New results on $gamma_{rm str}$ in 2D quantum gravity using Regge calculus

stata中将数据映射到0到100之间的
命令
- 使用`egen`命令：可以使用`egen`命令创建一个新变量，并将其值设置为原始变量除以100的结果。

例如，如果原始变量的名称为`x`，可以使用以下命令将其映射到0到100之间：
```
egen newvar=x/100
```
- 使用`mapvalues`命令：可以使用`mapvalues`命令将原始变量的值映射到指定的范围内。

例如，如果原始变量的名称为`x`，可以使用以下命令将其映射到0到100之间：```
mapvalues x, map(0 100)
```
请注意，具体的命令和语法可能会因Stata的版本和设置而有所不同。

在使用这些命令时，请确保仔细阅读Stata的帮助文件或参考相关文档，以确保正确使用。

hive高级函数Hive是一个基于Hadoop的数据仓库解决方案，它使用Hive查询语言（HQL）来处理和分析大型数据集。

Hive提供了许多内置的高级函数，用于对数据进行转换、过滤、聚合等操作。

在本文中，我们将探讨一些常用的Hive高级函数，并讨论它们的用法和应用场景。

一、字符串函数：1. CONCAT：将多个字符串连接在一起。

2. SUBSTRING：返回一个字符串的子串。

3. INSTR：返回一个字符串中第一次出现的指定子串的位置。

4. REPLACE：将一个字符串中的所有指定子串替换为新的子串。

5. TRIM：去除一个字符串两端的空格。

这些字符串函数在数据清洗和转换中非常有用，可以帮助我们从原始数据中提取需要的信息。

二、日期函数：1. TO_DATE：将一个字符串转换为日期格式。

2. YEAR/MONTH/DAY：从一个日期字段中提取年份、月份或日期。

3. DATE_ADD：将指定的时间间隔添加到一个日期字段。

4. DATE_DIFF：计算两个日期之间的天数差。

这些日期函数可以帮助我们对时间序列数据进行分析和计算，如计算每月销售额或计算两个日期之间的间隔。

三、数值函数：1. ROUND：对一个数值字段进行四舍五入。

2. ABS：返回一个数值字段的绝对值。

3. CEIL/FLOOR：对一个数值字段进行向上取整或向下取整。

4. POWER/SQRT：计算一个数值字段的幂或平方根。

这些数值函数在数据分析和统计计算中非常有用，可以帮助我们对数据进行处理和转换。

四、集合函数：1. COUNT：统计一个字段中非空值的数量。

2. SUM：计算一个字段中数值的总和。

3. AVG：计算一个字段中数值的平均值。

4. MAX/MIN：找到一个字段中数值的最大值或最小值。

这些集合函数常用于数据汇总和聚合分析，可以帮助我们计算数据的统计指标和概要信息。

除了上述提到的高级函数，Hive还提供了许多其他的内置函数，如条件函数（CASE WHEN）、窗口函数（OVER）、分组函数（GROUP BY）等。

a rXiv:n ucl-t h /441v28Ma y2The decay ρ0→π++π−+γand the coupling constant g ρσγA.Gokalp ∗and O.Yilmaz †Physics Department,Middle East Technical University,06531Ankara,Turkey(February 8,2008)Abstract The experimental branching ratio for the radiative decay ρ0→π++π−+γis used to estimate the coupling constant g ρσγfor a set of values of σ-meson parameters M σand Γσ.Our results are quite different than the values of this constant used in the literature.PACS numbers:12.20.Ds,13.40.HqTypeset using REVT E XThe radiative decay processρ0→π++π−+γhas been studied employing different approaches[1,5].There are two mechanisms that can contribute to this radiative decay: thefirst one is the internal bremsstrahlung where one of the charged pions from the decay ρ0→π++π−emits a photon,and the second one is the structural radiation which is caused by the internal transformation of theρ-meson quark structure.Since the bremsstrahlung is well described by quantum electrodynamics,different methods have been used to estimate the contribution of the structural radiation.Singer[1]calculated the amplitude for this decay by considering only the bremsstrahlung mechanism since the decayρ0→π++π−is the main decay mode ofρ0-meson.He also used the universality of the coupling of theρ-meson to pions and nucleons to determine the coupling constant gρππfrom the knowledge of the coupling constant gρter,Renard [3]studied this decay among other vector meson decays into2π+γfinal states in a gauge invariant way with current algebra,hard-pion and Ward-identities techniques.He,moreover, established the correspondence between these current algebra results and the structure of the amplitude calculated in the single particle approximation for the intermediate states.In corresponding Feynman diagrams the structural radiation proceeds through the intermediate states asρ0→S+γwhere the meson S subsequently decays into aπ+π−pair.He concluded that the leading term is the pion bremsstrahlung and that the largest contribution to the structural radiation amplitude results from the scalarσ-meson intermediate state.He used the rough estimate gρσγ≃1for the coupling constant gρσγwhich was obtained with the spin independence assumption in the quark model.The coupling constant gρππwas determined using the then available experimental decay rate ofρ-meson and also current algebra results as3.2≤gρππ≤4.9.On the other hand,the coupling constant gσππwas deduced from the assumed decay rateΓ≃100MeV for theσ-meson as gσππ=3.4with Mσ=400MeV. Furthermore,he observed that theσ-contribution modifies the shape of the photon spectrum for high momenta differently depending on the