Introducing Microsoft Research Podcast, intimate conversations with the researchers behind cutting edge discoveries and how they plan to change the world. And, Logical “and”. &&, “short circuit and” of Boolean expressions. Or, Logical “or”. , “short circuit or” of Boolean expressions. Not, Logical negation. Xor, Logical exclusive-or. ==>, Logical implication., Logical equivalence.

To correct some other statements: it is not correct that one can reuse all Matlab code in Octave or FreeMat. There are some classes of functions that are not well implemented at all in the other versions. I have large blocks of code that I have found it better to implement anew in environments that have approximately similar functionality for just these classes of functions.

Of the functionality that Matlab has that Octave does not, I have found surrogates in R, Python, and, to some extent, Java and C. Reimplementing libraries is harder than basic code. Pay attention to libraries. – Oct 20 '11 at 6:11 5.

Can you use R to replace MATLAB? I used MATLAB for years but switched primarily to R in the last 3 years. At this point, they have much more in common than not.

It partially depends on your field and use-case. And as, it also depends on which 'church you happen to frequent'. It's best if you look at the vs. For a specific task before you decide. A similar question and. (at the University of Maine) maintains an extensive, and is the best reference on the subject. You can also review.

Here are some of the things that I've observed in the past, none of which should be deal-breakers. • Generally, MATLAB has a better programming environment (e.g. Better documentation, better debuggers, better object browser) and is 'easier' to use (you can use MATLAB without doing any programming if you want). Allows you to visually program by connecting blocks in graphs.

By providing a better IDE with improved debugging, but it's still a step behind. • MATLAB is a little faster with the normal configuration (), although there are things that can be done to improve R performance if that becomes an issue.

• Since it's commercial, it also arguably has more 'products' (in the sense of integrated add-ons) and support (but you pay for it). For instance, it has things like which creates executable MATLAB programs that can be deployed. • So far as packages/toolkits are concerned, MATLAB has much more support for the physical sciences while R is stronger for statistics, which is not to say that the other can't perform these tasks. And they can both be easily extended. So, if ease-of-use isn't a primary concern (and there's no other business reason to avoid using an open-source tool), then I think that there's a real case to be made for using R.

It has a very strong community around it (the R mailing lists are amazing), is rapidly developing (see CRAN), and it's free (which isn't a small issue!). Edit: I would just add one further point to this: the book includes a chapter on the 'Essential Comparisons of the Matlab and R Languages'.

This covers some important syntax differences (such as the interpretation of a dot, or the meaning of square brackets []). The book itself is well worth reading for anyone interested in functional programming (in either language). R is an environment for statistical data analysis and graphics. MATLAB's origins are in numerical computation. The basic language implementations have many features in common if you use them for for data manipulation (e.g., matrix/vector operations).

R has statistical functionality hard to find elsewhere (>2000 Packages on ), and lots of statisticians use it. On the other hand, MATLAB has lots of (expensive) toolboxes for engineering applications like • image processing/image acquisition, • filter design, • fuzzy logic/fuzzy control, • partial differential equations, • etc. I have used both R and MATLAB to solve problems and construct models related to Environmental Engineering and there is a lot of overlap between the two systems. In my opinion, the advantages of MATLAB lie in specialized domain-specific applications. Some examples are: • Functions such as streamline that aid in fluid dynamics investigations. • Toolboxes such as the image processing toolset. I have not found a R package that provides an equivalent implementation of tools like the watershed algorithm.

In my opinion MATLAB provides far better interactive graphics capabilities. However, I think R produces better static print-quality graphics, depending on the application. MATLAB's symbolic math toolbox is also better integrated and more capable than R equivalents such as Ryacas or rSymPy.

The existence of the MATLAB compiler also allows systems based on MATLAB code to be deployed independently of the MATLAB environment-- although it's availability will depend on how much money you have to throw around. Another thing I should note is that the MATLAB debugger is one of the best I have worked with.

The principle advantage I see with R is the openness of the system and the ease with which it can be extended. This has resulted in an incredible diversity of packages on CRAN. I know Mathworks also maintains a repository of user-contributed toolboxes and I can't make a fair comparison as I have not used it that much.

