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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 from __future__ import absolute_import
22 from __future__ import print_function
23 import array
24 import copy
25 import fractions
26 import itertools
27 import logging
28 import math
29 import os
30 import re
31 import sys
32
33 import subprocess
34 import xml.dom.minidom as minidom
35 from xml.parsers.expat import ExpatError as XMLParsingError
36 import six
37 StringIO = six
38 from six.moves import range
39 from six.moves import zip
40 import io
41 if six.PY3:
42 file = io.IOBase
43
44 root_path = os.path.split(os.path.dirname(os.path.realpath( __file__ )))[0]
45 sys.path.append(os.path.join(root_path))
46 sys.path.append(os.path.join(root_path,os.pardir))
47 try:
48
49 import madgraph.various.misc as misc
50 from madgraph import MadGraph5Error
51 logger = logging.getLogger("madgraph.various.histograms")
52
53 except ImportError as error:
54
55 import internal.misc as misc
56 from internal import MadGraph5Error
57 logger = logging.getLogger("internal.histograms")
67 """A class to store lists of physics object."""
68
70 """Exception raised if an error occurs in the definition
71 or execution of a physics object list."""
72 pass
73
75 """Creates a new particle list object. If a list of physics
76 object is given, add them."""
77
78 list.__init__(self)
79
80 if init_list is not None:
81 for object in init_list:
82 self.append(object)
83
85 """Appends an element, but test if valid before."""
86
87 assert self.is_valid_element(object), \
88 "Object %s is not a valid object for the current list" % repr(object)
89
90 list.append(self, object)
91
92
94 """Test if object obj is a valid element for the list."""
95 return True
96
98 """String representation of the physics object list object.
99 Outputs valid Python with improved format."""
100
101 mystr = '['
102
103 for obj in self:
104 mystr = mystr + str(obj) + ',\n'
105
106 mystr = mystr.rstrip(',\n')
107
108 return mystr + ']'
109
110
111 -class Bin(object):
112 """A class to store Bin related features and function.
113 """
114
115 - def __init__(self, boundaries=(0.0,0.0), wgts=None, n_entries = 0):
116 """ Initializes an empty bin, necessarily with boundaries. """
117
118 self.boundaries = boundaries
119 self.n_entries = n_entries
120 if not wgts:
121 self.wgts = {'central':0.0}
122 else:
123 self.wgts = wgts
124
126 if name=='boundaries':
127 if not isinstance(value, tuple):
128 raise MadGraph5Error("Argument '%s' for bin property "+\
129 "'boundaries' must be a tuple."%str(value))
130 else:
131 for coordinate in value:
132 if isinstance(coordinate, tuple):
133 for dim in coordinate:
134 if not isinstance(dim, float):
135 raise MadGraph5Error("Coordinate '%s' of the bin"+\
136 " boundary '%s' must be a float."%str(dim,value))
137 elif not isinstance(coordinate, float):
138 raise MadGraph5Error("Element '%s' of the bin boundaries"+\
139 " specified must be a float."%str(bound))
140 elif name=='wgts':
141 if not isinstance(value, dict):
142 raise MadGraph5Error("Argument '%s' for bin uncertainty "+\
143 "'wgts' must be a dictionary."%str(value))
144 for val in value.values():
145 if not isinstance(val,float):
146 raise MadGraph5Error("The bin weight value '%s' is not a "+\
147 "float."%str(val))
148
149 super(Bin, self).__setattr__(name,value)
150
152 """ Accesses a specific weight from this bin."""
153 try:
154 return self.wgts[key]
155 except KeyError:
156 raise MadGraph5Error("Weight with ID '%s' is not defined for"+\
157 " this bin"%str(key))
158
160 """ Accesses a specific weight from this bin."""
161
162
163
164 assert(isinstance(wgt, float))
165
166 try:
167 self.wgts[key] = wgt
168 except KeyError:
169 raise MadGraph5Error("Weight with ID '%s' is not defined for"+\
170 " this bin"%str(key))
171
173 """ Add an event to this bin. """
174
175
176 if isinstance(weights, float):
177 weights = {'central': weights}
178
179 for key in weights:
180 if key == 'stat_error':
181 continue
182 try:
183 self.wgts[key] += weights[key]
184 except KeyError:
185 raise MadGraph5Error('The event added defines the weight '+
186 '%s which was not '%key+'registered in this histogram.')
187
188 self.n_entries += 1
189
190
191
192
193
194
195
197 """ Nice representation of this Bin.
198 One can order the weight according to the argument if provided."""
199
200 res = ["Bin boundaries : %s"%str(self.boundaries)]
201 if not short:
202 res.append("Bin weights :")
203 if order is None:
204 label_list = list(self.wgts.keys())
205 else:
206 label_list = order
207
208 for label in label_list:
209 try:
210 res.append(" -> '%s' : %4.3e"%(str(label),self.wgts[label]))
211 except KeyError:
212 pass
213 else:
214 res.append("Central weight : %4.3e"%self.get_weight())
215
216 return '\n'.join(res)
217
219 """ Apply a given function to all bin weights."""
220 self.wgts = func(self.wgts)
221
222 @classmethod
223 - def combine(cls, binA, binB, func):
224 """ Function to combine two bins. The 'func' is such that it takes
225 two weight dictionaries and merge them into one."""
226
227 res_bin = cls()
228 if binA.boundaries != binB.boundaries:
229 raise MadGraph5Error('The two bins to combine have'+\
230 ' different boundaries, %s!=%s.'%(str(binA.boundaries),str(binB.boundaries)))
231 res_bin.boundaries = binA.boundaries
232
233 try:
234 res_bin.wgts = func(binA.wgts, binB.wgts)
235 except Exception as e:
236 raise MadGraph5Error("When combining two bins, the provided"+\
237 " function '%s' triggered the following error:\n\"%s\"\n"%\
238 (func.__name__,str(e))+" when combining the following two bins:\n"+\
239 binA.nice_string(short=False)+"\n and \n"+binB.nice_string(short=False))
240
241 return res_bin
242
243 -class BinList(histograms_PhysicsObjectList):
244 """ A class implementing features related to a list of Bins. """
245
246 - def __init__(self, list = [], bin_range = None,
247 weight_labels = None):
248 """ Initialize a list of Bins. It is possible to define the range
249 as a list of three floats: [min_x, max_x, bin_width]"""
250
251 self.weight_labels = weight_labels
252 if bin_range:
253
254 if not self.weight_labels:
255 self.weight_labels = ['central', 'stat_error']
256 if len(bin_range)!=3 or any(not isinstance(f, float) for f in bin_range):
257 raise MadGraph5Error("The range argument to build a BinList"+\
258 " must be a list of exactly three floats.")
259 current = bin_range[0]
260 while current < bin_range[1]:
261 self.append(Bin(boundaries =
262 (current, min(current+bin_range[2],bin_range[1])),
263 wgts = dict((wgt,0.0) for wgt in self.weight_labels)))
264 current += bin_range[2]
265 else:
266 super(BinList, self).__init__(list)
267
269 """Test whether specified object is of the right type for this list."""
270
271 return isinstance(obj, Bin)
272
274 if name=='weight_labels':
275 if not value is None and not isinstance(value, list):
276 raise MadGraph5Error("Argument '%s' for BinList property '%s'"\
277 %(str(value),name)+' must be a list.')
278 elif not value is None:
279 for label in value:
280 if all((not isinstance(label,cls)) for cls in \
281 [str, int, float, tuple]):
282 raise MadGraph5Error("Element '%s' of the BinList property '%s'"\
283 %(str(value),name)+' must be a string, an '+\
284 'integer, a float or a tuple of float.')
285 if isinstance(label, tuple):
286 if len(label)>=1:
287 if not isinstance(label[0], (float, str)):
288 raise MadGraph5Error("Argument "+\
289 "'%s' for BinList property '%s'"%(str(value),name)+\
290 ' can be a tuple, but its first element must be a float or string.')
291 for elem in label[1:]:
292 if not isinstance(elem, (float,int,str)):
293 raise MadGraph5Error("Argument "+\
294 "'%s' for BinList property '%s'"%(str(value),name)+\
295 ' can be a tuple, but its elements past the first one must be either floats, integers or strings')
296
297
298 super(BinList, self).__setattr__(name, value)
299
301 """Appends an element, but test if valid before."""
302
303 super(BinList,self).append(object)
304
305 if len(self)==1 and self.weight_labels is None:
306 self.weight_labels = list(object.wgts.keys())
307
309 """ Nice representation of this BinList."""
310
311 res = ["Number of bin in the list : %d"%len(self)]
312 res.append("Registered weight labels : [%s]"%(', '.join([
313 str(label) for label in self.weight_labels])))
314 if not short:
315 for i, bin in enumerate(self):
316 res.append('Bin number %d :'%i)
317 res.append(bin.nice_string(order=self.weight_labels, short=short))
318
319 return '\n'.join(res)
320
322 """A mother class for all specific implementations of Histogram conventions
323 """
324
325 allowed_dimensions = None
326 allowed_types = []
327 allowed_axis_modes = ['LOG','LIN']
328
329 - def __init__(self, title = "NoName", n_dimensions = 2, type=None,
330 x_axis_mode = 'LIN', y_axis_mode = 'LOG', bins=None):
331 """ Initializes an empty histogram, possibly specifying
332 > a title
333 > a number of dimensions
334 > a bin content
335 """
336
337 self.title = title
338 self.dimension = n_dimensions
339 if not bins:
340 self.bins = BinList([])
341 else:
342 self.bins = bins
343 self.type = type
344 self.x_axis_mode = x_axis_mode
345 self.y_axis_mode = y_axis_mode
346
348 if name=='title':
349 if not isinstance(value, str):
350 raise MadGraph5Error("Argument '%s' for the histogram property "+\
351 "'title' must be a string."%str(value))
352 elif name=='dimension':
353 if not isinstance(value, int):
354 raise MadGraph5Error("Argument '%s' for histogram property "+\
355 "'dimension' must be an integer."%str(value))
356 if self.allowed_dimensions and value not in self.allowed_dimensions:
357 raise MadGraph5Error("%i-Dimensional histograms not supported "\
358 %value+"by class '%s'. Supported dimensions are '%s'."\
359 %(self.__class__.__name__,self.allowed_dimensions))
360 elif name=='bins':
361 if not isinstance(value, BinList):
362 raise MadGraph5Error("Argument '%s' for histogram property "+\
363 "'bins' must be a BinList."%str(value))
364 else:
365 for bin in value:
366 if not isinstance(bin, Bin):
367 raise MadGraph5Error("Element '%s' of the "%str(bin)+\
368 " histogram bin list specified must be a bin.")
369 elif name=='type':
370 if not (value is None or value in self.allowed_types or
371 self.allowed_types==[]):
372 raise MadGraph5Error("Argument '%s' for histogram"%str(value)+\
373 " property 'type' must be a string in %s or None."\
374 %([str(t) for t in self.allowed_types]))
375 elif name in ['x_axis_mode','y_axis_mode']:
376 if not value in self.allowed_axis_modes:
377 raise MadGraph5Error("Attribute '%s' of the histogram"%str(name)+\
378 " must be in [%s], ('%s' given)"%(str(self.allowed_axis_modes),
379 str(value)))
380
381 super(Histogram, self).__setattr__(name,value)
382
384 """ Nice representation of this histogram. """
385
386 res = ['<%s> histogram:'%self.__class__.__name__]
387 res.append(' -> title : "%s"'%self.title)
388 res.append(' -> dimensions : %d'%self.dimension)
389 if not self.type is None:
390 res.append(' -> type : %s'%self.type)
391 else:
392 res.append(' -> type : None')
393 res.append(' -> (x, y)_axis : ( %s, %s)'%\
394 (tuple([('Linear' if mode=='LIN' else 'Logarithmic') for mode in \
395 [self.x_axis_mode, self.y_axis_mode]])))
396 if short:
397 res.append(' -> n_bins : %s'%len(self.bins))
398 res.append(' -> weight types : [ %s ]'%
399 (', '.join([str(label) for label in self.bins.weight_labels]) \
400 if (not self.bins.weight_labels is None) else 'None'))
401
402 else:
403 res.append(' -> Bins content :')
404 res.append(self.bins.nice_string(short))
405
406 return '\n'.join(res)
407
409 """ Apply a given function to all bin weights."""
410
411 for bin in self.bins:
412 bin.alter_weights(func)
413
414 @classmethod
415 - def combine(cls, histoA, histoB, func):
416 """ Function to combine two Histograms. The 'func' is such that it takes
417 two weight dictionaries and merge them into one."""
418
419 res_histogram = copy.copy(histoA)
420 if histoA.title != histoB.title:
421 res_histogram.title = "[%s]__%s__[%s]"%(histoA.title,func.__name__,
422 histoB.title)
423 else:
424 res_histogram.title = histoA.title
425
426 res_histogram.bins = BinList([])
427 if len(histoA.bins)!=len(histoB.bins):
428 raise MadGraph5Error('The two histograms to combine have a '+\
429 'different number of bins, %d!=%d.'%(len(histoA.bins),len(histoB.bins)))
430
431 if histoA.dimension!=histoB.dimension:
432 raise MadGraph5Error('The two histograms to combine have a '+\
433 'different dimensions, %d!=%d.'%(histoA.dimension,histoB.dimension))
434 res_histogram.dimension = histoA.dimension
435
436 for i, bin in enumerate(histoA.bins):
437 res_histogram.bins.append(Bin.combine(bin, histoB.bins[i],func))
438
439
440
441 res_histogram.bins.weight_labels = [label for label in histoA.bins.\
442 weight_labels if label in res_histogram.bins.weight_labels] + \
443 sorted([label for label in res_histogram.bins.weight_labels if\
444 label not in histoA.bins.weight_labels])
445
446
447 return res_histogram
448
449
450
451
452 @staticmethod
454 """ Apply the multiplication to the weights of two bins."""
455
456 new_wgts = {}
457
458 new_wgts['stat_error'] = math.sqrt(
459 (wgtsA['stat_error']*wgtsB['central'])**2+
460 (wgtsA['central']*wgtsB['stat_error'])**2)
461
462 for label, wgt in wgtsA.items():
463 if label=='stat_error':
464 continue
465 new_wgts[label] = wgt*wgtsB[label]
466
467 return new_wgts
468
469 @staticmethod
471 """ Apply the division to the weights of two bins."""
472
473 new_wgts = {}
474 if wgtsB['central'] == 0.0:
475 new_wgts['stat_error'] = 0.0
476 else:
477
478 new_wgts['stat_error'] = math.sqrt(wgtsA['stat_error']**2+
479 ((wgtsA['central']*wgtsB['stat_error'])/
480 wgtsB['central'])**2)/wgtsB['central']
481
482 for label, wgt in wgtsA.items():
483 if label=='stat_error':
484 continue
485 if wgtsB[label]==0.0 and wgt==0.0:
486 new_wgts[label] = 0.0
487 elif wgtsB[label]==0.0:
488
489
490
491
492 new_wgts[label] = 0.0
493 else:
494 new_wgts[label] = wgt/wgtsB[label]
495
496 return new_wgts
497
498 @staticmethod
499 - def OPERATION(wgtsA, wgtsB, wgt_operation, stat_error_operation):
500 """ Apply the operation to the weights of two bins. Notice that we
501 assume here the two dict operands to have the same weight labels.
502 The operation is a function that takes two floats as input."""
503
504 new_wgts = {}
505 for label, wgt in wgtsA.items():
506 if label!='stat_error':
507 new_wgts[label] = wgt_operation(wgt, wgtsB[label])
508 else:
509 new_wgts[label] = stat_error_operation(wgt, wgtsB[label])
510
511
512
513
514
515 return new_wgts
516
517
518 @staticmethod
520 """ Apply the operation to the weights of a *single* bins.
521 The operation is a function that takes a single float as input."""
522
523 new_wgts = {}
524 for label, wgt in wgts.items():
525 if label!='stat_error':
526 new_wgts[label] = wgt_operation(wgt)
527 else:
528 new_wgts[label] = stat_error_operation(wgt)
529
530 return new_wgts
531
532 @staticmethod
533 - def ADD(wgtsA, wgtsB):
534 """ Implements the addition using OPERATION above. """
535 return Histogram.OPERATION(wgtsA, wgtsB,
536 (lambda a,b: a+b),
537 (lambda a,b: math.sqrt(a**2+b**2)))
538
539 @staticmethod
541 """ Implements the subtraction using OPERATION above. """
542
543 return Histogram.OPERATION(wgtsA, wgtsB,
544 (lambda a,b: a-b),
545 (lambda a,b: math.sqrt(a**2+b**2)))
546
547 @staticmethod
549 """ Implements the rescaling using SINGLEHISTO_OPERATION above. """
550
551 def rescaler(wgts):
552 return Histogram.SINGLEHISTO_OPERATION(wgts,(lambda a: a*factor),
553 (lambda a: a*factor))
554
555 return rescaler
556
557 @staticmethod
559 """ Implements the offset using SINGLEBIN_OPERATION above. """
560 def offsetter(wgts):
561 return Histogram.SINGLEHISTO_OPERATION(
562 wgts,(lambda a: a+offset),(lambda a: a))
563
564 return offsetter
565
567 """ Overload the plus function. """
568 if isinstance(other, Histogram):
569 return self.__class__.combine(self,other,Histogram.ADD)
570 elif isinstance(other, int) or isinstance(other, float):
571 self.alter_weights(Histogram.OFFSET(float(other)))
572 return self
573 else:
574 return NotImplemented, 'Histograms can only be added to other '+\
575 ' histograms or scalars.'
