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Display run errors | In the Status column of the Run Status dialog, mouse over a failed run to display a possible error message. |
Review error logs | In the Run Status dialog, right-click a failed run, and then select Open Run Folder. |
![design of experiments python tutorial design of experiments python tutorial](https://2022.help.altair.com/2022/inspire/en_us/images/figures/shared/tut_de_check_runs.png)
- When all runs are complete, close the Run Status dialog.
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![design of experiments python tutorial design of experiments python tutorial](https://2022.help.altair.com/2022/inspire/en_us/images/icons/shared/icon_review_reports_de.png)
Positive effects are shown in shades of blue and indicate that a positive change in the design variable results in a positive change on the response.
Negative effects are shown in shades of brown and indicate that a positive change in the design variable results in a negative change on the response. FA_Tube_B has a linear effect of -2.021 , meaning an increase in the tube base width will decrease the displacement response. The full linear effect from the minimum design variable of 96 mm to the maximum design variable of 144 mm would be approximately -2 mm .
For more information, see Linear Effects .
![design of experiments python tutorial design of experiments python tutorial](https://2022.help.altair.com/2022/inspire/en_us/images/icons/shared/icon_trade_off.png)
- For information on how to save and export the optimized shape, see Export Geometry and Results .
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Python Web Scraping Tutorial
- Introduction to Web Scraping
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Basics of Web Scraping
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Setting Up the Environment
- Beautifulsoup Installation - Python
- How to Install Requests in Python - For Windows, Linux, Mac
- Selenium Python Introduction and Installation
- How to Install Python Scrapy on Windows?
Extracting Data from Web Pages
- Implementing Web Scraping in Python with BeautifulSoup
- How to extract paragraph from a website and save it as a text file?
- Extract all the URLs from the webpage Using Python
- How to Scrape Nested Tags using BeautifulSoup?
- Extract all the URLs that are nested within <li> tags using BeautifulSoup
- Clean Web Scraping Data Using clean-text in Python
Fetching Web Pages
- GET and POST Requests Using Python
- BeautifulSoup - Scraping Paragraphs from HTML
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Searching and Extract for specific tags Beautifulsoup
- Python BeautifulSoup - find all class
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- Scrape Google Search Results using Python BeautifulSoup
- Get tag name using Beautifulsoup in Python
- Extracting an attribute value with beautifulsoup in Python
- BeautifulSoup - Modifying the tree
- Find the text of the given tag using BeautifulSoup
- Python | Remove spaces from a string
- Understanding Character Encoding
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- Locating single elements in Selenium Python
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Web scraping, the process of extracting data from websites, has emerged as a powerful technique to gather information from the vast expanse of the internet. In this tutorial, we’ll explore various Python libraries and modules commonly used for web scraping and delve into why Python 3 is the preferred choice for this task.
Essential Packages and Tools for Python Web Scraping
The latest version of Python , offers a rich set of tools and libraries specifically designed for web scraping, making it easier than ever to retrieve data from the web efficiently and effectively.
Table of Content
Requests Module
Beautifulsoup library, urllib module.
The requests library is used for making HTTP requests to a specific URL and returns the response. Python requests provide inbuilt functionalities for managing both the request and response.
Example: Making a Request
Python requests module has several built-in methods to make HTTP requests to specified URI using GET, POST, PUT, PATCH, or HEAD requests. A HTTP request is meant to either retrieve data from a specified URI or to push data to a server. It works as a request-response protocol between a client and a server. Here we will be using the GET request. The GET method is used to retrieve information from the given server using a given URI. The GET method sends the encoded user information appended to the page request.
![design of experiments python tutorial Python requests making GET request](https://media.geeksforgeeks.org/wp-content/uploads/20211023185655/PythonrequestsmakingGETrequest.png)
For more information, refer to our Python Requests Tutorial .
Beautiful Soup provides a few simple methods and Pythonic phrases for guiding, searching, and changing a parse tree: a toolkit for studying a document and removing what you need. It doesn’t take much code to document an application.
Beautiful Soup automatically converts incoming records to Unicode and outgoing forms to UTF-8. You don’t have to think about encodings unless the document doesn’t define an encoding, and Beautiful Soup can’t catch one. Then you just have to choose the original encoding. Beautiful Soup sits on top of famous Python parsers like LXML and HTML, allowing you to try different parsing strategies or trade speed for flexibility.
