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16 """Module for the handling of histograms, including Monte-Carlo error per bin
17 and scale/PDF uncertainties."""
18
19 from __future__ import division
20
21 import array
22 import copy
23 import fractions
24 import itertools
25 import logging
26 import math
27 import os
28 import re
29 import sys
30 import StringIO
31 import subprocess
32 import xml.dom.minidom as minidom
33 from xml.parsers.expat import ExpatError as XMLParsingError
34
35 root_path = os.path.split(os.path.dirname(os.path.realpath( __file__ )))[0]
36 sys.path.append(os.path.join(root_path))
37 sys.path.append(os.path.join(root_path,os.pardir))
38 try:
39
40 import madgraph.various.misc as misc
41 from madgraph import MadGraph5Error
42 logger = logging.getLogger("madgraph.various.histograms")
43
44 except ImportError, error:
45
46 import internal.misc as misc
47 from internal import MadGraph5Error
48 logger = logging.getLogger("internal.histograms")
58 """A class to store lists of physics object."""
59
61 """Exception raised if an error occurs in the definition
62 or execution of a physics object list."""
63 pass
64
66 """Creates a new particle list object. If a list of physics
67 object is given, add them."""
68
69 list.__init__(self)
70
71 if init_list is not None:
72 for object in init_list:
73 self.append(object)
74
76 """Appends an element, but test if valid before."""
77
78 assert self.is_valid_element(object), \
79 "Object %s is not a valid object for the current list" % repr(object)
80
81 list.append(self, object)
82
83
85 """Test if object obj is a valid element for the list."""
86 return True
87
89 """String representation of the physics object list object.
90 Outputs valid Python with improved format."""
91
92 mystr = '['
93
94 for obj in self:
95 mystr = mystr + str(obj) + ',\n'
96
97 mystr = mystr.rstrip(',\n')
98
99 return mystr + ']'
100
101
102 -class Bin(object):
103 """A class to store Bin related features and function.
104 """
105
106 - def __init__(self, boundaries=(0.0,0.0), wgts=None, n_entries = 0):
107 """ Initializes an empty bin, necessarily with boundaries. """
108
109 self.boundaries = boundaries
110 self.n_entries = n_entries
111 if not wgts:
112 self.wgts = {'central':0.0}
113 else:
114 self.wgts = wgts
115
117 if name=='boundaries':
118 if not isinstance(value, tuple):
119 raise MadGraph5Error, "Argument '%s' for bin property "+\
120 "'boundaries' must be a tuple."%str(value)
121 else:
122 for coordinate in value:
123 if isinstance(coordinate, tuple):
124 for dim in coordinate:
125 if not isinstance(dim, float):
126 raise MadGraph5Error, "Coordinate '%s' of the bin"+\
127 " boundary '%s' must be a float."%str(dim,value)
128 elif not isinstance(coordinate, float):
129 raise MadGraph5Error, "Element '%s' of the bin boundaries"+\
130 " specified must be a float."%str(bound)
131 elif name=='wgts':
132 if not isinstance(value, dict):
133 raise MadGraph5Error, "Argument '%s' for bin uncertainty "+\
134 "'wgts' must be a dictionary."%str(value)
135 for val in value.values():
136 if not isinstance(val,float):
137 raise MadGraph5Error, "The bin weight value '%s' is not a "+\
138 "float."%str(val)
139
140 super(Bin, self).__setattr__(name,value)
141
143 """ Accesses a specific weight from this bin."""
144 try:
145 return self.wgts[key]
146 except KeyError:
147 raise MadGraph5Error, "Weight with ID '%s' is not defined for"+\
148 " this bin"%str(key)
149
151 """ Accesses a specific weight from this bin."""
152
153
154
155 assert(isinstance(wgt, float))
156
157 try:
158 self.wgts[key] = wgt
159 except KeyError:
160 raise MadGraph5Error, "Weight with ID '%s' is not defined for"+\
161 " this bin"%str(key)
162
164 """ Add an event to this bin. """
165
166
167 if isinstance(weights, float):
168 weights = {'central': weights}
169
170 for key in weights:
171 if key == 'stat_error':
172 continue
173 try:
174 self.wgts[key] += weights[key]
175 except KeyError:
176 raise MadGraph5Error('The event added defines the weight '+
177 '%s which was not '%key+'registered in this histogram.')
178
179 self.n_entries += 1
180
181
182
183
184
185
186
188 """ Nice representation of this Bin.
189 One can order the weight according to the argument if provided."""
190
191 res = ["Bin boundaries : %s"%str(self.boundaries)]
192 if not short:
193 res.append("Bin weights :")
194 if order is None:
195 label_list = self.wgts.keys()
196 else:
197 label_list = order
198
199 for label in label_list:
200 try:
201 res.append(" -> '%s' : %4.3e"%(str(label),self.wgts[label]))
202 except KeyError:
203 pass
204 else:
205 res.append("Central weight : %4.3e"%self.get_weight())
206
207 return '\n'.join(res)
208
210 """ Apply a given function to all bin weights."""
211 self.wgts = func(self.wgts)
212
213 @classmethod
214 - def combine(cls, binA, binB, func):
215 """ Function to combine two bins. The 'func' is such that it takes
216 two weight dictionaries and merge them into one."""
217
218 res_bin = cls()
219 if binA.boundaries != binB.boundaries:
220 raise MadGraph5Error, 'The two bins to combine have'+\
221 ' different boundaries, %s!=%s.'%(str(binA.boundaries),str(binB.boundaries))
222 res_bin.boundaries = binA.boundaries
223
224 try:
225 res_bin.wgts = func(binA.wgts, binB.wgts)
226 except Exception as e:
227 raise MadGraph5Error, "When combining two bins, the provided"+\
228 " function '%s' triggered the following error:\n\"%s\"\n"%\
229 (func.__name__,str(e))+" when combining the following two bins:\n"+\
230 binA.nice_string(short=False)+"\n and \n"+binB.nice_string(short=False)
231
232 return res_bin
233
234 -class BinList(histograms_PhysicsObjectList):
235 """ A class implementing features related to a list of Bins. """
236
237 - def __init__(self, list = [], bin_range = None,
238 weight_labels = None):
239 """ Initialize a list of Bins. It is possible to define the range
240 as a list of three floats: [min_x, max_x, bin_width]"""
241
242 self.weight_labels = weight_labels
243 if bin_range:
244
245 if not self.weight_labels:
246 self.weight_labels = ['central', 'stat_error']
247 if len(bin_range)!=3 or any(not isinstance(f, float) for f in bin_range):
248 raise MadGraph5Error, "The range argument to build a BinList"+\
249 " must be a list of exactly three floats."
250 current = bin_range[0]
251 while current < bin_range[1]:
252 self.append(Bin(boundaries =
253 (current, min(current+bin_range[2],bin_range[1])),
254 wgts = dict((wgt,0.0) for wgt in self.weight_labels)))
255 current += bin_range[2]
256 else:
257 super(BinList, self).__init__(list)
258
260 """Test whether specified object is of the right type for this list."""
261
262 return isinstance(obj, Bin)
263
265 if name=='weight_labels':
266 if not value is None and not isinstance(value, list):
267 raise MadGraph5Error, "Argument '%s' for BinList property '%s'"\
268 %(str(value),name)+' must be a list.'
269 elif not value is None:
270 for label in value:
271 if all((not isinstance(label,cls)) for cls in \
272 [str, int, float, tuple]):
273 raise MadGraph5Error, "Element '%s' of the BinList property '%s'"\
274 %(str(value),name)+' must be a string, an '+\
275 'integer, a float or a tuple of float.'
276 if isinstance(label, tuple):
277 if len(label)>=1:
278 if not isinstance(label[0], (float, str)):
279 raise MadGraph5Error, "Argument "+\
280 "'%s' for BinList property '%s'"%(str(value),name)+\
281 ' can be a tuple, but its first element must be a float or string.'
282 for elem in label[1:]:
283 if not isinstance(elem, (float,int,str)):
284 raise MadGraph5Error, "Argument "+\
285 "'%s' for BinList property '%s'"%(str(value),name)+\
286 ' can be a tuple, but its elements past the first one must be either floats, integers or strings'
287
288
289 super(BinList, self).__setattr__(name, value)
290
292 """Appends an element, but test if valid before."""
293
294 super(BinList,self).append(object)
295
296 if len(self)==1 and self.weight_labels is None:
297 self.weight_labels = object.wgts.keys()
298
300 """ Nice representation of this BinList."""
301
302 res = ["Number of bin in the list : %d"%len(self)]
303 res.append("Registered weight labels : [%s]"%(', '.join([
304 str(label) for label in self.weight_labels])))
305 if not short:
306 for i, bin in enumerate(self):
307 res.append('Bin number %d :'%i)
308 res.append(bin.nice_string(order=self.weight_labels, short=short))
309
310 return '\n'.join(res)
311
313 """A mother class for all specific implementations of Histogram conventions
314 """
315
316 allowed_dimensions = None
317 allowed_types = []
318 allowed_axis_modes = ['LOG','LIN']
319
320 - def __init__(self, title = "NoName", n_dimensions = 2, type=None,
321 x_axis_mode = 'LIN', y_axis_mode = 'LOG', bins=None):
322 """ Initializes an empty histogram, possibly specifying
323 > a title
324 > a number of dimensions
325 > a bin content
326 """
327
328 self.title = title
329 self.dimension = n_dimensions
330 if not bins:
331 self.bins = BinList([])
332 else:
333 self.bins = bins
334 self.type = type
335 self.x_axis_mode = x_axis_mode
336 self.y_axis_mode = y_axis_mode
337
339 if name=='title':
340 if not isinstance(value, str):
341 raise MadGraph5Error, "Argument '%s' for the histogram property "+\
342 "'title' must be a string."%str(value)
343 elif name=='dimension':
344 if not isinstance(value, int):
345 raise MadGraph5Error, "Argument '%s' for histogram property "+\
346 "'dimension' must be an integer."%str(value)
347 if self.allowed_dimensions and value not in self.allowed_dimensions:
348 raise MadGraph5Error, "%i-Dimensional histograms not supported "\
349 %value+"by class '%s'. Supported dimensions are '%s'."\
350 %(self.__class__.__name__,self.allowed_dimensions)
351 elif name=='bins':
352 if not isinstance(value, BinList):
353 raise MadGraph5Error, "Argument '%s' for histogram property "+\
354 "'bins' must be a BinList."%str(value)
355 else:
356 for bin in value:
357 if not isinstance(bin, Bin):
358 raise MadGraph5Error, "Element '%s' of the "%str(bin)+\
359 " histogram bin list specified must be a bin."
360 elif name=='type':
361 if not (value is None or value in self.allowed_types or
362 self.allowed_types==[]):
363 raise MadGraph5Error, "Argument '%s' for histogram"%str(value)+\
364 " property 'type' must be a string in %s or None."\
365 %([str(t) for t in self.allowed_types])
366 elif name in ['x_axis_mode','y_axis_mode']:
367 if not value in self.allowed_axis_modes:
368 raise MadGraph5Error, "Attribute '%s' of the histogram"%str(name)+\
369 " must be in [%s], ('%s' given)"%(str(self.allowed_axis_modes),
370 str(value))
371
372 super(Histogram, self).__setattr__(name,value)
373
375 """ Nice representation of this histogram. """
376
377 res = ['<%s> histogram:'%self.__class__.__name__]
378 res.append(' -> title : "%s"'%self.title)
379 res.append(' -> dimensions : %d'%self.dimension)
380 if not self.type is None:
381 res.append(' -> type : %s'%self.type)
382 else:
383 res.append(' -> type : None')
384 res.append(' -> (x, y)_axis : ( %s, %s)'%\
385 (tuple([('Linear' if mode=='LIN' else 'Logarithmic') for mode in \
386 [self.x_axis_mode, self.y_axis_mode]])))
387 if short:
388 res.append(' -> n_bins : %s'%len(self.bins))
389 res.append(' -> weight types : [ %s ]'%
390 (', '.join([str(label) for label in self.bins.weight_labels]) \
391 if (not self.bins.weight_labels is None) else 'None'))
392
393 else:
394 res.append(' -> Bins content :')
395 res.append(self.bins.nice_string(short))
396
397 return '\n'.join(res)
398
400 """ Apply a given function to all bin weights."""
401
402 for bin in self.bins:
403 bin.alter_weights(func)
404
405 @classmethod
406 - def combine(cls, histoA, histoB, func):
407 """ Function to combine two Histograms. The 'func' is such that it takes
408 two weight dictionaries and merge them into one."""
409
410 res_histogram = copy.copy(histoA)
411 if histoA.title != histoB.title:
412 res_histogram.title = "[%s]__%s__[%s]"%(histoA.title,func.__name__,
413 histoB.title)
414 else:
415 res_histogram.title = histoA.title
416
417 res_histogram.bins = BinList([])
418 if len(histoA.bins)!=len(histoB.bins):
419 raise MadGraph5Error, 'The two histograms to combine have a '+\
420 'different number of bins, %d!=%d.'%(len(histoA.bins),len(histoB.bins))
421
422 if histoA.dimension!=histoB.dimension:
423 raise MadGraph5Error, 'The two histograms to combine have a '+\
424 'different dimensions, %d!=%d.'%(histoA.dimension,histoB.dimension)
425 res_histogram.dimension = histoA.dimension
426
427 for i, bin in enumerate(histoA.bins):
428 res_histogram.bins.append(Bin.combine(bin, histoB.bins[i],func))
429
430
431
432 res_histogram.bins.weight_labels = [label for label in histoA.bins.\
433 weight_labels if label in res_histogram.bins.weight_labels] + \
434 sorted([label for label in res_histogram.bins.weight_labels if\
435 label not in histoA.bins.weight_labels])
436
437
438 return res_histogram
439
440
441
442
443 @staticmethod
445 """ Apply the multiplication to the weights of two bins."""
446
447 new_wgts = {}
448
449 new_wgts['stat_error'] = math.sqrt(
450 (wgtsA['stat_error']*wgtsB['central'])**2+
451 (wgtsA['central']*wgtsB['stat_error'])**2)
452
453 for label, wgt in wgtsA.items():
454 if label=='stat_error':
455 continue
456 new_wgts[label] = wgt*wgtsB[label]
457
458 return new_wgts
459
460 @staticmethod
462 """ Apply the division to the weights of two bins."""
463
464 new_wgts = {}
465 if wgtsB['central'] == 0.0:
466 new_wgts['stat_error'] = 0.0
467 else:
468
469 new_wgts['stat_error'] = math.sqrt(wgtsA['stat_error']**2+
470 ((wgtsA['central']*wgtsB['stat_error'])/
471 wgtsB['central'])**2)/wgtsB['central']
472
473 for label, wgt in wgtsA.items():
474 if label=='stat_error':
475 continue
476 if wgtsB[label]==0.0 and wgt==0.0:
477 new_wgts[label] = 0.0
478 elif wgtsB[label]==0.0:
479
480
481
482
483 new_wgts[label] = 0.0
484 else:
485 new_wgts[label] = wgt/wgtsB[label]
486
487 return new_wgts
488
489 @staticmethod
490 - def OPERATION(wgtsA, wgtsB, wgt_operation, stat_error_operation):
491 """ Apply the operation to the weights of two bins. Notice that we
492 assume here the two dict operands to have the same weight labels.
493 The operation is a function that takes two floats as input."""
494
495 new_wgts = {}
496 for label, wgt in wgtsA.items():
497 if label!='stat_error':
498 new_wgts[label] = wgt_operation(wgt, wgtsB[label])
499 else:
500 new_wgts[label] = stat_error_operation(wgt, wgtsB[label])
501
502
503
504
505
506 return new_wgts
507
508
509 @staticmethod
511 """ Apply the operation to the weights of a *single* bins.
512 The operation is a function that takes a single float as input."""
513
514 new_wgts = {}
515 for label, wgt in wgts.items():
516 if label!='stat_error':
517 new_wgts[label] = wgt_operation(wgt)
518 else:
519 new_wgts[label] = stat_error_operation(wgt)
520
521 return new_wgts
522
523 @staticmethod
524 - def ADD(wgtsA, wgtsB):
525 """ Implements the addition using OPERATION above. """
526 return Histogram.OPERATION(wgtsA, wgtsB,
527 (lambda a,b: a+b),
528 (lambda a,b: math.sqrt(a**2+b**2)))
529
530 @staticmethod
532 """ Implements the subtraction using OPERATION above. """
533
534 return Histogram.OPERATION(wgtsA, wgtsB,
535 (lambda a,b: a-b),
536 (lambda a,b: math.sqrt(a**2+b**2)))
537
538 @staticmethod
540 """ Implements the rescaling using SINGLEHISTO_OPERATION above. """
541
542 def rescaler(wgts):
543 return Histogram.SINGLEHISTO_OPERATION(wgts,(lambda a: a*factor),
544 (lambda a: a*factor))
545
546 return rescaler
547
548 @staticmethod
550 """ Implements the offset using SINGLEBIN_OPERATION above. """
551 def offsetter(wgts):
552 return Histogram.SINGLEHISTO_OPERATION(
553 wgts,(lambda a: a+offset),(lambda a: a))
554
555 return offsetter
556
558 """ Overload the plus function. """
559 if isinstance(other, Histogram):
560 return self.__class__.combine(self,other,Histogram.ADD)
561 elif isinstance(other, int) or isinstance(other, float):
562 self.alter_weights(Histogram.OFFSET(float(other)))
563 return self
564 else:
565 return NotImplemented, 'Histograms can only be added to other '+\
566 ' histograms or scalars.'