The openness of R also extends to linking in compiled code. A while back I had a model written in Fortran and I was trying to decide between using R or MATLAB as a front-end to help prepare input and process results. I spent an hour reading about the MEX interface to compiled code. When I found that I would have to write and maintain a separate Fortran routine that did some intricate pointer juggling in order to manage the interface, I shelved MATLAB. The R interface consists of calling.Fortran( [subroutine name], [argument list]) and is simply quicker and cleaner.

In my experience moving from MATLAB to Python is an easier transition - Python with is closer to MATLAB in terms of style and features than R. There are also open source direct MATLAB clones and. There is certainly much that MATLAB can do that R can't - in my area MATLAB is used a lot for real time data aquisition - most hardware companies include MATLAB interfaces.

While this may be possible with R I imagine it would be a lot more involved. Also Simulink provides a whole area of functionality which I think is missing from R. I'm sure there is more but I'm not so familiar with R. Short answer: no, of course not.

While any set of mathematical software packages will have their overlaps, they will always have biases towards certain problem domains. These biases figure strongly in whether or not you want to use one of these packages. An example of what MATLAB can do that R cannot is interface to real-time hardware for signal processing/acquisition and control.

A model in MATLAB can be configured both to run in simulation on your machine before compiling the code to execute on a real system taking measured data as input and calculating appropriate outputs (what was before a simulation of a control system is now a fully functioning one). With the appropriate hardware board in your machine, you can run real-time control systems through a PC. R, by contrast, seems firmly set in the role of statistics, where I'm sure it out-performs what MATLAB can do. Similarly, is better than MATLAB at symbolic maths; Python is better than MATLAB at general programming; is better than all of them at actually creating graphs (er, I assume); and so on. As a user of both MATLAB and R, I think they are very different applications.

I myself have a background in computer science, etc. Brother S Keeper 6 4 Cracked. And I can't help thinking that R is by statisticians for statisticians whereas MATLAB is by programmers for programmers. R makes it very easy to visualize and compute all sorts of statistical stuff but I wouldn't use it to implement anything signal processing related if it was up to me. To sum up, if you want to do statistics, use R.

If you want to program, use MATLAB or some programming language.

Index of Packages Matching 'numpy' Package Weight* Description 12 NumPy: array processing for numbers, strings, records, and objects. 9 Extract image data into a 3D numpy array from a set of DICOM files. 9 Application for Django projects that adds some utilities and integration tools with Numpy.

9 A Python package which provides tools to convert files to and from IDX format (described at into numpy.ndarray. 9 Numpy extensions for set operations on nd-arrays, group_by operations, and related functionality 9 Missing NumPy functionalities 9 Ring buffer implementation for numpy 9 Sphinx extension to support docstrings in Numpy format 9 A Python utility library for working with expanded NumPy arrays. 8 A GPU-ready drop-in replacement for numpy 8 NumPy: array processing for numbers, strings, records, and objects. 8 Formats numpy matrices in an IPython Notebook 7 Gnumpy is a simple Python module that interfaces in a way almost identical to numpy, but does its computations on your computer's GPU, using Cudamat. 7 Toolbox for working with Numpy arrays.

7 NumPy: array processing for numbers, strings, records, and objects. 7 Numpy data serialization using msgpack 7 Nose plugin to set how floating-point errors are handled by numpy 7 more-reasonable core functionality for numpy 7 The interface between PYTHIA and NumPy 7 numpy recipe for python-for-android 7 The interface between ROOT and NumPy 7 Python functions for reading TOPAS result files 7 Wrapper of Numpy in Python3.

7 numpy array over zmq sockets 6 Module for converting directly from BSON to NumPy ndarrays and vice versa 6 Mapchete NumPy read/write extension 6 Utility functions for numpy, written in cython 6 A lightweight, pure Python, numpy compliant ndarray class 5 This project enables quering the Application Insights Analytics API while parsing the results for furthur processing using data analysis tools, such as numpy 5 Python package for splitting arrays into sub-arrays (i.e. Visual Assist 10 9 Keygen Download. Rectangular-tiling and rectangular-domain-decomposition), similar to ``numpy.array_split``. 5 Write a (list of) NumPy array(s) to an (animated) GIF.

