matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, *, filternorm=True, filterrad=4.0, resample=None, url=None, data=None, **kwargs)
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From:
改以下參數(shù)可以對(duì)圖片效果進(jìn)行調(diào)整：
舉個(gè)栗子：
這里分享下python，使用pil獲取圖層中出現(xiàn)次數(shù)最多顏色值的方法。
設(shè)備：magicbook
系統(tǒng)：Windows 11
軟件：python2014
1、首先打開(kāi)pycharm開(kāi)發(fā)工具，創(chuàng)建python項(xiàng)目，然后新建python文件。
2、定義函數(shù)countNum，傳入?yún)?shù)amn；然后使用字典，獲取字符串中的字符，統(tǒng)計(jì)出現(xiàn)的次數(shù)。
3、定義一個(gè)字符串變量cmn，然后調(diào)用函數(shù)countNum，傳入cmn，然后將返回的值給變量bmn，并打印。
4、保存代碼并運(yùn)行python文件，查看控制臺(tái)打印結(jié)果，如下圖所示就完成了。
轉(zhuǎn)為16進(jìn)制就是分別把紅綠藍(lán)三種顏色的色值轉(zhuǎn)為十六進(jìn)制，前面加個(gè)井號(hào)
by Nicolas P. Rougier, Bordeaux, November 2021.
PDF地址：
原文：
The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop only, some deal with 3D and large data, while others target flawless 2D rendering.
翻譯：
Python 科學(xué)可視化領(lǐng)域是巨大的（見(jiàn)下圖）。它由無(wú)數(shù)工具組成，從最通用和最有廣泛性的工具到更專業(yè)和達(dá)到機(jī)密級(jí)的工具。其中一些工具是基于社區(qū)的，而另一些則是由公司開(kāi)發(fā)的。有些是專門為 Web 制作的，有些僅適用于桌面，有些用于處理 3D 和大數(shù)據(jù)，而有些則針對(duì)完美的 2D 渲染。
原文：
Figure : The most important element of a figure is the figure itself. It is created when you call the figure method and we’ve already seen you can specify its size but you can also specify a background color (facecolor) as well as a title (suptitle). It is important to know that the background color won’t be used when you save the figure because the savefig function has also a facecolor argument (that is white by default) that will override your figure background color. If you don’t want any background you can specify transparent=True when you save the figure.
Axes : This is the second most important element that corresponds to the actual area where your data will be rendered. It is also called a subplot. You can have have one to many axes per figure and each is usually surrounded by four edges (left, top, right and bottom) that are called spines. Each of these spines can be decorated with major and minor ticks (that can point inward or outward), tick labels and a label. By default, matplotlib decorates only the left and bottom spines.
Axis : The decorated spines are called axis. The horizontal one is the xaxis and the vertical one is the yaxis. Each of them are made of a spine, major and minor ticks, major and minor ticks labels and an axis label.
Spines : Spines are the lines connecting the axis tick marks and noting the boundaries of the data area. They can be placed at arbitrary positions and may be visible or invisible.
Artist : Everything on the figure, including Figure, Axes, and Axis objects, is an artist. This includes Text objects, Line2D objects, collection objects, Patch objects. When the figure is rendered, all of the artists are drawn to the canvas. A given artist can only be in one Axes.
翻譯：
Figure（圖形）：圖形中最重要的元素是Figure本身。它是在你調(diào)用 figure 方法時(shí)創(chuàng)建的，我們已經(jīng)看到你可以指定它的大小，但你也可以指定背景顏色 (facecolor) 和標(biāo)題 (suptitle)。重要的是保存圖形時(shí)不會(huì)使用背景顏色，因?yàn)?savefig 函數(shù)也有一個(gè) facecolor 參數(shù)（默認(rèn)為白色），它將覆蓋圖形背景顏色。如果您不想要任何背景，您可以在保存圖形時(shí)指定 transparent=True。
Axes（軸域） ：這是第二個(gè)最重要的元素，對(duì)應(yīng)于將呈現(xiàn)數(shù)據(jù)的實(shí)際區(qū)域。它也被稱為子圖。每個(gè)圖形可以有一個(gè)到多個(gè)Axes ，每個(gè)軸通常被稱為spines的四個(gè)邊緣（左、上、右和下）包圍。這些spines中的每一個(gè)都可以裝飾有主要和次要刻度（可以指向內(nèi)或向外）、刻度標(biāo)簽和標(biāo)簽。默認(rèn)情況下，matplotlib 只裝飾左側(cè)和底部的Spines。
Axis（軸）：軸上的刻度稱為Axis。水平軸是 x 軸，垂直軸是 y 軸。它們中的每一個(gè)都由Spines、主要和次要刻度、刻度標(biāo)簽以及軸標(biāo)簽組成。
Spines（圖脊）：Spines 是連接軸刻度線和關(guān)注數(shù)據(jù)區(qū)域邊界的線。它們可以放置在任意位置并且可以是可見(jiàn)的或不可見(jiàn)的。
Artist（藝術(shù)家）：圖形上的所有內(nèi)容，包括圖形、軸和軸對(duì)象，都是 Artist 。這包括 Text 對(duì)象、Line2D 對(duì)象、集合對(duì)象、Patch 對(duì)象。當(dāng)圖形被渲染時(shí)，所有的 Artist 都被繪制到畫(huà)布上。給定的 Artist 只能在一個(gè) Axes 中。
原文：
Given the definition above, problems arise when how a visual is perceived differs significantly from the intent of the conveyer. Consequently, it is important to identify, as early as possible in the design process, the audience and the message the visual is to convey. The graphical design of the visual should be informed by this intent.
