Merge zizzer.eecs.umich.edu:/bk/m5
into zizzer.eecs.umich.edu:/.automount/ziff/z/binkertn/research/m5/work --HG-- extra : convert_revision : 8ef6ed2d770d45ac11d44a449e2c4f74ef656d87
This commit is contained in:
Nathan Binkert
commit
7ef8b40b3b
5 changed files with 476 additions and 506 deletions
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@ -103,6 +103,20 @@ class Node(object):
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def __str__(self):
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return self.name
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class Result(object):
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def __init__(self, x, y):
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self.data = {}
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self.x = x
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self.y = y
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def __contains__(self, run):
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return run in self.data
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def __getitem__(self, run):
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if run not in self.data:
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self.data[run] = [ [ 0.0 ] * self.y for i in xrange(self.x) ]
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return self.data[run]
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class Database(object):
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def __init__(self):
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self.host = 'zizzer.pool'
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@ -135,7 +149,23 @@ class Database(object):
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self.runs = None
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self.bins = None
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self.ticks = None
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self.__dict__['get'] = type(self).sum
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self.method = 'sum'
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self._method = type(self).sum
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def get(self, job, stat):
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run = self.allRunNames.get(job.name, None)
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if run is None:
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print 'run "%s" not found' % job
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return None
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from info import scalar, vector, value, total, len
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stat.system = self[job.system]
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if scalar(stat):
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return value(stat, run.run)
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if vector(stat):
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return values(stat, run.run)
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return None
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def query(self, sql):
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self.cursor.execute(sql)
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@ -203,7 +233,7 @@ class Database(object):
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self.query('select * from stats')
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import info
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for result in self.cursor.fetchall():
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stat = info.NewStat(StatData(result))
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stat = info.NewStat(self, StatData(result))
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self.append(stat)
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self.allStats.append(stat)
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self.allStatIds[stat.stat] = stat
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@ -421,30 +451,17 @@ class Database(object):
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def stdev(self, stat, bins, ticks):
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return self.outer('stddev', 'sum', stat, bins, ticks)
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def __getattribute__(self, attr):
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if attr != 'get':
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return super(Database, self).__getattribute__(attr)
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if self.__dict__['get'] == type(self).sum:
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return 'sum'
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elif self.__dict__['get'] == type(self).avg:
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return 'avg'
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elif self.__dict__['get'] == type(self).stdev:
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return 'stdev'
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else:
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return ''
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def __setattr__(self, attr, value):
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if attr != 'get':
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super(Database, self).__setattr__(attr, value)
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super(Database, self).__setattr__(attr, value)
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if attr != 'method':
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return
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if value == 'sum':
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self.__dict__['get'] = type(self).sum
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self._method = self.sum
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elif value == 'avg':
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self.__dict__['get'] = type(self).avg
