Package madgraph :: Package various :: Module histograms
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Source Code for Module madgraph.various.histograms

   1  #! /usr/bin/env python 
   2  ################################################################################ 
   3  # 
   4  # Copyright (c) 2010 The MadGraph5_aMC@NLO Development team and Contributors 
   5  # 
   6  # This file is a part of the MadGraph5_aMC@NLO project, an application which  
   7  # automatically generates Feynman diagrams and matrix elements for arbitrary 
   8  # high-energy processes in the Standard Model and beyond. 
   9  # 
  10  # It is subject to the MadGraph5_aMC@NLO license which should accompany this  
  11  # distribution. 
  12  # 
  13  # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch 
  14  # 
  15  ################################################################################ 
  16  """Module for the handling of histograms, including Monte-Carlo error per bin 
  17  and scale/PDF uncertainties.""" 
  18   
  19  from __future__ import division 
  20   
  21  import array 
  22  import copy 
  23  import fractions 
  24  import itertools 
  25  import logging 
  26  import math 
  27  import os 
  28  import re 
  29  import sys 
  30  import StringIO 
  31  import subprocess 
  32  import xml.dom.minidom as minidom 
  33  from xml.parsers.expat import ExpatError as XMLParsingError 
  34   
  35  root_path = os.path.split(os.path.dirname(os.path.realpath( __file__ )))[0] 
  36  sys.path.append(os.path.join(root_path))  
  37  sys.path.append(os.path.join(root_path,os.pardir)) 
  38  try: 
  39      # import from madgraph directory 
  40      import madgraph.various.misc as misc 
  41      from madgraph import MadGraph5Error 
  42      logger = logging.getLogger("madgraph.various.histograms") 
  43   
  44  except ImportError, error: 
  45      # import from madevent directory 
  46      import internal.misc as misc     
  47      from internal import MadGraph5Error 
  48      logger = logging.getLogger("internal.histograms") 
49 50 # I copy the Physics object list here so as not to add a whole dependency to 51 # base_objects which is annoying when using this histograms module from the 52 # bin/internal location of a process output (i.e. outside an MG5_aMC env.) 53 54 #=============================================================================== 55 # PhysicsObjectList 56 #=============================================================================== 57 -class histograms_PhysicsObjectList(list):
58 """A class to store lists of physics object.""" 59
60 - class PhysicsObjectListError(Exception):
61 """Exception raised if an error occurs in the definition 62 or execution of a physics object list.""" 63 pass
64
65 - def __init__(self, init_list=None):
66 """Creates a new particle list object. If a list of physics 67 object is given, add them.""" 68 69 list.__init__(self) 70 71 if init_list is not None: 72 for object in init_list: 73 self.append(object)
74
75 - def append(self, object):
76 """Appends an element, but test if valid before.""" 77 78 assert self.is_valid_element(object), \ 79 "Object %s is not a valid object for the current list" % repr(object) 80 81 list.append(self, object)
82 83
84 - def is_valid_element(self, obj):
85 """Test if object obj is a valid element for the list.""" 86 return True
87
88 - def __str__(self):
89 """String representation of the physics object list object. 90 Outputs valid Python with improved format.""" 91 92 mystr = '[' 93 94 for obj in self: 95 mystr = mystr + str(obj) + ',\n' 96 97 mystr = mystr.rstrip(',\n') 98 99 return mystr + ']'
100 #===============================================================================
101 102 -class Bin(object):
103 """A class to store Bin related features and function. 104 """ 105
106 - def __init__(self, boundaries=(0.0,0.0), wgts=None, n_entries = 0):
107 """ Initializes an empty bin, necessarily with boundaries. """ 108 109 self.boundaries = boundaries 110 self.n_entries = n_entries 111 if not wgts: 112 self.wgts = {'central':0.0} 113 else: 114 self.wgts = wgts
115
116 - def __setattr__(self, name, value):
117 if name=='boundaries': 118 if not isinstance(value, tuple): 119 raise MadGraph5Error, "Argument '%s' for bin property "+\ 120 "'boundaries' must be a tuple."%str(value) 121 else: 122 for coordinate in value: 123 if isinstance(coordinate, tuple): 124 for dim in coordinate: 125 if not isinstance(dim, float): 126 raise MadGraph5Error, "Coordinate '%s' of the bin"+\ 127 " boundary '%s' must be a float."%str(dim,value) 128 elif not isinstance(coordinate, float): 129 raise MadGraph5Error, "Element '%s' of the bin boundaries"+\ 130 " specified must be a float."%str(bound) 131 elif name=='wgts': 132 if not isinstance(value, dict): 133 raise MadGraph5Error, "Argument '%s' for bin uncertainty "+\ 134 "'wgts' must be a dictionary."%str(value) 135 for val in value.values(): 136 if not isinstance(val,float): 137 raise MadGraph5Error, "The bin weight value '%s' is not a "+\ 138 "float."%str(val) 139 140 super(Bin, self).__setattr__(name,value)
141
142 - def get_weight(self, key='central'):
143 """ Accesses a specific weight from this bin.""" 144 try: 145 return self.wgts[key] 146 except KeyError: 147 raise MadGraph5Error, "Weight with ID '%s' is not defined for"+\ 148 " this bin"%str(key)
149
150 - def set_weight(self, wgt, key='central'):
151 """ Accesses a specific weight from this bin.""" 152 153 # an assert is used here in this intensive function, so as to avoid 154 # slow-down when not in debug mode. 155 assert(isinstance(wgt, float)) 156 157 try: 158 self.wgts[key] = wgt 159 except KeyError: 160 raise MadGraph5Error, "Weight with ID '%s' is not defined for"+\ 161 " this bin"%str(key)
162
163 - def addEvent(self, weights = 1.0):
164 """ Add an event to this bin. """ 165 166 167 if isinstance(weights, float): 168 weights = {'central': weights} 169 170 for key in weights: 171 if key == 'stat_error': 172 continue 173 try: 174 self.wgts[key] += weights[key] 175 except KeyError: 176 raise MadGraph5Error('The event added defines the weight '+ 177 '%s which was not '%key+'registered in this histogram.') 178 179 self.n_entries += 1
180 181 #if 'stat_error' not in weights and 'central' in w: 182 # self.wgts['stat_error'] = self.wgts['central']/math.sqrt(float(self.n_entries)) 183 #else: 184 # self.wgts['stat_error'] = math.sqrt( self.wgts['stat_error']**2 + 185 # weights['stat_error']**2 ) 186
187 - def nice_string(self, order=None, short=True):
188 """ Nice representation of this Bin. 189 One can order the weight according to the argument if provided.""" 190 191 res = ["Bin boundaries : %s"%str(self.boundaries)] 192 if not short: 193 res.append("Bin weights :") 194 if order is None: 195 label_list = self.wgts.keys() 196 else: 197 label_list = order 198 199 for label in label_list: 200 try: 201 res.append(" -> '%s' : %4.3e"%(str(label),self.wgts[label])) 202 except KeyError: 203 pass 204 else: 205 res.append("Central weight : %4.3e"%self.get_weight()) 206 207 return '\n'.join(res)
208
209 - def alter_weights(self, func):
210 """ Apply a given function to all bin weights.""" 211 self.wgts = func(self.wgts)
212 213 @classmethod
214 - def combine(cls, binA, binB, func):
215 """ Function to combine two bins. The 'func' is such that it takes 216 two weight dictionaries and merge them into one.""" 217 218 res_bin = cls() 219 if binA.boundaries != binB.boundaries: 220 raise MadGraph5Error, 'The two bins to combine have'+\ 221 ' different boundaries, %s!=%s.'%(str(binA.boundaries),str(binB.boundaries)) 222 res_bin.boundaries = binA.boundaries 223 224 try: 225 res_bin.wgts = func(binA.wgts, binB.wgts) 226 except Exception as e: 227 raise MadGraph5Error, "When combining two bins, the provided"+\ 228 " function '%s' triggered the following error:\n\"%s\"\n"%\ 229 (func.__name__,str(e))+" when combining the following two bins:\n"+\ 230 binA.nice_string(short=False)+"\n and \n"+binB.nice_string(short=False) 231 232 return res_bin
233
234 -class BinList(histograms_PhysicsObjectList):
235 """ A class implementing features related to a list of Bins. """ 236
237 - def __init__(self, list = [], bin_range = None, 238 weight_labels = None):
239 """ Initialize a list of Bins. It is possible to define the range 240 as a list of three floats: [min_x, max_x, bin_width]""" 241 242 self.weight_labels = weight_labels 243 if bin_range: 244 # Set the default weight_labels to something meaningful 245 if not self.weight_labels: 246 self.weight_labels = ['central', 'stat_error'] 247 if len(bin_range)!=3 or any(not isinstance(f, float) for f in bin_range): 248 raise MadGraph5Error, "The range argument to build a BinList"+\ 249 " must be a list of exactly three floats." 250 current = bin_range[0] 251 while current < bin_range[1]: 252 self.append(Bin(boundaries = 253 (current, min(current+bin_range[2],bin_range[1])), 254 wgts = dict((wgt,0.0) for wgt in self.weight_labels))) 255 current += bin_range[2] 256 else: 257 super(BinList, self).__init__(list)
258
259 - def is_valid_element(self, obj):
260 """Test whether specified object is of the right type for this list.""" 261 262 return isinstance(obj, Bin)
263
264 - def __setattr__(self, name, value):
265 if name=='weight_labels': 266 if not value is None and not isinstance(value, list): 267 raise MadGraph5Error, "Argument '%s' for BinList property '%s'"\ 268 %(str(value),name)+' must be a list.' 269 elif not value is None: 270 for label in value: 271 if all((not isinstance(label,cls)) for cls in \ 272 [str, int, float, tuple]): 273 raise MadGraph5Error, "Element '%s' of the BinList property '%s'"\ 274 %(str(value),name)+' must be a string, an '+\ 275 'integer, a float or a tuple of float.' 276 if isinstance(label, tuple): 277 if len(label)>=1: 278 if not isinstance(label[0], (float, str)): 279 raise MadGraph5Error, "Argument "+\ 280 "'%s' for BinList property '%s'"%(str(value),name)+\ 281 ' can be a tuple, but its first element must be a float or string.' 282 for elem in label[1:]: 283 if not isinstance(elem, (float,int,str)): 284 raise MadGraph5Error, "Argument "+\ 285 "'%s' for BinList property '%s'"%(str(value),name)+\ 286 ' can be a tuple, but its elements past the first one must be either floats, integers or strings' 287 288 289 super(BinList, self).__setattr__(name, value)
290
291 - def append(self, object):
292 """Appends an element, but test if valid before.""" 293 294 super(BinList,self).append(object) 295 # Assign the weight labels to those of the first bin added 296 if len(self)==1 and self.weight_labels is None: 297 self.weight_labels = object.wgts.keys()
298
299 - def nice_string(self, short=True):
300 """ Nice representation of this BinList.""" 301 302 res = ["Number of bin in the list : %d"%len(self)] 303 res.append("Registered weight labels : [%s]"%(', '.join([ 304 str(label) for label in self.weight_labels]))) 305 if not short: 306 for i, bin in enumerate(self): 307 res.append('Bin number %d :'%i) 308 res.append(bin.nice_string(order=self.weight_labels, short=short)) 309 310 return '\n'.join(res)
311
312 -class Histogram(object):
313 """A mother class for all specific implementations of Histogram conventions 314 """ 315 316 allowed_dimensions = None 317 allowed_types = [] 318 allowed_axis_modes = ['LOG','LIN'] 319
320 - def __init__(self, title = "NoName", n_dimensions = 2, type=None, 321 x_axis_mode = 'LIN', y_axis_mode = 'LOG', bins=None):
322 """ Initializes an empty histogram, possibly specifying 323 > a title 324 > a number of dimensions 325 > a bin content 326 """ 327 328 self.title = title 329 self.dimension = n_dimensions 330 if not bins: 331 self.bins = BinList([]) 332 else: 333 self.bins = bins 334 self.type = type 335 self.x_axis_mode = x_axis_mode 336 self.y_axis_mode = y_axis_mode
337
338 - def __setattr__(self, name, value):
339 if name=='title': 340 if not isinstance(value, str): 341 raise MadGraph5Error, "Argument '%s' for the histogram property "+\ 342 "'title' must be a string."%str(value) 343 elif name=='dimension': 344 if not isinstance(value, int): 345 raise MadGraph5Error, "Argument '%s' for histogram property "+\ 346 "'dimension' must be an integer."%str(value) 347 if self.allowed_dimensions and value not in self.allowed_dimensions: 348 raise MadGraph5Error, "%i-Dimensional histograms not supported "\ 349 %value+"by class '%s'. Supported dimensions are '%s'."\ 350 %(self.__class__.__name__,self.allowed_dimensions) 351 elif name=='bins': 352 if not isinstance(value, BinList): 353 raise MadGraph5Error, "Argument '%s' for histogram property "+\ 354 "'bins' must be a BinList."%str(value) 355 else: 356 for bin in value: 357 if not isinstance(bin, Bin): 358 raise MadGraph5Error, "Element '%s' of the "%str(bin)+\ 359 " histogram bin list specified must be a bin." 360 elif name=='type': 361 if not (value is None or value in self.allowed_types or 362 self.allowed_types==[]): 363 raise MadGraph5Error, "Argument '%s' for histogram"%str(value)+\ 364 " property 'type' must be a string in %s or None."\ 365 %([str(t) for t in self.allowed_types]) 366 elif name in ['x_axis_mode','y_axis_mode']: 367 if not value in self.allowed_axis_modes: 368 raise MadGraph5Error, "Attribute '%s' of the histogram"%str(name)+\ 369 " must be in [%s], ('%s' given)"%(str(self.allowed_axis_modes), 370 str(value)) 371 372 super(Histogram, self).__setattr__(name,value)
373
374 - def nice_string(self, short=True):
375 """ Nice representation of this histogram. """ 376 377 res = ['<%s> histogram:'%self.__class__.__name__] 378 res.append(' -> title : "%s"'%self.title) 379 res.append(' -> dimensions : %d'%self.dimension) 380 if not self.type is None: 381 res.append(' -> type : %s'%self.type) 382 else: 383 res.append(' -> type : None') 384 res.append(' -> (x, y)_axis : ( %s, %s)'%\ 385 (tuple([('Linear' if mode=='LIN' else 'Logarithmic') for mode in \ 386 [self.x_axis_mode, self.y_axis_mode]]))) 387 if short: 388 res.append(' -> n_bins : %s'%len(self.bins)) 389 res.append(' -> weight types : [ %s ]'% 390 (', '.join([str(label) for label in self.bins.weight_labels]) \ 391 if (not self.bins.weight_labels is None) else 'None')) 392 393 else: 394 res.append(' -> Bins content :') 395 res.append(self.bins.nice_string(short)) 396 397 return '\n'.join(res)
398
399 - def alter_weights(self, func):
400 """ Apply a given function to all bin weights.""" 401 402 for bin in self.bins: 403 bin.alter_weights(func)
404 405 @classmethod
406 - def combine(cls, histoA, histoB, func):
407 """ Function to combine two Histograms. The 'func' is such that it takes 408 two weight dictionaries and merge them into one.""" 409 410 res_histogram = copy.copy(histoA) 411 if histoA.title != histoB.title: 412 res_histogram.title = "[%s]__%s__[%s]"%(histoA.title,func.__name__, 413 histoB.title) 414 else: 415 res_histogram.title = histoA.title 416 417 res_histogram.bins = BinList([]) 418 if len(histoA.bins)!=len(histoB.bins): 419 raise MadGraph5Error, 'The two histograms to combine have a '+\ 420 'different number of bins, %d!=%d.'%(len(histoA.bins),len(histoB.bins)) 421 422 if histoA.dimension!=histoB.dimension: 423 raise MadGraph5Error, 'The two histograms to combine have a '+\ 424 'different dimensions, %d!=%d.'%(histoA.dimension,histoB.dimension) 425 res_histogram.dimension = histoA.dimension 426 427 for i, bin in enumerate(histoA.bins): 428 res_histogram.bins.append(Bin.combine(bin, histoB.bins[i],func)) 429 430 # Reorder the weight labels as in the original histogram and add at the 431 # end the new ones which resulted from the combination, in a sorted order 432 res_histogram.bins.weight_labels = [label for label in histoA.bins.\ 433 weight_labels if label in res_histogram.bins.weight_labels] + \ 434 sorted([label for label in res_histogram.bins.weight_labels if\ 435 label not in histoA.bins.weight_labels]) 436 437 438 return res_histogram
439 440 # ================================================== 441 # Some handy function for Histogram combination 442 # ================================================== 443 @staticmethod
444 - def MULTIPLY(wgtsA, wgtsB):
445 """ Apply the multiplication to the weights of two bins.""" 446 447 new_wgts = {} 448 449 new_wgts['stat_error'] = math.sqrt( 450 (wgtsA['stat_error']*wgtsB['central'])**2+ 451 (wgtsA['central']*wgtsB['stat_error'])**2) 452 453 for label, wgt in wgtsA.items(): 454 if label=='stat_error': 455 continue 456 new_wgts[label] = wgt*wgtsB[label] 457 458 return new_wgts
459 460 @staticmethod
461 - def DIVIDE(wgtsA, wgtsB):
462 """ Apply the division to the weights of two bins.""" 463 464 new_wgts = {} 465 if wgtsB['central'] == 0.0: 466 new_wgts['stat_error'] = 0.0 467 else: 468 # d(x/y) = ( (dx/y)**2 + ((x*dy)/(y**2))**2 )**0.5 469 new_wgts['stat_error'] = math.sqrt(wgtsA['stat_error']**2+ 470 ((wgtsA['central']*wgtsB['stat_error'])/ 471 wgtsB['central'])**2)/wgtsB['central'] 472 473 for label, wgt in wgtsA.items(): 474 if label=='stat_error': 475 continue 476 if wgtsB[label]==0.0 and wgt==0.0: 477 new_wgts[label] = 0.0 478 elif wgtsB[label]==0.0: 479 # This situation is most often harmless and just happens in regions 480 # with low statistics, so I'll bypass the warning here. 481 # logger.debug('Warning:: A bin with finite weight was divided '+\ 482 # 'by a bin with zero weight.') 483 new_wgts[label] = 0.0 484 else: 485 new_wgts[label] = wgt/wgtsB[label] 486 487 return new_wgts
488 489 @staticmethod
490 - def OPERATION(wgtsA, wgtsB, wgt_operation, stat_error_operation):
491 """ Apply the operation to the weights of two bins. Notice that we 492 assume here the two dict operands to have the same weight labels. 493 The operation is a function that takes two floats as input.""" 494 495 new_wgts = {} 496 for label, wgt in wgtsA.items(): 497 if label!='stat_error': 498 new_wgts[label] = wgt_operation(wgt, wgtsB[label]) 499 else: 500 new_wgts[label] = stat_error_operation(wgt, wgtsB[label]) 501 # if new_wgts[label]>1.0e+10: 502 # print "stat_error_operation is ",stat_error_operation.__name__ 503 # print " inputs were ",wgt, wgtsB[label] 504 # print "for label", label 505 506 return new_wgts
507 508 509 @staticmethod
510 - def SINGLEHISTO_OPERATION(wgts, wgt_operation, stat_error_operation):
511 """ Apply the operation to the weights of a *single* bins. 512 The operation is a function that takes a single float as input.""" 513 514 new_wgts = {} 515 for label, wgt in wgts.items(): 516 if label!='stat_error': 517 new_wgts[label] = wgt_operation(wgt) 518 else: 519 new_wgts[label] = stat_error_operation(wgt) 520 521 return new_wgts
522 523 @staticmethod
524 - def ADD(wgtsA, wgtsB):
525 """ Implements the addition using OPERATION above. """ 526 return Histogram.OPERATION(wgtsA, wgtsB, 527 (lambda a,b: a+b), 528 (lambda a,b: math.sqrt(a**2+b**2)))
529 530 @staticmethod
531 - def SUBTRACT(wgtsA, wgtsB):
532 """ Implements the subtraction using OPERATION above. """ 533 534 return Histogram.OPERATION(wgtsA, wgtsB, 535 (lambda a,b: a-b), 536 (lambda a,b: math.sqrt(a**2+b**2)))
537 538 @staticmethod
539 - def RESCALE(factor):
540 """ Implements the rescaling using SINGLEHISTO_OPERATION above. """ 541 542 def rescaler(wgts): 543 return Histogram.SINGLEHISTO_OPERATION(wgts,(lambda a: a*factor), 544 (lambda a: a*factor))
545 546 return rescaler
547 548 @staticmethod
549 - def OFFSET(offset):
550 """ Implements the offset using SINGLEBIN_OPERATION above. """ 551 def offsetter(wgts): 552 return Histogram.SINGLEHISTO_OPERATION( 553 wgts,(lambda a: a+offset),(lambda a: a))
554 555 return offsetter 556
557 - def __add__(self, other):
558 """ Overload the plus function. """ 559 if isinstance(other, Histogram): 560 return self.__class__.combine(self,other,Histogram.ADD) 561 elif isinstance(other, int) or isinstance(other, float): 562 self.alter_weights(Histogram.OFFSET(float(other))) 563 return self 564 else: 565 return NotImplemented, 'Histograms can only be added to other '+\ 566 ' histograms or scalars.'
