diff options
Diffstat (limited to 'util/stats/info.py')
| -rw-r--r-- | util/stats/info.py | 669 |
1 files changed, 309 insertions, 360 deletions
diff --git a/util/stats/info.py b/util/stats/info.py index ae5d3211f..9932d7922 100644 --- a/util/stats/info.py +++ b/util/stats/info.py @@ -27,391 +27,347 @@ from __future__ import division import operator, re, types -source = None -display_run = 0 -global globalTicks -globalTicks = None - -def total(f): - if isinstance(f, FormulaStat): - v = f.value - else: - v = f - - f = FormulaStat() - if isinstance(v, (list, tuple)): - 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 isinstance(v, (list, tuple)): - 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]) - +def unproxy(proxy): + if hasattr(proxy, '__unproxy__'): + return proxy.__unproxy__() + + return proxy + +def scalar(stat): + stat = unproxy(stat) + assert(stat.__scalar__() != stat.__vector__()) + return stat.__scalar__() + +def vector(stat): + stat = unproxy(stat) + assert(stat.__scalar__() != stat.__vector__()) + return stat.__vector__() + +def value(stat, *args): + stat = unproxy(stat) + return stat.__value__(*args) + +def values(stat, run): + stat = unproxy(stat) + result = [] + for i in xrange(len(stat)): + val = value(stat, run.run, i) + if val is None: + return None + result.append(val) return result -def sums(x, y): - if isinstance(x, (list, tuple)): - return map(lambda x, y: x + y, x, y) - else: - return x + y - -def alltrue(seq): - return reduce(lambda x, y: x and y, seq) - -def allfalse(seq): - return not reduce(lambda x, y: x or y, seq) - -def enumerate(seq): - return map(None, range(len(seq)), seq) - -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 - if 'vc' not in self.__dict__: - self.vc = {} - - def __getattribute__(self, attr): - if attr == 'ticks': - if self.__dict__['ticks'] != globalTicks: - self.__dict__['value'] = None - self.__dict__['ticks'] = globalTicks - return self.__dict__['ticks'] - if attr == 'value': - if self.__dict__['ticks'] != globalTicks: - if self.__dict__['ticks'] != None and \ - len(self.__dict__['ticks']) == 1: - self.vc[self.__dict__['ticks'][0]] = self.__dict__['value'] - self.__dict__['ticks'] = globalTicks - if len(globalTicks) == 1 and self.vc.has_key(globalTicks[0]): - self.__dict__['value'] = self.vc[globalTicks[0]] - else: - self.__dict__['value'] = None - 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 - self.vc = {} - else: - super(Statistic, self).__setattr__(attr, value) +def total(stat, run): + return sum(values(stat, run)) - def getValue(self): - raise AttributeError, 'getValue() must be defined' - - def zero(self): - return False +def len(stat): + stat = unproxy(stat) + return stat.__len__() - def __ne__(self, other): - return not (self == other) +class Value(object): + def __scalar__(self): + raise AttributeError, "must define __scalar__ for %s" % (type (self)) + def __vector__(self): + raise AttributeError, "must define __vector__ for %s" % (type (self)) - 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 + return BinaryProxy(operator.__add__, self, other) def __sub__(self, other): - f = FormulaStat() - f.value = binaryop(operator.sub, self, other) - return f + return BinaryProxy(operator.__sub__, self, other) def __mul__(self, other): - f = FormulaStat() - f.value = binaryop(operator.mul, self, other) - return f + return BinaryProxy(operator.__mul__, self, other) + def __div__(self, other): + return BinaryProxy(operator.__div__, self, other) 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 + return BinaryProxy(operator.__truediv__, self, other) + def __floordiv__(self, other): + return BinaryProxy(operator.__floordiv__, self, other) + def __radd__(self, other): - f = FormulaStat() - f.value = binaryop(operator.add, other, self) - return f + return BinaryProxy(operator.__add__, other, self) def __rsub__(self, other): - f = FormulaStat() - f.value = binaryop(operator.sub, other, self) - return f + return BinaryProxy(operator.__sub__, other, self) def __rmul__(self, other): - f = FormulaStat() - f.value = binaryop(operator.mul, other, self) - return f + return BinaryProxy(operator.__mul__, other, self) + def __rdiv__(self, other): + return BinaryProxy(operator.__div__, other, self) 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 + return BinaryProxy(operator.__truediv__, other, self) + def __rfloordiv__(self, other): + return BinaryProxy(operator.__floordiv__, other, self) + 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 + return UnaryProxy(operator.__neg__, self) + def __pos__(self): + return UnaryProxy(operator.__pos__, self) + def __abs__(self): + return UnaryProxy(operator.