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self._method = self.avg
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elif value == 'stdev':
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self.__dict__['get'] = type(self).stdev
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self._method = self.stdev
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else:
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raise AttributeError, "can only set get to: sum | avg | stdev"
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@ -453,10 +470,12 @@ class Database(object):
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bins = self.bins
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if ticks is None:
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ticks = self.ticks
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sql = self.__dict__['get'](self, stat, bins, ticks)
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sql = self._method(self, stat, bins, ticks)
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self.query(sql)
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runs = {}
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xmax = 0
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ymax = 0
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for x in self.cursor.fetchall():
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data = Data(x)
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if not runs.has_key(data.run):
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@ -464,8 +483,17 @@ class Database(object):
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if not runs[data.run].has_key(data.x):
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runs[data.run][data.x] = {}
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xmax = max(xmax, data.x)
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ymax = max(ymax, data.y)
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runs[data.run][data.x][data.y] = data.data
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return runs
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results = Result(xmax + 1, ymax + 1)
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for run,data in runs.iteritems():
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result = results[run]
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for x,ydata in data.iteritems():
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for y,data in ydata.iteritems():
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result[x][y] = data
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return results
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def __getitem__(self, key):
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return self.stattop[key]
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@ -26,7 +26,7 @@
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class Value:
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def __init__(self, value, precision, percent = False):
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self.value = value
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self.value = float(value)
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self.precision = precision
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self.percent = percent
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def __str__(self):
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@ -90,61 +90,60 @@ class Print:
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class VectorDisplay:
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def display(self):
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if not self.value:
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return
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p = Print()
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p.flags = self.flags
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p.precision = self.precision
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if isinstance(self.value, (list, tuple)):
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if not len(self.value):
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return
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mytotal = reduce(lambda x,y: float(x) + float(y), self.value)
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mycdf = 0.0
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value = self.value
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if display_all:
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subnames = [ '[%d]' % i for i in range(len(value)) ]
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else:
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subnames = [''] * len(value)
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if self.__dict__.has_key('subnames'):
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for i,each in enumerate(self.subnames):
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if len(each) > 0:
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subnames[i] = '.%s' % each
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subdescs = [self.desc]*len(value)
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if self.__dict__.has_key('subdescs'):
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for i in xrange(min(len(value), len(self.subdescs))):
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subdescs[i] = self.subdescs[i]
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for val,sname,sdesc in map(None, value, subnames, subdescs):
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if mytotal > 0.0:
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mypdf = float(val) / float(mytotal)
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mycdf += mypdf
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if (self.flags & flags_pdf):
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p.pdf = mypdf
