【免费下载】Exmaple5——contour plots over maps

tecplot使用手册大部分是根据tecplot 9.0写的，不过应该10。

0等等也差不多。

一、简介tecplot包含两部分，一部分是数据的组织方式,另一部分是软件的基本操作.tecplot9。

0的三维数据显示功能大大增强了。

数据的组织方式和显示有很大关系。

数据的组织分成I,IJ，IJK组织.I组织类似行向量按照自然顺序排列。

二、tecplot的菜单结构File，Edit，View，Axis(XY,2D,3D）,Field，XY,Style，Data，Frame,Workspace,ToolsFrame modes有3D,用来表示表面、体积数据.2D表示2D field plots。

XY,S(ketch）。

layer有两种-——+zone layers,包括contour,vector等等.———+map layers，包括lines，symbols,bars等等。

针对XY—plotting。

针对的数据是XY方式组织的或者是I—ordered.三、tecplot的坐标系统包括:paper,frame,2D physical coord。

，3D physical coord.，paper左上角为原点.frame和2D，3D在左下角为原点.frame的长宽均为100.cell-centered data对于网格中心的数据,tecplot可以将其变换为网格节点上的数据。

可以通过Shift Cell-centered Data （Data menu）将其改变.Extract Data points可以有三种方法:——-+用鼠标选择离散点集——-+用鼠标画一个polyline，从某点开始———+用鼠标画一个geometry，从某点开始二进制数据格式比ASCII数据格式更快，因为他们占用更少的空间。

TECPLOT 的ASCII数据文件可以分成若干个RECORD：ZONE，TEXT，GEOMETRY,CUSTOM LABELS，这些RECORD排列在文件头后面。

