Twinned_data_refinement

Package‘BiDimRegression’October12,2022Version2.0.1Date2022-02-17Title Calculates the Bidimensional Regression Between Two2DConfigurationsImports Formula,methodsDepends R(>=4.1.0)Description Calculates the bidimensional regression between two2D configurations following the ap-proach by Tobler(1965).License GPL-3URL https:///package=BiDimRegression/,https:///alexander-pastukhov/bidim-regression/,https://alexander-pastukhov.github.io/bidim-regression/BugReports https:///alexander-pastukhov/bidim-regression/issues/ Encoding UTF-8NeedsCompilation noRepository CRANRoxygenNote7.1.2LazyData trueSuggests testthat,knitr,rmarkdown,dplyr,ggplot2VignetteBuilder knitrAuthor Claus-Christian Carbon[aut](<https:///0000-0002-3446-9347>), Alexander Pastukhov[aut,cre](<https:///0000-0002-8738-8591>)Maintainer Alexander Pastukhov<*****************************>Date/Publication2022-02-1713:02:10UTC12anova.lm2 R topics documented:anova.lm2 (2)BiDimRegression (3)CarbonExample1Data (4)CarbonExample2Data (5)CarbonExample3Data (6)EyegazeData (6)FriedmanKohlerData1 (7)FriedmanKohlerData2 (8)lm2 (8)NakayaData (10)predict.lm2 (11)Index12 anova.lm2Anova for lm2objectsDescriptionAnova for lm2objects,returns a table with pairwise comparisons between models or,if only one model was supplied,with the null model.Usage##S3method for class lm2anova(object,...)Argumentsobject an object of class"lm2"...further objects of class"lm2"Valuean anova data frameSee Alsolm2Exampleslm2euc<-lm2(depV1+depV2~indepV1+indepV2,NakayaData,transformation= Euclidean ) lm2aff<-lm2(depV1+depV2~indepV1+indepV2,NakayaData,transformation= Affine )anova(lm2euc,lm2aff)BiDimRegression3BiDimRegression Calculates the bidimensional regression between two2D configura-tionsDescriptionCalculates the bidimensional regression between two2D configurations using both Euclidean and Affine transformations following the approach by Tobler(1965).This function assumes strict data format and returns all coefficients and statistics in a single structure.Same functionality is now re-implemented in a R-friendly style,see lm2function.UsageBiDimRegression(coord)Argumentscoord table that must contain two columns for dependent variables(named depV1 and depV2)and two columns for independent variables(named indepV1andindepV2).Valuean S3class BiDimRegression containing all essential measures of the bidimensional regression •euclidean.r,affine.r-the regression coefficient,defined analogously to Pearson’s r.•euclidean.rsqr,affine.rsqr-the squared regression coefficient.•euclidean.diABSqr,affine.diABSqr-the squared distortion index for dependent vari-ables;following Waterman and Gordon’s(1984)extension of the bidimensional regression, it provides a measure of comparison of distortions,but the range of values is0to1following Friedman and Kohler(2003).•euclidean.dMaxABSqr,affine.dMaxABSqr-the maximal squared distortion index for de-pendent variables.•euclidean.diXYSqr,affine.diXYSqr-the distortion index for independent variables.•euclidean.dMaxXYSqr,affine.dMaxXYSqr-the maximal squared distortion index for in-dependent variables.•euclidean.scaleFactorX,affine.scaleFactorX-the scaling factor of thefirst dimension(1.0means no scaling;values below1.0indicate a contraction,values above1.0indicate anexpansion).•euclidean.scaleFactorY,affine.scaleFactorY-the scaling factor of the second dimen-sion.•euclidean.angleDEG,affine.angleDEG-the rotation angle in degrees.•euclidean.shear,affine.shear-shearing of the transformed configuration,always zero for the Euclidean transformation.•euclidean.ttestDF,affine.ttestDF-degrees of freedom(DF)for the t-tests regardingthe model parameters(alphas and betas).•euclidean.alpha1.*,euclidean.alpha2.*,affine.alpha1.*,affine.alpha2.*-in-tercept vectors,information includes.coeff for coefficient,.SE for standard error,tValuefor t-statistics,and pValue for significance.•euclidean.beta1.*,euclidean.beta2.*,affine.beta1.*,affine.beta2.*,affine.beta3.*, affine.beta4.*-slope vectors,information includes.coeff for coefficient,.SE for stan-dard error,tValue for t-statistics,and pValue for significance.•euclidean.fValue,affine.fValue-F-statistics,following the advice of Nakaya(1997).•euclidean.df1,affine.df1-degrees of freedom of the nominator used for the F-statisticspropagated by Nakaya(1997);df1=p-2,with p is the number of elements needed to calculatethe referring model:p=4for the Euclidean and p=6for the affine geometry Nakaya,1997,Table1.•euclidean.df2,affine.df2-degrees of freedom of the denominator used for the F-statisticspropagated by Nakaya(1997);df2=2n-p,with p is the number of elements needed to calcu-late the referring model(see df1)and n is the number of coordinate pairs.•euclidean.pValue,affine.pValue-the significance level based on the preceding F-statistics.•euclidean.dAICso,affine.dAICso-the AIC difference between the regarding bidimen-sional regression model and the bidimensional null model(S0)according to Nakaya(1997),formula56.•eucVSaff.*-statistical comparison between Euclidean and Affine models,include.fValuefor F-statistics,.df1and.df2for the degrees of freedom,.pValue for the significance level,and.dAIC for AIC difference between two models.See Alsolm2ExamplesresultingMeasures<-BiDimRegression(NakayaData)print(resultingMeasures)CarbonExample1Data Data from Carbon,C.C.(2013).BiDimRegression:BidimensionalRegression Modeling Using R.\Journal of Statistical Software,CodeSnippets,52(1),1-11(URL /v52/c01/)\DescriptionExample1from the domain of aesthetics to show how the method can be utilized for assessing thesimilarity of two portrayed persons,actually the Mona Lisa in the world famous Louvre version andthe only recently re-discovered Prado versionUsagedata(CarbonExample1Data)FormatA data frame with36observations on the following4variables.depV1a numeric vectordepV2a numeric vectorindepV1a numeric vectorindepV2a numeric vectorExamplesdata(CarbonExample1Data)##maybe str(CarbonExample1Data);plot(CarbonExample1Data)...CarbonExample2Data Data from Carbon,C.C.(2013).BiDimRegression:BidimensionalRegression Modeling Using R.\Journal of Statistical Software,CodeSnippets,52(1),1-11(URL /v52/c01/)\DescriptionExample2originates from the area of geography and inspects the accuracy of different maps of the city of Paris which were created over the last350years as compared to a recent mapUsagedata(CarbonExample2Data)FormatA data frame with13observations on the following4variables.depV1a numeric vectordepV2a numeric vectorindepV1a numeric vectorindepV2a numeric vectorExamplesdata(CarbonExample2Data)##maybe str(CarbonExample2Data);plot(CarbonExample2Data)...6EyegazeDataCarbonExample3Data Data from Carbon,C.C.(2013).BiDimRegression:BidimensionalRegression Modeling Using R.\Journal of Statistical Software,CodeSnippets,52(1),1-11(URL /v52/c01/)\DescriptionExample3focuses on demonstrating how good a cognitive map recalculated from averaged cogni-tive distance datafits with a related real mapUsagedata(CarbonExample3Data)FormatA data frame with10observations on the following4variables.depV1a numeric vectordepV2a numeric vectorindepV1a numeric vectorindepV2a numeric vectorExamplesdata(CarbonExample3Data)##maybe str(CarbonExample3Data);plot(CarbonExample3Data)...EyegazeData Eye gaze calibration dataDescriptionA dataset containing a monocular eye gaze recording with calibration sequence.Courtesy of Bam-berger Baby Institut(BamBI).UsageEyegazeDataFriedmanKohlerData17 FormatA data frame with365rows and6variables:time sample timestamp,in millisecondsx,y recorded gaze,in internal eye tracker unitstarget_x,target_y location of the calibration target on the screen,in pixelstarget index of the target within the sequence...FriedmanKohlerData1Data from Friedman,A.,&Kohler,B.(2003).Bidimensional regres-sion:Assessing the configural similarity and accuracy of cognitivemaps and other two-dimensional data sets.Psychological Methods,8(4),468-491.DescriptionData from Friedman,A.,&Kohler,B.(2003).Bidimensional regression:Assessing the configu-ral similarity and accuracy of cognitive maps and other two-dimensional data sets.Psychological Methods,8(4),468-491.Usagedata(FriedmanKohlerData1)FormatA data frame with4observations on the following4variables.depV1a numeric vectordepV2a numeric vectorindepV1a numeric vectorindepV2a numeric vectorExamplesdata(FriedmanKohlerData1)##maybe str(FriedmanKohlerData1);plot(FriedmanKohlerData1)...FriedmanKohlerData2Data from Friedman,A.,&Kohler,B.(2003).Bidimensional regres-sion:Assessing the configural similarity and accuracy of cognitivemaps and other two-dimensional data sets.Psychological Methods,8(4),468-491.DescriptionData from Friedman,A.,&Kohler,B.(2003).Bidimensional regression:Assessing the configu-ral similarity and accuracy of cognitive maps and other two-dimensional data sets.Psychological Methods,8(4),468-491.Usagedata(FriedmanKohlerData2)FormatA data frame with4observations on the following4variables.depV1a numeric vectordepV2a numeric vectorindepV1a numeric vectorindepV2a numeric vectorExamplesdata(FriedmanKohlerData2)##maybe str(FriedmanKohlerData2);plot(FriedmanKohlerData2)...lm2Fitting Bidimensional Regression ModelsDescriptionlm2is used tofit bidimensional linear regression models using Euclidean and Affine transformations following the approach by Tobler(1965).Usagelm2(formula,data,transformation)Argumentsformula a symbolic description of the model to befitted in the format A+B~C+D,whereA andB are dependent andC andD are independent variablesdata a data frame containing variables for the model.transformation the transformation to be used,either euclidean , affine ,or projective . Valuelm2returns an object of class"lm2".An object of class"lm"is a list containing at least the following components:transformation string with the transformation type(euclidean,affine,or projective)npredictors number of predictors used in the model:4for euclidean,6for affine,8for projective.df_model,df_residualdegrees of freedom for the model and for the residualstransformation_matrix3x3transformation matrixcoeff transformation coefficients,with a denoting the intercept terms.transformed_coeffscale,angle,and sheer coefficients,depends on transformation.fitted_values data frame containingfitted values for the original data setresiduals data frame containing residuals for the originalfitr.squared,adj.r.squaredR-squared and adjusted R-squared.F,p.value F-statistics and the corresponding p-value,given the df_model and df_residual degrees of freedom.dAIC Akaike Information Criterion(AIC)difference between the regression model and the null model.A negative values indicates that the regression model isbetter.See Nakaya(1997).distortion_indexDistortion index following Waterman and Gordon(1984),as adjusted by Fried-man and Kohler(2003)lm an underlying linear model for Euclidean and affine transformations.formula formula,describing input and output columnsdata data used tofit the modelCall function call information,incorporates the formula,transformation,and data. See Alsoanova.lm2BiDimRegression10NakayaData Exampleslm2euc<-lm2(depV1+depV2~indepV1+indepV2,NakayaData, euclidean )lm2aff<-lm2(depV1+depV2~indepV1+indepV2,NakayaData, affine )lm2prj<-lm2(depV1+depV2~indepV1+indepV2,NakayaData, projective )anova(lm2euc,lm2aff,lm2prj)predict(lm2euc)summary(lm2euc)NakayaData Data from Nakaya,T.(1997).Statistical inferences in bidimensionalregression models.Geographical Analysis,29(2),169-186.DescriptionData from Nakaya,T.(1997).Statistical inferences in bidimensional regression models.Geograph-ical Analysis,29(2),169-186.Usagedata(NakayaData)FormatA data frame with19observations on the following4variables.depV1a numeric vectordepV2a numeric vectorindepV1a numeric vectorindepV2a numeric vectorExamplesdata(NakayaData)##maybe str(NakayaData);plot(NakayaData)...predict.lm211 predict.lm2Predict method for Bidimensional Regression Model FitsDescriptionPredicted values based on the bidimensional regressional model object.Usage##S3method for class lm2predict(object,newdata,...)Argumentsobject an object of class"lm2"newdata An optional two column data frame with independent variables.If omitted,the fitted values are used....optional argumentsValuea two column data frame with predicted values for dependent variables.See Alsolm2Exampleslm2euc<-lm2(depV1+depV2~indepV1+indepV2,NakayaData,transformation= Euclidean ) predict(lm2euc,NakayaData[,3:4])Index∗datasetsCarbonExample1Data,4CarbonExample2Data,5CarbonExample3Data,6EyegazeData,6FriedmanKohlerData1,7FriedmanKohlerData2,8NakayaData,10anova.lm2,2,9BiDimRegression,3,9CarbonExample1Data,4CarbonExample2Data,5CarbonExample3Data,6EyegazeData,6FriedmanKohlerData1,7 FriedmanKohlerData2,8linear model,9lm2,2–4,8,11NakayaData,10predict.lm2,1112。

