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Jupyter Download For Mac10/25/2021
Navigating the Jupyter Notebook interface From an Anaconda terminal issue the following command to add the Gurobi channel to your default search list: conda config -add channels Now issue the following command to install the Gurobi package: conda install gurobiYou can remove the Gurobi package at any time by issuing the command: conda remove gurobiThe third step is to install a Gurobi license (if you haven’t already done so). You are now ready to use Gurobi from within Anaconda. Your next step is to launch either the Spyder IDE or Jupyter Notebook. You will be introduced to third-party APIs and will be shown how to manipulate images using the Python imaging library (pillow), how to apply optical character recognition to images to recognize text (tesseract and py-tesseract), and how to identify faces in images using the popular opencv library.Once the download has completed, double click on the downloaded. Pkg file and follow the installation instructions.Currently, code is typically published separately on GitHub or in another repository, where readers have to look up a footnote in the text to find out what scripts are being referenced, find the URL of the repository, go to the URL, look for the scripts, download them and associated data files, then run them. While publishers are increasingly open to the inclusion of links to supplementary code and other materials, such an arrangement inherently relegates them to secondary status relative to the written text.What if you could publish your scholarship in a format that gave equal weight to the prose and the code? The reality of current academic publication guidelines means that the forcible separation of your code and written argumentation may be a necessity, and their reunification may be impossible without navigating numerous obstacles. Scaling up computation with Jupyter notebooksWhen computation is an intrinsic part of your scholarship, how do you publish a scholarly argument in a way that makes the code as accessible and readable as the prose that accompanies it? In the humanities, the publication of scholarship primarily takes the form of written prose, in article or monograph form. Jupyter Notebooks for other programming languages
The notebook format is ideally suited for teaching, especially when students have different levels of technical proficiency and comfort with writing and editing code.The purpose of Jupyter notebooks is to provide a more accessible interface for code used in digitally-supported research or pedagogy. Multiple Programming Historian tutorials such as Text Mining in Python through the HTRC Feature Reader, and Extracting Illustrated Pages from Digital Libraries with Python, as well as other pedagogical materials for workshops, make reference to putting code in a Jupyter notebook or using Jupyter notebooks to guide learners while allowing them to freely remix and edit code. Within digital humanities literature, one can find references to Jupyter notebooks (split off from iPython, or interactive Python, notebooks in 2014) dating to 2015.Jupyter Notebooks have also gained traction within digital humanities as a pedagogical tool. Jupyter Software Package DoWhen notebooks can be useful in research and pedagogical contextsFor this lesson, we’ll work through a scenario of using Jupyter notebooks to analyze data, and then adapting that same notebook and data for classroom use.The lesson will also touch on more advanced topics related to Jupyter notebooks, such as: How to install, configure, and use the Jupyter notebook software package Do you want to organize your project workflow? Do you want to work through analyzing your data, keeping track of the things you try along the way? Do you want readers of your scholarship to be able to follow the theoretical and technical sides of your argument without switching between a PDF and a folder of scripts? Do you want to teach programming workshops that are more accessible to attendees with a range of technical backgrounds? Do you want to use or adapt notebooks that other people have written? Keep your goal in mind as you work through this lesson: depending on how you imagine using Jupyter notebooks, you may be able to skip sections that are mostly applicable in another context. Before you start this lesson, think about what you want to get from using Jupyter Notebooks. Gameboy mac emulatorRather than organizing code according to requirements that privilege the computer’s execution of the code, literate programming treats a program as literature understandable to human beings, prioritizing the programmer’s own thought process. Literate ComputingThe relationship between computer-readable code and human-readable text gained visibility within computer science in the 1970’s, when Donald Knuth proposed the “literate programming” paradigm. In fact, Jupyter notebooks are a great resource for people who are learning how to write code.Depending on the notebook you want to run, you may need to install some Python modules with pip, which assumes some familiarity with the command line (for Windows here, or Mac/Linux here).The lesson is written using Jupyter Notebook 6.0, but the UI and functionality of the software has been fairly consistent across versions. Using Jupyter Notebooks to scale up computation in environments like high-performance computing clustersThis lesson is suitable for intrepid beginners, assuming little by way of previous technical experience. Converting existing Python code to Jupyter Notebooks As early as the 1980s, notebook interfaces were available through software such as Wolfram Mathematica and MATLAB. 2Jupyter is neither the first nor the only example of computational notebooks. In practice it can be seen as a blend of a command-line environment such as the Unix shell with a word processor, since the resulting documents can be read like text, but contain blocks of code that were executed by the underlying computational system. 1Fernando Pérez, the creator of the iPython programming environment that ultimately became Project Jupyter, coined the term literate computing for the model used by Jupyter notebooks:A literate computing environment is one that allows users not only to execute commands but also to store in a literate document format the results of these commands along with figures and free-form text that can include formatted mathematical expressions. Literate programming tools are used to generate two outputs from the literate program: “tangled” code that can be executed by the computer, and “woven” formatted documentation. Omeka won’t let you install a plugin written as a Jupyter notebook), but the Jupyter environment can still be valuable for documenting code, teaching programming languages, and providing students with a space where they can easily experiment with provided examples. It may not make sense to implement projects in all of these languages using Jupyter notebooks (e.g. The name Jupyter is a reference to the three core languages supported by the project ( Julia, Python, and R), but kernels are available that make Jupyter compatible with tens of languages, including Ruby, PHP, Javascript, SQL, and Node.js. 3 They further developed this concept into The Art of Literary Text Analysis Spyral Notebooks.Jupyter has gained traction across many fields as an open-source environment that is compatible with numerous programming languages. Jupyter Lab is more complex, with a user environment more reminiscent of an Integrated Development Environment (discussed in previous Programming Historian tutorials for Windows, Mac, and Linux). Jupyter Notebook is widely-used and well-documented, and provides a simple file browser along with the environment for creating, editing, and running the notebooks. Ipynb extension), and the newer Jupyter Lab. AnacondaAnaconda is a free, open-source distribution of Python and R that comes with more than 1,400 packages, the Conda package manager for installing additional packages, and Anaconda Navigator, which allows you to manage environments (e.g. It’s easiest to use Anaconda to install Jupyter Notebook, but if you already have Python installed on your system and don’t want to deal with the large Anaconda package, you can run pip3 install jupyter (for Python 3). Both software packages are included in Anaconda, described below. Because of its comparative simplicity and ease of use for beginners, this tutorial uses Jupyter Notebook as the software for running notebook files. ![]()
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