Re: [yt-users] Overplotting equipotential contours from total potential

25 Sep 2013

      Hi Matt,

yes, I am using Enzo.
I am sure that my gravitational field does not include the particles, in
fact the gravity contribution of their masses is missing from the potential
(I am not using standard dark matter particles and I think the contribution
to gravity of this particle type is added after Enzo computes the standard
potential field). In fact if for example I plot the equipotential contours
for the default Grav_Potential I get just the equipotential surfaces for
the gas.

I dug in yt yesterday to understand what was going on and why it did not
like my potential, in fact I checked exactly the quantities you asked me
for bot Grav_Potential and my user defined Total_Grav_Potential (in cgs):

*In [1309]: grav_pot.min()*
*Out[1309]: -38532761511174.227*
*In [1310]: grav_pot.max()*
*Out[1310]: -1240331838330.0188*
*In [1311]: grav_pot.size*
*Out[1311]: 16777216*

*
In [1313]: tot_grav_pot.min()
Out[1313]: -417049888736805.5
In [1314]: tot_grav_pot.max()
Out[1314]: -3796359609406.9697
In [1315]: tot_grav_pot.size
Out[1315]: 16777216
*
*
*
I checked that the values were changing correctly as the sum of the the
three partial potentials (gas, part1 and part2), and they are ok. Also the
total length of the array is ok. So what I did next was defining
the Total_Grav_Potential field as

* def _Total_Grav_Potential(field,data): *

* #adds the current particle potential to the total potential*
* total_grav_potential = data['Grav_Potential'] *
* *
* return total_grav_potential*
*
*
so that it just returned the standard Grav_Potential field. Also in this
case the result was the same, i.e. I got the same error if clim was not
defined and no contours when clim was defined. Hence the problem was in how
the field was added to the yt fields.
I went checking the Creating Derived Fields page in the yt docs and the
fields.py file from my yt to see how fields were created, so I started
adding parameters to the add_field() function one at the time to understand
if they were responsible for the problem.
Turned out that the validators were the problem, in particular I had to
validate the following fields:

*
validators=ValidateDataField(["Grav_Potential","particle_position_x","particle_position_z","particle_position_z"])
*
*
*
which are the exactly the native Enzo fields that I used inside the
function definition (the used derived fields are instead ok!). So in the
end I used this:

* add_field("Total_Grav_Potential", \*
*        function=_Total_Grav_Potential, \*
*        take_log= False , \*
*        particle_type= True, \*
*
validators=ValidateDataField(["Grav_Potential","particle_position_x","particle_position_z","particle_position_z"]),
\*
*        vector_field= False, \*
*        units=r"\rm{erg}/\rm{g}")*

and now I get the contours.
I uploaded the plots here: http://postimg.org/gallery/5zomtyto/

The problem is solved but I do not really quite understand why the native
Enzo fields have to be validated.
Can I ask you if you can explain me what is happening in your opinion?

Concerning the coding suggestions, looks definitely better to do the
conversions at the end and use more derived fields, I will rewrite it
starting from your template.

Thank you very much for the help!
                                                      Roberto

