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author kaymccoy
date Fri, 12 Aug 2016 22:56:15 -0400
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15:2806b2ab4f2c 16:e5211663e357
1 # A translation of aggregate.pl into python! For analysis of Tn-Seq.
2 # This script requires BioPython just like calc_fitness.py, so you need it installed along with its dependencies if you want to run these scripts on your own.
3 # How to install BioPython and a list of its dependencies can be found here: http://biopython.org/DIST/docs/install/Installation.html
4 # K. McCoy
5
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12
13
14 ##### ARGUMENTS #####
15
16 def print_usage():
17 print "Aggregate.py's usage is as follows:" + "\n\n"
18 print "\033[1m" + "Required" + "\033[0m" + "\n"
19 print "-o" + "\t\t" + "Output file for aggregated data." + "\n"
20 print "\n"
21 print "\033[1m" + "Optional" + "\033[0m" + "\n"
22 print "-c" + "\t\t" + "Check for missing genes in the data set - provide a reference genome in genbank format. Missing genes will be sent to stdout." + "\n"
23 print "-m" + "\t\t" + "Place a mark in an extra column for this set of genes. Provide a file with a list of genes seperated by newlines." + "\n"
24 print "-x" + "\t\t" + "Cutoff: Don't include fitness scores with average counts (c1+c2)/2 < x (default: 0)" + "\n"
25 print "-b" + "\t\t" + "Blanks: Exclude -b % of blank fitness scores (scores where c2 = 0) (default: 0 = 0%)" + "\n"
26 print "-f" + "\t\t" + "An in-between file carrying information on the blank count found from calc_fitness or consol_fitness; one of two ways to pass a blank count to this script" + "\n"
27 print "-w" + "\t\t" + "Use weighted algorithm to calculate averages, variance, sd, se" + "\n"
28 print "-l" + "\t\t" + "Weight ceiling: maximum value to use as a weight (default: 999,999)" + "\n"
29 print "\n"
30 print "All remainder arguements will be treated as fitness files (those files created by calc_fitness.py)" + "\n"
31 print "\n"
32
33 import argparse
34 parser = argparse.ArgumentParser()
35 parser.add_argument("-o", action="store", dest="summary")
36 parser.add_argument("-c", action="store", dest="find_missing")
37 parser.add_argument("-m", action="store", dest="marked")
38 parser.add_argument("-x", action="store", dest="cutoff")
39 parser.add_argument("-b", action="store", dest="blank_pc")
40 parser.add_argument("-f", action="store", dest="blank_file")
41 parser.add_argument("-w", action="store", dest="weighted")
42 parser.add_argument("-l", action="store", dest="weight_ceiling")
43 parser.add_argument("fitnessfiles", nargs=argparse.REMAINDER)
44
45 arguments = parser.parse_args()
46
47 if not arguments.summary:
48 print "\n" + "You are missing a value for the -o flag. "
49 print_usage()
50 quit()
51
52 if not arguments.fitnessfiles:
53 print "\n" + "You are missing fitness file(s); these should be entered immediately after all the flags. "
54 print_usage()
55 quit()
56
57 # 999,999 is a trivial placeholder number
58
59 if (not arguments.weight_ceiling):
60 arguments.weight_ceiling = 999999
61
62 # Cutoff exists to discard positions with a low number of counted transcripts, because their fitness may not be as accurate - for the same reasoning that studies with low sample sizes can be innacurate.
63
64 if (not arguments.cutoff):
65 arguments.cutoff = 0
66
67 # Gets information from the txt output file of calc_fit / consol, if inputted
68
69 if arguments.blank_file:
70 with open(arguments.blank_file) as file:
71 blank_pc = file.read().splitlines()
72 arguments.blank_pc = float(blank_pc[0].split()[1])
73
74 if (not arguments.blank_pc):
75 arguments.blank_pc = 0
76
77
78
79
80
81 ##### SUBROUTINES #####
82
83 # A subroutine that calculates the average, variance, standard deviation (sd), and standard error (se) of a group of scores; for use when aggregating scores by gene later on
84
85 import math
86 def average(scores):
87 sum = 0
88 num = 0
89 for i in scores:
90 ###!!!