mass of theσ-meson.We like to note, however,that the nature of theσ-meson as a¯q q state in the naive quark model and therefore the estimation of the coupling constant gρσγin the quark model have been a subject ofcontroversy.Indeed,Jaffe[6,7]lately argued within the framework of lattice QCD calculation of pseudoscalar meson scattering amplitudes that the light scalar mesons are¯q2q2states rather than¯q q states.Recently,on the other hand,the coupling constant gρσγhas become an important input for the studies ofρ0-meson photoproduction on nucleons.The presently available data[8] on the photoproduction ofρ0-meson on proton targets near threshold can be described at low momentum transfers by a simple one-meson exchange model[9].Friman and Soyeur [9]showed that in this picture theρ0-meson photoproduction cross section on protons is given mainly byσ-exchange.They calculated theγσρ-vertex assuming Vector Dominance of the electromagnetic current,and their result when derived using an effective Lagrangian for theγσρ-vertex gives the value gρσγ≃2.71for this coupling ter,Titov et al.[10]in their study of the structure of theφ-meson photoproduction amplitude based on one-meson exchange and Pomeron-exchange mechanisms used the coupling constant gφσγwhich they calculated from the above value of gρσγinvoking unitary symmetry arguments as gφσγ≃0.047.They concluded that the data at low energies near threshold can accommodate either the second Pomeron or the scalar mesons exchange,and the differences between these competing mechanisms have profound effects on the cross sections and the polarization observables.It,therefore,appears of much interest to study the coupling constant gρσγthat plays an important role in scalar meson exchange mechanism from a different perspective other than Vector Meson Dominance as well.For this purpose we calculate the branching ratio for the radiative decayρ0→π++π−+γ,and using the experimental value0.0099±0.0016for this branching ratio[11],we estimate the coupling constant gρσγ.Our calculation is based on the Feynman diagrams shown in Fig.1.Thefirst two terms in thisfigure are not gauge invariant and they are supplemented by the direct term shown in Fig.1(c)to establish gauge invariance.Guided by Renard’s[3]current algebra results,we assume that the structural radiation amplitude is dominated byσ-meson intermediate state which is depicted in Fig. 1(d).We describe theρσγ-vertex by the effective LagrangianL int.ρσγ=e4πMρMρ)2 3/2.(3)The experimental value of the widthΓ=151MeV[11]then yields the value g2ρππ2gσππMσ π· πσ.(4) The decay width of theσ-meson that follows from this effective Lagrangian is given asΓσ≡Γ(σ→ππ)=g2σππ8 1−(2Mπ2iΓσ,whereΓσisgiven by Eq.(5).Since the experimental candidate forσ-meson f0(400-1200)has a width (600-1000)MeV[11],we obtain a set of values for the coupling constant gρσγby considering the ranges Mσ=400-1200MeV,Γσ=600-1000MeV for the parameters of theσ-meson.In terms of the invariant amplitude M(Eγ,E1),the differential decay probability for an unpolarizedρ0-meson at rest is given bydΓ(2π)31Γ= Eγ,max.Eγ,min.dEγ E1,max.E1,min.dE1dΓ[−2E2γMρ+3EγM2ρ−M3ρ2(2EγMρ−M2ρ)±Eγfunction ofβin Fig.5.This ratio is defined byΓβRβ=,Γtot.= Eγ,max.50dEγdΓdEγ≃constant.(10)ΓσM3σFurthermore,the values of the coupling constant gρσγresulting from our estimation are in general quite different than the values of this constant usually adopted for the one-meson exchange mechanism calculations existing in the literature.For example,Titov et al.