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p.cdf = mycdf
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if len(sname) == 0:
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continue
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p.name = self.name + sname
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p.desc = sdesc
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p.value = val
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p.display()
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if (self.flags & flags_total):
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if (p.__dict__.has_key('pdf')): del p.__dict__['pdf']
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if (p.__dict__.has_key('cdf')): del p.__dict__['cdf']
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p.name = self.name + '.total'
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p.desc = self.desc
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p.value = mytotal
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p.display()
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else:
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if not isinstance(self.value, (list, tuple)):
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p.name = self.name
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p.desc = self.desc
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p.value = self.value
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p.display()
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return
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mytotal = reduce(lambda x,y: float(x) + float(y), self.value)
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mycdf = 0.0
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value = self.value
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if display_all:
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subnames = [ '[%d]' % i for i in range(len(value)) ]
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else:
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subnames = [''] * len(value)
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if self.__dict__.has_key('subnames'):
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for i,each in enumerate(self.subnames):
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if len(each) > 0:
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subnames[i] = '.%s' % each
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subdescs = [self.desc]*len(value)
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if self.__dict__.has_key('subdescs'):
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for i in xrange(min(len(value), len(self.subdescs))):
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subdescs[i] = self.subdescs[i]
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for val,sname,sdesc in map(None, value, subnames, subdescs):
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if mytotal > 0.0:
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mypdf = float(val) / float(mytotal)
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mycdf += mypdf
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if (self.flags & flags_pdf):
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p.pdf = mypdf
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p.cdf = mycdf
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if len(sname) == 0:
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continue
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p.name = self.name + sname
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p.desc = sdesc
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p.value = val
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p.display()
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if (self.flags & flags_total):
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if (p.__dict__.has_key('pdf')): del p.__dict__['pdf']
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if (p.__dict__.has_key('cdf')): del p.__dict__['cdf']
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p.name = self.name + '.total'
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p.desc = self.desc
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p.value = mytotal
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p.display()
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@ -27,391 +27,347 @@
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from __future__ import division
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import operator, re, types
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source = None
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display_run = 0
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global globalTicks
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globalTicks = None
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def unproxy(proxy):
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if hasattr(proxy, '__unproxy__'):
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return proxy.__unproxy__()
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def total(f):
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if isinstance(f, FormulaStat):
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v = f.value
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else:
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v = f
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return proxy
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f = FormulaStat()
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if isinstance(v, (list, tuple)):
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f.value = reduce(operator.add, v)
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else:
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f.value = v
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def scalar(stat):
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stat = unproxy(stat)
