scientific-computing

I have a block matrix [A B C...] and a matrix D (all 2-dimensional). D has dimensions y-by-y, and A, B, C , etc are each z-by-y. Basically, what I want to compute is the matrix [D*(A'); D*(B'); D*(C');...] , where X ' refers to the transpose of X . However, I want to accomplish this without loops for speed considerations. I have been playing with the reshape command for several hours now, and I know how to use it in other cases, but this use case is different from the other ones and I cannot figure it out. I also would like to avoid using multi-dimensional matrices if at all possible. Honestly

I have a repeating signal that varies a little bit with each cycle of a process that repeats roughly every second, though the duration and the contents of each cycle vary from each other a little bit within some parameters. There are a thousand x,y coordinates for every second of my signal data. A small, but important, segment of the data within each cycle is corrupted, and I want to replace each corrupted segment with an upward facing parabola. For each data segment that needs to be replaced by the parabola, I have the x,y coordinates of three points. The vertex/minimum is one of those points

The gaussian_kde function in scipy.stats has a function evaluate that can returns the value of the PDF of an input point. I'm trying to use gaussian_kde to estimate the inverse CDF. The motivation is for generating Monte Carlo realizations of some input data whose statistical distribution is numerically estimated using KDE. Is there a method bound to gaussian_kde that serves this purpose? The example below shows how this should work for the case of a Gaussian distribution. First I show how to do the PDF calculation to set up the specific API I'm trying to achieve: import numpy as np from scipy

First of all: I am well aware that OpenCL does not magically make everything faster I am well aware that OpenCL has limitations So now to my question, i am used to do different scientific calculations using programming. Some of the things i work with is pretty intense in regards to the complexity and number of calculations. SO i was wondering, maybe i could speed things up bu using OpenCL. So, what i would love to hear from you all is answers to some of the following [bonus for links]: *What kind of calculations/algorithms/general problems is suitable for OpenCL *What is the general guidelines

I've currently got a project running on PiCloud that involves multiple iterations of an ODE Solver. Each iteration produces a NumPy array of about 30 rows and 1500 columns, with each iterations being appended to the bottom of the array of the previous results. Normally, I'd just let these fairly big arrays be returned by the function, hold them in memory and deal with them all at one. Except PiCloud has a fairly restrictive cap on the size of the data that can be out and out returned by a function, to keep down on transmission costs. Which is fine, except that means I'd have to launch

I am getting the error: Warning: invalid value encountered in log From Python and I believe the error is thrown by numpy (using version 1.5.0). However, since I am calling the "log" function in several places, I'm not sure where the error is coming from. Is there a way to get numpy to print the line number that generated this error? I assume the warning is caused by taking the log of a number that is small enough to be rounded to 0 or smaller (negative). Is that right? What is the usual origin of these warnings? Putting np.seterr(invalid='raise') in your code (before the errant log call) will