curve-fitting

This seems simple but I can't quite figure it out. I have a curve calculated from x,y data. Then I have a line. I want to find the x, y values for where the two intersect. Here is what I've got so far. It's super confusing and doesn't give the correct result. I can look at the graph and find the intersection x value and calculate the correct y value. I'd like to remove this human step. import numpy as np import matplotlib.pyplot as plt from pylab import * from scipy import linalg import sys import scipy.interpolate as interpolate import scipy.optimize as optimize w = np.array([0.0, 11

I am trying to figure out what it is I don't understand here. I am following http://www.scipy.org/Cookbook/FittingData and trying to fit a sine wave. The real problem is satellite magnetometer data which makes a nice sine wave on a spinning spacecraft. I created a dataset then am trying to fit it to recover the inputs. Here is my code: import numpy as np from scipy import optimize from scipy.optimize import curve_fit, leastsq import matplotlib.pyplot as plt class Parameter: def __init__(self, value): self.value = value def set(self, value): self.value = value def __call__(self): return self

I want to fit an exponential function y=x ** pw with a constant pw to fit through two datapoints. The scipy curve_fit function should optimise adj1 and adj2 . I have tried with the code below but couldn't get it to work. The curve does not go through the datapoints. How can I fix it? import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit def func(x, adj1,adj2): return np.round(((x+adj1) ** pw) * adj2, 2) x = [0.5,0.85] # two given datapoints to which the exponential function with power pw should fit y = [0.02,4] pw=15 popt, pcov = curve_fit(func, x, y) xf=np

I am new to R and I'm having some difficulty plotting an exponential curve using ggplot2. I have a set of data below. DATA X Y x y 1 0.6168111 37.20637 0.6168111 37.20637 2 0.5478698 24.17084 0.5478698 24.17084 3 0.6082697 26.21261 0.6082697 26.21261 4 0.6094899 26.14065 0.6094899 26.14065 5 0.6095040 38.56314 0.6095040 38.56314 6 0.6933108 36.78443 0.6933108 36.78443 7 0.5796637 27.82840 0.5796637 27.82840 8 0.4716866 30.63080 0.4716866 30.63080 9 0.5291792 29.78255 0.5291792 29.78255 10 1.2520000 33.12657 1.2520000 33.12657 11 1.2260000 31.81066 1.2260000 31.81066 12 1.2690000 59.91388 1

Original question I want to smooth my explanatory variable, something like Speed data of a vehicle, and then use this smoothed values. I searched a lot, and find nothing that directly is my answer. I know how to calculate the kernel density estimation ( density() or KernSmooth::bkde() ) but I don't know then how to calculate the smoothed values of speed. Re-edited question Thanks to @ZheyuanLi, I am able to better explain what I have and what I want to do. So I have re-edited my question as below. I have some speed measurement of a vehicle during a time, stored as a data frame vehicle : t

I have the option to add bounds to sio.curve_fit. Is there a way to expand upon this bounds feature that involves a function of the parameters? In other words, say I have an arbitrary function with two or more unknown constants. And then let's also say that I know the sum of all of these constants is less than 10. Is there a way I can implement this last constraint? import numpy as np import scipy.optimize as sio def f(x, a, b, c): return a*x**2 + b*x + c x = np.linspace(0, 100, 101) y = 2*x**2 + 3*x + 4 popt, pcov = sio.curve_fit(f, x, y, \ bounds = [(0, 0, 0), (10 - b - c, 10 - a - c, 10 - a

I am wondering if there is a class for fitting data to a sine curve. I found an algorithm for it here but it would take some time to first understand and then code... Before I go down that road I am wondering if the forum knows of a class that is already created for such mathematical modeling. CharlesB C# does not have a long tradition of scientific computing libraries... My take would be to get one of the numerous numerical computation routine packages (like on of them , or the Numerical Recipes) in C/C++, and wrap it under C#. See also this Math.SE post . On the commercial side, NMath