histogram

I have a set of data, and want to make an histogram of it. I need the bins to have the same size , by which I mean that they must contain the same number of objects, rather than the more common (numpy.histogram) problem of having equally spaced bins. This will naturally come at the expenses of the bins widths, which can - and in general will - be different. I will specify the number of desired bins and the data set, obtaining the bins edges in return. Example: data = numpy.array([1., 1.2, 1.3, 2.0, 2.1, 2.12]) bins_edges = somefunc(data, nbins=3) print(bins_edges) >> [1.,1.3,2.1,2.12] So the

How can I create a 3D histogram with R? For example, I have two variables to be counted for the number of times they fall in a defined two dimensional bin. So I have two variables in the X and Y axis, while the Z axis is the count of the two variables. cbeleites supports Monica have a look at package hexbin to calculate and display, or e.g. ggplot's stat_bin2d / stat_binhex for display. You get 2 spatial coordinates which is all your screen or paper can do plus a 3rd, colour-coded dimension. Note that How does one plot a 3D stacked histogram in R? is quite a duplicate of this question (but the

In Numpy 1.4.1, what is the simplest or most efficient way of calculating the histogram of a masked array? numpy.histogram and pyplot.hist do count the masked elements, by default! The only simple solution I can think of right now involves creating a new array with the non-masked value: histogram(m_arr[~m_arr.mask]) This is not very efficient, though, as this unnecessarily creates a new array. I'd be happy to read about better ideas! (Undeleting this as per discussion above...) I'm not sure whether or not the numpy developers would consider this a bug or expected behavior. I asked on the

I'm looking for a faster way to extract histogram data from an image. I'm currently using this piece of code that needs about 1200ms for a 6mpx JPEG image: ImageReader imageReader = (ImageReader) iter.next(); imageReader.setInput(is); BufferedImage image = imageReader.read(0); int height = image.getHeight(); int width = image.getWidth(); Raster raster = image.getRaster(); int[][] bins = new int[3][256]; for (int i = 0; i < width; i++) for (int j = 0; j < height; j++) { bins[0][raster.getSample(i, j, 0)]++; bins[1][raster.getSample(i, j, 1)]++; bins[2][raster.getSample(i, j, 2)]++; } Do you