IFCN Monthly real time data_maps 2015-04

合集下载

Utility for Caching Metafile Renditions For Matrix

Utility for Caching Metafile Renditions For MatrixTable of Contents Introduction (3)How does it work? (3)What is a metafile? (3)Supported Document Formats for Metafile Renditions (3)Steps for Creating Metafiles (4)i.For Files with XREFs (3D Assemblies or 2D files with XREFs) (4)ii.For all other files (4)Architecture (5)Administrator Guide (6)System Requirements (6)Installation (6)Manual Setup (7)Starting the Utility (9)Configuration (10)Setting Trigger Mechanisms in Matrix (10)i.On-Demand Mechanism (10)ii.Event-Based Mechanism (11)Enabling Icon Mail for Matrix Users (15)Feedback (16)General Inquiries (16)Sales Inquiries (16)Customer Support (16)IntroductionMetafile Utility uses the AutoVue server as a rendition tool to generate and check metafiles into the Matrix Product Data Management system (Matrix PDM). Once a metafile rendition is added to the native document, the next time the file is viewed in the AutoVue client, the AutoVue server fetches the metafile rendition instead of the native file.How does it work?Here is a brief description of how the utility handles requests for creating metafile renditions:1. The client application sends the rendition request to the PDM server.The actual request dispatched by the client depends on the triggering mechanism, which can be configured on the PDM server side. Examples of triggering mechanisms include check-in, promotion, or on-demand actions.2. The Server stores the request in a special queue item.Each queue item represents a request made by a client to generate a metafile rendition of the native file.3. The utility polls the queue items from the PDM at predefined intervals, and retrieves any pending requests.4. The utility sends relevant information of each request to the AutoVue server to generate the metafile rendition.5. The utility asks AutoVue server to check-in the metafile rendition to the PDM.The AutoVue server in turn passes the request to VueLink to add the metafile as a rendition to the nativedocument object in the PDM.What is a metafile?A metafile is a true replica of the original file. It is a lightweight format developed by Cimmetry. Its main advantage is performance enhancement of document viewing on the client side.Supported Document Formats for Metafile RenditionsThe utility generates metafile renditions for all Engineering and Vector formats that are supported by AutoVue Client-Server Edition. Currently, AutoVue Client-Server Edition supports many file formats, including 2D engineering drawings and 3D assemblies.Steps for Creating MetafilesFor Files with XREFs (3D Assemblies or 2D files with XREFs)Special care is given when creating a metafile for files with XREFs. For the utility to properly generate a metafile for a particular file, follow these steps:1. Launch the Matrix client application and login.2. Check in all the XREFs and the assembly, then define the relationship between the master file and the XREFs.3. If the On-Demand trigger mechanism is setup, select the Assembly business object, right-click and selectExecute > csiCreateQueueItemOnDemand from the popup menu.4. If the Event-Based trigger mechanism is setup, check out the Assembly and check it back into Matrix.For All Other FilesIf the Event-Based trigger mechanism is setup, the metafile request is generated as soon as the file is checked into Matrix.ArchitectureWhenever a predefined event occurs, such as a file check-in for example, Matrix invokes a server-side custom program called csiCreateQueueItem, which creates a custom object inside a Matrix vault. This custom object represents a unique Rendition Request.Meanwhile, a polling application retrieves these queue items from Matrix, using API provided by VueLink, extracts information from the QueueItem to identify the checked-in document. This information is added to an internal Queue, which is polled by a QueueReader. Each entry retrieved by the QueueReader is then dispatched to the AutoVue Client-Server Edition server using Vuebean API. Thereafter, the metafile is created and checked into Matrix by the AutoVue Client-Server Edition server, which interacts with VueLink for Matrix for this purpose. The status of the rendition, whether it is “Errored” or “Successful” is then sent back to the user by the Polling Application using Matrix Icon Mail.The Polling Application and the QueueReader run asynchronously, leading to greater efficiency.Administrator GuideSystem RequirementsThe utility itself is a Java application program and therefore can be run on any platform equipped with Java Virtual Machine (JVM).The following software must already be installed on the machine hosting this utility:•Sun's Java Runtime Environment (JRE) 1.4.2 or higher•Matrix Thick Client Applications v10.7- At least the MQL Application.The following software must also be available for the utility to work but may be installed on a different machine than the one hosting the utility:•AutoVue Client-Server Edition v19.1 (or any SP of v19.1)•Matrix Collaboration Kernel 10.7•VueLink for Matrix v19.1•Matrix Application Exchange Framework AEF 10.7•Matrix Engineering Central or Designer Central 10.7InstallationMetafile utility requires the AutoVue Server and VueLink. Before installing Metafile Utility with Matrix, make sure that Matrix, AutoVue Client-Server Edition, and VueLink for Matrix are properly installed and configured on your system, according to the manufacturer’s instructions, and are operating correctly.To install the utility for caching metafiles for Matrix, complete the steps outlined in the following sections:•Manual Setup•Setting Trigger Mechanism in Matrix•Enabling Icon Mail for Matrix Users•Starting the UtilityManual SetupTake the following steps to install Metafile Caching Utility manually. If you installed Metafile Caching Utility during the automatic installation of VueLink, skip the following steps.1. Create a folder called /cimmetry/metafile_utility. For example: c:/program files/cimmetry/metafile_utility2. Copy the content of the ‘metafile_utility’ folder found on the distribution CD to the folder created in previousstep.3. In the /metafile_utility/bin folder, open ‘run.bat’ in a text editor and modify the following settings:JDK_DIR Full path to the root folder where Sun’s JRE is installed. Example: c:\jdk1.3.1JARDIR Full path to the folder that contains metafile.jar along with other JAR files.PASSWORD Password of a valid Matrix account with administrative privileges.MX_CLIENT_JAR There are two entries in run.bat. You need to uncomment one entry only based onversion of Matrix you have installed. If you are using Matrix v9.5.2.0+,eMatrixServletRMI.jar should be in the classpath. Otherwise,eMatrixAppletDownloadXML.jar should be in the classpath.If you are using RIP mode, use full path to eMatrixAppletXML.jar instead ofeMatrixServletRMI.jar.Note:By default, the csiqcmatrix.properties file is located under same folder as metafile.jar. If this file is moved to another location, then you need to set the full path to PROPFILE.PROPFILE Full path to csiqcmatrix.properties file.4. In the /metafile_utility/bin folder, open the ‘csiqcmatrix.properties’ file in a text editor and modify thefollowing settings:JVUE_HOST The host server Name or IP where AutoVue Client-Server Edition Server isrunning.JVUE_PORT The Port Number to which AutoVue Client-Server Edition Server is listening. Bydefault, this value is 5099.MAX_THREADS For each request for rendition, the utility creates a new thread (process) thatinteracts with the AutoVue Server to perform the rendition as well as the check-in ofthe metafile. The maximum number of such threads that the polling client can runsimultaneously can be configured here. Increasing this number leads to quickerrendition because more files can be rendered at the same time. But it also leads toa greater load on the machine hosting this utility as well as the machine hostingAutoVue Server. This number cannot exceed the number of seats procured forAutoVue Client-Server Edition Server. For example, if you are running AutoVueClient-Server Edition server with a 50-user license, then the value supplied in thistext field may never exceed 50.QUEUE_CLIENT_PROP Full Path to csimfmatrix.properties properties file.5. In the /metafile_utility/bin folder, open ‘csimfmatrix.properties’ file in a text editor and modify the followingsettings:MX_HOST The host server Name or IP where the Application Server (e.g. Weblogic) hostingMatrix Thin Client applications is running.MX_PORT Any Matrix Thin Client installation needs a Webserver to be the point of entry for allrequests. The Webserver then transfers requests to the underlying application server.For example, when the Weblogic server is installed, it comes with a default Webserverthat listens to port 7001 by default and transfers all requests to Weblogic.The value supplied in this text field should be the port number to which the Webserver islistening.MX_CONNECT Matrix Collaboration Kernel type. RIP,RMI or EJB. Default is RIP.If using RIP mode, input MX_CONNECT is APPSERVER.MX_RMIPORT Port number if Matrix RMI server is used. Default is 1099.MX_USER Name of valid Matrix user with Administrative Privileges to Application Centrals.Default is Test Everything. The utility needs to sign into Matrix to access RenditionRequests. This user ID should have permissions to read and modify Business Objectsof type csiQueueItem. It should also have permissions to send icon mail notifications tousers about the status of their rendition requests.MX_VAULT Matrix vault name, which contains all the csiQueueItems. Care should be taken toensure that this is the same Vault that contains VueLink for Matrix specific businessobjects.MX_CSI_CONFIG This object (Cimmetry Configuration Business Object) is created whenVueLink for Matrix is installed. It contains all the required meta-data for VueLinkfunctionality. Since this utility depends on VueLink to function (please refer to thearchitecture diagram), the object name specified here should be accurate. CODEBASE Web path to the client file specified above (MX_CLIENT_JAR), with web app contextname. Example: /ematrix/WebClient/java/classesMX_FILEURL Web path to MxAutoVue servlet. Example: /ematrix/servlet/MxAutoVueServletMX_SVLT Web path to VueLink servlet. Example: /ematrix/com.cimmetry.vuelink.matrix.DMS6. In a text editor, open ‘schemaCreateBC.txt’ file found under /metafile_utility/bin folder and modify thefollowing settings:context_user Replace "__USER__" with the name of valid Matrix user who has administrativeprivileges. Default is creator.context_password Replace "__ PASSWORD __" with the password of valid Matrix user who hasadministrative privileges. Default is empty.context_vault Replace "__VAULT__” with Matrix Vault name.store_name Replace "__STORE__" with Matrix Store name.7. Launch Matrix MQL Application (mql.exe) and run the script contained in ‘schemaCreateBC.txt’ file. Forexample, at MQL prompt, type run c:/temp/schemaCreateBC.txt.Starting the UtilityInvoke ‘run.bat’ from the command line. It asks you for a password. Enter a password that belongs to a valid Matrix user who has administrative privileges.Make sure that Application Server (Weblogic or WebSphere) is running before invoking ‘run.bat’.ConfigurationSetting Trigger Mechanisms in MatrixMetafile Utility for Matrix takes advantage of the event mechanism provided by Matrix. Two kinds of triggering mechanisms are employed with Metafile Utility:The On-Demand (explicit) mechanism, metafile request is implemented with Matrix Thick-client. The user can initiate a metafile request when needed. The metafile Utility takes care of that request and creates the metafile.Note:• The On-Demand mechanism is recommended for files with XREFs.The Event-based (implicit) mechanism, the triggering method is involved with registering with a particular event such as the check-in event. That means once a document is checked in, an event, which is defined by Matrix is fired which invokes ‘csiCreateQueueItem’ program.Note:• The Event-Based mechanism is recommended for files without XREFs.On-Demand MechanismThe script file creates a program called ‘csiCreateQueueItemOnDemand’ within Matrix. This program functions exactly like the program “csiCreateQueueItem”. The difference is that “csiCreateQueueItem” is meant to be associated with Business Objects as a trigger while this program is to be associated with Business Objects as a method. The administrator may also associate the program “csiCreateQueueItem” with Business Object types other than CAD Model.1. Run Matrix Business application (eg.: business.exe).2. Open the Matrix CAD Model type in edit mode.3. In the Edit window that appears, click the Methods tab.4. Click Add.5. Type “csiCreateQueueItemOnDemand” in the Name field.6. Click Ok.Event-Based Mechanismii-1 csiCreateQueueItemThe script file creates a program called csiCreateQueueItem in Matrix. When invoked, this program creates a Business Object, which represents a request for Metafile Rendition. You can set the ‘Check-in’ trigger on CAD Model Business Objects to invoke this program. You can choose to associate the program with any other trigger.In addition to this, you can also associate the csiCreateQueueItem program with Business Object types other than CAD Model.1. Run Matrix Business application (eg.: business.exe).2. Open the Matrix CAD Model type in edit mode.3. In the Edit window that appears, click the Triggers tab.4. If the trigger you want is not in the list, click Add, then double-click the desired trigger.5. In the Edit Trigger window that appears, type the program name, i.e. csiCreateQueueItem, in the Action textfield.ii-2 csiCreateQueueItemMeetingThe script file creates a program called “csiCreateQueueItemMeeting” in Matrix. When invoked, this program creates a Business Object, which represents a request for Metafile Rendition. You can set the Create trigger on MeetingAttachment Relationship Business Objects to invoke this program. You can choose to associate the program with any other trigger.In addition to this, you can also associate the “csiCreateQueueItemMeeting” program with Business Object types other than MeetingAttachment.6. Run Matrix Business application (eg.: business.exe).7. Open the Matrix MeetingAttachment type in edit mode.8. In the Edit window that appears, click the Triggers tab.9. If the trigger you want is not in the list, click Add, then double-click the desired trigger.10. In the Edit Trigger window that appears, type the program name, i.e. csiCreateQueueItemMeeting, in the Actiontext field.11- Make sure the Meeting Attachments relationship is from Type Meeting to Type DOCUMENTS as well. In this case, the trigger would act on any type of document that is derived from type DOCUMENTS successfully.Enabling Icon Mail for Matrix UsersOnce the utility is installed successfully, you need to enable icon Mail for all users who will be creating Rendition Requests. This is needed because the utility relies on icon mail to send notification to users about the status of their requests.1. Run the Matrix Business application (eg.: business.exe).2. Select Object > Find from main menu.3. Select Person from the Object list, then click Find.4. From the search result, select the user who needs icon mail enabling.5. Select Object > Open > Edit from main menu.6. In the Edit window that appears, make sure the Icon Mail box is checked.FeedbackCimmetry System products are designed according to your needs. We would appreciate your feedback, comments and suggestions. We can be contacted by fax, e-mail or telephone. There is a feedback button on our Web page that activates an easy-to-use feedback form. Let us know what you think!General InquiriesTelephone: +1 514-735-3219Fax: (514) 735-6440E-mail: *****************Website: Sales InquiriesTelephone: +1 514-735-3219 or 1-800-361-1904Fax: (514) 735-6440E-mail: ******************Customer SupportTelephone: +1 514-735-9941Website: http://support。

