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id="recent-posts"><div class="recent-post-items"><div class="recent-post-item"><div class="post_cover left"><a href="/2026/01/12/Python%E5%AD%A6%E4%B9%A0_1/" title="python学习_1"><img class="post-bg" src="/img/python/1/image.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="python学习_1"></a></div><div class="recent-post-info"><a class="article-title" href="/2026/01/12/Python%E5%AD%A6%E4%B9%A0_1/" title="python学习_1">python学习_1</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2026-01-12T04:00:00.000Z" title="发表于 2026-01-12 12:00:00">2026-01-12</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a></span></div><div class="content">编程基础随着科技日益渗透我们的生活，希望我们能够成为技术的创造者，而不仅仅是技术的使用者。转变我们的视角，从技术的这一端(观察者)，转向另一端(技术创造者)，能够亲手打造属于自己的作品。 正在学习datawhale 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2025-12-16 12:00:00">2025-12-16</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F/">动力系统</a></span></div><div class="content">动力系统 ：尖点分岔、感染模型与跨临界分岔 文章地址：https://mp.weixin.qq.com/s/l4OAjdUqLcHML3-tEk-cDg 引言上次探讨了单分量一维系统。分析的焦点集中在单个控制参数$\mu$变化是，系统的不动点会发生怎么样的变化。分析了两种最基本的分岔类型： 鞍结分岔(Saddle-Node Bifurcation):其常态形式为$\partial_t u = -\mu+u*2$,他描述了不动点”凭空”产生和湮灭的最基本方式，是系统”引爆点”的数学原形。 叉式分岔(Pitchafork Bifurcation):其常态新形势为$\partial u=ru-u*3$,他以系统的对称性为前提，完美描述了物理学中（如金茨堡-朗道理论）的”自发性对称性破缺”现象。 上次课的内容都围绕一个核心问题：当单个控制参数$\mu$变化时，系统的定性行为会如何改变？ 本次课将在此基础上迈出关键一步，探讨由两个控制采纳数调控时更为复杂和丰富的动力学现象。为此，将深入研究尖点分岔(Cusp Bifurcation)。尖点分岔可以被叉式分岔的直接...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2025/12/09/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F_2/" title="动力系统_2"><img class="post-bg" src="/img/blog/lorenz.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="动力系统_2"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/12/09/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F_2/" title="动力系统_2">动力系统_2</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-12-09T04:00:00.000Z" title="发表于 2025-12-09 12:00:00">2025-12-09</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F/">动力系统</a></span></div><div class="content">动力系统 ：不动点、鞍点和叉形分岔 文章地址:https://mp.weixin.qq.com/s/6FLkKCKHE74SXdZM_r7gWw 引言这门课是讲解自组织与模式形成(Self-Organization and Pattern Formation, Prof. Erwin Frey, LMU Munich, Winter Semester) 课程链接：https://www.theorie.physik.uni-muenchen.de/lsfrey/research/biological_physics/2025_10_04/index.html视频链接：https://www.youtube.com/watch?v=apMmakW_090&list=PL2IEUF-u3gRfIw2cHFPqNHjNfQL5BxG5m $d$ (全导数) 和 $\partial$ (偏导数) 的区别我们一开始学的非线性动力学与混动这本书中一直都是使用$d$(全导数)，而在我们看论文的时候经常遇到的都是$\partial$ (偏导数) 全导数（$d$）和偏导数（$\par...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2025/12/02/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F_1/" title="动力系统_1"><img class="post-bg" src="/img/blog/lorenz.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="动力系统_1"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/12/02/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F_1/" title="动力系统_1">动力系统_1</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-12-02T04:00:00.000Z" title="发表于 2025-12-02 12:00:00">2025-12-02</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%8A%A8%E5%8A%9B%E7%B3%BB%E7%BB%9F/">动力系统</a></span></div><div class="content">动力系统的引入 文章地址：https://mp.weixin.qq.com/s/T6hJ-joBQNp-AL6EqDGWJg 引言这门课是讲解自组织与模式形成(Self-Organization and Pattern Formation, Prof. Erwin Frey, LMU Munich, Winter Semester) 课程链接：https://www.theorie.physik.uni-muenchen.de/lsfrey/research/biological_physics/2025_10_04/index.html视频链接：https://www.youtube.com/watch?v=apMmakW_090&list=PL2IEUF-u3gRfIw2cHFPqNHjNfQL5BxG5m 课程分为三个理论部分展开： 动力系统理论 (Dynamic Systems Theory) 相变与集体现象 (Phase Transitions & Collective Phenomena) 模式形成与自组织 (Pattern Formati...