Exmaple5——contour plots over maps

合集下载

3(上)图像可视化之对象图形法

• IDLgrImage
• IDLgrColorbar • IDLgrPalette • IDLgrFont
——
—— —— ——
图像对象，显示分类结果
颜色条对象颜色表对象字体对象，修改颜色表标注字体
快速可视化-用户界面构建
提供了与WIDGET_DRAW对应的显示控件
WIDGET_WINDOW 调试运行：\chapter10\Visualization\quick_window.pro
Dimension、Location和ViewPlane_Rect Eye-定义为视点的位置
运行 \chapter09\ViewImageDemo 运行 \chapter09\imageViewer.pro
对象图形法-基本图形对象
IDLgrView对象
Projection，与地理投影无关三维图像转二维显示时所用到的投影，分平行投影和透视投影两种。
事件处理
快速可视化-2048
事件处理
测试运行 \chapter10\Game2048.pro
快速可视化-显示分类图像
利用对象图形法显示ENVI遥感图像分类结果
调试运行\chapter10\test_quick_class.pro
显示分类图像-三种可视化方法对比
快速可视化
• Image • Colorbar —— —— 显示图像显示颜色条
图形可视化之
• 对象图形法 • 快速可视化
• 智能化编程工具
对象图形法
对象图形法-目录
概述对象的层次结构对象的基本操作基本图形对象
二维图形显示
三维图形显示对象的交互操作
编写类

NCL作图(XY图)

res@pmLegendDisplayMode="Always" res@pmLegendHeightF=0.1 res@pmLegendWidthF=0.15 res@pmLegendSide="Top" res@pmLegendOrthogonalPosF=-0.5 res@pmLegendhtF=0.012 ;Default: 0.02 res@lgPerimOn=False res@xyExplicitLegendLabels=(/"t0","t1"/) res@lgItemOrder=(/1,0/)
;res@xyLineColor="red"
;res@tmXMajorGrid=True ;res@tmYMajorGrid=True ;res@tmXMajorGridLineDashPattern=1 ;res@tmYMajorGridLineDashPattern=1
plot = gsn_csm_xy (wks,u&lat,u(:,:,{82}),res)
res@tmXBLabelFontHeightF=0.015
res@tmXBMinorValues=ispan(0,90,2)
res@xyMarkLineMode="Markers" res@xyMarker=2 res@xyMarkerColor="blue" res@xyMarkerSizeF=0.015 ;Default: .01
NCL Graphics
Types of graphics you can create with NCL
• Over 40 plotting templates • XY • Contour • Vector • Streamline • Overlays

学术部第五讲-NCL绘图

Step2 打开工作台
Some examples:
wks = gsn_open_wks(“x11”, “test”) ; X11 window wks = gsn_open_wks(“ps”, “test”) ; “test.ps” wks = gsn_open_wks(“png”, “wrf”) ; “wrf.png” wks = gsn_open_wks(“pdf”, “slp”) ; “slp.pdf”
• Specialized plots
– bar charts, skew-T, wind roses, taylor diagrams
Sample script 脚本样本
Overview ;******************************************* load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl" load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl" ;******************************************* begin ;******************************************* in = addfile ("myfile.nc","r"); pointer to file t = in->T ; read in data ;******************************************* ; create plot ;******************************************* wks = gsn_open_wks ("ps","ce") ; open ce.ps file ; choose colormap gsn_define_colormap (wks,"BlAqGrYeOrRe") res = True ; resource varb res@cnFillOn = True ; turn on color res@cnLinesOn = False ; no cn lines res@cnLevelSpacingF = 0.5 ; cn spacing res@gsnSpreadColors = True ; full colors res@lbAutoLabelStride = True ; nice lb labels plot = gsn_csm_contour_map (wks,t,res) end