Introductio nEffectively com municating tec hnical informat ion is a key par t of any engine ering and scien tific field. In this article, you’ll le arn tips on preparing yo ur simulated re sults for presen tations, assignm ents, and other publications to improve their r eadability, and get your message acros s.Tips will be cov ered for CODE V, LightTools Lu cidShape, and R Soft Photonic D evice Tools.CODE VGraphical outp ut from CODE V options appea r in output wind ows. Copying t his output from CODE V into yo ur report is eas y! Depending on t he analysis typ e, the output w indow will have a toolbar with various choices for you to edit the way your output is presented.Figure: CODE V P oint Spread Func tion chartEvery chart has a button to cop y the output to the clipboard, w hich can then b e pasted into yo ur report. In op tions such as M TF and Point Spread F unction (PSF), t he button is a d ropdown with “Copy to Clipboa rd”. In other op tions, such as s pot diagrams (S PO) and ray aberration c urves (RIM), a “Copy” button is available.ContentsTips on Technical Graphics (1)Introduction (1)CODE V (1)LightTools ...........................................................................3LucidShape .........................................................................5LucidShape CAA V5 Based ..............................................7RSoft Photonic Device Tools .. (10)vFigure: Copying charts from CODE V’s MTF option and Spot Diagram optionAnalysis options such as MTF and PSF have buttons to increase font size and line width to aid you in making your output more legible when pasted into results. There is even an “Apply Presentation/Report Template” button to quickly prepare the chart for presentations:Figure: Controls to adjust chart font size, line width, and switch to “Apply Presentation/Report Template”Figure: MTF chart copied using the default MTF template (on left), and Presentation/Report settings (on right).Notice the text in the chart on the right is easier to read.For more extensive chart customizations, you can press the Chart Properties button to access controls for chart colors, axes scales, and more.Figure: Some options have a toolbar button to customize chart properties furtherRight click on the chart to save the property settings as a template to use later, or apply the settings to all similar charts in the window.Figure: Right clicking on a chart gives you options to apply and save templates,apply settings to all charts in the window, and revert settings back to the defaultFor charts that don’t have editable properties, such as spot diagrams and ray aberration curves, you can extract the components in Microsoft products and edit the picture:Figure: Copy of a RIM curve in Microsoft Powerpoint. Right click to edit the picture (left).Example of edited picture after adjusting font and line sizes (right)The edited picture becomes a Microsoft drawing object. These objects can then be ungrouped and moved/modified independently. LightToolsGraphical output from LightTools analysis can be accessed from the Analysis menu and choosing the metric you’d like to assess (illuminance, intensity, luminance, etc.). The main output is to present illumination metrics using the LumViewer, but there are also other types of analysis for color charts, polarization, and more. Each chart has a toolbar to help you customize how the analysis is presented.Copying charts from the LumViewer is easy! Right click on the copy icon and choose “Copy to Clipboard”. You can then paste the chart into your document.Figure: LumViewer’s “Copy to Clipboard”You can make text more readable by adjusting the font size up and down.Figure: In the LumViewer toolbar, you can adjust text font sizevFigure: Irradiance LumViewer plot copied with default settings (on left),compared to chart copied after increasing the font size to be more readible.For more extensive chart customizations, you can press the Chart Properties button to access controls for chart colors, axes scales, and more!Figure: Analysis options have a toolbar button to customize chart properties furtherRight click on the LumViewer to save the property settings as a template to use later, or apply the settings to all similar charts.Figure: Right clicking on a LumViewer chart gives you options to apply andsave templates and apply settings to all charts in the window.For luminance measurements, you can overlay forward simulation results directly on the associated geometry in the V3D window. To enable this setting, go to the View menu > Simulation Results and choose True Color or False Color.Figure: False color illuminance overlaid on lens geometry in the LightTools 3D ViewLucidShapeGraphical output from LucidShape can be customized from context menus for the specific output. After you’ve opened the results, you can customize the appearance by right clicking on the chart.Figure: Analysis from LucidShapeTo copy a chart or GeoView from LucidShape, right click in the window’s area and select “Copy Bitmap”. You can now paste the chart from the clipboard to the desired destination.Figure: To copy output from a chart or the GeoView, right click and choose “Copy Bitmap”You can use other options from the context sensitive menu as well. For example, when plotting sensor data you can switch ISO lines off for a smoother view, and select “View Properties” to open further chart customizations:Figure: For chart customization, right click and choose “View Properties”In the properties window, you can change settings such as the axes ranges, the type of chart, the scale, and more:Figure: The data properties windows lets you customize the way sensor data is presentedYou can customize the GeoView too! The context menu has choices to set the background color and edit the global axis system attributes the Stock Scene selection.• From the GeoView’s Options menu > GeoView, you can set the background color and edit stock sceneFigure: GeoView with the global axis system (on left) and without the stock scene visibleFrom the GeoView’s Options menu > Global Settings, you can increase the line widths to make wireframes clearer to see inpresentations and reports.Figure: Wireframe view with default width (on left) and increased line width (on right)The GeoView toolbar has options to change orientation and the change surface rendering mode. For instance, you can switch between shaded/wireframe modes, and even display sensor results from “Display Light Data”.Figure: GeoView with shaded geometry (on left), wireframe geometry (middle), and Display Light Data (on right) LucidShape CAA VA BasedTo take a screenshot in CATIA you can go to Tools > Image > Capture:Click the 3rd Icon “Options”:In the second tab “Pixel”, you can check “White background”, “Anti-aliasing” and switch render quality to “Highest”:Click OK, and back with these icons, click the second one “Select Mode”, and then do a left click, mode and release the left click in CATIA: a rectangle appears:By clicking the corners of the rectangle and moving them, you can resize the area which will be taken as screenshot.With the CATIA controls, you can move/rotate your design: the screenshot rectangle is not moving:Once the screenshot size is adjusted, click the first icon “Capture”: a new window is opening and you can either save the screenshot, or copy it:Also, in case you want to take a screenshot from the same viewing position, you may want to create cameras: in a product, go to Infrastructure > Photo Studio:vOn the right of the screen, click “Create camera” after you chose the view you needed:In the CATIA tree, under Applications, the camera is created. You also see a glyph in the 3D view. By double clicking the camera, you will look at your design always from the same view. With a right click, properties, you can move the camera:vRSoft Photonic Device ToolsAll RSoft Photonic Device Tools plot data through an included plotting program called WinPLOT. You can customize the plot through an editor with a comprehensive set of commands.Figure: A customized line plot (on left), and contour plot (on right) from RSoft WinPLOTCopying a plot from WinPLOT is easy, just go to the File menu > Export Graph to save to a variety of formats.Figure: Export RSoft WinPLOT plots from the File menu > Export GraphEach plot file contains a series of commands that control how the plot is displayed. Click the “View Editor” button in the toolbar to see and edit the commands. Then click the “View Plot” button to return to the plot. For example, the “/tt” command sets the “Title at the Top” of the plot. See the WinPLOT manual for command documentation.Figure: Edit WinPLOT settings from the View Editor• Learn more about using WinPLOT to create high-quality graphics for publications:–Line plots–Contour plotsVisit /optical-solutions to learn more about our optical design software tools, services, and equipment. Or contact us at ******************* so we can help you with demos, trial licenses, and product pricing.©2022 Synopsys, Inc. All rights reserved. Synopsys is a trademark of Synopsys, Inc. in the United States and other countries. A list of Synopsys trademarks isavailable at /copyright.html. All other names mentioned herein are trademarks or registered trademarks of their respective owners.03/23/22.tempfile_10000038.。

海洋标量场数据二维可视化方法英文回答：Ocean scalar field data refers to the measurements of various scalar quantities in the ocean, such as temperature, salinity, dissolved oxygen, and chlorophyll concentration. Visualizing these data in a two-dimensional format is essential for understanding and analyzing patterns and trends in the ocean.There are several methods for visualizing ocean scalar field data in two dimensions. Two commonly used methods are contour plots and color maps.Contour plots represent scalar field data by drawing contour lines, which connect points of equal value. Each contour line represents a specific value of the scalar field, and the spacing between contour lines indicates the rate of change of the scalar field. For example, if we have temperature data, a contour plot can show areas of warm andcold water and the gradients between these regions. Contour plots are useful for identifying spatial patterns and boundaries in the ocean.Color maps, on the other hand, use a color scale to represent the values of the scalar field. Each color represents a specific value, and the intensity or brightness of the color indicates the magnitude of the scalar field. For example, a color map of chlorophyll concentration can show areas of high and low productivity in the ocean. Color maps are effective for visualizing the overall distribution and variability of scalar field data.In addition to contour plots and color maps, other visualization techniques can be used to enhance the understanding of ocean scalar field data. For example, vector plots can be used to show the direction and magnitude of ocean currents by using arrows. Streamlines can be used to depict the paths of water masses orparticles in the ocean. Scatter plots can be used to explore relationships between different scalar quantities, such as temperature and salinity.Overall, the choice of visualization method depends on the specific goals and characteristics of the ocean scalar field data. It is important to select a method that effectively communicates the information and insights derived from the data. By using a combination of visualizations, scientists and researchers can gain a comprehensive understanding of the complex dynamics of the ocean.中文回答：海洋标量场数据是指海洋中各种标量量值的测量数据，例如温度、盐度、溶解氧和叶绿素浓度。

C++ Primer英文版(第5版)《C++ Primer英文版(第5版)》基本信息作者： (美)李普曼(Lippman,S.B.) (美)拉乔伊(Lajoie,J.) (美)默Moo,B.E.) 出版社：电子工业出版社ISBN：9787121200380上架时间：2013-4-23出版日期：2013 年5月开本：16开页码：964版次：5-1所属分类：计算机 > 软件与程序设计 > C++ > C++内容简介计算机书籍 这本久负盛名的C++经典教程，时隔八年之久，终迎来史无前例的重大升级。

除令全球无数程序员从中受益，甚至为之迷醉的——C++大师Stanley B. Lippman的丰富实践经验，C++标准委员会原负责人Josée Lajoie对C++标准的深入理解，以及C++先驱Barbara E. Moo在C++教学方面的真知灼见外，更是基于全新的C++11标准进行了全面而彻底的内容更新。

非常难能可贵的是，《C++ Primer英文版(第5版)》所有示例均全部采用C++11标准改写，这在经典升级版中极其罕见——充分体现了C++语言的重大进展极其全面实践。

