Search Results for "numpy python 3.4"
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Showing 23 open source projects for "numpy python 3.4"
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Cloud tools for web scraping and data extractionAutomate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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PyVista
3D plotting and mesh analysis through a streamlined interface
3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). PyVista is a helper module for the Visualization Toolkit (VTK) that takes a different approach on interfacing with VTK through NumPy and direct array access. This package provides a Pythonic, well-documented interface exposing VTK’s powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets. This module can be used for scientific plotting for presentations and research papers as well as a supporting module for other mesh-dependent Python modules. ... -
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. -
Component Content Management System for Software DocumentationPaligo is an end-to-end Component Content Management System (CCMS) solution for technical documentation, policies and procedures, knowledge management, and more.
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VisPy
Main repository for Vispy
Vispy is an open-source, high-performance interactive visualization library in Python, designed for creating scientific visualizations and interactive plots. It leverages the power of modern Graphics Processing Units (GPUs) through OpenGL to render large datasets efficiently. Vispy supports a wide range of visualization types, including 2D plots, 3D visualizations, volume rendering, and more, making it suitable for scientific research, data analysis, and educational purposes. -
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JDF.jl
Julia DataFrames serialization format
...JDF.jl is a pure-Julia solution and there are a lot of ways to do nifty things like compression and encapsulating the underlying struture of the arrays that's hard to do in R and Python. E.g. Python's numpy arrays are C objects, but all the vector types used in JDF are Julia data types. -
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GemGIS
Spatial data processing for geomodeling
GemGIS is a Python-based, open-source geographic information processing library. It is capable of preprocessing spatial data such as vector data (shape files, geojson files, geopackages,…), raster data (tif, png,…), data obtained from online services (WCS, WMS, WFS) or XML/KML files (soon). Preprocessed data can be stored in a dedicated Data Class to be passed to the geomodeling package GemPy in order to accelerate the model-building process. Postprocessing of model results will allow export... -
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PDP-OmniSim
PDP-OmniSim simulating parallel and distributed processing systems
PDP-OmniSim 🧬 Scientific Overview PDP-OmniSim is an advanced computational framework for simulating parallel and distributed processing systems, with cutting-edge applications in computational neuroscience, distributed computing, and complex systems modeling. The framework provides researchers with robust tools for large-scale simulations of networked systems and their emergent behaviors. 🎯 Key Scientific Contributions 🔬 Interdisciplinary Research Domains Computational... -
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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. -
Tools to Build an Efficient Quality Management System.Sunday Business Systems is ideal for small to mid-sized manufacturing and service businesses looking for cost-effective, customizable quality management software to help them comply with industry standards and improve operational efficiency
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Padasip
Python Adaptive Signal Processing
Padasip (Python Adaptive Signal Processing) is a Python library tailored for adaptive filtering and online learning applications, particularly in signal processing and time series forecasting. It includes a variety of adaptive filter algorithms such as LMS, RLS, and their variants, offering real-time adaptation to changing environments. The library is lightweight, well-documented, and ideal for research, prototyping, or teaching purposes. Padasip supports both supervised and unsupervised... -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. -
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MCNPydE
MCNP data extraction and display software library
MCNPydE is a Python library for extracting data from MCNP output file. It requires Python, Matplotlib and Numpy. It is a data reduction tool for MCNP output for ease of results analysis and viewing. -
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pyspread
Python spreadsheet application
Pyspread is a non-traditional spreadsheet application that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet. Pyspread expects Python expressions in its grid cells, which makes a spreadsheet specific language obsolete. Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Dependencies + Python (>=2.7, <3.0) + numpy (>=1.1.0) + wxPython (>=2.8.10.1, Unicode version required) + matplotlib (>=1.1.1) + pycairo (>=1.8.8) Optional dependencies + python-gnupg (>=0.3.0, for opening own files without approval) + xlrd (>=0.9.2, for loading Excel® files) + xlwt (>=0.9.2, for saving Excel files, pyspread >=v0.3.0 required) + jedi (>=0.8.0, for tab completion and context help in the entry line, pyspread >=v0.3.0 required) + basemap (>=1.0.7, for the weather example pys file) -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
Data science solutions, insights, dashboards, machine learning, deployment. We start at 100GB. Vaex is a high-performance Python library for lazy Out-of-Core data frames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive... -
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Matplotlib is a python library for making publication quality plots using a syntax familiar to MATLAB users. Matplotlib uses numpy for numerics. Output formats include PDF, Postscript, SVG, and PNG, as well as screen display. As of matplotlib version 1.5, we are no longer making file releases available on SourceForge. Please visit http://matplotlib.org/users/installing.html for help obtaining matplotlib.
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wxRays
Visual x-ray data processor
Visual x-ray data processor. The routine, writed in Python using wxPython, NumPy, SciPy and mathplotlib libraries for practical and educational purpose. -
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Larch: Data Analysis for X-ray Spectra
Data Processing and Analysis for X-ray Spectroscopy and More
...Larch has been primarily developed for dealing with x-ray spectroscopic and scattering data, especially the kind of data collected at modern synchrotrons and x-ray sources. Larch is written in Python and relies heavily on the standard tools for scientific computing with Python (numpy, scipy, matplotlib, and h5py). -
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BriPy
A BRIC-seq data analysis package written in Python.
...Python2.7 or higher NumPy SciPy Matplotlib -
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pygolem
Python API for the GOLEM Tokamak discharge database
This simple Python API aims to provide a simple and easy to understand access to the discharge database of the GOLEM Tokamak. The scipy, numpy and matplotlib Python libraries are used for data analysis and plotting. -
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PyMaTi is a simple and easy to use GUI for numerical and scientific computing in Python. It surrounds well know packages NumPy and Matplotlib and provides possibility to immediately play with numerical python from intuitive user interface.
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PyPlotSuite aims to provide immediate, easy-to-use, GUI tools to display, analyze and create publication-quality 2D plots of 1D data series, images, 2D arrays. This project is based on (and would not exist without) pygtk, matplotlib and numpy.
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Quick reference for switching between mathematical computation environments for computer algebra, numeric processing and data visualisation. Examples are Matlab, IDL, SPlus, and their open-source counterparts Octave, Scilab, Python+NumPy and R.
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QME-DEV Workbench (wxScipy) is a data analysis workbench based on Python, SciPy, Numpy and MatPlotLib(PyLab) and uses wxPython for the GUI. It can be used for non-linear analysis with a large set of experimental or generated data. Load/Acquire / Save/ Gr
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