PAN Blend data-reduction-frameworks packages

Momentálne ADMesh iba načítava formát súboru STL, ktorý sa používa na aplikácie rýchlej tvorby prototypov, aj keď dokáže zapisovať súbory STL, VRML, OFF a DXF. Medzi niektoré vlastnosti admesh patria:

• vypĺňanie dier v sieti pridaním faziet
• opravovanie faziet spájaním blízko sa nachádzajúcich faziet
• opravovanie smerov normál (t.j. fazety by mali byť orientované v zápornom zmysle)
• odstránenie degenerovaných faziet (t.j. s dvomi alebo viacerými zhodnými vrcholmi)

bcolz provides columnar, chunked data containers that can be compressed in-memory and on-disk. Column storage allows for efficiently querying tables, as well as for cheap column addition and removal. It is based on NumPy, and uses it as the standard data container to communicate with bcolz objects, but it also comes with support for import/export facilities to/from HDF5/PyTables tables and Pandas dataframes.

This package contains the documentation.

Bitshuffle is an algorithm that rearranges typed, binary data for improving compression, as well as a python/C package that implements this algorithm within the Numpy framework.

The library can be used along side HDF5 to compress and decompress datasets and is integrated through the dynamically loaded filters framework. Bitshuffle is HDF5 filter number 32008.

Algorithmically, Bitshuffle is closely related to HDF5's Shuffle filter except it operates at the bit level instead of the byte level. Arranging a typed data array in to a matrix with the elements as the rows and the bits within the elements as the columns, Bitshuffle "transposes" the matrix, such that all the least-significant-bits are in a row, etc. This transpose is performed within blocks of data roughly 8kB long.

This does not in itself compress data, only rearranges it for more efficient compression. To perform the actual compression you will need a compression library. Bitshuffle has been designed to be well matched Marc Lehmann's LZF as well as LZ4. Note that because Bitshuffle modifies the data at the bit level, sophisticated entropy reducing compression libraries such as GZIP and BZIP are unlikely to achieve significantly better compression than simpler and faster duplicate-string-elimination algorithms such as LZF and LZ4. Bitshuffle thus includes routines (and HDF5 filter options) to apply LZ4 compression to each block after shuffling.

The Bitshuffle algorithm relies on neighbouring elements of a dataset being highly correlated to improve data compression. Any correlations that span at least 24 elements of the dataset may be exploited to improve compression.

Clpeak is a synthetic benchmarking tool to measure peak capabilities of opencl devices. It only measures the peak metrics that can be achieved using vector operations and does not represent a real-world use case

Fityk is a flexible and portable program for nonlinear fitting of analytical functions (especially peak-shaped) to data (usually experimental data). In other words, for nonlinear peak separation and analysis.

It was developed for analyzing diffraction patterns, but can be also used in other fields, since concepts and operations specific for crystallography are separated from the rest of the program.

Fityk offers various nonlinear fitting methods, subtracting background, calibrating data, easy placement of peaks and changing peak parameters, automation of common tasks with scripts, and much more. The main advantage of the program is flexibility - parameters of peaks can be arbitrarily bound to each other, eg. the width of a peak can be an independent variable, can be the same as the width of another peak or can be given by a complicated - common to all peaks - formula.

libjs-sphinxdoc is necessary for the Javascript stuff in the documentation.

Gnuplot je portabilný nástroj na interaktívne kreslenie dát a funkcií riadený z príkazového riadka, podporujúci mnoho výstupných formátov vrátane ovládačov mnohých tlačiarní, (La)TeX, (x)fig, Postscript atď. Výstup do X11 nájdete v balíku gnuplot-x11.

S dátovými súbormi a funkciami je možné pracovať pomocou jazyka podobného jazyku C. Dokáže vyhladzovať, prekladať krivkou, nelineárne prekladanie krivkou a pracovať s komplexnými číslami.

Tento balík slúži na inštaláciu plnohodnotného gnuplot (-qt, -x11 or -nox).

The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is used for easy access, manipulation, and visualization of earth science data. The format of the data may be either binary, GRIB, NetCDF, or HDF-SDS (Scientific Data Sets). GrADS has been implemented worldwide on a variety of commonly used operating systems and is freely distributed over the Internet.

GrADS uses a 4-Dimensional data environment: longitude, latitude, vertical level, and time. Data sets are placed within the 4-D space by use of a data descriptor file. GrADS interprets station data as well as gridded data, and the grids may be regular, non-linearly spaced, gaussian, or of variable resolution. Data from different data sets may be graphically overlaid, with correct spatial and time registration. Operations are executed interactively by entering FORTRAN-like expressions at the command line. A rich set of built-in functions are provided, but users may also add their own functions as external routines written in any programming language.