计量经济学第二次实验报告(利用所给数据（bothtwins data Excel文件）研究教育的工资回报率问题)一、实验内容：1、实验目的：利用所给数据（bothtwins data Excel文件）研究教育的工资回报率问题。

2、实验要求：运用Eviews软件进行数据分析，利用已知数据建立回归模型，考虑诸如遗漏变量和测量误差的模型内生性问题。

由于数据都是不同家庭的双胞胎数据，分析时请利用这一数据特征二、实验报告：（1）、问题提出随着社会的发展，教育的工资回报率问题被提上了日程。

对于影响工资回报率的因素我们愈加关注。

为了这一问题，我们利用所给数据（bothtwins data Excel文件）研究教育的工资回报率问题。

同时考虑诸如遗漏变量和测量误差的模型内生性问题。

（2）、指标选择根据分析问题的需要，依据指标数据可得性原则，我们选择经济含义明确并具有较好完整性和可比较性的数据（bothtwins data Excel文件）作为数据指标。

Age：年龄；age2：年龄平方；Daded：父亲受教育年数；Momed：母亲受教育年数；Hrwage：工资时薪；lwage：时薪工资对数值；female：是否为女性； white：是否为白人；first:是否为家中长子；Educ：受教育年数（自报）；Educ_t：双胞胎中另一个受教育年数（自报）；Eductt：双胞胎中某个提供的另外一个的受教育年数（互报）；Educt_t：此双胞胎的sibling提供的此双胞胎受教育年数（互报）。

（3）、数据来源数据由老师提供。

由于数据量过大，截取部分数据展示如下表1，具体数据参见附表1表1 数据（bothtwins data Excel文件）（部分数据）（4）、数据处理数据可直接用于建模分析，无需经过任何处理（5）、数据分析运用Eviews画出时薪工资与受教育程度的散点图，观察两者的相关性。

如图1时薪工资与受教育程度的散点图从图1可以看出时薪工资与受教育程度有一定正相关关系。

受虐狂周永恒Roy乐于被Twins整佚名【期刊名称】《时代风采》【年(卷),期】2002(000)024【摘要】拍戏不专心? Roy与Twins拍摄的电影《这个夏天有异性》刚刚上映不久,反应不俗。

一般捧过场的Fans都认为,这部戏的题材很新鲜,而且剧情搞笑。

不过Roy却因这部电影而传出不利传闻,近有报纸称,Roy拍戏期间很不专心,未上镜前不但不努力背剧本,只是顾着玩打电子游戏机,总是经常“吃缧丝”,NG频频,令电影工作人员怨声载道。

【总页数】2页(P46-47)【正文语种】中文【中图分类】J692【相关文献】1.Deformation twins and twinning at ambient temperature in cryomilled Al-Zn-Mg-Cu alloy powders [J],2.我和Twins的距离只有0．1公分——TWINS和全世界撒娇 [J],3.周永恒Roy为音乐而唱歌 [J],4.Data Collection on and Refinement of Twinned, Non-merohedrally-twinned Methyl 2-Aminopyrazine-3-carboxylate:RLATT for Indexing, and TwinRotMat for De-twinning [J], Ng Seik Weng5.Data Collection on and Refinement of Twinned,Non-merohedrally-twinned Methyl 2-Aminopyrazine-3-carboxylate：RLATT for Indexing,and TwinRotMat for De-twinning [J], Ng Seik Weng因版权原因，仅展示原文概要，查看原文内容请购买。