[10] uses the value gρσγ=2.71which they obtain from Friman and Soyeur’s[9]analysis ofρ-meson photoproduction using Vector Meson Dominance.It is interesting to note that in their study of pion dynamics in Quantum Hadrodynamics II,which is a renormalizable model constructed using local gauge invariance based on SU(2)group,that has the sameLagrangian densities for the vertices we use,Serot and Walecka[14]come to the conclusion that in order to be consistent with the experimental result that s-waveπN-scattering length is anomalously small,in their tree-level calculation they have to choose gσππ=12.Since they use Mσ=520MeV this impliesΓσ≃1700MeV.If we use these values in our analysis,we then obtain gρσγ=11.91.Soyeur[12],on the other hand,uses quite arbitrarly the values Mσ=500 MeV,Γσ=250MeV,which in our calculation results in the coupling constant gρσγ=6.08.We like to note,however,that these values forσ-meson parameters are not consistent with the experimental data onσ-meson[11].Our analysis and estimation of the coupling constant gρσγusing the experimental value of the branching ratio of the radiative decayρ0→π++π−+γgive quite different values for this coupling constant than used in the literature.Furthermore,since we obtain this coupling constant as a function ofσ-meson parameters,it will be of interest to study the dependence of the observables of the reactions,such as for example the photoproduction of vector mesons on nucleonsγ+N→N+V where V is the neutral vector meson, analyzed using one-meson exchange mechanism on these parameters.AcknowledgmentsWe thank Prof.Dr.M.P.Rekalo for suggesting this problem to us and for his guidance during the course of our work.We also wish to thank Prof.Dr.T.M.Aliev for helpful discussions.REFERENCES[1]P.Singer,Phys.Rev.130(1963)2441;161(1967)1694.[2]V.N.Baier and V.A.Khoze,Sov.Phys.JETP21(1965)1145.[3]S.M.Renard,Nuovo Cim.62A(1969)475.[4]K.Huber and H.Neufeld,Phys.Lett.B357(1995)221.[5]E.Marko,S.Hirenzaki,E.Oset and H.Toki,Phys.Lett.B470(1999)20.[6]R.L.Jaffe,hep-ph/0001123.[7]M.Alford and R.L.Jaffe,hep-lat/0001023.[8]Aachen-Berlin-Bonn-Hamburg-Heidelberg-Munchen Collaboration,Phys.Rev.175(1968)1669.[9]B.Friman and M.Soyeur,Nucl.Phys.A600(1996)477.[10]A.I.Titov,T.-S.H.Lee,H.Toki and O.Streltrova,Phys.Rev.C60(1999)035205.[11]Review of Particle Physics,Eur.Phys.J.C3(1998)1.[12]M.Soyeur,nucl-th/0003047.[13]S.I.Dolinsky,et al,Phys.Rep.202(1991)99.[14]B.D.Serot and J.D.Walecka,in Advances in Nuclear Physics,edited by J.W.Negeleand E.Vogt,Vol.16(1986).TABLESTABLE I.The calculated coupling constant gρσγfor differentσ-meson parametersΓσ(MeV)gρσγ500 6.97-6.00±1.58 8008.45±1.77600 6.16-6.68±1.85 80010.49±2.07800 5.18-9.11±2.64 90015.29±2.84900 4.85-10.65±3.14 90017.78±3.23Figure Captions:Figure1:Diagrams for the decayρ0→π++π−+γFigure2:The photon spectra for the decay width ofρ0→π++π−+γ.The contributions of different terms are indicated.Figure3:The pion energy spectra for the decay width ofρ0→π++π−+γ.The contri-butions of different terms are indicated.Figure4:The decay width ofρ0→π++π−+γas a function of minimum detected photon energy.Figure5:The ratio Rβ=Γβ。

goat工具箱函数Goat工具箱函数Goat工具箱是一个功能强大的开发工具，提供了许多实用的函数，可以帮助开发人员快速高效地进行开发工作。

以下是Goat工具箱中一些重要的函数及其用法。

1. 字符串处理函数1.1. Trim函数：用于去除字符串两端的空格或指定字符。

示例代码：```gostr := " Hello, World! "trimmedStr := goat.Trim(str)fmt.Println(trimmedStr) // 输出: "Hello, World!"```1.2. Split函数：用于将字符串按照指定的分隔符分割成多个子字符串。

示例代码：```gostr := "apple,banana,orange"strList := goat.Split(str, ",")fmt.Println(strList) // 输出: ["apple", "banana", "orange"]```1.3. Replace函数：用于将字符串中的指定子字符串替换为新的子字符串。

示例代码：```gostr := "Hello, World!"newStr := goat.Replace(str, "World", "Goat")fmt.Println(newStr) // 输出: "Hello, Goat!"```2. 文件处理函数2.1. ReadFile函数：用于读取指定路径下的文件内容，并返回文件内容的字符串。

示例代码：```gocontent, err := goat.ReadFile("/path/to/file.txt")if err != nil {fmt.Println("读取文件失败:", err)return}fmt.Println(content)```2.2. WriteFile函数：用于将指定的字符串内容写入到指定路径下的文件中。

数据清洗与整理中的常用函数及应用案例分析数据清洗与整理是数据科学中非常重要的一个环节，它涉及到对原始数据进行处理，以便得到高质量的数据用于进一步分析和建模。

本文将介绍几个常用的数据清洗与整理函数，并通过一些实际案例来说明它们的应用。

一、缺失值处理函数在实际数据中，常常会遇到缺失值的情况，即部分数据缺失或未记录。

缺失值对于数据分析和建模是有潜在影响的，因此需要对其进行处理。

常见的缺失值处理函数有：1. 删除函数(dropna)：删除包含缺失值的行或列。

例如，假设我们有一份销售数据，其中包含了销售额和销售量两列，我们想要删除包含缺失值的行，可以使用dropna函数进行处理。

2. 填充函数(fillna)：对缺失值进行填充。

例如，假设我们有一份学生考试成绩数据，其中某个学生的某门课程成绩缺失，我们可以使用fillna函数将缺失值填充为该门课程的平均值。

二、重复值处理函数重复值是指在数据中出现了相同的记录。

重复值对于数据分析和建模是没有意义的，因此需要对其进行处理。

常见的重复值处理函数有：1. 查找函数(duplicated)：查找数据中是否存在重复值。