书中丰富的教学辅助内容、醒目的知识点提示，以及精心组织的编程示范，让这本书在C++领域的权威地位更加不可动摇。

无论是初学者入门，或是中、高级程序员提升，本书均为不容置疑的首选。

目录《c++ primer英文版(第5版)》prefacechapter 1 getting started 11.1 writing a simple c++program 21.1.1 compiling and executing our program 31.2 afirstlookat input/output 51.3 awordaboutcomments 91.4 flowofcontrol 111.4.1 the whilestatement 111.4.2 the forstatement 131.4.3 readinganunknownnumberof inputs 141.4.4 the ifstatement 171.5 introducingclasses 191.5.1 the sales_itemclass 201.5.2 afirstlookatmemberfunctions 231.6 thebookstoreprogram. 24chaptersummary 26definedterms 26part i the basics 29chapter 2 variables and basic types 312.1 primitivebuilt-intypes 322.1.1 arithmetictypes 322.1.2 typeconversions 352.1.3 literals 382.2 variables 412.2.1 variabledefinitions 412.2.2 variabledeclarations anddefinitions 44 2.2.3 identifiers 462.2.4 scopeof aname 482.3 compoundtypes 502.3.1 references 502.3.2 pointers 522.3.3 understandingcompoundtypedeclarations 57 2.4 constqualifier 592.4.1 references to const 612.4.2 pointers and const 622.4.3 top-level const 632.4.4 constexprandconstantexpressions 652.5 dealingwithtypes 672.5.1 typealiases 672.5.2 the autotypespecifier 682.5.3 the decltypetypespecifier 702.6 definingourowndatastructures 722.6.1 defining the sales_datatype 722.6.2 using the sales_dataclass 742.6.3 writing our own header files 76 chaptersummary 78definedterms 78chapter 3 strings, vectors, and arrays 813.1 namespace usingdeclarations 823.2 library stringtype 843.2.1 defining and initializing strings 843.2.2 operations on strings 853.2.3 dealing with the characters in a string 90 3.3 library vectortype 963.3.1 defining and initializing vectors 973.3.2 adding elements to a vector 1003.3.3 other vectoroperations 1023.4 introducingiterators 1063.4.1 usingiterators 1063.4.2 iteratorarithmetic 1113.5 arrays 1133.5.1 definingandinitializingbuilt-inarrays 113 3.5.2 accessingtheelementsof anarray 1163.5.3 pointers andarrays 1173.5.4 c-stylecharacterstrings 1223.5.5 interfacingtooldercode 1243.6 multidimensionalarrays 125chaptersummary 131definedterms 131chapter 4 expressions 1334.1 fundamentals 1344.1.1 basicconcepts 1344.1.2 precedenceandassociativity 1364.1.3 orderofevaluation 1374.2 arithmeticoperators 1394.3 logical andrelationaloperators 1414.4 assignmentoperators 1444.5 increment anddecrementoperators 1474.6 thememberaccessoperators 1504.7 theconditionaloperator 1514.8 thebitwiseoperators 1524.9 the sizeofoperator 1564.10 commaoperator 1574.11 typeconversions 1594.11.1 thearithmeticconversions 1594.11.2 other implicitconversions 1614.11.3 explicitconversions 1624.12 operatorprecedencetable 166 chaptersummary 168definedterms 168chapter 5 statements 1715.1 simple statements 1725.2 statementscope 1745.3 conditional statements 1745.3.1 the ifstatement 1755.3.2 the switchstatement 1785.4 iterativestatements 1835.4.1 the whilestatement 1835.4.2 traditional forstatement 1855.4.3 range forstatement 1875.4.4 the do whilestatement 1895.5 jumpstatements 1905.5.1 the breakstatement 1905.5.2 the continuestatement 1915.5.3 the gotostatement 1925.6 tryblocks andexceptionhandling 1935.6.1 a throwexpression 1935.6.2 the tryblock 1945.6.3 standardexceptions 197 chaptersummary 199definedterms 199chapter 6 functions 2016.1 functionbasics 2026.1.1 localobjects 2046.1.2 functiondeclarations 2066.1.3 separatecompilation 2076.2 argumentpassing 2086.2.1 passingargumentsbyvalue 2096.2.2 passingargumentsbyreference 2106.2.3 constparametersandarguments 2126.2.4 arrayparameters 2146.2.5 main:handlingcommand-lineoptions 218 6.2.6 functionswithvaryingparameters 2206.3 return types and the returnstatement 222 6.3.1 functionswithnoreturnvalue 2236.3.2 functionsthatreturnavalue 2236.3.3 returningapointer toanarray 2286.4 overloadedfunctions 2306.4.1 overloadingandscope 2346.5 features forspecializeduses 2366.5.1 defaultarguments 2366.5.2 inline and constexprfunctions 2386.5.3 aids for debugging 2406.6 functionmatching 2426.6.1 argumenttypeconversions 2456.7 pointers tofunctions 247 chaptersummary 251definedterms 251chapter 7 classes 2537.1 definingabstractdatatypes 2547.1.1 designing the sales_dataclass 2547.1.2 defining the revised sales_dataclass 256 7.1.3 definingnonmemberclass-relatedfunctions 260 7.1.4 constructors 2627.1.5 copy,assignment, anddestruction 2677.2 accesscontrol andencapsulation 2687.2.1 friends 2697.3 additionalclassfeatures 2717.3.1 classmembersrevisited 2717.3.2 functions that return *this 2757.3.3 classtypes 2777.3.4 friendshiprevisited 