Example_of_Normal_Plot

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matlab考试题库及答案大学

matlab考试题库及答案大学# MATLAB考试题库及答案大学一、选择题1. MATLAB中用于生成0到1之间均匀分布随机数的函数是： - A. rand- B. randi- C. randperm- D. randn答案：A2. 下列哪个命令可以用于绘制函数图像？- A. plot- B. text- C. title- D. xlabel答案：A3. MATLAB中，以下哪个不是矩阵的属性？- A. size- B. length- C. rank- D. transpose答案：D4. 以下哪个函数可以用于求解线性方程组？- A. solve- B. linsolve- C. linprog- D. fsolve答案：A5. MATLAB中，用于计算矩阵特征值的函数是：- A. eig- B. eign- C. eigen- D. eigenvalue答案：A二、填空题1. MATLAB的基本数据单位是________。

答案：矩阵2. 使用MATLAB进行数值计算时，可以利用________来存储数据。

答案：变量3. MATLAB中的向量可以是________或________。

- 答案：行向量；列向量4. 矩阵的转置可以通过________操作实现。

答案：.'5. MATLAB中，使用________函数可以计算矩阵的行列式。

答案：det三、简答题1. 简述MATLAB中使用循环结构的注意事项。

答案：在MATLAB中使用循环结构时，应注意以下几点：确保循环体内部逻辑正确，避免无限循环；使用for循环时，循环变量的初始化和步长设置要合理；使用while循环时，循环条件要明确，确保循环能够正常退出。

2. 描述MATLAB中如何实现数组的多维索引。

答案：在MATLAB中，多维数组的索引可以通过使用圆括号来实现，每个维度的索引用逗号分隔。

例如，对于一个三维数组A，可以使用A(i,j,k)来访问第i行、第j列、第k层的元素。

plot_importance_的用法

plot_importance_的用法Plot_importance_的用法Plot_importance_是一个在机器学习中常用的函数，用于可视化特征的重要性。

通过使用plot_importance_，我们可以更好地理解和解释机器学习模型的预测结果，从而提供更多见解和洞察。

在本文中，我们将一步一步回答关于plot_importance_的用法的问题，以帮助读者了解该函数的功能和如何应用它来分析特征的重要性。

1. 为什么重要性分析是机器学习中的关键步骤？机器学习模型的目标是从输入特征中学习模式，以预测目标变量。

然而，并非所有的特征对预测结果都具有相同的重要性。

一些特征可能对预测结果的贡献更大，而其他特征可能对预测结果几乎没有影响。

通过进行特征的重要性分析，我们可以识别出对预测结果最为关键的特征。

这有助于我们更好地理解问题的本质并指导特征选择、特征工程和模型优化的过程。

因此，重要性分析是机器学习中的关键步骤。

2. plot_importance_的主要功能是什么？plot_importance_是一个用于绘制特征重要性的函数。

它可以帮助我们直观地理解机器学习模型对特征的重要性排序。

通过plot_importance_，我们可以获得特征的重要性分数，并将其以柱状图或其他可视化形式展示。

这使我们能够快速识别出对预测结果最具影响力的特征，并便于我们在特征选择或特征工程中采取相应的措施。

3. 如何使用plot_importance_函数进行特征重要性分析？使用plot_importance_函数进行特征重要性分析的步骤通常涉及以下几个方面：(a) 首先，我们需要训练一个机器学习模型，例如决策树、随机森林或梯度提升树。

(b) 在训练完模型后，我们可以从模型中提取特征重要性分数。

这通常通过计算或获取模型的相关属性来实现。

CalSim版本0.5.2产品说明说明书

Package‘CalSim’October12,2022Type PackageTitle The Calibration SimplexVersion0.5.2Author Johannes ResinMaintainer Johannes Resin<************************>Depends R(>=3.5),spatstat(>=2.0-0)Imports spatstat.geom,stats,ExactMultinomDescription Generates the calibration simplex(a generalization of the reliability diagram)for three-category probability forecasts,as proposed by Wilks(2013)<doi:10.1175/W AF-D-13-00027.1>. License GPL-2Encoding UTF-8LazyData trueRoxygenNote7.1.1NeedsCompilation yesRepository CRANDate/Publication2021-03-2513:20:11UTCR topics documented:calibration_simplex (2)plot.calibration_simplex (4)ternary_forecast_example (6)Index71calibration_simplex Calibration SimplexDescriptionGenerates an object of class calibration_simplex which can be used to assess the calibration of ternary probability forecasts.The Calibration Simplex can be seen as generalization of the reliability diagram for binary probability forecasts.For details on the interpretation of the calibration simplex, see Wilks(2013).Be aware that some minor changes have been made compared to the calibration simplex as suggested by Wilks(2013)(see note below).As a somewhat experimental feature,multinomial p-values can be used for uncertainty quantiﬁca-tion,that is,as a tool to judge whether the observed discrepancies may be merely coincidental or whether the predictions may in fact be miscalibrated,see Resin(2020,Section4.2).Usagecalibration_simplex(n,p1,p2,p3,obs,test_stat,percentagewise)##Default S3method:calibration_simplex(n=10,p1=NULL,p2=NULL,p3=NULL,obs=NULL,test_stat="LLR",percentagewise=FALSE)Argumentsn A natural number.p1A vector containing the forecasted probabilities for theﬁrst(1)category,e.g.below-normal.p2A vector containing the forecasted probabilities for the second(2)category,e.g.near-normal.p3A vector containing the forecasted probabilities for the third(3)category,e.g.above-normal.obs A vector containing the observed outcomes(Categories are encoded as1(e.g.below-normal),2(e.g.near-normal)and3(e.g.above-normal)).test_stat A string indicating which test statistic is to be used for the multinomial test in each bin.Options are"LLR"(log-likelihood ratio;default),"Chisq"(Pearson’schi-square)and"Prob"(probability mass statistic).See details percentagewise Logical,specifying whether probabilities are percentagewise(summing to100) or not(summing to1).DetailsOnly two of the three forecast probability vectors(p1,p2and p3)need to be speciﬁed.The p-values are based on multinomial tests comparing the observed frequencies within a bin with the average forecast probabilities within the bin as outlined in Resin(2020,Section4.2).The p-values are exact and do not rely on asymptotics,however,it is assumed that the true distribution (under the hypothesis of forecast calibration)within each bin is approximated well by the multi-nomial distribution.If n is small the approximation may be poor,resulting in unreliable p-values.p-Values less than0.0001are not exact but merely indicate a value less than0.0001.ValueA list with class"calibration_simplex"containingn As input by user or default.n_bins Computed from n.Number of hexagons.n_obs Total number of observations.freq Vector of length n_bins containing the number of observations within each bin.cond_rel_freq Matrix containing the observed outcome frequencies within each bin.cond_ave_prob Matrix containing the average forecast probabilities within each bin.pvals Exact multinomial p-values within each bin.See details.Object of class calibration_simplex.NoteIn contrast to the calibration simplex proposed by Daniel S.Wilks,2013,the simplex has been mirrored at the diagonal through the left bottom hexagon.The miscalibration error is by default calculated precisely(in each bin as the difference of the relative frequencies of each class and the average forecast probabilities)instead of approximately(using Wilks original formula).Approxi-mate errors can be used by setting true_error=FALSE when using plot.calibration_simplex. ReferencesDaniel S.Wilks,2013,The Calibration Simplex:A Generalization of the Reliability Diagram for Three-Category Probability Forecasts,Weather and Forecasting,28,1210-1218Resin,J.(2020),A Simple Algorithm for Exact Multinomial Tests,Preprint https:/// abs/2008.12682See Alsoplot.calibration_simplexternary_forecast_exampleExamplesattach(ternary_forecast_example)#see also documentation of sample data#?ternary_forecast_example#Calibrated forecast samplecalsim0=calibration_simplex(p1=p1,p3=p3,obs=obs0)plot(calsim0,use_pvals=TRUE)#with multinomial p-values#Overconfident forecast samplecalsim1=calibration_simplex(p1=p1,p3=p3,obs=obs1)plot(calsim1)#Underconfident forecast samplecalsim2=calibration_simplex(p1=p1,p3=p3,obs=obs2)plot(calsim2,use_pvals=TRUE)#with multinomial p-values#Unconditionally biased forecast samplecalsim3=calibration_simplex(p1=p1,p3=p3,obs=obs3)plot(calsim3)#Using a different number of binscalsim=calibration_simplex(n=4,p1=p1,p3=p3,obs=obs3)plot(calsim)calsim=calibration_simplex(n=13,p1=p1,p3=p3,obs=obs3)plot(calsim,#using some additional plotting parameters: error_scale=0.5,#errors are less pronounced(smaller shifts)min_bin_freq=100,#dots are plotted only for bins,#which contain at least100forecast-outcome pairs category_labels=c("below-normal","near-normal","above-normal"),main="Sample calibration simplex")detach(ternary_forecast_example)plot.calibration_simplexPlot Calibration SimplexDescriptionPlot Calibration SimplexUsage##S3method for class calibration_simplexplot(x,true_error=TRUE,error_scale=0.3,min_bin_freq=10,plot_error_scale=TRUE,scale_area=NULL,indicate_bins=TRUE,category_labels=c("1","2","3"),use_pvals=FALSE,alphas=c(0.1,0.01),...)Argumentsx Object of class calibration_simplextrue_error Logical,specifying whether to use true miscalibration errors or approximate miscalibration errors.error_scale A number specifying the magnitude of the miscalibration errors(greater0,usu-ally should be less than1,cf.note below).min_bin_freq A number.Lower bound for(absolute)frequencies,i.e.how many observations have to lie in a bin for it to be plotted.plot_error_scaleLogical,specifying whether to plot a scale showing the magnitude of miscali-bration errors.scale_area Optional.A number by which the areas of the points are e if points are to small or to big.indicate_bins Logical,specifying whether to connect points to their respective bin(center of hexagon).category_labelsA vector of length3containing the category names,e.g.c("1","2","3")(de-fault)use_pvals Logical,determines whether multinomial p-values are used for uncertainty quan-tiﬁcation,see details.alphas Vector of length2with values1>alphas[1]>alphas[2]>=0.0001.Only relevant if use_pvals=TRUE....Arguments concerning the title(e.g.main,cex.main,col.main and font.main) and subtitle(e.g.sub,cex.sub,col.sub and font.sub)may be passed here.DetailsIf multinomial p-values are used(use_pvals=TRUE),the dots are colored in the following way:•Blue:p-value greater alphas[1](0.1by default).•Orange:p-value between alphas[1]and alphas[2](0.1and0.01by default)•Red:p-value less than alphas[2](0.01by default)•Black:p-value is exactly0.This only happens if a category which is assigned0probabilityrealizes.6ternary_forecast_example Many small p-values(orange and red dots)indicate miscalibrated predictions,whereas many blue dots indicate that the predictions may in fact be calibrated.W ARNING:The use of the multinomial p-values is more of an experimental feature and may not yield reliable p-values,especially if n is small.For details regarding the calculation of the p-values see also calibration_simplex.NoteFor details on the meaning of the error scale,cf.Wilks,2013,especially Fig. 2.Note that the miscalibration error in each category is in"probability units"(as it is the average difference in relative frequency and forecast probability in each bin).ternary_forecast_exampleTernary probability forecast and observations.Description10,000realizations of a ternary probability forecast,which exhibits different characteristics,de-pending on the realizing outcome variable.Idealized forecast example,generated as described in Wilks(2013).Usagedata(ternary_forecast_example)FormatA data frame with10,000rows and6variables.p1forecast probability for outcome1p3forecast probability for outcome3obs0outcomes,such that the forecast is well-calibratedobs1outcomes,such that the forecast is overconﬁdentobs2outcomes,such that the forecast is underconﬁdentobs3outcomes,such that the forecast is unconditionally biasedSourceData generated by package author.ReferencesDaniel S.Wilks,2013,The Calibration Simplex:A Generalization of the Reliability Diagram for Three-Category Probability Forecasts,Weather and Forecasting,28,1210-1218Index∗datasetsternary_forecast_example,6 calibration_simplex,2,6CalSim(calibration_simplex),2plot.calibration_simplex,3,4ternary_forecast_example,3,67。

python中f.grid_sample函数用法

`f.grid_sample`函数是PyTorch中的一个函数，用于对输入的网格进行采样。

它的主要作用是将输入的网格映射到指定的特征图上。

这个函数通常用于实现卷积神经网络中的上采样操作。

函数的基本用法如下：```pythontorch.nn.functional.grid_sample(input, grid, mode='bilinear', padding_mode='zeros', align_corners=None)```参数说明：- `input`：输入的特征图，形状为(N, C, H, W)。

