If we add filtering as follows, import matplotlibįrom import gaussian_filterĬS = plt.contour(X, Y, gaussian_filter(Z, 5. Individual points dont show up on matplotlib if you dont specify their markersize and the marker. Z = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)Īnd try to pcolormesh/contour, plt.figure() Here: plt.plot (x (t, 1, 1), xprime (t, 1, 1)) Since t is one value and not a list because of your for loop, the X and y that you are trying to plot are just individual points. First, set up some dummy data (a Gaussian) and add noise, import matplotlib I've put together minimal example with Gaussian filter (and scipy) which I think looks like it may do what you want. But nothing I have tried thus far has created 4 simple contour lines atop my pcolormesh data. I have read through the contour demo, the API examples, the pcolormesh levels example, and this closely-related SO post (my data is already gridded, so the solution doesn't help). We will make use of some helper functions that will simplify plotting. using Plots using LinearAlgebra, ForwardDiff import PyPlot import Contour: contours, levels, level, lines, coordinates. That is, given a value for z, lines are drawn for connecting the (x,y) coordinates w. This covers plotting the typical 2D and 3D plots in Julia with the Plots package. ContourPlot has attribute HoldAll, and evaluates the f i and g i only after assigning specific numerical values to x and y. ContourPlot treats the variables x and y as local, effectively using Block. A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. At positions where f does not evaluate to a real number, holes are left so that the background to the contour plot shows through. When I plot only the output of pcolormesh, everything looks great. Answer (1 of 3): Thank you Samriddhi Sinha for the A2A. # Just pretend that the data are lying on the center of the gridĬont = plt.contour(x_grid,y_grid,h,4,colors='k',origin='lower') Pcm = plt.pcolormesh(x_grid,y_grid,h,cmap='jet') H = masked_log(h) # 'safe' log that replaces <0 elements by 0 in output Hist, X, Y = np.histogram2d(x, y, bins=nbins, weights=weights) I create the gridded data using import numpy as np I have some 2D data that I am displaying using pcolormesh that I would like to display a few contours on top of.

Countour plot vs parametir cplot