BulkObservables

class BulkObservables.BulkObservables(particle_objects_list: List[List[Particle]])[source]

Class to calculate bulk observables from a list of Particle objects. It is assumed that all necessary cuts were performed to the particle list before.

Examples

 1>>> from sparkx.Oscar import Oscar
 2>>> from sparkx.BulkObservables import BulkObservables
 3
 4>>> # ============================================================
 5>>> # 1. Load and filter input particles from simulation output
 6>>> # ============================================================
 7
 8>>> # You can obtain a list of Particle objects using either the
 9>>> # Oscar or Jetscape classes. Alternatively, you may use a
10>>> # custom user-defined list of Particle objects.
11>>> #
12>>> # In this example, we use the Oscar class and filter the output
13>>> # to include only charged particles.
14>>>
15>>> OSCAR_FILE_PATH = [Oscar_directory]/particle_lists.oscar
16>>> particle_objects_list = Oscar(OSCAR_FILE_PATH, filters={'charged_particles': True}).particle_objects_list()
17
18>>> # =====================================
19>>> # 2. Calculate bulk observables
20>>> # =====================================
21
22>>> # Initialize the BulkObservables class
23>>> bulk_observables = BulkObservables(particle_objects_list)
24
25>>> # Calculate dN/dy
26>>> histogram_dNdy = bulk_observables.dNdy()
27
28>>> # Calculate dN/dpT
29>>> histogram_dNdpT = bulk_observables.dNdpT()
30
31>>> # Calculate dN/dη
32>>> histogram_dNdEta = bulk_observables.dNdEta()
33
34>>> # Calculate dN/dmT
35>>> histogram_dNdmT = bulk_observables.dNdmT()
36
37>>> # Calculate mid-rapidity yield
38>>> mid_rapidity_yield = bulk_observables.mid_rapidity_yield()
39>>> print(mid_rapidity_yield)
40
41>>> # Calculate mid-rapidity mean pT
42>>> mid_rapidity_mean_pT = bulk_observables.mid_rapidity_mean_pT()
43>>> print(mid_rapidity_mean_pT)
44
45>>> # Calculate mid-rapidity mean mT
46>>> mid_rapidity_mean_mT = bulk_observables.mid_rapidity_mean_mT()
47>>> print(mid_rapidity_mean_mT)
Attributes:
particle_objects: ReadOnlyList

A read-only list of lists of Particle objects.

Methods

dNdy:

Calculate the event averaged yield \(\frac{dN}{dy}\).

dNdpT:

Calculate the event averaged yield \(\frac{dN}{dp_T}\).

dNdEta:

Calculate the event averaged yield \(\frac{dN}{d\eta}\)

dNdmT:

Calculate the event averaged yield \(\frac{dN}{dm_T}\).

mid_rapidity_yield:

Calculate the event-averaged particle yield at mid-rapidity.

mid_rapidity_mean_pT:

Calculate the event-averaged mean transverse momentum \(p_T\) at mid-rapidity.

mid_rapidity_mean_mT:

Calculate the event-averaged mean transverse mass \(m_T\) at mid-rapidity.
BulkObservables.dNdy(bin_properties: Tuple[int | float, int | float, int] | List[int | float] | None = None) → Histogram[source]

Calculate the event averaged yield \(\frac{dN}{dy}\)

Parameters:
bin_properties: tuple, list

Optional tuple (start, stop, num) for histogram binning. If not given, a default will be used

Returns:
Histogram

1D histogram containing the event averaged particle counts per rapidity bin.

BulkObservables.dNdpT(bin_properties: Tuple[int | float, int | float, int] | List[int | float] | None = None) → Histogram[source]

Calculate the event averaged yield \(\frac{dN}{dp_T}\)

Parameters:
bin_properties: tuple, list

Optional tuple (start, stop, num) for histogram binning. If not given, a default will be used

Returns:
Histogram

1D histogram containing the event averaged particle counts per transverse momentum bin.

BulkObservables.dNdEta(bin_properties: Tuple[int | float, int | float, int] | List[int | float] | None = None) → Histogram[source]

Calculate the event averaged yield \(\frac{dN}{d\eta}\)

Parameters:
bin_properties: tuple, list

Optional tuple (start, stop, num) for histogram binning. If not given, a default will be used

Returns:
Histogram

1D histogram containing the event averaged particle counts per pseudo-rapidity bin.

BulkObservables.dNdmT(bin_properties: Tuple[int | float, int | float, int] | List[int | float] | None = None) → Histogram[source]

Calculate the event averaged yield \(\frac{dN}{dm_T}\)

Parameters:
bin_properties: tuple, list

Optional tuple (start, stop, num) for histogram binning. If not given, a default will be used

Returns:
Histogram

1D histogram containing the event averaged particle counts per transverse mass bin.

BulkObservables.mid_rapidity_yield(y_width: float = 1.0, quantity: str = 'rapidity', divide_by_width: bool = False) → float[source]

Calculate the event-averaged particle yield at mid-rapidity.

Parameters:
y_widthfloat

The rapidity window width, centered at 0.

quantitystr

The rapidity-related method name (rapidity, pseudorapidity, etc.).

divide_by_widthbool

If True, return the estimated dN/dy by dividing by y_width.

Returns:
float

The event-averaged particle yield at mid-rapidity.

BulkObservables.mid_rapidity_mean_pT(y_width: float = 1.0, quantity: str = 'rapidity') → float[source]

Compute event-wise mean pT, then average over events that have at least one particle in mid-rapidity window.

Parameters:
y_widthfloat

The rapidity window width, centered at 0.

quantitystr

The rapidity-related method name (rapidity, pseudorapidity, etc.).

Returns:
float

The event-averaged mean transverse momentum pT at mid-rapidity.

BulkObservables.mid_rapidity_mean_mT(y_width: float = 1.0, quantity: str = 'rapidity') → float[source]

Compute event-wise mean mT, then average over events that have at least one particle in mid-rapidity window.

Parameters:
y_widthfloat

The rapidity window width, centered at 0.

quantitystr

The rapidity-related method name (rapidity, pseudorapidity, etc.).

Returns:
float

The event-averaged mean transverse mass mT at mid-rapidity.