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author kaymccoy
date Sat, 13 Aug 2016 00:03:18 -0400
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29:c67dd00f7e18 30:3e346e0dd55c
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
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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 unweighted_average(scores):
87 sum = 0
88 num = 0
89 i = 0
90 while i < len(scores):
91 if not scores[i]:
92 scores[i] = 0.0
93 sum += float(scores[i])
94 num += 1
95 i += 1
96 average = sum/num
97 xminusxbars = 0
98 while i < len(scores):
99 xminusxbars += (float(scores[i]) - average)**2
100 if len(scores) < 2:
101 variance = xminusxbars/(num-1)
102 else:
103 variance = 0
104 sd = math.sqrt(variance)
105 se = sd / math.sqrt(num)
106 return (average, variance, sd, se)
107
108 # 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
109 # For use when aggregating scores by gene later on, if the weighted argument is called
110
111 def weighted_average(scores,weights):
112 sum = 0
113 weighted_average = 0
114 weighted_variance = 0
115 top = 0
116 bottom = 0
117 i = 0
118 while i < len(weights):
119 if not scores[i]:
120 scores[i] = 0.0
121 top += float(weights[i])*float(scores[i])
122 bottom += float(weights[i])
123 i += 1
124 if bottom == 0:
125 return 0
126 weighted_average = top/bottom
127 top = 0
128 bottom = 0
129 i = 0
130 while i < len(weights):
131 top += float(weights[i]) * (float(scores[i]) - weighted_average)**2
132 bottom += float(weights[i])
133 i += 1
134 weighted_variance = top/bottom
135 weighted_stdev = math.sqrt(weighted_variance)
136 weighted_stder = weighted_stdev/math.sqrt(len(scores))
137 return (weighted_average, weighted_variance, weighted_stdev, weighted_stder)
138
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142
143
144
145
146
147
148 ##### AGGREGATION / CALCULATIONS #####
149
150 #Reads the genes which should be marked in the final aggregate file into an array
151
152 import os.path
153 if arguments.marked:
154 with open(arguments.marked) as file:
155 marked_set = file.read().splitlines()
156
157 #Creates a dictionary of dictionaries to contain a summary of all genes and their fitness values
158 #The fitness values and weights match up, so that the weight of gene_summary[locus]["w"][2] would be gene_summary[locus]["s"][2]
159
160 import csv
161 gene_summary = {}
162 for eachfile in arguments.fitnessfiles:
163 with open(eachfile) as csvfile:
164 lines = csv.reader(csvfile)
165 for line in lines:
166 locus = line[9]
167 w = line[12]
168 if w == 'nW':
169 continue
170 if not w:
171 w == 0
172 c1 = float(line[2])
173 c2 = float(line[3])
174 avg = (c1+c2)/2
175 if avg < float(arguments.cutoff):
176 continue
177 if avg > float(arguments.weight_ceiling):
178 avg = arguments.weight_ceiling
179 if locus not in gene_summary:
180 gene_summary[locus] = {"w" : [], "s": []}
181 gene_summary[locus]["w"].append(w)
182 gene_summary[locus]["s"].append(avg)
183
184 #If finding any missing gene loci is requested in the arguments, starts out by loading all the known features from a genbank file
185
186 from Bio import SeqIO
187 if (arguments.find_missing):
188 output = [["locus","mean","var","sd","se","gene","Total","Blank","Not Blank","Blank Removed","M\n"]]
189 handle = open(arguments.find_missing, "rU")
190 for record in SeqIO.parse(handle, "genbank"):
191 refname = record.id
192 features = record.features
193 handle.close()
194
195 #Goes through the features to find which are genes
196
197 for feature in features:
198 gene = ""
199 if feature.type == "gene":
200 locus = "".join(feature.qualifiers["locus_tag"])
201 if "gene" in feature.qualifiers:
202 gene = "".join(feature.qualifiers["gene"])
203 else:
204 continue
205
206 #Goes through the fitness scores of insertions within each gene, and removes whatever % of blank fitness scores were requested along with their corresponding weights
207
208 sum = 0
209 num = 0
210 avgsum = 0
211 blank_ws = 0
212 i = 0
213 if locus in gene_summary.keys():
214 for w in gene_summary[locus]["w"]:
215 if float(w) == 0:
216 blank_ws += 1
217 else:
218 sum += float(w)
219 num += 1
220 count = num + blank_ws
221 removed = 0
222 to_remove = int(float(arguments.blank_pc)*count)
223 if blank_ws > 0:
224 i = 0
225 while i < len(gene_summary[locus]["w"]):
226 w = gene_summary[locus]["w"][i]
227 if removed == to_remove:
228 break
229 if float(w) == 0:
230 del gene_summary[locus]["w"][i]
231 del gene_summary[locus]["s"][i]
232 removed += 1
233 i -= 1
234 i += 1
235
236 #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
237
238 if num == 0:
239 if (arguments.marked and locus in marked_set):
240 output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "M", "\n"])
241 else:
242 output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "\n"])
243
244 #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
245
246 else:
247 if not arguments.weighted:
248 (average, variance, stdev, stderr) = unweighted_average(gene_summary[locus]["w"])
249 else:
250 (average, variance, stdev, stderr) = weighted_average(gene_summary[locus]["w"],gene_summary[locus]["s"])
251 if (arguments.marked and locus in marked_set):
252 output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "M", "\n"])
253 else:
254 output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "\n"])
255
256 #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.
