Natural neighbor interpolation is a method for interpolating scattered data (i. shape) == 1: # `curves. Performance Comparison: C# (ILNumerics), FORTRAN, MATLAB and numpy – Part II February 6, 2012 haymo 5 Comments In the first part of my somehow lengthy comparison between Fortran, ILNumerics, Matlab and numpy, I gave some categorization insight into terms related to ‘performance’ and ‘language’. In practice this means that numba code running on NumPy arrays will execute with a level of efficiency close to that of C. max(rtData). We use the numpy. You can vote up the examples you like or vote down the ones you don't like. python - Scipy interpolation on a numpy array; python - Linear interpolation on a numpy array; python - Apply numpy interpolation function with index array; python - numpy interpolation to increase array size; python - Fitting a 3D array of data to a 1D function with numpy or scipy; python - Efficient 1D linear regression for each element of 3D. Data manipulation with numpy: tips and tricks, part 1¶. Numpy & Scipy / Linear Algebra 14. Video Frame Interpolation using Generative Adversial Networks Pytorch ,numpy ,python. Return type: A numpy. NET Numerics. When delta is 1. However, the element type of an array can be object which permits storing anything in the array. Python Numpy Tutorial. What is the difference between them? Is one of them superior in terms of accuracy or performance?. The data consist of 3D magnetic. python - Scipy interpolation on a numpy array; python - Linear interpolation on a numpy array; python - Apply numpy interpolation function with index array; python - numpy interpolation to increase array size; python - Fitting a 3D array of data to a 1D function with numpy or scipy; python - Efficient 1D linear regression for each element of 3D. Linear interpolation allows us to improve an estimate based on a set of x- and y-values. 3D Plotting functions for numpy arrays ¶ Visualization can be created in mlab by a set of functions operating on numpy arrays. Notice how the image with bilinear interpolation is much smoother, especially in the pattern on the front arm. Return type: A numpy. Masked values in the data. How Interpolate Shape works. Interpolates a 3D feature class from an input 2D feature class and a surface with the overlapping extent. SciPy and NumPy Travis Oliphant SIAM 2011 Mar 2, 2011 2. I'm looking to take the value of a function at a grid of input points, and interpolate it at arbitrary output points. py from scipy. DataArray or numpy. In the previous article, a simple statistical model was created to generated different beach slopes on a daily basis. However, for certain areas such as linear algebra, we may instead want to use matrix. 8k 3 25 48 I apologize for writing to an old thread, but I think it worth the confusion. If not given use the mask of the data or if it has no mask either assume all the data is unmasked. interpn on 3 dimensional data on a given form. Some of the operations covered by this tutorial may be useful for other kinds of multidimensional array processing than image processing. {"categories":[{"categoryid":387,"name":"app-accessibility","summary":"The app-accessibility category contains packages which help with accessibility (for example. shadedrelief. Bilinear interpolation solves the problem of mapping a rectangular texture to an arbitrary quad, with a different set of trade-offs from the projective mapping we saw previously. Processing results is a transform sequence (3D displacement field changing in time) and motion-compensated volume sequence. GitHub Gist: instantly share code, notes, and snippets. splev(x_vals, splines) ("spline evaluate") – evaluate the spline data returned by splrep, and use it to estimate y values. Unfortunately it seems to be maintained by one person who has very poor. griddata The code below illustrates the different kinds of interpolation method available for scipy. The matrix rank will tell us that. 70 KB, 29 pages and we collected some download links, you can download this pdf book for free. comme la suggestion de numpy ci-dessus était trop longue, je pourrais attendre donc voici la version de cython pour référence future. DataArray or numpy. 3d interpolation between two xyz coordinates. vstack([numpy. I would like to interpolate this data layer by layer (in the plane X, Y) because calculating each layer takes a lot of time. Sequence registration: This extension registers a sequence of volumes (3D+t a. NumPy extends python into a high-level language for manipulating numerical data, similiar to MATLAB. ), and SciPy includes some of these interpolation forms. interpolate. These plots are informative, but sometimes a 3D plot (typically of the function's absolute value) gives a much better view. Elements where the mask is equivalent to 1 (also True) are interpreted as masked and are ignored during the interpolation. 