GEOMPACK users guide
osgEarth用户手册

这个文件建立了一个地图“MyMap”,geocentric 类型,GeoTIFF 图片源 名称是“bluemarble”(GeoTiff 是包含地理信息的一种 Tiff 格式的文件)。驱 动 driver 属性告诉 osgearth 哪个驱动去加载这些图片,所有子元素针对特定 的驱动。
2.1.2.多重图像层 osgEarth 支持有多个图像源的地图。这允许你创建的地图时,在基础层上
featuregeometryosg的几何渲染矢量数据featurestencil使用模板缓冲技术褶皱化地形矢量数据simple加载外部模型并放置在场景图421featuregeometrymodeldriver建立矢量特征数据的osg几何形状如今这个驱动程序可以简单的将矢量数据嵌合进几何图形中同时会有一个可选择的高度偏移这样你可以将几何图形放置于地形上将来它将支持足迹挤压纹理化和其他一些功能
mu lt ipas s
复合材料由多个渲染的图像,通过对场 景图。这种技术对图像层, 你可 以有数量没有限制,但可能会影响性能 ,因为每个额外的层意味着 另一 个渲染传递的场景图。
10
示例:
<map> <options> <terrain> <compositor>multitexture</compositor> </terrain> ... </options> ...
optional
<image>
图像图层。随着 osgearth 的合成器 compositor 使用,你的电
脑图像显示硬件将决定<image>层可以显示的最大数量 。
optional
cfdrc软件翻译说明书样本

软件翻译说明书CFD-GEOM_V _User_Manual介绍模型产生网格产生模型能够产生结构和非结构网格结构网格产生是基于无限插补法( TFI) 。
这个模块支持同一的, 指数的, 几何的, 双曲线切线顺着edge的网格点分布。
网格产生在NURBS( 曲线曲面的非均匀有理B样条) 曲线直接经过曲线她们直接来完成的。
网格产生在NURBS 表面是经过NURBS数学表示式直接产生的节点, 或者计算TFI万个在表面的参数空间。
在几何模型和网格产生的一个很重要的步骤是定义布局。
CFD-GEOM使用一个底部-向上在标签几何单元如edges, faces和blocks。
●edges-在结构网格产生中, 一个edge是为面和体网格产生的基本元素。
一个edge是组成一个和多个的连接几何元素, 等等, 线和曲线, 有n个格点和n-1条线连接这些链接的网格点。
使用者连接这些edges形成edge set。
小心网格点顺着edge( edge 格点) 是不同于几何点产生, 应该使用几何产生工具( 几何点) , 尽管她们的坐标值能够相同。
●Face-使用者指定4个edge set( 极少用于产生布局阵列) 形成一个face,这基本构造成了面网格。
一个face是不同于surface表面的, 前者TFI来于edge set, 后者TFI来自NURBS表面。
用户采集一个或者多个face能够形成face set。
●Block-6个face sets( 极少用于产生拓扑) 联合形成一个block, 这个基本构造成了体网格的产生。
一个block是一个区分体积由face sets形成组成它的边界。
在一些例子中, 有可能组合成两个或者更多网格blocks形成一个单个的block。
形成了一个混合成的block。
这个TFI每个block的网格线依然保留在这个混合成的block。
后来定义一个拓扑的结果是一个结合一个或者多个blocks形成的单个或者多个域网格。
MKLE包用户指南说明书

Package‘MKLE’August21,2023Type PackageTitle Maximum Kernel Likelihood EstimationVersion1.0.1Author Thomas JakiMaintainer Thomas Jaki<**************************>Description Package for fast computation of the maximum kernel likelihood estimator(mkle). License GPLNeedsCompilation noRepository CRANDate/Publication2023-08-2108:02:38UTCR topics documented:MKLE-package (1)klik (3)mkle (4)mkle.ci (5)opt.bw (6)state (8)Index9 MKLE-package Maximum kernel likelihood estimationDescriptionComputes the maximum kernel likelihood estimator using fast fourier transforms.12MKLE-package DetailsPackage:MKLEType:PackageVersion: 1.01Date:2023-08-21License:GPLThe maximum kernel likelihood estimator is defined to be the valueˆθthat maximizes the estimated kernel likelihood based on the general location model,f(x|θ)=f0(x−θ).This model assumes that the mean associated with$f_0$is zero which of course implies that the mean of X i isθ.The kernel likelihood is the estimated likelihood based on the above model usinga kernel density estimate,ˆf(.|h,X1,...,X n),and is defined asˆL(θ|X1,...,X n)=ni=1ˆf(Xi−(¯X−θ)|h,X1,...,X n).The resulting estimator therefore is an estimator of the mean of X i.Author(s)Thomas JakiMaintainer:Thomas Jaki<*********************>ReferencesJaki T.,West R.W.(2008)Maximum kernel likelihood estimation.Journal of Computational and Graphical Statistics V ol.17(No4),976-993.Silverman,B.W.