![]() ![]() Plotting seismograms with increasing epicentral distance.Plotting track and trajectory of hurricanes on a topographic map.Plotting 1 arc-minute global relief map.Plotting the geospatial data clipped by coastlines.Pygmt: high-resolution topographic map in python.Three-dimensional perspective map of taiwan using gmt and pygmt.High-quality maps using the modern interface to the generic mapping tools.Non-linear curve fitting to a model with multiple observational variables.Monte carlo simulations to test for the correlation between two dataset. ![]() Locating earthquakes using geiger’s method.Numerical tests on travel time tomography.Simple wave modeling and hilbert transform in matlab.Introduction to the time series analysis.Introduction to the exploratory factor analysis.Estimation of the degrees of freedom for time series.Avoiding common mistakes in analyzing correlations of two time-series.Hypothesis test for the significance of linear trend.Easily integrate custom functions in matlab with python.The easy way to compute and visualize the time & frequency correlation.Monte carlo methods and earthquake location problem.Genetic algorithm: a highly robust inversion scheme for geophysical applications.Signal denoising using fourier analysis in python.Numerically solving initial value problems using the runge-kutta method.How effective is the signal denoising using the matlab based wavelet analysis.Numerical methods for scientific computation.Here's an improved loading, just in case # tell numpy the first 2 columns are int and the last 2 are floats The first 2 columns are supposed to be int and the last 2 ones are float. It's simple and regular, I would suggest testing on a more general one, if possible. Here's an example file, it's the same used for the scatter plot. How can get a decent 3D graph with minimal interpolation? Is there something like just linking the closest 3D points together?īy the way, my data is fairly regular, like they are organized as a set of 2D planes, or "slices", but I'd like to know if this is possible without making that assumption. And this is probably also true (for large values of "interpolation"). I was also told by some other people that interpolation is bad, because it forces a shape. I was told by some people that I absolutely need to interpolate to find a surface. I've read the doc about griddata, it says it returns aĢd float array - Array of values interpolated at (xi, yi) points. However, it "smooths" the curve, and interpolates to a regular set of points. I'm aware of this question explaining how to get a 3D surface out of irregular 3D data. I need to have a similar representation while elaborating my data as little as possible (to prevent distortions). There is a nice example in the gallery that draws a surface and the projection of contours (image below). My_data = np.genfromtxt(input_file, delimiter='\t', skiprows=0)Įrrors = my_data # 4th column (errors) Input_file = os.path.normpath('C:/Users/sturaroa/Documents/my_file.tsv') Let's keep the errors aside for the moment. Each line of this file has 3 coordinates and a standard deviation. I'm plotting a 3D scatter plot reading my values from a file. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |