python中maintain的作用
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Python内容推荐
Python库 | maintain_PlatoUtils-0.1.2.26.tar.gz
资源分类:Python库 所属语言:Python 资源全名:maintain_PlatoUtils-0.1.2.26.tar.gz 资源来源:官方 安装方法:https://lanzao.blog.csdn.net/article/details/101784059
Python库 | maintain_PlatoUtils-0.1.2.11-py3-none-any.whl
python库。 资源全名:maintain_PlatoUtils-0.1.2.11-py3-none-any.whl
基于Python Flask框架的共享自习室预约系统论文.doc
基于Python Flask框架的共享自习室预约系统 论文
Introduction to Python Programming and Developing GUI Applications with PyQT
Introduction to Python Programming and Developing GUI Applications with PyQT
基于Python的在线网盘系统的设计与实现
不包括代码哦 请谅解
基于python的小区监控图像拼接系统源码数据库论文.doc
基于python的小区监控图像拼接系统源码数据库论文.doc
QGIS Python Programming Cookbook
About This Book, Use Python and QGIS to create and transform data, produce appealing GIS visualizations, and build complex map layoutsLearn undocumented features of the new QGIS processing moduleA set of user-friendly recipes that can automate the entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes, Who This Book Is For, If you are a geospatial analyst who wants to learn more about automating everyday GIS tasks or a programmer who is responsible for building GIS applications,this book is for you. The short, reusable recipes make concepts easy to understand. You can build larger applications that are easy to maintain when they are put together., What You Will Learn, Build a library of reusable scripts with ScriptRunner Create, import, and edit geospatial data on disk or in memory Get to know more about dynamic mapping Create and add features to static maps Create a mapbook Reproject a vector layer Geolocate photos on a map Combine multiple rasters into one image, In Detail, QGIS Python Programming will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will come across recipes that will help you to compose static maps, create heavily customized maps, and add specialized labels and annotations. Apart from this, the book will also share a few tips and tricks based on different aspects of QGIS.
基于python+Django的校园疫情监控平台源码数据库论文.docx
基于python+Django的校园疫情监控平台源码数据库论文.docx
QGIS Python Programming Cookbook - Second Edition
QGIS Python Programming Cookbook - Second Edition by Joel Lawhead English | 14 Mar. 2017 | ASIN: B01MRP7T3B | 464 Pages | AZW3 | 9.26 MB Master over 170 recipes that will help you turn QGIS from a desktop GIS tool into a powerful automated geospatial framework About This Book Delve into the undocumented features of the QGIS API Get a set of user-friendly recipes that can automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes This book has a complete code upgrade to QGIS 2.18 and 30 new, valuable recipes Who This Book Is For This book is for geospatial analysts who want to learn more about automating everyday GIS tasks as well as programmers responsible for building GIS applications. The short, reusable recipes make concepts easy to understand and combine so you can build larger applications that are easy to maintain. What You Will Learn Use Python and QGIS to produce captivating GIS visualizations and build complex map layouts Find out how to effectively use the poorly-documented and undocumented features of the QGIS Python API Automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes Create, import, and edit geospatial data on disk or in-memory Change QGIS settings programmatically to control default behavior Automatically generate PDF map books Build dynamic forms for field input In Detail QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language—Python, we can write effective scripts that extend the core functionality of QGIS. Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we'll also share a few tips and tricks based on different aspects of QGIS. Style and approach This book follows a recipe-based problem-solution approach to address and dispel challenges faced when implementing and using QGIS on a regular basis.