APEX 3 / APEXII DUO Quick GuideTurning on the Low temperature (LT) device1.On the Cryostream Controller next to the APEX, press the START button (bluebutton/arrow in figure below).2.Once the controller is done starting up, and the “TEMP” display shows a temperature,press START again to begin cooling to 100 K. Wait ~30 minutes for the temperature to reach 100K and stabilize.3.If the crystal sample is no good and no one is going to be using the diffractometerafter you, please press the STOP button (red button/arrow in figure below) to turnoff the LT device.Login1.Click on Sample and select Login.2.Enter username (lowercase group name) and password (pjc).Create a New Sample1.Click or click Sample and select New.2.Enter sample name (e.g. 1234a).Note: Check white board or website to determine # of sample for your lab.3.Sample folder will be created in D:\frames\(username)\(sample name).Centering the CrystalNote: Take care when opening and closing the doors. DO NOT SLAM THE DOORS.When opening doors, an audible *click* should be heard when pressing the green“Open Doors” button. If the *click* is not heard, but the “Alarm” light is flashing,gently push in the handles until you hear the *click* then proceed to pull thehandles and open the doors. If the doors still won’t open, contact yourcrystallographer.ing APEXServer (monitor in enclosure), click Set Up on the left-hand menu andclick .2.On the bottom right, click Center.Note: If image is not moving, click in the VIDEO window.3.Screw the goniometer head on making sure the notches are aligned.e the goniometer key to adjust the crystal until centered in the crosshairs.5.On the right-hand side of the screen, click Spin Phi 90 to rotate the crystal andcontinue to adjust the position until centered. Repeat three more times and close the doors.Determine Unit Cell1.Click Evaluate on the left-hand menu.2.Click .3.Under “Manual Mode” on the right-hand side, click andclick Collect at the bottom right.Note: Spots should be well-defined circles and should not overlap. Samplesthat are twinned or multi-crystalline may have smeared or overlapping spots.If sample is stable, it may be worth finding another crystal.4.Click .5.Change the Min. I/sigma(l) value to 5 using the slider bar or manually enteringthe value.6.Click Harvest at the bottom right.Note: After the spots have been harvested, inspect the “Expected Resolution”table (see figure below). Crystals with an expected resolution greater than1.0Å for 20 s/° are generally too weak to give a publishable structure.Note: Click , and adjust the circle to check the best resolution spot at 10seconds, please use your best judgement for exposure time (better to err onthe side of caution and collect longer exposures).7.Click , then click Index at the bottom right.8.APEX3 will select the “best” unit cell. Click Accept at the bottom right.Note: If a method failed, two different unit cells were calculated or there area large number of overlapping/unselected spots, use CELL_NOW to calculatethe unit cell. (CELL_NOW instructions located at the end of the guide)9.Adjust the Tolerance value to 5.00 or move the slider all the way to the left.10.Click Refine until the Unit Cell parameters stop changing.11.Click Accept.12.Click .13.APEX3 will suggest possible Bravais lattices. Always select Triclinic P, unless yourcrystallographer suggests otherwise or you are performing a quick screening.Note: The correct lattice type may not always be the highest symmetrylattice. Therefore, collecting in Triclinic P ensures a sufficient amount of datafor any lattice option.14.Click .15.Adjust the Tolerance value to 5.00 or move the slider all the way to the left.16.Click Refine until the Unit Cell parameters stop changing.Note: Inspect the predicted overlay on the matrix frames. Make sure ALL thespots are picked/circled.17.Click Accept.18.Before setting up data c ollection…a.Inspect the “Expected resolution” table to determine the optimal exposuretime for good data for resolution out to 0.77Å.b.Check your unit cell versus known materials/starting materials using theX-ray facility website or the Cambridge Database.Note: If a full dataset is collected, you will be charged, regardless of whetherthe structure has been done before.c.Calculate the density of the crystal sample.e the formula: density = MW/V x 1.66 x Z, where MW is molecularweight and V is unit cell volume. Z can equal 1, 2, 4 or 8 depending onthe Bravais lattice. A reasonable density should be between 1.2 and2.0 g/cm3.Set Up Data Collection1.On the left-hand menu, click Collect, then .2.A djust “Resolution” in the top-right to 0.77Å for a heavy atom structure (Cl orheavier) or 0.83Å for a light atom structure (all atoms less than Cl).e the dropdown menu to a djust “Symmetry” to “Centrosymmetric (-1)” unlesscompound is known to be chiral. If compound is chiral, do not change originalselection of “Chiral (1).”4.Click and then click OK.Note: If crystal is possibly twinned or multi-crystalline, change “Crystal todetector distance” to 50-60mm.5.Click Select scan parameters… at the bottom.6.Adjust “Frame angle [degrees]” to 0.50.7.Adjust “Frame time [seconds]”(exposure time) according to “Expected resolution”table from Unit Cell Determination. (Max exposure of 30 seconds)8.Click OK. Take note of the “E xpected end time” listed at the bottom of the page.9.Pay attention to the “Average Multiplicity” value. If collecting as triclinic, but ahigher symmetry Bravais is possible, an “Average Multiplicity” of ~4 is acceptable. If crystal system is actually triclinic, click Determine strategy… and increase“Minimum multiplicity for 90% of the data” value until “Average Multiplicity” isgreater than 6 (usually 3.50 works).10.Click .11.Click in the bottom left corner.12.If the collection will end at night or over the weekend with no new sample to bemounted after, add “Thermostat Off” to the next available row.13.If exposure times are 5 seconds or less, change default “Exposures” from correlatedto uncorrelated using the dropdown menu.14.Click in the lower right corner.Note: If error occurs, resolve error. Typically, increasing detector distanceresolves issue. If error still occurs, contact your crystallographer.15.Click in lower right corner.Example experiment setup:Note: The above figure is an example, not all strategies will have the same number of scans.Integration of DataNote: Twinned and multi-crystalline samples require extra steps when processing data (integration, scaling, etc). Consult your crystallographer for further dataprocessing.1.On left-hand menu, click Reduce Data.2.Click .3.Change “Resolution Limit” value in top right corner to 0.77Å for heavy atomsstructures or 0.83Å for light atom structures.4.Click in the bottom right.5.Click More Options.6.C hange “Generate Mask with Fractional Lower Limit of Average Intensity” to 0.550and click OK.7.Click in the bottom right. Make sure the correct scan sets areselected and click Choose.8.ClickNote: Pay attention to the “Spot Shape Correlation” graph. The averagevalue should be around 0.6 or higher for usable data. Sudden drops in thegraph suggests something occurred during collection. Inspect the frames andconsult your crystallographer. In addition, pay attention to the “Spot ShapeProfile.” The spot should be well-defined and spherical.Scaling Data1.Click in left-hand menu.2.Pay attention to the “Input File,” which should be xxxx_0m.raw, and “Laue Group”and “Point Group,” which should match the Bravais lattice. (-1 for triclinic, 2/m for monoclinic, mmm for orthorhombic, etc.)3.Click in lower right corner.4.Click in the lower right corner.Note: Pay attention to the “Mean Weight” and “R(%).” Both graphs shouldplateau by the end of the refinement cycles. Mean Weight values >0.90 aregood and the lower the R(%), the better. If graph does not plateau/converge,increase “Number of Refinement Cycles” to >50. If it still does not converge,then the Bravais chosen is incorrect.5.Click Next in lower right corner.6.Click Finish in the bottom right corner.Generate .INS File.1.In left-hand menu, click Examine Data, then click2.Files for XPREP should be automatically selected, if not, browse for themo_xxxx_0m.p4p and mo_xxxx_0m.hkl files in the “work” folder.3.Click OK.4.In XPREP GUI window, press <Enter> on keyboard to accept default options.a.Make note of the R(sym) values for determining higher symmetry, lowervalues are better.b.Pay attention to the CFOM values as well as systematic absence values forspace group determination.c.When merging data sets, make note of the Completeness, Redundancy,Mean I/s values, and Rmerge/Rsigma values.Note: Completeness should be > 95% for publishable structure.5.When prompted, enter formula of compound. (Case and text sensitive)Note: XPREP will generate a Z value and calculated density based on formula.Reasonable density should be between 1.2 and 2.0.6.When prompted, enter output file name (no spaces). Default name is acceptable, ora custom name if testing multiple space groups.7.Continue pressing <Enter> until XPREP quits and window closes.Important!!: After the proper space group has been determined using XPREP,make sure to go back to “Determine Unit Cell” to change the Bravais to the correctsymmetry lattice, then re-integrate, re-scale and re-generate the .INS file.Structure Solution∙AUTOSTRUCTURE Method1.In the left-hand menu, click Find Structure.2.Click .3.Click and select appropriate .hkl file generated from XPREP.Note: Even though an .hkl file is loaded, you still need to reselect the latest.hkl file to update the unit cell parameters and lattice type determined fromthe .ins.4.Make sure the formula is correct (to the best of your knowledge) or the program willhave difficulty generating a solution.5.Click Start.∙Solve Structure Method1.Click .2.Click and select the appropriate .hkl/.ins files.3.Select the method for solving the structure and click Solve Structure.a.Intrinsic PhasingNote: Be sure that the unit cell and Bravais type match those from the XPREPand not the initial unit cell and lattice group from Determine Unit Cell.es XT to solve structure. Resulting .res has all atoms isotropicii.May generate more than one.res file, each solving the structure ina different, but related space group.iii.Select the appropriate .res file based on R values and Flack parameter (avoid 0.50 Flack value).iv.Be sure to select corresponding .res/.hkl files. The output files willhave “_a”, “_b”, and “_c” attached to file name.b.Direct Methodes XS, reads original space group determined in XPREP andrecorded in .ins file.ii.Output .res file will have some heavy atoms labeled, but all otheratoms will be Q-peaks.c.Patterson Methodi.Only recommended for difficult structures that did not work withprevious methods.es original space group determined in XPREP and recorded in .ins file.iii.Output .res file will only have heavy atoms assigned and Q-peakspresent.Note: If the molecule has disorder, AUTOSTRUCTURE will have troubleproducing a sensible solution. Be sure to try the other solution methods andlook for features in the Q-peaks such as phenyl rings, t-butyl groups, etc.stly, use the appropriate .res and .hkl files to refine the structure using theprogram of your choice. Have fun!Determine Unit Cell using CELL_NOW1.Click Evaluate on the left-hand menu.2.Click .3.Click .4.Adjust the Min. I/sigma(l) value to 1.75 by using the slider bar or manuallyentering the value.5.Click Harvest at the bottom right.6.Click Sample in the top-left corner of APEX3, then Export, then select P4P file…7.Under “Export For:”, select CELL_NOW.8.Leave the default .p4p name (samplename.p4p) and click OK.9.Click Sample in the top-left corner of APEX3, then click Run Command…10.Type “cell_now” and press <Enter>.11.Type in the name of the .p4p file, default is “samplename.p4p” and press <Enter>.12.Accept default .cn file name by pressing <Enter>.13.Begin the initial unit cell search by pressing <Enter>.14.Accept the default “superlattice threshold” by pressi ng <Enter>.15.Accept the default “minimum and maximum values for cell edge” by pressing<Enter>, unless cell lengths are less than 5 or greater than 45.a. A table of possible unit cells will be generated and ordered by calculatedFigure of Merit (FOM).16.Press <Enter> to accept default “Maximum deviation from integer index.”17.Press <Enter> to accept the best FOM unit cell. If a different cell is wanting to beselected, type the corresponding number from the list.18.Type “cell#a.p4p” as the first domain file name and press <Enter>.19.If a large number of “reflections not yet assigned to a domain” remains, press<Enter> to continue search for another domain and name as “cell#b.p4p.”Note: Make note of the FOM of the additional cells that are calculated (shouldbe greater than ~0.50).20.Type “Q” and press <Enter> to quit.21.Click Sample in the top-left corner of APEX3, then Import, then P4P/SPIN file.22.Click and select the last .p4p file made (e.g. cell1d if four domains werecalculated) and click Open.23.Select Import all and click OK.Note: The lattice type from CELL_NOW is not transferred when importing the.p4p file. If Bravais does not recognize the cell as the same type calculated inCELL_NOW (e.g P, C, etc.), then click Edit…next to the “Unit cells” box andmanually select the lattice type. You can skip the Bravais step if this is thecase.24.Click .25.APEX3 will suggest possible Bravais lattices. Always select Triclinic P, unless yourcrystallographer suggests otherwise or you are performing a quick screening.Note: The correct lattice type may not always be the highest symmetrylattice. Therefore, collecting in Triclinic P ensures sufficient data for any latticeoption.26.Click .27.Adjust the Tolerance value to 5.00 or move the slider all the way to the left.28.Click Refine until the Unit Cell parameters stop changing.Note: Inspect the predicted overlay on the matrix frames. Make sure ALL thespots are picked/circled.29.Click Accept.。