576
578 """ Overload the subtraction function. """
579 if isinstance(other, Histogram):
580 return self.__class__.combine(self,other,Histogram.SUBTRACT)
581 elif isinstance(other, int) or isinstance(other, float):
582 self.alter_weights(Histogram.OFFSET(-float(other)))
583 return self
584 else:
585 return NotImplemented, 'Histograms can only be subtracted to other '+\
586 ' histograms or scalars.'
587
589 """ Overload the multiplication function. """
590 if isinstance(other, Histogram):
591 return self.__class__.combine(self,other,Histogram.MULTIPLY)
592 elif isinstance(other, int) or isinstance(other, float):
593 self.alter_weights(Histogram.RESCALE(float(other)))
594 return self
595 else:
596 return NotImplemented, 'Histograms can only be multiplied to other '+\
597 ' histograms or scalars.'
598
600 """ Overload the multiplication function. """
601 if isinstance(other, Histogram):
602 return self.__class__.combine(self,other,Histogram.DIVIDE)
603 elif isinstance(other, int) or isinstance(other, float):
604 self.alter_weights(Histogram.RESCALE(1.0/float(other)))
605 return self
606 else:
607 return NotImplemented, 'Histograms can only be divided with other '+\
608 ' histograms or scalars.'
609
610 __truediv__ = __div__
611
612 -class HwU(Histogram):
613 """A concrete implementation of an histogram plots using the HwU format for
614 reading/writing histogram content."""
615
616 allowed_dimensions = [2]
617 allowed_types = []
618
619
620 output_formats_implemented = ['HwU','gnuplot']
621
622
623
624 mandatory_weights = {'xmin':'boundary_xmin', 'xmax':'boundary_xmax',
625 'central value':'central', 'dy':'stat_error'}
626
627
628
629
630
631 weight_header_start_re = re.compile('^##.*')
632
633
634
635 weight_header_re = re.compile(
636 '&\s*(?P<wgt_name>(\S|(\s(?!\s*(&|$))))+)(\s(?!(&|$)))*')
637
638
639
640
641
642 histo_start_re = re.compile('^\s*<histogram>\s*(?P<n_bins>\d+)\s*"\s*'+
643 '(?P<histo_name>(\S|(\s(?!\s*")))+)\s*"\s*$')
644
645 a_float_re = '[\+|-]?\d+(\.\d*)?([EeDd][\+|-]?\d+)?'
646 histo_bin_weight_re = re.compile('(?P<weight>%s|NaN)'%a_float_re,re.IGNORECASE)
647 a_int_re = '[\+|-]?\d+'
648
649
650 histo_end_re = re.compile(r'^\s*<\\histogram>\s*$')
651
652 weight_label_scale = re.compile('^\s*mur\s*=\s*(?P<mur_fact>%s)'%a_float_re+\
653 '\s*muf\s*=\s*(?P<muf_fact>%s)\s*$'%a_float_re,re.IGNORECASE)
654 weight_label_PDF = re.compile('^\s*PDF\s*=\s*(?P<PDF_set>\d+)\s*$')
655 weight_label_PDF_XML = re.compile('^\s*pdfset\s*=\s*(?P<PDF_set>\d+)\s*$')
656 weight_label_TMS = re.compile('^\s*TMS\s*=\s*(?P<Merging_scale>%s)\s*$'%a_float_re)
657 weight_label_alpsfact = re.compile('^\s*alpsfact\s*=\s*(?P<alpsfact>%s)\s*$'%a_float_re,
658 re.IGNORECASE)
659
660 weight_label_scale_adv = re.compile('^\s*dyn\s*=\s*(?P<dyn_choice>%s)'%a_int_re+\
661 '\s*mur\s*=\s*(?P<mur_fact>%s)'%a_float_re+\
662 '\s*muf\s*=\s*(?P<muf_fact>%s)\s*$'%a_float_re,re.IGNORECASE)
663 weight_label_PDF_adv = re.compile('^\s*PDF\s*=\s*(?P<PDF_set>\d+)\s+(?P<PDF_set_cen>\S+)\s*$')
664
665
667 """a class for histogram data parsing errors"""
668
669 @classmethod
671 """ From the format of the weight label given in argument, it returns
672 a string identifying the type of standard weight it is."""
673
674 if isinstance(wgt_label,str):
675 return 'UNKNOWN_TYPE'
676 if isinstance(wgt_label,tuple):
677 if len(wgt_label)==0:
678 return 'UNKNOWN_TYPE'
679 if isinstance(wgt_label[0],float):
680 return 'murmuf_scales'
681 if isinstance(wgt_label[0],str):
682 return wgt_label[0]
683 if isinstance(wgt_label,float):
684 return 'merging_scale'
685 if isinstance(wgt_label,int):
686 return 'pdfset'
687
688 return 'UNKNOWN_TYPE'
689
690
691 - def __init__(self, file_path=None, weight_header=None,
692 raw_labels=False, consider_reweights='ALL', selected_central_weight=None, **opts):
693 """ Read one plot from a file_path or a stream. Notice that this
694 constructor only reads one, and the first one, of the plots specified.
695 If file_path was a path in argument, it would then close the opened stream.
696 If file_path was a stream in argument, it would leave it open.
697 The option weight_header specifies an ordered list of weight names
698 to appear in the file specified.
699 The option 'raw_labels' specifies that one wants to import the
700 histogram data with no treatment of the weight labels at all
701 (this is used for the matplotlib output)."""
702
703 super(HwU, self).__init__(**opts)
704
705 self.dimension = 2
706
707 if file_path is None:
708 return
709 elif isinstance(file_path, str):
710 stream = open(file_path,'r')
711 elif isinstance(file_path, io.IOBase):
712 stream = file_path
713 elif isinstance(file_path, file):
714 stream = file_path
715 else:
716 raise MadGraph5Error("Argument file_path '%s' for HwU init"\
717 %str(file_path)+"ialization must be either a file path or a stream.")
718
719
720 if not weight_header:
721 weight_header = HwU.parse_weight_header(stream, raw_labels=raw_labels)
722
723 if not self.parse_one_histo_from_stream(stream, weight_header,
724 consider_reweights=consider_reweights,
725 selected_central_weight=selected_central_weight,
726 raw_labels=raw_labels):
727
728
729 super(Histogram,self).__setattr__('bins',None)
730
731
732 if isinstance(file_path, str):
733 stream.close()
734
735 - def addEvent(self, x_value, weights = 1.0):
736 """ Add an event to the current plot. """
737
738 for bin in self.bins:
739 if bin.boundaries[0] <= x_value < bin.boundaries[1]:
740 bin.addEvent(weights = weights)
741
742 - def get(self, name):
743
744 if name == 'bins':
745 return [b.boundaries[0] for b in self.bins]
746 else:
747 return [b.wgts[name] for b in self.bins]
748
766
768 """return two list of entry one with the minimum and one with the maximum value.
769 selector can be:
770 - a regular expression on the label name
771 - a function returning T/F (applying on the label name)
772 - a list of labels
773 - a keyword
774 """
775
776
777 if isinstance(selector, str):
778 if selector == 'QCUT':
779 selector = r'^Weight_MERGING=[\d]*[.]?\d*$'
780 elif selector == 'SCALE':
781 selector = r'(MUF=\d*[.]?\d*_MUR=([^1]\d*|1\d+)_PDF=\d*)[.]?\d*|(MUF=([^1]\d*|1\d+)[.]?\d*_MUR=\d*[.]?\d*_PDF=\d*)'
782 elif selector == 'ALPSFACT':
783 selector = r'ALPSFACT'
784 elif selector == 'PDF':
785 selector = r'(?:MUF=1_MUR=1_PDF=|MU(?:F|R)="1.0" MU(?:R|F)="1.0" PDF=")(\d*)'
786 if not mode:
787
788
789
790
791
792 pdfs = [int(re.findall(selector, n)[0]) for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)]
793 min_pdf, max_pdf = min(pdfs), max(pdfs)
794 if max_pdf - min_pdf > 100:
795 mode == 'min/max'
796 elif max_pdf <= 90000:
797 mode = 'hessian'
798 else:
799 mode = 'gaussian'
800 selections = [n for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)]
801 elif hasattr(selector, '__call__'):
802 selections = [n for n in self.bins[0].wgts if selector(n)]
803 elif isinstance(selector, (list, tuple)):
804 selections = selector
805
806
807 if not mode:
808 mode = 'min/max'
809
810
811 values = []
812 for s in selections:
813 values.append(self.get(s))
814
815
816 if not len(values):
817 return [0] * len(self.bins), [0]* len(self.bins)
818 elif len(values) ==1:
819 return values[0], values[0]
820
821
822
823 if mode == 'min/max':
824 min_value, max_value = [], []
825 for i in range(len(values[0])):
826 data = [values[s][i] for s in range(len(values))]
827 min_value.append(min(data))
828 max_value.append(max(data))
829 elif mode == 'gaussian':
830
831 min_value, max_value = [], []
832 for i in range(len(values[0])):
833 pdf_stdev = 0.0
834 data = [values[s][i] for s in range(len(values))]
835 sdata = sum(data)/len(data)
836 sdata2 = sum(x**2 for x in data)/len(data)
837 pdf_stdev = math.sqrt(max(sdata2 -sdata**2,0.0))
838 min_value.append(sdata - pdf_stdev)
839 max_value.append(sdata + pdf_stdev)
840
841 elif mode == 'hessian':
842
843
844 pdfs = [(int(re.findall(selector, n)[0]),n) for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)]
845 pdfs.sort()
846
847
848 if len(pdfs) % 2:
849
850 pdf1 = pdfs[0][0]
851 central = pdf1 -1
852 name = pdfs[0][1].replace(str(pdf1), str(central))
853 central = self.get(name)
854 else:
855 central = self.get(pdfs.pop(0)[1])
856
857
858 values = []
859 for _, name in pdfs:
860 values.append(self.get(name))
861
862
863 min_value, max_value = [], []
864 for i in range(len(values[0])):
865 pdf_up = 0
866 pdf_down = 0
867 cntrl_val = central[i]
868 for s in range(int((len(pdfs))/2)):
869 pdf_up += max(0.0,values[2*s][i] - cntrl_val,
870 values[2*s+1][i] - cntrl_val)**2
871 pdf_down += max(0.0,cntrl_val - values[2*s][i],
872 cntrl_val - values[2*s+1][i])**2
873
874 min_value.append(cntrl_val - math.sqrt(pdf_down))
875 max_value.append(cntrl_val + math.sqrt(pdf_up))
876
877
878
879
880 return min_value, max_value
881
911
913 """ Returns the string representation of this histogram using the
914 HwU standard."""
915
916 res = []
917 if print_header:
918 res.append(self.get_formatted_header())
919 res.extend([''])
920 res.append('<histogram> %s "%s"'%(len(self.bins),
921 self.get_HwU_histogram_name(format='HwU')))
922 for bin in self.bins:
923 if 'central' in bin.wgts:
924 res.append(' '.join('%+16.7e'%wgt for wgt in list(bin.boundaries)+
925 [bin.wgts['central'],bin.wgts['stat_error']]))
926 else:
927 res.append(' '.join('%+16.7e'%wgt for wgt in list(bin.boundaries)))
928 res[-1] += ' '.join('%+16.7e'%bin.wgts[key] for key in
929 self.bins.weight_labels if key not in ['central','stat_error'])
930 res.append('<\histogram>')
931 return res
932
933 - def output(self, path=None, format='HwU', print_header=True):
934 """ Ouput this histogram to a file, stream or string if path is kept to
935 None. The supported format are for now. Chose whether to print the header
936 or not."""
937
938 if not format in HwU.output_formats_implemented:
939 raise MadGraph5Error("The specified output format '%s'"%format+\
940 " is not yet supported. Supported formats are %s."\
941 %HwU.output_formats_implemented)
942
943 if format == 'HwU':
944 str_output_list = self.get_HwU_source(print_header=print_header)
945
946 if path is None:
947 return '\n'.join(str_output_list)
948 elif isinstance(path, str):
949 stream = open(path,'w')
950 stream.write('\n'.join(str_output_list))
951 stream.close()
952 elif isinstance(path, file):
953 path.write('\n'.join(str_output_list))
954
955
956 return True
957
960 """ Test whether the defining attributes of self are identical to histo,
961 typically to make sure that they are the same plots but from different
962 runs, and they can be summed safely. We however don't want to
963 overload the __eq__ because it is still a more superficial check."""
964
965 this_known_weight_labels = [label for label in self.bins.weight_labels if
966 HwU.get_HwU_wgt_label_type(label)!='UNKNOWN_TYPE']
967 other_known_weight_labels = [label for label in other.bins.weight_labels if
968 HwU.get_HwU_wgt_label_type(label)!='UNKNOWN_TYPE']
969 this_unknown_weight_labels = [label for label in self.bins.weight_labels if
970 HwU.get_HwU_wgt_label_type(label)=='UNKNOWN_TYPE']
971 other_unknown_weight_labels = [label for label in other.bins.weight_labels if
972 HwU.get_HwU_wgt_label_type(label)=='UNKNOWN_TYPE']
973
974 if self.title != other.title or \
975 set(this_known_weight_labels) != set(other_known_weight_labels) or \
976 (set(this_unknown_weight_labels) != set(other_unknown_weight_labels) and\
977 consider_unknown_weight_labels) or \
978 (self.type != other.type and consider_type) or \
979 self.x_axis_mode != self.x_axis_mode or \
980 self.y_axis_mode != self.y_axis_mode or \
981 any(b1.boundaries!=b2.boundaries for (b1,b2) in \
982 zip(self.bins,other.bins)):
983 return False
984
985 return True
986
987
988
989 @classmethod
991 """ Read a given stream until it finds a header specifying the weights
992 and then returns them."""
993
994 for line in stream:
995 if cls.weight_header_start_re.match(line):
996 header = [h.group('wgt_name') for h in
997 cls.weight_header_re.finditer(line)]
998 if any((name not in header) for name in cls.mandatory_weights):
999 raise HwU.ParseError("The mandatory weight names %s were"\
1000 %str(list(cls.mandatory_weights.keys()))+" are not all present"+\
1001 " in the following HwU header definition:\n %s"%line)
1002
1003
1004 if raw_labels:
1005
1006
1007 header = [ (h if h not in ['xmin','xmax'] else
1008 cls.mandatory_weights[h]) for h in header ]
1009
1010 return header
1011 else:
1012 header = [ (h if h not in cls.mandatory_weights else
1013 cls.mandatory_weights[h]) for h in header ]
1014
1015
1016
1017
1018 for i, h in enumerate(header):
1019 scale_wgt = HwU.weight_label_scale.match(h)
1020 PDF_wgt = HwU.weight_label_PDF.match(h)
1021 Merging_wgt = HwU.weight_label_TMS.match(h)
1022 alpsfact_wgt = HwU.weight_label_alpsfact.match(h)
1023 scale_wgt_adv = HwU.weight_label_scale_adv.match(h)
1024 PDF_wgt_adv = HwU.weight_label_PDF_adv.match(h)
1025 if scale_wgt_adv:
1026 header[i] = ('scale_adv',
1027 int(scale_wgt_adv.group('dyn_choice')),
1028 float(scale_wgt_adv.group('mur_fact')),
1029 float(scale_wgt_adv.group('muf_fact')))
1030 elif scale_wgt:
1031 header[i] = ('scale',
1032 float(scale_wgt.group('mur_fact')),
1033 float(scale_wgt.group('muf_fact')))
1034 elif PDF_wgt_adv:
1035 header[i] = ('pdf_adv',
1036 int(PDF_wgt_adv.group('PDF_set')),
1037 PDF_wgt_adv.group('PDF_set_cen'))
1038 elif PDF_wgt:
1039 header[i] = ('pdf',int(PDF_wgt.group('PDF_set')))
1040 elif Merging_wgt:
1041 header[i] = ('merging_scale',float(Merging_wgt.group('Merging_scale')))
1042 elif alpsfact_wgt:
1043 header[i] = ('alpsfact',float(alpsfact_wgt.group('alpsfact')))
1044
1045 return header
1046
1047 raise HwU.ParseError("The weight headers could not be found.")
1048
1049
1051 """ Parse the histogram name for tags which would set its various
1052 attributes."""
1053
1054 for i, tag in enumerate(histogram_name.split('|')):
1055 if i==0:
1056 self.title = tag.strip()
1057 else:
1058 stag = tag.split('@')
1059 if len(stag)==1 and stag[0].startswith('#'): continue
1060 if len(stag)!=2:
1061 raise MadGraph5Error('Specifier in title must have the'+\
1062 " syntax @<attribute_name>:<attribute_value>, not '%s'."%tag.strip())
1063
1064 stag = [t.strip().upper() for t in stag]
1065 if stag[0] in ['T','TYPE']:
1066 self.type = stag[1]
1067 elif stag[0] in ['X_AXIS', 'X']:
1068 self.x_axis_mode = stag[1]
1069 elif stag[0] in ['Y_AXIS', 'Y']:
1070 self.y_axis_mode = stag[1]
1071 elif stag[0] in ['JETSAMPLE', 'JS']:
1072 self.jetsample = int(stag[1])
1073 else:
1074 raise MadGraph5Error("Specifier '%s' not recognized."%stag[0])
1075
1077 """ Returns the histogram name in the HwU syntax or human readable."""