例如，假设我们有一份客户订单数据，其中包含了客户ID和订单号两列，我们可以使用duplicated函数查找是否存在相同的订单号。

2. 删除函数(drop_duplicates)：删除数据中的重复值。

例如，假设我们有一份产品销售数据，其中包含了产品ID和销售日期两列，我们可以使用drop_duplicates函数删除重复的销售记录。

三、字符串处理函数在数据清洗与整理中，经常会涉及到对字符串进行处理的情况。

常见的字符串处理函数有：1. 替换函数(replace)：替换字符串中的某个子串。

例如，假设我们有一份用户评论数据，其中包含了用户的评论内容，我们可以使用replace函数将其中的敏感词汇替换为"*”。

2. 分割函数(split)：将字符串按指定的分隔符进行分割，返回一个分割后的列表。

Package‘SCI’October12,2022Type PackageTitle Standardized Climate Indices Such as SPI,SRI or SPEIVersion1.0-2Date2016-05-02Author Lukas Gudmundsson&James H.StaggeMaintainer Lukas Gudmundsson<**************************.ch>Dependsfitdistrplus,lmomcoSuggests evdDescription Functions for generating Standardized Climate Indices(SCI).SCI is a transformation of(smoothed)climate(or environmental)time series that removes seasonality and forces the data totake values of the standard normal distribution.SCI wasoriginally developed for precipitation.In this case it isknown as the Standardized Precipitation Index(SPI).License GPL(>=2)NeedsCompilation noRepository CRANDate/Publication2016-05-0311:03:16R topics documented:SCI-package (2)dist.start (3)fitSCI (4)genlog (9)pe3 (10)Index1212SCI-package SCI-package Standardized Climate Indices Such as SPI,SRI or SPEIDescriptionFunctions for generating Standardized Climate Indices(SCI).SCI is a transformation of(smoothed) climate(or environmental)time series that removes seasonality and forces the data to take values of the standard normal distribution.SCI was originally developed for precipitation.In this case it is known as the Standardized Precipitation Index(SPI).DetailsPackage:SCIType:PackageVersion: 1.0-2Date:2016-05-02License:GPL(>=2)Author(s)Lukas Gudmundsson&James StaggeMaintainer:Lukas Gudmundsson<**************************.ch>ReferencesStagee,J.H.;Tallaksen,L.M.;Gudmundsson,L.;van Loon,A.;Stahl,K.:Candidate Distributions for Climatological Drought Indices(SPI and SPEI),2015,International Journal of Climatology,35, 4027-4040,doi:10.1002/joc.4267.Stagee,J.H.;Tallaksen,L.M.;Gudmundsson,L.;van Loon,A.;Stahl,K.:Response to comment on "Candidate Distributions for Climatological Drought Indices(SPI and SPEI)",2016,International Journal of Climatology,36,2132-2138,doi:10.1002/joc.4564.Examples##create artificial data,resembling precipitationset.seed(101)n.years<-60date<-rep(1:n.years,each=12)+1950+rep((0:11)/12,times=n.years)PRECIP<-(0.25*sin(2*pi*date)+0.3)*rgamma(n.years*12,shape=3,scale=1) PRECIP[PRECIP<0.1]<-0##apply SCI transformationspi.para<-fitSCI(PRECIP,first.mon=1,time.scale=6,distr="gamma",p0=TRUE)dist.start3 spi<-transformSCI(PRECIP,first.mon=1,obj=spi.para)plot(date,spi,t="l")dist.start Rough estimates for parameters of selected distributionsDescriptionProduces rough parameter estimates for specific distributions(distr)that are useful as starting values for maximum likelihood estimation.Usagedist.start(x,distr,...)lmom.start(x,distr=c("gamma","genlog","gev","gumbel","lnorm","norm","pe3","weibull"),...)mom.start(x,distr=c("gamma","gumbel","logis","lnorm","norm","weibull"),...)Argumentsx numeric vectordistr A character string"name"naming a distribution for which the corresponding density function(dname),the corresponding distribution function(pname)andthe quantile function(qname)must be defined(see for example GammaDist) ...arguments passed to other functions,currently not used.Detailslmom.start uses L-moments for parameter estimation.In most cases it relies on functionality of the lmomco package.Currently available distributions are:"gamma","genlog","gev","gumbel", "logis","lnorm","norm","pe3","weibull".mom.start uses moments(e.g.mean,standard deviation)for parameter estimation.Some estimates are precise,others only approximations that provide reasonable starting values.Currently available distributions are:"gamma","gumbel","logis","lnorm","norm","weibull".dist.start callsfirst lmom.start to estimate parameters.In case of failure mom.start is called, hopefully producing reasonable parameter estimates.Valuenamed list,names correspond to distribution parameters.In case of failure,the same list with NA values is returned.Author(s)Lukas Gudmundsson&James StaggeExampleslmom.start(rgamma(100,shape=0.5,rate=1),"gamma")mom.start(rgamma(100,shape=0.5,rate=1),"gamma")dist.start(rgamma(100,shape=0.5,rate=1),"gamma")fitSCI Standardized Climate Index(SCI)DescriptionfitSCI identifies parameters for the Standardized Climate Index(SCI)transformation.transformSCI applies the transformationUsagefitSCI(x,...)