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334 results for “python”
Dataset for the article "Why Python Developers Migrate to Pytest"
<p>Dataset for the proposed article "Why Python Developers Migrate to Pytest"</p> <p>Each file shows a tabular representation, mapping which resource presents advantages and disadvantages (why_developers_migrate_themes.csv), what type of code examples they have (why_developers_migrate_code_examples.csv), and what tips we can find (why_developers_migrate_tips.csv). </p>
SoftwareThatMatters - Analyzing the effect of introducing time as a component in Python dependency graphs
<p>This is a data set produced as a result of the research conducted during my bachelor thesis project "Analyzing the effect of introducing time as a component in Python dependency graphs". This set contains 3 parts:</p> <ul> <li>The raw data that was gathered in order to be processed.</li> <li>The processed data that was used for the construction of the graph.</li> <li>The results that were produced during this research.</li> </ul> <p>More information can be found on the repository of this research project: <a href="https://github.com/AndreiPurcaru/SoftwareThatMatters">SoftwareThatMatters</a></p> <p> </p>
How and Why Developers Migrate Python Tests [Master dissertation]
<p>Dataset for the master dissertation "How and Why Python Developers Migrate to Pytest".</p> <p>The `10_systems` zip file contains the aggregated and intermediate files for the systems used for precision and recall analysis.</p> <p>The `top_100_systems` zip file contains the aggregated and intermediate files for the top 100 python systems analyzes.</p> <p>The `rq5_*` files contain data to assess the advantages and disadvantages found in 100 issues or pull requests and in the Grey Literature Review. The columns indicate if the advantages (A) or disadvantages (D) are present or not.</p> <p>Lastly, the `rq6_*` files present a similar structure, with themes defined while performing a thematic analysis for qualitative research questions.</p>
Appendix for "Don't DIY: Automatically transform legacy Python code to support structural pattern matching"
<p>This is the appendix for paper "Don’t DIY: Automatically transform legacy Python code to support structural pattern matching" presented in SCAM 2022.</p> <p><strong>Abstract</strong></p> <p><em>As data becomes more and more complex as technology evolves, the need to support more complex data types in programming languages has grown. However, without proper storage and manipulation capabilities, handling such data can result in hard-to-read, difficult-to-maintain code. Therefore, programming languages continuously evolve to provide more and more ways to handle complex data. Python 3.10 introduced structural pattern matching, which serves this exact purpose: we can split complex data into relevant parts by examining its structure, and store them for later processing. Previously, we could only use the traditional conditional branching, which could have led to long chains of nested conditionals. Maintaining such code fragments can be cumbersome. In this paper, we present a complete framework to solve the aforementioned problem. Our software is capable of examining Python source code and transforming relevant conditionals into structural pattern matching. Moreover, it is able to handle nested conditionals and it is also easily extensible, thus the set of possible transformations can be easily increased.</em></p>
Artefact to our paper "An Empirical Study of Automated Unit Test Generation for Python"
<p><strong>Artefact for “An Empirical Study of Automated Unit Test Generation for Python”</strong></p> <p>Together with our paper “An Empirical Study of Automated Unit Test Generation for Python”, we provide this artefact for future use.</p> <p><strong>Pynguin Version</strong></p> <p>We used Pynguin 0.25.2 for our experiments. The releases of Pynguin are archieved by Zenodo, too. Pynguin 0.25.2 is available under DOI <a href="https://doi.org/10.5281/zenodo.6836225">10.5281/zenodo.6836225</a>.</p> <p><strong>Preparation of the Environment</strong></p> <p>We use the <a href="https://python-poetry.org"><code>poetry</code></a> dependency-management tool to manage all dependencies for this artefact. Install this tool if you have not done yet. Furthermore, let <code>poetry</code> create a virtual environment for the experiment by execution <code>poetry install</code>.</p> <p><strong>Execution of the Experiment</strong></p> <p>The execution scripts make several assumptions that are based on our infrastructure. We maintain a SLURM cluster infrastructure that defines different <code>constraints</code> for different machines.