r语言分解时间序列的高低通量

R语言是一种用于统计分析和数据可视化的编程语言，它在处理时间序列数据方面具有很高的灵活性和可扩展性。

分解时间序列的高低通滤波是一种常见的技术，用于将时间序列数据分解为趋势、季节性和残差三个部分。

在R语言中，有多种方法可以实现时间序列的高低通滤波，本文将介绍其中常用的几种方法。

1. 使用stats包中的stl函数在R语言中，stats包中提供了stl函数，可以用来对时间序列数据进行分解。

stl函数可以将时间序列数据分解为趋势、季节性和残差三个部分，其中季节性部分可以作为高通滤波的结果，趋势部分可以作为低通滤波的结果。

2. 使用forecast包中的dpose函数除了stats包中的stl函数外，forecast包中也提供了dpose函数，用于对时间序列数据进行分解。

dpose函数与stl函数类似，也可以将时间序列数据分解为趋势、季节性和残差三个部分。

通过调整函数的参数，可以得到不同的高低通滤波结果。

3. 使用Wavelet包中的dwt函数除了上述两种方法外，Wavelet包中的dwt函数也可以用来实现时间序列数据的高低通滤波。

dwt函数利用小波变换的方法，可以将时间序列数据分解为不同频率的成分，从而实现高低通滤波的效果。

4. 使用TTR包中的EMA函数另外，TTR包中的EMA函数也可以用来实现时间序列数据的高低通滤波。

EMA函数利用指数移动平均的方法，可以对时间序列数据进行低通滤波，从而得到平滑的趋势部分。

5. 使用signal包中的filter函数signal包中的filter函数也是一个用于实现时间序列数据的高低通滤波的工具。

filter函数可以基于给定的滤波器设计参数，对时间序列数据进行滤波处理，从而得到高低通滤波的结果。

在实际应用中，选择哪种方法取决于具体的数据特点和分析需求。

有时候需要同时使用多种方法进行对比，以选择最适合的高低通滤波方法。

无论采用哪种方法，都需要结合实际情况，合理调整参数，以获得准确和有效的分解结果。

mysql map返回科学计数法 -回复

mysql map返回科学计数法-回复MySQL是一种广泛使用的关系型数据库管理系统。

在MySQL中，使用MAP函数可以实现对字段进行映射操作。

本文将介绍如何使用MAP函数返回科学计数法作为查询结果。

MySQL的MAP函数是一种用于对字段进行映射操作的函数。

该函数接受多组键值对作为参数，并根据字段的值在键值对中进行匹配。

如果字段的值与任何一个键值对的键相等，则返回对应键值对的值作为结果。

如果字段的值与所有键值对的键都不相等，则返回NULL。

在本文的情景中，我们将使用MAP函数返回科学计数法作为查询结果。

假设我们有一个表格存储了某个物品的名称和其对应的数量，我们想要获得物品名称和对应数量的科学计数法表示。

首先，我们需要创建一个名为items的表格，其中包含名为item_name 和item_quantity的两个字段。

我们可以使用如下的MySQL语句创建该表格：CREATE TABLE items (item_name VARCHAR(50),item_quantity FLOAT);接下来，我们向items表格中插入一些示例数据。