(1986),Density Estimation for Statistics and Data Analysis,Chapman&Hall, 2nd ed.Examplesdata(state)mkle(state$CRIME)klik3 klik Kernel log likelihoodDescriptionThe function computes the kernel log likelihood for a givenˆθ.Usageklik(delta,data,kde,grid,min)Argumentsdelta the difference of the parameter theta for which the kernel log likelihood will be computed and the sample mean.data the data for which the kernel log likelihood will be computed.kde an object of the class"density".grid the stepsize between the x-values in kde.min the smallest x-value in kde.DetailsThis function is intended to be called through the function mkle and is optimized for fast computa-tion.ValueThe log likelihood based on the shifted kernel density estimator.Author(s)Thomas JakiReferencesJaki T.,West R.W.(2008)Maximum kernel likelihood estimation.Journal of Computational and Graphical Statistics V ol.17(No4),976-993.See Alsomkle4mkleExamplesdata(state)attach(state)bw<-2*sd(CRIME)kdensity<-density(CRIME,bw=bw,kernel="biweight",from=min(CRIME)-2*bw,to=max(CRIME)+2*bw,n=2^12)min<-kdensity$x[1]grid<-kdensity$x[2]-min#finds the kernel log likelihood at the sample meanklik(0,CRIME,kdensity,grid,min)mkle Maximum kernel likelihood estimationDescriptionComputes the maximum kernel likelihood estimator for a given dataset and bandwidth.Usagemkle(data,bw=2*sd(data),kernel=c("gaussian","epanechnikov","rectangular","triangular", "biweight","cosine","optcosine"),gridsize=2^14)Argumentsdata the data for which the estimator should be found.bw the smoothing bandwidth to be used.kernel a character string giving the smoothing kernel to be used.This must be oneof’"gaussian"’,’"rectangular"’,’"triangular"’,’"epanechnikov"’,’"biweight"’,’"cosine"’or’"optcosine"’,with default’"gaussian"’.May be abbreviated to aunique prefix(single letter).gridsize the number of points at which the kernel density estimator is to be evaluatedwith214as the default.DetailsThe default for the bandwidth is2s,which is the near-optimal value if a Gaussian kernel is used.Ifthe bandwidth is zero,the sample mean will be returned.Larger gridsize results in more acurate estimates but also longer computation times.The use ofgridsizes between211and220is recommended.ValueThe maximum kernel likelihood estimator.mkle.ci5Noteoptimize is used for the optimization and density is used to estimate the kernel density.Author(s)Thomas JakiReferencesJaki T.,West R.W.(2008)Maximum kernel likelihood estimation.Journal of Computational and Graphical Statistics V ol.17(No4),976-993.See AlsoklikExamplesdata(state)plot(density(state$CRIME))abline(v=mean(state$CRIME),col= red )abline(v=mkle(state$CRIME),col= blue )mkle.ci Confidence intervals for the maximum kernel likelihood estimatorDescriptionComputes different confidence intervals for the maximum kernel likelihood estimator for a given dataset and bandwidth.Usagemkle.ci(data,bw=2*sd(data),alpha=0.1,kernel=c("gaussian","epanechnikov", "rectangular","triangular","biweight","cosine","optcosine"),method=c("percentile","wald","boott"),B=1000,gridsize=2^14) Argumentsdata