3,195 research outputs found
Replication Data for: Downscaling approach to compare COVID-19 count data from databases aggregated atdifferent spatial scales
No full textThis file contains the code and data required to reproduce the study
Exploiting real-time 3d visualisation to enthuse students: A case study of using visual python in engineering
No full textWe describe our experience teaching programming and numerical methods to engineering students using Visual Python to exploit three dimensional real time visualisation. We describe the structure and content of this teaching module and evaluate the module after its delivery. We find that the students enjoy being able to visualise physical processes (even if these have effectively only 1 or 2 spatial degrees of freedom) and this improves the learning experience
ctypes. ctypes run!
No full textOne of the new features of Python 2.5 is the introduction of ctypes as a standard library module. At the simplest level, ctypes adds the standard C types to Python: signed and unsigned bytes, shorts, ints and longs; as well as structs, unions, pointers and functions. At run-time it can load a shared library (DLL) and import its symbols, allowing a Python application to make function calls into the library without any special preparation. ctypes can be used to wrap native libraries in place of interface generators such as SWIG, to manipulate memory and Python objects at the lowest level, and to prototype application development in other languages.This paper begins with a quick introduction to ctypes, shows some advanced techniques, and describes some examples of how it has been used by the author in his recent work
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.
Replication Data for: Predicting non-state terrorism worldwide
No full textThis folder contains all files required to replicate the work "Predicting non-state terrorism worldwide"
Evaluation of MIDI data processing function by Python + Mido library
No full textThis is a research note investigating the MIDI data processing function of the Mido library on Python. MIDI (Musical Instrument Digital Interface) is a standard for transmitting and receiving music performance information between electronic musical instruments and computers. Mido is a library that allows MIDI data to be handled as an object on the programming language Python. By using a library such as Mido, it can be expected to process the control and performance expression of various electronic musical instruments efficiently and flexibly. The author has made various attempts, such as converting information from various sensors into MIDI data based on certain rules, using a board computer such as Arduino or a personal computer. As a result of investigating the MIDI processing function of Mido this time, it was found to be sufficiently useful for the author's research. In the future, I would like to further explore the Python and Mido libraries and deepen my research activities.本稿はPython上のMidoライブラリによる、MIDIデータ処理機能について調べた研究ノートである。MIDI（Musical Instrument Digital Interface）とは，電子楽器やコンピュータ間で音楽の演奏情報を送受信するための規格である。またMidoは，MIDIデータをプログラム言語Python上のオブジェクトとして扱うことができるようにするためのライブラリで，Midoのようなライブラリを利用することで，様々な電子楽器の制御や演奏表現を効率よく，かつ柔軟に処理することが期待できる。著者はこれまでArduinoなどのボードコンピュータやパソコンを使い，各種センサーからの情報を一定ルールに基づきMIDIデータに変換するなど、様々な試みを行ってきたが，今回MidoによるMIDI処理機能を調べた結果、著者の研究に十分役立つことが分かった。今後はPythonやMidoライブラリをさらに探求し、より研究活動を深めて行きたい。departmental bulletin pape
Data for: A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2016
No full textA Bayesian Approach to Modelling Subnational Spatial Dynamics of Worldwide Non-State Terrorism, 2010 - 2016
A. Python, J. Illian, C. Jones-Todd, M. Blangiardo
J. R. Statist. Soc. A, (2018)
All results are obtained using R statistical software. We provide the R code and data needed to reproduce the research work.
For full replication, the R code listed below should be run in this order:
-first: from (1) to (2): generate input data for the statistical models;
-second: (4), (5) and (6): the L,S, and F model, respectively;
-third: (7): the results and plots of the L,S, and F model, respectively.
Since fitting the models are computationally intense, we also provide the outputs of the models as .RData files along with the R code used to produce them.