Only after identifying the message will it be worth the time to develop your figure, just as you would take the time to craft your words and sentences when writing an article only after deciding on the main points of the text.
A figure can be displayed on a variety of media, such as a poster, a computer monitor, a projection screen (as in an oral presentation), or a simple sheet of paper (as in a printed article). Each of these media represents different physical sizes for the figure, but more importantly, each of them also implies different ways of viewing and interacting with the figure.
Whether describing an experimental setup, introducing a new model, or presenting new results, you cannot explain everything within the figure itself—a figure should be accompanied by a caption. The caption explains how to read the figure and provides additional precision for what cannot be graphically represented.
All plots require at least some manual tuning of the different settings to better express the message, be it for making a precise plot more salient to a broad audience, or to choose the best colormap for the nature of the data.
Color is an important dimension in human vision and is consequently equally important in the design of a scientific figure.
What distinguishes a scientific figure from other graphical artwork is the presence of data that needs to be shown as objectively as possible.
Chartjunk refers to all the unnecessary or confusing visual elements found in a figure that do not improve the message (in the best case) or add confusion (in the worst case).
Remember, in science, message and readability of the figure is the most important aspect while beauty is only an option.
There exist many tools that can make your life easier when creating figures, and knowing a few of them can save you a lot of time.
翻譯：
如上所述，當(dāng)視覺(jué)所展示的與表達(dá)者的意圖明顯出現(xiàn)偏差時(shí)，就會(huì)出現(xiàn)問(wèn)題。 因此，在設(shè)計(jì)過(guò)程中盡早確定受眾和視覺(jué)傳達(dá)的信息非常重要。 視覺(jué)的圖形設(shè)計(jì)應(yīng)以此意圖為依據(jù)。
只有在確定了信息之后，才值得花時(shí)間開(kāi)發(fā)你的圖形，就像你在寫(xiě)一篇文章時(shí)，只有在確定了文本的要點(diǎn)之后，才會(huì)花時(shí)間精心制作你的單詞和句子。
圖形可以顯示在各種媒介上，例如海報(bào)、計(jì)算機(jī)顯示器、投影屏幕（如宣講會(huì)）或簡(jiǎn)單的紙（如印刷品）。每一個(gè)媒介都代表了圖形的不同物理尺寸，但更重要的是，每一個(gè)還提示了觀看者和與圖形互動(dòng)的不同方式。
無(wú)論是說(shuō)明如何設(shè)置實(shí)驗(yàn)、引入新模型還是展示新結(jié)果，圖形都無(wú)法解釋本身的所有內(nèi)容——圖形應(yīng)附有備注。備注解釋了如何閱讀該圖并為無(wú)法用圖形表示的內(nèi)容提供更精確的說(shuō)明。
所有的圖形都至少需要對(duì)不同的缺省值進(jìn)行一些手動(dòng)調(diào)整，以更好地表達(dá)信息，不僅是為了使圖形對(duì)廣大觀眾更加突出精確，還需要對(duì)數(shù)據(jù)的性質(zhì)選擇最佳顏色圖。
顏色是人類視覺(jué)中的一個(gè)重要維度，因此在科學(xué)人物的設(shè)計(jì)中同樣重要。
科學(xué)圖形與其他圖形藝術(shù)作品的區(qū)別在于，數(shù)據(jù)的展現(xiàn)需要盡可能客觀地顯示。
“無(wú)用圖形”是指在圖中存在的所有不必要或令人困惑的視覺(jué)元素，這些元素不會(huì)改善信息（在最好的情況下）或增加混亂（在最壞的情況下）。
請(qǐng)記住，在科學(xué)中，圖形的信息和可讀性是最重要的方面，而美化只是一種選擇。
有許多工具可以讓您在創(chuàng)建圖形時(shí)更輕松，了解其中的一些工具可以為您節(jié)省大量時(shí)間。
通過(guò)代碼來(lái)設(shè)置顏色。
可以使用語(yǔ)句，turtlecolor（"red"）通過(guò)這一句代碼，畫(huà)筆的顏色就被設(shè)置為紅色，以此類推。
Python由荷蘭數(shù)學(xué)和計(jì)算機(jī)科學(xué)研究學(xué)會(huì)的于1990年代初設(shè)計(jì)。
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