r语言fullset函数用法-回复R语言是一种广泛使用的统计分析和数据可视化的编程语言。

在R语言中，有许多可以帮助用户处理和分析数据的函数。

其中一个非常有用的函数是fullset函数。

本文将介绍fullset函数的用法，并一步一步回答有关这个函数的常见问题。

首先，让我们来了解一下fullset函数的定义。

在R语言中，fullset函数是一个用于创建一个包含所有可能值的完整平衡表的函数。

这个平衡表可以用于设计和分析实验，以及处理数据中缺失值的情况。

现在，让我们来看一下fullset函数的使用方法。

在R语言中，使用fullset 函数可以通过以下步骤进行：1. 安装和加载R语言中的`datarium`库。

Rinstall.packages("datarium")library(datarium)2. 使用fullset函数创建完整平衡表。

Rfull_table <- fullset(factor1, factor2, ...)在这里，`factor1`、`factor2`等是需要包含在平衡表中的因子变量。

你可以根据你的实际需要添加更多的因子变量。

3. 将完整平衡表应用于数据分析。

Ranalysis_result <- apply(full_table, 1, function(row) {# 进行数据分析的代码})在这里，`apply`函数用于逐行应用数据分析函数。

你可以根据自己的数据和分析方法进行适当的修改。

4. 使用完整平衡表处理数据中的缺失值。

Rcleaned_data <- merge(data, full_table, by = c("factor1", "factor2", ...), all = TRUE)在这里，`merge`函数用于将数据和完整平衡表进行合并。

这将填充数据中的缺失值，并使得每个可能的因子组合都有对应的数据。

双重差分法的stata命令-回复双重差分法（Double Difference method）是一种在处理含有面板数据（Panel Data）的实证研究中常用的统计方法。

该方法通过观察同一实体在时间上的变化和不同实体之间的差异，来解决内生性（Endogeneity）的问题。

在Stata软件中，有一些常用的命令可以实施双重差分法分析，包括`xtreg`、`areg`和`reghdfe`等。

本文将一步一步地回答关于Stata命令的使用问题，并介绍其背后的原理。

首先，我们需要了解何时应该使用双重差分法。

双重差分法通常用于面板数据的因果推断分析，特别适用于研究政策决策对实体间变量的影响。

例如，假设我们想研究一项政策改革对不同地区的经济增长率的影响。

我们需要收集每个地区多年的经济增长数据，并观察政策改革前后的变化。

双重差分法将帮助我们控制因其他潜在因素带来的内生性问题，以更准确地评估政策改革的影响。

接下来，我们将介绍如何使用Stata的命令实施双重差分法分析。

首先，我们需要加载面板数据，并确保数据已经按照实体和时间进行排序。

statause "面板数据文件名.dta", clear然后，我们可以使用`xtreg`命令来运行双重差分法。

`xtreg`命令是Stata 中用于拟合面板数据固定效应模型的命令。

stataxtreg 因变量自变量1 自变量2 自变量3, fe vce(cluster 实体标识)在上述命令中，`因变量`代表面板数据中的被解释变量，`自变量1`至`自变量3`代表我们感兴趣的解释变量。

