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IPython

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  Analyzed 11 months 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]

47.1K lines of code

68 current contributors

11 months since last commit

470 users on Open Hub

Activity Not Available
4.70103
   
0 Reviews
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Mostly written in Python
Licenses: BSD-3-Clause
Tags interactive library matplotlib notebook numpy parallelcomputing python scipy shell visualization

h5py

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  Analyzed 11 months 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]

17.6K lines of code

40 current contributors

11 months since last commit

11 users on Open Hub

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0.0
 
0 Reviews
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Mostly written in Python
Licenses: BSD-3-Clause
Tags binding cython h5 hdf hdf5 netcdf numerical numpy physics pyrex python science 3 more...

numexpr

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  Analyzed 11 months 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]

7.74K lines of code

3 current contributors

about 1 year since last commit

3 users on Open Hub

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0.0
 
0 Reviews
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Mostly written in C++
Licenses: mit
Tags numpy pytables python scipy

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
 
0 Reviews
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Mostly written in language not available
Licenses: No declared licenses
Tags jit_compile numpy python scipy

nitime

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  Analyzed 11 months 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]

9.7K lines of code

3 current contributors

about 1 year since last commit

1 users on Open Hub

Activity Not Available
4.0
   
0 Reviews
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Mostly written in Python
Licenses: bsd
Tags nipy numpy python scientific scipy signal-processing visualization

SciPy.in Conference

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

0 lines of code

0 current contributors

0 since last commit

1 users on Open Hub

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0.0
 
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Mostly written in language not available
Licenses: gpl3
Tags css django html javascript python scipy

SciPy Notebook

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  Analyzed 11 months 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 11 years since last commit

1 users on Open Hub

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0.0
 
0 Reviews
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Mostly written in C++
Licenses: gpl
Tags ide numpy python scipy

scientific-python-lectures

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  Analyzed 11 months ago

Lectures on scientific computing with python, as IPython notebooks.

234 lines of code

0 current contributors

about 1 year since last commit

1 users on Open Hub

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0.0
 
0 Reviews
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Mostly written in Python
Licenses: No declared licenses
Tags interactive ipython matplotlib numeric numpy python scientific scipy

SciExp2

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SciExp² (aka SciExp square or simply SciExp2) stands for Scientific Experiment Exploration, which contains a comprehensive framework for easing the workflow of creating, executing and evaluating experiments. The driving idea behind the provided abstractions is that of the need for quick and ... [More] effortless design-space exploration. That is, the definition and evaluation of experiments that are based on the permutation of different parameters in the design space. The framework is available in the form of Python modules which can be integrated into your own applications, but also comes with some interfaces to allow for quick writing without having to import a whole set of modules and instantiate objects of their corresponding classes. [Less]

0 lines of code

0 current contributors

0 since last commit

1 users on Open Hub

Activity Not Available
0.0
 
0 Reviews
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Mostly written in language not available
Licenses: gpl3_or_l...
Tags analysis design_space_exploration numpy science scipy statistics

scipy-lecture-notes

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  Analyzed 11 months ago

Tutorial material on the scientific Python ecosystem

8.75K lines of code

19 current contributors

12 months since last commit

1 users on Open Hub

Activity Not Available
0.0
 
0 Reviews
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Mostly written in Python
Licenses: No declared licenses
Tags matplotlib numeric numpy python scientific scikit scipy