pd_result 今天要说的问题不是这个,这个只是附带优化下 重点问题是关于读取xls时的一个警告 WARNING *** file size (1080329) not 512 + multiple of sector xlrd下的compdoc.py文件的第117-118行附近(看有的是119行)代码 print("WARNING *** file size (%d) not 512 + multiple of sector
SAP RETAIL初阶之MM41创建商品主数据界面里的Industry Sector SAP RETAIL系统里,使用事务代码MM41去创建商品主数据里,初始界面并不需要用户选择Industry Sector。 选择了物料类型,物料组,物料种类等栏位,输入门店代码,选择相关视图后回车,在Basic Data视图里看到Industry Sector字段值默认为1,如下图示: 这是什么配置起的作用? 点击该菜单节点,进入如下界面, Retail Industry Sector字段值为1. -完- 写于2022-1-19
2 and ends at sector 33 First usable sector is 34, last usable sector is 41943006 Partitions will be aligned on 2048-sector boundaries Total free space is 37748669 sectors (18.0 GiB) Number Start (sector 2 and ends at sector 33 First usable sector is 34, last usable sector is 41943006 Partitions will be aligned on 2048-sector boundaries Total free space is 41942973 sectors (20.0 GiB) Number Start (sector 2 and ends at sector 33 First usable sector is 34, last usable sector is 41943006 Partitions will be
The i-th sector contains the fi-th fragment of the file (1 ≤ fi ≤ n). It takes |a - b| time units to move the magnetic head from the sector a to the sector b. 1 to the sector 5, i.e. it takes 4 time units 2->3 means movement from the sector 5 to the sector 2 , i.e. it takes 3 time units 3->4 means movement from the sector 2 to the sector 4, i.e. it takes 2 time units 4->5 means movement from the sector 4 to the sector 3, i.e. it takes 1 time units So the
< ADDR_FLASH_SECTOR_1) && (Address >= ADDR_FLASH_SECTOR_0)) { sector = FLASH_SECTOR_0; } else if((Address < ADDR_FLASH_SECTOR_2) && (Address >= ADDR_FLASH_SECTOR_1)) { sector = FLASH_SECTOR { sector = FLASH_SECTOR_3; } else if((Address < ADDR_FLASH_SECTOR_5) && (Address >= ADDR_FLASH_SECTOR _4)) { sector = FLASH_SECTOR_4; } else if((Address < ADDR_FLASH_SECTOR_6) && (Address >= ADDR_FLASH_SECTOR _10)) { sector = FLASH_SECTOR_10; } else { sector = FLASH_SECTOR_11; } return sector
, hh, ss): """计算(head, sector, cylindar)""" s = sector % ss + 1 sector /= ss h = sector % hh sector /= hh c = sector & 0xFF s |= (sector >> 2) & 0xC0 :%d logical_sector_size:%d""" % (device_size, device_sector_number, logical_sector_size)) - 1 if (new_end_sector - new_start_sector + 1) * logical_sector_size > 0xFFFFFFFF * 512: sys.exit(1) new_end_sector = 0xFFFFFFFF * 512 / logical_sector_size + new_start_sector -
= 0; if ((Address < ADDR_FLASH_SECTOR_1) && (Address >= ADDR_FLASH_SECTOR_0)) { sector >= ADDR_FLASH_SECTOR_22)) { sector = FLASH_SECTOR_22; } else { sector = FLASH_SECTOR_23; } return sector; } uint16_t MEM_If_Erase_HS(uint32_t Add ADDR_FLASH_SECTOR_3 0x0800C000 #define ADDR_FLASH_SECTOR_4 0x08010000 #define ADDR_FLASH_SECTOR_ ADDR_FLASH_SECTOR_8 0x08080000 #define ADDR_FLASH_SECTOR_9 0x080A0000 #define ADDR_FLASH_SECTOR_
