Search Results for "python data analysis" - Page 58
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Showing 5178 open source projects for "python data analysis"
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DataStation Community Edition
App to easily query, script, and visualize data from every database
DataStation is an open-source data IDE for developers. It allows you to easily build graphs and tables with data pulled from SQL databases, logging databases, metrics databases, HTTP servers, and all kinds of text and binary files. Need to join or munge data? Write embedded scripts as needed in languages like Python, JavaScript, R or SQL. All in one application. Build reports with graphs, charts and tables. Script against data. Cross-platform: Windows, macOS, and Linux. Easily fetch your data... -
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Filecatman
A file categorization management program.
A file categorization management program, designed to make sorting, categorizing and accessing data simple and easy. -
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Guild AI
Experiment tracking, ML developer tools
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. Guild AI simplifies hyperparameter tuning by applying state-of-the-art algorithms through... -
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ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
ModelFox makes it easy to train, deploy, and monitor machine learning models. Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser. ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. Train a machine... -
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agglo
Multi facets CASE/AGL for easy project developments
Agglo is a CASE (Computer Aided Software Engineering) or AGL (Atelier Génie Logiciel), whose aim is to facilitate installation ans use of various tools (either existing opensource tools, or agglo tools), in several facets of project development: - requirements management - unit tests - automated integration tests - toolkit for various languages (cpp, c, python, shell, xsl) other facets are to come ultimately: - coverage tests - planification - integration with hudson - code quality... -
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Catalyst
Accelerated deep learning R&D
... something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant... -
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OpenVigil
Open pharmacovigilance data extraction, mining and analysis tool
OpenVigil provides a webinterface to analyse pharmacovigilance data, i.e., spontanous or systematic collections of treatments (drugs) and observed adverse events ("drug side effects"). FDA Adverse Event Reporting System (AERS) and other pharmacovigilance data (e.g., Canadian or German) are supported. The OpenVigil web-based analysis tools offer several analysis modes like extraction, filtering, mining of data and analyses via measurements of disproportionality like proportional reporting ratio... -
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gplearn
Genetic Programming in Python, with a scikit-learn inspired API
gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best... -
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Sneedacity
Audio Editor
... in Python, Perl, or any language that supports named pipes. Nyquist Very powerful built-in scripting language that may also be used to create plug-ins. Editing multi-track editing with sample accuracy and arbitrary sample rates. Accessibility for VI users. Analysis and visualization tools to analyze audio or other signal data. -
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The Python Computer Graphics Kit is a collection of Python modules that contain the basic types and functions to be able to create 3D computer graphics images (focusing on Pixar's RenderMan interface).
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seladb/pickledb-rs
PickleDB-rs is a lightweight and simple key-value store
PickleDB-RS is a lightweight, fast, and key-value store written in Rust. Inspired by the Python PickleDB, it provides a simple, NoSQL database for small applications that require persistent key-value storage without the overhead of full-fledged databases. It is designed for speed and ease of use, with a JSON-based storage format. -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
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MANTI
MANTI - Mastering Advanced N-Termini Interpretation
MANTI is a one-stop shop N-termini annotation & evaluation solution. MANTI was previously (un)known as muda.pl ahead of v3.7, the project was renamed to MANTI.pl with v3.7 on 2019-06-24. It congregates information from different MaxQuant or DiaNN/MSFragger output files into a master file suitable explicitly for protein neo-termini analyses. The central anchor for the data congregation is the modificationSpecificPeptides.txt or diann-output.pr_matrix.tsv file - additional data is inferred... -
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TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
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m2cgen
Transform ML models into a native code
m2cgen (Model 2 Code Generator) - is a lightweight library that provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#, Rust, Elixir). Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies. Some models force input data to be particular type during prediction phase... -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example... -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an... -
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Haircut_EQ
Fetches the historical Haircut data (%) of Equities and Gold Bonds
Haircut is the amount of margin money deducted while pledging the Equites/ Mutual Funds / Bonds for trading. A haircut value of 13% means that, if a share worth of Rs 100 is pledged for trading, then Rs. 13 will be deducted and Rs. 87 will be given as collateral margin for trading. It is based on previous closing price. This program is used to identify the real value of a share with date. It fetches the historical Haircut data (%) and Price of an equity listed in NSE, India at a selected... -
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RasCAL
We shoot neutron beams at things.
** This is a legacy site - RasCAL has now moved to... https://github.com/arwelHughes/RasCAL_2019 Please download from Github, as this will be continually updated** RasCAL is a Matlab based application for the analysis of neutron and x-ray reflectivity data. It is specifically written for analysis of neutron data taken at multiple contrasts. Rascal 1 is now a closed project, as we are hard at work on better an brighter things with Rascal 2. So, in the next couple of days I'll... -
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Texar-PyTorch
Integrating the Best of TF into PyTorch, for Machine Learning
... of this repository is maintained by Petuum Open Source. Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering highly usable and customizable modules superior to PyTorch native ones. Texar-PyTorch (this repo) and Texar-TF have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Data processing, model architectures, loss functions, training and inference algorithms, evaluation, etc. -
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CC-attack
Using Socks4/5 or http proxies to make a multithreading Http-flood
Using Socks4/5 or http proxies to make a multithreading Http-flood/Https-flood (cc) attack. -
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Binarytree
Python library for studying Binary Trees
Binarytree is Python library that lets you generate, visualize, inspect and manipulate binary trees. Skip the tedious work of setting up test data, and dive straight into practicing algorithms. Heaps and BSTs (binary search trees) are also supported. Binarytree supports another representation which is more compact but without the indexing properties. Traverse trees using different algorithms. -
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AugLy
A data augmentations library for audio, image, text, and video
AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations. Each modality’s augmentations are contained within its own sub-library. These sub-libraries include both function-based and class-based transforms, composition operators, and have the option to provide metadata about the transform applied, including its intensity. AugLy is a great library to utilize for augmenting your data in model training, or to evaluate... -
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FOML is an expressive logic rule language that supports object modeling, analysis, and inference. It naturally supports model-level activities, such as constraints (extending UML diagrams), dynamic compositional modeling, analysis and reasoning about models, model testing, design pattern modeling, specification of Domain Specific Modeling Languages, and meta-modeling. FOML can reason about: 1. The model meta-data (meta-model level reasoning, or syntax reasoning) 2. Data level: reason about...
