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334 results for “Python”
Figure 1 from: Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin P (2017) Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes 3: e12346. https://doi.org/10.3897/rio.3.e12346
Figure 1 - Laminar python pipeline, demonstrated using high-resolution MR data of a ferret brain. a) Binary images demarcating inner (grey-white matter interface, top) and outer (pial surface, bottom) boundaries of the cortex. b) Levelset representations of the same surfaces, where positive values are assigned to voxels outside of the volume deliminated by the surface, and negative values to voxels inside, each increasing in value with euclidean distance from the surface. c) Continuous equivolumetric intracortical depth, which models the positions of laminae relative to cortical morphology. d) Discrete representations of equivolumetric depth levels. e) T2 values, sampled at the six equivolumetric intracortical depths. Note that the equivolumetric laminae do not represent architectonic layers, but provide an anatomically meaningful coordinate system of cortical depth.
Updated dartmouth grid files for use in Isochrones python package
<p>These files replace the dartmouth stellar model grid files from https://zenodo.org/record/161241.</p>
Python codes used to plot figures for our submitted paper
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Dataset for Data Analysis and Visualization with Python for Social Scientists lesson
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binary_c V2.2.4 with binary_c-python V1.0.0 on Ubuntu 22.04 (Linux Gnome) desktop
<p>The <strong><em>binary_c</em></strong> software framework models the evolution of single, binary and multiple stars, including stellar evolution and nucleosynthesis. It is desgined for stellar population calculations, so is lightweight and versatile, and its support software contains tools for development and data analysis. <strong><em>binary_c</em></strong> is happy in standalone, virtual and HPC environments. Builds on <a href="https://en.wikipedia.org/wiki/Linux">Linux</a>, <a href="https://en.wikipedia.org/wiki/MacOS">OSX</a> and <a href="https://en.wikipedia.org/wiki/Windows_Subsystem_for_Linux">WSL</a>.</p> <p>This is version 2.2.4 of binary_c, with binary_c-python 1.0.0, installed on Kubuntu 24.04 desktop running a Linux kernel. Many useful tools are pre-installed, such as GCC, Clang, meson, ninja, valgrind, emacs, GSL, kcachegrind, gdb, gnuplot, and Jupyter so you can run binary_c-python's notebooks. Also included are commands to run either the latest stable versions or master versions straight from the gitlab repository.</p> <p>Homepage: <a href="https://binary_c.gitlab.io/">https://binary_c.gitlab.io/</a></p> <p>Login details:</p> <ul> <li>User: binary_c</li> <li>Password: binary_c</li> </ul> <p>The virtual machine is compressed with xzip, so you will need to decompress it before use.</p>
binary_c V2.2.4 with binary_c-python V1.0.0 on Kubuntu 24.04 (Linux) desktop
<p>The <strong><em>binary_c</em></strong> software framework models the evolution of single, binary and multiple stars, including stellar evolution and nucleosynthesis. It is desgined for stellar population calculations, so is lightweight and versatile, and its support software contains tools for development and data analysis. <strong><em>binary_c</em></strong> is happy in standalone, virtual and HPC environments. Builds on <a href="https://en.wikipedia.org/wiki/Linux">Linux</a>, <a href="https://en.wikipedia.org/wiki/MacOS">OSX</a> and <a href="https://en.wikipedia.org/wiki/Windows_Subsystem_for_Linux">WSL</a>.</p> <p>This is version 2.2.4 of binary_c, with binary_c-python 1.0.0, installed on Kubuntu 24.04 desktop running a Linux kernel. Many useful tools are pre-installed, such as GCC, Clang, meson, ninja, valgrind, emacs, GSL, kcachegrind, gdb, gnuplot, and Jupyter so you can run binary_c-python's notebooks. Also included are commands to run either the latest stable versions or master versions straight from the gitlab repository.</p> <p>Homepage: <a href="https://binary_c.gitlab.io/">https://binary_c.gitlab.io/</a></p> <p>Login details:</p> <ul> <li>User: binary_c</li> <li>Password: binary_c</li> </ul> <p>The virtual machine is compressed with xzip, so you will need to decompress it before use.</p>