私はPythonで3D bspline曲線を計算する必要があります。私はscipy. I want to get a uniform cloud of 3d points. It is often superior to linear barycentric interpolation, which is a commonly used method of interpolation provided by Scipy's griddata function. Vq = interp3(X,Y,Z,V,Xq,Yq,Zq) returns interpolated values of a function of three variables at specific query points using linear interpolation. (X,Y) instead of f(X,Y,Z), which is your 3D problem. I guess it's impossible, save for using the 3D Analyst Toolbar 'Interpolate' for every feature I create. Mlpy is a Python machine learning library built on top of NumPy/SciPy, the GNU Scientific Library. Many of the SciPy routines are Python "wrappers", that is, Python routines that provide a Python interface for numerical libraries and routines originally written in Fortran, C, or C++. PyRAF - Python interface to IRAF. dpi (scalar) – Resolution of saved images in dots per inch; k_parallel (numpy. Elements where the mask is equivalent to 1 (also True) are interpreted as masked and are ignored during the interpolation. As of version 0. The class is rather simple with only __init__() and __call__() implemented. 1D interpolation; 2D Interpolation (and above) Scope; Let's do it with Python; Neighbours and connectivity: Delaunay mesh; Nearest interpolation; Linear interpolation; Higher order interpolation; Comparison / Discussion; Tutorials; Traitement de signal; Image processing; Optimization. Data manipulation with numpy: tips and tricks, part 1¶. Discrete Sibson (Natural Neighbor) Interpolation. With Python using NumPy and SciPy you can read, extract information, modify, display, create and save image data. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and. amax() Python's numpy module provides a function to get the maximum value from a Numpy array i. The interpolated surface is smoother than corresponding surfaces obtained by bilinear interpolation or nearest-neighbor interpolation. The answer is, first you interpolate it to a regular grid. 3D position vector as a numpy. I've already used a splines to interpolate a missing simulated points. How can I solve a multidimensional interpolation problem? For example with MATLAB. mgrid); z0 corresponds to the true values for the coordinates; and f0, the function to define it (both, unknown in the practice). NumPy is the standard library for scientific computing with powerful tools to integrate with C and C++. In this tutorial, you will discover how to use Pandas in Python to both increase and decrease. GitHub Gist: instantly share code, notes, and snippets. resize() function Resizing an image can be done in many ways. interpolate) matplotlib is a python 2D/3D plotting library which:. NumPy or Numeric Python is a package for computation on homogenous n-dimensional arrays. On top of NumPy, another open-source library to Python is SciPy. An early demonstration suggests that 3D COUNTLESS may be as fast as about 4 Megavoxels/sec in Python/numpy, about 35x faster than 2D counting. atleast_2d numpy. This tutorial was contributed by Justin Johnson. Matplotlib provides basic 3D plotting in the mplot3d subpackage, whereas Mayavi provides a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine. broadcast numpy. However, the data I get is in the form of lists of different variables (x,y,z, temp, etc. interp(x, xp, fp, left=None, right=None, period=None) [source] One-dimensional linear interpolation. Interpolation on a regular grid in arbitrary dimensions. interpolation_order (int) – Splines of that order are used to interpolate. Two-dimensional interpolation with scipy. interpolate_with (other_tf, t) ¶ Interpolate with another rigid transformation. interpolate import interp1d from pylab import plot, axis, legend from numpy import linspace # sample values x = linspace(0,2*pi,6) y = sin(x) # Create a spline class for interpolation. Note that you can probably better use order=3 with a Catmull-Rom spline, which corresponds to the linear interpolation of the two quadratic polynomials. Day two introduces numeric data processing using NumPy arrays as well as displaying data as 2D and 3D plots. Reading and Writing a FITS File in Python. The returned tensor and ndarray share the same memory. interpolate. Vq = interp3(X,Y,Z,V,Xq,Yq,Zq) returns interpolated values of a function of three variables at specific query points using linear interpolation. X over and over again. linspace(0, 4, 12) y = np. For the polynomial fit, it is the higher order polynomials that sometimes causes the interpolation function to deviate quite far from the data. You may have observations at the wrong frequency. displacement : numpy array displacement vectors for each control point displacement is a NumPy array with displacement vectors. Scipy Interpolate RectBivariateSpline constructor DRF: Simple foreign key assignment with nested ser Filter query by type; Implement Curvilinear component analysis in Python periodic task using celery to delete a queryset re Stereo-Image and Depthmap to 3D-Scatterplot with P Random invalid syntax in python. vectorops module has functions for adding, subtracting, multiplying, normalizing, and interpolating. Interpolation by Splines KEY WORDS. Experienced NumPy users will have noticed some discrepancy between meshgrid and the mgrid, a function that is used just as often, for exactly the same purpose. Elements where the mask is equivalent to 1 (also True) are interpreted as masked and are ignored during the interpolation. 