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Python - Intro to Coding - Research Guides at Temple University

https://guides.temple.edu/c.php?g=995754&p=7207090

The recommended way to install python is to use Anaconda Distribution. Anaconda Distribution is a free, easy-to-install package manager, environment manager, and Python distribution with a collection of 1,500+ open source packages with free community support.

Downloading plain text from Internet Archive and Project ... - Sites

https://sites.temple.edu/tudsc/2016/06/08/downloading-plain-text-from-internet-archive-and-project-gutenberg-with-python/

For getting texts off of Gutenberg, I started with the Gutenberg package for Python by Clemens Wolff. In the fall, when I was doing this work, this was a well documented tool that can do a lot more when you work one text at a time than the fairly basic version I used below for bulk downloading.

Computer & Information Science (CIS) - Temple University

https://bulletin.temple.edu/undergraduate/courses/cis/

This course introduces computer programming using Python, a computer language which is widely used in industry, scientific research, game programming and web applications.

Qualitative Data Analysis and QDA Tools - Temple University

https://guides.temple.edu/qda/qualcoder

QualCoder works on Windows, macOS, and Linux operating systems. For version 3.5 and higher, if installing the source code or master code, you must have installed Python 3.10 or higher. A 64-bit VLC player must be installed to work with audio and video.

Enabling software using environment modules with Lmod — High ...

https://www.hpc.temple.edu/mhpc/hpc-technology/exercise6/modules.html

You will see that module load python is the same as module load python/3.13.3 since it is the only version and therefore the default. Create an empty Python 2.7.18 module file and inspect the output of module avail:

Biology (BIOL) | Temple University Bulletin

https://bulletin.temple.edu/undergraduate/courses/biol/

In addition to coverage of the basics of the Python language, topics will include: phylogenetic tree models, implementation of Markov models for biological problems, data structures and algorithms for the analysis of biological sequences, and the use of popular Python modules relevant for biological modeling.

HackerRank Solutions: Jesse and Cookies – Vahid E-Portfolio

https://sites.temple.edu/vahid/2022/03/05/hackerrank-solutions-jesse-and-cookies/

Problem Jesse loves cookies and wants the sweetness of some cookies to be greater than value . To do this, two cookies with the least sweetness are repeatedly mixed. This creates a special combined cookie with: sweetness Least sweet cookie 2nd least sweet cookie). This occurs until all the cookies have a sweetness . Given the sweetness of a number of cookies, determine the minimum number of ...

The OpenNARS implementation of the Non-Axiomatic Reasoning System

https://cis.temple.edu/~pwang/Publication/OpenNARS.pdf

NARS utilises the Non-Axiomatic Logic (NAL) [9] for inference and the Nars-ese language for representing statements. The language and the logic are outside the scope of this document. The aim of this paper is to describe the current implementation of NARS in detail. The following aspects of the implementa-tion are focused on: memory management with concept centric processing, non-deterministic ...

Choose QDA software - Qualitative Data Analysis and QDA Tools ...

https://guides.temple.edu/qda/choosing

This workshop is the second of the two-part workshop on five different Qualitative Data Analysis (QDA) tools – ATLAS.ti, NVivo, Dedoose, Taguette, and QualCoder. The workshop recaps essential and distinctive features demonstrated in the Part 1 recorded workshop and answered questions about the tools. The workshop discusses considerations for choosing among the five tools and provides ...

Physics (PHYS) | Temple University Bulletin

https://bulletin.temple.edu/courses/phys/

The course will describe ways to utilize both computational and data-centric approaches to enable the "virtual design" of functional materials. Applications based on both molecule systems and solid-state materials will be discussed. Machine learning and computational practices will be provided through Python-based weekly projects.