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20 maj 2019 · 5. i'm looking for the best way to create a contour plot using a numpy meshgrid. I have excel data in columns simplyfied looking like this: The x and y values define a 2d plane with the length (x-Axis) of 7 values and depth (y-Axis) of 2 values.
Create a 2-D grid with uniformly spaced x-coordinates and y-coordinates in the interval [-2,2]. x = -2:0.25:2; y = x; [X,Y] = meshgrid(x); Evaluate and plot the function f ( x , y ) = x e - x 2 - y 2 over the 2-D grid.
meshgrid (..) builds matrices or 3-D hypermatrices by replicating input vectors along 2 or 3 dimensions. To create arrays of size [nx, ny] instead of [ny, nx], or with more than 3 dimensions, please use ndgrid(x, y) or ndgrid(x, y, z,..).
20 paź 2021 · You can plot any function as long as you can apply it to the coordinates of the elements in a matrix. Here’s another example of a two-dimensional function: Z = XX ** 2 + (YY - XX) * 2
The meshgrid() function does exactly what we want it to. It takes two 1D arrays that together specify a 2D grid and return two 2D arrays that specify the x and y grid values at all grid points. With those two arrays (that are the same size) we can now use array math to create our temperature array:
numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy') [source] #. Return a tuple of coordinate matrices from coordinate vectors. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.
matplotlib and NumPy offer some interesting mechanisms that make the visualization of a 2D scalar field convenient. In this recipe, we show a very simple way to visualize a 2D scalar field. How to do it... The numpy.meshgrid() function generates the samples from an explicit 2D function.