91 print type(sum)
92 print type(i)
93 sum += i
94 num += 1
95 average = sum/num
96 xminusxbars = 0
97 for i in scores:
98 xminusxbars += (i - average)**2
99 variance = xminusxbars/(num-1)
100 sd = math.sqrt(variance)
101 se = sd / math.sqrt(num)
102 return (average, variance, sd, se)
103
104 # A subroutine that calculates the weighted average, variance, standard deviation (sd), and standard error (se) of a group of scores; the weights come from the number of reads each insertion location has
105 # For use when aggregating scores by gene later on, if the weighted argument is called
106
107 def weighted_average(scores,weights):
108 sum = 0
109 weighted_average = 0
110 weighted_variance = 0
111 top = 0
112 bottom = 0
113 i = 0
114 while i < len(weights):
115 if not scores[i]:
116 scores[i] = 0.0
117 top += float(weights[i])*float(scores[i])
118 bottom += float(weights[i])
119 i += 1
120 if bottom == 0:
121 return 0
122 weighted_average = top/bottom
123 top = 0
124 bottom = 0
125 i = 0
126 while i < len(weights):
127 top += float(weights[i]) * (float(scores[i]) - weighted_average)**2
128 bottom += float(weights[i])
129 i += 1
130 weighted_variance = top/bottom
131 weighted_stdev = math.sqrt(weighted_variance)
132 weighted_stder = weighted_stdev/math.sqrt(len(scores))
133 return (weighted_average, weighted_variance, weighted_stdev, weighted_stder)
134
135
136
137
138
139
140
141
142
143
144 ##### AGGREGATION / CALCULATIONS #####
145
146 #Reads the genes which should be marked in the final aggregate file into an array
147
148 import os.path
149 if arguments.marked:
150 with open(arguments.marked) as file:
151 marked_set = file.read().splitlines()
152
153 #Creates a dictionary of dictionaries to contain a summary of all genes and their fitness values
154 #The fitness values and weights match up, so that the weight of gene_summary[locus]["w"][2] would be gene_summary[locus]["s"][2]
155
156 import csv
157 gene_summary = {}
158 for eachfile in arguments.fitnessfiles:
159 with open(eachfile) as csvfile:
160 lines = csv.reader(csvfile)
161 for line in lines:
162 locus = line[9]
163 w = line[12]
164 if w == 'nW':
165 continue
166 if not w:
167 w == 0
168 c1 = float(line[2])
169 c2 = float(line[3])
170 avg = (c1+c2)/2
171 if avg < float(arguments.cutoff):
172 continue
173 if avg > float(arguments.weight_ceiling):
174 avg = arguments.weight_ceiling
175 if locus not in gene_summary:
176 gene_summary[locus] = {"w" : [], "s": []}
177 gene_summary[locus]["w"].append(w)
178 gene_summary[locus]["s"].append(avg)
179
180 #If finding any missing gene loci is requested in the arguments, starts out by loading all the known features from a genbank file
181
182 from Bio import SeqIO
183 if (arguments.find_missing):
184 output = [["locus","mean","var","sd","se","gene","Total","Blank","Not Blank","Blank Removed","M\n"]]
185 handle = open(arguments.find_missing, "rU")
186 for record in SeqIO.parse(handle, "genbank"):
187 refname = record.id
188 features = record.features
189 handle.close()
190
191 #Goes through the features to find which are genes
192
193 for feature in features:
194 gene = ""
195 if feature.type == "gene":
196 locus = "".join(feature.qualifiers["locus_tag"])
197 if "gene" in feature.qualifiers:
198 gene = "".join(feature.qualifiers["gene"])
199 else:
200 continue
201
202 #Goes through the fitness scores of insertions within each gene, and removes whatever % of blank fitness scores were requested along with their corresponding weights
203
204 #!!! issue = locus["w"] returning str not float for some reason!