Data may be displayed using a variety of graphical techniques: line and bar graphs, scatter plots, smoothed contours, shaded contours, streamlines, wind vectors, grid boxes, shaded grid boxes, and station model plots. Graphics may be output in PostScript or image formats. GrADS provides geophysically intuitive defaults, but the user has the option to control all aspects of graphics output.

GrADS has a programmable interface (scripting language) that allows for sophisticated analysis and display applications. Use scripts to display buttons and dropmenus as well as graphics, and then take action based on user point-and-clicks. GrADS can be run in batch mode, and the scripting language facilitates using GrADS to do long overnight batch jobs.

HDF5 (Hierarchical Data Format 5) is a file format for storing scientific data. These tools allow converting other formats to HDF5 and to visualize HDF5 files. They include:

• h5topng, which extracts a 2d slice of an HDF5 file and outputs a corresponding image in PNG format;
• h5totxt, which extracts 2d slices and outputs comma-delimited text (suitable for import into a spreadsheet);
• h5fromtxt, which converts simple text input into multi-dimensional numeric HDF5 datasets;
• h5fromh4, which converts HDF4 data to HDF5;
• h5tovtk, which converts HDF5 files to VTK files for visualization with VTK-aware programs;

HDF Compass is an experimental viewer program for HDF5 and related formats, designed to complement other more complex applications like HDFView. Strong emphasis is placed on clean minimal design, and maximum extensibility through a plugin system for new formats.

This package provides the HDF Compass application.

The external filter mechanism introduced with HDF5 1.8.12 allows applications to utilize custom filters not shipped by the HDF5 core library without recompiling your application. This package provides external filters for HDF5 for

• the LZ4 compression algorithm
• BZip2 compression

This package contains a filter for HDF5 that uses the Blosc compressor. By installing this filter, you can read and write HDF5 files with Blosc-compressed datasets.

H5Z-ZFP is a compression filter for HDF5 using the ZFP compression library, supporting lossy and lossless compression of floating point and integer data to meet bitrate, accuracy, and/or precision targets.

HDF5 (Hierarchical Data Format library, version 5) is a versatile, mature scientific software library designed for the fast, flexible storage of enormous amounts of data.

This package provides a plugin to the HDF5 LZF filter for the LZF compression library. Plugins are built for both serial (single processor) jobs (libhdf5-dev) and for multiprocessor (threaded) jobs (libhdf5-mpi-dev).

Hierarchical Data Format 5 (HDF5) is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O.

This package contains runtime tools for HDF5.

Dokáže zobrazovať, upravovať, analyzovať, spracúvať, ukladať a tlačiť 8- bitové, 16-bitové a 32-bitové obrázky. Číta mnohé obrazové formáty vrátane TIFF, GIF, JPEG, BMP, DICOM, FITS a „raw“. Podporuje „zásobníky“ - série obrázkov, ktoré sa nachádzajú v jednom okne.

Dokáže počítať plochy a štatistiky hodnôt pixlov používateľom vybraných oblastí. Dokáže merať vzdialenosti a uhly. Dokáže tvoriť histogramy hustoty a čiarové profilové kresby. Podporuje štandardné funkcie spracovania obrazu ako zmena kontrastu, zaostrovanie, vyhladzovanie, detekcia hrán a mediánové filtrovanie.

Priestorová kalibrácia poskytuje priestorové miery reálneho sveta ako milimetre. Tiež je dostupná kalibrácia hustoty a škály šedej.

ImageJ vyvíja Wayne Rasband (wayne@codon.nih.gov) na Research Services Branch, National Institute of Mental Health, Bethesda, Maryland, USA.

Jupyter Notebook je webový aplikácia umožňujúca vytvárať a zdieľať dokumenty obsahujúce živý kód, vzťahy, vizualizácie a vysvetľujúci text. Notebook podporuje viacero programovacích jazykov, zdieľanie a interaktívne ovládacie prvky.

Tento balík obsahuje podpríkazy jupyter „notebook“, „nbextension“, „serverextension“ a „bundlerextension“.

LabPlot provides an easy way to create, manage and edit plots. It allows you to produce plots based on data from a spreadsheet or on data imported from external files. Plots can be exported to several pixmap and vector graphic formats.