E3SMv2 for Fortran-Python interface
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Replication Package: Unboxing Default Argument Breaking Changes in 1 + 2 Data Science Libraries in Python
<p><strong>Replication Package</strong></p> <p>This repository contains data and source files needed to replicate our work described in the paper "Unboxing Default Argument Breaking Changes in Scikit Learn".</p> <p><strong>Requirements</strong></p> <p>We recommend the following requirements to replicate our study:</p> <ol> <li>Internet access</li> <li>At least 100GB of space</li> <li>Docker installed</li> <li>Git installed</li> </ol> <p><strong>Package Structure</strong></p> <p>We relied on Docker containers to provide a working environment that is easier to replicate. Specifically, we configure the following containers:</p> <ul> <li><code>data-analysis</code>, an R-based Container we used to run our data analysis.</li> <li><code>data-collection</code>, a Python Container we used to collect Scikit's default arguments and detect them in client applications.</li> <li><code>database</code>, a Postgres Container we used to store clients' data, obtainer from Grotov et al.</li> <li><code>storage</code>, a directory used to store the data processed in <code>data-analysis</code> and <code>data-collection</code>. This directory is shared in both containers.</li> <li><code>docker-compose.yml</code>, the Docker file that configures all containers used in the package.</li> </ul> <p>In the remainder of this document, we describe how to set up each container properly.</p> <p><strong>Using VSCode to Setup the Package</strong></p> <p>We selected VSCode as the IDE of choice because its extensions allow us to implement our scripts directly inside the containers. In this package, we provide configuration parameters for both <code>data-analysis</code> and <code>data-collection</code> containers. This way you can directly access and run each container inside it without any specific configuration.</p> <p>You first need to set up the containers</p> <pre><code>$ cd /replication/package/folder $ docker-compose build $ docker-compose up # Wait docker creating and running all containers </code></pre> <p>Then, you can open them in Visual Studio Code:</p> <ol> <li>Open VSCode in project root folder</li> <li>Access the command palette and select "Dev Container: Reopen in Container" <ol> <li>Select either <em>Data Collection</em> or <em>Data Analysis</em>.</li> </ol> </li> <li>Start working</li> </ol> <p>If you want/need a more customized organization, the remainder of this file describes it in detail.</p> <p><strong>Longest Road: Manual Package Setup</strong></p> <p><strong>Database Setup</strong></p> <p>The database container will automatically restore the dump in <code>dump_matroskin.tar</code> in its first launch. To set up and run the container, you should:</p> <p>Build an image:</p> <pre><code>$ cd ./database $ docker build --tag 'dabc-database' . $ docker image ls REPOSITORY TAG IMAGE ID CREATED SIZE dabc-database latest b6f8af99c90d 50 minutes ago 18.5GB </code></pre> <p>Create and enter inside the container:</p> <pre><code>$ docker run -it --name dabc-database-1 dabc-database $ docker exec -it dabc-database-1 /bin/bash root# psql -U postgres -h localhost -d jupyter-notebooks jupyter-notebooks=# \dt List of relations Schema | Name | Type | Owner --------+-------------------+-------+------- public | Cell | table | root public | Code_cell | table | root public | Md_cell | table | root public | Notebook | table | root public | Notebook_features | table | root public | Notebook_metadata | table | root public | repository | table | root </code></pre> <p>If you got the tables list as above, your database is properly setup.</p> <p>It is important to mention that this database is extended from the one provided by <a href="https://markdowntohtml.com/">Grotov et al.