data-fitting

I have an important question about the use of chi^2 test to constrain a parameter in cosmology. I appreciate your help. Please do not give this question negative rate (this question is important to me). Assume we have a data file ( data.txt ) concluding 600 data and this data file has 3 columns, first column is redshift(z), second column is observational dL(m_obs) and third column is error(err). As we know chi^2 function is chi^2=(m_obs-m_theo)**2/err**2 #chi^2=sigma((m_obs-m_theo)**2/err**2) from 1 to N=600 All of thing that we must calculate is putting z from given data file into our

I have a rather easy-to-understand question. I have a set of data and I want to estimate how good this data fit a standard normal distribution . To do so, I start with my code: [f_p,m_p] = hist(data,128); f_p = f_p/trapz(m_p,f_p); x_th = min(data):.001:max(data); y_th = normpdf(x_th,0,1); figure(1) bar(m_p,f_p) hold on plot(x_th,y_th,'r','LineWidth',2.5) grid on hold off Fig. 1 will look like the one below: Easy to see that the fit is quite poor, altough the bell-shape can be spotted. The main problem resides therefore in the variance of my data. To find out the proper number of occurrances my

I have the plot below and I want to fit it with 2 lines. Using python I manage to fit the upper part: def func(x,a,b): x=np.array(x) return a*(x**b) popt,pcov=curve_fit(func,up_x,up_y) And I want to fit the lower part with another line, but I want the line to pass through the point where the red one stars, so I can have a continuous function. So my question is how can I use curve_fit by giving a point the function has to pass through, but leaving the slope of the line to be calculated by python? (Or any other python package able to do it) A possible stepwise parametrisation of your model in

My question is pretty standard but can't find a solution of that. I have points=[x,y,z] and want to plot best fit line. I am using function given below (and Thanx Smith) % LS3DLINE.M Least-squares line in 3 dimensions. % % Version 1.0 % Last amended I M Smith 27 May 2002. % Created I M Smith 08 Mar 2002 % --------------------------------------------------------------------- % Input % X Array [x y z] where x = vector of x-coordinates, % y = vector of y-coordinates and z = vector of % z-coordinates. % Dimension: m x 3. % % Output % x0 Centroid of the data = point on the best-fit line. %

Intro In gnuplot there's a solution to create histogram from file named hist.dat what likes 1 2 2 2 3 by using commands binwidth=1 set boxwidth binwidth bin(x,width)=width*floor(x/width) + binwidth/2.0 plot [0:5][0:*] "hist.dat" u (bin($1,binwidth)):(1.0) smooth freq with boxes that generates a histogram like this one from other SO page . Question How can I fit my function to this histogram? I defined a Gaussian function and initialized its values by f(x) = a*exp(-((x-m)/s)**2) a=3; m=2.5; s=1 and in the output the function follow the histogram well. Unfortunatelly I cannot fit to this

Given that the fitting function is of type: I intend to fit such function to the experimental data (x,y=f(x)) that I have. But then I have some doubts: How do I define my fitting function when there's a summation involved? Once the function defined, i.e. def func(..) return ... is it still possible to use curve_fit from scipy.optimize? Because now there's a set of parameters s_i and r_i involved compared to the usual fitting cases where one has few single parameters. Finally are such cases treated completely differently? Feel a bit lost here, thanks for any help. This is very well within reach