Features include:

• project-based management of data
• project explorer for management and organization of created objects in different folders and sub-folders
• spreadsheet with basic functionality for manual data entry or for generation of uniform and non-uniform random numbers
• import of external ASCII data into the project for further editing and visualization
• export of spreadsheet to an ASCII file
• worksheet as the main parent object for plots, labels, etc; supports different layouts and zooming functions
• export of worksheet to different formats (PDF, EPS, PNG, and SVG)
• great variety of editing capabilities for properties of worksheet and its objects
• cartesian plots, created either from imported or manually created data sets or via mathematical equation
• definition of mathematical formulas is supported by syntax highlighting and completion and by the list of thematically grouped mathematical and physical constants and functions
• analysis of plotted data is supported by many zooming and navigation features
• linear and non-linear fits to data, several fit-models are predefined and custom models with arbitrary number of parameters can be provided

Quantitative estimate of elemental composition by spectroscopic and imaging techniques using X-ray fluorescence requires the availability of accurate data of X-ray interaction with matter. Although a wide number of computer codes and data sets are reported in literature, none of them is presented in the form of freely available library functions which can be easily included in software applications for X-ray fluorescence. This work presents a compilation of data sets from different published works and an xraylib interface in the form of callable functions. Although the target applications are on X-ray fluorescence, cross sections of interactions like photoionization, coherent scattering and Compton scattering, as well as form factors and anomalous scattering functions, are also available.

xraylib provides access to some of the most respected databases of physical data in the field of X-rays. The core of xraylib is a library, written in ANSI C, containing over 40 functions to be used to retrieve data from these databases. This C library can be directly linked with any program written in C, C++ or Objective-C.

This package contains the header files.

Quantitative estimate of elemental composition by spectroscopic and imaging techniques using X-ray fluorescence requires the availability of accurate data of X-ray interaction with matter. Although a wide number of computer codes and data sets are reported in literature, none of them is presented in the form of freely available library functions which can be easily included in software applications for X-ray fluorescence. This work presents a compilation of data sets from different published works and an xraylib interface in the form of callable functions. Although the target applications are on X-ray fluorescence, cross sections of interactions like photoionization, coherent scattering and Compton scattering, as well as form factors and anomalous scattering functions, are also available.

xraylib provides access to some of the most respected databases of physical data in the field of X-rays. The core of xraylib is a library, written in ANSI C, containing over 40 functions to be used to retrieve data from these databases. This C library can be directly linked with any program written in C, C++ or Objective-C.

xylib is a C++ library for reading files that contain x-y data from powder diffraction, spectroscopy and other experimental methods.

This package contains a small program xyconv that converts files supported by the xylib library to TSV (tab-separated values).

Search and export numerics from any text/ascii file. Data sets (scalar, vector, matrix) are given unique names, based on file content. Results can be generated for e.g. Matlab, YAML, IDL, Scilab, Octave, XML, HTML, and NeXus/HDF5.

This package contains the executable.

LZ4 je veľmi rýchly bezstratový komprimačný algoritmus poskytujúci rýchlosť komprimácie 400MB/s na jadro, ktorý je škálovateľný na viacjadrových CPU. Tiež má extrémne rýchle dekódovanie s rýchlosťou viacerých GB/s na jadro, pričom na viacjadrových systémov typicky dosahuje rýchlostné obmedzenia RAM.

Tento balík obsahuje nástroj používajúci liblz4.

Ngraph is the program to create scientific 2-dimensional graphs for researchers and engineers. This program can create advanced graphs which can't be created by spreadsheets. Graphs can be exported to postscript.

Octave is a (mostly MATLAB® compatible) high-level language, primarily intended for numerical computations. It provides a convenient command-line interface for solving linear and nonlinear problems numerically.

Octave can be dynamically extended with user-supplied C++ files.

Pandoc is a Haskell library for converting from one markup format to another, and a command-line tool that uses this library. The formats it can handle include

• light markup formats (many variants of Markdown, reStructuredText, AsciiDoc, Org-mode, Muse, Textile, txt2tags)
• HTML formats (HTML 4 and 5)
• Ebook formats (EPUB v2 and v3, FB2)
• Documentation formats (GNU TexInfo, Haddock)
• Roff formats (man, ms)
• TeX formats (LaTeX, ConTeXt)
• Typst
• XML formats (DocBook 4 and 5, JATS, TEI Simple, OpenDocument)
• Outline formats (OPML)
• Bibliography formats (BibTeX, BibLaTeX, CSL JSON, CSL YAML, RIS)
• Word processor formats (Docx, RTF, ODT)
• Interactive notebook formats (Jupyter notebook ipynb)
• Page layout formats (InDesign ICML)
• Wiki markup formats (MediaWiki, DokuWiki, TikiWiki, TWiki, Vimwiki, XWiki, ZimWiki, Jira wiki, Creole)
• Slide show formats (LaTeX Beamer, PowerPoint, Slidy, reveal.js, Slideous, S5, DZSlides)
• Data formats (CSV and TSV tables)
• PDF (via external programs such as pdflatex or wkhtmltopdf)

Pandoc can convert mathematical content in documents between TeX, MathML, Word equations, roff eqn, typst, and plain text. It includes a powerful system for automatic citations and bibliographies, and it can be customized extensively using templates, filters, and custom readers and writers written in Lua.

This package contains the pandoc tool.