2797.4 classscope 2827.4.1 namelookupandclassscope 2837.5 constructorsrevisited 2887.5.1 constructor initializerlist 2887.5.2 delegatingconstructors 2917.5.3 theroleof thedefaultconstructor 2937.5.4 implicitclass-typeconversions 2947.5.5 aggregateclasses 2987.5.6 literalclasses 2997.6 staticclassmembers 300chaptersummary 305definedterms 305contents xipart ii the c++ library 307chapter 8 the io library 3098.1 the ioclasses 3108.1.1 nocopyorassignfor ioobjects 3118.1.2 conditionstates 3128.1.3 managingtheoutputbuffer 3148.2 file input and output 3168.2.1 using file stream objects 3178.2.2 file modes 3198.3 stringstreams 3218.3.1 using an istringstream 3218.3.2 using ostringstreams 323chaptersummary 324definedterms 324chapter 9 sequential containers 3259.1 overviewof the sequentialcontainers 3269.2 containerlibraryoverview 3289.2.1 iterators 3319.2.2 containertypemembers 3329.2.3 begin and endmembers 3339.2.4 definingandinitializingacontainer 3349.2.5 assignment and swap 3379.2.6 containersizeoperations 3409.2.7 relationaloperators 3409.3 sequentialcontaineroperations 3419.3.1 addingelements toasequentialcontainer 3419.3.2 accessingelements 3469.3.3 erasingelements 3489.3.4 specialized forward_listoperations 3509.3.5 resizingacontainer 3529.3.6 containeroperationsmayinvalidateiterators 353 9.4 how a vectorgrows 3559.5 additional stringoperations 3609.5.1 other ways to construct strings 3609.5.2 other ways to change a string 3619.5.3 stringsearchoperations 3649.5.4 the comparefunctions 3669.5.5 numericconversions 3679.6 containeradaptors 368chaptersummary 372definedterms 372chapter 10 generic algorithms 37510.1 overview. 37610.2 afirstlookat thealgorithms 37810.2.1 read-onlyalgorithms 37910.2.2 algorithmsthatwritecontainerelements 380 10.2.3 algorithmsthatreordercontainerelements 383 10.3 customizingoperations 38510.3.1 passingafunctiontoanalgorithm 38610.3.2 lambdaexpressions 38710.3.3 lambdacapturesandreturns 39210.3.4 bindingarguments 39710.4 revisiting iterators 40110.4.1 insert iterators 40110.4.2 iostream iterators 40310.4.3 reverse iterators 40710.5 structureofgenericalgorithms 41010.5.1 thefive iteratorcategories 41010.5.2 algorithmparameterpatterns 41210.5.3 algorithmnamingconventions 41310.6 container-specificalgorithms 415 chaptersummary 417definedterms 417chapter 11 associative containers 41911.1 usinganassociativecontainer 42011.2 overviewof theassociativecontainers 423 11.2.1 defininganassociativecontainer 423 11.2.2 requirements onkeytype 42411.2.3 the pairtype 42611.3 operations onassociativecontainers 428 11.3.1 associativecontainer iterators 429 11.3.2 addingelements 43111.3.3 erasingelements 43411.3.4 subscripting a map 43511.3.5 accessingelements 43611.3.6 awordtransformationmap 44011.4 theunorderedcontainers 443 chaptersummary 447definedterms 447chapter 12 dynamicmemory 44912.1 dynamicmemoryandsmartpointers 45012.1.1 the shared_ptrclass 45012.1.2 managingmemorydirectly 45812.1.3 using shared_ptrs with new 46412.1.4 smartpointers andexceptions 46712.1.5 unique_ptr 47012.1.6 weak_ptr 47312.2 dynamicarrays 47612.2.1 newandarrays 47712.2.2 the allocatorclass 48112.3 usingthelibrary:atext-queryprogram 484 12.3.1 designof thequeryprogram 48512.3.2 definingthequeryprogramclasses 487 chaptersummary 491definedterms 491part iii tools for class authors 493chapter 13 copy control 49513.1 copy,assign, anddestroy 49613.1.1 thecopyconstructor 49613.1.2 thecopy-assignmentoperator 50013.1.3 thedestructor 50113.1.4 theruleofthree/five 50313.1.5 using = default 50613.1.6 preventingcopies 50713.2 copycontrol andresourcemanagement 51013.2.1 classesthatactlikevalues 51113.2.2 definingclassesthatactlikepointers 51313.3 swap 51613.4 acopy-controlexample 51913.5 classesthatmanagedynamicmemory 52413.6 movingobjects 53113.6.1 rvaluereferences 53213.6.2 moveconstructor andmoveassignment 53413.6.3 rvaluereferencesandmemberfunctions 544 chaptersummary 549definedterms 549chapter 14 overloaded operations and conversions 551 14.1 basicconcepts 55214.2 input andoutputoperators 55614.2.1 overloading the output operator [[55714.2.2 overloading the input operator ]]. 