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assert(stat.__scalar__() != stat.__vector__())
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return stat.__scalar__()
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return f
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def vector(stat):
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stat = unproxy(stat)
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assert(stat.__scalar__() != stat.__vector__())
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return stat.__vector__()
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def unaryop(op, f):
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if isinstance(f, FormulaStat):
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v = f.value
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else:
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v = f
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if isinstance(v, (list, tuple)):
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return map(op, v)
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else:
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return op(v)
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def zerodiv(lv, rv):
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if rv == 0.0:
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return 0.0
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else:
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return operator.truediv(lv, rv)
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def wrapop(op, lv, rv):
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if isinstance(lv, str):
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return lv
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if isinstance(rv, str):
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return rv
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return op(lv, rv)
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def same(lrun, rrun):
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for lx,rx in zip(lrun.keys(),rrun.keys()):
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if lx != rx:
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print 'lx != rx'
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print lx, rx
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print lrun.keys()
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print rrun.keys()
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return False
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for ly,ry in zip(lrun[lx].keys(),rrun[rx].keys()):
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if ly != ry:
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print 'ly != ry'
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print ly, ry
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print lrun[lx].keys()
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print rrun[rx].keys()
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return False
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return True
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def binaryop(op, lf, rf):
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result = {}
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if isinstance(lf, FormulaStat) and isinstance(rf, FormulaStat):
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lv = lf.value
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rv = rf.value
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theruns = []
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for r in lv.keys():
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if rv.has_key(r):
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if same(lv[r], rv[r]):
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theruns.append(r)
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else:
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raise AttributeError
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for run in theruns:
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result[run] = {}
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for x in lv[run].keys():
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result[run][x] = {}
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for y in lv[run][x].keys():
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result[run][x][y] = wrapop(op, lv[run][x][y],
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rv[run][x][y])
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elif isinstance(lf, FormulaStat):
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lv = lf.value
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for run in lv.keys():
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result[run] = {}
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for x in lv[run].keys():
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result[run][x] = {}
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for y in lv[run][x].keys():
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result[run][x][y] = wrapop(op, lv[run][x][y], rf)
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elif isinstance(rf, FormulaStat):
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rv = rf.value
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for run in rv.keys():
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result[run] = {}
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for x in rv[run].keys():
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result[run][x] = {}
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for y in rv[run][x].keys():
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result[run][x][y] = wrapop(op, lf, rv[run][x][y])
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def value(stat, *args):
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stat = unproxy(stat)
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return stat.__value__(*args)
