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IPython

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  Analyzed 7 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]

27.4K lines of code

86 current contributors

7 days since last commit

450 users on Open Hub

High Activity
4.70408
   
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h5py

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  Analyzed 8 days 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]

8.04K lines of code

17 current contributors

12 days since last commit

9 users on Open Hub

Moderate Activity
0.0
 
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numexpr

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  Analyzed over 1 year 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

over 1 year since last commit

3 users on Open Hub

Activity Not Available
0.0
 
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numba

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  Analyzed 8 days ago

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.

118K lines of code

21 current contributors

13 days since last commit

2 users on Open Hub

Very High Activity
0.0
 
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Licenses: No declared licenses

SciPy.in Conference

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  Analyzed 21 days ago

Webapp that runs http://scipy.in for organizing SciPy India Conference.

8.64K lines of code

0 current contributors

almost 6 years since last commit

1 users on Open Hub

Inactive
0.0
 
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PyWavelets

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  Analyzed 13 days ago

PyWavelets is a free, open source Python package for computing various kinds of Wavelet Transforms: • Forward and Inverse Discrete Wavelet Transform (1D, 2D, nD) • Forward and Inverse Stationary Wavelet Transform (1D and 2D) • Wavelet Packets decomposition and reconstruction (1D and 2D) • ... [More] Approximation of wavelet and scaling functions • Many built-in wavelet filters and custom wavelets supported • Single and double precision calculations • Real and complex-valued calculations [Less]

11.2K lines of code

10 current contributors

13 days since last commit

1 users on Open Hub

Low Activity
0.0
 
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SciTools

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  Analyzed over 1 year ago

SciTools is a Python package containing lots of useful tools for scientific computing in Python. The package is built on top of other widely used packages such as NumPy, SciPy, ScientificPython, Gnuplot, etc. SciTools also comes with a plotting interface called Easyviz, which is a unified ... [More] interface to various packages for scientific visualization and plotting. Both curve plots and more advanced 2D/3D visualization of scalar and vector fields are supported. The Easyviz interface was designed with three ideas in mind: 1) a simple, Matlab-like syntax; 2) a unified interface to lots of visualization engines (backends): Gnuplot, Matplotlib, Grace, Veusz, Pmw.Blt.Graph, PyX, Matlab, VTK, VisIt, OpenDX; and 3) a minimalistic interface which offers only basic control of plots: curves, linestyles, [Less]

32K lines of code

0 current contributors

almost 3 years since last commit

1 users on Open Hub

Activity Not Available
5.0
 
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pyQPCR

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  Analyzed 7 months ago

pyQPCR is a GUI application written in python that deals with quantitative PCR (QPCR) raw data. Using quantification cycle values extracted from QPCR instruments, it uses a proven and universally applicable model to give finalized quantification resu

15.4K lines of code

0 current contributors

about 2 years since last commit

1 users on Open Hub

Activity Not Available
0.0
 
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nitime

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  Analyzed 4 days ago

Nitime is a library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level ... [More] interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. [Less]

9.94K lines of code

3 current contributors

29 days since last commit

1 users on Open Hub

Very Low Activity
4.0
   
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SciPy Notebook

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  Analyzed 9 days ago

SciPy Notebook is a notebook-style editor to hack Python with the comfort of an editor and the interactivity of a console.

2.71K lines of code

0 current contributors

about 4 years since last commit

1 users on Open Hub

Inactive
0.0
 
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