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2025/11/11/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-6/" title="科研绘图_6"><img class="post-bg" src="/img/blog/cover.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="科研绘图_6"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/11/11/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-6/" title="科研绘图_6">科研绘图_6</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-11-11T04:00:00.000Z" title="发表于 2025-11-11 12:00:00">2025-11-11</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE/">科研绘图</a></span></div><div class="content">让你的数据动起来：用Python创建动画在很多情况下，静态图表可以展示最终结果，但在阐明其背后的过程时往往力不从心。在这种情况下，动画可以发挥显著作用。通过展示一系列帧，每一帧都描绘出流程中的一个步骤，您可以更好地吸引观众的注意力，并更有效地解释复杂的概念和工作流程。 本文使用python和Matplotlib通过动画的形式将一些概念展示出来 Animations using Matplotlib基于其绘图功能，Matplotlib 还提供了一个接口，可以使用 animation 模块生成动画。 动画是一系列帧，其中每一帧都对应于屏幕上的一个图形 Matplotlib的动画制作过程可以总两个不同的角度来理解: FuncAnimation:FuncAnimation是基于函数的方法来创建动画的。它使用用户提供的一个或多个函数来更新图形的状态，并按照一定的时间间隔连续地调用这些函数，从而实现动画效果。用户需要定义一个更新函数，该函数在每个时间步长上更新图形对象的属性，然后FuncAnimation会根据用户指定的帧数、时间间隔等参数来自动计算动画的帧序列。这种方法适用于需要根据时间...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2025/11/04/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-5/" title="科研绘图_5"><img class="post-bg" src="/img/blog/cover.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="科研绘图_5"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/11/04/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-5/" title="科研绘图_5">科研绘图_5</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-11-04T04:00:00.000Z" title="发表于 2025-11-04 12:00:00">2025-11-04</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE/">科研绘图</a></span></div><div class="content">一些实用小技巧matplotlib字体配置衬线和非衬线字体 衬线字体常用于正式文档和出版物，无衬线字体更加简洁现代，通常在屏幕上的可读性更好，也更适合用于演示文稿。对于中文来说，大体上对应于serif/sans-serif的字体是宋体系列/黑体系列，宋体常用于书籍等长篇文章；黑体笔画粗细基本一致，较为醒目，常用于标题、导语、和标志中，幻灯片里一般采用黑体。 举个栗子：2015年Google就是用了无衬线体代替了衬线体的Logo 更多详细字体内容参考 字体族matplotlib的字体配置有两个层级，首先是字体族(font.family)的配置，在matplotlib中，预置了五种基本的字体族，分别是： serif - 衬线字体 sans-serif - 无衬线字体 monospace - 等宽字体 cursive - 手写体风格的字体 fantasy - 装饰性或艺术性的字体 在代码中在，我们可以手动指定想要选择的字体 1234567891011import matplotlib.pyplot as plt# 更新rcParams来设定字体plt.rcPa...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2025/10/28/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-4/" title="科研绘图_4"><img class="post-bg" src="/img/blog/cover.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="科研绘图_4"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/10/28/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-4/" title="科研绘图_4">科研绘图_4</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-10-28T04:00:00.000Z" title="发表于 2025-10-28 12:00:00">2025-10-28</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE/">科研绘图</a></span></div><div class="content">一个问题对于下面这种图形保存方式，是否可以正确保存图形 123456789X = np.linspace(0, 10*np.pi, 1000)Y = np.sin(X)fig, ax = plt.subplots()ax.plot(X, Y)plt.show()plt.savefig('figure.png') 对于下面这种图形保存方式，是否可以正常保存 12345678X = np.linspace(0, 10*np.pi, 1000)Y = np.sin(X)fig, ax = plt.subplots()ax.plot(X, Y)plt.show()fig.savefig('plot.png') 尝试之后，可以感觉出来plt和fig这两种方式不同，我们应该知道良好的代码规范，在图片显示之前就对图片进行保存 一些画图过程中的常见参数在之前的学习中在实现绘图时,有一些函数和参数是没有特别说明的我们来大概说一下这些参数或者的作用 dpi = 300 在创建画布fig, ax = plt.subplots(figsize=...