def __init__(self, measure, beta, kappa, normed, coords, check_input):
if 'efpm' in measure:
raise ValueError('\'efpm\' no longer supported')
if 'efm' in measure:
raise ValueError('\'efm\' no longer supported')
# store measure object
self._measure = Measure(measure, beta, kappa, normed, coords, check_input)
def __init__(self, kwargs):
kwargs_check('EFBase', kwargs, allowed=MEASURE_KWARGS)
self._measure = Measure(kwargs.pop('measure'), **kwargs)
class EFPBase(with_metaclass(ABCMeta, object)):
def __init__(self, measure, beta, kappa, normed, coords, check_input):
if 'efpm' in measure:
raise ValueError('\'efpm\' no longer supported')
if 'efm' in measure:
raise ValueError('\'efm\' no longer supported')
# store measure object
self._measure = Measure(measure, beta, kappa, normed, coords,
check_input)
def get_zs_thetas_dict(self, event, zs, thetas):
if event is not None:
zs, thetas = self._measure.evaluate(event)
elif zs is None or thetas is None:
raise TypeError(
'if event is None then zs and/or thetas cannot also be None')
return zs, {w: thetas**w for w in self._weight_set}
@abstractproperty
def _weight_set(self):
pass
@property
def measure(self):
return self._measure.measure
@property
def beta(self):
return self._measure.beta
@property
def kappa(self):
return self._measure.kappa
@property
def normed(self):
return self._measure.normed
@property
def coords(self):
return self._measure.coords
@property
def check_input(self):
return self._measure.check_input
def _batch_compute_func(self, event):
return self.compute(event, batch_call=True)
@abstractmethod
def compute(self, *args, **kwargs):
pass
def batch_compute(self, events, n_jobs=None):
"""Computes the value of the EFP on several events.
**Arguments**
- **events** : array_like or `fastjet.PseudoJet`
- The events as an array of arrays of particles in coordinates
matching those anticipated by `coords`.
- **n_jobs** : _int_ or `None`
- The number of worker processes to use. A value of `None` will
use as many processes as there are CPUs on the machine.
**Returns**
- _1-d numpy.ndarray_
- A vector of the EFP value for each event.
"""
if n_jobs is None:
n_jobs = multiprocessing.cpu_count() or 1
chunksize = max(1, len(events) // n_jobs)
# setup processor pool
with create_pool(n_jobs) as pool:
results = np.asarray(
list(pool.map(self._batch_compute_func, events, chunksize)))
return results
class EFPBase(with_metaclass(ABCMeta, object)):
def __init__(self, measure, beta, kappa, normed, coords, check_input):
if 'efpm' in measure:
raise ValueError('\'efpm\' no longer supported')
if 'efm' in measure:
raise ValueError('\'efm\' no longer supported')
# store measure object
self._measure = Measure(measure, beta, kappa, normed, coords,
check_input)
def get_zs_thetas_dict(self, event, zs, thetas):
if event is not None:
zs, thetas = self._measure.evaluate(event)
elif zs is None or thetas is None:
raise TypeError(
'if event is None then zs and/or thetas cannot also be None')
return zs, {w: thetas**w for w in self._weight_set}
@abstractproperty
def _weight_set(self):
pass
@property
def measure(self):
return self._measure.measure
@property
def beta(self):
return self._measure.beta
@property
def kappa(self):
return self._measure.kappa
@property
def normed(self):
return self._measure.normed
@property
def check_input(self):
return self._measure.check_input
@property
def subslicing(self):
return self._measure.subslicing
def _batch_compute_func(self, event):
return self.compute(event, batch_call=True)
@abstractmethod
def compute(self, *args, **kwargs):
pass
def batch_compute(self, events, n_jobs=-1):
"""Computes the value of the EFP on several events.
**Arguments**
- **events** : array_like or `fastjet.PseudoJet`
- The events as an array of arrays of particles in coordinates
matching those anticipated by `coords`.
- **n_jobs** : _int_
- The number of worker processes to use. A value of `-1` will attempt
to use as many processes as there are CPUs on the machine.
**Returns**
- _1-d numpy.ndarray_
- A vector of the EFP value for each event.
"""
if n_jobs == -1:
try:
self.n_jobs = multiprocessing.cpu_count()
except:
self.n_jobs = 4 # choose reasonable value
# setup processor pool
chunksize = max(len(events) // self.n_jobs, 1)
if sys.version_info[0] == 3:
with multiprocessing.Pool(self.n_jobs) as pool:
results = np.asarray(
list(pool.imap(self._batch_compute_func, events,
chunksize)))
# Pool is not a context manager in python 2
else:
pool = multiprocessing.Pool(self.n_jobs)
results = np.asarray(
list(pool.imap(self._batch_compute_func, events, chunksize)))
pool.close()
return results