- Importing Libraries: The code imports the requests library for making HTTP requests and the BeautifulSoup class from the bs4 library for parsing HTML.
- Making a GET Request: It sends a GET request to ‘https://www.geeksforgeeks.org/python-programming-language/’ and stores the response in the variable r.
- Checking Status Code: It prints the status code of the response, typically 200 for success.
- Parsing the HTML : The HTML content of the response is parsed using BeautifulSoup and stored in the variable soup.
- Printing the Prettified HTML: It prints the prettified version of the parsed HTML content for readability and analysis.
![design of experiments python tutorial Python BeautifulSoup Parsing HTML](https://media.geeksforgeeks.org/wp-content/uploads/20211023191537/PythonBeautifulSoupParsingHTML.png)
Finding Elements by Class
Now, we would like to extract some useful data from the HTML content. The soup object contains all the data in the nested structure which could be programmatically extracted. The website we want to scrape contains a lot of text so now let’s scrape all those content. First, let’s inspect the webpage we want to scrape.
![design of experiments python tutorial findallbs4pythontutorial-copy](https://media.geeksforgeeks.org/wp-content/uploads/20240603111643/findallbs4pythontutorial-copy.webp)
In the above image, we can see that all the content of the page is under the div with class entry-content. We will use the find class. This class will find the given tag with the given attribute. In our case, it will find all the div having class as entry-content.
We can see that the content of the page is under the <p> tag. Now we have to find all the p tags present in this class. We can use the find_all class of the BeautifulSoup.
![design of experiments python tutorial find_all bs4](https://media.geeksforgeeks.org/wp-content/uploads/20210323152640/findallbs4.png)
For more information, refer to our Python BeautifulSoup .
Selenium is a popular Python module used for automating web browsers. It allows developers to control web browsers programmatically, enabling tasks such as web scraping, automated testing, and web application interaction. Selenium supports various web browsers, including Chrome, Firefox, Safari, and Edge, making it a versatile tool for browser automation.
Example 1: For Firefox
In this specific example, we’re directing the browser to the Google search page with the query parameter “geeksforgeeks”. The browser will load this page, and we can then proceed to interact with it programmatically using Selenium. This interaction could involve tasks like extracting search results, clicking on links, or scraping specific content from the page.
![design of experiments python tutorial for-firefox](https://media.geeksforgeeks.org/wp-content/uploads/20240308160217/for-firefox.png)
Example 2: For Chrome
- We import the webdriver module from the Selenium library.
- We specify the path to the web driver executable. You need to download the appropriate driver for your browser and provide the path to it. In this example, we’re using the Chrome driver.
- We create a new instance of the web browser using webdriver.Chrome() and pass the path to the Chrome driver executable as an argument.
- We navigate to a webpage by calling the get() method on the browser object and passing the URL of the webpage.
- We extract information from the webpage using various methods provided by Selenium. In this example, we retrieve the page title using the title attribute of the browser object.
- Finally, we close the browser using the quit() method.
![design of experiments python tutorial design of experiments python tutorial](https://media.geeksforgeeks.org/wp-content/uploads/20210226111840/scrapemultiplepagespythonselenium.png)
For more information, refer to our Python Selenium .
The lxml module in Python is a powerful library for processing XML and HTML documents. It provides a high-performance XML and HTML parsing capabilities along with a simple and Pythonic API. lxml is widely used in Python web scraping due to its speed, flexibility, and ease of use.
Here’s a simple example demonstrating how to use the lxml module for Python web scraping:
- We import the html module from lxml along with the requests module for sending HTTP requests.
- We define the URL of the website we want to scrape.
- We send an HTTP GET request to the website using the requests.get() function and retrieve the HTML content of the page.
- We parse the HTML content using the html.fromstring() function from lxml, which returns an HTML element tree.
- We use XPath expressions to extract specific elements from the HTML tree. In this case, we’re extracting the text content of all the <a> (anchor) elements on the page.
- We iterate over the extracted link titles and print them out.
The urllib module in Python is a built-in library that provides functions for working with URLs. It allows you to interact with web pages by fetching URLs (Uniform Resource Locators), opening and reading data from them, and performing other URL-related tasks like encoding and parsing. Urllib is a package that collects several modules for working with URLs, such as:
- urllib.request for opening and reading.