55814.3 arithmetic andrelationaloperators 56014.3.1 equalityoperators 56114.3.2 relationaloperators 56214.4 assignmentoperators 56314.5 subscriptoperator 56414.6 increment anddecrementoperators 56614.7 memberaccessoperators 56914.8 function-calloperator 57114.8.1 lambdasarefunctionobjects 57214.8.2 library-definedfunctionobjects 57414.8.3 callable objects and function 57614.9 overloading,conversions, andoperators 57914.9.1 conversionoperators 58014.9.2 avoidingambiguousconversions 58314.9.3 functionmatchingandoverloadedoperators 587 chaptersummary 590definedterms 590chapter 15 object-oriented programming 59115.1 oop:anoverview 59215.2 definingbaseandderivedclasses 59415.2.1 definingabaseclass 59415.2.2 definingaderivedclass 59615.2.3 conversions andinheritance 60115.3 virtualfunctions 60315.4 abstractbaseclasses 60815.5 accesscontrol andinheritance 61115.6 classscopeunder inheritance 61715.7 constructors andcopycontrol 62215.7.1 virtualdestructors 62215.7.2 synthesizedcopycontrol andinheritance 62315.7.3 derived-classcopy-controlmembers 62515.7.4 inheritedconstructors 62815.8 containers andinheritance 63015.8.1 writing a basketclass 63115.9 textqueriesrevisited 63415.9.1 anobject-orientedsolution 63615.9.2 the query_base and queryclasses 63915.9.3 thederivedclasses 64215.9.4 the evalfunctions 645chaptersummary 649definedterms 649chapter 16 templates and generic programming 65116.1 definingatemplate. 65216.1.1 functiontemplates 65216.1.2 classtemplates 65816.1.3 templateparameters 66816.1.4 membertemplates 67216.1.5 controlling instantiations 67516.1.6 efficiency and flexibility 67616.2 templateargumentdeduction 67816.2.1 conversions andtemplatetypeparameters 67916.2.2 function-templateexplicitarguments 68116.2.3 trailing return types and type transformation 683 16.2.4 functionpointers andargumentdeduction 68616.2.5 templateargumentdeductionandreferences 68716.2.6 understanding std::move 69016.2.7 forwarding 69216.3 overloadingandtemplates 69416.4 variadictemplates 69916.4.1 writingavariadicfunctiontemplate 70116.4.2 packexpansion 70216.4.3 forwardingparameterpacks 70416.5 template specializations 706chaptersummary 713definedterms 713part iv advanced topics 715chapter 17 specialized library facilities 71717.1 the tupletype 71817.1.1 defining and initializing tuples 71817.1.2 using a tuple toreturnmultiplevalues 72117.2 the bitsettype 72317.2.1 defining and initializing bitsets 723 17.2.2 operations on bitsets 72517.3 regularexpressions 72817.3.1 usingtheregularexpressionlibrary 729 17.3.2 thematchandregex iteratortypes 73417.3.3 usingsubexpressions 73817.3.4 using regex_replace 74117.4 randomnumbers 74517.4.1 random-numberengines anddistribution 745 17.4.2 otherkinds ofdistributions 74917.5 the iolibraryrevisited 75217.5.1 formattedinput andoutput 75317.5.2 unformattedinput/outputoperations 761 17.5.3 randomaccess toastream 763 chaptersummary 769definedterms 769chapter 18 tools for large programs 77118.1 exceptionhandling 77218.1.1 throwinganexception 77218.1.2 catchinganexception 77518.1.3 function tryblocks andconstructors 777 18.1.4 the noexceptexceptionspecification 779 18.1.5 exceptionclasshierarchies 78218.2 namespaces 78518.2.1 namespacedefinitions 78518.2.2 usingnamespacemembers 79218.2.3 classes,namespaces,andscope 79618.2.4 overloadingandnamespaces 80018.3 multiple andvirtual inheritance 80218.3.1 multiple inheritance 80318.3.2 conversions andmultiplebaseclasses 805 18.3.3 classscopeundermultiple inheritance 807 18.3.4 virtual inheritance 81018.3.5 constructors andvirtual inheritance 813 chaptersummary 816definedterms 816chapter 19 specialized tools and techniques 819 19.1 controlling memory allocation 82019.1.1 overloading new and delete 82019.1.2 placement newexpressions 82319.2 run-timetypeidentification 82519.2.1 the dynamic_castoperator 82519.2.2 the typeidoperator 82619.2.3 usingrtti 82819.2.4 the type_infoclass 83119.3 enumerations 83219.4 pointer toclassmember 83519.4.1 pointers todatamembers 83619.4.2 pointers tomemberfunctions 83819.4.3 usingmemberfunctions ascallableobjects 84119.5 nestedclasses 84319.6 union:aspace-savingclass 84719.7 localclasses 85219.8 inherentlynonportablefeatures 85419.8.1 bit-fields 85419.8.2 volatilequalifier 85619.8.3 linkage directives: extern "c" 857chaptersummary 862definedterms 862appendix a the library 865a.1 librarynames andheaders 866a.2 abrieftourof thealgorithms 870a.2.1 algorithms tofindanobject 871a.2.2 otherread-onlyalgorithms 872a.2.3 binarysearchalgorithms 873a.2.4 algorithmsthatwritecontainerelements 873a.2.5 partitioningandsortingalgorithms 875a.2.6 generalreorderingoperations 877a.2.7 permutationalgorithms 879a.2.8 setalgorithms forsortedsequences 880a.2.9 minimumandmaximumvalues 880a.2.10 numericalgorithms 881a.3 randomnumbers 882a.3.1 randomnumberdistributions 883a.3.2 randomnumberengines 884本图书信息来源：中国互动出版网。