</p> <p>Furthermore, we assume some paths to be present: we assume every computing machine to have writable mount points at <code>/local/${USER}</code> and <code>/local/hdd/${USER}</code>. On our machines, both are mount points on the local hard disk/SSD of the computing machines. Additionally, we have a shared mount <code>/scratch/${USER}</code>, which is mounted via NFS from a central file server. This mount point is also mounted on all computing machines.</p> <p>We assume the created and packaged Docker image to be located at <code>/scratch/lukasczy/pynguin.tar</code>. You can change this path by editing the XML files. These XML files contain the basic definitions of the jobs: they specify the SLURM constraint, the version of the Pynguin Docker container, the used Pynguin configurations as well as the modules used for the experiments. These modules have to reside under <code>projects</code>, as they come with this artefact.</p> <p>The Python script <code>execution.py</code> generates the actual run scripts from the XML file. It generates all scripts necessary to run a SLURM array job consisting of all runs for the experiment. Further general settings for the SLURM array job are present in this file.</p> <p>The Bash script <code>run_experiment.sh</code> executes the full execution pipeline; one has to specify the variable <code>EXPERIMENT_NAME</code> to match the name of the respective XML file who's defined experiment shall be executed.</p> <p><em>Important:</em> Executing the full experiment can take several days, depending on your computing infrastructure! We do therefore provide the raw result CSVs for further inspection.</p> <p><strong>Data Analysis</strong></p> <p>All raw data resides in the <code>data</code> folder:</p> <ul> <li><code>loc_data.csv</code> contains all information about the lines of code in each module. This file was created using the <code>extract_locs_and_types.py</code> script in the root folder. Please note that executing this script requires that the <code>cloc</code> utility tool is installed on your system's path.</li> <li><code>types.csv</code> and <code>types_per_module.csv</code> contain type information extract from the modules at different granularity level. They are also generated using the aforementioned script.</li> <li><code>results-assertion.csv.xz</code> contains the raw results from the experiment for RQ3 that evaluates the effectiveness of the assertions.</li> <li><code>results.csv.xz</code> contains the raw results from the experiment for RQ1 and RQ2.</li> </ul> <p>We provide the Jupyter Notebook that generated the plots, tables, and various LaTeX macros in the <code>notebooks</code> folder. Please note that if you want to reexecute this notebook, you might have to change the <code>PAPER_EXPORT_PATH</code> constant in cell <code>[2]</code> to a suitable location on your machine. Executing this notebook requires a installation of a TeX system to be available on your system because the plots are generated using <code>pdflatex</code> and <code>matplotlib</code>s pgf backend.</p> <p><strong>Further Data</strong></p> <p>The folder <code>projects</code> contains all the projects in the versions stated in our paper. The folder <code>run-logs</code> contains all the run logs from our experiment executions.</p>
Classroom: Python based Artificial Intelligence
<p>Dataset for deep learning training course</p> <p> </p> <p>1) Heart Segmentation of ventricles</p> <p>2) ImageNet</p> <p>3) CIFAR10</p> <p> </p> <p>Npz, tfrecords dataset type</p> <p> </p>
Supporting data tables and Python scripts for the paper: "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length"
<p>This repository contains all the data tables and Python scripts necessary to generate the results presented in Le Minor et al. (2022): "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length".</p>
2004.30 Apollo the Python-Slayer
You can copy, modify, and distribute this work, even for commercial purposes, all without asking permission. Learn more about The Cleveland Museum of Art's Open Access initiative: http://www.clevelandart.org/open-access-faqs *Apollo the Python-Slayer*, c. 350 BC. Attributed to Praxiteles (Greek, c. 400BC-c. 330BC). Bronze, copper, and stone inlay; base: 0.5 x 47.3 x 40.7 cm (3/16 x 18 5/8 x 16 in.); overall: 150 x 50.3 x 66.8 cm (59 1/16 x 19 13/16 x 26 5/16 in.). The Cleveland Museum of Art, Severance and Greta Millikin Purchase Fund 2004.30 Learn more on The Cleveland Museum of Art's Collection Online: https://www.clevelandart.org/art/2004.30 Source: Objaverse 1.0 / Sketchfab
Low Poly Colt Python
In English: The Colt Phyton, a weapon that went down in history in the year 1955 and is still remembered by many still. En Español: La Colt Phyton, un arma que pasó a la historia en el año 1955 y sigue siendo recordado por muchos aun. Source: Objaverse 1.0 / Sketchfab
2004.30.b Apollo the Python-Slayer
