Part of my *moving to python for science*.

I have lately been using gnuplot to monitor the progress and end results of my simulations. These are 2D hydrodynamic simulations, which involve 2D scalar and vector fields.

**2D plots**

With gnuplot, I used to run stuff like

plot ‘8000/points.dat’ u 1:2

for a 2D plot of a scalar field. On column 1 I have the x values of the coordinates and on column 2 the y values, so this just plots the coordinates. The “8000” is the directory for time step 8000, where the data file is saved.

OK, now running ipython –pylab I have to run

dt = pylab.loadtxt(‘8000/points.dat’)

ss=scatter(dt[:,0],dt[:,1],c=dt[:,11],s=50)

2D scatter plot with colors

Important things to notice:

- python starts numbering c-style: at 0! so, despite pylab having been designed in order to mimic matlab syntax, there is a clear departure here.
- it’s easy to assign a color to the points, in this case column 12 (11 for python) is a density field. The default color scale looks ok (it goes from blue to red).
- the size is set to 50 for a nice full-screen view.
- New plots will appear on top of this one. If this is not desired, close the window, or run pylab.clf()

**Vector plots**

This is quite easy in gnuplot:

plot ‘8000/points.dat’ u 1:2:($5/10):($6/10) w vec

On columns 5 and 6 I store the x and y components of the velocity. In this example they are reduced ten-fold in order to hace a decent plot (and this has to set by hand…).

Now we may run:

st=quiver(dt[:,0],dt[:,1],dt[:,4],dt[:,5],dt[11])

which does the same. A bonus is that the vector length is automatically calculated from the average vector length. This may the right solution, if not the option “scale=xx” may be used. The higher xx is, the shorter the arrows (??). The fifth entry is a color code, which does not look to good at present because, I think, they are taken as RGB values, unlike with scatter.

Vector plot — funny senseless colors

** 3D plots**

This one’s harder. To get a scatter 3D plot of three columns I used to run

splot ‘8000/points.dat’ u 1:2:12

Now, it’s more like

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

ax = fig.add_subplot(111, projection=’3d’)

ax.scatter(dt[:,0],dt[:,1],dt[:,11])

fig.show()

That produces a quite nice scatter plot with tick marks on the three axes.

3D scatter plot