567
568 - def __sub__(self, other):
569 """ Overload the subtraction function. """ 570 if isinstance(other, Histogram): 571 return self.__class__.combine(self,other,Histogram.SUBTRACT) 572 elif isinstance(other, int) or isinstance(other, float): 573 self.alter_weights(Histogram.OFFSET(-float(other))) 574 return self 575 else: 576 return NotImplemented, 'Histograms can only be subtracted to other '+\ 577 ' histograms or scalars.'
578
579 - def __mul__(self, other):
580 """ Overload the multiplication function. """ 581 if isinstance(other, Histogram): 582 return self.__class__.combine(self,other,Histogram.MULTIPLY) 583 elif isinstance(other, int) or isinstance(other, float): 584 self.alter_weights(Histogram.RESCALE(float(other))) 585 return self 586 else: 587 return NotImplemented, 'Histograms can only be multiplied to other '+\ 588 ' histograms or scalars.'
589
590 - def __div__(self, other):
591 """ Overload the multiplication function. """ 592 if isinstance(other, Histogram): 593 return self.__class__.combine(self,other,Histogram.DIVIDE) 594 elif isinstance(other, int) or isinstance(other, float): 595 self.alter_weights(Histogram.RESCALE(1.0/float(other))) 596 return self 597 else: 598 return NotImplemented, 'Histograms can only be divided with other '+\ 599 ' histograms or scalars.'
600 601 __truediv__ = __div__ 602
603 -class HwU(Histogram):
604 """A concrete implementation of an histogram plots using the HwU format for 605 reading/writing histogram content.""" 606 607 allowed_dimensions = [2] 608 allowed_types = [] 609 610 # For now only HwU output format is implemented. 611 output_formats_implemented = ['HwU','gnuplot'] 612 # Lists the mandatory named weights that must be specified for each bin and 613 # what corresponding label we assign them to in the Bin weight dictionary, 614 # (if any). 615 mandatory_weights = {'xmin':'boundary_xmin', 'xmax':'boundary_xmax', 616 'central value':'central', 'dy':'stat_error'} 617 618 # ======================== 619 # Weight name parser RE's 620 # ======================== 621 # This marks the start of the line that defines the name of the weights 622 weight_header_start_re = re.compile('^##.*') 623 # This is the format of a weight name specifier. It is much more complicated 624 # than necessary because the HwU standard allows for spaces from within 625 # the name of a weight 626 weight_header_re = re.compile( 627 '&\s*(?P<wgt_name>(\S|(\s(?!\s*(&|$))))+)(\s(?!(&|$)))*') 628 629 # ================================ 630 # Histo weight specification RE's 631 # ================================ 632 # The start of a plot 633 histo_start_re = re.compile('^\s*<histogram>\s*(?P<n_bins>\d+)\s*"\s*'+ 634 '(?P<histo_name>(\S|(\s(?!\s*")))+)\s*"\s*$') 635 # A given weight specifier 636 a_float_re = '[\+|-]?\d+(\.\d*)?([EeDd][\+|-]?\d+)?' 637 histo_bin_weight_re = re.compile('(?P<weight>%s|NaN)'%a_float_re,re.IGNORECASE) 638 a_int_re = '[\+|-]?\d+' 639 640 # The end of a plot 641 histo_end_re = re.compile(r'^\s*<\\histogram>\s*$') 642 # A scale type of weight 643 weight_label_scale = re.compile('^\s*mur\s*=\s*(?P<mur_fact>%s)'%a_float_re+\ 644 '\s*muf\s*=\s*(?P<muf_fact>%s)\s*$'%a_float_re,re.IGNORECASE) 645 weight_label_PDF = re.compile('^\s*PDF\s*=\s*(?P<PDF_set>\d+)\s*$') 646 weight_label_PDF_XML = re.compile('^\s*pdfset\s*=\s*(?P<PDF_set>\d+)\s*$') 647 weight_label_TMS = re.compile('^\s*TMS\s*=\s*(?P<Merging_scale>%s)\s*$'%a_float_re) 648 weight_label_alpsfact = re.compile('^\s*alpsfact\s*=\s*(?P<alpsfact>%s)\s*$'%a_float_re, 649 re.IGNORECASE) 650 651 weight_label_scale_adv = re.compile('^\s*dyn\s*=\s*(?P<dyn_choice>%s)'%a_int_re+\ 652 '\s*mur\s*=\s*(?P<mur_fact>%s)'%a_float_re+\ 653 '\s*muf\s*=\s*(?P<muf_fact>%s)\s*$'%a_float_re,re.IGNORECASE) 654 weight_label_PDF_adv = re.compile('^\s*PDF\s*=\s*(?P<PDF_set>\d+)\s+(?P<PDF_set_cen>\S+)\s*$') 655 656
657 - class ParseError(MadGraph5Error):
658 """a class for histogram data parsing errors"""
659 660 @classmethod
661 - def get_HwU_wgt_label_type(cls, wgt_label):
662 """ From the format of the weight label given in argument, it returns 663 a string identifying the type of standard weight it is.""" 664 665 if isinstance(wgt_label,str): 666 return 'UNKNOWN_TYPE' 667 if isinstance(wgt_label,tuple): 668 if len(wgt_label)==0: 669 return 'UNKNOWN_TYPE' 670 if isinstance(wgt_label[0],float): 671 return 'murmuf_scales' 672 if isinstance(wgt_label[0],str): 673 return wgt_label[0] 674 if isinstance(wgt_label,float): 675 return 'merging_scale' 676 if isinstance(wgt_label,int): 677 return 'pdfset' 678 # No clue otherwise 679 return 'UNKNOWN_TYPE'
680 681
682 - def __init__(self, file_path=None, weight_header=None, 683 raw_labels=False, consider_reweights='ALL', selected_central_weight=None, **opts):
684 """ Read one plot from a file_path or a stream. Notice that this 685 constructor only reads one, and the first one, of the plots specified. 686 If file_path was a path in argument, it would then close the opened stream. 687 If file_path was a stream in argument, it would leave it open. 688 The option weight_header specifies an ordered list of weight names 689 to appear in the file specified. 690 The option 'raw_labels' specifies that one wants to import the 691 histogram data with no treatment of the weight labels at all 692 (this is used for the matplotlib output).""" 693 694 super(HwU, self).__init__(**opts) 695 696 self.dimension = 2 697 698 if file_path is None: 699 return 700 elif isinstance(file_path, str): 701 stream = open(file_path,'r') 702 elif isinstance(file_path, file): 703 stream = file_path 704 else: 705 raise MadGraph5Error, "Argument file_path '%s' for HwU init"\ 706 %str(file_path)+"ialization must be either a file path or a stream." 707 708 # Attempt to find the weight headers if not specified 709 if not weight_header: 710 weight_header = HwU.parse_weight_header(stream, raw_labels=raw_labels) 711 712 if not self.parse_one_histo_from_stream(stream, weight_header, 713 consider_reweights=consider_reweights, 714 selected_central_weight=selected_central_weight, 715 raw_labels=raw_labels): 716 # Indicate that the initialization of the histogram was unsuccessful 717 # by setting the BinList property to None. 718 super(Histogram,self).__setattr__('bins',None) 719 720 # Explicitly close the opened stream for clarity. 721 if isinstance(file_path, str): 722 stream.close()
723
724 - def addEvent(self, x_value, weights = 1.0):
725 """ Add an event to the current plot. """ 726 727 for bin in self.bins: 728 if bin.boundaries[0] <= x_value < bin.boundaries[1]: 729 bin.addEvent(weights = weights)
730
731 - def get(self, name):
732 733 if name == 'bins': 734 return [b.boundaries[0] for b in self.bins] 735 else: 736 return [b.wgts[name] for b in self.bins]
737
738 - def add_line(self, names):
739 """add a column to the HwU. name can be a list""" 740 741 if isinstance(names, str): 742 names = [names] 743 else: 744 names = list(names) 745 #check if all the entry are new 746 for name in names[:]: 747 if name in self.bins[0].wgts: 748 logger.warning("name: %s is already defines in HwU.") 749 names.remove(name) 750 # 751 for name in names: 752 self.bins.weight_labels.append(name) 753 for bin in self.bins: 754 bin.wgts[name] = 0
755
756 - def get_uncertainty_band(self, selector, mode=0):
757 """return two list of entry one with the minimum and one with the maximum value. 758 selector can be: 759 - a regular expression on the label name 760 - a function returning T/F (applying on the label name) 761 - a list of labels 762 - a keyword 763 """ 764 765 # find the set of weights to consider 766 if isinstance(selector, str): 767 if selector == 'QCUT': 768 selector = r'^Weight_MERGING=[\d]*[.]?\d*$' 769 elif selector == 'SCALE': 770 selector = r'(MUF=\d*[.]?\d*_MUR=([^1]\d*|1\d+)_PDF=\d*)[.]?\d*|(MUF=([^1]\d*|1\d+)[.]?\d*_MUR=\d*[.]?\d*_PDF=\d*)' 771 elif selector == 'ALPSFACT': 772 selector = r'ALPSFACT' 773 elif selector == 'PDF': 774 selector = r'(?:MUF=1_MUR=1_PDF=|MU(?:F|R)="1.0" MU(?:R|F)="1.0" PDF=")(\d*)' 775 if not mode: 776 # pdfs=[] 777 ## for n in self.bins[0].wgts: 778 # misc.sprint( n) 779 # if re.search(selector,n, re.IGNORECASE): 780 # pdfs.append(int(re.findall(selector, n)[0])) 781 pdfs = [int(re.findall(selector, n)[0]) for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)] 782 min_pdf, max_pdf = min(pdfs), max(pdfs) 783 if max_pdf - min_pdf > 100: 784 mode == 'min/max' 785 elif max_pdf <= 90000: 786 mode = 'hessian' 787 else: 788 mode = 'gaussian' 789 selections = [n for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)] 790 elif hasattr(selector, '__call__'): 791 selections = [n for n in self.bins[0].wgts if selector(n)] 792 elif isinstance(selector, (list, tuple)): 793 selections = selector 794 795 # find the way to find the minimal/maximal curve 796 if not mode: 797 mode = 'min/max' 798 799 # build the collection of values 800 values = [] 801 for s in selections: 802 values.append(self.get(s)) 803 804 #sanity check 805 if not len(values): 806 return [0] * len(self.bins), [0]* len(self.bins) 807 elif len(values) ==1: 808 return values[0], values[0] 809 810 811 # Start the real work 812 if mode == 'min/max': 813 min_value, max_value = [], [] 814 for i in xrange(len(values[0])): 815 data = [values[s][i] for s in xrange(len(values))] 816 min_value.append(min(data)) 817 max_value.append(max(data)) 818 elif mode == 'gaussian': 819 # use Gaussian method (NNPDF) 820 min_value, max_value = [], [] 821 for i in xrange(len(values[0])): 822 pdf_stdev = 0.0 823 data = [values[s][i] for s in xrange(len(values))] 824 sdata = sum(data)/len(data) 825 sdata2 = sum(x**2 for x in data)/len(data) 826 pdf_stdev = math.sqrt(max(sdata2 -sdata**2,0.0)) 827 min_value.append(sdata - pdf_stdev) 828 max_value.append(sdata + pdf_stdev) 829 830 elif mode == 'hessian': 831 # For old PDF this is based on the set ordering -> 832 #need to order the pdf sets: 833 pdfs = [(int(re.findall(selector, n)[0]),n) for n in self.bins[0].wgts if re.search(selector,n, re.IGNORECASE)] 834 pdfs.sort() 835 836 # check if the central was put or not in this sets: 837 if len(pdfs) % 2: 838 # adding the central automatically 839 pdf1 = pdfs[0][0] 840 central = pdf1 -1 841 name = pdfs[0][1].replace(str(pdf1), str(central)) 842 central = self.get(name) 843 else: 844 central = self.get(pdfs.pop(0)[1]) 845 846 #rebuilt the collection of values but this time ordered correctly 847 values = [] 848 for _, name in pdfs: 849 values.append(self.get(name)) 850 851 #Do the computation 852 min_value, max_value = [], [] 853 for i in xrange(len(values[0])): 854 pdf_up = 0 855 pdf_down = 0 856 cntrl_val = central[i] 857 for s in range(int((len(pdfs))/2)): 858 pdf_up += max(0.0,values[2*s][i] - cntrl_val, 859 values[2*s+1][i] - cntrl_val)**2 860 pdf_down += max(0.0,cntrl_val - values[2*s][i], 861 cntrl_val - values[2*s+1][i])**2 862 863 min_value.append(cntrl_val - math.sqrt(pdf_down)) 864 max_value.append(cntrl_val + math.sqrt(pdf_up)) 865 866 867 868 869 return min_value, max_value
870
871 - def get_formatted_header(self):
872 """ Return a HwU formatted header for the weight label definition.""" 873 874 res = '##& xmin & xmax & ' 875 876 if 'central' in self.bins.weight_labels: 877 res += 'central value & dy & ' 878 879 others = [] 880 for label in self.bins.weight_labels: 881 if label in ['central', 'stat_error']: 882 continue 883 label_type = HwU.get_HwU_wgt_label_type(label) 884 if label_type == 'UNKNOWN_TYPE': 885 others.append(label) 886 elif label_type == 'scale': 887 others.append('muR=%6.3f muF=%6.3f'%(label[1],label[2])) 888 elif label_type == 'scale_adv': 889 others.append('dyn=%i muR=%6.3f muF=%6.3f'%(label[1],label[2],label[3])) 890 elif label_type == 'merging_scale': 891 others.append('TMS=%4.2f'%label[1]) 892 elif label_type == 'pdf': 893 others.append('PDF=%i'%(label[1])) 894 elif label_type == 'pdf_adv': 895 others.append('PDF=%i %s'%(label[1],label[2])) 896 elif label_type == 'alpsfact': 897 others.append('alpsfact=%d'%label[1]) 898 899 return res+' & '.join(others)
900
901 - def get_HwU_source(self, print_header=True):
902 """ Returns the string representation of this histogram using the 903 HwU standard.""" 904 905 res = [] 906 if print_header: 907 res.append(self.get_formatted_header()) 908 res.extend(['']) 909 res.append('<histogram> %s "%s"'%(len(self.bins), 910 self.get_HwU_histogram_name(format='HwU'))) 911 for bin in self.bins: 912 if 'central' in bin.wgts: 913 res.append(' '.join('%+16.7e'%wgt for wgt in list(bin.boundaries)+ 914 [bin.wgts['central'],bin.wgts['stat_error']])) 915 else: 916 res.append(' '.join('%+16.7e'%wgt for wgt in list(bin.boundaries))) 917 res[-1] += ' '.join('%+16.7e'%bin.wgts[key] for key in 918 self.bins.weight_labels if key not in ['central','stat_error']) 919 res.append('<\histogram>') 920 return res
921
922 - def output(self, path=None, format='HwU', print_header=True):
923 """ Ouput this histogram to a file, stream or string if path is kept to 924 None. The supported format are for now. Chose whether to print the header 925 or not.""" 926 927 if not format in HwU.output_formats_implemented: 928 raise MadGraph5Error, "The specified output format '%s'"%format+\ 929 " is not yet supported. Supported formats are %s."\ 930 %HwU.output_formats_implemented 931 932 if format == 'HwU': 933 str_output_list = self.get_HwU_source(print_header=print_header) 934 935 if path is None: 936 return '\n'.join(str_output_list) 937 elif isinstance(path, str): 938 stream = open(path,'w') 939 stream.write('\n'.join(str_output_list)) 940 stream.close() 941 elif isinstance(path, file): 942 path.write('\n'.join(str_output_list)) 943 944 # Successful writeout 945 return True
946
947 - def test_plot_compability(self, other, consider_type=True, 948 consider_unknown_weight_labels=True):
949 """ Test whether the defining attributes of self are identical to histo, 950 typically to make sure that they are the same plots but from different 951 runs, and they can be summed safely. We however don't want to 952 overload the __eq__ because it is still a more superficial check.""" 953 954 this_known_weight_labels = [label for label in self.bins.weight_labels if 955 HwU.get_HwU_wgt_label_type(label)!='UNKNOWN_TYPE'] 956 other_known_weight_labels = [label for label in other.bins.weight_labels if 957 HwU.get_HwU_wgt_label_type(label)!='UNKNOWN_TYPE'] 958 this_unknown_weight_labels = [label for label in self.bins.weight_labels if 959 HwU.get_HwU_wgt_label_type(label)=='UNKNOWN_TYPE'] 960 other_unknown_weight_labels = [label for label in other.bins.weight_labels if 961 HwU.get_HwU_wgt_label_type(label)=='UNKNOWN_TYPE'] 962 963 if self.title != other.title or \ 964 set(this_known_weight_labels) != set(other_known_weight_labels) or \ 965 (set(this_unknown_weight_labels) != set(other_unknown_weight_labels) and\ 966 consider_unknown_weight_labels) or \ 967 (self.type != other.type and consider_type) or \ 968 self.x_axis_mode != self.x_axis_mode or \ 969 self.y_axis_mode != self.y_axis_mode or \ 970 any(b1.boundaries!=b2.boundaries for (b1,b2) in \ 971 zip(self.bins,other.bins)): 972 return False 973 974 return True
975 976 977 978 @classmethod
979 - def parse_weight_header(cls, stream, raw_labels=False):
980 """ Read a given stream until it finds a header specifying the weights 981 and then returns them.""" 982 983 for line in stream: 984 if cls.weight_header_start_re.match(line): 985 header = [h.group('wgt_name') for h in 986 cls.weight_header_re.finditer(line)] 987 if any((name not in header) for name in cls.mandatory_weights): 988 raise HwU.ParseError, "The mandatory weight names %s were"\ 989 %str(cls.mandatory_weights.keys())+" are not all present"+\ 990 " in the following HwU header definition:\n %s"%line 991 992 # Apply replacement rules specified in mandatory_weights 993 if raw_labels: 994 # If using raw labels, then just change the name of the 995 # labels corresponding to the bin edges 996 header = [ (h if h not in ['xmin','xmax'] else 997 cls.mandatory_weights[h]) for h in header ] 998 # And return it with no further modification 999 return header 1000 else: 1001 header = [ (h if h not in cls.mandatory_weights else 1002 cls.mandatory_weights[h]) for h in header ] 1003 1004 # We use a special rule for the weight labeled as a 1005 # muR=2.0 muF=1.0 scale specification, in which case we store 1006 # it as a tuple 1007 for i, h in enumerate(header): 1008 scale_wgt = HwU.weight_label_scale.match(h) 1009 PDF_wgt = HwU.weight_label_PDF.match(h) 1010 Merging_wgt = HwU.weight_label_TMS.match(h) 1011 alpsfact_wgt = HwU.weight_label_alpsfact.match(h) 1012 scale_wgt_adv = HwU.weight_label_scale_adv.match(h) 1013 PDF_wgt_adv = HwU.weight_label_PDF_adv.match(h) 1014 if scale_wgt_adv: 1015 header[i] = ('scale_adv', 1016 int(scale_wgt_adv.group('dyn_choice')), 1017 float(scale_wgt_adv.group('mur_fact')), 1018 float(scale_wgt_adv.group('muf_fact'))) 1019 elif scale_wgt: 1020 header[i] = ('scale', 1021 float(scale_wgt.group('mur_fact')), 1022 float(scale_wgt.group('muf_fact'))) 1023 elif PDF_wgt_adv: 1024 header[i] = ('pdf_adv', 1025 int(PDF_wgt_adv.group('PDF_set')), 1026 PDF_wgt_adv.group('PDF_set_cen')) 1027 elif PDF_wgt: 1028 header[i] = ('pdf',int(PDF_wgt.group('PDF_set'))) 1029 elif Merging_wgt: 1030 header[i] = ('merging_scale',float(Merging_wgt.group('Merging_scale'))) 1031 elif alpsfact_wgt: 1032 header[i] = ('alpsfact',float(alpsfact_wgt.group('alpsfact'))) 1033 1034 return header 1035 1036 raise HwU.ParseError, "The weight headers could not be found."