我们可以使用INSERT INTO语句将数据插入到表格中，如下所示：INSERT INTO items (item_name, item_quantity)VALUES('item1', 1000),('item2', 2000),('item3', 3000),('item4', 4000),('item5', 5000);现在，我们可以编写查询语句来获取物品名称和对应数量的科学计数法表示。

我们可以使用MAP函数来实现这一目标。

以下是具有科学计数法结果的查询语句示例：SELECTitem_name,MAP(item_quantity,1000, '1e3',1000000, '1e6',1000000000, '1e9') AS scientific_notationFROM items;在上述查询语句中，我们使用MAP函数将item_quantity字段的值与多组键值对进行匹配。

动态时间归正法(dtw)路径优化方法

动态时间归正法(dtw)路径优化方法
动态时间归正法（DTW）是一种用于衡量两个时间序列之间相似性的方法。

它通过将一个时间序列拉伸或压缩，以便与另一个时间序列的长度一致，从而计算它们之间的相似性。

下面介绍DTW的路径优化方法：
1. 计算两个序列各个点之间的距离矩阵。

2. 寻找一条从矩阵左上角到右下角的路径，使得路径上的元素和最小。

3. 寻找最小路径的方法是，当前路径长度等于前一步的路径长度加上当前元素的大小。

路径上的某个元素（i，j），它的前一个元素只可能为左边的相
邻元素（i，j-1）、上面的相邻元素（i-1，j）或左上方的相邻元素（i-1，j-1）。

4. 重复上述步骤，直到找到最后的右下角元素，即为最小路径。

通过以上步骤，可以找到两个时间序列之间的最优匹配路径，从而计算它们之间的相似性。

这种方法适用于处理不同长度、不同速度的时间序列数据，如语音识别、手势识别等领域。

Real-time PCR for mRNA quantitation

INTRODUCTIONThe advent of real-time PCR and real-time reverse transcription PCR (real-time RT-PCR) has dramatically changed the field of measuring gene expression. Real-time PCR is the technique of collecting data throughout the PCR process as it occurs, thus combining amplification and detection into a single step. This is achieved using a variety of different fluorescent chemistries that correlate PCR product concentration to fluorescence intensity (1). Reactions are characterized by the point in time (or PCR cycle) where the target amplification is first detected. This value is usually referred to as cycle threshold (C t), the time at which fluorescence intensity is greater than background fluorescence. Conse-quently, the greater the quantity of target DNA in the starting material, the faster a significant increase in fluorescent signal will appear, yielding a lower C t (2).There are many benefits of using real-time PCR over other methods to quantify gene expression. It can produce quantitative data with an accurate dynamic range of 7 to 8 log orders of magnitude (3) and does not require post-amplification manipu-lation. Real-time PCR assays are 10,000- to 100,000-fold more sensitivethan RNase protection assays (4),1000-fold more sensitive than dot blothybridization (5), and can even detect asingle copy of a specific transcript (6).In addition, real-time PCR assays canreliably detect gene expression differ-ences as small as 23% between samples(7) and have lower coefficients ofvariation (cv; SYBR® Green at 14.2%;TaqMan® at 24%) than end point assayssuch as band densitometry (44.9%)and probe hybridization (45.1%) (8).Real-time PCR can also discriminatebetween messenger RNAs (mRNAs)with almost identical sequences,requires much less RNA templatethan other methods of gene expressionanalysis, and can be relatively high-throughput given the proper equipment.The major disadvantage to real-timePCR is that it requires expensiveequipment and reagents. In addition,due to its extremely high sensitivity,sound experimental design and an in-depth understanding of normalizationtechniques are imperative for accurateconclusions.The general steps performed duringa real-time PCR experiment, from RNAisolation to data analysis, are outlinedin Figure 1. This review is intendedto provide an overview of the manyfacets of real-time PCR, highlightingPCR theory, quantification methodsand models, data normalization, typesof detection chemistry, and causes ofvariation.THEORY OF REAL-TIME PCRPCR can be broken into four majorphases (Figure 2): the linear groundphase, early exponential phase, log-linear (also known as exponential)phase, and plateau phase (9). Duringthe linear ground phase (usuallythe first 10–15 cycles), PCR is justbeginning, and fluorescence emissionat each cycle has not yet risen abovebackground. Baseline fluorescenceis calculated at this time. At the earlyexponential phase, the amount offluorescence has reached a thresholdwhere it is significantly higher (usually10 times the standard deviation of thebaseline) than background levels. Thecycle at which this occurs is known asC t in ABI Prism®literature (AppliedBiosystems, Foster City, CA, USA) orcrossing point (CP) in LightCycler®literature (Roche Applied Science,Indianapolis, IN, USA) (2,10). Thisvalue is representative of the startingcopy number in the original templateand is used to calculate experimentalresults (2). During the log-linear phase,Real-time PCR for mRNA quantitationMarisa L. Wong and Juan F. MedranoReal-time PCR has become one of the most widely used methods of gene quantitation because it has a large dynamic range, boasts tremendous sensitivity, can be highly sequence-specific, has little to no post-amplification processing, and is amenable to increasing sample throughput. However, optimal benefit from these advantages requires a clear understanding of the many options available for running a real-time PCR experiment. Starting with the theory behind real-time PCR, this review discusses the key components of a real-time PCR experiment, including one-step or two-step PCR, absolute versus relative quantitation, mathematical mod-els available for relative quantitation and amplification efficiency calculations, types of normalization or data correction, and detection chemistries. In addition, the many causes of variation as well as methods to calculate intra- and inter-assay variation are addressed.University of California, Davis, CA, USABioTechniques 39:__-__ (July 2005)PCR reaches its optimal amplification period with the PCR product doubling after every cycle in ideal reaction conditions. Finally, the plateau stage is reached when reaction components become limited and the fluorescence intensity is no longer useful for data calculation (11).One-Step Versus Two-Step Real-Time PCRWhen quantifying mRNA, real-time PCR can be performed as either a one-step reaction, where the entire reaction from cDNA synthesis to PCR amplification is performed in a single tube, or as a two-step reaction, wherereverse transcription and PCR ampli-fication occur in separate tubes. Thereare several pros and cons associatedwith each method. One-step real-time PCR is thought to minimizeexperimental variation because bothenzymatic reactions occur in a singletube. However, this method uses anRNA starting template, which is proneto rapid degradation if not handledproperly. Therefore, a one-step reactionmay not be suitable in situations wherethe same sample is assayed on severaloccasions over a period of time. One-step protocols are also reportedly lesssensitive than two-step protocols (12).Two-step real-time PCR separatesthe reverse transcription reaction fromthe real-time PCR assay, allowingseveral different real-time PCR assayson dilutions of a single cDNA. Becausethe process of reverse transcriptionis notorious for its highly variablereaction efficiency (13), using dilutionsfrom the same cDNA template ensuresthat reactions from subsequent assayshave the same amount of templateas those assayed earlier. Data fromtwo-step real-time PCR is quite repro-ducible with Pearson correlationcoefficients ranging from 0.974 to0.988 (14). A two-step protocol may bepreferred when using a DNA bindingdye (such as SYBR Green I) becauseit is easier to eliminate primer-dimersthrough the manipulation of meltingtemperatures (T m s) (14). However,two-step protocols allow for increasedopportunities of DNA contamination inreal-time PCR.TYPES OF REAL-TIMEQUANTIFICATIONAbsolute QuantitationAbsolute quantitation uses seriallydiluted standards of known concen-trations to generate a standard curve.The standard curve produces a linearrelationship between C t and initialamounts of total RNA or cDNA,allowing the determination of theconcentration of unknowns basedon their C t values (2). This methodassumes all standards and sampleshave approximately equal amplifi-cation efficiencies (15). In addition,the concentration of serial dilutionsshould encompass the levels in theexperimental samples and stay withinthe range of accurately quantifiable anddetectable levels specific for both thereal-time PCR machine and assay.The PCR standard is a fragmentof double-stranded DNA (dsDNA),single-stranded DNA (ssDNA), orcRNA bearing the target sequence.A simple protocol for constructing acRNA standard for one-step PCR canbe found in Fronhoffs et al. (16), whilea DNA standard for two-step real-timePCR can be synthesized by cloning thetarget sequence into a plasmid (17),Figure 1. Steps performed when measuring gene expression using real-time PCR. RNA is first isolated and characterized for quantity and integrity. If performing a one-step reaction, RNA is used as a template for the real-time PCR assay, and reverse transcription occurs during the as-say. During a two-step reaction, cDNA is first synthesized and then used as a PCR template. The steps performed on the real-time PCR machine are shown in blue, the time during which raw fluorescence data are collected, adjusted, and manipulated to generate the output data used foranalysis. For normalizing results with multiple housekeeping genes, a normalization factor must be calculated for each individual sample. Dividing the fluorescent data by its normalization factor produces the normalized data, which is followed by statistical analysis.purifying a conventional PCR product (18), or directly synthesizing the target nucleic acid. The standard used must be a pure species. DNA standards have been shown to have a larger quantifi-cation range and greater sensitivity, reproducibility, and stability than RNA standards (19). However, a DNA standard cannot be used for a one-step real-time RT-PCR due to the absence of a control for the reverse transcription efficiency (20). Relative QuantitationDuring relative quantitation, changes in sample gene expression are measured based on either an external standard or a reference sample, also known as a calibrator (21). When using a calibrator, the results are expressed as a target/reference ratio. There are numerous mathematical models available to calculate the mean normalized gene expression from relative quantitation assays. Depending on the method employed, these can yield different results and thus discrepant measures of standard error (22,23). Table 1 shows a comparison of the different methods, with an explanation of each method to follow.Table 1. Characteristics of Relative Quantitation Methods Methods (Reference)Amplification Efficiency Correction Amplification Efficiency Calculation Amplification Efficiency Assumptions AutomatedExcel-BasedProgramStandard Curve (31)no standard curve no experimental sample variation noComparative C t (2-∆∆Ct ) (21)yes standard curve reference = target noPfaffl et al.