the data for which the confidence interval should be found.bw the smoothing bandwidth to be used.alpha the significance level.kernel a character string giving the smoothing kernel to be used.This must be one of’"gaussian"’,’"rectangular"’,’"triangular"’,’"epanechnikov"’,’"biweight"’,’"cosine"’or’"optcosine"’,with default’"gaussian"’,and may be abbreviatedto a unique prefix(single letter).method a character string giving the type of interval to be used.This must be one of ’"percentile"’,’"wald"’or’"boott"’.B number of resamples used to estimate the mean squared error with1000as thedefault.gridsize the number of points at which the kernel density estimator is to be evaluated with214as the default.DetailsThe method can be a vector of strings containing the possible choices.The bootstrap-t-interval can be very slow for large datasets and a large number of resamples as a two layered resampling is necessary.ValueA dataframe with the requested intervals.Author(s)Thomas JakiReferencesJaki T.,West R.W.(2008)Maximum kernel likelihood estimation.Journal of Computational and Graphical Statistics V ol.17(No4),976-993.Davison,A.C.and Hinkley,D.V.(1997),Bootstrap Methods and their Applications,Cambridge Series in Statistical and Probabilistic Mathematics,Cambridge University Press.See AlsomkleExamplesdata(state)mkle.ci(state$CRIME,method=c( wald , percentile ),B=100,gridsize=2^11)opt.bw Optimal bandwidth for the maximum kernel likelihood estimatorDescriptionEstimates the optimal bandwidth for the maximum kernel likelihood estimator using a Gaussian kernel for a given dataset using the bootstrap.Usageopt.bw(data,bws=c(sd(data),4*sd(data)),B=1000,gridsize=2^14)Argumentsdata the data for which the optimal bandwidth should be found.bws a vector with the upper and lower bound for the bandwidth.B number of resamples used to estimate the mean squared error with1000as thedefault.gridsize the number of points at which the kernel density estimator is to be evaluated with214as the default.DetailsThe bandwidth considered fall between one and4standard deviations.In addition the mse of the mkle for a bandwidth of zero will also be included.The estimation of the optimal bandwidth might take several minutes depending on the number of bootstrap resamples and the gridsize used.ValueThe estimated optimal bandwidth.NoteThe optimize is used for the optimization.Author(s)Thomas JakiReferencesJaki T.,West R.W.(2008)Maximum kernel likelihood estimation.Submitted to Journal of Com-putational and Graphical Statistics V ol.17(No4),976-993.Davison,A.C.and Hinkley,D.V.(1997),Bootstrap Methods and their Applications,Cambridge Series in Statistical and Probabilistic Mathematics,Cambridge University Press.See AlsomkleExamplesdata(state)opt.bw(state$CRIME,B=10)8state state Violent death in the USADescriptionThe dataset gives the number of violent death per100,000population per stateUsagedata(state)FormatA data frame with50observations on the following2variables.STATE a factor with levels AK AL AR AZ CA CO CT DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY CRIME a numeric vectorSourceShapiro,Robert~J.1998.Statistical Abstract of the United States.118edn.U.S.Bureau of the Census.Examplesdata(state)hist(state$CRIME)mkle(state$CRIME)Index∗datasetsstate,8∗distributionklik,3∗htestmkle.ci,5∗nonparametricklik,3mkle,4mkle.ci,5opt.bw,6∗packageMKLE-package,1∗univarmkle,4density,5klik,3,5MKLE(MKLE-package),1mkle,3,4,6,7MKLE-package,1mkle.ci,5opt.bw,6optimize,5,7state,89。
Geofram 4.0版本软件中文使用手册-P包

Geofram 4.0版本软件使用手册中国石油大学第一部分 基础知识一、系统介绍Geofram 4.0系统是 schlumber 公司针对解决测井、地震、油藏、地质以及综合研究等问题开发的较为完整的集成软件。
如图1-1所示,本软件共划分井眼地质(geolog )岩石物理(petrophysics )、油藏描述(reservoir)、地震(seismic )、可视化地震(visualization)和工具(utility)等六个部分。
系统管理配备3个基本管理工具:项目管理工具(project mandge )、工作流程管理器(process manage )及数据管理器(data manage )。
该系统具有如下特色:*基于Oracle 关系型数据库管理:使得解释参数、描述信息及离散、连续采样的信息数据,均以数据指针信息存放在项目数据库的目录中。
数据入库后不需知道数据以什么样格式存放再什么地方,只进行聚焦即可得到快速查询。
*可靠的安全性:设臵了用户存储权限,使得数据管理更为方便;*极大提高工作效率:合理的项目配臵,减少不必要的数据重复,使数据深度、单位、坐标系统等转换更为方便,省时;*强大的可操作性:所有程序军采用窗口、菜单选项交互运行,加大可视化程序。
图1-1二、系统登陆当在界面敲入用户名和密码之后,出现如图1-2所视界面,选择GeoFram 4.0.3进入GeoFram4.0版本系统,出现图1-3所示界面,选择其中一个用户,敲入密码(password ),当系统确认后在单击Application manager 即可完成系统的完全登陆。
出现如图1-4所示界面。
可选择进行下一步工作。
三、数据加载进入系统后,单击系统界面中的Data manager管理器,出现数据管理主图1-2图1-3图1-4图1-5窗口(如图1-4)。
选择loaders and unloaders 出现如图1-5窗口,其中ASCIL Load 表示ASCIL 数据加载;data load 为Dlis 数据加载;data save 表示数据存储。
Discovery教程(上册)

Discovery教程(上册)(基本操作、数据加载)目录GeoGraphix Discovery 教程说明 (1)第一章建立工区 (3)1建立一个工区 (3)第二章输入人文数据和井数据 (10)2输入人文数据 (10)3输入和查看数据服务公司的一般井数据 (15)4用Excel 表格输入普通井数据 (21)5给井输入ASCII文本文件 (28)6输入测井曲线 (41)第三章建立底图 (44)1显示人文地理/大地网格图层 (44)2图层显示属性(线条、文字等。
) (47)3底图属性(比例尺、标题等) (50)第四章在底图上添加井数据 ...... 错误!未定义书签。
1在底图上添加作业者、井名和井号、总深度等错误!未定义书签。
2在底图上显示WellBase图层 ............. 错误!未定义书签。
3建立研究区 ........................................... 错误!未定义书签。
4建立/显示WellBase 海拔深度图层 .. 错误!未定义书签。
5建立/显示WellBase 地层厚度图层 .. 错误!未定义书签。
第五章井数据查看和质量控制.... 错误!未定义书签。
1在底图上选井查看可用的井数据 ....... 错误!未定义书签。
第六章在地质剖面上进行分层和层位对比错误!未定义书签。
1通过GeoAtlas建立一个横剖面 ......... 错误!未定义书签。
2建立测井曲线显示模板 ....................... 错误!未定义书签。
3修改/定制剖面 ..................................... 错误!未定义书签。
4建立在GeoAtlas 中显示的剖面线 .... 错误!未定义书签。
5在XSection中的地层划分/地层对比错误!未定义书签。
第七章等高线地质解释............ 错误!未定义书签。
7在IsoMap图层上作海下构造顶部等高线错误!未定义书签。
IBM ATLAS POLICY SUITE V6.0.3 FIXPACK 8 用户手册说明书

IBM A TLAS P OLICY S UITE V6.0.3 F IX P ACK 8 README Release Date: September 12, 2018Contents1.I NTRODUCTION (3)1.1Overview (3)1.2Related links (3)2.S YSTEM R EQUIREMENTS (3)3.I NSTALLATION I NSTRUCTIONS (3)3.1Create Atlas Schema for Oracle Exadata [OPTIONAL] (3)3.1.1 Exadata Database Creation Steps (3)3.1.2 Atlas Fresh Schema Creation on Exadata (4)3.2Upgrade Atlas Database (4)3.3Deploying Atlas in JBoss 6.4 EAP (4)3.4Deploying Atlas in WebSphere (4)3.5Cognos 11 Support (5)3.6Known Issues (5)4.W HAT’S N EW IN THIS R ELEASE (6)4.1 Software Currency (6)4.2 SLM Implementation (6)4.3 Security Fixes (6)N OTICES,C OPYRIGHT L ICENSE AND T RADEMARKS (7)1.I NTRODUCTION1.1OverviewThis document provides an overview of installation