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'''
Created on Aug 10, 2011
@author: steger, gombos, matuszka
'''
from DataProcessing.MeasurementLevel import Ordinal, Ratio, Interval #Nominal
from math import sqrt
from DataProcessing.DataReader import DataReader
from DataProcessing.DataHeader import DataHeader, DataHeaderCell
from DataProcessing.DataSource import DataSource
from DataProcessing.Data import Data
from DataProcessing.DataError import AggregatorError
#FIXME: docs
class Aggregator(DataSource):
'''
classdocs
@cvar cn_count: the name of the column indicating the set size before aggregation
'''
cn_count = 'Count'
def __init__(self, datasource, cellrequest):
'''
Constructor
@param datasource: table of records to manipulate with
@type datasource: DataSource
@param cellrequest: a column wise projection of the table is carried out, this column is kept
@type cellrequest: CellRequest
'''
if not isinstance(datasource, DataSource):
raise AggregatorError("Wrong type of datasource %s" % datasource)
DataSource.__init__(self, dependency = datasource)
self._inputreader.extract(cellrequest = [cellrequest])
for c in self._inputreader.headercells():
break
if not c.dimension.level(self.dimension_compatible):
raise AggregatorError("The measurement level of input (%s) is not compatible with %s" % (c.dimension, self.name))
header = DataHeader("%sAggregate(%s)" % (self.name, datasource.name))
dimension = c.dimension
header.addColumn(DataHeaderCell(name = self.cn_count, dimension = dimension.manager["Countable"]))
self.cn_aggr = '%s(%s)' % (self.name, c.name)
header.addColumn(DataHeaderCell(name = self.cn_aggr, dimension = dimension, unit = c.unit))
self._data = Data(datasource.um, header)
self._record = self._data.getTemplate(size = 1)
# self.um = datasource.um
self.source = datasource
self._aggregate = None
#FIXME: ez a ketto cucc tenyleg kell?
def __len__(self):
return len(self._data)
@property
def writelock(self):
return self._data.writelock
@property
def aggregate(self):
self.process()
return self._aggregate
@property
def readerClass(self):
return DataReader
@property
def dimension_compatible(self):
raise AggregatorError("dimension_compatible property is not implemented in %s" % self)
class Sum(Aggregator):
def __init__(self, datasource, cellrequest):
Aggregator.__init__(self, datasource, cellrequest)
self._aggregate = 0
@property
def dimension_compatible(self):
return Interval
@property
def name(self):
return "Sum"
def _process(self):
if self._inputreader.sourceCleared.isSet():
self._inputreader.sourceCleared.clear()
self._aggregate = 0
self._inputreader.rewind()
changed = True
else:
changed = False
for (x,) in self._inputreader:
self._aggregate += float(x)
changed = True
if changed:
self._data.clear()
self._record.update(name = self.cn_aggr, values = (self._aggregate,))
self._record.update(name = self.cn_count, values = (len(self.source),))
self._data.saveRecord(self._record)
return self.CLEARED | self.EXPANDED
return self.PASS
class Min(Aggregator):
def __init__(self, datasource, cellrequest):
Aggregator.__init__(self, datasource, cellrequest)
@property
def dimension_compatible(self):
return Ordinal
@property
def name(self):
return "Min"
def _process(self):
if self._inputreader.sourceCleared.isSet():
self._inputreader.sourceCleared.clear()
self._aggregate = None
self._inputreader.rewind()
if self._aggregate is None:
samples = []
else:
samples = [self._aggregate]
for (x,) in self._inputreader:
samples.append( float(x) )
newvalue = min(samples)
if self._aggregate != newvalue:
self._aggregate = newvalue
self._data.clear()
self._record.update(name = self.cn_aggr, values = (self._aggregate,))