205
206 sum = 0
207 num = 0
208 avgsum = 0
209 blank_ws = 0
210 i = 0
211 if locus in gene_summary.keys():
212 for w in gene_summary[locus]["w"]:
213 if float(w) == 0:
214 blank_ws += 1
215 else:
216 sum += float(w)
217 num += 1
218 count = num + blank_ws
219 removed = 0
220 to_remove = int(float(arguments.blank_pc)*count)
221 if blank_ws > 0:
222 i = 0
223 while i < len(gene_summary[locus]["w"]):
224 w = gene_summary[locus]["w"][i]
225 if removed == to_remove:
226 break
227 if float(w) == 0:
228 del gene_summary[locus]["w"][i]
229 del gene_summary[locus]["s"][i]
230 removed += 1
231 i -= 1
232 i += 1
233
234 #If all the fitness values within a gene are empty, sets mean/var to 0.10 and Xs out sd/se; marks the gene if that's requested
235
236 if num == 0:
237 if (arguments.marked and locus in marked_set):
238 output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "M", "\n"])
239 else:
240 output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "\n"])
241
242 #Otherwise calls average() or weighted_average() to find the aggregate w / count / standard deviation / standard error of the insertions within each gene; marks the gene if that's requested
243
244 else:
245 if not arguments.weighted:
246 (average, variance, stdev, stderr) = average(gene_summary[locus]["w"])
247 else:
248 (average, variance, stdev, stderr) = weighted_average(gene_summary[locus]["w"],gene_summary[locus]["s"])
249 if (arguments.marked and locus in marked_set):
250 output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "M", "\n"])
251 else:
252 output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "\n"])
253
254 #If a gene doesn't have any insertions, sets mean/var to 0.10 and Xs out sd/se, plus leaves count through removed blank because there were no reads.
255
256 else:
257 if (arguments.marked and locus in marked_set):
258 output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "M", "\n"])
259 else:
260 output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "\n"])
261
262 #Writes the aggregated fitness file
263
264 with open(arguments.summary, "wb") as csvfile:
265 writer = csv.writer(csvfile)
266 writer.writerows(output)