requirements, enhancements, and defect fixes included in the IBM Atlas Policy Suite V6.0.3 Fix Pack 8 release.IMPORTANT: When upgrading from / installing over an existing version of Policy Atlas or Retention Syndication connector or eDiscovery Syndication connector, please take a backup of your current installation folder.6.0.3.8 installer MAY NOT take a backup of the current installation folder.1.2Related linksIBM Atlas 6.0.3.8 Readme page:https:///support/pages/ibm-atlas-policy-suite-version-603-fix-pack-8-readme2.S YSTEM R EQUIREMENTSPlease refer to System Requirements documented on the IBM Support Portal –https:///software/reports/compatibility/clarity/softwareReqsForProduct.html 3.I NSTALLATION I NSTRUCTIONSPlease refer to IBM Knowledge Center → Atlas eDiscovery Process Management 6.0.3 →Installing and upgrading IBM Atlas Policy SuiteURL: https:///support/knowledgecenter/en/SSXPJK_6.0.3/KC_ditamaps/atlas_6.0.3.htm 3.1Create Atlas Schema for Oracle Exadata [OPTIONAL]Note: Perform the next steps in this section 3.1, only if you are configuring Atlas with Oracle Exadata; else proceed to section 3.2 to Upgrade Atlas Database.Steps for Creating Atlas Schema for Oracle Exadata3.1.1 Exadata Database Creation Steps1. Ensure that Oracle 12.2 Exadata is installed.2. Create a pluggable database, either from Database Configuration assistant (User Interface) or manually, through SQL command prompt.3. Provide Open/Read/Write permission for the pluggable database that you created.3.1.2 Atlas Fresh Schema Creation on Exadata1.You can create fresh Atlas schema against newly created pluggable databasePlease refer tohttps:///support/knowledgecenter/en/SSXPJK_6.0.3/com.ibm.aps.install.doc/ap-sin05.html2. If you are trying to create PSSAPL user against CDB, then you must execute following command before creation of user- alter session set "_ORACLE_SCRIPT"=true;3.2Upgrade Atlas DatabasePlease refer to the Upgrade Database section from the document:https:///support/knowledgecenter/en/SSXPJK_6.0.3/com.ibm.aps.install.doc/apsup00. html•For Oracle Exadata , as Pluggable database acts as Oracle service, hence use this second form for the DBName: jdbc:oracle:thin:@//HostName:Port/DBName.•Earlier, the Atlas Extensions threw an exception with a CUSTOMFIELD15": invalid identifier error. If you have Atlas extensions deployed, you can fix this issue by performing the instructions in the following technote:/support/docview.wss?uid=swg219788553.3Deploying Atlas in JBoss 6.4 EAPJBoss 6.4 EAP requires explicit encoding setting to support umlaut characters.Solution: Add the following setting in standalone.xml after <extensions> settings:<system-properties><property name="org.apache.catalina.connector.URI_ENCODING" value="UTF-8"/> </system-properties>3.4Deploying Atlas in WebSphereBefore you begin IBM Atlas Policy Suite installation:Ensure that you have the WebSphere Application Server (WAS) - Base Edition installed on your system. Currently, the IBM Atlas Policy Suite supports only the Base edition of WAS.If you are runnin