Some uses of Pandoc require additional packages:

• SVG content in PDF output requires librsvg2-bin.
• YAML metadata in TeX-related output requires texlive-latex-extra.
• *.hs filters not set executable requires ghc.
• *.js filters not set executable requires nodejs.
• *.php filters not set executable requires php.
• *.pl filters not set executable requires perl.
• *.py filters not set executable requires python.
• *.rb filters not set executable requires ruby.
• *.r filters not set executable requires r-base-core.
• LaTeX output, and PDF output via PDFLaTeX, require texlive-latex-recommended.
• XeLaTeX output, and PDF output via XeLaTeX, require texlive-xetex.
• LuaTeX output, and PDF output via LuaTeX, require texlive-luatex.
• ConTeXt output, and PDF output via ConTeXt, require context.
• PDF output via wkhtmltopdf requires wkhtmltopdf.
• Roff man and roff ms output, and PDF output via roff ms, require groff.
• MathJax-rendered equations require libjs-mathjax.
• KaTeX-rendered equations require node-katex.
• option --csl may use styles in citation-style-language-styles.

PyMca is set of applications and Python libraries for analysis of X-ray fluorescence spectra.

The applications included in this package are:

• edfviewer - Display and inspection of data files in ESRF Data Format
• elementsinfo - Displays element specific X-ray data
• mca2edf - Converts files from SPEC MCA format to EDF
• peakidentifier - Displays X-ray fluorescence peaks in a given energy range
• pymcabatch - Batch fitting of spectra
• pymcapostbatch - Post-processing of batch fitting results
• pymca - Interactive data-analysis
• pymcaroitool - Region-of-interest (ROI) imaging tool

The PyMca toolkit can read data files in SPEC, ESRF data file (EDF), OMNIC, HDF5, AIFIRA and SupaVisio formats.

This are the scripts of the package.

Agate is a Python data analysis library that is optimized for humans instead of machines. It is an alternative to numpy and pandas that solves real-world problems with readable code.

Why agate?

• A readable and user-friendly API.
• A complete set of SQL-like operations.
• Unicode support everywhere.
• Decimal precision everywhere.
• Exhaustive user documentation.
• Pluggable extensions that add SQL integration, Excel support, and more.
• Designed with iPython, Jupyter and atom/hydrogen in mind.
• Pure Python. No C dependencies to compile.
• Exhaustive test coverage.
• MIT licensed and free for all purposes.
• Zealously zen.
• Made with love.

This package provides the documentation.

Dask is a flexible parallel computing library for analytics, containing two components.

1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads.
2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

This contains the documentation

Notebooks come alive when interactive widgets are used. Learning becomes an immersive and fun experience. Researchers can easily see how changing inputs to a model impact the results.

This package installs documentation.

MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors.

mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects).

This package provides HTML rendering of the user's manual.

Scikit-bio is a Python package providing data structures, algorithms, and educational resources for bioinformatics.

This is the HTML documentation for skbio.

scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations.

This package contains documentation and example scripts for python-skimage.

This package contains documentation and example scripts for python-sklearn.

PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data.

It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases.

• Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them).
• The tables are both enlargeable and compressible.
• I/O is buffered, so you can get very fast I/O, specially with large tables.
• Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well.
• It supports the complete set of NumPy objects.

This package includes the manual in HTML formats.

Agate je knižnica jazyka Python na analýzu dát optimalizovaná pre ľudí, nie pre počítače. Je alternatívou k numpy a pandas, ktorá umožňuje riešenie problémov z reálneho sveta pomocou čitateľného kódu.

Prečo agate?

• čitateľné a používateľsky prívetivé API
• kompletná množina operácií podobných SQL
• všade podpora Unicode
• všade desatinná presnosť
• vyčerpávajúca používateľská dokumentácia
• zásuvné rozšírenia pridávajúce integráciu s SQL, Excelom a iné
• navrhnutá s ohľadom na iPython, Jupyter a atom/hydrogen
• čistý Python, žiadne kompilované závislosti
• vyčerpávajúce pokrytie testami
• licencia MIT a neobmedzené využitie
• horlivo dodržiava zen
• vytvorená s láskou

Tento balík poskytuje moduly jazyka Python 3.

bcolz provides columnar, chunked data containers that can be compressed in-memory and on-disk. Column storage allows for efficiently querying tables, as well as for cheap column addition and removal. It is based on NumPy, and uses it as the standard data container to communicate with bcolz objects, but it also comes with support for import/export facilities to/from HDF5/PyTables tables and Pandas dataframes.

This package contains the modules for Python 3.

CodraFT is a generic signal and image processing software based on Python scientific libraries (such as NumPy, SciPy or scikit-image) and Qt graphical user interfaces (thanks to guidata and guiqwt.

CodraFT stands for "CODRA Filtering Tool".