1078
1079 type_map = {'NLO':'NLO', 'LO':'LO', 'AUX':'auxiliary histogram'}
1080
1081 if format=='human':
1082 res = self.title
1083 if not self.type is None:
1084 try:
1085 res += ', %s'%type_map[self.type]
1086 except KeyError:
1087 res += ', %s'%str('NLO' if self.type.split()[0]=='NLO' else
1088 self.type)
1089 if hasattr(self,'jetsample'):
1090 if self.jetsample==-1:
1091 res += ', all jet samples'
1092 else:
1093 res += ', Jet sample %d'%self.jetsample
1094
1095 return res
1096
1097 elif format=='human-no_type':
1098 res = self.title
1099 return res
1100
1101 elif format=='HwU':
1102 res = [self.title]
1103 res.append('|X_AXIS@%s'%self.x_axis_mode)
1104 res.append('|Y_AXIS@%s'%self.y_axis_mode)
1105 if hasattr(self,'jetsample'):
1106 res.append('|JETSAMPLE@%d'%self.jetsample)
1107 if self.type:
1108 res.append('|TYPE@%s'%self.type)
1109 return ' '.join(res)
1110
1111 - def parse_one_histo_from_stream(self, stream, all_weight_header,
1112 consider_reweights='ALL', raw_labels=False, selected_central_weight=None):
1113 """ Reads *one* histogram from a stream, with the mandatory specification
1114 of the ordered list of weight names. Return True or False depending
1115 on whether the starting definition of a new plot could be found in this
1116 stream."""
1117 n_bins = 0
1118
1119 if consider_reweights=='ALL' or raw_labels:
1120 weight_header = all_weight_header
1121 else:
1122 new_weight_header = []
1123
1124 for wgt_label in all_weight_header:
1125 if wgt_label in ['central','stat_error','boundary_xmin','boundary_xmax'] or\
1126 HwU.get_HwU_wgt_label_type(wgt_label) in consider_reweights:
1127 new_weight_header.append(wgt_label)
1128 weight_header = new_weight_header
1129
1130
1131 for line in stream:
1132 start = HwU.histo_start_re.match(line)
1133 if not start is None:
1134 self.process_histogram_name(start.group('histo_name'))
1135
1136
1137 if self.type == 'AUX':
1138 continue
1139 n_bins = int(start.group('n_bins'))
1140
1141
1142 self.bins = BinList(weight_labels = [ wgt_label for
1143 wgt_label in weight_header if wgt_label not in
1144 ['boundary_xmin','boundary_xmax']])
1145 break
1146
1147
1148 for line_bin in stream:
1149 bin_weights = {}
1150 boundaries = [0.0,0.0]
1151 for j, weight in \
1152 enumerate(HwU.histo_bin_weight_re.finditer(line_bin)):
1153 if j == len(all_weight_header):
1154 raise HwU.ParseError("There is more bin weights"+\
1155 " specified than expected (%i)"%len(weight_header))
1156 if selected_central_weight == all_weight_header[j]:
1157 bin_weights['central'] = float(weight.group('weight'))
1158 if all_weight_header[j] == 'boundary_xmin':
1159 boundaries[0] = float(weight.group('weight'))
1160 elif all_weight_header[j] == 'boundary_xmax':
1161 boundaries[1] = float(weight.group('weight'))
1162 elif all_weight_header[j] == 'central' and not selected_central_weight is None:
1163 continue
1164 elif all_weight_header[j] in weight_header:
1165 bin_weights[all_weight_header[j]] = \
1166 float(weight.group('weight'))
1167
1168
1169
1170
1171 if len(bin_weights)<(len(weight_header)-2):
1172 raise HwU.ParseError(" There are only %i weights"\
1173 %len(bin_weights)+" specified and %i were expected."%\
1174 (len(weight_header)-2))
1175 self.bins.append(Bin(tuple(boundaries), bin_weights))
1176 if len(self.bins)==n_bins:
1177 break
1178
1179 if len(self.bins)!=n_bins:
1180 raise HwU.ParseError("%i bin specification "%len(self.bins)+\
1181 "were found and %i were expected."%n_bins)
1182
1183
1184 for line_end in stream:
1185 if HwU.histo_end_re.match(line_end):
1186
1187
1188 if not raw_labels:
1189 self.trim_auxiliary_weights()
1190
1191 return True
1192
1193
1194 return False
1195
1197 """ Remove all weights which are auxiliary (whose name end with '@aux')
1198 so that they are not included (they will be regenerated anyway)."""
1199
1200 for i, wgt_label in enumerate(self.bins.weight_labels):
1201 if isinstance(wgt_label, str) and wgt_label.endswith('@aux'):
1202 for bin in self.bins:
1203 try:
1204 del bin.wgts[wgt_label]
1205 except KeyError:
1206 pass
1207 self.bins.weight_labels = [wgt_label for wgt_label in
1208 self.bins.weight_labels if (not isinstance(wgt_label, str)
1209 or (isinstance(wgt_label, str) and not wgt_label.endswith('@aux')) )]
1210
1211 - def set_uncertainty(self, type='all_scale',lhapdfconfig='lhapdf-config'):
1212 """ Adds a weight to the bins which is the envelope of the scale
1213 uncertainty, for the scale specified which can be either 'mur', 'muf',
1214 'all_scale' or 'PDF'."""
1215
1216 if type.upper()=='MUR':
1217 new_wgt_label = 'delta_mur'
1218 scale_position = 1
1219 elif type.upper()=='MUF':
1220 new_wgt_label = 'delta_muf'
1221 scale_position = 2
1222 elif type.upper()=='ALL_SCALE':
1223 new_wgt_label = 'delta_mu'
1224 scale_position = -1
1225 elif type.upper()=='PDF':
1226 new_wgt_label = 'delta_pdf'
1227 scale_position = -2
1228 elif type.upper()=='MERGING':
1229 new_wgt_label = 'delta_merging'
1230 elif type.upper()=='ALPSFACT':
1231 new_wgt_label = 'delta_alpsfact'
1232 else:
1233 raise MadGraph5Error(' The function set_uncertainty can'+\
1234 " only handle the scales 'mur', 'muf', 'all_scale', 'pdf',"+\
1235 "'merging' or 'alpsfact'.")
1236
1237 wgts_to_consider=[]
1238 label_to_consider=[]
1239 if type.upper() == 'MERGING':
1240
1241
1242
1243 wgts_to_consider.append([ label for label in self.bins.weight_labels if \
1244 HwU.get_HwU_wgt_label_type(label)=='merging_scale' ])
1245 label_to_consider.append('none')
1246
1247 elif type.upper() == 'ALPSFACT':
1248
1249
1250
1251 wgts_to_consider.append([ label for label in self.bins.weight_labels if \
1252 HwU.get_HwU_wgt_label_type(label)=='alpsfact' ])
1253 label_to_consider.append('none')
1254 elif scale_position > -2:
1255
1256 dyn_scales=[label[1] for label in self.bins.weight_labels if \
1257 HwU.get_HwU_wgt_label_type(label)=='scale_adv']
1258
1259 dyn_scales=[scale for n,scale in enumerate(dyn_scales) if scale not in dyn_scales[:n]]
1260 for dyn_scale in dyn_scales:
1261 wgts=[label for label in self.bins.weight_labels if \
1262 HwU.get_HwU_wgt_label_type(label)=='scale_adv' and label[1]==dyn_scale]
1263 if wgts:
1264 wgts_to_consider.append(wgts)
1265 label_to_consider.append(dyn_scale)
1266
1267 wgts=[label for label in self.bins.weight_labels if \
1268 HwU.get_HwU_wgt_label_type(label)=='scale']
1269
1270
1271
1272
1273 if wgts:
1274 wgts_to_consider.append(wgts)
1275 label_to_consider.append('none')
1276
1277
1278 if scale_position > -1:
1279 for wgts in wgts_to_consider:
1280 wgts_to_consider.remove(wgts)
1281 wgts = [ label for label in wgts if label[-scale_position]==1.0 ]
1282 wgts_to_consider.append(wgts)
1283 elif scale_position == -2:
1284
1285 pdf_sets=[label[2] for label in self.bins.weight_labels if \
1286 HwU.get_HwU_wgt_label_type(label)=='pdf_adv']
1287
1288 pdf_sets=[ii for n,ii in enumerate(pdf_sets) if ii not in pdf_sets[:n]]
1289 for pdf_set in pdf_sets:
1290 wgts=[label for label in self.bins.weight_labels if \
1291 HwU.get_HwU_wgt_label_type(label)=='pdf_adv' and label[2]==pdf_set]
1292 if wgts:
1293 wgts_to_consider.append(wgts)
1294 label_to_consider.append(pdf_set)
1295
1296 wgts = [ label for label in self.bins.weight_labels if \
1297 HwU.get_HwU_wgt_label_type(label)=='pdf']
1298 if wgts:
1299 wgts_to_consider.append(wgts)
1300 label_to_consider.append('none')
1301
1302 if len(wgts_to_consider)==0 or all(len(wgts)==0 for wgts in wgts_to_consider):
1303
1304 return (None,[None])
1305
1306
1307 if type=='PDF':
1308 use_lhapdf=False
1309 try:
1310 lhapdf_libdir=subprocess.Popen([lhapdfconfig,'--libdir'],\
1311 stdout=subprocess.PIPE).stdout.read().decode().strip()
1312 except:
1313 use_lhapdf=False
1314 else:
1315 try:
1316 candidates=[dirname for dirname in os.listdir(lhapdf_libdir) \
1317 if os.path.isdir(os.path.join(lhapdf_libdir,dirname))]
1318 except OSError:
1319 candidates=[]
1320 for candidate in candidates:
1321 if os.path.isfile(os.path.join(lhapdf_libdir,candidate,'site-packages','lhapdf.so')):
1322 sys.path.insert(0,os.path.join(lhapdf_libdir,candidate,'site-packages'))
1323 try:
1324 import lhapdf
1325 use_lhapdf=True
1326 break
1327 except ImportError:
1328 sys.path.pop(0)
1329 continue
1330
1331 if not use_lhapdf:
1332 try:
1333 candidates=[dirname for dirname in os.listdir(lhapdf_libdir+'64') \
1334 if os.path.isdir(os.path.join(lhapdf_libdir+'64',dirname))]
1335 except OSError:
1336 candidates=[]
1337 for candidate in candidates:
1338 if os.path.isfile(os.path.join(lhapdf_libdir+'64',candidate,'site-packages','lhapdf.so')):
1339 sys.path.insert(0,os.path.join(lhapdf_libdir+'64',candidate,'site-packages'))
1340 try:
1341 import lhapdf
1342 use_lhapdf=True
1343 break
1344 except ImportError:
1345 sys.path.pop(0)
1346 continue
1347
1348 if not use_lhapdf:
1349 try:
1350 import lhapdf
1351 use_lhapdf=True
1352 except ImportError:
1353 logger.warning("Failed to access python version of LHAPDF: "\
1354 "cannot compute PDF uncertainty from the "\
1355 "weights in the histograms. The weights in the HwU data files " \
1356 "still cover all PDF set members, "\
1357 "but the automatic computation of the uncertainties from "\
1358 "those weights might not be correct. \n "\
1359 "If the python interface to LHAPDF is available on your system, try "\
1360 "adding its location to the PYTHONPATH environment variable and the"\
1361 "LHAPDF library location to LD_LIBRARY_PATH (linux) or DYLD_LIBRARY_PATH (mac os x).")
1362
1363 if type=='PDF' and use_lhapdf:
1364 lhapdf.setVerbosity(0)
1365
1366
1367 position=[]
1368 labels=[]
1369 for i,label in enumerate(label_to_consider):
1370 wgts=wgts_to_consider[i]
1371 if label != 'none':
1372 new_wgt_labels=['%s_cen %s @aux' % (new_wgt_label,label),
1373 '%s_min %s @aux' % (new_wgt_label,label),
1374 '%s_max %s @aux' % (new_wgt_label,label)]
1375 else:
1376 new_wgt_labels=['%s_cen @aux' % new_wgt_label,
1377 '%s_min @aux' % new_wgt_label,
1378 '%s_max @aux' % new_wgt_label]
1379 try:
1380 pos=[(not isinstance(lab, str)) for lab in \
1381 self.bins.weight_labels].index(True)
1382 position.append(pos)
1383 labels.append(label)
1384 self.bins.weight_labels = self.bins.weight_labels[:pos]+\
1385 new_wgt_labels + self.bins.weight_labels[pos:]
1386 except ValueError:
1387 pos=len(self.bins.weight_labels)
1388 position.append(pos)
1389 labels.append(label)
1390 self.bins.weight_labels.extend(new_wgt_labels)
1391
1392 if type=='PDF' and use_lhapdf and label != 'none':
1393 p=lhapdf.getPDFSet(label)
1394
1395
1396 for bin in self.bins:
1397 if type!='PDF':
1398 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]]
1399 bin.wgts[new_wgt_labels[1]] = min(bin.wgts[label] \
1400 for label in wgts)
1401 bin.wgts[new_wgt_labels[2]] = max(bin.wgts[label] \
1402 for label in wgts)
1403 elif type=='PDF' and use_lhapdf and label != 'none' and len(wgts) > 1:
1404 pdfs = [bin.wgts[pdf] for pdf in sorted(wgts)]
1405 ep=p.uncertainty(pdfs,-1)
1406 bin.wgts[new_wgt_labels[0]] = ep.central
1407 bin.wgts[new_wgt_labels[1]] = ep.central-ep.errminus
1408 bin.wgts[new_wgt_labels[2]] = ep.central+ep.errplus
1409 elif type=='PDF' and use_lhapdf and label != 'none' and len(bin.wgts) == 1:
1410 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]]
1411 bin.wgts[new_wgt_labels[1]] = bin.wgts[wgts[0]]
1412 bin.wgts[new_wgt_labels[2]] = bin.wgts[wgts[0]]
1413 else:
1414 pdfs = [bin.wgts[pdf] for pdf in sorted(wgts)]
1415 pdf_up = 0.0
1416 pdf_down = 0.0
1417 cntrl_val = bin.wgts['central']
1418 if wgts[0][1] <= 90000:
1419
1420 if len(pdfs)>2:
1421 for i in range(int((len(pdfs)-1)/2)):
1422 pdf_up += max(0.0,pdfs[2*i+1]-cntrl_val,
1423 pdfs[2*i+2]-cntrl_val)**2
1424 pdf_down += max(0.0,cntrl_val-pdfs[2*i+1],
1425 cntrl_val-pdfs[2*i+2])**2
1426 pdf_up = cntrl_val + math.sqrt(pdf_up)
1427 pdf_down = cntrl_val - math.sqrt(pdf_down)
1428 else:
1429 pdf_up = bin.wgts[pdfs[0]]
1430 pdf_down = bin.wgts[pdfs[0]]
1431 elif wgts[0] in range(90200, 90303) or \
1432 wgts[0] in range(90400, 90433) or \
1433 wgts[0] in range(90700, 90801) or \
1434 wgts[0] in range(90900, 90931) or \
1435 wgts[0] in range(91200, 91303) or \
1436 wgts[0] in range(91400, 91433) or \
1437 wgts[0] in range(91700, 91801) or \
1438 wgts[0] in range(91900, 91931):
1439
1440 pdf_stdev = 0.0
1441 for pdf in pdfs[1:]:
1442 pdf_stdev += (pdf - cntrl_val)**2
1443 pdf_stdev = math.sqrt(pdf_stdev)
1444 pdf_up = cntrl_val+pdf_stdev
1445 pdf_down = cntrl_val-pdf_stdev
1446 else:
1447
1448 pdf_stdev = 0.0
1449 for pdf in pdfs[1:]:
1450 pdf_stdev += (pdf - cntrl_val)**2
1451 pdf_stdev = math.sqrt(pdf_stdev/float(len(pdfs)-2))
1452 pdf_up = cntrl_val+pdf_stdev
1453 pdf_down = cntrl_val-pdf_stdev
1454
1455 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]]
1456 bin.wgts[new_wgt_labels[1]] = pdf_down
1457 bin.wgts[new_wgt_labels[2]] = pdf_up
1458
1459
1460
1461 return (position,labels)
1462
1464 """ Select a specific merging scale for the central value of this Histogram. """
1465 if selected_label not in self.bins.weight_labels:
1466 raise MadGraph5Error("Selected weight label '%s' could not be found in this HwU."%selected_label)
1467
1468 for bin in self.bins:
1469 bin.wgts['central']=bin.wgts[selected_label]
1470
1471 - def rebin(self, n_rebin):
1472 """ Rebin the x-axis so as to merge n_rebin consecutive bins into a
1473 single one. """
1474
1475 if n_rebin < 1 or not isinstance(n_rebin, int):
1476 raise MadGraph5Error("The argument 'n_rebin' of the HwU function"+\
1477 " 'rebin' must be larger or equal to 1, not '%s'."%str(n_rebin))
1478 elif n_rebin==1:
1479 return
1480
1481 if self.type and 'NOREBIN' in self.type.upper():
1482 return
1483
1484 rebinning_list = list(range(0,len(self.bins),n_rebin))+[len(self.bins),]
1485 concat_list = [self.bins[rebinning_list[i]:rebinning_list[i+1]] for \
1486 i in range(len(rebinning_list)-1)]
1487
1488 new_bins = copy.copy(self.bins)
1489 del new_bins[:]
1490
1491 for bins_to_merge in concat_list:
1492 if len(bins_to_merge)==0:
1493 continue
1494 new_bins.append(Bin(boundaries=(bins_to_merge[0].boundaries[0],
1495 bins_to_merge[-1].boundaries[1]),wgts={'central':0.0}))
1496 for weight in self.bins.weight_labels:
1497 if weight != 'stat_error':
1498 new_bins[-1].wgts[weight] = \
1499 sum(b.wgts[weight] for b in bins_to_merge)
1500 else:
1501 new_bins[-1].wgts['stat_error'] = \
1502 math.sqrt(sum(b.wgts['stat_error']**2 for b in\
1503 bins_to_merge))
1504
1505 self.bins = new_bins
1506
1507 @classmethod
1509 """ Function to determine the optimal x-axis range when plotting
1510 together the histos in histo_list and considering the weights
1511 weight_labels"""
1512
1513
1514 if weight_labels is None:
1515 weight_labels = histo_list[0].bins.weight_labels
1516
1517 all_boundaries = sum([ list(bin.boundaries) for histo in histo_list \
1518 for bin in histo.bins if \
1519 (sum(abs(bin.wgts[label]) for label in weight_labels) > 0.0)] ,[])
1520
1521 if len(all_boundaries)==0:
1522 all_boundaries = sum([ list(bin.boundaries) for histo in histo_list \
1523 for bin in histo.bins],[])
1524 if len(all_boundaries)==0:
1525 raise MadGraph5Error("The histograms with title '%s'"\
1526 %histo_list[0].title+" seems to have no bins.")