数時間後、私は一見容易な3D補間をPythonで行うことができないようです。 Matlabのでは、私がしなければならなかったすべては Vi = interp3(x,y,z,V,xi,yi,zi) この使用scipyのダウンロードのndimage. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. 02 Nov 2018 by pierre. 3D interpolation of large array. I've already used a splines to interpolate a missing simulated points. These plots are informative, but sometimes a 3D plot (typically of the function's absolute value) gives a much better view. append(lon, [lllon, urlon, lllon, urlon]) temp = np. With Python using NumPy and SciPy you can read, extract information, modify, display, create and save image data. When delta is 1. Following is an example. interpolate. My favorite short cut is to define a view method for DS9 objects. NumPy and Python List. 1D Spline Interpolation # demo/interpolate/spline. If you have a nice notebook you'd like to add here, or you'd like to make some other edits, please see the SciPy-CookBook repository. Instead, it is common to import under the briefer name np:. In mathematics, bicubic interpolation is an extension of cubic interpolation for interpolating data points on a two-dimensional regular grid. GitHub Gist: instantly share code, notes, and snippets. Data manipulation with numpy: tips and tricks, part 2¶More examples on fast manipulations with data using numpy. array) – an Nd array with the final dimension being size 3. nan values along the z dimension, and I just want the changes to modify my existing array. Who would do a Near using two cursors and a slow geometry function?. 3D Plotting functions for numpy arrays¶ Visualization can be created in mlab by a set of functions operating on numpy arrays. I would like to interpolate this data layer by layer (in the plane X, Y) because calculating each layer takes a lot of time. Bilinear interpolation solves the problem of mapping a rectangular texture to an arbitrary quad, with a different set of trade-offs from the projective mapping we saw previously. The window is centred on a certain calendar day, for example: - April 13th, we take the values for April 11th, April 12th, April 13th, April 14th and April 15th of each year of the base period. However, as shown in the figure grid, there is a problem of block merging after interpolation. com , which are amazing. Computational Physics - D. I guess it's impossible, save for using the 3D Analyst Toolbar 'Interpolate' for every feature I create. What is a Contour Plot A contour plot is a graphical technique which portrays a 3-dimensional surface in two dimensions. pyplot as plt from scipy import interpolate. I have a test array with dimension (3,3,3) with nan values. One of the easiest way to do this is to use basemap. Few post ago, we have seen how to use the function numpy. interpolate. DTIC Science & Technology. A 3-vector can also be represented by a Quaternion object who's scalar part is 0 and vector part is the required 3-vector. Lagrange interpolation in python. Numpy has special functions which can work with array Some 3D functionality integration, interpolation ± linear algebra, Fourier transforms, signal and image. Note that you can probably better use order=3 with a Catmull-Rom spline, which corresponds to the linear interpolation of the two quadratic polynomials. Each circle has a radius of $10$ pixels and their centers are spaced $50$ pixels. This section addresses basic image manipulation and processing using the core scientific modules NumPy and SciPy. #!/usr/bin/env python """Generate an image from ASCII data. griddata(points, values, xi, method='linear', fill_value=nan, rescale=False) When slicing the code, reading files in to numpy is not the culprit, but the griddata is. On the plus side, bilinear interpolation doesn't produce as much of a faux-3D effect, and it always maintains uniform UV spacing along the quad's edges. Hi everyone, I've looked through the list here and in Numpy-users, and checked the 'net but can't find an answer to this problem (with luck, I've. map function for fast interpolation for 2d (in fact for any dimensional) dataset. [Fwd: 3D interpolation over irregular data]. You may have observations at the wrong frequency. 