- `grid`：输入的网格，形状为(N, H_out, W_out, 2)。

其中H_out和W_out分别表示输出特征图的高度和宽度。

- `mode`：插值模式，可选值为'bilinear'（双线性插值）和'nearest'（最近邻插值）。

默认值为'bilinear'。

- `padding_mode`：边界填充模式，可选值为'zeros'（用0填充）、'border'（用边界像素填充）和'reflection'（用镜像填充）。

默认值为'zeros'。

- `align_corners`：是否对齐角点。

如果为True，则输出特征图的大小与输入网格的大小相同；如果为False，则输出特征图的大小可能小于输入网格的大小。

默认值为None。

示例代码：```pythonimport torchimport torch.nn.functional as F# 创建一个4x4的特征图input = torch.randn(1, 3, 4, 4)# 创建一个4x4的网格grid = torch.randn(1, 4, 4, 2)# 使用双线性插值进行上采样output = F.grid_sample(input, grid, mode='bilinear', padding_mode='zeros')print(output.shape) # 输出：torch.Size([1, 3, 4, 4])```。

origin菜单栏的中文解释

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(179)Paired Sample Sign Test 配对样本符号检验 (180)Paired Sample Wilcoxon Signed Rank Test配对样本Wilcoxon符号秩检验 (181)Two-Sample Kolmogorov-Smirnov Test 双样本KS检验 (183)Mann-Whitney Test Mann-Whitney检验 (185)Kruskal-Wallis ANOV AKruskal-Wallis方差分析 (187)Moods Median Test 中位数检验 (188)Friedman ANOV A Friedman方差分析 (189)(NPH) K independent Samples K个独立样本 (191)(NPH) Paired Samples 配对样本 (193)(NPH) Two Independent Samples 两个独立样本 (195)Survival Analysis 生存分析 (197)Kaplan-Meier Estimator卡普兰-迈耶估计量 (198)Cox Model Estimator比例风险模型 (199)Weilbull fit Weibull拟合 (201)Multivariate Analysis 多元分析 (203)Principal Component Analysis 主成分分析 (203)K-Means Cluster Analysis K-均值聚类分析 (204)Hierarchical Cluster Analysis 层次聚类分析 (206)Discriminant Analysis 判别分析 (208)Power and Sample Size 功效和样本大小分析 (212)(PSS) One-Sample t-test 单样本t-检验 (212)(PSS) Two-Sample t-test 双样本t-检验 (213)(PSS)Paired t-test 配对样本t-检验 (214)(PSS)One-Way ANOV A 单因素方差分析 (215)ROC Curve受试者工作特征曲线 (216)Image 图像Image adjustments 图像调整 (220)Brightness 亮度 (221)Contrast 对比度 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(241)Binary 转换成二值图 (242)Dynamic Binary 转换成动态二值图 (243)Threshold 阈值 (244)RGB Merge 三原色合并 (245)RGB Splist 三原色拆分 (246)Image Scale 图像比例尺 (247)Geometric Transforms 几何变换 (248)Flip 翻转 (248)Rotate 旋转 (248)Shear 修剪 (249)Resize 调整尺寸 (251)AutoTrim 自动修整 (251)Offset 图像偏移 (252)Spatial Filtering 空间过滤 (254)Average 平均过滤 (255)Gaussian 高斯过滤 (255)Median 中值过滤 (256)Noise 噪音 (257)Edge 边缘.................................................................................................................... 258 Sharpen 锐化................................................................................................................ 258 Unsharpen Mask USM 锐化. (259)User Filter 用户滤镜 (260)Graph 图形 (262)Layer Contents 图层内容 (262)Plot Setup 绘图设置 (263)Add Plot to Layer 层中加图 (263)Add Error Bars 添加误差条 (264)Add Function Graph 添加函数曲线 (265)Rescale to Show All 重置坐标显示全部 (266)Layer Management 图层管理 (266)Add Axis Scrollbar 添加坐标滚动条 (266)New Layer (Axes) 新图层(坐标) (267)Extract to Graphs 提取图层 (268)Apply Palette to Color Map 应用调色板 (270)Merge Graph Windows (271)Speed Mode (271)Update Legend 更新图例 (272)New Legend 新图例 (273)New Enhanced Legend (273)New Table (274)New Color Scale (274)New XY Scaler 新XY比例尺............................................................................................ 275 Set Active Layer By Layer Icon Only ................................................................................... 275 Fit Layer to Graph 图层适合图形....................................................................................... 276 Fit Page to Layers 页面适合图层. (276)Exchange X-Y Axes 交换X-Y坐标 (277)Offset Grouped Data in Layer 偏移图层中的分组数据 (277)Convert to Standard Font Size (277)Data 数据 (278)Set Display Range 设置显示范围 (278)Reset to Full Range 显示全部数据 (280)Mark Data Range 标记数据范围 (280)Clear Data Markers 清除数据标记 (280)Analysis Marker 分析标记 (281)Lock Position 锁定位置 (282)Mask Data Point 给数据点加蒙板 (282)Move Data Point 移动数据点 (283)Remove Bad Data Points 清除坏数据 (285)Gadget 小工具 (285)Quick Fit 快速拟合 (285)Quick Sigmoidal Fit 快速反曲拟合 (287)Quick Peaks 快速峰拟合 (288)Rise Time 上升时间 (289)Cluster 聚类 (289)Statistics (291)Differentiate 微分 (292)Integrate 积分 (293)Interpolate 内插 (293)Intersect 交叉点 (294)FFT 快速傅立叶变换 (295)Vertical Cursor 垂直光标 (295)Layout 布局 (296)Add Graph 添加图形 (297)Add Worksheet 添加工作 (298)Set Picture Holder 开启图像定位 (298)Clear Picture Holder 清除图像定位 (299)New Table 新表格 (299)Global Speed Control 全局速度控制 (300)Tools 工具Options 选项 (301)System Variables 系统变量 (302)Protection 保护 (303)Fitting Function Builder 拟合函数创建器 (304)Fitting Function Organizer 拟合函数管理器 (305)Template Library 模板库 (306)Theme Organizer 主题管理 (306)Import Filters Manager 滤镜导入管理器 (307)Package Manager 包管理器 (308)Customer Menu Organizer 用户菜单管理器 (308)x-Function Builder x-函数创建器 (309)x-Function Script Samples x-函数脚本样本 (309)Copy Origin Sub-VI to LabVIEW User.lib (310)Set Group Folder Location 设置组文件夹位置 (310)Group Folder Manager 组文件夹管理器 (311)Transfer User Files 传递用户文件 (311)Pallet Editor 调色板编辑器 (312)Digitizer 数字转换器 (312)MATLAB Console MATLAB控制台 (313)Mathematica Link 连接Mathematica (314)Format 格式Menu 菜单 (315)Worksheet 工作表 (315)Column 列 (316)Cell 单元格 (317)Snap to Grid 对齐栅格 (317)Programming Control 编程管理 (318)Object Properties 对象属性 (319)Page Properties 页面属性 (320)Layer Properties 图层属性 (320)Plot Properties 图形属性 (321)Snap Layer to Grid 图层对齐栅格 (321)Snap Objects to Grid 对象对齐栅格 (321)Axes 坐标 (321)Axis Tick Labels 坐标刻度 (323)Axis Titles 坐标名称 (323)Windows 窗口Cascade 层叠窗口 (324)Tile Horizontally 水平显示 (324)Tile Vertically 垂直显示 (325)Arrange Ions 排列图标 (325)Refresh 刷新 (325)Duplicate副本 (326)Split 分割 (326)Properties 属性 (327)Command Window 命令窗口 (327)Script Window 脚本窗口 (328)Help 帮助Origin Toolbars 工具栏 (329)Standard 标准 (329)Edit 编辑 (330)Graph 图形 (330)2D Graph 二维图形 (331)3D and Contour Graph 三维图和等高线图 (333)3D Rotation 三维旋转 (334)Worksheet Data 工作表数据 (334)Column 列 (335)Layout 布局 (335)Mask 蒙板 (336)Tools 工具 (336)Object Edit 对象编辑 (337)Arrow 箭头 (338)Style 样式 (338)Format 格式 (339)Auto Update 自动更新 (339)Database 数据库 (340)Markers & Locks 标记和锁定 (340)。

Types of Fiction

Types of Fiction——By narratorial tone and designThe conventional realistic novelThe conventional realistic novel is regarded as a type of novel recording or ‘reflecting’ faithfully an actual way of life without idealization, dramatization, escapism, and other extravagant qualities of romance. Its literary method is based on detailed accuracy of description and it is mainly focused on showing everyday activities and life among the middle or lower class society or recognizing soberly the actual problems of life. Modern criticism frequently insists that realism is not a direct or simple reproduction of reality (a ‘slice of life’) but a system of conventions producing a lifelike illusion of some ‘real’ world outside the text, by processes of selection, exclusion, description, and manners of addressing the reader.Some outstanding works of the conventional realistic fiction include Honoré de Balzac's Illusions perdues (1837–43), Gustave Flaubert's Madame Bovary (1857) and Charles Dicken s’s David Copperfield (1849-1850).The satirical novelThe satirical novel is a type of novel whose aim is to expose foolishness in all its guises-vanity, hypocrisy, pedantry, idolatry, bigotry, sentimentality and to effect reform through such exposure. Its tone may vary from tolerant amusement to bitter indignation. It manifests the vices, folly, abuses or failings of individuals, institutions, or societies by means of ridicule, mockery, burlesque, irony or other methods.Several renowned examples are: William Shakespeare’s Merchant of Venice (1596-1597), Moliere’s L’avare (1668) and Mark Twain’s The Million Pound Note.The anti-novelThe anti-novel is a type of avant-garde novel and an experimental work of fiction that departs from traditional novelistic conventions by ignoring such elements as: coherent plot structure, consistent point of view, realistic character portrayal, dialogue, and human interest. Seeking to overcome readers' habits and challenge their expectations, anti-novelists deliberately avoid any intrusion of authorial personality, preferences, or values. The anti-novel usually fragments and distorts the experience of its characters, forcing the reader to construct the reality of the story from a disordered narrative.Some notable examples of the anti-novel are: Alain Robbe-Grillet’s Les Gommes (1953), Nathalie Sarraute’s Le Planetarium (1959) and Claude Simon’s La Route des Flandres (1960).FactionFaction, a portmanteau of "fact" and "fiction", is known also as non-fiction novel, which, broadly speaking, depicts real historical figures and actual events narrated woven together with fictitious allegations and using the storytelling techniques of fiction. Faction, or the non-fiction novel, is an otherwise loosely-defined and flexible genre.An example of faction is Norman Mailer's The Armies of the Night (1968).MetafictionMetafiction is known as fiction about fiction or Romantic irony in the context of Romantic works of literature. It is a kind of fiction that self-consciously and systematically addresses the devices of fiction and draws readers’attention to its status as an artifact by posing questions about the relationship between fiction and reality, usually using irony and self-reflection to expose the fictional illusion.Some celebrated cases are: John Fowles's The French Lieutenant's Woman (1969), Italo Calvino's Se una notte d'inverno un viaggatore (If on a winter's night a traveler, 1979) and Muriel Spark's The Comforters (1957).Fabulation/SurfictionFabulation is a type of modern fiction that openly delights in its self‐conscious verbal artifice and do not fit into the traditional categories of realism or (novelistic) romance. They violate, in a variety of ways, standard novelistic expectations by drastic—and sometimes highly successful—experiments with subject matter, form, style, temporal sequence, and fusions of the everyday, fantastic, mythical, and nightmarish, in renderings that blur traditional distinctions between what is serious or trivial, horrible or ludicrous, tragic or comic.Surfiction is a new kind of fiction which is now more often referred to as postmodernist. Rather than attempt to mirror some pre‐existing reality, surfiction abandons realism in favour of metafiction, self‐consciously advertising its own fictional status. It doesn’t attempt to be meaningful, truthful, or realistic.Some examples of fabulation/surfiction are John Simmons Barth’s The Sot-Weed Factor (1960), Lost in the Funhouse (1968) and William Gaddis’s J R (1975), A Frolic of His Own (1994).Magic realismMagic realism, a kind of modern fiction in which fabulous and fantastical events are included in a narrative that otherwise maintains the ‘reliable’ tone of objective realistic report. It is what happens when a highly detailed, realistic setting is invaded by something too strange to believe (for example: the fantastic attributes given to characters in such novels—levitation, flight, telepathy, telekinesis). Rather than explain reality using natural or physical laws, magical realist texts create a reality in which the relation between incidents, characters, and setting could not be based upon or justified by their status within the physical world or their normal acceptance by bourgeois mentality. It designates a tendency of the modern novel to reach beyond the confines of realism and draw upon the energies of fable, folktale and myth while retaining a strong contemporary social relevance.Gabriel García Márquez's Cien años de soledad (One Hundred Years of Solitude, 1967) is often cited as a leading example. Thus Juan Rulfo’s Pedro Páramo (1955) and Günter Grass's Die Blechtrommel (The Tin Drum, 1959) are also among the greatest magic realist novels.。

