Thanks so much!
On Wednesday, October 19, 2022 at 7:59:36 PM UTC+5:30 Emmanuel Charpentier
wrote:
> That can be done in Sage in a variety of ways . Here’s one :
>
> var("x, y")
> L = 3# Plotted function
> f =lambda x,y:cos(x)-2*y# Coloring# Colormap
> cm=colormaps["RdBu"]# We have to scale the colormap :# Range of values :
> possible shortcut via some analytical obviousnesses :# cmin = f(-3, 3).n()#
> cmax = f(0, -3)# Being dum and computing on grid :
> foo = [u for u in (-3,-2.97..3)]
> Mesh = [[f(u, v) for v in foo] for u in foo]
> cmin = min(flatten(Mesh))
> cmax = max(flatten(Mesh)
> crange = cmax-cmin
> cf = lambda x, y: f(x, y)/crange# Plot
> parametric_plot3d(lambda x, y, z:[x, y, f(x, y)], (-3, 3), (-3, 3),
> color=(cf, cm), aspect_ratio=[2, 2, 1])
>
> It turns out that plot3d(f, (-3, 3), (-3, 3), color=(cf, cm),
> aspect_ratio=[2, 2, 1]) *also* works, but the color= parameter is not
> documented as such for plot3d, only for parametric_plot3d.
>
> You may also try to play with list_plot3d, which I do not use as often.
> Lazy me…
>
> HTH,
>
> Le mercredi 19 octobre 2022 à 11:18:37 UTC+2, gauri...@gmail.com a écrit :
>
>> I want to create heat maps of a scalar field in SAGE. Here is equivalent
>> code in Matplotlib that does the job along with the resulting output. The
>> matplotlib code uses the .pcolormesh function. Is there an equivalent in
>> SAGE?
>>
>> The closest I can see are the functions that create contour plots as
>> given here
>> <https://doc.sagemath.org/html/en/reference/plotting/sage/plot/contour_plot.html>,
>>
>> but I don’t want the discrete bands that are created in those plots; for
>> contour plots that is of course natural because you want to see the shapes
>> of the individual level curves.
>>
>> However, I specifically want the continuous smearing as outputted by the
>> Matplotlib code below, which gives a “heat-map” of the 2D scalar function.
>>
>> import numpy as np
>> import matplotlib.pyplot as plt
>> import matplotlib.cm as cm
>>
>> fig,ax = plt.subplots()
>> L = 3
>> x = np.linspace(-L, L, 100)
>> y = np.linspace(-L, L, 100)
>> X, Y = np.meshgrid(x, y)
>> Z = np.cos(X) - 2*Y
>>
>> z_min, z_max = -np.abs(Z).max(), np.abs(Z).max()
>> c= ax.pcolormesh(X, Y, Z, cmap='RdBu', vmin=z_min, vmax=z_max)
>> fig.colorbar(c, ax=ax)
>>
>> ax.set_aspect(1.0)
>> ax.set_title("Heat map of scalar field",fontsize=20)
>> fig.set_size_inches((10,10))
>> plt.show()
>>
>>
>>
>> Thanks!
>>
>> Gaurish
>>
>>
>
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