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def values(stat, run):
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stat = unproxy(stat)
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result = []
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for i in xrange(len(stat)):
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val = value(stat, run.run, i)
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if val is None:
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return None
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result.append(val)
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return result
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def sums(x, y):
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if isinstance(x, (list, tuple)):
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return map(lambda x, y: x + y, x, y)
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else:
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return x + y
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def total(stat, run):
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return sum(values(stat, run))
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def alltrue(seq):
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return reduce(lambda x, y: x and y, seq)
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def len(stat):
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stat = unproxy(stat)
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return stat.__len__()
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def allfalse(seq):
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return not reduce(lambda x, y: x or y, seq)
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class Value(object):
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def __scalar__(self):
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raise AttributeError, "must define __scalar__ for %s" % (type (self))
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def __vector__(self):
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raise AttributeError, "must define __vector__ for %s" % (type (self))
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def enumerate(seq):
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return map(None, range(len(seq)), seq)
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def __add__(self, other):
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return BinaryProxy(operator.__add__, self, other)
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def __sub__(self, other):
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return BinaryProxy(operator.__sub__, self, other)
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def __mul__(self, other):
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return BinaryProxy(operator.__mul__, self, other)
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def __div__(self, other):
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return BinaryProxy(operator.__div__, self, other)
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def __truediv__(self, other):
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return BinaryProxy(operator.__truediv__, self, other)
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def __floordiv__(self, other):
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return BinaryProxy(operator.__floordiv__, self, other)
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def cmp(a, b):
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if a < b:
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return -1
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elif a == b:
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return 0
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else:
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return 1
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def __radd__(self, other):
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return BinaryProxy(operator.__add__, other, self)
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def __rsub__(self, other):
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return BinaryProxy(operator.__sub__, other, self)
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def __rmul__(self, other):
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return BinaryProxy(operator.__mul__, other, self)
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def __rdiv__(self, other):
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return BinaryProxy(operator.__div__, other, self)
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def __rtruediv__(self, other):
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return BinaryProxy(operator.__truediv__, other, self)
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def __rfloordiv__(self, other):
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return BinaryProxy(operator.__floordiv__, other, self)
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class Statistic(object):
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def __neg__(self):
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return UnaryProxy(operator.__neg__, self)
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def __pos__(self):
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return UnaryProxy(operator.__pos__, self)
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def __abs__(self):
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return UnaryProxy(operator.__abs__, self)
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def __init__(self, data):
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self.__dict__.update(data.__dict__)
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if not self.__dict__.has_key('value'):
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self.__dict__['value'] = None
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if not self.__dict__.has_key('bins'):