257
258 else:
259 if (arguments.marked and locus in marked_set):
260 output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "M", "\n"])
261 else:
262 output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "\n"])
263
264 #Writes the aggregated fitness file
265
266 with open(arguments.summary, "wb") as csvfile:
267 writer = csv.writer(csvfile)
268 writer.writerows(output)
269
270 #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
271 #This is never called through Galaxy since finding missing genes is just better than not finding them.
272
273 else:
274 output = [["Locus","W","Count","SD","SE","M\n"]]
275 for gene in gene_summary.keys():
276 sum = 0
277 num = 0
278 average = 0
279 if "w" not in gene_summary[gene]:
280 continue
281 for i in gene_summary[gene]["w"]:
282 sum += i
283 num += 1
284 average = sum/num
285 xminusxbars = 0
286 for i in w:
287 xminusxbars += (i-average)**2
288 if num > 1:
289 sd = math.sqrt(xminusxbars/(num-1))
290 se = sd / math.sqrt(num)
291 if (arguments.marked and locus in marked_set):
292 output.append([gene, average, num, sd, se, "M", "\n"])
293 else:
294 output.append([gene, average, num, sd, se, "\n"])
295 with open(arguments.summary, "wb") as csvfile:
296 writer = csv.writer(csvfile)
297 writer.writerows(output)
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393 #
394 # ~MMM=:DMMM?, +NMMO=,:~I8MMMMM8+, , ~I8MMMMMN87~?8NNMMN8: +NMND~ +MN= ,$MMMI ?M8, ,OM8, :MN+ =MM? ,MMDNMMD ,+DM8I, ,,:::~~~::::::::::
395 # IMMMNMM8I ,I8MM87~::+$8NNMMMMOI+=~~:, ,,:~=?$DNMMMMMMDOZI7ZDMMMD8I , , $M8+?8MM8I , 7MI +MN= ZMN, 8MD MMN8MMM, :$ONM8I+=:, ,,,::::~~~~~=====~:
396 # , ,DMNN7: , ,OMMN7==~::~=?8NNMMMMMMNNMMMMMMMMMMMN8OO8ODNMMMMMD~ , IMMNMN~ ,OM+ ,NM$ ,NMO, :MM$ , ,:::,,::::,, $MNMMNM, ,,, :?ONMMNN8?~,, , ,,,,,,,::~~=+++??=~
