47from __future__ import division
48import operator, re, types
49
50source = None
51display_run = 0
52global globalTicks
53globalTicks = None
54
55def total(f):
56 if isinstance(f, FormulaStat):
57 v = f.value
58 else:
59 v = f
60
61 f = FormulaStat()
62 if isinstance(v, (list, tuple)):
63 f.value = reduce(operator.add, v)
64 else:
65 f.value = v
66
67 return f
68
69def unaryop(op, f):
70 if isinstance(f, FormulaStat):
71 v = f.value
72 else:
73 v = f
74
75 if isinstance(v, (list, tuple)):
76 return map(op, v)
77 else:
78 return op(v)
79
80def zerodiv(lv, rv):
81 if rv == 0.0:
82 return 0.0
83 else:
84 return operator.truediv(lv, rv)
85
86def wrapop(op, lv, rv):
87 if isinstance(lv, str):
88 return lv
89
90 if isinstance(rv, str):
91 return rv
92
93 return op(lv, rv)
94
95def same(lrun, rrun):
96 for lx,rx in zip(lrun.keys(),rrun.keys()):
97 if lx != rx:
98 print 'lx != rx'
99 print lx, rx
100 print lrun.keys()
101 print rrun.keys()
102 return False
103 for ly,ry in zip(lrun[lx].keys(),rrun[rx].keys()):
104 if ly != ry:
105 print 'ly != ry'
106 print ly, ry
107 print lrun[lx].keys()
108 print rrun[rx].keys()
109 return False
110 return True
111
112
113def binaryop(op, lf, rf):
114 result = {}
115
116 if isinstance(lf, FormulaStat) and isinstance(rf, FormulaStat):
117 lv = lf.value
118 rv = rf.value
119
120 theruns = []
121 for r in lv.keys():
122 if rv.has_key(r):
123 if same(lv[r], rv[r]):
124 theruns.append(r)
125 else:
126 raise AttributeError
127
128 for run in theruns:
129 result[run] = {}
130 for x in lv[run].keys():
131 result[run][x] = {}
132 for y in lv[run][x].keys():
133 result[run][x][y] = wrapop(op, lv[run][x][y],
134 rv[run][x][y])
135 elif isinstance(lf, FormulaStat):
136 lv = lf.value
137 for run in lv.keys():
138 result[run] = {}
139 for x in lv[run].keys():
140 result[run][x] = {}
141 for y in lv[run][x].keys():
142 result[run][x][y] = wrapop(op, lv[run][x][y], rf)
143 elif isinstance(rf, FormulaStat):
144 rv = rf.value
145 for run in rv.keys():
146 result[run] = {}
147 for x in rv[run].keys():
148 result[run][x] = {}
149 for y in rv[run][x].keys():
150 result[run][x][y] = wrapop(op, lf, rv[run][x][y])
151
152 return result
153
154def sums(x, y):
155 if isinstance(x, (list, tuple)):
156 return map(lambda x, y: x + y, x, y)
157 else:
158 return x + y
159
160def alltrue(seq):
161 return reduce(lambda x, y: x and y, seq)
162
163def allfalse(seq):
164 return not reduce(lambda x, y: x or y, seq)
165
166def enumerate(seq):
167 return map(None, range(len(seq)), seq)
168
169def cmp(a, b):
170 if a < b:
171 return -1
172 elif a == b:
173 return 0
174 else:
175 return 1
176
177class Statistic(object):
178
179 def __init__(self, data):
180 self.__dict__.update(data.__dict__)
181 if not self.__dict__.has_key('value'):
182 self.__dict__['value'] = None
183 if not self.__dict__.has_key('bins'):
184 self.__dict__['bins'] = None
185 if not self.__dict__.has_key('ticks'):
186 self.__dict__['ticks'] = None
187 if 'vc' not in self.__dict__:
188 self.vc = {}
189
190 def __getattribute__(self, attr):
191 if attr == 'ticks':
192 if self.__dict__['ticks'] != globalTicks:
193 self.__dict__['value'] = None
194 self.__dict__['ticks'] = globalTicks
195 return self.__dict__['ticks']
196 if attr == 'value':
197 if self.__dict__['ticks'] != globalTicks:
198 if self.__dict__['ticks'] != None and \
199 len(self.__dict__['ticks']) == 1:
200 self.vc[self.__dict__['ticks'][0]] = self.__dict__['value']
201 self.__dict__['ticks'] = globalTicks
202 if len(globalTicks) == 1 and self.vc.has_key(globalTicks[0]):
203 self.__dict__['value'] = self.vc[globalTicks[0]]
204 else:
205 self.__dict__['value'] = None