1037 1038
1039 - def process_histogram_name(self, histogram_name):
1040 """ Parse the histogram name for tags which would set its various 1041 attributes.""" 1042 1043 for i, tag in enumerate(histogram_name.split('|')): 1044 if i==0: 1045 self.title = tag.strip() 1046 else: 1047 stag = tag.split('@') 1048 if len(stag)==1 and stag[0].startswith('#'): continue 1049 if len(stag)!=2: 1050 raise MadGraph5Error, 'Specifier in title must have the'+\ 1051 " syntax @<attribute_name>:<attribute_value>, not '%s'."%tag.strip() 1052 # Now list all supported modifiers here 1053 stag = [t.strip().upper() for t in stag] 1054 if stag[0] in ['T','TYPE']: 1055 self.type = stag[1] 1056 elif stag[0] in ['X_AXIS', 'X']: 1057 self.x_axis_mode = stag[1] 1058 elif stag[0] in ['Y_AXIS', 'Y']: 1059 self.y_axis_mode = stag[1] 1060 elif stag[0] in ['JETSAMPLE', 'JS']: 1061 self.jetsample = int(stag[1]) 1062 else: 1063 raise MadGraph5Error, "Specifier '%s' not recognized."%stag[0]
1064
1065 - def get_HwU_histogram_name(self, format='human'):
1066 """ Returns the histogram name in the HwU syntax or human readable.""" 1067 1068 type_map = {'NLO':'NLO', 'LO':'LO', 'AUX':'auxiliary histogram'} 1069 1070 if format=='human': 1071 res = self.title 1072 if not self.type is None: 1073 try: 1074 res += ', %s'%type_map[self.type] 1075 except KeyError: 1076 res += ', %s'%str('NLO' if self.type.split()[0]=='NLO' else 1077 self.type) 1078 if hasattr(self,'jetsample'): 1079 if self.jetsample==-1: 1080 res += ', all jet samples' 1081 else: 1082 res += ', Jet sample %d'%self.jetsample 1083 1084 return res 1085 1086 elif format=='human-no_type': 1087 res = self.title 1088 return res 1089 1090 elif format=='HwU': 1091 res = [self.title] 1092 res.append('|X_AXIS@%s'%self.x_axis_mode) 1093 res.append('|Y_AXIS@%s'%self.y_axis_mode) 1094 if hasattr(self,'jetsample'): 1095 res.append('|JETSAMPLE@%d'%self.jetsample) 1096 if self.type: 1097 res.append('|TYPE@%s'%self.type) 1098 return ' '.join(res)
1099
1100 - def parse_one_histo_from_stream(self, stream, all_weight_header, 1101 consider_reweights='ALL', raw_labels=False, selected_central_weight=None):
1102 """ Reads *one* histogram from a stream, with the mandatory specification 1103 of the ordered list of weight names. Return True or False depending 1104 on whether the starting definition of a new plot could be found in this 1105 stream.""" 1106 n_bins = 0 1107 1108 if consider_reweights=='ALL' or raw_labels: 1109 weight_header = all_weight_header 1110 else: 1111 new_weight_header = [] 1112 # Filter the weights to consider based on the user selection 1113 for wgt_label in all_weight_header: 1114 if wgt_label in ['central','stat_error','boundary_xmin','boundary_xmax'] or\ 1115 HwU.get_HwU_wgt_label_type(wgt_label) in consider_reweights: 1116 new_weight_header.append(wgt_label) 1117 weight_header = new_weight_header 1118 1119 # Find the starting point of the stream 1120 for line in stream: 1121 start = HwU.histo_start_re.match(line) 1122 if not start is None: 1123 self.process_histogram_name(start.group('histo_name')) 1124 # We do not want to include auxiliary diagrams which would be 1125 # recreated anyway. 1126 if self.type == 'AUX': 1127 continue 1128 n_bins = int(start.group('n_bins')) 1129 # Make sure to exclude the boundaries from the weight 1130 # specification 1131 self.bins = BinList(weight_labels = [ wgt_label for 1132 wgt_label in weight_header if wgt_label not in 1133 ['boundary_xmin','boundary_xmax']]) 1134 break 1135 1136 # Now look for the bin weights definition 1137 for line_bin in stream: 1138 bin_weights = {} 1139 boundaries = [0.0,0.0] 1140 for j, weight in \ 1141 enumerate(HwU.histo_bin_weight_re.finditer(line_bin)): 1142 if j == len(all_weight_header): 1143 raise HwU.ParseError, "There is more bin weights"+\ 1144 " specified than expected (%i)"%len(weight_header) 1145 if selected_central_weight == all_weight_header[j]: 1146 bin_weights['central'] = float(weight.group('weight')) 1147 if all_weight_header[j] == 'boundary_xmin': 1148 boundaries[0] = float(weight.group('weight')) 1149 elif all_weight_header[j] == 'boundary_xmax': 1150 boundaries[1] = float(weight.group('weight')) 1151 elif all_weight_header[j] == 'central' and not selected_central_weight is None: 1152 continue 1153 elif all_weight_header[j] in weight_header: 1154 bin_weights[all_weight_header[j]] = \ 1155 float(weight.group('weight')) 1156 1157 # For the HwU format, we know that exactly two 'weights' 1158 # specified in the weight_header are in fact the boundary 1159 # coordinate, so we must subtract two. 1160 if len(bin_weights)<(len(weight_header)-2): 1161 raise HwU.ParseError, " There are only %i weights"\ 1162 %len(bin_weights)+" specified and %i were expected."%\ 1163 (len(weight_header)-2) 1164 self.bins.append(Bin(tuple(boundaries), bin_weights)) 1165 if len(self.bins)==n_bins: 1166 break 1167 1168 if len(self.bins)!=n_bins: 1169 raise HwU.ParseError, "%i bin specification "%len(self.bins)+\ 1170 "were found and %i were expected."%n_bins 1171 1172 # Now jump to the next <\histo> tag. 1173 for line_end in stream: 1174 if HwU.histo_end_re.match(line_end): 1175 # Finally, remove all the auxiliary weights, but only if not 1176 # asking for raw labels 1177 if not raw_labels: 1178 self.trim_auxiliary_weights() 1179 # End of successful parsing this histogram, so return True. 1180 return True 1181 1182 # Could not find a plot definition starter in this stream, return False 1183 return False
1184
1185 - def trim_auxiliary_weights(self):
1186 """ Remove all weights which are auxiliary (whose name end with '@aux') 1187 so that they are not included (they will be regenerated anyway).""" 1188 1189 for i, wgt_label in enumerate(self.bins.weight_labels): 1190 if isinstance(wgt_label, str) and wgt_label.endswith('@aux'): 1191 for bin in self.bins: 1192 try: 1193 del bin.wgts[wgt_label] 1194 except KeyError: 1195 pass 1196 self.bins.weight_labels = [wgt_label for wgt_label in 1197 self.bins.weight_labels if (not isinstance(wgt_label, str) 1198 or (isinstance(wgt_label, str) and not wgt_label.endswith('@aux')) )]
1199
1200 - def set_uncertainty(self, type='all_scale',lhapdfconfig='lhapdf-config'):
1201 """ Adds a weight to the bins which is the envelope of the scale 1202 uncertainty, for the scale specified which can be either 'mur', 'muf', 1203 'all_scale' or 'PDF'.""" 1204 1205 if type.upper()=='MUR': 1206 new_wgt_label = 'delta_mur' 1207 scale_position = 1 1208 elif type.upper()=='MUF': 1209 new_wgt_label = 'delta_muf' 1210 scale_position = 2 1211 elif type.upper()=='ALL_SCALE': 1212 new_wgt_label = 'delta_mu' 1213 scale_position = -1 1214 elif type.upper()=='PDF': 1215 new_wgt_label = 'delta_pdf' 1216 scale_position = -2 1217 elif type.upper()=='MERGING': 1218 new_wgt_label = 'delta_merging' 1219 elif type.upper()=='ALPSFACT': 1220 new_wgt_label = 'delta_alpsfact' 1221 else: 1222 raise MadGraph5Error, ' The function set_uncertainty can'+\ 1223 " only handle the scales 'mur', 'muf', 'all_scale', 'pdf',"+\ 1224 "'merging' or 'alpsfact'." 1225 1226 wgts_to_consider=[] 1227 label_to_consider=[] 1228 if type.upper() == 'MERGING': 1229 # It is a list of list because we consider only the possibility of 1230 # a single "central value" in this case, so the outtermost list is 1231 # always of length 1. 1232 wgts_to_consider.append([ label for label in self.bins.weight_labels if \ 1233 HwU.get_HwU_wgt_label_type(label)=='merging_scale' ]) 1234 label_to_consider.append('none') 1235 1236 elif type.upper() == 'ALPSFACT': 1237 # It is a list of list because we consider only the possibility of 1238 # a single "central value" in this case, so the outtermost list is 1239 # always of length 1. 1240 wgts_to_consider.append([ label for label in self.bins.weight_labels if \ 1241 HwU.get_HwU_wgt_label_type(label)=='alpsfact' ]) 1242 label_to_consider.append('none') 1243 elif scale_position > -2: 1244 ##########: advanced scale 1245 dyn_scales=[label[1] for label in self.bins.weight_labels if \ 1246 HwU.get_HwU_wgt_label_type(label)=='scale_adv'] 1247 # remove doubles in list but keep the order! 1248 dyn_scales=[scale for n,scale in enumerate(dyn_scales) if scale not in dyn_scales[:n]] 1249 for dyn_scale in dyn_scales: 1250 wgts=[label for label in self.bins.weight_labels if \ 1251 HwU.get_HwU_wgt_label_type(label)=='scale_adv' and label[1]==dyn_scale] 1252 if wgts: 1253 wgts_to_consider.append(wgts) 1254 label_to_consider.append(dyn_scale) 1255 ##########: normal scale 1256 wgts=[label for label in self.bins.weight_labels if \ 1257 HwU.get_HwU_wgt_label_type(label)=='scale'] 1258 ## this is for the 7-point variations (excludes mur/muf = 4, 1/4) 1259 #wgts_to_consider = [ label for label in self.bins.weight_labels if \ 1260 # isinstance(label,tuple) and label[0]=='scale' and \ 1261 # not (0.5 in label and 2.0 in label)] 1262 if wgts: 1263 wgts_to_consider.append(wgts) 1264 label_to_consider.append('none') 1265 ##########: remove renormalisation OR factorisation scale dependence... 1266 1267 if scale_position > -1: 1268 for wgts in wgts_to_consider: 1269 wgts_to_consider.remove(wgts) 1270 wgts = [ label for label in wgts if label[-scale_position]==1.0 ] 1271 wgts_to_consider.append(wgts) 1272 elif scale_position == -2: 1273 ##########: advanced PDF 1274 pdf_sets=[label[2] for label in self.bins.weight_labels if \ 1275 HwU.get_HwU_wgt_label_type(label)=='pdf_adv'] 1276 # remove doubles in list but keep the order! 1277 pdf_sets=[ii for n,ii in enumerate(pdf_sets) if ii not in pdf_sets[:n]] 1278 for pdf_set in pdf_sets: 1279 wgts=[label for label in self.bins.weight_labels if \ 1280 HwU.get_HwU_wgt_label_type(label)=='pdf_adv' and label[2]==pdf_set] 1281 if wgts: 1282 wgts_to_consider.append(wgts) 1283 label_to_consider.append(pdf_set) 1284 ##########: normal PDF 1285 wgts = [ label for label in self.bins.weight_labels if \ 1286 HwU.get_HwU_wgt_label_type(label)=='pdf'] 1287 if wgts: 1288 wgts_to_consider.append(wgts) 1289 label_to_consider.append('none') 1290 1291 if len(wgts_to_consider)==0 or all(len(wgts)==0 for wgts in wgts_to_consider): 1292 # No envelope can be constructed, it is not worth adding the weights 1293 return (None,[None]) 1294 1295 # find and import python version of lhapdf if doing PDF uncertainties 1296 if type=='PDF': 1297 use_lhapdf=False 1298 try: 1299 lhapdf_libdir=subprocess.Popen([lhapdfconfig,'--libdir'],\ 1300 stdout=subprocess.PIPE).stdout.read().strip() 1301 except: 1302 use_lhapdf=False 1303 else: 1304 try: 1305 candidates=[dirname for dirname in os.listdir(lhapdf_libdir) \ 1306 if os.path.isdir(os.path.join(lhapdf_libdir,dirname))] 1307 except OSError: 1308 candidates=[] 1309 for candidate in candidates: 1310 if os.path.isfile(os.path.join(lhapdf_libdir,candidate,'site-packages','lhapdf.so')): 1311 sys.path.insert(0,os.path.join(lhapdf_libdir,candidate,'site-packages')) 1312 try: 1313 import lhapdf 1314 use_lhapdf=True 1315 break 1316 except ImportError: 1317 sys.path.pop(0) 1318 continue 1319 1320 if not use_lhapdf: 1321 try: 1322 candidates=[dirname for dirname in os.listdir(lhapdf_libdir+'64') \ 1323 if os.path.isdir(os.path.join(lhapdf_libdir+'64',dirname))] 1324 except OSError: 1325 candidates=[] 1326 for candidate in candidates: 1327 if os.path.isfile(os.path.join(lhapdf_libdir+'64',candidate,'site-packages','lhapdf.so')): 1328 sys.path.insert(0,os.path.join(lhapdf_libdir+'64',candidate,'site-packages')) 1329 try: 1330 import lhapdf 1331 use_lhapdf=True 1332 break 1333 except ImportError: 1334 sys.path.pop(0) 1335 continue 1336 1337 if not use_lhapdf: 1338 try: 1339 import lhapdf 1340 use_lhapdf=True 1341 except ImportError: 1342 logger.warning("Failed to access python version of LHAPDF: "\ 1343 "cannot compute PDF uncertainty from the "\ 1344 "weights in the histograms. The weights in the HwU data files " \ 1345 "still cover all PDF set members, "\ 1346 "but the automatic computation of the uncertainties from "\ 1347 "those weights might not be correct. \n "\ 1348 "If the python interface to LHAPDF is available on your system, try "\ 1349 "adding its location to the PYTHONPATH environment variable and the"\ 1350 "LHAPDF library location to LD_LIBRARY_PATH (linux) or DYLD_LIBRARY_PATH (mac os x).") 1351 1352 if type=='PDF' and use_lhapdf: 1353 lhapdf.setVerbosity(0) 1354 1355 # Place the new weight label last before the first tuple 1356 position=[] 1357 labels=[] 1358 for i,label in enumerate(label_to_consider): 1359 wgts=wgts_to_consider[i] 1360 if label != 'none': 1361 new_wgt_labels=['%s_cen %s @aux' % (new_wgt_label,label), 1362 '%s_min %s @aux' % (new_wgt_label,label), 1363 '%s_max %s @aux' % (new_wgt_label,label)] 1364 else: 1365 new_wgt_labels=['%s_cen @aux' % new_wgt_label, 1366 '%s_min @aux' % new_wgt_label, 1367 '%s_max @aux' % new_wgt_label] 1368 try: 1369 pos=[(not isinstance(lab, str)) for lab in \ 1370 self.bins.weight_labels].index(True) 1371 position.append(pos) 1372 labels.append(label) 1373 self.bins.weight_labels = self.bins.weight_labels[:pos]+\ 1374 new_wgt_labels + self.bins.weight_labels[pos:] 1375 except ValueError: 1376 pos=len(self.bins.weight_labels) 1377 position.append(pos) 1378 labels.append(label) 1379 self.bins.weight_labels.extend(new_wgt_labels) 1380 1381 if type=='PDF' and use_lhapdf and label != 'none': 1382 p=lhapdf.getPDFSet(label) 1383 1384 # Now add the corresponding weight to all Bins 1385 for bin in self.bins: 1386 if type!='PDF': 1387 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]] 1388 bin.wgts[new_wgt_labels[1]] = min(bin.wgts[label] \ 1389 for label in wgts) 1390 bin.wgts[new_wgt_labels[2]] = max(bin.wgts[label] \ 1391 for label in wgts) 1392 elif type=='PDF' and use_lhapdf and label != 'none' and len(wgts) > 1: 1393 pdfs = [bin.wgts[pdf] for pdf in sorted(wgts)] 1394 ep=p.uncertainty(pdfs,-1) 1395 bin.wgts[new_wgt_labels[0]] = ep.central 1396 bin.wgts[new_wgt_labels[1]] = ep.central-ep.errminus 1397 bin.wgts[new_wgt_labels[2]] = ep.central+ep.errplus 1398 elif type=='PDF' and use_lhapdf and label != 'none' and len(bin.wgts) == 1: 1399 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]] 1400 bin.wgts[new_wgt_labels[1]] = bin.wgts[wgts[0]] 1401 bin.wgts[new_wgt_labels[2]] = bin.wgts[wgts[0]] 1402 else: 1403 pdfs = [bin.wgts[pdf] for pdf in sorted(wgts)] 1404 pdf_up = 0.0 1405 pdf_down = 0.0 1406 cntrl_val = bin.wgts['central'] 1407 if wgts[0] <= 90000: 1408 # use Hessian method (CTEQ & MSTW) 1409 if len(pdfs)>2: 1410 for i in range(int((len(pdfs)-1)/2)): 1411 pdf_up += max(0.0,pdfs[2*i+1]-cntrl_val, 1412 pdfs[2*i+2]-cntrl_val)**2 1413 pdf_down += max(0.0,cntrl_val-pdfs[2*i+1], 1414 cntrl_val-pdfs[2*i+2])**2 1415 pdf_up = cntrl_val + math.sqrt(pdf_up) 1416 pdf_down = cntrl_val - math.sqrt(pdf_down) 1417 else: 1418 pdf_up = bin.wgts[pdfs[0]] 1419 pdf_down = bin.wgts[pdfs[0]] 1420 elif wgts[0] in range(90200, 90303) or \ 1421 wgts[0] in range(90400, 90433) or \ 1422 wgts[0] in range(90700, 90801) or \ 1423 wgts[0] in range(90900, 90931) or \ 1424 wgts[0] in range(91200, 91303) or \ 1425 wgts[0] in range(91400, 91433) or \ 1426 wgts[0] in range(91700, 91801) or \ 1427 wgts[0] in range(91900, 90931) or \ 1428 wgts[0] in range(92000, 92031): 1429 # PDF4LHC15 Hessian sets 1430 pdf_stdev = 0.0 1431 for pdf in pdfs[1:]: 1432 pdf_stdev += (pdf - cntrl_val)**2 1433 pdf_stdev = math.sqrt(pdf_stdev) 1434 pdf_up = cntrl_val+pdf_stdev 1435 pdf_down = cntrl_val-pdf_stdev 1436 elif wgts[0] in range(244400, 244501) or \ 1437 wgts[0] in range(244600, 244701) or \ 1438 wgts[0] in range(244800, 244901) or \ 1439 wgts[0] in range(245000, 245101) or \ 1440 wgts[0] in range(245200, 245301) or \ 1441 wgts[0] in range(245400, 245501) or \ 1442 wgts[0] in range(245600, 245701) or \ 1443 wgts[0] in range(245800, 245901) or \ 1444 wgts[0] in range(246000, 246101) or \ 1445 wgts[0] in range(246200, 246301) or \ 1446 wgts[0] in range(246400, 246501) or \ 1447 wgts[0] in range(246600, 246701) or \ 1448 wgts[0] in range(246800, 246901) or \ 1449 wgts[0] in range(247000, 247101) or \ 1450 wgts[0] in range(247200, 247301) or \ 1451 wgts[0] in range(247400, 247501): 1452 # use Gaussian (68%CL) method (NNPDF) 1453 pdf_stdev = 0.0 1454 pdf_diff = sorted([abs(pdf-cntrl_val) for pdf in pdfs[1:]]) 1455 pdf_stdev = pdf_diff[67] 1456 pdf_up = cntrl_val+pdf_stdev 1457 pdf_down = cntrl_val-pdf_stdev 1458 else: 1459 # use Gaussian (one sigma) method (NNPDF) 1460 pdf_stdev = 0.0 1461 for pdf in pdfs[1:]: 1462 pdf_stdev += (pdf - cntrl_val)**2 1463 pdf_stdev = math.sqrt(pdf_stdev/float(len(pdfs)-2)) 1464 pdf_up = cntrl_val+pdf_stdev 1465 pdf_down = cntrl_val-pdf_stdev 1466 # Finally add them to the corresponding new weight 1467 bin.wgts[new_wgt_labels[0]] = bin.wgts[wgts[0]] 1468 bin.wgts[new_wgt_labels[1]] = pdf_down 1469 bin.wgts[new_wgt_labels[2]] = pdf_up 1470 1471 # And return the position in self.bins.weight_labels of the first 1472 # of the two new weight label added. 1473 return (position,labels)
1474
1475 - def select_central_weight(self, selected_label):
1476 """ Select a specific merging scale for the central value of this Histogram. """ 1477 if selected_label not in self.bins.weight_labels: 1478 raise MadGraph5Error, "Selected weight label '%s' could not be found in this HwU."%selected_label 1479 1480 for bin in self.bins: 1481 bin.wgts['central']=bin.wgts[selected_label]
1482
1483 - def rebin(self, n_rebin):
1484 """ Rebin the x-axis so as to merge n_rebin consecutive bins into a 1485 single one. """ 1486 1487 if n_rebin < 1 or not isinstance(n_rebin, int): 1488 raise MadGraph5Error, "The argument 'n_rebin' of the HwU function"+\ 1489 " 'rebin' must be larger or equal to 1, not '%s'."%str(n_rebin) 1490 elif n_rebin==1: 1491 return 1492 1493 if self.type and 'NOREBIN' in self.type.upper(): 1494 return 1495 1496 rebinning_list = list(range(0,len(self.bins),n_rebin))+[len(self.bins),] 1497 concat_list = [self.bins[rebinning_list[i]:rebinning_list[i+1]] for \ 1498 i in range(len(rebinning_list)-1)] 1499 1500 new_bins = copy.copy(self.bins) 1501 del new_bins[:] 1502 1503 for bins_to_merge in concat_list: 1504 if len(bins_to_merge)==0: 1505 continue 1506 new_bins.append(Bin(boundaries=(bins_to_merge[0].boundaries[0], 1507 bins_to_merge[-1].boundaries[1]),wgts={'central':0.0})) 1508 for weight in self.bins.weight_labels: 1509 if weight != 'stat_error': 1510 new_bins[-1].wgts[weight] = \ 1511 sum(b.wgts[weight] for b in bins_to_merge) 1512 else: 1513 new_bins[-1].wgts['stat_error'] = \ 1514 math.sqrt(sum(b.wgts['stat_error']**2 for b in\ 1515 bins_to_merge)) 1516 1517 self.bins = new_bins
1518 1519 @classmethod
1520 - def get_x_optimal_range(cls, histo_list, weight_labels=None):
