def __init__(self, name, low, high, legalValues):
self.name = name
self.minimum = low
self.maximum = high
self.data = BTree()
class numericIndex(Persistent):
'''
Essentially an extension of durus.btree.BTree that accepts duplicate keys (via chaining)
'''
def __init__(self, name, low, high, legalValues):
self.name = name
self.minimum = low
self.maximum = high
self.data = BTree()
def __getItem__(self, key):
return self.data.get(key,[])
def __setItem__(self, key, value):
if self.minimum == None or key < self.minimum:
self.minimum = key
if self.maximum == None or key > self.maximum:
self.maximum = key
self.data[key] = value
def count(self, low, high):
return self.data.count_range(low,high,closed_start=True,closed_end=True)
def has_key(self, key):
return self.data.has_key(key)
def findNaturalMinAndMax(self):
if self.minimum == self.maximum:
self.minimum = 0
if self.maximum == 0:
self.minimum = -1
self.maximum = 1
self._p_note_change()
return
elif self.minimum > self.maximum:
temp = self.maximum
self.maximum = self.minimum
self.minimum = temp
span = self.maximum - self.minimum
if self.maximum > 0:
nearestTenMax = 10**math.ceil(math.log10(self.maximum))
elif self.maximum == 0:
nearestTenMax = 0
else:
nearestTenMax = -10**math.floor(math.log10(-self.maximum))
if self.minimum > 0:
nearestTenMin = 10**math.floor(math.log10(self.minimum))
elif self.minimum == 0:
nearestTenMin = 0
else:
nearestTenMin = -10**math.ceil(math.log10(-self.minimum))
# prefer nearestTenMax if the gap between it and self.maximum is less than 25% the span of the data
if abs(nearestTenMax - self.maximum) < 0.25*span:
self.maximum = nearestTenMax
# prefer zero if the gap between it and self.minimum is less than 50% the span of the data, then 25% for nearestTenMin
if self.minimum > 0 and self.minimum < 0.5*span:
self.minimum = 0
elif abs(self.minimum - nearestTenMin) < 0.25*span:
self.minimum = nearestTenMin
self._p_note_change()