`fe`选项表示拟合固定效应模型，`vce(cluster 实体标识)`选项用于计算异方差稳健的标准误。

另外一种可看作是`xtreg`的替代命令是`areg`。

这两个命令的使用方法非常相似，但是它们处理固定效应模型的原理略有不同。

`areg`命令假设实体固定效应与时间固定效应被完全包含在因变量和解释变量中。

Methods and Tutorials – Single Crystal DiffractionSingle crystal diffraction and magnetism▪Background material:Piccoli P. M. B., Koetzle T. F., Schultz A. J., “Single crystal neutron diffraction for the inorganic chemist - A practical guide”, Comments on Inorganic Chemistry,28, 3-38 (2007).Michels-Clark T. M., Savici A. T., Lynch V. E., Wang X. P., Hoffmann C. M., "Expanding Lorentz and spectrum corrections to large volumes of reciprocal space for single-crystal time-of-flight neutron diffraction", Journal of Applied Crystallography,49, 497-506 (2016).Sheldrick G. M., “Crystal structure refinement with SHELXL”, Acta Crystallographica Section C-Structural Chemistry,71, 3-8 (2015).Petricek V., Dusek M., Plasil J., “Crystallographic computing system Jana2006: solution and refinement of twinned structures”, Zeitschrift für Kristallographie - Crystalline Materials,231, 583-599 (2016).Coll in Broholm’s lecture on magnetic neutron scattering, online:http://cins.ca/docs/ss2013/lectures/Broholm.pdfWills A. S., “A walk through of the maths behind Bertaut’s method of representational analysis of magnetic structures”Petricek V., Henriques M. S., Dusek M., “Solution and Refinement of Magnetic Structures with Jana2006”, Acta Physica Polonica A130, 848-851 (2016).Wilson C. C., “Single crystal neutron diffraction from molecular materials”, World Scientific, 2000 (book).Squires G. L., "Introduction to the Theory of Thermal Neutron Scattering", CambridgeUniversity Press, 2012 (book).Lovesey S. W., "The Theory of Neutron Scattering from Condensed Matter Volume II", Oxford University Press, 1986 (book).Data reductionTOPAZ IntroductionNeutron TOF Data Reduction for Modulated Crystals (TOPAZ)Instruction videos for Single Crystal data reduction. (HB-2C data)Manuals for Single Crystal data reduction. (HB-2C data)Data analysisSHELX Tutorials and TalksSHELX for neutrons George SheldrickRefinement of small molecules against neutron data Xiaoping WangGSAS & EXPGUI GSAS/EXPGUI refinement example Xiaoping WangGSAS-II Single crystal structure refinement with TOF data in GSAS-II Robert Von Dreele (Argonne, 2016)JANA2006 Installation notes Vaclav Petricek and Margarida S. HenriquesTutorial A CsLiSO4: Structure with pseudomerohedric 3-fold twinningTutorial B Y(PO3)3: Modulated crystal structure with crenelTutorial C DyMn6Ge6: A conical magnetic structure with k1 = (0, 0, 0) and k2 = (0, 0, 0.1651) Tutorial D K2V3O8: Solution of (3+1)-dimensional incommensurately modulated structure with twinning (TOPAZ data)ISODISTORT Instructions Branton CampbellISOVIZ and ISOVIZQ InstallersTutorial ExercisesFULLPROFHands-on example Single Crystal, Commensurate Huibo CaoHands-on example Single Crystal, Incommensurate Huibo CaoBilbao Crystallographic Server Hands-on demonstration Manuel Perez-Mato▪SoftwareSHELX https://shelx.uni-goettingen.de/Program package for the determination of small and macromolecular crystal structuresShelXle https:///shelx/eingabe.phpA graphical interface for SHELXLOlex² Crystallography Software /SoftwareProgram package to solve, refine and finish small-molecule crystal structures using an intuitive user interfaceJANA http://jana.fzu.cz/Crystallographic Computing System for Standard and Modulated StructuresStructure solution and refinement of crystal / magnetic structures (CW and TOF neutron)GSAS & EXPGUI https:///xtal/software/downloads.htmlSoftware package to fit structural models to x-ray and neutron diffraction data. It can be used with both single-crystal and powder diffraction data (Rietveld analysis). EXPGUI is a Graphical user interface to GSASGSAS II https:///trac/pyGSASPython project for determination of crystal structures using both powder and single-crystal diffraction with extensive visualization capabilities.FullProf https://www.ill.eu/sites/fullprof/Crystallographic programs mainly developed for Rietveld analysis of neutron (constantwavelength, time of flight, nuclear and magnetic scattering) or X-ray powder diffraction data collected at constant or variable step in scattering angle 2theta.Isotropy Software Suite https:///iso/isotropy.phpA collection of software which applies group theoretical methods to the analysis of phasetransitions in crystalline solids.SARAh /spaces/willsgroup/software/Simulated annealing and representation analysis of magnetic structuresSpinWaveGenie https:///SpinWaveGenie/SpinWaveGenieLibrary for simplifying linear spin wave calculations of magnetic structuresVESTA https:///vesta/en/download.htmlA three-dimensional visualization system for crystal and magnetic structure analysisCCP14 Program repository /mirror.htmCollection of software for single crystal and powder diffraction.Diffuse Scattering▪Tutorials and Manuals:Data analysisrmc-discord Tutorial examples for the refinement of magnetic, occupational and displacive disorder for single crystal in two and three dimensions▪Softwarermc-discord https://zjmorgan.github.io/rmc-discord/An atomistic reverse Monte Carlo (RMC) refinement program for the analysis of diffusescattering from disordered single crystalsDISCUS_SUITE Diffuse program collection https:///tproffen/DiffuseCodeDiffuse scattering & defect structure simulation and refinement, including PDFs from n/X data Javelin https:///rosswhitfield/javelinPython program for X-ray and neutron single crystal nuclear and magnetic diffuse scattering analysisYELL https:///YellProgram/YellDiffuse scattering interpretation using the X-ray 3D-∆PDF refinementUltrafast calculation of diffuse-scattering patterns from atomistic modelsProtein Crystallography▪Background material:Blakeley M. P., Podjarny A. D., “Neutron macromolecular crystallography”,Emerging Topics in Life Science2, 39-55 (2018).Ashkar R., Bilheux H. Z., Bordallo H., Briber R., Callaway D. J. E., Cheng X.,. Chu X.-Q, Curtis J. E., Dadmun M., Fenimore P., Fushman D., Gabel F., Gupta K., Herberle F., Heinrich F., Hong L., Katsaras J., Kelman Z., Kharlampieva E., Kneller G. R., Kovalevsky A., Krueger S., Langan P., Lieberman R., Liu Y., Losche M., Lyman E., Mao Y., Marino J., Mattos C., Meilleur F., Moody P., Nickels J. D., O’Dell W. B., O’Neill H., Perez-Salas U., Peters J., Petridis L., Sokolov A. P., Stanley C., Wagner N., Weinrich M., Weiss K., Wymore T., Zhang Y., Smith J. C., “Neutron scattering in the biological sciences: progress and prospects”,Acta Crystallographica Section D 74, 1129-1168 (2018).Oksanen, E.; Chen, J. C.-H.; Fisher, S. Z., “Neutron Crystallography for the Study of Hydrogen Bonds in Macromolecules”,Molecules22, 596 (2017).Fisher Z., Jackson A., Kovalevsky A., Oksanen E., Wacklin H., Chapter 1 – Biological Structures, in Exp. Methods Phys. Sci., Ed. F. Fernandez-Alonso and D. Price, Elsevier, 49, pp 1-75 (2017).Niimura, N., Takimoto-Kamimura, M., Tanaka, I., “Neutron diffraction in studies of protein dynamics and functions Application of”, Encyclopedia of Analytical Chemistry: Applications, Theory and Instrumentation, 1-30 (2016).O’Dell, W. B., Bodenheimer, A. M., Meilleur, F., “Neutron protein crystallography: a complementary tool for locating hydrogens in proteins”, Archives of Biochemistry and Biophys ics 602, 48-60 (2016).Golden, E. A., Vrielink, A., “Looking for hydrogen atoms: neutron crystallography provides novel insights into protein structure and function”, Australian Journal of Chemistry67, 1751-1762 (2014).Langan, P., Chen, J. C.-H., “Seeing the chemistry in biology with neutron crystallography”, Physical Chemistry Chemical Phys ics15, 13705 (2013).Niimura, N. and Podjarny, A., “Neutron protein crystallography”, Oxford University Press. 250 pp. (2011)Glusker J., Kovalevsky A., Hanson L., Fisher Z., Mustyakimov M., Mason S., Forsyth T., Langan P., “Using Neutron Protein Crystallography to Understand Enzyme Mechanism”,Acta Crystallographica Section D 66, 1257-1261 (2010).Adams, P. D.; Mustyakimov, M.; Afonine, P. V.; Langan, P., “Generalized X-ray and Neutron Crystallographic Analysis: More Accurate and Complete Structures for Biological Macromolecules”, Acta Crystallographica Section D65, 567-573 (2009).Blakeley, M. P., “Neutron macromolecular crystallography”, Crystallography Reviews15, 157-218 (2009).Langan P., Fisher Z., Kovalevsky A., Mustyakimov M., Valone A. S., Unkefer C., Waltman M.J., Coates L., Adams P. D., Afonine P. V., Bennett B., Dealwis C., Schoenborn B. P., “Protein Structures by Spallation Neutron Crystallography”, Journal of Synchrotron Radiation15, 215-218 (2008).Niimura, N., Bau, R., “Neutron protein crystallography: beyond the folding structure of biological macromolecules”, Acta Crystallographica Section A 64, 12-22 (2008).Blakeley, M. P., Langan, P., Niimura, N., Podjarny, A. “Neutron crystallography: opportunities, challenges, and limitations”, Current Opinion in Structural Biol ogy 18, 593-600 (2008).▪Tutorials and Manuals:Data reductionMaNDi https:///neutrons/mandi_autoreduction_scriptsData analysisCCP4 CCP4 YouTube ChannelsPhenix Videos from Phenix workshops▪SoftwareCCP4 https:///Software for Macromolecular X-Ray CrystallographyPhenix https:///Software suite for the automated determination of molecular structures using X-raycrystallography and other methods。

pytorch nd维度格式-回复PyTorch是一个广泛应用于深度学习的开源机器学习库。

它支持高度并行化的计算，具有强大的GPU加速能力，并提供了一个直观的前端语言和丰富的工具生态系统。

PyTorch的nd维度格式是指在PyTorch中处理多维数据的方式。

本文将一步一步解释nd维度格式的概念、使用方法以及实际应用。

第一步：什么是nd维度格式？nd维度格式是指n维数据的表示方式。

在PyTorch中，我们可以使用张量（Tensor）来表示多维数据。

张量可以是0维（标量）、1维（向量）、2维（矩阵）以及更高维度的数组。

nd维度格式的灵活性使得PyTorch 可以处理各种形状和大小不同的数据。

第二步：如何创建nd维度格式的张量？在PyTorch中，我们可以使用`torch.Tensor()`函数来创建一个空的张量。

例如，我们可以使用以下代码创建一个3x3的矩阵张量：import torchtensor = torch.Tensor(3, 3)我们也可以使用Python中的列表（list）或NumPy数组来创建张量。