567
569 """ Overload the subtraction function. """
570 if isinstance(other, Histogram):
571 return self.__class__.combine(self,other,Histogram.SUBTRACT)
572 elif isinstance(other, int) or isinstance(other, float):
573 self.alter_weights(Histogram.OFFSET(-float(other)))
574 return self
575 else:
576 return NotImplemented, 'Histograms can only be subtracted to other '+\
577 ' histograms or scalars.'
578
580 """ Overload the multiplication function. """
581 if isinstance(other, Histogram):
582 return self.__class__.combine(self,other,Histogram.MULTIPLY)
583 elif isinstance(other, int) or isinstance(other, float):
584 self.alter_weights(Histogram.RESCALE(float(other)))
585 return self
586 else:
587 return NotImplemented, 'Histograms can only be multiplied to other '+\
588 ' histograms or scalars.'
589
591 """ Overload the multiplication function. """
592 if isinstance(other, Histogram):
593 return self.__class__.combine(self,other,Histogram.DIVIDE)
594 elif isinstance(other, int) or isinstance(other, float):
595 self.alter_weights(Histogram.RESCALE(1.0/float(other)))
596 return self
597 else:
598 return NotImplemented, 'Histograms can only be divided with other '+\
599 ' histograms or scalars.'
600
601 __truediv__ = __div__
602
603 -class HwU(Histogram):
604 """A concrete implementation of an histogram plots using the HwU format for
605 reading/writing histogram content."""
606
607 allowed_dimensions = [2]
608 allowed_types = []
609
610
611 output_formats_implemented = ['HwU','gnuplot']
612
613
614
615 mandatory_weights = {'xmin':'boundary_xmin', 'xmax':'boundary_xmax',
616 'central value':'central', 'dy':'stat_error'}
617
618
619
620
621
622 weight_header_start_re = re.compile('^##.*')
623
624
625
626 weight_header_re = re.compile(
627 '&\s*(?P<wgt_name>(\S|(\s(?!\s*(&|$))))+)(\s(?!(&|$)))*')
628
629
630
631
632
633 histo_start_re = re.compile('^\s*<histogram>\s*(?P<n_bins>\d+)\s*"\s*'+
634 '(?P<histo_name>(\S|(\s(?!\s*")))+)\s*"\s*$')
635
636 a_float_re = '[\+|-]?\d+(\.\d*)?([EeDd][\+|-]?\d+)?'
637 histo_bin_weight_re = re.compile('(?P<weight>%s|NaN)'%a_float_re,re.IGNORECASE)
638 a_int_re = '[\+|-]?\d+'
639
640
641 histo_end_re = re.compile(r'^\s*<\\histogram>\s*$')
642
643 weight_label_scale = re.compile('^\s*mur\s*=\s*(?P<mur_fact>%s)'%a_float_re+\
644 '\s*muf\s*=\s*(?P<muf_fact>%s)\s*$'%a_float_re,re.IGNORECASE)
645 weight_label_PDF = re.compile('^\s*PDF\s*=\s*(?P<PDF_set>\d+)\s*$')
646 weight_label_PDF_XML = re.compile('^\s*pdfset\s*=\s*(?P<PDF_set>\d+)\s*$')
647 weight_label_TMS = re.compile('^\s*TMS\s*=\s*(?P<Merging_scale>%s)\s*$'%a_float_re)
648 weight_label_alpsfact = re.compile('^\s*alpsfact\s*=\s*(?P<alpsfact>%s)\s*$'%a_float_re,
649 re.IGNORECASE)
650
651 weight_label_scale_adv = re.compile('^\s*dyn\s*=\s*(?P<dyn_choice>%s)'%a_int_re+\
652 '\s*mur\s*=\s*(?P<mur_fact>%s)'%a_float_re+\
653 '\s*muf\s*=\s*(?P<muf_fact>%s)\s*$'%a_float_re,re.IGNORECASE)
654 weight_label_PDF_adv = re.compile('^\s*PDF\s*=\s*(?P<PDF_set>\d+)\s+(?P<PDF_set_cen>\S+)\s*$')
655
656
658 """a class for histogram data parsing errors"""
659
660 @classmethod
662 """ From the format of the weight label given in argument, it returns
663 a string identifying the type of standard weight it is."""
664
665 if isinstance(wgt_label,str):
666 return 'UNKNOWN_TYPE'
667 if isinstance(wgt_label,tuple):
668 if len(wgt_label)==0:
669 return 'UNKNOWN_TYPE'
670 if isinstance(wgt_label[0],float):
671 return 'murmuf_scales'
672 if isinstance(wgt_label[0],str):
673 return wgt_label[0]
674 if isinstance(wgt_label,float):
675 return 'merging_scale'
676 if isinstance(wgt_label,int):
677 return 'pdfset'
678
679 return 'UNKNOWN_TYPE'
680
681
682 - def __init__(self, file_path=None, weight_header=None,
683 raw_labels=False, consider_reweights='ALL', selected_central_weight=None, **opts):
684 """ Read one plot from a file_path or a stream. Notice that this
685 constructor only reads one, and the first one, of the plots specified.
686 If file_path was a path in argument, it would then close the opened stream.
687 If file_path was a stream in argument, it would leave it open.
688 The option weight_header specifies an ordered list of weight names
689 to appear in the file specified.
690 The option 'raw_labels' specifies that one wants to import the
691 histogram data with no treatment of the weight labels at all
692 (this is used for the matplotlib output)."""
693
694 super(HwU, self).__init__(**opts)
695
696 self.dimension = 2
697
698 if file_path is None:
699 return
700 elif isinstance(file_path, str):
701 stream = open(file_path,'r')
702 elif isinstance(file_path, file):
703 stream = file_path
704 else:
705 raise MadGraph5Error, "Argument file_path '%s' for HwU init"\
706 %str(file_path)+"ialization must be either a file path or a stream."
707
708
709 if not weight_header:
710 weight_header = HwU.parse_weight_header(stream, raw_labels=raw_labels)
711
712 if not self.parse_one_histo_from_stream(stream, weight_header,
713 consider_reweights=consider_reweights,
714 selected_central_weight=selected_central_weight,
715 raw_labels=raw_labels):
716
717
718 super(Histogram,self).__setattr__('bins',None)
719
720
721 if isinstance(file_path, str):
722 stream.close()
723
724 - def addEvent(self, x_value, weights = 1.0):
725 """ Add an event to the current plot. """
726
727 for bin in self.bins:
728 if bin.boundaries[0] <= x_value < bin.boundaries[1]:
729 bin.addEvent(weights = weights)
730
731 - def get(self, name):
732
733 if name == 'bins':
734 return [b.boundaries[0] for b in self.bins]
735 else:
736 return [b.wgts[name] for b in self.bins]
737
755
757 """return two list of entry one with the minimum and one with the maximum value.
758 selector can be:
759 - a regular expression on the label name
760 - a function returning T/F (applying on the label name)
761 - a list of labels
762 - a keyword
763 """
764
765
766 if isinstance(selector, str):
767 if selector == 'QCUT':
768 selector = r'^Weight_MERGING=[\d]*[.]?\d*$'
769 elif selector == 'SCALE':
770 selector = r'(MUF=\d*[.]?\d*_MUR=([^1]\d*|1\d+)_PDF=\d*)[.]?\d*|(MUF=([^1]\d*|1\d+)[.]?\d*_MUR=\d*[.]?\d*_PDF=\d*)'
771 elif selector == 'ALPSFACT':
772 selector = r'ALPSFACT'
773 elif selector == 'PDF':
774 selector = r'(?:MUF=1_MUR=1_PDF=|MU(?:F|R)="1.0" MU(?:R|F)="1.0" PDF=")(\d*)'
775 if not mode:
776
777
778
779
780
781 pdfs = [int(re.findall(selector, n)[0]) for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)]
782 min_pdf, max_pdf = min(pdfs), max(pdfs)
783 if max_pdf - min_pdf > 100:
784 mode == 'min/max'
785 elif max_pdf <= 90000:
786 mode = 'hessian'
787 else:
788 mode = 'gaussian'
789 selections = [n for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)]
790 elif hasattr(selector, '__call__'):
791 selections = [n for n in self.bins[0].wgts if selector(n)]
792 elif isinstance(selector, (list, tuple)):
793 selections = selector
794
795
796 if not mode:
797 mode = 'min/max'
798
799
800 values = []
801 for s in selections:
802 values.append(self.get(s))
803
804
805 if not len(values):
806 return [0] * len(self.bins), [0]* len(self.bins)
807 elif len(values) ==1:
808 return values[0], values[0]
809
810
811
812 if mode == 'min/max':
813 min_value, max_value = [], []
814 for i in xrange(len(values[0])):
815 data = [values[s][i] for s in xrange(len(values))]
816 min_value.append(min(data))
817 max_value.append(max(data))
818 elif mode == 'gaussian':
819
820 min_value, max_value = [], []
821 for i in xrange(len(values[0])):
822 pdf_stdev = 0.0
823 data = [values[s][i] for s in xrange(len(values))]
824 sdata = sum(data)/len(data)
825 sdata2 = sum(x**2 for x in data)/len(data)
826 pdf_stdev = math.sqrt(max(sdata2 -sdata**2,0.0))
827 min_value.append(sdata - pdf_stdev)
828 max_value.append(sdata + pdf_stdev)
829
830 elif mode == 'hessian':
831
832
833 pdfs = [(int(re.findall(selector, n)[0]),n) for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)]
834 pdfs.sort()
835
836
837 if len(pdfs) % 2:
838
839 pdf1 = pdfs[0][0]
840 central = pdf1 -1
841 name = pdfs[0][1].replace(str(pdf1), str(central))
842 central = self.get(name)
843 else:
844 central = self.get(pdfs.pop(0)[1])
845
846
847 values = []
848 for _, name in pdfs:
849 values.append(self.get(name))
850
851
852 min_value, max_value = [], []
853 for i in xrange(len(values[0])):
854 pdf_up = 0
855 pdf_down = 0
856 cntrl_val = central[i]
857 for s in range(int((len(pdfs))/2)):
858 pdf_up += max(0.0,values[2*s][i] - cntrl_val,
859 values[2*s+1][i] - cntrl_val)**2
860 pdf_down += max(0.0,cntrl_val - values[2*s][i],
861 cntrl_val - values[2*s+1][i])**2
862
863 min_value.append(cntrl_val - math.sqrt(pdf_down))
864 max_value.append(cntrl_val + math.sqrt(pdf_up))
865
866
867
868
869 return min_value, max_value
870
900
902 """ Returns the string representation of this histogram using the
903 HwU standard."""
904
905 res = []
906 if print_header:
907 res.append(self.get_formatted_header())
908 res.extend([''])
909 res.append('<histogram> %s "%s"'%(len(self.bins),
910 self.get_HwU_histogram_name(format='HwU')))
911 for bin in self.bins:
912 if 'central' in bin.wgts:
913 res.append(' '.join('%+16.7e'%wgt for wgt in list(bin.boundaries)+
914 [bin.wgts['central'],bin.wgts['stat_error']]))
915 else:
916 res.append(' '.join('%+16.7e'%wgt for wgt in list(bin.boundaries)))
917 res[-1] += ' '.join('%+16.7e'%bin.wgts[key] for key in
918 self.bins.weight_labels if key not in ['central','stat_error'])
919 res.append('<\histogram>')
920 return res
921
922 - def output(self, path=None, format='HwU', print_header=True):
923 """ Ouput this histogram to a file, stream or string if path is kept to
924 None. The supported format are for now. Chose whether to print the header
925 or not."""
926
927 if not format in HwU.output_formats_implemented:
928 raise MadGraph5Error, "The specified output format '%s'"%format+\
929 " is not yet supported. Supported formats are %s."\
930 %HwU.output_formats_implemented
931
932 if format == 'HwU':
933 str_output_list = self.get_HwU_source(print_header=print_header)
934
935 if path is None:
936 return '\n'.join(str_output_list)
937 elif isinstance(path, str):
938 stream = open(path,'w')
939 stream.write('\n'.join(str_output_list))
940 stream.close()
941 elif isinstance(path, file):
942 path.write('\n'.join(str_output_list))
943
944
945 return True
946
949 """ Test whether the defining attributes of self are identical to histo,
950 typically to make sure that they are the same plots but from different
951 runs, and they can be summed safely. We however don't want to
952 overload the __eq__ because it is still a more superficial check."""
953
954 this_known_weight_labels = [label for label in self.bins.weight_labels if
955 HwU.get_HwU_wgt_label_type(label)!='UNKNOWN_TYPE']
956 other_known_weight_labels = [label for label in other.bins.weight_labels if
957 HwU.get_HwU_wgt_label_type(label)!='UNKNOWN_TYPE']
958 this_unknown_weight_labels = [label for label in self.bins.weight_labels if
959 HwU.get_HwU_wgt_label_type(label)=='UNKNOWN_TYPE']
960 other_unknown_weight_labels = [label for label in other.bins.weight_labels if
961 HwU.get_HwU_wgt_label_type(label)=='UNKNOWN_TYPE']
962
963 if self.title != other.title or \
964 set(this_known_weight_labels) != set(other_known_weight_labels) or \
965 (set(this_unknown_weight_labels) != set(other_unknown_weight_labels) and\
966 consider_unknown_weight_labels) or \
967 (self.type != other.type and consider_type) or \
968 self.x_axis_mode != self.x_axis_mode or \
969 self.y_axis_mode != self.y_axis_mode or \
970 any(b1.boundaries!=b2.boundaries for (b1,b2) in \
971 zip(self.bins,other.bins)):
972 return False
973
974 return True
975
976
977
978 @classmethod
980 """ Read a given stream until it finds a header specifying the weights
981 and then returns them."""
982
983 for line in stream:
984 if cls.weight_header_start_re.match(line):
985 header = [h.group('wgt_name') for h in
986 cls.weight_header_re.finditer(line)]
987 if any((name not in header) for name in cls.mandatory_weights):
988 raise HwU.ParseError, "The mandatory weight names %s were"\
989 %str(cls.mandatory_weights.keys())+" are not all present"+\
990 " in the following HwU header definition:\n %s"%line
991
992
993 if raw_labels:
994
995
996 header = [ (h if h not in ['xmin','xmax'] else
997 cls.mandatory_weights[h]) for h in header ]
998
999 return header
1000 else:
1001 header = [ (h if h not in cls.mandatory_weights else
1002 cls.mandatory_weights[h]) for h in header ]
1003
1004
1005
1006
1007 for i, h in enumerate(header):
1008 scale_wgt = HwU.weight_label_scale.match(h)
1009 PDF_wgt = HwU.weight_label_PDF.match(h)
1010 Merging_wgt = HwU.weight_label_TMS.match(h)
1011 alpsfact_wgt = HwU.weight_label_alpsfact.match(h)
1012 scale_wgt_adv = HwU.weight_label_scale_adv.match(h)
1013 PDF_wgt_adv = HwU.weight_label_PDF_adv.match(h)
1014 if scale_wgt_adv:
1015 header[i] = ('scale_adv',
1016 int(scale_wgt_adv.group('dyn_choice')),
1017 float(scale_wgt_adv.group('mur_fact')),
1018 float(scale_wgt_adv.group('muf_fact')))
1019 elif scale_wgt:
1020 header[i] = ('scale',
1021 float(scale_wgt.group('mur_fact')),
1022 float(scale_wgt.group('muf_fact')))
1023 elif PDF_wgt_adv:
1024 header[i] = ('pdf_adv',
1025 int(PDF_wgt_adv.group('PDF_set')),
1026 PDF_wgt_adv.group('PDF_set_cen'))
1027 elif PDF_wgt:
1028 header[i] = ('pdf',int(PDF_wgt.group('PDF_set')))
1029 elif Merging_wgt:
1030 header[i] = ('merging_scale',float(Merging_wgt.group('Merging_scale')))
1031 elif alpsfact_wgt:
1032 header[i] = ('alpsfact',float(alpsfact_wgt.group('alpsfact')))
1033
1034 return header
1035
1036 raise HwU.ParseError, "The weight headers could not be found."
1037
1038
1040 """ Parse the histogram name for tags which would set its various
1041 attributes."""
1042
1043 for i, tag in enumerate(histogram_name.split('|')):
1044 if i==0:
1045 self.title = tag.strip()
1046 else:
1047 stag = tag.split('@')
1048 if len(stag)==1 and stag[0].startswith('#'): continue
1049 if len(stag)!=2:
1050 raise MadGraph5Error, 'Specifier in title must have the'+\
1051 " syntax @<attribute_name>:<attribute_value>, not '%s'."%tag.strip()
1052
1053 stag = [t.strip().upper() for t in stag]
1054 if stag[0] in ['T','TYPE']:
1055 self.type = stag[1]
1056 elif stag[0] in ['X_AXIS', 'X']:
1057 self.x_axis_mode = stag[1]
1058 elif stag[0] in ['Y_AXIS', 'Y']:
1059 self.y_axis_mode = stag[1]
1060 elif stag[0] in ['JETSAMPLE', 'JS']:
1061 self.jetsample = int(stag[1])
1062 else:
1063 raise MadGraph5Error, "Specifier '%s' not recognized."%stag[0]
1064
1066 """ Returns the histogram name in the HwU syntax or human readable."""
