Search Results for "utilities"

...Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests.

Datumaro is a flexible Python-based dataset management framework and command-line tool for building, analyzing, transforming, and converting computer vision datasets in many popular formats. It supports importing and exporting annotations and images across a wide variety of standards like COCO, PASCAL VOC, YOLO, ImageNet, Cityscapes, and many more, enabling easy integration with different training pipelines and tools. Datumaro makes it easy to merge datasets, split them into...

Messy datasets? Missing values? missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. Just pip install missingno to get started. This quickstart uses a sample of the NYPD Motor Vehicle Collisions Dataset dataset. The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion. ...

...If upgrading from version v1.x.x, please download and install v2.0.0 first. IMPORTANT FIXES in respect to base v2.0.0 version: v.2.5.0 introduces the Differential Attenuation and Cube Viewer utilities, and migrates user database to *.json files v2.4.3 fixes a with K element in the fit-approx method v2.4.3 fixes and issue where saving plots with fit-approx or a auto-wizard could freeze the software v2.4.2 introduces Image Viewer to Mosaic v2.4.1 fixes an issue in merging H5 or EDF datasets with Mosaic Full changelog at https://linssab.github.io/history X-Ray Fluorescence Imaging Software for Multiple Samples is an open source software to manipulate and study macro-X-Ray Fluorescence (MA-XRF) datasets. ...

...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions.

Collection of utilities based on two basics classes: Matematica and VarData. Matematica) performs math operations on vectors and matrices for smoothing, interpolation, convolution, image processing... VarData) manipulate a structure of points connected by links. Addraw) openGL engine. ElPoly) analyze mechanical properties of polymer and membrane like structures.

Parse, analyze and -- most importantly -- use COBOL data definitions. This gives you access to COBOL data from Python programs. Write data analyzers, one-time data conversion utilities and Python programs that are part of COBOL systems. Really.

The Scientific Visualization Artist Tools (SVAT) include converters from the Visualization Toolkit (VTK) to RenderMan Interface Bytestream (RIB) (ASCII and binary files supported), Blender to RenderMan, compilable RenderMan shaders and other utilities.

Utilities for managing images of documents and their summaries produced during the discovery process for litigation.