14, 2018: In an upcoming article on COUNTLESS 3D, I will document speeds up to 24. base_repr numpy. interpolate is griddata, where you pass your points and values and interpolate in another set of points that you pass. In other objects (EArray, VLArray or Table) you can make use of the 'flavor' parameter in constructors to tell PyTables: "Hey, every time that I read from this object, please, return me an (rec)array with the appropriate flavor". mgrid); z0 corresponds to the true values for the coordinates; and f0, the function to define it (both, unknown in the practice). If input arrays are not one dimensional they will be internally flattened. One option available in scipy. Interpolation on a regular grid in arbitrary dimensions. In this code snippet, I present an implementation that creates per vertex normals from an indexed vertex array, and all without any loops. I'm trying to streamline a very large data collection workflow. In acubic splines interpolation, the input is the set of knots + first derivatives for each knot. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. The Scipy functions griddata and Rbf can both be used to interpolate randomly scattered n-dimensional data. As of version 0. 02 Nov 2018 by pierre. array), an n-dimensional array that is also present in some form in array-oriented languages such as Fortran 90, R, and MATLAB, as well as predecessors APL and J. DataFrame([1, 3, 4, np. = But there are=20 many differences. interpolate import interp1d from pylab import plot, axis, legend from numpy import linspace # sample values x = linspace(0,2*pi,6) y = sin(x) # Create a spline class for interpolation. map function for fast interpolation for 2d (in fact for any dimensional) dataset. pylab: A module to merge Matplotlib and NumPy together in an environment closer to MATLAB = pyplot+numpy Object-oriented way: The Pythonic way to interface with Matplotlib NOTE: The object-oriented is generally preferred for non-interactive plotting (i. totPts – total number of points on the global TIN surface. While many people can interpolate on an intuitive basis, the article below shows the formalized mathematical approach behind the intuition. For grayscale images (2D ndarray), set reps to (n vertical, n horizontal). [Fwd: 3D interpolation over irregular data]. Somehow I don' exactly get how the interpolation is taking place, so I don't know the correct way for the form of my input data. com Enthought, Inc. I am trying to use scipy. splrep(x_pts, y_pts)-returns a tuple representing the spline formulas needed scipy. pyds9 - Display numpy arrays in DS9. If the interpolation is 'none', then no interpolation is performed for the Agg, ps and pdf backends. Two commonly used approaches are linear interpolation and cubic splines. You'll need to examine the full size images to tell the differences. interpolate. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Rank and nullspace of a matrix 15. In this set of screencasts, we demonstrate methods to perform interpolation with the SciPy, the scientific computing library for Python. Welcome to a tutorial series, covering OpenCV, which is an image and video processing library with bindings in C++, C, Python, and Java. I'm trying to model a smooth path between several control points in three dimensions, the problem is that there doesn't appear to be an explanation on how to use splines to achieve this. reshape (1) if len (curves. 8, but not with later releases of either software. This library contains algorithms and mathematical tools to manipulate NumPy objects, with very definite scientific and engineering objectives. base_repr numpy. from a Python generator, list, or tuple (including list comprehensions, which are a very useful variant of the array-from-list method) using functions that are dedicated to generating numpy arrays, such as arange, linspace, etc. In the previous article, a simple statistical model was created to generated different beach slopes on a daily basis. Python3 + NumPy, SciPy, Matplotlib 2014年10月2日 櫻庭政夫 Python3 でグラフ描画や科学技術計算などの機能を利用することを目的に、Python 3. Vq = interp3(X,Y,Z,V,Xq,Yq,Zq) returns interpolated values of a function of three variables at specific query points using linear interpolation. In this code snippet, I present an implementation that creates per vertex normals from an indexed vertex array, and all without any loops. Python: Function Exploration with matplotlib. Data manipulation with numpy: tips and tricks, part 1¶. tile — NumPy v1. interp1d, but for 2-D arrays. If at least one of your data sets to be interpulated is on a grid, > you can use numpy. interpolate. shape[0]) corresponds to rows, while the second (camera. I am importing data from a tabbed. I then employ the same data to build a 2D interpolating function with the scipy module