##Default S3method:fitSCI(x,first.mon,time.scale,distr,p0,p0.center.mass=FALSE,scaling=c("no","max","sd"),mledist.par=list(),start.fun=dist.start,start.fun.fix=FALSE,warn=TRUE,...) transformSCI(x,...)##Default S3method:transformSCI(x,first.mon,obj,sci.limit=Inf,warn=TRUE,...)Argumentsx numeric vector,representing a monthly univariate time series.first.mon value in1:12indicating the month of thefirst element of xtime.scale The time scale(integer)of the SCI calculation.The time scale is the window length of an backward looking running mean.distr A character string"name"naming a distribution for which the corresponding density function(dname),the corresponding distribution function(pname)andthe quantile function(qname)must be defined(see for example GammaDist) p0if TRUE,model Probability of zero(precipitation)months is modeled with a mixed distribution as D(x)=p0+(1−p0)G(x),where G(x)>0is thereference distribution(e.g.Gamma)p0is the probability of a zero(precipitation)month.p0.center.mass If TRUE,the Probability of zero(precipitation)is estimated using a"center of mass"estimate based on the Weibull plotting position function(see details).Only applies if p0=TRUE.scaling Indicates whether to do some scaling of x prior to parameter identification."no"(the default)indicates no scaling."max"indicates scaling by the maximum of x,such that x<-x/max(x,na.rm=TRUE)."sd"stands for scaling by the standarddeviation.Scaling can stabilize parameter estimation.mledist.par named list that can be used to pass parameters to mledist in packagefitdistr-plus.start.fun Function with arguments x and distr estimating initial parameters of the func-tion distr for each month.The function should return a named list correspond-ing to the parameters of distr.(See also dist.start)start.fun.fix logical argument,indicating if parameter estimates of start.fun should be used if maximum likelihood estimation breaks down.This stabilizes the imple-mentation but can introduce biases in the resulting SCI.obj an object of class fitSCI,output from fitSCI.sci.limit Truncate absolute values of SCI that are lage than sci.limit.See details.warn Issue warnings if problems in parameter estimation occur....further arguments passed to methodsDetailsfitSCI estimates the parameters for transforming a meteorological and environmental time series toa Standardized Climate Index(SCI).transformSCI applies the standardisation.Typical SCI are theStandardized Precipitation Index(SPI),the Standardized Runoff Index(SRI)or the Standardized Precipitation Evapotranspiration Index(SPEI).To reduce biases in the presence of many zero(precipitation)events,the probability of these events (p0)can be estimated using a"center of mass"estimate based on the Weibull plotting position function(p0.center.mass=TRUE).Following Stagge et al.(2014)the probability of zero events isthen estimated as p0=n pn+1,where n p refers to the number of zero events and n is the sample size.The resulting mixed distribution used fro SCI transformation is thenD(x)=p0+(1−p0)G(x)if x>0n p+12(n+1)if x=0where G(x)>0is a model(e.g.gamma)distribution.Uncertainty in distribution parameters can cause unrealistically large(small)SCI values if values in x exceed the values used for parameter estimation(see fitSCI).Therefore transformSCI allows for a truncation of the SCI series such that abs(sci)<=sci.limit.The truncation can be disabled by setting sci.limit=Inf.ValuefitSCI returns an object of class"fitSCI"with the following