1067
1068 type_map = {'NLO':'NLO', 'LO':'LO', 'AUX':'auxiliary histogram'}
1069
1070 if format=='human':
1071 res = self.title
1072 if not self.type is None:
1073 try:
1074 res += ', %s'%type_map[self.type]
1075 except KeyError:
1076 res += ', %s'%str('NLO' if self.type.split()[0]=='NLO' else
1077 self.type)
1078 if hasattr(self,'jetsample'):
1079 if self.jetsample==-1:
1080 res += ', all jet samples'
1081 else:
1082 res += ', Jet sample %d'%self.jetsample
1083
1084 return res
1085
1086 elif format=='human-no_type':
1087 res = self.title
1088 return res
1089
1090 elif format=='HwU':
1091 res = [self.title]
1092 res.append('|X_AXIS@%s'%self.x_axis_mode)
1093 res.append('|Y_AXIS@%s'%self.y_axis_mode)
1094 if hasattr(self,'jetsample'):
1095 res.append('|JETSAMPLE@%d'%self.jetsample)
1096 if self.type:
1097 res.append('|TYPE@%s'%self.type)
1098 return ' '.join(res)
1099
1100 - def parse_one_histo_from_stream(self, stream, all_weight_header,
1101 consider_reweights='ALL', raw_labels=False, selected_central_weight=None):
1102 """ Reads *one* histogram from a stream, with the mandatory specification
1103 of the ordered list of weight names. Return True or False depending
1104 on whether the starting definition of a new plot could be found in this
1105 stream."""
1106 n_bins = 0
1107
1108 if consider_reweights=='ALL' or raw_labels:
1109 weight_header = all_weight_header
1110 else:
1111 new_weight_header = []
1112
1113 for wgt_label in all_weight_header:
1114 if wgt_label in ['central','stat_error','boundary_xmin','boundary_xmax'] or\
1115 HwU.get_HwU_wgt_label_type(wgt_label) in consider_reweights:
1116 new_weight_header.append(wgt_label)
1117 weight_header = new_weight_header
1118
1119
1120 for line in stream:
1121 start = HwU.histo_start_re.match(line)
1122 if not start is None:
1123 self.process_histogram_name(start.group('histo_name'))
1124
1125
1126 if self.type == 'AUX':
1127 continue
1128 n_bins = int(start.group('n_bins'))
1129
1130
1131 self.bins = BinList(weight_labels = [ wgt_label for
1132 wgt_label in weight_header if wgt_label not in
1133 ['boundary_xmin','boundary_xmax']])
1134 break
1135
1136
1137 for line_bin in stream:
1138 bin_weights = {}
1139 boundaries = [0.0,0.0]
1140 for j, weight in \
1141 enumerate(HwU.histo_bin_weight_re.finditer(line_bin)):
1142 if j == len(all_weight_header):
1143 raise HwU.ParseError, "There is more bin weights"+\
1144 " specified than expected (%i)"%len(weight_header)
1145 if selected_central_weight == all_weight_header[j]:
1146 bin_weights['central'] = float(weight.group('weight'))
1147 if all_weight_header[j] == 'boundary_xmin':
1148 boundaries[0] = float(weight.group('weight'))
1149 elif all_weight_header[j] == 'boundary_xmax':
1150 boundaries[1] = float(weight.group('weight'))
1151 elif all_weight_header[j] == 'central' and not selected_central_weight is None:
1152 continue
1153 elif all_weight_header[j] in weight_header:
1154 bin_weights[all_weight_header[j]] = \
1155 float(weight.group('weight'))
1156
1157
1158
1159
1160 if len(bin_weights)<(len(weight_header)-2):
1161 raise HwU.ParseError, " There are only %i weights"\
1162 %len(bin_weights)+" specified and %i were expected."%\
1163 (len(weight_header)-2)
1164 self.bins.append(Bin(tuple(boundaries), bin_weights))
1165 if len(self.bins)==n_bins:
1166 break
1167
1168 if len(self.bins)!=n_bins:
1169 raise HwU.ParseError, "%i bin specification "%len(self.bins)+\
1170 "were found and %i were expected."%n_bins
1171
1172
1173 for line_end in stream:
1174 if HwU.histo_end_re.match(line_end):
1175
1176
1177 if not raw_labels:
1178 self.trim_auxiliary_weights()
1179
1180 return True
1181
1182
1183 return False
1184
1186 """ Remove all weights which are auxiliary (whose name end with '@aux')
1187 so that they are not included (they will be regenerated anyway)."""
1188
1189 for i, wgt_label in enumerate(self.bins.weight_labels):
1190 if isinstance(wgt_label, str) and wgt_label.endswith('@aux'):
1191 for bin in self.bins:
1192 try:
1193 del bin.wgts[wgt_label]
1194 except KeyError:
1195 pass
1196 self.bins.weight_labels = [wgt_label for wgt_label in
1197 self.bins.weight_labels if (not isinstance(wgt_label, str)
1198 or (isinstance(wgt_label, str) and not wgt_label.endswith('@aux')) )]
1199
1200 - def set_uncertainty(self, type='all_scale',lhapdfconfig='lhapdf-config'):
1201 """ Adds a weight to the bins which is the envelope of the scale
1202 uncertainty, for the scale specified which can be either 'mur', 'muf',
1203 'all_scale' or 'PDF'."""
1204
1205 if type.upper()=='MUR':
1206 new_wgt_label = 'delta_mur'
1207 scale_position = 1
1208 elif type.upper()=='MUF':
1209 new_wgt_label = 'delta_muf'
1210 scale_position = 2
1211 elif type.upper()=='ALL_SCALE':
1212 new_wgt_label = 'delta_mu'
1213 scale_position = -1
1214 elif type.upper()=='PDF':
1215 new_wgt_label = 'delta_pdf'
1216 scale_position = -2
1217 elif type.upper()=='MERGING':
1218 new_wgt_label = 'delta_merging'
1219 elif type.upper()=='ALPSFACT':
1220 new_wgt_label = 'delta_alpsfact'
1221 else:
1222 raise MadGraph5Error, ' The function set_uncertainty can'+\
1223 " only handle the scales 'mur', 'muf', 'all_scale', 'pdf',"+\
1224 "'merging' or 'alpsfact'."
1225
1226 wgts_to_consider=[]
1227 label_to_consider=[]
1228 if type.upper() == 'MERGING':
1229
1230
1231
1232 wgts_to_consider.append([ label for label in self.bins.weight_labels if \
1233 HwU.get_HwU_wgt_label_type(label)=='merging_scale' ])
1234 label_to_consider.append('none')
1235
1236 elif type.upper() == 'ALPSFACT':
1237
1238
1239
1240 wgts_to_consider.append([ label for label in self.bins.weight_labels if \
1241 HwU.get_HwU_wgt_label_type(label)=='alpsfact' ])
1242 label_to_consider.append('none')
1243 elif scale_position > -2:
1244
1245 dyn_scales=[label[1] for label in self.bins.weight_labels if \
1246 HwU.get_HwU_wgt_label_type(label)=='scale_adv']
1247
1248 dyn_scales=[scale for n,scale in enumerate(dyn_scales) if scale not in dyn_scales[:n]]
1249 for dyn_scale in dyn_scales:
1250 wgts=[label for label in self.bins.weight_labels if \
1251 HwU.get_HwU_wgt_label_type(label)=='scale_adv' and label[1]==dyn_scale]
1252 if wgts:
1253 wgts_to_consider.append(wgts)
1254 label_to_consider.append(dyn_scale)
1255
1256 wgts=[label for label in self.bins.weight_labels if \
1257 HwU.get_HwU_wgt_label_type(label)=='scale']
1258
1259
1260
1261
1262 if wgts:
1263 wgts_to_consider.append(wgts)
1264 label_to_consider.append('none')
1265
1266
1267 if scale_position > -1:
1268 for wgts in wgts_to_consider:
1269 wgts_to_consider.remove(wgts)
1270 wgts = [ label for label in wgts if label[-scale_position]==1.0 ]
1271 wgts_to_consider.append(wgts)
1272 elif scale_position == -2:
1273
1274 pdf_sets=[label[2] for label in self.bins.weight_labels if \
1275 HwU.get_HwU_wgt_label_type(label)=='pdf_adv']
1276
1277 pdf_sets=[ii for n,ii in enumerate(pdf_sets) if ii not in pdf_sets[:n]]
1278 for pdf_set in pdf_sets:
1279 wgts=[label for label in self.bins.weight_labels if \
1280 HwU.get_HwU_wgt_label_type(label)=='pdf_adv' and label[2]==pdf_set]
1281 if wgts:
1282 wgts_to_consider.append(wgts)
1283 label_to_consider.append(pdf_set)
1284
1285 wgts = [ label for label in self.bins.weight_labels if \
1286 HwU.get_HwU_wgt_label_type(label)=='pdf']
1287 if wgts:
1288 wgts_to_consider.append(wgts)
1289 label_to_consider.append('none')
1290
1291 if len(wgts_to_consider)==0 or all(len(wgts)==0 for wgts in wgts_to_consider):
1292
1293 return (None,[None])
1294
1295
1296 if type=='PDF':
1297 use_lhapdf=False
1298 try:
1299 lhapdf_libdir=subprocess.Popen([lhapdfconfig,'--libdir'],\
1300 stdout=subprocess.PIPE).stdout.read().strip()
1301 except:
1302 use_lhapdf=False
1303 else:
1304 try:
1305 candidates=[dirname for dirname in os.listdir(lhapdf_libdir) \
1306 if os.path.isdir(os.path.join(lhapdf_libdir,dirname))]
1307 except OSError:
1308 candidates=[]
1309 for candidate in candidates:
1310 if os.path.isfile(os.path.join(lhapdf_libdir,candidate,'site-packages','lhapdf.so')):
1311 sys.path.insert(0,os.path.join(lhapdf_libdir,candidate,'site-packages'))
1312 try:
1313 import lhapdf
1314 use_lhapdf=True
1315 break
1316 except ImportError:
1317 sys.path.pop(0)
1318 continue
1319
1320 if not use_lhapdf:
1321 try:
1322 candidates=[dirname for dirname in os.listdir(lhapdf_libdir+'64') \
1323 if os.path.isdir(os.path.join(lhapdf_libdir+'64',dirname))]
1324 except OSError:
1325 candidates=[]
1326 for candidate in candidates:
1327 if os.path.isfile(os.path.join(lhapdf_libdir+'64',candidate,'site-packages','lhapdf.so')):
1328 sys.path.insert(0,os.path.join(lhapdf_libdir+'64',candidate,'site-packages'))
1329 try:
1330 import lhapdf
1331 use_lhapdf=True
1332 break
1333 except ImportError:
1334 sys.path.pop(0)
1335 continue
1336
1337 if not use_lhapdf:
1338 try:
1339 import lhapdf
1340 use_lhapdf=True
1341 except ImportError:
1342 logger.warning("Failed to access python version of LHAPDF: "\
1343 "cannot compute PDF uncertainty from the "\
1344 "weights in the histograms. The weights in the HwU data files " \
1345 "still cover all PDF set members, "\
1346 "but the automatic computation of the uncertainties from "\
1347 "those weights might not be correct. \n "\
1348 "If the python interface to LHAPDF is available on your system, try "\
1349 "adding its location to the PYTHONPATH environment variable and the"\
1350 "LHAPDF library location to LD_LIBRARY_PATH (linux) or DYLD_LIBRARY_PATH (mac os x).")
1351
1352 if type=='PDF' and use_lhapdf:
1353 lhapdf.setVerbosity(0)
1354
1355
1356 position=[]
1357 labels=[]
1358 for i,label in enumerate(label_to_consider):
1359 wgts=wgts_to_consider[i]
1360 if label != 'none':
1361 new_wgt_labels=['%s_cen %s @aux' % (new_wgt_label,label),
1362 '%s_min %s @aux' % (new_wgt_label,label),
1363 '%s_max %s @aux' % (new_wgt_label,label)]
1364 else:
1365 new_wgt_labels=['%s_cen @aux' % new_wgt_label,
1366 '%s_min @aux' % new_wgt_label,
1367 '%s_max @aux' % new_wgt_label]
1368 try:
1369 pos=[(not isinstance(lab, str)) for lab in \
1370 self.bins.weight_labels].index(True)
1371 position.append(pos)
1372 labels.append(label)
1373 self.bins.weight_labels = self.bins.weight_labels[:pos]+\
1374 new_wgt_labels + self.bins.weight_labels[pos:]
1375 except ValueError:
1376 pos=len(self.bins.weight_labels)
1377 position.append(pos)
1378 labels.append(label)
1379 self.bins.weight_labels.extend(new_wgt_labels)
1380
1381 if type=='PDF' and use_lhapdf and label != 'none':
1382 p=lhapdf.getPDFSet(label)
1383
1384
1385 for bin in self.bins:
1386 if type!='PDF':
1387 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]]
1388 bin.wgts[new_wgt_labels[1]] = min(bin.wgts[label] \
1389 for label in wgts)
1390 bin.wgts[new_wgt_labels[2]] = max(bin.wgts[label] \
1391 for label in wgts)
1392 elif type=='PDF' and use_lhapdf and label != 'none' and len(wgts) > 1:
1393 pdfs = [bin.wgts[pdf] for pdf in sorted(wgts)]
1394 ep=p.uncertainty(pdfs,-1)
1395 bin.wgts[new_wgt_labels[0]] = ep.central
1396 bin.wgts[new_wgt_labels[1]] = ep.central-ep.errminus
1397 bin.wgts[new_wgt_labels[2]] = ep.central+ep.errplus
1398 elif type=='PDF' and use_lhapdf and label != 'none' and len(bin.wgts) == 1:
1399 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]]
1400 bin.wgts[new_wgt_labels[1]] = bin.wgts[wgts[0]]
1401 bin.wgts[new_wgt_labels[2]] = bin.wgts[wgts[0]]
1402 else:
1403 pdfs = [bin.wgts[pdf] for pdf in sorted(wgts)]
1404 pdf_up = 0.0
1405 pdf_down = 0.0
1406 cntrl_val = bin.wgts['central']
1407 if wgts[0] <= 90000:
1408
1409 if len(pdfs)>2:
1410 for i in range(int((len(pdfs)-1)/2)):
1411 pdf_up += max(0.0,pdfs[2*i+1]-cntrl_val,
1412 pdfs[2*i+2]-cntrl_val)**2
1413 pdf_down += max(0.0,cntrl_val-pdfs[2*i+1],
1414 cntrl_val-pdfs[2*i+2])**2
1415 pdf_up = cntrl_val + math.sqrt(pdf_up)
1416 pdf_down = cntrl_val - math.sqrt(pdf_down)
1417 else:
1418 pdf_up = bin.wgts[pdfs[0]]
1419 pdf_down = bin.wgts[pdfs[0]]
1420 elif wgts[0] in range(90200, 90303) or \
1421 wgts[0] in range(90400, 90433) or \
1422 wgts[0] in range(90700, 90801) or \
1423 wgts[0] in range(90900, 90931) or \
1424 wgts[0] in range(91200, 91303) or \
1425 wgts[0] in range(91400, 91433) or \
1426 wgts[0] in range(91700, 91801) or \
1427 wgts[0] in range(91900, 91931):
1428
1429 pdf_stdev = 0.0
1430 for pdf in pdfs[1:]:
1431 pdf_stdev += (pdf - cntrl_val)**2
1432 pdf_stdev = math.sqrt(pdf_stdev)
1433 pdf_up = cntrl_val+pdf_stdev
1434 pdf_down = cntrl_val-pdf_stdev
1435 else:
1436
1437 pdf_stdev = 0.0
1438 for pdf in pdfs[1:]:
1439 pdf_stdev += (pdf - cntrl_val)**2
1440 pdf_stdev = math.sqrt(pdf_stdev/float(len(pdfs)-2))
1441 pdf_up = cntrl_val+pdf_stdev
1442 pdf_down = cntrl_val-pdf_stdev
1443
1444 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]]
1445 bin.wgts[new_wgt_labels[1]] = pdf_down
1446 bin.wgts[new_wgt_labels[2]] = pdf_up
1447
1448
1449
1450 return (position,labels)
1451
1453 """ Select a specific merging scale for the central value of this Histogram. """
1454 if selected_label not in self.bins.weight_labels:
1455 raise MadGraph5Error, "Selected weight label '%s' could not be found in this HwU."%selected_label
1456
1457 for bin in self.bins:
1458 bin.wgts['central']=bin.wgts[selected_label]
1459
1460 - def rebin(self, n_rebin):
1461 """ Rebin the x-axis so as to merge n_rebin consecutive bins into a
1462 single one. """
1463
1464 if n_rebin < 1 or not isinstance(n_rebin, int):
1465 raise MadGraph5Error, "The argument 'n_rebin' of the HwU function"+\
1466 " 'rebin' must be larger or equal to 1, not '%s'."%str(n_rebin)
1467 elif n_rebin==1:
1468 return
1469
1470 if self.type and 'NOREBIN' in self.type.upper():
1471 return
1472
1473 rebinning_list = list(range(0,len(self.bins),n_rebin))+[len(self.bins),]
1474 concat_list = [self.bins[rebinning_list[i]:rebinning_list[i+1]] for \
1475 i in range(len(rebinning_list)-1)]
1476
1477 new_bins = copy.copy(self.bins)
1478 del new_bins[:]
1479
1480 for bins_to_merge in concat_list:
1481 if len(bins_to_merge)==0:
1482 continue
1483 new_bins.append(Bin(boundaries=(bins_to_merge[0].boundaries[0],
1484 bins_to_merge[-1].boundaries[1]),wgts={'central':0.0}))
1485 for weight in self.bins.weight_labels:
1486 if weight != 'stat_error':
1487 new_bins[-1].wgts[weight] = \
1488 sum(b.wgts[weight] for b in bins_to_merge)
1489 else:
1490 new_bins[-1].wgts['stat_error'] = \
1491 math.sqrt(sum(b.wgts['stat_error']**2 for b in\
1492 bins_to_merge))
1493
1494 self.bins = new_bins
1495
1496 @classmethod
1498 """ Function to determine the optimal x-axis range when plotting
1499 together the histos in histo_list and considering the weights
1500 weight_labels"""
1501
1502
1503 if weight_labels is None:
1504 weight_labels = histo_list[0].bins.weight_labels
1505
1506 all_boundaries = sum([ list(bin.boundaries) for histo in histo_list \
1507 for bin in histo.bins if \
1508 (sum(abs(bin.wgts[label]) for label in weight_labels) > 0.0)] ,[])
1509
1510 if len(all_boundaries)==0:
1511 all_boundaries = sum([ list(bin.boundaries) for histo in histo_list \
1512 for bin in histo.bins],[])
1513 if len(all_boundaries)==0:
1514 raise MadGraph5Error, "The histograms with title '%s'"\
1515 %histo_list[0].title+" seems to have no bins."