267
268 #If finding missing genes is not requested, just finds the aggregate w / count / standard deviation / standard error of the insertions within each gene, and writes them to a file, plus marks the genes requested
269 #This is never called through Galaxy since finding missing genes is just better than not finding them.
270
271 else:
272 output = [["Locus","W","Count","SD","SE","M\n"]]
273 for gene in gene_summary.keys():
274 sum = 0
275 num = 0
276 average = 0
277 if "w" not in gene_summary[gene]:
278 continue
279 for i in gene_summary[gene]["w"]:
280 sum += i
281 num += 1
282 average = sum/num
283 xminusxbars = 0
284 for i in w:
285 xminusxbars += (i-average)**2
286 if num > 1:
287 sd = math.sqrt(xminusxbars/(num-1))
288 se = sd / math.sqrt(num)
289 if (arguments.marked and locus in marked_set):
290 output.append([gene, average, num, sd, se, "M", "\n"])
291 else:
292 output.append([gene, average, num, sd, se, "\n"])
293 with open(arguments.summary, "wb") as csvfile:
294 writer = csv.writer(csvfile)
295 writer.writerows(output)
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391 #
392 # ~MMM=:DMMM?, +NMMO=,:~I8MMMMM8+, , ~I8MMMMMN87~?8NNMMN8: +NMND~ +MN= ,$MMMI ?M8, ,OM8, :MN+ =MM? ,MMDNMMD ,+DM8I, ,,:::~~~::::::::::
393 # IMMMNMM8I ,I8MM87~::+$8NNMMMMOI+=~~:, ,,:~=?$DNMMMMMMDOZI7ZDMMMD8I , , $M8+?8MM8I , 7MI +MN= ZMN, 8MD MMN8MMM, :$ONM8I+=:, ,,,::::~~~~~=====~:
394 # , ,DMNN7: , ,OMMN7==~::~=?8NNMMMMMMNNMMMMMMMMMMMN8OO8ODNMMMMMD~ , IMMNMN~ ,OM+ ,NM$ ,NMO, :MM$ , ,:::,,::::,, $MNMMNM, ,,, :?ONMMNN8?~,, , ,,,,,,,::~~=+++??=~
395 # ,,:=+????+, I$ :ZMMN8$~:,,, ,:=?7$O8DD8O$7+==+$O8DNMMMMMMMMM$ ?$, == ,~, ~NM= 8MD, ,OM8ZMMO , ,::::::~~~:,, ?MNMMZ ,,,,,, ,+7ONMNMD8O$+~, ,,,,,,::~====::
396 # ,:=IONMMMMMMMMMMM8: ZN$: ,~DMMMND7=, ,,:~====:=$DNMMMMMMMN88MMMZ +N$ , ,7DN8= =MN, IMM =DMN7 ,,,,,,,,,, ,~?, ,,,,, , ~?$8MMMMNN8Z?~:,, ,:::,,,
397 #+ONMMMMMMNO7=:,, ,,+MMO, 7D$: ~OMNMNNMNNNNNNNNNNNMMMMMMMMMNMMMM?,~MMM8 ND, 7MM=, ?NN:, +MO, ?MM, ,,,, , :,:=$DMMMMMMN87=~, ,,,,,
398 #MMND8$: , 7NM7 , =?~,,, :?88DDDNNMNNNDNDD88Z?:, ZMM$ ,MMMO ,MM+ZNMM? ::ZNZ, +M$ ,MM?, , ,, ,,:=?Z8NMMMMMN8Z+=,