contourplot函数

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.。

CODE V 技术文档：如何准备模拟结果以进行演示、作业和其他出版物说明书

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.。

NCL_Bruyere教程

WRFUserARW.ncl
NCL libraries
Diagnostics (inline functions) + scriptsMainain/support MMM
WRF Users' Tutorial Mesoscale & Microscale Meteorological Division / NCAR
26 29 30 32 35 36 (.) (t) (o) (.) (b) (y) IS IS IS IS IS IS NOT NOT NOT NOT NOT NOT A A A A A A LEGAL LEGAL LEGAL LEGAL LEGAL LEGAL FUNCTION FUNCTION FUNCTION FUNCTION FUNCTION FUNCTION CODE CODE CODE CODE CODE CODE
o

wrf_real.ncl (start with a sample script)

Run NCL script
o
ncl wrf_real.ncl
WRF Users' Tutorial Mesoscale & Microscale Meteorological Division / NCAR
3
Home Directory
~/.hluresfile
Very Important

Required by NCL libraries Must be in your “~/” directory (home directory) Control
o o o o

color table ; font white/black background size of plot control characters

海洋标量场数据二维可视化方法

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。

matplotlib.pyplot中的contourf函数 -回复

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.。

NCL-online-Example

NCL online Example1.Standard subscripting设定数组v为：v = (/0,1,2,3,4,5,6,7,8,9/)则以下引用分别为：v1 = v(1:7) ; v1 contains 1,2,3,4,5,6,7 .v2 = v(1:7:3) ; v2 is an array containing the elements 1,4,7 .v3 = v(:4) ; v3 contains 0,1,2,3,4 (a missing initial integer indicates the beginning of the indices).v4 = v(8:) ; v4 contains 8,9 (a missing second index indicates the largest of the indices).v5 = v(:) ; v5 equals v (this is the same as setting v5 = v).v6 = v(2:4:-1) ; v6 contains 4,3,2 (in that order).;The algorithm is to find all numbers within the range of the firstcolon-separated pair, then step in reverse when the stride is negative.v7 = v(6) ; v7 is a scalar with value 6 .v8 = v(5:3) ; v8 contains 5,4,3 in that order (when the starting index is greater than the ending index, a reverse selection is done).v9 = v(::-1) ; v9 contains 9,8,...,0 in that order.2. Color Maps（1）用RGB值创建自己的颜色图编辑自己的颜色图，使用RGB值（Red、Green和Blue），定义一个2维的float数组，维数为n×3，第一个维数n代表颜色数（n），第二个维数代表RGB值。

contour-enhanced funnel plots指令 -回复

contour-enhanced funnel plots指令-回复如何使用contourenhanced funnel plots指令进行数据分析和结果可视化。

步骤1：背景介绍contourenhanced funnel plots指令是一种用于可视化和评估研究中选择性报告和出版偏倚的方法。

它是从传统的漏斗图（funnel plot）演变而来，通过在漏斗图中添加等高线（contour）线条来提供更多的信息。

漏斗图通过显示研究的精确性和效应大小之间的关系来帮助评估出版偏倚。

步骤2：数据准备在使用contourenhanced funnel plots指令之前，首先需要准备好需要进行分析的数据。

这些数据通常应该包括研究的精确性度量（例如标准误差）和效应大小度量（例如标准化抽样比例）。

步骤3：安装stata contourenhanced funnel plots指令在使用contourenhanced funnel plots指令之前，需要确保已在Stata 软件中正确安装了该指令。

在Stata的命令窗口中输入以下命令来安装contourenhanced funnel plots指令：ssc install contourenhancedfunnelplots步骤4：加载数据使用Stata的`use`命令加载准备好的数据集。

例如：use data.dta步骤5：生成contourenhanced funnel plots使用contourenhanced funnel plots指令来生成漏斗图，命令格式如下：contourenhancedfunnelplot effectsize se [, options]其中，`effectsize`是效应大小度量的变量名称，`se`是研究的精确性度量的变量名称。