397 # ,,:=+????+, I$ :ZMMN8$~:,,, ,:=?7$O8DD8O$7+==+$O8DNMMMMMMMMM$ ?$, == ,~, ~NM= 8MD, ,OM8ZMMO , ,::::::~~~:,, ?MNMMZ ,,,,,, ,+7ONMNMD8O$+~, ,,,,,,::~====::
398 # ,:=IONMMMMMMMMMMM8: ZN$: ,~DMMMND7=, ,,:~====:=$DNMMMMMMMN88MMMZ +N$ , ,7DN8= =MN, IMM =DMN7 ,,,,,,,,,, ,~?, ,,,,, , ~?$8MMMMNN8Z?~:,, ,:::,,,
399 #+ONMMMMMMNO7=:,, ,,+MMO, 7D$: ~OMNMNNMNNNNNNNNNNNMMMMMMMMMNMMMM?,~MMM8 ND, 7MM=, ?NN:, +MO, ?MM, ,,,, , :,:=$DMMMMMMN87=~, ,,,,,
400 #MMND8$: , 7NM7 , =?~,,, :?88DDDNNMNNNDNDD88Z?:, ZMM$ ,MMMO ,MM+ZNMM? ::ZNZ, +M$ ,MM?, , ,, ,,:=?Z8NMMMMMN8Z+=,
401 #: ~ZMM8~ ,,,, 8MM, +MMM, 7ZZI~=$OOZ$: ,:+???+, +MZ =MN= ,,, , ,,:~=?IIII$ZO88DDNNNNNNMMMMMMMMMMN~,
402 # ,:OMMM? ,,:,, 8MM ~NNM7 :OMMMMMMMMO: =M8, :NM~ = ,~?I, ,,,,,,,,,,,,,, ,~$DNMMMMMMMMMND8O888Z$II7777I??+++===:,
403 # ?DMM8?, OMN??NMM$ ~8MMMO?===7MMM8: ~NM= =NN: ,OM~ ,, +NMMN~ ,,,,, ,,,,,,,, ,?$O8NNMDDZ7?+=~:, , , ,
404 # , ~$MMMD+ , , ~MMNMMN~ : +NMMZ, NMNM~ 7MI , ZNN,, IMN, :DMNM+ ~+NMMMD~, ,,,,,,,, ,, ,,,,,:, +OMNMMOI~,
405 # , $MMMM$, , ,=?ODNMNNMNMMMMNNND~ ,$D$, , ,8MM8 ,MMM7 ,ZMNNMM= DMMNMMMMMMMMMMMMNI: , ,,,,,,,,, ,,, ?NMO=, ,
406 # ,~ZNMND7, ,,:~=+$DNNMMMMNDDDD888OZZZZ8NMMN IMMN: ,MMN~ +Z$+, ?NNNDO+:?O888OI, ,,,,,,, ,,, +MN+
407 # =ONMMM8~ , ,:=IDMMMMMMMND8$+:, , ,INMNZ :MMM~, , +MMD , ,, ,,::,,,, ,,:::, ?M8: ,
408 #8MMMNZ~ , =I$ONMMNDZ7?+, ,,=I8NMD7: DMMN DMN= ,:::, ,:~~=~~:,, :ZMMZI+: ,, ,,,,,,,
409 #MN7:,:=7ONMMMD$?=, , ~7ODNMM$+: ~MMM++7ZOZOO8O8D8$~ ,MM8 ,,,,::~~==~~:,, :+7DMMMMNNDD88OOZZZO88DNNNN8=, ,,,,,,,
410 #I,~+DMMNND7: , ,$MMMMMN7, $MMN :??+=~::,,,, NMD, ,:,,:::~=~~,,,, ,,=I$8DNMMMMMMMMMNMMMNZ: ,,,,,,
411 #DNMNN7:, ,+ONMMMMNI: NMM$ DMN= ,,,,:::::~~::,, ?DMMMMDZ=, ,,,
412 #N8$: ,,, ,:=?ONMMMD8Z+, ,,,, MMM= ZMMI ,=?$8NMMMMMMMMMMMN87=~~,,, :=ZMMMMD$~
413 # , ,=ZNMMMMMNI:, ,~?Z88888$=, ,:~+??~, MMM, IMM$ ,=ZNMMMMMN8$+~=~=~~===7ODNMMMN8DNMMMN+, ,,