self._record.update(name = self.cn_count, values = (len(self.source),))
self._data.saveRecord(self._record)
return self.CLEARED | self.EXPANDED
return self.PASS
class Max(Aggregator):
def __init__(self, datasource, cellrequest):
Aggregator.__init__(self, datasource, cellrequest)
@property
def dimension_compatible(self):
return Ordinal
@property
def name(self):
return "Max"
def _process(self):
if self._inputreader.sourceCleared.isSet():
print "MAX src cleared"
self._inputreader.sourceCleared.clear()
self._aggregate = None
self._inputreader.rewind()
if self._aggregate is None:
samples = []
else:
samples = [self._aggregate]
for (x,) in self._inputreader:
samples.append( float(x) )
print "SAMPLES", samples
newvalue = max(samples)
print "MAX", newvalue, "of", len(self.source)
if self._aggregate != newvalue:
print "SETTING"
self._aggregate = newvalue
self._data.clear()
self._record.update(name = self.cn_aggr, values = (self._aggregate,))
self._record.update(name = self.cn_count, values = (len(self.source),))
self._data.saveRecord(self._record)
print "XXX", self._record.record
return self.CLEARED | self.EXPANDED
return self.PASS
class Mean(Aggregator):
def __init__(self, datasource, cellrequest):
Aggregator.__init__(self, datasource, cellrequest)
self._sum = 0
@property
def dimension_compatible(self):
return Ratio
@property
def name(self):
return "Mean"
def _process(self):
changed = False
for (x,) in self._inputreader:
self._sum += float(x)
changed = True
if changed:
self._aggregate = self._sum / float(len(self.source))
self._data.clear()
self._record.update(name = self.cn_aggr, values = (self._aggregate,))
self._record.update(name = self.cn_count, values = (len(self.source),))
self._data.saveRecord(self._record)
return self.CLEARED | self.EXPANDED
return self.PASS
class Deviation(Aggregator):
def __init__(self, data, cellrequest):
Aggregator.__init__(self, data, cellrequest)
self._emp = True
@property
def empirical(self):
return self._emp
@empirical.setter
def empirical(self, emp):
self._emp = bool(emp)
@property
def dimension_compatible(self):
return Ratio
@property
def name(self):
return "StdDev"
def _process(self):
changed = False
aggr = 0
data = []
self._inputreader.rewind()
for (x,) in self._inputreader:
x = float(x)
aggr += x
data.append(x)
changed = True
if changed:
n = float(len(data))
avg = aggr / n
s2 = map(lambda x: (x-avg)*(x-avg), data)
if self.empirical:
self._aggregate = sqrt(sum(s2) / (n+1))
else:
self._aggregate = sqrt(sum(s2) / n)
self._data.clear()
self._record.update(name = self.cn_aggr, values = (self._aggregate,))
self._record.update(name = self.cn_count, values = (len(self.source),))
self._data.saveRecord(self._record)
return self.CLEARED | self.EXPANDED
return self.PASS
class Percentile(Aggregator):
def __init__(self, data, cellrequest):
self._percentile = .75
Aggregator.__init__(self, data, cellrequest)
@property
def percentile(self):
return self._percentile
@percentile.setter
def percentile(self, percentile):
self._percentile = max(0, min(1, float(percentile)))
@property
def dimension_compatible(self):
return Ordinal
@property
def name(self):
return "Percentile_%d%%" % int(round(100 * self.percentile))
def _process(self):
data = []
self._inputreader.rewind()
for (x,) in self._inputreader:
data.append(x)
data.sort()
n = len(data)
p = int((n - 1) * self.percentile)
if n % 2:
val = data[p]
else:
val = .5 * (data[p] + data[p+1])
if self._aggregate != val:
self._aggregate = val
self._data.clear()
self._record.update(name = self.cn_aggr, values = (self._aggregate,))
self._record.update(name = self.cn_count, values = (len(self.source),))
self._data.saveRecord(self._record)
return self.CLEARED | self.EXPANDED
return self.PASS
|
