gem5/util/stats/info.py
Nathan Binkert 2771abb6ed Totally re-do/reorganize the python part of the statistics code
Make the database creation/removal/cleanup code use python
Make formulas work with the database
Add support to do some graphing, but needs more work
Still need to work on vectors, 2d vectors, dists and vectordists

--HG--
extra : convert_revision : 1a88320dcc036a3751e8a036770766dce76a568c
2004-08-09 21:20:52 -04:00

724 lines
20 KiB
Python

from __future__ import division
import operator, re, types
source = None
display_run = 0
def issequence(t):
return isinstance(t, types.TupleType) or isinstance(t, types.ListType)
def total(f):
if isinstance(f, FormulaStat):
v = f.value
else:
v = f
f = FormulaStat()
if issequence(v):
f.value = reduce(operator.add, v)
else:
f.value = v
return f
def unaryop(op, f):
if isinstance(f, FormulaStat):
v = f.value
else:
v = f
if issequence(v):
return map(op, v)
else:
return op(v)
def zerodiv(lv, rv):
if rv == 0.0:
return 0.0
else:
return operator.truediv(lv, rv)
def wrapop(op, lv, rv):
if isinstance(lv, str):
return lv
if isinstance(rv, str):
return rv
return op(lv, rv)
def same(lv, rv):
for lrun,rrun in zip(lv.keys(),rv.keys()):
if lrun != rrun:
print 'lrun != rrun'
print lrun, rrun
print lv.keys()
print rv.keys()
return False
for lx,rx in zip(lv[lrun].keys(),rv[rrun].keys()):
if lx != rx:
print 'lx != rx'
print lx, rx
print lv[lrun].keys()
print rv[rrun].keys()
return False
for ly,ry in zip(lv[lrun][lx].keys(),rv[rrun][rx].keys()):
if ly != ry:
print 'ly != ry'
print ly, ry
print lv[lrun][lx].keys()
print rv[rrun][rx].keys()
return False
return True
def binaryop(op, lf, rf):
result = {}
if isinstance(lf, FormulaStat) and isinstance(rf, FormulaStat):
lv = lf.value
rv = rf.value
if not same(lv, rv):
raise AttributeError, "run,x,y not identical"
for run in lv.keys():
result[run] = {}
for x in lv[run].keys():
result[run][x] = {}
for y in lv[run][x].keys():
result[run][x][y] = wrapop(op, lv[run][x][y],
rv[run][x][y])
elif isinstance(lf, FormulaStat):
lv = lf.value
for run in lv.keys():
result[run] = {}
for x in lv[run].keys():
result[run][x] = {}
for y in lv[run][x].keys():
result[run][x][y] = wrapop(op, lv[run][x][y], rf)
elif isinstance(rf, FormulaStat):
rv = rf.value
for run in rv.keys():
result[run] = {}
for x in rv[run].keys():
result[run][x] = {}
for y in rv[run][x].keys():
result[run][x][y] = wrapop(op, lf, rv[run][x][y])
return result
def sums(x, y):
if issequence(x):
return map(lambda x, y: x + y, x, y)
else:
return x + y
def alltrue(list):
return reduce(lambda x, y: x and y, list)
def allfalse(list):
return not reduce(lambda x, y: x or y, list)
def enumerate(list):
return map(None, range(len(list)), list)
def cmp(a, b):
if a < b:
return -1
elif a == b:
return 0
else:
return 1
class Statistic(object):
def __init__(self, data):
self.__dict__.update(data.__dict__)
if not self.__dict__.has_key('value'):
self.__dict__['value'] = None
if not self.__dict__.has_key('bins'):
self.__dict__['bins'] = None
if not self.__dict__.has_key('ticks'):
self.__dict__['ticks'] = None
def __getattribute__(self, attr):
if attr == 'value':
if self.__dict__['value'] == None:
self.__dict__['value'] = self.getValue()
return self.__dict__['value']
else:
return super(Statistic, self).__getattribute__(attr)
def __setattr__(self, attr, value):
if attr == 'bins' or attr == 'ticks':
if attr == 'bins':
global db
if value is not None:
value = db.getBin(value)
elif attr == 'samples' and type(value) is str:
value = [ int(x) for x in value.split() ]
self.__dict__[attr] = value
self.__dict__['value'] = None
else:
super(Statistic, self).__setattr__(attr, value)
def getValue(self):
raise AttributeError, 'getValue() must be defined'
def zero(self):
return False
def __ne__(self, other):
return not (self == other)
def __str__(self):