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self.__dict__['bins'] = None
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if not self.__dict__.has_key('ticks'):
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self.__dict__['ticks'] = None
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if 'vc' not in self.__dict__:
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self.vc = {}
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class Scalar(Value):
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def __scalar__(self):
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return True
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def __getattribute__(self, attr):
|
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if attr == 'ticks':
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if self.__dict__['ticks'] != globalTicks:
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self.__dict__['value'] = None
|
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self.__dict__['ticks'] = globalTicks
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return self.__dict__['ticks']
|
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if attr == 'value':
|
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if self.__dict__['ticks'] != globalTicks:
|
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if self.__dict__['ticks'] != None and \
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len(self.__dict__['ticks']) == 1:
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self.vc[self.__dict__['ticks'][0]] = self.__dict__['value']
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self.__dict__['ticks'] = globalTicks
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if len(globalTicks) == 1 and self.vc.has_key(globalTicks[0]):
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self.__dict__['value'] = self.vc[globalTicks[0]]
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else:
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self.__dict__['value'] = None
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if self.__dict__['value'] == None:
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self.__dict__['value'] = self.getValue()
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return self.__dict__['value']
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else:
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return super(Statistic, self).__getattribute__(attr)
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|
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def __setattr__(self, attr, value):
|
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if attr == 'bins' or attr == 'ticks':
|
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if attr == 'bins':
|
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if value is not None:
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value = source.getBin(value)
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#elif attr == 'ticks' and type(value) is str:
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# value = [ int(x) for x in value.split() ]
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self.__dict__[attr] = value
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self.__dict__['value'] = None
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self.vc = {}
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else:
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super(Statistic, self).__setattr__(attr, value)
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|
||||
def getValue(self):
|
||||
raise AttributeError, 'getValue() must be defined'
|
||||
|
||||
def zero(self):
|
||||
def __vector__(self):
|
||||
return False
|
||||
|
||||
def __ne__(self, other):
|
||||
return not (self == other)
|
||||
def __value__(self, run):
|
||||
raise AttributeError, '__value__ must be defined'
|
||||
|
||||
def __str__(self):
|
||||
return '%f' % (float(self))
|
||||
class VectorItemProxy(Value):
|
||||
def __init__(self, proxy, index):
|
||||
self.proxy = proxy
|
||||
self.index = index
|
||||
|
||||
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 __scalar__(self):
|
||||
return True
|
||||
|
||||
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 __vector__(self):
|
||||
return False
|
||||
|
||||
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 __value__(self, run):
|
||||
return value(self.proxy, run, self.index)
|
||||
|
||||
class Vector(Value):
|
||||
def __scalar__(self):
|
||||
return False
|
||||
|
||||
class Scalar(Statistic,FormulaStat):
|
||||
def getValue(self):
|
||||
return source.data(self, self.bins, self.ticks)
|
||||
def __vector__(self):
|
||||
return True
|
||||
|
||||
def display(self):
|
||||
def __value__(self, run, index):
|
||||
raise AttributeError, '__value__ must be defined'
|
||||
|
||||
def __getitem__(self, index):
|
||||
return VectorItemProxy(self, index)
|
||||
|
||||
class ScalarConstant(Scalar):
|
||||
def __init__(self, constant):
|
||||
self.constant = constant
|
||||
def __value__(self, run):
|
||||
return self.constant
|
||||
|
||||
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 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
|
||||
|
||||
raise AttributeError, 'Only values can be wrapped'
|
||||
|
||||
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 __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])
|
||||
|
||||
class AttrProxy(Proxy):
|
||||
def __init__(self, proxy, attr):
|
||||
self.proxy = proxy
|
||||
self.attr = attr
|
||||
|
||||
def __unproxy__(self):
|
||||
return unproxy(getattr(unproxy(self.proxy), self.attr))
|
||||
|
||||
class ProxyGroup(object):
|
||||
def __init__(self, dict=None, **kwargs):
|
||||
self.__dict__['dict'] = {}
|
||||
|
||||
if dict is not None:
|
||||
self.dict.update(dict)
|
||||
|
||||
if kwargs:
|
||||
self.dict.update(kwargs)
|
||||
|
||||
def __getattr__(self, name):
|
||||
return Proxy(name, self.dict)
|
||||
|
||||
def __setattr__(self, attr, value):
|
||||
self.dict[attr] = value
|
||||
|
||||