步骤6：可选参数contourenhanced funnel plots指令还提供了一些可选的参数，以便对生成的图形进行自定义。

contour函数

contour函数介绍在数据可视化和机器学习中，contour函数是一个非常有用的工具。

它可以绘制二维数据集的等高线图，帮助我们直观地理解数据的分布和特征。

等高线图是通过在二维平面上绘制一系列等高线来表示数据的。

每条等高线代表相同数值的数据点。

通过观察等高线的形状、间距和高度变化，可以快速分析出数据中存在的模式和关系。

在这篇文档中，我们将介绍如何使用contour函数绘制等高线图。

我们将首先讲解contour函数的基本用法，然后给出一些实际例子，展示如何使用contour函数进行数据分析和可视化。

基本用法为了使用contour函数绘制等高线图，我们需要提供两个数组：X轴上的值数组和Y轴上的值数组。

这两个数组用来确定二维平面上的点坐标。

另外，我们还需要提供一个Z轴上的值数组，用来确定每个点的数值。

下面是使用contour函数绘制等高线图的基本步骤：1.导入所需的库：import numpy as npimport matplotlib.pyplot as plt2.创建X轴和Y轴上的值数组：x = np.linspace(-5, 5, 100)y = np.linspace(-5, 5, 100)X, Y = np.meshgrid(x, y)3.创建Z轴上的值数组，这里我们可以根据需要来定义一个函数或者使用随机数生成：Z = np.sin(np.sqrt(X**2+ Y**2))4.调用contour函数来绘制等高线图：plt.contour(X, Y, Z)plt.xlabel('X')plt.ylabel('Y')plt.title('Contour Plot')plt.show()运行上述代码，就可以获得一个简单的等高线图。

实例演示现在让我们通过一些具体的例子来演示contour函数的使用。

示例1：函数可视化我们首先使用contour函数来可视化一个简单的函数。

contour函数用法

contour函数用法contour函数是Python中matplotlib库中的一种函数，它可以将二维函数的数据表示为三维数据的图形表示形式，一般用来进行数据的可视化。

contour函数的原型为：matplotlib.pyplot.contour(X, Y, Z, levels=None,colors=None, origin=None, extent=None, corner_mask=None, hold=None, **kwargs)contour函数可以生成曲线图，曲线图可以显示出沿着某一方向上不变的曲线，在曲线图中，每条线代表当前方向上的某一种特定数值，以此来可视化某种属性的变化特征，同时我们可以从曲线图中发现某个函数在特定条件下的极值点，这样可以帮助我们定位数学优化问题中的极小值点。

contour函数有4个参数：X，Y，Z，levels，colors。

X和Y参数是该函数接受的两个变量，分别用于表示曲线图中的横轴和纵轴；Z参数接受一个变量，用于表示曲线图中不同点处的z 值；levels参数接受一个数组，用来表示曲线图中不同点处的z值所对应的不同等级，比如可以使用[0,2,4,6,8,10]来表示z值的6个等级；colors参数接受一个字符串，用来表示曲线图中不同等级所对应的颜色分布；contour函数的另一个重要参数是origin，它表示曲线图的坐标原点，它默认的值是“upper”，表示以图片左上角为原点，其他可选值还有lower”，表示以图片右下角为原点；extent表示图片框架，其中包括横轴和纵轴的最小值和最大值；corner_mask表示图片中每个点的mask值，可以用来移除图片中的某些点；使用contour函数进行曲线图可视化，需要根据实际需要修改contour函数参数，以达到最佳可视化效果。