1521 """ Function to determine the optimal x-axis range when plotting 1522 together the histos in histo_list and considering the weights 1523 weight_labels""" 1524 1525 # If no list of weight labels to consider is given, use them all. 1526 if weight_labels is None: 1527 weight_labels = histo_list[0].bins.weight_labels 1528 1529 all_boundaries = sum([ list(bin.boundaries) for histo in histo_list \ 1530 for bin in histo.bins if \ 1531 (sum(abs(bin.wgts[label]) for label in weight_labels) > 0.0)] ,[]) 1532 1533 if len(all_boundaries)==0: 1534 all_boundaries = sum([ list(bin.boundaries) for histo in histo_list \ 1535 for bin in histo.bins],[]) 1536 if len(all_boundaries)==0: 1537 raise MadGraph5Error, "The histograms with title '%s'"\ 1538 %histo_list[0].title+" seems to have no bins." 1539 1540 x_min = min(all_boundaries) 1541 x_max = max(all_boundaries) 1542 1543 return (x_min, x_max)
1544 1545 @classmethod
1546 - def get_y_optimal_range(cls,histo_list, labels=None, 1547 scale='LOG', Kratio = False):
1548 """ Function to determine the optimal y-axis range when plotting 1549 together the histos in histo_list and considering the weights 1550 weight_labels. The option Kratio is present to allow for the couple of 1551 tweaks necessary for the the K-factor ratio histogram y-range.""" 1552 1553 # If no list of weight labels to consider is given, use them all. 1554 if labels is None: 1555 weight_labels = histo_list[0].bins.weight_labels 1556 else: 1557 weight_labels = labels 1558 1559 all_weights = [] 1560 for histo in histo_list: 1561 for bin in histo.bins: 1562 for label in weight_labels: 1563 # Filter out bin weights at *exactly* because they often 1564 # come from pathological division by zero for empty bins. 1565 if Kratio and bin.wgts[label]==0.0: 1566 continue 1567 if scale!='LOG': 1568 all_weights.append(bin.wgts[label]) 1569 if label == 'stat_error': 1570 all_weights.append(-bin.wgts[label]) 1571 elif bin.wgts[label]>0.0: 1572 all_weights.append(bin.wgts[label]) 1573 1574 1575 sum([ [bin.wgts[label] for label in weight_labels if \ 1576 (scale!='LOG' or bin.wgts[label]!=0.0)] \ 1577 for histo in histo_list for bin in histo.bins], []) 1578 1579 all_weights.sort() 1580 if len(all_weights)!=0: 1581 partial_max = all_weights[int(len(all_weights)*0.95)] 1582 partial_min = all_weights[int(len(all_weights)*0.05)] 1583 max = all_weights[-1] 1584 min = all_weights[0] 1585 else: 1586 if scale!='LOG': 1587 return (0.0,1.0) 1588 else: 1589 return (1.0,10.0) 1590 1591 y_max = 0.0 1592 y_min = 0.0 1593 1594 # If the maximum is too far from the 90% max, then take the partial max 1595 if (max-partial_max)>2.0*(partial_max-partial_min): 1596 y_max = partial_max 1597 else: 1598 y_max = max 1599 1600 # If the maximum is too far from the 90% max, then take the partial max 1601 if (partial_min - min)>2.0*(partial_max-partial_min) and min != 0.0: 1602 y_min = partial_min 1603 else: 1604 y_min = min 1605 1606 if Kratio: 1607 median = all_weights[len(all_weights)//2] 1608 spread = (y_max-y_min) 1609 if abs(y_max-median)<spread*0.05 or abs(median-y_min)<spread*0.05: 1610 y_max = median + spread/2.0 1611 y_min = median - spread/2.0 1612 if y_min != y_max: 1613 return ( y_min , y_max ) 1614 1615 # Enforce the maximum if there is 5 bins or less 1616 if len(histo_list[0].bins) <= 5: 1617 y_min = min 1618 y_max = max 1619 1620 # Finally make sure the range has finite length 1621 if y_min == y_max: 1622 if max == min: 1623 y_min -= 1.0 1624 y_max += 1.0 1625 else: 1626 y_min = min 1627 y_max = max 1628 1629 return ( y_min , y_max )
1630
1631 -class HwUList(histograms_PhysicsObjectList):
1632 """ A class implementing features related to a list of Hwu Histograms. """ 1633 1634 # Define here the number of line color schemes defined. If you need more, 1635 # simply define them in the gnuplot header and increase the number below. 1636 # It must be <= 9. 1637 number_line_colors_defined = 8 1638
1639 - def is_valid_element(self, obj):
1640 """Test wether specified object is of the right type for this list.""" 1641 1642 return isinstance(obj, HwU) or isinstance(obj, HwUList)
1643
1644 - def __init__(self, file_path, weight_header=None, run_id=None, 1645 merging_scale=None, accepted_types_order=[], consider_reweights='ALL', 1646 raw_labels=False, **opts):
1647 """ Read one plot from a file_path or a stream. 1648 This constructor reads all plots specified in target file. 1649 File_path can be a path or a stream in the argument. 1650 The option weight_header specifies an ordered list of weight names 1651 to appear in the file or stream specified. It accepted_types_order is 1652 empty, no filter is applied, otherwise only histograms of the specified 1653 types will be kept, and in this specified order for a given identical 1654 title. The option 'consider_reweights' selects whether one wants to 1655 include all the extra scale/pdf/merging variation weights. Possible values 1656 are 'ALL' or a list of the return types of the function get_HwU_wgt_label_type(). 1657 The option 'raw_labels' specifies that one wants to import the 1658 histogram data with no treatment of the weight labels at all 1659 (this is used for the matplotlib output). 1660 """ 1661 1662 if isinstance(file_path, str): 1663 stream = open(file_path,'r') 1664 elif isinstance(file_path, file): 1665 stream = file_path 1666 else: 1667 return super(HwUList,self).__init__(file_path, **opts) 1668 1669 try: 1670 # Try to read it in XML format 1671 self.parse_histos_from_PY8_XML_stream(stream, run_id, 1672 merging_scale, accepted_types_order, 1673 consider_reweights=consider_reweights, 1674 raw_labels=raw_labels) 1675 except XMLParsingError: 1676 # Rewinding the stream 1677 stream.seek(0) 1678 # Attempt to find the weight headers if not specified 1679 if not weight_header: 1680 weight_header = HwU.parse_weight_header(stream,raw_labels=raw_labels) 1681 1682 # Select a specific merging scale if asked for: 1683 selected_label = None 1684 if not merging_scale is None: 1685 for label in weight_header: 1686 if HwU.get_HwU_wgt_label_type(label)=='merging_scale': 1687 if float(label[1])==merging_scale: 1688 selected_label = label 1689 break 1690 if selected_label is None: 1691 raise MadGraph5Error, "No weight could be found in the input HwU "+\ 1692 "for the selected merging scale '%4.2f'."%merging_scale 1693 1694 new_histo = HwU(stream, weight_header,raw_labels=raw_labels, 1695 consider_reweights=consider_reweights, 1696 selected_central_weight=selected_label) 1697 # new_histo.select_central_weight(selected_label) 1698 while not new_histo.bins is None: 1699 if accepted_types_order==[] or \ 1700 new_histo.type in accepted_types_order: 1701 self.append(new_histo) 1702 new_histo = HwU(stream, weight_header, raw_labels=raw_labels, 1703 consider_reweights=consider_reweights, 1704 selected_central_weight=selected_label) 1705 1706 # if not run_id is None: 1707 # logger.debug("The run_id '%s' was specified, but "%run_id+ 1708 # "format of the HwU plot source is the MG5aMC"+ 1709 # " so that the run_id information is ignored.") 1710 1711 # Order the histograms according to their type. 1712 titles_order = [h.title for h in self] 1713 def ordering_function(histo): 1714 title_position = titles_order.index(histo.title) 1715 if accepted_types_order==[]: 1716 type_precedence = {'NLO':1,'LO':2,None:3,'AUX':5} 1717 try: 1718 ordering_key = (title_position,type_precedence[histo.type]) 1719 except KeyError: 1720 ordering_key = (title_position,4) 1721 else: 1722 ordering_key = (title_position, 1723 accepted_types_order.index(histo.type)) 1724 return ordering_key
1725 1726 # The command below is to first order them in alphabetical order, but it 1727 # is often better to keep the order of the original HwU source. 1728 # self.sort(key=lambda histo: '%s_%d'%(histo.title, 1729 # type_order.index(histo.type))) 1730 self.sort(key=ordering_function) 1731 1732 # Explicitly close the opened stream for clarity. 1733 if isinstance(file_path, str): 1734 stream.close()
1735
1736 - def get_hist_names(self):
1737 """return a list of all the names of define histograms""" 1738 1739 output = [] 1740 for hist in self: 1741 output.append(hist.get_HwU_histogram_name()) 1742 return output
1743
1744 - def get_wgt_names(self):
1745 """ return the list of all weights define in each histograms""" 1746 1747 return self[0].bins.weight_labels
1748 1749
1750 - def get(self, name):
1751 """return the HWU histograms related to a given name""" 1752 for hist in self: 1753 if hist.get_HwU_histogram_name() == name: 1754 return hist 1755 1756 raise NameError, "no histogram with name: %s" % name
1757
1758 - def parse_histos_from_PY8_XML_stream(self, stream, run_id=None, 1759 merging_scale=None, accepted_types_order=[], 1760 consider_reweights='ALL', raw_labels=False):
1761 """Initialize the HwU histograms from an XML stream. Only one run is 1762 used: the first one if run_id is None or the specified run otherwise. 1763 Accepted type order is a filter to select histograms of only a certain 1764 type. The option 'consider_reweights' selects whether one wants to 1765 include all the extra scale/pdf/merging variation weights. 1766 Possible values are 'ALL' or a list of the return types of the 1767 function get_HwU_wgt_label_type().""" 1768 1769 run_nodes = minidom.parse(stream).getElementsByTagName("run") 1770 all_nodes = dict((int(node.getAttribute('id')),node) for 1771 node in run_nodes) 1772 selected_run_node = None 1773 weight_header = None 1774 if run_id is None: 1775 if len(run_nodes)>0: 1776 selected_run_node = all_nodes[min(all_nodes.keys())] 1777 else: 1778 try: 1779 selected_run_node = all_nodes[int(run_id)] 1780 except: 1781 selected_run_node = None 1782 1783 if selected_run_node is None: 1784 if run_id is None: 1785 raise MadGraph5Error, \ 1786 'No histogram was found in the specified XML source.' 1787 else: 1788 raise MadGraph5Error, \ 1789 "Histogram with run_id '%d' was not found in the "%run_id+\ 1790 "specified XML source." 1791 1792 # If raw weight label are asked for, then simply read the weight_labels 1793 # directly as specified in the XML header 1794 if raw_labels: 1795 # Filter empty weights coming from the split 1796 weight_label_list = [wgt.strip() for wgt in 1797 str(selected_run_node.getAttribute('header')).split(';') if 1798 not re.match('^\s*$',wgt)] 1799 ordered_weight_label_list = [w for w in weight_label_list if w not\ 1800 in ['xmin','xmax']] 1801 # Remove potential repetition of identical weight labels 1802 filtered_ordered_weight_label_list = [] 1803 for wgt_label in ordered_weight_label_list: 1804 if wgt_label not in filtered_ordered_weight_label_list: 1805 filtered_ordered_weight_label_list.append(wgt_label) 1806 1807 selected_weights = dict([ (wgt_pos, 1808 [wgt if wgt not in ['xmin','xmax'] else HwU.mandatory_weights[wgt]]) 1809 for wgt_pos, wgt in enumerate(weight_label_list) if wgt in 1810 filtered_ordered_weight_label_list+['xmin','xmax']]) 1811 1812 return self.retrieve_plots_from_XML_source(selected_run_node, 1813 selected_weights, filtered_ordered_weight_label_list, 1814 raw_labels=True) 1815 1816 # Now retrieve the header and save all weight labels as dictionaries 1817 # with key being properties and their values as value. If the property 1818 # does not defined a value, then put None as a value 1819 all_weights = [] 1820 for wgt_position, wgt_label in \ 1821 enumerate(str(selected_run_node.getAttribute('header')).split(';')): 1822 if not re.match('^\s*$',wgt_label) is None: 1823 continue 1824 all_weights.append({'POSITION':wgt_position}) 1825 for wgt_item in wgt_label.strip().split('_'): 1826 property = wgt_item.strip().split('=') 1827 if len(property) == 2: 1828 all_weights[-1][property[0].strip()] = property[1].strip() 1829 elif len(property)==1: 1830 all_weights[-1][property[0].strip()] = None 1831 else: 1832 raise MadGraph5Error, \ 1833 "The weight label property %s could not be parsed."%wgt_item 1834 1835 # Now make sure that for all weights, there is 'PDF', 'MUF' and 'MUR' 1836 # and 'MERGING' defined. If absent we specify '-1' which implies that 1837 # the 'default' value was used (whatever it was). 1838 # Also cast them in the proper type 1839 for wgt_label in all_weights: 1840 for mandatory_attribute in ['PDF','MUR','MUF','MERGING','ALPSFACT']: 1841 if mandatory_attribute not in wgt_label: 1842 wgt_label[mandatory_attribute] = '-1' 1843 if mandatory_attribute=='PDF': 1844 wgt_label[mandatory_attribute] = int(wgt_label[mandatory_attribute]) 1845 elif mandatory_attribute in ['MUR','MUF','MERGING','ALPSFACT']: 1846 wgt_label[mandatory_attribute] = float(wgt_label[mandatory_attribute]) 1847 1848 # If merging cut is negative, then pick only the one of the central scale 1849 # If not specified, then take them all but use the PDF and scale weight 1850 # of the central merging_scale for the variation. 1851 if merging_scale is None or merging_scale < 0.0: 1852 merging_scale_chosen = all_weights[2]['MERGING'] 1853 else: 1854 merging_scale_chosen = merging_scale 1855 1856 # Central weight parameters are enforced to be those of the third weight 1857 central_PDF = all_weights[2]['PDF'] 1858 # Assume central scale is one, unless specified. 1859 central_MUR = all_weights[2]['MUR'] if all_weights[2]['MUR']!=-1.0 else 1.0 1860 central_MUF = all_weights[2]['MUF'] if all_weights[2]['MUF']!=-1.0 else 1.0 1861 central_alpsfact = all_weights[2]['ALPSFACT'] if all_weights[2]['ALPSFACT']!=-1.0 else 1.0 1862 1863 # Dictionary of selected weights with their position as key and the 1864 # list of weight labels they correspond to. 1865 selected_weights = {} 1866 # Treat the first four weights in a special way: 1867 if 'xmin' not in all_weights[0] or \ 1868 'xmax' not in all_weights[1] or \ 1869 'Weight' not in all_weights[2] or \ 1870 'WeightError' not in all_weights[3]: 1871 raise MadGraph5Error, 'The first weight entries in the XML HwU '+\ 1872 ' source are not the standard expected ones (xmin, xmax, sigmaCentral, errorCentral)' 1873 selected_weights[0] = ['xmin'] 1874 selected_weights[1] = ['xmax'] 1875 1876 # =========== BEGIN HELPER FUNCTIONS =========== 1877 def get_difference_to_central(weight): 1878 """ Return the list of properties which differ from the central weight. 1879 This disregards the merging scale value for which any central value 1880 can be picked anyway.""" 1881 1882 differences = [] 1883 # If the tag 'Weight' is in the weight label, then this is 1884 # automatically considered as the Event weight (central) for which 1885 # only the merging scale can be different 1886 if 'Weight' in weight: 1887 return set([]) 1888 if weight['MUR'] not in [central_MUR, -1.0] or \ 1889 weight['MUF'] not in [central_MUF, -1.0]: 1890 differences.append('mur_muf_scale') 1891 if weight['PDF'] not in [central_PDF,-1]: 1892 differences.append('pdf') 1893 if weight['ALPSFACT'] not in [central_alpsfact, -1]: 1894 differences.append('ALPSFACT') 1895 return set(differences)
1896 1897 def format_weight_label(weight): 1898 """ Print the weight attributes in a nice order.""" 1899 1900 all_properties = weight.keys() 1901 all_properties.pop(all_properties.index('POSITION')) 1902 ordered_properties = [] 1903 # First add the attributes without value 1904 for property in all_properties: 1905 if weight[property] is None: 1906 ordered_properties.append(property) 1907 1908 ordered_properties.sort() 1909 all_properties = [property for property in all_properties if 1910 not weight[property] is None] 1911 1912 # then add PDF, MUR, MUF and MERGING if present 1913 for property in ['PDF','MUR','MUF','ALPSFACT','MERGING']: 1914 all_properties.pop(all_properties.index(property)) 1915 if weight[property]!=-1: 1916 ordered_properties.append(property) 1917 1918 ordered_properties.extend(sorted(all_properties)) 1919 1920 return '_'.join('%s%s'\ 1921 %(key,'' if weight[key] is None else '=%s'%str(weight[key])) for 1922 key in ordered_properties) 1923 # =========== END HELPER FUNCTIONS =========== 1924 1925 1926 # The central value is not necessarily the 3rd one if a different merging 1927 # cut was selected. 1928 if float(all_weights[2]['MERGING']) == merging_scale_chosen: 1929 selected_weights[2]=['central value'] 1930 else: 1931 for weight_position, weight in enumerate(all_weights): 1932 # Check if that weight corresponds to a central weight 1933 # (conventional label for central weight is 'Weight' 1934 if get_difference_to_central(weight)==set([]): 1935 # Check if the merging scale matches this time 1936 if weight['MERGING']==merging_scale_chosen: 1937 selected_weights[weight_position] = ['central value'] 1938 break 1939 # Make sure a central value was found, throw a warning if found 1940 if 'central value' not in sum(selected_weights.values(),[]): 1941 central_merging_scale = all_weights[2]['MERGING'] 1942 logger.warning('Could not find the central weight for the'+\ 1943 ' chosen merging scale (%f).\n'%merging_scale_chosen+\ 1944 'MG5aMC will chose the original central scale provided which '+\ 1945 'correspond to a merging scale of %s'%("'inclusive'" if 1946 central_merging_scale in [0.0,-1.0] else '%f'%central_merging_scale)) 1947 selected_weights[2]=['central value'] 1948 1949 # The error is always the third entry for now. 1950 selected_weights[3]=['dy'] 1951 1952 # Now process all other weights 1953 for weight_position, weight in enumerate(all_weights[4:]): 1954 # Apply special transformation for the weight label: 1955 # scale variation are stored as: 1956 # ('scale', mu_r, mu_f) for scale variation 1957 # ('pdf',PDF) for PDF variation 1958 # ('merging_scale',float) for merging scale 1959 # ('type',value) for all others (e.g. alpsfact) 1960 variations = get_difference_to_central(weight) 1961 # We know select the 'diagonal' variations where each parameter 1962 # is varied one at a time. 1963 1964 # Accept also if both pdf and mur_muf_scale differ because 1965 # the PDF used for the Event weight is often unknown but the 1966 # mu_r and mu_f variational weight specify it. Same story for 1967 # alpsfact. 1968 if variations in [set(['mur_muf_scale']),set(['pdf','mur_muf_scale'])]: 1969 wgt_label = ('scale',weight['MUR'],weight['MUF']) 1970 if variations in [set(['ALPSFACT']),set(['pdf','ALPSFACT'])]: 1971 wgt_label = ('alpsfact',weight['ALPSFACT']) 1972 if variations == set(['pdf']): 1973 wgt_label = ('pdf',weight['PDF']) 1974 if variations == set([]): 1975 # Unknown weight (might turn out to be taken as a merging variation weight below) 1976 wgt_label = format_weight_label(weight) 1977 1978 # Make sure the merging scale matches the chosen one 1979 if weight['MERGING'] != merging_scale_chosen: 1980 # If a merging_scale was specified, then ignore all other weights 1981 if merging_scale: 1982 continue 1983 # Otherwise consider them also, but for now only if it is for 1984 # the central value parameter (central PDF, central mu_R and mu_F) 1985 if variations == set([]): 1986 # We choose to store the merging variation weight labels as floats 1987 wgt_label = ('merging_scale', weight['MERGING']) 1988 # Make sure that the weight label does not already exist. If it does, 1989 # this means that the source has redundant information and that 1990 # there is no need to specify it again. 1991 if wgt_label in sum(selected_weights.values(),[]): 1992 continue 1993 1994 # Now register the selected weight 1995 try: 1996 selected_weights[weight_position+4].append(wgt_label) 1997 except KeyError: 1998 selected_weights[weight_position+4]=[wgt_label,] 1999 2000 if merging_scale and merging_scale > 0.0 and \ 2001 len(sum(selected_weights.values(),[]))==4: 2002 logger.warning('No additional variation weight was found for the '+\ 2003 'chosen merging scale %f.'%merging_scale) 2004 2005 # Make sure to use the predefined keywords for the mandatory weight labels 2006 for wgt_pos in selected_weights: 2007 for i, weight_label in enumerate(selected_weights[wgt_pos]): 2008 try: 2009 selected_weights[wgt_pos][i] = HwU.mandatory_weights[weight_label] 2010 except KeyError: 2011 pass 2012 2013 # Keep only the weights asked for 2014 if consider_reweights!='ALL': 2015 new_selected_weights = {} 2016 for wgt_position, wgt_labels in selected_weights.items(): 2017 for wgt_label in wgt_labels: 2018 if wgt_label in ['central','stat_error','boundary_xmin','boundary_xmax'] or\ 2019 HwU.get_HwU_wgt_label_type(wgt_label) in consider_reweights: 2020 try: 2021 new_selected_weights[wgt_position].append(wgt_label) 2022 except KeyError: 2023 new_selected_weights[wgt_position] = [wgt_label] 2024 selected_weights = new_selected_weights 2025 2026 # Cache the list of selected weights to be defined at each line 2027 weight_label_list = sum(selected_weights.values(),[]) 2028 2029 # The weight_label list to set to self.bins 2030 ordered_weight_label_list = ['central','stat_error'] 2031 for weight_label in weight_label_list: 2032 if not isinstance(weight_label, str): 2033 ordered_weight_label_list.append(weight_label) 2034 for weight_label in weight_label_list: 2035 if weight_label in ['central','stat_error','boundary_xmin','boundary_xmax']: 2036 continue 2037 if isinstance(weight_label, str): 2038 ordered_weight_label_list.append(weight_label) 2039 2040 # Now that we know the desired weights, retrieve all plots from the 2041 # XML source node. 2042 return self.retrieve_plots_from_XML_source(selected_run_node, 2043 selected_weights, ordered_weight_label_list, raw_labels=False) 2044