IDL在气象中的应用

U s i n g I D L i n M e t e o r o l o g yV e r s i o n4Andy HeapsAcknowledgementsSpecial thanks are due to the Reading University Meteorology department IDL users for all their help and suggestions in improving this guide. I am also much indebted to the pvwave-idl newsgroup and ITT-VIS support for many interesting discussions and for answering the really difficult questions. The cover picture is of icebergs in Sermilik fjord on the east coast of Greenland. At the top of the picture isthe edge of the inland ice cap.At the core of this guide are a set of routines for making plots that are commonly used in meteorology.File and postscript operatorsNCREAD - read a netCDF file.NCWRITE - write a netCDF file.POS - select the plot position in the postscript page.PSOPEN - open a postscript file.PSCLOSE - close a postscript file.Contour plotsCOLBAR - draw a colour bar.CON - draw a contour plot.LEVS - select contour levels.MAP - select a map projection.STIPPLE - plot stipples on a contour plot.Vector plotsVECT - plot vectors.Graph plotsEBAR - draw error bars.GPLOT - draw lines, text and symbols on plots.GSYM - a selection of plotting symbols.HIST - plot histograms.GeneralAXES - label axesCS - select a colour scale.GHELP - view guide procedure references documentation on-line.GSET - set up the plotting area.LEGEND - make a legend for the plot.PCON - convert pressure to height in km and vice-versa.REGRID - regrid longitude-latitude data using bi-linear interpolation.SCROP - convert input to a character string.The guide is split into three sections: plot and code examples, a guide to using IDL and guide routine reference. A full discussion of these routines and their options is available at the end of the guide. Setting up access to the guide routinesMeteorology department at the University of Reading users:addsetup idl idl_guide4to your .kshrc file and open a new terminal window.All other users:Running the examplesTo start up IDL and to compile and run the first example in this guide type the following commands at the UNIX command prompt:idlex1Typing q or pressing escape in the graphic window will quit the viewing of the plot and return to the IDL command prompt.PRO ex1d=NCREAD('gdata.nc')PSOPENCS, SCALE=1MAPLEVS, MIN=-32, MAX=32, STEP=4CON, FIELD=d.temp(*,*,0), X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)' PSCLOSEENDIn the first example we plot the temperature at 1000mb.The program ex1.pro is made up from the following components:PRO ex1 name the procedure.d=NCREAD('gdata.nc') - reads a netCDF file.This routine stores the requested data from a netCDF file in a structure called d. In this case all the netCDF variables are read.PSOPEN - opens a postscript file.CS, SCALE=1 - set the colour scale to scale number 1.MAP - sets the cylindrical map projection.LEVS, MIN=-32, MAX=32, STEP=4 - specify the plotting levels. In this case we select a minimum of 32, maximum of 32 and a step of 4.CON, FIELD=d.temp(*,*,0), X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)' This makes the contour plot. To the CON command we pass the temperature at the first level (1000mb), the longitudes and latitudes and a title. We know that level number 0 is 1000mb as:PRINT, d.p(0)1000.00PSCLOSE - close and view the postscript file.END - end the procedure.The program examples, data and library can be accessed by typing gex, gdata or glib at the UNIX command prompt. Start off by copying the example that most closely matches the plot you d like to make to your home directory. Then modify the code to try and inset your data/parameters into the code. i.e.gexcp ex1.pro ~cd ~nedit ex1.pro &Then edit the code as required in ex1.pro. This new version of ex1.pro takes precedence over the examples directory ex1.pro as it is in the current directory.If you are making changes to the ex1.pro program then a recompilation of the ex1.pro file is required:idl.compile ex1ex1As you become more familiar with the plotting routines you ll be able to write plotting code without reference to the example code.PRO ex2d=NCREAD('gdata.nc')PSOPENCS, SCALE=1MAP, LONMIN=-20, LONMAX=40, LATMIN=30, LATMAX=70LEVS, MIN=-20, MAX=20, STEP=2.5, NDECS=1CON, FIELD=d.temp(*,*,0), X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)' PSCLOSEENDIn the second example, we have zoomed into a region by setting the mapping usingMAP, LONMIN=-20, LONMAX=40, LATMIN=30, LATMAX=70The levels were also changed to better fit the temperature range over the plotted region. Notice that the levels were selected so that all the negative temperatures were shaded blue.In the LEVS command NDECS=1 was used to limit the number of decimal places of the levels to one.PRO ex3d=NCREAD('gdata.nc')PSOPENCS, SCALE=4MAP, /NHLEVS, MIN=15150, MAX=16650, STEP=150CON, FIELD=d.ht(*,*,6), X=d.lon, Y=t, CB_TITLE='Height (m)', $TITLE='Jan 1987 - 100mb Geopotential Height'PSCLOSEENDA northern hemisphere polar plot of geopotential height at 100mb can be made using the flag /NH to the MAP command.CS, SCALE=4 - We also used colour scale number four which fits in better with the selected levels.We know that level number 6 is 100mb as:PRINT, d.p(6)100.000The CON command was a little long to fit on one line so the IDL continuation symbol $ was used.PRO ex4d=NCREAD('gdata.nc')PSOPENCS, SCALE=1MAPLEVS, MIN=-32, MAX=32, STEP=4CON, FIELD=d.temp(*,*,0), X=d.lon, Y=t, TITLE='Jan 1987', /NOFILL PSCLOSEENDTo create an unfilled contour plot add the /NOFILL option to the CON command.PRO ex5d=NCREAD('strf_anom.nc')PSOPEN, XSIZE=20000, YSIZE=5000, CCOLOUR=7CS, SCALE=7MAP, LATMIN=0LEVS, MIN=-6, MAX=5, STEP=1CON, FIELD=d.strf/1e6, X=d.x, Y=d.y, TITLE='Streamfunction anomaly*1e6 s!E-1!N', $ NEGATIVE_STYLE=2, ZERO_THICK=200, /NOFILLPSCLOSEENDIn example 5 we modified:PSOPEN,XSIZE=20000, YSIZE=5000 - the plot size was manually set to be 20000x5000 pixels to give a 1:1 spatial correspondence between the longitude and latitude degrees.PSOPEN, CCOLOUR=7 - coastlines were also changed to colour 7 on SCALE=7, grey.CON options:NEGATIVE_STYLE=2 -negative contour lines to be dashed.ZERO_THICK=200 - set zero line to be twice as thick as normal.The character string s!E-1!N was used to make s-1 in the title.More on text formatting in section 11 of the brief introduction to IDL later after the examples.PRO ex6d=NCREAD('gdata.nc')PSOPENCS, SCALE=1MAP, LONMIN=-20, LONMAX=40, LATMIN=30, LATMAX=70LEVS, MIN=-20, MAX=20, STEP=2.5, NDECS=1CON, FIELD=d.temp(*,*,0), X=d.lon, Y=t, TITLE='Jan 1987', $ CB_TITLE='Temperature (Celsius)', /BLOCKPSCLOSEENDA block plot is where the individual cells of data are shaded according to their value. To make a block fill plot use the /BLOCK option to the CON command.PRO ex7d=NCREAD('gdata.nc')PSOPEN, XPLOTS=2, YPLOTS=2CS, SCALE=1LEVS, MIN=-32, MAX=32, STEP=4t1000=d.temp(*,*,0)MAPCON, FIELD=t1000, X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)' POS, XPOS=1, YPOS=2MAP, /MOLLWEIDECON, FIELD=t1000, X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)' POS, XPOS=2, YPOS=1MAP, /ROBINSON, LONMIN=0, LONMAX=360CON, FIELD=t1000, X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)', $ /CB_UNDERPOS, XPOS=2, YPOS=2MAP, SATELLITE=[0, 50]CON, FIELD=t1000, X=d.lon, Y=t, TITLE='Jan 1987', CB_TITLE='Temperature (Celsius)', $ /CB_RIGHTPSCLOSEENDIn example 7 four different map projections are plotted on a page and the POS command is used to specify each position. The default position for a plot is POS, XPOS=1, YPOS=1 so it is not necessary to set it for the first plot made.PRO ex8d=NCREAD('gdata.nc')PSOPEN, TICKLEN=-200, FONT=6, TFONT=6, TCHARSIZE=200, SPACE1=300, $ CB_WIDTH=75, CB_HEIGHT=200CS, SCALE=1MAPLEVS, MIN=-32, MAX=32, STEP=4CON, FIELD=d.temp(*,*,0), X=d.lon, Y=t, TITLE='Jan 1987',CB_TITLE='Temperature (Celsius)' PSCLOSEENDIn example 8 the following options to PSOPEN were changed:TICKLEN=-200 - set the tick length to be -200 pixels i.e. into the plot.FONT=6 - changed the general and title font to be Schoolbook.TCHARSIZE=200 -set title font to be twice normal size.SPACE1=300 -set the general spacing of text and lines to be 300 pixels.CB_WIDTH=75, CB_HEIGHT=200 -changed the colour bar to be 75% of the width of the plot and to be twice the normal height.See the reference section entry for PSOPEN for a full listing of all the options to PSOPEN.PRO ex9d=NCREAD('elnino.nc')PSOPENCS, COLS=[93, 123, 91, 267, 272, 367, 409, 419, 407]MAP, LONMIN=110, LONMAX=300, LATMIN=-50, LATMAX=40LEVS, MANUAL=['-1', '-0.5', '0.5', '1', '1.5', '2', '2.5', '3']CON, FIELD=d.sst, X=d.lon, Y=t, TITLE='October SST Anomaly', $CB_TITLE='Temperature (Celsius)'AXES, STEP=10PSCLOSEENDIn this plot we defined a simple colour scale to fit the temperature anomaly by selecting the colours by name. In addition the /NOAXES keyword was passed to the CON command and a separate call was made to the AXES command to specify an axis label every ten degrees. This was done because the longitude extent of 190 degrees would have produced a strange set of longitude and latitude labels. The levels were placed inside quotes as the levels contained both integer and floating point numbers. If this had not been done IDL would have translated the levels into a set of floating point numbers and the formatting would have been lost.CS, COLS=[93, 123, 91, 267, 272, 367, 409, 419, 407]was used to manually select a colour scale from a table of colours.PRO ex10PSOPENPOS, XSIZE=10000, YSIZE=8000, XOFFSET=2000, YOFFSET=6000d=NCREAD('orog_lr.nc')CS, SCALE=30MAP, LONMIN=50, LONMAX=150, LATMIN=0, LATMAX=60LEVS, MIN=250, MAX=6000, STEP=250, /EXACTCON, FIELD=d.ht, X=d.longitude, Y=titude, TITLE='Low resolution orography', $ /BLOCK, /NOLINES, /NOCOLBAR, /NOAXESAXES, XSTEP=20, YSTEP=10POS, XSIZE=10000, YSIZE=8000, XOFFSET=14000, YOFFSET=6000d=NCREAD('orog_hr.nc')MAP, LONMIN=50, LONMAX=150, LATMIN=0, LATMAX=60CON, FIELD=d.ht, X=d.longitude, Y=titude, TITLE='High resolution orography',$ /BLOCK, /NOLINES, /NOCOLBAR, /NOAXESAXES, XSTEP=20, YSTEP=10COLBAR, COORDS=[3000, 3000, 23000, 3700], LEVS=LINDGEN(24)*250+250, $ TITLE='Height (m)', /ALTPSCLOSEENDHere we plotted low and high resolution block fill orography over India and the Himalaya region. The individual colour bars were turned off with the /NOCOLBAR option to CON. A separate call to COLBAR was made to produce a colour bar for both plots. The /ALT option to COLBAR alternated the text top and bottom on the colour bar or the text would overlap.POS was used to manually place the plots using the supplied sizes and offsets. IDL uses 1000 pixels per centimetre in postscript plots. To see where your plot is being positioned just divide the POS options by 1000 to get centimetres.PRO ex11d=NCREAD('lc1.nc')PSOPENCS, SCALE=1, NCOLS=19MAP, LONMIN=0, LONMAX=110, LATMIN=20, LATMAX=75, /SECTORLEVS, MIN=-20, MAX=20, STEP=2, /EXACTCON, FIELD=d.temp, X=d.longitude, Y=titude, COL=INDGEN(21)+2, /NOFILL, /NOMAP LEVS, MANUAL=['-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.2', '0.4', '0.6', '0.8', '1.0']CON, FIELD=d.mslp, X=d.longitude, Y=titude, NEGATIVE_STYLE=2, /NOFILL, /NOMAP PSCLOSEENDIn example 11 some LC1 experiment data was plotted. The individual temperature contour colours were specified with the COL option to CON. A further plot of mean sea level pressure anomaly was then overlaid on this with another call to CON.PRO ex12d=NCREAD('orca2.nc')PSOPEN, YPLOTS=2, /PORTRAITCS, SCALE=1MAPLEVS, MIN=-2, MAX=30, STEP=2CON, FIELD=d.sst, X=d.longitude, Y=titude, CB_TITLE='Temperature (Celsius)'POS, YPOS=2MAP, LAND=12, OCEAN=8, /DRAWGPLOT, X=d.longitude, Y=titude, SYM=1, /NOLINES, SIZE=10AXESPSCLOSEENDThe top plot shows the Orca2 grid point locations with two northern poles and one southern pole. The bottom plot shows the sea surface temperature on this grid being contoured and plotted in the usual manner.PRO ex13d=NCREAD('gdata.nc')zonalu=TOTAL(d.u,1)/N_ELEMENTS(d.lon)PSOPEN, SPACE2=800LEVS, MIN=-32, MAX=32, STEP=4CS, SCALE=1GSET, XMIN=-90, XMAX=90, YMIN=1000, YMAX=0CON, FIELD=zonalu, X=t, Y=d.p, TITLE='Jan 1987 - Zonal Mean Zonal Wind', $ NEGATIVE_STYLE=2, ZERO_THICK=200,CB_TITLE='ms!E-1!N'AXES, XSTEP=30, XTITLE='Latitude', YSTEP=-250, YTITLE='Pressure (mb)'PSCLOSEENDIn this plot we used the IDL routine TOTAL to zonally average the zonal wind. The zonal wind field d.u is an array with the dimensions longitude, latitude, height. The statement (d.u,1)/N_ELEMENTS(d.lon) sums the array over the first dimension of longitude and then divides by the number of longitudes to get the zonal mean zonal wind.The plotting region was set using GSET before contouring the data with negative contours dashed and a thicker zero contour line.The plot to colour bar spacing was increased to 800 pixels with PSOPEN, SPACE2=800 so that the colour bar didn t overwrite