CodraFT is available as a stand-alone application or as an addon to your Python-Qt application thanks to advanced automation and embedding features.

Collection of perceptually uniform colormaps for use with Python plotting programs like bokeh, matplotlib, holoviews, and datashader based on the set of perceptually uniform colormaps created by Peter Kovesi at the Center for Exploration Targeting.

The colormap package provides simple utilities to convert colors between RGB, HEX, HLS, HUV and a class to easily build colormaps for matplotlib. All matplotlib colormaps and some R colormaps are available altogether. The plot_colormap method (see below) is handy to quickly pick up a colormaps and the test_colormap is useful to see test a new colormap.

This package installs the library for Python 3.

Keď používate matplotlib a vykresľujete viac ako jednu čiaru, je bežné chcieť cyklicky používať viac ako jeden štýl, ale logika vykresľovania sa tým môže rýchlo skomplikovať.

Na vyriešenie tohto problému a umožnenie jednoduchého cyklického prechádzania ľubovoľných kwargs bola vyvinutá kombinovateľná trieda „Cycler“.

Tento balík obsahuje verziu Cycler pre Python 3.

Dask is a flexible parallel computing library for analytics, containing two components.

1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads.
2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

This contains the Python 3 version.

Descartes allows the usage of geometric objects as matplotlib paths and patches.

This is the Python 3 version of the library.

Python 3 tools for reading European XFEL's HDF5 files.

Plotting data on Google Maps, the easy way. A matplotlib-like interface to generate the HTML and javascript to render all the data you’d like on top of Google Maps. Several plotting methods make creating exploratory map views effortless.

This package facilitates the creation and rendering of graph descriptions in the DOT language of the Graphviz graph drawing software from Python.

Create a graph object, assemble the graph by adding nodes and edges, and retrieve its DOT source code string. Save the source code to a file and render it with the Graphviz installation of your system.

Use the view option/method to directly inspect the resulting (PDF, PNG, SVG, etc.) file with its default application. Graphs can also be rendered and displayed within Jupyter notebooks.

This contains the Python 3 version.

A Python interface for the netCDF4 file-format that reads and writes local or remote HDF5 files directly via h5py or h5pyd, without relying on the Unidata netCDF library.

This package provides the modules for Python 3.

HDF5 for Python (h5py) is a general-purpose Python interface to the Hierarchical Data Format library, version 5. HDF5 is a versatile, mature scientific software library designed for the fast, flexible storage of enormous amounts of data.

From a Python programmer's perspective, HDF5 provides a robust way to store data, organized by name in a tree-like fashion. You can create datasets (arrays on disk) hundreds of gigabytes in size, and perform random-access I/O on desired sections. Datasets are organized in a filesystem-like hierarchy using containers called "groups", and accessed using the tradional POSIX /path/to/resource syntax.

H5py provides a simple, robust read/write interface to HDF5 data from Python. Existing Python and Numpy concepts are used for the interface; for example, datasets on disk are represented by a proxy class that supports slicing, and has dtype and shape attributes. HDF5 groups are presented using a dictionary metaphor, indexed by name.

This is a simple dependency package which depends on the serial or MPI build of h5py and provides test data files.

h5sparse is a Python library which helps create scipy sparse matrix in HDF5.

hdf5plugin provides h5py extensions to control HDF5 compression filters plugins installed in the system.

The package supports providing options to: * blosc * bitshuffle * lz4 * FCIDECOMP * ZFP * zstd

These filter plugins are not provided by this package and need to be installed using their own packages.

HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets that can be described as multidimensional arrays of a given signal (for example, a 2D array of spectra, also known as a spectrum image).

HyperSpy makes it straightforward to apply analytical procedures that operate on an individual signal to multidimensional arrays, as well as providing easy access to analytical tools that exploit the multidimensionality of the dataset.

Its modular structure makes it easy to add features to analyze many different types of signals.

ipyparallel is a Python package and collection of CLI scripts for controlling clusters for Jupyter. ipyparallel is the new home of IPython.parallel.

ipyparallel contains the following command-line scripts:

   ipcluster - start/stop a cluster
   ipcontroller - start a scheduler
   ipengine - start an engine

This package installs the Python 3 library and the CLI scripts.

Notebooks come alive when interactive widgets are used. Learning becomes an immersive and fun experience. Researchers can easily see how changing inputs to a model impact the results.

This package installs the library for Python 3 notebooks.

JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots). Joyplots are stacked, partially overlapping density plots. They are a nice way to plot data to visually compare distributions, especially those that change across one dimension (e.g., over time). Though hardly a new technique, they have become very popular lately thanks to the R packages ggridges and ggjoy.

Leather is the Python charting library for those who need charts now and don’t care if they’re perfect.

Why leather?