(26)yes standard curve sample = control REST aQ-Gene (23)yes standard curve sample = control Q-Geneb Gentle et al . (7)yesraw dataresearcher defines log-linear phase noLiu and Saint (22)yes raw datareference and target genes can have different efficiencies no DART-PCR (30)yes raw datastatistically defined log-linear phaseDART-PCR c C t , cycle threshold, DART-PCR, data analysis for real-time PCR; REST, relative expression software tool.a b c /cgi/content/full/31/14/e73/DC1Amplification efficiency. Ampli-fication efficiency of the reaction is an important consideration when performing relative quantitation. Past methods of calculating gene expression have assumed the amplification efficiency of the reaction is ideal, or 1, meaning the PCR product concentration doubles during every cycle within the exponential phase of the reaction (24). However, many PCRs do not have ideal amplification efficiencies, and calcula-tions without an appropriate correction factor may overestimate starting concentration (22). Current mathe-matical models make assumptions of reaction kinetics and usually require its accurate measurement (7,21,22,25,26).Traditionally, amplification efficiency of a reaction is calculated using data collected from a standard curve with the following formula (27): [Eq. 1]Exponential amplification = 10(-1/slope) Efficiency = [10(-1/slope)]-1The amplification efficiency of the reaction varies from being relatively stable in the early exponential phase and gradually declining to zero (22).This decay is due to the depletion of PCR components, the decline of polymerase activity, and competition with PCR products. Calcu-lation of amplification efficiency using a standard curve is not reflective of thischanging efficiency (28) and may overes-timate efficiencies (9). Because PCR results are based on C t , which are determined very early in the exponential phase of the reaction,these differences inamplification efficiency usually generate onlyminor differences in C tvalue (20). Nonetheless, after 26 cycles, a 5% difference in ampli-fication efficiency can result in a 2-fold difference of PCRproduct concentration (29).There are several alternate methods of calculating amplification efficiency based on raw data collected during PCR (7,9,22,25,30). During the exponential phase, the absolute fluores-cence increase at each PCR cycle for each individual sample reflects the true reaction kinetics of that sample. Consequently, data collected during the exponential phase can be log-trans-formed and plotted with the slope of the regression line representing the sample’s amplification efficiency. In the Liu and Saint (22) method, the individual researcher designates which cycles have exponential characteristics, while the method proposed by Tichopad et al. (9) uses a statistical calculation to define the period of exponential growth. Amplification efficiency calculated from raw data analysis is reportedly more accurate than when derived from a standard curve (9,25).Standard curve method for relative quantification. The quantity of each experimental sample is first deter-mined using a standard curve and then expressed relative to a single calibrator sample (31). The calibrator is desig-nated as 1-fold, with all experimentallyderived quantities reported as an n-fold difference relative to the calibrator. Because sample quantity is divided by calibrator quantity, standard curve units are eliminated, requiring only the relative dilution factors of the standards for quantification. This method is often applied when the amplification efficiencies of the reference and target genes are unequal (22). It is also the simplest method of quantification because it requires no preparation of exogenous standards, no quantification of calibrator samples, and is not based on complex mathematics. However, because this method does not incor-porate an endogenous control (usually a housekeeping gene), results must still be normalized.Comparative C t(2-∆∆Ct) method. The comparative C t method is a mathematical model that calculates changes in gene expression as a relative fold difference between an experi-mental and calibrator sample. While this method includes a correction for nonideal amplification efficiencies (i.e., not 1; Reference 21), the ampli-fication kinetics of the target gene and reference gene assays must be approxi-mately equal (32) because different efficiencies will generate errors when using this method (22). Consequently, a validation assay must be performed where serial dilutions are assayed for the target and reference gene and the results plotted with the log input concentration for each dilution on the x-axis, and the difference in C t (target-reference) for each dilution on the y-axis. If the absolute value of the slope of the line is less than 0.1, the comparative C t method may be used (21). The PCR product size should be kept small (less than 150 bp) and the reaction rigorously optimized (25). Because the comparative C t method does not require a standard curve, it is useful when assaying a large number of samples since all reaction wells are filled with sample reactions rather than standards.Pfaffl model. The Pfaffl model (26) combines gene quantification and normalization into a single calcu-lation. This model incorporates the amplification efficiencies of the target and reference (normalization) genes to correct for differences between the two assays. The relative expressionsoftware tool (REST©), which runsin Microsoft®Excel, automates dataanalysis using this model (33). RESTuses the Pairwise Fixed ReallocationRandomization Test©to calculateresult significance and will indicate ifthe reference gene used is suitable fornormalization.Q-Gene. Q-Gene is a fully compre-hensive Microsoft Excel-based softwareapplication that aids in the entireprocess of a real-time PCR experiment,from experimental planning and setupthrough data analysis and graphicalpresentation (23). Q-Gene calculatesthe mean normalized gene expressionwith standard errors using two differentmathematical models, both correctingfor amplification efficiencies. Thecalculated expression values are thencompared between two matched groupsto determine the expression of a samplerelative to a calibrator. The program alsoincludes several statistical tests such asthe paired or unpaired Student’s t-test,a Mann-Whitney U-test, Wilcoxonsigned-rank test, together withPearson’s correlation analysis to fullyassess the significance of experimentalresults. When running large or complexreal-time PCR experiments, having anorganized and automated method suchas Q-Gene can significantly expeditedata processing and management.Gentle et al. Gentle et al. (7)designed one of the first models inwhich both fold changes betweensamples and amplification efficienciesof experimental versus control samplesare calculated without the use ofstandard curves. Linear regressionanalyses of the mean of the raw logfluorescence data collected during theexponential phase of the PCR are usedto calculate the amplification efficiencyof each sample. By graphing the controland experimental samples together,they show that the vertical distancebetween the control and experimentallines is the log of the fold differencebetween the two, with the slopes ofthe lines representing the log of theiramplification efficiencies (7).Liu and Saint. Liu and Saint (22)developed a sigmoidal mathematicalmodel to quantitate and normalize geneexpression. Similar to Gentle et al. (7),this method calculates amplificationefficiencies from the actual slope ofthe amplification plot rather than astandard curve. The authors found thismethod was more accurate than thecomparative C t method with regard tothe varying amplification efficiencythroughout the PCR because the userFigure 2. Phases of the PCR amplification curve. The PCR amplification curve charts the accumula-tion of fluorescent emission at each reaction cycle. The curve can be broken into four different phases: the linear ground, early exponential, log-linear, and plateau phases. Data gathered from these phases are important for calculating background signal, cycle threshold (C t), and amplification efficiency. Rn is the intensity of fluorescent emission of the reporter dye divided by the intensity of fluorescent emissionof the passive dye (a reference dye incorporated into the PCR master mix to control for differences in master mix volume). ∆Rn is calculated as the difference in Rn values of a sample and either no template control or background, and thus represents the magnitude of signal generated during PCR. This graph was generated with ABI Prism SDS version 1.9 software (Applied Biosystems).defines which PCR cycles experience exponential growth and are used for the calculation (22).Amplification plot method. The amplification plot method uses a simple algorithm to calculate the amplification efficiency of every sample individually within the real-time PCR assay. These data are then used in the calculation forexpression quantitation (30). To easedata handling, Peirson et al. (30) havedeveloped a Microsoft Excel workbookentitled Data Analysis for Real-TimePCR (DART-PCR) that quickly calcu-lates all results from raw data.Absolute or Relative Quantitation:Pros and ConsAbsolute quantitation is consideredto be more labor-intensive than relativequantitation because of the necessityto create reliable standards for quanti-tation and include these standardsin every PCR (19). However, whenperforming relative quanti-tation, the data (C t) used forcomparison are arbitraryvalues and only applicableto the samples run withinthe same PCR. To comparesamples between twodifferent PCRs, it is necessaryto include a reference controlin every plate or run. In caseswhere data compared areassayed on different daysor in different laboratories,absolute quantitation may bepreferred because results arebased on a constant. In termsof fold-change data, absoluteand relative quantitationmethods produce comparableresults (30).ControlsThere are several typesof controls that ensure theintegrity of every step of thereal-time PCR process. DNAcontamination in the samplemay be accounted for with aminus reverse transcriptioncontrol. However, whenone has numerous samples,an alternate method toprevent the detection ofgenomic DNA is to designthe target PCR product tospan