scipy. mgrid); z0 corresponds to the true values for the coordinates; and f0, the function to define it (both, unknown in the practice). Interpolation on a regular grid in arbitrary dimensions. This section addresses basic image manipulation and processing using the core scientific modules NumPy and SciPy. = But there are=20 many differences. That procedure works great and is very fast. interpolate(). vmtkScripts reference. This book provides numerous recipes in mastering common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib. This function uses the RIP [Read/Interpolate/plot] code to calculate potential energy (CAPE) and convective inhibition (CIN) [J kg-1] for every grid point in the entire 3D domain (treating each grid point as a parcel). DataArray or numpy. complex64 k2xx (k) ¶ Private: the inverse FFT and image cropping (which is the reverse of _xx2k() method) k2y (k) ¶ Private: interpolation by the Sparse Matrix-Vector. Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist that integrate with NumPy. One of these is Numeric. LinearNDInterpolator(). pyplot as plt #Define mapframe lllon = -11 lllat = 49 urlon = 2 urlat = 61 # Make some toy data, random points + corners n = 10 # no of stations lat = np. dpi (scalar) – Resolution of saved images in dots per inch; k_parallel (numpy. Day three surveys the scientific algorithms available in SciPy including interpolation, integration, linear algebra, signal/image processing, optimization, and others. Linear Interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points. Returns: DataFrame. Often plotting a simple map is enough, but if you want to go a bit beyond qualitative comparison then you have to interpolate data from one grid to another. Numpy and Scipy libraries − For image manipuation and processing. bitwise_xor numpy. Some of the algorithms available are nearest neighbors, splines, and a wrapper around a library called Qhull. For 2-D interpolation, where xq and yq specify an m-by-n grid of query points, vq is an m-by-n array. Theano has been powering large-scale computationally intensive scientific investigations since 2007. I am extremely frustrated because after several hours I can't seem to be able to do a seemingly easy 3D interpolation in python. py -s nx,ny -c nc -t 'image title' Data should be one ASCII float per line, in the following order:: z[x1,y1] z[x2,y1]. Many of the SciPy routines are Python "wrappers", that is, Python routines that provide a Python interface for numerical libraries and routines originally written in Fortran, C, or C++. PyRAF - Python interface to IRAF. If you have a nice notebook you'd like to add here, or you'd like to make some other edits, please see the SciPy-CookBook repository. INTER_LINEAR for all resizing purposes. I have a large dataset (e. python,list,numpy,multidimensional-array. And if I understood it right, you will interpolate between these vectors in time. Flexible Data Ingestion. , 70 x 500 x 500) and want to interpolate points (for example to double the size). GitHub Gist: instantly share code, notes, and snippets. SciPy is a Python library of mathematical routines. tile() is convenient when arranging the same image repeatedly. Two commonly used approaches are linear interpolation and cubic splines. Neville's Algorithm. This book provides numerous recipes in mastering common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib. NumPy User Guide. As of version 0. Interpolation is the process of finding a value between two points on a line or curve. interpolate. I am working on a face recognition using 3D data from a special 3D imaging system. Read this in as a numpy array using scipy. I don't have any particular preference as to how to do it,. They are extracted from open source Python projects. Note that numpy:rank does not give you the matrix rank, but rather the number of dimensions of the array. A simpler way is to use pandas and numpy: pd. The results always pass through the original sampling of the function. coordinates. In practice, this could mean you can infer new, estimated location points between known location points to either create higher frequency data, or to fill in missing values. Interpolation and Extrapolation in 2D in Python/v3 Learn how to interpolation and extrapolate data in two dimensions Note: this page is part of the documentation for version 3 of Plotly. Here I will show how to prepare your data and how to perform interpolation. But the examples were all using software I don't use, so I tried to do it with python. NumPy manual contents¶. A note on the time dimension ¶ Although scikit-image does not currently provide functions to work specifically with time-varying 3D data, its compatibility with NumPy arrays allows us to work quite naturally with a 5D array of the shape (t, pln, row, col, ch):. Available with 3D Analyst license. If this is the case, I would recommend yout o just install anaconda instead of futzing around trying to figure out what the problem is. Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist that integrate with NumPy. I would like to create a 2-d array (surface) from this data, each cell in the array being an interpolated value based on the nearby z values. interpolate. interpolation, polynomial interpolation, spline. This should never happen. I have extracted frames from both videos and frames. interpolate. With Python using NumPy and SciPy you can read, extract information, modify, display, create and save image data. (X,Y) instead of f(X,Y,Z), which is your 3D problem. pyds9 - Display numpy arrays in DS9. You're probably using python-xy. I want to get a uniform cloud of 3d points. window_width ¶. Interpolation by Splines KEY WORDS. array), an n-dimensional array that is also present in some form in array-oriented languages such as Fortran 90, R, and MATLAB, as well as predecessors APL and J. For those interested the data comes from the FRGC. But the examples were all using software I don't use, so I tried to do it with python. Thank you for the reply. yi = y1 + (y2-y1) * (xi-x1) / (x2-x1) With some vectorized Numpy expressions we can select the relevant points from the dataset and apply the above function:. displacement : numpy array displacement vectors for each control point displacement is a NumPy array with displacement vectors. Note that you can probably better use order=3 with a Catmull-Rom spline, which corresponds to the linear interpolation of the two quadratic polynomials. Understand what splines are Why the spline is introduced Approximating functions by splines We have seen in previous lecture that a function f(x) can be interpolated at n+1 points in an interval [a;b] using a single polynomial p n(x) de ned over the. Here are the examples of the python api numpy. python,list,numpy,multidimensional-array. Interpolation. When delta is 1. The chapters on NumPy have been using arrays (NumPy Array Basics A and NumPy Array Basics B). array), an n-dimensional array that is also present in some form in array-oriented languages such as Fortran 90, R, and MATLAB, as well as predecessors APL and J. Piecewise Polynomial Interpolation If the number of data points is large, then polynomial interpolation becomes problematic since high-degree interpolation yields oscillatory polynomials, when the data may t a smooth function. Numpy & Scipy / Matplotlib 15. Oliphant [email protected] pyplot as plt x = np. It includes a user guide, full reference documentation, a developer guide, meta information, and “NumPy Enhancement Proposals” (which include the NumPy Roadmap and detailed plans for major new features). bitwise_and numpy. griddata(points, values, xi, method='linear', fill_value=nan, rescale=False) When slicing the code, reading files in to numpy is not the culprit, but the griddata is. With Python using NumPy and SciPy you can read, extract information, modify, display, create and save image data. They are extracted from open source Python projects. from a Python generator, list, or tuple (including list comprehensions, which are a very useful variant of the array-from-list method) using functions that are dedicated to generating numpy arrays, such as arange, linspace, etc. Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful environment for scientific computing. SciPy and NumPy: An Overview for Developers-2010kaiser ~~~~~ Want to learn SciPy and NymPy quickly? Cut through the complexity of online documentation with this concise and illustrated book, and discover how easily you can get up to speed with these Python libraries. We will use the Python programming language for all assignments in this course. Evaluating it at any other point gives the linear interpolation between the points. MATLAB=C2=AE and NumPy/SciPy have a lot in common. The functions can be used with all geometry types (except GeometryCollection), and 3D types are either preserved or supported by 3D affine transformations. The returned tensor and ndarray share the same memory. I want to get a uniform cloud of 3d points. py, which is not the most recent version. NumPy or Numeric Python is a package for computation on homogenous n-dimensional arrays. Another predecessor of NumPy is Numarray, which is a complete rewrite of Numeric but is deprecated as well. I am extremely frustrated because after several hours I can't seem to be able to do a seemingly easy 3D interpolation in python. Linear and nearest-neighbour interpolation are supported. NumPy provides a compact, typed container for homogenous arrays of data. sparray : a python binding of the Sparse module - alpha stage. You can vote up the examples you like or vote down the ones you don't like.