the x-axis title of Latitude.A separate call was made to AXES to label them with the specified axis labels. Note that a YSTEP= -250 was used as YMIN=1000 and YMAX=0.PRO ex14d=NCREAD('gdata.nc')zonalu=TOTAL(d.u,1)/N_ELEMENTS(d.lon)PSOPENCS, COLS=[464]LEVS, MANUAL=[-200, 0], /EXACTGSET, XMIN=-90, XMAX=90, YMIN=1000, YMAX=0.3, /YLOGCON, FIELD=zonalu, X=t, Y=d.p, TITLE='Jan 1987 - Zonal Mean Zonal Wind', $ CB_TITLE='ms!E-1!N', /NOLINES, /NOCOLBARLEVS, MIN=-100, MAX=35, STEP=5CON, FIELD=zonalu, X=t, Y=d.p, NEGATIVE_STYLE=2, ZERO_THICK=200, $ /NOFILLAXES, XSTEP=30, XTITLE='Latitude', YVALS=['1000', '300', '100', '30', '10', '3', '1', '0.3'], $ YTITLE='Pressure (mb)', /NORIGHTylabels=[0, 10, 20, 30, 40, 50, 56]yvals=PCON(ylabels, /TO_MB)AXES, /ONLYRIGHT, YVALS=yvals, YLABELS=ylabels, YTITLE='Height (km)'PSCLOSEENDHere we made a logarithmic y-axis by adding the /YLOG keyword to GSET.In this plot we used a single shade of grey for the negative values. A second call to CON was made to overlay the contour levels. Shading with grey is a common way of avoiding paying journal colour page charges.The right y-axis was labelled by height rather than pressure by using the PCON function to convert pressure to height. Be careful to not do the same for troposphere plots as the pressure height relation is only reliable for the stratosphere. The /NORIGHT and /ONLYRIGHT keywords to the AXES routine were used to plot the right y-axis in the correct way.PRO ex15d=NCREAD('stipple.nc')PSOPENCS, SCALE=11, NCOLS=14MAP, /ROBINSONLEVS, MIN=-30, MAX=30, STEP=5CON, FIELD=d.climatology, X=d.longitude, Y=titude, CB_TITLE='Temperature [Celcius]' STIPPLE, FIELD=d.climatology-d.jan1963, X=d.longitude, Y=titude, MIN=1, MAX=20 PSCLOSEENDIn example 15 the STIPPLE procedure is used to show and anomaly field on top of a contour plot. The contour plot is that of the 1957-2002 January climatology. The stippled field is that where the January 1963 temperature is one degree or less below this climatology.Stippling is commonly used to indicate anomalies and deviations.PRO ex16d=NCREAD('qbo.nc')PSOPEN, SPACE2=800CS, SCALE=1LEVS, MIN=-40, MAX=15, STEP=5GSET, XMIN=90.5, XMAX=1169.5, YMIN=1000, YMAX=10, /YLOGCON, FIELD=d.zonalu, X=d.time, Y=d.pressure, TITLE='Zonal Mean Zonal Wind', $ CB_TITLE='ms!E-1!N', ZERO_THICK=200yvals=[1000, 700, 500, 300, 200, 100, 70, 50, 30, 20, 10]xvals=[90.5, 480.5, 840.5, 1169.5]xlabels=['01/12/1978:12z', '01/01/1980:12z', '01/01/1981:12z', '01/12/1981:12z']AXES, XVALS=xvals, XLABELS=xlabels, XTITLE='Time', $YVALS=yvals, YLABELS=yvals, YTITLE='Pressure [hPa]'PSCLOSEENDIn this plot XVALS was associated with XLABELS to produce different labels on the x-axis other than the value at that point. We also set YVALS to be a string array so that we both set the YVALS and label them with the same array. A logarithmic y-axis was made and labelled with the pressure.PRO ex17d=NCREAD('u2.nc')PSOPENCS, SCALE=1GSET, XMIN=120, XMAX=240, YMIN=0, YMAX=199LEVS, MIN=-8, MAX=5, STEP=1CON, FIELD=d.u, X=d.longitude, Y=d.day, TITLE='Zonal wind',CB_TITLE='ms!E-1!N', $ /NOLINES, /NOAXESLEVS, MANUAL=[-1000, 0]CON, FIELD=d.u, X=d.longitude, Y=d.day, /NOLINELABELS, /NOFILLAXES, XVALS=[120,150,180,210,240], $XLABELS=['120E', '150E','DL','150W', '120W'], $YVALS=[12,43,74,102,133,163,194], $YLABELS=['Jan 1995','Feb 1995','Mar 1995','Apr 1995', $'May 1995','Jun 1995','Jul 1995']PSCLOSEENDHere we made a longitude-time plot. A separate call to CON was made draw a single zero contour line. The contour lines weren t drawn as this would have made seeing the structure within the data very difficult.PRO ex18PSOPENCS, SCALE=26MAP, OCEAN=2, LAND=14, /DRAWGPLOT, X=-120, Y=8, TEXT='Nino3 region', FONT=7xpts=[-150, -150, -90, -90, -150]ypts=[-5, 5, 5, -5, -5]GPLOT, X=xpts, Y=ypts, FILLCOL=17, THICK=400GPLOT, X=43, Y=55, TEXT='Moscow', ALIGN=0.0, VALIGN=0.5GPLOT, X=37, Y=55 , SYM=3, SIZE=200, COL=25GPLOT, X=103, Y=-30, TEXT='Australia', CHARSIZE=200, ORIENTATION=-90, COL=22 GPLOT, X=3000, Y=19000, TEXT='Text above plot and left justified', FONT=2, $ ALIGN=0.0, SIZE=150, /BOLD, /ITALIC, /DEVICEGPLOT, X=26700, Y=19000, TEXT='Text above plot and right justified', FONT=2, $ ALIGN=1.0, SIZE=150, /DEVICEAXESPSCLOSEENDExample 18 shows how to draw lines, text and symbols on plots. The locations of these are in data units which are specified by MAP (degrees) or GSET. If the keyword /DEVICE is used then the locations are in device or pixel coordinates. Using device coordinates means the bottom left corner is (0,0) and the size of the default A4 paper size is 29700x21600 pixels.PRO ex19d=NCREAD('vapour.nc')PSOPENCS, IDL=33, NCOLS=9MAPLEVS, MANUAL=['1E6', '5E6', '1E7', '5E7', '1E8', '5E8', '1E9', '5E9', '1E10', '5E10'], /EXACT CON, FIELD=d.vapour, X=d.longitude, Y=titude, TITLE='Total Emissions - January',$ CB_TITLE='H!I2!NO vapour(g)', /NOLINESPSCLOSEENDExample 19 shows a plot of some AERO2k aircraft water emission vapour total column data for January. LEVS is used with the MANUAL keyword and the levels are preserved by using quotes to be used in the colour bar. If the quotes weren t used then the levels would have been labelled as floating point numbers.PRO ex20d=NCREAD('gwinds.nc')PSOPENCS, SCALE=1MAP, LONMIN=10, LONMAX=120, LATMIN=-30, LATMAX=30, /DRAWVECT, U=d.u(*,*,3), V=d.v(*,*,3), X=d.longitude, Y=titude, $LENGTH=200, MAG=10, MUNITS='ms!E-1!N'AXES, STEP=10PSCLOSEENDIn ex20 the u and v components of the wind were selected at 870mb and were passed to the VECT routine for plotting. The mapping was set and drawn by adding the /DRAW option to MAP.PRO ex21d=NCREAD('gwinds.nc')PSOPEN, XPLOTS=2CS, SCALE=1MAP, /NH, /DRAWVECT, U=d.u(*,*,14), V=d.v(*,*,14), X=d.longitude, Y=titude, $MAG=10, MUNITS='ms!E-1!N'AXES, STEP=30POS, XPOS=2MAP, /NH, /DRAWVECT, U=d.u(*,*,14), V=d.v(*,*,14), X=d.longitude, Y=titude, $MAG=10, MUNITS='ms!E-1!N', PTS=40AXES, STEP=30PSCLOSEENDWith polar vectors, the regular latitudes contract to give a bulls eye effect as seen in the bottom plot. When the PTS=40 option is added to the VECT routine for polar plots the data is interpolated using bi-linear interpolation to 40 regularly spaced points in both the x and y directions. With the data interpolated in this manner it is much easier to see the wind direction than in the regularly plotted vectors at their original longitudes and latitudes.PRO ex22d=NCREAD('epflux.nc')PSOPENCS, SCALE=6, NCOLS=10LEVS, MIN=-2, MAX=2, STEP=0.5, NDECS=1GSET, XMIN=0, XMAX=90, YMIN=1000, YMAX=0, TITLE='EP Flux and Zonal Wind Anomalies' CON, X=titude, Y=d.plev, FIELD=d.u, ZERO_STYLE=2, /NOCOLBARVECT, X=titude, Y=d.plev, U=d.epfu, V=d.epfv, $MAG=['1E14', '1E19'], LENGTH=[100, 200], MUNITS=['m!E2!Ns!E-2!N', 'ms!E-2!NPa'] AXES, XSTEP=15, YSTEP=-200, YMINOR=-50, XTITLE='Latitude (N)', YTITLE='Pressure (hPa)' PSCLOSEENDSome plots have widely differing scales for the u and v components as in ex22. If your plot requires a precise angle for the vector then:1)PSOPEN, XSIZE=xsize, YSIZE=ysize will set your plot size in pixels. 1cm=1000 pixels.2)Use the LENGTH=length option to VECT to set your vector lengths in the x and y-directions. Theseare by default 100 (%) which is 300 pixels (0.3cm).3)Use MAG=mag option to VECT to set the magnitudes of the u and v-components.PRO ex23xpts=FINDGEN(361)PSOPENCS, SCALE=1GSET, XMIN=0, XMAX=360, YMIN=-1, YMAX=1GPLOT, X=xpts, Y=SIN(xpts*!DTOR)AXESPSCLOSEENDIn example 22 a plot of a sine curve is made. To convert from degrees to radians the scaling factor !DTOR is used. Calling AXES without any user supplied values gives default axis values that can be improved on by specifying some extra keywords, as shown in the next example.PRO ex24xpts=FINDGEN(361)ypts=[[SIN(xpts*!DTOR)], [COS(xpts*!DTOR)]]PSOPEN, THICK=200, CHARSIZE=150, XOFFSET=2000CS, SCALE=1GSET, XMIN=0, XMAX=360, YMIN=-1, YMAX=1AXES, XSTEP=30, XTITLE='Degrees', YTITLE='Amplitude', YSTEP=0.25, NDECS=2GPLOT, X=xpts, Y=ypts, /LEGEND, LEGPOS=3, COL=[3, 18], LABELS=['Sine', 'Cosine'] PSCLOSEENDIn example 23 sine and cosine curves are plotted and a legend was made. The axes were better labelled by specifying different steps and labels for the x and y-axes. To make the plot easier to see the lines were made thicker and the text larger with the options THICK=200, CHARSIZE=120 to PSOPEN. The plot was shifted slightly to the right with XOFFSET=2000 so that there was space for the y-axis title of Amplitude.PRO ex25d=NCREAD('fieldsite.nc')PSOPEN, THICK=200, CHARSIZE=140CS, SCALE=1GSET, XMIN=7, XMAX=25, YMIN=-5, YMAX=15, TITLE='Reading field site temperatures' labels=['Maximum', 'Minimum']GPLOT, X=d.day, Y=d.temperature, /LEGEND, LEGPOS=7, SYM=[1, 2], $ COL=[18, 3], LABELS=labelsxvals=INDGEN(10)*2+7xlabels=SCROP(xvals)+' Feb'AXES, XVALS=xvals, XLABELS=xlabels, YSTEP=5, YTITLE='Temp [Deg C]', MINOR=1 PSCLOSEENDExample 25 shows the minimum and maximum at the University of Reading field site. The data points have markers associated with them by using the SYM keyword to GPLOT.(Temperature data from /weather/citylist/World.htm)PRO ex26d=NCREAD('dailyt.nc')PSOPEN, THICK=200, CHARSIZE=150CS, SCALE=28GSET, XMIN=1, XMAX=365, YMIN=-30, YMAX=40, TITLE='Daily temperature'xvals=[1,31,59,90,120,151,181,212,243,273,304,334]months=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']cities=['Beijing', 'Mumbai', 'Moscow', 'Sydney', 'Winnipeg', 'Brasilia']cols=INDGEN(6)+2GPLOT, X=d.day, Y=d.temp, /LEGEND, LEGPOS=6, COL=cols, LABELS=citiesAXES, XVALS=xvals, XLABELS=months, XTITLE='Month', YSTEP=10, YTITLE='Degrees Celsius' PSCLOSEENDIn example 26 the x-values in days were translated into months and plotted using the AXES procedure. The legend position was also moved using the LEGPOS keyword to GPLOT to a central position as there was space there where no lines from the graph were drawn.(Data from /gmd/webdata/ccgg/trends/co2_mm_mlo.dat)PRO ex27d=NCREAD('maunaloa.nc')PSOPEN, THICK=200, CHARSIZE=150CS, SCALE=1GSET, XMIN=1958, XMAX=2010, YMIN=310, YMAX=390, $TITLE='Atmospheric CO!I2!N at Mauna Loa Observatory'GPLOT, X=d.time(0:186), Y=d.co2(0:186), COL=3GPLOT, X=d.time(186:N_ELEMENTS(d.time)-1), Y=d.co2(186:N_ELEMENTS(d.time)-1), COL=18 LEGEND, LEGPOS=1, COL=[3, 18], LABELS=['SIO', 'CDIAC']AXES, XVALS=INDGEN(6)*10+1960, XTITLE='Year', YSTEP=10, YTITLE='Concentration (ppm)' PSCLOSEENDIn this plot the CO2 concentration at Mauna Loa was plotted for two different stations. As the data came from two different stations this was differentiated by making into two curves. The crossover point (pt=186) is the date the monitoring station used was changed, February 1974. A separate call to the LEGEND procedure was made to form a legend for the two lines.PRO ex28d=NCREAD('gdata.nc')zonalt=TOTAL(d.temp,1)/N_ELEMENTS(d.lon)zonalu=TOTAL(d.u,1)/N_ELEMENTS(d.lon)PSOPEN, THICK=200, CHARSIZE=150CS, SCALE=28GSET, XMIN=-90, XMAX=90, YMIN=-30, YMAX=30, $TITLE='Zonal Mean Temperature and Zonal Mean Zonal Wind'GPLOT, X=t, Y=zonalt(*,0), COL=4AXES, YSTEP=10, YTITLE='Temperature (Celsius)', /ONLYLEFTAXES, XSTEP=30, XTITLE='Latitude', /NOLEFT, /NORIGHTGSET, XMIN=-90, XMAX=90, YMIN=-15, YMAX=15GPLOT, X=t, Y=zonalu(*,0), COL=7AXES, STEP=5, YTITLE='Zonal Wind ms!E-1!N', /ONLYRIGHTLEGEND, LEGPOS=9, COL=[7, 4], LABELS=['Zonal Wind', 'Temperature']PSCLOSEENDIn example 28 a graph with two independent y-axes is plotted. The /NORIGHT was used to omit the righty-axis. A second call was then made to GSET to reset the plotting limits. Finally another call to AXES was made with the /ONLYRIGHT keyword to draw the right y-axis.PRO ex29d=NCREAD('amo.nc')PSOPEN, THICK=300, CHARSIZE=150CS,SCALE=28GSET, XMIN=1870, XMAX=2000, YMIN=-0.3, YMAX=0.3, TITLE='AMO Index'GPLOT, X=d.year, Y=d.index, BELOW=4, ABOVE=2AXES, XSTEP=10, YSTEP=0.1, NDECS=1PSCLOSEENDIn example 29 the ABOVE and BELOW keywords to GPLOT were used to fill the curve above and below the zero line with different colours.PRO ex30d=NCREAD('gdata.nc')zonalu=TOTAL(d.temp,1)/N_ELEMENTS(d.lon)profiles=TRANSPOSE([zonalu(12, *), zonalu(36, *), zonalu(60, *)])labels=['60S', 'Equator', '60N']PSOPEN, THICK=200, CHARSIZE=150CS, SCALE=28GSET, XMIN=-90, XMAX=90, YMIN=1000, YMAX=0.3, /YLOG, $TITLE='Zonal Mean Zonal Wind (ms!E-1!N)'GPLOT, X=profiles, Y=d.p, /LEGEND, LEGPOS=9, COL=[2, 3, 4], LABELS=labelsAXES, XSTEP=30, XTITLE='Latitude', YTITLE='Pressure (mb)', /NORIGHT, $ YVALS=['1000', '300', '100', '30', '10', '3', '1', '0.3']ylabels=[0, 10, 20, 30, 40, 50, 56]yvals=PCON(ylabels, /TO_MB)AXES, /ONLYRIGHT, YVALS=yvals, YLABELS=ylabels, YTITLE='Height (km)'PSCLOSEENDHere we plot the zonal mean zonal winds at three latitudes on a logarithmic vertical y-axis. The IDL routine TRANSPOSE was used to rotate the profiles array so that the three lines were plotted vertically rather than horizontally.。