206 if self.__dict__['value'] == None:
207 self.__dict__['value'] = self.getValue()
208 return self.__dict__['value']
209 else:
210 return super(Statistic, self).__getattribute__(attr)
211
212 def __setattr__(self, attr, value):
213 if attr == 'bins' or attr == 'ticks':
214 if attr == 'bins':
215 if value is not None:
216 value = source.getBin(value)
217 #elif attr == 'ticks' and type(value) is str:
218 # value = [ int(x) for x in value.split() ]
219
220 self.__dict__[attr] = value
221 self.__dict__['value'] = None
222 self.vc = {}
223 else:
224 super(Statistic, self).__setattr__(attr, value)
225
226 def getValue(self):
227 raise AttributeError, 'getValue() must be defined'
228
229 def zero(self):
230 return False
231
232 def __ne__(self, other):
233 return not (self == other)
234
235 def __str__(self):
236 return '%f' % (float(self))
237
238class FormulaStat(object):
239 def __add__(self, other):
240 f = FormulaStat()
241 f.value = binaryop(operator.add, self, other)
242 return f
243 def __sub__(self, other):
244 f = FormulaStat()
245 f.value = binaryop(operator.sub, self, other)
246 return f
247 def __mul__(self, other):
248 f = FormulaStat()
249 f.value = binaryop(operator.mul, self, other)
250 return f
251 def __truediv__(self, other):
252 f = FormulaStat()
253 f.value = binaryop(zerodiv, self, other)
254 return f
255 def __mod__(self, other):
256 f = FormulaStat()
257 f.value = binaryop(operator.mod, self, other)
258 return f
259 def __radd__(self, other):
260 f = FormulaStat()
261 f.value = binaryop(operator.add, other, self)
262 return f
263 def __rsub__(self, other):
264 f = FormulaStat()
265 f.value = binaryop(operator.sub, other, self)
266 return f
267 def __rmul__(self, other):
268 f = FormulaStat()
269 f.value = binaryop(operator.mul, other, self)
270 return f
271 def __rtruediv__(self, other):
272 f = FormulaStat()
273 f.value = binaryop(zerodiv, other, self)
274 return f
275 def __rmod__(self, other):
276 f = FormulaStat()
277 f.value = binaryop(operator.mod, other, self)
278 return f
279 def __neg__(self):
280 f = FormulaStat()
281 f.value = unaryop(operator.neg, self)
282 return f
283 def __getitem__(self, idx):
284 f = FormulaStat()
285 f.value = {}
286 for key in self.value.keys():
287 f.value[key] = {}
288 f.value[key][0] = {}
289 f.value[key][0][0] = self.value[key][idx][0]
290 return f
291
292 def __float__(self):
293 if isinstance(self.value, FormulaStat):
294 return float(self.value)
295 if not self.value.has_key(display_run):
296 return (1e300*1e300)
297 if len(self.value[display_run]) == 1:
298 return self.value[display_run][0][0]
299 else:
300 #print self.value[display_run]
301 return self.value[display_run][4][0]
302 #raise ValueError
303
304 def display(self):
305 import display
306 d = display.VectorDisplay()
307 d.flags = 0
308 d.precision = 1
309 d.name = 'formula'
310 d.desc = 'formula'
311 val = self.value[display_run]
312 d.value = [ val[x][0] for x in val.keys() ]
313 d.display()
314
315
316class Scalar(Statistic,FormulaStat):
317 def getValue(self):
318 return source.data(self, self.bins, self.ticks)
319
320 def display(self):
321 import display
322 p = display.Print()
323 p.name = self.name
324 p.desc = self.desc
325 p.value = float(self)
326 p.flags = self.flags
327 p.precision = self.precision
328 if display.all or (self.flags & flags.printable):
329 p.display()
330
331 def comparable(self, other):
332 return self.name == other.name
333
334 def __eq__(self, other):
335 return self.value == other.value
336
337 def __isub__(self, other):
338 self.value -= other.value
339 return self
340
341 def __iadd__(self, other):
342 self.value += other.value