例如，以下代码创建了一个2x2的矩阵张量：import torchimport numpy as npdata = [[1, 2], [3, 4]]tensor = torch.Tensor(data)# 或者array = np.array([[1, 2], [3, 4]])tensor = torch.Tensor(array)另外，我们还可以使用`torch.zeros()`和`torch.ones()`函数创建指定大小的零矩阵和全一矩阵。

例如，以下代码创建了一个3x3的全零矩阵和一个2x2的全一矩阵：import torchzeros_tensor = torch.zeros(3, 3)ones_tensor = torch.ones(2, 2)第三步：如何使用nd维度格式的张量进行计算？PyTorch提供了丰富的张量操作函数，可以对nd维度格式的张量进行各种计算。

r语言venn差异基因-回复R语言是一种用于统计分析和绘图的开源编程语言。

在生物信息学研究中，R语言被广泛应用于差异基因分析。

差异基因分析是一种用于识别在不同条件下表达水平发生显著变化的基因的方法，对于理解生物体内基因调控机制和寻找潜在的治疗靶点非常重要。

Venn图是常用于展示多组数据的交集和差异的一种图形工具，用于直观地显示不同组之间的重叠和唯一的元素。

本文将介绍如何使用R语言生成Venn差异基因图，并解读其结果。

一、准备工作在进行差异基因分析之前，首先需要准备好需要分析的基因表达数据。

常见的数据来源包括转录组测序数据、芯片数据等。

本文以转录组测序数据为例进行介绍。

数据预处理包括数据质控、表达量计算、差异分析等步骤，这些步骤超出了本文的范围。

二、安装R包在进行差异基因分析之前，需要安装一些R语言中常用的包。

使用以下命令安装所需的包：Rinstall.packages("VennDiagram")install.packages("limma")三、加载数据加载数据的方法根据具体的数据格式而定。

一般来说，可以使用read.csv()函数加载常见的.csv格式的数据文件，或者使用read.table()函数加载其他格式的数据文件。

R# 读取数据文件data <- read.csv("data.csv")四、差异分析差异分析是找出在不同条件下基因表达水平发生差异的关键步骤。

差异分析常用于两组或多组样本之间的比较。

在R语言中，可以使用limma包进行差异分析。

以下是一个示例代码：Rlibrary(limma)# 设定组别group <- c("Control", "Treatment", "Control", "Treatment")# 创建试验设计矩阵design <- model.matrix(~0 + group)# 进行差异分析fit <- lmFit(data, design)fit <- contrasts.fit(fit, contrasts = c(-1, 1))fit <- eBayes(fit)# 提取差异基因results <- topTable(fit, coef = 1, adjust.method = "BH", sort.by = "P")diff_genes <- results[resultsadj.P.Val < 0.05, ]五、绘制Venn图在R语言中，可以使用VennDiagram包绘制Venn图。

主体间效应检验python -回复什么是主体间效应检验以及如何在Python中进行？主体间效应检验（Inter-subject effect testing）是一种用来比较不同主体或不同组之间的差异的统计分析方法。

它旨在探索个体或组之间在某个变量或指标上是否存在显著差异。

在统计学中，主体间效应检验经常被用作研究实验、社会科学研究以及医学实验的重要工具。

它可以帮助研究人员了解不同处理、干预或条件对主体产生的效应是否显著。

在Python中，我们可以使用多种方法来进行主体间效应检验。

下面将一步一步介绍如何使用Python进行主体间效应检验。

第一步是准备数据。

首先，我们需要收集所需的数据，并将其整理成可用于分析的格式。

通常，数据应包含被试或组别的标识符以及我们要比较的变量或指标的测量值。

第二步是导入所需的库。

在Python中，我们可以使用一些强大的库来进行主体间效应检验，如pandas、numpy和scipy.stats。

在开始之前，我们需要确保这些库已经安装并导入到我们的代码中。

接下来，我们可以加载我们的数据集，并进行必要的数据清洗和预处理。

如果数据集很大或格式较复杂，我们可以使用pandas库中的数据处理函数来简化操作。

第四步是选择适当的主体间效应检验方法。

在Python中，我们有多个主体间效应检验的方法可供选择，包括独立样本t检验、方差分析（ANOVA）、非参数检验（例如Mann-Whitney U检验和Kruskal-Wallis检验）等。

选择适当的方法取决于我们的研究设计和假设。

第五步是执行主体间效应检验。

使用所选择的方法，我们可以利用相应的Python函数对数据进行分析。

例如，如果选择了独立样本t检验，我们可以使用scipy库中的ttest_ind()函数进行分析，该函数接受两个样本的测量值作为输入，并返回t-statistic和p-value等统计结果。

第六步是解读结果。

一旦主体间效应检验完成，我们可以根据显示的统计结果进行结果解释。

stata hdidregress例子-回复什么是Stata中的hdidregress函数？Stata是一个常用的统计分析软件，它提供了一系列功能强大的数据处理和分析工具。

hdidregress是Stata中的一个命令，它是用于进行高维固定效应、异质性以及动态面板数据模型的估计。

在处理实证数据时，常常会遇到高维度固定效应和异质性这样的问题。

高维度固定效应指的是数据集中包含大量的个体和时间单位，而异质性则是指这些个体和时间单位之间存在差异。

hdidregress命令的目的就是解决这些问题，提供一种可靠的估计方法。

hdidregress命令的基本语法如下：hdidregress depvar_ind depvar_agn indepvars, first(panelvar) second(treatmentvars) covar(other_covars) ab(n) twostep其中，depvar_ind是因变量个体方差，depvar_agn是因变量时间方差，indepvars是自变量。