R语言绘图

R语言绘图画图例子 R Graph Cookbook例子 [html] view plain copy print ? span style =font-size:18px; / span [html] view plain copy print ? span style = font-size:18px; From： / span a href = span style = fon画图例子R Graph Cookbook例子[html] view plaincopyprint? [html] view plaincopyprint?From：<a href=""></a>今天突然找到一本专门教授R语言绘图的书，R Graph Cookbook，发现还不错。

当初喜欢上R语言就是因为它绘图特别好看。

下面把这本书的内容，经过我学习后，翻译并贴上了吧。

（我水平还不够，原创还不行呀，不过学习就是先模仿再创新的过程）之所以要贴到博客上来，我是怕有一天我也忘记了，好直接到我博客上来搜索一下，同时也方便了别人。

下面说的是用R语言绘制散点图。

其实绘制散点图，用plot（x,y）直接就可以了，不过这样绘制出来的散点图太简单了，不能达到使用的要求，所以要详细得设置一些参数，才能让图形更加的漂亮。

还是直接上代码吧，我已经做好注释的了。

1.plot(cars$dist~cars$speed,#y~x，cars是R自带的数据2.main="Relationship between car distance & speed",#标题3.xlab = "Speed(miles per hour)",#x轴标题4.ylab = "Distance travelled (miles)",#Y轴标题5.xlim = c(0,30),#设置x轴的取值区间为0到306.ylim = c(0,140),#设置y轴的取值区间为0到1407.xaxs = "i",#这里是设置x轴的风格，暂时没看明白有多大区别8.yaxs = "i",9.col = "red",#设置颜色10.pch = 19)#pch指代点的形状，用数字表示，可查看帮助文档11.#如果要保存图片怎么办呢？我觉得最简单的方法就是使用RStudio这个IDE，极其得好，可惜很多人都不知道。

ExplainingNormalQuantile-QuantilePlotsthrough...

