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IPython

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  Analyzed about 2 hours 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]

47.6K lines of code

68 current contributors

about 17 hours since last commit

471 users on Open Hub

High Activity
4.70103
   
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matplotlib

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  No analysis available

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 code. [Less]

0 lines of code

165 current contributors

0 since last commit

292 users on Open Hub

Activity Not Available
4.6
   
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Mostly written in language not available
Licenses: BSD-3-Clause

h5py

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  Analyzed about 14 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]

17.7K lines of code

40 current contributors

3 days since last commit

11 users on Open Hub

Moderate Activity
0.0
 
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Reinteract

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  No analysis available

Reinteract is a system for interactive experimentation with python.

0 lines of code

0 current contributors

0 since last commit

4 users on Open Hub

Activity Not Available
5.0
 
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Mostly written in language not available
Licenses: No declared licenses

rootpy

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  Analyzed about 21 hours ago

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

19.2K lines of code

2 current contributors

over 5 years since last commit

3 users on Open Hub

Inactive
5.0
 
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numexpr

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  Analyzed about 14 hours 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]

7.77K lines of code

3 current contributors

16 days since last commit

3 users on Open Hub

Moderate Activity
0.0
 
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pyspread

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  No analysis available

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]

0 lines of code

3 current contributors

0 since last commit

3 users on Open Hub

Activity Not Available
3.0
   
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Mostly written in language not available
Licenses: gpl3

numba

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  No analysis available

Numba is an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators). Its goal is to seamlessly integrate with the Python scientific software stack and produce optimized native code, as well as integrate with native foreign languages.

0 lines of code

60 current contributors

0 since last commit

2 users on Open Hub

Activity Not Available
0.0
 
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Mostly written in language not available
Licenses: No declared licenses

theano

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  Analyzed about 10 hours 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]

155K lines of code

14 current contributors

3 months since last commit

2 users on Open Hub

Very Low Activity
0.0
 
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Visual

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  Analyzed about 23 hours ago

Visual is a 3D graphics module for Python with a friendly API.

139K lines of code

0 current contributors

over 11 years since last commit

2 users on Open Hub

Inactive
5.0
 
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