Modelica.Blocks.Continuous.Derivative x_dot(start=1) This provides an approximation of the derivative. I gave the x as an input and got x_dot.y as the derivative with no problems.

python,arrays,numpy,curve-fitting,derivative

x=np.array([6,3,5,2,1,4,9,7,8]) y=np.array([2,1,3,5,7,9,8,10,7]) sort_idx = np.argsort(x) y=y[sort_idx] x=x[sort_idx] minm=np.array([],dtype=int) maxm=np.array([],dtype=int) length = y.size i=0 while i < length-1: if i < length - 1: while i < length-1 and y[i+1] >= y[i]: i+=1 if i != 0 and i < length-1: maxm = np.append(maxm,i) i+=1 if i < length - 1:...

You should set your source (From Workspace) to output one sample per time instant. Currently I think all of your data is going out at the same instant. Your simin in "From workspace" should be similar to the following struct for your case. simin.time = 0:20; simin.signals.values = (0:5:100)'; simin.signals.dimensions...

One approach would be to store the previous two values lines in two variables, and use a third variable to store the line. So you could get the local minimum like this: awk 'prev!=""&&prev<=prev2&&prev<=$2{print line}{prev2=prev;prev=$2;line=$0}' file and the local maximum like this: awk 'prev!=""&&prev>=prev2&&prev>=$2{print line}{prev2=prev;prev=$2;line=$0}' file ...

Let's say that f[i,j] is the value at node (i,j), and h is the size of space step. You already know how to calculate second order derivatives of f, for example fxx[i,j] = (f[i+1,j]-2*f[i,j]+f[i-1,j])/h^2 fyy[i,j] = (f[i,j+1]-2*f[i,j]+f[i,j-1])/h^2 fxy[i,j] = (f[i+1,j+1]-f[i+1,j-1]-f[i-1,j+1]+f[i-1,j-1])/h^2 These are of second degree of accuracy, that is the...

I think you would probably need to check out what Sobel operator does here: http://en.wikipedia.org/wiki/Sobel_operator...

You can use the pracma library, such as: library(pracma) dummy <- function(x) { z <- x[1]; y <- x[2] rez <- (z^2)*(y^3) rez } grad(dummy, c(1,2)) [1] 16 12 hessian(dummy, c(1,2)) [,1] [,2] [1,] 16 24 [2,] 24 12 ...

matlab,image-processing,derivative

First, your grayscale image should be represented as a matrix, with entries corresponding to brightness. Then use numerical gradient twice, like this: I = [1 2 3 4 ; 6 4 2 2 ; 4 5 0 7 ; 2 4 3 1]; % image [Ix, Iy] = gradient(I); %...

See from mathworks.com/help/symbolic/subs.html that you substitute a list of variables in {}: syms f pz u f = u*pz^3 second_div = diff(f,pz,2); subs(second_div,{pz,u},[3,4]) which gives 72 as expected....

python,function,for-loop,derivative

These kind of syntax errors can be somewhat tricky to diagnose: File "path/to/my/test.py", line 20 return y ^ SyntaxError: invalid syntax Since you know that there's most likely nothing wrong with the line in question (there is nothing wrong with the return y) the next thing to do is look...

Assume you have a structure S, S.t is the time vector and S.I is the current vector in each time in S.t. (both should be in the same length N). Now, if you want to approximate the derivative: dt = diff(S.t); % dt is the time intervals length, dt is...

input,modelica,derivative,dymola,openmodelica

Typically, this happens when the input signal feeds directly into a quantity that must be continuous (e.g., where a discontinuity would cause an impulse). The way I deal with this situations is to put a high gain first order filter on the input. This ensures that the actual signal is...

The second derivatives are given by the Hessian matrix. Here is a Python implementation for ND arrays, that consists in applying the np.gradient twice and storing the output appropriately, import numpy as np def hessian(x): """ Calculate the hessian matrix with finite differences Parameters: - x : ndarray Returns: an...

python,numpy,signal-processing,data-fitting,derivative

Have a look at the Savitzky-Gollay filter for an efficient local polynomial fitting. It is implemented, for instance, in scipy.signal.savgol_filter. The derivative of the fitted polynomial can be obtained with the deriv=1 argument....

You do not need this while loop at all. The code below will give you the output you want; it finds all local minima and all local maxima and stores them in minm and maxm, respectively. Please note: When you apply this to large datasets, make sure to smooth the...

matlab,anonymous-function,derivative

Here's one solution. function df = der(f) if isa(f, 'cfit') || isa(f, 'sfit') df = @(x) differentiate(f, x); elseif isa(f, 'sym') || isa(f, 'function_handle') syms r F = sym(f); df = matlabFunction(diff(F), 'Vars', r); % These next four lines have been added: c = df(ones(1,2)); if length(c) == 1 df...

matlab,symbolic-math,derivative

Note: I'm using R2014b. Symbolic Math functionality has changed greatly in recent versions and continues to do so. Users on different versions may need to do slightly different things to achieve the results below, which relies on accessing undocumented functionality. First, since this is about performance, it is sufficient to...

Until recently, the derivative function in InfluxDB was broken. In the newest versions it works fine.

c,math,recursion,calculus,derivative

I would implement it like this: double derivative(double (*f)(double), double x0, int order) { const double delta = 1.0e-6; double x1 = x0 - delta; double x2 = x0 + delta; if (order == 1) { double y1 = f(x1); double y2 = f(x2); return (y2 - y1) / (x2...

The answer you requested from solve depends on the number of terms in the summation. You haven't specified that. If you don't know that, you can specify it by symbols. Change the second arguments of both sums from simply j to j= a..b. I did this, and then I got...