Explaining Normal Quantile-Quantile Plots through Animation:The Water-Filling AnalogyRobert A.StineDepartment of StatisticsThe Wharton School of the University of PennsylvaniaPhiladelphia,PA19104-6340September9,2016AbstractA normal quantile-quantile(QQ)plot is an important diagnostic for checking the as-sumption of normality.Though useful,these plots confuse students in my introductory statistics classes.A water-ﬁlling analogy,however,intuitively conveys the underlying concept.This analogy characterizes a QQ plot as a parametric plot of the water levels in two graduallyﬁlling vases.Each vase takes its shape from a probability distribution or sample.If the vases share a common shape,then the water levels match throughout theﬁlling,and the QQ plot traces a diagonal line.An R package qqvases provides an interactive animation of this process and is suitable for classroom use.Key words:Education,diagnostic,simulation.1Figure1:Though hard to judge from the histogram,the normal QQ plot shows that the distribution of daily percentage changes in the value of Apple stock in2014-2015has thicker tails than a normal distribution.1IntroductionNormal QQ plots are an important visual diagnostic,but one that can be hard toexplain.Students quickly learn that if the data in a normal QQ plot deviate from adiagonal reference line,then the assumption of normality is questionable.For example,Figure1illustrates the diﬃculty of judging normality from a histogram.The data aredaily percentage changes in the value of Apple stock in2014-2015.The histogram isbell-shaped,but the distribution has thicker tails than anticipated by normality.Thedeviations from the diagonal line in the normal QQ plot imply that,in the extremes,the data extend farther out than expected under normality.While they recognize itsimportance,many of my students have treated this plot as a graphical“black box”:auseful diagnostic that relies on a magical mechanism.In the spirit of Brown and Kass(2009)and Cobb(2015),this paper oﬀers a heuristic that makes these“fundamentalconcepts accessible.”2Water-ﬁlling analogyA normal QQ plot compares the shape of the empirical distribution of a sample to theshape of a normal distribution.To set up the analogy,consider comparing the shapeof a continuous distribution to that of the normal.Quantile plots graph percentilesof the distributions and therein lies the diﬃculty for students.Many of my studentsFigure2:Diﬀerent water levels in two vases reﬂect the diﬀerent shapes of the underlying distributions.The pair in the left frame are10%full,then50%full in the middle frame,and ﬁnally80%full on the right.ﬁnd a cumulative distribution confusing enough without considering its inverse.A sim-ple analogy,however,makes quantile functions more ly,quantilefunctions are analogous to water levels as weﬁll transparent vases.Figure2illustrates the idea.The“vase”on the left of each frame of Figure2is formed by gluing a gamma density function with its mirror image.Similarly,the vaseon the right of each frame is a Gaussian vase.Both vases have the same“volume”(really,area).To limit the heights of the vases,both are truncated at the0.0005and0.9995percentiles.(I call these containers vases because of their resemblance to vaseplots(Benjamini,1988).)Now imagine simultaneouslyﬁlling the two vases at equalrates with water,as suggested by the sequence of plots in theﬁgure.The left frameshows the two vases,initially10%full.The middle frame shows themﬁlled to50%,and the right frame shows them80%full.If two vases have the same shape,then thewater levels match throughout theﬁlling.Otherwise,as in this example,oneﬁlls moreslowly than the other,and the levels diﬀer.The level in the gamma vase grows slowlyat the start of theﬁlling because it has a wide base.As theﬁlling proceeds,the levelin the gamma vase eventually catches up with the level in the Gaussian vase becauseboth vases hold the same amount.In this context,a normal QQ plot is the parametric plot of these water levels.The level of the Gaussian vase determines the coordinate on the x-axis,and the level ofthe other vase gives the coordinate for the y-axis.Figure3shows the normal quantileplot for the two vases in Figure2,as rendered by the accompanying application.Tomake the linkage between this graph and the vases explicit,theﬁgure displays halvesof the vases(the density functions)along the respective axes.The quantile plot addsFigure3:This view of the open-source application shows the normal QQ plot for a gamma distribution.Adjacent vases reinforce the water-ﬁlling analogy as the quantiles increase.a diagonal line to make it easier to identify curvature.Controls in the application shown in Figure3allow interactive modiﬁcations.The percentile slider controls the water level;for example,moving the slider to the rightincreases the water level and extends the curve in the plot.Other controls change thedistribution that deﬁnes the y-axis;choices include a normal distribution,the showngamma distribution(with shape parameter3),a beta distribution,t-distributions(with3and6degrees of freedom),and a mixture of a normal and gamma.3Empirical QQ plotsApplying this analogy to the normal QQ plot of data requires more work and imagina-tion for two reasons.First,it would not make sense toﬁll a“discrete vase”–the waterwould leak out.Second,normal QQ plots of data should include bands that indicatewhether deviations from the diagonal are large enough to imply a signiﬁcant departurefrom normality.To address theﬁrst problem,statistics oﬀers a variety of smooth density estimates, but these estimates are unfamiliar to students taking introductory statistics.For ex-ample,a kernel density provides a continuous density estimate,and these have beenused to enhance boxplots(Hintze and Nelson,1998).A kernel density estimate,how-ever,diverts attention from the QQ plot to itself.Rather than take that route,then,the accompanying software shows a histogram of data.(The statistics package JMPadopts a similar presentation.)Because observations in a tall histogram bin are rel-atively closely packed,the heights of the bins in the histogram inversely convey the average rate of water-ﬁlling within an interval.For the second problem,there are simple approximations that quantify the size of a departure from the reference line.Students recognize that there is a problem when they see deviations like those in Figure1,but the decision of whether a deviation is “large enough”(statistically signiﬁcant)is a tough call for a student who is new to QQ plots.Bands remove this subjectivity by indicating how much departure from the reference line can be produced by sampling variation.A student doesn’t have to guess whether the data drift far from the reference line;they can see whether points fall outside the bands.For setting the bounds in the normal quantile plot,a variety of elaborate methods are available.Aldor-Noiman,Brown,Buja,Rolke,and Stine(2013)review several powerful proposals,but the accompanying animation requires limits that can be com-puted quickly.With that in mind,bounds for the deviation from normalityﬁrst tabled in Lilliefors(1967)work nicely.These tables adjust for the use of estimated parameters in the normal distribution.The tables give the value cαsuch thatlim n P(supx|F n(x)− Φ(x)|≤cα/√n)≈1−α,(1)where F n denotes the empirical distribution of the data and Φdenotes the cumulative normal distribution with estimated parameters X and s2.The asymptotic critical valuefor sup x|F n(x)− Φ(x)|≈0.89/√n ifα=0.05.For example,Figure4shows an example of an normal QQ plot of a sample of200observations from a gamma density,ﬁlled to the75th percentile.Selecting the“Sampledistribution?”checkbox in the application dialog produces an empirical QQ plot.Ahistogram replaces the distribution on the y-axis.Points in this sample drift outsidethe limits,indicating a statistically signiﬁcant departure from normality.(These arehighlighted in red in theﬁgure.)4DiscussionI must admit that you do not need a computer animation to teach quantile plots thisway.The water-ﬁlling analogy alone seems to take the mystery out of QQ plots.IFigure4:This animation shows the normal QQ plot of a sample of200observations from a gamma distribution.sketch two vases side-by-side on a blackboard,say that each vase holds a liter,andgradually“ﬁll”the vases by coloring in the levels with chalk or a marker.Students arequick to recognize that the levels–the quantiles–remain equal only if the vases havethe same shape.In that case,a graph of the level of one vase versus the level of theother falls along a diagonal line.I then sketch a blackboard version of Figure2andask the students to tell me how the graph of the levels will look.This discussion is alsoa nice opportunity to convey what is meant by the shape of a distribution.For those who want to use software,the animation can be run either on-line or in-stalled locally.Those with less interest in R can run the application remotely by point-ing their browser to :3838/stine/qqvases/.Readers familiar with R can download the package qqvases from the CRAN repositoryor from links on my web page /∼stine/.The softwareexploits shiny,a library for R used here to render plots with interactive controls ina browser window(Chang,Cheng,Allaire,Xie,and McPherson,2015).Running thecommand qq vase plot locally allows the user to customize the application,such asadding more distributions and generating QQ plots of data.In addition to explaining QQ plots,the software can be used to illustrate other fundamental concepts,such as Type I errors(the quantile plot of a sample from anormal distribution has data outside the bands)and power(the bands in an QQ plotbecome tighter as the sample size grows).It can be surprising to see how hard it is torecognize that small samples from a gamma distribution diﬀer signiﬁcantly from thenormal.Some students do ask about the origin of the conﬁdence bands.I don’t have a simple analogy explaining those,so I use the question as an opportunity to advertise more advanced courses.It is not unusual to see quantile plots explained with the help of showing dis-tributions along the axes(e.g.,the cover and Figure3.4of Verzani,2005),but to my knowledge authors have not exploited the water-ﬁlling analogy and resulting animation. This analogy is used to explain normal quantile plots in a textbook citepstinefoster14, but Iﬁnd that it works better when animated.ReferencesAldor-Noiman,S.,Brown,L.D.,Buja,A.,Rolke,W.,and Stine,R.A.(2013),“The power to see:A new graphical test of normality,”The American Statistician,67, 249–260.Benjamini,Y.(1988),“Opening the box of a boxplot,”The American Statistician,42, 257–262.Brown,E.N.and Kass,R.E.(2009),“What is statistics?”The American Statistician, 63,105–110.Chang,W.,Cheng,J.,Allaire,J.,Xie,Y.,and McPherson,J.(2015),shiny:Web Application Framework for R,r package version0.11.1.Cobb,G.(2015),“Mere renovation is too little too late:We need to rethink our undergraduate curriculum from the ground up,”The American Statistician,69,266–281.Hintze,J.L.and Nelson,R.D.(1998),“Violin plots:A box plot-density trace syner-gism,”The American Statistician,52,181–184.Lilliefors,H.W.(1967),“On the Kolmogorov-Smirnov test for normality with mean and variance unknown,”Journal of the Amer.Statist.Assoc.,62,399–402.Verzani,J.(2005),Using R for Introductory Statistics,Boca Raton FL:Chapman& Hall/CRC.。

python中的normal函数

python中的normal函数Normal函数是Python中常用的一个函数，它具有多种功能和用途。

在本文中，我们将深入探讨Normal函数的用法和特点。

Normal函数可以用于生成随机数。

通过指定均值和标准差，我们可以使用Normal函数来模拟正态分布。

正态分布是自然界中广泛存在的一种分布形式，它在统计学和概率论中扮演着重要的角色。

通过生成随机数，我们可以对实际问题进行建模和分析。

例如，在金融领域中，我们可以使用Normal函数来模拟股票价格的波动情况，从而进行风险评估和投资决策。

Normal函数还可以用于数据处理和分析。

在数据科学领域中，我们经常需要对数据集进行标准化处理，以便进行更准确的分析和预测。

Normal函数可以帮助我们实现这个目标。

通过将数据集的每个值减去均值，再除以标准差，我们可以将数据集转化为标准正态分布。

这样一来，不同数据集之间就具有可比性，方便进行数据分析和建模。

Normal函数还可以用于概率计算和统计推断。

在统计学中，我们经常需要计算随机变量的概率分布和统计指标。

通过Normal函数，我们可以计算出正态分布的概率密度函数和累积分布函数，从而进行相关的推断和判断。

例如，在假设检验中，我们可以使用Normal 函数来计算p值，进而判断一个观察值是否在给定的假设下是显著的。

Normal函数还可以用于机器学习和人工智能领域。

在许多机器学习算法中，我们需要使用概率模型来对数据进行建模和预测。

正态分布是一种常用的概率模型，可以用于回归、分类和聚类等任务。

通过Normal函数，我们可以生成符合正态分布的随机数，从而模拟训练数据和测试数据。

这样一来，我们可以评估和改进机器学习算法的性能。

Normal函数在Python中具有广泛的应用。

无论是数据分析、统计推断还是机器学习，Normal函数都扮演着重要的角色。

通过了解和掌握Normal函数的用法，我们可以更好地处理和分析数据，从而做出更准确的决策和预测。

plot_model函数

plot_model函数plot_model函数是一种非常有用的函数，它可以帮助我们可视化机器学习模型的结构，从而更好地理解和调试模型。

在本文中，我们将介绍plot_model函数的基本用法和一些高级用法，以及如何在不同的机器学习框架中使用这个函数。

一、plot_model函数的基本用法plot_model函数是Python中的一个函数，它可以用来可视化机器学习模型的结构。

这个函数可以在不同的机器学习框架中使用，比如Keras、TensorFlow、PyTorch等。

在Keras中，我们可以使用如下代码来绘制一个模型的结构：```from keras.utils import plot_modelfrom keras.models import Sequentialfrom yers import Densemodel = Sequential()model.add(Dense(32, input_shape=(784,),activation='relu'))model.add(Dense(10, activation='softmax'))plot_model(model, to_file='model.png', show_shapes=True) ```这个代码会生成一个名为model.png的图片，图片中显示了模型的结构。

