211,894 research outputs found
An OpenModelica Python Interface and its use in PySimulator
No full textHow can Python users be empowered with the robust simulation, compilation and scripting abilities of a non-proprietary object-oriented, equation based modeling language such as Modelica? The immediate objective of this work is to develop an application programming interface for the OpenModelica modeling and simulation environment that would bridge the gap between the two agile programming languages Python and Modelica.
The Python interface to OpenModelica – OMPython, is both a tool and a functional library that allows Python users to realize the full capabilities of OpenModelica's scripting and simulation environment requiring minimal setup actions. OMPython is designed to combine both the simulation and model building processes. Thus domain experts (people writing the models) and computational engineers (people writing the solver code) can work on one unified tool that is industrially viable for optimization of Modelica models, while offering a flexible platform for algorithm development and research
Orange: data mining toolbox in Python
No full textOrange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining
Simulating Evolutionary Games: A Python-Based Introduction
No full textThis paper is an introduction to agent-based simulation using the Python programming language. The core objective of the paper is to enable students, teachers, and researchers immediately to begin social-science simulation projects in a general purpose programming language. This objective is facilitated by design features of the Python programming language, which we very briefly discuss. The paper has a 'tutorial' component, in that it is enablement-focused and therefore strongly application-oriented. As our illustrative application, we choose a classic agent-based simulation model: the evolutionary iterated prisoner's dilemma. We show how to simulate the iterated prisoner's dilemma with code that is simple and readable yet flexible and easily extensible. Despite the simplicity of the code, it constitutes a useful and easily extended simulation toolkit. We offer three examples of this extensibility: we explore the classic result that topology matters for evolutionary outcomes, we show how player type evolution is affected by payoff cardinality, and we show that strategy evaluation procedures can affect strategy persistence. Social science students and instructors should find that this paper provides adequate background to immediately begin their own simulation projects. Social science researchers will additionally be able to compare the simplicity, readability, and extensibility of the Python code with comparable simulations in other languages.Agent-Based Simulation, Python, Prisoner's Dilemma
A comparison of C, Matlab and Python as teaching languages in engineering
No full textWe describe and compare the programming languages C, MATLAB and Python as teaching languages for engineering students. We distinguish between two distinct phases in the process of converting a given problem into a computer program that can provide a solution: (i) finding an algorithmic solution and (ii) implementing this in a particular programming language. It is argued that it is most important for the understanding of the students to perform the first step whereas the actual implementation in a programming language is of secondary importance for the learning of problem-solving techniques. We therefore suggest to chose a well-structured teaching language that provides a clear and intuitive syntax and allows students to quickly express their algorithms. In our experience in engineering computing we find that MATLAB is much better suited than C for this task but the best choice in terms of clarity and functionality of the language is provided by Python
TextGridTools: A TextGrid Processing and Analysis Toolkit for Python
No full textBuschmeier H, Wlodarczak M. TextGridTools: A TextGrid Processing and Analysis Toolkit for Python. In: Tagungsband der 24. Konferenz zur Elektronischen Sprachsignalverarbeitung (ESSV 2013). Bielefeld, Germany; 2013: 152-157.In this paper we present TextGridTools, a free Python package for processing, querying and manipulating Praat's TextGrid files. TextGridTools improves on many deficiencies of Praat's embedded scripting language by providing a clean data model for TextGrid objects and their attributes, and offering functionality for common annotation-related tasks, for instance calculation of inter-annotator agreement measures. Owing to seamless integration with other Python tools, such as data analysis libraries and interactive interpreters, users gain access to a versatile and powerful computing environment without the need of repeated format conversions
Python a geoinformatikában
No full textA diplomamunka a Python és a térinformatika kapcsolatával, és egy kisebb geodéziai cég adatrendszerezési problémájával foglalkozik.gjGeográfus, GeoinformatikaMSc/M
Axelrod-Python/Axelrod: v4.9.0
No full textv4.9.0, 2020-04-07
New strategies, new classifier system and internal improvements/fixes.