414 # , ~?$ODMMMMNZ?: :II+~, ,=7= :?77?=:====?O+ ,,:,,, MMM, ?MM$ ,,,, :?ONMM8II=, , =DMMMM87=, ,,,,
415 # , ,, ,~I8MMMMMMN87?~:=+?7$ZOO88DD888O$I+~:, ~ZZ: ,$7,~??, ,?+ ,+Z8$?==??= MMM =MM$ :?ODNNNNNNNMMO: ,:?NNMNO= ,,,IMMMNZ, ,,,:,,,,,,,,,,,,,
416 #, ,~7DNMMMMMMMMMMMNNNNMMMNND8Z7II7$$$$ZODNNNMND$, :O$: , ,IN$, I+ +ZZ=,, 7+ MMM =ODDDDNNNNNN8= :MM$ ?DDNN8?::,, ,,7NMM8, 7NMNZ~, :OMMM$, , ,,:::~~:,,,,,,,,,,,,,,,,,
417 #?8NMMMMMMMMMMMN8I:,,, ~$NMMM$ :87 +DM$ ID+~78I, O7 :=+~ MMM , , ?MM7 $NMN$, :I8MMMMMMNMDNNNNNNNNNDD88ZI=: ,ZMM7, +MMN~ ,,,:::,, ,,:,::::,,,,,,
418 #MMMD8DNMDZ7=: ,:=+7ZNNNDOZ~ =DI , :7MM7, IDDZI, =DN88ZI77$N? NMM: $MM= ~: =OMNZ+ ,=7DMMMMMMNDDOOOOZ$7IIIII77$ZOO8NNMMD$+~ :OND~, ,, MMN= ,,,,,,,,,,,,,
419 #: ,,, , ,:+ZNMNNMMNO?: +8? +NMN7 , , ON~ 8MM$ NMD, ,7MMMN, 7MMO, ,:ONMMMMN8I: ,~ZNMNN$, ~MM? , ZMNND?~: ,,, ,, ,,,,,,
420 # , , ,:~?7$ZZ8NNMMMNO7I=, ~D+ ZNO=, , :NM$: =MMM NMO ~NMNMMM :ZDN$, ,,=7DMD$?=, :?ZMD$: :DMNOZZZ$: ,,,,, ~IDMMMMMMMMMMMMMMMMMMMMM8~
421 # , =DMNNNNNNNN8$= : , ,,~?ODNZ: :DM? =: ,MMM7 +MN~ ,MM8:NMN INMZ, ,=ONOI , +NMZ ,,+ZDMMMMMMMN+, ,,,,,,, ,~?$ODNNNNNNNMMMMMMMMD= ,
422 # ,:::::~7$I?=: ,~78NMNMMMMM? :+Z+ M8 $MM, =, DMMD NMN DMO OMM77NMI, ?8$~ , ~ZDDDNNNNNMMMMMMMMNMNNNNNDD8Z+:, IMN: I8DNMMMMN7~: ,,,,,, +$O$, ~$DMMMMMMMD~ ,, ,
423 # :I+ :ID? ~ZDNNNNMMNO?~,, ,:::::=7ONMNNNDMMMN$ ,=IONNMMMMZ:, ~MO ,7MMMI $MN, , :MMN ?8NDDNND$~ +MM~ ,MM~=MMMNMD, =ONNDDNMMMMMMMMMMMMNNND88DNNNNNMMMMMMMNDO7+~::,:,7MM+ =DMMMNNO? ~ZMMMMM7::~INMMMMMMMMMMMMMN8:
424 #:MMM+,INMMM: ,:~+78NMMMM8?==++++++???++??I$Z77$$$$$$$7II??I$ZZO8MMMMM8Z7~ ,IZI ~77+ ?MMMMD88MN8+~, +8MO$OMMMMMNMMMZ, ~=: OMM? ,MM? ZMM~MMMM8~ ,:+7$$$$ZZ7?==: :8MDOZ$ZZZODMMMMMM8+, ,:=?$ZZOOOOOOZ$: , , ,=8MNMMMMMMMMMMNDZ$7$MMMMMMMMMMMMMMMMMMNI+?I7ZDNMMMMMMMMMM$,