first参数用于指定个体固定效应变量，second参数用于指定时间固定效应变量。

covar参数则用于指定其他控制变量。

hdidregress命令的工作方式是将原有的动态面板数据模型转化为一个回归方程，并且通过引入固定效应和异质性的估计方法来解决数据集中面临的问题。

通过hdidregress命令进行估计后，可以得到相应的系数估计值和标准误差。

接下来，我们将通过一个具体的例子来演示如何使用hdidregress命令。

假设我们研究了100个国家在20年的经济增长情况，我们将经济增长率作为因变量，GDP、劳动力、教育水平和政府支出作为自变量。

我们还希望控制个体固定效应和时间固定效应。

首先，我们需要加载数据集并查看数据的整体情况。

use growth_data.dtadescribe数据集中包含的变量有国家代码、年份、经济增长率、GDP、劳动力、教育水平和政府支出。

Quick Start Users Guide forBruker SMART APEX II DiffractometerROUTINE DATA COLLECTIONSoftware version APEX2 v2.1-0J. Tanski 2/15/06, rev. S. MacMillan 1/14/07This guide is useful for routine data entry, collection and reduction.The APEX2 v.2 User Manual contains more information and instructions. Please see JMT for instructions on how to start the X-ray generator andcryostream.1.0There are two X-ray log books: a composition book for note taking anda binder that contains pertinent information and finished structuretables. Both notebooks should be kept up-to-date. Before starting a new data collection, begin a new page (odd pages only) in the composition log book.Each entry should ultimately contain the date, names of users (including students and non-Vassar users), sample filename, temperature, crystal size and description of the crystal.The format of the sample filename is the first and last initial of the researcher, the log book page number, and one element symbol that describes the compound. For example, if J oe T anski wants to collect data on a V anadium compound and writes information on page 373 of the log book, then the sample filename will be JT373V.The binder log book contains two forms that should be filled out as more information is obtained. The goal of these notebooks is to streamline the massive amount of information that flows into and around the X-ray facility. Please help us stay organized!Note: If the Apex2 window is already open, skip to Step 1.2iii1.1To begin, double click the BIS icon at the top of the desktop screen.BIS is the program that links the hardware and the APEX2 software used for set-up, collection and reduction. A dialogue box will appear;confirm that the distance is set to 5.483 cm and click OK.Information, including the distance (5.525 cm), detector temperature(~ -60°C) and generator status (20 kV, 5 mA), will appear in the BIS window.1.2Double click the APEX2 icon. Select I NSTRUMENT-C ONNECTION.The host name appears by default (Bruker3b-server); click C ONNECT.It will take about 15 seconds to establish the connection.i.To open an existing project, select S AMPLE-O PEN. A list of projectswill appear. Find the desired project, highlight it, and click OK.ii.To create a new project, select S AMPLE-N EW. A dialogue box will appear with three fields (name, group and folder).First, create a new folder:Folder: The default for this field is C:/frames/guest. Use the foldericon to browse to C:/frames/guest/APEXUsers/VassarUsers/User.*Create a new directory within the appropriate user folder using thenew folder icon () and name the folder as the sample filename.Hit ENTER to create the folder (you must hit enter!), select the folder,and then click OK.*Note: User is the actual name of the researcher (i.e. Tanski). Ifdata is being collected for a non-Vassar user, browse toC:/frames/guest/NonVassarUsers/User instead of the path givenabove.Name: Use the sample filename given in the X-ray log book (i.e.JT373V).Group: The default is “Users”; there is no need to change this field. The left side of the APEX2 window has a task bar with Setup, Evaluate, Collect, Integrate, Scale, Examine Data, Solve Structure, Refine Structure, Report and Instrument buttons. Use these buttons to navigate the program through the various stages of the experiment.1.3Select C ENTER C RYSTAL. The video microscope screen will open. Ifthe goiniometer is not zeroed and the crystal has not yet been mounted, click M OUNT (lower right corner of the APEX2 screen) and place the crystal on the goiniometer. Click R IGHT to drive the goiniometer to the right position; manually adjust the loop such that the edge of the loop is perpendicular to the plane of the video microscope screen. This will make centering the crystal easier.See p. 5-6 in the Apex2 User Manual for instructions on how to center the crystal.The crystal should not extend past two and a half rings on the crosshairs. The ÅÆ button can be used to measure the crystal (100 μm = 0.1 mm). Record the crystal size in the log book.When the crystal is centered, lower the video microscope window and click the “X” button (be sure not to click the APEX2 “X” above it, as this will exit the entire program) to close the centering window.1.4Select S ETUP-D ESCRIBE. Enter the molecular formula (with nospaces), the primary crystal color (if the crystal has no color, it is colourless, not n/a), dimension, and shape (i.e. block, plate, needle, bulk) in the appropriate fields. To write the information to the database, close the window by clicking the “X” button (not the APEX2 “X” above it).1.5Select E VALUATE-D ETERMINE U NIT C ELL. Click R UN (right side ofAPEX2 screen) to accept the default matrix collection time (5 seconds).It takes about 12 minutes to collect the matrix frames, harvest the spots and index the cell. There should be green check marks in each box as the indexing steps are completed. Record the unit cell in the log book.Click C LOSE when all the steps are done.If the cell is acceptable, skip to Step 1.7If the cell is not acceptable, it is possible to either manually determine the unit cell or to work further with the cell (suggested for weak or poor data, or for a suspicious unit cell).i.Manually determine the unit cell.a)To obtain a new unit cell from matrix runs only, delete the unit celland reflections by clicking the D ELETE buttons. Click H ARVESTS POTS and browse to find the first image of the matrix collection(matrix_01_0001.sfrm). Select the number of runs and images touse (3 and 20, respectively). It is best to be have an I/σ of ~10 onthe More/Fewer Spots scroll bar.1 Click H ARVEST at the bottomright of the screen.Click I NDEX, move the More Spots scroll bar towards “MoreSpots” (I/σ ~10). Click I NDEX at the bottom right of the screen.If a reasonable unit cell is obtained, select it and click A CCEPT.Continue with the instructions in Part ii below.b)To read in additional reflections from data frames, click onH ARVEST S POTS and browse to find the first image of the datacollection (samplefilename_01_0001.sfrm) and set the number ofruns (1) and number of images to use (50-100). It is best to behave an I/σ of ~10 on the More/Fewer Spots scroll bar. ClickH ARVEST at the bottom right of the screen.Continue with the instructions in Part ii below.ii.Working further with the unit cell.a)Move the More/Fewer reflections scroll bar towards “MoreReflections” and click R EFINE. Move the scroll bar stepwisetowards “Fewer Reflections”, refining once or twice each step,until the unit cell parameters have refined to at least 2 decimalplaces and the detector translation distance is minimized. It is alsoadvisable to alternate refinement parameters with the D ETECTORT RANSLATION and B EAM C ENTER boxes checked OR with theD ETECTOR O RIENTATION box checked. Stop moving theMore/Fewer reflections scroll bar to the right when a large portionof the reflections refined are removed from the refinement. Thespots on the frames should be predicted (covered by the bluecircles). Click A CCEPT.1 Adjusting the I/σ for harvesting spots and unit cell determination will often be necessary. For weak data, decrease I/σ; for very strong or twinned data, increase I/σ.b)Click B RAVAIS and select the appropriate lattice. After clickingA CCEPT, click R EFINE and move the More/Fewer Reflectionsscroll bar towards “More Reflections”. Continue to refine the cellas described above.c)Accept the unit cell and record it in the log book. If the detectortranslation distance did not refine to near zero, more reflectionswill have to be read in from data frames and the unit cell refinedfurther (Part i.b). If this must be done more than once, be sure tochange the number of the first data frame to one more than the lastframe read in the previous time.Click the “X” to close this window.Note: Data collection may be started even if there is no unit cell; itis possible to harvest spots from the data frames later to determinethe cell.1.6Skip this step unless necessary. Select C OLLECT-O RIENTED S CANS.Repeat the following procedure for each vector a, b, and c. Note that all of the scans may not be possible on our fixed chi platform.Click on a vector, then C ALCULATE, then G O T HERE. After the goiniometer drives to the correct position, click M AKE S CAN.Record the appearance of the axial photographs in the log book. For example, mirror symmetry will be observed for the b-axis of a monoclinic cell. Click the “X” to close this window.1.7Select C OLLECT-E XPERIMENT. To collect a standard hemisphere, skipto Step 1.8 (advised).To prepare a unique data collection strategy, select D ATA C OLLECTION S TRATEGY. Make sure the distance is 55.25 mm and the resolution is <0.75 Å. The program must “Scan” after changes are made. Set the firstexposure time to 10 seconds (30 for weakly diffracting crystals), hit ENTER, and click S AME.Under “Strategy”, select the length of data collection desired. 12 hours is a good place to start; the length of time will depend on the crystal quality and unit cell symmetry.Select Execute Refine Strategy; when the time, redundancy and completeness are acceptable, click S TOP.Select E XECUTE S ORT R UNS FOR C OMPLETENESS and click the “X” to close this window.1.8Select C OLLECT-E XPERIMENT.i.If collecting a standard hemisphere, click L OAD T ABLE, browse toC:\ and double click hemisphere.exp. The default exposure time is10 seconds; change this time accordingly. For example, weaklydiffracting crystals will require a 30 or 60 second exposure time.At the top of the screen, make sure the “Image Location” is thefolder of the correct project and that the “Filename” is the correctsample filename. Change these fields if they are incorrect.Click V ALIDATE and OK. Click E XECUTE. The data collection time depends on what was selected in Step 1.7.ii.If using a unique data collection strategy, right click the “Operations” box of the first line and select “Generator”; then clickA PPEND S TRATEGY. The runs determined in Step 1.7 should becopied into the table.At the top of the screen, make sure the “Image Location” is thefolder of the correct project and that the “Filename” is the correctsample filename. Change these fields if they are incorrect.Click V ALIDATE and OK. Click E XECUTE. The standard hemisphere collection takes about 5 hours with 10 second frames(13 hours with 30 second frames).iii.When the data collection is complete, select S CALE-C RYSTALF ACES and obtain a video. Select S ETUP-C ENTER C RYSTAL andclick R IGHT to orient the crystal such that it is visible in the videomiscroscope screen. Select F ILE-S AVE A S to save a picture of thecrystal in the current orientation.1.9If the data collection is finished, it is advisable to work on the unit cellbefore integrating the data. Refer to Step 1.5i.b to harvest more spots from the first data run. Once finished refining the cell, selectI NTEGRATE-I NTEGRATE I MAGES and then I MPORT R UNS FROME XPERIMENT.If the data collection is still running, select I NTEGRATE-I NTEGRATEI MAGES and then F IND R UNS; edit the frames to be used. Be sure tounclick the matrix runs.Use the default refinement options. Under “Integration Options”, select M ORE O PTIONS; change the “Generate Mask-Active Pixel Mask” to0.50 (this will account for the beamstop shadow) and uncheck the“Multiwire Reference Correction”.S TART I NTEGRATION.ClickThe most important information to follow is the correlation values and intensity. The correlation values are a measure of how well the reflections agree with their calculated positions. These values should, ideally, be equal to 1; in practice, anywhere from 0.5 to 0.9 is fine. The spot intensities should range from hundreds to thousands. If either of these does not look good, return to Step 1.5i.b and work more on the unit cell.See p. 7-11 to 7-13 in the Apex2 User Manual for more information on following the integration and viewing the results. Close the Saint Chart when the integration is finished, and click the “X” to close the integration setup window.1.10Scale the data only if working with a complete data set. Select S CALE-S CALE. The default parameters should be fine for normal data. Browse to find the first run (filename_01.raw) and check the O PEN A LL W ITHB ASE box; click O PEN. Unclick M ERGED B ATCHES unless the crystalsystem is triclinic or if there is not much data.Select the absorption type. Choose “Weak” for organic crystals and “Medium” for inorganic coordination compounds.N EXT, then R EFINE. The R(int) values should be low (less than Click10%) and the Mean Weight should approach 1. Click N EXT, and thenD ETERMINE E RROR M ODEL. Remove bad runs (R(int)>15%) byunchecking the box, and repeating the refinement. Click F INISH. TheE XIT AX SCALE button must be used to quit. See p. 7-16 to 7-21 in theApex2 User Manual for an explanation of the output.1.11Select E XAMINE D ATA-XPREP. See the Structure Solution andRefinement section of this Quick Start Guide for more detailed instructions.Browse to find the necessary files in the work subdirectory (filename_0m.p4p and filename_0m.hkl). Select filename_01.raw only if initial data from the first run is available; select filename_0m.raw if more than one run is available but the data collection is not finished.All of the default parameters should be fine for normal data - simply hit ENTER in each case. You must enter the molecular formula if it was not entered in Step 1.4. When prompted for an output filename, type “filename”; do not include the _0m of the previous file. Finally, type Y (yes) to overwrite the intensity data.1.12Select S OLVE S TRUCTURE-S TRUCTURE S OLUTION. If it does notautomatically come up, browse to find the instruction file (filename.ins) produced in Step 1.11. Click S OLVE S TRUCTURE. R(alpha) values less than 0.1, CFOM values less than 0.1 and RE values less than 0.3 are a good indication that the structure can be solved.1.13Select R EFINE S TRUCTURE-S TRUCTURE R EFINEMENT. In this area, thestructure can be viewed, the atoms assigned, and the data refined to give the final result. See the Structure Solution and Refinement section of this Quick Start Guide for instructions.1.14Finally, print structure diagrams (from XP) and data tables (XCIF) onthe IBM PC using the SHELXTL interface as described in the Printing the Results and Preparing the Files for Submission and Publication section of this Quick Start Guide.。

