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Showing 38 open source projects for "sample"
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Panda-Helper
Panda-Helper: Data profiling utility for Pandas DataFrames and Series
Panda-Helper is a simple data-profiling utility for Pandas DataFrames and Series. Assess data quality and usefulness with minimal effort. Quickly perform initial data exploration, so you can move on to more in-depth analysis. -
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Stan.jl
Stan.jl illustrates the usage of the 'single method' packages
...For most applications one of the "single method" packages, e.g. StanSample.jl, StanDiagnose.jl, etc., is a better choice for day-to-day use. To execute the most important method in Stan ("sample"), use StanSample.jl. Some Pluto notebook examples can be found in the repository. -
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mapcn
Beautiful map components, 100% Free, Zero config, one command setup
...The core idea is to enable agents to generalize learned behavior by representing continuous control policies in a compact, interpretable form that preserves smoothness and controllability. The project includes implementations that experiment with policy encoding, action decomposition techniques, and sample efficiency analysis in classic reinforcement learning environments. By organizing action outputs into meaningful, lower-dimensional manifolds, MapCN attempts to improve both learning convergence and transfer performance when compared to unguided continuous control baselines. -
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FriendsDon'tLetFriends
Friends don't let friends make certain types of data visualization
Friends don't let friends make certain types of data visualization - What are they and why are they bad. -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from... -
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Mixed-effects models in Julia
A Julia package for fitting (statistical) mixed-effects models
...If the potential levels of the covariate are fixed and reproducible, e.g. the levels for Sex could be "F" and "M", they are modeled with fixed-effects parameters. If the levels constitute a sample from a population, e.g. the Subject or the Item at a particular observation, they are modeled as random effects. -
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Apache Seata
High-performance, open source distributed transaction solution
Seata is a distributed transaction solution for microservices that provides consistent, cross-service commits without forcing every team to adopt the same persistence model. Its architecture separates responsibilities into a global coordinator and per-service participants, so business services remain decoupled while transactions are orchestrated centrally. Multiple modes are supported—AT (automatic, SQL-based with undo logs), TCC (try-confirm-cancel), Saga (long-running compensation), and... -
8
MCPower
MCPower — simple Monte Carlo power analysis for complex models
...It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation. -
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missingno
Missing data visualization module for Python
...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. At a glance, date, time, the distribution of injuries, and the contribution factor of the first vehicle appear to be completely populated, while geographic information seems mostly complete, but spottier. ... -
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Nonlinear Dynamics
A concise introduction interlaced with code
This repository holds material related with the textbook Nonlinear Dynamics: A Concise Introduction Interlaced with code, co-authored by George Datseris and Ulrich Parlitz. The textbook will be published by Springer-Nature, in the series Undergraduate Lecture Notes in Physics. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
...In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
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XISMuS
X-Ray Imaging Software for Multiple Samples
...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. XISMuS also works as a sample management tool, where you can easily change between datasets (samples) and compare, cross-interact and normalize them. -
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Bridge.jl
A statistical toolbox for diffusion processes
Statistics and stochastic calculus for Markov processes in continuous time, include univariate and multivariate stochastic processes such as stochastic differential equations or diffusions (SDE's) or Levy processes. -
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WhyLogs Java Library
Profile and monitor your ML data pipeline end-to-end
...Using approximate statistics allows the package to run on minimal infrastructure and monitor an entire dataset, rather than miss outliers and other anomalies by only using a sample of the data to calculate statistics. -
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OzGIS free open-source mapping system
Analysis and display of Census, business, government attribute data
...The system can be used to to support management decisions associated with, for example, government planning, marketing, sales, site and personnel location, advertising and research. Systems for Windows, Mac and Ubuntu, source code, sample data files and manuals are included. DOWNLOAD to your Downloads directory and unzip to Downloads/OzGIS ; see the Files tab for installation instructions. See the installation manual or "OzGIS Installation" Youtube video for help with getting started. I am unable to show OzGIS to you, so suggest that you search for "OzGIS lloyd" on Youtube for demonstration videos or "OzGIS" on Pinterest for example maps. ... -
16
OpenData
Open source coronavirus 2019 sample application using open data
...The purpose is to provide template code to access open data readily available from official government websites useful for regional data analysis, latest information at a glance with data visualisation, etc. Sample includes region like Australia, Austria, Belgium, Brazil, Canada, Chile, Czechia, Denmark, Estonia, Europe, Finland, France, Germany, Greece, Hong Kong, Iceland, Indonesia, Ireland, Israel, Italy, Japan, Luxembourg, Macau, Malaysia, Netherlands, Norway, Korea, Philippines, Poland,Portugal,Qatar,Romania,Saudi Arabia,Singapore,Slovenia,Spain,Sweden,Switzerland,Taiwan,Thailand,United Kingdom,United States of America,etc. with various data such as face mask maps, and more. -
17
nonechucks
Deal with bad samples in your dataset dynamically
nonechucks is a library that provides wrappers for PyTorch's datasets, samplers and transforms to allow for dropping unwanted or invalid samples dynamically. What if you have a dataset of 1000s of images, out of which a few dozen images are unreadable because the image files are corrupted? Or what if your dataset is a folder full of scanned PDFs that you have to OCRize, and then run a language detector on the resulting text, because you want only the ones that are in English? Or maybe you... -
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A set of Matlab functions which compute effect size statistics and (exact) confidence intervals for a wide range of data analysis situations, including two-sample-, oneway-, twoway- and contrast analyses as well as categorical data in tables.
