Projects tagged ‘numpy’

IPython

Analyzed 1 day 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]

47.6K lines of code

68 current contributors

4 days since last commit

471 users on Open Hub

High Activity

0 Reviews

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Mostly written in Python

Licenses: BSD-3-Clause

matplotlib

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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]

0 lines of code

165 current contributors

0 since last commit

292 users on Open Hub

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Mostly written in language not available

Licenses: BSD-3-Clause

Tags c++ chart charting charts development graph graphics graphing interactive ipython library numerical 7 more...

h5py

Analyzed 1 day 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]

17.7K lines of code

40 current contributors

6 days since last commit

11 users on Open Hub

Moderate Activity

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...

Reinteract

R

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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

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Mostly written in language not available

Licenses: No declared licenses

Tags development editor extensible graphics interactive numpy programming pygtk python shell visualization

rootpy

Analyzed about 5 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

0 Reviews

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Mostly written in Python

Licenses: gpl3

Tags c++ cern data data_formats hdf5 library numpy physics python root scientific visualization

numexpr

N

Analyzed 1 day 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

14 days since last commit

3 users on Open Hub

Moderate Activity

0 Reviews

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Mostly written in C++

Licenses: mit

Tags numpy pytables python scipy

pyspread

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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]

0 lines of code

3 current contributors

0 since last commit

3 users on Open Hub

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Mostly written in language not available

Licenses: gpl3

Tags csv linux matplotlib numpy office python spreadsheet wxpython

numba

N

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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

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Mostly written in language not available

Licenses: No declared licenses

Tags jit_compile numpy python scipy

theano

T

Analyzed about 22 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]

155K lines of code

14 current contributors

3 months since last commit

2 users on Open Hub

Very Low Activity

0 Reviews

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Mostly written in Python

Licenses: BSD-3-Clause

Tags cas compiler numpy python symbolic

Visual

Analyzed about 21 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

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Mostly written in Python

Licenses: python_li...

Tags 3d numpy opengl python

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