1516
1517 x_min = min(all_boundaries)
1518 x_max = max(all_boundaries)
1519
1520 return (x_min, x_max)
1521
1522 @classmethod
1525 """ Function to determine the optimal y-axis range when plotting
1526 together the histos in histo_list and considering the weights
1527 weight_labels. The option Kratio is present to allow for the couple of
1528 tweaks necessary for the the K-factor ratio histogram y-range."""
1529
1530
1531 if labels is None:
1532 weight_labels = histo_list[0].bins.weight_labels
1533 else:
1534 weight_labels = labels
1535
1536 all_weights = []
1537 for histo in histo_list:
1538 for bin in histo.bins:
1539 for label in weight_labels:
1540
1541
1542 if Kratio and bin.wgts[label]==0.0:
1543 continue
1544 if scale!='LOG':
1545 all_weights.append(bin.wgts[label])
1546 if label == 'stat_error':
1547 all_weights.append(-bin.wgts[label])
1548 elif bin.wgts[label]>0.0:
1549 all_weights.append(bin.wgts[label])
1550
1551
1552 sum([ [bin.wgts[label] for label in weight_labels if \
1553 (scale!='LOG' or bin.wgts[label]!=0.0)] \
1554 for histo in histo_list for bin in histo.bins], [])
1555
1556 all_weights.sort()
1557 if len(all_weights)!=0:
1558 partial_max = all_weights[int(len(all_weights)*0.95)]
1559 partial_min = all_weights[int(len(all_weights)*0.05)]
1560 max = all_weights[-1]
1561 min = all_weights[0]
1562 else:
1563 if scale!='LOG':
1564 return (0.0,1.0)
1565 else:
1566 return (1.0,10.0)
1567
1568 y_max = 0.0
1569 y_min = 0.0
1570
1571
1572 if (max-partial_max)>2.0*(partial_max-partial_min):
1573 y_max = partial_max
1574 else:
1575 y_max = max
1576
1577
1578 if (partial_min - min)>2.0*(partial_max-partial_min) and min != 0.0:
1579 y_min = partial_min
1580 else:
1581 y_min = min
1582
1583 if Kratio:
1584 median = all_weights[len(all_weights)//2]
1585 spread = (y_max-y_min)
1586 if abs(y_max-median)<spread*0.05 or abs(median-y_min)<spread*0.05:
1587 y_max = median + spread/2.0
1588 y_min = median - spread/2.0
1589 if y_min != y_max:
1590 return ( y_min , y_max )
1591
1592
1593 if len(histo_list[0].bins) <= 5:
1594 y_min = min
1595 y_max = max
1596
1597
1598 if y_min == y_max:
1599 if max == min:
1600 y_min -= 1.0
1601 y_max += 1.0
1602 else:
1603 y_min = min
1604 y_max = max
1605
1606 return ( y_min , y_max )
1607
1608 -class HwUList(histograms_PhysicsObjectList):
1609 """ A class implementing features related to a list of Hwu Histograms. """
1610
1611
1612
1613
1614 number_line_colors_defined = 8
1615
1617 """Test wether specified object is of the right type for this list."""
1618
1619 return isinstance(obj, HwU) or isinstance(obj, HwUList)
1620
1621 - def __init__(self, file_path, weight_header=None, run_id=None,
1622 merging_scale=None, accepted_types_order=[], consider_reweights='ALL',
1623 raw_labels=False, **opts):
1624 """ Read one plot from a file_path or a stream.
1625 This constructor reads all plots specified in target file.
1626 File_path can be a path or a stream in the argument.
1627 The option weight_header specifies an ordered list of weight names
1628 to appear in the file or stream specified. It accepted_types_order is
1629 empty, no filter is applied, otherwise only histograms of the specified
1630 types will be kept, and in this specified order for a given identical
1631 title. The option 'consider_reweights' selects whether one wants to
1632 include all the extra scale/pdf/merging variation weights. Possible values
1633 are 'ALL' or a list of the return types of the function get_HwU_wgt_label_type().
1634 The option 'raw_labels' specifies that one wants to import the
1635 histogram data with no treatment of the weight labels at all
1636 (this is used for the matplotlib output).
1637 """
1638
1639 if isinstance(file_path, str):
1640 stream = open(file_path,'r')
1641 elif isinstance(file_path, file):
1642 stream = file_path
1643 else:
1644 return super(HwUList,self).__init__(file_path, **opts)
1645
1646 try:
1647
1648 self.parse_histos_from_PY8_XML_stream(stream, run_id,
1649 merging_scale, accepted_types_order,
1650 consider_reweights=consider_reweights,
1651 raw_labels=raw_labels)
1652 except XMLParsingError:
1653
1654 stream.seek(0)
1655
1656 if not weight_header:
1657 weight_header = HwU.parse_weight_header(stream,raw_labels=raw_labels)
1658
1659
1660 selected_label = None
1661 if not merging_scale is None:
1662 for label in weight_header:
1663 if HwU.get_HwU_wgt_label_type(label)=='merging_scale':
1664 if float(label[1])==merging_scale:
1665 selected_label = label
1666 break
1667 if selected_label is None:
1668 raise MadGraph5Error, "No weight could be found in the input HwU "+\
1669 "for the selected merging scale '%4.2f'."%merging_scale
1670
1671 new_histo = HwU(stream, weight_header,raw_labels=raw_labels,
1672 consider_reweights=consider_reweights,
1673 selected_central_weight=selected_label)
1674
1675 while not new_histo.bins is None:
1676 if accepted_types_order==[] or \
1677 new_histo.type in accepted_types_order:
1678 self.append(new_histo)
1679 new_histo = HwU(stream, weight_header, raw_labels=raw_labels,
1680 consider_reweights=consider_reweights,
1681 selected_central_weight=selected_label)
1682
1683
1684
1685
1686
1687
1688
1689 titles_order = [h.title for h in self]
1690 def ordering_function(histo):
1691 title_position = titles_order.index(histo.title)
1692 if accepted_types_order==[]:
1693 type_precedence = {'NLO':1,'LO':2,None:3,'AUX':5}
1694 try:
1695 ordering_key = (title_position,type_precedence[histo.type])
1696 except KeyError:
1697 ordering_key = (title_position,4)
1698 else:
1699 ordering_key = (title_position,
1700 accepted_types_order.index(histo.type))
1701 return ordering_key
1702
1703
1704
1705
1706
1707 self.sort(key=ordering_function)
1708
1709
1710 if isinstance(file_path, str):
1711 stream.close()
1712
1720
1722 """ return the list of all weights define in each histograms"""
1723
1724 return self[0].bins.weight_labels
1725
1726
1727 - def get(self, name):
1728 """return the HWU histograms related to a given name"""
1729 for hist in self:
1730 if hist.get_HwU_histogram_name() == name:
1731 return hist
1732
1733 raise NameError, "no histogram with name: %s" % name
1734
1735 - def parse_histos_from_PY8_XML_stream(self, stream, run_id=None,
1736 merging_scale=None, accepted_types_order=[],
1737 consider_reweights='ALL', raw_labels=False):
1738 """Initialize the HwU histograms from an XML stream. Only one run is
1739 used: the first one if run_id is None or the specified run otherwise.
1740 Accepted type order is a filter to select histograms of only a certain
1741 type. The option 'consider_reweights' selects whether one wants to
1742 include all the extra scale/pdf/merging variation weights.
1743 Possible values are 'ALL' or a list of the return types of the
1744 function get_HwU_wgt_label_type()."""
1745
1746 run_nodes = minidom.parse(stream).getElementsByTagName("run")
1747 all_nodes = dict((int(node.getAttribute('id')),node) for
1748 node in run_nodes)
1749 selected_run_node = None
1750 weight_header = None
1751 if run_id is None:
1752 if len(run_nodes)>0:
1753 selected_run_node = all_nodes[min(all_nodes.keys())]
1754 else:
1755 try:
1756 selected_run_node = all_nodes[int(run_id)]
1757 except:
1758 selected_run_node = None
1759
1760 if selected_run_node is None:
1761 if run_id is None:
1762 raise MadGraph5Error, \
1763 'No histogram was found in the specified XML source.'
1764 else:
1765 raise MadGraph5Error, \
1766 "Histogram with run_id '%d' was not found in the "%run_id+\
1767 "specified XML source."
1768
1769
1770
1771 if raw_labels:
1772
1773 weight_label_list = [wgt.strip() for wgt in
1774 str(selected_run_node.getAttribute('header')).split(';') if
1775 not re.match('^\s*$',wgt)]
1776 ordered_weight_label_list = [w for w in weight_label_list if w not\
1777 in ['xmin','xmax']]
1778
1779 filtered_ordered_weight_label_list = []
1780 for wgt_label in ordered_weight_label_list:
1781 if wgt_label not in filtered_ordered_weight_label_list:
1782 filtered_ordered_weight_label_list.append(wgt_label)
1783
1784 selected_weights = dict([ (wgt_pos,
1785 [wgt if wgt not in ['xmin','xmax'] else HwU.mandatory_weights[wgt]])
1786 for wgt_pos, wgt in enumerate(weight_label_list) if wgt in
1787 filtered_ordered_weight_label_list+['xmin','xmax']])
1788
1789 return self.retrieve_plots_from_XML_source(selected_run_node,
1790 selected_weights, filtered_ordered_weight_label_list,
1791 raw_labels=True)
1792
1793
1794
1795
1796 all_weights = []
1797 for wgt_position, wgt_label in \
1798 enumerate(str(selected_run_node.getAttribute('header')).split(';')):
1799 if not re.match('^\s*$',wgt_label) is None:
1800 continue
1801 all_weights.append({'POSITION':wgt_position})
1802 for wgt_item in wgt_label.strip().split('_'):
1803 property = wgt_item.strip().split('=')
1804 if len(property) == 2:
1805 all_weights[-1][property[0].strip()] = property[1].strip()
1806 elif len(property)==1:
1807 all_weights[-1][property[0].strip()] = None
1808 else:
1809 raise MadGraph5Error, \
1810 "The weight label property %s could not be parsed."%wgt_item
1811
1812
1813
1814
1815
1816 for wgt_label in all_weights:
1817 for mandatory_attribute in ['PDF','MUR','MUF','MERGING','ALPSFACT']:
1818 if mandatory_attribute not in wgt_label:
1819 wgt_label[mandatory_attribute] = '-1'
1820 if mandatory_attribute=='PDF':
1821 wgt_label[mandatory_attribute] = int(wgt_label[mandatory_attribute])
1822 elif mandatory_attribute in ['MUR','MUF','MERGING','ALPSFACT']:
1823 wgt_label[mandatory_attribute] = float(wgt_label[mandatory_attribute])
1824
1825
1826
1827
1828 if merging_scale is None or merging_scale < 0.0:
1829 merging_scale_chosen = all_weights[2]['MERGING']
1830 else:
1831 merging_scale_chosen = merging_scale
1832
1833
1834 central_PDF = all_weights[2]['PDF']
1835
1836 central_MUR = all_weights[2]['MUR'] if all_weights[2]['MUR']!=-1.0 else 1.0
1837 central_MUF = all_weights[2]['MUF'] if all_weights[2]['MUF']!=-1.0 else 1.0
1838 central_alpsfact = all_weights[2]['ALPSFACT'] if all_weights[2]['ALPSFACT']!=-1.0 else 1.0
1839
1840
1841
1842 selected_weights = {}
1843
1844 if 'xmin' not in all_weights[0] or \
1845 'xmax' not in all_weights[1] or \
1846 'Weight' not in all_weights[2] or \
1847 'WeightError' not in all_weights[3]:
1848 raise MadGraph5Error, 'The first weight entries in the XML HwU '+\
1849 ' source are not the standard expected ones (xmin, xmax, sigmaCentral, errorCentral)'
1850 selected_weights[0] = ['xmin']
1851 selected_weights[1] = ['xmax']
1852
1853
1854 def get_difference_to_central(weight):
1855 """ Return the list of properties which differ from the central weight.
1856 This disregards the merging scale value for which any central value
1857 can be picked anyway."""
1858
1859 differences = []
1860
1861
1862
1863 if 'Weight' in weight:
1864 return set([])
1865 if weight['MUR'] not in [central_MUR, -1.0] or \
1866 weight['MUF'] not in [central_MUF, -1.0]:
1867 differences.append('mur_muf_scale')
1868 if weight['PDF'] not in [central_PDF,-1]:
1869 differences.append('pdf')
1870 if weight['ALPSFACT'] not in [central_alpsfact, -1]:
1871 differences.append('ALPSFACT')
1872 return set(differences)
1873
1874 def format_weight_label(weight):
1875 """ Print the weight attributes in a nice order."""
1876
1877 all_properties = weight.keys()
1878 all_properties.pop(all_properties.index('POSITION'))
1879 ordered_properties = []
1880
1881 for property in all_properties:
1882 if weight[property] is None:
1883 ordered_properties.append(property)
1884
1885 ordered_properties.sort()
1886 all_properties = [property for property in all_properties if
1887 not weight[property] is None]
1888
1889
1890 for property in ['PDF','MUR','MUF','ALPSFACT','MERGING']:
1891 all_properties.pop(all_properties.index(property))
1892 if weight[property]!=-1:
1893 ordered_properties.append(property)
1894
1895 ordered_properties.extend(sorted(all_properties))
1896
1897 return '_'.join('%s%s'\
1898 %(key,'' if weight[key] is None else '=%s'%str(weight[key])) for
1899 key in ordered_properties)
1900
1901
1902
1903
1904
1905 if float(all_weights[2]['MERGING']) == merging_scale_chosen:
1906 selected_weights[2]=['central value']
1907 else:
1908 for weight_position, weight in enumerate(all_weights):
1909
1910
1911 if get_difference_to_central(weight)==set([]):
1912
1913 if weight['MERGING']==merging_scale_chosen:
1914 selected_weights[weight_position] = ['central value']
1915 break
1916
1917 if 'central value' not in sum(selected_weights.values(),[]):
1918 central_merging_scale = all_weights[2]['MERGING']
1919 logger.warning('Could not find the central weight for the'+\
1920 ' chosen merging scale (%f).\n'%merging_scale_chosen+\
1921 'MG5aMC will chose the original central scale provided which '+\
1922 'correspond to a merging scale of %s'%("'inclusive'" if
1923 central_merging_scale in [0.0,-1.0] else '%f'%central_merging_scale))
1924 selected_weights[2]=['central value']
1925
1926
1927 selected_weights[3]=['dy']
1928
1929
1930 for weight_position, weight in enumerate(all_weights[4:]):
1931
1932
1933
1934
1935
1936
1937 variations = get_difference_to_central(weight)
1938
1939
1940
1941
1942
1943
1944
1945 if variations in [set(['mur_muf_scale']),set(['pdf','mur_muf_scale'])]:
1946 wgt_label = ('scale',weight['MUR'],weight['MUF'])
1947 if variations in [set(['ALPSFACT']),set(['pdf','ALPSFACT'])]:
1948 wgt_label = ('alpsfact',weight['ALPSFACT'])
1949 if variations == set(['pdf']):
1950 wgt_label = ('pdf',weight['PDF'])
1951 if variations == set([]):
1952
1953 wgt_label = format_weight_label(weight)
1954
1955
1956 if weight['MERGING'] != merging_scale_chosen:
1957
1958 if merging_scale:
1959 continue
1960
1961
1962 if variations == set([]):
1963
1964 wgt_label = ('merging_scale', weight['MERGING'])
1965
1966
1967
1968 if wgt_label in sum(selected_weights.values(),[]):
1969 continue
1970
1971
1972 try:
1973 selected_weights[weight_position+4].append(wgt_label)
1974 except KeyError:
1975 selected_weights[weight_position+4]=[wgt_label,]
1976
1977 if merging_scale and merging_scale > 0.0 and \
1978 len(sum(selected_weights.values(),[]))==4:
1979 logger.warning('No additional variation weight was found for the '+\
1980 'chosen merging scale %f.'%merging_scale)
1981
1982
1983 for wgt_pos in selected_weights:
1984 for i, weight_label in enumerate(selected_weights[wgt_pos]):
1985 try:
1986 selected_weights[wgt_pos][i] = HwU.mandatory_weights[weight_label]
1987 except KeyError:
1988 pass
1989
1990
1991 if consider_reweights!='ALL':
1992 new_selected_weights = {}
1993 for wgt_position, wgt_labels in selected_weights.items():
1994 for wgt_label in wgt_labels:
1995 if wgt_label in ['central','stat_error','boundary_xmin','boundary_xmax'] or\
1996 HwU.get_HwU_wgt_label_type(wgt_label) in consider_reweights:
1997 try:
1998 new_selected_weights[wgt_position].append(wgt_label)
1999 except KeyError:
2000 new_selected_weights[wgt_position] = [wgt_label]
2001 selected_weights = new_selected_weights
2002
2003
2004 weight_label_list = sum(selected_weights.values(),[])
2005
2006
2007 ordered_weight_label_list = ['central','stat_error']
2008 for weight_label in weight_label_list:
2009 if not isinstance(weight_label, str):
2010 ordered_weight_label_list.append(weight_label)
2011 for weight_label in weight_label_list:
2012 if weight_label in ['central','stat_error','boundary_xmin','boundary_xmax']:
2013 continue
2014 if isinstance(weight_label, str):
2015 ordered_weight_label_list.append(weight_label)
2016
2017
2018
2019 return self.retrieve_plots_from_XML_source(selected_run_node,
2020 selected_weights, ordered_weight_label_list, raw_labels=False)
2021
2024 """Given an XML node and the selected weights and their ordered list,
2025 import all histograms from the specified XML node."""