return '%f' % (float(self))
class FormulaStat(object):
def __add__(self, other):
f = FormulaStat()
f.value = binaryop(operator.add, self, other)
return f
def __sub__(self, other):
f = FormulaStat()
f.value = binaryop(operator.sub, self, other)
return f
def __mul__(self, other):
f = FormulaStat()
f.value = binaryop(operator.mul, self, other)
return f
def __truediv__(self, other):
f = FormulaStat()
f.value = binaryop(zerodiv, self, other)
return f
def __mod__(self, other):
f = FormulaStat()
f.value = binaryop(operator.mod, self, other)
return f
def __radd__(self, other):
f = FormulaStat()
f.value = binaryop(operator.add, other, self)
return f
def __rsub__(self, other):
f = FormulaStat()
f.value = binaryop(operator.sub, other, self)
return f
def __rmul__(self, other):
f = FormulaStat()
f.value = binaryop(operator.mul, other, self)
return f
def __rtruediv__(self, other):
f = FormulaStat()
f.value = binaryop(zerodiv, other, self)
return f
def __rmod__(self, other):
f = FormulaStat()
f.value = binaryop(operator.mod, other, self)
return f
def __neg__(self):
f = FormulaStat()
f.value = unaryop(operator.neg, self)
return f
def __getitem__(self, idx):
f = FormulaStat()
f.value = {}
for key in self.value.keys():
f.value[key] = {}
f.value[key][0] = {}
f.value[key][0][0] = self.value[key][idx][0]
return f
def __float__(self):
if isinstance(self.value, FormulaStat):
return float(self.value)
if not self.value.has_key(display_run):
return (1e300*1e300)
if len(self.value[display_run]) == 1:
return self.value[display_run][0][0]
else:
#print self.value[display_run]
return self.value[display_run][4][0]
#raise ValueError
def display(self):
import display
d = display.VectorDisplay()
d.flags = 0
d.precision = 1
d.name = 'formula'
d.desc = 'formula'
val = self.value[display_run]
d.value = [ val[x][0] for x in val.keys() ]
d.display()
class Scalar(Statistic,FormulaStat):
def getValue(self):
return source.data(self, self.bins)
def display(self):
import display
p = display.Print()
p.name = self.name
p.desc = self.desc
p.value = float(self)
p.flags = self.flags
p.precision = self.precision
if display.all or (self.flags & flags.printable):
p.display()
def comparable(self, other):
return self.name == other.name
def __eq__(self, other):
return self.value == other.value
def __isub__(self, other):
self.value -= other.value
return self
def __iadd__(self, other):
self.value += other.value
return self
def __itruediv__(self, other):
if not other:
return self
self.value /= other
return self
class Vector(Statistic,FormulaStat):
def getValue(self):
return source.data(self, self.bins);
def display(self):
import display
if not display.all and not (self.flags & flags.printable):
return
d = display.VectorDisplay()
d.__dict__.update(self.__dict__)
d.display()
def comparable(self, other):
return self.name == other.name and \
len(self.value) == len(other.value)
def __eq__(self, other):
if issequence(self.value) != issequence(other.value):
return false
if issequence(self.value):
if len(self.value) != len(other.value):
return False
else:
for v1,v2 in zip(self.value, other.value):
if v1 != v2:
return False
return True
else:
return self.value == other.value
def __isub__(self, other):
self.value = binaryop(operator.sub, self.value, other.value)
return self
def __iadd__(self, other):
self.value = binaryop(operator.add, self.value, other.value)
return self
def __itruediv__(self, other):
if not other:
return self
if issequence(self.value):
for i in xrange(len(self.value)):
self.value[i] /= other
else:
self.value /= other
return self
class Formula(Vector):
def getValue(self):
formula = re.sub(':', '__', self.formula)
x = eval(formula, source.stattop)
return x.value
def comparable(self, other):
return self.name == other.name and \
compare(self.dist, other.dist)
def __eq__(self, other):