1527
1528 x_min = min(all_boundaries)
1529 x_max = max(all_boundaries)
1530
1531 return (x_min, x_max)
1532
1533 @classmethod
1536 """ Function to determine the optimal y-axis range when plotting
1537 together the histos in histo_list and considering the weights
1538 weight_labels. The option Kratio is present to allow for the couple of
1539 tweaks necessary for the the K-factor ratio histogram y-range."""
1540
1541
1542 if labels is None:
1543 weight_labels = histo_list[0].bins.weight_labels
1544 else:
1545 weight_labels = labels
1546
1547 all_weights = []
1548 for histo in histo_list:
1549 for bin in histo.bins:
1550 for label in weight_labels:
1551
1552
1553 if Kratio and bin.wgts[label]==0.0:
1554 continue
1555 if scale!='LOG':
1556 all_weights.append(bin.wgts[label])
1557 if label == 'stat_error':
1558 all_weights.append(-bin.wgts[label])
1559 elif bin.wgts[label]>0.0:
1560 all_weights.append(bin.wgts[label])
1561
1562
1563 sum([ [bin.wgts[label] for label in weight_labels if \
1564 (scale!='LOG' or bin.wgts[label]!=0.0)] \
1565 for histo in histo_list for bin in histo.bins], [])
1566
1567 all_weights.sort()
1568 if len(all_weights)!=0:
1569 partial_max = all_weights[int(len(all_weights)*0.95)]
1570 partial_min = all_weights[int(len(all_weights)*0.05)]
1571 max = all_weights[-1]
1572 min = all_weights[0]
1573 else:
1574 if scale!='LOG':
1575 return (0.0,1.0)
1576 else:
1577 return (1.0,10.0)
1578
1579 y_max = 0.0
1580 y_min = 0.0
1581
1582
1583 if (max-partial_max)>2.0*(partial_max-partial_min):
1584 y_max = partial_max
1585 else:
1586 y_max = max
1587
1588
1589 if (partial_min - min)>2.0*(partial_max-partial_min) and min != 0.0:
1590 y_min = partial_min
1591 else:
1592 y_min = min
1593
1594 if Kratio:
1595 median = all_weights[len(all_weights)//2]
1596 spread = (y_max-y_min)
1597 if abs(y_max-median)<spread*0.05 or abs(median-y_min)<spread*0.05:
1598 y_max = median + spread/2.0
1599 y_min = median - spread/2.0
1600 if y_min != y_max:
1601 return ( y_min , y_max )
1602
1603
1604 if len(histo_list[0].bins) <= 5:
1605 y_min = min
1606 y_max = max
1607
1608
1609 if y_min == y_max:
1610 if max == min:
1611 y_min -= 1.0
1612 y_max += 1.0
1613 else:
1614 y_min = min
1615 y_max = max
1616
1617 return ( y_min , y_max )
1618
1619 -class HwUList(histograms_PhysicsObjectList):
1620 """ A class implementing features related to a list of Hwu Histograms. """
1621
1622
1623
1624
1625 number_line_colors_defined = 8
1626
1628 """Test wether specified object is of the right type for this list."""
1629
1630 return isinstance(obj, HwU) or isinstance(obj, HwUList)
1631
1632 - def __init__(self, file_path, weight_header=None, run_id=None,
1633 merging_scale=None, accepted_types_order=[], consider_reweights='ALL',
1634 raw_labels=False, **opts):
1635 """ Read one plot from a file_path or a stream.
1636 This constructor reads all plots specified in target file.
1637 File_path can be a path or a stream in the argument.
1638 The option weight_header specifies an ordered list of weight names
1639 to appear in the file or stream specified. It accepted_types_order is
1640 empty, no filter is applied, otherwise only histograms of the specified
1641 types will be kept, and in this specified order for a given identical
1642 title. The option 'consider_reweights' selects whether one wants to
1643 include all the extra scale/pdf/merging variation weights. Possible values
1644 are 'ALL' or a list of the return types of the function get_HwU_wgt_label_type().
1645 The option 'raw_labels' specifies that one wants to import the
1646 histogram data with no treatment of the weight labels at all
1647 (this is used for the matplotlib output).
1648 """
1649
1650 if isinstance(file_path, str):
1651 stream = open(file_path,'r')
1652 elif isinstance(file_path, file):
1653 stream = file_path
1654 else:
1655 return super(HwUList,self).__init__(file_path, **opts)
1656
1657 try:
1658
1659 self.parse_histos_from_PY8_XML_stream(stream, run_id,
1660 merging_scale, accepted_types_order,
1661 consider_reweights=consider_reweights,
1662 raw_labels=raw_labels)
1663 except XMLParsingError:
1664
1665 stream.seek(0)
1666
1667 if not weight_header:
1668 weight_header = HwU.parse_weight_header(stream,raw_labels=raw_labels)
1669
1670
1671 selected_label = None
1672 if not merging_scale is None:
1673 for label in weight_header:
1674 if HwU.get_HwU_wgt_label_type(label)=='merging_scale':
1675 if float(label[1])==merging_scale:
1676 selected_label = label
1677 break
1678 if selected_label is None:
1679 raise MadGraph5Error("No weight could be found in the input HwU "+\
1680 "for the selected merging scale '%4.2f'."%merging_scale)
1681
1682 new_histo = HwU(stream, weight_header,raw_labels=raw_labels,
1683 consider_reweights=consider_reweights,
1684 selected_central_weight=selected_label)
1685
1686 while not new_histo.bins is None:
1687 if accepted_types_order==[] or \
1688 new_histo.type in accepted_types_order:
1689 self.append(new_histo)
1690 new_histo = HwU(stream, weight_header, raw_labels=raw_labels,
1691 consider_reweights=consider_reweights,
1692 selected_central_weight=selected_label)
1693
1694
1695
1696
1697
1698
1699
1700 titles_order = [h.title for h in self]
1701 def ordering_function(histo):
1702 title_position = titles_order.index(histo.title)
1703 if accepted_types_order==[]:
1704 type_precedence = {'NLO':1,'LO':2,None:3,'AUX':5}
1705 try:
1706 ordering_key = (title_position,type_precedence[histo.type])
1707 except KeyError:
1708 ordering_key = (title_position,4)
1709 else:
1710 ordering_key = (title_position,
1711 accepted_types_order.index(histo.type))
1712 return ordering_key
1713
1714
1715
1716
1717
1718 self.sort(key=ordering_function)
1719
1720
1721 if isinstance(file_path, str):
1722 stream.close()
1723
1731
1733 """ return the list of all weights define in each histograms"""
1734
1735 return self[0].bins.weight_labels
1736
1737
1738 - def get(self, name):
1739 """return the HWU histograms related to a given name"""
1740 for hist in self:
1741 if hist.get_HwU_histogram_name() == name:
1742 return hist
1743
1744 raise NameError("no histogram with name: %s" % name)
1745
1746 - def parse_histos_from_PY8_XML_stream(self, stream, run_id=None,
1747 merging_scale=None, accepted_types_order=[],
1748 consider_reweights='ALL', raw_labels=False):
1749 """Initialize the HwU histograms from an XML stream. Only one run is
1750 used: the first one if run_id is None or the specified run otherwise.
1751 Accepted type order is a filter to select histograms of only a certain
1752 type. The option 'consider_reweights' selects whether one wants to
1753 include all the extra scale/pdf/merging variation weights.
1754 Possible values are 'ALL' or a list of the return types of the
1755 function get_HwU_wgt_label_type()."""
1756
1757 run_nodes = minidom.parse(stream).getElementsByTagName("run")
1758 all_nodes = dict((int(node.getAttribute('id')),node) for
1759 node in run_nodes)
1760 selected_run_node = None
1761 weight_header = None
1762 if run_id is None:
1763 if len(run_nodes)>0:
1764 selected_run_node = all_nodes[min(all_nodes.keys())]
1765 else:
1766 try:
1767 selected_run_node = all_nodes[int(run_id)]
1768 except:
1769 selected_run_node = None
1770
1771 if selected_run_node is None:
1772 if run_id is None:
1773 raise MadGraph5Error('No histogram was found in the specified XML source.')
1774 else:
1775 raise MadGraph5Error("Histogram with run_id '%d' was not found in the "%run_id+\
1776 "specified XML source.")
1777
1778
1779
1780 if raw_labels:
1781
1782 weight_label_list = [wgt.strip() for wgt in
1783 str(selected_run_node.getAttribute('header')).split(';') if
1784 not re.match('^\s*$',wgt)]
1785 ordered_weight_label_list = [w for w in weight_label_list if w not\
1786 in ['xmin','xmax']]
1787
1788 filtered_ordered_weight_label_list = []
1789 for wgt_label in ordered_weight_label_list:
1790 if wgt_label not in filtered_ordered_weight_label_list:
1791 filtered_ordered_weight_label_list.append(wgt_label)
1792
1793 selected_weights = dict([ (wgt_pos,
1794 [wgt if wgt not in ['xmin','xmax'] else HwU.mandatory_weights[wgt]])
1795 for wgt_pos, wgt in enumerate(weight_label_list) if wgt in
1796 filtered_ordered_weight_label_list+['xmin','xmax']])
1797
1798 return self.retrieve_plots_from_XML_source(selected_run_node,
1799 selected_weights, filtered_ordered_weight_label_list,
1800 raw_labels=True)
1801
1802
1803
1804
1805 all_weights = []
1806 for wgt_position, wgt_label in \
1807 enumerate(str(selected_run_node.getAttribute('header')).split(';')):
1808 if not re.match('^\s*$',wgt_label) is None:
1809 continue
1810 all_weights.append({'POSITION':wgt_position})
1811 for wgt_item in wgt_label.strip().split('_'):
1812 property = wgt_item.strip().split('=')
1813 if len(property) == 2:
1814 all_weights[-1][property[0].strip()] = property[1].strip()
1815 elif len(property)==1:
1816 all_weights[-1][property[0].strip()] = None
1817
1818
1819
1820
1821
1822
1823
1824
1825 for wgt_label in all_weights:
1826 for mandatory_attribute in ['PDF','MUR','MUF','MERGING','ALPSFACT']:
1827 if mandatory_attribute not in wgt_label:
1828 wgt_label[mandatory_attribute] = '-1'
1829 if mandatory_attribute=='PDF':
1830 wgt_label[mandatory_attribute] = int(wgt_label[mandatory_attribute])
1831 elif mandatory_attribute in ['MUR','MUF','MERGING','ALPSFACT']:
1832 wgt_label[mandatory_attribute] = float(wgt_label[mandatory_attribute])
1833
1834
1835
1836
1837 if merging_scale is None or merging_scale < 0.0:
1838 merging_scale_chosen = all_weights[2]['MERGING']
1839 else:
1840 merging_scale_chosen = merging_scale
1841
1842
1843 central_PDF = all_weights[2]['PDF']
1844
1845 central_MUR = all_weights[2]['MUR'] if all_weights[2]['MUR']!=-1.0 else 1.0
1846 central_MUF = all_weights[2]['MUF'] if all_weights[2]['MUF']!=-1.0 else 1.0
1847 central_alpsfact = all_weights[2]['ALPSFACT'] if all_weights[2]['ALPSFACT']!=-1.0 else 1.0
1848
1849
1850
1851 selected_weights = {}
1852
1853 if 'xmin' not in all_weights[0] or \
1854 'xmax' not in all_weights[1] or \
1855 'Weight' not in all_weights[2] or \
1856 'WeightError' not in all_weights[3]:
1857 raise MadGraph5Error('The first weight entries in the XML HwU '+\
1858 ' source are not the standard expected ones (xmin, xmax, sigmaCentral, errorCentral)')
1859 selected_weights[0] = ['xmin']
1860 selected_weights[1] = ['xmax']
1861
1862
1863 def get_difference_to_central(weight):
1864 """ Return the list of properties which differ from the central weight.
1865 This disregards the merging scale value for which any central value
1866 can be picked anyway."""
1867
1868 differences = []
1869
1870
1871
1872 if 'Weight' in weight:
1873 return set([])
1874 if weight['MUR'] not in [central_MUR, -1.0] or \
1875 weight['MUF'] not in [central_MUF, -1.0]:
1876 differences.append('mur_muf_scale')
1877 if weight['PDF'] not in [central_PDF,-1]:
1878 differences.append('pdf')
1879 if weight['ALPSFACT'] not in [central_alpsfact, -1]:
1880 differences.append('ALPSFACT')
1881 return set(differences)
1882
1883 def format_weight_label(weight):
1884 """ Print the weight attributes in a nice order."""
1885
1886 all_properties = list(weight.keys())
1887 all_properties.pop(all_properties.index('POSITION'))
1888 ordered_properties = []
1889
1890 for property in all_properties:
1891 if weight[property] is None:
1892 ordered_properties.append(property)
1893
1894 ordered_properties.sort()
1895 all_properties = [property for property in all_properties if
1896 not weight[property] is None]
1897
1898
1899 for property in ['PDF','MUR','MUF','ALPSFACT','MERGING']:
1900 all_properties.pop(all_properties.index(property))
1901 if weight[property]!=-1:
1902 ordered_properties.append(property)
1903
1904 ordered_properties.extend(sorted(all_properties))
1905
1906 return '_'.join('%s%s'\
1907 %(key,'' if weight[key] is None else '=%s'%str(weight[key])) for
1908 key in ordered_properties)
1909
1910
1911
1912
1913
1914 if float(all_weights[2]['MERGING']) == merging_scale_chosen:
1915 selected_weights[2]=['central value']
1916 else:
1917 for weight_position, weight in enumerate(all_weights):
1918
1919
1920 if get_difference_to_central(weight)==set([]):
1921
1922 if weight['MERGING']==merging_scale_chosen:
1923 selected_weights[weight_position] = ['central value']
1924 break
1925
1926 if 'central value' not in sum(list(selected_weights.values()),[]):
1927 central_merging_scale = all_weights[2]['MERGING']
1928 logger.warning('Could not find the central weight for the'+\
1929 ' chosen merging scale (%f).\n'%merging_scale_chosen+\
1930 'MG5aMC will chose the original central scale provided which '+\
1931 'correspond to a merging scale of %s'%("'inclusive'" if
1932 central_merging_scale in [0.0,-1.0] else '%f'%central_merging_scale))
1933 selected_weights[2]=['central value']
1934
1935
1936 selected_weights[3]=['dy']
1937
1938
1939 for weight_position, weight in enumerate(all_weights[4:]):
1940
1941
1942
1943
1944
1945
1946 variations = get_difference_to_central(weight)
1947
1948
1949
1950
1951
1952
1953
1954 if variations in [set(['mur_muf_scale']),set(['pdf','mur_muf_scale'])]:
1955 wgt_label = ('scale',weight['MUR'],weight['MUF'])
1956 if variations in [set(['ALPSFACT']),set(['pdf','ALPSFACT'])]:
1957 wgt_label = ('alpsfact',weight['ALPSFACT'])
1958 if variations == set(['pdf']):
1959 wgt_label = ('pdf',weight['PDF'])
1960 if variations == set([]):
1961
1962 wgt_label = format_weight_label(weight)
1963
1964
1965 if weight['MERGING'] != merging_scale_chosen:
1966
1967 if merging_scale:
1968 continue
1969
1970
1971 if variations == set([]):
1972
1973 wgt_label = ('merging_scale', weight['MERGING'])
1974
1975
1976
1977 if wgt_label in sum(list(selected_weights.values()),[]):
1978 continue
1979
1980
1981 try:
1982 selected_weights[weight_position+4].append(wgt_label)
1983 except KeyError:
1984 selected_weights[weight_position+4]=[wgt_label,]
1985
1986 if merging_scale and merging_scale > 0.0 and \
1987 len(sum(list(selected_weights.values()),[]))==4:
1988 logger.warning('No additional variation weight was found for the '+\
1989 'chosen merging scale %f.'%merging_scale)
1990
1991
1992 for wgt_pos in selected_weights:
1993 for i, weight_label in enumerate(selected_weights[wgt_pos]):
1994 try:
1995 selected_weights[wgt_pos][i] = HwU.mandatory_weights[weight_label]
1996 except KeyError:
1997 pass
1998
1999
2000 if consider_reweights!='ALL':
2001 new_selected_weights = {}
2002 for wgt_position, wgt_labels in selected_weights.items():
2003 for wgt_label in wgt_labels:
2004 if wgt_label in ['central','stat_error','boundary_xmin','boundary_xmax'] or\
2005 HwU.get_HwU_wgt_label_type(wgt_label) in consider_reweights:
2006 try:
2007 new_selected_weights[wgt_position].append(wgt_label)
2008 except KeyError:
2009 new_selected_weights[wgt_position] = [wgt_label]
2010 selected_weights = new_selected_weights
2011
2012
2013 weight_label_list = sum(list(selected_weights.values()),[])
2014
2015
2016 ordered_weight_label_list = ['central','stat_error']
2017 for weight_label in weight_label_list:
2018 if not isinstance(weight_label, str):
2019 ordered_weight_label_list.append(weight_label)
2020 for weight_label in weight_label_list:
2021 if weight_label in ['central','stat_error','boundary_xmin','boundary_xmax']:
2022 continue
2023 if isinstance(weight_label, str):
2024 ordered_weight_label_list.append(weight_label)
2025
2026
2027
2028 return self.retrieve_plots_from_XML_source(selected_run_node,
2029 selected_weights, ordered_weight_label_list, raw_labels=False)
2030
2033 """Given an XML node and the selected weights and their ordered list,
2034 import all histograms from the specified XML node."""
