matplotlib中matshow和imshow的区别

2023-10-25 08:40

本文主要是介绍matplotlib中matshow和imshow的区别,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

matplotlib中matshow和imshow的区别

1.matshow

如下,即在一个图形窗口中将数组作为矩阵展示

def matshow(A, fignum=None, **kwargs):"""Display an array as a matrix in a new figure window.The origin is set at the upper left hand corner and rows (firstdimension of the array) are displayed horizontally.  The aspectratio of the figure window is that of the array, unless this wouldmake an excessively short or narrow figure.Tick labels for the xaxis are placed on top.Parameters----------A : array-like(M, N)The matrix to be displayed.fignum : None or int or FalseIf *None*, create a new figure window with automatic numbering.If a nonzero integer, draw into the figure with the given number(create it if it does not exist).If 0, use the current axes (or create one if it does not exist)... note::Because of how `.Axes.matshow` tries to set the figure aspectratio to be the one of the array, strange things may happen if youreuse an existing figure.Returns-------image : `~matplotlib.image.AxesImage`Other Parameters----------------**kwargs : `~matplotlib.axes.Axes.imshow` arguments"""

效果图如下:

2.imshow

展示图像数据在一个二维普通光栅中

    def imshow(self, X, cmap=None, norm=None, aspect=None,interpolation=None, alpha=None, vmin=None, vmax=None,origin=None, extent=None, shape=None, filternorm=1,filterrad=4.0, imlim=None, resample=None, url=None, **kwargs):"""Display an image, i.e. data on a 2D regular raster.Parameters----------X : array-like or PIL imageThe image data. Supported array shapes are:- (M, N): an image with scalar data. The data is visualizedusing a colormap.- (M, N, 3): an image with RGB values (0-1 float or 0-255 int).- (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),i.e. including transparency.The first two dimensions (M, N) define the rows and columns ofthe image.Out-of-range RGB(A) values are clipped.cmap : str or `~matplotlib.colors.Colormap`, optionalThe Colormap instance or registered colormap name used to mapscalar data to colors. This parameter is ignored for RGB(A) data.Defaults to :rc:`image.cmap`.norm : `~matplotlib.colors.Normalize`, optionalThe `Normalize` instance used to scale scalar data to the [0, 1]range before mapping to colors using *cmap*. By default, a linearscaling mapping the lowest value to 0 and the highest to 1 is used.This parameter is ignored for RGB(A) data.aspect : {'equal', 'auto'} or float, optionalControls the aspect ratio of the axes. The aspect is of particularrelevance for images since it may distort the image, i.e. pixelwill not be square.This parameter is a shortcut for explicitly calling`.Axes.set_aspect`. See there for further details.- 'equal': Ensures an aspect ratio of 1. Pixels will be square(unless pixel sizes are explicitly made non-square in datacoordinates using *extent*).- 'auto': The axes is kept fixed and the aspect is adjusted sothat the data fit in the axes. In general, this will result innon-square pixels.If not given, use :rc:`image.aspect` (default: 'equal').interpolation : str, optionalThe interpolation method used. If *None*:rc:`image.interpolation` is used, which defaults to 'nearest'.Supported values are 'none', 'nearest', 'bilinear', 'bicubic','spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser','quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc','lanczos'.If *interpolation* is 'none', then no interpolation is performedon the Agg, ps, pdf and svg backends. Other backends will fall backto 'nearest'. Note that most SVG renders perform interpolation atrendering and that the default interpolation method they implementmay differ.See:doc:`/gallery/images_contours_and_fields/interpolation_methods`for an overview of the supported interpolation methods.Some interpolation methods require an additional radius parameter,which can be set by *filterrad*. Additionally, the antigrain imageresize filter is controlled by the parameter *filternorm*.alpha : scalar, optionalThe alpha blending value, between 0 (transparent) and 1 (opaque).This parameter is ignored for RGBA input data.vmin, vmax : scalar, optionalWhen using scalar data and no explicit *norm*, *vmin* and *vmax*define the data range that the colormap covers. By default,the colormap covers the complete value range of the supplieddata. *vmin*, *vmax* are ignored if the *norm* parameter is used.origin : {'upper', 'lower'}, optionalPlace the [0,0] index of the array in the upper left or lower leftcorner of the axes. The convention 'upper' is typically used formatrices and images.If not given, :rc:`image.origin` is used, defaulting to 'upper'.Note that the vertical axes points upward for 'lower'but downward for 'upper'.extent : scalars (left, right, bottom, top), optionalThe bounding box in data coordinates that the image will fill.The image is stretched individually along x and y to fill the box.The default extent is determined by the following conditions.Pixels have unit size in data coordinates. Their centers are oninteger coordinates, and their center coordinates range from 0 tocolumns-1 horizontally and from 0 to rows-1 vertically.Note that the direction of the vertical axis and thus the defaultvalues for top and bottom depend on *origin*:- For ``origin == 'upper'`` the default is``(-0.5, numcols-0.5, numrows-0.5, -0.5)``.- For ``origin == 'lower'`` the default is``(-0.5, numcols-0.5, -0.5, numrows-0.5)``.See the example :doc:`/tutorials/intermediate/imshow_extent` for amore detailed description.filternorm : bool, optional, default: TrueA parameter for the antigrain image resize filter (see theantigrain documentation).  If *filternorm* is set, the filternormalizes integer values and corrects the rounding errors. Itdoesn't do anything with the source floating point values, itcorrects only integers according to the rule of 1.0 which meansthat any sum of pixel weights must be equal to 1.0.  So, thefilter function must produce a graph of the proper shape.filterrad : float > 0, optional, default: 4.0The filter radius for filters that have a radius parameter, i.e.when interpolation is one of: 'sinc', 'lanczos' or 'blackman'.resample : bool, optionalWhen *True*, use a full resampling method.  When *False*, onlyresample when the output image is larger than the input image.url : str, optionalSet the url of the created `.AxesImage`. See `.Artist.set_url`.Returns-------image : `~matplotlib.image.AxesImage`Other Parameters----------------**kwargs : `~matplotlib.artist.Artist` propertiesThese parameters are passed on to the constructor of the`.AxesImage` artist.See also--------matshow : Plot a matrix or an array as an image.Notes-----Unless *extent* is used, pixel centers will be located at integercoordinates. In other words: the origin will coincide with the centerof pixel (0, 0).There are two common representations for RGB images with an alphachannel:-   Straight (unassociated) alpha: R, G, and B channels represent thecolor of the pixel, disregarding its opacity.-   Premultiplied (associated) alpha: R, G, and B channels representthe color of the pixel, adjusted for its opacity by multiplication.`~matplotlib.pyplot.imshow` expects RGB images adopting the straight(unassociated) alpha representation."""