399 #: ~ZMM8~ ,,,, 8MM, +MMM, 7ZZI~=$OOZ$: ,:+???+, +MZ =MN= ,,, , ,,:~=?IIII$ZO88DDNNNNNNMMMMMMMMMMN~,
400 # ,:OMMM? ,,:,, 8MM ~NNM7 :OMMMMMMMMO: =M8, :NM~ = ,~?I, ,,,,,,,,,,,,,, ,~$DNMMMMMMMMMND8O888Z$II7777I??+++===:,
401 # ?DMM8?, OMN??NMM$ ~8MMMO?===7MMM8: ~NM= =NN: ,OM~ ,, +NMMN~ ,,,,, ,,,,,,,, ,?$O8NNMDDZ7?+=~:, , , ,
402 # , ~$MMMD+ , , ~MMNMMN~ : +NMMZ, NMNM~ 7MI , ZNN,, IMN, :DMNM+ ~+NMMMD~, ,,,,,,,, ,, ,,,,,:, +OMNMMOI~,
403 # , $MMMM$, , ,=?ODNMNNMNMMMMNNND~ ,$D$, , ,8MM8 ,MMM7 ,ZMNNMM= DMMNMMMMMMMMMMMMNI: , ,,,,,,,,, ,,, ?NMO=, ,
404 # ,~ZNMND7, ,,:~=+$DNNMMMMNDDDD888OZZZZ8NMMN IMMN: ,MMN~ +Z$+, ?NNNDO+:?O888OI, ,,,,,,, ,,, +MN+
405 # =ONMMM8~ , ,:=IDMMMMMMMND8$+:, , ,INMNZ :MMM~, , +MMD , ,, ,,::,,,, ,,:::, ?M8: ,
406 #8MMMNZ~ , =I$ONMMNDZ7?+, ,,=I8NMD7: DMMN DMN= ,:::, ,:~~=~~:,, :ZMMZI+: ,, ,,,,,,,
407 #MN7:,:=7ONMMMD$?=, , ~7ODNMM$+: ~MMM++7ZOZOO8O8D8$~ ,MM8 ,,,,::~~==~~:,, :+7DMMMMNNDD88OOZZZO88DNNNN8=, ,,,,,,,
408 #I,~+DMMNND7: , ,$MMMMMN7, $MMN :??+=~::,,,, NMD, ,:,,:::~=~~,,,, ,,=I$8DNMMMMMMMMMNMMMNZ: ,,,,,,
409 #DNMNN7:, ,+ONMMMMNI: NMM$ DMN= ,,,,:::::~~::,, ?DMMMMDZ=, ,,,
410 #N8$: ,,, ,:=?ONMMMD8Z+, ,,,, MMM= ZMMI ,=?$8NMMMMMMMMMMMN87=~~,,, :=ZMMMMD$~
411 # , ,=ZNMMMMMNI:, ,~?Z88888$=, ,:~+??~, MMM, IMM$ ,=ZNMMMMMN8$+~=~=~~===7ODNMMMN8DNMMMN+, ,,
412 # , ~?$ODMMMMNZ?: :II+~, ,=7= :?77?=:====?O+ ,,:,,, MMM, ?MM$ ,,,, :?ONMM8II=, , =DMMMM87=, ,,,,
413 # , ,, ,~I8MMMMMMN87?~:=+?7$ZOO88DD888O$I+~:, ~ZZ: ,$7,~??, ,?+ ,+Z8$?==??= MMM =MM$ :?ODNNNNNNNMMO: ,:?NNMNO= ,,,IMMMNZ, ,,,:,,,,,,,,,,,,,
414 #, ,~7DNMMMMMMMMMMMNNNNMMMNND8Z7II7$$$$ZODNNNMND$, :O$: , ,IN$, I+ +ZZ=,, 7+ MMM =ODDDDNNNNNN8= :MM$ ?DDNN8?::,, ,,7NMM8, 7NMNZ~, :OMMM$, , ,,:::~~:,,,,,,,,,,,,,,,,,
415 #?8NMMMMMMMMMMMN8I:,,, ~$NMMM$ :87 +DM$ ID+~78I, O7 :=+~ MMM , , ?MM7 $NMN$, :I8MMMMMMNMDNNNNNNNNNDD88ZI=: ,ZMM7, +MMN~ ,,,:::,, ,,:,::::,,,,,,
416 #MMMD8DNMDZ7=: ,:=+7ZNNNDOZ~ =DI , :7MM7, IDDZI, =DN88ZI77$N? NMM: $MM= ~: =OMNZ+ ,=7DMMMMMMNDDOOOOZ$7IIIII77$ZOO8NNMMD$+~ :OND~, ,, MMN= ,,,,,,,,,,,,,
417 #: ,,, , ,:+ZNMNNMMNO?: +8? +NMN7 , , ON~ 8MM$ NMD, ,7MMMN, 7MMO, ,:ONMMMMN8I: ,~ZNMNN$, ~MM? , ZMNND?~: ,,, ,, ,,,,,,
