values_grid_interp = griddata((r, theta), values, (r_matrix,theta_matrix),method='linear') # #-- Plot... --- fig, ax = plt.subplots(subplot_kw=dict(projection='polar')) ax.contourf(theta_grid, r_grid, values_grid_interp) plt.show() pdb.set_trace()...
x <- matrix(c(36,97,33,89,45,99,51,93,47,88),2,5) boxplot(x,medlty="blank", #medlty="blank"就是把四分位盒式图(箱图)的须须去掉 names=c("A","B","C","D","E"), col="pink", boxwex=0.35) abline(h=71,col="navy", lwd=2, lty=5) 表示数据在最大最小之间位置的线型...
However, the data I would like to visualise, a 360 degree water surface elevation plot, seems a bit more detailed and could really use the benefits of a contour plot. I'll create a new issue about this topic in the Javascript library as suggested by @emmanuelle. Link to the issue ...
Top right plot (Cartesian tricontourf) is fine, bottom right (polar tricontourf) isn't. In particular there is a triangular region without any contours around 180 degrees. The theta calculation using np.arctan2 returns values in the range -pi to +pi, and some of the contour polygons ...
Also, note that the contour maps show contours of the raw sPV fields with no filtering of small regions applied. Figure 5 Open in figure viewerPowerPoint As in Figure 4 but for the second half of the split-like events listed in Table 1. Thirty-eight events in the 38 years from 1979/...