343 return self
344
345 def __itruediv__(self, other):
346 if not other:
347 return self
348 self.value /= other
349 return self
350
351class Vector(Statistic,FormulaStat):
352 def getValue(self):
353 return source.data(self, self.bins, self.ticks);
354
355 def display(self):
356 import display
357 if not display.all and not (self.flags & flags.printable):
358 return
359
360 d = display.VectorDisplay()
361 d.__dict__.update(self.__dict__)
362 d.display()
363
364 def comparable(self, other):
365 return self.name == other.name and \
366 len(self.value) == len(other.value)
367
368 def __eq__(self, other):
369 if isinstance(self.value, (list, tuple)) != \
370 isinstance(other.value, (list, tuple)):
371 return False
372
373 if isinstance(self.value, (list, tuple)):
374 if len(self.value) != len(other.value):
375 return False
376 else:
377 for v1,v2 in zip(self.value, other.value):
378 if v1 != v2:
379 return False
380 return True
381 else:
382 return self.value == other.value
383
384 def __isub__(self, other):
385 self.value = binaryop(operator.sub, self.value, other.value)
386 return self
387
388 def __iadd__(self, other):
389 self.value = binaryop(operator.add, self.value, other.value)
390 return self
391
392 def __itruediv__(self, other):
393 if not other:
394 return self
395 if isinstance(self.value, (list, tuple)):
396 for i in xrange(len(self.value)):
397 self.value[i] /= other
398 else:
399 self.value /= other
400 return self
401
402class Formula(Vector):
403 def getValue(self):
404 formula = re.sub(':', '__', self.formula)
405 x = eval(formula, source.stattop)
406 return x.value
407
408 def comparable(self, other):
409 return self.name == other.name and \
410 compare(self.dist, other.dist)
411
412 def __eq__(self, other):
413 return self.value == other.value
414
415 def __isub__(self, other):
416 return self
417
418 def __iadd__(self, other):
419 return self
420
421 def __itruediv__(self, other):
422 if not other:
423 return self
424 return self
425
426class SimpleDist(object):
427 def __init__(self, sums, squares, samples):
428 self.sums = sums
429 self.squares = squares
430 self.samples = samples
431
432 def getValue(self):
433 return 0.0
434
435 def display(self, name, desc, flags, precision):
436 import display
437 p = display.Print()
438 p.flags = flags
439 p.precision = precision
440
441 if self.samples > 0:
442 p.name = name + ".mean"
443 p.value = self.sums / self.samples
444 p.display()
445
446 p.name = name + ".stdev"
447 if self.samples > 1:
448 var = (self.samples * self.squares - self.sums ** 2) \
449 / (self.samples * (self.samples - 1))
450 if var >= 0:
451 p.value = math.sqrt(var)
452 else:
453 p.value = 'NaN'
454 else:
455 p.value = 0.0
456 p.display()
457
458 p.name = name + ".samples"
459 p.value = self.samples
460 p.display()
461
462 def comparable(self, other):
463 return True
464
465 def __eq__(self, other):
466 return self.sums == other.sums and self.squares == other.squares and \
467 self.samples == other.samples
468
469 def __isub__(self, other):
470 self.sums -= other.sums
471 self.squares -= other.squares
472 self.samples -= other.samples
473 return self
474
475 def __iadd__(self, other):
476 self.sums += other.sums
477 self.squares += other.squares
478 self.samples += other.samples
479 return self
480
481 def __itruediv__(self, other):
482 if not other:
483 return self
484 self.sums /= other
485 self.squares /= other
486 self.samples /= other
487 return self
488
489class FullDist(SimpleDist):
490 def __init__(self, sums, squares, samples, minval, maxval,
491 under, vec, over, min, max, bsize, size):
492 self.sums = sums
493 self.squares = squares