</a>. Basically, we added three columns in the table <code>Notebook_features</code> (<code>API_functions_calls</code>, <code>defined_functions_calls</code>, and<code>other_functions_calls</code>) containing the function calls performed by each client in the database.</p> <p><strong>Data Collection Setup</strong></p> <p>This container is responsible for collecting the data to answer our research questions. It has the following structure:</p> <ul> <li><code>dabcs.py</code>, extract DABCs from Scikit Learn source code, and export them to a CSV file.</li> <li><code>dabcs-clients.py</code>, extract function calls from clients and export them to a CSV file. We rely on a modified version of <a href="https://markdowntohtml.com/">Matroskin</a> to leverage the function calls. You can find the tool's source code in the `matroskin`` directory.</li> <li><code>Makefile</code>, commands to set up and run both <code>dabcs.py</code> and <code>dabcs-clients.py</code></li> <li><code>matroskin</code>, the directory containing the modified version of matroskin tool. We extended the library to collect the function calls performed on the client notebooks of Grotov's dataset.</li> <li><code>storage</code>, a docker volume where the data-collection should save the exported data. This data will be used later in <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>.</li> <li><code>requirements.txt</code>, Python dependencies adopted in this module.</li> </ul> <p>Note that the container will automatically configure this module for you, e.g., install dependencies, configure matroskin, download scikit learn source code, etc. For this, you must run the following commands:</p> <pre><code>$ cd ./data-collection $ docker build --tag "data-collection" . $ docker run -it -d --name data-collection-1 -v $(pwd)/:/data-collection -v $(pwd)/../storage/:/data-collection/storage/ data-collection $ docker exec -it data-collection-1 /bin/bash $ ls Dockerfile Makefile config.yml dabcs-clients.py dabcs.py matroskin storage requirements.txt utils.py </code></pre> <p>If you see project files, it means the container is configured accordingly.</p> <p><strong>Data Analysis Setup</strong></p> <p>We use this container to conduct the analysis over the data produced by the <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> container. It has the following structure:</p> <ul> <li><code>dependencies.R</code>, an R script containing the dependencies used in our data analysis.</li> <li><code>data-analysis.Rmd</code>, the R notebook we used to perform our data analysis</li> <li><code>datasets</code>, a docker volume pointing to the <code>storage</code> directory.</li> </ul> <p>Execute the following commands to run this container:</p> <pre><code>$ cd ./data-analysis $ docker build --tag "data-analysis" . $ docker run -it -d --name data-analysis-1 -v $(pwd)/:/data-analysis -v $(pwd)/../storage/:/data-collection/datasets/ data-analysis $ docker exec -it data-analysis-1 /bin/bash $ ls data-analysis.Rmd datasets dependencies.R Dockerfile figures Makefile </code></pre> <p>If you see project files, it means the container is configured accordingly.</p> <p>A note on <code>storage</code> shared folder</p> <p>As mentioned, the <code>storage</code> folder is mounted as a volume and shared between <code>data-collection</code> and <code>data-analysis</code> containers. We compressed the content of this folder due to space constraints. Therefore, before starting working on <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> or <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>, make sure you extracted the compressed files. You can do this by running the <code>Makefile</code> inside <code>storage</code> folder.</p> <pre><code>$ make unzip # extract files $ ls clients-dabcs.csv clients-validation.csv dabcs.csv Makefile scikit-learn-versions.csv versions.csv $ make zip # compress files $ ls csv-files.tar.gz Makefile</code></pre>
Figure 1 from: Amosu A, Mahmood H (2018) PyLogFinder: A Python Program for Graphical Geophysical Log Selection. Research Ideas and Outcomes 4: e23676. https://doi.org/10.3897/rio.4.e23676
Figure 1 Graphical representation of logs present in several LAS files. The green color represents logs present and the white color represent logs which are not present.