__abs__, self) + +class Scalar(Value): + def __scalar__(self): + return True - 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() + def __vector__(self): + return False + def __value__(self, run): + raise AttributeError, '__value__ must be defined' -class Scalar(Statistic,FormulaStat): - def getValue(self): - return source.data(self, self.bins, self.ticks) +class VectorItemProxy(Value): + def __init__(self, proxy, index): + self.proxy = proxy + self.index = index - 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 __scalar__(self): + return True - def comparable(self, other): - return self.name == other.name + def __vector__(self): + return False - def __eq__(self, other): - return self.value == other.value + def __value__(self, run): + return value(self.proxy, run, self.index) - def __isub__(self, other): - self.value -= other.value - return self +class Vector(Value): + def __scalar__(self): + return False - def __iadd__(self, other): - self.value += other.value - return self + def __vector__(self): + return True - def __itruediv__(self, other): - if not other: - return self - self.value /= other - return self + def __value__(self, run, index): + raise AttributeError, '__value__ must be defined' -class Vector(Statistic,FormulaStat): - def getValue(self): - return source.data(self, self.bins, self.ticks); + def __getitem__(self, index): + return VectorItemProxy(self, index) - def display(self): - import display - if not display.all and not (self.flags & flags.printable): - return +class ScalarConstant(Scalar): + def __init__(self, constant): + self.constant = constant + def __value__(self, run): + return self.constant - d = display.VectorDisplay() - d.__dict__.update(self.__dict__) - d.display() +class VectorConstant(Vector): + def __init__(self, constant): + self.constant = constant + def __value__(self, run, index): + return self.constant[index] + def __len__(self): + return len(self.constant) - def comparable(self, other): - return self.name == other.name and \ - len(self.value) == len(other.value) +def WrapValue(value): + if isinstance(value, (int, long, float)): + return ScalarConstant(value) + if isinstance(value, (list, tuple)): + return VectorConstant(value) + if isinstance(value, Value): + return value - def __eq__(self, other): - if isinstance(self.value, (list, tuple)) != \ - isinstance(other.value, (list, tuple)): - return False + raise AttributeError, 'Only values can be wrapped' - if isinstance(self.value, (list, tuple)): - 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 +class Statistic(object): + def __getattr__(self, attr): + if attr in ('data', 'x', 'y'): + result = self.source.data(self, self.bins, self.ticks) + self.data = result.data + self.x = result.x + self.y = result.y + return super(Statistic, self).__getattribute__(attr) - def __isub__(self, other): - self.value = binaryop(operator.sub, self.value, other.value) - return self + def __setattr__(self, attr, value): + if attr == 'stat': + raise AttributeError, '%s is read only' % stat + if attr in ('source', 'bins', 'ticks'): + if getattr(self, attr) != value: + if hasattr(self, 'data'): + delattr(self, 'data') + + super(Statistic, self).__setattr__(attr, value) + +class ValueProxy(Value): + def __getattr__(self, attr): + if attr == '__value__': + if scalar(self): + return self.__scalarvalue__ + if vector(self): + return self.__vectorvalue__ + if attr == '__len__': + if vector(self): + return self.__vectorlen__ + return super(ValueProxy, self).__getattribute__(attr) + +class UnaryProxy(ValueProxy): + def __init__(self, op, arg): + self.op = op + self.arg = WrapValue(arg) + + def __scalar__(self): + return scalar(self.arg) + + def __vector__(self): + return vector(self.arg) + + def __scalarvalue__(self, run): + val = value(self.arg, run) + if val is None: + return None + return self.op(val) + + def __vectorvalue__(self, run, index): + val = value(self.arg, run, index) + if val is None: + return None + return self.op(val) + + def __vectorlen__(self): + return len(unproxy(self.arg)) + +class BinaryProxy(ValueProxy): + def __init__(self, op, arg0, arg1): + super(BinaryProxy, self).