2035
2036
2037 for multiplicity_node in xml_node.getElementsByTagName("jethistograms"):
2038 multiplicity = int(multiplicity_node.getAttribute('njet'))
2039 for histogram in multiplicity_node.getElementsByTagName("histogram"):
2040
2041 if histogram.getAttribute("weight")!='all':
2042 continue
2043 new_histo = HwU()
2044 hist_name = '%s %s'%(str(histogram.getAttribute('name')),
2045 str(histogram.getAttribute('unit')))
2046
2047 new_histo.process_histogram_name('%s |JETSAMPLE@%d'%(hist_name,multiplicity))
2048
2049
2050 if new_histo.type == 'AUX':
2051 continue
2052
2053
2054
2055 new_histo.bins = BinList(weight_labels = ordered_weight_label_list)
2056 hist_data = str(histogram.childNodes[0].data)
2057 for line in hist_data.split('\n'):
2058 if line.strip()=='':
2059 continue
2060 bin_weights = {}
2061 boundaries = [0.0,0.0]
2062 for j, weight in \
2063 enumerate(HwU.histo_bin_weight_re.finditer(line)):
2064 try:
2065 for wgt_label in selected_weights[j]:
2066 if wgt_label == 'boundary_xmin':
2067 boundaries[0] = float(weight.group('weight'))
2068 elif wgt_label == 'boundary_xmax':
2069 boundaries[1] = float(weight.group('weight'))
2070 else:
2071 if weight.group('weight').upper()=='NAN':
2072 raise MadGraph5Error("Some weights are found to be 'NAN' in histogram with name '%s'"%hist_name+\
2073 " and jet sample multiplicity %d."%multiplicity)
2074 else:
2075 bin_weights[wgt_label] = \
2076 float(weight.group('weight'))
2077 except KeyError:
2078 continue
2079
2080 if len(bin_weights)!=len(ordered_weight_label_list):
2081 raise MadGraph5Error('Not all defined weights were found in the XML source.\n'+\
2082 '%d found / %d expected.'%(len(bin_weights),len(ordered_weight_label_list))+\
2083 '\nThe missing ones are: %s.'%\
2084 str(list(set(ordered_weight_label_list)-set(bin_weights.keys())))+\
2085 "\nIn plot with title '%s' and jet sample multiplicity %d."%\
2086 (hist_name, multiplicity))
2087
2088 new_histo.bins.append(Bin(tuple(boundaries), bin_weights))
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106 if not raw_labels:
2107 new_histo.trim_auxiliary_weights()
2108
2109
2110 self.append(new_histo)
2111
2112 - def output(self, path, format='gnuplot',number_of_ratios = -1,
2113 uncertainties=['scale','pdf','statitistical','merging_scale','alpsfact'],
2114 use_band = None,
2115 ratio_correlations=True, arg_string='',
2116 jet_samples_to_keep=None,
2117 auto_open=True,
2118 lhapdfconfig='lhapdf-config',
2119 assigned_colours=None):
2120 """ Ouput this histogram to a file, stream or string if path is kept to
2121 None. The supported format are for now. Chose whether to print the header
2122 or not."""
2123
2124 if len(self)==0:
2125 return MadGraph5Error, 'No histograms stored in the list yet.'
2126
2127 if not format in HwU.output_formats_implemented:
2128 raise MadGraph5Error("The specified output format '%s'"%format+\
2129 " is not yet supported. Supported formats are %s."\
2130 %HwU.output_formats_implemented)
2131
2132 if isinstance(path, str) and not any(ext in os.path.basename(path) \
2133 for ext in ['.Hwu','.ps','.gnuplot','.pdf']):
2134 output_base_name = os.path.basename(path)
2135 HwU_stream = open(path+'.HwU','w')
2136 else:
2137 raise MadGraph5Error("The path argument of the output function of"+\
2138 " the HwUList instance must be file path without its extension.")
2139
2140 HwU_output_list = []
2141
2142
2143 if format == 'HwU':
2144 HwU_output_list.extend(self[0].get_HwU_source(print_header=True))
2145 for histo in self[1:]:
2146 HwU_output_list.extend(histo.get_HwU_source())
2147 HwU_output_list.extend(['',''])
2148 HwU_stream.write('\n'.join(HwU_output_list))
2149 HwU_stream.close()
2150 return
2151
2152
2153 if format == 'gnuplot':
2154 gnuplot_stream = open(path+'.gnuplot','w')
2155
2156
2157 matching_histo_lists = HwUList([HwUList([self[0]])])
2158 for histo in self[1:]:
2159 matched = False
2160 for histo_list in matching_histo_lists:
2161 if histo.test_plot_compability(histo_list[0],
2162 consider_type=False, consider_unknown_weight_labels=True):
2163 histo_list.append(histo)
2164 matched = True
2165 break
2166 if not matched:
2167 matching_histo_lists.append(HwUList([histo]))
2168
2169 self[:] = matching_histo_lists
2170
2171
2172 coli=['col1','col2','col3','col4','col5','col6','col7','col8']
2173 colours={coli[0] : "#009e73",
2174 coli[1] : "#0072b2",
2175 coli[2] : "#d55e00",
2176 coli[3] : "#f0e442",
2177 coli[4] : "#56b4e9",
2178 coli[5] : "#cc79a7",
2179 coli[6] : "#e69f00",
2180 coli[7] : "black"}
2181 if assigned_colours:
2182 for index, item in enumerate(assigned_colours):
2183 if (item != None): colours[coli[index]]=item
2184
2185 replace_dict=colours
2186 replace_dict['arg_string']=arg_string
2187 replace_dict['output_base_name']=output_base_name
2188
2189
2190 gnuplot_output_list_v4 = [
2191 """
2192 ################################################################################
2193 #
2194 # This gnuplot file was generated by MadGraph5_aMC@NLO project, a program which
2195 # automatically generates Feynman diagrams and matrix elements for arbitrary
2196 # high-energy processes in the Standard Model and beyond. It also perform the
2197 # integration and/or generate events for these processes, at LO and NLO accuracy.
2198 #
2199 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
2200 #
2201 ################################################################################
2202 # %(arg_string)s
2203 reset
2204
2205 set lmargin 10
2206 set rmargin 0
2207 set terminal postscript portrait enhanced mono dashed lw 1.0 "Helvetica" 9
2208 # The pdf terminal offers transparency support, but you will have to adapt things a bit
2209 #set terminal pdf enhanced font "Helvetica 12" lw 1.0 dashed size 29.7cm, 21cm
2210 set key font ",9"
2211 set key samplen "2"
2212 set output "%(output_base_name)s.ps"
2213
2214 # This is the "PODO" color palette of gnuplot v.5, but with the order
2215 # changed: palette of colors selected to be easily distinguishable by
2216 # color-blind individuals with either protanopia or deuteranopia. Bang
2217 # Wong [2011] Nature Methods 8, 441.
2218
2219 set style line 1 lt 1 lc rgb "%(col1)s" lw 2.5
2220 set style line 11 lt 2 lc rgb "%(col1)s" lw 2.5
2221 set style line 21 lt 4 lc rgb "%(col1)s" lw 2.5
2222 set style line 31 lt 6 lc rgb "%(col1)s" lw 2.5
2223 set style line 41 lt 8 lc rgb "%(col1)s" lw 2.5
2224
2225 set style line 2 lt 1 lc rgb "%(col2)s" lw 2.5
2226 set style line 12 lt 2 lc rgb "%(col2)s" lw 2.5
2227 set style line 22 lt 4 lc rgb "%(col2)s" lw 2.5
2228 set style line 32 lt 6 lc rgb "%(col2)s" lw 2.5
2229 set style line 42 lt 8 lc rgb "%(col2)s" lw 2.5
2230
2231 set style line 3 lt 1 lc rgb "%(col3)s" lw 2.5
2232 set style line 13 lt 2 lc rgb "%(col3)s" lw 2.5
2233 set style line 23 lt 4 lc rgb "%(col3)s" lw 2.5
2234 set style line 33 lt 6 lc rgb "%(col3)s" lw 2.5
2235 set style line 43 lt 8 lc rgb "%(col3)s" lw 2.5
2236
2237 set style line 4 lt 1 lc rgb "%(col4)s" lw 2.5
2238 set style line 14 lt 2 lc rgb "%(col4)s" lw 2.5
2239 set style line 24 lt 4 lc rgb "%(col4)s" lw 2.5
2240 set style line 34 lt 6 lc rgb "%(col4)s" lw 2.5
2241 set style line 44 lt 8 lc rgb "%(col4)s" lw 2.5
2242
2243 set style line 5 lt 1 lc rgb "%(col5)s" lw 2.5
2244 set style line 15 lt 2 lc rgb "%(col5)s" lw 2.5
2245 set style line 25 lt 4 lc rgb "%(col5)s" lw 2.5
2246 set style line 35 lt 6 lc rgb "%(col5)s" lw 2.5
2247 set style line 45 lt 8 lc rgb "%(col5)s" lw 2.5
2248
2249 set style line 6 lt 1 lc rgb "%(col6)s" lw 2.5
2250 set style line 16 lt 2 lc rgb "%(col6)s" lw 2.5
2251 set style line 26 lt 4 lc rgb "%(col6)s" lw 2.5
2252 set style line 36 lt 6 lc rgb "%(col6)s" lw 2.5
2253 set style line 46 lt 8 lc rgb "%(col6)s" lw 2.5
2254
2255 set style line 7 lt 1 lc rgb "%(col7)s" lw 2.5
2256 set style line 17 lt 2 lc rgb "%(col7)s" lw 2.5
2257 set style line 27 lt 4 lc rgb "%(col7)s" lw 2.5
2258 set style line 37 lt 6 lc rgb "%(col7)s" lw 2.5
2259 set style line 47 lt 8 lc rgb "%(col7)s" lw 2.5
2260
2261 set style line 8 lt 1 lc rgb "%(col8)s" lw 2.5
2262 set style line 18 lt 2 lc rgb "%(col8)s" lw 2.5
2263 set style line 28 lt 4 lc rgb "%(col8)s" lw 2.5
2264 set style line 38 lt 6 lc rgb "%(col8)s" lw 2.5
2265 set style line 48 lt 7 lc rgb "%(col8)s" lw 2.5
2266
2267
2268 set style line 999 lt 1 lc rgb "gray" lw 2.5
2269
2270 safe(x,y,a) = (y == 0.0 ? a : x/y)
2271
2272 set style data histeps
2273 set key invert
2274
2275 """%(replace_dict)
2276 ]
2277
2278 gnuplot_output_list_v5 = [
2279 """
2280 ################################################################################
2281 #
2282 # This gnuplot file was generated by MadGraph5_aMC@NLO project, a program which
2283 # automatically generates Feynman diagrams and matrix elements for arbitrary
2284 # high-energy processes in the Standard Model and beyond. It also perform the
2285 # integration and/or generate events for these processes, at LO and NLO accuracy.
2286 #
2287 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
2288 #
2289 ################################################################################
2290 # %(arg_string)s
2291 reset
2292
2293 set lmargin 10
2294 set rmargin 0
2295 set terminal postscript portrait enhanced color "Helvetica" 9
2296 # The pdf terminal offers transparency support, but you will have to adapt things a bit
2297 #set terminal pdf enhanced font "Helvetica 12" lw 1.0 dashed size 29.7cm, 21cm
2298 set key font ",9"
2299 set key samplen "2"
2300 set output "%(output_base_name)s.ps"
2301
2302 # This is the "PODO" color palette of gnuplot v.5, but with the order
2303 # changed: palette of colors selected to be easily distinguishable by
2304 # color-blind individuals with either protanopia or deuteranopia. Bang
2305 # Wong [2011] Nature Methods 8, 441.
2306
2307 set style line 1 lt 1 lc rgb "%(col1)s" lw 1.3
2308 set style line 101 lt 1 lc rgb "%(col1)s" lw 1.3 dt (6,3)
2309 set style line 11 lt 2 lc rgb "%(col1)s" lw 1.3 dt (6,3)
2310 set style line 21 lt 4 lc rgb "%(col1)s" lw 1.3 dt (3,2)
2311 set style line 31 lt 6 lc rgb "%(col1)s" lw 1.3 dt (2,1)
2312 set style line 41 lt 8 lc rgb "%(col1)s" lw 1.3 dt (4,3)
2313
2314 set style line 2 lt 1 lc rgb "%(col2)s" lw 1.3
2315 set style line 102 lt 1 lc rgb "%(col2)s" lw 1.3 dt (6,3)
2316 set style line 12 lt 2 lc rgb "%(col2)s" lw 1.3 dt (6,3)
2317 set style line 22 lt 4 lc rgb "%(col2)s" lw 1.3 dt (3,2)
2318 set style line 32 lt 6 lc rgb "%(col2)s" lw 1.3 dt (2,1)
2319 set style line 42 lt 8 lc rgb "%(col2)s" lw 1.3 dt (4,3)
2320
2321 set style line 3 lt 1 lc rgb "%(col3)s" lw 1.3
2322 set style line 103 lt 1 lc rgb "%(col3)s" lw 1.3 dt (6,3)
2323 set style line 13 lt 2 lc rgb "%(col3)s" lw 1.3 dt (6,3)
2324 set style line 23 lt 4 lc rgb "%(col3)s" lw 1.3 dt (3,2)
2325 set style line 33 lt 6 lc rgb "%(col3)s" lw 1.3 dt (2,1)
2326 set style line 43 lt 8 lc rgb "%(col3)s" lw 1.3 dt (4,3)
2327
2328 set style line 4 lt 1 lc rgb "%(col4)s" lw 1.3
2329 set style line 104 lt 1 lc rgb "%(col4)s" lw 1.3 dt (6,3)
2330 set style line 14 lt 2 lc rgb "%(col4)s" lw 1.3 dt (6,3)
2331 set style line 24 lt 4 lc rgb "%(col4)s" lw 1.3 dt (3,2)
2332 set style line 34 lt 6 lc rgb "%(col4)s" lw 1.3 dt (2,1)
2333 set style line 44 lt 8 lc rgb "%(col4)s" lw 1.3 dt (4,3)
2334
2335 set style line 5 lt 1 lc rgb "%(col5)s" lw 1.3
2336 set style line 105 lt 1 lc rgb "%(col5)s" lw 1.3 dt (6,3)
2337 set style line 15 lt 2 lc rgb "%(col5)s" lw 1.3 dt (6,3)
2338 set style line 25 lt 4 lc rgb "%(col5)s" lw 1.3 dt (3,2)
2339 set style line 35 lt 6 lc rgb "%(col5)s" lw 1.3 dt (2,1)
2340 set style line 45 lt 8 lc rgb "%(col5)s" lw 1.3 dt (4,3)
2341
2342 set style line 6 lt 1 lc rgb "%(col6)s" lw 1.3
2343 set style line 106 lt 1 lc rgb "%(col6)s" lw 1.3 dt (6,3)
2344 set style line 16 lt 2 lc rgb "%(col6)s" lw 1.3 dt (6,3)
2345 set style line 26 lt 4 lc rgb "%(col6)s" lw 1.3 dt (3,2)
2346 set style line 36 lt 6 lc rgb "%(col6)s" lw 1.3 dt (2,1)
2347 set style line 46 lt 8 lc rgb "%(col6)s" lw 1.3 dt (4,3)
2348
2349 set style line 7 lt 1 lc rgb "%(col7)s" lw 1.3
2350 set style line 107 lt 1 lc rgb "%(col7)s" lw 1.3 dt (6,3)
2351 set style line 17 lt 2 lc rgb "%(col7)s" lw 1.3 dt (6,3)
2352 set style line 27 lt 4 lc rgb "%(col7)s" lw 1.3 dt (3,2)
2353 set style line 37 lt 6 lc rgb "%(col7)s" lw 1.3 dt (2,1)
2354 set style line 47 lt 8 lc rgb "%(col7)s" lw 1.3 dt (4,3)
2355
2356 set style line 8 lt 1 lc rgb "%(col8)s" lw 1.3
2357 set style line 108 lt 1 lc rgb "%(col8)s" lw 1.3 dt (6,3)
2358 set style line 18 lt 2 lc rgb "%(col8)s" lw 1.3 dt (6,3)
2359 set style line 28 lt 4 lc rgb "%(col8)s" lw 1.3 dt (3,2)
2360 set style line 38 lt 6 lc rgb "%(col8)s" lw 1.3 dt (2,1)
2361 set style line 48 lt 8 lc rgb "%(col8)s" lw 1.3 dt (4,3)
2362
2363
2364 set style line 999 lt 1 lc rgb "gray" lw 1.3
2365
2366 safe(x,y,a) = (y == 0.0 ? a : x/y)
2367
2368 set style data histeps
2369 set key invert
2370
2371 """%(replace_dict)
2372 ]
2373
2374
2375 try:
2376 p = subprocess.Popen(['gnuplot', '--version'], \
2377 stdout=subprocess.PIPE, stderr=subprocess.PIPE)
2378 except OSError:
2379
2380
2381 gnuplot_output_list=gnuplot_output_list_v5
2382 else:
2383 output, _ = p.communicate()
2384 output.decode()
2385 if not output:
2386 gnuplot_output_list=gnuplot_output_list_v5
2387 elif float(output.split()[1]) < 5. :
2388 gnuplot_output_list=gnuplot_output_list_v4
2389 else:
2390 gnuplot_output_list=gnuplot_output_list_v5
2391
2392
2393
2394
2395 block_position = 0
2396 for histo_group in self:
2397
2398 block_position = histo_group.output_group(HwU_output_list,
2399 gnuplot_output_list, block_position,output_base_name+'.HwU',
2400 number_of_ratios=number_of_ratios,
2401 uncertainties = uncertainties,
2402 use_band = use_band,
2403 ratio_correlations = ratio_correlations,
2404 jet_samples_to_keep=jet_samples_to_keep,
2405 lhapdfconfig = lhapdfconfig)
2406
2407
2408 gnuplot_output_list.extend([
2409 "unset multiplot",
2410 '!ps2pdf "%s.ps" &> /dev/null'%output_base_name])
2411 if auto_open:
2412 gnuplot_output_list.append(
2413 '!open "%s.pdf" &> /dev/null'%output_base_name)
2414
2415
2416 gnuplot_stream.write('\n'.join(gnuplot_output_list))
2417 HwU_stream.write('\n'.join(HwU_output_list))
2418 gnuplot_stream.close()
2419 HwU_stream.close()
2420
2421 logger.debug("Histograms have been written out at "+\
2422 "%s.[HwU|gnuplot]' and can "%output_base_name+\
2423 "now be rendered by invoking gnuplot.")
