Showing 81 - 100 results of 134 for search '(( python modular implementation ) OR ( ((python code) OR (python tool)) representing ))', query time: 0.33s Refine Results
  1. 81

    Soulware-Lite by Abhiram Gnyanijaya (21572942)

    Published 2025
    “…</p><p><br></p><p dir="ltr">The system is fully modular, built on Python + FastAPI, and integrated with Streamlit UI for visualizing alignment confidence and drift flags. …”
  2. 82

    Dataset for CNN-based Bayesian Calibration of TELEMAC-2D Hydraulic Model by Jose Zevallos (21379988)

    Published 2025
    “…</li><li>Files starting with <code>y_part</code> are flattened output arrays representing corresponding water depth values.…”
  3. 83

    Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet by Kang Yang (7323734)

    Published 2025
    “…</p><p><br></p><ul><li>00_Satellite-derived moulins: Moulins directly mapped from Sentinel-2 imagery, representing actual moulin positions;</li><li>01_Snapped moulins: Moulins snapped to DEM-modeled supraglacial drainage networks, primarily used for analyses;</li><li>02_Moulin recurrences: Recurring moulins determined from the snapped moulins;</li><li>03_Internally drained catchments: Internally drained catchment (IDC) associated with each moulin;</li><li>04_Surface meltwater runoff: surface meltwater runoff calculated from MAR for the study area, elevation bins, and IDCs; </li><li>05_DEM-derived: Topographic features modeled from ArcticDEM, including elevation bins, depressions and drainage networks;</li><li>06_GWR: Variables for conducting geographically weighted regression (GWR) analysis;</li></ul><p><br></p><ul><li>Code_01_Mapping moulins on the southwestern GrIS.ipynb: A Jupyter Notebook to analyze moulin distributions, reproducing most of the analyses and figures presented in the manuscript using the provided datasets;</li><li>Code_02_pre1_calculate Strain Rate from XY ice velocity.py: A preprocessing Python script to calculate strain rate for the GWR analysis;</li><li>Code_02_pre2_calculate Driving Stress from ice thickness and surface slope.py: A preprocessing Python script to calculate driving stress for the GWR analysis;</li><li>Code_02_GWR analysis.ipynb: A Jupyter Notebook to conduct the GWR analysis using the provided datasets.…”
  4. 84

    <b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b> by Puguang Zhao (19023065)

    Published 2025
    “…</p><p dir="ltr"> 'boxplot.py': This script is executed in Visual Studio Code, using Python 3.10.4 as the runtime environment.…”
  5. 85

    <b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

    Published 2025
    “…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata from Step 1</li><li><code>perception_module/trained_models/</code> - Pre-trained models</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python -m perception_module.pred \<br> --model-weights .…”
  6. 86

    MCCN Case Study 3 - Select optimal survey locality by Donald Hobern (21435904)

    Published 2025
    “…</p><p dir="ltr">This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:</p><ol><li>Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html" target="_blank">https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html</a></li><li>ANUCLIM Annual Mean Rainfall raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer</a></li><li>ANUCLIM Annual Mean Temperature raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer</a></li></ol><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Generate STAC metadata for layers from predefined configuratiion</li><li>Load data cube and exclude nodata values</li><li>Scale all variables to a 0.0-1.0 range</li><li>Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)</li><li>Select 10 random points within NSW</li><li>Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW</li><li>For every point in NSW, find the lowest environmental distance to any of the selected points</li><li>Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point</li><li>Clean up and save results to RO-Crate</li></ol><p><br></p>…”
  7. 87
  8. 88

    Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data by Jeanette Leue (22470409)

    Published 2025
    “…This Zenodo package includes: the full research paper (PDF), a complete Python implementation generating the LMC field and conductivity model, a numerical plot comparing discrete LMC values with the smoothed continuum field, a cover letter and supporting documentation. …”
  9. 89

    Attention and Cognitive Workload by Rui Varandas (11900993)

    Published 2025
    “…</p><p dir="ltr">The data for subject 2 do not include the 2nd part of the acquisition (python task) because the equipment stopped acquiring; subject 3 has the 1st (N-Back task and mental subtraction) and the 2nd part (python tutorial) together in the <code>First part</code> folder (file <code>D1_S3_PB_description.json</code> indicates the start and end of each task); subject 4 only has the mental subtraction task in the 1st part acquisition and in subject 8, the subtraction task data is included in the 2nd part acquisition, along with python task.…”
  10. 90