in circos.sectors: # Plot sector name sector.text(f"Sector: {sector.name}", r=110, size=15) # Create x positions & random y values x = np.arange(sector.start, sector.end) + 0.5 y = np.random.randint(0, 100, len(x)) # Plot lines track1 = sector.add_track((80, 100), r_pad_ratio in circos.sectors: sector.text(sector.name, size=10) fig = circos.plotfig() from pycirclize import in circos.sectors: sector.text(sector.name, r=120, size=10, color=chr_name2color[sector.name])
(18deg); } .sector:nth-child(3) { transform: rotate(54deg); } .sector:nth-child ); } .sector:nth-child(6) { transform: rotate(162deg); } .sector:nth-child(7) { ; } .sector:nth-child(2n) .sector-inner { background: #ffffff; } .sector-inner div class="sector">
3.首先读取sector0的信息(Sectors==>Read,在Starting Sector输入0后按Enter) 4.Sector 0结果显示如下,看到全为空,表明MBR被损坏(MBR包含启动code 、分区表、disk signature等信息) 5.需要从好的磁盘copy sector 0,Drives找到正常磁盘,以Drive0为例,双击PhysicalDrive0后选择Set Active。 6.读取sector0的信息(Sectors==>Read,在Starting Sector输入0后按Enter) 7.保存sector0,按照默认选项点击“保存”。 9.读取sector0的信息(Sectors==>Read,在Starting Sector输入0后按Enter) 10.导入第7步保存的sector0 信息(File==》Open File)。 11.Sectors==》Write,将sector 0 信息保存(在弹出界面依次选择是,Write it)。
意思是这个查询到的里面的计数单位都是512-byte,不管上层设置的block大小是多少,我们知道文件系统底层的sector就是512-byte,所以这个查询到的结果就可以跟当前的文件系统的sector 个数为20971520s 20971520s*512byte=10737418240byte=10485760KB=10240MB sector的大小一定,总rbd大小一定的情况下sector的数目也是一定的 =int(object)*4096*1024/512 print "object:"+str(object) print "sector:"+str(sector) incre= ,而sector也是会一一对应到后台的对象的,这个在本文当中得到了验证,所以整个逻辑就是,在文件系统层找到文件对应的sector位置,然后再在底层把sector和对象关系找好,就能从找到文件在对象当中的具体的位置 ,也就能定位并且能提取了,本篇是基于xfs的,其他文件系统只要能定位文件的sector,就可以在底层找到文件,这个以后会补充其他文件系统进来
sector.index即我们的区域索引第四列gene_id, labels指的是具体要标注上去的内容,也就是第三列基因名。 根据每个细胞类型对应的gene_id号进行上色,用highlight.sector给它们进行高亮以及标注。这里注意 track.index = 2,选择高亮第二圈。 利用circos.link进行绘图,前四个参数是必不可少的: (1)sector.index1是指箭头起点的索引。 (2)point1是指起点的宽度,因为我们是一条线,所以宽度为0。 (3)sector.index2 = lrid[i,6]是指箭头终点的索引。 (4)point2是指终点的宽度,同理,线是没有宽度的。 #画图 for(i in 1:nrow(lrid)){ circos.link(sector.index1 = lrid[i,3], point1 = 0, sector.index2 = lrid
Key B Using sector 00 as an exploit sector Sector: 12, type B, probe 0, distance 2924 ..... Sector: 14, type B, probe 1, distance 2868 ..... Sector: 14, type B, probe 2, distance 2894 ..... Sector: 14, type B, probe 3, distance 2880 ..... Sector: 14, type B, probe 4, distance 2844 ..... Sector: 14, type B, probe 5, distance 2892 ..... Sector: 14, type B, probe 6, distance 2876 ..... Sector: 14, type B, probe 7, distance 2892 ..... Sector: 14, type B, probe 8, distance 2920 .....
labels = paste0("LG",i), sector.index = paste0("LG",i),cex=0.5) } circos.trackPlotRegion labels = paste0("LG",i), sector.index = paste0("LG",i),cex=0.5) } circos.trackPlotRegion labels = paste0("LG",i), sector.index = paste0("LG",i),cex=0.5) } circos.text(x=-11,y=8 ,labels="染色体",sector.index = "LG1") circos.trackPlotRegion(factors=df$Chr,y=df$Y) circos.trackLines(df $Chr,df$X,df$Y,col=col) circos.text(x=-10,y=11,labels="SNP密度",sector.index = "LG1") circos.clear() ?