On 25 September 2013 07:22, Matthew Turk  wrote:
...
Hi Roberto,
On Mon, Sep 23, 2013 at 8:59 PM, trobolo dinni
 wrote:
...
Dear yt users,
I am running a binary star simulation with enzo, and in my box there is a
star made up of a gas envelope and a particle as core, and a secondary
star
represented by a single particle (no gas).
I would like to plot the equipotential surfaces of the system on a slice
plot, hence, since the default Grav_Potential field does not include the
particles potential, I created a custom made field as follows:
It looks like you're using Enzo -- is that right?  If so, are you sure
the Grav_Potential field does not include your particle's potentials?
If not, you may be able to apply them as analytic expressions.
Regardless, I've supplied some comments below:
...
def _Total_Grav_Potential(field,data):
#G in cgs
grav_const_cgs = 6.674e-8
#data for gas
gas_grav_potential_cgs = data['Grav_Potential']  *
(length_unit1/time_unit1)**2.0 #in units of velocity^2
gas_position_x_cgs = data['x'] * length_unit1
gas_position_y_cgs = data['y'] * length_unit1
gas_position_z_cgs = data['z'] * length_unit1
You like will want to apply the conversion at the end of your
computation.  You can also use fields like Radius and ParticleMass to
get things back in cm and g, respectively.  There are probably better
ways than what I am about to propose (Stephen Skory and Elizabeth
Tasker have both done things like this) but here's just something off
the top of my head.
f = data["Grav_Potential"].copy()
for i in range(len(data["ParticleMass"])):
    c = np.array([data["particle_position_%s" % ax] for ax in 'xyz'])
    data.set_field_parameter("center", c)
    R = data["Radius"] # an array of gas radii
    m = data["ParticleMass"][i]
    # compute grav potential with particle here ...
    f += ...
return f
...
#initialize the array for the total grav potential to the gas grav
potential
total_grav_potential_cgs = gas_grav_potential_cgs
#loops on the particles
for i in range(len(data['ParticleMassMsun'])):
#data of the current particle
current_particle_mass_cgs= data['ParticleMassMsun'][i] * mass_unit
current_particle_position_x_cgs = data['particle_position_x'][i] *
length_unit1
current_particle_position_y_cgs = data['particle_position_y'][i] *
length_unit1
current_particle_position_z_cgs = data['particle_position_z'][i] *
length_unit1
#computes the array of grav potential for the current particle
current_particle_grav_potential_cgs = -(grav_const_cgs *
current_particle_mass_cgs)/(((gas_position_x_cgs -
current_particle_position_x_cgs)**2.0 + (gas_position_y_cgs -
current_particle_position_y_cgs)**2.0 + (gas_position_z_cgs -
current_particle_position_z_cgs)**2.0)**0.5)
#adds the current particle potential to the total potential
total_grav_potential_cgs = total_grav_potential_cgs +
current_particle_grav_potential_cgs
return total_grav_potential_cgs
#adds the field to the available yt fields
add_field("Total_Grav_Potential", function=_Total_Grav_Potential,
take_log=True ,units=r"\rm{erg}/\rm{g}")
where the length unit and time unit variables have the function to
convert
my quantities in cgs.
If I then call my "Total_Grav_Potential" field, I get reasonable values,
but
when I try to inspect the shape of the equipotential surfaces on the
xy-plane by plotting the following:
sp = SlicePlot(pf, 'z', "Density", width = 1.0)
sp.annotate_velocity(factor=16, normalize=True)
sp.annotate_contour('Total_Grav_Potential',clim=(max_total_pot,min_total_pot))
...
sp.annotate_particles(1.0, p_size=50.0, marker='o', col='black')
sp.annotate_text((0.7,1.05), "time = "+str(pf.current_time)+"yr")
sp.save(plot_dir+"density_slice_z_"+str(index)+".png")
nothing happens, and the plot does not show any additional contour;
while if
I do the same with the default "Grav_Potential" field, the gas
equipotential
contours get plotted without problems.
I also tried to play with the "annotate_contour" options "ncont",
"factor"
and "clim" to refine my plot but the result is always the same.
Additionally, if I do not set the "clim" option the following error shows
up:
ValueError: zero-size array to minimum.reduce without identity
This message suggests something funny may be going on.  What is the result
of:
dd = pf.h.all_data()
dd["Total_Grav_Potential"].min(), dd["Total_Grav_Potential"].max(),
dd["Total_Grav_Potential"].size
...
I would like to ask if my custom definition of the Total_Grav_Potential
field is correct from a yt coding point of view and if I should modify it
(or if there is any improvement I can do).
Also, I would like to ask if you have any idea of what could be the
problem
...
with the plotting.
Let us know how it goes, and I hope this helps!
-Matt
...
Thanks for the help,
                                 Roberto
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