    Audio Datasets of belt conveyor rollers in mines by Juan Liu (19687435)

    Published 2024
    “…</li><li><b>Python Code: </b>This code validates the accuracy and usability of the audio feature datasets in real-time monitoring of belt conveyor roller operational states.…”
  11. 91

    GeoGraphNetworks: Shapefile-Derived Datasets for Accurate and Scalable Graphical Representations by Harish Sharma (18881935)

    Published 2025
    “…The rail line infrastructure of the USA is represented as a single network that covers the entire country and includes connectivity to Canada. …”
  12. 92

    Advancing Solar Magnetic Field Modeling by Carlos António (21257432)

    Published 2025
    “…<br><br>We developed a significantly faster Python code built upon a functional optimization framework previously proposed and implemented by our team. …”
  13. 93

    Social media images of China's terraces by Song Chen (19488280)

    Published 2025
    “…Geo-tagged images were collected using Weibo cookies and Python-based scraping tools (available at: https://github.com/dataabc/weibo-search). …”
  14. 94

    Reinforcement Learning based traffic steering inOpen Radio Access Network (ORAN)- oran-ts GitHub Repository by Aaradhy Sharma (21503465)

    Published 2025
    “…<p dir="ltr">This repository contains the simulation codebase for research on <b>Reinforcement Learning (RL) based Traffic Steering and Resource Allocation in Open Radio Access Networks (O-RAN)</b>. It features a modular Python framework implementing various RL agents (Q-Learning, SARSA, N-Step SARSA, DQN) and a traditional baseline evaluated in a realistic cellular network environment. …”
  15. 95

    Physiotherapist-Assisted Wrist Movement Protocol for EEG-Based Corticokinematic Coherence Assessment by Fanni Kovács (21733838)

    Published 2025
    “…</li></ol><h4><b>Movement File Structure</b></h4><p dir="ltr">Each entry contains:</p><ul><li><code><strong>time</strong></code>: a 32-bit unsigned integer indicating the timestamp in milliseconds,</li><li><code><strong>x</strong></code>, <code><strong>y</strong></code>, <code><strong>z</strong></code>: 16-bit signed integers representing acceleration along the respective axes,</li><li><code><strong>trigger</strong></code>: an 8-bit unsigned integer used to mark event-related triggers for synchronization with the EEG data (e.g., movement onset).…”
  16. 96

    Cognitive Fatigue by Rui Varandas (11900993)

    Published 2025
    “…<br></p><p dir="ltr"><b>HCI features</b> encompass keyboard, mouse, and screenshot data. Below is a Python code snippet for extracting screenshot files from the screenshots CSV file.…”
  17. 97

    Multisession fNIRS-EEG data of Post-Stroke Motor Recovery: Recordings During Intact and Paretic Hand Movements by Aleksandra Medvedeva (21221383)

    Published 2025
    “…The fNIRS .snirf files are accompanied by event files as .txt tables, containing arrays of event timestamps and corresponding event codes. The code for signal reading, preprocessing, and epoching is provided with the dataset in the “Preprocessing” file. …”
  18. 98

    Digital Twin for Chemical Sciences by Jin Qian (19339035)

    Published 2025
    “…The procedure for generating data in Figure 3 can be found in the demo notebook in Supplementary Code. The procedure for generating data of Figure 4 has been uploaded in fig4_figshare.zip file. …”
  19. 99

    Sonification of Warming Stripes by Christopher Harrison (9448751)

    Published 2025
    “…The sonification was produced using the STRAUSS sonification Python package.</p><p dir="ltr">Here we release:<br>1. …”
  20. 100

    Supervised Classification of Burned Areas Using Spectral Reflectance and Machine Learning by Baptista Boanha (22424668)

    Published 2025
    “…Six Python scripts are provided, each implementing a distinct machine learning algorithm—Random Forest, k-Nearest Neighbors (k-NN), Multi-Layer Perceptron (MLP), Decision Tree, Naïve Bayes, and Logistic Regression. …”