2045 - def retrieve_plots_from_XML_source(self, xml_node, 2046 selected_weights, ordered_weight_label_list,raw_labels=False):
2047 """Given an XML node and the selected weights and their ordered list, 2048 import all histograms from the specified XML node.""" 2049 2050 # We now start scanning all the plots 2051 for multiplicity_node in xml_node.getElementsByTagName("jethistograms"): 2052 multiplicity = int(multiplicity_node.getAttribute('njet')) 2053 for histogram in multiplicity_node.getElementsByTagName("histogram"): 2054 # We only consider the histograms with all the weight information 2055 if histogram.getAttribute("weight")!='all': 2056 continue 2057 new_histo = HwU() 2058 hist_name = '%s %s'%(str(histogram.getAttribute('name')), 2059 str(histogram.getAttribute('unit'))) 2060 # prepend the jet multiplicity to the histogram name 2061 new_histo.process_histogram_name('%s |JETSAMPLE@%d'%(hist_name,multiplicity)) 2062 # We do not want to include auxiliary diagrams which would be 2063 # recreated anyway. 2064 if new_histo.type == 'AUX': 2065 continue 2066 # Make sure to exclude the boundaries from the weight 2067 # specification 2068 # Order the weights so that the unreckognized ones go last 2069 new_histo.bins = BinList(weight_labels = ordered_weight_label_list) 2070 hist_data = str(histogram.childNodes[0].data) 2071 for line in hist_data.split('\n'): 2072 if line.strip()=='': 2073 continue 2074 bin_weights = {} 2075 boundaries = [0.0,0.0] 2076 for j, weight in \ 2077 enumerate(HwU.histo_bin_weight_re.finditer(line)): 2078 try: 2079 for wgt_label in selected_weights[j]: 2080 if wgt_label == 'boundary_xmin': 2081 boundaries[0] = float(weight.group('weight')) 2082 elif wgt_label == 'boundary_xmax': 2083 boundaries[1] = float(weight.group('weight')) 2084 else: 2085 if weight.group('weight').upper()=='NAN': 2086 raise MadGraph5Error, \ 2087 "Some weights are found to be 'NAN' in histogram with name '%s'"%hist_name+\ 2088 " and jet sample multiplicity %d."%multiplicity 2089 else: 2090 bin_weights[wgt_label] = \ 2091 float(weight.group('weight')) 2092 except KeyError: 2093 continue 2094 # For this check, we subtract two because of the bin boundaries 2095 if len(bin_weights)!=len(ordered_weight_label_list): 2096 raise MadGraph5Error, \ 2097 'Not all defined weights were found in the XML source.\n'+\ 2098 '%d found / %d expected.'%(len(bin_weights),len(ordered_weight_label_list))+\ 2099 '\nThe missing ones are: %s.'%\ 2100 str(list(set(ordered_weight_label_list)-set(bin_weights.keys())))+\ 2101 "\nIn plot with title '%s' and jet sample multiplicity %d."%\ 2102 (hist_name, multiplicity) 2103 2104 new_histo.bins.append(Bin(tuple(boundaries), bin_weights)) 2105 2106 # if bin_weights['central']!=0.0: 2107 # print '---------' 2108 # print 'multiplicity =',multiplicity 2109 # print 'central =', bin_weights['central'] 2110 # print 'PDF = ', [(key,bin_weights[key]) for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='pdf'] 2111 # print 'PDF min/max =',min(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='pdf'),max(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='pdf') 2112 # print 'scale = ', [(key,bin_weights[key]) for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='scale'] 2113 # print 'scale min/max =',min(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='scale'),max(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='scale') 2114 # print 'merging = ', [(key,bin_weights[key]) for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='merging_scale'] 2115 # print 'merging min/max =',min(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='merging_scale'),max(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='merging_scale') 2116 # print 'alpsfact = ', [(key,bin_weights[key]) for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='alpsfact'] 2117 # print 'alpsfact min/max =',min(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='alpsfact'),max(bin_weights[key] for key in bin_weights if HwU.get_HwU_wgt_label_type(key)=='alpsfact') 2118 # print '---------' 2119 # stop 2120 2121 # Finally remove auxiliary weights 2122 if not raw_labels: 2123 new_histo.trim_auxiliary_weights() 2124 2125 # And add it to the list 2126 self.append(new_histo)
2127
2128 - def output(self, path, format='gnuplot',number_of_ratios = -1, 2129 uncertainties=['scale','pdf','statitistical','merging_scale','alpsfact'], 2130 use_band = None, 2131 ratio_correlations=True, arg_string='', 2132 jet_samples_to_keep=None, 2133 auto_open=True, 2134 lhapdfconfig='lhapdf-config'):
2135 """ Ouput this histogram to a file, stream or string if path is kept to 2136 None. The supported format are for now. Chose whether to print the header 2137 or not.""" 2138 2139 if len(self)==0: 2140 return MadGraph5Error, 'No histograms stored in the list yet.' 2141 2142 if not format in HwU.output_formats_implemented: 2143 raise MadGraph5Error, "The specified output format '%s'"%format+\ 2144 " is not yet supported. Supported formats are %s."\ 2145 %HwU.output_formats_implemented 2146 2147 if isinstance(path, str) and not any(ext in os.path.basename(path) \ 2148 for ext in ['.Hwu','.ps','.gnuplot','.pdf']): 2149 output_base_name = os.path.basename(path) 2150 HwU_stream = open(path+'.HwU','w') 2151 else: 2152 raise MadGraph5Error, "The path argument of the output function of"+\ 2153 " the HwUList instance must be file path without its extension." 2154 2155 HwU_output_list = [] 2156 # If the format is just the raw HwU source, then simply write them 2157 # out all in sequence. 2158 if format == 'HwU': 2159 HwU_output_list.extend(self[0].get_HwU_source(print_header=True)) 2160 for histo in self[1:]: 2161 HwU_output_list.extend(histo.get_HwU_source()) 2162 HwU_output_list.extend(['','']) 2163 HwU_stream.write('\n'.join(HwU_output_list)) 2164 HwU_stream.close() 2165 return 2166 2167 # Now we consider that we are attempting a gnuplot output. 2168 if format == 'gnuplot': 2169 gnuplot_stream = open(path+'.gnuplot','w') 2170 2171 # Now group all the identified matching histograms in a list 2172 matching_histo_lists = HwUList([HwUList([self[0]])]) 2173 for histo in self[1:]: 2174 matched = False 2175 for histo_list in matching_histo_lists: 2176 if histo.test_plot_compability(histo_list[0], 2177 consider_type=False, consider_unknown_weight_labels=True): 2178 histo_list.append(histo) 2179 matched = True 2180 break 2181 if not matched: 2182 matching_histo_lists.append(HwUList([histo])) 2183 2184 self[:] = matching_histo_lists 2185 2186 # Write the gnuplot header 2187 gnuplot_output_list_v4 = [ 2188 """ 2189 ################################################################################ 2190 # 2191 # This gnuplot file was generated by MadGraph5_aMC@NLO project, a program which 2192 # automatically generates Feynman diagrams and matrix elements for arbitrary 2193 # high-energy processes in the Standard Model and beyond. It also perform the 2194 # integration and/or generate events for these processes, at LO and NLO accuracy. 2195 # 2196 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch 2197 # 2198 ################################################################################ 2199 # %s 2200 reset 2201 2202 set lmargin 10 2203 set rmargin 0 2204 set terminal postscript portrait enhanced mono dashed lw 1.0 "Helvetica" 9 2205 # The pdf terminal offers transparency support, but you will have to adapt things a bit 2206 #set terminal pdf enhanced font "Helvetica 12" lw 1.0 dashed size 29.7cm, 21cm 2207 set key font ",9" 2208 set key samplen "2" 2209 set output "%s.ps" 2210 2211 # This is the "PODO" color palette of gnuplot v.5, but with the order 2212 # changed: palette of colors selected to be easily distinguishable by 2213 # color-blind individuals with either protanopia or deuteranopia. Bang 2214 # Wong [2011] Nature Methods 8, 441. 2215 2216 set style line 1 lt 1 lc rgb "#009e73" lw 2.5 2217 set style line 11 lt 2 lc rgb "#009e73" lw 2.5 2218 set style line 21 lt 4 lc rgb "#009e73" lw 2.5 2219 set style line 31 lt 6 lc rgb "#009e73" lw 2.5 2220 set style line 41 lt 8 lc rgb "#009e73" lw 2.5 2221 2222 set style line 2 lt 1 lc rgb "#0072b2" lw 2.5 2223 set style line 12 lt 2 lc rgb "#0072b2" lw 2.5 2224 set style line 22 lt 4 lc rgb "#0072b2" lw 2.5 2225 set style line 32 lt 6 lc rgb "#0072b2" lw 2.5 2226 set style line 42 lt 8 lc rgb "#0072b2" lw 2.5 2227 2228 set style line 3 lt 1 lc rgb "#d55e00" lw 2.5 2229 set style line 13 lt 2 lc rgb "#d55e00" lw 2.5 2230 set style line 23 lt 4 lc rgb "#d55e00" lw 2.5 2231 set style line 33 lt 6 lc rgb "#d55e00" lw 2.5 2232 set style line 43 lt 8 lc rgb "#d55e00" lw 2.5 2233 2234 set style line 4 lt 1 lc rgb "#f0e442" lw 2.5 2235 set style line 14 lt 2 lc rgb "#f0e442" lw 2.5 2236 set style line 24 lt 4 lc rgb "#f0e442" lw 2.5 2237 set style line 34 lt 6 lc rgb "#f0e442" lw 2.5 2238 set style line 44 lt 8 lc rgb "#f0e442" lw 2.5 2239 2240 set style line 5 lt 1 lc rgb "#56b4e9" lw 2.5 2241 set style line 15 lt 2 lc rgb "#56b4e9" lw 2.5 2242 set style line 25 lt 4 lc rgb "#56b4e9" lw 2.5 2243 set style line 35 lt 6 lc rgb "#56b4e9" lw 2.5 2244 set style line 45 lt 8 lc rgb "#56b4e9" lw 2.5 2245 2246 set style line 6 lt 1 lc rgb "#cc79a7" lw 2.5 2247 set style line 16 lt 2 lc rgb "#cc79a7" lw 2.5 2248 set style line 26 lt 4 lc rgb "#cc79a7" lw 2.5 2249 set style line 36 lt 6 lc rgb "#cc79a7" lw 2.5 2250 set style line 46 lt 8 lc rgb "#cc79a7" lw 2.5 2251 2252 set style line 7 lt 1 lc rgb "#e69f00" lw 2.5 2253 set style line 17 lt 2 lc rgb "#e69f00" lw 2.5 2254 set style line 27 lt 4 lc rgb "#e69f00" lw 2.5 2255 set style line 37 lt 6 lc rgb "#e69f00" lw 2.5 2256 set style line 47 lt 8 lc rgb "#e69f00" lw 2.5 2257 2258 set style line 8 lt 1 lc rgb "black" lw 2.5 2259 set style line 18 lt 2 lc rgb "black" lw 2.5 2260 set style line 28 lt 4 lc rgb "black" lw 2.5 2261 set style line 38 lt 6 lc rgb "black" lw 2.5 2262 set style line 48 lt 7 lc rgb "black" lw 2.5 2263 2264 2265 set style line 999 lt 1 lc rgb "gray" lw 2.5 2266 2267 safe(x,y,a) = (y == 0.0 ? a : x/y) 2268 2269 set style data histeps 2270 set key invert 2271 2272 """%(arg_string,output_base_name) 2273 ] 2274 2275 gnuplot_output_list_v5 = [ 2276 """ 2277 ################################################################################ 2278 # 2279 # This gnuplot file was generated by MadGraph5_aMC@NLO project, a program which 2280 # automatically generates Feynman diagrams and matrix elements for arbitrary 2281 # high-energy processes in the Standard Model and beyond. It also perform the 2282 # integration and/or generate events for these processes, at LO and NLO accuracy. 2283 # 2284 # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch 2285 # 2286 ################################################################################ 2287 # %s 2288 reset 2289 2290 set lmargin 10 2291 set rmargin 0 2292 set terminal postscript portrait enhanced color "Helvetica" 9 2293 # The pdf terminal offers transparency support, but you will have to adapt things a bit 2294 #set terminal pdf enhanced font "Helvetica 12" lw 1.0 dashed size 29.7cm, 21cm 2295 set key font ",9" 2296 set key samplen "2" 2297 set output "%s.ps" 2298 2299 # This is the "PODO" color palette of gnuplot v.5, but with the order 2300 # changed: palette of colors selected to be easily distinguishable by 2301 # color-blind individuals with either protanopia or deuteranopia. Bang 2302 # Wong [2011] Nature Methods 8, 441. 2303 2304 set style line 1 lt 1 lc rgb "#009e73" lw 1.3 2305 set style line 101 lt 1 lc rgb "#009e73" lw 1.3 dt (6,3) 2306 set style line 11 lt 2 lc rgb "#009e73" lw 1.3 dt (6,3) 2307 set style line 21 lt 4 lc rgb "#009e73" lw 1.3 dt (3,2) 2308 set style line 31 lt 6 lc rgb "#009e73" lw 1.3 dt (2,1) 2309 set style line 41 lt 8 lc rgb "#009e73" lw 1.3 dt (4,3) 2310 2311 set style line 2 lt 1 lc rgb "#0072b2" lw 1.3 2312 set style line 102 lt 1 lc rgb "#0072b2" lw 1.3 dt (6,3) 2313 set style line 12 lt 2 lc rgb "#0072b2" lw 1.3 dt (6,3) 2314 set style line 22 lt 4 lc rgb "#0072b2" lw 1.3 dt (3,2) 2315 set style line 32 lt 6 lc rgb "#0072b2" lw 1.3 dt (2,1) 2316 set style line 42 lt 8 lc rgb "#0072b2" lw 1.3 dt (4,3) 2317 2318 2319 set style line 3 lt 1 lc rgb "#d55e00" lw 1.3 2320 set style line 103 lt 1 lc rgb "#d55e00" lw 1.3 dt (6,3) 2321 set style line 13 lt 2 lc rgb "#d55e00" lw 1.3 dt (6,3) 2322 set style line 23 lt 4 lc rgb "#d55e00" lw 1.3 dt (3,2) 2323 set style line 33 lt 6 lc rgb "#d55e00" lw 1.3 dt (2,1) 2324 set style line 43 lt 8 lc rgb "#d55e00" lw 1.3 dt (4,3) 2325 2326 set style line 4 lt 1 lc rgb "#f0e442" lw 1.3 2327 set style line 104 lt 1 lc rgb "#f0e442" lw 1.3 dt (6,3) 2328 set style line 14 lt 2 lc rgb "#f0e442" lw 1.3 dt (6,3) 2329 set style line 24 lt 4 lc rgb "#f0e442" lw 1.3 dt (3,2) 2330 set style line 34 lt 6 lc rgb "#f0e442" lw 1.3 dt (2,1) 2331 set style line 44 lt 8 lc rgb "#f0e442" lw 1.3 dt (4,3) 2332 2333 set style line 5 lt 1 lc rgb "#56b4e9" lw 1.3 2334 set style line 105 lt 1 lc rgb "#56b4e9" lw 1.3 dt (6,3) 2335 set style line 15 lt 2 lc rgb "#56b4e9" lw 1.3 dt (6,3) 2336 set style line 25 lt 4 lc rgb "#56b4e9" lw 1.3 dt (3,2) 2337 set style line 35 lt 6 lc rgb "#56b4e9" lw 1.3 dt (2,1) 2338 set style line 45 lt 8 lc rgb "#56b4e9" lw 1.3 dt (4,3) 2339 2340 set style line 6 lt 1 lc rgb "#cc79a7" lw 1.3 2341 set style line 106 lt 1 lc rgb "#cc79a7" lw 1.3 dt (6,3) 2342 set style line 16 lt 2 lc rgb "#cc79a7" lw 1.3 dt (6,3) 2343 set style line 26 lt 4 lc rgb "#cc79a7" lw 1.3 dt (3,2) 2344 set style line 36 lt 6 lc rgb "#cc79a7" lw 1.3 dt (2,1) 2345 set style line 46 lt 8 lc rgb "#cc79a7" lw 1.3 dt (4,3) 2346 2347 set style line 7 lt 1 lc rgb "#e69f00" lw 1.3 2348 set style line 107 lt 1 lc rgb "#e69f00" lw 1.3 dt (6,3) 2349 set style line 17 lt 2 lc rgb "#e69f00" lw 1.3 dt (6,3) 2350 set style line 27 lt 4 lc rgb "#e69f00" lw 1.3 dt (3,2) 2351 set style line 37 lt 6 lc rgb "#e69f00" lw 1.3 dt (2,1) 2352 set style line 47 lt 8 lc rgb "#e69f00" lw 1.3 dt (4,3) 2353 2354 set style line 8 lt 1 lc rgb "black" lw 1.3 2355 set style line 108 lt 1 lc rgb "black" lw 1.3 dt (6,3) 2356 set style line 18 lt 2 lc rgb "black" lw 1.3 dt (6,3) 2357 set style line 28 lt 4 lc rgb "black" lw 1.3 dt (3,2) 2358 set style line 38 lt 6 lc rgb "black" lw 1.3 dt (2,1) 2359 set style line 48 lt 8 lc rgb "black" lw 1.3 dt (4,3) 2360 2361 2362 set style line 999 lt 1 lc rgb "gray" lw 1.3 2363 2364 safe(x,y,a) = (y == 0.0 ? a : x/y) 2365 2366 set style data histeps 2367 set key invert 2368 2369 """%(arg_string,output_base_name) 2370 ] 2371 2372 # determine the gnuplot version 2373 try: 2374 p = subprocess.Popen(['gnuplot', '--version'], \ 2375 stdout=subprocess.PIPE, stderr=subprocess.PIPE) 2376 except OSError: 2377 # assume that version 4 of gnuplot is the default if 2378 # gnuplot could not be found 2379 gnuplot_output_list=gnuplot_output_list_v5 2380 else: 2381 output, _ = p.communicate() 2382 if float(output.split()[1]) < 5. : 2383 gnuplot_output_list=gnuplot_output_list_v4 2384 else: 2385 gnuplot_output_list=gnuplot_output_list_v5 2386 2387 2388 # Now output each group one by one 2389 # Block position keeps track of the gnuplot data_block index considered 2390 block_position = 0 2391 for histo_group in self: 2392 # Output this group 2393 block_position = histo_group.output_group(HwU_output_list, 2394 gnuplot_output_list, block_position,output_base_name+'.HwU', 2395 number_of_ratios=number_of_ratios, 2396 uncertainties = uncertainties, 2397 use_band = use_band, 2398 ratio_correlations = ratio_correlations, 2399 jet_samples_to_keep=jet_samples_to_keep, 2400 lhapdfconfig = lhapdfconfig) 2401 2402 # Now write the tail of the gnuplot command file 2403 gnuplot_output_list.extend([ 2404 "unset multiplot", 2405 '!ps2pdf "%s.ps" &> /dev/null'%output_base_name]) 2406 if auto_open: 2407 gnuplot_output_list.append( 2408 '!open "%s.pdf" &> /dev/null'%output_base_name) 2409 2410 # Now write result to stream and close it 2411 gnuplot_stream.write('\n'.join(gnuplot_output_list)) 2412 HwU_stream.write('\n'.join(HwU_output_list)) 2413 gnuplot_stream.close() 2414 HwU_stream.close() 2415 2416 logger.debug("Histograms have been written out at "+\ 2417 "%s.[HwU|gnuplot]' and can "%output_base_name+\ 2418 "now be rendered by invoking gnuplot.")