Disk /dev/sda: 10.7 GB, 10737418240 bytes, 20971520 sectors Units = sectors of 1 * 512 = 512 bytes Sector Disk /dev/sdb: 10.7 GB, 10737418240 bytes, 20971520 sectors Units = sectors of 1 * 512 = 512 bytes Sector /mapper/cl-root: 8585 MB, 8585740288 bytes, 16769024 sectors Units = sectors of 1 * 512 = 512 bytes Sector The device presents a logical sector size that is smaller than the physical sector size. (2048-20971519, default 2048):【默认回车】 Using default value 2048 Last sector, +sectors or +size{K,M,G}
执行修改 KV2 时,已经存在的 KV2 旧值被修改为已删除,sector1 状态被修改为脏状态,此后将 KV2 新值放入 sector1,发现 sector1 已经没有空间了,sector1 的状态还会被修改为已满状态 经过查找发现 KV3 的新值只能放到 sector2,放到 sector2 后将其修改为正在使用状态; 执行删除 KV1 时,找到 KV1 的位置,将其修改为已删除状态,sector1 状态已经为脏状态 执行添加 KV5 操作,由于 KV5 体积较大,sector2 放不下,所以只能放在一个新扇区 sector3 上,添加后,修改 sector3 状态为正在使用 执行添加 KV6 操作,KV6 也只能放在 sector3 下,将其放入 sector 3 后,发现 sector3 空间已满,所以将其修改已满状态。 就这样 sector1 上的 KV2 被搬运至了 sector2,腾空 sector1 后,又对其执行了格式化操作,这样整个 ENV 分区里又多了一个空状态的扇区。
2 and ends at sector 33 First usable sector is 34, last usable sector is 11721045134 Partitions will be aligned on 2048-sector boundaries Total free space is 2014 sectors (1007.0 KiB) Number Start (sector 2 and ends at sector 33 First usable sector is 34, last usable sector is 11721045134 Partitions will (sector) End (sector) Size Code Name 1 2048 1048578047 500.0 GiB 8300 2 and ends at sector 33 First usable sector is 34, last usable sector is 11721045134 Partitions will
实现思路大概如下 1、获取所有概念板块列表 通过 xtdata.get_sector_list()筛选名称以 GN开头的板块(概念板块标识) 。 (): try: download_sector_data() # 确保板块数据已下载 sector_list = xtdata.get_sector_list #print(concept_sectors) sector_gains = [] for sector in concept_sectors: stocks = xtdata.get_stock_list_in_sector(sector) # 获取板块成分股[3,4](@ref) #print(stocks) ((sector[2:], avg_gain)) # 去除前缀"gn",保留板块名称 # 按涨幅降序排序,取前5 top5 = sorted(sector_gains,
1)和逻辑扇区2(logical sector 2)。 假设我们要用Flash模拟EEPROM来存储DTC,我们都知道DTC是个很小的数据,但是更新可能会比较频繁,如下图: 图2 Flash模拟EEPROM示意图 首先,逻辑扇区1(logical sector 1)被写满,我们将最新的DTC数据复制进逻辑扇区2(logical sector 2)里面,然后擦除整片逻辑扇区1(logical sector 1),这时,逻辑扇区1(logical sector 1)状态变成(Not Actived)待工作片区,逻辑扇区2(logical sector 2)变成(Actived)工作片区,等逻辑扇区2(logical sector 2)写满再将最新数据复制到逻辑扇区 1(logical sector 1),然后擦除逻辑扇区2(logical sector 2),这样来回反复。
其实很简单 import streamlit as st import requests import datetime import pandas as pd import json def get_sector_data data = response.json() if "list" in data and data["list"]: sector_list = get_sector_data(selected_date, k) if sector_data: df = pd.DataFrame(sector_data) st.dataframe(df) # 添加个股查询功能 selected_sector_code = st.selectbox("选择板块代码查看个股信息", df["代码 "].tolist()) if selected_sector_code: stock_data = get_stock_data(selected_sector_code