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Python - Smooth scatter plot

I have followed the post here in order to smooth a 3D scatter plot I have.
My original scatter plot is,
And I would like to get a smooth plot like the following one, that was made using Mathematica,
In the post I mentioned, they use the trisurf function to get a smoother plot. So I though I could use the same to get a similar plot. However, what I get is
As you can see, the triangulation did not work properly. And I don't know how to fix it.
Does anybody know a way to fix this problem? Or is there any other function I could use to smooth my scatter plot?
I think I should mention that my scatter plot is NOT a surface, it is a volume.
Thank you.
Just to clarify this, I post my codes for the original and the trisurf plot eventhough there isn't much to see.
Scatter plot:
S=pd.read_csv("SeparableStatesGrafica.csv",header=None,names=
['P0','P1','P2','P3','P4'])
G=plt.figure().gca(projection='3d')
G.scatter(S['P1'], S['P3'], S['P0'],color='red')
G.set_xlabel("P1")
G.set_ylabel("P3")
G.set_zlabel("P0")
G.view_init(40,40)
plt.show()
Trisurf plot:
S=pd.read_csv("SeparableStatesGrafica.csv",header=None,names=
['P0','P1','P2','P3','P4'])
p0=S['P0'].values
p1=S['P1'].values
p3=S['P3'].values
fig = pylab.figure(figsize=pyplot.figaspect(.96))
ax = Axes3D(fig)
ax.plot_trisurf(p1, p3, p0)
ax.set_xlabel("p1")
ax.set_ylabel("p3")
ax.set_zlabel("p0")
ax.view_init(40,40)
plt.show()

uneven axis when using pl.imshow

I am having trouble when plotting an image using pylabs imshow. Well there is no problem while plotting but my data is uneven (approx. 32*850) so when I plot it, the y axis is very short compared to the x-axis and you can see an example here example image. I just want the image to be stretched out in the y-axis so it is easier to see.
The code I started with(excluded labels and so on) is:
pl.figure()
pl.imshow(fom_data, interpolation='nearest')
pl.show()
And after googling it I tried changing to
pl.figure(figsize=(6,10))
Which only made the white parts around it larger. I then tried to write it with pyplot instead since it was easier to find people discussing the same thing:
fig, ax = plt.imshow(fom_data,extent=[0,850,0,32],aspect='auto')
plt.show()
As I found in this example: Imshow: extent and aspect but then get the following error message : 'AxesImage' object is not iterable
I am obiusly no pro, but if you know where my brain is going wrong please explain.

Using pyplot:
plt.figure()
plt.imshow(my_image)
plt.axes().set_aspect(aspect="auto") # grab the current axes to set their aspect

Changing center of rotation in a Matplotlib 3D plot

I have a 3D line plot in matplotlib, created using the following code:
def view(self):
from mpl_toolkits.mplot3d import Axes3D #noqa
import matplotlib.pyplot as plt
ax = plt.figure().gca(projection='3d')
history = np.array(self.position_history)
x, y ,z = history[:, 0], history[:, 1], history[:, 2]
ax.plot(x, y, z)
plt.show()
Here history is an Mx3 array of points. This works fine and pops up a plot as expected. I am able to click and drag to modify the azimuth and elevation interactively. I am able to zoom by right-clicking and dragging.
However I am wondering if it is possible to modify the center point of the pan and zoom? I would like to zoom into the top-right, and then pan around with the top right as my center of rotation. If you have ever used solidworks or another CAD program, this is the behavior I am after. Is this doable? If not interactively, can I do it programmatically?
And finally, if none of this is possible in matplotlib, is there another library that can accomplish what I want?

I have also run into trouble in the past in terms of customizing mplot3d, rather unsuccessfully..
And finally, if none of this is possible in matplotlib, is there another library that can accomplish what I want?
you can do this with mayavi
here is a relevant stackoverflow answer for customizing how you interact with your plot
there are also various useful tips and tricks for animating in general and for using mayavi
(apologies if this isn't useful)

Not sure this will do what you need but you can define the center when you first plot. Here, "c_x" is the center of rotation on the x axis and the -/+ 200 defines the axis to be 200 units in both direction. Likewise for y and z.
If you had the user choose the center before plotting, that could work as a crude work around.
http://matplotlib.org/api/axes_api.html?highlight=set_xbound#matplotlib.axes.Axes.set_xbound
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.autoscale(enable=False,axis='both') #you will need this line to change the Z-axis
ax.set_xbound(c_x-200, c_x+200)
ax.set_ybound(c_y-200, c_y+200)
ax.set_zbound(c_z-200, c_z+200)

mayavi mlab get current axes

Is there a way of a procedure similar to plt.gca() to get a handle to the current axes. I first do a=mlab.surf(x, y, u2,warp_scale='auto')
and then
b=mlab.plot3d(yy, yy, (yy-40)**2 ,tube_radius=20.0)
but the origin of a and b are different and the plot looks incorrect. So I want to put b into the axes of a
In short, what would be the best way in mayavi to draw a surface and a line on same axes?

What you are expecting to be able to do from your matplotlib experience is not how mayavi axes work. In matplotlib the visualization is a child of the axes and the axes determines its coordinates. In mayavi or vtk, visualization sources consist of points in space. Axes are objects that surround a source and provide tick markings of the coordinate extent of those objects, that are not necessary for the visualizations, and where they exist they are children of sources.

You should remove the argument warp_scale='auto'
a=mlab.surf(x, y, u2)
and it will work.

Plotting points in python

I want to plot some (x,y) points on the same graph and I don't need any special features at all short of support for polar coordinates which would be nice but not necessary. It's mostly for visualizing my data. Is there a simple way to do this? Matplotlib seems like way more than I need right now. Are there any more basic modules available? What do You recommend?

Go with matplotlib Chance is that sometime in the future you might need to do more than just "simple" stuff and then you don't need to invest time learning a new plot-tool.
See this link for list of plotting tools for python...

Absolutely. Matplotlib is the way to go.
The pyplot module provides a nice interface to get simple plots up and running fast, especially if you are familiar with MatLab's plotting environment. Here is a simple example using pyplot:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
x_points = xrange(0,9)
y_points = xrange(0,9)
p = ax.plot(x_points, y_points, 'b')
ax.set_xlabel('x-points')
ax.set_ylabel('y-points')
ax.set_title('Simple XY point plot')
fig.show()

import matplotlib.pyplot as plt
x = range(1,10)
y = range(1,10)
plt.plot(x,y,'o')
plt.show()
Here's a simple line with made up x, y. Note: x and y are lists.
Their lengths should be equal or you'll get a error. Cheers!

I suggest the most good looking plotting library for Python: CairoPlot

You can use the Tkinter canvas widget. It uses rectangular coordinates but of course you can translate to polar. The canvas is pretty much just like it sounds -- a blank canvas on which you can draw points, lines, circles, rectangles, etc.

You could always write a plotting function that uses the turtle module from the standard library.

MathGL is GPL plotting library which have Python interface, arbitrary (including polar) curved coordinates, a lot of plot types, export to PNG, EPS, SVG, widgets, and so on. For 1D plot samples see here.

Have you tried to use pillow?
from PIL import Image, ImageDraw
#Set up canvas
img = Image.new (mode, size)
draw = ImageDraw.Draw (img)
#Draw your points
draw.point (xy, colour)