g PolicyAtlas and AtlasReports on the same WebSphere profile, atleast the following versions of WebSphere are required:•WAS 8.5.0.1•WAS 8.5.5.2•WAS 9.0.0.73.5Cognos 11 SupportThis release supports Atlas integration with Cognos 11 .Refer to the document for detail steps:https:///support/knowledgecenter/en/SSXPJK_6.0.3/com.ibm.aps.integra-tion.doc/apscg007.htmlUpdate BASE_URL provided in the document above, with the following new value:3.6Known IssuesThis section lists known limitations about installation.•When upgrading from / installing over an existing version of Policy Atlas or Retention Syn-dication connector or eDiscovery Syndication connector, please take a backup of your cur-rent installation folder.6.0.3.8 Installer MAY NOT take a backup of the current installation folder.•In previous releases there was a JBoss 6 EAP-specific .ear file, AtlasExtensions_jboss6eap.ear, for Atlas Extensions - this is no longer the case. Please use the AtlasExtensions.ear. •Known Documentation Issues with corrections can be found here –/support/docview.wss?uid=swg27044359•Atlas does not enforce email address uniqueness.In Atlas, duplicate email addresses are not allowed on People in Scope page. Thus, if a person with identical email address is directly added to Request Scope, Atlas shows an error message.However, there are three scenarios in which a duplicate email address can be added to Re-quest Scope, wherein Atlas does not show any error message.The three scenarios are as follows:o Whenever an email address is updated through Admin > Person to create a duplicate email address entry, Atlas does not show any error message.o Whenever a data source is created with a duplicate email address entry and that data source is added to the Request Scope, Atlas does not show any error message.o Whenever an organization is created with a duplicate email address entry and that organization is added to the Request Scope, Atlas does not show any error message.These three scenarios result in duplicate email addresses in Resource Scope without any error message from Atlas.•Confirmation Reminder for Global notice is not sent to Partially Sent Recipients.In the case of disruption of Policy Atlas application, if Global reminder is partially sent, then the Confirmation Reminders to the Global Notice will not be delivered to the recipients listed with reason Partially Sent. The confirmation reminders will be delivered only to the recipi-ents with reason Sent.Workaround: You should send the Global Reminder again. Ensure that the Atlas application is not terminated abruptly until the Global Reminder is delivered to all the recipients. 4.W HAT’S N EW IN THIS R ELEASEThe IBM Atlas Policy Suite 6.0.3 Fix Pack 8 consists of the following new features.4.1 Software CurrencyAdded Support•Cognos 11: For details on the configuration change needed, refer to the section 3.5 in the Installation Instructions section.