appletree.plugins.er_microphys

In appletree.plugins.er_microphys, we put some plugins to simulate ER quanta generation.

class appletree.plugins.er_microphys.IonizationER(llh_name: Optional[str] = None)

Bases: Plugin

depends_on: List[str] = ['num_quanta']

parameters: Tuple = ('nex_ni_ratio',)

provides: List[str] = ['num_ion']

simulate(key, parameters, num_quanta)

class appletree.plugins.er_microphys.Quanta(llh_name: Optional[str] = None)

Bases: Plugin

depends_on: List[str] = ['energy']

parameters: Tuple = ('w', 'fano')

provides: List[str] = ['num_quanta']

simulate(key, parameters, energy)

class appletree.plugins.er_microphys.RecombFluct(llh_name: Optional[str] = None)

Bases: Plugin

depends_on: List[str] = ['energy']

parameters: Tuple = ('rf0', 'rf1')

provides: List[str] = ['recomb_std']

simulate(key, parameters, energy)

class appletree.plugins.er_microphys.RecombinationER(llh_name: Optional[str] = None)

Bases: Plugin

depends_on: List[str] = ['num_quanta', 'num_ion', 'recomb']

provides: List[str] = ['num_photon', 'num_electron']

simulate(key, parameters, num_quanta, num_ion, recomb)

class appletree.plugins.er_microphys.TrueRecombER(llh_name: Optional[str] = None)

Bases: Plugin

depends_on: List[str] = ['recomb_mean', 'recomb_std']

provides: List[str] = ['recomb']

simulate(key, parameters, recomb_mean, recomb_std)

class appletree.plugins.er_microphys.mTI(llh_name: Optional[str] = None)

Bases: Plugin

depends_on: List[str] = ['energy']

parameters: Tuple = ('w', 'nex_ni_ratio', 'py0', 'py1', 'py2', 'py3', 'py4', 'field')

provides: List[str] = ['recomb_mean']

simulate(key, parameters, energy)