在TensorFlow中，我们可以使用如下代码来绘制一个模型的结构：```import tensorflow as tffrom tensorflow.keras.utils import plot_modelfrom tensorflow.keras.models import Sequentialfrom yers import Densemodel = Sequential()model.add(Dense(32, input_shape=(784,),activation='relu'))model.add(Dense(10, activation='softmax'))plot_model(model, to_file='model.png', show_shapes=True) ```这个代码也会生成一个名为model.png的图片，图片中显示了模型的结构。

flac3d5.0常用命令集锦

建模1、调用文件：①文件与工程在同一个文件夹，只写文件名即可：If the called file is located in the same folder as the FLAC3D project file, then only the file name need be entered with the CALL command.②不在同一个文件夹，全路径：Otherwise, the file may be called by specifying its complete path (e.g., c:\myfolder\file.dat ).Undo ;撤销上一条命令2、创建旋转缩放视图3、建模命令model mech mohr ;莫尔库伦模型model mech elastic ;弹性模型set grav 0, 0, -9.81 ; 重力加速度 negative z -direction. （垂直向下！常用的）下下面面这这代代码码，，是是沿沿着着--y y 方方向向的的重重力力加加速速度度，，注注意意区区别别！！！！！！！！gen zone brick size 6,8,8 p0 -10, -10, -20 ... ;省略号表示写不下后面继续p1 10, -10, -20 ...p2 -10, 10, -20 ...p3 -10, -10, 0plot zonegen zone brick size 6,8,8 p0 -10, -10, -20 ... ;不规则六面体p1 10, -10, -20 p2 -10, 10, -20 ...p3 -10, -10, 0 p4 10, 10, -20 ...p5 -10, 10, 10 p6 10, -10, 0 ...p7 10, 10, 10plot current plot Plot01plot clearplot zoneUndo ;撤销命令set logfile 127X1001.txtset log on truncateset log offlist zone prin range x 0 1 y 0 1 z 0 1 ;显示指定围各单元的主应力，结果如下Hist命令：①命令编号按顺序从1开始：each history is numbered sequentially from 1 as it is entered via the HISTORY command.②查找显示所有的his命令：Return to the Flac3D> prompt and typelist histfor a listing of the histories and their corresponding numbers.hist nstep 5 ;每5步记录1次。

第5-章统计数据代表值计算

第5章统计数据的标志值计算及其描述5.3 探索性统计分析前面介绍的统计资料描述方法通常是先将统计资料分组处理，然后把分组资料画成各种图分析其分布规律。

但这种传统的统计资料处理方法有一定的局限性，容易失去原始资料的真实性。

为了克服这一弊端，70年代开始国外已经出现了新的探索性资料分析方法。

探索性资料分析特征操作简便，能够准确地描述统计变量分布情况。

在这里只介绍最常用的茎叶图和箱线图。

5.3.1 箱线图（Boxplot）0 ╋箱线图是把第一四分位数，第二四分位数(中位数)，第三四分位数，四分位数范围，最大值及最小值画成一个箱子和一条线。

将箱子两侧超过四分位数差 1.5倍范围内的值用点线（--）连接起来，超出四分位数差1.5倍至3倍范围的值为零，超过3倍的特别大或特别小的异常值用*表示。

如果我们所研究的统计资料的分布是对称的，那么Q2垂直线将落在箱子的正中央。

如果Q2垂直线落在靠近Q3的位置，那么分布的长尾拖向左边;相反Q2垂直线落在靠近Q1的位置，那么分布的长尾拖向右边。

因此不同的箱线形状就反映出不同的分布特征(见图5-6)。

5.3.2 茎叶图（Stem-and-leafDisplay ）茎叶图将提供统计资料的分布形态、范围、集中程度等情报。

利用茎叶图主要有两个优点，首先它既能保1Q 2 Q 3 (B) 右偏分布Q 1 Q 2 Q 3 (A) 正态分布Q 1 Q 2 Q 3(C) 左偏分布图5-6 箱线图与分布形状留原始资料的真实情报，又能为准确计算平均数等代表值提供方便。

特别是对100个以下观测值的频数分布分析非常有用。

其次SAS for Windows 在统计量的分组中，茎叶图可将统计分组和画直方图一次完成，不会出现重复分组的可能。

茎叶图将树茎置于垂直线的左侧，树叶在垂直线的右侧按顺序排列。

绘制茎叶图，关键是要设计好树茎，这好比和资料分组时，先确定分组数和每组的组限一样重要。

茎叶图是以观测值的间隔为纵坐标（茎），观测值的末尾数字为横坐标（叶），叶的右边数字为相应区间内观测值的个数。

LaTeX画神经网络图

LaTeX绘制网络结点图的tikz库在控制论或者是智能领域，神经网络是经常接触到的，另外，研究网络时，也经常需要绘制网络结点图，下面介绍一个tikz库可以非常方便地绘制这类图。

The following example shows a Rearrangeable Clos Network：Kalman Filter System Model神经网络绘图包包的整体设计非常不错，使用也很方便，作者使用该包写了一个版面不错的文档。

Linear regression may be visualised as a graph. The output is simply the weighted sum of the inputs:Logistic regression is a powerful tool but it can only form simple hypotheses, since it operates on a linear combination of the input values (albeit applying a non-linear function as soon as possible). Neural networks are constructed from layers of such non-linear mixing elements, allowing development of more complex hypotheses. This is achieved by stacking4 logistic regression networks to produce more complex behaviour. The inclusion of extra non-linear mixing stages between the input and the output nodes can increase the complexity of the network, allowing it to develop more advanced hypotheses. This is relatively simple:The presence of multiple layers can be used to construct all the elementary logic gates. This in turn allows construction of advanced digital processing logic in neural networks – and this construction occurs automatically during the learning stage. Some examples are shown below, which take inputs of 0/1 and which return a positive output for true and a non-positive output for false:From these, it becomes trivial to construct other gates. Negatingthe values produces the inverted gates, and these can be used to construct more complex gates. Thus, neural networks may be understood as “self-designing microchips”, capable of both digital and analogue processing：OmnigraffleOmniGraffle是由The Omni Group制作的一款绘图软件，其只能于运行在Mac OS X和iPad平台之上，添加公式可以配合latexit使用。

coefplot语法

coefplot语法coefplot是一种用于可视化回归模型系数的R语言包，它可以提供直观的图形展示来比较不同模型的系数估计结果。

本文将介绍coefplot的语法和用法，以及如何利用该包进行回归模型系数的可视化分析。

一、coefplot的基本语法使用coefplot包，我们可以通过以下几个步骤来绘制回归模型的系数图：1. 安装并加载coefplot包：使用install.packages("coefplot")安装包，并使用library(coefplot)加载包。

2. 创建回归模型：使用lm()函数创建一个线性回归模型，并将其赋值给一个变量。

3. 提取模型系数：使用coef()函数提取回归模型的系数。

4. 绘制系数图：使用coefplot()函数来绘制系数图。

二、使用coefplot进行系数可视化coefplot提供了多种绘制系数图的方法，可以根据需要选择合适的方法。

下面将介绍几种常用的方法。

1. 简单的系数图最简单的系数图可以使用coefplot()函数来实现，代码如下：```model <- lm(y ~ x1 + x2 + x3, data = df)coefs <- coef(model)coefplot(coefs)```其中，y是因变量，x1、x2和x3是自变量，df是数据框。

2. 分组系数图当模型中包含分组变量时，我们可以使用facet参数来实现分组系数图。

代码如下：```model <- lm(y ~ x1 + x2 + x3 + group, data = df)coefs <- coef(model)coefplot(coefs, facet = ~ group)```其中，group是分组变量。

3. 置信区间系数图我们可以使用confint参数来添加置信区间到系数图中。

代码如下：```model <- lm(y ~ x1 + x2 + x3, data = df)coefs <- coef(model)confint <- confint(model)coefplot(coefs, confint = confint)```4. 水平线系数图我们可以使用hline参数来添加水平线到系数图中。

Minitab实战

Minitab实战有些时候我们在复制数据进入minitab后，由于分析的需要，需要对数据的排列进行处理，我们会有很多偷懒的方法，先看看下图的命令菜单。

它们分别为堆叠栏、堆叠栏块、堆叠行。

针对下图的数据我们在选择上面的堆叠栏时，出现如图的对话框，并如图输入指令时，堆叠行的对话框操作基本与堆叠栏相同，输出的不一致能够根据附图看出。

堆叠的操作在很多的分析中能够应用，如方差分析、测量系统分析、操纵图等等。

●转换栏操作1有些时候我们的数据需要从栏的顺序结构转换为行的结构，这时能够使用转换栏操作，如图。

●转换栏操作2利用转换栏的操作，针对下图1的数据，进行图2的对话框操作后的结果如图1。

==将A、B栏的数据转换为行的格式。

转换栏前后数据按序排列数据有的时候为了分析与观察的方便，需要将数据按某栏的数据大小进行排列，这是能够使用sort命令，对话框如图。

针对图1的数据按图2对话框操作以后的数据排列效果如图1。

栏运算1在进行minitab分析时，有的时候需要对数据进行简单的运算或者函数运算，完成这个功能的菜单操作如图1，出现的对话框及简要的操作方法如图2。

sample针对图中的C1栏进行简单运算与对数运算的结果如图。

行、栏统计1我们有的时候需要对某一栏或者行的数据进行一些基本统计，执行如图菜单可帮帮你。

在执行菜单后出现的对话框如图，此命令一次只提供一个基本统计参数的运算，包含：均指、与、方差、极值、缺失数据等，然后输入需要计算的栏或者行所在栏组就能够了。

标准化操作标准化结合与分布概念应该不是一个陌生的名词，minitab的操作菜单如图。

从出现的对话框来看，minitab提供5种标准化的方法：1.减去均值并除以标准差；2.减去均值；3.除以标准差；4.减去指定的值并除以指定的值；5.将数据标准化，使得数据变化在某个范围内。

什么是标准化结合？是标准化操作，比如将均值为μ，标准差为σ的正态分布转化为标准正态分布，其操作就是（x-μ）/σ，这个应用是很重要的。

DEGseq

X
→
N (log2(n1p1),
(
1 − p1 n1p1
)(log2e
)2)
=
N (µX ,
σX2 )
(1)
Y
→
N
(log2(n2p2),
(
1 − p2 n2p2
)(log2e)2
)
=
N (µY , σY2 )
(2)
Based on the assumption that C1 and C2 are independent (so X and Y are independent), the distributions of M and A can be obtained:
1
text ﬁle contains the gene expression values for the samples, a P-value and two kinds of Q-values which are calculated by the methods described in Benjamini and Hochberg (1995) and Storey and Tibshirani (2003) for each gene to denote its expression diﬀerence between libraries.
wanglikun@
Contents
1 Introduction
1

2 Getting started
2
3 Methods
2
3.1 MA-plot-based method with random sampling model . . . . . . . . . . . . . 2

stable difussion

stable difussionStable Diffusion是一个文本到图像的潜在扩散模型，由CompVis、Stability AI和LAION的研究人员和工程师创建。

它使用来自LAION-5B 数据库子集的512x512图像进行训练。

使用这个模型，可以生成包括人脸在内的任何图像，因为有开源的预训练模型，所以我们也可以在自己的机器上运行它，如下图所示。

如果你足够聪明和有创造力，你可以创造一系列的图像，然后形成一个视频。

例如，Xander Steenbrugge使用它和上图所示的输入提示创建了下面这段令人惊叹的《穿越时间》视频。

以下是他用来创作这幅创造性艺术作品的灵感和文本:本文首先介绍什么是Stable Diffusion，并讨论它的主要组成部分。

然后我们将使用模型以三种不同的方式创建图像，这三种方式从更简单到复杂。

Stable DiffusionStable Diffusion是一种机器学习模型，它经过训练可以逐步对随机高斯噪声进行去噪以获得感兴趣的样本，例如生成图像。

扩散模型有一个主要的缺点就是去噪过程的时间和内存消耗都非常昂贵。

这会使进程变慢，并消耗大量内存。

主要原因是它们在像素空间中运行，特别是在生成高分辨率图像时。

Latent diffusion通过在较低维度的潜空间上应用扩散过程而不是使用实际的像素空间来减少内存和计算成本。

所以Stable Diffusion引入了Latent diffusion的方式来解决这一问题计算代价昂贵的问题。

1、Latent diffusion的主要组成部分Latent diffusion有三个主要组成部分:自动编码器(VAE)自动编码器(VAE)由两个主要部分组成:编码器和解码器。