• A readable and user-friendly API.
• Optimized for exploratory charting.
• Produces scale-independent SVG charts.
• Completely type-agnostic. Chart your data, whatever it is.
• Designed with iPython, Jupyter and atom/hydrogen in mind.
• Pure Python. No C dependencies to compile.
• MIT licensed and free for all purposes.
• Zealously zen.
• Made with love.

This package provides the modules for Python 3.

Matplotlib je knižnica v čistom Pythone s cieľom priniesť do Pythonu vykresľovanie v publikačnej kvalite so syntaxou známou používateľom Matlabu. Ku všetkým vykresľovacím príkazom v rozhraní pylab je možné pristupovať buď prostredníctvom funkcionálneho rozhrania známeho používateľom Matlabu alebo prostredníctvom objektovo orientovaného rozhrania známeho používateľom Pythonu.

MPI for Python (mpi4py) poskytuje väzby štandardu (MPI Message Passing Interface) pre programovací jazyk Python, čím umožňuje ktorémukoľvek programu v Pythone využívať viacero procesorov.

mpi4py je postavený na špecifikácii MPI-1/MPI-2 a poskytuje objektovo orientované rozhranie, ktoré úzko sleduje väzby MPI-2 pre C++. Podporuje komunikáciu typu point-to-point (posielanie, prijímanie) a kolektívnu komunikáciu (vysielanie, roztrúsenie, zhromaždenie) akéhokoľvek objektu Pythonu, ktorý je možné serializovať pomocou Pickle a tiež optimalizovanú komunikáciu objektov Pythonu, ktorá sprístupňuje rozhranie jednosegmentového buffera (polia NumPy, zabudované objekty bajtov/reťazcov/polí).

This package provides functionality to make it easy to make your own scatter density maps, both for interactive and non-interactive use. Fast.

This package installs the library for Python 3.

mplcursors provides interactive data selection cursors for Matplotlib. It is inspired from mpldatacursor, with a much simplified API.

This package installs the library for Python 3.

A Python module to export matplotlib graphs, e.g. for mpld3.

This package provides the Python 3.x module.

The matplotlib basemap toolkit is a library for plotting 2D data on maps in Python. It is similar in functionality to the matlab mapping toolbox, the IDL mapping facilities, GrADS, or the Generic Mapping Tools. PyNGL and CDAT are other libraries that provide similar capabilities in Python.

Basemap does not do any plotting on its own, but provides the facilities to transform coordinates to one of 23 different map projections (using the PROJ.4 C library). Matplotlib is then used to plot contours, images, vectors, lines or points in the transformed coordinates. Shoreline, river and political boundary datasets (from Generic Mapping Tools) are provided, along with methods for plotting them. The GEOS library is used internally to clip the coastline and political boundary features to the desired map projection region.

Basemap provides facilities for reading data in netCDF and Shapefile formats, as well as directly over http using OPeNDAP. This functionality is provided through the PyDAP client, and a Python interface to the Shapefile C library.

Basemap is geared toward the needs of earth scientists, particular oceanographers and meteorologists. The author originally wrote Basemap to help in his research (climate and weather forecasting), since at the time CDAT was the only other tool in Python for plotting data on map projections. Over the years, the capabilities of Basemap have evolved as scientists in other disciplines (such as biology, geology and geophysics) requested and contributed new features.

This package contains the Python 3 version of python-mpltoolkits.basemap.

WARNING: this package is deprecated in favour of cartopy.

NetCDF version 4 has many features not found in earlier versions of the library and is implemented on top of HDF5. This module can read and write files in both the new netCDF 4 and the old netCDF 3 format, and can create files that are readable by HDF5 clients. The API is modelled after Scientific.IO.NetCDF, and should be familiar to users of that module.

Most new features of netCDF 4 are implemented, such as multiple unlimited dimensions, groups and zlib data compression. All the new numeric data types (such as 64 bit and unsigned integer types) are implemented. Compound and variable length (vlen) data types are supported, but the enum and opaque data types are not. Mixtures of compound and vlen data types (compound types containing vlens, and vlens containing compound types) are not supported.

This package contains the netCDF 4 module for Python 3.

This package consists of a couple of optimised tools for doing things that can roughly be considered "group-indexing operations". The most prominent tool is aggregate.

aggregate takes an array of values, and an array giving the group number for each of those values. It then returns the sum (or mean, or std, or any, ...etc.) of the values in each group. You have probably come across this idea before, using matlab accumarray, pandas groupby, or generally MapReduce algorithms and histograms.

There are different implementations of aggregate provided, based on plain numpy, numba and weave. Performance is a main concern, and so far this implementation comfortably beats similar implementations in other packages (check the benchmarks).

This package contains Python 3 bindings for the OpenCV (Open Computer Vision) library.

The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance.

OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction.