425 #=MMMZ$MMMMMM~ ,::::~==~~+I$8NMMMMMN$::::::::,,, ,,=ONDDO$7II?+~, , ,,$DD87: =NND= , ,+$$=:~, ,:, ,MMD NMI ,~?Z8DND88$?: 8MM$MMMZ:=~:,~~:::,,,=$DNMND$MM8, ~DMNMMMN?, :7$7?+==~=:,,,,,,,, ,,,,,,, ,,,,,,::,,,,,:::::::,,,::::::,, ,OM$,7MMMMMMNZ= , ~8MMMMMMMNDO7I7MMMMMMMMMMMMN8Z7+?NMMMD,,
426 #NMMMMMM? ,++, ~=I??= :7$ 7MM+ 7M7 ,:?ZZ$MMM8NMZ~+, :?INMMM? +$NMMMNZ: :+?I7$$Z$O88D88DDDDNNNNNNNNNNNNNNNNNNDDNDDDNNNNNMMNMMMMMMMNNNN$:??~ ,+II?=, , ,, ,?I??+=~, :MMMMMNM
427 #MMMMMD~ ,:=++++++++++=~,,, +MMN: ,MMD, :M$ ,INMNMMMMMMMMMM~~~?D, :OMM8: ,+$8Z$+,,$MMMMMD, :IODDDD
428 #N$~,,, ,~I$8NMMMMMMMMMNMMMMMMMMMMMMMMMMNNNN7: ZMMMM? NMMMNZ~:, +$: ,NMNI:::~?8MMM7I? IO ~I$ODNNNDND8OO$I?DMMMD$I8NMMMMMMMNMNMMMMMMM8=, ,
429 # ,:~?ONMMMMNNNDDD8NMD+, ,~?Z8NMNM8=, , $MNNNMM? :DMMMNDD8DZ7$+, ,8NMMNMNMMMD$: =8, ,INMMMNZI?====+I$$8DDNMMMMMMNNNMND7, :~$DMMMNMO
430 # ,?DMMMMMNNNMMM+::~++ZMMMM8ZZZZZZ$II77II7???=~:, ,+DMN7 =NMM8, ,NMMNMNMN$?I7I77OZ~ ~8D$~, ,MMMMMMNM8: :DMMM, , DMO=ZMZ,
431 # ,?OMMN87=, IZNNNMMMMMMMMMMND7IIII??III$8DDNN8Z+, , ~MM ?OMMMM~ ,MMMMMZ+ ~I= IMMMO$$= ?NNM? MN~ +NM:
432 # IMM8~ =MD, ?N= :,, ,+77?=+, $8MM7::OMMMMMMZ+ I+, ?NI , :MMM8$ NNI, 7M$
433 # ,MMN $MD 7M+ ~ODNZ~, :7MMN? , $MMMN= 7MNMN: +8+ ,D8~ =MMDD8Z= =NMD OM7,
434 # OMMD~ IMN: =8O:,~+$OO? :IZ+: :ZMMNM8= =DMMI ~8MM8= , ,, :8M?,$MM, ::, DM, =Z~ ,I8DDDN8$DMN~ MN ,=Z8DNDZZ= MD: ,
435 # ,,,,,,,, ?8NNMM8$I????++=+8MMMD$77$ZDMMNMMMDNOZ~ :IZ8DOI~, =MMMNMNM8I:, ,7M8I +MND7 ~MNDDM8~ 7MI MM7 :8MDDM7 8N= ~ID$, ,:Z$?:,,,=ONNMNMD= ?M$ :: +MI ,,,,,,::~~:::,,,,,,, ,+OMO:, ,
436 # ,,,::~===~~:, ,~+I$88DNNNNNNNNNDNDD8O$?~:, ,7DNMNDMMMN~,ZMND$8NNDZ=, $M? ?NNN7 =MO OMD$MM= MMMINMO $M7 DN: 8MMD: ?D~ :O8ZDMMMMD=, ,ZN$ ,INNMD= :NO ,:+8MMMMMMMMDI:,::,,
437 #=~~~::~~::, ~?78MMMDNMM? :+I$DD87?=, OMNDO$7$OI: 7N+ ?MNMN~ MMMNM$ 7M$ 7M$ +NMMO, ?N~ ~8+ ~7NMMNNOI= ~?ONZ+ ~ZNND,$M8 ~MO $MMMMMMMMMM7:::~~~~~~=+++===~~~:::,,
