Free streaming for heavy-ion collision initial conditions.
Free streaming and Landau matching for boost-invariant hydrodynamic initial conditions.
freestream is a Python implementation of pre-equilibrium free streaming for heavy-ion collisions, as described in
pip install freestream
The only requirements are numpy (1.8.0 or later) and scipy (0.14.0 or later).
freestream has an object-oriented interface through the FreeStreamer class, which takes three parameters:
freestream.FreeStreamer(initial, grid_max, time)
The initial array must contain a two-dimensional (boost-invariant) initial condition discretized onto a uniform square grid. It is then interpreted as a density profile of non-interacting massless partons at time τ = 0+.
The grid_max parameter sets the outermost edge of the grid, not the midpoint of the outer grid cell, e.g.
This is the same definition as the trento --grid-max parameter.
It is very important that the grid max is set correctly to avoid superluminal propagation.
Suppose initial is an n × n initial condition array with a grid max of 10.0 fm and we want to free stream for 1.0 fm. We first create a FreeStreamer object:
import freestream fs = freestream.FreeStreamer(initial, 10.0, 1.0)
We can now extract the various quantities needed to initialize hydro from fs.
Tuv = fs.Tuv()
Tuv is an n × n × 3 × 3 array containing the full tensor at each grid point. If we only want a certain component of the tensor, we can pass indices to the function:
T00 = fs.Tuv(0, 0)
T00 is an n × n array containing T00 at each grid point. This is purely for syntactic convenience: fs.Tuv(0, 0) is equivalent to fs.Tuv()[:, :, 0, 0].
e = fs.energy_density() # n x n u = fs.flow_velocity() # n x n x 3
We can also extract the individual components of flow velocity:
u1 = fs.flow_velocity(1) # n x n
Again, this is equivalent to fs.flow_velocity()[:, :, 1].
The shear pressure tensor πμν works just like Tμν:
pi = fs.shear_tensor() # n x n x 3 x 3 pi01 = fs.shear_tensor(0, 1) # n x n
The bulk viscous pressure Π depends on the equation of state P(e). By default, the ideal EoS P(e) = e/3 is used:
bulk = fs.bulk_pressure()
The bulk pressure is in fact zero with the ideal EoS, but there will be small nonzero values due to numerical precision.
To use another EoS, pass a callable object to bulk_pressure():
bulk = fs.bulk_pressure(eos)
For example, suppose we have a table of pressure and energy density we want to interpolate. We can use scipy.interpolate to construct a spline and pass it to bulk_pressure():
import scipy.interpolate as interp eos_spline = interp.InterpolatedUnivariateSpline(energy_density, pressure) bulk = fs.bulk_pressure(eos_spline)
The code should run in a few seconds, depending on the grid size. Computation time is proportional to the number of grid cells (i.e. n2).
Ensure that the grid is large enough to accommodate radial expansion. The code does not check for overflow.
FreeStreamer returns references to its internal arrays, so do not modify them in place—make copies!
FreeStreamer uses a two-dimensional cubic spline (scipy.interpolate.RectBivariateSpline) to construct a continuous initial condition profile from a discrete grid. This is very precise provided the grid spacing is small enough. The spline sometimes goes very slightly negative around sharp boundaries; FreeStreamer coerces these negative values to zero.
The script test.py contains unit tests and generates visualizations for qualitative inspection. To run the tests, install nose and run:
nosetests -v test.py
There are two unit tests:
These tests occasionally fail since there is a random component and the tolerance is somewhat stringent (every grid point must agree within 0.1%). When a test fails, it will print out a list of ratios (observed/expected). Typically the failures occur at the outermost grid cell where the system is very dilute, and even there it will only miss by ~0.2%.
To generate visualizations, execute test.py as a script with two arguments, the test case to visualize and a PDF output file. There are three test cases:
python test.py gaussian1 freestream.pdf
will run the gaussian1 test case and save results in freestream.pdf. The PDF contains visualizations of the initial state and everything that FreeStreamer computes. In each visualization, red colors indicate positive values, blue means negative, and the maximum absolute value of the array is annotated in the upper left.
The included script animate.py generates animations (like the one at the top of this page) from initial conditions saved in HDF5 format (e.g. trento events). It requires python3 with matplotlib and h5py, and of course freestream must be installed. To animate a trento event, first generate some events in HDF5 format then run the script:
trento Pb Pb 10 -o events.hdf ./animate.py events.hdf event_0 freestream.mp4
The first argument is the HDF5 filename, the second is the dataset to animate, and the last is the animation filename. Run ./animate.py --help for more information including options for the animation duration, framerate, colormap, etc.