Palettable (formerly brewer2mpl) is a library of color palettes for Python. It's written in pure Python with no dependencies, but it can supply color maps for matplotlib. You can use Palettable to customize matplotlib plots or supply colors for a web application.

This package installs the library for Python 3.

pandas balík jazyka Python poskytujúci rýchle, flexibilné a expresívne údajové štruktúry navrhnuté na zjednodušenie a zintuitívnenie práce s „relačnými“ dátami a dátami „s označením“. Jeho cieľom je byť základným stavebným blokom praktickej analýzy dát skutočného sveta v jazyku Python. pandas sa hodí na rôzne druhy dát:

• Tabuľkové dáta so stĺpcami heterogénnych typov, ako v tabuľke SQL alebo hárku tabuľkového kalkulátora.
• Zoradené a nezoradené dáta časových radov (nie nutne s pevnou frekvenciou).
• Ľubovoľné maticové dáta (homogénneho alebo heterogénneho typu) s označením riadkov a stĺpcov.
• Akékoľvek iné sady experimentálnych alebo štatistických dát. Dáta v skutočnosti nemusia byť označené, aby ste ich mohli umiestniť do údajovej štruktúry pandas.

Tento balík obsahuje verziu pre Python 3.

This package provides a periodic table of the elements with support for mass, density and xray/neutron scattering information.

Masses, densities and natural abundances come from the NIST Physics Laboratory, but do not represent a critical evaluation by NIST scientists.

Neutron scattering calculations use values collected by the Atomic Institute of the Austrian Universities. These values do corresponding to those from other packages, though there are some differences depending to the tables used. Bound coherent neutron scattering for gold in particular is significantly different from older value: 7.63(6) as measured in 1974 compared to 7.90(7) as measured in 1990.

X-ray scattering calculations use a combination of empirical and theoretical values from the LBL Center for X-ray Optics. These values differ from those given in other sources such as the International Tables for Crystallography, Volume C, and so may give different results from other packages.

This package installs the library for Python 3.

Versatile utility function to run external processes from Python, with many features:

• runs an external process and waits for it to finish
• does not deadlock, no matter the process stdout/stderr output behaviour
• returns the exit code, stdout, stderr (separately, both as bytestrings), and the total process runtime as a dictionary
• process can run in a custom environment, either as a modification of the current environment or in a new environment from scratch
• stdin can be fed to the process, the returned dictionary contains information how much was read by the process
• stdout and stderr is printed by default, can be disabled
• stdout and stderr can be passed to any arbitrary function for live processing (separately, both as unicode strings)
• optionally enforces a time limit on the process

Pweave is a scientific report generator and a literate programming tool for Python. Pweave can capture the results and plots from data analysis and works well with NumPy, SciPy and matplotlib. It is able to run Python code from source document and include the results and capture matplotlib plots in the output.

Utility package that includes:

• pyct.cmd: Makes various commands available to other packages. (Currently no sophisticated plugin system, just a try import/except in the other packages.) The same commands are available from within Python. Can either add new subcommands to an existing argparse based command if the module has an existing command, or create the entire command if the module has no existing command. Currently, there are commands for copying examples and fetching data. See
• pyct.build: Provides various commands to help package building, primarily as a convenience for project maintainers.

Pymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. These are some of the main features:

1.Highly flexible classes for the representation of Element, Site, Molecule, Structure objects.

1. Extensive input/output support, including support for VASP (http://cms.mpi.univie.ac.at/vasp/), ABINIT (http://www.abinit.org/), CIF, Gaussian, XYZ, and many other file formats.

2. Powerful analysis tools, including generation of phase diagrams, Pourbaix diagrams, diffusion analyses, reactions, etc.

3. Electronic structure analyses, such as density of states and band structure.

4. Integration with the Materials Project REST API, Crystallography Open Database.

This package provides the pymtgen module for Python 3.

Matplotlib includes a number of test utilities and decorators, but these are geared towards the nose testing framework. Pytest-mpl makes it easy to compare figures produced by tests to reference images when using pytest.

This package provides the pytest plugin for Python 3.

Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.

Some of the features that seaborn offers are

• Several built-in themes that improve on the default matplotlib aesthetics
• Tools for choosing color palettes to make beautiful plots that reveal patterns in your data
• Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data
• Tools that fit and visualize linear regression models for different kinds of independent and dependent variables
• A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate
• High-level abstractions for structuring grids of plots that let you easily build complex visualizations

This is the Python 3 version of the package.

Scikit-bio is a Python package providing data structures, algorithms, and educational resources for bioinformatics.

This is the package for Python3

scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations.

This package provides the Python 3 module.

scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality:

• Gaussian Mixture Models
• Manifold learning
• kNN
• SVM (via LIBSVM)

This package contains the Python 3 version.

sklearn-pandas provides a bridge between scikit-learn's machine learning methods and pandas data frames.

