zabbix获取一周内各个等级告警的次数

# encoding:UTF-8
import xlsxwriter
import datetime
import pymysql
import numpy as np
import pandas

__author__ = 'sanjing'
__data__ = '2019/06/14'


averagesql = """
    select from_unixtime(clock,'%Y-%m-%d') as time,
        count(DISTINCT eventid) as id    
        from alerts 
        WHERE mediatypeid = 5 
        AND message LIKE '%告警等级: Average%'
        AND SUBJECT  LIKE '%PROBLEM%'
        AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
        group by time
        order by time asc

"""

highsql = """
    select from_unixtime(clock,'%Y-%m-%d') as time,
        count(DISTINCT eventid) as id    
        from alerts 
        WHERE mediatypeid = 5 
        AND message LIKE '%告警等级: High%'
        AND SUBJECT  LIKE '%PROBLEM%'
        AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
        group by time
        order by time asc
"""

warningsql = """
    select from_unixtime(clock,'%Y-%m-%d') as time,
        count(DISTINCT eventid) as id    
        from alerts 
        WHERE mediatypeid = 5 
        AND message LIKE '%告警等级: Warning%'
        AND SUBJECT  LIKE '%PROBLEM%'
        AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
        group by time
        order by time asc
"""

informationsql = """
    select from_unixtime(clock,'%Y-%m-%d') as time,
        count(DISTINCT eventid) as id    
        from alerts 
        WHERE mediatypeid = 5 
        AND message LIKE '%告警等级: Information%'
        AND SUBJECT  LIKE '%PROBLEM%'
        AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
        group by time
        order by time asc
"""

disastersql = """
    select from_unixtime(clock,'%Y-%m-%d') as time,
        count(DISTINCT eventid) as id    
        from alerts 
        WHERE mediatypeid = 5 
        AND message LIKE '%告警等级: Diasater%'
        AND SUBJECT  LIKE '%PROBLEM%'
        AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
        group by time
        order by time asc
"""


#连接MySQL数据库
def get_count(sql):
    conn=pymysql.connect("xx.xx.xx.xx", "readonly", "xxxx", "zabbix", charset='utf8')
    cursor = conn.cursor()
    cursor.execute(sql)
    count = cursor.fetchall()
    # 将rows转化为数组
    rows = np.array(count)
    conn.close()
    return count


def coloum(data, weekendtime):
#创建一个excel文件
    workbook = xlsxwriter.Workbook(weekendtime +".xlsx")
#创建一个工作表，默认sheet1
    worksheet = workbook.add_worksheet()
    bold = workbook.add_format({'bold': 1})
#表头

    title = ['告警级别', '星期一','星期二','星期三','星期四','星期五','星期六','星期日']
#列名
    buname = ['information', 'warning','average', 'high', 'disaster']
# 定义数据formatter格式对象，设置边框加粗1像素
    formatter = workbook.add_format()
    formatter.set_border(1)
#定义格式：# 定义标题栏格式对象：边框加粗1像素，背景色为灰色，单元格内容居中、加粗
    title_formatter = workbook.add_format()
    title_formatter.set_border(1)
    title_formatter.set_bg_color('#cccccc')
    title_formatter.set_align('center')
    title_formatter.set_bold()
    chart_col = workbook.add_chart({'type': 'column'})
    def chart_series(row):
        chart_col.add_series(
            {
                'categories': '=Sheet1!$B$1:$H$1',
                'values': '=Sheet1!$B${}:$H${}'.format(row, row),
                'line': {'color': 'black'},
#                'name': '=Sheet1!$A${}'.format(row)
                'name': '=Sheet1!$A$' + row
            }
        )
# 下面分别以行和列的方式将标题栏、业务名称、流量数据写入单元格，并引用不同的格式对象
    worksheet.write_row('A1',title,title_formatter)
    worksheet.write_column('A2',buname,formatter)
    for i in range (2,7):
        worksheet.write_row('B{}'.format(i),data[i-2],formatter)
        print (i)
        chart_series(str(i))


    # 设置图表的title 和 x，y轴信息
    chart_col.set_title({'name': '告警统计/周'})
    chart_col.set_x_axis({'name': '告警级别'})
    chart_col.set_y_axis({'name': '告警次数'})
    # 设置图表的风格
#    chart_col.set_style(37)

    # 把图表插入到worksheet以及偏移
    worksheet.insert_chart('A10', chart_col, {'x_offset': 25, 'y_offset': 10})
    workbook.close()
#判断二维元组是否为空，长度是否满足要求，不满足则补0.
#输入为一个字典，判断是否为空，空则添加数据
def covertdata(jsondata,weektime):

    listkey = list(jsondata.keys())
    for i in weektime:
        j = i.strftime("%Y-%m-%d")
        if listkey:
            if j not in listkey:
                jsondata[j] = "0"
        else:
            jsondata[j] = "0"
#    print (jsondata)
#按照时间对字典进行排序
    sort = sorted(jsondata.items(), key=lambda d: d[0])
#将第二列取出来并转为列表
    array = np.array(sort)
    array2 = array[:, 1]
    list2 = array2.tolist()
    list3 = list(map(lambda x: float(x), list2))
    return list3
#    print (list2)


if __name__ == '__main__':
    yesterday = (datetime.date.today() + datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
    weeklist = pandas.date_range(end=yesterday, periods=7)
    informationdata = get_count(informationsql)
    informationlist = covertdata(dict(informationdata),weeklist)
    warningdata = get_count(warningsql)
    warninglist = covertdata(dict(warningdata),weeklist)
    averagedata = get_count(averagesql)
    averagelist = covertdata(dict(averagedata),weeklist)
    highdata = get_count(highsql)
    highlist = covertdata(dict(highdata),weeklist)
    disasterdata = get_count(disastersql)
    disasterlist = covertdata(dict(disasterdata),weeklist)
#    print (informationlist)
#    print (warninglist)
#    print (averagelist)
#    print (highlist)
#    print (disasterlist)
    data1 = [informationlist, warninglist, averagelist, highlist, disasterlist]
    coloum(data1,yesterday)

结果如图：