2026
2027
2028 for multiplicity_node in xml_node.getElementsByTagName("jethistograms"):
2029 multiplicity = int(multiplicity_node.getAttribute('njet'))
2030 for histogram in multiplicity_node.getElementsByTagName("histogram"):
2031
2032 if histogram.getAttribute("weight")!='all':
2033 continue
2034 new_histo = HwU()
2035 hist_name = '%s %s'%(str(histogram.getAttribute('name')),
2036 str(histogram.getAttribute('unit')))
2037
2038 new_histo.process_histogram_name('%s |JETSAMPLE@%d'%(hist_name,multiplicity))
2039
2040
2041 if new_histo.type == 'AUX':
2042 continue
2043
2044
2045
2046 new_histo.bins = BinList(weight_labels = ordered_weight_label_list)
2047 hist_data = str(histogram.childNodes[0].data)
2048 for line in hist_data.split('\n'):
2049 if line.strip()=='':
2050 continue
2051 bin_weights = {}
2052 boundaries = [0.0,0.0]
2053 for j, weight in \
2054 enumerate(HwU.histo_bin_weight_re.finditer(line)):
2055 try:
2056 for wgt_label in selected_weights[j]:
2057 if wgt_label == 'boundary_xmin':
2058 boundaries[0] = float(weight.group('weight'))
2059 elif wgt_label == 'boundary_xmax':
2060 boundaries[1] = float(weight.group('weight'))
2061 else:
2062 if weight.group('weight').upper()=='NAN':
2063 raise MadGraph5Error, \
2064 "Some weights are found to be 'NAN' in histogram with name '%s'"%hist_name+\
2065 " and jet sample multiplicity %d."%multiplicity
2066 else:
2067 bin_weights[wgt_label] = \
2068 float(weight.group('weight'))
2069 except KeyError:
2070 continue
2071
2072 if len(bin_weights)!=len(ordered_weight_label_list):
2073 raise MadGraph5Error, \
2074 'Not all defined weights were found in the XML source.\n'+\
2075 '%d found / %d expected.'%(len(bin_weights),len(ordered_weight_label_list))+\
2076 '\nThe missing ones are: %s.'%\
2077 str(list(set(ordered_weight_label_list)-set(bin_weights.keys())))+\
2078 "\nIn plot with title '%s' and jet sample multiplicity %d."%\
2079 (hist_name, multiplicity)
2080
2081 new_histo.bins.append(Bin(tuple(boundaries), bin_weights))
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099 if not raw_labels:
2100 new_histo.trim_auxiliary_weights()
2101
2102
2103 self.append(new_histo)
2104
2105 - def output(self, path, format='gnuplot',number_of_ratios = -1,
2106 uncertainties=['scale','pdf','statitistical','merging_scale','alpsfact'],
2107 use_band = None,
2108 ratio_correlations=True, arg_string='',
2109 jet_samples_to_keep=None,
2110 auto_open=True,
2111 lhapdfconfig='lhapdf-config'):
2112 """ Ouput this histogram to a file, stream or string if path is kept to
2113 None. The supported format are for now. Chose whether to print the header
2114 or not."""
2115
2116 if len(self)==0:
2117 return MadGraph5Error, 'No histograms stored in the list yet.'
2118
2119 if not format in HwU.output_formats_implemented:
2120 raise MadGraph5Error, "The specified output format '%s'"%format+\
2121 " is not yet supported. Supported formats are %s."\
2122 %HwU.output_formats_implemented
2123
2124 if isinstance(path, str) and not any(ext in os.path.basename(path) \
2125 for ext in ['.Hwu','.ps','.gnuplot','.pdf']):
2126 output_base_name = os.path.basename(path)
2127 HwU_stream = open(path+'.HwU','w')
2128 else:
2129 raise MadGraph5Error, "The path argument of the output function of"+\
2130 " the HwUList instance must be file path without its extension."
2131
2132 HwU_output_list = []
2133
2134
2135 if format == 'HwU':
2136 HwU_output_list.extend(self[0].get_HwU_source(print_header=True))
2137 for histo in self[1:]:
2138 HwU_output_list.extend(histo.get_HwU_source())
2139 HwU_output_list.extend(['',''])
2140 HwU_stream.write('\n'.join(HwU_output_list))
2141 HwU_stream.close()
2142 return
2143
2144
2145 if format == 'gnuplot':
2146 gnuplot_stream = open(path+'.gnuplot','w')
2147
2148
2149 matching_histo_lists = HwUList([HwUList([self[0]])])
2150 for histo in self[1:]:
2151 matched = False
2152 for histo_list in matching_histo_lists:
2153 if histo.test_plot_compability(histo_list[0],
2154 consider_type=False, consider_unknown_weight_labels=True):
2155 histo_list.append(histo)
2156 matched = True
2157 break
2158 if not matched:
2159 matching_histo_lists.append(HwUList([histo]))
2160
2161 self[:] = matching_histo_lists
2162
2163
2164 gnuplot_output_list_v4 = [
2165 """
2166 ################################################################################
2167 #
2168 # This gnuplot file was generated by MadGraph5_aMC@NLO project, a program which
2169 # automatically generates Feynman diagrams and matrix elements for arbitrary
2170 # high-energy processes in the Standard Model and beyond. It also perform the
2171 # integration and/or generate events for these processes, at LO and NLO accuracy.
2172 #
2173 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
2174 #
2175 ################################################################################
2176 # %s
2177 reset
2178
2179 set lmargin 10
2180 set rmargin 0
2181 set terminal postscript portrait enhanced mono dashed lw 1.0 "Helvetica" 9
2182 # The pdf terminal offers transparency support, but you will have to adapt things a bit
2183 #set terminal pdf enhanced font "Helvetica 12" lw 1.0 dashed size 29.7cm, 21cm
2184 set key font ",9"
2185 set key samplen "2"
2186 set output "%s.ps"
2187
2188 # This is the "PODO" color palette of gnuplot v.5, but with the order
2189 # changed: palette of colors selected to be easily distinguishable by
2190 # color-blind individuals with either protanopia or deuteranopia. Bang
2191 # Wong [2011] Nature Methods 8, 441.
2192
2193 set style line 1 lt 1 lc rgb "#009e73" lw 2.5
2194 set style line 11 lt 2 lc rgb "#009e73" lw 2.5
2195 set style line 21 lt 4 lc rgb "#009e73" lw 2.5
2196 set style line 31 lt 6 lc rgb "#009e73" lw 2.5
2197 set style line 41 lt 8 lc rgb "#009e73" lw 2.5
2198
2199 set style line 2 lt 1 lc rgb "#0072b2" lw 2.5
2200 set style line 12 lt 2 lc rgb "#0072b2" lw 2.5
2201 set style line 22 lt 4 lc rgb "#0072b2" lw 2.5
2202 set style line 32 lt 6 lc rgb "#0072b2" lw 2.5
2203 set style line 42 lt 8 lc rgb "#0072b2" lw 2.5
2204
2205 set style line 3 lt 1 lc rgb "#d55e00" lw 2.5
2206 set style line 13 lt 2 lc rgb "#d55e00" lw 2.5
2207 set style line 23 lt 4 lc rgb "#d55e00" lw 2.5
2208 set style line 33 lt 6 lc rgb "#d55e00" lw 2.5
2209 set style line 43 lt 8 lc rgb "#d55e00" lw 2.5
2210
2211 set style line 4 lt 1 lc rgb "#f0e442" lw 2.5
2212 set style line 14 lt 2 lc rgb "#f0e442" lw 2.5
2213 set style line 24 lt 4 lc rgb "#f0e442" lw 2.5
2214 set style line 34 lt 6 lc rgb "#f0e442" lw 2.5
2215 set style line 44 lt 8 lc rgb "#f0e442" lw 2.5
2216
2217 set style line 5 lt 1 lc rgb "#56b4e9" lw 2.5
2218 set style line 15 lt 2 lc rgb "#56b4e9" lw 2.5
2219 set style line 25 lt 4 lc rgb "#56b4e9" lw 2.5
2220 set style line 35 lt 6 lc rgb "#56b4e9" lw 2.5
2221 set style line 45 lt 8 lc rgb "#56b4e9" lw 2.5
2222
2223 set style line 6 lt 1 lc rgb "#cc79a7" lw 2.5
2224 set style line 16 lt 2 lc rgb "#cc79a7" lw 2.5
2225 set style line 26 lt 4 lc rgb "#cc79a7" lw 2.5
2226 set style line 36 lt 6 lc rgb "#cc79a7" lw 2.5
2227 set style line 46 lt 8 lc rgb "#cc79a7" lw 2.5
2228
2229 set style line 7 lt 1 lc rgb "#e69f00" lw 2.5
2230 set style line 17 lt 2 lc rgb "#e69f00" lw 2.5
2231 set style line 27 lt 4 lc rgb "#e69f00" lw 2.5
2232 set style line 37 lt 6 lc rgb "#e69f00" lw 2.5
2233 set style line 47 lt 8 lc rgb "#e69f00" lw 2.5
2234
2235 set style line 8 lt 1 lc rgb "black" lw 2.5
2236 set style line 18 lt 2 lc rgb "black" lw 2.5
2237 set style line 28 lt 4 lc rgb "black" lw 2.5
2238 set style line 38 lt 6 lc rgb "black" lw 2.5
2239 set style line 48 lt 7 lc rgb "black" lw 2.5
2240
2241
2242 set style line 999 lt 1 lc rgb "gray" lw 2.5
2243
2244 safe(x,y,a) = (y == 0.0 ? a : x/y)
2245
2246 set style data histeps
2247 set key invert
2248
2249 """%(arg_string,output_base_name)
2250 ]
2251
2252 gnuplot_output_list_v5 = [
2253 """
2254 ################################################################################
2255 #
2256 # This gnuplot file was generated by MadGraph5_aMC@NLO project, a program which
2257 # automatically generates Feynman diagrams and matrix elements for arbitrary
2258 # high-energy processes in the Standard Model and beyond. It also perform the
2259 # integration and/or generate events for these processes, at LO and NLO accuracy.
2260 #
2261 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
2262 #
2263 ################################################################################
2264 # %s
2265 reset
2266
2267 set lmargin 10
2268 set rmargin 0
2269 set terminal postscript portrait enhanced color "Helvetica" 9
2270 # The pdf terminal offers transparency support, but you will have to adapt things a bit
2271 #set terminal pdf enhanced font "Helvetica 12" lw 1.0 dashed size 29.7cm, 21cm
2272 set key font ",9"
2273 set key samplen "2"
2274 set output "%s.ps"
2275
2276 # This is the "PODO" color palette of gnuplot v.5, but with the order
2277 # changed: palette of colors selected to be easily distinguishable by
2278 # color-blind individuals with either protanopia or deuteranopia. Bang
2279 # Wong [2011] Nature Methods 8, 441.
2280
2281 set style line 1 lt 1 lc rgb "#009e73" lw 1.3
2282 set style line 101 lt 1 lc rgb "#009e73" lw 1.3 dt (6,3)
2283 set style line 11 lt 2 lc rgb "#009e73" lw 1.3 dt (6,3)
2284 set style line 21 lt 4 lc rgb "#009e73" lw 1.3 dt (3,2)
2285 set style line 31 lt 6 lc rgb "#009e73" lw 1.3 dt (2,1)
2286 set style line 41 lt 8 lc rgb "#009e73" lw 1.3 dt (4,3)
2287
2288 set style line 2 lt 1 lc rgb "#0072b2" lw 1.3
2289 set style line 102 lt 1 lc rgb "#0072b2" lw 1.3 dt (6,3)
2290 set style line 12 lt 2 lc rgb "#0072b2" lw 1.3 dt (6,3)
2291 set style line 22 lt 4 lc rgb "#0072b2" lw 1.3 dt (3,2)
2292 set style line 32 lt 6 lc rgb "#0072b2" lw 1.3 dt (2,1)
2293 set style line 42 lt 8 lc rgb "#0072b2" lw 1.3 dt (4,3)
2294
2295
2296 set style line 3 lt 1 lc rgb "#d55e00" lw 1.3
2297 set style line 103 lt 1 lc rgb "#d55e00" lw 1.3 dt (6,3)
2298 set style line 13 lt 2 lc rgb "#d55e00" lw 1.3 dt (6,3)
2299 set style line 23 lt 4 lc rgb "#d55e00" lw 1.3 dt (3,2)
2300 set style line 33 lt 6 lc rgb "#d55e00" lw 1.3 dt (2,1)
2301 set style line 43 lt 8 lc rgb "#d55e00" lw 1.3 dt (4,3)
2302
2303 set style line 4 lt 1 lc rgb "#f0e442" lw 1.3
2304 set style line 104 lt 1 lc rgb "#f0e442" lw 1.3 dt (6,3)
2305 set style line 14 lt 2 lc rgb "#f0e442" lw 1.3 dt (6,3)
2306 set style line 24 lt 4 lc rgb "#f0e442" lw 1.3 dt (3,2)
2307 set style line 34 lt 6 lc rgb "#f0e442" lw 1.3 dt (2,1)
2308 set style line 44 lt 8 lc rgb "#f0e442" lw 1.3 dt (4,3)
2309
2310 set style line 5 lt 1 lc rgb "#56b4e9" lw 1.3
2311 set style line 105 lt 1 lc rgb "#56b4e9" lw 1.3 dt (6,3)
2312 set style line 15 lt 2 lc rgb "#56b4e9" lw 1.3 dt (6,3)
2313 set style line 25 lt 4 lc rgb "#56b4e9" lw 1.3 dt (3,2)
2314 set style line 35 lt 6 lc rgb "#56b4e9" lw 1.3 dt (2,1)
2315 set style line 45 lt 8 lc rgb "#56b4e9" lw 1.3 dt (4,3)
2316
2317 set style line 6 lt 1 lc rgb "#cc79a7" lw 1.3
2318 set style line 106 lt 1 lc rgb "#cc79a7" lw 1.3 dt (6,3)
2319 set style line 16 lt 2 lc rgb "#cc79a7" lw 1.3 dt (6,3)
2320 set style line 26 lt 4 lc rgb "#cc79a7" lw 1.3 dt (3,2)
2321 set style line 36 lt 6 lc rgb "#cc79a7" lw 1.3 dt (2,1)
2322 set style line 46 lt 8 lc rgb "#cc79a7" lw 1.3 dt (4,3)
2323
2324 set style line 7 lt 1 lc rgb "#e69f00" lw 1.3
2325 set style line 107 lt 1 lc rgb "#e69f00" lw 1.3 dt (6,3)
2326 set style line 17 lt 2 lc rgb "#e69f00" lw 1.3 dt (6,3)
2327 set style line 27 lt 4 lc rgb "#e69f00" lw 1.3 dt (3,2)
2328 set style line 37 lt 6 lc rgb "#e69f00" lw 1.3 dt (2,1)
2329 set style line 47 lt 8 lc rgb "#e69f00" lw 1.3 dt (4,3)
2330
2331 set style line 8 lt 1 lc rgb "black" lw 1.3
2332 set style line 108 lt 1 lc rgb "black" lw 1.3 dt (6,3)
2333 set style line 18 lt 2 lc rgb "black" lw 1.3 dt (6,3)
2334 set style line 28 lt 4 lc rgb "black" lw 1.3 dt (3,2)
2335 set style line 38 lt 6 lc rgb "black" lw 1.3 dt (2,1)
2336 set style line 48 lt 8 lc rgb "black" lw 1.3 dt (4,3)
2337
2338
2339 set style line 999 lt 1 lc rgb "gray" lw 1.3
2340
2341 safe(x,y,a) = (y == 0.0 ? a : x/y)
2342
2343 set style data histeps
2344 set key invert
2345
2346 """%(arg_string,output_base_name)
2347 ]
2348
2349
2350 try:
2351 p = subprocess.Popen(['gnuplot', '--version'], \
2352 stdout=subprocess.PIPE, stderr=subprocess.PIPE)
2353 except OSError:
2354
2355
2356 gnuplot_output_list=gnuplot_output_list_v5
2357 else:
2358 output, _ = p.communicate()
2359 if float(output.split()[1]) < 5. :
2360 gnuplot_output_list=gnuplot_output_list_v4
2361 else:
2362 gnuplot_output_list=gnuplot_output_list_v5
2363
2364
2365
2366
2367 block_position = 0
2368 for histo_group in self:
2369
2370 block_position = histo_group.output_group(HwU_output_list,
2371 gnuplot_output_list, block_position,output_base_name+'.HwU',
2372 number_of_ratios=number_of_ratios,
2373 uncertainties = uncertainties,
2374 use_band = use_band,
2375 ratio_correlations = ratio_correlations,
2376 jet_samples_to_keep=jet_samples_to_keep,
2377 lhapdfconfig = lhapdfconfig)
2378
2379
2380 gnuplot_output_list.extend([
2381 "unset multiplot",
2382 '!ps2pdf "%s.ps" &> /dev/null'%output_base_name])
2383 if auto_open:
2384 gnuplot_output_list.append(
2385 '!open "%s.pdf" &> /dev/null'%output_base_name)
2386
2387
2388 gnuplot_stream.write('\n'.join(gnuplot_output_list))
2389 HwU_stream.write('\n'.join(HwU_output_list))
2390 gnuplot_stream.close()
2391 HwU_stream.close()
2392
2393 logger.debug("Histograms have been written out at "+\
2394 "%s.[HwU|gnuplot]' and can "%output_base_name+\
2395 "now be rendered by invoking gnuplot.")