- urllib.parse for parsing URLs
- urllib.error for the exceptions raised
- urllib.robotparser for parsing robot.txt files
If urllib is not present in your environment, execute the below code to install it.
Here’s a simple example demonstrating how to use the urllib module to fetch the content of a web page:
- We define the URL of the web page we want to fetch.
- We use urllib.request.urlopen() function to open the URL and obtain a response object.
- We read the content of the response object using the read() method.
- Since the content is returned as bytes, we decode it to a string using the decode() method with ‘utf-8’ encoding.
- Finally, we print the HTML content of the web page.
![design of experiments python tutorial uutt](https://media.geeksforgeeks.org/wp-content/uploads/20240308160419/uutt.png)
The pyautogui module in Python is a cross-platform GUI automation library that enables developers to control the mouse and keyboard to automate tasks. While it’s not specifically designed for web scraping, it can be used in conjunction with other web scraping libraries like Selenium to interact with web pages that require user input or simulate human actions.
In this example, pyautogui is used to perform scrolling and take a screenshot of the search results page obtained by typing a query into the search input field and clicking the search button using Selenium.
The schedule module in Python is a simple library that allows you to schedule Python functions to run at specified intervals. It’s particularly useful in web scraping in Python when you need to regularly scrape data from a website at predefined intervals, such as hourly, daily, or weekly.
- We import the necessary modules: schedule, time, requests, and BeautifulSoup from the bs4 package.
- We define a function scrape_data() that performs the web scraping task. Inside this function, we send a GET request to a website (replace ‘https://example.com’ with the URL of the website you want to scrape), parse the HTML content using BeautifulSoup, extract the desired data, and print it.
- We schedule the scrape_data() function to run every hour using schedule.every().hour.do(scrape_data).
- We enter a main loop that continuously checks for pending scheduled tasks using schedule.run_pending() and sleeps for 1 second between iterations to prevent the loop from consuming too much CPU.
![design of experiments python tutorial design of experiments python tutorial](https://media.geeksforgeeks.org/wp-content/uploads/20210629200554/Screenshot20210629at80230PM.png)
Why Python3 for Web Scraping?
Python’s popularity for web scraping stems from several factors:
- Ease of Use : Python’s clean and readable syntax makes it easy to understand and write code, even for beginners. This simplicity accelerates the development process and reduces the learning curve for web scraping tasks.
- Rich Ecosystem : Python boasts a vast ecosystem of libraries and frameworks tailored for web scraping. Libraries like BeautifulSoup, Scrapy, and Requests simplify the process of parsing HTML, making data extraction a breeze.
- Versatility : Python is a versatile language that can be used for a wide range of tasks beyond web scraping. Its flexibility allows developers to integrate web scraping seamlessly into larger projects, such as data analysis, machine learning, or web development.
- Community Support : Python has a large and active community of developers who contribute to its libraries and provide support through forums, tutorials, and documentation. This wealth of resources ensures that developers have access to assistance and guidance when tackling web scraping challenges.
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Parametric modeling and numerical simulation of a three-dimensional random aggregate model of lime–sand piles based on python–abaqus.
![ORCID design of experiments python tutorial](https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1718874496)
1. Introduction
1.1. research on mesoscopic model modeling, 1.2. research ideas and purposes, 2. meso-model modeling of lime–sand pile, 2.1. aggregate gradation test, 2.2. determination of basic parameters of aggregate, 2.3. the generation of aggregate, 2.4. delivery of aggregate, 3. numerical simulation of lime–sand pile meso-model, 3.1. determination and verification of microscopic parameters, 3.2. mesh generation of mesoscopic model, 3.3. static simulation analysis, 3.3.1. different mixing ratio simulation analysis, 3.3.2. simulation analysis of different heights, 4. conclusions and suggestions, author contributions, data availability statement, conflicts of interest.