下面举例说明contour 函数的使用方法。

比如，需要构建一个曲线图的可视化，该曲线图上有5个点（x,y）分别为：(0,1)，(1,0)，(2,2)，(3,1)，(4,2)，它们各自的z值为：0，1，4，9，16，那么可以构建以下代码：import matplotlib.pyplot as pltx = [0, 1, 2, 3, 4]y = [1, 0, 2, 1, 2]z = [0, 1, 4, 9, 16]#置x,y,z的值X, Y, Z = np.meshgrid(x, y, z)#置曲线图的样式plt.contour(X, Y, Z, levels=[0, 4, 9, 16], colors=[r b g y])#置曲线图的标题plt.title(Contour Plot#示曲线图plt.show()以上即是contour函数的应用方法，可以根据实际需求修改不同参数，达到最佳可视化效果。

m_contourf函数

m_contourf函数详解`m_contourf` 函数通常是在基于地图投影的绘图中使用的Matplotlib函数。

该函数用于在地图上填充等高线区域，其中等高线表示相同数值的数据。

以下是`m_contourf` 函数的一般形式：```pythonm_contourf(lon, lat, data, kwargs)```其中：- `lon` 和`lat` 是经度和纬度的数组，用于定义数据点的位置。

- `data` 是与`lon` 和`lat` 对应的数据值。

- `kwargs` 是可选的关键字参数，用于设置其他绘图选项。

下面是一些常用的关键字参数：1. `levels`：定义填充颜色的级别或数值。

```pythonlevels = [1, 2, 3, 4, 5]```2. `cmap`：设置填充颜色的颜色映射。

```pythoncmap = 'viridis'```3. `extend`：定义颜色映射范围是否延伸。

```pythonextend = 'both' # 或'min' 或'max'```4. `alpha`：设置填充颜色的透明度。

```pythonalpha = 0.7```5. `ax`：指定绘图的轴对象，如果没有指定，将使用当前轴。

这是一个简单的例子：```pythonimport numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.basemap import Basemap# 创建一个Basemap对象m = Basemap(projection='mill', llcrnrlat=-60, urcrnrlat=60, llcrnrlon=-180, urcrnrlon=180, resolution='c')# 生成一些示例数据lon = np.linspace(-180, 180, 360)lat = np.linspace(-60, 60, 120)lon, lat = np.meshgrid(lon, lat)data = np.sin(np.radians(lat))# 使用m_contourf函数绘制填充等高线图m.contourf(lon, lat, data, levels=20, cmap='viridis', alpha=0.7)# 添加其他地图元素（例如海岸线、国界线等）m.drawcoastlines()m.drawcountries()# 显示图形plt.show()```这个例子创建了一个地图投影对象（Basemap），并使用`m_contourf` 函数在地图上填充等高线区域。

basemap contourf参数详解

basemap contourf参数详解basemap是Python中的一个库，用于绘制地图和地理数据可视化。

其中的contourf参数是用来绘制等高线填充图的。

本文将详细介绍basemap库中的contourf参数的使用方法和常见应用场景。

一、contourf参数简介contourf是basemap库中用于绘制等高线填充图的函数。

它可以根据给定的数据和经纬度坐标，绘制出地理区域内的等高线填充图，以展示数据在空间上的分布情况。

二、contourf参数的语法contourf函数的语法如下：```pythoncontourf(x, y, data, levels, **kwargs)```其中，x和y是数据的经纬度坐标，data是要绘制的数据，levels 是等高线的分层数量。

除了这些必要参数外，contourf函数还接受一些可选的关键字参数(kwargs)，用于设置等高线填充图的样式和属性。

三、常见参数的使用方法1. x和y参数x和y参数是数据的经纬度坐标，可以是一维或二维数组。

当x和y是一维数组时，它们表示每个数据点的经度和纬度。

当x和y是二维数组时，它们表示数据在地理区域内的经纬度网格。

在使用contourf函数之前，需要先将数据与经纬度坐标对应好。

2. data参数data参数是要绘制的数据，可以是一维或二维数组。

当data是一维数组时，它表示每个数据点的数值。

当data是二维数组时，它表示数据在地理区域内的网格。

在使用contourf函数之前，需要确保数据与经纬度坐标对应好。

3. levels参数levels参数用于设置等高线的分层数量。

可以是一个整数，表示等高线的数量；也可以是一个一维数组，表示每个等高线的数值。

当使用整数时，contourf函数会自动根据数据的范围和数量来确定等高线的数值。

四、常见应用场景等高线填充图常用于展示地理区域内的数据分布情况，尤其适用于气象、地质、地理等领域的数据可视化。

Exmaple6——vector plots over maps

Exmaple6——vector plots over maps该示例从netCDF文件中读取数据，并且展示如何在各种地图投影上重复展示矢量图，如何细化矢量数据，如何增加图的大小。