5 numpy array with labeled axes 5 A C++ and NumPy-compatible Python API for acquiring, processing and encoding video streams in real time 5 Save and load numpy arrays as PNG images 5 Numpy and nd4j interop 5 Alternative approach to defining and viewing NumPy's arrays 5 Implementation of the Observer pattern for NumPy arrays 5 Efficient python (NumPy) neural network library. 5 Streaming operations on NumPy arrays 5 numpy-based multimedia library 5 Write numpy array(s) to a PNG or animated PNG file. 5 Library to make reading, writing and modifying both binary and ascii STL files easy. 5 5 A tiny bit of NumPy for Transcrypt using JavaScript typed arrays 5 Numpy extension module for efficient search of first array index that compares true 5 BMP image handler for Python (to numpy ndarray or PIL) native C.pyd 5 Easily create 1D and 2D films of NumPy arrays. 5 XPM image file loader for Python (to numpy ndarray or PIL) native C.pyd 5 Conversion between QImages and numpy.ndarrays. 5 A pickleable wrapper for sharing NumPy ndarrays between processes using POSIX shared memory. 5 Share numpy arrays between processes 5 Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

5 A Python module for reading and writing WAV files using numpy arrays. 5 Out-of-core NumPy arrays 5 Python Matplotlib, Numpy library to manage wind data, draw windrose (also known as a polar rose plot) 4 A fast python graph library based on numpy and scipy.

4 Lazy evaluation to treat functions as numpy arrays. 4 Generate country flags with numpy and pandas 4 cython cdef-class to facilitate numpy-arrays as attributes 4 NumPy arrays with named axes and named indices. 4 Create easy-to-use Query objects that can apply on NumPy structured arrays, astropy Table, and Pandas DataFrame. 4 Package used for reading hyperspectral captures 4 Light weight numpy, scipy, and matplotlib wrapper 4 Adds support for generating datetime to Hypothesis 4 Subclass of numpy.matrix behaving as matrices in matlab.

4 Common 2-D NumPy operations. 4 4 Optimised tools for group-indexing operations: aggregated sum and more. 4 A very lightweight implementation of distributed arrays 4 Python and numpy port of Nicholas Higham's m*lab test matrices 4 Visualizes 3d NumPy arrays using Matplotlib and PyQt4. 3 Return Numpy arrays as formatted LaTeX arrays. 3 Fast Dot Products on Pretty Big Data (powered by Bcolz) 3 Bohrium NumPy 3 A non-distributed numpy-based analysis module focusing on the manipulation, grouping and filtering of data from various sources. Bops also has map-reduce functionality. 3 Fast NumPy array functions written in Cython 3 Fast NumPy array functions written in C 3 Library for converting python numpy datastructures to the ROOT output format.

3 Compute B-spline basis functions via Cox - de Boor algorithm. 3 2d color plotting tool 3 A minmax implementation in Cython for use with NumPy 3 Utility for exploring the dataset 3 A library to provide Table data constructs over SQLite Databases 3 decimalpy - A Decimal based version of numpy 3 A port of the Dual-Tree Complex Wavelet Transform MATLAB toolbox. 3 Cython interface between the numpy arrays and the Matrix/Array classes of the Eigen C++ library 3 The interface between FastJet and NumPy 3 What's the fastest way to sum a NumPy array?

3 FFVideo is a python extension makes possible to access to decoded frames at two format: PIL.Image or numpy.ndarray. 3 A parallel and scalable library for matrix operations 3 ftz: flush denormal numbers in numpy arrays to zero 3 Numpy-based vectorized geospatial functions 3 GH Python Remote is a package to get Rhinoceros3D/Grasshopper and Python to collaborate better: connect an external python instance to Grasshopper, and vice-versa. 3 automatically load multi-dimensional Tiff and Gif files and file sequences as numpy arrays using PIL 3 A scientific image viewer and toolkit 3 Fast, scalable & beautiful scientific visualisation 3 Gnuplot-based plotting for numpy 3 HDF REST Server 3 2D particle playground 3 Extra features for Python's JSON: comments, order, numpy, pandas, datetimes, and many more! Simple but customizable. 3 Label the rows, columns, any dimension, of your NumPy arrays. 3 a Python package that provides a lazily-evaluated numerical array class, larray, based on and compatible with NumPy arrays. 3 python class that implements a general least-squares fit of a linear model using numpy matrix inversion 3 A wrapper for numpy arrays providing named axes, interpolation, iteration, disk persistence and numerical calcs 3 Statistical functions, goodness-of-fit tests and special and special distributions not implemented in scipy/numpy.