stata中egen生成缺失值的规则-回复Stata中的egen命令是一个非常强大的数据处理工具，它可以用于生成缺失值，以及进行各种基于数据变量的计算。

在本文中，我们将详细介绍egen命令和生成缺失值的规则。

egen命令的基本语法如下：egen 变量名= 函数名(变量列表) [, options]其中，变量名是您希望生成的新变量的名称，函数名是指定的egen函数的名称，变量列表是您希望在函数中使用的变量列表。

options是可选项，可以用于进一步调整函数的行为。

现在，让我们逐步回答与egen生成缺失值有关的问题。

1. 为什么要生成缺失值？生成缺失值是在数据分析中常见的一项任务。

有时候，某些变量的值可能是无法获取到的，或者数据缺失的原因没有办法找到。

在这种情况下，为了保持数据的完整性和一致性，我们需要在缺失的数据位置上生成缺失值。

2. 如何使用egen生成缺失值？在Stata中，我们可以使用egen命令的missing函数来生成缺失值。

missing函数会根据指定的规则对数据进行判断，并在符合条件的位置上生成缺失值。

例如，我们可以使用以下命令在变量x上生成缺失值：egen missing_x = missing(x)这个命令将会在missing_x变量中根据变量x的值生成相应的缺失值。

如果x的值为缺失值或者为非数字值（比如字符串），那么missing_x将被设置为1，否则设为0。

3. 怎样根据特定条件生成缺失值？除了使用missing函数外，egen命令还提供了一些其他的函数，可以根据特定的条件生成缺失值。

例如，我们可以使用egen的cond函数对变量进行条件判断，并在符合条件的位置上生成缺失值：egen cond_x = cond(x < 0, x, .)这个命令将会在cond_x变量中根据x的值生成相应的缺失值。

如果x小于0，那么cond_x将被设置为x的值，否则设为缺失值。

与cond函数类似的还有ifelse函数，它可以根据不同的条件生成不同的值或缺失值：egen ifelse_x = ifelse(x < 0, x, .)这个命令将在ifelse_x变量中根据x的值生成相应的缺失值。

refinement modules 细化模型Global and Local Cascade Refinement in testing，我们使用了Global Step和Local Step来处理高分辨率的分割细化通过使用相同训练的refinement module。

Global step考虑的是resize image 和repair structure，Local step是细化细节。

Global StepGlobal step采用3级级联refine the whole image。

由于全分辨率图像GPU无法处理，我们对输入进行下采样，使长轴具有长度L，同时保持相同的纵横比。

在第一级级联后，输入通道的一个被双线性上采样的coarse output代替。

一直重复到最后。

这种设计使我们的网络能够逐步修复分割错误，同时保持初始分割中的细节。

通过多个levels，我们可以粗略地描述对象，并在粗层次中修复较大的误差，并使用粗层次提供的更健壮的特征来关注精细层次中的边界精度。

Local Step超高分辨率图像在GPU处理很困难，而且在训练和测试数据的巨大改变也会引起分割质量下降。

我们利用级联模型首先使用下采样图片执行global refinement，然后使用image crops来自于higher分辨率image执行local refinement。

这些crops能使Local step不用高分辨率训练数据就能处理高分辨率图像，因为Global step已经考虑到image context。

总结边缘的细化效果还是可以的，但是推理是Cascade，local step 都要2次，时间消耗大。

pytorch nd维度格式-回复PyTorch是一种开源的深度学习框架，广泛用于构建和训练神经网络。

在PyTorch中，nd维度格式是指张量的维度。

张量在PyTorch中是一种多维数组，类似于NumPy的ndarray。

张量可以是一维、二维或更高维。

在本文中，我们将详细介绍PyTorch中的nd维度格式，包括如何创建、操作和使用张量。

1. 张量的创建：在PyTorch中，我们可以使用torch.tensor()函数来创建张量。

这个函数接受一个数据结构作为输入，例如列表、元组或NumPy数组。

以下是创建一维和二维张量的示例代码：pythonimport torch# 创建一维张量tensor_1d = torch.tensor([1, 2, 3, 4, 5])# 创建二维张量tensor_2d = torch.tensor([[1, 2, 3], [4, 5, 6]])2. 张量的属性：张量有几个重要的属性，包括形状（shape）、数据类型（dtype）和设备（device）。

形状指示张量的维度大小，数据类型指示张量的元素类型，设备指示张量所在的计算设备（如CPU或GPU）。

以下是查询张量属性的示例代码：pythonimport torchtensor = torch.tensor([[1, 2, 3], [4, 5, 6]])# 查询张量的形状print(tensor.shape) # 输出: torch.Size([2, 3])# 查询张量的数据类型print(tensor.dtype) # 输出: torch.int64# 查询张量所在的设备print(tensor.device) # 输出: cpu3. 张量的索引和切片：与NumPy类似，我们可以使用索引和切片操作来访问和修改张量的元素。

以下是使用索引和切片操作的示例代码：pythonimport torchtensor = torch.tensor([[1, 2, 3], [4, 5, 6]])# 访问张量的单个元素print(tensor[0, 1]) # 输出: tensor(2)# 修改张量的单个元素tensor[0, 1] = 9print(tensor) # 输出: tensor([[1, 9, 3], [4, 5, 6]])# 使用切片操作访问子张量sub_tensor = tensor[:, :2]print(sub_tensor) # 输出: tensor([[1, 9], [4, 5]])4. 张量的数学运算：张量支持各种数学运算，包括加法、减法、乘法和除法。