hpmc.analyze¶
Overview
hpmc.analyze.sdf 
Compute the scale distribution function. 
Details
Compute properties of hard particle configurations.

class
hoomd.hpmc.analyze.
sdf
(mc, filename, xmax, dx, navg, period, overwrite=False, phase=0)¶ Compute the scale distribution function.
Parameters:  mc (
hoomd.hpmc.integrate
) – MC integrator.  filename (str) – Output file name.
 xmax (float) – Maximum x value at the right hand side of the rightmost bin (distance units).
 dx (float) – Bin width (distance units).
 navg (int) – Number of times to average before writing the histogram to the file.
 period (int) – Number of timesteps between histogram evaluations.
 overwrite (bool) – Set to True to overwrite filename instead of appending to it.
 phase (int) – When 1, start on the current time step. When >= 0, execute on steps where (step + phase) % period == 0.
sdf
computes a distribution function of scale parameters \(x\). For each particle, it finds the smallest scale factor \(1+x\) that would cause the particle to touch one of its neighbors and records that in the histogram \(s(x)\). The histogram is discrete and \(s(x_i) = s[i]\) where \(x_i = i \cdot dx + dx/2\).In an NVT simulation, the extrapolation of \(s(x)\) to \(x = 0\), \(s(0+)\) is related to the pressure.
\[\frac{P}{kT} = \rho \left(1 + \frac{s(0+)}{2d} \right)\]where \(d\) is the dimensionality of the system and \(\rho\) is the number density.
Extrapolating \(s(0+)\) is not trivial. Here are some suggested parameters, but they may not work in all cases.
 xmax = 0.02
 dx = 1e4
 Polynomial curve fit of degree 5.
In systems near densest packings,
dx=1e5
may be needed along with either a smaller xmax or a smaller region to fit. A good rule of thumb might be to fit a region wherenumpy.sum(s[0:n]*dx)
~ 0.5  but this needs further testing to confirm.sdf
averages navg histograms together before writing them out to a text file in a plain format: “timestep bin_0 bin_1 bin_2 .... bin_n”.sdf
works well with restartable jobs. Ensure thatnavg*period
is an integer fraction \(1/k\) of the restart period. Thensdf
will have written the final output to its file just before the restart gets written. The new data needed for the next line of values is entirely collected after the restart.Warning
sdf
does not compute correct pressures for simulations with concave particles.Numpy extrapolation code:
def extrapolate(s, dx, xmax, degree=5): # determine the number of values to fit n_fit = int(math.ceil(xmax/dx)); s_fit = s[0:n_fit]; # construct the x coordinates x_fit = numpy.arange(0,xmax,dx) x_fit += dx/2; # perform the fit and extrapolation p = numpy.polyfit(x_fit, s_fit, degree); return numpy.polyval(p, 0.0);
Examples:
mc = hpmc.integrate.sphere(seed=415236) analyze.sdf(mc=mc, filename='sdf.dat', xmax=0.02, dx=1e4, navg=100, period=100) analyze.sdf(mc=mc, filename='sdf.dat', xmax=0.002, dx=1e5, navg=100, period=100)

disable
()¶ Disable the analyzer.
Examples:
my_analyzer.disable()
Executing the disable command will remove the analyzer from the system. Any
hoomd.run()
command executed after disabling an analyzer will not use that analyzer during the simulation. A disabled analyzer can be reenabled withenable()
.

set_period
(period)¶ Changes the period between analyzer executions
Parameters: period (int) – New period to set (in time steps) Examples:
analyzer.set_period(100) analyzer.set_period(1)
While the simulation is running (
hoomd.run()
, the action of each analyzer is executed every period time steps. Changing the period does not change the phase set when the analyzer was first created.
 mc (