Advanced Python Programming.epub
What You Will Learn Use NumPy and pandas to import and manipulate datasets Achieve native performance with Cython and Numba Write asynchronous code using asyncio and RxPy Design highly scalable programs with application scaffolding Explore abstract methods to maintain data consistency Clone objects using the prototype pattern Use the adapter pattern to make incompatible interfaces compatible Employ the strategy pattern to dynamically choose an algorithm
LargeScaleMachineLearningwithPython.pdf
Large Scale Machine Learning with Python [PDF + EPUB + CODE] Packt Publishing | August 4, 2016 | English | 439 pages Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
micropython-stubber:为不同的micropython固件生成并使用存根以与vscode和_或pylint一起使用
在VSCode中提高MicroPython的生产力 在现代编辑器中如此普遍的智能和代码替换功能对于MicroPython项目而言并非一帆风顺。 虽然语言是Python,但是所使用的模块与CPython不同,并且不同的端口具有不同的模块和类,或者具有不同参数的同一类。 在现代的编辑器中编写MicroPython代码不需要涉及保持浏览器打开以检查用于读取传感器,点亮LED或发送网络请求的确切参数。 幸运的是,有了一些其他配置和数据,就有可能使编辑者了解您对MicroPython的了解。 即使您运行开/关自定义固件版本。 为了实现这一点,需要做一些事情: 使用PEP 484类型提示的固件中本机/已启用模块的存根文件 VSCode Python扩展的特定配置 皮林特的具体配置 禁止与MicroPython主体冲突或代码优化的警告。 有了这些,VSCode将在很大程度上了解MicroPyt
Python 形态学梯度批量 边缘增强报告
Python 形态学梯度批量 边缘增强报告 批量对二值图做形态学梯度增强,输出 grad_*.png、morph_grad_report.csv 与梯度幅值柱状图。 功能: · 缺省自动生成二值演示图 · 批量形态学梯度 · morph_grad_report.csv · 多图对比预览网格 · 梯度幅度柱状图 · 打包时预跑 output/preview 压缩包含可运行源码、依赖与说明,按 README 安装后即可复现。
Python CNN MNIST 手写数字识别
Python CNN MNIST 手写数字识别 轻量卷积网络在 MNIST 上训练分类,输出混淆矩阵、history.csv 与损失曲线,数据自动下载。 功能: · MNIST 手写数字 · 双层卷积池化 CNN · 混淆矩阵 · 损失曲线 · 自动下载数据 · 打包时预跑 output/preview 压缩包含可运行源码、依赖与说明,按 README 安装后即可复现。
Python 随机森林 Wine 多分类 特征重要性
Python 随机森林 Wine 多分类 特征重要性 随机森林在 Wine 数据集上三分类,输出混淆矩阵、特征重要性图与 report.csv。 功能: · Wine 三分类 · 随机森林 · 混淆矩阵 · 特征重要性条形图 · report.csv · 打包时预跑 output/preview 压缩包含可运行源码、依赖与说明,按 README 安装后即可复现。
Python QDA Iris 二次判别 决策边界
Python QDA Iris 二次判别 决策边界 二次判别分析在 Iris 上三分类,输出混淆矩阵、前两维决策边界图与 report.csv。 功能: · Iris QDA 二次判别 · 混淆矩阵 · 前两维决策边界 · report.csv · 可复现划分 · 打包时预跑 output/preview 压缩包含可运行源码、依赖与说明,按 README 安装后即可复现。
Python XGBoost 房价回归 特征重要性
Python XGBoost 房价回归 特征重要性 合成房屋表格特征训练 XGBoost 回归,输出 RMSE/MAE/R2、特征重要性图与预测散点图。 功能: · 合成房价表格数据 · XGBoost 回归 · RMSE/MAE/R2 · 特征重要性图 · 预测散点图 · 打包时预跑 output/preview 压缩包含可运行源码、依赖与说明,按 README 安装后即可复现。
Python PDF批量转灰度 体积对比报告
Python PDF批量转灰度 体积对比报告 批量将彩色 PDF 转为灰度版,输出 gray_*.pdf、gray_report.csv 与体积对比柱状图,缺省自动生成演示 PDF。 功能: · 缺省生成彩色演示 PDF · 批量转灰度 PDF · gray_report.csv · 体积对比柱状图 · PyMuPDF 渲染 · 打包时预跑 output/preview 压缩包含可运行源码、依赖与说明,按 README 安装后即可复现。
maintain_frame11111.rar maintain_frame11111.rar
maintain_frame11111.rar maintain_frame11111.rar
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maintain_frame22222.rar maintain_frame22222.rar
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