an exon/exon boundary.Variation in the efficiency ofthe reverse transcriptase aswell as the amount of RNAadded into the reaction canbe accounted for using anendogenous control, whichis a nucleic acid alreadypresent in an individualsample. The use of endog-enous controls is discussedin detail in the sectionentitled Normalization. PCRmaster mix volume has beenFigure 3. Real-time PCR detection chemistries. Probe sequences are shown in blue while target DNA sequences are shown in black. Primers are indicated by horizontal arrowheads. Not all unlabeled PCR primers are shown. Oligo,oligonucleotide.shown to be a factor in PCR amplifi-cation efficiency such that differences in master mix volume in reactions using the same amount of starting template have different amplification efficiencies (22). A passive reference dye (such as ROX) is often included in the master mix to account for subtle differences in PCR master mix volumes as well as non-PCR-related fluctuations in fluorescence signal. Problems with the PCR master mix itself can be accounted for using an exogenous control, which is a synthesized construct of charac-terized RNA or DNA spiked into each reaction (34).NormalizationNormalization of gene expression data is used to correct sample-to-sample variation. Starting material obtained from different individuals usually varies in tissue mass or cell number, RNA integrity or quantity, or experimental treatment. Under ideal conditions, mRNA levels can be standardized to cell number, but when using whole tissue samples, this type of normalization is impossible (35). Therefore, real-time PCR results are usually normalized against a control gene that may also serve as a positivecontrol for the reaction. The idealcontrol gene should be expressed inan unchanging fashion regardless ofexperimental conditions, includingdifferent tissue or cell types, devel-opmental stage, or sample treatment.Because there is no one gene that meetsthis criterion for every experimentalcondition, it is necessary to validatethe expression stability of a controlgene for the specific requirements of anexperiment prior to its use for normal-ization (36).Housekeeping genes (mRNA).Traditionally, genes thought to havestable expression have been employedas controls in gene expression assays.Due to the increased sensitivity anddynamic range of real-time PCR overtraditional quantitation techniques,many of the well-known housekeepinggenes such as GAPDH andβ-actin havebeen shown to be affected by differenttreatments, biological processes, andeven different tissues or cell types(reviewed in depth in Reference 11).Consequently, normalization with asingle housekeeping gene can falselybias results. When using a house-keeping gene for normalization, it isabsolutely imperative to validate itsstability with one’s own samples ratherthan relying on previously publishedmaterials.Ribosomal RNA (rRNA). rRNAis another possible reference gene fornormalization. Of the two main rRNAs,28S and 18S, 28S is considered morerepresentative of mRNA integritybecause 18S may remain intact insamples with degraded mRNA (37).There are several problems with using28S rRNA to normalize mRNA genemeasurements. rRNAs are transcribedwith a different polymerase thanmRNAs, so changes in polymeraseactivity may not affect both types ofRNA expression equally (38). This islikely reflected in the fact that rRNAexpression tends to be less affectedby treatments that significantly altermRNA expression (39). Varying ratiosof rRNA to mRNA have been reported(40) and, given the extreme abundanceof 28S rRNA in a total RNA sample [ina 10-µg total RNA sample, on average2 µg are 18S rRNA and 5.5 µg are 28SrRNA (Technical Bulletin #151, www./techlib/tb/tb_151.html;Reference 40a)], it may be impossibleto accurately measure both 28S anda rare transcript in the same RNA orcDNA dilution. Lastly, rRNA, whichTable 2. Characteristics of Detection ChemistriesDetection Chemistries Specificity MultiplexCapabilitySpecificOligonucleotideRequiredAllelicDiscriminationCostDNA Binding Dyes two PCR primers No No No$Hybridization Probe Four Oligonucleotide Method two PCR primers;two specific probesYes Yes Yes$$$Hybridization Probe Three Oligonucleotide Method two PCR primers;one specific probeYes Yes Yes$$$Hydrolysis Probes two PCR primers;one specific probeYes Yes Yes$$$Molecular Beacons two PCR primers;one specific probeYes Yes Yes$$$Scorpions one PCR primer;one primer/probeYes Yes Yes$$$ Sunrise Primers two PCR primers Yes Yes Yes$$$ LUX Primers two PCR primers Yes Yes No$$ $$$, very expensive; $$, moderately expensive; $, inexpensive. LUX, light upon extension.lacks a poly(A) tail, cannot be measured if an oligo(dT) or gene-specific primer has been used for reverse transcription.Total RNA. Gene expression measurements may be normalized against total RNA concentration (11). RNA quantitation can be performed via RiboGreen®RNA (Molecular Probes, Eugene, OR, USA) quantifi-cation or the Agilent 2100 BioAnalyzer (Agilent Technologies, Palo Alto, CA, USA); spectrophotometry may not have the sensitivity and accuracy required for this measurement. There are several inherent problems with this approach: total RNA levels are affected by cellular processes, RNA quality and reverse transcription efficiency are not considered, normalization is only as accurate as the RNA quantifi-cation, and, in situations where RNA is extracted from a microdissected tissue, all recovered RNA may be needed for the real-time PCR assay itself (35).Multiple mRNAs. Given the many disadvantages of using cell number, mRNA, rRNA, or total RNA for normalization purposes, a new method of employing multiple housekeeping genes has emerged to minimize these problems (19,35,41). Multiple housekeeping genes are assayed and a normalization factor is calculated from the geometric mean of their expression levels (19,35). In this method, the expression stability of several (10–13) different housekeeping genes in the samples of interest are measured to identify the genes most suitable for an individual experiment. Microarray results may be exploited to identify potential normalization candidates (42). A list of housekeeping genes can be found in Vandesompele et al.(35). The expression stability of candidate control genes is determined with either geNorm (35) or BestKeeper (19), which are both Microsoft Excel applets that estimate gene stability through numerous pair-wise compar-isons. geNorm can be downloaded at medgen.ugent.be/~jvdesomp/genorm, and BestKeeper can be downloaded at . While the use of multiple housekeeping genes may be the most labor-intensive method, it is also the most conservative method of data normalization.DETECTION CHEMISTRIESA diagram of all real-time PCRdetection chemistries discussed in thisreview can be seen in Figure 3, with acomparison of their characteristics inTable 2.DNA Binding DyesDNA binding dyes emit fluores-cence when bound to dsDNA (Figure3A). As the double-stranded PCRproduct accumulates during cycling,more dye can bind and emit fluores-cence. Thus, the fluorescence intensityincreases proportionally to dsDNAconcentration (43). This technique isvery flexible because one dye can beused for different gene assays. Conse-quently, multiplexing reactions is notpossible. Because DNA binding dyesdo not bind in a sequence-specificmanner, these assays are prone to falsepositives (44). Accurate results demanda specific PCR, which can be confirmedvia dissociation curve analysis, wherethe presence of different PCR productsis reflected in the number of first-derivative melting peaks (45) or gelanalysis (46). A protocol for SYBRGreen I PCR master mix can be foundin Ramos-Payen et al. (47).Hybridization ProbesHybridization probes can be utilizedin either a four or three oligonucle-otide manner (for a short review, seeReference 48) (Figure 3B). The fouroligonucleotide method consists of twoPCR primers and two sequence-specificprobes that bind adjacent to eachother in a head-to-tail arrangement.The upstream probe is labeled withan acceptor dye on the 3′ end, and thedownstream probe with a donor dye onthe 5′ end (49), allowing the donor andacceptor fluorophores to experiencean increase in fluorescence resonanceenergy transfer (FRET) when bound(48). The three oligonucleotide methodis similar to the four oligonucleotidemethod, except that the upstream PCRprimer is labeled with an acceptor dyeon the 3′end, and thus replaces thefunction of one of the probes from thefour oligonucleotide method.In both cases, the downstream probecan be designed to cover a mutation siteand discriminate between known allelesand detect new alleles simultaneously(50). Alleles are identified and differ-entiated via dissociation curve (48). Asingle melting curve can distinguishup to four different T m s, and six differ-ently labeled probes may be multi-plexed, theoretically allowing a run of24 assays in a single tube (48). Whilemultiplex reactions are theoretically asimple way to increase the efficiencyof data collection, in reality it is avery technically challenging processthat requires extensive optimization toensure that reactions do not competewith each other (34).Hydrolysis ProbesHydrolysis probes, exemplified bythe TaqMan chemistry, also knownas 5′nuclease assay, fluoresce uponprobe hydrolysis to detect PCRproduct accumulation (Figure 3C).The sequence-specific probe is labeledwith a reporter dye on the 5′ end anda quencher dye on the 3′end (24),which allows the quencher to reducethe reporter fluorescence intensity byFRET when the probe is intact (51).While both hydrolysis and hybrid-ization probes rely on FRET to alterthe intensity of fluorescence emission,the energy transfer works in oppositemanners in these two chemistries.FRET reduces fluorescence intensityin hydrolysis probes and increasesintensity in hybridization probes.When annealed to the target sequence,the bound and quenched probe will bedegraded by the DNA polymerase’s 5′nuclease ability during the extensionstep of the PCR. Probe degradationallows for separation of the reporterfrom the quencher dye, resultingin increased fluorescence emission(2,24).Minor groove binders (MGBs), suchas dihydrocyclopyrroloindole tripeptide(DPI3), may be added to these probes toincrease their T m and allow the use of ashorter probe (52). These probes are notonly less expensive to produce but havereduced background fluorescence and alarger dynamic range due to increasedefficiency of reporter quenching (52).。