__init__() + self.op = op + self.arg0 = WrapValue(arg0) + self.arg1 = WrapValue(arg1) + + def __scalar__(self): + return scalar(self.arg0) and scalar(self.arg1) + + def __vector__(self): + return vector(self.arg0) or vector(self.arg1) + + def __scalarvalue__(self, run): + val0 = value(self.arg0, run) + val1 = value(self.arg1, run) + if val0 is None or val1 is None: + return None + return self.op(val0, val1) + + def __vectorvalue__(self, run, index): + if scalar(self.arg0): + val0 = value(self.arg0, run) + if vector(self.arg0): + val0 = value(self.arg0, run, index) + if scalar(self.arg1): + val1 = value(self.arg1, run) + if vector(self.arg1): + val1 = value(self.arg1, run, index) + + if val0 is None or val1 is None: + return None + + return self.op(val0, val1) + + def __vectorlen__(self): + if vector(self.arg0) and scalar(self.arg1): + return len(self.arg0) + if scalar(self.arg0) and vector(self.arg1): + return len(self.arg1) + + len0 = len(self.arg0) + len1 = len(self.arg1) + + if len0 != len1: + raise AttributeError, \ + "vectors of different lengths %d != %d" % (len0, len1) + + return len0 + +class Proxy(Value): + def __init__(self, name, dict): + self.name = name + self.dict = dict + + def __unproxy__(self): + return unproxy(self.dict[self.name]) + + def __getitem__(self, index): + return ItemProxy(self, index) + + def __getattr__(self, attr): + return AttrProxy(self, attr) + +class ItemProxy(Proxy): + def __init__(self, proxy, index): + self.proxy = proxy + self.index = index + + def __unproxy__(self): + return unproxy(unproxy(self.proxy)[self.index]) - def __iadd__(self, other): - self.value = binaryop(operator.add, self.value, other.value) - return self +class AttrProxy(Proxy): + def __init__(self, proxy, attr): + self.proxy = proxy + self.attr = attr - def __itruediv__(self, other): - if not other: - return self - if isinstance(self.value, (list, tuple)): - for i in xrange(len(self.value)): - self.value[i] /= other - else: - self.value /= other - return self + def __unproxy__(self): + return unproxy(getattr(unproxy(self.proxy), self.attr)) -class Formula(Vector): - def getValue(self): - formula = re.sub(':', '__', self.formula) - x = eval(formula, source.stattop) - return x.value +class ProxyGroup(object): + def __init__(self, dict=None, **kwargs): + self.__dict__['dict'] = {} - def comparable(self, other): - return self.name == other.name and \ - compare(self.dist, other.dist) + if dict is not None: + self.dict.update(dict) - def __eq__(self, other): - return self.value == other.value + if kwargs: + self.dict.update(kwargs) - def __isub__(self, other): - return self + def __getattr__(self, name): + return Proxy(name, self.dict) - def __iadd__(self, other): - return self + def __setattr__(self, attr, value): + self.dict[attr] = value - def __itruediv__(self, other): - if not other: - return self - return self +class ScalarStat(Statistic,Scalar): + def __value__(self, run): + if run not in self.data: + return None + return self.data[run][0][0] + + def display(self, run=None): + import display + p = display.Print() + p.name = self.name + p.desc = self.desc + p.value = value(self, run) + p.flags = self.flags + p.precision = self.precision + if display.all or (self.flags & flags.printable): + p.display() + +class VectorStat(Statistic,Vector): + def __value__(self, run, item): + if run not in self.data: + return None + return self.data[run][item][0] + + def __len__(self): + return self.x -class SimpleDist(object): + def display(self, run=None): + import display + d = display.VectorDisplay() + d.name = self.name + d.desc = self.desc + d.value = [ value(self, run, i) for i in xrange(len(self)) ] + d.flags = self.flags + d.precision = self.precision + d.display() + +class Formula(Value): + def __getattribute__(self, attr): + if attr not in ( '__scalar__', '__vector__', '__value__', '__len__' ): + return super(Formula, self).__getattribute__(attr) + + formula = re.sub(':', '__', self.formula) + value = eval(formula, self.source.stattop) + return getattr(value, attr) + +class SimpleDist(Statistic): 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() @@ -482,9 +438,6 @@ class FullDist(SimpleDist): self.bsize = bsize self.size = size - def getValue(self): - return 0.0 - def display(self, name, desc, flags, precision): import display p = display.Print() @@ -574,9 +527,6 @@ class FullDist(SimpleDist): 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): @@ -606,9 +556,6 @@ class Dist(Statistic): 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): @@ -694,9 +641,6 @@ class VectorDist(Statistic): 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): @@ -748,20 +692,25 @@ class Vector2d(Statistic): return self return self -def NewStat(data): +def NewStat(source, data): stat = None if data.type == 'SCALAR': - stat = Scalar(data) + stat = ScalarStat() elif data.type == 'VECTOR': - stat = Vector(data) + stat = VectorStat() elif data.type == 'DIST': - stat = Dist(data) + stat = Dist() elif data.type == 'VECTORDIST': - stat = VectorDist(data) + stat = VectorDist() elif data.type == 'VECTOR2D': - stat = Vector2d(data) + stat = Vector2d() elif data.type == 'FORMULA': - stat = Formula(data) + stat = Formula() + + stat.__dict__['source'] = source + stat.__dict__['bins'] = None + stat.__dict__['ticks'] = None + stat.__dict__.update(data.__dict__) return stat |