Open Source Python Scientific/Engineering Software - Page 57
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Python Scientific/Engineering Software
View 202 business solutionsBrowse free open source Python Scientific/Engineering Software and projects below. Use the toggles on the left to filter open source Python Scientific/Engineering Software by OS, license, language, programming language, and project status.
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PLASMAKIN: a chemical kinetics package
A library to compute the electron and chemical kinetics on plasmas
PLASMAKIN is a package to handle physical and chemical data used in plasma physics modeling and to compute gas-phase and gas-surface kinetics data: particle production and loss rates, photon emission spectra and energy exchange rates. -
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PLC Technology!
PLC Technology!
PLC Technology! is about Automotion, Programmable Logic Controller, IIoT... -
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PLGS Assistant
Easing the identification of high quality PLGS identified peptides
This software tool assists in the identification of PLGS identified peptides that provides high quality HDX information. The output file is formated so it can be directly imported into DynamX. This tool requires that the user has python installed. -
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PLWM is a Python package, containing classes suitable for implementing a window manager. PLWM is also a window manager, using the PLWM package.
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PPLine
SNP calling, annotation and gene/transcripts expression quantification
PPLine is a Python-based suite aimed to process raw RNA-seq or Exome-seq data. PPLine provides: - read mapping (STAR/Tophat2/bowtie/bowtie2), including novel splice junsctions discovery - gene and transcript expression estimation (HTSeq-count/Cufflinks) - SNP calling with BQSR and indel realignment (samtools/GATK) - variant annotation (Annovar) - novel transcripts discovery (Cufflinks) - predicting proteotypic peptides and creating ref/alt proteins fasta-database - integration of the results -
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PQS
Petoron Quantum Standard (PQS)
Petoron Quantum Standard (PQS) v1.2 A fully custom, offline encryption standard - built entirely from scratch. PQS v1.2 is a minimalistic, self-contained encryption engine for secure file protection, with zero reliance on external cryptographic libraries. PBKDF2-HMAC-SHA256 (200k iterations, adjustable) for password hardening. Key separation via BLAKE2s - independent keys for encryption and MAC. BLAKE2s-MAC authentication - 16-byte tag, any modification = instant rejection. Streaming keystream generator - secure, large-scale XOR without key reuse. Fake padding (HEAD/TAIL) - obfuscates binary boundaries and payload structure. Precise size encoding - restores original payload exactly. Supports files up to 128 MB per encrypted block. No dependencies, fully offline, cross-platform Python implementation. PQS is designed for high-assurance security, full auditability, and deterministic offline use - without unnecessary complexity. https://github.com/01alekseev/PQS -
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PS-Drone
Programming a Parrot AR.Drone 2.0 with Python - The Easy Way
The PS-Drone-API is a full featured SDK, written in and for Python, for Parrot's AR.Drone 2.0. It was designed to be easy to learn, but it offers the full set of the possibilities of the AR.Drone 2.0, including Sensor-Data (aka NavData), Configuration and full Video-support. The video function is not restricted to mere viewing, it is also possible to analyze video images data using OpenCV2. Obviously, the PS-Drone is perfect for teaching purposes; however, even the requirements for professional purposes can be satisfied. PS-Drone comes with a tutorial, explaining its most important commands and the drone's most important sensor values. The examples are easy to understand for people with little programming experience. A full list of commands and a description of all sensor data is available in a detailed documentation. It took several months to create PS-Drone, so it would be nice to get some donations for further development (e.g. Parrot's Bebop) and as a appreciation. -
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PSSPT
PSSPT (Protein Secondary Structure Prediction Tool)
PSSPT (Protein Secondary Structure Prediction Tool) Please, visit our GitHub page for more detail. -
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PTBC
irreversible encryption with time self-destruction
Petoron Time Burn Cipher (PTBC) A promising cipher based on self-destruction of time, complete absence of traces and impossibility of key selection. Key features: AES-CFB encryption with per-file IV Argon2id key derivation with 256MB RAM resistance TTL (time to live) built directly into the encrypted file HMAC-SHA512 integrity verification Self-deleting mode (--autowipe) and one-time access marking (--onetime) No file metadata, no recovery, no password resets Philosophy: If you lose the password - itโs over. If time expires - itโs gone. PTBC doesnโt forgive, doesnโt forget, and doesnโt leak. https://github.com/01alekseev/PTBC -
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PTools : a C++/Python library for macromolecular docking
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The Python Unified Membrane Analysis Toolkit is a suite of python tools for analyzing molecular dynamics simulations of membranes.