多点地质统计学python -回复

多点地质统计学python -回复"多点地质统计学python"是一种基于Python编程语言的地质统计学方法，它通过使用多点统计技术来分析地质数据和建立地质模型。

多点地质统计学python可以用于地质建模、矿产资源评估、油气勘探等地质领域。

本文将逐步介绍多点地质统计学python的基本原理、数据处理步骤以及应用案例。

第一步：理解多点地质统计学的基本原理多点地质统计学是一种从多个样本点中提取信息进行推断的统计学方法，它考虑了地质数据在空间上的相关性和变异性。

这种方法的基本原理是通过对多个样本点进行统计分析，揭示地质变量的分布和空间结构。

多点地质统计学方法在地质学领域得到广泛应用，可以实现对地质数据的建模和预测。

第二步：了解多点统计技术在python中的实现Python是一种功能强大且易于使用的编程语言，具有丰富的数据处理和分析库，例如NumPy、Pandas和Matplotlib等。

这些库提供了处理大型地质数据集和执行统计分析的工具。

在python中，可以使用这些库来实现多点地质统计学方法，例如多点统计算法、样本点选择和空间变异性分析等。

第三步：处理地质数据集在使用多点地质统计学python之前，首先需要准备一个地质数据集。

这个数据集包含了地质变量的观测值，例如地层厚度、属性值或矿石品位等。

地质数据集通常以表格形式存储，可以使用Pandas库将数据导入到python环境中进行处理。

第四步：分析地质数据集在有了地质数据集之后，可以使用多点地质统计学python来分析数据。

首先，可以使用NumPy库计算地质数据的统计指标，例如均值、方差和协方差等。

这些统计指标可以用来揭示地质变量的中心趋势和空间关系。

接下来，可以使用多点统计算法来分析地质数据的变异性和空间结构。

其中，常见的多点地质统计学方法包括变差函数、半变异函数和变异权重等。

这些方法可以用来评估地质数据的变异性，并揭示地质变量之间的空间关联性。

时间维度生成函数的方法

时间维度生成函数的方法
生成函数是计算机科学中一种强大的工具，它用于表示和操作离散数据结构，如序列、集合、图等。

在时间维度上，生成函数可以用于表示和操作时间序列数据。

在时间维度上，生成函数可以使用以下方法：
1. 递归：递归是一种将问题分解为更小的子问题的方法。

在时间维度上，递归可以用于生成时间序列数据。

例如，斐波那契数列是一种经典的递归时间序列，可以使用递归生成。

2. 迭代：迭代是一种重复执行某个过程的方法。

在时间维度上，迭代可以用于生成时间序列数据。

例如，可以使用迭代生成等差数列、等比数列等时间序列。

3. 随机：随机是一种不确定性的方法。

在时间维度上，随机可以用于生成随机时间序列数据。

例如，可以使用随机数生成器生成随机正弦波、随机噪声等时间序列。

4. 组合：组合是一种将多个方法组合在一起的方法。

在时间维度上，组合可以用于生成更复杂的时间序列数据。

例如，可以将递归、迭代和随机等方法组合在一起生成更复杂的时间序列。

以上是几种常用的在时间维度上生成函数的方法，具体使用哪种方法取决于具体的应用场景和需求。

MySQL数据库的时空数据处理和地理空间分析实验

MySQL数据库的时空数据处理和地理空间分析实验引言：MySQL是一种常用的关系型数据库管理系统，广泛应用于各种领域。

而随着时空数据和地理空间数据的不断涌现，数据库管理系统需要具备处理这些特殊数据类型的能力。

本文将介绍MySQL数据库在时空数据处理和地理空间分析方面的实验。

一、时空数据处理时空数据是指具有时间和空间组成的数据集合，对于很多应用领域来说具有重要的意义。

MySQL数据库在时空数据处理方面提供了一些内建函数和扩展功能。

1. 时间处理函数MySQL提供了许多内建函数来处理时间数据类型，如DATE、TIME和TIMESTAMP。

通过这些函数，我们可以进行时间数据的提取、计算和格式化。

例如，我们可以使用DATE_FORMAT函数将时间数据按照指定的格式进行显示：SELECT DATE_FORMAT('2022-07-15', '%Y年%m月%d日');输出结果为：2022年07月15日2. 空间数据处理函数MySQL 5.7版本及以上提供了对地理空间数据类型的支持，包括Point、LineString和Polygon等。

通过空间数据处理函数，我们可以进行空间数据的查询、分析和转换。

例如，我们可以使用ST_Distance函数计算两个点之间的距离：SELECT ST_Distance(Point(1, 2), Point(4, 6));输出结果为：53. 时空数据索引对于大规模的时空数据集合，高效的数据索引是必不可少的。

MySQL通过引入空间索引（Spatial Index）来提高时空数据查询的性能。

空间索引采用了R树（R-tree）数据结构来组织数据，可以实现高效的数据检索和空间查询。

通过创建空间索引，我们可以在数据库中快速查找满足特定空间条件的数据。

二、地理空间分析实验地理空间分析是在地理空间数据基础上进行的数据处理和分析过程。

MySQL 数据库提供了一些地理空间分析的功能，可以帮助我们进行各种地理空间数据的处理和分析。

r语言广义差分法代码

r语言广义差分法代码广义差分法（Generalized Difference Method）是一种用于时间序列分析和预测的方法，广泛应用于经济学、金融学、统计学等领域。

在R语言中，可以使用一些包（如forecast、TTR）来实现广义差分法的相关功能。

首先，我们需要导入相关的包。

使用`library()`函数可以导入已经安装好的包。

例如，使用`library(forecast)`可以导入forecast 包，使用`library(TTR)`可以导入TTR包。

接下来，我们需要准备时间序列数据。

假设我们有一个名为data的数据框，其中包含一个时间变量Date和一个数值变量Value。

可以使用`ts()`函数将Value转换为时间序列对象。

例如，可以使用以下代码将Value转换为时间序列对象：`ts_data <- ts(data$Value, frequency = 12)`。

这里的frequency参数表示时间序列的频率，如果数据是每个月的，则频率为12。

进行广义差分法之前，我们通常需要对时间序列进行平稳性检验和差分操作。

可以使用`adf.test()`函数来进行单位根检验。

例如，可以使用以下代码对时间序列对象ts_data进行单位根检验：`adf.test(ts_data)`。

如果检验结果中的p-value小于0.05，则可以认为时间序列是平稳的；否则，需要进行差分操作。

进行差分操作可以使用`diff()`函数。

例如，可以使用以下代码对时间序列对象ts_data进行一阶差分：`diff_data <-diff(ts_data)`。

这将生成一个新的时间序列对象diff_data，其中包含了ts_data的一阶差分结果。

接下来，我们可以使用广义差分法进行时间序列建模和预测。

可以使用`auto.arima()`函数自动选择ARIMA模型，并使用`forecast()`函数进行预测。

例如，可以使用以下代码选择ARIMA模型并预测未来12个时间点的值：```model <- auto.arima(ts_data)forecast <- forecast(model, h = 12)```这将生成一个预测对象forecast，其中包含了未来12个时间点的预测值。