同一个矩阵展示效果如下:

与上面对比我们可以看到图像的坐标默认是不同的。

 

详细可参阅官方文档。

https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.matshow.html#matplotlib.axes.Axes.matshow

https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow

 

 

这篇关于matplotlib中matshow和imshow的区别的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!


原文地址:https://blog.csdn.net/IT_flying625/article/details/102594258
本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处:http://www.chinasem.cn/article/281380

相关文章

Conda与Python venv虚拟环境的区别与使用方法详解

《Conda与Pythonvenv虚拟环境的区别与使用方法详解》随着Python社区的成长,虚拟环境的概念和技术也在不断发展,:本文主要介绍Conda与Pythonvenv虚拟环境的区别与使用... 目录前言一、Conda 与 python venv 的核心区别1. Conda 的特点2. Python v

Go语言中make和new的区别及说明

《Go语言中make和new的区别及说明》:本文主要介绍Go语言中make和new的区别及说明,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录1 概述2 new 函数2.1 功能2.2 语法2.3 初始化案例3 make 函数3.1 功能3.2 语法3.3 初始化

深度解析Spring Boot拦截器Interceptor与过滤器Filter的区别与实战指南

《深度解析SpringBoot拦截器Interceptor与过滤器Filter的区别与实战指南》本文深度解析SpringBoot中拦截器与过滤器的区别,涵盖执行顺序、依赖关系、异常处理等核心差异,并... 目录Spring Boot拦截器(Interceptor)与过滤器(Filter)深度解析:区别、实现

Before和BeforeClass的区别及说明

《Before和BeforeClass的区别及说明》:本文主要介绍Before和BeforeClass的区别及说明,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录Before和BeforeClass的区别一个简单的例子当运行这个测试类时总结Before和Befor

Android学习总结之Java和kotlin区别超详细分析

《Android学习总结之Java和kotlin区别超详细分析》Java和Kotlin都是用于Android开发的编程语言,它们各自具有独特的特点和优势,:本文主要介绍Android学习总结之Ja... 目录一、空安全机制真题 1:Kotlin 如何解决 Java 的 NullPointerExceptio

使用Python和Matplotlib实现可视化字体轮廓(从路径数据到矢量图形)

《使用Python和Matplotlib实现可视化字体轮廓(从路径数据到矢量图形)》字体设计和矢量图形处理是编程中一个有趣且实用的领域,通过Python的matplotlib库,我们可以轻松将字体轮廓... 目录背景知识字体轮廓的表示实现步骤1. 安装依赖库2. 准备数据3. 解析路径指令4. 绘制图形关键

Python中OpenCV与Matplotlib的图像操作入门指南

《Python中OpenCV与Matplotlib的图像操作入门指南》:本文主要介绍Python中OpenCV与Matplotlib的图像操作指南,本文通过实例代码给大家介绍的非常详细,对大家的学... 目录一、环境准备二、图像的基本操作1. 图像读取、显示与保存 使用OpenCV操作2. 像素级操作3.

Linux中的more 和 less区别对比分析

《Linux中的more和less区别对比分析》在Linux/Unix系统中,more和less都是用于分页查看文本文件的命令,但less是more的增强版,功能更强大,:本文主要介绍Linu... 目录1. 基础功能对比2. 常用操作对比less 的操作3. 实际使用示例4. 为什么推荐 less?5.

Java 关键字transient与注解@Transient的区别用途解析

《Java关键字transient与注解@Transient的区别用途解析》在Java中,transient是一个关键字,用于声明一个字段不会被序列化,这篇文章给大家介绍了Java关键字transi... 在Java中,transient 是一个关键字,用于声明一个字段不会被序列化。当一个对象被序列化时,被

解读@ConfigurationProperties和@value的区别

《解读@ConfigurationProperties和@value的区别》:本文主要介绍@ConfigurationProperties和@value的区别及说明,具有很好的参考价值,希望对大家... 目录1. 功能对比2. 使用场景对比@ConfigurationProperties@Value3. 核