Search Results for "sdcc setup for windows"
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Showing 13 open source projects for "sdcc setup for windows"
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City Map Poster Generator
Transform your favorite cities into beautiful, minimalist designs
maptoposter is a code-driven poster generator that turns any city into a minimalist, print-style map artwork with consistent typography and themed color palettes. It is built around a simple command-line flow where you pass a city and country, and the tool fetches the relevant map geometry and renders it into a clean composition that looks like a design product rather than a raw GIS export. The repository includes a library of predefined themes that change the overall look (for example,... -
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The Missing Semester
The Missing Semester of Your CS Education
The Missing Semester is a course and repository that teaches the engineering skills often skipped in traditional computer science curricula: command-line fluency, shell scripting, editors, version control, debugging, data wrangling, and automation. It includes lecture notes, exercises, and sample solutions that encourage hands-on practice rather than passive reading. The curriculum demystifies tools like bash, vim, git, and make, showing how to combine them into efficient workflows that... -
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Amazon Braket Default Simulator
An implementation of a quantum simulator that you can run locally
The Amazon Braket Default Simulator is a Python open-source library that provides an implementation of a quantum simulator that you can run locally. You can use the simulator to test quantum tasks that you construct for the Amazon Braket SDK before you submit them to the Amazon Braket service for execution. You must have the Amazon Braket SDK installed to use the local simulator. Follow the instructions in the README for setup. If you want to contribute to the project, be sure to run unit... -
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NVIDIA Isaac Lab
Unified framework for robot learning built on NVIDIA Isaac Sim
Isaac Lab is an open-source modular robotics learning framework built atop Isaac Sim. It simplifies research workflows across reinforcement learning, imitation learning, and motion planning by offering robust, GPU-accelerated simulation with realistic sensor and physics fidelity—ideal for sim-to-real robot training. Compatible and optimized for use with Isaac Sim versions (e.g., Sim 5.0 and 4.5). GPU-accelerated, high-fidelity physics and sensor simulation suitable for complex learning... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI. -
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HistogramsApp
Application that generates KDE-PDP plots from geochronological data
HistogramsApp is a Python 3.6 application that generates (KDE and PDP) from geochronological data .HistogramsApp allows to interactively setup plot parameters such as the bandwidth and the peak detection sensibility. To cite the application please refer to: 1) https://www.tandfonline.com/doi/abs/10.1080/00206814.2021.1954556?journalCode=tigr20 Rodriguez-Corcho, A. F., Rojas-Agramonte, Y., Barrera-Gonzalez, J. A., Marroquin-Gomez, M. P., Bonilla-Correa, S., Izquierdo-Camacho, D.,... -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually... -
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CIF2Cell
Generating cells for electronic structure calculations from CIF files
CIF2Cell is a tool to generate the geometrical setup for various electronic structure codes from a CIF (Crystallographic Information Framework) file. The program currently supports output for a number of popular electronic structure programs, including ABINIT, ASE, CASTEP, CP2K, CPMD, CRYSTAL09, Elk, EMTO, Exciting, Fleur, FHI-aims, Hutsepot, MOPAC, Quantum Espresso, RSPt, Siesta, SPR-KKR, VASP. Also exports some related formats like .coo, .cfg and .xyz-files. The program has been published... -
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Collective Mind Technology
plugin-based framework for systematic and reproducible experimentation
New version moved to http://github.com/ctuning/ck Collective Mind framework (cM) is an open-source plugin-based schema-free repository and infrastructure for collaborative, systematic and reproducible research and experimentation. This 3rd version (started in 2006) helps to implement, preserve, share and reproduce the whole experimental setup as connected modules and data. cM uses crowdsourcing to leverage knowledge and computational resources of multiple users. For example, it... -
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This toolkit allows complete control of a microscopy setup from Labview, Matlab, Scilab, Python, .Net, VB, IgorPro, Mathematica and more. Included is a standalone program for image acquisition and scripting control of a scientific microscope.
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AVPython embeds the Python programming language within ArcView GIS 3.x. This project will also encompass efforts to build a similar bridge between Python and ArcObjects. An integrated, redistributable Python / PyWin32 setup wizard is also published here.
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jrgp is a strong-typed Genetic Programming system, which features a graphical interface (gool) to setup and run GP-problems and a tool (fs-d) that greatly simplifies the definition of a GP-problem.
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