2419
2420 - def output_group(self, HwU_out, gnuplot_out, block_position, HwU_name, 2421 number_of_ratios = -1, 2422 uncertainties = ['scale','pdf','statitistical','merging_scale','alpsfact'], 2423 use_band = None, 2424 ratio_correlations = True, 2425 jet_samples_to_keep=None, 2426 lhapdfconfig='lhapdf-config'):
2427 2428 """ This functions output a single group of histograms with either one 2429 histograms untyped (i.e. type=None) or two of type 'NLO' and 'LO' 2430 respectively.""" 2431 2432 # This function returns the main central plot line, making sure that 2433 # negative distribution are displayed in dashed style 2434 def get_main_central_plot_lines(HwU_name, block_position, color_index, 2435 title, show_mc_uncertainties): 2436 """ Returns two plot lines, one for the negative contributions in 2437 dashed and one with the positive ones in solid.""" 2438 2439 template = "'%(hwu)s' index %(ind)d using (($1+$2)/2):%(data)s%(stat_col)s%(stat_err)s%(ls)s%(title)s" 2440 template_no_stat = "'%(hwu)s' index %(ind)d using (($1+$2)/2):%(data)s%(ls)s%(title)s" 2441 rep_dic = {'hwu':HwU_name, 2442 'ind':block_position, 2443 'ls':' ls %d'%color_index, 2444 'title':" title '%s'"%title, 2445 'stat_col': ':4', 2446 'stat_err': ' w yerrorbar', 2447 'data':'3', 2448 'linetype':''} 2449 2450 # This would be the original output 2451 # return [template_no_stat%rep_dic]+\ 2452 # ([template%rep_dic] if show_mc_uncertainties else []) 2453 2454 # The use of sqrt(-1) is just a trick to prevent the line to display 2455 res = [] 2456 rep_dic['data'] = '($3 < 0 ? sqrt(-1) : $3)' 2457 res.append(template_no_stat%rep_dic) 2458 rep_dic['title'] = " title ''" 2459 if show_mc_uncertainties: 2460 res.append(template%rep_dic) 2461 rep_dic['data'] = '($3 >= 0 ? sqrt(-1) : abs($3))' 2462 rep_dic['ls'] = ' ls %d'%(100+color_index) 2463 res.append(template_no_stat%rep_dic) 2464 if show_mc_uncertainties: 2465 res.append(template%rep_dic) 2466 return res
2467 2468 # This bool can be modified later to decide whether to use uncertainty 2469 # bands or not 2470 # ======== 2471 def get_uncertainty_lines(HwU_name, block_position, 2472 var_pos, color_index,title, ratio=False, band=False): 2473 """ Return a string line corresponding to the plotting of the 2474 uncertainty. Band is to chose wether to display uncertainty with 2475 a band or two lines.""" 2476 2477 # This perl substitution regular expression copies each line of the 2478 # HwU source and swap the x1 and x2 coordinate of the second copy. 2479 # So if input is: 2480 # 2481 # blabla 2482 # +0.01e+01 0.3 4 5 6 2483 # +0.03e+01 0.5 7 8 9 2484 # ... 2485 # 2486 # The output will be 2487 # 2488 # blabla 2489 # +0.01e+01 0.3 4 5 6 2490 # 0.3 +0.01e+01 4 5 6 2491 # +0.03e+01 0.5 7 8 9 2492 # 0.5 +0.03e+01 7 8 9 2493 # ... 2494 # 2495 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} $+{rest}\n/g'" 2496 # Gnuplot escapes the antislash, so we must esacape then once more O_o. 2497 # Gnuplot doesn't have raw strings, what a shame... 2498 copy_swap_re = copy_swap_re.replace('\\','\\\\') 2499 # For the ratio, we must divide by the central value 2500 position = '(safe($%d,$3,1.0)-1.0)' if ratio else '%d' 2501 if not band: 2502 return ["'%s' index %d using (($1+$2)/2):%s ls %d title '%s'"\ 2503 %(HwU_name,block_position, position%(var_pos),color_index,title), 2504 "'%s' index %d using (($1+$2)/2):%s ls %d title ''"\ 2505 %(HwU_name,block_position, position%(var_pos+1),color_index)] 2506 else: 2507 return [' "<%s %s" index %d using 1:%s:%s with filledcurve ls %d fs transparent solid 0.2 title \'%s\''%\ 2508 (copy_swap_re,HwU_name,block_position, 2509 position%var_pos,position%(var_pos+1),color_index,title)] 2510 # ======== 2511 2512 2513 layout_geometry = [(0.0, 0.5, 1.0, 0.4 ), 2514 (0.0, 0.35, 1.0, 0.15), 2515 (0.0, 0.2, 1.0, 0.15)] 2516 layout_geometry.reverse() 2517 2518 # Group histograms which just differ by jet multiplicity and add their 2519 # sum as first plot 2520 matching_histo_lists = HwUList([HwUList([self[0]])]) 2521 for histo in self[1:]: 2522 matched = False 2523 for histo_list in matching_histo_lists: 2524 if hasattr(histo, 'jetsample') and histo.jetsample >= 0 and \ 2525 histo.type == histo_list[0].type: 2526 matched = True 2527 histo_list.append(histo) 2528 break 2529 if not matched: 2530 matching_histo_lists.append(HwUList([histo])) 2531 2532 # For each group of histograms with different jet multiplicities, we 2533 # define one at the beginning which is the sum. 2534 self[:] = [] 2535 for histo_group in matching_histo_lists: 2536 # First create a plot that sums all jet multiplicities for each type 2537 # (that is, only if jet multiplicities are defined) 2538 if len(histo_group)==1: 2539 self.append(histo_group[0]) 2540 continue 2541 # If there is already a histogram summing them, then don't create 2542 # a copy of it. 2543 if any(hist.jetsample==-1 for hist in histo_group if 2544 hasattr(hist, 'jetsample')): 2545 self.extend(histo_group) 2546 continue 2547 summed_histogram = copy.copy(histo_group[0]) 2548 for histo in histo_group[1:]: 2549 summed_histogram = summed_histogram + histo 2550 summed_histogram.jetsample = -1 2551 self.append(summed_histogram) 2552 self.extend(histo_group) 2553 2554 # Remove the curve of individual jet samples if they are not desired 2555 if not jet_samples_to_keep is None: 2556 self[:] = filter(lambda histo: 2557 (not hasattr(histo,'jetsample')) or (histo.jetsample == -1) or 2558 (histo.jetsample in jet_samples_to_keep), self) 2559 2560 # This function is to create the ratio histograms if the user turned off 2561 # correlations. 2562 def ratio_no_correlations(wgtsA, wgtsB): 2563 new_wgts = {} 2564 for label, wgt in wgtsA.items(): 2565 if wgtsB['central']==0.0 and wgt==0.0: 2566 new_wgts[label] = 0.0 2567 continue 2568 elif wgtsB['central']==0.0: 2569 # It is ok to skip the warning here. 2570 # logger.debug('Warning:: A bin with finite weight '+ 2571 # 'was divided by a bin with zero weight.') 2572 new_wgts[label] = 0.0 2573 continue 2574 new_wgts[label] = (wgtsA[label]/wgtsB['central']) 2575 return new_wgts 2576 2577 # First compute the ratio of all the histograms from the second to the 2578 # number_of_ratios+1 ones in the list to the first histogram. 2579 n_histograms = len(self) 2580 ratio_histos = HwUList([]) 2581 # A counter to keep track of the number of ratios included 2582 n_ratios_included = 0 2583 for i, histo in enumerate(self[1:]): 2584 if not hasattr(histo,'jetsample') or histo.jetsample==self[0].jetsample: 2585 n_ratios_included += 1 2586 else: 2587 continue 2588 2589 if number_of_ratios >=0 and n_ratios_included > number_of_ratios: 2590 break 2591 2592 if ratio_correlations: 2593 ratio_histos.append(histo/self[0]) 2594 else: 2595 ratio_histos.append(self[0].__class__.combine(histo, self[0], 2596 ratio_no_correlations)) 2597 if self[0].type=='NLO' and self[1].type=='LO': 2598 ratio_histos[-1].title += '1/K-factor' 2599 elif self[0].type=='LO' and self[1].type=='NLO': 2600 ratio_histos[-1].title += 'K-factor' 2601 else: 2602 ratio_histos[-1].title += ' %s/%s'%( 2603 self[1].type if self[1].type else '(%d)'%(i+2), 2604 self[0].type if self[0].type else '(1)') 2605 # By setting its type to aux, we make sure this histogram will be 2606 # filtered out if the .HwU file output here would be re-loaded later. 2607 ratio_histos[-1].type = 'AUX' 2608 self.extend(ratio_histos) 2609 2610 # Compute scale variation envelope for all diagrams 2611 if 'scale' in uncertainties: 2612 (mu_var_pos,mu) = self[0].set_uncertainty(type='all_scale') 2613 else: 2614 (mu_var_pos,mu) = (None,[None]) 2615 2616 if 'pdf' in uncertainties: 2617 (PDF_var_pos,pdf) = self[0].set_uncertainty(type='PDF',lhapdfconfig=lhapdfconfig) 2618 else: 2619 (PDF_var_pos,pdf) = (None,[None]) 2620 2621 if 'merging_scale' in uncertainties: 2622 (merging_var_pos,merging) = self[0].set_uncertainty(type='merging') 2623 else: 2624 (merging_var_pos,merging) = (None,[None]) 2625 if 'alpsfact' in uncertainties: 2626 (alpsfact_var_pos,alpsfact) = self[0].set_uncertainty(type='alpsfact') 2627 else: 2628 (alpsfact_var_pos,alpsfact) = (None,[None]) 2629 2630 uncertainties_present = list(uncertainties) 2631 if PDF_var_pos is None and 'pdf' in uncertainties_present: 2632 uncertainties_present.remove('pdf') 2633 if mu_var_pos is None and 'scale' in uncertainties_present: 2634 uncertainties_present.remove('scale') 2635 if merging_var_pos is None and 'merging' in uncertainties_present: 2636 uncertainties_present.remove('merging') 2637 if alpsfact_var_pos is None and 'alpsfact' in uncertainties_present: 2638 uncertainties_present.remove('alpsfact') 2639 no_uncertainties = len(uncertainties_present)==0 2640 2641 # If the 'use_band' option is None we should adopt a default which is 2642 try: 2643 uncertainties_present.remove('statistical') 2644 except: 2645 pass 2646 if use_band is None: 2647 # For clarity, it is better to only use bands only for one source 2648 # of uncertainty 2649 if len(uncertainties_present)==0: 2650 use_band = [] 2651 elif len(uncertainties_present)==1: 2652 use_band = uncertainties_present 2653 elif 'scale' in uncertainties_present: 2654 use_band = ['scale'] 2655 else: 2656 use_band = [uncertainties_present[0]] 2657 2658 for histo in self[1:]: 2659 if (not mu_var_pos is None) and \ 2660 mu_var_pos != histo.set_uncertainty(type='all_scale')[0]: 2661 raise MadGraph5Error, 'Not all histograms in this group specify'+\ 2662 ' scale uncertainties. It is required to be able to output them'+\ 2663 ' together.' 2664 if (not PDF_var_pos is None) and\ 2665 PDF_var_pos != histo.set_uncertainty(type='PDF',\ 2666 lhapdfconfig=lhapdfconfig)[0]: 2667 raise MadGraph5Error, 'Not all histograms in this group specify'+\ 2668 ' PDF uncertainties. It is required to be able to output them'+\ 2669 ' together.' 2670 if (not merging_var_pos is None) and\ 2671 merging_var_pos != histo.set_uncertainty(type='merging')[0]: 2672 raise MadGraph5Error, 'Not all histograms in this group specify'+\ 2673 ' merging uncertainties. It is required to be able to output them'+\ 2674 ' together.' 2675 if (not alpsfact_var_pos is None) and\ 2676 alpsfact_var_pos != histo.set_uncertainty(type='alpsfact')[0]: 2677 raise MadGraph5Error, 'Not all histograms in this group specify'+\ 2678 ' alpsfact uncertainties. It is required to be able to output them'+\ 2679 ' together.' 2680 2681 2682 # Now output the corresponding HwU histogram data 2683 for i, histo in enumerate(self): 2684 # Print the header the first time only 2685 HwU_out.extend(histo.get_HwU_source(\ 2686 print_header=(block_position==0 and i==0))) 2687 HwU_out.extend(['','']) 2688 2689 # First the global gnuplot header for this histogram group 2690 global_header =\ 2691 """ 2692 ################################################################################ 2693 ### Rendering of the plot titled '%(title)s' 2694 ################################################################################ 2695 2696 set multiplot 2697 set label "%(title)s" font ",13" at graph 0.04, graph 1.05 2698 set xrange [%(xmin).4e:%(xmax).4e] 2699 set bmargin 0 2700 set tmargin 0 2701 set xtics nomirror 2702 set ytics nomirror 2703 set mytics %(mxtics)d 2704 %(set_xtics)s 2705 set key horizontal noreverse maxcols 1 width -4 2706 set label front 'MadGraph5\_aMC\@NLO' font "Courier,11" rotate by 90 at graph 1.02, graph 0.04 2707 """ 2708 2709 # Now the header for each subhistogram 2710 subhistogram_header = \ 2711 """#-- rendering subhistograms '%(subhistogram_type)s' 2712 %(unset label)s 2713 %(set_format_y)s 2714 set yrange [%(ymin).4e:%(ymax).4e] 2715 set origin %(origin_x).4e, %(origin_y).4e 2716 set size %(size_x).4e, %(size_y).4e 2717 set mytics %(mytics)d 2718 %(set_ytics)s 2719 %(set_format_x)s 2720 %(set_yscale)s 2721 %(set_ylabel)s 2722 %(set_histo_label)s 2723 plot \\""" 2724 replacement_dic = {} 2725 2726 replacement_dic['title'] = self[0].get_HwU_histogram_name(format='human-no_type') 2727 # Determine what weight to consider when computing the optimal 2728 # range for the y-axis. 2729 wgts_to_consider = ['central'] 2730 if not mu_var_pos is None: 2731 for mu_var in mu_var_pos: 2732 wgts_to_consider.append(self[0].bins.weight_labels[mu_var]) 2733 wgts_to_consider.append(self[0].bins.weight_labels[mu_var+1]) 2734 wgts_to_consider.append(self[0].bins.weight_labels[mu_var+2]) 2735 if not PDF_var_pos is None: 2736 for PDF_var in PDF_var_pos: 2737 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var]) 2738 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var+1]) 2739 wgts_to_consider.append(self[0].bins.weight_labels[PDF_var+2]) 2740 if not merging_var_pos is None: 2741 for merging_var in merging_var_pos: 2742 wgts_to_consider.append(self[0].bins.weight_labels[merging_var]) 2743 wgts_to_consider.append(self[0].bins.weight_labels[merging_var+1]) 2744 wgts_to_consider.append(self[0].bins.weight_labels[merging_var+2]) 2745 if not alpsfact_var_pos is None: 2746 for alpsfact_var in alpsfact_var_pos: 2747 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var]) 2748 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var+1]) 2749 wgts_to_consider.append(self[0].bins.weight_labels[alpsfact_var+2]) 2750 2751 (xmin, xmax) = HwU.get_x_optimal_range(self[:2],\ 2752 weight_labels = wgts_to_consider) 2753 replacement_dic['xmin'] = xmin 2754 replacement_dic['xmax'] = xmax 2755 replacement_dic['mxtics'] = 10 2756 replacement_dic['set_xtics'] = 'set xtics auto' 2757 2758 # Add the global header which is now ready 2759 gnuplot_out.append(global_header%replacement_dic) 2760 2761 # Now add the main plot 2762 replacement_dic['subhistogram_type'] = '%s and %s results'%( 2763 str(self[0].type),str(self[1].type)) if len(self)>1 else \ 2764 'single diagram output' 2765 (ymin, ymax) = HwU.get_y_optimal_range(self[:2], 2766 labels = wgts_to_consider, scale=self[0].y_axis_mode) 2767 2768 # Force a linear scale if the detected range is negative 2769 if ymin< 0.0: 2770 self[0].y_axis_mode = 'LIN' 2771 2772 # Already add a margin on upper bound. 2773 if self[0].y_axis_mode=='LOG': 2774 ymax += 10.0 * ymax 2775 ymin -= 0.1 * ymin 2776 else: 2777 ymax += 0.3 * (ymax - ymin) 2778 ymin -= 0.3 * (ymax - ymin) 2779 2780 replacement_dic['ymin'] = ymin 2781 replacement_dic['ymax'] = ymax 2782 replacement_dic['unset label'] = '' 2783 (replacement_dic['origin_x'], replacement_dic['origin_y'], 2784 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop() 2785 replacement_dic['mytics'] = 10 2786 # Use default choise for the main histogram 2787 replacement_dic['set_ytics'] = 'set ytics auto' 2788 replacement_dic['set_format_x'] = "set format x ''" if \ 2789 (len(self)-n_histograms>0 or not no_uncertainties) else "set format x" 2790 replacement_dic['set_ylabel'] = 'set ylabel "{/Symbol s} per bin [pb]"' 2791 replacement_dic['set_yscale'] = "set logscale y" if \ 2792 self[0].y_axis_mode=='LOG' else 'unset logscale y' 2793 replacement_dic['set_format_y'] = "set format y '10^{%T}'" if \ 2794 self[0].y_axis_mode=='LOG' else 'unset format' 2795 2796 replacement_dic['set_histo_label'] = "" 2797 gnuplot_out.append(subhistogram_header%replacement_dic) 2798 2799 # Now add the main layout 2800 plot_lines = [] 2801 uncertainty_plot_lines = [] 2802 n=-1 2803 2804 for i, histo in enumerate(self[:n_histograms]): 2805 n=n+1 2806 color_index = n%self.number_line_colors_defined+1 2807 # Label to appear for the lower curves 2808 title = [] 2809 if histo.type is None and not hasattr(histo, 'jetsample'): 2810 title.append('%d'%(i+1)) 2811 else: 2812 if histo.type: 2813 title.append('NLO' if \ 2814 histo.type.split()[0]=='NLO' else histo.type) 2815 if hasattr(histo, 'jetsample'): 2816 if histo.jetsample!=-1: 2817 title.append('jet sample %d'%histo.jetsample) 2818 else: 2819 title.append('all jet samples') 2820 2821 title = ', '.join(title) 2822 # Label for the first curve in the upper plot 2823 if histo.type is None and not hasattr(histo, 'jetsample'): 2824 major_title = 'central value for plot (%d)'%(i+1) 2825 else: 2826 major_title = [] 2827 if not histo.type is None: 2828 major_title.append(histo.type) 2829 if hasattr(histo, 'jetsample'): 2830 if histo.jetsample!=-1: 2831 major_title.append('jet sample %d'%histo.jetsample) 2832 else: 2833 major_title.append('all jet samples') 2834 else: 2835 major_title.append('central value') 2836 major_title = ', '.join(major_title) 2837 2838 if not mu[0] in ['none',None]: 2839 major_title += ', dynamical\_scale\_choice=%s'%mu[0] 2840 if not pdf[0] in ['none',None]: 2841 major_title += ', PDF=%s'%pdf[0].replace('_','\_') 2842 2843 # Do not show uncertainties for individual jet samples (unless first 2844 # or specified explicitely and uniquely) 2845 if not (i!=0 and hasattr(histo,'jetsample') and histo.jetsample!