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 = float(self)
|
||||
p.value = value(self, run)
|
||||
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
|
||||
class VectorStat(Statistic,Vector):
|
||||
def __value__(self, run, item):
|
||||
if run not in self.data:
|
||||
return None
|
||||
return self.data[run][item][0]
|
||||
|
||||
def __eq__(self, other):
|
||||
return self.value == other.value
|
||||
def __len__(self):
|
||||
return self.x
|
||||
|
||||
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, self.ticks);
|
||||
|
||||
def display(self):
|
||||
def display(self, run=None):
|
||||
import display
|
||||
if not display.all and not (self.flags & flags.printable):
|
||||
return
|
||||
|
||||
d = display.VectorDisplay()
|
||||
d.__dict__.update(self.__dict__)
|
||||
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()
|
||||
|
||||
def comparable(self, other):
|
||||
return self.name == other.name and \
|
||||
len(self.value) == len(other.value)
|
||||
class Formula(Value):
|
||||
def __getattribute__(self, attr):
|
||||
if attr not in ( '__scalar__', '__vector__', '__value__', '__len__' ):
|
||||
return super(Formula, self).__getattribute__(attr)
|
||||
|
||||
def __eq__(self, other):
|
||||
if isinstance(self.value, (list, tuple)) != \
|
||||
isinstance(other.value, (list, tuple)):
|
||||
return False
|
||||
|
||||
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
|
||||
|
||||
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 isinstance(self.value, (list, tuple)):
|
||||
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
|
||||
value = eval(formula, self.source.stattop)
|
||||
return getattr(value, attr)
|
||||
|
||||
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):
|
||||
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
|
||||
|
||||
|
|
|
|||
|
|
@ -26,24 +26,8 @@
|
|||
#
|
||||
# Authors: Nathan Binkert
|
||||
|
||||
class dbinfo(object):
|
||||
def get(self, job, stat):
|
||||
import info
|
||||
|
||||
run = info.source.allRunNames.get(job.name, None)
|
||||
if run is None:
|
||||
print 'run "%s" not found' % job
|
||||
return None
|
||||
|
||||
stat.system = info.source[job.system]
|
||||
info.display_run = run.run;
|
||||
val = float(stat)
|
||||
if val == 1e300*1e300:
|
||||
return None
|
||||
return val
|
||||
|
||||
class StatOutput(object):
|
||||
def __init__(self, name, jobfile, stat=None, info=dbinfo(), binstats=None):
|
||||
def __init__(self, name, jobfile, info, stat=None, binstats=None):
|
||||
self.name = name
|
||||
self.jobfile = jobfile
|
||||
self.stat = stat
|
||||
|
|
@ -141,7 +125,7 @@ class StatOutput(object):
|
|||
data = zeros((len(groupopts), len(baropts)), Float)
|
||||
data = [ [ None ] * len(baropts) for i in xrange(len(groupopts)) ]
|
||||
enabled = False
|
||||
stacked = None
|
||||
stacked = 0
|
||||
for g,gopt in enumerate(groupopts):
|
||||
for b,bopt in enumerate(baropts):
|
||||
job = self.jobfile.job(options + [ gopt, bopt ])
|
||||
|
|
@ -149,17 +133,28 @@ class StatOutput(object):
|
|||
continue
|
||||
|
||||
val = self.info.get(job, self.stat)
|
||||
if val is None:
|
||||
val = 0.0
|
||||
curstacked = isinstance(val, (list, tuple))
|
||||
if stacked is None:
|
||||
stacked = curstacked
|
||||
else:
|
||||
if stacked != curstacked:
|
||||
raise ValueError, "some stats stacked, some not"
|
||||
if isinstance(val, (list, tuple)):
|
||||
if len(val) == 1:
|
||||
val = val[0]
|
||||
else:
|
||||
stacked = len(val)
|
||||
|
||||
data[g][b] = val
|
||||
|
||||
if stacked == 0:
|
||||
for i in xrange(len(groupopts)):
|
||||
for j in xrange(len(baropts)):
|
||||
if data[i][j] is None:
|
||||
data[i][j] = 0.0
|
||||
else:
|
||||
for i in xrange(len(groupopts)):
|
||||
for j in xrange(len(baropts)):
|
||||
val = data[i][j]
|
||||
if val is None:
|
||||
data[i][j] = [] * stacked
|
||||
elif len(val) != stacked:
|
||||
raise ValueError, "some stats stacked, some not"
|
||||
|
||||
data = array(data)
|
||||
if data.sum() == 0:
|
||||
continue
|
||||
|
|
@ -167,7 +162,10 @@ class StatOutput(object):
|
|||
bar_descs = [ opt.desc for opt in baropts ]
|
||||
group_descs = [ opt.desc for opt in groupopts ]
|
||||
if stacked:
|
||||
legend = self.info.rcategories
|
||||
try:
|
||||
legend = self.info.rcategories
|
||||
except:
|
||||
legend = [ str(i) for i in xrange(stacked) ]
|
||||
else:
|
||||
legend = bar_descs
|
||||
|
||||
|
|
|
|||
|
|
@ -98,29 +98,27 @@ def commands(options, command, args):
|
|||
|
||||
raise CommandException
|
||||
|
||||
import db, info
|
||||
info.source = db.Database()
|
||||
info.source.host = options.host
|
||||
info.source.db = options.db
|
||||
info.source.passwd = options.passwd
|
||||
info.source.user = options.user
|
||||
info.source.connect()
|
||||
#info.source.update_dict(globals())
|
||||
import db
|
||||
source = db.Database()
|
||||
source.host = options.host
|
||||
source.db = options.db
|
||||
source.passwd = options.passwd
|
||||
source.user = options.user
|
||||
source.connect()
|
||||
#source.update_dict(globals())
|
||||
|
||||
if type(options.get) is str:
|
||||
info.source.get = options.get
|
||||
if type(options.method) is str:
|
||||
source.method = options.method
|
||||
|
||||
if options.runs is None:
|
||||
runs = info.source.allRuns
|
||||
runs = source.allRuns
|
||||
else:
|
||||
rx = re.compile(options.runs)
|
||||
runs = []
|
||||
for run in info.source.allRuns:
|
||||
for run in source.allRuns:
|
||||
if rx.match(run.name):
|
||||
runs.append(run)
|
||||
|
||||
info.display_run = runs[0].run
|
||||
|
||||
if command == 'runs':
|
||||
user = None
|
||||
opts, args = getopts(args, '-u')
|
||||
|
|
@ -129,14 +127,14 @@ def commands(options, command, args):
|
|||
for o,a in opts:
|
||||
if o == '-u':
|
||||
user = a
|
||||
info.source.listRuns(user)
|
||||
source.listRuns(user)
|
||||
return
|
||||
|
||||
if command == 'stats':
|
||||
if len(args) == 0:
|
||||
info.source.listStats()
|
||||
source.listStats()
|
||||
elif len(args) == 1:
|
||||
info.source.listStats(args[0])
|
||||
source.listStats(args[0])
|
||||
else:
|
||||
raise CommandException
