I have been using scipy.optimize.leastsq to fit some data. I would like to get some confidence intervals on these estimates so I look into the cov_x output but the documentation is very unclear as to what this is and how to get the covariance matrix for my parameters from this.
I am looking for how to resample a numpy array representing image data at a new size, preferably having a choice of the interpolation method (nearest, bilinear, etc.). I know there is
I have trouble properly understanding numpy.where() despite reading the doc, this post and this other post. Can someone provide step-by-step commented examples with 1D and 2D arrays? Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them … Read more
In R I can create the desired output by doing:
I am trying to filter a noisy heart rate signal with python. Because heart rates should never be above about 220 beats per minute, I want to filter out all noise above 220 bpm. I converted 220/minute into 3.66666666 Hertz and then converted that Hertz to rad/s to get 23.0383461 rad/sec.
This Q&A is intended as a canonical(-ish) concerning two-dimensional (and multi-dimensional) interpolation using scipy. There are often questions concerning the basic syntax of various multidimensional interpolation methods, I hope to set these straight too.
This question is intended to be a canonical duplicate target
I intend to fit a 2D Gaussian function to images showing a laser beam to get its parameters like FWHM and position. So far I tried to understand how to define a 2D Gaussian function in Python and how to pass x and y variables to it.
I would like to get the index of a 2 dimensional Numpy array that matches a row. For example, my array is this:
I recently discovered Conda after I was having trouble installing SciPy, specifically on a Heroku app that I am developing.