The R code and data are provided in "R_code.zip", which contains the following folders:
(1) GTD
GTD.R: extraction of terrorism data from the Global Terrorism database (GTD)
Input: GTDsource.csv (file downloaded Jan 2017 from GTD)
Output: GTDworld.csv used in R code (2):
(2) DATA
Data_paper.R: extraction of covariate data based on GTD events locations
Input: covariates data with different formats in covariate.zip
Output: paper_data.RData used in R code (4) to (9)
(3) FUNCTIONS
functions.R: functions to facilitate running the spatio-temporal models in R-INLA
used in in R code (4) to (9)
(4) L-MODEL
L_model.R: the Bernoulli space-time models of the lethality of terrorism
Inputs: paper_data.RData (2) and functions.R (3)
Outputs:
-models with different sets of covariates: L0.Rdata to L7.RData ;
-selected final model: L7.Rdata
-models for plotting: L7.Rdata
-robustness test models: Lrob1.Rdata, Lmesh2.RData, Lmesh3.RData
-predictive models with different sets of covariates: L7pred.Rdata, L0pred.Rdata
(5) S-MODEL
S_model.R: the Poisson space-time models of the severity of lethal terrorism
Inputs: paper_data.RData (2) and functions.R (3)
Outputs:
-models with different sets of covariates: S0.Rdata to S7.RData ;
-selected final model: S4.Rdata
-models for plotting: S4plot.Rdata
-robustness test models: bernrob1.Rdata, bernmesh2.RData, bernmesh3.RData
-predictive models with different sets of covariates: bern0pred.Rdata, bern4pred.Rdata
(6) F-MODEL
F_model.R: the Poisson space-time models of the frequency of lethal terrorism
Inputs: paper_data.RData (2) and functions.R (3)
Outputs:
-models with different sets of covariates: F0.Rdata to F7.RData ;
-selected final model: Ffinal.Rdata
-models for plotting: F3plot.Rdata
-robustness test models: Frob1.Rdata, Ffinalagg075.RData, Ffinalagg025.RData,Ffinalagg100.RData, Ffinalagg150.RData
-predictive models with different sets of covariates: F0pred.Rdata, F6pred.Rdata
(7) RESULTS
-Results_Lmodel.R: generate the results of the L-models and the graphics in the manuscript
Inputs: paper_data.RData (2), functions.R (3), L-model outputs (4)
Outputs: plots and figures
-Results_Smodel.R: generate the results of the S-models and the graphics in the manuscript
Inputs: paper_data.RData (2), functions.R (3), S-model outputs (5)
Outputs: plots and figures
-Results_Fmodel.R: generate the results of the F-models and the graphics in the manuscript
Inputs: paper_data.RData (2), functions.R (3), F-model outputs (6), and country.shp
Outputs: plots and figures
Contact (first author):
Dr Andre Python
Malaria Atlas Project | University of Oxford
Big Data Institute | Li Ka Shing Centre for Health Information and Discovery
Old Road Campus | Headington | Oxford | OX3 7LF | United Kingdom
E: [email protected]
W: www.map.ox.ac.uk | www.bdi.ox.ac.u
An Accessible Python based Author Identification Process
No full textAn author identification process using Python tools and using the Federalist Papers as a case study
Netherlands eScience Center Python Template
No full textSpend less time setting up and configuring your new Python packages and comply with the
Netherlands eScience Center Software Development Guide
from the start.
Added
Instructions to add your existing code to directory generated by the NLeSC Python template #202
Keywords to questionaire #270
Next step issue generation workflow #228
Next step issue for SonarCloud integration #234
Next step issue for Zenodo integration #235
Next step issue for Read the Docs #236
Next step issue for citation data #237
Next step issue for linting #238
Next steps documentation #240
Support for sub packages in distro #160
Tests for api doc generation #213
CI Tests on Windows #140 #223
.pylintrc file
Valid license name and first author name in CITATION.cff
SonarCloud integration for code quality and coverage #89
Read the Docs #78
Changed
Always generate API docs #176
Have 100% test coverage in generated code #88
Removed
Automatic publish to PyPi after GitHub release #196
</ul
kivy/python-for-android: v2022.09.04
No full textHighlights:
This is the last release that defaults to Python 3.9 when building hostpython3 and python3. The next one will target Python 3.10
This is the last release that uses Android NDK 23b by default, the next one will use Android NDK 25
This is the last release that defaults to target API 27, the next one will default to target API 31, following the new requirement from Google for apps that need to be distributed on Play Store.
In order to fully support API 31 and as a propedeutic change for new features in Kivy, in the next release, python-for-android will use the new SDL2 releases.
Full changelog:
liblzma: Use p4a_install instead of install, as a file named INSTALL is already present. #2663 (misl6)
Force --platform=linux/amd64 in Dockerfile #2660 (misl6)
Remove six and enum34 dependency #2657 (misl6)
Update supported Python versions #2656 (misl6)
Fixes some E275 - assert is a keyword. #2647 (misl6)
Updates matplotlib, fixes an issue related to shared libc++ #2645 (misl6)
RTSP support for ffmpeg #2644 (alicakici1234)
Fixes TypeError: str.join() takes exactly one argument (2 given) in hostpython3/__init__.py", line 69 #2642 (Furtif)
Resolve absolute path to local recipes #2640 (dbnicholson)
Merges master into develop after release 2022.07.20 #2639 (misl6)
Fix webview Back button behaviour #2636 (interlark)
Add icon-bg and icon-fg to fix_args #2633 (danigm)
Remove stray - in output file name #2581 (dbnicholson)
Add option for adding files to res/xml without touching manifest #2330 (rambo
- …
