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.BulkObservables import BulkObservables 2 3>>> # Initialize the BulkObservables class 4>>> bulk_observables = BulkObservables(particle_objects_list) 5 6>>> # Calculate dN/dy 7>>> histogram_dNdy = bulk_observables.dNdy() 8 9>>> # Calculate dN/dpT 10>>> histogram_dNdpT = bulk_observables.dNdpT() 11 12>>> # Calculate dN/dη 13>>> histogram_dNdEta = bulk_observables.dNdEta() 14 15>>> # Calculate dN/dmT 16>>> histogram_dNdmT = bulk_observables.dNdmT() 17 18>>> # Calculate mid-rapidity yield 19>>> mid_rapidity_yield = bulk_observables.mid_rapidity_yield() 20>>> print(mid_rapidity_yield) 21 22>>> # Calculate mid-rapidity mean pT 23>>> mid_rapidity_mean_pT = bulk_observables.mid_rapidity_mean_pT() 24>>> print(mid_rapidity_mean_pT) 25 26>>> # Calculate mid-rapidity mean mT 27>>> mid_rapidity_mean_mT = bulk_observables.mid_rapidity_mean_mT() 28>>> 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') float[source]
Calculate the event-averaged particle yield at mid-rapidity.
- Parameters:
- y_width: float
- The rapidity window width, centered at 0, within which
particles are counted. The default value is 1, meaning the function will count particles with rapidity between -0.5 and 0.5.
- quantity: str
The quantity to be used for the rapidity calculation (rapidity, pseudorapidity, spacetime_rapidity).
- Returns:
- particle_counter / num_events: float
The average number of particles per event that fall within the specified rapidity range.
- BulkObservables.mid_rapidity_mean_pT(y_width: float = 1.0, quantity: str = 'rapidity') float[source]
Calculate the event-averaged mean transverse momentum \(p_T\) at mid-rapidity. It is assumed that detector cuts have been performed on the particle list.
- Parameters:
- y_width: float
- The rapidity window width, centered at 0, within which
particles are counted. The default value is 1, meaning the function will count particles with rapidity between -0.5 and 0.5.
- quantity: str
The quantity to be used for the rapidity calculation (rapidity, pseudorapidity, spacetime_rapidity).
- Returns:
- particle_counter / num_events: float
The average pT of particles per event that fall within the specified rapidity range.
- BulkObservables.mid_rapidity_mean_mT(y_width: float = 1.0, quantity: str = 'rapidity') float[source]
Calculate the event-averaged mean transverse mass \(m_T\) at mid-rapidity.
- Parameters:
- y_width: float
- The rapidity window width, centered at 0, within which
particles are counted. The default value is 1, meaning the function will count particles with rapidity between -0.5 and 0.5.
- quantity: str
The quantity to be used for the rapidity calculation (rapidity, pseudorapidity, spacetime_rapidity).
- Returns:
- particle_counter / num_events: float
The average mT of particles per event that fall within the specified rapidity range.