418 # , , ,:~?7$ZZ8NNMMMNO7I=, ~D+ ZNO=, , :NM$: =MMM NMO ~NMNMMM :ZDN$, ,,=7DMD$?=, :?ZMD$: :DMNOZZZ$: ,,,,, ~IDMMMMMMMMMMMMMMMMMMMMM8~
419 # , =DMNNNNNNNN8$= : , ,,~?ODNZ: :DM? =: ,MMM7 +MN~ ,MM8:NMN INMZ, ,=ONOI , +NMZ ,,+ZDMMMMMMMN+, ,,,,,,, ,~?$ODNNNNNNNMMMMMMMMD= ,
420 # ,:::::~7$I?=: ,~78NMNMMMMM? :+Z+ M8 $MM, =, DMMD NMN DMO OMM77NMI, ?8$~ , ~ZDDDNNNNNMMMMMMMMNMNNNNNDD8Z+:, IMN: I8DNMMMMN7~: ,,,,,, +$O$, ~$DMMMMMMMD~ ,, ,
421 # :I+ :ID? ~ZDNNNNMMNO?~,, ,:::::=7ONMNNNDMMMN$ ,=IONNMMMMZ:, ~MO ,7MMMI $MN, , :MMN ?8NDDNND$~ +MM~ ,MM~=MMMNMD, =ONNDDNMMMMMMMMMMMMNNND88DNNNNNMMMMMMMNDO7+~::,:,7MM+ =DMMMNNO? ~ZMMMMM7::~INMMMMMMMMMMMMMN8:
422 #:MMM+,INMMM: ,:~+78NMMMM8?==++++++???++??I$Z77$$$$$$$7II??I$ZZO8MMMMM8Z7~ ,IZI ~77+ ?MMMMD88MN8+~, +8MO$OMMMMMNMMMZ, ~=: OMM? ,MM? ZMM~MMMM8~ ,:+7$$$$ZZ7?==: :8MDOZ$ZZZODMMMMMM8+, ,:=?$ZZOOOOOOZ$: , , ,=8MNMMMMMMMMMMNDZ$7$MMMMMMMMMMMMMMMMMMNI+?I7ZDNMMMMMMMMMM$,
423 #=MMMZ$MMMMMM~ ,::::~==~~+I$8NMMMMMN$::::::::,,, ,,=ONDDO$7II?+~, , ,,$DD87: =NND= , ,+$$=:~, ,:, ,MMD NMI ,~?Z8DND88$?: 8MM$MMMZ:=~:,~~:::,,,=$DNMND$MM8, ~DMNMMMN?, :7$7?+==~=:,,,,,,,, ,,,,,,, ,,,,,,::,,,,,:::::::,,,::::::,, ,OM$,7MMMMMMNZ= , ~8MMMMMMMNDO7I7MMMMMMMMMMMMN8Z7+?NMMMD,,
424 #NMMMMMM? ,++, ~=I??= :7$ 7MM+ 7M7 ,:?ZZ$MMM8NMZ~+, :?INMMM? +$NMMMNZ: :+?I7$$Z$O88D88DDDDNNNNNNNNNNNNNNNNNNDDNDDDNNNNNMMNMMMMMMMNNNN$:??~ ,+II?=, , ,, ,?I??+=~, :MMMMMNM
425 #MMMMMD~ ,:=++++++++++=~,,, +MMN: ,MMD, :M$ ,INMNMMMMMMMMMM~~~?D, :OMM8: ,+$8Z$+,,$MMMMMD, :IODDDD
426 #N$~,,, ,~I$8NMMMMMMMMMNMMMMMMMMMMMMMMMMNNNN7: ZMMMM? NMMMNZ~:, +$: ,NMNI:::~?8MMM7I? IO ~I$ODNNNDND8OO$I?DMMMD$I8NMMMMMMMNMNMMMMMMM8=, ,
427 # ,:~?ONMMMMNNNDDD8NMD+, ,~?Z8NMNM8=, , $MNNNMM? :DMMMNDD8DZ7$+, ,8NMMNMNMMMD$: =8, ,INMMMNZI?====+I$$8DDNMMMMMMNNNMND7, :~$DMMMNMO
428 # ,?DMMMMMNNNMMM+::~++ZMMMM8ZZZZZZ$II77II7???=~:, ,+DMN7 =NMM8, ,NMMNMNMN$?I7I77OZ~ ~8D$~, ,MMMMMMNM8: :DMMM, , DMO=ZMZ,
429 # ,?OMMN87=, IZNNNMMMMMMMMMMND7IIII??III$8DDNN8Z+, , ~MM ?OMMMM~ ,MMMMMZ+ ~I= IMMMO$$= ?NNM? MN~ +NM:
430 # IMM8~ =MD, ?N= :,, ,+77?=+, $8MM7::OMMMMMMZ+ I+, ?NI , :MMM8$ NNI, 7M$
431 # ,MMN $MD 7M+ ~ODNZ~, :7MMN? , $MMMN= 7MNMN: +8+ ,D8~ =MMDD8Z= =NMD OM7,
432 # OMMD~ IMN: =8O:,~+$OO? :IZ+: :ZMMNM8= =DMMI ~8MM8= , ,, :8M?,$MM, ::, DM, =Z~ ,I8DDDN8$DMN~ MN ,=Z8DNDZZ= MD: ,