2424
2425 - def output_group(self, HwU_out, gnuplot_out, block_position, HwU_name,
2426 number_of_ratios = -1,
2427 uncertainties = ['scale','pdf','statitistical','merging_scale','alpsfact'],
2428 use_band = None,
2429 ratio_correlations = True,
2430 jet_samples_to_keep=None,
2431 lhapdfconfig='lhapdf-config'):
2432
2433 """ This functions output a single group of histograms with either one
2434 histograms untyped (i.e. type=None) or two of type 'NLO' and 'LO'
2435 respectively."""
2436
2437
2438
2439 def get_main_central_plot_lines(HwU_name, block_position, color_index,
2440 title, show_mc_uncertainties):
2441 """ Returns two plot lines, one for the negative contributions in
2442 dashed and one with the positive ones in solid."""
2443
2444 template = "'%(hwu)s' index %(ind)d using (($1+$2)/2):%(data)s%(stat_col)s%(stat_err)s%(ls)s%(title)s"
2445 template_no_stat = "'%(hwu)s' index %(ind)d using (($1+$2)/2):%(data)s%(ls)s%(title)s"
2446 rep_dic = {'hwu':HwU_name,
2447 'ind':block_position,
2448 'ls':' ls %d'%color_index,
2449 'title':" title '%s'"%title,
2450 'stat_col': ':4',
2451 'stat_err': ' w yerrorbar',
2452 'data':'3',
2453 'linetype':''}
2454
2455
2456
2457
2458
2459
2460 res = []
2461 rep_dic['data'] = '($3 < 0 ? sqrt(-1) : $3)'
2462 res.append(template_no_stat%rep_dic)
2463 rep_dic['title'] = " title ''"
2464 if show_mc_uncertainties:
2465 res.append(template%rep_dic)
2466 rep_dic['data'] = '($3 >= 0 ? sqrt(-1) : abs($3))'
2467 rep_dic['ls'] = ' ls %d'%(100+color_index)
2468 res.append(template_no_stat%rep_dic)
2469 if show_mc_uncertainties:
2470 res.append(template%rep_dic)
2471 return res
2472
2473
2474
2475
2476 def get_uncertainty_lines(HwU_name, block_position,
2477 var_pos, color_index,title, ratio=False, band=False):
2478 """ Return a string line corresponding to the plotting of the
2479 uncertainty. Band is to chose wether to display uncertainty with
2480 a band or two lines."""
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500 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} $+{rest}\n/g'"
2501
2502
2503 copy_swap_re = copy_swap_re.replace('\\','\\\\')
2504
2505 position = '(safe($%d,$3,1.0)-1.0)' if ratio else '%d'
2506 if not band:
2507 return ["'%s' index %d using (($1+$2)/2):%s ls %d title '%s'"\
2508 %(HwU_name,block_position, position%(var_pos),color_index,title),
2509 "'%s' index %d using (($1+$2)/2):%s ls %d title ''"\
2510 %(HwU_name,block_position, position%(var_pos+1),color_index)]
2511 else:
2512 return [' "<%s %s" index %d using 1:%s:%s with filledcurve ls %d fs transparent solid 0.2 title \'%s\''%\
2513 (copy_swap_re,HwU_name,block_position,
2514 position%var_pos,position%(var_pos+1),color_index,title)]
2515
2516
2517
2518 layout_geometry = [(0.0, 0.5, 1.0, 0.4 ),
2519 (0.0, 0.35, 1.0, 0.15),
2520 (0.0, 0.2, 1.0, 0.15)]
2521 layout_geometry.reverse()
2522
2523
2524
2525 matching_histo_lists = HwUList([HwUList([self[0]])])
2526 for histo in self[1:]:
2527 matched = False
2528 for histo_list in matching_histo_lists:
2529 if hasattr(histo, 'jetsample') and histo.jetsample >= 0 and \
2530 histo.type == histo_list[0].type:
2531 matched = True
2532 histo_list.append(histo)
2533 break
2534 if not matched:
2535 matching_histo_lists.append(HwUList([histo]))
2536
2537
2538
2539 self[:] = []
2540 for histo_group in matching_histo_lists:
2541
2542
2543 if len(histo_group)==1:
2544 self.append(histo_group[0])
2545 continue
2546
2547
2548 if any(hist.jetsample==-1 for hist in histo_group if
2549 hasattr(hist, 'jetsample')):
2550 self.extend(histo_group)
2551 continue
2552 summed_histogram = copy.copy(histo_group[0])
2553 for histo in histo_group[1:]:
2554 summed_histogram = summed_histogram + histo
2555 summed_histogram.jetsample = -1
2556 self.append(summed_histogram)
2557 self.extend(histo_group)
2558
2559
2560 if not jet_samples_to_keep is None:
2561 self[:] = [histo for histo in self if (not hasattr(histo,'jetsample')) or (histo.jetsample == -1) or
2562 (histo.jetsample in jet_samples_to_keep)]
2563
2564
2565
2566 def ratio_no_correlations(wgtsA, wgtsB):
2567 new_wgts = {}
2568 for label, wgt in wgtsA.items():
2569 if wgtsB['central']==0.0 and wgt==0.0:
2570 new_wgts[label] = 0.0
2571 continue
2572 elif wgtsB['central']==0.0:
2573
2574
2575
2576 new_wgts[label] = 0.0
2577 continue
2578 new_wgts[label] = (wgtsA[label]/wgtsB['central'])
2579 return new_wgts
2580
2581
2582
2583 n_histograms = len(self)
2584 ratio_histos = HwUList([])
2585
2586 n_ratios_included = 0
2587 for i, histo in enumerate(self[1:]):
2588 if not hasattr(histo,'jetsample') or histo.jetsample==self[0].jetsample:
2589 n_ratios_included += 1
2590 else:
2591 continue
2592
2593 if number_of_ratios >=0 and n_ratios_included > number_of_ratios:
2594 break
2595
2596 if ratio_correlations:
2597 ratio_histos.append(histo/self[0])
2598 else:
2599 ratio_histos.append(self[0].__class__.combine(histo, self[0],
2600 ratio_no_correlations))
2601 if self[0].type=='NLO' and self[1].type=='LO':
2602 ratio_histos[-1].title += '1/K-factor'
2603 elif self[0].type=='LO' and self[1].type=='NLO':
2604 ratio_histos[-1].title += 'K-factor'
2605 else:
2606 ratio_histos[-1].title += ' %s/%s'%(
2607 self[1].type if self[1].type else '(%d)'%(i+2),
2608 self[0].type if self[0].type else '(1)')
2609
2610
2611 ratio_histos[-1].type = 'AUX'
2612 self.extend(ratio_histos)
2613
2614
2615 if 'scale' in uncertainties:
2616 (mu_var_pos,mu) = self[0].set_uncertainty(type='all_scale')
2617 else:
2618 (mu_var_pos,mu) = (None,[None])
2619
2620 if 'pdf' in uncertainties:
2621 (PDF_var_pos,pdf) = self[0].set_uncertainty(type='PDF',lhapdfconfig=lhapdfconfig)
2622 else:
2623 (PDF_var_pos,pdf) = (None,[None])
2624
2625 if 'merging_scale' in uncertainties:
2626 (merging_var_pos,merging) = self[0].set_uncertainty(type='merging')
2627 else:
2628 (merging_var_pos,merging) = (None,[None])
2629 if 'alpsfact' in uncertainties:
2630 (alpsfact_var_pos,alpsfact) = self[0].set_uncertainty(type='alpsfact')
2631 else:
2632 (alpsfact_var_pos,alpsfact) = (None,[None])
2633
2634 uncertainties_present = list(uncertainties)
2635 if PDF_var_pos is None and 'pdf' in uncertainties_present:
2636 uncertainties_present.remove('pdf')
2637 if mu_var_pos is None and 'scale' in uncertainties_present:
2638 uncertainties_present.remove('scale')
2639 if merging_var_pos is None and 'merging' in uncertainties_present:
2640 uncertainties_present.remove('merging')
2641 if alpsfact_var_pos is None and 'alpsfact' in uncertainties_present:
2642 uncertainties_present.remove('alpsfact')
2643 no_uncertainties = len(uncertainties_present)==0
2644
2645
2646 try:
2647 uncertainties_present.remove('statistical')
2648 except:
2649 pass
2650 if use_band is None:
2651
2652
2653 if len(uncertainties_present)==0:
2654 use_band = []
2655 elif len(uncertainties_present)==1:
2656 use_band = uncertainties_present
2657 elif 'scale' in uncertainties_present:
2658 use_band = ['scale']
2659 else:
2660 use_band = [uncertainties_present[0]]
2661
2662 for histo in self[1:]:
2663 if (not mu_var_pos is None) and \
2664 mu_var_pos != histo.set_uncertainty(type='all_scale')[0]:
2665 raise MadGraph5Error('Not all histograms in this group specify'+\
2666 ' scale uncertainties. It is required to be able to output them'+\
2667 ' together.')
2668 if (not PDF_var_pos is None) and\
2669 PDF_var_pos != histo.set_uncertainty(type='PDF',\
2670 lhapdfconfig=lhapdfconfig)[0]:
2671 raise MadGraph5Error('Not all histograms in this group specify'+\
2672 ' PDF uncertainties. It is required to be able to output them'+\
2673 ' together.')
2674 if (not merging_var_pos is None) and\
2675 merging_var_pos != histo.set_uncertainty(type='merging')[0]:
2676 raise MadGraph5Error('Not all histograms in this group specify'+\
2677 ' merging uncertainties. It is required to be able to output them'+\
2678 ' together.')
2679 if (not alpsfact_var_pos is None) and\
2680 alpsfact_var_pos != histo.set_uncertainty(type='alpsfact')[0]:
2681 raise MadGraph5Error('Not all histograms in this group specify'+\
2682 ' alpsfact uncertainties. It is required to be able to output them'+\
2683 ' together.')
2684
2685
2686
2687 for i, histo in enumerate(self):
2688
2689 HwU_out.extend(histo.get_HwU_source(\
2690 print_header=(block_position==0 and i==0)))
2691 HwU_out.extend(['',''])
2692
2693
2694 global_header =\
2695 """
2696 ################################################################################
2697 ### Rendering of the plot titled '%(title)s'
2698 ################################################################################
2699
2700 set multiplot
2701 set label "%(title)s" font ",13" at graph 0.04, graph 1.05
2702 set xrange [%(xmin).4e:%(xmax).4e]
2703 set bmargin 0
2704 set tmargin 0
2705 set xtics nomirror
2706 set ytics nomirror
2707 set mytics %(mxtics)d
2708 %(set_xtics)s
2709 set key horizontal noreverse maxcols 1 width -4
2710 set label front 'MadGraph5\_aMC\@NLO' font "Courier,11" rotate by 90 at graph 1.02, graph 0.04
2711 """
2712
2713
2714 subhistogram_header = \
2715 """#-- rendering subhistograms '%(subhistogram_type)s'
2716 %(unset label)s
2717 %(set_format_y)s
2718 set yrange [%(ymin).4e:%(ymax).4e]
2719 set origin %(origin_x).4e, %(origin_y).4e
2720 set size %(size_x).4e, %(size_y).4e
2721 set mytics %(mytics)d
2722 %(set_ytics)s
2723 %(set_format_x)s
2724 %(set_yscale)s
2725 %(set_ylabel)s
2726 %(set_histo_label)s
2727 plot \\"""
2728 replacement_dic = {}
2729
2730 replacement_dic['title'] = self[0].get_HwU_histogram_name(format='human-no_type')
2731
2732
2733 wgts_to_consider = ['central']
2734 if not mu_var_pos is None:
2735 for mu_var in mu_var_pos:
2736 wgts_to_consider.append(self[0].bins.weight_labels[mu_var])
2737 wgts_to_consider.append(self[0].bins.weight_labels[mu_var+1])
2738 wgts_to_consider.append(self[0].bins.weight_labels[mu_var+2])
2739 if not PDF_var_pos is None:
2740 for PDF_var in PDF_var_pos:
2741 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var])
2742 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var+1])
2743 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var+2])
2744 if not merging_var_pos is None:
2745 for merging_var in merging_var_pos:
2746 wgts_to_consider.append(self[0].bins.weight_labels[merging_var])
2747 wgts_to_consider.append(self[0].bins.weight_labels[merging_var+1])
2748 wgts_to_consider.append(self[0].bins.weight_labels[merging_var+2])
2749 if not alpsfact_var_pos is None:
2750 for alpsfact_var in alpsfact_var_pos:
2751 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var])
2752 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var+1])
2753 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var+2])
2754
2755 (xmin, xmax) = HwU.get_x_optimal_range(self[:2],\
2756 weight_labels = wgts_to_consider)
2757 replacement_dic['xmin'] = xmin
2758 replacement_dic['xmax'] = xmax
2759 replacement_dic['mxtics'] = 10
2760 replacement_dic['set_xtics'] = 'set xtics auto'
2761
2762
2763 gnuplot_out.append(global_header%replacement_dic)
2764
2765
2766 replacement_dic['subhistogram_type'] = '%s and %s results'%(
2767 str(self[0].type),str(self[1].type)) if len(self)>1 else \
2768 'single diagram output'
2769 (ymin, ymax) = HwU.get_y_optimal_range(self[:2],
2770 labels = wgts_to_consider, scale=self[0].y_axis_mode)
2771
2772
2773 if ymin< 0.0:
2774 self[0].y_axis_mode = 'LIN'
2775
2776
2777 if self[0].y_axis_mode=='LOG':
2778 ymax += 10.0 * ymax
2779 ymin -= 0.1 * ymin
2780 else:
2781 ymax += 0.3 * (ymax - ymin)
2782 ymin -= 0.3 * (ymax - ymin)
2783
2784 replacement_dic['ymin'] = ymin
2785 replacement_dic['ymax'] = ymax
2786 replacement_dic['unset label'] = ''
2787 (replacement_dic['origin_x'], replacement_dic['origin_y'],
2788 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop()
2789 replacement_dic['mytics'] = 10
2790
2791 replacement_dic['set_ytics'] = 'set ytics auto'
2792 replacement_dic['set_format_x'] = "set format x ''" if \
2793 (len(self)-n_histograms>0 or not no_uncertainties) else "set format x"
2794 replacement_dic['set_ylabel'] = 'set ylabel "{/Symbol s} per bin [pb]"'
2795 replacement_dic['set_yscale'] = "set logscale y" if \
2796 self[0].y_axis_mode=='LOG' else 'unset logscale y'
2797 replacement_dic['set_format_y'] = "set format y '10^{%T}'" if \
2798 self[0].y_axis_mode=='LOG' else 'unset format'
2799
2800 replacement_dic['set_histo_label'] = ""
2801 gnuplot_out.append(subhistogram_header%replacement_dic)
2802
2803
2804 plot_lines = []
2805 uncertainty_plot_lines = []
2806 n=-1
2807
2808 for i, histo in enumerate(self[:n_histograms]):
2809 n=n+1
2810 color_index = n%self.number_line_colors_defined+1
2811
2812 title = []
2813 if histo.type is None and not hasattr(histo, 'jetsample'):
2814 title.append('%d'%(i+1))
2815 else:
2816 if histo.type:
2817 title.append('NLO' if \
2818 histo.type.split()[0]=='NLO' else histo.type)
2819 if hasattr(histo, 'jetsample'):
2820 if histo.jetsample!=-1:
2821 title.append('jet sample %d'%histo.jetsample)
2822 else:
2823 title.append('all jet samples')
2824
2825 title = ', '.join(title)
2826
2827 if histo.type is None and not hasattr(histo, 'jetsample'):
2828 major_title = 'central value for plot (%d)'%(i+1)
2829 else:
2830 major_title = []
2831 if not histo.type is None:
2832 major_title.append(histo.type)
2833 if hasattr(histo, 'jetsample'):
2834 if histo.jetsample!=-1:
2835 major_title.append('jet sample %d'%histo.jetsample)
2836 else:
2837 major_title.append('all jet samples')
2838 else:
2839 major_title.append('central value')
2840 major_title = ', '.join(major_title)
2841
2842 if not mu[0] in ['none',None]:
2843 major_title += ', dynamical\_scale\_choice=%s'%mu[0]
2844 if not pdf[0] in ['none',None]:
2845 major_title += ', PDF=%s'%pdf[0].replace('_','\_')
2846
2847
2848
2849 if not (i!=0 and hasattr(histo,'jetsample') and histo.jetsample!=-1 and \
2850 not (jet_samples_to_keep and len(jet_samples_to_keep)==1 and
2851 jet_samples_to_keep[0] == histo.jetsample)):
2852
2853 uncertainty_plot_lines.append({})
2854
2855
2856
2857
2858
2859
2860
2861
2862 if not mu_var_pos is None and len(mu_var_pos)>0:
2863 if 'scale' in use_band:
2864 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines(
2865 HwU_name, block_position+i, mu_var_pos[0]+4, color_index+10,
2866 '%s, scale variation'%title, band='scale' in use_band)
2867 else:
2868 uncertainty_plot_lines[-1]['scale'] = \
2869 ["sqrt(-1) ls %d title '%s'"%(color_index+10,'%s, scale variation'%title)]
2870
2871 if not PDF_var_pos is None and len(PDF_var_pos)>0:
2872 if 'pdf' in use_band:
2873 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines(
2874 HwU_name,block_position+i, PDF_var_pos[0]+4, color_index+20,
2875 '%s, PDF variation'%title, band='pdf' in use_band)
2876 else:
2877 uncertainty_plot_lines[-1]['pdf'] = \
2878 ["sqrt(-1) ls %d title '%s'"%(color_index+20,'%s, PDF variation'%title)]
2879
2880 if not merging_var_pos is None and len(merging_var_pos)>0:
2881 if 'merging_scale' in use_band:
2882 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines(
2883 HwU_name,block_position+i, merging_var_pos[0]+4, color_index+30,
2884 '%s, merging scale variation'%title, band='merging_scale' in use_band)
2885 else:
2886 uncertainty_plot_lines[-1]['merging_scale'] = \
2887 ["sqrt(-1) ls %d title '%s'"%(color_index+30,'%s, merging scale variation'%title)]
2888
2889 if not alpsfact_var_pos is None and len(alpsfact_var_pos)>0:
2890 if 'alpsfact' in use_band:
2891 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines(
2892 HwU_name,block_position+i, alpsfact_var_pos[0]+4, color_index+40,
2893 '%s, alpsfact variation'%title, band='alpsfact' in use_band)
2894 else:
2895 uncertainty_plot_lines[-1]['alpsfact'] = \
2896 ["sqrt(-1) ls %d title '%s'"%(color_index+40,'%s, alpsfact variation'%title)]
2897
2898
2899
2900
2901
2902
2903
2904
2905 plot_lines.extend(
2906 get_main_central_plot_lines(HwU_name, block_position+i,
2907 color_index, major_title, 'statistical' in uncertainties))
2908
2909
2910 if not mu_var_pos is None:
2911 for j,mu_var in enumerate(mu_var_pos):
2912 if j!=0:
2913 n=n+1
2914 color_index = n%self.number_line_colors_defined+1
2915 plot_lines.append(
2916 "'%s' index %d using (($1+$2)/2):%d ls %d title '%s'"\
2917 %(HwU_name,block_position+i,mu_var+3,color_index,\