return self.value == other.value
def __isub__(self, other):
return self
def __iadd__(self, other):
return self
def __itruediv__(self, other):
if not other:
return self
return self
class SimpleDist(object):
def __init__(self, sums, squares, samples):
self.sums = sums
self.squares = squares
self.samples = samples
def getValue(self):
return 0.0
def display(self, name, desc, flags, precision):
import display
p = display.Print()
p.flags = flags
p.precision = precision
if self.samples > 0:
p.name = name + ".mean"
p.value = self.sums / self.samples
p.display()
p.name = name + ".stdev"
if self.samples > 1:
var = (self.samples * self.squares - self.sums ** 2) \
/ (self.samples * (self.samples - 1))
if var >= 0:
p.value = math.sqrt(var)
else:
p.value = 'NaN'
else:
p.value = 0.0
p.display()
p.name = name + ".samples"
p.value = self.samples
p.display()
def comparable(self, other):
return True
def __eq__(self, other):
return self.sums == other.sums and self.squares == other.squares and \
self.samples == other.samples
def __isub__(self, other):
self.sums -= other.sums
self.squares -= other.squares
self.samples -= other.samples
return self
def __iadd__(self, other):
self.sums += other.sums
self.squares += other.squares
self.samples += other.samples
return self
def __itruediv__(self, other):
if not other:
return self
self.sums /= other
self.squares /= other
self.samples /= other
return self
class FullDist(SimpleDist):
def __init__(self, sums, squares, samples, minval, maxval,
under, vec, over, min, max, bsize, size):
self.sums = sums
self.squares = squares
self.samples = samples
self.minval = minval
self.maxval = maxval
self.under = under
self.vec = vec
self.over = over
self.min = min
self.max = max
self.bsize = bsize
self.size = size
def getValue(self):
return 0.0
def display(self, name, desc, flags, precision):
import display
p = display.Print()
p.flags = flags
p.precision = precision
p.name = name + '.min_val'
p.value = self.minval
p.display()
p.name = name + '.max_val'
p.value = self.maxval
p.display()
p.name = name + '.underflow'
p.value = self.under
p.display()
i = self.min
for val in self.vec[:-1]:
p.name = name + '[%d:%d]' % (i, i + self.bsize - 1)
p.value = val
p.display()
i += self.bsize
p.name = name + '[%d:%d]' % (i, self.max)
p.value = self.vec[-1]
p.display()
p.name = name + '.overflow'
p.value = self.over
p.display()
SimpleDist.display(self, name, desc, flags, precision)
def comparable(self, other):
return self.min == other.min and self.max == other.max and \
self.bsize == other.bsize and self.size == other.size
def __eq__(self, other):
return self.sums == other.sums and self.squares == other.squares and \
self.samples == other.samples
def __isub__(self, other):
self.sums -= other.sums
self.squares -= other.squares
self.samples -= other.samples
if other.samples:
self.minval = min(self.minval, other.minval)
self.maxval = max(self.maxval, other.maxval)
self.under -= under
self.vec = map(lambda x,y: x - y, self.vec, other.vec)
self.over -= over
return self
def __iadd__(self, other):
if not self.samples and other.samples:
self = other
return self
self.sums += other.sums
self.squares += other.squares
self.samples += other.samples
if other.samples:
self.minval = min(self.minval, other.minval)
self.maxval = max(self.maxval, other.maxval)
self.under += other.under
self.vec = map(lambda x,y: x + y, self.vec, other.vec)
self.over += other.over
return self
def __itruediv__(self, other):
if not other:
return self
self.sums /= other
self.squares /= other
self.samples /= other
if self.samples:
self.under /= other
for i in xrange(len(self.vec)):
self.vec[i] /= other
self.over /= other
return self
class Dist(Statistic):
def getValue(self):
return 0.0
def display(self):
import display
if not display.all and not (self.flags & flags.printable):