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Click here to enlarge figure
Lime Type | Name | CaO + MgO | MgO | CO | SO |
---|
Siliceous lime | Siliceous lime90 | CL90-Q | ≥90 | ≤5 | ≤4 | ≤2 |
Type of Soil | Natural Water Content W (%) | Natural Density (g/cm³) | Plasticity Index | Compression Modulus (MPa) | Liquidity Factor | Shearing Strength |
---|
C/kpa | Φ |
---|
Loess | 18.8 | 1.75 | 12.8 | 13.46 | <0 | 30.0 | 23.0 |
Aggregate Type | Young’s Modulus (MPa) | Poisson’s Ratio | Natural Density (g/cm³) | Equivalent Coefficient of Linear Expansion (10 /°C) |
---|
Lime matrix | 8000 | 0.30 | 2.50 | 9.40 |
Sand aggregate | 10 | 0.20 | 1.50 | - |
Loess aggregate | 23 | 0.40 | 1.75 | - |
Aggregate Type | Grid Type | Grid Size | Element Number |
---|
Lime matrix | C3D4 | 3 mm | 634,800 |
Sand aggregate | C3D4 | 1 mm | 932,225 |
Loess aggregate | C3D4 | 1 mm | 233,056 |
Category | Experimental Value (kN) | Simulation Value (kN) | Relative Error/% |
---|
S-4:5:1 | 8.58 | 8.81 | 2.68 |
M-5:4:1 | 12.37 | 12.61 | 1.94 |
L-6:3:1 | 18.48 | 18.89 | 2.22 |
Groups | Experimental Value (kN) | Analogue Value (kN) | Relative Error/% |
---|
M-50 | 12.37 | 12.61 | 1.94 |
M-100 | 12.42 | 12.57 | 1.21 |
M-150 | 12.33 | 12.52 | 1.54 |
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Yuan, J.; Si, J.; Qiao, Y.; Sun, W.; Qiao, S.; Niu, X.; Zhou, M.; Ju, J. Parametric Modeling and Numerical Simulation of a Three-Dimensional Random Aggregate Model of Lime–Sand Piles Based on Python–Abaqus. Buildings 2024 , 14 , 1842. https://doi.org/10.3390/buildings14061842
Yuan J, Si J, Qiao Y, Sun W, Qiao S, Niu X, Zhou M, Ju J. Parametric Modeling and Numerical Simulation of a Three-Dimensional Random Aggregate Model of Lime–Sand Piles Based on Python–Abaqus. Buildings . 2024; 14(6):1842. https://doi.org/10.3390/buildings14061842
Yuan, Jia, Jianhui Si, Yong Qiao, Wenshuo Sun, Shibo Qiao, Xiaoyu Niu, Ming Zhou, and Junpeng Ju. 2024. "Parametric Modeling and Numerical Simulation of a Three-Dimensional Random Aggregate Model of Lime–Sand Piles Based on Python–Abaqus" Buildings 14, no. 6: 1842. https://doi.org/10.3390/buildings14061842
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Introduction. Design of Experiment (DOE) is an important activity for any scientist, engineer, or statistician planning to conduct experimental analysis. This exercise has become critical in this age of rapidly expanding the field of data science and associated statistical modeling and machine learning.A well-planned DOE can give a researcher meaningful data set to act upon with the optimal ...
An introduction to Design of Experiments (DOE) with python with a simple case study with and without interactions. Photo by Edge2Edge Media on Unsplash Introduction. In this article, I want to ...
The pyDOE package is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs. Hint. All available designs can be accessed after a simple import statement: >>> from pyDOE import *.
Want to learn more? Take the full course at https://learn.datacamp.com/courses/experimental-design-in-python at your own pace. More than a video, you'll lear...
Introduction¶. Design of Experiment (DOE) is an important activity for any scientist, engineer, or statistician planning to conduct experimental analysis. This exercise has become critical in this age of rapidly expanding field of data science and associated statistical modeling and machine learning.A well-planned DOE can give a researcher meaningful data set to act upon with optimal number ...
Design of experiments for Python. pyDOE2: An experimental design package for python. pyDOE2 is a fork of the pyDOE package that is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.. This fork came to life to solve bugs and issues that remained unsolved in the original package.
Design of experiments for Python. pyDOE3: An experimental design package for python¶. pyDOE3 is fork of pyDOE2 which is a fork of pyDOE. As for pyDOE2 wrt to pyDOE, pyDOE3 came to life to solve bugs and issues that remained unsolved in pyDOE2.. The pyDOE3 package is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.
dexpy - Design of Experiments (DOE) in Python. dexpy is a Design of Experiments (DOE) package based on the Design-Expert ® software from Stat-Ease, Inc. If you're new to the area of DOE, here is a primer to help get you started. The primary purpose of this package is to construct experimental designs. After performing your experiment, you ...