这个示例从一些netCDF文件中读取数据，并且在不同的地图投影上创建三种矢量图。

为改变矢量和地图设置resources。

运行这个示例，必须下载以下文件：gsun06n.ncl，然后键入：ncl gsun06n.ncl。

示例6代码及解释1. load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"2.3. begin4.dir = ncargpath("data")5.uf = addfile(dir+"/cdf/Ustorm.cdf","r") ; Open three netCDF files.6.vf = addfile(dir+"/cdf/Vstorm.cdf","r")7.tf = addfile(dir+"/cdf/Tstorm.cdf","r")打开含有风暴数据的三个netCDF文件。

uf、vf和tf是在addfile函数中特定的三个引用。

8.9.u = uf->u(0,:,:) ; Get u, v, lat, lon data.10.v = vf->v(0,:,:)t = uf->u&lat12.lon = uf->u&lon在2个netCDF文件引用中读取变量u和V以及坐标变量lat和lon，uf和vf并且将它们存储到本地的NCL变量中。

uf->u和vf->v是风速矢量数据的三维数组（同样大小），第一个维度代表时间，第二个维度代表纬度，第三个维度代表经度（用print(getfilevardims(uf,"u"))可以验证）。

Exmaple5——contour plots over maps

Exmaple5——contour plots over maps这个示例从netCDF文件中读取数据，并且展示如何命名维度。

如何获得现有的颜色图并且改变它的值，如何在各种地图投影上叠加等值线图，如何填充特定的等值线，如何掩蔽，如何在你任意想要的位置绘制文本字符串。

这个示例从netCDF文件中读取数据并且在不同的地图投影上创建了4个等值线图。

从等值线和地图两个方面改变了resources。

运行这个示例，必须下载以下文件：gsun05n.ncl，然后键入：ncl gsun05n.ncl 示例5代码及解释1. load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"2.3. begin4.data_dir = ncargpath("data") ; Get ready to open three netCDF files.5.cdf_file1 = addfile(data_dir + "/cdf/941110_P.cdf","r")6.cdf_file2 = addfile(data_dir + "/cdf/sstdata_netcdf.nc","r")7.cdf_file3 = addfile(data_dir + "/cdf/Pstorm.cdf","r")打开三个netCDF文件，cdf_file1、cdf_file2和cdf_file3是在addfile函数中指定的三个文件。

8.9.psl = cdf_file1->Psl ; Store some data from the three netCDF10.sst = cdf_file2->sst ; files to local variables.11.pf = cdf_file3->p从刚刚打开的三个netCDF中读取一些数据，并且将它们存储在本地的NCL 变量中。

lighttools中文说明

第一章介绍翻译：郑一狼照明作为光学领域中的一个重要部分，对于很多公司和它们的产品来说正变得越来越重要。

可是，直到现在对于照明系统设计和分析来说还没有可供广泛选择的专业软件，能够应用于照明系统的软件也很难使用。

正因为如此，目前照明系统的设计通常是建立系统的模型然后测试此模型。

Li gh tT oo ls的照明模块是为了满足照明行业的需要而编写的。

它通过计算机建模对照明系统进行精确的定量分析，从而帮助你更加快速有效的开发照明产品。

一.什么是L i gh t To o l s照明模块？Li gh tT oo ls照明模块是L i gh tT oo ls核心模块的可选的扩展模块。

它使用非序列性光线追迹并且基于元件进行建模，帮助你模拟完整的光学系统，包括系统的光源、光学元件和机械结构。

Li gh tT oo ls照明模块完全与Li gh t To ols核心模块相结合，并且添加了新的菜单和命令。

因为两个模块是相互结合的，一旦你熟悉了Li gh tT oo l s核心模块，那么在你了解了Li gh t To ol s照明模块的一些特性后，将很快能够使用L ig ht To ol s照明模块。