编码器将把图像转换成低维的潜在表示形式，该表示形式将作为下一个组件U_Net 的输入。

解码器将做相反的事情，它将把潜在的表示转换回图像。

在Latent diffusion训练过程中，利用编码器获得正向扩散过程中输入图像的潜表示(latent)。

Example3

Example nº 3 Beam Evolutive AnalysisCivilFEM Manual of Advanced Examples Example nº 3 – Table of Contents3EXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIS (1)3.1AIM (1)3.2DESCRIPTION OF THE EXAMPLE (1)3.3RESULTS TO BE OBTAINED (2)3.4CALCULATION LOG (3)3.4.1Introduction (3)3.4.2Model generation (4)3.4.3Solution (6)3.4.4Postprocess (7)3.5RESULTS (7)3.5.1Load Step 1: bending moments law (7)3.5.2Load Step 1: distribution of normal stresses (8)3.5.3Load Step 2: bending moments law (8)3.5.4Load Step 2: distribution of normal stresses (9)3.5.5Load Step 3: bending moments law (9)3.5.6Load Step 3: distribution of normal stresses (10)3.5.7Load Step 4: bending moments law (10)3.5.8Load Step 4: distribution of normal stresses (11)3.6SUMMARY (11)EXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIS 3 EXAMPLE Nº 3: BEAMEVOLUTIVE ANALYSIS3.1 AIMThe aim of this example is to practice the approach, with CivilFEM, to the analysis evolutive structures.For it, a simple structure has been chosen (bisupported beam) with a composite concrete - steel section.3.2 DESCRIPTION OF THE EXAMPLEIn the attached figure the structural scheme of the bisupported beam is shown, along with a detail of the section, that is made up by a concrete slab and an opened steel box.The constructive process is the following:EXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSISDay 1: Construction of the steel box.Day 7: Concrete upper slab is built.Day 35: The structure is loaded.The structure is loaded with the following actions:1. Self weight for the following densities:Steel 7850 kg/m3Concrete 2500 kg/m32. Dead load: 20 kN/m3. Surface load: 9 kN/m24. Punctual load at center of span: 600 kN DATA:GEOMETRYL = 20.00 mb slab= 5.40 mh slab= 0.20 mh box= 0.80 mb box= 3.20 mb bas e = 1.50 mb flange= 0.40 mt web= 0.02 mt base= 0.04 mt flange= 0.03 mCONCRETEC35/45 (Eurocode 3)STEELFe360 (Eurocode 3)3.3 RESULTS TO BE OBTAINEDBending moment laws for the following times:EXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIST = 1 daysT = 7.1 daysT = 35 days before applying the rest of loadsT = 35 days after applying the rest of loadsStress distribution in the section of the center of the span for the previoustimes.3.4 CALCULATION LOG3.4.1 IntroductionDue to the changes originated in the cross section throughout the time as a result of the proposed constructive process, it is necessary to activate the option of evolutive analysis. This option is within the Bridges Module.There are several possibilities to definite the cross section. It has been chosen to build it in ANSYS by means of auxiliary MESH200 elements and to capture it using the CivilFEM cross section import utility.The construction of the finite elements model is made by means of direct generation of nodes and elements.In phase of solution the following load states are solved:T = 1 days: As the calculation time is less than the activation time of concrete, the self weight of the structure corresponds to the weight of the metallic box and it is supported solely by the section corresponding to the box. It is necessary tosolve this load state so that CivilFEM can calculate the strains of the structure at the moment at which the concrete appears.T = 7.1 days: The concrete is 0.1 days old. It contributes with its self weight but without resistance. This age (0.1 days) is smaller than the first age defined in the time dependent properties, and therefore the material does not have any rigidity.The self weight is the one corresponding to the concrete and the steel but this weight is only supported by the metallic box.T = 35 days, before placing the rest of loads: In this case, that could be omitted, it can be verified that the forces and moments are the same as the ones in the previous state, because even though the concrete now has a rigiditycorresponding to 28 days, the actions on the structure have not changed.T = 35 days, after applying the rest of loads: In this case the forces and moments come from the action of the self weight and the rest of loads, with the singularity that the self weight is only supported by the steel box and the rest of loads aredistributed on the complete section, as it can be observed in the stress distribution results.EXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSISAfter solving these states the distribution of bending moments on the structure and the distribution of normal stresses in the central section of the span are plotted.3.4.2 Model generation~CFACTIV,NLBR,Y ! Activate Bridge module~CFCONFG,EVL,ACTIV,1 ! Activate Evolutive Analysis! ParametersL = 20.00B_SLAB = 5.40H_SLAB = 0.20H_BOX = 0.80B_BOX = 3.20B_BASE = 1.50B_FLANGE= 0.40T_WEB = 0.02T_BASE = 0.04T_FLANGE= 0.03! Title/TITLE,Beam evolutive Analysis! Setup~UNITS,SI~CODESEL,EC3,EC2,,,EC8/PREP7! Preprocessor! -------------------------------------------------------------------! Materials~CFMP,1,LIB,STEEL,EC3,Fe 360 !Structural Steel~CFMP,2,LIB,CONCRETE,EC2,C35/45 !Concrete~CFMP,2,DATGEN,TAct,,7 !Concrete Activation time! Element typesET,1,200,0 ! Linear auxiliary elementsET,2,200,6 ! Cuadrilateral auxiliary elementsET,3,BEAM54 ! Elastic 2D Beam! Cross Section modeling! Solid ModelingK, 1,-B_SLAB/2,0K, 2, B_SLAB/2,0K, 3,-B_SLAB/2,-H_SLABK, 4, B_SLAB/2,-H_SLABK, 5,-B_BOX/2,-H_SLAB-T_FLANGE/2K, 6,-B_BOX/2+B_FLANGE,-H_SLAB-T_FLANGE/2K, 7,-B_BASE/2,-H_SLAB-H_BOX+T_BASE/2K, 8, B_BASE/2,-H_SLAB-H_BOX+T_BASE/2K, 9, B_BOX/2-B_FLANGE,-H_SLAB-T_FLANGE/2K,10, B_BOX/2,-H_SLAB-T_FLANGE/2A,1,3,4,2L,5,6L,6,7L,7,8L,8,9L,9,10! Attributes and meshESIZE,0.10 ! Global element sizeR,1,T_BASE,T_BASEMAT,1 $ TYPE,1 $ REAL,1 ! AttributesLMESH,7 ! Line meshR,2,T_WEB,T_WEBMAT,1 $ TYPE,1 $ REAL,2 ! AttributesEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSISLMESH,6,8,2 ! Line mesh R,3,T_FLANGE,T_FLANGEMAT,1 $ TYPE,1 $ REAL,3 ! Attributes LMESH,5,9,4 ! Line mesh MAT,2 $ TYPE,2 ! Attributes AMESH,1 ! Line mesh! Capture cross sectionLOCAL,11,0,0,0,0, , ,-90 ! Local coordinate system ~SEC2DIN,1,11 ! Capture section~SECMDF,1,STRPROP,ASEC,,8 ! Use homogenized properties! Delete auxiliary mesh CSYS,0ACLEAR,ALL $ LCLEAR,ALLADELE,ALL $ LDELE,ALL $ KDELE,ALL! Beam propertyEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIS~BMSHPRO,1,BEAM,1,1,,,54,1,1, ! Real constants generation ! NodesN, 1,0,0N,21,L,0FILL! ElementsMAT,1 $ TYPE,3 $ REAL,1E,1,2EGEN,20,1,-1,! Boundary conditionsD, 1,UX,0D, 1,UY,0D,21,UY,03.4.3 Solution/SOLU! Solution! --------------------------------------------------! Load Step 1: T= 1 day~ACTTIME,1,0 ! Analysis timeACEL,0,9.81,0SOLVE! Load Step 2: T = 7.1 days~ACTTIME,7.1,0 ! Analysis timeSOLVE! Load Step 3: T = 35 days, before loading~ACTTIME,35,0 ! Analysis timeSOLVE! Load Step 4: T = 35 days, after loadingSFBEAM,ALL,1,PRES,20000+9000*B_SLABF,11,FY,-600000SOLVEEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIS 3.4.4 Postprocess/POST1! Postprocessing! --------------------------------------------------~CFSET,,1 ! Read Load Step 1~PLLSFOR,M,Z,-3, ! MZ Plot~PLCSSTR,BEAM,11,I,SX ! Stress distribution for element 11, end I~CFSET,,2 ! Read Load Step 2~PLLSFOR,M,Z,-3, ! MZ Plot~PLCSSTR,BEAM,11,I,SX ! Stress distribution for element 11, end I~CFSET,,3 ! Read Load Step 3~PLLSFOR,M,Z,-3, ! MZ Plot~PLCSSTR,BEAM,11,I,SX ! Stress distribution for element 11, end I~CFSET,,4 ! Read Load Step 4~PLLSFOR,M,Z,-3, ! MZ Plot~PLCSSTR,BEAM,11,I,SX ! Stress distribution for element 11, end I3.5 RESULTS3.5.1 Load Step 1: bending moments lawEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIS3.5.2 Load Step 1: distribution of normal stresses3.5.3 Load Step 2: bending moments lawEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSISCivilFEM Manual of Advanced Examples - Ingeciber, S.A.. 3-93.5.4 Load Step 2: distribution of normal stresses3.5.5 Load Step 3: bending moments lawEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSIS3-10 CivilFEM Manual of Advanced Examples - Ingeciber, S.A..3.5.6 Load Step 3: distribution of normal stresses3.5.7 Load Step 4: bending moments lawEXAMPLE Nº 3: BEAM EVOLUTIVE ANALYSISCivilFEM Manual of Advanced Examples - Ingeciber, S.A.. 3-113.5.8Load Step 4: distribution of normal stresses3.6 SUMMARYThis exercise allows the CivilFEM user to practiceThe creation of cross sections through an ANSYS auxiliary MESH200 modeland its later capture to CivilFEM The concepts relative to the activation time and material age plus the analysis of the influence this has in the distribution of forces, moments and stresses.。

数据通信原理实验指导书

实验一编码与译码一、实验学时：2学时二、实验类型：验证型三、实验仪器：安装Matlab软件的PC机一台四、实验目的：用MATLAB仿真技术实现信源编译码、过失操纵编译码,并计算误码率。

在那个实验中咱们将观看到二进制信息是如何进行编码的。

咱们将要紧了解：1．目前用于数字通信的基带码型2．过失操纵编译码五、实验内容：1．经常使用基带码型（1）利用MATLAB 函数wave_gen 来产生代表二进制序列的波形,函数wave_gen 的格式是：wave_gen(二进制码元,‘码型’,Rb)此处Rb 是二进制码元速度,单位为比特/秒（bps）。

产生如下的二进制序列：>> b = [1 0 1 0 1 1];利用Rb=1000bps 的单极性不归零码产生代表b的波形且显示波形x，填写图1-1：>> x = wave_gen(b,‘unipolar_nrz’,1000);>> waveplot(x)（2）用如下码型重复步骤（1）（提示：能够键入“help wave_gen”来获取帮忙），并做出相应的记录：a 双极性不归零码b 单极性归零码c 双极性归零码d 曼彻斯特码(manchester)x 10-3x 10-3图1-1 单极性不归零码图1-2双极性不归零码x 10-3x 10-32．过失操纵编译码（1）利用MATLAB 函数encode 来对二进制序列进行过失操纵编码, 函数encode 的格式是：A ．code = encode(msg,n,k,'linear/fmt',genmat)B ．code = encode(msg,n,k,'cyclic/fmt',genpoly)C ．code = encode(msg,n,k,'hamming/fmt',prim_poly)其中A ．用于产生线性分组码，B ．用于产生循环码，C ．用于产生hamming 码，msg 为待编码二进制序列，n 为码字长度，k 为分组msg 长度，genmat 为生成矩阵，维数为k*n ，genpoly 为生成多项式,缺省情形下为cyclpoly(n,k)。

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