2396
2397 - def output_group(self, HwU_out, gnuplot_out, block_position, HwU_name,
2398 number_of_ratios = -1,
2399 uncertainties = ['scale','pdf','statitistical','merging_scale','alpsfact'],
2400 use_band = None,
2401 ratio_correlations = True,
2402 jet_samples_to_keep=None,
2403 lhapdfconfig='lhapdf-config'):
2404
2405 """ This functions output a single group of histograms with either one
2406 histograms untyped (i.e. type=None) or two of type 'NLO' and 'LO'
2407 respectively."""
2408
2409
2410
2411 def get_main_central_plot_lines(HwU_name, block_position, color_index,
2412 title, show_mc_uncertainties):
2413 """ Returns two plot lines, one for the negative contributions in
2414 dashed and one with the positive ones in solid."""
2415
2416 template = "'%(hwu)s' index %(ind)d using (($1+$2)/2):%(data)s%(stat_col)s%(stat_err)s%(ls)s%(title)s"
2417 template_no_stat = "'%(hwu)s' index %(ind)d using (($1+$2)/2):%(data)s%(ls)s%(title)s"
2418 rep_dic = {'hwu':HwU_name,
2419 'ind':block_position,
2420 'ls':' ls %d'%color_index,
2421 'title':" title '%s'"%title,
2422 'stat_col': ':4',
2423 'stat_err': ' w yerrorbar',
2424 'data':'3',
2425 'linetype':''}
2426
2427
2428
2429
2430
2431
2432 res = []
2433 rep_dic['data'] = '($3 < 0 ? sqrt(-1) : $3)'
2434 res.append(template_no_stat%rep_dic)
2435 rep_dic['title'] = " title ''"
2436 if show_mc_uncertainties:
2437 res.append(template%rep_dic)
2438 rep_dic['data'] = '($3 >= 0 ? sqrt(-1) : abs($3))'
2439 rep_dic['ls'] = ' ls %d'%(100+color_index)
2440 res.append(template_no_stat%rep_dic)
2441 if show_mc_uncertainties:
2442 res.append(template%rep_dic)
2443 return res
2444
2445
2446
2447
2448 def get_uncertainty_lines(HwU_name, block_position,
2449 var_pos, color_index,title, ratio=False, band=False):
2450 """ Return a string line corresponding to the plotting of the
2451 uncertainty. Band is to chose wether to display uncertainty with
2452 a band or two lines."""
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472 copy_swap_re = r"perl -pe 's/^\s*(?<x1>[\+|-]?\d+(\.\d*)?([EeDd][\+|-]?\d+)?)\s*(?<x2>[\+|-]?\d+(\.\d*)?([EeDd][\+|-]?\d+)?)(?<rest>.*)\n/ $+{x1} $+{x2} $+{rest}\n$+{x2} $+{x1} $+{rest}\n/g'"
2473
2474
2475 copy_swap_re = copy_swap_re.replace('\\','\\\\')
2476
2477 position = '(safe($%d,$3,1.0)-1.0)' if ratio else '%d'
2478 if not band:
2479 return ["'%s' index %d using (($1+$2)/2):%s ls %d title '%s'"\
2480 %(HwU_name,block_position, position%(var_pos),color_index,title),
2481 "'%s' index %d using (($1+$2)/2):%s ls %d title ''"\
2482 %(HwU_name,block_position, position%(var_pos+1),color_index)]
2483 else:
2484 return [' "<%s %s" index %d using 1:%s:%s with filledcurve ls %d fs transparent solid 0.2 title \'%s\''%\
2485 (copy_swap_re,HwU_name,block_position,
2486 position%var_pos,position%(var_pos+1),color_index,title)]
2487
2488
2489
2490 layout_geometry = [(0.0, 0.5, 1.0, 0.4 ),
2491 (0.0, 0.35, 1.0, 0.15),
2492 (0.0, 0.2, 1.0, 0.15)]
2493 layout_geometry.reverse()
2494
2495
2496
2497 matching_histo_lists = HwUList([HwUList([self[0]])])
2498 for histo in self[1:]:
2499 matched = False
2500 for histo_list in matching_histo_lists:
2501 if hasattr(histo, 'jetsample') and histo.jetsample >= 0 and \
2502 histo.type == histo_list[0].type:
2503 matched = True
2504 histo_list.append(histo)
2505 break
2506 if not matched:
2507 matching_histo_lists.append(HwUList([histo]))
2508
2509
2510
2511 self[:] = []
2512 for histo_group in matching_histo_lists:
2513
2514
2515 if len(histo_group)==1:
2516 self.append(histo_group[0])
2517 continue
2518
2519
2520 if any(hist.jetsample==-1 for hist in histo_group if
2521 hasattr(hist, 'jetsample')):
2522 self.extend(histo_group)
2523 continue
2524 summed_histogram = copy.copy(histo_group[0])
2525 for histo in histo_group[1:]:
2526 summed_histogram = summed_histogram + histo
2527 summed_histogram.jetsample = -1
2528 self.append(summed_histogram)
2529 self.extend(histo_group)
2530
2531
2532 if not jet_samples_to_keep is None:
2533 self[:] = filter(lambda histo:
2534 (not hasattr(histo,'jetsample')) or (histo.jetsample == -1) or
2535 (histo.jetsample in jet_samples_to_keep), self)
2536
2537
2538
2539 def ratio_no_correlations(wgtsA, wgtsB):
2540 new_wgts = {}
2541 for label, wgt in wgtsA.items():
2542 if wgtsB['central']==0.0 and wgt==0.0:
2543 new_wgts[label] = 0.0
2544 continue
2545 elif wgtsB['central']==0.0:
2546
2547
2548
2549 new_wgts[label] = 0.0
2550 continue
2551 new_wgts[label] = (wgtsA[label]/wgtsB['central'])
2552 return new_wgts
2553
2554
2555
2556 n_histograms = len(self)
2557 ratio_histos = HwUList([])
2558
2559 n_ratios_included = 0
2560 for i, histo in enumerate(self[1:]):
2561 if not hasattr(histo,'jetsample') or histo.jetsample==self[0].jetsample:
2562 n_ratios_included += 1
2563 else:
2564 continue
2565
2566 if number_of_ratios >=0 and n_ratios_included > number_of_ratios:
2567 break
2568
2569 if ratio_correlations:
2570 ratio_histos.append(histo/self[0])
2571 else:
2572 ratio_histos.append(self[0].__class__.combine(histo, self[0],
2573 ratio_no_correlations))
2574 if self[0].type=='NLO' and self[1].type=='LO':
2575 ratio_histos[-1].title += '1/K-factor'
2576 elif self[0].type=='LO' and self[1].type=='NLO':
2577 ratio_histos[-1].title += 'K-factor'
2578 else:
2579 ratio_histos[-1].title += ' %s/%s'%(
2580 self[1].type if self[1].type else '(%d)'%(i+2),
2581 self[0].type if self[0].type else '(1)')
2582
2583
2584 ratio_histos[-1].type = 'AUX'
2585 self.extend(ratio_histos)
2586
2587
2588 if 'scale' in uncertainties:
2589 (mu_var_pos,mu) = self[0].set_uncertainty(type='all_scale')
2590 else:
2591 (mu_var_pos,mu) = (None,[None])
2592
2593 if 'pdf' in uncertainties:
2594 (PDF_var_pos,pdf) = self[0].set_uncertainty(type='PDF',lhapdfconfig=lhapdfconfig)
2595 else:
2596 (PDF_var_pos,pdf) = (None,[None])
2597
2598 if 'merging_scale' in uncertainties:
2599 (merging_var_pos,merging) = self[0].set_uncertainty(type='merging')
2600 else:
2601 (merging_var_pos,merging) = (None,[None])
2602 if 'alpsfact' in uncertainties:
2603 (alpsfact_var_pos,alpsfact) = self[0].set_uncertainty(type='alpsfact')
2604 else:
2605 (alpsfact_var_pos,alpsfact) = (None,[None])
2606
2607 uncertainties_present = list(uncertainties)
2608 if PDF_var_pos is None and 'pdf' in uncertainties_present:
2609 uncertainties_present.remove('pdf')
2610 if mu_var_pos is None and 'scale' in uncertainties_present:
2611 uncertainties_present.remove('scale')
2612 if merging_var_pos is None and 'merging' in uncertainties_present:
2613 uncertainties_present.remove('merging')
2614 if alpsfact_var_pos is None and 'alpsfact' in uncertainties_present:
2615 uncertainties_present.remove('alpsfact')
2616 no_uncertainties = len(uncertainties_present)==0
2617
2618
2619 try:
2620 uncertainties_present.remove('statistical')
2621 except:
2622 pass
2623 if use_band is None:
2624
2625
2626 if len(uncertainties_present)==0:
2627 use_band = []
2628 elif len(uncertainties_present)==1:
2629 use_band = uncertainties_present
2630 elif 'scale' in uncertainties_present:
2631 use_band = ['scale']
2632 else:
2633 use_band = [uncertainties_present[0]]
2634
2635 for histo in self[1:]:
2636 if (not mu_var_pos is None) and \
2637 mu_var_pos != histo.set_uncertainty(type='all_scale')[0]:
2638 raise MadGraph5Error, 'Not all histograms in this group specify'+\
2639 ' scale uncertainties. It is required to be able to output them'+\
2640 ' together.'
2641 if (not PDF_var_pos is None) and\
2642 PDF_var_pos != histo.set_uncertainty(type='PDF',\
2643 lhapdfconfig=lhapdfconfig)[0]:
2644 raise MadGraph5Error, 'Not all histograms in this group specify'+\
2645 ' PDF uncertainties. It is required to be able to output them'+\
2646 ' together.'
2647 if (not merging_var_pos is None) and\
2648 merging_var_pos != histo.set_uncertainty(type='merging')[0]:
2649 raise MadGraph5Error, 'Not all histograms in this group specify'+\
2650 ' merging uncertainties. It is required to be able to output them'+\
2651 ' together.'
2652 if (not alpsfact_var_pos is None) and\
2653 alpsfact_var_pos != histo.set_uncertainty(type='alpsfact')[0]:
2654 raise MadGraph5Error, 'Not all histograms in this group specify'+\
2655 ' alpsfact uncertainties. It is required to be able to output them'+\
2656 ' together.'
2657
2658
2659
2660 for i, histo in enumerate(self):
2661
2662 HwU_out.extend(histo.get_HwU_source(\
2663 print_header=(block_position==0 and i==0)))
2664 HwU_out.extend(['',''])
2665
2666
2667 global_header =\
2668 """
2669 ################################################################################
2670 ### Rendering of the plot titled '%(title)s'
2671 ################################################################################
2672
2673 set multiplot
2674 set label "%(title)s" font ",13" at graph 0.04, graph 1.05
2675 set xrange [%(xmin).4e:%(xmax).4e]
2676 set bmargin 0
2677 set tmargin 0
2678 set xtics nomirror
2679 set ytics nomirror
2680 set mytics %(mxtics)d
2681 %(set_xtics)s
2682 set key horizontal noreverse maxcols 1 width -4
2683 set label front 'MadGraph5\_aMC\@NLO' font "Courier,11" rotate by 90 at graph 1.02, graph 0.04
2684 """
2685
2686
2687 subhistogram_header = \
2688 """#-- rendering subhistograms '%(subhistogram_type)s'
2689 %(unset label)s
2690 %(set_format_y)s
2691 set yrange [%(ymin).4e:%(ymax).4e]
2692 set origin %(origin_x).4e, %(origin_y).4e
2693 set size %(size_x).4e, %(size_y).4e
2694 set mytics %(mytics)d
2695 %(set_ytics)s
2696 %(set_format_x)s
2697 %(set_yscale)s
2698 %(set_ylabel)s
2699 %(set_histo_label)s
2700 plot \\"""
2701 replacement_dic = {}
2702
2703 replacement_dic['title'] = self[0].get_HwU_histogram_name(format='human-no_type')
2704
2705
2706 wgts_to_consider = ['central']
2707 if not mu_var_pos is None:
2708 for mu_var in mu_var_pos:
2709 wgts_to_consider.append(self[0].bins.weight_labels[mu_var])
2710 wgts_to_consider.append(self[0].bins.weight_labels[mu_var+1])
2711 wgts_to_consider.append(self[0].bins.weight_labels[mu_var+2])
2712 if not PDF_var_pos is None:
2713 for PDF_var in PDF_var_pos:
2714 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var])
2715 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var+1])
2716 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var+2])
2717 if not merging_var_pos is None:
2718 for merging_var in merging_var_pos:
2719 wgts_to_consider.append(self[0].bins.weight_labels[merging_var])
2720 wgts_to_consider.append(self[0].bins.weight_labels[merging_var+1])
2721 wgts_to_consider.append(self[0].bins.weight_labels[merging_var+2])
2722 if not alpsfact_var_pos is None:
2723 for alpsfact_var in alpsfact_var_pos:
2724 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var])
2725 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var+1])
2726 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var+2])
2727
2728 (xmin, xmax) = HwU.get_x_optimal_range(self[:2],\
2729 weight_labels = wgts_to_consider)
2730 replacement_dic['xmin'] = xmin
2731 replacement_dic['xmax'] = xmax
2732 replacement_dic['mxtics'] = 10
2733 replacement_dic['set_xtics'] = 'set xtics auto'
2734
2735
2736 gnuplot_out.append(global_header%replacement_dic)
2737
2738
2739 replacement_dic['subhistogram_type'] = '%s and %s results'%(
2740 str(self[0].type),str(self[1].type)) if len(self)>1 else \
2741 'single diagram output'
2742 (ymin, ymax) = HwU.get_y_optimal_range(self[:2],
2743 labels = wgts_to_consider, scale=self[0].y_axis_mode)
2744
2745
2746 if ymin< 0.0:
2747 self[0].y_axis_mode = 'LIN'
2748
2749
2750 if self[0].y_axis_mode=='LOG':
2751 ymax += 10.0 * ymax
2752 ymin -= 0.1 * ymin
2753 else:
2754 ymax += 0.3 * (ymax - ymin)
2755 ymin -= 0.3 * (ymax - ymin)
2756
2757 replacement_dic['ymin'] = ymin
2758 replacement_dic['ymax'] = ymax
2759 replacement_dic['unset label'] = ''
2760 (replacement_dic['origin_x'], replacement_dic['origin_y'],
2761 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop()
2762 replacement_dic['mytics'] = 10
2763
2764 replacement_dic['set_ytics'] = 'set ytics auto'
2765 replacement_dic['set_format_x'] = "set format x ''" if \
2766 (len(self)-n_histograms>0 or not no_uncertainties) else "set format x"
2767 replacement_dic['set_ylabel'] = 'set ylabel "{/Symbol s} per bin [pb]"'
2768 replacement_dic['set_yscale'] = "set logscale y" if \
2769 self[0].y_axis_mode=='LOG' else 'unset logscale y'
2770 replacement_dic['set_format_y'] = "set format y '10^{%T}'" if \
2771 self[0].y_axis_mode=='LOG' else 'unset format'
2772
2773 replacement_dic['set_histo_label'] = ""
2774 gnuplot_out.append(subhistogram_header%replacement_dic)
2775
2776
2777 plot_lines = []
2778 uncertainty_plot_lines = []
2779 n=-1
2780
2781 for i, histo in enumerate(self[:n_histograms]):
2782 n=n+1
2783 color_index = n%self.number_line_colors_defined+1
2784
2785 title = []
2786 if histo.type is None and not hasattr(histo, 'jetsample'):
2787 title.append('%d'%(i+1))
2788 else:
2789 if histo.type:
2790 title.append('NLO' if \
2791 histo.type.split()[0]=='NLO' else histo.type)
2792 if hasattr(histo, 'jetsample'):
2793 if histo.jetsample!=-1:
2794 title.append('jet sample %d'%histo.jetsample)
2795 else:
2796 title.append('all jet samples')
2797
2798 title = ', '.join(title)
2799
2800 if histo.type is None and not hasattr(histo, 'jetsample'):
2801 major_title = 'central value for plot (%d)'%(i+1)
2802 else:
2803 major_title = []
2804 if not histo.type is None:
2805 major_title.append(histo.type)
2806 if hasattr(histo, 'jetsample'):
2807 if histo.jetsample!=-1:
2808 major_title.append('jet sample %d'%histo.jetsample)
2809 else:
2810 major_title.append('all jet samples')
2811 else:
2812 major_title.append('central value')
2813 major_title = ', '.join(major_title)
2814
2815 if not mu[0] in ['none',None]:
2816 major_title += ', dynamical\_scale\_choice=%s'%mu[0]
2817 if not pdf[0] in ['none',None]:
2818 major_title += ', PDF=%s'%pdf[0].replace('_','\_')
2819
2820
2821
2822 if not (i!=0 and hasattr(histo,'jetsample') and histo.jetsample!=-1 and \
2823 not (jet_samples_to_keep and len(jet_samples_to_keep)==1 and
2824 jet_samples_to_keep[0] == histo.jetsample)):
2825
2826 uncertainty_plot_lines.append({})
2827
2828
2829
2830
2831
2832
2833
2834
2835 if not mu_var_pos is None and len(mu_var_pos)>0:
2836 if 'scale' in use_band:
2837 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines(
2838 HwU_name, block_position+i, mu_var_pos[0]+4, color_index+10,
2839 '%s, scale variation'%title, band='scale' in use_band)
2840 else:
2841 uncertainty_plot_lines[-1]['scale'] = \
2842 ["sqrt(-1) ls %d title '%s'"%(color_index+10,'%s, scale variation'%title)]
2843
2844 if not PDF_var_pos is None and len(PDF_var_pos)>0:
2845 if 'pdf' in use_band:
2846 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines(
2847 HwU_name,block_position+i, PDF_var_pos[0]+4, color_index+20,
2848 '%s, PDF variation'%title, band='pdf' in use_band)
2849 else:
2850 uncertainty_plot_lines[-1]['pdf'] = \
2851 ["sqrt(-1) ls %d title '%s'"%(color_index+20,'%s, PDF variation'%title)]
2852
2853 if not merging_var_pos is None and len(merging_var_pos)>0:
2854 if 'merging_scale' in use_band:
2855 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines(
2856 HwU_name,block_position+i, merging_var_pos[0]+4, color_index+30,
2857 '%s, merging scale variation'%title, band='merging_scale' in use_band)
2858 else:
2859 uncertainty_plot_lines[-1]['merging_scale'] = \
2860 ["sqrt(-1) ls %d title '%s'"%(color_index+30,'%s, merging scale variation'%title)]
2861
2862 if not alpsfact_var_pos is None and len(alpsfact_var_pos)>0:
2863 if 'alpsfact' in use_band:
2864 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines(
2865 HwU_name,block_position+i, alpsfact_var_pos[0]+4, color_index+40,
2866 '%s, alpsfact variation'%title, band='alpsfact' in use_band)
2867 else:
2868 uncertainty_plot_lines[-1]['alpsfact'] = \
2869 ["sqrt(-1) ls %d title '%s'"%(color_index+40,'%s, alpsfact variation'%title)]
2870
2871
2872
2873
2874
2875
2876
2877
2878 plot_lines.extend(
2879 get_main_central_plot_lines(HwU_name, block_position+i,
2880 color_index, major_title, 'statistical' in uncertainties))
2881
2882
2883 if not mu_var_pos is None:
2884 for j,mu_var in enumerate(mu_var_pos):
2885 if j!=0:
2886 n=n+1
2887 color_index = n%self.number_line_colors_defined+1
2888 plot_lines.append(
2889 "'%s' index %d using (($1+$2)/2):%d ls %d title '%s'"\
