绘制柱状图
import matplotlib.pyplot
def 订购时间绘图():
global 订购时间
'''
每一天的，X轴24个点位；
以铃音为单位，每个日期为x轴
'''
color_list=["red","orange","yellow","green","blue","purple",]
for toneid,value in 订购时间.items():
total_width, n = 1, len(订购时间[toneid])
width = total_width / n #每根柱子的宽度
matplotlib.pyplot.clf() #清空
matplotlib.rc("font",family='FangSong')
index=0
for 日期,times in value.items():
label_str=日期.replace(' 00:00:00','')
time_dict=订购时间划分(times)
num_list=time_dict.values()
time_list=time_dict.keys()
x=[int(t)-(total_width-width)/n + width*index for t in time_list]
matplotlib.pyplot.bar(x,num_list,width=width,align='edge',color=color_list[index],label=label_str)
matplotlib.pyplot.legend() #使得图例生效
index+=1
matplotlib.pyplot.title('{}-订购'.format(toneid))
matplotlib.pyplot.xlabel('小时')
matplotlib.pyplot.ylabel('数量')
matplotlib.pyplot.savefig(r"D:\BaiduSyncdisk\01工作\脚本\data\绘图\{}.png".format(toneid));
绘制折线图
import matplotlib.pyplot
def draw_charts_v1(hostname:str,datetime_flag:int)->None:
'''
本函数使用百分百绘制图表
hostname：主机名
datetime_flag：时间范围标志
0：最近24小时
1：最近一周
2：最近一个月
'''
global result_dict,datetime_flag_dict
data_dict = result_dict[hostname]
# print(matplotlib.pyplot.style.available)
matplotlib.pyplot.clf() #清空
'''
['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
'''
matplotlib.pyplot.style.use('bmh') #图形风格：bmh、ggplot、dark_background、fivethirtyeight和grayscale。
matplotlib.pyplot.figure(figsize=(16,10)) #长宽，单位为百像素。即16表示1600像素
matplotlib.rc("font",family='FangSong') #字体
#matplotlib.pyplot.xticks(rotation=1) #X轴间隔
matplotlib.pyplot.yticks(numpy.linspace(0,1.2,num=13)) #Y轴间隔
matplotlib.pyplot.ylim(0,1.2) #调整Y轴值范围
server_state = [int(x) for x in data_dict['server_state']] #转换下类型才不会出问题。
# server_state_y = (server_state-numpy.min(server_state))/(numpy.max(server_state)-numpy.min(server_state))
matplotlib.pyplot.plot(data_dict['reg_date'],server_state,label='服务状态')
matplotlib.pyplot.plot(data_dict['reg_date'],data_dict['活跃线程占比'],label='活跃线程占比')
matplotlib.pyplot.plot(data_dict['reg_date'],data_dict['cpu使用率'],label='cpu使用率')
matplotlib.pyplot.plot(data_dict['reg_date'],data_dict['内存使用率'],label='内存使用率')
matplotlib.pyplot.plot(data_dict['reg_date'],data_dict['硬盘使用率'],label='硬盘使用率')
pic_name='{} {} 状态折线图'.format(hostname,datetime_flag_dict[datetime_flag])
matplotlib.pyplot.title(pic_name)
matplotlib.pyplot.xlabel('时间')
matplotlib.pyplot.ylabel('百分比')
matplotlib.pyplot.legend()
matplotlib.pyplot.savefig(r"{}{}.png".format(check_folder(datetime_flag),pic_name))
matplotlib.pyplot.close(); #显式关闭。
将小图汇总生成大图
def Merge_pic(path:str,width:int,height:int,png_num:int=19):
'''
本函数用于将生成的所有图表合并成一张大图，生成的大图将以.jpg作为文件后缀
path：路径，里面应存储有待合并的图片
width：图表的宽度，单位像素
height：图表的高度，单位像素
png_num：图片总数，默认值19，将按照4*5的方式排列
'''
#获取图片名称
images_files = [f for f in os.listdir(path) if f.endswith('.png')]
#初始化大图的宽度和高度
jpg_width=4*width
jpg_height=5*height
#创建一个空白的大图
new_image= PIL.Image.new('RGB',(jpg_width,jpg_height),'#FFFFFF') #初始化为白色
#设置初始位置
x_offset=0
y_offset=0
#循环读取每张图片并拼接到大图上
for y in range(5):
for x in range(4):
idx = x+y*4
if(19<=idx):
continue
img = PIL.Image.open(path + images_files[idx])
new_image.paste(img,(x_offset,y_offset))
#更新位置信息
x_offset += img.width
x_offset=0
y_offset+=img.height
#保存
new_image.save('{}所有主机-{}状态折线图.jpg'.format(path,os.path.basename(path.replace('\\',''))))
标签：seaborn,pyplot,matplotlib,生成,v0,width,柱状图,dict,图片 From： https://www.cnblogs.com/love-DanDan/p/18022579