contourplot函数Contour plot, also known as a level plot or isocontour plot, is a graphical representation of a three-dimensional surface on a two-dimensional plane. It is widely used in various scientific and engineering fields to visualize the relationship between two continuous variables, often representing a third variable through contour lines or shaded regions.In this article, we will explore the contourplot function, which is a powerful tool in data visualization. We will discuss its definition, how it works, its applications, and some examples to illustrate its usage.Definition:Contourplot is a function commonly found in data visualization libraries, such as Matplotlib in Python or ggplot2 in R. It takes in three variables, x, y, and z. The x and y variables represent the coordinates on the two-dimensional plane, while the z variable represents the value associated with each coordinate pair.The contourplot function then generates a contour plot by creating isocontour lines or shaded regions that connect points of equal z-values. These contour lines or regions help us understand the structure and behavior of the underlying surface, identifying areas of high or low values.How does it work?To generate a contour plot, the contourplot function first creates agrid of x and y values spanning the range of the data. By default, the grid is evenly spaced, but it can also be customized to manage the resolution of the plot.For each point in the grid, the contourplot function calculates the associated z-value based on the given function or dataset. It then connects points with the same z-value, producing a set of contour lines or shaded regions.The spacing between contour lines, also known as contour levels, can be customized to highlight specific regions of interest. Higher contour levels create a more detailed plot, while lower levels smooth out the plot.Applications:Contour plots are widely used in various scientific and engineering fields due to their ability to visualize complex data patterns and relationships. Some common applications include:1. Physical Sciences: Contour plots are used to represent physical phenomena such as temperature distribution, pressure gradients, and electrostatic potential. They aid in understanding the behavior of these variables in space.2. Environmental Studies: Contour plots are used to represent environmental variables such as precipitation, wind speed, and pollutant concentration. They help identify patterns and areas of interest, assisting in decision-making processes.3. Geology and Geography: Contour plots are used to represent elevation, topography, and bathymetry. They are essential tools in studying landforms, geological processes, and oceanographic features.4. Engineering and Design: Contour plots are used to represent stress distribution, heat conduction, fluid flow, and other variables in various engineering applications. They aid in optimizing designs, identifying problem areas, and visualizing simulation results.Examples:Let's explore a few examples of contourplot usage using the Matplotlib library in Python:Example 1: Temperature DistributionSuppose we have a set of temperature measurements in a two-dimensional space. We can use a contour plot to visualize the temperature distribution.```pythonimport numpy as npimport matplotlib.pyplot as plt# Generate x, y coordinatesx = np.linspace(-2, 2, 100)y = np.linspace(-2, 2, 100)X, Y = np.meshgrid(x, y)# Generate temperature valuesZ = np.sin(X) * np.cos(Y)# Create a contour plotplt.contourf(X, Y, Z, cmap='coolwarm')plt.colorbar()plt.title('Temperature Distribution')plt.xlabel('X')plt.ylabel('Y')plt.show()```In this example, we generate a grid of x and y values using`np.linspace`. We then calculate the temperature values (z-values) based on a mathematical function `Z = sin(X) * cos(Y)`. Finally, we use the `contourf` function to create a filled contour plot. We add a color bar, a title, and labels to enhance the plot's readability.Example 2: Pollutant ConcentrationSuppose we have measurements of pollutant concentrations in a two-dimensional space. We can use a contour plot to visualize the spatial distribution of pollutants.```pythonimport numpy as npimport matplotlib.pyplot as plt# Generate x, y coordinatesx = np.linspace(-5, 5, 200)y = np.linspace(-5, 5, 200)X, Y = np.meshgrid(x, y)# Generate pollutant concentration valuesZ = np.exp(-X**2 - Y**2)# Create a contour plotplt.contour(X, Y, Z, cmap='YlOrRd')plt.colorbar()plt.title('Pollutant Concentration')plt.xlabel('X')plt.ylabel('Y')plt.show()```In this example, we generate a grid of x and y values using`np.linspace`. We then calculate the pollutant concentration values (z-values) based on a mathematical function `Z = exp(-X^2 - Y^2)`. Finally, we use the `contour` function to create a contour plot. We add a color bar, a title, and labels to enhance the plot's readability.Conclusion:Contour plots are invaluable tools in data visualization, allowing us to understand complex relationships between variables in a two-dimensional space. They are widely used in various scientific and engineering fields to analyze and interpret data patterns.The contourplot function, available in data visualization libraries, enables us to generate contour plots by connecting points of equalz-values with contour lines or shaded regions. By customizing the contour levels, we can focus on specific regions of interest.Through examples, we have demonstrated how contour plots can be used to visualize temperature distributions and pollutant concentrations. With the help of the Matplotlib library in Python, we can easily generate and customize contour plots to suit our data visualization needs.In conclusion, contourplot functions are powerful tools in visualizing data relationships and patterns, and they play a vital role in understanding complex data in various scientific and engineering fields.。