438 # ,~ONMMNO~, :?8NNMN8?~ZNMMMMMMMMMNMMNDOI=~NM? =MMM+ NMMMO 8M$ ,MM, $MMMN =D, ,?N~ , ?NMMMNNNDDO8DDD887, ,$ND= :O$NMZ IMO ,IMMMMN$, 7NO, ,,,,,:,,,
439 # ,~IDMNN8OI, :~+$DMMMMMMMN87+=~~~~?ZDNNMM, ?NM? :NMN :MM7 ~MN ~DMMMN O~ :DD, ,MMMMNNOI:,, , ,=ZODD8D? :MMM: NMZ +8NM8, ,, :~ ,,~:,
440 # ,INNMMNNO+ :=78NNZIIONMNNDMMN ,7D~ :MN~ MNMM?=MM $? OD, ,IMD ,:8MNMNNNNNNNNMMNM7: :8~ ZND OMN7, :NNMMMMN :8MMMMMNZ=:
441 # :O88NMMN$=~:, ?MMN88NMMMD =MN? =, :NM +7$NO, =N8 , OMI, ,, 7M: ZDDND? MMO ,, +MM8?OMN: ,::
442 # :$NMMMNMM8I: ,8MD, +MMN= ,=?77=:ZMMM7 ?MN ~$+ 7M7 ,DZ :M~ IMM8: ,8MN, , ,,,, :MMD,=DMM+,?O8= , ,
443 # ,=I77$ONNNZ?+ONMZ =$D ,+=::+$8NNMNDNMD7?, ?MN :~~: ~D7 ,NI ,M7 ~?ZNZ?, 8MM~ =ZMMN~$MMMMMMMNMMD$ONMMMMD:
444 # ~DMM= ~I7=, =DMMMD7MMM, :+?=?O$: ~N: ~MN88D$: NMM=, ~MMMMMMMMMMMMOZMMMMMM7::8MMI
445 # , :DNNI :~IDMMND :::=?II?==~::ZN7=+I$ZZZ8DZ+~~: IMMM~ ~MMMNMMMMMI~:7NMMMMD7,: +NM8,
446 # :NMM7, ~MM+ ,,,:~==~~~: , OMMN: ?NNMMMMM8, ~NMNO, =MMMN?,,
447 # ,,,,,,,, ,$8MMO= ~M8 +MNO: ,MM~ , :: ,~, ,,:::,,,,
448 # ,::,,,, ~ONND+ OD+ ?NMD, ?? ,::::::,,
449 # :=+??=:,,,,, ~?$D87I: ~Z? =$DM$: ,::,,,,,,,,
450 # ,:~==~, :+=, $NNNNZ~ ,,:~~: , :DMMMI
451 # ,~~~~~:,,, ,,, ,~IMMMN8O+, ,:~?7$Z7~, :ZDMNNDNM8ZI
452 # ,,~~:,, , :$DNNMMN8?, , ,~7ZOO?: ,:$NMMMM? :7NMMD?,
453 # ,:~~:,,, $NMMMMMNMNO?~ ~?ODDD$=, :?8MMMMMD?~7NMMMD$~ :ONMMN?
454 # ,,,,,,,,, ?$DMMMMMMMMMMD$?=~, ,~7ZZODN87????I$ODNMDOZ$7I: ~$ZDMD7==~ =$ZDND$++~,
455 # ~?++=~~, ~+?II7ZNMMMMMM8$$$?~, ,~?II7Z8DDOZ$77II+: ~?IZDM8O$I~, :??$8MMNZZ7+,
456 # , , ,+D7 :=IZ8NMMMNNMNNO$= ,:?ZDNNMMMNMND8Z7=, =ZNMMMMMD?,, :ONMNMMMN?,
457 # ,MMMM7 ,,::~IONNMMNNDDD8OZI=, ,::::=+I$ONMNNNNDDDNNMMMMMMMMMD87: ,:~=ZNNNDOI, ~7$ZO8DDNNNNDD8O+:,
458 #~, +MMMMDI?~ ,~+Z8DNDNNMMMMDD$+:,,, , ,~?7O8DNNNNNNMMMMMNOOZI??++?IZ88$: ,,~ZDMMMMMMMMMMMMMMMMMMNNNNNMMMNND8$=,,
459 # ,~: NMMMMOZMM8: ,7DZ~ ,,~IONMMMMMMMMMNDZ+~: , ,=I8DNMMMMMNMNMMMMMDNMMMMNMMMNNNDDDDD8O8Z$7II+++IZDDMMNMN$,,