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2025/10/21/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-3/" title="科研绘图_3"><img class="post-bg" src="/img/blog/cover.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="科研绘图_3"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/10/21/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-3/" title="科研绘图_3">科研绘图_3</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-10-21T04:00:00.000Z" title="发表于 2025-10-21 12:00:00">2025-10-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE/">科研绘图</a></span></div><div class="content">坐标图形布局画图的四个步骤初始化–数据准备–绘制图形–观察保存 这个图形是否能正常显示 12345678import numpy as npimport matplotlib.pyplot as pltX = np.arange(10)Y = np.random.uniform(1, 10, 10)ax.bar(X, Y) 通过上面的例子可以看出，figure(图形)和坐标图形(axes)的存在是必须的，这是显示绘图的基础元素。那么这二者的关系的是什么。首先figure是最顶层的容器，可以理解为一个总的空白的布，做一个比喻就是你做ppt时候的页面，然后再在上面添加元素，Axes也就是在figure上创建的坐标系（子图），其实这个才是我们直观理解的图，可以说是次顶级容器。我们之后所有的图操作都是在Axes上建立的。 二者之间的关系： 一个Figure可以包含一个或多个Axes对象，而一个Axes对象一定属于且仅属于一个Figure。（就是一个图形只会属于那一页PPT） 注意区分Axes不是Axis(轴)，Axes包含了Axis 简单说一下Figure的作用，他最大的作用...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2025/10/14/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-2/" title="科研绘图_2"><img class="post-bg" src="/img/blog/cover.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="科研绘图_2"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/10/14/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE-2/" title="科研绘图_2">科研绘图_2</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-10-14T04:00:00.000Z" title="发表于 2025-10-14 12:00:00">2025-10-14</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%A7%91%E7%A0%94%E7%BB%98%E5%9B%BE/">科研绘图</a></span></div><div class="content">Matplotlib🎨核心图形元素 画布 (Figure) - 顶层容器，承载所有图形元素 坐标图形 (Axes) - 绘图核心区域，包含大部分图层属性 轴 (Axis) - 坐标轴线，控制数据展示范围 艺术对象 (Artist) - 所有图形元素的基类 按照元素体系分类🔧 基础类元素 (Primitive) 点 (point) 线 (line) 文本 (text) 网格 (grid) 标题 (title) 图例 (legend) 📦 容器类元素 (Container) 图形 (Figure) - 基础图形容器 坐标图形 (Axes) - 绘图核心容器 轴 (Axis) - 坐标轴容器 刻度 (Tick) - 刻度容器 详细元素说明 画布 (Figure) 功能: 顶层图形容器 可配置参数: 画布大小 (figsize) 分辨率 (dpi) 背景颜色 特性: 可划分为多个子图区域 坐标图形 (Axes) 别名: 子图 核心功能: 数据展示区域 组成元素: 轴脊 (spine) 图名 (title) 刻度 (tick) 轴标签 (axis label)...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2025/10/05/Macroscopic%20Description_2015/" title="文献阅读_Macroscopic Description for Networks of Spiking Neurons"><img class="post-bg" src="/img/article/globe.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="文献阅读_Macroscopic Description for Networks of Spiking Neurons"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/10/05/Macroscopic%20Description_2015/" title="文献阅读_Macroscopic Description for Networks of Spiking Neurons">文献阅读_Macroscopic Description for Networks of Spiking Neurons</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-10-05T04:00:00.000Z" title="发表于 2025-10-05 12:00:00">2025-10-05</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%AE%BA%E6%96%87/">论文</a></span></div><div class="content">🧠 论文基本信息 标题：Macroscopic Description for Networks of Spiking Neurons 作者：Ernest Montbrió1, Diego Pazó2, and Alex Roxin3 发表出处：Physical Review X 发表时间：2015 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