matplot contourf函数解释说明1. 引言1.1 概述Matplotlib是一款功能强大的Python绘图库，广泛应用于数据可视化与科学计算领域。

其中contourf函数是Matplotlib中一个重要的绘图函数之一，用于绘制等高线图。

1.2 文章结构本文将从以下几个方面对matplot contourf函数进行深入讲解和说明：引言、Matplotlib库简介、Contour绘图原理与基本使用方法、Contourf函数进阶应用技巧以及结论与展望。

在引言部分，我们将对文章的主题和内容进行简要概述，阐明本文旨在详细介绍matplot contourf函数的相关知识和应用技巧。

1.3 目的本文旨在帮助读者全面理解Matplotlib库的重要性，并详细介绍contour绘图在数据可视化中的应用场景。

同时，通过深入讲解和实例演示contourf函数的原理、参数和用法，使读者能够掌握该函数的基本使用方法。

此外，还将介绍如何利用contourf函数进行颜色填充设置、线型修改以及标签显示方式调整等进阶技巧，并通过实例分析展示如何利用contourf函数进行二维数据可视化展示。

最后，我们将总结contourf函数的主要特点与优势，并对其未来的研究和应用方向进行展望。

通过阅读本文，读者将能够深入了解matplot contourf函数的使用方法，掌握其在数据可视化中的重要性和应用场景，并在实际项目中充分利用其功能特点进行数据可视化展示。

接下来，我们将介绍Matplotlib库的概述及其重要性。

2. Matplotlib库简介:2.1 理解Matplotlib库:Matplotlib是一个功能强大的Python绘图工具库，用于创建高质量的静态、动态和交互式可视化图形。

它是一个开源项目，被广泛应用于数据分析、科学研究以及工程领域。

Matplotlib提供了一系列函数和方法，可以轻松地绘制各种类型的图表，包括线型图、散点图、柱状图等。

matplotlib.pyplot中的contourf函数-回复Introduction:The contourf function in matplotlib.pyplot is a powerful tool for visualizing and presenting 2D scalar fields. It allows us to create filled contour plots, where each contour represents a specific value of the scalar field. In this article, we will explore the various features and uses of contourf, step by step, to understand how it can be used to effectively communicate complex data.I. Understanding Contours:To begin, it's important to understand what contours represent in a 2D plot. A contour is a line that connects points of equal value on a graph. In the case of contourf, these lines are filled to create a visual representation of the scalar field.II. Preparing the Data:Before we can use contourf, we need to have the data we want to plot. Typically, this data is in the form of a 2D grid with corresponding scalar values. For example, let's say we have adataset with temperature readings from different regions on a map. We can arrange these readings in a grid and assign corresponding temperatures to each point on the grid.III. Plotting Contours:Once we have our data prepared, we can start plotting the contours. To do this, we first import the necessary libraries, including matplotlib.pyplot. We then create a figure and an axes object using the subplots function.Next, we use the contourf function to plot the contours on the axes object. We pass in the data grid and specify the levels at which we want contours to appear. The levels parameter can be a single value, which will generate a contour at that value, or a range of values to generate multiple contours.Additionally, contourf allows us to define color maps using the cmap parameter, which determines how the colors are assigned to the different contour levels. We can choose from predefined color maps or create our own custom color maps.IV. Fine-tuning the Plot:Once the basic contours are plotted, we can enhance the visualization by adding annotations, labels, and other elements. For example, we can use the contour function to overlay line contours on top of the filled contours. We can also add a color bar to indicate the range of values represented by the colors.Moreover, contourf provides options to customize the appearance of the plot. We can adjust the line width, line color, and line style of the contours. We can also change the color and transparency of the filled areas. These options allow us to create visually appealing and informative plots.V. Advanced Features:Contourf offers advanced features to handle complex data and improve the clarity of the plot. We can use the linestyles parameter to create special contour lines, such as dashed or dotted lines, to highlight specific features. We can also control the spacing between the contour levels using the extend parameter, which enables smooth transitions between adjacent contours.Furthermore, contourf allows us to combine multiple plots in a single figure, incorporating subplots and insets. This capability is particularly useful when comparing different datasets or presenting detailed views of specific regions.VI. Applications:Contourf is widely used in a variety of fields, including meteorology, geophysics, and engineering. It can be applied to visualize weather patterns, fluid dynamics, topographic maps, and more. With its ability to effectively represent complex scalar fields, contourf helps researchers and analysts gain insights and make informed decisions based on the data.Conclusion:In summary, the contourf function in matplotlib.pyplot is a powerful tool for visualizing 2D scalar fields. With its flexible options for customizing contours, colors, and annotations, contourf enables us to create informative and visually appealingplots. By understanding the step-by-step process of using contourf, we can effectively communicate complex data and enhance our exploration of the underlying patterns and relationships.。