Example_of_Normal_Plot
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。
(c) 一旦我们获得了特征重要性分数，我们可以使用plot_importance_函数将其可视化展示出来。
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 (221)Gamma伽玛值 (221)Hue 色调 (222)Invert 图像色彩翻转 (222)Saturation 饱和度 (223)Histcontrast 直方图对比度 (223)Histequalize 直方图均衡 (223)Auto Level 自动色阶 (224)Color Level 色阶调整 (224)Function LUT 函数搜寻表 (225)Leveling 层次调整 (226)Balance 色彩平衡 (227)Color Replace 颜色替换 (228)Arithmetic transformation 算术变换 (228)Alpha Blend 透明混合 (229)Simple Math 简单数学运算 (230)Math Function 数学函数 (232)Pixel Logic 像素逻辑 (233)Subtract Background 减背景 (234)Extract to XYZ 图像转数据 (235)Morphological Filter 形态学过滤 (236)Replace Background 背景替换 (238)Subtract Interpolated Background 内插背景减影 (238)Conversion 转换 (239)Convert to Data 转成数值 (239)Color to Gray 转换成灰度图....................................................................................... 240 Convert to Image 转换成图像. (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? <span style="font-size:18px;"></span>[html] view plaincopyprint?<span style="font-size:18px;">From：</span><a href=""><span style="font-size:18px;"></span></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.<span style="font-size:18px;">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。