494 self.samples = samples
495 self.minval = minval
496 self.maxval = maxval
497 self.under = under
498 self.vec = vec
499 self.over = over
500 self.min = min
501 self.max = max
502 self.bsize = bsize
503 self.size = size
504
505 def getValue(self):
506 return 0.0
507
508 def display(self, name, desc, flags, precision):
509 import display
510 p = display.Print()
511 p.flags = flags
512 p.precision = precision
513
514 p.name = name + '.min_val'
515 p.value = self.minval
516 p.display()
517
518 p.name = name + '.max_val'
519 p.value = self.maxval
520 p.display()
521
522 p.name = name + '.underflow'
523 p.value = self.under
524 p.display()
525
526 i = self.min
527 for val in self.vec[:-1]:
528 p.name = name + '[%d:%d]' % (i, i + self.bsize - 1)
529 p.value = val
530 p.display()
531 i += self.bsize
532
533 p.name = name + '[%d:%d]' % (i, self.max)
534 p.value = self.vec[-1]
535 p.display()
536
537
538 p.name = name + '.overflow'
539 p.value = self.over
540 p.display()
541
542 SimpleDist.display(self, name, desc, flags, precision)
543
544 def comparable(self, other):
545 return self.min == other.min and self.max == other.max and \
546 self.bsize == other.bsize and self.size == other.size
547
548 def __eq__(self, other):
549 return self.sums == other.sums and self.squares == other.squares and \
550 self.samples == other.samples
551
552 def __isub__(self, other):
553 self.sums -= other.sums
554 self.squares -= other.squares
555 self.samples -= other.samples
556
557 if other.samples:
558 self.minval = min(self.minval, other.minval)
559 self.maxval = max(self.maxval, other.maxval)
560 self.under -= under
561 self.vec = map(lambda x,y: x - y, self.vec, other.vec)
562 self.over -= over
563 return self
564
565 def __iadd__(self, other):
566 if not self.samples and other.samples:
567 self = other
568 return self
569
570 self.sums += other.sums
571 self.squares += other.squares
572 self.samples += other.samples
573
574 if other.samples:
575 self.minval = min(self.minval, other.minval)
576 self.maxval = max(self.maxval, other.maxval)
577 self.under += other.under
578 self.vec = map(lambda x,y: x + y, self.vec, other.vec)
579 self.over += other.over
580 return self
581
582 def __itruediv__(self, other):
583 if not other:
584 return self
585 self.sums /= other
586 self.squares /= other
587 self.samples /= other
588
589 if self.samples:
590 self.under /= other
591 for i in xrange(len(self.vec)):
592 self.vec[i] /= other
593 self.over /= other
594 return self
595
596class Dist(Statistic):
597 def getValue(self):
598 return 0.0
599
600 def display(self):
601 import display
602 if not display.all and not (self.flags & flags.printable):
603 return
604
605 self.dist.display(self.name, self.desc, self.flags, self.precision)
606
607 def comparable(self, other):
608 return self.name == other.name and \
609 self.dist.compareable(other.dist)
610
611 def __eq__(self, other):
612 return self.dist == other.dist
613
614 def __isub__(self, other):
615 self.dist -= other.dist
616 return self
617
618 def __iadd__(self, other):
619 self.dist += other.dist
620 return self
621
622 def __itruediv__(self, other):
623 if not other:
624 return self
625 self.dist /= other
626 return self
627
628class VectorDist(Statistic):
629 def getValue(self):
630 return 0.0
631
632 def display(self):
633 import display
634 if not display.all and not (self.flags & flags.printable):
635 return
636
637 if isinstance(self.dist, SimpleDist):
638 return
639
640 for dist,sn,sd,i in map(None, self.dist, self.subnames, self.subdescs,
641 range(len(self.dist))):
642 if len(sn) > 0:
643 name = '%s.%s' % (self.name, sn)
644 else:
645 name = '%s[%d]' % (self.name, i)