|
||||
|
||||
|
|
@ -144,9 +142,9 @@ def commands(options, command, args):
|
|||
|
||||
if command == 'bins':
|
||||
if len(args) == 0:
|
||||
info.source.listBins()
|
||||
source.listBins()
|
||||
elif len(args) == 1:
|
||||
info.source.listBins(args[0])
|
||||
source.listBins(args[0])
|
||||
else:
|
||||
raise CommandException
|
||||
|
||||
|
|
@ -154,9 +152,9 @@ def commands(options, command, args):
|
|||
|
||||
if command == 'formulas':
|
||||
if len(args) == 0:
|
||||
info.source.listFormulas()
|
||||
source.listFormulas()
|
||||
elif len(args) == 1:
|
||||
info.source.listFormulas(args[0])
|
||||
source.listFormulas(args[0])
|
||||
else:
|
||||
raise CommandException
|
||||
|
||||
|
|
@ -166,7 +164,7 @@ def commands(options, command, args):
|
|||
if len(args):
|
||||
raise CommandException
|
||||
|
||||
info.source.listTicks(runs)
|
||||
source.listTicks(runs)
|
||||
return
|
||||
|
||||
if command == 'stability':
|
||||
|
|
@ -177,8 +175,8 @@ def commands(options, command, args):
|
|||
merge = int(args[0])
|
||||
except ValueError:
|
||||
usage()
|
||||
stats = info.source.getStat(args[1])
|
||||
info.source.get = "sum"
|
||||
stats = source.getStat(args[1])
|
||||
source.method = 'sum'
|
||||
|
||||
def disp(*args):
|
||||
print "%-20s %12s %12s %4s %5s %5s %5s %10s" % args
|
||||
|
|
@ -195,18 +193,17 @@ def commands(options, command, args):
|
|||
|
||||
#loop through all the selected runs
|
||||
for run in runs:
|
||||
info.display_run = run.run;
|
||||
runTicks = info.source.retTicks([ run ])
|
||||
runTicks = source.retTicks([ run ])
|
||||
#throw away the first one, it's 0
|
||||
runTicks.pop(0)
|
||||
info.globalTicks = runTicks
|
||||
source.ticks = runTicks
|
||||
avg = 0
|
||||
stdev = 0
|
||||
numoutsideavg = 0
|
||||
numoutside1std = 0
|
||||
numoutside2std = 0
|
||||
pairRunTicks = []
|
||||
if float(stat) == 1e300*1e300:
|
||||
if value(stat, run.run) == 1e300*1e300:
|
||||
continue
|
||||
for t in range(0, len(runTicks)-(merge-1), merge):
|
||||
tempPair = []
|
||||
|
|
@ -215,17 +212,17 @@ def commands(options, command, args):
|
|||
pairRunTicks.append(tempPair)
|
||||
#loop through all the various ticks for each run
|
||||
for tick in pairRunTicks:
|
||||
info.globalTicks = tick
|
||||
avg += float(stat)
|
||||
source.ticks = tick
|
||||
avg += value(stat, run.run)
|
||||
avg /= len(pairRunTicks)
|
||||
for tick in pairRunTicks:
|
||||
info.globalTicks = tick
|
||||
val = float(stat)
|
||||
source.ticks = tick
|
||||
val = value(stat, run.run)
|
||||
stdev += pow((val-avg),2)
|
||||
stdev = math.sqrt(stdev / len(pairRunTicks))
|
||||
for tick in pairRunTicks:
|
||||
info.globalTicks = tick
|
||||
val = float(stat)
|
||||
source.ticks = tick
|
||||
val = value(stat, run.run)
|
||||
if (val < (avg * .9)) or (val > (avg * 1.1)):
|
||||
numoutsideavg += 1
|
||||
if (val < (avg - stdev)) or (val > (avg + stdev)):
|
||||
|
|
@ -264,9 +261,9 @@ def commands(options, command, args):
|
|||
if options.ticks:
|
||||
if not options.graph:
|
||||
print 'only displaying sample %s' % options.ticks
|
||||
info.globalTicks = [ int(x) for x in options.ticks.split() ]
|
||||
source.ticks = [ int(x) for x in options.ticks.split() ]
|
||||
|
||||
from output import StatOutput
|
||||
import output
|
||||
|
||||
def display():
|
||||
if options.graph:
|
||||
|
|
@ -280,12 +277,12 @@ def commands(options, command, args):
|
|||
raise CommandException
|
||||
|
||||
if command == 'stat':
|
||||
stats = info.source.getStat(args[0])
|
||||
stats = source.getStat(args[0])
|
||||
if command == 'formula':
|
||||
stats = eval(args[0])
|
||||
|
||||
for stat in stats:
|
||||
output = StatOutput(stat.name, options.jobfile)
|
||||
output = output.StatOutput(stat.name, options.jobfile, source)
|
||||
output.stat = stat
|
||||
output.label = stat.name
|
||||
display()
|
||||
|
|
@ -295,12 +292,11 @@ def commands(options, command, args):
|
|||
if len(args):
|
||||
raise CommandException
|
||||
|
||||
system = info.source.__dict__[options.system]
|
||||
|
||||
from proxy import ProxyGroup
|
||||
sim_ticks = info.source['sim_ticks']
|
||||
sim_seconds = info.source['sim_seconds']
|
||||
proxy = ProxyGroup(system = info.source[options.system])
|
||||
system = source.__dict__[options.system]
|
||||
from info import ProxyGroup
|
||||
sim_ticks = source['sim_ticks']
|
||||
sim_seconds = source['sim_seconds']
|
||||
proxy = ProxyGroup(system = source[options.system])
|
||||
system = proxy.system
|
||||
|
||||
etherdev = system.tsunami.etherdev0
|
||||
|
|
@ -309,7 +305,7 @@ def commands(options, command, args):
|
|||
packets = etherdev.rxPackets + etherdev.txPackets
|
||||
bps = etherdev.rxBandwidth + etherdev.txBandwidth
|
||||
|
||||
output = StatOutput(command, options.jobfile)
|
||||
output = output.StatOutput(command, options.jobfile, source)
|
||||
|
||||
if command == 'usertime':
|
||||
import copy
|
||||
|
|
@ -460,7 +456,7 @@ if __name__ == '__main__':
|
|||
options.user = getpass.getuser()
|
||||
options.runs = None
|
||||
options.system = 'client'
|
||||
options.get = None
|
||||
options.method = None
|
||||
options.binned = False
|
||||
options.graph = False
|
||||
options.ticks = False
|
||||
|
|
@ -469,7 +465,7 @@ if __name__ == '__main__':
|
|||
options.jobfile = None
|
||||
options.all = False
|
||||
|
||||
opts, args = getopts(sys.argv[1:], '-BEFG:Jad:g:h:j:pr:s:u:T:')
|
||||
opts, args = getopts(sys.argv[1:], '-BEFJad:g:h:j:m:pr:s:u:T:')
|
||||
for o,a in opts:
|
||||
if o == '-B':
|
||||
options.binned = True
|
||||
|
|
@ -477,8 +473,6 @@ if __name__ == '__main__':
|
|||
options.printmode = 'E'
|
||||
if o == '-F':
|
||||
options.printmode = 'F'
|
||||
if o == '-G':
|
||||
options.get = a
|
||||
if o == '-a':
|
||||
options.all = True
|
||||
if o == '-d':
|
||||
|
|
@ -492,6 +486,8 @@ if __name__ == '__main__':
|
|||
jobfilename = None
|
||||
if o == '-j':
|
||||
jobfilename = a
|
||||
if o == '-m':
|
||||
options.method = a
|
||||
if o == '-p':
|
||||
options.passwd = getpass.getpass()
|
||||
if o == '-r':
|
||||
|
|
|
|||
Loading…
Reference in a new issue