=-1 and \ 2846 not (jet_samples_to_keep and len(jet_samples_to_keep)==1 and 2847 jet_samples_to_keep[0] == histo.jetsample)): 2848 2849 uncertainty_plot_lines.append({}) 2850 2851 # We decide to show uncertainties in the main plot only if they 2852 # are part of a monocolor band. Otherwise, they will only be 2853 # shown in the first subplot. Notice that plotting 'sqrt(-1)' 2854 # is just a trick so as to have only the key printed with no 2855 # line 2856 2857 # Show scale variation for the first central value if available 2858 if not mu_var_pos is None and len(mu_var_pos)>0: 2859 if 'scale' in use_band: 2860 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines( 2861 HwU_name, block_position+i, mu_var_pos[0]+4, color_index+10, 2862 '%s, scale variation'%title, band='scale' in use_band) 2863 else: 2864 uncertainty_plot_lines[-1]['scale'] = \ 2865 ["sqrt(-1) ls %d title '%s'"%(color_index+10,'%s, scale variation'%title)] 2866 # And now PDF_variation if available 2867 if not PDF_var_pos is None and len(PDF_var_pos)>0: 2868 if 'pdf' in use_band: 2869 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines( 2870 HwU_name,block_position+i, PDF_var_pos[0]+4, color_index+20, 2871 '%s, PDF variation'%title, band='pdf' in use_band) 2872 else: 2873 uncertainty_plot_lines[-1]['pdf'] = \ 2874 ["sqrt(-1) ls %d title '%s'"%(color_index+20,'%s, PDF variation'%title)] 2875 # And now merging variation if available 2876 if not merging_var_pos is None and len(merging_var_pos)>0: 2877 if 'merging_scale' in use_band: 2878 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines( 2879 HwU_name,block_position+i, merging_var_pos[0]+4, color_index+30, 2880 '%s, merging scale variation'%title, band='merging_scale' in use_band) 2881 else: 2882 uncertainty_plot_lines[-1]['merging_scale'] = \ 2883 ["sqrt(-1) ls %d title '%s'"%(color_index+30,'%s, merging scale variation'%title)] 2884 # And now alpsfact variation if available 2885 if not alpsfact_var_pos is None and len(alpsfact_var_pos)>0: 2886 if 'alpsfact' in use_band: 2887 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines( 2888 HwU_name,block_position+i, alpsfact_var_pos[0]+4, color_index+40, 2889 '%s, alpsfact variation'%title, band='alpsfact' in use_band) 2890 else: 2891 uncertainty_plot_lines[-1]['alpsfact'] = \ 2892 ["sqrt(-1) ls %d title '%s'"%(color_index+40,'%s, alpsfact variation'%title)] 2893 2894 # plot_lines.append( 2895 # "'%s' index %d using (($1+$2)/2):3 ls %d title '%s'"\ 2896 # %(HwU_name,block_position+i,color_index, major_title)) 2897 # if 'statistical' in uncertainties: 2898 # plot_lines.append( 2899 # "'%s' index %d using (($1+$2)/2):3:4 w yerrorbar ls %d title ''"\ 2900 # %(HwU_name,block_position+i,color_index)) 2901 plot_lines.extend( 2902 get_main_central_plot_lines(HwU_name, block_position+i, 2903 color_index, major_title, 'statistical' in uncertainties)) 2904 2905 # Add additional central scale/PDF curves 2906 if not mu_var_pos is None: 2907 for j,mu_var in enumerate(mu_var_pos): 2908 if j!=0: 2909 n=n+1 2910 color_index = n%self.number_line_colors_defined+1 2911 plot_lines.append( 2912 "'%s' index %d using (($1+$2)/2):%d ls %d title '%s'"\ 2913 %(HwU_name,block_position+i,mu_var+3,color_index,\ 2914 '%s dynamical\_scale\_choice=%s' % (title,mu[j]))) 2915 # And now PDF_variation if available 2916 if not PDF_var_pos is None: 2917 for j,PDF_var in enumerate(PDF_var_pos): 2918 if j!=0: 2919 n=n+1 2920 color_index = n%self.number_line_colors_defined+1 2921 plot_lines.append( 2922 "'%s' index %d using (($1+$2)/2):%d ls %d title '%s'"\ 2923 %(HwU_name,block_position+i,PDF_var+3,color_index,\ 2924 '%s PDF=%s' % (title,pdf[j].replace('_','\_')))) 2925 2926 # Now add the uncertainty lines, those not using a band so that they 2927 # are not covered by those using a band after we reverse plo_lines 2928 for one_plot in uncertainty_plot_lines: 2929 for uncertainty_type, lines in one_plot.items(): 2930 if not uncertainty_type in use_band: 2931 plot_lines.extend(lines) 2932 # then those using a band 2933 for one_plot in uncertainty_plot_lines: 2934 for uncertainty_type, lines in one_plot.items(): 2935 if uncertainty_type in use_band: 2936 plot_lines.extend(lines) 2937 2938 # Reverse so that bands appear first 2939 plot_lines.reverse() 2940 2941 # Add the plot lines 2942 gnuplot_out.append(',\\\n'.join(plot_lines)) 2943 2944 # Now we can add the scale variation ratio 2945 replacement_dic['subhistogram_type'] = 'Relative scale and PDF uncertainty' 2946 2947 if 'statistical' in uncertainties: 2948 wgts_to_consider.append('stat_error') 2949 2950 # This function is just to temporarily create the scale ratio histogram with 2951 # the hwu.combine function. 2952 def rel_scale(wgtsA, wgtsB): 2953 new_wgts = {} 2954 for label, wgt in wgtsA.items(): 2955 if label in wgts_to_consider: 2956 if wgtsB['central']==0.0 and wgt==0.0: 2957 new_wgts[label] = 0.0 2958 continue 2959 elif wgtsB['central']==0.0: 2960 # It is ok to skip the warning here. 2961 # logger.debug('Warning:: A bin with finite weight '+ 2962 # 'was divided by a bin with zero weight.') 2963 new_wgts[label] = 0.0 2964 continue 2965 new_wgts[label] = (wgtsA[label]/wgtsB['central']) 2966 if label != 'stat_error': 2967 new_wgts[label] -= 1.0 2968 else: 2969 new_wgts[label] = wgtsA[label] 2970 return new_wgts 2971 2972 histos_for_subplots = [(i,histo) for i, histo in enumerate(self[:n_histograms]) if 2973 ( not (i!=0 and hasattr(histo,'jetsample') and histo.jetsample!=-1 and \ 2974 not (jet_samples_to_keep and len(jet_samples_to_keep)==1 and 2975 jet_samples_to_keep[0] == histo.jetsample)) )] 2976 2977 # Notice even though a ratio histogram is created here, it 2978 # is not actually used to plot the quantity in gnuplot, but just to 2979 # compute the y range. 2980 (ymin, ymax) = HwU.get_y_optimal_range([histo[1].__class__.combine( 2981 histo[1],histo[1],rel_scale) for histo in histos_for_subplots], 2982 labels = wgts_to_consider, scale='LIN') 2983 2984 # Add a margin on upper and lower bound. 2985 ymax = ymax + 0.2 * (ymax - ymin) 2986 ymin = ymin - 0.2 * (ymax - ymin) 2987 replacement_dic['unset label'] = 'unset label' 2988 replacement_dic['ymin'] = ymin 2989 replacement_dic['ymax'] = ymax 2990 if not no_uncertainties: 2991 (replacement_dic['origin_x'], replacement_dic['origin_y'], 2992 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop() 2993 replacement_dic['mytics'] = 2 2994 # replacement_dic['set_ytics'] = 'set ytics %f'%((int(10*(ymax-ymin))/10)/3.0) 2995 replacement_dic['set_ytics'] = 'set ytics auto' 2996 replacement_dic['set_format_x'] = "set format x ''" if \ 2997 len(self)-n_histograms>0 else "set format x" 2998 replacement_dic['set_ylabel'] = 'set ylabel "%s rel.unc."'\ 2999 %('(1)' if self[0].type==None else '%s'%('NLO' if \ 3000 self[0].type.split()[0]=='NLO' else self[0].type)) 3001 replacement_dic['set_yscale'] = "unset logscale y" 3002 replacement_dic['set_format_y'] = 'unset format' 3003 3004 3005 tit='Relative uncertainties w.r.t. central value' 3006 if n_histograms > 1: 3007 tit=tit+'s' 3008 # if (not mu_var_pos is None and 'scale' not in use_band): 3009 # tit=tit+', scale is dashed' 3010 # if (not PDF_var_pos is None and 'pdf' not in use_band): 3011 # tit=tit+', PDF is dotted' 3012 replacement_dic['set_histo_label'] = \ 3013 'set label "%s" font ",9" front at graph 0.03, graph 0.13' % tit 3014 # Simply don't add these lines if there are no uncertainties. 3015 # This meant uncessary extra work, but I no longer car at this point 3016 if not no_uncertainties: 3017 gnuplot_out.append(subhistogram_header%replacement_dic) 3018 3019 # Now add the first subhistogram 3020 plot_lines = [] 3021 uncertainty_plot_lines = [] 3022 n=-1 3023 for (i,histo) in histos_for_subplots: 3024 n=n+1 3025 k=n 3026 color_index = n%self.number_line_colors_defined+1 3027 # Plot uncertainties 3028 if not mu_var_pos is None: 3029 for j,mu_var in enumerate(mu_var_pos): 3030 uncertainty_plot_lines.append({}) 3031 if j==0: 3032 color_index = k%self.number_line_colors_defined+1 3033 else: 3034 n=n+1 3035 color_index = n%self.number_line_colors_defined+1 3036 # Add the central line only if advanced scale variation 3037 if j>0 or mu[j]!='none': 3038 plot_lines.append( 3039 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\ 3040 %(HwU_name,block_position+i,mu_var+3,color_index)) 3041 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines( 3042 HwU_name, block_position+i, mu_var+4, color_index+10,'', 3043 ratio=True, band='scale' in use_band) 3044 if not PDF_var_pos is None: 3045 for j,PDF_var in enumerate(PDF_var_pos): 3046 uncertainty_plot_lines.append({}) 3047 if j==0: 3048 color_index = k%self.number_line_colors_defined+1 3049 else: 3050 n=n+1 3051 color_index = n%self.number_line_colors_defined+1 3052 # Add the central line only if advanced pdf variation 3053 if j>0 or pdf[j]!='none': 3054 plot_lines.append( 3055 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\ 3056 %(HwU_name,block_position+i,PDF_var+3,color_index)) 3057 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines( 3058 HwU_name, block_position+i, PDF_var+4, color_index+20,'', 3059 ratio=True, band='pdf' in use_band) 3060 if not merging_var_pos is None: 3061 for j,merging_var in enumerate(merging_var_pos): 3062 uncertainty_plot_lines.append({}) 3063 if j==0: 3064 color_index = k%self.number_line_colors_defined+1 3065 else: 3066 n=n+1 3067 color_index = n%self.number_line_colors_defined+1 3068 if j>0 or merging[j]!='none': 3069 plot_lines.append( 3070 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\ 3071 %(HwU_name,block_position+i,merging_var+3,color_index)) 3072 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines( 3073 HwU_name, block_position+i, merging_var+4, color_index+30,'', 3074 ratio=True, band='merging_scale' in use_band) 3075 if not alpsfact_var_pos is None: 3076 for j,alpsfact_var in enumerate(alpsfact_var_pos): 3077 uncertainty_plot_lines.append({}) 3078 if j==0: 3079 color_index = k%self.number_line_colors_defined+1 3080 else: 3081 n=n+1 3082 color_index = n%self.number_line_colors_defined+1 3083 if j>0 or alpsfact[j]!='none': 3084 plot_lines.append( 3085 "'%s' index %d using (($1+$2)/2):(safe($%d,$3,1.0)-1.0) ls %d title ''"\ 3086 %(HwU_name,block_position+i,alpsfact_var+3,color_index)) 3087 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines( 3088 HwU_name, block_position+i, alpsfact_var+4, color_index+40,'', 3089 ratio=True, band='alpsfact' in use_band) 3090 3091 if 'statistical' in uncertainties: 3092 plot_lines.append( 3093 "'%s' index %d using (($1+$2)/2):(0.0):(safe($4,$3,0.0)) w yerrorbar ls %d title ''"%\ 3094 (HwU_name,block_position+i,color_index)) 3095 3096 plot_lines.append("0.0 ls 999 title ''") 3097 3098 # Now add the uncertainty lines, those not using a band so that they 3099 # are not covered by those using a band after we reverse plo_lines 3100 for one_plot in uncertainty_plot_lines: 3101 for uncertainty_type, lines in one_plot.items(): 3102 if not uncertainty_type in use_band: 3103 plot_lines.extend(lines) 3104 # then those using a band 3105 for one_plot in uncertainty_plot_lines: 3106 for uncertainty_type, lines in one_plot.items(): 3107 if uncertainty_type in use_band: 3108 plot_lines.extend(lines) 3109 3110 # Reverse so that bands appear first 3111 plot_lines.reverse() 3112 # Add the plot lines 3113 if not no_uncertainties: 3114 gnuplot_out.append(',\\\n'.join(plot_lines)) 3115 3116 # We finish here when no ratio plot are asked for. 3117 if len(self)-n_histograms==0: 3118 # Now add the tail for this group 3119 gnuplot_out.extend(['','unset label','', 3120 '################################################################################']) 3121 # Return the starting data_block position for the next histogram group 3122 return block_position+len(self) 3123 3124 # We can finally add the last subhistograms for the ratios. 3125 ratio_name_long='(' 3126 for i, histo in enumerate(self[:n_histograms]): 3127 if i==0: continue 3128 ratio_name_long+='%d'%(i+1) if histo.type is None else ('NLO' if \ 3129 histo.type.split()[0]=='NLO' else histo.type) 3130 ratio_name_long+=')/' 3131 ratio_name_long+=('(1' if self[0].type==None else '(%s'%('NLO' if \ 3132 self[0].type.split()[0]=='NLO' else self[0].type))+' central value)' 3133 3134 ratio_name_short = 'ratio w.r.t. '+('1' if self[0].type==None else '%s'%('NLO' if \ 3135 self[0].type.split()[0]=='NLO' else self[0].type)) 3136 3137 replacement_dic['subhistogram_type'] = '%s ratio'%ratio_name_long 3138 replacement_dic['set_ylabel'] = 'set ylabel "%s"'%ratio_name_short 3139 3140 (ymin, ymax) = HwU.get_y_optimal_range(self[n_histograms:], 3141 labels = wgts_to_consider, scale='LIN',Kratio = True) 3142 3143 # Add a margin on upper and lower bound. 3144 ymax = ymax + 0.2 * (ymax - ymin) 3145 ymin = ymin - 0.2 * (ymax - ymin) 3146 replacement_dic['unset label'] = 'unset label' 3147 replacement_dic['ymin'] = ymin 3148 replacement_dic['ymax'] = ymax 3149 (replacement_dic['origin_x'], replacement_dic['origin_y'], 3150 replacement_dic['size_x'], replacement_dic['size_y']) = layout_geometry.pop() 3151 replacement_dic['mytics'] = 2 3152 # replacement_dic['set_ytics'] = 'set ytics %f'%((int(10*(ymax-ymin))/10)/10.0) 3153 replacement_dic['set_ytics'] = 'set ytics auto' 3154 replacement_dic['set_format_x'] = "set format x" 3155 replacement_dic['set_yscale'] = "unset logscale y" 3156 replacement_dic['set_format_y'] = 'unset format' 3157 replacement_dic['set_histo_label'] = \ 3158 'set label "%s" font ",9" at graph 0.03, graph 0.13'%ratio_name_long 3159 # 'set label "NLO/LO (K-factor)" font ",9" at graph 0.82, graph 0.13' 3160 gnuplot_out.append(subhistogram_header%replacement_dic) 3161 3162 uncertainty_plot_lines = [] 3163 plot_lines = [] 3164 3165 # Some crap to get the colors right I suppose... 3166 n=-1 3167 n=n+1 3168 if not mu_var_pos is None: 3169 for j,mu_var in enumerate(mu_var_pos): 3170 if j!=0: n=n+1 3171 if not PDF_var_pos is None: 3172 for j,PDF_var in enumerate(PDF_var_pos): 3173 if j!=0: n=n+1 3174 if not merging_var_pos is None: 3175 for j,merging_var in enumerate(merging_var_pos): 3176 if j!=0: n=n+1 3177 if not alpsfact_var_pos is None: 3178 for j,alpsfact_var in enumerate(alpsfact_var_pos): 3179 if j!=0: n=n+1 3180 3181 for i_histo_ratio, histo_ration in enumerate(self[n_histograms:]): 3182 n=n+1 3183 k=n 3184 block_ratio_pos = block_position+n_histograms+i_histo_ratio 3185 color_index = n%self.number_line_colors_defined+1 3186 # Now add the subhistograms 3187 plot_lines.append( 3188 "'%s' index %d using (($1+$2)/2):3 ls %d title ''"%\ 3189 (HwU_name,block_ratio_pos,color_index)) 3190 if 'statistical' in uncertainties: 3191 plot_lines.append( 3192 "'%s' index %d using (($1+$2)/2):3:4 w yerrorbar ls %d title ''"%\ 3193 (HwU_name,block_ratio_pos,color_index)) 3194 3195 # Then the scale variations 3196 if not mu_var_pos is None: 3197 for j,mu_var in enumerate(mu_var_pos): 3198 uncertainty_plot_lines.append({}) 3199 if j==0: 3200 color_index = k%self.number_line_colors_defined+1 3201 else: 3202 n=n+1 3203 color_index = n%self.number_line_colors_defined+1 3204 # Only print out the additional central value for advanced scale variation 3205 if j>0 or mu[j]!='none': 3206 plot_lines.append( 3207 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\ 3208 %(HwU_name,block_ratio_pos,mu_var+3,color_index)) 3209 uncertainty_plot_lines[-1]['scale'] = get_uncertainty_lines( 3210 HwU_name, block_ratio_pos, mu_var+4, color_index+10,'', 3211 band='scale' in use_band) 3212 if not PDF_var_pos is None: 3213 for j,PDF_var in enumerate(PDF_var_pos): 3214 uncertainty_plot_lines.append({}) 3215 if j==0: 3216 color_index = k%self.number_line_colors_defined+1 3217 else: 3218 n=n+1 3219 color_index = n%self.number_line_colors_defined+1 3220 # Only print out the additional central value for advanced pdf variation 3221 if j>0 or pdf[j]!='none': 3222 plot_lines.append( 3223 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\ 3224 %(HwU_name,block_ratio_pos,PDF_var+3,color_index)) 3225 uncertainty_plot_lines[-1]['pdf'] = get_uncertainty_lines( 3226 HwU_name, block_ratio_pos, PDF_var+4, color_index+20,'', 3227 band='pdf' in use_band) 3228 if not merging_var_pos is None: 3229 for j,merging_var in enumerate(merging_var_pos): 3230 uncertainty_plot_lines.append({}) 3231 if j==0: 3232 color_index = k%self.number_line_colors_defined+1 3233 else: 3234 n=n+1 3235 color_index = n%self.number_line_colors_defined+1 3236 if j>0 or merging[j]!='none': 3237 plot_lines.append( 3238 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\ 3239 %(HwU_name,block_ratio_pos,merging_var+3,color_index)) 3240 uncertainty_plot_lines[-1]['merging_scale'] = get_uncertainty_lines( 3241 HwU_name, block_ratio_pos, merging_var+4, color_index+30,'', 3242 band='merging_scale' in use_band) 3243 if not alpsfact_var_pos is None: 3244 for j,alpsfact_var in enumerate(alpsfact_var_pos): 3245 uncertainty_plot_lines.append({}) 3246 if j==0: 3247 color_index = k%self.number_line_colors_defined+1 3248 else: 3249 n=n+1 3250 color_index = n%self.number_line_colors_defined+1 3251 if j>0 or alpsfact[j]!='none': 3252 plot_lines.append( 3253 "'%s' index %d using (($1+$2)/2):%d ls %d title ''"\ 3254 %(HwU_name,block_ratio_pos,alpsfact_var+3,color_index)) 3255 uncertainty_plot_lines[-1]['alpsfact'] = get_uncertainty_lines( 3256 HwU_name, block_ratio_pos, alpsfact_var+4, color_index+40,'', 3257 band='alpsfact' in use_band) 3258 3259 # Now add the uncertainty lines, those not using a band so that they 3260 # are not covered by those using a band after we reverse plo_lines 3261 for one_plot in uncertainty_plot_lines: 3262 for uncertainty_type, lines in one_plot.items(): 3263 if not uncertainty_type in use_band: 3264 plot_lines.extend(lines) 3265 # then those using a band 3266 for one_plot in uncertainty_plot_lines: 3267 for uncertainty_type, lines in one_plot.items(): 3268 if uncertainty_type in use_band: 3269 plot_lines.extend(lines) 3270 3271 plot_lines.append("1.0 ls 999 title ''") 3272 3273 # Reverse so that bands appear first 3274 plot_lines.reverse() 3275 # Add the plot lines 3276 gnuplot_out.append(',\\\n'.join(plot_lines)) 3277 3278 # Now add the tail for this group 3279 gnuplot_out.extend(['','unset label','', 3280 '################################################################################']) 3281 3282 # Return the starting data_block position for the next histogram group 3283 return block_position+len(self) 3284