判断时间段在某个时间区间的函数

判断时间段在某个时间区间的函数
本文将介绍如何编写一个判断时间段是否在某个时间区间内的函数。

该函数可以用于各种应用程序中，例如计算机程序、网站和移动应用程序。

本文将涵盖以下主题：
1. 时间的表示方法：介绍时间的不同表示方法，例如UNIX时间戳、ISO 8601时间格式等。

2. 时间区间的表示方法：介绍时间区间的表示方法，例如开区间、闭区间等。

3. 函数设计：介绍如何设计一个判断时间段是否在某个时间区间内的函数，包括输入参数、输出结果等。

4. 实现方法：介绍如何使用编程语言实现该函数，包括使用if 语句、逻辑运算符等。

5. 测试和调试：介绍如何测试和调试该函数，包括使用单元测试、集成测试等方法。

通过本文的学习，读者将能够了解如何编写一个实用的时间段判断函数，并且可以将该函数应用到各种实际应用程序中。

- 1 -。

动态时间规整计算例子

动态时间规整计算例子
动态时间规整（DTW，Dynamic Time Warping）是一种用于测量两个时间序列之间相似度的方法，即使两个序列在时间和速度上有所不同，也可以计算它们的相似度。

以下是一个简单的动态时间规整（DTW）计算例子：假设我们有两个时间序列：
●序列A:[1,3,4,2,5]
●序列B:[1,2,2,4,3,5]
现在我们想要计算这两个序列之间的相似度，我们可以使用动态时间规整算法来进行计算。

步骤：
1.创建距离矩阵：计算两个序列中每对点之间的距离（如欧氏距离、曼哈顿距
离等）。

2.初始化动态规划矩阵：创建一个矩阵来存储计算过程中的临时值。

3.动态规划计算：根据以下规则填充动态规划矩阵：
●初始化第一行和第一列为无穷大，矩阵第一行和第一列的值代表A或B的子
序列和空序列之间的最小距离。

●对于矩阵中的其他位置，计算当前位置的值为当前位置值与相邻三个位置中
的最小值（即对角线、上方、左方）加上当前位置的距离。

4.回溯路径：根据动态规划矩阵，找出路径，得到最小距离。

在示例中，我们可以通过手动计算或编写代码来执行动态时间规整算法，计算序列A和序列B之间的距离。

这个例子可以帮助理解DTW算法是如何在时间序列相似度比较中工作的。

时空轨迹碰撞算法

时空轨迹碰撞算法全文共四篇示例，供读者参考第一篇示例：时空轨迹碰撞算法（Temporal Trace Collision Algorithm，简称TTCA）是一种用于处理大规模时空轨迹数据的高效算法。

随着移动设备的普及和位置信息的普遍记录，时空轨迹数据的规模不断增大，如何高效地处理这些数据成为了研究的焦点之一。

TTCA算法便是为了解决这一问题而设计的。

我们需要了解什么是时空轨迹数据。

时空轨迹数据是指随着时间变化而变化的物体在空间中的运动路径数据。

移动设备的定位信息、多辆车辆的轨迹记录等都属于时空轨迹数据。

这些数据通常包含了大量的时间戳和坐标点，是一种典型的时空数据。

在处理时空轨迹数据时，我们通常会涉及到轨迹的相似性匹配、轨迹的聚类分析、轨迹的异常检测等问题。

而时空轨迹碰撞算法正是解决轨迹的相似性匹配问题的一种重要算法。

其基本思想是在时空轨迹数据中寻找两个不同轨迹之间的碰撞点，即两个轨迹在时间和空间上出现重合的情况。

通过识别这种碰撞点，我们可以对轨迹进行匹配或者聚类，进而得到更深入的分析结果。

那么，时空轨迹碰撞算法是如何实现的呢？在算法的实现过程中，我们首先需要将时空轨迹数据表示成一种便于计算的形式。

通常我们可以将轨迹表示为一系列时间戳和坐标点的序列。

然后，我们可以使用一些特定的数据结构和算法来高效地寻找碰撞点。

一个常用的方法是使用R树（R-tree）索引结构来加速碰撞点的查找。

R树是一种多维索引结构，可以高效地支持范围查询和近邻查询等操作。

通过将时空轨迹数据存储在R树中，我们可以快速地找到有重叠部分的轨迹，并识别其碰撞点。

我们还可以结合一些距离度量和相似性算法来进一步精确地计算碰撞点的位置。

在实际应用中，时空轨迹碰撞算法可以被广泛用于城市交通监测、移动设备定位、社交网络分析等领域。

在城市交通监测中，我们可以利用此算法识别交通事故发生的位置和时间，帮助交通管理部门及时处理。

在移动设备定位中，我们可以通过匹配用户的轨迹，提高位置服务的准确性和个性化度。

今晚月色真美的python代码

今晚月色真美简介月色是一种美丽而神秘的自然现象，常常给人以宁静和悠远的感觉。

今晚月色真美是一种常用来形容夜晚的诗意句子，它让人联想到宁静的夜晚、明亮的月光和无垠的宇宙。

在这篇文章中，我们将使用Python代码探索和呈现今晚月色真美的魅力。

月相计算在开始探索今晚月色真美之前，我们需要先了解一些基本知识。

月相是指月球表面在地球上观察到的不同形状。

通过计算月相，我们可以确定今晚月亮的形状和亮度。

1. 计算方法计算月相的方法有多种，其中最常用的是基于月球的黄道坐标和地球的经度、纬度。

根据这些参数，我们可以使用天文计算库来计算今晚月亮的具体相位。

2. Python代码示例下面是一个使用Python计算今晚月相的简单示例代码：import ephemfrom datetime import datetimeobs = ephem.Observer()t = '40.7128' # 纬度obs.lon = '-74.0060' # 经度obs.date = datetime.utcnow()moon = ephem.Moon()pute(obs)phase = moon.phase / 100 # 将phase转换为0~1的范围if phase == 0:print("今晚是新月")elif 0 < phase < 0.5:print("今晚是上弦月")elif phase == 0.5:print("今晚是满月")else:print("今晚是下弦月")通过设置观测者的经度和纬度，使用ephem库计算得到今晚的月相。

根据月相的数值范围，我们可以判断今晚是新月、上弦月、满月还是下弦月。

月亮的魅力的确，今晚月色真美。

月亮作为夜空中最明亮的天体之一，给人带来了许多美丽和神秘的感觉。

1. 月亮的亮度月亮的亮度是指其反射阳光的能力，这也是我们看到的月亮明亮的原因。

c语言五个点判断趋势的方法

c语言五个点判断趋势的方法（原创版3篇）目录（篇1）一、C 语言简介二、五个点判断趋势的方法1.移动平均线法2.指数平滑法3.线性回归法4.相关系数法5.随机指标法正文（篇1）一、C 语言简介C 语言是一种通用的、过程式的计算机程序设计语言。

它的设计目标是提供一种能以简单、清晰、高效的方式编写程序的语言，以满足操作系统、设备驱动、应用程序等各个领域的需求。

C 语言具有丰富的语法结构和强大的功能，广泛应用于嵌入式系统、操作系统、游戏开发等领域。

二、五个点判断趋势的方法在 C 语言中，我们可以使用以下五种方法来判断数据的趋势：1.移动平均线法移动平均线法是一种常见的时间序列分析方法，用于消除数据中的短期波动，以便更清晰地观察长期趋势。

其基本思想是将一段时间内的数据求和，然后除以时间间隔，得到一个平均值。

随着时间推移，不断更新数据，计算新的平均值，从而得到一个移动平均线序列。

当移动平均线呈现上升趋势时，说明数据整体呈上升趋势；反之，则呈下降趋势。

2.指数平滑法指数平滑法是一种基于指数加权的移动平均法，用于预测和消除时间序列数据中的季节性波动。

其基本思想是给最近的数据赋予更大的权重，而给较早的数据赋予较小的权重。

这样，随着时间的推移，数据的权重会逐渐减小，从而减小季节性波动的影响。

3.线性回归法线性回归法是一种通过建立线性模型来研究两个或多个变量之间关系的方法。

在时间序列分析中，线性回归法可以用于预测未来的趋势。

其基本思想是选择一个线性函数，使得这个函数与数据点的误差的平方和最小。

通过求解线性回归方程，可以得到趋势线的斜率和截距，从而预测未来的趋势。

4.相关系数法相关系数法是一种衡量两个变量之间线性相关程度的方法。

在时间序列分析中，可以用相关系数来衡量数据的趋势。

其基本思想是计算数据之间的协方差和标准差，然后计算它们的比值。

相关系数的值介于 -1 和 1 之间，当相关系数接近 1 时，说明数据呈正相关；当相关系数接近 -1 时，说明数据呈负相关；当相关系数接近 0 时，说明数据之间没有明显的线性关系。