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MOIRAI
Simple Scientific Workflow System for CAGE Analysis
Cap analysis of gene expression (CAGE) is a sequencing based technology to capture the 5’ ends of RNAs in a biological sample. After mapping, a CAGE peak on the genome indicates the position of an active transcriptional start site (TSS) and the number of reads correspond to its expression level. CAGE is prominently used in both the FANTOM and ENCODE project. MOIRAI is a compact yet flexible workflow system designed to carry out the main steps in data processing and analysis of CAGE data. ... -
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TESTIMAGES
Testing images for scientific purposes
The TESTIMAGES archive is a huge and free collection of sample images designed for analysis and quality assessment of different kinds of displays and image processing techniques. The archive includes more than 2 million images originally acquired and divided in three different categories: SAMPLING and SAMPLING_PATTERNS (aimed at testing resampling algorithms), COLOR (aimed at testing color rendering on different displays) and PATTERNS (aimed at testing the rendering of standard geometrical patterns). ... -
21
QuickRNASeq
A pipeline for large scale RNA-seq data analysis
...QuickRNASeq takes advantage of parallel computing resources, a careful selection of previously published algorithms for RNA-seq read mapping, counting and quality control, and a three-stage strategy to build a fully automated workflow. We also implemented built-in functionalities to detect sample swapping or mislabeling in large-scale RNA-seq studies. Our pipeline significantly lifts large-scale RNA-seq data analysis to the next level of automation and visualization. Please take a look at our test run project report and get a taste of its rich interactive visualization features. http://baohongz.github.io/QuickRNASeq QuickRNASeq user guide can be downloaded or is accessible online as well http://baohongz.github.io/QuickRNASeq/guide.html For more on QuickRNASeq, please see our paper http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-2356-9 Enjoy QuickRNASeq -
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Sample Level Musical Timeline
Sample Level Modulation of Musical Timeline
Sample Level Modulation of Musical Timeline Mingfeng Zhang Dept. of Electrical and Computer Engineering, University of Rochester In this toolbox we provide signal processing tools to allocate music events (samples of musical notes) to specified time locations with sample level accuracy. In this implementation, we use computational tools to add in micro-timing variations in J.S. -
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Python Open Source Echosounder Toolkit
real-time visualization of network data broadcasts from echosounders
During acoustic surveys of marine ecosystems, fisheries scientists need to quickly interpret large amounts of echosounder data and decide whether and where to sample for targeted organisms. The Python Open Source Echosounder Toolkit (pyOSET) was designed to facilitate this process by providing near real-time visualization of network data broadcasts from multiple echosounder systems, and to serve as a framework for implementation of algorithms to detect, locate, and identify fish or the seabed. ... -
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PRADA
PRADA : Pipeline for RNA-Sequencing Data Analysis
Massively parallel sequencing of cDNA reverse transcribed from RNA (RNASeq) provides an accurate estimate of the quantity and composition of mRNAs. To characterize the transcriptome through the analysis of RNA-seq data, we developed PRADA. PRADA focuses on the processing and analysis of gene expression estimates, supervised and unsupervised gene fusion identification, and supervised intragenic deletion identification. PRADA currently supports 7 modules to process and identify... -
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KMeansAniX
Animation of kmeans clustering using X Window System
Open source animation of kmeans clustering in X Window System using the C++ libplotter library. Supports Linux, Mac, and BSD. Includes common initialization methods such as Forgy, Macqueen, random, and angular. Sample videos are available through the Files Tab above. The SVN repo is accessible thorugh the Code Tab above. Requires a C++ compiler, libplot-dev, and libncurses5-dev Mac alternative to libplot-dev: macports plotutils +x11