return
self.dist.display(self.name, self.desc, self.flags, self.precision)
def comparable(self, other):
return self.name == other.name and \
self.dist.compareable(other.dist)
def __eq__(self, other):
return self.dist == other.dist
def __isub__(self, other):
self.dist -= other.dist
return self
def __iadd__(self, other):
self.dist += other.dist
return self
def __itruediv__(self, other):
if not other:
return self
self.dist /= other
return self
class VectorDist(Statistic):
def getValue(self):
return 0.0
def display(self):
import display
if not display.all and not (self.flags & flags.printable):
return
if isinstance(self.dist, SimpleDist):
return
for dist,sn,sd,i in map(None, self.dist, self.subnames, self.subdescs,
range(len(self.dist))):
if len(sn) > 0:
name = '%s.%s' % (self.name, sn)
else:
name = '%s[%d]' % (self.name, i)
if len(sd) > 0:
desc = sd
else:
desc = self.desc
dist.display(name, desc, self.flags, self.precision)
if (self.flags & flags.total) or 1:
if isinstance(self.dist[0], SimpleDist):
disttotal = SimpleDist( \
reduce(sums, [d.sums for d in self.dist]),
reduce(sums, [d.squares for d in self.dist]),
reduce(sums, [d.samples for d in self.dist]))
else:
disttotal = FullDist( \
reduce(sums, [d.sums for d in self.dist]),
reduce(sums, [d.squares for d in self.dist]),
reduce(sums, [d.samples for d in self.dist]),
min([d.minval for d in self.dist]),
max([d.maxval for d in self.dist]),
reduce(sums, [d.under for d in self.dist]),
reduce(sums, [d.vec for d in self.dist]),
reduce(sums, [d.over for d in self.dist]),
dist[0].min,
dist[0].max,
dist[0].bsize,
dist[0].size)
name = '%s.total' % (self.name)
desc = self.desc
disttotal.display(name, desc, self.flags, self.precision)
def comparable(self, other):
return self.name == other.name and \
alltrue(map(lambda x, y : x.comparable(y),
self.dist,
other.dist))
def __eq__(self, other):
return alltrue(map(lambda x, y : x == y, self.dist, other.dist))
def __isub__(self, other):
if issequence(self.dist) and issequence(other.dist):
for sd,od in zip(self.dist, other.dist):
sd -= od
else:
self.dist -= other.dist
return self
def __iadd__(self, other):
if issequence(self.dist) and issequence(other.dist):
for sd,od in zip(self.dist, other.dist):
sd += od
else:
self.dist += other.dist
return self
def __itruediv__(self, other):
if not other:
return self
if issequence(self.dist):
for dist in self.dist:
dist /= other
else:
self.dist /= other
return self
class Vector2d(Statistic):
def getValue(self):
return 0.0
def display(self):
import display
if not display.all and not (self.flags & flags.printable):
return
d = display.VectorDisplay()
d.__dict__.update(self.__dict__)
if self.__dict__.has_key('ysubnames'):
ysubnames = list(self.ysubnames)
slack = self.x - len(ysubnames)
if slack > 0:
ysubnames.extend(['']*slack)
else:
ysubnames = range(self.x)
for x,sname in enumerate(ysubnames):
o = x * self.y
d.value = self.value[o:o+self.y]
d.name = '%s[%s]' % (self.name, sname)
d.display()
if self.flags & flags.total:
d.value = []
for y in range(self.y):
xtot = 0.0
for x in range(self.x):
xtot += self.value[y + x * self.x]
d.value.append(xtot)
d.name = self.name + '.total'
d.display()
def comparable(self, other):
return self.name == other.name and self.x == other.x and \
self.y == other.y
def __eq__(self, other):
return True
def __isub__(self, other):
return self
def __iadd__(self, other):
return self
def __itruediv__(self, other):
if not other:
return self
return self
def NewStat(data):
stat = None
if data.type == 'SCALAR':
stat = Scalar(data)
elif data.type == 'VECTOR':
stat = Vector(data)
elif data.type == 'DIST':
stat = Dist(data)
elif data.type == 'VECTORDIST':
stat = VectorDist(data)
elif data.type == 'VECTOR2D':
stat = Vector2d(data)
elif data.type == 'FORMULA':
stat = Formula(data)
return stat