3 Pure NumPy practice with third-party operator integration. 3 MKL-based FFT transforms for NumPy arrays 3 Monary performs high-performance column queries on MongoDB 3 Convenience wrappers around numpy histograms 3 A multiple-tau algorithm for Python/NumPy. 3 Multivariate Polynomial fitting with NumPy 3 A package for science using numpy, matplotlib, readthedocs, etc. 3 A package for science using numpy, matplotlib, readthedocs, etc.

3 NamedMatrix, a numpy matrix wrapper class. 3 Library to parse Nanonis files. 3 Fast, discrete natural neighbor interpolation in 3D on a CPU. 3 Tools for numpy ndarray 3 Simple and powerfull neural network library for python 3 np = numpy++: numpy with added convenience functionality 3 A Python package for working with NumPy arrays and ctypes arrays. 3 Deep Learning Library based on pure Numpy 3 nphelper - convenient numpy helper functions 3 A HUSL color space conversion library that works with numpy 3 Enhancements to Numpy 3 Cross-platform, NumPy based module for reading TDMS files produced by LabView.

3 numbyte - numerical bytearray - c++ numerical buffer interface extending bytearray into numpy-like, 2d array 3 Numcube extends the functionality of numpy multidimensional arrays by adding named and annotated axes. 3 An alternative OpenCV wrapper 3 Utility library for detecting and removing outliers from normally distributed datasets 3 A Python package extending NumPy and SciPy to allow specification of numbers and arrays with physical units. 3 Extra functions built on NumPy, SciPy, pandas, matplotlib, etc. 3 PyContracts is a Python package that allows to declare constraints on function parameters and return values. Contracts can be specified using Python3 annotations, in a decorator, or inside a docstring:type: and:rtype: tags.

PyContracts supports a basic type system, variables binding, arithmetic constraints, and has several specialized contracts (notably for Numpy arrays), as well as an extension API. 3 Pure Numpy Implementation of the Coherent Point Drift Algorithm 3 A fast and flexible numpy-based wrapper for CPLex's Optimization Suite. 3 Python wrapper for CULAtools 3 A DataFrame (table like datastructure) for Python, similar to R's dataframe based on numpy arrays 3 Allow numpy arrays as source of texture data for pyglet. 3 Pure Python geodesy tools 3 Python interface to the NCSA HDF4 library 3 it allows you to use ipopt (an optimization tool) via python 3 The interface between FastJet and NumPy 3 small nogil-compatible linear equation system solver 3 PyOpenCV - A Python wrapper for OpenCV 2.x using Boost.Python and NumPy 3 Python Solver via Sympy + SciPy/NumPy for Stochastic Differential Equations! 3 f2py and numpy based wrappers for SLALIB 3 An audio library based on PortAudio, CFFI and NumPy 3 An audio library based on libsndfile, CFFI and NumPy 3 A Python library to write a table in various formats: CSV / Elasticsearch / HTML / JavaScript / JSON / Jupyter Notebook / LaTeX / LTSV / Markdown / MediaWiki / NumPy / Excel / Pandas / Python / reStructuredText / SQLite / TOML / TSV. 3 Python-HDF4: Python interface to the NCSA HDF4 library. 3 Numpy/Scipy implementations of state-of-the-art image thresholding algorithms 3 Seamless Numpy-UBlas interoperability 3 Quantum Hilbert Space Tensors in Python and Sage 3 Support for physical quantities with units, based on numpy 3 ctypes utilities for faster and easier simulation programming in C and Python 3 Fast and direct raster I/O for use with Numpy and SciPy 3 Pure Python implementation of the Ramer-Douglas-Peucker algorithm 3 Python/NumPy implementation of IDL's rebin function.