Tags : Browse Projects

Select a tag to browse associated projects and drill deeper into the tag cloud.

IPython

Compare

  Analyzed 15 days ago

IPython: Productive Interactive Computing IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: - Powerful interactive Python shells (terminal-, Qt- and web-based). - Support for interactive data visualization and use of GUI ... [More] toolkits. - Flexible, embeddable interpreters to load into your own projects. - Tools for high level and interactive parallel computing. [Less]

27.5K lines of code

89 current contributors

about 1 month since last commit

450 users on Open Hub

High Activity
4.70408
   
I Use This

matplotlib

Compare

  Analyzed about 3 hours ago

matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shells (similar to matlab or mathematica), web application servers ... [More] , and six graphical user interface toolkits. It is possible to generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of codes. [Less]

240K lines of code

187 current contributors

3 days since last commit

262 users on Open Hub

Very High Activity
4.6
   
I Use This

h5py

Compare

  Analyzed about 2 hours ago

The h5py package is a Pythonic interface to the HDF5 binary data format. It lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets ... [More] can be stored in a single file, categorized and tagged however you want. H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. You can iterate over datasets in a file, or check out the .shape or .dtype attributes of datasets; you don't need to know anything special about HDF5 to get started. Best of all, the files you create are in a standard binary format you can exchange with other people, including those who use programs like IDL and MATLAB. [Less]

8.12K lines of code

18 current contributors

8 days since last commit

9 users on Open Hub

Moderate Activity
0.0
 
I Use This

DeVIDE

Compare

  Analyzed almost 2 years ago

DeVIDE, or the Delft Visualisation and Image processing Development Environment, is a cross-platform software framework for the rapid prototyping, testing and deployment of visualisation and image processing algorithms. The software was developed within the Visualisation group. DeVIDE's primary (and ... [More] currently only) front-end is a data-flow boxes-and-lines network editor. In this regard, it is very similar to AVS, OpenDX, Khoros or VISSION. DeVIDE integrates functionality from libraries such as VTK, ITK, GDCM, DCMTK, numpy and matplotlib. It is being very actively developed. [Less]

263K lines of code

0 current contributors

about 6 years since last commit

5 users on Open Hub

Activity Not Available
4.0
   
I Use This

InVesalius

Compare

  Analyzed over 1 year ago

InVesalius is a medical public software which aims to assist diagnosis and surgical planning. Based on a sequence of two dimensional (2D) DICOM images, acquired through computed tomography or magnetic resonance equipments, InVesalius software allows the reconstruction of virtual three dimensional ... [More] (3D) models, correspondent to the anatomical region of interest. Both volume rendering and 3D surface export to STL, OBJ and other file-formats are supported. The software has shown to be versatile, contributing with various fields, among which medicine, odontology, veterinary, archeology and engineering. InVesalius is multi-platform (GNU Linux, Windows and MacOS X) and internationalized (English, Portuguese, French and Spanish). [Less]

286K lines of code

4 current contributors

over 6 years since last commit

5 users on Open Hub

Activity Not Available
5.0
 
I Use This

Reinteract

Compare

  Analyzed 4 days ago

Reinteract is a system for interactive experimentation with python.

15.1K lines of code

0 current contributors

almost 6 years since last commit

4 users on Open Hub

Inactive
5.0
 
I Use This
Licenses: No declared licenses

rootpy

Compare

  Analyzed 3 days ago

A pythonic layer on top of the ROOT framework's PyROOT bindings.

19.2K lines of code

5 current contributors

5 months since last commit

4 users on Open Hub

Low Activity
5.0
 
I Use This

numexpr

Compare

  Analyzed almost 2 years ago

What it isThe numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C ... [More] and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations. Why It WorksThere are two extremes to array expression evaluation. Each binary operation can run separately over the array elements and return a temporary array. This is what NumPy does: 2*a + 3*b uses three temporary arrays as large as a or b. This strategy wastes memory (a problem if the arrays are large). It is also not a good use of CPU cache memory because the results of 2*a and 3*b will not be in cache for the final addition if the arrays are large. The other extreme is to loop over each element: for i in xrange(len(a)): c[i] = 2*a[i] + 3*b[i]This conserves memory and is good for the cache, but on each iteration Python must check the type of each operand and select the correct routine for each operation. All but the first such checks are wasted, as the input arrays are not changing. numexpr uses an in-between approach. Arrays are handled in chunks (the first pass uses 256 elements). As Python code, it looks something like this: for i in xrange(0, len(a), 256): r0 = a[i:i+256] r1 = b[i:i+256] multiply(r0, 2, r2) multiply(r1, 3, r3) add(r2, r3, r2) c[i:i+256] = r2The 3-argument form of add() stores the result in the third argument, instead of allocating a new array. This achieves a good balance between cache and branch prediction. The virtual machine is written entirely in C, which makes it faster than the Python above. For more info about numexpr, read the Numexpr's Overview written by the original author (David M. Cooke). Examples of UseUsing it is simple: >>> import numpy as np >>> import numexpr as ne >>> a = np.arange(1e6) # Choose large arrays for high performance >>> b = np.arange(1e6) >>> ne.evaluate("a + 1") # a simple expression array([ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, ..., 9.99998000e+05, 9.99999000e+05, 1.00000000e+06]) >>> ne.evaluate('a*b-4.1*a > 2.5*b') # a more complex one array([False, False, False, ..., True, True, True], dtype=bool)and fast... :-) >>> timeit a**2 + b**2 + 2*a*b 10 loops, best of 3: 33.3 ms per loop >>> timeit ne.evaluate("a**2 + b**2 + 2*a*b") 100 loops, best of 3: 7.96 ms per loop # 4.2x faster than NumPy [Less]

13.2K lines of code

19 current contributors

almost 2 years since last commit

3 users on Open Hub

Activity Not Available
0.0
 
I Use This

pyspread

Compare

  Analyzed 3 days ago

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 ... [More] cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Pyspread is designed for Linux and other GTK platforms. Windows is also supported. [Less]

26.2K lines of code

2 current contributors

3 months since last commit

3 users on Open Hub

Low Activity
3.0
   
I Use This

theano

Compare

  Analyzed about 1 hour ago

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: * tight integration with numpy – Use numpy.ndarray in Theano-compiled functions. * transparent use of a GPU – Perform ... [More] data-intensive calculations up to 140x faster than with CPU.(float32 only) * efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs. * speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny. * dynamic C code generation – Evaluate expressions faster. * extensive unit-testing and self-verification – Detect and diagnose many types of mistake. github repository: http://github.com/Theano/Theano [Less]

154K lines of code

89 current contributors

8 days since last commit

2 users on Open Hub

Very High Activity
0.0
 
I Use This