2890 %(HwU_name,block_position+i,mu_var+3,color_index,\
2891 '%s dynamical\_scale\_choice=%s' % (title,mu[j])))
2892
2893 if not PDF_var_pos is None:
2894 for j,PDF_var in enumerate(PDF_var_pos):
2895 if j!=0:
2896 n=n+1
2897 color_index = n%self.number_line_colors_defined+1
2898 plot_lines.append(
2899 "'%s' index %d using (($1+$2)/2):%d ls %d title '%s'"\
2900 %(HwU_name,block_position+i,PDF_var+3,color_index,\
2901 '%s PDF=%s' % (title,pdf[j].replace('_','\_'))))
2902
2903
2904
2905 for one_plot in uncertainty_plot_lines:
2906 for uncertainty_type, lines in one_plot.items():
2907 if not uncertainty_type in use_band:
2908 plot_lines.extend(lines)
2909
2910 for one_plot in uncertainty_plot_lines:
2911 for uncertainty_type, lines in one_plot.items():
2912 if uncertainty_type in use_band:
2913 plot_lines.extend(lines)
2914
2915
2916 plot_lines.reverse()
2917
2918
2919 gnuplot_out.append(',\\\n'.join(plot_lines))
2920
2921
2922 replacement_dic['subhistogram_type'] = 'Relative scale and PDF uncertainty'
2923
2924 if 'statistical' in uncertainties:
2925 wgts_to_consider.append('stat_error')
2926
2927
2928
2929 def rel_scale(wgtsA, wgtsB):
2930 new_wgts = {}
2931 for label, wgt in wgtsA.items():
2932 if label in wgts_to_consider:
2933 if wgtsB['central']==0.0 and wgt==0.0:
2934 new_wgts[label] = 0.0
2935 continue
2936 elif wgtsB['central']==0.0:
2937
2938
2939
2940 new_wgts[label] = 0.0
2941 continue
2942 new_wgts[label] = (wgtsA[label]/wgtsB['central'])
2943 if label != 'stat_error':
2944 new_wgts[label] -= 1.0
2945 else:
2946 new_wgts[label] = wgtsA[label]
2947 return new_wgts
2948
2949 histos_for_subplots = [(i,histo) for i, histo in enumerate(self[:n_histograms]) if
2950 ( not (i!=0 and hasattr(histo,'jetsample') and histo.jetsample!=-1 and \
2951 not (jet_samples_to_keep and len(jet_samples_to_keep)==1 and
2952 jet_samples_to_keep[0] == histo.jetsample)) )]
2953
2954
2955
2956
2957 (ymin, ymax) = HwU.get_y_optimal_range([histo[1].__class__.combine(
2958 histo[1],histo[1],rel_scale) for histo in histos_for_subplots],
2959 labels = wgts_to_consider, scale='LIN')
2960
2961
2962 ymax = ymax + 0.2 * (ymax - ymin)
2963 ymin = ymin - 0.2 * (ymax - ymin)
2964 replacement_dic['unset label'] = 'unset label'
2965 replacement_dic['ymin'] = ymin
2966 replacement_dic['ymax'] = ymax
2967 if not no_uncertainties:
2968 (replacement_dic['origin_x'], replacement_dic['origin_y'],
2969 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop()
2970 replacement_dic['mytics'] = 2
2971
2972 replacement_dic['set_ytics'] = 'set ytics auto'
2973 replacement_dic['set_format_x'] = "set format x ''" if \
2974 len(self)-n_histograms>0 else "set format x"
2975 replacement_dic['set_ylabel'] = 'set ylabel "%s rel.unc."'\
2976 %('(1)' if self[0].type==None else '%s'%('NLO' if \
2977 self[0].type.split()[0]=='NLO' else self[0].type))
2978 replacement_dic['set_yscale'] = "unset logscale y"
2979 replacement_dic['set_format_y'] = 'unset format'
2980
2981
2982 tit='Relative uncertainties w.r.t. central value'
2983 if n_histograms > 1:
2984 tit=tit+'s'
2985
2986
2987
2988
2989 replacement_dic['set_histo_label'] = \
2990 'set label "%s" font ",9" front at graph 0.03, graph 0.13' % tit
2991
2992
2993 if not no_uncertainties:
2994 gnuplot_out.append(subhistogram_header%replacement_dic)
2995
2996
2997 plot_lines = []
2998 uncertainty_plot_lines = []
2999 n=-1
3000 for (i,histo) in histos_for_subplots:
3001 n=n+1
3002 k=n
3003 color_index = n%self.number_line_colors_defined+1
3004
3005 if not mu_var_pos is None:
3006 for j,mu_var in enumerate(mu_var_pos):
3007 uncertainty_plot_lines.append({})
3008 if j==0:
3009 color_index = k%self.number_line_colors_defined+1
3010 else:
3011 n=n+1
3012 color_index = n%self.number_line_colors_defined+1
3013
3014 if j>0 or mu[j]!='none':
3015 plot_lines.append(
3016 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3017 %(HwU_name,block_position+i,mu_var+3,color_index))
3018 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines(
3019 HwU_name, block_position+i, mu_var+4, color_index+10,'',
3020 ratio=True, band='scale' in use_band)
3021 if not PDF_var_pos is None:
3022 for j,PDF_var in enumerate(PDF_var_pos):
3023 uncertainty_plot_lines.append({})
3024 if j==0:
3025 color_index = k%self.number_line_colors_defined+1
3026 else:
3027 n=n+1
3028 color_index = n%self.number_line_colors_defined+1
3029
3030 if j>0 or pdf[j]!='none':
3031 plot_lines.append(
3032 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3033 %(HwU_name,block_position+i,PDF_var+3,color_index))
3034 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines(
3035 HwU_name, block_position+i, PDF_var+4, color_index+20,'',
3036 ratio=True, band='pdf' in use_band)
3037 if not merging_var_pos is None:
3038 for j,merging_var in enumerate(merging_var_pos):
3039 uncertainty_plot_lines.append({})
3040 if j==0:
3041 color_index = k%self.number_line_colors_defined+1
3042 else:
3043 n=n+1
3044 color_index = n%self.number_line_colors_defined+1
3045 if j>0 or merging[j]!='none':
3046 plot_lines.append(
3047 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3048 %(HwU_name,block_position+i,merging_var+3,color_index))
3049 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines(
3050 HwU_name, block_position+i, merging_var+4, color_index+30,'',
3051 ratio=True, band='merging_scale' in use_band)
3052 if not alpsfact_var_pos is None:
3053 for j,alpsfact_var in enumerate(alpsfact_var_pos):
3054 uncertainty_plot_lines.append({})
3055 if j==0:
3056 color_index = k%self.number_line_colors_defined+1
3057 else:
3058 n=n+1
3059 color_index = n%self.number_line_colors_defined+1
3060 if j>0 or alpsfact[j]!='none':
3061 plot_lines.append(
3062 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3063 %(HwU_name,block_position+i,alpsfact_var+3,color_index))
3064 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines(
3065 HwU_name, block_position+i, alpsfact_var+4, color_index+40,'',
3066 ratio=True, band='alpsfact' in use_band)
3067
3068 if 'statistical' in uncertainties:
3069 plot_lines.append(
3070 "'%s' index %d using (($1+$2)/2):(0.0):(safe($4,$3,0.0)) w yerrorbar ls %d title ''"%\
3071 (HwU_name,block_position+i,color_index))
3072
3073 plot_lines.append("0.0 ls 999 title ''")
3074
3075
3076
3077 for one_plot in uncertainty_plot_lines:
3078 for uncertainty_type, lines in one_plot.items():
3079 if not uncertainty_type in use_band:
3080 plot_lines.extend(lines)
3081
3082 for one_plot in uncertainty_plot_lines:
3083 for uncertainty_type, lines in one_plot.items():
3084 if uncertainty_type in use_band:
3085 plot_lines.extend(lines)
3086
3087
3088 plot_lines.reverse()
3089
3090 if not no_uncertainties:
3091 gnuplot_out.append(',\\\n'.join(plot_lines))
3092
3093
3094 if len(self)-n_histograms==0:
3095
3096 gnuplot_out.extend(['','unset label','',
3097 '################################################################################'])
3098
3099 return block_position+len(self)
3100
3101
3102 ratio_name_long='('
3103 for i, histo in enumerate(self[:n_histograms]):
3104 if i==0: continue
3105 ratio_name_long+='%d'%(i+1) if histo.type is None else ('NLO' if \
3106 histo.type.split()[0]=='NLO' else histo.type)
3107 ratio_name_long+=')/'
3108 ratio_name_long+=('(1' if self[0].type==None else '(%s'%('NLO' if \
3109 self[0].type.split()[0]=='NLO' else self[0].type))+' central value)'
3110
3111 ratio_name_short = 'ratio w.r.t. '+('1' if self[0].type==None else '%s'%('NLO' if \
3112 self[0].type.split()[0]=='NLO' else self[0].type))
3113
3114 replacement_dic['subhistogram_type'] = '%s ratio'%ratio_name_long
3115 replacement_dic['set_ylabel'] = 'set ylabel "%s"'%ratio_name_short
3116
3117 (ymin, ymax) = HwU.get_y_optimal_range(self[n_histograms:],
3118 labels = wgts_to_consider, scale='LIN',Kratio = True)
3119
3120
3121 ymax = ymax + 0.2 * (ymax - ymin)
3122 ymin = ymin - 0.2 * (ymax - ymin)
3123 replacement_dic['unset label'] = 'unset label'
3124 replacement_dic['ymin'] = ymin
3125 replacement_dic['ymax'] = ymax
3126 (replacement_dic['origin_x'], replacement_dic['origin_y'],
3127 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop()
3128 replacement_dic['mytics'] = 2
3129
3130 replacement_dic['set_ytics'] = 'set ytics auto'
3131 replacement_dic['set_format_x'] = "set format x"
3132 replacement_dic['set_yscale'] = "unset logscale y"
3133 replacement_dic['set_format_y'] = 'unset format'
3134 replacement_dic['set_histo_label'] = \
3135 'set label "%s" font ",9" at graph 0.03, graph 0.13'%ratio_name_long
3136
3137 gnuplot_out.append(subhistogram_header%replacement_dic)
3138
3139 uncertainty_plot_lines = []
3140 plot_lines = []
3141
3142
3143 n=-1
3144 n=n+1
3145 if not mu_var_pos is None:
3146 for j,mu_var in enumerate(mu_var_pos):
3147 if j!=0: n=n+1
3148 if not PDF_var_pos is None:
3149 for j,PDF_var in enumerate(PDF_var_pos):
3150 if j!=0: n=n+1
3151 if not merging_var_pos is None:
3152 for j,merging_var in enumerate(merging_var_pos):
3153 if j!=0: n=n+1
3154 if not alpsfact_var_pos is None:
3155 for j,alpsfact_var in enumerate(alpsfact_var_pos):
3156 if j!=0: n=n+1
3157
3158 for i_histo_ratio, histo_ration in enumerate(self[n_histograms:]):
3159 n=n+1
3160 k=n
3161 block_ratio_pos = block_position+n_histograms+i_histo_ratio
3162 color_index = n%self.number_line_colors_defined+1
3163
3164 plot_lines.append(
3165 "'%s' index %d using (($1+$2)/2):3 ls %d title ''"%\
3166 (HwU_name,block_ratio_pos,color_index))
3167 if 'statistical' in uncertainties:
3168 plot_lines.append(
3169 "'%s' index %d using (($1+$2)/2):3:4 w yerrorbar ls %d title ''"%\
3170 (HwU_name,block_ratio_pos,color_index))
3171
3172
3173 if not mu_var_pos is None:
3174 for j,mu_var in enumerate(mu_var_pos):
3175 uncertainty_plot_lines.append({})
3176 if j==0:
3177 color_index = k%self.number_line_colors_defined+1
3178 else:
3179 n=n+1
3180 color_index = n%self.number_line_colors_defined+1
3181
3182 if j>0 or mu[j]!='none':
3183 plot_lines.append(
3184 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3185 %(HwU_name,block_ratio_pos,mu_var+3,color_index))
3186 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines(
3187 HwU_name, block_ratio_pos, mu_var+4, color_index+10,'',
3188 band='scale' in use_band)
3189 if not PDF_var_pos is None:
3190 for j,PDF_var in enumerate(PDF_var_pos):
3191 uncertainty_plot_lines.append({})
3192 if j==0:
3193 color_index = k%self.number_line_colors_defined+1
3194 else:
3195 n=n+1
3196 color_index = n%self.number_line_colors_defined+1
3197
3198 if j>0 or pdf[j]!='none':
3199 plot_lines.append(
3200 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3201 %(HwU_name,block_ratio_pos,PDF_var+3,color_index))
3202 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines(
3203 HwU_name, block_ratio_pos, PDF_var+4, color_index+20,'',
3204 band='pdf' in use_band)
3205 if not merging_var_pos is None:
3206 for j,merging_var in enumerate(merging_var_pos):
3207 uncertainty_plot_lines.append({})
3208 if j==0:
3209 color_index = k%self.number_line_colors_defined+1
3210 else:
3211 n=n+1
3212 color_index = n%self.number_line_colors_defined+1
3213 if j>0 or merging[j]!='none':
3214 plot_lines.append(
3215 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3216 %(HwU_name,block_ratio_pos,merging_var+3,color_index))
3217 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines(
3218 HwU_name, block_ratio_pos, merging_var+4, color_index+30,'',
3219 band='merging_scale' in use_band)
3220 if not alpsfact_var_pos is None:
3221 for j,alpsfact_var in enumerate(alpsfact_var_pos):
3222 uncertainty_plot_lines.append({})
3223 if j==0:
3224 color_index = k%self.number_line_colors_defined+1
3225 else:
3226 n=n+1
3227 color_index = n%self.number_line_colors_defined+1
3228 if j>0 or alpsfact[j]!='none':
3229 plot_lines.append(
3230 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3231 %(HwU_name,block_ratio_pos,alpsfact_var+3,color_index))
3232 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines(
3233 HwU_name, block_ratio_pos, alpsfact_var+4, color_index+40,'',
3234 band='alpsfact' in use_band)
3235
3236
3237
3238 for one_plot in uncertainty_plot_lines:
3239 for uncertainty_type, lines in one_plot.items():
3240 if not uncertainty_type in use_band:
3241 plot_lines.extend(lines)
3242
3243 for one_plot in uncertainty_plot_lines:
3244 for uncertainty_type, lines in one_plot.items():
3245 if uncertainty_type in use_band:
3246 plot_lines.extend(lines)
3247
3248 plot_lines.append("1.0 ls 999 title ''")
3249
3250
3251 plot_lines.reverse()
3252
3253 gnuplot_out.append(',\\\n'.join(plot_lines))
3254
3255
3256 gnuplot_out.extend(['','unset label','',
3257 '################################################################################'])
3258
3259
3260 return block_position+len(self)
3261
3262
3263
3264
3265 -def plot_ratio_from_HWU(path, ax, hwu_variable, hwu_numerator, hwu_denominator, *args, **opts):
3266 """INPUT:
3267 - path can be a path to HwU or an HwUList instance
3268 - ax is the matplotlib frame where to do the plot
3269 - hwu_variable is the histograms to consider
3270 - hwu_numerator is the numerator of the ratio plot
3271 - hwu_denominator is the denominator of the ratio plot
3272 OUTPUT:
3273 - adding the curves to the plot
3274 - return the HwUList
3275 """
3276
3277 if isinstance(path, str):
3278 hwu = HwUList(path, raw_labels=True)
3279 else:
3280 hwu = path
3281
3282 if 'hwu_denominator_path' in opts:
3283 print 'found second hwu'
3284 if isinstance(opts['hwu_denominator_path'],str):
3285 hwu2 = HwUList(path, raw_labels=True)
3286 else:
3287 hwu2 = opts['hwu_denominator_path']
3288 del opts['hwu_denominator_path']
3289 else:
3290 hwu2 = hwu
3291
3292
3293 select_hist = hwu.get(hwu_variable)
3294 select_hist2 = hwu2.get(hwu_variable)
3295 bins = select_hist.get('bins')
3296 num = select_hist.get(hwu_numerator)
3297 denom = select_hist2.get(hwu_denominator)
3298 ratio = [num[i]/denom[i] if denom[i] else 1 for i in xrange(len(bins))]
3299 if 'drawstyle' not in opts:
3300 opts['drawstyle'] = 'steps'
3301 ax.plot(bins, ratio, *args, **opts)
3302 return hwu
3303
3304 -def plot_from_HWU(path, ax, hwu_variable, hwu_central, *args, **opts):
3305 """INPUT:
3306 - path can be a path to HwU or an HwUList instance
3307 - ax is the matplotlib frame where to do the plot
3308 - hwu_variable is the histograms to consider
3309 - hwu_central is the central curve to consider
3310 - hwu_error is the error band to consider (optional: Default is no band)
3311 - hwu_error_mode is how to compute the error band (optional)
3312 OUTPUT:
3313 - adding the curves to the plot
3314 - return the HwUList
3315 - return the line associated to the central (can be used to get the color)
3316 """
3317
3318
3319 if 'hwu_error' in opts:
3320 hwu_error = opts['hwu_error']
3321 del opts['hwu_error']
3322 else:
3323 hwu_error = None
3324
3325 if 'hwu_error_mode' in opts:
3326 hwu_error_mode = opts['hwu_error_mode']
3327 del opts['hwu_error_mode']
3328 else:
3329 hwu_error_mode = None
3330
3331 if 'hwu_mult' in opts:
3332 hwu_mult = opts['hwu_mult']
3333 del opts['hwu_mult']
3334 else:
3335 hwu_mult = 1
3336
3337 if isinstance(path, str):
3338 hwu = HwUList(path, raw_labels=True)
3339 else:
3340 hwu = path
3341
3342
3343 select_hist = hwu.get(hwu_variable)
3344 bins = select_hist.get('bins')
3345 central_value = select_hist.get(hwu_central)
3346 if hwu_mult != 1:
3347 central_value = [hwu_mult*b for b in central_value]
3348 if 'drawstyle' not in opts:
3349 opts['drawstyle'] = 'steps'
3350 H, = ax.plot(bins, central_value, *args, **opts)
3351
3352
3353 if hwu_error:
3354 if not 'hwu_error_mode' in opts:
3355 opts['hwu_error_mode']=None
3356 h_min, h_max = select_hist.get_uncertainty_band(hwu_error, mode=hwu_error_mode)
3357 if hwu_mult != 1:
3358 h_min = [hwu_mult*b for b in h_min]
3359 h_max = [hwu_mult*b for b in h_max]
3360 fill_between_steps(bins, h_min, h_max, ax=ax, facecolor=H.get_color(),
3361 alpha=0.5, edgecolor=H.get_color(),hatch='/')
3362
3363 return hwu, H
3364
3365
3366
3367
3368
3369
3370 if __name__ == "__main__":
3371 main_doc = \
3372 """ For testing and standalone use. Usage:
3373 python histograms.py <.HwU input_file_path_1> <.HwU input_file_path_2> ... --out=<output_file_path.format> <options>
3374 Where <options> can be a list of the following:
3375 '--help' See this message.