components:dist.para A column matrix containing the parameters of distribution distr for each month.Row names correspond to the distribution parameters.If p0=TUE anadditional row named P0is introduced,indicating the probability of zero(pre-cipitation)events.dist.para.flag an vector indicating possible issues occurring throughout parameter estimation.Possible values are:0.no problems occurred;1.starting values could notbe estimated;2.mledist crashed with unknown error;3.mledist did notconverge;4.all values in this month are NA;5.all values in this month areconstant,distribution not defined.time.scale The time scale(integer)of the SCI calculation.distr A character string"name"naming a distribution usedp0logical indicating whether probability of zero(precipitation)events is esti-mated separately.p0.center.mass logical indicating whether probability of zero(precipitation)events is esti-mated using the"centre of mass"estimator(see Stagge et al.(2014)for details).scaling numeric value that has been used to scale x(see argument scaling).A value of1results from scaling="no",other values are the maximum value or thestandard deviation of x,depending on the choice of the parameter scaling.call the function calltransformSCI returns a numeric vector containing the SCI,having values of the standard normal distribution.NoteThis function is intended to be used together with transformSCI.Author(s)Lukas Gudmundsson&James StaggeReferencesStagee,J.H.;Tallaksen,L.M.;Gudmundsson,L.;van Loon,A.;Stahl,K.:Candidate Distributions for Climatological Drought Indices(SPI and SPEI),2015,International Journal of Climatology,35, 4027-4040,doi:10.1002/joc.4267.Stagee,J.H.;Tallaksen,L.M.;Gudmundsson,L.;van Loon,A.;Stahl,K.:Response to comment on "Candidate Distributions for Climatological Drought Indices(SPI and SPEI)",2016,International Journal of Climatology,36,2132-2138,doi:10.1002/joc.4564.McKee,T.;Doesken,N.&Kleist,J.:The relationship of drought frequency and duration to time scales Preprints,8th Conference on Applied Climatology,1993,179-184.Shukla,S.&Wood,A.W.:Use of a standardized runoff index for characterizing hydrologic drought Geophysical Research Letters,2008,35,L02405.Vicente-Serrano,S.M.;Begueria,S.&Lopez-Moreno,J.I.:A Multiscalar Drought Index Sensitive to Global Warming:The Standardized Precipitation Evapotranspiration Index J.Climate,Journal of Climate,American Meteorological Society,2009,23,1696-1718.See Alsodist.startExamples####generate artificial data##set.seed(101)n.years<-60date<-rep(1:n.years,each=12)+1950+rep((0:11)/12,times=n.years)##PrecipitationPRECIP<-(0.25*sin(2*pi*date)+0.3)*rgamma(n.years*12,shape=3,scale=1) PRECIP[PRECIP<0.1]<-0##Potential EvapotranspirationPET<-0.5*sin(2*pi*date)+1.2+rnorm(n.years*12,0,0.2)##display test datamatplot(date,cbind(PRECIP,PET),t=c("h","l"),col=c("blue","red"),lty=1)legend("topright",legend=c("PRECIPitation","temperature"),fill=c("blue","red"))####example SPI##spi.para<-fitSCI(PRECIP,first.mon=1,distr="gamma",time.scale=6,p0=TRUE)spi.paraspi<-transformSCI(PRECIP,first.mon=1,obj=spi.para)plot(date,spi,t="l")####effect of time.scale on SPI##spi.1.para<-fitSCI(PRECIP,first.mon=1,time.scale=1,distr="gamma",p0=TRUE)spi.12.para<-fitSCI(PRECIP,first.mon=1,time.scale=12,distr="gamma",p0=TRUE)spi.1<-transformSCI(PRECIP,first.mon=1,obj=spi.1.para)spi.12<-transformSCI(PRECIP,first.mon=1,obj=spi.12.para)matplot(date,cbind(spi.1,spi.12),t="l",lty=1,col=c("red","blue"),lwd=c(1,2))legend("topright",legend=c("time.scale=1","time.scale=12"),fill=c("red","blue"))####example SPEI##if(require(evd)){spei.para<-fitSCI(PRECIP-PET,first.mon=1,time.scale=6,distr="gev",p0=FALSE)spei<-transformSCI(PRECIP-PET,first.mon=1,obj=spei.para)plot(date,spei,t="l")}####effect of changing different distribution for SPEI computation##spei.genlog.para<-fitSCI(PRECIP-PET,first.mon=1,time.scale=6,distr="genlog",p0=FALSE) spei.genlog<-transformSCI(PRECIP-PET,first.mon=1,obj=spei.genlog.para)if(require(evd)){lines(date,spei.genlog,col="red")}else{plot(date,spei.genlog,t="l")} ##in this case:only limited effect.