In particular, it provides:

• a way to map DataFrame columns to transformations, which are later recombined into features
• a way to cross-validate a pipeline that takes a pandas DataFrame as input.

This is the Python 3 version of the package.

PyTables is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data.

It is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code (generated using Cython), makes it a fast, yet extremely easy to use tool for interactively save and retrieve very large amounts of data. One important feature of PyTables is that it optimizes memory and disk resources so that they take much less space (between a factor 3 to 5, and more if the data is compressible) than other solutions, like for example, relational or object oriented databases.

• Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them).
• The tables are both enlargeable and compressible.
• I/O is buffered, so you can get very fast I/O, specially with large tables.
• Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well.
• It supports the complete set of NumPy objects.

Image and meta-data can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH, ImageJ, MicroManager, FluoView, SEQ and GEL files.

Only a subset of the TIFF specification is supported, mainly uncompressed and losslessly compressed 2**(0 to 6) bit integer, 16, 32 and 64-bit float, grayscale and RGB(A) images, which are commonly used in bio-scientific imaging. Specifically, reading JPEG/CCITT compressed image data or EXIF/IPTC/GPS/XMP meta-data is not implemented. Only primary info records are read for STK, FluoView, MicroManager, and NIH image formats.

TIFF, the Tagged Image File Format, is under the control of Adobe Systems. BigTIFF allows for files greater than 4 GB. STK, LSM, FluoView, SEQ, GEL, and OME-TIFF, are custom extensions defined by MetaMorph, Carl Zeiss MicroImaging, Olympus, Media Cybernetics, Molecular Dynamics, and the Open Microscopy Environment consortium respectively.

A lightweight pure-Python derivative of Enthought Traits, used for configuring Python objects.

It powers the config system of IPython and Jupyter.

This package contains the library for Python 3.

xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures.

It provides a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels.

This package provides the Python 3 library

X-ray Reference Data in SQLite library, including a Python3 interface. The database contains data about the interactions of X-rays with matter.

This package installs the library for Python 3.

Zarr is a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. Some highlights:

• Create N-dimensional arrays with any NumPy dtype.
• Chunk arrays along any dimension.
• Compress chunks using the fast Blosc meta-compressor or alternatively using zlib, BZ2 or LZMA.
• Store arrays in memory, on disk, inside a Zip file, on S3, ...
• Read an array concurrently from multiple threads or processes.
• Write to an array concurrently from multiple threads or processes.
• Organize arrays into hierarchies via groups.
• Use filters to preprocess data and improve compression.

A remote desktop service that launches virtual machines and displays them in your browser. Place virtual machine files (ISO, QCOW2, VDI, VMDK...) into /var/lib/qemu-web-desktop/machines, add their name in the /etc/qemu-web-desktop/machines.conf file, and run qwdctl refresh. You can tune the service settings in the /etc/qemu-web-desktop/config.pl This project is also named Data Analysis Remote Treatment Service (DARTS).

Once installed, connect to http://server/qemu-web-desktop

Scilab je balík maticového vedeckého softvéru. Scilab obsahuje stovky zabudovaných matematických funkcií, bohaté údajové štruktúry (vrátane polynómov, racionálnych funkcií, lineárnych systémov, zoznamov atď.) a dodáva sa s množstvom konkrétnych sád nástrojov na riadenie, spracovanie signálov atď.

Tento balík poskytuje Xcos, grafický editor na návrh hybridných modelov dynamických systémov.Modely možno navrhovať, načítať, ukladať, kompilovať a simulovať. Xcos je stabilné a výkonné riešenie priemyselných a akademických potrieb, poskytujúce funkcie na modelovanie mechanických systémov (automobilový a letecký priemysel, ...), hydraulických okruhov (priehrady, modelovanie rúr, ...), riadiacich systémov atď. Tiež obsahuje možnosti Modelica.

Minimálnu verziu Scilabu môžete nainštalovať z balíka „scilab-cli“.

The silx project aims at providing a collection of Python packages to support the development of data assessment, reduction and analysis applications at synchrotron radiation facilities. It aims at providing reading/writing different file formats, data reduction routines and a set of Qt widgets to browse and visualize data.

The current version provides :

• reading HDF5 file format (with support of SPEC file format)
• histogramming
• fitting
• 1D and 2D visualization using multiple backends (matplotlib or OpenGL)
• image plot widget with a set of associated tools (See changelog file).
• Unified browser for HDF5, SPEC and image file formats supporting inspection and visualization of n-dimensional datasets.
• Unified viewer (silx view filename) for HDF5, SPEC and image file formats
• OpenGL-based widget to display 3D scalar field with isosurface and cutting plane.

This uses the Python 3 version of the package.