•Oracle Exadata 12.2: For details on Atlas schema creation on Oracle Exadata, refer to the section 3.1 in the Installation Instructions section.4.2 SLM ImplementationAtlas – SLM implementation enables reporting of usage and License metrics for different Atlas products.These metrics are captured in an XML file called SLM Tag file. These SLM tag files can be then consumed by the IBM License Metrics Tool for reporting and auditing.Refer to the IBM Atlas Policy Suite User Guide (https:///support/pages/ibm-atlas-pol-icy-suite-–-slm-users-guide) for the details of how to configure Atlas for SLM and how to interpret the license metrics count.4.3 Security FixesThis release has upgraded the following open source jar files for fixing security vulnerabilities in the earlier versions.1.c3p0-0.9.5.4.jarmons-beanutils-1.9.2.jarmons-codec-1.7.jar4.xstream-1.4.11.1.jar5._Builder.js from Dojo toolkit 1.14N OTICES,C OPYRIGHT L ICENSE AND T RADEMARKSNoticesThis information was developed for products and services offered in the U.S.A. IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service.IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not give you any license to these patents. 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GEO数据库教程

GEO数据库教程展开全文转载自:果子学生信,这是一个干货极多,老师极好的公众号上次更新了一个GEO数据框的帖子,包含了GEO分析的主要组成部分,其中有些区域为了代码复用,不是很容易懂。
现在对其进行注释,并增加KEGG分析的内容,力求能让他变成最有诚意的GEO数据分析教程,文末还录制了一个友好的导学视频,希望物尽其用。
以下是正文:GEO数据库包罗万象,对于每个领域的科研工作者很有帮助。
我最开始分析GEO芯片主要使用别人的代码,跑流程。
但是绝对不能出现报错,一报错就束手无策。
遇到比较困难的地方就使用别人的小工具来代替。
比如,ID转换,GO分析等。
直到去年我才能够完全使用R语言实现。
这两年,网上的教程帖子井喷一样涌现,大有教师超过学员的趋势。
但是,GEO的分析跟TCGA不一样,里面要考虑的细节比较多。
比如我们今天要解决以下几个问题:1.GEO数据如何方便地下载?2.数据什么时候需要标准化?3.什么时候做log转换?4.芯片探针如何转换?5.差异基因如何获得?6.配对样本的对比矩阵如何构建?7.配对样本如何作图展示?8.热图火山图究竟在什么?9.GO分析,KEGG分析怎么做,如何解释?看文献得到GSE号,这是数据的名片,GSE32575,文献中提及的GSE号在这个网址输入这个号我们可以知道这个芯片的信息/geo/query/acc.cgiGEO官网点击进去后,会有这个芯片的介绍比如,这个芯片一开始出现的时候是干什么的?summary认真读一读就知道作者为什么要做这个芯片。
如果还不是很清楚,可以往下看,可以看到实验设计,甚至还有这个芯片当时发表的文章。
下载下来读一读,可以看看当时这个芯片的哪些信息被使用了,还有哪些没被使用,我们可以提出新的科研假设,用别人的信息去支持。
而这也是芯片数据挖掘的目的:用别人的数据支持自己的科研假设。
在往下看还有平台信息GPL6102,这个信息可以帮助我们来注释文件。
图中的48表示这个芯片做了48个样本,每个样本都写明是什么样本。
surpac基础指南

测量数据库 块体模型 绘图文件 风格文件 宏文件
示例 pit1.str pit1.dtm geo.ddb
ug.sdb blk.mdl pit.swf styles.ssi mcr.tcl
内容
宏文件:包含录制的操作过程或二次开 线文件:表示点和线,存储点的位置、 DTM 地质数据库:在 测量数据库:在 块体模型:使用块体将实体模型规格化 绘图文件:将打印内容保存为绘图文件 风格文件:线和面的颜色及显示方式保 :表示表面和实体模型,存储点 Surpac和关系型测量 和关系型钻孔 描述值等信息 连接成面的方式等信息 数据库之间建立关联 并储存各种信息 以备打印 存在风格文件中 发程序的 TCL脚本文件
Gemcom Surpac 三维矿业软件培训
基础概念和基本操作
Gemcom Surpac™
1. 软件的安装与运行
2. 软件的注册
3. 设置工作目录
4. 在快捷方式中设置 默认工作目录
任务:运行 Gemcom Surpac
Gemcom Surpac™
一. 用户界面
1. 2. 3. 菜单栏 工具栏 导航面板
点3
点4
点5
点n
Y
X
Z
D1
描述字段
Dn
Gemcom Surpac™
一. 线文件原理
2. 线文件的结构
① 头行记录 ② 轴线记录 ③ 线号 ④ 北坐标(Y)
①
②
⑤ 东坐标(X)
⑥ 高程(Z) ⑦ D1 ⑧ D2 ⑨ D3 ⑩ 段结束 ⑪ 文件结束
⑩
③
④ ⑤ ⑥ ⑦ ⑧ ⑨
⑪
Gemcom Surpac™
2;6;9
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(5) mdf2d: This directory contains routines for further subdividing the convex decomposition of
wa ipsohlytogocnaalllirnecgliuodnebEaQseDdIoSn2.aTmheeshlediustmridbuf2ti.ofncofnutnacitnioinng
error code indicator produced by some routines; if it is nonzero, see the Errcodes le for a brief
description of the error.