##generally:optimal choice of distribution:user responsibility.####use a30year reference period for SPI parameter estimation##sel.date<-date>=1970&date<2000spi.ref.para<-fitSCI(PRECIP[sel.date],first.mon=1,distr="gamma",time.scale=6,p0=TRUE) ##apply the the parameters of the reference period to all data##also outside the reference periodspi.ref<-transformSCI(PRECIP,first.mon=1,obj=spi.ref.para)plot(date,spi.ref,t="l",col="blue",ylim=c(-5,5),lwd=2)lines(date[sel.date],spi.ref[sel.date],col="red",lwd=3)legend("bottom",legend=c("reference period","extrapolation period"),fill=c("red","blue"), horiz=TRUE)####use"start.fun.fix"in instances where maximum likelyhood estimation fails####force failure of maximum likelyhood estimation by adding"strange"value##a warning should be issuedxx<-PRECIP-PET;xx[300]<-1000spei.para<-fitSCI(xx,first.mon=2,time.scale=1,p0=FALSE,distr="gev")spei.para$dist.para##use start.fun,usually ment for estimating inital values for##parameter optimisation if maximum likelihood estimation failsspei.para<-fitSCI(xx,first.mon=2,time.scale=1,p0=FALSE,distr="gev",start.fun.fix=TRUE)spei.para$dist.para####usage of sci.limit to truncate unrealistic SCI values##PRECIP.mod<-PRECIPPRECIP.mod[300]<-100##introduce spuriously large valuespi.mod.para<-fitSCI(PRECIP.mod,first.mon=1,time.scale=3,p0=TRUE,distr="gamma")plot(transformSCI(PRECIP.mod,first.mon=1,obj=spi.mod.para,sci.limit=Inf), t="l",col="blue",lwd=2)lines(transformSCI(PRECIP.mod,first.mon=1,obj=spi.mod.para,sci.limit=4),col="red")####how to modify settings of function"mledist"used for parameter identification####identify parameters with standard settingsspi.para<-fitSCI(PRECIP,first.mon=1,distr="gamma",time.scale=6,p0=TRUE)##add lower and upper limits for parameter identificationlower.lim<-apply(spi.para$dist.para,1,min)-0.5*apply(spi.para$dist.para,1,sd) upper.lim<-apply(spi.para$dist.para,1,max)+0.5*apply(spi.para$dist.para,1,sd)spi.para.limit<-fitSCI(PRECIP,first.mon=1,distr="gamma",time.scale=6,p0=TRUE,mledist.par=list(lower=lower.lim,upper=upper.lim))####how to write an own start.fun##(required if distributions not mentioned in"dist.start"are used)####function with same arguments as"dist.start"genlog9 my.start<-function(x,distr="gamma"){###code based on"mmedist"in package"fitdistrplus"ppar<-try({n<-length(x)m<-mean(x)v<-(n-1)/n*var(x)shape<-m^2/vrate<-m/vlist(shape=shape,rate=rate)},TRUE)if(class(ppar)=="try-error")##function has to be able to return NA parameters ppar<-list(shape=NA,rate=NA)return(ppar)}my.start(PRECIP)spi.para<-fitSCI(PRECIP,first.mon=1,time.scale=6,p0=TRUE,distr="gamma",start.fun=my.start)genlog Generalized Logistic DistributionDescriptionDensity,distribution and quantile function of the generalized logistic distributionUsagepgenlog(q,shape,scale,location)dgenlog(x,shape,scale,location)qgenlog(p,shape,scale,location)Argumentsx,q vector of quantiles.p vector of probabilities.shape shape parameterscale scale parameterlocation location parameterDetailsThe functions of the genlog family are a reimplementation of the Generalized Logistic Distribution in the lmomco package,making the code compatible with the standard nomenclature for distri-bution in R.The original functions in lmomco are pdfglo(density function),quaglo(quantile function)and cdfglo(distribution function).Valuedgenlog gives the density(pdf),pgenlog gives the distribution function(cdf),and qgenlog gives the quantile function(inverse cdf).10pe3 Author(s)James Stagge&Lukas GudmundssonReferencesAsquith,W.H.,2013:lmomco–L-moments,trimmed L-moments,L-comoments,censored L-moments,and many distributions.R package version1.7.8,Tech University,Lubbock,Texas. Examplesdgenlog(1,shape=1,scale=2,location=3)pe3Pearson Type III distributionDescriptionDensity,distribution and quantile function of the Pearson Type III distributionUsagedpe3(x,shape,scale,location)ppe3(q,shape,scale,location)qpe3(p,shape,scale,location)Argumentsx,q