646
647 if len(sd) > 0:
648 desc = sd
649 else:
650 desc = self.desc
651
652 dist.display(name, desc, self.flags, self.precision)
653
654 if (self.flags & flags.total) or 1:
655 if isinstance(self.dist[0], SimpleDist):
656 disttotal = SimpleDist( \
657 reduce(sums, [d.sums for d in self.dist]),
658 reduce(sums, [d.squares for d in self.dist]),
659 reduce(sums, [d.samples for d in self.dist]))
660 else:
661 disttotal = FullDist( \
662 reduce(sums, [d.sums for d in self.dist]),
663 reduce(sums, [d.squares for d in self.dist]),
664 reduce(sums, [d.samples for d in self.dist]),
665 min([d.minval for d in self.dist]),
666 max([d.maxval for d in self.dist]),
667 reduce(sums, [d.under for d in self.dist]),
668 reduce(sums, [d.vec for d in self.dist]),
669 reduce(sums, [d.over for d in self.dist]),
670 dist[0].min,
671 dist[0].max,
672 dist[0].bsize,
673 dist[0].size)
674
675 name = '%s.total' % (self.name)
676 desc = self.desc
677 disttotal.display(name, desc, self.flags, self.precision)
678
679 def comparable(self, other):
680 return self.name == other.name and \
681 alltrue(map(lambda x, y : x.comparable(y),
682 self.dist,
683 other.dist))
684
685 def __eq__(self, other):
686 return alltrue(map(lambda x, y : x == y, self.dist, other.dist))
687
688 def __isub__(self, other):
689 if isinstance(self.dist, (list, tuple)) and \
690 isinstance(other.dist, (list, tuple)):
691 for sd,od in zip(self.dist, other.dist):
692 sd -= od
693 else:
694 self.dist -= other.dist
695 return self
696
697 def __iadd__(self, other):
698 if isinstance(self.dist, (list, tuple)) and \
699 isinstance(other.dist, (list, tuple)):
700 for sd,od in zip(self.dist, other.dist):
701 sd += od
702 else:
703 self.dist += other.dist
704 return self
705
706 def __itruediv__(self, other):
707 if not other:
708 return self
709 if isinstance(self.dist, (list, tuple)):
710 for dist in self.dist:
711 dist /= other
712 else:
713 self.dist /= other
714 return self
715
716class Vector2d(Statistic):
717 def getValue(self):
718 return 0.0
719
720 def display(self):
721 import display
722 if not display.all and not (self.flags & flags.printable):
723 return
724
725 d = display.VectorDisplay()
726 d.__dict__.update(self.__dict__)
727
728 if self.__dict__.has_key('ysubnames'):
729 ysubnames = list(self.ysubnames)
730 slack = self.x - len(ysubnames)
731 if slack > 0:
732 ysubnames.extend(['']*slack)
733 else:
734 ysubnames = range(self.x)
735
736 for x,sname in enumerate(ysubnames):
737 o = x * self.y
738 d.value = self.value[o:o+self.y]
739 d.name = '%s[%s]' % (self.name, sname)
740 d.display()
741
742 if self.flags & flags.total:
743 d.value = []
744 for y in range(self.y):
745 xtot = 0.0
746 for x in range(self.x):
747 xtot += self.value[y + x * self.x]
748 d.value.append(xtot)
749
750 d.name = self.name + '.total'
751 d.display()
752
753 def comparable(self, other):
754 return self.name == other.name and self.x == other.x and \
755 self.y == other.y
756
757 def __eq__(self, other):
758 return True
759
760 def __isub__(self, other):
761 return self
762
763 def __iadd__(self, other):
764 return self
765
766 def __itruediv__(self, other):
767 if not other:
768 return self
769 return self
770
771def NewStat(data):
772 stat = None
773 if data.type == 'SCALAR':
774 stat = Scalar(data)
775 elif data.type == 'VECTOR':
776 stat = Vector(data)
777 elif data.type == 'DIST':
778 stat = Dist(data)
779 elif data.type == 'VECTORDIST':
780 stat = VectorDist(data)
781 elif data.type == 'VECTOR2D':
782 stat = Vector2d(data)
783 elif data.type == 'FORMULA':
784 stat = Formula(data)
785
786 return stat
787
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