def set_shape(self, shape):
"Sets the shape parameter for a Gamma distribution over rates."
if self._rate_het_type > RateHetType.LAST_GAMMA:
raise TypeError("RateHetManager.set_shape can only be used with gamma distributions")
if shape > _MIN_GAMMA_SHAPE:
self._rate_list = None # this will trigger recalculation of the rates
self._shape = shape
self._state_hash_dirty = True
casrvo_set_shape(self._asrv, shape)
else:
raise ValueError("Shape must be > %f" % _MIN_GAMMA_SHAPE)
def __init__(self,
rate_het_type=RateHetType.ARBITRARY_RATES,
**kwargs):
"""Currently just supports the discrete approximation of
gamma-distributed rates.
`rate_het_type` indicates the distribution used to calculate rates
this should be a facet of the RateHetType "enum"
"""
if rate_het_type > RateHetType.MAX_RATE_HET_TYPE:
raise ValueError("Illegal value for rate_het_type")
self._state_hash = None
self._state_hash_dirty = True
self._rate_het_type = rate_het_type
num_categories = kwargs.get("num_categories", 0)
rates = kwargs.get("rates")
probabilities = kwargs.get("probabilities")
if not num_categories:
if rates:
num_categories = len(rates)
elif probabilities:
num_categories = len(probabilities)
else:
raise ValueError('At least one of "num_categories", "rates", or "probabilities" arguments must be used')
if num_categories < 1 or int(num_categories) != num_categories:
raise ValueError("num_categories must be a positive integer")
self._num_cat = num_categories
if probabilities is None:
self._probabilities = [1.0/num_categories] * num_categories
self._rate_list = [1.0] * num_categories
if rate_het_type <= RateHetType.LAST_GAMMA:
shape = float(kwargs.get("shape", 0.5))
if shape <= 0.0:
raise ValueError("shape must be greater than 0.0")
s = max(_MIN_GAMMA_SHAPE, shape)
a = casrvo_ctor(s, num_categories, rate_het_type)
casrvo_set_shape(a, shape)
assert(a is not None)
self._asrv = a
self._rate_list = None
self.shape = s
if rates:
self.rates = rates
if probabilities:
self.probabilities = probabilities
self._prob_hash = None