vector of quantiles.p vector of probabilities.shape shape parameterscale scale parameterlocation location parameterDetailsThe functions of the pe3family are a reimplementation of the Pearson Type III Distribution in the lmomco package,making the code compatible with the standard nomenclature for distributions in R.The original functions in lmomco are pdfpe3(density function),quape3(quantile function)and cdfpe3(distribution function).Valuedpe3gives the density(pdf),ppe3gives the distribution function(cdf),and qpe3gives the quantile function(inverse cdf).pe311Author(s)James Stagge&Lukas GudmundssonReferencesAsquith,W.H.,2013:lmomco–L-moments,trimmed L-moments,L-comoments,censored L-moments,and many distributions.R package version1.7.8,Tech University,Lubbock,Texas. Examplesdpe3(1,shape=1,scale=2,location=3)Index∗distributiongenlog,9pe3,10∗packageSCI-package,2∗tsfitSCI,4cdfglo,9cdfpe3,10dgenlog(genlog),9dist.start,3,5,6dpe3(pe3),10 fitdistrplus,5fitSCI,4,5gamma,3GammaDist,3,4genlog,3,9gev,3gumbel,3lmom.start(dist.start),3 lnorm,3logis,3mledist,5,6mom.start(dist.start),3 norm,3pdfglo,9pdfpe3,10pe3,3,10pgenlog(genlog),9ppe3(pe3),10qgenlog(genlog),9qpe3(pe3),10quaglo,9quape3,10SCI(SCI-package),2SCI-package,2transformSCI,6transformSCI(fitSCI),4weibull,312。

去除缺失值的函数名称去除缺失值是数据清洗过程中不可或缺的一步，在实际操作中，选择合适的函数名称能够帮助我们轻松地处理数据。

本文将为大家介绍几个常用的去除缺失值的函数名称及其用法。

一、dropna()dropna() 是 Pandas 包中最常用的去除缺失值函数。

该函数可以根据缺失值的位置删除该行或该列的值。

语法：DataFrame.dropna(subset=None, axis=0, inplace=False, how='any')其中：- subset：指定处理的列，默认为 None ，即处理所有列； - axis：指定轴方向，0 表示删除行，1 表示删除列； - inplace：默认为 False，表示返回新的数据，若设为 True，表示改变原有的 DataFrame； - how：指定删除方式，‘any’：只要存在一个缺失值就删除，‘all’：全部为缺失值才删除。

示例：```python # 导入模块 import pandas as pd import numpy as np# 创建含有缺失值的 DataFrame 对象 data ={'name':['Tom','Jerry','Mike','Mary','Linda','Sam'], 'age':[30,np.nan,21,29,20,np.nan], 'gender':['M','M','M','F','F','M'],'income':[2000,np.nan,5000,3500,np.nan,4500]} df = pd.DataFrame(data) print(df)# 删除 DataFrame 中含有缺失值的行df.dropna(inplace=True) print(df)# 删除 DataFrame 中含有缺失值的列df.dropna(axis=1,inplace=True) print(df) ```二、fillna()fillna() 函数可以用于填充缺失值，也可以用于对特定列进行填充。

GAMIT群友交流（持续更新）(2011-03-15 21:37:15)2011-3-15最新版的GAMIT才能处理2011年数据（10.4）而且还要装最新的更新包1、armake.f文件中判断时段信息时默认是1980-2010年，所以将2010年改成你希望的年份；2、重新编译，搞定！在 fixdrv文件夹下，打开armake.f文件，将2010改为2100.然后再gamit文件夹下运行./install_software.即重新编译安装。

2011-3-16用超快速星历时，需要设置什么吗？sh_gamit -orbit IGSU2011-04-11-----------------------------------------------------------------------------Q:FATAL :110410:1654: 5.0 MODELb/read_antex_head: ANTEX version > 1.3FATAL :110410:1654: 5.0 MODEL/model: GAMIT.fatal exists: MODE L not executed……FATAL :110410:1654: 5.0 AUTCLN/autcln: GAMIT.fatal exists: AU TCLN not executedA:工程里面存在的错误，将其删掉把调用的antex里comment删掉几行Q:嗯那个走过去又有fatal No valid cfiles read 继续求解释A:用没有生成文件的，纯准备文件重算试试---lee_ws2011-04-27这个错误主要是因为用的antmod。

dat文件版本是1.4的你用之前1.3的就没有问题了thank lee ws for ur answer-------------------------------------------------------------------------------2011-04-25Q:为什么gamit不能处理2011年的数据啊？A:修改fixdrv/amake.f中2010为2099即可----------------------------------------------2011-04-27Q：GAMIT计算出来基线后，各位怎么解算台站坐标的A：不是用QOCA嘛，这个软件要申请，网平差。

teradata 字符函数Teradata数据库提供了许多内置的字符函数，用于处理和操作字符串数据。

这些字符函数可以用于从字符串中提取子串、转换字符串的大小写、删除空格、连接字符串等操作。

下面我将介绍一些常用的Teradata字符函数：1. SUBSTRING函数，SUBSTRING函数用于从字符串中提取子串。

语法为SUBSTRING(string FROM start FOR length)，其中string是要提取子串的字符串，start是起始位置，length是要提取的长度。

2. TRIM函数，TRIM函数用于删除字符串中的前导或尾随空格。

语法为TRIM(leading/trailing/both trim_character FROM string)，可以根据需要删除字符串开头、结尾或两端的指定字符。

3. UPPER和LOWER函数，UPPER函数用于将字符串转换为大写，LOWER函数用于将字符串转换为小写。

4. POSITION函数，POSITION函数用于查找子串在字符串中的位置。

语法为POSITION(substring IN string)，返回子串在字符串中的起始位置。

5. CONCAT函数，CONCAT函数用于连接两个或多个字符串。

语法为CONCAT(string1, string2, ...)，可以连接任意个字符串。

除了上述函数外，Teradata还提供了许多其他字符函数，如REPLACE用于替换字符串中的子串，OCTET_LENGTH用于返回字符串的字节数，CHAR_LENGTH用于返回字符串的字符数等等。

总的来说，Teradata的字符函数提供了丰富的功能，可以满足对字符串数据进行各种操作的需求。

通过灵活运用这些字符函数，可以高效地处理和操作字符串数据。