2918 '%s dynamical\_scale\_choice=%s' % (title,mu[j])))
2919
2920 if not PDF_var_pos is None:
2921 for j,PDF_var in enumerate(PDF_var_pos):
2922 if j!=0:
2923 n=n+1
2924 color_index = n%self.number_line_colors_defined+1
2925 plot_lines.append(
2926 "'%s' index %d using (($1+$2)/2):%d ls %d title '%s'"\
2927 %(HwU_name,block_position+i,PDF_var+3,color_index,\
2928 '%s PDF=%s' % (title,pdf[j].replace('_','\_'))))
2929
2930
2931
2932 for one_plot in uncertainty_plot_lines:
2933 for uncertainty_type, lines in one_plot.items():
2934 if not uncertainty_type in use_band:
2935 plot_lines.extend(lines)
2936
2937 for one_plot in uncertainty_plot_lines:
2938 for uncertainty_type, lines in one_plot.items():
2939 if uncertainty_type in use_band:
2940 plot_lines.extend(lines)
2941
2942
2943 plot_lines.reverse()
2944
2945
2946 gnuplot_out.append(',\\\n'.join(plot_lines))
2947
2948
2949 replacement_dic['subhistogram_type'] = 'Relative scale and PDF uncertainty'
2950
2951 if 'statistical' in uncertainties:
2952 wgts_to_consider.append('stat_error')
2953
2954
2955
2956 def rel_scale(wgtsA, wgtsB):
2957 new_wgts = {}
2958 for label, wgt in wgtsA.items():
2959 if label in wgts_to_consider:
2960 if wgtsB['central']==0.0 and wgt==0.0:
2961 new_wgts[label] = 0.0
2962 continue
2963 elif wgtsB['central']==0.0:
2964
2965
2966
2967 new_wgts[label] = 0.0
2968 continue
2969 new_wgts[label] = (wgtsA[label]/wgtsB['central'])
2970 if label != 'stat_error':
2971 new_wgts[label] -= 1.0
2972 else:
2973 new_wgts[label] = wgtsA[label]
2974 return new_wgts
2975
2976 histos_for_subplots = [(i,histo) for i, histo in enumerate(self[:n_histograms]) if
2977 ( not (i!=0 and hasattr(histo,'jetsample') and histo.jetsample!=-1 and \
2978 not (jet_samples_to_keep and len(jet_samples_to_keep)==1 and
2979 jet_samples_to_keep[0] == histo.jetsample)) )]
2980
2981
2982
2983
2984 (ymin, ymax) = HwU.get_y_optimal_range([histo[1].__class__.combine(
2985 histo[1],histo[1],rel_scale) for histo in histos_for_subplots],
2986 labels = wgts_to_consider, scale='LIN')
2987
2988
2989 ymax = ymax + 0.2 * (ymax - ymin)
2990 ymin = ymin - 0.2 * (ymax - ymin)
2991 replacement_dic['unset label'] = 'unset label'
2992 replacement_dic['ymin'] = ymin
2993 replacement_dic['ymax'] = ymax
2994 if not no_uncertainties:
2995 (replacement_dic['origin_x'], replacement_dic['origin_y'],
2996 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop()
2997 replacement_dic['mytics'] = 2
2998
2999 replacement_dic['set_ytics'] = 'set ytics auto'
3000 replacement_dic['set_format_x'] = "set format x ''" if \
3001 len(self)-n_histograms>0 else "set format x"
3002 replacement_dic['set_ylabel'] = 'set ylabel "%s rel.unc."'\
3003 %('(1)' if self[0].type==None else '%s'%('NLO' if \
3004 self[0].type.split()[0]=='NLO' else self[0].type))
3005 replacement_dic['set_yscale'] = "unset logscale y"
3006 replacement_dic['set_format_y'] = 'unset format'
3007
3008
3009 tit='Relative uncertainties w.r.t. central value'
3010 if n_histograms > 1:
3011 tit=tit+'s'
3012
3013
3014
3015
3016 replacement_dic['set_histo_label'] = \
3017 'set label "%s" font ",9" front at graph 0.03, graph 0.13' % tit
3018
3019
3020 if not no_uncertainties:
3021 gnuplot_out.append(subhistogram_header%replacement_dic)
3022
3023
3024 plot_lines = []
3025 uncertainty_plot_lines = []
3026 n=-1
3027 for (i,histo) in histos_for_subplots:
3028 n=n+1
3029 k=n
3030 color_index = n%self.number_line_colors_defined+1
3031
3032 if not mu_var_pos is None:
3033 for j,mu_var in enumerate(mu_var_pos):
3034 uncertainty_plot_lines.append({})
3035 if j==0:
3036 color_index = k%self.number_line_colors_defined+1
3037 else:
3038 n=n+1
3039 color_index = n%self.number_line_colors_defined+1
3040
3041 if j>0 or mu[j]!='none':
3042 plot_lines.append(
3043 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3044 %(HwU_name,block_position+i,mu_var+3,color_index))
3045 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines(
3046 HwU_name, block_position+i, mu_var+4, color_index+10,'',
3047 ratio=True, band='scale' in use_band)
3048 if not PDF_var_pos is None:
3049 for j,PDF_var in enumerate(PDF_var_pos):
3050 uncertainty_plot_lines.append({})
3051 if j==0:
3052 color_index = k%self.number_line_colors_defined+1
3053 else:
3054 n=n+1
3055 color_index = n%self.number_line_colors_defined+1
3056
3057 if j>0 or pdf[j]!='none':
3058 plot_lines.append(
3059 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3060 %(HwU_name,block_position+i,PDF_var+3,color_index))
3061 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines(
3062 HwU_name, block_position+i, PDF_var+4, color_index+20,'',
3063 ratio=True, band='pdf' in use_band)
3064 if not merging_var_pos is None:
3065 for j,merging_var in enumerate(merging_var_pos):
3066 uncertainty_plot_lines.append({})
3067 if j==0:
3068 color_index = k%self.number_line_colors_defined+1
3069 else:
3070 n=n+1
3071 color_index = n%self.number_line_colors_defined+1
3072 if j>0 or merging[j]!='none':
3073 plot_lines.append(
3074 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3075 %(HwU_name,block_position+i,merging_var+3,color_index))
3076 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines(
3077 HwU_name, block_position+i, merging_var+4, color_index+30,'',
3078 ratio=True, band='merging_scale' in use_band)
3079 if not alpsfact_var_pos is None:
3080 for j,alpsfact_var in enumerate(alpsfact_var_pos):
3081 uncertainty_plot_lines.append({})
3082 if j==0:
3083 color_index = k%self.number_line_colors_defined+1
3084 else:
3085 n=n+1
3086 color_index = n%self.number_line_colors_defined+1
3087 if j>0 or alpsfact[j]!='none':
3088 plot_lines.append(
3089 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\
3090 %(HwU_name,block_position+i,alpsfact_var+3,color_index))
3091 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines(
3092 HwU_name, block_position+i, alpsfact_var+4, color_index+40,'',
3093 ratio=True, band='alpsfact' in use_band)
3094
3095 if 'statistical' in uncertainties:
3096 plot_lines.append(
3097 "'%s' index %d using (($1+$2)/2):(0.0):(safe($4,$3,0.0)) w yerrorbar ls %d title ''"%\
3098 (HwU_name,block_position+i,color_index))
3099
3100 plot_lines.append("0.0 ls 999 title ''")
3101
3102
3103
3104 for one_plot in uncertainty_plot_lines:
3105 for uncertainty_type, lines in one_plot.items():
3106 if not uncertainty_type in use_band:
3107 plot_lines.extend(lines)
3108
3109 for one_plot in uncertainty_plot_lines:
3110 for uncertainty_type, lines in one_plot.items():
3111 if uncertainty_type in use_band:
3112 plot_lines.extend(lines)
3113
3114
3115 plot_lines.reverse()
3116
3117 if not no_uncertainties:
3118 gnuplot_out.append(',\\\n'.join(plot_lines))
3119
3120
3121 if len(self)-n_histograms==0:
3122
3123 gnuplot_out.extend(['','unset label','',
3124 '################################################################################'])
3125
3126 return block_position+len(self)
3127
3128
3129 ratio_name_long='('
3130 for i, histo in enumerate(self[:n_histograms]):
3131 if i==0: continue
3132 ratio_name_long+='%d'%(i+1) if histo.type is None else ('NLO' if \
3133 histo.type.split()[0]=='NLO' else histo.type)
3134 ratio_name_long+=')/'
3135 ratio_name_long+=('(1' if self[0].type==None else '(%s'%('NLO' if \
3136 self[0].type.split()[0]=='NLO' else self[0].type))+' central value)'
3137
3138 ratio_name_short = 'ratio w.r.t. '+('1' if self[0].type==None else '%s'%('NLO' if \
3139 self[0].type.split()[0]=='NLO' else self[0].type))
3140
3141 replacement_dic['subhistogram_type'] = '%s ratio'%ratio_name_long
3142 replacement_dic['set_ylabel'] = 'set ylabel "%s"'%ratio_name_short
3143
3144 (ymin, ymax) = HwU.get_y_optimal_range(self[n_histograms:],
3145 labels = wgts_to_consider, scale='LIN',Kratio = True)
3146
3147
3148 ymax = ymax + 0.2 * (ymax - ymin)
3149 ymin = ymin - 0.2 * (ymax - ymin)
3150 replacement_dic['unset label'] = 'unset label'
3151 replacement_dic['ymin'] = ymin
3152 replacement_dic['ymax'] = ymax
3153 (replacement_dic['origin_x'], replacement_dic['origin_y'],
3154 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop()
3155 replacement_dic['mytics'] = 2
3156
3157 replacement_dic['set_ytics'] = 'set ytics auto'
3158 replacement_dic['set_format_x'] = "set format x"
3159 replacement_dic['set_yscale'] = "unset logscale y"
3160 replacement_dic['set_format_y'] = 'unset format'
3161 replacement_dic['set_histo_label'] = \
3162 'set label "%s" font ",9" at graph 0.03, graph 0.13'%ratio_name_long
3163
3164 gnuplot_out.append(subhistogram_header%replacement_dic)
3165
3166 uncertainty_plot_lines = []
3167 plot_lines = []
3168
3169
3170 n=-1
3171 n=n+1
3172 if not mu_var_pos is None:
3173 for j,mu_var in enumerate(mu_var_pos):
3174 if j!=0: n=n+1
3175 if not PDF_var_pos is None:
3176 for j,PDF_var in enumerate(PDF_var_pos):
3177 if j!=0: n=n+1
3178 if not merging_var_pos is None:
3179 for j,merging_var in enumerate(merging_var_pos):
3180 if j!=0: n=n+1
3181 if not alpsfact_var_pos is None:
3182 for j,alpsfact_var in enumerate(alpsfact_var_pos):
3183 if j!=0: n=n+1
3184
3185 for i_histo_ratio, histo_ration in enumerate(self[n_histograms:]):
3186 n=n+1
3187 k=n
3188 block_ratio_pos = block_position+n_histograms+i_histo_ratio
3189 color_index = n%self.number_line_colors_defined+1
3190
3191 plot_lines.append(
3192 "'%s' index %d using (($1+$2)/2):3 ls %d title ''"%\
3193 (HwU_name,block_ratio_pos,color_index))
3194 if 'statistical' in uncertainties:
3195 plot_lines.append(
3196 "'%s' index %d using (($1+$2)/2):3:4 w yerrorbar ls %d title ''"%\
3197 (HwU_name,block_ratio_pos,color_index))
3198
3199
3200 if not mu_var_pos is None:
3201 for j,mu_var in enumerate(mu_var_pos):
3202 uncertainty_plot_lines.append({})
3203 if j==0:
3204 color_index = k%self.number_line_colors_defined+1
3205 else:
3206 n=n+1
3207 color_index = n%self.number_line_colors_defined+1
3208
3209 if j>0 or mu[j]!='none':
3210 plot_lines.append(
3211 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3212 %(HwU_name,block_ratio_pos,mu_var+3,color_index))
3213 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines(
3214 HwU_name, block_ratio_pos, mu_var+4, color_index+10,'',
3215 band='scale' in use_band)
3216 if not PDF_var_pos is None:
3217 for j,PDF_var in enumerate(PDF_var_pos):
3218 uncertainty_plot_lines.append({})
3219 if j==0:
3220 color_index = k%self.number_line_colors_defined+1
3221 else:
3222 n=n+1
3223 color_index = n%self.number_line_colors_defined+1
3224
3225 if j>0 or pdf[j]!='none':
3226 plot_lines.append(
3227 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3228 %(HwU_name,block_ratio_pos,PDF_var+3,color_index))
3229 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines(
3230 HwU_name, block_ratio_pos, PDF_var+4, color_index+20,'',
3231 band='pdf' in use_band)
3232 if not merging_var_pos is None:
3233 for j,merging_var in enumerate(merging_var_pos):
3234 uncertainty_plot_lines.append({})
3235 if j==0:
3236 color_index = k%self.number_line_colors_defined+1
3237 else:
3238 n=n+1
3239 color_index = n%self.number_line_colors_defined+1
3240 if j>0 or merging[j]!='none':
3241 plot_lines.append(
3242 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3243 %(HwU_name,block_ratio_pos,merging_var+3,color_index))
3244 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines(
3245 HwU_name, block_ratio_pos, merging_var+4, color_index+30,'',
3246 band='merging_scale' in use_band)
3247 if not alpsfact_var_pos is None:
3248 for j,alpsfact_var in enumerate(alpsfact_var_pos):
3249 uncertainty_plot_lines.append({})
3250 if j==0:
3251 color_index = k%self.number_line_colors_defined+1
3252 else:
3253 n=n+1
3254 color_index = n%self.number_line_colors_defined+1
3255 if j>0 or alpsfact[j]!='none':
3256 plot_lines.append(
3257 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\
3258 %(HwU_name,block_ratio_pos,alpsfact_var+3,color_index))
3259 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines(
3260 HwU_name, block_ratio_pos, alpsfact_var+4, color_index+40,'',
3261 band='alpsfact' in use_band)
3262
3263
3264
3265 for one_plot in uncertainty_plot_lines:
3266 for uncertainty_type, lines in one_plot.items():
3267 if not uncertainty_type in use_band:
3268 plot_lines.extend(lines)
3269
3270 for one_plot in uncertainty_plot_lines:
3271 for uncertainty_type, lines in one_plot.items():
3272 if uncertainty_type in use_band:
3273 plot_lines.extend(lines)
3274
3275 plot_lines.append("1.0 ls 999 title ''")
3276
3277
3278 plot_lines.reverse()
3279
3280 gnuplot_out.append(',\\\n'.join(plot_lines))
3281
3282
3283 gnuplot_out.extend(['','unset label','',
3284 '################################################################################'])
3285
3286
3287 return block_position+len(self)
3288
3289
3290
3291
3292 -def plot_ratio_from_HWU(path, ax, hwu_variable, hwu_numerator, hwu_denominator, *args, **opts):
3293 """INPUT:
3294 - path can be a path to HwU or an HwUList instance
3295 - ax is the matplotlib frame where to do the plot
3296 - hwu_variable is the histograms to consider
3297 - hwu_numerator is the numerator of the ratio plot
3298 - hwu_denominator is the denominator of the ratio plot
3299 OUTPUT:
3300 - adding the curves to the plot
3301 - return the HwUList
3302 """
3303
3304 if isinstance(path, str):
3305 hwu = HwUList(path, raw_labels=True)
3306 else:
3307 hwu = path
3308
3309 if 'hwu_denominator_path' in opts:
3310 print('found second hwu')
3311 if isinstance(opts['hwu_denominator_path'],str):
3312 hwu2 = HwUList(path, raw_labels=True)
3313 else:
3314 hwu2 = opts['hwu_denominator_path']
3315 del opts['hwu_denominator_path']
3316 else:
3317 hwu2 = hwu
3318
3319
3320 select_hist = hwu.get(hwu_variable)
3321 select_hist2 = hwu2.get(hwu_variable)
3322 bins = select_hist.get('bins')
3323 num = select_hist.get(hwu_numerator)
3324 denom = select_hist2.get(hwu_denominator)
3325 ratio = [num[i]/denom[i] if denom[i] else 1 for i in range(len(bins))]
3326 if 'drawstyle' not in opts:
3327 opts['drawstyle'] = 'steps'
3328 ax.plot(bins, ratio, *args, **opts)
3329 return hwu
3330
3331 -def plot_from_HWU(path, ax, hwu_variable, hwu_central, *args, **opts):
3332 """INPUT:
3333 - path can be a path to HwU or an HwUList instance
3334 - ax is the matplotlib frame where to do the plot
3335 - hwu_variable is the histograms to consider
3336 - hwu_central is the central curve to consider
3337 - hwu_error is the error band to consider (optional: Default is no band)
3338 - hwu_error_mode is how to compute the error band (optional)
3339 OUTPUT:
3340 - adding the curves to the plot
3341 - return the HwUList
3342 - return the line associated to the central (can be used to get the color)
3343 """
3344
3345
3346 if 'hwu_error' in opts:
3347 hwu_error = opts['hwu_error']
3348 del opts['hwu_error']
3349 else:
3350 hwu_error = None
3351
3352 if 'hwu_error_mode' in opts:
3353 hwu_error_mode = opts['hwu_error_mode']
3354 del opts['hwu_error_mode']
3355 else:
3356 hwu_error_mode = None
3357
3358 if 'hwu_mult' in opts:
3359 hwu_mult = opts['hwu_mult']
3360 del opts['hwu_mult']
3361 else:
3362 hwu_mult = 1
3363
3364 if isinstance(path, str):
3365 hwu = HwUList(path, raw_labels=True)
3366 else:
3367 hwu = path
3368
3369
3370 select_hist = hwu.get(hwu_variable)
3371 bins = select_hist.get('bins')
3372 central_value = select_hist.get(hwu_central)
3373 if hwu_mult != 1:
3374 central_value = [hwu_mult*b for b in central_value]
3375 if 'drawstyle' not in opts:
3376 opts['drawstyle'] = 'steps'
3377 H, = ax.plot(bins, central_value, *args, **opts)
3378
3379
3380 if hwu_error:
3381 if not 'hwu_error_mode' in opts:
3382 opts['hwu_error_mode']=None
3383 h_min, h_max = select_hist.get_uncertainty_band(hwu_error, mode=hwu_error_mode)
3384 if hwu_mult != 1:
3385 h_min = [hwu_mult*b for b in h_min]
3386 h_max = [hwu_mult*b for b in h_max]
3387 fill_between_steps(bins, h_min, h_max, ax=ax, facecolor=H.get_color(),
3388 alpha=0.5, edgecolor=H.get_color(),hatch='/')
3389
3390 return hwu, H
3391
3392
3393
3394
3395
3396
3397 if __name__ == "__main__":
3398 main_doc = \
3399 """ For testing and standalone use. Usage:
3400 python histograms.py <.HwU input_file_path_1> <.HwU input_file_path_2> ... --out=<output_file_path.format> <options>
3401 Where <options> can be a list of the following:
3402 '--help' See this message.