Real-time Navigation System 指南说明书

NAVIGATIONLearn how to enter a destination and operate the navigation system.Basic Navigation OperationA real-time navigation system uses GPS and a map database to show your current location and help guide you to a desired destination. You can operate the system using voice commands or the touchscreen.To reduce the potential for driver distraction, certain manual functions may be available.Steering Wheel ControlsUse the steering wheel controls to control the system.NAVIGATION*SOURCE button: Press to enter a selection after pressing and holding the MENU button.MENU button: From the map screen, press and hold to display Go Home and other options.+ / - buttons: Scroll the list after pressing and holding the MENU button.BACK button:Press to cancel a command or operation. TALK button: Press to givenavigation commands.Navigation Display and ControlspositionVoice Prompt VolumeAdjust volume for navigation and voice command confirmation. This does not affect audio or Bluetooth ® HandsFreeLink ® volume, which is controlled by the volume bar.1. 2. 3. 4. Voice Prompt1. 2.3. 4. Voice Recognition Tips• • To bypass the system prompts, simply press the Talk button again to say your command.• When the Talk button is pressed, all available voice commands are highlighted in yellow.• Speak in a clear, natural voice.• Reduce all background noise.• Adjust the dashboard and side vents away from the microphone in the ceiling.• Close the windows and moonroof.Entering a DestinationUse multiple methods to enter a destination. Home AddressStore your home address in the system so you can easily route to it.1.From the Home screen, select Settings. 2. Select Navigation.3. Select Edit Go Home (1 or 2).4. Enter the address, and select OK when complete.When your home address is stored, you can press the Talk button and say “Go home” at any time. You can also select Go home from the navigation menu. Street AddressEnter a street address using voice commands. Press the Talk button before saying each command.You can also enter the address manually when the vehicle is stopped. From the map screen, press MENU and select Address. Follow the prompts.U.S. models1. From the Voice Portal screen, say “Address.”2. The system prompts you to say the entireaddress. For example, say “1-2-3-4 Main Street, Los Angeles, California.”3. 4. Say “Set as destination”Canadian models1. From the Voice Portal screen, say “Address.”2. Follow the prompts.Place CategoryEnter a destination or point of interest using voice commands. Press the Talk button before saying each command.You can also enter the place name manually when the vehicle is stopped. From the map screen, press MENU and select Place Name. Follow the prompts.1. From the Voice Portal screen, say “Place name .” 2. Say a category, for example “ATM/Bank .”3. Say the name of the place, for example “ABC Bank .”4. The system interprets the name. If necessary, you may have to clarify similar matches.5. Say “Set as destination” to set the route.Find Nearest PlaceEnter a destination by category and distance using voice commands. Press the Talk button before saying each command.1. Say “Find nearest” and a place. For example, “Find nearest ATM.”2. A list of the nearest places in your area is displayed by shortest distance to destination.3. Say the number next to the destination you want to select.Next guidance pointEstimated time to destinationDistance to destination Calculated route Taking a Detour You can calculate a detour route manually if you encounter a road closure or other obstacle.6.Say “Set as destination ” to set the route.1.From the map screen, say “Find nearest ATM.”2.A list of the nearest ATMs in yourarea is displayed by shortest distance to ing the “Find Nearest” Command Press and release the T alk button before you say a command.。

第章时间序列平滑预测法

第三章时间序列平滑预测法
• 时间序列预测法，是将预测对象的历史数据按照时间的顺序排列成为时间序列，然后分析它随时间的变化趋势，外推预测对象的未来值。这样，就把影响预测对象变化的一切因素由“时间”综合起来描述了。
• 时间序列分析预测可分为确定性时间序列预测法和随机性时间序列预测法。
第1节时间序列概述
一、简单移动平均法
设时间序列为：y1, y2…,yt, …；简单移动平均公
式(3.2.1)为：
Mt
yt
yt1 N
ytN 1
t ≥ N (3.2.1)
式中：Mt为t期移动平均数；N为移动平均的项数。式（3.2.1）表明当t向前移动一个时期，就增
加一个新数据，去掉一个远期数据，得到一个新的平均数。由于它不断的“吐故纳新”，逐期向
预测值 yˆt α=0.5
219.1 223.4 216.95 212.775 218.7875 223.8438 230.2719 231.3359 237.468 237.934 244.567 243.7335 246.1667 246.2334
y2003
6
151.968
表3.2.2 某商店1991年－2002年利润及加权移动平均预测值表单位：万元
年份 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003年预测值
利润 120.87 125.58 131.66 130.42 130.38 135.54 144.25 147.82 148.57 148.61 149.76 154.56
148.69
3.8
150.375
160
150

时间序列异常值探测 matlab

时间序列异常值探测 matlab时间序列异常值探测是一种重要的数据分析技术，它可以帮助我们发现时间序列中的异常值，从而更好地理解数据的特征和趋势。

在本文中，我们将介绍如何使用Matlab进行时间序列异常值探测。

我们需要了解什么是时间序列。

时间序列是指在一段时间内收集到的数据序列，例如股票价格、气温、销售额等。

时间序列通常具有一定的周期性和趋势性，因此我们需要对其进行分析和预测。

在时间序列中，异常值是指与其他数据点相比具有显著不同的数据点。

异常值可能是由于测量误差、数据录入错误或其他未知因素引起的。

因此，我们需要对时间序列进行异常值探测，以便更好地理解数据的特征和趋势。

Matlab是一种强大的数据分析工具，它提供了许多用于时间序列异常值探测的函数和工具。

以下是一些常用的Matlab函数和工具： 1. boxplot函数：boxplot函数可以用于绘制箱线图，箱线图可以帮助我们发现时间序列中的异常值。

2. zscore函数：zscore函数可以用于计算时间序列中每个数据点的标准分数，标准分数可以帮助我们发现与其他数据点相比具有显著不同的数据点。

3. isoutlier函数：isoutlier函数可以用于判断时间序列中的数据点是否为异常值，它可以根据不同的方法进行判断，例如基于中位数的方法、基于均值的方法等。

4. tscollection函数：tscollection函数可以用于创建时间序列集合，它可以帮助我们对多个时间序列进行异常值探测和分析。

除了以上函数和工具外，Matlab还提供了许多其他的函数和工具，例如histogram函数、scatter函数、timeseries函数等，它们都可以用于时间序列异常值探测。

时间序列异常值探测是一种重要的数据分析技术，它可以帮助我们发现时间序列中的异常值，从而更好地理解数据的特征和趋势。

Matlab是一种强大的数据分析工具，它提供了许多用于时间序列异常值探测的函数和工具，我们可以根据具体的需求选择合适的函数和工具进行分析。

平滑预测法公式

平滑预测法公式
平滑预测法（smoothing forecasting method）是一个应用于把历史数据作为基础，预测未来结果的模型，是应用在大多数商业统计学和管理决策分析里的预测技术。

它能够从非平稳数据，即变化频繁的数据里提取预测信息。

平滑预测法的公式可以将一组历史数据进行平滑处理，从而减少计算量，并且可以有效地反映出宏观背景及其他影响属性，易于进行预测分析。

平滑预测法公式如下：
预测量F等于当前月份Yn与前一个月份Yn-1的调和平均，即：F= (2*Yn +(n-1)*Yn-1) / (n+1)
其中，Yn表示当前月份的数值，Yn-1表示前一个月份的数值，n 代表月份的前后数字差值，并以该取值表示月份数，比如3月分得到的n值为2，4月的n值为3，以此类推。

在此，通过平滑预测法，能够有效估算到未来月份的数据，提前做出大致的规划和预判，加大流程的可控性，确保未来的预测结果的可行性和准确性。

dttf值域

DTTF（Day-to-Time Forecasting）是一种时间序列预测方法，它可以根据历史数据对未来一段时间内的数值进行预测。

在DTTF 中，时间序列数据被分为多个时间段，每个时间段都有一个对应的预测值。

DTTF 的值域通常是实数集合，即包括所有可能的实数值。

具体来说，DTTF 的值域取决于所预测的时间序列数据的类型和预测的时间段长度。

例如，如果预测的是气温数据，那么DTTF 的值域通常是介于-40℃到50℃之间的实数集合。

需要注意的是，DTTF 的值域并不是固定不变的，它可能会随着时间序列数据的变化而发生变化。

例如，如果时间序列数据中出现了异常值，那么预测值可能会超出原来的值域范围。

因此，在使用DTTF 进行预测时，需要对预测值进行合理的处理和解释，以避免出现误判或误解。