You can copy, modify, and distribute this work, even for commercial purposes, all without asking permission. Learn more about The Cleveland Museum of Art's Open Access initiative: http://www.clevelandart.org/open-access-faqs *Apollo the Python-Slayer*, c. 350 BC. Attributed to Praxiteles (Greek, c. 400BC-c. 330BC). Bronze, copper and stone inlay; overall: 25.9 x 8.8 x 8.7 cm (10 3/16 x 3 7/16 x 3 7/16 in.). The Cleveland Museum of Art, Severance and Greta Millikin Purchase Fund 2004.30.b Learn more on The Cleveland Museum of Art's Collection Online: https://www.clevelandart.org/art/2004.30.b Source: Objaverse 1.0 / Sketchfab
Replication Kit for Paper: "Are There Any Unit Tests? An Empirical Study on Unit Testing in Open Source Python Projects"
<p>Replication Kit for the Paper "Are there any Unit Tests? An Empirical Study on Open Source Python Projects" by Fabian Trautsch, Jens Grabowski.</p> <p>You can cite the paper via:</p> <p>@inproceedings{trautsch2017there,<br> title={Are There Any Unit Tests? An Empirical Study on Unit Testing in Open Source Python Projects},<br> author={Trautsch, Fabian and Grabowski, Jens},<br> booktitle={Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (ICST)},<br> pages={207--218},<br> year={2017},<br> organization={IEEE}<br> }</p> <p> </p> <p>Contents:<br> 1) Used version of the vcsSHARK<br> - located in “vcsSHARK”<br> 2) Used version of the testImpSHARK<br> - located in “testImpSHARK”<br> 3) Analysis implementations<br> - located in “testImpSHARK/testimpshark/analysis”<br> 4) Raw Data CSV Files<br> - located in “testImpSHARK/testimpshark/analysis/data<br> 5) Raw MongoDB<br> - located in “mongo_backup”</p> <p><br> Usage:<br> 1) Usage instructions for the vcsSHARK is given on its github homepage (http://ftrautsch.github.io/vcsSHARK/index.html) or directly in the “vcsSHARK/pyvcsshark/main.py” file</p> <p>2) Usage instructions for the testImpSHARK:<br> - if only one revision should be analyzed use “testImpSHARK/main.py”<br> - if all revisions should be analyzed use “testImpSHARK/execution.py”<br> - in both files concrete instructions can be found</p> <p>3) Each analysis file is commented. For some of them (rq1_boxplot.py and rq4.py the connection to the MongoDB must be changed). For the R files, the path to the data must be adapted. Otherwise, the files can be directly executed.</p> <p>4) The MongoDB can be restored via:<br> mongorestore --gzip --archive=smartshark040816.gz --db smartshark --host <HOST> --port <PORT> --username <USERNAME> --password <PASSWORD> --authenticationDatabase <AUTHENTICATION_DATABASE></p> <p><br> Tests:<br> 1) The tests can be run directly via the unittest framework of python: e.g., python -m unittest tests/test_common.py</p>
Supporting Data for pyCSEP: An Enhanced Python Toolkit for Earthquake Forecast Developers
<p>Contains the reproducibility package for pyCSEP: An Enhanced Python Toolkit for Earthquake Forecast Developers</p> <p>Please view the README.md in this archive for instructions on how to run the reproducibility package.</p> <p>The source code for pyCSEP used in this reproducibility package can be viewed on GitHub <a href="https://github.com/SCECcode/pycsep/releases/tag/v0.5.2">here</a>.</p>
Radiographic data of 'Python killing a Gnu' by Antoine-Louis Barye, The J. Paul Getty Museum collection
<p><strong>Summary</strong></p> <p>This submission contains radiographic data including small metal markers of the sculpture <a href="https://www.getty.edu/art/collection/object/103RQ1"><em>Python Killing a Gnu </em>(1840s–1860s), Antoine-Louis Barye (French, 1796 - 1875), the J. Paul Getty Museum collection</a> number 85.SE.48. (h 27.9 cm, w 39.1 cm, d 20.5 cm.)</p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the X-ray facility located at the J. Paul Getty Museum, Los Angeles. Full details can be found in [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The folder contains:</p> <ul> <li>darks_flat: folder containing darkfield and flatfield images, <em>frame*.tif</em></li> <li>data: raw (unprocessed or uncorrected) projection data, <em>frame*.tif</em></li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). Accompanying code can be found here.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>References</strong></p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p> <p><em> </em></p> <p> </p>
Demo datasets used by the openECCI Python library