The Python code of the Latent Dirichlet Allocation analysis
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Supplementary material 5 from: Currylow AF, Falk BG, Yackel Adams AA, Romagosa CM, Josimovich JM, Rochford MR, Cherkiss MS, Nafus MG, Hart KM, Mazzotti FJ, Snow RW, Reed RN (2022) Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA. NeoBiota 78: 129-158. https://doi.org/10.3897/neobiota.78.93788
Total number of adult Burmese python (Python molurus bivittatus)
Supplementary material 1 from: Currylow AF, Falk BG, Yackel Adams AA, Romagosa CM, Josimovich JM, Rochford MR, Cherkiss MS, Nafus MG, Hart KM, Mazzotti FJ, Snow RW, Reed RN (2022) Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA. NeoBiota 78: 129-158. https://doi.org/10.3897/neobiota.78.93788
Total Burmese python (Python molurus bivittatus)
Supplementary material 3 from: Currylow AF, Falk BG, Yackel Adams AA, Romagosa CM, Josimovich JM, Rochford MR, Cherkiss MS, Nafus MG, Hart KM, Mazzotti FJ, Snow RW, Reed RN (2022) Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA. NeoBiota 78: 129-158. https://doi.org/10.3897/neobiota.78.93788
The potential clutch sizes
Supplementary material 6 from: Currylow AF, Falk BG, Yackel Adams AA, Romagosa CM, Josimovich JM, Rochford MR, Cherkiss MS, Nafus MG, Hart KM, Mazzotti FJ, Snow RW, Reed RN (2022) Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA. NeoBiota 78: 129-158. https://doi.org/10.3897/neobiota.78.93788
Juvenile Burmese python (Python molurus bivittatus)
Supplementary material 2 from: Currylow AF, Falk BG, Yackel Adams AA, Romagosa CM, Josimovich JM, Rochford MR, Cherkiss MS, Nafus MG, Hart KM, Mazzotti FJ, Snow RW, Reed RN (2022) Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA. NeoBiota 78: 129-158. https://doi.org/10.3897/neobiota.78.93788
Relationship of snout-vent lengths (SVLs) and tail lengths to total lengths
Supplementary material 4 from: Currylow AF, Falk BG, Yackel Adams AA, Romagosa CM, Josimovich JM, Rochford MR, Cherkiss MS, Nafus MG, Hart KM, Mazzotti FJ, Snow RW, Reed RN (2022) Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA. NeoBiota 78: 129-158. https://doi.org/10.3897/neobiota.78.93788
Percent of total Burmese python (Python molurus bivittatus)
Supplementary material 1 from: Guzy JC, Falk BG, Smith BJ, Willson JD, Reed RN, Aumen NG, Avery ML, Bartoszek IA, Campbell E, Cherkiss MS, Claunch NM, Currylow AF, Dean T, Dixon J, Engeman R, Funck S, Gibble R, Hengstebeck KC, Humphrey JS, Hunter ME, Josimovich JM, Ketterlin J, Kirkland M, Mazzotti FJ, McCleery R, Miller MA, McCollister M, Parker MR, Pittman SE, Rochford M, Romagosa C, Roybal A, Snow RW, Spencer MM, Waddle JH, Yackel Adams AA, Hart KM (2023) Burmese pythons in Florida: A synthesis of biology, impacts, and management tools. NeoBiota 80: 1-119. https://doi.org/10.3897/neobiota.80.90439
FWCSFWMD mass length
Supplementary material 2 from: Guzy JC, Falk BG, Smith BJ, Willson JD, Reed RN, Aumen NG, Avery ML, Bartoszek IA, Campbell E, Cherkiss MS, Claunch NM, Currylow AF, Dean T, Dixon J, Engeman R, Funck S, Gibble R, Hengstebeck KC, Humphrey JS, Hunter ME, Josimovich JM, Ketterlin J, Kirkland M, Mazzotti FJ, McCleery R, Miller MA, McCollister M, Parker MR, Pittman SE, Rochford M, Romagosa C, Roybal A, Snow RW, Spencer MM, Waddle JH, Yackel Adams AA, Hart KM (2023) Burmese pythons in Florida: A synthesis of biology, impacts, and management tools. NeoBiota 80: 1-119. https://doi.org/10.3897/neobiota.80.90439
Python removals WIMS data used in synthesis
Supplementary material 1 from: Toudonou C, Elwin A, Penner J, Coulthard E, Norrey J, Megson D, Rödel M-O, Sinsin B, Harrington LA, Auliya M, D'Cruze N (2022) Seeking serpents: Ball python trade in Benin, West Africa. Nature Conservation 50: 85-114. https://doi.org/10.3897/natureconservation.50.86352
Ball python trade questionnaire, Benin
MODES DATA - python scripts
<p>MODES DATA - a few python scripts to visualize the data</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.