Free streaming for heavy-ion collision initial conditions.

## Project description

*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

- J. Liu, C. Shen, U. Heinz, “Pre-equilibrium evolution effects on heavy-ion collision observables”, PRC 91 064906 (2015), arXiv:1504.02160 [nucl-th].
- W. Broniowski, W. Florkowski, M. Chojnacki, A. Kisiel, “Free-streaming approximation in early dynamics of relativistic heavy-ion collisions”, PRC 80 034902 (2009), arXiv:0812.3393 [nucl-th].

## Installation

Simply run

pip install freestream

The only requirements are numpy (1.8.0 or later) and scipy (0.14.0 or later).

## Usage

`freestream` has an object-oriented interface through the `FreeStreamer` class, which takes three parameters:

freestream.FreeStreamer(initial, grid_max, time)

where

`initial`is a square array containing the initial state,`grid_max`is the*x*and*y*maximum of the grid in fm, i.e. half the grid width (see following example),`time`is the time to free stream in fm/c.

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.

- A 200 × 200 grid with a max of 10.0 fm has cell edges at -10.00, -9.90, …, +10.00 and cell midpoints at -9.95, -9.85, …, +9.95.
- A 201 × 201 grid with a max of 10.05 fm has cell edges at -10.05, -9.95, …, +10.05 and cell midpoints at -10.00, -9.90, …, +10.00.

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`.

### Energy-momentum tensor *T*^{μν}

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 *T*^{00} at each grid point.
This is purely for syntactic convenience: `fs.Tuv(0, 0)` is equivalent to `fs.Tuv()[:, :, 0, 0]`.

### Energy density *e* and flow velocity *u*^{μ}

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]`.

### Shear tensor π^{μν} and bulk pressure Π

The shear pressure tensor π^{μν} works just like *T*^{μν}:

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)

### Other notes

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. *n*^{2}).

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!

## Testing and internals

`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:

- Comparison against an analytic solution for a symmetric Gaussian initial state (computed in Mathematica).
- Comparison against a randomly-generated initial condition without interpolation.

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:

`gaussian1`, a narrow symmetric Gaussian centered at the origin.`gaussian2`, a wider asymmetric Gaussian offset from the origin.`random`, a randomly-generated initial condition (this is not in any way realistic, it’s only for visualization).

For example:

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.

## Animations

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.

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