<p>Demo datasets used by the openECCI Python library for Electron Channelling Contrast Imaging (ECCI) analysis of crystal defects in scanning electron microscope (SEM).</p> <h2>EBSD map</h2> <table> <tbody><tr> <th><strong>File name</strong></th> <th>ebsd_map/20kv_26nA_15mm WD_4x4 bin_fcc_Fe Map.ctf</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>A set of experimental EBSD map from a polycrystalline fcc austenitic stainless steel sample of (664 x 499) acquired using an electron beam of 20kV, 26nA, working distance 15mm, binning 4x4 in a JEOL JSM-7001F FEGSEM equipped with Oxford Instruments Nordlys Max2 EBSD detector.</td> </tr> </tbody> </table> <table> <tbody><tr> <th><strong>File name</strong></th> <th>ebsd_map/20kv_26nA_15mm WD_4x4 binning Si Map.ctf</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>A set of experimental EBSD map from a single crystal Si[001] specimen of (48 x 35) acquired using an electron beam of 20kV, 26nA, working distance 15mm, binning 4x4 in a JEOL JSM-7001F FEGSEM equipped with Oxford Instruments Nordlys Max2 EBSD detector.</td> </tr> </tbody> </table> <h2>EBSD master pattern</h2> <table> <tbody><tr> <th><strong>File name</strong></th> <th>ebsd_master_pattern/Fe-master-20kV.h5</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>EBSD master pattern of FCC Fe at 20kV simulated using <a href="https://github.com/EMsoft-org/EMsoft">EMsoft package ver. 5.0.20221208</a>.</td> </tr> </tbody> </table> <table> <tbody><tr> <th><strong>File name</strong></th> <th>ebsd_master_pattern/Si-master-20kV.h5</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>EBSD master pattern of Si at 20kV simulated using <a href="https://github.com/EMsoft-org/EMsoft">EMsoft package ver. 5.0.20221208</a>.</td> </tr> </tbody> </table> <h2>Electron Channelling Pattern (ECP) from FCC austenitic stainless steel specimen</h2> <table> <tbody><tr> <th><strong>File name</strong></th> <th>fcc_fe/01_Si_ref.tif</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>Reference ECP acquired from Si[001] attached with the austenitic stainless steel specimen.</td> </tr> </tbody> </table> <table> <tbody><tr> <th><strong>File name</strong></th> <th>fcc_fe/01_steel_overview.tif</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>A low magnification BSE image acquired from the region has been EBSD mapped on the austenitic stainless steel specimen.</td> </tr> </tbody> </table> <table> <tbody><tr> <th><strong>File name</strong></th> <th>fcc_fe/steel_ECP_xxx.tif & fcc_fe/steel_SEM_xxx.tif</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>Images pairs of higher magnification BSE images and the ECP acquired from the corresponding region.</td> </tr> </tbody> </table> <h2>ECP pattern from Si[001] wafer at different stage orientations</h2> <table> <tbody><tr> <th><strong>File name</strong></th> <th>si_wafer/TFS/Si_xtilt_xrot_xxx.tif</th> </tr> </tbody><tbody> <tr> <td><strong>Description</strong></td> <td>ECP acquired from Si[001] attached with the austenitic stainless steel specimen with different SEM stage tilt/rotation combinations.</td> </tr> </tbody> </table>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
FAIRmat Tutorial 9: Plugins: Python schemas and parsers
<p>NOMAD is a research data management platform for materials science. NOMAD Oasis allows you to operate the popular NOMAD service for your own lab, with your rules, and on your resources. You can adopt NOMAD Oasis to implement your institutes data policies and to work with your specific data types and workflows.</p> <p>This tutorial aims to introduce participants to the new plugin mechanism in NOMAD and teach them how to develop and integrate their own Python schemas and parsers to a NOMAD Oasis. Plugins enable you to alter how NOMAD processes data and therefore allow for more powerful customisations than the custom schemas presented in past tutorials. Participants will learn how to enable the conversion of new materials science data formats into NOMAD's standardised and machine-readable format. NOMAD plugins can be contributed to the community to further promote reproducibility and transparency in materials science.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Eurasian cooling paper datasets and python scripts
<div>This repository contains model output datasets and python scripts that I used tp produce figures those presented in this study.</div> <div> <div> </div> </div>
Data From the Brutus Python Package
<p>Data downloaded from and pre-processed using the brutus python package.</p> <p> </p> <pre>If any of this data is used please make sure to cite: Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository <strong>[http://archive.ics.uci.edu/ml]</strong>. Irvine, CA: University of California, School of Information and Computer Science. </pre>
Data for: ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data
<p>Data associated with the ColiCoords software paper</p>
Python functions -- cross-validation methods from a data-driven perspective
<p>This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation (SP-CV), dissimilarity quantification by adversarial validation (AVD), and dissimilarity-adaptive cross-validation (DA-CV). The description of how to run codes is in Readme.txt. The descriptions of functions are in functions.docx.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.