def yield_rows(self):
row = SummaryRow()
isect_time = 0.0
for item in Utils.combinations(*self.condcols.veckey_pairs(self.zeros)):
row.level = len(item)
row.suppress = False
if row.level in self.levels:
row.type_string = ['0'] * len(self.condcols)
colnames = []
colvalues = []
intersect_rows = []
for var_val, var_rows, suppress, condcol in item:
row.type_string[condcol.index] = '1'
intersect_rows.append(var_rows)
colnames.append(condcol.name)
colvalues.append(var_val)
if suppress:
row.suppress = True
isect_start = time.time()
if len(intersect_rows) == 0:
row.count = len(self.filtered_ds)
row.extract = self.filtered_ds
else:
if len(intersect_rows) == 1:
cellrows = intersect_rows[0]
else:
cellrows = soomfunc.intersect(*intersect_rows)
row.count = len(cellrows)
row.extract = DatasetTake(self.dataset, cellrows)
isect_time += time.time() - isect_start
row.colnames = tuple(colnames)
row.colvalues = tuple(colvalues)
yield row
soom.info('Summarise intersect() time: %.3f' % isect_time)
def near(self, lhsrowwords, rhsrowwords, mask=_BEFORE | _AFTER):
# only check matching rows
rows = soomfunc.intersect(lhsrowwords[0], rhsrowwords[0])
lhswords = lhsrowwords[1]
rhswords = rhsrowwords[1]
words = {}
_BEFORE = self._BEFORE
_AFTER = self._AFTER
nearness = self.nearness
for row in rows:
hits = sets.Set()
# this is O(n*n) and could be improved
# find all hits and then remove duplicates
for left in lhswords[row]:
for right in rhswords[row]:
if (mask & _BEFORE) and right - nearness <= left <= right:
hits.add(left)
hits.add(right)
if (mask & _AFTER) and right <= left <= right + nearness:
hits.add(left)
hits.add(right)
if hits:
hits = list(hits)
hits.sort()
words[row] = Numeric.array(hits, Numeric.Int)
# remove rows that have no hits left
rows = Numeric.array(filter(lambda r: r in words, rows), Numeric.Int)
return rows, words
def yield_rows(self):
row = SummaryRow()
isect_time = 0.0
for item in Utils.combinations(
*self.condcols.veckey_pairs(self.zeros)):
row.level = len(item)
row.suppress = False
if row.level in self.levels:
row.type_string = ['0'] * len(self.condcols)
colnames = []
colvalues = []
intersect_rows = []
for var_val, var_rows, suppress, condcol in item:
row.type_string[condcol.index] = '1'
intersect_rows.append(var_rows)
colnames.append(condcol.name)
colvalues.append(var_val)
if suppress:
row.suppress = True
isect_start = time.time()
if len(intersect_rows) == 0:
row.count = len(self.filtered_ds)
row.extract = self.filtered_ds
else:
if len(intersect_rows) == 1:
cellrows = intersect_rows[0]
else:
cellrows = soomfunc.intersect(*intersect_rows)
row.count = len(cellrows)
row.extract = DatasetTake(self.dataset, cellrows)
isect_time += time.time() - isect_start
row.colnames = tuple(colnames)
row.colvalues = tuple(colvalues)
yield row
soom.info('Summarise intersect() time: %.3f' % isect_time)
def factor(self):
comparison = self.comparison()
f = comparison
while self._peek('"and"', '"or"', 'END', '"\\\\)"') == '"and"':
self._scan('"and"')
comparison = self.comparison()
f = soomfunc.intersect(f, comparison)
return f
def factor(self):
comparison = self.comparison()
f = comparison
while self._peek('"and"', '"or"', 'END', '"\\\\)"') == '"and"':
self._scan('"and"')
comparison = self.comparison()
f = soomfunc.intersect(f, comparison)
return f
def intersect(self, lhsrowwords, rhsrowwords):
# find matching rows
rows = soomfunc.intersect(lhsrowwords[0], rhsrowwords[0])
lhswords = lhsrowwords[1]
rhswords = rhsrowwords[1]
# almalgamate word hits
words = {}
for r in rows:
words[r] = soomfunc.union(lhswords[r], rhswords[r])
return rows, words
def op_between(self, value, filter_keys):
try:
start, end = value
except (ValueError, TypeError):
raise ExpressionError('between(start, end)')
if type(self.data) is MA.MaskedArray:
resmap_ge = greater_equal(self.data, start).filled()
resmap_lt = less(self.data, end).filled()
else:
resmap_ge = Numeric.greater_equal(self.data, start)
resmap_lt = Numeric.less(self.data, end)
vectors = soomfunc.intersect(Numeric.nonzero(resmap_ge),
NUmeric.nonzero(resmap_lt))
return vectors
def follow(self, lhsrowwords, rhsrowwords):
# find matching rows
rows = soomfunc.intersect(lhsrowwords[0], rhsrowwords[0])
lhswords = lhsrowwords[1]
rhswords = rhsrowwords[1]
# almalgamate word hits
words = {}
for row in rows:
hits = sets.Set()
# this is O(n*n) and could be improved
# find all hits and remove duplicates
for left in lhswords[row]:
for right in rhswords[row]:
if right == left + 1:
hits.add(left)
hits.add(right)
if hits:
hits = list(hits)
hits.sort()
words[row] = Numeric.array(hits, Numeric.Int)
# remove rows that have no hits left
rows = Numeric.array(filter(lambda r: r in words, rows), Numeric.Int)
return rows, words
def get_inverted(self):
inverted = {}
for value in self._src_col.inverted.keys():
inverted[value] = soomfunc.intersect(self._src_col.inverted[value],
self._all_record_ids)
return inverted
print " >%8d: %d" % (pow(10, v) - 1, c)
if 1:
calls = 0
result = []
vl = stats('vector length distribution')
cvl = stats('cache vector length distribution')
for veckey in combinations(*values):
if len(veckey) > 0:
values, rows = zip(*veckey)
if len(rows) == 1:
rows = rows[0]
else:
calls += 1
rows = intersect(*rows)
vl.add(len(rows))
if len(values) > 1 and len(values) < len(cols):
cvl.add(len(rows))
# result.append((values, rows))
elapsed = time() - st
print '%d intersect calls, %.3f sec, %.3f sec per call' % (
calls, elapsed, elapsed / calls)
vl.report()
cvl.report()
if 0:
calls = 0
cache = {}
for veckey in combinations(*values):
if len(veckey) > 0:
print " >%8d: %d" % (pow(10, v) - 1, c)
if 1:
calls = 0
result = []
vl = stats('vector length distribution')
cvl = stats('cache vector length distribution')
for veckey in combinations(*values):
if len(veckey) > 0:
values, rows = zip(*veckey)
if len(rows) == 1:
rows = rows[0]
else:
calls += 1
rows = intersect(*rows)
vl.add(len(rows))
if len(values) > 1 and len(values) < len(cols):
cvl.add(len(rows))
# result.append((values, rows))
elapsed = time() - st
print '%d intersect calls, %.3f sec, %.3f sec per call' % (calls, elapsed,
elapsed / calls)
vl.report()
cvl.report()
if 0:
calls = 0
cache = {}
for veckey in combinations(*values):
if len(veckey) > 0:
def get_inverted(self):
inverted = {}
for value in self._src_col.inverted.keys():
inverted[value] = soomfunc.intersect(self._src_col.inverted[value],
self._all_record_ids)
return inverted
def isect(a, b): # redundant, I think.
return soomfunc.intersect(vector1_val1, vector2_val2)
