![]() ![]() The minimum of this plot is nothing, but the x position of the first minimum. What this line does is substitute min_y, when the second column (whose minimum we extracted before) is equal to the minimum, and an undefined value, 1/0, otherwise. Having done that, we retrieve the positions of the minimum and maximum, by calling a dummy plot on the columns Set label 2 'Maximum' at max_pos_x, max_y 0.3 centreįirst, we retrieve the values of the minimum and the maximum by using a dummy plot. Set label 1 'Minimum' at min_pos_x, min_y-0.3 centre ![]() Set arrow 2 from max_pos_x, max_y 0.2 to max_pos_x, max_y 0.02 lw 0.5 Set arrow 1 from min_pos_x, min_y-0.2 to min_pos_x, min_y-0.02 lw 0.5 # Automatically adding an arrow at a position that depends on the min/max The rest of the plot is trivial, and this script results in the following graph: Therefore, if we take the square root of the sum of residuals divided by the number of degrees of freedom plus one, we get the standard deviation. However, we know that the number of degrees of freedoms is one less, than the number of data points, for we fit a function with a single parameter. One of them is the sum of the residuals, which is called FIT_WSSR, while another is the number of degrees of freedom, FIT_NDF. What we utilise here is the fact that the fit function also sets a couple of variables. Mean_y w l lt 3, 'stats2.dat' u 1:2 w p pt 7 lt 1 ps 1 Mean_y stddev_y with filledcurves y1=mean_y lt 1 lc rgb "#bbbbdd", \ Plot mean_y-stddev_y with filledcurves y1=mean_y lt 1 lc rgb "#bbbbdd", \ Set label 2 gprintf("Standard deviation = %g", stddev_y) at 2, min_y-0.35 Set label 1 gprintf("Mean = %g", mean_y) at 2, min_y-0.2 # Plotting the range of standard deviation with a shaded background Stddev_y = sqrt(FIT_WSSR / (FIT_NDF 1 )) Easy enough, and in just a couple of lines, we created this figure In the plotting section, we produce three labels, that tell us something about the data set, and plot the data range with shaded region. You can convince yourself that this returns the average of the data set. This plot does nothing but fills in the values of the minimum and maximum of the data set. Max_y with filledcurves y1=mean_y lt 1 lc rgb "#bbddbb", \Īt the beginning of our script, we just produce some dummy data, and call a dummy plot. Plot min_y with filledcurves y1=mean_y lt 1 lc rgb "#bbbbdd", \ Set label 3 gprintf("Mean = %g", mean_y) at 2, max_y 0.35 Set label 2 gprintf("Maximum = %g", max_y) at 2, max_y 0.2 Set label 1 gprintf("Minimum = %g", min_y) at 2, min_y-0.2 If I just naively include a plot command (through fprintf), then the plot shows up for the first time after some 20 or 30 iterations (depending on the size of the lattice).# Plotting the minimum and maximum ranges with a shaded background ![]() ![]() Now, I want to visualize the matrix at every iteration, so for that, I need to call gnuplot (through the same pipe pointer) in every iteration, so that the existing plot is updated. This means the plot window does not show up at a specific time of your choice, and in fact shows up at the end of the program. However, I notice that the plot window appears only after the pipe is closed - presumably it is then that gnuplot is actually executed the first time, with all the arguments sent through the pipe. I then use a pipe to call gnuplot from within this program, to plot the file. The idea is to store the state of the lattice in a matrix, with each site storing the value 1 for spin up, and -1 for spin down.Ī computer program I've written in C with a friend generates a data file with the state of the matrix. I want to visualize the system at every iteration of the Monte Carlo loop. Before I compute any thermodynamic quantities though, I want to study the energetics of the system with only nearest-neighbour interactions. So I'm trying to solve the 2D Ising Model using a simple Monte Carlo algorithm, for small square lattices, imposing periodic boundary conditions. ![]()
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