objects.filter(job_name__icontains=job).values_list(
'job_meanmoney', ))
# print(a)
mm = {
'0-2000': 0,
'2000-4000': 0,
'4000-6000': 0,
'6000-8000': 0,
'8000-10000': 0,
'10000-15000': 0,
'15000-20000': 0,
'20000-30000': 0,
'30000以上': 0
}
for i in a:
if int(i[0]) < 2000:
mm['0-2000'] += 1
elif int(i[0]) < 4000:
mm['2000-4000'] += 1
elif int(i[0]) < 6000:
mm['4000-6000'] += 1
elif int(i[0]) < 8000:
mm['6000-8000'] += 1
elif int(i[0]) < 10000:
mm['8000-10000'] += 1
elif int(i[0]) < 15000:
mm['10000-15000'] += 1
elif int(i[0]) < 20000:
mm['15000-20000'] += 1
elif int(i[0]) < 30000:
mm['20000-30000'] += 1
else:
mm['30000以上'] += 1
# ss = {}
# for i in mm:
# if mm.count(i) >= 1:
# ss[i] = mm.count(i)
key = list(mm.keys())
value = list(mm.values())
pie = Pie(title="薪资所占比例图",
title_pos='left',
width=1000,
height=350,
title_text_size=18)
# pie.add(
# "",
# key,
# value,
# radius=[35, 60],
# center=[65, 50],
# legend_pos="80%",
# legend_orient="vertical",
# )
# 选图
# pie.add("", key, value, radius=[35, 50], label_text_color=None, title_pos='left',
# is_label_show=True, legend_orient='vertical')
#
# pie.add("商品A", key, value, center=[65, 50], is_random=True,
# radius=[35, 50], rosetype='radius', legend_pos='left', legend_orient='vertical')
pie.add("薪资频数分布图",
key,
value,
center=[45, 50],
is_random=True,
radius=[35, 60],
rosetype='area',
legend_pos='left',
legend_orient='vertical',
is_legend_show=False,
is_label_show=True)
return pie
def qianChengWuYou(position, city):
start_url = "https://search.51job.com/list/{},000000,0000,00,9,99,{},2,{}.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare="
city_code = {
"北京": "010000",
"上海": "020000",
"广州": "030200",
"深圳": "040000",
"武汉": "180200",
"西安": "200200",
"杭州": "080200",
"南京": "070200",
"成都": "090200",
"重庆": "060000",
"东莞": "030800",
"大连": "230300",
"沈阳": "230200",
"苏州": "070300",
"昆明": "250200",
"长沙": "190200",
"合肥": "150200",
"宁波": "080300",
"郑州": "170200",
"天津": "050000",
"青岛": "120300",
"济南": "120200",
"哈尔滨": "220200",
"长春": "240200",
"福州": "110200"
}
headers = {
"Accept":
"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
"Host":
"search.51job.com",
"Referer":
start_url.format(quote(city_code[city]), quote(position), 1),
"User-Agent":
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36",
}
rsp = requests.get(start_url.format(quote(city_code[city]),
quote(position), 1),
headers=headers)
html = etree.HTML(rsp.content)
pages = html.xpath(
'//*[@id="resultList"]/div[2]/div[5]/text()')[1].replace(" / ", "")
districts = dict()
districts = qianChengWuYou_parse_html(html, districts)
for i in range(2, int(pages)):
headers["Referer"] = start_url.format(quote(city_code[city]),
quote(position), i)
rsp = requests.get(start_url.format(quote(city_code[city]),
quote(position), 1),
headers=headers)
html = etree.HTML(rsp.content)
districts = qianChengWuYou_parse_html(html, districts)
time.sleep(0.02)
pie = Pie("前程无忧职位信息统计",
"{}{}职位行政区分布".format(city, position),
title_pos='center',
width=1300)
columns = districts.keys()
data = districts.values()
# 加入数据，设置坐标位置为【75，50】，上方的colums选项取消显示，显示label标签
pie.add("统计",
columns,
data,
center=[50, 60],
is_legend_show=False,
is_label_show=True)
# 保存图表
pie.render(
"/home/itianeru/PycharmProjects/RHFW/RHFW/pjob/templates/pjob/{}_{}.html"
.format(city, position))