433 # ,,,,,,,, ?8NNMM8$I????++=+8MMMD$77$ZDMMNMMMDNOZ~ :IZ8DOI~, =MMMNMNM8I:, ,7M8I +MND7 ~MNDDM8~ 7MI MM7 :8MDDM7 8N= ~ID$, ,:Z$?:,,,=ONNMNMD= ?M$ :: +MI ,,,,,,::~~:::,,,,,,, ,+OMO:, ,
434 # ,,,::~===~~:, ,~+I$88DNNNNNNNNNDNDD8O$?~:, ,7DNMNDMMMN~,ZMND$8NNDZ=, $M? ?NNN7 =MO OMD$MM= MMMINMO $M7 DN: 8MMD: ?D~ :O8ZDMMMMD=, ,ZN$ ,INNMD= :NO ,:+8MMMMMMMMDI:,::,,
435 #=~~~::~~::, ~?78MMMDNMM? :+I$DD87?=, OMNDO$7$OI: 7N+ ?MNMN~ MMMNM$ 7M$ 7M$ +NMMO, ?N~ ~8+ ~7NMMNNOI= ~?ONZ+ ~ZNND,$M8 ~MO $MMMMMMMMMM7:::~~~~~~=+++===~~~:::,,
436 # ,~ONMMNO~, :?8NNMN8?~ZNMMMMMMMMMNMMNDOI=~NM? =MMM+ NMMMO 8M$ ,MM, $MMMN =D, ,?N~ , ?NMMMNNNDDO8DDD887, ,$ND= :O$NMZ IMO ,IMMMMN$, 7NO, ,,,,,:,,,
437 # ,~IDMNN8OI, :~+$DMMMMMMMN87+=~~~~?ZDNNMM, ?NM? :NMN :MM7 ~MN ~DMMMN O~ :DD, ,MMMMNNOI:,, , ,=ZODD8D? :MMM: NMZ +8NM8, ,, :~ ,,~:,
438 # ,INNMMNNO+ :=78NNZIIONMNNDMMN ,7D~ :MN~ MNMM?=MM $? OD, ,IMD ,:8MNMNNNNNNNNMMNM7: :8~ ZND OMN7, :NNMMMMN :8MMMMMNZ=:
439 # :O88NMMN$=~:, ?MMN88NMMMD =MN? =, :NM +7$NO, =N8 , OMI, ,, 7M: ZDDND? MMO ,, +MM8?OMN: ,::
440 # :$NMMMNMM8I: ,8MD, +MMN= ,=?77=:ZMMM7 ?MN ~$+ 7M7 ,DZ :M~ IMM8: ,8MN, , ,,,, :MMD,=DMM+,?O8= , ,
441 # ,=I77$ONNNZ?+ONMZ =$D ,+=::+$8NNMNDNMD7?, ?MN :~~: ~D7 ,NI ,M7 ~?ZNZ?, 8MM~ =ZMMN~$MMMMMMMNMMD$ONMMMMD:
442 # ~DMM= ~I7=, =DMMMD7MMM, :+?=?O$: ~N: ~MN88D$: NMM=, ~MMMMMMMMMMMMOZMMMMMM7::8MMI
443 # , :DNNI :~IDMMND :::=?II?==~::ZN7=+I$ZZZ8DZ+~~: IMMM~ ~MMMNMMMMMI~:7NMMMMD7,: +NM8,
444 # :NMM7, ~MM+ ,,,:~==~~~: , OMMN: ?NNMMMMM8, ~NMNO, =MMMN?,,
445 # ,,,,,,,, ,$8MMO= ~M8 +MNO: ,MM~ , :: ,~, ,,:::,,,,
446 # ,::,,,, ~ONND+ OD+ ?NMD, ?? ,::::::,,
447 # :=+??=:,,,,, ~?$D87I: ~Z? =$DM$: ,::,,,,,,,,
448 # ,:~==~, :+=, $NNNNZ~ ,,:~~: , :DMMMI
449 # ,~~~~~:,,, ,,, ,~IMMMN8O+, ,:~?7$Z7~, :ZDMNNDNM8ZI
450 # ,,~~:,, , :$DNNMMN8?, , ,~7ZOO?: ,:$NMMMM? :7NMMD?,
451 # ,:~~:,,, $NMMMMMNMNO?~ ~?ODDD$=, :?8MMMMMD?~7NMMMD$~ :ONMMN?
452 # ,,,,,,,,, ?$DMMMMMMMMMMD$?=~, ,~7ZZODN87????I$ODNMDOZ$7I: ~$ZDMD7==~ =$ZDND$++~,
453 # ~?++=~~, ~+?II7ZNMMMMMM8$$$?~, ,~?II7Z8DDOZ$77II+: ~?IZDM8O$I~, :??$8MMNZZ7+,
454 # , , ,+D7 :=IZ8NMMMNNMNNO$= ,:?ZDNNMMMNMND8Z7=, =ZNMMMMMD?,, :ONMNMMMN?,
