gem5/util/stats/info.py
Ali Saidi 21c6dd80d7 fix a display bug
add option to limit results to a set of ticks
fix ticks code to work

util/stats/info.py:
    change samples -> ticks and pass all parameters
util/stats/stats.py:
    add option to select a set of ticks and fix display bug

--HG--
extra : convert_revision : eca80a8c6bb75cf82bf1624f3d0170690b2928af
2005-01-13 23:59:39 -05:00

721 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(lrun, rrun):
for lx,rx in zip(lrun.keys(),rrun.keys()):
if lx != rx:
print 'lx != rx'
print lx, rx
print lrun.keys()
print rrun.keys()
return False
for ly,ry in zip(lrun[lx].keys(),rrun[rx].keys()):
if ly != ry:
print 'ly != ry'
print ly, ry
print lrun[lx].keys()
print 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
theruns = []
for r in lv.keys():
if rv.has_key(r):
if same(lv[r], rv[r]):
theruns.append(r)
else:
raise AttributeError
for run in theruns:
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':
if value is not None:
value = source.getBin(value)
elif attr == 'ticks' 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, self.ticks)
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