如果你是使用L i g h t T o o l s的新手，那么我们推荐你在学习L i gh tT oo l s照明模块之前先熟悉Li g ht To ol s核心模块，以此来熟悉Li gh tT oo l s的基本特性和操作。

Li gh tT oo ls照明模块使用非序列性光线追迹，这点和2D及3D设计视图是一致的，但是和I ma gi ng Pa th模块不一样，I m ag in g P a th模块只使用序列性光线追迹。

二．照明系统基础大部分照明系统拥有以下这些特性，所有以下这些特性都可以在Li gh tT oo l s中被模拟。

●系统有一个或更多的照明光源，通常成一定角度并且不均匀地分布在三维空间中。

●我们需要在系统的若干位置分析照度。

2+部分阶乘设计

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Exmaple5——contour plots over maps这个示例从netCDF文件中读取数据，并且展示如何命名维度。

这个示例从netCDF文件中读取数据并且在不同的地图投影上创建了4个等值线图。

从等值线和地图两个方面改变了resources。

psl是一个在纬度/经度格点测量的压力值的2维数组，sst是一个在纬度/经度和12个不同时间步长上测量的海洋表面温度的3维数组。

pf是在纬度/经度格点和64个不同时间步长上测量的压力值的3维数组。

请注意：本地NCL变量和netCDF文件变量中所使用的不同的变量名字（如，pf和cdf_file->p）。

这是可以选择的，此示例中是为了展示你可以给你想要命名的变量随意命名。

12.13.psl@nlon = dimsizes(psl&lon) ; Store the sizes of the lat/lon14.psl@nlat = dimsizes(psl&lat) ; arrays as attributes of the psl15.pf@nlon = dimsizes(pf&lon) ; and pf variables.16.pf@nlat = dimsizes(pf&lat)Psl和pf都包含叫做“lat”和“lon”的坐标变量，因此获得这些属性变量的常量并且把它们的值以psf和pf的属性存储起来。

17.18.sst!1 = "lat" ; Name dimensions 0 and 1 of sst19.sst!2 = "lon" ; "lat" and "lon.20.sst&lat = cdf_file2->lat ; Create coordinate variables21.sst&lon = cdf_file2->lon ; for sst and store the sizes22.sst@nlon = dimsizes(sst&lon) ; of the arrays as attributes23.sst@nlat = dimsizes(sst&lat) ; of sst.sst唯一的坐标变量是“time”，和它的第一个维度相对应（这是在先前的文件中事先确定的）。

使用iscoord函数来确定变量是否含有该特定的坐标变量。

在18-21行中，因为sst只有第一个维度是命名过的，你可以给sst创建named dimensions和叫做“lat”和“lon”的第二个、第三个坐标变量。

在22-23行，存储lat和lon数组的长度作为sst变量。

请记住：为了创建另一个变量的坐标变量，它必须与变量的一个named dimension有相同的名字。

24.25.wks = gsn_open_wks("x11","gsun05n") ; Open a workstation.26.27. ;----------- Begin first plot -----------------------------------------在地理图上绘制等值线图。

28.29.resources = True30.31.resources@sfXCStartV = min(psl&lon) ; Define where contour plot32.resources@sfXCEndV= max(psl&lon) ; should lie on the map plot.33.resources@sfYCStartV = min(psl&lat)34.resources@sfYCEndV= max(psl&lat)为了在一个地图上重复显示任何图，必须在地图上指定你想要显示地图的经度和纬度值。