455 # ,MMMM7 ,,::~IONNMMNNDDD8OZI=, ,::::=+I$ONMNNNNDDDNNMMMMMMMMMD87: ,:~=ZNNNDOI, ~7$ZO8DDNNNNDD8O+:,
456 #~, +MMMMDI?~ ,~+Z8DNDNNMMMMDD$+:,,, , ,~?7O8DNNNNNNMMMMMNOOZI??++?IZ88$: ,,~ZDMMMMMMMMMMMMMMMMMMNNNNNMMMNND8$=,,
457 # ,~: NMMMMOZMM8: ,7DZ~ ,,~IONMMMMMMMMMNDZ+~: , ,=I8DNMMMMMNMNMMMMMDNMMMMNMMMNNNDDDDD8O8Z$7II+++IZDDMMNMN$,,
458 # :~: MMMMMNMMM+ :, +?DOI~ ,:~?7$$ZODDNMNN8Z7??+=~:, ,~=+?7I?=:,,,,:=?I7$$$$$$$ZZZOO8DDNNMMNNNNNMMMMNMZ+
459 # ~=, MMMMMMMM8 ,NNNO , ,~?ZDNMNNNNNNND8O7?:,,, ,~=7DMMMMMMMMMZ~
460 # ,:+~ ,MMMMMMMMO ~N8: ,,:~, ,:~:,,, ,:~, ,:~~=+++?7$O8DNNNNNMMN8$=, ~+$ZO8DNNMMNNNND8OOOZ$+:, :=+I8MMMMDOI
461 # ,=+ MMMMMMMMMDMZ, ~?: ,,,,, ,:~~~, ,NN?$NMMMMMMMMMMMN87~:,,,=+=:,,,,,,:?NMMNMMMMMMNNDNDI8MMM+ ,
462 # +=, $MMMN?$NMN, ,+?+:, ,,:~~:, :=~, 8M= , :7ONNNO?~, , :$NNMMMMMMMMMMMMMN,
463 # ,+? ,NNO,, , :ODNNNZ: :?777I= IMO ,,=$Z7+~ ,?NNMMNZ: :7NMMMMMNMMMM8 , ,IDND~
464 # ,=~, 7I :+I+~::,, ~?I~ , ,~=~==: ZMZ ?8I: :I$DND$?~ :?$8MMN$?: ?ZDMMMMMMMM7 +$NMMMMMI
465 # ~~ , ,=+=: ,== ::, , ZMM: ?NN8: ,7NMMNI, ,$NMMMO: =NMMMNMM+ I8NMMMM,
466 # ~= ,~:,,~~, ,:, ,,,, , ,, +MN8 ,~OMM8Z, :+ZMNDOI =IMMN8= ,=NNMO ?NMMMD
467 # ::, ,,,:: ,:, ,, =MMO~ , ?NMMMI, ,INMMMDI, ~DMMN+, NMO ?DNO~
468 # ,~, ,,,::, ,, , ,, ,INMND+: :MMM+ ~ZDDMMMD?~:$MMMMMMMMMMM?
469 # ,, :~~:, ,:, ,, :ZMMMMNNMMMN, ,=ONMMMMNM$,,,,,, ,,
470 #Z, :, ,, ,~=~, ,, ,=?7$I= , :~~~, ,,,,
471 #MD= :: ,,,, , ,, ,,,,
472 #MNMI :: ,,, ,,,,
473 #7MMM? , ,~: :~:, ,:,
474 # OMMMO: ~NMD, :=: :+=: ,,,
475 # ~MMMN8: NMMM~ +?: ,:=++~ ,:,,
476 # $MMMMMNI,?MMMMZ ,DM7 ,=I= ~+~~:, ,:,,,
477 # +MMO~OMMMMMMMMD MMM7 , , =$~ ?OZ+ ,:,
478 # ~NMN: :+DNN7MMMNMMMDODMMN+ :+?, +77+~: :::,
479 # IMM+ ,NMMMMMMMMN?NM7 ~7+, , :+$I, , +8? :+~
480 # =NMD, NMMMMMM8~ ?NN, =ND? ,=??: ,8NMMMM= :++
481 # +DO, ~ZZ$ODI :8M~ :=I8NMNMMD+, ,:~~=: , +8MO$DMD+ ~===~: :~~:
482 # :$~ 7NMD$, ZMM8I? ,~=: , ZNN7,:MM8~,OMNMD8DMM= ++:
483 # =ZNOI: ?DND= ,?I~ ,,,: ,$ND+ ?NMNNNNN7+, 7MN, ,=+~
484 # :ZI: :, ,=7: ::,, $NN= 7NMMMM7: :DMMMN8NMMDDMN7 ,::,
485 # ~?= ,,, ,ZM= DMMD8 ?MMMMNN7, ,I+ :~~,
486 # =+, ,,, ,, =Z8: +$~
487 # :~ ,,~~,
488 # :, ::,
489 # ::, ,:::,
490 # ,, ,~
491 # ,, ,,,
492 #
493 #