Cleanup the tests:
https://github.com/Axelrod-Python/Axelrod/pull/1308
Create function to handle internal file paths:
https://github.com/Axelrod-Python/Axelrod/pull/1307
Fix bug in Result set:
https://github.com/Axelrod-Python/Axelrod/pull/1305
Improve and expand LR Player's docstring
https://github.com/Axelrod-Python/Axelrod/pull/1303
New strategy classifier mechanism:
https://github.com/Axelrod-Python/Axelrod/pull/1300
Add new Gradual strategy:
https://github.com/Axelrod-Python/Axelrod/pull/1299
Add missing author to docs bibliography:
https://github.com/Axelrod-Python/Axelrod/pull/1295
Suppress numpy warnings:
https://github.com/Axelrod-Python/Axelrod/pull/1292
Fix documentation:
https://github.com/Axelrod-Python/Axelrod/pull/1291
Fix FirstByDowning:
https://github.com/Axelrod-Python/Axelrod/pull/1285
Add citations:
https://github.com/Axelrod-Python/Axelrod/pull/1281
https://github.com/Axelrod-Python/Axelrod/compare/v4.9.0...v4.8.
Hardware-accelerated interactive data visualization for neuroscience in Python.
No full textLarge datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization
Axelrod-Python/Axelrod: v3.9.0
No full textv3.9.0, 2017-10-13
<p>New strategies, a minor bug fix and a small documentation improvement.</p>
<ul>
<li>Add the Bush Mosteller strategy
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1002">https://github.com/Axelrod-Python/Axelrod/pull/1002</a></li>
<li>Add k42r from Fortran code
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1135">https://github.com/Axelrod-Python/Axelrod/pull/1135</a></li>
<li>Add MemoryDecay
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1137">https://github.com/Axelrod-Python/Axelrod/pull/1137</a></li>
<li>Add k32r from Fortran code
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1138">https://github.com/Axelrod-Python/Axelrod/pull/1138</a></li>
<li>Add Random Tit For Tat
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1136">https://github.com/Axelrod-Python/Axelrod/pull/1136</a></li>
<li>Add k42r from Fortran code
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1139">https://github.com/Axelrod-Python/Axelrod/pull/1139</a></li>
<li>Add reference to documentation
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1134">https://github.com/Axelrod-Python/Axelrod/pull/1134</a></li>
<li>Fix minor bug in the fingerprints
<a href="https://github.com/Axelrod-Python/Axelrod/pull/1140">https://github.com/Axelrod-Python/Axelrod/pull/1140</a></li>
</ul>
<p>Here are all the commits for this PR:
<a href="https://github.com/Axelrod-Python/Axelrod/compare/v3.9.0...v3.8.1">https://github.com/Axelrod-Python/Axelrod/compare/v3.9.0...v3.8.1</a></p>
Axelrod-Python/Axelrod: v4.10.0
No full textv4.10.0, 2020-08-12
Major rewrite of the random seeding (which fixes a reproducibility bug when
using parallel processing), support for python 3.8 and various
documentation/internal fixes.
Move CI to github actions
https://github.com/Axelrod-Python/Axelrod/pull/1309
https://github.com/Axelrod-Python/Axelrod/pull/1322
https://github.com/Axelrod-Python/Axelrod/pull/1327
Sort all import statements using isort
https://github.com/Axelrod-Python/Axelrod/pull/1351
Add a test that all strategies have names
https://github.com/Axelrod-Python/Axelrod/pull/1354
Add a function to automatically check what information is used by strategies
https://github.com/Axelrod-Python/Axelrod/pull/1355
https://github.com/Axelrod-Python/Axelrod/pull/1331
Minor documentation fixes
https://github.com/Axelrod-Python/Axelrod/pull/1321
https://github.com/Axelrod-Python/Axelrod/pull/1329
https://github.com/Axelrod-Python/Axelrod/pull/1357
https://github.com/Axelrod-Python/Axelrod/pull/1358
https://github.com/Axelrod-Python/Axelrod/pull/1363
Add python 3.8 support
https://github.com/Axelrod-Python/Axelrod/pull/1366
Improve tests
https://github.com/Axelrod-Python/Axelrod/pull/1332
https://github.com/Axelrod-Python/Axelrod/pull/1333
https://github.com/Axelrod-Python/Axelrod/pull/1359
Fix RevisedDowning
https://github.com/Axelrod-Python/Axelrod/pull/1323
https://github.com/Axelrod-Python/Axelrod/compare/v4.10.0...v4.9.
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