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PYroMat
Thermodynamic properties in python
PYroMat brings the thermodynamic properties of nearly 1,000 species to Python users with only a few keystrokes. Entropy, enthalpy, specific heats, inverse functions, and more are available from ideal gases, their mixtures, and multi-phase steam. Accurate, fast, and tested; PYroMat is the open-source solution for students, researchers, design engineers, and tinkerers everywhere who need thermodynamic properties. -
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Paddle Quantum
Paddle Quantum
Paddle Quantum (้ๆกจ) is the world's first cloud-integrated quantum machine learning platform based on Baidu PaddlePaddle. It supports the building and training of quantum neural networks, making PaddlePaddle the first deep-learning framework in China. Paddle Quantum is feature-rich and easy to use. It provides comprehensive API documentation and tutorials help users get started right away. Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing (QC). It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for the scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts. -
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ParaSim
Parallelized calculation of molecular similarities
Diversity assessments and comparisons of large compound databases require calculating similarities of millions of compounds in an affordable time. ParaSim addresses this challenge by parallelizing the calculations according to the number of computing cores available on a single machine. It is optimized for the throughput of very large numbers of query structures against very large numbers of reference structures. As as special feature, ParaSim allows to store and and to access frequently queried datasets as persistant objects in memory for short response times. ParaSim calculates chemical similarities based on binary structural fingerprints. It does not compute fingerprints by itself but relies on third party software to do so. Basically, all types of structural fingerprints which can be stored in an array of bits (a bitset) can be used by ParaSim. See the Wiki (https://sourceforge.net/p/parasim/wiki/Documentation/) for detailed documentation. FEEDBACK HIGHLY APPRECIATED! -
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PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.
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PCSIM is a tool for distributed simulation of heterogeneous networks composed of different model neurons and synapses. The development of PCSIM was supported by the FACETS EU project.
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ParamIT
a Toolset for Molecular Mechanical Force Field Parameterization
ParamIT is a toolkit aiding the development of molecular mechanical force field parameterization of small, drag like, molecules within CHARMM general force field (CGenFF) protocol. The developed toolkit helps the researchers in following ways: 1) automating the creation of multiple input files for quantum and molecular mechanics programs, 2) automating the output analysis and 3) substitute the use of full MM programs with a faster specialized one. The developed tools include: 1) generator of molecule-water complexes with graphical user interface (GUI), 2) semi-automatic frequency analysis using symbolic potential energy distribution matrix and comparison of optimized internal coordinates, 3) GUI for charge fitting with three modes: manual, Monte-Carlo sampling or brute force, and 4) GUI for dihedral terms fitting. The usage of these tools decreases the labor effort, lowers manual input errors and reduces the time needed for accurate MM parameterization efforts. -
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ParticleStats is software for analysing tracked particles from RNA Localization experiments. Analysis is performed on the X and Y coordinated of the tracked RNA particles
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Path Sim contains a collection of tools for analysing Biochemical pathways with respect to stochastic behavior and cell-to-cell variations.
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Pathomx
Workflow-based data analysis built on IPython
Pathomx is a workflow-based tool for the analysis and visualisation of experimental data. Initially created as a tool for metabolomic data analysis is has been extended and can now be used for any scientific and non-scientific data analysis. -
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Pena is an acronym for "Pena Encryption for Automata". This framework will help you to run encrypted representations of programs.
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Peptide Vaccine Analysis Tool (PVAT) is an optimization software that predicts the best possible peptide stretches in a given protein sequence based on two factors: 1. the surface exposure of the peptide stretches, and 2. their susceptibility to mutation.
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Perceval
An open source framework for programming photonic quantum computers
An open-source framework for programming photonic quantum computers. Through a simple object-oriented Python API, Perceval provides tools for composing circuits from linear optical components, defining single-photon sources, manipulating Fock states, running simulations, reproducing published experimental papers and experimenting with a new generation of quantum algorithms. It aims to be a companion tool for developing photonic circuits โ for simulating and optimizing their design, modeling both the ideal and realistic behaviors, and proposing a normalized interface to control them through the concept of backends. -
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Retrieve statistics from SNMP-aware devices, store them in a database and graphically display them for monitoring and notification purposes.