Design of experiments are an important part of scientific research. It is a methodology for choosing the best set of experiments to get data that will help y...
pyDOE2: An experimental design package for python. pyDOE2 is a fork of the pyDOE package that is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs. This fork came to life to solve bugs and issues that remained unsolved in the original package.
Design of experiments for Python. Navigation. Project description ; Release history ; Download files ; Verified details These details have been verified by PyPI ... Tags DOE, design of experiments, experimental design, optimization, statistics, python . Classifiers. Development Status. 5 - Production/Stable ...
Design of Experiment (DOE) is an important activity for any scientist, engineer, or statistician planning to conduct experimental analysis. This exercise has become critical in this age of rapidly expanding field of data science and associated statistical modeling and machine learning. A well-planned DOE can give a researcher meaningful data ...
pyDOE3: An experimental design package for python. pyDOE3 is a fork of the pyDOE2 package that is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs. This fork came to life to solve bugs and issues that remained unsolved in the original package.
pyDOE3: An experimental design package for python pyDOE3 is a fork of the pyDOE2 package that is designed to help the scientist, engineer, statistician, etc., to construct appropriate experimental designs.
An important point while running a DOE, however, is the ability to look for the maximum response of a system. In this article we will employ some very basic tools available with python to address such a point: given the result of a full factorial DOE with 2 levels, how to plan and execute the next runs in order to achieve a maximum.
Upon completion of this lesson, you should be able to: understand the issues and principles of Design of Experiments (DOE), understand experimentation is a process, list the guidelines for designing experiments, and. recognize the key historical figures in DOE. 1.1 - A Quick History of the Design of Experiments (DOE)
Optimize design from a set of constrained equations — an analytical model derived from first principles — that likely weave together with nonlinearities. Found in sim.py, which the tutorial explains. Improve experimental modeling and design efficiency by fitting a model of what happens in the true world.
Experimental design is fundamental to research, but formal methods to identify good designs are lacking. Advances in Bayesian statistics and machine learning offer algorithm-based ways to identify good experimental designs. Adaptive design optimization (ADO; Cavagnaro, Myung, Pitt, & Kujala, 2010; Myung, Cavagnaro, & Pitt, 2013) is one such method. It works by maximizing the informativeness ...
The provided DoE are: stratified DoE (axial, factorial, composite, box) random (bootstrap, LHS, MonteCarlo, importance sampling) deterministic (fixed, Gauss product, tensor product, Smolyak) cross validation (K-Fold, leave one out) low discrepancy (Faure, Halton, reverse Halton, Haselgrove and, of course, Sobol') optimized LHS (from Monte-Carlo ...
DoEgen: A Python Library for Optimised Design of Experiment Generation and Evaluation. DoEgen is a Python library aiming to assist in generating optimised Design of Experiments (DoE), evaluating design efficiencies, and analysing experiment results. In a first step, optimised designs can be automatically generated and efficiencies evaluated for ...
Evaluate designs by using geometric variables and applying a design-of-experiments (DOE) or optimization method. Manufacture. Set up and run a basic porosity or thinning analysis. Print 3D. Preapare and run an additive manufacturing simulation, and export a file for 3D printing. Inspire Python API
The primary types of experimental design include: Pre-experimental Research Design. True Experimental Research Design. Quasi-Experimental Research Design. Statistical Experimental Design. Pre-experimental Research Design. A preliminary approach where groups are observed after implementing cause and effect factors to determine the need for ...
W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.
Complete System Design Tutorial; Software Design Patterns; System Design Roadmap; Top 10 System Design Interview Questions and Answers; Interview Corner. Company Preparation; ... In this tutorial, we'll explore various Python libraries and modules commonly used for web scraping and delve into why Python 3 is the preferred choice for this task.
A lime-sand pile is a three-phase particle composite material composed of a lime matrix, sand, and a loess aggregate at the meso level. Establishing a random aggregate model that can reflect the actual aggregate gradation, content, and morphology is the premise of numerical simulations of the meso-mechanics of lime-sand piles. In this paper, the secondary development of Abaqus 2022 is ...