3376 '--gnuplot' or '' output the histograms read to gnuplot
3377 '--HwU' to output the histograms read to the raw HwU source.
3378 '--types=<type1>,<type2>,...' to keep only the type<i> when importing histograms.
3379 '--titles=<title1>,<title2>,...' to keep only the titles which have any of 'title<i>' in them (not necessarily equal to them)
3380 '--n_ratios=<integer>' Specifies how many curves must be considerd for the ratios.
3381 '--no_open' Turn off the automatic processing of the gnuplot output.
3382 '--show_full' to show the complete output of what was read.
3383 '--show_short' to show a summary of what was read.
3384 '--simple_ratios' to turn off correlations and error propagation in the ratio.
3385 '--sum' To sum all identical histograms together
3386 '--average' To average over all identical histograms
3387 '--rebin=<n>' Rebin the plots by merging n-consecutive bins together.
3388 '--assign_types=<type1>,<type2>,...' to assign a type to all histograms of the first, second, etc... files loaded.
3389 '--multiply=<fact1>,<fact2>,...' to multiply all histograms of the first, second, etc... files by the fact1, fact2, etc...
3390 '--no_suffix' Do no add any suffix (like '#1, #2, etc..) to the histograms types.
3391 '--lhapdf-config=<PATH_TO_LHAPDF-CONFIG>' give path to lhapdf-config to compute PDF certainties using LHAPDF (only for lhapdf6)
3392 '--jet_samples=[int1,int2]' Specifies what jet samples to keep. 'None' is the default and keeps them all.
3393 '--central_only' This option specifies to disregard all extra weights, so as to make it possible
3394 to take the ratio of plots with different extra weights specified.
3395 '--keep_all_weights' This option specifies to keep in the HwU produced all the weights, even
3396 those which are not known (i.e. that is scale, PDF or merging variation)
3397 For chosing what kind of variation you want to see on your plot, you can use the following options
3398 '--no_<type>' Turn off the plotting of variations of the chosen type
3399 '--only_<type>' Turn on only the plotting of variations of the chosen type
3400 '--variations=['<type1>',...]' Turn on only the plotting of the variations of the list of chosen types
3401 '--band=['<type1>',...]' Chose for which variations one should use uncertainty bands as opposed to lines
3402 The types can be: pdf, scale, stat, merging or alpsfact
3403 For the last two options one can use ...=all to automatically select all types.
3404
3405 When parsing an XML-formatted plot source output by the Pythia8 driver, the file names can be appended
3406 options as suffixes separated by '|', as follows:
3407 python histograms.py <XML_source_file_name>@<option1>@<option2>@etc..
3408 These options can be
3409 'run_id=<integer>' Specifies the run_ID from which the plots should be loaded.
3410 By default, the first run is considered and the ones that follow are ignored.
3411 'merging_scale=<float>' This option allows to specify to import only the plots corresponding to a specific
3412 value for the merging scale.
3413 A value of -1 means that only the weights with the same merging scale as the central weight are kept.
3414 By default, all weights are considered.
3415 """
3416
3417 possible_options=['--help', '--gnuplot', '--HwU', '--types','--n_ratios',\
3418 '--no_open','--show_full','--show_short','--simple_ratios','--sum','--average','--rebin', \
3419 '--assign_types','--multiply','--no_suffix', '--out', '--jet_samples',
3420 '--no_scale','--no_pdf','--no_stat','--no_merging','--no_alpsfact',
3421 '--only_scale','--only_pdf','--only_stat','--only_merging','--only_alpsfact',
3422 '--variations','--band','--central_only', '--lhapdf-config','--titles',
3423 '--keep_all_weights']
3424 n_ratios = -1
3425 uncertainties = ['scale','pdf','statistical','merging_scale','alpsfact']
3426
3427 use_band = None
3428 auto_open = True
3429 ratio_correlations = True
3430 consider_reweights = ['pdf','scale','murmuf_scales','merging_scale','alpsfact']
3431
3432 - def log(msg):
3433 print "histograms.py :: %s"%str(msg)
3434
3435 if '--help' in sys.argv or len(sys.argv)==1:
3436 log('\n\n%s'%main_doc)
3437 sys.exit(0)
3438
3439 for arg in sys.argv[1:]:
3440 if arg.startswith('--'):
3441 if arg.split('=')[0] not in possible_options:
3442 log('WARNING: option "%s" not valid. It will be ignored' % arg)
3443
3444 arg_string=' '.join(sys.argv)
3445
3446 OutName = ""
3447 for arg in sys.argv[1:]:
3448 if arg.startswith('--out='):
3449 OutName = arg[6:]
3450
3451 accepted_types = []
3452 for arg in sys.argv[1:]:
3453 if arg.startswith('--types='):
3454 accepted_types = [(type if type!='None' else None) for type in \
3455 arg[8:].split(',')]
3456
3457 accepted_titles = []
3458 for arg in sys.argv[1:]:
3459 if arg.startswith('--titles='):
3460 accepted_titles = [(type if type!='None' else None) for type in \
3461 arg[9:].split(',')]
3462
3463 assigned_types = []
3464 for arg in sys.argv[1:]:
3465 if arg.startswith('--assign_types='):
3466 assigned_types = [(type if type!='None' else None) for type in \
3467 arg[15:].split(',')]
3468
3469 jet_samples_to_keep = None
3470
3471 lhapdfconfig = ['lhapdf-config']
3472 for arg in sys.argv[1:]:
3473 if arg.startswith('--lhapdf-config='):
3474 lhapdfconfig = arg[16:]
3475
3476 no_suffix = False
3477 if '--no_suffix' in sys.argv:
3478 no_suffix = True
3479
3480 if '--central_only' in sys.argv:
3481 consider_reweights = []
3482
3483 if '--keep_all_weights' in sys.argv:
3484 consider_reweights = 'ALL'
3485
3486 for arg in sys.argv[1:]:
3487 if arg.startswith('--n_ratios='):
3488 n_ratios = int(arg[11:])
3489
3490 if '--no_open' in sys.argv:
3491 auto_open = False
3492
3493 variation_type_map={'scale':'scale','merging':'merging_scale','pdf':'pdf',
3494 'stat':'statistical','alpsfact':'alpsfact'}
3495
3496 for arg in sys.argv:
3497 try:
3498 opt, value = arg.split('=')
3499 except ValueError:
3500 continue
3501 if opt=='--jet_samples':
3502 jet_samples_to_keep = eval(value)
3503 if opt=='--variations':
3504 uncertainties=[variation_type_map[type] for type in eval(value,
3505 dict([(key,key) for key in variation_type_map.keys()]+
3506 [('all',variation_type_map.keys())]))]
3507 if opt=='--band':
3508 use_band=[variation_type_map[type] for type in eval(value,
3509 dict([(key,key) for key in variation_type_map.keys()]+
3510 [('all',[type for type in variation_type_map.keys() if type!='stat'])]))]
3511
3512 if '--simple_ratios' in sys.argv:
3513 ratio_correlations = False
3514
3515 for arg in sys.argv:
3516 if arg.startswith('--no_') and not arg.startswith('--no_open'):
3517 uncertainties.remove(variation_type_map[arg[5:]])
3518 if arg.startswith('--only_'):
3519 uncertainties= [variation_type_map[arg[7:]]]
3520 break
3521
3522
3523
3524 if isinstance(consider_reweights, list):
3525 naming_map={'pdf':'pdf','scale':'scale',
3526 'merging_scale':'merging_scale','alpsfact':'alpsfact'}
3527 for key in naming_map:
3528 if (not key in uncertainties) and (naming_map[key] in consider_reweights):
3529 consider_reweights.remove(naming_map[key])
3530
3531 n_files = len([_ for _ in sys.argv[1:] if not _.startswith('--')])
3532 histo_norm = [1.0]*n_files
3533
3534 for arg in sys.argv[1:]:
3535 if arg.startswith('--multiply='):
3536 histo_norm = [(float(fact) if fact!='' else 1.0) for fact in \
3537 arg[11:].split(',')]
3538
3539 if '--average' in sys.argv:
3540 histo_norm = [hist/float(n_files) for hist in histo_norm]
3541
3542 log("=======")
3543 histo_list = HwUList([])
3544 for i, arg in enumerate(sys.argv[1:]):
3545 if arg.startswith('--'):
3546 break
3547 log("Loading histograms from '%s'."%arg)
3548 if OutName=="":
3549 OutName = os.path.basename(arg).split('.')[0]+'_output'
3550
3551 file_specification = arg.split('@')
3552 filename = file_specification.pop(0)
3553 file_options = {}
3554 for option in file_specification:
3555 opt, value = option.split('=')
3556 if opt=='run_id':
3557 file_options[opt]=int(value)
3558 if opt=='merging_scale':
3559 file_options[opt]=float(value)
3560 else:
3561 log("Unreckognize file option '%s'."%option)
3562 sys.exit(1)
3563 new_histo_list = HwUList(filename, accepted_types_order=accepted_types,
3564 consider_reweights=consider_reweights, **file_options)
3565
3566 if len(accepted_titles)>0:
3567 new_histo_list = HwUList(histo for histo in new_histo_list if
3568 any(t in histo.title for t in accepted_titles))
3569 for histo in new_histo_list:
3570 if no_suffix or n_files==1:
3571 continue
3572 if not histo.type is None:
3573 histo.type += '|'
3574 else:
3575 histo.type = ''
3576
3577
3578
3579
3580
3581
3582 try:
3583 suffix = assigned_types[i]
3584 except IndexError:
3585 suffix = "#%d"%(i+1)
3586 try:
3587 histo.type = histo.type[:histo.type.index('#')] + suffix
3588 except ValueError:
3589 histo.type += suffix
3590
3591 if i==0 or all(_ not in ['--sum','--average'] for _ in sys.argv):
3592 for j,hist in enumerate(new_histo_list):
3593 new_histo_list[j]=hist*histo_norm[i]
3594 histo_list.extend(new_histo_list)
3595 continue
3596
3597 if any(_ in sys.argv for _ in ['--sum','--average']):
3598 for j, hist in enumerate(new_histo_list):
3599
3600 hist.test_plot_compability(histo_list[j])
3601
3602 histo_list[j] += hist*histo_norm[i]
3603
3604 log("A total of %i histograms were found."%len(histo_list))
3605 log("=======")
3606
3607 n_rebin = 1
3608 for arg in sys.argv[1:]:
3609 if arg.startswith('--rebin='):
3610 n_rebin = int(arg[8:])
3611
3612 if n_rebin > 1:
3613 for hist in histo_list:
3614 hist.rebin(n_rebin)
3615
3616 if '--gnuplot' in sys.argv or all(arg not in ['--HwU'] for arg in sys.argv):
3617
3618 histo_list.output(OutName, format='gnuplot',
3619 number_of_ratios = n_ratios,
3620 uncertainties=uncertainties,
3621 ratio_correlations=ratio_correlations,
3622 arg_string=arg_string,
3623 jet_samples_to_keep=jet_samples_to_keep,
3624 use_band=use_band,
3625 auto_open=auto_open,
3626 lhapdfconfig=lhapdfconfig)
3627
3628 log("%d histograms have been output in " % len(histo_list)+\
3629 "the gnuplot format at '%s.[HwU|gnuplot]'." % OutName)
3630 if auto_open:
3631 command = 'gnuplot %s.gnuplot'%OutName
3632 try:
3633 subprocess.call(command,shell=True,stderr=subprocess.PIPE)
3634 except:
3635 log("Automatic processing of the gnuplot card failed. Try the"+\
3636 " command by hand:\n%s"%command)
3637 else:
3638 sys.exit(0)
3639
3640 if '--HwU' in sys.argv:
3641 log("Histograms data has been output in the HwU format at "+\
3642 "'%s.HwU'."%OutName)
3643 histo_list.output(OutName, format='HwU')
3644 sys.exit(0)
3645
3646 if '--show_short' in sys.argv or '--show_full' in sys.argv:
3647 for i, histo in enumerate(histo_list):
3648 if i!=0:
3649 log('-------')
3650 log(histo.nice_string(short=(not '--show_full' in sys.argv)))
3651 log("=======")
3656 ''' Fills a hole in matplotlib: fill_between for step plots.
3657 Parameters :
3658 ------------
3659 x : array-like
3660 Array/vector of index values. These are assumed to be equally-spaced.
3661 If not, the result will probably look weird...
3662 y1 : array-like
3663 Array/vector of values to be filled under.
3664 y2 : array-Like
3665 Array/vector or bottom values for filled area. Default is 0.
3666 **kwargs will be passed to the matplotlib fill_between() function.
3667 '''
3668
3669 if ax is None:
3670 ax = plt.gca()
3671
3672
3673
3674
3675 xx= []; [(xx.append(d),xx.append(d)) for d in x]; xx = xx[1:]
3676
3677 xstep = x[1] -x[0]
3678
3679 xx.append(xx[-1] + xstep)
3680
3681
3682 if h_align == 'mid':
3683 xx = [X-xstep/2. for X in xx]
3684 elif h_align == 'right':
3685 xx = [X-xstep for X in xx]
3686
3687
3688 yy1 = []; [(yy1.append(d),yy1.append(d)) for d in y1]
3689 if isinstance(y1, list):
3690 yy2 = []; [(yy2.append(d),yy2.append(d)) for d in y2]
3691 else:
3692 yy2=y2
3693 if len(yy2) != len(yy1):
3694 yy2 = []; [(yy2.append(d),yy2.append(d)) for d in y2]
3695
3696
3697 ax.fill_between(xx, yy1, y2=yy2, **kwargs)
3698
3699 return ax
3700
3701