这是通过在重复显示的图上的经度坐标指定X轴坐标，在纬度坐标指定Y轴坐标来完成的。

对于一个等值线图，如果你的网格是等间距分布的，那么你可以使用ScalarField resources sfXCStartV和sfXCEndV来设定X轴的最小值和最大值，使用sfYCStartV和sfYCEndV来设定Y轴的最小值和最大值。

如果你有等间距分布的数组值来表示坐标轴，那么你可以使用sfXArray和sfYArray resources来设置数组值。

如果你的网格不是等间距分布的，你必须使用在example 2示例2中描述的sfXArray和sfYArray来确定坐标轴的范围。

在这个示例中，使用*StartV和*EndV resources来确定纬度、经度坐标轴的最小值和最大值。

因为psl&lat和psl&lon是psl的坐标变量，这就意味着它们代表了测量psl 的维度值和经度值。

因此，使用这些坐标变量莱维重复显示的等值线图提供经度/维度值的最小值和最大值。

min和max函数能够提取任何维度的数组并且返回该数组的最小值和最大值。

请注意：如果你没有为确定X和Y轴的最小值和最大值设定任何的ScalarField resources，那么最小值的默认值是0.0，最大值的默认值分别是n-1和m-1，n是psl在X方向上的点的个数，m是psl在Y方向上的点的个数。

这将导致等值线图会在纬度/经度（0.,0.,）和（m-1,n-1）角落显示，而并没有任何意义。

35.36.map = gsn_contour_map(wks,psl,resources) ; Draw contours over a map.在地图上创建并且绘制psl变量的等值线图。

默认的地图投影是圆柱等距投影。

gsn_contour_map函数的第一个参数是对于前一个调用gsn_open_wks的返回的工作站变量。

接下来的参数是将要绘制的2维标量场，可以是float、double 或者integer类型。

最后的参数是逻辑值，表明是否设置了任何的resources。

请注意：等值线图覆盖了整张梯度。

这是因为在31-34行中指定的纬度值是从-90~90，经度值是从-180~180（可以通过使用print来将这些值打印出来而验证）。

非常重要，请注意：gsn_contour_map函数实际上是创建了两个图，等值线图和地图。

地图是从函数返回的，等值线图是作为叫做“contour”地图的一个变量返回的。

当你在一个图上覆盖另外一个图时，被覆盖的图叫做“基图”，覆盖在基图上的图叫做“覆盖图”。

函数gsn_*创建这些覆盖图通常以函数值范围基图，同时覆盖图通常是基图的属性。

当你使用setvalues语句来改变resourcees时，这些信息变得非常有用，见example 9示例9。

37.38. ;----------- Begin second plot -----------------------------------------在正投影上绘制sst（海洋表面温度）第一个时间步长的等值线图，并且为以stipple类型来填充等值线、隐蔽陆地上的等值线使等值线图显得稍大来设置一些resources。

介绍一些等值线绘制顺序和地图组件的概念（这是为了使你得到想要的隐蔽的效果）。

39.40.getvalues wks ; Retrieve the default color map.41."wkColorMap" : cmap42.end getvalues使用getvalues获得使用的默认的颜色图并且将它存储在当地变量中（在该例中是cmap，但是你可以随意称呼该变量）。

颜色图和你用gsn_open_wks中调用而打开的工作站是相联系的，因此它是“Workstation”resources组中的一部分，以“wk”开头。

default color map默认的颜色图有32个条目（每个条目都是RGB值），所以NCL为cmap分配了一个32×3的float数组。

在示例4example 4中更加详细的描述了getvalues语句。

43.44.cmap(0,:) = (/1.,1.,1./) ; Change background to white.45.cmap(1,:) = (/0.,0.,0./) ; and foreground to black.46.gsn_define_colormap(wks, cmap)颜色index0和1分别代表了背景色和前景色，在默认情况下，背景色是黑色而前景色是白色。

使用刚刚获得的颜色图（cmap），将背景色设为白色，前景色设为黑色。

用RGB值来设置颜色，白色用（1.,1.,1.）表示、黑色用（0.,0.,0.）表示。