3285 ################################################################################ 3286 ## matplotlib related function 3287 ################################################################################ 3288 -def plot_ratio_from_HWU(path, ax, hwu_variable, hwu_numerator, hwu_denominator, *args, **opts):
3289 """INPUT: 3290 - path can be a path to HwU or an HwUList instance 3291 - ax is the matplotlib frame where to do the plot 3292 - hwu_variable is the histograms to consider 3293 - hwu_numerator is the numerator of the ratio plot 3294 - hwu_denominator is the denominator of the ratio plot 3295 OUTPUT: 3296 - adding the curves to the plot 3297 - return the HwUList 3298 """ 3299 3300 if isinstance(path, str): 3301 hwu = HwUList(path, raw_labels=True) 3302 else: 3303 hwu = path 3304 3305 if 'hwu_denominator_path' in opts: 3306 print 'found second hwu' 3307 if isinstance(opts['hwu_denominator_path'],str): 3308 hwu2 = HwUList(path, raw_labels=True) 3309 else: 3310 hwu2 = opts['hwu_denominator_path'] 3311 del opts['hwu_denominator_path'] 3312 else: 3313 hwu2 = hwu 3314 3315 3316 select_hist = hwu.get(hwu_variable) 3317 select_hist2 = hwu2.get(hwu_variable) 3318 bins = select_hist.get('bins') 3319 num = select_hist.get(hwu_numerator) 3320 denom = select_hist2.get(hwu_denominator) 3321 ratio = [num[i]/denom[i] if denom[i] else 1 for i in xrange(len(bins))] 3322 if 'drawstyle' not in opts: 3323 opts['drawstyle'] = 'steps' 3324 ax.plot(bins, ratio, *args, **opts) 3325 return hwu
3326
3327 -def plot_from_HWU(path, ax, hwu_variable, hwu_central, *args, **opts):
3328 """INPUT: 3329 - path can be a path to HwU or an HwUList instance 3330 - ax is the matplotlib frame where to do the plot 3331 - hwu_variable is the histograms to consider 3332 - hwu_central is the central curve to consider 3333 - hwu_error is the error band to consider (optional: Default is no band) 3334 - hwu_error_mode is how to compute the error band (optional) 3335 OUTPUT: 3336 - adding the curves to the plot 3337 - return the HwUList 3338 - return the line associated to the central (can be used to get the color) 3339 """ 3340 3341 # Handle optional parameter 3342 if 'hwu_error' in opts: 3343 hwu_error = opts['hwu_error'] 3344 del opts['hwu_error'] 3345 else: 3346 hwu_error = None 3347 3348 if 'hwu_error_mode' in opts: 3349 hwu_error_mode = opts['hwu_error_mode'] 3350 del opts['hwu_error_mode'] 3351 else: 3352 hwu_error_mode = None 3353 3354 if 'hwu_mult' in opts: 3355 hwu_mult = opts['hwu_mult'] 3356 del opts['hwu_mult'] 3357 else: 3358 hwu_mult = 1 3359 3360 if isinstance(path, str): 3361 hwu = HwUList(path, raw_labels=True) 3362 else: 3363 hwu = path 3364 3365 3366 select_hist = hwu.get(hwu_variable) 3367 bins = select_hist.get('bins') 3368 central_value = select_hist.get(hwu_central) 3369 if hwu_mult != 1: 3370 central_value = [hwu_mult*b for b in central_value] 3371 if 'drawstyle' not in opts: 3372 opts['drawstyle'] = 'steps' 3373 H, = ax.plot(bins, central_value, *args, **opts) 3374 3375 # Add error band 3376 if hwu_error: 3377 if not 'hwu_error_mode' in opts: 3378 opts['hwu_error_mode']=None 3379 h_min, h_max = select_hist.get_uncertainty_band(hwu_error, mode=hwu_error_mode) 3380 if hwu_mult != 1: 3381 h_min = [hwu_mult*b for b in h_min] 3382 h_max = [hwu_mult*b for b in h_max] 3383 fill_between_steps(bins, h_min, h_max, ax=ax, facecolor=H.get_color(), 3384 alpha=0.5, edgecolor=H.get_color(),hatch='/') 3385 3386 return hwu, H
3387 3388 3389 3390 3391 3392 3393 if __name__ == "__main__": 3394 main_doc = \ 3395 """ For testing and standalone use. Usage: 3396 python histograms.py <.HwU input_file_path_1> <.HwU input_file_path_2> ... --out=<output_file_path.format> <options> 3397 Where <options> can be a list of the following: 3398 '--help' See this message. 3399 '--gnuplot' or '' output the histograms read to gnuplot 3400 '--HwU' to output the histograms read to the raw HwU source. 3401 '--types=<type1>,<type2>,...' to keep only the type<i> when importing histograms. 3402 '--titles=<title1>,<title2>,...' to keep only the titles which have any of 'title<i>' in them (not necessarily equal to them) 3403 '--n_ratios=<integer>' Specifies how many curves must be considerd for the ratios. 3404 '--no_open' Turn off the automatic processing of the gnuplot output. 3405 '--show_full' to show the complete output of what was read. 3406 '--show_short' to show a summary of what was read. 3407 '--simple_ratios' to turn off correlations and error propagation in the ratio. 3408 '--sum' To sum all identical histograms together 3409 '--average' To average over all identical histograms 3410 '--rebin=<n>' Rebin the plots by merging n-consecutive bins together. 3411 '--assign_types=<type1>,<type2>,...' to assign a type to all histograms of the first, second, etc... files loaded. 3412 '--multiply=<fact1>,<fact2>,...' to multiply all histograms of the first, second, etc... files by the fact1, fact2, etc... 3413 '--no_suffix' Do no add any suffix (like '#1, #2, etc..) to the histograms types. 3414 '--lhapdf-config=<PATH_TO_LHAPDF-CONFIG>' give path to lhapdf-config to compute PDF certainties using LHAPDF (only for lhapdf6) 3415 '--jet_samples=[int1,int2]' Specifies what jet samples to keep. 'None' is the default and keeps them all. 3416 '--central_only' This option specifies to disregard all extra weights, so as to make it possible 3417 to take the ratio of plots with different extra weights specified. 3418 '--keep_all_weights' This option specifies to keep in the HwU produced all the weights, even 3419 those which are not known (i.e. that is scale, PDF or merging variation) 3420 For chosing what kind of variation you want to see on your plot, you can use the following options 3421 '--no_<type>' Turn off the plotting of variations of the chosen type 3422 '--only_<type>' Turn on only the plotting of variations of the chosen type 3423 '--variations=['<type1>',...]' Turn on only the plotting of the variations of the list of chosen types 3424 '--band=['<type1>',...]' Chose for which variations one should use uncertainty bands as opposed to lines 3425 The types can be: pdf, scale, stat, merging or alpsfact 3426 For the last two options one can use ...=all to automatically select all types. 3427 3428 When parsing an XML-formatted plot source output by the Pythia8 driver, the file names can be appended 3429 options as suffixes separated by '|', as follows: 3430 python histograms.py <XML_source_file_name>@<option1>@<option2>@etc.. 3431 These options can be 3432 'run_id=<integer>' Specifies the run_ID from which the plots should be loaded. 3433 By default, the first run is considered and the ones that follow are ignored. 3434 'merging_scale=<float>' This option allows to specify to import only the plots corresponding to a specific 3435 value for the merging scale. 3436 A value of -1 means that only the weights with the same merging scale as the central weight are kept. 3437 By default, all weights are considered. 3438 """ 3439 3440 possible_options=['--help', '--gnuplot', '--HwU', '--types','--n_ratios',\ 3441 '--no_open','--show_full','--show_short','--simple_ratios','--sum','--average','--rebin', \ 3442 '--assign_types','--multiply','--no_suffix', '--out', '--jet_samples', 3443 '--no_scale','--no_pdf','--no_stat','--no_merging','--no_alpsfact', 3444 '--only_scale','--only_pdf','--only_stat','--only_merging','--only_alpsfact', 3445 '--variations','--band','--central_only', '--lhapdf-config','--titles', 3446 '--keep_all_weights'] 3447 n_ratios = -1 3448 uncertainties = ['scale','pdf','statistical','merging_scale','alpsfact'] 3449 # The list of type of uncertainties for which to use bands. None is a 'smart' default 3450 use_band = None 3451 auto_open = True 3452 ratio_correlations = True 3453 consider_reweights = ['pdf','scale','murmuf_scales','merging_scale','alpsfact']
3454 3455 - def log(msg):
3456 print "histograms.py :: %s"%str(msg)
3457 3458 if '--help' in sys.argv or len(sys.argv)==1: 3459 log('\n\n%s'%main_doc) 3460 sys.exit(0) 3461 3462 for arg in sys.argv[1:]: 3463 if arg.startswith('--'): 3464 if arg.split('=')[0] not in possible_options: 3465 log('WARNING: option "%s" not valid. It will be ignored' % arg) 3466 3467 arg_string=' '.join(sys.argv) 3468 3469 OutName = "" 3470 for arg in sys.argv[1:]: 3471 if arg.startswith('--out='): 3472 OutName = arg[6:] 3473 3474 accepted_types = [] 3475 for arg in sys.argv[1:]: 3476 if arg.startswith('--types='): 3477 accepted_types = [(type if type!='None' else None) for type in \ 3478 arg[8:].split(',')] 3479 3480 accepted_titles = [] 3481 for arg in sys.argv[1:]: 3482 if arg.startswith('--titles='): 3483 accepted_titles = [(type if type!='None' else None) for type in \ 3484 arg[9:].split(',')] 3485 3486 assigned_types = [] 3487 for arg in sys.argv[1:]: 3488 if arg.startswith('--assign_types='): 3489 assigned_types = [(type if type!='None' else None) for type in \ 3490 arg[15:].split(',')] 3491 3492 jet_samples_to_keep = None 3493 3494 lhapdfconfig = ['lhapdf-config'] 3495 for arg in sys.argv[1:]: 3496 if arg.startswith('--lhapdf-config='): 3497 lhapdfconfig = arg[16:] 3498 3499 no_suffix = False 3500 if '--no_suffix' in sys.argv: 3501 no_suffix = True 3502 3503 if '--central_only' in sys.argv: 3504 consider_reweights = [] 3505 3506 if '--keep_all_weights' in sys.argv: 3507 consider_reweights = 'ALL' 3508 3509 for arg in sys.argv[1:]: 3510 if arg.startswith('--n_ratios='): 3511 n_ratios = int(arg[11:]) 3512 3513 if '--no_open' in sys.argv: 3514 auto_open = False 3515 3516 variation_type_map={'scale':'scale','merging':'merging_scale','pdf':'pdf', 3517 'stat':'statistical','alpsfact':'alpsfact'} 3518 3519 for arg in sys.argv: 3520 try: 3521 opt, value = arg.split('=') 3522 except ValueError: 3523 continue 3524 if opt=='--jet_samples': 3525 jet_samples_to_keep = eval(value) 3526 if opt=='--variations': 3527 uncertainties=[variation_type_map[type] for type in eval(value, 3528 dict([(key,key) for key in variation_type_map.keys()]+ 3529 [('all',variation_type_map.keys())]))] 3530 if opt=='--band': 3531 use_band=[variation_type_map[type] for type in eval(value, 3532 dict([(key,key) for key in variation_type_map.keys()]+ 3533 [('all',[type for type in variation_type_map.keys() if type!='stat'])]))] 3534 3535 if '--simple_ratios' in sys.argv: 3536 ratio_correlations = False 3537 3538 for arg in sys.argv: 3539 if arg.startswith('--no_') and not arg.startswith('--no_open'): 3540 uncertainties.remove(variation_type_map[arg[5:]]) 3541 if arg.startswith('--only_'): 3542 uncertainties= [variation_type_map[arg[7:]]] 3543 break 3544 3545 # Now remove from the weights considered all those not deemed necessary 3546 # in view of which uncertainties are selected 3547 if isinstance(consider_reweights, list): 3548 naming_map={'pdf':'pdf','scale':'scale', 3549 'merging_scale':'merging_scale','alpsfact':'alpsfact'} 3550 for key in naming_map: 3551 if (not key in uncertainties) and (naming_map[key] in consider_reweights): 3552 consider_reweights.remove(naming_map[key]) 3553 3554 n_files = len([_ for _ in sys.argv[1:] if not _.startswith('--')]) 3555 histo_norm = [1.0]*n_files 3556 3557 for arg in sys.argv[1:]: 3558 if arg.startswith('--multiply='): 3559 histo_norm = [(float(fact) if fact!='' else 1.0) for fact in \ 3560 arg[11:].split(',')] 3561 3562 if '--average' in sys.argv: 3563 histo_norm = [hist/float(n_files) for hist in histo_norm] 3564 3565 log("=======") 3566 histo_list = HwUList([]) 3567 for i, arg in enumerate(sys.argv[1:]): 3568 if arg.startswith('--'): 3569 break 3570 log("Loading histograms from '%s'."%arg) 3571 if OutName=="": 3572 OutName = os.path.basename(arg).split('.')[0]+'_output' 3573 # Make sure to process the potential XML options appended to the filename 3574 file_specification = arg.split('@') 3575 filename = file_specification.pop(0) 3576 file_options = {} 3577 for option in file_specification: 3578 opt, value = option.split('=') 3579 if opt=='run_id': 3580 file_options[opt]=int(value) 3581 if opt=='merging_scale': 3582 file_options[opt]=float(value) 3583 else: 3584 log("Unreckognize file option '%s'."%option) 3585 sys.exit(1) 3586 new_histo_list = HwUList(filename, accepted_types_order=accepted_types, 3587 consider_reweights=consider_reweights, **file_options) 3588 # We filter now the diagrams whose title doesn't match the constraints 3589 if len(accepted_titles)>0: 3590 new_histo_list = HwUList(histo for histo in new_histo_list if 3591 any(t in histo.title for t in accepted_titles)) 3592 for histo in new_histo_list: 3593 if no_suffix or n_files==1: 3594 continue 3595 if not histo.type is None: 3596 histo.type += '|' 3597 else: 3598 histo.type = '' 3599 # Firs option is to give a bit of the name of the source HwU file. 3600 #histo.type += " %s, #%d"%\ 3601 # (os.path.basename(arg).split('.')[0][:3],i+1) 3602 # But it is more elegant to give just the number. 3603 # Overwrite existing number if present. We assume here that one never 3604 # uses the '#' in its custom-defined types, which is a fair assumptions. 3605 try: 3606 suffix = assigned_types[i] 3607 except IndexError: 3608 suffix = "#%d"%(i+1) 3609 try: 3610 histo.type = histo.type[:histo.type.index('#')] + suffix 3611 except ValueError: 3612 histo.type += suffix 3613 3614 if i==0 or all(_ not in ['--sum','--average'] for _ in sys.argv): 3615 for j,hist in enumerate(new_histo_list): 3616 new_histo_list[j]=hist*histo_norm[i] 3617 histo_list.extend(new_histo_list) 3618 continue 3619 3620 if any(_ in sys.argv for _ in ['--sum','--average']): 3621 for j, hist in enumerate(new_histo_list): 3622 # First make sure the plots have the same weight labels and such 3623 hist.test_plot_compability(histo_list[j]) 3624 # Now let the histogram module do the magic and add them. 3625 histo_list[j] += hist*histo_norm[i] 3626 3627 log("A total of %i histograms were found."%len(histo_list)) 3628 log("=======") 3629 3630 n_rebin = 1 3631 for arg in sys.argv[1:]: 3632 if arg.startswith('--rebin='): 3633 n_rebin = int(arg[8:]) 3634 3635 if n_rebin > 1: 3636 for hist in histo_list: 3637 hist.rebin(n_rebin) 3638 3639 if '--gnuplot' in sys.argv or all(arg not in ['--HwU'] for arg in sys.argv): 3640 # Where the magic happens: 3641 histo_list.output(OutName, format='gnuplot', 3642 number_of_ratios = n_ratios, 3643 uncertainties=uncertainties, 3644 ratio_correlations=ratio_correlations, 3645 arg_string=arg_string, 3646 jet_samples_to_keep=jet_samples_to_keep, 3647 use_band=use_band, 3648 auto_open=auto_open, 3649 lhapdfconfig=lhapdfconfig) 3650 # Tell the user that everything went for the best 3651 log("%d histograms have been output in " % len(histo_list)+\ 3652 "the gnuplot format at '%s.[HwU|gnuplot]'." % OutName) 3653 if auto_open: 3654 command = 'gnuplot %s.gnuplot'%OutName 3655 try: 3656 subprocess.call(command,shell=True,stderr=subprocess.PIPE) 3657 except: 3658 log("Automatic processing of the gnuplot card failed. Try the"+\ 3659 " command by hand:\n%s"%command) 3660 else: 3661 sys.exit(0) 3662 3663 if '--HwU' in sys.argv: 3664 log("Histograms data has been output in the HwU format at "+\ 3665 "'%s.HwU'."%OutName) 3666 histo_list.output(OutName, format='HwU') 3667 sys.exit(0) 3668 3669 if '--show_short' in sys.argv or '--show_full' in sys.argv: 3670 for i, histo in enumerate(histo_list): 3671 if i!=0: 3672 log('-------') 3673 log(histo.nice_string(short=(not '--show_full' in sys.argv))) 3674 log("=======")
3675 3676 ######## Routine from https://gist.github.com/thriveth/8352565 3677 ######## To fill for histograms data in matplotlib 3678 -def fill_between_steps(x, y1, y2=0, h_align='right', ax=None, **kwargs):
3679 ''' Fills a hole in matplotlib: fill_between for step plots. 3680 Parameters : 3681 ------------ 3682 x : array-like 3683 Array/vector of index values. These are assumed to be equally-spaced. 3684 If not, the result will probably look weird... 3685 y1 : array-like 3686 Array/vector of values to be filled under. 3687 y2 : array-Like 3688 Array/vector or bottom values for filled area. Default is 0. 3689 **kwargs will be passed to the matplotlib fill_between() function. 3690 ''' 3691 # If no Axes opject given, grab the current one: 3692 if ax is None: 3693 ax = plt.gca() 3694 3695 3696 # First, duplicate the x values 3697 #duplicate the info # xx = numpy.repeat(2)[1:] 3698 xx= []; [(xx.append(d),xx.append(d)) for d in x]; xx = xx[1:] 3699 # Now: the average x binwidth 3700 xstep = x[1] -x[0] 3701 # Now: add one step at end of row. 3702 xx.append(xx[-1] + xstep) 3703 3704 # Make it possible to change step alignment. 3705 if h_align == 'mid': 3706 xx = [X-xstep/2. for X in xx] 3707 elif h_align == 'right': 3708 xx = [X-xstep for X in xx] 3709 3710 # Also, duplicate each y coordinate in both arrays 3711 yy1 = []; [(yy1.append(d),yy1.append(d)) for d in y1] 3712 if isinstance(y1, list): 3713 yy2 = []; [(yy2.append(d),yy2.append(d)) for d in y2] 3714 else: 3715 yy2=y2 3716 if len(yy2) != len(yy1): 3717 yy2 = []; [(yy2.append(d),yy2.append(d)) for d in y2] 3718 3719 # now to the plotting part: 3720 ax.fill_between(xx, yy1, y2=yy2, **kwargs) 3721 3722 return ax
3723 ######## end routine from https://gist.github.com/thriveth/835256 3724