3403 '--gnuplot' or '' output the histograms read to gnuplot
3404 '--HwU' to output the histograms read to the raw HwU source.
3405 '--types=<type1>,<type2>,...' to keep only the type<i> when importing histograms.
3406 '--titles=<title1>,<title2>,...' to keep only the titles which have any of 'title<i>' in them (not necessarily equal to them)
3407 '--n_ratios=<integer>' Specifies how many curves must be considerd for the ratios.
3408 '--no_open' Turn off the automatic processing of the gnuplot output.
3409 '--show_full' to show the complete output of what was read.
3410 '--show_short' to show a summary of what was read.
3411 '--simple_ratios' to turn off correlations and error propagation in the ratio.
3412 '--colours=<colour1>,<colour2>,...' to assign a non-default colour to GnuPlot histograms (max 8 colours)
3413 '--sum' To sum all identical histograms together
3414 '--average' To average over all identical histograms
3415 '--rebin=<n>' Rebin the plots by merging n-consecutive bins together.
3416 '--assign_types=<type1>,<type2>,...' to assign a type to all histograms of the first, second, etc... files loaded.
3417 '--multiply=<fact1>,<fact2>,...' to multiply all histograms of the first, second, etc... files by the fact1, fact2, etc...
3418 '--no_suffix' Do no add any suffix (like '#1, #2, etc..) to the histograms types.
3419 '--lhapdf-config=<PATH_TO_LHAPDF-CONFIG>' give path to lhapdf-config to compute PDF certainties using LHAPDF (only for lhapdf6)
3420 '--jet_samples=[int1,int2]' Specifies what jet samples to keep. 'None' is the default and keeps them all.
3421 '--central_only' This option specifies to disregard all extra weights, so as to make it possible
3422 to take the ratio of plots with different extra weights specified.
3423 '--keep_all_weights' This option specifies to keep in the HwU produced all the weights, even
3424 those which are not known (i.e. that is scale, PDF or merging variation)
3425 For chosing what kind of variation you want to see on your plot, you can use the following options
3426 '--no_<type>' Turn off the plotting of variations of the chosen type
3427 '--only_<type>' Turn on only the plotting of variations of the chosen type
3428 '--variations=['<type1>',...]' Turn on only the plotting of the variations of the list of chosen types
3429 '--band=['<type1>',...]' Chose for which variations one should use uncertainty bands as opposed to lines
3430 The types can be: pdf, scale, stat, merging or alpsfact
3431 For the last two options one can use ...=all to automatically select all types.
3432
3433 When parsing an XML-formatted plot source output by the Pythia8 driver, the file names can be appended
3434 options as suffixes separated by '|', as follows:
3435 python histograms.py <XML_source_file_name>@<option1>@<option2>@etc..
3436 These options can be
3437 'run_id=<integer>' Specifies the run_ID from which the plots should be loaded.
3438 By default, the first run is considered and the ones that follow are ignored.
3439 'merging_scale=<float>' This option allows to specify to import only the plots corresponding to a specific
3440 value for the merging scale.
3441 A value of -1 means that only the weights with the same merging scale as the central weight are kept.
3442 By default, all weights are considered.
3443 """
3444
3445 possible_options=['--help', '--gnuplot', '--HwU', '--types','--n_ratios',\
3446 '--no_open','--show_full','--show_short','--simple_ratios','--sum','--average','--rebin', \
3447 '--assign_types','--multiply','--no_suffix', '--out', '--jet_samples',
3448 '--no_scale','--no_pdf','--no_stat','--no_merging','--no_alpsfact',
3449 '--only_scale','--only_pdf','--only_stat','--only_merging','--only_alpsfact',
3450 '--variations','--band','--central_only', '--lhapdf-config','--titles',
3451 '--keep_all_weights','--colours']
3452 n_ratios = -1
3453 uncertainties = ['scale','pdf','statistical','merging_scale','alpsfact']
3454
3455 use_band = None
3456 auto_open = True
3457 ratio_correlations = True
3458 consider_reweights = ['pdf','scale','murmuf_scales','merging_scale','alpsfact']
3459
3460 - def log(msg):
3461 print("histograms.py :: %s"%str(msg))
3462
3463 if '--help' in sys.argv or len(sys.argv)==1:
3464 log('\n\n%s'%main_doc)
3465 sys.exit(0)
3466
3467 for arg in sys.argv[1:]:
3468 if arg.startswith('--'):
3469 if arg.split('=')[0] not in possible_options:
3470 log('WARNING: option "%s" not valid. It will be ignored' % arg)
3471
3472 arg_string=' '.join(sys.argv)
3473
3474 OutName = ""
3475 for arg in sys.argv[1:]:
3476 if arg.startswith('--out='):
3477 OutName = arg[6:]
3478
3479 accepted_types = []
3480 for arg in sys.argv[1:]:
3481 if arg.startswith('--types='):
3482 accepted_types = [(type if type!='None' else None) for type in \
3483 arg[8:].split(',')]
3484
3485 accepted_titles = []
3486 for arg in sys.argv[1:]:
3487 if arg.startswith('--titles='):
3488 accepted_titles = [(type if type!='None' else None) for type in \
3489 arg[9:].split(',')]
3490
3491 assigned_types = []
3492 for arg in sys.argv[1:]:
3493 if arg.startswith('--assign_types='):
3494 assigned_types = [(type if type!='None' else None) for type in \
3495 arg[15:].split(',')]
3496
3497 assigned_colours = []
3498 for arg in sys.argv[1:]:
3499 if arg.startswith('--colours='):
3500 assigned_colours = [(colour if colour!='None' else None) for colour in \
3501 arg[10:].split(',')]
3502
3503 jet_samples_to_keep = None
3504
3505 lhapdfconfig = ['lhapdf-config']
3506 for arg in sys.argv[1:]:
3507 if arg.startswith('--lhapdf-config='):
3508 lhapdfconfig = arg[16:]
3509
3510 no_suffix = False
3511 if '--no_suffix' in sys.argv:
3512 no_suffix = True
3513
3514 if '--central_only' in sys.argv:
3515 consider_reweights = []
3516
3517 if '--keep_all_weights' in sys.argv:
3518 consider_reweights = 'ALL'
3519
3520 for arg in sys.argv[1:]:
3521 if arg.startswith('--n_ratios='):
3522 n_ratios = int(arg[11:])
3523
3524 if '--no_open' in sys.argv:
3525 auto_open = False
3526
3527 variation_type_map={'scale':'scale','merging':'merging_scale','pdf':'pdf',
3528 'stat':'statistical','alpsfact':'alpsfact'}
3529
3530 for arg in sys.argv:
3531 try:
3532 opt, value = arg.split('=')
3533 except ValueError:
3534 continue
3535 if opt=='--jet_samples':
3536 jet_samples_to_keep = eval(value)
3537 if opt=='--variations':
3538 uncertainties=[variation_type_map[type] for type in eval(value,
3539 dict([(key,key) for key in variation_type_map.keys()]+
3540 [('all',list(variation_type_map.keys()))]))]
3541 if opt=='--band':
3542 use_band=[variation_type_map[type] for type in eval(value,
3543 dict([(key,key) for key in variation_type_map.keys()]+
3544 [('all',[type for type in variation_type_map.keys() if type!='stat'])]))]
3545
3546 if '--simple_ratios' in sys.argv:
3547 ratio_correlations = False
3548
3549 for arg in sys.argv:
3550 if arg.startswith('--no_') and not arg.startswith('--no_open'):
3551 uncertainties.remove(variation_type_map[arg[5:]])
3552 if arg.startswith('--only_'):
3553 uncertainties= [variation_type_map[arg[7:]]]
3554 break
3555
3556
3557
3558 if isinstance(consider_reweights, list):
3559 naming_map={'pdf':'pdf','scale':'scale',
3560 'merging_scale':'merging_scale','alpsfact':'alpsfact'}
3561 for key in naming_map:
3562 if (not key in uncertainties) and (naming_map[key] in consider_reweights):
3563 consider_reweights.remove(naming_map[key])
3564
3565 n_files = len([_ for _ in sys.argv[1:] if not _.startswith('--')])
3566 histo_norm = [1.0]*n_files
3567
3568 for arg in sys.argv[1:]:
3569 if arg.startswith('--multiply='):
3570 histo_norm = [(float(fact) if fact!='' else 1.0) for fact in \
3571 arg[11:].split(',')]
3572
3573 if '--average' in sys.argv:
3574 histo_norm = [hist/float(n_files) for hist in histo_norm]
3575
3576 log("=======")
3577 histo_list = HwUList([])
3578 for i, arg in enumerate(sys.argv[1:]):
3579 if arg.startswith('--'):
3580 break
3581 log("Loading histograms from '%s'."%arg)
3582 if OutName=="":
3583 OutName = os.path.basename(arg).split('.')[0]+'_output'
3584
3585 file_specification = arg.split('@')
3586 filename = file_specification.pop(0)
3587 file_options = {}
3588 for option in file_specification:
3589 opt, value = option.split('=')
3590 if opt=='run_id':
3591 file_options[opt]=int(value)
3592 if opt=='merging_scale':
3593 file_options[opt]=float(value)
3594 else:
3595 log("Unreckognize file option '%s'."%option)
3596 sys.exit(1)
3597 new_histo_list = HwUList(filename, accepted_types_order=accepted_types,
3598 consider_reweights=consider_reweights, **file_options)
3599
3600 if len(accepted_titles)>0:
3601 new_histo_list = HwUList(histo for histo in new_histo_list if
3602 any(t in histo.title for t in accepted_titles))
3603 for histo in new_histo_list:
3604 if no_suffix or n_files==1:
3605 continue
3606 if not histo.type is None:
3607 histo.type += '|'
3608 else:
3609 histo.type = ''
3610
3611
3612
3613
3614
3615
3616 try:
3617 suffix = assigned_types[i]
3618 except IndexError:
3619 suffix = "#%d"%(i+1)
3620 try:
3621 histo.type = histo.type[:histo.type.index('#')] + suffix
3622 except ValueError:
3623 histo.type += suffix
3624
3625 if i==0 or all(_ not in ['--sum','--average'] for _ in sys.argv):
3626 for j,hist in enumerate(new_histo_list):
3627 new_histo_list[j]=hist*histo_norm[i]
3628 histo_list.extend(new_histo_list)
3629 continue
3630
3631 if any(_ in sys.argv for _ in ['--sum','--average']):
3632 for j, hist in enumerate(new_histo_list):
3633
3634 hist.test_plot_compability(histo_list[j])
3635
3636 histo_list[j] += hist*histo_norm[i]
3637
3638 log("A total of %i histograms were found."%len(histo_list))
3639 log("=======")
3640
3641 n_rebin = 1
3642 for arg in sys.argv[1:]:
3643 if arg.startswith('--rebin='):
3644 n_rebin = int(arg[8:])
3645
3646 if n_rebin > 1:
3647 for hist in histo_list:
3648 hist.rebin(n_rebin)
3649
3650 if '--gnuplot' in sys.argv or all(arg not in ['--HwU'] for arg in sys.argv):
3651
3652 histo_list.output(OutName, format='gnuplot',
3653 number_of_ratios = n_ratios,
3654 uncertainties=uncertainties,
3655 ratio_correlations=ratio_correlations,
3656 arg_string=arg_string,
3657 jet_samples_to_keep=jet_samples_to_keep,
3658 use_band=use_band,
3659 auto_open=auto_open,
3660 lhapdfconfig=lhapdfconfig,
3661 assigned_colours=assigned_colours)
3662
3663 log("%d histograms have been output in " % len(histo_list)+\
3664 "the gnuplot format at '%s.[HwU|gnuplot]'." % OutName)
3665 if auto_open:
3666 command = 'gnuplot %s.gnuplot'%OutName
3667 try:
3668 subprocess.call(command,shell=True,stderr=subprocess.PIPE)
3669 except:
3670 log("Automatic processing of the gnuplot card failed. Try the"+\
3671 " command by hand:\n%s"%command)
3672 else:
3673 sys.exit(0)
3674
3675 if '--HwU' in sys.argv:
3676 log("Histograms data has been output in the HwU format at "+\
3677 "'%s.HwU'."%OutName)
3678 histo_list.output(OutName, format='HwU')
3679 sys.exit(0)
3680
3681 if '--show_short' in sys.argv or '--show_full' in sys.argv:
3682 for i, histo in enumerate(histo_list):
3683 if i!=0:
3684 log('-------')
3685 log(histo.nice_string(short=(not '--show_full' in sys.argv)))
3686 log("=======")
3691 ''' Fills a hole in matplotlib: fill_between for step plots.
3692 Parameters :
3693 ------------
3694 x : array-like
3695 Array/vector of index values. These are assumed to be equally-spaced.
3696 If not, the result will probably look weird...
3697 y1 : array-like
3698 Array/vector of values to be filled under.
3699 y2 : array-Like
3700 Array/vector or bottom values for filled area. Default is 0.
3701 **kwargs will be passed to the matplotlib fill_between() function.
3702 '''
3703
3704 if ax is None:
3705 ax = plt.gca()
3706
3707
3708
3709
3710 xx= []; [(xx.append(d),xx.append(d)) for d in x]; xx = xx[1:]
3711
3712 xstep = x[1] -x[0]
3713
3714 xx.append(xx[-1] + xstep)
3715
3716
3717 if h_align == 'mid':
3718 xx = [X-xstep/2. for X in xx]
3719 elif h_align == 'right':
3720 xx = [X-xstep for X in xx]
3721
3722
3723 yy1 = []; [(yy1.append(d),yy1.append(d)) for d in y1]
3724 if isinstance(y1, list):
3725 yy2 = []; [(yy2.append(d),yy2.append(d)) for d in y2]
3726 else:
3727 yy2=y2
3728 if len(yy2) != len(yy1):
3729 yy2 = []; [(yy2.append(d),yy2.append(d)) for d in y2]
3730
3731
3732 ax.fill_between(xx, yy1, y2=yy2, **kwargs)
3733
3734 return ax
3735
3736