460 # :~: MMMMMNMMM+ :, +?DOI~ ,:~?7$$ZODDNMNN8Z7??+=~:, ,~=+?7I?=:,,,,:=?I7$$$$$$$ZZZOO8DDNNMMNNNNNMMMMNMZ+
461 # ~=, MMMMMMMM8 ,NNNO , ,~?ZDNMNNNNNNND8O7?:,,, ,~=7DMMMMMMMMMZ~
462 # ,:+~ ,MMMMMMMMO ~N8: ,,:~, ,:~:,,, ,:~, ,:~~=+++?7$O8DNNNNNMMN8$=, ~+$ZO8DNNMMNNNND8OOOZ$+:, :=+I8MMMMDOI
463 # ,=+ MMMMMMMMMDMZ, ~?: ,,,,, ,:~~~, ,NN?$NMMMMMMMMMMMN87~:,,,=+=:,,,,,,:?NMMNMMMMMMNNDNDI8MMM+ ,
464 # +=, $MMMN?$NMN, ,+?+:, ,,:~~:, :=~, 8M= , :7ONNNO?~, , :$NNMMMMMMMMMMMMMN,
465 # ,+? ,NNO,, , :ODNNNZ: :?777I= IMO ,,=$Z7+~ ,?NNMMNZ: :7NMMMMMNMMMM8 , ,IDND~
466 # ,=~, 7I :+I+~::,, ~?I~ , ,~=~==: ZMZ ?8I: :I$DND$?~ :?$8MMN$?: ?ZDMMMMMMMM7 +$NMMMMMI
467 # ~~ , ,=+=: ,== ::, , ZMM: ?NN8: ,7NMMNI, ,$NMMMO: =NMMMNMM+ I8NMMMM,
468 # ~= ,~:,,~~, ,:, ,,,, , ,, +MN8 ,~OMM8Z, :+ZMNDOI =IMMN8= ,=NNMO ?NMMMD
469 # ::, ,,,:: ,:, ,, =MMO~ , ?NMMMI, ,INMMMDI, ~DMMN+, NMO ?DNO~
470 # ,~, ,,,::, ,, , ,, ,INMND+: :MMM+ ~ZDDMMMD?~:$MMMMMMMMMMM?
471 # ,, :~~:, ,:, ,, :ZMMMMNNMMMN, ,=ONMMMMNM$,,,,,, ,,
472 #Z, :, ,, ,~=~, ,, ,=?7$I= , :~~~, ,,,,
473 #MD= :: ,,,, , ,, ,,,,
474 #MNMI :: ,,, ,,,,
475 #7MMM? , ,~: :~:, ,:,
476 # OMMMO: ~NMD, :=: :+=: ,,,
477 # ~MMMN8: NMMM~ +?: ,:=++~ ,:,,
478 # $MMMMMNI,?MMMMZ ,DM7 ,=I= ~+~~:, ,:,,,
479 # +MMO~OMMMMMMMMD MMM7 , , =$~ ?OZ+ ,:,
480 # ~NMN: :+DNN7MMMNMMMDODMMN+ :+?, +77+~: :::,
481 # IMM+ ,NMMMMMMMMN?NM7 ~7+, , :+$I, , +8? :+~
482 # =NMD, NMMMMMM8~ ?NN, =ND? ,=??: ,8NMMMM= :++
483 # +DO, ~ZZ$ODI :8M~ :=I8NMNMMD+, ,:~~=: , +8MO$DMD+ ~===~: :~~:
484 # :$~ 7NMD$, ZMM8I? ,~=: , ZNN7,:MM8~,OMNMD8DMM= ++:
485 # =ZNOI: ?DND= ,?I~ ,,,: ,$ND+ ?NMNNNNN7+, 7MN, ,=+~
486 # :ZI: :, ,=7: ::,, $NN= 7NMMMM7: :DMMMN8NMMDDMN7 ,::,
487 # ~?= ,,, ,ZM= DMMD8 ?MMMMNN7, ,I+ :~~,
488 # =+, ,,, ,, =Z8: +$~
489 # :~ ,,~~,
490 # :, ::,
491 # ::, ,:::,
492 # ,, ,~
493 # ,, ,,,
494 #
495 #