(1) general: This directory contains general non-geometric routines, e.g. initializing common
(8) basic3d: This directory contains basic 3-D geometric routines, e.g. compute the circumsphere,
volume, minimum solid angle, or radius ratio of a tetrahedron. Routines which a user may wish to call include BARYTH, CCSPH, OPSIDE, RADRTH, SANGMN, VOLTH.
GEOMPACK Users' Guide
Barry Joe Department of Computing Science
University of Alberta Edmonton, Alberta, Canada T6G 2H1
Phone: (403) 492-5757 Email: barry@cs.ualberta.ca
July 15, 1993
1 Introduction
GEOMPACK is a mathematical software package, written in standard Fortran 77, for the GEneration Of Meshes using GEOMetric algorithms. An introduction to GEOMPACK is provided in
or more main driver programs (in les with names beginning summary and statistical information to output unit 7. The
main programs may be modi ed depending on the application. Some directories have a Data
This work was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada.
1
(2) basic2d: This directory contains basic 2-D geometric routines, e.g. compute the signed area
2 Directories of routines
Currently, the routines of GEOMPACK are organized into the 15 directories listed below. Each
dwiirtehctDorRy )c.oSnotaminesmaaMinapkreogrleamans dwornitee
Joe91c]. The routines of this package are available via anonymous ftp from menaik.cs.ualberta.ca; see the pub/geompack directory. This user guide only covers material not described in the ref-
(10) deltrkd: This directory contains routines for constructing a k-D Delaunay triangulation (k
2) using the incremental approach and local transformations as described in Joe93a]. Routines which a user may wish to call include BNSRTK, CCSPHK, DTRIMK, DTRISK, DTRIWK, SMPXLS, WALKTK.
(9) deltr3d: This directory contains routines for constructing a 3-D Delaunay triangulation using
the incremental approach and local transformations (or face swaps) as described in Joe89, Joe91a], and for improving a 3-D triangulation based on a tetrahedron shape measure such as minimum solid angle or radius ratio Joe93c, LiJ93]. Routines which a user may wish to call include BNSRT3, DTRIS3, DTRIW3, IMPTR3, TETLST, WALKT3.
blocks, sorting, returning CPU time. The only non-portable routines of the package are the
timer routines in les gtime.f and clock.c; the Fortran GTIME routine calls the C clock
routine. If the Unix operating system is not used, then the timer routines should be replaced or the calls to GTIME in the main programs should be removed. Routines which a user may wish to call (in a main program or subroutine) include DHPSRT, GTIME, IHPSRT, INITCB, PRIME, RANDPT, ROTIAR.
JoS86]. Routines the user-supplied
which a user may mesh distribution
function UMDF2 may be modi ed, and UMDF2 may be renamed.
(6) triang2d: This directory contains routines for generating a Delaunay triangular mesh in each
(3) vispol: This directory contains routines for computing the visibilitห้องสมุดไป่ตู้ polygon of a simple
polygon from a viewpoint using the algorithm in JoS87, Joe90]. Routines which a user may wish to call include ROTIPG, VISPOL, VISVRT, VORNBR.
erences, so algorithmic details and experimental results are not provided here. In Section 2, the organization of GEOMPACK into directories of routines is brie y described. In Section 3, the input format and data structures are brie y described for the method of JoS86] which generates convex polygon decompositions and triangular meshes in 2-D polygonal regions. In Section 4, the input format and data structures are brie y described for the method of Joe93b] which generates convex polyhedron decompositions and tetrahedral meshes in 3-D polyhedral regions.
subdirectory which contains input les, e.g. describing polygonal or polyhedral regions. In addition
to the directories, there is an Errcodes le of error codes and descriptions. IERR is an integer
the incremental approach and edge swaps; they are similar to the routines in Slo87] except that a bounding triangle is not needed. Routines which a user may wish to call include BNSRT2, DTRIS2, DTRIW2, WALKT2.